November 2024 arXiv papers — page 53

Do-Hyun Park, Min-Wook Jeon, Jinwoo Jeong, Isaac Sim

Electronic warfare support (ES) systems intercept adversary radar signals and estimate various types of signal information, including modulation schemes. The accurate and rapid identification of modulation schemes under conditions of very low signal power remains a significant challenge for ES systems. This paper proposes a recognition model based on a noise

Perry Hart, Kuen-Bang Hou

We contribute to the theory of (homotopy) colimits inside homotopy type theory. The heart of our work characterizes the connection between (graph-indexed) colimits in a type universe and colimits in coslices of the universe, called coslice colimits. To derive this characterization, we give a construction of coslice colimits that is tailored to reveal the con

Fengyuan Liu, Nikhil Kandpal, Colin Raffel

The influence of contextual input on the behavior of large language models (LLMs) has prompted the development of context attribution methods that aim to quantify each context span's effect on an LLM's generations. The leave-one-out (LOO) error, which measures the change in the likelihood of the LLM's response when a given span of the context is removed, pro

Arvind Mohan, Ashesh Chattopadhyay, Jonah Miller

Differentiable Programming for scientific machine learning (SciML) has recently seen considerable interest and success, as it directly embeds neural networks inside PDEs, often called as NeuralPDEs, derived from first principle physics. Therefore, there is a widespread assumption in the community that NeuralPDEs are more trustworthy and generalizable than bl

Yixin Dong, Charlie F. Ruan, Yaxing Cai, Ruihang Lai

The applications of LLM Agents are becoming increasingly complex and diverse, leading to a high demand for structured outputs that can be parsed into code, structured function calls, and embodied agent commands. These developments bring significant demands for structured generation in LLM inference. Context-free grammar is a flexible approach to enable struc

Karsten Roth, Zeynep Akata, Dima Damen, Ivana Balažević

Large-scale multimodal representation learning successfully optimizes for zero-shot transfer at test time. Yet the standard pretraining paradigm (contrastive learning on large amounts of image-text data) does not explicitly encourage representations to support few-shot adaptation. In this work, we propose a simple, but carefully designed extension to multimo

Zhenxiong Tan, Songhua Liu, Xingyi Yang, Qiaochu Xue

We present OminiControl, a novel approach that rethinks how image conditions are integrated into Diffusion Transformer (DiT) architectures. Current image conditioning methods either introduce substantial parameter overhead or handle only specific control tasks effectively, limiting their practical versatility. OminiControl addresses these limitations through

Fei Ye

We show that the second Jacobian ideal of a hypersurface can be decomposed such that a power of the Jacobian ideal becomes a factor. As an application of the decomposition, we present an elementary proof establishing that the second Nash blow-up algebra of a hypersurface singularity is a contact invariant.

Silin Zhou, Shuo Shang, Lisi Chen, Christian S. Jensen

Trajectory representation learning (TRL) maps trajectories to vectors that can then be used for various downstream tasks, including trajectory similarity computation, trajectory classification, and travel-time estimation. However, existing TRL methods often produce vectors that, when used in downstream tasks, yield insufficiently accurate results. A key reas

Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam

We show that deep neural networks achieve dimension-independent rates of convergence for learning structured densities such as those arising in image, audio, video, and text applications. More precisely, we demonstrate that neural networks with a simple $L^2$-minimizing loss achieve a rate of $n^{-1/(4+r)}$ in nonparametric density estimation when the underl

Tomasz Krajewski, Marek Lewicki, Martin Vasar, Ville Vaskonen

We study the evolution of growing vacuum bubbles. The bubble walls interact with the surrounding fluid and may, consequently, reach a terminal velocity. If the mean free path of the particles in the fluid is much shorter than the bubble wall thickness, the fluid is locally in thermal equilibrium and the wall's terminal velocity can be determined by entropy c

Gérard Besson, Gilles Courtois, Sa'ar Hersonsky

We provide intrinsic conditions on the geometry of horospheres in a closed, negatively curved Riemannian manifold of dimension greater than or equal to 3, which guarantee that the sectional curvature is constant.

