nils-herrmann/clean_scirepeval huggingface dataset:
Contain This , Unleashing Docker for HPC

Containers are a lightweight virtualization method for running multiple isolated Linux systems under a common host operating system. Container-based computing is revolutionizing the way applications are developed and deployed. A new ecosystem has emerged around the Docker platform to enable container based computing. However, this revolution has yet to reach the HPC community. In this paper, we provide background on Linux Containers and Docker, and how they can be of value to the scientific and HPC community. We will explain some of the use cases that motivate the need for user defined images and the uses of Docker. We will describe early work in deploying and integrating Docker into an HPC environment, and some of the pitfalls and challenges we encountered. We will discuss some of the security implications of using Docker and how we have addressed those for a shared user system typical of HPC centers. We will also provide performance measurements to illustrate the low overhead of containers. While our early work has been on cluster-based/CS-series systems, we will describe some preliminary assessment of supporting Docker on Cray XC series supercomputers, and a potential partnership with Cray to explore the feasibility and approaches to using Docker on large systems. Docker; User Defined Images; containers; HPC systems
An investigation on the feasibility of cross-project defect prediction

Software defect prediction helps to optimize testing resources allocation by identifying defect-prone modules prior to testing. Most existing models build their prediction capability based on a set of historical data, presumably from the same or similar project settings as those under prediction. However, such historical data is not always available in practice. One potential way of predicting defects in projects without historical data is to learn predictors from data of other projects. This paper investigates defect predictions in the cross-project context focusing on the selection of training data. We conduct three large-scale experiments on 34 data sets obtained from 10 open source projects. Major conclusions from our experiments include: (1) in the best cases, training data from other projects can provide better prediction results than training data from the same project; (2) the prediction results obtained using training data from other projects meet our criteria for acceptance on the average level, defects in 18 out of 34 cases were predicted at a Recall greater than 70% and a Precision greater than 50%; (3) results of cross-project defect predictions are related with the distributional characteristics of data sets which are valuable for training data selection. We further propose an approach to automatically select suitable training data for projects without historical data. Prediction results provided by the training data selected by using our approach are comparable with those provided by training data from the same project.
Non-contact video-based vital sign monitoring using ambient light and auto-regressive models.

Remote sensing of the reflectance photoplethysmogram using a video camera typically positioned 1 m away from the patient's face is a promising method for monitoring the vital signs of patients without attaching any electrodes or sensors to them. Most of the papers in the literature on non-contact vital sign monitoring report results on human volunteers in controlled environments. We have been able to obtain estimates of heart rate and respiratory rate and preliminary results on changes in oxygen saturation from double-monitored patients undergoing haemodialysis in the Oxford Kidney Unit. To achieve this, we have devised a novel method of cancelling out aliased frequency components caused by artificial light flicker, using auto-regressive (AR) modelling and pole cancellation. Secondly, we have been able to construct accurate maps of the spatial distribution of heart rate and respiratory rate information from the coefficients of the AR model. In stable sections with minimal patient motion, the mean absolute error between the camera-derived estimate of heart rate and the reference value from a pulse oximeter is similar to the mean absolute error between two pulse oximeter measurements at different sites (finger and earlobe). The activities of daily living affect the respiratory rate, but the camera-derived estimates of this parameter are at least as accurate as those derived from a thoracic expansion sensor (chest belt). During a period of obstructive sleep apnoea, we tracked changes in oxygen saturation using the ratio of normalized reflectance changes in two colour channels (red and blue), but this required calibration against the reference data from a pulse oximeter.
The Cubli: A cube that can jump up and balance

This paper introduces the Cubli, a 15×15×15 cm cube that can jump up and balance on a corner. Momentum wheels mounted on three faces of the cube  rotate at high angular velocities and then brake suddenly, causing the Cubli to jump up. Once the Cubli has almost reached the corner stand-up position, controlled motor torques are applied to make it balance on its corner. This paper tracks the development of the Cubli's one dimensional prototype at ETH Zurich and presents preliminary results.
Explaining Predictions of Non-Linear Classifiers in NLP

Layer-wise relevance propagation (LRP) is a recently proposed technique for explaining predictions of complex non-linear classifiers in terms of input variables. In this paper, we apply LRP for the first time to natural language processing (NLP). More precisely, we use it to explain the predictions of a convolutional neural network (CNN) trained on a topic categorization task. Our analysis highlights which words are relevant for a specific prediction of the CNN. We compare our technique to standard sensitivity analysis, both qualitatively and quantitatively, using a “word deleting” perturbation experiment, a PCA analysis, and various visualizations. All experiments validate the suitability of LRP for explaining the CNN predictions, which is also in line with results reported in recent image classification studies.
Highlighting Diverse Concepts in Documents

