May 2024 arXiv papers — page 75

Jinzhao Wang, Shunyu Yao

Quantum energy teleportation (QET) is the phenomenon in which locally inaccessible energy is activated as extractable work through collaborative local operations and classical communication (LOCC) with an entangled partner. It closely resembles the more well-known quantum information teleportation (QIT) where quantum information can be sent through an entang

Ignacio Loaiza, Danial Motlagh, Kasra Hejazi, Modjtaba Shokrian Zini

Response theory has a successful history of connecting experimental observations with theoretical predictions. Of particular interest is the optical response of matter, from which spectroscopy experiments can be modelled. However, the calculation of response properties for quantum systems is often prohibitively expensive, especially for nonlinear spectroscop

Shadi Ali Ahmad, Wissam Chemissany, Marc S. Klinger, Robert G. Leigh

All physical observations are made relative to a reference frame, which is a system in its own right. If the system of interest admits a group symmetry, the reference frame observing it must transform commensurately under the group to ensure the covariance of the combined system. We point out that the crossed product is a way to realize quantum reference fra

Davide Racco, Sarunas Verner, Wei Xue

We investigate the gravitational production of a scalar field $\chi$ with a mass exceeding the Hubble scale during inflation $m_\chi \gtrsim H_I$, employing both analytical and numerical approaches. We demonstrate that the steepest descent method effectively captures the epochs and yields of gravitational production in a compact and simple analytical framewo

Carl Beadle, Andrea Caputo, Sebastian A. R. Ellis

We consider resonant wave-like dark matter conversion into low-frequency radio waves in the Earth's ionosphere. Resonant conversion occurs when the dark matter mass and the plasma frequency coincide, defining a range $m_{ \text{DM} } \sim 10^{-9} - 10^{-8}$ eV where this approach is best suited. Owing to the non-relativistic nature of dark matter and the typ

Dario L. Lorenzoni, David I. Kaiser, Evan McDonough

We demonstrate that "natural inflation", also known as "axion inflation", can be compatible with Planck 2018 measurements of the cosmic microwave background, while predicting an exponentially small tensor-to-scalar ratio, e.g., $r\sim 10^{-15}$. The strong suppression of $r$ arises from dynamics of the radial component of the complex scalar field, whose phas

Jonah Herzog-Arbeitman, Jiabin Yu, Dumitru Călugăru, Haoyu Hu

Although the strongly interacting flat bands in twisted bilayer graphene (TBG) have been approached using the minimal Bistritzer-MacDonald (BM) Hamiltonian, there is mounting evidence that strain and lattice relaxation are essential in correctly determining the order of the correlated insulator groundstates. These effects can be incorporated in an enhanced c

Marco Bornstein, Amrit Singh Bedi, Abdirisak Mohamed, Furong Huang

Standard federated learning (FL) approaches are vulnerable to the free-rider dilemma: participating agents can contribute little to nothing yet receive a well-trained aggregated model. While prior mechanisms attempt to solve the free-rider dilemma, none have addressed the issue of truthfulness. In practice, adversarial agents can provide false information to

Naveen Sendhilnathan, Ajoy S. Fernandes, Michael J. Proulx, Tanya R. Jonker

Although eye-tracking technology is being integrated into more VR and MR headsets, the true potential of eye tracking in enhancing user interactions within XR settings remains relatively untapped. Presently, one of the most prevalent gaze applications in XR is input control; for example, using gaze to control a cursor for pointing. However, our eyes evolved

Enver Aman, Karthik C. S., Sharath Punna

In the Euclidean $k$-means problems we are given as input a set of $n$ points in $\mathbb{R}^d$ and the goal is to find a set of $k$ points $C\subseteq \mathbb{R}^d$, so as to minimize the sum of the squared Euclidean distances from each point in $P$ to its closest center in $C$. In this paper, we formally explore connections between the $k$-coloring problem

Chakradhar Rangi, Herbert F Fotso, Hanna Terletska, Juana Moreno

We introduce an extension of the non-equilibrium dynamical mean field theory to incorporate the effects of static random disorder in the dynamics of a many-particle system by integrating out different disorder configurations resulting in an effective time-dependent density-density interaction. We use this method to study the non-equilibrium transient dynamic

Davide Bilò, Giordano Colli, Luca Forlizzi, Stefano Leucci

Given an undirected connected graph $G = (V(G), E(G))$ on $n$ vertices, the minimum Monitoring Edge-Geodetic Set (MEG-set) problem asks to find a subset $M \subseteq V(G)$ of minimum cardinality such that, for every edge $e \in E(G)$, there exist $x,y \in M$ for which all shortest paths between $x$ and $y$ in $G$ traverse $e$. We show that, for any constant

