October 2023 arXiv papers — page 43

Ze-Chun Hu, Xue Peng, Renming Song, Yuan Tan

In this paper, we study the asymptotic behavior of the number of rarely visited edges (i.e., edges that visited only once) of a simple symmetric random walk on $\mathbb{Z}$. Let $\alpha(n)$ be the number of rarely visited edges up to time $n$. First, we evaluate $\mathbb{E}(\alpha(n))$, show that $n\to \mathbb{E}(\alpha(n))$ is non-decreasing in $n$ and that

Eyal Segalis, Dani Valevski, Danny Lumen, Yossi Matias

Text-to-image diffusion models achieved a remarkable leap in capabilities over the last few years, enabling high-quality and diverse synthesis of images from a textual prompt. However, even the most advanced models often struggle to precisely follow all of the directions in their prompts. The vast majority of these models are trained on datasets consisting o

Chen Liu, Hongyu Zang, Xin Li, Yong Heng

Image-based Reinforcement Learning is a practical yet challenging task. A major hurdle lies in extracting control-centric representations while disregarding irrelevant information. While approaches that follow the bisimulation principle exhibit the potential in learning state representations to address this issue, they still grapple with the limited expressi

Boda Lin, Xinyi Zhou, Binghao Tang, Xiaocheng Gong

Pre-trained language models have been widely used in dependency parsing task and have achieved significant improvements in parser performance. However, it remains an understudied question whether pre-trained language models can spontaneously exhibit the ability of dependency parsing without introducing additional parser structure in the zero-shot scenario. I

Thomas Guilmeau, Nicola Branchini, Emilie Chouzenoux, Víctor Elvira

Adaptive importance sampling (AIS) algorithms are widely used to approximate expectations with respect to complicated target probability distributions. When the target has heavy tails, existing AIS algorithms can provide inconsistent estimators or exhibit slow convergence, as they often neglect the target's tail behaviour. To avoid this pitfall, we propose a

Linping Qu, Shenghui Song, Chi-Ying Tsui, Yuyi Mao

Because of its privacy-preserving capability, federated learning (FL) has attracted significant attention from both academia and industry. However, when being implemented over wireless networks, it is not clear how much communication error can be tolerated by FL. This paper investigates the robustness of FL to the uplink and downlink communication error. Our

John Pharo, Yicheng Guo, Guillermo Barro Calvo, Teja Teppala

We present gas-phase metallicity measurements for 583 emission line galaxies at $0.3<z<0.85$, including 388 dwarf galaxies with $log(M_{\star}/M_{\odot}) < 9.5$, and explore the dependence of the metallicity on the stellar mass and star formation properties of the galaxies. Metallicities are determined through the measurement of emission lines in very deep (

Lingxiao Wang, Yan Li, Barry Graubard, Hormuzd Katki

Accurate cancer risk estimation is crucial to clinical decision-making, such as identifying high-risk people for screening. However, most existing cancer risk models incorporate data from epidemiologic studies, which usually cannot represent the target population. While population-based health surveys are ideal for making inference to the target population,

Koki Tokeshi, Vincent Vennin

Most high-energy constructions that realise a phase of cosmic inflation contain many degrees of freedom. Yet, cosmological observations are all consistent with single-field embeddings. We show how volume selection effects explain this apparent paradox. Because of quantum diffusion, different regions of space inflate by different amounts. In regions that infl

Timur Sudak, Sebastian Tschiatschek

We consider the problem of learning Variational Autoencoders (VAEs), i.e., a type of deep generative model, from data with missing values. Such data is omnipresent in real-world applications of machine learning because complete data is often impossible or too costly to obtain. We particularly focus on improving a VAE's amortized posterior inference, i.e., th

Diogo Lavado, Cláudia Soares, Alessandra Micheletti

Regularizing Deep Neural Networks (DNNs) is essential for improving generalizability and preventing overfitting. Fixed penalty methods, though common, lack adaptability and suffer from hyperparameter sensitivity. In this paper, we propose a novel approach to DNN regularization by framing the training process as a constrained optimization problem. Where the d

Qizhen Wu, Kexin Liu, Lei Chen

Reinforcement learning suffers from limitations in real practices primarily due to the number of required interactions with virtual environments. It results in a challenging problem because we are implausible to obtain a local optimal strategy with only a few attempts for many learning methods. Hereby, we design an improved reinforcement learning method base

