May 2024 arXiv papers — page 57

Chenbo Shi, Jin Pan, Xin Gu, Shichen Liang

This paper presents a novel approach for computing substructure characteristic modes. This method leverages electromagnetic scattering matrices and spherical wave expansion to directly decompose electromagnetic fields. Unlike conventional methods that rely on the impedance matrix generated by the method of moments (MoM), our technique simplifies the problem

G. P. Singh, Romanshu Garg, Ashutosh Singh

In this study, we construct a theoretical framework based on the generalized Hubble parameter form which may arise within the particle creation, viscous and $f(R)$ gravity theory. The Hubble parameter is scrutinized for its compatibility with the observational data relevant to the late-time universe. By using Bayesian statistical techniques based on $\chi^{2

Jeremiah Birrell, Markos A. Katsoulakis, Luc Rey-Bellet, Benjamin J. Zhang

We present a novel method for training score-based generative models which uses nonlinear noising dynamics to improve learning of structured distributions. Generalizing to a nonlinear drift allows for additional structure to be incorporated into the dynamics, thus making the training better adapted to the data, e.g., in the case of multimodality or (approxim

Hao Sun, Mihaela van der Schaar

Aligning Large Language Models (LLMs) is crucial for enhancing their safety and utility. However, existing methods, primarily based on preference datasets, face challenges such as noisy labels, high annotation costs, and privacy concerns. In this work, we introduce Alignment from Demonstrations (AfD), a novel approach leveraging high-quality demonstration da

S. Vaidya, A. Hernández-Melián, J. P. Tidey, S. P. M. Curley

We investigate the magnetic properties of $S=1$ antiferromagnetic diamond lattice, Ni$X_{2}$(pyrimidine)$_{2}$ ($X$ = Cl, Br), hosting a single-ion anisotropy (SIA) orientation which alternates between neighbouring sites. Through neutron diffraction measurements of the $X$ = Cl compound, the ordered state spins are found to align collinearly along a pseudo-e

Ruikai Cui, Xibin Song, Weixuan Sun, Senbo Wang

Large Reconstruction Models have made significant strides in the realm of automated 3D content generation from single or multiple input images. Despite their success, these models often produce 3D meshes with geometric inaccuracies, stemming from the inherent challenges of deducing 3D shapes solely from image data. In this work, we introduce a novel framewor

Christopher Chong, Dmitry E. Pelinovsky, Guido Schneider

We prove the existence of a class of time-localized and space-periodic breathers (called q-gap breathers) in nonlinear lattices with time-periodic coefficients. These q-gap breathers are the counterparts to the classical space-localized and time-periodic breathers found in space-periodic systems. Using normal form transformations, we establish rigorously the

Claudia Alfes, Paul Kiefer, Jan Mazáč

In this article, we show that Fourier eigenmeasures supported on spheres with radii given by a locally finite sequence, which we call $k$-spherical measures, correspond to Fourier series exhibiting a modular-type transformation behaviour with respect to the metaplectic group. A familiar subset of such Fourier series comprises holomorphic modular forms. This

Xiankang He, Guangkai Xu, Bo Zhang, Hao Chen

Monocular camera calibration is a key precondition for numerous 3D vision applications. Despite considerable advancements, existing methods often hinge on specific assumptions and struggle to generalize across varied real-world scenarios, and the performance is limited by insufficient training data. Recently, diffusion models trained on expansive datasets ha

William L. Tong, Cengiz Pehlevan

In-context learning (ICL), the remarkable ability to solve a task from only input exemplars, is often assumed to be a unique hallmark of Transformer models. By examining commonly employed synthetic ICL tasks, we demonstrate that multi-layer perceptrons (MLPs) can also learn in-context. Moreover, MLPs, and the closely related MLP-Mixer models, learn in-contex

Misbah Yaqoob, Fabian Kammerbauer, Tom G. Saunderson, Vitaliy I. Vasyuchka

The manipulation of magnetization via Magnetic torques is one of the most important phenomena in spintronics. In thin films, conventionally, a charge current flowing in a heavy metal is used to generate transverse spin currents and to exert torques on the magnetization of an adjacent ferromagnetic thin film layer. Here, in contrast to the typically employed

Ingo Blakowski, Dmitrii Zendrikov, Cristiano Capone, Giacomo Indiveri

Achieving energy efficiency in learning is a key challenge for artificial intelligence (AI) computing platforms. Biological systems demonstrate remarkable abilities to learn complex skills quickly and efficiently. Inspired by this, we present a hardware implementation of model-based reinforcement learning (MBRL) using spiking neural networks (SNNs) on mixed-

