December 2023 arXiv papers — page 29

Xinyu Ren, Ruixuan Wang

This research presents a novel active detection model utilizing deep reinforcement learning to accurately detect traffic objects in real-world scenarios. The model employs a deep Q-network based on LSTM-CNN that identifies and aligns target zones with specific categories of traffic objects through implementing a top-down approach with efficient feature extra

Taro Yoshino

We introduce a mock toric variety, a generalization of a toric variety. For a non-toric example, Del-Pezzo surfaces are mock toric varieties. These new varieties inherit some properties of mock toric varieties. In application, we give sufficient conditions for the concrete construction of a strictly toroidal model of a hypersurface in a mock toric variety.

Kui Wang, Tao Yu, Zongdian Li, Kei Sakaguchi

The concept of a digital twin (DT) plays a pivotal role in the ongoing digital transformation and has achieved significant strides for various wireless applications in recent years. In particular, the field of autonomous vehicles is a domain that is ripe for exploiting the concept of DT. Nevertheless, there are many challenges that include holistic considera

Khalid El-Awady

We investigate the construction of generative models capable of encoding physical constraints that can be hard to express explicitly. For the problem of inverse material design, where one seeks to design a material with a prescribed set of properties, a significant challenge is ensuring synthetic viability of a proposed new material. We encode an implicit da

Yongle Zhang, Ting Kei Pong, Shiqi Xu

We revisit and adapt the extended sequential quadratic method (ESQM) in [3] for solving a class of difference-of-convex optimization problems whose constraints are defined as the intersection of level sets of Lipschitz differentiable functions and a simple compact convex set. Particularly, for this class of problems, we develop a variant of ESQM, called ESQM

Xicong Shen, Yang Liu, Huiqi Liu, Jue Hong

Fine-tuning is a prominent technique to adapt a pre-trained language model to downstream scenarios. In parameter-efficient fine-tuning, only a small subset of modules are trained over the downstream datasets, while leaving the rest of the pre-trained model frozen to save computation resources. In recent years, a popular productization form arises as Model-as

Johann Kay Ann Tan, Siu-Kit Lau, Yoshimi Hasegawa

This study investigates the aural and visual factors that influence appropriateness perception in soundscape evaluations in residential spaces, where people may spend most of their time in. Appropriateness in soundscape is derived from the expectation of sound sources in a specific environment, place, or function heard by a listener. The appropriateness of s

Xiangmin Liu, Rui Ge, Chengyu Chen, Jiangwei Wu

Thin film lithium niobate (TFLN) has become an platform for modern integrated circuits due to its excellent optical properties. With the development of rare earth ion doped TFLN, important breakthroughs of on-chip microlasers has emerged and show significant application for optical communication, computing and quantum photonics. However, challenges still rem

Xinran Li, Jun Zhang

Effective communication protocols in multi-agent reinforcement learning (MARL) are critical to fostering cooperation and enhancing team performance. To leverage communication, many previous works have proposed to compress local information into a single message and broadcast it to all reachable agents. This simplistic messaging mechanism, however, may fail t

Rafael I. Rofa

A graceful labelling of a tree T = (V,E), where V is the set of vertices of the tree and E is its edge set, is a bijective function f from V to the set consisting of the numbers 0, 1, ... |E| inclusive, such that if edge uv is assigned the value |f(u)-f(v)| then the edge labels are distinct numbers of the set consisting of the numbers 1, 2, ..., |E| inclusiv

Tianhao Shi, Yang Zhang, Zhijian Xu, Chong Chen

Adapting Large Language Models for Recommendation (LLM4Rec) has shown promising results. However, the challenges of deploying LLM4Rec in real-world scenarios remain largely unexplored. In particular, recommender models need incremental adaptation to evolving user preferences, while the suitability of traditional incremental learning methods within LLM4Rec re

Federico Ambrosino, Shota Komatsu

We study analytic properties and integrable structures of the meson spectrum in large $N_c$ QCD$_2$. We show that the integral equation that determines the masses of the mesons, often called the 't Hooft equation, is equivalent to finding solutions to a TQ-Baxter equation. Our analysis extends some of previous results by Fateev et al.\ to general quark masse

Nobuharu Nakajima

The physical interpretation of weak measurements has been the subject of much debate. It is known that anomalous phenomena and results that appear in weak measurements are essentially related to the phase of the quantum system being measured. Consideration of the phase is important to clarify its physical interpretation. In classical wave optics, there has l

