December 2023 arXiv papers — page 123

Yuhui Liu

Let lambda_1, \lambda_2, \lambda_3, \lambda_4 be non-zero real numbers, not all negative, with \lambda_1/\lambda_2 irrational and algebraic. Suppose that \mathcal{V} is a well-spaced sequence and \delta >0. In this paper, it is proved that for any \varepsilon >0, the number of v \in \mathcal{V} with v \leqslant N for which |\lambda_1 p_1^2 + \lambda_2 p_2^3+

Haoran Wei, Lingyu Kong, Jinyue Chen, Liang Zhao

Modern Large Vision-Language Models (LVLMs) enjoy the same vision vocabulary -- CLIP, which can cover most common vision tasks. However, for some special vision task that needs dense and fine-grained vision perception, e.g., document-level OCR or chart understanding, especially in non-English scenarios, the CLIP-style vocabulary may encounter low efficiency

Qishan Liu, Kenny C. Y. Ng

Primordial black holes~(PBHs) formed in the early Universe are well-motivated dark matter~(DM) candidates over a wide range of masses. These PBHs could emit detectable signals in the form of photons, electrons, and neutrinos through Hawking radiation. We consider the null observations of astrophysical $\bar{\nu}_{e}$ flux from several neutrino detectors and

Ian Ball

This note gives simpler proofs of the variational and multiple priors representations in Maccheroni et al. (2006) and Gilboa and Schmeidler (1989).

Prithvijit Chattopadhyay, Bharat Goyal, Boglarka Ecsedi, Viraj Prabhu

Synthetic data (SIM) drawn from simulators have emerged as a popular alternative for training models where acquiring annotated real-world images is difficult. However, transferring models trained on synthetic images to real-world applications can be challenging due to appearance disparities. A commonly employed solution to counter this SIM2REAL gap is unsupe

Chong-Bin Chen

We revisit inflation coupled with vector fields employing kinetic coupling in the comoving gauge. It is known that there is a cumulative effect $IN^2$ on the curvature power spectrum. For a large number of e-foldings $N$, this contribution is so significant that it could violate observational constraints when the ratio of kinetic energy between the vector fi

Eliot Kapit, Brandon A. Barton, Sean Feeney, George Grattan

A canonical feature of the constraint satisfaction problems in NP is approximation hardness, where in the worst case, finding sufficient-quality approximate solutions is exponentially hard for all known methods. Fundamentally, the lack of any guided local minimum escape method ensures both exact and approximate classical approximation hardness, but the equiv

Reynald Affeldt, Jacques Garrigue, Takafumi Saikawa

One can perform equational reasoning about computational effects with a purely functional programming language thanks to monads. Even though equational reasoning for effectful programs is desirable, it is not yet mainstream. This is partly because it is difficult to maintain pencil-and-paper proofs of large examples. We propose a formalization of a hierarchy

Tomoyuki Tokuue, Tomoaki Ishiyama

Gravitational $N$-body simulations calculate numerous interactions between particles. The tree algorithm reduces these calculations by constructing a hierarchical oct-tree structure and approximating gravitational forces on particles. Over the last three decades, the tree algorithm has been extensively used in large-scale simulations, and its parallelization

Binxiao Huang, Jason Chun Lok Li, Jie Ran, Boyu Li

Conventional super-resolution (SR) schemes make heavy use of convolutional neural networks (CNNs), which involve intensive multiply-accumulate (MAC) operations, and require specialized hardware such as graphics processing units. This contradicts the regime of edge AI that often runs on devices strained by power, computing, and storage resources. Such a chall

Sage Shaw, Zachary P Kilpatrick

Traveling waves of neural activity emerge in cortical networks both spontaneously and in response to stimuli. The spatiotemporal structure of waves can indicate the information they encode and the physiological processes that sustain them. Here, we investigate the stimulus-response relationships of traveling waves emerging in adaptive neural fields as a mode

Michael Chitayat, Adrien Dubouloz

We show that a $3$-dimensional Pham-Brieskorn hypersurface $\{ X_0^{a_0} + X_1^{a_1} + X_2^{a_2} + X_3^{a_3}=0\}$ in $\mathbb{A}^4$ such that $\min\{a_0, a_1, a_2, a_3 \} \geq 2$ and at most one element $i$ of $\{0,1,2,3\}$ satisfies $a_i = 2$ does not admit a non-trivial action of the additive group $\mathbb{G}_a$.

