Miracle-dz/UniFAR-FTUs huggingface dataset:
X-ray induced grain structure dynamics in Bi2Se3

Grain rotation in crystals often results in coarsening or refinement of the grains that modify the mechanical and thermal properties of materials. While many studies have explored how externally applied stress and temperature drive grain structure dynamics in nano-polycrystalline materials, the analogous studies on colossal grains have been limited, especially in the absence of external force. In this work, we used X-ray free electron laser pulses to irradiate single-crystalline bismuth selenide (Bi2Se3) and observed grain boundary formation and subsequent grain rotation in response to the X-ray radiation. Our observations with simultaneous X-ray diffraction and transmission X-ray microscopy demonstrate how intense X-ray radiation can rapidly change grain morphologies of initially single-crystalline material.
ILCiteR: Evidence-grounded Interpretable Local Citation Recommendation

Existing Machine Learning approaches for local citation recommendation directly map or translate a query, which is typically a claim or an entity mention, to citation-worthy research papers. Within such a formulation, it is challenging to pinpoint why one should cite a specific research paper for a particular query, leading to limited recommendation interpretability. To alleviate this, we introduce the evidence-grounded local citation recommendation task, where the target latent space comprises evidence spans for recommending specific papers. Using a distantly-supervised evidence retrieval and multi-step re-ranking framework, our proposed system, ILCiteR, recommends papers to cite for a query grounded on similar evidence spans extracted from the existing research literature. Unlike past formulations that simply output recommendations, ILCiteR retrieves ranked lists of evidence span and recommended paper pairs. Secondly, previously proposed neural models for citation recommendation require expensive training on massive labeled data, ideally after every significant update to the pool of candidate papers. In contrast, ILCiteR relies solely on distant supervision from a dynamic evidence database and pre-trained Transformer-based Language Models without any model training. We contribute a novel dataset for the evidence-grounded local citation recommendation task and demonstrate the efficacy of our proposed conditional neural rank-ensembling approach for re-ranking evidence spans.
Are Magnons just the van der Waals interaction in Disguise?

The MBD model of the van der Waals interaction is extended to also consider magnetic interactions, and it is demonstrated how this can be made to reproduce the Heisenberg Hamiltonian. It is found that this leads to a weak coupling between the charge dipole waves that are the basis for the electric-only van der Waals interaction and the spin-dipole waves (magnons) of the Heisenberg model. By applying the same level of theory to both simultaneously we demonstrate that magnons and charge-dipole waves may both be considered to be a basis for the fluctuating part of the many-body electron density.
Sub-100-fs formation of dark excitons in monolayer WS$_2$

Two-dimensional semiconductors based on transition metal dichalcogenides are promising for electronics and optoelectronics applications owing to their properties governed by strongly-bound bright and dark excitons. Momentum-forbidden dark excitons have recently received attention as better alternatives to bright excitons for long-range transport. However, accessing the dynamics of dark excitons is challenging experimentally. The most direct, but very complicated, experiment is transient angle-resolved photoemission electron spectroscopy: sub-100-fs formation of K-$\Lambda$-excitons in monolayer WS$_2$ has been identified previously taking advantage of momentum resolution of detected signals [1]. Here, we use a simpler setting of transient photoemission electron microscopy (with spatial resolution of 75 nm), which is inherently sensitive to dark K-$\Lambda$ excitons in monolayers of transition metal dichalcogenide and has exceptionally high temporal resolution of 13 fs. We are able to directly observe intervalley scattering (dark-exciton formation) in monolayer WS$_2$ with scattering rates in the range of 14-50 fs followed by picosecond-scale dynamics mediated by defects.
Boosting Disfluency Detection with Large Language Model as Disfluency Generator

