Miracle-dz/UniFAR-FTUs huggingface dataset:
INACIA: Integrating Large Language Models in Brazilian Audit Courts: Opportunities and Challenges

This paper introduces INACIA ( In strução A ssistida c om I nteligência A rtificial), a groundbreaking system designed to integrate Large Language Models (LLMs) into the operational framework of Brazilian Federal Court of Accounts (TCU). The system automates various stages of case analysis, including basic information extraction, admissibility examination, Periculum in mora and Fumus boni iuris analyses, and recommendations generation. Through a series of experiments, we demonstrate INACIA’s potential in extracting relevant information from case documents, evaluating its legal plausibility, and formulating propositions for judicial decision-making. Utilizing a validation dataset alongside LLMs, our evaluation methodology presents a novel approach to assessing system performance, correlating highly with human judgment. These results underscore INACIA’s potential in complex legal task handling while also acknowledging the current limitations. This study discusses possible improvements and the broader implications of applying AI in legal contexts, suggesting that INACIA represents a significant step towards integrating AI in legal systems globally, albeit with cautious optimism grounded in the empirical findings.
Scalable Cyclic Schedulers for Age of Information Optimization in Large-Scale Status Update Systems

We study cyclic scheduling for generate-at-will (GAW) multi-source status update systems with heterogeneous service times and packet drop probabilities, with the aim of minimizing the weighted sum age of information (AoI), called system AoI, or the weighted sum peak AoI (PAoI), called system PAoI. In particular, we obtain well-performing cyclic schedulers which can easily scale to thousands of information sources and which also have low online implementation complexity. The proposed schedulers are comparatively studied against existing scheduling algorithms in terms of computational load and system AoI/PAoI performance, to validate their effectiveness.
Dynamic models augmented by hierarchical data: an application of estimating HIV epidemics at sub-national level.

Dynamic models have been successfully used in producing estimates of HIV epidemics at the national level due to their epidemiological nature and their ability to estimate prevalence, incidence, and mortality rates simultaneously. Recently, HIV interventions and policies have required more information at sub-national levels to support local planning, decision-making and resource allocation. Unfortunately, many areas lack sufficient data for deriving stable and reliable results, and this is a critical technical barrier to more stratified estimates. One solution is to borrow information from other areas within the same country. However, directly assuming hierarchical structures within the HIV dynamic models is complicated and computationally time-consuming. In this article, we propose a simple and innovative way to incorporate hierarchical information into the dynamical systems by using auxiliary data. The proposed method efficiently uses information from multiple areas within each country without increasing the computational burden. As a result, the new model improves predictive ability and uncertainty assessment.
TOVAC: Tele-operated Vehicle Admission Control and Routing

Tele-operated Driving (ToD) is a challenging use case for mobile network operators. Video captured by the built-in vehicle cameras must be streamed meeting a latency requirement of 5 ms with a 99.999% reliability. Although 5G offers high bandwidth, ultra-low latencies and high reliability; ToD service requirements are violated due to bad channel conditions. Ignoring the channel state may lead to over-estimate the number of ToD vehicles that can meet the service requirements, hence comprising the vehicle security. To fill this gap, in this letter we propose TOVAC, an algorithm that guarantees ToD service requirements by taking adequate admission control and routing decisions. This is achieved by using a channel-based capacity graph that determines the maximum number of vehicles that can be tele-operated in any road section. We evaluate TOVAC considering cellular deployments from Turin and show that, unlike a state of the art solution, TOVAC guarantees the ToD service requirements.
Near surface seismoelectrics in comparative field studies

