March 2024 arXiv papers — page 162

David L. Craig, Natalia Ares, Erik M. Gauger

Differentiable models of physical systems provide a powerful platform for gradient-based algorithms, with particular impact on parameter estimation and optimal control. Quantum systems present a particular challenge for such characterisation and control, owing to their inherently stochastic nature and sensitivity to environmental parameters. To address this

Manuel Furlan, Pavel Putrov

We describe a method to implement finite group global and gauged $q$-form symmetries into the axiomatic structure of $d$-dimensional Topological Quantum Field Theory (TQFT) in terms of bordisms decorated by cohomology classes. Namely, on a manifold with a boundary, the gauge field is considered as a class in an appropriate relative cohomology group. It is de

Driss Bennis, Ayoub Bouziri

Let R be a commutative ring with identity and S a multiplicative subset of R. The aim of this paper is to study the class of commutative rings in which every S-flat module is flat (resp., projective). An R-module M is said to be S-flat if the localization of M at S, M_S, is a flat R_S-module. Commutative rings R for which all S-flat R-modules are flat are ch

Marcus Nóbrega Gomes, Ahmad F. Taha, Luis Miguel C. Rápallo, Eduardo M. Mendiondo

Detention ponds can mitigate flooding and improve water quality by allowing the settlement of pollutants. Typically, they are operated with fully open orifices and weirs (i.e., passive control). Active controls can improve the performance of these systems: orifices can be retrofitted with controlled valves and spillways can have controllable gates. The real-

David Itkin, Martin Larsson

In the framework of stochastic portfolio theory we introduce rank volatility stabilized models for large equity markets over long time horizons. These models are rank-based extensions of the volatility stabilized models introduced by Fernholz & Karatzas in 2005. On the theoretical side we establish global existence of the model and ergodicity of the induced

Benjamin James Pethybridge

This note clarifies and extends results on the complex SYK model to the solvable q = 2 case. We calculate the four point function OPE of fermions in the low energy CFT, implying the existence of a tower of integer-weight operators in the IR. We comment on the lack of a mode breaking conformal symmetry in this special case of SYK and the consequences for defo

Melih Şahin, Beyza E. Ortlek, Ozgur B. Akan

Molecular communication (MC) enables information transfer through molecules at the nano-scale. This paper presents new and optimized source coding (data compression) methods for MC. In a recent paper, prefix source coding was introduced into the field, through an MC-adapted version of the Huffman coding. We first show that while MC-adapted Huffman coding imp

Jacqueline G. Cavazos, Nia Nixon

Collaborative problem-solving (CPS) is a vital skill used both in the workplace and in educational environments. CPS is useful in tackling increasingly complex global, economic, and political issues and is considered a central 21st century skill. The increasingly connected global community presents a fruitful opportunity for creative and collaborative proble

Abhilash Chenreddy, Erick Delage

The field of Contextual Optimization (CO) integrates machine learning and optimization to solve decision making problems under uncertainty. Recently, a risk sensitive variant of CO, known as Conditional Robust Optimization (CRO), combines uncertainty quantification with robust optimization in order to promote safety and reliability in high stake applications

M. S. Le, G. A. Hine, A. Goffin, J. P. Palastro

We show that the dynamics of high-intensity laser pulses undergoing self-focused propagation in a nonlinear medium can be understood in terms of the topological constraints imposed by the formation and evolution of spatiotemporal optical vortices (STOVs). STOVs are born from point phase defects on the sides of the pulse nucleated by spatiotemporal phase shea

Luigi De Rosa, Jaemin Park

We prove that any sequence of vanishing viscosity Leray-Hopf solutions to the periodic two-dimensional incompressible Navier-Stokes equations does not display anomalous dissipation if the initial vorticity is a measure with positive singular part. A key step in the proof is the use of the Delort-Majda concentration-compactness argument to exclude formation o

Maria T. Baldassarre, Danilo Caivano, Berenice Fernandez Nieto, Domenico Gigante

In recent months, the social impact of Artificial Intelligence (AI) has gained considerable public interest, driven by the emergence of Generative AI models, ChatGPT in particular. The rapid development of these models has sparked heated discussions regarding their benefits, limitations, and associated risks. Generative models hold immense promise across mul

Nicola Piovesan, Antonio De Domenico, Fadhel Ayed

The increasing interest in Large Language Models (LLMs) within the telecommunications sector underscores their potential to revolutionize operational efficiency. However, the deployment of these sophisticated models is often hampered by their substantial size and computational demands, raising concerns about their viability in resource-constrained environmen

