November 2024 arXiv papers — page 74

David Noever, Forrest McKee

This research introduces a novel evaluation framework designed to assess large language models' (LLMs) ability to acknowledge uncertainty on 675 fundamentally unsolvable problems. Using a curated dataset of graduate-level grand challenge questions with intentionally unknowable answers, we evaluated twelve state-of-the-art LLMs, including both open and closed

Abdullah Al Mamun, Akid Abrar, Mizanur Rahman, M Sabbir Salek

The rise of quantum computing threatens traditional cryptographic algorithms that secure Transportation Cyber-Physical Systems (TCPS). Shor's algorithm poses a significant threat to RSA and ECC, while Grover's algorithm reduces the security of symmetric encryption schemes, such as AES. The objective of this paper is to underscore the urgency of transitioning

Yubin Lu, Xiaofan Li, Chun Liu, Qi Tang

Extracting governing physical laws from computational or experimental data is crucial across various fields such as fluid dynamics and plasma physics. Many of those physical laws are dissipative due to fluid viscosity or plasma collisions. For such a dissipative physical system, we propose a framework to learn the corresponding laws of the systems based on t

Yaşar Utku Alçalar, Merve Gülle, Mehmet Akçakaya

Physics-driven deep learning (PD-DL) approaches have become popular for improved reconstruction of fast magnetic resonance imaging (MRI) scans. Though PD-DL offers higher acceleration rates than existing clinical fast MRI techniques, their use has been limited outside specialized MRI centers. A key challenge is generalization to rare pathologies or different

Shen Li, Lei Jiang, Wei Wang, Hongwei Hu

This paper shows a proof-of-concept that, given a typical 3-channel images but in a randomly permuted channel order, a model (termed as Chanel-Orderer) with ad-hoc inductive biases in terms of both architecture and loss functions can accurately predict the channel ordering and knows how to make it right. Specifically, Chanel-Orderer learns to score each of t

Dennis Quayesam, Jacob Akubire, Oliveira Darkwah

Predicting Sea Surface Temperature (SST) in the Great Barrier Reef (GBR) region is crucial for the effective management of its fragile ecosystems. This study provides a rigorous comparative analysis of several machine learning techniques to identify the most effective method for SST prediction in this area. We evaluate the performance of ridge regression, Le

Zhaodong Yang, Yunhai Han, Ai-Ping Hu, Harish Ravichandar

We present Asymmetric Dexterity (AsymDex), a novel and simple reinforcement learning (RL) framework that can efficiently learn a large class of bimanual skills in multi-fingered hands without relying on demonstrations. Two crucial insights enable AsymDex to reduce the observation and action space dimensions and improve sample efficiency. First, true ambidext

Jiayang Ao, Yanbei Jiang, Qiuhong Ke, Krista A. Ehinger

Understanding and reconstructing occluded objects is a challenging problem, especially in open-world scenarios where categories and contexts are diverse and unpredictable. Traditional methods, however, are typically restricted to closed sets of object categories, limiting their use in complex, open-world scenes. We introduce Open-World Amodal Appearance Comp

Tianbi Zhang, Lukas Berners, Jakub Holzer, T. Ben Britton

Recent advances in scanning electron microscope (SEM) based Kikuchi diffraction have demonstrated the important potential for reflection and transmission methods, like transmission Kikuchi diffraction (TKD) and electron backscatter diffraction (EBSD). Furthermore, with the advent of compact direct electron detectors (DED) it has been possible to place the de

Siyuan Jin, Zhendong Bei, Bichao Chen, Yong Xia

Traditional banks face significant challenges in digital transformation, primarily due to legacy system constraints and fragmented ownership. Recent incidents show that such fragmentation often results in superficial incident resolutions, leaving root causes unaddressed and causing recurring failures. We introduce a novel approach to post-incident analysis,

Chen Xu, Jie Wang, Yingjie Jing, Fujia Li

The standing waves existed in radio telescope data are primarily due to reflections among the instruments, which significantly impact the spectrum quality of the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Eliminating these standing waves for FAST is challenging given the constant changes in their phases and amplitudes. Over a ten-second pe

Pachara Sawettamalya, Huacheng Yu

For any $\{0,1\}$-valued function $f$, its \emph{$n$-folded XOR} is the function $f^{\oplus n}$ where $f^{\oplus n}(X_1, \ldots, X_n) = f(X_1) \oplus \cdots \oplus f(X_n)$. Given a procedure for computing the function $f$, one can apply a ``naive" approach to compute $f^{\oplus n}$ by computing each $f(X_i)$ independently, followed by XORing the outputs. Thi

