November 2024 arXiv papers — page 73

Charles O'Neill, Alim Gumran, David Klindt

A recent line of work has shown promise in using sparse autoencoders (SAEs) to uncover interpretable features in neural network representations. However, the simple linear-nonlinear encoding mechanism in SAEs limits their ability to perform accurate sparse inference. Using compressed sensing theory, we prove that an SAE encoder is inherently insufficient for

Zhi Luo, Xiyuan Yang, Pan Zhou, Di Wang

Manipulating the interaction trajectories between the intelligent agent and the environment can control the agent's training and behavior, exposing the potential vulnerabilities of reinforcement learning (RL). For example, in Cyber-Physical Systems (CPS) controlled by RL, the attacker can manipulate the actions of the adopted RL to other actions during the t

Jinghong Su, Yi-bao Zhang, Cheng Wang, Lei Yi

The presence of dispersed-phase droplets can result in a notable increase in the system's drag. However, our understanding of the mechanism underlying this phenomenon remains limited. In this study, we use three-dimensional direct numerical simulations with a modified multi-marker volume-of-fluid method to investigate liquid-liquid two-phase turbulence in a

Wei-Wei Zhang, Zheping Wu, Hengyue Jia, Wei Zhao

Quantum mechanics empowers the emergence of quantum advantages in various fields, including quantum algorithms. Quantum PageRank is a promising tool for a future quantum internet. Recently, arbitrary phase rotations (APR) have been introduced in the underlying Szegedy's quantum walk of quantum PageRank algorithm. In this work, we thoroughly study the role AP

Inge S. Helland

In this article, the weakest possible theorem providing a foundation for the Hilbert space formalism of quantum theory is stated. The necessary postulates are formulated, and the mathematics is spelt out in detail. It is argued that, from this approach, a general epistemic interpretation of quantum mechanics is natural. Some applications to the Bell experime

Xianda Guo, Ruijun Zhang, Yiqun Duan, Yuhang He

Accurate spatial reasoning in outdoor environments - covering geometry, object pose, and inter-object relationships - is fundamental to downstream tasks such as mapping, motion forecasting, and high-level planning in autonomous driving. We introduce SURDS, a large-scale benchmark designed to systematically evaluate the spatial reasoning capabilities of visio

Linlin Tian, Yixuan Tian, Bohan Li, Guoqing Li

This paper explores the optimal investment problem of a renewal risk model with generalized Erlang distributed interarrival times. The phases of the Erlang interarrival time is assumed to be observable. The price of the risky asset is driven by the constant elasticity of variance model (CEV) and the insurer aims to maximize the exponential utility of the ter

Xun Zhu, Gao-Chan Yong

Based on the AMPT model, which incorporates both hadronic and quark degrees of freedom, we studied the productions of lambda, kaon, proton, and pion in reaction systems $^{40}$Ca+$^{40}$Ca, $^{48}$Ca+$^{48}$Ca, and $^{197}$Au+$^{197}$Au. It is found that the ratios of identical particle emissions from heavy and light reaction systems, especially the emission

Anika Tahsin Meem

In modern times, the cryptocurrency market is one of the world's most rapidly rising financial markets. The cryptocurrency market is regarded to be more volatile and illiquid than traditional markets such as equities, foreign exchange, and commodities. The risk of this market creates an uncertain condition among the investors. The purpose of this research is

David Mascareñas, Andre Green, Ashlee Liao, Michael Torrez

We demonstrate the suitability of high dynamic range, high-speed, neuromorphic event-based, dynamic vision sensors for metallic additive manufacturing and welding for in-process monitoring applications. In-process monitoring to enable quality control of mission critical components produced using metallic additive manufacturing is of high interest. However, t

Zhi-Min Wu, Gao-Chan Yong, Qingfeng Li

We propose a hybrid equation of state (VDF+MIT EoS) to describe the hadron-quark phase transition in dense nuclear matter. By coupling this EoS with the AMPT-HC transport model and comparing to recent experimental data on proton and $\Lambda$ directed flow $v_1$, we constrain the transition to likely occur near $5\rho_0$--$6\rho_0$, ruling out transitions be

Martti Hanhisalo, Mohammad Sajjad Mirmoosa, Tero Setälä, Łukasz Rudnicki

Light is known to exhibit quantum uncertainty in terms of its amplitude, phase, and polarization. However, quantum uncertainty related to coherence, which is also a fundamental physical property of light, has not been considered to date. Here, we formulate and explore the concept of quantum optical coherence uncertainty. We focus on the first-order coherence

