Miracle-dz/UniFAR-FTUs huggingface dataset:
Separable Physics-Informed Neural Networks for the solution of elasticity problems

A method for solving elasticity problems based on separable physics-informed neural networks (SPINN) in conjunction with the deep energy method (DEM) is presented. Numerical experiments have been carried out for a number of problems showing that this method has a significantly higher convergence rate and accuracy than the vanilla physics-informed neural networks (PINN) and even SPINN based on a system of partial differential equations (PDEs). In addition, using the SPINN in the framework of DEM approach it is possible to solve problems of the linear theory of elasticity on complex geometries, which is unachievable with the help of PINNs in frames of partial differential equations. Considered problems are very close to the industrial problems in terms of geometry, loading, and material parameters.
Drilling on the Goban Spur: Objectives, Regional Geological Setting, and Operational Summary

Deep Sea Drilling Project Leg 80 investigated the structural evolution and depositional history of the Goban Spur and the adjacent Porcupine Abyssal Plain (Fig. 1). This is the second DSDP leg devoted to documenting the development of the continental margin of northwest Europe. During the first leg (Leg 48), Sites 400 to 402 were drilled on the Armorican margin, 250 km southeast of the Goban Spur (Montadert, Roberts, et al., 1979). The Leg 48 investigation was incomplete because the drill string was lost at the deepest site (400), but the results were nevertheless significant for understanding the evolution of passive margins. Structural relationships and fossil assemblages indicate that the rifting of the Armorican margin created submarine relief of as much as 2000 m (Montadert, Roberts, et al., 1979). Seafloor spreading began no later than the early Aptian and was accompanied by rapid crustal subsidence in the form of regional downwarping and tilting. Subsidence was greatest near the ocean/continent juncture and diminished farther toward the continent, where the crust is thicker. Subsidence has been minimal since the Eocene, and deposition since the Eocene has been strongly influenced by oceanic circulation patterns and water chemistry. As the Cenozoic deposition proceeded, the continental slope grew outward, growth clearly recorded in the lithofacies and unconformities penetrated by the Leg 48 drill holes. We placed Sites 548 to 551 along a transect across the continent/ocean boundary on the sediment-starved Goban Spur (Figs. 1 and 2) to attempt the following: (1) to establish the geologic ages, lithofacies, depositional environments, and subsidence rates of the pre-, syn-, and post-rift phases of development of the continental margin; (2) to determine the duration of rifting and the age and nature of the post-rift unconformity; (3) to document the age and nature of significant post-rift chronostratigraphic boundaries and prominent seismic unconformities and assess the relationships between these fea-
A Comparison Between Lie Group- and Lie Algebra- Based Potential Functions for Geometric Impedance Control

In this paper, a comparison analysis between geometric impedance controls (GICs) derived from two different potential functions on SE(3) for robotic manipulators is presented. The first potential function is defined on the Lie group, utilizing the Frobenius norm of the configuration error matrix. The second potential function is defined utilizing the Lie algebra, i.e., log-map of the configuration error. Using a differential geometric approach, the detailed derivation of the distance metric and potential function on SE(3) is introduced. The GIC laws are respectively derived from the two potential functions, followed by extensive comparison analyses. In the qualitative analysis, the properties of the error function and control laws are analyzed, while the performances of the controllers are quantitatively compared using numerical simulation.
On the Constrained CAV Platoon Control Problem

The main objective of the connected and automated vehicle (CAV) platoon control problem is to regulate CAVs' position while ensuring stability and accounting for vehicle dynamics. Although this problem has been studied in the literature, existing research has some limitations. This paper presents two new theoretical results that address these limitations: (i) the synthesis of unrealistic high-gain control parameters due to the lack of a systematic way to incorporate the lower and upper bounds on the control parameters, and (ii) the performance sensitivity to the communication delay due to inaccurate Taylor series approximation. To be more precise, taking advantage of the wellknown Pade approximation, this paper proposes a constrained CAV platoon controller synthesis that (i) systematically incorporates the lower and upper bounds on the control parameters, and (ii) significantly improves the performance sensitivity to the communication delay. The effectiveness of the presented results is verified through conducting extensive numerical simulations. The proposed controller effectively attenuates the stop-and-go disturbance -- a single cycle of deceleration followed by acceleration -- amplification throughout the mixed platoon (consisting of CAVs and human-driven vehicles). Modern transportation systems will benefit from the proposed CAV controls in terms of effective disturbance attenuation as it will potentially reduce collisions.
Exploiting separation-dependent coherence to boost optical resolution

