July 2023 arXiv papers — page 4

Bo Li, David Saad

In the context of epidemic spreading, many intricate dynamical patterns can emerge due to the cooperation of different types of pathogens or the interaction between the disease spread and other failure propagation mechanism. To unravel such patterns, simulation frameworks are usually adopted, but they are computationally demanding on big networks and subject

Haonan Wang, Jie Liu, Jie Tang, Gangshan Wu

Heatmap-based methods have become the mainstream method for pose estimation due to their superior performance. However, heatmap-based approaches suffer from significant quantization errors with downscale heatmaps, which result in limited performance and the detrimental effects of intermediate supervision. Previous heatmap-based methods relied heavily on addi

Gabriela Barenboim, Luigi Del Debbio, Johannes Hirn, Veronica Sanz

We use Google's MusicVAE, a Variational Auto-Encoder with a 512-dimensional latent space to represent a few bars of music, and organize the latent dimensions according to their relevance in describing music. We find that, on average, most latent neurons remain silent when fed real music tracks: we call these "noise" neurons. The remaining few dozens of laten

Giampiero Palatucci, Mirco Piccinini

We deal with a wide class of nonlinear nonlocal equations led by integro-differential operators of order $(s,p)$, with summability exponent $p \in (1,\infty)$ and differentiability exponent $s\in (0,1)$, whose prototype is the fractional subLaplacian in the Heisenberg group. We present very recent boundedness and regularity estimates (up to the boundary) for

Ali K. Uncu, James H. Davenport, Matthew England

This paper accompanies a new dataset of non-linear real arithmetic problems for the SMT-LIB benchmark collection. The problems come from an automated proof procedure of Gerhold--Kauers, which is well suited for solution by SMT. The problems of this type have not been tackled by SMT-solvers before. We describe the proof technique and give one new such proof t

Candida Bowtell, Robert Hancock, Joseph Hyde

For graphs $G, H_1,\dots,H_r$, write $G \to (H_1, \ldots, H_r)$ to denote the property that whenever we $r$-colour the edges of $G$, there is a monochromatic copy of $H_i$ in colour $i$ for some $i \in \{1,\dots,r\}$. Mousset, Nenadov and Samotij proved an upper bound on the threshold function for the property that $G_{n,p} \to (H_1,\dots,H_r)$, thereby reso

Rihito Nagase, Takahiro Sagawa

The tradeoff relation between speed and cost is a central issue in designing fast and efficient information processing devices. We derive an achievable bound on thermodynamic cost for obtaining information through finite-time (non-quasi-static) measurements. Our proof is based on optimal transport theory, which enables us to identify the explicit protocol to

Zoé Verchère, Sourour Elloumi, Andrea Simonetto

Variational quantum algorithms have been advocated as promising candidates to solve combinatorial optimization problems on near-term quantum computers. Their methodology involves transforming the optimization problem into a quadratic unconstrained binary optimization (QUBO) problem. While this transformation offers flexibility and a ready-to-implement circui

Andres Fernandez Herrero, Siqing Zhang

Fix a smooth projective family of curves $C \to S$ and a split reductive group scheme $G$ over a Noetherian base scheme $S$. For any (possibly nonreduced) fixed relative Cartier divisor $D$, we provide a treatment of the moduli of $G$-bundles on the fibers of $C$ equipped with $t$-connections with pole orders bounded by $D$. Under mild assumptions on the cha

Darshan Chakrabarti, Jelena Diakonikolas, Christian Kroer

Coordinate descent methods are popular in machine learning and optimization for their simple sparse updates and excellent practical performance. In the context of large-scale sequential game solving, these same properties would be attractive, but until now no such methods were known, because the strategy spaces do not satisfy the typical separable block stru

Matthew C. Russell

We provide a refinement of MacMahon's partition identity on sequence-avoiding partitions, and use it to produce another mod 6 partition identity. In addition, we show that our technique also extends to cover Andrews's generalization of MacMahon's identity. Our proofs are bijective in nature, exploiting a theorem of Xiong and Keith.

