April 2023 arXiv papers — page 13

Jai Prakash, Michele Vignati, Edoardo Sabbioni

Vehicle teleoperation has potential applications in fallback solutions for autonomous vehicles, remote delivery services, and hazardous operations. However, network delays and limited situational awareness can compromise teleoperation performance and increase the cognitive workload of human operators. To address these issues, we previously introduced the nov

Anastasis Stathopoulos, Georgios Pavlakos, Ligong Han, Dimitris Metaxas

This paper shows that it is possible to learn models for monocular 3D reconstruction of articulated objects (e.g., horses, cows, sheep), using as few as 50-150 images labeled with 2D keypoints. Our proposed approach involves training category-specific keypoint estimators, generating 2D keypoint pseudo-labels on unlabeled web images, and using both the labele

Mirac Suzgun, Stuart M. Shieber, Dan Jurafsky

We introduce string2string, an open-source library that offers a comprehensive suite of efficient algorithms for a broad range of string-to-string problems. It includes traditional algorithmic solutions as well as recent advanced neural approaches to tackle various problems in string alignment, distance measurement, lexical and semantic search, and similarit

Xin Chen, Ben Kang, Jiawen Zhu, Dong Wang

In this paper, we introduce a new sequence-to-sequence learning framework for RGB-based and multi-modal object tracking. First, we present SeqTrack for RGB-based tracking. It casts visual tracking as a sequence generation task, forecasting object bounding boxes in an autoregressive manner. This differs from previous trackers, which depend on the design of in

Fabio Benatti, Giovanni Gramegna, Stefano Mancini, Gibbs Nwemadji

Although different architectures of quantum perceptrons have been recently put forward, the capabilities of such quantum devices versus their classical counterparts remain debated. Here, we consider random patterns and targets independently distributed with biased probabilities and investigate the storage capacity of a continuous quantum perceptron model tha

Zane M. Rossi, Isaac L. Chuang

The study of classical algorithms is supported by an immense understructure, founded in logic, type, and category theory, that allows an algorithmist to reason about the sequential manipulation of data irrespective of a computation's realizing dynamics. As quantum computing matures, a similar need has developed for an assurance of the correctness of high-lev

Nikolaos Gkanatsios, Ayush Jain, Zhou Xian, Yunchu Zhang

Language is compositional; an instruction can express multiple relation constraints to hold among objects in a scene that a robot is tasked to rearrange. Our focus in this work is an instructable scene-rearranging framework that generalizes to longer instructions and to spatial concept compositions never seen at training time. We propose to represent languag

Johannes Zenn, Robert Bamler

Annealed Importance Sampling (AIS) moves particles along a Markov chain from a tractable initial distribution to an intractable target distribution. The recently proposed Differentiable AIS (DAIS) (Geffner and Domke, 2021; Zhang et al., 2021) enables efficient optimization of the transition kernels of AIS and of the distributions. However, we observe a low e

John Z. Zhang, Shuo Yang, Gengshan Yang, Arun L. Bishop

We present SLoMo: a first-of-its-kind framework for transferring skilled motions from casually captured "in the wild" video footage of humans and animals to legged robots. SLoMo works in three stages: 1) synthesize a physically plausible reconstructed key-point trajectory from monocular videos; 2) optimize a dynamically feasible reference trajectory for the

Greg Huber, Craig Knecht, Walter Trump, Robert M. Ziff

As a toy model of chiral interactions in crowded spaces, we consider sphinx tilings in finite regions of the triangular lattice. The sphinx tiles, hexiamonds composed of six equilateral triangles in the shape of a stylized sphinx, come in left and right enantiomorphs. Regions scaled up from the unit sphinx by an integer factor ("Sphinx frames") require tiles

Mariana Jaber, Marius Peper, Wojciech A. Hellwing, Miguel Angel Aragon-Calvo

Voids possess a very complex internal structure and dynamics. Using $N$-body simulations we study the hierarchical nature of sub-structures present in the cosmic web (CW). We use the SpineWeb method which provides a complete characterization of the CW into its primary constituents: voids, walls, filaments, and nodes. We aim to characterize the inner composit

Jean-Jacques Forneron, Liang Zhong

Generalized and Simulated Method of Moments are often used to estimate structural Economic models. Yet, it is commonly reported that optimization is challenging because the corresponding objective function is non-convex. For smooth problems, this paper shows that convexity is not required: under conditions involving the Jacobian of the moments, certain algor

Shipra Agrawal, Yiding Feng, Wei Tang

We consider a novel pricing and advertising framework in which a seller not only sets the product price but also designs flexible advertising schemes to influence customers' valuations of the product. We impose no structural restriction on the seller's feasible advertising strategies and allow her to advertise the product by disclosing or concealing

