Miracle-dz/UniFAR-FTUs huggingface dataset:
H-TSP: Hierarchically Solving the Large-Scale Travelling Salesman Problem

We propose an end-to-end learning framework based on hierarchical reinforcement learning, called H-TSP, for addressing the large-scale Traveling Salesman Problem (TSP). The proposed H-TSP constructs a solution of a TSP instance starting from the scratch relying on two components: the upper-level policy chooses a small subset of nodes (up to 200 in our experiment) from all nodes that are to be traversed, while the lower-level policy takes the chosen nodes as input and outputs a tour connecting them to the existing partial route (initially only containing the depot). After jointly training the upper-level and lower-level policies, our approach can directly generate solutions for the given TSP instances without relying on any time-consuming search procedures. To demonstrate effectiveness of the proposed approach, we have conducted extensive experiments on randomly generated TSP instances with different numbers of nodes. We show that H-TSP can achieve comparable results (gap 3.42% vs. 7.32%) as SOTA search-based approaches, and more importantly, we reduce the time consumption up to two orders of magnitude (3.32s vs. 395.85s). To the best of our knowledge, H-TSP is the first end-to-end deep reinforcement learning approach that can scale to TSP instances of up to 10000 nodes. Although there are still gaps to SOTA results with respect to solution quality, we believe that H-TSP will be useful for practical applications, particularly those that are time-sensitive e.g., on-call routing and ride hailing service.
Practical Differentially Private and Byzantine-resilient Federated Learning

Privacy and Byzantine resilience are two indispensable requirements for a federated learning (FL) system. Although there have been extensive studies on privacy and Byzantine security in their own track, solutions that consider both remain sparse. This is due to difficulties in reconciling privacy-preserving and Byzantine-resilient algorithms. In this work, we propose a solution to such a two-fold issue. We use our version of differentially private stochastic gradient descent (DP-SGD) algorithm to preserve privacy and then apply our Byzantine-resilient algorithms. We note that while existing works follow this general approach, an in-depth analysis on the interplay between DP and Byzantine resilience has been ignored, leading to unsatisfactory performance. Specifically, for the random noise introduced by DP, previous works strive to reduce its seemingly detrimental impact on the Byzantine aggregation. In contrast, we leverage the random noise to construct a first-stage aggregation that effectively rejects many existing Byzantine attacks. Moreover, based on another property of our DP variant, we form a second-stage aggregation which provides a final sound filtering. Our protocol follows the principle of co-designing both DP and Byzantine resilience. We provide both theoretical proof and empirical experiments to show our protocol is effective: retaining high accuracy while preserving the DP guarantee and Byzantine resilience. Compared with the previous work, our protocol 1) achieves significantly higher accuracy even in a high privacy regime; 2) works well even when up to 90% distributive workers are Byzantine.
De novo reconstruction of satellite repeat units from sequence data

Satellite DNA are long tandemly repeating sequences in a genome and may be organized as high-order repeats (HORs). They are enriched in centromeres and are challenging to assemble. Existing algorithms for identifying satellite repeats either require the complete assembly of satellites or only work for simple repeat structures without HORs. Here we describe Satellite Repeat Finder (SRF), a new algorithm for reconstructing satellite repeat units and HORs from accurate reads or assemblies without prior knowledge on repeat structures. Applying SRF to real sequence data, we showed that SRF could reconstruct known satellites in human and well-studied model organisms. We also found satellite repeats are pervasive in various other species, accounting for up to 12% of their genome contents but are often underrepresented in assemblies. With the rapid progress on genome sequencing, SRF will help the annotation of new genomes and the study of satellite DNA evolution even if such repeats are not fully assembled.
Saturn's Interior After the Cassini Grand Finale

