February 2024 arXiv papers — page 75

Zilinghan Li, Shilan He, Pranshu Chaturvedi, Volodymyr Kindratenko

Federated learning enables multiple data owners to collaboratively train robust machine learning models without transferring large or sensitive local datasets by only sharing the parameters of the locally trained models. In this paper, we elaborate on the design of our Advanced Privacy-Preserving Federated Learning (APPFL) framework, which streamlines end-to

Naihao Deng, Zhenjie Sun, Ruiqi He, Aman Sikka

In this paper, we investigate the effectiveness of various LLMs in interpreting tabular data through different prompting strategies and data formats. Our analyses extend across six benchmarks for table-related tasks such as question-answering and fact-checking. We introduce for the first time the assessment of LLMs' performance on image-based table represent

Giulio Audagnotto, Antonino Di Piazza

The classical dynamics and the construction of quantum states in a plane wave curved spacetime are examined, paying particular attention to the similarities with the case of an electromagnetic plane wave in flat spacetime. A natural map connecting the dynamics of a particle in the Rosen metric and the motion of a charged particle in an electromagnetic plane

Paul Krzakala, Junjie Yang, Rémi Flamary, Florence d'Alché-Buc

We propose Any2graph, a generic framework for end-to-end Supervised Graph Prediction (SGP) i.e. a deep learning model that predicts an entire graph for any kind of input. The framework is built on a novel Optimal Transport loss, the Partially-Masked Fused Gromov-Wasserstein, that exhibits all necessary properties (permutation invariance, differentiability an

Nóra Frankl, Attila Jung, István Tomon

Two celebrated extensions of Helly's theorem are the Fractional Helly theorem of Katchalski and Liu (1979) and the Quantitative Volume theorem of B\'ar\'any, Katchalski, and Pach (1982). Improving on several recent works, we prove an optimal combination of these two results. We show that given a family $\mathcal{F}$ of $n$ convex sets in $\mathbb{R}^d$ such

Michela Lorandi, Anya Belz

The performance of NLP methods for severely under-resourced languages cannot currently hope to match the state of the art in NLP methods for well resourced languages. We explore the extent to which pretrained large language models (LLMs) can bridge this gap, via the example of data-to-text generation for Irish, Welsh, Breton and Maltese. We test LLMs on thes

Leigh Lapworth

L-QLES is an open source python code for generating 1D, 2D and 3D Laplacian operators and associated Poisson equations and their classical solutions. Its goal is to provide quantum algorithm developers with a flexible test case framework where features of industrial applications can be incorporated without the need for end-user domain knowledge or reliance o

Christophe Roux, Max Zimmer, Sebastian Pokutta

Federated Learning (FL) algorithms using Knowledge Distillation (KD) have received increasing attention due to their favorable properties with respect to privacy, non-i.i.d. data and communication cost. These methods depart from transmitting model parameters and instead communicate information about a learning task by sharing predictions on a public dataset.

Oleksandr Balabanov, Hampus Linander

Fine-tuning large language models can improve task specific performance, although a general understanding of what the fine-tuned model has learned, forgotten and how to trust its predictions is still missing. We derive principled uncertainty quantification for fine-tuned LLMs with posterior approximations using computationally efficient low-rank adaptation e

Marc Fossorier, Mahdi Shakiba-Herfeh, Huazi Zhang

In this paper, the OSD algorithm is modified to perform a limited GE with $O(N^3 \min\{R, 1-R\}^3)$ complexity for an $(N,K)$ linear block code of rate $R=K/N$.

Riccardo Miccini, Alessandro Cerioli, Clément Laroche, Tobias Piechowiak

Despite the recent advances in model compression techniques for deep neural networks, deploying such models on ultra-low-power embedded devices still proves challenging. In particular, quantization schemes for Gated Recurrent Units (GRU) are difficult to tune due to their dependence on an internal state, preventing them from fully benefiting from sub-8bit qu

Miri Varshavsky-Hassid, Roy Hirsch, Regev Cohen, Tomer Golany

The incorporation of Denoising Diffusion Models (DDMs) in the Text-to-Speech (TTS) domain is rising, providing great value in synthesizing high quality speech. Although they exhibit impressive audio quality, the extent of their semantic capabilities is unknown, and controlling their synthesized speech's vocal properties remains a challenge. Inspired by recen

A. Armatol, C. Augier, I. C. Bandac, D. Baudin

BINGO is a project aiming to set the grounds for large-scale bolometric neutrinoless double-beta-decay experiments capable of investigating the effective Majorana neutrino mass at a few meV level. It focuses on developing innovative technologies (a detector assembly, cryogenic photodetectors and active veto) to achieve a very low background index, of the ord

