text:
## Terms
* Semantic Segmentation: Traditional segmentation divides the image in visually similar patches. Semantic segmentation on the other hand divides the image in semantically meaningful patches. This usually means to classify each pixel (e.g.: This pixel belongs to a cat, that pixel belongs to a dog, the other pixel is background).
## Main ideas
* Complete neural networks which were trained for image classification can be used as a convolution. Those networks can be trained on Image Net (e.g. VGG, AlexNet, GoogLeNet)
* Use upsampling to (1) reduce training and prediction time (2) improve consistency of output. (See [What are deconvolutional layers?](http://datascience.stackexchange.com/a/12110/8820) for an explanation.)
## How FCNs work
1. Train a neural network for image classification which is trained on input images of a fixed size ($d \times w \times h$)
2. Interpret the network as a single convolutional filter for each output neuron (so $k$ output neurons means you have $k$ filters) over the complete image area on which the original network was trained.
3. Run the network as a CNN over an image of any size (but at least $d \times w \times h$) with a stride $s \in \mathbb{N}_{\geq 1}$
4. If $s > 1$, then you need an upsampling layer (deconvolutional layer) to convert the coarse output into a dense output.
## Nice properties
* FCNs take images of arbitrary size and produce an image of the same output size.
* Computationally efficient
## See also:
> They allow you to treat the convolutional neural network as one giant filter. You can then spatially apply the neural net as a convolution to images larger than the original training image size, getting a spatially dense output.
> Let's say you train a neural net (with some loss function) with a convolutional layer (3 x 3, stride of 2), pooling layer (3 x 3, stride of 2), and a fully connected layer with 10 units, using 25 x 25 images. Note that the receptive field size of each max pooling unit is 7 x 7, so the pooling output is 5 x 5. You can convert the fully connected layer to to a set of 10 5 x 5 convolutional filters (unit strides). If you do that, the entire net can be treated as a filter with receptive field size 35 x 35 and stride of 4. You can then take that net and apply it to a 50 x 50 image, and you'd get a 3 x 3 x 10 spatially dense output.
So the hypervector is just a big vector created from a network:
`"We concatenate features from some or all of the feature
maps in the network into one long vector for every location
which we call the hypercolumn at that location. As an
example, using pool2 (256 channels), conv4 (384 channels)
and fc7 (4096 channels) from the architecture of [28] would
lead to a 4736 dimensional vector."`
So how exactly do we construct the vector?
Each activation map results in a single element of the resulting hypervector. The corresponding pixel location in each activation map is used as if the activation maps were all scaled to the size of the original image.
The paper shows the below formula for the calculation. Here $\mathbf{f}_i$ is the value of the pixel in the scaled space and each $\mathbf{F}_{k}$ are points in the activation map. $\alpha_{ik}$ scales the known values to produce the midway points.
$$\mathbf{f}_i = \sum_k \alpha_{ik} \mathbf{F}_{k}$$
Then the fully connected layers are simply appended to complete the vector.
So this gives us a representation for each pixel but is it a good one? The later layers will have the input pixel in their receptive field. After the first few layers it is expected that the spatial constraint is not strong.
## Introduction
* [Link to Paper](http://arxiv.org/pdf/1412.6071v4.pdf)
* Spatial pooling layers are building blocks for Convolutional Neural Networks (CNNs).
* Input to pooling operation is a $N_{in}$ x $N_{in}$ matrix and output is a smaller matrix $N_{out}$ x $N_{out}$.
* Pooling operation divides $N_{in}$ x $N_{in}$ square into $N^2_{out}$ pooling regions $P_{i, j}$.
* $P_{i, j}$ ⊂ $\{1, 2, . . . , N_{in}\}$ $\forall$ $(i, j) \in \{1, . . . , N_{out} \}^2$
* Refers to 2x2 max-pooling layer.
* Popular choice for max-pooling operation.
### Advantages of MP2
* Fast.
* Quickly reduces the size of the hidden layer.
* Encodes a degree of invariance with respect to translations and elastic distortions.
### Issues with MP2
* Disjoint nature of pooling regions.
* Since size decreases rapidly, stacks of back-to-back CNNs are needed to build deep networks.
* Reduces the spatial size of the image by a factor of *α*, where *α ∈ (1, 2)*.
* Introduces randomness in terms of choice of pooling region.
* Pooling regions can be chosen in a *random* or *pseudorandom* manner.
* Pooling regions can be *disjoint* or *overlapping*.
## Generating Pooling Regions
* Let $a_i$ and $b_i$ be 2 increasing sequences of integers, starting at 1 and ending at $N_{in}$.
* Increments are either 1 or 2.
* For *disjoint regions, $P = [a_{i−1}, a_{i − 1}] × [b_{j−1}, b_{j − 1}]$
* For *overlapping regions, $P = [a_{i−1}, a_i] × [b_{j−1}, b_j 1]$
* Pooling regions can be generated *randomly* by choosing the increment randomly at each step.
* To generate pooling regions in a *peusdorandom* manner, choose $a_i$ = ceil($\alpha | (i+u))$, where $\alpha \in (1, 2)$ with some $u \in (0, 1)$.
* Each FMP layer uses a different pair of sequence.
* An FMP network can be thought of as an ensemble of similar networks, with each different pooling-region configuration defining a different member of the ensemble.
## Observations
* *Random* FMP is good on its own but may underfit when combined with dropout or training data augmentation.
* *Pseudorandom* approach generates more stable pooling regions.
* *Overlapping* FMP performs better than *disjoint* FMP.
## Weakness
* No justification is provided for the observations mentioned above.
* It needs to be seen how performance is affected if the pooling layer in architectures like GoogLeNet.
#### Introduction
* The paper demonstrates how simple CNNs, built on top of word embeddings, can be used for sentence classification tasks.
* [Link to the paper](https://arxiv.org/abs/1408.5882)
* [Implementation](https://github.com/shagunsodhani/CNN-Sentence-Classifier)
#### Architecture
* Pad input sentences so that they are of the same length.
* Map words in the padded sentence using word embeddings (which may be either initialized as zero vectors or initialized as word2vec embeddings) to obtain a matrix corresponding to the sentence.
* Apply convolution layer with multiple filter widths and feature maps.
* Apply max-over-time pooling operation over the feature map.
* Concatenate the pooling results from different layers and feed to a fully-connected layer with softmax activation.
* Softmax outputs probabilistic distribution over the labels.
* Use dropout for regularisation.
#### Hyperparameters
* RELU activation for convolution layers
* Filter window of 3, 4, 5 with 100 feature maps each.
* Dropout - 0.5
* Gradient clipping at 3
* Batch size - 50
* Adadelta update rule.
#### Variants
* CNN-rand
* Randomly initialized word vectors.
* CNN-static
* Uses pre-trained vectors from word2vec and does not update the word vectors.
* CNN-non-static
* Same as CNN-static but updates word vectors during training.
* CNN-multichannel
* Uses two set of word vectors (channels).
* One set is updated and other is not updated.
#### Datasets
* Sentiment analysis datasets for Movie Reviews, Customer Reviews etc.
* Classification data for questions.
* Maximum number of classes for any dataset - 6
#### Strengths
* Good results on benchmarks despite being a simple architecture.
* Word vectors obtained by non-static channel have more meaningful representation.
#### Weakness
* Small data with few labels.
* Results are not very detailed or exhaustive.
#### Introduction
* Open-domain Question Answering (Open QA) - efficiently querying large-scale knowledge base(KB) using natural language.
* Two main approaches:
* Information Retrieval
* Transform question (in natural language) into a valid query(in terms of KB) to get a broad set of candidate answers.
* Perform fine-grained detection on candidate answers.
* Semantic Parsing
* Interpret the correct meaning of the question and convert it into an exact query.
* Limitations:
* Human intervention to create lexicon, grammar, and schema.
* This work builds upon the previous work where an embedding model learns low dimensional vector representation of words and symbols.
* [Link](https://arxiv.org/abs/1406.3676) to the paper.
#### Task Definition
* Input - Training set of questions (paired with answers).
* KB providing a structure among the answers.
* Answers are entities in KB and questions are strings with one identified KB entity.
* The paper has used FREEBASE as the KB.
* Datasets
* WebQuestions - Built using FREEBASE, Google Suggest API, and Mechanical Turk.
* FREEBASE triplets transformed into questions.
* Clue Web Extractions dataset with entities linked with FREEBASE triplets.
* Dataset of paraphrased questions using WIKIANSWERS.
#### Embedding Questions and Answers
* Model learns low-dimensional vector embeddings of words in question entities and relation types of FREEBASE such that questions and their answers are represented close to each other in the joint embedding space.
* Scoring function $S(q, a)$, where $q$ is a question and $a$ is an answer, generates high score if $a$ answers $q$.
* $S(q, a) = f(q)^{T} . g(a)$
* $f(q)$ maps question to embedding space.
* $f(q) = W \phi (q)$
* $W$ is a matrix of dimension $K * N$
* $K$ - dimension of embedding space (hyper parameter).
* $N$ - total number of words/entities/relation types.
* $\psi(q)$ - Sparse Vector encoding the number of times a word appears in $q$.
* Similarly, $g(a) = W \psi (a)$ maps answer to embedding space.
* $\psi(a)$ gives answer representation, as discussed below.
#### Possible Representations of Candidate Answers
* Answer represented as a **single entity** from FREEBASE and TBD is a one-of-N encoded vector.
* Answer represented as a **path** from question to answer. The paper considers only one or two hop paths resulting in 3-of-N or 4-of-N encoded vectors(middle entities are not recorded).
* Encode the above two representations using **subgraph representation** which represents both the path and the entire subgraph of entities connected to answer entity as a subgraph. Two embedding representations are used to differentiate between entities in path and entities in the subgraph.
* SubGraph approach is based on the hypothesis that including more information about the answers would improve results.
#### Training and Loss Function
* Minimize margin based ranking loss to learn matrix $W$.
* Stochastic Gradient Descent, multi-threaded with Hogwild.
#### Multitask Training of Embeddings
* To account for a large number of synthetically generated questions, the paper also multi-tasks the training of model with paraphrased prediction.
* Scoring function $S_{prp} (q1, q2) = f(q1)^{T} f(q2)$, where $f$ uses the same weight matrix $W$ as before.
* High score is assigned if $q1$ and $q2$ belong to same paraphrase cluster.
* Additionally, the model multitasks the task of mapping embeddings of FREEBASE entities (mids) to actual words.
#### Inference
* For each question, a candidate set is generated.
* The answer (from candidate set) with the highest set is reported as the correct answer.
* Candidate set generation strategy
* $C_1$ - All KB triplets containing the KB entity from the question forms a candidate set. Answers would be limited to 1-hop paths.
* $C_2$ - Rank all relation types and keep top 10 types and add only those 2-hop candidates where the selected relations appear in the path.
#### Results
* $C_2$ strategy outperforms $C_1$ approach supporting the hypothesis that a richer representation for answers can store more information.
* Proposed approach outperforms the baseline methods but is outperformed by an ensemble of proposed approach with semantic parsing via paraphrasing model.
Spatial Pyramid Pooling (SPP) is a technique which allows Convolutional Neural Networks (CNNs) to use input images of any size, not only $224\text{px} \times 224\text{px}$ as most architectures do. (However, there is a lower bound for the size of the input image).
## Idea
* Convolutional layers operate on any size, but fully connected layers need fixed-size inputs
* Solution:
* Add a new SPP layer on top of the last convolutional layer, before the fully connected layer
* Use an approach similar to bag of words (BoW), but maintain the spatial information. The BoW approach is used for text classification, where the order of the words is discarded and only the number of occurences is kept.
* The SPP layer operates on each feature map independently.
* The output of the SPP layer is of dimension $k \cdot M$, where $k$ is the number of feature maps the SPP layer got as input and $M$ is the number of bins.
Example: We could use spatial pyramid pooling with 21 bins:
* 1 bin which is the max of the complete feature map
* 4 bins which divide the image into 4 regions of equal size (depending on the input size) and rectangular shape. Each bin gets the max of its region.
* 16 bins which divide the image into 4 regions of equal size (depending on the input size) and rectangular shape. Each bin gets the max of its region.
## Evaluation
* Pascal VOC 2007, Caltech101: state-of-the-art, without finetuning
* ImageNet 2012: Boosts accuracy for various CNN architectures
* ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014: Rank #2
## Code
The paper claims that the code is [here](http://research.microsoft.com/en-us/um/people/kahe/), but this seems not to be the case any more.
People have tried to implement it with Tensorflow ([1](http://stackoverflow.com/q/40913794/562769), [2](https://github.com/fchollet/keras/issues/2080), [3](https://github.com/tensorflow/tensorflow/issues/6011)), but by now no public working implementation is available.
## Related papers
* [Atrous Convolution](https://arxiv.org/abs/1606.00915)
This paper introduces a neural network architecture
that is deeper and wider, yet optimizing for computational
efficiency by approximating the expected sparse structure
(following from Arora et al's work) using readily available
dense blocks. An ensemble of 7 models (all with the same
architecture but different image sampling) achieved top spot
in the classification task at ILSVRC2014.
"Their main result states that if the probability distribution of the data-set is representable by a large, very sparse deep neural network, then the optimal network topology can be constructed layer by layer by analyzing the correlation statistics of the activations of the last layer and clustering neurons with highly correlated outputs."
Main contributions:
- A more generalized exploration of the NIN architecture,
called the Inception module.
- 1x1 convolutions to capture dense information clusters
- 3x3 and 5x5 to capture more spatially spread out
- Ratio of 3x3 and 5x5 to 1x1 convolutions increases as we go deeper
as features of higher abstraction are less spatially
- To avoid the blow-up of output channels cause by merging outputs
of convolutional layers and pooling layer, they use 1x1 convolutions
for dimensionality reduction. This has the added benefit of another
layer of non-linearity (and thus increasing discriminative capability).
- Multiple intermediate layers are tied to the objective function. Since
features produced by intermediate layers of a deep network are
supposed to be very discriminative, and to strengthen the gradient signal
passing through them during back-propagation, they attach auxiliary classifiers
to intermediate layers.
- During training, they do a weighted sum of this loss with the total loss
of the network.
- At test time, these auxiliary networks are discarded.
- Architecture: average pooling, 1x1 convolution (for dimensionality reduction),
dropout, linear layer with softmax.
## Strengths
- Excellent results on ILSVRC2014.
## Weaknesses / Notes
- Even though the authors try to explain some of the intuition, most of
the design decisions seem arbitrary.
Adam is like RMSProp with momentum. The (simplified) update [[Stanford CS231n]](https://cs231n.github.io/neural-networks-3/#ada) looks as follows:
m = beta1*m + (1-beta1)*dx
v = beta2*v + (1-beta2)*(dx**2)
x += - learning_rate * m / (np.sqrt(v) + eps)
TLDR; The authors show that applying dropout to only the **non-recurrent** connections (between layers of the same timestep) in an LSTM works well, improving the scores on various sequence tasks.
#### Data Sets and model performance
- PTB Language Modeling Perplexity: 78.4
- Google Icelandic Speech Dataset WER Accuracy: 70.5
- WMT'14 English to French Machine Translation BLEU: 29.03
- MS COCO Image Caption Generation BLEU: 24.3
TLDR; The authors empirically evaluate seq2seq Neural Machine Translation systems. They find that performance degrades significantly as sentences get longer, and as the number of unknown words in the source sentence increases. Thus, they propose that more investigation into how to deal with large vocabularies and long-range dependencies is needed. The authors also present a new gated recursive convolutional network (grConv) architecture, which consists of a binary tree using GRU units. While this network architecture does not perform as well as the RNN encoder, it seems to be learning grammatical properties represented in the gate activations in an unsupervised fashion.
#### Key Points
- GrConv: Neuron computed as combination between left and right neuron in previous layer, gated with the activations of those neurons. 3 gates: Left, right, reset.
- In experiments, encoder varies between RNN and grConv. Decoder is always RNN.
- Model size is only 500MB. 30k vocabulary. Only trained on sentences <= 30 tokens. Networks not trained to convergence.
- Beam search with scores normalized by sequence length to choose translations.
- Hypothesis is that fixed vector representation is a bottleneck, or that decoder is not powerful enough.
#### Notes/Questions
- THe network is only trained on sequences <= 30 tokens. Can we really expect it to perform well on long sequences? Long sequences may inherently have grammatical structures that cannot be observed in short sequences.
- There's a mistake in the new activation formula, wrong time superscript, should be (t-1).
TLDR; The authors propose a novel "attention" mechanism that they evaluate on a Machine Translation task, achieving new state of the art (and large improvements in dealing with long sentences). Standard seq2seq models typically try to encode the input sequence into a fixed length vector (the last hidden state) based on which the decoder generates the output sequence. However, it is unreasonable to assume the all necessary information can be encoded in this one vector. Thus, the authors let the decoder depend on a attention vector, which based on the weighted sum (expectation) of the input hidden states. The attention weights are learned jointly, as part of the network architecture.
#### Data Sets and model performance
Bidirectional GRU, 1000 hidden units. Multilayer maxout to compute output probabilities in decoder.
#### Key Takeaways
- Attention mechanism is a weighted sum of the hidden states computed by the encoder. The weights come from a softmax-normalized attention function (a perceptron in this paper), which are learned during training.
- Attention can be expensive, because it must be evaluated for each encoder-decoder output pair, resulting in a len(x) * len(y) matrix.
- The attention mechanism improves performance across the board, but has a particularly large affect on long sentences, confirming the hyptohesis that the fixed vector encoding is a bottleneck.
- The authors use a bidirectional-GRU, concatenating both hidden states into a final state at each time step.
- It is easy to visualize the attention matrix (for a single input-ouput sequence pair). The authors show that in the case of English to French translations the matrix has large values on the diagonal, showing the these two languages are well aligned in terms of word order.
#### Question/Notes
- The attention mechanism seems limited in that it computes a simple weighted average. What about more complex attention functions that allow input states to interact?
TLDR; The authors propose Neural Turing Machines (NTMs). A NTM consists of a memory bank and a controller network. The controller network (LSTM or MLP in this paper) controls read/write heads by focusing their attention softly, using a distribution over all memory addresses. It can learn the parameters for two addressing mechanisms: Content-based addressing ("find similar items") and location-based addressing. NTMs can be trained end-to-end using gradient descent. The authors evaluate NTMs on program generations tasks and compare their performance against that of LSTMs. Tasks include copying, recall, prediction, and sorting binary vectors. While both LSTMs and NTMs seems to perform well on training data, only NTMs are able to generalize to longer sequences.
#### Key Observations
- Controller network tried with LSTM or MLP. Which one works better is task-dependent, but LSTM "cache" can be a bottleneck.
- Controller size, number of read/write heads, and memory size are hyperparameters.
- Monitoring the memory addressing shows that the NTM actually learns meaningful programs.
- Number LSTM parameters grow quadratically with hidden unit size due to recurrent connection, not so for NTMs, leading to models with fewer parameters.
- Example problems are very small, typically using sequences 8 bit vectors.
#### Notes/Questions
- At what length to NTMs stop to work? Would've liked to see where results get significantly worse.
- Can we automatically transform fuzzy NTM programs into deterministic ones?
TLDR; The authors show that seq2seq LSTM networks (2 layers, 400-dims) can learn to evaluate short Python programs (loops, conditionals, addition, subtraction, multiplication). The program code is fed one character at a time, and the LSTM is tasked with generating an output number (12 character vocab). The authors also present a new curriculum learning strategy, where the network is fed with a sensible mixture of easy and increasingly difficult examples, allowing it to gradually build up the concepts required to evaluate these programs.
#### Key Points
- LSTM unrolled for 50 steps, 2 layer, 400 cells per layer, ~2.5M parameters. Gradient norm constrained to 5.
- 3 Curriculum Learning strategies: 1. Naive (increase example difficulty) 2. Mixed: Randomly sample easy and hard problems, 3. Combined: Sample from Naive and Mixed strategy. Mixed or Combined almost always performs better.
- Output Vocabulary: 10 digits, minus, dot
- For evaluation teacher forcing is used: Feed correct output when generating target sequence
- Evaluation Tasks: Program Evaluation, Addition, Memorization
- Tricks: Reverse Input sequence, Double input sequence. Seem to make big difference.
- Nesting loops makes the tasks difficult since LSTMs can't deal with compositionality.
- Feeding easy examples and before hard examples may require the LSTM to restructure its memory.
#### Notes / Questions
- I wonder if there's a relation between regularization/dropout and curriculum learning. The authors propose that mixing example difficulty forces a more general representation. Shouldn't dropout be doing a similar thing?
TLDR; Authors apply 3-layer seq2seq LSTM with 256 units and attention mechanism to consituency parsing task and achieve new state of the art. Attention made a huge difference for a small dataset (40k examples), but less so for a noisy large dataset (~11M examples).
#### Data Sets and model performance
- WSJ (40k examples): 90.5
- Large distantly supervised corpus (90k gold examples, 11M noisy examples): 92.8
#### Key Takeaways
- The authors use existing parsers to label a large dataset to be used for training. The trained model then outperforms the "teacher" parsers. A possible explanation is that errors of supervising parsers look like noise to the more powerful LSTM model. These results are extremely valuable, as data is typically the limiting factor, but existing models almost always exist.
- Attention mechanism can lead to huge improvements on small data sets.
- All of the learned LSTM models were able to deal with long (~70 tokens) sentences without a significant impact of performance.
- Reversing the input in seq2seq tasks is common. However, reversing resulted in only a 0.2 point bump in accuracy.
- Pre-trained word vectors bumped scores by 0.4 (92.9 -> 94.3) only.
#### Notes/Questions
- How much does the ouput data representation matter? The authors linearized the parse tree using depth-first traversal and parentheses. Are there more efficient representations that may lead to better results?
- How much does the noise in the auto-labeled training data matter when compared to the data size? Are there systematic errors in the auto-labeled data that put a ceiling on model performance?
- Bidirectional LSTM?
This technique is a combination of two powerful machine learning algorithms:
- convolutional neural networks are excellent at image classification, i.e., finding out what is seen on an input image,
- recurrent neural networks that are capable of processing a sequence of inputs and outputs, therefore it can create sentences of what is seen on the image.
Combining these two techniques makes it possible for a computer to describe in a sentence what is seen on an input image.
A paper in the intersection for Computer Vision and Machine Learning. They simplify networks by replacing max-pooling by convolutions with higher stride.
* introduce a new variant of the "deconvolution approach" for visualizing features learned by CNNs, which can be applied to a broader range of network structures than existing approaches
## Datasets
competitive or state of the art performance on several object recognition datasets (CIFAR-10, CIFAR-100, ImageNet)
This paper is about the analysis of CNNs. It seems to be extremely similar to what Zeiler & Fergus did. I can't see the contribution. Only cited 7 times, although it is from December 2014 -> I suggest to read the Zeiler & Fergus paper instead.
## Related
* 2013, Zeiler & Fergus: [Visualizing and Understanding Convolutional Networks ](http://www.shortscience.org/paper?bibtexKey=journals/corr/ZeilerF13#martinthoma)
## Errors
* " Section ?? provides"
# Conditional Generative Adversarial Nets
## Introduction
* Conditional version of [Generative Adversarial Nets (GAN)](https://gist.github.com/shagunsodhani/1f9dc0444142be8bd8a7404a226880eb) where both generator and discriminator are conditioned on some data **y** (class label or data from some other modality).
* [Link to the paper](https://arxiv.org/abs/1411.1784)
## Architecture
* Feed **y** into both the generator and discriminator as additional input layers such that **y** and input are combined in a joint hidden representation.
## Experiment
### Unimodal Setting
* Conditioning MNIST images on class labels.
* *z* (random noise) and **y** mapped to hidden layers with ReLu with layer sizes of 200 and 1000 respectively and are combined to obtain ReLu layer of dimensionality 1200.
* Discriminator maps *x* (input) and **y** to maxout layers and the joint maxout layer is fed to sigmoid layer.
* Results do not outperform the state-of-the-art results but do provide a proof-of-the-concept.
### Multimodal Setting
* Map images (from Flickr) to labels (or user tags) to obtain the one-to-many mapping.
* Extract image and text features using convolutional and language model.
* Generative Model
* Map noise and convolutional features to a single 200 dimensional representation.
* Discriminator Model
* Combine the representation of word vectors (corresponding to tags) and images.
## Future Work
* While the results are not so good, they do show the potential of Conditional GANs, especially in the multimodal setting.
The goal of this method is to create a feature representation $f$ of an input $x$ that is domain invariant over some domain $d$. The feature vector $f$ is obtained from $x$ using an encoder network (e.g. $f = G_f(x)$).
The reason this is an issue is that the input $x$ is correlated with $d$ and this can confuse the model to extract features that capture differences in domains instead of differences in classes. Here I will recast the problem differently from in the paper:
**Problem:** Given a conditional probability $p(x|d=0)$ that may be different from $p(x|d=1)$:
$$p(x|d=0) \stackrel{?}{\ne} p(x|d=1)$$
we would like it to be the case that these distributions are equal.
$$p(G_f(x) |d=0) = p(G_f(x)|d=1)$$
$$p(f|d=0) = p(f|d=1)$$
Of course this is an issue if some class label $y$ is correlated with $d$ meaning that we may hurt the performance of a classifier that now may not be able to predict $y$ as well as before.
The paper proposes adding a domain classifier network to the feature vector using a reverse gradient layer. This layer simply flips the sign on the gradient. Here is an example in [Theano](https://github.com/Theano/Theano):
class ReverseGradient(theano.gof.Op):
def grad(self, input, output_gradients):
return [-output_gradients[0]]
You then train this domain network as if you want it to correctly predict the domain (appending it's error to your loss function). As the domain network learns new ways to correctly predict an output these gradients will be flipped and the information in feature vector $f$ will be removed.
There are two major hyper parameters of the method. The number of dimensions at the bottleneck is one but it is linked to your network. The second is a scalar on the gradient so you can increase or decrease the effect of the gradient on the embedding.
#### Problem addressed:
A fast way of finding adversarial examples, and a hypothesis for the adversarial examples
#### Summary:
This paper tries to explain why adversarial examples exists, the adversarial example is defined in another paper \cite{arxiv.org/abs/1312.6199}. The adversarial example is kind of counter intuitive because they normally are visually indistinguishable from the original example, but leads to very different predictions for the classifier. For example, let sample $x$ be associated with the true class $t$. A classifier (in particular a well trained dnn) can correctly predict $x$ with high confidence, but with a small perturbation $r$, the same network will predict $x+r$ to a different incorrect class also with high confidence.
This paper explains that the exsistence of such adversarial examples is more because of low model capacity in high dimensional spaces rather than overfitting, and got some empirical support on that. It also shows a new method that can reliably generate adversarial examples really fast using `fast sign' method. Basically, one can generate an adversarial example by taking a small step toward the sign direction of the objective. They also showed that training along with adversarial examples helps the classifier to generalize.
#### Novelty:
A fast method to generate adversarial examples reliably, and a linear hypothesis for those examples.
#### Datasets:
#### Resources:
Talk of the paper https://www.youtube.com/watch?v=Pq4A2mPCB0Y
#### Presenter:
Yingbo Zhou
This week I read upon [GraphX](https://amplab.cs.berkeley.edu/wp-content/uploads/2014/02/graphx.pdf), a distributed graph computation framework that unifies graph-parallel and data-parallel computation. Graph-parallel systems efficiently express iterative algorithms (by exploiting the static graph structure) but do not perform well on operations that require a more general view of the graph like operations that move data out of the graph. Data-parallel systems perform well on such tasks but directly implementing graph algorithms on data-parallel systems is inefficient due to complex joins and excessive data movement. This is the gap that GraphX fills in by allowing the same data to be viewed and operated upon both as a graph and as a table.
### Preliminaries
Let $G = (V, E)$ be a graph where $V = \{1, ..., n\}$ is the set of vertices and $E$ is the set of $m$ directed edges. Each directed edge is a tuple of the form $(i, j) \in E$ where $i \in V$ is the source vertex and $j \in V$ is the target vertex. The vertex properties are represented as $P_V(i)$ where $i \in V$ and edge properties as $P_E (i, j)$ for edge $(i, j) \in E$. The collection of all the properties is $P = (P_V, P_E)$. The combination of graph structure and properties defines a property graph $G(P) = (V, E, P)$.
Graph-Parallel Systems consist of a property graph $G = (V, E, P)$ and a vertex-program $Q$ that is instantiated simultaneously on all the vertices. The execution on vertex $v$, called $Q(v)$, interacts with execution on the adjacent vertices by message passing or shared state and can read/modify properties on the vertex, edges and adjacent vertices. $Q$ can run in two different modes:
* **bulk-synchronous mode** - all vertex programs run concurrently in a sequence of super-steps.
* **asynchronous mode** - vertex programs run as and when resources are available and impose constraints on whether neighbouring vertex-programs can run concurrently.
**Gather-Apply-Scatter (GAS)** decomposition model breaks down a vertex-program into purely edge-parallel and vertex-parallel stages. The associative *gather* function collects the inbound messages on the vertices, the *apply* function operates only on the vertices and updates its value and the *scatter* function computes the message to be sent along each edge and can be safely executed in parallel.
GrapX uses bulk-synchronous model and adopts the GAS decomposition model.
### GraphX Data Model
The GraphX Data Model consists of immutable collections and property graphs. Collections consist of unordered tuples (key-value pairs) and are used to represent unstructured data. The property graph combines the structural information (in the form of collections of vertices and edges) with properties describing this structure. Properties are just collections of form $(i, P_V (i))$ and $((i, j), P_E (i, j))$. The collection of vertices and edges are represented using RDDs (Resilient Distributed Datasets). Edges can be partitioned as per a user defined function. Within a partition, edges are clustered by source vertex id and there is an unclustered index on target vertex id. The vertices are hash partitioned by id and stored in a hash index within a partition. Each vertex partition contains a bitmask which allows for set intersection and filtering. It also contains a routing table that logically maps a vertex id to set of edge partitions containing the adjacent edges. This table is used when constructing triplets and is stored as a compressed bitmap.
### Operators
Other than standard data-parallel operators like `filter`, `map`, `leftJoin`, and `reduceByKey`, GraphX supports following graph-parallel operators:
* `graph` - constructs property graph given a collection of edges and vertices.
* `vertices`, `edges` - decompose the graph into a collection of vertices or edges by extracting vertex or edge RDDs.
* `mapV`, `mapE` - transform the vertex or edge collection.
* `triplets` -returns collection of form $((i, j), (P_V (i), P_E (i, j), P_V (j)))$. The operator essentially requires a multiway join between vertex and edge RDD. This operation is optimized by shifting the site of joins to edges, using the routing table, so that only vertex data needs to be shuffled.
* `leftJoin` - given a collection of vertices and a graph, returns a new graph which incorporates the property of matching vertices from the given collection into the given graph without changing the underlying graph structure.
* `subgraph` - returns a subgraph of the original graph by applying predicates on edges and vertices
* `mrTriplets` (MapReduce triplet) - logical composition of triplets followed by map and reduceByKey. It is the building block of graph-parallel algorithms.
All these operators can be expressed in terms on relational operators and can be composed together to express different graph-parallel abstractions. The paper shows how these operators can be used to construct a enhanced version of Pregel based on GAS. It also shows how to express connected components algorithm and `coarsen` operator.
### Structural Index Reuse
Collections and graphs, being immutable, share the structural indexes associated within each vertex and edge partition to both reduce memory overhead and accelerate local graph operations. Most of the operators preserve the structural indexes to reuse them. For operators like subgraph which restrict the graph, the bitmask is used to construct the restricted view.
### Distributed Join Optimization
##### Incremental View Maintenance
The number of vertices that change between different steps of iterative graph algorithms decreases as the computation converges. After each operation, GraphX tracks which vertices have been changed by maintaining a bit mask. When materializing a vertex view, it uses values from the previous view for vertices which have not changed and ships only those vertices which are changed. This also allows for another optimization when using the `mrTriplets` operation: `mrTriplets` support an optional argument called *skipStale*. when this option is enabled, the `mrTriplets` function does not apply on edges origination from vertices that have not changed since its last iteration. This optimization uses the same bitmask that incremental views were using.
##### Automatic Join elimination
GraphX has implemented a JVM bytecode analyzer that determines whether source/target vertex attributes are referenced in a mrTriplet UDF (for map) or not. Since edges already contain the vertex ids, a 3-way join can be brought down to 2-way join if only source/target vertex attributes are needed (as in PageRank algorithm) or the join can be completely eliminated if none of the vertex attributes are referenced.
### Sequential Scan vs Index Scan
Using structural indices, while reduces computation cost in iterative algorithms, prevents physical data from shrinking. To counter this issue, GraphX switches from sequential scan to bitmap index scan when the fraction of active vertices drops below 0.8. Since edges are clustered by source vertex id, bitmap index scan can efficiently join edges and vertexes together.
### Other Optimizations
* Though GraphX uses Spark's shuffle mechanism, it materializes shuffled data in memory itself, unlike Spark which materializes shuffle data in disk and relies on OS buffer cache to cache the data. The rationale behind this modification is that graph algorithms tend to be communication intensive and inability to control when buffers are flushed can lead to additional overhead.
* When implementing join step, vertices routed to the same target are batched, converted from row-orientation to column-orientation and compressed by LZF algorithm and then sent to their destination.
* During shuffling, integers are encoded using a variable encoding scheme where for each byte, the first 7 bits encode the value, and the highest order bit indicates if another byte is needed for encoding the value. So smaller integers can be encoded with fewer bytes and since, in most cases, vertex ids are smaller than 64 bits, the technique helps to reduce an amount of data to be moved.
### System Evaluation
GraphX was evaluated against graph algorithms implemented over Spark 0.8.1, Giraph 1.0 and GraphLab 2.2 for both graph-parallel computation tasks and end-to-end graph analytic pipelines. Key observations:
* GraphLab benefits from its native runtime and performs best among all the implementations for both PageRank and Connected Components algorithm.
* For connected components algorithm, Giraph benefits from using edge cuts but suffers from Hadoop overhead.
* GraphX outperforms idiomatic implementation of PageRank on Spark, benefitting from various optimizations discussed earlier.
* As more machines are added, GraphX does not scale linearly but it still outperforms the speedup achieved by GraphLab (for PageRank).
* GraphX outperforms Giraph and GraphLab for a multi-step, end-to-end graph analytics pipeline that parses Wikipedia articles to make a link graph, runs PageRank on the link graph and joins top 20 articles with their text.
GraphX provides a small set of core graph-processing operators, implemented on top of relational operators, by efficiently encoding graphs as a collection of edges and vertices with two indexing data structures. While it does lag behind specialised systems like Giraph and GraphLab in terms of graph-parallel computation tasks, GraphX does not aim at speeding up such tasks. It instead aims to provide an efficient workflow in end-to-end graph analytics system by combining data-parallel and graph-parallel computations in the same framework. Given that it does outperform all the specialised systems in terms of end-to-end runtime for graph pipelines and makes the development process easier by eliminating the need to learn and maintain multiple systems, it does seem to be a promising candidate for the use case it is attempting to solve.
This paper hypothesizes that a CNN trained for scene classification automatically
discovers meaningful object detectors, representative of the scene categories,
without any explicit object-level supervision. This claim is backed by well-designed
experiments which are a natural extension of the primary insight that since scenes
are composed of objects (a typical bedroom would have a bed, lamp; art gallery would
have paintings, etc), a CNN that performs reasonable well on scene recognition
must be localizing objects in intermediate layers.
## Strengths
- Demonstrates the difference in learned representations in Places-CNN and ImageNet-CNN.
- The top 100 images that have the largest average activation per layer are picked and it's shown that earlier layers such as pool1 prefer similar images for both networks while deeper layers tend to be more specialized to the specific task of scene or object categorization i.e. ~75% of the top 100 images that show high activations for fc7 belong to ImageNet for ImageNet-CNN and Places for Places-CNN.
- Simplifies input images to identify salient regions for classification.
- The input image is simplified by iteratively removing segments that cause the least decrease in classification score until the image is incorrectly classified. This leads them to the minimal image representation (sufficient and necessary) that is needed by the network to correctly recognize scenes, and many of these contain objects that provide discriminative information for scene classification.
- Visualizes the 'empirical receptive fields' of units.
- The top K images with highest activations for a given unit are identified. To identify which regions of the image lead to high unit activations, the image is replicated with occluders at different regions. The occluded images are passed through the network and large changes in activation indicate important regions. This leads them to generate feature maps and finally to empirical receptive fields after appropriate centre-calibration, which are more localized and smaller than the theoretical size.
- Studies the visual concepts / semantics captured by units.
- AMT workers are surveyed on the segments that maximally activate units. They're asked to tag the visual concept, mark negative samples and provide the level of abstraction (from simple elements and colors to objects and scenes). Plot of distribution of semantic categories at each layer shows that deeper layers do capture higher levels of abstraction and Places-CNN units indeed discover more objects than ImageNet-CNN units.
## Weaknesses / Notes
- Unclear as to how they obtain soft, grayed out images from the iterative segmentation methodology in the first approach where they generate minimal image representations needed for accurate classification. I would assume these regions to be segmentations with black backgrounds and hard boundaries. Perez et al. (2013) might have details regarding this.


