May 2023 arXiv papers — page 4

Manchang Jin, Gaosheng Liu, Kunshu Hu, Xin Luo

Recent learning-based approaches have achieved significant progress in light field (LF) image super-resolution (SR) by exploring convolution-based or transformer-based network structures. However, LF imaging has many intrinsic physical priors that have not been fully exploited. In this paper, we analyze the coordinate transformation of the LF imaging process

Pere Masjuan, Alejandro Miranda, Pablo Roig

We compute for the first time the $\tau$ data-driven Euclidean windows for the hadronic vacuum polarization contribution to the muon $g-2$. We show that $\tau$-based results agree with the available lattice window evaluations and with the full result. On the intermediate window, where all lattice evaluations are rather precise and agree, $\tau$-based results

Sharmila Karumuri, Ilias Bilionis

Inverse problems, i.e., estimating parameters of physical models from experimental data, are ubiquitous in science and engineering. The Bayesian formulation is the gold standard because it alleviates ill-posedness issues and quantifies epistemic uncertainty. Since analytical posteriors are not typically available, one resorts to Markov chain Monte Carlo samp

Yang Yang, Jian Wu, Xiangman Song, Derun Wu

Hit rate is a key performance metric in predicting process product quality in integrated industrial processes. It represents the percentage of products accepted by downstream processes within a controlled range of quality. However, optimizing hit rate is a non-convex and challenging problem. To address this issue, we propose a data-driven quasi-convex approa

Che-Ping Tsai, Jiong Zhang, Eli Chien, Hsiang-Fu Yu

We introduce a novel class of sample-based explanations we term high-dimensional representers, that can be used to explain the predictions of a regularized high-dimensional model in terms of importance weights for each of the training samples. Our workhorse is a novel representer theorem for general regularized high-dimensional models, which decomposes the m

Haider Shoaib, Hina Tabassum

Vehicle-to-infrastructure (V2I) communication is becoming indispensable for successful roll-out of connected and autonomous vehicles (CAVs). While increasing the CAVs' speed improves the average CAV traffic flow, it increases communication handoffs (HOs) thus reducing wireless data rates. Furthermore, unplanned density of active base-stations (BSs) may resul

Thomas-Peter Fries, Michael W. Kaiser

A mechanical model and numerical method for structural membranes implied by all isosurfaces of a level-set function in a three-dimensional bulk domain are proposed. The mechanical model covers large displacements in the context of the finite strain theory and is formulated based on the tangential differential calculus. Alongside curved two-dimensional membra

Onur Mutlu

Memory-centric computing aims to enable computation capability in and near all places where data is generated and stored. As such, it can greatly reduce the large negative performance and energy impact of data access and data movement, by fundamentally avoiding data movement and reducing data access latency & energy. Many recent studies show that memory-cent

Jishnu Ray Chowdhury, Cornelia Caragea

We propose Beam Tree Recursive Cell (BT-Cell) - a backpropagation-friendly framework to extend Recursive Neural Networks (RvNNs) with beam search for latent structure induction. We further extend this framework by proposing a relaxation of the hard top-k operators in beam search for better propagation of gradient signals. We evaluate our proposed models in d

Chenghao Yang, Fan Yin, He He, Kai-Wei Chang

Despite the popularity of Shapley Values in explaining neural text classification models, computing them is prohibitive for large pretrained models due to a large number of model evaluations. In practice, Shapley Values are often estimated with a small number of stochastic model evaluations. However, we show that the estimated Shapley Values are sensitive to

Junwei Lu, Jin Yin, Tianxi Cai

Due to the increasing adoption of electronic health records (EHR), large scale EHRs have become another rich data source for translational clinical research. Despite its potential, deriving generalizable knowledge from EHR data remains challenging. First, EHR data are generated as part of clinical care with data elements too detailed and fragmented for resea

Roy Haller, Melissa Osterwalder, Gergő Fülöp, Joost Ridderbos

Three-dimensional topological Dirac semimetals have recently gained significant attention, since they possess exotic quantum states. When constructing Josephson junctions utilizing these materials as the weak link, the fractional ac Josephson effect emerges in the presence of a topological supercurrent contribution. We investigate the ac Josephson effect in

Michal Johanis, Václav Kryštof, Luděk Zajíček

Our paper is a complement to a recent article by D. Azagra and C. Mudarra (2021). We show how older results on semiconvex functions with modulus $\omega$ easily imply extension theorems for $C^{1,\omega}$-smooth functions on super-reflexive Banach spaces which are versions of some theorems of Azagra and Mudarra. We present also some new interesting consequen

