May 2024 arXiv papers — page 56

Saeed Azad, Suraj Khanal, Daniel R. Herber, Gaofeng Jia

A promising direction towards reducing the levelized cost of energy for wave energy converter (WEC) farms is to improve their performance. WEC design studies generally focus on a single design domain (e.g., geometry, control, or layout) to improve the farm's performance under simplifying assumptions, such as regular waves. This strategy, however, has resulte

Adam Baybutt

We motivate the study of the crypto asset class with eleven empirical facts, and study the drivers of crypto asset returns through the lens of univariate factors. We argue crypto assets are a new, attractive, and independent asset class. In a novel and rigorously built panel of crypto assets, we examine pricing ability of sixty three asset characteristics to

Enrico Maria Renzi, Emanuele Galiffi, Xiang Ni, Andrea Alù

Polar dielectrics with low crystal symmetry and sharp phonon resonances can support hyperbolic shear polaritons - highly confined surface modes with frequency-dependent optical axes and asymmetric dissipation features. So far, these modes have been observed only in bulk natural materials at mid-infrared frequencies, with properties limited by available cryst

Inwon Kim, Antoine Mellet, Jeremy Sheung-Him Wu

This paper addresses congested transport, which can be described, at macroscopic scales, by a continuity equation with a pressure variable generated from the hard-congestion constraint (maximum value of the density). The main goal of the paper is to show that, in one spatial dimension, this continuum PDE can be derived as the mean-field limit of a system of

M. Mattern, Ph. André, A. Zavagno, D. Russeil

Despite recent progress, the question of what regulates the star formation efficiency in galaxies remains one of the most debated problems in astrophysics. According to the dominant picture, star formation (SF) is regulated by turbulence and feedback, and the SFE is 1-2% per local free-fall time. In an alternate scenario, the SF rate in galactic disks is lin

Blake Bordelon, Hamza Tahir Chaudhry, Cengiz Pehlevan

In this work, we analyze various scaling limits of the training dynamics of transformer models in the feature learning regime. We identify the set of parameterizations that admit well-defined infinite width and depth limits, allowing the attention layers to update throughout training--a relevant notion of feature learning in these models. We then use tools f

Ryan Donald, Brendan Hertel, Stephen Misenti, Yan Gu

Robot skill learning and execution in uncertain and dynamic environments is a challenging task. This paper proposes an adaptive framework that combines Learning from Demonstration (LfD), environment state prediction, and high-level decision making. Proactive adaptation prevents the need for reactive adaptation, which lags behind changes in the environment ra

A. V. Guglielmi, B. I. Klain

After the main shock of an earthquake the aftershocks are observed. According to Omori's law, the frequency of aftershocks decreases hyperbolically over time. We noticed that, strictly speaking, Omori's law paradoxically contradicts the law of energy conservation. The contradiction is that the excitation of each aftershock consumes a finite portion of the so

Futoshi Futami, Masahiro Fujisawa

While the expected calibration error (ECE), which employs binning, is widely adopted to evaluate the calibration performance of machine learning models, theoretical understanding of its estimation bias is limited. In this paper, we present the first comprehensive analysis of the estimation bias in the two common binning strategies, uniform mass and uniform w

Jocelyn Shen, Yubin Kim, Mohit Hulse, Wazeer Zulfikar

Modeling empathy is a complex endeavor that is rooted in interpersonal and experiential dimensions of human interaction, and remains an open problem within AI. Existing empathy datasets fall short in capturing the richness of empathy responses, often being confined to in-lab or acted scenarios, lacking longitudinal data, and missing self-reported labels. We

Archismita Dalal, Iraitz Montalban, Narendra N. Hegade, Alejandro Gomez Cadavid

We study a job shop scheduling problem for an automatized robot in a high-throughput laboratory and a travelling salesperson problem with recently proposed digitized counterdiabatic quantum optimization (DCQO)algorithms. In DCQO, we find the solution of an optimization problem via an adiabatic quantum dynamics, which is accelerated with counterdiabatic proto

Michael Munn, Benoit Dherin, Javier Gonzalvo

Many of the recent remarkable advances in computer vision and language models can be attributed to the success of transfer learning via the pre-training of large foundation models. However, a theoretical framework which explains this empirical success is incomplete and remains an active area of research. Flatness of the loss surface and neural collapse have

Jinbo Peng, Zhe Chen, Zheng Lin, Haoxuan Yuan

Due to sophisticated deployments of all kinds of wireless networks (e.g., 5G, Wi-Fi, Bluetooth, LEO satellite, etc.), multiband signals distribute in a large bandwidth (e.g., from 70 MHz to 8 GHz). Consequently, for network monitoring and spectrum sharing applications, a sniffer for extracting physical layer information, such as structure of packet, with low