We present a review of Saturn's interior structure and thermal evolution, with a particular focus on work in the past 5 years. Data from the Cassini mission, including a precise determination of the gravity field from the Grand Finale orbits, and the still ongoing identification of ring wave features in Saturn's C-ring tied to seismic modes in the planet, have led to dramatic advances in our understanding of Saturn's structure. Models that match the gravity field suggest that differential rotation, as seen in the visible atmosphere, extends down to at least a depth of 10,000 km (1/6$^{\rm th}$ the planet's radius). At greater depths, a variety of different investigations all now point to a deep Saturn rotation rate of 10 hours and 33 minutes. There is very compelling evidence for a central heavy element concentration (``core''), that in most recent models is 12-20 Earth masses. Ring seismology strongly suggests that the core is not entirely compact, but is dilute (mixed in with the overlying H/He), and has a substantial radial extent, perhaps out to around one-half of the planet's radius. A wide range of thermal evolution scenarios can match the planet's current luminosity, with progress on better quantifying the helium rain scenario hampered by Saturn's poorly known atmospheric helium abundance. We discuss the relevance of magnetic field data on understanding the planet's current interior structure. We point towards additional future work that combines seismology and gravity within a framework that includes differential rotation, and the utility of a Saturn entry probe.
On the Capacity Region of Reconfigurable Intelligent Surface Assisted Symbiotic Radios

In this paper, we are interested in reconfigurable intelligent surface (RIS)-assisted symbiotic radio (SR) systems, where an RIS assists a primary transmission by passive beam-forming and simultaneously acts as a secondary transmitter to modulate its own information by periodically adjusting its reflecting coefficients. The above modulation scheme innately enables a new multiplicative multiple access channel (M-MAC), in which the primary and secondary signals are superposed in a multiplicative and additive manner. To pursue the fundamental performance limits of the M-MAC, we focus on the characterization of the capacity region of such systems. Due to the passive nature of RISs, the transmitted signal of the RIS should satisfy the peak power constraint. Under this constraint at the RIS as well as the average power constraint at the primary transmitter (PTx), we analyze the capacity-achieving distributions of the transmitted signals and the optimal reflecting coefficients of the RIS. Then, we derive the maximum achievable rates for both primary and secondary transmissions and characterize the rate region of the M-MAC. It is observed that the secondary transmission can achieve the maximum rate when the PTx transmits signals with the constant envelope. Furthermore, the rate region of the M - MAC is strictly convex and larger than that of the conventional TDMA scheme.
Investigating the Nature of 3D Generalization in Deep Neural Networks

Visual object recognition systems need to generalize from a set of 2D training views to novel views. The question of how the human visual system can generalize to novel views has been studied and modeled in psychology, computer vision, and neuroscience. Modern deep learning architectures for object recognition generalize well to novel views, but the mechanisms are not well understood. In this paper, we characterize the ability of common deep learning architectures to generalize to novel views. We formulate this as a supervised classification task where labels correspond to unique 3D objects and examples correspond to 2D views of the objects at different 3D orientations. We consider three common models of generalization to novel views: (i) full 3D generalization, (ii) pure 2D matching, and (iii) matching based on a linear combination of views. We find that deep models generalize well to novel views, but they do so in a way that differs from all these existing models. Extrapolation to views beyond the range covered by views in the training set is limited, and extrapolation to novel rotation axes is even more limited, implying that the networks do not infer full 3D structure, nor use linear interpolation. Yet, generalization is far superior to pure 2D matching. These findings help with designing datasets with 2D views required to achieve 3D generalization. Code to reproduce our experiments is publicly available: https://github.com/shoaibahmed/investigating_3d_generalization. git
Planar Hall effect in topological Weyl and nodal-line semimetals