In porous saturated media, seismic compressional waves can cause measurable electric signals via electrokinetic coupling. Despite increasing interest in the seismoelectric method, little material has been published on actual field measurements. In order to address this deficiency, several sites in Northern Germany were visited at different times to investigate the repeatability and variability of these seismoelectric signals. In general, there is a strong correlation between the overall quality of seismoelectric data and the quality of the associated seismic data. Factors like the electrical resistivity or electrode coupling are less significant. A strong dependence of the seismoelectric signals on the pH value could be shown with borehole seismoelectric measurements. A hodogram analysis of three-component seismoelectric data reveals that the polarization mainly follows theoretical predictions. The measurements, interpretations and comparisons with other methods presented in this work will help to assess the chances of successful application of seismoelectric measurements in near surface geophysical studies.
MoSECroT: Model Stitching with Static Word Embeddings for Crosslingual Zero-shot Transfer

Transformer-based pre-trained language models (PLMs) have achieved remarkable performance in various natural language processing (NLP) tasks. However, pre-training such models can take considerable resources that are almost only available to high-resource languages. On the contrary, static word embeddings are easier to train in terms of computing resources and the amount of data required. In this paper, we introduce MoSECroT Model Stitching with Static Word Embeddings for Crosslingual Zero-shot Transfer), a novel and challenging task that is especially relevant to low-resource languages for which static word embeddings are available. To tackle the task, we present the first framework that leverages relative representations to construct a common space for the embeddings of a source language PLM and the static word embeddings of a target language. In this way, we can train the PLM on source-language training data and perform zero-shot transfer to the target language by simply swapping the embedding layer. However, through extensive experiments on two classification datasets, we show that although our proposed framework is competitive with weak baselines when addressing MoSECroT, it fails to achieve competitive results compared with some strong baselines. In this paper, we attempt to explain this negative result and provide several thoughts on possible improvement.
Distributed Monitoring for Data Distribution Shifts in Edge-ML Fraud Detection

The digital era has seen a marked increase in financial fraud. edge ML emerged as a promising solution for smartphone payment services fraud detection, enabling the deployment of ML models directly on edge devices. This approach enables a more personalized real-time fraud detection. However, a significant gap in current research is the lack of a robust system for monitoring data distribution shifts in these distributed edge ML applications. Our work bridges this gap by introducing a novel open-source framework designed for continuous monitoring of data distribution shifts on a network of edge devices. Our system includes an innovative calculation of the Kolmogorov-Smirnov (KS) test over a distributed network of edge devices, enabling efficient and accurate monitoring of users behavior shifts. We comprehensively evaluate the proposed framework employing both real-world and synthetic financial transaction datasets and demonstrate the framework's effectiveness.
A Comparison of Star-formation Histories Derived from UniverseMachine and LEGA-C at 0.6 < z < 1

In this work, we compare star formation histories of massive (10.5 <log(M*/M⊙)< 12) galaxies in the UniverseMachine model to those measured from the Large Early Galaxy Astrophysics Census (LEGA-C) at 0.6 < z < 1. Following the LEGA-C study, we investigate how 50% (t 50) and 90% (t 90) formation timescales depend on total stellar mass. We find good agreement between the observed and model timescales for the star-forming population Δ t SF ≲ 1 Gyr across the full mass range. In contrast, the observed age-mass correlation is weaker for the quiescent population compared to UniverseMachine models (Δt Q ≲ 2 Gyr), especially at the high-mass end. This indicates continued star formation or additional processes in the most massive quiescent galaxies, a behavior not accounted for in the UniverseMachine model.
MGNet: Learning Correspondences via Multiple Graphs

Learning correspondences aims to find correct correspondences (inliers) from the initial correspondence set with an uneven correspondence distribution and a low inlier rate, which can be regarded as graph data. Recent advances usually use graph neural networks (GNNs) to build a single type of graph or simply stack local graphs into the global one to complete the task. But they ignore the complementary relationship between different types of graphs, which can effectively capture potential relationships among sparse correspondences. To address this problem, we propose MGNet to effectively combine multiple complementary graphs. To obtain information integrating implicit and explicit local graphs, we construct local graphs from implicit and explicit aspects and combine them effectively, which is used to build a global graph. Moreover, we propose Graph Soft Degree Attention (GSDA) to make full use of all sparse correspondence information at once in the global graph, which can capture and amplify discriminative features. Extensive experiments demonstrate that MGNet outperforms state-of-the-art methods in different visual tasks. The code is provided in https://github.com/DAILUANYUAN/MGNet-2024AAAI.
Quantum enhanced balanced heterodyne readout for differential interferometry