Si Zhang, Philip W. L. Fong

Protection domains are one of the most enduring concepts in Access Control. Entities with identical access control characteristics are grouped under the same protection domain, and domain-based policies assign access privileges to the protection domain as a whole. With the advent of the Internet of Things (IoT), devices play the roles of both subjects and ob

Haoyu Wei, Runzhe Wan, Lei Shi, Rui Song

Many real-world bandit applications are characterized by sparse rewards, which can significantly hinder learning efficiency. Leveraging problem-specific structures for careful distribution modeling is recognized as essential for improving estimation efficiency in statistics. However, this approach remains under-explored in the context of bandits. To address

Wenzhi Gao, Zhaonan Qu, Madeleine Udell, Yinyu Ye

We consider the problem of finding the optimal diagonal preconditioner for a positive definite matrix. Although this problem has been shown to be solvable and various methods have been proposed, none of the existing approaches are scalable to matrices of large dimension, or when access is limited to black-box matrix-vector products, thereby significantly lim

Chengxin Chen, Pengyuan Zhang

One persistent challenge in deep learning based speech emotion recognition (SER) is the unconscious encoding of emotion-irrelevant factors (e.g., speaker or phonetic variability), which limits the generalization of SER in practical use. In this paper, we propose DSNet, a Disentangled Siamese Network with neutral calibration, to meet the demand for a more rob

Kohei Akase

We consider the Cauchy problem for a quadratic derivative nonlinear Schr\"odinger equation whose nonlinearity is a linear combination of $\partial_x (u^2)$ and $\partial_x (|u|^2)$. We prove the local well-posedness in the $L^2$-based Sobolev space $H^s(\mathbb{R})$ for $s\ge 0$ with bounded primitives. Moreover, we prove the global well-posedness in $H^s(\m

Rui Song, Fausto Giunchiglia, Yingji Li, Mingjie Tian

Cross-domain text classification aims to transfer models from label-rich source domains to label-poor target domains, giving it a wide range of practical applications. Many approaches promote cross-domain generalization by capturing domain-invariant features. However, these methods rely on unlabeled samples provided by the target domains, which renders the m

Bo Peng, Bingjun Zhu, Danil Dmitriev, Jun Zhang

Radiometer is a light-induced aerodynamic propulsive device under the rarefied gas environment, which holds great potential for the next-gen near-space flight. However, its practical applications are hindered by the weak propulsion forces on the conventional radiometer vanes. Herein, this material-aerodynamics cross-disciplinary study develops novel radiomet

Qi Hu, Haoran Li, Jiaxin Bai, Zihao Wang

In the era of large language models (LLMs), efficient and accurate data retrieval has become increasingly crucial for the use of domain-specific or private data in the retrieval augmented generation (RAG). Neural graph databases (NGDBs) have emerged as a powerful paradigm that combines the strengths of graph databases (GDBs) and neural networks to enable eff

Jiawei Wen

In recent years, considerable attention has been devoted to the regularization models due to the presence of high-dimensional data in scientific research. Sparse support vector machine (SVM) are useful tools in high-dimensional data analysis, and they have been widely used in the area of econometrics. Nevertheless, the non-smoothness of objective functions a

Seokhoon Jeong, Assentay Makhmud

Recent Large Language Models (LLMs) have shown remarkable capabilities in mimicking fictional characters or real humans in conversational settings. However, the realism and consistency of these responses can be further enhanced by providing richer information of the agent being mimicked. In this paper, we propose a novel approach to generate more realistic a

Yulei Wang, Xin Cheng, Yang Guo, Jinhan Guo

Context. Three-dimensional (3D) reconnection is an important mechanism for efficiently releasing energy during astrophysical eruptive events, which is difficult to be quantitatively analyzed especially within turbulent plasmas. Aims. In this paper, an efficient method for identifying locations and configurations of 3D reconnection from MHD data is developed.

