nils-herrmann/clean_scirepeval huggingface dataset:
Reduction of walking time in the distribution center of De Bijenkorf

In many distribution centers, there is a constant pressure to reduce the order throughput times. One such distribution center is the DC of De Bijenkorf, a retail organization in The Netherlands with 7 subsidiaries and a product assortment of about 300,000 SKUs (stock keeping units). The orders for the subsidiaries are picked manually in this warehouse, which is very labor intensive. Furthermore many shipments have to be finished at about the same time, which leads to peak loads in the picking process. The picking process is therefore a costly operation. In this study we have investigated the possibilities to pick the orders more efficiently, without altering the storage or material handling equipment used or the storage strategies. It appeared to be possible to obtain a reduction between 17 and 34% in walking time, by simply routing the pickers more efficiently. The amount of walking time reduction depends on the routing algorithm used. The largest saving is obtained by using an optimal routing algorithm that has been developed for De Bijenkorf. The main reason for this substantial reduction in walking time, is the change from one-sided picking to two-sided picking in the narrow aisles. It is even possible to obtain a further reduction in walking time by clustering the orders. Small orders can be combined on one pick cart and can be picked in a single route. The combined picking of several orders (constrained by the size of the orders and the cart capacity) leads to a total reduction of about 60% in walking time, using a simple order clustering strategy in combination with a newly developed routing strategy. The reduction in total order picking time and hence the reduction in the number of pickers is about 19%.
Adaptation in CRISPR-Cas Systems.

Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) proteins constitute an adaptive immune system in prokaryotes. The system preserves memories of prior infections by integrating short segments of foreign DNA, termed spacers, into the CRISPR array in a process termed adaptation. During the past 3 years, significant progress has been made on the genetic requirements and molecular mechanisms of adaptation. Here we review these recent advances, with a focus on the experimental approaches that have been developed, the insights they generated, and a proposed mechanism for self- versus non-self-discrimination during the process of spacer selection. We further describe the regulation of adaptation and the protein players involved in this fascinating process that allows bacteria and archaea to harbor adaptive immunity.
Machine Learning Based Session Drop Prediction in LTE Networks and Its SON Aspects

Abnormal bearer session release (i.e. bearer session drop) in cellular telecommunication networks may seriously impact the quality of experience of mobile users. The latest mobile technologies enable high granularity real-time reporting of all conditions of individual sessions, which gives rise to use data analytics methods to process and monetize this data for network optimization. One such example for analytics is Machine Learning (ML) to predict session drops well before the end of session. In this paper a novel ML method is presented that is able to predict session drops with higher accuracy than using traditional models. The method is applied and tested on live LTE data offline. The high accuracy predictor can be part of a SON function in order to eliminate the session drops or mitigate their effects.
Three-phase Flyback-Boost DC-DC converter with three-phase high frequency isolation

This paper presents a new modulation strategy of the three-phase DC-DC Boost converter with high frequency isolation. This work has as its main objective to present a solution to deal with the forbidden region found in the study of the referred converter. The proposed modulation keeps the main characteristics of the original topology, which are: the voltage and current frequencies in the output filter are three times the switching frequency, input current ripple reduction, high frequency isolation, moreover, when the converter works within duty ratio up to 1/3, it makes it possible to precharge the output capacitor, reducing the start-up converter' currents. A theoretical analysis of the operating stages and the experimental results are presented to a prototype operating as a Flyback-Boost with switching frequency of 20 kHz, input voltage from 24 V to 120 V, output voltage from 52 V to 450 V and output power from 650 W to 2025 W.
An iterative improvement process for agile software development

Agile software development of short iterative cycles offers an opportunity for rapid, visible and motivating software process improvement (SPI). The agile principles suggest the regular reflections of agile project teams for improving the efficiency and adaptation of the process. However, current literature provides little support or empirical evidence for conducting such improvement efficiently, systematically and in a validated manner. Thus, this article proposes an Iterative Improvement Process for conducting SPI within individual agile project teams, which aims at increasing the ability of software developers to improve the development process based on their experiences and context knowledge. The approach has been trialed within a multiple case study of five consecutive case projects where both qualitative and quantitative research data has been systematically collected. The empirical data confirms the positive effects of the Iterative Improvement Process on the software development projects and reveals the willingness of software developers to participate in the Iterative Improvement activities due to the rapid and visible changes in their working practices.
Discretized Streams: A Fault-Tolerant Model for Scalable Stream Processing

