nils-herrmann/clean_scirepeval huggingface dataset:
The underlying structure of visuospatial working memory in children with mathematical learning disability.

This study examined visual, spatial-sequential, and spatial-simultaneous working memory (WM) performance in children with mathematical learning disability (MLD) and low mathematics achievement (LMA) compared with typically developing (TD) children. Groups were matched on reading decoding performance and verbal intelligence. Besides statistical significance testing, we used bootstrap confidence interval estimation and computed effect sizes. Children were individually tested with six computerized tasks, two for each visuospatial WM subcomponent. We found that both MLD and LMA children had low visuospatial WM function in both spatial-simultaneous and spatial-sequential WM tasks. The WM deficit was most expressed in MLD children and less in LMA children. This suggests that WM scores are distributed along a continuum with TD children achieving top scores and MLD children achieving low scores. The theoretical and practical significance of findings is discussed.  What is already known on this subject? Working memory plays an important role in mathematical achievement. Children with mathematical learning disability (MLD) usually have low working memory resources. Conflicting results have been reported concerning the role of VSWM in individuals with MLD. What the present study adds? Children with different degree of impairment in math achievement and typically developing children were tested. Visual, spatial-sequential, and spatial-simultaneous working memory tasks were examined. Only spatial-sequential and spatial-simultaneous working memory tasks discriminated the two impairments groups.
Biconvex Relaxation for Semidefinite Programming in Computer Vision

References  Wang, P., Shen, C., van den Hengel, A., A fast semidefinite approach to solving binary quadratic problems. CVPR 2013.  Candes, E.J., Li, X., Soltanolkotabi, M., Phase retrieval via wirtinger flow: Theory and algorithms.  Transaction on Information Theory, 2015.  Zheng, Q., Lafferty, J., A convergent gradient descent algorithm for rank minimization and semidefinite programming from random linear measurements. NIPS 2015.  Netrapalli, P., Jain, P., Sanghavi, S., Phase retrieval using alternating minimization. NIPS 2013. Many important problems requires solving for low rank SDPs with PSD constraint matrices.
5D Covariance tracing for efficient defocus and motion blur

The rendering of effects such as motion blur and depth-of-field requires costly 5D integrals. We accelerate their computation through adaptive sampling and reconstruction based on the prediction of the anisotropy and bandwidth of the integrand. For this, we develop a new frequency analysis of the 5D temporal light-field, and show that first-order motion can be handled through simple changes of coordinates in 5D. We further introduce a compact representation of the spectrum using the covariance matrix and Gaussian approximations. We derive update equations for the 5 × 5 covariance matrices for each atomic light transport event, such as transport, occlusion, BRDF, texture, lens, and motion. The focus on atomic operations makes our work general, and removes the need for special-case formulas. We present a new rendering algorithm that computes 5D covariance matrices on the image plane by tracing paths through the scene, focusing on the single-bounce case. This allows us to reduce sampling rates when appropriate and perform reconstruction of images with complex depth-of-field and motion blur effects.
Semantic parsing of street scenes from video

Semantic models of the environment can significantly improve navigation and decision making capabilities of autonomous robots or enhance level of human and robot interaction. We present a novel approach for semantic segmentation of street scene images into coherent regions, while simultaneously categorizing each region as one of the predefined categories representing commonly encountered object and background classes. We formulate the segmentation on small blob-based superpixels and exploit a visual vocabulary tree as an intermediate image representation. The main novelty of our approach is the introduction of an explicit model of spatial co-occurrence of visual words associated with superpixels and utilization of appearance, geometry and contextual cues in a probabilistic framework. We demonstrate how individual cues contribute towards global segmentation accuracy and how their combination yields superior performance compared with the best known method on the challenging benchmark dataset which exhibits diversity of street scenes with varying viewpoints, a large number of categories, captured in daylight and dusk.
Gate sizing: finFETs vs 32nm bulk MOSFETs

FinFET devices promise to replace traditional MOSFETs because of superior ability in controlling leakage and minimizing short channel effects while delivering a strong drive current. We investigate in this paper gate sizing of finFET devices, and we provide a comparison with 32nm bulk CMOS. Wider finFET devices are built utilizing multiple parallel fins between the source and drain. Independent gating of the finFET's double gates allows significant reduction in leakage current. We perform temperature-aware circuit optimization by modeling delay using temperature-dependent parameters, and by imposing constraints that limit the maximum allowable number of parallel fins. We show that finFET circuits are superior in performance and produce less static power when compared to 32nm circuits.
CRF-CNN: Modeling Structured Information in Human Pose Estimation