Alexander F. Ritter, Filip Živanović

Using Morse-Bott-Floer spectral sequences, we describe a filtration by ideals on quantum cohomology for symplectic manifolds with a Hamiltonian $S^1$-action that extends to a pseudoholomorphic $\mathbb{C}^*$-action. These spaces include all Conical Symplectic Resolutions, in particular all Quiver Varieties. Our spectral sequences give explicit descriptions o

Zane M. Rossi, Victor M. Bastidas, William J. Munro, Isaac L. Chuang

Quantum singular value transformation (QSVT) enables the application of polynomial functions to the singular values of near arbitrary linear operators embedded in unitary transforms, and has been used to unify, simplify, and improve most quantum algorithms. QSVT depends on precise results in representation theory, with the desired polynomial functions acting

Nikolaos Gkanatsios, Mayank Singh, Zhaoyuan Fang, Shubham Tulsiani

We present Analogical Networks, a model that encodes domain knowledge explicitly, in a collection of structured labelled 3D scenes, in addition to implicitly, as model parameters, and segments 3D object scenes with analogical reasoning: instead of mapping a scene to part segments directly, our model first retrieves related scenes from memory and their corres

Chengyue Wu, Teng Wang, Yixiao Ge, Zeyu Lu

Foundation models have achieved great advances in multi-task learning with a unified interface of unimodal and multimodal tasks. However, the potential of such multi-task learners has not been exploited during transfer learning. In this work, we present a universal parameter-efficient transfer learning method, termed Predict-Interpolate Tuning ($\pi$-Tuning)

Yier Lin, Li-Cheng Tsai

We consider the $n$-point, fixed-time large deviations of the KPZ equation with the narrow wedge initial condition. The scope consists of concave-configured, upper-tail deviations and a wide range of scaling regimes that allows time to be short, unit-order, and long. We prove the $n$-point large deviation principle and characterize, with proof, the correspon

Jonathan Mandelbaum, Holger Jäkel, Laurent Schmalen

Low-density parity-check codes together with belief propagation (BP) decoding are known to be well-performing for large block lengths. However, for short block lengths there is still a considerable gap between the performance of the BP decoder and the maximum likelihood decoder. Different ensemble decoding schemes such as, e.g., the automorphism ensemble dec

María Barroso, Carlos María Alaíz, Ángela Fernández, Jose Luis Torrecilla

Nowadays many real-world datasets can be considered as functional, in the sense that the processes which generate them are continuous. A fundamental property of this type of data is that in theory they belong to an infinite-dimensional space. Although in practice we usually receive finite observations, they are still high-dimensional and hence dimensionality

Hengyi Wang, Jingwen Wang, Lourdes Agapito

We present Co-SLAM, a neural RGB-D SLAM system based on a hybrid representation, that performs robust camera tracking and high-fidelity surface reconstruction in real time. Co-SLAM represents the scene as a multi-resolution hash-grid to exploit its high convergence speed and ability to represent high-frequency local features. In addition, Co-SLAM incorporate

Gyungin Shin, Samuel Albanie, Weidi Xie

Segmentation is a core computer vision competency, with applications spanning a broad range of scientifically and economically valuable domains. To date, however, the prohibitive cost of annotation has limited the deployment of flexible segmentation models. In this work, we propose Zero-shot Unsupervised Transfer Instance Segmentation (ZUTIS), a framework th

Li-Cheng Tsai

We analyze the high moments of the Stochastic Heat Equation (SHE) via a transformation to the attractive Brownian Particles (BPs), which are Brownian motions interacting via pairwise attractive drift. In those scaling regimes where the particles tend to cluster, we prove a Large Deviation Principle (LDP) for the empirical measure of the attractive BPs. Under

Sølve Eidnes, Kjetil Olsen Lye

Pseudo-Hamiltonian neural networks (PHNN) were recently introduced for learning dynamical systems that can be modelled by ordinary differential equations. In this paper, we extend the method to partial differential equations. The resulting model is comprised of up to three neural networks, modelling terms representing conservation, dissipation and external f

Nurettin Turan, Benedikt Fesl, Michael Koller, Michael Joham

We propose a versatile feedback scheme for both single- and multi-user multiple-input multiple-output (MIMO) frequency division duplex (FDD) systems. Particularly, we propose utilizing a Gaussian mixture model (GMM) with a reduced number of parameters for codebook construction, feedback encoding, and precoder design. The GMM is fitted offline at the base sta

