nils-herrmann/clean_scirepeval huggingface dataset:
Occupational therapy using a sensory integrative approach: a case study of effectiveness.

This article presents a case report of a child with poor sensory processing and describes the disorders impact on the child's occupational behavior and the changes in occupational performance during 10 months of occupational therapy using a sensory integrative approach (OT-SI).



Retrospective chart review of assessment data and analysis of parent interview data are reviewed. Progress toward goals and objectives is measured using goal attainment scaling. Themes from parent interview regarding past and present occupational challenges are presented.



Notable improvements in occupational performance are noted on goal attainment scales, and these are consistent with improvements in behavior. Parent interview data indicate noteworthy progress in the child's ability to participate in home, school, and family activities.



This case report demonstrates a model for OT-SI. The findings support the theoretical underpinnings of sensory integration theory: that improvement in the ability to process and integrate sensory input will influence adaptive behavior and occupational performance. Although these findings cannot be generalized, they provide preliminary evidence supporting the theory and the effectiveness of this approach.
Remote plethysmographic imaging using ambient light.

Plethysmographic signals were measured remotely (> 1m) using ambient light and a simple consumer level digital camera in movie mode. Heart and respiration rates could be quantified up to several harmonics. Although the green channel featuring the strongest plethysmographic signal, corresponding to an absorption peak by (oxy-) hemoglobin, the red and blue channels also contained plethysmographic information. The results show that ambient light photo-plethysmography may be useful for medical purposes such as characterization of vascular skin lesions (e.g., port wine stains) and remote sensing of vital signs (e.g., heart and respiration rates) for triage or sports purposes.
Detecting parametric objects in large scenes by Monte Carlo sampling

Point processes constitute a natural extension of Markov random fields (MRF), designed to handle parametric objects. They have shown efficiency and competitiveness for tackling object extraction problems in vision. Simulating these stochastic models is however a difficult task. The performances of the existing samplers are limited in terms of computation time and convergence stability, especially on large scenes. We propose a new sampling procedure based on a Monte Carlo formalism. Our algorithm exploits the Markovian property of point processes to perform the sampling in parallel. This procedure is embedded into a data-driven mechanism so that the points are distributed in the scene in function of spatial information extracted from the input data. The performances of the sampler are analyzed through a set of experiments on various object detection problems from large scenes, including comparisons to the existing algorithms. The sampler is also tested as optimization algorithm for MRF-based labeling problems.
Practical Designs of Brain–Computer Interfaces Based on the Modulation of EEG Rhythms

A brain–computer interface (BCI) is a communication channel which does not depend on the brain’s normal output pathways of peripheral nerves and muscles [1–3]. It supplies paralyzed patients with a new approach to communicate with the environment. Among various brain monitoring methods employed in current BCI research, electroencephalogram (EEG) is the main interest due to its advantages of low cost, convenient operation and non-invasiveness. In present-day EEG-based BCIs, the following signals have been paid much attention: visual evoked potential (VEP), sensorimotor mu/beta rhythms, P300 evoked potential, slow cortical potential (SCP), and movement-related cortical potential (MRCP). Details about these signals can be found in chapter “Brain Signals for Brain–Computer Interfaces”. These systems offer some practical solutions (e.g., cursor movement and word processing) for patients with motor disabilities. In this chapter, practical designs of several BCIs developed in Tsinghua University will be introduced. First of all, we will propose the paradigm of BCIs based on the modulation of EEG rhythms and challenges confronting practical system designs. In Sect. 2, modulation and demodulation methods of EEG rhythms will be further explained. Furthermore, practical designs of a VEP-based BCI and a motor imagery based BCI will be described in Sect. 3. Finally, Sect. 4 will present some real-life application demos using these practical BCI systems.
Egos inflating over time: a cross-temporal meta-analysis of the Narcissistic Personality Inventory.

A cross-temporal meta-analysis found that narcissism levels have risen over the generations in 85 samples of American college students who completed the 40-item forced-choice Narcissistic Personality Inventory (NPI) between 1979 and 2006 (total n=16,475). Mean narcissism scores were significantly correlated with year of data collection when weighted by sample size (beta=.53, p<.001). Since 1982, NPI scores have increased 0.33 standard deviation. Thus, almost two-thirds of recent college students are above the mean 1979-1985 narcissism score, a 30% increase. The results complement previous studies finding increases in other individualistic traits such as assertiveness, agency, self-esteem, and extraversion.
Path following control for tracked vehicles based on slip-compensating odometry

