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We study an online forecasting setting in which, over $T$ rounds, $N$ strategic experts each report a forecast to a mechanism, the mechanism selects one forecast, and then the outcome is revealed. In any given round, each expert has a belief about the outcome, but the expert wishes to select its report so as to maximize the total number of times it is selected. The goal of the mechanism is to obtain low belief regret: the difference between its cumulative loss (based on its selected forecasts) and the cumulative loss of the best expert in hindsight (as measured by the experts' beliefs). We consider exactly truthful mechanisms for non-myopic experts, meaning that truthfully reporting its belief strictly maximizes the expert's subjective probability of being selected in any future round. Even in the full-information setting, it is an open problem to obtain the first no-regret exactly truthful mechanism in this setting. We develop the first no-regret mechanism for this setting via an online extension of the Independent-Event Lotteries Forecasting Competition Mechanism (I-ELF). By viewing this online I-ELF as a novel instance of Follow the Perturbed Leader (FPL) with noise based on random walks with loss-dependent perturbations, we obtain $\tilde{O}(\sqrt{T N})$ regret. Our results are fueled by new tail bounds for Poisson binomial random variables that we develop. We extend our results to the bandit setting, where we give an exactly truthful mechanism obtaining $\tilde{O}(T^{2/3} N^{1/3})$ regret; this is the first no-regret result even among approximately truthful mechanisms., Comment: 44 pages

Dynamic Text-Attributed Graphs (DyTAGs) have numerous real-world applications, e.g. social, collaboration, citation, communication, and review networks. In these networks, nodes and edges often contain text descriptions, and the graph structure can evolve over time. Future link prediction, edge classification, relation generation, and other downstream tasks on DyTAGs require powerful representations that encode structural, temporal, and textual information. Although graph neural networks (GNNs) excel at handling structured data, encoding temporal information within dynamic graphs remains a significant challenge. In this work, we propose LLM-driven Knowledge Distillation for Dynamic Text Attributed Graph (LKD4DyTAG) with temporal encoding to address these challenges. We use a simple, yet effective approach to encode temporal information in edges so that graph convolution can simultaneously capture both temporal and structural information in the hidden representations. To leverage LLM's text processing capabilities for learning richer representations on DyTAGs, we distill knowledge from LLM-driven edge representations (based on a neighborhood's text attributes) into saptio-temporal representations using a lightweight GNN model that encodes temporal and structural information. The objective of knowledge distillation enables the GNN to learn representations that more effectively encode the available structural, temporal, and textual information in DyTAG. We conducted extensive experimentation on six real-world DyTAG datasets to verify the effectiveness of our approach LKD4DyTAG for future link prediction and edge classification task. The results show that our approach significantly improves the performance of downstream tasks compared to the baseline models., Comment: Accepted at the AI4TS: AI for Time Series Analysis workshop, AAAI 2025

Rare earth chalcogenides provide a great platform to study exotic quantum phenomena such as superconductivity and charge density waves. Among various interesting properties, the coupling between magnetism and electronic transport has attracted significant attention. Here, we report the investigation of such coupling in {alpha}-Gd2Se3 single crystals through magnetic, calorimetric, and transport property measurements. {alpha}-Gd2Se3 is found to display an antiferromagnetic ground state below 11 K with metamagnetic spin-flop transitions. The magnetic fluctuations remain strong above the transition temperature. Transport measurements reveal an overall metallic transport behavior with a large negative magnetoresistance of ~ 65% near the magnetic transition temperature, together with positive MR near the field-induced spin-flop transitions, which can be understood in terms of the suppression of spin scattering by the magnetic field., Comment: 13 Pages, 5 figures, 1 Table