Misaki Mizumoto, Masahiro Tsujimoto, Renata S. Cumbee, Megan E. Eckart

The spectroscopic performance of x-ray instruments can be affected at high count rates. The effects and mitigation in the optical chain, such as x-ray attenuation filters or de-focusing mirrors, are widely discussed, but those in the signal chain are not. Using the Resolve x-ray microcalorimeter onboard the XRISM satellite, we discuss the effects observed du

Satoru Hayami

Magnetic skyrmions with topologically nontrivial spin textures form a variety of periodic structures depending on microscopic interactions and lattice symmetry. We theoretically investigate a transformation between triangular and square skyrmion crystals against an external magnetic field in a polar tetragonal magnet. By performing the simulated annealing fo

Hui Liu, Xiaojin Zhang, Yingying Zhang

We first introduce the notion of $CM$-$\tau$-tilting free algebras as the generalization of $CM$-free algebras and show the homological properties of $CM$-$\tau$-tilting free algebras. Then we give a bijection between Gorenstein projective $\tau$-rigid modules and certain modules by using an equivalence established by Kong and Zhang. Finally, we give a parti

Padmavathi Venkatraman, Wynn Jacobson-Galan

The light-curve evolution of a supernova contains information of the exploding star. Early-time photometry of a variety of explosive transients, including Calcium-rich transients and type IIb/Ibc and IIP supernovae shows evidence for an early light curve peak as a result of the explosion's shock wave passing through extended material (i.e., shock cooling emi

Y. Liu, T. T. Liu, Q. Q. Yang, G. Tian

Magnon frequency comb provides opportunities for exploring magnon nonlinear effects and measuring the transmission magnon frequency in magnets, whose controllability becomes vital for modulating the operating frequency and improving the measurement accuracy. Nevertheless, such controllable frequency comb remains to be explored. In this work, we investigate t

Haiyang Sun, Zheng Lian, Chenglong Wang, Kang Chen

There remain two critical challenges that hinder the development of ERC. Firstly, there is a lack of exploration into mining deeper insights from the data itself for conversational emotion tasks. Secondly, the systems exhibit vulnerability to random modality feature missing, which is a common occurrence in realistic settings. Focusing on these two key challe

He Zhang, Xinyang Li, Christine Qiu, Xinyi Fu

This preliminary study investigated user experiences in VR horror games, highlighting fear-triggering and gender-based differences in perception. By utilizing a scientifically validated and specially designed questionnaire, we successfully collected questionnaire data from 23 subjects for an early empirical study of fear induction in a virtual reality gaming

Bo-Nan Jiang

We present a two-state Kalman estimator of gravity acceleration and evaluate its performance by numerical simulations and post-measurement demonstration with real-world atomic gravimetry. We show that the estimator-enhanced gravimetry significantly improves upon both short-term sensitivity and long-term stability. The estimates of gravity acceleration demons

Chunchao Wen, Jianfa Zhang, Shiqiao Qin, Zhihong Zhu

It was proved that the joint operation of electromagnetic reciprocity and $n$-fold ($n\geq3$) rotational symmetry would secure arbitrary polarization-independent backscattering efficiency [Phys. Rev. B \textbf{103}, 045422 (2021)]. Here we remove the restriction of reciprocity and study the backscatterings of plane waves by rotationally symmetric magneto-opt

Saman Lak, Rajeev Jaiman

In this paper, we numerically study the oscillatory dynamics associated with the tip vortex cavitation over an elliptical hydrofoil section using our 3D variational multiphase flow solver at a Reynolds number of $Re=8.95 \times 10^5$ via dynamic subgrid-scale modeling and homogeneous mixture theory. To begin, we examine the grid resolution requirements and i

Xiongfei Zhao, Gerui Zhang, Yain-Whar Si

As coin-based rewards dwindle, transaction fees play an important role as mining incentives in Bitcoin. In this paper, we propose a novel mechanism called Efficient Dynamic Transaction Storage (EDTS) for dynamically allocating transactions among blocks to achieve efficient storage utilization. By leveraging a combination of Cuckoo Filter and Dynamic Transact

Fangyuan Wang, Anqing Duan, Peng Zhou, Shengzeng Huo

The challenges inherent in long-horizon tasks in robotics persist due to the typical inefficient exploration and sparse rewards in traditional reinforcement learning approaches. To address these challenges, we have developed a novel algorithm, termed Explicit-Implicit Subgoal Planning (EISP), designed to tackle long-horizon tasks through a divide-and-conquer

Wenhao Wu, Weiwei Wang, Shengjiang Kong

Clustering is a fundamental unsupervised representation learning task with wide application in computer vision and pattern recognition. Deep clustering utilizes deep neural networks to learn latent representation, which is suitable for clustering. However, previous deep clustering methods, especially image clustering, focus on the features of the data itself

