March 2024 arXiv papers — page 113

Manuel R. Izquierdo, Miguel Bezares, Steven Liebling, Carlos Palenzuela

The LIGO-Virgo-Kagra collaboration has observed gravitational waves consistent with the mergers of a black hole and a neutron star, namely GW200105 and GW200115, providing evidence for such cataclysmic events. Although no electromagnetic counterpart was reported for either of these two events, under certain conditions black hole--neutron star mergers are exp

Weijian Chen, Maryam Abbasi, Serra Erdamar, Jacob Muldoon

We experimentally study the transient dynamics of a dissipative superconducting qubit under periodic drive towards its nonequilibrium steady states. The corresponding stroboscopic evolution, given by the qubit states at times equal to integer multiples of the drive period, is determined by a (generically non-Hermitian) Floquet Liouvillian. The drive period c

Colin Orion Chandler, Chadwick A. Trujillo, William J. Oldroyd, Jay K. Kueny

We present the Citizen Science program Active Asteroids and describe discoveries stemming from our ongoing project. Our NASA Partner program is hosted on the Zooniverse online platform and launched on 2021 August 31, with the goal of engaging the community in the search for active asteroids -- asteroids with comet-like tails or comae. We also set out to iden

Sophia Yi, Adrien Kuntz, Enrico Barausse, Emanuele Berti

In the aftermath of a binary black hole merger event, the gravitational wave signal emitted by the remnant black hole is modeled as a superposition of damped sinusoids known as quasinormal modes. While the dominant quasinormal modes originating from linear black hole perturbation theory have been studied extensively in this post-merger "ringdown" phase, more

Chengyao Wang, Li Jiang, Xiaoyang Wu, Zhuotao Tian

Self-supervised 3D representation learning aims to learn effective representations from large-scale unlabeled point clouds. Most existing approaches adopt point discrimination as the pretext task, which assigns matched points in two distinct views as positive pairs and unmatched points as negative pairs. However, this approach often results in semantically i

Huan-ang Gao, Mingju Gao, Jiaju Li, Wenyi Li

Semantic image synthesis (SIS) shows good promises for sensor simulation. However, current best practices in this field, based on GANs, have not yet reached the desired level of quality. As latent diffusion models make significant strides in image generation, we are prompted to evaluate ControlNet, a notable method for its dense control capabilities. Our inv

Yuhang Zheng, Xiangyu Chen, Yupeng Zheng, Songen Gu

Constructing a 3D scene capable of accommodating open-ended language queries, is a pivotal pursuit, particularly within the domain of robotics. Such technology facilitates robots in executing object manipulations based on human language directives. To tackle this challenge, some research efforts have been dedicated to the development of language-embedded imp

Haochen Luo, Jindong Gu, Fengyuan Liu, Philip Torr

Different from traditional task-specific vision models, recent large VLMs can readily adapt to different vision tasks by simply using different textual instructions, i.e., prompts. However, a well-known concern about traditional task-specific vision models is that they can be misled by imperceptible adversarial perturbations. Furthermore, the concern is exac

Piotr Nawrot, Adrian Łańcucki, Marcin Chochowski, David Tarjan

Transformers have emerged as the backbone of large language models (LLMs). However, generation remains inefficient due to the need to store in memory a cache of key-value representations for past tokens, whose size scales linearly with the input sequence length and batch size. As a solution, we propose Dynamic Memory Compression (DMC), a method for online ke

Akhil Kedia, Mohd Abbas Zaidi, Sushil Khyalia, Jungho Jung

In spite of their huge success, transformer models remain difficult to scale in depth. In this work, we develop a unified signal propagation theory and provide formulae that govern the moments of the forward and backward signal through the transformer model. Our framework can be used to understand and mitigate vanishing/exploding gradients, rank collapse, an

Lingyi Hong, Shilin Yan, Renrui Zhang, Wanyun Li

Visual object tracking aims to localize the target object of each frame based on its initial appearance in the first frame. Depending on the input modility, tracking tasks can be divided into RGB tracking and RGB+X (e.g. RGB+N, and RGB+D) tracking. Despite the different input modalities, the core aspect of tracking is the temporal matching. Based on this com

Csaba Vincze, Márk Oláh, Ábris Nagy

In the paper we investigate locally symmetric polynomial metrics in special cases of Riemannian and Finslerian surfaces. The Riemannian case will be presented by a collection of basic results (regularity of second root metrics) and formulas up to Gauss curvature. In case of Finslerian surfaces we formulate necessary and sufficient conditions for a locally sy