Tracked vehicles have the advantage of stable locomotion on uneven terrain, and, as a result, such mechanisms are used for locomotion on outdoor robots, including those used for search and rescue. However, such mechanisms always slip when a tracked vehicle follows a curve, and the slippage generates large accumulated positioning errors in the vehicle compared with conventional wheeled mobile robots. To improve the accuracy of the odometry and enable a path-following control, the estimation of the track slippage is essential. In this paper, we propose an improved method of odometry for tracked vehicles to follow a straight line or a curve. In this method, the vehicle estimates the slip ratios using two encoders (attached to the actuators) and a gyro-sensor. Based on the improved odometry, the path-following control of tracked vehicles is significantly improved. The validity of the method was confirmed with experiments involving our tracked vehicle on several types of surfaces.
Unsupervised Learning of Object Landmarks by Factorized Spatial Embeddings

Learning automatically the structure of object categories remains an important open problem in computer vision. In this paper, we propose a novel unsupervised approach that can discover and learn landmarks in object categories, thus characterizing their structure. Our approach is based on factorizing image deformations, as induced by a viewpoint change or an object deformation, by learning a deep neural network that detects landmarks consistently with such visual effects. Furthermore, we show that the learned landmarks establish meaningful correspondences between different object instances in a category without having to impose this requirement explicitly. We assess the method qualitatively on a variety of object types, natural and man-made. We also show that our unsupervised landmarks are highly predictive of manually-annotated landmarks in face benchmark datasets, and can be used to regress these with a high degree of accuracy.
Better cross company defect prediction

How can we find data for quality prediction? Early in the life cycle, projects may lack the data needed to build such predictors. Prior work assumed that relevant training data was found nearest to the local project. But is this the best approach? This paper introduces the Peters filter which is based on the following conjecture: When local data is scarce, more information exists in other projects. Accordingly, this filter selects training data via the structure of other projects. To assess the performance of the Peters filter, we compare it with two other approaches for quality prediction. Within-company learning and cross-company learning with the Burak filter (the state-of-the-art relevancy filter). This paper finds that: 1) within-company predictors are weak for small data-sets; 2) the Peters filter+cross-company builds better predictors than both within-company and the Burak filter+cross-company; and 3) the Peters filter builds 64% more useful predictors than both within-company and the Burak filter+cross-company approaches. Hence, we recommend the Peters filter for cross-company learning.
A Wideband Dual-Mode SIW Cavity-Backed Triangular-Complimentary-Split-Ring-Slot (TCSRS) Antenna

A cavity-backed triangular-complimentary-split-ring-slot (TCSRS) antenna based on substrate integrated waveguide (SIW) is proposed in this communication. Proposed antenna element is designed and implemented at 28 and 45 GHz for the fifth generation (5G) of wireless communications. The principle of the proposed antenna element is investigated first then the arrays with two and four elements are designed for high-gain operation. Antennas prototype along with their arrays are fabricated using standard printed circuit board (PCB) process at both frequencies-28 and 45 GHz. Measured result shows that the 16.67% impedance bandwidth at 28 GHz and 22.2% impedance bandwidth at 45 GHz is achieved, while maintaining the same substrate height at both frequencies. The measured peak gains of the 2 × 2 antenna array at 30 or 50 GHz are 13.5 or 14.4 dBi, respectively.
Efficient and agile storage management in software defined environments

management in software defined environments A. Alba G. Alatorre C. Bolik A. Corrao T. Clark S. Gopisetty R. Haas R. I. Kat B. S. Langston N. S. Mandagere D. Noll S. Padbidri R. Routray Y. Song C.-H. Tan A. Traeger The IT industry is experiencing a disruptive trend for which the entire data center infrastructure is becoming software defined and programmable. IT resources are provisioned and optimized continuously according to a declarative and expressive specification of the workload requirements. The software defined environments facilitate agile IT deployment and responsive data center configurations that enable rapid creation and optimization of value-added services for clients. However, this fundamental shift introduces new challenges to existing data center management solutions. In this paper, we focus on the storage aspect of the IT infrastructure and investigate its unique challenges as well as opportunities in the emerging software defined environments. Current state-of-the-art software defined storage (SDS) solutions are discussed, followed by our novel framework to advance the existing SDS solutions. In addition, we study the interactions among SDS, software defined compute (SDC), and software defined networking (SDN) to demonstrate the necessity of a holistic orchestration and to show that joint optimization can significantly improve the effectiveness and efficiency of the overall software defined environments.
Wireless data center networking

Data centers play a key role in the expansion of cloud computing. However, the efficiency of data center networks is limited by oversubscription. The typical unbalanced traffic distributions of a DCN further aggravate the problem. Wireless networking, as a complementary technology to Ethernet, has the flexibility and capability to provide feasible approaches to handle the problem. In this article, we analyze the challenges of DCNs and articulate the motivations of employing wireless in DCNs. We also propose a hybrid Ethernet/wireless DCN architecture and a mechanism to dynamically schedule wireless transmissions based on traffic demands. Our simulation study demonstrates the effectiveness of the proposed wireless DCN.
Database Issues in Knowledge Discovery and Data Mining

