nils-herrmann/clean_scirepeval huggingface dataset:
Virtual Teams : What Do We Know and Where Do We Go From Here ?

In this paper, we review the research on virtual teams in an effort to assess the state of the literature. We start with an examination of the definitions of virtual teams used and propose an integrative definition that suggests that all teams may be defined in terms of their extent of virtualness. Next, we review findings related to team inputs, processes, and outcomes, and identify areas of agreement and inconsistency in the literature on virtual teams. Based on this review, we suggest avenues for future research, including methodological and theoretical considerations that are important to advancing our understanding of virtual teams.
Three Knowledge Management Strategies: Knowledge Hierarchies, Knowledge Markets, and Knowledge Communities

1 Bob Zmud was the accepting Senior Editor for this article. 2 Much research has been done on organizational forms, particularly hierarchies, communities, and markets. A classic article that describes how firms can be organized is Ouchi, W. G., “Markets, Bureaucracies, and Clans,” Administrative Science Quarterly (25), 1980, pp. 129-141. Three more recent articles that examine the use of markets, hierarchies, and communities that are internal to the firm are Adler, P. S., “Market, Hierarchy, and Trust: The Knowledge Economy and the Future of Capitalism,” Organization Science (12:2), 2001, pp. 214-234; Barney, J. B., “Firm Resources and Sustained Competitive Advantage,” Journal of Management (17), 1991, pp. 99-120; and Conner, K. R. and Prahalad, C. K., “A Resource-based Theory of the Firm: Knowledge Versus Opportunism,” Organization Science (7:5), 1996, pp. 477-501. 3 Note that we use the term “reuse” because our focus is the knowledge management strategy rather than the user per se. 4 Alavi, M. and Leidner, D. E., “Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues,” MIS Quarterly (25:1), 2001,  Grant, R. M., “Prospering in Dynamically Competitive Environments: Organizational Capability as Knowledge Integration,” THREE KNOWLEDGE MANAGEMENT STRATEGIES: KNOWLEDGE HIERARCHIES, KNOWLEDGE MARKETS, AND KNOWLEDGE COMMUNITIES
3D Fluid Flow Estimation with Integrated Particle Reconstruction

The standard approach to densely reconstruct the motion in a volume of fluid is to inject high-contrast tracer particles and record their motion with multiple high-speed cameras. Almost all existing work processes the acquired multi-view video in two separate steps: first, a per-frame reconstruction of the particles, usually in the form of soft occupancy likelihoods in a voxel representation; followed by 3D motion estimation, with some form of dense matching between the precomputed voxel grids from different time steps. In this sequential procedure, the first step cannot use temporal consistency considerations to support the reconstruction, while the second step has no access to the original, highresolution image data. We show, for the first time, how to jointly reconstruct both the individual tracer particles and a dense 3D fluid motion field from the image data, using an integrated energy minimization. Our hybrid Lagrangian/Eulerian model explicitly reconstructs individual particles, and at the same time recovers a dense 3D motion field in the entire domain. Making particles explicit greatly reduces the memory consumption and allows one to use the high-resolution input images for matching. Whereas the dense motion field makes it possible to include physical a-priori constraints and account for the incompressibility and viscosity of the fluid. The method exhibits greatly (≈70%) improved results over a recent baseline with two separate steps for 3D reconstruction and motion estimation. Our results with only two time steps are comparable to those of state-of-the-art tracking-based methods that require much longer sequences.
A comprehensive study of the predictive accuracy of dynamic change-impact analysis