Many “big data” applications need to act on data arriving in real time. However, current programming models for distributed stream processing are relatively low-level, often leaving the user to worry about consistency of state across the system and fault recovery. Furthermore, the models that provide fault recovery do so in an expensive manner, requiring either hot replication or long recovery times. We propose a new programming model, discretized streams (D-Streams), that offers a high-level functional API, strong consistency, and efficient fault recovery. D-Streams support a new recovery mechanism that improves efficiency over the traditional replication and upstream backup schemes in streaming databases— parallel recovery of lost state—and unlike previous systems, also mitigate stragglers. We implement D-Streams as an extension to the Spark cluster computing engine that lets users seamlessly intermix streaming, batch and interactive queries. Our system can process over 60 million records/second at sub-second latency on 100 nodes.
Mobile phones in education: Challenges and opportunities for learning

The millennials use mobile phones on a daily basis to keep in touch with family and friends (Lenhart 2010). However, the role of mobile phones in education needs to be close examined as educators strive to incorporate mobile leaning devices in the classroom. Consequently, schools will not only need to evaluate their school curriculums but also recognize the power in the digital devices to engage, enable, and empower Gen-M and iGen learners. Therefore, the purpose of this article is to provide a rationale for the need for administrators to design guidelines for schools planning to adopt mobile phones in their curricula. Additionally, this article is intended to stimulate reflections on effective ways to adopt mobile phones in education to engage learners.
Detecting Communities in Large Networks by Iterative Local Expansion

Much structured data of scientific interest can be represented as networks, where sets of nodes or vertices are joined together in pairs by links or edges. Although these networks may belong to different research areas, there is one property that many of them do have in common: the network community structure, which means that there exists densely connected groups of vertices, with only sparser connections between groups. Identifying community structure in networks has attracted much research attention. However, most existing approaches require structure information of the graph in question to be completely accessible, which is impractical for some large networks, e.g., the World Wide Web (WWW). In this paper, we propose a community discovery algorithm for large networks that iteratively finds communities based on local information only. We compare our algorithm with previous global approaches to show its scalability. Experimental results on real world networks, such as the co-purchase network from Amazon, verify the feasibility and effectiveness of our approach.
Random Phase Textures: Theory and Synthesis

This paper explores the mathematical and algorithmic properties of two sample-based texture models: random phase noise (RPN) and asymptotic discrete spot noise (ADSN). These models permit to synthesize random phase textures. They arguably derive from linearized versions of two early Julesz texture discrimination theories. The ensuing mathematical analysis shows that, contrarily to some statements in the literature, RPN and ADSN are different stochastic processes. Nevertheless, numerous experiments also suggest that the textures obtained by these algorithms from identical samples are perceptually similar. The relevance of this study is enhanced by three technical contributions providing solutions to obstacles that prevented the use of RPN or ADSN to emulate textures. First, RPN and ADSN algorithms are extended to color images. Second, a preprocessing is proposed to avoid artifacts due to the nonperiodicity of real-world texture samples. Finally, the method is extended to synthesize textures with arbitrary size from a given sample.
Design of a printed wide band Log-periodic antenna dipole array with a new feeding tecnique

A new feeding technique for printed Log-periodic dipole arrays (LPDAs) is presented, and used to design a printed LPDA operating between 4 and 18 GHz. The antenna has been designed using CST MICROWAVE STUDIO 2010, and the simulation results show that the antenna can be used as an Ultra Wideband Antenna in the range 6-9 GHz.
Randomized Prior Functions for Deep Reinforcement Learning

Dealing with uncertainty is essential for e cient reinforcement learning. There is a growing literature on uncertainty estimation for deep learning from fixed datasets, but many of the most popular approaches are poorlysuited to sequential decision problems. Other methods, such as bootstrap sampling, have no mechanism for uncertainty that does not come from the observed data. We highlight why this can be a crucial shortcoming and propose a simple remedy through addition of a randomized untrainable ‘prior’ network to each ensemble member. We prove that this approach is e cient with linear representations, provide simple illustrations of its e cacy with nonlinear representations and show that this approach scales to large-scale problems far better than previous attempts.
Learning to rank for spatiotemporal search

