nils-herrmann/clean_scirepeval huggingface dataset:
PageRank on an evolving graph

One of the most important features of the Web graph and social networks is that they are constantly evolving. The classical computational paradigm, which assumes a fixed data set as an input to an algorithm that terminates, is inadequate for such settings. In this paper we study the problem of computing PageRank on an evolving graph. We propose an algorithm that, at any moment in the time and by crawling a small portion of the graph, provides an estimate of the PageRank that is close to the true PageRank of the graph at that moment. We will also evaluate our algorithm experimentally on real data sets and on randomly generated inputs. Under a stylized model of graph evolution, we show that our algorithm achieves a provable performance guarantee that is significantly better than the naive algorithm that crawls the nodes in a round-robin fashion.
Learning Adversary-Resistant Deep Neural Networks

Deep neural networks (DNNs) have proven to be quite effective in a vast array of machine learning tasks, with recent examples in cyber security and autonomous vehicles. Despite the superior performance of DNNs in these applications, it has been recently shown that these models are susceptible to a particular type of attack that exploits a fundamental flaw in their design. This attack consists of generating particular synthetic examples referred to as adversarial samples. These samples are constructed by slightly manipulating real datapoints in order to “fool” the original DNN model, forcing it to mis-classify previously correctly classified samples with high confidence. Addressing this flaw in the model is essential if DNNs are to be used in critical applications such as those in cyber security. Previous work has provided various defense mechanisms by either augmenting the training set or enhancing model complexity. However, after a thorough analysis, we discover that DNNs protected by these defense mechanisms are still susceptible to adversarial samples, indicating that there are no theoretical guarantees of resistance provided by these mechanisms. To the best of our knowledge, we are the first to investigate this issue shared across previous research work and to propose a unifying framework for protecting DNN models by integrating a data transformation module with the DNN. More importantly, we provide a theoretical guarantee for protection under our proposed framework. We evaluate our method and several other existing solutions on both MNIST, CIFAR-10, and a malware dataset, to demonstrate the generality of our proposed method and its potential for handling cyber security applications. The results show that our framework provides better resistance compared to state-of-art solutions while experiencing negligible degradation in accuracy.
Despeckling of medical ultrasound images

Speckle noise is an inherent property of medical ultrasound imaging, and it generally tends to reduce the image resolution and contrast, thereby reducing the diagnostic value of this imaging modality. As a result, speckle noise reduction is an important prerequisite, whenever ultrasound imaging is used for tissue characterization. Among the many methods that have been proposed to perform this task, there exists a class of approaches that use a multiplicative model of speckled image formation and take advantage of the logarithmical transformation in order to convert multiplicative speckle noise into additive noise. The common assumption made in a dominant number of such studies is that the samples of the additive noise are mutually uncorrelated and obey a Gaussian distribution. The present study shows conceptually and experimentally that this assumption is oversimplified and unnatural. Moreover, it may lead to inadequate performance of the speckle reduction methods. The study introduces a simple preprocessing procedure, which modifies the acquired radio-frequency images (without affecting the anatomical information they contain), so that the noise in the log-transformation domain becomes very close in its behavior to a white Gaussian noise. As a result, the preprocessing allows filtering methods based on assuming the noise to be white and Gaussian, to perform in nearly optimal conditions. The study evaluates performances of three different, nonlinear filters - wavelet denoising, total variation filtering, and anisotropic diffusion - and demonstrates that, in all these cases, the proposed preprocessing significantly improves the quality of resultant images. Our numerical tests include a series of computer-simulated and in vivo experiments.
Generative adversarial networks: introduction and outlook

Recently, generative adversarial networks U+0028 GANs U+0029 have become a research focus of artificial intelligence. Inspired by two-player zero-sum game, GANs comprise a generator and a discriminator, both trained under the adversarial learning idea. The goal of GANs is to estimate the potential distribution of real data samples and generate new samples from that distribution. Since their initiation, GANs have been widely studied due to their enormous prospect for applications, including image and vision computing, speech and language processing, etc. In this review paper, we summarize the state of the art of GANs and look into the future. Firstly, we survey GANs U+02BC proposal background, theoretic and implementation models, and application fields. Then, we discuss GANs U+02BC advantages and disadvantages, and their development trends. In particular, we investigate the relation between GANs and parallel intelligence, with the conclusion that GANs have a great potential in parallel systems research in terms of virtual-real interaction and integration. Clearly, GANs can provide substantial algorithmic support for parallel intelligence.
DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction

Distant supervision can effectively label data for relation extraction, but suffers from the noise labeling problem. Recent works mainly perform soft bag-level noise reduction strategies to find the relatively better samples in a sentence bag, which is suboptimal compared with making a hard decision of false positive samples in sentence level. In this paper, we introduce an adversarial learning framework, which we named DSGAN, to learn a sentencelevel true-positive generator. Inspired by Generative Adversarial Networks, we regard the positive samples generated by the generator as the negative samples to train the discriminator. The optimal generator is obtained until the discrimination ability of the discriminator has the greatest decline. We adopt the generator to filter distant supervision training dataset and redistribute the false positive instances into the negative set, in which way to provide a cleaned dataset for relation classification. The experimental results show that the proposed strategy significantly improves the performance of distant supervision relation extraction comparing to state-of-the-art systems.
Collective context-aware topic models for entity disambiguation

A crucial step in adding structure to unstructured data is to identify references to entities and disambiguate them. Such disambiguated references can help enhance readability and draw similarities across different pieces of running text in an automated fashion. Previous research has tackled this problem by first forming a catalog of entities from a knowledge base, such as Wikipedia, and then using this catalog to disambiguate references in unseen text. However, most of the previously proposed models either do not use all text in the knowledge base, potentially missing out on discriminative features, or do not exploit word-entity proximity to learn high-quality catalogs. In this work, we propose topic models that keep track of the context of every word in the knowledge base; so that words appearing within the same context as an entity are more likely to be associated with that entity. Thus, our topic models utilize all text present in the knowledge base and help learn high-quality catalogs. Our models also learn groups of co-occurring entities thus enabling collective disambiguation. Unlike most previous topic models, our models are non-parametric and do not require the user to specify the exact number of groups present in the knowledge base. In experiments performed on an extract of Wikipedia containing almost 60,000 references, our models outperform SVM-based baselines by as much as 18% in terms of disambiguation accuracy translating to an increment of almost 11,000 correctly disambiguated references.
A Padded Encoding Scheme to Accelerate Scans by Leveraging Skew

In-memory data analytic systems that use vertical bit-parallel scan methods generally use encoding techniques. We observe that in such environments, there is an opportunity to turn skew in both the data and predicate distributions (usually a problem for query processing) into a benefit that can be leveraged to encode the column values. This paper proposes a padded encoding scheme to address this opportunity. The proposed scheme creates encodings that map common attribute values to codes that can easily be distinguished from other codes by only examining a few bits in the full code. Consequently, scans on columns stored using the padded encoding scheme can safely prune the computation without examining all the bits in the code, thereby reducing the memory bandwidth and CPU cycles that are consumed when evaluating scan queries. Our padded encoding method results in a fixed-length encoding, as fixed-length encodings are easier to manage. However, the proposed padded encoding may produce longer (fixed-length) codes than those produced by popular order-preserving encoding methods, such as dictionary-based encoding. This additional space overhead has the potential to negate the gains from early pruning of the scan computation. However, as we demonstrate empirically, the additional space overhead is generally small, and the padded encoding scheme provides significant performance improvements.
Deep Patient Similarity Learning for Personalized Healthcare

Predicting patients’ risk of developing certain diseases is an important research topic in healthcare. Accurately identifying and ranking the similarity among patients based on their historical records is a key step in personalized healthcare. The electric health records (EHRs), which are irregularly sampled and have varied patient visit lengths, cannot be directly used to measure patient similarity due to the lack of an appropriate representation. Moreover, there needs an effective approach to measure patient similarity on EHRs. In this paper, we propose two novel deep similarity learning frameworks which simultaneously learn patient representations and measure pairwise similarity. We use a convolutional neural network (CNN) to capture local important information in EHRs and then feed the learned representation into triplet loss or softmax cross entropy loss. After training, we can obtain pairwise distances and similarity scores. Utilizing the similarity information, we then perform disease predictions and patient clustering. Experimental results show that CNN can better represent the longitudinal EHR sequences, and our proposed frameworks outperform state-of-the-art distance metric learning methods.
Modeling Event Importance for Ranking Daily News Events

