March 2024 arXiv papers — page 74

Paul C. Duffell, Abigail Polin, Soham Mandal

We demonstrate in a proof-of-concept numerical hydrodynamics calculation that the narrow radial filamentary structures seen in Pa 30 could be generated through highly efficient cooling (e.g. via line emission) in the ejecta. Efficient cooling in the ejecta causes a drop of pressure support in Rayleigh-Taylor fingers, leading them to be compressed, and suppre

Rwiddhi Chakraborty, Adrian Sletten, Michael Kampffmeyer

Group robustness strategies aim to mitigate learned biases in deep learning models that arise from spurious correlations present in their training datasets. However, most existing methods rely on the access to the label distribution of the groups, which is time-consuming and expensive to obtain. As a result, unsupervised group robustness strategies are sough

Eric Sakk

In this work, a symbolic dynamical formulation based upon discrete iterative mappings derived from the Collatz conjecture is introduced. It is demonstrated that this formulation naturally induces a ternary alphabet useful for characterizing the expansive and dissipative behavior of generated itineraries. Furthermore, local and quasi-global analyses indicate

Yufei Zhu, Han Fan, Andrey Rudenko, Martin Magnusson

Long-term human motion prediction (LHMP) is essential for safely operating autonomous robots and vehicles in populated environments. It is fundamental for various applications, including motion planning, tracking, human-robot interaction and safety monitoring. However, accurate prediction of human trajectories is challenging due to complex factors, including

Zhiyue Luo, Jun Xu, Fanglin Chen

Traffic signal control is important in intelligent transportation system, of which cooperative control is difficult to realize but yet vital. Many methods model multi-intersection traffic networks as grids and address the problem using multi-agent reinforcement learning (RL). Despite these existing studies, there is an opportunity to further enhance our unde

Meet Doshi, Raj Dabre, Pushpak Bhattacharyya

In this paper, we explore the utility of translationese as synthetic data created using machine translation for pre-training language models (LMs) for low-resource languages (LRLs). Our simple methodology consists of translating large amounts of web-crawled monolingual documents (clean) into the LRLs, followed by filtering the translated documents using tiny

Leilei Liu, Jieheng Zeng

For a commutative Gorenstein Noetherian ring $R$, we construct an affine scheme $X$ solely from DG singularity category $S_{dg}(R)$ of $R$ such that there is a finite surjective morphism $X \rightarrow \mathrm{Spec}(R /I)$, where $\mathrm{Spec}(R /I)$ is the singular locus in $\mathrm{Spec}(R)$. As an application, for two such rings with equivalent DG singul

Christian Apostoli, Patrick Kelly, Annette Lopez, Kaelyn Dauer

We leverage random phase approximation and unbiased auxiliary-field quantum Monte Carlo methods to compute dynamical correlations for a dilute homogeneous two-dimensional attractive Fermi gas. Our main purpose is to quantitatively study the collective excitations of the system to generate robust benchmark results and to shed light into fermionic superfluidit

Moroni Santiago-García, Shunashi G Castillo-López, Arturo Camacho-Guardian

Lattice polarons, quasiparticles arising from the interaction between an impurity and its surrounding bosonic environment confined to a lattice system, have emerged as a platform for generating complex few-body states, probing many-body phenomena, and addressing long-standing problems in physics. In this study, we employ a variational ansatz to investigate t

Pratapaditya Bej, Vinod Jayakeerthi

The evolution of Quantum Key Distribution (QKD) relies on innovative methods to enhance its security and efficiency. Unextendible Product Bases (UPBs) hold promise in quantum cryptography due to their inherent indistinguishability, yet they are underutilized in QKD protocols. This work introduces a protocol utilizing UPBs to establish quantum keys between di

Michael Dellaiera, Cyann Plard, Thomas Vuillaume, Alexandre Benoit

The Cherenkov Telescope Array Observatory (CTAO) is the next generation of observatories employing the imaging air Cherenkov technique for the study of very high energy gamma rays. The deployment of deep learning methods for the reconstruction of physical attributes of incident particles has evinced promising outcomes when conducted on simulations. However,

Kaifeng Bu

We investigate the extremality of stabilizer states to reveal their exceptional role in the space of all $n$-qubit/qudit states. We establish uncertainty principles for the characteristic function and the Wigner function of states, respectively. We find that only stabilizer states achieve saturation in these principles. Furthermore, we prove a general theore