Yihe Wang, Nan Huang, Taida Li, Yujun Yan

Medical time series (MedTS) data, such as Electroencephalography (EEG) and Electrocardiography (ECG), play a crucial role in healthcare, such as diagnosing brain and heart diseases. Existing methods for MedTS classification primarily rely on handcrafted biomarkers extraction and CNN-based models, with limited exploration of transformer-based models. In this

Ebubechukwu O. Ilo-Okeke, Tim Byrnes

We present a theory for quantum nondemolition (QND) measurements of an atomic ensemble in the presence of spontaneous emission. We derive the master equation that governs the evolution of the ground state of the atoms and the quantum state of light. Solving the master equation exactly without invoking the Holstein-Primakoff approximation and projecting out t

Satoya Imai, Augusto Smerzi, Luca Pezzè

A central task in quantum metrology is to exploit quantum correlations to outperform classical sensitivity limits. Metrologically useful entanglement is identified when the quantum Fisher information (QFI) exceeds a separability bound for a given parameter-encoding Hamiltonian. However, so far, only results for linear Hamiltonians are well-established. Here,

Asunción Jiménez-Cordero, Salvador Pineda, Juan Miguel Morales

The Rank Pricing Problem (RPP) is a challenging bilevel optimization problem with binary variables whose objective is to determine the optimal pricing strategy for a set of products to maximize the total benefit, given that customer preferences influence the price for each product. Traditional methods for solving RPP are based on exact approaches which may b

Iuliia Dmitrieva, Sergey Babkin, Adam S. Charles

Closed-loop neuroscience experimentation, where recorded neural activity is used to modify the experiment on-the-fly, is critical for deducing causal connections and optimizing experimental time. A critical step in creating a closed-loop experiment is real-time inference of neural activity from streaming recordings. One challenging modality for real-time pro

Benjamin Gallusser, Martin Weigert

Cell tracking is a ubiquitous image analysis task in live-cell microscopy. Unlike multiple object tracking (MOT) for natural images, cell tracking typically involves hundreds of similar-looking objects that can divide in each frame, making it a particularly challenging problem. Current state-of-the-art approaches follow the tracking-by-detection paradigm, i.

Leonardo Defilippis, Bruno Loureiro, Theodor Misiakiewicz

In this work we investigate the generalization performance of random feature ridge regression (RFRR). Our main contribution is a general deterministic equivalent for the test error of RFRR. Specifically, under a certain concentration property, we show that the test error is well approximated by a closed-form expression that only depends on the feature map ei

Chao Chen Ye, Yuliia Kreminska, Jianting Ye, Jagoda Sławińska

Topological insulators have been studied intensively over the last decades. Among these materials, three-dimensional (3D) zirconium pentatelluride (ZrTe$_5$) stands out as one of the most intriguing for both theoretical and experimental studies because of its diverse range of distinct topological phases. In this work, we employ density functional theory to s

Tim Treu, Marvin Klinger, Noah Oefele, Prachi Telang

The manifold of energetically degenerate configurations arising from competing interactions in frustrated magnets gives rise to an enhanced entropy at lowest temperatures, which can be utilized for adiabatic demagnetization refrigeration (ADR). We review structural and magnetic properties of various Yb- and Gd-based oxides featuring frustration related to di

D. S. Smirnov, E. L. Ivchenko

The optical orientation and alignment of excitons in semiconductor indirect gap quantum dots have been studied theoretically. A special regime is analyzed in which the energy of the hyperfine interaction of an electron with lattice nuclei is small compared to the exchange splitting between bright and dark excitonic levels, but is comparable to the anisotropi

Saswata Roy, Alen Senanian, Christopher S. Wang, Owen C. Wetherbee

Spins and oscillators are foundational to much of physics and applied sciences. For quantum information, a spin 1/2 exemplifies the most basic unit, a qubit. High angular momentum spins (HAMSs) and harmonic oscillators provide multi-level manifolds (e.g., qudits) which have the potential for hardware-efficient protected encodings of quantum information and s

Karandeep J. Singh

In this note, we use give some algebraic applications of a previous result by the author which compares the deformations parameterized by the Maurer-Cartan elements of a differential graded Lie algebra, and a differential graded Lie subalgebra: It gives a criterion for the map on the space of Maurer-Cartan elements up to gauge equivalence, induced by the inc

