October 2023 arXiv papers — page 104

L. Brugnano, K. Burrage, P. Burrage, F. Iavernaro

In this paper we consider the numerical solution of fractional differential equations. In particular, we study a step-by-step graded mesh procedure based on an expansion of the vector field using orthonormal Jacobi polynomials. Under mild hypotheses, the proposed procedure is capable of getting spectral accuracy. A few numerical examples are reported to conf

Chengxing He, Robert R. Jones

We have used quantum control to suppress the impact of random atom positions on coherent population transfer within atom pairs, enabling the observation of dipole-dipole driven Rabi oscillations in a Rydberg gas with hundreds of atoms. The method exploits the reduced coupling-strength sensitivity of the off-resonant Rabi frequency, and coherently amplifies t

Hanbin Wang, Jie Xu, Zhiguo Yang

In this paper, we present a novel second-order generalised rotational discrete gradient scheme for numerically approximating the orthonormal frame gradient flow of biaxial nematic liquid crystals. This scheme relies on reformulating the original gradient flow system into an equivalent generalised "rotational" form. A second-order discrete gradient approximat

Muhammad Abdullah Naeem, Amir Khazraei, Miroslav Pajic

High dimensional random dynamical systems are ubiquitous, including -- but not limited to -- cyber-physical systems, daily return on different stocks of S&P 1500 and velocity profile of interacting particle systems around McKeanVlasov limit. Mathematically, underlying phenomenon can be captured via a stable $n$-dimensional linear transformation `$A$' and add

Chao Zheng, Yan-Qiu Zhang, Shao-Lin Xiong, Cheng-Kui Li

The early afterglow of a Gamma-ray burst (GRB) can provide critical information on the jet and progenitor of the GRB. The extreme brightness of GRB 221009A allows us to probe its early afterglow in unprecedented detail. In this letter, we report comprehensive observation results of the early afterglow of GRB 221009A (from $T_0$+660 s to $T_0$+1860 s, where $

Eyup B. Unlu, Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva

We describe a machine-learning-based surrogate model for reproducing the Bayesian posterior distributions for exoplanet atmospheric parameters derived from transmission spectra of transiting planets with typical retrieval software such as TauRex. The model is trained on ground truth distributions for seven parameters: the planet radius, the atmospheric tempe

Yuxiang Wu, Guanting Dong, Weiran Xu

Zero-shot Dialogue State Tracking (DST) addresses the challenge of acquiring and annotating task-oriented dialogues, which can be time-consuming and costly. However, DST extends beyond simple slot-filling and requires effective updating strategies for tracking dialogue state as conversations progress. In this paper, we propose ParsingDST, a new In-Context Le

Katrin Fässler, Ivan Yuri Violo

We introduce new flatness coefficients, which we call $\iota$-numbers, for Ahlfors $k$-regular sets in metric spaces ($k\in \mathbb{N}$). Using these coefficients for $k=1$, we characterize uniform $1$-rectifiability in rather general metric spaces, completing earlier work by Hahlomaa and Schul. Our proof proceeds by quantifying an isometric embedding theore

Nandi Moksnes, William Usher

An increasing number of studies using energy system optimisation models are conducted with higher spatial and temporal resolution. This comes with a computational cost which places a limit on the size, complexity, and detail of the model. In this paper, we explore the relative importance of structural aspects of energy system models, spatial and temporal res

Maxim Koroteev, Yaroslav Borisov, Pavel Frolov

We propose a new scheme to re-compress JPEG images in a lossless way. Using a JPEG image as an input the algorithm partially decodes the signal to obtain quantized DCT coefficients and then re-compress them in a more effective way.

Andrey Mukhin, Roman Krivonos, Alexey Vikhlinin, Brian Grefenstette

The large side aperture of the NuSTAR telescope for unfocused photons (so-called stray light) is a known source of rich astrophysical information. To support many studies based on the NuSTAR stray light data, we present a fully automatic method for determining detector area suitable for background analysis and free from any kind of focused X-ray flux. The me

Max Johansson Saarijärvi, Erik Sjöqvist

We propose iSWAP-type quantum gates based on geometric phases purely associated with paths on the Schmidt sphere [Phys. Rev. A 62, 022109 (2000)]. These geometric Schmidt gates can entangle qubit pairs to an arbitrary degree; in particular, they can create maximally entangled states from product states by an appropriate choice of base point on the Schmidt sp

Yunfeng Shi, Li Wen

In this paper we study the lattice quasi-periodic operators with power-law long-range hopping and meromorphic monotone potentials, and diagonalize the operators via a Nash-Moser iteration scheme. As applications, we obtain uniform power-law localization, uniform dynamical localization and Lipschitz continuity of the integrated density of states (IDS) for suc