Fotis Farakos, Matteo Morittu, George Tringas

We discuss minimally supersymmetric AdS$_3$ flux vacua of massive type IIA supergravity on G2-orientifolds. We find that configurations that are scale-separated can be within finite distance from non scale-separated ones, while both remain at large volume, weak coupling and have moduli stabilization. The transition is achieved with the use of a D4-brane modu

Wojciech Górecki

The Heisenberg limit provides a fundamental bound on the achievable estimation precision with a limited number of $N$ resources used (e.g., atoms, photons, etc.). Using entangled quantum states makes it possible to scale the precision with $N$ better than when resources would be used independently. Consequently, the optimal use of all resources involves accu

Pingchuan Ma, Peter Yichen Chen, Bolei Deng, Joshua B. Tenenbaum

We propose a hybrid neural network (NN) and PDE approach for learning generalizable PDE dynamics from motion observations. Many NN approaches learn an end-to-end model that implicitly models both the governing PDE and constitutive models (or material models). Without explicit PDE knowledge, these approaches cannot guarantee physical correctness and have limi

Dalal El Youssoufi, Maria-Rosa L. Cioni, Nikolay Kacharov, Cameron P. M. Bell

We present a kinematic analysis of the Small Magellanic Cloud using 3700 spectra extracted from the European Southern Observatory archive. We used data from Gaia and near-infrared photometry to select stellar populations and discard Galactic foreground stars. The sample includes main-sequence, red giant branch and red clump stars, observed with the Fibre Lar

Gwenaëlle Dufour, Steven B. Charnley, Johan E. Lindberg

We have studied gas-grain chemical models of interstellar clouds to search for nonlinear dynamical evolution. A prescription is given for producing oscillatory solutions when a bistable solution exists in the gas-phase chemistry and we demonstrate the existence of limit cycle and relaxation oscillation solutions. As the autocatalytic chemical processes under

Gabriele Losi, Omar Chehaimi, M. Clelia Righi

High throughput first-principles calculations, based on solving the quantum mechanical many-body problem for hundreds of materials in parallel, have been successfully applied to advance many materials-based technologies, from batteries to hydrogen storage. However, this approach has not yet been adopted to systematically study solid-solid interfaces and thei

Claudio Bonati, Andrea Pelissetto, Ettore Vicari

We investigate some general properties of linear gauge fixings and gauge-field correlators in lattice models with noncompact U(1) gauge symmetry. In particular, we show that, even in the presence of a gauge fixing, some gauge-field observables (like the photon-mass operator) are not well-defined, depending on the specific gauge fixing adopted and on its impl

Xiaoyu Tian, Tao Jiang, Longfei Yun, Yucheng Mao

Robotic perception requires the modeling of both 3D geometry and semantics. Existing methods typically focus on estimating 3D bounding boxes, neglecting finer geometric details and struggling to handle general, out-of-vocabulary objects. 3D occupancy prediction, which estimates the detailed occupancy states and semantics of a scene, is an emerging task to ov

Albert Yu Sun, Varun Nair, Elliot Schumacher, Anitha Kannan

A wave of new task-based virtual assistants has been fueled by increasingly powerful large language models (LLMs), such as GPT-4 (OpenAI, 2023). A major challenge in deploying LLM-based virtual conversational assistants in real world settings is ensuring they operate within what is admissible for the task. To overcome this challenge, the designers of these v

Bo Berndtsson, Vlassis Mastrantonis, Yanir A. Rubinstein

This article introduces $L^p$ versions of the support function of a convex body $K$ and associates to these canonical $L^p$-polar bodies $K^{\circ, p}$ and Mahler volumes $\mathcal{M}_p(K)$. Classical polarity is then seen as $L^\infty$-polarity. This one-parameter generalization of polarity leads to a generalization of the Mahler conjectures, with a subtle

Nils Brouwer, Steffen Hirtle, Baerbel Rethfeld

We study the relaxation dynamics of laser-excited non-equilibrium electron distributions in the valence- and conduction band of a dielectric. We apply Boltzmann collision integrals to trace the influence of different scattering mechanisms on the energy- and particle density of electrons and holes. Our results show a two-timescale behavior of the equilibratio

Matteo Mio, Ralph Sarkis, Valeria Vignudelli

We present a generalisation of the theory of quantitative algebras of Mardare, Panangaden and Plotkin where (i) the carriers of quantitative algebras are not restricted to be metric spaces and can be arbitrary fuzzy relations or generalised metric spaces, and (ii) the interpretations of the algebraic operations are not required to be nonexpansive. Our main r

