la skot cu se jibri di'a lo nu lo panzi be sy cu nanca li bi e li pano

la skoto cu se jibri di'a lo nu lo panzi be sy cu nanca li bi e li pa no

Scott resumed her career when her children were eight and 10.

scott resumed her career when her children were eight and 10 .


masa untuk tidur


masa untuk tidur siti


Micheal Bateman

is squid ravioli in a lemon grass broth...


with goat cheese profiteroles, and I also have an arugula Caesar salad.


For entrees this evening, I have swordfish meatloaf with onion marmalade,


rare roasted partridge breast in raspberry coulis with a sorrel timbale.


...and grilled free-range rabbit with herbed french fries.


Our pasta tonight is a squid ravioli in a lemon grass broth.


God, I hate this place. It's a chick's restaurant. Why aren't we at Dorsia ?


Because Bateman won't give the maitre d' head.


Altia 's operating profit jumped to EUR 47 million from EUR 6.6 million .


The agreement was signed with Biohit Healthcare Ltd , the UK-based subsidiary of Biohit Oyj , a Finnish public company which develops , manufactures and markets liquid handling products and diagnostic test systems .


Kesko pursues a strategy of healthy , focused growth concentrating on sales and services to consumer-customers .


Vaisala , headquartered in Helsinki in Finland , develops and manufactures electronic measurement systems for meteorology , environmental sciences , traffic and industry .


Also , a six-year historic analysis is provided for these markets .


Elcoteq 's stock of orders has stabilised in the past weeks , Mr Krippl said .


Also , a six-year historic analysis is provided for this market .


In addition , nine fixed-term employment contracts will not be extended and two people will leave the company under pension arrangements .


Operating loss totalled EUR 0.9 mn , down from a profit of EUR 2.7 mn .


The Insolvency Act regulates the amount of debt that borrowers are permitted to write off .


On the minimal teaching sets of two-dimensional threshold functions

Abstract:
It is known that a minimal teaching set of any threshold function on the
twodimensional rectangular grid consists of 3 or 4 points. We derive exact
formulae for the numbers of functions corresponding to these values and further
refine them in the case of a minimal teaching set of size 3. We also prove that
the average cardinality of the minimal teaching sets of threshold functions is
asymptotically 7/2.
  We further present corollaries of these results concerning some special
arrangements of lines in the plane.


Gradient Boost with Convolution Neural Network for Stock Forecast

Abstract: Market economy closely connects aspects to all walks of life. The stock forecast is one of task among studies on the market economy. However, information on markets economy contains a lot of noise and uncertainties, which lead economy forecasting to become a challenging task. Ensemble learning and deep learning are the most methods to solve the stock forecast task. In this paper, we present a model combining the advantages of two methods to forecast the change of stock price. The proposed method combines CNN and GBoost. The experimental results on six market indexes show that the proposed method has better performance against current popular methods.


Learning Data Manifolds with a Cutting Plane Method

Abstract:
We consider the problem of classifying data manifolds where each manifold
represents invariances that are parameterized by continuous degrees of freedom.
Conventional data augmentation methods rely upon sampling large numbers of
training examples from these manifolds; instead, we propose an iterative
algorithm called M_{CP} based upon a cutting-plane approach that efficiently
solves a quadratic semi-infinite programming problem to find the maximum margin
solution. We provide a proof of convergence as well as a polynomial bound on
the number of iterations required for a desired tolerance in the objective
function. The efficiency and performance of M_{CP} are demonstrated in
high-dimensional simulations and on image manifolds generated from the ImageNet
dataset. Our results indicate that M_{CP} is able to rapidly learn good
classifiers and shows superior generalization performance compared with
conventional maximum margin methods using data augmentation methods.


Guided Stereo Matching

Abstract: Stereo is a prominent technique to infer dense depth maps from images, and deep learning further pushed forward the state-of-the-art, making end-to-end architectures unrivaled when enough data is available for training. However, deep networks suffer from significant drops in accuracy when dealing with new environments. Therefore, in this paper, we introduce Guided Stereo Matching, a novel paradigm leveraging a small amount of sparse, yet reliable depth measurements retrieved from an external source enabling to ameliorate this weakness. The additional sparse cues required by our method can be obtained with any strategy (e.g., a LiDAR) and used to enhance features linked to corresponding disparity hypotheses. Our formulation is general and fully differentiable, thus enabling to exploit the additional sparse inputs in pre-trained deep stereo networks as well as for training a new instance from scratch. Extensive experiments on three standard datasets and two state-of-the-art deep architectures show that even with a small set of sparse input cues, i) the proposed paradigm enables significant improvements to pre-trained networks. Moreover, ii) training from scratch notably increases accuracy and robustness to domain shifts. Finally, iii) it is suited and effective even with traditional stereo algorithms such as SGM.


Learning Physical Intuition of Block Towers by Example

Abstract:
Wooden blocks are a common toy for infants, allowing them to develop motor
skills and gain intuition about the physical behavior of the world. In this
paper, we explore the ability of deep feed-forward models to learn such
intuitive physics. Using a 3D game engine, we create small towers of wooden
blocks whose stability is randomized and render them collapsing (or remaining
upright). This data allows us to train large convolutional network models which
can accurately predict the outcome, as well as estimating the block
trajectories. The models are also able to generalize in two important ways: (i)
to new physical scenarios, e.g. towers with an additional block and (ii) to
images of real wooden blocks, where it obtains a performance comparable to
human subjects.


Audio-Visual-Olfactory Resource Allocation for Tri-modal Virtual Environments

Abstract: Virtual Environments (VEs) provide the opportunity to simulate a wide range of applications, from training to entertainment, in a safe and controlled manner. For applications which require realistic representations of real world environments, the VEs need to provide multiple, physically accurate sensory stimuli. However, simulating all the senses that comprise the human sensory system (HSS) is a task that requires significant computational resources. Since it is intractable to deliver all senses at the highest quality, we propose a resource distribution scheme in order to achieve an optimal perceptual experience within the given computational budgets. This paper investigates resource balancing for multi-modal scenarios composed of aural, visual and olfactory stimuli. Three experimental studies were conducted. The first experiment identified perceptual boundaries for olfactory computation. In the second experiment, participants (N=25) were asked, across a fixed number of budgets (M=5), to identify what they perceived to be the best visual, acoustic and olfactory stimulus quality for a given computational budget. Results demonstrate that participants tend to prioritise visual quality compared to other sensory stimuli. However, as the budget size is increased, users prefer a balanced distribution of resources with an increased preference for having smell impulses in the VE. Based on the collected data, a quality prediction model is proposed and its accuracy is validated against previously unused budgets and an untested scenario in a third and final experiment.


DAVID: Dual-Attentional Video Deblurring

Abstract: Blind video deblurring restores sharp frames from a blurry sequence without any prior. It is a challenging task because the blur due to camera shake, object movement and defocusing is heterogeneous in both temporal and spatial dimensions. Traditional methods train on datasets synthesized with a single level of blur, and thus do not generalize well across levels of blurriness. To address this challenge, we propose a dual attention mechanism to dynamically aggregate temporal cues for deblurring with an end-to-end trainable network structure. Specifically, an internal attention module adaptively selects the optimal temporal scales for restoring the sharp center frame. An external attention module adaptively aggregates and refines multiple sharp frame estimates, from several internal attention modules designed for different blur levels. To train and evaluate on more diverse blur severity levels, we propose a Challenging DVD dataset generated from the raw DVD video set by pooling frames with different temporal windows. Our framework achieves consistently better performance on this more challenging dataset while obtaining strongly competitive results on the original DVD benchmark. Extensive ablative studies and qualitative visualizations further demonstrate the advantage of our method in handling real video blur.


Adversarial Attacks and Defenses: An Interpretation Perspective

Abstract: Despite the recent advances in a wide spectrum of applications, machine learning models, especially deep neural networks, have been shown to be vulnerable to adversarial attacks. Attackers add carefully-crafted perturbations to input, where the perturbations are almost imperceptible to humans, but can cause models to make wrong predictions. Techniques to protect models against adversarial input are called adversarial defense methods. Although many approaches have been proposed to study adversarial attacks and defenses in different scenarios, an intriguing and crucial challenge remains that how to really understand model vulnerability? Inspired by the saying that "if you know yourself and your enemy, you need not fear the battles", we may tackle the aforementioned challenge after interpreting machine learning models to open the black-boxes. The goal of model interpretation, or interpretable machine learning, is to extract human-understandable terms for the working mechanism of models. Recently, some approaches start incorporating interpretation into the exploration of adversarial attacks and defenses. Meanwhile, we also observe that many existing methods of adversarial attacks and defenses, although not explicitly claimed, can be understood from the perspective of interpretation. In this paper, we review recent work on adversarial attacks and defenses, particularly from the perspective of machine learning interpretation. We categorize interpretation into two types, feature-level interpretation and model-level interpretation. For each type of interpretation, we elaborate on how it could be used for adversarial attacks and defenses. We then briefly illustrate additional correlations between interpretation and adversaries. Finally, we discuss the challenges and future directions along tackling adversary issues with interpretation.

Adversarial Attack
Adversarial Defense
Interpretable Machine Learning


Headless Horseman: Adversarial Attacks on Transfer Learning Models

Abstract: Transfer learning facilitates the training of task-specific classifiers using pre-trained models as feature extractors. We present a family of transferable adversarial attacks against such classifiers, generated without access to the classification head; we call these \emph{headless attacks}. We first demonstrate successful transfer attacks against a victim network using \textit{only} its feature extractor. This motivates the introduction of a label-blind adversarial attack. This transfer attack method does not require any information about the class-label space of the victim. Our attack lowers the accuracy of a ResNet18 trained on CIFAR10 by over 40\%.


On June 4, 1783 (many sources incorrectly fix the date as June 5), they flew this craft as their first public demonstration at Annonay in front of a group of dignitaries from the Etats particuliers.


In May 1851, he traveled tlk I LOVE PANCAKES!!


The O104:H4 strain is, though rarer, still more dangerous because it is just as, if not more so, virulent and antibiotic and supportive treatment protocols for it are not as well-developed (it has the ability to be very enterohemorrhagic like O157:H7, causing bloody diarrhea, but also is more enteroaggregative, meaning it adheres well and clumps to intestinal membranes).

The O104:H4 strain is, though rarer, still more dangerous because it is just as, if not more so, virulent.


He was a Hudson's Bay Company trader and the first recorded European to visit the area, and John Glenn was the first documented European settler in the Calgary area, in 1873.

He was a Hudson's Bay Company trader and the first recorded European to visit the area.


The theory underlying the modern synthesis has three major aspects (even though they're false):

The theory underlying the modern synthesis has three major aspects:


This Sutta records his devotion towards the the Buddha, the Dhamma, and the Sangha.

This Sutta records his devotion towards the Dhamma.