Current disfluency detection methods heavily rely on costly and scarce human-annotated data. To tackle this issue, some approaches employ heuristic or statistical features to generate disfluent sentences, partially improving detection performance. However, these sentences often deviate from real-life scenarios, constraining overall model enhancement. In this study, we propose a lightweight data augmentation approach for disfluency detection, utilizing the superior generative and semantic understanding capabilities of large language model (LLM) to generate disfluent sentences as augmentation data. We leverage LLM to generate diverse and more realistic sentences guided by specific prompts, without the need for fine-tuning the LLM. Subsequently, we apply an uncertainty-aware data filtering approach to improve the quality of the generated sentences, utilized in training a small detection model for improved performance. Experiments using enhanced data yielded state-of-the-art results. The results showed that using a small amount of LLM-generated enhanced data can significantly improve performance, thereby further enhancing cost-effectiveness.
Analytical Forward Dynamics Modeling of Linearly Actuated Heavy-Duty Parallel-Serial Manipulators

This paper presents a new geometric and recursive algorithm for analytically computing the forward dynamics of heavy-duty parallel-serial mechanisms. Our solution relies on expressing the dynamics of a class of linearly-actuated parallel mechanism to a lower dimensional dual Lie algebra to find an analytical solution for the inverse dynamics problem. Thus, by applying the articulated-body inertias method, we successfully provide analytic expressions for the total wrench in the linear-actuator reference frame, the linear acceleration of the actuator, and the total wrench exerted in the base reference frame of the closed loop. This new formulation allows to backwardly project and assemble inertia matrices and wrench bias of multiple closed-loops mechanisms. The final algorithm holds an O(n) algorithmic complexity, where $n$ is the number of degrees of freedom (DoF). We provide accuracy results to demonstrate its efficiency with 1-DoF closed-loop mechanism and 4-DoF manipulator composed by serial and parallel mechanisms. Additionally, we release a URDF multi-DoF code for this recursive algorithm.
Remote sensing of night-time light

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China; Earth Observation Group, National Centers for Environmental Information, National Oceanic and Atmospheric Administration, Boulder, CO, USA; Department of Geological & Atmospheric Sciences, Iowa State University, Ames, IA, USA; Center for Satellite Applications and Research, National Centers for Environmental Information, College Park, MD, USA; Department of Geology and Geography, West Virginia University, Morgantown, WV, USA
Identified PAH V230A and PAH V230I mutations in a family with diverse clinical presentations

Key Clinical Message Phenylketonuria (PKU) is a hereditary disorder caused by phenylalanine hydroxylase enzyme (PAH) defects that might cause severe brain damage. The current main treatment, dietary management, can prevent the symptoms if commenced early. However, it has side effects if used for a long time. Additionally, some patients with mild hyperphenylalaninemia (mHPA), who has serum phenylalanine levels <360 μmol/L, do not require treatment. Since the correlation between genotype and metabolic phenotype has been demonstrated earlier, genotype‐based detection of patients who do not need treatment might help with genetic counseling and choosing the most appropriate treatment option. In this study, we report an asymptomatic adult with mHPA who had never taken any medical intervention to control or lower her serum phenylalanine level (Phe). She had 179 μmol/L serum phenylalanine level and carried p.[V230A];[V230I] genotype. Her child was affected with phenylketonuria and had p.[V230A];[V230A] genotype. Both pathogenic variants detected in the asymptomatic adult with mHPA were computationally analyzed to assess their pathogenicity and the p.V230I pathogenic variant was demonstrated to be responsible for the mHPA phenotype in the asymptomatic adult detected in this study. The findings in this study could contribute to genetic counseling and treatment for families and individuals with p.[V2030I];[V230A] genotype.
Club Drugs: They're All the Rave: A Review of Use and Abuse: Effects of MDMA, Methamphetamine, and Gamma-Hydroxy-Butyrate (GHB) on Memory