Jonathan Zheng, Alan Ritter, Wei Xu

The performance of Large Language Models (LLMs) degrades from the temporal drift between data used for model training and newer text seen during inference. One understudied avenue of language change causing data drift is the emergence of neologisms -- new word forms -- over time. We create a diverse resource of recent English neologisms by using several popu

Sebastian Koch, Narunas Vaskevicius, Mirco Colosi, Pedro Hermosilla

Current approaches for 3D scene graph prediction rely on labeled datasets to train models for a fixed set of known object classes and relationship categories. We present Open3DSG, an alternative approach to learn 3D scene graph prediction in an open world without requiring labeled scene graph data. We co-embed the features from a 3D scene graph prediction ba

Muhammad Fajrin, Joseph J. Armstrong, Jonathan C. Tan, Juan Farias

In their early, formative stages star clusters can undergo rapid dynamical evolution leading to strong gravitational interactions and ejection of "runaway" stars at high velocities. While O/B runaway stars have been well studied, lower-mass runaways are so far very poorly characterised, even though they are expected to be much more common. We carried out spe

Özkan Karabacak, Horia Cornean, Rafael Wisniewski

Stochastic convergence of discrete time Markov processes has been analysed based on a dual Lyapunov approach. Using some existing results on ergodic theory of Markov processes, it has been shown that existence of a properly subinvariant function (counterpart of the Lyapunov density in deterministic systems) implies sweeping of a Markov process out of the set

Nicole Arulanantham, M. K. McClure, Klaus Pontoppidan, Tracy L. Beck

We present JWST MIRI MRS observations of the edge-on protoplanetary disk around the young sub-solar mass star Tau 042021, acquired as part of the Cycle 1 GO program "Mapping Inclined Disk Astrochemical Signatures (MIDAS)." These data resolve the mid-IR spatial distributions of H$_2$, revealing X-shaped emission extending to ~200 au above the disk midplane wi

Anna Martin-Boyle, Aahan Tyagi, Marti A. Hearst, Dongyeop Kang

Numerous AI-assisted scholarly applications have been developed to aid different stages of the research process. We present an analysis of AI-assisted scholarly writing generated with ScholaCite, a tool we built that is designed for organizing literature and composing Related Work sections for academic papers. Our evaluation method focuses on the analysis of

Manu Gupta, J. K. Thalmann, A. M. Veronig

In order to improve our understanding on the pre-requisites of eruptive solar flares, we study and compare different measures that characterize the eruptive potential of solar active regions - the critical height for torus instability as a local measure and the helicity ratio as a global measure - with the structural properties of the underlying magnetic fie

Evelyn Macdonald, Kristen Menou, Christopher Lee, Adiv Paradise

We have shown in a recent study, using 3D climate simulations, that dayside land cover has a substantial impact on the climate of a synchronously rotating temperate rocky planet such as Proxima Centauri b. Building on that result, we generate synthetic transit spectra from our simulations to assess the impact of these land-induced climate uncertainties on wa

William P. Maxam, James C. Davis

Cybersecurity is a major challenge for large organizations. Traditional cybersecurity defense is reactive. Cybersecurity operations centers keep out adversaries and incident response teams clean up after break-ins. Recently a proactive stage has been introduced: Cyber Threat Hunting (TH) looks for potential compromises missed by other cyber defenses. TH is m

Merlin Christ, Tobias Dyckerhoff, Tashi Walde

We introduce notions of lax semiadditive and lax additive $(\infty,2)$-categories, categorifying the classical notions of semiadditive and additive 1-categories. To establish a well-behaved axiomatic framework, we develop a calculus of lax matrices and use it to prove that in locally cocomplete $(\infty,2)$-categories lax limits and lax colimits agree and ar

Thomas Prohaszka, Lukas Neumann, Markus Haltmeier

Image reconstruction in Multispectral Computed Tomography (MSCT) requires solving a challenging nonlinear inverse problem, commonly tackled via iterative optimization algorithms. Existing methods necessitate computing the derivative of the forward map and potentially its regularized inverse. In this work, we present a simple yet highly effective algorithm fo

Ruiyang Zhou

Levenshtein transformer (LevT) is a non-autoregressive machine translation model with high decoding efficiency and comparable translation quality in terms of bleu score, due to its parallel decoding and iterative refinement procedure. Are there any deficiencies of its translations and what improvements could be made? In this report, we focus on LevT's decode

Mosh Levy, Alon Jacoby, Yoav Goldberg

This paper explores the impact of extending input lengths on the capabilities of Large Language Models (LLMs). Despite LLMs advancements in recent times, their performance consistency across different input lengths is not well understood. We investigate this aspect by introducing a novel QA reasoning framework, specifically designed to assess the impact of i

Ming Yang

This article provides several theorems regarding the existence of limit for multivariable function, among which Theorem 1 and Theorem 3 relax the requirement for sequence of Heine's definition of limit. These results address the question of which paths need to be considered to determine the existence of limit for multivariable function.

