nils-herrmann/clean_scirepeval huggingface dataset:
Learning Neural Network Classifiers with Low Model Complexity

Modern neural network architectures for large-scale learning tasks have substantially higher model complexities, which makes understanding, visualizing and training these architectures difficult. Recent contributions to deep learning techniques have focused on architectural modifications to improve parameter efficiency and performance. In this paper, we derive a continuous and differentiable error functional for a neural network that minimizes its empirical error as well as a measure of the model complexity. The latter measure is obtained by deriving a differentiable upper bound on the Vapnik-Chervonenkis (VC) dimension of the classifier layer of a class of deep networks. Using standard backpropagation, we realize a training rule that tries to minimize the error on training samples, while improving generalization by keeping the model complexity low. We demonstrate the effectiveness of our formulation (the Low Complexity Neural Network-LCNN) across several deep learning algorithms, and a variety of large benchmark datasets. We show that hidden layer neurons in the resultant networks learn features that are crisp, and in the case of image datasets, quantitatively sharper. Our proposed approach yields benefits across a wide range of architectures, in comparison to and in conjunction with methods such as Dropout and Batch Normalization, and our results strongly suggest that deep learning techniques can benefit from model complexity control methods such as the LCNN learning rule.
Sensor Deployment and Target Localization Based on Virtual Forces

The effectiveness of cluster-based distributed sensor networks depends to a large extent on the coverage provided by the sensor deployment. We propose a virtual force algorithm (VFA) as a sensor deployment strategy to enhance the coverage after an initial random placement of sensors. For a given number of sensors, the VFA algorithm attempts to maximize the sensor field coverage. A judicious combination of attractive and repulsive forces is used to determine virtual motion paths and the rate of movement for the randomly-placed sensors. Once the effective sensor positions are identified, a one-time movement with energy consideration incorporated is carried out, i.e., the sensors are redeployed to these positions. We also propose a novel probabilistic target localization algorithm that is executed by the cluster head. The localization results are used by the cluster head to query only a few sensors (out of those that report the presence of a target) for more detailed information. Simulation results are presented to demonstrate the effectiveness of the proposed approach.
On the Use of Weighted Linear Combination Method in GIS: Common and Best Practice Approaches

The weighted linear combination (WLC) technique is a decision rule for deriving composite maps using GIS. It is one of the most often used decision models in GIS. The method, however, is frequently applied without full understanding of the assumptions underling this approach. In many case studies, the WLC model has been applied incorrectly and with dubious results because analysts (decision makers) have ignored or been unaware of the assumptions. This paper provides a critical overview of the current practice with respect to GIS/WLC and suggests the best practice approach.
THE PLACE OF MISES’S HUMAN ACTION IN THE DEVELOPMENT OF MODERN ECONOMIC THOUGHT

The core of any system of economic theory is the explanation of how prices are determined. As Mises (1998, p. 235) himself put it, “Economics is mainly concerned with the analysis of the determination of money prices of goods and services exchanged on the market.” Thus, the core of Human Action is parts three and four (pp. 201–684), entitled, respectively, “Economic Calculation” and “Catallactics or Economics of the Market Society.” In these two parts, comprising 484 pages, there is presented for the first time a complete and systematic theory of how actual market prices are determined. Of course, Mises did not create this theory out of whole cloth. In fact, the theory of price elaborated in Human Action represents the crowning achievement of the Austrian School of economics. It is the culmination of the approach to price theory originated by Carl Menger in 1871 and developed further by a handful of brilliant economists of the generation intervening between Menger and Mises. These latter included especially Eugen von Böhm-Bawerk, J.B. Clark, Phillip H. Wicksteed, Frank A. Fetter, and Herbert J. Davenport. Unfortunately, for reasons to be explained below, the entire Mengerian approach went into decline after World War I and had lapsed into nearly complete dormancy by the mid-1930s. Mises’s outstanding contribution in Human Action was to singlehandedly revive this approach and elaborate it into a coherent and systematic theory of price determination. This article is divided into sections, section 1 describes the development of the Mengerian approach to price theory up until World War I, by which time it had reached the zenith of its international influence. Section 2 describes its amazingly rapid decline and suggests four reasons for it, including two fundamental theoretical
Guiding Reinforcement Learning Exploration Using Natural Language

In this work we present a technique for using natural language to help reinforcement learning generalize to unseen environments using neural machine translation techniques. These techniques are then integrated into policy shaping to make it more effective at learning in unseen environments. We evaluate this technique using the popular arcade game, Frogger, and show that our modified policy shaping algorithm improves over a Q-learning agent as well as a baseline version of policy shaping.
Syscoin: A Peer-to-Peer Electronic Cash System with Blockchain-Based Services for E-Business