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Sharon Farmer
dates de séjour
pays d'origine
lien internet
projet de recherche
Medieval Paris and the Mediterranean: The Origins of the Paris Silk Industry in the Thirteenth Century
During the 2013-2014 academic year I intend to complete a monograph that will expand our understanding of Mediterranean immigration into medieval Northern France by examining the origins of the Parisian silk industry during the thirteenth century. The first scholarly examination of the origins of the silk cloth industry in late thirteenth-century Paris, this monograph will also provide the first book-length case study of the role that skilled Mediterranean artisans played in transforming the material culture of Northern France during the age of the crusades. Its discussion of women's role in the silk industry will shed important light on gendered differences between the Northern French silk textile industry, on the one hand, and the linen and wool industries on the other. Its discussion of the role of Jews and Jewish converts in the Parisian silk industry will transform our understanding of Jewish economic activity in Northwest Europe at this time.
I will participate in the weekly seminar of IEA, and am prepared to give a presentation to that semimar whenever asked to do so. In March, I will give a presentation in Didier Lett's seminar on "Famille, parenté et genre au Moyen Âge (XIIe-XVe siècles)," at the Université de Paris 7, on the role of women in the three Parisian textile industries – silk, wool and linen.
Ce résident fait partie du programme de mobilité européenne EURIAS


