Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Anatomy Education with Generative AI-based Virtual Assistants in Immersive Virtual Reality Environments

Virtual reality (VR) and interactive 3D visualization systems have enhanced educational experiences and environments, particularly in complicated subjects such as anatomy education. VR-based systems surpass the potential limitations of traditional training approaches in facilitating interactive engagement among students. However, research on embodied virtual assistants that leverage generative artificial intelligence (AI) and verbal communication in the anatomy education context is underrepresented. In this work, we introduce a VR environment with a generative AI-embodied virtual assistant to support participants in responding to varying cognitive complexity anatomy questions and enable verbal communication. We assessed the technical efficacy and usability of the proposed environment in a pilot user study with 16 participants. We conducted a within-subject design for virtual assistant configuration (avatar- and screen-based), with two levels of cognitive complexity (knowledge- and analysis-based). The results reveal a significant difference in the scores obtained from knowledge- and analysis-based questions in relation to avatar configuration. Moreover, results provide insights into usability, cognitive task load, and the sense of presence in the proposed virtual assistant configurations. Our environment and results of the pilot study offer potential benefits and future research directions beyond medical education, using generative AI and embodied virtual agents as customized virtual conversational assistants.
Upscaling and Effective Behavior for Two-Phase Porous-Medium Flow using a Diffuse Interface Model

We investigate two-phase flow in porous media and derive a two-scale model, which incorporates pore-scale phase distribution and surface tension into the effective behavior at the larger Darcy scale. The free-boundary problem at the pore scale is modeled using a diffuse interface approach in the form of a coupled Allen-Cahn Navier-Stokes system with an additional momentum flux due to surface tension forces. Using periodic homogenization and formal asymptotic expansions, a two-scale model with cell problems for phase evolution and velocity contributions is derived. We investigate the computed effective parameters and their relation to the saturation for different fluid distributions, in comparison to commonly used relative permeability saturation curves. The two-scale model yields non-monotone relations for relative permeability and saturation. The strong dependence on local fluid distribution and effects captured by the cell problems highlights the importance of incorporating pore-scale information into the macro-scale equations.
Efficient Federated Low Rank Matrix Recovery via Alternating GD and Minimization: A Simple Proof

This note provides a significantly simpler and shorter proof of our sample complexity guarantee for solving the low rank column-wise sensing problem using the Alternating Gradient Descent (GD) and Minimization (AltGDmin) algorithm. AltGDmin was developed and analyzed for solving this problem in our recent work. We also provide an improved guarantee.
Integrating Tick-level Data and Periodical Signal for High-frequency Market Making

We focus on the problem of market making in high-frequency trading. Market making is a critical function in financial markets that involves providing liquidity by buying and selling assets. However, the increasing complexity of financial markets and the high volume of data generated by tick-level trading makes it challenging to develop effective market making strategies. To address this challenge, we propose a deep reinforcement learning approach that fuses tick-level data with periodic prediction signals to develop a more accurate and robust market making strategy. Our results of market making strategies based on different deep reinforcement learning algorithms under the simulation scenarios and real data experiments in the cryptocurrency markets show that the proposed framework outperforms existing methods in terms of profitability and risk management.
Deciphering Long-Range Order in Active Matter: Insights from Swimming Bacteria in Quasi-2D and Electrokinetic Janus Particles

Emergent order resulting from spontaneous symmetry breakings has been a central topic in statistical physics. Active matter systems composed of nonequilibrium elements exhibit a diverse range of fascinating phenomena beyond equilibrium physics. One striking example is the emergent long-range orientational order in two dimensions, which is prohibited in equilibrium systems. The existence of long-range order in active matter systems was predicted first by a numerical model and proven analytically by dynamic renormalization group analysis. Experimental evidence for long-range order with giant number fluctuations has been provided in some experimental systems including microswimmers such as swimming bacteria and electrokinetic Janus particles. In this review, we provide a pedagogical introduction to the theoretical descriptions of long-range order in collective motion of active matter systems and an overview of the experimental efforts in the two prototypical microswimmer experimental systems. We also offer critical assessments of how and when such long-range order can be achieved in experimental systems. By comparing numerical, theoretical, and experimental results, we discuss the future challenges in active matter physics.
General Physical Properties of Fermi Blazars

