May 2024 arXiv papers — page 110

Visal So, Midhuna Duraisamy Suganthi, Abhishek Menon, Mingjian Zhu

Electron transfer is at the heart of many fundamental physical, chemical, and biochemical processes essential for life. The exact simulation of these reactions is often hindered by the large number of degrees of freedom and by the essential role of quantum effects. Here, we experimentally simulate a paradigmatic model of molecular electron transfer using a m

Camilo García-Cely, Giacomo Landini, Óscar Zapata

QCD-like theories in which the dark matter (DM) of the Universe is hypothesized to be a thermal relic in the form of a dark pion has been extensively investigated, with most studies neglecting the CP-violating $\theta$-angle associated with the topological vacuum. We point out that a non-vanishing $\theta$ could potentially trigger resonant number-changing p

Robert Fleischer, Eleftheria Malami, Anders Rehult, K. Keri Vos

The leptonic decay $B_s \to \mu^+\mu^-$ is both rare and theoretically clean, making it an excellent probe for New Physics searches. Due to its helicity suppression in the Standard Model, this decay is particularly sensitive to new (pseudo)-scalar contributions. We present a new strategy for detecting CP-violating New Physics contributions of this kind, expl

Thomas W. Baumgarte, Stuart L. Shapiro

We present self-consistent numerical simulations in general relativity of putative primordial black holes inside neutron stars. Complementing a companion paper in which we assumed the black hole mass $m$ to be much smaller than the mass $M_*$ of the neutron star, thereby justifying a point-mass treatment, we here consider black holes with masses large enough

Kimi C. Kreilgaard, Charlotte A. Mason, Fergus Cullen, Ryan Begley

The escape fraction of ionising photons from galaxies ($f_\mathrm{esc}$) is a key parameter for understanding how intergalactic hydrogen became reionised, but it remains mostly unconstrained. Measurements have been limited to the average value in galaxy ensembles and handfuls of individual detections. To help understand which mechanisms govern ionising photo

Robert J. Saskowski

Gibbons-Hawking-York (GHY) terms are typically neglected when performing dimensional reductions of gravitational theories. We consider the reduction of such terms for both two-derivative and four-derivative theories in general dimensions. We demonstrate a robust consistency wherein the GHY term in the original, higher-dimensional theory translates directly t

Eric Lescano, David Osten

The known stringy non-relativistic (NR) limit of the universal NS-NS sector of supergravity has a finite Lagrangian due to non-trivial cancellations of divergent parts coming from the metric and the $B$-field. We demonstrate that in Double Field Theory (DFT) and generalised geometry these cancellations already happen at the level of the generalised metric, w

M. Leemker, A. S. Booth, E. F. van Dishoeck, L. Wölfer

High-resolution observations show that typically both the dust and the gas in nearby extended protoplanetary disks are structured, possibly related to radial and azimuthal variations in the disk density and/or chemistry. The aim of this work is to identify the expected location and intensity of rings seen in molecular line emission of HCN, CN, C$_2$H, NO, [C

Neil Dowling, Maxwell T. West, Angus Southwell, Azar C. Nakhl

Despite their ever more widespread deployment throughout society, machine learning algorithms remain critically vulnerable to being spoofed by subtle adversarial tampering with their input data. The prospect of near-term quantum computers being capable of running {quantum machine learning} (QML) algorithms has therefore generated intense interest in their ad

Paulo Luz, Sante Carloni

We study adiabatic, radial perturbations of static, self-gravitating perfect fluids within the theory of general relativity employing a new perturbative formalism. We show that by considering a radially static observer, the description of the perturbations can be greatly simplified with respect to the standard comoving treatment. The new perturbation equatio

Masha Baryakhtar, Olivier Simon, Zachary J. Weiner

The fundamental constants at recombination can differ from their present-day values due to degeneracies in cosmological parameters, raising the possibility of yet-undiscovered physics coupled directly to the Standard Model. We study the cosmology of theories in which a new, hyperlight scalar field modulates the electron mass and fine-structure constant at ea

Xin Qiao, Matteo Poggi, Pengchao Deng, Hao Wei

Integrating an RGB camera into a ToF imaging system has become a significant technique for perceiving the real world. The RGB guided ToF imaging system is crucial to several applications, including face anti-spoofing, saliency detection, and trajectory prediction. Depending on the distance of the working range, the implementation schemes of the RGB guided To

Jorge G. Russo

In the context of Kaluza-Klein theories, the time dilation of charged particles in an external field depends on the charge in a specific way. Experimental tests are proposed to search for extra dimensions using this distinctive feature.

