nils-herrmann/clean_scirepeval huggingface dataset:
Asymptotic minimax regret for data compression, gambling, and prediction

For problems of data compression, gambling, and prediction of individual sequences 1 the following questions arise. Given a target family of probability mass functions ( 1 ), how do we choose a probability mass function ( 1 ) so that it approximately minimizes the maximum regret /belowdisplayskip10ptminus6pt max (log 1 ( 1 ) log 1 ( 1 )̂) and so that it achieves the best constant in the asymptotics of the minimax regret, which is of the form ( 2) log( 2 ) + + (1), where is the parameter dimension? Are there easily implementable strategies that achieve those asymptotics? And how does the solution to the worst case sequence problem relate to the solution to the corresponding expectation version min max (log 1 ( 1 ) log 1 ( 1 ))? In the discrete memoryless case, with a given alphabet of size , the Bayes procedure with the Dirichlet(1 2 1 2) prior is asymptotically maximin. Simple modifications of it are shown to be asymptotically minimax. The best constant is = log( (1 2) ( ( 2)) which agrees with the logarithm of the integral of the square root of the determinant of the Fisher information. Moreover, our asymptotically optimal strategies for the worst case problem are also asymptotically optimal for the expectation version. Analogous conclusions are given for the case of prediction, gambling, and compression when, for each observation, one has access to side information from an alphabet of size . In this setting the minimax regret is shown to be ( 1) 2 log 2 + + (1)
Mining behavioral economics to design persuasive technology for healthy choices

Influence through information and feedback has been one of the main approaches of persuasive technology. We propose another approach based on behavioral economics research on decision-making. This approach involves designing the presentation and timing of choices to encourage people to make self-beneficial decisions. We applied three behavioral economics persuasion techniques - the default option strategy, the planning strategy, and the asymmetric choice strategy - to promote healthy snacking in the workplace. We tested the strategies in three experimental case studies using a human snack deliverer, a robot, and a snack ordering website. The default and the planning strategies were effective, but they worked differently depending on whether the participants had healthy dietary lifestyles or not. We discuss designs for persuasive technologies that apply behavioral economics.
Unsupervised language model adaptation via topic modeling based on named entity hypotheses

Language model (LM) adaptation is often achieved by combining a generic LM with a topic-specific model that is more relevant to the target document. Unlike previous work on unsupervised LM adaptation, in this paper we propose to leverage named entity (NE) information for topic analysis and LM adaptation. We investigate two topic modeling approaches, latent Dirichlet allocation (LDA) and clustering, and proposed a new mixture topic model for LDA based LM adaptation. Our experiments for N-best list rescoring have shown that this new adaptation framework using NE information and topic analysis outperforms the baseline generic N-gram LM based on a state-of-the-art Mandarin recognition system.
Achieving the Secrecy Capacity of Wiretap Channels Using Polar Codes

Suppose that Alice wishes to send messages to Bob through a communication channel C1, but her transmissions also reach an eavesdropper Eve through another channel C2. This is the wiretap channel model introduced by Wyner in 1975. The goal is to design a coding scheme that makes it possible for Alice to communicate both reliably and securely. Reliability is measured in terms of Bob's probability of error in recovering the message, while security is measured in terms of the mutual information between the message and Eve's observations. Wyner showed that the situation is characterized by a single constant Cs, called the secrecy capacity, which has the following meaning: for all ε >; 0, there exist coding schemes of rate R ≥ Cs-ε that asymptotically achieve the reliability and security objectives. However, his proof of this result is based upon a random-coding argument. To date, despite consider able research effort, the only case where we know how to construct coding schemes that achieve secrecy capacity is when Eve's channel C2 is an erasure channel, or a combinatorial variation thereof. Polar codes were recently invented by Arikan; they approach the capacity of symmetric binary-input discrete memoryless channels with low encoding and decoding complexity. In this paper, we use polar codes to construct a coding scheme that achieves the secrecy capacity for a wide range of wiretap channels. Our construction works for any instantiation of the wiretap channel model, as long as both C1 and C2 are symmetric and binary-input, and C2 is degraded with respect to C1. Moreover, we show how to modify our construction in order to provide strong security, in the sense defined by Maurer, while still operating at a rate that approaches the secrecy capacity. In this case, we cannot guarantee that the reliability condition will also be satisfied unless the main channel C1 is noiseless, although we believe it can be always satisfied in practice.
How Sustainable are Model Software Artifacts in the Context of Model Driven Software Engineering