Dmytro Pavlichenko, Sven Behnke

Many objects, such as tools and household items, can be used only if grasped in a very specific way - grasped functionally. Often, a direct functional grasp is not possible, though. We propose a method for learning a dexterous pre-grasp manipulation policy to achieve human-like functional grasps using deep reinforcement learning. We introduce a dense multi-c

Lin Huang, Weisheng Li, Yujuan Tan, Linlin Shen

In this study, the structural problems of the YOLOv5 model were analyzed emphatically. Based on the characteristics of fine defects in artificial leather, four innovative structures, namely DFP, IFF, AMP, and EOS, were designed. These advancements led to the proposal of a high-performance artificial leather fine defect detection model named YOLOD. YOLOD demo

James H. Davenport, Matthew England

Cylindrical Algebraic Decomposition (CAD) by projection and lifting requires many iterated univariate resultants. It has been observed that these often factor, but to date this has not been used to optimise implementations of CAD. We continue the investigation into such factorisations, writing in the specific context of SC-Square.

M. C. J. Bootsma, K. M. D. Chan, O. Diekmann, H. Inaba

The aim of this short note is twofold. We formulate the general Kermack-McKendrick epidemic model incorporating static heterogeneity and show how it simplifies to a scalar Renewal Equation (RE) when separable mixing is assumed. A key feature is that all information about the heterogeneity is encoded in one nonlinear real valued function of a real variable. I

Mingzhi Wang, Guanghai Guo, Pengfei Yan, Songbai Chen

We investigate the shadows of Konoplya-Zhidenko naked singularity. In the spacetime of Konoplya-Zhidenko naked singularity, not only can unstable retrograde light ring (LR) exist, but also unstable prograde LR, leading to the formation of a complete photon sphere (PS). Due to the absence of an event horizon, a dark disc-shaped shadow does not appear; instead

Jaehyeok Jin, Eok Kyun Lee, Gregory A. Voth

This paper series aims to establish a complete correspondence between fine-grained (FG) and coarse-grained (CG) dynamics by way of excess entropy scaling (introduced in Paper I). While Paper II successfully captured translational motions in CG systems using a hard sphere mapping, the absence of rotational motions in single-site CG models introduces differenc

Aparajita Bhattacharyya, Kornikar Sen, Ujjwal Sen

Energy extraction from quantum batteries by means of completely positive trace-preserving (CPTP) maps leads to the concept of CPTP-local passive states, which identify bipartite states from which no energy can be squeezed out by applying any CPTP map to a particular subsystem. We prove, for arbitrary dimension, that if a state is CPTP-local passive with resp

Ashish Marisetty, Prathistith Raj M, Praneeth Nemani, Venkanna Udutalapally

Malnutrition poses a significant threat to global health, resulting from an inadequate intake of essential nutrients that adversely impacts vital organs and overall bodily functioning. Periodic examinations and mass screenings, incorporating both conventional and non-invasive techniques, have been employed to combat this challenge. However, these approaches

Matthew J. Madison, Stefanie A Wind, Lientje Maas, Kazuhiro Yamaguchi

Diagnostic classification models (DCMs) are psychometric models designed to classify examinees according to their proficiency or non-proficiency of specified latent characteristics. These models are well-suited for providing diagnostic and actionable feedback to support formative assessment efforts. Several DCMs have been developed and applied in different s

Andrew Pocklington, Aashish A. Clerk

Photonic and bosonic systems subject to incoherent, wide-bandwidth driving cannot typically reach stable finite-density phases using only non-dissipative Hamiltonian nonlinearities; one instead needs nonlinear losses, or a finite pump bandwidth. We describe here a very general mechanism for circumventing this common limit, whereby Hamiltonian interactions ca

Sonali Gangwar, Rajamanickam Ravisankar, S. I. Mistakidis, Paulsamy Muruganandam

We investigate the ground state and dynamics of one-dimensional spin-orbit coupled (SOC) quantum droplets within the extended Gross-Pitaevskii approach. As the SOC wavenumber increases, stripe droplet patterns emerge, with a flat-top background, for larger particle numbers. The surface energy decays following a power-law with respect to the interactions. At

Zhiying Jiang, Zengxi Zhang, Jinyuan Liu, Xin Fan

Multi-spectral image stitching leverages the complementarity between infrared and visible images to generate a robust and reliable wide field-of-view (FOV) scene. The primary challenge of this task is to explore the relations between multi-spectral images for aligning and integrating multi-view scenes. Capitalizing on the strengths of Graph Convolutional Net

Jiaqi Miao

Through the utilization of smart materials and well-designed structures, functional surfaces have been developed to enable small-scale liquid/solid manipulation tasks, thereby facilitating crucial applications in the fields of microfluidics, soft robotics, and biomedical engineering. However, the design of functional systems with flexible, tunable, and multi

