nils-herrmann/clean_scirepeval huggingface dataset:
C4: the continuously concurrent compacting collector

C4, the Continuously Concurrent Compacting Collector, an updated generational form of the Pauseless GC Algorithm , is introduced and described, along with details of its implementation on modern X86 hardware. It uses a read barrier to support concur- rent compaction, concurrent remapping, and concurrent incremental update tracing. C4 differentiates itself from other generational garbage collectors by supporting simultaneous-generational concurrency: the different generations are collected using concurrent (non stop-the-world) mechanisms that can be simultaneously and independently active. C4 is able to continuously perform concurrent young generation collections, even during long periods of concurrent full heap collection, allowing C4 to sustain high allocation rates and maintain the efficiency typical to generational collectors, without sacrificing response times or reverting to stop-the-world operation. Azul systems has been shipping a commercial implementation of the Pauseless GC mechanism, since 2005. Three successive generations of Azul's Vega series systems relied on custom multi-core processors and a custom OS kernel to deliver both the scale and features needed to support Pauseless GC. In 2010, Azul released its first software-only commercial implementation of C4 for modern commodity X86 hardware, using Linux kernel enhancements to support the required feature set. We discuss implementa- tion details of C4 on X86, including the Linux virtual and physical memory management enhancements that were used to support the high rate of virtual memory operations required for sustained pauseless operation. We discuss updates to the collector's manage- ment of the heap for efficient generational collection and provide throughput and pause time data while running sustained workloads.
Explaining the Infant ’ s Object Concept : Beyond the Perception / Cognition Dichotomy

Some of the most exciting research in recent cognitive science has involved the demonstration that young infants possess a remarkable array of discriminative abilities. Infants a few months old have been shown to have a substantial amount of ‘initial knowledge’ about objects, in domains such as physics and arithmetic (for recent reviews and overviews, see Baillargeon, 1995; Carey, 1995; Spelke, 1994; Spelke, Gutheil, & Van de Walle, 1995; Spelke, Vishton, & von Hofsten, 1995). In this chapter we will be concerned with what such results tell us about the structure of the infant’s mind — in other words with what the phrase ‘initial knowledge’ means in terms of the underlying cognitive architecture. For the duration of this chapter, we will adopt Spelke’s phrase ‘initial knowledge’ without scare-quotes, having recognized that it is precisely the meaning of this phrase which is at issue. Traditional discussions have often addressed the nature of initial knowledge in terms of an implicit dichotomy between ‘perception’ and ‘cognition’ (e.g. Bogartz, Shinskey, & Speaker, 1997; Kellman, 1988; Leslie, 1988; Spelke, 1988a, 1988b). From within such a dichotomy, ‘perception’ has often been found want-
Outranking methods in support of supplier selection

Initial purchasing decisions such as make-or-buy decisions and supplier selection are decisions of strategic importance to companies. The nature of these decisions usually is complex and unstructured. Management Science techniques might be helpful tools for this kind of decision making problems. So far, however, the application of outranking methods in purchasing decisions has not been suggested in purchasing or operations research literature. In this paper we show by means of a supplier selection example, that an outranking approach may be very well suited as a decision-making tool for initial purchasing decisions.
Knowledge-based approaches in software documentation: A systematic literature review

Software documents are core artifacts produced and consumed in documentation activity in the software lifecycle. Meanwhile, knowledge-based approaches have been extensively used in software development for decades, however, the software engineering community lacks a comprehensive understanding on how knowledge-based approaches are used in software documentation, especially documentation of software architecture design.  The objective of this work is to explore how knowledge-based approaches are employed in software documentation, their influences to the quality of software documentation, and the costs and benefits of using these approaches.  We use a systematic literature review method to identify the primary studies on knowledge-based approaches in software documentation, following a pre-defined review protocol.  Sixty studies are finally selected, in which twelve quality attributes of software documents, four cost categories, and nine benefit categories of using knowledge-based approaches in software documentation are identified. Architecture understanding is the top benefit of using knowledge-based approaches in software documentation. The cost of retrieving information from documents is the major concern when using knowledge-based approaches in software documentation.  The findings of this review suggest several future research directions that are critical and promising but underexplored in current research and practice: (1) there is a need to use knowledge-based approaches to improve the quality attributes of software documents that receive less attention, especially credibility, conciseness, and unambiguity; (2) using knowledge-based approaches with the knowledge content in software documents which gets less attention in current applications of knowledge based approaches in software documentation, to further improve the practice of software documentation activity; (3) putting more focus on the application of software documents using the knowledge-based approaches (knowledge reuse, retrieval, reasoning, and sharing) in order to make the most use of software documents; and (4) evaluating the costs and benefits of using knowledge-based approaches in software documentation qualitatively and quantitatively.
Ontogenesis of Agency in Machines : A Multidisciplinary Review