Deep convolutional neural networks (CNN) have achieved great success. On the other hand, modeling structural information has been proved critical in many vision problems. It is of great interest to integrate them effectively. In a classical neural network, there is no message passing between neurons in the same layer. In this paper, we propose a CRF-CNN framework which can simultaneously model structural information in both output and hidden feature layers in a probabilistic way, and it is applied to human pose estimation. A message passing scheme is proposed, so that in various layers each body joint receives messages from all the others in an efficient way. Such message passing can be implemented with convolution between features maps in the same layer, and it is also integrated with feedforward propagation in neural networks. Finally, a neural network implementation of endto-end learning CRF-CNN is provided. Its effectiveness is demonstrated through experiments on two benchmark datasets.
Raimond: Quantitative Data Extraction from Twitter to Describe Events

Social media play a decisive role in communicating and spreading information during global events. In particular, real-time microblogging platforms such as Twitter have become prevalent. Researchers have used microblogging for a number of tasks, including past events analysis, predictions, and information retrieval. Nevertheless, little attention has been given to quantitative data extraction. In this paper, we address two questions: can we develop a mechanism to extract quantitative data from a collection of tweets, and can we use the salient findings to describe an event? To answer the first question, we introduce Raimond, a virtual text curator, specialized in quantitative data extraction from Twitter. To address the second question, we use our system on three events and evaluate its output using a crowdsourcing strategy. We demonstrate the e↵ectiveness of our approach with a number of real world examples.
Test Case Generation by Symbolic Execution: Basic Concepts, a CLP-Based Instance, and Actor-Based Concurrency

The focus of this tutorial is white-box test case generation (TCG) based on symbolic execution. Symbolic execution consists in executing a program with the contents of its input arguments being symbolic variables rather than concrete values. A symbolic execution tree characterizes the set of execution paths explored during the symbolic execution of a program. Test cases can be then obtained from the successful branches of the tree. The tutorial is split into three parts: (1) The first part overviews the basic techniques used in TCG to ensure termination, handling heap-manipulating programs, achieving compositionality in the process and guiding TCG towards interesting test cases. (2) In the second part, we focus on a particular implementation of the TCG framework in constraint logic programming (CLP). In essense, the imperative object-oriented program under test is automatically transformed into an equivalent executable CLP-translated program. The main advantage of CLP-based TCG is that the standard mechanism of CLP performs symbolic execution for free. The PET system is an open-source software that implements this approach. (3) Finally, in the last part, we study the extension of TCG to actor-based concurrent programs.
A unified approach for motion and force control of robot manipulators: The operational space formulation

A framework for the analysis and control of manipulator systems with respect to the dynamic behavior of their end-effectors is developed. First, issues related to the description of end-effector tasks that involve constrained motion and active force control are discussed. The fundamentals of the operational space formulation are then presented, and the unified approach for motion and force control is developed. The extension of this formulation to redundant manipulator systems is also presented, constructing the end-effector equations of motion and describing their behavior with respect to joint forces. These results are used in the development of a new and systematic approach for dealing with the problems arising at kinematic singularities. At a singular configuration, the manipulator is treated as a mechanism that is redundant with respect to the motion of the end-effector in the subspace of operational space orthogonal to the singular direction.
Second-order complex random vectors and normal distributions

We formulate as a deconvolution problem the causalhoncausal non-Gaussian multichannel autoregressive (AR) parameter estimation problem. The super exponential aljporithm presented in a recent paper by Shalvi and Weinstein is generalized to the vector case. We present an adaptive implementation that is very attractive since it is higher order statistics (HOS) based b u t does not present the high comlputational complexity of methods proposed up to now.
Co-operative downloading in vehicular ad-hoc wireless networks

Increasing need for people to be "connected"; while at the same time remain as mobile as ever poses several interesting issues in wireless networks. It is conceivable in the near-future that wireless "hotspots" experience flash crowds-like traffic arrival pattern. A common phenomena in the Internet today characterized by sudden and unpredicted increase in popularity of on-line content. In this paper, we propose SPAWN, a cooperative strategy for content delivery and sharing in future vehicular networks. We study the issues involved in using such a strategy from the standpoint of vehicular ad-hoc networks. In particular, we show that not only content server but also wireless access network load reduction is critical. We propose a "communication efficient" swarming protocol which uses a gossip mechanism that leverages the inherent broadcast nature of the wireless medium, and a piece-selection strategy that takes proximity into account in decisions to exchange pieces. We show through simulation that gossip incorporates location-awareness into peer selection, while incurring low messaging overhead, and consequently enhancing the swarming protocol performance. We develop an analytical model to characterize the performance of SPAWN.
Bring you to the past: Automatic Generation of Topically Relevant Event Chronicles

