LLukas22/scidocs huggingface dataset:
Automated gesture tracking in head-fixed mice

BACKGROUND
The preparation consisting of a head-fixed mouse on a spherical or cylindrical treadmill offers unique advantages in a variety of experimental contexts. Head fixation provides the mechanical stability necessary for optical and electrophysiological recordings and stimulation. Additionally, it can be combined with virtual environments such as T-mazes, enabling these types of recording during diverse behaviors.


NEW METHOD
In this paper we present a low-cost, easy-to-build acquisition system, along with scalable computational methods to quantitatively measure behavior (locomotion and paws, whiskers, and tail motion patterns) in head-fixed mice locomoting on cylindrical or spherical treadmills.


EXISTING METHODS
Several custom supervised and unsupervised methods have been developed for measuring behavior in mice. However, to date there is no low-cost, turn-key, general-purpose, and scalable system for acquiring and quantifying behavior in mice.


RESULTS
We benchmark our algorithms against ground truth data generated either by manual labeling or by simpler methods of feature extraction. We demonstrate that our algorithms achieve good performance, both in supervised and unsupervised settings.


CONCLUSIONS
We present a low-cost suite of tools for behavioral quantification, which serve as valuable complements to recording and stimulation technologies being developed for the head-fixed mouse preparation.
Pupil and salivary indicators of autonomic dysfunction in autism spectrum disorder.

Dysregulated tonic pupil size has been reported in autism spectrum disorder (ASD). Among the possible sources of this dysregulation are disruptions in the feedback loop between norepinephrine (NE) and hypothalamic systems. In the current study, we examined afternoon levels of salivary alpha-amylase (sAA, a putative correlate of NE) and cortisol (used to assess stress-based responses) in two independent samples of children with ASD. We found a larger pupil size and lower sAA levels in ASD, compared to typical and clinical age-matched controls. This was substantiated at the individual level, as sAA levels were strongly correlated with tonic pupil size. Relatively little diurnal variation in sAA taken in the home environment in the ASD group was also observed, while typical controls showed a significant linear increase throughout the day. Results are discussed in terms of potential early biomarkers and the elucidation of underlying neural dysfunction in ASD.
Towards Quantum Programs Verification: From Quipper Circuits to QPMC

We present a translation from the quantum programming language Quipper to the QPMC model checker, with the main aim of verifying Quipper programs. We implemented and tested our translation on several quantum algorithms, including Grover’s quantum search.
Hop, a Fast Server for the Diffuse Web

The diffuse Web is an alternative way of using the Web 2.0 infrastructure for building personal diffuse applications. Systems that let users tune the temperature of their house with a cell-phone, check that the shutters are closed with a PDA, or select the music to be played on a Hi-Fi system with a PC are examples of the targeted applications. Diffuse Web applications have similarities with Web 2.0 applications: i) they rely on fast bi-directional interactions between servers and clients, and ii) they make extensive use of non-cachable dynamic contents. On the other hand, diffuse applications have also an important difference with respect to traditional Web applications: they generally do not need to deal with a huge number of simultaneous users. That is, diffuse Web applications are built on top of standard technologies but they use it differently. Therefore they demand different optimizations and tunings. Hop (http://hop.inria.fr) is a platform designed for building and running diffuse Web applications. Its software development kit contains two compilers, one interpreter, and a bootstrapped Web server. That is, the Hop Web server is implemented in Hop. This paper shows that this implementation strategy allows Hop to dramatically outperform the popular mainstream Web servers for delivering dynamic contents. Contrary to most servers, Hop delivers static and dynamic contents at a comparable pace. The paper details the implementation of the Hop Web server.
Neuropathy and Related Findings in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study