Yihao Liu, Xiangyu Chen, Xianzheng Ma, Xintao Wang

Image processing is a fundamental task in computer vision, which aims at enhancing image quality and extracting essential features for subsequent vision applications. Traditionally, task-specific models are developed for individual tasks and designing such models requires distinct expertise. Building upon the success of large language models (LLMs) in natura

John H. Miller, Martha Y. Suárez-Villagrán, Johnathan O. Sanderson

The charge density wave (CDW) is a condensate that often forms in layered materials. It is known to carry electric current \emph{en masse}, but the transport mechanism remains poorly understood at the microscopic level. Its quantum nature is revealed by several lines of evidence. Experiments often show lack of CDW displacement when biased just below the thre

Haozhe Kong, Huasheng Xie, Jizhong Sun

Ion cyclotron emission (ICE) holds great potential as a diagnostic tool for fast ions in fusion devices. The theory of magnetoacoustic cyclotron instability (MCI), as an emission mechanism for ICE, states that MCI is driven by a velocity distribution of fast ions that approximates a drift ring beam. The influence of key parameters on the linear MCI is system

Shengbing Deng, Qiaoran Wu

In this paper, we consider the existence of normalized solutions for the following $p$-Laplacian equation \begin{equation*} \left\{\begin{array}{ll} -\Delta_{p}u-V(x)\lvert u\rvert^{p-2}u+\lambda\lvert u\rvert^{p-2}u=\lvert u\rvert^{q-2}u&\mbox{in}\ \mathbb{R}^N, \int_{\mathbb{R}^N}\lvert u\rvert^pdx=a^p, \end{array}\right. \end{equation*} where $N\geqslant

Xiang Zhang, Changhao Wang, Lingfeng Sun, Zheng Wu

Learning contact-rich manipulation skills is essential. Such skills require the robots to interact with the environment with feasible manipulation trajectories and suitable compliance control parameters to enable safe and stable contact. However, learning these skills is challenging due to data inefficiency in the real world and the sim-to-real gap in simula

Ciro Ciliberto, Rick Miranda, Joaquim Roé

We show the existence of cones over 8-dimensional rational spheres at the boundary of the Mori cone of the blow-up of the plane at $s\geq 13$ very general points. This gives evidence for De Fernex's strong $\Delta$-conjecture, which is known to imply Nagata's conjecture. This also implies the existence of a multitude of good and wonderful rays as defined in

Yayu Guo, Mejdi Azaiez, Chuanju Xu

In this paper, we propose and analyze an efficient numerical method for the anisotropic phase field dendritic crystal growth model, which is challenging because we are facing the nonlinear coupling and anisotropic coefficient in the model. The proposed method is a two-step scheme. In the first step, an intermediate solution is computed by using BDF schemes o

Ziniu Li, Tian Xu, Yushun Zhang, Zhihang Lin

Reinforcement Learning from Human Feedback (RLHF) is key to aligning Large Language Models (LLMs), typically paired with the Proximal Policy Optimization (PPO) algorithm. While PPO is a powerful method designed for general reinforcement learning tasks, it is overly sophisticated for LLMs, leading to laborious hyper-parameter tuning and significant computatio

Patrick Koppenburg

The LHCb experiment is in operation since 2009 and has provided measurements of the KM matrix with unprecedented precision. 50 years after the original paper we are in the position of pinning down the parameters of the theory, or possibly to show its limitations. In these proceedings the status of LHCb is shown in a historical perspective, along with some an

Jonathon Mellor, Rachel Christie, James Guilder, Robert S Paton

Accurate and representative data is vital for precisely reporting the impact of influenza in healthcare systems. Northern hemisphere winter 2022/23 experienced the most substantial influenza wave since the COVID-19 pandemic began in 2020. Simultaneously, new data streams become available within health services because of the pandemic. Comparing these data, s

Alireza Maleki, Ha H. Nguyen, Ebrahim Bedeer, Robert Barton

Chirps spread spectrum (CSS) modulation is the heart of long-range (LoRa) modulation used in the context of long-range wide area network (LoRaWAN) in internet of things (IoT) scenarios. Despite being a proprietary technology owned by Semtech Corp., LoRa modulation has drawn much attention from the research and industry communities in recent years. However, t

Abhinav Dahiya, Stephen L. Smith

This paper addresses the challenge of enabling a single robot to effectively assist multiple humans in decision-making for task planning domains. We introduce a comprehensive framework designed to enhance overall team performance by considering both human expertise in making the optimal decisions and robot influence on human decision-making. Our model integr