Simon Akesson, Frances A. Santos

Providing external knowledge to Large Language Models (LLMs) is a key point for using these models in real-world applications for several reasons, such as incorporating up-to-date content in a real-time manner, providing access to domain-specific knowledge, and contributing to hallucination prevention. The vector database-based Retrieval Augmented Generation

Michael Gutperle, Yan-Yan Li, Dikshant Rathore, Konstantinos Roumpedakis

We study non-invertible defects in two-dimensional $S_N$ orbifold CFTs. We construct universal defects which do not depend on the details of the seed CFT and hence exist in any orbifold CFT. Additionally, we investigate non-universal defects arising from the topological defects of the seed CFT. We argue that there exist universal defects that are non-trivial

Natalia Korolkova, Luis Sánchez-Soto, Gerd Leuchs

Quantum entanglement describes superposition states in multi-dimensional systems, at least two partite, which cannot be factorized and are thus non-separable. Non-separable states exist also in classical theories involving vector spaces. In both cases, it is possible to violate a Bell-like inequality. This has led to controversial discussions, which we resol

Joanna Berteaud, Christopher Eckner, Francesca Calore, Maïca Clavel

Millisecond pulsars (MSPs) are abundant in globular clusters (GCs), which offer favorable environments for their creation. While the advent of recent, powerful facilities led to a rapid increase in MSP discoveries in GCs through pulsation searches, detection biases persist. In this work, we investigate the ability of current and future detections in GCs to c

Ummay Kulsum, Haotian Zhu, Bowen Xu, Marcelo d'Amorim

Recent work in automated program repair (APR) proposes the use of reasoning and patch validation feedback to reduce the semantic gap between the LLMs and the code under analysis. The idea has been shown to perform well for general APR, but its effectiveness in other particular contexts remains underexplored. In this work, we assess the impact of reasoning an

Seul-Ki Bac, Florian le Mardelé, Jiashu Wang, Mykhaylo Ozerov

Probing the quantum geometry and topology in condensed matter systems has relied heavily on static electronic transport experiments in magnetic fields. Yet, contact-free optical measurements have rarely been explored. Magnetic dichroism (MCD), the nonreciprocal absorption of circular polarized light, was theoretically linked to the quantized anomalous Hall e

Ted Lentsch, Holger Caesar, Dariu M. Gavrila

Unsupervised 3D object detection methods have emerged to leverage vast amounts of data without requiring manual labels for training. Recent approaches rely on dynamic objects for learning to detect mobile objects but penalize the detections of static instances during training. Multiple rounds of self-training are used to add detected static instances to the

Yongsheng Yu, Jiebo Luo

Conventional demographic inference methods have predominantly operated under the supervision of accurately labeled data, yet struggle to adapt to shifting social landscapes and diverse cultural contexts, leading to narrow specialization and limited accuracy in applications. Recently, the emergence of large multimodal models (LMMs) has shown transformative po

Eddel Elí Ojeda Avilés, Daniel Olmos-Liceaga, Jae-Hun Jung

Partial differential equations (PDEs) with multiple scales or those defined over sufficiently large domains arise in various areas of science and engineering and often present problems when approximating the solutions numerically. Machine learning techniques are a relatively recent method for solving PDEs. Despite the increasing number of machine learning st

Pedro B. Melo, Sílvio M. Duarte Queirós, Welles A. M. Morgado

In this paper, we investigate the stochastic thermodynamics of Fisher information (FI), meaning we characterize both the \textit{fluctuations} of FI, introducing a parastatistics of that quantity, and thermodynamic quantities. We introduce two initial conditions: an equilibrium initial condition and a minimum entropy initial condition, both under a protocol

Yue Zhang, Hehe Fan, Yi Yang

To bridge the gap between vision and language modalities, Multimodal Large Language Models (MLLMs) usually learn an adapter that converts visual inputs to understandable tokens for Large Language Models (LLMs). However, most adapters generate consistent visual tokens, regardless of the specific objects of interest mentioned in the prompt. Since these adapter

Sreyan Ghosh, Chandra Kiran Reddy Evuru, Sonal Kumar, Utkarsh Tyagi

Large Vision-Language Models (LVLMs) often produce responses that misalign with factual information, a phenomenon known as hallucinations. While hallucinations are well-studied, the exact causes behind them remain underexplored. In this paper, we first investigate the root causes of hallucinations in LVLMs. Our findings reveal that existing mitigation techni

Aaron Defazio, Xingyu Alice Yang, Harsh Mehta, Konstantin Mishchenko

Existing learning rate schedules that do not require specification of the optimization stopping step T are greatly out-performed by learning rate schedules that depend on T. We propose an approach that avoids the need for this stopping time by eschewing the use of schedules entirely, while exhibiting state-of-the-art performance compared to schedules across

