nils-herrmann/clean_scirepeval huggingface dataset:
Consistent penetration depth estimation for deformable collision response

Penalty approaches can be used to efficiently resolve collisions of dynamically simulated rigid and deformable objects. These approaches compute penalty forces based on the penetration depth of intersecting objects and there exist many algorithms for estimating the exact penetration depth. However, in discrete-time simulations, this information can cause non-plausible collision responses in case of large penetrations or due to the object discretization. In this paper, we present a method to compute consistent penetration depth information in order to reduce collision response artifacts inherent to existing penetration depth approaches. The method considers a set of close surface features to avoid discontinuous penetration depths. Further, a propagation scheme is applied in case of large penetrations to avoid non-plausible, inconsistent penetration depth information.
Memory training impacts short-term changes in aging white matter: a longitudinal diffusion tensor imaging study.

A growing body of research indicates benefits of cognitive training in older adults, but the neuronal mechanisms underlying the effect of cognitive intervention remains largely unexplored. Neuroimaging methods are sensitive to subtle changes in brain structure and show potential for enhancing our understanding of both aging- and training-related neuronal plasticity. Specifically, studies using diffusion tensor imaging (DTI) suggest substantial changes in white matter (WM) in aging, but it is not known whether cognitive training might modulate these structural alterations. We used tract-based spatial statistics (TBSS) optimized for longitudinal analysis to delineate the effects of 8 weeks intensive memory training on WM microstructure. 41 participants (mean age 61 years) matched for age, sex and education were randomly assigned to an intervention or control group. All participants underwent MRI-scanning and neuropsychological assessments at the beginning and end of the study. Longitudinal analysis across groups revealed significant increase in frontal mean diffusivity (MD), indicating that DTI is sensitive to WM structural alterations over a 10-week interval. Further, group analysis demonstrated positive effects of training on the short-term changes. Participants in the training group showed a relative increase in fractional anisotropy (FA) compared with controls. Further, a significant relationship between memory improvement and change in FA was found, suggesting a possible functional significance of the reported changes. The training effect on FA seemed to be driven by a relative decrease in radial diffusivity, which might indicate a role for myelin-related processes in WM plasticity.
THE X-BAR THEORY OF PHRASE STRUCTURE

X-bar theory is widely regarded as a substantive theory of phrase structure properties in natural languages. In this paper we will demonstrate that a formalization of its content reveals very little substance in its claims. We state and discuss six conditions that encapsulate the claims of X-bar theory: Lexicality — each nonterminal is a projection of a preterminal; Succession — each X dominates an X for all n ≥ 0; Uniformity — all maximal projections have the same bar-level; Maximality — all non-heads are maximal projections; Centrality — the start symbol is a maximal projection; and Optionality — all and only non-heads are optional. We then consider recent proposals to ‘eliminate’ base components from transformational grammars and to reinterpret X-bar theory as a set of universal constraints holding for all languages at D-structure, arguing that this strategy fails. We show that, as constraints on phrase structure rule systems, the X-bar conditions have hardly any effect on the descriptive power of grammars, and that the principles with the most chance of making some descriptive difference are the least adhered to in practice. Finally, we reconstruct X-bar theory in a way that makes no reference to the notion of bar-level but instead makes the notion ‘head of’ the central one.∗
An extended set of Haar-like features for rapid object detection

Recently Viola et al.  have introduced a rapid object detection scheme based on a boosted cascade of simple features. In this paper we introduce a novel set of rotated haar-like features, which significantly enrich this basic set of simple haar-like features and which can also be calculated very efficiently. At a given hit rate our sample face detector shows off on average a 10% lower false alarm rate by means of using these additional rotated features. We also present a novel post optimization procedure for a given boosted cascade improving on average the false alarm rate further by 12.5%. Using both enhancements the number of false detections is only 24 at a hit rate of 82.3% on the CMU face set .
Artificial General Intelligence: Concept, State of the Art, and Future Prospects

In recent years broad community of researchers has emerged, focusing on the original ambitious goals of the AI field – the creation and study of software or hardware systems with general intelligence comparable to, and ultimately perhaps greater than, that of human beings. This paper surveys this diverse community and its progress. Approaches to defining the concept of Artificial General Intelligence (AGI) are reviewed including mathematical formalisms, engineering, and biology inspired perspectives. The spectrum of designs for AGI systems includes systems with symbolic, emergentist, hybrid and universalist characteristics. Metrics for general intelligence are evaluated, with a conclusion that, although metrics for assessing the achievement of human-level AGI may be relatively straightforward (e.g. the Turing Test, or a robot that can graduate from elementary school or university), metrics for assessing partial progress remain more controversial and problematic.
Personality judgments based on physical appearance.

