Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact and realization of increased class separability on the small sample size problem in hyperspectral classification

A hyperspectral image is often represented by a high-dimensional dataset for analysis. Under the condition of small sample sizes, the Hughes phenomenon often occurs in high-dimensional classification because of poorly estimated class parameters. Solutions to the Hughes phenomenon have been focused on sample sizes and dimensionality to improve parameter estimation. Considering the Hughes phenomenon as an interaction of two counter-effects between degrading parameter estimation and increasing class separability with dimensionality, this study raises class separability to compensate for the degradation of accuracy in parameter estimation. Instead of merely taking the lowpass (LP) filter as a smoothing technique in image processing, this study presents the LP filter as a means of increasing class separability from the standpoint of pattern recognition. An increase in class separability not only mitigates the Hughes phenomenon but also facilitates parameter estimation in joint supervised-unsupervised learning based on the expectation and maximization (EM) algorithm. LP filtering may hurt classification performance if the filtered, high-frequency contents embed discriminant information. This study overcomes the problem by extracting frequency-based features and forming an augmented feature set. Our experiments demonstrate the feasibility of a spatial filter for the improvement of class separability and parameter estimation in classification.
Human mobility generation with physics-enhanced network

Modeling human mobility helps to understand how people are accessing resources and physically contacting with each other in cities, and thus contributes to various applications such as urban planning, epidemic control, and location-based advertisement. Next location prediction is one decisive task in individual human mobility modeling and is usually viewed as sequence modeling, solved with Markov or RNN-based methods. However, the existing models paid little attention to the logic of individual travel decisions and the reproducibility of the collective behavior of population. To this end, we propose a Causal and Spatial-constrained Long and Short-term Learner (CSLSL) for next location prediction. CSLSL utilizes a causal structure based on multi-task learning to explicitly model the " when → what → where ", a.k. a. " time → activity → location " decision logic. We next propose a spatial-constrained loss function as an auxiliary task, to ensure the consistency between the predicted and actual spatial distribution of travelers’ destinations. Moreover, CSLSL adopts modules named Long and Short-term Capturer (LSC) to learn the transition regularities across different time spans. Extensive experiments on three real-world datasets show promising performance improvements of CSLSL over base-lines and confirm the effectiveness of introducing the causality and consistency constraints. The implementation is available at https://github.com/urbanmobility/CSLSL.
A method for simplifying phase-height mapping in phase measurement profilometry

Phase-height mapping algorithm is the key technology of phase measurement profilometry (PMP). Because of lens distortion of the projector, phase-height mapping is not simple linear transform and the mapping procedure become complex. A method is introduced to simplify phase-height mapping. The detail of this method is expressed as following: (1) Two suits of sinusoidal gratings which are perpendicular to each other are projected to the calibration target respectively. (2) The position of each target mark in calibration target is estimated using standard image processing technique. (3) Distortion coefficients of projector are estimated from the phase of target marks and their positions, according to the camera model. (4) Ideal phase distribution for projecting is designed. (5) According to the camera distortion model, distorting and transforming the ideal phase distribution, distorted phase distribution in the image plane is acquired. Then the phase-shifting sinusoidal fringes with distorted phase are generated. The anamorphic sinusoidal fringes are distorted inversely during projecting because projecting process is the reverse process of imaging. Therefore, the ideal sinusoidal fringes are projected. The phase-height mapping of PMP system can be ideally expressed and the mapping procedure is simplified. A practical PMP measurement system was constructed and the distortion coefficients are estimated by calibrating the system. They are k1=-6.989×10-2 and p1=5.957×10-3. Then the distorted sinusoidal fringes are generated and projected for calibrating. Distortion coefficients are estimated again. They are k1=-7.882×10-3 and p1=-3.777×10-3. It is found from the experimental results that the distortion of projector is reduced a lot after grating correction.
Impulse‐response method to predict echo‐responses from targets of complex geometry. Part I: Theory

A theoretical model is proposed for the complete solution of the acoustical problem of the radiation of transient ultrasonic pulses, their scattering by targets of arbitrary shape and acoustic impedance and their reception. The model combines Kirchhoff’s retarded potential formula with the impulse‐response approach to the field radiated into fluids by wideband pistonlike sources. Kirchhoff’s formula is made explicit with three geometrical assumptions. Further assumptions and stated approximations allow a generalization of the reciprocity principle from its already‐known form for point targets. This leads to a formula for the complete acoustical impulse response which is explicit, very compact and easy to compute, requiring integration only over the insonified area of the target. Throughout, particular attention is given to the hypotheses and approximations made in deriving the model with clear definition of its domain of applicability.
More than Syntaxes: Investigating Semantics to Zero-shot Cross-lingual Relation Extraction and Event Argument Role Labelling