While Bitcoin (Peer-to-Peer Electronic Cash) [Nak]solved the double spend problem and provided work withtimestamps on a public ledger, it has not to date extendedthe functionality of a blockchain beyond a transparent andpublic payment system. Satoshi Nakamoto's original referenceclient had a decentralized marketplace service which was latertaken out due to a lack of resources [Deva]. We continued withNakamoto's vision by creating a set of commercial-grade ser-vices supporting a wide variety of business use cases, includinga fully developed blockchain-based decentralized marketplace,secure data storage and transfer, and unique user aliases thatlink the owner to all services controlled by that alias.
Hierarchical Approaches for Reinforcement Learning in Parameterized Action Space

We explore Deep Reinforcement Learning in a parameterized action space. Specifically, we investigate how to achieve sample-efficient end-to-end training in these tasks. We propose a new compact architecture for the tasks where the parameter policy is conditioned on the output of the discrete action policy. We also propose two new methods based on the state-of-the-art algorithms Trust Region Policy Optimization (TRPO) and Stochastic Value Gradient (SVG) to train such an architecture. We demonstrate that these methods outperform the state of the art method, Parameterized Action DDPG, on test domains.
Data Compression

This paper surveys a variety of data compression methods spanning almost 40 years of research, from the work of Shannon, Fano, and Huffman in the late 1940s to a technique developed in 1986. The aim of data compression is to reduce redundancy in stored or communicated data, thus increasing effective data density. Data compression has important application in the areas of file storage and distributed systems. Concepts from information theory as they relate to the goals and evaluation of data compression methods are discussed briefly. A framework for evaluation and comparison of methods is constructed and applied to the algorithms presented. Comparisons of both theoretical and empirical natures are reported, and possibilities for future research are suggested.
Online Learning of Relaxed CCG Grammars for Parsing to Logical Form

We consider the problem of learning to parse sentences to lambda-calculus representations of their underlying semantics and present an algorithm that learns a weighted combinatory categorial grammar (CCG). A key idea is to introduce non-standard CCG combinators that relax certain parts of the grammar—for example allowing flexible word order, or insertion of lexical items— with learned costs. We also present a new, online algorithm for inducing a weighted CCG. Results for the approach on ATIS data show 86% F-measure in recovering fully correct semantic analyses and 95.9% F-measure by a partial-match criterion, a more than 5% improvement over the 90.3% partial-match figure reported by He and Young (2006).
Hierarchical Representations for Efficient Architecture Search

We explore efficient neural architecture search methods and show that a simple yet powerful evolutionary algorithm can discover new architectures with excellent performance. Our approach combines a novel hierarchical genetic representation scheme that imitates the modularized design pattern commonly adopted by human experts, and an expressive search space that supports complex topologies. Our algorithm efficiently discovers architectures that outperform a large number of manually designed models for image classification, obtaining top-1 error of 3.6% on CIFAR-10 and 20.3% when transferred to ImageNet, which is competitive with the best existing neural architecture search approaches. We also present results using random search, achieving 0.3% less top-1 accuracy on CIFAR-10 and 0.1% less on ImageNet whilst reducing the search time from 36 hours down to 1 hour.
Error tracking in a clinical biochemistry laboratory.

We report our results for the systematic recording of all errors in a standard clinical laboratory over a 1-year period.



Recording was performed using a commercial database program. All individuals in the laboratory were allowed to report errors. The testing processes were classified according to function, and errors were classified as pre-analytical, analytical, post-analytical, or service-related, and then further divided into descriptive subgroups. Samples were taken from hospital wards (38.6%), outpatient clinics (25.7%), general practitioners (29.4%), and other hospitals.



A total of 1189 errors were reported in 1151 reports during the first year, corresponding to an error rate of 1 error for every 142 patients, or 1 per 1223 tests. The majority of events were due to human errors (82.6%), and only a few (4.3%) were the result of technical errors. Most of the errors (81%) were pre-analytical. Of the remainder, 10% were analytical, 8% were post-analytical, and 1% was service-related. Nearly half of the errors (n=550) occurred with samples received from general practitioners or clinical hospital wards. Identification errors were relatively common when non-technicians collected blood samples.



