nils-herrmann/clean_scirepeval huggingface dataset:
MASK: Redesigning the GPU Memory Hierarchy to Support Multi-Application Concurrency

Graphics Processing Units (GPUs) exploit large amounts of threadlevel parallelism to provide high instruction throughput and to efficiently hide long-latency stalls. The resulting high throughput, along with continued programmability improvements, have made GPUs an essential computational resource in many domains. Applications from different domains can have vastly different compute and memory demands on the GPU. In a large-scale computing environment, to efficiently accommodate such wide-ranging demands without leaving GPU resources underutilized, multiple applications can share a single GPU, akin to how multiple applications execute concurrently on a CPU. Multi-application concurrency requires several support mechanisms in both hardware and software. One such key mechanism is virtual memory, which manages and protects the address space of each application. However, modern GPUs lack the extensive support for multi-application concurrency available in CPUs, and as a result suffer from high performance overheads when shared by multiple applications, as we demonstrate. We perform a detailed analysis of which multi-application concurrency support limitations hurt GPU performance the most. We find that the poor performance is largely a result of the virtual memory mechanisms employed in modern GPUs. In particular, poor address translation performance is a key obstacle to efficient GPU sharing. State-of-the-art address translation mechanisms, which were designed for single-application execution, experience significant inter-application interference when multiple applications spatially share the GPU. This contention leads to frequent misses in the shared translation lookaside buffer (TLB), where a single miss can induce long-latency stalls for hundreds of threads. As a result, the GPU often cannot schedule enough threads to successfully hide the stalls, which diminishes system throughput and becomes a first-order performance concern. Based on our analysis, we propose MASK, a new GPU framework that provides low-overhead virtual memory support for the concurrent execution of multiple applications. MASK consists of three novel address-translation-aware cache and memory management mechanisms that work together to largely reduce the overhead of address translation: (1) a token-based technique to reduce TLB contention, (2) a bypassing mechanism to improve the effectiveness of cached address translations, and (3) an application-aware memory scheduling scheme to reduce the interference between address translation and data requests. Our evaluations show that MASK restores much of the throughput lost to TLB contention. Relative to a state-of-the-art GPU TLB, MASK improves system throughput by 57.8%, improves IPC throughput by 43.4%, and reduces applicationlevel unfairness by 22.4%. MASK's system throughput is within 23.2% of an ideal GPU system with no address translation overhead.
Nonblocking and orphan-free message logging protocols

Currently existing message logging protocols demonstrate a classic pessimistic vs. optimistic tradeoff. It is shown that the optimistic-pessimistic tradeoff is not inherent to the problem of message logging. The authors construct a message-logging protocol that has the positive features of both optimistic and pessimistic protocols: the protocol prevents orphans and allows simple failure recovery; however, it requires no blocking in failure-free runs. Furthermore, this protocol does not introduce any additional message overhead as compared to one implemented for a system in which messages may be lost but processes do not crash.
Exact Synthesis and Implementation of New High-Order Wideband Marchand Baluns

New ultra-wideband high-order Marchand baluns with one microstrip unbalanced port and two microstrip balanced ports are proposed in this paper. The proposed Marchand baluns are synthesized based on an -plane high-pass prototype using the Richards' transformation. The responses of the synthesized high-order Marchand baluns are exactly predicted at all real frequencies. All circuit elements are commensurate, which means the electrical lengths of all transmission line elements are a quarter-wavelength long at the center frequency. Two fifth-order Marchand baluns with reflection coefficients of 20.53 and 21.71 dB corresponding to 131% and 152% bandwidth, respectively, are synthesized and realized using the combinations of microstrip lines, slotlines, and coplanar striplines. Simulated and measured results are presented.
DeepCas: An End-to-end Predictor of Information Cascades

Information cascades, effectively facilitated by most social network platforms, are recognized as a major factor in almost every social success and disaster in these networks. Can cascades be predicted? While many believe that they are inherently unpredictable, recent work has shown that some key properties of information cascades, such as size, growth, and shape, can be predicted by a machine learning algorithm that combines many features. These predictors all depend on a bag of hand-crafting features to represent the cascade network and the global network structures. Such features, always carefully and sometimes mysteriously designed, are not easy to extend or to generalize to a different platform or domain. Inspired by the recent successes of deep learning in multiple data mining tasks, we investigate whether an end-to-end deep learning approach could effectively predict the future size of cascades. Such a method automatically learns the representation of individual cascade graphs in the context of the global network structure, without hand-crafted features or heuristics. We find that node embeddings fall short of predictive power, and it is critical to learn the representation of a cascade graph as a whole. We present algorithms that learn the representation of cascade graphs in an end-to-end manner, which significantly improve the performance of cascade prediction over strong baselines including feature based methods, node embedding methods, and graph kernel methods. Our results also provide interesting implications for cascade prediction in general.
Instant Scene Modeler for Crime Scene Reconstruction

