Miracle-dz/UniFAR-FTUs huggingface dataset:
Low Energy Event Reconstruction and Selection in Super-Kamiokande-III

Super–Kamiokande–I studied low energy neutrino interactions above 4.5 MeV. Photo-cathode coverage has been restored to 40% in Super-Kamiokande-III in order to observe Cherenkov events with an energy even below 4.5 MeV. This is motivated by the transition of solar neutrino oscillations between vacuum and matter-dominated oscillations near 3 MeV and delayed neutron detection from inverse-beta interactions. I will discuss the progress in event reconstruction and background suppression as well as future measurements which a lower analysis and hardware energy threshold will make possible.
Locating the university lecture as a contemporary educational practice

Abstract The university lecture has attracted much critical evaluation over a long period. Yet it remains resilient in the face of such scepticism. However, the project reported here finds that a sample of experienced lecturers fail to recognise the terms of this critique in their own practice. They uniformly describe contrary approaches. These are characterised in terms of three communication priorities: orchestration, enactment, and dialogue. An emphasis on the dialogic nature of exposition suggested an exploratory intervention in which students collaborated with a peer around a recorded lecture. Transcripts of these sessions indicate the way in which lectures can prompt a ‘conversational’ reaction within their audiences. The overall pattern of findings reported here helps to interpret the resilience of the live lecture. The success of online courses implies that lecturing co-presence is central to the resilience of lecturing in mainstream education. This is interpreted in relation to practitioners’ continued protection of three imperatives within live exposition: community, conviviality and conversation.
MRI thigh measurements predict whole‐body skeletal muscle mass in patients with type 2 diabetes: a comparison with DXA

Sarcopenia is an age‐related loss of skeletal muscle mass (SMM) and function, associated with falls, frailty, and functional decline. It is more prevalent and often accelerated in people with Type 2 diabetes (T2D), especially when co‐existing with obesity (sarcopenic obesity). Accurate whole‐body SMM measurement, feasible using dual‐energy X‐ray absorptiometry (DXA) or magnetic resonance imaging (MRI), has utility both in clinical practice and in epidemiological and mechanistic research, considering the dual mechanical and metabolic function of skeletal muscle. Compared with MRI, DXA may underestimate age‐related muscle mass by up to 30%, and so direct comparison of DXA/MRI‐derived SMM measurements may be invalid in patients with obesity and T2D, who have potentially even more pronounced sarcopenia/sarcopenic obesity. We aimed to validate single‐slice or multiple‐slice measures of SMM, using MRI, with whole‐body SMM measures derived from DXA scans of appendicular lean soft tissue, specifically in patients with obesity and T2D.
CCP: Configurable Crowd Profiles

Diversity among agents’ behaviors and heterogeneity in virtual crowds in general, is an important aspect of crowd simulation as it is crucial to the perceived realism and plausibility of the resulting simulations. Heterogeneous crowds constitute the pillar in creating numerous real-life scenarios such as museum exhibitions, which require variety in agent behaviors, from basic collision avoidance to more complex interactions both among agents and with environmental features. Most of the existing systems optimize for specific behaviors such as goal seeking, and neglect to take into account other behaviors and how these interact together to form diverse agent profiles. In this paper, we present a RL-based framework for learning multiple agent behaviors concurrently. We optimize the agent policy by varying the importance of the selected behaviors (goal seeking, collision avoidance, interaction with environment, and grouping) while training; essentially we have a reward function that changes dynamically during training. The importance of each separate sub-behavior is added as input to the policy, resulting in the development of a single model capable of capturing as well as enabling dynamic run-time manipulation of agent profiles; thus allowing configurable profiles. Through a series of experiments, we verify that our system provides users with the ability to design virtual scenes; control and mix agent behaviors thus creating personality profiles, and assign different profiles to groups of agents. Moreover, we demonstrate that interestingly the proposed model generalizes to situations not seen in the training data such as a) crowds with higher density, b) behavior weights that are outside the training intervals and c) to scenes with more intricate environment layouts. Code, data and trained policies for this paper are at https://github.com/veupnea/CCP.
Learning Soccer Juggling Skills with Layer-wise Mixture-of-Experts

Learning physics-based character controllers that can successfully integrate diverse motor skills using a single policy remains a challenging problem. We present a system to learn control policies for multiple soccer juggling skills, based on deep reinforcement learning. We introduce a task-description framework for these skills which facilitates the specification of individual soccer juggling tasks and the transitions between them. Desired motions can be authored using interpolation of crude reference poses or based on motion capture data. We show that a layer-wise mixture-of-experts architecture offers significant benefits. During training, transitions are chosen with the help of an adaptive random walk, in support of efficient learning. We demonstrate foot, head, knee, and chest juggles, foot stalls, the challenging around-the-world trick, as well as robust transitions. Our work provides a significant step towards realizing physics-based characters capable of the precision-based motor skills of human athletes. Code is available at https://github.com/ZhaomingXie/soccer_juggle_release.
Remarks on Interpolation in Reproducing Kernel Hilbert Spaces