Boyuan Chen, Mingzhi Zhu, Brendan Dolan-Gavitt, Muhammad Shafique

The rapid advancement of large language models (LLMs) has significantly improved code completion tasks, yet the trade-off between accuracy and computational cost remains a critical challenge. While using larger models and incorporating inference-time self-testing algorithms can significantly improve output accuracy, they incur substantial computational expen

Trambak Bhattacharyya, Eugenio Megias, Airton Deppman

Using the Plastino-Plastino (PP) equation, we calculate transport coefficients of the heavy-quarks traversing inside the quark-gluon plasma, and generalize their relationship with differential energy loss. The PP equation indicates anomalous diffusion of the probe particles and yields a quasi-exponential stationary distribution obtained also from the nonaddi

Shen Yuan, Haotian Liu, Hongteng Xu

While following different technical routes, both low-rank and orthogonal adaptation techniques can efficiently adapt large-scale pre-training models in specific tasks or domains based on a small piece of trainable parameters. In this study, we bridge the gap between these two techniques, proposing a simple but effective adaptation method based on Householder

Nasser Mohammed, Anna Ordog, Rebecca A. Booth, Andrea Bracco

A direct consequence of Faraday rotation is that the polarized radio sky does not resemble the total intensity sky at long wavelengths. We analyze G137+7, which is undetectable in total intensity but appears as a depolarization feature. We use the first polarization maps from the Canadian Hydrogen Intensity Mapping Experiment. Our $400-729$ MHz bandwidth and

Wei Wu, Xiaoxin Feng, Ziyan Gao, Yuheng Kan

Data-driven autonomous driving motion generation tasks are frequently impacted by the limitations of dataset size and the domain gap between datasets, which precludes their extensive application in real-world scenarios. To address this issue, we introduce SMART, a novel autonomous driving motion generation paradigm that models vectorized map and agent trajec

Lorenzo Baldassari, Ali Siahkoohi, Josselin Garnier, Knut Solna

This work introduces a sampling method capable of solving Bayesian inverse problems in function space. It does not assume the log-concavity of the likelihood, meaning that it is compatible with nonlinear inverse problems. The method leverages the recently defined infinite-dimensional score-based diffusion models as a learning-based prior, while enabling prov

Matthew Dawes

In arXiv:1710.01672, we obtained general type results for orthogonal modular varieties associated with moduli spaces of compact hyperk\"ahler manifolds of deformation generalised Kummer type (also known as 'deformation generalised Kummer varieties'). The orthogonal modular varieties were defined in terms of an integer 2d, corresponding to the degree of polar

Kosuke Osawa, Javier Jimenez

The causal relevance of local flow conditions in wall-bounded turbulence is analysed using ensembles of interventional experiments in which the effect of perturbing the flow within a small cell is monitored at some future time. When this is done using the relative amplification of the perturbation energy, causality depends on the flow conditions within the c

Jiyuan Tan, Jose Blanchet, Vasilis Syrgkanis

Recent progress in Neural Causal Models (NCMs) showcased how identification and partial identification of causal effects can be automatically carried out via training of neural generative models that respect the constraints encoded in a given causal graph [Xia et al. 2022, Balazadeh et al. 2022]. However, formal consistency of these methods has only been pro

Reetwika Basu, Eric Anderson, Chinmay Deval, Kelsey Herndon

In an era where informed decision-making is paramount for sustainable development and effective resource management, the role of Earth observations (EO) in shaping economic landscapes cannot be overstated. EO, facilitated by satellites, sensors, and data analytics, is a cornerstone for evidence-based policymaking, risk mitigation, and resource allocation. SE

Thomas Ågotnes, Hans van Ditmarsch, Tim French

This paper demonstrates the undecidability of a number of logics with quantification over public announcements: arbitrary public announcement logic (APAL), group announcement logic (GAL), and coalition announcement logic (CAL). In APAL we consider the informative consequences of any announcement, in GAL we consider the informative consequences of a group of

Rachel Carrington, Paul Fearnhead

Quantifying uncertainty in detected changepoints is an important problem. However it is challenging as the naive approach would use the data twice, first to detect the changes, and then to test them. This will bias the test, and can lead to anti-conservative p-values. One approach to avoid this is to use ideas from post-selection inference, which conditions

Anna V. Maltsev, Yasir Z. Barlas, Adina Hazan, Rui Zhang

Biological systems, particularly the brain, are frequently analyzed as networks, conveying mechanistic insights into their function and pathophysiology. This is the first study of a functional network of cardiac tissue. We use calcium imaging to obtain two functional networks in a subsidiary but essential pacemaker of the heart, the atrioventricular node (AV