Shanglong Liu, Lin Qi, Junyu Dong, Wenxiang Gu

In this work, we concentrate on exciting the intrinsic local consistency of stereo matching through the incorporation of superpixel soft constraints, with the objective of mitigating inaccuracies at the boundaries of predicted disparity maps. Our approach capitalizes on the observation that neighboring pixels are predisposed to belong to the same object and

Zheng Zhang, Qiong Wu, Pingyi Fan, Nan Cheng

To address communication latency issues, the Third Generation Partnership Project (3GPP) has defined Cellular-Vehicle to Everything (C-V2X) technology, which includes Vehicle-to-Vehicle (V2V) communication for direct vehicle-to-vehicle communication. However, this method requires vehicles to autonomously select communication resources based on the Semi-Persi

Prerna Patil, Eurika Kaiser, J Nathan Kutz, Steven Brunton

This work explores the intersection of time-delay embeddings, periodic orbit theory, and symbolic dynamics. Time-delay embeddings have been effectively applied to chaotic time series data, offering a principled method to reconstruct relevant information of the full attractor from partial time series observations. In this study, we investigate the structure o

Milutin Obradović, Nikola Tuneski

For $f\in \mathcal{S}$, the class univalent functions in the unit disk $\mathbb{D}$ and given by $f(z)=z+\sum_{n=2}^{\infty} a_n z^n$ for $z\in \mathbb{D}$, we improve previous bounds for the second and third Hankel determinants in case when either $a_2=0,$ or $a_3=0$. We also improve an upper bound for the coefficient difference $|a_4|-|a_3|$ when $f\in \ma

David Mason, Ed Bennett, Biagio Lucini, Maurizio Piai

First-order phase transitions in the early universe have rich phenomenological implications, such as the production of a potentially detectable signal of stochastic relic background gravitational waves. The hypothesis that new, strongly coupled dynamics, hiding in a new dark sector, could be detected in this way, via the telltale signs of its confinement/dec

Yunkee Chae, Eunsik Shin, Suntae Hwang, Seungryeol Paik

Lyrics generation presents unique challenges, particularly in achieving precise syllable control while adhering to song form structures such as verses and choruses. Conventional line-by-line approaches often lead to unnatural phrasing, underscoring the need for more granular syllable management. We propose a framework for lyrics generation that enables multi

Arnaud Guillin, D I Lu, Boris Nectoux, Liming Wu

In this work, we investigate the compactness and the long time behavior of killed Feynman-Kac semigroups of various processes arising from statistical physics with very general singular Schr{\"o}dinger potentials. The processes we consider cover a large class of processes used in statistical physics, with strong links with quantum mechanics and (local or not

Byung Gyu Chae

A lensless digital holography enables wide-field microscopic imaging without the limitations imposed by optical lens performance. However, conventional holographic imaging often relies on magnifying optical systems to compensate for the low resolution of holograms captured by image sensors. The spatial resolution of the reconstructed image is fundamentally c

Ziqi Jia, Xiaoyang Qu, Chenghao Liu, Jianzong Wang

Label Distribution Learning (LDL) is an effective approach for handling label ambiguity, as it can analyze all labels at once and indicate the extent to which each label describes a given sample. Most existing LDL methods consider the number of labels to be static. However, in various LDL-specific contexts (e.g., disease diagnosis), the label count grows ove

E. Caffau, P. Bonifacio, L. Monaco, M. Steffen

Context: The small- to intermediate-mass ($M <0.8 M_\odot$), most metal-poor stars that formed in the infancy of the Universe are still shining today in the sky. They are very rare, but their discovery and investigation brings new knowledge on the formation of the first stellar generations. Aims: SDSS J102915.14+172927.9 is one of the most metal-poor star kn

Shuai Zhang, Gao-Chan Yong, J. L. Rodríguez-Sánchez, J. Cugnon

In the relentless pursuit of expanding the periodic table, the discovery of element 119 remains elusive, despite two decades of dedicated research efforts. The traditional fusion-evaporation approach, although fruitful in the past, now appears to be approaching its operational limits. This scenario sets the stage for considering innovative methodologies esse

Jean-François Coulombel, Grégory Faye

In this article we study the spectral, linear and nonlinear stability of stationary shock profile solutions to the Lax-Wendroff scheme for hyperbolic conservation laws. We first clarify the spectral stability of such solutions depending on the convexity of the flux for the underlying conservation law. The main contribution of this article is a detailed study

Yongdong Luo, Xiawu Zheng, Guilin Li, Shukang Yin

Existing large video-language models (LVLMs) struggle to comprehend long videos correctly due to limited context. To address this problem, fine-tuning long-context LVLMs and employing GPT-based agents have emerged as promising solutions. However, fine-tuning LVLMs would require extensive high-quality data and substantial GPU resources, while GPT-based agents

