October 2023 arXiv papers — page 142

Ming Ni, Rong-Long Ma, Zhen-Zhen Kong, Xiao Xue

With one- and two-qubit gate fidelities approaching the fault-tolerance threshold for spin qubits in silicon, how to scale up the architecture and make large arrays of spin qubits become the more pressing challenges. In a scaled-up structure, qubit-to-qubit connectivity has crucial impact on gate counts of quantum error correction and general quantum algorit

Daniel Graves

Rook-Brauer algebras are a family of diagram algebras. They contain many interesting subalgebras: rook algebras, Brauer algebras, Motzkin algebras, Temperley-Lieb algebras and symmetric group algebras. In this paper, we generalize the rook-Brauer algebras and their subalgebras by allowing more structured diagrams. We introduce equivariance by labelling edges

Pratyankara Narasimhan, Stephan Humeniuk, Ananda Roy, Victor Drouin-Touchette

The presence of competing interactions due to geometry leads to frustration in quantum spin models. As a consequence, the ground state of such systems often displays a large degeneracy that can be lifted due to thermal or quantum effects. One such example is the antiferromagnetic Ising model on the Kagome lattice. It was shown that while the same model on th

I. Mereminskiy, A. Lutovinov, S. Molkov, R. Krivonos

We report on the detection of type-C quasi-periodic oscillations during the initial stages of the outburst of Swift J1727.8-1613 in 2023. Using data of the INTEGRAL observatory along with the data of the SRG/ART-XC and Swift/XRT telescopes the fast growth of the QPO frequency was traced. We present a hard X-ray lightcurve that covers the initial stages of th

Runqiu Wang, Ran Dai, Ying Huang, Marian L. Neuhouser

The rapidly expanding field of metabolomics presents an invaluable resource for understanding the associations between metabolites and various diseases. However, the high dimensionality, presence of missing values, and measurement errors associated with metabolomics data can present challenges in developing reliable and reproducible methodologies for disease

Philipp Naumann, Xiaojun Wu

We study the structure of the Albanese map for K\"ahler manifolds with nef anticanonical bundle. First, we give a result for fourfolds whose Albanse torus is an elliptic curve. In the general case of any dimension, we look at two cases: The general fiber of the Albanese map is a Calabi-Yau manifold or a projective space. In the first case, we show that the m

Mengzhou Xia, Tianyu Gao, Zhiyuan Zeng, Danqi Chen

The popularity of LLaMA (Touvron et al., 2023a;b) and other recently emerged moderate-sized large language models (LLMs) highlights the potential of building smaller yet powerful LLMs. Regardless, the cost of training such models from scratch on trillions of tokens remains high. In this work, we study structured pruning as an effective means to develop small

Wentao Cao, Jonas Hirsch, Dominik Inauen

For any $\theta<\frac{1}{3}$, we show that very weak solutions to the two-dimensional Monge-Amp\`ere equation with regularity $C^{1,\theta}$ are dense in the space of continuous functions. This result is shown by a convex integration scheme involving a subtle decomposition of the defect at each stage. The decomposition diagonalizes the defect and, in additio

Anni Zou, Zhuosheng Zhang, Hai Zhao, Xiangru Tang

Large language models (LLMs) have unveiled remarkable reasoning capabilities by exploiting chain-of-thought (CoT) prompting, which generates intermediate reasoning chains to serve as the rationale for deriving the answer. However, current CoT methods either simply employ general prompts such as Let's think step by step, or heavily rely on pre-defined task-sp

Vitonofrio Crismale, Simone Del Vecchio, Tommaso Monni, Stefano Rossi

The set of states on ${\rm CCR}(\ch)$, the CCR algebra of a separable Hilbert space $\ch$, is here looked at as a natural object to obtain a non-commutative version of Freedman's theorem for unitarily invariant stochastic processes. In this regard, we provide a complete description of the compact convex set of states of ${\rm CCR}(\ch)$ that are invariant un

Yufei Bo, Yiheng Duan, Shuo Shao, Meixia Tao

Semantic communications have emerged as a new paradigm for improving communication efficiency by transmitting the semantic information of a source message that is most relevant to a desired task at the receiver. Most existing approaches typically utilize neural networks (NNs) to design end-to-end semantic communication systems, where NN-based semantic encode

Ian Gemp, Luke Marris, Georgios Piliouras

We propose the first loss function for approximate Nash equilibria of normal-form games that is amenable to unbiased Monte Carlo estimation. This construction allows us to deploy standard non-convex stochastic optimization techniques for approximating Nash equilibria, resulting in novel algorithms with provable guarantees. We complement our theoretical analy