Each clinical laboratory should record errors in a structured manner. A relation database is a useful tool for the recording and extraction of data, as the database can be structured to reflect the workflow at each individual laboratory.
AVA: A Video Dataset of Spatio-Temporally Localized Atomic Visual Actions

This paper introduces a video dataset of spatio-temporally localized Atomic Visual Actions (AVA). The AVA dataset densely annotates 80 atomic visual actions in 437 15-minute video clips, where actions are localized in space and time, resulting in 1.59M action labels with multiple labels per person occurring frequently. The key characteristics of our dataset are: (1) the definition of atomic visual actions, rather than composite actions; (2) precise spatio-temporal annotations with possibly multiple annotations for each person; (3) exhaustive annotation of these atomic actions over 15-minute video clips; (4) people temporally linked across consecutive segments; and (5) using movies to gather a varied set of action representations. This departs from existing datasets for spatio-temporal action recognition, which typically provide sparse annotations for composite actions in short video clips. AVA, with its realistic scene and action complexity, exposes the intrinsic difficulty of action recognition. To benchmark this, we present a novel approach for action localization that builds upon the current state-of-the-art methods, and demonstrates better performance on JHMDB and UCF101-24 categories. While setting a new state of the art on existing datasets, the overall results on AVA are low at 15.8% mAP, underscoring the need for developing new approaches for video understanding.
Neuroanatomy for the Neuroscientist

The brain and spinal cord form the central nervous system. The brain is the part of the central nervous system that is housed in the cranium/skull. It consists of the brain stem, diencephalon, cerebellum, and cerebrum. At the foramen magnum, the highest cervical segment of the spinal cord is continuous with the lowest level of the medulla of the brain stem. The spinal nerves from the sacral, lumbar, thoracic, and cervical levels of the spinal cord form the lower part of the peripheral nervous system and record general sensations of pain, temperature touch, and pressure. The 12 cranial nerves attached to the brain form the upper part of the peripheral nervous system and record general sensations of pain, temperature touch, and pressure, but in addition we now find the presence of the special senses of smell, vision, hearing, balance, and taste. The blood supply to the brain originates from the first major arterial branches from the heart insuring that over 20% of the entire supply of oxygenated blood flows directly into the brain.
Search Based Software Engineering: A Comprehensive Analysis and Review of Trends Techniques and Applications

In the past five years there has been a dramatic increase in work on Search Based Software Engineering (SBSE), an approach to software engineering in which search based optimisation algorithms are used to address problems in Software Engineering. SBSE has been applied to problems throughout the Software Engineering lifecycle, from requirements and project planning to maintenance and re-engineering. The approach is attractive because it offers a suite of adaptive automated and semi-automated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This paper provides a review and classification of literature on SBSE. The paper identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.
MEDIA CLOUD : WHEN MEDIA REVOLUTION MEETS RISE OF CLOUD COMPUTING

Media content has become the major traffic of Internet and will keep on increasing rapidly. Various innovative media applications, services, devices have emerged and people tend to consume more media contents. We are meeting a media revolution. But media processing requires great capacity and capability of computing resources. Meanwhile cloud computing has emerged as a prosperous technology and the cloud computing platform has become a fundamental facility providing various services, great computing power, massive storage and bandwidth with modest cost. The integration of cloud computing and media processing is therefore a natural choice for both of them, and hence comes forth the media cloud. In this paper we make a comprehensive overview on the recent media cloud research work. We first discuss the challenges of the media cloud, and then summarize its architecture, the processing, and its storage and delivery mechanisms. As the result, we propose a new architecture for the media cloud. At the end of this paper, we make suggestions on how to build a media cloud and propose several future research topics as the conclusion.
Antimicrobial and synergistic effects of some essential oils to fight against microbial pathogens – a review

Hemali Padalia, Pooja Moteriya, Yogesh Baravalia and Sumitra Chanda* 1 Phytochemical, Pharmacological and Microbiological Laboratory, Department of Biosciences (UGC-CAS), Saurashtra University, Rajkot-360005, Gujarat, India 2 Phytochemical, Pharmacological and Microbiological Laboratory, Department of Biochemistry, Saurashtra University, Rajkot-360005, Gujarat, India * Corresponding author: email: svchanda@gmail.com
Directional water collection on wetted spider silk