Shreyas Verma, Kien Tran, Yusuf Ali, Guangyu Min

Reducing and detecting hallucinations in large language models is an open research problem. In this project, we attempt to leverage recent advances in the field of uncertainty estimation to reduce hallucinations in frozen large language models. Epistemic neural networks have recently been proposed to improve output joint distributions for large pre-trained m

Su Jia, Nathan Kallus, Christina Lee Yu

We consider experimentation in the presence of non-stationarity, inter-unit (spatial) interference, and carry-over effects (temporal interference), where we wish to estimate the global average treatment effect (GATE), the difference between average outcomes having exposed all units at all times to treatment or to control. We suppose spatial interference is d

Xiongfei Zhao, Yain-Whar Si

In China's Greater Bay Area (Guangdong-Hong Kong-Macao), the increasing use of Blockchain technology in financial services has the potential to generate benefits for many stakeholders. Blockchains are known for their distinctive features, such as decentralized architecture, tamper-proof data structures, and traceable transactions. These features make Blockch

Tung Nguyen, Alex Scott, Paul Seymour

We confirm a conjecture of Fox, Pach, and Suk, that for every $d>0$, there exists $c>0$ such that every $n$-vertex graph of VC-dimension at most $d$ has a clique or stable set of size at least $n^c$. This implies that, in the language of model theory, every graph definable in NIP structures has a clique or anti-clique of polynomial size, settling a conjectur

Danny Goodacre

This report is concerned with the efficiency of numerical methods for simulating quantum spin systems, with the aim to implement an improved method for simulation of a time-dependent Hamiltonian that displays chirped pulses at a high frequency. Working in the density matrix formulation of quantum systems, we study evolution under the Liouville-von Neumann eq

Wen-Xiang Chen, Zi-Yang Huang

We know that Kerr black holes are stable for specific conditions.In this article, we use algebraic methods to prove the stability of the Kerr black hole against certain scalar perturbations. This provides new results for the previously obtained superradiant stability conditions of Kerr black hole. Hod proved that Kerr black holes are stable to massive pertur

Jiayin Sun, Qiulei Dong

Open-set image recognition (OSR) aims to both classify known-class samples and identify unknown-class samples in the testing set, which supports robust classifiers in many realistic applications, such as autonomous driving, medical diagnosis, security monitoring, etc. In recent years, open-set recognition methods have achieved more and more attention, since

Xiao-Feng Zhou, Yu-Chen Zhuang, Mo-Han Zhang, Hao Sheng

In a molecule formed by two atoms, energy difference between bonding and antibonding orbitals should depend on distance of the two atoms. However, exploring molecular orbitals of two natural atoms with tunable distance has remained an outstanding experimental challenge. Graphene quantum dots (GQDs) can be viewed as relativistic artificial atoms, therefore, o

Tomáš Votroubek, Tomáš Kroupa

We show how to compute globally optimal solutions to inverse kinematics (IK) by formulating the problem as an indefinite quadratically constrained quadratic program. Our approach makes it feasible to solve IK instances of generic redundant manipulators. We demonstrate the performance on randomly generated designs and on real-world robots with up to ten revol

Lu Li, Huangxing Li

We explored decision-making dynamics in social systems, referencing the 'herd behavior' from prior studies where individuals follow preceding choices without understanding the underlying reasons. While previous research highlighted a preference for the optimal choice without external influences, our study introduced principals or external guides, adding comp

Wuji Zhang, Ruifang Wu, Chunfang Sun, Chunfeng Wu

We propose a theoretical approach for entangling two Dicke states in a periodic modulated quantum system. By considering two qubit ensembles that are nonuniformly coupled to a common resonator, we can derive an effective Hamiltonian whose energy levels depend nonlinearly on the excitation number of each qubit ensemble. More simplified effective Hamiltonian c

Haiwei Xue, Sicheng Yang, Zhensong Zhang, Zhiyong Wu

Audio-driven co-speech human gesture generation has made remarkable advancements recently. However, most previous works only focus on single person audio-driven gesture generation. We aim at solving the problem of conversational co-speech gesture generation that considers multiple participants in a conversation, which is a novel and challenging task due to t

Tadao Hoshino, Takahide Yanagi

This study considers testing the specification of spillover effects in causal inference. We focus on experimental settings in which the treatment assignment mechanism is known to researchers. We develop a new randomization test utilizing a hierarchical relationship between different exposures. Compared with existing approaches, our approach is essentially ap