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clip from the final episode of season 4. This one features Al Capone who happens to be one of the best characters on television and is played to perfection by British actor Stephen Graham. I'd love to see Capone get his own spinoff series after Boardwalk Empire wraps up next year.
This is the 4th post from this series and the 2nd appearance of this vintage Jameson bottle. Click here to watch the pervious scenes. Very sad to hear that season 5 will be the last. Hopefully the series will go out with a bang.
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Sunday, 11 May 2014
Another Vintage Jameson Sighting in Boardwalk Empire
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Friday, 2 May 2014
Scotch Cinema Will Return After These Messages
It has been quite a while since the last scotch sighting but the hiatus is almost over. Plenty of new clips coming up. The site is currently going through some renovations so in the meantime, here are some interesting whisky commercials to hold everyone over. Haven't done any of these commercial breaks in ages. The ones below are rather quirky to say the least. One is fake (I'll let you figure out which one), the Duran Duran advert is just completely bizarre, and the McEwan's Scotch Beer clip might be the worst commercial I've ever seen. Even though the god awful song has been stuck in my head all day.
Click here for some of the whisky commercials we've featured in previous posts.
Click here for some of the whisky commercials we've featured in previous posts.
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Waitrose demands a 5% cut from suppliers
As Tesco and Sainsbury's traded insults this week over whether there is a supermarket price war or not, The Grocer has learnt of suppliers already coming under pressure from two other rivals looking to shore up margins and boost value…
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Brad Womack, The Bachelor's clean-cut Texan, had some troubles growing up – and he's not ashamed to admit it.
With tabloid reports this week highlighting a few alleged brushes with the law he had as a teenager, including carrying a forged driver's license, public intoxication and passing a bad check, Womack explains to PEOPLE how he learned from his mistakes.
"I'm sure I wouldn't be the first to admit that I had a couple of missteps when I was a teenager – some 20 odd years ago. Some of the claims have been blown out of proportion but I took responsibility for my actions and took immediate measures to remedy the situation," Womack, now 38, says.
"It was just part of my growing pains back when I was still young and na ve. You learn from your experiences good and bad – and you move on. It ultimately builds character."


