October 2023 arXiv papers — page 107

S. J. Molyneux, G. Calistro Rivera, C. De Breuck, C. M. Harrison

We present a comprehensive study of the molecular gas properties of 17 Type 2 quasars at $z <$ 0.2 from the Quasar Feedback Survey (L$_{[OIII]}$ > $10^{42.1}$ $\rm ergs^{-1}$), selected by their high [OIII] luminosities and displaying a large diversity of radio jet properties, but dominated by LIRG-like galaxies. With these data, we are able to investigate t

Yu Feng, Mingchang Liu, Hao Wu

We consider global 2-SLE$_{\kappa}$ $(\eta_1, \eta_2)$ in a topological rectangle with $\kappa\in (4,8)$. We derive the law of a random hitting point of the curves and show that, conditional on this random hitting point, the pair of two curves has the same law as Gaussian free field flow lines with proper boundary data. Using a similar idea, we derive the as

Ravi Gautam, Takamasa Hirai, Abdulkareem Alasli, Hosei Nagano

Functional materials such as magnetic, thermoelectric, and battery materials have been revolutionized through nanostructure engineering. However, spin caloritronics, an advancing field based on spintronics and thermoelectrics with fundamental physics studies, has focused only on uniform materials without complex microstructures. Here, we show how nanostructu

Dongkyu Lee, Ziqi Wang, Junho Song

Various simulation-based and analytical methods have been developed to evaluate the seismic fragilities of individual structures. However, a community's seismic safety and resilience are substantially affected by network reliability, determined not only by component fragilities but also by network topology and commodity/information flows. However, seismic re

Antonio Di Crescenzo, Antonella Iuliano, Verdiana Mustaro, Gabriella Verasani

We investigate the effects of the resetting mechanism to the origin for a random motion on the real line characterized by two alternating velocities $v_1$ and $v_2$. We assume that the sequences of random times concerning the motions along each velocity follow two independent geometric counting processes of intensity $\lambda$, and that the resetting times a

Shahnam Ghanbari Saheli, Jennifer Lin, Huanzhi Hu, Frank Krüger

We determine the phase diagrams of anisotropic Kitaev-Heisenberg models on the honeycomb lattice using parton mean-field theories based on different Majorana fermion representations of the $S=1/2$ spin operators. Firstly, we use a two-dimensional Jordan-Wigner transformation (JWT) involving a semi-infinite snake string operator. In order to ensure that the f

David Schwickert, Andreas Przystawik, Dian Diaman, Detlef Kip

The coupling of electronic and nuclear motion in polyatomic molecules is at the heart of attochemistry. The molecular properties, transient structures and reaction mechanism of these many-body quantum objects are defined on the level of electrons and ions by molecular wave functions and their coherent superposition, respectively. In the present contribution

Guillermo P. Curbera, Susumu Okada, Werner J. Ricker

We present a detailed survey of recent developments in the study of the finite Hilbert transform and its corresponding inversion problem in rearrangement invariant spaces on $(-1,1)$.

Jia-Yu Chen, Mai-Ying Duan, Fu-Hu Liu, Khusniddin K. Olimov

In this article, the multi-region structure of transverse momentum ($p_T$) spectra of identified particles produced in relativistic collisions is studied by the multi-component standard distribution (the Boltzmann, Fermi-Dirac, or Bose-Einstein distribution) in the framework of a multi-source thermal model. Results are interpreted in the framework of string

Huayang Li, Tian Lan, Zihao Fu, Deng Cai

There are a number of diverging hypotheses about the neural text degeneration problem, i.e., generating repetitive and dull loops, which makes this problem both interesting and confusing. In this work, we aim to advance our understanding by presenting a straightforward and fundamental explanation from the data perspective. Our preliminary investigation revea

Rui Guo, Zhao-Yu Li, Juntai Shen, Shude Mao

The vertical phase-space spiral (snail) is a direct sign of dis-equilibrium of Milky Way's disc. Nevertheless, the wrapping of the phase snail contains information of the vertical potential. We propose a novel method to measure the vertical potential utilizing the intersections between the snail and $z$/$V_{z}$ axes, for which we know the maximum vertical he

Luigi Sigillo, Eleonora Grassucci, Aurelio Uncini, Danilo Comminiello

Neural network generalizability is becoming a broad research field due to the increasing availability of datasets from different sources and for various tasks. This issue is even wider when processing medical data, where a lack of methodological standards causes large variations being provided by different imaging centers or acquired with various devices and

Oliver Daisey, Tom Ducat

We describe a Laurent phenomenon for the Cayley plane, which is the homogeneous variety associated to the cominuscule representation of $E_6$. The corresponding Laurent phenomenon algebra has finite type and appears in a natural sequence of LPAs indexed by the $E_n$ Dynkin diagrams for $n\leq6$. We conjecture the existence of a further finite type LPA, assoc