Denis Gessert, Wolfhard Janke, Martin Weigel

The population annealing algorithm is a population-based equilibrium version of simulated annealing. It can sample thermodynamic systems with rough free-energy landscapes more efficiently than standard Markov chain Monte Carlo alone. A number of parameters can be fine-tuned to improve the performance of the population annealing algorithm. While there is some

John E. Lane, Philip T. Metzger, Christopher D. Immer, Xiaoyi Li

A mathematical model and software implementation developed to predict trajectories of single lunar dust particles acted on by a high velocity gas flow is discussed. The model uses output from a computation fluid dynamics (CFD) or direct simulation Monte Carlo (DSMC) simulation of a rocket nozzle hot gas jet. The gas density, velocity vector field, and temper

Mohamed El Amine Seddik, Mastane Achab, Henrique Goulart, Merouane Debbah

In this paper, we propose a nested matrix-tensor model which extends the spiked rank-one tensor model of order three. This model is particularly motivated by a multi-view clustering problem in which multiple noisy observations of each data point are acquired, with potentially non-uniform variances along the views. In this case, data can be naturally represen

Jinbin Zhao, Peitao Liu, Jiantao Wang, Jiangxu Li

Superhard materials with good fracture toughness have found wide industrial applications, which necessitates the development of accurate hardness and fracture toughness models for efficient materials design. Although several macroscopic models have been proposed, they are mostly semiempirical based on prior knowledge or assumptions, and obtained by fitting l

Sebastian Rocks, Robert S. Hoy

Motivated in part by the recent observation of liquid glass in suspensions of ellipsoidal colloids, we examine the structure of jammed ellipse packings over a much wider range of particle aspect ratios ($\alpha$) than has been previously attempted. We determine $\phi_{\rm J}(\alpha)$ to high precision, and find empirical analytic formulae that predict $\phi_

B. G. Zakharov

We calculate the medium modification factor $I_{pA}$ for the photon-tagged jet fragmentation functions for scenario with the quark-gluon plasma formation in $pA$ and $pp$ collisions. We perform calculations of radiative and collisional parton energy loss in the quark-gluon plasma with running $\alpha_s$ which has a plateau around $Q\sim \kappa T$ with $\kapp

Filipa C. R. Peres, Rafael Wagner, Ernesto F. Galvão

Recently, cat states have been used to heuristically improve the runtime of a classical simulator of quantum circuits based on the diagrammatic ZX-calculus. Here we investigate the use of cat-state injection within the quantum circuit model. We explore a family of cat states, $\left| \mathrm{cat}_m^* \right>$, and describe circuit gadgets using them to concu

Jaejun Lee, Chanyoung Chung, Joyce Jiyoung Whang

Inductive knowledge graph completion has been considered as the task of predicting missing triplets between new entities that are not observed during training. While most inductive knowledge graph completion methods assume that all entities can be new, they do not allow new relations to appear at inference time. This restriction prohibits the existing method

Mordehai Milgrom

I present a new class of nonrelativistic, modified-gravity MOND theories. The three gravitational degrees of freedom of these ``TRIMOND'' theories are the MOND potential and two auxiliary potentials, one of which emerges as the Newtonian potential. Their Lagrangians involve a function of three acceleration variables -- the gradients of the potentials. So, th

Martin Fränzle, Paul Kröger, Sarah Winter, Martin Zimmermann

We compare games under delayed control and delay games, two types of infinite games modelling asynchronicity in reactive synthesis. In games under delayed control both players suffer from partial informedness due to symmetrically delayed communication, while in delay games, the protagonist has to grant lookahead to the alter player. Our first main result, th

Nikolas Kuhn

We construct moduli stacks of stable sheaves for surfaces fibered over marked nodal curves by using expanded degenerations. These moduli stacks carry a virtual class and therefore give rise to enumerative invariants. In the case of a surface with two irreducible components glued along a smooth divisor, we prove a degeneration formula that relates the moduli

Junang Li, Chih-Wei Joshua Liu, Michal Szurek, Nikta Fakhri

Thermodynamic irreversibility is a crucial property of living matter. Irreversible processes maintain spatiotemporally complex structures and functions characteristic of living systems. In high-dimensional biological dynamics, robust and general quantification of irreversibility remains a challenging task due to experimental noise and nonlinear interactions

Yijia Zhang, Yibo Han, Shijie Cao, Guohao Dai

Running out of GPU memory has become a main bottleneck for large-scale DNN training. How to reduce the memory footprint during training has received intensive research attention. We find that previous gradient accumulation reduces activation memory but fails to be compatible with gradient memory reduction due to a contradiction between preserving gradients a

