Miracle-dz/UniFAR-FTUs huggingface dataset:
Psychiatric Admissions of Children and Adolescents Across School Periods and Daylight-Saving Transitions.

Objectives This study sought to examine fluctuations in admissions to a child and adolescent inpatient psychiatry unit in relation to school breaks, school starts, as well as time change transitions in and out of Daylight-Saving Time (DST). Methods Five years (2012-2017) of youth inpatient admissions to a pediatric hospital in Ontario were retrieved (n=2,498). A sub-sample was grouped weekly, starting on the Sunday of each week for a total of 260 weekly time bins. The number of admissions during in and out of school periods, school starts in the fall and winter semester, and time change transitions were compared. Results Admissions were significantly higher during school periods as opposed to out of school periods, and significantly increased from prior- to post-school starts. No significant difference in admission rates were found in and out of DST changes. Weekly time series analyses for DST changes and monthly time series analyses for school starts did not identify a significant seasonality in admissions. Conclusions These findings suggest that school periods and school onset may be significant stressors associated with an increased rate of psychiatric admissions. The presence of potential compensating factors is proposed to explain the lack of relationship between pedopsychiatric admissions and time change transitions.
The association between depression symptom endorsement and glycemic outcomes in adolescents with type 1 diabetes

The prevalence of depression among adolescents with type 1 diabetes is estimated to be 2–3 times higher than in the general population. In adults with type 1 diabetes and depression, short‐term outcomes are worse compared to individuals just diagnosed with type 1 diabetes. This study aims to determine if depressive symptom endorsement is associated with glycemic outcomes and short‐term complications in adolescents with type 1 diabetes.
VEGF loaded porcine decellularized adipose tissue derived hydrogel could enhance angiogenesis in vitro and in vivo

Abstract Decellularized adipose tissue (DAT) has been widely applied in soft tissue regeneration, however, DAT may play a promising role in accelerating wound healing because of suitable physical characteristics and biological properties. In this research, we fabricated the DAT hydrogel and the VEGF loaded heparinized-DAT hydrogel (VEGF hydrogel) and evaluated their efficiency in full-thickness skin wound model. We designed one method to encapsulate VEGF to hep-DAT hydrogel in order to control VEGF release rate. Result showed that the VEGF release could last up to 3 day, and 1 ml hep-DAT hydrogel (5 mg/ml) could bind up to (64.521 ± 11.550) ng VEGF which was 4.2 times to that of DAT hydrogels. Moreover, the VEGF released in 3 days still preserved biological activities that the released VEGF could enhance tube formation of HUVECs in vitro. Otherwise, the VEGF hydrogel could significantly accelerate wound healing compared with DAT hydrogel and VEGF injection, collagen deposition and newly formed vessels in the VEGF hydrogel groups were also higher than those of other groups. We believed that the VEGF hydrogel could be one attractive biomaterial for potential clinical applications.
Cost-utility analysis of transcranial direct current stimulation therapy with and without virtual illusion for neuropathic pain for adults with spinal cord injury in Canada

Objective To undertake a cost-utility analysis comparing virtual illusion (VI) and transcranial direct current stimulation (tDCS) combination therapy, tDCS alone and standard pharmacological care in Ontario, Canada from a societal perspective over a three-month time horizon. Design Cost-utility analysis using Markov model methods Setting Community setting in Ontario, Canada. Participants Individuals with spinal cord injury and neuropathic pain (NP) resistant to pharmacological therapy. Interventions Virtual illusion and transcranial direct current stimulation, transcranial direct current stimulation alone and standard pharmacological therapy. Outcome Measures Incremental costs, quality adjusted life years (QALY) and incremental cost effectiveness ratio Results The incremental cost effectiveness ratio of VI and tDCS therapy cost is $3,396 per QALY (2020 Canadian dollars) when compared to standard care. The incremental cost per QALY of tDCS therapy alone is $33,167. VI and tDCS therapy had lower incremental costs (−$519) and higher incremental QALYs (0.026) compared to tDCS alone. From a public healthcare payer perspective, there is a 74% probability that VI and tDCS therapy and 54% probability that tDCS alone would be cost effective at a $50,000 per QALY willingness-to-pay threshold. Our findings remained relatively robust in various scenario analyses. Conclusion Our findings suggest that at three-months after therapy, VI and tDCS combination therapy may be more cost effective than tDCS therapy alone. Based on conventional health technology funding thresholds, VI and tDCS combination therapy merits consideration for the treatment of NP in adults with spinal cord injuries.
Automatic Post-Editing for Vietnamese

