Miracle-dz/UniFAR-FTUs huggingface dataset:
Eccentric Exercise: Adaptations and Applications for Health and Performance

The goals of this narrative review are to provide a brief overview of the muscle and tendon adaptations to eccentric resistance exercise and address the applications of this form of training to aid rehabilitative interventions and enhance sports performance. This work is centered on the author contributions to the Special Issue entitled “Eccentric Exercise: Adaptations and Applications for Health and Performance”. The major themes from the contributing authors include the need to place greater attention on eccentric exercise mode selection based on training goals and individual fitness level, optimal approaches to implementing eccentric resistance exercise for therapeutic purposes, factors that affect the use of eccentric exercise across the lifespan, and general recommendations to integrate eccentric exercise in athletic training regimens. The authors propose that movement velocity and the absorption or recovery of kinetic energy are critical components of eccentric exercise programming. Regarding the therapeutic use of eccentric resistance training, patient-level factors regarding condition severity, fitness level, and stage of rehabilitation should govern the plan of care. In athletic populations, use of eccentric exercise may improve movement competency and promote improved safety and performance of sport-specific tasks. Eccentric resistance training is a viable option for youth, young adults, and older adults when the exercise prescription appropriately addresses program goals, exercise tolerability, and compliance. Despite the benefits of eccentric exercise, several key questions remain unanswered regarding its application underscoring the need for further investigation.
Modeling the influence of motor skills on literacy in third grade: Contributions of executive functions and handwriting

Several nonlanguage factors influence literacy development, and motor skills are among those most studied. Despite the publication of several studies that have supported the existence of this relationship, the type of influence and underlying mechanisms have been little explored. Herein, we propose modeling the relationship between motor skills and literacy through structural equation modeling, testing the contribution of executive functions and handwriting skills as the possible mediators of this relationship. In a study of 278 third-grade children, we used a wide range of measures related to written language (reading, spelling, reading comprehension, and written production), fine motor skills (dominant hand, nondominant hand, and bimanual dexterity), executive functions (verbal and visuospatial working memory, inhibition, and shifting), and handwriting. Structural equation modeling of the relationship between these different variables indicated that in the third grade, the influence of fine motor skills on literacy is fully mediated by both executive functions and handwriting skills. These motor skills effects are observed for both low levels of processing (reading, spelling) and high levels of processing (reading comprehension, written production). The results are discussed in terms of the potential mechanisms underlying different literacy skills and their implications for pedagogical programs.
Comparable performance on a spatial memory task in data collected in the lab and online

Online data collection offers a wide range of benefits including access to larger and more diverse populations, together with a reduction in the experiment cycle. Here we compare performance in a spatial memory task, in which participants had to estimate object locations following viewpoint shifts, using data from a controlled lab-based setting and from an unsupervised online sample. We found that the data collected in a conventional laboratory setting and those collected online produced very similar results, although the online data was more variable with standard errors being about 10% larger than those of the data collected in the lab. Overall, our findings suggest that spatial memory studies using static images can be successfully carried out online with unsupervised samples. However, given the higher variability of the online data, it is recommended that the online sample size is increased to achieve similar standard errors to those obtained in the lab. For the current study and data processing procedures, this would require an online sample 25% larger than the lab sample.
Step In, Step Out from the First Lockdown: An Exploration of COVID-19 Perceptions in France and Quebec

Objective. The objective of this research was to describe and analyze the role of psychological and behavioral factors on perceptions of COVID-19 in France and Quebec at three different times during the pandemic. Design. We conducted three qualitative and quantitative studies (Study 1 N = 255, Study 2 N = 230, Study 3 N = 143). Participants were asked to evaluate psychological and behavioral measures: at the beginning of lockdown (Study 1), during lockdown (Study 2), and during lockdown exit (Study 3). Results. Results of Study 1 show that perceptions of COVID-19 are organized around fear and a sense of threat. During the lockdown, participants mentioned for the first time the health practices to prevent the spread of COVID-19 (Study 2). Psychological and social impacts constitute a central theme in participants’ discourse (Study 2 and 3). Conclusions. The results show that perceptions of risk during a pandemic are socially constructed. Perceptions seem to be influenced by the political and health management of a territory and by the evolution of behavioral and psychological responses.
Preferences of older people for environmental attributes of local parks: The use of choice-based conjoint analysis

