October 2023 arXiv papers — page 95

Dawid J. Kopiczko, Tijmen Blankevoort, Yuki M. Asano

Low-rank adapation (LoRA) is a popular method that reduces the number of trainable parameters when finetuning large language models, but still faces acute storage challenges when scaling to even larger models or deploying numerous per-user or per-task adapted models. In this work, we present Vector-based Random Matrix Adaptation (VeRA), which significantly r

Hongyu Wang, Shuming Ma, Li Dong, Shaohan Huang

The increasing size of large language models has posed challenges for deployment and raised concerns about environmental impact due to high energy consumption. In this work, we introduce BitNet, a scalable and stable 1-bit Transformer architecture designed for large language models. Specifically, we introduce BitLinear as a drop-in replacement of the nn.Line

Aleyah Dawkins, Rachel Kirsch

Ore in 1961 determined the maximum number of edges in graphs not containing a Hamiltonian cycle, and Tur\'{a}n in 1941 found the maximum number of edges in graphs not containing a $K_{r+1}$. Motivated by the work of Adamus in 2009 and Ferrero and Lesniak in 2018 on the maximum number of edges in $r$-partite non-Hamiltonian graphs, we find the maximum number

Ming Zhong, Chenxin An, Weizhu Chen, Jiawei Han

Large Language Models (LLMs) inherently encode a wealth of knowledge within their parameters through pre-training on extensive corpora. While prior research has delved into operations on these parameters to manipulate the underlying implicit knowledge (encompassing detection, editing, and merging), there remains an ambiguous understanding regarding their tra

Thomas Decker, Michael Lebacher, Volker Tresp

Concept-based explanation methods, such as Concept Activation Vectors, are potent means to quantify how abstract or high-level characteristics of input data influence the predictions of complex deep neural networks. However, applying them to industrial prediction problems is challenging as it is not immediately clear how to define and access appropriate conc

Philipp Schoenegger, Peter S. Park

Accurately predicting the future would be an important milestone in the capabilities of artificial intelligence. However, research on the ability of large language models to provide probabilistic predictions about future events remains nascent. To empirically test this ability, we enrolled OpenAI's state-of-the-art large language model, GPT-4, in a three-mon

Youngjoong Kwon, Lingjie Liu, Henry Fuchs, Marc Habermann

Generating controllable and photorealistic digital human avatars is a long-standing and important problem in Vision and Graphics. Recent methods have shown great progress in terms of either photorealism or inference speed while the combination of the two desired properties still remains unsolved. To this end, we propose a novel method, called DELIFFAS, which

Zhen Xu, Sida Peng, Haotong Lin, Guangzhao He

This paper targets high-fidelity and real-time view synthesis of dynamic 3D scenes at 4K resolution. Recently, some methods on dynamic view synthesis have shown impressive rendering quality. However, their speed is still limited when rendering high-resolution images. To overcome this problem, we propose 4K4D, a 4D point cloud representation that supports har

Suthee Ruangwises

Nondango is a pencil puzzle consisting of a rectangular grid partitioned into regions, with some cells containing a white circle. The player has to color some circles black such that every region contains exactly one black circle, and there are no three consecutive circles (horizontally, vertically, or diagonally) having the same color. In this paper, we pro

Pierre Fernandez, Guillaume Couairon, Teddy Furon, Matthijs Douze

The rapid growth of transformer-based models increases the concerns about their integrity and ownership insurance. Watermarking addresses this issue by embedding a unique identifier into the model, while preserving its performance. However, most existing approaches require to optimize the weights to imprint the watermark signal, which is not suitable at scal

Guneykan Ozgul, Xiantao Li, Mehrdad Mahdavi, Chunhao Wang

We present quantum algorithms for sampling from non-logconcave probability distributions in the form of $\pi(x) \propto \exp(-\beta f(x))$. Here, $f$ can be written as a finite sum $f(x):= \frac{1}{N}\sum_{k=1}^N f_k(x)$. Our approach is based on quantum simulated annealing on slowly varying Markov chains derived from unadjusted Langevin algorithms, removing

Diogo A. Gomes, Michele Ricciardi

The primary objective of this paper is to understand first-order, time-dependent mean-field games with Neumann boundary conditions, a question that remains under-explored in the literature. This matter is particularly relevant given the importance of boundary conditions in crowd models. In our model, the Neumann conditions result from players entering the do

Raúl Felipe

Our purpose with this paper is, in first place, to recast the space of quiddity sequences corresponding to usual frieze patterns as a different type of SET operad, and second to introduce and study $\mathfrak{M}$-quiddity sequences where $\mathfrak{M}$ is a monodromy block matrix of order two. Also, we examine some related topic as are the possibility of to