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Heated Seats, Turbo Charged, Aluminum Wheels, Trailer Hitch, iPod/MP3 Input, Satellite Radio, Back-Up Camera, DRIVER CONFIDENCE PACKAGE. EPA 29 MPG Hwy/22 MPG City! LT trim, NIGHTFALL GRAY METALLIC exterior and JET BLACK interior SEE MORE!KEY FEATURES INCLUDEBack-Up Camera, Satellite Radio, iPod/MP3 Input. MP3 Player, Onboard Communications System, Aluminum Wheels, Privacy Glass, Keyless Entry, Child Safety Locks, Steering Wheel Controls.OPTION PACKAGESCONFIDENCE & CONVENIENCE PACKAGE includes (B26) Driver Confidence Package and (ZQ2) Driver Convenience Package content, LICENSE PLATE FRONT MOUNTING PACKAGE, TRANSMISSION, 9-SPEED AUTOMATIC, ELECTRONICALLY-CONTROLLED WITH OVERDRIVE includes Driver Shift Control (STD), DRIVER CONVENIENCE PACKAGE includes (BTV) Remote Start, (CJ2) dual-zone automatic climate control, (KA1) driver and front passenger heated seats, (N34) leather-wrapped steering wheel, (UG1) Universal Home Remote and (TB5) power liftgate, UNIVERSAL HOME REMOTE includes garage door opener, programmable, ENGINE, 2.0L TURBO, 4-CYLINDER, SIDI, VVT (252 hp [188.0 kW] @ 5500 rpm, 260 lb-ft of torque [353.0 N-m] @ 2500 - 4500 rpm) (STD), DRIVER CONFIDENCE PACKAGE includes (UD7) Rear Park Assist, (UFG) Rear Cross-Traffic Alert, (UKC) Side Blind Zone Alert with Lane Change Alert and (DWK) outside heated power-adjustable manual-folding body-color mirrors with turn signal indicators, LT PREFERRED EQUIPMENT GROUP includes Standard Equipment, MIRRORS, OUTSIDE HEATED POWER-ADJUSTABLE, MANUAL-FOLDING, BODY-COLOR with turn signal indicators, SEATS, HEATED DRIVER AND FRONT PASSENGERMORE ABOUT USFormerly Ivan Leonard Chevrolet serving Birmingham metro area for over 40 years. Now a Hendrick team dealership with access to over 6,000 used cars.Prices are before state and local taxes, licensing fees and dealership closing charge. Errors in equipment and pricing may exist, please contact the dealership to confirm details. All vehicles are subject to prior sale.This Dealership checks the vehicle recall status prior to making any pre-owned vehicle available for sale. Manufacturers and/or NHTSA may announce additional recalls at any time. To inquire about the recall status of this vehicle, visit: httpss://vinrcl.safercar.gov/vin/
Price may exclude tax, title, tags, governmental fees, any emissions testing charges and any finance charges (if applicable). Unless otherwise stated separately in the vehicle details, price does not include documentation, processing, administrative, closing or similar fees. Current mileage may vary due to test drives and vehicle relocation.


