April 2024 arXiv papers — page 190

Deog Ki Hong, Stephen J. Lonsdale

We show that a stable vortex soliton, carrying a constant magnetic flux, exists in the homogeneous medium of axions with a constant time-derivative. Axions can be bound in the vortex, having energy less than the axion mass. If the observed magnetic fields in galaxies are those of the vortex, the axion-photon coupling has to be smaller than $10^{-17}\,{\rm Ge

Chenfei Shi, Wenchang Hou, Hanbin Deng, Bikash Patra

Kagome lattice can host abundant exotic quantum states such as superconductivity and charge density wave (CDW). Recently, successive orders of A-type antiferromagnetism (AFM), CDW and canted AFM have been manifested upon cooling in kagome FeGe. However, the mechanism of CDW and interaction with magnetism remains unclear. Here we investigate the evolution of

Jaejung Seol, Seojun Kim, Jaejun Yoo

Visual layout plays a critical role in graphic design fields such as advertising, posters, and web UI design. The recent trend towards content-aware layout generation through generative models has shown promise, yet it often overlooks the semantic intricacies of layout design by treating it as a simple numerical optimization. To bridge this gap, we introduce

Maximilian Weiherer, Andreea Dogaru, Shreya Kapoor, Hannah Schieber

As we all know, writing scientific papers together with our beloved colleagues is a truly remarkable experience (partially): endless discussions about the same useless paragraph over and over again, followed by long days and long nights -- both at the same time. What a wonderful ride it is! What a beautiful life we have. But wait, there's one tiny little pro

Tomoyuki Takenawa

A geometric study is given for the 4-dimensional Garnier system. By the resolution of indeterminacy, the group of its B\"aklund transformations is lifted to a group of pseudo-isomorphisms between rational varieties obtained from ${\mathbb P}^2 \times {\mathbb P}^2$ by 10 or 21 blow-ups. The root basis is discussed in the N\'eron-Severi bilattices for the spa

Hajime Inoue

An ejection mechanism of relativistic jets from slim disks is studied. Since the radiation pressure is dominant in the slim disk, radiative energy flow arises along the pressure gradient in the vertical direction. The divergence of the radiative flux tells us that the radiative energy flow from a bottom layer near the equatorial plane is absorbed by another

Yuru Xiao, Xianming Liu, Deming Zhai, Kui Jiang

Neural Radiance Field (NeRF) technology has made significant strides in creating novel viewpoints. However, its effectiveness is hampered when working with sparsely available views, often leading to performance dips due to overfitting. FreeNeRF attempts to overcome this limitation by integrating implicit geometry regularization, which incrementally improves

Mario Zitelli

Optical solitons in multimode fibers have been predicted 40 years ago and extensively investigated theoretically. Transmission experiments in nonlinear multimode fibers have gained renewed interest, motivated by their potential to extend the capacity of long-distance transmission systems; only in the last few years, new experiments have revealed unexpected p

Weicong Qin, Zhongxiang Sun

With the advancement of Artificial Intelligence (AI) and Large Language Models (LLMs), there is a profound transformation occurring in the realm of natural language processing tasks within the legal domain. The capabilities of LLMs are increasingly demonstrating unique roles in the legal sector, bringing both distinctive benefits and various challenges. This

Hao Chen, Yuqi Hou, Chenyuan Qu, Irene Testini

Human perception of the world is shaped by a multitude of viewpoints and modalities. While many existing datasets focus on scene understanding from a certain perspective (e.g. egocentric or third-person views), our dataset offers a panoptic perspective (i.e. multiple viewpoints with multiple data modalities). Specifically, we encapsulate third-person panoram

Xin'ao Feng, Yaohua Sun, Mugen Peng

Mega low earth orbit (LEO) satellite constellation is promising in achieving global coverage with high capacity. However, forwarding packets in mega constellation faces long end-to-end delay caused by multi-hop routing and high-complexity routing table construction, which will detrimentally impair the network transmission efficiency. To overcome this issue,

Ruowen Zhao, Zhengyi Wang, Yikai Wang, Zihan Zhou

3D content generation has wide applications in various fields. One of its dominant paradigms is by sparse-view reconstruction using multi-view images generated by diffusion models. However, since directly reconstructing triangle meshes from multi-view images is challenging, most methodologies opt to an implicit representation (such as NeRF) during the sparse

Ang Bian, Wei Li, Hangjie Yuan, Chengrong Yu

Model generalization ability upon incrementally acquiring dynamically updating knowledge from sequentially arriving tasks is crucial to tackle the sensitivity-stability dilemma in Continual Learning (CL). Weight loss landscape sharpness minimization seeking for flat minima lying in neighborhoods with uniform low loss or smooth gradient is proven to be a stro