Hongkai Chen, Zixin Luo, Yurun Tian, Xuyang Bai

Identifying robust and accurate correspondences across images is a fundamental problem in computer vision that enables various downstream tasks. Recent semi-dense matching methods emphasize the effectiveness of fusing relevant cross-view information through Transformer. In this paper, we propose several improvements upon this paradigm. Firstly, we introduce

Yuan Sui, Yufei He, Nian Liu, Xiaoxin He

Large Language Models (LLMs) are often challenged by generating erroneous or hallucinated responses, especially in complex reasoning tasks. Leveraging Knowledge Graphs (KGs) as external knowledge sources has emerged as a viable solution. However, existing KG-enhanced methods, either retrieval-based or agent-based, encounter difficulties in accurately retriev

Qiji Zhou, Ruochen Zhou, Zike Hu, Panzhong Lu

Recent advancements in Chain-of-Thought (CoT) and related rationale-based works have significantly improved the performance of Large Language Models (LLMs) in complex reasoning tasks. With the evolution of Multimodal Large Language Models (MLLMs), enhancing their capability to tackle complex multimodal reasoning problems is a crucial frontier. However, incor

Eleonora Vanzan, Sarah Libanore, Lorenzo Valbusa Dall'Armi, Nicola Bellomo

Primordial black holes still represent a viable candidate for a significant fraction, if not for the totality, of dark matter. If these compact objects have masses of order tens of solar masses, their coalescence can be observed by current and future ground-based gravitational wave detectors. Therefore, finding new gravitational wave signatures associated wi

Ganggui Ding, Canyu Zhao, Wen Wang, Zhen Yang

Benefiting from large-scale pre-trained text-to-image (T2I) generative models, impressive progress has been achieved in customized image generation, which aims to generate user-specified concepts. Existing approaches have extensively focused on single-concept customization and still encounter challenges when it comes to complex scenarios that involve combini

Marit O. E. Steiner, Julen S. Pedernales, Martin B. Plenio

We explore the potential of levitating solids embedded with non-permanent, optically controllable electron spins, which can be used to hyperpolarize their nuclear spin environment with exceptionally long lifetimes. For example, pentacene-doped naphthalene, which will also serve as our prime example, can achieve bulk polarization exceeding $80\,\%$ at cryogen

Xuyang Ge, Fukang Zhu, Wentao Shu, Junxuan Wang

Circuit analysis of any certain model behavior is a central task in mechanistic interpretability. We introduce our circuit discovery pipeline with Sparse Autoencoders (SAEs) and a variant called Transcoders. With these two modules inserted into the model, the model's computation graph with respect to OV and MLP circuits becomes strictly linear. Our methods d

Thomas D. P. Edwards, James Alvey, Justin Alsing, Nam H. Nguyen

Scaling laws for large language models (LLMs) have provided useful guidance in training ever larger models for predictable performance gains. Time series forecasting shares a similar sequential structure to language, and is amenable to large-scale transformer architectures. Here we show that foundational decoder-only time series transformer models exhibit an

Vahid Jebraeeli, Bo Jiang, Derya Cansever, Hamid Krim

In the era of big data, the sheer volume and complexity of datasets pose significant challenges in machine learning, particularly in image processing tasks. This paper introduces an innovative Autoencoder-based Dataset Condensation Model backed by Koopman operator theory that effectively packs large datasets into compact, information-rich representations. In

Chong Mou, Mingdeng Cao, Xintao Wang, Zhaoyang Zhang

Despite significant advancements in video generation and editing using diffusion models, achieving accurate and localized video editing remains a substantial challenge. Additionally, most existing video editing methods primarily focus on altering visual content, with limited research dedicated to motion editing. In this paper, we present a novel attempt to R

Konstantinos Pitas, Julyan Arbel

We propose a simple and effective method to estimate the uncertainty of closed-source deep neural network image classification models. Given a base image, our method creates multiple transformed versions and uses them to query the top-1 prediction of the closed-source model. We demonstrate significant improvements in the calibration of uncertainty estimates

Arko Banerjee, Kia Rahmani, Joydeep Biswas, Isil Dillig

Among approaches for provably safe reinforcement learning, Model Predictive Shielding (MPS) has proven effective at complex tasks in continuous, high-dimensional state spaces, by leveraging a backup policy to ensure safety when the learned policy attempts to take risky actions. However, while MPS can ensure safety both during and after training, it often hin