Aakash Gautam, Khushboo Gandhi, Jessica Eileen Sendejo

Challenges faced by formerly incarcerated individuals in the United States raise questions about our society's ability to truly provide second chances. This paper presents the outcomes of our ongoing collaboration with a non-profit organization dedicated to reentry support. We highlight the multifaceted challenges individuals face during their reentry journe

N. Phan-Thien, M. A. Kanso, A. J. Giacomin

We derive the constitutive equation for a suspension of lopsided Hookean dumbbells. By lopsided, we mean that one bead is larger than the other. We find that all results derived for symmetric Hookean dumbbells can be taken over for lopsided ones by replacing$ \frac{2}{\zeta}$ with $\frac{1}{\zeta_1}+\frac{1}{\zeta_2}$, where ${\zeta}$, ${\zeta}_1$ and ${\zet

Abdelrahman Abdelhamed, Mahmoud Afifi, Alec Go

Large language models (LLMs) have been effectively used for many computer vision tasks, including image classification. In this paper, we present a simple yet effective approach for zero-shot image classification using multimodal LLMs. Using multimodal LLMs, we generate comprehensive textual representations from input images. These textual representations ar

Vasiliki Evdoridou, Núria Fagella, Lukas Geyer, Leticia Pardo-Simón

One of the fundamental distinctions in McMullen and Sullivan's description of the Teichm\"uller space of a complex dynamical system is between discrete and indiscrete grand orbit relations. We investigate these on the Fatou set of transcendental entire maps and provide criteria to distinguish between the two types. Furthermore, we show that discrete and indi

Fan Xu, Lei Zhang, Bin Liu

In this paper, we study the initial-boundary value problem for the stochastic Landau-Lifshitz-Baryakhtar (SLLBar) equation with Stratonovich-type noise in bounded domains $\mathcal{O}\subset\mathbb{R}^d$, $d=1,2,3$. Our main results can be briefly described as follows: (1) for $d=1,2,3$ and any $\mathbf{u}_0\in\mathbb{H}^1$, the SLLBar equation admits a uniq

Benoit Gaujac, Jérémie Donà, Liviu Copoiu, Timothy Atkinson

Representation learning and \emph{de novo} generation of proteins are pivotal computational biology tasks. Whilst natural language processing (NLP) techniques have proven highly effective for protein sequence modelling, structure modelling presents a complex challenge, primarily due to its continuous and three-dimensional nature. Motivated by this discrepanc

Umme Ayman Koana, Quang Hy Le, Shadikur Rahman, Chris Carlson

Effective ownership of software artifacts, particularly code, is crucial for accountability, knowledge sharing, and code quality enhancement. Researchers have proposed models linking ownership of software artifacts with developer performance and code quality. Our study aims to systematically examine various ownership models and provide a structured literatur

Nicolai Steinke, Daniel Göhring, Raùl Rojas

The precise point cloud ground segmentation is a crucial prerequisite of virtually all perception tasks for LiDAR sensors in autonomous vehicles. Especially the clustering and extraction of objects from a point cloud usually relies on an accurate removal of ground points. The correct estimation of the surrounding terrain is important for aspects of the driva

Mohammad H. Shekarriz, Dhananjay Thiruvady, Asef Nazari, Rhyd Lewis

For $0<\rho\leq 1$, a $\rho$-happy vertex $v$ in a coloured graph $G$ has at least $\rho\cdot \mathrm{deg}(v)$ same-colour neighbours, and a $\rho$-happy colouring (aka soft happy colouring) of $G$ is a vertex colouring that makes all the vertices $\rho$-happy. A community is a subgraph whose vertices are more adjacent to themselves than the rest of the vert

Wenhan Chang, Tianqing Zhu, Heng Xu, Wenjian Liu

In current AI era, users may request AI companies to delete their data from the training dataset due to the privacy concerns. As a model owner, retraining a model will consume significant computational resources. Therefore, machine unlearning is a new emerged technology to allow model owner to delete requested training data or a class with little affecting o

James Hinns, David Martens

The rise of deep learning in image classification has brought unprecedented accuracy but also highlighted a key issue: the use of 'shortcuts' by models. Such shortcuts are easy-to-learn patterns from the training data that fail to generalise to new data. Examples include the use of a copyright watermark to recognise horses, snowy background to recognise husk

