April 2024 arXiv papers — page 43

Eugene Koh, Rohan Shawn Sunil, Hilbert Yuen In Lam, Marek Mutwil

Life finds a way. For sessile organisms like plants, the need to adapt to changes in the environment is even more poignant. For humanity, the need to develop crops that can grow in diverse environments and feed our growing population is an existential one. The advent of the genomics era enabled the generation of high-throughput data and computational methods

Ryosuke Hyakuna

The Cauchy problem for the nonlinear Schr\"odinger equation is called unconditionally well posed in a data space $E$ if it is well posed in the usual sense and the solution is unique in the space $C([0,T]; E)$. In this paper, this notion of unconditional well-posedness is redefined so that it covers $L^p$-based Sobolev spaces as data space $E$ and it is equi

Vivek Anand, Bharat Lohani, Gaurav Pandey, Rakesh Mishra

Autonomous vehicles (AVs) heavily rely on LiDAR perception for environment understanding and navigation. LiDAR intensity provides valuable information about the reflected laser signals and plays a crucial role in enhancing the perception capabilities of AVs. However, accurately simulating LiDAR intensity remains a challenge due to the unavailability of mater

Zhen Ma, Shuhan Zheng, Yingqi Chen, Ruokai Xu

Quantum spin liquids (QSLs) represent a novel state where spins are highly entangled but do not order even at zero temperature due to strong quantum fluctuations. Such a state is mostly studied in Heisenberg models defined on geometrically frustrated lattices. Here, we turn to a new triangular-lattice antiferromagnet PrMgAl$_{11}$O$_{19}$, in which the inter

Aobo Liang, Xingguo Jiang, Yan Sun, Xiaohou Shi

Long-term time series forecasting (LTSF) provides longer insights into future trends and patterns. Over the past few years, deep learning models especially Transformers have achieved advanced performance in LTSF tasks. However, LTSF faces inherent challenges such as long-term dependencies capturing and sparse semantic characteristics. Recently, a new state s

Xin Jiang, Hao Tang, Rui Yan, Jinhui Tang

Fine-grained image retrieval (FGIR) is to learn visual representations that distinguish visually similar objects while maintaining generalization. Existing methods propose to generate discriminative features, but rarely consider the particularity of the FGIR task itself. This paper presents a meticulous analysis leading to the proposal of practical guideline

Philip Müller, Georgios Kaissis, Daniel Rueckert

Report generation models offer fine-grained textual interpretations of medical images like chest X-rays, yet they often lack interactivity (i.e. the ability to steer the generation process through user queries) and localized interpretability (i.e. visually grounding their predictions), which we deem essential for future adoption in clinical practice. While t

Zahra Nazemian

For every infinite cardinal number $\kappa$, $\kappa$-monoids and their realization have recently been introduced and studied by Nazemian and Smertnig. A $\kappa$-monoid $H$ has a realization to a ring $R$ if there exists an element $x \in H$ such that $H$ is $\aleph_1 ^{-}$-braided over $\text{add}(\aleph_0 x)$, and $\text{add}(\aleph_0 x)$, as $\aleph_0$-m

M. W. Duszka, M. F. Rode, A. L. Sobolewski

A novel, computationally designed, class of triangular-shape organic molecules with an inverted singlet-triplet (IST) energy gap is investigated with the aid of ab initio methods of electronic structure theory. The considered molecular systems have a form of cyclic oligomers and their common feature is electronic conjugation localized along the molecular rim

Marius van der Put, Jaap Top

There is an abundance of equations of Painlev\'e type besides the classical Painlev\'e equations. Classifications have been computed by the Japanese school. Here we consider Painlev\'e type equations induced by isomonodromic families of linear ODE's having at most ${z=0}$ and $z=\infty$ as singularities. Requiring that the formal data at the singularities pr

Kaiwen Xue, Yuhao Zhou, Shen Nie, Xu Min

Bayesian flow networks (BFNs) iteratively refine the parameters, instead of the samples in diffusion models (DMs), of distributions at various noise levels through Bayesian inference. Owing to its differentiable nature, BFNs are promising in modeling both continuous and discrete data, while simultaneously maintaining fast sampling capabilities. This paper ai

Jag Mohan Singh, Raghavendra Ramachandra

Face Recognition Systems (FRS) are widely used in commercial environments, such as e-commerce and e-banking, owing to their high accuracy in real-world conditions. However, these systems are vulnerable to facial morphing attacks, which are generated by blending face color images of different subjects. This paper presents a new method for generating 3D face m