Traian Rebedea, Razvan Dinu, Makesh Sreedhar, Christopher Parisien

NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems. Guardrails (or rails for short) are a specific way of controlling the output of an LLM, such as not talking about topics considered harmful, following a predefined dialogue path, using a particular language style, and more. There are sever

Kieran Wood, Samuel Kessler, Stephen J. Roberts, Stefan Zohren

Forecasting models for systematic trading strategies do not adapt quickly when financial market conditions rapidly change, as was seen in the advent of the COVID-19 pandemic in 2020, causing many forecasting models to take loss-making positions. To deal with such situations, we propose a novel time-series trend-following forecaster that can quickly adapt to

Nick Rekuski

Using a recent description of the geometric stability manifold, we show the geometric stability manifold associated to any smooth projective complex surface is contractible. We then use this result to demonstrate infinitely many new families of surfaces whose stability manifold is contractible.

Jonas Landgraf, Christa Flühmann, Thomas Fösel, Florian Marquardt

The selective number-dependent arbitrary phase (SNAP) gates form a powerful class of quantum gates, imparting arbitrarily chosen phases to the Fock states of a cavity. However, for short pulses, coherent errors limit the performance. Here we demonstrate in theory and experiment that such errors can be completely suppressed, provided that the pulse times exce

Yu Chen, Xinyuan Qian, Zexu Pan, Kainan Chen

The prevailing noise-resistant and reverberation-resistant localization algorithms primarily emphasize separating and providing directional output for each speaker in multi-speaker scenarios, without association with the identity of speakers. In this paper, we present a target speaker localization algorithm with a selective hearing mechanism. Given a referen

N. A. Abdulov, X. Chen, A. V. Kotikov, A. V. Lipatov

The low-x behavior of the linearly polarized gluon density h_{g}(x,k_t^2, Q^2) in nuclei is studied in the rescaling model at small transverse momentum k_t.

Grant C. Forbes, Parth Katlana, Zeydy Ortiz

Abstractive text summarization has garnered increased interest as of late, in part due to the proliferation of large language models (LLMs). One of the most pressing problems related to generation of abstractive summaries is the need to reduce "hallucinations," information that was not included in the document being summarized, and which may be wholly incorr

Rahul Ghosal, Marcos Matabuena

We develop a new method for multivariate scalar on multidimensional distribution regression. Traditional approaches typically analyze isolated univariate scalar outcomes or consider unidimensional distributional representations as predictors. However, these approaches are sub-optimal because: i) they fail to utilize the dependence between the distributional

SeungKyu Kim, Hyun-Jic Oh, Seonghui Min, Won-Ki Jeong

With the emergence of the Segment Anything Model (SAM) as a foundational model for image segmentation, its application has been extensively studied across various domains, including the medical field. However, its potential in the context of histopathology data, specifically in region segmentation, has received relatively limited attention. In this paper, we

Chuang Li, Yan Zhang, Min-Yen Kan, Haizhou Li

Previous zero-shot dialogue state tracking (DST) methods only apply transfer learning, ignoring unlabelled data in the target domain. We transform zero-shot DST into few-shot DST by utilising such unlabelled data via joint and self-training methods. Our method incorporates auxiliary tasks that generate slot types as inverse prompts for main tasks, creating s

L. Q. Huston, D. Y. Popov, A. Weiland, M. M. Bordelon

Uranium ditelluride (UTe$_2$) has attracted recent interest due to its unique superconducting properties, which include the potential for a topological odd-parity superconducting state. Recently, ac-calorimetry measurements under pressure indicate a change in the ground state of UTe$_2$ from superconducting to antiferromagnetic at 1.4 GPa. Here, we investiga

Rongjun Qin, Guixiang Zhang, Yang Tang

Recent deep learning-based methods outperform traditional learning methods on remote sensing (RS) semantic segmentation/classification tasks. However, they require large training datasets and are generally known for lack of transferability due to the highly disparate RS image content across different geographical regions. Yet, there is no comprehensive analy

Carles Padró

Efficient deterministic algorithms to construct representations of lattice path matroids over finite fields are presented. They are built on known constructions of hierarchical secret sharing schemes, a recent characterization of hierarchical matroid ports, and the existence of isolating weight functions for lattice path matroids whose values are polynomial

Christian Brand, Alfred Hucht, Hamid Mehdipour, Giriraj Jnawali

The critical behavior of the order-disorder phase transition in the buckled dimer structure of the Si(001) surface is investigated both theoretically by means of first-principles calculations and experimentally by spot profile analysis low-energy electron diffraction (SPA-LEED). We use density functional theory (DFT) with three different functionals commonly