Genrich Zeller, Desedea Diaz Barrero, Paul Wiesen, Simon Niemes

In this work, we report on studies of graphene exposed to tritium gas in a controlled environment. The single layer graphene on a $\textrm{SiO}_2$/Si substrate was exposed to 400 mbar of $\textrm{T}_2$ for a total time of $\approx$ 55 h. The resistivity of the graphene sample was measured $\textit{in situ}$ during tritium exposure using the Van der Pauw meth

Toai Luong

The Cahn-Hilliard equation is a widely used model for describing phase separation processes in a binary mixture. In this paper, we investigate the viscous Cahn-Hilliard equation with a degenerate, phase-dependent mobility. We define the concept of a weak solution and establish the existence of such a solution by taking limits of solutions to the viscous Cahn

Spandan Minwalla

We perform a matched asymptotic expansion to find an analytic formula for the trajectory of a light ray in a Schwarzschild metric, in a power series expansion in the deviation of the impact parameter from its critical value. We present results valid to second sub leading order in this expansion. We use these results to find an analytic expansion for the angu

Florian Desmons, Thomas Milcent, Anne-Virginie Salsac, Mirco Ciallella

In this paper, we present a computational framework based on fully Eulerian models for fluid-structure interaction for the numerical simulation of biological capsules. The flexibility of such models, given by the Eulerian treatment of the interface and deformations, allows us to easily deal with the large deformations experienced by the capsule. The modeling

Sonja Groß, Amartya Ganguly, Hendrik Dietz, Sami Haddadin

We propose the next evolution of the artificial sense of touch, including an in-depth examination of the latest advancements in tactile sensing technology and the challenges that remain. We delve into the forefront of DNA and nanomaterials that enable the design of functionalized nanostructures in combination with the advantages of auto-assembly mechanisms.

Niki Maria Foteinopoulou, Ioannis Patras

Facial Expression Recognition (FER) is a crucial task in affective computing, but its conventional focus on the seven basic emotions limits its applicability to the complex and expanding emotional spectrum. To address the issue of new and unseen emotions present in dynamic in-the-wild FER, we propose a novel vision-language model that utilises sample-level t

Anjali Gopal, Nathan Helm-Burger, Lennart Justen, Emily H. Soice

Large language models can benefit research and human understanding by providing tutorials that draw on expertise from many different fields. A properly safeguarded model will refuse to provide "dual-use" insights that could be misused to cause severe harm, but some models with publicly released weights have been tuned to remove safeguards within days of intr

Jessica Echterhoff, An Yan, Kyungtae Han, Amr Abdelraouf

Concept bottleneck models have been successfully used for explainable machine learning by encoding information within the model with a set of human-defined concepts. In the context of human-assisted or autonomous driving, explainability models can help user acceptance and understanding of decisions made by the autonomous vehicle, which can be used to rationa

Masahiro Kato, Kota Matsui, Ryo Inokuchi

Consider a scenario where we have access to train data with both covariates and outcomes while test data only contains covariates. In this scenario, our primary aim is to predict the missing outcomes of the test data. With this objective in mind, we train parametric regression models under a covariate shift, where covariate distributions are different betwee

Yohei Saito, Owen Ganter, Chao Shang, Kenichiro Hashimoto

We present results of the coefficient of thermal expansion for the frustrated quasi-two-dimensional molecular conductor $\theta$-(BEDT-TTF)$_2$RbZn(SCN)$_4$ for temperatures 1.5 K $\leq T \leq$ 290 K. A pronounced first-order phase transition anomaly is observed at the combined charge-order/structural transition at 215 K. Furthermore, clear evidence is found

Kai Song, Yaoxing Bian, Ku Wu, Hongrui Liu

Single-pixel imaging can collect images at the wavelengths outside the reach of conventional focal plane array detectors. However, the limited image quality and lengthy computational times for iterative reconstruction still impede the practical application of single-pixel imaging. Recently, deep learning has been introduced into single-pixel imaging, which h

George Janelidze, Manuela Sobral

By a closure space we will mean a pair $(A,\mathcal{C})$, in which $A$ is a set and $\mathcal{C}$ a set of subsets of $A$ closed under arbitrary intersections. The purpose of this paper is to initiate a development of descent theory of closure spaces, with our main results being: (a) characterization of descent morphisms of closure spaces; (b) in the categor

Margherita Lelli-Chiesa, Andreas Leopold Knutsen, Alessandro Verra

The moduli space R_{g,2n} parametrizes double covers of smooth curves of genus g ramified at 2n points. We will prove the (uni)rationality of R_{g,2}, R_{g,4} and R_{g,6} in low genera.