Andrei Martinez-Finkelshtein, Rafael Morales

Information about the behavior of zeros of classical families of multiple or Hermite-Pad\'e orthogonal polynomials as functions of the intrinsic parameters of the family is scarce. We establish the interlacing properties of the zeros of Angelesco-Jacobi polynomials when one of the three main parameters is increased by 1, extending the work of dos Santos (201

Jau-Uei Chen, Tamás L. Horváth, Tan Bui-Thanh

We present a divergence-free and $H(div)$-conforming hybridized discontinuous Galerkin (HDG) method and a computationally efficient variant called embedded-HDG (E-HDG) for solving stationary incompressible viso-resistive magnetohydrodynamic (MHD) equations. The proposed E-HDG approach uses continuous facet unknowns for the vector-valued solutions (velocity a

Chris MacLeod, Evgenia Nitishinskaya, Buck Shlegeris

We review the cumulant decomposition (a way of decomposing the expectation of a product of random variables (e.g. $\mathbb{E}[XYZ]$) into a sum of terms corresponding to partitions of these variables.) and the Wick decomposition (a way of decomposing a product of (not necessarily random) variables into a sum of terms corresponding to subsets of the variables

Yuhao Ye, Akiyoshi Yamada, Yuto Kinoshita, Jinhua Wang

Bulk bismuth has a complex Landau spectrum. The small effective masses and the large g-factors are anisotropic. The chemical potential drifts at high magnetic fields. Moreover, twin boundaries further complexify the interpretation of the data by producing extra anomalies in the extreme quantum limit. Here, we present a study of angle dependence of magnetores

Bowen Jin, Wentao Zhang, Yu Zhang, Yu Meng

In real-world scenarios, texts in a graph are often linked by multiple semantic relations (e.g., papers in an academic graph are referenced by other publications, written by the same author, or published in the same venue), where text documents and their relations form a multiplex text-attributed graph. Mainstream text representation learning methods use pre

Anwar Said, Mudassir Shabbir, Tyler Derr, Waseem Abbas

Graph Neural Networks (GNNs) have shown remarkable merit in performing various learning-based tasks in complex networks. The superior performance of GNNs often correlates with the availability and quality of node-level features in the input networks. However, for many network applications, such node-level information may be missing or unreliable, thereby lim

Michael A. Calkins, Talal AlRefae, Angel Hernandez, Ming Yan

Quasi-static Rayleigh-B\'enard convection with an imposed horizontal magnetic field is investigated numerically for Chandrasekhar numbers up to $Q=10^6$ with stress free boundary conditions. Both $Q$ and the Rayleigh number ($Ra$) are varied to identify the various dynamical regimes that are present in this system. We find three primary regimes: (I) a two-di

Alvaro Carbonero, Alexandre Duval, Victor Schmidt, Santiago Miret

The use of machine learning for material property prediction and discovery has traditionally centered on graph neural networks that incorporate the geometric configuration of all atoms. However, in practice not all this information may be readily available, e.g.~when evaluating the potentially unknown binding of adsorbates to catalyst. In this paper, we inve

E. Cristo, E. Esparza Borges, N. C. Santos, O. Demangeon

The development of state-of-the-art spectrographs has ushered in a new era in the detection and characterization of exoplanetary systems. Our objective is to utilize the high-resolution and precision capabilities of the ESPRESSO instrument to detect and measure the broad-band transmission spectrum of HD 189733b's atmosphere. Additionally, we aim to employ an

Zhenlan Ji, Pingchuan Ma, Zongjie Li, Shuai Wang

While code generation has been widely used in various software development scenarios, the quality of the generated code is not guaranteed. This has been a particular concern in the era of large language models (LLMs)- based code generation, where LLMs, deemed a complex and powerful black-box model, is instructed by a high-level natural language specification

Shuvro Chowdhury, Kerem Y. Camsari

The slowing down of Moore's Law has led to a crisis as the computing workloads of Artificial Intelligence (AI) algorithms continue skyrocketing. There is an urgent need for scalable and energy-efficient hardware catering to the unique requirements of AI algorithms and applications. In this environment, probabilistic computing with p-bits emerged as a scalabl

László Erdős, Joscha Henheik, Jana Reker, Volodymyr Riabov

We prove that a class of weakly perturbed Hamiltonians of the form $H_\lambda = H_0 + \lambda W$, with $W$ being a Wigner matrix, exhibits prethermalization. That is, the time evolution generated by $H_\lambda$ relaxes to its ultimate thermal state via an intermediate prethermal state with a lifetime of order $\lambda^{-2}$. Moreover, we obtain a general rel

Nepal Banerjee

Here we have simulated effect of quenched type random-bond disorder during the XY transition.Here we have studied the spontaneous magnetization(M),heat-capacity(Cv) with T.Here we notice a spontaneous symmetry breaking and observe quasi long range order (QLRO) at ground state in presence of this type of bond-random disorder.

