nils-herrmann/clean_scirepeval huggingface dataset:
Techniques for Interpretable Machine Learning

Interpretable machine learning tackles the important problem that humans cannot understand the behaviors of complex machine learning models and how these classifiers arrive at a particular decision. Although many approaches have been proposed, a comprehensive understanding of the achievements and challenges is still lacking. This paper provides a survey covering existing techniques and methods to increase the interpretability of machine learning models and also discusses the crucial issues to consider in future work such as interpretation design principles and evaluation metrics in order to push forward the area of interpretable machine learning.
The Cancer Genome Atlas Pan-Cancer analysis project

The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumors to discover molecular aberrations at the DNA, RNA, protein and epigenetic levels. The resulting rich data provide a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages. The Pan-Cancer initiative compares the first 12 tumor types profiled by TCGA. Analysis of the molecular aberrations and their functional roles across tumor types will teach us how to extend therapies effective in one cancer type to others with a similar genomic profile.
Information-Centric Wireless Networks with Mobile Edge Computing

In order to better accommodate the dramatically increasing demand for data caching and computing services, storage and computation capabilities should be endowed to some of the intermediate nodes within the network. In this paper, we design a novel virtualized heterogeneous networks framework aiming at enabling content caching and computing. With the virtualization of the whole system, the communication, computing and caching resources can be shared among all users associated with different virtual service providers. We formulate the virtual resource allocation strategy as a joint optimization problem, where the gains of not only virtualization but also caching and computing are taken into consideration in the proposed architecture. In addition, a distributed algorithm based on alternating direction method of multipliers is adopted to solve the formulated problem, in order to reduce the computational complexity and signaling overhead. Finally, extensive simulations are presented to show the effectiveness of the proposed scheme under different system parameters.
The Edge of Violence : Towards Telling the Difference Between Violent and Non-Violent Radicalization

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Real-Time Data Analytics in Sensor Networks

The proliferation of Wireless Sensor Networks (WSNS) in the past decade has provided the bridge between the physical and digital worlds, enabling the monitoring and study of physical phenomena at a granularity and level of detail that was never before possible. In this study, we review the efforts of the research community with respect to two important problems in the context of WSNS: real-time collection of the sensed data, and real-time processing of these data series.
Huffman Based LZW Lossless Image Compression Using Retinex Algorithm

Image compression is an application of data compression that encodes the original image with few bits. The objective of image compression is to reduce irrelevance and redundancy of the image data in order to be able to store or transmit data in an efficient form. So image compression can reduce the transmit time over the network and increase the speed of transmission. In Lossless image compression no data loss when the compression Technique is done. In this research, a new lossless compression scheme is presented and named as Huffman Based LZW Lossless Image Compression using Retinex Algorithm which consists of three stages: In the first stage, a Huffman coding is used to compress the image. In the second stage all Huffman code words are concatenated together and then compressed with LZW coding and decoding. In the third stage the Retinex algorithm are used on compressed image for enhance the contrast of image and improve the quality of image. This Proposed Technique is used to increase the compression ratio (CR), Peak signal of Noise Ratio (PSNR), and Mean Square Error (MSE) in the MATLAB Software. Compression, Encode, Decode, Huffman, LZW.
Plant-based nutrition for healthcare professionals: implementing diet as a primary modality in the prevention and treatment of chronic disease

Cardiovascular disease remains the world's leading cause of death. Yet, we have known for decades that the vast majority of atherosclerosis and its subsequent morbidity and mortality are influenced predominantly by diet. This paper will describe a health-promoting whole food, plant-based diet; delineate macro- and micro-nutrition, emphasizing specific geriatric concerns; and offer guidance to physicians and other healthcare practitioners to support patients in successfully utilizing nutrition to improve their health.
Ethnic Polarization , Potential Con ict , and Civil Wars