Yan Wang, Heidi Ann Scharf Donovan, Sabit Hassan, Mailhe Alikhani

Patients who effectively manage their symptoms often demonstrate higher levels of engagement in conversations and interventions with healthcare practitioners. This engagement is multifaceted, encompassing cognitive and socio-affective dimensions. Consequently, it is crucial for AI systems to understand the engagement in natural conversations between patients

Luigi Bonati, Enrico Trizio, Andrea Rizzi, Michele Parrinello

Identifying a reduced set of collective variables is critical for understanding atomistic simulations and accelerating them through enhanced sampling techniques. Recently, several methods have been proposed to learn these variables directly from atomistic data. Depending on the type of data available, the learning process can be framed as dimensionality redu

Aryo Pradipta Gema, Dominik Grabarczyk, Wolf De Wulf, Piyush Borole

Knowledge graphs are powerful tools for representing and organising complex biomedical data. Several knowledge graph embedding algorithms have been proposed to learn from and complete knowledge graphs. However, a recent study demonstrates the limited efficacy of these embedding algorithms when applied to biomedical knowledge graphs, raising the question of w

Richard Stiskalek, Harry Desmond

Galaxies have been observed to exhibit a level of simplicity unexpected in the complex galaxy formation scenario posited by standard cosmology. This is particularly apparent in their dynamics, where scaling relations display much regularity and little intrinsic scatter. However, the parameters responsible for this simplicity have not been identified. Using t

David Benisty, Philippe Brax, Anne-Christine Davis

The impact of light scalars coupled conformally and disformally to matter on the geodetic and frame-dragging (FD) precessions is calculated. For larger frequencies the disformal interaction becomes increasingly relevant. We use several satellite experiments and Pulsar time of arrival (ToA) measurements to derive bounds on the couplings, combining the Gravity

Lisa T. Weinbrenner, Lina Vandré, Tim Coopmans, Otfried Gühne

Quantum information science may lead to technological breakthroughs in computing, cryptography and sensing. For the implementation of these tasks, however, complex devices with many components are needed and the quantum advantage may easily be spoiled by failure of few parts only. A paradigmatic example are quantum networks. There, not only noise sources lik

Shutaro Nakaoka

We prove the Pieri formulas for Schur multiple zeta functions, which are generalizations of the Pieri formulas proved by Nakasuji and Takeda for hook type Schur multiple zeta functions. Moreover, we also prove the Littlewood-Richardson rule for Schur multiple zeta functions. In the course of their proofs, we regard the `truncated' version of Schur multiple z

Parker Glenn, Parag Pravin Dakle, Preethi Raghavan

In addressing the task of converting natural language to SQL queries, there are several semantic and syntactic challenges. It becomes increasingly important to understand and remedy the points of failure as the performance of semantic parsing systems improve. We explore semantic parse correction with natural language feedback, proposing a new solution built

J. M. Wrobel, R. C. Walker

If the construction of the ngVLA begins in 2026, its sensitivity is expected to match that of the VLA by late 2029. At that juncture it is anticipated that open-skies observing will cease on the VLA and commence on the ngVLA. We suggest that during 2026-2029 the VLA be held in a customized final configuration encompassing portions of its standard A, B, C and

Ziyi Ni, Minglun Han, Feilong Chen, Linghui Meng

Enhancing automatic speech recognition (ASR) performance by leveraging additional multimodal information has shown promising results in previous studies. However, most of these works have primarily focused on utilizing visual cues derived from human lip motions. In fact, context-dependent visual and linguistic cues can also benefit in many scenarios. In this

Robert Tjarko Lange, Henning Sprekeler

Is the lottery ticket phenomenon an idiosyncrasy of gradient-based training or does it generalize to evolutionary optimization? In this paper we establish the existence of highly sparse trainable initializations for evolution strategies (ES) and characterize qualitative differences compared to gradient descent (GD)-based sparse training. We introduce a novel

Hye-jin Shim, Rosa González Hautamäki, Md Sahidullah, Tomi Kinnunen

Shortcut learning, or `Clever Hans effect` refers to situations where a learning agent (e.g., deep neural networks) learns spurious correlations present in data, resulting in biased models. We focus on finding shortcuts in deep learning based spoofing countermeasures (CMs) that predict whether a given utterance is spoofed or not. While prior work has address

Lili Su, Ming Xiang, Jiaming Xu, Pengkun Yang

Federated learning is a decentralized machine learning framework that enables collaborative model training without revealing raw data. Due to the diverse hardware and software limitations, a client may not always be available for the computation requests from the parameter server. An emerging line of research is devoted to tackling arbitrary client unavailab