Xiaogang Xu, Kun Zhou, Tao Hu, Jiafei Wu

Low-Light Video Enhancement (LLVE) seeks to restore dynamic or static scenes plagued by severe invisibility and noise. In this paper, we present an innovative video decomposition strategy that incorporates view-independent and view-dependent components to enhance the performance of LLVE. We leverage dynamic cross-frame correspondences for the view-independen

Denis Nikolaevich Sob'yanin

In light of the discovery of the long-period radio pulsar PSR J0901-4046, it is interesting to revisit a question about how the magnetized neutron star slows down its rotation. In the case of a weak or liquid outer crust, the mechanism of spin-down becomes unclear because the braking stress cannot then be transmitted from the surface to the main bulk of the

Zhuoyan Luo, Yinghao Wu, Tianheng Cheng, Yong Liu

The newly proposed Generalized Referring Expression Segmentation (GRES) amplifies the formulation of classic RES by involving complex multiple/non-target scenarios. Recent approaches address GRES by directly extending the well-adopted RES frameworks with object-existence identification. However, these approaches tend to encode multi-granularity object inform

Samik Dutta, Nayantara Gupta

The Large High Altitude Air Shower Observatory (LHAASO) has detected very high energy gamma rays from the LINER galaxy NGC 4278, which has a low luminosity active galactic nucleus, and symmetric mildly relativistic S-shaped twin jets detected by radio observations. Few low-luminosity active galactic nuclei are detected in gamma rays due to their faintness. E

Alessandro Borghi, Tobias Breiten, Serkan Gugercin

We consider the problem of constructing reduced models for large scale systems with poles in general domains in the complex plane (as opposed to, e.g., the open left-half plane or the open unit disk). Our goal is to design a model reduction scheme, building upon theoretically established methodologies, yet encompassing this new class of models. To this aim,

Luis Miguel Pazos-Outón, Cristina Nader Vasconcelos, Anton Raichuk, Anurag Arnab

Protecting and restoring forest ecosystems is critical for biodiversity conservation and carbon sequestration. Forest monitoring on a global scale is essential for prioritizing and assessing conservation efforts. Satellite-based remote sensing is the only viable solution for providing global coverage, but to date, large-scale forest monitoring is limited to

Zhisheng Zhang, Pengyang Huang

In recent years, the remarkable advancements in deep neural networks have brought tremendous convenience. However, the training process of a highly effective model necessitates a substantial quantity of samples, which brings huge potential threats, like unauthorized exploitation with privacy leakage. In response, we propose a framework named HiddenSpeaker, e

An-Hsi Chen, Qiangsheng Lu, Eitan Hershkovitz, Miguel L. Crespillo

Realizing topological superconductivity by integrating high-transition-temperature ($T_C$) superconductors with topological insulators can open new paths for quantum computing applications. Here, we report a new approach for increasing the superconducting transition temperature ($T_{C}^{onset}$) by interfacing the unconventional superconductor Fe(Te,Se) with

Alcides Buss, Bartosz Kwaśniewski, Andrew McKee, Adam Skalski

We extend the theory of Fourier--Stieltjes algebras to the category of twisted actions by \'etale groupoids on arbitrary C*-bundles, generalizing theories constructed previously by B\'{e}dos and Conti for twisted group actions on unital C*-algebras, and by Renault and others for groupoid C*-algebras, in each case motivated by the classical theory of Fourier-

Simen Gaure, Stefanos Koffas, Stjepan Picek, Sondre Rønjom

Large Language Models (LLMs) have gained significant popularity recently. LLMs are susceptible to various attacks but can also improve the security of diverse systems. However, besides enabling more secure systems, how well do open source LLMs behave as covertext distributions to, e.g., facilitate censorship-resistant communication? In this paper, we explore

Jianzhi Yang, Pedro V. P. Cunha, Carlos A. R. Herdeiro

The Einstein field equations, or generalizations thereof, are difficult to solve analytically. On the other hand, numerical solutions of the same equations have become increasingly common, in particular concerning compact objects. Whereas analytic approximations to each individual solution within a numerical family have been proposed, proxies for whole famil

Madeleine Geers, Oscar Fabelo, Matthew J. Cliffe, Laura Cañadillas-Delgado

Three solid solutions of [CH$_3$NH$_3$]Co$_x$Ni$_{1-x}$(HCOO)$_3$, with $x$ = 0.25 (1), 0.50 (2) and 0.75 (3), were synthesized and their nuclear structures and magnetic properties were characterized using single crystal neutron diffraction and magnetization measurements. At room temperature, all three compounds crystallize in the Pnma orthorhombic space gro