Roger Sewell

Shannon defined the mutual information between two variables. We illustrate why the true mutual information between a variable and the predictions made by a prediction algorithm is not a suitable measure of prediction quality, but the apparent Shannon mutual information (ASI) is; indeed it is the unique prediction quality measure with either of two very diff

Ioannis Katsantonis, Anna Tasolamprou, Eleftherios Economou, Thomas Koschny

We demonstrate a simple, low-cost and ultracompact chiral resonant metasurface design, which, by strong local coupling to a quantum gain medium (quantum emitters), allows to implement an ultra-thin metasurface laser, capable of generating tunable circularly polarized coherent lasing output. According to our detailed numerical investigations the lasing emissi

Radha Krishnan, Beng Yee Gan, Yu-Ling Hsueh, A. M. Saffat-Ee Huq

While traditionally considered a deleterious effect in quantum dot spin qubits, the spin-orbit interaction is recently being revisited as it allows for rapid coherent control by on-chip AC electric fields. For electrons in bulk silicon, SOC is intrinsically weak, however, it can be enhanced at surfaces and interfaces, or through atomic placement. Here we sho

Rui Wang, Deshi Li, Qingqing Wu, Kaitao Meng

Unmanned aerial vehicles (UAVs) have received plenty of attention due to their high flexibility and enhanced communication ability, nonetheless, the limited onboard energy restricts UAVs' application on persistent data collection missions in large areas. In this paper, we propose a rechargeable UAV-assisted periodic data collection scheme, where a UAV is dis

Ziheng Chen, Jia Wang, Jun Zhuang, Abbavaram Gowtham Reddy

The right to be forgotten (RTBF) seeks to safeguard individuals from the enduring effects of their historical actions by implementing machine-learning techniques. These techniques facilitate the deletion of previously acquired knowledge without requiring extensive model retraining. However, they often overlook a critical issue: unlearning processes bias. Thi

Roi Makov, Francisco Guinea, Ady Stern

We study the formation and properties of perfectly-flat zero energy bands in a multi-layer graphene systems in the chiral limit. Employing the degrees of freedoms of the multi-layer system, such as relative twist-angle and relative shifts, in a way that preserves a set of symmetries, we define a two-dimensional parameter plane that hosts lines of two and fou

Jacob Pfau, William Merrill, Samuel R. Bowman

Chain-of-thought responses from language models improve performance across most benchmarks. However, it remains unclear to what extent these performance gains can be attributed to human-like task decomposition or simply the greater computation that additional tokens allow. We show that transformers can use meaningless filler tokens (e.g., '......') in place

Christos Sardianos, Chrysostomos Symvoulidis, Matthias Schlögl, Iraklis Varlamis

The developing field of enhanced diagnostic techniques in the diagnosis of infectious diseases, constitutes a crucial domain in modern healthcare. By utilizing Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) data and incorporating machine learning algorithms into one platform, our research aims to tackle the ongoing issue of precise infection identific

Jokin Alcibar, Jose I. Aizpurua, Ekhi Zugastia, Oier Penagarikano

Health monitoring of remote critical infrastructure is a complex and expensive activity due to the limited infrastructure accessibility. Inspection drones are ubiquitous assets that enhance the reliability of critical infrastructures through improved accessibility. However, due to the harsh operation environment, it is crucial to monitor their health to ensu

Ahmed Bhayat, Martin Suda

The support for higher-order reasoning in the Vampire theorem prover has recently been completely reworked. This rework consists of new theoretical ideas, a new implementation, and a dedicated strategy schedule. The theoretical ideas are still under development, so we discuss them at a high level in this paper. We also describe the implementation of the calc

Cheng-En Lee, Kuo-Yu Liao, Hsiao-Wen Yu, Ruhui Zhang

In this paper, we present a framework for convolutional coded Poisson receivers (CCPRs) that incorporates spatially coupled methods into the architecture of coded Poisson receivers (CPRs). We use density evolution equations to track the packet decoding process with the successive interference cancellation (SIC) technique. We derive outer bounds for the stabi

Karl-Theodor Sturm

Metric measure spaces with synthetic Ricci bounds have attracted great interest in recent years, accompanied by spectacular breakthroughs and deep new insights. In this survey, I will provide a brief introduction to the concept of lower Ricci bounds as introduced by Lott-Villani and myself, and illustrate some of its geometric, analytic and probabilistic con

A. Socci, G. Sabatini, M. Padovani, S. Bovino

Cosmic rays regulate the dynamics and the chemical processes in the densest and coldest regions of the ISM. Still, the determination of the cosmic-ray ionisation rate of H$_2$ (${\zeta^{\rm ion}_{{\rm H}_2}}$) is plagued by uncertainties in the adopted chemical networks and the analysis techniques. This work aims to homogeneously estimate the ${\zeta^{\rm io

Zhiyu He, Jiayu Li, Weizhi Ma, Min Zhang

Nowadays, personalized recommender systems play an increasingly important role in music scenarios in our daily life with the preference prediction ability. However, existing methods mainly rely on users' implicit feedback (e.g., click, dwell time) which ignores the detailed user experience. This paper introduces Electroencephalography (EEG) signals to person

J. Andres Montoya

We prove that the class LOGSPACE (L, for short) is different from the class NP.