Y. Jiang, M. Gupta, C. Riggert, M. Pendharkar

Many recipes for realizing topological superconductivity rely on broken time-reversal symmetry, which is often attained by applying a substantial external magnetic field. Alternatively, using magnetic materials can offer advantages through low-field operation and design flexibility on the nanoscale. Mechanisms for lifting spin degeneracy include exchange cou

George C. Ţurcaş

Let $q > 5$ be a prime and $K$ a quadratic number field. In this article we extend a previous result of Najman and the author and prove that if $E/K$ is an elliptic curve with potentially multiplicative reduction at all primes $\mathfrak q \mid q$, then $E$ does not have prime isogenies of degree greater than $71$ and different from $q$. As an application to

Trevor D. Wooley

Consider a set of integers $\mathscr A$ having finite diameter $X$, and a system of simultaneous polynomial equations to be solved over $\mathscr A$. In many circumstances, it is known that the number of solutions of this system is $O(X^\epsilon |\mathscr A|^\theta)$ for a suitable $\theta>0$ and any $\epsilon>0$. These estimates become worse than trivial wh

Zhao Huang, Christopher Lane, Sarah E. Grefe, Snehasish Nandy

Dirac materials have been proposed as a new class of electron-based detectors for light dark-matter (DM) scattering or absorption, with predicted sensitivities far exceeding superconductors and superfluid helium. The superiority of Dirac materials originates from a significantly reduced in-medium dielectric response winning over the suppression of DM scatter

Yier Lin

We obtain the multi-point positive integer Lyapunov exponents of the Stochastic Heat Equation (SHE) and provide three expressions for them. We prove the result by matching the upper and lower bounds for the Lyapunov exponents. The upper bound is obtained by analyzing the contour integral formula in [Borodin-Corwin 2014]. For the lower bound, we apply an indu

Fatma Gamza Düzgün, Antonio Iannizzotto, Vincenzo Vespri

We prove a general clustering result for the fractional Sobolev space $W^{s,p}$: whenever the positivity set of a function $u$ in a square has measure bounded from below by a multiple of the cube's volume, and the $W^{s,p}$-seminorm of $u$ is bounded from above by a convenient power of the cube's side, then $u$ is positive in a universally reduced cube. Our

Eden Kuperwasser, Wojciech Samotij

Given a family of graphs $\mathcal{F}$ and an integer $r$, we say that a graph is $r$-Ramsey for $\mathcal{F}$ if any $r$-colouring of its edges admits a monochromatic copy of a graph from $\mathcal{F}$. The threshold for the classic Ramsey property in the binomial random graph, where $\mathcal{F}$ consists of one graph, was located in the celebrated work of

Simon Eberle, Hui Yu

For the obstacle problem with a nonlinear operator, we characterize the space of global solutions with compact contact sets. This is achieved by constructing a bijection onto a class of quadratic polynomials describing the asymptotic behavior of solutions.

Pietro Melzi, Christian Rathgeb, Ruben Tolosana, Ruben Vera-Rodriguez

Face recognition systems have significantly advanced in recent years, driven by the availability of large-scale datasets. However, several issues have recently came up, including privacy concerns that have led to the discontinuation of well-established public datasets. Synthetic datasets have emerged as a solution, even though current synthesis methods prese

Colin Defant, Rachana Madhukara, Hugh Thomas

The first author recently introduced toric promotion, an operator that acts on the labelings of a graph $G$ and serves as a cyclic analogue of Sch\"utzenberger's promotion operator. Toric promotion is defined as the composition of certain toggle operators, listed in a natural cyclic order. We consider more general permutoric promotion operators, which are de

Saud Hakem Al Harbi, Lionel Nganyewou Tidjon, Foutse Khomh

Integrating ethical practices into the AI development process for artificial intelligence (AI) is essential to ensure safe, fair, and responsible operation. AI ethics involves applying ethical principles to the entire life cycle of AI systems. This is essential to mitigate potential risks and harms associated with AI, such as algorithm biases. To achieve thi

Meng Chen, Chen Jiang, Jianshi Yan

We answer an open problem of the first author and Zhang (see Open Problem 6.4 (3) in Math. Z. 258 (2008), 565-585) and prove that, for any nonsingular projective $3$-fold of general type with the geometric genus greater than 201, the bicanonical system is not composed of any pencil of surfaces.