Hank Flury, Jan Hannig, Richard Smith

The H\"usler-Reiss distribution describes the limit of the pointwise maxima of a bivariate normal distribution. This distribution is defined by a single parameter, $\lambda$. We provide asymptotic theory for maximum likelihood estimation of $\lambda$ under a block maxima approach. Our work assumes independent and identically distributed bivariate normal rand

Takamasa Ando

We explore topological manipulations in one spatial dimension, which are defined for a system with a global symmetry and map the system to another one with a dual symmetry. In particular, we discuss fusion category symmetries enhanced by the invariance of the actions of topological manipulations, i.e., self-$G$-alities for topological manipulations. Based on

Francesco A. Genco

We present a logical framework that enables us to define a formal theory of computational trust in which this notion is analysed in terms of epistemic attitudes towards the possible objects of trust and in relation to existing evidence in favour of the trustworthiness of these objects. The framework is based on a quantified epistemic and justification logic

Ruoyu Wang, Zhipeng Yang, Zinan Zhao, Xinyan Tong

The development of a general purpose service robot for daily life necessitates the robot's ability to deploy a myriad of fundamental behaviors judiciously. Recent advancements in training Large Language Models (LLMs) can be used to generate action sequences directly, given an instruction in natural language with no additional domain information. However, whi

Steven N. Karp, Evgeny Mukhin, Vitaly Tarasov

A quasi-exponential is an entire function of the form $e^{cu}p(u)$, where $p(u)$ is a polynomial and $c \in \mathbb{C}$. Let $V = \langle e^{h_1u}p_1(u), \dots, e^{h_Nu}p_N(u) \rangle$ be a vector space with a basis of quasi-exponentials. We show that if $h_1, \dots, h_N$ are nonnegative and all of the complex zeros of the Wronskian $\operatorname{Wr}(V)$ ar

M. Toslali, S. Qasim, S. Parthasarathy, F. A. Oliveira

Distributed tracing has become a fundamental tool for diagnosing performance issues in the cloud by recording causally ordered, end-to-end workflows of request executions. However, tracing in production workloads can introduce significant overheads due to the extensive instrumentation needed for identifying performance variations. This paper addresses the tr

Akash Dhasade, Anne-Marie Kermarrec, Tuan-Anh Nguyen, Rafael Pires

Federated learning (FL) is a machine learning approach where nodes collaboratively train a global model. As more nodes participate in a round of FL, the effectiveness of individual model updates by nodes also diminishes. In this study, we increase the effectiveness of client updates by dividing the network into smaller partitions, or cohorts. We introduce Co

Fabian Schneider, Duc-Lam Duong, Matti Lassas, Maarten V. de Hoop

Score-based diffusion models (SDMs) have emerged as a powerful tool for sampling from the posterior distribution in Bayesian inverse problems. However, existing methods often require multiple evaluations of the forward mapping to generate a single sample, resulting in significant computational costs for large-scale inverse problems. To address this, we propo

David Lindsay, Sian Lindsay

Confidence region prediction is a practically useful extension to the commonly studied pattern recognition problem. Instead of predicting a single label, the constraint is relaxed to allow prediction of a subset of labels given a desired confidence level 1-delta. Ideally, effective region predictions should be (1) well calibrated - predictive regions at conf

Margaux Zaffran, Julie Josse, Yaniv Romano, Aymeric Dieuleveut

Predictive uncertainty quantification is crucial in decision-making problems. We investigate how to adequately quantify predictive uncertainty with missing covariates. A bottleneck is that missing values induce heteroskedasticity on the response's predictive distribution given the observed covariates. Thus, we focus on building predictive sets for the respon

Sungwoo Oh, Donggyu Kim

We introduce GECKO, a bilingual large language model (LLM) optimized for Korean and English, along with programming languages. GECKO is pretrained on the balanced, high-quality corpus of Korean and English employing LLaMA architecture. In this report, we share the experiences of several efforts to build a better data pipeline for the corpus and to train our

Harry Gingold, Jocelyn Quaintance

Certain measurements in celestial mechanics necessitate having the origin O of a Cartesian coordinate system (CCS) coincide with a point mass. For the two and three body problems we show mathematical inadequacies in Newton's celestial mechanics equations (NCME) when the origin of a coordinate system coincides with a point mass. A certain system of equations

Yuxuan Zhao

Automated Valet Parking (AVP) is a crucial component of advanced autonomous driving systems, focusing on the endpoint task within the "human-vehicle interaction" process to tackle the challenges of the "last mile".The perception module of the automated parking algorithm has evolved from local perception using ultrasonic radar and global scenario precise map

Yuxuan Zhao

As the trend of moving away from high-precision maps gradually emerges in the autonomous driving industry,traditional planning algorithms are gradually exposing some problems. To address the high real-time, high precision, and high trajectory quality requirements posed by the automatic parking task under real-time perceived local maps,this paper proposes an