Kasra Kassai, Tasos Dagiuklas, Satwat Bashir, Muddesar Iqbal

This study investigates the application of advanced machine learning models, specifically Long Short-Term Memory (LSTM) networks and Gradient Booster models, for accurate energy consumption estimation within a Kubernetes cluster environment. It aims to enhance sustainable computing practices by providing precise predictions of energy usage across various com

Yulin Gong, Rachel Bean

We demonstrate that pairwise peculiar velocity correlations for galaxy clusters can be directly reconstructed from the kinematic Sunyaev-Zel'dovich (kSZ) signature imprinted in the CMB using a machine learning model with a gradient boosting algorithm trained on high-fidelity kSZ simulations. The machine learning model is trained using six to eight cluster fe

Ben Forrás

Let $\mathcal G\simeq H\rtimes\Gamma$ be the semidirect product of a finite group $H$ and $\Gamma\simeq\mathbb Z_p$. Let $F/\mathbb Q_p$ be a finite extension with ring of integers $\mathcal O_F$. Then the total ring of quotients $\mathcal Q^F(\mathcal G)$ of the completed group ring $\mathcal O_{F}[[\mathcal G]]$ is a semisimple ring. We determine its Wedde

N. van Remortel, M. Colomer Molla, B. Clerbaux, A. De Roeck

Heavy neutrinos with masses in the MeV range can in principle simultaneously explain the light neutrino masses and the origin of baryonic matter in the universe. The strongest constraints on their properties come from their potential impact on the formation of light elements in the early universe. Since these constraints rely on assumptions about the cosmic

R. Gnana Praveen, Jahangir Alam

Although person or identity verification has been predominantly explored using individual modalities such as face and voice, audio-visual fusion has recently shown immense potential to outperform unimodal approaches. Audio and visual modalities are often expected to pose strong complementary relationships, which plays a crucial role in effective audio-visual

Kexin Zhang, Brianna Cochran, Ruijia Chen, Lance Hartung

First responders (FRs) navigate hazardous, unfamiliar environments in the field (e.g., mass-casualty incidents), making life-changing decisions in a split second. AR head-mounted displays (HMDs) have shown promise in supporting them due to its capability of recognizing and augmenting the challenging environments in a hands-free manner. However, the design sp

Lucy Qin, Vaughn Hamilton, Sharon Wang, Yigit Aydinalp

As many as 8 in 10 adults share intimate content such as nude or lewd images. Sharing such content has significant benefits for relationship intimacy and body image, and can offer employment. However, stigmatizing attitudes and a lack of technological mitigations put those sharing such content at risk of sexual violence. An estimated 1 in 3 people have been

Razvan M. Tudoran

To each arbitrary given general geometric structure on $\mathbb{R}^{n}$, we associate a pair of compatible Fourier transforms, that prove to appear naturally in the framework of Poisson's summation formula for full lattices. We study their properties and the compatibility with the classical $n-$dimensional Fourier transform. In the case of a positive definit

Akshay Gupte, Melanie Siebenhofer, Angelika Wiegele

Computing the edge expansion of a graph is a famously hard combinatorial problem for which there have been many approximation studies. We present two variants of exact algorithms using semidefinite programming (SDP) to compute this constant for any graph. The first variant uses the SDP relaxation first to reduce the search space considerably. The problem is

Lin Xu, Ningxin Peng, Daquan Zhou, See-Kiong Ng

Dialogue state tracking (DST) aims to record user queries and goals during a conversational interaction achieved by maintaining a predefined set of slots and their corresponding values. Current approaches decide slot values opaquely, while humans usually adopt a more deliberate approach by collecting information from relevant dialogue turns and then reasonin

Riccardo Zuliani, Efe C. Balta, John Lygeros

Model mismatch and process noise are two frequently occurring phenomena that can drastically affect the performance of model predictive control (MPC) in practical applications. We propose a principled way to tune the cost function and the constraints of linear MPC schemes to improve the closed-loop performance and robust constraint satisfaction on uncertain

R. Gnana Praveen, Jahangir Alam

Person or identity verification has been recently gaining a lot of attention using audio-visual fusion as faces and voices share close associations with each other. Conventional approaches based on audio-visual fusion rely on score-level or early feature-level fusion techniques. Though existing approaches showed improvement over unimodal systems, the potenti