Arjun Kumar, Tobias Toll

We present a model of proton geometry where the number and size of gluon density hotspots in the proton's thickness function evolves with the resolution scale of the event given by the Mandelstam $t$ variable in exclusive diffractive $ep$ collisions. We use the impact-parameter dependent saturation dipole model bSat/IPSat, as well as its linearised (non-satu

Diana Riazi, Giacomo Livan

An abundance of literature has shown that the injection of noise into complex socio-economic systems can improve their resilience. This study aims to understand whether the same applies in the context of information diffusion in social networks. Specifically, we aim to understand whether the injection of noise in a social network of agents seeking to uncover

George Siachamis, Kyriakos Psarakis, Marios Fragkoulis, Arie van Deursen

Stream processing in the last decade has seen broad adoption in both commercial and research settings. One key element for this success is the ability of modern stream processors to handle failures while ensuring exactly-once processing guarantees. At the moment of writing, virtually all stream processors that guarantee exactly-once processing implement a va

Anna Menacher, Thomas E. Nichols, Timothy D. Johnson, Jian Kang

In addressing the challenge of analysing the large-scale Adolescent Brain Cognition Development (ABCD) fMRI dataset, involving over 5,000 subjects and extensive neuroimaging data, we propose a scalable Bayesian scalar-on-image regression model for computational feasibility and efficiency. Our model employs a relaxed-thresholded Gaussian process (RTGP), integ

Akihiro Fujii, Anh Khoa Augustin Lu, Koji Shimizu, Satoshi Watanabe

Materials design aims to discover novel compounds with desired properties. However, prevailing strategies face critical trade-offs. Conventional element-substitution approaches readily and adaptively incorporate various domain knowledge but remain confined to a narrow search space. In contrast, deep generative models efficiently explore vast compositional la

Jérôme Carrand

The Sinai billiard map $T$ on the two-torus, i.e., the periodic Lorentz gaz, is a discontinuous map. Assuming finite horizon and another condition we introduce -- namely \emph{negligible singularities} -- we prove that the metric pressure map associated with the billiard map $T$ is upper semi-continuous, as well as the compactness of the set of $T$-invariant

Francesco Zola, Lander Segurola, Erin King, Martin Mullins

Tools for fighting cyber-criminal activities using new technologies are promoted and deployed every day. However, too often, they are unnecessarily complex and hard to use, requiring deep domain and technical knowledge. These characteristics often limit the engagement of law enforcement and end-users in these technologies that, despite their potential, remai

Giannis Daras, Alexandros G. Dimakis, Constantinos Daskalakis

Ambient diffusion is a recently proposed framework for training diffusion models using corrupted data. Both Ambient Diffusion and alternative SURE-based approaches for learning diffusion models from corrupted data resort to approximations which deteriorate performance. We present the first framework for training diffusion models that provably sample from the

Mirthe Dankloff, Vanja Skoric, Giovanni Sileno, Sennay Ghebreab

AI algorithms used in the public sector, e.g., for allocating social benefits or predicting fraud, often involve multiple public and private stakeholders at various phases of the algorithm's life-cycle. Communication issues between these diverse stakeholders can lead to misinterpretation and misuse of algorithms. We investigate the communication processes fo

Diego Martínez, Federico Vigolo

In this memoir we develop a framework to study rigidity problems for Roe-like C*-algebras of countably generated coarse spaces. The main goal is to give a complete and self-contained solution to the problem of C*-rigidity for proper (extended) metric spaces. Namely, we show that (stable) isomorphisms among Roe algebras always give rise to coarse equivalences

Sheng Zhang, Jixuan Shi, Yibo Liang, Yuedong Sun

Distributing quantum entanglement between distant parties is a significant but difficult task in quantum information science, as it can enable numerous applications but suffers from exponential decay in the quantum channel. Quantum repeater is one of the most promising approaches towards this goal. In a quantum repeater protocol, it is essential that the ent

Maxime Federico, Hans-Rudolf Jauslin

We analyze through the expectation value of the energy density the spatial nonlocality of single photons emitted by the spontaneous decay of a Hydrogen atom. By using a minimal coupling between the quantized electromagnetic field and the atom, we compute the state of the photon under the assumption that only a single-photon is produced. The calculations are

Lisabeth Marie Santana, Chandralekha Singh

This research focuses on the experiences of seven undergraduate women who were majoring in physics in a medium-size physics department at a small liberal arts college. In the semi-structured, empathetic interviews we conducted, the women discussed how they decided to major in physics, their interactions with their peers and instructors, who supported them du