Mirco Ciallella, Lorenzo Micalizzi, Victor Michel-Dansac, Philipp Öffner

In this work, we present a high-order finite volume framework for the numerical simulation of shallow water flows. The method is designed to accurately capture complex dynamics inherent in shallow water systems, particularly suited for applications such as tsunami simulations. The arbitrarily high-order framework ensures precise representation of flow behavi

Miguel Spínola, Shashank Saxena, Prateek Gupta, Brandon Runnels

Grain boundary (GB) properties greatly influence the mechanical, electrical, and thermal response of polycrystalline materials. Most computational studies of GB properties at finite temperatures use molecular dynamics (MD), which is computationally expensive, limited in the range of accessible timescales, and requires cumbersome techniques like thermodynamic

Xingjian Zhang, Zhaokai Pan, Guoding Liu

Quantum theory promises computational speed-ups over classical approaches. The celebrated Gottesman-Knill Theorem implies that the full power of quantum computation resides in the specific resource of "magic" states -- the secret sauce to establish universal quantum computation. However, it is still questionable whether magic indeed brings the believed quant

Thomas Depian, Martin Nöllenburg, Soeren Terziadis, Markus Wallinger

Boundary labeling is a technique in computational geometry used to label sets of features in an illustration. It involves placing labels along an axis-parallel bounding box and connecting each label with its corresponding feature using non-crossing leader lines. Although boundary labeling is well-studied, semantic constraints on the labels have not been inve

Niklas Wretblad, Fredrik Gordh Riseby, Rahul Biswas, Amin Ahmadi

Text-to-SQL, which involves translating natural language into Structured Query Language (SQL), is crucial for enabling broad access to structured databases without expert knowledge. However, designing models for such tasks is challenging due to numerous factors, including the presence of 'noise,' such as ambiguous questions and syntactical errors. This study

Simon Dirmeier, Ye Hong, Fernando Perez-Cruz

Diffusion probabilistic models (DPMs) have rapidly evolved to be one of the predominant generative models for the simulation of synthetic data, for instance, for computer vision, audio, natural language processing, or biomolecule generation. Here, we propose using DPMs for the generation of synthetic individual location trajectories (ILTs) which are sequence

Semih Cayci, Atilla Eryilmaz

We analyze recurrent neural networks with diagonal hidden-to-hidden weight matrices, trained with gradient descent in the supervised learning setting, and prove that gradient descent can achieve optimality \emph{without} massive overparameterization. Our in-depth nonasymptotic analysis (i) provides improved bounds on the network size $m$ in terms of the sequ

Emanuele Marconato, Samuele Bortolotti, Emile van Krieken, Antonio Vergari

Neuro-Symbolic (NeSy) predictors that conform to symbolic knowledge - encoding, e.g., safety constraints - can be affected by Reasoning Shortcuts (RSs): They learn concepts consistent with the symbolic knowledge by exploiting unintended semantics. RSs compromise reliability and generalization and, as we show in this paper, they are linked to NeSy models bein

S. Johanan Joysingh, P. Vijayalakshmi, T. Nagarajan

A novel feature, based on the chirp z-transform, that offers an improved representation of the underlying true spectrum is proposed. This feature, the chirp MFCC, is derived by computing the Mel frequency cepstral coefficients from the chirp magnitude spectrum, instead of the Fourier transform magnitude spectrum. The theoretical foundations for the proposal,

Jiahe Chen, Jinkun Cao, Dahua Lin, Kris Kitani

To predict future trajectories, the normalizing flow with a standard Gaussian prior suffers from weak diversity. The ineffectiveness comes from the conflict between the fact of asymmetric and multi-modal distribution of likely outcomes and symmetric and single-modal original distribution and supervision losses. Instead, we propose constructing a mixed Gaussi