Ethan Weber, Riley Peterlinz, Rohan Mathur, Frederik Warburg

We recover the underlying 3D structure from images of cartoons and anime depicting the same scene. This is an interesting problem domain because images in creative media are often depicted without explicit geometric consistency for storytelling and creative expression-they are only 3D in a qualitative sense. While humans can easily perceive the underlying 3D

Jorge Enrique García-Farieta, Andrés Balaguera-Antolínez, Francisco-Shu Kitaura

We assess the effectiveness of a non-parametric bias model in generating mock halo catalogues for modified gravity (MG) cosmologies, relying on the distribution of dark matter from either MG or $\Lambda$CDM. We aim to generate halo catalogues that effectively capture the distinct impact of MG, ensuring high accuracy in both two- and three-point statistics fo

Wenqin Chen, Xiao-Wei Zhang, Ying Su, Ting Cao

We present a quantum theory of phonon magnetic moment in doped Dirac semimetals. Our theory is based on an emergent gauge field approach to the electron-phonon coupling, applicable to both gapless and gapped systems. We find that the magnetic moment is directly proportional to the electrical Hall conductivity through the phonon Hall viscosity. Our theory is

Peiying Zhang, Nanxuan Zhao, Jing Liao

Vector graphics are widely used in digital art and highly favored by designers due to their scalability and layer-wise properties. However, the process of creating and editing vector graphics requires creativity and design expertise, making it a time-consuming task. Recent advancements in text-to-vector (T2V) generation have aimed to make this process more a

Zheng Gu, Shiyuan Yang, Jing Liao, Jing Huo

Visual In-Context Learning (ICL) has emerged as a promising research area due to its capability to accomplish various tasks with limited example pairs through analogical reasoning. However, training-based visual ICL has limitations in its ability to generalize to unseen tasks and requires the collection of a diverse task dataset. On the other hand, existing

Yunfan Jiang, Chen Wang, Ruohan Zhang, Jiajun Wu

Learning in simulation and transferring the learned policy to the real world has the potential to enable generalist robots. The key challenge of this approach is to address simulation-to-reality (sim-to-real) gaps. Previous methods often require domain-specific knowledge a priori. We argue that a straightforward way to obtain such knowledge is by asking huma

Ruiqi Gao, Aleksander Holynski, Philipp Henzler, Arthur Brussee

Advances in 3D reconstruction have enabled high-quality 3D capture, but require a user to collect hundreds to thousands of images to create a 3D scene. We present CAT3D, a method for creating anything in 3D by simulating this real-world capture process with a multi-view diffusion model. Given any number of input images and a set of target novel viewpoints, o

Tao Feng, Chuanyang Jin, Jingyu Liu, Kunlun Zhu

The evolution of artificial intelligence (AI) has profoundly impacted human society, driving significant advancements in multiple sectors. AGI, distinguished by its ability to execute diverse real-world tasks with efficiency and effectiveness comparable to human intelligence, reflects a paramount milestone in AI evolution. While existing studies have reviewe

Joachim Harnois-Deraps, Sven Heydenreich, Benjamin Giblin, Nicolas Martinet

We analyse the fourth data release of the Kilo Degree Survey (KiDS-1000) and extract cosmological parameter constraints based on the cosmic shear peak count statistics. Peaks are identified in aperture mass maps in which the filter is maximally sensitive to angular scales in the range 2-4arcmin, probing deep into the non-linear regime of structure formation.

Sahel Sharifymoghaddam, Shivani Upadhyay, Wenhu Chen, Jimmy Lin

Recently, Large Vision Language Models (LVLMs) have unlocked many complex use cases that require Multi-Modal (MM) understanding (e.g., image captioning or visual question answering) and MM generation (e.g., text-guided image generation or editing) capabilities. To further improve the output fidelityof LVLMs we introduce UniRAG, a plug-and-play technique that

Fares Fourati, Vaneet Aggarwal, Mohamed-Slim Alouini

In complex environments with large discrete action spaces, effective decision-making is critical in reinforcement learning (RL). Despite the widespread use of value-based RL approaches like Q-learning, they come with a computational burden, necessitating the maximization of a value function over all actions in each iteration. This burden becomes particularly

Dylan Reynolds, Gustavo M. Monteiro, Sriram Ganeshan

We adapt the fluid description of Fractional Quantum Hall (FQH) states, as seen in (arXiv:2203.06516), to model a system of interacting two-component bosons. This system represents the simplest physical realization of an interacting bosonic Symmetry-Protected Topological (SPT) phase, also known as the integer quantum Hall effect (IQHE) of bosons. In particul