Cheng Peng, Xi Yang, Aokun Chen, Zehao Yu

Objective To solve major clinical natural language processing (NLP) tasks using a unified text-to-text learning architecture based on a generative large language model (LLM) via prompt tuning. Methods We formulated 7 key clinical NLP tasks as text-to-text learning and solved them using one unified generative clinical LLM, GatorTronGPT, developed using GPT-3

Fei Wu, Kung-Sik Chan

Time series of matrix-valued data are increasingly available in various areas including economics, finance, social science, among others. These data may shed light on the inter-dynamical relationships between two sets of attributes, for instance, countries and economic indices. The matrix autoregressive (MAR) model provides a parsimonious approach for analyz

Zhaoxuan Tan, Meng Jiang

User modeling (UM) aims to discover patterns or learn representations from user data about the characteristics of a specific user, such as profile, preference, and personality. The user models enable personalization and suspiciousness detection in many online applications such as recommendation, education, and healthcare. Two common types of user data are te

Chuan-Peng Zhang, M. Zhu, P. Jiang, C. Cheng

The FAST All Sky HI survey (FASHI) was designed to cover the entire sky observable by the Five-hundred-meter Aperture Spherical radio Telescope (FAST), spanning approximately 22000 square degrees of declination between -14 deg and +66 deg, and in the frequency range of 1050-1450 MHz, with the expectation of eventually detecting more than 100000 HI sources. B

Feihu Liu

Let $\langle A\rangle$ be the numerical semigroup generated by relatively prime positive integers $\{a_1,a_2,...,a_n\}$. The quotient of $\langle A\rangle$ with respect to a positive integer $p$ is defined by $\frac{\langle A\rangle}{p}=\{x\in \mathbb{N} \mid px\in \langle A\rangle\}$. The quotient $\frac{\langle A\rangle}{p}$ is known to be a semigroup but

Qirui Yang, Qihua Cheng, Huanjing Yue, Le Zhang

Raw low light image enhancement (LLIE) has achieved much better performance than the sRGB domain enhancement methods due to the merits of raw data. However, the ambiguity between noisy to clean and raw to sRGB mappings may mislead the single-stage enhancement networks. The two-stage networks avoid ambiguity by decoupling the two mappings but usually have lar

Gabriel Cuomo, Zohar Komargodski, Siwei Zhong

We discuss rapidly rotating states of a superfluid. We concentrate on the Giant-Vortex (GV) state, which is a coherent rotating solution with a macroscopic hole at the center. We show that, for any trap, the fluctuations obey an approximately chiral dispersion relation, describing arbitrary shape deformations moving with the speed of the ambient superfluid.

Ming Shan Hee, Aditi Kumaresan, Nguyen Khoi Hoang, Nirmalendu Prakash

The rise of social media platforms has brought about a new digital culture called memes. Memes, which combine visuals and text, can strongly influence public opinions on social and cultural issues. As a result, people have become interested in categorizing memes, leading to the development of various datasets and multimodal models that show promising results

Nirmalendu Prakash, Han Wang, Nguyen Khoi Hoang, Ming Shan Hee

The proliferation of social media has given rise to a new form of communication: memes. Memes are multimodal and often contain a combination of text and visual elements that convey meaning, humor, and cultural significance. While meme analysis has been an active area of research, little work has been done on unsupervised multimodal topic modeling of memes, w

Yi Chen, Yuying Ge, Yixiao Ge, Mingyu Ding

The pursuit of artificial general intelligence (AGI) has been accelerated by Multimodal Large Language Models (MLLMs), which exhibit superior reasoning, generalization capabilities, and proficiency in processing multimodal inputs. A crucial milestone in the evolution of AGI is the attainment of human-level planning, a fundamental ability for making informed

Rayyan Abdalla

High-resolution time-frequency (TF) analysis plays crucial role in characterizing multicomponent signal (MCSs) and estimating oscillatory properties. Linear time-frequency representations (TFRs) such as classical short-time Fourier transform (STFT) and continuous wavelet transform (CWT) incur constrained TF resolution and energy diffusion in both time and fr

Yuqin Yang, Saber Salehkaleybar, Negar Kiyavash

We study the problem of identifying the unknown intervention targets in structural causal models where we have access to heterogeneous data collected from multiple environments. The unknown intervention targets are the set of endogenous variables whose corresponding exogenous noises change across the environments. We propose a two-phase approach which in the