Ryoga Fukuhara, Makoto Morishita, Takahiro Katagiri, Masatoshi Kawai

In this paper, support vector machine (SVM) performance was assessed utilizing a quantum-inspired complementary metal-oxide semiconductor (CMOS) annealer. The primary focus during performance evaluation was the accuracy rate in binary classification problems. A comparative analysis was conducted between SVM running on a CPU (classical computation) and execut

Maniraman Periyasamy, Axel Plinge, Christopher Mutschler, Daniel D. Scherer

The study of variational quantum algorithms (VQCs) has received significant attention from the quantum computing community in recent years. These hybrid algorithms, utilizing both classical and quantum components, are well-suited for noisy intermediate-scale quantum devices. Though estimating exact gradients using the parameter-shift rule to optimize the VQC

Zhu Liao, Victor Quétu, Van-Tam Nguyen, Enzo Tartaglione

Unstructured pruning is a popular compression method for efficiently reducing model parameters. However, while it effectively decreases the number of parameters, it is commonly believed that unstructured pruning cannot shorten the computational critical path, i.e., the maximum number of layers traversed during forward propagation. In this paper, we study whe

Xin Jin, Tiejun Lv, Wei Ni, Zhipeng Lin

Dual-function-radar-communication (DFRC) is a promising candidate technology for next-generation networks. By integrating hybrid analog-digital (HAD) beamforming into a multi-user millimeter-wave (mmWave) DFRC system, we design a new reconfigurable subarray (RS) architecture and jointly optimize the HAD beamforming to maximize the communication sum-rate and

Elia Fusi, Ramiro A. Lafuente, James Stanfield

We develop a framework inspired by Lauret's "bracket flow" to study the generalized Ricci flow, as introduced by Streets, on discrete quotients of Lie groups. As a first application, we establish global existence on solvmanifolds in arbitrary dimensions, a result which is new even for the pluriclosed flow. We also define a notion of generalized Ricci soliton

Chitrak Karan, Abhishek Chaudhuri, Debasish Chaudhuri

We consider a two-dimensional, tangentially active, semi-flexible, self-avoiding polymer to find a dynamical re-entrant transition between motile open chains and spinning achiral spirals with increasing activity. Utilizing probability distributions of the turning number, we ascertain the comparative stability of the spiral structure and present a detailed ph

W. Zeng

We propose a tilt-assisted chirality Hall effect in the normal metal-superconductor (NS) junctions based on the time-reversal broken type-I Weyl semimetals. It is found that the chirality-contrasting skew reflection occurs at the NS interface due to the tilt of the Weyl cones, which is responsible for the nonzero transverse chirality Hall currents. Distinct

Tianyu Guo, Sai Praneeth Karimireddy, Michael I. Jordan

Collaboration between different data centers is often challenged by heterogeneity across sites. To account for the heterogeneity, the state-of-the-art method is to re-weight the covariate distributions in each site to match the distribution of the target population. Nevertheless, this method could easily fail when a certain site couldn't cover the entire pop

Georgios E. Pavlou, Vasiliki Pavlidou, Vagelis Harmandaris

Inverse problems are prevalent in numerous scientific and engineering disciplines, where the objective is to determine unknown parameters within a physical system using indirect measurements or observations. The inherent challenge lies in deducing the most probable parameter values that align with the collected data. This study introduces an algorithm for re

Peican Zhu, Zechen Pan, Yang Liu, Jiwei Tian

Graph Neural Network (GNN)-based fake news detectors apply various methods to construct graphs, aiming to learn distinctive news embeddings for classification. Since the construction details are unknown for attackers in a black-box scenario, it is unrealistic to conduct the classical adversarial attacks that require a specific adjacency matrix. In this paper

Xiang Gao, Yuqi Zhang

Recent style transfer problems are still largely dominated by Generative Adversarial Network (GAN) from the perspective of cross-domain image-to-image (I2I) translation, where the pivotal issue is to learn and transfer target-domain style patterns onto source-domain content images. This paper handles the problem of translating real pictures into traditional