Using symmetry analysis and semiclassical Boltzmann equation, we theoretically explore the planar Hall effect (PHE) in three-dimensional materials. We demonstrate that PHE is a general phenomenon that can occur in various systems regardless of band topology. Both the Lorentz force and Berry curvature effects can induce significant PHE, and the leading contributions of both effects linearly depend on the electric and magnetic fields. The Lorentz force and Berry curvature PHE coefficient possess only antisymmetric and symmetric parts, respectively. Both contributions respect the same crystalline symmetry constraints but differ under time-reversal symmetry. Remarkably, for topological Weyl semimetal, the Berry curvature PHE coefficient is a constant that does not depends on the Fermi energy, while the Lorentz force contribution linearly increases with the Fermi energy, resulting from the linear dispersion of the Weyl point. Furthermore, we find that the PHE in topological nodal line semimetals is mainly induced by the Lorentz force, as the Berry curvature in these systems vanishes near the nodal line. Our study not only highlights the significance of the Lorentz force in PHE, but also reveals its unique characteristics, which will be beneficial for determining the Lorentz force contribution experimentally.
Considerations for Minimizing Data Collection Biases for Eliciting Natural Behavior in Human-Robot Interaction

Many of us researchers take extra measures to control for known-unknowns. However, unknown-unknowns can, at best, be negligible, but otherwise, they could produce unreliable data that might have dire consequences in real-life downstream applications. Human-Robot Interaction standards informed by empirical data could save us time and effort and provide us with the path toward the robots of the future. To this end, we share some of our pilot studies, lessons learned, and how they affected the outcome of our experiments. While these aspects might not be publishable in themselves, we hope our work might save time and effort for other researchers towards their research and serve as additional considerations for discussion at the workshop.
CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image Understanding

Self-supervised learning (SSL) has gained wide-spread attention in the remote sensing (RS) and Earth observation (EO) communities owing to its ability to learn task-agnostic representations without human-annotated labels. Nevertheless, most existing RS SSL methods are limited to learning either global semantic separable or local spatial perceptible representations. We argue that this learning strategy is suboptimal in the realm of RS since the required representations for different RS downstream tasks are often varied and complex. In this study, we proposed a unified SSL framework that is better suited for RS image representation learning. The proposed SSL framework, contrastive mask image distillation (CMID), is capable of learning representations with both global semantic separability and local spatial perceptibility by combining contrastive learning (CL) with masked image modeling (MIM) in a self-distillation way. Furthermore, our CMID learning framework is architecture-agnostic, which is compatible with both convolutional neural networks (CNNs) and vision transformers (ViTs), allowing CMID to be easily adapted to a variety of deep learning (DL) applications for RS understanding. Comprehensive experiments have been carried out on four downstream tasks (i.e., scene classification, semantic segmentation, object detection, and change detection) and the results show that models pretrained using CMID achieve a better performance than other state-of-the-art SSL methods on multiple downstream tasks. The code and pretrained models will be made available at https://github.com/NJU-LHRS/official-CMIDhttps://github.com/NJU-LHRS/official-CMID to facilitate SSL research and speed up the development of RS images DL applications.
A model for seagrass species competition: dynamics of the symmetric case.

We propose a general population dynamics model for two seagrass species growing and interacting in two spatial dimensions. The model includes spatial terms accounting for the clonal growth characteristics of seagrasses, and coupling between species through the net mortality rate. We consider both intraspecies and interspecies facilitative and competitive interactions, allowing density-dependent interaction mechanisms. Here we study the case of very similar species with reciprocal interactions, which allows reducing the number of the model parameters to just four, and whose bifurcation structure can be considered the backbone of the complete system. We find that the parameter space can be divided into ten regions with qualitatively different bifurcation diagrams. These regimes can be further grouped into just five regimes with different ecological interpretations. Our analysis allows the classification of all possible density distributions and dynamical behaviors of meadows with two coexisting species.
Simulation training in suicide risk assessment and intervention: a systematic review and meta-analysis