Johanna Rogvall, Roshan Singh, Morgane Vacher, Marcus Lundberg

Photochemistry and photophysics processes involve structures far from equilibrium. In these reactions, there is often strong coupling between nuclear and electronic degrees of freedom. For first-row transition metals, K$\beta$ X-ray emission spectroscopy (XES) is a sensitive probe of electronic structure due to the direct overlap between the valence orbitals

Karl Tamberg, Hayretdin Bahsi

Despite various approaches being employed to detect vulnerabilities, the number of reported vulnerabilities shows an upward trend over the years. This suggests the problems are not caught before the code is released, which could be caused by many factors, like lack of awareness, limited efficacy of the existing vulnerability detection tools or the tools not

Huy V. Vo, Vasil Khalidov, Timothée Darcet, Théo Moutakanni

Self-supervised features are the cornerstone of modern machine learning systems. They are typically pre-trained on data collections whose construction and curation typically require extensive human effort. This manual process has some limitations similar to those encountered in supervised learning, e.g., the crowd-sourced selection of data is costly and time

Wencong Wang, Jacob Kokinda, Jiazhen Li, Qing Gu

Our recent research on type-I quadrature parity-time (PT) symmetry, utilizing an open twin-beam system, not only enables observing genuine quantum photonic PT symmetry amid phase-sensitive amplification (PSA) and loss in the presence of Langevin noise but also reveals additional classical-to-quantum (C2Q) transitions in quadrature and relative-intensity nois

Seyed Moein Ayyoubzadeh, Kourosh Shahnazari

Homograph disambiguation, the task of distinguishing words with identical spellings but different meanings, poses a substantial challenge in natural language processing. In this study, we introduce a novel dataset tailored for Persian homograph disambiguation. Our work encompasses a thorough exploration of various embeddings, evaluated through the cosine sim

D. Fasano, A. J. Winter, M. Benisty, G. Rosotti

Detecting protoplanets during their formation stage is an important but elusive goal of modern astronomy. Kinematic detections via the spiral wakes in the gaseous disc are a promising avenue to achieve this goal. We aim to test the applicability to observations in the low and intermediate planet mass regimes of a commonly used semi-analytical model for plane

Tim C. Rese, Nils Japke, Sebastian Koch, Tobias Pfandzelter

In a continuous deployment setting, Function-as-a-Service (FaaS) applications frequently receive updated releases, each of which can cause a performance regression. While continuous benchmarking, i.e., comparing benchmark results of the updated and the previous version, can detect such regressions, performance variability of FaaS platforms necessitates thous

Modesto Orozco-Ruiz, Nguyen H. Le, Florian Mintert

We show that combining ideas from the fields of quantum invariants and of optimal control can be used to design optimal quantum control solutions without explicit reference to quantum states. The states are specified only implicitly in terms of operators to which they are eigenstates. The scaling in numerical effort of the resultant approach is not given by

John S. McCaskill, Thomas Maeke, Dominic Funke, Pierre Mayr

Lablets are autonomous microscopic particles with programmable CMOS electronics that canvcontrol electrokinetic phenomena and electrochemical reactions in solution via actuator and sensor microelectrodes. The lablets are designed to be rechargeable using an integrated supercapacitor, and to allow docking to one another or to a smart surface for interchange o

Wee Kiat New, Kai-Kit Wong, Hao Xu, Farshad Rostami Ghadi

Fluid antenna system (FAS) has recently surfaced as a promising technology for the upcoming sixth generation (6G) wireless networks. Unlike traditional antenna system (TAS) with fixed antenna location, FAS introduces a flexible component in which the radiating element can switch its position within a predefined space. This capability allows FAS to achieve ad

Sylvain Lannebère, David E. Fernandes, Tiago A. Morgado, Mário G. Silveirinha

Here, we present an exploratory study of the potential applications of electrically biased materials that possess a nonreciprocal and non-Hermitian electromagnetic response analogous to the electronic response of field-effect transistors. The most distinctive feature of such materials is their chiral-gain, meaning that their response can be active or dissipa

Jiantong Jiang, Zeyi Wen, Peiyu Yang, Atif Mansoor

Probabilistic graphical models (PGMs) serve as a powerful framework for modeling complex systems with uncertainty and extracting valuable insights from data. However, users face challenges when applying PGMs to their problems in terms of efficiency and usability. This paper presents Fast-PGM, an efficient and open-source library for PGM learning and inferenc

Felix Dangel, Johannes Müller, Marius Zeinhofer

Physics-informed neural networks (PINNs) are infamous for being hard to train. Recently, second-order methods based on natural gradient and Gauss-Newton methods have shown promising performance, improving the accuracy achieved by first-order methods by several orders of magnitude. While promising, the proposed methods only scale to networks with a few thousa