The problem of resolving point-like light sources not only serves as a benchmark for optical resolution but also holds various practical applications ranging from microscopy to astronomy. In this research, we aim to resolve two thermal sources sharing arbitrary mutual coherence using the spatial mode demultiplexing technique. Our analytical study includes scenarios where the coherence and the emission rate depend on the separation between the sources, and is not limited to the faint sources limit. We consider the fluorescence of two interacting dipoles to demonstrate that the dependence of emission characteristics on the parameter of interest can boost the sensitivity of the estimation and noticeably prolong the duration of information decay.
Exploring filament galaxies using {\em AstroSat}/ UVIT

We present results from our deep Far-ultraviolet (FUV) survey using {\em AstroSat}/UVIT of a filamentary structure at $z$ $\sim$ $0.072$. A total of four filaments comprising 58 galaxies were probed in our study. We detect 18 filament galaxies in our FUV observation. All filament galaxies are further classified based on their photometric color, nuclear activity, and morphology. The filaments contain galaxies with mixed stellar population types and structures. We do not detect galaxies in our UVIT survey up to a distance of 0.4~Mpc from the filament axis, implying a dearth of recent star formation in the inner region of filaments. We witness an increase in FUV specific star formation rate (sSFR) of filament galaxies with increasing distance from the filament spine (D$_{\rm fil}$). On the contrary, no such gradient in FUV sSFR with cluster-centric distance is observed in the case of cluster galaxies. The high stellar mass filament galaxies (M$_\star$ $\sim$ 10$^{11}$ M$_\odot$) were more star-forming than cluster galaxies in a fixed mass range. The FUV morphology of some filament galaxies detected in the filament outskirts (D$_{\rm fil}$ $\sim$ 0.9~Mpc) is comparable to or slightly extended than their optical counterpart. The mass assembly of galaxies probed by estimating $(FUV-r)$ color gradients show that more centrally star-forming galaxies reside closer to the filament axis regardless of stellar mass. Our results prove that the likelihood of merger interaction and gas starvation increases on approaching the filament spine. We report a definitive and in-homogeneous impact of filaments on the galaxies residing inside them.
Few-Shot Learning for Chronic Disease Management: Leveraging Large Language Models and Multi-Prompt Engineering with Medical Knowledge Injection

This study harnesses state-of-the-art AI technology for chronic disease management, specifically in detecting various mental disorders through user-generated textual content. Existing studies typically rely on fully supervised machine learning, which presents challenges such as the labor-intensive manual process of annotating extensive training data for each disease and the need to design specialized deep learning architectures for each problem. To address such challenges, we propose a novel framework that leverages advanced AI techniques, including large language models and multi-prompt engineering. Specifically, we address two key technical challenges in data-driven chronic disease management: (1) developing personalized prompts to represent each user's uniqueness and (2) incorporating medical knowledge into prompts to provide context for chronic disease detection, instruct learning objectives, and operationalize prediction goals. We evaluate our method using four mental disorders, which are prevalent chronic diseases worldwide, as research cases. On the depression detection task, our method (F1 = 0.975~0.978) significantly outperforms traditional supervised learning paradigms, including feature engineering (F1 = 0.760) and architecture engineering (F1 = 0.756). Meanwhile, our approach demonstrates success in few-shot learning, i.e., requiring only a minimal number of training examples to detect chronic diseases based on user-generated textual content (i.e., only 2, 10, or 100 subjects). Moreover, our method can be generalized to other mental disorder detection tasks, including anorexia, pathological gambling, and self-harm (F1 = 0.919~0.978).
Assessment of Sports Concussion in Female Athletes: A Role for Neuroinformatics?