Adib Hasan, Mardavij Roozbehani, Munther Dahleh

This paper introduces WeatherFormer, a transformer encoder-based model designed to learn robust weather features from minimal observations. It addresses the challenge of modeling complex weather dynamics from small datasets, a bottleneck for many prediction tasks in agriculture, epidemiology, and climate science. WeatherFormer was pretrained on a large pretr

A. B. Balantekin, Anna M. Suliga

Motivated by the growing interest in the applications of quantum information science in astrophysical settings, especially for the neutrino transport in compact objects where three-flavors of neutrinos need to be mapped on qutrits, we review properties of one- and two-qudit systems. We contrast two-qubit and two-qudits systems by pointing out how some of the

Jiuqi Wang, Ethan Blaser, Hadi Daneshmand, Shangtong Zhang

Traditionally, reinforcement learning (RL) agents learn to solve new tasks by updating their neural network parameters through interactions with the task environment. However, recent works demonstrate that some RL agents, after certain pretraining procedures, can learn to solve unseen new tasks without parameter updates, a phenomenon known as in-context rein

Diwei Huang, Kunyang Lin, Peihao Chen, Qing Du

Few-shot audio-visual acoustics modeling seeks to synthesize the room impulse response in arbitrary locations with few-shot observations. To sufficiently exploit the provided few-shot data for accurate acoustic modeling, we present a *map-guided* framework by constructing acoustic-related visual semantic feature maps of the scenes. Visual features preserve s

S. Tian, H. Gao, G. Hong, S. Wang

Existing deep learning methods have made significant progress in gait representation learning. Quantization can facilitate the application of gait models as a model-agnostic general compression technique. Typically, appearance-based models binarize inputs into silhouette sequences. However, mainstream quantization methods prioritize minimizing task loss over

Benjamin C. Lee, David Brooks, Arthur van Benthem, Udit Gupta

Computing is at a moment of profound opportunity. Emerging applications -- such as capable artificial intelligence, immersive virtual realities, and pervasive sensor systems -- drive unprecedented demand for computer. Despite recent advances toward net zero carbon emissions, the computing industry's gross energy usage continues to rise at an alarming rate, o

Xiaoxin Shen, Eman Alashwali, Lorrie Faith Cranor

When companies release marketing materials aimed at promoting their privacy practices or highlighting specific privacy features, what do they actually communicate to consumers? In this paper, we explore the impact of privacy marketing on: (1) consumers' attitudes toward the organizations providing the campaigns, (2) overall privacy awareness, and (3) the act

Dhananjaya Sahoo, A. Y. Deo, Madhu, Khamosh Yadav

High-spin states in the transitional $^{217}$Ac nucleus are established up to 3.8 MeV excitation energy and $I^{\pi} =$ 41/2$^+$ with the addition of around 20 new transitions. The structure of the yrast and near-yrast states below the 29/2$^+$ isomer is revisited. The inconsistencies in the level schemes reported earlier are resolved. The level structure ab

Benjamin Sorkin, Gil Ariel, Tomer Markovich

Stochastic thermodynamics is a developing theory for systems out of thermal equilibrium. It allows to formulate a wealth of nontrivial relations among thermodynamic quantities such as heat dissipation, excess work, and entropy production in generic nonequilibrium stochastic processes. A key quantity for the derivation of these relations is the propagator - t

Aditi Chandra, Marcelo O. Magnasco

We examine the dynamical properties of a single-layer convolutional recurrent network with a smooth sigmoidal activation function, for small values of the inputs and when the convolution kernel is unitary, so all eigenvalues lie exactly at the unit circle. Such networks have a variety of hallmark properties: the outputs depend on the inputs via compressive n

Steven Charlton

We prove that the weight 6, depth 3, multiple polylogarithm $ \mathrm{Li}_{4,1,1}((xyz)^{-1}, x, y) $, or rather its more natural `divergent' incarnation $ \mathrm{Li}_{3;1,1,1}(x,y,z) $, satisfies the 6-fold anharmonic symmetries of the dilogarithm $ \mathrm{Li}_2 $, $ \lambda \mapsto 1-\lambda $ and $ \lambda \mapsto \lambda^{-1} $, in each of $x$, $y$ and

Md Ahasanuzzaman, Gustavo A. Oliva, Ahmed E. Hassan

Predicting potential long-time contributors (LTCs) early allows project maintainers to effectively allocate resources and mentoring to enhance their development and retention. Mapping programming language expertise to developers and characterizing projects in terms of how they use programming languages can help identify developers who are more likely to beco

Aditya Paul, Crystal Noel

Sympathetic cooling is a technique often employed to mitigate motional heating in trapped-ion quantum computers. However, choosing system parameters such as number of coolants and cooling duty cycle for optimal gate performance requires evaluating trade-offs between motional errors and other slower errors such as qubit dephasing. The optimal parameters depen