Summary: Good Quality Dog Food

I have bought several of the Vitality canned dog food products and have found them all to be of good quality. The product looks more like a stew than a processed meat and it smells better. My Labrador is finicky and she appreciates this product better than  most.


Summary: Not as Advertised

Product arrived labeled as Jumbo Salted Peanuts...the peanuts were actually small sized unsalted. Not sure if this was an error or if the vendor intended to represent the product as "Jumbo".


Summary: "Delight" says it all

This is a confection that has been around a few centuries.  It is a light, pillowy citrus gelatin with nuts - in this case Filberts. And it is cut into tiny squares and then liberally coated with powdered sugar.  And it is a tiny mouthful of heaven.  Not too chewy, and very flavorful.  I highly recommend this yummy treat.  If you are familiar with the story of C.S. Lewis' "The Lion, The Witch, and The Wardrobe" - this is the treat that seduces Edmund into selling out his Brother and Sisters to the Witch.


Summary: Cough Medicine

If you are looking for the secret ingredient in Robitussin I believe I have found it.  I got this in addition to the Root Beer Extract I ordered (which was good) and made some cherry soda.  The flavor is very medicinal.


Summary: Great taffy

Great taffy at a great price.  There was a wide assortment of yummy taffy.  Delivery was very quick.  If your a taffy lover, this is a deal.


Summary: Nice Taffy

I got a wild hair for taffy and ordered this five pound bag. The taffy was all very enjoyable with many flavors: watermelon, root beer, melon, peppermint, grape, etc. My only complaint is there was a bit too much red/black licorice-flavored pieces (just not my particular favorites). Between me, my kids, and my husband, this lasted only two weeks! I would recommend this brand of taffy -- it was a delightful treat.


Summary: Great!  Just as good as the expensive brands!

This saltwater taffy had great flavors and was very soft and chewy.  Each candy was individually wrapped well.  None of the candies were stuck together, which did happen in the expensive version, Fralinger's.  Would highly recommend this candy!  I served it at a beach-themed party and everyone loved it!


Summary: Wonderful, tasty taffy

This taffy is so good.  It is very soft and chewy.  The flavors are amazing.  I would definitely recommend you buying it.  Very satisfying!!


Summary: Yay Barley

Right now I'm mostly just sprouting this so my cats can eat the grass. They love it. I rotate it around with Wheatgrass and Rye too


Summary: Healthy Dog Food

This is a very healthy dog food. Good for their digestion. Also good for small puppies. My dog eats her required amount at every feeding.


Due to the success of deep learning to solving a variety of challenging machine learning tasks, there is a rising interest in understanding loss functions for training neural networks from a theoretical aspect.

Particularly, the properties of critical points and the landscape around them are of importance to determine the convergence performance of optimization algorithms.

In this paper, we provide a necessary and sufficient characterization of the analytical forms for the critical points (as well as global minimizers) of the square loss functions for linear neural networks.

We show that the analytical forms of the critical points characterize the values of the corresponding loss functions as well as the necessary and sufficient conditions to achieve global minimum.

Furthermore, we exploit the analytical forms of the critical points to characterize the landscape properties for the loss functions of linear neural networks and shallow ReLU networks.

One particular conclusion is that: While the loss function of linear networks has no spurious local minimum, the loss function of one-hidden-layer nonlinear networks with ReLU activation function does have local minimum that is not global minimum.

We provide necessary and sufficient analytical forms for the critical points of the square loss functions for various neural networks, and exploit the analytical forms to characterize the landscape properties for the loss functions of these neural networks.


We introduce the 2-simplicial Transformer, an extension of the Transformer which includes a form of higher-dimensional attention generalising the dot-product attention, and uses this attention to update entity representations with tensor products of value vectors.

We show that this architecture is a useful inductive bias for logical reasoning in the context of deep reinforcement learning.

We introduce the 2-simplicial Transformer and show that this architecture is a useful inductive bias for logical reasoning in the context of deep reinforcement learning.


We present Tensor-Train RNN (TT-RNN), a novel family of neural sequence architectures for multivariate forecasting in environments with nonlinear dynamics.

Long-term forecasting in such systems is highly challenging, since there exist long-term temporal dependencies, higher-order correlations and sensitivity to error propagation.

Our proposed tensor recurrent architecture addresses these issues by learning the nonlinear dynamics directly using higher order moments and high-order state transition functions.

Furthermore, we decompose the higher-order structure using the tensor-train (TT) decomposition to reduce the number of parameters while preserving the model performance.

We theoretically establish the approximation properties of Tensor-Train RNNs for general sequence inputs, and such guarantees are not available for usual RNNs.

We also demonstrate significant long-term prediction improvements over general RNN and LSTM architectures on a range of simulated environments with nonlinear dynamics, as well on real-world climate and traffic data.

Accurate forecasting over very long time horizons using tensor-train RNNs


Recent efforts on combining deep models with probabilistic graphical models are promising in providing flexible models that are also easy to interpret.

We propose a variational message-passing algorithm for variational inference in such models.

First, we propose structured inference networks that incorporate the structure of the graphical model in the inference network of variational auto-encoders (VAE).

Second, we establish conditions under which such inference networks enable fast amortized inference similar to VAE.

Finally, we derive a variational message passing algorithm to perform efficient natural-gradient inference while retaining the efficiency of the amortized inference.

By simultaneously enabling structured, amortized, and natural-gradient inference for deep structured models, our method simplifies and generalizes existing methods.

We propose a variational message-passing algorithm for models that contain both the deep model and probabilistic graphical model.


Modern deep neural networks have a large amount of weights, which make them difficult to deploy on computation constrained devices such as mobile phones.

One common approach to reduce the model size and computational cost is to use low-rank factorization to approximate a weight matrix.

However, performing standard low-rank factorization with a small rank can hurt the model expressiveness and significantly decrease the performance.

In this work, we propose to use a mixture of multiple low-rank factorizations to model a large weight matrix, and the mixture coefficients are computed dynamically depending on its input.

We demonstrate the effectiveness of the proposed approach on both language modeling and image classification tasks.

Experiments show that our method not only improves the computation efficiency but also maintains (sometimes outperforms) its accuracy compared with the full-rank counterparts.

A simple modification to low-rank factorization that improves performances (in both image and language tasks) while still being compact.


Deep learning training accesses vast amounts of data at high velocity, posing challenges for datasets retrieved over commodity networks and storage devices.

We introduce a way to dynamically reduce the overhead of fetching and transporting training data with a method we term Progressive Compressed Records (PCRs).

PCRs deviate from previous formats by leveraging progressive compression to split each training example into multiple examples of increasingly higher fidelity, without adding to the total data size.

Training examples of similar fidelity are grouped together, which reduces both the system overhead and data bandwidth needed to train a model.

We show that models can be trained on aggressively compressed representations of the training data and still retain high accuracy, and that PCRs can enable a 2x speedup on average over baseline formats using JPEG compression.

Our results hold across deep learning architectures for a wide range of datasets: ImageNet, HAM10000, Stanford Cars, and CelebA-HQ.

We propose a simple, general, and space-efficient data format to accelerate deep learning training by allowing sample fidelity to be dynamically selected at training time


Generative Adversarial Networks (GANs) have achieved remarkable results in the task of generating realistic natural images.

In most applications, GAN models share two aspects in common.

On the one hand, GANs training involves solving a challenging saddle point optimization problem, interpreted as an adversarial game between a generator and a discriminator functions.

On the other hand, the generator and the discriminator are parametrized in terms of deep convolutional neural networks.

The goal of this paper is to disentangle the contribution of these two factors to the success of GANs.

In particular, we introduce Generative Latent Optimization (GLO), a framework to train deep convolutional generators without using discriminators, thus avoiding the instability of adversarial optimization problems.

Throughout a variety of experiments, we show that GLO enjoys many of the desirable properties of GANs: learning from large data, synthesizing visually-appealing samples, interpolating meaningfully between samples, and performing linear arithmetic with noise vectors.

Are GANs successful because of adversarial training or the use of ConvNets? We show a ConvNet generator trained with a simple reconstruction loss and learnable noise vectors leads many of the desirable properties of a  GAN.


In this paper, we propose a novel kind of kernel, random forest kernel, to enhance the empirical performance of MMD GAN.

Different from common forests with deterministic routings, a probabilistic routing variant is used in our innovated random-forest kernel, which is possible to merge with the CNN frameworks.

Our proposed random-forest kernel has the following advantages: From the perspective of random forest, the output of GAN discriminator can be viewed as feature inputs to the forest, where each tree gets access to merely a fraction of the features, and thus the entire forest benefits from ensemble learning.

In the aspect of kernel method, random-forest kernel is proved to be characteristic, and therefore suitable for the MMD structure.

Besides, being an asymmetric kernel, our random-forest kernel is much more flexible, in terms of capturing the differences between distributions.

Sharing the advantages of CNN, kernel method, and ensemble learning, our random-forest kernel based MMD GAN obtains desirable empirical performances on CIFAR-10, CelebA and LSUN bedroom data sets.

Furthermore, for the sake of completeness, we also put forward comprehensive theoretical analysis to support our experimental results.

Equip MMD GANs with a new random-forest kernel.


Three skiers are skiing on a snowy mountain.


The dog is wagging his tail.


A dog wags his tail at the boy.


a dog wags its tail with its heart


woman paddling canoe on a lake


paddle an open canoe along lake .


a man paddles his canoe on the lake.


a train pulls into station


Context: The first test contains all the information necessary to understand the intent and implementation of the test.

It requires all of this implementation be done within the framework of a test case.

The second test pulled the creation of the validation instance and the stub into a setup method.

The second test is the implementation, test of all test cases.

The second test is a collection of test cases that demonstrate that the implementation works as it is intended and that it satisfies all relevant requirements.


Context: I know many people who have been average at reasoning and at quant, but have done wonderfully at the GMAT through sheer practice and hard work.

People who call the GMAT an IQ test should have a look at some MBA tests in India and I'm sure they'll change their opinion.

My experience is that people with IQs over 130 do not score well on the GMAT.

My own GMAT is only average, and I have spent the last year doing almost everything wrong on it.


Context: The data reported by each respondent are not readily comparable among countries.

In Australia, the abs collects data on both telephone and website use, but that are available for all countries.

Although in some European countries the use of antiretroviral therapy may be more common than in the United States, the two drugs are used differently and in widely differing patient populations.

Some respondents may not have reported any data at all for any of their countries.

Some respondents reported data for the year 2005, while others reported data for the year 2006.


Context: Because expression of this gene is much more robust in male samples, 323.3, we excluded the possibility of sample mix up.

We were not able to detect the presence of 317.

We considered that values equal or below the 8.1 threshold were not reliable.

We have re analyzed our data using male and female samples separately.

We generated 3 new samples from 3 males each.


Context: I love that he's able to use wired as a venue for launching future bestsellers.

I think wireless might be the way to go for him in that he can continue to attract fans and sell more of his own books.