Xinchi Huang, Taichi Kosugi, Hirofumi Nishi, Yu-ichiro Matsushita

During the noisy intermediate-scale quantum (NISQ) era, it is important to optimize the quantum circuits in circuit depth and gate count, especially entanglement gates, including the CNOT gate. Among all the unitary operators, diagonal unitary matrices form a special class that plays a crucial role in many quantum algorithms/subroutines. Based on a natural g

Jonathan Tonglet, Manon Reusens, Philipp Borchert, Bart Baesens

Question answering over hybrid contexts is a complex task, which requires the combination of information extracted from unstructured texts and structured tables in various ways. Recently, In-Context Learning demonstrated significant performance advances for reasoning tasks. In this paradigm, a large language model performs predictions based on a small set of

Sajal Kaur Minhas, Morgan Sangeux, Julia Polak, Michelle Carey

A typical gait analysis requires the examination of the motion of nine joint angles on the left-hand side and six joint angles on the right-hand side across multiple subjects. Due to the quantity and complexity of the data, it is useful to calculate the amount by which a subject's gait deviates from an average normal profile and to represent this deviation a

James Salsbury, Jeremy Oakley, Steven Julious, Lisa Hampson

An assurance calculation is a Bayesian alternative to a power calculation. One may be performed to aid the planning of a clinical trial, specifically setting the sample size or to support decisions about whether or not to perform a study. Immuno-oncology is a rapidly evolving area in the development of anticancer drugs. A common phenomenon that arises in tri

C. W. J. Beenakker

We calculate the elongation or contraction force $F$ on a point contact (length $L$) connecting two superconductors with a phase difference $\phi$. When $L$ is small compared to the superconducting coherence length $\xi_0$ this force is given by $F=-(\Delta_0/\pi\xi_0)\ln(\xi_0/L)\cos\phi$ per spin-degenerate transverse mode. Quantum fluctuations in states f

Yichi Zhang, Zhuo Chen, Lingbing Guo, Yajing Xu

Large language model (LLM) based knowledge graph completion (KGC) aims to predict the missing triples in the KGs with LLMs. However, research about LLM-based KGC fails to sufficiently harness LLMs' inference proficiencies, overlooking critical structural information integral to KGs. In this paper, we explore methods to incorporate structural information into

Masih Aminbeidokhti, Fidel A. Guerrero Peña, Heitor Rapela Medeiros, Thomas Dubail

Data augmentation is one of the most effective techniques for regularizing deep learning models and improving their recognition performance in a variety of tasks and domains. However, this holds for standard in-domain settings, in which the training and test data follow the same distribution. For the out-of-domain case, where the test data follow a different

Artem Kalmykov

We construct an element in a completion of the universal enveloping algebra of $\mathfrak{gl}_N$, which we call the Kirillov projector, that connects the topics of the title: on the one hand, it is defined using the evaluation homomorphism from the Yangian of $\mathfrak{gl}_N$, on the other hand, it gives a canonical projection onto the space of Whittaker ve

Karim Farid, Simon Schrodi, Max Argus, Thomas Brox

Counterfactual explanations have emerged as a promising method for elucidating the behavior of opaque black-box models. Recently, several works leveraged pixel-space diffusion models for counterfactual generation. To handle noisy, adversarial gradients during counterfactual generation -- causing unrealistic artifacts or mere adversarial perturbations -- they

Zikun Chen, Han Zhao, Parham Aarabi, Ruowei Jiang

Generative Adversarial Networks (GANs) can synthesize realistic images, with the learned latent space shown to encode rich semantic information with various interpretable directions. However, due to the unstructured nature of the learned latent space, it inherits the bias from the training data where specific groups of visual attributes that are not causally

Caoyun Fan, Wenqing Chen, Jidong Tian, Yitian Li

Counterfactually-Augmented Data (CAD) -- minimal editing of sentences to flip the corresponding labels -- has the potential to improve the Out-Of-Distribution (OOD) generalization capability of language models, as CAD induces language models to exploit domain-independent causal features and exclude spurious correlations. However, the empirical results of CAD

Arjun Sharma, Donald L. Koch

A finite difference scheme is used to develop a numerical method to solve the flow of an unbounded viscoelastic fluid with zero to moderate inertia around a prolate spheroidal particle. The equations are written in prolate spheroidal coordinates, and the shape of the particle is exactly resolved as one of the coordinate surfaces representing the inner bounda