Yuanhao Li, Badong Chen, Natsue Yoshimura, Yasuharu Koike

Sparse Bayesian learning has promoted many effective frameworks for brain activity decoding, especially for the reconstruction of muscle activity. However, existing sparse Bayesian learning mainly employs Gaussian distribution as error assumption in the reconstruction task, which is not necessarily the truth in the real-world application. On the other hand,

Mrinnoy M. Gohain, Kalyan Bhuyan

In this paper, in an FLRW background and a perfect fluid equation of state, we explore the possibility of the realization of an emergent scenario in a 4D regularized extension of Einstein-Gauss-Bonnet gravity, with the field equations particularly expressed in terms of scalar-tensor degrees of freedom. By assuming non-zero spatial curvature ($k = \pm 1$), th

Jens Kötters, Stefan E. Schmidt

Conjunctive table algebras are introduced and axiomatically characterized. A conjunctive table algebra is a variant of SPJR algebra (a weaker form of relational algebra), which corresponds to conjunctive queries with equality. The table operations relate to logical operations (e.g. column deletion corresponds to existential quantification). This enables a co

Jaemin Park

In this paper, we investigate the long-time behavior of the two-dimensional incompressible Boussinesq system with kinematic viscosity in a periodic channel, focusing on instability and asymptotic stability near hydrostatic equilibria. Firstly, we prove that any hydrostatic equilibrium reveals long-time instability when the initial data are perturbed in Sobol

Asato Seshita, Aichi Yamashita, Takeshi Fujita, Takayoshi Katase

As thermoelectric generators can convert waste heat into electricity, they play an important role in energy harvesting. The metal chalcogenide AgBiSe2 is one of the high-performance thermoelectric materials with low lattice thermal conductivity (klat), but it exhibits temperature-dependent crystal structural transitions from hexagonal to rhombohedral, and fi

Li Yang, Zhipeng Luo, Shiming Zhang, Fei Teng

With the digitization of modern cities, large data volumes and powerful computational resources facilitate the rapid update of intelligent models deployed in smart cities. Continual learning (CL) is a novel machine learning paradigm that constantly updates models to adapt to changing environments, where the learning tasks, data, and distributions can vary ov

Özlem Tuğfe Demir, Emil Björnson

Following the promising beamforming gains offered by reconfigurable intelligent surfaces (RISs), a new hardware architecture, known as \emph{beyond diagonal RIS (BD-RIS)}, has recently been proposed. This architecture enables controllable signal flows between the RIS elements, thereby providing greater design flexibility. However, the physics-imposed symmetr

K. Abdurasulov, J. Adashev, Z. Normatov, Sh. Solijonova

This article is devoted to the classification of anti-dendriform algebras that are associated with associativity. They are characterized as algebras with two operations whose sum is associative. In particular, the paper is devoted to classifying anti-dendriform algebras associated with null-filiform associative algebras and three-dimensional algebras. It is

Xiaoxiao Liang, Haoyu Yang, Kang Liu, Bei Yu

Optical proximity correction (OPC) is a vital step to ensure printability in modern VLSI manufacturing. Various OPC approaches based on machine learning have been proposed to pursue performance and efficiency, which are typically data-driven and hardly involve any particular considerations of the OPC problem, leading to potential performance or efficiency bo

Jinfeng Xu, Siyuan Yang, Xianzhi Li, Yuan Tang

Existing point cloud semantic segmentation networks cannot identify unknown classes and update their knowledge, due to a closed-set and static perspective of the real world, which would induce the intelligent agent to make bad decisions. To address this problem, we propose a Probability-Driven Framework (PDF) for open world semantic segmentation that include

Hang Zhou, Chenglong Wang, Yimin Hu, Tong Xiao

Reinforcement learning with human feedback for aligning large language models (LLMs) trains a reward model typically using ranking loss with comparison pairs.However, the training procedure suffers from an inherent problem: the uncontrolled scaling of reward scores during reinforcement learning due to the lack of constraints while training the reward model.T

Rolf Bader, Simon Linke, Stefanie Gernert

A nonlinear-dynamical algorithm for city planning is proposed as an Impulse Pattern Formulation (IPF) for predicting relevant parameters like health, artistic freedom, or financial developments of different social or political stakeholders over the cause of a planning process. The IPF has already shown high predictive precision at low computational cost in m

Tiberiu Burlacu, Danny Lathouwers, Zoltán Perkó

$\textbf{Purpose:}$ To assess the viability of a physics-based, deterministic and adjoint-capable algorithm for performing treatment planning system independent dose calculations and for computing dosimetric differences caused by anatomical changes. $\textbf{Methods:}$ A semi-numerical approach is employed to solve two partial differential equations for the

E. Jesina, M. Harrison, H. Andras-Letanovszky, D. Krug

We present an analysis on Physics Club's supremacy over the Astronomy Club at the University of Arizona. Through a thorough investigation of each club's history and content, and subsequent diligent calculations, we have proven without a doubt that the Physics Club is superior to Astronomy Club.

