nils-herrmann/clean_scirepeval huggingface dataset:
Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery

Linear spectral mixture analysis (LSMA) is a widely used technique in remote sensing to estimate abundance fractions of materials present in an image pixel. In order for an LSMA-based estimator to produce accurate amounts of material abundance, it generally requires two constraints imposed on the linear mixture model used in LSMA, which are the abundance sum-to-one constraint and the abundance nonnegativity constraint. The first constraint requires the sum of the abundance fractions of materials present in an image pixel to be one and the second imposes a constraint that these abundance fractions be nonnegative. While the first constraint is easy to deal with, the second constraint is difficult to implement since it results in a set of inequalities and can only be solved by numerical methods. Consequently, most LSMA-based methods are unconstrained and produce solutions that do not necessarily reflect the true abundance fractions of materials. In this case, they can only be used for the purposes of material detection, discrimination, and classification, but not for material quantification. In this paper, we present a fully constrained least squares (FCLS) linear spectral mixture analysis method for material quantification. Since no closed form can be derived for this method, an efficient algorithm is developed to yield optimal solutions. In order to further apply the designed algorithm to unknown image scenes, an unsupervised least squares error (LSE)-based method is also proposed to extend the FCLS method in an unsupervised manner. A series of computer simulations and real hyperspectral data experiments were conducted to demonstrate the performance of the proposed FCLS LSMA approach in material quantification.
The path to personalized medicine.

n engl j med 363;4 nejm.org july 22, 2010 301 to human illness, identified genetic variability in patients’ responses to dozens of treatments, and begun to target the molecular causes of some diseases. In addition, scientists are developing and using diagnostic tests based on genetics or other molecular mechanisms to better predict patients’ responses to targeted therapy. The challenge is to deliver the benefits of this work to patients. As the leaders of the National Institutes of Health (NIH) and the Food and Drug Administration (FDA), we have a shared vision of personalized medicine and the scientific and regulatory structure needed to support its growth. Together, we have been focusing on the best ways to develop new therapies and optimize prescribing by steering patients to the right drug at the right dose at the right time. We recognize that myriad obstacles must be overcome to achieve these goals. These include scientific challenges, such as determining which genetic markers have the most clinical significance, limiting the off-target effects of gene-based therapies, and conducting clinical studies to identify genetic variants that are correlated with a drug response. There are also policy challenges, such as finding a level of regulation for genetic tests that both protects patients and encourages innovation. To make progress, the NIH and the FDA will invest in advancing translational and regulatory science, better define regulatory pathways for coordinated approval of codeveloped diagnostics and therapeutics, develop risk-based approaches for appropriate review of diagnostics to more accurately assess their validity and clinical utility, and make information about tests readily available. Moving from concept to clinical use requires basic, translational, and regulatory science. On the basic-science front, studies are identifying many genetic variations underlying the risks of both rare and common diseases. These newly discovered genes, proteins, and pathways can represent powerful new drug targets, but currently there is insufficient evidence of a downstream market to entice the private sector to explore most of them. To fill that void, the NIH and the FDA will The Path to Personalized Medicine
Automatic Recognition of Deceptive Facial Expressions of Emotion

Humans modify facial expressions in order to mislead observers regarding their true emotional states. Being able to recognize the authenticity of emotional displays is notoriously difficult for human observers. Evidence in experimental psychology shows that discriminative facial responses are short and subtle. This suggests that such behavior would be easier to distinguish when captured in high resolution at an increased frame rate. We are proposing SASE-FE, the first dataset of genuine and deceptive facial expressions of emotions for automatic recognition1 . We show that overall the problem of recognizing deceptive facial expressions can be successfully addressed by learning spatio-temporal representations of the data. For this purpose, we propose a method that aggregates features along fiducial trajectories in a deeply learnt feature space. Interesting additional results show that on average it is easier to distinguish among genuine expressions than deceptive ones and that certain emotion pairs are more difficult to distinguish than others.
Why Deep Neural Networks?

Recently there has been much interest in understanding why deep neural networks are preferred to shallow networks. In this paper, we show that, for a large class of piecewise smooth functions, the number of neurons needed by a shallow network to approximate a function is exponentially larger than the corresponding number of neurons needed by a deep network for a given degree of function approximation. First, we consider univariate functions on a bounded interval and require a neural network to achieve an approximation error of ε uniformly over the interval. We show that shallow networks (i.e., networks whose depth does not depend on ε) require Ω(poly(1/ε)) neurons while deep networks (i.e., networks whose depth grows with 1/ε) require O(polylog(1/ε)) neurons. We then extend these results to certain classes of important multivariate functions. Our results are derived for neural networks which use a combination of rectifier linear units (ReLUs) and binary step units, two of the most popular type of activation functions. Our analysis builds on this simple observation that the binary approximation of a real number in the interval [0, 1] can be represented by a deep neural network which uses a “small” number of neurons.
Steganalysis of LSB matching in grayscale images