M. Askari, P. Broumand, M. Javidi

This study presents a novel coupled mechano-electro-chemical formulation for predicting stress corrosion cracking (SCC) phenomena in steel structures using the phase field method. SCC is a complex damage process that arises from the interaction between mechanical loading and corrosion in a corrosive electrolyte environment. The proposed formulation introduce

Emily K. Deibert, Ernst J. W. de Mooij, Ray Jayawardhana, Jake D. Turner

We present high-resolution transmission spectroscopy of WASP-76b with GRACES/Gemini North obtained as part of the ExoGemS survey. With a broad spectral range of 400-1050 nm and a relatively high resolution of ~66,000, these observations are particularly well-suited to searching for atomic and molecular atmospheric species via the Doppler cross-correlation te

M. Zendra, F. Borgonovi, G. L. Celardo, S. Gurvitz

We present a non-standard Hubbard model applicable to arbitrary single-particle potential profiles and inter-particle interactions. Our approach involves a novel treatment of Wannier functions, free from the ambiguities of conventional methods and applicable to finite systems without periodicity constraints. To ensure the consistent evaluation of Wannier fun

Igor V. Erovenko, Mark Broom

We consider a finite structured population of mobile individuals that strategically explore a network using a Markov movement model and interact with each other via a public goods game. We extend the model of Erovenko et al. (2019) from complete, circle, and star graphs to various random networks to further investigate the effect of network topology on the e

László Györfi, Tamás Linder, Harro Walk

We study the excess minimum risk in statistical inference, defined as the difference between the minimum expected loss in estimating a random variable from an observed feature vector and the minimum expected loss in estimating the same random variable from a transformation (statistic) of the feature vector. After characterizing lossless transformations, i.e.

Muruhan Rathinam, Mingkai Yu

Stochastic reaction network models arise in intracellular chemical reactions, epidemiological models and other population process models, and are a class of continuous time Markov chains which have the nonnegative integer lattice as state space. We consider the problem of estimating the conditional probability distribution of a stochastic reaction network gi

Urmila Chadayammuri, Michelle Ntampaka, John ZuHone, Àkos Bogdàn

Galaxy cluster mass functions are a function of cosmology, but mass is not a direct observable, and systematic errors abound in all its observable proxies. Mass-free inference can bypass this challenge, but it requires large suites of simulations spanning a range of cosmologies and models for directly observable quantities. In this work, we devise a U-net -

Alfredo Navarra, Francesco Piselli

Programmable Matter (PM) has been widely investigated in recent years. It refers to some kind of matter with the ability to change its physical properties (e.g., shape or color) in a programmable way. One reference model is certainly Amoebot, with its recent canonical version (DISC 2021). Along this line, with the aim of simplification and to better address

Yongming Wen

Assume $\mathcal{L}=-\Delta+V$ is a Schr\"{o}dinger operator on $\mathbb{R}^d$, where $V$ belongs to certain reverse H\"{o}lder class $RH_\sigma$ with $\sigma\geq d/2$. We consider the class of $A_{p,q}$ weights associated to $\mathcal{L}$, denoted by $A_{p,q}^{\mathcal{L}}(\mathbb{R}^d)$, which include the classical Muckenhoupt $A_{p,q}(\mathbb{R}^d)$ weigh

Inga Kamp, Thomas Henning, Aditya M. Arabhavi, Giulio Bettoni

The understanding of planet formation has changed recently, embracing the new idea of pebble accretion. This means that the influx of pebbles from the outer regions of planet-forming disks to their inner zones could determine the composition of planets and their atmospheres. The solid and molecular components delivered to the planet-forming region can be bes

Rudy Arthur

The Valeriepieris circle is the smallest circle that can be draw on the globe containing half of the world's population. The Valeriepieris (VP) circle acts as a spatial median, effectively splitting spatial data into two halves in a unique way. In this paper the idea of the VP circle is generalised and a fast algorithm and corresponding software package to c

Yining Ma, Qadeer Khan, Daniel Cremers

In this work, we propose a learning based neural model that provides both the longitudinal and lateral control commands to simultaneously navigate multiple vehicles. The goal is to ensure that each vehicle reaches a desired target state without colliding with any other vehicle or obstacle in an unconstrained environment. The model utilizes an attention based

Chimdessa Gashu Feyisa, H. H. Jen

Entangled states are an important resource for quantum information processing and for the fundamental understanding of quantum physics. An intriguing open question would be whether entanglement can improve the performance of quantum heat engines in particular. One of the promising platforms to address this question is to use entangled atoms as a non-thermal