Giulio Ortali, Alessandro Gabbana, Imre Atmodimedjo, Alessandro Corbetta

We present a new class of equivariant neural networks, hereby dubbed Lattice-Equivariant Neural Networks (LENNs), designed to satisfy local symmetries of a lattice structure. Our approach develops within a recently introduced framework aimed at learning neural network-based surrogate models Lattice Boltzmann collision operators. Whenever neural networks are

David Cruz-Uribe, Kabe Moen, Yuanzhen Shao

In this paper we study the degenerate parabolic $p$-Laplacian,$ \partial_t u - v^{-1}{\rm div}(|\sqrt{Q} \nabla u|^{p-2} Q \nabla u)=0$, where the degeneracy is controlled by a matrix $Q$ and a weight $v$. With mild integrability assumptions on $Q$ and $v$, we prove the existence and uniqueness of solutions on any interval $[0,T]$. If we further assume the e

Li Meng, Morten Goodwin, Anis Yazidi, Paal Engelstad

The expanding research on manifold-based self-supervised learning (SSL) builds on the manifold hypothesis, which suggests that the inherent complexity of high-dimensional data can be unraveled through lower-dimensional manifold embeddings. Capitalizing on this, DeepInfomax with an unbalanced atlas (DIM-UA) has emerged as a powerful tool and yielded impressiv

Ali Mohammadi Ruzbahani

Integrating blockchain technology with the Internet of Things offers transformative possibilities for enhancing network security and privacy in the contemporary digital landscape, where interconnected devices and expansive networks are ubiquitous. This paper explores the pivotal role of artificial intelligence in bolstering blockchain-enabled IoT systems, po

Nathan Wycoff

Regression trees have emerged as a preeminent tool for solving real-world regression problems due to their ability to deal with nonlinearities, interaction effects and sharp discontinuities. In this article, we rather study regression trees applied to well-behaved, differentiable functions, and determine the relationship between node parameters and the local

Xin Qiu, Risto Miikkulainen

With the widespread application of Large Language Models (LLMs) to various domains, concerns regarding the trustworthiness of LLMs in safety-critical scenarios have been raised, due to their unpredictable tendency to hallucinate and generate misinformation. Existing LLMs do not have an inherent functionality to provide the users with an uncertainty/confidenc

Hugh Dance, Benjamin Bloem-Reddy

Estimating joint distributions (a.k.a. couplings) over counterfactual outcomes is central to personalized decision-making and treatment risk assessment. Two emergent frameworks with identifiability guarantees are: (i) bijective structural causal models (SCMs), which are flexible but brittle to mis-specified latent noise; and (ii) optimal-transport (OT) metho

Md. Toukir Ahmed, Md Wadud Ahmed, Ocean Monjur, Jason Lee Emmert

As the demand for food surges and the agricultural sector undergoes a transformative shift towards sustainability and efficiency, the need for precise and proactive measures to ensure the health and welfare of livestock becomes paramount. In the context of the broader agricultural landscape outlined, the application of Hyperspectral Imaging (HSI) takes on pr

Fedor Pakhomov, Giovanni Soldà

The famous theorem of Higman states that for any well-quasi-order (wqo) $Q$ the embeddability order on finite sequences over $Q$ is also wqo. In his celebrated 1965 paper, Nash-Williams established that the same conclusion holds even for all the transfinite sequences with finite range, thus proving a far reaching generalization of Higman's theorem. In the pr

Amar Halilovic, Senka Krivic

To bring robots into human everyday life, their capacity for social interaction must increase. One way for robots to acquire social skills is by assigning them the concept of identity. This research focuses on the concept of \textit{Explanation Identity} within the broader context of robots' roles in society, particularly their ability to interact socially a

Nicola Bertozzi, Claudio Bonanno

When dealing with an orbit determination problem, uncertainties naturally arise from intrinsic errors related to observation devices and approximation models. Following the least squares method and applying approximation schemes such as the differential correction, uncertainties can be geometrically summarized in confidence regions and estimated by confidenc

Weizhou Liu, Jiaze Li, Xuhui Chen, Fei Hou

This paper presents a new method, Diffusing Winding Gradients (DWG), for reconstructing watertight 3D surfaces from unoriented point clouds. Our method exploits the alignment between the gradients of the generalized winding number (GWN) field and globally consistent normals to orient points effectively. Starting with an unoriented point cloud, DWG initially