Zijian Ding, Alison Smith-Renner, Wenjuan Zhang, Joel R. Tetreault

To explore how humans can best leverage LLMs for writing and how interacting with these models affects feelings of ownership and trust in the writing process, we compared common human-AI interaction types (e.g., guiding system, selecting from system outputs, post-editing outputs) in the context of LLM-assisted news headline generation. While LLMs alone can g

Gang Zhao, Yidong Shi, Shudong Lu, Xinjie Yang

Document-level event extraction (DEE) faces two main challenges: arguments-scattering and multi-event. Although previous methods attempt to address these challenges, they overlook the interference of event-unrelated sentences during event detection and neglect the mutual interference of different event roles during argument extraction. Therefore, this paper

Milad Shafiee, Guillaume Bellegarda, Auke Ijspeert

Learning a locomotion policy for quadruped robots has traditionally been constrained to a specific robot morphology, mass, and size. The learning process must usually be repeated for every new robot, where hyperparameters and reward function weights must be re-tuned to maximize performance for each new system. Alternatively, attempting to train a single poli

Antonios Kamariotis, Eleni Chatzi

Engineers are often faced with the decision to select the most appropriate model for simulating the behavior of engineered systems, among a candidate set of models. Experimental monitoring data can generate significant value by supporting engineers toward such decisions. Such data can be leveraged within a Bayesian model updating process, enabling the uncert

Emir Syahreza Fadhilla, Ardian Nata Atmaja, Bobby Eka Gunara, Mir Faizal

The supersymmetric baby-Skyrme model is an interesting field theoretical model, and its BPS states have been studied using the usual methods. Here, we propose a novel method to rigorously obtain both topologically stable BPS and non-BPS states in the $\mathcal{N}=2$ baby Skyrme Model. It is observed that the BPS states found using this novel method coincide

Xiaochen Zhu, Xinjian Luo, Yuncheng Wu, Yangfan Jiang

Split Learning (SL) has emerged as a practical and efficient alternative to traditional federated learning. While previous attempts to attack SL have often relied on overly strong assumptions or targeted easily exploitable models, we seek to develop more capable attacks. We introduce SDAR, a novel attack framework against SL with an honest-but-curious server

Nuno M. Guerreiro, Ricardo Rei, Daan van Stigt, Luisa Coheur

Widely used learned metrics for machine translation evaluation, such as COMET and BLEURT, estimate the quality of a translation hypothesis by providing a single sentence-level score. As such, they offer little insight into translation errors (e.g., what are the errors and what is their severity). On the other hand, generative large language models (LLMs) are

Gang Zhao, Xiaocheng Gong, Xinjie Yang, Guanting Dong

Most current Event Extraction (EE) methods focus on the high-resource scenario, which requires a large amount of annotated data and can hardly be applied to low-resource domains. To address EE more effectively with limited resources, we propose the Demonstration-enhanced Schema-guided Generation (DemoSG) model, which benefits low-resource EE from two aspects

Haoke Zhang, Yue Wang, Juntao Li, Xiabing Zhou

Large Language Models~(LLMs) have demonstrated incredible capabilities in understanding, generating, and manipulating languages. Through human-model interactions, LLMs can automatically understand human-issued instructions and output the expected contents, which can significantly increase working efficiency. In various types of real-world demands, editing-or

Martin Werner Licht

We construct finite element de~Rham complexes of higher and possibly non-uniform polynomial order in finite element exterior calculus (FEEC). Starting from the finite element differential complex of lowest-order, known as the complex of Whitney forms, we incrementally construct the higher-order complexes by adjoining exact local complexes associated to simpl

Georgia Christopoulou, Cono Di Paola, Floris Eelke Elzinga, Aurelie Jallat

Catalytic processes are vital in the chemical industry, with nitrogen-to-ammonia conversion being a major industrial process. Designing catalysts relies on computational chemistry methods like Density Functional Theory (DFT), which have limitations in accuracy, especially for complex materials. Quantum computing advancements offer promise for precise ab-init

Kai Chen, Chunwei Wang, Kuo Yang, Jianhua Han

The rapid development of large language models (LLMs) has not only provided numerous opportunities but also presented significant challenges. This becomes particularly evident when LLMs inadvertently generate harmful or toxic content, either unintentionally or because of intentional inducement. Existing alignment methods usually direct LLMs toward the favora