Eden Frenkel, Tej Chajed, Oded Padon, Sharon Shoham

This paper lays a practical foundation for using abstract interpretation with an abstract domain that consists of sets of quantified first-order logic formulas. This abstract domain seems infeasible at first sight due to the complexity of the formulas involved and the enormous size of sets of formulas (abstract elements). We introduce an efficient representa

Batoul Banihashemi, Ted Jacobson

The gravitational path integral is usually implemented with a covariant action by analogy with other gauge field theories, but the gravitational case is different in important ways. A key difference is that the integrand has an essential singularity, which occurs at zero lapse where the spacetime metric degenerates. The lapse integration contour required to

Woo-Ram Lee, Nathan M. Myers, V. W. Scarola

We present a hybrid quantum algorithm for estimating gaps in many-body energy spectra, supported by an analytic proof of its inherent resilience to state preparation and measurement errors, as well as mid-circuit multi-qubit depolarizing noise. Our analysis extends to a broader class of Markovian noise, employing error mitigation strategies that optimize the

Jingkang Yang, Jun Cen, Wenxuan Peng, Shuai Liu

We are living in a three-dimensional space while moving forward through a fourth dimension: time. To allow artificial intelligence to develop a comprehensive understanding of such a 4D environment, we introduce 4D Panoptic Scene Graph (PSG-4D), a new representation that bridges the raw visual data perceived in a dynamic 4D world and high-level visual underst

Hervé Dutrieux, Robert G. Edwards, Colin Egerer, Joseph Karpie

We present an exploration of the unpolarized isovector proton generalized parton distributions (GPDs) $H^{u-d}(x, \xi, t)$ and $E^{u-d}(x, \xi, t)$ in the pseudo-distribution formalism using distillation. Taking advantage of the large kinematic coverage made possible by this approach, we present results on the moments of GPDs up to the order $x^3$ -- includi

Wen Yin, Shota Nakagawa, Tamaki Murokoshi, Makoto Hattori

We investigate a novel scenario involving asymmetric keV-range dark matter (DM) in the form of right-handed (sterile) neutrinos. Based on the Fermi-Dirac distribution, we demonstrate that asymmetric fermionic DM forms a Fermi degenerate gas, making it potentially colder than symmetric fermionic DM. This setup simultaneously accounts for the Universe's baryon

Jiawei Ge, Debarghya Mukherjee, Jianqing Fan

As machine learning models are increasingly deployed in dynamic environments, it becomes paramount to assess and quantify uncertainties associated with distribution shifts. A distribution shift occurs when the underlying data-generating process changes, leading to a deviation in the model's performance. The prediction interval, which captures the range of li

Yu Gui, Ying Jin, Zhimei Ren

Before deploying outputs from foundation models in high-stakes tasks, it is imperative to ensure that they align with human values. For instance, in radiology report generation, reports generated by a vision-language model must align with human evaluations before their use in medical decision-making. This paper presents Conformal Alignment, a general framewo

Tianhe Ren, Qing Jiang, Shilong Liu, Zhaoyang Zeng

This paper introduces Grounding DINO 1.5, a suite of advanced open-set object detection models developed by IDEA Research, which aims to advance the "Edge" of open-set object detection. The suite encompasses two models: Grounding DINO 1.5 Pro, a high-performance model designed for stronger generalization capability across a wide range of scenarios, and Groun

Rhea Sanjay Sukthanker, Arber Zela, Benedikt Staffler, Aaron Klein

The increasing size of language models necessitates a thorough analysis across multiple dimensions to assess trade-offs among crucial hardware metrics such as latency, energy consumption, GPU memory usage, and performance. Identifying optimal model configurations under specific hardware constraints is becoming essential but remains challenging due to the com

Spencer K. Passmore, Alaric L. Sanders, Andrew V. Martin, Harry M. Quiney

Serial femtosecond X-ray crystallography (SFX) captures the structure and dynamics of biological macromolecules at high spatial and temporal resolutions. The ultrashort pulse produced by an X-ray free electron laser (XFEL) 'outruns' much of the radiation damage that impairs conventional crystallography. However, the rapid onset of 'electronic damage' due to

Omar Alrabiah, Jesse Goodman, Jonathan Mosheiff, João Ribeiro

We prove that random low-degree polynomials (over $\mathbb{F}_2$) are unbiased, in an extremely general sense. That is, we show that random low-degree polynomials are good randomness extractors for a wide class of distributions. Prior to our work, such results were only known for the small families of (1) uniform sources, (2) affine sources, and (3) local so