Gabriele Grifò, Annalisa Iuorio, Frits Veerman

In this work, an extension of the 1D Klausmeier model that accounts for the toxicity compounds is considered and the occurrence of travelling stripes is investigated. Numerical simulations are firstly conducted to capture the qualitative behaviours of the pulse-type solutions and, then, geometric singular perturbation theory is used to prove the existence of

J. Dudouet, G. Colombi, D. Reygadas Tello, C. Michelagnoli

The first systematic experimental study of the neutron-rich Br isotopes with two complementary state-of-the-art techniques is presented. These isotopes have been populated in the fission process at two different facilities, GANIL and ILL. New spectroscopic information has been obtained for odd-even $^{87-93}$Br isotopes and the experimental results have been

Alkis Kalavasis, Amin Karbasi, Grigoris Velegkas, Felix Zhou

We study computational aspects of algorithmic replicability, a notion of stability introduced by Impagliazzo, Lei, Pitassi, and Sorrell [2022]. Motivated by a recent line of work that established strong statistical connections between replicability and other notions of learnability such as online learning, private learning, and SQ learning, we aim to underst

Md Abrar Jahin, Asef Shahriar, Md Al Amin

Accurate demand forecasting is crucial for optimizing supply chain management. Traditional methods often fail to capture complex patterns from seasonal variability and special events. Despite advancements in deep learning, interpretable forecasting models remain a challenge. To address this, we introduce the Multi-Channel Data Fusion Network (MCDFN), a hybri

Chantal David, Lucile Devin, Ezra Waxman

We study the low-lying zeros of a family of $L$-functions attached to the CM elliptic curve $E_d \;:\; y^2 = x^3 - dx$, for each odd and square-free integer $d$. Specifically, upon writing the $L$-function of $E_d$ as $L(s-\frac12, \xi_d)$ for the appropriate Gr\"ossencharakter $\xi_d$ of conductor $\mathfrak{f}_d$, we consider the collection $\mathcal{F}_d$

Hafsa El Hafyani, Bastien Pasdeloup, Camille Yver, Pierre Romenteau

Multimodal object detection has shown promise in remote sensing. However, multimodal data frequently encounter the problem of low-quality, wherein the modalities lack strict cell-to-cell alignment, leading to mismatch between different modalities. In this paper, we investigate multimodal object detection where only one modality contains the target object and

Chuan Chen, Jian-Yu Lu, Xu-Yang Chen, Zhen-Yu Wang

Multi-squeezed states, also known as generalized squeezed states, are valuable quantum non-Gaussian resources, because they can feature non-classical properties such as large phase-space Wigner negativities. In this work, we introduce a novel shortcuts to adiabaticity (STA) method for the fast preparation of multi-squeezed states. In contrast to previous STA

Tim Adamo, Wei Bu, Piotr Tourkine, Bin Zhu

Celestial scattering amplitudes for massless particles are Mellin transforms of momentum-space scattering amplitudes with respect to the energies of the external particles, and behave as conformal correlators on the celestial sphere. However, there are few explicit cases of well-defined celestial amplitudes, particularly for gravitational theories: the mixin

Ionut-Vlad Modoranu, Mher Safaryan, Grigory Malinovsky, Eldar Kurtic

We propose a new variant of the Adam optimizer called MicroAdam that specifically minimizes memory overheads, while maintaining theoretical convergence guarantees. We achieve this by compressing the gradient information before it is fed into the optimizer state, thereby reducing its memory footprint significantly. We control the resulting compression error v

Niklas Hellmer, Jan Spaliński

In this paper, we introduce new density-sensitive bifiltrations for data using the framework of Dowker complexes. Previously, Dowker complexes were studied to address directional or bivariate data whereas density-sensitive bifiltrations on \v{C}ech and Vietoris--Rips complexes were suggested to make them more robust, while increasing computational complexity

Aagrah Agnihotri, Jouni Suhonen, Hong Joo Kim

We give for the first time, theoretical estimates of unknown rare electron-capture (EC) decay branchings of $^{44}$Ti, $^{57}$Co, and $^{139}$Ce, relevant for searches of (exotic) dark-matter particles. The nuclear-structure calculations have been done exploiting the nuclear shell model (NSM) with well-established Hamiltonians and an advanced theory of $\bet

Laurent Hascoët, Jean-Luc Bouchot, Shreyas Sunil Gaikwad, Sri Hari Krishna Narayanan

Checkpointing is a cornerstone of data-flow reversal in adjoint algorithmic differentiation. Checkpointing is a storage/recomputation trade-off that can be applied at different levels, one of which being the call tree. We are looking for good placements of checkpoints onto the call tree of a given application, to reduce run time and memory footprint of its a