An interpolation problem in reproducing kernel Hilbert spaces is formulated and solved in terms of the minimal solution and the elements of an auxiliary reproducing kernel Hilbert space, subject to a norm constraint. The scheme is illustrated with two particular reproducing kernel Hilbert spaces: the Arveson space and the Bergman space, where the general theorem leads to convenient parametrizations of the solution sets.
Predicting User Interests Based on Their Latest Web Activities

—This paper discusses and proposes a method for predicting information interesting to users based on their recent online behavior. Analysis of user activities on the web aims to investigate and acquire some information about user interests through websites. Therefore, we assume that recent interests of users can be predicted by analyzing the characteristics of acquired web contents. Our proposed method identiﬁes these user interests based on the clicked log of web advertisement by using neural networks, and makes it possible to predict information by regression to the learned user model. It means that ﬂexible information service can be constructed using predictions based on user presence. The evaluation indicates that the method is effective and practical in comparison to the conventional model which statistically analyzes web activities.
Optimal mating of Pinus taeda L. under different scenarios using differential evolution algorithm

A newly developed software, AgMate, was used to perform optimized mating for monoecious Pinus taeda L. breeding. Using a computational optimization procedure called differential evolution (DE), AgMate was applied under different breeding population sizes scenarios (50, 100, 150, 200, 250) and candidate contribution scenarios (max use of each candidate was set to 1 or 8), to assess its efficiency in maximizing the genetic gain while controlling inbreeding. Real pedigree data set from North Carolina State University Tree Improvement Co-op with 962 Pinus taeda were used to optimize objective functions accounting for coancestry of parents and expected genetic gain and inbreeding of the future progeny. AgMate results were compared with those from another widely used mating software called MateSel (Kinghorn, 1999). For the proposed mating list for 200 progenies, AgMate resulted in an 83.7% increase in genetic gain compared with the candidate population. There was evidence that AgMate performed similarly to MateSel in managing coancestry and expected genetic gain, but MateSel was superior in avoiding inbreeding in proposed mate pairs. The developed algorithm was computationally efficient in maximizing the objective functions and flexible for practical application in monoecious diploid conifer breeding. Study implications A dataset from a breeding population of loblolly pine (Pinus taeda L.) was analyzed using an optimal mating software, AgMate (developed by the authors), to optimize the selection, contribution, and mating of candidates simultaneously. The software helps breeders make decisions on which tree to cross with which tree and how many times, such that the trees are not related to each other and would result in the best performing progenies. AgMate is effective in meeting the breeding objectives for monoecious species. The open-source, easy-to-use, and flexible AgMate software, also available as a website, is invaluable in helping breeders to create optimal matings for future generations, which balance the pursuit of maximizing genetic gain while maintaining genetic diversity.
Wnt5A Signaling Regulates Gut Bacterial Survival and T Cell Homeostasis

Gut commensal bacterial diversity and T cell homeostasis are crucial entities of the host innate immune network, yet the molecular details of host-directed signaling pathways that sustain the steady state of gut bacterial colonization and T cell activation remain unclear. Here, we describe the protective role of a Wnt5A-actin axis in the survival of several gut bacterial commensals and its necessity in shaping gut bacterial colonization and the associated T cell repertoire. ABSTRACT In light of the demonstrated antagonism of Wnt5A signaling toward the growth of several bacterial pathogens, it was important to study the influence of Wnt5A on gut-resident bacteria and its outcome. Here, we demonstrate that in contrast to inhibiting the survival of the established gut pathogen Salmonella enterica, Wnt5A clearly promotes the survival of the common gut commensals Enterococcus faecalis and Lactobacillus rhamnosus within macrophages through a self-perpetuating Wnt5A-actin axis. A Wnt5A-actin axis furthermore regulates the subsistence of the natural bacterial population of the Peyer’s patches, as is evident from the diminution in the countable bacterial CFU therein through the application of Wnt5A signaling and actin assembly inhibitors. Wnt5A dependency of the gut-resident bacterial population is also manifested in the notable difference between the bacterial diversities associated with the feces and Peyer’s patches of Wnt5A heterozygous mice, which lack a functional copy of the Wnt5A gene, and their wild-type counterparts. Alterations in the gut commensal bacterial population resulting from either the lack of a copy of the Wnt5A gene or inhibitor-mediated attenuation of Wnt5A signaling are linked with significant differences in cell surface major histocompatibility complex (MHC) II levels and regulatory versus activated CD4 T cells associated with the Peyer’s patches. Taken together, our findings reveal the significance of steady state Wnt5A signaling in shaping the gut commensal bacterial population and the T cell repertoire linked to it, thus unveiling a crucial control device for the maintenance of gut bacterial diversity and T cell homeostasis. IMPORTANCE Gut commensal bacterial diversity and T cell homeostasis are crucial entities of the host innate immune network, yet the molecular details of host-directed signaling pathways that sustain the steady state of gut bacterial colonization and T cell activation remain unclear. Here, we describe the protective role of a Wnt5A-actin axis in the survival of several gut bacterial commensals and its necessity in shaping gut bacterial colonization and the associated T cell repertoire. This study opens up new avenues of investigation into the role of the Wnt5A-actin axis in protection of the gut from dysbiosis-related inflammatory disorders.
Combining Conflict-based Search and Agent-based Modeling for Evacuation Problems (Extended Abstract)