Bhanu Prakash Pant

At the beginning of 2020, MAGIC reported a very-high-energy (VHE) flaring activity from the FSRQ QSO B1420+326. It is now the fourth known most distant blazar (z=0.682) with an observed VHE gamma-ray emission. In this work, we investigate the effect of photon-axionlike particle (ALP) oscillations in the gamma-ray spectra measured by Fermi-LAT and MAGIC aroun

Jian Ma

In this paper we propose to apply copula entropy (CE) to photometric redshifts. CE is used to measure the correlations between photometric measurements and redshifts and then the measurements associated with high CEs are selected for predicting redshifts. We verified the proposed method on the SDSS quasar data. Experimental results show that the accuracy of

Martin Dechant, Susanne Poeller, Benedikt Hosp, Olga Lukashova-Sanz

Trust is essential for our interactions with others but also with artificial intelligence (AI) based systems. To understand whether a user trusts an AI, researchers need reliable measurement tools. However, currently discussed markers mostly rely on expensive and invasive sensors, like electroencephalograms, which may cause discomfort. The analysis of mouse

Samit Dasgupta

This is an exposition of our joint work with Kakde, Silliman, and Wang, in which we prove a version of Ribet's Lemma for $\mathrm{GL}_2$ in the residually indistinguishable case. We suppose we are given a Galois representation taking values in the total ring of fractions of a complete reduced Noetherian local ring $\mathbf{T}$, such that the characteristic p

Kaleem Peeroo, Peter Popov, Vladimir Stankovic

The Data Distribution Service (DDS) is a widely used communication specification for real-time mission-critical systems that follow the principles of publish-subscribe middleware. DDS has an extensive set of quality of service (QoS) parameters allowing a thorough customisation of the intended communication. An extensive survey of the performance of the imple

Xingchen Li, Yifan Duan, Beibei Wang, Haojie Ren

In multimodal perception systems, achieving precise extrinsic calibration between LiDAR and camera is of critical importance. Previous calibration methods often required specific targets or manual adjustments, making them both labor-intensive and costly. Online calibration methods based on features have been proposed, but these methods encounter challenges s

Felix Ali Mehmeti, Kaïs Ammari, Serge Nicaise

We study the free Schr\"odinger equation on finite metric graphs with infinite ends. We give sufficient conditions to obtain the $L^1$ to $L^\infty$ time decay rate at least $t^{-1/2}$. These conditions allow certain metric graphs with circles and/or with commensurable lengths of the bounded edges. Further we study the dynamics of the probability flow betwee

Prashant Singh, Karel Proesmans

Measuring entropy production of a system directly from the experimental data is highly desirable since it gives a quantifiable measure of the time-irreversibility for non-equilibrium systems and can be used as a cost function to optimize the performance of the system. Although numerous methods are available to infer the entropy production of stationary syste

James Leiner, Brian Manzo, Aaditya Ramdas, Wesley Tansey

Controlling false positives (Type I errors) through statistical hypothesis testing is a foundation of modern scientific data analysis. Existing causal structure discovery algorithms either do not provide Type I error control or cannot scale to the size of modern scientific datasets. We consider a variant of the causal discovery problem with two sets of nodes

Muhammad Ihsan Khalil

This study introduces two innovative methodologies aimed at augmenting energy efficiency in satellite-to-ground communication systems through the integration of multiple Reflective Intelligent Surfaces (RISs). The primary objective of these methodologies is to optimize overall energy efficiency under two distinct scenarios. In the first scenario, denoted as

Felix Draxler, Peter Sorrenson, Lea Zimmermann, Armand Rousselot

Normalizing Flows are generative models that directly maximize the likelihood. Previously, the design of normalizing flows was largely constrained by the need for analytical invertibility. We overcome this constraint by a training procedure that uses an efficient estimator for the gradient of the change of variables formula. This enables any dimension-preser

Meghdad Yazdani-Hamid, Mehdi Biderang, Alireza Akbari

We undertake a theoretical analysis to probe the Kerr spectrum within the superconducting phase of strontium ruthenate, where the Kerr rotation experiments demonstrate the existence of a superconducting state with broken time reversal symmetry. We find that spin-orbit coupling changes the hybridization along the Fermi surface's diagonal zone mainly affects H

Kohei Sato, Hiromasa Watanabe, Takeshi Yamazaki

Traditionally, there has been a method to extract the charge radius of a hadron based on the fits of its form factor with some model assumptions. In contrast, a completely different method has been proposed, which does not depend on the models. In this report, we explore several improvements to this model-independent method for analyzing the pion charge radi