Zhi-Kang Xiong, Zhen-Lai Wang, Y. Liu, Meng Wen

Classical vector waves can possess intricate spin angular momenta (SAM), which are \emph{perpendicular} to the propagation direction, as revealed by the recent recognition of surprisingly transverse SAM in electromagnetic (EM) fields. In this paper, we employ the Hertz potential method to define structured vector fields and analytically decompose the SAM of

Kiarash Parvizi

Priority queues are fundamental data structures with widespread applications in various domains, including graph algorithms and network simulations. Their performance critically impacts the overall efficiency of these algorithms. Traditional priority queue implementations often face cache-related performance bottlenecks, especially in modern computing enviro

Chung Wing Chan, Daihui Wu, Kaiyao Qiao, Kin Long Fong

Chiral active particles (CAPs) are self-propelling particles that break time-reversal symmetry by orbiting or spinning, leading to intriguing behaviors. Here, we examined the dynamics of CAPs moving in 2D lattices of disk obstacles through active Brownian dynamics simulations and granular experiments with grass seeds. We find that the effective diffusivity o

Manuel Madeira, Dorina Thanou, Pascal Frossard

Graph-based representation approaches have been proven to be successful in the analysis of biomedical data, due to their capability of capturing intricate dependencies between biological entities, such as the spatial organization of different cell types in a tumor tissue. However, to further enhance our understanding of the underlying governing biological me

Z. H. Xue, K. J. Lee, X. D. Gao, R. X. Xu

The axion is well motivated in physics. It solves the strong charge conjugation-parity reversal problem CP in fundamental physics and the dark matter problem in astronomy. Its interaction with the electromagnetic field has been expected but never detected experimentally. Such particles may convert to radio waves in the environment with a strong magnetic fiel

Yakov Itin

Acoustic axes are spatial directions in media (often crystals) where at least two of the three acoustic waves have the same phase velocity. Identification of such directions for materials with specific elasticity parameters is both theoretically fascinating and practical in acoustic applications. In this paper, we introduce the notion of the reduced acoustic

Jesse Campion Loth, Amarpreet Rattan

We show that for the product of two fixed point free conjugacy classes, the average number of cycles is always very similar. Specifically, our main result is that for a randomly chosen pair of fixed point free permutations of cycle types $\alpha$ and $\beta$, the average number of cycles in their product is between $H_n-3$ and $H_n+1$, where $H_n$ is the har

Jonathan Eckhardt, Aleksey Kostenko

We extend the inverse spectral transform for the conservative Camassa-Holm flow on the line to a class of initial data that requires strong decay at one endpoint but only mild boundedness-type conditions at the other endpoint. The latter condition appears to be close to optimal in a certain sense for the well-posedness of the conservative Camassa-Holm flow.

José Santana Costa, Ali Tahzibi

For a class of volume preserving partially hyperbolic diffeomorphisms (or non-uniformly Anosov) $f\colon {\T}^d\rightarrow{\T}^d$ homotopic to linear Anosov automorphism, we show that the sum of the positive (negative) Lyapunov exponents of $f$ is bounded above (resp. below) by the sum of the positive (resp. negative) Lyapunov exponents of its linearization.

Dominik Macko, Patrik Goldschmidt, Peter Pištek, Daniela Chudá

Constant evolution and the emergence of new cyberattacks require the development of advanced techniques for defense. This paper aims to measure the impact of a supervised filter (classifier) in network anomaly detection. We perform our experiments by employing a hybrid anomaly detection approach in network flow data. For this purpose, we extended a state-of-

M. F. Sousa, J. G. Coelho, J. C. N. de Araujo, C. Guidorzi

Double white-dwarf (DWD) mergers are relevant astrophysical sources expected to produce massive, highly-magnetized WDs, supernovae (SNe) Ia, and neutron stars (NSs). Although they are expected to be numerous sources in the sky, their detection has evaded the most advanced transient surveys. This article characterizes the optical transient expected from DWD m

Guanqi Chen, Lei Yang, Guanhua Chen, Jia Pan

The ability to navigate robots with natural language instructions in an unknown environment is a crucial step for achieving embodied artificial intelligence (AI). With the improving performance of deep neural models proposed in the field of vision-and-language navigation (VLN), it is equally interesting to know what information the models utilize for their d

Veronica Ballerini, Björn Bornkamp, Alessandra Mattei, Fabrizia Mealli

In clinical trials, patients may discontinue treatments prematurely, breaking the initial randomization and, thus, challenging inference. Stakeholders in drug development are generally interested in going beyond the Intention-To-Treat (ITT) analysis, which provides valid causal estimates of the effect of treatment assignment but does not inform on the effect