Camilla Fiorini, Clément Flint, Louis Fostier, Emmanuel Franck

Symbolic Regression (SR) is a widely studied field of research that aims to infer symbolic expressions from data. A popular approach for SR is the Sparse Identification of Nonlinear Dynamical Systems (SINDy) framework, which uses sparse regression to identify governing equations from data. This study introduces an enhanced method, Nested SINDy, that aims to

D. L. Helis, A. Melchiorre, A. Puiu, G. Benato

In this paper, we present the first measurement of a Gallium Arsenide crystal working as low-temperature calorimeter for direct Dark Matter (DM) searches within the DAREDEVIL (DARk-mattEr DEVIces for Low energy detection) project. In the quest for direct dark matter detection, innovative approaches to lower the detection thershold and explore the sub-GeV mas

Paula L. Pagano, Massimo Borghi, Federica Moroni, Alice Viola

Optical microresonators are characterized by a comb of resonances that preserve similar characteristics over a broad spectral interval. However, for many applications it is beneficial to selectively control of the quality factor (Q) of one or only some resonances. In this work we propose and experimentally validate the use of a resonant interferometric coupl

N. Schneider, V. Ossenkopf-Okada, E. Keilmann, M. Roellig

High-latitude intermediate-velocity clouds (IVCs) are part of the Milky Way's HI halo and originate from either a galactic fountain process or extragalactic gas infall. They are partly molecular and can most of the time be identified in CO. Some of these regions also exhibit high-velocity cloud (HVC) gas, which is mostly atomic, and gas at local velocities (

Bin Xu, Fengwen Mu, Yingzhou Liu, Rulei Guo

Thermal boundary resistance (TBR) in semiconductor-on-diamond structure bottlenecks efficient heat dissipation in electronic devices. In this study, to reduce the TBR between GaN and diamond, surface-activated bonding with a hybrid SiOx-Ar ion source was applied to achieve an ultrathin interfacial layer. The simultaneous surface activation and slow depositio

Mateusz Klimaszewski, Piotr Andruszkiewicz, Alexandra Birch

Modular deep learning is the state-of-the-art solution for lifting the curse of multilinguality, preventing the impact of negative interference and enabling cross-lingual performance in Multilingual Pre-trained Language Models. However, a trade-off of this approach is the reduction in positive transfer learning from closely related languages. In response, we

Folco Bertini Baldassini, Mustafa Shukor, Matthieu Cord, Laure Soulier

Large Language Models have demonstrated remarkable performance across various tasks, exhibiting the capacity to swiftly acquire new skills, such as through In-Context Learning (ICL) with minimal demonstration examples. In this work, we present a comprehensive framework for investigating Multimodal ICL (M-ICL) in the context of Large Multimodal Models. We con

Andrea Merlina, Thiago Garrett, Roman Vitenberg

Proof-of-Work (PoW) blockchains have emerged as a robust and effective consensus mechanism in open environments, leading to widespread deployment with numerous cryptocurrency platforms and substantial investments. However, the commonly deployed PoW implementations are all based on solving cryptographic puzzles. Researchers have been pursuing the compelling i

Yang Liu, Binglin Chen, Yongsen Zheng, Lechao Cheng

Metro Origin-Destination (OD) prediction is a crucial yet challenging spatial-temporal prediction task in urban computing, which aims to accurately forecast cross-station ridership for optimizing metro scheduling and enhancing overall transport efficiency. Analyzing fine-grained and comprehensive relations among stations effectively is imperative for metro O

Yu Feng, Tianrui Ma, Yuhao Zhu, Xuan Zhang

Eye tracking is becoming an increasingly important task domain in emerging computing platforms such as Augmented/Virtual Reality (AR/VR). Today's eye tracking system suffers from long end-to-end tracking latency and can easily eat up half of the power budget of a mobile VR device. Most existing optimization efforts exclusively focus on the computation pipeli

Matic Belak Vivod, Zvonko Jagličić, Graham King, Thomas C. Hansen

Fluoridoargentates(II) represent a fascinating class of silver(II) compounds that exhibit structural and magnetic similarities to cuprate superconductors. However, their synthesis is challenging, leaving their properties largely unexplored. In this study, mechanochemistry is introduced as a new technique for the synthesis of fluoridoargentates(II), which avo

Akshay Thakur, Souvik Chakraborty

We propose a neural operator framework, termed mixture density nonlinear manifold decoder (MD-NOMAD), for stochastic simulators. Our approach leverages an amalgamation of the pointwise operator learning neural architecture nonlinear manifold decoder (NOMAD) with mixture density-based methods to estimate conditional probability distributions for stochastic ou

