March 2024 arXiv papers — page 32

Wolfgang Oehm, Pavel Kroupa

Simulations of structure formation in the standard cold dark matter cosmological model quantify the dark matter halos of galaxies. Taking into account dynamical friction between the dark matter halos, we investigate the past orbital dynamical evolution of the Magellanic Clouds in the presence of the Galaxy. Our calculations are based on a three-body model of

Abdelrahman Shaker, Syed Talal Wasim, Martin Danelljan, Salman Khan

Recently, transformer-based approaches have shown promising results for semi-supervised video object segmentation. However, these approaches typically struggle on long videos due to increased GPU memory demands, as they frequently expand the memory bank every few frames. We propose a transformer-based approach, named MAVOS, that introduces an optimized and d

Jiamian Wang, Guohao Sun, Pichao Wang, Dongfang Liu

The increasing prevalence of video clips has sparked growing interest in text-video retrieval. Recent advances focus on establishing a joint embedding space for text and video, relying on consistent embedding representations to compute similarity. However, the text content in existing datasets is generally short and concise, making it hard to fully describe

Muhammad Hamza Mughal, Rishabh Dabral, Ikhsanul Habibie, Lucia Donatelli

Gestures play a key role in human communication. Recent methods for co-speech gesture generation, while managing to generate beat-aligned motions, struggle generating gestures that are semantically aligned with the utterance. Compared to beat gestures that align naturally to the audio signal, semantically coherent gestures require modeling the complex intera

Junke Wang, Dongdong Chen, Chong Luo, Bo He

The core of video understanding tasks, such as recognition, captioning, and tracking, is to automatically detect objects or actions in a video and analyze their temporal evolution. Despite sharing a common goal, different tasks often rely on distinct model architectures and annotation formats. In contrast, natural language processing benefits from a unified

Qingping Sun, Yanjun Wang, Ailing Zeng, Wanqi Yin

Expressive human pose and shape estimation (a.k.a. 3D whole-body mesh recovery) involves the human body, hand, and expression estimation. Most existing methods have tackled this task in a two-stage manner, first detecting the human body part with an off-the-shelf detection model and inferring the different human body parts individually. Despite the impressiv

Kashyap Chitta, Daniel Dauner, Andreas Geiger

SLEDGE is the first generative simulator for vehicle motion planning trained on real-world driving logs. Its core component is a learned model that is able to generate agent bounding boxes and lane graphs. The model's outputs serve as an initial state for rule-based traffic simulation. The unique properties of the entities to be generated for SLEDGE, such as

Chhote Lal Shah, Dipanjan Majumdar, Chandan Bose, Sunetra Sarkar

Effects of chord-wise flexibility as an instrument to control chaotic transitions in the wake of a flexible flapping foil have been studied here using an immersed boundary method-based in-house fluid-structure-interaction solver. The ability of the flapping foil at an optimum level of flexibility to inhibit chaotic transition, otherwise encountered in a simi

Yunzhou Song, Jiahui Lei, Ziyun Wang, Lingjie Liu

We propose a novel test-time optimization approach for efficiently and robustly tracking any pixel at any time in a video. The latest state-of-the-art optimization-based tracking technique, OmniMotion, requires a prohibitively long optimization time, rendering it impractical for downstream applications. OmniMotion is sensitive to the choice of random seeds,

Miloš S. Kurilić

$\mathop{\rm rp}\nolimits ({\mathbb B})$ denotes the reduced power ${\mathbb B}^\omega /\Phi$ of a Boolean algebra ${\mathbb B}$, where $\Phi$ is the Fr\'{e}chet filter $\Phi$ on $\omega$. We investigate iterated reduced powers ($\mathop{\rm rp}\nolimits ^0 ({\mathbb B})={\mathbb B}$ and $\mathop{\rm rp}\nolimits ^{n+1} ({\mathbb B} )=\mathop{\rm rp}\nolimit

Eleonora Lopez, Eleonora Grassucci, Debora Capriotti, Danilo Comminiello

Hypercomplex neural networks are gaining increasing interest in the deep learning community. The attention directed towards hypercomplex models originates from several aspects, spanning from purely theoretical and mathematical characteristics to the practical advantage of lightweight models over conventional networks, and their unique properties to capture b

Caleb Lammers, Sam Hadden, Norman Murray

To improve our understanding of orbital instabilities in compact planetary systems, we compare suites of $N$-body simulations against numerical integrations of simplified dynamical models. We show that, surprisingly, dynamical models that account for small sets of resonant interactions between the planets can accurately recover $N$-body instability times. Th