Hyeongjun Kwon, Jinhyun Jang, Jin Kim, Kwonyoung Kim

Visual scenes are naturally organized in a hierarchy, where a coarse semantic is recursively comprised of several fine details. Exploring such a visual hierarchy is crucial to recognize the complex relations of visual elements, leading to a comprehensive scene understanding. In this paper, we propose a Visual Hierarchy Mapper (Hi-Mapper), a novel approach fo

Bo Zou, Chao Yang, Yu Qiao, Chengbin Quan

Significant advancements in video question answering (VideoQA) have been made thanks to thriving large image-language pretraining frameworks. Although these image-language models can efficiently represent both video and language branches, they typically employ a goal-free vision perception process and do not interact vision with language well during the answ

Achintya Kundu, Fabian Lim, Aaron Chew, Laura Wynter

Supernet training of LLMs is of great interest in industrial applications as it confers the ability to produce a palette of smaller models at constant cost, regardless of the number of models (of different size / latency) produced. We propose a new method called Multistage Low-rank Fine-tuning of Super-transformers (MLFS) for parameter-efficient supernet tra

Yijin Choi, Jongkyung Shin, Chiehyeon Lim

An increasing number of retailers are expanding their channels to the offline and online domains, transforming them into multi-channel retailers. This transition emphasizes the need for cross-channel recommendations. Given that each retail channel represents a separate domain with a unique context, this can be regarded as a cross-domain recommendation (CDR).

Fang Liu, Yang Liu, Lin Shi, Zhen Yang

The rise of Large Language Models (LLMs) has significantly advanced various applications on software engineering tasks, particularly in code generation. Despite the promising performance, LLMs are prone to generate hallucinations, which means LLMs might produce outputs that deviate from users' intent, exhibit internal inconsistencies, or misaligned with the

Nguyen Dung Chinh, Le Tri Dat, Vinh N. T. Pham, Tran Duong Anh-Tai

We theoretically investigate the nonlinear effects of a magnetic field on the relaxation process of exciton-polaritons toward Bose-Einstein condensation in GaAs quantum wells. Our study reveals that the modification of the exciton's effective mass, Rabi splitting, and dispersion significantly alters the relaxation rate of polaritons as they approach condensa

Taikei Fujii, Takahiko Nobukawa

We give a connection formula for the Jackson integral of Riemann-Papperitz type. This includes a solution of the connection problem for the variant of $q$-hypergeometric equation of degree three introduced by Hatano-Matsunawa-Sato-Takemura. Using this formula we show a linear relation for the Kajihara's $q$-hypergeometric series $W^{M,2}$, whose $q$-differen

Xiupeng Chen, Jacquelien M. A. Scherpen, Nima Monshizadeh

Demand response has been a promising solution for accommodating renewable energy in power systems. In this study, we consider a demand response scheme within a distribution network facing an energy supply deficit. The utility company incentivizes load aggregators to adjust their pre-scheduled energy consumption and generation to match the supply. Each aggreg

Toru Takahashi

This study proposes a versatile and efficient optimisation method for discrete coils that induce a magnetic field by their steady currents. The prime target is gradient coils for MRI (Magnetic Resonance Imaging). The derivative (gradient) of the $z$-component the magnetic field, which is calculated by the Biot--Savart's law, with respect to the $z$-coordinat

Yifan Zhu, Ruiyao Ma, Baihua Zheng, Xiangyu Ke

Similarity search, the task of identifying objects most similar to a given query object under a specific metric, has gathered significant attention due to its practical applications. However, the absence of coordinate information to accelerate similarity search and the high computational cost of measuring object similarity hinder the efficiency of existing C

Shi Chen, Kenji Fukushima, Yusuke Shimada

We investigate detailed properties of imaginary rotating matter with gluons and quarks at high temperature. Previously, we showed that imaginary rotation induces perturbative confinement of gluons at the rotation center. We perturbatively calculate the Polyakov loop potential and find inhomogeneous confinement above a certain threshold of imaginary angular v