Andrzej Grzesik, Justyna Jaworska, Bartłomiej Kielak, Aliaksandra Novik

A classical Tur\'an problem asks for the maximum possible number of edges in a graph of a given order that does not contain a particular graph $H$ as a subgraph. It is well-known that the chromatic number of $H$ is the graph parameter which describes the asymptotic behavior of this maximum. Here, we consider an analogous problem for oriented graphs, where co

Mira Gergácz, Ákos Kereszturi

The search for ephemeral liquid water on Mars is an ongoing activity. After the recession of the seasonal polar ice cap on Mars, small water ice patches may be left behind in shady places due to the low thermal conductivity of the Martian surface and atmosphere. During late spring and early summer, these patches may be exposed to direct sunlight and warm up

Chun-Wei Tsai, Yi-Cheng Yang, Tzu-Chieh Tang, Che-Wei Hsu

Most metaheuristic algorithms rely on a few searched solutions to guide later searches during the convergence process for a simple reason: the limited computing resource of a computer makes it impossible to retain all the searched solutions. This also reveals that each search of most metaheuristic algorithms is just like a ballpark guess. To help address thi

Maoyuan Ye, Jing Zhang, Shanshan Zhao, Juhua Liu

End-to-end text spotting aims to integrate scene text detection and recognition into a unified framework. Dealing with the relationship between the two sub-tasks plays a pivotal role in designing effective spotters. Although Transformer-based methods eliminate the heuristic post-processing, they still suffer from the synergy issue between the sub-tasks and l

Hongxu Jiang, Muhammad Imran, Preethika Muralidharan, Anjali Patel

Micro-ultrasound (micro-US) is a novel 29-MHz ultrasound technique that provides 3-4 times higher resolution than traditional ultrasound, potentially enabling low-cost, accurate diagnosis of prostate cancer. Accurate prostate segmentation is crucial for prostate volume measurement, cancer diagnosis, prostate biopsy, and treatment planning. However, prostate

Benjamin Sambale

A finite group G with center Z is of central type if there exists a fully ramified character $\lambda\in\mathrm{Irr}(Z)$, i.e. the induced character $\lambda^G$ is a multiple of an irreducible character. Howlett-Isaacs have shown that G is solvable in this situation. A corresponding theorem for p-Brauer characters was proved by Navarro-Sp\"ath-Tiep under the

Amílcar Branquinho, Ana Foulquié-Moreno, Assil Fradi, Manuel Mañas

In this work we show how to get advantage from the Riemann--Hilbert analysis in order to obtain information about the matrix orthogonal polynomials and functions of second kind associated with a weight matrix. We deduce properties for the recurrence relation coefficients from differential properties of the weight matrix. We take the matrix polynomials of Her

Hye-jin Shim, Jee-weon Jung, Tomi Kinnunen

Audio anti-spoofing for automatic speaker verification aims to safeguard users' identities from spoofing attacks. Although state-of-the-art spoofing countermeasure(CM) models perform well on specific datasets, they lack generalization when evaluated with different datasets. To address this limitation, previous studies have explored large pre-trained models,

Thomas D. Cohen, Hyunwoo Oh

The rodeo algorithm has been proposed recently as an efficient method in quantum computing for projection of a given initial state onto a state of fixed energy for systems with discrete spectra. In the initial formulation of the rodeo algorithm these times were chosen randomly via a Gaussian distribution with fixed RMS times. In this paper it is shown that s

Emanuele Marconato, Stefano Teso, Antonio Vergari, Andrea Passerini

Neuro-Symbolic (NeSy) predictive models hold the promise of improved compliance with given constraints, systematic generalization, and interpretability, as they allow to infer labels that are consistent with some prior knowledge by reasoning over high-level concepts extracted from sub-symbolic inputs. It was recently shown that NeSy predictors are affected b

Jun-Peng Li, Sai Wang, Zhi-Chao Zhao, Kazunori Kohri

Primordial non-Gaussianity encodes vital information of the physics of the early universe, particularly during the inflationary epoch. To explore the local-type primordial non-Gaussianity $f_{\mathrm{NL}}$, we study the anisotropies in gravitational wave background induced by the linear cosmological scalar perturbations during radiation domination in the ear

Ziyang Chen, Yongsheng Pan, Yiwen Ye, Hengfei Cui

Although recent years have witnessed the great success of convolutional neural networks (CNNs) in medical image segmentation, the domain shift issue caused by the highly variable image quality of medical images hinders the deployment of CNNs in real-world clinical applications. Domain generalization (DG) methods aim to address this issue by training a robust

Nadya Serebriakova, Andrew Tkachenko, Sarah Gebruers, Dominic M. Bowman

Modern stellar structure and evolution theory experiences a lack of observational calibrations for the interior physics of intermediate- and high-mass stars. This leads to discrepancies between theoretical predictions and observed phenomena mostly related to angular momentum and element transport. Analyses of large samples of massive stars connecting state-o

Defang Chen, Zhenyu Zhou, Jian-Ping Mei, Chunhua Shen

Recent years have witnessed significant progress in developing effective training and fast sampling techniques for diffusion models. A remarkable advancement is the use of stochastic differential equations (SDEs) and their marginal-preserving ordinary differential equations (ODEs) to describe data perturbation and generative modeling in a unified framework.