Jade Brisson, Bruno Colbois

In this note, we investigate the Steklov spectrum of the warped product $[0,L]\times_h \Sigma$ equipped with the metric $dt^2+h(t)^2g_\Sigma$, where $\Sigma$ is a compact surface. We find sharp upper bounds for the Steklov eigenvalues in terms of the eigenvalues of the Laplacian on $\Sigma$. We apply our method to the case of metric of revolution on the 3-di

Yulu Gong, Jiaxin Huang, Bo Liu, Jingyu Xu

The paragraph is grammatically correct and logically coherent. It discusses the importance of mobile terminal cloud computing migration technology in meeting the demands of evolving computer and cloud computing technologies. It emphasizes the need for efficient data access and storage, as well as the utilization of cloud computing migration technology to pre

Marlon L. Bause, Wolfgang Herrmann, Laura G. Spitler

[...] We present a singlepulse search method, improving on commonly used neural network classifiers thanks to the filtering of radio frequency interference based on its spectral variance and the magnetar's rotation. With this approach, we were able to lower the signal to noise ratio (S/N) detection threshold from 8 to 5. This allowed us to find over 115,000

Alessandro Ferretti

The ALICE Muon IDentifier is composed of 72 single-gap bakelite Resistive Plate Chambers, which have been operational since 2009 in maxi-avalanche mode (discrimination threshold:7 mV without amplification) with a Tetrafluoroethane/Isobutane/Sulfur Hexafluoride gas mixture, undergoing counting rates of the order of tens of Hz/cm^2. In this talk, the long-term

Wesley Fussner, Simon Santschi

We exhaustively classify varieties of BL-algebras with the amalgamation property, showing that there are only countably many of them and solving an open problem of Montagna. As a consequence of this classification, we obtain a complete description of which axiomatic extensions of H\'{a}jek's basic fuzzy logic BL have the deductive interpolation property. Alo

Eyal Buks

The problem of quantum measurement can be partially resolved by incorporating a process of spontaneous disentanglement into quantum dynamics. We propose a modified master equation, which contains a nonlinear term giving rise to both spontaneous disentanglement and thermalisation. We find that the added nonlinear term enables limit cycle steady states, which

Mathias Marconi, Karin Alfaro-Bittner, Lucas Sarrazin, Massimo Giudici

The behavior of a dynamical system can exhibit abrupt changes when it crosses a tipping point. To prevent catastrophic events, it is useful to analyze indicators of the incoming bifurcation, as the divergence of the relaxation time of the system when approaching the critical point. However, this phenomenon, called critical slowing down (CSD), is hardly measu

Timothy H. McNicholl

We study presentations of $C^*(X)$ that are evaluative over a presentation of $X$ in that $(f,p) \mapsto f(p)$ is computable. We prove existence-uniqueness theorems for such presentations. We use our methods to prove an effective Banach-Stone Theorem for unital commutative $C^*$ algebras. We also apply our results to the computable categoricity of $C^*$ alge

Haotian Wu, Maojun Zhang, Yulin Shao, Krystian Mikolajczyk

Acquiring and utilizing accurate channel state information (CSI) can significantly improve transmission performance, thereby holding a crucial role in realizing the potential advantages of massive multiple-input multiple-output (MIMO) technology. Current prevailing CSI feedback approaches improve precision by employing advanced deep-learning methods to learn

Jung Won Cho, Victoria Gould, Nik Ruškuc, Dandan Yang

We show that any graph product of residually finite monoids is residually finite. As a special case we obtain that any free product of residually finite monoids is residually finite. The corresponding results for graph products of semigroups follow.

Kazuki Enomoto, Shinya Kanemura, Sora Taniguchi

The electric dipole moment is examined in a three-loop neutrino mass model with dark matter originally proposed in Aoki et al. [Phys. Rev. Lett. 102, 051805 (2009)]. The model contains a $CP$-violating phase in the Higgs potential which plays an important role in electroweak baryogenesis and is thus expected to explain the baryon asymmetry of the Universe si

Ileana Montoya Perez, Parisa Movahedi, Valtteri Nieminen, Antti Airola

Background: Synthetic data has been proposed as a solution for sharing anonymized versions of sensitive biomedical datasets. Ideally, synthetic data should preserve the structure and statistical properties of the original data, while protecting the privacy of the individual subjects. Differential privacy (DP) is currently considered the gold standard approac

Agrim Gupta, Adel Heidari, Jiaming Jin, Dinesh Bharadia

Connectivity on-the-go has been one of the most impressive technological achievements in the 2010s decade. However, multiple studies show that this has come at an expense of increased carbon footprint, that also rivals the entire aviation sector's carbon footprint. The two major contributors of this increased footprint are (a) smartphone batteries which affe

