November 2024 arXiv papers — page 59

Diego Barberena, Matthew P. A. Fisher

We study in detail the postselection problem in a specific model: bosons hopping on a lattice subjected to continuous local measurements of quadrature observables. We solve the model analytically and show that the postselection overhead can be reduced by postprocessing the entire measurement record into one or two numbers for each trajectory and then postsel

Rayhana Amjad, Rob van Glabbeek, Liam O'Connor

LTL3 is a multi-valued variant of Linear-time Temporal Logic for runtime verification applications. The semantic descriptions of LTL3 in previous work are given only in terms of the relationship to conventional LTL. Our approach, by contrast, gives a full model-based inductive accounting of the semantics of LTL3, in terms of families of definitive prefix set

Cinzia Di Giusto, Laetitia Laversa, Kirstin Peters

We revisit the problem of synchronisability for communicating automata, i.e., whether the language of send messages for an asynchronous system is the same as the language of send messages with a synchronous communication. The un/decidability of the problem depends on the specific asynchronous semantics considered as well as the topology (the communication fl

Hans Hüttel, Lars Jensen, Chris Oliver Paulsen, Julian Teule

We study the data-parallel language BUTF, inspired by the Futhark language for array programming. We give a translation of BUTF into a version of the pi-calculus with broadcasting and labeled names. The translation is both complete and sound. Moreover, we propose a cost model by annotating translated BUTF processes. This is used for a complexity analysis of

Francisco Arrieta Zuccalli, Pedro Massey, Demetrio Stojanoff

Let $A\in\mathbb C^{n\times n}$ and let $\mathcal X\subset \mathbb C^n$ be an $A$-invariant subspace with $\dim \mathcal X=d\geq 1$, corresponding to exterior eigenvalues of $A$. Given an initial subspace $\mathcal V\subset \mathbb C^n$ with $\dim \mathcal V=r\geq d$, we search for expansions of $\mathcal V$ of the form $\mathcal V+A(\mathcal W_0)$, where $\

John C. Forbes, Michele T. Bannister, Chris Lintott, Angus Forrest

Upcoming surveys are likely to discover a new sample of interstellar objects (ISOs) within the Solar System, but questions remain about the origin and distribution of this population within the Galaxy. ISOs are ejected from their host systems with a range of velocities, spreading out into tidal streams - analogous to the stellar streams routinely observed fr

Sai Ramana Kiran Pinnama Raju, Rishabh Singh, Manoj Velmurugan, Nitin J. Sanket

Optical flow estimation is a critical task for tiny mobile robotics to enable safe and accurate navigation, obstacle avoidance, and other functionalities. However, optical flow estimation on tiny robots is challenging due to limited onboard sensing and computation capabilities. In this paper, we propose EdgeFlowNet , a high-speed, low-latency dense optical f

Nisa Ulumuddin, Sandra Korte-Kerzel, Zhuocheng Xie

This study investigates the site occupancy preferences of Al in Ta(Fe$_{1-x}$Al$_x$)$_2$ Laves phases using first-principles calculations, covering Al concentrations from 0 to 50 at.\%. Al atoms exhibit a strong preference for $2a$ Wyckoff sites, with configurations becoming more energetically favorable as these sites reach full occupancy at high Al concentr

Yaqi Wang, Haipei Xu

Recently, as Large Language Models (LLMs) have shown impressive emerging capabilities and gained widespread popularity, research on LLM-based search agents has proliferated. In real-world situations, users often input contextual and highly personalized queries to chatbots, challenging LLMs to capture context and generate appropriate answers. However, much of

Shenglai Zeng, Jiankun Zhang, Bingheng Li, Yuping Lin

Retrieval-Augmented Generation (RAG) systems have shown promise in enhancing the performance of Large Language Models (LLMs). However, these systems face challenges in effectively integrating external knowledge with the LLM's internal knowledge, often leading to issues with misleading or unhelpful information. This work aims to provide a systematic study on

Vijay Prakash, Kevin Lee, Arkaprabha Bhattacharya, Danny Yuxing Huang

Answering end user security questions is challenging. While large language models (LLMs) like GPT, LLAMA, and Gemini are far from error-free, they have shown promise in answering a variety of questions outside of security. We studied LLM performance in the area of end user security by qualitatively evaluating 3 popular LLMs on 900 systematically collected en

Saikat Banerjee, Peter Carbonetto, Matthew Stephens

Variational empirical Bayes (VEB) methods provide a practically attractive approach to fitting large, sparse, multiple regression models. These methods usually use coordinate ascent to optimize the variational objective function, an approach known as coordinate ascent variational inference (CAVI). Here we propose alternative optimization approaches based on