We study the general physical properties of Fermi blazars using the Fermi fourth source catalog data (4FGL-DR2). The quasi-simultaneous multiwavelength data of Fermi blazars are fitted by using the one-zone leptonic model to obtain some physical parameters, such as jet power, magnetic field, and Doppler factor. We study the distributions of the derived physical parameters as a function of black hole mass and accretion disk luminosity. The main results are as follows. (1) For a standard thin accretion disk, the jet kinetic power of most flat-spectrum radio quasars can be explained by the Blandford–Payne (BP) mechanism. However, the jet kinetic power of most BL Lacertae objects (BL Lacs) cannot be explained by either the Blandford–Znajek mechanism or the BP mechanism. The BL Lacs may have advection-dominated accretion flows surrounding their massive black holes. (2) After excluding the redshift, there is a moderately strong correlation between the jet kinetic power and jet radiation power and the accretion disk luminosity for Fermi blazars. These results confirm a close connection between jet and accretion. The jet kinetic power is slightly larger than the accretion disk luminosity for Fermi blazars. (3) There is a significant correlation between jet kinetic power and gamma-ray luminosity and radio luminosity for Fermi blazars, which suggests that gamma-ray luminosity and radio luminosity can be used to indicate the jet kinetic power.
Vorticity of polariton condensates in rotating traps

This work is inspired by recent experiments on the formation of vortices in exciton-polariton condensates placed in rotating optical traps. We study theoretically the dynamics of formation of such vortices and elucidate the fundamental role of the mode competition effect in determining the properties of stationary polariton states triggered by stimulated scattering of exciton-polaritons. The interplay between linear and non-linear effects is shown to result in a peculiar polariton dynamics. However, near the lasing threshold, the predominant contribution of the nonlinear effects is the saturation of the linear gain.
Data-driven dynamical coarse-graining for condensed matter systems.

Simulations of condensed matter systems often focus on the dynamics of a few distinguished components but require integrating the full system. A prime example is a molecular dynamics simulation of a (macro)molecule in a solution, where the molecule(s) and the solvent dynamics need to be integrated, rendering the simulations computationally costly and often unfeasible for physically/biologically relevant time scales. Standard coarse graining approaches can reproduce equilibrium distributions and structural features but do not properly include the dynamics. In this work, we develop a general data-driven coarse-graining methodology inspired by the Mori-Zwanzig formalism, which shows that macroscopic systems with a large number of degrees of freedom can be described by a few relevant variables and additional noise and memory terms. Our coarse-graining method consists of numerical integrators for the distinguished components, where the noise and interaction terms with other system components are substituted by a random variable sampled from a data-driven model. The model is parameterized using data from multiple short-time full-system simulations, and then, it is used to run long-time simulations. Applying our methodology to three systems-a distinguished particle under a harmonic and a bistable potential and a dimer with two metastable configurations-the resulting coarse-grained models are capable of reproducing not only the equilibrium distributions but also the dynamic behavior due to temporal correlations and memory effects. Remarkably, our method even reproduces the transition dynamics between metastable states, which is challenging to capture correctly. Our approach is not constrained to specific dynamics and can be extended to systems beyond Langevin dynamics, and, in principle, even to non-equilibrium dynamics.
Miniaturized Graph Convolutional Networks with Topologically Consistent Pruning

Magnitude pruning is one of the mainstream methods in lightweight architecture design whose goal is to extract subnetworks with the largest weight connections. This method is known to be successful, but under very high pruning regimes, it suffers from topological inconsistency which renders the extracted subnetworks disconnected, and this hinders their generalization ability. In this paper, we devise a novel magnitude pruning method that allows extracting subnetworks while guarantying their topological consistency. The latter ensures that only accessible and co-accessible -- impactful -- connections are kept in the resulting lightweight networks. Our solution is based on a novel reparametrization and two supervisory bi-directional networks which implement accessibility/co-accessibility and guarantee that only connected subnetworks will be selected during training. This solution allows enhancing generalization significantly, under very high pruning regimes, as corroborated through extensive experiments, involving graph convolutional networks, on the challenging task of skeleton-based action recognition.
Imputing Phylogenetic Trees Using Tropical Polytopes over the Space of Phylogenetic Trees

When we apply comparative phylogenetic analyses to genome data, it poses a significant problem and challenge that some of the given species (or taxa) often have missing genes (i.e., data). In such a case, we have to impute a missing part of a gene tree from a sample of gene trees. In this short paper, we propose a novel method to infer the missing part of a phylogenetic tree using an analogue of a classical linear regression in the setting of tropical geometry. In our approach, we consider a tropical polytope, a convex hull with respect to the tropical metric closest to the data points. We show a condition that we can guarantee that an estimated tree from the method has at most a Robinson–Foulds (RF) distance of four from the ground truth, and computational experiments with simulated data and empirical data from Clavicipitaceae, which contains more than 4000 genes, show the method works well.
A modern framework for jet tagger development