Hawau Olamide Toyin, Amirbek Djanibekov, Ajinkya Kulkarni, Hanan Aldarmaki

We present ArTST, a pre-trained Arabic text and speech transformer for supporting open-source speech technologies for the Arabic language. The model architecture follows the unified-modal framework, SpeechT5, that was recently released for English, and is focused on Modern Standard Arabic (MSA), with plans to extend the model for dialectal and code-switched

Hervé Bergeron, Jean-Pierre Gazeau, Przemysław Małkiewicz, Patrick Peter

We have discovered a class of dynamically stable coherent states for motion on the half-line. The regularization of the half-line boundary and the consequent quantum motion are expounded within the framework of covariant affine quantization, although alternative approaches are also feasible. The former approach is rooted in affine coherent states and offers

Wei Fang, Yanqi Chen, Jianhao Ding, Zhaofei Yu

Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties. As the emerging spiking deep learning paradigm attracts increasing interest, traditional programming frameworks cannot meet the demands of the automatic differentiation, parallel comp

Gesesew R. Habtie, Ramkrishna Das, Ruchi Pandey, N. M. Ashok

We present the results of the investigation of the nova V1674 Her (2021), recognised as the swiftest classical nova, with $t_2 \sim 0.90$ days. The distance to the nova is estimated to be 4.97 kpc. The mass and radius of the WD are calculated to be $\sim~1.36~M_\odot$ and $\sim 0.15~R_\oplus$, respectively. Over the course of one month following the outburst

Gerald Ebmer, Adam Loch, Minh Nhat Vu, Germain Haessig

Real-time applications for autonomous operations depend largely on fast and robust vision-based localization systems. Since image processing tasks require processing large amounts of data, the computational resources often limit the performance of other processes. To overcome this limitation, traditional marker-based localization systems are widely used sinc

M. Basil Altaie

Time in relativity theory has a status different from that adopted by standard quantum mechanics, where time is considered as a parameter measured with reference to an external absolute Newtonian frame. This status strongly restricts its role in the dynamics of systems and hinders any formulation to merge quantum mechanics with general relativity, specifical

Yiming Lin, Xiao-Bo Jin, Qiufeng Wang, Kaizhu Huang

Panoramic Narrative Grounding (PNG) is an emerging visual grounding task that aims to segment visual objects in images based on dense narrative captions. The current state-of-the-art methods first refine the representation of phrase by aggregating the most similar $k$ image pixels, and then match the refined text representations with the pixels of the image

Vincent Canel, Xiaoping Jia, Michel Campillo, Ioan Ionescu

We study the transition from cohesive to noncohesive granular states of synthetic rocks under oedometric loading, combining simultaneous measurements of ultrasound velocity and acoustic emissions. Our samples are agglomerates made of glass beads bonded with a few percent of cement, either ductile or brittle. These cemented granular samples exhibit an inelast

Javier Matulich, Evelyn Rodríguez

In this paper we analyze the asymptotic symmetries of the three-dimensional Chern-Simons supergravity for a supersymmetric extension of the semi-simple enlargement of the Poincar\'e algebra, also known as AdS-Lorentz superalgebra, which is characterized by two fermionic generators. We propose a consistent set of asymptotic boundary conditions for the aforeme

Yixin Wu, Ning Yu, Michael Backes, Yun Shen

Malicious or manipulated prompts are known to exploit text-to-image models to generate unsafe images. Existing studies, however, focus on the passive exploitation of such harmful capabilities. In this paper, we investigate the proactive generation of unsafe images from benign prompts (e.g., a photo of a cat) through maliciously modified text-to-image models.

Yi-Wei Jiang, Wei-Han Tan, Hua-Xing Chen, Er-Liang Cui

We study strong decays of the $\phi(2170)$, along with its possible partner $X(2436)$, as two fully-strange tetraquark states of $J^{PC} = 1^{--}$. We consider seven decay channels: $\phi \eta$, $\phi \eta^\prime$, $\phi f_0(980)$, $\phi f_1(1420)$, $h_1(1415) \eta$, $h_1(1415) \eta^\prime$, and $h_1(1415) f_1(1420)$. Some of these channels are kinematically

Qiong Wu, Qiangwei Yin, Sijie Zhang, Tianchen Hu

We report the significant enhancement on ultrafast terahertz optical conductivity and the unexpected formation of a polaronic-like state in semiconductor Mn3Si2Te6 at room temperature. With the absorption of pump photons, the low-frequency terahertz photoconductivity spectrum exhibits a significant rise, quickly forming a broad peak and subsequently shifting

