Miracle-dz/UniFAR-FTUs huggingface dataset:
Wechsler Adult Intelligence Scale—Fourth Edition (WAIS-IV)

Used as a general test the intelligence, the Wechsler Adult Intelligence Scale –Fourth Edition (WAISIV) was developed to assess cognitive ability for adults. This instruments aids in examining the relationship between intellectual functional and memory. A common purpose for the WAIS is for educational planning and placement with older adolescents and adults. The test includes 11 subtests with various types of format. Approximately 60 to 90 minutes is required for completion. Author David Wechsler Reliability and Validity The Wechsler Adult Intelligence is a well-established scale and it has fairly high consistency. Over a two to twelve week time period, the test-retest reliabilities ranged from 0.70 (7 subscales) to 0.90 (2 subscales). Inter-scorer coefficients were very high, all being above 0.90. According to the test manual, the instrument targets three are – psychoeducational disability, neuropsychiatic and organic dysfunction, and giftedness. The WAIS correlated highly with the Stanford-Binet IV test (0.88) and had high concordance with various measures: memory, language, dexterity, motor speed, attention, and cognitive ability. Obtaining the WAIS-IV Pearson Assessments Administration, Analysis and Reporting Statistics Solutions consists of a team of professional methodologists and statisticians that can assist the student or professional researcher in administering the survey instrument, collecting the data, conducting the analyses and explaining the results. For additional information on these services, click here.
Mining Automatically Estimated Poses from Video Recordings of Top Athletes

Abstract Human pose detection systems based on state-of-the-art DNNs are about to be extended, adapted and re-trained to fit the application domain of specific sports. Therefore, plenty of noisy pose data will soon be available from videos recorded at a regular and frequent basis. This work is among the first to develop mining algorithms that can mine the expected abundance of noisy and annotation-free pose data from video recordings in individual sports. Using swimming as an example of a sport with dominant cyclic motion, we show how to determine unsupervised time-continuous cycle speeds and temporally striking poses as well as measure unsupervised cycle stability over time. The average error in cycle length estimation across all strokes is 0.43 frames at 50 fps compared to manual annotations. Additionally, we use long jump as an example of a sport with a rigid phase-based motion to present a technique to automatically partition the temporally estimated pose sequences into their respective phases with a mAP of 0.89. This enables the extraction of performance relevant, pose-based metrics currently used by national professional sports associations. Experimental results prove the effectiveness of our mining algorithms, which can also be applied to other cycle-based or phase-based types of sport.
Training Rats Using Water Rewards Without Water Restriction

High-throughput behavioral training of rodents has been a transformative development for systems neuroscience. Water or food restriction is typically required to motivate task engagement. We hypothesized a gap between physiological water need and hedonic water satiety that could be leveraged to train rats for water rewards without water restriction. We show that when Citric Acid (CA) is added to water, female rats drink less, yet consume enough to maintain long term health. With 24 h/day access to a visual task with water rewards, rats with ad lib CA water performed 84% ± 18% as many trials as in the same task under water restriction. In 2-h daily sessions, rats with ad lib CA water performed 68% ± 13% as many trials as under water restriction. Using reward sizes <25 μl, rats with ad lib CA performed 804 ± 285 trials/day in live-in sessions or 364 ± 82 trials/day in limited duration daily sessions. The safety of CA water amendment was previously shown for male rats, and the gap between water need and satiety was similar to what we observed in females. Therefore, it is likely that this method will generalize to male rats, though this remains to be shown. We conclude that at least in some contexts rats can be trained using water rewards without water restriction, benefitting both animal welfare and scientific productivity.
Potential Benefit of Intra-operative Administration of Ketorolac on Breast Cancer Recurrence According to the Patient’s Body Mass Index

