Miracle-dz/UniFAR-FTUs huggingface dataset:
Revisiting the Lindemann's criterion from a minimal viscosity perspective

A revisiting of the Lindemann criterion under the recently established minimal viscosity formulation is proposed which uncovers intriguing insights into the melting process. The approach suggests that melting involves competition between the electron rest energy and the thermal one caused by temperature increase when considering a defect-free crystal structure. However, when accounting for the role of vacancies in the melting process, the analysis suggests that the key competing factors are the thermal energy and the one needed for vacancy migration. The estimated value of the Lindemann constant, in this case, is close to the reported values and with recent simulations studying the correlation between Born and Lindemann melting criteria.
Use of Graph Neural Networks in Aiding Defensive Cyber Operations

In an increasingly interconnected world, where information is the lifeblood of modern society, regular cyber-attacks sabotage the confidentiality, integrity, and availability of digital systems and information. Additionally, cyber-attacks differ depending on the objective and evolve rapidly to disguise defensive systems. However, a typical cyber-attack demonstrates a series of stages from attack initiation to final resolution, called an attack life cycle. These diverse characteristics and the relentless evolution of cyber attacks have led cyber defense to adopt modern approaches like Machine Learning to bolster defensive measures and break the attack life cycle. Among the adopted ML approaches, Graph Neural Networks have emerged as a promising approach for enhancing the effectiveness of defensive measures due to their ability to process and learn from heterogeneous cyber threat data. In this paper, we look into the application of GNNs in aiding to break each stage of one of the most renowned attack life cycles, the Lockheed Martin Cyber Kill Chain. We address each phase of CKC and discuss how GNNs contribute to preparing and preventing an attack from a defensive standpoint. Furthermore, We also discuss open research areas and further improvement scopes.
Siamese Networks with Soft Labels for Unsupervised Lesion Detection and Patch Pretraining on Screening Mammograms

Self-supervised learning has become a popular way to pretrain a deep learning model and then transfer it to perform downstream tasks. However, most of these methods are developed on large-scale image datasets that contain natural objects with clear textures, outlines, and distinct color contrasts. It remains uncertain whether these methods are equally effective for medical imaging, where the regions of interest often blend subtly and indistinctly with the surrounding tissues. In this study, we propose an alternative method that uses contralateral mammograms to train a neural network to encode similar embeddings when a pair contains both normal images and different embeddings when a pair contains normal and abnormal images. Our approach leverages the natural symmetry of human body as weak labels to learn to distinguish abnormal lesions from background tissues in a fully unsupervised manner. Our findings suggest that it's feasible by incorporating soft labels derived from the Euclidean distances between the embeddings of the image pairs into the Siamese network loss. Our method demonstrates superior performance in mammogram patch classification compared to existing self-supervised learning methods. This approach not only leverages a vast amount of image data effectively but also minimizes reliance on costly labels, a significant advantage particularly in the field of medical imaging.
Gamma-rays and Neutrinos from Giant Molecular Cloud Populations in the Galactic Plane

The recent detection of significant neutrino flux from the inner Galactic plane by the IceCube detector has provided us valuable insights on the spectrum of cosmic rays in our Galaxy. This flux can be produced either by a population of Galactic point sources or by diffused emission from cosmic ray interactions with the interstellar medium or by a mixture of both. In this work, we compute diffused gamma-ray and neutrino fluxes produced by a population of giant molecular clouds (GMCs) in our Galaxy, assuming different parametrizations of the Galactic diffused cosmic ray distribution. In particular, we take into account two main cases: (I) constant cosmic ray luminosity in our Galaxy, and (II) space-dependent cosmic ray luminosity based on the supernovae distribution in our Galaxy. For Case-I, we found that the neutrino flux from GMCs is a factor of $\sim 10$ below compared to $\pi^0$ and KRA$_\gamma$ best-fitted models of IceCube observations at $10^5$ GeV. Instead, for Case-II the model can explain up to $\sim 90 \%$ of the neutrino flux at that energy. Moreover, for this scenario IceCube detector could be able to detect neutrino events from the Galactic centre regions. We then calculated the gamma-ray and neutrino fluxes from individual GMCs and noticed that several current and future Cherenkov telescopes and neutrino observatories have the right sensitivities to study these objects. In particular, very neutrino-bright region such as Aquila Rift is favourable for detection by the IceCube-Gen2 observatory.
MatSynth: A Modern PBR Materials Dataset