Travis Gagie

The Burrows-Wheeler Transform (BWT) moves characters with similar contexts in a text together, where a character's context consists of the characters immediately following it. We say that a property has contextual locality if characters with similar contexts tend to have the same or similar values (``tags'') of that property. We argue that if we consider a r

Pau Pujolas, Jack Rossbach

Trade imbalances significantly alter the welfare implications of tariffs. Using an illustrative model, we show that trade deficits enhance a country's ability to alter its terms of trade, and thereby benefit from tariffs. Greater trade deficits imply higher optimal, or welfare maximizing, tariffs. We compute optimal unilateral and Nash equilibrium tariffs be

Enze Liu, Elisa Luo, Shawn Shan, Geoffrey M. Voelker

The success of generative AI relies heavily on training on data scraped through extensive crawling of the Internet, a practice that has raised significant copyright, privacy, and ethical concerns. While few measures are designed to resist a resource-rich adversary determined to scrape a site, crawlers can be impacted by a range of existing tools such as robo

Berkay Becu, Santanu S. Dey, Feng Qiu, Alinson S. Xavier

Many operations related optimization problems involve repeatedly solving similar mixed integer linear programming (MILP) instances with the same constraint matrix but differing objective coefficients and right-hand-side values. The goal of this paper is to generate good cutting-planes for such instances using historical data. Gomory mixed integer cuts (GMIC)

Natasha Latouf, Michael D. Himes, Avi M. Mandell, Michael Dane Moore

We deploy a newly-generated set of geometric albedo spectral grids to examine the detectability of methane (CH4) in the reflected-light spectrum of an Earth-like exoplanet at visible and near-infrared wavelengths with a future exoplanet imaging mission. By quantifying the detectability as a function of signal-to-noise ratio (SNR) and molecular abundance, we

Radmila Sazdanovic, Daniel Scofield

Recently, big data techniques such as machine learning and topological data analysis have made their way to theoretical mathematics. Motivated by the recent work with polynomial invariants for knots, we use manifold learning and topological data analysis techniques to explore the structure and properties of the point cloud consisting of the chromatic polynom

Jiankang Chen, Ling Deng, Zhiyong Gan, Wei-Shi Zheng

Out-of-Distribution (OOD) detection is crucial when deploying machine learning models in open-world applications. The core challenge in OOD detection is mitigating the model's overconfidence on OOD data. While recent methods using auxiliary outlier datasets or synthesizing outlier features have shown promising OOD detection performance, they are limited due

E-Ro Nguyen, Hieu Le, Dimitris Samaras, Michael S. Ryoo

Generalized Referring expressions can describe one object, several related objects, or none at all. Existing generalized referring segmentation (GRES) models treat all cases alike, predicting a single binary mask and ignoring how linguistic phrases correspond to distinct visual instances. To this end, we reformulate GRES as an instance-level reasoning proble

Laia Domingo, Mahdi Chehimi

Breast cancer remains the leading cause of cancer-related mortality among women worldwide, necessitating the meticulous examination of mammograms by radiologists to characterize abnormal lesions. This manual process demands high accuracy and is often time-consuming, costly, and error-prone. Automated image segmentation using artificial intelligence offers a

V. V. Braguta, M. N. Chernodub, Ya. A. Gershtein, A. A. Roenko

Recently, lattice simulations of SU(3) Yang-Mills theory revealed that rotating hot gluon matter in thermal equilibrium possesses a novel inhomogeneous phase consisting of the deconfinement phase located in the center region, which is spatially separated from the confinement phase in the periphery. This inhomogeneous two-phase structure is also expected to b

Gabriel Cortês, Nuno Lourenço, Penousal Machado

Artificial Intelligence (AI) has driven innovations and created new opportunities across various sectors. However, leveraging domain-specific knowledge often requires automated tools to design and configure models effectively. In the case of Deep Neural Networks (DNNs), researchers and practitioners usually resort to Neural Architecture Search (NAS) approach

Mykola Moroz

We found a counterexample to the conjecture of Karvatskyi and Pratsiovytyi concerning the topological type of the achievement set of an intermediate series (Proceedings of the International Geometry Center, 2023. https://doi.org/10.15673/pigc.v16i3.2519). This conjecture is based on an analogy with the squeeze theorem from calculus. We also proposed an impro

Lakshmikar R. Polamreddy, Kalyan Roy, Sheng-Han Yueh, Deepshikha Mahato

The scarcity of accessible medical image data poses a significant obstacle in effectively training deep learning models for medical diagnosis, as hospitals refrain from sharing their data due to privacy concerns. In response, we gathered a diverse dataset named MedImgs, which comprises over 250,127 images spanning 61 disease types and 159 classes of both hum

Emanuele Mendicelli, David Schaich

Supersymmetric models are grounded in the intriguing concept of a hypothetical symmetry that relates bosonic and fermionic particles. This symmetry has profound implications, offering valuable extensions to the Standard Model of particle physics and fostering connections to theories of quantum gravity. However, lattice studies exploring the non-perturbative