Weizhao Cai, Vasily S. Minkov, Ying Sun, Panpan Kong

The discovery of high-temperature superconductivity above 240 K in binary La-H and Y-H systems inspired further predictions of even higher transition temperatures in compounds such as YH10 and MgH6, which are likely to be dynamically unstable. Ternary superhydrides provide alternative pathways to stabilize desired near-room temperature superconducting phases

Manbir S Gulati, Paul F Roysdon

Autoregressive and Masked Transformers are incredibly effective as generative models and classifiers. While these models are most prevalent in NLP, they also exhibit strong performance in other domains, such as vision. This work contributes to the exploration of transformer-based models in synthetic data generation for diverse application domains. In this pa

Shandong Yuan

Sentence classification is one of the basic tasks of natural language processing. Convolution neural network (CNN) has the ability to extract n-grams features through convolutional filters and capture local correlations between consecutive words in parallel, so CNN is a popular neural network architecture to dealing with the task. But restricted by the width

Rayyan Abdalla

Complex-valued neural networks (CVNNs) have recently been successful in various pioneering areas which involve wave-typed information and frequency-domain processing. This work addresses different structures and classification of CVNNs. The theory behind complex activation functions, implications related to complex differentiability and special activations f

Yung-Chin Chen, Shimpei Ando, Daichi Fujiki, Shinya Takamaeda-Yamazaki

To address the 'memory wall' problem in NN hardware acceleration, we introduce HALO-CAT, a software-hardware co-design optimized for Hidden Neural Network (HNN) processing. HALO-CAT integrates Layer-Penetrative Tiling (LPT) for algorithmic efficiency, reducing intermediate result sizes. Furthermore, the architecture employs an activation-localized computing-

Daisheng Jin, Jiangbei Hu, Baixin Xu, Yuxin Dai

In this study, we introduce a novel two-stage technique for decomposing and reconstructing facial features from sparse-view images, a task made challenging by the unique geometry and complex skin reflectance of each individual. To synthesize 3D facial models more realistically, we endeavor to decouple key facial attributes from the RGB color, including geome

Rayyan Abdalla

Wireless communication systems generally endure severe fading and interference caused by the time-dispersive channel. Major transmission distortion is produced by channel multipath propagation and overlap of subsequent symbols. To counteract channel response, equalization techniques are employed to operate on channel output and recover transmitted signal at

Shenglin Xu, Qinfu Zhao, Ronglan Zhang, Bingbing Suo

The practical application of Na-based solid-state electrolytes (SSEs) is limited by their low level of conduction. To evaluate the impact of tetrahedral anion groups on carrier migration, we designed a set of anti-perovskite SSEs theoretically based on the previously reported Na4OBr2, including Na4O(BH4)2, Na4O(BF4)2, and Na4O(AlH4)2. It is essential to note

Zhongliang Zhou, Mengxuan Hu, Mariah Salcedo, Nathan Gravel

Artificial intelligence (AI), particularly machine learning and deep learning models, has significantly impacted bioinformatics research by offering powerful tools for analyzing complex biological data. However, the lack of interpretability and transparency of these models presents challenges in leveraging these models for deeper biological insights and for

Rahul Venkatesh, Honglin Chen, Kevin Feigelis, Daniel M. Bear

The ability to understand physical dynamics is critical for agents to act in the world. Here, we use Counterfactual World Modeling (CWM) to extract vision structures for dynamics understanding. CWM uses a temporally-factored masking policy for masked prediction of video data without annotations. This policy enables highly effective "counterfactual prompting"

Xinglong Chen, Junjie Zhang, A. S. Thind, S. Sharma

We report the discovery of a novel form of Ruddlesden-Popper (RP) oxide, which stands as the first example of long-range, coherent polymorphism in this class of inorganic solids. Rather than the well-known, uniform stacking of perovskite blocks ubiquitously found in RP phases, this newly discovered polymorph of the bilayer RP phase $La_{3}Ni_{2}O_{7}$adopts

Congrong Ren, Xin Liang, Hanqi Guo

We explore an error-bounded lossy compression approach for reducing scientific data associated with 2D/3D unstructured meshes. While existing lossy compressors offer a high compression ratio with bounded error for regular grid data, methodologies tailored for unstructured mesh data are lacking; for example, one can compress nodal data as 1D arrays, neglectin

Viqar Husain, Hassan Mehmood

We present a 4-dimensional generally covariant gauge theory which leads to the Gauss constraint but lacks both the Hamiltonian and spatial diffeomorphism constraints. The canonical theory therefore resembles Yang-Mills theory without the Hamiltonian. We describe its observables, quantization, and some generalizations.