Raquel Sánchez-Barquilla, Francisco Martín Vega, Alberto M. Ruiz, Na Hyun Jo

WTe$_2$ stands out as a semimetal presenting Fermi level quantum oscillations in most measured quantities under magnetic fields. However, the electronic band structure above and below the Fermi level has not been explored completely. Here we study the electronic band structure of WTe$_2$ by quasiparticle interference with Scanning Tunneling Microscopy (STM)

J. M. García-Calcines

In the context of the Lusternik-Schnirelmann category, researcher T. Srinivasan demonstrated that when the space under consideration is an absolute neighborhood retract, its category can be realized through arbitrary subsets, not necessarily open ones. The primary aim of this survey is to illustrate how this result has been extended to the case of topologica

Noufissa Hafidi, Jawad H'michane

In this paper, we introduce and study a new concept of unbounded order demi Dunford-Pettis operators. Namely, we investigate some properties for this new class of operators and we study its connection with other known operators, we also establish important results about power unbounded order demi Dunford-Pettis operators, and domination property.

D. Pugliese, Z. Stuchlık

We examine the shadow cast by a Kerr black hole, focusing on constraints on photons corresponding to different shadow boundaries. The photons are related to different orbital ranges and impact parameter values, creating a map of the shadow boundaries. Our analysis fixes also the conditions under which it is possible to observe an "imprint" of the black hole

Alessandro Bacchetta, Valerio Bertone, Chiara Bissolotti, Giuseppe Bozzi

We present an extraction of the unpolarized transverse-momentum-dependent parton distribution and fragmentation functions that takes into account possible differences between quark flavors and final-state hadrons. The extraction is based on experimental measurements from Drell-Yan processes and semi-inclusive deep-inelastic scattering, whose combination is e

Md Shahin Ali, Md Manjurul Ahsan, Lamia Tasnim, Sadia Afrin

Data privacy has become a major concern in healthcare due to the increasing digitization of medical records and data-driven medical research. Protecting sensitive patient information from breaches and unauthorized access is critical, as such incidents can have severe legal and ethical complications. Federated Learning (FL) addresses this concern by enabling

Ali Mohammadi Ruzbahani

The increasing demand for electricity, coupled with the rise in greenhouse gas emissions, necessitates the integration of Renewable Energy Sources (RESs) into power grids. However, the fluctuating nature of RESs introduces new challenges in energy management. The Internet of Energy (IoE) framework provides a solution by enabling real-time monitoring, dynamic

Paolo Arnaudo, Giulio Bonelli, Alessandro Tanzini

We compute new exact analytic expressions for one-loop scalar effective actions in Kerr (A)dS black hole (BH) backgrounds in four and five dimensions. These are computed by the connection coefficients of the Heun equation via a generalization of the Gelfand-Yaglom formalism to second-order linear ODEs with regular singularities. The expressions we find are i

Ziqiao Ma, Zekun Wang, Joyce Chai

Humans are efficient language learners and inherently social creatures. Our language development is largely shaped by our social interactions, for example, the demonstration and feedback from caregivers. Contrary to human language learning, recent advancements in large language models have primarily adopted a non-interactive training paradigm, and refined pr

Sayan Kumar Ray, NZ Jhanjhi, Akbar Hossain

The problem of improving the handover performance in Long Term Evolution-Advanced (LTE-A) networks has not been fully solved yet. Traditionally, the selection of the target Evolved Node B (TeNB) in the handover procedure is based on the signal strength measurements, which may not produce a reliable handover. A reliable handover method may reduce the instance

Andreas Brandhuber, Graham R. Brown, Paolo Pichini, Gabriele Travaglini

We study the binary dynamics of two Kerr black holes with arbitrary spin vectors in the presence of parity-even and parity-odd cubic deformations of gravity. We first derive the tree-level Compton amplitudes for a Kerr black hole in cubic gravity, which we then use to compute the two-to-two amplitudes of the massive bodies to leading order in the deformation

Yuting Wang, Jinpeng Wang, Bin Chen, Tao Dai

Given a text query, partially relevant video retrieval (PRVR) aims to retrieve untrimmed videos containing relevant moments. Due to the lack of moment annotations, the uncertainty lying in clip modeling and text-clip correspondence leads to major challenges. Despite the great progress, existing solutions either sacrifice efficiency or efficacy to capture var

Theosamuele Signor, Paula Jofré, Luis Martí, Nayat Sánchez-Pi

The chemical composition of a star's atmosphere reflects the chemical composition of its birth environment. Therefore, it should be feasible to recognize stars born together that have scattered throughout the galaxy, solely based on their chemistry. This concept, known as "strong chemical tagging", is a major objective of spectroscopic studies, but has yet t

Tahereh Ramezani, Ernst Paunzen, Caiyun Xia, Katerina Pivonkova

We studied blue horizontal branch stars (BHBs), and calculated their radial velocities. Spectra of these stars have been obtained with moderate signal-to-noise ratio for five blue horizontal-branch stars using the 2 meter telescope and Echelle Spectrograph in Ondrejov observatory, Czech republic.