Background Nonsteroidal anti-inflammatory drugs (NSAIDs) are currently used in some countries as analgesics in primary cancer surgery. Retrospective studies suggest that NSAIDs could reduce breast cancer recurrences. Because NSAIDs also act on biological mechanisms present in patients with increased adiposity, we aimed at assessing whether the intra-operative administration of ketorolac or diclofenac would be associated with a reduction of recurrence in patients with elevated body mass index (BMI). Methods We considered two institutional retrospective series of 827 and 1007 patients evaluating the administration of ketorolac (n = 529 with, n = 298 without) or diclofenac (n = 787 with, n = 220 without). The BMI subgroups were defined as less than 25 kg/m2 (lean) and 25 or more kg/m2 (overweight and obese). Cumulative incidence estimation of distant metastases as well as Fine-Gray and Dixon-Simon models was used. These analyses were adjusted for clinico-pathological variables. All statistical tests were two-sided. Results The administration of ketorolac was statistically significantly associated with decreased incidence of distant recurrences (adjusted hazard ratio [aHR]= 0.59, 95% confidence interval [CI] = 0.37 to 0.96, P = .03). In particular, the association was evident in the high-body mass index (BMI) group of patients (aHR = 0.55, 95% CI = 0.31 to 0.96, P = .04). The administration of diclofenac was not statistically significantly associated with decreased incidence of distant recurrences, either in the global population or in the BMI subgroups. Conclusions These results show that the intra-operative administration of ketorolac, but not diclofenac, is statistically significantly associated with a reduction of distant recurrences in patients with increased BMI. Altogether, this study points to a potentially important repositioning of ketorolac in the intra-operative treatment of patients with elevated BMI that, if prospectively validated, might be as impactful as and cheaper than adjuvant systemic anticancer therapies.
Developing an automated writing placement system for ESL learners

ABSTRACT There are quite a few challenges in the development of an automated writing placement model for non-native English learners, among them the fact that exams that encompass the full range of language proficiency exhibited at different stages of learning are hard to design. However, acquisition of appropriate training data that are relevant to the task at hand is essential in the development of the model. Using the Cambridge Learner Corpus writing scores, which have been subsequently benchmarked to Common European Framework of Reference for Languages (CEFR) levels, we conceptualize the task as a supervised machine learning problem, and primarily focus on developing a generic writing model. Such an approach facilitates the modeling of truly consistent, internal marking criteria regardless of the prompt delivered, which has the additional advantage of requiring smaller dataset sizes and not necessarily requiring re-training or tuning for new tasks. The system is developed to predict someone’s proficiency level on the CEFR scale, which allows learners to point to a specific standard of achievement. We furthermore integrate our model into Cambridge English Write & ImproveTM—a freely available, cloud-based tool that automatically provides diagnostic feedback to non-native English language learners at different levels of granularity—and examine its use.
Stop the KillFies! Using Deep Learning Models to Identify Dangerous Selfies

Selfies have become a prominent medium for self-portrayal on social media. Unfortunately, certain social media users go to extreme lengths to click selfies, which puts their lives at risk. Two hundred and sixteen individuals have died since March 2014 until January 2018 while trying to click selfies. It is imperative to be able to identify dangerous selfies posted on social media platforms to be able to build an intervention for users going to extreme lengths for clicking such selfies. In this work, we propose a convolutional neural network based classifier to identify dangerous selfies posted on social media using only the image (no metadata). We show that our proposed approach gives an accuracy of $98%$ and performs better than previous methods.
Evaluation of Depression in Subacute Low Back Pain: A Case Control Study.