Francisco Teixeira, Alberto Abad, Bhiksha Raj, Isabel Trancoso

Speaker embeddings are ubiquitous, with applications ranging from speaker recognition and diarization to speech synthesis and voice anonymisation. The amount of information held by these embeddings lends them versatility, but also raises privacy concerns. Speaker embeddings have been shown to contain information on age, sex, health and more, which speakers m

Lyubov Kotova, Alexei Platonov, Vladimir Kochereshko

Time-resolved and time-integrated circularly polarized photoluminescence of excitons and trions in external magnetic fields up to 10 T has been studied in undoped and n-type doped quantum well structures based on ZnSe. In an undoped structure, a circular polarization of photoluminescence induced by magnetic fields corresponded to the Boltzmann distribution o

Samuel Chevalier

Power system optimization problems which include the nonlinear AC power flow equations require powerful and robust numerical solution algorithms. Within this sub-field of nonlinear optimization, interior point methods have come to dominate the solver landscape. Over the last decade, however, a number of efficient numerical optimizers have emerged from the fi

V. E. Timofeev, D. N. Aristov

We discuss the Goldstone mode of skyrmion crystal in a model of two-dimenssional ferromagnet with Dzyaloshinskii-Moriya interaction in magnetic field. We use stereographic projection approach to construct skyrmion crystal and consider skyrmion's displacement field. The small overlap of the individual skyrmion images restricts the potential energy to the inte

Ren-Jian Wang, Ke Xue, Yutong Wang, Peng Yang

Diversity plays a significant role in many problems, such as ensemble learning, reinforcement learning, and combinatorial optimization. How to define the diversity measure is a longstanding problem. Many methods rely on expert experience to define a proper behavior space and then obtain the diversity measure, which is, however, challenging in many scenarios.

Bruce Hoeneisen

The formation of galaxies with warm dark matter is approximately adiabatic. The cold dark matter limit is singular and requires relaxation. In these lecture notes we develop, step-by-step, the physics of galaxies with warm dark matter, and their formation. The theory is validated with observed spiral galaxy rotation curves. These observations constrain the p

Juo-Tung Chen, Chien-Ming Huang

Large language models offer new ways of empowering people to program robot applications-namely, code generation via prompting. However, the code generated by LLMs is susceptible to errors. This work reports a preliminary exploration that empirically characterizes common errors produced by LLMs in robot programming. We categorize these errors into two phases:

Pouya Mehralian, Bagher BabaAli, Ashena Gorgan Mohammadi

Self-supervised learning offers an efficient way of extracting rich representations from various types of unlabeled data while avoiding the cost of annotating large-scale datasets. This is achievable by designing a pretext task to form pseudo labels with respect to the modality and domain of the data. Given the evolving applications of online handwritten tex

Stefan Rhys Jeske, Jonathan Klein, Dominik L. Michels, Jan Bender

Neural shape representation generally refers to representing 3D geometry using neural networks, e.g., computing a signed distance or occupancy value at a specific spatial position. In this paper we present a neural-network architecture suitable for accurate encoding of 3D shapes in a single forward pass. Our architecture is based on a multi-scale hybrid syst

Anshuk Uppal, Kristoffer Stensbo-Smidt, Wouter Boomsma, Jes Frellsen

In variational inference, the benefits of Bayesian models rely on accurately capturing the true posterior distribution. We propose using neural samplers that specify implicit distributions, which are well-suited for approximating complex multimodal and correlated posteriors in high-dimensional spaces. Our approach introduces novel bounds for approximate infe

Gabriel Lourenço, André Milagre, Rui Santos, João P. Silva

We propose a set of precision muon-related observables that serve as a tool to constrain new physics models. Using LEP's precision measurements on the $Z$-boson pole, we derive bounds on the new physics quantum contributions to the decay $Z \to \mu^+ \mu^-$. We show that the new precision observables have a real impact on two specific models that solve the $

Lisa Alazraki, Lluis Castrejon, Mostafa Dehghani, Fantine Huot

This paper studies ensembling in the era of Large Vision-Language Models (LVLMs). Ensembling is a classical method to combine different models to get increased performance. In the recent work on Encyclopedic-VQA the authors examine a wide variety of models to solve their task: from vanilla LVLMs, to models including the caption as extra context, to models au