An event chronicle provides people with an easy and fast access to learn the past. In this paper, we propose the first novel approach to automatically generate a topically relevant event chronicle during a certain period given a reference chronicle during another period. Our approach consists of two core components – a timeaware hierarchical Bayesian model for event detection, and a learning-to-rank model to select the salient events to construct the final chronicle. Experimental results demonstrate our approach is promising to tackle this new problem.
Genital injuries in adults.

The examination of the rape victim should focus on the therapeutic, forensic and psychological needs of the individual patient. One aspect will be an examination for ano-genital injuries. From a medical perspective, they tend to be minor and require little in the way of treatment. They must be considered when assessing the risk of blood-borne viruses and the need for prophylaxis. From a forensic perspective, an understanding of genital injury rates, type of injury, site and healing may assist the clinician to interpret the findings in the context of the allegations that have been made. There are many myths and misunderstandings about ano-genital injuries and rape. The clinician has a duty to dispel these.
Fast software polar decoders

Among error-correcting codes, polar codes are the first to provably achieve channel capacity with an explicit construction. In this work, we present software implementations of a polar decoder that leverage the capabilities of modern general-purpose processors to achieve an information throughput in excess of 200 Mbps, a throughput well suited for software-defined-radio applications. We also show that, for a similar error-correction performance, the throughput of polar decoders both surpasses that of LDPC decoders targeting general-purpose processors and is competitive with that of state-of-the-art software LDPC decoders running on graphic processing units.
Combining metaheuristics with mathematical programming, constraint programming and machine learning

During the last years, interest on hybrid metaheuristics has risen considerably in the field of optimization and machine learning. The best results found for many optimization problems in science and industry are obtained by hybrid optimization algorithms. Combinations of optimization tools such as metaheuristics, mathematical programming, constraint programming and machine learning, have provided very efficient optimization algorithms. Four different types of combinations are considered in this paper: (1) Combining metaheuristics with complementary metaheuristics. (2) Combining metaheuristics with exact methods frommathematical programmingapproacheswhich aremostly used in the operations research community. (3) Combining metaheuristics with constraint programming approaches developed in the artificial intelligence community. (4) Combining metaheuristics with machine learning and data mining techniques.
Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers

Many sources of fluctuation contribute to the fMRI signal, and this makes identifying the effects that are truly related to the underlying neuronal activity difficult. Independent component analysis (ICA) - one of the most widely used techniques for the exploratory analysis of fMRI data - has shown to be a powerful technique in identifying various sources of neuronally-related and artefactual fluctuation in fMRI data (both with the application of external stimuli and with the subject "at rest"). ICA decomposes fMRI data into patterns of activity (a set of spatial maps and their corresponding time series) that are statistically independent and add linearly to explain voxel-wise time series. Given the set of ICA components, if the components representing "signal" (brain activity) can be distinguished form the "noise" components (effects of motion, non-neuronal physiology, scanner artefacts and other nuisance sources), the latter can then be removed from the data, providing an effective cleanup of structured noise. Manual classification of components is labour intensive and requires expertise; hence, a fully automatic noise detection algorithm that can reliably detect various types of noise sources (in both task and resting fMRI) is desirable. In this paper, we introduce FIX ("FMRIB's ICA-based X-noiseifier"), which provides an automatic solution for denoising fMRI data via accurate classification of ICA components. For each ICA component FIX generates a large number of distinct spatial and temporal features, each describing a different aspect of the data (e.g., what proportion of temporal fluctuations are at high frequencies). The set of features is then fed into a multi-level classifier (built around several different classifiers). Once trained through the hand-classification of a sufficient number of training datasets, the classifier can then automatically classify new datasets. The noise components can then be subtracted from (or regressed out of) the original data, to provide automated cleanup. On conventional resting-state fMRI (rfMRI) single-run datasets, FIX achieved about 95% overall accuracy. On high-quality rfMRI data from the Human Connectome Project, FIX achieves over 99% classification accuracy, and as a result is being used in the default rfMRI processing pipeline for generating HCP connectomes. FIX is publicly available as a plugin for FSL.
A Single-Stage SEPIC PFC Converter for Multiple Lighting LED Lamps