We show the underpinnings of a method for summarizing documents: it ingests a document and automatically highlights a small set of sentences that are expected to cover the different aspects of the document. The sentences are picked using simple coverage and orthogonality criteria. We describe a novel combinatorial formulation that captures exactly the document-summarization problem, and we develop simple and efficient algorithms for solving it. We compare our algorithms with many popular document-summarization techniques via a broad set of experiments on real data. The results demonstrate that our algorithms work well in practice and give high-quality summaries.
A High Voltage Gain DC–DC Converter Integrating Coupled-Inductor and Diode–Capacitor Techniques

The high-voltage gain converter is widely employed in many industry applications, such as photovoltaic systems, fuel cell systems, electric vehicles, and high-intensity discharge lamps. This paper presents a novel single-switch high step-up nonisolated dc-dc converter integrating coupled inductor with extended voltage doubler cell and diode-capacitor techniques. The proposed converter achieves extremely large voltage conversion ratio with appropriate duty cycle and reduction of voltage stress on the power devices. Moreover, the energy stored in leakage inductance of coupled inductor is efficiently recycled to the output, and the voltage doubler cell also operates as a regenerative clamping circuit, alleviating the problem of potential resonance between the leakage inductance and the junction capacitor of output diode. These characteristics make it possible to design a compact circuit with high static gain and high efficiency for industry applications. In addition, the unexpected high-pulsed input current in the converter with coupled inductor is decreased. The operating principles and the steady-state analyses of the proposed converter are discussed in detail. Finally, a prototype circuit is implemented in the laboratory to verify the performance of the proposed converter.
FreeDrawer: a free-form sketching system on the responsive workbench

A sketching system for spline-based free-form surfaces on the Responsive Workbench is presented. We propose 3D tools for curve drawing and deformation techniques for curves and surfaces, adapted to the needs of designers. The user directly draws curves in the virtual environment, using a tracked stylus as an input device. A curve network can be formed, describing the skeleton of a virtual model. The non-dominant hand positions and orients the model while the dominant hand uses the editing tools. The curves and the resulting skinning surfaces can interactively be deformed.
G-CNN: An Iterative Grid Based Object Detector

We introduce G-CNN, an object detection technique based on CNNs which works without proposal algorithms. G-CNN starts with a multi-scale grid of fixed bounding boxes. We train a regressor to move and scale elements of the grid towards objects iteratively. G-CNN models the problem of object detection as finding a path from a fixed grid to boxes tightly surrounding the objects. G-CNN with around 180 boxes in a multi-scale grid performs comparably to Fast R-CNN which uses around 2K bounding boxes generated with a proposal technique. This strategy makes detection faster by removing the object proposal stage as well as reducing the number of boxes to be processed.
The Green Vehicle Routing Problem

This work introduces the Green Vehicle Routing Problem (GVRP). The GVRP is an extension of the well-known vehicle routing problem (VRP). Moreover, the GVRP includes an objective function that minimizes weighted distance. Minimizing weighted distance reduces fuel consumption and consequently CO2 emissions. Therefore, the GVRP is more environmentally friendly than traditional versions of the VRP. This work presents a Mixed Integer Linear Program formulation for the problem and a Local Search algorithm to find local optima. Also, the problem is illustrated using a small problem instance.
Enterprise resource planning systems' impact on accounting processes

The advent of the IT-led era and the increased competition have forced companies to react to the new changes in order to remain competitive. Enterprise resource planning (ERP) systems offer distinct advantages in this new business environment as they lower operating costs, reduce cycle times and (arguably) increase customer satisfaction. This study examines, via an exploratory survey of 26 companies, the underlying reasons why companies choose to convert from conventional information systems (IS) to ERP systems and the changes brought in, particularly in the accounting process. The aim is not only to understand the changes and the benefits involved in adopting ERP systems compared with conventional IS, but also to establish the best way forward in future ERP applications. The empirical evidence confirms a number of changes in the accounting process introduced with the adoption of ERP systems.
Untangling invariant object recognition