Ludwig Stage, Dimka Karastoyanova

To benefit from the abundance of data and the insights it brings data processing pipelines are being used in many areas of research and development in both industry and academia. One approach to automating data processing pipelines is the workflow technology, as it also supports collaborative, trial-and-error experimentation with the pipeline architecture in

Jianwei Yang, Hao Zhang, Feng Li, Xueyan Zou

We present Set-of-Mark (SoM), a new visual prompting method, to unleash the visual grounding abilities of large multimodal models (LMMs), such as GPT-4V. As illustrated in Fig. 1 (right), we employ off-the-shelf interactive segmentation models, such as SEEM/SAM, to partition an image into regions at different levels of granularity, and overlay these regions

David Jensen, Brian LaMacchia, Ufuk Topcu, Pamela Wisniewski

Algorithmic robustness refers to the sustained performance of a computational system in the face of change in the nature of the environment in which that system operates or in the task that the system is meant to perform. Below, we motivate the importance of algorithmic robustness, present a conceptual framework, and highlight the relevant areas of research

Yaofang Liu, Xiaodong Cun, Xuebo Liu, Xintao Wang

The vision and language generative models have been overgrown in recent years. For video generation, various open-sourced models and public-available services have been developed to generate high-quality videos. However, these methods often use a few metrics, e.g., FVD or IS, to evaluate the performance. We argue that it is hard to judge the large conditiona

Quentin Bouniot, Ievgen Redko, Anton Mallasto, Charlotte Laclau

In the last decade, we have witnessed the introduction of several novel deep neural network (DNN) architectures exhibiting ever-increasing performance across diverse tasks. Explaining the upward trend of their performance, however, remains difficult as different DNN architectures of comparable depth and width -- common factors associated with their expressiv

B. E. Tetarenko, A. W. Shaw, P. A. Charles

The recurring transient outbursts in low-mass X-ray binaries (LMXBs) provide ideal laboratories to study the accretion process. Unlike their supermassive relatives, LMXBs are far too small and distant to be imaged directly. Fortunately, phase-resolved spectroscopy can provide an alternative diagnostic to study their highly complex, time-dependent accretion d

Amanda Burcroff

Kostka coefficients appear in the representation theory of the general linear group and enumerate semistandard Young tableaux of fixed shape and content. The $r$-Kostka cone is the real polyhedral cone generated by pairs of partitions with at most $r$ parts, written as non-increasing $r$-tuples, such that the corresponding Kostka coefficient is nonzero. We p

Ana Aleksandric, Hanani Pankaj, Gabriela Mustata Wilson, Shirin Nilizadeh

Cyberbullying and online harassment have serious negative psychological and emotional consequences for the victims, such as decreased life satisfaction, suicidal ideation, self-harming behaviors, depression, anxiety, and others. Most of the prior works assessed people's emotional responses via questionnaires, while social media platforms contain data that co

Minghan Chu, Weicheng Qian

Turbulent flows are of central importance across applications in science and engineering problems. For design and analysis, scientists and engineers use Computational Fluid Dynamics (CFD) simulations using turbulence models. Turbulent models are limited approximations, introducing epistemic uncertainty in CFD results. For reliable design and analysis, we req

Haesom Sung, Sungtae Cho, Che Ming Ko, Su Houng Lee

We extend the recent study of $K_{1}/K^{*}$ enhancement as a signature of chiral symmetry restoration in heavy ion collisions at the Large Hadron Collider (LHC) via the kinetic approach to include the effects due to non-unity hadron fugacities during the evolution of produced hadronic matter and the temperature-dependent $K_1$ mass. Although the effect of no

Klaus Paschek, Mijin Lee, Dmitry A. Semenov, Thomas K. Henning

Aqueous chemistry within carbonaceous planetesimals is promising for synthesizing prebiotic organic matter essential to all life. Meteorites derived from these planetesimals delivered these life building blocks to the early Earth, potentially facilitating the origins of life. Here, we studied the formation of vitamin B$_3$ as it is an important precursor of

David R. Morrison, Benjamin Sung

We study $6$d $\mathcal{N} = (1,0)$ supergravity theories arising in the frozen phase of F-theory. For each of the known global models, we construct an F-theory compactification in the unfrozen phase with an identical non-abelian gauge algebra and massless matter content. Two such low energy effective theories are then distinguished through gauge enhancement