S. Paron

The formation of stars, particularly the high-mass star formation, poses several still open questions. Nowadays, thanks to the most modern telescopes and instruments, we are able to observe and analyse many physical and chemical processes involved in the birth of massive stars. This work introduces to the interstellar medium, cradle of the stars, and makes f

Omid Mirzaeedodangeh, Farhad Mehdifar, Dimos V. Dimarogonas

In this paper, we present a novel 3D formation control scheme for directed graphs in a leader-follower configuration, achieving (almost) global convergence to the desired shape. Specifically, we introduce three controlled variables representing bispherical coordinates that uniquely describe the formation in 3D. Acyclic triangulated directed graphs (a class o

Ruoxuan Bai, Jingxuan Yang, Weiduo Gong, Yi Zhang

Intelligent systems are increasingly integral to our daily lives, yet rare safety-critical events present significant latent threats to their practical deployment. Addressing this challenge hinges on accurately predicting the probability of safety-critical events occurring within a given time step from the current state, a metric we define as 'criticality'.

Soon-Tae Hong, Yong-Wan Kim, Young-Jai Park

In this paper, we study tidal forces in the Schwarzschild black hole whose metric includes explicitly a generalized uncertainty principle (GUP) effect. We also investigate interesting features of the geodesic equations and tidal effects dependent on the GUP parameter $\alpha$ related to a minimum length. Then, by solving geodesic deviation equations explicit

Claudio Bonanno, Jorge Luis Dasilva Golán, Massimo D'Elia, Margarita García Pérez

We investigate the role of topology on the lattice determination of the $\mathrm{SU}(3)$ strong coupling renormalized via gradient flow. To deal with the topological freezing of standard local algorithms, the definition of the coupling is usually projected onto the zero topological sector. However, it is not obvious that this definition is not biased by the

Ziyuan Zhu, Xiao Chen, Wei Wang

Bicoherence is a way to measure the phase coupling of triplets of Fourier frequencies. We use this method to analyze quasi-periodic oscillations (QPOs) in the black hole X-ray binary MAXI J1535$-$571 during its 2017 September-October outburst. The bicoherence provides an interesting new diagnostic to uncover QPO behaviour and the relationships between QPO ha

Hamed Taghavian, Florian Dorfler, Mikael Johansson

An iterative learning algorithm is presented for continuous-time linear-quadratic optimal control problems where the system is externally symmetric with unknown dynamics. Both finite-horizon and infinite-horizon problems are considered. It is shown that the proposed algorithm is globally convergent to the optimal solution and has some advantages over adaptiv

Wadim Zudilin

We relate two different solutions of a Mahler equation; one solution is only defined at certain roots of unity, while the other is an analytic function inside the unit disk.

Marius Ghergu, Jack McNicholl

We discuss the existence and nonexistence of solutions to the steady-state Gierer-Meinhardt system $$ \begin{cases} \displaystyle -\Delta u=\frac{u^p}{v^q}+\lambda \rho(x) \,, u>0 &\quad\mbox{ in }\mathbb{R}^N\setminus K,\\[0.1in] \displaystyle -\Delta v=\frac{u^m}{v^s} \,, v>0 &\quad\mbox{ in }\mathbb{R}^N\setminus K,\\[0.1in] \displaystyle \;\;\; \frac{\pa

Simon Stock, Davood Babazadeh, Christian Becker, Spyros Chatzivasileiadis

While the uncertainty in generation and demand increases, accurately estimating the dynamic characteristics of power systems becomes crucial for employing the appropriate control actions to maintain their stability. In our previous work, we have shown that Bayesian Physics-informed Neural Networks (BPINNs) outperform conventional system identification method

Zhengqi Xu, Jun Xu, Ai-Guo Wu, Shuning Wang

Satellite attitude cotrol is a crucial part of aerospace technology, and model predictive control(MPC) is one of the most promising controllers in this area, which will be less effective if real-time online optimization can not be achieved. Explicit MPC converts the online calculation into a table lookup process, however the solution is difficult to obtain i

Yanyuan Qiao, Zheng Yu, Longteng Guo, Sihan Chen

Multimodal large language models (MLLMs) have attracted widespread interest and have rich applications. However, the inherent attention mechanism in its Transformer structure requires quadratic complexity and results in expensive computational overhead. Therefore, in this work, we propose VL-Mamba, a multimodal large language model based on state space model