Ashwani Anand, Sylvain Schmitz, Lia Schütze, Georg Zetzsche

Well-structured transition systems (WSTS) are an abstract family of systems that encompasses a vast landscape of infinite-state systems. By requiring a well-quasi-ordering (wqo) on the set of states, a WSTS enables generic algorithms for classic verification tasks such as coverability and termination. However, even for systems that are WSTS like vector addit

Jamie Bernardi, Gabriel Mukobi, Hilary Greaves, Lennart Heim

Existing strategies for managing risks from advanced AI systems often focus on affecting what AI systems are developed and how they diffuse. However, this approach becomes less feasible as the number of developers of advanced AI grows, and impedes beneficial use-cases as well as harmful ones. In response, we urge a complementary approach: increasing societal

Thomas D. Cohen, Hyunwoo Oh

The cost and the error of the adiabatic theorem for preparing the final eigenstate are discussed in terms of path length. Previous studies in terms of the norm of the Hamiltonian and its derivatives with the spectral gap are limited in their ability to describe the cost of adiabatic state preparation for certain physically large systems. We argue that total

Rodrigo Maier, Manuella Corrêa e Silva

In this paper charged black holes are obtained assuming that a Born-Infeld electrodynamics may arise from an interaction between the electromagnetic field and a vacuum component. In this context Cauchy horizons do not appear in the maximal analytical extension once an event horizon is formed so that the interior spacetime does not suffer from any sort of ins

Yuexiang Zhai, Hao Bai, Zipeng Lin, Jiayi Pan

Large vision-language models (VLMs) fine-tuned on specialized visual instruction-following data have exhibited impressive language reasoning capabilities across various scenarios. However, this fine-tuning paradigm may not be able to efficiently learn optimal decision-making agents in multi-step goal-directed tasks from interactive environments. To address t

Hei Lam, Yangqian Yan

Motivated by the observation of the breakdown of quantization for the Thouless pump in the presence of strong interaction by ETH [Walter et. al. Nat. Phys. 19, 1471 (2023), Viebahn et. al. arXiv:2308.03756], we study the interplay of strong interaction and topology in the (1+1)-dimensional interacting Rice-Mele model. We point out that the quantization of th

Alexander Dietmüller, Romain Jacob, Laurent Vanbever

Machine learning (ML) is a powerful tool to model the complexity of communication networks. As networks evolve, we cannot only train once and deploy. Retraining models, known as continual learning, is necessary. Yet, to date, there is no established methodology to answer the key questions: With which samples to retrain? When should we retrain? We address the

Feng Ruan

We investigate the uniform convergence of subdifferential mappings from empirical risk to population risk in nonsmooth, nonconvex stochastic optimization. This question is key to understanding how empirical stationary points approximate population ones, yet characterizing this convergence remains a fundamental challenge due to the set-valued and nonsmooth na

Jianhao Chen, Haoyuan Ouyang, Junyang Ren, Wentao Ding

Facts extraction is pivotal for constructing knowledge graphs. Recently, the increasing demand for temporal facts in downstream tasks has led to the emergence of the task of temporal fact extraction. In this paper, we specifically address the extraction of temporal facts from natural language text. Previous studies fail to handle the challenge of establishin

Farid Thaalba, Giulia Ventagli, Thomas P. Sotiriou

In this note, we discuss the effect of light, non-gauge, bosonic degrees of freedom on the exterior spacetime of an exotic compact object. We show that such fields generically introduce large deviations from black hole spacetimes of General Relativity near and outside the surfaces of ultra-compact exotic objects unless one assumes they totally decouple from

Adrian Bulat, Yassine Ouali, Georgios Tzimiropoulos

Despite noise and caption quality having been acknowledged as important factors impacting vision-language contrastive pre-training, in this paper, we show that the full potential of improving the training process by addressing such issues is yet to be realized. Specifically, we firstly study and analyze two issues affecting training: incorrect assignment of

Jutho Haegeman, Laurens Lootens, Quinten Mortier, Alexander Stottmeister

We develop a Hamiltonian formalism for simulating interacting chiral fermions on the lattice while preserving unitarity and locality and without breaking the chiral symmetry. The fermion doubling problem is circumvented by constructing a Fock space endowed with a semi-definite norm. When projecting our theory on the the single-particle sector, we recover the

Marçal Comajoan Cara, Gopal Ramesh Dahale, Zhongtian Dong, Roy T. Forestano

We introduce a hybrid quantum-classical vision transformer architecture, notable for its integration of variational quantum circuits within both the attention mechanism and the multi-layer perceptrons. The research addresses the critical challenge of computational efficiency and resource constraints in analyzing data from the upcoming High Luminosity Large H