How does agency arise in biocognitive systems? Can the emergence of agency be explained by physical laws? We review these and related questions in philosophy, psychology, biology, and physics. Based on the review we ask the questions: i) Can machines have agency? and, if so, ii) How can we build machines with agency? We examine existing work in artificial intelligence and machine learning on selfmotivated, self-teaching, and self-developing systems with respect to the ontogenesis (“coming into being”) of agency in computational systems. The impact of these “autogenic” systems or machine agency on science, technology, and humanity will be discussed.
Broadband transition from dielectric-filled to air-filled Substrate Integrated Waveguide for low loss and high power handling millimeter-wave Substrate Integrated Circuits

Air-filled Substrate Integrated Waveguide (SIW) based on multilayer Printed Circuit Board (PCB) process is proposed in this paper for millimeter-wave applications that require low cost, high performances and compactness. This air-filled SIW allows for substantial loss reduction and power handling enhancement. Its fabrication involves three layers. The top and bottom substrates can consist of a low cost standard substrate such as FR-4 on which base-band or digital circuits can be implemented so to obtain a very compact, high performance and low cost millimeter-wave system. At Ka-band, it is shown that air-filled SIW compared to dielectric-filled SIW based on Rogers RT/Duroid 5880 and 6002 reduces losses by a mean value of 0.054 dB/cm and 0.11 dB/cm and increases average power handling capability by 6 dB and 8 dB, respectively. To allow interconnects of the proposed air-filled SIW with dielectric-filled SIW, a broadband transition is presented. The design steps of this transition are detailed. For demonstration purposes, a back-to-back transition operating over the Ka-band is fabricated. It achieves a matching of better than -15 dB and an insertion loss of 0.6 ±0.2 dB (0.3 ±0.1 dB for the transition) from 27 to 40 GHz.
The effectiveness of the combined use of VIX and Support Vector Machines on the prediction of S&P 500

The aim of this research is to analyse the effectiveness of the Chicago Board Options Exchange Market Volatility Index (VIX) when used with Support Vector Machines (SVMs) in order to forecast the weekly change in the S&P 500 index. The data provided cover the period between 3 January 2000 and 30 December 2011. A trading simulation is implemented so that statistical efficiency is complemented by measures of economic performance. The inputs retained are traditional technical trading rules commonly used in the analysis of equity markets such as Relative Strength Index, Moving Average Convergence Divergence, VIX and the daily return of the S&P 500. The SVM identifies the best situations in which to buy or sell in the market. The two outputs of the SVM are the movement of the market and the degree of set membership. The obtained results show that SVM using VIX produce better results than the Buy and Hold strategy or SVM without VIX. The influence of VIX in the trading system is particularly significant when bearish periods appear. Moreover, the SVM allows the reduction in the Maximum Drawdown and the annualised standard deviation.
A low cost ultra-wideband pulse transceiver

A low cost pulse transceiver has been developed for measuring the electrical properties of materials. The transceiver generates an ultra wide band (UWB) pulse as well as samples the received pulse. The pulse generator has been designed using a silicon-germanium (SiGe) analog comparator which is a possible alternative to step recovery diodes. The pulse is received by an extended time sampling circuit. The sampling circuit presented is an alternative design that does not require the use of a broadband balun.
Leveraging Legacy Code to Deploy Desktop Applications on the Web

Xax is a browser plugin model that enables developers to leverage existing tools, libraries, and entire programs to deliver feature-rich applications on the web. Xax employs a novel combination of mechanisms that collectively provide security, OS-independence, performance, and support for legacy code. These mechanisms include memory-isolated native code execution behind a narrow syscall interface, an abstraction layer that provides a consistent binary interface across operating systems, system services via hooks to existing browser mechanisms, and lightweight modifications to existing tool chains and code bases. We demonstrate a variety of applications and libraries from existing code bases, in several languages, produced with various tool chains, running in multiple browsers on multiple operating systems. With roughly two person-weeks of effort, we ported 3.3 million lines of code to Xax, including a PDF viewer, a Python interpreter, a speech synthesizer, and an OpenGL pipeline.
Recommender Systems for Large-scale E-Commerce : Scalable Neighborhood Formation Using Clustering

Recommender systems apply knowledge discovery techniques to the problem of making personalized product recommendations during a live customer interaction. These systems, especially the k-nearest neighbor collaborative filtering based ones, are achieving widespread success in E-commerce nowadays. The tremendous growth of customers and products in recent years poses some key challenges for recommender systems. These are: producing high quality recommendations and performing many recommendations per second for millions of customers and products. New recommender system technologies are needed that can quickly produce high quality recommendations, even for very large-scale problems. We address the performance issues by scaling up the neighborhood formation process through the use of clustering techniques.
Implicit memory for words heard during sleep