Sophie Xhonneux, Alessandro Sordoni, Stephan Günnemann, Gauthier Gidel

Large language models (LLMs) are vulnerable to adversarial attacks that can bypass their safety guardrails. In many domains, adversarial training has proven to be one of the most promising methods to reliably improve robustness against such attacks. Yet, in the context of LLMs, current methods for adversarial training are hindered by the high computational c

S. K. Panja, E. Harikumar, R. P. Malik

We obtain the off-shell nilpotent Becchi-Rouet-Stora-Tyutin (BRST) and anti-BRST symmetry transformations (corresponding to the infinitesimal classical gauge symmetry transformations) for the modified massive three $(2+1)$-dimensional (3D) Abelian 2-form gauge theory with a single pseudo-scalar field. The latter field (having the negative kinetic term and a

Bill Psomas, Ioannis Kakogeorgiou, Nikos Efthymiadis, Giorgos Tolias

This work introduces composed image retrieval to remote sensing. It allows to query a large image archive by image examples alternated by a textual description, enriching the descriptive power over unimodal queries, either visual or textual. Various attributes can be modified by the textual part, such as shape, color, or context. A novel method fusing image-

Ivo Petrov, Dimitar I. Dimitrov, Maximilian Baader, Mark Niklas Müller

Federated learning works by aggregating locally computed gradients from multiple clients, thus enabling collaborative training without sharing private client data. However, prior work has shown that the data can actually be recovered by the server using so-called gradient inversion attacks. While these attacks perform well when applied on images, they are li

Vishal Vivek Saley, Rocktim Jyoti Das, Dinesh Raghu, Mausam

End-to-end Task-Oriented Dialog (TOD) systems typically require extensive training datasets to perform well. In contrast, large language model (LLM) based TOD systems can excel even with limited data due to their ability to learn tasks through in-context exemplars. However, these models lack alignment with the style of responses in training data and often ge

W. Bate, D. B. Jess, S. D. T. Grant, A. Hillier

The magnetic geometry of the solar atmosphere, combined with projection effects, makes it difficult to accurately map the propagation of ubiquitous waves in fibrillar structures. These waves are of interest due to their ability to carry energy into the chromosphere and deposit it through damping and dissipation mechanisms. To this end, the Interferometric Bi

Ethan Harvey, Mikhail Petrov, Michael C. Hughes

We pursue transfer learning to improve classifier accuracy on a target task with few labeled examples available for training. Recent work suggests that using a source task to learn a prior distribution over neural net weights, not just an initialization, can boost target task performance. In this study, we carefully compare transfer learning with and without

Antonio Amariti, Simone Rota

We classify all possible charge lattices and 1-form symmetry groups for $\mathcal{N}=2$ SCFTs with characteristic dimension $\varkappa \neq \{1,2\}$. For rank-$r$ SCFTs that are not stacks of lower rank theories the order of the 1-form symmetry group can be 1,2,3,4 and $r+1$. As an application of the classification we show that $\mathcal{N}=2$ $S$-folds and

Maxence Corman, Luis Lehner, William E. East, Guillaume Dideron

Studying the dynamical, nonlinear regime of modified theories of gravity remains a theoretical challenge that limits our ability to test general relativity. Here we consider two generally applicable, but approximate methods for treating modifications to full general relativity that have been used to study binary black hole mergers and other phenomena in this

Geremy Loachamín Suntaxi, Paris Papavasileiou, Eleni D. Koronaki, Dimitrios G. Giovanis

This work introduces a comprehensive approach utilizing data-driven methods to elucidate the deposition process regimes in Chemical Vapor Deposition (CVD) reactors and the interplay of physical mechanism that dominate in each one of them. Through this work, we address three key objectives. Firstly, our methodology relies on process outcomes, derived by a det

Hanchen Tai, Qingdong He, Jiangning Zhang, Yijie Qian

Open-vocabulary 3D scene understanding presents a significant challenge in the field. Recent works have sought to transfer knowledge embedded in vision-language models from 2D to 3D domains. However, these approaches often require prior knowledge from specific 3D scene datasets, limiting their applicability in open-world scenarios. The Segment Anything Model

Kristóf Németh, Dániel Hadházi

Recent results in the literature indicate that artificial neural networks (ANNs) can outperform the dynamic factor model (DFM) in terms of the accuracy of GDP nowcasts. Compared to the DFM, the performance advantage of these highly flexible, nonlinear estimators is particularly evident in periods of recessions and structural breaks. From the perspective of p

Julian Haas, Ragnar Mogk, Annette Bieniusa, Mira Mezini

Mixed-consistency programming models assist programmers in designing applications that provide high availability while still ensuring application-specific safety invariants. However, existing models often make specific system assumptions, such as building on a particular database system or having baked-in coordination strategies. This makes it difficult to a

Tang-Kai Lee, Xinrui Zhao

In this paper, we prove that for any asymptotically conical self-shrinker, there exists an embedded closed hypersurface such that the mean curvature flow starting from it develops a singularity modeled on the given shrinker. The main technique is the Wa\.zewski box argument, used by Stolarski in the proof of the corresponding theorem in the Ricci flow case.