Xiang Li, Gagan Agrawal, Ruoming Jin, Rajiv Ramnath

We consider the problem of constructing embeddings of large attributed graphs and supporting multiple downstream learning tasks. We develop a graph embedding method, which is based on extending deep metric and unbiased contrastive learning techniques to 1) work with attributed graphs, 2) enabling a mini-batch based approach, and 3) achieving scalability. Bas

Yu-Shan Zhang, Wei Dang, Kai Wang, Yong-Bo Tang

Using relativistic multiconfiguration Dirac-Hartree-Fock method, we calculate the hyperfine-structure properties of the $2s2p$ $^3\!P_{J}$ state in $^9$Be. The hyperfine-structure properties encompass first-order hyperfine-structure parameters, as well as second-order and third-order corrections arising from the hyperfine mixing of different $2s2p$ $^3\!P_{J

Tiancheng Sun, Shaolan Bi, Xunzhou Chen, Yuxi

This study investigates the temporal and spatial variations in lithium abundance within the Milky Way using a sample of 22,034 main-sequence turn-off (MSTO) stars and subgiants, characterised by precise stellar ages, 3D NLTE (non-local thermodynamic equilibrium) lithium abundances, and birth radii. Our results reveal a complex variation in lithium abundance

Zhuoyin Dai, Di Wu, Xiaoli Xu, Yong Zeng

Channel knowledge map (CKM) is a promising paradigm shift towards environment-aware communication and sensing by providing location-specific prior channel knowledge before real-time communication. Although CKM is particularly appealing for dense networks such as cell-free networks, it remains a challenge to efficiently generate CKMs in dense networks. For a

Allen Hao Huang, Imanol Schlag

Our work proposes a novel approach to designing activation functions by focusing on their gradients and deriving the corresponding activation functions using integration. We introduce the Expanded Integral of the Exponential Linear Unit (xIELU), a trainable piecewise activation function derived by integrating trainable affine transformations applied to the E

Phu Nguyen, Daniel Polani, Stas Tiomkin

The reward function is an essential component in robot learning. Reward directly affects the sample and computational complexity of learning, and the quality of a solution. The design of informative rewards requires domain knowledge, which is not always available. We use the properties of the dynamics to produce system-appropriate reward without adding exter

Zhuohan Gu, Jiayi Yao, Kuntai Du, Junchen Jiang

As large language models (LLMs) show impressive performance on complex tasks, they still struggle with longer contextual understanding and high computational costs. To balance efficiency and quality, we introduce LLMSteer, a fine-tuning-free framework that enhances LLMs through query-independent attention steering. Tested on popular LLMs and datasets, LLMSte

Anique Tahir, Lu Cheng, Manuel Sandoval, Yasin N. Silva

Social media discourse involves people from different backgrounds, beliefs, and motives. Thus, often such discourse can devolve into toxic interactions. Generative Models, such as Llama and ChatGPT, have recently exploded in popularity due to their capabilities in zero-shot question-answering. Because these models are increasingly being used to ask questions

Meng Gao, Xin Li, Yu Tian, Peng Yang

We construct a soliton train configuration with holographic superfluid model under AdS soliton background. We investigate the stability of a soliton train using Bloch waves under two distinct quantization schemes. Upon imposing a minor perturbation on the soliton train system, it has been observed that there exist two elastic modes and one phonon mode. Most

Emad Mohamed, Shruti Tiwari, Sheena Christabel Pravin

This research presents an advanced AI-powered ultrasound imaging system that incorporates real-time image processing, organ tracking, and voice commands to enhance the efficiency and accuracy of diagnoses in clinical practice. Traditional ultrasound diagnostics often require significant time and introduce a degree of subjectivity due to user interaction. The

Sebastian Janampa, Marios Pattichis

Line segment detection is a fundamental low-level task in computer vision, and improvements in this task can impact more advanced methods that depend on it. Most new methods developed for line segment detection are based on Convolutional Neural Networks (CNNs). Our paper seeks to address challenges that prevent the wider adoption of transformer-based methods

Yuxuan Chen, Rongpeng Li, Zhifeng Zhao, Honggang Zhang

We present MERLOT, a scalable mixture-of-expert (MoE) based refinement of distilled large language model optimized for encrypted traffic classification. By applying model distillation techniques in a teacher-student paradigm, compact models derived from GPT-2-base retain high classification accuracy while minimizing computational costs. These models function

Adnan, Kyungbae Park

Twisted torus knots are a generalization of torus knots, obtained by introducing additional full twists to adjacent strands of the torus knots. In this article, we present an explicit formula for the Alexander polynomial of twisted torus knots. Our approach utilizes a presentation of the knot group of twisted torus knots combined with Fox's free differential