Chunwei Liu, Enrique Noriega-Atala, Adarsh Pyarelal, Clayton T Morrison

The global output of academic publications exceeds 5 million articles per year, making it difficult for humans to keep up with even a tiny fraction of scientific output. We need methods to navigate and interpret the artifacts -- texts, graphs, charts, code, models, and datasets -- that make up the literature. This paper evaluates various methods for extracti

Anjie Jiang, Kangtong Mo, Satoshi Fujimoto, Michael Taylor

Solar trajectory monitoring is a pivotal challenge in solar energy systems, underpinning applications such as autonomous energy harvesting and environmental sensing. A prevalent failure mode in sustained solar tracking arises when the predictive algorithm erroneously diverges from the solar locus, erroneously anchoring to extraneous celestial or terrestrial

Viranjan Bhattacharyya, Tyler Ard, Rongyao Wang, Ardalan Vahidi

In this paper, a multi-agent motion planning problem is studied aiming to minimize energy consumption of connected automated vehicles (CAVs) in lane change scenarios. We model this interactive motion planning as a generalized Nash equilibrium problem and formalize how vehicle-to-vehicle intention sharing enables solution of the game between multiple CAVs as

Yang Liu, Siao Liu, Xiaoguang Zhu, Jielin Li

Video Anomaly Detection (VAD) aims to automatically analyze spatiotemporal patterns in surveillance videos collected from open spaces to detect anomalous events that may cause harm, such as fighting, stealing, and car accidents. However, vision-based surveillance systems such as closed-circuit television often capture personally identifiable information. The

Isuru Thilakasiri, Tharaka Fonseka, Isuri Mapa, Roshan Godaliyadda

Massive changes in many aspects related to social groups of different socioeconomic backgrounds were caused by the COVID-19 pandemic and as a result, the overall state of mental health was severely affected globally. This study examined how the pandemic affected Sri Lankan citizens representing a range of socioeconomic backgrounds in terms of their mental he

Benjamin Levine, Javier Sánchez, Chihway Chang, Anja von der Linden

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will survey the southern sky to create the largest galaxy catalog to date, and its statistical power demands an improved understanding of systematic effects such as source overlaps, also known as blending. In this work we study how blending introduces a bias in the number counts of galaxies

Mackenzie Weygandt Mathis

Biological intelligence is inherently adaptive -- animals continually adjust their actions based on environmental feedback. However, creating adaptive artificial intelligence (AI) remains a major challenge. The next frontier is to go beyond traditional AI to develop "adaptive intelligence," defined here as harnessing insights from biological intelligence to

Devora Chait-Roth, Kedar S. Namjoshi

Smart contracts form the core of Web3 applications. Contracts mediate the transfer of cryptocurrency, making them irresistible targets for hackers. We introduce ASP, a system aimed at easing the construction of provably secure contracts. The Asp system consists of three closely-linked components: a programming language, a defensive compiler, and a proof chec

Andreas Leopold Knutsen, Sara Torelli

Under the assumption that the adjusted Brill-Noether number $\widetilde{\rho}$ is at least $-g$, we prove that the Brill-Noether loci in $\mathcal{M}_{g,n}$ of pointed curves carrying pencils with prescribed ramification at the marked points have a component of the expected codimension with pointed curves having Brill-Noether varieties of pencils of the mini

Will Pazner

In this paper, we construct and analyze preconditioners for the interior penalty discontinuous Galerkin discretization posed in the space $H(\mathrm{div})$. These discretizations are used as one component in exactly divergence-free pressure-robust discretizations for the Stokes problem. Three preconditioners are presently considered: a subspace correction pr

Sizhe Wang, Wenwen Li

This study introduces a novel approach to terrain feature classification by incorporating spatial point pattern statistics into deep learning models. Inspired by the concept of location encoding, which aims to capture location characteristics to enhance GeoAI decision-making capabilities, we improve the GeoAI model by a knowledge driven approach to integrate

Sándor Vágvölgyi

We show that for any ground term equation systems $E$ and $F$, (1) the union of the generated congruences by $E$ and $F$ is a congruence on the ground term algebra if and only if there exists a ground term equation system $H$ such that the congruence generated by $H$ is equal to the union of the congruences generated by $E$ and $F$ if and only if the congrue

José Holanda

The magnetic spin Hall effect arises from a reactive counterpart of the dissipative spin response that is responsible for the ordinary spin Hall effect. This interpretation is supported by the dependence of spin Hall effect signals on the reversal of magnetic order parameters and can be explained in terms of the symmetries of well-defined linear response fun