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Many users need different browser extensions to make the web search more comfortable. Sometimes browser add-ons are in the right way useful and can amaze their users with beneficial discoveries. Betweentimes browser add-ons can be downloaded by manifold unknown programs making users be astonished with the new installed browser add-on. If you have the same problem and you want to eliminate the ads together with the unwanted add-on that creates them, so you are on the right page. On this page you will find several powerful methods that will help you to remove TornPlusTV from your browser.
Install TornPlusTV Removal Tool:
- Eliminates all component parts generated by TornPlusTV.
- Is able to solve browser problems and protect browser settings.
- Has System and Network Guards, so you can forget about computer threats.
What is TornPlusTV?
TornPlusTV is not a malicious browser add-on because it cannot infect other computers, but it can be downloaded with diverse free programs. It sensitises that TornPlusTV is a PC infection. But, in fact, it is only an nerve-racking browser extension that tortures you with promotional report in the form of pop-ups. Almost every user has undesirable browser extensions like TornPlusTV and is eager to remove it.
Ways of TornPlusTV removal
1. Install TornPlusTV Removal Tool:
2. Uninstall TornPlusTV from Control Panel:
- Click Start button
- Select Control Panel
- Find and highlight the annoying program and select Uninstall
3. Uninstall TornPlusTV manually:
- No information
- No information
- No information
Remove TornPlusTV from browsers
Delete TornPlusTV from Firefox:
- Help=>Troubleshooting Information
- Reset Firefox
- Finish
Eliminate TornPlusTV from IE:
- Run inetcpl.cpl
- Advanced=>Reset
- Delete Personal Settings=>Reset=>Close
Remove TornPlusTV from Google Chrome:
- C:\Users\"your username"\AppData\Local\Google\Chrome\Application\User Data
- Rename Default folder to Default2
- Open Google Chrome
Get rid of TornPlusTV from Opera:
- Do not open Opera
- Remove Profile and Cache folders in:
- C:\Users\User_Name\AppData\Roaming\Opera Software\Opera Stable
- C:\Users\User_Name\AppData\Local\Opera Software\Opera Stable
- Open Opera
Remove TornPlusTV from Safari:
- Open your browser
- Settings->Reset Safari
- Reset
Why is TornPlusTV dangerous?
Every user should understand that all the programs for browsers including TornPlusTV, no matter if they are functional, are the areas of weakness for a browser where they are installed. The browser vendors release periodic updates, due to which the browser safety is protected. The presence of any browser add-on can make the try to defend the security of the browser impracticable. And, consequently, some online offenders are able to infect your computer or thieve your sensible info.
If you are not able to fancy your life without browser extensions, then I can advise you to install add-ons from dependable vendors only, may be it will help you to keep your system safe. But you should understand that it is better not to install any extensions at all. But if you still are eager to install any add-on in that case you should be prepared that this program will get compliance to see your sensitive data that you enter on various webpages and can pass it to the third parties. You should realize that to download a browser add-on is the same situation that to install a program. Will you install software if you did not hear about its vendor? I recommend you not to. So, you also should be careful downloading the browser add-on that you are going to install.
TornPlusTV, as a promotional extension, is more dangerous, because you cannot be certain that the popping-up advertising information is genuine and that the website that is mentioned in the ad is secure. If you want to make use of TornPlusTV or any other browser extension that demonstrates you special deals then you should be ready that it can trick you.
Are Manual Removal Methods Safe?
You should know that manual ways of removal that delete TornPlusTV from browsers are 100% secure and you can execute them not being afraid that you damage your system. Also these ways of removal are very effective, so you will get the wanted result: the browser add-on will be eliminated together will all its manifestations. It is also an easy technique; it yields solely to the automatic method of removal. But there is another manual removal instruction using which you should delete files, folders and registry entries.
This method is not harmful in situation if you are able to follow it gingerly or let the experienced person perform it. In order to make this instruction safer you should do the backup of the registry. Do not forget that only the enumerated elements of the extension should be removed and no other ones differently your PC will not load. If you do not understand what registry key is and where it can be, then you should use automatic way of removal in order not to damage your computer.
But the most effective and the most harmless instructions are automatic. Downloading any antimalware software that has TornPlusTV in its base of viruses, you will remove this unwanted browser extension promptly and the virus will not bother you any more since the antivirus will protect your system for the future.
The Best TornPlusTV Removal Tool
Do you want to download the program that is able to uninstall TornPlusTV perfectly? So I want to advise you Spyhunter because it is the best TornPlusTV Removal Tool and even more. This utility will detect and eliminate all possible browser add-ons and, besides, is able to protect your system, deleting all present-day viruses.
Spyhunter is a software written by Enigma Software. It includes antispyware and antimalware features, so then there are no viral elements that cannot be eliminated with it. Spyhunter has a regularly updated signature base that protects your system from various zero-day viral programs.
The recent version of Spyhunter not only modify its interface to more easy-to-use and fine, still this utility does not conflict with other antispyware tools that you can download. Also this tool works faster and protects better.
Spyhunter gives real-time protection, has Integrated SpyHunter Compact OS that let to remove the most stubborn viruses and the dependable live technical support. Spyhunter is compatible with Windows 98/ME/NT/2000/XP/2003/Vista/Seven/8, it needs 256 MB of RAM, 75 MB of available hard disk space.
Protect Your Computer With Antivirus
I hope this page was helpful for TornPlusTV Removal. But if you want to guard your system from other similar browser extensions and from all other dangerous programs, then you can download any antimalware software. You should download a program that has a broadened virus base, regular update and real-time protection. Merely such a tool can keep your system safe and sound. It is better to scan your computer for viruses at least weekly. Should have a large base of viruses and everyday updates. The most modern antiviruses has real-time protection. It is the 100% guard that is able to detect computer threats on your computer when it is just attempting to infect your system.
If you are unaware what antimalware to choose, then I recommend you to download Spyhunter. This program will perfectly delete all probable malicious objects and will produce a real-time hurdle to guard the system from the incoming viral programs. It is very easy to scan your system with Spyhunter and it is so powerful that is able to detect and delete trojans and other hidden viral programs. If you download any helpful program for you work, but Spyhunter reveals it as malicious software and you are absolutely sure that the program cannot b a computer infection, so you can add it to Exclusions list and Spyhunter will not detect it as a virus. If you need to scan just a single folder, then you are welcome to choose Custom Malware Fixes option. You can also use Process Guard or Registry Guard of the antivirus program for your convenience.
How to use Spyhunter
You are welcome to left-click the link on the page in order to download Spyhunter. Open that installer and execute the demanded actions. Set the language that is comfortable for you. Fortunately Spyhunter provides a lot of them, being helpful and handy. But not only this makes Spyhunter very well-known all over the world, but also its great capacity to guard users computer.
When the installation is finished the software needs update, usually this procedure should begin by itself, if not, in that case you are welcome to do it. When the patch is installed, you should begin a Scan. You are welcome to eliminate the tick in the Quick Scan check box, if you would like to scan your computer completely. But usually Quick scan is enough to define all the computer infections and this option spare your time and effort.
Look through the list of the found viruses when the scan will be completed. If you find needed software in the list, so you should delete the tick near it, otherwise Spyhunter will remove the software. It deals with the rare and special utilities that users download for their work. But usually, there is no need to eliminate any ticks. After that you need to left-click Fix Threats button. If you have already had the license, then the enumerated malware will be deleted. In case you did not, you will have the opportunity to pay for the license key.
About Author: Material provided by: Alesya Orlova