Lulu Zhang, Ni Zhao, Yongbiao Yang, Junye Zhao

The spin transverse relaxation time (T_2) of atoms is an important indicator for precision measurement. Several methods have been proposed to characterize the T_2 of atoms. In this paper, the T_2 of rubidium (Rb) atomic vapor in the same cell was measured using four measuring methods, namely spin noise spectrum signal fitting, improved free induction decay (

Roozbeh Yousefzadeh

We study the understanding of deep neural networks from the scope in which they are trained on. While the accuracy of these models is usually impressive on the aggregate level, they still make mistakes, sometimes on cases that appear to be trivial. Moreover, these models are not reliable in realizing what they do not know leading to failures such as adversar

Luca Nalon, Jeremy T. Tyson

The H-type deviation, $\delta({\mathbb G})$, of a step two Carnot group ${\mathbb G}$ quantifies the extent to which ${\mathbb G}$ deviates from the geometrically and algebraically tractable class of Heisenberg-type (H-type) groups. In an earlier paper, the author defined this notion and used it to provide new analytic characterizations for the class of H-ty

Mei Wang, Weihong Deng, Sen Su

Ancient history relies on the study of ancient characters. However, real-world scanned oracle characters are difficult to collect and annotate, posing a major obstacle for oracle character recognition (OrCR). Besides, serious abrasion and inter-class similarity also make OrCR more challenging. In this paper, we propose a novel unsupervised domain adaptation

Fangxun Shu, Lei Zhang, Hao Jiang, Cihang Xie

This paper presents Audio-Visual LLM, a Multimodal Large Language Model that takes both visual and auditory inputs for holistic video understanding. A key design is the modality-augmented training, which involves the integration of modality-specific tokens engineered to activate the appropriate visual and/or auditory encoder selectively. This mechanism is pi

Muneera Bano, Didar Zowghi, Vincenzo Gervasi

The growing presence of Artificial Intelligence (AI) in various sectors necessitates systems that accurately reflect societal diversity. This study seeks to envision the operationalization of the ethical imperatives of diversity and inclusion (D&I) within AI ecosystems, addressing the current disconnect between ethical guidelines and their practical implemen

Md. Sohel Rana, Razu Ahmed, Md. Sajidul Islam, R. S. Islam

In recent years, the investigation of novel materials for various technological applications has gained much importance in materials science research. Tri-molybdenum phosphide (Mo3P), a promising transition metal phosphide (TMP), has gathered significant attention due to its unique structural and electronic properties, which already make it potentially valua

Candi Zheng, Yuan Lan

Popular guidance for denoising diffusion probabilistic model (DDPM) linearly combines distinct conditional models together to provide enhanced control over samples. However, this approach overlooks nonlinear effects that become significant when guidance scale is large. To address this issue, we propose characteristic guidance, a guidance method that provides

Mu Tian, Xiaohui Chen, Yi Gao

Many deep learning based automated medical image segmentation systems, in reality, face difficulties in deployment due to the cost of massive data annotation and high latency in model iteration. We propose a dynamic interactive learning framework that addresses these challenges by integrating interactive segmentation into end-to-end weak supervised learning

Prakhar Srivastava, Ruihan Yang, Gavin Kerrigan, Gideon Dresdner

In climate science and meteorology, high-resolution local precipitation (rain and snowfall) predictions are limited by the computational costs of simulation-based methods. Statistical downscaling, or super-resolution, is a common workaround where a low-resolution prediction is improved using statistical approaches. Unlike traditional computer vision tasks, w

Hanqing Liu, Hatem Brahmi, Carla Boix-Constant, Herre S. J. van der Zant

Heat transport in two-dimensions is fundamentally different from that in three dimensions. As a consequence, the thermal properties of 2D materials are of great interest, both from scientific and application point of view. However, few techniques are available for accurate determination of these properties in ultrathin suspended membranes. Here, we present a

Federico Cassano, Luisa Li, Akul Sethi, Noah Shinn

A significant amount of research is focused on developing and evaluating large language models for a variety of code synthesis tasks. These include synthesizing code from natural language, synthesizing tests from code, and synthesizing explanations of code. In contrast, the behavior of instructional code editing with LLMs is understudied. These are tasks in