Driven by the need to address labor shortages and meet the demands of a rapidly growing population, robotic automation has become a critical component in precision agriculture. Leaf-level hyperspectral spectroscopy is shown to be a powerful tool for phenotyping, monitoring crop health, identifying essential nutrients within plants as well as detecting diseases and water stress. This work introduces RoMu4o, a robotic manipulation unit for orchard operations offering an automated solution for proximal hyperspectral leaf sensing. This ground robot is equipped with a 6DOF robotic arm and vision system for real-time deep learning-based image processing and motion planning. We developed robust perception and manipulation pipelines that enable the robot to successfully grasp target leaves and perform spectroscopy. These frameworks operate synergistically to identify and extract the 3D structure of leaves from an observed batch of foliage, propose 6D poses, and generate collision-free constraint-aware paths for precise leaf manipulation. The end-effector of the arm features a compact design that integrates an independent lighting source with a hyperspectral sensor, enabling high-fidelity data acquisition while streamlining the calibration process for accurate measurements. Our ground robot is engineered to operate in unstructured orchard environments. However, the performance of the system is evaluated in both indoor and outdoor plant models. The system demonstrated reliable performance for 1-LPB hyperspectral sampling, achieving 95% success rate in lab trials and 79% in field trials. Field experiments revealed an overall success rate of 70% for autonomous leaf grasping and hyperspectral measurement in a pistachio orchard. The open-source repository is available at: https://github.com/mehradmrt/UCM-AgBot-ROS2

In a recent advance, Nb3Cl8 two-dimensional crystals with a kagome lattice and electronic topological flat bands has been experimentally fabricated (Nano Lett. 2022, 22, 4596). In this work motivated by the aforementioned progress, we conduct first-principles calculations to explore the structural, phonon dispersion relations, single-layer exfoliation energies and mechanical features of the Nb3X8 (X=Cl, Br, I) nanosheets. Acquired phonon dispersion relations reveal the dynamical stability of the Nb3X8 (X=Cl, Br, I) monolayers. In order to isolate single-layer crystals from bulk counterparts, we predicted exfoliation energies of 0.24, 0.27 and 0.28 J/m2, for the Nb3Cl8, Nb3Br8 and Nb3I8 monolayers, respectively, which are noticeably lower than that of the graphene. We found that the Nb3X8 monolayers are relatively strong nanosheets with isotropic elasticity and anisotropic tensile strength. It is moreover shown that by increasing the atomic weight of halogen atoms in the Nb3X8 nanosheets, mechanical characteristics decline. Presented results provide a useful vision about the key physical properties of novel 2D systems of Nb3X8 (X=Cl, Br, I).

Understanding the fracture mechanisms in composite materials across scales, from nano- to micro-scales, is essential for an in-depth understanding of the reinforcement mechanisms and designing the next generation of lightweight, high-strength composites. However, conventional methods struggle to model the complex fracture behavior of nanocomposites, particularly at the fiber-matrix interface. The phase-field regularized cohesive fracture model has proven to be effective in simulating crack initiation, branching, and propagation; however, capturing the cohesive fracture strength at smaller scales remains a significant challenge. This study introduces a novel approach that combines an energy-based star-convex decomposition cohesive phase-field fracture model with molecular dynamics simulations to explore the thickness dependency of nanocomposite mechanical properties. The proposed framework enables hierarchical modeling of carbon-nitride nanosheet-reinforced composites' mechanical and fracture behaviors. The developed model could elucidate complex fracture processes across different scales and highlight critical scaling effects. This methodology provides an efficient solution for uncovering hierarchical fracture mechanisms in reinforced nanocomposites, offering valuable insights into their fracture behavior and strengthening mechanisms.

Photonic integrated circuits (PICs) have been acknowledged as the promising platforms for the applications in data communication, Lidar in autonomous driving vehicles, innovative sensor technology, etc. Since the demonstration of optical components individually, integration of both electronics and photonics for functional devices on a common platform has been a key technology driver enhancing the stability and scalability of integrated photonic technologies. Recently, we proposed to use sapphire as a high-performance PIC platform, which enables a fully integrated solution to include a complete set of components with light source, modulator, light detection, passive devices, silicon on sapphire control circuit all-in-one sapphire platform to achieve high-performance low-cost mixed-signal optical links. In parallel to developing ac-tive components such as group III-V lasers on sapphire, in this work, the performance of group III-V straight waveguides on sapphire was systemically studied. The refractive indices contrast between GaAs, InP, GaSb, and sapphire are sufficiently high to achieve low loss over a broad optical wavelength. The calculated loss at wavelengths of 1330 nm, 1550 nm, and 2000 nm for the GaAs, InP, and GaSb rib waveguides are 0.32 dB/cm, 0.67 dB/cm, and 0.70 dB/cm, re-spectively. Since the fundamental element to construct all passive building blocks is the straight waveguide, results from this work would allow us to assess other basic passive building blocks., Comment: 15 pages, 5 figures