Saba Mansour, Emily Logan, James F. Antaki, Mahdi Esmaily

Cardiovascular surgeries and mechanical circulatory support devices create non-physiological blood flow conditions that can be detrimental, especially for pediatric patients. A source of complications is mechanical red blood cell (RBC) damage induced by the localized supraphysiological shear fields. To understand such complications, we introduce a multi-scal

Xinhao Zhong, Siyu Jiao, Yao Zhao, Yunchao Wei

Current Semi-Supervised Object Detection (SSOD) methods enhance detector performance by leveraging large amounts of unlabeled data, assuming that both labeled and unlabeled data share the same label space. However, in open-set scenarios, the unlabeled dataset contains both in-distribution (ID) classes and out-of-distribution (OOD) classes. Applying semi-supe

Haifeng Wen, Nicolò Michelusi, Osvaldo Simeone, Hong Xing

Over-the-air federated learning (FL), i.e., AirFL, leverages computing primitively over multiple access channels. A long-standing challenge in AirFL is to achieve coherent signal alignment without relying on expensive channel estimation and feedback. This paper proposes NCAirFL, a CSI-free AirFL scheme based on unbiased non-coherent detection at the edge ser

Daizhan Cheng

Under the assumption that a finite signal with different sampling lengths or different sampling frequencies is considered as equivalent, the signal space is considered as the quotient space of $\mathbb{R}^{\infty}$ over equivalence. The topological structure and the properties of signal space are investigated. Using them some characteristics of semi-tensor p

Miguel Licona, Joaquín Tey

It is known that if a plane graph is graceful (resp. near-graceful), then its semidual is conservative (resp. near-conservative). In this work we prove that the semidual of a plane graph of size $M$ consisting of two nested cycles is conservative if $M \equiv 0,3 \pmod 4$, and near-conservative otherwise. We also show that for a given integer $m_1 \geq 3$, t

Youka Kaku, Yasusada Nambu

We investigate the interplay between quantum theory and gravity by exploring gravitational lensing and Einstein ring images in a weak gravitational field induced by a mass source in spatial quantum superposition. We analyze a quantum massless scalar field propagating in two distinct models of gravity: the first quantized Newtonian gravity (QG) model, which g

Moritz Rietschel, Fang Guo, Kyle Steinfeld

Architects adopt visual scripting and parametric design tools to explore more expansive design spaces (Coates, 2010), refine their thinking about the geometric logic of their design (Woodbury, 2010), and overcome conventional software limitations (Burry, 2011). Despite two decades of effort to make design scripting more accessible, a disconnect between a des

Feng-Yu Wang

In this paper we introduce some recent progresses on the convergence rate in Wasserstein distance for empirical measures of Markov processes. For diffusion processes on compact manifolds possibly with reflecting or killing boundary conditions, the sharp convergence rate as well as renormalization limits are presented in terms of the dimension of the manifold

Zehao Li, Zhouchen Lin, Yijie Peng

We study parameter inference in simulation-based stochastic models where the analytical form of the likelihood is unknown. The main difficulty is that score evaluation as a ratio of noisy Monte Carlo estimators induces bias and instability, which we overcome with a ratio-free nested multi-time-scale (NMTS) stochastic approximation (SA) method that simultaneo

Henggeng Han, Song Wang, Xue Li, Chuanjie Zheng

The magnetic dynamo mechanism of giant stars remains an open question, which can be explored by investigating their activity-rotation relations with multiple proxies. By using the data from the LAMOST and \emph{GALEX} surveys, we carried out a comprehensive study of activity-rotation relations of evolved stars based on \cahk lines, $\rm{H\alpha}$ lines and n

Zhili Gong, Zitong Wei, Jeremy D. Brown

The absence of physical interfaces creates challenges when interacting with touchscreen technology. This study aims to investigate an innovative haptic solution for interacting with graphical user interfaces. A motorized shape-changing rotary knob interface, HapKnob, has been developed, achieving seven distinctive shape configurations and various force feedb

Ning Ding, Yehui Tang, Haochen Qin, Zhenli Zhou

In order to reduce the computational complexity of large language models, great efforts have been made to to improve the efficiency of transformer models such as linear attention and flash-attention. However, the model size and corresponding computational complexity are constantly scaled up in pursuit of higher performance. In this work, we present MemoryFor

Zimeng Zeng, Xiaoming Zhang, Shunhong Zhang, Jian Wu

The quest for topological superconductors triggers revived interests in resolving non-s-wave pairing channels mediated by phonons. While density functional theory and density functional perturbtaion theory have established a powerful framework to calculate electron-phonon couplings in real materials in a first-principles way, its application is largely limit