Karen Wintersperger, Florian Dommert, Thomas Ehmer, Andrey Hoursanov

We present an industrial end-user perspective on the current state of quantum computing hardware for one specific technological approach, the neutral atom platform. Our aim is to assist developers in understanding the impact of the specific properties of these devices on the effectiveness of algorithm execution. Based on discussions with different vendors an

Juraj Lagin, Simon Bilik

In this paper we present a method to analyze the inner structure of the composite FRP rebar, namely the shift of the real center of gravity with a respect to the geometrical center of rebar and changes of cross-sectional characteristics. We propose an automated pipeline based on classical computer vision techniques and on the ratio between the glass fibers a

M. M. Boyce, A. M. Hopkins, S. Riggi, L. Rudnick

We present a comparison between the performance of a selection of source finders using a new software tool called Hydra. The companion paper, Paper~I, introduced the Hydra tool and demonstrated its performance using simulated data. Here we apply Hydra to assess the performance of different source finders by analysing real observational data taken from the Ev

Tingxiang Fan, Bowen Shen, Yinqiang Zhang, Chuye Zhang

Despite the increasing prevalence of robots in daily life, their navigation capabilities are still limited to environments with prior knowledge, such as a global map. To fully unlock the potential of robots, it is crucial to enable them to navigate in large-scale unknown and changing unstructured scenarios. This requires the robot to construct an accurate st

M. M. Boyce, A. M. Hopkins, S. Riggi, L. Rudnick

The latest generation of radio surveys are now producing sky survey images containing many millions of radio sources. In this context it is highly desirable to understand the performance of radio image source finder (SF) software and to identify an approach that optimises source detection capabilities. We have created Hydra to be an extensible multi-SF and c

Oluwatosin Ogundare, Srinath Madasu, Nathanial Wiggins

Large Language Models (LLMs) have shown great potential in solving complex problems in various fields, including oil and gas engineering and other industrial engineering disciplines like factory automation, PLC programming etc. However, automatic identification of strong and weak solutions to fundamental physics equations governing several industrial process

Enrico Pedretti, Paolo Restuccia, M. Clelia Righi

Molecular adsorption is the first important step of many surface-mediated chemical processes, from catalysis to tribology. This phenomenon is controlled by physical/chemical interactions, which can be accurately described by first principles calculations. In recent years, several computational tools have been developed to study molecular adsorption based on

Felix M. Haehl, Ying Zhao

When two particles collide in an asymptotically AdS spacetime with high enough energy and small enough impact parameter, they can form a black hole. Motivated by dual quantum circuit considerations, we propose a threshold condition for black hole formation. Intuitively the condition can be understood as the onset of overlap of the butterfly cones describing

Mehmet Karahan, Mertcan Inal, Cosku Kasnakoglu

In this study, a fault-tolerant super-twisting sliding mode controller with a control allocation system for a quadrotor aircraft is proposed. Super twisting sliding mode control is a robust control technique that handles a system with a relative degree equal to one. A super-twisting sliding mode controller is proposed because of its robustness to uncertainti

Matthew P. Leighton, Laurent Kreplak, Andrew D. Rutenberg

We investigate the elastic properties of anisotropic elastomers with a double-twist director field, which is a model for collagen fibrils or blue phases. We observe a significant Poynting-like effect, coupling torsion (fibril twist) and extension. For freely-rotating boundary conditions, we identify a structural bistability at very small extensional strains

Christopher Mastandrea, Chih-Chun Chien

A transition of quantum walk induced by classical randomness changes the probability distribution of the walker from a two-peak structure to a single-peak one when the random parameter exceeds a critical value. We first establish the generality of the localization by showing its emergence in the presence of random rotation or translation. The transition poin

Ignacio R. Sola, Vladimir S. Malinovsky, Jaewook Ahn, Seokmin Shin

By controlling the temporal and spatial features of light, we propose a novel protocol to prepare two-qubit entangling gates on atoms trapped at close distance, which could potentially speed up the operation of the gate from the sub-micro to the nanosecond scale. The protocol is robust to variations in the pulse areas and the position of the atoms, by virtue

Sushant Sachdeva, Yibin Zhao

A symmetric matrix is called a Laplacian if it has nonpositive off-diagonal entries and zero row sums. Since the seminal work of Spielman and Teng (2004) on solving Laplacian linear systems in nearly linear time, several algorithms have been designed for the task. Yet, the work of Kyng and Sachdeva (2016) remains the simplest and most practical sequential so

Edward Parker, Michael J. D. Vermeer

The academic literature contains many estimates of the resources required to operate a cryptanalytically relevant quantum computer (CRQC) in terms of rather abstract quantities like the number of qubits. But to our knowledge, there have not been any estimates of these requirements in terms of more familiar economic resources like money or electricity. We dem