We introduce MatSynth, a dataset of 4,000+ CC0 ultra-high resolution PBR materials. Materials are crucial components of virtual relightable assets, defining the interaction of light at the surface of geometries. Given their importance, significant research effort was dedicated to their representation, creation and acquisition. However, in the past 6 years, most research in material acquisiton or generation relied either on the same unique dataset, or on company-owned huge library of procedural materials. With this dataset we propose a significantly larger, more diverse, and higher resolution set of materials than previously publicly available. We carefully discuss the data collection process and demonstrate the benefits of this dataset on material acquisition and generation applications. The complete data further contains metadata with each material's origin, license, category, tags, creation method and, when available, descriptions and physical size, as well as 3M+ renderings of the augmented materials, in 1K, under various environment lightings. The MatSynth dataset is released through the project page at: https://www.gvecchio. com/matsynth.
Distinguishing Oceans of Water from Magma on Mini-Neptune K2-18b

Mildly irradiated mini-Neptunes have densities potentially consistent with them hosting substantial liquid-water oceans (“Hycean” planets). The presence of CO2 and simultaneous absence of ammonia (NH3) in their atmospheres has been proposed as a fingerprint of such worlds. JWST observations of K2-18b, the archetypal Hycean, have found the presence of CO2 and the depletion of NH3 to <100 ppm; hence, it has been inferred that this planet may host liquid-water oceans. In contrast, climate modeling suggests that many of these mini-Neptunes, including K2-18b, may likely be too hot to host liquid water. We propose a solution to this discrepancy between observation and climate modeling by investigating the effect of a magma ocean on the atmospheric chemistry of mini-Neptunes. We demonstrate that atmospheric NH3 depletion is a natural consequence of the high solubility of nitrogen species in magma at reducing conditions; precisely the conditions prevailing where a thick hydrogen envelope is in communication with a molten planetary surface. The magma ocean model reproduces the present JWST spectrum of K2-18b to ≲3σ, suggesting this is as credible an explanation for current observations as the planet hosting a liquid-water ocean. Spectral areas that could be used to rule out the magma ocean model include the >4 μm region, where CO2 and CO features dominate: magma ocean models suggest a systematically lower CO2/CO ratio than estimated from free-chemistry retrieval, indicating that deeper observations of this spectral region may be able to distinguish between oceans of liquid water and magma on mini-Neptunes.
Room-temperature Magnetic Thermal Switching by Suppressing Phonon-Magnon Scattering

Thermal switching materials, whose thermal conductivity can be controlled externally, show great potential in contemporary thermal management. Manipulating thermal transport properties through magnetic fields has been accomplished in materials that exhibit a high magnetoresistance. However, it is generally understood that the lattice thermal conductivity attributed to phonons is not significantly impacted by the magnetic fields. In this study, we experimentally demonstrate the significant impact of phonon-magnon scattering on the thermal conductivity of the rare-earth metal gadolinium near room temperature, which can be controlled by a magnetic field to realize thermal switching. Using first-principles lattice dynamics and spin-lattice dynamics simulations, we attribute the observed change in phononic thermal conductivity to field-suppressed phonon-magnon scattering. This research suggests that phonon-magnon scattering in ferromagnetic materials is crucial for determining their thermal conductivity, opening the door to innovative magnetic-field-controlled thermal switching materials.
Orbital hall effect: from intrinsic mechanism to experimental evidence

Comparing with the spin of electron, the electronic orbitals, which have been long ignored in non-equilibrium transport, are getting more and more attentions, due to the prediction and experimental verification of orbital Hall effect (OHE) recently. In this report, the recent advances, including intrinsic mechanisms, direct and indirect experimental observations of OHE, are reviewed comprehensively. The orbital current, generated by OHE, shows much more fundamental features than spin current in explaining Hall conductivity and manipulating magnetization electronically. Theoretically, in momentum space, the OHE is proposed to originate from electric-field driven non-equilibrium orbital texture, based on tight-binding model. However, current experimental works still stay on the level to reveal how the OHE behaves after orbital current is generated, and the unambiguous experimental evidence to verify the generation mechanism of OHE is still missing, limiting the interpretation of OHE. Angle-resolved photoemission spectroscopy with polarized incident photon, on the other hand, could be used to identify the orbital textures on energy band and reveal the non-equilibrium nature of orbital-momentum locking behind OHE, which may help to provide the direct experimental evidence to examine the intrinsic mechanism of OHE, and reveal the nature of wave packet with non-vanishing orbital angular momentum.
Modeling Online Paging in Multi-Core Systems