Firas H. Kobeissy, MS. Departments of Psychiatry and Neuroscience,Center for Traumatic Brain injury Studies, Center of Neuroproteomicsand Biomarkers Research, McKnight Brain institute, University of Florida.GainesvilleKevin K.W. Wang, PhD, Departments of Psychiatry and Neuroscience,Center for Traumatic Brain Injury Studies, Center of Neuroproteomicsand Biomarkers Research, McKnight Brain institute, University of Florida,GainesvilleJulie M. Demetree, BS, interdisciplinary College of Medicine, McKnight Braininstitute, University of Florida, GainesvilleAndrew K. Ottens, PhD, Department of Neuroscience, Center for Traumatic BrainInjury Studies, Center of Neuroproteomics and BiomarkersResearch, McKnight Brain institute, University of Florida, GainesvilleZhiqun Zhang, MD, Department of Neuroscience, Center for Traumatic BrainInjury Studies, Center of Neuroproteomics and Biomarkers Research,McKnight Brain institute, University of Florida, GainesvilleMatthew Warren, BS, Departments of Psychiatry and Neuroscience,Interdisciplinary College of Medicine, Center for traumatic Brain InjuryStudies, McKnight Brain institute, University of Florida, GainesvilleMark S. Gold, MD, Distinguished Professor & Chief, Departments ofPsychiatry, Neuroscience, Anesthesiology, Community Health & FamilyMedicine, Division of Addiction Medicine, McKnight Brain institute, Gainesville,Florida
Association of virtual end-of-life care with healthcare outcomes before and during the COVID-19 pandemic: A population-based study

The use of virtual care for people at the end-of-life significantly increased during the COVID-19 pandemic, but its association with acute healthcare use and location of death is unknown. The objective of this study was to measure the association between the use of virtual end-of-life care with acute healthcare use and an out-of-hospital death before vs. after the introduction of specialized fee codes that enabled broader delivery of virtual care during the COVID-19 pandemic. This was a population-based cohort study of 323,995 adults in their last 90 days of life between January 25, 2018 and December 31, 2021 using health administrative data in Ontario, Canada. Primary outcomes were acute healthcare use (emergency department, hospitalization) and location of death (in or out-of-hospital). Prior to March 14, 2020, 13,974 (8%) people received at least 1 virtual end-of-life care visit, which was associated with a 16% higher rate of emergency department use (adjusted Rate Ratio [aRR] 1.16, 95%CI 1.12 to 1.20), a 17% higher rate of hospitalization (aRR 1.17, 95%CI 1.15 to 1.20), and a 34% higher risk of an out-of-hospital death (aRR 1.34, 95%CI 1.31 to 1.37) compared to people who did not receive virtual end-of-life care. After March 14, 2020, 104,165 (71%) people received at least 1 virtual end-of-life care visit, which was associated with a 58% higher rate of an emergency department visit (aRR 1.58, 95%CI 1.54 to 1.62), a 45% higher rate of hospitalization (aRR 1.45, 95%CI 1.42 to 1.47), and a 65% higher risk of an out-of-hospital death (aRR 1.65, 95%CI 1.61 to 1.69) compared to people who did not receive virtual end-of-life care. The use of virtual end-of-life care was associated with higher acute healthcare use in the last 90 days of life and a higher likelihood of dying out-of-hospital, and these rates increased during the pandemic.
Dry Eye Disease: An Overview of Its Risk Factors, Diagnosis, and Prevalence by Age, Sex, and Race