Crime scene reconstruction is a labor-intensive but an essential task of police investigation. Currently, the police spend a lot of time at the crime scene taking many measurements and photos, and then spend more time at the computer recreating the crime scene drawings manually. Automatic generation of photo-realistic three-dimensional calibrated models using a hand-held device is highly desirable for crime scene reconstruction as well as for many other applications. In this paper, we present the instant Scene Modeler (iSM), a 3D imaging system that automatically creates 3D models using an off-the-shelf hand-held stereo camera. The user points the camera at a scene of interest and the system will create a photo-realistic 3D calibrated model automatically within minutes. Forensics field tests have been carried out with promising results and the advantages of 3D imaging for this application are described.
Accounting for the Contradictory Organizational Consequences of Information Technology: Theoretical Directions and Methodological Implications

Although much contemporary thought considers advanced information technologies as either determinants or enablers of radical organizational change, empirical studies have revealed inconsistent findings to support the deterministic logic implicit in such arguments. This paper reviews the contradictory empirical findings both across studies and within studies, and proposes the use of theories employing a logic of opposition to study the organizational consequences of information technology. In contrast to a logic of determination, a logic of opposition explains organizational change by identifying forces both promoting change and impeding change. Four specific theories are considered: organizational politics, organizational culture, institutional theory, and organizational learning. Each theory is briefly described to illustrate its usefulness to the problem of explaining information technology’s role in organizational change. Four methodological implications of using these theories are also discussed: empirical identification of opposing forces, statement of opposing hypotheses, process research, and employing multiple interpretations.
Understanding Sustained Participation in Transactional Virtual Communities

Two research gaps in prior studies on knowledge sharing in virtual communities (VCs) are identified. First, prior studies have focused on VCs with no explicit rewards system, whereas VCs using a competition-based reward system (e.g., transactional VCs) is not explored. Second, prior studies have concentrated on the determinants of initial participation rather than sustained participation. In this study, considering that a social learning process is involved in sustained participation, task complexity and self-efficacy – two social learning factors – are proposed to moderate the relationship between motivations and sustained participation. A filed survey with 205 subjects in a transactional virtual community was conducted to test the research model. According to findings, extrinsic and intrinsic motivations significantly influence knowledge sharing intention. Negative interaction effect between extrinsic motivation and task complexity, as well as positive interaction effect between intrinsic motivation and self-efficacy, are observed as well. Implications and future research are discussed.
Unimpeded permeation of water through helium-leak-tight graphene-based membranes.

Permeation through nanometer pores is important in the design of materials for filtration and separation techniques and because of unusual fundamental behavior arising at the molecular scale. We found that submicrometer-thick membranes made from graphene oxide can be completely impermeable to liquids, vapors, and gases, including helium, but these membranes allow unimpeded permeation of water (H(2)O permeates through the membranes at least 10(10) times faster than He). We attribute these seemingly incompatible observations to a low-friction flow of a monolayer of water through two-dimensional capillaries formed by closely spaced graphene sheets. Diffusion of other molecules is blocked by reversible narrowing of the capillaries in low humidity and/or by their clogging with water.
Describing and forecasting video access patterns

Computer systems are increasingly driven by workloads that reflect large-scale social behavior, such as rapid changes in the popularity of media items like videos. Capacity planners and system designers must plan for rapid, massive changes in workloads when such social behavior is a factor. In this paper we make two contributions intended to assist in the design and provisioning of such systems.We analyze an extensive dataset consisting of the daily access counts of hundreds of thousands of YouTube videos. In this dataset, we find that there are two types of videos: those that show rapid changes in popularity, and those that are consistently popular over long time periods. We call these two types rarely-accessed and frequently-accessed videos, respectively. We observe that most of the videos in our data set clearly fall in one of these two types. In this work, we study the frequently-accessed videos by asking two questions: first, is there a relatively simple model that can describe its daily access patterns? And second, can we use this simple model to predict the number of accesses that a video will have in the near future, as a tool for capacity planning? To answer these questions we develop a framework for characterization and forecasting of access patterns. We show that for frequently-accessed videos, daily access patterns can be extracted via principal component analysis, and used efficiently for forecasting.
Semantic Audiovisual Data Fusion for Automatic Emotion Recognition