This paper presents a SEPIC PFC converter for driving multiple lighting LED lamps. With the aid of this converter, high power factor and high efficiency can be achieved by a simple single-stage circuit with low device stress features. A burst-mode dimming method is used to regulate the current and brightness of multiple LED lamps. A laboratory prototype is also built and tested. With the prototype, high power factor can remain under universal input voltage operation and individual lamp brightness control.
Subregional hippocampal atrophy predicts Alzheimer's dementia in the cognitively normal

Atrophic changes of the hippocampus are typically regarded as an early sign of Alzheimer's dementia (AD). Using the radial distance atrophy mapping approach, we compared the longitudinal MRI data of 10 cognitively normal elderly subjects who remained normal at 3-year and 6-year follow-up (NL-NL) and 7 cognitively normal elderly subjects who were diagnosed with mild cognitive impairment (MCI) 2.8 (range 2.0-3.9) and with AD 6.8 years (range 6.1-8.2) after baseline (NL-MCI(AD)). 3D statistical maps revealed greater hippocampal atrophy in the NL-MCI(AD) relative to the NL-NL group at baseline (left p=0.05; right p=0.06) corresponding to 10-15% CA1, and 10-25% subicular atrophy, and bilateral differences at 3-year follow-up (left p=0.001, right p<0.02) corresponding to 10-30% subicular, 10-20% CA1, and 10-20% newly developed CA2-3 atrophy. This preliminary study suggests that excess CA1 and subicular atrophy is present in cognitively normal individuals predestined to decline to amnestic MCI, while progressive involvement of the CA1 and subiculum, and atrophy spreading to the CA2-3 subfield in amnestic MCI, suggests future diagnosis of AD.
Moderating variables of music training-induced neuroplasticity: a review and discussion

A large body of literature now exists to substantiate the long-held idea that musicians' brains differ structurally and functionally from non-musicians' brains. These differences include changes in volume, morphology, density, connectivity, and function across many regions of the brain. In addition to the extensive literature that investigates these differences cross-sectionally by comparing musicians and non-musicians, longitudinal studies have demonstrated the causal influence of music training on the brain across the lifespan. However, there is a large degree of inconsistency in the findings, with discordance between studies, laboratories, and techniques. A review of this literature highlights a number of variables that appear to moderate the relationship between music training and brain structure and function. These include age at commencement of training, sex, absolute pitch (AP), type of training, and instrument of training. These moderating variables may account for previously unexplained discrepancies in the existing literature, and we propose that future studies carefully consider research designs and methodologies that control for these variables.
Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease.

Recent evidence suggests that some brain areas act as hubs interconnecting distinct, functionally specialized systems. These nexuses are intriguing because of their potential role in integration and also because they may augment metabolic cascades relevant to brain disease. To identify regions of high connectivity in the human cerebral cortex, we applied a computationally efficient approach to map the degree of intrinsic functional connectivity across the brain. Analysis of two separate functional magnetic resonance imaging datasets (each n = 24) demonstrated hubs throughout heteromodal areas of association cortex. Prominent hubs were located within posterior cingulate, lateral temporal, lateral parietal, and medial/lateral prefrontal cortices. Network analysis revealed that many, but not all, hubs were located within regions previously implicated as components of the default network. A third dataset (n = 12) demonstrated that the locations of hubs were present across passive and active task states, suggesting that they reflect a stable property of cortical network architecture. To obtain an accurate reference map, data were combined across 127 participants to yield a consensus estimate of cortical hubs. Using this consensus estimate, we explored whether the topography of hubs could explain the pattern of vulnerability in Alzheimer's disease (AD) because some models suggest that regions of high activity and metabolism accelerate pathology. Positron emission tomography amyloid imaging in AD (n = 10) compared with older controls (n = 29) showed high amyloid-beta deposition in the locations of cortical hubs consistent with the possibility that hubs, while acting as critical way stations for information processing, may also augment the underlying pathological cascade in AD.
Fisheye menus

We introduce “fisheye menus” which apply traditional fisheye graphical visualization techniques to linear menus. This provides for an efficient mechanism to select items from long menus, which are becoming more common as menus are used to select data items in, for example, ecommerce applications. Fisheye menus dynamically change the size of menu items to provide a focus area around the mouse pointer. This makes it possible to present the entire menu on a single screen without requiring buttons, scrollbars, or hierarchies. A pilot study with 10 users compared user preference of fisheye menus with traditional pull-down menus that use scrolling arrows, scrollbars, and hierarchies. Users preferred the fisheye menus for browsing tasks, and hierarchical menus for goal-directed tasks.