L. Peri, G. A. Oakes, L. Cochrane, C. J. B. Ford

Semiconductor quantum dots operated dynamically are the basis of many quantum technologies such as quantum sensors and computers. Hence, modelling their electrical properties at microwave frequencies becomes essential to simulate their performance in larger electronic circuits. Here, we develop a self-consistent quantum master equation formalism to obtain th

Katherine Castro, Ignacio R. Solá, Juan J. Omiste

In this study, we present a revision of the Quantum Optimal Control Theory (QOCT) originally proposed by Rabitz et al (Phys. Rev. A 37, 49504964 (1988)), which has broad applications in physical and chemical physics. First, we identify the QOCT equations as the Euler-Lagrange equations of the functional associated to the control scheme. In this framework we

Alexander Geng, Ali Moghiseh, Claudia Redenbach, Katja Schladitz

Quantum computers possess the potential to process data using a remarkably reduced number of qubits compared to conventional bits, as per theoretical foundations. However, recent experiments have indicated that the practical feasibility of retrieving an image from its quantum encoded version is currently limited to very small image sizes. Despite this constr

Michal Hrbek, Lorenzo Martini

We show that the cotilting heart associated to a tilting complex $T$ is a locally coherent and locally coperfect Grothendieck category (i.e. an Ind-completion of a small artinian abelian category) if and only if $T$ is product-complete. We then apply this to the specific setting of the derived category of a commutative noetherian ring $R$. If $\dim(R)<\infty

Zbigniew Palmowski, Meral Şimşek, Apostolos D. Papaioannou

In this paper, we solve exit problems for a level-dependent L\'evy process which is exponentially killed with a killing intensity that depends on the present state of the process. Moreover, we analyse the respective resolvents. All identities are given in terms of new generalisations of scale functions (counterparts of the scale function from the theory of L

Belén Pulido, Alba M. Franco-Pereira, Rosa E. Lillo

With the rapid growth of data generation, advancements in functional data analysis (FDA) have become essential, especially for approaches that handle multiple variables at the same time. This paper introduces a novel formulation of the epigraph and hypograph indices, along with their generalized expressions, specifically designed for multivariate functional

S. Chevalier, C. Babusiaux, T. Merle, F. Arenou

The recent Gaia third data release (DR3) has brought some new exciting data about stellar binaries. It provides new opportunities to fully characterize more stellar systems and contribute to enforce our global knowledge of stars behaviour. By combining the new Gaia non-single stars catalog with double-lined spectroscopic binaries (SB2), one can determine the

Vincent Lemaire, Fabrice Clérot, Marc Boullé

Variable selection or importance measurement of input variables to a machine learning model has become the focus of much research. It is no longer enough to have a good model, one also must explain its decisions. This is why there are so many intelligibility algorithms available today. Among them, Shapley value estimation algorithms are intelligibility metho

Nathan Haut, Wolfgang Banzhaf, Bill Punch

This paper examines various methods of computing uncertainty and diversity for active learning in genetic programming. We found that the model population in genetic programming can be exploited to select informative training data points by using a model ensemble combined with an uncertainty metric. We explored several uncertainty metrics and found that diffe

Navid Abbasi, Karl Landsteiner

The holographic system described by Einstein-Maxwell-Chern-Simons dynamics in the bulk of AdS exhibits a chiral magnetic effect and a quantum critical point. Through numerical calculations, we find that the butterfly velocity can serve as a new identifier for the quantum critical point in this system. We show that the critical point is the point at which the

Kevin Qinghong Lin, Pengchuan Zhang, Joya Chen, Shraman Pramanick

Video Temporal Grounding (VTG), which aims to ground target clips from videos (such as consecutive intervals or disjoint shots) according to custom language queries (e.g., sentences or words), is key for video browsing on social media. Most methods in this direction develop taskspecific models that are trained with type-specific labels, such as moment retrie

Shan Ali, Chaima Boufaied, Domenico Bianculli, Paula Branco

Growth in system complexity increases the need for automated log analysis techniques, such as Log-based Anomaly Detection (LAD). While deep learning (DL) methods have been widely used for LAD, traditional machine learning (ML) techniques can also perform well depending on the context and dataset. Semi-supervised techniques deserve the same attention as they

Haozhen Zhang, Le Yu, Xi Xiao, Qing Li

Encrypted traffic classification is receiving widespread attention from researchers and industrial companies. However, the existing methods only extract flow-level features, failing to handle short flows because of unreliable statistical properties, or treat the header and payload equally, failing to mine the potential correlation between bytes. Therefore, i