Purpose – This study aims to examine the environmental attributes relevant to older people's preferences for neighbourhood open spaces. It also aims to explore the association between the relative importance of different environmental attributes and personal and social characteristics in a sample of older people covering a range of geographic locations in the UK.Design/methodology/approach – Choice‐based conjoint analysis (CBC) was used to obtain responses from a sample of 237 older people (60 years +) living in the UK. A total of 13 environmental attributes were identified from earlier qualitative and quantitative studies. Participants were asked to choose a preferred park from a pair of hypothetical neighbourhood parks differing on four of the attributes presented in a questionnaire. The questionnaire included 14 such tasks.Findings – The results suggest that older people preferred a neighbourhood park which is without nuisance, has cafes and toilets, many trees and plants, light traffic en route, wildl.
Preliminary Findings on Visual Event-Related Potential P3 in Asymptomatic Patients with Cerebral Small Vessel Disease

Background Cerebral small vessel disease is the primary cause of cognitive impairment. Therefore, early recognition is of great significance. Some studies have shown that asymptomatic cerebral small vessel disease (aCSVD) patients have abnormal neurocognitive function, but this is not readily apparent at the initial stage. The objective of this paper was to assess visual spatial attention by event-related potential (ERP) examination and to analyze the relationship between ERP data and clinical characteristics in patients with aCSVD. Methods We selected 25 aCSVD patients and enrolled 23 age-matched normal subjects as the control group. We measured the latency and amplitude of original/corresponding differential ERP components using the modified visual oddball paradigm, which included a standard stimulus, target stimulus, and new stimulus. Additionally, we selected aberrant ERP components to study the correlations between the ERP data and clinical characteristics of the patients with aCSVD. Results We found not only lower amplitude but also significantly longer P3 latency in the aCSVD patients. The above results were further verified by analyzing the different components (target minus standard and novel minus standard) of P3. Furthermore, abnormal ERPs in the aCSVD patients were closely related to the changes observed with imaging. Conclusion It was demonstrated that the speed and capability of processing visual spatial information was impaired in aCSVD patients compared with healthy controls. Thus, ERP examination could detect the presence of attentional deficits and might become a rapid and sensitive method for the early diagnosis of aCSVD. However, its availability needs further investigation.
Encoder-decoder models for chest X-ray report generation perform no better than unconditioned baselines

High quality radiology reporting of chest X-ray images is of core importance for high-quality patient diagnosis and care. Automatically generated reports can assist radiologists by reducing their workload and even may prevent errors. Machine Learning (ML) models for this task take an X-ray image as input and output a sequence of words. In this work, we show that ML models for this task based on the popular encoder-decoder approach, like ‘Show, Attend and Tell’ (SA&T) have similar or worse performance than models that do not use the input image, called unconditioned baseline. An unconditioned model achieved diagnostic accuracy of 0.91 on the IU chest X-ray dataset, and significantly outperformed SA&T (0.877) and other popular ML models (p-value < 0.001). This unconditioned model also outperformed SA&T and similar ML methods on the BLEU-4 and METEOR metrics. Also, an unconditioned version of SA&T obtained by permuting the reports generated from images of the test set, achieved diagnostic accuracy of 0.862, comparable to that of SA&T (p-value ≥ 0.05).
Ringed seal (Pusa hispida) breeding habitat on the landfast ice in northwest Alaska during spring 1983 and 1984

There has been significant sea ice loss associated with climate change in the Pacific Arctic, with unquantified impacts to the habitat of ice-obligate marine mammals such as ringed seals (Pusa hispida). Ringed seals maintain breathing holes and excavate subnivean lairs on sea ice to provide protection from weather and predators during birthing, nursing, and resting. However, there is limited baseline information on the snow and ice habitat, distribution, density, and configuration of ringed seal structures (breathing holes, simple haul-out lairs, and pup lairs) in Alaska. Here, we describe historic field records from two regions of the eastern Chukchi Sea (Kotzebue Sound and Ledyard Bay) collected during spring 1983 and 1984 to quantify baseline ringed seal breeding habitat and map the distribution of ringed seal structures using modern geospatial tools. Of 490 structures located on pre-established study grids by trained dogs, 29% were pup lairs (25% in Kotzebue Sound and 33% in Ledyard Bay). Grids in Ledyard Bay had greater overall density of seal structures than those in Kotzebue Sound (8.6 structures/km2 and 7.1 structures/km2), but structures were larger in Kotzebue Sound. Pup lairs were located in closer proximity to other structures and characterized by deeper snow and greater ice deformation than haul-out lairs or simple breathing holes. At pup lairs, snow depths averaged 74.9 cm (range 37–132 cm), with ice relief nearby averaging 76 cm (range 31–183 cm), and ice deformation 29.9% (range 5–80%). We compare our results to similar studies conducted in other geographic regions and discuss our findings in the context of recent declines in extent and duration of seasonal cover of landfast sea ice and snow deposition on sea ice. Ultimately, additional research is needed to understand the effects of recent environmental changes on ringed seals, but our study establishes a baseline upon which future research can measure pup habitat in northwest Alaska.
Forecasting intermittent and sparse time series: A unified probabilistic framework via deep renewal processes