Automatic post-editing (APE) is an important remedy for reducing errors of raw translated texts that are produced by machine translation (MT) systems or software-aided translation. In this paper, we present a systematic approach to tackle the APE task for Vietnamese. Specifically, we construct the first large-scale dataset of 5M Vietnamese translated and corrected sentence pairs. We then apply strong neural MT models to handle the APE task, using our constructed dataset. Experimental results from both automatic and human evaluations show the effectiveness of the neural MT models in handling the Vietnamese APE task.
Controller Interventions to Mitigate Potential Air Traffic Conflicts

An experiment was conducted to obtain empirical evidence that controllers, in addition to detecting and resolving expected losses-of-separation, intervene to mitigate potential conflicts that would not result in a loss of separation assuming proper compliance by both pilots. Participant controllers controlled traffic that contained aircraft expected to lose separation as well as others that would not lose separation, but were configured in such a way that a loss of separation could occur quickly if one of the aircraft did not comply with their clearance. Binary logistic regressions performed on the aircraft that were subjected to control by the aircraft provides evidence that this mitigation set improves the predictive power of a model to identify aircraft to which the participants applied control. Moreover, a regression performed on the aircraft for whom controllers provided informative commands, particularly traffic identification calls, demonstrated differences between the loss-of-separation aircraft and the mitigation aircraft. The results have implications for understanding strategies used by air traffic controllers when deciding for what aircraft controllers will intervene.
Measuring Uncertainty in Translation Quality Evaluation (TQE)

From both human translators (HT) and machine translation (MT) researchers’ point of view, translation quality evaluation (TQE) is an essential task. Translation service providers (TSPs) have to deliver large volumes of translations which meet customer specifications with harsh constraints of required quality level in tight time-frames and costs. MT researchers strive to make their models better, which also requires reliable quality evaluation. While automatic machine translation evaluation (MTE) metrics and quality estimation (QE) tools are widely available and easy to access, existing automated tools are not good enough, and human assessment from professional translators (HAP) are often chosen as the golden standard (CITATION). Human evaluations, however, are often accused of having low reliability and agreement. Is this caused by subjectivity or statistics is at play? How to avoid the entire text to be checked and be more efficient with TQE from cost and efficiency perspectives, and what is the optimal sample size of the translated text, so as to reliably estimate the translation quality of the entire material? This work carries out such a motivated research to correctly estimate the confidence intervals (CITATION) depending on the sample size of translated text, e.g. the amount of words or sentences, that needs to be processed on TQE workflow step for confident and reliable evaluation of overall translation quality. The methodology we applied for this work is from Bernoulli Statistical Distribution Modelling (BSDM) and Monte Carlo Sampling Analysis (MCSA).
A Simple Algorithm for Multiple-Source Shortest Paths in Planar Digraphs

Given an $n$-vertex planar embedded digraph $G$ with non-negative edge weights and a face $f$ of $G$, Klein presented a data structure with $O(n\log n)$ space and preprocessing time which can answer any query $(u,v)$ for the shortest path distance in $G$ from $u$ to $v$ or from $v$ to $u$ in $O(\log n)$ time, provided $u$ is on $f$. This data structure is a key tool in a number of state-of-the-art algorithms and data structures for planar graphs. Klein's data structure relies on dynamic trees and the persistence technique as well as a highly non-trivial interaction between primal shortest path trees and their duals. The construction of our data structure follows a completely different and in our opinion very simple divide-and-conquer approach that solely relies on Single-Source Shortest Path computations and contractions in the primal graph. Our space and preprocessing time bound is $O(n\log |f|)$ and query time is $O(\log |f|)$ which is an improvement over Klein's data structure when $f$ has small size.
FACE: facial automaton for conveying emotions

