December 2023 arXiv papers — page 49

Nazlı Doğan

The aim of this paper is to define Toeplitz operators between K\"othe spaces, especially power series spaces. We determine the conditions for continuity and compactness of these operators. We define the concept of S-tameness of a family of continuous operators. We construct some conditions on S-tameness for the families consisting of Toeplitz operators.

Jiayu Lin, Rong Ye, Meng Han, Qi Zhang

Counter-argument generation -- a captivating area in computational linguistics -- seeks to craft statements that offer opposing views. While most research has ventured into paragraph-level generation, sentence-level counter-argument generation beckons with its unique constraints and brevity-focused challenges. Furthermore, the diverse nature of counter-argum

Wei Wang, Bo Zeng

Mixed integer sets have a strong modeling capacity to describe practical systems. Nevertheless, incorporating a mixed integer set often renders an optimization formulation drastically more challenging to compute. In this paper, we study how to effectively solve two-stage robust mixed integer programs built upon decision-dependent uncertainty (DDU). We partic

Changhun Yang

We study the long time behavior of small solutions to the semi-relativistic Hartree equations in two dimension. The nonlinear term is convolved with the singular potential $|x|^{-\gamma}$ for $1<\gamma<2$, which is referred to as short-range interaction potential in the sense of scattering phenomenon. We establish the scattering results for small solutions i

Anju Panthi, Kaushar Vaidya

We study an open cluster NGC 6940 using \textit{AstroSat}/UVIT data and other archival data. This is an intermediate age cluster ($\sim$ 1 Gyr), located at about 770 pc distance, harboring several exotic populations apart from normal single and binary stars. We identify members of this cluster using a machine learning algorithm, ML-MOC and identify 492 membe

Keqiang Sun, Dor Litvak, Yunzhi Zhang, Hongsheng Li

We introduce a new method for learning a generative model of articulated 3D animal motions from raw, unlabeled online videos. Unlike existing approaches for 3D motion synthesis, our model requires no pose annotations or parametric shape models for training; it learns purely from a collection of unlabeled web video clips, leveraging semantic correspondences d

Miseul Kim, Zhenyu Piao, Jihyun Lee, Hong-Goo Kang

In this paper, we propose a neural articulation-to-speech (ATS) framework that synthesizes high-quality speech from articulatory signal in a multi-speaker situation. Most conventional ATS approaches only focus on modeling contextual information of speech from a single speaker's articulatory features. To explicitly represent each speaker's speaking style as w

Srinivasa Pranav, José M. F. Moura

Peer-to-peer deep learning algorithms are enabling distributed edge devices to collaboratively train deep neural networks without exchanging raw training data or relying on a central server. Peer-to-Peer Learning (P2PL) and other algorithms based on Distributed Local-Update Stochastic/mini-batch Gradient Descent (local DSGD) rely on interleaving epochs of tr

Matthew J Simpson, Nizhum Rahman, Alexander KY Tam

Reaction-diffusion models are often used to describe biological invasion, where populations of individuals that undergo random motility and proliferation lead to moving fronts. Many models of biological invasion are extensions of the Fisher-KPP model that describes the evolution of a 1D population density as a result of linear diffusion and logistic growth.

Kemal Erdem Yenin, Reha Oguz Sayin, Kuzey Arar, Kadir Kaan Atalay

Multimodal optimization is often encountered in engineering problems, especially when different and alternative solutions are sought. Evolutionary algorithms can efficiently tackle multimodal optimization thanks to their features such as the concept of population, exploration/exploitation, and being suitable for parallel computation. This paper investigates

Miseul Kim, Zhenyu Piao, Jihyun Lee, Hong-Goo Kang

Decoding spoken speech from neural activity in the brain is a fast-emerging research topic, as it could enable communication for people who have difficulties with producing audible speech. For this task, electrocorticography (ECoG) is a common method for recording brain activity with high temporal resolution and high spatial precision. However, due to the ri

Masayuki Kawakita

We completely prove the ACC for minimal log discrepancies on smooth threefolds. It implies on smooth threefolds the ACC for a-lc thresholds, the uniform m-adic semi-continuity of minimal log discrepancies and the boundedness of the log discrepancy of some divisor that computes the minimal log discrepancy.