David Klindt, Sophia Sanborn, Francisco Acosta, Frédéric Poitevin

Single neurons in neural networks are often interpretable in that they represent individual, intuitively meaningful features. However, many neurons exhibit $\textit{mixed selectivity}$, i.e., they represent multiple unrelated features. A recent hypothesis proposes that features in deep networks may be represented in $\textit{superposition}$, i.e., on non-ort

António Farinhas, José G. C. de Souza, André F. T. Martins

Large language models (LLMs) are becoming a one-fits-many solution, but they sometimes hallucinate or produce unreliable output. In this paper, we investigate how hypothesis ensembling can improve the quality of the generated text for the specific problem of LLM-based machine translation. We experiment with several techniques for ensembling hypotheses produc

Anna Maltsev, Mohammed Osman

We consider N x N matrices with complex entries that are perturbed by a complex Gaussian matrix with small variance. We prove that if the unperturbed matrix satisfies certain local laws then the bulk correlation functions are universal in the large N limit. Assuming the entries are independent and identically distributed with a common distribution that has f

Adam Block, Dylan J. Foster, Akshay Krishnamurthy, Max Simchowitz

This work studies training instabilities of behavior cloning with deep neural networks. We observe that minibatch SGD updates to the policy network during training result in sharp oscillations in long-horizon rewards, despite negligibly affecting the behavior cloning loss. We empirically disentangle the statistical and computational causes of these oscillati

Amin Nassiri-Rad, Kosar Asadi

In this paper, we investigate the scalar-induced gravitational waves in single-field non-attractor inflation for the Pulsar Timing Arrays data. Our model comprises three phases of inflation: the first and third phases are slow-roll inflation, while the second phase is a period of non-attractor inflation. We analyze the model's predictions for various values

Chenyu Zhang, Chen Zhang, Juntian Qu, Xiang Qian

All-around, real-time navigation and sensing across the water environments by miniature soft robotics are promising, for their merits of small size, high agility and good compliance to the unstructured surroundings. In this paper, we propose and demonstrate a mantas-like soft aquatic robot which propels itself by flapping-fins using rolled dielectric elastom

Osamu Fukushima

The existence of $p$-form symmetry in $(d+1)$-dimensional quantum field is known to always lead to the breakdown of the eigenstate thermalization hypothesis (ETH) for certain $(d-p)$-dimensional operators other than symmetry operators under some assumptions. The assumptions include the mixing of symmetry sectors within a given energy shell, which is rather c

Seoung-Hun Kang, Myeongjun Kang, Sang Woon Hwang, Sinchul Yeom

In the quest for efficient and cost-effective photovoltaic absorber materials beyond silicon, considerable attention has been directed toward exploring alternatives. One such material, zincblende-derived Cu2ZnSnS4 (CZTS), has shown promise due to its ideal band-gap size and high absorption coefficient. However, challenges such as structural defects and secon

Brenda S. Ferrari, Matteo Manica, Ronaldo Giro, Teodoro Laino

Polymers are candidate materials for a wide range of sustainability applications such as carbon capture and energy storage. However, computational polymer discovery lacks automated analysis of reaction pathways and stability assessment through retro-synthesis. Here, we report the first extension of transformer-based language models to polymerization reaction

Nitya Ravi, Kelly A. Douglass, Regina Demina

We present a study of the ratio of visible mass to total mass in spiral galaxies to better understand the relative amount of dark matter present in galaxies of different masses and evolutionary stages. Using the velocities of the H-alpha emission line measured in spectroscopic observations from the Sloan Digital Sky Survey (SDSS) MaNGA Data Release 17 (DR17)

Mikhail Ignatev, Mikhail Venchakov

Let $U$ be the unitriangular group over a finite field. We consider an interesting class of irreducible complex characters of $U$, so-called characters of depth 2. This is a next natural step after characters of maximal and submaximal dimension, whose description is already known. We explicitly describe the support of a character of depth 2 by a system of de

Dongliang Cao, Paul Roetzer, Florian Bernard

We propose a novel unsupervised learning approach for non-rigid 3D shape matching. Our approach improves upon recent state-of-the art deep functional map methods and can be applied to a broad range of different challenging scenarios. Previous deep functional map methods mainly focus on feature extraction and aim exclusively at obtaining more expressive featu

Alexander Duncan, Wenbo Niu, Jinhyung Park

The gonality conjecture, proved by Ein--Lazarsfeld, asserts that the gonality of a nonsingular projective curve of genus $g$ can be detected from its syzygies in the embedding given by a line bundle of sufficiently large degree. An effective result obtained by Rathmann says that any line bundle of degree at least 4g-3 would work in the gonality theorem. In t