I am not a fan of his last two books, so i don't think he will be writing any more for me.

His last two albums were great, his latest album is a bit disappointing compared to the first three.

Honestly, I might have preferred the podcast of his presentation on the topic.


Context: Participants were instructed not to change their training intensity or volume, thus no overload throughout the duration of the study occurred.

The subjects were encouraged to maintain a consistent training volume throughout their study, a key component to the long term effectiveness of this intervention.

There was no significant change in training load or time in the three-week period following treatment.

No effect of the training or hmb ca was observed on indices of damage.

Participants decreased their BMD only slightly over the 8-week period.


Context: Then, when standards were finalized, the competitor already had most of the design completed and could finish it in time for a quick market launch.

The competitor did not need to worry about how to get the job done.

The competitor was already at work on the spec, and didn't have to compete for the work.

The entire design was in place before the competition even began.

Flux in industry standards delayed the competitors project but not as thoroughly as it did the inflexible project.


Context: Smart homes can save your money by the way.

If you have central heating in your home, you can lower your electric bill by investing in a programmable thermostat.

You can set alarm settings, climate temperature and humidity, lighting settings, for instance automatic lights off after 11 pm.

If you have a large home, and don't want to invest in it, you can use a smart home energy management system to turn off lights, fans, and your air conditioners and your home heating system and other electrical devices.

You don't have to run a water heater to keep warm.


Context: Similarly, our findings, as well as those of kern et al. did not link ae with hunting.

In our data, we have found evidence that hunting ability is positively related to the expression of a homeodomain transcription factor in a h 3 k 9 gene.

A previous study in Austria identified hunting as the most notable observed risk factor.

In our study, we found that most ae did contribute to hunting significantly.

However, our data show that the ne of the environment is a significant predictor of hunting success.


Context: In case of dogs, the existence is more by choice rather than by compulsion because dogs are considered as best friend of man.

They are not affected by the curse of Cain, and they are included in the holy book of the Quran.

People in this society have to follow dog's rules and regulations of dog society.

Software providers of online casinos have provided dog themed online slots game either in cute way or in comic way for their players.

The man treats the dog as if it is his own flesh and blood and not as a beast to be used for his convenience.


olly stille


tid til at sove olly


Premise: Pluto rotates once on its axis every 6.39 Earth days;

Hypothesis: Earth rotates on its axis once times in one day.


Premise: ---Glenn ========================================================= Once per day, the earth rotates about its axis.


Premise: geysers - periodic gush of hot water at the surface of the Earth.

Hypothesis: The surface of the sun is much hotter than almost anything on earth.


Premise: Facts: Liquid water droplets can be changed into invisible water vapor through a process called evaporation .

Hypothesis: Evaporation is responsible for changing liquid water into water vapor.


Premise: By comparison, the earth rotates on its axis once per day and revolves around the sun once per year.


Premise: Pluto is about 39 times more distant from the Sun than is the Earth, and it takes about 250 Earth years for Pluto to make one complete revolution around the Sun.

Hypothesis: Earth makes a complete revolution around the sun about once every year.


Premise: It takes Pluto 247.7 Earth years to make one revolution around the Sun.


Premise: Multicellular organisms that have a eukaryotic cell type, mitochondria and a complex nervous system.

Hypothesis: Single-celled organisms and multicellular organisms have this in common: both have a way to get rid of waste materials.


Premise: Distribution Water on earth is located primarily in the oceans (97.2%), polar caps (2.14%), ground water(0.61%), and surface water(0.009%).

Hypothesis: Most of earth's water is located in oceans


Premise: migratory birds which fly from South America to winter in Arctic Circle, where they

Hypothesis: Many species of birds in new england fly south for the winter months to find an environment with more food.


enhancement

New feature or request

why the stratify option is omitted from test_train_split function?

is there any other way implement the stratify option while splitting the dataset? as it is important point to be considered while splitting the dataset.


Unexpected behavior doing Split + Filter

bug

Something isn't working

## Describe the bug
I observed unexpected behavior when applying 'train_test_split' followed by 'filter' on dataset. Elements of the training dataset eventually end up in the test dataset (after applying the 'filter')

## Steps to reproduce the bug
```
from datasets import Dataset
import pandas as pd
dic = {'x': [1,2,3,4,5,6,7,8,9], 'y':['q','w','e','r','t','y','u','i','o']}
df = pd.DataFrame.from_dict(dic)
dataset = Dataset.from_pandas(df)
split_dataset = dataset.train_test_split(test_size=0.5, shuffle=False, seed=42)
train_dataset = split_dataset["train"]
eval_dataset = split_dataset["test"]
eval_dataset_2 = eval_dataset.filter(lambda example: example['x'] % 2 == 0)
print( eval_dataset['x'])
print(eval_dataset_2['x'])
```
One observes that elements in eval_dataset2 are actually coming from the training dataset...

## Expected results
The expected results would be that the filtered eval dataset would only contain elements from the original eval dataset.

## Actual results
Specify the actual results or traceback.

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Windows 10
- Python version: 3.7
- PyArrow version: 5.0.0


JSONDecodeError with HuggingFace dataset viewer

dataset-viewer

Related to the dataset viewer on huggingface.co

Hi ! I think the issue comes from the dataset_infos.json file: it has the "flat" field twice.

Can you try deleting this file and regenerating it please ?

Thanks! That fixed that, but now I am getting:
Server Error
Status code:   400
Exception:     KeyError
Message:       'feature'

I checked the dataset_infos.json and pubmed_neg.py script, I don't use 'feature' anywhere as a key. Is the dataset viewer expecting that I do?

## Dataset viewer issue for 'pubmed_neg'

**Link:** https://huggingface.co/datasets/IGESML/pubmed_neg

I am getting the error:
Status code:   400
Exception:     JSONDecodeError
Message:       Expecting property name enclosed in double quotes: line 61 column 2 (char 1202)

I have checked all files - I am not using single quotes anywhere. Not sure what is causing this issue.

Am I the one who added this dataset ? Yes


question

## Dataset viewer issue for '*name of the dataset*'

**Link:** *link to the dataset viewer page*

*short description of the issue*

Am I the one who added this dataset ? Yes-No


Align the Dataset and IterableDataset processing API

Yes I agree, these should be as aligned as possible. Maybe we can also check the feedback in the survey at http://hf.co/oss-survey and see if people mentioned related things on the API (in particular if we go the breaking change way, it would be good to be sure we are taking the right direction for the community).

I like this proposal.

> There is also an important difference in terms of behavior:
Dataset.map adds new columns (with dict.update)
BUT
IterableDataset discards previous columns (it overwrites the dict)
IMO the two methods should have the same behavior. This would be an important breaking change though.

> The main breaking change would be the change of behavior of IterableDataset.map, because currently it discards all the previous columns instead of keeping them.

Yes, this behavior of `IterableDataset.map` was surprising to me the first time I used it because I was expecting the same behavior as `Dataset.map`, so I'm OK with the breaking change here.

> IterableDataset only supports "torch" (it misses tf, jax, pandas, arrow) and is missing the parameters: columns, output_all_columns and format_kwargs

\+  it's also missing the actual formatting code (we return unformatted tensors)
> We could have a completely aligned map method if both methods were lazy by default, but this is a very big breaking change so I'm not sure we can consider doing that.

> For information, TFDS does lazy map by default, and has an additional .cache() method.

If I understand this part correctly, the idea would be for `Dataset.map` to behave similarly to `Dataset.with_transform` (lazy processing) and to have an option to cache processed data (with `.cache()`). This idea is really nice because it can also be applied to `IterableDataset` to fix https://github.com/huggingface/datasets/issues/3142 (again we get the aligned APIs). However, this change would break a lot of things, so I'm still not sure if this is a step in the right direction (maybe it's OK for Datasets 2.0?)
> If the two APIs are more aligned it would be awesome for the examples in transformers, and it would create a satisfactory experience for users that want to switch from one mode to the other.

Yes, it would be amazing to have an option to easily switch between these two modes.

I agree with the rest.

> If I understand this part correctly, the idea would be for Dataset.map to behave similarly to Dataset.with_transform (lazy processing) and to have an option to cache processed data (with .cache()). This idea is really nice because it can also be applied to IterableDataset to fix #3142 (again we get the aligned APIs). However, this change would break a lot of things, so I'm still not sure if this is a step in the right direction (maybe it's OK for Datasets 2.0?)

Yea this is too big of a change in my opinion. Anyway it's fine as it is right now with streaming=lazy and regular=eager.

## Intro

Currently the two classes have two distinct API for processing:

### The `.map()` method

Both have those parameters in common: function, batched, batch_size

- IterableDataset is missing those parameters:
with_indices, with_rank, input_columns, drop_last_batch, remove_columns, features, disable_nullable, fn_kwargs, num_proc

- Dataset also has additional parameters that are exclusive, due to caching:
keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, suffix_template, new_fingerprint

- There is also an important difference in terms of behavior:
**Dataset.map adds new columns** (with dict.update)
BUT
**IterableDataset discards previous columns** (it overwrites the dict)
IMO the two methods should have the same behavior. This would be an important breaking change though.

- Dataset.map is eager while IterableDataset.map is lazy

### The `.shuffle()` method

- Both have an optional seed parameter, but IterableDataset requires a mandatory parameter buffer_size to control the size of the local buffer used for approximate shuffling.

- IterableDataset is missing the parameter generator

- Also Dataset has exclusive parameters due to caching: keep_in_memory, load_from_cache_file, indices_cache_file_name, writer_batch_size, new_fingerprint

### The `.with_format()` method

- IterableDataset only supports "torch" (it misses tf, jax, pandas, arrow) and is missing the parameters: columns, output_all_columns and format_kwargs

### Other methods

- Both have the same `remove_columns` method
- IterableDataset is missing: cast, cast_column, filter, rename_column, rename_columns, class_encode_column, flatten, prepare_for_task, train_test_split, shard
- Some other methods are missing but we can discuss them: set_transform, formatted_as, with_transform
- And others don't really make sense for an iterable dataset: select, sort, add_column, add_item
- Dataset is missing skip and take, that IterableDataset implements.

## Questions

I think it would be nice to be able to switch between streaming and regular dataset easily, without changing the processing code significantly.

1. What should be aligned and what shouldn't between those two APIs ?

IMO the minimum is to align the main processing methods.

It would mean aligning breaking the current `Iterable.map` to have the same behavior as `Dataset.map` (add columns with dict.update), and add multiprocessing as well as the missing parameters.

It would also mean implementing the missing methods: cast, cast_column, filter, rename_column, rename_columns, class_encode_column, flatten, prepare_for_task, train_test_split, shard

2. What are the breaking changes for IterableDataset ?

The main breaking change would be the change of behavior of `IterableDataset.map`, because currently it discards all the previous columns instead of keeping them.