BACKGROUND Low back pain (LBP) is the most common musculoskeletal disorder affecting the general population and it is believed to be associated with depression. OBJECTIVE The study aim was to describe and compare the impact in a sample of people with subacute LBP (SLBP) and patients without LBP with normalized reference values in the light of the scores obtained with regard using the Beck Depression Inventory (BDI). STUDY DESIGN This was a case-control study. SETTINGS Physiotherapist area at a care center. METHODS A sample of 164 participants of a mean age of 41.45 ± 0.97 came to a physical therapy clinic where self-reported data were registered, informants' professional activity was determined, and the scores obtained were compared in the BDI. RESULTS Total BDI scores at SLBP and their controls were 21.52 ± 6.93 (11 - 43) and 17.30 ± 5.09 (11 - 30), respectively (P < 0.001). The SLBP patient has 2.12 times more likely to have moderate depression (OR 2.12 (1.07 - 4.18) and 18.82 times more likely to have serious depression (OR 18.82 (1.06 - 331.81) compared to their controls (P < 0.05). LIMITATIONS The study was not a randomized controlled trial. Although primary outcome data were self-reported, the assessor was not blinded. CONCLUSIONS People with SLBP also have a significant increase in depression based on BDI scores, regardless of gender.
Assessing School Turnaround: Evidence from Ohio

Policy makers have struggled to find successful approaches to address concentrated, persistent low school achievement. While NCLB and the School Improvement Grant (SIG) program have devoted significant time and attention to turnaround, very little empirical evidence substantiates whether and how these efforts work. This study employs a comparative interrupted time series (CITS) to examine a sample of 20 Ohio schools that participated in a school turnaround program and finds participating schools experienced meaningful improvements in student achievement after completing the 2-year program, which persisted and grew in the 2 years subsequent to the completion of the program. Improved student achievement is not wholly concentrated within specific performance categories, suggesting that participation in the program is associated with increases in overall student performance rather than focusing only on students at the margin of proficiency. These results provide some of the first causal evidence of the potential efficacy of focused school improvement efforts.
A survey of emotion recognition in speech

Emotions play a significant role in human perception and decision making.For a long time research on emotion intelligence has been done in the fields of psychology and cognitive science.Along with the development of artificial intelligence these years,the combination of emotion intelligence and computer technology brings the research area of affective computing.Automatic recognition of human emotion is the first step toward affective computing.Speech as the most important medium of human communication contains lots of emotional information,and how to automatically recognize speakers’ emotional state is the subject of attention by researchers from different fields.This paper surveys the current state of the art in several key issues related to emotion recognition by speech,including the theory of emotion,the collecting of emotional speech database,the emotional speech feature,and the recognition algorithm.Finally several key issues for future research are presented.
PAPANIKOLOPOULOS Panoramic Imaging for Miniature Robots

O mnidirectional sensors hold a great promise for robot localization and navigation. However, the limited payload volume of miniature robots makes the use of omnidirectional vision sensors almost impossible. We overcame this problem by moving the robot around in order to create panoramic images. In addition to the general problems for building mosaics (i.e., computing the warping functions), this framework also has to take into account the noisy images delivered by the miniature robot. Two methods are presented. The solution for the general case allows for large rotations and a zoom factor. A special case, in which the homography is approximated by translations caused by minor changes in the optical system, is also considered, as computation time can be reduced significantly in this case. Panoramic images require expensive lenses and custom-made hardware that, due to volume constraints, many robots (especially miniature ones) cannot accommodate. As a more cost effective alternative, a robot in front of a complex scene could restitute this landscape by taking several pictures and then trying to piece them together. This process, called “mosaicking images,” tries to recreate a continuous picture from several overlapping images taken from the same scene. However, the result of this intuitive approach will be poor because of perspective distortions that appear when the camera is spun around its axes. Hostage and disaster rescue missions, as well as toxic atmosphere surveillance, are two examples in which the use of robots can be beneficial to save human lives. We have developed a heterogeneous robot team consisting of two kinds of robots for these environments. The cylindrical Scout robot [1] is 11 cm long, 4 cm in diameter, and is equipped with a video camera (Figure 1). Locomotion is accomplished through a unique combination of rolling and jumping. The much larger Ranger robot is used to deploy Scouts in their area of operation and provides for computational resources. Remote human rescue personnel desire to be provided with a complete and high-resolution view of a Scout’s surrounding area. This is achieved by using the Scout’s video camera as a visual sensor for mosaicking images. The small size of the Scout and its limited transmission power complicate the creation of mosaics because of noise in the images. Two video transmission frequencies are available. At the 900 MHz range, the signal penetrates objects or walls more easily than at the 2.4 GHz range. However, the higher frequency provides a clearer signal under the condition of line-of-sight. The remainder of this article gives an overview of related work and introduces the proposed method for mosaicking images. Experimental results are presented, and the article closes with a conclusion and suggestions for future research directions. BY CHRISTIAN C. DOS SANTOS, SASCHA A. STOETER, PAUL E. RYBSKI, AND NIKOLAOS P. PAPANIKOLOPOULOS Panoramic Imaging for Miniature Robots
Tensor Methods for Nonlinear Matrix Completion