Pouya Haghi, Ryan Marshall, Po Hao Chen, Anthony Skjellum

Offload of MPI collectives to network devices, e.g., NICs and switches, is being implemented as an effective mechanism to improve application performance by reducing inter- and intra-node communication and bypassing MPI software layers. Given the rich deployment of accelerators and programmable NICs/switches in data centers, we posit that there is an opportu

P. Punta, J. A. Lay, A. M. Moro

Background: Reactions with halo nuclei from deformed regions exhibit important deviations from the inert core+valence picture. Structure and reaction formalisms have recently been extended or adapted to explore the possibility of exciting the underlying core. Purpose: We will study up to what extent transfer reactions involving halo nuclei $^{11}$Be and $^{1

Niladri Patra

Consider the moduli space, $\mathcal{M}_{3},$ of cubic polynomials over $\mathbb{C}$, with a marked critical point. Let $\mathscr{S}_{k,n}$ be the set of all points in $\mathcal{M}_{3}$ for which the marked critical point is strictly $(k,n)$-preperiodic. Milnor conjectured that the affine algebraic curves $\mathscr{S}_{k,n}$ are irreducible, for all $k \geq

Francesco Camilli, Pierluigi Contucci, Emanuele Mingione

The overlap distribution of the Sherrington-Kirkpatrick model on the Nishimori line has been proved to be self averaging for large volumes. Here we study the joint distribution of the rescaled overlaps around their common mean and prove that it converges to a Gaussian vector.

Alice Balbi, Andreas S. Skeidsvoll, Henrik Koch

Time-dependent equation-of-motion coupled cluster (TD-EOM-CC) is used to simulate impulsive stimulated x-ray Raman scattering (ISXRS) of ultrashort laser pulses by neon, carbon monoxide, pyrrole, and p-aminophenol. The TD-EOM-CC equations are expressed in the basis of field-free EOM-CC states, where the calculation of the core-excited states is simplified th

A. Ignesti, B. Vulcani, A. Botteon, B. Poggianti

Wide-field radio continuum observations of galaxy clusters are revealing an increasing number of spiral galaxies hosting tens of kpc-long radio tails produced by the nonthermal interstellar medium being displaced by the ram pressure. We present a semi-empirical model for the multi-frequency radio continuum emission from ram pressure stripped tails based on t

Tapio Helin, Nuutti Hyvönen, Jarno Maaninen, Juha-Pekka Puska

The aim of magnetorelaxometry imaging is to determine the distribution of magnetic nanoparticles inside a subject by measuring the relaxation of the superposition magnetic field generated by the nanoparticles after they have first been aligned using an external activation magnetic field that has subsequently been switched off. This work applies techniques of

Muhammad Imran, Brianna Nguyen, Jake Pensa, Sara M. Falzarano

Early diagnosis of prostate cancer significantly improves a patient's 5-year survival rate. Biopsy of small prostate cancers is improved with image-guided biopsy. MRI-ultrasound fusion-guided biopsy is sensitive to smaller tumors but is underutilized due to the high cost of MRI and fusion equipment. Micro-ultrasound (micro-US), a novel high-resolution ultras

John F. Barry, Reed A. Irion, Matthew H. Steinecker, Daniel K. Freeman

Quantum sensors offer unparalleled precision, accuracy, and sensitivity for a variety of measurement applications. We report a compact magnetometer based on a ferrimagnetic sensing element in an oscillator architecture that circumvents challenges common to other quantum sensing approaches such as limited dynamic range, limited bandwidth, and dependence on va

Brennon Maistry, Absalom E. Ezugwu

Breast cancer is a prevalent form of cancer among women, with over 1.5 million women being diagnosed each year. Unfortunately, the survival rates for breast cancer patients in certain third-world countries, like South Africa, are alarmingly low, with only 40% of diagnosed patients surviving beyond five years. The inadequate availability of resources, includi

Ryota Okumura, Tadahiro Taniguchi, Yosinobu Hagiwara, Akira Taniguchi

In this study, we explore the emergence of symbols during interactions between individuals through an experimental semiotic study. Previous studies investigate how humans organize symbol systems through communication using artificially designed subjective experiments. In this study, we have focused on a joint attention-naming game (JA-NG) in which participan