Maxime Lecomte, Alexandre Journeaux, Loan Renaud, Jean Dalibard

Dipolar gases like erbium and dysprosium have a dense spectrum of resonant loss features associated with their strong anisotropic interaction potential. These resonances display various behaviours with density and temperature, implying diverse microscopic properties. Here, we quantitatively investigate the low-field ($B < 6\,\text{G}$) loss features in ultra

Bo Jiang, Zitian Wang, Xixi Wang, Ziyan Zhang

Existing visual change detectors usually adopt CNNs or Transformers for feature representation learning and focus on learning effective representation for the changed regions between images. Although good performance can be obtained by enhancing the features of the change regions, however, these works are still limited mainly due to the ignorance of mining t

Guangwei Chen, Rafael Vazquez, Junfei Qiao, Miroslav Krstic

In this paper, we investigate the rapid stabilization of N-layer Timoshenko composite beams with anti-damping and anti-stiffness at the uncontrolled boundaries. The problem of stabilization for a two-layer composite beam has been previously studied by transforming the model into a 1-D hyperbolic PIDE-ODE form and then applying backstepping to this new system

Lu Xu

We consider a scalar conservation law with source in a bounded open interval $\Omega\subseteq\mathbb R$. The equation arises from the macroscopic evolution of an interacting particle system. The source term models an external effort driving the solution to a given function $\varrho$ with an intensity function $V:\Omega\to\mathbb R_+$ that grows to infinity a

Yi Chen

In this paper, a class of systems in which auxiliary batteries fed by fossil fuel generation and wind farms work together to supply power to the grid are modeled and strategies are solved to minimize the consumption of fossil fuels while still allowing the total energy to satisfy the energy demand of the grid. The wind resource is modeled using stochastic di

Madhur Khurana, Himanshu Chaudhary, Ujjal Debnath, Alok Sardar

In our study, we have adopted the framework of Horava-Lifshitz gravity to model the Universe's dark matter and dark energy components. Specifically, we have considered two recent parametrizations for dark energy models: the CBDRM-type and CADMM-type parameterizations. In our analysis, we have explicitly expressed the Hubble parameter, denoted as $H(z)$, for

Jesús Domínguez-Gómez, Isabel Pérez, Tomás Ruiz-Lara, Reynier F. Peletier

Void galaxies are essential for understanding the physical processes that drive galaxy evolution because they are less affected by external factors than galaxies in denser environments, that is, in filaments, walls, and clusters. The stellar metallicity of a galaxy traces the accumulated fossil record of the star formation through the entire life of the gala

René Hafner, Nils Wolfgramm, Peter Klein, Herbert M. Urbassek

Environmental pollution by man-made toxic and persistent organic compounds, found throughout the world in surface and groundwater, has various negative effects on aquatic life systems and even humans. Therefore, it is important to develop and improve water treatment technologies capable of removing such substances from wastewater or purifying drinking water.

Federica Mantegazzini, Felix Ahrens, Matteo Borghesi, Paolo Falferi

A wide-bandwidth and low-noise amplification chain in the microwave regime is crucial for the efficient read-out of quantum systems based on superconducting detectors, such as Microwave Kinetic Inductance Detectors (MKIDs), Transition Edge Sensors (TESs), Magnetic Microcalorimeters (MMCs), and RF cavities, as well as qubits. Kinetic Inductance Travelling Wav

Andreas Happe, Aaron Kaplan, Juergen Cito

Penetration-testing is crucial for identifying system vulnerabilities, with privilege-escalation being a critical subtask to gain elevated access to protected resources. Language Models (LLMs) presents new avenues for automating these security practices by emulating human behavior. However, a comprehensive understanding of LLMs' efficacy and limitations in p

Quan Zhou, Jakub Marecek

Fairness holds a pivotal role in the realm of machine learning, particularly when it comes to addressing groups categorised by protected attributes, e.g., gender, race. Prevailing algorithms in fair learning predominantly hinge on accessibility or estimations of these protected attributes, at least in the training process. We design a single group-blind proj

MD S Q Zulkar Nine, Tevfik Kosar, Fatih Bulut, Jinho Hwang

Network Function Virtualization (NFV) platforms consume significant energy, introducing high operational costs in edge and data centers. This paper presents a novel framework called GreenNFV that optimizes resource usage for network function chains using deep reinforcement learning. GreenNFV optimizes resource parameters such as CPU sharing ratio, CPU freque