Jiawei Jiang, Chengkai Han, Wenjun Jiang, Wayne Xin Zhao

As deep learning technology advances and more urban spatial-temporal data accumulates, an increasing number of deep learning models are being proposed to solve urban spatial-temporal prediction problems. However, there are limitations in the existing field, including open-source data being in various formats and difficult to use, few papers making their code

Robert J. McCann

We give a simplified approach to Kunzinger & Saemann's theory of Lorentzian length spaces in the globally hyperbolic case; these provide a nonsmooth framework for general relativity. We close a gap in the regularly localizable setting, by showing consistency of two potentially different notions of timelike geodesic segments used in the literature. In the smo

Yichen Xie, Chenfeng Xu, Marie-Julie Rakotosaona, Patrick Rim

By identifying four important components of existing LiDAR-camera 3D object detection methods (LiDAR and camera candidates, transformation, and fusion outputs), we observe that all existing methods either find dense candidates or yield dense representations of scenes. However, given that objects occupy only a small part of a scene, finding dense candidates a

Noriyoshi Fukaya, Masayuki Hayashi

We consider a double power nonlinear Schr\"odinger equation which possesses the algebraically decaying stationary solution $\phi_0$ as well as exponentially decaying standing waves $e^{i\omega t}\phi_\omega(x)$ with $\omega>0$. It is well-known from the general theory that stability properties of standing waves are determined by the derivative of $\omega\map

Gaia Fontana, Tiziano Peraro

Intersection numbers are rational scalar products among functions that admit suitable integral representations, such as Feynman integrals. Using these scalar products, the decomposition of Feynman integrals into a basis of linearly independent master integrals is reduced to a projection. We present a new method for computing intersection numbers that only us

Christoph Thieme, Marilia Ramos, Ingrid B. Utne, Ali Mosleh

The International Workshop for Autonomous System Safety (IWASS) is a joint effort by the B. John Garrick Institute for the Risk Sciences at the University of California Los Angeles (UCLA-GIRS) and the Norwegian University of Science and Technology (NTNU). IWASS is an invitation-only event designed to be a platform for cross-industrial and interdisciplinary e

Solomon Ubani, Suleyman Olcay Polat, Rodney Nielsen

In this paper, we investigate the use of data obtained from prompting a large generative language model, ChatGPT, to generate synthetic training data with the aim of augmenting data in low resource scenarios. We show that with appropriate task-specific ChatGPT prompts, we outperform the most popular existing approaches for such data augmentation. Furthermore

Filip Klubička, Vasudevan Nedumpozhimana, John D. Kelleher

The goal of this paper is to learn more about how idiomatic information is structurally encoded in embeddings, using a structural probing method. We repurpose an existing English verbal multi-word expression (MWE) dataset to suit the probing framework and perform a comparative probing study of static (GloVe) and contextual (BERT) embeddings. Our experiments

Yuheng Bu, Harsha Vardhan Tetali, Gholamali Aminian, Miguel Rodrigues

We analyze the generalization ability of joint-training meta learning algorithms via the Gibbs algorithm. Our exact characterization of the expected meta generalization error for the meta Gibbs algorithm is based on symmetrized KL information, which measures the dependence between all meta-training datasets and the output parameters, including task-specific

Ramandeep Gill, Jonathan Granot

Exceptionally bright gamma-ray burst (GRB) afterglows can reveal the angular structure of their jets. GRB jets appear to have a narrow core (of half-opening angle $\theta_c$), beyond which their kinetic energy drops as a power-law with angle $\theta$ from the jet's symmetry axis, $E_{k,\rm iso}(\theta)\propto[1+(\theta/\theta_c)^2]^{-a/2}$. The power-law ind

Benjamin L. Hess, Jay J. Ague

Intracrystalline diffusion is an invaluable tool for estimating timescales of geological events. Diffusion is typically modeled using gradients in chemical potential. However, chemical potential is derived for uniform pressure and temperature conditions and therefore cannot be used to model diffusion when there are gradients in stress. Internal stress variat

Aviv Netanyahu, Abhishek Gupta, Max Simchowitz, Kaiqing Zhang

Machine learning systems, especially with overparameterized deep neural networks, can generalize to novel test instances drawn from the same distribution as the training data. However, they fare poorly when evaluated on out-of-support test points. In this work, we tackle the problem of developing machine learning systems that retain the power of overparamete