Despite the crucial role of physical appearance in forming first impressions, little research has examined the accuracy of personality impressions based on appearance alone. This study examined the accuracy of observers' impressions on 10 personality traits based on full-body photographs using criterion measures based on self and peer reports. When targets' posture and expression were constrained (standardized condition), observers' judgments were accurate for extraversion, self-esteem, and religiosity. When targets were photographed with a spontaneous pose and facial expression (spontaneous condition), observers' judgments were accurate for almost all of the traits examined. Lens model analyses demonstrated that both static cues (e.g., clothing style) and dynamic cues (e.g., facial expression, posture) offered valuable personality-relevant information. These results suggest that personality is manifested through both static and expressive channels of appearance, and observers use this information to form accurate judgments for a variety of traits.
Learning Semantic Scene Models by Trajectory Analysis

In this paper, we describe an unsupervised learning framework to segment a scene into semantic regions and to build semantic scene models from longterm observations of moving objects in the scene. First, we introduce two novel similarity measures for comparing trajectories in far-field visual surveillance. The measures simultaneously compare the spatial distribution of trajectories and other attributes, such as velocity and object size, along the trajectories. They also provide a comparison confidence measure which indicates how well the measured image-based similarity approximates true physical similarity. We also introduce novel clustering algorithms which use both similarity and comparison confidence. Based on the proposed similarity measures and clustering methods, a framework to learn semantic scene models by trajectory analysis is developed. Trajectories are first clustered into vehicles and pedestrians, and then further grouped based on spatial and velocity distributions. Different trajectory clusters represent different activities. The geometric and statistical models of structures in the scene, such as roads, walk paths, sources and sinks, are automatically learned from the trajectory clusters. Abnormal activities are detected using the semantic scene models. The system is robust to low-level tracking errors.
A Framework for Large-Scale Detection of Web Site Defacements

Web site defacement, the process of introducing unauthorized modifications to a Web site, is a very common form of attack. In this paper we describe and evaluate experimentally a framework that may constitute the basis for a defacement detection service capable of monitoring thousands of remote Web sites systematically and automatically.
 In our framework an organization may join the service by simply providing the URLs of the resources to be monitored along with the contact point of an administrator. The monitored organization may thus take advantage of the service with just a few mouse clicks, without installing any software locally or changing its own daily operational processes. Our approach is based on anomaly detection and allows monitoring the integrity of many remote Web resources automatically while remaining fully decoupled from them, in particular, without requiring any prior knowledge about those resources.
 We evaluated our approach over a selection of dynamic resources and a set of publicly available defacements. The results are very satisfactory: all attacks are detected while keeping false positives to a minimum. We also assessed performance and scalability of our proposal and we found that it may indeed constitute the basis for actually deploying the proposed service on a large scale.
Using Visual Text Mining to Support the Study Selection Activity in Systematic Literature Reviews

A systematic literature review (SLR) is a methodology used to aggregate all relevant existing evidence to answer a research question of interest. Although crucial, the process used to select primary studies can be arduous, time consuming, and must often be conducted manually.  We propose a novel approach, known as 'Systematic Literature Review based on Visual Text Mining' or simply SLR-VTM, to support the primary study selection activity using visual text mining (VTM) techniques.  We conducted a case study to compare the performance and effectiveness of four doctoral students in selecting primary studies manually and using the SLR-VTM approach. To enable the comparison, we also developed a VTM tool that implemented our approach. We hypothesized that students using SLR-VTM would present improved selection performance and effectiveness.  Our results show that incorporating VTM in the SLR study selection activity reduced the time spent in this activity and also increased the number of studies correctly included.  Our pilot case study presents promising results suggesting that the use of VTM may indeed be beneficial during the study selection activity when performing an SLR.
Design and implementation of fine pitch COB LED display

LED displays are widely used in advertisement, transportation, and commercial signages. Conventional LED display modules consist of a LED array assembly and a driving circuit board, inside an external housing. The dot pitch of conventional LED displays is typically 2.0 mm or above, which is relatively coarse. In some applications, such as the screen of personal mobile LED devices, a higher resolution with the dot pitch smaller than <;1.5 mm is needed due to the short viewing distance. Therefore, technologies for implementing fine pitch LED displays are in demand. This paper introduces a new type of LED display with the chip-on-board (COB) technology. The dot pitch of this new type of COB LED display may be reduced to 0.5 mm. The configurations for monochromatic and tricolored display modules are discussed. The reliability studies on the COB LED display are also presented. The results of ball bond shear, wire pull, junction temperature and thermal cycling tests indicate that the fine pitch COB LED display is feasible and reliable.
MemorySanitizer: Fast detector of uninitialized memory use in C++