Sándor Lökös, Gábor Kasza

In high energy heavy ion collisions a new state of matter, the strongly coupled quark gluon plasma is formed that exhibits the similar properties as our Universe had just a couple of microseconds after the Big Bang, hence such collisions are usually referred as Little Bangs. Subsequent investigations showed that the created medium is a nearly perfect fluid w

Mohammadali Razeghi, Jean Spiece, Valentin Fonck, Yao Zhang

Solid-state cooling devices offer compact, quiet, reliable and environmentally friendly solutions that currently rely primarily on the thermoelectric (TE) effect. Despite more than two centuries of research, classical thermoelectric coolers suffer from low efficiency which hampers wider application. In this study, the less researched Anomalous Ettingshausen

Yifan Wang, Haodi Ma, Daisy Zhe Wang

Large language model (LLM) has marked a pivotal moment in the field of machine learning and deep learning. Recently its capability for query planning has been investigated, including both single-modal and multi-modal queries. However, there is no work on the query optimization capability of LLM. As a critical (or could even be the most important) step that s

Hanlin Wu, Sheng Li, Yan Lyu, Yucheng Guo

Growth parameters play a significant role in the crystal quality and physical properties of layered materials. Here we present a case study on a van der Waals magnetic NbFeTe2 material. Two different types of polymorphic NbFeTe2 phases, synthesized at different temperatures, display significantly different behaviors in crystal symmetry, electronic structure,

Kenji Shimomura, Masatoshi Sato

Non-Hermiticity enables macroscopic accumulation of bulk states, named non-Hermitian skin effects. The non-Hermitian skin effects are well-established for single-particle systems, but their proper characterization for general systems is elusive. Here, we propose a general criterion of non-Hermitian skin effects, which works for any finite-dimensional system

Sara Joksovic, Jovana Stanojev, Natasa Samardzic, Branimir Bajac

The discovery of transparent electrodes led to the development of optoelectronic devices such as OLEDs, LCDs, touchscreens, IR sensors, etc. Since ITO has many drawbacks in respect of its production cost and limited transparency in IR, carbon nanotubes (CNTs) have been a potential replacement for ITO due to their exceptional electrical and optical properties

Ben Lasscock, Altay Sansal, Alejandro Valenciano

This article presents a self-supervised generative AI approach to seismic data processing and interpretation using a Masked AutoEncoder (MAE) with a Vision Transformer (ViT) backbone. We modified the MAE-ViT architecture to process 3D seismic mini-cubes to analyze post-stack seismic data. The MAE model can semantically categorize seismic features, demonstrat

Ilias Chalkidis, Stephanie Brandl

Instruction-finetuned Large Language Models inherit clear political leanings that have been shown to influence downstream task performance. We expand this line of research beyond the two-party system in the US and audit Llama Chat in the context of EU politics in various settings to analyze the model's political knowledge and its ability to reason in context

Milou Temmink, Ewine F. van Dishoeck, Sierra L. Grant, Benoit Tabone

The MRS mode of the JWST-MIRI instrument has been shown to be a powerful tool to characterise the molecular gas emission of the inner region of planet-forming disks. Here, we analyse the spectrum of the compact T-Tauri disk DR Tau, which is complemented by high spectral resolution (R~60000-90000) CO ro-vibrational observations. Various molecular species, inc

Ewa Damek, Sebastian Mentemeier

In recent works on the theory of machine learning, it has been observed that heavy tail properties of Stochastic Gradient Descent (SGD) can be studied in the probabilistic framework of stochastic recursions. In particular, G\"{u}rb\"{u}zbalaban et al. (arXiv:2006.04740) considered a setup corresponding to linear regression for which iterations of SGD can be

Adian Liusie, Yassir Fathullah, Mark J. F. Gales

Large Language Models (LLMs) have demonstrated impressive zero-shot capabilities and versatility in NLP tasks, however they sometimes fail to maintain crucial invariances for specific tasks. One example is permutation sensitivity, where LLMs' outputs may significantly vary depending on the order of the input options. While debiasing techniques can mitigate t

Phillip Y. Lee, Minhyuk Sung

When an image generation process is guided by both a text prompt and spatial cues, such as a set of bounding boxes, do these elements work in harmony, or does one dominate the other? Our analysis of a pretrained image diffusion model that integrates gated self-attention into the U-Net reveals that spatial grounding often outweighs textual grounding due to th

Chengzhe Feng, Yanan Sun, Ke Li, Pan Zhou

As Pre-trained Language Models (PLMs), a popular approach for code intelligence, continue to grow in size, the computational cost of their usage has become prohibitively expensive. Prompt learning, a recent development in the field of natural language processing, emerges as a potential solution to address this challenge. In this paper, we investigate the eff