In recent years both the number and the size of organisational databases have increased rapidly. However, although available processing power has also grown, the increase in stored data has not necessarily led to a corresponding increase in useful information and knowledge. This has led to a growing interest in the development of tools capable of harnessing the increased processing power available to better utilise the potential of stored data. The terms “Knowledge Discovery in Databases” and “Data Mining” have been adopted for a field of research dealing with the automatic discovery of knowledge implicit within databases. Data mining is useful in situations where the volume of data is either too large or too complicated for manual processing or, to a lesser extent, where human experts are unavailable to provide knowledge. The success already attained by a wide range of data mining applications has continued to prompt further investigation into alternative data mining techniques and the extension of data mining to new domains. This paper surveys, from the standpoint of the database systems community, current issues in data mining research by examining the architectural and process models adopted by knowledge discovery systems, the different types of discovered knowledge, the way knowledge discovery systems operate on different data types, various techniques for knowledge discovery and the ways in which discovered knowledge is used.
Implicit Calibration of a Remote Gaze Tracker

We describe a system designed to monitor the gaze of a user working naturally at a computer workstation. The system consists of three cameras situated between the keyboard and the monitor. Free head movements are allowed within a three-dimensional volume approximately 40 centimeters in diameter. Two fixed, wide-field "face" cameras equipped with active-illumination systems enable rapid localization of the subject's pupils. A third steerable "eye" camera has a relatively narrow field of view, and acquires the images of the eyes which are used for gaze estimation. Unlike previous approaches which construct an explicit three-dimensional representation of the subject's head and eye, we derive mappings for steering control and gaze estimation using a procedure we call implicit calibration. Implicit calibration is performed by collecting a "training set" of parameters and associated measurements, and solving for a set of coefficients relating the measurements back to the parameters of interest. Preliminary data on three subjects indicate an median gaze estimation error of ap-proximately 0.8 degree.
WU-CRISPR: characteristics of functional guide RNAs for the CRISPR/Cas9 system

The CRISPR/Cas9 system has been rapidly adopted for genome editing. However, one major issue with this system is the lack of robust bioinformatics tools for design of single guide RNA (sgRNA), which determines the efficacy and specificity of genome editing. To address this pressing need, we analyze CRISPR RNA-seq data and identify many novel features that are characteristic of highly potent sgRNAs. These features are used to develop a bioinformatics tool for genome-wide design of sgRNAs with improved efficiency. These sgRNAs as well as the design tool are freely accessible via a web server, WU-CRISPR ( http://crispr.wustl.edu ).
The Human Factor in

We discuss the importance of understanding psychological aspects of phishing, and review some recent findings. Given these findings, we critique some commonly used security practices and suggest and review alternatives, including educational approaches. We suggest a few techniques that can be used to assess and remedy threats remotely, without requiring any user involvement. We conclude by discussing some approaches to anticipate the next wave of threats, based both on psychological and technical insights. 1 What Will Consumers Believe? There are several reasons why it is important to understand what consumers will find believable. First of all, it is crucial for service providers to know their vulnerabilities (and those of their clients) in order to assess their exposure to risks and the associated liabilities. Second, recognizing what the vulnerabilities are translates into knowing from where the attacks are likely to come; this allows for suitable technical security measures to be deployed to detect and protect against attacks of concern. It also allows for a proactive approach in which the expected vulnerabilities are minimized by the selection and deployment of appropriate email and web templates, and the use of appropriate manners of interaction. Finally, there are reasons for why understanding users is important that are not directly related to security: Knowing what consumers will believe—and will not believe—means a better ability to reach the consumers with information they do not expect, whether for reasons of advertising products or communicating alerts. Namely, given the mimicry techniques used by phishers, there is a risk that consumers incorrectly classify legitimate messages as attempts to attack them. Being aware of potential pitfalls may guide decisions that facilitate communication. While technically knowledgeable, specialists often make the mistake of believing that security measures that succeed in protecting them are sufficient to protect average consumers. For example, it was for a long time commonly held among security practitioners that the widespread deployment of SSL would eliminate phishing once consumers become aware of the risks and nature of phishing attacks. This, very clearly, has not been the case, as supported both by reallife observations and by experiments [48]. This can be ascribed to a lack of attention to security among typical users [47, 35], but also to inconsistent or inappropriate security education — whether implicit or not. An example of a common procedure that indirectly educates user is the case of lock symbols. Many financial institutions place a lock symbol in the content portion of the login page to indicate that a secure connection will be established as the user submits his credentials. This is to benefit from the fact that users have been educated to equate an SSL lock with a higher level of security. However, attackers may also place lock icons in the content of the page, whether they intend to establish an SSL connection or not. Therefore, the use of the lock
Combining Multiple Dynamic Models and Deep Learning Architectures for Tracking the Left Ventricle Endocardium in Ultrasound Data