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|1,100 sq. ft.
|Property type
|Minimum Stay
|9 - 28 nights
This top floor (3rd fl.) walk-up condo is 1100 sq ft (100 sq meters) with 14 ft ceilings, central air conditioning, large screened balcony, en-suite laundry, dishwasher, in-sink disposal. Facing south with a view of a lake, this clean and bright condo includes two LCD TVs, a DVD player, a fully equiped kitchen, beach chairs, towels, linens etc. Phone line, cable TV, and internet are included.
Our condo is centrally located in North Naples and the gated community features a private sun/shade swimming pool, whirlpool, racquetball court, full gym, barbecue grill, tennis court and children's play area. Only 10 minutes from the beautiful white sand beaches of the Gulf Coast. Less than a 15 minute drive from the high-end dining and shopping areas of historic Downtown Naples, Waterside Shops and newly built Mercatto complex. Close to grocery stores, mall and 20 cinema theatre. 25 minutes from Southwest Florida International Airport (RSW).
Local Experience: The owners of this property have been visiting the Naples area for over a decade and can share their knowledge of the region to help you make the most of your time in Southwest Florida. They are also avid golfers and can help recommend local golf courses. The condo complex has recently replaced pool furniture and upgraded some of the exercise equipment as well as the racquetball court. The owners are happy to make special arrangements for your stay including regular cleaning, newspaper or mail delivery, and enhanced cable TV packages. This can be discussed prior to rental................Nearest Supermarket: Publix at 2 miles...........
Public Golf Courses: dozens within 20 miles...........
Movie Theatres: 5 miles............
Miromar Outlet Mall: 15 miles............
Short Get-Aways: Walt Disney World is approximately 3 hours north and could be visited on a day trip, however most visitors spend a night or two in Orlando before returning to Naples.
Miami and South Beach, home to the art deco district and famous beaches is only a 2 hour drive east. West Palm Beach, another popular destination, is about 2.5 hours northeast.
Key West is a popular laid back and picturesque town at the southern most point of the US. To reach the town it is a 5 hour drive through the Florida Keys, however there is a high speed ferry from Naples that reaches Key West in about 3 hours. Many people take this option as an easy and relaxing day trip.
Everglades National Park is less than 30 minutes to the East of Naples and provides excellent wildlife tours for bird watchers and alligator chasers.
Alternative Airports: Alternative airports are West Palm Beach (PBI), Fort Lauderdale (FLL) and Miami (MIA) with frequent international flights. If you rent a car at these airports you can return the car to any of the three airports without additional fees. Sarasota (SRQ) is about 1.5 hours north but is a small airport serving the Eastern US. The local airport (8 mi) is Naples (APF) and has few commercial flights. There are flights offered from Atlanta and other parts of Florida to Naples airport.
Owned by two Canadians who have family in the Naples area and visit regularly. We are knowledgable about the area and treat this vacation rental as our own home.
Mark Halpren purchased this condo in 2010
Dear Marc, please excuse us, that we can only report today we had very nice days in Naples and we can have very nice excursions. Your apartment was to our fullest satisfaction and we did not miss anything. Many thanks for everything. We would love to come back again.
Many greetings from Duisburg
Dirk and Christiane
SEE ADDITIONAL INFO FOR CONDO COMPLEX: http://www.thereserveatnaples.org
Other Activities: Everglades excursions, airboats, outlet mall
Golf: Dozens of public golf courses within a 25 minute drive. Decent championship courses charge $50 to $70 during the peak season and half that in the off-season. Premium courses are over $100. Rates include carts and are generally mandatory. Golf clubs may be stored in our storage locker on the main floor of the building.
This was our first experience with VRBO and it was definitely a winner! The condo we rented in Naples was in an ideal location close to beaches, restaurants, and shopping. It was also spotlessly clean when we arrived with everything in good working order. We were particularly delighted with the view which looked out over a lake and fountain that was lit each night. The development has a really nice pool that we enjoyed on several non-beach days as well as an exercise room that has the basics and is also very clean. Mark could not have been nicer to work with and was patient with us as "VRBO beginners". He sent us detailed instructions prior to our arrival, as well as suggestions for fun things to do in Naples. This unit and its owners would definitely be our first choice for a return stay in Naples. It provided the perfect combination of convenience, quality, and value for our money. We were really sorry to leave!
Nice gated property with pool, workout room. We stayed for 3 weeks and wanted a property more inland and not right in the beach. Good location and still easy to get to the beaches. The unit was clean and well equipped.
This was our second stay at this condo. For us, this is a great place relax. We made full use of the sun flat overlooking the lake, the warm pool, and the hot tub. If you like shopping at Bealls, Ross, TJ Maxx, Burlington, etc., they are all located very close by. We dined at Outback, Olive Garden, Rib City, Pinchers, Steak and Shake, Perkins .... most dining places imaginable are all close by. The Naples Pier/Beach is close by, as well as downtown Naples, where you can shop or watch the Lamborghini's, Ferrari's and Rolles Royce's drive by.
Thanks to Mark and Brent for another nice stay!
John and Sandi
I absolutely loved my visit. The place was as advertised - even better. It was very clean when I arrived. I had 2 very minor issues while I was there and the owners got it taken care of immediately. Mark was great to deal with via email... very understanding, polite and respectful. I'm going back later this year. I think that says it all.
This place is about as good as it gets. It was cleaned thoroughly upon arrival. There are 2 bedroom areas, each with their own bathroom. Also included was a washer and dryer, which very handy for long stays. Pretty much everything was included except for food.
The balcony overlooks a lake with a number of fountains that light up at night. There is some wildlife like birds diving for food, and we also saw a large turtle. The pool area is absolutely awesome - heated to about 88-90F, and a hot tub heated to around 102-104F. Adjacent to the pool and lake is a sun tanning flat.
As my wife can attest to, there is tons of shopping nearby. There are plenty of restaurants, grocery stores, and a liquor/beer store that has just about every brand that you can imagine. There are beaches nearby - we spent the day at the Naples Pier and beach (a great spot). My daughter and wife went to the Naples Zoo nearby........ There are plenty of day excursions very close by.
As it says in the title, we're coming back for the month February 2016. Thanks to Mark and Brent for a fantastic stay.
John and Sandi K.
Orillia, Canada
We stayed in the appartment january-february 2013 for a winter escape from the cold north. The Reserve is not a real holiday resort, but more a normal resident condominion area. Topfloor appartment, quiet neighbours. Sourroundings well organized and well kept. Area large enough for a 15 minuts walk.
We did not use the pool-area etc. much, but it seemed rather well kept. When sunbathing we experienced the furniture around needed to be cleaned (greasy and some dirty).
The appartment is well equipped, has good beds and well-functioning kitchen. Bathrooms normal standard for an appartment of this type.
The view to the lake is unbeatable and the top level, south orientation gives much light.
The owners are really attentive, responsive and responsible.
The only drawback to this nice appartment is the US-75 only 200-250 yards away. Our own appartment in Europe is at a street with high trafic load and we have of course 3-layer special windows etc., but none of the appartments at The Reserve has any noise dampening devices. For people living in central parts of big towns, this might not be a problem, but for us, it meant, that we did not use the nice patio and moved to the 2nd bedroom (which in no ways was bad), which is more protected, than the master bedroom.
We can really recommend this appartment, also for longer stays.
Margit and Henrik (65/70) seniors from Europe
|Condo registration
|Exit cleaning
May accept personal checks or PayPal for deposit depending on the country of origin.
30% deposit due to hold dates - remainder upon arrival. Payment to be made in USD through BMO Harris Bank.