Nisa Ara, Rudranil Basu, Emil Mathew, Indrakshi Raychowdhury

Identifying topological phases for a strongly correlated theory remains a non-trivial task, as defining order parameters, such as Berry phases, is not straightforward. Quantum information theory is capable of identifying topological phases for a theory that exhibits quantum phase transition with a suitable definition of order parameters that are related to d

Abdesslem Layeb

This paper introduces the Trochoid Search Optimization Algorithm (TSO), a novel metaheuristic leveraging the mathematical properties of trochoid curves. The TSO algorithm employs a unique combination of simultaneous translational and rotational motions inherent in trochoids, fostering a refined equilibrium between explorative and exploitative search capabili

Zhixiang Su, Di Wang, Chunyan Miao, Lizhen Cui

Aiming to accurately predict missing edges representing relations between entities, which are pervasive in real-world Knowledge Graphs (KGs), relation prediction plays a critical role in enhancing the comprehensiveness and utility of KGs. Recent research focuses on path-based methods due to their inductive and explainable properties. However, these methods f

Wonseok Lee, Sanggyu Chong, Jihan Kim

In this study, a versatile methodology for initiating polymerization from monomers in highly cross-linked materials is investigated. As polymerization progresses, force-field parameters undergo continuous modification due to the formation of new chemical bonds. This dynamic process not only impacts the atoms directly involved in bonding, but also influences

Heng Ma, Yan-Xia Ren

The logarithmic correction for the order of the maximum of a two-type reducible branching Brownian motion on the real line exhibits a double jump when the parameters (the ratio of the diffusion coefficients of the two types of particles, and the ratio of the branching rates the two types of particles) cross the boundary of the anomalous spreading region iden

Alex Morehead, Jeffrey Ruffolo, Aadyot Bhatnagar, Ali Madani

Generative models of macromolecules carry abundant and impactful implications for industrial and biomedical efforts in protein engineering. However, existing methods are currently limited to modeling protein structures or sequences, independently or jointly, without regard to the interactions that commonly occur between proteins and other macromolecules. In

Chengen Lai, Shengli Song, Shiqi Meng, Jingyang Li

Natural language explanation in visual question answer (VQA-NLE) aims to explain the decision-making process of models by generating natural language sentences to increase users' trust in the black-box systems. Existing post-hoc methods have achieved significant progress in obtaining a plausible explanation. However, such post-hoc explanations are not always

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

Using a data sample corresponding to an integrated luminosity of 10.9 fb$^{-1}$ collected at center-of-mass energies from 4.16 to 4.34 GeV with the BESIII detector, we search for the decay $\chi_{c1}(3872) \to \pi^{+}\pi^{-}\chi_{c1}$ in the radiative production $e^{+}e^{-} \to \gamma \chi_{c1}(3872)$. No significant signal is observed, and the ratio for the

Saimin Chen Zhang

This research introduces a revolutionary paradigm for HetNet management, presenting an innovative algorithmic framework that transcends traditional notions of network capacity enhancement. Our exploration delves into the intricate interplay among distinct components, weaving together metaheuristic algorithms, Neural Networks optimization, and Federated Learn

Bo Wang, Kan Chen, Lu Meng, Shi-Lin Zhu

We investigate the mass spectrum of the molecular pentaquarks composed of a baryon and a meson. We establish the underlying relations among the near-threshold interactions of the molecular tetraquark and pentaquark systems. We find the existence of the molecule candidates in the $\Sigma_c\bar{D}^{(\ast)}$, $DD^\ast$, and $D\bar{D}^\ast$ systems indicates a s

Lata Thakur, Yuji Hirono

We study the interplay of non-equilibrium properties of a quark-gluon plasma (QGP) and heavy quarkonia. For this purpose, we compute the quarkonium spectral functions in a bulk-viscous QGP. We take into account the bulk viscous nature of the medium by modifying the distribution functions of thermal quarks and gluons. This modification affects the dielectric

Arijit Dutta, Efe Sen, Jun-Hui Zheng, Monika Aidelsburger

The anomalous Floquet Anderson insulator (AFAI) has been theoretically predicted in step-wise periodically driven models, but its stability under more general driving protocols hasn't been determined. We show that adding disorder to the anomalous Floquet topological insulator realized with a continuous driving protocol in the experiment by K. Wintersperger e

Griffin Johnston, Jason O'Neill

Given a vector $\alpha = (\alpha_1, \ldots, \alpha_k) \in \mathbb{F}_2^k$, we say a collection of subsets $\mathcal{F}$ satisfies $\alpha$-intersection pattern modulo $2$ if all $i$-wise intersections consisting of $i$ distinct sets from $\mathcal{F}$ have size $\alpha_i \pmod{2}$. In this language, the classical oddtown and eventown problems correspond to v

Sergei D. Odintsov, Simone D'Onofrio, Tanmoy Paul

In spirit of the recently proposed four-parameter generalized entropy of apparent horizon, we investigate inflationary cosmology where the matter field inside of the horizon is dominated by a scalar field with a power law potential (i.e., the form of $\phi^n$ where $\phi$ is the scalar field under consideration). Actually without any matter inside of the hor