Computational Fluid Dynamics (CFD) is an indispensable method of fluid modelling in engineering applications, reducing the need for physical prototypes and testing for tasks such as design optimisation and performance analysis. Depending on the complexity of the system under consideration, models ranging from low to high fidelity can be used for prediction, allowing significant speed-up. However, the choice of model requires information about the actual dynamics of the flow regime. Correctly identifying the regions/clusters of flow that share the same dynamics has been a challenging research topic to date. In this study, we propose a novel hybrid approach to flow clustering. It consists of characterising each sample point of the system with equation-based features, i.e. features are budgets that represent the contribution of each term from the original governing equation to the local dynamics at each sample point. This was achieved by applying the Sparse Identification of Nonlinear Dynamical systems (SINDy) method pointwise to time evolution data. The method proceeds with equation-based clustering using the Girvan-Newman algorithm. This allows the detection of communities that share the same physical dynamics. The algorithm is implemented in both Eulerian and Lagrangian frameworks. In the Lagrangian, i.e. dynamic approach, the clustering is performed on the trajectory of each point, allowing the change of clusters to be represented also in time. The performance of the algorithm is first tested on a flow around a cylinder. The construction of the dynamic clusters in this test case clearly shows the evolution of the wake from the steady state solution through the transient to the oscillatory solution. Dynamic clustering was then successfully tested on turbulent flow data. Two distinct and well-defined clusters were identified and their temporal evolution was reconstructed.

Large language models (LLMs) pre-trained on massive corpora have demonstrated impressive few-shot learning capability on many NLP tasks. Recasting an NLP task into a text-to-text generation task is a common practice so that generative LLMs can be prompted to resolve it. However, performing document-level relation extraction (DocRE) tasks with generative LLM models is still challenging due to the structured output format of DocRE, which complicates the conversion to plain text. Limited information available in few-shot samples and prompt instructions induce further difficulties and challenges in relation extraction for mentioned entities in a document. In this paper, we represent the structured output as a graph-style triplet rather than natural language expressions and leverage generative LLMs for the DocRE task. Our approach, the Graph-DPEP framework is grounded in the reasoning behind triplet explanation thoughts presented in natural language. In this framework, we first introduce a ``decomposed-plug" method for performing the generation from LLMs over prompts with type-space decomposition to alleviate the burden of distinguishing all relation types. Second, we employ a verifier for calibrating the generation and identifying overlooked query entity pairs. Third, we develop "ensemble-play", reapplying generation on the entire type list by leveraging the reasoning thoughts embedded in a sub-graph associated with the missing query pair to address the missingness issue. Through extensive comparisons with existing prompt techniques and alternative Language Models (LLMs), our framework demonstrates superior performance on publicly available benchmarks in experiments.

Continuous arterial blood pressure (ABP) monitoring is invasive but essential for hemodynamic monitoring. Recent techniques have reconstructed ABP non-invasively using pulsatile signals but produced inaccurate systolic and diastolic blood pressure (SBP and DBP) values and were sensitive to individual variability. ArterialNet integrates generalized pulsatile-to-ABP signal translation and personalized feature extraction using hybrid loss functions and regularization. We validated ArterialNet using the MIMIC-III dataset and achieved a root mean square error (RMSE) of 5.41 mmHg, with at least a 58% lower standard deviation. ArterialNet reconstructed ABP with an RMSE of 7.99 mmHg in remote health scenarios. ArterialNet achieved superior performance in ABP reconstruction and SBP and DBP estimations, with significantly reduced subject variance, demonstrating its potential in remote health settings. We also ablated ArterialNet architecture to investigate the contributions of each component and evaluated its translational impact and robustness by conducting a series of ablations on data quality and availability.

Machine learning has revolutionized the modeling of clinical timeseries data. Using machine learning, a Deep Neural Network (DNN) can be automatically trained to learn a complex mapping of its input features for a desired task. This is particularly valuable in Electronic Health Record (EHR) databases, where patients often spend extended periods in intensive care units (ICUs). Machine learning serves as an efficient method for extract meaningful information. However, many state-of-the-art (SOTA) methods for training DNNs demand substantial volumes of labeled data, posing significant challenges for clinics in terms of cost and time. Self-supervised learning offers an alternative by allowing practitioners to extract valuable insights from data without the need for costly labels. Yet, current SOTA methods often necessitate large data batches to achieve optimal performance, increasing computational demands. This presents a challenge when working with long clinical timeseries data. To address this, we propose an efficient method of contrastive pretraining tailored for long clinical timeseries data. Our approach utilizes an estimator for negative pair comparison, enabling effective feature extraction. We assess the efficacy of our pretraining using standard self-supervised tasks such as linear evaluation and semi-supervised learning. Additionally, our model demonstrates the ability to impute missing measurements, providing clinicians with deeper insights into patient conditions. We demonstrate that our pretraining is capable of achieving better performance as both the size of the model and the size of the measurement vocabulary scale. Finally, we externally validate our model, trained on the MIMIC-III dataset, using the eICU dataset. We demonstrate that our model is capable of learning robust clinical information that is transferable to other clinics.