Djamel eddine Kebiche, Paolo Giordano

By means of several examples, we motivate that universal properties are the simplest way to solve a given mathematical problem, explaining in this way why they appear everywhere in mathematics. In particular, we present the co-universal property of Schwartz distributions, as the simplest way to have derivatives of continuous functions, Colombeau algebra as t

Chuang Liu, Zelin Yao, Yibing Zhan, Xueqi Ma

Graph Transformers (GTs) have demonstrated their advantages across a wide range of tasks. However, the self-attention mechanism in GTs overlooks the graph's inductive biases, particularly biases related to structure, which are crucial for the graph tasks. Although some methods utilize positional encoding and attention bias to model inductive biases, their ef

Koichiro Furutani, Giovanni Midei, Andrea Perali, Luca Salasnich

We study the amplitude and phase fluctuations of the Ginzburg-Landau quasiorder parameter for superconductors in two spatial dimensions. Starting from the mean-field critical temperature $T_{\mathrm{c}0}$, we calculate the beyond-mean-field critical temperature $T_{\rm c}$ by including thermal fluctuations of the quasiorder parameter within the Gaussian leve

Ludovico Bruni Bruno, Giacomo Elefante

In this work we describe and test the construction of least squares Whitney forms based on weights. If, on the one hand, the relevance of such a family of differential forms is nowadays clear in numerical analysis, on the other hand the selection of performing sets of supports (hence of weights) for projecting onto high order Whitney forms turns often to be

Kento Kawaharazuka, Kei Okada, Masayuki Inaba

When a robot executes a task, it is necessary to model the relationship among its body, target objects, tools, and environment, and to control its body to realize the target state. However, it is difficult to model them using classical methods if the relationship is complex. In addition, when the relationship changes with time, it is necessary to deal with t

Pierre Monmarché, Julien Reygner

Non-linear versions of log-Sobolev inequalities, that link a free energy to its dissipation along the corresponding Wasserstein gradient flow (i.e. corresponds to Polyak-Lojasiewicz inequalities in this context), are known to provide global exponential long-time convergence to the free energy minimizers, and have been shown to hold in various contexts. Howev

Mauro D'Onofrio, Cesare Chiosi

Context: Using the Illustris-1 and IllustrisTNG-100 simulations we investigate the properties of the Fundamental Plane (FP), that is the correlation between the effective radius Re, the effective surface intensity Ie and the central stellar velocity dispersion (sigma) of galaxies, at different cosmic epochs. Aims: Our aim is to study the properties of galaxi

Felix Klaedtke

This paper presents the monitoring tool POLIMON for checking system behavior at runtime against specifications expressed as formulas in the real-time logic MTL or its extension with the freeze quantifier. The tool's distinguishing feature is that POLIMON can receive messages describing the system events out of order. Furthermore, since POLIMON processes rece

Rasmus L. Olsen, Sina Hassani, Troels Pedersen, Jakob Gulddahl Rasmussen

Widely available measurement equipment in electrical distribution grids, such as power-quality measurement devices, substation meters, or customer smart meters do not provide phasor measurements due to the lack of high resolution time synchronisation. Instead such measurement devices allow to obtain magnitudes of voltages and currents and the local phase ang

Per Bäck, Johan Richter

We prove several new versions of Hilbert's basis theorem for non-associative Ore extensions, non-associative skew Laurent polynomial rings, non-associative skew power series rings, and non-associative skew Laurent series rings. For non-associative skew Laurent polynomial rings, we show that both a left and a right version of Hilbert's basis theorem hold. For

Ankit Vani, Bac Nguyen, Samuel Lavoie, Ranjay Krishna

Selective attention helps us focus on task-relevant aspects in the constant flood of our sensory input. This constraint in our perception allows us to robustly generalize under distractions and to new compositions of perceivable concepts. Transformers employ a similar notion of attention in their architecture, but representation learning models with transfor

Michiel van der Meer, Neele Falk, Pradeep K. Murukannaiah, Enrico Liscio

Active Learning (AL) addresses the high costs of collecting human annotations by strategically annotating the most informative samples. However, for subjective NLP tasks, incorporating a wide range of perspectives in the annotation process is crucial to capture the variability in human judgments. We introduce Annotator-Centric Active Learning (ACAL), which i

Jinfu Liu, Baiqiao Yin, Jiaying Lin, Jiajun Wen

Skeleton-based action recognition has gained considerable traction thanks to its utilization of succinct and robust skeletal representations. Nonetheless, current methodologies often lean towards utilizing a solitary backbone to model skeleton modality, which can be limited by inherent flaws in the network backbone. To address this and fully leverage the com