Today, software is pervasive in our daily lives and its sustainability and environmental impact have become major factors in the development of software systems. Due to increasing of software complexity, it is widely acknowledged that it is impossible to test every aspect of system software. One method of increasing understanding between the software developer and the customer, of deepening the understanding of how software works, and ultimately of reducing the complexity of software, is through the use of models. Model driven software engineering (MDSE) is an established approach for developing complex software systems. This work presents a first attempt at reshaping existing basic dimensions for software sustainability in terms of MDSE. Moreover, we describe 8 quality aspects and their relationships with each other. We also present a set of guidelines to identify the 8 quality aspects, and the paths to measure sustainability in terms of MDSE. Finally, we describe an example with a set of hypothetical scenarios where we show how our quality aspects could be helpful to measure and analyze the degree of sustainability in the context of MDSE.  Sustainability, Green, Model Driven Software Engineering, MDSE, MDSE Sustainability, MDE, Model Sustainability, Green IN models.
Fourier Transform Infrared Spectroscopy (FT-IR) and Simple Algorithm Analysis for Rapid and Non-Destructive Assessment of Developmental Cotton Fibers

With cotton fiber growth or maturation, cellulose content in cotton fibers markedly increases. Traditional chemical methods have been developed to determine cellulose content, but it is time-consuming and labor-intensive, mostly owing to the slow hydrolysis process of fiber cellulose components. As one approach, the attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy technique has also been utilized to monitor cotton cellulose formation, by implementing various spectral interpretation strategies of both multivariate principal component analysis (PCA) and 1-, 2- or 3-band/-variable intensity or intensity ratios. The main objective of this study was to compare the correlations between cellulose content determined by chemical analysis and ATR FT-IR spectral indices acquired by the reported procedures, among developmental Texas Marker-1 (TM-1) and immature fiber (im) mutant cotton fibers. It was observed that the R value, CIIR, and the integrated intensity of the 895 cm-1 band exhibited strong and linear relationships with cellulose content. The results have demonstrated the suitability and utility of ATR FT-IR spectroscopy, combined with a simple algorithm analysis, in assessing cotton fiber cellulose content, maturity, and crystallinity in a manner which is rapid, routine, and non-destructive.
Multiagent Optimization System for Solving the Traveling Salesman Problem (TSP)

The multiagent optimization system (MAOS) is a nature-inspired method, which supports cooperative search by the self-organization of a group of compact agents situated in an environment with certain sharing public knowledge. Moreover, each agent in MAOS is an autonomous entity with personal declarative memory and behavioral components. In this paper, MAOS is refined for solving the traveling salesman problem (TSP), which is a classic hard computational problem. Based on a simplified MAOS version, in which each agent manipulates on extremely limited declarative knowledge, some simple and efficient components for solving TSP, including two improving heuristics based on a generalized edge assembly recombination, are implemented. Compared with metaheuristics in adaptive memory programming, MAOS is particularly suitable for supporting cooperative search. The experimental results on two TSP benchmark data sets show that MAOS is competitive as compared with some state-of-the-art algorithms, including the Lin-Kernighan-Helsgaun, IBGLK, PHGA, etc., although MAOS does not use any explicit local search during the runtime. The contributions of MAOS components are investigated. It indicates that certain clues can be positive for making suitable selections before time-consuming computation. More importantly, it shows that the cooperative search of agents can achieve an overall good performance with a macro rule in the switch mode, which deploys certain alternate search rules with the offline performance in negative correlations. Using simple alternate rules may prevent the high difficulty of seeking an omnipotent rule that is efficient for a large data set.
CME Common Craniofacial Anomalies : Facial Clefts and Encephaloceles

The wide variety of craniofacial malformations makes classification difficult. A simple classification system allows an overview of the current understanding of the causes, assessments, and treatments of the most frequently encountered craniofacial anomalies. Facial clefts and encephaloceles are reviewed with respect to their diverse causes, pathogenesis, anatomical features, and treatments. Approaches to the surgical treatment of these conditions are reviewed.
On the Role of Emotion in Embodied Cognitive Architectures: From Organisms to Robots