Victor K. Khamesi, Niall M. Adams, Dean A. Bodenham, Edward A. K. Cohen

Detecting changes in data streams is a vital task in many applications. There is increasing interest in changepoint detection in the online setting, to enable real-time monitoring and support prompt responses and informed decision-making. Many approaches assume stationary sequences before encountering an abrupt change in the mean or variance. Notably less at

David V. Svintradze

We have developed dynamic manifold solutions for the Navier-Stokes equations using an extension of differential geometry called the calculus for moving surfaces. Specifically, we have shown that the geometric solutions to the Navier-Stokes equations can take the form of fluctuating spheres, constant mean curvature surfaces, generic wave equations for compres

Byung-Kwan Lee, Chae Won Kim, Beomchan Park, Yong Man Ro

The rapid development of large language and vision models (LLVMs) has been driven by advances in visual instruction tuning. Recently, open-source LLVMs have curated high-quality visual instruction tuning datasets and utilized additional vision encoders or multiple computer vision models in order to narrow the performance gap with powerful closed-source LLVMs

Kobe Bruyninckx, Daan Huybrechs, Karl Meerbergen

Boundary integral equations lead to dense system matrices when discretized, yet they are data-sparse. Using the $\mathcal{H}$-matrix format, this sparsity is exploited to achieve $\mathcal{O}(N\log N)$ complexity for storage and multiplication by a vector. This is achieved purely algebraically, based on low-rank approximations of subblocks, and hence the for

Mounir Hajli

In this short note, we investigate the generalization of Lehmer's problem to finitely generated fields over $\mathbb{Q}$.

Shuhan Liu, Yunfan Zhou, Lu Ying, Yuan Tian

Finding the root causes of anomalies in cloud computing systems quickly is crucial to ensure availability and efficiency since accurate root causes can guide engineers to take appropriate actions to address the anomalies and maintain customer satisfaction. However, it is difficult to investigate and identify the root causes based on large-scale and high-dime

Jakob Struye, Filip Lemic, Jeroen Famaey

Achieving high-quality wireless interactive Extended Reality (XR) will require multi-gigabit throughput at extremely low latency. The Millimeter-Wave (mmWave) frequency bands, between 24 and 300GHz, can achieve such extreme performance. However, maintaining a consistently high Quality of Experience with highly mobile users is challenging, as mmWave communica

Monalisa Mohapatra, Pritam Kumar Bhoi, Gopal Krishna Panda

Positive integers with all digits equal are called repdigits. In this paper, we find all balancing and Lucas-balancing numbers, which can be expressed as the difference of two repdigits. The method of proof involves the application of Baker's theory for linear forms in logarithms of algebraic numbers and the Baker-Davenport reduction procedure.

Jean P. Martins

The multidimensional knapsack problem (MKP) is an NP-hard combinatorial optimization problem whose solution is determining a subset of maximum total profit items that do not violate capacity constraints. Due to its hardness, large-scale MKP instances are usually a target for metaheuristics, a context in which effective feasibility maintenance strategies are

Maxence Faldor, Jenny Zhang, Antoine Cully, Jeff Clune

Open-ended and AI-generating algorithms aim to continuously generate and solve increasingly complex tasks indefinitely, offering a promising path toward more general intelligence. To accomplish this grand vision, learning must occur within a vast array of potential tasks. Existing approaches to automatically generating environments are constrained within man

Anslem Uche Amaonyeiro, Murphy Egwe

We introduce the notion of tempered ultradistributions in classical Morrey spaces by preserving their respective properties. Moreover we investigate some embedding results within the scale of classical Morrey spaces (local Morrey space $\mathcal{L}^{p,\beta}(\mathbb{C}^{n},\mu)$ or global Morrey space $L^{p,\beta}(\mathbb{C}^{n})$ where the underlying functi

Janan Arslan, Henri Chhoa, Ines Khemir, Romain Valabregue

Summary: Medical researchers obtain knowledge about the prevention and treatment of disability and disease using physical measurements and image data. To assist in this endeavor, feature extraction packages are available that are designed to collect data from the image structure. In this study, we aim to augment current works by adding to the current mix of

F. Z. Zeraatgari, F. Hafezianzadeh, Y. -X. Zhang, A. Mosallanezhad

Aims. We explore machine learning techniques to forecast star formation rate, stellar mass, and metallicity across galaxies with redshifts ranging from 0.01 to 0.3. Methods. Leveraging CatBoost and deep learning architectures, we utilize multiband optical and infrared photometric data from SDSS and AllWISE, trained on the SDSS MPA-JHU DR8 catalogue. Results.