We address the problem of evacuation from the heuristic search perspective combined with agent-based modeling (ABM). The evacuation problem is modeled as a navigation of multiple agents in a known environment. The environment is divided into a danger and a safe zone while the task of agents is to move from the danger zone to the safe zone in a collision-free manner. Unlike previous approaches that model the environment as a discrete graph with agents placed in its vertices, at most one agent per vertex, our approach adopts various continuous aspects such as a grid-based embedding of the environment into 2D space and continuous line of sight of agents. In addition to this, we adopt hierarchical structure of our multi-agent system in which so called leading agents are more informed and are capable of performing multi-agent pathfinding (MAPF) via centralized algorithms like conflict-based search (CBS) while so called following agents with limited knowledge about other agents are modeled using simple local rules. Our experimental evaluation indicates that suggested hierarchical modeling approach can serve as a tool for studying the progress and the efficiency of evacuation processes in different environments.
Computational Investigation on the Effects of Two-Way Wing Morphing Strategy in the Critical Mach Number

The elasto-flexible wing morphing is an advanced methodology to achieve higher lift-to-drag (L/D) ratio for the subsonic wing configurations at the given flow conditions. The present article describes mechanisms of continuous morphing of a swept back wing for achieving effective delay in the critical Mach number ( M cr ) distribution over the wing surface with higher L/D ratio. Firstly, typical wing sweep angles of a Jet transport airplane ranging from 25 0 to 32 0 are considered for the present investigation towards sweep morphing. Then, the planform morphing is done by increasing the value of mean aerodynamic chord by 2.5% to 7.5% from its base line value. A novel computational simulation technique is utilized to observe the value of M cr over the wing at each 0.5% incremental chord values. The computational fluid dynamic analysis of a 3-D doubly tapered wing model is done at 0 0 angle of attack and free stream Mach number about 0.85. A NACA 6-series supercritical airfoil is selected for the wing design that resembles the wing configuration of an A300-B4 airplane. The outcome of the investigation has revealed reduced drag benefits near the transonic regime through the two way wing morphing strategy and the results are presented with applications.
Generalized Longest Path Problems

The longest simple path and snake-in-a-box are combinatorial search problems of considerable research interest. We create a common framework of longest constrained path in a graph that contains these two problems, as well as other interesting maximum path problems, as special cases. We analyze properties of this general framework, produce bounds on the path length that can be used as admissible heuristics for all problem types therein. For the special cases of longest simple path and snakes, these heuristics are shown to reduce the number of expansions when searching for a maximal path, which in some cases leads to reduced search time despite the significant overhead of computing these heuristics.
FIBS-Unet: Feature Integration and Block Smoothing Network for Single Image Dehazing

The dehazing algorithms are based on the hazy simulation equation to remove haze and restore the input image feature maps by estimating the intensity coefficient of the atmospheric light source and the scattering coefficient of the atmosphere. However, the coefficient prediction isn’t good, resulting in artifact noise in the dehazed output image. The increasing expansion of deep learning algorithms in computer vision applications to combat noise and interference in the hazy picture is growing. This paper proposed an efficient framework for Feature Integration and Block Smoothing (FIBS-Unet) Unet architecture using encoder-decoder processing with intensity attention block. We modified the Res2Net residual block with customized convolution and added instance normalization to improve the encoder feature extraction efficiency. Besides, we designed the Intensity Attention Block (IAB) using Sub-Pixel Layer and convolution ( $1\times 1$ ) to amplify input feature and fusion feature maps. We developed an efficient decoder employing sub-pixel convolutions, concatenations, contrive convolutions, and multipliers to recover smooth and high-quality feature maps at the framework. The proposed FIBS-Unet has minimized the Mean Absolute Error (MAE) at perceptual loss function with the RESIDE dataset. We calculated the Peak Signal-to-Noise Ratio (PSNR), the Similarity Index Measure (SSIM), and a subjective visual color difference to evaluate the model’s effectiveness. The proposed FIBS-Unet achieved better quality dehazing image results of PSNR:34.122 and SSIM:0.9890 in the outdoor scenarios at dense haze and backlight image for the Synthetic Objective Testing Set (SOTS). Our extensive experimental results specify that proposed FIBS-Unet is extendable to real-time applications.
Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review