The paper describes a novel technique for the recognition of emotions from multimodal data. We focus on the recognition of the six prototypic emotions. The results from the facial expression recognition and from the emotion recognition from speech are combined using a bi-modal multimodal semantic data fusion model that determines the most probable emotion of the subject. Two types of models based on geometric face features for facial expression recognition are being used, depending on the presence or absence of speech. In our approach we define an algorithm that is robust to changes of face shape that occur during regular speech. The influence of phoneme generation on the face shape during speech is removed by using features that are only related to the eyes and the eyebrows. The paper includes results from testing the presented models.
Development of a positive psychology intervention for patients with acute
cardiovascular disease

The management of depression and other negative psychological states in cardiac patients has been a focus of multiple treatment trials, though such trials have not led to substantial improvements in cardiac outcomes. In contrast, there has been minimal focus on interventions to increase positive psychological states in cardiac patients, despite the fact that optimism and other positive states have been associated with superior cardiovascular outcomes. Our objective was to develop an 8-week, phone-based positive psychology intervention for patients hospitalized with acute cardiac disease (acute coronary syndrome or decompensated heart failure). Such an intervention would consist of positive psychology exercises adapted for this specific population, and it would need to be feasible for practitioners and patients in real-world settings. By adapting exercises that were previously validated in healthy individuals, we were able to generate a positive psychology telemedicine intervention for cardiac patients that focused on optimism, kindness, and gratitude. In addition, we successfully created a companion treatment manual for subjects to enhance the educational aspects of the intervention and facilitate completion of exercises. Finally, we successfully performed a small pilot trial of this intervention, and found that the positive psychology intervention appeared to be feasible and well-accepted in a cohort of patients with acute cardiac illness. Future studies should further develop this promising intervention and examine its impact on psychological and medical outcomes in this vulnerable population of cardiac patients.
Kinect Deform: Enhanced 3D Reconstruction of Non-rigidly Deforming Objects

In this work we propose KinectDeform, an algorithm which targets enhanced 3D reconstruction of scenes containing non-rigidly deforming objects. It provides an innovation to the existing class of algorithms which either target scenes with rigid objects only or allow for very limited non-rigid deformations or use precomputed templates to track them. KinectDeform combines a fast non-rigid scene tracking algorithm based on octree data representation and hierarchical voxel associations with a recursive data filtering mechanism. We analyze its performance on both real and simulated data and show improved results in terms of smoothness and feature preserving 3D reconstructions with reduced noise.
Youth Computational Participation in the Wild: Understanding Experience and Equity in Participating and Programming in the Online Scratch Community

Most research in primary and secondary computing education has focused on understanding learners within formal classroom communities, leaving aside the growing number of promising informal online programming communities where young users contribute, comment, and collaborate on programs to facilitate learning. In this article, we examined trends in computational participation in Scratch, an online community with over 1 million registered youth designers. Drawing on a random sample of 5,004 youth programmers and their activities over 3 months in early 2012, we examined programming concepts used in projects in relation to level of participation, gender, and length of membership of Scratch programmers. Latent class analysis results identified the same four groups of programmers in each month based on the usage of different programming concepts and showed how membership in these groups shifted in different ways across time. Strikingly, the largest group of project creators (named Loops) used the simplest and fewest programming concepts. Further, this group was the most stable in membership and was disproportionately female. In contrast, the more complex programming groups (named Variables, Low Booleans, and High Booleans) showed much movement across time. Further, the Low Booleans and High Booleans groups, the only groups to use “and,” “or,” and “not” statements in their programs, were disproportionately male. In the discussion, we address the challenges of analyzing young learners’ programming in informal online communities and opportunities for designing more equitable computational participation.
Performance Analysis, Mapping, and Multiobjective Optimization of a Hybrid Robotic Machine Tool