Saurabh Shivpuje, Manas Sajjan, Yuchen Wang, Zixuan Hu

Adaptive Variational Quantum Dynamics (AVQD) algorithms offer a promising approach to providing quantum-enabled solutions for systems treated within the purview of open quantum dynamical evolution. In this study, we employ the unrestricted vectorization variant of AVQD to simulate and benchmark various non-unitarily evolving systems. We exemplify how constru

01. AI, :, Alex Young, Bei Chen

We introduce the Yi model family, a series of language and multimodal models that demonstrate strong multi-dimensional capabilities. The Yi model family is based on 6B and 34B pretrained language models, then we extend them to chat models, 200K long context models, depth-upscaled models, and vision-language models. Our base models achieve strong performance

Joseph W. Cutler, Craig Disselkoen, Aaron Eline, Shaobo He

Cedar is a new authorization policy language designed to be ergonomic, fast, safe, and analyzable. Rather than embed authorization logic in an application's code, developers can write that logic as Cedar policies and delegate access decisions to Cedar's evaluation engine. Cedar's simple and intuitive syntax supports common authorization use-cases with readab

Bilal Faye, Hanane Azzag, Mustapha Lebbah, Djamel Bouchaffra

Low-cost cross-modal representation learning is crucial for deriving semantic representations across diverse modalities such as text, audio, images, and video. Traditional approaches typically depend on large specialized models trained from scratch, requiring extensive datasets and resulting in high resource and time costs. To overcome these challenges, we i

Sven Raum

We prove that the twisted group C*-algebra of an acylindrically hyperbolic group -- not necessarily having trivial finite radical -- has stable rank one.

Henrik Glavind Clausen, Pierre Rouchon, Rafal Wisniewski

In this work, we consider the problem of online (real-time, single-shot) estimation of static or slow-varying parameters along quantum trajectories in quantum dynamical systems. Based on the measurement signal of a continuously-monitored quantum system, we propose a recursive algorithm for computing the maximum likelihood estimate of unknown parameters using

L. Cerrigone, G. Umana, C. Trigilio, K. M. Menten

CRL 618 is a post-AGB star that has started to ionize its ejecta. Its central HII region has been observed over the last 40 years and has steadily increased in flux density at radio wavelengths. In this paper, we present data that we obtained with the Very Large Array in its highest frequency band (43 GHz) in 2011 and compare these with archival data in the

David Parmenter, Mark Pollicott

There are several known constructions of equilibrium states for H\"older continuous potentials in the context of both subshifts of finite type and uniformly hyperbolic systems. In this article we present another method of building such measures, formulated in the unified and more general setting of Smale spaces. This simultaneously extends the authors' previ

Julie Butler, Morten Hjorth-Jensen, Justin Lietz

The homogeneous electron gas is a system which has many applications in chemistry and physics. However, its infinite nature makes studies at the many-body level complicated due to long computational run times. Because it is size extensive, coupled cluster theory is capable of studying the homogeneous electron gas, but it still poses a large computational cha

Kevin Hinze, Shaikh Saad

Scalar leptoquarks naturally arise within unified theories, offering a promising avenue for addressing one of the most significant challenges of the Standard Model--the existence of non-zero neutrino masses. In this work, we present a unified theory based on the SU(5) gauge group, where neutrino mass appears at the two-loop level via the propagation of scala

Shichen Dong, Wen Cheng, Jiayu Qin, Wei Wang

The emergence of LLMs has ignited a fresh surge of breakthroughs in NLP applications, particularly in domains such as question-answering systems and text generation. As the need for longer context grows, a significant bottleneck in model deployment emerges due to the linear expansion of the Key-Value (KV) cache with the context length. Existing methods prima

Alex Havrilla, Yuqing Du, Sharath Chandra Raparthy, Christoforos Nalmpantis

Reinforcement Learning from Human Feedback (\textbf{RLHF}) has emerged as a dominant approach for aligning LLM outputs with human preferences. Inspired by the success of RLHF, we study the performance of multiple algorithms that learn from feedback (Expert Iteration, Proximal Policy Optimization (\textbf{PPO}), Return-Conditioned RL) on improving LLM reasoni

C. A. Middelburg

Belnap-Dunn logic, also knows as the logic of First-Degree Entailment, is a logic that can serve as the underlying logic of theories that are inconsistent or incomplete. For various reasons, different expansions of Belnap-Dunn logic with non-classical connectives have been studied. This paper investigates the question whether those expansions are interdefina

Qilang Ye, Zitong Yu, Rui Shao, Xinyu Xie

This paper focuses on the challenge of answering questions in scenarios that are composed of rich and complex dynamic audio-visual components. Although existing Multimodal Large Language Models (MLLMs) can respond to audio-visual content, these responses are sometimes ambiguous and fail to describe specific audio-visual events. To overcome this limitation, w