Antony Sikorski, Daniel McKenzie, Douglas Nychka

In geostatistics, traditional spatial models often rely on the Gaussian Process (GP) to fit stationary covariances to data. It is well known that this approach becomes computationally infeasible when dealing with large data volumes, necessitating the use of approximate methods. A powerful class of methods approximate the GP as a sum of basis functions with r

Weixiang Zhao, Yulin Hu, Zhuojun Li, Yang Deng

Safety alignment of large language models (LLMs) has been gaining increasing attention. However, current safety-aligned LLMs suffer from the fragile and imbalanced safety mechanisms, which can still be induced to generate unsafe responses, exhibit over-safety by rejecting safe user inputs, and fail to preserve general utility after safety alignment. To this

Lionel J. Dmello, Laurens T. Ligthart, David Gross

While there exist theories that have states "more strongly entangled" than quantum theory, in the sense that they show CHSH values above Tsirelson's bound, all known examples of such theories have a strictly smaller set of measurements. Therefore, in tasks which require both bipartite states and measurements, they do not perform better than QM. One of the si

Arthur N. Montanari, François Lamoline, Robert Bereza, Jorge Gonçalves

Data-driven modeling of dynamical systems often faces numerous data-related challenges. A fundamental requirement is the existence of a unique set of parameters for a chosen model structure, an issue commonly referred to as identifiability. Although this problem is well studied for ordinary differential equations (ODEs), few studies have focused on the more

Kaelan Donatella, Samuel Duffield, Maxwell Aifer, Denis Melanson

Second-order training methods have better convergence properties than gradient descent but are rarely used in practice for large-scale training due to their computational overhead. This can be viewed as a hardware limitation (imposed by digital computers). Here we show that natural gradient descent (NGD), a second-order method, can have a similar computation

Shimao Zhang, Changjiang Gao, Wenhao Zhu, Jiajun Chen

Recently, Large Language Models (LLMs) have shown impressive language capabilities. While most of the existing LLMs have very unbalanced performance across different languages, multilingual alignment based on translation parallel data is an effective method to enhance the LLMs' multilingual capabilities. In this work, we discover and comprehensively investig

Antongiulio Fornasiero, Noa Lavi, Sonia L'Innocente, Vincenzo Mantova

A classical tool in the study of real closed fields are the fields $K((G))$ of generalized power series (i.e., formal sums with well-ordered support) with coefficients in a field $K$ of characteristic 0 and exponents in an ordered abelian group $G$. In this paper we enlarge the family of ordinals $\alpha$ of non-additively principal Cantor degree for which $

Adam R. Fraser, A. J. Crilly, N. P. L. Niasse, D. A. Chapman

Global microphysics models are required for the modelling of high-energy-density physics (HEDP) experiments, the improvement of which are critical to the path to inertial fusion energy. This work presents further developments to the atomic and microphysics code, SpK, part of the numerical modelling suite of Imperial College London and First Light Fusion. We

Ashok Kumar Pathak, Ritik Soni

In this paper, we first define the multivariate tempered space-fractional Poisson process (MTSFPP) by time-changing the multivariate Poisson process with an independent tempered {\alpha}-stable subordinator. Its distributional properties, the mixture tempered time and space variants and their PDEs connections are studied. Then we define the multivariate temp

Noam Koren, Kira Radinsky

Multivariate time series forecasting is a pivotal task in several domains, including financial planning, medical diagnostics, and climate science. This paper presents the Neural Fourier Transform (NFT) algorithm, which combines multi-dimensional Fourier transforms with Temporal Convolutional Network layers to improve both the accuracy and interpretability of

Jing Long, Guanhua Ye, Tong Chen, Yang Wang

The rapid expansion of Location-Based Social Networks (LBSNs) has highlighted the importance of effective next Point-of-Interest (POI) recommendations, which leverage historical check-in data to predict users' next POIs to visit. Traditional centralized deep neural networks (DNNs) offer impressive POI recommendation performance but face challenges due to pri

Xin Cheng, Xiuying Chen, Shuqi Li, Di Luo

Time series prediction is crucial for understanding and forecasting complex dynamics in various domains, ranging from finance and economics to climate and healthcare. Based on Transformer architecture, one approach involves encoding multiple variables from the same timestamp into a single temporal token to model global dependencies. In contrast, another appr