Zihao Zhao, Sheng Wang, Qian Wang, Dinggang Shen

Obtaining large-scale radiology reports can be difficult for medical images due to various reasons, limiting the effectiveness of contrastive pre-training in the medical image domain and underscoring the need for alternative methods. In this paper, we propose eye-tracking as an alternative to text reports, as it allows for the passive collection of gaze sign

Sahil Khose, Anisha Pal, Aayushi Agarwal, Deepanshi

Real-world aerial scene understanding is limited by a lack of datasets that contain densely annotated images curated under a diverse set of conditions. Due to inherent challenges in obtaining such images in controlled real-world settings, we present SkyScenes, a synthetic dataset of densely annotated aerial images captured from Unmanned Aerial Vehicle (UAV)

Renxiang Guan, Zihao Li, Xianju Li, Chang Tang

High-dimensional and complex spectral structures make the clustering of hyperspectral images (HSI) a challenging task. Subspace clustering is an effective approach for addressing this problem. However, current subspace clustering algorithms are primarily designed for a single view and do not fully exploit the spatial or textural feature information in HSI. I

Yuxiao Wu, Zhichen Pan, Lei Qian, Scott Ransom

We present the discovery of three pulsars in the Globular Cluster (GC) M15 (NGC 7078) by the Five-hundred-meter Aperture Spherical radio Telescope (FAST). PSR J2129+1210J (M15J) is a millisecond pulsar with a spin period of 11.84 ms and a dispersion measure of 66.68 pc cm-3. Both PSR J2129+1210K and L (M15K and L) are long-period pulsars with spin periods of

M. Mylova, N. Afshordi

Cuscuton field theory is an extension of general relativity that does not introduce additional propagating degrees of freedom, or violate relativistic causality. We construct a general geometric description of the cuscuton field theory by introducing curvature corrections to both the volume (potential) and the surface (kinetic) terms in the original cuscuton

Michael Levin

Let (X,Z) be a minimal dynamical system on a compact metric X and k an integer such that mdim X< k. We show that (X,Z) admits an equivariant embedding in the shift (D^k)^Z where D is a superdendrite.

Sung Hwan Mun, Min Hyun Han, Canyeong Moon, Nam Soo Kim

In recent years, there have been studies to further improve the end-to-end neural speaker diarization (EEND) systems. This letter proposes the EEND-DEMUX model, a novel framework utilizing demultiplexed speaker embeddings. In this work, we focus on disentangling speaker-relevant information in the latent space and then transform each separated latent variabl

Michael Levin

Let (X,Z) be a dynamical system on a compact metric X and let X be the countable union of closed invariant subsets X_i, i in N. We prove that mdim X =sup {mdim X_i : i in N}.

Haotian Zhang, Semujju Stuart Dereck, Zhicheng Wang, Xianwei Lv

Although the applications of artificial intelligence especially deep learning had greatly improved various aspects of intelligent manufacturing, they still face challenges for wide employment due to the poor generalization ability, difficulties to establish high-quality training datasets, and unsatisfactory performance of deep learning methods. The emergence

Yue Wu, Yongzhe Yuan, Xiaolong Fan, Xiaoshui Huang

We propose a new framework that formulates point cloud registration as a denoising diffusion process from noisy transformation to object transformation. During training stage, object transformation diffuses from ground-truth transformation to random distribution, and the model learns to reverse this noising process. In sampling stage, the model refines rando

Xinfang Zhang, Zhihao Wu, Gregory A. L. White, Zhongcheng Xiang

The development of fault-tolerant quantum processors relies on the ability to control noise. A particularly insidious form of noise is temporally correlated or non-Markovian noise. By combining randomized benchmarking with supervised machine learning algorithms, we develop a method to learn the details of temporally correlated noise. In particular, we can le

Riley Britten, Michael Kinyon, Kenneth Kunen, J. D. Phillips

We study loops which are universal (that is, isotopically invariant) with respect to the property of flexibility ($xy\cdot x = x\cdot yx$). We also weaken this to semi-universality, that is, loops in which every left and right isotope is flexible, but not necessarily every isotope. One of our main results is that universally flexible, inverse property loops

Christian Hugo Hoffmann, Frederik F. Flöther

Artificial intelligence (AI) recently had its 'iPhone moment' and adoption has drastically accelerated. Quantum computing appears poised to follow suit over the next years. However, while there has been discourse about how to use AI responsibly, there is still little appreciation and awareness among executives, managers, and practitioners about the broader e