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Well victim 'had suffered injuries'
A man whose body was found in a well had been assaulted, police said today.
An initial post mortem examination did not prove conclusive in providing a cause of death and further tests will be carried out, Scotland Yard said.
However, the examination did reveal injuries consistent with an assault, a spokesman added.
The body was discovered by two workmen doing clearing work in the front garden of a large house in Audley Drive, Warlingham, Surrey, on Friday and recovered by specialist police officers.
Seven men aged between 21 and 27 who were arrested on suspicion of murder have been bailed to a date in late December, pending further investigation.
The post mortem examination, carried out at East Surrey Hospital mortuary, confirmed that the body is male.
The Yard spokesman said inquiries continue to try to establish the victim's identity.
Detective Chief Inspector Cliff Lyons said: "This is an unusual case and I would appeal for anybody who has concerns about a missing person, or who believes they may know something about this individual or incident, to call my officers.
"The post mortem examination has revealed this person, who we now know is a white male, suffered injuries before being placed into the well. We are treating the incident as a murder."
Mr Lyons said yesterday that judging by the size of the body it was likely to be an adult.
He told reporters at the scene: "It's not been there for an extended period of time; it will be a matter of weeks at the most."
He was not prepared to discuss who lived in the house or a suggestion that the body was wrapped in carpet. He could not confirm whether the body was intact.
Asked about claims by local residents that there had been quite a bit of trouble in the past couple of years with the people who lived in the house, and that police had been called many times, he said: "The residents have expressed concern, there is intelligence to support that notion, yes."
The Croydon Advertiser newspaper named the workmen who found the body as Jack Duncan, 17, and Rory Mulholland, 21.
They had been asked to do some gardening at the house on Thursday and returned the following day to clear up.
Jack said: "There were some brambles on top of the well and while I was using a rake to clear them I noticed there was something covering the top of the well.
"It was like a paving slab. Rory lifted it off and we saw a blue thing in water, like something was covered in tarpaulin.
"I got some gutter piping and I was poking it and spinning it around, which was unravelling where it had been wrapped up."
He said he saw the shape of two legs, then a backside, and realised it was a body.
"It was definitely a man. You could tell by the size of his thighs, he had builder's legs. It was definitely an adult. It still had skin and hair, so couldn't have been there for very long."
Jack then called the police.
The pair had been asked to do some gardening at the house by a friend.
When they arrived at the house on Thursday, a group of Polish men appeared to be moving out.
"They looked like they were in a hurry but I didn't think anything of it," said Jack. "When we came back on the Friday, no one was left."
Anyone with information was urged to call police on 020 8721 4961 or Crimestoppers anonymously on 0800 555111.


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German Chancellor Angela Merkel has no plans for a cabinet reshuffle after her party lost power in a state election for the third time in her second term, her spokesman said Monday.
"The chancellor has no such plans," Steffen Seibert told a regular government briefing after Sunday's election saw Merkel's Christian Democrats (CDU) voted out of office in Baden-Wuerttemberg after 58 years.
One figure seen by German newspapers as ripe for the chop is Economy Minister Rainer Bruederle from Merkel's national coalition partners, the pro-business Free Democrats (FDP).
An embarrassing media report last week quoted Bruederle as saying that Merkel's recent decision to suspend an extension to nuclear power plants' operating period was motivated by the state election.
This appeared to confirm the suspicions of many voters, with one survey showing 70 percent thought the chancellor's announcement smacked of "electioneering."
The defeat in Baden-Wuerttemberg was the third in Merkel's almost 18-month-old second term, having lost Hamburg in February and North Rhine-Westphalia in May, and with it control of the Bundesrat upper house.
The FDP only just cleared the five-percent hurdle needed to gain seats in the Baden-Wuerttemberg state parliament, but failed to do so in another election the same day in Rhineland-Palatinate.
The party also missed out in Saxony-Anhalt last Sunday, and the results are likely to increase pressure on FDP leader Guido Westerwelle, the vice chancellor and foreign minister.
Bruederle's spokeswoman Sarah Schneid said that he intended to carry on in his job.


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Realfast acquired by zipLogix
Realtor-owned zipLogix -- the provider of zipForm electronic forms and transaction management and digital signature applications -- has acquired real estate forms company Realfast Inc. and will discontinue Realfast's Realfast2Go offering. Fraser, Mich.-based zipLogix is offering all current Realfast users access to its products, zipForm 6 Standard, zipForm 6 Professional and zipVault.
"The acquisition of Realfast will significantly contribute to our strength and momentum in the marketplace," said zipLogix Chairman Walt McDonald in a statement. "We are extremely excited to offer incoming Realfast customers our flagship product, zipForm, which is used by more real estate professionals than any other software in the business."
Founded in 1991, zipLogix is a joi...


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GraphicRiver Halloween Design Elements 5803963
Category: Stock Vector | Comment: ( 0 )
GraphicRiver Halloween Design Elements 5803963
Stock Vector \ Conceptual \ Seasons \ Holidays \ Halloween
Scrapbook \ Scrapbooking \ Scary \ Pumpkin \ October \ Orange \ Season \ Trick
Stock Vector - Halloween Design Elements 5803963 by shlemina @ GraphicRiver
Stock Vector \ Conceptual \ Seasons \ Holidays \ Halloween
Graphics Files Included: JPG Image, Vector EPS; Layered: No; Minimum Adobe CS Version: CS.
Collection of halloween design elements
Halloween Icons; Halloween Stickers; Halloween Design Elements; Lace Hang Tags with Bows; Labels with Bows; Card Notes with Gift Bows; Twine Bows.
Keywords: autumn, background, card, celebration, collection, decor, decoration, evil, ghost, halloween, haunting, holiday, horror, illustration, jack, lantern, october, orange, pumpkin, scary, scrapbook, scrapbooking, season, set, smile, squash, template, treat, trick, yellow.


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We may have mentioned (okay, we DEFINITELY have mentioned) the importance of using friend lists for maintaining your privacy on Facebook, but I'm not sure that we've mentioned that it can actually help you when marketing on Facebook.
By creating different friend lists, you can target who sees what information. We always talk about ways to keep business contacts from viewing your personal stuff. Now, we ask you to consider creating content that is only viewable by your business contact list. Why? Because this way, you can create lots of great content, provide tons of great information, and not innondate your favorite aunt, your nursery school buddies, and your best friends with industry info. Privacy settings get better every day. Have you visited your privacy settings lately? If not, go do it. Like, right now. :)
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Join Gay Snare! Download full-length 31min 12sec movie now!
Look at this pretty twink and how he quietly sits on that chair, blindfolded, and ready for something to happen. Something really did happened, as Tomm stepped in. Tomm is a big guy, but he's not afraid to show his feeling and gay sexuality. He begins to lick the skinny dude and then, take off his pants.
- amadeus
- : 5min 55sec
- : 27 Mar 2014
- Exclusive video


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You can only message people who are following you. If you'd like to message this person, simply join our Recruiter Pro Plan.
All things Music Production at Sony ATV's Film & TV Production Music Division.
I am an Apple Certified IOS & Mobile hardware Technician, responsible for triage, diagnosis, and repair of Apple hardware and software systems. In this role my job is to offer solutions to both simple & complex technological problems, while overcoming any objections to solutions. This role needs me to always be able to explain technology to a wide array users within the Apple technological ecosystem; leading to a plethora of opportunities to educate and more importantly be a facilitator to the learning customer.
As a Specialist at Apple, I have trained to be extremely IT proficient on Mac OSX systems. In my job role I help create, maintain and often repair the energy and the excitement around the Apple products, by providing the right solution in the form of support and assistance. Apple is dedicated to delivering a customer service experience that is unlike any other and this starts with discovering a consumers' needs and their potential issues. With the timely support that I provide I have learnt the invaluable skill of problem solving, communication and attention to detail. Key Competencies Acquired: - Ability to deliver great customer experiences in a very active environment and to be invigorated by constant personal interaction. - Strong interest in technology, particularly Apple products, and agility at learning new products and features. - Strong communication skills that let you converse as freely and comfortably with small groups as with individual customers.
Music Producer & Artist
The main objective that I set myself in this role is to 'ensure that I am always proactive and continuingly providing the best service possible'. In this position at LC Designs I was responsible for generating, processing and maintaining the B2B sales received from independent clients. I was the first point of contact when customers were placing orders and/or making enquiries, I also handled the sales generated online via the company's website, and amazon. With this in mind I also had a hand in re-editing the website and liaising with designers to make aesthetic changes, meeting and discussing them with directors and through this began leading and generating additional sales through the new social media pages such as Facebook & Twitter. Quality Checking: This part of my job role includes using my knowledge and initiative to check goods that have been received and deciding whether the quality of the product is of a standard accepted by LC Designs and would be of a customer and consequently reporting back to the Quality Check Department for further analysis and correspondence with manufactures.
In this, which was my first job, I worked in the Communications and Media Unit at Kent County Council, in which I performed essential administration functions within a small team, ensuring the smooth day-to-day running of this indispensable group. One of my main jobs other than administration was assisting my colleague in the production of the tri- weekly Media Monitoring & social coverage pack that was distributed to staff and councillors at Kent County Council.
Certificate of Higher Education in Music Production; FHEQ Level 4
Merit - National Diploma in Music Technology; Level 3
11 GCSEs


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Power up for a killer 3D combat game -- the ultimate in-your-face battle
of the Ballz! Face up to 8 unique characters and 8 bosses that punch,
kick, club, slam, humiliate and fart on their opponents. Check your
damage rating on instant replay, or morph into another character to
really faze your opposition.


