Miracle-dz/UniFAR-FTUs huggingface dataset:
RPM: RF-Based Pose Machines

Radio-frequency (RF) based human sensing technologies, due to their great practical value in various applications and privacy-preserving nature, have gained tremendous attention in recent years. However, without fully exploiting the characteristics of radio signals, the performance of existing methods are still limited. First, RF features of the moving human body have different representations in dimensions such as channel and scale, which is challenging when performing feature fusion. Besides, the human body is specularly reflective with respect to the radar, which means the human body cannot be fully captured by a single RF snapshot. Therefore, the radar signal reflected by the human body is sparse and incomplete, which is difficult to extract high-quality features for 3D human pose estimation. In this paper, we present the RF-based Pose Machines (RPM), a novel framework which can generate 3D skeletons from RF signals. Considering the characteristics of RF signals, RPM includes several modules to overcome the challenges. Firstly, a Feature Fusion Network (FFN) is designed to effectively fuse radio signals from horizontal and vertical planes based on the channels' correlation and maintain high-quality feature via a multi-scale fusion block. A Spatio-Temporal Attention network is then designed to reconstruct 3D skeletons from the sparse and incomplete RF signals. Specifically, a spatial attention module is designed to model non-local relationships among joints and reconstruct body parts that a single RF snapshot cannot capture. Afterwards, a temporal attention module is proposed to refine 3D pose based on temporal coherency learned from frame queries. To evaluate the performance of our RPM framework, we construct a large-scale dataset of synchronized 3 d skeletons and RF signals, RFSkeleton3D. Our experimental results show that RPM locates 3D key points of the human body with an average error of $5.71\;\mathrm{cm}$ and maintains its performance in new environments with occlusion or bad illumination. The dataset and codes will be made in public.
An Adaptive Multi-Mode Navigation Method with Intelligent Virtual Sensor Based on Long Short-Term Memory in GNSS Restricted Environment

Aiming at the problem of fast divergence of pure inertial navigation system without correction under the condition of GNSS restricted environment, this paper proposes a multi-mode navigation method with an intelligent virtual sensor based on long short-term memory (LSTM). The training mode, predicting mode, and validation mode for the intelligent virtual sensor are designed. The modes are switching flexibly according to GNSS rejecting situation and the status of the LSTM network of the intelligent virtual sensor. Then the inertial navigation system (INS) is corrected, and the availability of the LSTM network is also maintained. Meanwhile, the fireworks algorithm is adopted to optimize the learning rate and the number of hidden layers of LSTM hyperparameters to improve the estimation performance. The simulation results show that the proposed method can maintain the prediction accuracy of the intelligent virtual sensor online and shorten the training time according to the performance requirements adaptively. Under small sample conditions, the training efficiency and availability ratio of the proposed intelligent virtual sensor are improved significantly more than the neural network (BP) as well as the conventional LSTM network, improving the navigation performance in GNSS restricted environment effectively and efficiently.
Polygon visibility decompositions with applications

Many problems in Computational Geometry involve a simple polygon P and a family of geometric objects, say s, contained in P. For example, if s is the family of chords of P then we may want to find the longest chord in P. Alternatively, given a chord of P, we may wish to determine the areas of the two subpolygons of P determined by the chord. Let Π be a polygonal decomposition of a polygon P. We call Π a visibility decomposition with respect to s if, for any object g ∈ s, we can cover g with o(|P|) of the subpolygons of Π. We investigate the application of visibility decompositions of polygons to solving problems of the forms described. Any visibility decomposition Π of a polygon P that we construct will have the property that, for some class of polygons ℘ where the polygons in ℘ have useful properties, Π ⊆ ℘. Furthermore, the properties of ℘ will be key to solving any problems we solve on P using Π. Some of the visibility decomposition classes we investigate are already known in the literature, for example weakly edge visible polygon decompositions. We make improvements relating to these decomposition classes and in some cases we also find new applications for them. We also develop several new polygon visibility decomposition classes. We then use these decomposition classes to solve a number of problems on polygons including the circular ray shooting problem and the largest axis-aligned rectangle problem. It is noteworthy that the solutions to problems that we provide are usually more efficient and always simpler than alternative solutions.
Occupational performance in Huntington’s disease: A cross-sectional study