Tommaso Bacchielli, Lorenzo Pucci, Enrico Paolini, Andrea Giorgetti

This work proposes a low-complexity estimation approach for an orthogonal time frequency space (OTFS)-based integrated sensing and communication (ISAC) system. In particular, we first define four low-dimensional matrices used to compute the channel matrix through simple algebraic manipulations. Secondly, we establish an analytical criterion, independent of s

João J. Xarez

Starting with a symmetric monoidal adjunction with certain properties, one derives another symmetric monoidal adjunction with the same properties between the respective categories of all V-categories. If one begins with a reflection of a full replete subcategory, the derived adjunction is also a reflection of the same kind. Semi-left-exactness (also called a

Daniel Owusu Adu, Daniel Keliher

A result of Hohloch links the theory of integer partitions with the Monge formulation of the optimal transport problem, giving the optimal transport map between (Young diagrams of) integer partitions and their corresponding symmetric partitions. Our aim is to extend Hohloch's result to the higher dimensional case. In doing so, we show the Kantorovich formula

D. A. Kozlov, J. Ziegler, N. N. Mikhailov, Z. D. Kvon

An experimental study of Landau levels (LLs) in a system of two-dimensional massless Dirac fermions based on a critical thickness HgTe quantum well has been carried out. The magnetotransport and the capacitive response have been investigated simultaneously. It is shown that the formation of Shubnikov-de Haas (SdH) oscillations associated with odd v filling f

Matthew Powell

It is known that the Lyapunov exponent for multifrequency analytic cocycles is weak-H\"older continuous in cocycle for certain Diophantine frequencies, and that this implies certain regularity of the integrated density of states in energy for Jacobi operators. In this paper, we establish the pointwise modulus of continuity in both cocycle and frequency and o

Stefania De Curtis, Luigi Delle Rose, Felix Egle, Stefano Moretti

We show how effects of compositeness emerging in a Composite 2-Higgs Doublet Model can enter Standard Model (SM)-like Higgs pair production at the Large Hadron Collider in both resonant and non-resonant mode. Such effects can arise from modified trilinear Higgs self-couplings and top-Yukawa couplings as well as from loops of new heavy quarks and additional q

Xi Cen, Qianjun He, Zichen Song, Zihan Wang

Two classes of fractional type variable weights are established in this paper. The first kind of weights ${A_{\vec p( \cdot ),q( \cdot )}}$ are variable multiple weights, which are characterized by the weighted variable boundedness of multilinear fractional type operators, called multilinear Hardy--Littlewood--Sobolev theorem on weighted variable Lebesgue sp

Giovanni Catino, Yanyan Li, Dario D. Monticelli, Alberto Roncoroni

In this paper we classify positive solutions to the critical semilinear elliptic equation in $\mathbb{H}^n$. We prove that they are the Jerison-Lee's bubbles, provided $n=1$ or $n\geq 2$ and a suitable control at infinity holds. The proofs are based on a classical Jerison-Lee's differential identity and on pointwise/integral estimates recently obtained for c

Dominik Bullach, Daniel Macias Castillo

We derive new cases of conjectures of Rubin and of Burns--Kurihara--Sano concerning derivatives of Dirichlet $L$-series at $s = 0$ in $p$-elementary extensions of number fields for arbitrary prime numbers $p$. In naturally arising examples of such extensions one therefore obtains annihilators of class groups from $S$-truncated Dirichlet $L$-series for `large

Xiaochong Lan, Chen Gao, Depeng Jin, Yong Li

Stance detection automatically detects the stance in a text towards a target, vital for content analysis in web and social media research. Despite their promising capabilities, LLMs encounter challenges when directly applied to stance detection. First, stance detection demands multi-aspect knowledge, from deciphering event-related terminologies to understand

Kamal Taha

This survey paper offers a comprehensive review of methodologies utilizing machine learning (ML) classification techniques for identifying wafer defects in semiconductor manufacturing. Despite the growing body of research demonstrating the effectiveness of ML in wafer defect identification, there is a noticeable absence of comprehensive reviews on this subje

Giacomo Morpurgo, Louk Rademaker, Christophe Berthod, Thierry Giamarchi

We study the Hall constant in a homogeneous two-dimensional fluid of correlated electrons immersed in a perpendicular magnetic field, with special focus on the regime of low carrier density. The model consists of a one-band tight-binding model and a momentum-independent causal self-energy, representing interaction-induced correlations effects that are restri

Jincheng Lu, Zi Wang, Jie Ren, Chen Wang

Quantum thermodynamics with microscopic inelastic scattering processes has been intensively investigated in recent years. Here, we apply quantum master equation combined with full counting statistics approach to investigate the role of quantum coherence on the periodically-driven inelastic heat engine. We demonstrate that the inelastic quantum heat engine ex