Maria Vlachou, Craig Macdonald

Query Performance Prediction (QPP) estimates the effectiveness of a search engine's results in response to a query without relevance judgments. Traditionally, post-retrieval predictors have focused upon either the distribution of the retrieval scores, or the coherence of the top-ranked documents using traditional bag-of-words index representations. More rece

Dennis Gramlich, Carsten W. Scherer, Hannah Häring, Christian Ebenbauer

In this work, we develop a method based on robust control techniques to synthesize robust time-varying state-feedback policies for finite, infinite, and receding horizon control problems subject to convex quadratic state and input constraints. To ensure constraint satisfaction of our policy, we employ (initial state)-to-peak gain techniques. Based on this id

Gabriela Kováčová, Birgit Rudloff

We consider the problem of projecting a convex set onto a subspace, or equivalently formulated, the problem of computing a set obtained by applying a linear mapping to a convex feasible set. This includes the problem of approximating convex sets by polyhedrons. The existing literature on convex projections provides methods for bounded convex sets only, in th

Anwesha Maharana, Karel Plets, Alexey Isavnin, Stefaan Poedts

A magnetised flux rope model, "Flux Rope in 3D" (FRi3D) is used in the framework of European Heliospheric Forecasting Information Asset (EUHFORIA) for studying the evolution and propagation of coronal mass ejections (CME). In this paper, we rectify the mistake in the mentioned magnetic field profile of the FRi3D model used in Maharana et al., 2022, and we cl

Somnath Basu Roy Chowdhury, Nicholas Monath, Ahmad Beirami, Rahul Kidambi

Fairness, especially group fairness, is an important consideration in the context of machine learning systems. The most commonly adopted group fairness-enhancing techniques are in-processing methods that rely on a mixture of a fairness objective (e.g., demographic parity) and a task-specific objective (e.g., cross-entropy) during the training process. Howeve

Bingyu Zhu, Qingyun Sun, Jianxin Li, Daqing Li

Evaluating node influence is fundamental for identifying key nodes in complex networks. Existing methods typically rely on generic indicators to rank node influence across diverse networks, thereby ignoring the individualized features of each network itself. Actually, node influence stems not only from general features but the multi-scale individualized info

Dany Lachance-Quirion, Marc-Antoine Lemonde, Jean Olivier Simoneau, Lucas St-Jean

The Gottesman-Kitaev-Preskill (GKP) code encodes a logical qubit into a bosonic system with resilience against single-photon loss, the predominant error in most bosonic systems. Here we present experimental results demonstrating quantum error correction of GKP states based on reservoir engineering of a superconducting device. Error correction is made autonom

Pedro Afonso Marques, Samuel Ahizi, Miguel Alfonso Mendez

The thermal management of cryogenic storage tanks requires advanced control strategies to minimize the boil-off losses produced by heat leakages and sloshing-enhanced heat and mass transfer. This work presents a data-assimilation approach to calibrate a 0D thermodynamic model for cryogenic fuel tanks from data collected in real time from multiple tanks. The

Muhan Zhang

In the realm of deep learning, the self-attention mechanism has substantiated its pivotal role across a myriad of tasks, encompassing natural language processing and computer vision. Despite achieving success across diverse applications, the traditional self-attention mechanism primarily leverages linear transformations for the computation of query, key, and

Rui Wen, Tianhao Wang, Michael Backes, Yang Zhang

Large Language Models (LLMs) are powerful tools for natural language processing, enabling novel applications and user experiences. However, to achieve optimal performance, LLMs often require adaptation with private data, which poses privacy and security challenges. Several techniques have been proposed to adapt LLMs with private data, such as Low-Rank Adapta

Alexandre Delory, Daniel A. Kiefer, Maxime Lanoy, Antonin Eddi

To produce sounds, we adjust the tension of our vocal folds to shape their properties and control the pitch. This efficient mechanism offers inspiration for designing reconfigurable materials and adaptable soft robots. However, understanding how flexible structures respond to a significant static strain is not straightforward. This complexity also limits the

Ian MacLaren, Enrique Frutos-Myro, Steven Zeltmann, Colin Ophus

An approach for the crystallographic mapping of two-phase alloys on the nanoscale using a combination of scanned precession electron diffraction and open-source python libraries is introduced in this paper. This method is demonstrated using the example of a two-phase alpha / beta titanium alloy. The data was recorded using a direct electron detector to colle

