Miracle-dz/UniFAR-FTUs huggingface dataset:
Vision-based Relative Detection and Tracking for Teams of Micro Aerial Vehicles

In this paper, we address the vision-based detection and tracking problems of multiple aerial vehicles using a single camera and Inertial Measurement Unit (IMU) as well as the corresponding perception consensus problem (i.e., uniqueness and identical IDs across all observing agents). We design several vision-based decentralized Bayesian multi-tracking filtering strategies to resolve the association between the incoming unsorted measurements obtained by a visual detector algorithm and the tracked agents. We compare their accuracy in different operating conditions as well as their scalability according to the number of agents in the team. This analysis provides useful insights about the most appropriate design choice for the given task. We further show that the proposed perception and inference pipeline which includes a Deep Neural Network (DNN) as visual target detector is lightweight and capable of concurrently running control and planning with Size, Weight, and Power (SWaP) constrained robots on-board. Experimental results show the effective tracking of multiple drones in various challenging scenarios such as heavy occlusions.
Active Exploration for Inverse Reinforcement Learning

Inverse Reinforcement Learning (IRL) is a powerful paradigm for inferring a reward function from expert demonstrations. Many IRL algorithms require a known transition model and sometimes even a known expert policy, or they at least require access to a generative model. However, these assumptions are too strong for many real-world applications, where the environment can be accessed only through sequential interaction. We propose a novel IRL algorithm: Active exploration for Inverse Reinforcement Learning (AceIRL), which actively explores an unknown environment and expert policy to quickly learn the expert's reward function and identify a good policy. AceIRL uses previous observations to construct confidence intervals that capture plausible reward functions and find exploration policies that focus on the most informative regions of the environment. AceIRL is the first approach to active IRL with sample-complexity bounds that does not require a generative model of the environment. AceIRL matches the sample complexity of active IRL with a generative model in the worst case. Additionally, we establish a problem-dependent bound that relates the sample complexity of AceIRL to the suboptimality gap of a given IRL problem. We empirically evaluate AceIRL in simulations and find that it significantly outperforms more naive exploration strategies.
Rank-Based Decomposable Losses in Machine Learning: A Survey

Recent works have revealed an essential paradigm in designing loss functions that differentiate individual losses versus aggregate losses. The individual loss measures the quality of the model on a sample, while the aggregate loss combines individual losses/scores over each training sample. Both have a common procedure that aggregates a set of individual values to a single numerical value. The ranking order reflects the most fundamental relation among individual values in designing losses. In addition, decomposability, in which a loss can be decomposed into an ensemble of individual terms, becomes a significant property of organizing losses/scores. This survey provides a systematic and comprehensive review of rank-based decomposable losses in machine learning. Specifically, we provide a new taxonomy of loss functions that follows the perspectives of aggregate loss and individual loss. We identify the aggregator to form such losses, which are examples of set functions. We organize the rank-based decomposable losses into eight categories. Following these categories, we review the literature on rank-based aggregate losses and rank-based individual losses. We describe general formulas for these losses and connect them with existing research topics. We also suggest future research directions spanning unexplored, remaining, and emerging issues in rank-based decomposable losses.
A Flexible Zero-Inflated Poisson-Gamma model with application to microbiome read counts

In microbiome studies, it is of interest to use a sample from a population of microbes, such as the gut microbiota community, to estimate the population proportion of these taxa. However, due to biases introduced in sampling and preprocessing steps, these observed taxa abundances may not reﬂect true taxa abundance patterns in the ecosystem. Repeated measures including longitudinal study designs may be potential solutions to mitigate the discrepancy between observed abundances and true underlying abundances. Yet, widely observed zero-inﬂation and over-dispersion issues can distort downstream statistical analyses aiming to associate taxa abundances with covariates of interest. To this end, we propose a Zero-Inﬂated Poisson Gamma (ZIPG) framework to address the aforementioned challenges. From a perspective of measurement errors, we accommodate the discrepancy between observations and truths by decomposing the mean parameter in Poisson regression into a true abundance level and a multiplicative measurement of sampling variability from the microbial ecosystem. Then, we provide ﬂexible modeling by connecting both mean abundance and the variability to different covariates, and build valid statistical inference procedures for both parameter estimation and hypothesis testing. Through comprehensive simulation studies and real data applications, the proposed ZIPG method provides signiﬁcant insights into distinguished differential variability and abundance. and PG for the inference of β 1 and only ZIPG-bWald, ZIPG-pbWald, and PG for the inference on β ∗ 1 , because inference of the dispersion parameter is not applicable in pscl and NBZIMM. Results are presented based on L = 1 , 000 Monte Carlo replicates for each scenario. The performances of ZIPG-bWald and ZIPG-pbWald are evaluated using B = 200 bootstrap samples, which are numerically sufﬁcient and stable based on our experience.
Depression among medical students