Thodoris Lykouris, Wentao Weng

High-stakes applications rely on combining Artificial Intelligence (AI) and humans for responsive and reliable decision making. For example, content moderation in social media platforms often employs an AI-human pipeline to promptly remove policy violations without jeopardizing legitimate content. A typical heuristic estimates the risk of incoming content an

Paola Martire, Cristiano Longarini, Giuseppe Lodato, Giovanni P. Rosotti

In recent years the gas kinematics probed by molecular lines detected with ALMA has opened a new window to study protoplanetary disks. High spatial and spectral resolution observations have revealed the complexity of protoplanetary disk structure and correctly interpreting these data allow us to gain a better comprehension of the planet formation process. We

Theresa Stadler, Bogdan Kulynych, Michael C. Gastpar, Nicolas Papernot

The promise of least-privilege learning -- to find feature representations that are useful for a learning task but prevent inference of any sensitive information unrelated to this task -- is highly appealing. However, so far this concept has only been stated informally. It thus remains an open question whether and how we can achieve this goal. In this work,

Tom Bocklisch, Thomas Werkmeister, Daksh Varshneya, Alan Nichol

We describe a system for building task-oriented dialogue systems combining the in-context learning abilities of large language models (LLMs) with the deterministic execution of business logic. LLMs are used to translate between the surface form of the conversation and a domain-specific language (DSL) which is used to progress the business logic. We compare o

Zihan Qiu, Zeyu Huang, Youcheng Huang, Jie Fu

The feed-forward networks (FFNs) in transformers are recognized as a group of key-value neural memories to restore abstract high-level knowledge. In this work, we conduct an empirical ablation study on updating keys (the 1st layer in the FFNs layer) or values (the 2nd layer in the FFNs layer). We compare those two methods in various knowledge editing and fin

Louis Ohl, Pierre-Alexandre Mattei, Mickaël Leclercq, Arnaud Droit

Trees are convenient models for obtaining explainable predictions on relatively small datasets. Although there are many proposals for the end-to-end construction of such trees in supervised learning, learning a tree end-to-end for clustering without labels remains an open challenge. As most works focus on interpreting with trees the result of another cluster

Jonas Beck, Nathanael Bosch, Michael Deistler, Kyra L. Kadhim

Ordinary differential equations (ODEs) are widely used to describe dynamical systems in science, but identifying parameters that explain experimental measurements is challenging. In particular, although ODEs are differentiable and would allow for gradient-based parameter optimization, the nonlinear dynamics of ODEs often lead to many local minima and extreme

Alessandro Cucinotta, Andrea Mondino

The goal of this note is to prove the Half Space Property for RCD(0,N) spaces, namely that if (X,d,m) is a parabolic RCD(0,N) space and $ C \subset X \times \mathbb{R}$ is locally the boundary of a perimeter minimizing set and it is contained in a half space, then $C$ is a locally finite union of horizontal slices. The same result is proved for RCD(K,N) spac

Argyrios Christodoulou, Natalia Jurga

Self-projective sets are natural fractal sets which describe the action of a semigroup of matrices on projective space. In recent years there has been growing interest in studying the dimension theory of self-projective sets, as well as progress in the understanding of closely related objects such as Furstenberg measures. The aim of this survey is twofold: f

A. Eckey, A. Golub, F. C. Salgado, S. Villalba-Chávez

Near-future experiments intend to detect strong-field Breit-Wheeler pair creation from the collision between bremsstrahlung bursts containing GeV-$\gamma$ quanta and high-intensity laser pulses. In this theoretical study, we investigate the influence of laser focusing, radiation reaction and a broad bremsstrahlung $\gamma$ spectrum on the energy and angular

Simone Verzellesi

We provide integral representation and $\Gamma$-compactness results for anisotropic local functionals depending on arbitrary Lipschitz continuous vector fields. In particular, neither bracket-generating assumptions nor linear independence conditions are required.

Michal Bouška, Přemysl Šůcha, Antonín Novák, Zdeněk Hanzálek

In this paper, we investigate the use of the deep learning method for solving a well-known NP-hard single machine scheduling problem with the objective of minimizing the total tardiness. We propose a deep neural network that acts as a polynomial-time estimator of the criterion value used in a single-pass scheduling algorithm based on Lawler's decomposition a

Jun Zhan, Junqi Dai, Jiasheng Ye, Yunhua Zhou

We introduce AnyGPT, an any-to-any multimodal language model that utilizes discrete representations for the unified processing of various modalities, including speech, text, images, and music. AnyGPT can be trained stably without any alterations to the current large language model (LLM) architecture or training paradigms. Instead, it relies exclusively on da