Denis Boyer, Satya N. Majumdar

We study the relaxation of a Brownian particle with long range memory under confinement in one dimension. The particle diffuses in an arbitrary confining potential and resets at random times to previously visited positions, chosen with a probability proportional to the local time spent there by the particle since the initial time. This model mimics an animal

Hans Cruz-Prado, Octavio Castaños, Giuseppe Marmo, Francisco Nettel

We construct the vector field associated to the GKLS generator for systems described by Gaussian states. This vector field is defined on the dual space of the algebra of operators, restricted to operators quadratic in position and momentum. It is shown that the GKLS dynamics accepts a decomposition principle, that is, this vector field can be decomposed in t

Madeline Clyburn, Jonathan Zrake

We present a theoretical study of the gravitational wave (GW) driven inspirals of accreting black hole binaries with mass $M = 10^7 M_\odot$ and mass ratios between $10^{-3}$ and $10^{-1}$. Our results are based on analytic estimates, and grid-based hydrodynamics simulations run for many thousands of binary orbits before the merger. We show that the GW inspi

Alexander Wietfeld, Sebastian Schmidt, Wolfgang Kellerer

This work presents a novel flow-based molecular communication (MC) testbed using spectral sensing and ink intensity estimation to enable real-time multi-molecule (MUMO) transmission. MUMO communication opens up crucial opportunities for increased throughput as well as implementing more complex coding, modulation, and resource allocation strategies for MC tes

Rainer Dick

Coherent states are a well-established tool of quantum optics to describe electromagnetic waves in terms of photons. However, they do not describe the near-field regime of radiation sources. Instead, we generically use classical solutions of Maxwell's equations to describe radiation in the near-field regime. The classical solutions provide linear relations b

Agnieszka Sorensen, Paul Sorensen

We perform a finite-size scaling analysis of net-proton number cumulants in Au+Au collisions at center-of-mass energies between $\sqrt{s_{\rm{NN}}} = 2.4$ and $200~\rm{GeV}$ to search for evidence of a critical point in the QCD phase diagram. We use the second-order susceptibility and Binder cumulant, whose scaling with the system size is studied by using th

Alexander Golovnev, Zeyu Guo, Pooya Hatami, Satyajeet Nagargoje

For $S\subseteq \mathbb{F}^n$, consider the linear space of restrictions of degree-$d$ polynomials to $S$. The Hilbert function of $S$, denoted $\mathrm{h}_S(d,\mathbb{F})$, is the dimension of this space. We obtain a tight lower bound on the smallest value of the Hilbert function of subsets $S$ of arbitrary finite grids in $\mathbb{F}^n$ with a fixed size $

Tuo Zhang, Jinyue Yuan, Salman Avestimehr

Numerous recent works aim to enhance the efficacy of Large Language Models (LLMs) through strategic prompting. In particular, the Optimization by PROmpting (OPRO) approach provides state-of-the-art performance by leveraging LLMs as optimizers where the optimization task is to find instructions that maximize the task accuracy. In this paper, we revisit OPRO f

Noga Alon, Zhihan Jin, Benny Sudakov

We prove the following variant of Helly's classical theorem for Hamming balls with a bounded radius. For $n>t$ and any (finite or infinite) set $X$, if in a family of Hamming balls of radius $t$ in $X^n$, every subfamily of at most $2^{t+1}$ balls have a common point, so do all members of the family. This is tight for all $|X|>1$ and all $n>t$. The proof of

Prabhanjan Ananth, Fatih Kaleoglu, Henry Yuen

Unclonable cryptography is concerned with leveraging the no-cloning principle to build cryptographic primitives that are otherwise impossible to achieve classically. Understanding the feasibility of unclonable encryption, one of the key unclonable primitives, satisfying indistinguishability security in the plain model has been a major open question in the ar

Vasudevarao Allu, Abhishek Pandey

In this paper, we introduce the concept of quasihyperbolically visible spaces. As a tool, we study the connection between the Gromov boundary and the metric boundary.

Youngjoon Jang, Ji-Hoon Kim, Junseok Ahn, Doyeop Kwak

The goal of this work is to simultaneously generate natural talking faces and speech outputs from text. We achieve this by integrating Talking Face Generation (TFG) and Text-to-Speech (TTS) systems into a unified framework. We address the main challenges of each task: (1) generating a range of head poses representative of real-world scenarios, and (2) ensuri

Christian Internò, Elena Raponi, Markus Olhofer, Ali Raza

The practical deployment of Federated Learning (FL) on resource-constrained devices is fundamentally limited by the high cost of training large models and the instability caused by heterogeneous (non-IID) client data. Conventional pruning methods often treat data heterogeneity as a problem to be mitigated. In this work, we introduce a paradigm shift: we refr

Fernando Szechtman

Consider the Macdonald group $G(\alpha,\beta)=\langle A,B\,|\, A^{[A,B]}=A^\alpha,\, B^{[B,A]}=B^\beta\rangle$, where $\alpha$ and $\beta$ are integers different from one. We fill a gap in Macdonald's original proof that $G(\alpha,\beta)$ is nilpotent, and find the order and nilpotency class of each Sylow subgroup of $G(\alpha,\beta)$.