When we fall asleep, our awareness of the surrounding world fades. Yet, the sleeping brain is far from being dormant and recent research unraveled the preservation of complex sensory processing during sleep. In wakefulness, such processes usually lead to the formation of long-term memory traces, being it implicit or explicit. We examined here the consequences upon awakening of the processing of sensory information at a high level of representation during sleep. Participants were instructed to classify auditory stimuli as words or pseudo-words, through left and right hand responses, while transitioning toward sleep. An analysis of the electroencephalographic (EEG) signal revealed the preservation of lateralized motor activations in response to sounds, suggesting that stimuli were correctly categorized during sleep. Upon awakening, participants did not explicitly remember words processed during sleep and failed to distinguish them from new words (old/new recognition test). However, both behavioral and EEG data indicate the presence of an implicit memory trace for words presented during sleep. In addition, the underlying neural signature of such implicit memories markedly differed from the explicit memories formed during wakefulness, in line with dual-process accounts arguing for two independent systems for explicit and implicit memory. Thus, our results reveal that implicit learning mechanisms can be triggered during sleep and provide a novel approach to explore the neural implementation of memory without awareness.
Tutorial on agent-based modeling and simulation

Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of autonomous, interacting agents. ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use electronic laboratories to support their research. Some have gone so far as to contend that ABMS is a third way of doing science besides deductive and inductive reasoning. Computational advances have made possible a growing number of agent-based applications in a variety of fields. Applications range from modeling agent behavior in the stock market and supply chains, to predicting the spread of epidemics and the threat of bio-warfare, from modeling consumer behavior to understanding the fall of ancient civilizations, to name a few. This tutorial describes the theoretical and practical foundations of ABMS, identifies toolkits and methods for developing ABMS models, and provides some thoughts on the relationship between ABMS and traditional modeling techniques.
A detection-based multiple object tracking method

In this paper we describe a method for tracking multiple objects whose number is unknown and varies during tracking. Based on preliminary results of object detection in each image which may have missing and/or false detection, the multiple object tracking method keeps a graph structure where it maintains multiple hypotheses about the number and the trajectories of the objects in the video. The image information drives the process of extending and pruning the graph, and determines the best hypothesis to explain the video. While the image-based object detection makes a local decision, the tracking process confirms and validates the detection through time, therefore, it can be regarded as temporal detection which makes a global decision across time. The multiple object tracking method gives feedbacks which are predictions of object locations to the object detection module. Therefore, the method integrates object detection and tracking tightly. The most possible hypothesis provides the multiple object tracking result. The experimental results are presented.
Do Differences in Password Policies Prevent Password Reuse?

Password policies were originally designed to make users pick stronger passwords. However, research has shown that they often fail to achieve this goal. In a systematic audit of the top 100 web sites in Germany, we explore if diversity in current real-world password policies prevents password reuse. We found that this is not the case: we are the first to show that a single password could hypothetically fulfill 99% of the policies under consideration. This is especially problematic because password reuse exposes users to similar risks as weak passwords. We thus propose a new approach for policies that focuses on password reuse and respects other websites to determine if a password should be accepted. This re-design takes current user behavior into account and potentially boosts the usability and security of password-based authentication.
Proliferation, cell cycle and apoptosis in cancer

Beneath the complexity and idiopathy of every cancer lies a limited number of 'mission critical' events that have propelled the tumour cell and its progeny into uncontrolled expansion and invasion. One of these is deregulated cell proliferation, which, together with the obligate compensatory suppression of apoptosis needed to support it, provides a minimal 'platform' necessary to support further neoplastic progression. Adroit targeting of these critical events should have potent and specific therapeutic consequences.
Sorry, I only speak natural language: a pattern-based, data-driven and guided approach to mapping natural language queries to SPARQL

We present a new interface based on natural language to support users in specifying their queries with respect to RDF datasets. The approach relies on a number of predefined patterns that uniquely determine a type of SPARQL query. The approach is incremental and assisted in that it guides a user step by step in specifying a query by incrementally parsing the input and providing suggestions for completion at every stage. The methodology to specify the patterns is informed by empirical distributions of SPARQL query types as found in standard query logs. So far, we have implemented the approach using two patterns only as proof-of-concept. The coverage of the pattern library will be extended in the future. We will also provide an evaluation of the approach on the well-known QALD dataset.
On the Consistency of Top-k Surrogate Losses

