Miracle-dz/UniFAR-FTUs huggingface dataset:
Motor imagery during action observation in virtual reality: the impact of watching myself performing at a level I have not yet achieved

ABSTRACT Feedforward modeling, the creation of one’s own behaviour that is potentially achievable in the future, can support motor performance and learning. While this has been shown for sequences of motor actions, it remains to be tested whether feedforward modelling is beneficial for single complex motor actions. Using an immersive, state-of-the-art, low-latency Cave Automatic Virtual Environment (CAVE), we compared motor imagery during action observation (AOMI) of oneself performing at one’s current skill level against AOMI of oneself performing at an achievable future skill level. We performed 3D scans and created a ready-to-animate virtual human of each participant. During acquisition, participants observed an avatar of themselves performing either one of their own previously executed squats (Me-Novice) or observed an avatar of themselves performing a skilled squat (Me-Skilled), whilst simultaneously imagining the feelings and sensations associated with movement execution. Findings revealed an advantage for the Me-Skilled group as compared to the Me-Novice group in motor performance and cognitive representation structure, while self-efficacy improved in both groups. In comparison to watching and imagining oneself performing at the current novice skill level, watching and imagining oneself performing at a more advanced skill level prevented from making errors in motor performance and led to perceptual-cognitive scaffolding as shown by functional changes in underlying representations. Simultaneous imagery whilst observing future states of action may therefore help to establish cognitive prerequisites that enable better motor performance. To this end, virtual reality is a promising tool to create learning environments that exceed an individual’s current performance level.
Improve the Deep Learning Models in Forestry Based on Explanations and Expertise

In forestry studies, deep learning models have achieved excellent performance in many application scenarios (e.g., detecting forest damage). However, the unclear model decisions (i.e., black-box) undermine the credibility of the results and hinder their practicality. This study intends to obtain explanations of such models through the use of explainable artificial intelligence methods, and then use feature unlearning methods to improve their performance, which is the first such attempt in the field of forestry. Results of three experiments show that the model training can be guided by expertise to gain specific knowledge, which is reflected by explanations. For all three experiments based on synthetic and real leaf images, the improvement of models is quantified in the classification accuracy (up to 4.6%) and three indicators of explanation assessment (i.e., root-mean-square error, cosine similarity, and the proportion of important pixels). Besides, the introduced expertise in annotation matrix form was automatically created in all experiments. This study emphasizes that studies of deep learning in forestry should not only pursue model performance (e.g., higher classification accuracy) but also focus on the explanations and try to improve models according to the expertise.
Removing Boilerplate and Duplicate Content from Web Corpora

V poslednich letech se Web stal oblibeným zdrojem textových dat pro lingvistický výzkum. Web poskytuje obrovske množstvi textů v řadě jazyků. Nicmeně, aby bylo možne z Webu vytvařet kolekce (textove korpusy) vhodne pro aplikaci v oblasti zpracovani přirozeneho jazyka, je třeba vyřesit řadu problemů. Tato prace řesi dva z nich: odstraňovani sablon a odstraňovani duplicitnich a podobných (teměř duplicitnich) dat. Na větsině webových stranek je hlavni obsah zasazen do sablony, ktera může obsahovat např. navigacni linky, reklamy, hlavicky a paticky apod. Pokud bychom texty z sablon zahrnuli do korpusu, výsledkem může být nadměrný výskyt slov typu domů, hledej, tisk apod. Tato prace poskytuje přehled běžně použivaných technik pro odstraňovani sabon a rovněž nabizi nový heuristický přistup k tomuto problemu. Prace srovnava navržnou metodu s aktualnimi algoritmy na dostupných datových kolekcich. Mnoho textů se na Webu vyskytuje vicenasobně (např. zrcadlene stranky, revize dokumentů, citace v diskuznich forech apod.). Duplicitni data narusuji statisticke udaje odvozene z korpusů a způsobuji potiže uživatelům, kteři ve webových korpusech hledaji lingvisticke informace. Problem identifikace duplicitnich a blizkých webových stranek byl již podrobně studovan v kontextu webových vyhledavaců. Nicmeně problem je zde typicky omezen pouze na identifikaci teměř totožných dokumentů. Dvojice webových stranek, ktere obsahuji v nezanedbatelnem množstvi jak identicke casti, tak i casti rozdilne, nejsou webovými vyhledavaci považovany za blizke. V oblasti textových korpusů jsou ovsem zdrojem problemů jakekoliv duplicity. Vzhledem k tomu, že identifikaci podobnosti středni urovně ve velkých textových kolekcich zatim ve vědeckých kruzich nebylo věnovano přilis pozornosti, jednim z cilů teto prace je zaplnit tuto mezeru.
Assessment of Oral Manifestations and Oral Health in Hospitalized Patients with COVID-19: Machine Learning and Statistical Analysis