Thore E. Moe, Tiago M. D. Pereira, Luc Rouppe van der Voort, Mats Carlsson

We aim to compare and contrast the typical shapes of synthetic Ca II 854.2 nm spectra found in Bifrost simulations having different magnetic activity with the spectral shapes found in a quiet Sun observation from the Swedish 1-m Solar Telescope (SST). We use clustering techniques to extract the typical Ca II 854.2 nm profile shapes synthesized from Bifrost s

Massimo Fornasier, Peter Richtárik, Konstantin Riedl, Lukang Sun

Consensus-based optimization (CBO) is a versatile multi-particle metaheuristic optimization method suitable for performing nonconvex and nonsmooth global optimizations in high dimensions. It has proven effective in various applications while at the same time being amenable to a theoretical convergence analysis. In this paper, we explore a variant of CBO, whi

Marek Kubis, Paweł Skórzewski, Marcin Sowański, Tomasz Ziętkiewicz

In a spoken dialogue system, an NLU model is preceded by a speech recognition system that can deteriorate the performance of natural language understanding. This paper proposes a method for investigating the impact of speech recognition errors on the performance of natural language understanding models. The proposed method combines the back transcription pro

Moritz Hardt, Celestine Mendler-Dünner

Predictions in the social world generally influence the target of prediction, a phenomenon known as performativity. Self-fulfilling and self-negating predictions are examples of performativity. Of fundamental importance to economics, finance, and the social sciences, the notion has been absent from the development of machine learning that builds on the stati

Stephanie Brandl, Emanuele Bugliarello, Ilias Chalkidis

In order to build reliable and trustworthy NLP applications, models need to be both fair across different demographics and explainable. Usually these two objectives, fairness and explainability, are optimized and/or examined independently of each other. Instead, we argue that forthcoming, trustworthy NLP systems should consider both. In this work, we perform

Mehrshad Saadatinia, Armin Salimi-Badr

In this study, we leverage a deep learning-based method for the automatic diagnosis of schizophrenia using EEG brain recordings. This approach utilizes generative data augmentation, a powerful technique that enhances the accuracy of the diagnosis. To enable the utilization of time-frequency features, spectrograms were extracted from the raw signals. After ex

Haifeng Wen, Hong Xing, Osvaldo Simeone

Addressing the communication bottleneck inherent in federated learning (FL), over-the-air FL (AirFL) has emerged as a promising solution, which is, however, hampered by deep fading conditions. In this paper, we propose AirFL-Mem, a novel scheme designed to mitigate the impact of deep fading by implementing a \emph{long-term} memory mechanism. Convergence bou

Peixuan Han, Zhenghao Liu, Zhiyuan Liu, Chenyan Xiong

The anchor-document data derived from web graphs offers a wealth of paired information for training dense retrieval models in an unsupervised manner. However, unsupervised data contains diverse patterns across the web graph and often exhibits significant imbalance, leading to suboptimal performance in underrepresented or difficult groups. In this paper, we i

Yijun Tang, Himadri Shekhar Dhar, Rupert F. Oulton, Robert A. Nyman

The study of temporal coherence in a Bose-Einstein condensate of photons can be challenging, especially in the presence of correlations between the photonic modes. In this work, we use a microscopic, multimode model of photonic condensation inside a dye-filled microcavity and the quantum regression theorem, to derive an analytical expression for the equation

Alexandre Amice, Peter Werner, Russ Tedrake

We present an efficient method for certifying non-collision for piecewise-polynomial motion plans in algebraic reparametrizations of configuration space. Such motion plans include those generated by popular randomized methods including RRTs and PRMs, as well as those generated by many methods in trajectory optimization. Based on Sums-of-Squares optimization,

Jan de Leeuw, Zaharah Bukhsh, Yingqian Zhang

Within the domain of e-commerce retail, an important objective is the reduction of parcel loss during the last-mile delivery phase. The ever-increasing availability of data, including product, customer, and order information, has made it possible for the application of machine learning in parcel loss prediction. However, a significant challenge arises from t

Amila Ravinath, Bikshapathi Gouda, Italo Atzeni, Antti Tölli

We propose uplink power control (PC) methods for massive multiple-input multiple-output systems with 1-bit analog-to-digital converters, which are specifically tailored to address the non-monotonic data detection performance with respect to the transmit power of the user equipment (UE). Considering a single UE, we design a multi-amplitude pilot sequence to c

Chris Salahub, Wayne Oldford

A common approach to evaluating the significance of a collection of $p$-values combines them with a pooling function, in particular when the original data are not available. These pooled $p$-values convert a sample of $p$-values into a single number which behaves like a univariate $p$-value. To clarify discussion of these functions, a telescoping series of a