Sudipta Das, Rivu Gupta, Himadri Shekhar Dhar, Aditi Sen De

The telecloning protocol distributes quantum states from a single sender to multiple receivers via a shared entangled state by exploiting the notions of teleportation and approximate cloning. We investigate the optimal telecloning fidelities obtained using both Gaussian and non-Gaussian shared resources. When the shared non-Gaussian state is created by subtr

Zhichao Huang, Rong Ye, Tom Ko, Qianqian Dong

Given the great success of large language models (LLMs) across various tasks, in this paper, we introduce LLM-ST, a novel and effective speech translation model constructed upon a pre-trained LLM. By integrating the large language model (LLM) with a speech encoder and employing multi-task instruction tuning, LLM-ST can produce accurate timestamped transcript

Harsha Vardhan Tetali, Joel B. Harley, Benjamin D. Haeffele

With the recent success of representation learning methods, which includes deep learning as a special case, there has been considerable interest in developing techniques that incorporate known physical constraints into the learned representation. As one example, in many applications that involve a signal propagating through physical media (e.g., optics, acou

Yifei Sun, Qi Zhu, Yang Yang, Chunping Wang

Recently, the paradigm of pre-training and fine-tuning graph neural networks has been intensively studied and applied in a wide range of graph mining tasks. Its success is generally attributed to the structural consistency between pre-training and downstream datasets, which, however, does not hold in many real-world scenarios. Existing works have shown that

Zanpeng Yin, Daisuke Jido

We study the theoretical structure of compositeness with explicit energy dependence, and find a possible explanation for the difficulty in the interpretation of compositeness of deuteron. Compositeness of deuteron is calculated as larger than one in many methods like weak-binding limit. Even though it is widely assumed that the energy dependence in interacti

Ananya Bhattacharjee, Yuchen Zeng, Sarah Yi Xu, Dana Kulzhabayeva

Traditional interventions for academic procrastination often fail to capture the nuanced, individual-specific factors that underlie them. Large language models (LLMs) hold immense potential for addressing this gap by permitting open-ended inputs, including the ability to customize interventions to individuals' unique needs. However, user expectations and pot

Eric Macke, Iurii Timrov, Nicola Marzari, Lucio Colombi Ciacchi

We present an orbital-resolved extension of the Hubbard $U$ correction to density-functional theory (DFT). Compared to the conventional shell-averaged approach, the prediction of energetic, electronic and structural properties is strongly improved, particularly for compounds characterized by both localized and hybridized states in the Hubbard manifold. The n

Rustem Bolat, Jose M. Guevara, Philipp Leinen, Marvin Knol

The discrete and charge-separated nature of matter - electrons and nuclei - results in local electrostatic fields that are ubiquitous in nanoscale structures and are determined by their shape, material, and environment. Such fields are relevant in catalysis, nanoelectronics and quantum nanoscience, and their control will become even more important as the dev

Zhongyang Guo, Guanran Jiang, Zhongdan Zhang, Peng Li

This paper introduces "Shai" a 10B level large language model specifically designed for the asset management industry, built upon an open-source foundational model. With continuous pre-training and fine-tuning using a targeted corpus, Shai demonstrates enhanced performance in tasks relevant to its domain, outperforming baseline models. Our research includes

Chenxu Zhang, Chao Wang, Jianfeng Zhang, Hongyi Xu

The generation of emotional talking faces from a single portrait image remains a significant challenge. The simultaneous achievement of expressive emotional talking and accurate lip-sync is particularly difficult, as expressiveness is often compromised for the accuracy of lip-sync. As widely adopted by many prior works, the LSTM network often fails to captur

Yang Lu, Jordan Yu, Shou-Hsuan Stephen Huang

This study explores the idea of AI Personality or AInality suggesting that Large Language Models (LLMs) exhibit patterns similar to human personalities. Assuming that LLMs share these patterns with humans, we investigate using human-centered psychometric tests such as the Myers-Briggs Type Indicator (MBTI), Big Five Inventory (BFI), and Short Dark Triad (SD3

Naba Jyoti Gogoi, Prabwal Phukon

We study the thermodynamic topology of 4D Euler-Heisenberg-AdS (EHAdS) black hole and higher-order QED corrected Euler-Heisenberg-AdS black hole in different ensembles using generalized off-shell free energy. In this approach, black holes are viewed as defects in the thermodynamic space. We work in two ensembles: canonical ensemble in which the charge is kep