This paper presents a new tool to perform various steps in jet tagger development in an efficient and comprehensive way. A common data structure is used for training, as well as for performance evaluation in data. The introduction of this new framework reduces the amount of data to be stored while accomplishing the same tasks, and shortens waiting times between algorithm development and data-to-simulation results becoming available from months to days, taking typical CMS experiment pipelines as a reference. Proper utilization of high-throughput systems enables first data-to-simulation studies with a recent neural network architecture, Particle Transformer, adapted to jet flavour tagging. Unlike official implementations of the collaboration, the new framework allows investigating different variants, like different training paradigms, and their impact on data/simulation agreement, without producing any new large files on disk, and within the same run of the analysis framework. Besides being more time- and storage-efficient and thus enabling the first results of that kind to be available just few hours after finishing neural network training, the framework is currently the only realization capable of studying how adversarial techniques affect data/simulation agreement for tagger algorithm outputs as well as inputs.
Characters in
 p
 -adic Heisenberg and lattice vertex operator algebras

We study characters of states in $p$-adic vertex operator algebras. In particular, we show that the image of the character map for both the $p$-adic Heisenberg and $p$-adic lattice vertex operator algebras contain infinitely-many non-classical $p$-adic modular forms which are not contained in the image of the algebraic character map.
Universal properties of repulsive self-propelled particles and attractive driven particles

Motility-induced phase separation (MIPS) is a nonequilibrium phase separation that has a different origin from equilibrium phase separation induced by attractive interactions. Similarities and differences in collective behaviors between these two types of phase separation have been intensely discussed. Here, to study another kind of similarity between MIPS and attraction-induced phase separation under a nonequilibrium condition, we perform simulations of active Brownian particles with uniaxially anisotropic self-propulsion (uniaxial ABPs) in two dimensions. We find that (i) long-range density correlation appears in the homogeneous state, (ii) anisotropic particle configuration appears in MIPS, where the anisotropy removes the possibility of microphase separation suggested for isotropic ABPs [X. -Q. Shi et al., Phys. Rev. Lett. 125, 168001 (2020)], and (iii) critical phenomena for the anisotropic MIPS presumably belong to the universality class for two-dimensional uniaxial ferromagnets with dipolar long-range interactions. Properties (i)-(iii) are common to the well-studied randomly driven lattice gas (RDLG), which is a particle model that undergoes phase separation by attractive interactions under external driving forces, suggesting that the origin of phase separation is not essential for macroscopic behaviors of uniaxial ABPs and RDLG. Based on the observations in uniaxial ABPs, we construct a coarse-grained Langevin model, which shows properties (i)-(iii) and corroborates the generality of the findings.
Optimal Execution Using Reinforcement Learning

This work is about optimal order execution, where a large order is split into several small orders to maximize the implementation shortfall. Based on the diversity of cryptocurrency exchanges, we attempt to extract cross-exchange signals by aligning data from multiple exchanges for the first time. Unlike most previous studies that focused on using single-exchange information, we discuss the impact of cross-exchange signals on the agent's decision-making in the optimal execution problem. Experimental results show that cross-exchange signals can provide additional information for the optimal execution of cryptocurrency to facilitate the optimal execution process.
Robust Roadside Perception: an Automated Data Synthesis Pipeline Minimizing Human Annotation

Recently, advancements in vehicle-to-infrastructure communication technologies have elevated the significance of infrastructure-based roadside perception systems for cooperative driving. This paper delves into one of its most pivotal challenges: data insufficiency. The lacking of high-quality labeled roadside sensor data with high diversity leads to low robustness, and low transfer-ability of current roadside perception systems. In this paper, a novel solution is proposed to address this problem that creates synthesized training data using Augmented Reality. A Generative Adversarial Network is then applied to enhance the reality further, that produces a photo-realistic synthesized dataset that is capable of training or fine-tuning a roadside perception detector which is robust to different weather and lighting conditions. Our approach was rigorously tested at two key intersections in Michigan, USA: the Mcity intersection and the State St./Ellsworth Rd roundabout. The Mcity intersection is located within the Mcity test field, a controlled testing environment. In contrast, the State St./Ellsworth Rd intersection is a bustling roundabout notorious for its high traffic flow and a significant number of accidents annually. Experimental results demonstrate that detectors trained solely on synthesized data exhibit commendable performance across all conditions. Furthermore, when integrated with labeled data, the synthesized data can notably bolster the performance of pre-existing detectors, especially in adverse conditions.
Thompson sampling for improved exploration in GFlowNets