Wei Tao, Yucheng Zhou, Yanlin Wang, Wenqiang Zhang

In software development, resolving the emergent issues within GitHub repositories is a complex challenge that involves not only the incorporation of new code but also the maintenance of existing code. Large Language Models (LLMs) have shown promise in code generation but face difficulties in resolving Github issues, particularly at the repository level. To o

Anoop Kini, Andreas Jansche, Timo Bernthaler, Gerhard Schneider

FastCAR is a novel task consolidation approach in Multi-Task Learning (MTL) for a classification and a regression task, despite task heterogeneity with only subtle correlation. It addresses object classification and continuous property variable regression, a crucial use case in science and engineering. FastCAR involves a labeling transformation approach that

K. Prabhu, S. Raghunathan, M. Millea, G. Lynch

We forecast constraints on cosmological parameters enabled by three surveys conducted with SPT-3G, the third-generation camera on the South Pole Telescope. The surveys cover separate regions of 1500, 2650, and 6000 ${\rm deg}^{2}$ to different depths, in total observing 25% of the sky. These regions will be measured to white noise levels of roughly 2.5, 9, a

Qiyuan He, Jinghao Wang, Ziwei Liu, Angela Yao

Conditional diffusion models can create unseen images in various settings, aiding image interpolation. Interpolation in latent spaces is well-studied, but interpolation with specific conditions like text or poses is less understood. Simple approaches, such as linear interpolation in the space of conditions, often result in images that lack consistency, smoot

Suyanpeng Zhang, Sze-chuan Suen, Han Yu, Maged Dessouky

During the COVID-19 pandemic, there were over three million infections in Los Angeles County (LAC). To facilitate distribution when vaccines first became available, LAC set up six mega-sites for dispensing a large number of vaccines to the public. To understand if another choice of mega-site location would have improved accessibility and health outcomes, and

Henry Towsner

In this paper we give an ordinal analysis of the theory of second order arithmetic. We do this by working with proof trees -- that is, "deductions" which may not be well-founded. Working in a suitable theory, we are able to represent functions on proof trees as yet further proof trees satisfying a suitable analog of well-foundedness. Iterating this process a

Samir Khaki, Konstantinos N. Plataniotis

We introduce the $\textbf{O}$ne-shot $\textbf{P}$runing $\textbf{T}$echnique for $\textbf{I}$nterchangeable $\textbf{N}$etworks ($\textbf{OPTIN}$) framework as a tool to increase the efficiency of pre-trained transformer architectures $\textit{without requiring re-training}$. Recent works have explored improving transformer efficiency, however often incur co

Sherwin Bahmani, Xian Liu, Wang Yifan, Ivan Skorokhodov

Recent techniques for text-to-4D generation synthesize dynamic 3D scenes using supervision from pre-trained text-to-video models. However, existing representations for motion, such as deformation models or time-dependent neural representations, are limited in the amount of motion they can generate-they cannot synthesize motion extending far beyond the boundi

Rui Pan, Xiang Liu, Shizhe Diao, Renjie Pi

The machine learning community has witnessed impressive advancements since large language models (LLMs) first appeared. Yet, their massive memory consumption has become a significant roadblock to large-scale training. For instance, a 7B model typically requires at least 60 GB of GPU memory with full parameter training, which presents challenges for researche

Longtao Zheng, Zhiyuan Huang, Zhenghai Xue, Xinrun Wang

General virtual agents need to handle multimodal observations, master complex action spaces, and self-improve in dynamic, open-domain environments. However, existing environments are often domain-specific and require complex setups, which limits agent development and evaluation in real-world settings. As a result, current evaluations lack in-depth analyses t

Devansh R. Agrawal, Dimitra Panagou

This paper presents two algorithms for multi-agent dynamic coverage in spatiotemporal environments, where the coverage algorithms are informed by the method of data assimilation. In particular, we show that by explicitly modeling the environment using a Gaussian Process (GP) model, and considering the sensing capabilities and the dynamics of a team of robots

Zehao Wang, Yuping Wang, Zhuoyuan Wu, Hengbo Ma

The confluence of the advancement of Autonomous Vehicles (AVs) and the maturity of Vehicle-to-Everything (V2X) communication has enabled the capability of cooperative connected and automated vehicles (CAVs). Building on top of cooperative perception, this paper explores the feasibility and effectiveness of cooperative motion prediction. Our method, CMP, take