OBJECTIVE To describe the development and progression of neuropathy and related findings among patients with type 1 diabetes who participated in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study. RESEARCH DESIGN AND METHODS The main diabetic peripheral neuropathy (DPN) outcome was assessed using clinical symptoms, signs, and nerve conduction study results during DCCT and repeated in EDIC year 13/14. Cardiovascular autonomic neuropathy (CAN) was assessed by R-R response to paced breathing, Valsalva ratio, and blood pressure response to standing during DCCT and in EDIC years 13/14 and 16/17. Additionally, symptoms reflecting neuropathic pain and autonomic function (including hypoglycemia awareness) were collected yearly in EDIC using standardized questionnaires; peripheral neuropathy was also assessed annually using the Michigan Neuropathy Screening Instrument. Assessments of genitourinary function were collected at EDIC year 10. RESULTS Intensive therapy during the DCCT significantly reduced the risk of DPN and CAN at DCCT closeout (64% and 45%, respectively, P < 0.01). The prevalence and incidence of DPN and CAN remained significantly lower in the DCCT intensive therapy group compared with the DCCT conventional therapy group through EDIC year 13/14. CONCLUSIONS The persistent effects of prior intensive therapy on neuropathy measures through 14 years of EDIC largely mirror those observed for other diabetes complications. DCCT/EDIC provides important information on the influence of glycemic control, and the clinical course of diabetic neuropathy, and, most important, on how to prevent neuropathy in type 1 diabetes.
Story Ending Generation with Incremental Encoding and Commonsense Knowledge

Generating a reasonable ending for a given story context, i.e., story ending generation, is a strong indication of story comprehension. This task requires not only to understand the context clues which play an important role in planning the plot, but also to handle implicit knowledge to make a reasonable, coherent story. In this paper, we devise a novel model for story ending generation. The model adopts an incremental encoding scheme to represent context clues which are spanning in the story context. In addition, commonsense knowledge is applied through multi-source attention to facilitate story comprehension, and thus to help generate coherent and reasonable endings. Through building context clues and using implicit knowledge, the model is able to produce reasonable story endings. Automatic and manual evaluation shows that our model can generate more reasonable story endings than state-of-the-art baselines. 1
Are Psychology University Student Gamblers Representative of Non-university Students and General Gamblers? A Comparative Analysis

Students recruited from psychology undergraduate university populations are commonly used in psychology research, including gambling studies. However, the extent to which the use of this subpopulation produces findings that can be extrapolated to other groups is questionable. The present study was designed to compare results from university-recruited psychology student gamblers to those obtained from a sample of gamblers recruited from the general population that also included students. An online survey measuring gambling behavior and Internet gambling, attitudes and knowledge about gambling and problem gambling severity was posted on websites accessed by gamblers. Participants were recruited from two sources, a psychology undergraduate university population (n = 461) and online websites (n = 4,801). Results showed university-recruited students differed significantly from both adults and students recruited from the general population in respect to demographic variables and gambling behavior. Psychology undergraduate students were younger, more likely to be female, and had lower incomes. When relevant demographic variables were controlled, psychology undergraduate students were found to gamble less frequently, at different times, and to be at lower-risk for gambling-related problems, but had more irrational beliefs and more negative attitudes towards gambling than gamblers recruited from the general population. Results suggest that caution should be used in extrapolating findings from research using university-recruited psychology student gamblers to wide community populations due to differences related to gambling thoughts, attitudes and behaviors.
Intensified chemotherapy and dose-reduced involved-field radiotherapy in patients with early unfavorable Hodgkin's lymphoma: final analysis of the German Hodgkin Study Group HD11 trial.

PURPOSE
Combined-modality treatment consisting of four to six cycles of chemotherapy followed by involved-field radiotherapy (IFRT) is the standard of care for patients with early unfavorable Hodgkin's lymphoma (HL). It is unclear whether treatment results can be improved with more intensive chemotherapy and which radiation dose needs to be applied.