Xuelin Qian, Yu Wang, Simian Luo, Yinda Zhang

Auto-regressive models have achieved impressive results in 2D image generation by modeling joint distributions in grid space. In this paper, we extend auto-regressive models to 3D domains, and seek a stronger ability of 3D shape generation by improving auto-regressive models at capacity and scalability simultaneously. Firstly, we leverage an ensemble of publ

Tuna Demircik, Domingo Gallegos, Umut Gürsoy, Matti Järvinen

We study energy-momentum and charge transport in strongly interacting holographic quantum field theories in an anisotropic thermal state by contrasting three different holographic methods to compute transport coefficients: standard holographic calculation of retarded Greens functions, a method based on the null-focusing equation near horizon and the novel me

Evan Wonisch, Jonas Mensing, Andreas Heuer

Exploiting physical processes for fast and energy-efficient computation bears great potential in the advancement of modern hardware components. This paper explores non-linear charge tunneling in nanoparticle networks, controlled by external voltages. The dynamics are described by a master equation, which describes the development of a distribution function o

Jueon Eom, Seyeon Jeong, Taekyoung Kwon

Fuzzing is an effective bug-finding technique but it struggles with complex systems like JavaScript engines that demand precise grammatical input. Recently, researchers have adopted language models for context-aware mutation in fuzzing to address this problem. However, existing techniques are limited in utilizing coverage guidance for fuzzing, which is rathe

Mark L. Lewis

Let $G$ be a $p$-group for some prime $p$. Let $n$ be the positive integer so that $|G:Z(G)| = p^n$. Suppose $A$ is a maximal abelian subgroup of $G$. Let $$p^l = {\rm max} \{|Z(C_G (g)):Z(G)| : g \in G \setminus Z(G)\},$$ $$p^b = {\rm max} \{|cl(g)| : g \in G \setminus Z(G) \},$$ and $p^a = |A:Z(G)|$. Then we show that $a \ge n/(b+l)$.

Haolin Chen, Philip N. Garner

We are motivated primarily by the adaptation of text-to-speech synthesis models; however we argue that more generic parameter-efficient fine-tuning (PEFT) is an appropriate framework to do such adaptation. Nevertheless, catastrophic forgetting remains an issue with PEFT, damaging the pre-trained model's inherent capabilities. We demonstrate that existing Bay

Run-Ze Fan, Xuefeng Li, Haoyang Zou, Junlong Li

The quality of finetuning data is crucial for aligning large language models (LLMs) with human values. Current methods to improve data quality are either labor-intensive or prone to factual errors caused by LLM hallucinations. This paper explores elevating the quality of existing instruction data to better align with human values, introducing a simple and ef

Tian Wang

Let $A/K$ be an absolutely simple abelian surface defined over a number field $K$. We give unconditional upper bounds for the number of prime ideals $\mathfrak{p}$ of $K$ with norm up to $x$ such that $A$ has supersingular reduction at $\mathfrak{p}$. These bounds are obtained in three distinct settings, depending on the endomorphism algebra of $A$, namely,

Paul Breiding, Pierpaola Santarsiero

Subspace varieties are algebraic varieties whose elements are tensors with bounded multilinear rank. In this paper, we compute their degrees by computing their volumes.

Xinwei Guo, Yujun Li, Yafeng Peng, Xuetao Wei

As AIGC has impacted our society profoundly in the past years, ethical issues have received tremendous attention. The most urgent one is the AIGC copyright dilemma, which can immensely stifle the development of AIGC and greatly cost the entire society. Given the complexity of AIGC copyright governance and the fact that no perfect solution currently exists, p

Minh Nhat Pham, Chun-Jen Su, Yu-Ching Huang, Kun-Ta Lin

Intermolecular interactions are crucial in determining the morphology of solution-processed semiconducting polymer thin films. However, these random interactions often lead to disordered or short-range ordered structures. Achieving long-range order in these films has been a challenge due to limited control over microscopic interactions in current techniques.

Jenny Taylor, Michael Papenbrock, Tobias Stockmanns, Ralf Kliemt

A new generation of experiments is being developed, where the challenge of separating rare signal processes from background at high intensities requires a change of trigger paradigm. At the future PANDA experiment at FAIR, hardware triggers will be abandoned and instead a purely software-based system will be used. This requires novel reconstruction methods w

Alexander Bendeck, Dennis Bromley, Vidya Setlur

Natural language and search interfaces intuitively facilitate data exploration and provide visualization responses to diverse analytical queries based on the underlying datasets. However, these interfaces often fail to interpret more complex analytical intents, such as discerning subtleties and quantifiable differences between terms like "bump" and "spike" i

Thomas Eiter, Ana Leonor Silvestre

This paper is concerned with integral representations and asymptotic expansions of solutions to the time-periodic incompressible Navier-Stokes equations for fluid flow in the exterior of a rigid body that moves with constant velocity. Using the time-periodic Oseen fundamental solution, we derive representation formulas for solutions with suitable regularity.