Weijia Zhang, Chun Kai Ling, Xuanhui Zhang

Censoring is the central problem in survival analysis where either the time-to-event (for instance, death), or the time-tocensoring (such as loss of follow-up) is observed for each sample. The majority of existing machine learning-based survival analysis methods assume that survival is conditionally independent of censoring given a set of covariates; an assu

Wei Wang, Peizheng Li, Angela Doufexi, Mark A Beach

Reconfigurable intelligent surface (RIS) technology is receiving significant attention as a key enabling technology for 6G communications, with much attention given to coverage infill and wireless power transfer. However, relatively little attention has been paid to the radiation pattern fidelity, for example, sidelobe suppression. When considering multi-use

Mingchao Liang, Erik Leitinger, Florian Meyer

In this work, we develop a multipath-based simultaneous localization and mapping (SLAM) method that can directly be applied to received radio signals. In existing multipath-based SLAM approaches, a channel estimator is used as a preprocessing stage that reduces data flow and computational complexity by extracting features related to multipath components (MPC

Yongyang Cai, Khyati Malik, Hyeseon Shin

We develop a dynamic multi-region climate-economy model with emissions trading and solve for the dynamic Nash equilibrium under noncooperation, where each region follows Paris Agreement-based emissions caps. The permit price reaches $923 per ton of carbon by 2050, and global temperature rises to 1.7 degrees Celsius above pre-industrial levels by 2100. The re

Ka Ming Law, Arashdeep S. Thind, Mihir Pendharkar, Sahil J. Patel

We report the formation of Mn-rich regions at the interface of Co2FexMn1-xSi thin films grown on GaAs substrates by molecular beam epitaxy (MBE). Scanning transmission electron microscopy (STEM) with electron energy loss (EEL) spectrum imaging reveals that each interfacial region: (1) is 1-2 nm wide, (2) occurs irrespective of the Fe/Mn composition ratio and

Zonghai Yao, Nandyala Siddharth Kantu, Guanghao Wei, Hieu Tran

The advancement in healthcare has shifted focus toward patient-centric approaches, particularly in self-care and patient education, facilitated by access to Electronic Health Records (EHR). However, medical jargon in EHRs poses significant challenges in patient comprehension. To address this, we introduce a new task of automatically generating lay definition

Boris Blok, Chang Wu

We study the $z_g$ distribution for heavy flavour, i.e. bottom and charm quark, jets propagating through the dense QCD medium. We extend the late emission approximation for Armesto-Salgado-Wiedemann (ASW) formula to heavy flavours. % We consider both the normalised and $N_{\rm jet}$ normalised $z_g$ distributions, and the ratio of the latter distributions to

Jovan Žigić

This report provides an investigation into solving the Kuramoto-Sivashinsky equation in two spatial dimensions (2DKS) using a pseudo-spectral method on various rectangular periodic domains. The Kuramoto-Sivashinsky equation is a fluid dynamics model that exhibits dynamical features that are highly dependent on the length of the periodic domain. The goals of

Kazuo Yamazaki

The momentum formulation of the surface quasi-geostrophic equations consists of two nonlinear terms, besides the pressure term, one of which cannot be written in a divergence form. When the anti-divergence operator is applied to such nonlinear terms, in general, one cannot take advantage of the differentiation operator of order minus one unless the nonlinear

Eggon Viana

We study the beta deformation of the superstring in $AdS_5\times S^5$ at all orders in the deformation parameter, using the pure spinor formalism. This is necessary to study the regime of strong deformation parameter, which in the field side is related to fishnet theories. We compare the pure spinor sigma model approach to the previously known supergravity d

Efthalia Traianou, Thomas P. Krichbaum, José L. Gómez, Rocco Lico

One of the most well-known extragalactic sources in the sky, quasar 3C 454.3, shows a curved parsec-scale jet that has been exhaustively monitored with very-long-baseline interferometry (VLBI) over the recent years. In this work, we present a comprehensive analysis of four years of high-frequency VLBI observations at 43 GHz and 86 GHz, between 2013-2017, in

Huiqun Li, Hanhan Zhou, Yifei Zou, Dongxiao Yu

Value function factorization has achieved great success in multi-agent reinforcement learning by optimizing joint action-value functions through the maximization of factorized per-agent utilities. To ensure Individual-Global-Maximum property, existing works often focus on value factorization using monotonic functions, which are known to result in restricted