PATIENTS AND METHODS
Patients age 16 to 75 years with newly diagnosed early unfavorable HL were randomly assigned in a 2 × 2 factorial design to one of the following treatment arms: four cycles of doxorubicin, bleomycin, vinblastine, and dacarbazine (ABVD) + 30 Gy of IFRT; four cycles of ABVD + 20 Gy of IFRT; four cycles of bleomycin, etoposide, doxorubicin, cyclophosphamide, vincristine, procarbazine, and prednisone (BEACOPP(baseline)) + 30 Gy of IFRT; or four cycles of BEACOPP(baseline) + 20 Gy of IFRT.


RESULTS
With a total of 1,395 patients included, the freedom from treatment failure (FFTF) at 5 years was 85.0%, overall survival was 94.5%, and progression-free survival was 86.0%. BEACOPP(baseline) was more effective than ABVD when followed by 20 Gy of IFRT (5-year FFTF difference, 5.7%; 95% CI, 0.1% to 11.3%). However, there was no difference between BEACOPP(baseline) and ABVD when followed by 30 Gy of IFRT (5-year FFTF difference, 1.6%; 95% CI, -3.6% to 6.9%). Similar results were observed for the radiotherapy question; after four cycles of BEACOPP(baseline), 20 Gy was not inferior to 30 Gy (5-year FFTF difference, -0.8%; 95% CI, -5.8% to 4.2%), whereas inferiority of 20 Gy cannot be excluded after four cycles of ABVD (5-year FFTF difference, -4.7%; 95% CI, -10.3% to 0.8%). Treatment-related toxicity occurred more often in the arms with more intensive therapy.


CONCLUSION
Moderate dose escalation using BEACOPP(baseline) did not significantly improve outcome in early unfavorable HL. Four cycles of ABVD should be followed by 30 Gy of IFRT.
Minimizing nfa's and regular expressions

We show inapproximability results concerning minimization of nondeterministic finite automata (nfa’s) as well as regular expressions relative to given nfa’s, regular expressions or deterministic finite automata (dfa’s). We show that it is impossible to efficiently minimize a given nfa or regular expression with n states, transitions, resp. symbols within the factor o(n), unless P = PSPACE. Our inapproximability results for a given dfa with n states are based on cryptographic assumptions and we show that any efficient algorithm will have an approximation factor of at least n poly(log n) . Our setup also allows us to analyze the minimum consistent dfa problem. Classification: Automata and Formal Languages, Computational Complexity, Approximability
Robust Convolutional Neural Networks for Image Recognition

Recently image recognition becomes vital task using several methods. One of the most interesting used methods is using Convolutional Neural Network (CNN). It is widely used for this purpose. However, since there are some tasks that have small features that are considered an essential part of a task, then classification using CNN is not efficient because most of those features diminish before reaching the final stage of classification. In this work, analyzing and exploring essential parameters that can influence model performance. Furthermore different elegant prior contemporary models are recruited to introduce new leveraging model. Finally, a new CNN architecture is proposed which achieves state-of-the-art classification results on the different challenge benchmarks. The experimented are conducted on MNIST, CIFAR-10, and CIFAR-100 datasets. Experimental results showed that the results outperform and achieve superior results comparing to the most contemporary approaches. Keywords—Convolutional Neural Network; Image recognition; Multiscale input images
Forward and Inverse Kinematics Models for a 5-dof Pioneer 2 Robot Arm

Manipulator control is one of the main research areas in robotics, requiring in the first instance the manipulator model. Using the Denavit-Hartenberg methodology this paper develops both forward and inverse kinematics models for a 5-dof Pioneer 2 robot arm. This paper also includes background information about robot arms, especially the Pioneer 2 robot arm, and discusses some implementation issues.
Vaginal delivery and pelvic floor dysfunction: current evidence and implications for future research

Vaginal delivery is the major risk factor for the development of pelvic organ prolapse and urinary and fecal incontinence, resulting from damage to the pelvic floor muscles, nerves and connective tissue. This article reviews the perineal trauma mechanism during vaginal delivery and discusses implications of current and future research projects.
Speech emotion recognition using convolutional and Recurrent Neural Networks