Renxiang Guan, Zihao Li, Chujia Song, Guo Yu

Spatial correlations between different ground objects are an important feature of mining land cover research. Graph Convolutional Networks (GCNs) can effectively capture such spatial feature representations and have demonstrated promising results in performing hyperspectral imagery (HSI) classification tasks of complex land. However, the existing GCN-based H

Sangjun Sim, Min Yong Jeong, Hyunggeun Lee, Dong Hyun David Lee

To understand the alkali-metal-dependent material properties of recently discovered $A$V$_{3}$Sb$_{5}$ ($A$ = K, Rb, and Cs), we conducted a detailed electronic structure analysis based on first-principles density functional theory calculations. Contrary to the case of $A$ = K and Rb, the energetic positions of the low-lying Van Hove singularities are revers

Haokai Hong, Wanyu Lin, Ming Yang, Kay Chen Tan

Can we train a 3D molecule generator using data from dense regions to generate samples in sparse regions? This challenge can be framed as an out-of-distribution (OOD) generation problem. While prior research on OOD generation predominantly targets property shifts, structural shifts -- such as differences in molecular scaffolds or functional groups -- represe

Bharath Keshavamurthy, Nicolo Michelusi

This work describes the orchestration of a fleet of rotary-wing Unmanned Aerial Vehicles (UAVs) for harvesting prioritized traffic from random distributions of heterogeneous users with Multiple Input Multiple Output (MIMO) capabilities. In a finite-horizon offline setting, the goal is to optimize the beam-forming design, the 3D UAV positioning and trajectory

David Persson, Nicolas Boullé, Daniel Kressner

The randomized singular value decomposition (SVD) has become a popular approach to computing cheap, yet accurate, low-rank approximations to matrices due to its efficiency and strong theoretical guarantees. Recent work by Boull\'e and Townsend (FoCM, 2023) presents an infinite-dimensional analog of the randomized SVD to approximate Hilbert-Schmidt operators.

Ling Wang, Runfa Chen, Yikai Wang, Fuchun Sun

Unsupervised non-rigid point cloud shape correspondence underpins a multitude of 3D vision tasks, yet itself is non-trivial given the exponential complexity stemming from inter-point degree-of-freedom, i.e., pose transformations. Based on the assumption of local rigidity, one solution for reducing complexity is to decompose the overall shape into independent

Al Jay Lan J. Alamin, Bryan S. Hernandez

Power law systems have been studied extensively due to their wide-ranging applications, particularly in chemistry. In this work, we focus on power law systems that can be decomposed into stoichiometrically independent subsystems. We show that for such systems where the ranks of the augmented matrices containing the kinetic order vectors of the underlying sub

Kanishk Gandhi, Denise Lee, Gabriel Grand, Muxin Liu

Language models are rarely shown fruitful mistakes while training. They then struggle to look beyond the next token, suffering from a snowballing of errors and struggling to predict the consequence of their actions several steps ahead. In this paper, we show how language models can be taught to search by representing the process of search in language, as a f

Mohammad R. Garousi

This study delves into the dimensional reduction of the classical effective action of heterotic string theory on a circle, along with its T-duality symmetry, with the aim of identifying the bosonic couplings. To achieve this, we propose a truncation scheme for the generalized Buscher rules and the reduced action, specifically targeting the truncation of the

Georges Sadaka, Pierre Jolivet, Efstathios G. Charalampidis, Ionut Danaila

We present and distribute a parallel finite-element toolbox written in the free software FreeFem for computing the Bogoliubov-de Gennes (BdG) spectrum of stationary solutions to one- and two-component Gross-Pitaevskii (GP) equations, in two or three spatial dimensions. The parallelization of the toolbox relies exclusively upon the recent interfacing of FreeF

M. Zubair, Saira Waheed, Quratulien Muneer, M. Ahmad

The present work is devoted to explore some interesting cosmological features of a newly proposed theory of gravity namely $\mathcal{F}(R,L_m,T)$ theory, where $R$ and $T$ represent the Ricci scalar and trace of energy momentum-tensor, respectively. Firstly, a non-equilibrium thermodynamical description is considered on the apparent horizon of the Friedmann'

Kohei Takehira

This paper discusses the number of points for which the dynamical canonical height is less than or equal to a given value. The height function is a fundamental and important tool in number theory to capture the ``number-theoretic complexity" of a point. Asymptotic formulas for the number of points in projective space below a given height have been studied by