François Dubois

The present contribution is in the field of quantum modelling of macroscopic phenomena. The focus is on one enigmatic aspect of quantum physics, namely the Einstein-Podolsky-Rosen paradox and entanglement. After a review of the state of the art concerning macroscopic quantum effects and quantum interaction, this contribution proposes a link between embryolog

Zahra Ghalenovi, Masoumeh Moazzen Sorkhi, Amir Hossein Sovizi

In the present work we investigate the mass spectrum and semileptonic decays of double charm and bottom baryon states using the hypercentral quark model. We solve the six-dimensional Schr\"odinger equation via deep learning and particle swarm optimization techniques to improve the speed and accuracy. Then, we predict the masses of the ground and excited stat

Tony J. Puthenpurakal

Let $K$ be a field and let $R = K[X_1, \ldots, X_m]$ with $m \geq 2$. Give $R$ the standard grading. Let $I$ be a homogeneous ideal of height $g$. Assume $1 \leq g \leq m -1$. Suppose $H^i_I(R) \neq 0$ for some $i \geq 0$. We show (1) $H^i_I(R)_n \neq 0$ for all $n \leq -m$. (2) if Supp $H^i_I(R) \neq \{ (X_1, \ldots, X_m)\}$ then $H^i_I(R)_n \neq 0$ for all

Xulong Zhang, Xiaoyang Qu, Haoxiang Shi, Chunguang Xiao

This paper proposes a novel 3D speech-to-animation (STA) generation framework designed to address the shortcomings of existing models in producing diverse and emotionally resonant animations. Current STA models often generate animations that lack emotional depth and variety, failing to align with human expectations. To overcome these limitations, we introduc

Zhongchi Zhang, Zihan Zhao, Huaichuan Wang, Ken Deng

We generated a one-dimensional quantum gas confined in an elongated optical dipole trap instead of 2D optical lattices. The sample, comprising thousands of atoms, spans several hundred micrometers and allows for independent control of temperature and chemical potential using Feshbach resonance. This allows us to directly observe and investigate the spatial d

B. A. Richards, N. Ristoff, J. Smits, A. Jeronimo Perez

Superparamagnetic iron-oxide nanoparticles (SPIONs) are promising probes for biomedical imaging, but the heterogeneity of their magnetic properties is difficult to characterize with existing methods. Here, we perform widefield imaging of the stray magnetic fields produced by hundreds of isolated ~30-nm SPIONs using a magnetic microscope based on nitrogen-vac

Kenta Nagase, Soki Oshima, Hikaru Takahashi, Takashi Mukaiyama

Using radio-frequency (RF) spectroscopy, we measure the $p$-wave contacts in a Fermi gas of $^6$Li atoms near the $p$-wave Feshbach resonance. The RF spectrum exhibits clear asymptotic behavior, characterized by $\tilde{\omega}^{-1/2}$ and $\tilde{\omega}^{-3/2}$ dependencies. The magnetic-field dependence of the $p$-wave contacts agrees reasonably well with

Kefeng Liu, Yang Shen

Building on our recent work, we construct the Penrose transformations of the cohomology groups of homogeneous line bundles on flag domains $D = G_\R / T$, where $G_\R$ is of Hermitian type. We provide sufficient conditions for the injectivity of the Penrose transformation and identify conditions under which the Penrose transformation of the automorphic cohom

Jie Chen, Hua Mao, Chuanbin Liu, Zhu Wang

Leveraging the diversity and quantity of data provided by various graph-structured data augmentations while preserving intrinsic semantic information is challenging. Additionally, successive layers in graph neural network (GNN) tend to produce more similar node embeddings, while graph contrastive learning aims to increase the dissimilarity between negative p

Yifan Jing, Tingxiang Zou

We establish a group-action version of the Szemer\'edi-Trotter theorem over any field, extending Bourgain's result for the group $\mathrm{SL}_2(k)$. As an Elekes-Szab\'o-type application, we obtain quantitative bounds on the number of collinear triples on reducible cubic surfaces in $\mathbb{P}^3(k)$, where $k = \mathbb{F}_{q}$ and $k = \mathbb{C}$, thereby

Lunjia Hu, Kevin Tian, Chutong Yang

Recent work on supervised learning [GKR+22] defined the notion of omnipredictors, i.e., predictor functions $p$ over features that are simultaneously competitive for minimizing a family of loss functions $\mathcal{L}$ against a comparator class $\mathcal{C}$. Omniprediction requires approximating the Bayes-optimal predictor beyond the loss minimization parad