We consider the problem of detecting spatial domain least significant bit (LSB) matching steganography in grayscale images, which has proved much harder than for its counterpart, LSB replacement. We use the histogram characteristic function (HCF), introduced by Harmsen for the detection of steganography in color images but ineffective on grayscale images. Two novel ways of applying the HCF are introduced: calibrating the output using a downsampled image and computing the adjacency histogram instead of the usual histogram. Extensive experimental results show that the new detectors are reliable, vastly more so than those previously known.
Attention-based sequence-to-sequence model for speech recognition: development of state-of-the-art system on LibriSpeech and its application to non-native English

Recent research has shown that attention-based sequence-tosequence models such as Listen, Attend, and Spell (LAS) yield comparable results to state-of-the-art ASR systems on various tasks. In this paper, we describe the development of such a system and demonstrate its performance on two tasks: first we achieve a new state-of-the-art word error rate of 3.43% on the test clean subset of LibriSpeech English data; second on non-native English speech, including both read speech and spontaneous speech, we obtain very competitive results compared to a conventional system built with the most updated Kaldi recipe.
A Toolkit for Generating Sentences from Context-Free Grammars

Producing sentences from a grammar, according to various criteria, is required in many applications. It is also a basic building block for grammar engineering. This paper presents a toolkit for context-free grammars, which mainly consists of several algorithms for sentence generation or enumeration and for coverage analysis for context-free grammars. The toolkit deals with general context-free grammars. Besides providing implementations of algorithms, the toolkit also provides a simple graphical user interface, through which the user can use the toolkit directly. The toolkit is implemented in Java and is available at http://lcs.ios.ac.cn/ hiwu/toolkit.php. In the paper, the overview of the toolkit and the description of the GUI are presented, and experimental results and preliminary applications of the toolkit are also contained.
Labor Costs and Foreign Direct Investment : A Panel VAR Approach

This paper examines the endogenous interaction between labor costs and Foreign Direct Investment (FDI) in the OECD countries via the Panel VAR approach under system GMM estimates for the period 1995–2009. The available data allows identifying the relevance of the components of labor costs, and allows a detailed analysis across different sectors. Empirical findings have revealed that sectoral composition of FDI and the decomposition of labor costs play a significant role in investigating the dynamic association between labor costs and FDI. Further, results suggest that labor market policies should focus on productivity-enhancing tools in addition to price hindering tools.
Classifying software visualization tools using the Bloom's taxonomy of cognitive domain

There are a lot of software visualization tools existing for various purposes. Therefore, how to choose the right visualization tool for a specific task becomes an important issue. Although there are a few taxonomies for classifying visualization tools, each has its own defects. This paper proposes a new classification schema based on the widely used Bloom's cognitive taxonomy. We summarize a set of questions for each level of the Bloom taxonomy using the well defined verbs, to identify if the visualization tool belongs to the individual cognitive level, which in turn can determine the purposes of the tool. We also conduct case studies on four tools to evaluate this new approach. The result demonstrates that the new classification schema can provide good guidance for people to choose a useful visualization tool.
A Survey of Secure Routing Protocols in Multi-Hop Cellular Networks

Multi-hop networks are expected to be an important part of 5G mobile systems. In a multi-hop cellular network (MCN), the nodes assist each other in relaying packets towards their destinations. The structures and operations of multi-hop networks make them particularly vulnerable to certain types of attack. Hence, security measures to counter these attacks are necessary. In this paper, we provide an overview of the secure routing protocols for multi-hop networks and classify them into MCN Type-1 and Type-0 categories for device-to-device communications, and the Internet-of-Things category for machine-to-machine communications. Our focus is on the applied cryptographic techniques and the security mechanisms in secure routing protocols. We propose an evaluation framework incorporating different security aspects, vulnerabilities, and levels of deployability to compare a number of secure routing protocols. Moreover, we review the secure routing paths in software-defined networking as a solution for existing challenges in multi-hop networks. Some open research problems are highlighted as possible directions for future studies.
Modeling 2D Appearance Evolution for 3D Object Categorization