Irfan Nafiz Shahan, Pulok Ahmed Auvi

Voice recognition and speaker identification are vital for applications in security and personal assistants. This paper presents a lightweight 1D-Convolutional Neural Network (1D-CNN) designed to perform speaker identification on minimal datasets. Our approach achieves a validation accuracy of 97.87%, leveraging data augmentation techniques to handle backgro

Utsithon Chaichompoo, Kritsada Sangkhanan

Let $X$ be a nonempty set and $T(X)$ the full transformation semigroup on $X$. For any equivalence relation $E$ on $X$, define a subsemigroup $T_{E^*}(X)$ of $T(X)$ by $$ T_{E^*}(X)=\{\alpha\in T(X):\text{for all}\ x,y\in X, (x,y)\in E\Leftrightarrow (x\alpha,y\alpha)\in E\}. $$ We have the regular part of $T_{E^*}(X)$, denoted by $\mathrm{Reg}(T)$, is the l

Matheus Fabri, Davide Polvara

We investigate the perturbative integrability of two-dimensional massive quantum field theories with polynomial-like interactions and show that any theory of such class which is purely elastic at the tree level is also purely elastic at one loop. To preserve the elasticity, the physical renormalized masses of the theory must differ from the classical ones by

Jürgen Hausen, Katharina Király

We give an explicit description of all quasismooth, rational, projective surfaces of Picard number one that admit a non-trivial torus action and have an integral canonical self intersection number.

Thomas Schmidt, Xin Ying Lee, Maximilian P. Wolkersdorfer

Stockart et al. (2025) recommend guidelines for best practices in the field of unconscious cognition. However, they condone the repeatedly criticized technique of excluding trials with high visibility ratings or of participants with high sensitivity for the critical stimulus. Based on standard signal detection theory for discrimination judgments, we show tha

Lahoucine Bouhouch, Yassine Dakir, Abdallah Slaoui, Rachid Ahl Laamara

In this paper, we explore the construction of Planar Maximally Entangled (PME) states from phase states. PME states form a class of $n$-partite states in which any subset of adjacent particles whose size is less than or equal to half the total number of particles is in a fully entangled state. This property is essential to ensuring the robustness and stabili

E. Aprile, J. Aalbers, K. Abe, S. Ahmed Maouloud

We report on a blinded search for dark matter with single- and few-electron signals in the first science run of XENONnT relying on a novel detector response framework that is physics-model-dependent. We derive 90\% confidence upper limits for dark matter-electron interactions. Heavy and light mediator cases are considered for the standard halo model and dark

Jan Bednarik, Erroll Wood, Vasileios Choutas, Timo Bolkart

Nowadays, it is possible to scan faces and automatically register them with high quality. However, the resulting face meshes often need further processing: we need to stabilize them to remove unwanted head movement. Stabilization is important for tasks like game development or movie making which require facial expressions to be cleanly separated from rigid h

Miguel Espinosa, Chenhongyi Yang, Linus Ericsson, Steven McDonagh

The Segment Anything Model (SAM) was originally designed for label-agnostic mask generation. Does this model also possess inherent semantic understanding, of value to broader visual tasks? In this work we follow a multi-staged approach towards exploring this question. We firstly quantify SAM's semantic capabilities by comparing base image encoder efficacy un

Adrian Carmona, Fatemeh Elahi, Christiane Scherb, Pedro Schwaller

We present a minimal composite dark matter model, based on a $SU(N_d)$ dark sector with $n_f$ dark quarks and a heavy t-channel mediator. For $n_f\geq 4$, the dark flavor symmetry guarantees the stability of a subset of the dark pions, which serve as our dark matter candidates. Their relic abundance is determined by co-scattering or co-annihilation with the

Matthew McQuinn, Casey McGrath

The microhertz frequency band of gravitational waves probes the merger of supermassive black holes as well as many other gravitational wave phenomena. However, space-interferometry methods that use test masses would require further development of test-mass isolation systems to detect anticipated astrophysical events. We propose an approach that avoids onboar

Steven Charlton, Andrei Matveiakin, Danylo Radchenko, Daniil Rudenko

We define a Hopf algebra of polylogarithms of an arbitrary field, which is a candidate for a conjectural Hopf algebra of framed mixed Tate motives. Our definition is elementary and mimics Goncharov's construction of higher Bloch groups. We also discuss the Hodge and motivic realizations of the Hopf algebra of polylogarithms.