Felipe Gutiérrez Rojas, Sébastien Bouquillon, Rene A. Mendez, Hernan Pulgar

FRIPON is an efficient ground-based network for the detection and characterization of fireballs, which was initiated in France in 2016 with over one hundred cameras and which has been very successfully extended to Europe and Canada with one hundred more stations. After seven successful years of operation in the northern hemisphere, it seems necessary to exte

Peng Zhao, Jiehua Zhang, Bowen Peng, Longguang Wang

Network binarization exhibits great potential for deployment on resource-constrained devices due to its low computational cost. Despite the critical importance, the security of binarized neural networks (BNNs) is rarely investigated. In this paper, we present ARBiBench, a comprehensive benchmark to evaluate the robustness of BNNs against adversarial perturba

Bin Chen, Zezhou Hu

In this note, we discuss the bulk reconstruction of massless free fields in flat space from the highest-weight representation of boundary Carrollian conformal field theory (CCFT). We expand the bulk field as a sum of infinite descendants of a primary state defined in the boundary CCFT, and discuss the Lorentz invariant bulk-boundary propagator in detail for

Yuhua Liu, Satoko Takahashi, Masahiro Machida, Kohji Tomisaka

We present the Atacama Large Millimeter/submillimeter Array (ALMA) observations of linearly polarized 1.1 mm continuum emission at $\sim$0.14" (55 au) resolution and CO ($J$=2$-$1) emission at $\sim$1.5" (590 au) resolution towards one prestellar (MMS 4), four Class 0 (MMS$\,$1, MMS$\,$3, MMS$\,$5, and MMS$\,$6), one Class I (MMS$\,$7), and one flat-spectrum

Lu-Meng Liu, Song-Jie Li, Zhen Wang, Jun Xu

We propose to use spectator particle yield ratios to probe the configuration of $\alpha$ clusters in $^{12}$C and $^{16}$O by their collisions at RHIC and LHC energies. The idea is illustrated based on initial density distributions with various $\alpha$-cluster configurations generated by a microscopic cluster model, and without $\alpha$ clusters from mean-f

Koichiro Yaji, Shunsuke Tsuda

Harnessing electron spin is crucial in developing energy-saving and high-speed devices for the next generation. In this scheme, visualizing spin-polarized electronic states aids in designing and developing new materials and devices. Spin-resolved photoemission spectroscopy provides information on the spin-polarized electronic states. To investigate the spin-

Nickalas K. Reynolds, John J. Tobin, Patrick D. Sheehan, Sarah I. Sadavoy

We present a statistical characterization of circumstellar disk orientations toward 12 protostellar multiple systems in the Perseus molecular cloud using the Atacama Large Millimeter/submillimeter Array at Band 6 (1.3 mm) with a resolution of 25 mas (8 au). This exquisite resolution enabled us to resolve the compact inner disk structures surrounding the comp

Uri Ronen

We show that the group algebras $\mathbb{C}[\text{SL}_3(\mathbb{F}_3[t]/(t^3))]$ and $\mathbb{C}[\text{SL}_3(\mathbb{Z}/27)]$ are not isomorphic, as well as $\mathbb{C}[\text{SL}_4(\mathbb{F}_2[t]/(t^3))]$ and $\mathbb{C}[\text{SL}_4(\mathbb{Z}/8)]$, by computing the number of conjugacy classes in those groups using MAGMA's calculator. Similarly, we reproduc

Xianyao Chen, Ka-Kit Tung

A catastrophic collapse of the Atlantic Meridional Overturning Circulation (AMOC) will have serious impacts on global climate. Based on eight AMOC proxies across the Atlantic basin Boers claimed to have found Early Warning Signals (EWS) that point to its imminent collapse. Here we show that common to all eight of Boers' AMOC proxies are artificial rises in v

Haoqin Sun, Shiwan Zhao, Xuechen Wang, Wenjia Zeng

Multimodal emotion recognition (MMER) is an active research field that aims to accurately recognize human emotions by fusing multiple perceptual modalities. However, inherent heterogeneity across modalities introduces distribution gaps and information redundancy, posing significant challenges for MMER. In this paper, we propose a novel fine-grained disentang

Miracle Chibuzor Marcel, Idris Abubakar Sani, Jorbedom Leelabari Gerald, Privatus Pius

We report new measurements of the position angle and separation of the double star WDS 03245+5938 STI 450, based on our observations, Gaia EDR3, and historical data. We find that the position angle and separation are 209.7{\deg} and 7.68", respectively, showing slight changes from the previous values of 210{\deg} and 7.742". We also find that the distances b

Ryan Campbell, Junsang Yoon

This paper investigates the impact of using gradient norm reward signals in the context of Automatic Curriculum Learning (ACL) for deep reinforcement learning (DRL). We introduce a framework where the teacher model, utilizing the gradient norm information of a student model, dynamically adapts the learning curriculum. This approach is based on the hypothesis