Per Berglund, Tristan Hübsch, Djordje Minic

We elaborate on the new understanding of the cosmological constant and the gauge hierarchy problems in the context of string theory in its metastring formulation, based on the concepts of modular spacetime and Born geometry. The interplay of phase space (and Born geometry), the Bekenstein bound, the mixing between ultraviolet (UV) and infrared (IR) physics a

Sierra Dunn, Ryan Goh, Benjamin Krewson

We study the modulational dynamics of striped patterns formed in the wake of a planar directional quench. Such quenches, which move across a medium and nucleate pattern-forming instabilities in their wake, have been shown in numerous applications to control and select the wavenumber and orientation of striped phases. In the context of the prototypical comple

Sombit Dey, Assem Sadek, Gianluca Monaci, Boris Chidlovskii

Navigation of terrestrial robots is typically addressed either with localization and mapping (SLAM) followed by classical planning on the dynamically created maps, or by machine learning (ML), often through end-to-end training with reinforcement learning (RL) or imitation learning (IL). Recently, modular designs have achieved promising results, and hybrid al

Giulia Comini, Manuel Sam Ribeiro, Fan Yang, Heereen Shim

Phonetic information and linguistic knowledge are an essential component of a Text-to-speech (TTS) front-end. Given a language, a lexicon can be collected offline and Grapheme-to-Phoneme (G2P) relationships are usually modeled in order to predict the pronunciation for out-of-vocabulary (OOV) words. Additionally, post-lexical phonology, often defined in the f

Zahra Esmaeilbeig, Mojtaba Soltanalian

This paper revisits two prominent adaptive filtering algorithms, namely recursive least squares (RLS) and equivariant adaptive source separation (EASI), through the lens of algorithm unrolling. Building upon the unrolling methodology, we introduce novel task-based deep learning frameworks, denoted as Deep RLS and Deep EASI. These architectures transform the

Elena Wittemyer, Ian Abraham

We present a method for image-guided exploration for mobile robotic systems. Our approach extends ergodic exploration methods, a recent exploration approach that prioritizes complete coverage of a space, with the use of a learned image classifier that automatically detects objects and updates an information map to guide further exploration and localization o

Donghwan Lee, Han-Dong Lim, Do Wan Kim

The main goal of this paper is to investigate continuous-time distributed dynamic programming (DP) algorithms for networked multi-agent Markov decision problems (MAMDPs). In our study, we adopt a distributed multi-agent framework where individual agents have access only to their own rewards, lacking insights into the rewards of other agents. Moreover, each a

Yushan Li, Zitong Wang, Jianping He, Cailian Chen

Preserving the topology from being inferred by external adversaries has become a paramount security issue for network systems (NSs), and adding random noises to the nodal states provides a promising way. Nevertheless, recent works have revealed that the topology cannot be preserved under i.i.d. noises in the asymptotic sense. How to effectively characterize

Gonçalo Mordido, Pranshu Malviya, Aristide Baratin, Sarath Chandar

Sharpness-aware minimization (SAM) methods have gained increasing popularity by formulating the problem of minimizing both loss value and loss sharpness as a minimax objective. In this work, we increase the efficiency of the maximization and minimization parts of SAM's objective to achieve a better loss-sharpness trade-off. By taking inspiration from the Loo

Yuuya Chiba

Absence of local conserved quantities is often required, such as for thermalization or for the validity of response theory. Although many studies have discussed whether thermalization occurs in the Ising chain with longitudinal and transverse fields, rigorous results on local conserved quantities of this model have still been lacking. Here, we rigorously pro

Yun Zeng, Deren Han, Yansheng Su, Jiaxin Xie

The problem of finding a solution to the linear system $Ax = b$ with certain minimization properties arises in numerous scientific and engineering areas. In the era of big data, the stochastic optimization algorithms become increasingly significant due to their scalability for problems of unprecedented size. This paper focuses on the problem of minimizing a

L. J. Zhang

Recently some critical problems and challenges have been exposed, hindering the development and practical application of SSLBs, such as the low room temperature ionic conductivity of solid electrolyte, the risk of short circuit caused by lithium dendrite piercing the electrolyte, etc. In order to address these challenges, it's essential to obtain in-depth in

Giovanni Pighizzini, Luca Prigioniero

We introduce and investigate forgetting 1-limited automata, which are single-tape Turing machines that, when visiting a cell for the first time, replace the input symbol in it by a fixed symbol, so forgetting the original contents. These devices have the same computational power as finite automata, namely they characterize the class of regular languages. We