Akshay Paruchuri, Samuel Ehrenstein, Shuxian Wang, Inbar Fried

Monocular depth estimation in endoscopy videos can enable assistive and robotic surgery to obtain better coverage of the organ and detection of various health issues. Despite promising progress on mainstream, natural image depth estimation, techniques perform poorly on endoscopy images due to a lack of strong geometric features and challenging illumination e

Xinyu Zhao, Hao Yan, Yongming Liu

A large volume of accident reports is recorded in the aviation domain, which greatly values improving aviation safety. To better use those reports, we need to understand the most important events or impact factors according to the accident reports. However, the increasing number of accident reports requires large efforts from domain experts to label those re

Asad Mahmood, Thang X. Vu, Symeon Chatzinotas, Björn Ottersten

This work investigates the application of Beyond Diagonal Intelligent Reflective Surface (BD-IRS) to enhance THz downlink communication systems, operating in a hybrid: reflective and transmissive mode, to simultaneously provide services to indoor and outdoor users. We propose an optimization framework that jointly optimizes the beamforming vectors and phase

Ang Yang, Jinlou Ma, Lei Ying

The conventional wisdom suggests that transports of conserved quantities in non-integrable quantum many-body systems at high temperatures are diffusive. However, we discover a counterexample of this paradigm by uncovering anomalous hydrodynamics in a spin-1/2 XXZ chain with power-law couplings. This model, classified as non-integrable due to its Wigner-Dyson

Constance Ferragu, Philomene Chagniot, Vincent Coyette

In recent literature, few-shot classification has predominantly been defined by the N-way k-shot meta-learning problem. Models designed for this purpose are usually trained to excel on standard benchmarks following a restricted setup, excluding the use of external data. Given the recent advancements in large language and vision models, a question naturally a

Mundankulu Kabongo

The functional relation of the Riemann z\^eta function provides us with neither the nature nor the expression of z\^eta at positive odd numbers. From the function $F(z)=\frac{z^{-2n}}{e^z-1}$, we find a functional relation involving $\zeta(4n- 1)$, $\zeta(2p)$ and $\zeta(4n-1-2p)$. It is given by: \begin{equation} \zeta(4n-1)=\frac{1}{2n-1}\sum_{p=1}^{2n-2}\

Sachita Nishal, Charlotte Li, Nicholas Diakopoulos

News organizations today rely on AI tools to increase efficiency and productivity across various tasks in news production and distribution. These tools are oriented towards stakeholders such as reporters, editors, and readers. However, practitioners also express reservations around adopting AI technologies into the newsroom, due to the technical and ethical

Hong Liu, Chong Shangguan, Jozef Skokan, Zixiang Xu

We establish a novel connection between the well-known chromatic threshold problem in extremal combinatorics and the celebrated $(p,q)$-theorem in discrete geometry. In particular, for a graph $G$ with bounded clique number and a natural density condition, we prove a $(p,q)$-theorem for an abstract convexity space associated with $G$. Our result strengthens

Mubashir Noman, Mustansar Fiaz, Hisham Cholakkal, Salman Khan

Deep learning has shown remarkable success in remote sensing change detection (CD), aiming to identify semantic change regions between co-registered satellite image pairs acquired at distinct time stamps. However, existing convolutional neural network and transformer-based frameworks often struggle to accurately segment semantic change regions. Moreover, tra

Erik G. Larsson, Joao Vieira, Pål Frenger

We present a reciprocity calibration method for dual-antenna repeaters in wireless networks. The method uses bi-directional measurements between two network nodes, A and B, where for each bi-directional measurement, the repeaters are configured in different states. The nodes A and B could be two access points in a distributed MIMO system, or they could be a

Sarper Aydın, Orhan Eren Akgün, Stephanie Gil, Angelia Nedić

In this work, we consider the consensus problem in which legitimate agents share their values over an undirected communication network in the presence of malicious or faulty agents. Different from the previous works, we characterize the conditions that generalize to several scenarios such as intermittent faulty or malicious transmissions, based on trust obse

Anton Alekseev, Andrew Neitzke, Xiaomeng Xu, Yan Zhou

We consider an $n\times n$ system of ODEs on $\mathbb{P}^1$ with a simple pole $A$ at $z=0$ and a double pole $u={\rm diag}(u_1, \dots, u_n)$ at $z=\infty$. This is the simplest situation in which the monodromy data of the system are described by upper and lower triangular Stokes matrices $S_\pm$, and we impose reality conditions which imply $S_-=S_+^\dagger