Wentian Zhang

This paper studies the effect of antitrust enforcement on venture capital (VC) investments and VC-backed companies. To establish causality, I exploit the DOJ's decision to close several antitrust field offices in 2013, which reduced the antitrust enforcement in areas near the closed offices. I find that the reduction in antitrust enforcement causes a signifi

Wilson de Souza Junior, Taufik Abrao

In this work, we address the energy efficiency (EE) maximization problem in a downlink communication system utilizing reconfigurable intelligent surface (RIS) in a multi-user massive multiple-input multiple-output (mMIMO) setup with zero-forcing (ZF) precoding. The channel between the base station (BS) and RIS operates under a Rician fading with Rician facto

Andreas Papaefstathiou, Gilberto Tetlalmatzi-Xolocotzi

We investigate multi-Higgs boson production at proton colliders, in a framework involving anomalous interactions, focusing on triple Higgs boson production. We consider modifications to the Higgs boson self-couplings, to the Yukawa interactions, as well as new contact interactions of Higgs bosons with either quarks or gluons. To this end, we have developed a

Tomoyuki Morimae, Alexander Poremba, Takashi Yamakawa

We study digital signatures with revocation capabilities and show two results. First, we define and construct digital signatures with revocable signing keys from the LWE assumption. In this primitive, the signing key is a quantum state which enables a user to sign many messages and yet, the quantum key is also revocable, i.e., it can be collapsed into a clas

Jiaming Zhou, Shiwan Zhao, Yaqi Liu, Wenjia Zeng

The success of retrieval-augmented language models in various natural language processing (NLP) tasks has been constrained in automatic speech recognition (ASR) applications due to challenges in constructing fine-grained audio-text datastores. This paper presents kNN-CTC, a novel approach that overcomes these challenges by leveraging Connectionist Temporal C

Srujan Meesala, David Lake, Steven Wood, Piero Chiappina

Entanglement is an extraordinary feature of quantum mechanics. Sources of entangled optical photons were essential to test the foundations of quantum physics through violations of Bell's inequalities. More recently, entangled many-body states have been realized via strong non-linear interactions in microwave circuits with superconducting qubits. Here we demo

Pratyusha Sharma, Jordan T. Ash, Dipendra Misra

Transformer-based Large Language Models (LLMs) have become a fixture in modern machine learning. Correspondingly, significant resources are allocated towards research that aims to further advance this technology, typically resulting in models of increasing size that are trained on increasing amounts of data. This work, however, demonstrates the surprising re

Zhoumeng Wang

Recommender systems utilizing explicit feedback have witnessed significant advancements and widespread applications over the past years. However, generating recommendations in few-shot scenarios remains a persistent challenge. Recently, large language models (LLMs) have emerged as a promising solution for addressing natural language processing (NLP) tasks, t

Yang Liu, Haoqin Sun, Geng Chen, Qingyue Wang

Speech emotion recognition (SER) performance deteriorates significantly in the presence of noise, making it challenging to achieve competitive performance in noisy conditions. To this end, we propose a multi-level knowledge distillation (MLKD) method, which aims to transfer the knowledge from a teacher model trained on clean speech to a simpler student model

Tao Wu, Tie Luo, Donald C. Wunsch

The capacity to generalize to future unseen data stands as one of the utmost crucial attributes of deep neural networks. Sharpness-Aware Minimization (SAM) aims to enhance the generalizability by minimizing worst-case loss using one-step gradient ascent as an approximation. However, as training progresses, the non-linearity of the loss landscape increases, r

Jung-Ho Hong, Woo-Jeoung Nam, Kyu-Sung Jeon, Seong-Whan Lee

Revealing the transparency of Deep Neural Networks (DNNs) has been widely studied to describe the decision mechanisms of network inner structures. In this paper, we propose a novel post-hoc framework, Unfold and Conquer Attribution Guidance (UCAG), which enhances the explainability of the network decision by spatially scrutinizing the input features with res

Zongchen Chen, Dan Mikulincer, Daniel Reichman, Alexander S. Wein

The Metropolis process (MP) and Simulated Annealing (SA) are stochastic local search heuristics that are often used in solving combinatorial optimization problems. Despite significant interest, there are very few theoretical results regarding the quality of approximation obtained by MP and SA (with polynomially many iterations) for NP-hard optimization probl

Lorenzo Gavassino

We propose a general procedure for evaluating, directly from microphysics, the constitutive relations of heat-conducting fluids in regimes of large fluxes of heat. Our choice of hydrodynamic formalism is Carter's two-fluid theory, which happens to coincide with \"{O}ttinger's GENERIC theory for relativistic heat conduction. This is a natural framework, as it