The correctness of software is affected by its constant changes. For that reason, developers use change-impact analysis to identify early the potential consequences of changing their software. Dynamic impact analysis is a practical technique that identifies potential impacts of changes for representative executions. However, it is unknown how reliable its results are because their accuracy has not been studied. This paper presents the first comprehensive study of the predictive accuracy of dynamic impact analysis in two complementary ways. First, we use massive numbers of random changes across numerous Java applications to cover all possible change locations. Then, we study more than 100 changes from software repositories, which are representative of developer practices. Our experimental approach uses sensitivity analysis and execution differencing to systematically measure the precision and recall of dynamic impact analysis with respect to the actual impacts observed for these changes. Our results for both types of changes show that the most cost-effective dynamic impact analysis known is surprisingly inaccurate with an average precision of 38-50% and average recall of 50-56% in most cases. This comprehensive study offers insights on the effectiveness of existing dynamic impact analyses and motivates the future development of more accurate impact analyses.
The Referential Reader: A Recurrent Entity Network for Anaphora Resolution

We present a new architecture for storing and accessing entity mentions during online text processing. While reading the text, entity references are identified, and may be stored by either updating or overwriting a cell in a fixedlength memory. The update operation implies coreference with the other mentions that are stored in the same cell; the overwrite operations causes these mentions to be forgotten. By encoding the memory operations as differentiable gates, it is possible to train the model end-to-end, using both a supervised anaphora resolution objective as well as a supplementary language modeling objective. Evaluation on a dataset of pronoun-name anaphora demonstrates that the model achieves state-of-the-art performance with purely left-to-right processing of the text.
Partially Blind Deblurring of Barcode from Out-of-Focus Blur

This paper addresses the nonstationary out-of-focus (OOF) blur removal in the application of barcode reconstruction. We propose a partially blind deblurring method when partial knowledge of the clean barcode is available. In particular, we consider an image formation model based on geometrical optics, which involves the point-spread function (PSF) for the OOF blur. With the known information, we can estimate a low-dimensional representation of the PSF using the Levenberg– Marquardt algorithm. Once the PSF is obtained, the deblurred image is computed by solving a quadratic program. We find that imposing a [0,1] box constraint is often good enough to enforce binary signal. Experiments on real data demonstrate that the forward model is physically realistic and our partially blind deblurring method can yield good reconstructions.
Design and evaluation of novel pH-sensitive ureido-conjugated chitosan/TPP nanoparticles targeted to Helicobacter pylori.

The covalently modified ureido-conjugated chitosan/TPP multifunctional nanoparticles have been developed as targeted nanomedicine delivery system for eradication of Helicobacter pylori. H. pylori can specifically express the urea transport protein on its membrane to transport urea into cytoplasm for urease to produce ammonia, which protects the bacterium in the acid milieu of stomach. The clinical applicability of topical antimicrobial agent is needed to eradicate H. pylori in the infected fundal area. In this study, we designed and synthesized two ureido-conjugated chitosan derivatives UCCs-1 and UCCs-2 for preparation of multifunctional nanoparticles. The process was optimized in order to prepare UCCs/TPP nanoparticles for encapsulation of amoxicillin. The results showed that the amoxicillin-UCCs/TPP nanoparticles exhibited favorable pH-sensitive characteristics, which could procrastinate the release of amoxicillin at gastric acids and enable the drug to deliver and target to H. pylori at its survival region effectively. Compared with unmodified amoxicillin-chitosan/TPP nanoparticles, a more specific and effective H. pylori growth inhibition was observed for amoxicillin-UCCs/TPP nanoparticles. Drug uptake analysis tested by flow cytometry and confocal laser scanning microscopy verified that the uptake of FITC-UCCs-2/TPP nanoparticles was associated with urea transport protein on the membrane of H. pylori and reduced with the addition of urea as competitive transport substrate. These findings suggest that the multifunctional amoxicillin-loaded nanoparticles have great potential for effective therapy of H. pylori infection. They may also serve as pharmacologically effective nanocarriers for oral targeted delivery of other therapeutic drugs to treat H. pylori.
A Derandomized Switching Lemma and an Improved Derandomization of AC0