Transformers have a quadratic scaling of computational complexity with input size, which limits the input context window size of large language models (LLMs) in both training and inference. Meanwhile, retrieval-augmented generation (RAG) besed models can better handle longer contexts by using a retrieval system to filter out unnecessary information. However, most RAG methods only perform retrieval based on the initial query, which may not work well with complex questions that require deeper reasoning. We introduce a novel approach, Inner Loop Memory Augmented Tree Retrieval (ILM-TR), involving inner-loop queries, based not only on the query question itself but also on intermediate findings. At inference time, our model retrieves information from the RAG system, integrating data from lengthy documents at various levels of abstraction. Based on the information retrieved, the LLM generates texts stored in an area named Short-Term Memory (STM) which is then used to formulate the next query. This retrieval process is repeated until the text in STM converged. Our experiments demonstrate that retrieval with STM offers improvements over traditional retrieval-augmented LLMs, particularly in long context tests such as Multi-Needle In A Haystack (M-NIAH) and BABILong.

Objective: realtime monitoring of invasive ventilation (iV) in intensive care units (ICUs) plays a crucial role in ensuring prompt interventions and better patient outcomes. However, conventional methods often overlook valuable insights embedded within clinical notes, relying solely on tabular data. In this study, we propose an innovative approach to enhance iV risk monitoring by incorporating clinical notes into the monitoring pipeline through using language models for text summarization. Results: We achieve superior performance in all metrics reported by the state-of-the-art in iV risk monitoring, namely: an AUROC of 0.86, an AUC-PR of 0.35, and an AUCt of up to 0.86. We also demonstrate that our methodology allows for more lead time in flagging iV for certain time buckets. Conclusion: Our study underscores the potential of integrating clinical notes and language models into realtime iV risk monitoring, paving the way for improved patient care and informed clinical decision-making in ICU settings.

Automated persistent and fine-grained monitoring of orchards at the individual tree or fruit level helps maximize crop yield and optimize resources such as water, fertilizers, and pesticides while preventing agricultural waste. Towards this goal, we present a 4D spatio-temporal metric-semantic mapping method that fuses data from multiple sensors, including LiDAR, RGB camera, and IMU, to monitor the fruits in an orchard across their growth season. A LiDAR-RGB fusion module is designed for 3D fruit tracking and localization, which first segments fruits using a deep neural network and then tracks them using the Hungarian Assignment algorithm. Additionally, the 4D data association module aligns data from different growth stages into a common reference frame and tracks fruits spatio-temporally, providing information such as fruit counts, sizes, and positions. We demonstrate our method's accuracy in 4D metric-semantic mapping using data collected from a real orchard under natural, uncontrolled conditions with seasonal variations. We achieve a 3.1 percent error in total fruit count estimation for over 1790 fruits across 60 apple trees, along with accurate size estimation results with a mean error of 1.1 cm. The datasets, consisting of LiDAR, RGB, and IMU data of five fruit species captured across their growth seasons, along with corresponding ground truth data, will be made publicly available at: https://4d-metric-semantic-mapping.org/

Engineering magnetism in layered magnets could result in novel phenomena related to two-dimensional (2D) magnetism, which can be useful for fundamental research and practical applications. Extensive doping efforts such as substitution and intercalation have been adopted to tune antiferromagnetic (AFM) properties in M2P2X6 compounds. The substitutional doping in this material family has mainly focused on bimetallic substitution. Recently, the metal substitution can also be extended to more than two metal elements, leading to medium and high-entropy alloys (MEAs and HEAs), which are fairly underexplored in layered magnetic systems including M2P2X6. In this work, we explored the magnetic properties of the previously unreported Cu- and Cr-substituted Ni2P2S6 i.e., CuxNi2(1-x)CrxP2S6. Our study reveals a relatively systematic evolution of AFM phases with substitution than that observed in traditional bimetallic substitution in M2P2X6. Furthermore, the Cu and Cr substitutions in Ni2P2S6 are found to enhance the ferromagnetic (FM) correlation, which is also accompanied by a possible weak FM phase at low temperatures for the intermediate compositions from 0.32 to 0.80. Our work provides a strategy to establish ferromagnetism in AFM M2P2X6 that can also be used for property tuning in other layered magnets.