Conventional heterodyne readout schemes are now under reconsideration due to the realization of techniques to evade its inherent 3 dB signal-to-noise penalty. The application of high-frequency, spectrally entangled, two-mode squeezed states can further improve the readout sensitivity of audio-band signals. In this paper, we experimentally demonstrate quantum-enhanced heterodyne readout of two spatially distinct interferometers with direct optical signal combination, circumventing the 3 dB heterodyne signal-to-noise penalty. Applying a high-frequency, spectrally entangled, two-mode squeezed state, we show further signal-to-noise improvement of an injected audio band signal of 3.5 dB. This technique is applicable for quantum-limited high-precision experiments, with application to searches for quantum gravity, gravitational wave detection and wavelength-multiplexed quantum communication.
Higher-order tensor methods for minimizing difference of convex functions

Higher-order tensor methods were recently proposed for minimizing smooth convex and nonconvex functions. Higher-order algorithms accelerate the convergence of the classical first-order methods thanks to the higher-order derivatives used in the updates. The purpose of this paper is twofold. Firstly, to show that the higher-order algorithmic framework can be generalized and successfully applied to (nonsmooth) difference of convex functions, namely, those that can be expressed as the difference of two smooth convex functions and a possibly nonsmooth convex one. We also provide examples when the subproblem can be solved efficiently, even globally. Secondly, to derive a complete convergence analysis for our higher-order difference of convex functions (HO-DC) algorithm. In particular, we prove that any limit point of the HO-DC iterative sequence is a critical point of the problem under consideration, the corresponding objective value is monotonically decreasing and the minimum value of the norms of its subgradients converges globally to zero at a sublinear rate. The sublinear or linear convergence rates of the iterations are obtained under the Kurdyka-Lojasiewicz property.
Safe Low-Altitude Navigation in Steep Terrain with Fixed-Wing Aerial Vehicles

Fixed-wing aerial vehicles provide an efficient way to navigate long distances or cover large areas for environmental monitoring applications. By design, they also require large open spaces due to limited maneuverability. However, strict regulatory and safety altitude limits constrain the available space. Especially in complex, confined, or steep terrain, ensuring the vehicle does not enter an inevitable collision state(ICS) can be challenging. In this work, we propose a strategy to find safe paths that do not enter an ICS while navigating within tight altitude constraints. The method uses periodic paths to efficiently classify ICSs. A sampling-based planner creates collision-free and kinematically feasible paths that begin and end in safe periodic (circular) paths. We show that, in realistic terrain, using circular periodic paths can simplify the goal selection process by making it yaw agnostic and constraining yaw. We demonstrate our approach by dynamically planning safe paths in real-time while navigating steep terrain on a flight test in complex alpine terrain.
Microswimmers knead nematic into cholesteric

The hydrodynamic stresses created by active particles can destabilise orientational order present in the system. This is manifested, for example, by the appearance of a bend instability in active nematics or in quasi-2-dimensional living liquid crystals consisting of swimming bacteria in thin nematic films. Using large-scale scale hydrodynamics simulations, we study a system consisting of spherical microswimmers within a 3-dimensional nematic liquid crystal. We observe a spontaneous chiral symmetry breaking, where the uniform nematic state is kneaded into a continously twisting state, corresponding to a helical director configuration akin to a cholesteric liquid crystal. The transition arises from the hydrodynamic coupling between the liquid crystalline elasticity and the swimmer flow fields, leading to a twist-bend instability of the nematic order. It is observed for both pusher (extensile) and puller (contractile) swimmers. Further, we show that the liquid crystal director and particle trajectories are connected: in the cholesteric state the particle trajectories become helicoidal.
Structure-Preserving Physics-Informed Neural Networks With Energy or Lyapunov Structure