This paper presents MemorySanitizer, a dynamic tool that detects uses of uninitialized memory in C and C++. The tool is based on compile time instrumentation and relies on bit-precise shadow memory at run-time. Shadow propagation technique is used to avoid false positive reports on copying of uninitialized memory.
 MemorySanitizer finds bugs at a modest cost of 2.5x in execution time and 2x in memory usage; the tool has an optional origin tracking mode that provides better reports with moderate extra overhead. The reports with origins are more detailed compared to reports from other similar tools; such reports contain names of local variables and the entire history of the uninitialized memory including intermediate stores. In this paper we share our experience in deploying the tool at a large scale and demonstrate the benefits of compile-time instrumentation over dynamic binary instrumentation.
HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads

The production environment for analytical data management applications is rapidly changing. Many enterprises are shifting away from deploying their analytical databases on high-end proprietary machines, and moving towards cheaper, lower-end, commodity hardware, typically arranged in a shared-nothing MPP architecture, often in a virtualized environment inside public or private “clouds”. At the same time, the amount of data that needs to be analyzed is exploding, requiring hundreds to thousands of machines to work in parallel to perform the analysis. There tend to be two schools of thought regarding what technology to use for data analysis in such an environment. Proponents of parallel databases argue that the strong emphasis on performance and efficiency of parallel databases makes them wellsuited to perform such analysis. On the other hand, others argue that MapReduce-based systems are better suited due to their superior scalability, fault tolerance, and flexibility to handle unstructured data. In this paper, we explore the feasibility of building a hybrid system that takes the best features from both technologies; the prototype we built approaches parallel databases in performance and efficiency, yet still yields the scalability, fault tolerance, and flexibility of MapReduce-based systems.
Maximum Torque per Volt operation and stability improvement of PMSM in deep flux-weakening Region

The stability problem with current vector control in the deep flux-weakening region is analyzed and a new flux-weakening control scheme to operate the PMSM in the MTPV (Maximum Torque per Volt) region is proposed. The proposed control scheme significantly improves stability and increases speed range while maintaining maximum possible torque capability. The transition between different operation regions in the proposed control scheme proves to be smooth. Simulation and experimental tests are carried out to verify the theoretical analysis.
Acoustic feature prediction from semantic features for expressive speech using deep neural networks

The goal of the study is to predict acoustic features of expressive speech from semantic vector space representations. Though a lot of successful work was invested in expressiveness analysis and prediction, the results often involve manual labeling, or indirect prediction evaluation such as speech synthesis. The proposed analysis aims at direct acoustic feature prediction and comparison to original acoustic features from an audiobook. The audiobook is mapped in a semantic vector space. A set of acoustic features is extracted from the same utterances, involving iVectors trained on MFCC and F0 basis. Two regression models are trained with the semantic coordinates, DNNs and a baseline CART. Later, semantic and acoustic context features are combined for the prediction. The prediction is achieved successfully using the DNNs. A closer analysis shows that the prediction works best for larger utterances or utterances with specific contexts, and worst for general short utterances and proper names.
Measuring problem video game playing in adolescents.

Some researchers suggest that for some people, video game playing is an addictive behaviour similar to substance dependence. Our aim was to design and validate a scale to measure the problems associated with the apparently addictive use of all types of video games and video game systems, because there is no instrument at the present time that can be used for this purpose.



We reviewed the DSM-IV criteria for substance dependence and for pathological gambling, as well as the literature on the addictions in order to design a short scale (PVP; problem video game playing) that is quick and easy to apply.



The scale was administered to 223 Spanish adolescents aged between 13 and 18 years. The study was carried out in Granada and Algeciras, Spain.



Psychometric analyses show that the PVP seems to be unidimensional and has acceptable internal consistency (Cronbach's alpha) at 0.69. The pattern of associations between the scale scores and alternative measures of problem play supports its construct validity (higher total scores in the scale were associated with higher frequency of play, mean and longest times per session, self and parents' perception of playing to excess, and scores in the Severity of Dependence Scale).