We describe a new pseudorandom generator for AC0. Our generator ε-fools circuits of depth d and size M and uses a seed of length Ŏ(log<sup>d+4</sup> M/ε). The previous best construction for $d \geq 3$ was due to Nisan, and had seed length Ŏ(log<sup>2d+6</sup> M/ε). A seed length of O(log<sup>2d+Ω(1)</sup> M) is best possible given Nisan-type generators and the current state of circuit lower bounds. Seed length Ω(log<sup>d</sup> M/ε) is a barrier for any pseudorandom generator construction given the current state of circuit lower bounds. For d=2, a pseudorandom generator of seed length Ŏ(log<sup>2</sup> M/ε) was known. Our generator is based on a "pseudorandom restriction'' generator which outputs restrictions that satisfy the conclusions of the Hastad Switching Lemma and that uses a seed of polylogarithmic length.
Towards Exploring Data-Intensive Scientific Applications at Extreme Scales through Systems and Simulations

The state-of-the-art storage architecture of high-performance computing systems was designed decades ago, and with today's scale and level of concurrency, it is showing significant limitations. Our recent work proposed a new architecture to address the I/O bottleneck of the conventional wisdom, and the system prototype (FusionFS) demonstrated its effectiveness on up to 16 K nodes-the scale on par with today's largest supercomputers. The main objective of this paper is to investigate FusionFS's scalability towards exascale. Exascale computers are predicted to emerge by 2018, comprising millions of cores and billions of threads. We built an event-driven simulator (FusionSim) according to the FusionFS architecture, and validated it with FusionFS's traces. FusionSim introduced less than 4 percent error between its simulation results and FusionFS traces. With FusionSim we simulated workloads on up to two million nodes and find out almost linear scalability of I/O performance; results justified FusionFS's viability for exascale systems. In addition to the simulation work, this paper extends the FusionFS system prototype in the following perspectives: (1) the fault tolerance of file metadata is supported, (2) the limitations of the current system design is discussed, and (3) a more thorough performance evaluation is conducted, such as N-to-1 metadata write, system efficiency, and more platforms such as Amazon Cloud.
Systemc-clang: An open-source framework for analyzing mixed-abstraction SystemC models

This work presents an open-source framework called systemc-clang for analyzing SystemC models that consist of a mixture of register-transfer level, and transaction-level components. The framework statically parses mixed-abstraction SystemC models, and represents them using an intermediate representation. This intermediate representation captures the structural information about the model, and certain behavioural semantics of the processes in the model. This representation can be used for multiple purposes such as static analysis of the model, code transformations, and optimizations. We describe with examples, the key details in implementing systemc-clang, and show an example of constructing a plugin that analyzes the intermediate representation to discover opportunities for parallel execution of SystemC processes. We also experimentally evaluate the capabilities of this framework with a subset of examples from the SystemC distribution including register-transfer, and transaction-level models.
Organizing a Global Coordinate System from Local Information on an Ad Hoc Sensor Network

We demonstrate that it is possible to achieve accurate localization and tracking of a target in a randomly placed wireless sensor network composed of inexpensive components of limited accuracy. The crucial enabler for this is a reasonably accurate local coordinate system aligned with the global coordinates. We present an algorithm for creating such a coordinate system without the use of global control, globally accessible beacon signals, or accurate estimates of inter-sensor distances. The coordinate system is robust and automatically adapts to the failure or addition of sensors. Extensive theoretical analysis and simulation results are presented. Two key theoretical results are: there is a critical minimum average neighborhood size of 15 for good accuracy and there is a fundamental limit on the resolution of any coordinate system determined strictly from local communication. Our simulation results show that we can achieve position accuracy to within 20% of the radio range even when there is variation of up to 10% in the signal strength of the radios. The algorithm improves with finer quantizations of inter-sensor distance estimates: with 6 levels of quantization position errors better than 10% achieved. Finally we show how the algorithm gracefully generalizes to target tracking tasks.
Robust and Discriminative Self-Taught Learning