Over the past decade, the intricacies of sports-related concussions among female athletes have become readily apparent. Traditional clinical methods for diagnosing concussions suffer limitations when applied to female athletes, often failing to capture subtle changes in brain structure and function. Advanced neuroinformatics techniques and machine learning models have become invaluable assets in this endeavor. While these technologies have been extensively employed in understanding concussion in male athletes, there remains a significant gap in our comprehension of their effectiveness for female athletes. With its remarkable data analysis capacity, machine learning offers a promising avenue to bridge this deficit. By harnessing the power of machine learning, researchers can link observed phenotypic neuroimaging data to sex-specific biological mechanisms, unraveling the mysteries of concussions in female athletes. Furthermore, embedding methods within machine learning enable examining brain architecture and its alterations beyond the conventional anatomical reference frame. In turn, allows researchers to gain deeper insights into the dynamics of concussions, treatment responses, and recovery processes. To guarantee that female athletes receive the optimal care they deserve, researchers must employ advanced neuroimaging techniques and sophisticated machine-learning models. These tools enable an in-depth investigation of the underlying mechanisms responsible for concussion symptoms stemming from neuronal dysfunction in female athletes. This paper endeavors to address the crucial issue of sex differences in multimodal neuroimaging experimental design and machine learning approaches within female athlete populations, ultimately ensuring that they receive the tailored care they require when facing the challenges of concussions.
Snow-Distribution Modelling in the Ammassalik Region, South East Greenland

The Ammassalik region is characterized by a strong alpine relief, with altitudes up to 1,000 m. Glaciers are located mainly on the western side of ridges. The climate is low arctic, with annual precipitation amounts of more than 1,000 mm, which falls mainly as snow. Furthermore very strong storms occur frequently throughout the region. All together these factors support strong snow redistribution by wind, which likely explains the glacier locations, and also explains the observed regional runoff differences. The aim of this study is to apply the Liston & Sturm snow-transport model (SnowTran-3D) to elucidate the snow distribution according to the actual climatic conditions. A digital terrain model was used to determine the terrain forcing of the wind field. Precipitation data from the Ammassalik meteorological station were corrected for aerodynamic errors and orographic effects. Wind, temperature and humidity were obtained from a station located on a nunatak 515 m.a.s.l. at the equilibrium line on the Mittivakkat Glacier. The recorded winter accumulation (balance) of snow on the glacier was used for model calibration and testing. Significant snow transport from east-facing slopes to west-facing slopes was confirmed by the model. The drift accumulations were greatest at the head of the glacier, just on the lee side of the ridge east of the glacier. In some areas, as much as 10% of the precipitation was returned to the atmosphere by blowing-snow sublimation. An average snow water equivalent of 113 cm was obtained (not including some minor areas having snow depths as great as 4 m). These results compare well with glacier observations of 114 cm collected in May 1998 (during the field survey the 4 m areas are omitted because of crevasse hazards). Future work will use the model to test scenarios that include changes in wind regime. 1
Families over the integral Bernstein Center and Tate cohomology of local Base change lifts for GL(n, F)