Despite tremendous variation in the appearance of visual objects, primates can recognize a multitude of objects, each in a fraction of a second, with no apparent effort. However, the brain mechanisms that enable this fundamental ability are not understood. Drawing on ideas from neurophysiology and computation, we present a graphical perspective on the key computational challenges of object recognition, and argue that the format of neuronal population representation and a property that we term 'object tangling' are central. We use this perspective to show that the primate ventral visual processing stream achieves a particularly effective solution in which single-neuron invariance is not the goal. Finally, we speculate on the key neuronal mechanisms that could enable this solution, which, if understood, would have far-reaching implications for cognitive neuroscience.
Conditional Bernoulli Mixtures for Multi-label Classification

Multi-label classification is an important machine learning task wherein one assigns a subset of candidate labels to an object. In this paper, we propose a new multi-label classification method based on Conditional Bernoulli Mixtures. Our proposed method has several attractive properties: it captures label dependencies; it reduces the multi-label problem to several standard binary and multi-class problems; it subsumes the classic independent binary prediction and power-set subset prediction methods as special cases; and it exhibits accuracy and/or computational complexity advantages over existing approaches. We demonstrate two implementations of our method using logistic regressions and gradient boosted trees, together with a simple training procedure based on Expectation Maximization. We further derive an efficient prediction procedure based on dynamic programming, thus avoiding the cost of examining an exponential number of potential label subsets. Experimental results show the effectiveness of the proposed method against competitive alternatives on benchmark datasets.
Fully-Trainable Deep Matching

Deep Matching (DM) is a popular high-quality method for quasi-dense image matching. Despite its name, however, the original DM formulation does not yield a deep neural network that can be trained end-to-end via backpropagation. In this paper, we remove this limitation by rewriting the complete DM algorithm as a convolutional neural network. This results in a novel deep architecture for image matching that involves a number of new layer types and that, similar to recent networks for image segmentation, has a U-topology. We demonstrate the utility of the approach by improving the performance of DM by learning it end-to-end on an image matching task.
Design of a high frequency low voltage CMOS operational amplifier

A method is presented in this paper for the design of a high frequency CMOS operational amplifier (OpAmp) which operates at 3V power supply using tsmc 0.18 micron CMOS technology. The OPAMP designed is a two-stage CMOS OPAMP followed by an output buffer. This Operational Transconductance Amplifier (OTA) employs a Miller capacitor and is compensated with a current buffer compensation technique. The unique behaviour of the MOS transistors in saturation region not only allows a designer to work at a low voltage, but also at a high frequency. Designing of two-stage op-amps is a multi-dimensional-optimization problem where optimization of one or more parameters may easily result into degradation of others. The OPAMP is designed to exhibit a unity gain frequency of 2.02GHz and exhibits a gain of 49.02dB with a 60.5 0 phase margin. As compared to the conventional approach, the proposed compensation method results in a higher unity gain frequency under the same load condition. Design has been carried out in Tanner tools. Simulation results are verified using S-edit and W-edit.
A Performance Evaluation and Examination of Open-Source Erasure Coding Libraries for Storage

Over the past five years, large-scale storage installations have required fault-protection beyond RAID-5, leading to a flurry of research on and development of erasure codes for multiple disk failures. Numerous open-source implementations of various coding techniques are available to the general public. In this paper, we perform a head-to-head comparison of these implementations in encoding and decoding scenarios. Our goals are to compare codes and implementations, to discern whether theory matches practice, and to demonstrate how parameter selection, especially as it concerns memory, has a significant impact on a code’s performance. Additional benefits are to give storage system designers an idea of what to expect in terms of coding performance when designing their storage systems, and to identify the places where further erasure coding research can have the most impact.
On Improving Deep Reinforcement Learning for POMDPs

Deep Reinforcement Learning (RL) recently emerged as one of the most competitive approaches for learning in sequential decision making problems with fully observable environments, e.g., computer Go. However, very little work has been done in deep RL to handle partially observable environments. We propose a new architecture called Action-specific Deep Recurrent QNetwork (ADRQN) to enhance learning performance in partially observable domains. Actions are encoded by a fully connected layer and coupled with a convolutional observation to form an action-observation pair. The time series of actionobservation pairs are then integrated by an LSTM layer that learns latent states based on which a fully connected layer computes Q-values as in conventional Deep Q-Networks (DQNs). We demonstrate the effectiveness of our new architecture in several partially observable domains, including flickering Atari games.
Patterning Technique for Generating Arbitrary Anisotropic Impedance Surfaces

Anisotropic impedance surfaces have been demonstrated to be useful for a variety of applications ranging from antennas, to surface wave guiding, to control of scattering. To increase their anisotropy requires elongated unit cells which have reduced symmetry and thus are not easily arranged into arbitrary patterns. We discuss the limitations of existing patterning techniques, and explore options for generating anisotropic impedance surfaces with arbitrary spatial variation. We present an approach that allows a wide range of anisotropic impedance profiles, based on a point-shifting method combined with a Voronoi cell generation technique. This approach can be used to produce patterns which include highly elongated cells with varying orientation, and cells which can smoothly transition between square, rectangular, hexagonal, and other shapes with a wide range of aspect ratios. We demonstrate a practical implementation of this technique which allows us to define gaps between the cells to generate impedance surfaces, and we use it to implement a simple example of a structure which requires smoothly varying impedance, in the form of a planar Luneberg lens. Simulations of the lens are verified by measurements, validating our pattern generation technique.
To do or to have? That is the question.