The top-k error is often employed to evaluate performance for challenging classification tasks in computer vision as it is designed to compensate for ambiguity in ground truth labels. This practical success motivates our theoretical analysis of consistent top-k classification. To this end, we define top-k calibration as a necessary and sufficient condition for consistency, for bounded below loss functions. Unlike prior work, our analysis of top-k calibration handles non-uniqueness of the predictor scores, and extends calibration to consistency – providing a theoretically sound basis for analysis of this topic. Based on the topk calibration analysis, we propose a rich class of top-k calibrated Bregman divergence surrogates. Our analysis continues by showing previously proposed hinge-like top-k surrogate losses are not top-k calibrated and thus inconsistent. On the other hand, we propose two new hinge-like losses, one which is similarly inconsistent, and one which is consistent. Our empirical results highlight theoretical claims, confirming our analysis of the consistency of these losses.
Higher-Order Inference for Multi-class Log-Supermodular Models

Higher-order models have been shown to be very useful for a plethora of computer vision tasks. However, existing techniques have focused mainly on MAP inference. In this paper, we present the first efficient approach towards approximate Bayesian marginal inference in a general class of high-order, multi-label attractive models, where previous techniques slow down exponentially with the order (clique size). We formalize this task as performing inference in log-supermodular models under partition constraints, and present an efficient variational inference technique. The resulting optimization problems are convex and yield bounds on the partition function. We also obtain a fully factorized approximation to the posterior, which can be used in lieu of the true complicated distribution. We empirically demonstrate the performance of our approach by comparing it to traditional inference methods on a challenging high-fidelity multi-label image segmentation dataset. We obtain state-of-the-art classification accuracy for MAP inference, and substantially improved ROC curves using the approximate marginals.
Sound-model-based acoustic source localization using distributed microphone arrays

Acoustic source localization and sound recognition are common acoustic scene analysis tasks that are usually considered separately. In this paper, a new source localization technique is proposed that works jointly with an acoustic event detection system. Given the identities and the end-points of simultaneous sounds, the proposed technique uses the statistical models of those sounds to compute a likelihood score for each model and for each signal at the output of a set of null-steering beamformers per microphone array. Those scores are subsequently combined to find the MAP-optimal event source positions in the room. Experimental work is reported for a scenario consisting of meeting-room acoustic events, either isolated or overlapped with speech. From the localization results, which are compared with those from the SRP-PHAT technique, it seems that the proposed model-based approach can be an alternative to current techniques for event-based localization.
Low-Rank Kernel Learning with Bregman Matrix Divergences

In this paper, we study low-rank matrix nearness problems, with a focus on learning low-rank positive semidefinite (kernel) matrices for machine learning applications. We propose efficient algorithms that scale linearly in the number of data points and quadratically in the rank of the input matrix. Existing algorithms for learning kernel matrices often scale poorly, with running times that are cubic in the number of data points. We employ Bregman matrix divergences as the measures of nearness—these divergences are natural for learning low-rank kernels since they preserve rank as well as positive semidefiniteness. Special cases of our framework yield faster algorithms for various existing learning problems, and experimental results demonstrate that our algorithms can effectively learn both low-rank and full-rank kernel matrices.
Spatiotemporal Dynamics of Word Processing in the Human Brain

We examined the spatiotemporal dynamics of word processing by recording the electrocorticogram (ECoG) from the lateral frontotemporal cortex of neurosurgical patients chronically implanted with subdural electrode grids. Subjects engaged in a target detection task where proper names served as infrequent targets embedded in a stream of task-irrelevant verbs and nonwords. Verbs described actions related to the hand (e.g, throw) or mouth (e.g., blow), while unintelligible nonwords were sounds which matched the verbs in duration, intensity, temporal modulation, and power spectrum. Complex oscillatory dynamics were observed in the delta, theta, alpha, beta, low, and high gamma (HG) bands in response to presentation of all stimulus types. HG activity (80-200 Hz) in the ECoG tracked the spatiotemporal dynamics of word processing and identified a network of cortical structures involved in early word processing. HG was used to determine the relative onset, peak, and offset times of local cortical activation during word processing. Listening to verbs compared to nonwords sequentially activates first the posterior superior temporal gyrus (post-STG), then the middle superior temporal gyrus (mid-STG), followed by the superior temporal sulcus (STS). We also observed strong phase-locking between pairs of electrodes in the theta band, with weaker phase-locking occurring in the delta, alpha, and beta frequency ranges. These results provide details on the first few hundred milliseconds of the spatiotemporal evolution of cortical activity during word processing and provide evidence consistent with the hypothesis that an oscillatory hierarchy coordinates the flow of information between distinct cortical regions during goal-directed behavior.