Kaito Kimura, Justin Lyle, Yuya Otake, Ryo Takahashi

This paper provides a method to get a noetherian equicharacteristic local UFD with an isolated singularity from a given noetherian complete equicharacteristic local ring, preserving certain properties. This is applied to invesitgate the (non)vanishing of Ext modules. It is proved that there exist a Gorenstein local UFD $A$ having an isolated singularity such

Yijun Tang, Himadri Shekhar Dhar, Rupert F. Oulton, Robert A. Nyman

The temporal coherence of an ideal Bose gas increases as the system approaches the Bose-Einstein condensation threshold from below, with coherence time diverging at the critical point. However, counter-examples have been observed for condensates of photons formed in an externally pumped, dye-filled microcavity, wherein the coherence time decreases rapidly fo

Thiziri Nait-Saada, Alireza Naderi, Jared Tanner

The infinitely wide neural network has been proven a useful and manageable mathematical model that enables the understanding of many phenomena appearing in deep learning. One example is the convergence of random deep networks to Gaussian processes that allows a rigorous analysis of the way the choice of activation function and network weights impacts the tra

H. K. Avetissian, G. A. Musayelyan, G. F. Mkrtchian

This article aims to study the linear and nonlinear optical response of inversion symmetric graphene quantum dots (GQDs) in the presence of on-site disorder or vacancies. The presence of disorder or vacancy breaks the special inversion symmetry leading to the emergence of intense Hall-type anomalous harmonics. This phenomenon is attributed to the intrinsic t

Yoan Géran

Logical frameworks can be used to translate proofs from a proof system to another one. For this purpose, we should be able to encode the theory of the proof system in the logical framework. The Lambda Pi calculus modulo theory is one of these logical frameworks. Powerful theories such as pure type systems with an infinite hierarchy of universes have been enc

Kun Fang

Measuring the electron diffusion coefficient is the most straightforward task in the study of gamma-ray pulsar halos. The updated measurements of the spatial morphology and spectrum of the Geminga halo by the High-Altitude Water Cherenkov (HAWC) experiment enable us to constrain parameters beyond the diffusion coefficient, including the size of the slow-diff

Eugenio Megias, Manuel Perez-Victoria, Mariano Quiros

Unstable particles decay sooner or later, so they are not described by asymptotic one-particle states and they should not be included as independent states in unitarity relations such as the optical theorem. The same applies to any countable collection of unstable particles. We show that the behaviour of unparticle stuff, that is, a continuous collection of

Huiwen Yang, Lingying Huang, Subhrakanti Dey, Ling Shi

In recent years, over-the-air aggregation has been widely considered in large-scale distributed learning, optimization, and sensing. In this paper, we propose the over-the-air federated policy gradient algorithm, where all agents simultaneously broadcast an analog signal carrying local information to a common wireless channel, and a central controller uses t

Fanhao Jia, Shaowen Xu, Shunbo Hu, Jianguo Chen

A comprehensive understand of the intrinsic piezoelectric anisotropy stemming from diverse chemical and physical factors is a key step for the rational design of highly anisotropic materials. We performed high-throughput calculations on tetragonal ABO3 perovskites to investigate the piezoelectricity and the interplay between lattice, displacement, polarizati

Adnen Abdessaied, Lei Shi, Andreas Bulling

We propose $\mathbb{VD}$-$\mathbb{GR}$ - a novel visual dialog model that combines pre-trained language models (LMs) with graph neural networks (GNNs). Prior works mainly focused on one class of models at the expense of the other, thus missing out on the opportunity of combining their respective benefits. At the core of $\mathbb{VD}$-$\mathbb{GR}$ is a novel

Sheng-Hsuan Huang, Thomas Dirmeier, Golnoush Shafiee, Kaisa Laiho

Crystalline Whispering Gallery Mode Resonators (WGMRs) have been shown to facilitate versatile sources of quantum states that can efficiently interact with atomic systems. These features make WGMRs an efficient platform for quantum information processing. Here, we experimentally show that it is possible to generate polarization entanglement from WGMRs by usi

Bernard J. Giron Castro, Christophe Peucheret, Darko Zibar, Francesco Da Ros

Among the promising advantages of photonic computing over conventional computing architectures is the potential to increase computing efficiency through massive parallelism by using the many degrees of freedom provided by photonics. Here, we numerically demonstrate the simultaneous use of time and frequency (equivalently wavelength) multiplexing to solve thr