Heikki Mäntysaari, Yossathorn Tawabutr

We present the first fully consistent calculation of inclusive $\pi^0$ production at forward rapidities at next-to-leading order (NLO) accuracy in proton-lead collisions at $\sqrt{s}=8.16$ TeV within the Color Glass Condensate approach. The center-of-mass energy dependence is determined by the Balitsky-Kovchegov equation with the initial condition constraine

Diego Marcondes, Junior Barrera

The machine learning of lattice operators has three possible bottlenecks. From a statistical standpoint, it is necessary to design a constrained class of operators based on prior information with low bias, and low complexity relative to the sample size. From a computational perspective, there should be an efficient algorithm to minimize an empirical error ov

M. Dhillon, K. K. Kataria

In this paper, we study the merging and splitting of generalized counting processes (GCPs). First, we study the merging of a finite number of independent GCPs and then extend it to the case of countably infinite. The merged process is observed to be a GCP with increased arrival rates. It is shown that a packet of jumps arrives in the merged process according

Anh Xuan Do, Nguyen Lam, Guozhen Lu

We investigate necessary and sufficient conditions on the weights for the Hardy-Rellich inequalities to hold, and propose a new way to use the notion of Bessel pair to establish the optimal Hardy-Rellich type inequalities. Our results sharpened earlier Hardy-Rellich and Rellich type inequalities in the literature. We also study several results about the symm

Jan Hammelmann, Marcus Bluhm, Marlene Nahrang, Hannah Elfner

We study the evolution of critical fluctuations in an expanding system within a hadronic transport approach. The initialization of the system with critical fluctuations is achieved by coupling the ideal hadron resonance gas cumulants to the ones from the 3d Ising model and generating the net and total particle number distribution from the principle of maximu

Pierre Auclair-Desrotour, Mohammad Farhat, Gwenaël Boué, Mickaël Gastineau

Recent observations and theoretical progress made about the history of the Earth-Moon system suggest that tidal dissipation in oceans primarily drives the long term evolution of orbital systems hosting ocean planets. Particularly, they emphasise the key role played by the geometry of land-ocean distributions in this mechanism. However, the complex way contin

Jiashi Chen, Wei Wang

We present a spectral analysis of \textit{Insight}-HXMT observations of the low-mass X-ray binary 4U 1543-47 which locates in our Milky Way galaxy during the 2021 outburst. We focus on the observations in its soft state, and attempt to determine the spin of the black hole candidate through Thermal-Continuum Fitting (CF) method. The spin derived from CF metho

Alexandra Barancová, Melvin Wevers, Nanne van Noord

This paper explores the capacity of computer vision models to discern temporal information in visual content, focusing specifically on historical photographs. We investigate the dating of images using OpenCLIP, an open-source implementation of CLIP, a multi-modal language and vision model. Our experiment consists of three steps: zero-shot classification, fin

Bohan Yang, Han Zhang

The purpose of this article is twofold: to prove a pointwise equidistribution theorem with an error rate for almost smooth functions, which strengthens the main result of Kleinbock, Shi and Weiss (2017); and to obtain a L\'evy-Khintchin theorem for weighted best approximations, which extends the main theorem of Cheung and Chevallier (2019). To do so, we empl

Ruilin Shi, Zach Walsh, Xingxing Yu

The $\textit{planar Tur\'an number}$ $\textrm{ex}_{\mathcal P}(n,H)$ of a graph $H$ is the maximum number of edges in an $n$-vertex planar graph without $H$ as a subgraph. Let $C_k$ denote the cycle of length $k$. The planar Tur\'an number $\textrm{ex}_{\mathcal P}(n,C_k)$ is known for $k\le 7$. We show that dense planar graphs with a certain connectivity pr

Haifeng Zou, Xiaowen Xu, Chen-Song Zhang

Efficiently solving sparse linear algebraic equations is an important research topic of numerical simulation. Commonly used approaches include direct methods and iterative methods. Compared with the direct methods, the iterative methods have lower computational complexity and memory consumption, and are thus often used to solve large-scale sparse linear equa

Yulong Shi, Mingwei Sun, Yongshuai Wang, Jiahao Ma

Owing to advancements in deep learning technology, Vision Transformers (ViTs) have demonstrated impressive performance in various computer vision tasks. Nonetheless, ViTs still face some challenges, such as high computational complexity and the absence of desirable inductive biases. To alleviate these issues, {the potential advantages of combining eagle visi

George Yiasemis, Nikita Moriakov, Jan-Jakob Sonke, Jonas Teuwen

Cardiac magnetic resonance imaging is a valuable non-invasive tool for identifying cardiovascular diseases. For instance, Cine MRI is the benchmark modality for assessing the cardiac function and anatomy. On the other hand, multi-contrast (T1 and T2) mapping has the potential to assess pathologies and abnormalities in the myocardium and interstitium. However

