nils-herrmann/clean_scirepeval huggingface dataset:
Apache REEF: Retainable Evaluator Execution Framework

Resource Managers like YARN and Mesos have emerged as a critical layer in the cloud computing system stack, but the developer abstractions for leasing cluster resources and instantiating application logic are very low level. This flexibility comes at a high cost in terms of developer effort, as each application must repeatedly tackle the same challenges (e.g., fault tolerance, task scheduling and coordination) and reimplement common mechanisms (e.g., caching, bulk-data transfers). This article presents REEF, a development framework that provides a control plane for scheduling and coordinating task-level (data-plane) work on cluster resources obtained from a Resource Manager. REEF provides mechanisms that facilitate resource reuse for data caching and state management abstractions that greatly ease the development of elastic data processing pipelines on cloud platforms that support a Resource Manager service. We illustrate the power of REEF by showing applications built atop: a distributed shell application, a machine-learning framework, a distributed in-memory caching system, and a port of the CORFU system. REEF is currently an Apache top-level project that has attracted contributors from several institutions and it is being used to develop several commercial offerings such as the Azure Stream Analytics service.
"I've Got Nothing to Lose": Consumers' Risk Perceptions and Protective Actions after the Equifax Data Breach

Equifax, one of the three major U.S. credit bureaus, experienced a large-scale data breach in 2017. We investigated consumers’ mental models of credit bureaus, how they perceive risks from this data breach, whether they took protective measures, and their reasons for inaction through 24 semi-structured interviews. We find that participants’ mental models of credit bureaus are incomplete and partially inaccurate. Although many participants were aware of and concerned about the Equifax breach, few knew whether they were affected, and even fewer took protective measures after the breach. We find that this behavior is not primarily influenced by accuracy of mental models or risk awareness, but rather by costs associated with protective measures, optimism bias in estimating one’s likelihood of victimization, sources of advice, and a general tendency towards delaying action until harm has occurred. We discuss legal, technical and educational implications and directions towards better protecting consumers in the credit reporting system.
PAC-Bayes Control: Synthesizing Controllers that Provably Generalize to Novel Environments

Our goal is to synthesize controllers for robots that provably generalize well to novel environments given a dataset of example environments. The key technical idea behind our approach is to leverage tools from generalization theory in machine learning by exploiting a precise analogy (which we present in the form of a reduction) between robustness of controllers to novel environments and generalization of hypotheses in supervised learning. In particular, we utilize the Probably Approximately Correct (PAC)-Bayes framework, which allows us to obtain upper bounds (that hold with high probability) on the expected cost of (stochastic) controllers across novel environments. We propose control synthesis algorithms that explicitly seek to minimize this upper bound. The corresponding optimization problem can be solved efficiently using convex optimization (Relative Entropy Programming in particular) in the setting where we are optimizing over a finite control policy space. In the more general setting of continuously parameterized controllers, we minimize this upper bound using stochastic gradient descent. We present examples of our approach in the context of obstacle avoidance control with depth measurements. Our simulated examples demonstrate the potential of our approach to provide strong generalization guarantees on controllers for robotic systems with continuous state and action spaces, nonlinear dynamics, and partially observable state via sensor measurements.
When Acquisition Spoils Retention: Direct Selling vs. Delegation Under CRM

T widespread implementation of customer relationship management technologies in business has allowed companies to increasingly focus on both acquiring and retaining customers. The challenge of designing incentive mechanisms that simultaneously focus on customer acquisition and customer retention comes from the fact that customer acquisition and customer retention are usually separate but intertwined tasks that make providing proper incentives more difficult. The present study develops incentive mechanisms that simultaneously address acquisition and retention of customers with an emphasis on the interactions between them. The main focus of this study is to examine the impact of the negative effect of acquisition on retention, i.e., the spoiling effect, on firm performance under direct selling and delegation of customer acquisition. Our main finding is that the negative effect of acquisition on retention has a significant impact on acquisition and retention efforts and firm profit. In particular, when the customer acquisition and retention are independent, the firm’s profit is higher under direct selling than under delegation; however, when acquisition spoils retention, interestingly, the firm’s profit may be higher under delegation. Our analysis also finds that the spoiling effect not only reduces the optimal acquisition effort but may also reduce retention effort under both direct selling and delegation. Comparing the optimal efforts under direct selling and delegation, the acquisition effort is always lower under delegation regardless of the spoiling effect, but the retention effort may be higher under delegation with the spoiling effect. Furthermore, when the customer antagonism effect from price promotions is considered, our main results hold regarding the firm’s preferences between direct selling and delegation, which demonstrates the robustness of our model.
English Cognate Object Construction : A Usage-based , Construction Grammar Approach ∗