Do experiences make people happier than material possessions? In two surveys, respondents from various demographic groups indicated that experiential purchases-those made with the primary intention of acquiring a life experience--made them happier than material purchases. In a follow-up laboratory experiment, participants experienced more positive feelings after pondering an experiential purchase than after pondering a material purchase. In another experiment, participants were more likely to anticipate that experiences would make them happier than material possessions after adopting a temporally distant, versus a temporally proximate, perspective. The discussion focuses on evidence that experiences make people happier because they are more open to positive reinterpretations, are a more meaningful part of one's identity, and contribute more to successful social relationships.
BiblioViz: a system for visualizing bibliography information

The InfoVis 2004 contest led to the development of several bibliography visualization systems. Even though each of these systems offers some unique views of the bibliography data, there is no single best system offering all the desired views. We have thus studied how to consolidate the desirable functionalities of these systems into a cohesive design. We have also designed a few novel visualization methods. This paper presents our findings and creation: BiblioViz, a bibliography visualization system that gives the maximum number of views of the data using a minimum number of visualization constructs in a unified fashion.
Spectrum management in cognitive radio ad hoc networks

The problem of spectrum scarcity and inefficiency in spectrum usage will be addressed by the newly emerging cognitive radio paradigm that allows radios to opportunistically transmit in the vacant portions of the spectrum already assigned to licensed users. For this, the ability for spectrum sensing, spectrum sharing, choosing the best spectrum among the available options, and dynamically adapting transmission parameters based on the activity of the licensed spectrum owners must be integrated within cognitive radio users. Specifically in cognitive radio ad hoc networks, distributed multihop architecture, node mobility, and spatio-temporal variance in spectrum availability are some of the key distinguishing factors. In this article the important features of CRAHNs are presented, along with the design approaches and research challenges that must be addressed. Spectrum management in CRAHNs comprises spectrum sensing, sharing, decision, and mobility. In this article each of these functions are described in detail from the viewpoint of multihop infra-structureless networks requiring cooperation among users.
FMCW MIMO Radar System for Frequency-Division Multiple TX-Beamforming

In this paper, a prototype automotive radar sensor is presented that is capable of generating simultaneously multiple transmit (TX) beams. The system is based on a four-channel 77-GHz frequency-modulated continuous-wave (FMCW) radar system. The number of beams, their radiated power, steering angle, and beam pattern can be changed adaptively. This is achieved by the utilization of orthogonal waveforms applied to different beams in combination with digital beamforming on the receive side. Key components are vector modulators in the TX path controlled by digital-to-analog converters. The performance of the system is shown in measurements focused on beam pattern, signal-to-noise ratio, and susceptibility in case of interfering targets at cross-range. Measurement results are discussed and compared to theory and simulations. Furthermore, crest factor minimization of the vector modulator's control signals is introduced and used to increase the achievable TX power, which will be also shown in measurements.
As Time Goes by: Discovering Eras in Evolving Social Networks

Within the large body of research in complex network analysis, an important topic is the temporal evolution of networks. Existing approaches aim at analyzing the evolution on the global and the local scale, extracting properties of either the entire network or local patterns. In this paper, we focus instead on detecting clusters of temporal snapshots of a network, to be interpreted as eras of evolution. To this aim, we introduce a novel hierarchical clustering methodology, based on a dissimilarity measure (derived from the Jaccard coefficient) between two temporal snapshots of the network. We devise a framework to discover and browse the eras, either in top-down or a bottom-up fashion, supporting the exploration of the evolution at any level of temporal resolution. We show how our approach applies to real networks, by detecting eras in an evolving co-authorship graph extracted from a bibliographic dataset; we illustrate how the discovered temporal clustering highlights the crucial moments when the network had profound changes in its structure. Our approach is finally boosted by introducing a meaningful labeling of the obtained clusters, such as the characterizing topics of each discovered era, thus adding a semantic dimension to our analysis.