Miracle-dz/UniFAR-FTUs huggingface dataset:
Evidence for an Excitonic Insulator State in 
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The excitonic insulator (EI) is an exotic ground state of narrow-gap semiconductors and semimetals arising from spontaneous condensation of electron-hole pairs bound by attractive Coulomb interaction. Despite research on EIs dating back to half a century ago, their existence in real materials remains a subject of ongoing debate. In this study, through systematic experimental and theoretical investigations, we provide evidence for the existence of an EI ground state in a van der Waals compound Ta$_2$Pd$_3$Te$_5$. Density-functional-theory calculations suggest that it is a semimetal with a small band overlap, whereas various experiments exhibit an insulating ground state with a clear band gap. Upon incorporating electron-hole Coulomb interaction into our calculations, we obtain an EI phase where the electronic symmetry breaking opens a many-body gap. Angle-resolved photoemission spectroscopy measurements exhibit that the band gap is closed with a significant change in the dispersions as the number of thermally excited charge carriers becomes sufficiently large in both equilibrium and nonequilibrium states. Structural measurements reveal a slight breaking of crystal symmetry with exceptionally small lattice distortion in the insulating state, which cannot account for the significant gap opening. Therefore, we attribute the insulating ground state with a gap opening in Ta$_2$Pd$_3$Te$_5$ to exciton condensation, where the coupling to the symmetry-breaking electronic state induces a subtle change in the crystal structure.
Images in Discrete Choice Modeling: Addressing Data Isomorphism in Multi-Modality Inputs

This paper explores the intersection of Discrete Choice Modeling (DCM) and machine learning, focusing on the integration of image data into DCM's utility functions and its impact on model interpretability. We investigate the consequences of embedding high-dimensional image data that shares isomorphic information with traditional tabular inputs within a DCM framework. Our study reveals that neural network (NN) components learn and replicate tabular variable representations from images when co-occurrences exist, thereby compromising the interpretability of DCM parameters. We propose and benchmark two methodologies to address this challenge: architectural design adjustments to segregate redundant information, and isomorphic information mitigation through source information masking and inpainting. Our experiments, conducted on a semi-synthetic dataset, demonstrate that while architectural modifications prove inconclusive, direct mitigation at the data source shows to be a more effective strategy in maintaining the integrity of DCM's interpretable parameters. The paper concludes with insights into the applicability of our findings in real-world settings and discusses the implications for future research in hybrid modeling that combines complex data modalities. Full control of tabular and image data congruence is attained by using the MIT moral machine dataset, and both inputs are merged into a choice model by deploying the Learning Multinomial Logit (L-MNL) framework.
Superhedging Supermartingales

Supermartingales are here defined on a non-probabilistic setting and can be interpreted solely in terms of superhedging operations. The classical expectation operator is replaced by a pair of subadditive operators one of them providing a class of null sets and the other one acting as an outer integral. These operators are motivated by a financial theory of no-arbitrage pricing. Such a setting extends the classical stochastic framework by replacing the path space of the process by a trajectory set, while also providing a financial/gambling interpretation based on the notion of superhedging. The paper proves analogues of the following classical results: Doob's supermartingale decomposition and Doob's pointwise convergence theorem for nonnegative supermartingales. The approach shows how linearity of the expectation operator can be circumvented and how integrability properties in the proposed setting lead to the special case of (hedging) martingales while no integrability conditions are required for the general supermartingale case.
Opportunities for the direct manipulation of a phase-driven Andreev spin qubit

In a Josephson junction, the transfer of Cooper pairs from one superconductor to the other one can be associated with the formation of Andreev bound states. In a Josephson junction made with a semiconducting nanowire, the spin degeneracy of these Andreev states can be broken thanks to the presence of spin-orbit coupling and a finite phase difference between the two superconducting electrodes. The lifting of the spin degeneracy opened the way to the realization of Andreev spin qubits that do not require the application of a large magnetic field. So far the operation of these qubits relied on a Raman process involving two microwave tones and a third Andreev state [M. Hays et al., Science 373, 430 (2021)]. Still, time-reversal preserving impurities in the nanowire allow for spin-flip scattering processes. Here, using the formalism of scattering matrices, we show that these processes generically couple Andreev states with opposite spins. In particular, the non-vanishing current matrix element between them allows for the direct manipulation of phase-driven Andreev spin qubits, thereby circumventing the use of the above-mentioned Raman process.
Prototype-based Cross-Modal Object Tracking