The English cognate object (CO) construction like laugh a nervous laugh raises intriguing analytic and empirical questions. They include (a) what kind of verb licenses the CO, (b) what is the grammatical status of the CO (including its argumenthood), and (c) what are the semantic/pragmatic contributions of the construction? In answering these questions and to see real usages of the construction, in this paper we have investigated English corpora like the COCA (Corpus of Contemporary American English) and suggest a lexicalist perspective. In particular, we assume that there are two different types of the construction, EVENTIVE-CO and REFERENTIAL-CO, based on the object’s referential property. This difference in the referential power leads to many syntactic differences between the two types. In addition, we show that the uses of the CO selecting verbs are much more flexible than the literature has suggested. As a way of accounting for these variations, we sketch a Construction Grammar view in which argument structure constructions, lexical semantics, and constructional constraints are all interacting together to license the construction in question.
Modeling of a Silicon-Carbide MOSFET With Focus on Internal Stray Capacitances and Inductances, and Its Verification

This paper describes a compact model for a silicon-carbide (SiC) MOSFET, which features both characterization and modeling of a new drain-gate capacitor. In addition, the internal gate resistance and the internal stray inductances of the discrete package are experimentally evaluated and modeled by a newly-proposed method. The gate drive circuit is also modeled and involved in this simulation. The comprehensive modeling procedure is also considered, which offers an efficient modeling process. The model shows excellent agreements in transient behavior of both gate drive circuit and main power circuit between experimental and simulated waveforms. The comparison shows accurate reproducibility of a rate of drain-source voltage change and a rate of drain-current change within an error of 15%, which could not be realized with the conventional stray capacitance models. This successful validation indicates that the developed model would offer a new modeling concept and be expected to open up the possibilities of the simulation study for evaluating and designing detailed performance of SiC-MOSFETs.
Incremental dialog processing in a task-oriented dialog

Incremental Dialog Processing (IDP) enables Spoken Dialog Systems to gradually process minimal units of user speech in order to give the user an early system response. In this paper, we present an application of IDP that shows its effectiveness in a task-oriented dialog system. We have implemented an IDP strategy and deployed it for one month on a real-user system. We compared the resulting dialogs with dialogs produced over the previous month without IDP. Results show that the incremental strategy significantly improved system performance by eliminating long and often off-task utterances that generally produce poor speech recognition results. User behavior is also affected; the user tends to shorten utterances after being interrupted by the system.
Convolutional Neural Network based sentiment analysis using Adaboost combination

Sentimental polarity detection has long been a hot task in natural language processing since its applications range from product feedback analysis to user statement understanding. Recently a lot of machine learning approaches have been proposed in the literature, e.g., SVM, Naive Bayes, recursive neural network, auto-encoders and etc. Among these different models, Convolutional Neural Network (CNN) architecture have also demonstrated profound efficiency in NLP tasks including sentiment classification. In CNN, the width of convolutional filter functions alike number N in N-grams model. Thus, different filter lengths may influence the performance of CNN classifier. In this paper, we want to study the possibility of leveraging the contribution of different filter lengths and grasp their potential in the final polarity of the sentence. We then use Adaboost to combine different classifiers with respective filter sizes. The experimental study on commonly used datasets has shown its potential in identifying the different roles of specific N-grams in a sentence respectively and merging their contribution in a weighted classifier.
Can Airborne Ground Penetrating Radars Explore Groundwater in Hyper-Arid Regions?

Groundwater provides roughly 43% of the water used globally for irrigated agriculture. Understanding, predicting, and managing the environmental processes that define the natural capital of Earth’s changing groundwater is one of the most pressing societal challenges of the 21st century. To understand the influence of the dynamics in the vadose zone on terrestrial ecosystems, and to estimate the future sustainability of groundwater resources, a regional and eventually global assessment of water table depth is required. To enable observations of the hydrologic systems’ dynamics, the feasibility of an airborne ground penetrating radar (GPR) system is considered as a first step to effectively provide both large spatial coverage and short revisit times. Such a capability has the potential to enable large-scale surveys to directly observe the shallow subsurface hydrologic processes. To evaluate the capabilities of such a system, we start with a review of soil and subsurface material properties, with a focus on hyper-arid regions. Using first principles, results from literature reviews, and recent field measurements, we then investigate the effects of attenuation and surface clutter to identify the potential capabilities and challenges of an airborne GPR to investigate the spatio-temporal dynamics of the vadose zone. In this paper, we arrive at a qualified “yes” as an answer the title’s question. With low radar frequencies (on the order of 10 MHz or less), adequate ground clutter rejection, and medium or higher vadose zone soil resistivity, the detection of water table depths of 50 m and beyond are feasible.
What Color Is Your Jacobian? Graph Coloring for Computing Derivatives