ABSTRACT Purpose. Suicide is a major cause of preventable death worldwide. Adequate training in risk assessment and intervention is key to suicide prevention. The use of simulation (role plays, simulated patients, virtual reality…) for practical training is a promising tool in mental health. The purpose of this study was to assess the effectiveness of simulation training in suicide risk assessment and intervention for healthcare professionals and gatekeepers. Methods. We conducted a systematic review in Medline and PsycINFO up to 31 July 2021 of randomized controlled trials (RCTs), non-randomized controlled trials, and pre/post-test studies. RCTs were furthermore included in a meta-analysis. We assessed the methodological quality of all studies with the Medical Education Research Study Quality Instrument, and the Cochrane Risk of Bias tool 2.0 for RCTs. Primary outcomes were changes in Kirkpatrick criteria: attitudes, skills, knowledge, behaviors, and patient outcomes. Results. We included 96 articles representing 43,656 participants. Most pre/post-test (n = 65) and non-randomized controlled (n = 14) studies showed significant improvement in attitudes, skills, knowledge, and behaviors. The meta-analysis of 11 RCTs showed positive changes in attitudes immediately after training and at 2–4 months post-training; in self-perceived skills at 6 months post-training; but not in factual knowledge. Studies assessing benefits for patients are still limited. Conclusions. The heterogeneity of methodological designs, interventions, and trained populations combined with a limited number of RCTs and studies on patients’ outcomes limit the strength of the evidence. However, preliminary findings suggest that simulation is promising for practical training in suicidal crisis intervention and should be further studied.
Pre-COVID and COVID experience of objective structured clinical examination as a learning tool for post-graduate residents in Obstetrics & Gynecology-a quality improvement study

Objective Due to its comprehensive, reliable, and valid format, the objective structured clinical examination (OSCE) is the gold standard for assessing the clinical competency of medical students. In the present study, we evaluated the importance of the OSCE as a learning tool for postgraduate (PG) residents assessing their junior undergraduate students. We further aimed to analyze quality improvement during the pre-coronavirus disease (COVID) and COVID periods. Methods This quality-improvement interventional study was conducted at the Department of Obstetrics and Gynecology. The PG residents were trained to conduct the OSCE. A formal feedback form was distributed to 22 participants, and their responses were analyzed using a five-point Likert scale. Fishbone analysis was performed, and the ‘plan-do-study-act’ (PDSA) cycle was implemented to improve the OSCE. Results Most of the residents (95%) believed that this examination system was extremely fair and covered a wide range of clinical skills and knowledge. Further, 4.5% believed it was more labor- and resource intensive and time-consuming. Eighteen (81.8%) residents stated that they had learned all three domains: communication skills, time management skills, and a stepwise approach to clinical scenarios. The PDSA cycle was run eight times, resulting in a dramatic improvement (from 30% to 70%) in the knowledge and clinical skills of PGs and the standard of OSCE. Conclusion The OSCE can be used as a learning tool for young assessors who are receptive to novel tools. The involvement of PGs in the OSCE improved their communication skills and helped overcome human resource limitations while manning various OSCE stations.
Delayed Leukoreduction of whole blood with a platelet‐sparing filter to increase low‐titer group O whole blood production in the United States

Demand for low‐titer Group O whole blood (LTOWB) is increasing for trauma. The whole blood (WB) platelet‐sparing (WB‐SP) filter enables leukoreduction (LR) while retaining platelet quantity and function; however, in the United States WB must be filtered and placed in the cold within 8 h of collection. A longer processing window would facilitate improved logistics and supply of LR‐WB to meet the growing medical need. This study evaluated the impact of increasing filtration timing from <8 h to <12 h on the quality of LR‐WB.
Evaluation of a Structured Screening Assessment to Detect Isolated Rapid Eye Movement Sleep Behavior Disorder

Isolated rapid eye movement (REM) sleep behavior disorder (iRBD) cohorts have provided insights into the earliest neurodegenerative processes in α‐synucleinopathies. Even though polysomnography (PSG) remains the gold standard for diagnosis, an accurate questionnaire‐based algorithm to identify eligible subjects could facilitate efficient recruitment in research.
UNILATERAL STRENGTH TRAINING AFTER TOTAL KNEE ARTHROPLASTY LEADS TO SIMILAR OR BETTER EFFECTS ON STRENGTH AND FLEXIBILITY THAN BILATERAL STRENGTH TRAINING – A RANDOMIZED CONTROLLED PILOT STUDY