Anıl Gürses, Gautham Reddy, Saad Masrur, Özgür Özdemir

Digital twins (DTs), which are virtual environments that simulate, predict, and optimize the performance of their physical counterparts, hold great promise in revolutionizing next-generation wireless networks. While DTs have been extensively studied for wireless networks, their use in conjunction with autonomous vehicles featuring programmable mobility remai

Yichi Zhang, Yuchen Zhang, Lipeng Zhu, Sa Xiao

In this correspondence, we propose a movable antenna (MA)-aided multi-user hybrid beamforming scheme with a sub-connected structure, where multiple movable sub-arrays can independently change their positions within different local regions. To maximize the system sum rate, we jointly optimize the digital beamformer, analog beamformer, and positions of subarra

Pascal Marquet, Max Planck

An English (2024) translation by P. Marquet of 3 German papers about "Die Quantenhypothese f\"ur Molekeln mit mehreren Freiheitsgraden (The quantum hypothesis for molecules with multiple degrees of freedom)" and "Bemerkung \"uber die Entropiekonstante zweiatomiger Gase (Remarks on the entropy constant of diatomic gases)" by Max Planck (1915,a,b,c)

Cheng Chen, Shoki Ohta, Takayuki Nishio, Mohamed Wahib

This study explores the feasibility of adapting CSI-guided imaging across varied environments. Focusing on continuous model learning through continuous updates, we investigate CSI-Imager's adaptability in dynamically changing settings, specifically transitioning from an office to an industrial environment. Unlike traditional approaches that may require retra

Bryan S. Hernandez, Patrick Vincent N. Lubenia, Eduardo R. Mendoza

This work introduces a new method for comparing two reaction networks of the same or closely related systems through their embedded networks in terms of the shared set of species. Hence, we call this method the Common Species Embedded Networks (CSEN) analysis. Using this approach, we conduct a comparison of existing reaction networks associated with Wnt sign

Renhua Gu, Xiangfeng Meng

SemEval-2024 Task 8 provides a challenge to detect human-written and machine-generated text. There are 3 subtasks for different detection scenarios. This paper proposes a system that mainly deals with Subtask B. It aims to detect if given full text is written by human or is generated by a specific Large Language Model (LLM), which is actually a multi-class t

Gousia Habib, Shaima Qureshi, Malik ishfaq

Biomedical image analysis is of paramount importance for the advancement of healthcare and medical research. Although conventional convolutional neural networks (CNNs) are frequently employed in this domain, facing limitations in capturing intricate spatial and temporal relationships at the pixel level due to their reliance on fixed-sized windows and immutab

Pascal Marquet, Max Planck

This is an English (annotated) translation of the German paper by Max Planck (1916) "On the absolute entropy of monatomic bodies" (\"Uber die absolute Entropie einatomiger K\"orper).

Haitao Li, You Chen, Zhekai Ge, Qingyao Ai

Legal retrieval techniques play an important role in preserving the fairness and equality of the judicial system. As an annually well-known international competition, COLIEE aims to advance the development of state-of-the-art retrieval models for legal texts. This paper elaborates on the methodology employed by the TQM team in COLIEE2024.Specifically, we exp

Gousia Habib, Ishfaq Ahmed Malik, Jameel Ahmad, Imtiaz Ahmed

Batch Normalization is an important approach to advancing deep learning since it allows multiple networks to train simultaneously. A problem arises when normalizing along the batch dimension because B.N.'s error increases significantly as batch size shrinks because batch statistics estimates are inaccurate. As a result, computer vision tasks like detection,

Sergey Rybakov

In this paper, we prove a refinement of the Katsura theorem on finite group actions on abelian surfaces such that the quotient is birational to a $K3$ surface. As an application, we compute traces of Frobenius on the Neron--Severi groups of supersingular generalized Kummer surfaces over finite fields.

Gene Byrd, Sethanne Howard

Many Hubble type S galaxies have a flat rotation curve, V extending into the outer disk. The surface brightness there is too low to create V from stars of a reasonable mass-to-light ratio. To maintain a stable disk, a massive dark matter halo is usually assumed to dominate in creating V. Gravitational arm amplification is used here to estimate whether V is c

Jihoo Kim, Wonho Song, Dahyun Kim, Yunsu Kim

This paper introduces Evalverse, a novel library that streamlines the evaluation of Large Language Models (LLMs) by unifying disparate evaluation tools into a single, user-friendly framework. Evalverse enables individuals with limited knowledge of artificial intelligence to easily request LLM evaluations and receive detailed reports, facilitated by an integr