This paper analyzes the relationship between ethnic fractionalization, polarization, and conict. In recent years many authors have found empirical evidence that ethnic fractionalization has a negative e¤ect on growth. One mechanism that can explain this nexus is the e¤ect of ethnic heterogeneity on rent-seeking activities and the increase in potential conict, which is negative for investment. However the empirical evidence supporting the e¤ect of ethnic fractionalization on the incidence of civil conicts is very weak. Although ethnic fractionalization may be important for growth, we argue that the channel is not through an increase in potential ethnic conict. We discuss the appropriateness of indices of polarization to capture conictive dimensions. We develop a new measure of ethnic heterogeneity that satises the basic properties associated with the concept of polarization. The empirical section shows that this index of ethnic polarization is a signicant variable in the explanation of the incidence of civil wars. This result is robust to the presence of other indicators of ethnic heterogeneity, alternative sources of data for the construction of the indicators, and alternative data for civil wars.  We are grateful for comments by Antonio Villar, Joan Esteban, Paul Collier, Tim Besley and two anonymous referees. We thank the participants of seminars at the World Bank, Institut de la Mediterranea, Toulouse, Brown University, the European Economic Association Meetings and the Winter Meetings of the Econometric Society. We would like to thank Sergio Kurlat, Bill Easterly and Anke Hoe­ er for sharing their data with us. Financial support from the BBVA Foundation and the Spanish Secretary of Science and Technology (SEC2003-04429) is kindly acknowledged
Deep Learning in Medical Imaging: General Overview.

The artificial neural network (ANN)-a machine learning technique inspired by the human neuronal synapse system-was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging.
A Unitary Approach to Lexical Pragmatics : Relevance , Inference and Ad Hoc Concepts

According to recent work in the new field of lexical pragmatics, the meanings of words are frequently pragmatically adjusted and fine-tuned in context, so that their contribution to the proposition expressed is different from their lexically encoded sense. Well-known examples include lexical narrowing (e.g. ‘drink’ used to mean ALCOHOLIC DRINK), approximation (or loosening) (e.g. ‘flat’ used to mean RELATIVELY FLAT) and metaphorical extension (e.g. ‘bulldozer’ used to mean FORCEFUL PERSON). These three phenomena are often studied in isolation from each other and given quite distinct kinds of explanation. In this chapter, we will propose a more unified account. We will try to show that narrowing, loosening and metaphorical extension are simply different outcomes of a single interpretive process which creates an ad hoc concept, or occasion-specific sense, based on interaction among encoded concepts, contextual information and pragmatic expectations or principles. We will outline an inferential account of the lexical adjustment process using the framework of relevance theory, and compare it with some alternative accounts.  This work is part of an AHRC-funded project ‘A Unified Theory of Lexical Pragmatics’ (AR16356). We are grateful to our research assistants, Patricia Kolaiti, Tim Wharton and, in particular, Rosa Vega Moreno, whose PhD work on metaphor we draw on in this paper, and to Vladimir Žegarac, François Recanati, Nausicaa Pouscoulous, Paula Rubio Fernandez and Hanna Stoever, for helpful discussions. We would also like to thank Dan Sperber for sharing with us many valuable insights on metaphor and on lexical pragmatics more generally.
Physics-based trajectory optimization for grasping in cluttered environments

Grasping an object in a cluttered, unorganized environment is challenging because of unavoidable contacts and interactions between the robot and multiple immovable (static) and movable (dynamic) obstacles in the environment. Planning an approach trajectory for grasping in such situations can benefit from physics-based simulations that describe the dynamics of the interaction between the robot manipulator and the environment. In this work, we present a physics-based trajectory optimization approach for planning grasp approach trajectories. We present novel cost objectives and identify failure modes relevant to grasping in cluttered environments. Our approach uses rollouts of physics-based simulations to compute the gradient of the objective and of the dynamics. Our approach naturally generates behaviors such as choosing to push objects that are less likely to topple over, recognizing and avoiding situations which might cause a cascade of objects to fall over, and adjusting the manipulator trajectory to push objects aside in a direction orthogonal to the grasping direction. We present results in simulation for grasping in a variety of cluttered environments with varying levels of density of obstacles in the environment. Our experiments in simulation indicate that our approach outperforms a baseline approach that considers multiple straight-line trajectories modified to account for static obstacles by an aggregate success rate of 14% with varying degrees of object clutter.
Diversity and dynamics of dendritic signaling.