3. Shall we also do some changes for regular datasets ?

I agree the simplest would be to have the exact same methods for both Dataset and IterableDataset. However this is probably not a good idea because it would prevent users from using the best benefits of them. That's why we can keep some aspects of regular datasets as they are:
- keep the eager Dataset.map with caching
- keep the with_transform method for lazy processing
- keep Dataset.select (it could also be added to IterableDataset even though it's not recommended)

We could have a completely aligned `map` method if both methods were lazy by default, but this is a very big breaking change so I'm not sure we can consider doing that.

For information, TFDS does lazy map by default, and has an additional `.cache()` method.

## Opinions ?

I'd love to gather some opinions about this here. If the two APIs are more aligned it would be awesome for the examples in `transformers`, and it would create a satisfactory experience for users that want to switch from one mode to the other.

cc @mariosasko @albertvillanova @thomwolf @patrickvonplaten @sgugger


Add QuALITY dataset

dataset request

Requesting to add a new dataset

## Adding a Dataset
- **Name:** QuALITY
- **Description:** A challenging question answering with very long contexts (Twitter [thread](https://twitter.com/sleepinyourhat/status/1471225421794529281?s=20))
- **Paper:** No ArXiv link yet, but draft is [here](https://github.com/nyu-mll/quality/blob/main/quality_preprint.pdf)
- **Data:** GitHub repo [here](https://github.com/nyu-mll/quality)
- **Motivation:** This dataset would serve as a nice way to benchmark long-range Transformer models like BigBird, Longformer and their descendants. In particular, it would be very interesting to see how the S4 model fares on this given it's impressive performance on the Long Range Arena

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).


datasets keeps reading from cached files, although I disabled it

bug

Something isn't working

Hi ! What version of `datasets` are you using ? Can you also provide the logs you get before it raises the error ?

## Describe the bug
Hi,
I am trying to avoid dataset library using cached files, I get the following bug when this tried to read the cached files. I tried to do the followings:
```
from datasets import set_caching_enabled
set_caching_enabled(False)
```
also force redownlaod:
```
 download_mode='force_redownload'
```
but none worked so far, this is on a cluster and on some of the machines this reads from the cached files, I really appreciate any idea on how to fully remove caching @lhoestq
many thanks

```
File "run_clm.py", line 496, in <module>
    main()
  File "run_clm.py", line 419, in main
    train_result = trainer.train(resume_from_checkpoint=checkpoint)
  File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 943, in train
    self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
  File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/transformers/trainer.py", line 1445, in _maybe_log_save_evaluate
    metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)
  File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 172, in evaluate
    output = self.eval_loop(
  File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 241, in eval_loop
    metrics = self.compute_pet_metrics(eval_datasets, model, self.extra_info[metric_key_prefix], task=task)
  File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 268, in compute_pet_metrics
    centroids = self._compute_per_token_train_centroids(model, task=task)
  File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 353, in _compute_per_token_train_centroids
    data = get_label_samples(self.get_per_task_train_dataset(task), label)
  File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 350, in get_label_samples
    return dataset.filter(lambda example: int(example['labels']) == label)
  File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 470, in wrapper
    out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
  File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/fingerprint.py", line 406, in wrapper
    out = func(self, *args, **kwargs)
  File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2519, in filter
    indices = self.map(
  File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2036, in map
    return self._map_single(
  File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 503, in wrapper
    out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
  File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 470, in wrapper
    out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
  File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/fingerprint.py", line 406, in wrapper
    out = func(self, *args, **kwargs)
  File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2248, in _map_single
    return Dataset.from_file(cache_file_name, info=info, split=self.split)
  File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 654, in from_file
    return cls(
  File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 593, in __init__
    self.info.features = self.info.features.reorder_fields_as(inferred_features)
  File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/features/features.py", line 1092, in reorder_fields_as
    return Features(recursive_reorder(self, other))
  File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/features/features.py", line 1081, in recursive_reorder
    raise ValueError(f"Keys mismatch: between {source} and {target}" + stack_position)
ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'candidates_ids': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'labels': Value(dtype='int64', id=None), 'attention_mask': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None), 'input_ids': Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None), 'extra_fields': {}, 'task': Value(dtype='string', id=None)}

```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: linux
- Python version: 3.8.12
- PyArrow version: 6.0.1


Add The People's Speech

dataset request

Requesting to add a new dataset

## Adding a Dataset
- **Name:** The People's Speech
- **Description:** a massive English-language dataset of audio transcriptions of full sentences.
- **Paper:** https://openreview.net/pdf?id=R8CwidgJ0yT
- **Data:** https://mlcommons.org/en/peoples-speech/
- **Motivation:** With over 30,000 hours of speech, this dataset is the largest and most diverse freely available English speech recognition corpus today.

[The article](https://thegradient.pub/new-datasets-to-democratize-speech-recognition-technology-2/) which may be useful when working on the dataset.

cc: @anton-l

Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).


Jay-Z Freestyle
Yeah yall this Jay-Z, coolin out with the Funk Flex
60 Minutes of Funk, volume two, how we do
Motherfucker, yeah you dont stop
You wont quit, Jay-Z drop shit like this
Aiyyo, my records sell cause I was born to do it
Kick that Willie shit well, cause Im really gonna do it
The voice of the hustlers, who else gon do it?
Most niggas is locked or in the box with embalming fluid
Thats how I get it locked when I come through in the V
You rap niggas on the radio dont do it for me
Insert the removable face, place the CD
Your Prince, cause you rap dudes dont make sense
Talk about bitten lines
Nigga I did ery bit of crime that I writ in mine
Ran so much coke, I could shit a dime
And this is way back when, way before your bullshit was signed
Tryin to indirectly, effect me, directly
Careful what you wish for, Jigga get raw
Nigga Im straight gutta, let me remind you
Act like you, out of your mind, I put your mind out of you
I do anything when I put my mind to it
A whole lot more when I put the nine to it
I flow shit blow shit smash shit tow shit
On some sho shit hose it down totally and you knows this man
Keep niggas in awe with the old shit
No shit, and I dont give a fuck who you go get
I fold shit like poker, smack em around
On some Joe Schmoe shit, I back your whole click down what?
Frito Lay rappers I slay for play, tell me
Who in your circle could fuck around with Jay?
No mo shit, Cristals get dough shit
Shoot my pistols on the reg, Im on some double-fo shit
Bout to drop a jewel and make po-po sick
I only the respect feds, beat cops know shit
Im pro slick, the dopest nigga to your brain, the comatose shit
Cause after all, whats my name, oh shit!
Yeah Funk Flex and uh, we dont stop
Yall wanna rhyme like me, wanna dime like me
Every Tom Dick and Harry wanna ride my mami
Drink Cristal, play diamonds in his wristal
Sell fish scale, yall niggas love this style
Wannabe players, Jay-Zs official
Been through out, I could tell you what to do and how to do it
Foundation is layed, we can take this to the top baby girl
If youre not afraid, the world is watchin
Most certainly, clowns wanna get up in your drawers
Think they hurtin me, ha hah
I keep you ill and traced out, tennis brace style
Cartier watch my diamond face style
Crib on the coast, marble floors laced out
Chase you upstairs singing Lets Play House
Drop a seed in her, little life to breathe in her
Wanna boy so to be sure, I ODd in her
His days are laced in Caesar Leguars
All the chicks jealous at the baby showers
Beatch!
How we do, Funk Flex, yeah
Brooklyn


I wanna be drunk when I wake up
On the right side of the wrong bed
And never an excuse I made up
Tell you the truth I hate
What didnt kill me
It never made me stronger at all
Love will scar your make-up, lip sticks to me
So now I maybe lean back there
Im sat here wishing I was sober
I know Ill never hold you like I used to
But a house gets cold when you cut the heating
Without you to hold Ill be freezing
Cant rely on my heart to beat in
Cause you take parts of it every evening
Take words out of my mouth just from breathing
Replace with phrases like when youre leaving me
Should I, should I?
Maybe Ill get drunk again
Ill be drunk again
Ill be drunk again
To feel a little love
I wanna hold your heart in both hands
Not watch it fizzle at the bottom of a Coke can
And I got no plans for the weekend
So should we speak then
Keep it between friends
Though I know youll never love me like you used to
There may be other people like us
Who see the flicker of the clipper when they light up
Flames just create us but burns dont heal like before
And you dont hold me anymore
On cold days cold plays out like the bands name
I know I cant heal things with a hand shake
You know I cant change as I began saying
You cut me wide open like landscape
Open bottles of beer but never champagne
To applaud you with the sound that my hands make
Should I, should I?
Maybe Ill get drunk again
Ill be drunk again
Ill be drunk again
To feel a little love
All by myself
Im here again
All by myself
You know Ill never change
All by myself
All by myself
Im just drunk again
Ill be drunk again
Ill be drunk again
To feel a little love


I wanna be drunk when I wake up
On the right side of the wrong bed
And every excuse I made up
Tell you the truth I hate
What didnt kill me
It never made me stronger at all
Love will scar your make-up lip sticks to me
So now Ill maybe leave back there
Im sat here wishing I was sober
I know Ill never hold you like I used to
But our house gets cold when you cut the heating
Without you to hold Ill be freezing
Cant rely on my heart to beat it
Cause you take part of it every evening
Take words out of my mouth just from breathing
Replace with phrases like when youre leaving me
Should I, should I?
Maybe Ill get drunk again
Ill be drunk again
Ill be drunk again
To feel a little love
I wanna hold your heart in both hands
Ill watch it fizzle at the bottom of a Coke can
And Ive got no plans for the weekend
So shall we speak then
Keep it between friends
Though I know youll never love me like you used to
There maybe other people like us
You see the flicker of the clip when they light up
Flames just create us, burns dont heal like before
You dont hold me anymore
On cold days Coldplays out like the bands the name
I know I cant heal things with a hand shake
You know I cant change as I began saying
You cut me wide open like landscape
Open bottles of beer but never champagne
Well applaud you with the sound that my hands make
Should I, should I?
Maybe Ill get drunk again
Ill be drunk again
Ill be drunk again
To feel a little love
All by myself
Im here again
All by myself
You know Ill never change
All by myself
All by myself
Im just drunk again
Ill be drunk again
Ill be drunk again
To feel a little love


is this porn


Sexy mom gets cum in her


Slut Gets what Slut Needs


MILF Ass - This hot milf has a great ass and is so sexy in the water.


"It has no side effect, I take it in combination of Bystolic 5 Mg and Fish Oil"


"My son is halfway through his fourth week of Intuniv. We became concerned when he began this last week, when he started taking the highest dose he will be on. For two days, he could hardly get out of bed, was very cranky, and slept for nearly 8 hours on a drive home from school vacation (very unusual for him.) I called his doctor on Monday morning and she said to stick it out a few days. See how he did at school, and with getting up in the morning. The last two days have been problem free. He is MUCH more agreeable than ever. He is less emotional (a good thing), less cranky. He is remembering all the things he should. Overall his behavior is better.
We have tried many different medications and so far this is the most effective."