The computational modeling of emotion has been an area of growing interest in cognitive robotics research in recent years, but also a source of contention regarding how to conceive of emotion and how to model it. In this paper, emotion is characterized as (a) closely connected to embodied cognition, (b) grounded in homeostatic bodily regulation, and (c) a powerful organizational principle—affective modulation of behavioral and cognitive mechanisms—that is ‘useful’ in both biological brains and robotic cognitive architectures. We elaborate how emotion theories and models centered on core neurological structures in the mammalian brain, and inspired by embodied, dynamical, and enactive approaches in cognitive science, may impact on computational and robotic modeling. In light of the theoretical discussion, work in progress on the development of an embodied cognitive-affective architecture for robots is presented, incorporating aspects of the theories discussed.
Pupil: an open source platform for pervasive eye tracking and mobile gaze-based interaction

In this paper we present Pupil -- an accessible, affordable, and extensible open source platform for pervasive eye tracking and gaze-based interaction. Pupil comprises 1) a light-weight eye tracking headset, 2) an open source software framework for mobile eye tracking, as well as 3) a graphical user interface to playback and visualize video and gaze data. Pupil features high-resolution scene and eye cameras for monocular and binocular gaze estimation. The software and GUI are platform-independent and include state-of-the-art algorithms for real-time pupil detection and tracking, calibration, and accurate gaze estimation. Results of a performance evaluation show that Pupil can provide an average gaze estimation accuracy of 0.6 degree of visual angle (0.08 degree precision) with a processing pipeline latency of only 0.045 seconds.
A hybrid design for high performance large-scale sorting on FPGA

Sorting is a key kernel in numerous big data application including database operations, graphs and text analytics. Due to low control overhead, parallel bitonic sorting networks are usually employed for hardware implementations to accelerate sorting. Although a typical implementation of merge sort network can lead to low latency and small memory usage, it suffers from low throughput due to the lack of parallelism in the final stage. We analyze a pipelined merge sort network, showing its theoretical limits in terms of latency, memory and, throughput. To increase the throughput, we propose a merge sort based hybrid design where the final few stages in the merge sort network are replaced with “folded” bitonic merge networks. In these “folded” networks, all the interconnection patterns are realized by streaming permutation networks (SPN). We present a theoretical analysis to quantify latency, memory and throughput of our proposed design. Performance evaluations are performed by experiments on Xilinx Virtex-7 FPGA with post place-androute results. We demonstrate that our implementation achieves a throughput close to 10 GBps, outperforming state-of-the-art implementation of sorting on the same hardware by 1.2x, while preserving lower latency and higher memory efficiency.
Extending associative classifier to detect helpful online reviews with uncertain classes

While online product reviews are valuable sources of information to facilitate consumers’ purchase decisions, it is deemed meaningful and important to distinguish helpful reviews from unhelpful ones for consumers facing huge amounts of reviews nowadays. Thus, in light of review classification, this paper proposes a novel approach to identifying review helpfulness. In doing so, a Bayesian inference is introduced to estimate the probabilities of the reviews belonging to respective classes, which differs from the traditional approach that only assigns class labels in a binary manner. Furthermore, an extended fuzzy associative classifier, namely GARCfp, is developed to train review helpfulness classification models based on review class probabilities and fuzzily partitioned review feature values. Finally, data experiments conducted on the reviews from amazon.com reveal the effectiveness of the proposed approach.
Vision enhancement using a short wavelength light-absorbing filter.

This report illustrates photographically the adverse influence of short wavelength-induced light-scattering and autofluorescence on image quality, and the improvement of image quality that results by filtering out light wavelengths shorter than 480 nm. It provides additional data on the improvement in human vision (under conditions of excessive intraocular light-scattering and fluorescence) by filters that prevent short wavelength radiant energy from entering the eye.
Rules with parameters in modal logic I

We study admissibility of inference rules and unification with parameters in transitive modal logics (extensions of K4), in particular we generalize various results on parameterfree admissibility and unification to the setting with parameters. Specifically, we give a characterization of projective formulas generalizing Ghilardi’s characterization in the parameter-free case, leading to new proofs of Rybakov’s results that admissibility with parameters is decidable and unification is finitary for logics satisfying suitable frame extension properties (called cluster-extensible logics in this paper). We construct explicit bases of admissible rules with parameters for cluster-extensible logics, and give their semantic description. We show that in the case of finitely many parameters, these logics have independent bases of admissible rules, and determine which logics have finite bases. As a sideline, we show that cluster-extensible logics have various nice properties: in particular, they are finitely axiomatizable, and have an exponential-size model property. We also give a rather general characterization of logics with directed (filtering) unification. In the sequel, we will use the same machinery to investigate the computational complexity of admissibility and unification with parameters in cluster-extensible logics, and we will adapt the results to logics with unique top cluster (e.g., S4.2) and superintuitionistic logics.
Distributed Circuit Plasticity: New Clues for the Cerebellar Mechanisms of Learning