Intermittency are a common and challenging problem in demand forecasting. We introduce a new, unified framework for building probabilistic forecasting models for intermittent demand time series, which incorporates and allows to generalize existing methods in several directions. Our framework is based on extensions of well-established model-based methods to discrete-time renewal processes, which can parsimoniously account for patterns such as aging, clustering and quasi-periodicity in demand arrivals. The connection to discrete-time renewal processes allows not only for a principled extension of Croston-type models, but additionally for a natural inclusion of neural network based models—by replacing exponential smoothing with a recurrent neural network. We also demonstrate that modeling continuous-time demand arrivals, i.e., with a temporal point process, is possible via a trivial extension of our framework. This leads to more flexible modeling in scenarios where data of individual purchase orders are directly available with granular timestamps. Complementing this theoretical advancement, we demonstrate the efficacy of our framework for forecasting practice via an extensive empirical study on standard intermittent demand data sets, in which we report predictive accuracy in a variety of scenarios.
Individual dairy cow identification based on lightweight convolutional neural network

In actual farms, individual livestock identification technology relies on large models with slow recognition speeds, which seriously restricts its practical application. In this study, we use deep learning to recognize the features of individual cows. Alexnet is used as a skeleton network for a lightweight convolutional neural network that can recognise individual cows in images with complex backgrounds. The model is improved for multiple multiscale convolutions of Alexnet using the short-circuit connected BasicBlock to fit the desired values and avoid gradient disappearance or explosion. An improved inception module and attention mechanism are added to extract features at multiple scales to enhance the detection of feature points. In experiments, side-view images of 13 cows were collected. The proposed method achieved 97.95% accuracy in cow identification with a single training time of only 6 s, which is one-sixth that of the original Alexnet. To verify the validity of the model, the dataset and experimental parameters were kept constant and compared with the results of Vgg16, Resnet50, Mobilnet V2 and GoogLenet. The proposed model ensured high accuracy while having the smallest parameter size of 6.51 MB, which is 1.3 times less than that of the Mobilnet V2 network, which is famous for its light weight. This method overcomes the defects of traditional methods, which require artificial extraction of features, are often not robust enough, have slow recognition speeds, and require large numbers of parameters in the recognition model. The proposed method works with images with complex backgrounds, making it suitable for actual farming environments. It also provides a reference for the identification of individual cows in images with complex backgrounds.
Two-stage Temporal Modelling Framework for Video-based Depression Recognition using Graph Representation

Video-based automatic depression analysis provides a fast, objective and repeatable self-assessment solution, which has been widely developed in recent years. While depression clues may be reflected by human facial behaviours of various temporal scales, most existing approaches either focused on modelling depression from short-term or video-level facial behaviours. In this sense, we propose a two-stage framework that models depression severity from multi-scale short-term and video-level facial behaviours. The short-term depressive behaviour modelling stage first deep learns depression-related facial behavioural features from multiple short temporal scales, where a Depression Feature Enhancement (DFE) module is proposed to enhance the depression-related clues for all temporal scales and remove non-depression noises. Then, the video-level depressive behaviour modelling stage proposes two novel graph encoding strategies, i.e., Sequential Graph Representation (SEG) and Spectral Graph Representation (SPG), to re-encode all short-term features of the target video into a video-level graph representation, summarizing depression-related multi-scale video-level temporal information. As a result, the produced graph representations predict depression severity using both short-term and long-term facial beahviour patterns. The experimental results on AVEC 2013 and AVEC 2014 datasets show that the proposed DFE module constantly enhanced the depression severity estimation performance for various CNN models while the SPG is superior than other video-level modelling methods. More importantly, the result achieved for the proposed two-stage framework shows its promising and solid performance compared to widely-used one-stage modelling approaches.
The relative trace formula in electromagnetic scattering and boundary layer operators

This paper establishes trace-formulae for a class of operators defined in terms of the functional calculus for the Laplace operator on divergence-free vector fields with relative and absolute boundary conditions on Lipschitz domains in $\mathbb{R}^3$. Spectral and scattering theory of the absolute and relative Laplacian is equivalent to the spectral analysis and scattering theory for Maxwell equations. The trace-formulae allow for unbounded functions in the functional calculus that are not admissible in the Birman-Krein formula. In special cases the trace-formula reduces to a determinant formula for the Casimir energy that is being used in the physics literature for the computation of the Casimir energy for objects with metallic boundary conditions. Our theorems justify these formulae in the case of electromagnetic scattering on Lipschitz domains, give a rigorous meaning to them as the trace of certain trace-class operators, and clarifies the function spaces on which the determinants need to be taken.
Embedding Principle: a hierarchical structure of loss landscape of deep neural networks