Background: This study aimed to investigate the oral health presentations of coronavirus disease 2019 (COVID-19) inpatients using statistical analysis and machine learning methods before infection, during hospitalization, and after discharge from the hospital. Methods: This cross-sectional study was conducted on 140 hospitalized COVID-19 patients with reverse transcription-polymerase chain reaction diagnosis and severe symptoms. Demographic data, clinical characteristics, oral health habits, and oral manifestations in three periods (i.e., before infection, during hospitalization, and after discharge from the hospital) were recorded through a questionnaire and oral examination. Statistical analysis and machine learning methods were used for the analysis of patients’ data. Results: Xerostomia, dysgeusia, hypogeusia, halitosis, and a metallic taste were the most frequent oral symptoms during hospitalization, with the incidence of 68.6%, 51.4%, 49.3%, 31.4%, and 29.3% in patients, respectively. Using tobacco significantly increased the incidence of xerostomia, dysgeusia, hypogeusia, halitosis, and a metallic taste during hospitalization (P = 0.011, P = 0.001, P = 0.002, P = 0.0001, and P = 0.0001, respectively). Smoking led to increasing dysgeusia, hypogeusia, halitosis, and a metallic taste during hospitalization (P = 0.019, P = 0.014, P = 0.013, and P = 0.006, respectively). The micro-average receiver operating characteristic (ROC) curve analysis revealed that the machine learning logistic regression model achieved the highest area under the ROC curve with a value of 0.83. Conclusions: Xerostomia and dysgeusia are the most common oral symptoms of COVID-19 patients and could be used to predict COVID-19 infection. Dysgeusia correlates with xerostomia, and it is hypothesized that xerostomia is an etiologic factor for dysgeusia. The early detection of COVID-19 can help reduce the enormous burden on healthcare systems, and machine learning is advantageous for this purpose.
Natural Language Processing for Requirements Engineering: A Systematic Mapping Study

Context: NLP4RE (Natural Language Processing for Requirements Engineering) is an area of research and development that seeks to apply natural language processing (NLP) techniques, tools and resources to the requirements engineering (RE) process, to support human analysts to carry out various linguistic analysis tasks on textual requirements documents, such as detecting language issues, identifying key domain concepts and establishing requirements traceability links. Objective: This article reports on a mapping study that surveys the landscape of NLP4RE research to provide a holistic understanding of the field. Method : Following the guidance of systematic review, the mapping study is directed by five research questions, cutting across five aspects of NLP4RE research, concerning the state of the literature, the state of empirical research, the research focus, the state of tool development, and the usage of NLP technologies. Results : (i) The mapping study identified a total of 404 primary studies relevant to NLP4RE, which were published over the past 36 years and from 170 different venues; (ii) most of these studies (67.08%) are solution proposals, assessed by a laboratory experiment or an example application, while only a small percentage (7%) are assested in industrial settings; (iii) a large proportion of the studies (42.70%) focus on the requirements analysis phase, with quality defect detection as their central task and requirements specification as their commonly processed document type; (iv) 130 NLP4RE tools (i.e., RE specific NLP tools) are extracted from these studies, but only 15 of them (11.54%) available for download; (v) 231 different NLP technologies are also identified, comprising 140 NLP techniques, 66 NLP tools and 25 NLP resources, but most of them – particularly those novel NLP techniques and specialized tools – are used infrequently; by contrast, commonly used NLP technologies are traditional analysis techniques (e.g., POS tagging and tokenization), general-purpose tools (e.g., Stanford CoreNLP and GATE) and generic language lexicons (WordNet and British National Corpus). Conclusion : The mapping study not only provides a collection of the literature in NLP4RE, but also, more importantly, establishes a structure to frame the existing literature: through categorization, synthesis and conceptualization of the main theoretical concepts and relationships that encompass both RE and NLP aspects. Our work thus produces a conceptual framework of NLP4RE. The framework is used to identify research gaps and directions, to highlight technology transfer needs, and to encourage more synergies between the RE community, the NLP one, and the software and systems practitioners. Our results can be used as a starting point to frame future studies according to a well-defined terminology and can be expanded as new technologies and novel solutions emerge.
Introduction