Syntactic dependency structures are commonly utilized as language-agnostic features to solve the word order difference issues in zero-shot cross-lingual relation and event extraction tasks. However, while sentences in multiple forms can be employed to express the same meaning, the syntactic structure may vary considerably in specific scenarios. To fix this problem, we find semantics are rarely considered, which could provide a more consistent semantic analysis of sentences and be served as another bridge between different languages. Therefore, in this paper, we introduce Syntax and Semantic Driven Network (SSDN) to equip syntax and semantic knowledge across languages simultaneously. Specifically, predicate-argument structures from semantic role labelling are explicitly incorporated into word representations. Then, a semantic-aware relational graph convolutional network (Sem-RGCN) and a transformer-based encoder are utilized to model both semantic dependency and syntactic dependency structures, respectively. Finally, a fusion module is introduced to integrate output representations adaptively. We conduct experiments on the widely-used ACE2005 English, Chinese, and Arabic datasets. The evaluation results demonstrate that the proposed method achieves the state-of-the-art performance. Further study also indicates SSDN could produce robust representations that facilitate the transfer operations across languages.
Recognize News Transition from Collective Behavior for News Recommendation

In the news recommendation, users are overwhelmed by thousands of news daily, which makes the users’ behavior data have high sparsity. Therefore, only considering a single user’s personalized preferences cannot support the news recommendation. How to improve the relatedness of news and users and reduce data sparsity has become a hot issue. Recent studies have attempted to use graph models to enrich the relationship between users and news, but they are still limited to modeling the historical behaviors of a single user. To fill the gap, we integrate user-news relationships and the overall user historical clicked news sequences to construct a global heterogeneous transition graph. And a refinement approach is proposed to recognize the news transition patterns in the graph. Based on the global heterogeneous transition graph, we propose a heterogeneous transition graph attention network to capture the common behavior patterns of most users to enhance the representation of user interest. Fusing the users’ personalized and common interest, we propose the GAINRec model to recommend news effectively. Extensive experiments are conducted on two public news recommendation datasets, and the results show the superiority of the proposed GAINRec model compared with the state-of-the-art news recommendation models. The implementation of our model is available at https://github.com/newsrec/GAINRec.
Technological advances in three-dimensional skin tissue engineering

Abstract Tissue engineering is an enabling technology that can be used to repair, replace, and regenerate different types of biological tissues and holds great potential in various biomedical applications. As the first line of defense for the human body, the skin has a complex structure. When skin is injured by trauma or disease, the skin tissues may regenerate under natural conditions, though often resulting in irreversible and aesthetically unpleasant scarring. The development of skin tissue engineering strategies was reviewed. Although the traditional approaches to skin tissue engineering have made good progress, they are still unable to effectively deal with large-area injuries or produce full-thickness grafts. In vitro three-dimensional (3D) skin constructs are good skin equivalent substitutes and they have promoted many major innovative discoveries in biology and medicine. 3D skin manufacturing technology can be divided into two categories: scaffold-free and scaffold-based. The representatives of traditional scaffold-free approaches are transwell/Boyden chamber approach and organotypic 3D skin culture. Because of its low cost and high repeatability, the scaffold-free 3D skin model is currently commonly used for cytotoxicity analysis, cell biochemical analysis, and high-throughput cell function. At present, many drug experiments use artificial skin developed by traditional approaches to replace animal models. 3D bioprinting technology is a scaffold-based approach. As a novel tissue manufacturing technology, it can quickly design and build a multi-functional human skin model. This technology offers new opportunities to build tissues and organs layer by layer, and it is now used in regenerative medicine to meet the increasing need for tissues and organs suitable for transplantation. 3D bioprinting can generate skin substitutes with improved quality and high complexity for wound healing and in vitro disease modeling. In this review, we analyze different types of conventional techniques to engineer skin and compare them with 3D bioprinting. We also summarized different types of equipment, bioinks, and scaffolds used in 3D skin engineering. In these skin culture techniques, we focus on 3D skin bioprinting technology. While 3D bioprinting technology is still maturing and improvements to the techniques and protocols are required, this technology holds great promise in skin-related applications.
Multiphase segmentation of digital material images