Hongyu Wang, Jiayu Xu, Senwei Xie, Ruiping Wang

Multilingual capability is an essential aspect for large multimodal models, since they are usually deployed across various countries and languages. However, most existing benchmarks for multilingual multimodal reasoning struggle to differentiate between models of varying performance; even language models without visual capabilities can easily achieve high sc

Shengchao Chen, Guodong Long, Jing Jiang, Chengqi Zhang

This paper demonstrates that pre-trained language models (PLMs) are strong foundation models for on-device meteorological variables modeling. We present LM-Weather, a generic approach to taming PLMs, that have learned massive sequential knowledge from the universe of natural language databases, to acquire an immediate capability to obtain highly customized m

Jennifer Gross, Sofia Ouhbi

The advancement of software sustainability encounters notable challenges, underscoring the necessity for understanding these challenges to facilitate significant progress and pave the way for effective solutions to advance software sustainability. This paper outlines key challenges identified in literature based on findings from a tertiary study. Challenges

Rudolf Herdt, Peter Maass

We investigate how generated structures of GANs correlate with their activations in hidden layers, with the purpose of better understanding the inner workings of those models and being able to paint structures with unconditionally trained GANs. This gives us more control over the generated images, allowing to generate them from a semantic segmentation map wh

Thomas Massoni

We present a new construction of codimension-one foliations from pairs of contact structures in dimension three. This constitutes a converse result to a celebrated theorem of Eliashberg and Thurston on approximations of foliations by contact structures. Under suitable hypotheses on the initial contact pairs, the foliations we construct are taut, allowing us

Ignacio García-Marco, Philippe Gimenez, Mario González-Sánchez

In this paper, we explore when the Betti numbers of the coordinate rings of a projective monomial curve and one of its affine charts are identical. Given an infinite field $k$ and a sequence of relatively prime integers $a_0 = 0 < a_1 < \cdots < a_n = d$, we consider the projective monomial curve $\mathcal{C}\subset\mathbb{P}_k^{\,n}$ of degree $d$ parametri

Thomas De Min, Massimiliano Mancini, Stéphane Lathuilière, Subhankar Roy

Prompt tuning has emerged as an effective rehearsal-free technique for class-incremental learning (CIL) that learns a tiny set of task-specific parameters (or prompts) to instruct a pre-trained transformer to learn on a sequence of tasks. Albeit effective, prompt tuning methods do not lend well in the multi-label class incremental learning (MLCIL) scenario (

Dario Fenoglio, Gabriele Dominici, Pietro Barbiero, Alberto Tonda

Federated Learning (FL), a privacy-aware approach in distributed deep learning environments, enables many clients to collaboratively train a model without sharing sensitive data, thereby reducing privacy risks. However, enabling human trust and control over FL systems requires understanding the evolving behaviour of clients, whether beneficial or detrimental

Victoria M. Orlando, Iván L. Degano, Pablo A. Lotito

The effective design and management of public transport systems are essential to ensure the best service for users. The performance of a transport system will depend heavily on user behaviour. In the common-lines problem approach, users choose which lines to use based on the best strategy for them. While Wardrop equilibrium has been studied for the common-li

Xinyi Hou, Yanjie Zhao, Shenao Wang, Haoyu Wang

The rapid advancements in Large Language Models (LLMs) have revolutionized natural language processing, with GPTs, customized versions of ChatGPT available on the GPT Store, emerging as a prominent technology for specific domains and tasks. To support academic research on GPTs, we introduce GPTZoo, a large-scale dataset comprising 730,420 GPT instances. Each

Sambatriniaina H. A. Rajohnson, Renée C. Kraan-Korteweg, Hao Chen, Bradley S. Frank

We conducted a search for HI emission of the gas-rich galaxies in the Vela region ($260^{\circ} \leq \ell \leq 290^{\circ}, -2^{\circ} \leq b \leq 1^{\circ}$) to explore the Vela Supercluster (VSCL) at $V_\mathrm{hel} \sim 18000$ km s$^{-1}$, largely obscured by Galactic dust. Within the mostly RFI-free band ($250 < V_\mathrm{hel} < 25000$ km s$^{-1}$) of Me

Ishan Patwardhan, Shubham Gandhi, Om Khare, Amit Joshi

The rapid advancement in Large Language Models has been met with significant challenges in their training processes, primarily due to their considerable computational and memory demands. This research examines parallelization techniques developed to address these challenges, enabling the efficient and scalable training of Large Language Models. A comprehensi