Anka Reuel, Amelia Hardy, Chandler Smith, Max Lamparth

AI models are increasingly prevalent in high-stakes environments, necessitating thorough assessment of their capabilities and risks. Benchmarks are popular for measuring these attributes and for comparing model performance, tracking progress, and identifying weaknesses in foundation and non-foundation models. They can inform model selection for downstream ta

Sixiao Zhang, Cheng Long, Wei Yuan, Hongxu Chen

In the era of large foundation models, data has become a crucial component in building high-performance AI systems. As the demand for high-quality and large-scale data continues to rise, data copyright protection is attracting increasing attention. In this work, we explore the problem of data watermarking for sequential recommender systems, where a watermark

Zhu Siyuan, Li Tao, Chen Zhongshan, Li Xin

{This paper proposes an angle measurement method based on Electronic Speckle Pattern Interferometry (ESPI) using a Michelson interferometer. By leveraging different principles within the same device, this method achieves complementary advantages across various angle ranges, enhancing measurement accuracy while maintaining high robustness. By utilizing CCD to

Fan Wang, Min Fang, Xiaoting Fu, Yang Chen

Recent literature reports a color deviation between observed Gaia color-magnitude diagrams (CMDs) and theoretical model isochrone predictions, particularly in the very low-mass regime. To assess its impact on cluster age determination via isochrone fitting, we quantified the color deviations for three benchmark clusters, Hyades, Pleiades, and Praesepe, both

Skyler Seto, Maartje ter Hoeve, Richard He Bai, Natalie Schluter

Large language models are trained on massive scrapes of the web, as required by current scaling laws. Most progress is made for English, given its abundance of high-quality pretraining data. For most other languages, however, such high quality pretraining data is unavailable. In this work, we study how to boost pretrained model performance in a target langua

Huan Huang, Hongliang Zhang, Jide Yuan, Luyao Sun

Intelligent omni-surfaces (IOSs) with 360-degree electromagnetic radiation significantly improves the performance of wireless systems, while an adversarial IOS also poses a significant potential risk for physical layer security. In this paper, we propose a "DISCO" IOS (DIOS) based fully-passive jammer (FPJ) that can launch omnidirectional fully-passive jammi

Sanju Vaidya, Jeff Chang

In this paper, we will construct formulas and bounds for Neighborhood Degree-based indices of graphs and describe graphs that attain the bounds. Furthermore, we will establish a lower bound for the spectral radius of any graph.

Pengcheng Zhou, Zhengyang Fang, Zhongliang Yang, Zhili Zhou

In recent years, there has been an increasing number of information hiding techniques based on network streaming media, focusing on how to covertly and efficiently embed secret information into real-time transmitted network media signals to achieve concealed communication. The misuse of these techniques can lead to significant security risks, such as the spr

Naoya Hatta, Taichi Ishitani, Ryota Shioya

Veryl, a hardware description language based on SystemVerilog, offers optimized syntax tailored for logic design, ensuring synthesizability and simplifying common constructs. It prioritizes interoperability with SystemVerilog, allowing for smooth integration with existing projects while maintaining high readability. Additionally, Veryl includes a comprehensi

Dexin Wang, Chunsheng Liu, Faliang Chang, Yichen Xu

Decision-making in robotics using denoising diffusion processes has increasingly become a hot research topic, but end-to-end policies perform poorly in tasks with rich contact and have limited controllability. This paper proposes Hierarchical Diffusion Policy (HDP), a new imitation learning method of using objective contacts to guide the generation of robot

Xiaobao Wei, Peng Chen, Guangyu Li, Ming Lu

Gaze estimation encounters generalization challenges when dealing with out-of-distribution data. To address this problem, recent methods use neural radiance fields (NeRF) to generate augmented data. However, existing methods based on NeRF are computationally expensive and lack facial details. 3D Gaussian Splatting (3DGS) has become the prevailing representat

Siwen Jiao, Yangyi Fang, Baoyun Peng, Wangqun Chen

Recent advancements in Visual Language Models (VLMs) have made them crucial for visual question answering (VQA) in autonomous driving, enabling natural human-vehicle interactions. However, existing methods often struggle in dynamic driving environments, as they usually focus on static images or videos and rely on downsampling to manage computational costs. T

Ning Chen, Zhanpeng Hou, Zhaolong Teng

A flavor-unified theory based on the simple Lie algebra of ${\mathfrak{s}\mathfrak{u}}(8)$ was previously proposed to generate the observed Standard Model quark/lepton mass hierarchies and the Cabibbo-Kobayashi-Maskawa mixing pattern due to their non-universal symmetry properties. A level-$1$ affine Lie algebra of $\widehat{ \mathfrak{s}\mathfrak{u} }(8)_{ k