Jinmin Wang, Zhizhang Xie, Guoliang Yu, Bo Zhu

We prove an $\ell^p$-version of the coarse Baum-Connes conjecture for spaces that coarsely embedds into $\ell^q$-spaces for any $p$ and $q$ in $[1,\infty)$.

Lars Malmqvist

Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of natural language processing tasks. However, their tendency to exhibit sycophantic behavior - excessively agreeing with or flattering users - poses significant risks to their reliability and ethical deployment. This paper provides a technical survey of sycophancy in

Ryan McConnell, Seungly Oh

We utilize a modulation restricted normal form approach to establish local well-posedness of the periodic Korteweg-de Vries equation in $H^s(\mathbb{T})$ for $s> -\frac23$. This work creates an analogue of the mKdV result by Nakanishi, Takaoka, and Tsutsumi for KdV, extending the currently best-known result of $s \geq -\frac12$ without utilizing the theory o

Sarah Griebel, Becca Cohen, Lucian Li, Jaihyun Park

Measures of textual similarity and divergence are increasingly used to study cultural change. But which measures align, in practice, with social evidence about change? We apply three different representations of text (topic models, document embeddings, and word-level perplexity) to three different corpora (literary studies, economics, and fiction). In every

Zizhao Wu, Jian Shi, Xuan Deng, Cheng Zhang

Point cloud completion aims to infer a complete shape from its partial observation. Many approaches utilize a pure encoderdecoder paradigm in which complete shape can be directly predicted by shape priors learned from partial scans, however, these methods suffer from the loss of details inevitably due to the feature abstraction issues. In this paper, we prop

David Pinto-Ramos, Karin Alfaro-Bittner, René G. Rojas, Marcel G. Clerc

Nonreciprocal coupling can alter the transport properties of material media, producing striking phenomena such as unidirectional amplification of waves, boundary modes, or self-assembled pattern formation. It is responsible for nonlinear convective instabilities in nonlinear systems that drive topological dissipative solitons in a single direction, producing

Federico Fabrizi, Saeed Goudarzi, Sana Khan, Tauheed Mohammad

Metal-halide perovskites (MHPs) have exciting optoelectronic properties and are under investigation for various applications, such as photovoltaics, light-emitting diodes, and lasers. An essential step towards exploiting the full potential of this class of materials is their large-scale, on-chip integration with high-resolution, top-down patterning. The deve

Andrea Butturini, Robert Benaiges-Fernandez, Octavi Fors, Daniel Garcia-Castellanos

The intense debate about the presence of methane in the Martian atmosphere has stimulated the study of methanogens adapted to terrestrial habitats that mimic Martian environments. We examinate the environmental conditions, energy sources and ecology of terrestrial methanogens thriving in deep crystalline fractures, sub-sea hypersaline lakes and subglacial wa

Ziyao Li, Shang-Ling Hsu, Cyrus Shahabi

Understanding human mobility behavior is crucial for numerous applications, including crowd management, location-based recommendations, and the estimation of pandemic spread. Machine learning models can predict the Points of Interest (POIs) that individuals are likely to visit in the future by analyzing their historical visit patterns. Previous studies addre

Christopher A. J. Duncan, Michael L. Brown

We show, using the pseudo-$C_\ell$ technique, how to estimate cosmic shear and galaxy-galaxy lensing power spectra that are insensitive to the effects of multiple sources of lensing bias including source-lens clustering, magnification bias and obscuration effects. All of these effects are of significant concern for ongoing and near-future Stage-IV cosmic she

Sergey Khoperskov, Glenn van de Ven, Matthias Steinmetz, Bridget Ratcliffe

We introduce a novel orbit superposition method designed to reconstruct the stellar density structure, kinematics, and chemical abundance distribution of the entire Milky Way by leveraging 6D phase-space information from its resolved stellar populations, limited by the spatial coverage of APOGEE DR17.