In the low-rank matrix completion (LRMC) problem, the low-rank assumption means that the columns (or rows) of the matrix to be completed are points on a low-dimensional linear algebraic variety. This paper extends this thinking to cases where the columns are points on a low-dimensional nonlinear algebraic variety, a problem we call Low Algebraic Dimension Matrix Completion (LADMC). Matrices whose columns belong to a union of subspaces are an important special case. We propose a LADMC algorithm that leverages existing LRMC methods on a tensorized representation of the data. For example, a second-order tensorized representation is formed by taking the Kronecker product of each column with itself, and we consider higher order tensorizations as well. This approach will succeed in many cases where traditional LRMC is guaranteed to fail because the data are low-rank in the tensorized representation but not in the original representation. We provide a formal mathematical justification for the success of our method. In particular, we give bounds of the rank of these data in the tensorized representation, and we prove sampling requirements to guarantee uniqueness of the solution. We also provide experimental results showing that the new approach outperforms existing state-of-the-art methods for matrix completion under a union of subspaces model.
Deep Learning for Text Classification in Azure Infrastructure April 15

With the increasing computing power and exploding volumes of data in the past few years, researchers have made continuous breakthroughs in deep learning technology including its application to natural language processing (NLP). However, deep learning models for Chinese language processing are still underdeveloped. Inspired by the potential of Chinese NLP with deep learning, this project aims at exploring and developing different deep learning models for sentiment analysis of Chinese text, classifying short Chinese sentences according to their underlying sentiments. Cooperating with Microsoft, this project has designed, implemented, and evaluated several deep learning models of mainly two basic architectures, namely RNN and CNN, which leverage recurrent neural networks to capture the high-level semantics of Chinese text. In order to improve the accuracy of the models, this project has conducted a series of experiments to optimize the dataset configuration and model’s hyperparameters. As an industry-based project, this project adopts Microsoft Cognitive Toolkit as the deep learning frameworks, and the experiments were powered by Microsoft Azure, the Microsoft could computing platform.
Toward a Better Understanding of How to Develop Software Under Stress - Drafting the Lines for Future Research

The software is often produced under significant time constraints. Our idea is to understand the effects of various software development practices on the performance of developers working in stressful environments, and identify the best operating conditions for software developed under stressful conditions collecting data through questionnaires, non-invasive software measurement tools that can collect measurable data about software engineers and the software they develop, without intervening their activities, and biophysical sensors and then try to recreated also in different processes or key development practices such conditions.
Genesis of Basic and Multi-Layer Echo State Network Recurrent Autoencoders for Efficient Data Representations