Recently, there has been growing interest in using physics-informed neural networks (PINNs) to solve differential equations. However, the preservation of structure, such as energy and stability, in a suitable manner has yet to be established. This limitation could be a potential reason why the learning process for PINNs is not always efficient and the numerical results may suggest nonphysical behavior. Besides, there is little research on their applications on downstream tasks. To address these issues, we propose structure-preserving PINNs to improve their performance and broaden their applications for downstream tasks. Firstly, by leveraging prior knowledge about the physical system, a structure-preserving loss function is designed to assist the PINN in learning the underlying structure. Secondly, a framework that utilizes structure-preserving PINN for robust image recognition is proposed. Here, preserving the Lyapunov structure of the underlying system ensures the stability of the system. Experimental results demonstrate that the proposed method improves the numerical accuracy of PINNs for partial differential equations. Furthermore, the robustness of the model against adversarial perturbations in image data is enhanced.
Accelerating Maximal Biclique Enumeration on GPUs

Maximal Biclique Enumeration (MBE) holds critical importance in graph theory with applications extending across fields such as bioinformatics, social networks, and recommendation systems. However, its computational complexity presents barriers for efficiently scaling to large graphs. To address these challenges, we introduce cuMBE, a GPU-optimized parallel algorithm for MBE. Utilizing a unique data structure, called compact array, cuMBE eradicates the need for recursion, thereby significantly minimizing dynamic memory requirements and computational overhead. The algorithm utilizes a hybrid parallelism approach, in which GPU thread blocks handle coarse-grained tasks associated with part of the search process. Besides, we implement three fine-grained optimizations within each thread block to enhance performance. Further, we integrate a work-stealing mechanism to mitigate workload imbalances among thread blocks. Our experiments reveal that cuMBE achieves an geometric mean speedup of 4.02x and 4.13x compared to the state-of-the-art serial algorithm and parallel CPU-based algorithm on both common and real-world datasets, respectively.
Universal hard-edge statistics of non-Hermitian random matrices

Random matrix theory is a powerful tool for understanding spectral correlations inherent in quantum chaotic systems. Despite diverse applications of non-Hermitian random matrix theory, the role of symmetry remains to be fully established. Here, we comprehensively investigate the impact of symmetry on the level statistics around the spectral origin -- hard-edge statistics -- and complete the classification of spectral statistics in all the 38 symmetry classes of non-Hermitian random matrices. Within this classification, we discern 28 symmetry classes characterized by distinct hard-edge statistics from the level statistics in the bulk of spectra, which are further categorized into two groups, namely the Altland-Zirnbauer$_0$ classification and beyond. We introduce and elucidate quantitative measures capturing the universal hard-edge statistics for all the symmetry classes. Furthermore, through extensive numerical calculations, we study various open quantum systems in different symmetry classes, including quadratic and many-body Lindbladians, as well as non-Hermitian Hamiltonians. We show that these systems manifest the same hard-edge statistics as random matrices and that their ensemble-average spectral distributions around the origin exhibit emergent symmetry conforming to the random-matrix behavior. Our results establish a comprehensive understanding of non-Hermitian random matrix theory and are useful in detecting quantum chaos or its absence in open quantum systems.
REACT: Autonomous Intrusion Response System for Intelligent Vehicles