A. L. Harris, S. Fritzsche

The elastic scattering of spinless vortex electrons on realistic target atoms has been investigated. In particular, expressions are derived in different approximations for the elastic angular-differential cross sections. We develop a distorted wave formalism that includes the effect of the atomic potential on the impinging vortex electron and compare this to

Jonas von der Heyden, Nils Schlüter, Philipp Binfet, Martin Asman

Smart grids feature a bidirectional flow of electricity and data, enhancing flexibility, efficiency, and reliability in increasingly volatile energy grids. However, data from smart meters can reveal sensitive private information. Consequently, the adoption of smart meters is often restricted via legal means and hampered by limited user acceptance. Since mete

Charles Radin, Lorenzo Sadun

We consider entropy-optimal graphons associated with extreme and near-extreme constraints on the densities of edges and triangles. We prove that the optimizers for near-extreme constraints are unique and multipodal and are perturbations of the previously known unique optimzers for extreme constraints. This proves the existence of infinitely many phases. We d

Selma Husanovic, Ginger Egberts, Alexander Heinlein, Fred Vermolen

Burn injuries present a significant global health challenge. Among the most severe long-term consequences are contractures, which can lead to functional impairments and disfigurement. Understanding and predicting the evolution of post-burn wounds is essential for developing effective treatment strategies. Traditional mathematical models, while accurate, are

Oluwole Jaiyeoba, Abdullah T. Mughrabi, Morteza Baradaran, Beenish Gul

Graph analytics are vital in fields such as social networks, biomedical research, and graph neural networks (GNNs). However, traditional CPUs and GPUs struggle with the memory bottlenecks caused by large graph datasets and their fine-grained memory accesses. While specialized graph accelerators address these challenges, they often support only moderate-sized

Syed Mujtaba Hassan, Shahid Hussain, Abdul Samad

Many NP-hard graph problems become easy for some classes of graphs. For example, coloring is easy for bipartite graphs, but NP-hard in general. So we can ask question like when does a hard problem become easy? What is the minimum substructure for which the problem remains hard? We use the notion of boundary classes to study such questions. In this paper, we

Alexandre Falcão, Konrad Tywoniuk

Jet suppression and modification is a hallmark feature of heavy-ion collisions. This can be attributed to an accumulated set of effects, including radiative and elastic energy loss and reabsorption of thermalized energy within the jet cone, which are encoded in a quenching weight, determining the probability distribution for a shift of the $p_T$ (energy loss

Arthur Elwing Torres, Edleno Silva de Moura, Altigran Soares da Silva, Mario A. Nascimento

Named Entity Recognition (NER) is a machine learning task that traditionally relies on supervised learning and annotated data. Acquiring such data is often a challenge, particularly in specialized fields like medical, legal, and financial sectors. Those are commonly referred to as low-resource domains, which comprise long-tail entities, due to the scarcity o

Meng Ye, Bingyu Xin, Bangwei Guo, Leon Axel

Multi-planar tagged MRI is the gold standard for regional heart wall motion evaluation. However, accurate recovery of the 3D true heart wall motion from a set of 2D apparent motion cues is challenging, due to incomplete sampling of the true motion and difficulty in information fusion from apparent motion cues observed on multiple imaging planes. To solve the

Noor Saud Abd, Noor Walid Khalid, Basim Hussein Ali

In the current digital age, the volume of data generated by various cyber activities has become enormous and is constantly increasing. The data may contain valuable insights that can be harnessed to improve cyber security measures. However, much of this data is unclassified and qualitative, which poses significant challenges to traditional analysis methods.

Daigo Koizumi, Shingo Kisanuki, Kenta Monden, Yusuke Kousaka

We report a new family of chiral intermetallic superconductors TaAl$_{x}$Ge$_{2-x}$. The mother compound TaGe$_2$ has a C40-type chiral hexagonal crystal structure with a pair of enantiomorphic space groups of $P6{_2}22$ and $P6{_4}22$. By substituting Ge with Al, TaAl$_{x}$Ge$_{2-x}$ polycrystals with the C40 structure were synthesized with Al substitution

Thuan Nguyen, Jiangshan Zhang, Jiming Jiang

We propose a random-effects approach to missing values for generalized linear mixed model (GLMM) analysis. The method converts a GLMM with missing covariates to another GLMM without missing covariates. The standard GLMM analysis tools for longitudinal data then apply. The method applies, in particular, to the cases of linear mixed models and logistic regress

Alessandro Cosenza, Michael Goldman, Melanie Koser

We consider a branched transport problem with weakly imposed boundary conditions. This problem arises as a reduced model for pattern formation in type-I superconductors. For this model, it is conjectured that the dimension of the boundary measure is non-integer. We prove this conjecture in a simplified 2D setting, under the (strong) assumption of Ahlfors reg