Yiwei Chen, Chao Tang, Amir Aghabiglou, Chung San Chu

We propose a new approach for non-Cartesian magnetic resonance image reconstruction. While unrolled architectures provide robustness via data-consistency layers, embedding measurement operators in Deep Neural Network (DNN) can become impractical at large scale. Alternative Plug-and-Play (PnP) approaches, where the denoising DNNs are blind to the measurement

Manuel Saavedra, Manuel Stadlbauer

We study recurrent operators from a new perspective by introducing the notion of hyper-recurrent operators and establish robust connections with quasi-rigid operators. For example, we prove that a recurrent operator on a separable Banach space is quasi-rigid if and only if it is a linear factor of a hyper-recurrent operator, and show that the quasi-rigid ope

Xander Byrne, Romain A. Meyer, Emanuele Paolo Farina, Eduardo Bañados

Of the hundreds of $z\gtrsim6$ quasars discovered to date, only one is known to be gravitationally lensed, despite the high lensing optical depth expected at $z\gtrsim6$. High-redshift quasars are typically identified in large-scale surveys by applying strict photometric selection criteria, in particular by imposing non-detections in bands blueward of the Ly

Mohammad Shahab Sepehri, Zalan Fabian, Mahdi Soltanolkotabi

The landscape of computational building blocks of efficient image restoration architectures is dominated by a combination of convolutional processing and various attention mechanisms. However, convolutional filters, while efficient, are inherently local and therefore struggle with modeling long-range dependencies in images. In contrast, attention excels at c

Bhaskar Mitra

Information retrieval (IR) research must understand and contend with the social implications of the technology it produces. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by div

Martin Donati, Ludovic Godard-Cadillac, Dragos Iftimie

In this paper, we study the point-vortex dynamics with positive intensities. We show that in the half-plane and in a disk, collapses of point vortices with the boundary in finite time are impossible, hence the solution of the dynamics is global in time. We also give some necessary conditions for the existence of collapses with the boundary in general smooth

Simone Loreti, Margreth Keiler, Andreas Zischg

While a social event, such as a concert or a food festival, is a common experience to people, a natural disaster is experienced by a fewer individuals. The ordinary and common ground experience of social events could be therefore used to better understand the complex impacts of uncommon, but devastating natural events on society, such as floods. Based on thi

Kerui Ren, Lihan Jiang, Tao Lu, Mulin Yu

The recent 3D Gaussian splatting (3D-GS) has shown remarkable rendering fidelity and efficiency compared to NeRF-based neural scene representations. While demonstrating the potential for real-time rendering, 3D-GS encounters rendering bottlenecks in large scenes with complex details due to an excessive number of Gaussian primitives located within the viewing

Sérgio R. A. Gomes, Alexandre C. M. Correia

At present, the main satellites of Uranus are not involved in any low order mean motion resonance (MMR). However, owing to tides raised in the planet, Ariel and Umbriel most likely crossed the 5/3 MMR in the past. Previous studies on this resonance passage relied on limited time-consuming $N-$body simulations or simplified models focusing solely on the effec

Sérgio R. A. Gomes, Alexandre C. M. Correia

Mutual gravitational interactions between the five major Uranian satellites raise small quasi-periodic fluctuations on their orbital elements. At the same time, tidal interactions between the satellites and the planet induce a slow outward drift of the orbits, while damping the eccentricities and the inclinations. In this paper, we revisit the current and ne

Evgeni Dimitrov, Alisa Knizel

We introduce a two-parameter family of probability distributions, indexed by $\beta/2 = \theta > 0$ and $K \in \mathbb{Z}_{\geq 0}$, that are called $\beta$-Krawtchouk corners processes. These measures are related to Jack symmetric functions, and can be thought of as integrable discretizations of $\beta$-corners processes from random matrix theory, or altern

Samuele Fracassi, Simone Traverso, Niccolò Traverso Ziani, Matteo Carrega

The simultaneous breaking of time-reversal and inversion symmetry can lead to peculiar effects in Josephson junctions, such as the anomalous Josephson effect or supercurrent rectification, which is a dissipationless analog of the diode effect. Due to their impact in new quantum technologies, it is important to find robust platforms and external means to mani

Md Mushfiqur Azam, Kevin Desai

Egocentric human pose estimation aims to estimate human body poses and develop body representations from a first-person camera perspective. It has gained vast popularity in recent years because of its wide range of applications in sectors like XR-technologies, human-computer interaction, and fitness tracking. However, to the best of our knowledge, there is n