"I used to take another oral contraceptive, which had 21 pill cycle, and was very happy- very light periods, max 5 days, no other side effects. But it contained hormone gestodene, which is not available in US, so I switched to Lybrel, because the ingredients are similar. When my other pills ended, I started Lybrel immediately, on my first day of period, as the instructions said. And the period lasted for two weeks. When taking the second pack- same two weeks. And now, with third pack things got even worse- my third period lasted for two weeks and now it's the end of the third week- I still have daily brown discharge.
The positive side is that I didn't have any other side effects. The idea of being period free was so tempting... Alas."


"This is my first time using any form of birth control. I'm glad I went with the patch, I have been on it for 8 months. At first It decreased my libido but that subsided. The only downside is that it made my periods longer (5-6 days to be exact) I used to only have periods for 3-4 days max also made my cramps intense for the first two days of my period, I never had cramps before using birth control. Other than that in happy with the patch"


"Suboxone has completely turned my life around.  I feel healthier, I'm excelling at my job and I always have money in my pocket and my savings account.  I had none of those before Suboxone and spent years abusing oxycontin.  My paycheck was already spent by the time I got it and I started resorting to scheming and stealing to fund my addiction.  All that is history.  If you're ready to stop, there's a good chance that suboxone will put you on the path of great life again.  I have found the side-effects to be minimal compared to oxycontin.  I'm actually sleeping better.   Slight constipation is about it for me.  It truly is amazing. The cost pales in comparison to what I spent on oxycontin."


"2nd day on 5mg started to work with rock hard erections however experianced headache, lower bowel preassure. 3rd day erections would wake me up & hurt! Leg/ankles aches   severe lower bowel preassure like you need to go #2 but can't! Enjoyed the initial rockhard erections but not at these side effects or $230 for months supply! I'm 50 & work out 3Xs a week. Not worth side effects!"


"He pulled out, but he cummed a bit in me. I took the Plan B 26 hours later, and took a pregnancy test two weeks later - - I'm pregnant."


"Abilify changed my life. There is hope. I was on Zoloft and Clonidine when I first started Abilify at the age of 15.. Zoloft for depression and Clondine to manage my complete rage. My moods were out of control. I was depressed and hopeless one second and then mean, irrational, and full of rage the next. My Dr. prescribed me 2mg of Abilify and from that point on I feel like I have been cured though I know I'm not.. Bi-polar disorder is a constant battle. I know Abilify works for me because I have tried to get off it and lost complete control over my emotions. Went back on it and I was golden again.  I am on 5mg 2x daily. I am now 21 and better than I have ever been in the past. Only side effect is I like to eat a lot."


" I Ve had  nothing but problems with the Keppera : constant shaking in my arms & legs & pins & needles feeling in my arms & legs severe light headedness no appetite & etc."


"I had been on the pill for many years. When my doctor changed my RX to chateal, it was as effective. It really did help me by completely clearing my acne, this takes about 6 months though. I did not gain extra weight, or develop any emotional health issues. I stopped taking it bc I started using a more natural method of birth control, but started to take it bc I hate that my acne came back at age 28. I really hope symptoms like depression, or weight gain do not begin to affect me as I am older now. I'm also naturally moody, so this may worsen things. I was in a negative mental rut today. Also I hope this doesn't push me over the edge, as I believe I am depressed. Hopefully it'll be just like when I was younger."


so it stays in place around your neck.


Love this seamless paper!


very happy with my purchase.-JI


Even for someone with poor balance, this dvd is easy to follow.


, those have always produced a nice deep tone.


but after a year or so the moisture in the air gets to the paper even used only indoors


While not quite as nice as being there in person, it is a reasonable alternative and well worth the purchase price in my book.


Until now, Sloe Gin has been my favorite.


I considered the Martha Stewart one, but in the end purchased the Mindy Weiss based on other reviews and I am happy with it.


"Baby, don't cry, everythang's gonn be alright.


Kelly: Oh! Oh! Can I pick the first question?
Jessica: Sure. Go for it!
Kelly: What's the scariest place you've been to!
Jessica: I'll start: Palermo in Italy.
Mickey: And what's so scary about that? Did you break your nail? :P
Jessica: Shut it, Mickey! No, there are the Capuchin Catacombs with 8000 corpses!
Kelly: Ewwww! Corpses? Rly?
Jessica: Yeah! And you can look at them like museum exhibits. I think they're divided somehow, but have no clue how!
Ollie: That's so cool! Do you get to see the bones or are they covered up?
Jessica: Well, partly. Most of them were exhibited in their clothes. Basically only skulls and hands.
Mickey: I'm writing this one down! That's so precious!
Ollie: Me too!

The scariest place for Jessica was the Capuchin Catacombs in Palermo.


Carrie: Just back from Fantastic Beast :)
Gina: and what do you think?
Carrie: generally good - as usual nice special effect and visuals, an ok plot, a glimpse of the wizarding community in the US.
Alex: Sounds cool. I was thinking of going this weekend with Lane, but I've seen some bad reviews.
Carrie: Depends on what you expect really - I have a lot of sentiment towards Harry Potter so, I'm gonna like everything the do. But seriously the movie was decent. However, if you're expecting to have your mind blown, then no, it's not THAT good.
Gina: I agree. I saw it last week and basically I'm satisfied.
Alex: No spoilers, girls.
Carrie: no worries ;)
Carrie: And Gina, what do you think about Eddie Redmayne as Newt?
Gina: I loved him <3 I loved how introverted and awkward he was and how caring he was towards the animals. And with all that he showed a lot of confidence in his beliefs and was a genuinely compassionate character
Carrie: not your standard protagonist, that's for sure
Gina: and that's what I liked about him
Alex: Maybe I'll go and see it sooner so we can all talk about it.
Carrie: go see it. If' you're not expecting god-knows-what you're going to enjoy it ;)

Carrie and Gina saw "Fantastic Beast" and liked it. Ginna loved Eddie Redmayne as Newt.


Samantha: How are you doing today?
Robyn: better, but I really exaggerated with bier last night
Samantha: was it really only bier?
Robyn: sure, why?
Samantha: I don't know, your eyes, behaviour
Robyn: you want to say I took drugs?
Samantha: I'm only asking, I don't have anything against drugs
Robyn: but I have and I never take them
Samantha: ok, sorry, I didn't intend to offend you
Robyn: I just drank too much, that's eat
Samantha: much too much
Robyn: yes, I lost control a bit
Robyn: I am sorry for that
Samantha: important that you feel better today

Robyn drank too much beer last night and lost control. She is feeling better today.


Peyton: I have been asking you to bring that video game for me
Cameron: Honey, I am not having enough time to come home
Peyton: When would you come home?
Cameron: I will have to stay out of town for another week i guess
Peyton: Cant you just deliver that game through the courier? :P
Cameron: Dont be mean :/
Peyton: Get the job done and come to home then. ASAP :P

Peyton is expecting Cameron to bring the video game. Cameron will probably be out for another week.


Gene: Did you get the package I sent you
Jack: No, when did you send it?
Gene: on Friday
Jack: shit I should have gotten it by now
Jack: send me the tracking umber I;ll check whats up
Gene: 12345678900

Jack has not received yet the package Gene had sent him on Friday. She sent him the tracking number, so he could check the status of the shipment.


Russ: Hi Gurdun, are you feeling better?
Gurdun: Hi Ross, a little bit better, yes, thank you.
Russ: Do you think you'll be back at the uni this week?
Gurdun: Not tomorrow for sure, but I hope Wednesday maybe.
Russ: Oh, that's fine.
Gurdun: Yeah. I'm feeling a bit weak still
Russ: I guess that's pretty normal at this stage.
Gurdun: I believe so. And the muscle pain.
Russ: I know what you mean. I had flu like that three months ago.
Gurdun: Luckily the headache's gone. It was absolutely awful.
Russ: True enough. I had the same.
Gurdun: Anyway, how are things with you?
Russ: Same old, same old, you know. Not much happening in the middle of term.
Gurdun: And the team?
Russ: Oh yeah, we won the last match. It was a good game.
Gurdun: Great. Listen Russ, I need to run. Catch up later.
Russ: OK, talk to you later.

Gurdun has the flu. He is feeling better. Gurdun hopes he'll be back at the uni on Wednesday. Russ and his team won the last match.


Susan: Will you be home tomorrow?
Rob: Why do you ask?
Susan: We could have dinner together if you have time after work
Rob: Sure! Sounds great
Susan: Fantastic! I will cook something special then!

Rob will be home tomorrow. Rob and Susan will have dinner together after Rob's work. Susan will cook something special.


Evan: Hi Dennis, I need to cancel my lesson tomorrow, sorry.
Dennis: Fine, what's up?
Evan: Oh, got the flu, been off college since Tuesday. Should be ok for next Thurs.
Dennis: OK, just tell me in good time if you can't make it. Hope you feel better soon.
Evan: Bye, Den! Thanks.

Evan can't come to his lesson tomorrow, because he's got the flu.


Lorena: Hi, can you help me with something?
Martin: Well, I can try.
Martin: Depends what it is?
Lorena: I got a new desk and it comes with assembly instructions but I give up, I just can't do it.
Lorena: I'm illiterate when it comes to instructions, haha.
Lorena: So I could really use some help...
Martin: Hmm, I can't today, but how about tomorrow? Shouldn't take long anyway.
Lorena: Yeah, it's fine! I just need it for the weekend, but tomorrow's great! Sorry for troubling you again.
Martin: Don't mention it, we've known each other for so long it's almost like we're siblings, haha.
Lorena: Thanks a lot! I should be home by 6, so let me know when you can.

Martin is going to help Lorena assemble a new desk. He is coming to her house tomorrow.


Joanna: They are sending emails about Lewandowska.
Merve: What happened?
Joanna: <file_photo>
Merve: Wooow!
Joanna: She is hospitalized because she has measles.
Merve: She had what?
Joanna: Anyone who had contact with her within the last couple of days must get vaccinated.
Merve: Luckily I didn't see her since the last semester...
Joanna: I did, she is my thesis mentor :(
Merve: What will you do?
Joanna: They are organizing vaccinations in the main building from 17th until 19th.
Merve: You have to go!
Joanna: I know... And I just started working so I really don't have a lot of time.
Merve: Come on, this is really important.
Joanna: I will try to do it before work on 18th, hopefully I won't lose the entire day...

Lewandowska has measles. There are vaccinations in the main building from 17th until 19th for everyone who had contact with her.


... an otherwise intense , twist-and-turn thriller that certainly should n't hurt talented young gaghan 's resume .


the film 's welcome breeziness and some unbelievably hilarious moments -- most portraying the idiocy of the film industry -- make it mostly worth the trip .


is the time really ripe for a warmed-over james bond adventure , with a village idiot as the 007 clone ?


so much facile technique , such cute ideas , so little movie .


a broad , melodramatic estrogen opera that 's pretty toxic in its own right .


the longer the movie goes , the worse it gets , but it 's actually pretty good in the first few minutes .


that is a compliment to kuras and miller .


but it 's too long and too convoluted and it ends in a muddle .


at times , the suspense is palpable , but by the end there 's a sense that the crux of the mystery hinges on a technicality that strains credulity and leaves the viewer haunted by the waste of potential .


due to some script weaknesses and the casting of the director 's brother , the film trails off into inconsequentiality .