Yiqun Mei, Yu Zeng, He Zhang, Zhixin Shu

At the core of portrait photography is the search for ideal lighting and viewpoint. The process often requires advanced knowledge in photography and an elaborate studio setup. In this work, we propose Holo-Relighting, a volumetric relighting method that is capable of synthesizing novel viewpoints, and novel lighting from a single image. Holo-Relighting lever

Haoyu Zhen, Xiaowen Qiu, Peihao Chen, Jincheng Yang

Recent vision-language-action (VLA) models rely on 2D inputs, lacking integration with the broader realm of the 3D physical world. Furthermore, they perform action prediction by learning a direct mapping from perception to action, neglecting the vast dynamics of the world and the relations between actions and dynamics. In contrast, human beings are endowed w

Jiazhi Yang, Shenyuan Gao, Yihang Qiu, Li Chen

In this paper, we introduce the first large-scale video prediction model in the autonomous driving discipline. To eliminate the restriction of high-cost data collection and empower the generalization ability of our model, we acquire massive data from the web and pair it with diverse and high-quality text descriptions. The resultant dataset accumulates over 2

Kirill Alpin

A unitary transformation is applied to the Hubbard model, which maps the Hubbard interaction to a single particle term. The resulting Hamiltonian consists of unconstrained fermions, which is then mapped to a Hamiltonian of spinless fermions coupled to pseudospins. The fermions are integrated out using second order perturbation theory in $1/U$, resulting in a

Eric Zelikman, Georges Harik, Yijia Shao, Varuna Jayasiri

When writing and talking, people sometimes pause to think. Although reasoning-focused works have often framed reasoning as a method of answering questions or completing agentic tasks, reasoning is implicit in almost all written text. For example, this applies to the steps not stated between the lines of a proof or to the theory of mind underlying a conversat

Sid Maibach, Eveliina Peltola

The conformal anomaly and the Virasoro algebra are fundamental aspects of 2D conformal field theory and conformally covariant models in planar random geometry. In this article, we explicitly derive the Virasoro algebra from an axiomatization of the conformal anomaly in terms of real determinant lines, one-dimensional vector spaces associated to Riemann surfa

SangEun Han, Igor F. Herbut

The canonical Gross-Neveu model for $N$ two-component Dirac fermions in $2+1$ dimensions suffers a continuous phase transition at a critical interaction $g_{c1} \sim 1/N$ at large $N$, at which its continuous symmetry $\text{SO}(2N)$ is preserved and a discrete (Ising) symmetry becomes spontaneously broken. A recent mean-field calculation, however, points to

Guo Chen, Yifei Huang, Jilan Xu, Baoqi Pei

Understanding videos is one of the fundamental directions in computer vision research, with extensive efforts dedicated to exploring various architectures such as RNN, 3D CNN, and Transformers. The newly proposed architecture of state space model, e.g., Mamba, shows promising traits to extend its success in long sequence modeling to video modeling. To assess

Fangfu Liu, Hanyang Wang, Weiliang Chen, Haowen Sun

Recent years have witnessed the strong power of 3D generation models, which offer a new level of creative flexibility by allowing users to guide the 3D content generation process through a single image or natural language. However, it remains challenging for existing 3D generation methods to create subject-driven 3D content across diverse prompts. In this pa

Nonia Vaquero-Sabater, Abel Carreras, Román Orús, Nicholas J. Mayhall

The Adaptive Derivative-Assembled Pseudo-Trotter Variational Quantum Eigensolver (ADAPT-VQE) has emerged as a pivotal promising approach for electronic structure challenges in quantum chemistry with noisy quantum devices. Nevertheless, to surmount existing technological constraints, this study endeavors to enhance ADAPT-VQE's efficacy. Leveraging insights fr

A. Mishra, G. Mamatsashvili, M. Seilmayer, F. Stefani

The effects of thermal convection on turbulence in accretion discs, and particularly its interplay with the magnetorotational instability (MRI), are of significant astrophysical interest. Despite extensive theoretical and numerical studies, such an interplay has not been explored experimentally. We conduct linear analysis of the azimuthal version of MRI (AMR

Anastasis Stathopoulos, Ligong Han, Dimitris Metaxas

We present Score-Guided Human Mesh Recovery (ScoreHMR), an approach for solving inverse problems for 3D human pose and shape reconstruction. These inverse problems involve fitting a human body model to image observations, traditionally solved through optimization techniques. ScoreHMR mimics model fitting approaches, but alignment with the image observation i