There are two distinct approaches to improving the quality of protein NMR structures during refinement: all-atom force fields and accumulated knowledge-assisted methods that include Rosetta. Mao et al. reported that, for 40 proteins, Rosetta increased the accuracies of their NMR-determined structures with respect to the X-ray crystal structures (Mao et al., J. Am. Chem. Soc. 136, 1893 (2014)). In this study, we calculated 32 structures of those studied by Mao et al. using all-atom force field and implicit solvent model, and we compared the results with those obtained from Rosetta. For a single protein, using only the experimental NOE-derived distances and backbone torsion angle restraints, 20 of the lowest energy structures were extracted as an ensemble from 100 generated structures. Restrained simulated annealing by molecular dynamics simulation searched conformational spaces with a total time step of 1-ns. The use of GPU-accelerated AMBER code allowed the calculations to be completed in hours using a single GPU computer—even for proteins larger than 20 kDa. Remarkably, statistical analyses indicated that the structures determined in this way showed overall higher accuracies to their X-ray structures compared to those refined by Rosetta (p-value < 0.01). Our data demonstrate the capability of sophisticated atomistic force fields in refining NMR structures, particularly when they are coupled with the latest GPU-based calculations. The straightforwardness of the protocol allows its use to be extended to all NMR structures.
EnergyHair: Sketch-Based Interactive Guide Hair Design Using Physics-Inspired Energy

This study introduces a new sketch-based hairstyle authoring in- terface for virtual characters captured using multi-view stereo. Individual hairs are typically modeled by interpolating guide hairs, which are downsampled hair strands representing the overall hair shapes. Unlike conventional geometric modeling, we incorporate the physical properties of hairs, such as gravity, collision, and bending resistance, in guide hair modeling. The use of physics-related shape optimization in the interface allows natural-looking 3D hair shapes to be modeled from minimal user specifications. We provide an in- teractive sketch tool that allows the user to specify hair shapes from multiple viewing angles. Our interface generates feedback regard- ing how much a designed sketch conforms to the laws of physics, enabling the user to strike a balance between artistic intention and physical naturalness. We further introduce an automatic sketch suggestion system based on the hair orientation obtained using image filtering.
Planar -Band 4 4 Nolen Matrix in SIW Technology

In this paper, a 4 4 Nolen matrix beam-forming network for multibeam antenna applications is designed and demonstrated at 12.5-GHz center frequency. The structure is implemented using substrate integrated waveguide (SIW) technology for its attractive advantages including compact size, low loss, light weight, and planar form well suitable for high-density integration with other microwave and millimeter-wave planar integrated circuits. SIW cruciform couplers are used as fundamental building blocks for their wide range of coupling factors and their specific topology well adapted to the serial feeding topology of a Nolen matrix. The network performances are investigated over a 500-MHz frequency bandwidth ranging from 12.25 to 12.75 GHz. The matrix definition based on SIW cruciform couplers is similar to its microstrip counterpart in terms of coupling factors and phase delays. The whole network is fabricated. Measured results are in good agreement with the theoretical predictions, thus validating the proposed design concept. Using this matrix with a four radiating elements array antenna enables us to investigate the impact of the proposed matrix on the beam pointing angles versus frequency.
The African Journal of Information Systems The African Journal of Information Systems