Johannes Schneider, Kilic Sinem, Daniel Stockhammer

Generative AI (GenAI) is transforming creative fields shaping our culture and our heritage. We focus on widespread interactions between clients and (creative) specialists highlighting a change in interaction patterns leading to a shift from the use of expert creativity towards AI-supported client creativity. More specifically, we explore the case of architec

Ji-Hun Oh, Kianoush Falahkheirkhah, John Cheville, Rohit Bhargava

Histopathologic analysis of stained tissue remains central to biomedical research and clinical care. Virtual staining (VS) offers a promising alternative, with potential to reduce costs and streamline workflows, yet hallucinations pose serious risks to clinical reliability. Here, we formalize the problem of hallucination detection in VS and propose a scalabl

Samuel Bernard-Bernardet, Benjamin Apffel

The spinorial ball is an electronic manipulable device that we recently introduced to discuss the origin of spin-1/2 from rotations group representation, without relying on the quantum mechanics framework. Nevertheless, it is also a macroscopic visualization of a quantum two-level system, and can thus be used to gain intuition on some generic features of qub

Jakob Lenschen, Rosalie Labbe, Nils Drotleff, Markus Fuhrmann

We propose and test a cryogenic setup comprising dielectric waveguides for mm-wave frequencies in the range of 75-110 GHz and temperatures down to 10 mK. The targeted applications are quantum technologies at millimeter-wave frequencies, which require measurements at low photon numbers and noise. We show that the high density polyethylene waveguides combine a

Do-Hyun Park, Min-Wook Jeon, Hyoung-Nam Kim

The rising demand for detecting hazardous situations has led to increased interest in radar-based human activity recognition (HAR). Conventional radar-based HAR methods predominantly rely on micro-Doppler spectrograms for recognition tasks. However, conventional spectrograms employ a fixed resolution regardless of the varying characteristics of human activit

Yiran Qiao, Yateng Tang, Xiang Ao, Qi Yuan

Online inclusive financial services encounter significant financial risks due to their expansive user base and low default costs. By real-world practice, we reveal that utilizing longer-term user payment behaviors can enhance models' ability to forecast financial risks. However, learning long behavior sequences is non-trivial for deep sequential models. Addi

David Pinto-Ramos

Nonlinear waves are a robust phenomenon observed in complex systems ranging from mechanics to ecology. Fronts are fundamental due to their robustness against perturbations and capacity to propagate one state over another. Controlling and understanding these waves is then fundamental to make use of their properties. Their velocity is one of the most important

David John Needham, John Billingham

In the third part of this series of papers, we address the same Cauchy problem that was considered in part 1, namely the nonlocal Fisher-KPP equation in one spatial dimension, $u_t = D u_{xx} + u(1-\phi_T*u)$, where $\phi_T*u$ is a spatial convolution with the top hat kernel, $\phi_T(y) \equiv H\left(\frac{1}{4}-y^2\right)$, except that now we include a spec

ShengQuan Zhou

We introduce a Markov-functional approach to construct local volatility models that are calibrated to a discrete set of marginal distributions. The method is inspired by and extends the volatility interpolation of Bass (1983) and Conze and Henry-Labord\`ere (2022). The method is illustrated with efficient numerical algorithms in the cases where the construct

Mehdi Amara, Christine Opagiste, Natalya Yu. Shitsevalova

In some intermetallic compounds, the crystallographic structure allows for an unusual latitude of movement of lanthanide ions inside so-called cages. Examples of such magnetic cage systems include the rare-earth hexaborides RB6 and filled skutterudites RPt4Ge12 series. In both instances, the rare-earth site, at the center of the cage, is of high symmetry, wh

Huilin Chen, Lei Wang, Yifan Chen, Tom Gedeon

Video action recognition has made significant strides, but challenges remain in effectively using both spatial and temporal information. While existing methods often focus on either spatial features (e.g., object appearance) or temporal dynamics (e.g., motion), they rarely address the need for a comprehensive integration of both. Capturing the rich temporal

Valentin Barriere

Deep Learning models tend to learn correlations of patterns on huge datasets. The bigger these systems are, the more complex are the phenomena they can detect, and the more data they need for this. The use of Artificial Intelligence (AI) is becoming increasingly ubiquitous in our society, and its impact is growing everyday. The promises it holds strongly dep

Mateus Marra, Daniel Smania

Given two H\"older potentials $\phi_+$ and $\psi_-$ for the unilateral shift, we define anisotropic Banach spaces of distributions on the bilateral shift space with a finite alphabet. On these spaces, the transfer operator for the bilateral shift is quasicompact with a spectral gap, and the unique Gibbs state associated with $\phi_+$ spans its $1$-eigenspace

Jyoti Dua, Hiran H Lathabai, Vivek Kumar Singh

Despite the importance of Indo-US research collaboration, it is intriguing to note that measurement and characterization of dynamics of Indo-US research collaboration is relatively underexplored. Therefore, in this work, we investigate major patterns in Indo-US collaboration with respect to certain key aspects using suitable scientometric notions and indicat