Juan Polo, Wayne J. Chetcuti, Anna Minguzzi, Andreas Osterloh

We investigate the effects of a static impurity, modeled by a localized barrier, in a one-dimensional mesoscopic system comprised of strongly correlated repulsive SU($N$)-symmetric fermions. For a mesoscopic sized ring under the effect of an artificial gauge field, we analyze the energy spectrum, the particle density and the current flowing through the impur

Haowei Xu, Guoqing Wang, Changhao Li, Hao Tang

Non-Hermiticity and chirality are two fundamental properties known to give rise to various intriguing phenomena. However, the interplay between these properties has been rarely explored. In this work, we bridge this gap by introducing an off-diagonal non-Hermitian spin-spin interaction mediated by chiral phonons. This interaction arises from the spin-selecti

Spencer A Hill, Simona Bordoni, Jonathan L Mitchell, Juan M Lora

The poleward extent of Earth's zonal-mean Hadley cells varies across seasons and years, which would be nice to capture in a simple theory. A plausible, albeit diagnostic, candidate from Hill et al (2022) combines the conventional two-layer, quasi-geostrophic, baroclinic instability-based framework with a less conventional assumption: that each cell's upper-b

Tom Shachar

We study the physics of 2 and 3 mutually intersecting conformal defects forming wedges and corners in general dimension. For 2 defects we derive the beta function of the edge interactions for infinite and semi-infinite wedges and study them in the tricritical model in $d=3-\epsilon$ as an example. We discuss the dependency of the edge anomalous dimension on

Junhui Mi, Rahul D. Tendulkar, Sarah M. C. Sittenfeld, Sujata Patil

Methods to handle missing data have been extensively explored in the context of estimation and descriptive studies, with multiple imputation being the most widely used method in clinical research. However, in the context of clinical risk prediction models, where the goal is often to achieve high prediction accuracy and to make predictions for future patients

Melanie J. Rowland, Caroline V. Morley, Brittany E. Miles, Genaro Suárez

The coldest Y spectral type brown dwarfs are similar in mass and temperature to cool and warm ($\sim$200 -- 400 K) giant exoplanets. We can therefore use their atmospheres as proxies for planetary atmospheres, testing our understanding of physics and chemistry for these complex, cool worlds. At these cold temperatures, their atmospheres are cold enough for w

L. R. Patrick, I. Negueruela

In this article we study the nature of the recently identified populations of hot companions to red supergiant stars (RSGs). To this end, we compile the literature on the most well characterised systems with the aim of better understanding the hot companions identified with ultra-violet photometry and confirmed with Hubble Space Telescope spectra in the Loca

Muhammad Ali, Alister Burr

Infrastructure-less Multi-hop Wireless Networks are the backbone for mission critical communications such as in disaster and battlefield scenarios. However, interference signals in the wireless channel cause losses to transmission in wireless networks resulting in a reduced network throughput and making efficient transmission very challenging. Therefore, tec

Taha Koleilat, Hojat Asgariandehkordi, Hassan Rivaz, Yiming Xiao

Recent advancements in vision-language models (VLMs), such as CLIP, have demonstrated substantial success in self-supervised representation learning for vision tasks. However, effectively adapting VLMs to downstream applications remains challenging, as their accuracy often depends on time-intensive and expertise-demanding prompt engineering, while full model

Maria Radionova, Alexander Okhotin

In this paper, different variants of reversible finite automata are compared, and their hierarchy by the expressive power is established. It is shown that one-way reversible automata with multiple initial states (MRFA) recognize strictly more languages than sweeping reversible automata (sRFA), which are in turn stronger than one-way reversible automata with

L. F. Melo, M. A. Solís-Prosser, O. Jiménez, A. Delgado

In a general optimized measurement scheme for discriminating between nonorthogonal quantum states, the error rate is minimized under the constraint of a fixed rate of inconclusive outcomes (FRIO). This so-called optimal FRIO measurement encompasses the standard and well known minimum-error and optimal unambiguous (or maximum-confidence) discrimination strate

John-Michael Eberhard, Amy E. Reines, Hansung B. Gim, Jeremy Darling

We present a systematic search for radio active galactic nuclei (AGNs) in dwarf galaxies using recent observations taken by the Very Large Array Sky Survey (VLASS). To select these objects, we first establish a criterion to identify radio-excess AGNs using the infrared-radio correlation (IRRC) parameter, $q$, that describes the tight relation between radio a

Hongxu Chen, Runshi Li, Bowei Zhu, Zhen Wang

Low-rank adaptations (LoRA) are widely used to fine-tune large models across various domains for specific downstream tasks. While task-specific LoRAs are often available, concerns about data privacy and intellectual property can restrict access to training data, limiting the acquisition of a multi-task model through gradient-based training. In response, LoRA