Cross-modal object tracking is an important research topic in the field of information fusion, and it aims to address imaging limitations in challenging scenarios by integrating switchable visible and near-infrared modalities. However, existing tracking methods face some difficulties in adapting to significant target appearance variations in the presence of modality switch. For instance, model update based tracking methods struggle to maintain stable tracking results during modality switching, leading to error accumulation and model drift. Template based tracking methods solely rely on the template information from first frame and/or last frame, which lacks sufficient representation ability and poses challenges in handling significant target appearance changes. To address this problem, we propose a prototype-based cross-modal object tracker called ProtoTrack, which introduces a novel prototype learning scheme to adapt to significant target appearance variations, for cross-modal object tracking. In particular, we design a multi-modal prototype to represent target information by multi-kind samples, including a fixed sample from the first frame and two representative samples from different modalities. Moreover, we develop a prototype generation algorithm based on two new modules to ensure the prototype representative in different challenges.
Kernel Heterogeneity Improves Sparseness of Natural Images Representations

Both biological and artificial neural networks inherently balance their performance with their operational cost, which balances their computational abilities. Typically, an efficient neuromorphic neural network is one that learns representations that reduce the redundancies and dimensionality of its input. This is for instance achieved in sparse coding, and sparse representations derived from natural images yield representations that are heterogeneous, both in their sampling of input features and in the variance of those features. Here, we investigated the connection between natural images' structure, particularly oriented features, and their corresponding sparse codes. We showed that representations of input features scattered across multiple levels of variance substantially improve the sparseness and resilience of sparse codes, at the cost of reconstruction performance. This echoes the structure of the model's input, allowing to account for the heterogeneously aleatoric structures of natural images. We demonstrate that learning kernel from natural images produces heterogeneity by balancing between approximate and dense representations, which improves all reconstruction metrics. Using a parametrized control of the kernels' heterogeneity used by a convolutional sparse coding algorithm, we show that heterogeneity emphasizes sparseness, while homogeneity improves representation granularity. In a broader context, these encoding strategy can serve as inputs to deep convolutional neural networks. We prove that such variance-encoded sparse image datasets enhance computational efficiency, emphasizing the benefits of kernel heterogeneity to leverage naturalistic and variant input structures and possible applications to improve the throughput of neuromorphic hardware.
ESBMC v7.4: Harnessing the Power of Intervals

ESBMC implements many state-of-the-art techniques for model checking. We report on new and improved features that allow us to obtain verification results for previously unsupported programs and properties. ESBMC employs a new static interval analysis of expressions in programs to increase verification performance. This includes interval-based reasoning over booleans and integers, forward and backward contractors, and particular optimizations related to singleton intervals because of their ubiquity. Other relevant improvements concern the verification of concurrent programs, as well as several operational models, internal ones, and also those of libraries such as pthread and the C mathematics library. An extended memory safety analysis now allows tracking of memory leaks that are considered still reachable.
Dimension-free comparison estimates for suprema of some canonical processes

We obtain error approximation bounds between expected suprema of canonical processes that are generated by random vectors with independent coordinates and expected suprema of Gaussian processes. In particular, we obtain a sharper proximity estimate for Rademacher and Gaussian complexities. Our estimates are dimension-free, and depend only on the geometric parameters and the numerical complexity of the underlying index set.
Plan, Posture and Go: Towards Open-World Text-to-Motion Generation

Conventional text-to-motion generation methods are usually trained on limited text-motion pairs, making them hard to generalize to open-world scenarios. Some works use the CLIP model to align the motion space and the text space, aiming to enable motion generation from natural language motion descriptions. However, they are still constrained to generate limited and unrealistic in-place motions. To address these issues, we present a divide-and-conquer framework named PRO-Motion, which consists of three modules as motion planner, posture-diffuser and go-diffuser. The motion planner instructs Large Language Models (LLMs) to generate a sequence of scripts describing the key postures in the target motion. Differing from natural languages, the scripts can describe all possible postures following very simple text templates. This significantly reduces the complexity of posture-diffuser, which transforms a script to a posture, paving the way for open-world generation. Finally, go-diffuser, implemented as another diffusion model, estimates whole-body translations and rotations for all postures, resulting in realistic motions. Experimental results have shown the superiority of our method with other counterparts, and demonstrated its capability of generating diverse and realistic motions from complex open-world prompts such as"Experiencing a profound sense of joy". The project page is available at https://moonsliu.github. io/Pro-Motion.
ChatGPT, Llama, can you write my report? An experiment on assisted digital forensics reports written using (Local) Large Language Models