Dominic Widdows, Amit Bhattacharyya

Quantum computers are expected to contribute more efficient and accurate ways of modeling economic processes. Quantum hardware is currently available at a relatively small scale, but effective algorithms are limited by the number of logic gates that can be used, before noise from gate inaccuracies tends to dominate results. Some theoretical algorithms that h

M. Lytova, F. Fillion-Gourdeau, S. Vallières, F. Légaré

The scattered field formalism is combined to the particle-in-cell method to model relativistic laser-plasma dynamics in complex field configurations. Despite the strong nonlinearity of the interactions, we demonstrate the validity of this technique theoretically, by using the semi-linear property of Maxwell-Vlasov equations, and empirically, by performing a

Yingxu He, Qiqi Sun

Automatic image captioning is a promising technique for conveying visual information using natural language. It can benefit various tasks in satellite remote sensing, such as environmental monitoring, resource management, disaster management, etc. However, one of the main challenges in this domain is the lack of large-scale image-caption datasets, as they re

Lin-Qing Gao, Xiao-Jun Bi, Jun Li, Run-Min Yao

The LHAASO collaboration recently reported the measurement of the gamma-ray spectra of GRB 221009A, which is the brightest burst ever, covering an energy range from 0.3 $\mathrm{TeV}$ to about 10 $\mathrm{TeV}$. Based on the observation, we investigate the ALP-photon oscillation effect in the host galaxy of GRB 221009A and the Milky Way. The ${\rm CL_s}$ met

Domenico Trotta, Timothy S. Horbury, David Lario, Rami Vainio

How thermal particles are accelerated to suprathermal energies is an unsolved issue, crucial for many astrophysical systems. We report novel observations of irregular, dispersive enhancements of the suprathermal particle population upstream of a high-Mach number interplanetary shock. We interpret the observed behavior as irregular "injections" of supratherma

Gugan Thoppe, L. A. Prashanth, Sanjay Bhat

We tackle the problem of estimating risk measures of the infinite-horizon discounted cost within a Markov cost process. The risk measures we study include variance, Value-at-Risk (VaR), and Conditional Value-at-Risk (CVaR). First, we show that estimating any of these risk measures with $\epsilon$-accuracy, either in expected or high-probability sense, requir

Zhen-Hua Zhang

In this paper, some subtleties in the measurement of the transverse polarization of the produced hadrons on symmetric colliders -- such as the Large Hadron Collider when conducting the $pp$ collisions -- are revealed. It can be proved that the transverse polarization of the produced particles with opposite pseudorapidity takes exactly opposite values if the

Jonathan S. Bloom, Dan Saracino

In \cite{striker2018rowmotion} Striker generalized Cameron and Fon-Der-Flaass's notion of a toggle group. In this paper we begin the study of transitive generalized toggle groups that contain a cycle. We first show that if such a group has degree $n$ and contains a transposition or a 3-cycle then the group contains $A_n$. Using the result about transposition

Imran Khan

With momentum increasing in the use of social robots as long-term assistive and collaborative partners, humans developing social bonds with these artificial agents appears to be inevitable. In human-human dyads, social bonding plays a powerful role in regulating behaviours, emotions, and even health. If this is to extend to human-robot dyads, the phenomenolo

Neha Gianchandani, Mahsa Dibaji, Johanna Ospel, Fernando Vega

Brain aging is a regional phenomenon, a facet that remains relatively under-explored within the realm of brain age prediction research using machine learning methods. Voxel-level predictions can provide localized brain age estimates that can provide granular insights into the regional aging processes. This is essential to understand the differences in aging

Radu Ignat, Mickael Nahon, Luc Nguyen

We prove that the degree-one vortex solution is the unique minimizer for the Ginzburg--Landau functional for gradient fields (that is, the Aviles--Giga model) in the unit ball $B^N$ in dimension $N \geq 4$ and with respect to its boundary value. A similar result is also proved for $\mathbb{S}^N$-valued maps in the theory of micromagnetics. Two methods are pr

Mathieu de Kruijf, Grayson M. Noah, Alberto Gomez-Saiz, John J. L. Morton

Silicon technology offers the enticing opportunity for monolithic integration of quantum and classical electronic circuits. However, the power consumption levels of classical electronics may compromise the local chip temperature and hence the fidelity of qubit operations. Here, we utilize a quantum-dot-based thermometer embedded in an industry-standard silic

Cheuk Wing Edmond Lam, Kartik Regulagadda, Matteo Donati, Abinash Tripathy

Jumping of coalescing condensate droplets from superhydrophobic surfaces is an interesting phenomenon which yields marked heat transfer enhancement over the more explored gravity-driven droplet removal mode in surface condensation, a phase change process of central interest to applications ranging from energy to water harvesting. However, when condensate mic