Yassin El Karrouchi, Tobias Weth

Inspired by recent work of Ferone and Volzone arXiv:2007.13195, we derive sufficient conditions for the validity and non-validity of a boundary version of Talenti's comparison principle in the context of Dirichlet-Poisson problems for the fractional Laplacian $(-\Delta)^s$ in the unit ball $\Omega= B_1(0) \subset \mathbb{R}^N$. In particular, our results imp

Mengshuo Jia, Zeyu Cui, Gabriela Hug

The integration of experimental technologies with large language models (LLMs) is transforming scientific research. It positions AI as a versatile research assistant rather than a mere problem-solving tool. In the field of power systems, however, managing simulations -- one of the essential experimental technologies -- remains a challenge for LLMs due to the

Mateus C. Silva, Rafael A. Melo, Mauricio G. C. Resende, Marcio C. Santos

Given a graph G=(V,E), a connected Grundy coloring is a proper vertex coloring that can be obtained by a first-fit heuristic on a connected vertex sequence. A first-fit coloring heuristic is one that attributes to each vertex in a sequence the lowest-index color not used for its preceding neighbors. A connected vertex sequence is one in which each element, e

Aayush Saxena, Alex J. Cameron, Harley Katz, Andrew J. Bunker

The UV continuum slope of galaxies, $\beta$, is a powerful diagnostic. Understanding the redshift evolution of $\beta$ and its dependence on key galaxy properties can shed light on the evolution of galaxy physical properties over cosmic time. In this study, we present $\beta$ measurements for 295 spectroscopically confirmed galaxies at $5.5<z<14.3$ selected

Zhenghao Zhong

A distinctive duality present in 3d $\mathcal{N}=4$ theories is the 3d mirror symmetry. Under this duality, the Coulomb (Higgs) branch of one theory corresponds to the Higgs (Coulomb) branch of its mirror dual. This paper is divided into two parts. In the first part, we examine quiver gauge theories constructed from unitary gauge groups arranged in the shape

G. Sabatini, S. Bovino, E. Redaelli, F. Wyrowski

Deuterium fractionation is a well-established evolutionary tracer in low-mass star formation, but its applicability to the high-mass regime remains an open question. The abundances and ratios of deuterated species have often been proposed as reliable evolutionary indicators for different stages of the high-mass star formation. We investigate the role of N$_2

Saquib Hassan, Georges Obied, John March-Russell

We argue for the existence of bounds on the tensions of $p$-branes in de Sitter space in terms of the Hubble rate and the strength of a class of Chern-Simons-like couplings. The world-volume couplings involve Abelian 1-form gauge fields in the bulk and possibly field strengths intrinsic to the brane. In many cases these couplings are the D-brane Chern-Simons

Duarte Feiteira, José P. S. Lemos, Oleg B. Zaslavskii

The Penrose process, a process that transfers energy from a black hole to infinity, together with the BSW mechanism, which uses collisions of ingoing particles at the event horizon of a black hole to locally produce large amounts of energy, is studied in a combined description for a $d$ dimensional extremal Reissner-Nordstr\"om black hole spacetime with nega

Jan-Niklas Herre, Qiyu Liu, Roman Rausch, Christoph Karrasch

We investigate the ergodicity-to-localization transition in interacting fermion systems subjected to a spatially uniform electric field. For that we employ the recently proposed Tensorflow Equations (TFE), a type of continuous unitary flow equations. This enables us to iteratively determine an approximate diagonal basis of the quantum many-body system. We pr

Swapnaneel Dey, Michael G. Jones, David J. Sand, Nicolas Mazziotti

We present a catalog of 34 new candidate (13 high confidence) isolated, young stellar systems within the Virgo galaxy cluster identified through a citizen science search of public optical and ultraviolet imaging. "Blue blobs" are a class of blue, faint, isolated, extremely low stellar mass, and metal-rich star-forming clouds embedded in the hot intracluster

Jin Ye, Ying Chen, Yanjun Li, Haoyu Wang

Computed Tomography (CT) is one of the most popular modalities for medical imaging. By far, CT images have contributed to the largest publicly available datasets for volumetric medical segmentation tasks, covering full-body anatomical structures. Large amounts of full-body CT images provide the opportunity to pre-train powerful models, e.g., STU-Net pre-trai

Toni Bertólez-Martínez, Ivan Esteban, Rasmi Hajjar, Olga Mena

The cosmological upper bound on the total neutrino mass is the dominant limit on this fundamental parameter. Recent observations-soon to be improved-have strongly tightened it, approaching the lower limit set by oscillation data. Understanding its physical origin, robustness, and model-independence becomes pressing. Here, we explicitly separate for the first