S. Farid Ghahari, Alborz Ghofrani, Jian Zhang, Ertugrul Taciroglu

To accurately analyze structures, soil-structure interaction effects must be taken into account. One approach is to create a complete finite element model of the full system wherein the soil is represented as a semi-infinite domain. This direct method is frequently adopted in research studies, but it is typically avoided in engineering practice due to the la

Tuna Han Salih Meral, Enis Simsar, Federico Tombari, Pinar Yanardag

Images produced by text-to-image diffusion models might not always faithfully represent the semantic intent of the provided text prompt, where the model might overlook or entirely fail to produce certain objects. Existing solutions often require customly tailored functions for each of these problems, leading to sub-optimal results, especially for complex pro

Caiqing Jian, Yongbin Qin, Lihui Wang

Gland instance segmentation is an essential but challenging task in the diagnosis and treatment of adenocarcinoma. The existing models usually achieve gland instance segmentation through multi-task learning and boundary loss constraint. However, how to deal with the problems of gland adhesion and inaccurate boundary in segmenting the complex samples remains

M. R. Bridson, A. W. Reid, R. Spitler

We prove that there exist finitely presented, residually finite groups that are profinitely rigid in the class of all finitely presented groups but not in the class of all finitely generated groups. These groups are of the form $\Gamma \times \Gamma$ where $\Gamma$ is a profinitely rigid 3-manifold group; we describe a family of such groups with the property

Zeyu Shen, Anilesh Krishnaswamy, Janardhan Kulkarni, Kamesh Munagala

In this paper, we consider differentially private classification when some features are sensitive, while the rest of the features and the label are not. We adapt the definition of differential privacy naturally to this setting. Our main contribution is a novel adaptation of AdaBoost that is not only provably differentially private, but also significantly out

Suyash Pratap

The emergence of dynamic rerouting in multi-modal transportation networks has emerged as a crucial area in operations research, revolutionizing routine optimization. The review study analyzes multiple research publications on algorithms and techniques related to dynamic rerouting to give a thorough summary of the state of research in this field and provide f

Diego C. P. Blanco, Eduardo Martini, Kenzo Sasaki, André V. G. Cavalieri

Spectral proper orthogonal decomposition (SPOD) is an increasingly popular modal analysis method in the field of fluid dynamics due to its specific properties: a linear system forced with white noise should have SPOD modes identical to response modes from resolvent analysis. The SPOD, coupled with the Welch method for spectral estimation, may require long ti

Sangwon Hyun, Mingyu Guo, M. Ali Babar

Large-Language Models (LLMs) have shifted the paradigm of natural language data processing. However, their black-boxed and probabilistic characteristics can lead to potential risks in the quality of outputs in diverse LLM applications. Recent studies have tested Quality Attributes (QAs), such as robustness or fairness, of LLMs by generating adversarial input

Seth Neel, Peter Chang

This is the first survey of the active area of AI research that focuses on privacy issues in Large Language Models (LLMs). Specifically, we focus on work that red-teams models to highlight privacy risks, attempts to build privacy into the training or inference process, enables efficient data deletion from trained models to comply with existing privacy regula

Xuechen Liu, Xin Wang, Erica Cooper, Xiaoxiao Miao

In this study, we introduce a novel cross-modal retrieval task involving speaker descriptions and their corresponding audio samples. Utilizing pre-trained speaker and text encoders, we present a simple learning framework based on contrastive learning. Additionally, we explore the impact of incorporating speaker labels into the training process. Our findings

Xiao Zhang, David Yunis, Michael Maire

We present an approach for analyzing grouping information contained within a neural network's activations, permitting extraction of spatial layout and semantic segmentation from the behavior of large pre-trained vision models. Unlike prior work, our method conducts a holistic analysis of a network's activation state, leveraging features from all layers and o

Shuning Lin, Yong Chen

Due to the dynamic characteristics of instantaneity and steepness, employing domain decomposition techniques for simulating rogue wave solutions is highly appropriate. Wherein, the backward compatible PINN (bc-PINN) is a temporally sequential scheme to solve PDEs over successive time segments while satisfying all previously obtained solutions. In this work,

Runze Chen, Dongwook Go, Stefan Bluegel, Weisheng Zhao

Orbitronics is an emerging and fascinating field that explores the utilization of the orbital degree of freedom of electrons for information processing. An increasing number of orbital phenomena are being currently discovered, with spin-orbit coupling mediating the interplay between orbital and spin effects, thus providing a wealth of control mechanisms and

Ziyi Yin, Rafael Orozco, Mathias Louboutin, Felix J. Herrmann

We introduce a probabilistic technique for full-waveform inversion, employing variational inference and conditional normalizing flows to quantify uncertainty in migration-velocity models and its impact on imaging. Our approach integrates generative artificial intelligence with physics-informed common-image gathers, reducing reliance on accurate initial veloc

Ziheng Zeng, Kellen Tan Cheng, Srihari Venkat Nanniyur, Jianing Zhou

Idiomatic expression (IE) processing and comprehension have challenged pre-trained language models (PTLMs) because their meanings are non-compositional. Unlike prior works that enable IE comprehension through fine-tuning PTLMs with sentences containing IEs, in this work, we construct IEKG, a commonsense knowledge graph for figurative interpretations of IEs.