Thermal management at silicon-diamond interface is critical for advancing high-performance electronic and optoelectronic devices. In this study, we calculate the interfacial thermal conductance between silicon and diamond using machine learning (ML) interatomic potentials trained on density functional theory (DFT) data. Using non-equilibrium molecular dynamics (NEMD) simulations, we compute the interfacial thermal conductance (ITC) for various system sizes. Our results show a closer agreement with experimental data than those obtained using traditional semi-empirical potentials such as Tersoff and Brenner which overestimate ITC by a factor of about 3. In addition, we analyze the frequency-dependent heat transfer spectrum, providing insights into the contributions of different phonon modes to the interfacial thermal conductance. The ML potential accurately captures the phonon dispersion relations and lifetimes, in good agreement with DFT calculations and experimental observations. It is shown that the Tersoff potential predicts higher phonon group velocities and phonon lifetimes compared to the DFT results. Furthermore, it predicts higher interfacial bonding strength, which is consistent with higher interfacial thermal conductance as compared to the ML potential. This study highlights the use of the ML interatomic potential to improve the accuracy and computational efficiency of thermal transport simulations in complex material systems.

Recently, layered materials transition metal thiophosphate MPX3 (M = transition metals, X = S or Se) have gained significant attention because of their rich magnetic, optical, and electronic properties. Specifically, the diverse magnetic structures and the robustness of magnetism in the two-dimensional limit have made them prominent candidates to study two-dimensional magnetism. Numerous efforts such as substitutions and interlayer intercalations have been made to tune the properties of these materials, which has greatly deepened the understanding of the underlying mechanisms that govern the properties. In this work, we focus on modifying the magnetism of Ising-type antiferromagnet FePS3 using electrochemical lithium intercalation. Our work unveils the effectiveness of electrochemical intercalation as a controllable tool to modulating magnetism, including tuning magnetic ordering temperature and inducing low temperature spin-glass state, offering an approach for implementing this material into applications.

Nowadays, three-dimensional reconstruction is used in various fields like computer vision, computer graphics, mixed reality and digital twin. The three-dimensional reconstruction of cultural heritage objects is one of the most important applications in this area which is usually accomplished by close range photogrammetry. The problem here is that the images are often noisy, and the dense image matching method has significant limitations to reconstruct the geometric details of cultural heritage objects in practice. Therefore, displaying high-level details in three-dimensional models, especially for cultural heritage objects, is a severe challenge in this field. In this paper, the shape from polarization method has been investigated, a passive method with no drawbacks of active methods. In this method, the resolution of the depth maps can be dramatically increased using the information obtained from the polarization light by rotating a linear polarizing filter in front of a digital camera. Through these polarized images, the surface details of the object can be reconstructed locally with high accuracy. The fusion of polarization and photogrammetric methods is an appropriate solution for achieving high resolution three-dimensional reconstruction. The surface reconstruction assessments have been performed visually and quantitatively. The evaluations showed that the proposed method could significantly reconstruct the surfaces' details in the three-dimensional model compared to the photogrammetric method with 10 times higher depth resolution.

Today, three-dimensional reconstruction of objects has many applications in various fields, and therefore, choosing a suitable method for high resolution three-dimensional reconstruction is an important issue and displaying high-level details in three-dimensional models is a serious challenge in this field. Until now, active methods have been used for high-resolution three-dimensional reconstruction. But the problem of active three-dimensional reconstruction methods is that they require a light source close to the object. Shape from polarization (SfP) is one of the best solutions for high-resolution three-dimensional reconstruction of objects, which is a passive method and does not have the drawbacks of active methods. The changes in polarization of the reflected light from an object can be analyzed by using a polarization camera or locating polarizing filter in front of the digital camera and rotating the filter. Using this information, the surface normal can be reconstructed with high accuracy, which will lead to local reconstruction of the surface details. In this paper, an end-to-end deep learning approach has been presented to produce the surface normal of objects. In this method a benchmark dataset has been used to train the neural network and evaluate the results. The results have been evaluated quantitatively and qualitatively by other methods and under different lighting conditions. The MAE value (Mean-Angular-Error) has been used for results evaluation. The evaluations showed that the proposed method could accurately reconstruct the surface normal of objects with the lowest MAE value which is equal to 18.06 degree on the whole dataset, in comparison to previous physics-based methods which are between 41.44 and 49.03 degree.