Ruhi Shah, Eduardo Martín-Martínez, T. Rick Perche

We analyze how non-relativistic effective models for the magnetic coupling of a spin to the electromagnetic field (proportional to $\hat{\boldsymbol{\sigma}}\cdot \boldsymbol{B}$) emerge from a full quantum field theoretical description of charged fermionic fields with the quantum electromagnetic field. This allows us to keep track of relativistic correction

Yuhao Dong, Zuyan Liu, Hai-Long Sun, Jingkang Yang

Large Language Models (LLMs) demonstrate enhanced capabilities and reliability by reasoning more, evolving from Chain-of-Thought prompting to product-level solutions like OpenAI o1. Despite various efforts to improve LLM reasoning, high-quality long-chain reasoning data and optimized training pipelines still remain inadequately explored in vision-language ta

Vipul Arora, Esty Kelman, Uri Meir

Linearity testing has been a focal problem in property testing of functions. We combine different known techniques and observations about linearity testing in order to resolve two recent versions of this task. First, we focus on the online manipulations model introduced by Kalemaj, Raskhodnikova and Varma (ITCS 2022 \& Theory of Computing 2023). In this mode

Omri Avrahami, Or Patashnik, Ohad Fried, Egor Nemchinov

Diffusion models have revolutionized the field of content synthesis and editing. Recent models have replaced the traditional UNet architecture with the Diffusion Transformer (DiT), and employed flow-matching for improved training and sampling. However, they exhibit limited generation diversity. In this work, we leverage this limitation to perform consistent

Tianbin Li, Yanzhou Su, Wei Li, Bin Fu

Despite significant advancements in general AI, its effectiveness in the medical domain is limited by the lack of specialized medical knowledge. To address this, we formulate GMAI-VL-5.5M, a multimodal medical dataset created by converting hundreds of specialized medical datasets with various annotations into high-quality image-text pairs. This dataset offer

Lei Zhu, Xinjiang Wang, Wayne Zhang, Rynson W. H. Lau

Convolutions (Convs) and multi-head self-attentions (MHSAs) are typically considered alternatives to each other for building vision backbones. Although some works try to integrate both, they apply the two operators simultaneously at the finest pixel granularity. With Convs responsible for per-pixel feature extraction already, the question is whether we still

Hossein Taghinejad, Kohtaro Yamakawa, Xiaoxi Huang, Yuanqi Lyu

Diffusive propagation of spin waves and their quanta - magnons - in the archetypal magnetic insulator yttrium iron garnet (YIG) is under a surge of research for low-power and low-loss data communication. However, operation under external magnetic fields reduces magnon diffusion length, attenuates the voltage amplitude at measurement terminals, and complicate

Jun Xiang, Jun Chen

Safety is extremely important for urban flights of autonomous Unmanned Aerial Vehicles (UAVs). Risk-aware path planning is one of the most effective methods to guarantee the safety of UAVs. This type of planning can be represented as a Constrained Shortest Path (CSP) problem, which seeks to find the shortest route that meets a predefined safety constraint. S

Tanaporn Na Narong, Hongquan Li, Joshua Tong, Mario Dueñas

An inter-combination transition in Yb enables a novel approach for rapidly imaging magnetic field variations with excellent spatial and temporal resolution and accuracy. This quantum imaging magnetometer reveals "dark stripes" that are contours of constant magnetic field visible by eye or capturable by standard cameras. These dark lines result from a combina

Ritwik Gupta, Leah Walker, Andrew W. Reddie

U.S. export controls on semiconductors are widely known to be permeable, with the People's Republic of China (PRC) steadily creating state-of-the-art artificial intelligence (AI) models with exfiltrated chips. This paper presents the first concrete, public evidence of how leading PRC AI labs evade and circumvent U.S. export controls. We examine how Chinese c

Meiyu Zhong, Ravi Tandon

Adversarial training is one of the predominant techniques for training classifiers that are robust to adversarial attacks. Recent work, however has found that adversarial training, which makes the overall classifier robust, it does not necessarily provide equal amount of robustness for all classes. In this paper, we propose the use of mixup for the problem o

Zhuoman Liu, Weicai Ye, Yan Luximon, Pengfei Wan

Realistic simulation of dynamic scenes requires accurately capturing diverse material properties and modeling complex object interactions grounded in physical principles. However, existing methods are constrained to basic material types with limited predictable parameters, making them insufficient to represent the complexity of real-world materials. We intro