Abstract Multiphase segmentation of pore-scale features and identification of mineralogy from digital images of materials is critical for many applications in the natural resources sector. However, the materials involved (rocks, catalyst pellets, and synthetic alloys) have complex and unpredictable composition. Algorithms that can be extended for multiphase segmentation of images of these materials are relatively few and very human-intensive. Challenges lie in designing algorithms that are context free, can function with less training data, and can handle the unpredictability of material composition. Semisupervised algorithms have shown success in classification in situations characterized by limited training data; they use unlabeled data in addition to labeled data to produce classification. The segmentation obtained can be more accurate than fully supervised learning approaches. This work proposes using a semisupervised clustering algorithm named Continuous Iterative Guided Spectral Class Rejection (CIGSCR) toward multiphase segmentation of digital scans of materials. CIGSCR harnesses spectral cohesion, splitting the intensity histogram of the input image down into clusters. This splitting provides the foundation for classification strategies that can be implemented as postprocessing steps to get the final segmentation. One classification strategy is presented. Micro-computed tomography scans of rocks are used to present the results. It is demonstrated that CIGSCR successfully enables distinguishing features up to the uniqueness of grayscale values, and extracting features present in full image stacks (3D), including features not presented in the training data. Results including instances of success and limitations are presented. Scalability to data sizes $ \mathcal{O}\left({10}^9\right) $ voxels is briefly discussed.
Observations of Hα Intensity Oscillations in a Flare Ribbon

High-cadence Hα blue wing observations of a C9.6 solar flare obtained at Big Bear Solar Observatory using the Rapid Dual Imager are presented. Wavelet and time-distance methods were used to study oscillatory power along the ribbon, finding periods of 40-80 s during the impulsive phase of the flare. A parametric study found statistically significant intensity oscillations with amplitudes of 3% of the peak flare amplitude, periods of 69 s (14.5 mHz) and oscillation decay times of 500 s. These measured properties are consistent with the existence of flare-induced acoustic waves within the overlying loops.
Observational prospective study of social media, smartphone use and self-harm in a clinical sample of young people: study protocol

Introduction Young people are the most frequent users of social media and smartphones and there has been an increasing speculation about the potential negative impacts of their use on mental health. This has coincided with a sharp increase in the levels of self-harm in young people. To date, studies researching this potential association are predominantly cross-sectional and reliant on self-report data, which precludes the ability to objectively analyse behaviour over time. This study is one of the first attempts to explore temporal patterns of real-world usage prior to self-harm, to identify whether there are usage patterns associated with an increased risk. Methods and analysis To study the mechanisms by which social media and smartphone use underpin self-harm in a clinical sample of young people, the Social media, Smartphone use and Self-harm in Young People (3S-YP) study uses a prospective, observational study design. Up to 600 young people aged 13–25 years old from secondary mental health services will be recruited and followed for up to 6 months. Primary analysis will compare real-world data in the 7 days leading up to a participant or clinician recorded self-harm episode, to categorise patterns of problematic usage. Secondary analyses will explore potential mediating effects of anxiety, depression, sleep disturbance, loneliness and bullying. Ethics and dissemination This study was approved by the National Research Ethics Service, London - Riverside, as well as by the Joint Research and Development Office of the Institute of Psychiatry, Psychology and Neuroscience and South London and Maudsley NHS Foundation Trust (SLaM), and the SLaM Clinical Research Interactive Search (CRIS) Oversight Committee. The findings from this study will be disseminated through peer-reviewed scientific journals, conferences, websites, social media and stakeholder engagement activities. Trial registration number NCT04601220.
Systematic, comprehensive, evidence-based approach to identify neuroprotective interventions for motor neuron disease: using systematic reviews to inform expert consensus

Objectives Motor neuron disease (MND) is an incurable progressive neurodegenerative disease with limited treatment options. There is a pressing need for innovation in identifying therapies to take to clinical trial. Here, we detail a systematic and structured evidence-based approach to inform consensus decision making to select the first two drugs for evaluation in Motor Neuron Disease-Systematic Multi-arm Adaptive Randomised Trial (MND-SMART: NCT04302870), an adaptive platform trial. We aim to identify and prioritise candidate drugs which have the best available evidence for efficacy, acceptable safety profiles and are feasible for evaluation within the trial protocol. Methods We conducted a two-stage systematic review to identify potential neuroprotective interventions. First, we reviewed clinical studies in MND, Alzheimer’s disease, Huntington’s disease, Parkinson’s disease and multiple sclerosis, identifying drugs described in at least one MND publication or publications in two or more other diseases. We scored and ranked drugs using a metric evaluating safety, efficacy, study size and study quality. In stage two, we reviewed efficacy of drugs in MND animal models, multicellular eukaryotic models and human induced pluripotent stem cell (iPSC) studies. An expert panel reviewed candidate drugs over two shortlisting rounds and a final selection round, considering the systematic review findings, late breaking evidence, mechanistic plausibility, safety, tolerability and feasibility of evaluation in MND-SMART. Results From the clinical review, we identified 595 interventions. 66 drugs met our drug/disease logic. Of these, 22 drugs with supportive clinical and preclinical evidence were shortlisted at round 1. Seven drugs proceeded to round 2. The panel reached a consensus to evaluate memantine and trazodone as the first two arms of MND-SMART. Discussion For future drug selection, we will incorporate automation tools, text-mining and machine learning techniques to the systematic reviews and consider data generated from other domains, including high-throughput phenotypic screening of human iPSCs.
Understanding Rural School Teachers’ Professional Agency and Its Relationship to Social Structure