Xin-Yun Hu, Xiao-Xiong Zeng, Li-Fang Li, Peng Xu

With the help of AdS/CFT correspondence, the Einstein ring of a charged black hole in conformal gravity has been studied. Imposing an oscillating Gauss source on one side of the AdS boundary which propagates in the bulk, we derive the response function on the other side of the boundary. With the proposed wave optics system, we observe the Einstein ring as ex

Ali Kaan Kurbanzade, Ansaar M. Baig, Sanjay Mehrotra

Unmanned aerial vehicles, commonly known as drones, have emerged as a disruptive technology with the potential to revolutionize operations across various industries. Drones are the fast-growing internet-of-things technology and are estimated to have a $100 billion market value in the next decade. Exploring drone operations through research has the potential

Norio Ota

This molecular orbital analysis predicts that pure carbon graphene molecules would play an important role on astronomically observed Diffuse Interstellar Bands (DIB), rather than fullerene. Laboratory experiments precisely coincided with observed DIB bands as studied by E. Cambell et al., which were considered to originate from mono-cation fullerene-(C$_{60}

Fan Bu, Tuomas Hytönen, Dachun Yang, Wen Yuan

This is the last one of three successive articles by the authors on matrix-weighted Besov-type and Triebel--Lizorkin-type spaces $\dot B^{s,\tau}_{p,q}(W)$ and $\dot F^{s,\tau}_{p,q}(W)$. In this article, the authors establish the molecular and the wavelet characterizations of these spaces. Furthermore, as applications, the authors obtain the optimal bounded

Fan Bu, Tuomas Hytönen, Dachun Yang, Wen Yuan

This article is the second one of three successive articles of the authors on the matrix-weighted Besov-type and Triebel--Lizorkin-type spaces. In this article, we obtain the sharp boundedness of almost diagonal operators on matrix-weighted Besov-type and Triebel--Lizorkin-type sequence spaces. These results not only possess broad generality but also improve

Eldar Kurtic, Torsten Hoefler, Dan Alistarh

Pruning large language models (LLMs) from the BERT family has emerged as a standard compression benchmark, and several pruning methods have been proposed for this task. The recent ``Sparsity May Cry'' (SMC) benchmark put into question the validity of all existing methods, exhibiting a more complex setup where many known pruning methods appear to fail. We rev

Peng Qu, Huimin Yu, Xiaomin Zhang

In this paper, one-dimensional nonisentropic compressible Euler equations with linear damping $\alpha(x)\rho u$ are analyzed.~We want to explore the conditions under which a subsonic temporal periodic boundary can trigger a time-periodic $C^{1}$ solution. To achieve this aim, we use a technically constructed iteration scheme and give the sufficient condition

Katsumasa Yoshikawa, Takato Yamazaki, Masaya Ohagi, Tomoya Mizumoto

In recent years, large language models (LLMs) have rapidly proliferated and have been utilized in various tasks, including research in dialogue systems. We aimed to construct a system that not only leverages the flexible conversational abilities of LLMs but also their advanced planning capabilities to reduce the speaking load on human interlocutors and effic

Kieran Barvenik, Zachary Coogan, Gabriele Librandi, Matteo Pezzulla

Soft and lightweight grippers have greatly enhanced the performance of robotic manipulators in handling complex objects with varying shape, texture, and stiffness. However, the combination of universal grasping with passive sensing capabilities still presents challenges. To overcome this limitation, we introduce a fluidic soft gripper, named the ``Pac-Man''

Yuki Mifune, Ryo Takahashi

Let $R$ be a commutative noetherian local ring with residue field $k$. Denote by $\mathsf{D^b}(R)$ the bounded derived category of finitely generated $R$-modules. In this paper, we study the structure of the Verdier quotient $\mathsf{D^b}(R)/\mathsf{thick}(R\oplus k)$. We give necessary and sufficient conditions for it to admit an additive generator.