This paper proposes a voxel-based approach for creating a digital twin of an urban environment that is capable of efficiently managing smart spaces. The paper explains the registration and localization procedure of the point cloud dataset, which uses the KISS ICP for scan point cloud combination and the RANSAC method for the initial alignment of the combined point cloud. The mobile mapping point cloud using Riegl VMX-250 serves as the reference map, and Velodyne scans are used for localization purposes. The point-to-plane iterative closest-point method is then employed to refine the alignment. The paper evaluates the efficacy of the proposed method by calculating the errors between the estimated and ground truth positions. The results indicate that the voxel-based approach is capable of accurately estimating the position of the sensor platform, which are applicable for various use cases. A specific use case in the context is smart parking space management, which is described and initial visualization results are shown.

Modern vision transformers leverage visually inspired local interaction between pixels through attention computed within window or grid regions, in contrast to the global attention employed in the original ViT. Regional attention restricts pixel interactions within specific regions, while sparse attention disperses them across sparse grids. These differing approaches pose a challenge between maintaining hierarchical relationships vs. capturing a global context. In this study, drawing inspiration from atrous convolution, we propose Atrous Attention, a blend of regional and sparse attention that dynamically integrates both local and global information while preserving hierarchical structures. Based on this, we introduce a versatile, hybrid vision transformer backbone called ACC-ViT, tailored for standard vision tasks. Our compact model achieves approximately 84% accuracy on ImageNet-1K with fewer than 28.5 million parameters, outperforming the state-of-the-art MaxViT by 0.42% while requiring 8.4% fewer parameters., Comment: Accepted as a Workshop Paper at T4V@CVPR2024. arXiv admin note: substantial text overlap with arXiv:2403.04200

Film boiling has practical applications in the current technology including steam power plants, cooling of electronic devices and emergency cooling systems. A finite difference/front tracking method is used to simulate film boiling at high density ratios on a horizontal plate subject to a constant wall heat flux. The grid resolution is relatively high (768 grids per width of the domain). The flow is dominated by Rayleigh-Taylor instability as well as Kelvin-Helmholtz instability. The flow structure includes the roll up of the interface between the gas and liquid. This happens at high density ratio (1000) where the difference between the gas and the liquid velocities across the interface is large. The jump in tangential velocity is an order of magnitude smaller at a lower density ratio (100). Hence, there is no roll up at lower density ratio. The flow is also influenced by vortex development as a result of the baroclinic term in the vorticity transport equation. The density gradient is large at the interface at high density ratio which tends to increase the baroclinic term. The plot of pressure gradient and density gradient shows that they are not parallel in the roll up regions. As a result, vortices in small scales develop that shed in the gas phase. The plot of the enstrophy with time shows that it shows smooth variation that match for two grid resolutions, however at a specific time enstrophies become spiky, and they depart from each other at two grid resolutions. The spiky behavior of enstrophy is due to vortex shedding in the roll up region., Comment: Submitted to Acta Mechanica

Electrocardiogram (ECG) signals, profiling the electrical activities of the heart, are used for a plethora of diagnostic applications. However, ECG systems require multiple leads or channels of signals to capture the complete view of the cardiac system, which limits their application in smartwatches and wearables. In this work, we propose a modally reduced representation learning method for ECG signals that is capable of generating channel-agnostic, unified representations for ECG signals. Through joint optimization of reconstruction and alignment, we ensure that the embeddings of the different channels contain an amalgamation of the overall information across channels while also retaining their specific information. On an independent test dataset, we generated highly correlated channel embeddings from different ECG channels, leading to a moderate approximation of the 12-lead signals from a single-channel embedding. Our generated embeddings can work as competent features for ECG signals for downstream tasks., Comment: Accepted as a Workshop Paper at TS4H@ICLR2024