Tsai Hor Chan, Kin Wai Lau, Jiajun Shen, Guosheng Yin

Uncertainty estimation aims to evaluate the confidence of a trained deep neural network. However, existing uncertainty estimation approaches rely on low-dimensional distributional assumptions and thus suffer from the high dimensionality of latent features. Existing approaches tend to focus on uncertainty on discrete classification probabilities, which leads

Viktor Pěč, Vitaly A. Kudryavtsev, Henrique M. Araújo, Timothy J. Sumner

Muon-induced neutrons can lead to potentially irreducible backgrounds in rare event search experiments. We have investigated the implication of laboratory depth on the muon-induced background in a future dark matter experiment capable of reaching the so-called neutrino floor. Our simulation study focused on a xenon-based detector with 70 tonnes of active mas

Cor Kraaikamp, Niels Langeveld

Recently a new class of continued fraction algorithms, the $(N,\alpha$)-expansions, was introduced for each $N\in\mathbb{N}$, $N\geq 2$ and $\alpha \in (0,\sqrt{N}-1]$. Each of these continued fraction algorithms has only finitely many possible digits. These $(N,\alpha)$-expansions `behave' very different from many other (classical) continued fraction algori

Oren Barkan, Yuval Asher, Amit Eshel, Yehonatan Elisha

We present Learning to Explain (LTX), a model-agnostic framework designed for providing post-hoc explanations for vision models. The LTX framework introduces an "explainer" model that generates explanation maps, highlighting the crucial regions that justify the predictions made by the model being explained. To train the explainer, we employ a two-stage proce

Zhi-Gang Wang, Xiao-Song Yang

We study the hadronic coupling constants in the two-body strong decays of the fully-charm tetraquark states with the $J^{PC}=0^{++}$, $1^{+-}$ and $2^{++}$ via the QCD sum rules based on rigorous quark-hadron duality. Then we obtain the hadronic coupling constants and partial decay widths therefore total decay widths, which support assigning the $X(6552)$ as

Tianlong Li, Wenhao Liu, Muling Wu, Shihan Dou

Personality is a crucial factor that shapes human communication patterns, thereby regulating the personalities of large language models (LLMs) holds significant potential in enhancing their user experiences. Previous approaches either relied on fine-tuning LLMs on specific corpora or required manually crafted prompts to evoke specific personalities from LLMs

Marco Antônio Athayde de Aguiar Vieira, Anderson Rocha Tavares, Renato Perez Ribas

Board games are a great source of entertainment for all ages, as they create a competitive and engaging environment, as well as stimulating learning and strategic thinking. It is common for digital versions of board games, as any other type of digital games, to offer the option to select the difficulty of the game. This is usually done by customizing the sea

S. Bellavia, S. Gratton, B. Morini, Ph. L. Toint

An algorithm for unconstrained non-convex optimization is described, which does not evaluate the objective function and in which minimization is carried out, at each iteration, within a randomly selected subspace. It is shown that this random approximation technique does not affect the method's convergence nor its evaluation complexity for the search of an $

Ruichao Yang, Wei Gao, Jing Ma, Hongzhan Lin

In recent years, we witness the explosion of false and unconfirmed information (i.e., rumors) that went viral on social media and shocked the public. Rumors can trigger versatile, mostly controversial stance expressions among social media users. Rumor verification and stance detection are different yet relevant tasks. Fake news debunking primarily focuses on

Anna Hunstig, Sebastian Peitz, Hendrik Rose, Torsten Meier

The prediction of photon echoes is a crucial technique for understanding optical quantum systems. However, it typically requires numerous simulations with varying parameters and input pulses, rendering numerical studies computationally expensive. This article investigates the use of data-driven surrogate models based on the Koopman operator to accelerate thi

Thomas Edridge, Manon Kok

Ferromagnetic materials in indoor environments give rise to disturbances in the ambient magnetic field. Maps of these magnetic disturbances can be used for indoor localisation. A Gaussian process can be used to learn the spatially varying magnitude of the magnetic field using magnetometer measurements and information about the position of the magnetometer. T

Satoshi Ohya

It is known that three-body contact interactions in one-dimensional $n(\geq3)$-body problems of nonidentical particles can be topologically nontrivial: they are all classified by unitary irreducible representations of the pure twin group $PT_{n}$. It was, however, unknown how such interactions are described in the Hamiltonian formalism. In this paper, we stu

Joseph Shaji Rebeirro, Muhib Omar, Till Lenz, Omkar Dhungel

A wide-field magnetometer utilizing nitrogen-vacancy (NV) centers in diamond that does not require microwaves is demonstrated. It is designed for applications where microwaves need to be avoided, such as magnetic imaging of biological or conductive samples. The system exploits a magnetically sensitive feature of NV centers near the ground state level anticro