Abdullah Rashwan, Jiageng Zhang, Ali Taalimi, Fan Yang

In recent years, transformer-based models have dominated panoptic segmentation, thanks to their strong modeling capabilities and their unified representation for both semantic and instance classes as global binary masks. In this paper, we revisit pure convolution model and propose a novel panoptic architecture named MaskConver. MaskConver proposes to fully u

Jiangchao Qiu, Sai Ravela, Kerry Emanuel

The risk of extreme storm tides to Bangladesh's low-lying and densely populated coastal regions, already vulnerable to tropical cyclones, remains poorly quantified under a warming climate. Here, using a statistical-physical downscaling approach, our multimodel large-ensemble projections under the IPCC6 SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios show that Ban

Ahmet Orun, Emre Orun, Fatih Kurugollu

Cyber-attacks keep threatening global networks and information infrastructures. The threat is getting more and more destructive and hard to counter day by day as the global networks continue to enlarge exponentially with limited security counter-measures. As this fact requires more sophisticated methods and techniques in urgency, a multidisciplinary remote c

Chengyu Zhou, Yuqi Su, Tangbin Xia, Xiaolei Fang

Multilinear Principal Component Analysis (MPCA) is a widely utilized method for the dimension reduction of tensor data. However, the integration of MPCA into federated learning remains unexplored in existing research. To tackle this gap, this article proposes a Federated Multilinear Principal Component Analysis (FMPCA) method, which enables multiple users to

Cyrus Vachha

We present a pipeline for integrating NeRFs into traditional compositing VFX pipelines using Nerfstudio, an open-source framework for training and rendering NeRFs. Our approach involves using Blender, a widely used open-source 3D creation software, to align camera paths and composite NeRF renders with meshes and other NeRFs, allowing for seamless integration

Jifeng Shen, Teng Guo, Xin Zuo, Heng Fan

Global feature based Pedestrian Attribute Recognition (PAR) models are often poorly localized when using Grad-CAM for attribute response analysis, which has a significant impact on the interpretability, generalizability and performance. Previous researches have attempted to improve generalization and interpretation through meticulous model design, yet they o

Mehmet S. Ismail, Ronald Peeters

It is well known that ex ante social preferences and expected utility are not always compatible. In this note, we introduce a novel framework that naturally separates social preferences from selfish preferences to answer the following question: What specific forms of social preferences can be accommodated within the expected utility paradigm? In a departure

Alex Stivala

Exponential-family random graph models (ERGMs) are a family of network models originating in social network analysis, which have also been applied to biological networks. Advances in estimation algorithms have increased the practical scope of these models to larger networks, however it is still not always possible to estimate a model without encountering pro

Alexey Arefiev, I-Lin Yeh, Louise Willingale

Direct laser acceleration of electrons is an important energy deposition mechanism for laser-irradiated plasmas that is particularly effective at relativistic laser intensities in the presence of quasi-static laser-driven plasma electric and magnetic fields. These radial electric and azimuthal magnetic fields provide transverse electron confinement by induci

Yusaku Shinoda, Yoshihiko Okamoto, Youichi Yamakawa, Hiroshi Takatsu

The electronic properties of ScPdGe and ScPdSi, crystallizing in the hexagonal ZrNiAl and orthorhombic TiNiSi structures, respectively, are investigated. ScPdGe and ScPdSi are found to show bulk superconductivity below 0.9 and 1.7 K, respectively, based on electrical resistivity and heat capacity data measured using synthesized polycrystalline samples. First

Subhasish Mukherjee, Ian Tice

We introduce a scale of anisotropic Sobolev spaces defined through a three-parameter family of Fourier multipliers and study their functional analytic properties. These spaces arise naturally in PDE when studying traveling wave solutions, and we give some simple applications of the spaces in this direction.