It is a widely accepted fact that data representations intervene noticeably in machine learning tools. The more they are well defined the better the performance results are. Feature extraction-based methods such as autoencoders are conceived for finding more accurate data representations from the original ones. They efficiently perform on a specific task in terms of 1) high accuracy, 2) large short term memory and 3) low execution time. Echo State Network (ESN) is a recent specific kind of Recurrent Neural Network which presents very rich dynamics thanks to its reservoir-based hidden layer. It is widely used in dealing with complex non-linear problems and it has outperformed classical approaches in a number of tasks including regression, classification, etc. In this paper, the noticeable dynamism and the large memory provided by ESN and the strength of Autoencoders in feature extraction are gathered within an ESN Recurrent Autoencoder (ESN-RAE). In order to bring up sturdier alternative to conventional reservoir-based networks, not only single layer basic ESN is used as an autoencoder, but also Multi-Layer ESN (ML-ESN-RAE). The new features, once extracted from ESN's hidden layer, are applied to classification tasks. The classification rates rise considerably compared to those obtained when applying the original data features. An accuracy-based comparison is performed between the proposed recurrent AEs and two variants of an ELM feed-forward AEs (Basic and ML) in both of noise free and noisy environments. The empirical study reveals the main contribution of recurrent connections in improving the classification performance results.
Dietary fish oil and DHA down-regulate antigen-activated CD4+ T-cells while promoting the formation of liquid-ordered mesodomains

We have demonstrated previously that n-3 PUFA endogenously produced by fat-1 transgenic mice regulate CD4+ T-cell function by affecting the formation of lipid rafts, liquid-ordered mesodomains in the plasma membrane. In the present study, we tested the effects of dietary sources of n-3 PUFA, i.e. fish oil (FO) or purified DHA, when compared with an n-6 PUFA-enriched maize oil control diet in DO11.10 T-cell receptor transgenic mice. Dietary n-3 PUFA were enriched in CD4+ T-cells, resulting in the increase of the n-3:n-6 ratio. Following antigen-specific CD4+ T-cell activation by B-lymphoma cells pulsed with the ovalbumin 323–339 peptide, the formation of liquid-ordered mesodomains at the immunological synapse relative to the whole CD4+ T-cell, as assessed by Laurdan labelling, was increased (P< 0·05) in the FO-fed group. The FO diet also suppressed (P< 0·05) the co-localisation of PKCθ with ganglioside GM1 (monosialotetrahexosylganglioside), a marker for lipid rafts, which is consistent with previous observations. In contrast, the DHA diet down-regulated (P< 0·05) PKCθ signalling by moderately affecting the membrane liquid order at the immunological synapse, suggesting the potential contribution of the other major n-3 PUFA components of FO, including EPA.
Controlling liquids using meshes

We present an approach for artist-directed animation of liquids using multiple levels of control over the simulation, ranging from the overall tracking of desired shapes to highly detailed secondary effects such as dripping streams, separating sheets of fluid, surface waves and ripples. The first portion of our technique is a volume preserving morph that allows the animator to produce a plausible fluid-like motion from a sparse set of control meshes. By rasterizing the resulting control meshes onto the simulation grid, the mesh velocities act as boundary conditions during the projection step of the fluid simulation. We can then blend this motion together with uncontrolled fluid velocities to achieve a more relaxed control over the fluid that captures natural inertial effects. Our method can produce highly detailed liquid surfaces with control over sub-grid details by using a mesh-based surface tracker on top of a coarse grid-based fluid simulation. We can create ripples and waves on the fluid surface attracting the surface mesh to the control mesh with spring-like forces and also by running a wave simulation over the surface mesh. Our video results demonstrate how our control scheme can be used to create animated characters and shapes that are made of water.
Research on direct torque control of switched reluctance motor with improved commutation strategy

Direct torque control (DTC) of switched reluctance motor (SRM) is one of the simple and effective torque control methods aim at the reduction of torque ripple and the acquisition of high accuracy torque control. However, due to the nonlinearity of SRM, the control quality can be worse during commutation. This paper analyzes the principle of conventional DTC and explains why the torque is not control well when commutation occurs. A new commutation strategy considering the SRM nature and using the prediction of torque change rate is proposed in this paper. Simulation results show that the proposed method has largely improved the torque control quality compared with the traditional DTC.