Objective Unilateral strength training has been shown to be more effective than bilateral training to increase strength in healthy people. The aims of this study were to test the feasibility of unilateral strength training during total knee arthroplasty (TKA) rehabilitation and compare it with standard bilateral training. Methods Twenty-four TKA patients from an inpatient rehabilitation programme were randomly assigned to unilateral and bilateral strength training groups. Both groups completed 6 strength training sessions during 3 weeks of rehabilitation. Isometric strength, knee joint flexibility, knee circumference, chair rise and walking abilities, and perceived exertion and pain were assessed before and after the training period. Results Both training groups increased isometric strength of both legs (+17–25%) and flexibility of the affected leg (+7.6%). Improvements in isometric strength of the healthy leg (+23% vs +11%) and flexibility of the affected leg (+10.7% vs +4.5%) were greater in the unilateral training group. Chair rise and 2-min walk test results improved in both groups, to the same extent. Perceived exertion decreased only in the unilateral training group (–20%), while perceived pain did not change in either group. Conclusion This study showed the feasibility of unilateral strength training in TKA rehabilitation. Strength and flexibility improved similarly or better with unilateral strength training compared with standard bilateral strength training. Future studies should analyse the efficacy of prolonged unilateral strength training following TKA. LAY ABSTRACT The rehabilitation process following a total knee replacement surgery is aimed at regaining strength and flexibility of the knee joint and improving the movements of daily living, such as walking and standing up from a chair. However, to date, it is not known which kind of strength training is most effective in achieving this aim within a rehabilitation programme. During standard rehabilitation, both legs are trained. The current study tested whether training of the affected leg only is feasible, and compared it with standard strength training of both legs. Training the affected leg only resulted in greater improvements in flexibility and similar or greater improvements in strength. Both types of training resulted in improvements in walking and standing up from a chair. In conclusion, strength training of the affected leg only is a feasible alternative to standard strength training with both legs in total knee replacement rehabilitation.
Effect of probiotic supplementation on chemotherapy- and radiotherapy-related diarrhoea in patients with cancer: an umbrella review of systematic reviews and meta-analyses

Abstract To date, several systematic reviews and meta-analyses (SRMA) have investigated the effects of probiotics, but the certainty of the evidence for an effect on chemotherapy and radiotherapy-related diarrhoea has not been assessed. We conducted an overview of SRMA, searching MEDLINE, Scopus, and ISI Web of Science from inception up to February 2022. We summarised the findings of eligible SRMA. Subsequently, we included randomised clinical trials (RCT) from the SRMA in meta-analyses, using a quality effects model to calculate the OR and 95 % CI for each outcome. We used ‘A Measurement Tool to Assess Systematic Reviews’ and the Cochrane risk of bias tool to assess the methodological quality of the SRMA and their RCT, respectively. We used the ‘Grading of Recommendations Assessment, Development, and Evaluation’. We included thirteen SRMA, which reported pooled effect sizes for chemotherapy and radiotherapy-related diarrhoea based on a total of eighteen RCT. Our meta-analyses demonstrated statistically significant beneficial effects from probiotics on all outcomes, except stool consistency; diarrhoea (any grade) OR 0·35 (95 % CI 0·22, 0·54), grade ≥ 2 diarrhoea 0·43 (0·25, 0·74), grade ≥ 3 diarrhoea 0·30 (0·15, 0·59), use of medication 0·49 (0·27, 0·88), soft stool 1·10 (0·44, 2·76) and watery stool 0·52 (0·29, 1·29). Probiotics use can reduce the incidence of diarrhoea in cancer patients in chemotherapy and radiotherapy, but the certainty of evidence for significant outcomes was very low and low.