Zi-Yu Dong, Teng Ma, Alex Pomarol, Francesco Sciotti

We analyze causality and unitarity constraints in graviton scattering amplitudes, aiming to establish new bounds on theories with $U(1)$-gravitational anomalies, such as axion models or strongly-coupled gauge theories. For this purpose, we show the necessity of coupling these theories to gravity. We obtain a universal scale $\Lambda_{\rm caus}$ at which stat

Shourya Bose, Yijiang Li, Amy Van Sant, Yu Zhang

Accurate short-term energy consumption forecasting for commercial buildings is crucial for smart grid operations. While smart meters and deep learning models enable forecasting using past data from multiple buildings, data heterogeneity from diverse buildings can reduce model performance. The impact of increasing dataset heterogeneity in time series forecast

Luchao Qi, Jiaye Wu, Bang Gong, Annie N. Wang

Facial aging is a complex process, highly dependent on multiple factors like gender, ethnicity, lifestyle, etc., making it extremely challenging to learn a global aging prior to predict aging for any individual accurately. Existing techniques often produce realistic and plausible aging results, but the re-aged images often do not resemble the person's appear

Younghun Roh, Yuanhao Wei, Eric Ruppert, Panagiota Fatourou

Many concurrent algorithms require processes to perform fetch-and-add operations on a single memory location, which can be a hot spot of contention. We present a novel algorithm called Aggregating Funnels that reduces this contention by spreading the fetch-and-add operations across multiple memory locations. It aggregates fetch-and-add operations into batche

Benoit Semelin, Romain Mériot, Ashutosh Mishra, David Cornu

The 21 cm signal from the Epoch of Reionization will be observed with the up-coming Square Kilometer Array (SKA). SKA should yield a full tomography of the signal which opens the possibility to explore its non-Gaussian properties. How can we extract the maximum information from the tomography and derive the tightest constraint on the signal? In this work, in

Lan Jiang, Yuchao Zheng, Miao Yu, Haiqing Zhang

Accurate brain tumor segmentation remains a challenging task due to structural complexity and great individual differences of gliomas. Leveraging the pre-eminent detail resilience of CRF and spatial feature extraction capacity of V-net, we propose a multimodal 3D Volume Generative Adversarial Network (3D-vGAN) for precise segmentation. The model utilizes Pse

Dmitry Khudoteplov, Elena Lanina, Alexey Sleptsov

We develop a method of constructing a kernel of Lie algebra weight system. A main tool we use in the analysis is Vogel's $\Lambda$ algebra and the surrounding framework. As an example of a developed technique we explicitly provide all Jacobi diagrams lying in the kernel of $\mathfrak{sl}_N$ weight system at low orders. We also discuss consequences of the pre

Jiahui Liu, Saachi Mutreja, Henry Yuen

We study a longstanding question of Aaronson and Kuperberg on whether there exists a classical oracle separating $\mathsf{QMA}$ from $\mathsf{QCMA}$. Settling this question in either direction would yield insight into the power of quantum proofs over classical proofs. We show that such an oracle exists if a certain quantum pseudorandomness conjecture holds.

Yishui Zhou, Min-Kai Lee, Sabreen Hammouda, Sheetal Devi

Magnetic kagome metals have attracted tremendous research interests recently, because they represent an ideal playground for exploring the fascinating interplay between their intrinsically inherited topologically non-trivial electron band structures, magnetism and electronic correlation effects, and the resultant novel electronic/magnetic states and emergent

Rongyu Wei, Francesco Albarelli, Jun Li, Vittorio Giovannetti

A Doppler radar is a device that employs the Doppler effect to estimate the radial velocity of a moving target at a distance. Traditional radars are based on a classical description of the electromagnetic radiation, but in principle their performance can be improved employing entangled quantum probe states. For target detection, i.e. hypothesis testing, a qu

Felix Thelen, Rico Zehl, Jan Lukas Bürgel, Diederik Depla

Magnetron sputtering is an essential technique in combinatorial materials science, enabling the efficient synthesis of thin-film materials libraries with continuous compositional gradients. For exploring multidimensional search spaces, minimizing preliminary experiments is essen-tial, as numerous materials libraries are required to adequately cover the space

Satwik Kundu, Swaroop Ghosh

With the growing interest in Quantum Machine Learning (QML) and the increasing availability of quantum computers through cloud providers, addressing the potential security risks associated with QML has become an urgent priority. One key concern in the QML domain is the threat of data poisoning attacks in the current quantum cloud setting. Adversarial access

Ricardo Gama, Ricardo Cunha, Daniel Fuertes, Carlos R. del-Blanco

Research on Reinforcement Learning (RL) approaches for discrete optimization problems has increased considerably, extending RL to areas classically dominated by Operations Research (OR). Vehicle routing problems are a good example of discrete optimization problems with high practical relevance, for which RL techniques have achieved notable success. Despite t

Kenny Peng, Nikhil Garg, Jon Kleinberg

The gold standard in human-AI collaboration is complementarity -- when combined performance exceeds both the human and algorithm alone. We investigate this challenge in binary classification settings where the goal is to maximize 0-1 accuracy. Given two or more agents who can make calibrated probabilistic predictions, we show a "No Free Lunch"-style result.