Priya Rani, Gaurav Negi, Theodorus Fransen, John P. McCrae

The present paper introduces new sentiment data, MaCMS, for Magahi-Hindi-English (MHE) code-mixed language, where Magahi is a less-resourced minority language. This dataset is the first Magahi-Hindi-English code-mixed dataset for sentiment analysis tasks. Further, we also provide a linguistics analysis of the dataset to understand the structure of code-mixin

Yuxiang Ma, Arpit Agarwal, Sandra Q. Liu, Wenzhen Yuan

Compliant grippers enable robots to work with humans in unstructured environments. In general, these grippers can improve with tactile sensing to estimate the state of objects around them to precisely manipulate objects. However, co-designing compliant structures with high-resolution tactile sensing is a challenging task. We propose a simulation framework fo

S. M. Iftiquar, J. Yi

Numerical simulation of a solar cell can provide various information that can be useful to maximize its power conversion efficiency (PCE). In that respect we carried out a set of numerical simulation using AFORS-HET simulation program. Separately, in order to get a better understanding, the optical absorption in individual layers devices were analyzed. Curre

Philipp Nazari, Oliver Lemke, Davide Guidobene, Artiom Gesp

Deep Graph Neural Networks struggle with oversmoothing. This paper introduces a novel, physics-inspired GNN model designed to mitigate this issue. Our approach integrates with existing GNN architectures, introducing an entropy-aware message passing term. This term performs gradient ascent on the entropy during node aggregation, thereby preserving a certain d

Konstantinos Kanellopoulos, Konstantinos Sgouras, F. Nisa Bostanci, Andreas Kosmas Kakolyris

The unprecedented growth in data demand from emerging applications has turned virtual memory (VM) into a major performance bottleneck. Researchers explore new hardware/OS co-designs to optimize VM across diverse applications and systems. To evaluate such designs, researchers rely on various simulation methodologies to model VM components.Unfortunately, curre

Jaidev Dasgupta

This work explores a possible course of evolution of mathematics in ancient times in India when there was no script, no place-value system, and no zero. Reviewing examples of time-reckoning, large numbers, sacrificial altar-making, and astronomy, it investigates the role of concrete objects, natural events, rituals and names in context-dependent arithmetic,

Hitesh Kandala, Jianfeng Gao, Jianwei Yang

We present Pix2Gif, a motion-guided diffusion model for image-to-GIF (video) generation. We tackle this problem differently by formulating the task as an image translation problem steered by text and motion magnitude prompts, as shown in teaser fig. To ensure that the model adheres to motion guidance, we propose a new motion-guided warping module to spatiall

Apolline Mellot, Antoine Collas, Sylvain Chevallier, Denis Engemann

Combining electroencephalogram (EEG) datasets for supervised machine learning (ML) is challenging due to session, subject, and device variability. ML algorithms typically require identical features at train and test time, complicating analysis due to varying sensor numbers and positions across datasets. Simple channel selection discards valuable data, leadin

Ryan Kavanagh, Brigitte Pientka

We present Most, a process language with message-observing session types. Message-observing session types extend binary session types with type-level computation to specify communication protocols that vary based on messages observed on other channels. Hence, Most allows us to express global invariants about processes, rather than just local invariants, in a

S. Lai, J. Utehs, A. Wilhahn, O. Bach

A neural network for software compensation was developed for the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL). The neural network uses spatial and temporal event information from the AHCAL and energy information, which is expected to improve sensitivity to shower development and the neutron fraction of the hadron shower. The neural network me

Jianghao Xu, Di Yang

By solving the infinitesimal Galilean symmetry for the KdV hierarchy, we obtain an explicit expression for the corresponding one-parameter Lie group, which we call the Galilean symmetry of the KdV hierarchy. As an application, we establish an explicit relationship between the non-abelian Born--Infeld partition function and the generalized Br\'ezin--Gross--Wi

Palak Jain, Adam Smith, Connor Wagaman

We describe the first algorithms that satisfy the standard notion of node-differential privacy in the continual release setting (i.e., without an assumed promise on input streams). Previous work addresses node-private continual release by assuming an unenforced promise on the maximum degree in a graph, but leaves open whether such a bound can be verified or

Julian Rodemann, Federico Croppi, Philipp Arens, Yusuf Sale

Bayesian optimization (BO) with Gaussian processes (GP) has become an indispensable algorithm for black box optimization problems. Not without a dash of irony, BO is often considered a black box itself, lacking ways to provide reasons as to why certain parameters are proposed to be evaluated. This is particularly relevant in human-in-the-loop applications of