Mina Karimi, Kaushik Bhattacharya

Reactive transport in permeable porous media is relevant for a variety of applications, but poses a significant challenge due to the range of length and time scales. Multiscale methods that aim to link microstructure with the macroscopic response of geo-materials have been developed, but require the repeated solution of the small-scale problem and provide th

Aleix Boquet-Pujadas, Jérôme Hardouïn, Junhao Wen, Jordi Ignés-Mullol

We present a framework to take new measurements in nematic systems that contain active elements such as molecular motors. Spatio-temporal fields of stress, traction, velocity, pressure, and forces are estimated jointly from microscopy images alone. Our inverse-problem approach ensures that these fields comply with physical laws and are accurate at system bou

E. Kapsabelis, Emmanuel N. Saridakis, P. C. Stavrinos

We present for the first time a Friedmann-like construction in the framework of an osculating Finsler-Randers-Sasaki geometry. In particular, we consider a vector field in the metric on a Lorentz tangent bundle, and thus the curvatures of horizontal and vertical spaces, as well as the extra contributions of torsion and non-linear connection, provide an intri

Pratiksha Thaker, Amrith Setlur, Zhiwei Steven Wu, Virginia Smith

Public pretraining is a promising approach to improve differentially private model training. However, recent work has noted that many positive research results studying this paradigm only consider in-distribution tasks, and may not apply to settings where there is distribution shift between the pretraining and finetuning data -- a scenario that is likely whe

Fahime Shahrokh, Nasser Ghadiri, Rasoul Samani, Milad Moradi

Biomedical Named Entity Recognition (NER) is a fundamental task of Biomedical Natural Language Processing for extracting relevant information from biomedical texts, such as clinical records, scientific publications, and electronic health records. The conventional approaches for biomedical NER mainly use traditional machine learning techniques, such as Condit

Tianyuan Jin, Hao-Lun Hsu, William Chang, Pan Xu

We study the multi-agent multi-armed bandit (MAMAB) problem, where $m$ agents are factored into $\rho$ overlapping groups. Each group represents a hyperedge, forming a hypergraph over the agents. At each round of interaction, the learner pulls a joint arm (composed of individual arms for each agent) and receives a reward according to the hypergraph structure

Xinglin Xiao, Yijie Wang, Nan Xu, Yuqi Wang

The difficulty of the information extraction task lies in dealing with the task-specific label schemas and heterogeneous data structures. Recent work has proposed methods based on large language models to uniformly model different information extraction tasks. However, these existing methods are deficient in their information extraction capabilities for Chin

Xinyi Wang, Shaukat Ali, Tao Yue, Paolo Arcaini

Test case optimization (TCO) reduces software testing cost while preserving its effectiveness, but solving TCO problems for large-scale and complex systems requires substantial computational resources. Quantum approximate optimization algorithms (QAOAs) are promising combinatorial optimization algorithms that rely on quantum computational resources, with the

Eitan Tadmor

We prove that Runge-Kutta (RK) methods for numerical integration of arbitrarily large systems of Ordinary Differential Equations are linearly stable. Standard stability arguments -- based on spectral analysis, resolvent condition or strong stability, fail to secure the stability of arbitrarily large RK systems. We explain the failure of different approaches,

Rafael B. Andrist, Gaofeng Huang

We prove the density property for generalized Calogero--Moser spaces with inner degrees of freedom. This allows us to describe the holomorphic automorphism group of these complex affine manifolds. These generalized Calogero--Moser spaces can also be understood as quiver varieties corresponding to moduli spaces of $\mathrm{SU}(2)$ instantons on a non-commutat

Franck Ramaharo, Gerzhino Rasolofomanana

We investigate the predictive power of different machine learning algorithms to nowcast Madagascar's gross domestic product (GDP). We trained popular regression models, including linear regularized regression (Ridge, Lasso, Elastic-net), dimensionality reduction model (principal component regression), k-nearest neighbors algorithm (k-NN regression), support

Yiyu Tang

The following question was proposed by Avi Wigderson and Yuval Wigderson: Is it possible to use the method in their paper(The uncertainty principle: variations on a theme) to prove Heisenberg uncertainty principle in higher dimension R^d, and get the correct dependence of the constant on d? We answer this question affirmatively, and also prove some generaliz

Pierce Ellingson, Farhad Jafari

Determination of linear combination of exponential functions with unknown rate constants from its sampled values is a problem of considerable interest. Here we present a constructive and explicit solution to this problem. Moments of such linear combinations appear in this construction.