Yuto Mochizuki, Masahiro Tsujimoto, Richard L. Kelley, Bert Vander Meulen

The Fe K$\alpha$ fluorescence line emission in X-ray spectra is a powerful diagnostic tool for various astrophysical objects to reveal the distribution of cold matter around photo-ionizing sources. The advent of the X-ray microcalorimeter onboard the \textit{XRISM} satellite will bring new constraints on the emission line. We present one of the first such re

Mircea Lică, Ojas Shirekar, Baptiste Colle, Chirag Raman

Embodied agents powered by large language models (LLMs), such as Voyager, promise open-ended competence in worlds such as Minecraft. However, when powered by open-weight LLMs they still falter on elementary tasks after domain-specific fine-tuning. We propose MindForge, a generative-agent framework for cultural lifelong learning through explicit perspective t

Samuel Hwang, Noah G. Singer, Santhoshini Velusamy

In the maximum directed cut problem, the input is a directed graph $G=(V,E)$, and the goal is to pick a partition $V = S \cup (V \setminus S)$ of the vertices such that as many edges as possible go from $S$ to $V\setminus S$. Oblivious algorithms, introduced by Feige and Jozeph (Algorithmica'17), are a simple class of algorithms for this problem. These algor

Atharva Gundawar, Karthik Valmeekam, Mudit Verma, Subbarao Kambhampati

Previous work has attempted to boost Large Language Model (LLM) performance on planning and scheduling tasks through a variety of prompt engineering techniques. While these methods can work within the distributions tested, they are neither robust nor predictable. This limitation can be addressed through compound LLM architectures where LLMs work in conjuncti

Jonggeun Hwang, Dongha Shin, Brendan T. Deveney, Manhee Lee

The hydrogen bond (HB) network of water under confinement has been predicted to have distinct structures from that of bulk water. However, direct measurement of the structure has not been achieved. Here, we present experimental evidence of confinement-induced ice formation in water. We directly probe the HB network of a water nano-meniscus formed and confine

Zhihan Liu, Shenao Zhang, Yongfei Liu, Boyi Liu

Direct preference learning offers a promising and computation-efficient beyond supervised fine-tuning (SFT) for improving code generation in coding large language models (LMs). However, the scarcity of reliable preference data is a bottleneck for the performance of direct preference learning to improve the coding accuracy of code LMs. In this paper, we intro

Daewa Kim, Demetrio Labate, Kamrun Mily, Annalisa Quaini

The mathematical modeling of crowds is complicated by the fact that crowds possess the behavioral ability to develop and adapt moving strategies in response to the context. For example, in emergency situations, people tend to alter their walking strategy in response to fear. To be able to simulate these situations, we consider a kinetic model of crowd dynami

Runlong Yu, Chonghao Qiu, Robert Ladwig, Paul C. Hanson

This paper introduces a \textit{Process-Guided Learning (Pril)} framework that integrates physical models with recurrent neural networks (RNNs) to enhance the prediction of dissolved oxygen (DO) concentrations in lakes, which is crucial for sustaining water quality and ecosystem health. Unlike traditional RNNs, which may deliver high accuracy but often lack

Yuan Yuan, Jingtao Ding, Chonghua Han, Zhi Sheng

Urban spatio-temporal flow prediction, encompassing traffic flows and crowd flows, is crucial for optimizing city infrastructure and managing traffic and emergency responses. Traditional approaches have relied on separate models tailored to either grid-based data, representing cities as uniform cells, or graph-based data, modeling cities as networks of nodes

Hao Ju, Shaofei Huang, Si Liu, Zhedong Zheng

Existing approaches to drone visual geo-localization predominantly adopt the image-based setting, where a single drone-view snapshot is matched with images from other platforms. Such task formulation, however, underutilizes the inherent video output of the drone and is sensitive to occlusions and viewpoint disparity. To address these limitations, we formulat

Adarsh Salagame, Eric Sihite, Alireza Ramezani

Navigating rugged terrain and steep slopes is a challenge for mobile robots. Conventional legged and wheeled systems struggle with these environments due to limited traction and stability. Northeastern University's COBRA (Crater Observing Bio-inspired Rolling Articulator), a novel multi-modal snake-like robot, addresses these issues by combining traditional