I. P. Fernando, D. Keller

Deep Neural Networks (DNNs) are a powerful and flexible tool for information extraction and modeling. In this study, we use DNNs to extract the Sivers functions by globally fitting Semi- Inclusive Deep Inelastic Scattering (SIDIS) and Drell-Yan (DY) data. To make predictions of this Transverse Momentum-dependent Distribution (TMD), we construct a minimally b

Denis R. Candido, Sigurdur I. Erlingsson, Hamed Gramizadeh, João Vitor I. Costa

Shubnikov-de Haas (SdH) oscillations have served as a paradigmatic experimental probe and tool for extracting key semiconductor parameters such as carrier density, effective mass, Zeeman splitting with g-factor $g^*$, quantum scattering times and spin-orbit (SO) coupling parameters. Here, we derive for the first time an analytical formulation for the SdH osc

Jacopo Germano, Francesco Emanuele Stradi, Gianmarco Genalti, Matteo Castiglioni

We study online learning in episodic constrained Markov decision processes (CMDPs), where the learner aims at collecting as much reward as possible over the episodes, while satisfying some long-term constraints during the learning process. Rewards and constraints can be selected either stochastically or adversarially, and the transition function is not known

Akira Lentfert, Anulekha De, Laura Scheuer, Benjamin Stadtmüller

The remagnetization process after ultrafast demagnetization can be described by relaxation mechanisms between the spin, electron, and lattice reservoirs. Thereby, collective spin excitations in form of spin waves and their angular momentum transfer play an important role on the longer timescales. In this work, we address the question whether the strength of

Nathan X. Roth, Stefanie N. Milam, Michael A. DiSanti, Geronimo L. Villanueva

The extraordinary 2021 September-October outburst of Centaur 29P/Schwassmann-Wachmann 1 afforded an opportunity to test the composition of primitive Kuiper disk material at high sensitivity. We conducted nearly simultaneous multi-wavelength spectroscopic observations of 29P/Schwassmann-Wachmann 1 using iSHELL at the NASA Infrared Telescope Facility and nFLAS

Lucía Gómez Álvarez, Sebastian Rudolph, Hannes Strass

Standpoint EL is a multi-modal extension of the popular description logic EL that allows for the integrated representation of domain knowledge relative to diverse standpoints or perspectives. Advantageously, its satisfiability problem has recently been shown to be in PTime, making it a promising framework for large-scale knowledge integration. In this paper,

Ignacio R. Sola, Seokmin Shin, Bo Y. Chang

We characterize the set of optimal protocols for two-qubit entangling gates through a mechanism analysis based on quantum pathways, which allows us to compare and rank the different solutions. As an example of a flexible platform with a rich landscape of protocols, we consider trapped neutral atoms excited to Rydberg states by different pulse sequences that

Lucas Pascotti Valem, Daniel Carlos Guimarães Pedronette

Person Re-ID has been gaining a lot of attention and nowadays is of fundamental importance in many camera surveillance applications. The task consists of identifying individuals across multiple cameras that have no overlapping views. Most of the approaches require labeled data, which is not always available, given the huge amount of demanded data and the dif

Qiang Miao, Thomas Barthel

We explain why and numerically confirm that there are no barren plateaus in the energy optimization of isometric tensor network states (TNS) for extensive Hamiltonians with finite-range interactions which are, for example, typical in condensed matter physics. Specifically, we consider matrix product states (MPS) with open boundary conditions, tree tensor net

Niklas Hahn, Michalis Xefteris

In this paper, we consider the $k$-Covering Canadian Traveller Problem ($k$-CCTP), which can be seen as a variant of the Travelling Salesperson Problem. The goal of $k$-CCTP is finding the shortest tour for a traveller to visit a set of locations in a given graph and return to the origin. Crucially, unknown to the traveller, up to $k$ edges of the graph are

Maniraman Periyasamy, Marc Hölle, Marco Wiedmann, Daniel D. Scherer

Deep reinforcement learning (DRL) often requires a large number of data and environment interactions, making the training process time-consuming. This challenge is further exacerbated in the case of batch RL, where the agent is trained solely on a pre-collected dataset without environment interactions. Recent advancements in quantum computing suggest that qu

Yonatan Bitton, Shlomi Cohen-Ganor, Ido Hakimi, Yoad Lewenberg

One of the exciting capabilities of recent language models for dialog is their ability to independently search for relevant information to ground a given dialog response. However, obtaining training data to teach models how to issue search queries is time and resource consuming. In this work, we propose q2d: an automatic data generation pipeline that generat

Terry Yue Zhuo

Recent advancements in the field of natural language generation have facilitated the use of large language models to assess the quality of generated text. Although these models have shown promising results in tasks such as machine translation and summarization, their applicability in code intelligence tasks remains limited without human involvement. The comp