3D object categorization is a non-trivial task in computer vision encompassing many real-world applications. We pose the problem of categorizing 3D polygon meshes as learning appearance evolution from multi-view 2D images. Given a corpus of 3D polygon meshes, we first render the corresponding RGB and depth images from multiple viewpoints on a uniform sphere. Using rank pooling, we propose two methods to learn the appearance evolution of the 2D views. Firstly, we train view-invariant models based on a deep convolutional neural network (CNN) using the rendered RGB-D images and learn to rank the first fully connected layer activations and, therefore, capture the evolution of these extracted features. The parameters learned during this process are used as the 3D shape representations. In the second method, we learn the aggregation of the views from the outset by employing the ranking machine to the rendered RGB- D images directly, which produces aggregated 2D images which we term as ``3D shape images". We then learn CNN models on this novel shape representation for both RGB and depth which encode salient geometrical structure of the polygon. Experiments on the ModelNet40 and ModelNet10 datasets show that the proposed method consistently outperforms existing state-of-the-art algorithms in 3D shape recognition.
Automated detection of smuggled high-risk security threats using Deep Learning

The security infrastructure is ill-equipped to detect and deter the smuggling of non-explosive devices that enable terror attacks such as those recently perpetrated in western Europe. The detection of so-called “small metallic threats” (SMTs) in cargo containers currently relies on statistical risk analysis, intelligence reports, and visual inspection of X-ray images by security officers. The latter is very slow and unreliable due to the difficulty of the task: objects potentially spanning less than 50 pixels have to be detected in images containing more than 2 million pixels against very complex and cluttered backgrounds. In this contribution, we demonstrate for the first time the use of Convolutional Neural Networks (CNNs), a type of Deep Learning, to automate the detection of SMTs in fullsize X-ray images of cargo containers. Novel approaches for dataset augmentation allowed to train CNNs from-scratch despite the scarcity of data available. We report fewer than 6% false alarms when detecting 90% SMTs synthetically concealed in streamof-commerce images, which corresponds to an improvement of over an order of magnitude over conventional approaches such as Bag-of-Words (BoWs). The proposed scheme offers potentially super-human performance for a fraction of the time it would take for a security officers to carry out visual inspection (processing time is approximately 3.5s per container image).
EARNING INDEPENDENT CAUSAL MECHANISMS

Independent causal mechanisms are a central concept in the study of causality with implications for machine learning tasks. In this work we develop an algorithm to recover a set of (inverse) independent mechanisms relating a distribution transformed by the mechanisms to a reference distribution. The approach is fully unsupervised and based on a set of experts that compete for data to specialize and extract the mechanisms. We test and analyze the proposed method on a series of experiments based on image transformations. Each expert successfully maps a subset of the transformed data to the original domain, and the learned mechanisms generalize to other domains. We discuss implications for domain transfer and links to recent trends in generative modeling.
A 500mA analog-assisted digital-LDO-based on-chip distributed power delivery grid with cooperative regulation and IR-drop reduction in 65nm CMOS

With the die area of modern processors growing larger and larger, the IR drop across the power supply rail due to its parasitic resistance becomes considerable. There is an urgent demand for local power regulation to reduce the IR drop and to enhance transient response. A distributed power-delivery grid (DPDG) is an attractive solution for large area power-supply applications. The dual-loop distributed micro-regulator in  achieves a tight regulation and fast response, but suffers from large ripple and high power consumption due to the comparator-based regulator. Digital low-dropout regulators (DLDOs)  can be used as local micro-regulators to implement a DPDG, due to their low-voltage operation and process scalability. Adaptive control [3], asynchronous 3D pipeline control [4], analog-assisted tri-loop control [5], and event-driven PI control [6] are proposed to enhance the transient response speed. However, digital LDOs suffer from the intrinsic limitations of large output ripple and narrow current range. This paper presents an on-chip DPDg with cooperative regulation based on an analog-assisted digital LDO (AADLDO), which inherits the merits of low output ripple and sub-LSB current supply ability from the analog control, and the advantage of low supply voltage operation and adaptive fast response from the digital control.
A Graduated Assignment Algorithm for Graph Matching

A graduated assignment algorithm for graph matching is presented which is fast and accurate even in the presence of high noise. By combining graduated nonconvexity, two-way (assignment) constraints, and sparsity, large improvements in accuracy and speed are achieved. Its low order computational complexity [O(/m), where land mare the number of links in the two graphs] and robustness in the presence of noise offer advantages over traditional combinatorial approaches. The algorithm, not restricted to any special class of graph, is applied to subgraph isomorphism, weighted graph matching, and attributed relational graph matching. To illustrate the performance of the algorithm, attributed relational graphs derived from objects are matched. Then, results from twenty-five thousand experiments conducted on 100 node random graphs of varying types (graphs with only zero-one links, weighted graphs, and graphs with node attributes and multiple link types) are reported. No comparable results have been reported by any other graph matching algorithm before in the research literature. Twenty-five hundred control experiments are conducted using a relaxation labeling algorithm and large improvements in accuracy are demonstrated.
Identifying Anomalies in Graph Streams Using Change Detection