Let $p$ and $l$ be distinct odd primes, and let $F$ be a $p$-adic field. Let $\pi$ be a generic smooth integral representation of ${\rm GL}_n(F)$ over an $\overline{\mathbb{Q}}_l$-vector space. Let $E$ be a finite Galois extension of $F$ with $[E:F]=l$. Let $\Pi$ be the base change lift of $\pi$ to the group ${\rm GL}_n(E)$. Let $\mathbb{W}^0(\Pi, \psi_E)$ be the lattice of $\overline{\mathbb{Z}}_l$-valued functions in the Whittaker model of $\Pi$, with respect to a standard ${\rm Gal}(E/F)$-equivaraint additive character $\psi_E:E\rightarrow \overline{\mathbb{Q}}_l^\times$. We show that the unique generic sub-quotient of the zero-th Tate cohomology group of $\mathbb{W}^0(\Pi, \psi_E)$ is isomorphic to the Frobenius twist of the unique generic sub-quotient of the mod-$l$ reduction of $\pi$. We first prove a version of this result for a family of smooth generic representations of ${\rm GL}_n(E)$ over the integral Bernstein center of ${\rm GL}_n(F)$. Our methods use the theory of Rankin-selberg convolutions and simple identities of local $\gamma$-factors. The results of this article remove the hypothesis that $l$ does not divide the pro-order of ${\rm GL}_{n-1}(F)$ in our previous work.
ULTRA: Unleash LLMs' Potential for Event Argument Extraction through Hierarchical Modeling and Pair-wise Refinement

Structural extraction of events within discourse is critical since it avails a deeper understanding of communication patterns and behavior trends. Event argument extraction (EAE), at the core of event-centric understanding, is the task of identifying role-specific text spans (i.e., arguments) for a given event. Document-level EAE (DocEAE) focuses on arguments that are scattered across an entire document. In this work, we explore the capabilities of open source Large Language Models (LLMs), i.e., Flan-UL2, for the DocEAE task. To this end, we propose ULTRA, a hierarchical framework that extracts event arguments more cost-effectively -- the method needs as few as 50 annotations and doesn't require hitting costly API endpoints. Further, it alleviates the positional bias issue intrinsic to LLMs. ULTRA first sequentially reads text chunks of a document to generate a candidate argument set, upon which ULTRA learns to drop non-pertinent candidates through self-refinement. We further introduce LEAFER to address the challenge LLMs face in locating the exact boundary of an argument span. ULTRA outperforms strong baselines, which include strong supervised models and ChatGPT, by 9.8% when evaluated by the exact match (EM) metric.
TEPI: Taxonomy-aware Embedding and Pseudo-Imaging for Scarcely-labeled Zero-shot Genome Classification

A species' genetic code or genome encodes valuable evolutionary, biological, and phylogenetic information that aids in species recognition, taxonomic classification, and understanding genetic predispositions like drug resistance and virulence. However, the vast number of potential species poses significant challenges in developing a general-purpose whole genome classification tool. Traditional bioinformatics tools have made notable progress but lack scalability and are computationally expensive. Machine learning-based frameworks show promise but must address the issue of large classification vocabularies with long-tail distributions. In this study, we propose addressing this problem through zero-shot learning using TEPI, Taxonomy-aware Embedding and Pseudo-Imaging. We represent each genome as pseudo-images and map them to a taxonomy-aware embedding space for reasoning and classification. This embedding space captures compositional and phylogenetic relationships of species, enabling predictions in extensive search spaces. We evaluate TEPI using two rigorous zero-shot settings and demonstrate its generalization capabilities qualitatively on curated, large-scale, publicly sourced data.
Magnetism and spin dynamics of an S=3/2 frustrated trillium lattice antiferromagnet K2CrTi(PO4)3