Jeanne Lin, Dmitry E. Pelinovsky, Bjorn de Rijk

We are interested in the dynamics of interfaces, or zeros, of shock waves in general scalar viscous conservation laws with a locally Lipschitz continuous flux function, such as the modular Burgers' equation. We prove that all interfaces coalesce within finite time, leaving behind either a single interface or no interface at all. Our proof relies on mass and

Sanja Stark, Emilie Frost, Marvin Nebel-Wenner

Managing complex Cyber-Physical Energy Systems (CPES) requires solving various optimization problems with multiple objectives and constraints. As distributed control architectures are becoming more popular in CPES for certain tasks due to their flexibility, robustness, and privacy protection, multi-objective optimization must also be distributed. For this pu

Lei Li, Tianfang Zhang, Xinglin Zhang, Jiaqi Liu

Within the domain of medical analysis, extensive research has explored the potential of mutual learning between Masked Autoencoders(MAEs) and multimodal data. However, the impact of MAEs on intermodality remains a key challenge. We introduce MedFLIP, a Fast Language-Image Pre-training method for Medical analysis. We explore MAEs for zero-shot learning with c

Manuel V. Gnann, Rik W. S. Westdorp, Joris van Winden

We study a parametrically forced nonlinear Schr\"odinger (PFNLS) equation, driven by multiplicative translation-invariant noise. We show that a solitary wave in the stochastic equation is orbitally stable on a timescale which is exponential in the inverse square of the noise strength. We give explicit expressions for the phase shift and fluctuations around t

Vladislav D. Kurilovich, Benjamin Remez, Leonid I. Glazman

A transmon qubit embedded in a high-impedance environment acts in a way dual to a conventional Josephson junction. In analogy to the AC Josephson effect, biasing of the transmon by a direct current leads to the oscillations of voltage across it. These oscillations are known as the Bloch oscillations. We find the Bloch oscillations spectrum, and show that the

Benjamin Remez, Vladislav D. Kurilovich, Maximilian Rieger, Leonid I. Glazman

Recently developed Josephson junction array transmission lines implement strong-coupling circuit electrodynamics compatible with a range of superconducting quantum devices. They provide both the high impedance which allows for strong quantum fluctuations, and photon modes with which to probe a quantum device, such as a small Josephson junction. In this high-

Axel Fünfhaus, Marius Möller, Thilo Kopp, Roser Valentí

Motivated by recently reported magnetic-field induced topological phases in ultracold atoms and correlated Moir\'e materials, we investigate topological phase transitions in a minimal model consisting of interacting spinless fermions described by the Hofstadter model on a square lattice. For interacting lattice Hamiltonians in the presence of a commensurate

Bindi S. Brook, Graeham R. Douglas, Oliver E. Jensen, Sonal Mistry

This report relates to a study group hosted by the EPSRC funded network, Integrating data-driven BIOphysical models into REspiratory MEdicine (BIOREME), and supported by The Insigneo Institute and The Knowledge Transfer Network. The BIOREME network hosts events, including this study group, to bring together multi-disciplinary researchers, clinicians, compani

Vladislav Vysotsky

A switching random walk, commonly known under the misnomer `oscillating random walk', is a real-valued Markov chain whose distribution of increments is determined by the sign of the current position. We explicitly identify an invariant measure of this chain and study its uniqueness, up to a constant factor, within the class of locally finite invariant measur

Yuri N. Lima, Victor P. Goncalves, André V. Giannini

The production of $B$ mesons at forward rapidities is strongly sensitive to the behavior of the gluon and bottom distribution functions for small and large values of the Bjorken - $x$ variable. In this exploratory study, we estimate the cross - section for the $B^{\pm}$ meson production in the kinematic range probed by the LHCb detector and that will be anal

Luigi Liquori, Michael Mendler

Building on the standard theory of process algebra with priorities, we identify a new scheduling mechanism, called "constructive reduction" which is designed to capture the essence of synchronous programming. The distinctive property of this evaluation strategy is to achieve determinacy-by-construction for multi-cast concurrent communication with shared memo

Zhi-Zhang Peng, Cheng-Jun Fang, Zong-Kuan Guo

We calculate the tensor bispectrum mediated by an excited scalar field during inflation and find that the bispectrum peaks in the squeezed configuration, which is different from that of gravitational waves induced by enhanced curvature perturbations re-entering the horizon in the radiation-dominated era. Measuring the bispectrum provides a promising way to d