Agricultural information can enhance smallholder farmers’ knowledge and decision-making ability, where the ubiquity of ICTs has resulted in an exponential increase of ICT based agricultural information services. However, there is still poor utilization of agricultural information. This study examines challenges that impede smallholder farmers from deriving value from agricultural information despite availability of information services. A mixed-method research approach was employed to gather data from 200 farmers using semi-structured interviews, Likert scale surveys and five focus group discussions. The findings indicate that the challenges faced include low education levels, poor quality information, poorly designed information channels, and high cost of information access. Suggested strategies include training, improving design of information channels, stakeholder collaboration, translating information into local languages and interventions in accelerating use of ICTs. This study will be significant in implementation of impactful information dissemination initiatives. Subsequent research should be focused on investigating the effectiveness of the suggested strategies.
Infinite mixture chaining: Efficient temporal construction of word meaning

Word meanings extend over time due to a functional need for maintaining communicative expressivity within a compact lexicon. Previous scholars have suggested that word meanings extend via a process of chaining, whereby novel items link to existing ones close in semantic space. Recent work has for- malized this idea using computational models grounded typically in the exemplar and prototype theories of categorization that are either memory-intensive or simplistic in representation. We propose an alternative account of chaining that op- timizes cognitive efficiency by trading off representational accuracy with memory complexity. We operationalize this effi- cient chaining as an infinite mixture model and show how it constructs the internal representations of word meaning adap- tively through time while predicting the historical development of English verb meanings with precision and limited resources. ,
Beyond Pragmatism: Internal and External Impacts of Hiring Professors of Teaching at Research-Intensive Universities

Teaching focused positions have grown in popularity in higher education and provide novel opportunities to transform undergraduate science, technology, engineering, and mathematics (STEM) education. The University of California (UC) system employs a unique teaching-focused faculty position, the Lecturer with Potential Security of Employment (L(P)SOE), who we refer to as Professors of Teaching. The Professor of Teaching position is a tenure-track position with primary expectations in teaching with additional scholarly activity and service responsibilities. We present findings from interviews with administrative faculty in STEM departments at three UC campuses to identify reasons for hiring Professors of Teaching, their impacts, and potential barriers to success. Through Durkheim’s division of labour and Wenger’s community of practice frameworks, we explore the role of Professors of Teaching within STEM departments. Overall, this study highlights the value of teaching-focused faculty in research-intensive universities and argues for departments to reconsider the value of education for teaching-focused faculty to maximize their potential as agents of change.
Data-Driven Optimization for Protein Design: Workflows, Algorithms and Metrics

Recent works have successfully demonstrated the ability of deep neural networks in predicting important properties such as fitness and stability from protein sequences via supervised learning. However, the use of learned deep neural network models for the task of designing de novo proteins that maximize a certain fitness value with backbones from scratch remains under-explored. In this paper, we study the problem of designing proteins where the optimization be carried out in a purely data-driven, “offline” manner, by utilizing databases of experimental data collected from wet lab evaluations. Synthesis of proteins proposed by the algorithm in an experimental setup in a wet lab, which incurs a big manual overhead for designers, is not allowed. Such an offline optimization problem require that a practitioner make several several design choices: a designer must decide what data distribution to train on, how their method would be evaluated, and must additionally devise workflows for tuning the optimization method they wish to use. In this paper, we perform a systematic study of various design choices that arise in in protein design, grounded in the problem of optimizing for protein stability, and use these insights to propose workflows, protocols and metrics to assist practitioners in effectively applying such data-driven approaches to protein design problems.
National Evaluation of Safe Start Promising Approaches Results Appendix H: Miami, Florida