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UK fertility scientists permitted to hand-pick embryos
The authority overseeing fertility treatment in Britain has given scientists in London permission to select embryos for fertility treatment which are free from genes which can cause a form of colon cancer.
The team at University College, London has been granted a licence by the fertility watchdog to screen embryos for a gene that causes a type of colon cancer.
If one of the parents carries the gene there is normally a 50 per cent chance it would be passed onto any of their children.
Now they will be able to go through IVF treatment with any embryos being screened for the gene, using a technique known as 'free implantation genetic diagnosis'.
Only embryos that are free of the gene will be used.
The screening technique is already being used by clinics around the country to check for other disorders, such as cystic fibrosis.
Topics: genetics, united-kingdom
First posted


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Tuesday, February 2, 2016
Always Watching (Elite Guardians, #1) by Lynette Eason
The bodyguards of Elite Guardians Agency have more than just skill and prowess in common--they're also all women. When it becomes clear that popular psychiatrist and radio personality Wade Savage has a stalker, his father secretly hires Elite Guardians in order to protect his son.
But when Wade's bodyguard is attacked and nearly killed, agency owner Olivia Edwards must step in and fill the gap. Olivia's skills are about to be tested to the limit as Wade's stalker moves from leaving innocent gifts at his door to threatening those closest to him. Olivia has the feeling that she's next on the list. And to complicate things even further, she realizes that her heart may be in as much danger as her client.
About The Author
My Review
Who would want to hurt a generous man with a young daughter, whom has lost her mother, and a woman she loved that was going to marry her dad? Wade Savage's father has hired Olivia Edwards Elite Guardians to protect his son, he has a stalker, with people being attacked, and suspicious deaths, and you never know what is going to happen next.
If you love suspense this is the book for you, it gives and give right to the end. You will make up your mind, and I was sure I knew whom was responsible, but guess what, I was very wrong, and very surprised to whom was the culprit.
This is as fast pace as you can get, and still have an even flowing story, picking this one up might keep you up at night, but you will not be disappointed.
I received this book through Revell Reads Blogger Program, and was not required to give a positive review.
Subscribe to: Post Comments (Atom)


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What is Global Entry?
"Global Entry is a U.S. Customs and Border Protection (CBP) program that allows expedited clearance for per-approved, low-risk travelers upon arrival in the United States." -Global Entry Website. So you get fast tracked at customs coming back into the United States and you also get TSA per-approved for all domestic travel for five years.
After our adventure today I thought I would provide some tips for Global Entry. So here it goes:
General Global Entry Information:
- Global Entry requires an online application, $100 fee, and an in person interview with a Customs and Border Control officer at a Global Entry Enrollment Center.
- Are you eligible for Global Entry? Check here: http://www.cbp.gov/global-entry/faqs This program is for "low-risk travelers" and I heard a story of a DUI from twenty years ago stopping someone from being eligible. I have never had a background check like this before....
- Soon after you have completed the application you will have an opportunity to schedule a face-to-face appointment at a Global Entry Enrollment Center. Schedule your appointment and be sure to make note of the documents you need to bring with you (Passport, proof of residency, acceptance letter.....).
I followed all the directions above and thought I was doing a pretty good job of being ahead of the game for my Iceland trip. When I went to schedule my interview, the local office was booked until I was already in Iceland so I scheduled an interview at the Otay Mesa Enrollment Center, right on the border of Mexico.
Photo by Little Sister
The Little Sister had also submitted an application but was not able to schedule an appointment online at the Otay Mesa Enrollment Center for the same day as my interview. The day before I was scheduled to go, I called them to see if they had any availability for her around the same time and they did! We booked her an appointment and worked out a carpool plan.
On this same day I called the Middle Sister who had an appointment closer to our Iceland departure date at the LAX Enrollment Center and said she might be able to call the Long Beach Enrollment Center (the place we all wanted to have our face-to-face interviews) to see if they had any availability earlier than her scheduled date and she was able to book an appointment that day!
I didn't really think about any of this until today but the Little Sister and I could of totally called Long Beach to for an interview. Of course I think of this as we are driving to almost Mexico for our interview. No worries, we had breakfast with a friend, but next time, I will call Long Beach for an appointment.
Tip #1 - Call the enrollment office you would like to have your interview in to see if they have availability. Save yourself a drive to Mexico......
My next tip seemed obvious to me when I was dealing with it, but not obvious at the time I was booking appointments.
Tip #2 - Book appointments early in the day. Bring a book or something to do while you wait.
My appointment was at 11am and the Little Sister had her appointment at 11:45am. We were able to check in 30 minutes before our appointments. They were running about 30 minutes behind schedule when we checked in and then lunch happened. The Little Sister finished her appointment at 1pm. If possible, book appointments early in the day. We did both bring a book, so it could of been worse, but it also could have been avoided.
Overall the interview was painless and the Customs and Border Control Officer is just making sure you are "low-risk" to be able to recommend you for Global Entry. Even after the interview you still have to be offically approved by someone else before you have Global Entry. Once you do, they send you a card (with your photo that you took during the interview) and you are good to go. I was concerned I might not get the card in time for my Iceland trip and the Officer told me I don't need it. My passport is what I scan at the Custom's kiosk coming back into the county and my Global Entry Membership number is the number I enter when purchasing airfare to receive TSA Priority.
All in all not a bad experience. I am thinking it will be worth the money spent and look forward to TSA Priority and not waitng in Custom's Lines! Leave me a comment if you have used Global Entry or have tips for expediting International travel. I am always looking for tips!


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Home › Steel Shelving Unit Label › Browse
black stained wooden bookshelf on cream wall paper. brown hickory wood mixed chrome metal based shelving unit with tow door panel and 5 tray. modern black acrylic wall shelf with 5 racks. cubit white wooden modular shelving unit built in bookshelves . brown wooden mixed metal bookshelf for storage organizer....
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SERDANG: Perdana University was launched by Prime Minister Datuk Seri Najib Tun Razak on Monday.
Former Prime Minister Tun Dr Mahathir Mohamad was named the university's first chancellor.
The university, a collaboration with world-renowned Johns Hopkins and the Royal College of Surgeons, saw the first intake of 30 medical students.
In his speech, Najib said the presence of the university would help put Malaysia on the world map for excellence in education and research in medicine.
About Perdana University
In collaboration with Johns Hopkins Medicine, Perdana University Graduate School of Medicine is an integrated academic medical center with Johns Hopkins values: excellence, discovery and innovation in medical education, research and patient care. The University encompasses a graduate school of medicine, a teaching hospital and a research enterprise. See a printable brochure that provides a comprehensive overview of Perdana University Graduate School of Medicine.
Perdana University offers:
- A collaboration with the Johns Hopkins University School of Medicine, a global leader in medical education, research and patient care
- A post-baccalaureate medical school that includes a fully integrated teaching hospital and basic and clinical science research enterprise
- A graduate medical education after the American model
- A curriculum based upon Genes to Society, an innovative approach to medical education researched, developed, streamlined and used by the Johns Hopkins School of Medicine in Baltimore, Maryland, USA
- Student-centered medical education with outstanding permanent faculty, as well as visiting faculty from the Johns Hopkins University School of Medicine
- A commitment to innovative research


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In this presentation at Smart Data 2016, the speaker Peter Rozek shares his understanding of a quick history of data vizualization, what the user experience of data visuzliazation should be, and how to make your data beatiful.
The UX of Data Visualization
May 16, 2016
Posted in Data visualization | Comments (0) Post a comment.
If you enjoyed this, then you should check out The UX Bookmark and UX Quotes too.


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I Like Honey Lovers Candy
Personal Stories, Advice, and Support
HahaI have no idea what honey lovers is but if it tastes like the old bit-o-honey that we used to get as kids I would love to try it. It sounds like a candy I would like. Is it available in the states? I have a confession here and that is I read this group quickly and I thought it...
fungirlmmm 46-50, F 2 Responses 0 Jul 6, 2010


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|Lithuania: MSC meets with the Baltic Sea fishermen in Klaipeda
|Friday, 01 June 2012 13:22
The Lithuanian Ministry of Agriculture on its website has reported a meeting with representatives of the Marine Stewardship Council (MSC) on 15 May 2012 in Klaipeda,
at which the MSC certification programme was presented to Baltic Sea fishermen.
The Marine Stewardship Council is an independent, global, non-profit organisation that manages a widely recognised sustainability label for fisheries. The MSC environmental standard for responsible fisheries and the MSC Chain of Custody standard for seafood traceability together ensure that the raw material in fish and seafood products carrying the MSC logo has been sourced from a sustainable fishery. To determine whether the fisheries meet the MSC standard requirements three main aspects have to be taken into account:
Participants at the meeting heard that in the Baltic Sea region, Danish, Swedish and German fisheries are already certified to the MSC standard, while Polish fishermen are currently considering it. Speakers noted that the fishing industry is increasingly aware that priority should be given to sustainable fisheries products and is committed to encouraging fishermen to adopt responsible fishing practices. This is the only way the industry can meet the increasing demand for sustainably produced fishery products.