Otavio Citton, Nestor Caticha

Motivated by the possible applications that a better understanding of consciousness might bring, we follow Tononi's idea and calculate analytically a complexity index for two systems of Ising spins with parallel update dynamics, the homogeneous and a modular infinite range models. Using the information geometry formulation of integrated information theory, w

Porfirio L. León Álvarez

Given a group $G$ and an integer $n\geq 0$ we consider the family $\mathcal{F}_n$ of all virtually abelian subgroups of $G$ of rank at most $n$. In this article we prove that for each $n\ge2$ the Bredon cohomology, with respect to the family $\mathcal{F}_n$, of a free abelian group with rank $k > n$ is nontrivial in dimension $k+n$; this answers a question o

David Bachmann, Michail Lianeris, Stavros Komineas

We demonstrate the existence and study in detail the features of chiral bimerons which are static solutions in an easy-plane magnet with the Dzyaloshinskii-Moriya (DM) interaction. These are skyrmionic textures with an integer topological charge and they present essential analogies to the meron configurations introduced in the context of quark confinement in

Benjamin Cellini, Burak Boyacıoğlu, Floris van Breugel

A dynamical system is observable if there is a one-to-one mapping from the system's measured outputs and inputs to all of the system's states. Analytical and empirical tools exist for quantifying the (full state) observability of linear and nonlinear systems; however, empirical tools for evaluating the observability of individual state variables are lacking.

Ian C. Matheson, Renu Malhotra

Mean plane measurements of the Kuiper Belt from observational data are of interest for their potential to test dynamical models of the solar system. Recent measurements have yielded inconsistent results. Here we report a measurement of the Kuiper Belt's mean plane with a sample size more than twice as large as in previous measurements. The sample of interest

Philipp G. Grützmacher, Michele Cutini, Edoardo Marquis, Manel Rodríguez Ripoll

Transition metal dichalcogenide (TMD) coatings have attracted enormous scientific and industrial interest due to their outstanding tribological behavior. The paradigmatic example is MoS2, even though selenides and tellurides have demonstrated superior tribological properties. Here, we describe an innovative in-operando conversion of Se nano-powders into lubr

Bingchen Zhao, Oisin Mac Aodha

We explore the problem of Incremental Generalized Category Discovery (IGCD). This is a challenging category incremental learning setting where the goal is to develop models that can correctly categorize images from previously seen categories, in addition to discovering novel ones. Learning is performed over a series of time steps where the model obtains new

Baiyu Li, Hang Ma

We introduce a new problem formulation, Double-Deck Multi-Agent Pickup and Delivery (DD-MAPD), which models the multi-robot shelf rearrangement problem in automated warehouses. DD-MAPD extends both Multi-Agent Pickup and Delivery (MAPD) and Multi-Agent Path Finding (MAPF) by allowing agents to move beneath shelves or lift and deliver a shelf to an arbitrary

Giovanni Garberoglio, Christof Gaiser, Roberto M. Gavioso, Allan H. Harvey

Recent advances regarding the interplay between ab initio calculations and metrology are reviewed, with particular emphasis on gas-based techniques used for temperature and pressure measurements. Since roughly 2010, several thermophysical quantities - in particular, virial and transport coefficients - can be computed from first principles without uncontrolle

Florian McLelland, Floris van Breugel

Understanding the structure, quantity, and type of snow in mountain landscapes is crucial for assessing avalanche safety, interpreting satellite imagery, building accurate hydrology models, and choosing the right pair of skis for your weekend trip. Currently, such characteristics of snowpack are measured using a combination of remote satellite imagery, weath

Akash Sinha, Aritra Ghosh, Bijan Bagchi

It is well known that the Hamiltonian of an $n$-dimensional isotropic oscillator admits an $SU(n)$ symmetry, making the system maximally superintegrable. However, the dynamical symmetries of the anisotropic oscillator are much more subtle. We introduce a novel set of canonical transformations that map an $n$-dimensional anisotropic oscillator to the correspo

Chiara Bernardini

We show that the prescribed Gaussian curvature equation in $\mathbb{R}^2$ $$-\Delta u= (1-|x|^p) e^{2u},$$ has solutions with prescribed total curvature equal to $\Lambda:=\int_{\mathbb{R}^2}(1-|x|^p)e^{2u}dx\in \mathbb{R}$, if and only if $$p\in(0,2) \qquad \text{and} \qquad (2+p)\pi\le\Lambda<4\pi$$ and prove that such solutions remain compact as $\Lambda\