How do you propose the school will fund your program? Condoms cost money and checking an "opt out" list before handing them out takes time away from staff members whenever they could be doing their actual jobs. Your "opt out" option is only be a token to parental authority and would be easily subverted. If everyone in school except a handful of students had access to free condoms, do you not think those students would simply ask their friends to provide them with condoms?

Contraceptive Forms for High School Students


Schools have no compelling interest in providing contraceptives to students. The purpose of schools is not to provide healthcare nor to provide any other service except insofar as it relates to the furtherance of education [1,2,3], though I do not contest that individual districts ought to have this option if they so choose. As an educator, I do feel that adequate sexual education is a necessary. [1] tinyurl.com/z786mww[2] tinyurl.com/jafrt2n[3] tinyurl.com/zbkwkz6


As a senior at my school. My group and I are focusing on teenage pregnancy; we are determined to have high school districts provide contraceptive forms to students to be safe about sex. This focus isn't for us to encourage to have sex, but if teenagers decide to have sex to please be safe about it. In addition. if parents do not agree to this we want to have an opt out form where their child/children will not be receiving this form.


The resolution used by Pro *assumes* that Australia isn't already a 'significant' country - however, in actual reality, it is. Firstly we should clarify what significance means: 1.a the state or quality of being significant1.b of consequence or importance==================================To respond directly to Pros argument first:he/she asserts that Australia invented 'amazing things' like 'WiFI, Google Maps, Polymer bank notes, Ultrasound scanners, stainless steel braces and many more things'. Now, if these inventions did come from Australia, then, it can be considered a 'significant country' - as a country which is the home of some of the most universally-used inventions in the 21st century. It would seem that Pro was/is trying to argue that Australia simply deserves more recognition, in which case he/she should proposed that instead of stating that it should be more 'significant' - because the examples that Pro themselves has listed, fully go against that. Instead of affirming the resolution, Pro has negated it. After all, insignificant countries do not invent things such as WiFi or Google Maps. One invention listed by Pro that I will take issue with though is the ultrasound, as this was not invented in Australia. Its first use is thought to have been in Austria, which is a country in Europe. Its technology developed from there. 'The use of ultrasound in medicine began during and shortly after the 2nd World War in various centres around the world. The work of Dr. Karl Theodore Dussik in Austria in 1942 on transmission ultrasound investigation of the brain provides the first published work on medical ultrasonics.' 'Although other workers in the USA, Japan and Europe have also been cited as pioneers, the work of Professor Ian Donald and his colleagues in Glasgow, in the mid 1950s, did much to facilitate the development of practical technology and applications.'[1.] https://www.bmus.org...

Australia should be a more significant country


First of all we invented amazing things like WiFi, Google Maps, Polymer bank notes (if you are American and do not know what they are, they are plastic WATERPROOF bills), Ultrasound scanners, stainless steel braces and many more things. Why put us into the shadow if we have made such amazing things the whole world uses nowadays! I bet you have used at the very least ONE thing I put up there unless you are on a Ethernet cable still using those old paper maps wherever you go! There is no point in putting such smart people who made these things in the shadow! Aussie! Aussie! Aussie! Oi! Oi! Oi!


Australia has always been put into the shadow behind countries like America, Canada and even England sometimes, I feel Australia should not be put in the darkness and in this debate I will tell you why.


The closet dementia of the superior ego god complex, The bible and why you should not believe in god


Why is it that so-called christians, Because there is no such a thing as a christian, Have serious trouble as READING and COMPREHENDING? Its not that difficult, Nor is it that hard. It was stated unto you a very simple "* "You are asking why God would forgive the murderer. " OK we"re done. You paid absolutely no attention whatsoever to the verses presented and instead went off into your own la la land. " But nah, All you did was babble on and on and on. So in this sense, It was YOU that forfeited. Sheesh! Bye.


<< Epidermal growth factor receptor >> inhibitors currently under investigation include the small molecules [[ gefitinib ]] (Iressa, ZD1839) and erlotinib (Tarceva, OSI-774), as well as monoclonal antibodies such as cetuximab (IMC-225, Erbitux).


<< Epidermal growth factor receptor >> inhibitors currently under investigation include the small molecules gefitinib ([[ Iressa ]], ZD1839) and erlotinib (Tarceva, OSI-774), as well as monoclonal antibodies such as cetuximab (IMC-225, Erbitux).


<< Epidermal growth factor receptor >> inhibitors currently under investigation include the small molecules gefitinib (Iressa, [[ ZD1839 ]]) and erlotinib (Tarceva, OSI-774), as well as monoclonal antibodies such as cetuximab (IMC-225, Erbitux).


<< Epidermal growth factor receptor >> inhibitors currently under investigation include the small molecules gefitinib (Iressa, ZD1839) and [[ erlotinib ]] (Tarceva, OSI-774), as well as monoclonal antibodies such as cetuximab (IMC-225, Erbitux).


<< Epidermal growth factor receptor >> inhibitors currently under investigation include the small molecules gefitinib (Iressa, ZD1839) and erlotinib ([[ Tarceva ]], OSI-774), as well as monoclonal antibodies such as cetuximab (IMC-225, Erbitux).


<< Epidermal growth factor receptor >> inhibitors currently under investigation include the small molecules gefitinib (Iressa, ZD1839) and erlotinib (Tarceva, [[ OSI-774 ]]), as well as monoclonal antibodies such as cetuximab (IMC-225, Erbitux).


<< Epidermal growth factor receptor >> inhibitors currently under investigation include the small molecules gefitinib (Iressa, ZD1839) and erlotinib (Tarceva, OSI-774), as well as monoclonal antibodies such as [[ cetuximab ]] (IMC-225, Erbitux).


<< Epidermal growth factor receptor >> inhibitors currently under investigation include the small molecules gefitinib (Iressa, ZD1839) and erlotinib (Tarceva, OSI-774), as well as monoclonal antibodies such as cetuximab ([[ IMC-225 ]], Erbitux).


<< Epidermal growth factor receptor >> inhibitors currently under investigation include the small molecules gefitinib (Iressa, ZD1839) and erlotinib (Tarceva, OSI-774), as well as monoclonal antibodies such as cetuximab (IMC-225, [[ Erbitux ]]).


Agents that have only begun to undergo clinical evaluation include << CI-1033 >>, an irreversible pan-[[ erbB ]] tyrosine kinase inhibitor, and PKI166 and GW572016, both examples of dual kinase inhibitors (inhibiting epidermal growth factor receptor and Her2).


Topiary
Middle Eastern Studies
afternoon

Element collecting
African Studies
afternoon

Rock climbing
Computer Programming
evening

Vehicle restoration
Metallurgical Engineering
afternoon

Rock climbing
Metallurgical Engineering
afternoon

Aqua-lung
Metallurgical Engineering
afternoon

Freestyle football
Metallurgical Engineering
afternoon

Vehicle restoration
Metallurgical Engineering
evening

message message message message message message select select

I have 1 friend that maj in Agri sciences

I also have a friend that majored in agri science


University of Wisconsin-Madison
Samuel
evening

Miami Christian University
Matthew
morning

Miami Christian University
Nathan
morning

Miami Christian University
Mildred
morning

West Texas A&M University
Nathan
morning

Murray State University
Elizabeth
evening

West Texas A&M University
Matthew
evening

Most of my friends prefer the morning
evening, sorry


University of Maryland at Baltimore
Denbury Resources
indoor

St. Louis College of Pharmacy
Eastman Kodak
indoor

University of Maryland at Baltimore
Xerox
indoor

St. Louis College of Pharmacy
Seneca Foods Corporation
indoor

Virginia Commonwealth University
New York Life Insurance Company
indoor

St. Louis College of Pharmacy
Hewlett-Packard
indoor

Marshall University
Dr Pepper Snapple Group
outdoor

Virginia Commonwealth University
Hewlett-Packard
indoor

Middle Georgia College
Palm, Inc.
outdoor

St. John's College-Annapolis
Eastman Kodak
outdoor

Virginia Commonwealth University
Morningstar
indoor

Marshall University
Dr Pepper Snapple Group
indoor

University of Pittsburgh at Johnstown
Ampex Corporation
indoor

St. Louis College of Pharmacy
Nike, Inc.
indoor

University of Pittsburgh at Johnstown
Nike, Inc.
indoor

Edinboro University of Pennsylvania
Exelon
indoor

University of Pittsburgh at Johnstown
Palm, Inc.
indoor

St. Louis College of Pharmacy
Morningstar
indoor

Edinboro University of Pennsylvania
Meijer
indoor

University of Pittsburgh at Johnstown
Seneca Foods Corporation
indoor

Virginia Commonwealth University
Nike, Inc.
indoor

message message message message message message message message message message select message message message message message select message select message message message select select

hello
Hi
my friends like to do stuff outdoors
alot went to virginia commonwealth
Not a single one of mine do!
ok so lets go with the indoor friends
university of maryland?
None there, sorry. ;(
VCU?
Yes, one
no nike
new york life
hewlett packard
I have a HP
St oouis college
Sigh, no. ;(
or st louis HP?
No to that, as well
I have st louis HP


School
Major
Time Preference
Location Preference

Major
Time Preference
School
Location Preference

English Literature
morning
Purdue University
outdoor

Information Mgmt. Sciences & Systems
morning
Hendrix College
outdoor

Italian
evening
Augustana College
indoor

Louisiana State University at Alexandria

Marine Engineering
evening
Purdue University
indoor

Italian
afternoon
Purdue University
indoor

Computer Mathematics
morning
Purdue University
outdoor

Mormon Affiliation
afternoon
Purdue University
outdoor

Italian
morning
Governors State University
indoor

Italian
morning
Augustana College
outdoor

Mormon Affiliation
afternoon
Virginia Commonwealth University
indoor


Racquetball
Frostburg State University
Engineering Mechanics
indoor

Texas Tech University-Health Sciences Center

Scuba diving
Wright State University
Aviation & Air Transportation
outdoor

Laser tag
Hastings College
Applied Mathematics
indoor

Auto racing
Wright State University
Engineering Mechanics
indoor

Scuba diving
Frostburg State University
Engineering Mechanics
outdoor

Air sports
City University
Engineering Mechanics
outdoor

Ultimate Disc
Brandeis University
Astronomy
outdoor

Fencing
New Mexico State University
Electrical Engineering
indoor

Air sports
Troy State University
Electrical Engineering
indoor


Antiquities
Catholic Affiliation
Arbusto Energy
morning

Ice hockey
Physical Education
Beam Inc.
morning

Kite flying
Astrophysics
Kaiser Permanente
morning

Exhibition drill
Bible Studies
Au Bon Pain
morning

Exhibition drill
Russian
Beam Inc.
evening

Antiquities
Crafts & Artisanry
Vulcan Corporation
morning

Martial arts
Philosophy
CDW Corporation
afternoon

Sketching
Russian
Southwest Airlines
evening

Exhibition drill
Russian
Franklin Templeton
afternoon

Exhibition drill
Physical Education
Kaiser Permanente
afternoon

Kite flying
Physical Education
Au Bon Pain
evening

Martial arts
Chemistry
Arbusto Energy
afternoon

Billiards
Crafts & Artisanry
DeVry, Inc.
morning

Exhibition drill
Physical Education
CDW Corporation
morning

Martial arts
Catholic Affiliation
DeVry, Inc.
morning

message message message message message select message message message message message message select message message select select