Generative flow networks (GFlowNets) are amortized variational inference algorithms that treat sampling from a distribution over compositional objects as a sequential decision-making problem with a learnable action policy. Unlike other algorithms for hierarchical sampling that optimize a variational bound, GFlowNet algorithms can stably run off-policy, which can be advantageous for discovering modes of the target distribution. Despite this flexibility in the choice of behaviour policy, the optimal way of efficiently selecting trajectories for training has not yet been systematically explored. In this paper, we view the choice of trajectories for training as an active learning problem and approach it using Bayesian techniques inspired by methods for multi-armed bandits. The proposed algorithm, Thompson sampling GFlowNets (TS-GFN), maintains an approximate posterior distribution over policies and samples trajectories from this posterior for training. We show in two domains that TS-GFN yields improved exploration and thus faster convergence to the target distribution than the off-policy exploration strategies used in past work.
Empowering NLG: Offline Reinforcement Learning for Informal Summarization in Online Domains

Our research introduces an innovative Natural Language Generation (NLG) approach that aims to optimize user experience and alleviate the workload of human customer support agents. Our primary objective is to generate informal summaries for online articles and posts using an offline reinforcement learning technique. In our study, we compare our proposed method with existing approaches to text generation and provide a comprehensive overview of our architectural design, which incorporates crawling, reinforcement learning, and text generation modules. By presenting this original approach, our paper makes a valuable contribution to the field of NLG by offering a fresh perspective on generating natural language summaries for online content. Through the implementation of Empowering NLG, we are able to generate higher-quality replies in the online domain. The experimental results demonstrate a significant improvement in the average"like"score, increasing from 0.09954378 to 0.5000152. This advancement has the potential to enhance the efficiency and effectiveness of customer support services and elevate the overall user experience when consuming online content.
Radar Cross Section Reduction of Microstrip Patch Antenna using Metamaterial Techniques

Radar cross section (RCS) reduction has become one of the critical research areas in recent years. The RCS of the target should be small to avoid detection. Different methods are used to reduce RCS, but the major challenge with many RCS minimization methodologies is that, it may deteriorate some antenna parameters. When antenna mode RCS is considered; structural mode RCS, and antenna parameters are critical, as the structure should be an antenna and a RCS reducing structure simultaneously. The techniques like applying Radar Absorption Material (RAM) entirely over the target, deployment of Energy Band Gap (EBG) structures, the use of passive, active cancellation, and polarization conversion are prevalent methods to reduce RCS. The manifestation of metamaterial property in an antenna results in the antenna's electromagnetic characteristics becoming negative for a particular bandwidth. Thus the RCS of the antenna can be reduced to a minimum range by loading the metamaterial structures. This paper discusses the application of polarization conversion method (PCM), L-structured and Square-structured fractal metamaterial antenna for RCS reduction. This paper reports the simulation, fabrication, and testing of the above antennas with their performance comparison. The antennas are designed for 4.3GHz frequency with a total dimension of 80mmx80mmx1.6mm. Antenna parameters like return loss, gain, radiation pattern, and bandwidth are analyzed along with the RCS. The L-structured metamaterial antenna implemented has a 29.37% larger bandwidth than the reference patch antenna with a gain of 2.94dB with a return loss of -28.28dB.
Machine learning for sports betting: should predictive models be optimised for accuracy or calibration?

—Sports betting’s recent federal legalisation in the USA coincides with the golden age of machine learning. If bettors can leverage data to reliably predict the probability of an outcome, they can recognise when the bookmaker’s odds are in their favour. As sports betting is a multi-billion dollar industry in the USA alone, identifying such opportunities could be extremely lucrative. Many researchers have applied machine learning to the sports outcome prediction problem, generally using accuracy to evaluate the performance of predictive models. We hypothesise that for the sports betting problem, model calibration is more important than accuracy. To test this hypothesis, we train models on NBA data over several seasons and run betting experiments on a single season, using published odds. We show that optimising the predictive model for calibration leads to greater returns than optimising for accuracy, on average (return on investment of +34.69% versus -35.17%) and in the best case (+36.93% versus +5.56%). These ﬁndings suggest that for sports betting (or any probabilistic decision-making problem), calibration is a more important metric than accuracy. Sports bettors who wish to increase proﬁts should therefore optimise their predictive model for calibration. Impact Statement —Sports bettors have an abundance of publicly available data at their disposal. This data can be leveraged to identify proﬁtable betting opportunities, using machine learning. However, bettors risk losing money if the model’s predictions are unreliable. Bettors generally assume the most reliable predictions come from the most accurate model, when in fact they come from the most well-calibrated model. We show that optimising the sports betting model for calibration leads to greater proﬁts than optimising for accuracy. The returns generated by our calibration-optimised betting systems were 69.86% higher on average, and 31.37% higher when comparing best case returns. With no such comparison between calibration and accuracy present in the literature, our results serve as a benchmark for evaluation of future research. In addition to helping bettors generate greater proﬁts, our ﬁndings may also suggest well-calibrated models are more useful than highly accurate models for probabilistic decision-making, generally.