We deal with the problem of ranking news events on a daily basis for large news corpora, an essential building block for news aggregation. News ranking has been addressed in the literature before but with individual news articles as the unit of ranking. However, estimating event importance accurately requires models to quantify current day event importance as well as its significance in the historical context. Consequently, in this paper we show that a cluster of news articles representing an event is a better unit of ranking as it provides an improved estimation of popularity, source diversity and authority cues. In addition, events facilitate quantifying their historical significance by linking them with long-running topics and recent chain of events. Our main contribution in this paper is to provide effective models for improved news event ranking.
 To this end, we propose novel event mining and feature generation approaches for improving estimates of event importance. Finally, we conduct extensive evaluation of our approaches on two large real-world news corpora each of which span for more than a year with a large volume of up to tens of thousands of daily news articles. Our evaluations are large-scale and based on a clean human curated ground-truth from Wikipedia Current Events Portal. Experimental comparison with a state-of-the-art news ranking technique based on language models demonstrates the effectiveness of our approach.
Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study

Recently, methods based on local image features have shown promise for texture and object recognition tasks. This paper presents a large-scale evaluation of an approach that represents images as distributions (signatures or histograms) of features extracted from a sparse set of keypoint locations and learns a Support Vector Machine classifier with kernels based on two effective measures for comparing distributions, the Earth Mover’s Distance and the χ2 distance. We first evaluate the performance of our approach with different keypoint detectors and descriptors, as well as different kernels and classifiers. We then conduct a comparative evaluation with several state-of-the-art recognition methods on four texture and five object databases. On most of these databases, our implementation exceeds the best reported results and achieves comparable performance on the rest. Finally, we investigate the influence of background correlations on recognition performance via extensive tests on the PASCAL database, for which ground-truth object localization information is available. Our experiments demonstrate that image representations based on distributions of local features are surprisingly effective for classification of texture and object images under challenging real-world conditions, including significant intra-class variations and substantial background clutter.
Bundling mobile base station and wireless energy transfer: Modeling and optimization

Wireless energy transfer is a promising technology to fundamentally address energy and lifetime problems in a wireless sensor network (WSN). On the other hand, it has been well recognized that a mobile base station has significant advantages over a static one. In this paper, we study the interesting problem of co-locating the mobile base station on the wireless charging vehicle (WCV). The goal is to minimize energy consumption of the entire system while ensuring none of the sensor nodes runs out of energy. We develop a mathematical model for this complex problem. Instead of studying the general problem formulation (OPT-t), which is time-dependent, we show that it is sufficient to study a special subproblem (OPT-s) which only involves space-dependent variables. Subsequently, we develop a provably near-optimal solution to OPT-s. The novelty of this research mainly resides in the development of several solution techniques to tackle a complex problem that is seemingly intractable at first glance. In addition to addressing a challenging and interesting problem in a WSN, we expect the techniques developed in this research can be applied to address other related networking problems involving time-dependent movement, flow routing, and energy consumption.
A user attention model for video summarization

Automatic generation of video summarization is one of the key techniques in video management and browsing. In this paper, we present a generic framework of video summarization based on the modeling of viewer's attention. Without fully semantic understanding of video content, this framework takes advantage of understanding of video content, this framework takes advantage of computational attention models and eliminates the needs of complex heuristic rules in video summarization. A set of methods of audio-visual attention model features are proposed and presented. The experimental evaluations indicate that the computational attention based approach is an effective alternative to video semantic analysis for video summarization.
Conceptual integration and metaphor: an event-related potential study.

Event-related brain potentials (ERPs) were recorded from 18 normal adults as they read sentences that ended with words used literally, metaphorically, or in an intermediate literal mapping condition. In the latter condition, the literal sense of the word was used in a way that prompted readers to map conceptual structure from a different domain. ERPs measured from 300 to 500 msec after the onset of the sentence-final words differed as a function of metaphoricity: Literal endings elicited the smallest N400, metaphors the largest N400, whereas literal mappings elicited an N400 of intermediate amplitude. Metaphoric endings also elicited a larger posterior positivity than did either literal or literal mapping words. Consistent with conceptual blending theory, the results suggest that the demands of conceptual integration affect the difficulty of both literal and metaphorical language.
Riemannian Metric Learning for Symmetric Positive Definite Matrices