We present a new statistical pattern recognition approach for the problem of left ventricle endocardium tracking in ultrasound data. The problem is formulated as a sequential importance resampling algorithm such that the expected segmentation of the current time step is estimated based on the appearance, shape, and motion models that take into account all previous and current images and previous segmentation contours produced by the method. The new appearance and shape models decouple the affine and nonrigid segmentations of the left ventricle to reduce the running time complexity. The proposed motion model combines the systole and diastole motion patterns and an observation distribution built by a deep neural network. The functionality of our approach is evaluated using a dataset of diseased cases containing 16 sequences and another dataset of normal cases comprised of four sequences, where both sets present long axis views of the left ventricle. Using a training set comprised of diseased and healthy cases, we show that our approach produces more accurate results than current state-of-the-art endocardium tracking methods in two test sequences from healthy subjects. Using three test sequences containing different types of cardiopathies, we show that our method correlates well with interuser statistics produced by four cardiologists.
Single Lgr5 stem cells build cryptvillus structures in vitro without a mesenchymal niche

The intestinal epithelium is the most rapidly self-renewing tissue in adult mammals. We have recently demonstrated the presence of about six cycling Lgr5+ stem cells at the bottoms of small-intestinal crypts. Here we describe the establishment of long-term culture conditions under which single crypts undergo multiple crypt fission events, while simultanously generating villus-like epithelial domains in which all differentiated cell types are present. Single sorted Lgr5+ stem cells can also initiate these cryptvillus organoids. Tracing experiments indicate that the Lgr5+ stem-cell hierarchy is maintained in organoids. We conclude that intestinal cryptvillus units are self-organizing structures, which can be built from a single stem cell in the absence of a non-epithelial cellular niche.
Reinforced Continual Learning

Most artificial intelligence models are limited in their ability to solve new tasks faster, without forgetting previously acquired knowledge. The recently emerging paradigm of continual learning aims to solve this issue, in which the model learns various tasks in a sequential fashion. In this work, a novel approach for continual learning is proposed, which searches for the best neural architecture for each coming task via sophisticatedly designed reinforcement learning strategies. We name it as Reinforced Continual Learning. Our method not only has good performance on preventing catastrophic forgetting but also fits new tasks well. The experiments on sequential classification tasks for variants of MNIST and CIFAR-100 datasets demonstrate that the proposed approach outperforms existing continual learning alternatives for deep networks.
Sparse Gaussian Processes for Bayesian Optimization

Bayesian optimization schemes often rely on Gaussian processes (GP). GP models are very flexible, but are known to scale poorly with the number of training points. While several efficient sparse GP models are known, they have limitations when applied in optimization settings. We propose a novel Bayesian optimization framework that uses sparse online Gaussian processes. We introduce a new updating scheme for the online GP that accounts for our preference during optimization for regions with better performance. We apply this method to optimize the performance of a free-electron laser, and demonstrate empirically that the weighted updating scheme leads to substantial improvements to performance in optimization.
Optimization of a Fuzzy-Logic-Control-Based MPPT Algorithm Using the Particle Swarm Optimization Technique

In this paper, an asymmetrical fuzzy-logic-control (FLC)-based maximum power point tracking (MPPT) algorithm for photovoltaic (PV) systems is presented. Two membership function (MF) design methodologies that can improve the effectiveness of the proposed asymmetrical FLC-based MPPT methods are then proposed. The first method can quickly determine the input MF setting values via the power–voltage (P–V) curve of solar cells under standard test conditions (STC). The second method uses the particle swarm optimization (PSO) technique to optimize the input MF setting values. Because the PSO approach must target and optimize a cost function, a cost function design methodology that meets the performance requirements of practical photovoltaic generation systems (PGSs) is also proposed. According to the simulated and experimental results, the proposed asymmetrical FLC-based MPPT method has the highest fitness value, therefore, it can successfully address the tracking speed/tracking accuracy dilemma compared with the traditional perturb and observe (P&O) and symmetrical FLC-based MPPT algorithms. Compared to the conventional FLC-based MPPT method, the obtained optimal asymmetrical OPEN ACCESS Energies 2015, 8 5339 FLC-based MPPT can improve the transient time and the MPPT tracking accuracy by 25.8% and 0.98% under STC, respectively.