Yijiong Yu

Recent advancements in the field of large language models, particularly through the Chain of Thought (CoT) approach, have demonstrated significant improvements in solving complex problems. However, existing models either tend to sacrifice detailed reasoning for brevity due to user preferences, or require extensive and expensive training data to learn complic

Bin Chen, Jian Zhang

Recent years have witnessed the success of deep networks in compressed sensing (CS), which allows for a significant reduction in sampling cost and has gained growing attention since its inception. In this paper, we propose a new practical and compact network dubbed PCNet for general image CS. Specifically, in PCNet, a novel collaborative sampling operator is

Nabarun Deb, Promit Ghosal, Bodhisattva Sen

In this paper we propose and study a class of nonparametric, yet interpretable measures of association between two random vectors $X$ and $Y$ taking values in $\mathbb{R}^{d_1}$ and $\mathbb{R}^{d_2}$ respectively ($d_1, d_2\ge 1$). These nonparametric measures -- defined using the theory of reproducing kernel Hilbert spaces coupled with optimal transport --

Feng Gao, Chao Yu, Yu Wang, Yi Wu

Accurate motion control in the face of disturbances within complex environments remains a major challenge in robotics. Classical model-based approaches often struggle with nonlinearities and unstructured disturbances, while RL-based methods can be fragile when encountering unseen scenarios. In this paper, we propose a novel framework, Neural Internal Model C

Ming Lu, Shiquan Ruan

From a category $\mathcal{A}$ with an involution $\varrho$, we introduce $\varrho$-complexes, which are a generalization of (bounded) complexes, periodic complexes and modules of $\imath$quiver algebras. The homological properties of the category $\mathcal{C}_\varrho(\mathcal{A})$ of $\varrho$-complexes are given to make the machinery of semi-derived Ringel-

Narges Golmirzaee, David H. Wood

This study concerns the far-field boundary conditions (BCs) for airfoil simulations at high incidence where the lift and drag are comparable in magnitude and the moment is significant. A NACA 0012 airfoil was simulated at high Reynolds number with the Spalart-Allmaras turbulence model in incompressible, steady flow. We use the impulse form of the lift, drag,

Jingyu Zhuang, Di Kang, Linchao Bao, Liang Lin

Text-driven avatar generation has gained significant attention owing to its convenience. However, existing methods typically model the human body with all garments as a single 3D model, limiting its usability, such as clothing replacement, and reducing user control over the generation process. To overcome the limitations above, we propose DAGSM, a novel pipe

Hao Zhou, Zhanning Gao, Zhili Chen, Maosheng Ye

In light of the dynamic nature of autonomous driving environments and stringent safety requirements, general MLLMs combined with CLIP alone often struggle to accurately represent driving-specific scenarios, particularly in complex interactions and long-tail cases. To address this, we propose the Hints of Prompt (HoP) framework, which introduces three key enh

Bowen Zhou, Kenji Watanabe, Takashi Taniguchi

We studied daisy-chained extraordinary magnetoresistance (EMR) devices based on high quality monolayer graphene encapsulated in hexagonal boron nitride (h-BN) at room temperature. The largest magnetoresistance (MR) achieved in our devices is 4.6 x 10^7 %, the record for EMR devices to date. The magnetic field sensitivity, dR/dB, reaches 104 kohm/T, exceeding

Adara M. Blaga, Antonella Nannicini

We introduce the concept of generalized almost plastic structure, and, on a pseudo-Riemannian manifold endowed with two $(1,1)$-tensor fields satisfying some compatibility conditions, we construct a family of generalized almost plastic structures and characterize their integrability with respect to a given affine connection on the manifold.

Shuman Peng, Arash Khoeini, Sharan Vaswani, Martin Ester

The quality of self-supervised pre-trained embeddings on out-of-distribution (OOD) data is poor without fine-tuning. A straightforward and simple approach to improving the generalization of pre-trained representation to OOD data is the use of deep ensembles. However, obtaining an effective ensemble in the embedding space with only unlabeled data remains an u

Kevin Godin-Dubois, Karine Miras, Anna V. Kononova

Traditional approaches to training agents have generally involved a single, deterministic environment of minimal complexity to solve various tasks such as robot locomotion or computer vision. However, agents trained in static environments lack generalization capabilities, limiting their potential in broader scenarios. Thus, recent benchmarks frequently rely

Huan Wang, Rui Shi, Zhaochen Liu, Jing Wang

The pursuit of a lattice analogue for Landau levels has been a central theme in condensed matter physics. Although the correspondence between Chern bands and the lowest Landau level has been widely studied, a lattice realization of the first Landau level remains elusive. Here we construct a minimal lattice model that provides a concrete orbital description o