The human face is the main organ of expression, capable of transmitting emotions that are almost instantly recognised by fellow beings. In this paper, we describe the development of a lifelike facial display based on the principles of biomimetic engineering. A number of paradigms that can be used for developing believable emotional displays, borrowing from elements of anthropomorphic mechanics and control, and materials science, are outlined. These are used to lay down the technological and philosophical premises necessary to construct a man-machine interface for expressing emotions through a biomimetic mechanical head. Applications in therapy to enhance social skills and understanding emotion in people with autism are discussed.
Composite material repairs to metallic airframe components

The repair of metal structures by using composite materials is a technology that was pioneered in Australia, and excellent results have been obtained in over 500 repairs of corrosion and fatigue cracking. This paper will present details of the first in-service composite material repair applied to metal structure on USAF C-141B operational aircraft. This engineering study evaluated five locations representing both fatigue and corrosion cracking susceptible areas on the C-141B strategic airlift transport aircraft. Repairs using either boron/epoxy or graphite/epoxy are beneficial for crack retardation, end fastener effect relief, and field stress reduction. The most obvious time saving feature of these repairs is that no fastener removal or installation is involved. Extensive structural analysis utilizing finite element modeling was done to ensure that the repair design not only would have the requisite strength and durability but also would reduce the inspection burden after the aircraft was returned to service. A test program was also undertaken to substantiate the analytical investigation. Typical results from the finite element models as well as the specimen and component tests will be presented. Finally, the step-by-step procedure by which an actual boron/epoxy repair was applied to a fatigue crack on a C-141 center wing will be reported and "lessons learned" will summarize the benefits of applying the technology.
On Krull-Schmidt bicategories

We study the existence and uniqueness of direct sum decompositions in additive bicategories. We find a simple definition of Krull–Schmidt bicategories, for which we prove the uniqueness of decompositions into indecomposable objects as well as a characterization in terms of splitting of idempotents and properties of 2-cell endomorphism rings. Examples of Krull–Schmidt bicategories abound, with many arising from the various flavors of 2-dimensional linear representation theory.
PhysXNet: A Customizable Approach for Learning Cloth Dynamics on Dressed People

We introduce PhysXNet, a learning-based approach to predict the dynamics of deformable clothes given 3D skeleton motion sequences of humans wearing these clothes. The proposed model is adaptable to a large variety of garments and changing topologies, without need of being retrained. Such simulations are typically carried out by physics engines that require manual human expertise and are subject to computationally intensive computations. PhysXNet, by contrast, is a fully differentiable deep network that at inference is able to estimate the geometry of dense cloth meshes in a matter of milliseconds, and thus, can be readily deployed as a layer of a larger deep learning architecture. This efficiency is achieved thanks to the specific parameterization of the clothes we consider, based on 3D UV maps encoding spatial garment displacements. The problem is then formulated as a mapping between the human kinematics space (represented also by 3D UV maps of the undressed body mesh) into the clothes displacement UV maps, which we learn using a conditional GAN with a discriminator that enforces feasible deformations. We train simultaneously our model for three garment templates, tops, bottoms and dresses for which we simulate deformations under 50 different human actions. Nevertheless, the UV map representation we consider allows encapsulating many different cloth topologies, and at test we can simulate garments even if we did not specifically train for them. A thorough evaluation demonstrates that PhysXNet delivers cloth deformations very close to those computed with the physical engine, opening the door to be effectively integrated within deep learning pipelines.
Gas Dynamics in the Star-forming Region G18.148–0.283: Is It a Manifestation of Two Colliding Molecular Clouds?