With rapid developments in the design of deep architecture models and learning algorithms, methods referred to as deep learning have come to be widely used in a variety of research areas such as pattern recognition, classification, and signal processing. Deep learning methods are being applied in various recognition tasks such as image, speech, and music recognition. Convolutional Neural Networks (CNNs) especially show remarkable recognition performance for computer vision tasks. In addition, Recurrent Neural Networks (RNNs) show considerable success in many sequential data processing tasks. In this study, we investigate the result of the Speech Emotion Recognition (SER) algorithm based on CNNs and RNNs trained using an emotional speech database. The main goal of our work is to propose a SER method based on concatenated CNNs and RNNs without using any traditional hand-crafted features. By applying the proposed methods to an emotional speech database, the classification result was verified to have better accuracy than that achieved using conventional classification methods.
Extrasynaptic exocytosis and its mechanisms: a source of molecules mediating volume transmission in the nervous system

We review the evidence of exocytosis from extrasynaptic sites in the soma, dendrites, and axonal varicosities of central and peripheral neurons of vertebrates and invertebrates, with emphasis on somatic exocytosis, and how it contributes to signaling in the nervous system. The finding of secretory vesicles in extrasynaptic sites of neurons, the presence of signaling molecules (namely transmitters or peptides) in the extracellular space outside synaptic clefts, and the mismatch between exocytosis sites and the location of receptors for these molecules in neurons and glial cells, have long suggested that in addition to synaptic communication, transmitters are released, and act extrasynaptically. The catalog of these molecules includes low molecular weight transmitters such as monoamines, acetylcholine, glutamate, gama-aminobutiric acid (GABA), adenosine-5-triphosphate (ATP), and a list of peptides including substance P, brain-derived neurotrophic factor (BDNF), and oxytocin. By comparing the mechanisms of extrasynaptic exocytosis of different signaling molecules by various neuron types we show that it is a widespread mechanism for communication in the nervous system that uses certain common mechanisms, which are different from those of synaptic exocytosis but similar to those of exocytosis from excitable endocrine cells. Somatic exocytosis has been measured directly in different neuron types. It starts after high-frequency electrical activity or long experimental depolarizations and may continue for several minutes after the end of stimulation. Activation of L-type calcium channels, calcium release from intracellular stores and vesicle transport towards the plasma membrane couple excitation and exocytosis from small clear or large dense core vesicles in release sites lacking postsynaptic counterparts. The presence of synaptic and extrasynaptic exocytosis endows individual neurons with a wide variety of time- and space-dependent communication possibilities. Extrasynaptic exocytosis may be the major source of signaling molecules producing volume transmission and by doing so may be part of a long duration signaling mode in the nervous system.
Reading Habits Among Students and its Effect on Academic Performance : A Study of Students of Koforidua Polytechnic

The study sought to assess the reading habits among students and their effects on their academic performance. The study was conducted in Koforidua Polytechnic situated in the Eastern Region of Ghana. A questionnaire was used for the data collection. The data collected were analyzed quantitatively with the use of Statistical Package for Social Science (SPSS). Figures and tables were used to present the results of findings. From the total of 1052 copies of questionnaire distributed, 1000 copies, representing 95.0% were filled and returned. The findings showed that majority of the respondents acknowledge the importance of reading, 81.9% of the respondents neither read novel nor fiction within the last two semesters while 62.0% of the respondents only read for the purpose of passing examination. The study confirmed that reading habit has influence on academic performance and there is a relationship between reading habit and academic performance. The study recommended among others that lecturers should be advised to stop providing handouts to students but rather encourage them to use the library for research and also, the current system of assessing students should be given a second thought with respect to the formulae of assessment.
Users — The Hidden Software Product Quality Experts?: A Study on How App Users Report Quality Aspects in Online Reviews