Patricia Mateiu, Adrian Groza

We tackle the task of enriching ontologies by automatically translating natural language sentences into Description Logic. Since Large Language Models (LLMs) are the best tools for translations, we fine-tuned a GPT-3 model to convert Natural Language sentences into OWL Functional Syntax. We employ objective and concise examples to fine-tune the model regardi

Ayshea Bains, Trent English, Nickolas Solomey

This research is to determine at Earth the high-energy neutrino flux coming from the galactic core, and from the many other accretion disks within the galactic core. It is estimated there are 10,000 such accretion disk within the cubic parsec of the galactic core alone and many more in the galactic core halo. There are various neutrino detectors, such as Ice

Andrew J. S. Hamilton

The spinors of the group Spin($N$) of rotations in $N$ spacetime dimensions are indexed by a bitcode with [$N$/2] bits. A well-known promising grand unified group that contains the standard-model group is Spin(10). Fermions in the standard model are described by five bits $yzrgb$, consisting of two weak bits $y$ and $z$, and three color bits $r$, $g$, $b$. I

Zhiguang Liao, Lei Chen, Guijing Duan, Yiming Wang

Motivated by the recent discovery of superconductivity in La$_3$Ni$_2$O$_7$ under pressure, we discuss the basic ingredients of a model that captures its microscopic physics under pressure tuning. We anchor our description in terms of the spectroscopic evidence of strong correlations in this system. In a bilayer Hubbard model including the Ni $3d$ $x^2-y^2$

Gustavo Pinto, Isadora Cardoso-Pereira, Danilo Monteiro Ribeiro, Danilo Lucena

As a way of addressing increasingly sophisticated problems, software professionals face the constant challenge of seeking improvement. However, for these individuals to enhance their skills, their process of studying and training must involve feedback that is both immediate and accurate. In the context of software companies, where the scale of professionals

Javed Lindner, David Dahmen, Michael Krämer, Moritz Helias

Bayesian inference and kernel methods are well established in machine learning. The neural network Gaussian process in particular provides a concept to investigate neural networks in the limit of infinitely wide hidden layers by using kernel and inference methods. Here we build upon this limit and provide a field-theoretic formalism which covers the generali

M. M. Amaan Valiuddin, Christiaan G. A. Viviers, Ruud J. G. van Sloun, Peter H. N. de With

Data uncertainties, such as sensor noise, occlusions or limitations in the acquisition method can introduce irreducible ambiguities in images, which result in varying, yet plausible, semantic hypotheses. In Machine Learning, this ambiguity is commonly referred to as aleatoric uncertainty. In image segmentation, latent density models can be utilized to addres

Stefan Jaric, Anastasiia Kudriavtseva, Nikita Nekrasov, Alexey V. Orlov

Measuring NT-proBNP biomarker is recommended for preliminary diagnostics of the heart failure. Recent studies suggest a possibility of early screening of biomarkers in saliva for non-invasive identification of cardiac diseases at the point-of-care. However, NT-proBNP concentrations in saliva can be thousand time lower than in blood plasma, going down to pg/m

T. M. A. Fink

The number of ordered factorizations and the number of recursive divisors are two related arithmetic functions that are recursively defined. But it is hard to construct explicit representations of these functions. Taking advantage of their recursive definition and a geometric interpretation, we derive three closed-form expressions for them both. These expres

Maylin Menzel, Tobias M. Wolff, Johannes W. Dietrich, Matthias A. Müller

Although hyperthyroidism is a common disease, the pharmaceutical therapy is based on a trial-and-error approach. We extend a mathematical model of the pituitary-thyroid feedback loop such that the intake of one antithyroid agent, namely methimazole (MMI), can be considered and use a model predictive control (MPC) scheme to determine suitable dosages.

Vevake Balaraman, Arash Eshghi, Ioannis Konstas, Ioannis Papaioannou

The ability to handle miscommunication is crucial to robust and faithful conversational AI. People usually deal with miscommunication immediately as they detect it, using highly systematic interactional mechanisms called repair. One important type of repair is Third Position Repair (TPR) whereby a speaker is initially misunderstood but then corrects the misu

Guedong Park, Hyukjoon Kwon, Hyunseok Jeong

The Wigner function formalism has played a pivotal role in examining the non-classical aspects of quantum states and their classical simulatability. Nevertheless, its application in qubit systems faces limitations due to negativity induced by Clifford gates. In this work, we propose a novel classical simulation method for qubit Clifford circuits based on the