Jesse Atuhurra, Hiroyuki Shindo, Hidetaka Kamigaito, Taro Watanabe

Many attempts have been made in multilingual NLP to ensure that pre-trained language models, such as mBERT or GPT2 get better and become applicable to low-resource languages. To achieve multilingualism for pre-trained language models (PLMs), we need techniques to create word embeddings that capture the linguistic characteristics of any language. Tokenization

Patrick Darwinkel

We explored leveraging state-of-the-art deep learning, big data, and natural language processing to enhance the detection of vulnerable web server versions. Focusing on improving accuracy and specificity over rule-based systems, we conducted experiments by sending various ambiguous and non-standard HTTP requests to 4.77 million domains and capturing HTTP res

Nurettin Sergin, Jiayu Huang, Tzyy-Shuh Chang, Hao Yan

One important characteristic of modern fault classification systems is the ability to flag the system when faced with previously unseen fault types. This work considers the unknown fault detection capabilities of deep neural network-based fault classifiers. Specifically, we propose a methodology on how, when available, labels regarding the fault taxonomy can

Bruno Bertrand, Pascale Defraigne, Aurélien Hees, Alexandra Sheremet

This study presents bounds on transient variations of fundamental constants, with typical timescales ranging from minutes to months, using clocks in space. The underlying phenomenology describing such transient variations relies on models for Dark Matter (DM) which suggest possible encounters of macroscopic compact objects with the Earth, due to the motion o

Henry Kenlay, Frédéric A. Dreyer, Aleksandr Kovaltsuk, Dom Miketa

Antibodies are proteins produced by the immune system that can identify and neutralise a wide variety of antigens with high specificity and affinity, and constitute the most successful class of biotherapeutics. With the advent of next-generation sequencing, billions of antibody sequences have been collected in recent years, though their application in the de

Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger

3D Gaussian Splatting (3DGS) has recently revolutionized radiance field reconstruction, achieving high quality novel view synthesis and fast rendering speed without baking. However, 3DGS fails to accurately represent surfaces due to the multi-view inconsistent nature of 3D Gaussians. We present 2D Gaussian Splatting (2DGS), a novel approach to model and reco

Andrey Gromov, Kushal Tirumala, Hassan Shapourian, Paolo Glorioso

How is knowledge stored in an LLM's weights? We study this via layer pruning: if removing a certain layer does not affect model performance in common question-answering benchmarks, then the weights in that layer are not necessary for storing the knowledge needed to answer those questions. To find these unnecessary parameters, we identify the optimal block of

Carlos Gomes, Thomas Brunschwiler

As repositories of large scale data in earth observation (EO) have grown, so have transfer and storage costs for model training and inference, expending significant resources. We introduce Neural Embedding Compression (NEC), based on the transfer of compressed embeddings to data consumers instead of raw data. We adapt foundation models (FM) through learned n

Umesh Bhatt, Sarvesh Pandey

The Ethereum Improvement Proposal 3675 (EIP-3675) marks a significant shift, transitioning from a Proof of Work (PoW) to a Proof of Stake (PoS) consensus mechanism. This transition resulted in a staggering 99.95% decrease in energy consumption. However, the transition prompts two critical questions: (1). How does EIP-3675 affect miners' dynamics? and (2). Ho

Yonghao Xu, Amanda Berg, Leif Haglund

Utilizing satellite imagery for wildfire detection presents substantial potential for practical applications. To advance the development of machine learning algorithms in this domain, our study introduces the \textit{Sen2Fire} dataset--a challenging satellite remote sensing dataset tailored for wildfire detection. This dataset is curated from Sentinel-2 mult

Chao Liang, Jianwen Jiang, Tianyun Zhong, Gaojie Lin

Talking face generation technology creates talking videos from arbitrary appearance and motion signal, with the "arbitrary" offering ease of use but also introducing challenges in practical applications. Existing methods work well with standard inputs but suffer serious performance degradation with intricate real-world ones. Moreover, efficiency is also an i

Qingyang Zhang

Pearson's Chi-squared test, though widely used for detecting association between categorical variables, exhibits low statistical power in large sparse contingency tables. To address this limitation, two novel permutation tests have been recently developed: the distance covariance permutation test and the U-statistic permutation test. Both leverage the distan

Gan Pei, Jiangning Zhang, Menghan Hu, Zhenyu Zhang

Deepfake is a technology dedicated to creating highly realistic facial images and videos under specific conditions, which has significant application potential in fields such as entertainment, movie production, digital human creation, to name a few. With the advancements in deep learning, techniques primarily represented by Variational Autoencoders and Gener