A serial-parallel hybrid machine tool is expected to integrate the respective features of pure serial/parallel mechanism. The traditional method of hybridization is to connect n (n ≥ 2) mechanisms bottom to head, in which at least one should be a parallel mechanism. One unique approach called Mechanism Hybridization is to embed one serial mechanism inside of a pure parallel mechanism, which greatly changes its overall performance. Based on this idea, an X-Y gantry system including a five-axis hybrid manipulator is developed, which is expected to be applied as the next generation of computer numerical control machine. The inverse kinematics and Jacobian matrix are derived. Since performance improvement is one of the most important factors that greatly affect the application potential of hybrid manipulators in different industry fields, to deeply investigate the comprehensive features, the local/global performance indexes of stiffness, dexterity, and manipulability are mathematically modeled and mapped. A discrete-boundary-searching method is developed to calculate and visualize the workspace. Pareto-based evolutionary multiobjective performance optimization is implemented to simultaneously improve the four indexes, and the representative nondominated solutions are listed. The proposed methodologies are generic and applicable for the design, modeling, and improvement of other parallel/hybrid robotic machine tools.
Co-Clustering with Side Information for Text mining

Many of the text mining applications contain a huge amount of information from document in the form of text. This text can be very helpful for Text Clustering. This text also includes various kind of other information known as Side Information or Metadata. Examples of this side information include links to other web pages, title of the document, author name or date of Publication which are present in the text document. Such metadata may possess a lot of information for the clustering purposes. But this Side information may be sometimes noisy. Using such Side Information for producing clusters without filtering it, can result to bad quality of Clusters. So we use an efficient Feature Selection method to perform the mining process to select that Side Information which is useful for Clustering so as to maximize the advantages from using it. The proposed technique, CCSI (Co-Clustering with Side Information) system makes use of the process of Co-Clustering or Two-mode clustering which is a data mining technique that allows concurrently clustering of the rows and columns of a matrix.
Detecting People in Artwork with CNNs

CNNs have massively improved performance in object detection in photographs. However research into object detection in artwork remains limited. We show state-of-the-art performance on a challenging dataset, People-Art, which contains people from photos, cartoons and 41 different artwork movements. We achieve this high performance by fine-tuning a CNN for this task, thus also demonstrating that training CNNs on photos results in overfitting for photos: only the first three or four layers transfer from photos to artwork. Although the CNN’s performance is the highest yet, it remains less than 60% AP, suggesting further work is needed for the cross-depiction problem.
Seven Guiding Scenarios for Information Visualization Evaluation

We take a new, scenario based look at evaluation in information visualization. Our seven scenarios, evaluating visual data analysis and reasoning, evaluating user performance, evaluating user experience, evaluating environments and work practices, evaluating communication through visualization, automated evaluation of visualizations, and evaluating collaborative data analysis were derived through an extensive literature review of over 800 visualization publications. These scenarios are described through their goals, the types of questions they embody and illustrated through example studies. Through this broad survey and the distillation of these scenarios we make two contributions. One, we encapsulate the current practices in the information visualization research community and, two, we provide a different approach to reaching decisions about what might be the most effective evaluation of a given information visualization. For example, if the research goals or evaluative questions are known they can be used to map to specific scenarios, where practical existing examples can be considered for effective evaluation approaches.
A line in the sand: a wireless sensor network for target detection, classification, and tracking

Intrusion detection is a surveillance problem of practical import that is well suited to wireless sensor networks. In this paper, we study the application of sensor networks to the intrusion detection problem and the related problems of classifying and tracking targets. Our approach is based on a dense, distributed, wireless network of multi-modal resource-poor sensors combined into loosely coherent sensor arrays that perform in situ detection, estimation, compression, and exfiltration. We ground our study in the context of a security scenario called “A Line in the Sand” and accordingly define the target, system, environment, and fault models. Based on the performance requirements of the scenario and the sensing, communication, energy, and computation ability of the sensor network, we explore the design space of sensors, signal processing algorithms, communications, networking, and middleware services. We introduce the influence field, which can be estimated from a network of binary sensors, as the basis for a novel classifier. A contribution of our work is that we do not assume a reliable network; on the contrary, we quantitatively analyze the effects of network unreliability on application performance. Our work includes multiple experimental deployments of over 90 sensors nodes at MacDill Air Force Base in Tampa, Florida, as well as other field experiments of comparable scale. Based on these experiences, we identify a set of key lessons and articulate a few of the challenges facing extreme scaling to tens or hundreds of thousands of sensor nodes.