M. Sifft, A. Kurzmann, J. Kerski, R. Schott

The blinking statistics of quantum emitters and their corresponding Markov models play an important role in high resolution microscopy of biological samples as well as in nano-optoelectronics and many other fields of science and engineering. Current methods for analyzing the blinking statistics like the full counting statistics or the Viterbi algorithm break

Chao Liang, Linchao Zhu, Humphrey Shi, Yi Yang

Modern deep learning systems are data-hungry. Learning with web data is one of the feasible solutions, but will introduce label noise inevitably, which can hinder the performance of deep neural networks. Sample selection is an effective way to deal with label noise. The key is to separate clean samples based on some criterion. Previous methods pay more atten

Yunli Wang, Zhiqiang Wang, Jian Yang, Shiyang Wen

Cascade ranking is widely used for large-scale top-k selection problems in online advertising and recommendation systems, and learning-to-rank is an important way to optimize the models in cascade ranking. Previous works on learning-to-rank usually focus on letting the model learn the complete order or top-k order, and adopt the corresponding rank metrics (e

Clement Fung, Chen Qiu, Aodong Li, Maja Rudolph

Anomaly detection is the task of identifying abnormal samples in large unlabeled datasets. While the advent of foundation models has produced powerful zero-shot anomaly detection methods, their deployment in practice is often hindered by the absence of labeled validation data -- without it, their detection performance cannot be evaluated reliably. In this wo

Ankit Bende, Simranjeet Singh, Chandan Kumar Jha, Tim Kempen

Memristor-aided logic (MAGIC) design style holds a high promise for realizing digital logic-in-memory functionality. The ability to implement a specific gate in a MAGIC design style hinges on the SET-to-RESET threshold ratio. The TaOx memristive devices exhibit distinct SET-to-RESET ratios, enabling the implementation of OR and NOT operations. As the adoptio

Ilia Krasikov

We show that the normalised ultraspherical polynomials, $G_n^{(\lambda)}(x)=C_n^{(\lambda)}(x)/C_n^{(\lambda)}(1)$, satisfy the following stronger version of Tur\'{a}n inequality, $$|x|^\theta \left(G_n^{(\lambda)}(x)\right)^2 -G_{n-1}^{(\lambda)}(x)G_{n+1}^{(\lambda)}(x) \ge 0 ,\;\;\;|x| \le 1, $$ where $\theta=4/(2-\lambda)$ if $-1/2 <\lambda \le 0$, and $

Federico Coro

We consider the diphoton production in hadronic collisions at the next-to-next-to-leading order (NNLO) in perturbative QCD, taking into account for the first time the full top quark mass dependence. We present the computation of the two-loop form factors for diphoton production in the quark annihilation channel, that are relevant for the phenomenological stu

Lingsheng Meng, Yong Liang Guan, Yao Ge, Zilong Liu

This paper studies Flag sequences for low-complexity delay-Doppler estimation by exploiting their distinctive peak-curtain ambiguity functions (AFs). Unlike the existing Flag sequence designs that are limited to prime lengths and periodic auto-AFs, we aim to design Flag sequence sets of arbitrary lengths with low (nontrivial) periodic/aperiodic auto- and cro

Federica Mennuni, Addolorata Salvatore

We study the quasilinear equation $(P)\qquad - {\rm div} (a(x,u,\nabla u)) +A_t(x,u,\nabla u) + |u|^{p-2}u\ =\ g(x,u) \qquad \hbox{in $\R^N$,} $ with $N\ge 3$ and $p > 1$. Here, we suppose $A : \R^N \times \R \times \R^N \to \R$ is a given ${C}^{1}$-Carath\'eodory function which grows as $|\xi|^p$ with $A_t(x,t,\xi) = \frac{\partial A}{\partial t}(x,t,\xi)$,

Bin Liu, Xiaodong Liu, Xiaoyu Jia, Fei Li

The active asteroid 311P is one of the two targets of a planned Chinese asteroid exploration mission Tianwen-2. During 2013, 311P experienced several mass-loss events and exhibited multiple comet-like tails. Here we analyze the morphology and surface brightness of the tails to investigate the dust environment around the nucleus and mechanism of activities. W

A. Khokhriakova, W. Becker, G. Ponti, M. Sasaki

Context. Extended gamma-ray TeV emission (TeV halos) around middle-aged pulsars has been detected. A proposed model to explain these TeV halos is that electrons from a degree-wide Pulsar Wind Nebula (PWN) get up-scattered by cosmic microwave background photons through inverse Compton processes. However, no X-ray degree-wide faint diffuse PWNe have been found