In this article we consider the problem of mapping a noisy estimate of a user's current location to a semantically meaningful point of interest, such as a home, restaurant, or store. Despite the poor accuracy of GPS on current mobile devices and the relatively high density of places in urban areas, it is possible to predict a user's location with considerable precision by explicitly modeling both places and users and by combining a variety of signals about a user's current context. Places are often simply modeled as a single latitude and longitude when in fact they are complex entities existing in both space and time and shaped by the millions of people that interact with them. Similarly, models of users reveal complex but predictable patterns of mobility that can be exploited for this task. We propose a novel spatial search algorithm that infers a user's location by combining aggregate signals mined from billions of foursquare check-ins with real-time contextual information. We evaluate a variety of techniques and demonstrate that machine learning algorithms for ranking and spatiotemporal models of places and users offer significant improvement over common methods for location search based on distance and popularity.
A Knowledge-Intensive Model for Prepositional Phrase Attachment

Prepositional phrases (PPs) express crucial information that knowledge base construction methods need to extract. However, PPs are a major source of syntactic ambiguity and still pose problems in parsing. We present a method for resolving ambiguities arising from PPs, making extensive use of semantic knowledge from various resources. As training data, we use both labeled and unlabeled data, utilizing an expectation maximization algorithm for parameter estimation. Experiments show that our method yields improvements over existing methods including a state of the art dependency parser.
Looking forward to looking backward: the misprediction of regret.

Decisions are powerfully affected by anticipated regret, and people anticipate feeling more regret when they lose by a narrow margin than when they lose by a wide margin. But research suggests that people are remarkably good at avoiding self-blame, and hence they may be better at avoiding regret than they realize. Four studies measured people's anticipations and experiences of regret and self-blame. In Study 1, students overestimated how much more regret they would feel when they "nearly won" than when they "clearly lost" a contest. In Studies 2, 3a, and 3b, subway riders overestimated how much more regret and self-blame they would feel if they "nearly caught" their trains than if they "clearly missed" their trains. These results suggest that people are less susceptible to regret than they imagine, and that decision makers who pay to avoid future regrets may be buying emotional insurance that they do not actually need.
Secure Multiparty Computation for Privacy-Preserving Data Mining

In this paper, we survey the basic paradigms and notions of secure multiparty computation and discuss their relevance to the field of privacy-preserving data mining. In addition to reviewing definitions and constructions for secure multiparty computation, we discuss the issue of efficiency and demonstrate the difficulties involved in constructing highly efficient protocols. We also present common errors that are prevalent in the literature when secure multiparty computation techniques are applied to privacy-preserving data mining. Finally, we discuss the relationship between secure multiparty computation and privacy-preserving data mining, and show which problems it solves and which problems it does not.
Hierarchical Graph Representation Learning with Differentiable Pooling

Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction. However, current GNN methods are inherently flat and do not learn hierarchical representations of graphs—a limitation that is especially problematic for the task of graph classification, where the goal is to predict the label associated with an entire graph. Here we propose DIFFPOOL, a differentiable graph pooling module that can generate hierarchical representations of graphs and can be combined with various graph neural network architectures in an end-to-end fashion. DIFFPOOL learns a differentiable soft cluster assignment for nodes at each layer of a deep GNN, mapping nodes to a set of clusters, which then form the coarsened input for the next GNN layer. Our experimental results show that combining existing GNN methods with DIFFPOOL yields an average improvement of 5–10% accuracy on graph classification benchmarks, compared to all existing pooling approaches, achieving a new state-of-the-art on four out of five benchmark data sets.
Function-based, biologically inspired concept generation

The natural world provides numerous cases for inspiration in engineering design. Biological organisms, phenomena, and strategies, which we refer to as biological systems, provide a rich set of analogies. These systems provide insight into sustainable and adaptable design and offer engineers billions of years of valuable experience, which can be used to inspire engineering innovation. This research presents a general method for functionally representing biological systems through systematic design techniques, leading to the conceptualization of biologically inspired engineering designs. Functional representation and abstraction techniques are used to translate biological systems into an engineering context. The goal is to make the biological information accessible to engineering designers who possess varying levels of biological knowledge but have a common understanding of engineering design. Creative or novel engineering designs may then be discovered through connections made between biology and engineering. To assist with making connections between the two domains concept generation techniques that use biological information, engineering knowledge, and automatic concept generation software are employed. Two concept generation approaches are presented that use a biological model to discover corresponding engineering components that mimic the biological system and use a repository of engineering and biological information to discover which biological components inspire functional solutions to fulfill engineering requirements. Discussion includes general guidelines for modeling biological systems at varying levels of fidelity, advantages, limitations, and applications of this research. The modeling methodology and the first approach for concept generation are illustrated by a continuous example of lichen.
Designing Optimized MPI Broadcast and Allreduce for Many Integrated Core (MIC) InfiniBand Clusters