Yanan Wang, Dheeraj R. Pasham, Diego Altamirano, Andres Gurpide

The tidal disruption of a star around a supermassive black hole (SMBH) offers a unique opportunity to study accretion onto a SMBH on a human-timescale. We present results from our 1000+ days NICER, Swift and Chandra monitoring campaign of AT 2019avd, a nuclear transient with TDE-like properties. Our primary finding is that approximately 225 days following th

Yu-Hong Li, Facheng Jin, Yujun Yang, Fei Li

We have investigated the angle-resolved ATI spectrum of oriented molecules in the IR+XUV co-rotating circular laser fields. According to the different roles of IR and XUV laser in the ionization process, we purposefully adjust the photon energy of XUV and the intensity of IR laser to make the ionization spectrum of the molecule distributed in a suitable mome

Eric Breyer, Alan Liu

This paper presents an exploration, implementations, and revisions of probabilistic sets and maps, specifically focusing on Bloomier filters and related data structures. The paper introduces the Fuse XORier Lookup Table (FXLT), an enhanced version of the Bloomier Filter incorporating spatial coupling, linear construction, and optimizations. The authors provi

Chongjun Tu, Peng Ye, Weihao Lin, Hancheng Ye

Improving the efficiency of Neural Architecture Search (NAS) is a challenging but significant task that has received much attention. Previous works mainly adopted the Differentiable Architecture Search (DARTS) and improved its search strategies or modules to enhance search efficiency. Recently, some methods have started considering data reduction for speedup

Elliott Tammaro, Hunter Angle, Edmund Mbadu

A ubiquitous feature of quantum mechanical theories is the existence of states of superposition. This is expected to be no different for a quantum gravity theory. Guided by this consideration and others we consider a framework in which classical reference frames may be in superposition relative to one another. Mirroring standard quantum mechanics we introduc

Li Shao, Hu Zhan, Chao Liu, Haonan Chi

We tested a new model of CMOS detector manufactured by the Gpixel Inc, for potential space astronomical application. In laboratory, we obtain some bias images under the typical application environment. In these bias images, clear random row noise pattern is observed. The row noise also contains some characteristic spatial frequencies. We quantitatively estim

S. Mashdour, A. Schmeink, R. C. de Lamare, J. P. Sales

Resource allocation is a fundamental task in cell-free (CF) massive multi-input multi-output (MIMO) systems, which can effectively improve the network performance. In this paper, we study the downlink of CF MIMO networks with network clustering and linear precoding, and develop a sequential multiuser scheduling and power allocation scheme. In particular, we

Hai Zhang, Chunwei Wu, Guitao Cao, Hailing Wang

Editing real images authentically while also achieving cross-domain editing remains a challenge. Recent studies have focused on converting real images into latent codes and accomplishing image editing by manipulating these codes. However, merely manipulating the latent codes would constrain the edited images to the generator's image domain, hindering the att

Geoffrey Fox, Mary P Thomas, Sajal Bhatia, Marisa Brazil

This document describes a two-day meeting held for the Principal Investigators (PIs) of NSF CyberTraining grants. The report covers invited talks, panels, and six breakout sessions. The meeting involved over 80 PIs and NSF program managers (PMs). The lessons recorded in detail in the report are a wealth of information that could help current and future PIs,

Siyang Luo, Ziyi Jiang, Zhenghan Chen, Xiaoxuan Liang

Despite the remarkable accomplishments of graph neural networks (GNNs), they typically rely on task-specific labels, posing potential challenges in terms of their acquisition. Existing work have been made to address this issue through the lens of unsupervised domain adaptation, wherein labeled source graphs are utilized to enhance the learning process for ta

Hideki Miyachi

In this paper, we discuss the boundary behavior of bounded pluriharmonic functions on the Teichm\"uller space. We will show a version of the Fatou theorem that every bounded pluriharmonic function admits the radial limits along the Teichm\"uller geodesic rays, and a version of the F. and M. Riesz theorem that the radial limit of a non-constant bounded holomo

Benjamin Billot, Neel Dey, Daniel Moyer, Malte Hoffmann

Rigid motion tracking is paramount in many medical imaging applications where movements need to be detected, corrected, or accounted for. Modern strategies rely on convolutional neural networks (CNN) and pose this problem as rigid registration. Yet, CNNs do not exploit natural symmetries in this task, as they are equivariant to translations (their outputs sh

Viktor Schlegel, Abhinav Ramesh Kashyap, Thanh-Tung Nguyen, Tsung-Han Yang

Computerised clinical coding approaches aim to automate the process of assigning a set of codes to medical records. While there is active research pushing the state of the art on clinical coding for hospitalized patients, the outpatient setting -- where doctors tend to non-hospitalised patients -- is overlooked. Although both settings can be formalised as a

Pankaj Kumar Debnath, Barnali Chakrabarti, Mantile Leslie Lekala

The quench dynamics of a strongly interacting bosons on quartic and sextic trap are studied by solving the time dependent many-boson Schrodinger equation numerically exactly. The dynamics is addressed by the key measures of one-body density in conjugate space and information entropy. For both cases, rich many-body dynamics is exhibited and loss of Bose-Fermi