D. Hudson Smith, John Paul Lineberger, George H. Baker

Many medical ultrasound video recognition tasks involve identifying key anatomical features regardless of when they appear in the video suggesting that modeling such tasks may not benefit from temporal features. Correspondingly, model architectures that exclude temporal features may have better sample efficiency. We propose a novel multi-head attention archi

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Using data samples collected at center-of-mass energies between 2.000 and 3.080 GeV with the BESIII detector operating at the BEPCII collider, a partial-wave analysis is performed on the process $e^+e^-\to\eta\pi^+\pi^-$. In addition to the dominant $e^+e^-\to\rho\eta$ component, the $e^+e^-\to a_2(1320)\pi$ process is also sizeable, contributing up to 24% o

Mathieu Roget, Giuseppe Di Molfetta

A discrete time quantum walk is known to be the single-particle sector of a quantum cellular automaton. Searching in this mathematical framework has interested the community since a long time. However, most results consider spatial search on regular graphs. This work introduces a new quantum walk-based searching scheme, designed to search nodes or edges on a

B. C. Hiesmayr, W. Krzemień, M. Bała

In medical applications -- such as positron emission tomography (PET) -- $511$keV photons that experience Compton scattering are studied. We present a consistent framework based on error-correction channels to fully describe the quantum information-theoretic content of high energetic photons undergoing Compton scattering, characterized by the Klein-Nishina f

Lochan Basyal, Mihir Sanghvi

Text summarization is a critical Natural Language Processing (NLP) task with applications ranging from information retrieval to content generation. Leveraging Large Language Models (LLMs) has shown remarkable promise in enhancing summarization techniques. This paper embarks on an exploration of text summarization with a diverse set of LLMs, including MPT-7b-

Ilyes Batatia

This work proposes a geometric insight into equivariant message passing on Riemannian manifolds. As previously proposed, numerical features on Riemannian manifolds are represented as coordinate-independent feature fields on the manifold. To any coordinate-independent feature field on a manifold comes attached an equivariant embedding of the principal bundle

Hua-Peng Gu, Hai-Tian Wang, Lijing Shao

Whether astrophysical black holes (BHs) can have charge is a question to be addressed by observations. In the era of gravitational wave (GW) astronomy, one can constrain the charge of a merged BH remnant using the merger-ringdown signal of the GW data. Extending earlier studies, we analyze five GW events in GWTC-3, assuming Kerr-Newman BHs. Our results show

Matthew Patterson, Christopher O'Reilly, Jon Robson, Tim Woollings

The coupled nature of the ocean-atmosphere system frequently makes understanding the direction of causality difficult in ocean-atmosphere interactions. This study presents a method to decompose turbulent heat fluxes into a component which is directly forced by atmospheric circulation, and a residual which is assumed to be primarily `ocean-forced'. This metho

Markus J. Buehler

For centuries, researchers have sought out ways to connect disparate areas of knowledge. While early scholars (Galileo, da Vinci, etc.) were experts across fields, specialization has taken hold later. With the advent of Artificial Intelligence, we can now explore relationships across areas (e.g., mechanics-biology) or disparate domains (e.g., failure mechani

Arghajit Jana, Debjit Chatterjee, Hsiang-Kuang Chang, Sachindra Naik

We present the results obtained from the spectral studies of black hole X-ray binary GX~339--4 using \astrosat~ observations during its 2021 outburst. \astrosat~ observed the source in the intermediate state for $\sim600$ ks. The combined spectra of SXT and LAXPC in the $0.7-25$ keV energy range are studied with phenomenological and physical models. The spec

Andreas Grivas, Antonio Vergari, Adam Lopez

Sigmoid output layers are widely used in multi-label classification (MLC) tasks, in which multiple labels can be assigned to any input. In many practical MLC tasks, the number of possible labels is in the thousands, often exceeding the number of input features and resulting in a low-rank output layer. In multi-class classification, it is known that such a lo

Gino Bishop, Simone Montangero, Frank K. Wilhelm

We perform exact diagonalization and time evolution of the Lechner-Hauke-Zoller (LHZ) annealing architecture [Science Advances 1(9), e1500838 (2015)] for ten physical qubits. Thereby, on a training set consisting of $2400$ problem instances, we perform the optimization task of tuning the local fields with the goal to identify a set of fixed optimal annealing

Jian Ding, Yumou Fei, Yuanzheng Wang

In this paper, we study the problem of recovering the latent vertex correspondence between two correlated random graphs with vastly inhomogeneous and unknown edge probabilities between different pairs of vertices. Inspired by and extending the matching algorithm via degree profiles by Ding, Ma, Wu and Xu (2021), we obtain an efficient matching algorithm as l