R. Bombin, F. Mazzanti, J. Boronat

We report quantum Monte Carlo results of harmonically confined quantum Bose dipoles within a range of interactions covering the evolution from a gas phase to the formation of an array of droplets. Scaling the experimental setup to a computationally accessible domain we characterize that evolution in qualitative agreement with experiments. Our microscopic app

Zijun Long, George Killick, Richard McCreadie, Gerardo Aragon Camarasa

Robotic vision applications often necessitate a wide range of visual perception tasks, such as object detection, segmentation, and identification. While there have been substantial advances in these individual tasks, integrating specialized models into a unified vision pipeline presents significant engineering challenges and costs. Recently, Multimodal Large

Fuyuki Ando, Takamasa Hirai, Ken-ichi Uchida

A transverse thermoelectric generator for magnetic-field-free and high-density power generation utilizing the anomalous Nernst effect is constructed and its performance is characterized. By alternately stacking two different permanent magnets with the large coercivity and anomalous Nernst coefficients of opposite sign, transverse thermoelectric voltage and p

Chao Tao, Aoran Hu, Rong Xiao, Haifeng Li

Data-driven deep learning methods have shown great potential in cropland mapping. However, due to multiple factors such as attributes of cropland (topography, climate, crop type) and imaging conditions (viewing angle, illumination, scale), croplands under different scenes demonstrate a great domain gap. This makes it difficult for models trained in the speci

B. Cseh, G. Csörnyei, L. Szabados, B. Csák

Context. Binary Cepheids play an important role in investigating the calibration of the classical Cepheid period-luminosity relationship. Therefore a thorough study of individual Cepheids belonging to binary systems is necessary. Aims. Our aim is to determine the orbit of the binary system V1344 Aql using newly observed and earlier published spectroscopic an

Panayiota Katsamba, Matthew D. Butler, Lyndon Koens, Thomas D. Montenegro-Johnson

We present an asymptotic theory for solving the dynamics of slender autophoretic loops and knots. Our formulation is valid for non-intersecting 3D centrelines, with arbitrary chemical patterning and varying (circular) cross-sectional radius, allowing a broad class of slender active loops and knots to be studied. The theory is amenable to closed-form solution

in-Zou Zhao, Jiao-Jiao Song, Qi-Yi Wu, Hao Liu

Synchrotron-based angle-resolved photoemission spectroscopy was employed to investigate the temperature evolution of the Yb 4f spectral for surface and bulk in the Kondo lattice YbCu$_2$Si$_2$.Our study quantitatively distinguishes between the surface and bulk hybridization processes, revealing that the onset temperatures for both surface and bulk hybridizat

Sonja Kraiczy, Edith Elkind

Participatory Budgeting (PB) is a form of participatory democracy in which citizens select a set of projects to be implemented, subject to a budget constraint. The Method of Equal Shares (MES), introduced in [18], is a simple iterative method for this task, which runs in polynomial time and satisfies a demanding proportionality axiom (Extended Justified Repr

Jin Sung Kim, Ying Shuai Quan, Chung Choo Chung

This paper proposes Koopman operator-based Stochastic Model Predictive Control (K-SMPC) for enhanced lateral control of autonomous vehicles. The Koopman operator is a linear map representing the nonlinear dynamics in an infinite-dimensional space. Thus, we use the Koopman operator to represent the nonlinear dynamics of a vehicle in dynamic lane-keeping situa

Mikko Majamaa

Non-terrestrial networks (NTNs) will complement terrestrial networks (TNs) in 5G and beyond, which can be attributed to recent deployment and standardization activities. Maximizing the efficiency of NTN communications is critical to unlock its full potential and reap its numerous benefits. One method to make communications more efficient is by the usage of m

Phan Van Thien

Let $m \ge n$, $\phi_{n,m}: \mathbb P^n \to \mathbb P^m$, $\phi_{n,m}(a_1, \ldots, a_n)=(a_1, \ldots, a_n, 0, \ldots, 0)$, be the embedding, $Z=m_1P_1+\cdots+m_sP_s$ be fat points in $\mathbb P^n$ and $\phi_{n,m}(Z)=m_1\phi_{n,m}(P_1)+\cdots+m_s\phi_{n,m}(P_s)$ be fat points in $\mathbb P^m$. We show the relation between the regularity index, Hilbert functio

Jhe-Yu Liou, Stephanie Forrest, Carole-Jean Wu

Parallel accelerators, such as GPUs, are key enablers for large-scale Machine Learning (ML) applications. However, ML model developers often lack detailed knowledge of the underlying system architectures, while system programmers usually do not have a high-level understanding of the ML model that runs on the specific system. To mitigate this gap between two