Introduction: Huntington’s disease (HD) is an autosomal dominant neurodegenerative disorder characterized by motor dysfunction, cognitive impairment, and psychiatric disorders. These symptoms cause functional limitations in occupational performance. This study aimed to describe the difficulties in self-care, productivity, and leisure activities and to analyze the patients’ own perception and satisfaction and the association with sex, age, disability, HD severity, and quality of life. Method: We conducted an observational, retrospective study in 38 patients with HD without cognitive impairment at the Burgos University Hospital. We assessed occupational performance, quality of life, and HD severity with the Canadian Occupational Performance Measure, Short-Form Health Survey 12, Unified Huntington’s Disease Rating Scale, Total Functional Capacity, and the Problems Behavior Assessment. Results: One hundred sixty-five occupations (median 4, range 2–7) were categorized as self-care (50.90%), leisure (30.30%), and productivity (18.79%). Patients reported a higher degree of satisfaction with the activities prioritized than with the degree of performance. Elderly HD patients reported more satisfaction in leisure and women in self-care. Self-ratings of satisfaction in productivity were associated with higher levels of mental wellbeing. Conclusions: HD patients have problems with their daily life occupations, particularly with self-care. This underlines the need for assessment tools that capture the complexity of occupations.
Research on risk management and control strategy of uranium resource procurement in China

ABSTRACT As the scale of nuclear power in China continues to expand, the problem of external dependence on uranium resources has become more and more prominent. The guarantee of uranium resources is facing great risks, especially the procurement risks. Therefore, the procurement risks of uranium resources based on the analytic hierarchy process and fuzzy comprehensive assessment method from multiple perspectives is analyzed in this paper. The results show that the risk values of target region selection, supplier selection and uranium mining are 73.6, 71.3 and 71.8 respectively, which are the main risks. The risk values of procurement contract and procurement cost are 69.3 and 67.5, which are relatively low. They can be regarded as the secondary risks. In addition, the countermeasures of uranium resource procurement risk are put forward from six aspects including risk mitigation, risk prevention, risk transfer, risk avoidance, risk retention and risk backup measures. It is helpful to guide the risk management of China’s uranium resources in procurement and transportation activities
Formative Development of a Weight Management Intervention for Adolescents with Type 1 Diabetes Mellitus and Obesity

The prevalence of overweight and obesity in youth with type 1 diabetes mellitus (T1D) now exceeds that of youth without T1D. Comorbid T1D and excess adiposity are associated with multiple serious negative health outcomes. Unfortunately, youth with T1D are often excluded from and/or not referred to standard behavioral lifestyle interventions. This is often attributed to the complexities of managing T1D and an effort not to overburden persons who have T1D. Furthermore, standard behavioral weight management intervention recommendations can be perceived as contradicting T1D disease management (e.g., removing sugar-sweetened beverages from diet, energy balance with exercise, and caloric restriction). A weight management intervention specifically designed for youth with T1D is needed to provide treatment to youth with comorbid T1D and overweight/obesity. The current study interviewed adolescents with T1D and overweight/obesity (n = 12), their caregivers (n = 12), and pediatric endocrinologists (n = 9) to understand (a) whether they would be interested in a weight management intervention adapted for youth with T1D and (b) specific adaptations they would want and need. Five central themes emerged following applied thematic analysis: (1) program content, (2) programmatic messaging, (3) program structure, (4) social support, and (5) eating disorder risk. Results provide detailed recommendations for the adaptation of a behavioral weight management intervention for youth with T1D.
The Effect of Sequential and Simultaneous Supplementation of Waste-Derived Volatile Fatty Acids and Methanol as Alternative Carbon Source Blend for Wastewater Denitrification

Supplementation of alternative carbon sources is a technological bottleneck, particularly in post-denitrification processes due to stringent effluent nitrogen levels. This study focuses on enhancing the sustainability of wastewater treatment practices by partially replacing conventionally used fossil-derived methanol with organic waste-derived volatile fatty acids (VFAs) in moving bed biofilm reactors (MBBRs). In this regards, results of denitrification batch assays with sequential or simultaneous addition of VFA effluent from acidogenic fermentation of potato starch residue (AD-VFAPPL) and chicken manure (AD-VFACKM), simulated synthetic VFAs solutions (sVFAs), and methanol as carbon source were presented and discussed. Although methanol has proven superior in the conversion of nitrate to nitrite, VFAs are more effective when it comes to reducing nitrite. Although solely added AD-VFAPPL had a slower denitrification capability (0.56 ± 0.13 mgNOx-N removed/m2/day) than methanol (1.04 ± 0.46 mgNOx-N removed/m2/day), up to 50% of the methanol can be replaced by waste-derived AD-VFAPPL and achieve comparable performance (1.08 ± 0.07 mgNOx-N removed/m2/day) with the pure methanol. This proves that the co-addition of VFAs together with methanol can fully compete with pure methanol in performance, providing a promising opportunity for wastewater treatment plants to potentially reduce their carbon footprint and become more sustainable in practice while benefiting from recovered nutrients from waste.
A Neural Network Separation Approach for the Inclusion of Static Friction in Nonlinear Static Models of Industrial Robots