Shi-Xian Sun, Long-Xing Huang, Zhen-Hua Zhao, Yong-Qiang Wang

In this article, we investigate soliton solutions in a system involving a charged Dirac field minimally coupled to Einstein gravity and the Bardeen field. We analyze the impact of two key parameters on the properties of the solution family: the magnetic charge $p$ of the Bardeen field and the electric charge $q$ of the Dirac field. We discover that the intro

Shreyansh Pitroda, Eric Sihite, Kaushik Venkatesh Krishnamurthy, Chenghao Wang

Our work aims to make significant strides in understanding unexplored locomotion control paradigms based on the integration of posture manipulation and thrust vectoring. These techniques are commonly seen in nature, such as Chukar birds using their wings to run on a nearly vertical wall. In this work, we show quadratic programming with contact constraints wh

Yunuo Zhang, Baiting Luo, Ayan Mukhopadhyay, Daniel Stojcsics

Efficient path optimization for drones in search and rescue operations faces challenges, including limited visibility, time constraints, and complex information gathering in urban environments. We present a comprehensive approach to optimize UAV-based search and rescue operations in neighborhood areas, utilizing both a 3D AirSim-ROS2 simulator and a 2D simul

Tathagata Sadhukhan, Manit Paul, Raaz Dwivedi

Nearest neighbor (NN) algorithms have been extensively used for missing data problems in recommender systems and sequential decision-making systems. Prior theoretical analysis has established favorable guarantees for NN when the underlying data is sufficiently smooth and the missingness probabilities are lower bounded. Here we analyze NN with non-smooth non-

Zihan Wang, Songlin Li, Lingyan Hao, Xinyu Hu

As video generation models advance rapidly, assessing the quality of generated videos has become increasingly critical. Existing metrics, such as Fr\'echet Video Distance (FVD), Inception Score (IS), and ClipSim, measure quality primarily in latent space rather than from a human visual perspective, often overlooking key aspects like appearance and motion con

Saman Ahmadi, Guido Tack, Daniel Harabor, Philip Kilby

The rapid adoption of electric vehicles (EVs) in modern transport systems has made energy-aware routing a critical task in their successful integration, especially within large-scale transport networks. In cases where an EV's remaining energy is limited and charging locations are not easily accessible, some destinations may only be reachable through an energ

Minsoo Kim, Vladimir Dvorkin, Jip Kim

Dynamic line rating (DLR) is a promising solution to increase the utilization of transmission lines by adjusting ratings based on real-time weather conditions. Accurate DLR forecast at the scheduling stage is thus necessary for system operators to proactively optimize power flows, manage congestion, and reduce the cost of grid operations. However, the DLR fo

Challen Enninful Adu, César E. Ramos Chuquiure, Bohao Zhang, Ram Vasudevan

Generating optimal trajectories for high-dimensional robotic systems in a time-efficient manner while adhering to constraints is a challenging task. This paper introduces PHLAME, which applies pseudospectral collocation and spatial vector algebra to efficiently solve the Affine Geometric Heat Flow (AGHF) Partial Differential Equation (PDE) for trajectory opt

Naoki Seto

We discuss the usability of the gravitational wave detector LISA for studying the orientational distribution of compact white dwarf binaries in the Galactic bulge. We pay special attention to measuring the dipole pattern of the distribution around the Galactic rotation axis. Based on our new formulation, which leverages the parity properties of the involved

Zihan Wang, Brian Liang, Varad Dhat, Zander Brumbaugh

Understanding robot behaviors and experiences through natural language is crucial for developing intelligent and transparent robotic systems. Recent advancement in large language models (LLMs) makes it possible to translate complex, multi-modal robotic experiences into coherent, human-readable narratives. However, grounding real-world robot experiences into

Natalia Cortés, J. Hernández-Tecorralco, L. Meza-Montes, R. de Coss

We explore quantum-thermodynamic effects in a phosphorous (P)-doped graphene monolayer subjected to biaxial tensile strain. Introducing substitutional P atoms in the graphene lattice generates a tunable spin magnetic moment controlled by the strain control parameter $\varepsilon$. This leads to a magnetic quantum phase transition (MQPT) at zero temperature m

Yu Lu, Zhong-Xi Shen, Shao-Ming Fei, Zhi-Xi Wang

The detection of entanglement in a bipartite state is a crucial issue in quantum information science. Based on realignment of density matrices and the vectorization of the reduced density matrices, we introduce a new set of separability criteria. The proposed separability criteria can detect more entanglement than the previous separability criteria. Moreover

Gerard Castro-López, Javier Gómez-Serrano

V-states are uniformly rotating vortex patches of the incompressible 2D Euler equation and the only known explicit examples are circles and ellipses. In this paper, we prove the existence of non-convex V-states with analytic boundary which are far from the known examples. To prove it, we use a combination of analysis of the linearized operator at an approxim