C. Y. Jiang, B. L. Chen, K. W. Chen, J. C. Jiao

We report results of magnetic susceptibility, specific heat, and muon spin relaxation ($\mu$SR) measurements on the polycrystalline Ba$_6$Nd$_2$Ti$_4$O$_{17}$, a disorder-free triangular-lattice antiferromagnet. The absence of long-range magnetic order or spin freezing is confirmed down to 30~mK, much less than the Curie-Weiss temperature -1.8~K. The magneti

Jing Ren, Pei Wang, Hanlong Li, Yuhan Wu

In recent years, terrestrial laser scanning technology has been widely used to collect tree point cloud data, aiding in measurements of diameter at breast height, biomass, and other forestry survey data. Since a single scan from terrestrial laser systems captures data from only one angle, multiple scans must be registered and fused to obtain complete tree po

Yifan Yang, Qiao Jin, Robert Leaman, Xiaoyu Liu

The remarkable capabilities of Large Language Models (LLMs) make them increasingly compelling for adoption in real-world healthcare applications. However, the risks associated with using LLMs in medical applications have not been systematically characterized. We propose using five key principles for safe and trustworthy medical AI: Truthfulness, Resilience,

Zenghu Li, Run Zhang

We study the max-type recursive model introduced by Hu and Shi (J. Stat. Phys., 2018), which generalizes the model of Derrida and Retaux (J. Stat. Phys., 2014). The class of geometric-type marginal distributions is preserved by the model with a geometric offspring distribution. We give some long-time asymptotic expansions of the parameters of the marginal di

Zexiong Fang, Qing Cheng

In this paper, we improve the original Lagrange multiplier approach \cite{ChSh22,ChSh_II22} and introduce a new energy correction approach to construct a class of robust, positivity/bound-preserving, mass conserving and energy dissipative schemes for Keller-Segel equations which only need to solve several linear Poisson like equations. To be more specific, w

Koray Düztaş

We show that the thermal radiation derived by Hawking can be smoothly extended to the $T=0$ limit for Kerr black holes. The emission of the modes with $\omega > m\Omega $ comes to a halt as the surface gravity vanishes. However, Kerr black holes smoothly continue to radiate both in bosonic and fermionic modes with $\omega < m\Omega$, at the $T=0$ limit. We d

Ryo Takahashi, Eric Whitmire, Roger Boldu, Shiu Ng

Smart rings for subtle, reliable finger input offer an attractive path for ubiquitous interaction with wearable computing platforms. However, compared to ordinary rings worn for cultural or fashion reasons, smart rings are much bulkier and less comfortable, largely due to the space required for a battery, which also limits the space available for sensors. Th

Nathan Roberts, Brook Salter, Jack Binysh, Peter J. Mosley

The breaking and enforcing of symmetries is a crucial ingredient in designing topologically robust materials. While magnetic fields can break time-reversal symmetry to create Chern insulators in electronic and microwave systems, at optical frequencies natural materials cannot respond to magnetic fields, which presents a challenge for the scalable exploitatio

Hans-Christian Herbig, Christopher W. Seaton, Lillian Whitesell

We construct canonical measures, referred to as Hilbert measures, on orbit spaces of classical coregular representations of the orthogonal groups $\operatorname{O}_m$. We observe that the measures have singularities along non-principal strata of the orbit space if and only if the number of copies of the defining representation of $\operatorname{O}_m$ is equa

Guillaume Cébron, Patrick Oliveira Santos, Pierre Youssef

We establish a central limit theorem for the sum of $\epsilon$-independent random variables, extending both the classical and free probability setting. Central to our approach is the use of graphon limits to characterize the limiting distribution, which depends on the asymptotic structure of the underlying graphs governing $\epsilon$-independence. This frame

Ismael Ribeiro de Assis, Ingrid Mertig, Börge Göbel

Skyrmions are nano-sized magnetic whirls attractive for spintronic applications due to their innate stability. They can emulate the characteristic behavior of various spintronic and electronic devices such as spin-torque nano-oscillators, artificial neurons and synapses, logic devices, diodes, and ratchets. Here, we show that skyrmions can emulate the physic

Haiyue Kang, John F. Kam, Gary J. Mooney, Lloyd C. L. Hollenberg

We scheme an efficient and reusable approach to quantum teleportation that allows cyclic teleportation of a two-qubit graph state around a quantum hamster wheel -- a ring of qubits entangled as a one-dimensional line prepared on the 20-qubit Quantinuum H1-1 ion-trap quantum processor. The qubits on the ring are periodically measured and reused to achieve a t