Dibakar Roychowdhury

We study various perturbations and their holographic interpretation for non-Abelian T-dual of $ AdS_5 \times S^5 $ where the T-duality is applied along the $ SU(2) $ of $ AdS_5 $. This paper focuses on two types of perturbations, namely the scalar and the vector fields on NATD of $ AdS_5 \times S^5 $. For scalar perturbations, the corresponding solutions cou

Junpeng Tan, Xin Zhang, Yao Lv, Xiangmin Xu

Although the use of multiple stacks can handle slice-to-volume motion correction and artifact removal problems, there are still several problems: 1) The slice-to-volume method usually uses slices as input, which cannot solve the problem of uniform intensity distribution and complementarity in regions of different fetal MRI stacks; 2) The integrity of 3D spac

C. Will, M. Wiesinger, P. Micke, H. Yildiz

We demonstrate a new temperature record for image-current mediated sympathetic cooling of a single proton in a cryogenic Penning trap by laser-cooled $^9$Be$^+$. An axial mode temperature of 170 mK is reached, which is a 15-fold improvement compared to the previous best value. Our cooling technique is applicable to any charged particle, so that the measureme

Rujie Wu, Xiaojian Ma, Zhenliang Zhang, Wei Wang

We introduce Bongard-OpenWorld, a new benchmark for evaluating real-world few-shot reasoning for machine vision. It originates from the classical Bongard Problems (BPs): Given two sets of images (positive and negative), the model needs to identify the set that query images belong to by inducing the visual concepts, which is exclusively depicted by images fro

Petra Benke, Florian Rösch, Eduardo Ros, Matthias Kadler

With the emergence of very high energy astronomy (VHE; E>100 GeV), new open questions were presented to astronomers studying the multi-wavelength emission from blazars. Answers to these open questions, such as the Doppler crisis, and finding the location of the high-energy activity have eluded us thus far. Recently, quasi-simultaneous multi-wavelength monito

Soumitra Dey, Chinedu Izuchukwu, Adeolu Taiwo, Simeon Reich

In this paper we study a class of split variational inclusion (SVI) and regularized split variational inclusion (RSVI) problems in real Hilbert spaces. We discuss various analytical properties of the net generated by the RSVI and establish the existence and uniqueness of the solution to the RSVI. Using analytical properties of this net and under certain assu

Hamza Djelouat, Mikko J. Sillanpää, Markus Leinonen, Markku Juntti

This paper addresses the joint user identification and channel estimation (JUICE) problem in machine-type communications under the practical spatially correlated channels model with unknown covariance matrices. Furthermore, we consider an MTC network with hierarchical user activity patterns following an event-triggered traffic mode. Therein the users are dis

Tomas M. Bosschieter, Zifei Xu, Hui Lan, Benjamin J. Lengerich

Although most pregnancies result in a good outcome, complications are not uncommon and can be associated with serious implications for mothers and babies. Predictive modeling has the potential to improve outcomes through better understanding of risk factors, heightened surveillance for high risk patients, and more timely and appropriate interventions, thereb

I. Bailleul, M. Hoshino

We prove a convergence result for a large class of random models that encompasses the case of the BPHZ models used in the study of singular stochastic PDEs. We introduce for that purpose a useful variation on the notion of regularity structure called a regularity-integrability structure. It allows to deal in a single elementary setting with models on a usual

Kaushal Kumar Kesharpu, Evgenii A. Kochetov, Alvaro Ferraz

We show that one dimensional (1D) topological superconductivity can be placed in the context of phenomena associated with strongly correlated electron systems. Here we propose a system consisting of a one-dimensional chain of strongly correlated fermions placed on a superconducting (SC) substrate that exhibits a spin-singlet extended $s$-wave pairing. Strong

Huawei Wu, Jing Yang, Keqin Feng

The circular external difference family and its strong version, which themselves are of independent combinatorial interest, were proposed as variants of the difference family to construct new unconditionally secure non-malleable threshold schemes. In this paper, we present new results regarding the construction and non-existence of (strong) circular external

Matthias Schaufelberger, Reinald Peter Kühle, Andreas Wachter, Frederic Weichel

Introduction: Photogrammetric surface scans provide a radiation-free option to assess and classify craniosynostosis. Due to the low prevalence of craniosynostosis and high patient restrictions, clinical data is rare. Synthetic data could support or even replace clinical data for the classification of craniosynostosis, but this has never been studied systemat