Mahakaran Sandhu, John Chen, Dana Matthews, Matthew A Spence

Proteins evolve through complex sequence spaces, with fitness landscapes serving as a conceptual framework that links sequence to function. Fitness landscapes can be smooth, where multiple similarly accessible evolutionary paths are available, or rugged, where the presence of multiple local fitness optima complicate evolution and prediction. Indeed, many pro

Mahsa Sheikholeslami, Navid Mazrouei, Yousof Gheisari, Afshin Fasihi

Traditional drug design faces significant challenges due to inherent chemical and biological complexities, often resulting in high failure rates in clinical trials. Deep learning advancements, particularly generative models, offer potential solutions to these challenges. One promising algorithm is DrugGPT, a transformer-based model, that generates small mole

Ursula Hamenstädt, Frieder Jäckel

For every $n\geq 4$ we construct infinitely many mutually not homotopic closed manifolds of dimension $n$ which admit a negatively curved Einstein metric but no locally symmetric metric.

Harsha Vardhan Khurdula, Basem Rizk, Indus Khaitan, Janit Anjaria

Current benchmarks for evaluating Vision Language Models (VLMs) often fall short in thoroughly assessing model abilities to understand and process complex visual and textual content. They typically focus on simple tasks that do not require deep reasoning or the integration of multiple data modalities to solve an original problem. To address this gap, we intr

Sebastian Bahamonde, Jorge Gigante Valcarcel

We analyse the stability issue of the vector and axial modes of the torsion and nonmetricity tensors around general backgrounds in the framework of cubic Metric-Affine Gravity. We show that the presence of cubic order invariants defined from the curvature, torsion and nonmetricity tensors allow the cancellation of the well-known instabilities arising in the

Hongzhe Zhou, Dong Lai

The physical origin of the recently identified slow-moving temperature fluctuations in accretion disks around super-massive black holes (SMBHs) cannot be accounted for by reverberation models. In this work, we propose that large-scale dynamos (LSDs) operating in accretion disks could generate quasi-periodic perturbations in the turbulence viscosity, thereby

Xuanhui Mao, Qian Xu, Liang Jiang

Quantum error correction (QEC) is essential for reliable quantum information processing. Targeting a particular error channel, both the encoding and the recovery channel can be optimized through a biconvex optimization to give a high-performance, noise-adapted QEC scheme. We solve the biconvex optimization by the technique of alternating semi-definite progra

Minjoon Jung, Junbin Xiao, Byoung-Tak Zhang, Angela Yao

Video large language models (Video-LLMs) can temporally ground language queries and retrieve video moments. Yet, such temporal comprehension capabilities are neither well-studied nor understood. So we conduct a study on prediction consistency -- a key indicator for robustness and trustworthiness of temporal grounding. After the model identifies an initial mo

Ming Yin, Zongsheng Cao, Qiqing Xia, Chenyang Tu

Knowledge graphs (KGs) serve as a vital backbone for a wide range of AI applications, including natural language understanding and recommendation. A promising yet underexplored direction is numerical reasoning over KGs, which involves inferring new facts by leveraging not only symbolic triples but also numerical attribute values (e.g., length, weight). Howev

Wei Jiang, Tong Chen, Xinyi Gao, Wentao Zhang

The rapid spread of rumors on social media has posed significant challenges to maintaining public trust and information integrity. Since an information cascade process is essentially a propagation tree, recent rumor detection models leverage graph neural networks to additionally capture information propagation patterns, thus outperforming text-only solutions

Edward McDugald, Arvind Mohan, Darren Engwirda, Agnese Marcato

We investigate the potential of an attention-based neural network architecture, the Senseiver, for sparse sensing in tsunami forecasting. Specifically, we focus on the Tsunami Data Assimilation Method, which generates forecasts from tsunameter networks. Our model is used to reconstruct high-resolution tsunami wavefields from extremely sparse observations, in

Michael A. Fedderke, Sergey Sibiryakov

The gravitational-lensing parallax of gamma-ray bursts (GRBs) is an intriguing probe of primordial black hole (PBH) dark matter in the asteroid-mass window, $2\times 10^{-16}M_{\odot} \lesssim M_{\text{PBH}} \lesssim 5 \times 10^{-12}M_{\odot}$. Recent work in the literature has shown exciting potential reach for this "picolensing" signal if a future space m