Zeyu Liu, Weicong Liang, Zhanhao Liang, Chong Luo

Visual text rendering poses a fundamental challenge for contemporary text-to-image generation models, with the core problem lying in text encoder deficiencies. To achieve accurate text rendering, we identify two crucial requirements for text encoders: character awareness and alignment with glyphs. Our solution involves crafting a series of customized text en

Zhishuai Liu, Pan Xu

Distributionally robust offline reinforcement learning (RL), which seeks robust policy training against environment perturbation by modeling dynamics uncertainty, calls for function approximations when facing large state-action spaces. However, the consideration of dynamics uncertainty introduces essential nonlinearity and computational burden, posing unique

Vibashan VS, Shubhankar Borse, Hyojin Park, Debasmit Das

In this paper, we introduce an open-vocabulary panoptic segmentation model that effectively unifies the strengths of the Segment Anything Model (SAM) with the vision-language CLIP model in an end-to-end framework. While SAM excels in generating spatially-aware masks, it's decoder falls short in recognizing object class information and tends to oversegment wi

Xiaozhou Feng, Matteo Ippoliti

The dynamics of quantum entanglement plays a central role in explaining the emergence of thermal equilibrium in isolated many-body systems. However, entanglement is notoriously hard to measure. Recent works have introduced a notion of pseudoentanglement describing ensembles of many-body states that, while only weakly entangled, cannot be efficiently distingu

Christian Austin, Sara Pollock, Yunrong Zhu

In this paper, we propose, analyze and demonstrate a dynamic momentum method to accelerate power and inverse power iterations with minimal computational overhead. The method can be applied to real diagonalizable matrices, is provably convergent with acceleration in the symmetric case, and does not require a priori spectral knowledge. We review and extend bac

Marco Bochicchio, Mauro Papinutto, Francesco Scardino

The present paper is the first installment where, extending our previous work in pure Yang-Mills (YM) theory, we compute the generating functional of correlators of collinear twist-$2$ operators that enter the components of balanced superfields -- i.e., superfields with an equal number of dotted and undotted indices in their spinor representation -- in $\mat

Chaoyang Wang, Xiangtai Li, Henghui Ding, Lu Qi

In-context segmentation has drawn increasing attention with the advent of vision foundation models. Its goal is to segment objects using given reference images. Most existing approaches adopt metric learning or masked image modeling to build the correlation between visual prompts and input image queries. This work approaches the problem from a fresh perspect

Yuhan Guo, Hanning Shao, Can Liu, Kai Xu

Generative text-to-image models, which allow users to create appealing images through a text prompt, have seen a dramatic increase in popularity in recent years. However, most users have a limited understanding of how such models work and it often requires many trials and errors to achieve satisfactory results. The prompt history contains a wealth of informa

João Morais, Ahmed Alkhateeb

Localization in outdoor wireless systems typically requires transmitting specific reference signals to estimate distance (trilateration methods) or angle (triangulation methods). These cause overhead on communication, need a LoS link to work well, and require multiple base stations, often imposing synchronization or specific hardware requirements. Fingerprin

Yanlai Yang, Matt Jones, Michael C. Mozer, Mengye Ren

We explore the training dynamics of neural networks in a structured non-IID setting where documents are presented cyclically in a fixed, repeated sequence. Typically, networks suffer from catastrophic interference when training on a sequence of documents; however, we discover a curious and remarkable property of LLMs finetuned sequentially in this setting: t

Jungmin Kim, Nanfang Yu, Zongfu Yu

In the context of visual perception, the optical signal from a scene is transferred into the electronic domain by detectors in the form of image data, which are then processed for the extraction of visual information. In noisy and weak-signal environments such as thermal imaging for night vision applications, however, the performance of neural computing task

Brandon McKinzie, Zhe Gan, Jean-Philippe Fauconnier, Sam Dodge

In this work, we discuss building performant Multimodal Large Language Models (MLLMs). In particular, we study the importance of various architecture components and data choices. Through careful and comprehensive ablations of the image encoder, the vision language connector, and various pre-training data choices, we identified several crucial design lessons.