G. Vivek, Debabrata Mondal, Subhadeep Chakraborty, S. Sinha

We investigate semiclassical dynamics of a coupled atom-photon interacting system described by a dimer of anisotropic Dicke model in the presence of photon loss, exhibiting a rich variety of non-linear dynamics. Based on symmetries and dynamical classification, we characterize and chart out various dynamical phases in a phase diagram. A key feature of this s

Anlei Zhang, Wei Cui

With the rapid development of quantum computing technology, we have entered the era of noisy intermediate-scale quantum (NISQ) computers. Therefore, designing quantum algorithms that adapt to the hardware conditions of current NISQ devices and can preliminarily solve some practical problems has become the focus of researchers. In this paper, we focus on quan

Hui Zhou, Robert Latham, Ken Raffenetti, Yanfei Guo

The progression of communication in the Message Passing Interface (MPI) is not well defined, yet it is critical for application performance, particularly in achieving effective computation and communication overlap. The opaque nature of MPI progress poses significant challenges in advancing MPI within modern high-performance computing (HPC) practices. Firstl

Nian Liu, Xiaoxin He, Thomas Laurent, Francesco Di Giovanni

Spectral graph convolution, an important tool of data filtering on graphs, relies on two essential decisions: selecting spectral bases for signal transformation and parameterizing the kernel for frequency analysis. While recent techniques mainly focus on standard Fourier transform and vector-valued spectral functions, they fall short in flexibility to model

Guy Ohayon, Michael Elad, Tomer Michaeli

Fairness in image restoration tasks is the desire to treat different sub-groups of images equally well. Existing definitions of fairness in image restoration are highly restrictive. They consider a reconstruction to be a correct outcome for a group (e.g., women) only if it falls within the group's set of ground truth images (e.g., natural images of women); o

Shuaiqi Wang, Rongzhe Wei, Mohsen Ghassemi, Eleonora Kreacic

Data sharing enables critical advances in many research areas and business applications, but it may lead to inadvertent disclosure of sensitive summary statistics (e.g., means or quantiles). Existing literature only focuses on protecting a single confidential quantity, while in practice, data sharing involves multiple sensitive statistics. We propose a novel

Siyi Wu, Julie Y. A. Cachia, Feixue Han, Bingsheng Yao

The human-computer interaction (HCI) research community has a longstanding interest in exploring the mismatch between users' actual experiences and expectation toward new technologies, for instance, large language models (LLMs). In this study, we compared users' (N = 38) initial expectations against their post-interaction perceptions of two LLM-powered menta

Mamuka Jibladze, Evgeny Kuznetsov

An embedding of arbitrary Heyting algebra H into a reduct from the variety of Kuznetsov-Muravitsky algebras is constructed. An algebraic proof is given that this reduct belongs to the variety of Heyting algebras generated by H.

Zeki Kazan, Jerome P. Reiter

We describe Bayesian inference for the parameters of Gaussian models of bounded data protected by differential privacy. Using this setting, we demonstrate that analysts can and should take constraints imposed by the bounds into account when specifying prior distributions. Additionally, we provide theoretical and empirical results regarding what classes of de

Huanjin Yao, Wenhao Wu, Taojiannan Yang, YuXin Song

Do we fully leverage the potential of visual encoder in Multimodal Large Language Models (MLLMs)? The recent outstanding performance of MLLMs in multimodal understanding has garnered broad attention from both academia and industry. In the current MLLM rat race, the focus seems to be predominantly on the linguistic side. We witness the rise of larger and high

Anthony Ozier-Lafontaine, Polina Arsenteva, Franck Picard, Bertrand Michel

Kernel-based testing has revolutionized the field of non-parametric tests through the embedding of distributions in an RKHS. This strategy has proven to be powerful and flexible, yet its applicability has been limited to the standard two-sample case, while practical situations often involve more complex experimental designs. To extend kernel testing to any d

Tyler Bell, Avinash Mudireddy, Ivan Johnson-Eversoll, Soura Dasgupta

We prove a new asymptotic un-equipartition property for the perplexity of long texts generated by a language model and present supporting experimental evidence from open-source models. Specifically we show that the logarithmic perplexity of any large text generated by a language model must asymptotically converge to the average entropy of its token distribut

Édouard Bonnet

Motivated by an induced counterpart of treewidth sparsifiers (i.e., sparse subgraphs keeping the treewidth large) provided by the celebrated Grid Minor theorem of Robertson and Seymour [JCTB '86] or by a classic result of Chekuri and Chuzhoy [SODA '15], we show that for any natural numbers $t$ and $w$, and real $\varepsilon > 0$, there is an integer $W := W(