Xiaoze Liu, Feijie Wu, Tianyang Xu, Zhuo Chen

The advent of Large Language Models (LLMs) has significantly transformed the AI landscape, enhancing machine learning and AI capabilities. Factuality issue is a critical concern for LLMs, as they may generate factually incorrect responses. In this paper, we propose GraphEval to evaluate an LLM's performance using a substantially large test dataset. Specifica

Shubham Kejriwal, Vojtech Witzany, Michal Zajacek, Dheeraj R. Pasham

Extreme-mass-ratio inspirals (EMRIs) are one of the primary targets for the recently adopted millihertz gravitational-wave (GW) observatory LISA. Some previous studies have argued that a fraction of all EMRIs form in matter-rich environments, and can potentially explain the dozens of soft X-ray band ($\sim 10^{-1} \rm keV$), low-frequency ($\sim 0.1$ mHz) pe

Wenfan Ou, Sheng Bi

Fueled by advances in both robust optimization theory and reinforcement learning (RL), robust Markov Decision Processes (RMDPs) have garnered increasing attention due to their powerful capability for sequential decision-making under uncertainty. In this paper, we provide a comprehensive overview of the theoretical foundations and recent developments in RMDPs

Alexander A. Voronov

This is a survey of Rational Homotopy Theory, intended for a Mathematical Physics readership.

Zhonghao Shi, Ellen Landrum, Amy O' Connell, Mina Kian

Socially assistive robots (SARs) have shown great success in providing personalized cognitive-affective support for user populations with special needs such as older adults, children with autism spectrum disorder (ASD), and individuals with mental health challenges. The large body of work on SAR demonstrates its potential to provide at-home support that comp

Xiao Fan, Qingwen Wu, Jiancheng Wu, Xiangli Lei

The presence of young stars, aged around several million years and situated within the range of $\sim 0.04-1$ pc from our Galactic center raises a question about their origins and dynamical evolutions. Their kinematics provide an opportunity to explore their formation or possible subsequent dynamical evolution. If Sagittarius A* was active in the past as sug

Gousia Habib, Tausifa jan Saleem, Sheikh Musa Kaleem, Tufail Rouf

Deep learning techniques have been demonstrated to surpass preceding cutting-edge machine learning techniques in recent years, with computer vision being one of the most prominent examples. However, deep learning models suffer from significant drawbacks when deployed in resource-constrained environments due to their large model size and high complexity. Know

Gil Kalai, Yosef Rinott, Tomer Shoham

Considerable effort in experimental quantum computing is devoted to noisy intermediate scale quantum computers (NISQ computers). Understanding the effect of noise is important for various aspects of this endeavor including notable claims for achieving quantum supremacy and attempts to demonstrate quantum error correcting codes. In this paper we use Fourier m

Zhenyu Hou, Yilin Niu, Zhengxiao Du, Xiaohan Zhang

ChatGLM is a free-to-use AI service powered by the ChatGLM family of large language models (LLMs). In this paper, we present the ChatGLM-RLHF pipeline -- a reinforcement learning from human feedback (RLHF) system -- designed to enhance ChatGLM's alignment with human preferences. ChatGLM-RLHF encompasses three major components: the collection of human prefere

H. V. Ragavendra, Anjan Kumar Sarkar, Shiv K. Sethi

In recent years, the detection of gravitational waves by LIGO and PTA collaborations have raised the intriguing possibility of excess matter power at small scales. Such an increase can be achieved by ultra slow roll (USR) phase during inflationary epoch. We constrain excess power over small scales within the framework of such models using cosmological datase

Takenori Kataoka, Masato Kurihara

In this paper, we study the module-theoretic structure of classical Iwasawa modules. More precisely, for a finite abelian $p$-extension $K/k$ of totally real fields and the cyclotomic $\mathbb{Z}_p$-extension $K_{\infty}/K$, we consider $X_{K_{\infty},S}={\rm Gal}(M_{K_{\infty},S}/K_{\infty})$ where $S$ is a finite set of places of $k$ containing all ramifyi

Yunsong Wang, Hanlin Chen, Gim Hee Lee

Recent advancements in vision-language foundation models have significantly enhanced open-vocabulary 3D scene understanding. However, the generalizability of existing methods is constrained due to their framework designs and their reliance on 3D data. We address this limitation by introducing Generalizable Open-Vocabulary Neural Semantic Fields (GOV-NeSF), a

Ji-Eun Han, Jun-Seok Koh, Hyeon-Tae Seo, Du-Seong Chang

We present a novel end-to-end personality-based synthetic dialogue data generation pipeline, specifically designed to elicit responses from large language models via prompting. We design the prompts to generate more human-like dialogues considering real-world scenarios when users engage with chatbots. We introduce PSYDIAL, the first Korean dialogue dataset f