Communication between neurons in the brain occurs primarily through synapses made onto elaborate treelike structures called dendrites. New electrical and optical recording techniques have led to tremendous advances in our understanding of how dendrites contribute to neuronal computation in the mammalian brain. The varied morphology and electrical and chemical properties of dendrites enable a spectrum of local and long-range signaling, defining the input-output relationship of neurons and the rules for induction of synaptic plasticity. In this way, diversity in dendritic signaling allows individual neurons to carry out specialized functions within their respective networks.
The Fraud Diamond : Considering the Four Elements of Fraud

Despite intense efforts to stamp out corruption, misappropriation of assets, and fraudulent financial reporting, it appears that fraud in its various forms is a problem that is increasing in frequency and severity. KPMG’s Fraud Survey 2003 documented a marked increase in overall fraud levels since its 1998 survey, with employee fraud by far the most common type of fraud. The 2003 survey also noted that fraudulent financial reporting had more than doubled from 1998. This trend is consistent with the unprecedented recent spate of large accounting frauds (Enron, WorldCom), as well as the increased number of accounting restatements and SEC enforcement actions in recent years.
Learning Semantic Similarity for Very Short Texts

Levering data on social media, such as Twitter and Facebook, requires information retrieval algorithms to become able to relate very short text fragments to each other. Traditional text similarity methods such as tf-idf cosine-similarity, based on word overlap, mostly fail to produce good results in this case, since word overlap is little or non-existent. Recently, distributed word representations, or word embeddings, have been shown to successfully allow words to match on the semantic level. In order to pair short text fragments -- as a concatenation of separate words -- an adequate distributed sentence representation is needed, in existing literature often obtained by naively combining the individual word representations. We therefore investigated several text representations as a combination of word embeddings in the context of semantic pair matching. This paper investigates the effectiveness of several such naive techniques, as well as traditional tf-idf similarity, for fragments of different lengths. Our main contribution is a first step towards a hybrid method that combines the strength of dense distributed representations -- as opposed to sparse term matching -- with the strength of tf-idf based methods to automatically reduce the impact of less informative terms. Our new approach outperforms the existing techniques in a toy experimental set-up, leading to the conclusion that the combination of word embeddings and tf-idf information might lead to a better model for semantic content within very short text fragments.
Learning time-series shapelets

Shapelets are discriminative sub-sequences of time series that best predict the target variable. For this reason, shapelet discovery has recently attracted considerable interest within the time-series research community. Currently shapelets are found by evaluating the prediction qualities of numerous candidates extracted from the series segments. In contrast to the state-of-the-art, this paper proposes a novel perspective in terms of learning shapelets. A new mathematical formalization of the task via a classification objective function is proposed and a tailored stochastic gradient learning algorithm is applied. The proposed method enables learning near-to-optimal shapelets directly without the need to try out lots of candidates. Furthermore, our method can learn true top-K shapelets by capturing their interaction. Extensive experimentation demonstrates statistically significant improvement in terms of wins and ranks against 13 baselines over 28 time-series datasets.
A low-power, high-performance approximate multiplier with configurable partial error recovery

Approximate circuits have been considered for error-tolerant applications that can tolerate some loss of accuracy with improved performance and energy efficiency. Multipliers are key arithmetic circuits in many such applications such as digital signal processing (DSP). In this paper, a novel approximate multiplier with a lower power consumption and a shorter critical path than traditional multipliers is proposed for high-performance DSP applications. This multiplier leverages a newly-designed approximate adder that limits its carry propagation to the nearest neighbors for fast partial product accumulation. Different levels of accuracy can be achieved through a configurable error recovery by using different numbers of most significant bits (MSBs) for error reduction. The approximate multiplier has a low mean error distance, i.e., most of the errors are not significant in magnitude. Compared to the Wallace multiplier, a 16-bit approximate multiplier implemented in a 28nm CMOS process shows a reduction in delay and power of 20% and up to 69%, respectively. It is shown that by utilizing an appropriate error recovery, the proposed approximate multiplier achieves similar processing accuracy as traditional exact multipliers but with significant improvements in power and performance.
DVQA: Understanding Data Visualizations via Question Answering

Bar charts are an effective way to convey numeric information, but today's algorithms cannot parse them. Existing methods fail when faced with even minor variations in appearance. Here, we present DVQA, a dataset that tests many aspects of bar chart understanding in a question answering framework. Unlike visual question answering (VQA), DVQA requires processing words and answers that are unique to a particular bar chart. State-of-the-art VQA algorithms perform poorly on DVQA, and we propose two strong baselines that perform considerably better. Our work will enable algorithms to automatically extract numeric and semantic information from vast quantities of bar charts found in scientific publications, Internet articles, business reports, and many other areas.
Ultrasonographically determined pedicled breast reduction in severe gigantomastia.