Runyang Feng, Yixing Gao, Tze Ho Elden Tse, Xueqing Ma

Denoising diffusion probabilistic models that were initially proposed for realistic image generation have recently shown success in various perception tasks (e.g., object detection and image segmentation) and are increasingly gaining attention in computer vision. However, extending such models to multi-frame human pose estimation is non-trivial due to the pr

Simon Kornblith, Lala Li, Zirui Wang, Thao Nguyen

Image captioning is conventionally formulated as the task of generating captions for images that match the distribution of reference image-caption pairs. However, reference captions in standard captioning datasets are short and may not uniquely identify the images they describe. These problems are further exacerbated when models are trained directly on image

Timothy Parker, Umberto Grandi, Emiliano Lorini

Responsibility anticipation is the process of determining if the actions of an individual agent may cause it to be responsible for a particular outcome. This can be used in a multi-agent planning setting to allow agents to anticipate responsibility in the plans they consider. The planning setting in this paper includes partial information regarding the initi

Chunfeng Cui, Liqun Qi

The Moore-Penrose inverse is a genuine extension of the matrix inverse. Given a complex matrix, there uniquely exists another complex matrix satisfying the four Moore-Penrose conditions, and if the original matrix is nonsingular, it is exactly the inverse of that matrix. In the last one and half decade, in the study of approximate synthesis in kinematic, two

Jan Nienhaus, Matthias Wink

We prove that there exists a gradient expanding Ricci soliton asymptotic to any given cone over the product of a round sphere and a Ricci flat manifold. In particular we obtain asymptotically conical expanding Ricci solitons with positive scalar curvature on $\mathbb{R}^3 \times S^1.$ More generally we construct continuous families of gradient expanding Ricc

Leticia Pardo-Simón, David J. Sixsmith

A major open question in transcendental dynamics asks if it is possible for points in a wandering domain to have bounded orbits, and more strongly, for a wandering domain to iterate only in a bounded domain. In this paper we give a partial answer to this question, by constructing a bounded wandering domain that spends, in a precise sense, nearly all of its t

Abdolreza Taheri, Robert Pettersson, Pelle Gustafsson, Joni Pajarinen

A sizable part of the fleet of heavy-duty machinery in the construction equipment industry uses the conventional valve-controlled load-sensing hydraulics. Rigorous climate actions towards reducing CO$_{2}$ emissions has sparked the development of solutions to lower the energy consumption and increase the productivity of the machines. One promising solution t

Mingyuan Fan, Chengyu Wang, Cen Chen, Yang Liu

Diffusion models and large language models have emerged as leading-edge generative models, revolutionizing various aspects of human life. However, the practical implementations of these models have also exposed inherent risks, bringing to the forefront their evil sides and sparking concerns regarding their trustworthiness. Despite the wealth of literature on

Guangyan Zhang, Thomas Merritt, Manuel Sam Ribeiro, Biel Tura-Vecino

Neural text-to-speech systems are often optimized on L1/L2 losses, which make strong assumptions about the distributions of the target data space. Aiming to improve those assumptions, Normalizing Flows and Diffusion Probabilistic Models were recently proposed as alternatives. In this paper, we compare traditional L1/L2-based approaches to diffusion and flow-

Leonardo de la Cruz

We study tree-level biadjoint scalar amplitudes in the language of $D$-modules. We construct left ideals in the Weyl algebra $D$ that allow a holonomic representation of $n$-point amplitudes in terms of the linear partial differential equations they satisfy. The resulting representation encodes the simple pole and recursive properties of the amplitude.

Manh Hong Duong, Amit Einav

In this work we study the convergence to equilibrium for a (potentially) degenerate nonlinear and nonlocal McKean-Vlasov equation. We show that the solution to this equation is related to the solution of a linear degenerate and/or defective Fokker-Planck equation and employ recent sharp convergence results to obtain an easily computable (and many times sharp

Raghav Soni, Daniel Harnack, Hannah Isermann, Sotaro Fushimi

Legged locomotion is arguably the most suited and versatile mode to deal with natural or unstructured terrains. Intensive research into dynamic walking and running controllers has recently yielded great advances, both in the optimal control and reinforcement learning (RL) literature. Hopping is a challenging dynamic task involving a flight phase and has the

Xiaoyu Li, Tao Xie, Dedong Liu, Jinghan Gao

3D Multi-object tracking (MOT) empowers mobile robots to accomplish well-informed motion planning and navigation tasks by providing motion trajectories of surrounding objects. However, existing 3D MOT methods typically employ a single similarity metric and physical model to perform data association and state estimation for all objects. With large-scale moder