Jon Kleinberg, Sigal Oren, Emily Ryu, Éva Tardos

A fundamental component in the theoretical school choice literature is the problem a student faces in deciding which schools to apply to. Recent models have considered a set of schools of different selectiveness and a student who is unsure of their strength and can apply to at most $k$ schools. Such models assume that the student cares solely about maximizin

Farzad Pourkamali, Nicolas Macris

We consider estimating a matrix from noisy observations coming from an arbitrary additive bi-rotational invariant perturbation. We propose an estimator which is optimal among the class of rectangular rotational invariant estimators and can be applied irrespective of the prior on the signal. For the particular case of Gaussian noise, we prove the optimality o

Gang Chen, Laurentiu Rodina, Congkao Wen

In this letter, starting from a kinematic Hopf algebra, we first construct a closed-form formula for all Bern-Carrasco-Johansson (BCJ) numerators in Yang-Mills (YM) theory with infinite orders of $\alpha'$ corrections, known as $\rm DF^2+YM$ theory, when coupled to two heavy particles which can be removed through a simple factorization limit. The full $\alph

Yonghoon Lee, Edgar Dobriban, Eric Tchetgen Tchetgen

We study the problem of conditional predictive inference on multiple outcomes missing at random (MAR) -- or equivalently, under covariate shift. While the weighted conformal prediction offers a tool for inference under covariate shift with a marginal coverage guarantee, procedures with conditional coverage guarantees are often desired in many applications to

Cristiana Tiago, Sten Roar Snare, Jurica Sprem, Kristin McLeod

Currently, medical image domain translation operations show a high demand from researchers and clinicians. Amongst other capabilities, this task allows the generation of new medical images with sufficiently high image quality, making them clinically relevant. Deep Learning (DL) architectures, most specifically deep generative models, are widely used to gener

Viktoria Yurgens, Yannik Fontana, Andrea Corazza, Brendan J. Shields

The nitrogen-vacancy center in diamond, owing to its optically addressable and long-lived electronic spin, is an attractive resource for the generation of remote entangled states. However, the center's low native fraction of coherent photon emission, $\sim$3\%, strongly reduces the achievable spin-photon entanglement rates. Here, we couple a nitrogen-vacancy

Sarah Brauner, Christopher Eur, Elizabeth Pratt, Raluca Vlad

Positive geometries were introduced by Arkani-Hamed--Bai--Lam as a method of computing scattering amplitudes in theoretical physics. We show that a positive geometry from a polytope admits a log resolution of singularities to another positive geometry. Our result states that the regions in a wonderful compactification of a hyperplane arrangement complement,

Rajni Kant Bansal, Enrique Mallada, Patricia Hidalgo-Gonzalez

Electricity markets typically clear in two stages: a day-ahead market and a real-time market. In this paper, we propose market mechanisms for a two-stage multi-interval electricity market with energy storage, generators, and demand uncertainties. We consider two possible mixed bidding strategies: storage first bids cycle depths in the day ahead and then char

Aletta Lucia Meinsma, Thomas Rothe, W. Gudrun Reijnierse, Ionica Smeets

Quantum technology is expected to have an impact on society. Scientists warn against the use of certain frames because they may create barriers to effective science communication. We studied 385 Dutch newspaper articles for the use of these frames. Newspapers commonly explained quantum concepts when mentioning quantum technology. They also regularly framed q

Irene Garcia-Camacho, Alberta Longhini, Michael Welle, Guillem Alenyà

The field of robotics faces inherent challenges in manipulating deformable objects, particularly in understanding and standardising fabric properties like elasticity, stiffness, and friction. While the significance of these properties is evident in the realm of cloth manipulation, accurately categorising and comprehending them in real-world applications rema

Siddharth Vishwanath, Hyungsuk Tak

We propose a variant of Hamiltonian Monte Carlo (HMC), called the Repelling-Attracting Hamiltonian Monte Carlo (RAHMC), for sampling from multimodal distributions. The key idea that underpins RAHMC is a departure from the conservative dynamics of Hamiltonian systems, which form the basis of traditional HMC, and turning instead to the dissipative dynamics of

Diego Hernández-Juárez, Mónica Rodríguez, Miriam Peña

We have developed a method to determine the most reliable distances for a large group of planetary nebulae. For this purpose, we analyze the distances obtained from \textit{Gaia} parallaxes and three determinations of statistical distances. The most reliable distance is derived for 2211 objects, and uncertainties for these distances are calculated in a homog