Autonomous and connected vehicles are rapidly evolving, integrating numerous technologies and software. This progress, however, has made them appealing targets for cybersecurity attacks. As the risk of cyber threats escalates with this advancement, the focus is shifting from solely preventing these attacks to also mitigating their impact. Current solutions rely on vehicle security operation centers, where attack information is analyzed before deciding on a response strategy. However, this process can be time-consuming and faces scalability challenges, along with other issues stemming from vehicle connectivity. This paper proposes a dynamic intrusion response system integrated within the vehicle. This system enables the vehicle to respond to a variety of incidents almost instantly, thereby reducing the need for interaction with the vehicle security operation center. The system offers a comprehensive list of potential responses, a methodology for response evaluation, and various response selection methods. The proposed solution was implemented on an embedded platform. Two distinct cyberattack use cases served as the basis for evaluating the system. The evaluation highlights the system's adaptability, its ability to respond swiftly, its minimal memory footprint, and its capacity for dynamic system parameter adjustments. The proposed solution underscores the necessity and feasibility of incorporating dynamic response mechanisms in smart vehicles. This is a crucial factor in ensuring the safety and resilience of future smart mobility.
Entropy based flux limiting scheme for conservation laws

The construction of high-resolution shock-capturing schemes is vital in producing highly accurate gravitational waveforms from neutron star binaries. The entropy based flux limiting (EFL) scheme is able to perform fast converging binary neutron star merger simulations reaching up to fourth-order convergence in the gravitational waveform phase. Here, we extend the applicability of the EFL method beyond special/general relativistic hydrodynamics to scalar conservation laws and show how to treat systems without a thermodynamic entropy. This is an indication that the method has universal applicability to any system of partial differential equations that can be written in conservation form. We also present some further very challenging special/general relativistic hydrodynamics applications of the EFL method and use it to construct eccentricity reduced initial data for a specific neutron star binary and show up to optimal fifth-order convergence in the gravitational waveform phase for this simulation.
Coulomb universality

Motivated by a number of realizations of long-range interacting systems, including ultra-cold atomic and molecular gases, we study a neutral plasma with power-law interactions longer-ranged than Coulombic. We find that beyond a crossover length, such interactions are universally screened down to a standard Coulomb form in all spatial dimensions. This implies, counter-intuitively, that in two dimensions, such a"super-Coulombic"gas undergoes the same Kosterlitz-Thouless transition as a conventional Coulomb gas at an elevated temperature that we calculate. We also predict that above two dimensions, even when naively the bare potential is confining, there is no confined phase of the plasma at any nonzero temperature. In addition, we show that power-law potentials that fall off more rapidly than Coulombic are screened down to a shorter ranged power-law, rather than an exponential Debye-Huckel Yukawa form. We expect these prediction to be testable in simulations, and hope they will inspire experimental studies in various platforms.
Nematic quantum disordered state in FeSe

The unusual quantum-disordered magnetic ground state intertwined with superconductivity and electronic nematicity in FeSe has been a research focus in iron-based superconductors. However, the intrinsic spin excitations across the entire Brillouin zone in detwinned FeSe, which forms the basis for a microscopic understanding of the magnetic state and superconductivity, remain to be determined. Here, we use inelastic neutron scattering to map out the spin excitations of FeSe dewtinned with a uniaxial-strain device. We find that the stripe spin excitations (Q=(1, 0)/(0, 1)) exhibit the $C_2$ symmetry up to $E\approx120$ meV, while the N{\'e}el spin excitations (Q=(1, 1)) retain their $C_4$ symmetry in the nematic state. The temperature dependence of the difference in the spin excitations at Q=(1, 0) and (0, 1) for temperatures above the structural phase transition unambiguously shows the establishment of the nematic quantum disordered state. The similarity of the N\'eel excitations in FeSe and NaFeAs suggests that the N\'eel excitations are driven by the enhanced electron correlations in the $3d_{xy}$ orbital. By determining the key features of the stripe excitations and fitting their dispersions using a Heisenberg Hamiltonian with biquadratic interaction ($J_1$-$K$-$J_2$), we establish a spin-interaction phase diagram and conclude that FeSe is close to a crossover region between the antiferroquadrupolar, N\'eel, and stripe ordering regimes. The results provide an experimental basis for establishing a microscopic theoretical model to describe the origin and intertwining of the emergent orders in iron-based superconductors.