The lack of training data is a common challenge in many machine learning problems, which is often tackled by semi-supervised learning methods or transfer learning methods. The former requires unlabeled images from the same distribution as the labeled ones and the latter leverages labeled images from related homogenous tasks. However, these restrictions often cannot be satisfied. To address this, we propose a novel robust and discriminative self-taught learning approach to utilize any unlabeled data without the above restrictions. Our new approach employs a robust loss function to learn the dictionary, and enforces the structured sparse regularization to automatically select the optimal dictionary basis vectors and incorporate the supervision information contained in the labeled data. We derive an efficient iterative algorithm to solve the optimization problem and rigorously prove its convergence. Promising results in extensive experiments have validated the proposed approach.
Synthetic aperture radar processing by a multiple scale neural system for boundary and surface representation

A neural network model of boundary segmentation and surface representation is developed to process images containing range data gathered by a synthetic aperture radar SAR sensor The boundary and surface processing are accomplished by an improved Boundary Contour System BCS and Feature Contour System FCS respectively that have been derived from analyses of perceptual and neurobiological data BCS FCS processing makes structures such as motor vehicles roads and buildings more salient and interpretable to human observers than they are in the original imagery Early processing by ON cells and OFF cells embedded in shunting center surround network models preprocessing by lateral geniculate nucleus LGN Such preprocessing compensates for illumination gradients normalizes input dynamic range and extracts local ratio contrasts ON cell and OFF cell outputs are combined in the BCS to de ne oriented lters that model cortical simple cells Pooling ON and OFF outputs at simple cells overcomes complementary processing de ciencies of each cell type along concave and convex contours and enhances simple cell sensitivity to image edges Oriented lter outputs are recti ed and outputs sensitive to opposite contrast polarities are pooled to de ne complex cells The complex cells output to stages of short range spatial competition or endstopping and orientational competition among hypercomplex cells Hypercomplex cells activate long range cooperative bipole cells that begin to group image boundaries Nonlinear feedback between bipole cells and hypercomplex cells segments image regions by cooperatively completing and regularizing the most favored boundaries while suppressing image noise and weaker boundary groupings Boundary segmentation is performed by three copies of the BCS at small medium and large lter scales whose subsequent interaction distances covary with the size of the lter Filling in of multiple surface representations occurs within the FCS at each scale via a boundary gated di usion process Di usion is activated by the normalized LGN ON and OFF outputs within ON and OFF lling in domains Di usion is restricted to the regions de ned by gating signals from the corresponding BCS boundary segmentation The lled in opponent ON and OFF signals are subtracted to form double opponent surface representations These surface representations are shown by any of three methods to be sensitive to both image ratio contrasts and background luminance The three scales of surface representation are then added to yield a nal multiple scale output The BCS and FCS are shown to perform favorably in comparison to several other techniques for speckle removal
The NALP3/NLRP3 Inflammasome Instigates Obesity-Induced Autoinflammation and Insulin Resistance

The emergence of chronic inflammation during obesity in the absence of overt infection or well-defined autoimmune processes is a puzzling phenomenon. The Nod-like receptor (NLR) family of innate immune cell sensors, such as the nucleotide-binding domain, leucine-rich–containing family, pyrin domain–containing-3 (Nlrp3, but also known as Nalp3 or cryopyrin) inflammasome are implicated in recognizing certain nonmicrobial originated 'danger signals' leading to caspase-1 activation and subsequent interleukin-1β (IL-1β) and IL-18 secretion. We show that calorie restriction and exercise-mediated weight loss in obese individuals with type 2 diabetes is associated with a reduction in adipose tissue expression of Nlrp3 as well as with decreased inflammation and improved insulin sensitivity. We further found that the Nlrp3 inflammasome senses lipotoxicity-associated increases in intracellular ceramide to induce caspase-1 cleavage in macrophages and adipose tissue. Ablation of Nlrp3 in mice prevents obesity-induced inflammasome activation in fat depots and liver as well as enhances insulin signaling. Furthermore, elimination of Nlrp3 in obese mice reduces IL-18 and adipose tissue interferon-γ (IFN-γ) expression, increases naive T cell numbers and reduces effector T cell numbers in adipose tissue. Collectively, these data establish that the Nlrp3 inflammasome senses obesity-associated danger signals and contributes to obesity-induced inflammation and insulin resistance.
Multicell High-Current Rectifier