Rohith Peddi, Saurabh, Ayush Abhay Shrivastava, Parag Singla

Spatio-Temporal Scene Graphs (STSGs) provide a concise and expressive representation of dynamic scenes by modeling objects and their evolving relationships over time. However, real-world visual relationships often exhibit a long-tailed distribution, causing existing methods for tasks like Video Scene Graph Generation (VidSGG) and Scene Graph Anticipation (SG

Juno C. L. Chan, Eungwang Seo, Alvin K. Y. Li, Heather Fong

Gravitational lensing by compact, small-scale intervening masses causes frequency-dependent distortions to gravitational-wave events. The optimal signal-to-noise ratio (SNR) is often used as a proxy for the detectability of exotic signals in gravitational-wave searches. In reality, the detectability of such signals in a matched-filtering search requires comp

Xu Chen, Zida Cheng, Yuangang Pan, Shuai Xiao

Existing click-through rate (CTR) prediction works have studied the role of feature interaction through a variety of techniques. Each interaction technique exhibits its own strength, and solely using one type usually constrains the model's capability to capture the complex feature relationships, especially for industrial data with enormous input feature fiel

Bing Cao, Quanhao Lu, Jiekang Feng, Qilong Wang

The dynamic imbalance of the fore-background is a major challenge in video object counting, which is usually caused by the sparsity of target objects. This remains understudied in existing works and often leads to severe under-/over-prediction errors. To tackle this issue in video object counting, we propose a density-embedded Efficient Masked Autoencoder Co

Jared Fernandez, Luca Wehrstedt, Leonid Shamis, Mostafa Elhoushi

Dramatic increases in the capabilities of neural network models in recent years are driven by scaling model size, training data, and corresponding computational resources. To develop the exceedingly large networks required in modern applications, such as large language models (LLMs), model training is distributed across tens of thousands of hardware accelera

Ruibao Wang, Bonan Yan

Processing-in-memory (PIM) is a promising choice for accelerating deep neural networks (DNNs) featuring high efficiency and low power. However, the rapid upscaling of neural network model sizes poses a crucial challenge for the limited on-chip PIM capacity. When the PIM presumption of "pre-loading DNN weights/parameters only once before repetitive computing"

Minguk Jang, Hye Won Chung

Test-time adaptation (TTA) is an effective approach to mitigate performance degradation of trained models when encountering input distribution shifts at test time. However, existing TTA methods often suffer significant performance drops when facing additional class distribution shifts. We first analyze TTA methods under label distribution shifts and identify

Yifei Zhang, Tianxu Jiang, Bo Pan, Jingyu Wang

Explaining the decision-making processes of Artificial Intelligence (AI) models is crucial for addressing their "black box" nature, particularly in tasks like image classification. Traditional eXplainable AI (XAI) methods typically rely on unimodal explanations, either visual or textual, each with inherent limitations. Visual explanations highlight key regio

Hung Vinh Tran, Tong Chen, Guanhua Ye, Quoc Viet Hung Nguyen

Content-based Recommender Systems (CRSs) play a crucial role in shaping user experiences in e-commerce, online advertising, and personalized recommendations. However, due to the vast amount of categorical features, the embedding tables used in CRS models pose a significant storage bottleneck for real-world deployment, especially on resource-constrained devic

M. G. Guarcello, V. Almendros-Abad, J. B. Lovell, K. Monsch

The typically large distances, extinction, and crowding of Galactic supermassive star clusters have so far hampered the identification of their very low mass members, required to extend our understanding of star and planet formation, and early stellar evolution, to starburst. This situation has now evolved thanks to the James Webb Space Telescope (JWST), and

Shuai Dong, Junyi Yang, Xiaoqi Peng, Hongyang Shang

Transformer model has gained prominence as a popular deep neural network architecture for neural language processing (NLP) and computer vision (CV) applications. However, the extensive use of nonlinear operations, like softmax, poses a performance bottleneck during transformer inference and comprises up to 40% of the total latency. Hence, we propose innovati

Carlos A. Benavides-Gallego, Eduard Larrañaga

Black hole accretion disks are a fascinating topic in astrophysics, as they play a crucial role in several high-energy situations. This paper investigates the optical appearance of scalar hairy black holes (SHBHs) with asymmetric potential, a numerical solution obtained in Phys. Rev. D 73, 084002 (2006) and discussed in Phys.Rev.D 108 (2023) 4, 044020. Since

Maximillian Newman, John Wakeley, Wai-Tong Louis Fan

We introduce a stochastic model of a population with overlapping generations and arbitrary levels of self-fertilization versus outcrossing. We study how the global graph of reproductive relationships, or population pedigree, influences the genealogical relationships of a sample of two gene copies at a genetic locus. Specifically, we consider a diploid Moran