Competing magnetic interactions, frustration driven quantum fluctuations, and spin correlations offer an ideal route for the experimental realization of emergent quantum phenomena and exotic quasi particle excitations in 3D frustrated magnets. In this context, trillium lattice, wherein magnetic ions decorate a three-dimensional chiral network of corner-shared equilateral triangular motifs, provides a viable ground. Herein, we present the crystal structure, magnetic susceptibility, specific heat, electron spin-resonance, muSR results on the polycrystalline samples of K2CrTi(PO4)3 wherein the Cr3+ ions form a perfect trillium lattice without any detectable anti-site disorder. The Curie-Weiss fit of the magnetic susceptibility data above 100 K yields a Curie-Weiss temperature of -23 K, which indicates the presence of dominant antiferromagnetic interactions between Cr3+ (S=3/2) moments. The specific heat measurements reveal the occurrence of two consecutive phase transitions, at temperatures TL = 4.3 K and TH = 8 K, corresponding to two different magnetic phases and it unveils the existence of short-range spin correlations above the ordering temperature TH. The power-law behavior of ESR linewidth suggests the persistence of short-range spin correlations over a relatively wide critical region in agreement with the specific heat results. The muSR resultsprovide concrete evidence of two different phases corresponding to two transitions, coupled with thecritical slowing down of spin fluctuations above TL and persistent spin dynamics below TL, consistent with the thermodynamic results. Moreover, the muSR results reveal the coexistence of static and dynamic local magnetic fields below TL, signifying the presence of complex magnetic phases owing to the entwining of spin correlations and competing magnetic interactions in this three-dimensional frustrated magnet.
Virtualized Delta, sharp, and pass moves for oriented virtual knots and links

We study virtualized Delta, sharp, and pass moves for oriented virtual links, and give necessary and sufficient conditions for two oriented virtual links to be related by the local moves. In particular, they are unknotting operations for oriented virtual knots. We provide lower bounds for the unknotting numbers and prove that they are best possible.
DenoSent: A Denoising Objective for Self-Supervised Sentence Representation Learning

Contrastive-learning-based methods have dominated sentence representation learning. These methods regularize the representation space by pulling similar sentence representations closer and pushing away the dissimilar ones and have been proven effective in various NLP tasks, e.g., semantic textual similarity (STS) tasks. However, it is challenging for these methods to learn fine-grained semantics as they only learn from the inter-sentence perspective, i.e., their supervision signal comes from the relationship between data samples. In this work, we propose a novel denoising objective that inherits from another perspective, i.e., the intra-sentence perspective. By introducing both discrete and continuous noise, we generate noisy sentences and then train our model to restore them to their original form. Our empirical evaluations demonstrate that this approach delivers competitive results on both semantic textual similarity (STS) and a wide range of transfer tasks, standing up well in comparison to contrastive-learning-based methods. Notably, the proposed intra-sentence denoising objective complements existing inter-sentence contrastive methodologies and can be integrated with them to further enhance performance. Our code is available at https://github.com/xinghaow99/DenoSent.
AdCorDA: Classifier Refinement via Adversarial Correction and Domain Adaptation

This paper describes a simple yet effective technique for refining a pretrained classifier network. The proposed AdCorDA method is based on modification of the training set and making use of the duality between network weights and layer inputs. We call this input space training. The method consists of two stages - adversarial correction followed by domain adaptation. Adversarial correction uses adversarial attacks to correct incorrect training-set classifications. The incorrectly classified samples of the training set are removed and replaced with the adversarially corrected samples to form a new training set, and then, in the second stage, domain adaptation is performed back to the original training set. Extensive experimental validations show significant accuracy boosts of over 5% on the CIFAR-100 dataset. The technique can be straightforwardly applied to refinement of weight-quantized neural networks, where experiments show substantial enhancement in performance over the baseline. The adversarial correction technique also results in enhanced robustness to adversarial attacks.
Automated Programmatic Performance Analysis of Parallel Programs

Developing efficient parallel applications is critical to advancing scientific development but requires significant performance analysis and optimization. Performance analysis tools help developers manage the increasing complexity and scale of performance data, but often rely on the user to manually explore low-level data and are rigid in how the data can be manipulated. We propose a Python-based API, Chopper, which provides high-level and flexible performance analysis for both single and multiple executions of parallel applications. Chopper facilitates performance analysis and reduces developer effort by providing configurable high-level methods for common performance analysis tasks such as calculating load imbalance, hot paths, scalability bottlenecks, correlation between metrics and CCT nodes, and causes of performance variability within a robust and mature Python environment that provides fluid access to lower-level data manipulations. We demonstrate how Chopper allows developers to quickly and succinctly explore performance and identify issues across applications such as AMG, Laghos, LULESH, Quicksilver and Tortuga.