The cerebellum is involved in learning and memory of sensory motor skills. However, the way this process takes place in local microcircuits is still unclear. The initial proposal, casted into the Motor Learning Theory, suggested that learning had to occur at the parallel fiber–Purkinje cell synapse under supervision of climbing fibers. However, the uniqueness of this mechanism has been questioned, and multiple forms of long-term plasticity have been revealed at various locations in the cerebellar circuit, including synapses and neurons in the granular layer, molecular layer and deep-cerebellar nuclei. At present, more than 15 forms of plasticity have been reported. There has been a long debate on which plasticity is more relevant to specific aspects of learning, but this question turned out to be hard to answer using physiological analysis alone. Recent experiments and models making use of closed-loop robotic simulations are revealing a radically new view: one single form of plasticity is insufficient, while altogether, the different forms of plasticity can explain the multiplicity of properties characterizing cerebellar learning. These include multi-rate acquisition and extinction, reversibility, self-scalability, and generalization. Moreover, when the circuit embeds multiple forms of plasticity, it can easily cope with multiple behaviors endowing therefore the cerebellum with the properties needed to operate as an effective generalized forward controller.
Supplier selection based on hierarchical potential support vector machine

Supplier selection is an important and widely studied topic since it has significant impact on purchasing management in supply chain. Recently, support vector machine has received much more attention from researchers, while studies on supplier selection based on it are few. In this paper, a new support vector machine technology, potential support vector machine, is introduced and then combined with decision tree to address issues on supplier selection including feature selection, multiclass classification and so on. So, hierarchical potential support vector machine and hierarchical system of features are put forward in the paper, and experiments show the proposed methodology has much better generalization performance and less computation consumptions than standard support vector machine.
2 Setting Up a Polymerase Chain Reaction Laboratory

One of the most important attributes of the polymerase chain reaction (PCR) is its exquisite sensitivity. However, the high sensitivity of PCR also renders it prone to falsepositive results because of, for example, exogenous contamination. Good laboratory practice and specific anti-contamination strategies are essential to minimize the chance of contamination. Some of these strategies, for example, physical separation of the areas for the handling samples and PCR products, may need to be taken into consideration during the establishment of a laboratory. In this chapter, different strategies for the detection, avoidance, and elimination of PCR contamination will be discussed.
The Ripple Effects of Vulnerability: The Effects of a Robot's Vulnerable Behavior on Trust in Human-Robot Teams

Successful teams are characterized by high levels of trust between team members, allowing the team to learn from mistakes, take risks, and entertain diverse ideas. We investigated a robot's potential to shape trust within a team through the robot's expressions of vulnerability. We conducted a between-subjects experiment (N = 35 teams, 105 participants) comparing the behavior of three human teammates collaborating with either a social robot making vulnerable statements or with a social robot making neutral statements. We found that, in a group with a robot making vulnerable statements, participants responded more to the robot's comments and directed more of their gaze to the robot, displaying a higher level of engagement with the robot. Additionally, we discovered that during times of tension, human teammates in a group with a robot making vulnerable statements were more likely to explain their failure to the group, console team members who had made mistakes, and laugh together, all actions that reduce the amount of tension experienced by the team. These results suggest that a robot's vulnerable behavior can have "ripple effects" on their human team members' expressions of trust-related behavior.
Proteomics of heavy metal toxicity in plants.

Plants endure a variety of abiotic and biotic stresses, all of which cause major limitations to production. Among abiotic stressors, heavy metal contamination represents a global environmental problem endangering humans, animals, and plants. Exposure to heavy metals has been documented to induce changes in the expression of plant proteins. Proteins are macromolecules directly responsible for most biological processes in a living cell, while protein function is directly influenced by posttranslational modifications, which cannot be identified through genome studies. Therefore, it is necessary to conduct proteomic studies, which enable the elucidation of the presence and role of proteins under specific environmental conditions. This review attempts to present current knowledge on proteomic techniques developed with an aim to detect the response of plant to heavy metal stress. Significant contributions to a better understanding of the complex mechanisms of plant acclimation to metal stress are also discussed.