Mental health disorders and mental health-related problems are an increasingly significant public health problem among the student population world-wide. Current data suggest that there is a high prevalence of depressive symptoms among medical students, which retains in their later professional carrier. The aim of this study is to highlight the prevalence, predictive factors, consequences and treatment opportunities for medical students' depression, by reviewing previous papers. Most of the studies conclude that the prevalence of depression among medical students is higher than in the general population. The prevalence of depressive symptoms ranges from 10% to 60%. Depression of medical students is associated with poorer quality of life, impaired academic productivity and increased usage of benzodiazepines. Medical students are less likely to use the appropriate medical treatment than the general population despite the seemingly better access to care. Only 20-30% of the depressed students ask for help in the appropriate health services and the same percentage of them use adequate therapy. The high prevalence of psychological distress among medical students indicates the need for a different way of psychological and social support for improvements of mental health and quality of life in future physicians.
TS2-Net: Token Shift and Selection Transformer for Text-Video Retrieval

Text-Video retrieval is a task of great practical value and has received increasing attention, among which learning spatial-temporal video representation is one of the research hotspots. The video encoders in the state-of-the-art video retrieval models usually directly adopt the pre-trained vision backbones with the network structure fixed, they therefore can not be further improved to produce the fine-grained spatial-temporal video representation. In this paper, we propose Token Shift and Selection Network (TS2-Net), a novel token shift and selection transformer architecture, which dynamically adjusts the token sequence and selects informative tokens in both temporal and spatial dimensions from input video samples. The token shift module temporally shifts the whole token features back-and-forth across adjacent frames, to preserve the complete token representation and capture subtle movements. Then the token selection module selects tokens that contribute most to local spatial semantics. Based on thorough experiments, the proposed TS2-Net achieves state-of-the-art performance on major text-video retrieval benchmarks, including new records on MSRVTT, VATEX, LSMDC, ActivityNet, and DiDeMo. Code will be available at https://github.com/ yuqi657/ts2_net .
Job prospects and labour mobility in China

China's structural changes have brought new challenges to its regional employment structures, entailing labour redistribution. Until now, Chinese migration research with a forward-looking perspective and on bilateral longitudinal determinants at the prefecture city level is almost non-existent. This paper investigates the effects of job prospects on individual migration decisions across prefecture boundaries. To this end, we created proxy variables for wage and employment prospects, introduced reference-dependence to a dynamic discrete choice model, and estimated corresponding empirical specifications with a unique quasi-panel of 66,427 individuals from 283 cities during 1997–2017. To address multilateral resistance to migration resulting from the future attractiveness, we exploited various monadic and dyadic fixed effects. Multilevel logit models and two-step system GMM estimation were adopted for the robustness check. Our primary findings are that a 10% increase in the ratio of sector-based employment prospects in cities of destination to cities of origin raises the probability of migration by 1.281–2.185 percentage points, and the effects tend to be stronger when the scale of the ratio is larger. Having a family migration network causes an increase of approximately 6 percentage points in migratory probabilities. Further, labour migrants are more likely to be male, unmarried, younger, or more educated. Our results suggest that the ongoing industrial reform in China influences labour mobility between cities, providing important insights for regional policymakers to prevent brain drain and to attract relevant talent.
Gauged U(1)Lμ−Lτ symmetry and two-zero textures of inverse neutrino mass matrix in light of muon (g − 2)

In the framework of anomaly free [Formula: see text] model, singlet scalar field with nonzero [Formula: see text] charge gives rise to massive gauge boson [Formula: see text] through spontaneous symmetry breaking. [Formula: see text] leads to one loop contribution to the muon anomalous magnetic moment. These scalar fields may, also, appear in the structure of right-handed neutrino mass matrix, thus, connecting the possible explanation of muon [Formula: see text] and low-energy neutrino phenomenology through vevs associated with the scalar fields. In this work, we consider textures of inverse neutrino mass matrix [Formula: see text] wherein any two elements of the mass matrix are zero. In this ansatz, with Dirac neutrino mass matrix diagonal, the zero(s) of right-handed Majorana neutrino mass matrix correspond to zero(s) in the low-energy effective neutrino mass matrix (within Type-I seesaw). We have realized two such textures of [Formula: see text] accommodating the muon [Formula: see text] and low-energy neutrino phenomenology. The requirement of successful explanation of muon [Formula: see text], further, constrains the allowed parameter space of the model and results in sharp correlations amongst neutrino mixing angles and CP invariants. The model explains muon [Formula: see text] for [Formula: see text] in the range (0.035–0.100 GeV) and [Formula: see text] which is found to be consistent with constraints coming from the current experiments CCFR, COHERENT, BABAR while being within sensitivities of future experiments such as NA62 and NA64.
Fidelity susceptibility as a diagnostic of the commensurate-incommensurate transition: A revisit of the programmable Rydberg chain