Rachel Wang, Ivan Y. Vasko, Tai Phan, Forrest Mozer

We present the analysis of 2,033 current sheets (CS) observed aboard four Cluster spacecraft in a pristine solar wind. Four-spacecraft estimates of the CS normal and propagation velocity are compared with different single-spacecraft estimates. The Minimum Variance Analysis (MVA) of the magnetic field is shown to be highly inaccurate in estimating the normal.

Nick Higginbottom, Nicolas Scepi, Christian Knigge, Knox S. Long

Disc winds are a common feature in accreting astrophysical systems on all scales. In active galactic nuclei (AGN) and accreting white dwarfs (AWDs), specifically, radiation pressure mediated by spectral lines is a promising mechanism for driving these outflows. Previous hydrodynamical simulations have largely supported this idea, but relied on highly approxi

Chafiq Benhida, Raúl E. Curto, George R. Exner

Geometrically regular weighted shifts (in short, GRWS) are those with weights $α(N,D)$ given by $α_n (N,D) = \sqrt{\frac{p^n + N}{p^n + D}}$, where $p > 1$ and $(N,D)$ is fixed in the open unit square $ (-1, 1)\times (-1, 1)$. We study here the zone of pairs $ (M,P)$ for which the weight $\frac{α(N,D) }{ α(M,P) }$ gives rise to a moment infinitely divisible

Xiao-Feng Shi, Yan Lu

Nuclear-spin entangling gates with divalent atoms can be executed by one global laser pulse when $Δ_{\text{Z}}<Ω$, where $Δ_{\text{Z}}$ is the Zeeman-splitting-dominated frequency difference for the clock-Rydberg transitions of the two nuclear-spin qubit states and $Ω$ is the maximal Rabi frequency. Concerning the sensitivity of Rydberg-state energy to magne

Boris Vexler, Jakob Wagner

In this work we consider the two dimensional instationary Navier-Stokes equations with homogeneous Dirichlet/no-slip boundary conditions. We show error estimates for the fully discrete problem, where a discontinuous Galerkin method in time and inf-sup stable finite elements in space are used. Recently, best approximation type error estimates for the Stokes p

Max Auer

We obtain a Poisson Limit for return times to small sets for product systems. Only one factor is required to be hyperbolic while the second factor is only required to satisfy polynomial deviation bounds for ergodic sums. In particular, the second fact can be either elliptic or parabolic. As an application of our main result, several maps of the form Anosov m

Kartik Venkatraman, Yongqiang Cheng, Alexandra Moy, Jordan A. Hachtel

Ion transport across solid solid interfaces is often slower than through the bulk of a material, impeding the charge and discharge rate of batteries. Designing highly conductive interfaces is challenging due to the need to probe ion conduction at individual interfaces and correlate it with the local structure. In this study, we address this challenge by enab

Diego Cifuentes, Santanu S. Dey, Jingye Xu

We consider sensitivity analysis for Mixed Binary Quadratic Programs (MBQPs) with respect to changing right-hand-sides (rhs). We show that even if the optimal solution of a given MBQP is known, it is NP-hard to approximate the change in objective function value with respect to changes in rhs. Next, we study algorithmic approaches to obtaining dual bounds for

Jingyan Li

This document describes a search for charged-lepton flavor violation (CLFV) in the production and decay of top quarks using 138 fb$^{-1}$ of data collected by the CMS experiment at a center-of-mass energy of 13 TeV. Events are selected for analysis if they contain an opposite-sign electron-muon pair, a third charged lepton (electron or muon), at least one je

Lekan Molu

We propose reaching steps towards the real-time strain control of multiphysics, multiscale continuum soft robots. To study this problem fundamentally, we ground ourselves in a model-based control setting enabled by mathematically precise dynamics of a soft robot prototype. Poised to integrate, rather than reject, inherent mechanical nonlinearities for embodi

Yujian Liu, Yang Zhang, Tommi Jaakkola, Shiyu Chang

Despite diffusion models' superior capabilities in modeling complex distributions, there are still non-trivial distributional discrepancies between generated and ground-truth images, which has resulted in several notable problems in image generation, including missing object errors in text-to-image generation and low image quality. Existing methods that atte

Weixing Zhang

Blended modeling is an emerging paradigm involving seamless interaction between multiple notations for the same underlying modeling language. We focus on a model-driven engineering (MDE) approach based on meta-models to develop textual languages to improve the blended modeling capabilities of modeling tools. In this thesis, we propose an approach that can su