Patrick L. Combettes, Diego J. Cornejo

In minimization models for image recovery and data analysis problems, loss functions and linear operators are typically aggregated as an average of composite terms. Each term in the aggregate models a desired property of the ideal solution arising from the \emph{a priori} knowledge and the observed data. We propose an alternative minimization model based on

Vincent Blümer, Celal Soyarslan, Ton van den Boogaard

We present a methodology for the generative reconstruction of 3D Volume Elements (VE) for numerical multiscale analysis of Ti-6Al-4V processed by Additive Manufacturing (AM). The basketweave morphology, which is typically dominant in AM-processed Ti-6Al-4V, is analyzed in conventional Electron Backscatter Diffusion (EBSD) micrographs. Prior \b{eta}-grain rec

Jeanne Colbois, Fabien Alet, Nicolas Laflorencie

Despite enormous efforts devoted to the study of the many-body localization (MBL) phenomenon, the nature of the high-energy behavior of the Heisenberg spin chain in a strong random magnetic field is lacking consensus. Here, we take a step back by exploring the weak interaction limit starting from the Anderson localized (AL) insulator. Through shift-invert di

Evgeny Stemasov, Simon Demharter, Max Rädler, Jan Gugenheimer

Extended Reality (XR) allows in-situ previewing of designs to be manufactured through Personal Fabrication (PF). These in-situ interactions exhibit advantages for PF, like incorporating the environment into the design process. However, design-for-fabrication in XR often happens through either highly complex 3D-modeling or is reduced to rudimentary adaptation

Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan

Causal inference has shown potential in enhancing the predictive accuracy, fairness, robustness, and explainability of Natural Language Processing (NLP) models by capturing causal relationships among variables. The emergence of generative Large Language Models (LLMs) has significantly impacted various NLP domains, particularly through their advanced reasonin

Ruixiang Jiang, Lingbo Liu, Changwen Chen

Despite the demonstrated parameter efficiency of prompt-based fusion, its limited adaptivity and expressiveness hinder its effectiveness for multimodal applications at scale. In this paper, we present the first comprehensive study addressing these limitations. Our key motivation is to ``divide and conquer'' the vanilla prompt, traditionally shared across all

Melanie Roschewitz, Fabio De Sousa Ribeiro, Tian Xia, Galvin Khara

Contrastive pretraining is well-known to improve downstream task performance and model generalisation, especially in limited label settings. However, it is sensitive to the choice of augmentation pipeline. Positive pairs should preserve semantic information while destroying domain-specific information. Standard augmentation pipelines emulate domain-specific

Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis, Anastasios Doulamis

Predictions in the form of probability distributions are crucial for effective decision-making. Quantile regression enables such predictions within spatial prediction settings that aim to create improved precipitation datasets by merging remote sensing and gauge data. However, ensemble learning of quantile regression algorithms remains unexplored in this con

Sebastian Engelke, Armeen Taeb

Extremal graphical models encode the conditional independence structure of multivariate extremes and provide a powerful tool for quantifying the risk of rare events. Prior work on learning these graphs from data has focused on the setting where all relevant variables are observed. For the popular class of H\"usler-Reiss models, we propose the \texttt{eglaten

Megha Srivastava, Simran Arora, Dan Boneh

The increasing compute demands of AI systems have led to the emergence of services that train models on behalf of clients lacking necessary resources. However, ensuring correctness of training and guarding against potential training-time attacks, such as data poisoning and backdoors, poses challenges. Existing works on verifiable training largely fall into t

Jian-Song Hong, Su-Qi Zhang, Xin Liu, Xiong-Jun Liu

Non-Abelian anyons have garnered extensive attention for obeying exotic non-Abelian statistics and having potential applications to fault-tolerant quantum computing. While the prior research has predominantly focused on non-Abelian statistics without the necessity of symmetry protection, recent progresses have shown that symmetries can play essential roles a

Fco. Italo G. Carvalho, Raul Victor de O. Paiva, Tarcisio F. Maciel, Victor F. Monteiro

In fifth generation (5G) wireless cellular networks, millimeter wave spectrum opens room for several potential improvements in throughput, reliability, latency, among other aspects. However, it also brings challenges, such as a higher influence of blockage which may significantly limit the coverage. In this context, network-controlled repeaters (NCRs) are ne

Sergio Luigi Cacciatori, Samuel Grushevsky, Alexander A. Voronov

We present a complete computation of superstring scattering amplitudes at tree level, for the case of Neveu-Schwarz insertions. Mathematically, this is to say that we determine explicitly the superstring measure on the moduli space $\mathcal{M}_{0,n,0}$ of super Riemann surfaces of genus zero with $n \ge 3$ Neveu-Schwarz punctures. While, of course, an expre

Gregory Coppola

Given the emergent reasoning abilities of large language models, information retrieval is becoming more complex. Rather than just retrieve a document, modern information retrieval systems advertise that they can synthesize an answer based on potentially many different documents, conflicting data sources, and using reasoning. We review recent literature and a