Ming-Rui Li, Shao-Kai Jian

Quantum electrodynamics (QED) is a cornerstone of particle physics and also finds diverse applications in condensed matter systems. Despite its significance, the dynamics of quantum electrodynamics under a quantum quench remains inadequately explored. In this paper, we investigate the nonequilibrium regime of quantum electrodynamics following a global quantu

Yu-Zheng Lin, Muntasir Mamun, Muhtasim Alam Chowdhury, Shuyu Cai

The escalating complexity of modern computing frameworks has resulted in a surge in the cybersecurity vulnerabilities reported to the National Vulnerability Database (NVD) by practitioners. Despite the fact that the stature of NVD is one of the most significant databases for the latest insights into vulnerabilities, extracting meaningful trends from such a l

Philip Broadbridge, Illia Donhauzer, Andriy Olenko

The paper examines stochastic diffusion within an expanding space-time framework. It starts with providing a rationale for the considered model and its motivation from cosmology where the expansion of space-time is used in modelling various phenomena. Contrary to other results in the literature, the considered in this paper general stochastic model takes int

Minh-Quan Viet Bui, Jongmin Park, Jihyong Oh, Munchurl Kim

Neural Radiance Fields (NeRF), initially developed for static scenes, have inspired many video novel view synthesis techniques. However, the challenge for video view synthesis arises from motion blur, a consequence of object or camera movements during exposure, which hinders the precise synthesis of sharp spatio-temporal views. In response, we propose a nove

Mou Sun, Tao Li, Wotao Yin

This report provides a comprehensive analysis of the performance of MindOpt Adapter for CPLEX 12.9 in benchmark testing. CPLEX, recognized as a robust Mixed Integer Programming (MIP) solver, has faced some scrutiny regarding its performance on MIPLIB 2017 when configured to default settings. MindOpt Adapter aims to enhance CPLEX's performance by automaticall

Ce Jin, Manish Purohit, Zoya Svitkina, Erik Vee

We study scheduling of computation graphs to minimize peak memory consumption, an increasingly critical task due to the surge in popularity of large deep-learning models. This problem corresponds to the weighted version of the classical one-shot black pebbling game. We propose the notion of a dominant schedule to capture the idea of finding the ``best'' sche

Hanamichi Kawamura

In this paper, we introduce formal sine functions whose coefficients are elements of a generalized harmonic algebra and investigate their properties corresponding to the classical addition formula and Pythagorean theorem. By taking their image under a linear map with some conditions, they coincide with the classical sine function. Moreover, we show that one

Michael Ridley, Emily Adlam

We investigate two types of temporal symmetry in quantum mechanics. The first type, time symmetry, refers to the inclusion of opposite time orientations on an equivalent physical footing. The second, event symmetry, refers to the inclusion of all time instants in a history sequence on an equivalent physical footing. We find that recent time symmetric interpr

Congyu Liao, Xiaozhi Cao, Siddharth Srinivasan Iyer, Sophie Schauman

Purpose: This study aims to develop a high-resolution whole-brain multi-parametric quantitative MRI approach for simultaneous mapping of myelin-water fraction (MWF), T1, T2, and proton-density (PD), all within a clinically feasible scan time. Methods: We developed 3D ViSTa-MRF, which combined Visualization of Short Transverse relaxation time component (ViSTa

Zixuan Huang, Stefan Stojanov, Anh Thai, Varun Jampani

We study the problem of single-image zero-shot 3D shape reconstruction. Recent works learn zero-shot shape reconstruction through generative modeling of 3D assets, but these models are computationally expensive at train and inference time. In contrast, the traditional approach to this problem is regression-based, where deterministic models are trained to dir

Sarkis Der Wartanian, Baydaa Al Ayoubi

This research is about COVID-19, which is a contagious virus that reached many countries, including Lebanon. Monitoring the outbreak, researchers have been involved in introducing COVID-19 targeting vaccines. Already facing financial and political issues, Lebanon was further affected by the COVID-19 outbreak. The hereby research tends to design the optimum v

Samuel J. Aronson, Kalotina Machini, Jiyeon Shin, Pranav Sriraman

Background. Large Language Models (LLMs) hold promise for improving genetic variant literature review in clinical testing. We assessed Generative Pretrained Transformer 4's (GPT-4) performance, nondeterminism, and drift to inform its suitability for use in complex clinical processes. Methods. A 2-prompt process for classification of functional evidence was o

Katsuki Aoki, Yu-tin Huang

In this work, we study the analytic properties of S-matrix for unstable particles, which is defined as the residues on the unphysical sheets where unstable poles reside. We demonstrate that anomalous thresholds associated with UV physics are unavoidable for unstable particles. This is in contrast to stable particles, where the anomalous thresholds are due to