Heyuan Yao, Zhenhua Song, Yuyang Zhou, Tenglong Ao

In this work, we present MoConVQ, a novel unified framework for physics-based motion control leveraging scalable discrete representations. Building upon vector quantized variational autoencoders (VQ-VAE) and model-based reinforcement learning, our approach effectively learns motion embeddings from a large, unstructured dataset spanning tens of hours of motio

R. P. Schmidt, S. Ramakrishna, A. A. Peshkov, N. Huntemann

The twisted light modes used in modern atomic physics experiments can be contaminated by small admixtures of plane wave radiation. Although these admixtures hardly reveal themselves in the beam intensity profile, they may seriously affect the outcome of high precision spectroscopy measurements. In the present study we propose a method for diagnosing such a p

Anne-Maria Ernvall-Hytönen, Tapani Matala-aho

We are interested in finding an explicit estimate to the binomial sum $Q_n(x)=\sum_{k=0}^{n} k! {n\choose k}^2 (-x)^{k}$ at $x=1$ for $n=0,1,2,\ldots$. Despite of its own interest the polynomial $Q_n(x)$ is important as the denominator in the Pad\'e identity of the Euler's factorial series $E(x) = \sum_{k=0}^{\infty} k! x^k$ as well as its close connection t

Kai Lv, Hang Yan, Qipeng Guo, Haijun Lv

Large language models have achieved remarkable success, but their extensive parameter size necessitates substantial memory for training, thereby setting a high threshold. While the recently proposed low-memory optimization (LOMO) reduces memory footprint, its optimization technique, akin to stochastic gradient descent, is sensitive to hyper-parameters and ex

Ayana Pinheiro de Castro Santana, Luís Henrique de Miranda

In this paper we prove the existence and regularity of weak solutions for the following system \begin{align*} \begin{cases} -\mbox{div}(M(x)\nabla u) + g(x,u,v) = f \ \ \mbox{in} \ \ \Omega\\ -\mbox{div}(M(x)\nabla v) = h(x,u,v) \ \ \mbox{in} \ \ \Omega\\ \ \ \ \ \ u=v=0 \ \ \mbox{on} \ \ \partial \Omega, \end{cases} \end{align*} where $\Omega$ is an open bo

Shunsuke Kitou, Akitoshi Nakano, Masato Imaizumi, Yuiga Nakamura

The metal-insulator transition (MIT) in vanadium dioxide VO$_2$ due to V-V dimerization has been extensively discussed for decades. While it is widely acknowledged that electron correlations, Peierls instabilities, and molecular orbital formations are crucial for understanding the MIT of VO$_2$, the primary origin of the MIT remains controversial. In this st

Iulian D. Toader

No, but the paper argues that Bohr understood his correspondence principle, or at least an aspect of that principle expressed by the notion of rational generalization, as grounded in Hankel's principle of permanence, adapted to new historical and theoretical contexts. This is shown to illuminate some otherwise obscure aspects of Bohr's approach to quantum th

Yuji Zhang, Jing Li, Wenjie Li

Language features are evolving in real-world social media, resulting in the deteriorating performance of text classification in dynamics. To address this challenge, we study temporal adaptation, where models trained on past data are tested in the future. Most prior work focused on continued pretraining or knowledge updating, which may compromise their perfor

Shuo Sun, Yuchen Zhang, Jiahuan Yan, Yuze Gao

The success of ChatGPT has ignited an AI race, with researchers striving to develop new large language models (LLMs) that can match or surpass the language understanding and generation abilities of commercial ones. In recent times, a number of models have emerged, claiming performance near that of GPT-3.5 or GPT-4 through various instruction-tuning methods.

Shiyuan Hu, Fanlong Meng

Many eukaryotic microorganisms propelled by multiple flagella can swim very rapidly with distinct gaits. Here, we model a three-dimensional mutiflagellate swimming strategy, resembling the microalgae, and investigate the effects of interflagella hydrodynamic interactions (iHIs) on the swimming performance. When the flagella are actuated synchronously, the sw

Viros Sriskaran, Jerome Alozy, Rafael Ballabriga, Michael Campbell

The Medipix4 chip is the latest member in the Medipix/Timepix family of hybrid pixel detector chips aimed at high-rate spectroscopic X-ray imaging using high-Z materials. It can be tiled on all 4 sides making it ideal for constructing large-area detectors with minimal dead area. The chip is designed to read out a sensor of 320 x 320 pixels with dimensions of

Fabio Azzalini, Tommaso Dolci, Marco Vagaggini

With the increasing availability of patient data, modern medicine is shifting towards prospective healthcare. Electronic health records offer a variety of information useful for clinical patient characterization and the development of predictive models, given that similar medical histories often lead to analogous health progressions. One application is the p

Stephan Schlemmer, Eline Plaar, Divita Gupta, Weslley Guilherme Dias de Paiva Silva