The emergence of co-processors such as Intel Many Integrated Cores (MICs) is changing the landscape of supercomputing. The MIC is a memory constrained environment and its processors also operate at slower clock rates. Furthermore, the communication characteristics between MIC processes are also different compared to communication between host processes. Communication libraries that do not consider these architectural subtleties cannot deliver good communication performance. The performance of MPI collective operations strongly affect the performance of parallel applications. Owing to the challenges introduced by the emerging heterogeneous systems, it is critical to fundamentally re-design collective algorithms to ensure that applications can fully leverage the MIC architecture. In this paper, we propose a generic framework to optimize the performance of important collective operations, such as, MPI Bcast, MPI Reduce and MPI Allreduce, on Intel MIC clusters. We also present a detailed analysis of the compute phases in reduce operations for MIC clusters. To the best of our knowledge, this is the first paper to propose novel designs to improve the performance of collectives on MIC clusters. Our designs improve the latency of the MPI Bcast operation with 4,864 MPI processes by up to 76%. We also observe up to 52.4% improvements in the communication latency of the MPI Allreduce operation with 2K MPI processes on heterogeneous MIC clusters. Our designs also improve the execution time of the WindJammer application by up to 16%.
CoAPthon: Easy development of CoAP-based IoT applications with Python

The Internet of Things (IoT) vision foresees billions of devices seamlessly integrated into information systems. In this context, the Constrained Application Protocol (CoAP) has been defined as a technology enabler to allow applications to interact with physical objects. In this work we present CoAPthon, an open-source Python-based CoAP library, which aims at simplifying the development of CoAP-enabled IoT applications. The library offers software developers a simple and easy-to-use programming interface to exploit CoAP as a communication protocol for rapid prototyping and deployment of IoT systems. The CoAPthon library is fully compliant with the CoAP RFC and implements in addition popular extensions such as the block-wise transfer and resource observing.
Modelling the dependence structure of financial assets : A survey of four copulas

Understanding and quantifying dependence is at the core of all modelling efforts in financial econometrics. The linear correlation coefficient, which is the far most used measure to test dependence in the financial community and also elsewhere, is only a measure of linear dependence. This means that it is a meaningful measure of dependence if asset returns are well represented by an elliptical distribution. Outside the world of elliptical distributions, however, using the linear correlation coefficient as a measure of dependence may lead to misleading conclusions. Hence, alternative methods for capturing co-dependency should be considered. One class of alternatives are copula-based dependence measures. In this survey we consider two parametric families of copulas; the copulas of normal mixture distributions and Archimedean copulas.
Neural mechanisms of language comprehension: Challenges to syntax

In 1980, the N400 event-related potential was described in association with semantic anomalies within sentences. When, in 1992, a second waveform, the P600, was reported in association with syntactic anomalies and ambiguities, the story appeared to be complete: the brain respected a distinction between semantic and syntactic representation and processes. Subsequent studies showed that the P600 to syntactic anomalies and ambiguities was modulated by lexical and discourse factors. Most surprisingly, more than a decade after the P600 was first described, a series of studies reported that semantic verb-argument violations, in the absence of any violations or ambiguities of syntax can evoke robust P600 effects and no N400 effects. These observations have raised fundamental questions about the relationship between semantic and syntactic processing in the brain. This paper provides a comprehensive review of the recent studies that have demonstrated P600s to semantic violations in light of several proposed triggers: semantic-thematic attraction, semantic associative relationships, animacy and semantic-thematic violations, plausibility, task, and context. I then discuss these findings in relation to a unifying theory that attempts to bring some of these factors together and to link the P600 produced by semantic verb-argument violations with the P600 evoked by unambiguous syntactic violations and syntactic ambiguities. I suggest that normal language comprehension proceeds along at least two competing neural processing streams: a semantic memory-based mechanism, and a combinatorial mechanism (or mechanisms) that assigns structure to a sentence primarily on the basis of morphosyntactic rules, but also on the basis of certain semantic-thematic constraints. I suggest that conflicts between the different representations that are output by these distinct but interactive streams lead to a continued combinatorial analysis that is reflected by the P600 effect. I discuss some of the implications of this non-syntactocentric, dynamic model of language processing for understanding individual differences, language processing disorders and the neuroanatomical circuitry engaged during language comprehension. Finally, I suggest that that these two processing streams may generalize beyond the language system to real-world visual event comprehension.