Shishir Khandelwal, Weijian Chen, Kater W. Murch, Géraldine Haack

Chiral state transfer along closed loops in the vicinity of an exceptional point is one of the many counter-intuitive observations in non-Hermitian physics. The application of this property beyond proof-of-principle in quantum physics, is an open question. In this work, we demonstrate chiral state conversion between singlet and triplet Bell states through fu

Charles Audet, Jean Bigeon, Romain Couderc, Michael Kokkolaras

This paper addresses risk averse constrained optimization problems where the objective and constraint functions can only be computed by a blackbox subject to unknown uncertainties. To handle mixed aleatory/epistemic uncertainties, the problem is transformed into a conditional value-at-risk (CVaR) constrained optimization problem. General inequality constrain

Fernando López, Jordi Luque, Carlos Segura, Pablo Gómez

Voice-based interfaces rely on a wake-up word mechanism to initiate communication with devices. However, achieving a robust, energy-efficient, and fast detection remains a challenge. This paper addresses these real production needs by enhancing data with temporal alignments and using detection based on two phases with multi-resolution. It employs two models:

Zhe-Kai Yi, Peng-Jie Guo, Hui Liang, Yi-Ran Li

Anomalous Hall effect (AHE), one of the most important electronic transport phenomena, generally appears in ferromagnetic materials but is rare in materials without magnetic elements. Here, we present a study of La$_3$MgBi$_5$, whose band structure carries multitype Dirac fermions. Although magnetic elements are absent in La$_3$MgBi$_5$, clear signals of AHE

Chenglin Fan, Ping Li, Hanyu Peng

The densest subgraph of a large graph usually refers to some subgraph with the highest average degree, which has been extended to the family of $p$-means dense subgraph objectives by~\citet{veldt2021generalized}. The $p$-mean densest subgraph problem seeks a subgraph with the highest average $p$-th-power degree, whereas the standard densest subgraph problem

Guomin Liu, Jian Song, Meng Wang

For a class of stochastic delay evolution equations driven by cylindrical $Q$-Wiener process, we study the Pontryagin's maximum principle for the stochastic recursive optimal control problem. The delays are given as moving averages with respect to general finite measures and appear in all the coefficients of the control system. In particular, the final cost

Yazhou Zhang, Mengyao Wang, Youxi Wu, Prayag Tiwari

Large language models (LLMs) and their variants have shown extraordinary efficacy across numerous downstream natural language processing (NLP) tasks, which has presented a new vision for the development of NLP. Despite their remarkable performance in natural language generating (NLG), LLMs lack a distinct focus on the emotion understanding domain. As a resul

Yibin Xu, Jingyi Zheng, Boris Düdder, Tijs Slaats

Sharding is essential for improving blockchain scalability. Existing protocols overlook diverse adversarial attacks, limiting transaction throughput. This paper presents Reticulum, a groundbreaking sharding protocol addressing this issue, boosting blockchain scalability. Reticulum employs a two-phase approach, adapting transaction throughput based on runtime

Jing-Chen Peng, Shaoxiong Yao, Kris Hauser

Soft-bubble tactile sensors have the potential to capture dense contact and force information across a large contact surface. However, it is difficult to extract contact forces directly from observing the bubble surface because local contacts change the global surface shape significantly due to membrane mechanics and air pressure. This paper presents a model

Tapendu Rana, Michael Ruzhansky

In this article, we explore the boundedness properties of pseudo-differential operators on radial sections of line bundles over the Poincar\'e upper half plane, even when dealing with symbols of limited regularity. We first prove the boundedness of these operators when the symbol is smooth. To achieve this, we establish a connection between the operator norm

Divya K. Srivastava, J. Mason Lilly, Karen M. Feigh

AI recommender systems are sought for decision support by providing suggestions to operators responsible for making final decisions. However, these systems are typically considered black boxes, and are often presented without any context or insight into the underlying algorithm. As a result, recommender systems can lead to miscalibrated user reliance and dec

Vishal Sunder, Beulah Karrolla, Eric Fosler-Lussier

In this work, we explore how a real time reading tracker can be built efficiently for children's voices. While previously proposed reading trackers focused on ASR-based cascaded approaches, we propose a fully end-to-end model making it less prone to lags in voice tracking. We employ a pointer network that directly learns to predict positions in the ground tr