Graph coloring has been employed since the 1980s to efficiently compute sparse Jacobian and Hessian matrices using either finite differences or automatic differentiation. Several coloring problems occur in this context, depending on whether the matrix is a Jacobian or a Hessian, and on the specifics of the computational techniques employed. We consider eight variant vertex coloring problems here. This article begins with a gentle introduction to the problem of computing a sparse Jacobian, followed by an overview of the historical development of the research area. Then we present a unifying framework for the graph models of the variant matrix estimation problems. The framework is based upon the viewpoint that a partition of a matrix into structurally orthogonal groups of columns corresponds to distance-2 coloring an appropriate graph representation. The unified framework helps integrate earlier work and leads to fresh insights; enables the design of more efficient algorithms for many problems; leads to new algorithms for others; and eases the task of building graph models for new problems. We report computational results on two of the coloring problems to support our claims. Most of the methods for these problems treat a column or a row of a matrix as an atomic entity, and partition the columns or rows (or both). A brief review of methods that do not fit these criteria is provided. We also discuss results in discrete mathematics and theoretical computer science that intersect with the topics considered here.
What Benefits do they Bring? A Case Study Analysis on Enterprise Social Networks

Over the last years, Enterprise Social Networks (ESN) have gained increasing attention both in academia and practice, resulting in a large number of publications dealing with ESN. Among them is a large number of case studies describing the benefits of ESN in each individual case. Based on the different research objects they focus, various benefits are described. However, an overview of the benefits achieved by using ESN is missing and will, thus, be elaborated in this article (research question 1). Further, we cluster the identified benefits to more generic categories and finally classify them to the capabilities of traditional IT as presented by Davenport and Short (1990) to determine if new capabilities of IT arise using ESN (research question 2). To address our research questions, we perform a qualitative content analysis on 37 ESN case studies. As a result, we identify 99 individual benefits, classify them to the capabilities of traditional IT, and define a new IT capability named Social Capital. Our results can, e.g., be used to align and expand current ESN success measurement approaches.
Market efficiency and accounting research : a discussion of ‘ capital market research in accounting ’ by S .

Much of capital market research in accounting over the past 20 years has assumed that the price adjustment process to information is instantaneous and/or trivial. This assumption has had an enormous influence on the way we select research topics, design empirical tests, and interpret research findings. In this discussion, I argue that price discovery is a complex process, deserving of more attention. I highlight significant problems associated with a na.ıve view of market efficiency, and advocate a more general model involving noise traders. Finally, I discuss the implications of recent evidence against market efficiency for future research.
Deep Dynamical Modeling and Control of Unsteady Fluid Flows

The design of flow control systems remains a challenge due to the nonlinear nature of the equations that govern fluid flow. However, recent advances in computational fluid dynamics (CFD) have enabled the simulation of complex fluid flows with high accuracy, opening the possibility of using learning-based approaches to facilitate controller design. We present a method for learning the forced and unforced dynamics of airflow over a cylinder directly from CFD data. The proposed approach, grounded in Koopman theory, is shown to produce stable dynamical models that can predict the time evolution of the cylinder system over extended time horizons. Finally, by performing model predictive control with the learned dynamical models, we are able to find a straightforward, interpretable control law for suppressing vortex shedding in the wake of the cylinder.
The DSM diagnostic criteria for gender identity disorder in adolescents and adults.

Apart from some general issues related to the Gender Identity Disorder (GID) diagnosis, such as whether it should stay in the DSM-V or not, a number of problems specifically relate to the current criteria of the GID diagnosis for adolescents and adults. These problems concern the confusion caused by similarities and differences of the terms transsexualism and GID, the inability of the current criteria to capture the whole spectrum of gender variance phenomena, the potential risk of unnecessary physically invasive examinations to rule out intersex conditions (disorders of sex development), the necessity of the D criterion (distress and impairment), and the fact that the diagnosis still applies to those who already had hormonal and surgical treatment. If the diagnosis should not be deleted from the DSM, most of the criticism could be addressed in the DSM-V if the diagnosis would be renamed, the criteria would be adjusted in wording, and made more stringent. However, this would imply that the diagnosis would still be dichotomous and similar to earlier DSM versions. Another option is to follow a more dimensional approach, allowing for different degrees of gender dysphoria depending on the number of indicators. Considering the strong resistance against sexuality related specifiers, and the relative difficulty assessing sexual orientation in individuals pursuing hormonal and surgical interventions to change physical sex characteristics, it should be investigated whether other potentially relevant specifiers (e.g., onset age) are more appropriate.
CAVE 2 : A Hybrid Reality Environment for Immersive Simulation and Information Analysis