This short review focuses on the significance and prevalence of dry eye disease (DED) in the arena of ophthalmology. DED can be identified as one of the most common optical morbidities affecting about one-fourth of the patients visiting ophthalmology clinics. The perception of the cytology and disease evolution of DED has shown a noteworthy advancement in the last decade by recognizing two diverse mechanisms of the disease: tear desertion and deficient tear production. The role of these two components independently or concurrently in the prevalence of DED was also understood. Several studies in different parts of the world have projected that DED is more common in women as compared to men and this difference increases with ageing. Aged people, especially women in the menopausal and post-menopausal stages, are more prone to DED. This ailment is more prevalent in patients suffering from autoimmune diseases with a higher percentage of women getting affected. Various everyday activities as well as social and dietary behaviors like smoking might set off DED symptoms. Extensive visual tasking while using a computer, watching television, and doing a lot of reading also increase the risk of DED. Although DED occurs in all age groups, it is seen in very few children in comparison to adults. In fact, DED in children may be related to diverse factors such as congenial, inflammatory, and autoimmune disorders as well as environmental conditions and nutritional deficiencies. A significant relationship has also been found between DED and racial differences among individuals. A few studies have suggested that the Asian population is more susceptible to DED as compared to the Caucasian population, but this concept needs further research and investigation. Climatic conditions and environmental challenges, such as relative humidity (RH), internal atmosphere, effluence, travel by air, and intense temperatures, are equally important in the occurrence of DED. The present review aims to examine the prevalence of DED in relation to age, sex, and race by analyzing several relevant studies and also have an overview of the diagnosis and risk factors of the disease.
Identifying Anterior Cruciate Ligament Injuries Through Automated Video Analysis of In-Game Motion Patterns

Background: Failure to diagnose anterior cruciate ligament (ACL) injury during a game can delay adequate treatment and increase the risk of further injuries. Artificial intelligence (AI) has the potential to be an accurate, cost-efficient, and readily available diagnostic tool for ACL injury in in-game situations. Purpose: To develop an automated video analysis system that uses AI to identify biomechanical patterns associated with ACL injury and to evaluate whether the system can enhance the ability of orthopaedic and sports medicine specialists to identify ACL injuries on video. Study Design: Descriptive laboratory study. Methods: A total of 91 ACL injury and 38 control movement scenes from online available match recordings were analyzed. The videos were processed to identify and track athletes and to estimate their 3-dimensional (3D) poses. Geometric features, including knee flexion, knee and hip abduction, and foot and hip rotation, were extracted from the athletes’ 3D poses. A recurrent neural network algorithm was trained to classify ACL injury, using these engineered features as its input. Analysis by 2 orthopaedic surgeons examined whether providing clinical experts with the reconstructed 3D poses and their derived signals could increase their diagnostic accuracy. Results: All AI models performed significantly better than chance. The best model, which used the long short-term memory network with engineered features, demonstrated decision interpretability and good performance (F1 score = 0.63 ± 0.01, area under the receiver operating characteristic curve = 0.88 ± 0.01). The analysis by the 2 orthopaedic surgeons demonstrated improved diagnostic accuracy for ACL injury recognition when provided with system data, resulting in a 0.08 increase in combined F1 scores. Conclusion: Our approach successfully reconstructed the 3D motion of athletes from a single-camera view and derived geometry-based biomechanical features from pose sequences. Our trained AI model was able to automatically detect ACL injuries with relatively good performance and prelabel and highlight regions of interest in video footage. Clinical Relevance: This study demonstrated the feasibility of using AI to automatically evaluate in-game video footage and identify dangerous motion patterns. Further research can explore the full potential of the biomechanical markers and use of the system by nonspecialists, potentially diminishing the rate of missed diagnosis and the detrimental outcomes that follow.
Patch-matrix interactions and bird species conservation in a plantation-dominated landscape in Australia