Patrick Bourg, Rodrigo Panosso Macedo, Andrew Spiers, Benjamin Leather

Quasinormal modes (QNMs) uniquely describe the dominant piece of the gravitational-wave ringdown of postmerger black holes. While the linear QNM regime has been extensively studied, recent work has highlighted the importance of second-perturbative-order, quadratic QNMs (QQNMs) arising from the nonlinear coupling of linear QNMs. Previous attempts to quantify

Evgeny Redekop, Canxun Zhang, Heonjoon Park, Jiaqi Cai

In the absence of time reversal symmetry, orbital magnetization provides a sensitive probe of topology and interactions, with particularly rich phenomenology in Chern insulators where topological edge states carry large equilibrium currents. Here, we use a nanoscale superconducting sensor to map the magnetic fringe fields in twisted bilayers of MoTe$_2$, whe

Caroline E. P. Robin, Martin J. Savage

Toward an improved understanding of the role of quantum information in nuclei and exotic matter, we examine the quantum magic (non-stabilizerness) in low-energy strong interaction processes. As stabilizer states can be prepared efficiently using classical computers, and include classes of entangled states, it is quantum magic and fluctuations in quantum magi

Illya Bakurov, Nathan Haut, Wolfgang Banzhaf

Sharpness-Aware Minimization (SAM) was recently introduced as a regularization procedure for training deep neural networks. It simultaneously minimizes the fitness (or loss) function and the so-called fitness sharpness. The latter serves as a measure of the nonlinear behavior of a solution and does so by finding solutions that lie in neighborhoods having uni

Charles Raude, K R Prajwal, Liliane Momeni, Hannah Bull

In this work, our goals are two fold: large-vocabulary continuous sign language recognition (CSLR), and sign language retrieval. To this end, we introduce a multi-task Transformer model, CSLR2, that is able to ingest a signing sequence and output in a joint embedding space between signed language and spoken language text. To enable CSLR evaluation in the lar

Yan B. Bandeira, Victor P. Goncalves, Wolfgang Schäfer

The cross-section for the production of an electroweak gauge boson ($G = W^{\pm}, Z^0, \gamma$) at forward rapidities in $pp$ collisions is derived within the color - dipole $S$ - matrix framework. We present the full expressions for the differential cross-section of the $q p \rightarrow G X$ process in the impact parameter and transverse momentum spaces, co

Diego García-Martín, Paolo Braccia, M. Cerezo

Parametrized and random unitary (or orthogonal) $n$-qubit circuits play a central role in quantum information. As such, one could naturally assume that circuits implementing symplectic transformations would attract similar attention. However, this is not the case, as $\mathbb{SP} (d/2)$ -- the group of $d\times d$ unitary symplectic matrices -- has thus far

Mikhail Gennadievich Belov, Vladislav Gennadievich Malyshkin

The problem of an optimal mapping between Hilbert spaces $IN$ of $\left|\psi\right\rangle$ and $OUT$ of $\left|\phi\right\rangle$ based on a set of wavefunction measurements (within a phase) $\psi_l \to \phi_l$, $l=1\dots M$, is formulated as an optimization problem maximizing the total fidelity $\sum_{l=1}^{M} \omega^{(l)} \left|\langle\phi_l|\mathcal{U}|\p

Junpeng Zhang, Qing Li, Liang Lin, Quanshi Zhang

This paper investigates the dynamics of a deep neural network (DNN) learning interactions. Previous studies have discovered and mathematically proven that given each input sample, a well-trained DNN usually only encodes a small number of interactions (non-linear relationships) between input variables in the sample. A series of theorems have been derived to p

Melissa Cruz Torres, Tobias Huber, Minakshi Nayak, Gilberto Tetlalmatzi-Xolocotzi

In this contribution a summary of the activities of Working Group 5 (WG5) presented during the 12th International Workshop on the CKM Unitarity Triangle (CKM2023) is reported. This includes new results on $\phi_{3}/\gamma$ measurements using $B\to DK$ decays, search for $CP$ violation using charmless $B$ decays and $b$-Baryon decays, measurement of branching