Generative AIs, especially Large Language Models (LLMs) such as ChatGPT or Llama, have advanced significantly, positioning them as valuable tools for digital forensics. While initial studies have explored the potential of ChatGPT in the context of investigations, the question of to what extent LLMs can assist the forensic report writing process remains unresolved. To answer the question, this article first examines forensic reports with the goal of generalization (e.g., finding the `average structure' of a report). We then evaluate the strengths and limitations of LLMs for generating the different parts of the forensic report using a case study. This work thus provides valuable insights into the automation of report writing, a critical facet of digital forensics investigations. We conclude that combined with thorough proofreading and corrections, LLMs may assist practitioners during the report writing process but at this point cannot replace them.
Convolution Neural Network Model Framework to Predict Microscale Drag Force for Turbulent Flow in Porous Media

Convolution Neural Networks (CNN) are well-suited to model the nonlinear relationship between the microscale geometry of porous media and the corresponding flow distribution, thereby accurately and efficiently coupling the flow behavior at the micro- and macro- scale levels. In this paper, we have identified the challenges involved in implementing CNNs for macroscale model closure in the turbulent flow regime, particularly in the prediction of the drag force components arising from the microscale level. We report that significant error is incurred in the crucial data preparation step when the Reynolds averaged pressure and velocity distributions are interpolated from unstructured stretched grids used for Large Eddy Simulation (LES) to the structured uniform grids used by the CNN model. We show that the range of the microscale velocity values is 10 times larger than the range of the pressure values. This invalidates the use of the mean squared error loss function to train the CNN model for multivariate prediction. We have developed a CNN model framework that addresses these challenges by proposing a conservative interpolation method and a normalized mean squared error loss function. We simulated a model dataset to train the CNN for turbulent flow prediction in periodic porous media composed of cylindrical solid obstacles with square cross-section by varying the porosity in the range 0.3 to 0.88. We demonstrate that the resulting CNN model predicts the pressure and viscous drag forces with less than 10% mean absolute error when compared to LES while offering a speedup of O(10^6).
Attacking Byzantine Robust Aggregation in High Dimensions

Training modern neural networks or models typically requires averaging over a sample of high-dimensional vectors. Poisoning attacks can skew or bias the average vectors used to train the model, forcing the model to learn specific patterns or avoid learning anything useful. Byzantine robust aggregation is a principled algorithmic defense against such biasing. Robust aggregators can bound the maximum bias in computing centrality statistics, such as mean, even when some fraction of inputs are arbitrarily corrupted. Designing such aggregators is challenging when dealing with high dimensions. However, the first polynomial-time algorithms with strong theoretical bounds on the bias have recently been proposed. Their bounds are independent of the number of dimensions, promising a conceptual limit on the power of poisoning attacks in their ongoing arms race against defenses. In this paper, we show a new attack called HIDRA on practical realization of strong defenses which subverts their claim of dimension-independent bias. HIDRA highlights a novel computational bottleneck that has not been a concern of prior information-theoretic analysis. Our experimental evaluation shows that our attacks almost completely destroy the model performance, whereas existing attacks with the same goal fail to have much effect. Our findings leave the arms race between poisoning attacks and provable defenses wide open.
Latest Pleistocene and Holocene glacial events in the Colonia valley, Northern Patagonia Icefield, southern Chile

The Northern Patagonia Icefield (NPI) is the primary glaciated terrain worldwide at its latitude (46.5–47.5°S), and constraining its glacial history provides unique information for reconstructing Southern Hemisphere paleoclimate. The Colonia Glacier is the largest outlet glacier draining the eastern NPI. Ages were determined using dendrochronology, lichenometry, radiocarbon, cosmogenic 10Be and optically stimulated luminescence. Dated moraines in the Colonia valley defined advances at 13.2 ± 0.95, 11.0 ± 0.47 and 4.96 ± 0.21 ka, with the last being the first constraint on the onset of Neoglaciation for the eastern NPI from a directly dated landform. Dating in the tributary Cachet valley, which contains an ice‐dammed lake during periods of Colonia Glacier expansion, defined an advance at ca. 2.95 ± 0.21 ka, periods of advancement at 810 ± 49 cal a BP and 245 ± 13 cal a BP, and retreat during the intervening periods. Recent Colonia Glacier thinning, which began in the late 1800s, opened a lower‐elevation outlet channel for Lago Cachet Dos in ca. 1960. Our data provide the most comprehensive set of Latest Pleistocene and Holocene ages for a single NPI outlet glacier and expand previously developed NPI glacial chronologies.
Multiagent Copilot Approach for Shared Autonomy between Human EEG and TD3 Deep Reinforcement Learning