Hybrid Reality Environments represent a new kind of visualization spaces that blur the line between virtual environments and high resolution tiled display walls. This paper outlines the design and implementation of the CAVE2 TM Hybrid Reality Environment. CAVE2 is the world’s first near-seamless flat-panel-based, surround-screen immersive system. Unique to CAVE2 is that it will enable users to simultaneously view both 2D and 3D information, providing more flexibility for mixed media applications. CAVE2 is a cylindrical system of 24 feet in diameter and 8 feet tall, and consists of 72 near-seamless, off-axisoptimized passive stereo LCD panels, creating an approximately 320 degree panoramic environment for displaying information at 37 Megapixels (in stereoscopic 3D) or 74 Megapixels in 2D and at a horizontal visual acuity of 20/20. Custom LCD panels with shifted polarizers were built so the images in the top and bottom rows of LCDs are optimized for vertical off-center viewingallowing viewers to come closer to the displays while minimizing ghosting. CAVE2 is designed to support multiple operating modes. In the Fully Immersive mode, the entire room can be dedicated to one virtual simulation. In 2D model, the room can operate like a traditional tiled display wall enabling users to work with large numbers of documents at the same time. In the Hybrid mode, a mixture of both 2D and 3D applications can be simultaneously supported. The ability to treat immersive work spaces in this Hybrid way has never been achieved before, and leverages the special abilities of CAVE2 to enable researchers to seamlessly interact with large collections of 2D and 3D data. To realize this hybrid ability, we merged the Scalable Adaptive Graphics Environment (SAGE) a system for supporting 2D tiled displays, with Omegalib a virtual reality middleware supporting OpenGL, OpenSceneGraph and Vtk applications.
Fast Fashion : Business Model Overview and Research Opportunities

Fast fashion is a business model that offers (the perception of) fashionable clothes at affordable prices. From an operations standpoint, fast fashion requires a highly responsive supply chain that can support a product assortment that is periodically changing. Though the underlying principles are simple, the successful execution of the fast-fashion business model poses numerous challenges. We present a careful examination of this business model and discuss its execution by analyzing the most prominent firms in the industry. We then survey the academic literature for research that is specifically relevant or directly related to fast fashion. Our goal is to expose the main components of fast fashion and to identify untapped research opportunities.
BROCCOLI: Software for fast fMRI analysis on many-core CPUs and GPUs

Analysis of functional magnetic resonance imaging (fMRI) data is becoming ever more computationally demanding as temporal and spatial resolutions improve, and large, publicly available data sets proliferate. Moreover, methodological improvements in the neuroimaging pipeline, such as non-linear spatial normalization, non-parametric permutation tests and Bayesian Markov Chain Monte Carlo approaches, can dramatically increase the computational burden. Despite these challenges, there do not yet exist any fMRI software packages which leverage inexpensive and powerful graphics processing units (GPUs) to perform these analyses. Here, we therefore present BROCCOLI, a free software package written in OpenCL (Open Computing Language) that can be used for parallel analysis of fMRI data on a large variety of hardware configurations. BROCCOLI has, for example, been tested with an Intel CPU, an Nvidia GPU, and an AMD GPU. These tests show that parallel processing of fMRI data can lead to significantly faster analysis pipelines. This speedup can be achieved on relatively standard hardware, but further, dramatic speed improvements require only a modest investment in GPU hardware. BROCCOLI (running on a GPU) can perform non-linear spatial normalization to a 1 mm(3) brain template in 4-6 s, and run a second level permutation test with 10,000 permutations in about a minute. These non-parametric tests are generally more robust than their parametric counterparts, and can also enable more sophisticated analyses by estimating complicated null distributions. Additionally, BROCCOLI includes support for Bayesian first-level fMRI analysis using a Gibbs sampler. The new software is freely available under GNU GPL3 and can be downloaded from github (https://github.com/wanderine/BROCCOLI/).
Point-to-multipoint in-band mmwave backhaul for 5G networks

Cost-effective and scalable wireless backhaul solutions are essential for realizing the 5G vision of providing gigabits per second anywhere. Not only is wireless backhaul essential to support network densification based on small cell deployments, but also for supporting very low latency inter-BS communication to deal with intercell interference. Multiplexing backhaul and access on the same frequency band (in-band wireless backhaul) has obvious cost benefits from the hardware and frequency reuse perspective, but poses significant technology challenges. We consider an in-band solution to meet the backhaul and inter-BS coordination challenges that accompany network densification. Here, we present an analysis to persuade the readers of the feasibility of in-band wireless backhaul, discuss realistic deployment and system assumptions, and present a scheduling scheme for inter- BS communications that can be used as a baseline for further improvement. We show that an inband wireless backhaul for data backhauling and inter-BS coordination is feasible without significantly hurting the cell access capacities.