Constantin Ulrich, Catherine Knobloch, Julius C. Holzschuh, Tassilo Wald

Despite considerable strides in developing deep learning models for 3D medical image segmentation, the challenge of effectively generalizing across diverse image distributions persists. While domain generalization is acknowledged as vital for robust application in clinical settings, the challenges stemming from training with a limited Field of View (FOV) rem

V. V. Belov, W. V. Pogosov

We conduct a numerical investigation of the dynamics of the central spin model in the presence of measurement processes. This model holds promise for experimental exploration due to its topology, which facilitates the natural distinction of a central particle and the quantum bath as different subsystems, allowing for the examination of entanglement phase tra

P. Woitke, J. Drażkowska, H. Lammer, K. Kadam

Ca-Al-rich inclusions (CAIs) are the oldest dated solid materials in the solar system, found as light-coloured crystalline ingredients in meteorites. Their formation time is commonly associated with age zero of the Solar System. Yet, the physical and chemical processes that once led to the formation of these sub-millimetre to centimetre-sized mineral particl

Shu Liu, Yan Xu, Tongming Wan, Xiaoyan Kui

Facial expression recognition (FER) plays a significant role in our daily life. However, annotation ambiguity in the datasets could greatly hinder the performance. In this paper, we address FER task via label distribution learning paradigm, and develop a dual-branch Adaptive Distribution Fusion (Ada-DF) framework. One auxiliary branch is constructed to obtai

Yunhua Fu, Renbiao Tao, Lifei Zhang, Shijie Li

Nominally anhydrous minerals (NAMs) composing Earth's and planetary rocks incorporate microscopic amounts of volatiles. However, volatile distribution in NAMs and their effect on physical properties of rocks remain controversial. Thus, constraining trace volatile concentrations in NAMs is tantamount to our understanding of the evolution of rocky planets and

Weichen Fan, Markus Ludwig, Ian Rousseau, Ivo Arabadzhiev

Supercontinua are broadband spectra that are essential to optical spectroscopy, sensing, imaging, and metrology. They are generated from ultrashort laser pulses through nonlinear frequency conversion in fibers, bulk media, and chip-integrated waveguides. For any generating platform, balancing the competing criteria of strong nonlinearity, transparency, and a

Valerio Bertone, Alexander Karlberg

We present a benchmark comparison of the massless inclusive deep-inelastic-scattering (DIS) structure functions up to $\mathcal{O}(\alpha_s^3)$ in perturbative QCD. The comparison is performed using the codes APFEL++ and HOPPET within the framework of the variable-flavour-number scheme and over a broad kinematic range relevant to the extraction of parton dis

Chunjie Xiao, Ting Hou, Weihai Zhang

In this paper, exponential stability of discrete-time Markov jump linear systems (MJLSs) with the Markov chain on a Borel space $(\Theta, \mathcal{B}(\Theta))$ is studied, and bounded real lemmas (BRLs) are given. The work generalizes the results from the previous literature that considered only the Markov chain taking values in a countable set to the scenar

Zerui Chen, Shizhe Chen, Etienne Arlaud, Ivan Laptev

In this work, we aim to learn a unified vision-based policy for multi-fingered robot hands to manipulate a variety of objects in diverse poses. Though prior work has shown benefits of using human videos for policy learning, performance gains have been limited by the noise in estimated trajectories. Moreover, reliance on privileged object information such as

Saeed S. I. Almishal, Leixin Miao, Yueze Tan, George N. Kotsonis

Interest in high-entropy inorganic compounds originates from their ability to stabilize cations and anions in local environments that rarely occur at standard temperature and pressure. This leads to new crystalline phases in many-cation formulations with structures and properties that depart from conventional trends. The highest-entropy homogeneous and rando

Jinseo Jeong, Junseo Koo, Qimeng Zhang, Gunhee Kim

Existing NeRF-based inverse rendering methods suppose that scenes are exclusively illuminated by distant light sources, neglecting the potential influence of emissive sources within a scene. In this work, we confront this limitation using LDR multi-view images captured with emissive sources turned on and off. Two key issues must be addressed: 1) ambiguity ar

Eugenio Lippiello, Lucilla de Arcangelis, Cataldo Godano

The identification of patterns in space, time, and magnitude, which could potentially encode the subsequent earthquake magnitude, represents a significant challenge in earthquake forecasting. A pivotal aspect of this endeavor involves the search for correlations between earthquake magnitudes, a task greatly hindered by the incompleteness of instrumental cata