Calvin Bao, Marine Carpuat

Authorship obfuscation techniques hold the promise of helping people protect their privacy in online communications by automatically rewriting text to hide the identity of the original author. However, obfuscation has been evaluated in narrow settings in the NLP literature and has primarily been addressed with superficial edit operations that can lead to unn

Lauritz van Luijk

We consider quantum systems with energy constraints relative to a reference Hamiltonian. In general, quantum channels and continuous-time dynamics need not satisfy energy conservation. Physically meaningful channels, however, only introduce a finite amount of energy to the system, and continuous-time dynamics only increase the energy gradually over time. We

M. Viviani, S. Baeßler, L. Barrón-Palos, N. Birge

Recently, the n$^3$He collaboration reported a measurement of the parity-violating (PV) proton directional asymmetry $A_{\mathrm {PV}} = (1.55\pm 0.97~\mathrm {(st\ at)} \pm 0.24~\mathrm {(sys)})\times 10^{-8}$ in the capture reaction of ${}^3$He$(\vec {n},{\mathrm p}){}^3$H at meV incident neutron energies. The result increased the limited inventory of prec

Sergio Hernandez F., Christophe Peucheret, Francesco Da Ros, Darko Zibar

The rate and reach of directly-modulated laser links is often limited by the interplay between chirp and fiber chromatic dispersion. We address this by optimizing the transmitter, receiver, bias and peak-to-peak current to the laser jointly. Our approach outperforms Volterra post-equalization at various symbol rates.

Anshul Pundhir, Balasubramanian Raman, Pravendra Singh

Deep learning models, particularly Convolutional Neural Networks (CNNs), have demonstrated exceptional performance in diagnosing skin diseases, often outperforming dermatologists. However, they have also unveiled biases linked to specific demographic traits, notably concerning diverse skin tones or gender, prompting concerns regarding fairness and limiting t

Xianzheng Ma, Brandon Smart, Yash Bhalgat, Shuai Chen

As large language models (LLMs) evolve, their integration with 3D spatial data (3D-LLMs) has seen rapid progress, offering unprecedented capabilities for understanding and interacting with physical spaces. This survey provides a comprehensive overview of the methodologies enabling LLMs to process, understand, and generate 3D data. Highlighting the unique adv

George Shaikovski, Adam Casson, Kristen Severson, Eric Zimmermann

Foundation models in computational pathology promise to unlock the development of new clinical decision support systems and models for precision medicine. However, there is a mismatch between most clinical analysis, which is defined at the level of one or more whole slide images, and foundation models to date, which process the thousands of image tiles conta

Richard Wen, Hunter McCoy, David Tench, Guido Tagliavini

Adaptive filters, such as telescoping and adaptive cuckoo filters, update their representation upon detecting a false positive to avoid repeating the same error in the future. Adaptive filters require an auxiliary structure, typically much larger than the main filter and often residing on slow storage, to facilitate adaptation. However, existing adaptive fil

Keenon Werling, Janelle Kaneda, Alan Tan, Rishi Agarwal

While reconstructing human poses in 3D from inexpensive sensors has advanced significantly in recent years, quantifying the dynamics of human motion, including the muscle-generated joint torques and external forces, remains a challenge. Prior attempts to estimate physics from reconstructed human poses have been hampered by a lack of datasets with high-qualit

Giorgos Kotsovolis

We show that the spectrum of every $\mathbb{R}-$isotropic homogeneous binary form $P$ of degree $n\geq3$ is an interval of the form $[0,M_P],$ where $M_P$ is some positive constant. This completes the discussion around a conjecture of Mordell from 1940 (disproved by Davenport) regarding the existence of spectral gaps for binary cubic forms and further settle

Xuanfan Ni, Piji Li

Recent efforts have evaluated large language models (LLMs) in areas such as commonsense reasoning, mathematical reasoning, and code generation. However, to the best of our knowledge, no work has specifically investigated the performance of LLMs in natural language generation (NLG) tasks, a pivotal criterion for determining model excellence. Thus, this paper

Hao Yan, Thomas D. Latoza, Ziyu Yao

Chat LLMs such as GPT-3.5-turbo and GPT-4 have shown promise in assisting humans in coding, particularly by enabling them to conversationally provide feedback. However, current approaches assume users have expert debugging skills, limiting accessibility for non-professional programmers. In this paper, we first explore Chat LLMs' limitations in assisting non-

Andrei Constantinescu, Diana Ghinea, Jakub Sliwinski, Roger Wattenhofer

The distributed computing literature considers multiple options for modeling communication. Most simply, communication is categorized as either synchronous or asynchronous. Synchronous communication assumes that messages get delivered within a publicly known timeframe and that parties' clocks are synchronized. Asynchronous communication, on the other hand, o