Deep reinforcement learning (RL) algorithms enable the development of fully autonomous agents that can interact with the environment. Brain-computer interface (BCI) systems decipher human implicit brain signals regardless of the explicit environment. In this study, we integrated deep RL and BCI to improve beneficial human interventions in autonomous systems and the performance in decoding brain activities by considering environmental factors. Shared autonomy was allowed between the action command decoded from the electroencephalography (EEG) of the human agent and the action generated from the twin delayed DDPG (TD3) agent for a given environment. Our proposed copilot control scheme with a full blocker (Co-FB) significantly outperformed the individual EEG (EEG-NB) or TD3 control. The Co-FB model achieved a higher target approaching score, lower failure rate, and lower human workload than the EEG-NB model. The Co-FB control scheme had a higher invisible target score and level of allowed human intervention than the TD3 model. We also proposed a disparity d-index to evaluate the effect of contradicting agent decisions on the control accuracy and authority of the copilot model. We found a significant correlation between the control authority of the TD3 agent and the performance improvement of human EEG classification with respect to the d-index. We also observed that shifting control authority to the TD3 agent improved performance when BCI decoding was not optimal. These findings indicate that the copilot system can effectively handle complex environments and that BCI performance can be improved by considering environmental factors. Future work should employ continuous action space and different multi-agent approaches to evaluate copilot performance.
Efficacy of Machine-Generated Instructions

Large"instruction-tuned"language models (i.e., finetuned to respond to instructions) have demonstrated a remarkable ability to generalize zero-shot to new tasks. Nevertheless, they depend heavily on human-written instruction data that is often limited in quantity, diversity, and creativity, therefore hindering the generality of the tuned model. We conducted a quantitative study to figure out the efficacy of machine-generated annotations, where we compare the results of a fine-tuned BERT model with human v/s machine-generated annotations. Applying our methods to the vanilla GPT-3 model, we saw that machine generated annotations were 78.54% correct and the fine-tuned model achieved a 96.01% model performance compared to the performance with human-labelled annotations. This result shows that machine-generated annotations are a resource and cost effective way to fine-tune down-stream models.
Pronóstico de caudales con Filtro de Kalman Discreto en el río Turbio

Se propuso la implementacion del algoritmo del Filtro de Kalman Discreto (DKF) junto con un modelo autorregresivo con entrada exogena (ARX) para realizar el pronostico de caudales a corto plazo con 24, 48, 72 y 96 horas de anticipacion en la cuenca del rio Turbio, localizada en el estado de Guanajuato y parte del estado de Jalisco, Mexico, vulnerable a inundaciones durante los periodos de lluvias que se presentan normalmente en la zona. La informacion de precipitacion y caudal disponible con las que se realizaron los pronosticos corresponde a las series de los anos 2003 y 2004. Los resultados obtenidos indican que los pronosticos realizados un paso hacia adelante, es decir, con un tiempo de 24 horas de anticipacion, presentaron los mejores ajustes en terminos de Nash-Sutcliffe, MSE y RMSE, que los pronosticos realizados a 48, 72 y 96 horas de anticipacion.
Global Rank Sum Test: An Efficient Rank-Based Nonparametric Test for Large Scale Online Experiment

Online experiments are widely used for improving online services. While doing online experiments, The student t-test is the most widely used hypothesis testing technique. In practice, however, the normality assumption on which the t-test depends on may fail, which resulting in untrustworthy results. In this paper, we first discuss the question of when the t-test fails, and thus introduce the rank-sum test. Next, in order to solve the difficulties while implementing rank-sum test in large online experiment platforms, we proposed a global-rank-sum test method as an improvement for the traditional one. Finally, we demonstrate that the global-rank-sum test is not only more accurate and has higher statistical power than the t-test, but also more time efficient than the traditional rank-sum test, which eventually makes it possible for large online experiment platforms to use.
Logic-Scaffolding: Personalized Aspect-Instructed Recommendation Explanation Generation using LLMs

The unique capabilities of Large Language Models (LLMs), such as the natural language text generation ability, position them as strong candidates for providing explanation for recommendations. However, despite the size of the LLM, most existing models struggle to produce zero-shot explanations reliably. To address this issue, we propose a framework called Logic-Scaffolding, that combines the ideas of aspect-based explanation and chain-of-thought prompting to generate explanations through intermediate reasoning steps. In this paper, we share our experience in building the framework and present an interactive demonstration for exploring our results.
On the smooth locus of affine Schubert varieties

We give a simple and uniform proof of a conjecture of Haines-Richarz characterizing the smooth locus of Schubert varieties in twisted affine Grassmannians. Our method is elementary and avoids any representation theoretic techniques, instead relying on a combinatorial analysis of tangent spaces of Schubert varieties.