Peter Sidajaya, Aloysius Dewen Lim, Baichu Yu, Valerio Scarani

Bell's theorem states that Local Hidden Variables (LHVs) cannot fully explain the statistics of measurements on some entangled quantum states. It is natural to ask how much supplementary classical communication would be needed to simulate them. We study two long-standing open questions in this field with neural network simulations and other tools. First, we

Lukas Koch, Matthias Ruf, Mathias Schäffner

We prove the absence of a Lavrentiev gap for vectorial integral functionals of the form $$ F: g+W_0^{1,1}(\Omega)^m\to\mathbb{R}\cup\{+\infty\},\qquad F(u)=\int_\Omega W(x,\mathrm{D} u)\,\mathrm{d}x, $$ where the boundary datum $g:\Omega\subset \mathbb{R}^d\to\mathbb{R}^m$ is sufficiently regular, $\xi\mapsto W(x,\xi)$ is convex and lower semicontinuous, sat

Ece Takmaz, Nicolo' Brandizzi, Mario Giulianelli, Sandro Pezzelle

Dialogue participants may have varying levels of knowledge about the topic under discussion. In such cases, it is essential for speakers to adapt their utterances by taking their audience into account. Yet, it is an open question how such adaptation can be modelled in computational agents. In this paper, we model a visually grounded referential game between

Pedro H. de Freitas Pimenta, Daniel A. Stariolo

We present a thorough numerical analysis of the relaxational dynamics of the Sherrington-Kirkpatrick spherical model with an additive non-disordered perturbation for large but finite sizes $N$. In the thermodynamic limit and at low temperatures, the perturbation is responsible for a phase transition from a spin glass to a ferromagnetic phase. We show that fi

Yan Wang, Feng Shu, Zhihong Zhuang, Rongen Dong

In this paper, the dominant factor affecting the performance of active intelligent reflecting surface (IRS) aided wireless communication networks in Rayleigh fading channel, namely the average signal-to-noise ratio (SNR) $\gamma_0$ at IRS, is studied. Making use of the weak law of large numbers, its simple asymptotic expression is derived as the number $N$ o

Jaclyn R. Lunger, Jessica Karaguesian, Hoje Chun, Jiayu Peng

Green hydrogen production is crucial for a sustainable future, but current catalysts for the oxygen evolution reaction (OER) suffer from slow kinetics, despite many efforts to produce optimal designs, particularly through the calculation of descriptors for activity. In this study, we develop a dataset of density functional theory calculations of bulk and sur

Robben E. Migacz, Guillaume Durey, Jesse T. Ault

Microparticles migrate in response to gradients in solute concentration through diffusiophoresis and diffusioosmosis. Merging streams of fluid with distinct solute concentrations is a common strategy for producing a steady concentration gradient with continuous flow in microfluidic devices; the solute concentration gradient and consequent diffusiophoresis ar

Conglei Xu, Kun Shen, Hongguang Sun, Yang Xu

Global sentence information is crucial for sequence labeling tasks, where each word in a sentence must be assigned a label. While BiLSTM models are widely used, they often fail to capture sufficient global context for inner words. Previous work has proposed various RNN variants to integrate global sentence information into word representations. However, thes

Michael G. Scheer, Biao Lian

Kekul\'e-O order in graphene, which has recently been realized experimentally, induces Dirac electron masses on the order of $m \sim 100 \text{meV}$. We show that twisted bilayer graphene in which one or both layers have Kekul\'e-O order exhibits nontrivial flat electronic bands on honeycomb and kagome lattices. When only one layer has Kekul\'e-O order, ther

Jen-tse Huang, Wenxiang Jiao, Man Ho Lam, Eric John Li

Recent research has focused on examining Large Language Models' (LLMs) characteristics from a psychological standpoint, acknowledging the necessity of understanding their behavioral characteristics. The administration of personality tests to LLMs has emerged as a noteworthy area in this context. However, the suitability of employing psychological scales, ini

Eng Keat Hng

Garbe, Hladký, Šileikis and Skerman [Ann. Inst. Henri Poincaré Probab. Stat., 60 (2024), pp. 2878-2922] recently introduced a general class of random graph processes called flip processes and proved that the typical evolution of these discrete-time random graph processes corresponds to certain continuous-time deterministic graphon trajectories. We obtain a c