Student retention is an essential measurement metric in education, indicated by retention rates, which are accumulated as students re-enroll from one academic year to the next. High retention rates can be obtained if institutions aim to provide appropriate support and teaching methods among the various practices to prevent students from deferring their studies. To address this pressing challenge faced by educational institutions, the underlying factors and the methodological aspects of building robust predictive models are reviewed and scrutinized. Educational Data Mining (EDM) and Learning Analytics (LA) have been widely adopted for knowledge discovery from educational data sources, improving the teaching practice, and identifying at-risk students. Various predictive techniques are applied in LA, such as Machine Learning (ML), Statistical Analysis, and Deep Learning (DL). To gain an in-depth review of these techniques, academic publications have been reviewed to highlight their potential to resolve Student Retention issues in education. Additionally, the paper presents a taxonomy of ML approaches and a comprehensive review of the success factors and the features that are not indicative of student performance in three different learning environments: Traditional Learning, Blended Learning, and Online Learning. The survey reveals that supervised ML and DL techniques are broadly applied in Student Retention. However, the application of ensemble and unsupervised learning clustering techniques supporting the heterogenous and homogenous groups of students is generally lacking. Moreover, static and traditional features are commonly used in student performance, ignoring vital factors such as educators-related, cognitive, and personal data. Furthermore, the paper highlights open challenges for future research directions.
Online Tensor Robust Principal Component Analysis

Online robust principal component analysis (RPCA) algorithms recursively decompose incoming data into low-rank and sparse components. However, they operate on data vectors and cannot directly be applied to higher-order data arrays (e.g. video frames). In this paper, we propose a new online robust PCA algorithm that preserves the multi-dimensional structure of data. Our algorithm is based on the recently proposed tensor singular value decomposition (T-SVD). We develop a convex optimization-based approach to recover the sparse component; and subsequently, update the low-rank component using incremental T-SVD. We propose an efficient tensor convolutional extension to the fast iterative shrinkage thresholding algorithm (FISTA) to produce a fast algorithm to solve this optimization problem. We demonstrate tensor-RPCA with the application of background foreground separation in a video stream. The foreground component is modeled as a sparse signal. The background component is modeled as a gradually changing low-rank subspace. Extensive experiments on real-world videos are presented and results demonstrate the effectiveness of our online tensor robust PCA.
Unsupervised Complex-Valued Sparse Feature Learning for PolSAR Image Classification

Deep learning has powerful feature extraction abilities and has achieved promising results in polarimetric synthetic aperture radar (PolSAR) image classification. However, the labeled samples of PolSAR images are generally limited, which could lead to the overfitting of deep networks and the inefficiency of deep features. To overcome this problem, in this article, we propose a complex-valued enforcing population and lifetime sparsity (CV-EPLS) model to extract nonredundant sparse features from PolSAR images. CV-EPLS achieves unsupervised learning of sparse polarimetric features with limited and unlabeled samples, including amplitude and phase information in multiple polarimetric channels. Concretely, CV-EPLS defines an activation metric function to achieve strong population sparsity. Additionally, a grid search strategy is designed to ensure that activation items are evenly distributed among the sparse targets, thus forming strong lifetime sparsity. In this way, CV-EPLS constructs the complex sparse matrices and extracts discriminative sparse features in an unsupervised way, with the dependence of features being effectively reduced. Experimental results on PolSAR images demonstrate the effectiveness of CV-EPLS in the extraction of features and its application to image classification.
Multifeature Fusion Keyword Extraction Algorithm Based on TextRank

Keyword extraction is the predecessor of many tasks, and its results directly affect search, recommendation, classification, and other tasks. In this study, we take Chinese text as the research object and propose a multi-feature fusion keyword extraction algorithm combined with BERT semantics and K-Truss graph(BSKT). The BSKT algorithm is based on the TextRank algorithm, which combines BERT semantic features, K-Truss features, and other features. First, the BSKT algorithm obtains the word vectors from the BERT pretraining model to calculate the semantic difference, which is used to optimize the iterative process of the TextRank word graph. Then, the BSKT algorithm obtains its K-Truss graph by decomposing the TextRank word graph and obtains the truss level feature of the word. Finally, by combining the word IDF and truss level features, the BSKT algorithm scores the words to extract keywords. Experimental results show that the BSKT algorithm achieves better performance than the latest keyword extraction algorithm SCTR in the task of extracting 1–10 keywords. Furthermore, the increment in F1 increased by 11.2% when the BSKT algorithm was used to extract three keywords from the Sensor dataset.