Static friction modeling is a critical task to have the accurate robot model. In this article, a neural network separation approach to include nonlinear static friction in models of industrial robots is proposed. For this purpose, the terms corresponding to static friction within the overall robot mathematical model are separable terms treated independently from the rest of the model. The separation modeling process is accomplished by first determining the mathematical model for the system by excluding the friction terms and estimating its parameter values. This part of the model corresponds to gravitational terms only. Because persistency of excitation is required to maintain high accuracy and avoid singularity in the estimations, data with large variations across multiple joint angles are gathered for estimation purposes and a weighted least squares approach is used. This estimation results in a highly accurate static mathematical model for industrial robots. Results from the weighted least squares estimation are compared with the original least squares estimation, ridge regression, a least absolute shrinkage and selection operator, and an elastic net to show superior performance. After modeling the gravitational terms of the model, a multilayer perceptron neural network is used to identify static friction forces in the model from the experimental data. This is required in the case of a robot with multiple degrees of freedom because the friction of each joint is a function of several other joint angles acting upon it; making the solution complex and difficult to be obtained through other friction modeling methods. The experimental results obtained from a Universal Robots-UR5 demonstrate the high accuracy of the proposed modeling methodology under static conditions, and future work will consider the implementation of dynamic terms to integrate friction forces during movement.
Optical Properties of Electrospun Nanofiber Mats

Electrospun nanofiber mats are usually applied in fields where their high specific surface area and small pore sizes are important, such as biotechnology or filtration. Optically, they are mostly white due to scattering from the irregularly distributed, thin nanofibers. Nevertheless, their optical properties can be modified and become highly important for different applications, e.g., in sensing devices or solar cells, and sometimes for investigating their electronic or mechanical properties. This review gives an overview of typical optical properties of electrospun nanofiber mats, such as absorption and transmission, fluorescence and phosphorescence, scattering, polarized emission, dyeing and bathochromic shift as well as the correlation with dielectric constants and the extinction coefficient, showing which effects may occur and can be measured by which instruments or used for different applications.
Long-term semiregular dust formation by the WC9+B0I system WR 70

1 Institute for Astronomy, University of Edinburgh, Royal Observatory, Edinburgh EH9 3HJ; 2 Space Research Organization Netherlands, Sorbonnelaan 2, NL-3584 CA Utrecht, The Netherlands; 3 Astronomical Institute `Anton Pannekoek', University of Amsterdam, Science Park 904, NL-1098 XH Amsterdam, The Netherlands; 4 South African Astronomical Observatory, P.O. Box 9, 7935 Observatory, South Africa; 5 Astronomy, Cosmology and Gravitation Centre, Astronomy Department, University of Cape Town, 7700 Rondebosch, South Africa; 6 Service d'Astrophysique DSM/IRFU/SAp CEA Saclay, L'Orme des Merisiers, b^atiment 709, F-91191 Gif-sur-Yvette France; 7 Atacama Large Millimetre/submillimetre Array (ALMA), Alonso de Cordova 3107, Vitacura, Santiago 763 0355, Chile
Un Algoritmo GRASP para resolver el problema de la programación de tareas dependientes en máquinas diferentes (task scheduling)

--- Operation’s planning for Software Development has been a complicated by-solve problem experienced since the golden age of the use methodologies whose rule those process. In which it is used, as well as in the efficiency and sophistication of the algorithms that try to solve the problems that appear in a software project, since its origin in the middle of the 20th century. The previously mentioned problem is known within algorithmic as task scheduling and it is defined as follows: given a group of tasks (operations) to be scheduled within a group of machines (or human resources, or human-machine resources), find an appropriate execution order. It is a NP-difficult complexity problem, so it justifies the usage of heuristic methods to obtain approximate solutions. This thesis presents a GRASP heuristic goal to solve the variant in which tasks are dependent and executing entities are different one from the other: now it could be possible the planning of the operation s from the inception’s RUP phase. We are remarking in particular, two disciplines of RUP methodology: business modeling and requirement. Both a greedy algorithm and a GRASP heuristic goal with two relaxation parameters have been developed. Innovative proposition because until now nobody has tried to solve the problem this way. Likewise a mathematical model for the specific variant of the problem to be considered is shown. To show efficiency of the GRASP algorithm, we developed a prototype program that executes and compares the results obtained by greedy and GRASP algorithms. The GRASP algorithm improves by 6% the results of the greedy algorithm, for instances with up to 12500 variables involved. Finally we measured the quality of these results with those of the mathematical model which would obtain the exact solution for smaller instances, taking advantage of software that solves linear programming problems: the GRASP algorithm got close to the exact result within a range of 95 to 99%, and even equaled it in some tests. Keywords: Task scheduling, GRASP algorithm, Software development, resource planning in software projects, RUP.
Inverse modelling of two-phase flow: calibration of relative permeability curves