We report the results obtained from a multiwavelength study of the H ii region G18.148−0.283 using the upgraded Giant Metrewave Radio Telescope at 1350 MHz, along with other archival data. In addition to the radio continuum emission, we have detected the H169α and H170α radio recombination lines toward G18.148−0.283 using a correlator bandwidth of 100 MHz. The moment-1 map of the ionized gas reveals a velocity gradient of approximately 10 km s−1 across the radio continuum peaks. The 12CO (J = 3−2) molecular line data from the CO High-Resolution Survey (COHRS) also show the presence of two velocity components that are very close to the velocities detected in the ionized gas. The spectrum and position–velocity diagram from CO emission reveal molecular gas at an intermediate-velocity range bridging the velocity components. We see mid-infrared absorption and far-infrared emission establishing the presence of a filamentary infrared dark cloud, the extent of which includes the targeted H ii region. The magnetic field inferred from dust polarization is perpendicular to the filament within the H ii region. We have also identified two O9 stars and 30 young stellar objects toward the target using data from the Two Micron All Sky Survey (2MASS), UKIRT Infrared Deep Sky Survey (UKIDSS), and Galactic Legacy Infrared Midplane Survey Extraordinaire (GLIMPSE). Cumulatively, this suggests that the region is the site of a cloud–cloud collision that has triggered massive star formation and subsequent formation of an H ii region.
Automatic Analysis of Linguistic Features in Journal Articles of Different Academic Impacts with Feature Engineering Techniques

English research articles (RAs) are an essential genre in academia, so the attempts to employ NLP to assist the development of academic writing ability have received considerable attention in the last two decades. However, there has been no study employing feature engineering techniques to investigate the linguistic features of RAs of different academic impacts (i.e., the papers of high/moderate citation times published in the journals of high/moderate impact factors). This study attempts to extract micro-level linguistic features in high- and moderate-impact journal RAs, using feature engineering methods. We extracted 25 highly relevant features from the Corpus of English Journal Articles through feature selection methods. All papers in the corpus deal with COVID-19 medical empirical studies. The selected features were then validated of the classification performance in terms of consistency and accuracy through supervised machine learning methods. Results showed that 24 linguistic features such as the overlapping of content words between adjacent sentences, the use of third-person pronouns, auxiliary verbs, tense, emotional words provide consistent and accurate predictions for journal articles with different academic impacts. Lastly, the random forest model is shown to be the best model to fit the relationship between these 24 features and journal articles with high and moderate impacts. These findings can be used to inform academic writing courses and lay the foundation for developing automatic evaluation systems for L2 graduate students.
The Tri-Pants Graph of the Twice-Punctured Torus

We investigate the structure of the tri-pants graph, a simplicial graph introduced by Maloni and Palesi, whose vertices correspond to particular collections of homotopy classes of simple closed curves of the twice-punctured torus, called tri-pants, and whose edges connect two vertices whenever the corresponding pants differ by suitable elementary moves. In particular, by examining the relationship between the tri-pants graph and the dual of the Farey complex, we prove that the tri-pants graph is connected and it has infinite diameter.
Isoperimetric and Poincaré Inequalities on Non-Self-Similar Sierpiński Sponges: the Borderline Case

Abstract In this paper we construct a large family of examples of subsets of Euclidean space that support a 1-Poincaré inequality yet have empty interior. These examples are formed from an iterative process that involves removing well-behaved domains, or more precisely, domains whose complements are uniform in the sense of Martio and Sarvas. While existing arguments rely on explicit constructions of Semmes families of curves, we include a new way of obtaining Poincaré inequalities through the use of relative isoperimetric inequalities, after Korte and Lahti. To do so, we further introduce the notion of of isoperimetric inequalities at given density levels and a way to iterate such inequalities. These tools are presented and apply to general metric measure measures. Our examples subsume the previous results of Mackay, Tyson, and Wildrick regarding non-self similar Sierpiński carpets, and extend them to many more general shapes as well as higher dimensions.
Best of Both Worlds: Practical and Theoretically Optimal Submodular Maximization in Parallel