Web requests are growing exponentially since the 90s due to the rapid development of the Internet. This process was further accelerated by the introduction of cloud services. It has been observed statistically that memory or web requests generally follow power-law distribution, Breslau et al. INFOCOM'99. That is, the $i^{\text{th}}$ most popular web page is requested with a probability proportional to $1 / i^{\alpha}$ ($\alpha>0$ is a constant). Furthermore, this study, which was performed more than 20 years ago, indicated Zipf-like behavior, i.e., that $\alpha \le 1$. Surprisingly, the memory access traces coming from petabyte-size modern cloud systems not only show that $\alpha$ can be bigger than one but also illustrate a shifted power-law distribution -- called Pareto type II or Lomax. These previously not reported phenomenon calls for statistical explanation. Our first contribution is a new statistical {\it multi-core power-law} model indicating that double-power law can be attributed to the presence of multiple cores running many virtual machines in parallel on such systems. We verify experimentally the applicability of this model using the Kolmogorov-Smirnov test (K-S test). The second contribution of this paper is a theoretical analysis indicating why LRU and LFU-based algorithms perform well in practice on data satisfying power-law or multi-core assumptions. We provide an explanation by studying the online paging problem in the stochastic input model, i.e., the input is a random sequence with each request independently drawn from a page set according to a distribution $\pi$. We derive formulas (as a function of the page probabilities in $\pi$) to upper bound their ratio-of-expectations, which help in establishing O(1) performance ratio given the random sequence following power-law and multi-core power-law distributions.
Designing Heterogeneous LLM Agents for Financial Sentiment Analysis

Large language models (LLMs) have drastically changed the possible ways to design intelligent systems, shifting the focuses from massive data acquisition and new modeling training to human alignment and strategical elicitation of the full potential of existing pre-trained models. This paradigm shift, however, is not fully realized in financial sentiment analysis (FSA), due to the discriminative nature of this task and a lack of prescriptive knowledge of how to leverage generative models in such a context. This study investigates the effectiveness of the new paradigm, i.e., using LLMs without fine-tuning for FSA. Rooted in Minsky's theory of mind and emotions, a design framework with heterogeneous LLM agents is proposed. The framework instantiates specialized agents using prior domain knowledge of the types of FSA errors and reasons on the aggregated agent discussions. Comprehensive evaluation on FSA datasets show that the framework yields better accuracies, especially when the discussions are substantial. This study contributes to the design foundations and paves new avenues for LLMs-based FSA. Implications on business and management are also discussed.
Modal decomposition synthesis for localized nonlinear losses at termination of a woodwind model: Influence on sound characteristics

A sound synthesis model for woodwind instruments is developed using modal decomposition of the input impedance, accounting for viscothermal losses as well as localized nonlinear losses at the end of the resonator. To extend the definition of the input impedance to the nonlinear domain, the method incorporates a dependence on the RMS acoustic velocity at a geometric discontinuity. The poles and residues resulting from the modal decomposition are then fitted with respect to this velocity. Thus, the pressure-flow relation defined by the resonator is completed by new equations which account for the dependence with the RMS velocity at the end of the tube. The ability of the model to reproduce the bifurcation diagram of a reed instrument was confirmed in a previous article from the authors [1]. The present work focuses on the influence of localized nonlinear losses on the acoustic pressure signal. Results are discussed with the observations of [2].
Multi-relational Graph Diffusion Neural Network with Parallel Retention for Stock Trends Classification

Stock trend classification remains a fundamental yet challenging task, owing to the intricate time-evolving dynamics between and within stocks. To tackle these two challenges, we propose a graph-based representation learning approach aimed at predicting the future movements of multiple stocks. Initially, we model the complex time-varying relationships between stocks by generating dynamic multi-relational stock graphs. This is achieved through a novel edge generation algorithm that leverages information entropy and signal energy to quantify the intensity and directionality of inter-stock relations on each trading day. Then, we further refine these initial graphs through a stochastic multi-relational diffusion process, adaptively learning task-optimal edges. Subsequently, we implement a decoupled representation learning scheme with parallel retention to obtain the final graph representation. This strategy better captures the unique temporal features within individual stocks while also capturing the overall structure of the stock graph. Comprehensive experiments conducted on real-world datasets from two US markets (NASDAQ and NYSE) and one Chinese market (Shanghai Stock Exchange: SSE) validate the effectiveness of our method. Our approach consistently outperforms state-of-the-art baselines in forecasting next trading day stock trends across three test periods spanning seven years. Datasets and code have been released (https://github.com/pixelhero98/MGDPR).
The patrimony of wooden churches, built between 1531 and 2015, in the Land of Maramureș, Romania

ABSTRACT The map and temporal scale of the territory known as ‘The Land of Maramureș’ outlines a real heritage treasure, built in historical time and formed of 74 wooden churches. Varying in terms of architecture, dimensions and cult, the wooden churches are indeed heritage objects, 33 of them being on the list of historical monuments in Romania, while 5 are included in the UNESCO world heritage list. An impressive database, which includes the edifices built during 1531–2015 period, is processed, analyzed, synthesized and mapped in this project. By using cartographic methods and specific programs (ArcGis, CorelDRAW), suggestive pictograms are generated, expressing relevant elements for the proposed purpose: spatiality, a series of technical details (height, architectural style, etc.), monument type and oldness. Such a complex material is useful in the process of territorial planning and organization from the point of view of those concerned with issues of urban development and tourism as well as for the general public as well.
Eating Speed Measurement Using Wrist-Worn IMU Sensors in Free-Living Environments