Chen Huang, Xinwei Yang, Yang Deng, Wenqiang Lei

Recent efforts have aimed to improve AI machines in legal case matching by integrating legal domain knowledge. However, successful legal case matching requires the tacit knowledge of legal practitioners, which is difficult to verbalize and encode into machines. This emphasizes the crucial role of involving legal practitioners in high-stakes legal case matchi

Roberto Macrì Demartino, Leonardo Egidi, Nicola Torelli

Over the last few years, there has been a growing interest in the prediction and modelling of competitive sports outcomes, with particular emphasis placed on this area by the Bayesian statistics and machine learning communities. In this paper, we have carried out a comparative evaluation of statistical and machine learning models to assess their predictive p

Xinru Zhang, Ni Ou, Berke Doga Basaran, Marco Visentin

Brain lesion segmentation plays an essential role in neurological research and diagnosis. As brain lesions can be caused by various pathological alterations, different types of brain lesions tend to manifest with different characteristics on different imaging modalities. Due to this complexity, brain lesion segmentation methods are often developed in a task-

Anoopa Joshi, Parvinder Singh, Atul Kumar

This article comprehensively explores matrices and their prerequisites for achieving positive semidefiniteness. The study delves into a series of theorems concerning pure quantum states in the context of weighted graphs. The main objective of this study is to establish a graph-theoretic framework for the study of quantum discord and to identify the necessary

Anderson de Andrade, Ivan Bajić

We identify an issue in multi-task learnable compression, in which a representation learned for one task does not positively contribute to the rate-distortion performance of a different task as much as expected, given the estimated amount of information available in it. We interpret this issue using the predictive $\mathcal{V}$-information framework. In lear

Bibek Poudel, Adam Cook, Sekou Traore, Shelah Ameli

Effective communication, specifically through documentation, is the beating heart of collaboration among contributors in software development. Recent advancements in language models (LMs) have enabled the introduction of a new type of actor in that ecosystem: LM-powered assistants capable of code generation, optimization, and maintenance. Our study investiga

Nai-Hui Chia, Daniel Liang, Fang Song

We establish connections between state tomography, pseudorandomness, quantum state synthesis, and circuit lower bounds. In particular, let $\mathfrak{C}$ be a family of non-uniform quantum circuits of polynomial size and suppose that there exists an algorithm that, given copies of $|\psi \rangle$, distinguishes whether $|\psi \rangle$ is produced by $\mathfr

Candido Martin Gonzalez, Jacques Rabie, Juana Sanchez-Ortega

The group scheme of ternary automorphisms of a perfect finite dimensional evolution algebra A is computed. The main advantage of using group schemes is that it allows to apply the Lie functor to determine the Lie algebra of ternary derivations of A. Using the generalised inverse of a matrix, we provide a precise classification of all ternary derivations of a

Samet Uzun, Purnanand Elango, Pierre-Loic Garoche, Behcet Acikmese

This paper introduces a generalized mean-based C^1-smooth robustness measure over discrete-time signals (D-GMSR) for signal temporal logic (STL) specifications. In conjunction with its C1-smoothness, D-GMSR is proven to be both sound and complete. Furthermore, it demonstrates favorable gradient properties and addresses locality and masking problems, which ar

Illia E. Rohozhkin

We construct $(2n+1)\times (2n+1)$ matrices corresponding to a motion of points on the plane from the point of view of Delaunay triangulations. We define a homomorphism from the pure braid group on ($n+3$) strands to the general linear group $\text{GL}_{2n+1}(\mathbb{Q})$.

Simon Biquard

The cosmic microwave background (CMB) anisotropies are a powerful probe of the early universe, and have largely contributed to establishing the current standard cosmological model. To extract the information encoded in those tiny variations, one must first compress the raw, time-domain data collected by a telescope into maps of the sky at the observed freque

Mitali Bafna, Jun-Ting Hsieh, Pravesh K. Kothari

We develop a new approach for approximating large independent sets when the input graph is a one-sided spectral expander - that is, the uniform random walk matrix of the graph has its second eigenvalue bounded away from 1. Consequently, we obtain a polynomial time algorithm to find linear-sized independent sets in one-sided expanders that are almost $3$-colo

Pierre Fleury

Cosmic shear is a key probe of modern cosmology. Amongst its challenges are shape noise and intrinsic alignments, both due to our ignorance of the unlensed shape of the source galaxies. I argue here that Einstein rings may be used as standard shapes to measure the external shear along their line of sight. In the Euclid era, this new observable is expected to