Yuemei Xu, Ling Hu, Jiayi Zhao, Zihan Qiu

Based on the foundation of Large Language Models (LLMs), Multilingual LLMs (MLLMs) have been developed to address the challenges faced in multilingual natural language processing, hoping to achieve knowledge transfer from high-resource languages to low-resource languages. However, significant limitations and challenges still exist, such as language imbalance

Akila de Silva, Nicholas Tee, Omkar Ghanekar, Fahim Hasan Khan

Vortices are studied in various scientific disciplines, offering insights into fluid flow behavior. Visualizing the boundary of vortices is crucial for understanding flow phenomena and detecting flow irregularities. This paper addresses the challenge of accurately extracting vortex boundaries using deep learning techniques. While existing methods primarily t

Jaehyeon Moon, Dohyung Kim, Junyong Cheon, Bumsub Ham

Post-training quantization (PTQ) is an efficient model compression technique that quantizes a pretrained full-precision model using only a small calibration set of unlabeled samples without retraining. PTQ methods for convolutional neural networks (CNNs) provide quantization results comparable to full-precision counterparts. Directly applying them to vision

Bidushi Sharma, Dhiren Kumar Basnet

In this paper we take a deeper look at the self conjugate reciprocal (SCR) polynomials, which towards the end of the paper aid the construction of new classes of permutation polynomials of simpler forms over $\mathbb{F}_{q^{2}}$. The paper focuses on the conditions required for a certain class of degree 2 and degree 3 SCR polynomials to have no roots in $\mu

Kieran Mastel, William Slofstra

The recent MIP*=RE theorem of Ji, Natarajan, Vidick, Wright, and Yuen shows that the complexity class MIP* of multiprover proof systems with entangled provers contains all recursively enumerable languages. Prior work of Grilo, Slofstra, and Yuen [FOCS '19] further shows (via a technique called simulatable codes) that every language in MIP* has a perfect zero

Jia Gong, Lin Geng Foo, Yixuan He, Hossein Rahmani

Sign Language Translation (SLT) is a challenging task that aims to translate sign videos into spoken language. Inspired by the strong translation capabilities of large language models (LLMs) that are trained on extensive multilingual text corpora, we aim to harness off-the-shelf LLMs to handle SLT. In this paper, we regularize the sign videos to embody lingu

Zhiyuan Cheng, Zhaoyi Liu, Tengda Guo, Shiwei Feng

Pixel-wise regression tasks (e.g., monocular depth estimation (MDE) and optical flow estimation (OFE)) have been widely involved in our daily life in applications like autonomous driving, augmented reality and video composition. Although certain applications are security-critical or bear societal significance, the adversarial robustness of such models are no

Lisong C. Sun, Neel P. Bhatt, Jonathan C. Liu, Zhiwen Fan

Simultaneous localization and mapping is essential for position tracking and scene understanding. 3D Gaussian-based map representations enable photorealistic reconstruction and real-time rendering of scenes using multiple posed cameras. We show for the first time that using 3D Gaussians for map representation with unposed camera images and inertial measureme

Chen Chen, Daochang Liu, Chang Xu

Pretrained diffusion models and their outputs are widely accessible due to their exceptional capacity for synthesizing high-quality images and their open-source nature. The users, however, may face litigation risks owing to the models' tendency to memorize and regurgitate training data during inference. To address this, we introduce Anti-Memorization Guidanc

Beomyoung Kim, Myeong Yeon Yi, Joonsang Yu, Young Joon Yoo

This paper presents a new practical training method for human matting, which demands delicate pixel-level human region identification and significantly laborious annotations. To reduce the annotation cost, most existing matting approaches often rely on image synthesis to augment the dataset. However, the unnaturalness of synthesized training images brings in

S. Ohkubo

$\alpha$+$\alpha$ scattering has a long history since the first experiment by Rutherford and Chadwick in 1927 and has been studied thoroughly experimentally and theoretically. However, $\alpha$+$\alpha$ scattering has never been paid attention from the viewpoint of refractive scattering. I have successfully analyzed the experimental angular distributions in

John McGreevy, Tarun Grover

Apparent violations of Naturalness may be explained by positing the existence of an omniscient but disinterested and possibly fallible Observer who regularly performs von Neumann measurements on us (and everything else). We comment briefly on the implications for the construction of scalable quantum computers.