Nobuyuki Yoshioka, Seiseki Akibue, Hayata Morisaki, Kento Tsubouchi

In fault-tolerant quantum computing, errors in unitary gate synthesis is comparable with noise inherent in the gates themselves. While mixed synthesis can suppress such coherent errors quadratically, there is no clear understanding on its remnant error, which hinders us from designing a holistic and practical error countermeasure. In this work, we propose th

Rui Miao, Kaixiong Zhou, Yili Wang, Ninghao Liu

Graph neural networks (GNNs) have exhibited prominent performance in learning graph-structured data. Considering node classification task, based on the i.i.d assumption among node labels, the traditional supervised learning simply sums up cross-entropy losses of the independent training nodes and applies the average loss to optimize GNNs' weights. But differ

Niloy Sikder, Md. Al-Masrur Khan, Anupam Kumar Bairagi, Mehedi Masud

Viruses are submicroscopic agents that can infect all kinds of lifeforms and use their hosts' living cells to replicate themselves. Despite having some of the simplest genetic structures among all living beings, viruses are highly adaptable, resilient, and given the right conditions, are capable of causing unforeseen complications in their hosts' bodies. Due

Gao Tianci

This paper presents an innovative application of Transformer-XL for long sequence tasks in robotic learning from demonstrations (LfD). The proposed framework effectively integrates multi-modal sensor inputs, including RGB-D images, LiDAR, and tactile sensors, to construct a comprehensive feature vector. By leveraging the advanced capabilities of Transformer-

Andreas Bucher, Birgit Schenk, Mateusz Dolata, Gerhard Schwabe

Workplace learning is used to train employees systematically, e.g., via e-learning or in 1:1 training. However, this is often deemed ineffective and costly. Whereas pure e-learning lacks the possibility of conversational exercise and personal contact, 1:1 training with human instructors involves a high level of personnel and organizational costs. Hence, peda

Junaid Majeed Bhat

We present a two-dimensional model of a Fermionic wire which shows a power-law conductance behavior despite the presence of uncorrelated disorder along the direction of the transport. The power-law behavior is attributed to the presence of energy eigenstates of diverging localization length below some energy cutoff, $E_c$. To study transport, we place the wi

Vladislav Trifonov, Alexander Rudikov, Oleg Iliev, Yuri M. Laevsky

Large linear systems are ubiquitous in modern computational science and engineering. The main recipe for solving them is the use of Krylov subspace iterative methods with well-designed preconditioners. Recently, GNNs have been shown to be a promising tool for designing preconditioners to reduce the overall computational cost of iterative methods by construct

Chong Xiang, Tong Wu, Zexuan Zhong, David Wagner

Retrieval-augmented generation (RAG) is susceptible to retrieval corruption attacks, where malicious passages injected into retrieval results can lead to inaccurate model responses. We propose RobustRAG, the first defense framework with certifiable robustness against retrieval corruption attacks. The key insight of RobustRAG is an isolate-then-aggregate stra

Klaus Bernhard, Christopher Lloyd

Discs in long-period eclipsing binary systems are rare and can lead to extraordinary eclipsing events. ZTF J185259.31+124955.2 was identified as a candidate disc-eclipsing system through a continuing search programme of ZTF variables with a near-IR excess in the WISE data. Examination of the combined ZTF and ATLAS photometry shows seven eclipses since 2017 w

Itzhak Fouxon, Alexander M. Leshansky

Linear shear flow bounded by a plane wall is an idealization that occurs in microfluidic devices and many other applications. Perfect plane approximation neglects surface irregularities and discrete particles adsorbed at the surface. Here we study the disturbance to the linear shear flow due to the particle(s) rigidly attached to the surface. We first revisi

Bowen Wang, Hongyu Li, Bin Liao, Ziyang Cheng

This paper investigates a hardware-efficient massive multiple-input multiple-output integrated sensing and communication (MIMO-ISAC) system with 1-bit analog-to-digital converters (ADCs)/digital-to-analog converters (DACs). The proposed system, referred to as 1BitISAC, employs 1-bit DACs at the ISAC transmitter and 1-bit ADCs at the sensing receiver, achievi