Erik Nascimento, Diego Mesquita, Samuel Kaski, Amauri H Souza

Deep neural networks are notoriously miscalibrated, i.e., their outputs do not reflect the true probability of the event we aim to predict. While networks for tabular or image data are usually overconfident, recent works have shown that graph neural networks (GNNs) show the opposite behavior for node-level classification. But what happens when we are predict

Weitao Yang, Paul W. Ayers

We extend ground-state density-functional theory to excited states and provide the theoretical formulation for the widely used $\Delta SCF$ method for calculating excited-state energies and densities. As the electron density alone is insufficient to characterize excited states, we formulate excited-state theory using the defining variables of a noninteractin

Pedro Agostini, Tolga Altinoluk, Néstor Armesto

We analyze the effects of next-to-eikonal corrections on dijet production in Deep Inelastic Scattering off nuclear targets in the framework of the Color Glass Condensate. They require the knowledge of correlators of fields in the target beyond those computed in the standard McLerran-Venugopalan model, specifically those between transverse and boost-enhanced

Victor M. Baez, Haoran Zhao, Nihal Abdurahiman, Nikhil V. Navkar

Given starting and ending positions and velocities, $L_2$ bounds on the acceleration and velocity, and the restriction to no more than two constant control inputs, this paper provides routines to compute the minimal-time path. Closed form solutions are provided for reaching a position in minimum time with and without a velocity bound, and for stopping at the

Victor Gracia, Pablo Krupa, Daniel Limon, Teodoro Alamo

Model Predictive Control (MPC) is a popular control approach due to its ability to consider constraints, including input and state restrictions, while minimizing a cost function. However, in practice, these constraints can result in feasibility issues, either because the system model is not accurate or due to the existence of external disturbances. To mitiga

Reza Dastbasteh, Farzad Padashnick, Pedro M. Crespo, Markus Grassl

Let $a$ and $b$ be two non-zero elements of a finite field $\mathbb{F}_q$, where $q>2$. It has been shown that if $a$ and $b$ have the same multiplicative order in $\mathbb{F}_q$, then the families of $a$-constacyclic and $b$-constacyclic codes over $\mathbb{F}_q$ are monomially equivalent. In this paper, we investigate the monomial equivalence of $a$-consta

Jiyong Li, Dilshod Azizov, Yang Li, Shangsong Liang

Recently, because of the high-quality representations of contrastive learning methods, rehearsal-based contrastive continual learning has been proposed to explore how to continually learn transferable representation embeddings to avoid the catastrophic forgetting issue in traditional continual settings. Based on this framework, we propose Contrastive Continu

Boris Epstein, Will Ma

We study the inventory placement problem of splitting $Q$ units of a single item across warehouses in advance of a downstream online matching problem that represents the dynamic fulfillment decisions of an e-commerce retailer. This is a challenging problem both theoretically, due to the computational complexity of the downstream matching problem, and practic

Markus Kleinau

Let $V$ and $W$ be quiver representations over $\mathbb{F}_1$ and let $K$ be a field. The scalar extensions $V^K$ and $W^K$ are quiver representations over $K$ with a distinguished, very well-behaved basis. We construct a basis of $\mathrm{Hom}_{KQ}(V^K,W^K)$ generalising the well-known basis of the morphism spaces between string and tree modules. We use thi

ShengYun Peng, Aishwarya Chakravarthy, Seongmin Lee, Xiaojing Wang

Tables convey factual and quantitative data with implicit conventions created by humans that are often challenging for machines to parse. Prior work on table recognition (TR) has mainly centered around complex task-specific combinations of available inputs and tools. We present UniTable, a training framework that unifies both the training paradigm and traini

Martin Houde, Will McCutcheon, Nicolás Quesada

In this note we summarize four important matrix decompositions commonly used in quantum optics, namely the Takagi/Autonne, Bloch-Messiah/Euler, Iwasawa, and Williamson decompositions. The first two of these decompositions are specialized versions of the singular-value decomposition when applied to symmetric or symplectic matrices. The third factors any sympl

Alberto Cerezo, Isabel Fernandez, Pablo Mira

We construct, for any $H\in \mathbb{R}$, infinitely many free boundary annuli in geodesic balls of $\mathbb{S}^3$ with constant mean curvature $H$ and a discrete, non-rotational, symmetry group. Some of these free boundary CMC annuli are actually embedded if $H\geq 1/\sqrt{3}$. We also construct embedded, non-rotational, free boundary CMC annuli in geodesic