Yasuko Kawahata

This paper is a preliminary report of the research plan and a digest of the results and discussions. On research note explores the complex dynamics of fake news dissemination and fact-checking costs within the framework of information markets and analyzes the equilibrium between supply and demand using the concepts of droop quotas, Meek's method, and margina

Masaya Ohagi

Online social networks often create echo chambers where people only hear opinions reinforcing their beliefs. An echo chamber often generates polarization, leading to conflicts caused by people with radical opinions, such as the January 6, 2021, attack on the US Capitol. The echo chamber has been viewed as a human-specific problem, but this implicit assumptio

Jean-Marie Frère

Glueballs are the most obvious, but this far not fully tested prediction of Quantum Chromodynamics. The lowest expected glueball states, in particular $0^{++}$ states are difficult to characterize, since they share the quantum numbers of the "vacuum" and of a number of possible quark-antiquark states. In this paper, we argue that the more easily identifiable

Charlotte Dugourd-Camus, Claudia P. Ferreira, Mostafa Adimy

Antibodies play an essential role in the immune response to viral infections, vaccination, or antibody therapy. Nevertheless, they can be either protective or harmful during the immune response. Moreover, competition or cooperation between mixed antibodies can enhance or reduce this protective or harmful effect. Using the laws of chemical reactions, we propo

Donato Pertici, Alberto Dolcetti

We study some properties of $SU_n$ endowed with the Frobenius metric $\phi$, which is, up to a positive constant multiple, the unique bi-invariant Riemannian metric on $SU_n$. In particular we express the distance between $P, Q \in SU_n$ in terms of eigenvalues of $P^*Q$; we compute the diameter of $(SU_n, \phi)$ and we determine its diametral pairs; we prov

Shengpeng Ji, Minghui Fang, Jialong Zuo, Ziyue Jiang

In recent years, large language models have achieved significant success in generative tasks related to speech, audio, music, and other signal domains. A crucial element of these models is the discrete acoustic codecs, which serve as an intermediate representation replacing the mel-spectrogram. However, there exist several gaps between discrete codecs and do

Olivier Devauchelle, Piotr Szymczak, Piotr Nowakowski

In modern democracies, the outcome of elections and referendums is often remarkably tight. The repetition of these divisive events are the hallmark of a split society; to the physicist, however, it is an astonishing feat for such large collections of diverse individuals. Many sociophysics models reproduce the emergence of collective human behavior with inter

Zhi-Yun Tang, Xianhua Tang

In this paper, we give a first negative answer to a question proposed by Li and Lin (Arch Ration Mech Anal 203(3): 943-968, 2012). Meanwhile we also give a second positive answer to the Li-Lin's open problem. The first positive answer was given by G. Cerami, X. Zhong and W. Zou (Calc. Var. Partial Differential Equations, 54(2): 1793-1829, 2015).

Hiroyuki Sato, Keisuke Suzuki, Atsushi Hashizume, Ryoichi Hanazawa

Progressive cognitive decline spanning across decades is characteristic of Alzheimer's disease (AD). Various predictive models have been designed to realize its early onset and study the long-term trajectories of cognitive test scores across populations of interest. Research efforts have been geared towards superimposing patients' cognitive test scores with

Yuanchi Zhang, Yile Wang, Zijun Liu, Shuo Wang

While large language models (LLMs) have been pre-trained on multilingual corpora, their performance still lags behind in most languages compared to a few resource-rich languages. One common approach to mitigate this issue is to translate training data from resource-rich languages into other languages and then continue training. However, using the data obtain

Riley Shipley, Garrett Hooten, David Boehme, Derek Schafer

While application profiling has been a mainstay in the HPC community for years, profiling of MPI and other communication middleware has not received the same degree of exploration. This paper adds to the discussion of MPI profiling, contributing two general-purpose profiling methods as well as practical applications of these methods to an existing implementa

Chengyi Ju, Jiannong Cao, Yu Yang, Zhen-Qun Yang

In the era of modern education, addressing cross-school learner diversity is crucial, especially in personalized recommender systems for elective course selection. However, privacy concerns often limit cross-school data sharing, which hinders existing methods' ability to model sparse data and address heterogeneity effectively, ultimately leading to suboptima