Gabriel Chua, Shing Yee Chan, Shaun Khoo

Large Language Models (LLMs) are prone to off-topic misuse, where users may prompt these models to perform tasks beyond their intended scope. Current guardrails, which often rely on curated examples or custom classifiers, suffer from high false-positive rates, limited adaptability, and the impracticality of requiring real-world data that is not available in

Kassahun H Betre, Yan X Zhang, Carter Edmond

We study structural and enumerative aspects of pure simplicial complexes and clique complexes. We prove a necessary and sufficient condition for any simplicial complex to be a clique complex that depends only on the list of facets. We also prove a theorem that a class of ``triangle-intersection free" pure clique complexes are uniquely determined up to isomor

Yuhan Qian, Yifan Yi, Jun Shao, Yanyao Yi

Master protocol trials use a single overarching protocol to evaluate multiple interventions, diseases, or disease subtypes, where individuals are often randomized to different subsets of intervention arms based on individual characteristics, enrollment timing, and intervention availability. While offering increased flexibility, this constrained and non-unifo

Wassim El Ahmar, Dhanvin Kolhatkar, Farzan Nowruzi, Robert Laganiere

Multiple Object Tracking (MOT) in thermal imaging presents unique challenges due to the lack of visual features and the complexity of motion patterns. This paper introduces an innovative approach to improve MOT in the thermal domain by developing a novel box association method that utilizes both thermal object identity and motion similarity. Our method merge

Jorge David Castaño-Yepes, J. M. Cabrera-Terán, Cristian Felipe Ramirez-Gutierrez

We study the influence of thermal fluctuations on the two-time correlation functions of bosonic baths within a superstatistics framework by assuming that fluctuations follow the gamma distribution. We further establish a connection between superstatistics and Tsallis non-additive thermodynamics by introducing a temperature-renormalizing parameter. Our result

Zdenek Sekanina

Comet ATLAS (C/2024 S1) is a bright dwarf sungrazer, the second Kreutz comet discovery from the ground this century, 13 years after comet Lovejoy (C/2011 W3). The Population II membership of comet ATLAS sets it apart from the overwhelming majority of other bright dwarf sungrazers, most of them classified as members of Populations I, Pe, or Pre-I in the conte

Jake Buzhardt, C. Ricardo Constante-Amores, Michael D. Graham

This work explores the relationship between state space methods and Koopman operator-based methods for predicting the time-evolution of nonlinear dynamical systems. We demonstrate that extended dynamic mode decomposition with dictionary learning (EDMD-DL), when combined with a state space projection, is equivalent to a neural network representation of the no

Antonio Russo, Gian Paolo Incremona, Patrizio Colaneri

This paper addresses the problem of stabilization of switched affine systems under dwell-time constraint, giving guarantees on the bound of the quadratic cost associated with the proposed state switching control law. Specifically, two switching rules are presented relying on the solution of differential Lyapunov inequalities and Lyapunov-Metzler inequalities

Jozef Hanč, Martina Hančová, Dominik Borovský

Ratio statistics and distributions are fundamental in various disciplines, including linear regression, metrology, nuclear physics, operations research, econometrics, biostatistics, genetics, and engineering. In this work, we introduce two novel computational approaches for evaluating ratio distributions using open data science tools and modern numerical qua

George J. Wilkie, Paul K. Romano, R. Michael Churchill

Modern tooling is demanded for predicting the transport and reaction characteristics of atoms and molecules, especially in the context of magnetic confinement fusion. DEGAS2, among the most common and capable tools currently in use, shares many fundamental similarities with the OpenMC framework, which was primarily developed for neutron and photon transport.

Ioannis Lambadaris, Ahmed Sid-Ali, Wei Sun, Yiqiang Q. Zhao

Mean-field limits have been used now as a standard tool in approximations, including for networks with a large number of nodes. Statistical inference on mean-filed models has attracted more attention recently mainly due to the rapid emergence of data-driven systems. However, studies reported in the literature have been mainly limited to continuous models. In

Samuel Filgueira da Silva, Mehmet Fatih Ozkan, Faissal El Idrissi, Prashanth Ramesh

Accurate modeling of lithium ion (li-ion) batteries is essential for enhancing the safety, and efficiency of electric vehicles and renewable energy systems. This paper presents a data-inspired approach for improving the fidelity of reduced-order li-ion battery models. The proposed method combines a Genetic Algorithm with Sequentially Thresholded Ridge Regres

Bernd Rosenow, Adrian Del Maestro

One-dimensional bosonic systems, such as helium confined to nanopores, exhibit Luttinger liquid behavior characterized by density waves as collective excitations. We investigate the impact of a scattering potential on a low dimensional quantum liquid. We consider a microscopic model of $^4$He inside a perturbed nanopore with a localized constriction, and emp