Nils Carqueville

The orbifold construction via topological defects in quantum field theory can either be understood as a state sum construction internal to a given ambient theory, or as the procedure of (identifying and) gauging ordinary and "non-invertible" symmetries. Here we explain how this is rigorously understood in the case of topological QFTs. We provide various exam

Adrián Andrada, Alejandro Tolcachier

We study complex solvmanifolds $\Gamma\backslash G$ with holomorphically trivial canonical bundle. We show that the trivializing section of this bundle can be either invariant or non-invariant by the action of $G$. First we characterize the existence of invariant trivializing sections in terms of the Koszul 1-form $\psi$ canonically associated to $(\mathfrak

Yin Yifan, Li Shunli, Wu Ke

This study suggests an idea that radiation in a periodic leaky-wave antenna (PLWA) should be considered to be produced with the fundamental mode, regardless of whether it is fast-wave or slow-wave. The idea is different from the conventional PLWA theory, which considers it a fact that a PLWA produces radiation with its fast-wave space harmonic when the funda

Maximilian Protte, Timon Schapeler, Jan Sperling, Tim J. Bartley

Superconducting nanowire single-photon detectors (SNSPDs) have been widely used to study the discrete nature of quantum states of light in the form of photon-counting experiments. We show that SNSPDs can also be used to study continuous variables of optical quantum states by performing homodyne detection at a bandwidth of $400~\mathrm{kHz}$. By measuring the

Marcin Briański, Jędrzej Hodor, Hoang La, Piotr Micek

For every integer $n$ with $n \geq 6$, we prove that the Boolean dimension of a poset consisting of all the subsets of $\{1,\dots,n\}$ equipped with the inclusion relation is strictly less than $n$.

Thomas Braure, Delphine Lazaro, David Hateau, Vincent Brandon

Computed Tomography (CT) is a prominent example of Imaging Inverse Problem highlighting the unrivaled performances of data-driven methods in degraded measurements setups like sparse X-ray projections. Although a significant proportion of deep learning approaches benefit from large supervised datasets, they cannot generalize to new experimental setups. In con

Jiashi Yang

In this paper, a phenomenological theory of saturated ferromagnetoelastic conductors is established using a multi-continuum model and the classical laws of mechanics, thermodynamics and electromagnetics. The theory is nonlinear and is valid for large deformations and strong electromagnetic fields. The constitutive relations in the theory satisfy the saturati

Osvaldo Gramaxo Freitas, Juan Calderón Bustillo, José A. Font, Solange Nunes

We evaluate several neural-network architectures, both convolutional and recurrent, for gravitational-wave time-series feature extraction by performing point parameter estimation on noisy waveforms from binary-black-hole mergers. We build datasets of 100,000 elements for each of four different waveform models (or approximants) in order to test how approximan

Ning Jiang, Ziying Zhou, Jiazheng Zhu, Yibo Wang

Recently, three optical tidal disruption event (TDE) candidates discovered by the Zwicky Transient Facility (ZTF) have been suggested to be coincident with high-energy neutrinos. They all exhibit unusually strong dust infrared (IR) echoes, with their peak times matching the neutrino arrival time even better than the optical peaks. We hereby report on two new

Alexander Schröder, Leon van Velzen, Maurits Kelder, Sascha Schäfer

Novel event-based electron detector platforms provide an avenue to extend the temporal resolution of electron microscopy into the ultrafast domain. Here, we characterize the timing accuracy of a detector based on a TimePix3 architecture using femtosecond electron pulse trains as a reference. With a large dataset of event clusters triggered by individual inci

Paola Loreti, Daniela Sforza, Masahiro Yamamoto

We consider initial boundary value problems for time fractional diffusion-wave equations: $$ d_t^{\alpha} u = -Au + \mu(t)f(x) $$ in a bounded domain where $\mu(t)f(x)$ describes a source and $\alpha \in (0,1) \cup (1,2)$, and $-A$ is a symmetric ellitpic operator with repect to the spatial variable $x$. We assume that $\mu(t) = 0$ for $t > T$:some time and

Arinbjörn Kolbeinsson, Luca Foschini

Data scarcity is a common obstacle in medical research due to the high costs associated with data collection and the complexity of gaining access to and utilizing data. Synthesizing health data may provide an efficient and cost-effective solution to this shortage, enabling researchers to explore distributions and populations that are not represented in exist