Peter K. S. Dunsby, Orlando Luongo, Marco Muccino, Vineshree Pillay

We consider a double polytropic cosmological fluid and demonstrate that, when one constituent resembles a bare cosmological constant while the other emulates a generalized Chaplygin gas, a good description of the Universe's large-scale dynamics is obtained. In particular, our double polytropic reduces to the Murnaghan equation of state, whose applications ar

Qiqi Hou, Farzad Farhadzadeh, Amir Said, Guillaume Sautiere

The rise of new video modalities like virtual reality or autonomous driving has increased the demand for efficient multi-view video compression methods, both in terms of rate-distortion (R-D) performance and in terms of delay and runtime. While most recent stereo video compression approaches have shown promising performance, they compress left and right view

Haoyuan Li, Salman Toor

The evolution of data architecture has seen the rise of data lakes, aiming to solve the bottlenecks of data management and promote intelligent decision-making. However, this centralized architecture is limited by the proliferation of data sources and the growing demand for timely analysis and processing. A new data paradigm, Data Mesh, is proposed to overcom

Dipayan Chakraborty, Florent Foucaud, Michael A. Henning, Tuomo Lehtilä

An $\textit{identifying code}$ of a closed-twin-free graph $G$ is a set $S$ of vertices of $G$ such that any two vertices in $G$ have a distinct intersection between their closed neighborhood and $S$. It was conjectured that there exists a constant $c$ such that for every connected closed-twin-free graph $G$ of order $n$ and maximum degree $\Delta$, the grap

Andreea Iana, Goran Glavaš, Heiko Paulheim

Digital news platforms use news recommenders as the main instrument to cater to the individual information needs of readers. Despite an increasingly language-diverse online community, in which many Internet users consume news in multiple languages, the majority of news recommendation focuses on major, resource-rich languages, and English in particular. Moreo

Zhesheng Liu, Mihail Zervos

We consider a stochastic impulse control problem that is motivated by applications such as the optimal exploitation of a natural resource. In particular, we consider a stochastic system whose uncontrolled state dynamics are modelled by a non-explosive positive linear diffusion. The control that can be applied to this system takes the form of one-sided impuls

Philip Sosnin, Calvin Tsay

Neural networks offer a computationally efficient approximation of model predictive control, but they lack guarantees on the resulting controlled system's properties. Formal certification of neural networks is crucial for ensuring safety, particularly in safety-critical domains such as autonomous vehicles. One approach to formally certify properties of neura

Andrea Ferrario, Alberto Termine, Alessandro Facchini

Human-centered explainable AI (HCXAI) advocates for the integration of social aspects into AI explanations. Central to the HCXAI discourse is the Social Transparency (ST) framework, which aims to make the socio-organizational context of AI systems accessible to their users. In this work, we suggest extending the ST framework to address the risks of social mi

Marc Coppens

For a chain of cycles $\Gamma$ we prove Cliff($\Gamma$)=gon($\Gamma$)-2.

Marco Fava

Extending the definition of $V$-stability conditions, given by Viviani in a recent preprint, we introduce the notion of universal stability conditions. Building on results by Pagani and Tommasi, we show that fine compactified universal Jacobians, that is, fine compactified Jacobians over the moduli spaces of stable pointed curves $\overline{\mathcal{M}}_{g,n

Yurui Qian, Qi Cai, Yingwei Pan, Yehao Li

Diffusion models have recently brought a powerful revolution in image generation. Despite showing impressive generative capabilities, most of these models rely on the current sample to denoise the next one, possibly resulting in denoising instability. In this paper, we reinterpret the iterative denoising process as model optimization and leverage a moving av

Souhail Hadgi, Lei Li, Maks Ovsjanikov

Transfer learning has long been a key factor in the advancement of many fields including 2D image analysis. Unfortunately, its applicability in 3D data processing has been relatively limited. While several approaches for point cloud transfer learning have been proposed in recent literature, with contrastive learning gaining particular prominence, most existi

Hao-Chung Cheng, Nilanjana Datta, Nana Liu, Theshani Nuradha

Quantum hypothesis testing (QHT) has been traditionally studied from the information-theoretic perspective, wherein one is interested in the optimal decay rate of error probabilities as a function of the number of samples of an unknown state. In this paper, we study the sample complexity of QHT, wherein the goal is to determine the minimum number of samples