Yanlin Li, Abimbola Abolarinwa, Suraj Ghosh, Shyamal Kumar Hui

In this article we derive gradient estimation for positive solution of the equation \begin{equation*} (\partial_t-\Delta_f)u = A(u)p(x,t) + B(u)q(x,t) + \mathcal{G}(u) \end{equation*} on a weighted Riemannian manifold evolving along the $(k,m)$ super Perelman-Ricci flow \begin{equation*} \frac{\partial g}{\partial t}(x,t)+2Ric_f^m(g)(x,t)\ge -2kg(x,t). \end{

Zuheng Kang, Yayun He, Jianzong Wang, Junqing Peng

Single-model systems often suffer from deficiencies in tasks such as speaker verification (SV) and image classification, relying heavily on partial prior knowledge during decision-making, resulting in suboptimal performance. Although multi-model fusion (MMF) can mitigate some of these issues, redundancy in learned representations may limits improvements. To

Federico Nudo

In this paper, we introduce two one-parameter families of quadratic polynomial enrichments designed to enhance the accuracy of the classical Crouzeix--Raviart finite element. These enrichments are realized by using weighted line integrals as enriched linear functionals and quadratic polynomial functions as enrichment functions. To validate the effectiveness

Junfeng Tian, Rui Wang, Cong Li, Yudong Zhou

This report details the development and key achievements of our latest language model designed for custom large language models. The advancements introduced include a novel Online Data Scheduler that supports flexible training data adjustments and curriculum learning. The model's architecture is fortified with state-of-the-art techniques such as Rotary Posit

Jie Song, GuanWen Fang, Shuo Ba, Zesen Lin

Morphological classification conveys abundant information on the formation, evolution, and environment of galaxies. In this work, we refine the two-step galaxy morphological classification framework ({\tt\string USmorph}), which employs a combination of unsupervised machine learning (UML) and supervised machine learning (SML) techniques, along with a self-co

Tianyu Yan, Zifu Wan, Xinhao Deng, Pingping Zhang

Recently, Segment Anything Model (SAM) shows exceptional performance in generating high-quality object masks and achieving zero-shot image segmentation. However, as a versatile vision model, SAM is primarily trained with large-scale natural light images. In underwater scenes, it exhibits substantial performance degradation due to the light scattering and abs

Marina Barinova

In the paper we consider an $\Omega$-stable 3-diffeomorphism, chain recurrent set of which consists of isolated periodic points and expanding attractors of codimension 1, orientable or not. We estimate a minimum number of isolated periodic points using information about the structure of the attractors.

Lorenz Halbeisen, Norbert Hungerbühler, Arman Shamsi Zargar

A pair $(a, b)$ of positive integers is a pythagorean pair if $a^2 + b^2$ is a square. A pythagorean pair $(a, b)$ is called a pythapotent pair of degree $h$ if there is another pythagorean pair $(k,l)$, which is not a multiple of $(a,b)$, such that $(a^hk, b^hl)$ is a pythagorean pair. To each pythagorean pair $(a, b)$ we assign an elliptic curve $\Gamma_{a

Bruce G. Elmegreen, Natalia Lahen

The collective properties of star clusters are investigated using a simulation of the collision between two dwarf galaxies. The characteristic power law of the cluster mass function, N(M), with a logarithmic slope d\log N/d\log M ~ -1, is present from cluster birth and remains throughout the simulation. The maximum mass of a young cluster scales with the sta

Orazio Pontorno, Luca Guarnera, Sebastiano Battiato

Deepfakes, synthetic images generated by deep learning algorithms, represent one of the biggest challenges in the field of Digital Forensics. The scientific community is working to develop approaches that can discriminate the origin of digital images (real or AI-generated). However, these methodologies face the challenge of generalization, that is, the abili

Jiaqi Liu, Ziran Wang, Peng Hang, Jian Sun

Cooperative Adaptive Cruise Control (CACC) represents a quintessential control strategy for orchestrating vehicular platoon movement within Connected and Automated Vehicle (CAV) systems, significantly enhancing traffic efficiency and reducing energy consumption. In recent years, the data-driven methods, such as reinforcement learning (RL), have been employed

Bidyut Hazarika, Prabwal Phukon

In this study, we investigate the thermodynamic topology of the Kerr-Sen-Ads black hole in restricted phase space. In the restricted phase space, a new parameter, central charge $C$, and its conjugate parameter $\mu$ are introduced, omitting the well-known $PdV$ term in the first law of black hole thermodynamics. We study the local and global topology of the