Wanghan Xu, Fenghua Ling, Wenlong Zhang, Tao Han

Data-driven artificial intelligence (AI) models have made significant advancements in weather forecasting, particularly in medium-range and nowcasting. However, most data-driven weather forecasting models are black-box systems that focus on learning data mapping rather than fine-grained physical evolution in the time dimension. Consequently, the limitations

Elena Garcia-Morato, Maria Jesus Algar, Cesar Alfaro, Felipe Ortega

Approximate nearest neighbour (ANN) search has become a central task in modern data-intensive applications, particularly when operating on large, heterogeneous, or high-dimensional datasets. However, many existing ANN methods struggle in such scenarios, either because they rely on metric assumptions or because their indexing strategies are not well suited to

Adrien Corenflos, Zheng Zhao, Simo Särkkä, Jens Sjölund

Given an unconditional diffusion model targeting a joint model $\pi(x, y)$, using it to perform conditional simulation $\pi(x \mid y)$ is still largely an open question and is typically achieved by learning conditional drifts to the denoising SDE after the fact. In this work, we express \emph{exact} conditional simulation within the \emph{approximate} diffus

Antonio J. Martínez Aparicio, Francescantonio Oliva, Francesco Petitta

In this paper we provide a complete characterization of the regularity properties of the solutions associated to the homogeneous Dirichlet problem \begin{equation*} \begin{cases} \displaystyle - \Delta_1 u= h(u)f & \text{in } \Omega, \\ \newline u=0 & \text{on } \partial \Omega, \end{cases} \end{equation*} where $\Omega\subset\mathbb{R}^N$ is a bounded open

Xin Cheng, Xun Wang, Xingxing Zhang, Tao Ge

This paper introduces xRAG, an innovative context compression method tailored for retrieval-augmented generation. xRAG reinterprets document embeddings in dense retrieval--traditionally used solely for retrieval--as features from the retrieval modality. By employing a modality fusion methodology, xRAG seamlessly integrates these embeddings into the language

Michael Kilgour, Mark Tuckerman, Jutta Rogal

Representations are a foundational component of any modelling protocol, including on molecules and molecular solids. For tasks that depend on knowledge of both molecular conformation and 3D orientation, such as the modelling of molecular dimers, clusters, or condensed phases, we desire a rotatable representation that is provably complete in the types and pos

Marco Romagnoni, Vincenzo Guidi, Laura Bandiera, Davide De Salvador

Bent crystal are widely used as optics for X-rays, but via the phenomenon of planar channeling they may act as waveguide for relativistic charged particles beam as well, outperforming some of the traditional technologies currently employed. A physical description of the phenomenon and the resulting potential for applications in a particle accelerator is repo

Manuel A. Espinosa-García, Ahtziri González, Yesenia Villicaña-Molina

In this paper we study the space $\mathbb{L}(n)$ of $n$-gons in the plane degenerated to segments. We prove that this space is a smooth real submanifold of $\mathbb{C}^n$, and describe its topology in terms of the manifold $\mathbb{M}(n)$ of $n$-gons degenerated to segments and with the first vertex at 0. We show that $\mathbb{M}(n)$ and $\mathbb{L}(n)$ cont

Po-Wei Huang, Patrick Rebentrost

Classical algorithms for market equilibrium computation such as proportional response dynamics face scalability issues with Internet-based applications such as auctions, recommender systems, and fair division, despite having an almost linear runtime in terms of the product of buyers and goods. In this work, we provide the first quantum algorithm for market e

Jiajie Zhao, Zhiwei Bai, Yaoyu Zhang

Overparameterized models like deep neural networks have the intriguing ability to recover target functions with fewer sampled data points than parameters (see arXiv:2307.08921). To gain insights into this phenomenon, we concentrate on a single-neuron target recovery scenario, offering a systematic examination of how initialization and sample size influence t

Aurora Ramírez, Mario Berrios, José Raúl Romero, Robert Feldt

Test case prioritisation (TCP) is a critical task in regression testing to ensure quality as software evolves. Machine learning has become a common way to achieve it. In particular, learning-to-rank (LTR) algorithms provide an effective method of ordering and prioritising test cases. However, their use poses a challenge in terms of explainability, both globa

Shifan Zhao, Jiaying Lu, Ji Yang, Edmond Chow

Gaussian Process Regression (GPR) is widely used in statistics and machine learning for prediction tasks requiring uncertainty measures. Its efficacy depends on the appropriate specification of the mean function, covariance kernel function, and associated hyperparameters. Severe misspecifications can lead to inaccurate results and problematic consequences, e