The antisymmetric C-H stretching vibration $\nu_3$ ($^2\Pi$ $\leftarrow$ $^2\Pi$) of ionized acetylene, C$_2$H$_2^+$, has been revisited using a cryogenic 22-pole ion trap machine. Two action spectroscopic techniques, the novel leak-out spectroscopy (LOS) method and the more established laser-induced reactions (LIR) method, are applied and compared. Mass sel

Patrik I. Sund, Ravitej Uppu, Stefano Paesani, Peter Lodahl

Boson sampling is a specialised algorithm native to the quantum photonic platform developed for near-term demonstrations of quantum advantage over classical computers. While clear useful applications for such near-term pre-fault-tolerance devices are not currently known, reaching a quantum advantage regime serves as a useful benchmark for the hardware. Here,

Xiaoshuai Song, Yutao Mou, Keqing He, Yueyan Qiu

In a practical dialogue system, users may input out-of-domain (OOD) queries. The Generalized Intent Discovery (GID) task aims to discover OOD intents from OOD queries and extend them to the in-domain (IND) classifier. However, GID only considers one stage of OOD learning, and needs to utilize the data in all previous stages for joint training, which limits i

Leyou Xu, Bo Zhou

A graph is $t$-tough if the deletion of any set of, say, $m$ vertices from the graph leaves a graph with at most $\frac{m}{t}$ components. In 1973, Chv\'{a}tal suggested the problem of relating toughness to factors in graphs. In 1985, Enomoto et al. showed that each $2$-tough graph with at least three vertices has a $2$-factor, but for any $\epsilon>0$, ther

Minh Ha Quang

This work presents an explicit description of the Fisher-Rao Riemannian metric on the Hilbert manifold of equivalent centered Gaussian measures on an infinite-dimensional Hilbert space. We show that the corresponding quantities from the finite-dimensional setting of Gaussian densities on Euclidean space, including the Riemannian metric, Levi-Civita connectio

T. Aramaki, M. Boezio, S. E. Boggs, V. Bonvicini

Compilation of papers presented by the GAPS Collaboration at the 38th International Cosmic Ray Conference (ICRC), held July 26 through August 3, 2023 in Nagoya, Japan.

Jing Xiong, Jianhao Shen, Ye Yuan, Haiming Wang

Automated theorem proving (ATP) has become an appealing domain for exploring the reasoning ability of the recent successful generative language models. However, current ATP benchmarks mainly focus on symbolic inference, but rarely involve the understanding of complex number combination reasoning. In this work, we propose TRIGO, an ATP benchmark that not only

Dejan Porjazovski, Yaroslav Getman, Tamás Grósz, Mikko Kurimo

Large pre-trained models are essential in paralinguistic systems, demonstrating effectiveness in tasks like emotion recognition and stuttering detection. In this paper, we employ large pre-trained models for the ACM Multimedia Computational Paralinguistics Challenge, addressing the Requests and Emotion Share tasks. We explore audio-only and hybrid solutions

Laxmi Prasad Naik, Tamal Ghosh, Sumit Mukherjee, Chiranjib Mitra

The macroscopic limit at which the quantum-to-classical transition occurs remains as one of the long-standing questions in the foundations of quantum theory. There are evidences that the macroscopic limit to which the quantumness of a system persists depends on the degree of interaction due to the measurement processes. For instance, with a system having a c

Justin Lien

A hypergraph as a generalization of graphs records higher-order interactions among nodes, yields a more flexible network model, and allows non-linear features for a group of nodes. In this article, we propose a hypergraph echo state network (HypergraphESN) as a generalization of graph echo state network (GraphESN) designed for efficient processing of hypergr

Xiaoshuai Song, Keqing He, Pei Wang, Guanting Dong

The tasks of out-of-domain (OOD) intent discovery and generalized intent discovery (GID) aim to extend a closed intent classifier to open-world intent sets, which is crucial to task-oriented dialogue (TOD) systems. Previous methods address them by fine-tuning discriminative models. Recently, although some studies have been exploring the application of large

Dou Li, Kangqi Liu, Pengfei Wang, Songbai Kang

In this study, a dual-interrogation (DI) method was used to suppress the light shift in the Rb 778 nm 5S1/2-5D5/2 two-photon transition (TPT) optical frequency standard. The approach used an auxiliary system to calibrate the light shift of the primary system in real time to mitigate the absolute light shift and suppress the sensitivity of the system to the o

Gyunam Park, Sevde Aydin, Cuneyt Ugur, Wil M. P. van der Aalst

Process mining, a technique turning event data into business process insights, has traditionally operated on the assumption that each event corresponds to a singular case or object. However, many real-world processes are intertwined with multiple objects, making them object-centric. This paper focuses on the emerging domain of object-centric process mining,