Many biological surfaces in both the plant and animal kingdom possess unusual structural features at the micro- and nanometre-scale that control their interaction with water and hence wettability. An intriguing example is provided by desert beetles, which use micrometre-sized patterns of hydrophobic and hydrophilic regions on their backs to capture water from humid air. As anyone who has admired spider webs adorned with dew drops will appreciate, spider silk is also capable of efficiently collecting water from air. Here we show that the water-collecting ability of the capture silk of the cribellate spider Uloborus walckenaerius is the result of a unique fibre structure that forms after wetting, with the ‘wet-rebuilt’ fibres characterized by periodic spindle-knots made of random nanofibrils and separated by joints made of aligned nanofibrils. These structural features result in a surface energy gradient between the spindle-knots and the joints and also in a difference in Laplace pressure, with both factors acting together to achieve continuous condensation and directional collection of water drops around spindle-knots. Submillimetre-sized liquid drops have been driven by surface energy gradients or a difference in Laplace pressure, but until now neither force on its own has been used to overcome the larger hysteresis effects that make the movement of micrometre-sized drops more difficult. By tapping into both driving forces, spider silk achieves this task. Inspired by this finding, we designed artificial fibres that mimic the structural features of silk and exhibit its directional water-collecting ability.
Structure and Validity of Self-Concept Clarity Measures

We examine the structure and validity of existing measures of selfconcept clarity  (SCC). We document six different measurement strategies that have been employed in the self-concept clarity literature, review existing research on their relationships with each other and with self-esteem, and present in-progress research designed to examine their structure and validity. We conclude that these measures largely reflect different constructs and that they demonstrate distinct patterns of relationships with criteria previously examined in the self-concept clarity literature. Further, we examine incremental validity over self-esteem, noting that measures of self-concept clarity demonstrate considerably weaker relationships with criteria once self-esteem is controlled for in the analyses. We discuss measurement of selfconcept clarity, placing special emphasis on understanding potentially diverse measures of SCC-related constructs, the role of self-esteem in selfconcept  clarity research, and potential cultural boundedness of extant assessment strategies.
Using Measures of Risk Perception to Predict Information Security Behavior: Insights from Electroencephalography (EEG)

Special Issue Anthony Vance Brigham Young University anthony@vance.name Bonnie Brinton Anderson Brigham Young University bonnie_anderson@byu.edu C. Brock Kirwan Brigham Young University kirwan@byu.edu Users’ perceptions of risks have important implications for information security because individual users’ actions can compromise entire systems. Therefore, there is a critical need to understand how users perceive and respond to information security risks. Previous research on perceptions of information security risk has chiefly relied on self-reported measures. Although these studies are valuable, risk perceptions are often associated with feelings—such as fear or doubt—that are difficult to measure accurately using survey instruments. Additionally, it is unclear how these self-reported measures map to actual security behavior. This paper contributes to this topic by demonstrating that risk-taking behavior is effectively predicted using electroencephalography (EEG) via event-related potentials (ERPs). Using the Iowa Gambling Task, a widely used technique shown to be correlated with real-world risky behaviors, we show that the differences in neural responses to positive and negative feedback strongly predict users’ information security behavior in a separate laboratory-based computing task. In addition, we compare the predictive validity of EEG measures to that of self-reported measures of information security risk perceptions. Our experiments show that self-reported measures are ineffective in predicting security behaviors under a condition in which information security is not salient. However, we show that, when security concerns become salient, self-reported measures do predict security behavior. Interestingly, EEG measures significantly predict behavior in both salient and non-salient conditions, which indicates that EEG measures are a robust predictor of security behavior.
Minimap and miniasm: fast mapping and de novo assembly for noisy long sequences

Single Molecule Real-Time (SMRT) sequencing technology and Oxford Nanopore technologies (ONT) produce reads over 10 kb in length, which have enabled high-quality genome assembly at an affordable cost. However, at present, long reads have an error rate as high as 10-15%. Complex and computationally intensive pipelines are required to assemble such reads.



We present a new mapper, minimap and a de novo assembler, miniasm, for efficiently mapping and assembling SMRT and ONT reads without an error correction stage. They can often assemble a sequencing run of bacterial data into a single contig in a few minutes, and assemble 45-fold Caenorhabditis elegans data in 9 min, orders of magnitude faster than the existing pipelines, though the consensus sequence error rate is as high as raw reads. We also introduce a pairwise read mapping format and a graphical fragment assembly format, and demonstrate the interoperability between ours and current tools.



https://github.com/lh3/minimap and https://github.com/lh3/miniasm


CONTACT
hengli@broadinstitute.org



Supplementary data are available at Bioinformatics online.
An Integrated Boost Resonant Converter for Photovoltaic Applications

Effective photovoltaic power conditioning requires efficient power conversion and accurate maximum power point tracking to counteract the effects of panel mismatch, shading, and general variance in power output during a daily cycle. In this paper, the authors propose an integrated boost resonant converter with low component count, galvanic isolation, simple control, as well as high efficiency across a wide input and load range. Provided is a discussion of the converter synthesis, key operational features, converter design procedure, and loss analysis, as well as experimental verification by way of a 250-W prototype with a California Energy Commission efficiency of 96.8%.