Alexander Hazeltine

The Adams conjecture states that the local theta correspondence sends a local Arthur packet to another local Arthur packet. M{\oe}glin confirmed the conjecture when lifting to groups of sufficiently high rank and also showed that it fails in low rank. Recently, Baki\'c and Hanzer described when the Adams conjecture holds in low rank for a representation in a

Jonas Larson

In this paper, we analyze the harmonically driven Jaynes-Cummings and Lipkin-Meshkov-Glick models using both numerical integration of time-dependent Hamiltonians and Floquet theory. For a separation of time-scales between the drive and intrinsic Rabi oscillations in the former model, the driving results in an effective periodic reversal of time. The correspo

Grigory Uskov, Sergey Sazonov, Marat Gilfanov, Igor Lapshov

In the fall of 2019, during the in-flight calibration phase of the SRG observatory, the onboard eROSITA and Mikhail Pavlinsky ART-XC telescopes carried out a series of observations of PG 1634+706 - one of the most luminous (an X-ray luminosity $\sim 10^{46}$ erg/s) quasars in the Universe at $z<2$. Approximately at the same dates this quasar was also observe

Francesca Ronchini, Luca Comanducci, Fabio Antonacci

In recent years, text-to-audio models have revolutionized the field of automatic audio generation. This paper investigates their application in generating synthetic datasets for training data-driven models. Specifically, this study analyzes the performance of two environmental sound classification systems trained with data generated from text-to-audio models

Chulhong Min, Utku Günay Acer, SiYoung Jang, Sangwon Choi

The miniaturization of AI accelerators is paving the way for next-generation wearable applications within wearable technologies. We introduce Mojito, an AI-native runtime with advanced MLOps designed to facilitate the development and deployment of these applications on wearable devices. It emphasizes the necessity of dynamic orchestration of distributed reso

Frédéric Lang, Matthias Volk

This volume contains the proceedings of MARS 2024, the sixth workshop on Models for Formal Analysis of Real Systems, held as part of ETAPS 2024, the European Joint Conferences on Theory and Practice of Software. The MARS workshops bring together researchers from different communities who are developing formal models of real systems in areas where complex mod

Daniel Arnström, André M. H. Teixeira

Safety filters ensure that only safe control actions are executed. We propose a simple and stealthy false-data injection attack for deactivating such safety filters; in particular, we focus on deactivating safety filters that are based on control-barrier functions. The attack injects false sensor measurements to bias state estimates to the interior of a safe

Philip Lippmann, Matthijs T. J. Spaan, Jie Yang

Language models (LMs) have achieved impressive accuracy across a variety of tasks but remain vulnerable to high-confidence misclassifications, also referred to as unknown unknowns (UUs). These UUs cluster into blind spots in the feature space, leading to significant risks in high-stakes applications. This is particularly relevant for smaller, lightweight LMs

Bhawna Piryani, Jamshid Mozafari, Adam Jatowt

Question answering (QA) and Machine Reading Comprehension (MRC) tasks have significantly advanced in recent years due to the rapid development of deep learning techniques and, more recently, large language models. At the same time, many benchmark datasets have become available for QA and MRC tasks. However, most existing large-scale benchmark datasets have b

Josu Etxezarreta Martinez, Olatz Sanz Larrarte, Javier Oliva del Moral, Reza Dastbasteh

Zero-noise extrapolation (ZNE) stands as the most widespread quantum error mitigation technique in order to aim the recovery of noise-free expectation values of observables of interest by means of Noisy Intermediate-Scale Quantum (NISQ) machines. Recently, Otten and Gray proposed a multidimensional generalization of polynomial ZNE for systems where there is

Daniel Spitz

Considering homogeneous four-dimensional space-time geometries within real projective geometry provides a mathematically well-defined framework to discuss their deformations and limits without the appearance of coordinate singularities. On Lie algebra level the related conjugacy limits act isomorphically to concatenations of contractions. We axiomatically in

Léo Simpson, Jonas Asprion, Simon Muntwiler, Johannes Köhler

This paper introduces a novel optimization-based approach for parametric nonlinear system identification. Building upon the prediction error method framework, traditionally used for linear system identification, we extend its capabilities to nonlinear systems. The predictions are computed using a moving horizon state estimator with a constant arrival cost. E

Roberta Bianchini, Michele Coti Zelati, Lucas Ertzbischoff

We investigate the hydrostatic approximation for inviscid stratified fluids, described by the two-dimensional Euler-Boussinesq equations in a periodic channel. Through a perturbative analysis of the hydrostatic homogeneous setting, we exhibit a stratified steady state violating the Miles-Howard criterion and generating a growing mode, both for the linearized