Namitha Liyanage, Yue Wu, Siona Tagare, Lin Zhong

A fault-tolerant quantum computer must decode and correct errors faster than they appear to prevent exponential slowdown due to error correction. The Union-Find (UF) decoder is promising with an average time complexity slightly higher than $O(d^3)$. We report a distributed version of the UF decoder that exploits parallel computing resources for further speed

V. N. Chuvil'deev, A. V. Nokhrin, C. V. Likhnitskii, A. A. Murashov

A hot salt corrosion (HSC) test was performed on the fine-grained titanium alpha-alloy Ti-2.5Al-2.6Zr (Russian industrial alloy PT-7M). The ultrafine-grained (UFG) microstructure in the titanium alpha-alloy was formed via cold Rotary Swaging. The grain size and volume fraction of the recrystallized microstructure in the alloy were varied by choosing appropri

X. -Y. Fu, Z. -Y. Guo, Q. -H. Wang, R. -C. Wang

Due to disparate formation mechanisms, as for central hot-spot ignition and fast ignition, the initial temperatures of electron and ions usually differs from each other in the hot spot. Considering the percipient dependence of fusion cross-section and energy losses on temperature, this difference manifests the inadequacy of the equilibrium theoretical model

Heisnam Shanjit Singh, Chiranjeeb Singha, Sraban Kumar Upadhyaya

In this work, we investigate the tunneling phenomenon of a charged Dirac particle emerging from a thermal horizon of a hot NUT-Kerr-Newman-Kasuya-Anti-de Sitter (HNKNK-AdS) black hole. Considering the tunneling formalism, we report the Hawking temperature of the charged Dirac particle through the horizon and the heat capacity of the HNKNK-AdS black hole. It

Hao-Chung Cheng, Li Gao

Strong converse theorems refer to the study of impossibility results in information theory. In particular, Mosonyi and Ogawa established a one-shot strong converse bound for quantum hypothesis testing [Comm. Math. Phys, 334(3), 2014], which servers as a primitive tool for establishing a variety of tight strong converse theorems in quantum information theory.

Xinyi He, Jiaru Zou, Yun Lin, Mengyu Zhou

Large Language Models have revolutionized code generation ability by converting natural language descriptions into executable code. However, generating complex code within real-world scenarios remains challenging due to intricate structures, subtle bugs, understanding of advanced data types, and lack of supplementary contents. To address these challenges, we

Moritz Drescher, Manfred Salmhofer, Tilman Enss

The functional determinant approach (FDA) is a simple method to compute exactly certain observables for ideal quantum systems and has been successfully applied to the Fermi polaron problem to obtain the dynamical overlap and spectral function. Unfortunately, its application to Bosonic ultracold gases is prohibited by the failure of the grand canonical ensemb

Ema Dimastrogiovanni, Matteo Fasiello, Alexandros Papageorgiou

We consider the case of axion-like particles (ALPs) during inflation. When coupled to a non-Abelian gauge sector via a Chern-Simons term, ALPs support an intriguing, testable, phenomenology with very distinctive features including chiral primordial gravitational waves. For sufficiently small values of the gauge vev and coupling, scalar perturbations in the g

Thiago Araujo

PySymmPol is a Python package designed for efficient manipulation of symmetric polynomials. It provides functionalities for working with various types of symmetric polynomials, including elementary, homogeneous, monomial symmetric, (skew-) Schur, and Hall-Littlewood polynomials. In addition to polynomial operations, PySymmPol offers tools to explore key prop

Iwona Kotko, Sambaran Banerjee, Krzysztof Belczynski

The two systems, namely, Gaia~BH1 and Gaia~BH2, that have been confirmed as dormant (i.e., no X-ray emission detected) black hole (BH) - low-mass star binaries in the latest Gaia mission data release (DR3) are intriguing in the context of their formation and evolution. Both systems consist of $\sim9$ $\mathrm{M}_{\odot}$ BH and $\sim1$ $\mathrm{M}_{\odot}$ s

N. A. Carella

Let $1\leq a<q$ be a pair of small integers such that $\gcd(a,q)=1$ and let $x>1$ be a large number. This note discusses the existence of a short sequence of primes $p\equiv a\bmod q$ between two squares $x^2$ and $(x+1)^2$.