Abstract Research on the role of teacher agency has been growing significantly during the last decade. Agentic teachers act purposefully and constructively to direct their professional development and contribute to the quality of education, especially in rural areas. Professional development is mutually constituted between teachers and socio-cultural conditions (e.g., circumstances, physical artifacts, power relations, and school cultures) that constrain or enable their agency. This study explored teacher professional agency, and its relationship to teacher perceived social structure, using two newly developed scales: (1) the Teacher Professional Agency (TPA) and (2) the Teacher-Perceived Social Structure (TPSS). A total number of 592 teachers working at rural schools in western China completed the questionnaire. The findings revealed that the two scales were valid and reliable instruments for measuring teacher professional agency, and teacher perceived social structure. Findings show a modestly-moderately correlation between teachers’ professional identity and their perceived social identity. Collegial relationships, followed by school climate and social identity, have the strongest impact on teachers’ professional agency.
School-wide positive behavioral interventions and supports and students with extensive support needs: a scoping review

School-wide Positive Behavioral Interventions and Supports (SWPBIS) is an inclusive multi-tiered system of behavioral supports that has been widely adopted by K-12 schools in the United States. SWPBIS focuses on creating safe, equitable, and inclusive school environments and has been linked to both positive behavioral and academic outcomes for students and improved perceptions of efficacy and job satisfaction for school personnel. However, there remain concerns about the involvement of students with extensive support needs (ESN) in SWPBIS despite calls to action in 2006 and 2016 for research in this area. Addressing these concerns, we conducted a scoping review to examine the current research literature on SWPBIS and students with ESN. We found that only 10 studies have been conducted since the 2006 call to action. Studies primarily focused on stakeholder perspectives regarding the importance or availability of SWPBIS for students with ESN. Although few studies examined SWPBIS effectiveness, findings from these studies lend support to the effectiveness of Tier 1 SWPBIS for students with ESN. We describe several key implications for supporting the inclusion of students with ESN in SWPBIS and future research initiatives.
An Efficient Trajectory Clustering Framework Based Relative Distance

along with more and more trajectory dataset being collected into application servers, the research in trajectory clustering has become increasingly important topic. This paper proposes a new mobile object trajectory Clustering algorithm (Trajectory Clustering based Improved Minimum Hausdorff Distance under Translation, TraClustMHD). In this framework, improved Minimum Hausdorff Distance under Translation is presented to measure the similarity between sub-segments. In additional, R-Tree is employed to improve the efficiency. The experimental results showed that this algorithm better than based on Hausdorff distance and based on line Hausdorff distance has good trajectory clustering performance.
A review on materials and processes for carbon dioxide separation and capture

In today’s world, owing to industrial expansion, urbanization, the rapid growth of the human population, and the high standard of living, the utilization of the most advanced technologies is unavoidable. The enhanced anthropogenic activities worldwide result in a continuous increase in global warming potential, thereby raising a global concern. The constant rise in global warming potential forces the world to mitigate greenhouse gases, particularly carbon dioxide. Carbon dioxide is considered as the primary contributor responsible for global warming and climatic changes. The global anthropogenic carbon dioxide emissions released into the atmosphere can eventually deteriorate the environment and endanger the ecosystem. Combating global warming is one of the main challenges in achieving sustainable development. Carbon capture and storage is a potential solution to mitigate carbon dioxide emissions. There are three main methods for carbon capture and storage: post-combustion, pre-combustion, and oxy-fuel combustion. Among them, post-combustion is used in thermal power plants and industrial sectors, all of which contribute a significant amount of carbon dioxide. Different techniques such as physical and chemical absorption, physical and chemical adsorption, membrane separation, and cryogenic distillation used for carbon capture are thoroughly discussed and presented. Currently, there are various materials including absorbents, adsorbents, and membranes used in carbon dioxide capture. Still, there is a search for new and novel materials and processes for separating and capturing carbon dioxide. This review article provides a comprehensive review of different methods, techniques, materials, and processes used for separating and capturing carbon dioxide from significant stationary point sources.