David R. Mortensen, Valentina Izrailevitch, Yunze Xiao, Hinrich Schütze

Lexical-syntactic flexibility, in the form of conversion (or zero-derivation) is a hallmark of English morphology. In conversion, a word with one part of speech is placed in a non-prototypical context, where it is coerced to behave as if it had a different part of speech. However, while this process affects a large part of the English lexicon, little work ha

C. A. Lindstrøm, J. Beinortaitė, J. Björklund Svensson, L. Boulton

Radio-frequency particle accelerators are engines of discovery, powering high-energy physics and photon science, but are also large and expensive due to their limited accelerating fields. Plasma-wakefield accelerators (PWFAs) provide orders-of-magnitude stronger fields in the charge-density wave behind a particle bunch travelling in a plasma, promising parti

Chenning Tong

We propose a phenomenological model for thermal convection at high Rayleigh numbers. It hypothesizes existence of a high-Reynolds-number turbulent boundary layer near each horizontal plate, which is shown to be convective. The convective logarithmic friction law of Tong and Ding (2020) is used to relate the large-scale velocity to the induced friction veloci

Connor Pryor, Quan Yuan, Jeremiah Liu, Mehran Kazemi

Dialog Structure Induction (DSI) is the task of inferring the latent dialog structure (i.e., a set of dialog states and their temporal transitions) of a given goal-oriented dialog. It is a critical component for modern dialog system design and discourse analysis. Existing DSI approaches are often purely data-driven, deploy models that infer latent states wit

Shuyi Chen, Shixiang Zhu

Machine learning models have shown exceptional prowess in solving complex issues across various domains. However, these models can sometimes exhibit biased decision-making, resulting in unequal treatment of different groups. Despite substantial research on counterfactual fairness, methods to reduce the impact of multivariate and continuous sensitive variable

Ignacio Soto, Maria Calderon, Oscar Amador, Manuel Urueña

In recent years, the use of cellular network technologies to provide communication-based applications to vehicles has received considerable attention. 3GPP, the standardization body responsible for cellular networks specifications, is developing technologies to meet the requirements of vehicular communication applications, and the research community is testi

Nicolo Gusmeroli, Andrea Bettinelli

The shift design and the personnel scheduling problem is known to be a difficult problem. It is a real-world problem which has lots of applications in the organization of companies. Solutions are usually found by dividing the problem in two steps: first the shifts are created, then the employees are assigned to them by respecting a bunch of constraints. The

Drew Scott, Satyanarayana G. Manyam, David W. Casbeer, Manish Kumar

Multi Agent Path Finding (MAPF) seeks the optimal set of paths for multiple agents from respective start to goal locations such that no paths conflict. We address the MAPF problem for a fleet of hybrid-fuel unmanned aerial vehicles which are subject to location-dependent noise restrictions. We solve this problem by searching a constraint tree for which the s

Abdelrahman Abdallah, Mahmoud Kasem, Mahmoud Abdalla, Mohamed Mahmoud

In this paper, we address the significant gap in Arabic natural language processing (NLP) resources by introducing ArabicaQA, the first large-scale dataset for machine reading comprehension and open-domain question answering in Arabic. This comprehensive dataset, consisting of 89,095 answerable and 3,701 unanswerable questions created by crowdworkers to look

Chia-Hao Chiang, Zheng-Han Huang, Liwen Liu, Hsin-Chien Liang

Human activities accelerate consumption of fossil fuels and produce greenhouse gases, resulting in urgent issues today: global warming and the climate change. These indirectly cause severe natural disasters, plenty of lives suffering and huge losses of agricultural properties. To mitigate impacts on our lands, scientists are developing renewable, reusable, a

Abdelrhman Werby, Chenguang Huang, Martin Büchner, Abhinav Valada

Recent open-vocabulary robot mapping methods enrich dense geometric maps with pre-trained visual-language features. While these maps allow for the prediction of point-wise saliency maps when queried for a certain language concept, large-scale environments and abstract queries beyond the object level still pose a considerable hurdle, ultimately limiting langu

Antoine Théberge, Maxime Descoteaux, Pierre-Marc Jodoin

Reinforcement learning (RL)-based tractography is a competitive alternative to machine learning and classical tractography algorithms due to its high anatomical accuracy obtained without the need for any annotated data. However, the reward functions so far used to train RL agents do not encapsulate anatomical knowledge which causes agents to generate spuriou