Inverse modelling of relative permeability curves in the context of two-phase flow is presented. Using a finite-difference approximation to the one-dimensional equation of immiscible two-phase flow in a heterogeneous reservoir, an inversion procedure is proposed that calibrates the parameters that describe the shape of the relative permeability curves so that both pressure and saturation data are matched by the model. Because the inversion is carried out within a stochastic framework, the resulting permeability spatial distribution represents a possible reservoir (or aquifer) model. The philosophy of the inversion method is founded on the self-calibrating method. Examples are used to investigate the sensitivity of results to fluid properties.
Pixels and their neighbors: Finite volume

We take you on the journey from continuous equations to their discrete matrix representations using the finite-volume method for the direct current (DC) resistivity problem. These techniques are widely applicable across geophysical simulation types and have their parallels in finite element and finite difference. We show derivations visually, as you would on a whiteboard, and have provided an accompanying notebook at http://github.com/seg to explore the numerical results using SimPEG (Cockett et al., 2015).
ALSI-Transformer: Transformer-Based Code Comment Generation With Aligned Lexical and Syntactic Information

Code comments explain the operational process of a computer program and increase the long-term productivity of programming tasks such as debugging and maintenance. Therefore, developing methods that automatically generate natural language comments from programming code is required. With the development of deep learning, various excellent models in the natural language processing domain have been applied for comment generation tasks, and recent studies have improved performance by simultaneously using the lexical information of the code token and the syntactical information obtained from the syntax tree. In this paper, to improve the accuracy of automatic comment generation, we introduce a novel syntactic sequence, Code-Aligned Type sequence (CAT), to align the order and length of lexical and syntactic information, and we propose a new neural network model, Aligned Lexical and Syntactic information-Transformer (ALSI-Transformer), based on a transformer that encodes the aligned multi-modal information with convolution and embedding aggregation layers. Through in-depth experiments, we compared ALSI-Transformer with current baseline methods using standard machine translation metrics and demonstrate that the proposed method achieves state-of-the-art performance in code comment generation.
iCatcher+: Robust and Automated Annotation of Infants’ and Young Children’s Gaze Behavior From Videos Collected in Laboratory, Field, and Online Studies

Technological advances in psychological research have enabled large-scale studies of human behavior and streamlined pipelines for automatic processing of data. However, studies of infants and children have not fully reaped these benefits because the behaviors of interest, such as gaze duration and direction, still have to be extracted from video through a laborious process of manual annotation, even when these data are collected online. Recent advances in computer vision raise the possibility of automated annotation of these video data. In this article, we built on a system for automatic gaze annotation in young children, iCatcher, by engineering improvements and then training and testing the system (referred to hereafter as iCatcher+) on three data sets with substantial video and participant variability (214 videos collected in U.S. lab and field sites, 143 videos collected in Senegal field sites, and 265 videos collected via webcams in homes; participant age range = 4 months–3.5 years). When trained on each of these data sets, iCatcher+ performed with near human-level accuracy on held-out videos on distinguishing “LEFT” versus “RIGHT” and “ON” versus “OFF” looking behavior across all data sets. This high performance was achieved at the level of individual frames, experimental trials, and study videos; held across participant demographics (e.g., age, race/ethnicity), participant behavior (e.g., movement, head position), and video characteristics (e.g., luminance); and generalized to a fourth, entirely held-out online data set. We close by discussing next steps required to fully automate the life cycle of online infant and child behavioral studies, representing a key step toward enabling robust and high-throughput developmental research.
Metastatic Castration-Resistant Prostate Cancer, Immune Checkpoint Inhibitors, and Beyond

The therapeutic landscape of several genitourinary malignancies has been revolutionized by the development of immune checkpoint inhibitors (ICIs); however, the utility of immunotherapies in prostate cancer has been limited, partly due to the immunologically “cold” tumor terrain of prostate cancer. As of today, pembrolizumab is the only immune checkpoint inhibitor approved for the treatment of metastatic castration resistant prostate cancer (mCRPC) in a select group of patients with high microsatellite instability (MSI-H), deficient mismatch repair (dMMR), or high tumor mutational burden (TMB). Looking ahead, several combinatorial approaches with ICIs involving radioligands, radiotherapy, PARP inhibitors, interleukin inhibitors, and cancer vaccines are exploring a potential synergistic effect. Furthermore, B7-H3 is an alternative checkpoint that may hold promise in adding to the treatment landscape of mCRPC. This review aims to summarize previous monotherapy and combination therapy trials of ICIs as well as novel immunotherapy combination therapeutic strategies and treatment targets in mCRPC.