Ilyass Moummad, Nicolas Farrugia, Romain Serizel, Jeremy Froidevaux

Multi-label imbalanced classification poses a significant challenge in machine learning, particularly evident in bioacoustics where animal sounds often co-occur, and certain sounds are much less frequent than others. This paper focuses on the specific case of classifying anuran species sounds using the dataset AnuraSet, that contains both class imbalance and

Xiaolong Du, Andrew Benson, Zhichao Carton Zeng, Tommaso Treu

The internal structure and abundance of dark matter halos and subhalos are powerful probes of the nature of dark matter. In order to compare observations with dark matter models, accurate theoretical predictions of these quantities are needed. We present a fast and accurate method to describe the tidal evolution of subhalos within their parent halo, based on

Sebastián Barbas Laina, Simon Boche, Sotiris Papatheodorou, Dimos Tzoumanikas

Autonomous navigation is needed for several robotics applications. In this paper we present an autonomous Micro Aerial Vehicle (MAV) system which purely relies on cost-effective and light-weight passive visual and inertial sensors to perform large-scale autonomous navigation in outdoor,unstructured and cluttered environments. We leverage visual-inertial simu

Chetana Jain, Rahul Sharma, Biswajit Paul

We report here results from pulse arrival time delay analysis of the eclipsing high mass X-ray binary pulsar LMC X-4 using observations made with the Rossi X-ray Timing Explorer, XMM-Newton, NuSTAR and AstroSat. Combining the orbital parameters determined from these observations with the historical measurements dating back to 1998, we have extended the $T_{\

Alex Green, Tony Wong, Remy Indebetouw, Omnarayani Nayak

To investigate the effects of stellar feedback on the gravitational state of giant molecular clouds (GMCs), we study $^{12}$CO and $^{13}$CO ALMA maps of nine GMCs distributed throughout the Large Magellanic Cloud (LMC), the nearest star-forming galaxy to our own. We perform noise and resolution matching on the sample, working at a common resolution of 3.5 a

Haiwen Huang, Songyou Peng, Dan Zhang, Andreas Geiger

Names are essential to both human cognition and vision-language models. Open-vocabulary models utilize class names as text prompts to generalize to categories unseen during training. However, the precision of these names is often overlooked in existing datasets. In this paper, we address this underexplored problem by presenting a framework for "renovating" n

Evgeny Stemasov, Tobias Wagner, Ali Askari, Jessica Janek

Hybrid board games (HBGs) augment their analog origins digitally (e.g., through apps) and are an increasingly popular pastime activity. Continuous world and character development and customization, known to facilitate engagement in video games, remain rare in HBGs. If present, they happen digitally or imaginarily, often leaving physical aspects generic. We d

John Rodman, James Juno, Bhuvana Srinivasan

The Rayleigh-Taylor (RT) instability is ubiquitously observed, yet has traditionally been studied using ideal fluid models. Collisionality can vary strongly across the fluid interface, and previous work demonstrates the necessity of kinetic models to completely capture dynamics in certain collisional regimes. Where previous kinetic simulations used spatially

Vlatko Vedral

We solve the infinite potential well problem using the methods of Heisenberg's matrix mechanics. In addition to being of educational value, the matrix mechanics allows us to deal with various unphysical issues caused by this potential in a seemingly unproblematic fashion. We also show how to treat many particles within this representation.

Íñigo Zubeldia, Boris Bolliet

We introduce cosmocnc, a Python package for computing the number count likelihood of galaxy cluster catalogues in a fast, flexible and accurate way. cosmocnc offers three types of likelihoods: an unbinned, a binned, and an extreme value likelihood. It also supports the addition of stacked cluster data, which is modelled consistently with the cluster catalogu

Niket Kathiriya, Hossein Haeri, Cindy Chen, Kshitij Jerath

Many modern systems, such as financial, transportation, and telecommunications systems, are time-sensitive in the sense that they demand low-latency predictions for real-time decision-making. Such systems often have to contend with continuous unbounded data streams as well as concept drift, which are challenging requirements that traditional regression techn

Federico Giannessi, Simone Di Cataldo, Santanu Saha, Lilia Boeri

This paper introduces the HEX (High-pressure Elemental Xstals) database, a complete database of the ground-state crystal structures of the first 57 elements of the periodic table, from H to La, at 0, 100, 200 and 300 GPa. HEX aims to provide a unified reference for high-pressure research, by compiling all available experimental information on elements at hig

U. D. Jentschura, L. T. Giorgini

The recursive Neville algorithm allows one to calculate interpolating functions recursively. Upon a judicious choice of the abscissas used for the interpolation (and extrapolation), this algorithm leads to a method for convergence acceleration. For example, one can use the Neville algorithm in order to successively eliminate inverse powers of the upper limit