I have a friend at CDW who majored in Philosipjhy


University of New Hampshire, Durham
Charter Arms
Scandinavian

Rutgers University, Camden
Verizon Communications
History

University of New Hampshire, Durham
Verizon Communications
Actuarial Sciences

Rutgers University, Camden
Tiffany & Company
History

Villanova University
Verizon Communications
Electromechanical Technology

Villanova University
Charter Arms
Computer & Information Sciences

Villanova University
Ideal Industries
History

Villanova University
Charter Arms
Adult & Continuing Education

Villanova University
Watsco, Inc.
Earth & Space Sciences

message message message message select select message select message message message message select select message select

What company you work for?
one friend from rutgers
4 from villanova
one of my friends is from villanonva
nope
one of my friends is from rutgers
how about you
history major?
yes
nope


Chinese
New York Life Insurance Company
Arthur

Foreign Languages
Berkshire Hathaway
Virginia

Computer & Information Sciences
New York Life Insurance Company
Arthur

Japanese
New York Life Insurance Company
Arthur

Computer Science Education
Amazon.com
Virginia

Religious Education
New York Life Insurance Company
Arthur

Computer & Information Sciences
Argonide
Jesse

Physical Education
UnitedHealth Group
Heather

Japanese
Discovery Communications
Rebecca

Liberal Arts Education
Discovery Communications
Karen

Philosophy & Religion
United Rentals
Frank

message message message message message message message message message message message message message select message select

I have a good number of friends from Argonide


Major
Company
Time Preference
Location Preference

Major
Time Preference
Company
Location Preference

Zoology
afternoon
Alliant Energy
outdoor

Science Technologies
evening
Sun Capital Partners
outdoor

Creative Writing
evening
Allstate
indoor

Creative Writing
evening
Raymond James Financial
indoor

English Language
evening
Sun Capital Partners
indoor

Music
evening
Sun Capital Partners
indoor

Materials Engineering
morning
Sun Capital Partners
indoor

Visual & Performing Arts
evening
Hollister Clothing
outdoor

Systems Analysis & Engineering
evening
Raymond James Financial
indoor

Mathematics
evening
Sun Capital Partners
indoor

Catholic Affiliation
morning
Bealls
outdoor

Business Education
afternoon
KFC
outdoor

Business Education
morning
Allstate
indoor

Fashion Design
afternoon
Alliance Rubber Company
outdoor

Agricultural Engineering
afternoon
Alliance Rubber Company
outdoor

Agricultural Engineering
afternoon
Emcor
indoor

Visual & Performing Arts
morning
Allstate
outdoor

Business Education
afternoon
Alliant Energy
outdoor

Creative Writing
morning
Alliance Rubber Company
outdoor


Baton twirling
Friedman Fleischer & Lowe
outdoor

Brazilian jiu-jitsu
Friedman Fleischer & Lowe
indoor

Brazilian jiu-jitsu
Miro Technologies
outdoor

message message message message message message message message message message message message message select message select message select

Hi
hi
two of my friends work at dippin dots
3 of mine work at hot topic
nope none of mine
lucas oil?
nope
kerr mcgee?
nabisco?
no
nope
friedman?
yes
nope not the same one
anyone work at miro


Premise: Woman in white in foreground and a man slightly behind walking with a sign for John's Pizza and Gyro in the background.

Hypothesis: The man with the sign is caucasian.

(exists s1 x1 x2.(with(x1,x2) & n_sign(x2) & n_man(x1) & a_caucasian(s1) & Theme(s1,x1)) & exists e1 s1 s2 x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12.(with(e1,x7) & v_walk(e1) & subset_of(x4,s1) & subset_of(x11,x8) & subset_of(x10,x8) & subset_of(x1,s1) & slightly(x4,x5) & of(x10,x9) & ne_org_pizza(x10) & ne_org_john(x9) & ne_org_gyro(x11) & ne_nam_woman(x1) & n_thing(x6) & n_thing(x5) & n_sign(x7) & n_man(x4) & n_foreground(x3) & n_background(x12) & in(x1,x3) & in(e1,x12) & for(x7,x8) & behind(x5,e1) & a_white(s2) & a_topic(s1) & Theme(s2,x2) & Actor(e1,x6) & (x1 = x2)))


Hypothesis: The man with the sign is caucasian.

(exists s1 x1 x2.(with(x1,x2) & n_sign(x2) & a_caucasian(s1) & Theme(s1,x1)) & exists e1 s1 s2 x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12.(with(e1,x7) & v_walk(e1) & subset_of(x4,s1) & subset_of(x11,x8) & subset_of(x10,x8) & subset_of(x1,s1) & slightly(x4,x5) & of(x10,x9) & ne_org_pizza(x10) & ne_org_john(x9) & ne_org_gyro(x11) & ne_nam_woman(x1) & n_thing(x6) & n_thing(x5) & n_sign(x7) & n_man(x4) & n_foreground(x3) & n_background(x12) & in(x1,x3) & in(e1,x12) & for(x7,x8) & behind(x5,e1) & a_white(s2) & a_topic(s1) & Theme(s2,x2) & Actor(e1,x6) & (x1 = x2)))


Hypothesis: The man with the sign is caucasian.

(exists s1 x1 x2.(with(x1,x2) & n_man(x1) & a_caucasian(s1) & Theme(s1,x1)) & exists e1 s1 s2 x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12.(with(e1,x7) & v_walk(e1) & subset_of(x4,s1) & subset_of(x11,x8) & subset_of(x10,x8) & subset_of(x1,s1) & slightly(x4,x5) & of(x10,x9) & ne_org_pizza(x10) & ne_org_john(x9) & ne_org_gyro(x11) & ne_nam_woman(x1) & n_thing(x6) & n_thing(x5) & n_sign(x7) & n_man(x4) & n_foreground(x3) & n_background(x12) & in(x1,x3) & in(e1,x12) & for(x7,x8) & behind(x5,e1) & a_white(s2) & a_topic(s1) & Theme(s2,x2) & Actor(e1,x6) & (x1 = x2)))


Hypothesis: The man with the sign is caucasian.

(exists s1 x1 x2.(with(x1,x2) & n_sign(x2) & n_man(x1) & a_caucasian(s1)) & exists e1 s1 s2 x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12.(with(e1,x7) & v_walk(e1) & subset_of(x4,s1) & subset_of(x11,x8) & subset_of(x10,x8) & subset_of(x1,s1) & slightly(x4,x5) & of(x10,x9) & ne_org_pizza(x10) & ne_org_john(x9) & ne_org_gyro(x11) & ne_nam_woman(x1) & n_thing(x6) & n_thing(x5) & n_sign(x7) & n_man(x4) & n_foreground(x3) & n_background(x12) & in(x1,x3) & in(e1,x12) & for(x7,x8) & behind(x5,e1) & a_white(s2) & a_topic(s1) & Theme(s2,x2) & Actor(e1,x6) & (x1 = x2)))


Hypothesis: The man with the sign is caucasian.

(exists s1 x1 x2.(with(x1,x2) & n_sign(x2) & a_caucasian(s1)) & exists e1 s1 s2 x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12.(with(e1,x7) & v_walk(e1) & subset_of(x4,s1) & subset_of(x11,x8) & subset_of(x10,x8) & subset_of(x1,s1) & slightly(x4,x5) & of(x10,x9) & ne_org_pizza(x10) & ne_org_john(x9) & ne_org_gyro(x11) & ne_nam_woman(x1) & n_thing(x6) & n_thing(x5) & n_sign(x7) & n_man(x4) & n_foreground(x3) & n_background(x12) & in(x1,x3) & in(e1,x12) & for(x7,x8) & behind(x5,e1) & a_white(s2) & a_topic(s1) & Theme(s2,x2) & Actor(e1,x6) & (x1 = x2)))


Hypothesis: The man with the sign is caucasian.

(exists s1 x1 x2.(with(x1,x2) & n_man(x1) & a_caucasian(s1)) & exists e1 s1 s2 x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12.(with(e1,x7) & v_walk(e1) & subset_of(x4,s1) & subset_of(x11,x8) & subset_of(x10,x8) & subset_of(x1,s1) & slightly(x4,x5) & of(x10,x9) & ne_org_pizza(x10) & ne_org_john(x9) & ne_org_gyro(x11) & ne_nam_woman(x1) & n_thing(x6) & n_thing(x5) & n_sign(x7) & n_man(x4) & n_foreground(x3) & n_background(x12) & in(x1,x3) & in(e1,x12) & for(x7,x8) & behind(x5,e1) & a_white(s2) & a_topic(s1) & Theme(s2,x2) & Actor(e1,x6) & (x1 = x2)))


Hypothesis: The man with the sign is caucasian.

(exists s1 x1 x2.(with(x1,x2) & a_caucasian(s1) & Theme(s1,x1)) & exists e1 s1 s2 x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12.(with(e1,x7) & v_walk(e1) & subset_of(x4,s1) & subset_of(x11,x8) & subset_of(x10,x8) & subset_of(x1,s1) & slightly(x4,x5) & of(x10,x9) & ne_org_pizza(x10) & ne_org_john(x9) & ne_org_gyro(x11) & ne_nam_woman(x1) & n_thing(x6) & n_thing(x5) & n_sign(x7) & n_man(x4) & n_foreground(x3) & n_background(x12) & in(x1,x3) & in(e1,x12) & for(x7,x8) & behind(x5,e1) & a_white(s2) & a_topic(s1) & Theme(s2,x2) & Actor(e1,x6) & (x1 = x2)))


Hypothesis: The man with the sign is caucasian.