Xuenan Xu, Xiaohang Xu, Zeyu Xie, Pingyue Zhang

Recently, there has been an increasing focus on audio-text cross-modal learning. However, most of the existing audio-text datasets contain only simple descriptions of sound events. Compared with classification labels, the advantages of such descriptions are significantly limited. In this paper, we first analyze the detailed information that human description

Gilles Dejaegere, Mahmoud Sakr

This study introduces time-windowed variations of three established trajectory simplification algorithms. These new algorithms are specifically designed to be used in contexts with bandwidth limitations. We present the details of these algorithms and highlight the differences compared to their classical counterparts. To evaluate their performance, we conduct

Yunsong Zhou, Linyan Huang, Qingwen Bu, Jia Zeng

Embodied scene understanding serves as the cornerstone for autonomous agents to perceive, interpret, and respond to open driving scenarios. Such understanding is typically founded upon Vision-Language Models (VLMs). Nevertheless, existing VLMs are restricted to the 2D domain, devoid of spatial awareness and long-horizon extrapolation proficiencies. We revisi

Austin Rothermich, Jacqueline K. Faherty, Daniella Bardalez-Gagliuffi, Adam C. Schneider

We report the identification of 89 new systems containing ultracool dwarf companions to main sequence stars and white dwarfs, using the citizen science project Backyard Worlds: Planet 9 and cross-reference between Gaia and CatWISE2020. Thirty-two of these companions and thirty-three host stars were followed up with spectroscopic observations, with companion

Prakash Chandra Chhipa, Meenakshi Subhash Chippa, Kanjar De, Rajkumar Saini

Perspective distortion (PD) causes unprecedented changes in shape, size, orientation, angles, and other spatial relationships of visual concepts in images. Precisely estimating camera intrinsic and extrinsic parameters is a challenging task that prevents synthesizing perspective distortion. Non-availability of dedicated training data poses a critical barrier

Laura Bollmers, Tobias Babai-Hemati, Boris Koppitz, Christof Eigner

Lithium niobate and lithium tantalate are among the most widespread materials for nonlinear, integrated photonics. Mixed crystals with arbitrary Nb-Ta ratios provide a new degree of freedom to tune materials properties, such as the birefringence, but also leverage the advantages of the singular compounds, for example, by combining the thermal stability of li

Dibyayan Chakraborty, Antoine Dailly, Florent Foucaud, Ralf Klasing

In this paper, we study a dynamic analogue of the Path Cover problem, which can be solved in polynomial-time in directed acyclic graphs. A temporal digraph has an arc set that changes over discrete time-steps, if the underlying digraph (the union of all the arc sets) is acyclic, then we have a temporal DAG. A temporal path is a directed path in the underlyin

Léopold Maytié, Benjamin Devillers, Alexandre Arnold, Rufin VanRullen

Humans perceive the world through multiple senses, enabling them to create a comprehensive representation of their surroundings and to generalize information across domains. For instance, when a textual description of a scene is given, humans can mentally visualize it. In fields like robotics and Reinforcement Learning (RL), agents can also access informatio

Zailan Zhang, Jiuxiang Zhang, Gangqiang Zhou, Jiyuan Xu

Germanium arsenide (GeAs) is a layered semiconductor with remarkably anisotropic physical, thermoelectric and optical properties, and a promising candidate for multifunctional devices based on in-plane polarization dependent response. Understanding the underlying mechanism of such devices requires the knowledge of GeAs electronic band structure and of the ho

Guangyu Zhao, Tianyue Wu, Yeke Chen, Fei Gao

Animals learn to adapt speed of their movements to their capabilities and the environment they observe. Mobile robots should also demonstrate this ability to trade-off aggressiveness and safety for efficiently accomplishing tasks. The aim of this work is to endow flight vehicles with the ability of speed adaptation in prior unknown and partially observable c

Qiushi Liu, Yuxiang Yang

Extensive research has been dedicated to the asymptotic theory of quantum metrology, where the goal is to determine the ultimate precision limit of quantum channel estimation when many accesses to the channel are allowed. The ultimate limit has been well established, but in general a noiseless and controllable ancilla is required for attaining it. Little is

CMS Collaboration

A search for long-lived heavy neutrinos (N) in the decays of B mesons produced in proton-proton collisions at $\sqrt{s}$ = 13 TeV is presented. The data sample corresponds to an integrated luminosity of 41.6 fb$^{-1}$ collected in 2018 by the CMS experiment at the CERN LHC, using a dedicated data stream that enhances the number of recorded events containing