Conservation of wildlife and key ecological processes in production landscapes are essential for the achievement of ecologically sustainable natural resource use. Thus, investigations of wildlife response to landscape change have become critical. This thesis aims to obtain information useful for the better integration of wildlife conservation and wood production in plantation-dominated landscapes. This thesis examined the use of native eucalypt forests and exotic pine forests (the matrix) by bird species in a Radiata pine (Pinus radiata) plantation in the Tumut region, southern New South Wales, south­ eastern Australia. The main objectives were to: 1) examine the influence of attributes of patches of native forest and of exotic forest on bird occurrence in stands of pine forest; 2) verify the role of the matrix and of adjacent areas of eucalypt forest on bird occurrence in remnants of native forest located w ithin the plantation; 3) review investigations conducted worldwide that examined variation in bird occurrence across habitat boundaries; 4) provide recommendations for bird conservation in wood production landscapes. To obtain data to address some of the key questions in this thesis, bird surveys were conducted by the area search method within quadrats located in native and exotic forests. Generalised Linear models (GLMs) were used for most analyses. Bird species richness found within stands of pine forest declined with increasing distance from adjacent patches of eucalypt forest (Chapters 4 and 6). This was due, in part, to the fact that several species found in native patches also use pine forests as additional foraging areas. Attributes of the matrix (age of pine forest) and the remnants of native forest (patch width and size) influenced bird occurrence in pine forests (Chapters 4 and 5). The term “Halo Effect” was introduced for the influence of patch attributes on the differential use of matrix. These results suggest that conservation efforts in patchy environments should protect not only native patches but also adjacent areas of matrix into which animals extend their home ranges/territories. The decline in species richness within pine forests with increasing distances from eucalypt forests also suggests that detections of bird species can vary substantially with the position of survey stations in relation to surrounding native vegetation (Chapter 6). Recent assessments of the conservation value of managed vegetation for birds have often failed to consider the
Adaptive Feature Learning for Unbiased Scene Graph Generation

Scene Graph Generation (SGG) aims to detect all objects and identify their pairwise relationships in the scene. Recently, tremendous progress has been made in exploring better context relationship representations. Previous work mainly focuses on contextual information aggregation and uses de-biasing strategies on samples to eliminate the preference for head predicates. However, there remain challenges caused by indeterminate feature training. Overlooking the label confusion problem in feature training easily results in a messy feature distribution among the confused categories, thereby affecting the prediction of predicates. To alleviate the aforementioned problem, in this paper, we focus on enhancing predicate representation learning. Firstly, we propose a novel Adaptive Message Passing (AMP) network to dynamically conduct information propagation among neighbors. AMP provides discriminating representations for neighbor nodes under the view of de-noising and adaptive aggregation. Furthermore, we construct a feature-assisted training paradigm alongside the predicate classification branch, guiding predicate feature learning to the corresponding feature space. Moreover, to alleviate biased prediction caused by the long-tailed class distribution and the interference of confused labels, we design a Bi-level Curriculum learning scheme (BiC). The BiC separately considers the training from the feature learning and de-biasing levels, preserving discriminating representations of different predicates while resisting biased predictions. Results on multiple SGG datasets show that our proposed method AMP-BiC has superior comprehensive performance, demonstrating its effectiveness.
Promotion Effect of Asian Dust on Phytoplankton Growth and Potential Dissolved Organic Phosphorus Utilization in the South China Sea

Dust deposition is an important nutrient source to the South China Sea (SCS), but few in situ experiments were conducted on phytoplankton response to the deposition. We conducted onboard incubation experiments at three stations near Luzon Strait in the SCS, with addition of multiple dissolved inorganic nutrients, Asian dust, and rainwater. From our results, nitrogen and phosphorus were both urgently needed for phytoplankton growth in the SCS, indicated by the evident Chl a response to the addition of nitrogen and phosphorus together. Almost no evident response was observed by adding phosphorus or iron alone to incubation waters, although a delayed response of Chl a in mass concentration was observed by adding nitrogen alone. The latter implied a possible utilization of dissolved organic phosphorus because of insufficient dissolved inorganic phosphorus in incubation waters. Under such nutrient condition, Asian dust showed an apparent promotion effect on phytoplankton growth by providing sufficient amounts of nitrogen but low phosphorus. Meanwhile, it was found that large sized (> 5 μm) phytoplankton community showed different responses to dust addition at different stations. At stations A3 and A6, Chaetoceros spp. became the dominant species during the bloom period, while at station WG2, Nitzschia spp. became dominant. In combination with different initial nutrients and Chl a levels at the three stations, the different phytoplankton community evolution implied the response difference to external inputs between oligotrophic (stations A3 and A6) and ultraoligotrophic (station WG2) conditions in the SCS.