Letian Zhang, Xiaotong Zhai, Zhongkai Zhao, Yongshuo Zong

Counterfactual reasoning, a fundamental aspect of human cognition, involves contemplating alternatives to established facts or past events, significantly enhancing our abilities in planning and decision-making. In light of the advancements in current multi-modal large language models, we explore their effectiveness in counterfactual reasoning. To facilitate

Qingfa Xiao, Shuangyin Li, Lei Chen

Prompt-based learning's efficacy across numerous natural language processing tasks has led to its integration into dense passage retrieval. Prior research has mainly focused on enhancing the semantic understanding of pre-trained language models by optimizing a single vector as a continuous prompt. This approach, however, leads to a semantic space collapse; i

Yong Liu, Tengge Hu, Haoran Zhang, Haixu Wu

The recent boom of linear forecasting models questions the ongoing passion for architectural modifications of Transformer-based forecasters. These forecasters leverage Transformers to model the global dependencies over temporal tokens of time series, with each token formed by multiple variates of the same timestamp. However, Transformers are challenged in fo

Anna Sztyber-Betley, Filip Kołodziej, Jan Betley, Piotr Duszak

Contract bridge is a game characterized by incomplete information, posing an exciting challenge for artificial intelligence methods. This paper proposes the BridgeHand2Vec approach, which leverages a neural network to embed a bridge player's hand (consisting of 13 cards) into a vector space. The resulting representation reflects the strength of the hand in t

Sören Wilkening, Andreea-Iulia Lefterovici, Lennart Binkowski, Michael Perk

Here we present two novel contributions for achieving quantum advantage in solving difficult optimisation problems, both in theory and foreseeable practice. (1) We introduce the "Quantum Tree Generator", an approach to generate in superposition all feasible solutions of a given instance, yielding together with amplitude amplification the optimal solutions fo

Kaican Li, Yifan Zhang, Lanqing Hong, Zhenguo Li

Out-of-distribution (OOD) generalization is a complicated problem due to the idiosyncrasies of possible distribution shifts between training and test domains. Most benchmarks employ diverse datasets to address this issue; however, the degree of the distribution shift between the training domains and the test domains of each dataset remains largely fixed. Thi

Ran Gao, Feng Wu, Hantao Sun, Jianjun Chen

Introducing disorderness in the superconducting materials has been considered promising to enhance the electromagnetic impedance and realize noise-resilient superconducting qubits. Despite a number of pioneering implementations, the understanding of the correlation between the material disorderness and the qubit coherence is still developing. Here, we demons

Maissâ Boughrara

We consider a class of blow-up solutions for perturbed nonlinear heat equations involving gradient terms. We first prove the single point blow-up property for this equation and determine its final blow-up profile. We also give a sharper description of its blow-up behaviour, where we take as a profile some suitably chosen solution of the unperturbed semilinea

Christoph Schoenfeld, Lennart Feuerer, Alexander-Cornelius Heinrich, Alfred Leitenstorfer

A thin-disk regenerative amplifier with 3 kHz repetition rate pumps a second-order nonlinear mixing scheme providing femtosecond transients of maximum electric field strength beyond 330 MV/cm at a center frequency of 45 THz. This value surpasses field conditions present at sub-molecular dimensions of matter. The inherent competition between efficiency and ba

Peng Qian, Hong-Ze Xu, Peng Zhao, Xiao Li

Addressing the paramount need for precise calibration in superconducting quantum qubits, especially in frequency control, this study introduces a novel calibration scheme harnessing the principles of Many-Body Localization (MBL). While existing strategies, such as Google's snake algorithm, have targeted optimization of qubit frequency parameters, our MBL-bas

D. S. Bondar, V. I. Maslov, I. N. Onishchenko

The paper considers the excitation of a wakefield in a metal-density plasma using a chain of x-ray laser pulses. The profiling parameters and the necessary parameters of laser pulses for obtaining stable high-quality bunches are found. An essential problem is the destruction of self-injected bunches in the course of their motion. The results of the study are

Man-Ho Ho

In this paper, we prove the real part of the Riemann-Roch-Grothendieck theorem for complex flat vector bundles at the differential form level.