Vadym Zayets

The paper investigates the systematic dependencies of the anisotropy field and the strength of spin-orbit (SO) interaction on gate voltage in Ta/FeB/MgO nanomagnets. Our findings reveal an intriguing opposite polarity in the gate-voltage dependencies of the anisotropy field and the coefficient of SO interaction across all studied nanomagnets. This opposite p

Jean-Pierre Magnot

We describe basic diffeological structures related to splittings and Grassmannians for infinite dimensional vector spaces. We analyze and expand the notion of non-commutative cross-ratio and prove its smoothness. Then we illustrate this theory by examples, with some of them extracted from the existing literature related to infinite dimensional (Banach) Grass

Thibaud van den Hove

We construct the geometric Satake equivalence for quasi-split reductive groups over nonarchimedean local fields, using \'etale Artin-Tate motives with $\mathbb{Z}[\frac{1}{p}]$-coefficients. We consider local fields of both equal and mixed characteristic. Along the way, we extend the work of Gaussent--Littelmann on the connection between LS galleries and MV

S. Mahmud, M. A. Ali, M. M. Hossain, M. M. Uddin

This paper presents the phase stability, opto-electronic and thermo-electric behavior of X2AuYZ6 (X = Cs, Rb; Z = Cl/Br/I) double perovskite halides by using the DFT method. The compounds belong to the cubic arrangement and are verified by the tolerance and octahedral factor. Formation enthalpy and binding energy meet the requirements of structural stability

Reinhard Heckel, Mathews Jacob, Akshay Chaudhari, Or Perlman

Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction. It focuses on DL approaches and architectures designed to improve image quality, accelerate scans, and add

Yuta Saito, Himan Abdollahpouri, Jesse Anderton, Ben Carterette

Short- and long-term outcomes of an algorithm often differ, with damaging downstream effects. A known example is a click-bait algorithm, which may increase short-term clicks but damage long-term user engagement. A possible solution to estimate the long-term outcome is to run an online experiment or A/B test for the potential algorithms, but it takes months o

Chenxuan Cui, Ying Chen, Qinxin Wang, David R. Mortensen

Proto-form reconstruction has been a painstaking process for linguists. Recently, computational models such as RNN and Transformers have been proposed to automate this process. We take three different approaches to improve upon previous methods, including data augmentation to recover missing reflexes, adding a VAE structure to the Transformer model for proto

Wataru Ishizaki, Kunihito Ioka

We investigate parametric decay instability (PDI) of circularly polarized Alfv\'en wave into daughter acoustic wave and backward Alfv\'en wave in magnetically-dominated plasma, in which the magnetization parameter $\sigma$ (energy density ratio of background magnetic field to matter) exceeds unity. We analyze relativistic magnetohydrodynamics (MHD), focusing

Daizhan Cheng, Xiao Zhang, Zhengping Ji, Changxi Li

A novel model reduction framework for large-scale complex systems is proposed by introducing function-type dynamic control systems via the dimension-keeping semi-tensor product (DK-STP) of matrices. Utilizing bridge matrices, the DK-STP facilitates the construction of an approximate observer-based realization (OR) of a linear control system in the form of a

Zhaoyang Chu, Yao Wan, Qian Li, Yang Wu

Vulnerability detection is crucial for ensuring the security and reliability of software systems. Recently, Graph Neural Networks (GNNs) have emerged as a prominent code embedding approach for vulnerability detection, owing to their ability to capture the underlying semantic structure of source code. However, GNNs face significant challenges in explainabilit

Sehyun Ryu, Jonggyu Jang, Hyun Jong Yang

The concept of differential privacy (DP) can quantitatively measure privacy loss by observing the changes in the distribution caused by the inclusion of individuals in the target dataset. The DP, which is generally used as a constraint, has been prominent in safeguarding datasets in machine learning in industry giants like Apple and Google. A common methodol

Lu-Chuan Liu, Cheng Wu, Wei Li, Yu-Ao Chen

Long baseline diffraction-limited optical aperture synthesis technology by interferometry plays an important role in scientific study and practical application. In contrast to amplitude (phase) interferometry, intensity interferometry -- which exploits the quantum nature of light to measure the photon bunching effect in thermal light -- is robust against atm

Tie Liu, Xuming Fang, Rong He

Generative Diffusion Models (GDMs), have made significant strides in modeling complex data distributions across diverse domains. Meanwhile, Deep Reinforcement Learning (DRL) has demonstrated substantial improvements in optimizing Wi-Fi network performance. Wi-Fi optimization problems are highly challenging to model mathematically, and DRL methods can bypass