J. E. Horvath, R. R. Fernandes, T. P. Idiart

The goal of this article is to give an overview of the current limitations and epistemological barriers in Science and Scientific Philosophy from a very general point of view. We first list and define the types of knowledge nous, doxa and episteme, and the Subject-Observer and Object(s) of study, to proceed showing the different types of barriers that diffic

Oveis Sheibani

In this contribution, the measurement of prompt ${\Lambda_{\rm c}^+\rm /D^0}$ ratio as a function of multiplicity in p--Pb collisions at mid-rapidity at $ \sqrt{s_{\text{NN}}} = 5.02 \;\text{TeV}$ is discussed. By performing this measurement as a function of multiplicity in pp and p--Pb collisions, we can evaluate the $p_{\rm T}$-differential baryon to meson

Jitender Kumar, Dan Yudilevich, Ariel Smooha, Inbar Zohar

Investigating spin and charge noise in strongly correlated electron systems is a valuable way to analyze their physical properties and unlock new phases of matter. In this context, nitrogen-vacancy (NV) center-based magnetometry has been proven to be a versatile sensor for various classes of magnetic materials in broad temperature and frequency ranges. Here,

Katherine Xu, Lingzhi Zhang, Jianbo Shi

Our brain can effortlessly recognize objects even when partially hidden from view. Seeing the visible of the hidden is called amodal completion; however, this task remains a challenge for generative AI despite rapid progress. We propose to sidestep many of the difficulties of existing approaches, which typically involve a two-step process of predicting amoda

Anna Kh. Balci, Lars Diening, Abner J. Salgado

We consider the numerical approximation of variational problems with orthotropic growth, that is those where the integrand depends strongly on the coordinate directions with possibly different growth in each direction. Under realistic regularity assumptions we derive optimal error estimates. These estimates depend on the existence of an orthotropically stabl

Yinuo Du, Hanying Zhao, Yang Liu, Xinlei Yu

Accurate localization and perception are pivotal for enhancing the safety and reliability of vehicles. However, current localization methods suffer from reduced accuracy when the line-of-sight (LOS) path is obstructed, or a combination of reflections and scatterings is present. In this paper, we present an integrated localization and sensing method that deli

Said Boulite, Abdellatif Elgrou, Lahcen Maniar, Omar Oukdach

In this paper, we continue the study of some controllability issues for the forward stochastic parabolic equation with dynamic boundary conditions. The main novelty in the present paper consists of considering only one control without extra forces in the noise parts. Utilizing an adequate spectral inequality and the iterative Lebeau-Robiano strategy, we firs

Paulina Stevia Nouwou Mindom, Amin Nikanjam, Foutse Khomh

Nowadays, we are witnessing an increasing adoption of Artificial Intelligence (AI) to develop techniques aimed at improving the reliability, effectiveness, and overall quality of software systems. Deep reinforcement learning (DRL) has recently been successfully used for automation in complex tasks such as game testing and solving the job-shop scheduling prob

Soheila Khajoui, Saeid Dehyadegari, Sayyed Abdolmajid Jalaee

The present study aimed to forecast the exports of a select group of Organization for Economic Co-operation and Development (OECD) countries and Iran using the neural networks. The data concerning the exports of the above countries from 1970 to 2019 were collected. The collected data were implemented to forecast the exports of the investigated countries for

Peter Braun-Munzinger, Krzysztof Redlich, Anar Rustamov, Johanna Stachel

The study of event-by-event fluctuations of net-baryon number in a subspace of full phase space is a promising direction for deciphering the structure of strongly interacting matter created in head-on collisions of relativistic heavy nuclei. Such fluctuations are generally suppressed by exact baryon number conservation. Moreover, the suppression is stronger

Wentao Zhu

Transformers have achieved promising results on a variety of tasks. However, the quadratic complexity in self-attention computation has limited the applications, especially in low-resource settings and mobile or edge devices. Existing works have proposed to exploit hand-crafted attention patterns to reduce computation complexity. However, such hand-crafted p

Ahmed Ayman

The potential for augmenting the segmentation of brain tumors through the use of few-shot learning is vast. Although several deep learning networks (DNNs) demonstrate promising results in terms of segmentation, they require a substantial quantity of training data in order to produce suitable outcomes. Furthermore, a major issue faced by most of these models

Yixuan Pang

We provide a much shorter but even more powerful proof of an algebraic identity, which can be used to establish the direct and the converse inequality under Type IV superorthogonality. As an application, we obtain the optimal order of the formal constant in the direct inequality. This order turns out to be also sharp for Type III superorthogonality. When $p\