Shilong Liu, Stéphane Virally, Gabriel Demontigny, Patrick Cusson

Frequency synthesis and spectro-temporal control of optical wave packets are central to ultrafast science, with supercontinuum (SC) generation standing as one remarkable example. Through passive manipulation, femtosecond (fs) pulses from nJ-level lasers can be transformed into octave-spanning spectra, supporting few-cycle pulse outputs when coupled with exte

Yutong Bu, Julianne Chung

Solving large-scale Bayesian inverse problems presents significant challenges, particularly when the exact (discretized) forward operator is unavailable. These challenges often arise in image processing tasks due to unknown defects in the forward process that may result in varying degrees of inexactness in the forward model. Moreover, for many large-scale pr

Jordan A. Gusdorff, Pia Bhatia, Trey T. Shin, Alexandra Sofia Uy-Tioco

Hexagonal boron nitride (hBN) plays a central role in nanoelectronics and nanophotonics. Moreover, hBN hosts room-temperature quantum emitters and optically addressable spins, making it promising for quantum sensing and quantum photonics. Despite many investigations of their optical properties, however, the emitters' chemical structure remains unclear, as do

Parisa Naghdi, Mohammad Mahdi Shiraz Bhurwani, Ahmad Rahmatpour, Parmita Mondal

This study evaluates a multimodal machine learning framework for predicting treatment outcomes in intracranial aneurysms (IAs). Combining angiographic parametric imaging (API), patient biomarkers, and disease morphology, the framework aims to enhance prognostic accuracy. Data from 340 patients were analyzed, with separate deep neural networks processing quan

Yu Zhao, Huifeng Yin, Bo Zeng, Hao Wang

Currently OpenAI o1 sparks a surge of interest in the study of large reasoning models (LRM). Building on this momentum, Marco-o1 not only focuses on disciplines with standard answers, such as mathematics, physics, and coding -- which are well-suited for reinforcement learning (RL) -- but also places greater emphasis on open-ended resolutions. We aim to addre

Ofer Dagan, Tyler Becker, Zachary N. Sunberg

When human operators of cyber-physical systems encounter surprising behavior, they often consider multiple hypotheses that might explain it. In some cases, taking information-gathering actions such as additional measurements or control inputs given to the system can help resolve uncertainty and determine the most accurate hypothesis. The task of optimizing t

Jun Xiang, Drake Essick, Luiz Gonzalez Bautista, Junfei Xie

Models for trajectory prediction are an essential component of many advanced air mobility studies. These models help aircraft detect conflict and plan avoidance maneuvers, which is especially important in Unmanned Aircraft systems (UAS) landing management due to the congested airspace near vertiports. In this paper, we propose a landing trajectory prediction

Enrico Fini, Mustafa Shukor, Xiujun Li, Philipp Dufter

We introduce a novel method for pre-training of large-scale vision encoders. Building on recent advancements in autoregressive pre-training of vision models, we extend this framework to a multimodal setting, i.e., images and text. In this paper, we present AIMV2, a family of generalist vision encoders characterized by a straightforward pre-training process,

Yiming Zhang, Zhuokai Zhao, Zhaorun Chen, Zenghui Ding

Recent advancements in multimodal large language models (MLLMs) have opened new avenues for video understanding. However, achieving high fidelity in zero-shot video tasks remains challenging. Traditional video processing methods rely heavily on fine-tuning to capture nuanced spatial-temporal details, which incurs significant data and computation costs. In co

Martin Matak, Karl Van Wyk, Tucker Hermans

Coordinating the motion of robots with high degrees of freedom (DoF) to grasp objects gives rise to many challenges. In this paper, we propose a novel imitation learning approach to learn a policy that directly predicts 23 DoF grasp trajectories from a partial point cloud provided by a single, fixed camera. At the core of the approach is a second-order geome

Lidia J. Gomes Da Silva

\texttt{DiscoTEX} is a highly accurate numerical algorithm for computing numerical weak-form solutions to distributionally sourced partial differential equations (PDE)s. The aim of this second paper, succeeding \cite{da2024discotex}, is to present its extension up to twelve orders. This will be demonstrated by computing numerical weak-form solutions to the d