Stevo Todorcevic, Jing Zhang

We investigate higher dimensional chain conditions, where the largeness notion is given by Fubini products of a given ideal. From strong saturation properties of an ideal, we derive abstractly versions of higher dimensional $\Delta$-system lemma, which imply many posets, including any finite support iteration of $\sigma$-centered posets and measure algebras,

Shanshan Xu, Leon Staufer, T. Y. S. S Santosh, Oana Ichim

Recognizing vulnerability is crucial for understanding and implementing targeted support to empower individuals in need. This is especially important at the European Court of Human Rights (ECtHR), where the court adapts Convention standards to meet actual individual needs and thus ensures effective human rights protection. However, the concept of vulnerabili

Yu Chen, Zihan Tan

We study the following characterization problem. Given a set $T$ of terminals and a $(2^{|T|}-2)$-dimensional vector $\pi$ whose coordinates are indexed by proper subsets of $T$, is there a graph $G$ that contains $T$, such that for all subsets $\emptyset\subsetneq S\subsetneq T$, $\pi_S$ equals the value of the min-cut in $G$ separating $S$ from $T\setminus

Mircea Mironenco, Patrick Forré

Invariance and equivariance to geometrical transformations have proven to be very useful inductive biases when training (convolutional) neural network models, especially in the low-data regime. Much work has focused on the case where the symmetry group employed is compact or abelian, or both. Recent work has explored enlarging the class of transformations us

Ignace Bossuyt, Stefan Vandewalle, Giovanni Samaey

We propose a micro-macro parallel-in-time Parareal method for scalar McKean-Vlasov stochastic differential equations (SDEs). In the algorithm, the fine Parareal propagator is a Monte Carlo simulation of an ensemble of particles, while an approximate ordinary differential equation (ODE) description of the mean and the variance of the particle distribution is

Meysam Bagheri, Sudeshna Roy, Thorsten Pöschel

We consider a liquid bridge between two identical spheres and provide approximate expressions for the capillary force and the exposed surface area of the liquid bridge as functions of the liquid bridge's total volume and the sphere separation distance. The radius of the spheres and the solid-liquid contact angle are parameters that enter the expressions. The

Christian J. Steinmetz, Thomas Walther, Joshua D. Reiss

Noise reduction techniques based on deep learning have demonstrated impressive performance in enhancing the overall quality of recorded speech. While these approaches are highly performant, their application in audio engineering can be limited due to a number of factors. These include operation only on speech without support for music, lack of real-time capa

Stefan Arnold, Nils Kemmerzell, Annika Schreiner

Differential Privacy (DP) has been tailored to address the unique challenges of text-to-text privatization. However, text-to-text privatization is known for degrading the performance of language models when trained on perturbed text. Employing a series of interpretation techniques on the internal representations extracted from BERT trained on perturbed pre-t

Rafeeq Syed, Cornelis J. G. J. Uiterwaal

A novel characterization method to measure the pulse duration of ultrafast near-IR pulses is introduced, which uses simple tabletop optics, is relatively inexpensive, and is expected to work in a broad wavelength range. Our diagnostic tool quantitatively characterizes the laser pulse duration of any near-IR wavelength assuming a Gaussian pulse shape with a l

Alejandro Antón Ruiz, Samar Hosseinzadegan, John Kvarnstrand, Klas Arvidsson

In this paper, we propose quantifying the radiated power of phased arrays or, in general, directive antennas, by the Constrained-View Radiated Power (CVRP). The constrained view shall be interpreted here as the Field-of-View (FoV) of an antenna that defines a region in space where focusing the radiated power is highly desired. In the limiting cases, we have

Langlin Huang, Shuhao Gu, Zhuocheng Zhang, Yang Feng

Conventional neural machine translation (NMT) models typically use subwords and words as the basic units for model input and comprehension. However, complete words and phrases composed of several tokens are often the fundamental units for expressing semantics, referred to as semantic units. To address this issue, we propose a method Semantic Units for Machin

Joé Brendel

For a broad class of symplectic manifolds of dimension at least six, we find the following new phenomenon: there exist local exotic Lagrangian tori. More specifically, let $X$ be a geometrically bounded symplectic manifold of dimension at least six. We show that every open subset of $X$ contains infinitely many Lagrangian tori which are distinct up to symple

Samuel Fransson, David Tilly, Robin Strand

The MR-Linac can enable real-time radiotherapy adaptation. However, real-time image acquisition is restricted to 2D to obtain sufficient spatial resolution, hindering accurate 3D segmentation. By reducing spatial resolution fast 3D imaging is feasible. Our study evaluates how much the spatial resolution of MR-images can be reduced without compromising a deep