Tianze Hao, Yuguang Shi, Yukai Sun

In this paper, without assuming that manifolds are spin, we prove that if a compact orientable, and connected Riemannian manifold $(M^{n},g)$ with scalar curvature $R_{g}\geq 6$ admits a non-zero degree and $1$-Lipschitz map to $(\mathbb{S}^{3}\times \mathbb{T}^{n-3},g_{\mathbb{S}^{3}}+g_{\mathbb{T}^{n-3}})$, for $4\leq n\leq 7$, then $(M^{n},g)$ is locally

I. Kupčić, J. Kordić

The current-dipole conductivity formula for doped three-dimensional Dirac semimetals is derived by using a modified gauge-invariant tight-binding approach. In a heavily doped regime, the effective number of charge carriers $n_{\alpha \alpha}^{\rm eff}$ in the Drude contribution is found to be by a factor of 4 larger than the nominal electron concentration $n

Simone Rossi, Ankit Singh, Thomas Hannagan

The elusive nature of gradient-based optimization in neural networks is tied to their loss landscape geometry, which is poorly understood. However recent work has brought solid evidence that there is essentially no loss barrier between the local solutions of gradient descent, once accounting for weight-permutations that leave the network's computation unchan

Guanlin Meng

This paper aims to explore the potential of combining Deep Reinforcement Learning (DRL) with Knowledge Distillation (KD) by distilling various DRL algorithms and studying their distillation effects. By doing so, the computational burden of deep models could be reduced while maintaining the performance. The primary objective is to provide a benchmark for eval

Guanting Dong, Tingfeng Hui, Zhuoma GongQue, Jinxu Zhao

Recently, prompt-based generative frameworks have shown impressive capabilities in sequence labeling tasks. However, in practical dialogue scenarios, relying solely on simplistic templates and traditional corpora presents a challenge for these methods in generalizing to unknown input perturbations. To address this gap, we propose a multi-task demonstration b

Geraldo F. Oliveira, Alain Kohli, David Novo, Ataberk Olgun

The growing volume of data in modern applications has led to significant computational costs in conventional processor-centric systems. Processing-in-memory (PIM) architectures alleviate these costs by moving computation closer to memory, reducing data movement overheads. UPMEM is the first commercially available PIM system, featuring thousands of in-order p

D. V. Ushakov, A. A. Afonenko, R. A. Khabibullin, M. A. Fadeev

Due to their high optical phonon energies GaInP/AlGaInP heterostructures are a promising active medium to solve the problem of creating compact semiconductor sources with an operating frequency range of 5.5-7 THz. In this work, the temperature dependences of gain and absorption at 6.8 THz have been calculated for a GaInP/AlGaInP-based quantum-cascade laser (

Anuradha Gupta, Rahul Mansotra

In this paper, we give common coincidence point and common fixed point theorems for four self maps in the setting of generalized TAC-contraction in partial b-metric space. Also, we give an example to authenticate the viability of the results.

Lihui Xue, Zhihao Wang, Xueqian Wang, Gang Li

Existing satellite remote sensing change detection (CD) methods often crop original large-scale bi-temporal image pairs into small patch pairs and then use pixel-level CD methods to fairly process all the patch pairs. However, due to the sparsity of change in large-scale satellite remote sensing images, existing pixel-level CD methods suffer from a waste of

Renzo Testa, Alex Rodriguez, Alberto d'Onofrio, Andrea Trombettoni

Optimizing the probability of quantum tunneling between two states, while keeping the resources of the underlying physical system constant, is a task of key importance due to its critical role in various applications. We show that, by applying Machine Learning techniques when the system is coupled to an ancilla, one optimizes the parameters of both the ancil

Marvin Edelmann, Yi Hua, Mikhail Pergament, Franz X. Kärtner

We demonstrate strong performance enhancement of an all polarization-maintaining fiber oscillator mode-locked using NPE in a linear self-stabilized fiber interferometer via suppression of cross-phase modulation (XPM). Numerical simulations reveal that XPM significantly affects the saturable absorber dynamics resulting in distortions of mode-locked steady-sta

Massimo Orazio Spata, Sebastiano Battiato, Alessandro Ortis, Francesco Rundo

Inverse modelling with deep learning algorithms involves training deep architecture to predict device's parameters from its static behaviour. Inverse device modelling is suitable to reconstruct drifted physical parameters of devices temporally degraded or to retrieve physical configuration. There are many variables that can influence the performance of an in