Eating speed is an important indicator that has been widely scrutinized in nutritional studies. The relationship between eating speed and several intake-related problems such as obesity, diabetes, and oral health has received increased attention from researchers. However, existing studies mainly use self-reported questionnaires to obtain participants' eating speed, where they choose options from slow, medium, and fast. Such a non-quantitative method is highly subjective and coarse in individual level. In this study, we propose a novel approach to measure eating speed in free-living environments automatically and objectively using wrist-worn inertial measurement unit (IMU) sensors. Specifically, a temporal convolutional network combined with a multi-head attention module (TCN-MHA) is developed to detect bites (including eating and drinking gestures) from free-living IMU data. The predicted bite sequences are then clustered to eating episodes. Eating speed is calculated by using the time taken to finish the eating episode to divide the number of bites. To validate the proposed approach on eating speed measurement, a 7-fold cross validation is applied to the self-collected fine-annotated full-day-I (FD-I) dataset, and a hold-out experiment is conducted on the full-day-II (FD-II) dataset. The two datasets are collected from 61 participants in free-living environments with a total duration of 513 h, which are publicly available. Experimental results shows that the proposed approach achieves a mean absolute percentage error (MAPE) of 0.110 and 0.146 in the FD-I and FD-II datasets, respectively, showcasing the feasibility of automated eating speed measurement. To the best of our knowledge, this is the first study investigating automated eating speed measurement.
Gait Recognition in the Wild: A Large-scale Benchmark and NAS-based Baseline

Gait benchmarks empower the research community to train and evaluate high-performance gait recognition systems. Even though growing efforts have been devoted to cross-view recognition, academia is restricted by current existing databases captured in the controlled environment. In this paper, we contribute a new benchmark and strong baseline for Gait REcognition in the Wild (GREW). The GREW dataset is constructed from natural videos, which contain hundreds of cameras and thousands of hours of streams in open systems. With tremendous manual annotations, the GREW consists of 26K identities and 128K sequences with rich attributes for unconstrained gait recognition. Moreover, we add a distractor set of over 233K sequences, making it more suitable for real-world applications. Compared with prevailing predefined cross-view datasets, the GREW has diverse and practical view variations, as well as more naturally challenging factors. To the best of our knowledge, this is the first large-scale dataset for gait recognition in the wild. Equipped with this benchmark, we dissect the unconstrained gait recognition problem, where representative appearance-based and model-based methods are explored. The proposed GREW benchmark proves to be essential for both training and evaluating gait recognizers in unconstrained scenarios. In addition, we propose the Single Path One-Shot neural architecture search with uniform sampling for Gait recognition, named SPOSGait, which is the first NAS-based gait recognition model. In experiments, SPOSGait achieves state-of-the-art performance on the CASIA-B, OU-MVLP, Gait3D, and GREW benchmarks, outperforming existing approaches by a large margin. The code will be released at https://github.com/XiandaGuo/SPOSGait.
How does the primate brain combine generative and discriminative computations in vision?

Vision is widely understood as an inference problem. However, two contrasting conceptions of the inference process have each been influential in research on biological vision as well as the engineering of machine vision. The first emphasizes bottom-up signal flow, describing vision as a largely feedforward, discriminative inference process that filters and transforms the visual information to remove irrelevant variation and represent behaviorally relevant information in a format suitable for downstream functions of cognition and behavioral control. In this conception, vision is driven by the sensory data, and perception is direct because the processing proceeds from the data to the latent variables of interest. The notion of “inference” in this conception is that of the engineering literature on neural networks, where feedforward convolutional neural networks processing images are said to perform inference. The alternative conception is that of vision as an inference process in Helmholtz’s sense, where the sensory evidence is evaluated in the context of a generative model of the causal processes that give rise to it. In this conception, vision inverts a generative model through an interrogation of the sensory evidence in a process often thought to involve top-down predictions of sensory data to evaluate the likelihood of alternative hypotheses. The authors include scientists rooted in roughly equal numbers in each of the conceptions and motivated to overcome what might be a false dichotomy between them and engage the other perspective in the realm of theory and experiment. The primate brain employs an unknown algorithm that may combine the advantages of both conceptions. We explain and clarify the terminology, review the key empirical evidence, and propose an empirical research program that transcends the dichotomy and sets the stage for revealing the mysterious hybrid algorithm of primate vision.