AJ Piergiovanni, Anelia Angelova

Image-language learning has made unprecedented progress in visual understanding. These developments have come at high costs, as contemporary vision-language models require large model scales and amounts of data. We here propose a much easier recipe for image-language learning, which produces effective models, outperforming bigger and more expensive ones, oft

Fei Ni, Jianye Hao, Yao Mu, Yifu Yuan

Recently, diffusion model shines as a promising backbone for the sequence modeling paradigm in offline reinforcement learning(RL). However, these works mostly lack the generalization ability across tasks with reward or dynamics change. To tackle this challenge, in this paper we propose a task-oriented conditioned diffusion planner for offline meta-RL(MetaDif

Ofir Nabati, Guy Tennenholtz, Shie Mannor

We present a representation-driven framework for reinforcement learning. By representing policies as estimates of their expected values, we leverage techniques from contextual bandits to guide exploration and exploitation. Particularly, embedding a policy network into a linear feature space allows us to reframe the exploration-exploitation problem as a repre

Ilias Chronopoulos, Katerina Chrysikou, George Kapetanios, James Mitchell

In this paper we study neural networks and their approximating power in panel data models. We provide asymptotic guarantees on deep feed-forward neural network estimation of the conditional mean, building on the work of Farrell et al. (2021), and explore latent patterns in the cross-section. We use the proposed estimators to forecast the progression of new C

Sohum Thakkar, Skander Kazdaghli, Natansh Mathur, Iordanis Kerenidis

Quantum algorithms have the potential to enhance machine learning across a variety of domains and applications. In this work, we show how quantum machine learning can be used to improve financial forecasting. First, we use classical and quantum Determinantal Point Processes to enhance Random Forest models for churn prediction, improving precision by almost 6

Yi Gu, Yoshito Otake, Keisuke Uemura, Masaki Takao

Musculoskeletal diseases such as sarcopenia and osteoporosis are major obstacles to health during aging. Although dual-energy X-ray absorptiometry (DXA) and computed tomography (CT) can be used to evaluate musculoskeletal conditions, frequent monitoring is difficult due to the cost and accessibility (as well as high radiation exposure in the case of CT). We

Casey Blacker, Pavel Tsyganenko

We compute the geodesic curvature of logarithmic spirals on surfaces of constant Gaussian curvature. In addition, we show that the asymptotic behavior of the geodesic curvature is independent of the curvature of the ambient surface. We also show that, at a fixed distance from the center of the spiral, the geodesic curvature is continuously differentiable as

Paul Dütting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard

Maximizing monotone submodular functions under a matroid constraint is a classic algorithmic problem with multiple applications in data mining and machine learning. We study this classic problem in the fully dynamic setting, where elements can be both inserted and deleted in real-time. Our main result is a randomized algorithm that maintains an efficient dat

Hong Nhung Nguyen, Seongwook Lee, Tien Tung Nguyen, Yong Hwa Kim

Concentration of drivers on traffic is a vital safety issue; thus, monitoring a driver being on road becomes an essential requirement. The key purpose of supervision is to detect abnormal behaviours of the driver and promptly send warnings to him her for avoiding incidents related to traffic accidents. In this paper, to meet the requirement, based on radar s

Georgios Zacharopoulos, Ilias Bournias, Verner Vlacic, Lukas Cavigelli

When utilized effectively, Supercloud heterogeneous systems have the potential to significantly enhance performance. Our ReDSEa tool-chain automates the mapping, load balancing, scheduling, parallelism, and overlapping processes for the Triangular System Solver (TS) on a heterogeneous system consisting of a Huawei Kunpeng ARM multi-core CPU and an Ascend 910

Juraj Kardos, Wouter Edeling, Diana Suleimenova, Derek Groen

Sensitivity analysis is an important tool used in many domains of computational science to either gain insight into the mathematical model and interaction of its parameters or study the uncertainty propagation through the input-output interactions. In many applications, the inputs are stochastically dependent, which violates one of the essential assumptions

Jie Gu, Amir-Kian Kashani-Poor, Albrecht Klemm, Marcos Marino

We obtain analytic and numerical results for the non-perturbative amplitudes of topological string theory on arbitrary, compact Calabi-Yau manifolds. Our approach is based on the theory of resurgence and extends previous special results to the more general case. In particular, we obtain explicit trans-series solutions of the holomorphic anomaly equations. Ou

Terry Yue Zhuo, Zhou Yang, Zhensu Sun, Yufei Wang

The increasingly popular adoption of deep learning models in many critical source code tasks motivates the development of data augmentation (DA) techniques to enhance training data and improve various capabilities (e.g., robustness and generalizability) of these models. Although a series of DA methods have been proposed and tailored for source code models, t