Ankush Saxena, V. P. S. Awana

We report a new member of topological insulator (TI) family i.e., Mn$_2$Sb$_2$Te$_5$, which belongs to MnSb$_2$Te$_4$ family and is a sister compound of Mn$_2$Bi$_2$Te$_5$. An antiferromagnetic layer of (MnTe)$_2$ has been inserted between quintuple layers of Sb$_2$Te$_3$. The crystal structure and chemical composition of as grown Mn$_2$Sb$_2$Te$_5$ crystal

Alexandr Polujan, Enes Pasalic, Sadmir Kudin, Fengrong Zhang

The concatenation of four Boolean bent functions $f=f_1||f_2||f_3||f_4$ is bent if and only if the dual bent condition $f_1^* + f_2^* + f_3^* + f_4^* =1$ is satisfied. However, to specify four bent functions satisfying this duality condition is in general quite a difficult task. Commonly, to simplify this problem, certain connections between $f_i$ are assume

Young Chan Seo

This paper analyzes the excessive risk perception of Korea as one of the causes of the international dispute over the import of Fukushima food between Korea and Japan. To do this, it compares the perception of Fukushima food among Koreans and people from other countries through a survey and identifies the factors that affect the perception through a linear r

Joshua Frisch, Eduardo Silva

We give a complete description of the Poisson boundary of wreath products $A\wr B= \bigoplus_{B} A\rtimes B$ of countable groups $A$ and $B$, for probability measures $\mu$ with finite entropy where lamp configurations stabilize almost surely. If, in addition, the projection of $\mu$ to $B$ is Liouville, we prove that the Poisson boundary of $(A\wr B,\mu)$ i

Xingjian Du, Zhesong Yu, Jiaju Lin, Bilei Zhu

Music tagging is a task to predict the tags of music recordings. However, previous music tagging research primarily focuses on close-set music tagging tasks which can not be generalized to new tags. In this work, we propose a zero-shot music tagging system modeled by a joint music and language attention (JMLA) model to address the open-set music tagging prob

Yunfan Shao, Linyang Li, Junqi Dai, Xipeng Qiu

Large language models (LLMs) can be used to serve as agents to simulate human behaviors, given the powerful ability to understand human instructions and provide high-quality generated texts. Such ability stimulates us to wonder whether LLMs can simulate a person in a higher form than simple human behaviors. Therefore, we aim to train an agent with the profil

Zain Taufique, Antonio Miele, Pasi Liljeberg, Anil Kanduri

DNN inference can be accelerated by distributing the workload among a cluster of collaborative edge nodes. Heterogeneity among edge devices and accuracy-performance trade-offs of DNN models present a complex exploration space while catering to the inference performance requirements. In this work, we propose adaptive workload distribution for DNN inference, j

Gyula Lakos

We review and provide simplified proofs related to the Magnus expansion, and improve convergence estimates. Observations and improvements concerning the Baker--Campbell--Hausdorff expansion are also made. In this Part IA, we consider uniform convexity. Notions of uniformly convex algebras are discussed, and uniform convexity is shown to improve convergence e

Ángel Merino, José González-Cabañas, Ángel Cuevas, Rubén Cuevas

The literature has shown that combining a few non-Personal Identifiable Information (non-PII) is enough to make a user unique in a dataset including millions of users. This work demonstrates that a combination of a few non-PII items can be activated to nanotarget users. We demonstrate that the combination of the location and {5} rare ({13} random) skills in

Abhik Digar

In this article, we introduce a geometrical notion, property strongly UC which is stronger than property UC and prove the existence of best approximations for a new class of almost cyclic $\psi$-contraction maps in a metric space. As a particular case, we obtain the main results of [Sadiq Basha, S., Best approximation theorems for almost cyclic contractions.

Huao Li, Yu Quan Chong, Simon Stepputtis, Joseph Campbell

While Large Language Models (LLMs) have demonstrated impressive accomplishments in both reasoning and planning, their abilities in multi-agent collaborations remains largely unexplored. This study evaluates LLM-based agents in a multi-agent cooperative text game with Theory of Mind (ToM) inference tasks, comparing their performance with Multi-Agent Reinforce

Ryota Ueda, Kazuhiko Kuroki, Tatsuya Kaneko

We investigate the staggered correlation of the on-site pairs, the so-called $\eta$-pairing correlation, induced by pump electric fields in the Hubbard model on the ladder lattice. Employing the time-evolution method based on exact diagonalization, we compute the photoinduced $\eta$-pairing correlation with different strengths of the interchain hopping. When

Eleonora Di Nezza, Stefano Trapani, Antonio Trusiani

In this note, we generalize the notion of entropy for potentials in a relative full Monge-Amp\`ere mass $\mathcal{E}(X, \theta, \phi)$, for a model potential $\phi$. We then investigate stability properties of this condition with respect to blow-ups and perturbation of the cohomology class. We also prove a Moser-Trudinger type inequality with general weight