Baiyu Liu, Shasha Xu

In this paper, we investigate a nonlocal equation involving the logarithmic Laplacian with indefinite nonlinearities: \begin{equation*} \left\{ \begin{array}{ll} L_\Delta u(x)=a(x_n)f(u), & x\in\Omega, \\ u(x)=0,& x\in \mathbb{R}^n\backslash\Omega. \end{array} \right. \end{equation*} Here, $\Omega$ represents a Lipschitz coercive epigraph. To achieve our obj

V. V. Syshchenko, A. I. Tarnovsky, V. I. Dronik, A. Yu. Isupov

The motion of charged particles in a crystal in the axial channeling regime can be both regular and chaotic. The chaos in quantum case manifests itself in the statistical properties of the energy levels set. These properties have been studied previously for the electrons channeling along [110] direction of the silicon crystal, in the case when the classical

Ioannis Florakis, John Rizos

We systematically develop the explicit map between string vacua constructed in the Free Fermionic Formulation and their $\mathbb{Z}_2^N$ toroidal orbifold counterparts. We illustrate the map in various example classes of models, including cases relevant for string phenomenology, as well as in theories where space-time supersymmetry is broken by the stringy S

Menachem Stern, Sam Dillavou, Dinesh Jayaraman, Douglas J. Durian

As the size and ubiquity of artificial intelligence and computational machine learning (ML) models grow, their energy consumption for training and use is rapidly becoming economically and environmentally unsustainable. Neuromorphic computing, or the implementation of ML in hardware, has the potential to reduce this cost. In particular, recent laboratory prot

Nian Li, Chen Gao, Mingyu Li, Yong Li

The advent of artificial intelligence has led to a growing emphasis on data-driven modeling in macroeconomics, with agent-based modeling (ABM) emerging as a prominent bottom-up simulation paradigm. In ABM, agents (e.g., households, firms) interact within a macroeconomic environment, collectively generating market dynamics. Existing agent modeling typically e

Giuliano Migliorini, Duccio Fanelli

Stochastic resonance is a well established phenomenon, which proves relevant for a wide range of applications, of broad trans-disciplinary breath. Consider a one dimensional bistable stochastic system, characterized by a deterministic double well potential and shaken by an additive noise source. When subject to an external periodic drive, and for a proper ch

Ilyes Batatia, Lars L. Schaaf, Huajie Chen, Gábor Csányi

Graph Neural Networks (GNNs), especially message-passing neural networks (MPNNs), have emerged as powerful architectures for learning on graphs in diverse applications. However, MPNNs face challenges when modeling non-local interactions in graphs such as large conjugated molecules, and social networks due to oversmoothing and oversquashing. Although Spectral

Pierre Le Jeune

Most contributions on Few-Shot Object Detection (FSOD) evaluate their methods on natural images only, yet the transferability of the announced performance is not guaranteed for applications on other kinds of images. We demonstrate this with an in-depth analysis of existing FSOD methods on aerial images and observed a large performance gap compared to natural

Philippe Michaud-Jacobs, Filip Najman

We determine all the quadratic points on the genus $13$ modular curve $X_0(163)$, thus completing the answer to a recent question of Banwait, the second-named author, and Padurariu. In doing so, we investigate a curious phenomenon involving a cubic point with complex multiplication on the curve $X_0(163)$. This cubic point prevents us, due to computational r

Rachid Zeghlache, Pierre-Henri Conze, Mostafa El Habib Daho, Yihao Li

Longitudinal analysis in medical imaging is crucial to investigate the progressive changes in anatomical structures or disease progression over time. In recent years, a novel class of algorithms has emerged with the goal of learning disease progression in a self-supervised manner, using either pairs of consecutive images or time series of images. By capturin

Zeyuan Zhou, Huipeng Gu, Guoliang Ju, Wei Xing

We proposed a parallel-in-time method based on preconditioner for Biot's consolidation model in poroelasticity. In order to achieve a fast and stable convergence for the matrix system of the Biot's model, we design two preconditioners with approximations of the Schur complement. The parallel-in-time method employs an inverted time-stepping scheme that iterat

Qiong Nan, Qiang Sheng, Juan Cao, Yongchun Zhu

Both accuracy and timeliness are key factors in detecting fake news on social media. However, most existing methods encounter an accuracy-timeliness dilemma: Content-only methods guarantee timeliness but perform moderately because of limited available information, while social con-text-based ones generally perform better but inevitably lead to latency becaus