The Miami Safe Start Program developed two distinct interventions within domestic violence shelters, transitional housing and homeless shelters, and Juvenile Dependency Court service providers to improve outcomes for children (ages 6 months– 12 years) exposed to violence. The interventions included the Infant Mental Health (IMH) model, a clinical assessment and dyadic treatment for infants to children age 5, and group therapy called Heroes for children ages 5 through 12. A full description of the interventions can be found in National Evaluation of Safe Start Promising Approaches: Assessing Program Implementation (Schultz et al., 2010). The two interventions were evaluated separately. The evaluations of these programs consisted of quasi-experimental studies in domestic violence and homeless shelters, with two intervention shelters and one comparison shelter. This design was augmented for the dyadic therapy component for court-referred children, with a randomized controlled study with a six-month wait-list control group and randomization occurring at the family level. For the IMH study, Miami enrolled 90 families, but only 44 percent were retained after six months. Participants in the study were largely black or other minorities and impoverished, with caregivers reporting an average of 1.8 types of violence exposure at baseline. Participants were drawn from two settings: domestic violence or homeless shelters and dependency court. According to caregiver reports, about 10 percent of children scored in the clinical range for child posttraumatic stress disorder (PTSD) symptoms, and about 20 percent of caregivers reported parenting stress in the clinical range. All of the families who were retained at six months received Child-Parent Psychotherapy (CPP), and the vast majority received case management services (89 percent), with an average of about 11 and nine sessions or meetings, respectively. Given the number of participants in this study, we had a 49-percent percent chance to detect a medium effect size of 0.63 at six months. Because of low sample sizes and the wait-list control group design for court-referred families, only six-month outcomes could be examined. Intent-to-treat analyses showed no differences between the intervention and comparison group at six months, although some significant changes were detected within the intervention group over time (child’s violence exposure, caregiver’s violence exposure, and caregiver’s personal problems), as
Nuclear dynamics of singlet exciton ﬁssion: A direct observation in pentacene single crystals

Singlet exciton ﬁssion (SEF) is a key process in the development of eﬃcient opto-electronic de-vices. An aspect that is rarely probed directly, and yet has a tremendous impact on SEF properties, is the nuclear structure and dynamics involved in this process. Here we directly observe the nuclear dynamics accompanying the SEF process in single crystal pentacene using femtosecond electron diﬀraction. The data reveal coherent atomic motions at 1 THz, incoherent motions, and an anisotropic lattice distortion representing the polaronic character of the triplet excitons. Combining molecular dynamics simulations, time-dependent density functional theory and experimental structure factor analysis, the coherent motions are identiﬁed as collective sliding motions of the pentacene molecules along their long axis. Such motions modify the excitonic coupling between adjacent molecules. Our ﬁndings reveal that long-range motions play a decisive part in the disintegration of the electronically correlated triplet pairs, and shed light on why SEF occurs on ultrafast timescales. π ∆ E Furthermore, recent ultrafast spectroscopy studies in combination with theory indicate that SEF is intrinsically linked to nuclear These call for an experimental probe that can access structural changes at the femtosecond In this work we directly reveal the structural dynamics accompanying the SEF process in single crystal pentacene using the method of femtosecond electron diﬀraction (FED), supported by real-time time-dependent density-functional theory (RT-TDDFT) and molecular dynamics simulations (MDS). Pentacene is the most widely studied SEF material
RELATIVE NAVIGATION FOR THE ORION VEHICLE

The Orion vehicle is being designed to provide manned spaceﬂight capability af- ter the retirement of the Space Shuttle in 2010. Orion will provide access to both the International Space Station and the Moon. In both cases, the vehicle is required to perform rendezvous and therefore requires a relative navigation ﬁlter. This pa-per documents the preliminary analysis performed by the Orion Navigation team, and reviews the current Orion relative navigation architecture as well as the sensors available for rendezvous and proximity operations.