Beomyoung Kim, Donghyun Kim, Sung Ju Hwang

This paper presents a fresh perspective on the role of saliency maps in weakly-supervised semantic segmentation (WSSS) and offers new insights and research directions based on our empirical findings. We conduct comprehensive experiments and observe that the quality of the saliency map is a critical factor in saliency-guided WSSS approaches. Nonetheless, we f

Ryota Masuki, Takuya Nomoto, Ryotaro Arita, Terumasa Tadano

Inspired by the recent discovery of a new polar metal LiReO$_3$ by K. Murayama, \textit{et al}, we calculate the temperature($T$)-dependent crystal structures of Li$B$O3 with $B$ = Ta, W, Re, Os, using the self-consistent phonon (SCPH) theory. We have reproduced the experimentally observed polar-nonpolar structural phase transitions and the transition temper

Heemin Yang, Jaesung Rim, Seungyong Lee, Seung-Hwan Baek

In this paper, we present GyroDeblurNet, a novel single-image deblurring method that utilizes a gyro sensor to resolve the ill-posedness of image deblurring. The gyro sensor provides valuable information about camera motion that can improve deblurring quality. However, exploiting real-world gyro data is challenging due to errors from various sources. To hand

Tianyu Huang, Liangzu Peng, René Vidal, Yun-Hui Liu

Given an input set of $3$D point pairs, the goal of outlier-robust $3$D registration is to compute some rotation and translation that align as many point pairs as possible. This is an important problem in computer vision, for which many highly accurate approaches have been recently proposed. Despite their impressive performance, these approaches lack scalabi

Yuanhao Zeng, Min Wang, Yihang Wang, Yingxia Shao

Large Language Models (LLMs) have excelled in various tasks but perform better in high-resource scenarios, which presents challenges in low-resource scenarios. Data scarcity and the inherent difficulty of adapting LLMs to specific tasks compound the challenge. To address the twin hurdles, we introduce \textbf{Leverage Learning}. We present a streamlined impl

Bo Zou, Chao Yang, Yu Qiao, Chengbin Quan

Existing methods to fine-tune LLMs, like Adapter, Prefix-tuning, and LoRA, which introduce extra modules or additional input sequences to inject new skills or knowledge, may compromise the innate abilities of LLMs. In this paper, we propose LLaMA-Excitor, a lightweight method that stimulates the LLMs' potential to better follow instructions by gradually payi

Leda Wang, Zhixiang Zhang, Edgar Dobriban

Randomized algorithms can be used to speed up the analysis of large datasets. In this paper, we develop a unified methodology for statistical inference via randomized sketching or projections in two of the most fundamental problems in multivariate statistical analysis: least squares and PCA. The methodology applies to fixed datasets -- i.e., is data-conditio

L. -J. Li, S. -B. Qian, L. -Y. Zhu, X. -D. Shi

We re-examine the Bailey diagrams of fundamental mode RR Lyrae stars from the perspective of horizontal branch (HB) evolution, identifying evolutionary effects as the probable direct cause of the Oosterhoff dichotomy. By establishing empirical relationships between pulsation amplitude and average effective temperature, and utilizing pulsation period relation

K. Mahesh Krishna

Let $(\{f_n\}_{n=1}^\infty, \{\tau_n\}_{n=1}^\infty)$ and $(\{g_n\}_{n=1}^\infty, \{\omega_n\}_{n=1}^\infty)$ be unbounded continuous p-Schauder frames ($0<p<1$) for a disc Banach space $\mathcal{X}$. Then for every $x \in ( \mathcal{D}(\theta_f) \cap\mathcal{D}(\theta_g))\setminus\{0\}$, we show that \begin{align}\label{UB} (1) \quad \quad \quad \quad \|\th

Rongjie Li, Yu Wu, Xuming He

Generative vision-language models (VLMs) have shown impressive performance in zero-shot vision-language tasks like image captioning and visual question answering. However, improving their zero-shot reasoning typically requires second-stage instruction tuning, which relies heavily on human-labeled or large language model-generated annotation, incurring high l

Dong He, Jie Fan, Xin Gao, Yu Gao

The dark photon is a promising candidate for the dark matter which comprises most of the matter in our visible Universe. Via kinetic mixing with the Standard Model it can also be resonantly converted to photons in an electromagnetic cavity, offering novel experimental possibilities for the discovery and study of dark matter. We report the results of a pathfi

Dongyuan Xiao, Ryunosuke Mori

In this paper, we investigate the spreading properties of solutions of the Aoki-Shida-Shigesada model. This model is a three-component reaction-diffusion system that delineates the geographical expansion of an initially localized population of farmers into a region occupied by hunter-gatherers. By considering the scenario where farmers and hunter-gatherers p