For the problem of maximizing a monotone, submodular function with respect to a cardinality constraint $k$ on a ground set of size $n$, we provide an algorithm that achieves the state-of-the-art in both its empirical performance and its theoretical properties, in terms of adaptive complexity, query complexity, and approximation ratio; that is, it obtains, with high probability, query complexity of $O(n)$ in expectation, adaptivity of $O(\log(n))$, and approximation ratio of nearly $1-1/e$. The main algorithm is assembled from two components which may be of independent interest. The first component of our algorithm, LINEARSEQ, is useful as a preprocessing algorithm to improve the query complexity of many algorithms. Moreover, a variant of LINEARSEQ is shown to have adaptive complexity of $O( \log (n / k) )$ which is smaller than that of any previous algorithm in the literature. The second component is a parallelizable thresholding procedure THRESHOLDSEQ for adding elements with gain above a constant threshold. Finally, we demonstrate that our main algorithm empirically outperforms, in terms of runtime, adaptive rounds, total queries, and objective values, the previous state-of-the-art algorithm FAST in a comprehensive evaluation with six submodular objective functions.
Quantum Fisher information of a teleported state in Heisenberg XXX chain with x-component of Dzyaloshinskii-Moriya interaction

This article investigates the quantum Fisher information associated with a teleported state in a Heisenberg XXX chain with the x-component of the Dzyaloshinskii-Moriya interaction. The correspondence between the output state and the fidelity of entanglement teleportation expressions was constructed utilizing physical variables associated with the selected system. Our findings suggest that temperature, the spin coupling constant J , and the x-components Dx may all contribute to the degree of intricacy between states and, therefore, to the possibilities of teleportation protocols. Additionally, these results suggest that the states’ separability needs either a high-temperature regime, strong spin-orbit coupling through the Dzyaloshinskii-Moriya interaction, or a ferromagnetic chain. Entanglement-based teleportation is improbable. The system states grow increasingly entangled even with an antiferromagnetic chain, weak spin-orbit coupling, or low temperature. As a consequence, the channel becomes entangled, making the teleportation protocol conceivable and feasible. PACS numbers: 03.67. Lx, 03.67. Hk, 75.10. Jm
Novel Weight Update Scheme for Hardware Neural Network based on Synaptic Devices Having Abrupt LTP or LTD Characteristics

Mitigating nonlinear weight update characteristics is one of the main challenges in designing neural networks based on synaptic devices. This paper presents a novel weight update method named conditional reverse update scheme (CRUS) for hardware neural network (HNN) consisting of synaptic devices with highly nonlinear or abrupt conductance update characteristics. We formulate a linear optimization method of conductance in synaptic devices to reduce the average deviation of weight changes from those calculated by the Stochastic Gradient Rule (SGD) algorithm. We introduce a metric called update noise (UN) to analyze the training dynamics during training. We then design a weight update rule that reduces the UN averaged over the training process. The optimized network achieves >90% accuracy on the MNIST dataset under highly nonlinear long-term potentiation (LTP) and long-term depression (LTD) conditions while using inaccurate and infrequent conductance sensing. Furthermore, the proposed method shows better accuracy than previously reported nonlinear weight update mitigation techniques under the same hardware specifications and device conditions. It also exhibits robustness to temporal variations in conductance updates. We expect our scheme to relieve design requirements in device and circuit engineering and serve as a practical technique that can be applied to future HNNs.
A four‐degree‐of‐freedom microprehensile microrobot on chip

This paper presents a cheap and easy‐to‐produce microprehensile microrobot on chip (MMOC). This four‐degree‐of‐freedom (DOFs) microprehensor is able to grip, hold and release submillimetric‐sized objects. The research conducted relied heavily on the design of a simple and efficient monolithic piezoelectric two‐DOF actuator, requiring no further motion transformation system and asking for no supplementary guiding system. The integration of all these functions in a single part eliminates nearly all assembly concerns. Each finger of the gripper is an actuator, called a duo‐bimorph, which provides higher deflections than piezoelectric tubes. The paper presents the developed MMOC prototype, comments its performances and details the functioning of the duo‐bimorph.