Hèctor Loopez Hidalgo, Michel Boeglin, David Kahn, Josiane Mothe

Semantic relations among entities are a widely accepted method for relation extraction. PromptORE (Prompt-based Open Relation Extraction) was designed to improve relation extraction with Large Language Models on generalistic documents. However, it is less effective when applied to historical documents, in languages other than English. In this study, we intro

Jonathan Fabrizio

This article presents an easy distance field-based collision detection scheme to detect collisions of an object with its environment. Through the clever use of back-face culling and z-buffering, the solution is precise and very easy to implement. Since the complete scheme relies on the graphics pipeline, the collision detection is performed by the GPU. It is

Changrui Liu, Shengling Shi, Bart De Schutter

Model mismatch often poses challenges in model-based controller design. This paper investigates model predictive control (MPC) of uncertain linear systems with input constraints, focusing on stability and closed-loop infinite-horizon performance. The uncertainty arises from a parametric mismatch between the true and the estimated system under the matrix Frob

Kunjal Panchal, Nisarg Parikh, Sunav Choudhary, Lijun Zhang

Finetuning large language models (LLMs) in federated learning (FL) settings has become increasingly important as it allows resource-constrained devices to finetune a model using private data. However, finetuning LLMs using backpropagation requires excessive memory (especially from intermediate activations) for resource-constrained devices. While Forward-mode

Rinat I. Dumaev, Sergei A. Molodyakov, Lev V. Utkin

To increase the transparency of modern computer-aided diagnosis (CAD) systems for assessing the malignancy of lung nodules, an interpretable model based on applying the generalized additive models and the concept-based learning is proposed. The model detects a set of clinically significant attributes in addition to the final malignancy regression score and l

Shahid Ismail, Moises Diaz, Cristina Carmona-Duarte, Jose Manuel Vilar

Lameness is one of the costliest pathological problems affecting dairy animals. It is usually assessed by trained veterinary clinicians who observe features such as gait symmetry or gait parameters as step counts in real-time. With the development of artificial intelligence, various modular systems have been proposed to minimize subjectivity in lameness asse

Hantao Yao, Rui Zhang, Lu Yu, Yongdong Zhang

Prompt tuning based on Context Optimization (CoOp) effectively adapts visual-language models (VLMs) to downstream tasks by inferring additional learnable prompt tokens. However, these tokens are less discriminative as they are independent of the pre-trained tokens and fail to capture input-specific knowledge, such as class-aware textual or instance-aware vis

Byomakesh Mahapatra, Deepika Gupta, Pankaj Kumar Sharma

The deployment of beyond fifth-generation (5G) infrastructure over disaster-affected regions, temporary hotspot situations (e.g., massive gatherings, etc.), complex terrains (e.g., sea, hills, marshes, etc.) poses numerous challenges for cellular service providers. Recently, unmanned aerial vehicles (UAVs) have emerged as potential candidates to overcome the

Marzena Ciszak, Alessio Belenchia, Antonello Ortolan, Francesco Marino

Several approaches to quantum gravity lead to nonlocal modifications of fields' dynamics. This, in turn, can give rise to nonlocal modifications of quantum mechanics at non-relativistic energies. Here, we analyze the nonlocal Schr\"{o}dinger evolution of a quantum harmonic oscillator in one such scenario, where the problem can be addressed without the use of

Alexander Tyurin, Kaja Gruntkowska, Peter Richtárik

In practical distributed systems, workers are typically not homogeneous, and due to differences in hardware configurations and network conditions, can have highly varying processing times. We consider smooth nonconvex finite-sum (empirical risk minimization) problems in this setup and introduce a new parallel method, Freya PAGE, designed to handle arbitraril

Ziyun Cui, Ziyang Zhang, Guangzhi Sun, Wen Wu

Advances in large language models raise the question of how alignment techniques will adapt as models become increasingly complex and humans will only be able to supervise them weakly. Weak-to-Strong mimics such a scenario where weak model supervision attempts to harness the full capabilities of a much stronger model. This work extends Weak-to-Strong to Weak

Zeyu Wang, Tianyi Jiang, Yao Lu, Xiaoze Bao

Recently, few-shot molecular property prediction (FSMPP) has garnered increasing attention. Despite impressive breakthroughs achieved by existing methods, they often overlook the inherent many-to-many relationships between molecules and properties, which limits their performance. For instance, similar substructures of molecules can inspire the exploration of

Martin Milanič, Nevena Pivač

A minimal separator in a graph is an inclusion-minimal set of vertices that separates some fixed pair of nonadjacent vertices. A graph class is said to be tame if there exists a polynomial upper bound for the number of minimal separators of every graph in the class, and feral if it contains arbitrarily large graphs with exponentially many minimal separators.