[Context and motivation] Research on eliciting requirements from a large number of online reviews using automated means has focused on functional aspects. Assuring the quality of an app is vital for its success. This is why user feedback concerning quality issues should be considered as well [Question/problem] But to what extent do online reviews of apps address quality characteristics? And how much potential is there to extract such knowledge through automation? [Principal ideas/results] By tagging online reviews, we found that users mainly write about "usability" and "reliability", but the majority of statements are on a subcharacteristic level, most notably regarding "operability", "adaptability", "fault tolerance", and "interoperability". A set of 16 language patterns regarding "usability" correctly identified 1,528 statements from a large dataset far more efficiently than our manual analysis of a small subset. [Contribution] We found that statements can especially be derived from online reviews about qualities by which users are directly affected, although with some ambiguity. Language patterns can identify statements about qualities with high precision, though the recall is modest at this time. Nevertheless, our results have shown that online reviews are an unused Big Data source for quality requirements.
E-learning Motivation and Educational Portal Acceptance in Developing Countries

Purpose – The purpose of this paper is to: empirically validate a modified unified theory of acceptance and use of technology (UTAUT) model by adding an “e-learning motivation” construct in the South American context; try to determine the role of e-learning motivation in the use and adoption of e-learning systems and conversely the effect of technology on students’ e-learning motivation; and to test region and gender as moderators in the model. Design/methodology/approach – A survey method was used to collect data from 47 schools located at different regions: the coast, Andes, and jungle of Peru. The partial least square technique was used for data analysis. Findings – It was found that “e-learning motivation” and “social influence” had a positive influence on behavioural intention, while “facilitating condition” had no effect on e-learning portal use. Furthermore, use behaviour had a positive influence on e-learning motivation. Also found was the moderating role of “region”. Research limitations/implications – The analysis is carried out in a single country, thus, caution should be taken in generalisation of the results. Practical implications – The findings will help policy makers and practitioners in developing countries to better understand students’ e-learning motivation. Originality/value – By adopting the UTAUT model, a new construct of “e-learning motivation” is added, and applied to the South American context.
Model Predictive Control of Vehicles on Urban Roads for Improved Fuel Economy

Energy consumption of a vehicle is greatly influenced by its driving behavior in highly interacting urban traffic. Strategies for fuel efficient driving have been studied and experimented with in various conceptual frameworks. This paper presents a novel control system to drive a vehicle efficiently on roads containing varying traffic and signals at intersections for improved fuel economy. The system measures the relevant information of the current road and traffic, predicts the future states of the preceding vehicle, and computes the optimal vehicle control input using model predictive control (MPC). A typical control objective is chosen to maximize fuel economy by regulating a safe head-distance or cruising at the optimal velocity under bounded driving torque condition. The proposed vehicle control system is evaluated in urban traffic containing thousands of diverse vehicles using the microscopic traffic simulator AIMSUN. Simulation results show that the vehicles controlled by the proposed MPC method significantly improve their fuel economy.
Online consumer retention: contingent effects of online shopping habit and online shopping experience

Received: 28 January 2005 Revised: 18 August 2005 2nd Revision: 12 June 2006 3rd Revision: 6 January 2007 4th Revision: 14 June 2007 Accepted: 20 September 2007 Abstract In this study, we further develop the information systems continuance model in the context of online shopping, using a contingency theory that accounts for the roles of online shopping habit and online shopping experience. Specifically, we argue and empirically demonstrate that although conceptually distinct, online shopping habit and online shopping experience have similar effects on repurchase intention. They both have positive mediated effects through satisfaction and moderate the relationship between satisfaction and online repurchase intention. The results of a survey study involving 122 online customers provide strong support for our research model. We also identify after-sale service, transaction efficiency, security, convenience, and cost savings as important online shopping usefulness drivers. Theoretical and practical implications include establishing a contingency theory to more fully explain online customer retention as well as guidelines for development of customer relationship management initiatives. European Journal of Information Systems (2007) 16, 780–792. doi:10.1057/palgrave.ejis.3000711