Our results confirm that the excessive use of video games is associated with a number of problems which resemble a dependence syndrome, and the PVP appears as a useful instrument for the measurement of such problems.
Heterogeneity-aware data regeneration in distributed storage systems

Distributed storage systems provide large-scale reliable data storage services by spreading redundancy across a large group of storage nodes. In such big systems, node failures take place on a regular basis. When a node fails or leaves the system, to maintain the same level of redundancy, it is expected to regenerate the redundant data at a replacement node as soon as possible. Previous studies aim to minimize the network traffic in the regeneration process, but in practical networks, where link capacities vary in a wide range, minimizing network traffic does not always mean minimizing regeneration time. Considering the heterogeneous link capacities, Li et al. proposed a tree-structured regeneration scheme, called RCTREE, to bypass the low-capacitated link encountered in direct transmissions. However, we find that RCTREE may rapidly lose data integrity after several regenerations. In this paper, we reconsider the problem of minimizing regeneration time in networks with heterogeneous link capacities. We derive the minimum amount of data to be transmitted through each link to preserve data integrity. We prove that building an optimal regeneration tree is NP-complete and propose a heuristic algorithm for a near-optimal solution. We further introduce a flexible regeneration scheme, which allows providers to generate different amount of coded data. Simulation results show that the flexible tree-structured regeneration scheme can reduce the regeneration time significantly.
The Effect of Data Sampling When Using Random Forest on Imbalanced Bioinformatics Data

Ensemble learning is a powerful tool that has shown promise when applied towards bioinformatics datasets. In particular, the Random Forest classifier has been an effective and popular algorithm due to its relatively good classification performance and its ease of use. However, Random Forest does not account for class imbalance which is known for decreasing classification performance and increasing bias towards the majority class. In this study, we seek to determine if the inclusion of data sampling will improve the performance of the Random Forest classifier. In order to test the effect of data sampling, we used Random Undersampling along with two post-sampling class distribution ratios: 35:65 and 50:50 (minority:majority). Additionally, we also built inductive models with Random Forest when no data sampling technique was applied, so we can observe the true effect of the data sampling. All three options were tested on a series of fifteen imbalanced bioinformatics datasets. Our results show that data sampling does improve the classification performance of Random Forest, especially when using the 50:50 post-sampling class distribution ratio. However, statistical analysis shows that the increase in performance is not statistically significant. Thus, we can state that while data sampling does improve the classification performance of Random Forest, it is not a necessary step as the classifier is fairly robust to imbalanced data on its own.
Auto-Join: Joining Tables by Leveraging Transformations

Traditional equi-join relies solely on string equality comparisons to perform joins. However, in scenarios such as adhoc data analysis in spreadsheets, users increasingly need to join tables whose join-columns are from the same semantic domain but use different textual representations, for which transformations are needed before equi-join can be performed. We developed Auto-Join, a system that can automatically search over a rich space of operators to compose a transformation program, whose execution makes input tables equi-join-able. We developed an optimal sampling strategy that allows Auto-Join to scale to large datasets efficiently, while ensuring joins succeed with high probability. Our evaluation using real test cases collected from both public web tables and proprietary enterprise tables shows that the proposed system performs the desired transformation joins efficiently and with high quality.
Modelling biological processes using workflow and Petri Net models

Biological processes can be considered at many levels of detail, ranging from atomic mechanism to general processes such as cell division, cell adhesion or cell invasion. The experimental study of protein function and gene regulation typically provides information at many levels. The representation of hierarchical process knowledge in biology is therefore a major challenge for bioinformatics. To represent high-level processes in the context of their component functions, we have developed a graphical knowledge model for biological processes that supports methods for qualitative reasoning.



We assessed eleven diverse models that were developed in the fields of software engineering, business, and biology, to evaluate their suitability for representing and simulating biological processes. Based on this assessment, we combined the best aspects of two models: Workflow/Petri Net and a biological concept model. The Workflow model can represent nesting and ordering of processes, the structural components that participate in the processes, and the roles that they play. It also maps to Petri Nets, which allow verification of formal properties and qualitative simulation. The biological concept model, TAMBIS, provides a framework for describing biological entities that can be mapped to the workflow model. We tested our model by representing malaria parasites invading host erythrocytes, and composed queries, in five general classes, to discover relationships among processes and structural components. We used reachability analysis to answer queries about the dynamic aspects of the model.


AVAILABILITY
The model is available at http://smi.stanford.edu/projects/helix/pubs/process-model/.