Satoru Koda, Ikuya Morikawa

Deep neural networks (DNNs) deployed in a cloud often allow users to query models via the APIs. However, these APIs expose the models to model extraction attacks (MEAs). In this attack, the attacker attempts to duplicate the target model by abusing the responses from the API. Backdoor-based DNN watermarking is known as a promising defense against MEAs, where

Sneha Sudhir Shetiya, Vikas Vyas, Shreyas Renukuntla

This paper describes how to proficiently prevent software defects in autonomous vehicles, discover and correct defects if they are encountered, and create a higher level of assurance in the software product development phase. It also describes how to ensure high assurance on software reliability.

Luka Milanovic, Greg Ferrero, Robin Oswald, Thomas Kinder

Laser diode injection-locking is a commonly used method to amplify laser light, while preserving its spectral properties. Fluctuations in the environmental conditions can cause injection-locking to fail, especially when operating with low seed powers or with a swept seed frequency. We present a method inspired by the H\"ansch-Couillaud scheme to monitor and

Gang Zhao, Ximing Zhang, Chenji Lu, Hui Zhao

Effective query-item relevance modeling is pivotal for enhancing user experience and safeguarding user satisfaction in e-commerce search systems. Recently, benefiting from the vast inherent knowledge, Large Language Model (LLM) approach demonstrates strong performance and long-tail generalization ability compared with previous neural-based specialized releva

Yusuke Aso, Satoshi Ohashi, Hauyu Baobab Liu, Wenrui Xu

The disk mass and substructure in young stellar objects suggest that planet formation may start at the protostellar stage through the growth of dust grains. To accurately estimate the grain size at the protostellar stage, we have observed the Class I protostar TMC-1A using the Jansky Very Large Array (VLA) at the Q (7 mm) and Ka (9 mm) bands at a resolution

Samuel Creedon, Volodymyr Mazorchuk

We prove that, when $n$ goes to infinity, Kostant's problem has negative answer for almost all simple highest weight modules in the principal block of the BGG category $\mathcal{O}$ for the Lie algebra $\mathfrak{sl}_n(\mathbb{C})$.

Wenli Huang, Ye Deng, Yang Wu, Jinjun Wang

Cloud cover can significantly hinder the use of remote sensing images for Earth observation, prompting urgent advancements in cloud removal technology. Recently, deep learning strategies have shown strong potential in restoring cloud-obscured areas. These methods utilize convolution to extract intricate local features and attention mechanisms to gather long-

Vijit V. Nautiyal

We theoretically explore the finite-time performance of a quantum thermochemical engine using a harmonically trapped 1D Bose gas in the quasicondensate regime as the working fluid. Operating on an Otto cycle, the engine's unitary work strokes involve quenches of interatomic interactions, treating the fluid as a closed many-body quantum system evolving dynami

Ashish Bastola, Nishant Luitel, Hao Wang, Danda Pani Paudel

While deep learning-based models like transformers, have revolutionized time-series and vision tasks, they remain highly susceptible to noise and often overfit on noisy patterns rather than robust features. This issue is exacerbated in vision transformers, which rely on pixel-level details that can easily be corrupt. To address this, we leverage the discrete

Murtaza Rangwala, Rajkumar Buyya

The rapid evolution of Internet of Things (IoT) environments has created an urgent need for secure and trustworthy distributed computing systems, particularly when dealing with heterogeneous devices and applications where centralized trust cannot be assumed. This paper proposes TrustMesh, a novel blockchain-enabled framework that addresses these challenges t

Kwangwoo Lee

Using the properties of generalized Fibonacci numbers, we determine the automorphism groups of some K3 surfaces with Picard number 2. Conversely, using the automorphisms of K3 surfaces with Picard number 2, we prove the criterion for a given integer n is to be a generalized Fibonacci number. Moreover, we show that the generalized k-th Fibonacci number divide

Madhav Narayan Bhat, Marco Russo, Luca P. Carloni, Giuseppe Di Guglielmo

Here we present a technique for using machine learning (ML) for single-qubit gate synthesis on field programmable logic for a superconducting transmon-based quantum computer based on simulated studies. Our approach is multi-stage. We first bootstrap a model based on simulation with access to the full statevector for measuring gate fidelity. We next present a

Sanghyeob Song, Jaihyun Lew, Hyemi Jang, Sungroh Yoon

Estimating the homography between two images is crucial for mid- or high-level vision tasks, such as image stitching and fusion. However, using supervised learning methods is often challenging or costly due to the difficulty of collecting ground-truth data. In response, unsupervised learning approaches have emerged. Most early methods, though, assume that th