Pintu Debnath

In [On $IP^{\star}$sets and central sets, Combinatorica, 14 (1994) 269-277], N. Hindman and V.Bergelson proved additive $IP^{\star}$-sets contain finite sums and finite products of a single sequence. An analogous study was made by A. Sisto in [Exponential triples, Electronics Journal of Combinatorics, 18 (2011), no. 147], where he proved that multiplicative

Tristram de Piro

Given a charge and current distribution with compact support, the associated potentials and fields are generally not integrable in the classical sense. However, it is convenient to be able to define their Fourier transform in order to create solutions to the wave equation. This paper develops the technology for this by considering the class of quasi split no

Runyu Ma, Jelle Luijkx, Zlatan Ajanovic, Jens Kober

In robot manipulation, Reinforcement Learning (RL) often suffers from low sample efficiency and uncertain convergence, especially in large observation and action spaces. Foundation Models (FMs) offer an alternative, demonstrating promise in zero-shot and few-shot settings. However, they can be unreliable due to limited physical and spatial understanding. We

Lukas Gohla, Andreas Thom

For $d \geq 4$ and $p$ a sufficiently large prime, we construct a lattice $\Gamma \leq {\rm PSp}_{2d}(\mathbb Q_p),$ such that its universal central extension cannot be sofic if $\Gamma$ satisfies some weak form of stability in permutations. In the proof, we make use of high-dimensional expansion phenomena and, extending results of Lubotzky, we construct new

Leonidas Liponis

This paper introduces a new method for redefining the Roman factorial using universally applicable functions that are not expressed in closed form. We present a set of foundational functions, similar to Boolean operations, to simplify the factorial expression. Through a systematic process of generalization, termed generalization process, we aim to use these

Dhurim Cakiqi, Max A. Little

Causal identification in causal Bayes nets (CBNs) is an important tool in causal inference allowing the derivation of interventional distributions from observational distributions where this is possible in principle. However, most existing formulations of causal identification using techniques such as d-separation and do-calculus are expressed within the mat

Afrina Tabassum, Dung Tran, Trung Dang, Ismini Lourentzou

Masked Autoencoders (MAEs) learn rich low-level representations from unlabeled data but require substantial labeled data to effectively adapt to downstream tasks. Conversely, Instance Discrimination (ID) emphasizes high-level semantics, offering a potential solution to alleviate annotation requirements in MAEs. Although combining these two approaches can add

Juyoung Jeong, David Sossa

The commutation principle proved by Ram\'irez, Seeger, and Sossa (SIAM J Optim 23:687-694, 2013) in the setting of Euclidean Jordan algebras says that for a Fr\'echet differentiable function $\Theta$ and a spectral function $F$, any local minimizer or maximizer $a$ of $\Theta+F$ over a spectral set $\mathcal{E}$ operator commutes with the gradient of $\Theta

Qunjie Zhou, Maxim Maximov, Or Litany, Laura Leal-Taixé

In this work, we propose the use of Neural Radiance Fields (NeRF) as a scene representation for visual localization. Recently, NeRF has been employed to enhance pose regression and scene coordinate regression models by augmenting the training database, providing auxiliary supervision through rendered images, or serving as an iterative refinement module. We e

J. S. Dowker

Some exact high temperature expansions are derived using a temperature inversion symmetry of the internal energy for conformal scalars and spinors on the Einstein Universe.

Neharika Valecha, Jesus Omar Lacruz, Michael Lentmaier, Joerg Widmer

mmWave communication has come up as the unexplored spectrum for 5G services. With new standards for 5G NR positioning, more off-the-shelf platforms and algorithms are needed to perform indoor positioning. An object can be accurately positioned in a room either by using an angle and a delay estimate or two angle estimates or three delay estimates. We propose

Florian Ginzel, Michael Fellner, Christian Ertler, Lars R. Schreiber

Motivated by the prospect of a two-dimensional square-lattice geometry for semiconductor spin qubits, we explore the realization of the Parity Architecture with quantum dots (QDs). We present sequences of spin shuttling and quantum gates that implement the Parity Quantum Approximate Optimization Algorithm (QAOA) on a lattice constructed of identical unit cel

Mohammad Aali, Jun Liu

Control barrier functions (CBFs) have recently introduced a systematic tool to ensure system safety by establishing set invariance. When combined with a nominal control strategy, they form a safety-critical control mechanism. However, the effectiveness of CBFs is closely tied to the system model. In practice, model uncertainty can compromise safety guarantee