J. N. Kämmerer, S. Masis, K. Hambardzumyan, P. Lenhard

The microwave-driven dynamics of the superconducting phase difference across a Josephson junction is now widely employed in superconducting qubits and quantum circuits. With the typical energy level separation frequency of several GHz, cooling these quantum devices to the ground state requires temperatures below 100 mK. Pushing the operation frequency of sup

Kegang Wang, Jiankai Tang, Yantao Wei, Mingxuan Liu

Remote photoplethysmography (rPPG) extracts PPG signals from subtle color changes in facial videos, showing strong potential for health applications. However, most rPPG methods rely on intensity differences between consecutive frames, missing long-term signal variations affected by motion or lighting artifacts, which reduces accuracy. This paper introduces T

Tolga Altinoluk, Guillaume Beuf, Swaleha Mulani

We derive the full next-to-eikonal (NEik) corrections to the gluon propagator from before to after traversing a highly boosted gluon background field, including corrections both beyond the shockwave limit and beyond the static limit in particular. After summarizing the results of the full NEik corrections to the before-to-after quark propagator computed in o

Till Freihaut, Giorgia Ramponi

Multi-agent Inverse Reinforcement Learning (MAIRL) aims to recover agent reward functions from expert demonstrations. We characterize the feasible reward set in Markov games, identifying all reward functions that rationalize a given equilibrium. However, equilibrium-based observations are often ambiguous: a single Nash equilibrium can correspond to many rewa

Vivek Kumar Singh, Prashasti Singh, Anurag Kanaujia, Abhirup Nandy

Science and scientific research activities, in addition to the involvement of the researchers, require resources like research infrastructure, materials and reagents, databases and computational tools, journal subscriptions and publication charges etc. In order to meet these requirements, researchers try to attract research funding from different funding sou

Dennis Zaritsky, Vedant Chandra, Charlie Conroy, Ana Bonaca

The Magellanic Stream (MS) has long been known to contain multiple H I strands and corresponding stellar populations are beginning to be discovered. Combining a sample of 17 stars from the H3 ("Hectochelle in the Halo at High Resolution") survey with 891 stars drawn from the Gaia DR3 catalog, we trace stars along a sub-dominant strand of the MS, as defined b

Tomas Berriel Martins, Martin R. Oswald, Javier Civera

This paper presents an Open-Vocabulary Online 3D semantic mapping pipeline, that we denote by its acronym OVO. Given a sequence of posed RGB-D frames, we detect and track 3D segments, which we describe using CLIP vectors. These are computed from the viewpoints where they are observed by a novel CLIP merging method. Notably, our OVO has a significantly lower

Stefan Kober

Integer programs (IPs) on constraint matrices with bounded subdeterminants are conjectured to be solvable in polynomial time. We give a strongly polynomial time algorithm to solve IPs where the constraint matrix has bounded subdeterminants and at most two non-zeros per row after removing a constant number of rows and columns. This result extends the work by

Ömer Öztürkoğlu, Gökberk Özsakallı, Syed Shah Sultan Mohiuddin Qadri

This study examines a home healthcare scheduling and routing problem (HHSRP) with a lunch break requirement. This problem especially consists of lunch break constraints for caregivers in addition to other typical features of the HHSRP in literature such as hard time window constraints for both patients and caregivers and patient preferences. The objective is

Hong Ding, Ziming Wang, Yi Ding, Hongjie Lin

Addressing the challenge of ensuring safety in ever-changing and unpredictable environments, particularly in the swiftly advancing realm of autonomous driving in today's 5G wireless communication world, we present Navigation Secure (NavSecure). This vision-based navigation framework merges the strengths of world models with crucial safety-focused decision-ma

Tao Zhang, Ziqi Zhang, Zongyang Ma, Yuxin Chen

Advanced Multimodal Large Language Models (MLLMs) struggle with recent Knowledge-based Visual Question Answering (VQA) tasks, such as INFOSEEK and Encyclopedic-VQA, due to their limited and frozen knowledge scope, often leading to ambiguous and inaccurate responses. Thus, multimodal Retrieval-Augmented Generation (mRAG) is naturally introduced to provide MLL

Zachary Akridge, Zachary Bradshaw

The problem of regularity and uniqueness are open for the supercritically dissipative surface quasi-geostrophic equations in certain classes. In this note we examine the extent to which small or large scales are necessarily active both for the temperature in a hypothetical blow-up scenario and for the error in hypothetical non-uniqueness scenarios, the latte