Dharmendra Kumar, Swarnendu Sil

We prove up to the boundary $\mathrm{BMO}$ estimates for linear Maxwell-Hodge type systems for $\mathbb{R}^{N}$-valued differential $k$-forms $u$ in $n$ dimensions \begin{align*} \left\lbrace \begin{aligned} d^\ast \left( A(x) du \right) &= f &&\text{ in } \Omega, d^\ast \left( B(x) u\right) &= g &&\text{ in } \Omega, \end{aligned} \right. \end{align*} with

Yi Xu, Masami Ouchi, Kimihiko Nakajima, Yuichi Harikane

We study outflows in 130 galaxies with -22<MUV<-16 at z=3-9 identified in JWST NIRSpec and NIRCam WFSS data taken by the ERO, CEERS, FRESCO, GLASS, and JADES programs. We identify 30 out of the 130 galaxies with broad components of FWHM~200-700 km s$^{-1}$ in the emission lines of H${\alpha}$ and [OIII] that trace ionized outflows, and find no excesses from

Xiaobing Ni, Jiaheng Ruan, Mengke Ge, Wendi Sun

This paper proposes an application mapping algorithm, BandMap, for coarse-grained reconfigurable array (CGRA), which allocates the bandwidth in PE array according to the transferring demands of data, especially the data with high spatial reuse, to reduce the routing PEs. To cover bandwidth allocation, BandMap maps the data flow graphs (DFGs), abstracted from

Xiaoxiang Yu, Zeling Shao, Zhiguo Li

In this paper, we give the classification of circulant graphs $C(\mathbb{Z}_{n},S)$ with $|S|=2$ and completely solve the dispersability of circulant graphs $C(\mathbb{Z}_{n},\{1, k\})$.

Xiaoyong Chu, Josef Pradler

Thermal freeze-out offers an attractive explanation of the dark matter density free from fine-tuning of initial conditions. For dark matter with a mass below tens of MeV, photons, electrons, and neutrinos are the only available direct Standard Model annihilation products. Using a full three-sector abundance calculation, we determine the minimal mass of dark

Shun-ichi Kimura, Takahiro Yamashita

Yama Nim is a two heaps Nim game introduced in the second author's Master Thesis, where the player takes more than $2$ tokens from one heap, and return $1$ token to the other heap. Triangular Nim is a generalization, where the player takes several tokens from one heap, and return some tokens (at least one token) to the other heap, so that the total number of

Simone Manti, Alessandro Lucantonio

Computational modeling is a key resource to gather insight into physical systems in modern scientific research and engineering. While access to large amount of data has fueled the use of Machine Learning (ML) to recover physical models from experiments and increase the accuracy of physical simulations, purely data-driven models have limited generalization an

Lluis Marti-Magro, Luis Labarga

Because gadolinium (Gd) has the highest thermal neutron capture cross section, resulting in an 8 MeV gamma cascade upon capture, it has been proposed for dissolution in water Cherenkov detectors to achieve efficient neutron tagging capabilities. While metallic Gd is insoluble in water, several compounds are very easy to dissolve. Gadolinium sulfate, Gd$_2$(S

Yu Gao, Huaqiao Zhang, Wei Xu

Under the local gravitational field, perturbations from high-frequency gravitational waves can cause a vertical shift of the M\"ossbauer resonance height. Considering a stationary scheme with the $^{109}$Ag isotope, we demonstrate that the extremely high precision of M\"ossbauer resonance allows for competitive gravitational wave sensitivity from KHz up to a

Xinyu Zhang, Zhiyuan Xiao, Qingrui Zhang, Wei Pan

The Central Pattern Generator (CPG) is adept at generating rhythmic gait patterns characterized by consistent timing and adequate foot clearance. Yet, its open-loop configuration often compromises the system's control performance in response to environmental variations. On the other hand, Reinforcement Learning (RL), celebrated for its model-free properties,

Binke Xia, Jingzheng Huang, Chen Fang, Hongjing Li

The weak value amplification technique has been proved useful for precision metrology in both theory and experiment. To explore the ultimate performance of weak value amplification for multi-parameter estimation, we investigate a general weak measurement formalism with assistance of high-order Hermite-Gaussian pointer and quantum Fisher information matrix. T

Ahmed Elzanaty, Jiuyu Liu, Anna Guerra, Francesco Guidi

The upcoming 6G technology is expected to operate in near-field (NF) radiating conditions thanks to high-frequency and electrically large antenna arrays. Although several studies have already addressed this possibility, it is worth noting that NF models introduce higher complexity, the justification for which is not always evident in terms of performance imp

Caizhen He, Hai Wang, Long Chen, Tong Luo

Object detection is the central issue of intelligent traffic systems, and recent advancements in single-vehicle lidar-based 3D detection indicate that it can provide accurate position information for intelligent agents to make decisions and plan. Compared with single-vehicle perception, multi-view vehicle-road cooperation perception has fundamental advantage