JungHyun Bae, Rose Montgomery, Stylianos Chatzidakis

For decades, the application of muon tomography to spent nuclear fuel (SNF) cask imaging has been theoretically evaluated and experimentally verified by many research groups around the world, including Los Alamos National Laboratory in the United States, Canadian Nuclear Laboratory in Canada, the National Institute for Nuclear Physics in Italy, and Toshiba i

Shinji Mukohyama, Kazufumi Takahashi, Keitaro Tomikawa, Vicharit Yingcharoenrat

The Effective Field Theory (EFT) of perturbations on an arbitrary background geometry with a timelike scalar profile was recently constructed in the context of scalar-tensor theories. In this paper, we use this EFT to study quasinormal frequencies of odd-parity perturbations on a static and spherically symmetric black hole background. Keeping a set of operat

F. Venturi, A. Rincón Romero, T. Hussain

Despite recent advances in suspension-based thermal spray techniques, there is still a need to widen the capability of available thermal spray setups to handle suspension to explore new compositions and improve the properties and performance of existing ones. In this work, a novel setup for injecting liquid-based feedstock in a liquid-fuelled high velocity o

Wen Zhou, Bowei Dong, Nikolaos Farmakidis, Xuan Li

Electronically reprogrammable photonic circuits based on phase-change chalcogenides present an avenue to resolve the von-Neumann bottleneck; however, implementation of such hybrid photonic-electronic processing has not achieved computational success. Here, we achieve this milestone by demonstrating an in-memory photonic-electronic dot-product engine, one tha

Dennis Haitz, Boris Jutzi, Markus Ulrich, Miriam Jaeger

This work represents a large step into modern ways of fast 3D reconstruction based on RGB camera images. Utilizing a Microsoft HoloLens 2 as a multisensor platform that includes an RGB camera and an inertial measurement unit for SLAM-based camera-pose determination, we train a Neural Radiance Field (NeRF) as a neural scene representation in real-time with th

Ke Alexander Wang, Matthew E. Levine, Jiaxin Shi, Emily B. Fox

Traditional models of glucose-insulin dynamics rely on heuristic parameterizations chosen to fit observations within a laboratory setting. However, these models cannot describe glucose dynamics in daily life. One source of failure is in their descriptions of glucose absorption rates after meal events. A meal's macronutritional content has nuanced effects on

Zheheng Jiang, Hossein Rahmani, Sue Black, Bryan M. Williams

Recently, deep learning based approaches have shown promising results in 3D hand reconstruction from a single RGB image. These approaches can be roughly divided into model-based approaches, which are heavily dependent on the model's parameter space, and model-free approaches, which require large numbers of 3D ground truths to reduce depth ambiguity and strug

Linwei Chen, Ying Fu, Kaixuan Wei, Dezhi Zheng

Existing instance segmentation techniques are primarily tailored for high-visibility inputs, but their performance significantly deteriorates in extremely low-light environments. In this work, we take a deep look at instance segmentation in the dark and introduce several techniques that substantially boost the low-light inference accuracy. The proposed metho

Joseph Neilson, Pietro Cataldi, Brian Derby

Flexible strain gauges with 88% optical transmittance, of reduced graphene oxide (rGO) on poly dimethylsiloxne membranes, are produced form monolayers of graphene oxide assembled into densely packed sheets at an immiscible hexane/water interface and subsequently reduced in HI vapor to increase electrical conductivity. Pre-straining and relaxing the membranes

Nour-Eddine Abouelkhir, Abdallah Slaoui, Hanane El Hadfi, Rachid Ahl Laamara

Recently, the Hilbert-Schmidt speed, as a special class of quantum statistical speed, has been reported to improve the interferometric phase in single-parameter quantum estimation. Here, we test this concept in the multiparameter scenario where two laser phases are estimated in a theoretical model consisting of a three-level atom interacting with two classic

Michael R. Fellows, Mario Grobler, Nicole Megow, Amer E. Mouawad

The dynamics of real-world applications and systems require efficient methods for improving infeasible solutions or restoring corrupted ones by making modifications to the current state of a system in a restricted way. We propose a new framework of solution discovery via reconfiguration for constructing a feasible solution for a given problem by executing a

Kaizhong Deng, Baoru Huang, Daniel S. Elson

The increasing prevalence of prostate cancer has led to the widespread adoption of Robotic-Assisted Surgery (RAS) as a treatment option. Sentinel lymph node biopsy (SLNB) is a crucial component of prostate cancer surgery and requires accurate diagnostic evidence. This procedure can be improved by using a drop-in gamma probe, SENSEI system, to distinguish can