We prove a general Embedding Principle of loss landscape of deep neural networks (NNs) that unravels a hierarchical structure of the loss landscape of NNs, i.e., loss landscape of an NN contains all critical points of all the narrower NNs. This result is obtained by constructing a class of critical embeddings which map any critical point of a narrower NN to a critical point of the target NN with the same output function. By discovering a wide class of general compatible critical embeddings, we provide a gross estimate of the dimension of critical submanifolds embedded from critical points of narrower NNs. We further prove an irreversiblility property of any critical embedding that the number of negative/zero/positive eigenvalues of the Hessian matrix of a critical point may increase but never decrease as an NN becomes wider through the embedding. Using a special realization of general compatible critical embedding, we prove a stringent necessary condition for being a"truly-bad"critical point that never becomes a strict-saddle point through any critical embedding. This result implies the commonplace of strict-saddle points in wide NNs, which may be an important reason underlying the easy optimization of wide NNs widely observed in practice.
A separation logic for negative dependence

Formal reasoning about hashing-based probabilistic data structures often requires reasoning about random variables where when one variable gets larger (such as the number of elements hashed into one bucket), the others tend to be smaller (like the number of elements hashed into the other buckets). This is an example of negative dependence, a generalization of probabilistic independence that has recently found interesting applications in algorithm design and machine learning. Despite the usefulness of negative dependence for the analyses of probabilistic data structures, existing verification methods cannot establish this property for randomized programs. To fill this gap, we design LINA, a probabilistic separation logic for reasoning about negative dependence. Following recent works on probabilistic separation logic using separating conjunction to reason about the probabilistic independence of random variables, we use separating conjunction to reason about negative dependence. Our assertion logic features two separating conjunctions, one for independence and one for negative dependence. We generalize the logic of bunched implications (BI) to support multiple separating conjunctions, and provide a sound and complete proof system. Notably, the semantics for separating conjunction relies on a non-deterministic, rather than partial, operation for combining resources. By drawing on closure properties for negative dependence, our program logic supports a Frame-like rule for negative dependence and monotone operations. We demonstrate how LINA can verify probabilistic properties of hash-based data structures and balls-into-bins processes.
Application of ASSR in the Hearing Aid Selection Process

The present study investigated the possible use of the auditory steady state response (ASSR) technique in hearing aid fitting. The relationship between the real ear insertion gain (REIG) and ASSR gain (unaided ASSR threshold vs. aided ASSR threshold) was examined. Thirty subjects with mild to moderately severe sensorineural hearing loss with age range of 15 years to 50 years (mean age 32 years) participated in the study. Based on the degree, configuration and type of hearing loss, two digital BTE hearing aids were preselected. For each subject insertion gain and ASSR gain were measured at 500 Hz, 1 kHz, 2 kHz and 4 kHz. The hearing aid that best matched with the target curve during insertion gain measurement was selected for the ASSR measurement. ASSR gain and REIG were highly correlated with significance values p < . 05. Paired sample t tests revealed that there was no significant difference at all test frequencies. The ASSR technique shows great promise in hearing aid fitting for those who can not reliably respond on behavioral testing.
Nd:YAG fourth harmonic (266-nm) generation for corneal reshaping procedure: An ex-vivo experimental study

Corneal reshaping is a common medical procedure utilized for the correction of different vision disorders relying on the ablation effect of the UV pulsed lasers, especially excimer lasers (ArF) at 193 nm. This wavelength is preferred in such medical procedures since laser radiation at 193 nm exhibits an optimum absorption by corneal tissue. However, it is also significantly absorbed by the water content of the cornea resulting in an unpredictability in the clinical results, as well as the high service and operation cost of the commercial ArF excimer laser device. Consequently, other types of solid-state UV pulsed lasers have been introduced. The present work investigates the ablation effect of solid-state laser at 266 nm in order to be utilized in corneal reshaping procedures. Different number of pulses has been applied to Polymethyl Methacrylate (PMMA) and ex-vivo rabbit cornea to evaluate the ablation effect of the produced laser radiation. PMMA target experienced ellipse-like ablated areas with a conical shape in the depth. The results revealed an almost constant ablation area regardless the number of laser pulses, which indicates the stability of the produced laser beam, whereas the ablation depth increases only with increasing the number of laser pulses. Examination of the ex-vivo cornea showed a significant tissue undulation, minimal thermal damage, and relatively smooth ablation surfaces. Accordingly, the obtained 266-nm laser specifications provide promising alternative to the traditional 193-nm excimer laser in corneal reshaping procedure.