Over the past few years, symmetric positive definite matrice s (SPD) have been receiving considerable attention from computer v ision community. Though various distance measures have been proposed in the p ast for comparing SPD matrices, the two most widely-used measures are affin e-invariant distance and log-Euclidean distance. This is because these two measures are true geodesic distances induced by Riemannian geometry. In this work, we focus on the log-Euclidean Riemannian geometry and propose a data-d riven approach for learning Riemannian metrics/geodesic distances for SPD ma trices. We show that the geodesic distance learned using the proposed approach p erforms better than various existing distance measures when evaluated on face m tching and clustering tasks. Notations – I denotes the identity matrix of appropriate size. – 〈 , 〉 denotes an inner product. – Sn denotes the set of n× n symmetric matrices. – S n denotes the set of n× n symmetric positive definite matrices. – TpM denotes the tangent space to the manifold M at the pointp ∈ M. – ‖ ‖F denotes the matrix Frobenius norm. – Chol(P) denotes the lower triangular matrix obtained from t he Cholesky decomposition of a matrix P. – exp() and log() denote matrix exponential and logarithm res pectively. – ∂ ∂x and ∂ 2 ∂x represent partial derivatives. 2 Raviteja Vemulapalli, David W. Jacobs
Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies

Intelligent behaviour in the real-world requires the ability to acquire new knowledge from an ongoing sequence of experiences while preserving and reusing past knowledge. We propose a novel algorithm for unsupervised representation learning from piece-wise stationary visual data: Variational Autoencoder with Shared Embeddings (VASE). Based on the Minimum Description Length principle, VASE automatically detects shifts in the data distribution and allocates spare representational capacity to new knowledge, while simultaneously protecting previously learnt representations from catastrophic forgetting. Our approach encourages the learnt representations to be disentangled, which imparts a number of desirable properties: VASE can deal sensibly with ambiguous inputs, it can enhance its own representations through imagination-based exploration, and most importantly, it exhibits semantically meaningful sharing of latents between different datasets. Compared to baselines with entangled representations, our approach is able to reason beyond surface-level statistics and perform semantically meaningful cross-domain inference.
Neural mechanisms of brain–computer interface control

Brain-computer interfaces (BCIs) enable people with paralysis to communicate with their environment. Motor imagery can be used to generate distinct patterns of cortical activation in the electroencephalogram (EEG) and thus control a BCI. To elucidate the cortical correlates of BCI control, users of a sensory motor rhythm (SMR)-BCI were classified according to their BCI control performance. In a second session these participants performed a motor imagery, motor observation and motor execution task in a functional magnetic resonance imaging (fMRI) scanner. Group difference analysis between high and low aptitude BCI users revealed significantly higher activation of the supplementary motor areas (SMA) for the motor imagery and the motor observation tasks in high aptitude users. Low aptitude users showed no activation when observing movement. The number of activated voxels during motor observation was significantly correlated with accuracy in the EEG-BCI task (r=0.53). Furthermore, the number of activated voxels in the right middle frontal gyrus, an area responsible for processing of movement observation, correlated (r=0.72) with BCI-performance. This strong correlation highlights the importance of these areas for task monitoring and working memory as task goals have to be activated throughout the BCI session. The ability to regulate behavior and the brain through learning mechanisms involving imagery such as required to control a BCI constitutes the consequence of ideo-motor co-activation of motor brain systems during observation of movements. The results demonstrate that acquisition of a sensorimotor program reflected in SMR-BCI-control is tightly related to the recall of such sensorimotor programs during observation of movements and unrelated to the actual execution of these movement sequences.
BOOTSTRAPPING TECHNIQUES TO IMPROVE THE BANDWIDTH OF TRANSIMPEDANCE AMPLIFIERS C .

Transimpedance amplifiers using voltage feedback operational amplifiers are widely used for current to voltage conversion in applications when a moderatehigh bandwidth and a high sensitivity are required, such as photodiodes, photomultipliers, electron multipliers and capacitive sensors. The conventional circuit presents a virtual earth to the input and at low frequencies, the input capacitance is usually not a significant concem. However, at high frequencies and especially for high sensitivity circuits, the total input capacitance can severely limit the available bandwidth from the circuit [1,6]. The input capacitance in effect constitutes part of the feedback network of the op-amp and hence reduces the available loop gain at high frequencies. In some cases a high input capacitance can cause the circuit to have a lightly damped or unstable dynamic response. Lag compensation by simply adding feedback capacitance is generally used to guarantee stability, however this approach does not permit the full gain-bandwidth characteristic of the op-amp to be fully exploited.
Mining for bidding strategies on ebay

Millions of people participate in online auctions on websites such as eBay. The data available in these public markets offer interesting opportunities to study Internet auctions. We explore techniques for identifying common bidding patterns on eBay using data from eBay video game console auctions. The analysis reveals that there are certain bidding behaviors that appear frequently in the data, some of which have been previously identified and others which are new. We propose new attributes of bidding engagements and rules for classifying strategies. In addition, we suggest economic motivations that might lead to the identified behaviors.