Clara Menzen, Marnix Fetter, Manon Kok

We present a mapping algorithm to compute large-scale magnetic field maps in indoor environments with approximate Gaussian process (GP) regression. Mapping the spatial variations in the ambient magnetic field can be used for localization algorithms in indoor areas. To compute such a map, GP regression is a suitable tool because it provides predictions of the

Weijie Chen, Haoyu Wang, Shicai Yang, Lei Zhang

We do not pursue a novel method in this paper, but aim to study if a modern text-to-image diffusion model can tailor any task-adaptive image classifier across domains and categories. Existing domain adaptive image classification works exploit both source and target data for domain alignment so as to transfer the knowledge learned from the labeled source data

Simmo Saan, Michael Schwarz, Julian Erhard, Helmut Seidl

Witnesses record automated program analysis results and make them exchangeable. To validate correctness witnesses through abstract interpretation, we introduce a novel abstract operation unassume. This operator incorporates witness invariants into the abstract program state. Given suitable invariants, the unassume operation can accelerate fixpoint convergenc

Paul Youssef, Osman Alperen Koraş, Meijie Li, Jörg Schlötterer

Pre-trained Language Models (PLMs) are trained on vast unlabeled data, rich in world knowledge. This fact has sparked the interest of the community in quantifying the amount of factual knowledge present in PLMs, as this explains their performance on downstream tasks, and potentially justifies their use as knowledge bases. In this work, we survey methods and

Yuxin Cao, Yian Li, Yumeng Zhu, Derui Wang

Anti-spoofing detection has become a necessity for face recognition systems due to the security threat posed by spoofing attacks. Despite great success in traditional attacks, most deep-learning-based methods perform poorly in 3D masks, which can highly simulate real faces in appearance and structure, suffering generalizability insufficiency while focusing o

Palak Jain, Livio Baldini Soares, Tom Kwiatkowski

We present 1-Pager the first system that answers a question and retrieves evidence using a single Transformer-based model and decoding process. 1-Pager incrementally partitions the retrieval corpus using constrained decoding to select a document and answer string, and we show that this is competitive with comparable retrieve-and-read alternatives according t

Yixuan Liang, Jiahao Yan, Dongran Si, Lin Chen

We show that the partial transpose of $9\times 9$ positive semidefinite matrices do not have inertia (4,1,4) and (3,2,4). It solves an open problem in "LINEAR AND MULTILINEAR ALGEBRA, Changchun Feng et al, 2022". We apply our results to construct some inertia, as well as present the list of all possible inertia of partial transpose of $12\times 12$ positive

Chengpeng Li, Zhengyi Yang, Jizhi Zhang, Jiancan Wu

Reinforcement learning (RL) has been widely applied in recommendation systems due to its potential in optimizing the long-term engagement of users. From the perspective of RL, recommendation can be formulated as a Markov decision process (MDP), where recommendation system (agent) can interact with users (environment) and acquire feedback (reward signals).How

Kevin Nadaud, Guillaume F. Nataf, Nazir Jaber, Micka Bah

Sub-coercive field non-linearities in $0.5(\text{Ba}_{0.7}\text{Ca}_{0.3}\text{TiO}_{3})-0.5(\text{BaZr}_{0.2}\text{Ti}_{0.8}\text{O}_{3})$ (BCTZ 50/50) thin film elaborated using pulsed laser deposition are studied using permittivity and phase angle of the third harmonic measurements as function of the AC measuring field $E_{\mathit{AC}}$ and temperature. T

Toochukwu Aka, Shankar Narayan

Multiphase flow in parallel channels is often an efficient approach to manage heat and energy distribution in engineering systems. However, two-phase flow with heating in parallel channels is prone to maldistribution, resulting in sub-optimal performance and in some cases, permanent damage. This challenge requires accurate flow modeling in parallel channels

Mateusz Kwaśnicki

We give a short proof of simplicity of the eigenvalues of the fractional Laplace operator in an interval, a result shown recently by Fall, Ghimenti, Micheletti and Pistoia [Calc. Var. Partial Differ. Equ. 62 (2023), #233].

Ketan M. Patel, Saurabh K. Shukla

It is well-known that the $SU(5)$ grand unified theory, with the standard model quarks and leptons unified in $\overline{5}$ and $10$ and the electroweak Higgs doublet residing in $5$ dimensional representations, leads to relation, $Y_d=Y_e^T$, between the Yukawa couplings of the down-type quarks and the charged leptons. We show that this degeneracy can be l