Zhengfu He, Xuyang Ge, Qiong Tang, Tianxiang Sun

Sparse dictionary learning has been a rapidly growing technique in mechanistic interpretability to attack superposition and extract more human-understandable features from model activations. We ask a further question based on the extracted more monosemantic features: How do we recognize circuits connecting the enormous amount of dictionary features? We propo

Alfred Galichon, Antoine Jacquet

Matching problems with linearly transferable utility (LTU) generalize the well-studied transferable utility (TU) case by relaxing the assumption that utility is transferred one-for-one within matched pairs. We show that LTU matching problems can be reframed as nonzero-sum games between two players, thus generalizing a result from von Neumann. The underlying

B. Rusen Argun, Yu Fu, Antonia Statt

Rigid bodies, made of smaller composite beads, are commonly used to simulate anisotropic particles with molecular dynamics or Monte Carlo methods. To accurately represent the particle shape and to obtain smooth and realistic effective pair interactions between two rigid bodies, each body may need to contain hundreds of spherical beads. Given an interacting p

Naquee Rizwan, Paramananda Bhaskar, Mithun Das, Swadhin Satyaprakash Majhi

There is a rapid increase in the use of multimedia content in current social media platforms. One of the highly popular forms of such multimedia content are memes. While memes have been primarily invented to promote funny and buoyant discussions, malevolent users exploit memes to target individuals or vulnerable communities, making it imperative to identify

Yoshihiko Hasegawa, Tomohiro Nishiyama

Thermodynamic tradeoff relations quantify the fundamental concept of ``no free lunch'' in the physical world, suggesting that faster and more precise physical processes come at a higher thermodynamic cost. The key elements in these tradeoff relations are the thermodynamic uncertainty relation and speed limit, which are closely tied to information inequalitie

Anastasiia Ciers, Alexander Jung, Joachim Ciers, Laurentius Radit Nindito

High-\Qm{} mechanical resonators are crucial for applications where low noise and long coherence time are required, as mirror suspensions, quantum cavity optomechanical devices, or nanomechanical sensors. Tensile strain in the material enables the use of dissipation dilution and strain engineering techniques, which increase the mechanical quality factor. The

Ziyue Wang, Chi Chen, Yiqi Zhu, Fuwen Luo

With the bloom of Large Language Models (LLMs), Multimodal Large Language Models (MLLMs) that incorporate LLMs with pre-trained vision models have recently demonstrated impressive performance across diverse vision-language tasks. However, they fall short to comprehend context involving multiple images. A primary reason for this shortcoming is that the visual

Sinwoo Cho, Omar Barrera, Jack Kramer, Vakhtang Chulukhadze

This paper reports the first piezoelectric acoustic filter in periodically poled piezoelectric film (P3F) lithium niobate (LiNbO3) at 23.8 GHz with low insertion loss (IL) of 1.52 dB and 3-dB fractional bandwidth (FBW) of 19.4%. The filter features a compact footprint of 0.64 mm2. The third-order ladder filter is implemented with electrically coupled resonat

Yuxia Wang, Zenan Zhai, Haonan Li, Xudong Han

Many studies have demonstrated that large language models (LLMs) can produce harmful responses, exposing users to unexpected risks when LLMs are deployed. Previous studies have proposed comprehensive taxonomies of the risks posed by LLMs, as well as corresponding prompts that can be used to examine the safety mechanisms of LLMs. However, the focus has been a

Xuanhua He, Ke Cao, Keyu Yan, Rui Li

Pan-sharpening involves integrating information from low-resolution multi-spectral and high-resolution panchromatic images to generate high-resolution multi-spectral counterparts. While recent advancements in the state space model, particularly the efficient long-range dependency modeling achieved by Mamba, have revolutionized computer vision community, its

Dorothee Henke, Henri Lefebvre, Martin Schmidt, Johannes Thürauf

It is well-known that coupling constraints in linear bilevel optimization can lead to disconnected feasible sets, which is not possible without coupling constraints. However, there is no difference between linear bilevel problems with and without coupling constraints w.r.t. their complexity-theoretical hardness. In this note, we prove that, although there is

Manuel Schürch, Laura Boos, Viola Heinzelmann-Schwarz, Gabriele Gut

AI-driven precision oncology has the transformative potential to reshape cancer treatment by leveraging the power of AI models to analyze the interaction between complex patient characteristics and their corresponding treatment outcomes. New technological platforms have facilitated the timely acquisition of multimodal data on tumor biology at an unprecedente