Figen Yilmaz, Siddharth Singh, Martijn F. S. Zwanenburg, Jinlun Hu

Superconducting circuits are being employed for large-scale quantum devices, and a pertinent challenge is to perform accurate numerical simulations of device parameters. One of the most advanced methods for analyzing superconducting circuit designs is the energy participation ratio (EPR) method, which constructs quantum Hamiltonians based on the energy distr

Junzhang Liu, Tingkai Liu, Yueyuan Sui, Stephen Xia

We introduce ElastiFormer, a post-training technique that adapts pretrained Transformer models into an elastic counterpart with variable inference time compute. ElastiFormer introduces small routing modules (as low as .00006% additional trainable parameters) to dynamically selects subsets of network parameters and input tokens to be processed by each layer o

Jakub Rondomanski, José D. Cojal González, Jürgen P. Rabe, Carlos-Andres Palma

We present a non-trivial metric tensor field on the space of 2-by-2 real-valued, symmetric matrices whose Levi-Civita connection renders frames of eigenvectors parallel. This results in fundamental reimagining of the space of symmetric matrices as a curved manifold (rather than a flat vector space) and reduces the computation of eigenvectors of one-parameter

Hao Liu, Yutong He, Bing Xiong, Changzheng Sun

High-performance electro-optic modulators play a critical role in modern telecommunication networks and intra-datacenter interconnects. Low driving voltage, large electro-optic bandwidth, compact device size, and multi-band operation ability are essential for various application scenarios, especially energy-efficient high-speed data transmission. However, it

Zeyang Li, Navid Azizan

Multi-agent reinforcement learning (MARL) has achieved notable success in cooperative tasks, demonstrating impressive performance and scalability. However, deploying MARL agents in real-world applications presents critical safety challenges. Current safe MARL algorithms are largely based on the constrained Markov decision process (CMDP) framework, which enfo

Benjamin J. Brown

The color code has been invaluable for the development of the theory of fault-tolerant logic gates using transversal rotations. Three-dimensional examples of the color code have shown us how its structure, specifically the intersection of the supports of logical operators, can give rise to non-Clifford $T$ and $CCZ$ gates. Here we present a color code with a

Yu Xu, Fan Tang, Juan Cao, Yuxin Zhang

Diffusion Transformers (DiTs) have exhibited robust capabilities in image generation tasks. However, accurate text-guided image editing for multimodal DiTs (MM-DiTs) still poses a significant challenge. Unlike UNet-based structures that could utilize self/cross-attention maps for semantic editing, MM-DiTs inherently lack support for explicit and consistent i

Hanaa Zitane, Delfim F. M. Torres

This paper deals with the gradient stability and the gradient stabilizability of Caputo time fractional diffusion linear systems. First, we give sufficient conditions that allow the gradient Mittag-Leffler and strong stability, where we use a direct method based essentially on the spectral properties of the system dynamic. Moreover, we consider a class of li

Simone Colombani, Dimitri Ognibene, Giuseppe Boccignone

In recent years, research in the area of human-robot interaction has focused on developing robots capable of understanding complex human instructions and performing tasks in dynamic and diverse environments. These systems have a wide range of applications, from personal assistance to industrial robotics, emphasizing the importance of robots interacting flexi

Jörg Neveling, Andreas Osterloh

The square root of the threetangle is calculated for the transverse XY-model with an integrability-breaking in-plane field component. To be in a regime of quasi-solvability of the convex roof, here we concentrate here on a 4-site model Hamiltonian. In general, the field and hence a mixing of the odd/even sectors, has a detrimental effect on the threetangle,

Binbin Gu, Juncheng Fang, Faisal Nawab

In database applications involving sensitive data, the dual imperatives of data confidentiality and provable query processing are important. This paper introduces PoneglyphDB, a database system that leverages non-interactive zero-knowledge proofs (ZKP) to support both confidentiality and provability. Unlike traditional databases, PoneglyphDB enhances confide

Chenhuan Wang, Wenbin Zhao

We consider gravitational particle production (GPP) of dark matter (DM) under a supergravity framework, where the $\alpha$-attractor inflation model is used. The particle spectrum is computed numerically and the DM number density is obtained. We show how the DM mass, gravitino mass and inflation model parameters modify the results, and find the reheating tem

Xiaoqing Luo, Rixing Huang, Dangyuan Lei, Guangyuan Li

Plasmonic nanoantennas containing nano-gaps support "hotspots" for greatly enhanced light-matter interactions, but suffer from inherent high losses, a long-standing issue that hinders practical applications. Here we report a strategy to significantly suppress the losses of plasmonic dimer nanoantennas. Specifically, by introducing the concept of cooperative