The free nipple breast reduction method has certain disadvantages, such as nipple hyposensitivity, loss of lactation, and loss of projection. To eliminate these risks, the authors describe a patient-based breast reduction technique in which the major supplier vessels of the nipple-areola complex were determined by color Doppler ultrasonography. Pedicles containing these vessels were designed for reductions.



Sixteen severe gigantomastia patients with a mean age of 41 years (range, 23 to 60 years) were included in the study. Major nipple-areola complex perforators were determined with 13- to 5-MHz linear probe Doppler ultrasonography before surgery. Pedicles were designed according to the vessel locations, and reductions were performed with superomedial-, superolateral-, or mediolateral-based designs.



Different combinations of internal mammary and lateral thoracic artery perforator-based reductions were achieved. None of the patients had areola necrosis. Mean reduction weight was 1795 g (range, 1320 to 2280) per breast.



Instead of using standard markings for severe gigantomastia patients, custom-made and sonographically determined pedicles were used. This technique can be considered as a "guide" for the surgeon during very large breast reductions.
Update on developments with SGLT2 inhibitors in the management of type 2 diabetes

The importance of the kidney's role in glucose homeostasis has gained wider understanding in recent years. Consequently, the development of a new pharmacological class of anti-diabetes agents targeting the kidney has provided new treatment options for the management of type 2 diabetes mellitus (T2DM). Sodium glucose co-transporter type 2 (SGLT2) inhibitors, such as dapagliflozin, canagliflozin, and empagliflozin, decrease renal glucose reabsorption, which results in enhanced urinary glucose excretion and subsequent reductions in plasma glucose and glycosylated hemoglobin concentrations. Modest reductions in body weight and blood pressure have also been observed following treatment with SGLT2 inhibitors. SGLT2 inhibitors appear to be generally well tolerated, and have been used safely when given as monotherapy or in combination with other oral anti-diabetes agents and insulin. The risk of hypoglycemia is low with SGLT2 inhibitors. Typical adverse events appear to be related to the presence of glucose in the urine, namely genital mycotic infection and lower urinary tract infection, and are more often observed in women than in men. Data from long-term safety studies with SGLT2 inhibitors and from head-to-head SGLT2 inhibitor comparator studies are needed to fully determine their benefit-risk profile, and to identify any differences between individual agents. However, given current safety and efficacy data, SGLT2 inhibitors may present an attractive option for T2DM patients who are failing with metformin monotherapy, especially if weight is part of the underlying treatment consideration.
Fitness Trackers: Fit for Health but Unfit for Security and Privacy

Wearable devices for fitness tracking and health monitoring have gained considerable popularity and become one of the fastest growing smart devices market. More and more companies are offering integrated health and activity monitoring solutions for fitness trackers. Recently insurances are offering their customers better conditions for health and condition monitoring. However, the extensive sensitive information collected by tracking products and accessibility by third party service providers poses vital security and privacy challenges on the employed solutions. In this paper, we present our security analysis of a representative sample of current fitness tracking products on the market. In particular, we focus on malicious user setting that aims at injecting false data into the cloud-based services leading to erroneous data analytics. We show that none of these products can provide data integrity, authenticity and confidentiality.
GRAPH PRIORS FOR DEEP NEURAL NETWORKS

In this work we explore how gene-gene interaction graphs can be used as a prior for the representation of a model to construct features based on known interactions between genes. Most existing machine learning work on graphs focuses on building models when data is confined to a graph structure. In this work we focus on using the information from a graph to build better representations in our models. We use the percolate task, determining if a path exists across a grid for a set of node values, as a proxy for gene pathways. We create variants of the percolate task to explore where existing methods fail. We test the limits of existing methods in order to determine what can be improved when applying these methods to a real task. This leads us to propose new methods based on Graph Convolutional Networks (GCN) that use pooling and dropout to deal with noise in the graph prior.
Elastic Allocation of Docker Containers in Cloud Environments

Docker containers wrap up a piece of software together with everything it needs for the execution and enable to easily run it on any machine. For their execution in the Cloud, we need to identify an elastic set of virtual machines that can accommodate those containers, while considering the diversity of their requirements. In this paper, we briefly describe our formulation of the Elastic provisioning of Virtual machines for Container Deployment (EVCD), which takes explicitly into account the heterogeneity of container requirements and virtual machine resources. Afterwards, we evaluate the EVCD formulation with the aim of demonstrating its flexibility in optimizing multiple QoS metrics.