Yunhao Gou, Kai Chen, Zhili Liu, Lanqing Hong

Multimodal large language models (MLLMs) have shown impressive reasoning abilities. However, they are also more vulnerable to jailbreak attacks than their LLM predecessors. Although still capable of detecting the unsafe responses, we observe that safety mechanisms of the pre-aligned LLMs in MLLMs can be easily bypassed with the introduction of image features

Fabio Maresca, Filippo Grazioli, Antonio Albanese, Vincenzo Sciancalepore

The tremendous hype around autonomous driving is eagerly calling for emerging and novel technologies to support advanced mobility use cases. As car manufactures keep developing SAE level 3+ systems to improve the safety and comfort of passengers, traffic authorities need to establish new procedures to manage the transition from human-driven to fully-autonomo

Yunchuan Zhang, Sangwoo Park, Osvaldo Simeone

In many applications, ranging from logistics to engineering, a designer is faced with a sequence of optimization tasks for which the objectives are in the form of black-box functions that are costly to evaluate. Furthermore, higher-fidelity evaluations of the optimization objectives often entail a larger cost. Existing multi-fidelity black-box optimization s

Pei-Xin Shen, Zhide Lu, Jose L. Lado, Mircea Trif

Persistent currents circulate continuously without requiring external power sources. Here, we extend their theory to include dissipation within the framework of non-Hermitian quantum Hamiltonians. Using Green's function formalism, we introduce a non-Hermitian Fermi-Dirac distribution and derive an analytical expression for the persistent current that relies

Ciaràn Rogers, Guido de Marchi, Bernhard Brandl

In this letter we present the first systematic spectroscopic measurements of the Near-InfraRed (NIR) hydrogen recombination lines Paschen alpha ($Pa_{\alpha}$ $\lambda = 1.875 \mu m$) and Brackett beta ($Br_{\beta}$ $\lambda = 2.626 \mu m$), produced by pre-main-sequence (PMS) stars. These stars, consisting of T Tauri and Herbig AeBe stars are located in the

Laura Fernández-Becerra, Miguel Ángel González-Santamarta, Ángel Manuel Guerrero-Higueras, Francisco Javier Rodríguez-Lera

The deployment of autonomous agents in environments involving human interaction has increasingly raised security concerns. Consequently, understanding the circumstances behind an event becomes critical, requiring the development of capabilities to justify their behaviors to non-expert users. Such explanations are essential in enhancing trustworthiness and sa

Ruoshi Liu, Junbang Liang, Sruthi Sudhakar, Huy Ha

Paper is a cheap, recyclable, and clean material that is often used to make practical tools. Traditional tool design either relies on simulation or physical analysis, which is often inaccurate and time-consuming. In this paper, we propose PaperBot, an approach that directly learns to design and use a tool in the real world using paper without human intervent

Ali Nouri, Beatriz Cabrero-Daniel, Fredrik Törner, Hȧkan Sivencrona

DevOps is a necessity in many industries, including the development of Autonomous Vehicles. In those settings, there are iterative activities that reduce the speed of SafetyOps cycles. One of these activities is "Hazard Analysis & Risk Assessment" (HARA), which is an essential step to start the safety requirements specification. As a potential approach to in

Mourad Choulli

We establish near-optimal quantitative uniqueness of continuation for solutions of evolution equations vanishing on the lateral boundary. These results were obtained simply by combining existing observability inequalities and energy estimates.

Ruixuan Liu, Tianhao Wang, Yang Cao, Li Xiong

The pre-training and fine-tuning paradigm has demonstrated its effectiveness and has become the standard approach for tailoring language models to various tasks. Currently, community-based platforms offer easy access to various pre-trained models, as anyone can publish without strict validation processes. However, a released pre-trained model can be a privac

Romane Gros, Omar Rodriguez-Nunez, Leonard Felger, Stefano Moriconi

Neuro-oncological surgery is the primary brain cancer treatment, yet it faces challenges with gliomas due to their invasiveness and the need to preserve neurological function. Hence, radical resection is often unfeasible, highlighting the importance of precise tumor margin delineation to prevent neurological deficits and improve prognosis. Imaging Mueller po

He Zhang, Chang Liu, Zun Wang, Xinran Wei

Predicting the mean-field Hamiltonian matrix in density functional theory is a fundamental formulation to leverage machine learning for solving molecular science problems. Yet, its applicability is limited by insufficient labeled data for training. In this work, we highlight that Hamiltonian prediction possesses a self-consistency principle, based on which w