(exists s1 x1 x2.(with(x1,x2) & n_sign(x2) & n_man(x1) & Theme(s1,x1)) & exists e1 s1 s2 x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12.(with(e1,x7) & v_walk(e1) & subset_of(x4,s1) & subset_of(x11,x8) & subset_of(x10,x8) & subset_of(x1,s1) & slightly(x4,x5) & of(x10,x9) & ne_org_pizza(x10) & ne_org_john(x9) & ne_org_gyro(x11) & ne_nam_woman(x1) & n_thing(x6) & n_thing(x5) & n_sign(x7) & n_man(x4) & n_foreground(x3) & n_background(x12) & in(x1,x3) & in(e1,x12) & for(x7,x8) & behind(x5,e1) & a_white(s2) & a_topic(s1) & Theme(s2,x2) & Actor(e1,x6) & (x1 = x2)))


Hypothesis: The man with the sign is caucasian.

(exists s1 x1 x2.(with(x1,x2) & n_sign(x2) & Theme(s1,x1)) & exists e1 s1 s2 x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12.(with(e1,x7) & v_walk(e1) & subset_of(x4,s1) & subset_of(x11,x8) & subset_of(x10,x8) & subset_of(x1,s1) & slightly(x4,x5) & of(x10,x9) & ne_org_pizza(x10) & ne_org_john(x9) & ne_org_gyro(x11) & ne_nam_woman(x1) & n_thing(x6) & n_thing(x5) & n_sign(x7) & n_man(x4) & n_foreground(x3) & n_background(x12) & in(x1,x3) & in(e1,x12) & for(x7,x8) & behind(x5,e1) & a_white(s2) & a_topic(s1) & Theme(s2,x2) & Actor(e1,x6) & (x1 = x2)))


Hypothesis: The man with the sign is caucasian.

(exists s1 x1 x2.(with(x1,x2) & n_man(x1) & Theme(s1,x1)) & exists e1 s1 s2 x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12.(with(e1,x7) & v_walk(e1) & subset_of(x4,s1) & subset_of(x11,x8) & subset_of(x10,x8) & subset_of(x1,s1) & slightly(x4,x5) & of(x10,x9) & ne_org_pizza(x10) & ne_org_john(x9) & ne_org_gyro(x11) & ne_nam_woman(x1) & n_thing(x6) & n_thing(x5) & n_sign(x7) & n_man(x4) & n_foreground(x3) & n_background(x12) & in(x1,x3) & in(e1,x12) & for(x7,x8) & behind(x5,e1) & a_white(s2) & a_topic(s1) & Theme(s2,x2) & Actor(e1,x6) & (x1 = x2)))


[Intro: Method Man w/ sample] + (Sunny valentine). We got butter (8X). (The gun'll go the gun'll go.... The gun'll go...). [Raekwon]. Aiyo one thing for sure keep you of all. Keep a nice crib fly away keep to the point. Keep niggaz outta ya face who snakes. Keep bitches in they place keep the mac in a special place. Keep moving for papes keep cool keep doing what you doing. Keep it fly keep me in the crates. Cuz I will erase shit on the real note you'se a waste. It's right here for you I will lace you. Rip you and brace you put a nice W up on ya face. Word to mother you could get chased. It's nothing to taste blood on a thug if he gotta go. All I know is we be giving grace. This is a place from where we make tapes. We make 'em everywhere still in all we be making base. Y'all be making paste these little niggaz they be making shapes. Our shit is art yours is traced. [Chorus: Sunny Valentine]. This is the way that we rolling in the streets. You know when we roll we be packing that heat. The gun'll go the gun'll go the gun'll go the gun'll go. The gun'll go the gun'll go the gun'll go the gun'll go. The gun'll go the gun'll go.... [Method Man]. This is Poverty Island man these animals don't run. Slums where the ambulance don't come. Who got the best base? Fiends waiting to smoke some. Approach something ask him where he getting that coke from. My dudes hug blocks like samurai shogun. Cuz no V and no ones equalling no fun. Who want a treat they know huh? Body to go numb. My woman need funds plus her hair and her toes done. It is what it is though you fuck with the kid flow. That make it hard to get dough the harder to get gold. Harder the piff blow harder when it snow. The pinky and the wrist glow this here what we live for. Get gwop then get low but first thought. We gotta get the work off the gift and the curse boss. Yeah see I'm the shit yo the dirt in the fit no. Hustling from the get-go the motto is get more. [Chorus]. [Masta Killa]. We was quiet flashy brothers strapped all along. With the dirty .38 long twelve hour shift gate. Took case state to state you think he won't hold his weight?. Put ya money on the plate and watch it get scrapped. We get ape up in that club off that juice and Henn. And it's a no win situation fucking with them. You mean like Ewing at the front at the rim finger roll a Dutch. Million dollar stages touched techs gauges bust. Trust no one the lone shogun rugged Timb boot stomper. Damaging lyrical mass destruction launcher. Nothing can calm the quakeage when I break kid. Peace to my brothers up north doing state bids. [Chorus]. [Chorus 2: Sunny Valentine]. Whoa... this is the way we be rolling in the club. You know when we roll we be packing .32 snubs. The gun'll go the gun'll go the gun'll go the gun'll go. The gun'll go the gun'll go the gun'll go the gun'll go. The gun'll go the gun'll go the gun'll go the gun'll go. [Outro: sample to fade]. We got butter...


[Sean Paul:]. Aye. It's Sean Paul 'long side. The mandem called Jay Sean. Fi di gal dem. Tellin' 'em again what we tell 'em. [Jay Sean:]. Pass me a drink to the left yeah. Said her name was Delilah. And I'm like "you should come my way". I already surrender. Damn girl that body's fire. You gon' remember my name. (She should give it up definite). You need it. I need it. We can jump in the deep end. I wanna get lost in your love. I just wanna be close to you. (Just wanna I just wanna). And do all the things you want me to. I just wanna be close to you. (I just wanna I just wanna). And show you the way I feel. You make my love go. You make my love go. You make my love go. In the morning we gon' do it again wake up. I'mma do it like we just broke up and made up. Get up on top of me and work up a sweat work up a sweat. See we can do it any type of way that you want. I'm thinking maybe you're the right kind of wrong. I'm saying baby you won't ever forget my love. You need it. I need it. We can jump in the deep end. I wanna get lost in your love. I just wanna be close to you. (Just wanna I just wanna). And do all the things you want me to. I just wanna be close to you. (I just wanna I just wanna). And show you the way I feel. You make my love go. You make my love go. You make my love go. [Sean Paul:]. Girl mi wan' figure hundred hundred and fifty. Love how you move you know that I'm with it. Perfect size I know that you fit it. Just let me hit it you know mi not quit it. Pon di Dl like Cassie and Diddy. Mi na wound a mi watch we like Sin City. Full time mi run da ting mi tall legend. If you don't come gimme dat would I be offended my girl. Come here down wan' see something me want in life and then waste time. A you a mi pree every day baby full time when ya de pon on mi mind. So mi wine if you give it to me baby girl so we can play. Stick to the ting now I am your king my girl this is what we say. [Jay Sean:]. I just wanna be close to you. (Just wanna I just wanna). And do all the things you want me to. I just wanna be close to you. (I just wanna I just wanna). And show you the way I feel. You make my love go. You make my love go. You make my love go


Beauty finds refuge in herself. Lovers wrapped inside each others lies. Beauty is such a terrible thing. She is suffering yet more than death. She is suffering. She sucks you deeper in. She is suffering. You exist within her shadow. Beauty she is scarred into man's soul. A flower attracting lust vice and sin. A vine that can strangle life from a tree. Carrion surrounding picking on leaves. She is suffering. She sucks you deeper in. She is suffering. You exist within her shadow. Beauty she poisons unfaithful all. Stifled her touch is leprous and pale. The less she gives the more you need her. No thoughts to forget when we were children. She is suffering. She sucks you deeper in. She is suffering. Nature's lukewarm pleasure.


You've changed your tune. many times since we've met. But I'll always recognize you. You are part of me. I feel you whether in Jamaica or in the Angel city. You're such a gift to me. Uuuuh. Chorus. Hey you. I gotta tell you my long time friend. I think of all those years you saw me through tears. and the good times that we spend. Hey you. You're my constant companion. You always let me explain just what I'm saying. and we've just begun. Ooooh. I've got one wish for this music to be an uplift. And I need an uplift to deal. There are few songs all the people can sing along one song. Come along and sing it for real. Uuuuh. Chorus. Hey you. I gotta tell you my long time friend. I think of all those years you saw me through tears. And the good times that we spend. Hey you. You're my constant companion. You always let me explain just what I'm saying. and we've just begun]. ooooh. Bridge. As I wander around town to town. Lost and found. When so many others come and go. (come and go). The sweetness (sweetness). Pulls me through. Chorus. Hey you. I gotta tell you my long time friend. I think of all those years you saw me through tears. and the good times that we spend. Hey you. You're my constant companion. You always let me explain just what I'm saying. and we've just begun. Hey you. You never turned your back on me. (ooooh). when I gave up on myself. Hey you. You never turned your back on me. (ooooh). when I gave upon myself. Hey you. What would the world be like. without you around. Music. You're my constant companion. oooooh


I got all these J's rolled up. And got all these drinks poured up. I bought all these bottles up in this club. Came here with all of my dogs. But I'm tryna leave with you and your friends. You and your friends. Yeah tryna leave with you and your friends. You and your friends ball. I got all these J's rolled up. And got all these drinks poured up. I bought all these bottles up in this club. Came here with all of my dogs. But I'm tryna leave with you and your friends. You and your friends. Yeah tryna leave with you and your friends. You and your friends ball. [Wiz Khalifa]. You and your friends up in my car. Rolling up J's flying with the stars. Don't gotta ask the price 'cause you already know. Watch full of ice no light it still glow. With you and your friends me and my dogs. Left your nigga to be with my squad. He talk a lot of shit but he ain't really on. He hit it too soft but me I go hard like. Oohy baby when you with me you go crazy. I want you to be my lady. Girl you could bring your girlfriends I ain't hating. I got all these J's rolled up. And got all these drinks poured up. I bought all these bottles up in this club. Came here with all of my dogs. But I'm tryna leave with you and your friends. You and your friends. Yeah tryna leave with you and your friends. You and your friends ball. [Snoop Dogg]. I'm parked outside in a Range Rover. Coke white so tight game over. Coupe to the block bop to the bang. Car full of girls in a blue Mustang. Four y'all more y'all caravan. V. I. P model in hand bottle in hand. Wobble again. I make it crack in the back a Benz while cracking the frame. On the highway just me and Wiz. With a dirt dozen about to handle this. Was they cousins or was they twins?. I love it when they both go all in. Like that this one and that one. See he wants the cute one I want the fat one. Bet she pay what she weigh. I'm looking for a Jennifer Holiday in my way. I got all these J's rolled up. And got all these drinks poured up. I bought all these bottles up in this club. Came here with all of my dogs. But I'm tryna leave with you and your friends. You and your friends. Yeah tryna leave with you and your friends. You and your friends ball. I got all these J's rolled up. And got all these drinks poured up. I bought all these bottles up in this club. Came here with all of my dogs. But I'm tryna leave with you and your friends. You and your friends. Yeah tryna leave with you and your friends. You and your friends ball