Manoj Kumar Shah, Richard A. Soref, Diandian Zhang, Wei Du

Photonic integrated circuits (PICs) have been acknowledged as the promising platforms for the applications in data communication, Lidar in autonomous driving vehicles, innovative sensor technology, etc. Since the demonstration of optical components individually, integration of both electronics and photonics for functional devices on a common platform has bee

Hao-Hao Peng, Ren-Hong Fang

We study the polarization of an electron scattered by different static potentials. The initial state of the electron is chosen as a wavepacket to construct the definite orbital angular momentum, and the final polarization of the electron, scattered by different static potentials such as vector, pseudovector, scalar and pseudoscalar potentials, is calculated.

Guofeng Yang, Yu Li, Yong He, Zhenjiang Zhou

UAV remote sensing technology has become a key technology in crop breeding, which can achieve high-throughput and non-destructive collection of crop phenotyping data. However, the multidisciplinary nature of breeding has brought technical barriers and efficiency challenges to knowledge mining. Therefore, it is important to develop a smart breeding goal tool

Shimon Murai, Heming Sun, Jiro Katto

Supported by powerful generative models, low-bitrate learned image compression (LIC) models utilizing perceptual metrics have become feasible. Some of the most advanced models achieve high compression rates and superior perceptual quality by using image captions as sub-information. This paper demonstrates that using a large multi-modal model (LMM), it is pos

Angel Hsing-Chi Hwang, Q. Vera Liao, Su Lin Blodgett, Alexandra Olteanu

Given the rising proliferation and diversity of AI writing assistance tools, especially those powered by large language models (LLMs), both writers and readers may have concerns about the impact of these tools on the authenticity of writing work. We examine whether and how writers want to preserve their authentic voice when co-writing with AI tools and wheth

Andrey R. Kolovsky

We analyze quantum transport of charged fermionic particles in the tight-binding lattice connecting two particle reservoirs (the leads). If the lead chemical potentials are different they create an electric field which tilts the lattice. We study the effect of this tilt on quantum transport in the presence of weak relaxation/decoherence processes in the latt

Malte Hassler

We introduce a simplified model of planar first passage percolation where weights along vertical edges are deterministic. We show that the limit shape has a flat edge in the vertical direction if and only if the random distribution of the horizontal edges has an atom at the infimum of its support. Furthermore, we present bounds on the upper and lower derivat

Lee Cohen, Yishay Mansour, Shay Moran, Han Shao

Precision and Recall are fundamental metrics in machine learning tasks where both accurate predictions and comprehensive coverage are essential, such as in multi-label learning, language generation, medical studies, and recommender systems. A key challenge in these settings is the prevalence of one-sided feedback, where only positive examples are observed du

Hamed Shirzad, Honghao Lin, Ameya Velingker, Balaji Venkatachalam

Transductive tasks on graphs differ fundamentally from typical supervised machine learning tasks, as the independent and identically distributed (i.i.d.) assumption does not hold among samples. Instead, all train/test/validation samples are present during training, making them more akin to a semi-supervised task. These differences make the analysis of the mo

Vladislav Chertenkov, Lev Shchur

The main question raised in the article is whether a neural network trained on a spin lattice model in one universality class can be used to test a model in another universality class. The quantities of interest are the critical phase transition temperature and the correlation length exponent. In other words, the question of transfer learning is how ``univer

Yuchen Yang, Xuanyi Liu, Xing Gao, Zhihang Zhong

Recent unsupervised methods for monocular 3D pose estimation have endeavored to reduce dependence on limited annotated 3D data, but most are solely formulated in 2D space, overlooking the inherent depth ambiguity issue. Due to the information loss in 3D-to-2D projection, multiple potential depths may exist, yet only some of them are plausible in human struct

Tiancheng Gu, Kaicheng Yang, Xiang An, Ziyong Feng

The objective of Radiology Report Generation (RRG) is to automatically generate coherent textual analyses of diseases based on radiological images, thereby alleviating the workload of radiologists. Current AI-based methods for RRG primarily focus on modifications to the encoder-decoder model architecture. To advance these approaches, this paper introduces an

Hanwei Liu, Huiling Cai, Qingcheng Lin, Xuefeng Li

Annotation ambiguity caused by the inherent subjectivity of visual judgment has always been a major challenge for Facial Expression Recognition (FER) tasks, particularly for largescale datasets from in-the-wild scenarios. A potential solution is the evaluation of relatively objective emotional distributions to help mitigate the ambiguity of subjective annota