Anomaly detection in graph streams requires both the discovery of normative subgraph patterns in the stream and then the identification of subgraphs that are unexpected deviations from the normative patterns. Both of these processes are computationally complex, and therefore we should only update them when necessary. We present an approach based on a change detection metric used for graph sampling that selectively updates the normative patterns only when significant change has occurred. Using a batch processing method on the graph stream, we show that the change detection approach significantly reduces running times while maintaining the accuracy of more exhaustive approaches. CCS Concepts • Information systems~Data stream mining • Computing methodologies~Anomaly detection.
Autism genome-wide copy number variation reveals ubiquitin and neuronal genes

Autism spectrum disorders (ASDs) are childhood neurodevelopmental disorders with complex genetic origins. Previous studies focusing on candidate genes or genomic regions have identified several copy number variations (CNVs) that are associated with an increased risk of ASDs. Here we present the results from a whole-genome CNV study on a cohort of 859 ASD cases and 1,409 healthy children of European ancestry who were genotyped with ∼550,000 single nucleotide polymorphism markers, in an attempt to comprehensively identify CNVs conferring susceptibility to ASDs. Positive findings were evaluated in an independent cohort of 1,336 ASD cases and 1,110 controls of European ancestry. Besides previously reported ASD candidate genes, such as NRXN1 (ref. 10) and CNTN4 (refs 11, 12), several new susceptibility genes encoding neuronal cell-adhesion molecules, including NLGN1 and ASTN2, were enriched with CNVs in ASD cases compared to controls (P = 9.5 × 10-3). Furthermore, CNVs within or surrounding genes involved in the ubiquitin pathways, including UBE3A, PARK2, RFWD2 and FBXO40, were affected by CNVs not observed in controls (P = 3.3 × 10-3). We also identified duplications 55 kilobases upstream of complementary DNA AK123120 (P = 3.6 × 10-6). Although these variants may be individually rare, they target genes involved in neuronal cell-adhesion or ubiquitin degradation, indicating that these two important gene networks expressed within the central nervous system may contribute to the genetic susceptibility of ASD.
Hierarchical Attention Networks

We propose a novel attention network, which accurately attends to target objects of various scales and shapes in images through multiple stages. The proposed network enables multiple layers to estimate attention in a convolutional neural network (CNN). The hierarchical attention model gradually suppresses irrelevant regions in an input image using a progressive attentive process over multiple CNN layers. The attentive process in each layer determines whether to pass or suppress feature maps for use in the next convolution. We employ local contexts to estimate attention probability at each location since it is difficult to infer accurate attention by observing a feature vector from a single location only. The experiments on synthetic and real datasets show that the proposed attention network outperforms traditional attention methods in various attribute prediction tasks.
Horizon occlusion culling for real-time rendering of hierarchical terrains

We present a technique to perform occlusion culling for hierarchical terrains at run-time. The algorithm is simple to implement and requires minimal pre-processing and additional storage, yet leads to 2-4 times improvement in framerate for views with high degrees of occlusion. Our method is based on the well-known occlusion horizon algorithm. We show how to adapt the algorithm for use with hierarchical terrains. The occlusion horizon is constructed as the terrain is traversed in an approximate front to back ordering. Regions of the terrain are compared to the horizon to determine when they are completely occluded from the viewpoint. Culling these regions leads to significant savings in rendering.
SORT: A Self-ORganizing Trust Model for Peer-to-Peer Systems

Open nature of peer-to-peer systems exposes them to malicious activity. Building trust relationships among peers can mitigate attacks of malicious peers. This paper presents distributed algorithms that enable a peer to reason about trustworthiness of other peers based on past interactions and recommendations. Peers create their own trust network in their proximity by using local information available and do not try to learn global trust information. Two contexts of trust, service, and recommendation contexts, are defined to measure trustworthiness in providing services and giving recommendations. Interactions and recommendations are evaluated based on importance, recentness, and peer satisfaction parameters. Additionally, recommender's trustworthiness and confidence about a recommendation are considered while evaluating recommendations. Simulation experiments on a file sharing application show that the proposed model can mitigate attacks on 16 different malicious behavior models. In the experiments, good peers were able to form trust relationships in their proximity and isolate malicious peers.
Research on significance of VCPU scheduling for SR-IOV on NUMA platform

Virtualization is vital important in server consolidation. Using virtualization technology, a single physical machine can provide multiple isolated virtual machine for users, which will improve the overall system resources usage. However, In virtualization environment, Non-Uniform Memory Access Architecture (NUMA) problem is also a very important parameter that affects server's performance. To solve NUMA problem, many solutions have been proposed to reduce its performance impact on virtual machines and improve the overall system performance. Hardware Single Root I/O Virtualization (SR-IOV) is a solution for high performance network processing, but when SR-IOV technology is running on a NUMA platform, it also generates the traditional problem about NUMA.