Grgur Kovač, Rémy Portelas, Masataka Sawayama, Peter Ford Dominey

The standard way to study Large Language Models (LLMs) with benchmarks or psychology questionnaires is to provide many different queries from similar minimal contexts (e.g. multiple choice questions). However, due to LLMs' highly context-dependent nature, conclusions from such minimal-context evaluations may be little informative about the model's behavior i

Myung Gyo Oh, Hong Eun Ahn, Leo Hyun Park, Taekyoung Kwon

Neural language models (LMs) are vulnerable to training data extraction attacks due to data memorization. This paper introduces a novel attack scenario wherein an attacker adversarially fine-tunes pre-trained LMs to amplify the exposure of the original training data. This strategy differs from prior studies by aiming to intensify the LM's retention of its pr

David G. Clark, Manuel Beiran

Neural circuits comprise multiple interconnected regions, each with complex dynamics. The interplay between local and global activity is thought to underlie computational flexibility, yet the structure of multiregion neural activity and its origins in synaptic connectivity remain poorly understood. We investigate recurrent neural networks with multiple regio

Leo Hyun Park, Jaeuk Kim, Myung Gyo Oh, Jaewoo Park

Deep learning models continue to advance in accuracy, yet they remain vulnerable to adversarial attacks, which often lead to the misclassification of adversarial examples. Adversarial training is used to mitigate this problem by increasing robustness against these attacks. However, this approach typically reduces a model's standard accuracy on clean, non-adv

Peter Reinholdt, Erik Rosendahl Kjellgren, Juliane Holst Fuglsbjerg, Karl Michael Ziems

We explore Davidson methods for obtaining excitation energies and other linear response properties within quantum self-consistent linear response (q-sc-LR) theory. Davidson-type methods allow for obtaining only a few selected excitation energies without explicitly constructing the electronic Hessian since they only require the ability to perform Hessian-vect

Renqiu Xia, Bo Zhang, Hancheng Ye, Xiangchao Yan

Recently, many versatile Multi-modal Large Language Models (MLLMs) have emerged continuously. However, their capacity to query information depicted in visual charts and engage in reasoning based on the queried contents remains under-explored. In this paper, to comprehensively and rigorously benchmark the ability of the off-the-shelf MLLMs in the chart domain

Yean Cheng, Renjie Wan, Shuchen Weng, Chengxuan Zhu

Though Neural Radiance Fields (NeRF) can produce colorful 3D representations of the world by using a set of 2D images, such ability becomes non-existent when only monochromatic images are provided. Since color is necessary in representing the world, reproducing color from monochromatic radiance fields becomes crucial. To achieve this goal, instead of manipul

Mafalda Malafaia, Thalea Schlender, Peter A. N. Bosman, Tanja Alderliesten

In the health domain, decisions are often based on different data modalities. Thus, when creating prediction models, multimodal fusion approaches that can extract and combine relevant features from different data modalities, can be highly beneficial. Furthermore, it is important to understand how each modality impacts the final prediction, especially in high

Charlotte Vermeylen, Marc Van Barel

In this paper a new Riemannian rank adaptive method (RRAM) is proposed for the low-rank tensor completion problem (LRTCP) formulated as a least-squares optimization problem on the algebraic variety of tensors of bounded tensor-train (TT) rank. The method iteratively optimizes over fixed-rank smooth manifolds using a Riemannian conjugate gradient algorithm fr

Jianshu Hu, Yunpeng Jiang, Paul Weng

Various data augmentation techniques have been recently proposed in image-based deep reinforcement learning (DRL). Although they empirically demonstrate the effectiveness of data augmentation for improving sample efficiency or generalization, which technique should be preferred is not always clear. To tackle this question, we analyze existing methods to bett

Mirko Daumann, Thomas Dahm

We study the broadening of initially localized wave packets in a quasi one-dimensional diamond ladder with interacting, spinless fermions. The lattice possesses a flat band causing localization. We place special focus on the transition away from the flat band many-body localized case by adding very weak dispersion. By doing so, we allow propagation of the wa

Dinh An Ngo, Thanh Dat Nguyen, Thi Le Chi Dang, Huy Hoan Le

Cheating in online exams has become a prevalent issue over the past decade, especially during the COVID-19 pandemic. To address this issue of academic dishonesty, our "Exam Monitoring System: Detecting Abnormal Behavior in Online Examinations" is designed to assist proctors in identifying unusual student behavior. Our system demonstrates high accuracy and sp