In recent years, programmable Rydberg atom arrays have been widely used to simulate new quantum phases and phase transitions, generating great interest among theorists and experimentalists. Based on the large-scale density matrix renormalization group method, the ground-state phase diagram of one-dimensional Rydberg chains is investigated with ﬁdelity susceptibility as an eﬃcient diagnostic method. We ﬁnd that the competition between Rydberg blockade and external detuning produces unconventional phases and phase transitions. As the Rydberg blockade radius increases, the phase transition between disordered and period-3 density-wave ordered phases changes from the standard three-state Potts to the unconventional chiral universality class. As the radius increases further, a very narrow intermediate ﬂoating phase begins to appear. This result provides a con-cise physical picture to illustrate the numerical results. Compared with previous studies, this work provides more evidence for non-conformal quantum phase transitions in programmable quantum simulators from the perspective of quantum information, showing that ﬁdelity susceptibility can be used to study commensurate-incommensurate phase transitions.
The relationship between whole-body motion and upper limb control for reaching and balance

Two functions of the upper limb are reaching and maintaining balance. To successfully perform these roles requires a mechanism for detecting and compensating for body sway. Here I investigate this possible mechanism by using vestibular stimulation to evoke responses in both the upper and lower limbs. I demonstrate that the evoked arm movements are scaled to the degree of sensed whole-body motion, and only operate when reaching within an earth-fixed reference frame. These findings suggest that vestibular signals contribute to maintaining reach accuracy during unexpected body motion which would otherwise take the limb off-target. I also show that the arm responds to vestibularly sensed body motion for balance. When firmly grasping a stationary support, upper limb forces are coordinated with ground reaction forces to produce a counteractive whole-body sway response. In contrast, during light grasp (<1N grip force) the arm does not actively engage in balance. Instead, it simply acts as a passive sensor to provide feedback of body motion which improves balance. Finally, even though postural control during stance has been successfully modelled as an inverted pendulum, my results suggest that the nervous system does not transform light touch feedback into a signal of rotation about the ankle joint.
ChronosPerseus: Randomized Point-based Value Iteration with Importance Sampling for POSMDPs

In reinforcement learning, agents have successfully used environments modeled with Markov decision processes (MDPs). However, in many problem domains, an agent may suffer from noisy observations or random times until its subsequent decision. While partially observable Markov decision processes (POMDPs) have dealt with noisy observations, they have yet to deal with the unknown time aspect. Of course, one could discretize the time, but this leads to Bellman's Curse of Dimensionality. To incorporate continuous sojourn-time distributions in the agent's decision making, we propose that partially observable semi-Markov decision processes (POSMDPs) can be helpful in this regard. We extend \citet{Spaan2005a} randomized point-based value iteration (PBVI) \textsc{Perseus} algorithm used for POMDP to POSMDP by incorporating continuous sojourn time distributions and using importance sampling to reduce the solver complexity. We call this new PBVI algorithm with importance sampling for POSMDPs -- \textsc{ChronosPerseus}. This further allows for compressed complex POMDPs requiring temporal state information by moving this information into state sojourn time of a POMSDP. The second insight is that keeping a set of sampled times and weighting it by its likelihood can be used in a single backup; this helps further reduce the algorithm complexity. The solver also works on episodic and non-episodic problems. We conclude our paper with two examples, an episodic bus problem and a non-episodic maintenance problem.
On Non-Negative Quadratic Programming in Geometric Optimization

We present experimental and theoretical results on a method that applies a numerical solver iteratively to solve several non-negative quadratic programming problems in geometric optimization. The method gains efficiency by exploiting the potential sparsity of the intermediate solutions. We implemented the method to call quadprog of MATLAB iteratively. In comparison with a single call of quadprog, we obtain a 10-fold speedup on two proximity graph problems in $\mathbb{R}^d$ on some public data sets, a 10-fold speedup on the minimum enclosing ball problem on random points in a unit cube in $\mathbb{R}^d$, and a 5-fold speedup on the polytope distance problem on random points from a cube in $\mathbb{R}^d$ when the input size is significantly larger than the dimension; we also obtain a 2-fold or more speedup on deblurring some gray-scale space and thermal images via non-negative least square. We compare with two minimum enclosing ball software by G\"{a}rtner and Fischer et al.; for 1000 nearly cospherical points or random points in a unit cube, the iterative method overtakes the software by G\"{a}rtner at 20 dimensions and the software by Fischer et al. at 170 dimensions. In the image deblurring experiments, the iterative method compares favorably with other software that can solve non-negative least square, including FISTA with backtracking, SBB, FNNLS, and lsqnonneg of MATLAB. We analyze theoretically the number of iterations taken by the iterative scheme to reduce the gap between the current solution value and the optimum by a factor $e$. Under certain assumptions, we prove a bound proportional to the square root of the number of variables.
Anomalous behaviour in loss-gradient based interpretability methods