Haoxuan Yuan, Zhe Chen, Zheng Lin, Jinbo Peng

Low Earth Orbit satellite Internet has recently been deployed, providing worldwide service with non-terrestrial networks. With the large-scale deployment of both non-terrestrial and terrestrial networks, limited spectrum resources will not be allocated enough. Consequently, dynamic spectrum sharing is crucial for their coexistence in the same spectrum, where

Yunyao Mao, Xiaoyang Liu, Wengang Zhou, Zhenbo Lu

Text-driven human motion generation, as one of the vital tasks in computer-aided content creation, has recently attracted increasing attention. While pioneering research has largely focused on improving numerical performance metrics on given datasets, practical applications reveal a common challenge: existing methods often overfit specific motion expressions

Jacob Bamberger, Federico Barbero, Xiaowen Dong, Michael M. Bronstein

The dominant paradigm for learning on graph-structured data is message passing. Despite being a strong inductive bias, the local message passing mechanism suffers from pathological issues such as over-smoothing, over-squashing, and limited node-level expressivity. To address these limitations we propose Bundle Neural Networks (BuNN), a new type of GNN that o

Luke Eilers, Raoul-Martin Memmesheimer, Sven Goedeke

State-of-the-art neural network training methods depend on the gradient of the network function. Therefore, they cannot be applied to networks whose activation functions do not have useful derivatives, such as binary and discrete-time spiking neural networks. To overcome this problem, the activation function's derivative is commonly substituted with a surrog

Fumihiro Naokawa, Toshiya Namikawa, Kai Murai, Ippei Obata

We point out that the rotation angle $\beta$ of cosmic birefringence, which is a recently reported parity-violating signal in the cosmic microwave background (CMB), has a phase ambiguity of $n\pi \,(n\in\mathbb{Z})$. This ambiguity has a significant impact on the interpretation of the origin of cosmic birefringence. Assuming an axion-like particle as the ori

Gwenn Le Gonidec, Maria Méndez Real, Guillaume Bouffard, Jean-Christophe Prévotet

Over the past few years, several research groups have introduced innovative hardware designs for Trusted Execution Environments (TEEs), aiming to secure applications against potentially compromised privileged software, including the kernel. Since 2015, a new class of software-enabled hardware attacks leveraging energy management mechanisms has emerged. These

Sérgio Luiz E. F. da Silva, G. Kaniadakis

The statistical basis for conventional full-waveform inversion (FWI) approaches is commonly associated with Gaussian statistics. However, errors are rarely Gaussian in non-linear problems like FWI. In this work, we investigate the portability of a new objective function for FWI applications based on the graph-space optimal transport and $\kappa$-generalized

Leah LeJeune, Navid Ghaffarzadegan, Lauren Childs, Omar Saucedo

The recent pandemic emphasized the need to consider the role of human behavior in shaping epidemic dynamics. In particular, it is necessary to extend beyond the classical epidemiological structures to fully capture the interplay between the spread of disease and how people respond. Here, we focus on the challenge of incorporating change in human behavior in

Shunsuke Yasunaga, Daisuke Jido, Takatsugu Ishikawa

The $\Lambda N$ invariant mass spectra for the reactions $K^-d\rightarrow\pi^-\Lambda p$ and $K^-d\rightarrow\pi^0\Lambda n$ are calculated for experimental study of isospin symmetry breaking in the $\Lambda N$ scattering at low energies, the difference in the scattering lengths and effective ranges of $\Lambda p$ and $\Lambda n$ systems. The calculations ar

Cui Yufang

Nevanlinna-Pick interpolation problem has been widely studied in recent decades, however, the known algorithm is not simplistic and robust enough. This paper provide a new method to solve the Nevanlinna-Pick interpolation problem with degree constraint. It is based on the covariance extension equation proposed by Byrnes and Lindquist. A reformulation of the

Achraf Zinihi, Moulay Rchid Sidi Ammi, Matthias Ehrhardt, Ahmed Bachir

This article conducts an in-depth investigation of a new spatio-temporal model for the cocaine-heroin epidemiological model with vital dynamics, incorporating the Laplacian operator. The study rigorously establishes the existence, uniqueness, non-negativity, and boundedness of solutions for the proposed model. In addition, the local stability of both a drug-

Yijin Zeng, Niall M. Adams, Dean A. Bodenham

In many real-world applications, it is common that a proportion of the data may be missing or only partially observed. We develop a novel two-sample testing method based on the Maximum Mean Discrepancy (MMD) which accounts for missing data in both samples, without making assumptions about the missingness mechanism. Our approach is based on deriving the mathe