Swasti Shreya Mishra, Kuldeep Kulkarni, Duygu Ceylan, Balaji Vasan Srinivasan

We propose a diffusion model-based approach, FloAtControlNet to generate cinemagraphs composed of animations of human clothing. We focus on human clothing like dresses, skirts and pants. The input to our model is a text prompt depicting the type of clothing and the texture of clothing like leopard, striped, or plain, and a sequence of normal maps that captur

Simone Colombani, Luca Brini, Dimitri Ognibene, Giuseppe Boccignone

Robots are increasingly being used in dynamic environments like workplaces, hospitals, and homes. As a result, interactions with robots must be simple and intuitive, with robots perception adapting efficiently to human-induced changes. This paper presents a robot control architecture that addresses key challenges in human-robot interaction, with a particular

Uditha Muthumala, Yuxuan Zhang, Luciano Sebastian Martinez-Rau, Sebastian Bader

This paper compares machine learning approaches with different input data formats for the classification of acoustic emission (AE) signals. AE signals are a promising monitoring technique in many structural health monitoring applications. Machine learning has been demonstrated as an effective data analysis method, classifying different AE signals according t

Debayan Jana, Abhik Basu

We study a generalized Kardar-Parisi-Zhang (KPZ) equation [Jana et al., Phys. Rev. E 109, L032104 (2024)] that sets the paradigm for universality in roughening of growing nonequilibrium surfaces without any conservation laws but with competing local and nonlocal nonlinear effects. This equation in two dimensions exhibits two distinct strong coupling regimes:

Johannes Aastrup, Jesper M. Grimstrup

Starting with a Dirac operator on a configuration space of $SU(2)$ gauge connections we consider its fluctuations with inner automorphisms. We show that a certain type of twisted inner fluctuations leads to a Dirac operator whose square gives the Hamiltonian of Yang-Mills quantum field theory coupled to a fermionic sector that consist of one-form fermions. W

J. Peña-Rodríguez, J. Förtsch, C. Pauly, K. -H. Kampert

We present a Python module for simulating Silicon Photo-Multipliers, Avalanche Photo-Diodes, and Multi-Pixel Photon Counters. This module allows users to perform noise analyses: Dark Count Rate, crosstalk, and afterpulsing. Furthermore, the simulation framework novelty is the capability of simulating assemblies of SiPM arrays (MPPCa) for large area detectors

Qingzhong Deng, Ahmed H. El-Saeed, Alaa Elshazly, Guy Lepage

Ring resonators are crucial in silicon photonics for various applications, but conventional designs face performance trade-offs. Here a third-order polynomial interconnected circular (TOPIC) bend is proposed to revolutionize the ring designs fundamentally. The TOPIC bend has a unique feature of continuous curvature and curvature derivative, which is theoreti

Keda Tao, Can Qin, Haoxuan You, Yang Sui

Video large language models (VLLMs) have significantly advanced recently in processing complex video content, yet their inference efficiency remains constrained because of the high computational cost stemming from the thousands of visual tokens generated from the video inputs. We empirically observe that, unlike single image inputs, VLLMs typically attend vi

Shammi Kumar, Raja Sen, Mamta Arya, Sankar Dhar

In this report, the optical properties of Ta doped SrTiO3 (STO) due to its potential in transparent conducting oxides (TCOs) is explored by a combination of theoretical studies based on density functional theory and spectroscopic ellipsometry. To achieve this theoretically, we vary the concentration of Ta from 0 - 12.5% in SrTi1-xTaxO3 system by substitution

Ilia Mazin, Yu Zhang

The hybridization between light and matter is fundamental for achieving cavity-induced control over quantum materials, necessitating accurate ab initio methods for their analysis. Among these, the quantum electrodynamics Hartree-Fock framework stands out as an essential mean field approximation for electron-electron and electron-photon interactions, forming

Jong-In Yang, Jooyoo Hong

Recently contact angle hysteresis in two-dimensional droplets lying on a solid surface has been studied extensively in terms of static friction due to pinning forces at contact points. Here we propose a method to determine the coefficient of static friction using two-dimensional horizontal capillary bridges. This method requires only the measurement of capil

Charalampos Katsis, Elisa Bertino

Zero Trust (ZT) is a security paradigm aiming to curtail an attacker's lateral movements within a network by implementing least-privilege and per-request access control policies. However, its widespread adoption is hindered by the difficulty of generating proper rules due to the lack of detailed knowledge of communication requirements and the characteristic