Francesco Casini, Rouven Frassek, Cristian Giardinà

We study the stirring process with $N-1$ species on a generic graph $G=(V,\mathcal{E})$ with reservoirs. The multispecies stirring process generalizes the symmetric exclusion process, which is recovered in the case $N=2$. We prove the existence of a dual process defined on an extended graph $\widetilde{G}=(\widetilde{V},\widetilde{\mathcal{E})}$ which includ

Marina Ghisi, Massimo Gobbino

We consider an abstract linear wave equation with a time-dependent dissipation that decays at infinity with the so-called scale invariant rate, which represents the critical case. We do not assume that the coefficient of the dissipation term is smooth, and we investigate the effect of its oscillations on the decay rate of solutions. We prove a decay estimate

Wen-Tao Xu, Rui-Zhen Huang, Guang-Ming Zhang

The formalism of the Rokhsar-Kivelson (RK) model has been frequently used to study topological phase transitions in 2D in terms of the deformed wavefunctions, which are RK-type wavefunctions. A key drawback of the deformed wavefunctions is that the obtained quantum critical points are RK-type, in the sense that the equal-time correlation functions are descri

Konstantin Rodionenko, Maxim Mazanov, Maxim A. Gorlach

Crystalline topological insulators have recently become a powerful platform for realizing photonic topological states from microwaves to the visible. Appropriate geometric symmetries of the lattice are at the core of their functionality. Here we put forward an alternative approach to craft those systems by designing the internal symmetries of the Hamiltonian

R. B. Di Renna, R. C. de Lamare

This paper proposes an iterative detection and decoding (IDD) scheme and an approach to improve the selection of access points (APs) in uplink cell-free massive multiple-antenna systems. A cost-effective scheme for selection of APs based on local log-likelihood ratios (LLRs) is developed that provides sufficient statistics to the central processing unit and

Ali Raza Mirza

This thesis presents studies performed on open quantum systems, that is, quantum systems interacting with their surrounding environment. Such systems are important not only in understanding the quantum-to-classical transition but also for the practical implementation of modern quantum technologies. In studies of open quantum systems performed to date, a very

Leonid Yavits

Fast parallel search capabilities on large datasets provided by content addressable memories (CAM) are required across multiple application domains. However compared to RAM, CAMs feature high area overhead and power consumption, and as a result, they scale poorly. The proposed solution, DRAMA, enables CAM, ternary CAM (TCAM) and approximate (similarity) sear

Gianluca Francica, Luca Dell'Anna

Fluctuation theorems are fundamental results in nonequilibrium thermodynamics beyond the linear response regime. Among these, the paradigmatic Tasaki-Crooks fluctuation theorem relates the statistics of the works done in a forward out-of-equilibrium quantum process and in a corresponding backward one. In particular, the initial states of the two processes ar

Kalpa Subbaih, Bharath Kumar Bolla

The surge of e-commerce reviews has presented a challenge in manually annotating the vast volume of reviews to comprehend their underlying aspects and sentiments. This research focused on leveraging weakly supervised learning to tackle aspect category learning and the sentiment classification of reviews. Our approach involves the generation of labels for bot

Mintu Karmakar, Swarnajit Chatterjee, Raja Paul, Heiko Rieger

We numerically study a discretized Vicsek model (DVM) with particles orienting in $q$ possible orientations in two dimensions. The study probes the significance of anisotropic orientation and microscopic interaction on the macroscopic behavior. The DVM is an off-lattice flocking model like the active clock model [ACM; EPL {\bf 138}, 41001 (2022)] but the dyn

George Gui, Olivier Toubia

Large Language Models (LLMs) have shown impressive potential to simulate human behavior. We identify a fundamental challenge in using them to simulate experiments: when LLM-simulated subjects are blind to the experimental design (as is standard practice with human subjects), variations in treatment systematically affect unspecified variables that should rema

Mehdi Monemi, Mohammad Amir Fallah, Mehdi Rasti, Matti Latva-Aho

Spot beamfocusing (SBF) is the process of focusing the signal power in a small spot-like region in the 3D space, which can be either hard-tuned (HT) using traditional tools like lenses and mirrors or electronically reconfigured (ER) using modern large-scale intelligent surface phased arrays. ER-SBF can be a key enabling technology (KET) for the next-generati