Nicholas Sung, Liu Zheng, Pingfeng Wang, Faez Ahmed

Our study introduces a Generative AI method that employs a cooling-guided diffusion model to optimize the layout of battery cells, a crucial step for enhancing the cooling performance and efficiency of battery thermal management systems. Traditional design processes, which rely heavily on iterative optimization and extensive guesswork, are notoriously slow a

Zikang Liu, Kun Zhou, Wayne Xin Zhao, Dawei Gao

Visual instruction tuning is the key to building large vision language models~(LVLMs), which can greatly improve the task generalization and solving capabilities by learning a mixture of instruction data from diverse visual tasks. Previous work mostly collects multiple existing visual instruction datasets via heuristic ways for training (even more than a mil

Wan-Zhe Feng, Jinzheng Li, Pran Nath

Production of gravitational waves in the early universe is discussed in a cosmologically consistent analysis within a first order phase transition involving a hidden sector feebly coupled with the visible sector. Each sector resides in its own heat bath leading to a potential dependent on two temperatures, and on two fields: one a standard model Higgs and th

Kristin DeVleming, Lena Ji, Patrick Kennedy-Hunt, Ming Hao Quek

We describe the 6-dimensional compact K-moduli space of Fano threefolds in deformation family No 2.18. These Fano threefolds are double covers of $\mathbb P^1\times\mathbb P^2$ branched along smooth $(2,2)$-surfaces, and Cheltsov--Fujita--Kishimoto--Park proved that any smooth Fano threefold in this family is K-stable. A member of family No 2.18 admits the s

Julien Calbert, Sébastien Mattenet, Antoine Girard, Raphaël M. Jungers

We introduce the concept of memoryless concretization relation (MCR) to describe abstraction within the context of controller synthesis. This relation is a specific instance of alternating simulation relation (ASR), where it is possible to simplify the controller architecture. In the case of ASR, the concretized controller needs to simulate the concurrent ev

David Vokrouhlický, David Nesvorný, Scott Tremaine

Modified Newtonian dynamics (MOND), which postulates a breakdown of Newton's laws of gravity/dynamics below some critical acceleration threshold, can explain many otherwise puzzling observational phenomena on galactic scales. MOND competes with the hypothesis of dark matter, which successfully explains the cosmic microwave background and large-scale structur

Iason Tsardanidis, Alkiviadis Koukos, Vasileios Sitokonstantinou, Thanassis Drivas

Uninterrupted optical image time series are crucial for the timely monitoring of agricultural land changes, particularly in grasslands. However, the continuity of such time series is often disrupted by clouds. In response to this challenge, we propose an innovative deep learning method that integrates cloud-free optical (Sentinel-2) observations and weather-

T. Thongmeearkom, C. J. Clark, R. P. Breton, M. Burgay

Redbacks are millisecond pulsar binaries with low mass, irradiated companions. These systems have a rich phenomenology that can be used to probe binary evolution models, pulsar wind physics, and the neutron star mass distribution. A number of high-confidence redback candidates have been identified through searches for variable optical and X-ray sources withi

Shuai Ma, Xinru Wang, Ying Lei, Chuhan Shi

In AI-assisted decision-making, it is crucial but challenging for humans to achieve appropriate reliance on AI. This paper approaches this problem from a human-centered perspective, "human self-confidence calibration". We begin by proposing an analytical framework to highlight the importance of calibrated human self-confidence. In our first study, we explore

Qiyuan Wang, Yanzhe Liu, Shang Zhao, Rong Liu

For robotic surgical videos, instrument presence annotations are typically recorded with video streams, which offering the potential to reduce the manually annotated costs for segmentation. However, weakly supervised surgical instrument segmentation with only instrument presence labels has been rarely explored in surgical domain due to the highly under-const

Ilona-Stefana Ninca, Ingo Bloch, Ben Bruers, Vitaliy Fadeyev

The breakdown voltage of silicon sensors is known to be affected by the ambient humidity. To understand the sensor's humidity sensitivity, Synopsys TCAD was used to simulate n-in-p sensors for different effective relative humidities. Photon emission of hot electrons was imaged with a microscope to locate breakdown in the edge-region of the sensor. The Top-Tr

Yi-Lun Liao, Tess Smidt, Muhammed Shuaibi, Abhishek Das

Understanding the interactions of atoms such as forces in 3D atomistic systems is fundamental to many applications like molecular dynamics and catalyst design. However, simulating these interactions requires compute-intensive ab initio calculations and thus results in limited data for training neural networks. In this paper, we propose to use denoising non-e