Do June Min, Veronica Perez-Rosas, Kenneth Resnicow, Rada Mihalcea

In this paper, we study the problem of multi-reward reinforcement learning to jointly optimize for multiple text qualities for natural language generation. We focus on the task of counselor reflection generation, where we optimize the generators to simultaneously improve the fluency, coherence, and reflection quality of generated counselor responses. We intr

Shubham Negi, Utkarsh Saxena, Deepika Sharma, Kaushik Roy

Analog Compute-in-Memory (CiM) accelerators are increasingly recognized for their efficiency in accelerating Deep Neural Networks (DNN). However, their dependence on Analog-to-Digital Converters (ADCs) for accumulating partial sums from crossbars leads to substantial power and area overhead. Moreover, the high area overhead of ADCs constrains the throughput

Takuya Machida

Quantum walks are referred to as quantum analogs to random walks in mathematics. They have been studied as quantum algorithms in quantum information for quantum computers. There are two types of quantum walks. One is the discrete-time quantum walk and the other is the continuous-time quantum walk. We study a continuous-time quantum walk on the half line and

Anh-Kiet Duong, Hoàng-Ân Lê, Minh-Tan Pham

In the realm of Federated Learning (FL) applied to remote sensing image classification, this study introduces and assesses several innovative communication strategies. Our exploration includes feature-centric communication, pseudo-weight amalgamation, and a combined method utilizing both weights and features. Experiments conducted on two public scene classif

Bowen Zheng, Zihan Lin, Enze Liu, Chen Yang

Nowadays, reading or writing comments on captivating videos has emerged as a critical part of the viewing experience on online video platforms. However, existing recommender systems primarily focus on users' interaction behaviors with videos, neglecting comment content and interaction in user preference modeling. In this paper, we propose a novel recommendat

Honghao Wang, Qingqing Wu, Wen Chen

This paper investigates the utility of movable antenna (MA) assistance for the multiple-input single-output (MISO) interference channel. We exploit an additional design degree of freedom provided by MA to enhance the desired signal and suppress interference so as to reduce the total transmit power of interference network. To this end, we jointly optimize the

Tobias Simon

The Kr\"otz-Stanton Extension Theorem states that the orbit map of a K-finite vector in a Hilbert representation of a linear Lie group extends to a holomorphic map to a principal fibre bundle over the complex crown domain associated to the Riemannian symmetric space $G/K$. We extend this theorem to arbitrary connected semisimple Lie groups and prove polynomi

Christian Reiher

This survey on graphs of large girth consists of two parts. The first deals with some aspects of algebraic and extremal graph theory loosely related to the Moore bound. Our point of departure for the second, Ramsey theoretic, part are some constructions of graphs with large chromatic number and large girth; this will lead us to a discussion of the recent gir

Yu Deng, Duomin Wang, Baoyuan Wang

In this paper, we propose a novel learning approach for feed-forward one-shot 4D head avatar synthesis. Different from existing methods that often learn from reconstructing monocular videos guided by 3DMM, we employ pseudo multi-view videos to learn a 4D head synthesizer in a data-driven manner, avoiding reliance on inaccurate 3DMM reconstruction that could

Francesco Borando, Guido Tiana

Protein-mediated interactions are ubiquitous in the cellular environment, and particularly in the nucleus, where they are responsible for the structuring of chromatin. We show through molecular--dynamics simulations of a polymer surrounded by binders that the strength of the binder-polymer interaction separates an equilibrium from a non-equilibrium regime. I

Daniel Ginsberg, Igor Rodnianski

We consider the long-time behavior of irrotational solutions of the three-dimensional compressible Euler equations with shocks, hypersurfaces of discontinuity across which the Rankine-Hugoniot conditions for irrotational flow hold. Our analysis is motivated by Landau's analysis of spherically-symmetric shock waves, who predicted that at large times, not just

Sander Borst, Leon Eifler, Ambros Gleixner

This paper presents the integration of constraint propagation and dual proof analysis in an exact, roundoff-error-free MIP solver. The authors employ safe rounding methods to ensure that all results remain provably correct, while sacrificing as little computational performance as possible in comparison to a pure floating-point implementation. The study also

Nicolas Shiaelis, Luke A. Clifton, Andrew R. McCluskey

Neutron reflectometry is a critical tool for investigating the structure of thin films and interfaces. However, the misapplication of the Born approximation to reflection geometry leads some to assume that the minimum thickness that may be probed by neutron reflectometry is limited by the Q-range of the measurement. In this study, we use model-dependent anal