Stable partitioned techniques for simulating unsteady fluid-structure interaction (FSI) are known to be computationally expensive when high added-mass is involved. Multiple coupling strategies have been developed to accelerate these simulations, but often use predictors in the form of simple finite-difference extrapolations. In this work, we propose a non-intrusive data-driven predictor that couples reduced-order models of both the solid and fluid subproblems, providing an initial guess for the nonlinear problem of the next time step calculation. Each reduced order model is composed of a nonlinear encoder-regressor-decoder architecture and is equipped with an adaptive update strategy that adds robustness for extrapolation. In doing so, the proposed methodology leverages physics-based insights from high-fidelity solvers, thus establishing a physics-aware machine learning predictor. Using three strongly coupled FSI examples, this study demonstrates the improved convergence obtained with the new predictor and the overall computational speedup realized compared to classical approaches.

Magnetic topological semimetals LnSbTe (Ln = Lanthanide) have attracted intensive attention because of the presence of interplay between magnetism, topological, and electron correlations depending on the choices of magnetic Ln elements. Recently, varying Sb-Te composition has been found to effectively control the electronic and magnetic states in LnSbxTe$_{2-x}$. With this motivation, we report the evolution of magnetic properties with Sb-Te substitution in NdSb$_x$Te$_{2-x+\delta}$. Our work reveals the interesting non-monotonic change in magnetic ordering temperature with varying composition stoichiometry. In addition, reducing the Sb content x drives the reorientation of moments from in-plane (ab-plane) to out-of-plane (c-axis) direction that results in the distinct magnetic structures for two end compounds NdTe$_2$ ($x = 0$) and NdSbTe ($x = 1$). Furthermore, the moment orientation in NdSb$_x$Te$_{2-x+\delta}$ is also found to be strongly tunable upon application of weak magnetic field, leading to rich magnetic phases depending on the composition stoichiometry, temperature, and magnetic field. Such strong tuning of magnetism in this material establishes it as a promising platform for investigating tunable topological states and correlated topological physics., Comment: 35 pages, 5 figures

In one view of the classical game of prediction with expert advice with binary outcomes, in each round, each expert maintains an adversarially chosen belief and honestly reports this belief. We consider a recently introduced, strategic variant of this problem with selfish (reputation-seeking) experts, where each expert strategically reports in order to maximize their expected future reputation based on their belief. In this work, our goal is to design an algorithm for the selfish experts problem that is incentive-compatible (IC, or \emph{truthful}), meaning each expert's best strategy is to report truthfully, while also ensuring the algorithm enjoys sublinear regret with respect to the expert with the best belief. Freeman et al. (2020) recently studied this problem in the full information and bandit settings and obtained truthful, no-regret algorithms by leveraging prior work on wagering mechanisms. While their results under full information match the minimax rate for the classical ("honest experts") problem, the best-known regret for their bandit algorithm WSU-UX is $O(T^{2/3})$, which does not match the minimax rate for the classical ("honest bandits") setting. It was unclear whether the higher regret was an artifact of their analysis or a limitation of WSU-UX. We show, via explicit construction of loss sequences, that the algorithm suffers a worst-case $\Omega(T^{2/3})$ lower bound. Left open is the possibility that a different IC algorithm obtains $O(\sqrt{T})$ regret. Yet, WSU-UX was a natural choice for such an algorithm owing to the limited design room for IC algorithms in this setting., Comment: Accepted to AISTATS 2024

Tuning magnetic properties in layered magnets is an important route to realize novel phenomenon related to two-dimensional (2D) magnetism. Recently, tuning antiferromagnetic (AFM) properties through substitution and intercalation techniques have been widely studied in MPX3 compounds. Interesting phenomena, such as diverse AFM structures and even the signatures of ferrimagnetism, have been reported. However, long-range ferromagnetic (FM) ordering has remained elusive. In this work, we explored the magnetic properties of the previously unreported Cr-substituted NiPS3. We found that Cr substitution is extremely efficient in controlling spin orientation in NiPS3. Our study reveals a field-induced spin polarization in lightly (9%) Cr-substituted NiPS3, which is likely attributed to the attenuation of AFM interactions and magnetic anisotropy due to Cr doping. Our work provides a possible strategy to achieve FM phase in AFM MPX3, which could be useful for investigating 2D magnetism as well as potential device applications.