Wenbin An, Feng Tian, Wenkai Shi, Yan Chen

Discovering fine-grained categories from coarsely labeled data is a practical and challenging task, which can bridge the gap between the demand for fine-grained analysis and the high annotation cost. Previous works mainly focus on instance-level discrimination to learn low-level features, but ignore semantic similarities between data, which may prevent these

Alexandr Buryak, Mikhail Troshkin

We prove that the DR hierarchy corresponding to the family of F-cohomological field theories without unit considered in a previous work of the first author together with D. Gubarevich can be ``trivialized'', i.e. reduced to two copies of the KdV hierarchy, using a simple nonlinear reciprocal transformation. This gives the first manifestation of a role of non

Yao Qianxiang, Bin Jiang

This study introduces the concept of "structural beauty" as an objective computational approach for evaluating the aesthetic appeal of images. Through the utilization of the Segment anything model (SAM), we propose a method that leverages recursive segmentation to extract finer-grained substructures. Additionally, by reconstructing the hierarchical structure

Ben Li, Yanfang Zhang, Jing Wang, Wenhao Wang

The continuous advancements in ultrafast lasers, characterized by high pulse energy, great average power, and ultrashort pulse duration, have opened up new frontiers and applications in various fields such as high-energy-density science. In this study, we investigated the implementation of non-Hermitian nonlinear parametric amplification by introducing anti-

Chelsea Huynh, Anna Ma, Michael Strand

Achieving accurate approximations to solutions of large linear systems is crucial, especially when those systems utilize real-world data. A consequence of using real-world data is that there will inevitably be missingness. Current approaches for dealing with missing data, such as deletion and imputation, can introduce bias. Recent studies proposed an adaptat

Zhongxiao Jia, Kailiang Zhang

We propose a CJ-FEAST GSVDsolver to compute a partial generalized singular value decomposition (GSVD) of a large matrix pair $(A,B)$ with the generalized singular values in a given interval. The solver is a highly nontrivial extension of the FEAST eigensolver for the (generalized) eigenvalue problem and CJ-FEAST SVDsolver for the SVD problem. For a partial G

Johannes M. Henn, Calum Milloy, Kai Yan

The general structure of infrared divergences in the scattering of massive particles is captured by the soft anomalous dimension matrix. The latter can be computed from a correlation function of multiple Wilson lines. The state-of-the-art two-loop result has a tantalizingly simple structure that is not manifest in the calculations. We argue that the complexi

Yu. L. Bolotin, V. V. Yanovsky

At present, there is practically no doubt that general relativity is closely related to gravity. Moreover, after the work of Jacobson, Padmanabhan and others, it became clear that a thermodynamic interpretation of Einstein's relativistic equations is possible. On the other hand, we are witnessing the conceptual problems of the SCM (the problem of the cosmolo

Makoto Yamada, Yuki Takezawa, Guillaume Houry, Kira Michaela Dusterwald

In this study, we delve into the problem of self-supervised learning (SSL) utilizing the 1-Wasserstein distance on a tree structure (a.k.a., Tree-Wasserstein distance (TWD)), where TWD is defined as the L1 distance between two tree-embedded vectors. In SSL methods, the cosine similarity is often utilized as an objective function; however, it has not been wel

Lorenzo Portinale

This survey has been written in occasion of the School and Workshop about Optimal Transport on Quantum Structures at Erd\"os Center in September 2022. We discuss some recent results on noncommutative entropic optimal transport problems and their relation to the study of the ground-state energy of a finite-dimensional composite quantum system at positive temp

Adam Roegiest, Radha Chitta, Jonathan Donnelly, Maya Lash

In many legal processes being able to action on the concrete implication of a legal question can be valuable to automating human review or signalling certain conditions (e.g., alerts around automatic renewal). To support such tasks, we present a form of legal question answering that seeks to return one (or more) fixed answers for a question about a contract

Hengjie Lin, Furen Deng, Yan Gong, Xuelei Chen

Ultralight axion (ULA) can be one of the potential candidates for dark matter. The extremely low mass of the ULA can lead to a de Broglie wavelength the size of galaxies which results in a suppression of the growth of structure on small scales. In this work, we forecast the constraint on the ULA particle mass $m_{\text{a}}$ and relative fraction to dark matt

Konstantinos Lampropoulos, Apostolis Zarras, Eftychia Lakka, Polyanthi Barmpaki

The healthcare sector is increasingly vulnerable to cyberattacks due to its growing digitalization. Patient data, including medical records and financial information, are at risk, potentially leading to identity theft and patient safety concerns. The European Union and other organizations identify key areas for healthcare system improvement, yet the industry