A multicell rectifier (MC) structure with N + 2 redundancy is presented. The topology is based on power cells implemented with the integrated gate commuted thyristors (IGCTs) to challenge the SCR standard industry solution for the past 35 years. This rectifier is a reliable, compact, efficient, nonpolluting alternative and cost-effective solution for electrolytic applications. Its structure, based on power cells, enables load shedding to ensure power delivery even in the event of power cell failures. It injects quasi-sinusoidal input currents and provides unity power factor without the use of passive or active filters. A complete evaluation based on IEEE standards 493-1997 and IEEE C57.18.10 for average downtime, failures rates, and efficiency is included. For comparison purposes, results are shown against conventional systems known for their high efficiency and reliability.
Learning to Search via Retrospective Imitation

We study the problem of learning a good search policy from demonstrations for combinatorial search spaces. We propose retrospective imitation learning, which, after initial training by an expert, improves itself by learning from its own retrospective solutions. That is, when the policy eventually reaches a feasible solution in a search tree after making mistakes and backtracks, it retrospectively constructs an improved search trace to the solution by removing backtracks, which is then used to further train the policy. A key feature of our approach is that it can iteratively scale up, or transfer, to larger problem sizes than the initial expert demonstrations, thus dramatically expanding its applicability beyond that of conventional imitation learning. We showcase the effectiveness of our approach on two tasks: synthetic maze solving, and integer program based risk-aware path planning.
A Modular Functor Which is Universal for Quantum Computation

We show that the topological modular functor from Witten–Chern–Simons theory is universal for quantum computation in the sense that a quantum circuit computation can be efficiently approximated by an intertwining action of a braid on the functor’s state space. A computational model based on Chern–Simons theory at a fifth root of unity is defined and shown to be polynomially equivalent to the quantum circuit model. The chief technical advance: the density of the irreducible sectors of the Jones representation has topological implications which will be considered elsewhere.
On Human Intellect and Machine Failures: Troubleshooting Integrative Machine Learning Systems

We study the problem of troubleshooting machine learning systems that rely on analytical pipelines of distinct components. Understanding and fixing errors that arise in such integrative systems is difficult as failures can occur at multiple points in the execution workflow. Moreover, errors can propagate, become amplified or be suppressed, making blame assignment difficult. We propose a human-in-the-loop methodology which leverages human intellect for troubleshooting system failures. The approach simulates potential component fixes through human computation tasks and measures the expected improvements in the holistic behavior of the system. The method provides guidance to designers about how they can best improve the system. We demonstrate the effectiveness of the approach on an automated image captioning system that has been pressed into real-world use.
B cells as therapeutic targets in autoimmune neurological disorders

B cells have a fundamental role in the pathogenesis of various autoimmune neurological disorders, not only as precursors of antibody-producing cells, but also as important regulators of the T-cell activation process through their participation in antigen presentation, cytokine production, and formation of ectopic germinal centers in the intermeningeal spaces. Two B-cell trophic factors—BAFF (B-cell-activating factor) and APRIL (a proliferation-inducing ligand)—and their receptors are strongly upregulated in many immunological disorders of the CNS and PNS, and these molecules contribute to clonal expansion of B cells in situ. The availability of monoclonal antibodies or fusion proteins against B-cell surface molecules and trophic factors provides a rational approach to the treatment of autoimmune neurological diseases. This article reviews the role of B cells in autoimmune neurological disorders and summarizes the experience to date with rituximab, a B-cell-depleting monoclonal antibody against CD20, for the treatment of relapsing–remitting multiple sclerosis, autoimmune neuropathies, neuromyelitis optica, paraneoplastic neurological disorders, myasthenia gravis, and inflammatory myopathies. It is expected that ongoing controlled trials will establish the efficacy and long-term safety profile of anti-B-cell agents in several autoimmune neurological disorders, as well as exploring the possibility of a safe and synergistic effect with other immunosuppressants or immunomodulators.