Loss-gradients are used to interpret the decision making process of deep learning models. In this work, we evaluate loss-gradient based attribution methods by occluding parts of the input and comparing the performance of the occluded input to the original input. We observe that the occluded input has better performance than the original across the test dataset under certain conditions. Similar behaviour is observed in sound and image recognition tasks. We explore different loss-gradient attribution methods, occlusion levels and replacement values to explain the phenomenon of performance improvement under occlusion.
SPIRAL: A Superlinearly Convergent Incremental Proximal Algorithm for Nonconvex Finite Sum Minimization

We introduce SPIRAL, a SuPerlinearly convergent Incremental pRoximal ALgorithm, for solving nonconvex regularized finite sum problems under a relative smoothness assumption. Each iteration of SPIRAL consists of an inner and an outer loop. It combines incremental gradient updates with a linesearch that has the remarkable property of never being triggered asymptotically, leading to superlinear convergence under mild assumptions at the limit point. Simulation results with L-BFGS directions on different convex, nonconvex, and non-Lipschitz differentiable problems show that our algorithm, as well as its adaptive variant, are competitive to the state of the art.
SepLUT: Separable Image-adaptive Lookup Tables for Real-time Image Enhancement

Image-adaptive lookup tables (LUTs) have achieved great success in real-time image enhancement tasks due to their high efficiency for modeling color transforms. However, they embed the complete transform, including the color component-independent and the component-correlated parts, into only a single type of LUTs, either 1D or 3D, in a coupled manner. This scheme raises a dilemma of improving model expressiveness or efficiency due to two factors. On the one hand, the 1D LUTs provide high computational efficiency but lack the critical capability of color components interaction. On the other, the 3D LUTs present enhanced component-correlated transform capability but suffer from heavy memory footprint, high training difficulty, and limited cell utilization. Inspired by the conventional divide-and-conquer practice in the image signal processor, we present SepLUT (separable image-adaptive lookup table) to tackle the above limitations. Specifically, we separate a single color transform into a cascade of component-independent and component-correlated sub-transforms instantiated as 1D and 3D LUTs, respectively. In this way, the capabilities of two sub-transforms can facilitate each other, where the 3D LUT complements the ability to mix up color components, and the 1D LUT redistributes the input colors to increase the cell utilization of the 3D LUT and thus enable the use of a more lightweight 3D LUT. Experiments demonstrate that the proposed method presents enhanced performance on photo retouching benchmark datasets than the current state-of-the-art and achieves real-time processing on both GPUs and CPUs.
FashionViL: Fashion-Focused Vision-and-Language Representation Learning

Large-scale Vision-and-Language (V+L) pre-training for representation learning has proven to be effective in boosting various downstream V+L tasks. However, when it comes to the fashion domain, existing V+L methods are inadequate as they overlook the unique characteristics of both the fashion V+L data and downstream tasks. In this work, we propose a novel fashion-focused V+L representation learning framework, dubbed as FashionViL. It contains two novel fashion-specific pre-training tasks designed particularly to exploit two intrinsic attributes with fashion V+L data. First, in contrast to other domains where a V+L data point contains only a single image-text pair, there could be multiple images in the fashion domain. We thus propose a Multi-View Contrastive Learning task for pulling closer the visual representation of one image to the compositional multimodal representation of another image+text. Second, fashion text (e.g., product description) often contains rich fine-grained concepts (attributes/noun phrases). To exploit this, a Pseudo-Attributes Classification task is introduced to encourage the learned unimodal (visual/textual) representations of the same concept to be adjacent. Further, fashion V+L tasks uniquely include ones that do not conform to the common one-stream or two-stream architectures (e.g., text-guided image retrieval). We thus propose a flexible, versatile V+L model architecture consisting of a modality-agnostic Transformer so that it can be flexibly adapted to any downstream tasks. Extensive experiments show that our FashionViL achieves a new state of the art across five downstream tasks. Code is available at https://github.com/BrandonHanx/mmf.