Zelin He, Ying Sun, Jingyuan Liu, Runze Li

We consider the transfer learning problem in the high dimensional linear regression setting, where the feature dimension is larger than the sample size. To learn transferable information, which may vary across features or the source samples, we propose an adaptive transfer learning method that can detect and aggregate the feature-wise (F-AdaTrans) or sample-

Victor H. González, Artem Litvinenko, Akash Kumar, Roman Khymyn

The ever increasing demand for computational power combined with the predicted plateau for the miniaturization of existing silicon-based technologies has made the search for low power alternatives an industrial and scientifically engaging problem. In this work, we explore spintronics-based Ising machines as hardware computation accelerators. We start by pres

Haoyu Wang, Basel Halak, Jianjie Ren, Ahmad Atamli

This study introduces a refined Flooding Injection Rate-adjustable Denial-of-Service (DoS) model for Network-on-Chips (NoCs) and more importantly presents DL2Fence, a novel framework utilizing Deep Learning (DL) and Frame Fusion (2F) for DoS detection and localization. Two Convolutional Neural Networks models for classification and segmentation were develope

Chaoqun Yang, Xiaowei Liang, Zhiguo Shi, Heng Zhang

This paper addresses the problem of group target tracking (GTT), wherein multiple closely spaced targets within a group pose a coordinated motion. To improve the tracking performance, the labeled random finite sets (LRFSs) theory is adopted, and this paper develops a new kind of LRFSs, i.e., augmented LRFSs, which introduces group information into the defini

Yipeng Wang, Yu Yu

The power spectrum, as a statistic in Fourier space, is commonly numerically calculated using the fast Fourier transform method to efficiently reduce the computational costs. To alleviate the systematic bias known as aliasing due to the insufficient sampling, the interlacing technique was proposed. We derive the analytical form of the shot noise under the in

Amir Zeldes, Tatsuya Aoyama, Yang Janet Liu, Siyao Peng

In this article we present Enhanced Rhetorical Structure Theory (eRST), a new theoretical framework for computational discourse analysis, based on an expansion of Rhetorical Structure Theory (RST). The framework encompasses discourse relation graphs with tree-breaking, non-projective and concurrent relations, as well as implicit and explicit signals which gi

Hui-Chun Wu

Some giant pulses and fast radio bursts exhibit notable circular polarization, which remains unexplained and carries significant implications for their emission mechanisms. In this study, we identify multiple nanoshot pairs uniformly spaced by approximately 21 $\mu$s within a giant pulse emitted by the Crab pulsar. Among these pairs, a subset displays left-h

Seyong Kim, Alexander Rothkopf

We construct a discrete non-hermitean momentum operator, which implements faithfully the non self-adjoint nature of momentum for a particle in a box. Its eigenfunctions are strictly limited to the interior of the box in the continuum limit, with the quarter wave as first non-trivial eigenstate. We show how to construct the corresponding hermitean Hamiltonian

V. F. Suleimanov, A. S. Tavleev, V. Doroshenko, K. Werner

Supersoft X-ray sources (SSSs) are accreting white dwarfs (WDs) with stable or recurrent thermonuclear burning on their surfaces. High-resolution X-ray spectra of such objects are rather complex, often consist of several components, and are difficult to interpret accurately. The main emission source is the hot surface of the WD and the emergent radiation can

Djamahl Etchegaray, Zi Huang, Tatsuya Harada, Yadan Luo

In this work, we tackle the limitations of current LiDAR-based 3D object detection systems, which are hindered by a restricted class vocabulary and the high costs associated with annotating new object classes. Our exploration of open-vocabulary (OV) learning in urban environments aims to capture novel instances using pre-trained vision-language models (VLMs)

David J. Goldie, Stafford Withington, Christopher N. Thomas

We consider and compare candidate superconducting detector technologies that might be applied to the readout of cavity-axion haloscopes and similar fundamental physics experiments. We conclude that a transition edge sensor (TES) configured with ballistic-phonon thermal isolation operated with a superconducting transition temperature of order 30 mK would prov

Christopher N. Thomas, Stafford Withington, David J. Goldie

We consider how superconducting microwave detector technology might be applied to the readout of cavity-axion haloscopes and similar fundamental physics experiments. Expressions for the sensitivity of two detection schemes are derived: 1) a dispersive spectrometer, and 2) a direct-conversion/homodyne receiver using detectors as mixing elements. In both cases

H. Zhang, Z. Qiao, H. Wang, B. Duan

Conversational artificial intelligence can already independently engage in brief conversations with clients with psychological problems and provide evidence-based psychological interventions. The main objective of this study is to improve the effectiveness and credibility of the large language model in psychological intervention by creating a specialized age