March 2023 arXiv papers — page 28

Bhaskar Shukla, David Dudal, Subhash Mahapatra

We investigate both from a qualitative as well as quantitative perspective the emergence of chaos in the QCD confining string in a magnetic field from a holographic viewpoint. We use an earlier developed bottom-up solution of the Einstein-Maxwell-Dilaton action that mimics QCD and its thermodynamics quite well. Surprisingly, our predictions depend on the use

Peter Henderson, Xuechen Li, Dan Jurafsky, Tatsunori Hashimoto

Existing foundation models are trained on copyrighted material. Deploying these models can pose both legal and ethical risks when data creators fail to receive appropriate attribution or compensation. In the United States and several other countries, copyrighted content may be used to build foundation models without incurring liability due to the fair use do

Qi Wu, Joseph A. Insley, Victor A. Mateevitsi, Silvio Rizzi

Implicit neural representations (INRs) have emerged as a powerful tool for compressing large-scale volume data. This opens up new possibilities for in situ visualization. However, the efficient application of INRs to distributed data remains an underexplored area. In this work, we develop a distributed volumetric neural representation and optimize it for in

Hongyu Zhao, Kangrui Wang, Mo Yu, Hongyuan Mei

Language models have been shown to perform remarkably well on a wide range of natural language processing tasks. In this paper, we propose LEAP, a novel system that uses language models to perform multi-step logical reasoning and incorporates explicit planning into the inference procedure. Explicit planning enables the system to make more informed reasoning

Xiaohui Rong, Xingguo Qi, Quan Zhou, Libin Kang

Na-ion batteries (NIBs), which are recognized as a next-generation alternative technology for energy storage, still suffer from commercialization constraints due to the lack of low-cost, high-performance cathode materials. Since our first discovery of Cu$^{3+}$/Cu$^{2+}$ electrochemistry in 2014, numerous Cu-substituted/doped materials have been designed for

Frederik Van Daele, Olivier Honnay, Steven Janssens, Hanne De Kort

Climate change and the resulting increased drought frequencies pose considerable threats to forest herb populations, especially when compounded by additional environmental challenges. Specifically, habitat fragmentation may disrupt climate adaptation and cause shifts in mating systems. To examine this, we conducted a garden experiment with Primula elatior of

Zhengyang Liu, Zeyu Ren, Zihe Wang

We continue the study of the performance for fixed-price mechanisms in the bilateral trade problem, and improve approximation ratios of welfare-optimal mechanisms in several settings. Specifically, in the case where only the buyer distribution is known, we prove that there exists a distribution over different fixed-price mechanisms, such that the approximati

Mingjian Liang, Junjie Hu, Chenyu Bao, Hua Feng

Recently, RGB-Thermal based perception has shown significant advances. Thermal information provides useful clues when visual cameras suffer from poor lighting conditions, such as low light and fog. However, how to effectively fuse RGB images and thermal data remains an open challenge. Previous works involve naive fusion strategies such as merging them at the

Chengcheng Han, Zhanhong Lei, Weihao Liao

Type II seesaw leptogenesis simultaneously explains the origin of neutrino masses, the baryon asymmetry of our universe, and the inflation. The Large Hadron Collider(LHC) provides an opportunity to directly test type II seesaw leptogenesis by looking for the predicted triplet Higgs. In this paper, we perform an analysis of the detection prospect for the trip

Jinsheng Pan, Weihong Qi, Zichen Wang, Hanjia Lyu

There is a broad consensus that news media outlets incorporate ideological biases in their news articles. However, prior studies on measuring the discrepancies among media outlets and further dissecting the origins of thematic differences suffer from small sample sizes and limited scope and granularity. In this study, we use a large dataset of 1.8 million ne

Jiayu Shang, Cheng Peng, Herui Liao, Xubo Tang

Motivation: There is accumulating evidence showing the important roles of bacteriophages (phages) in regulating the structure and functions of the microbiome. However, lacking an easy-to-use and integrated phage analysis software hampers microbiome-related research from incorporating phages in the analysis. Results: In this work, we developed a web server, P

Yunfeng Hou, Ching-Yen Weng, Peng Li

In discrete-event systems, to save sensor resources, the agent continuously adjusts sensor activation decisions according to a sensor activation policy based on the changing observations. However, new challenges arise for sensor activations in networked discrete-event systems, where observation delays and control delays exist between the sensor systems and t

Chengxi Li, Kai Fan, Jiajun Bu, Boxing Chen

Song translation requires both translation of lyrics and alignment of music notes so that the resulting verse can be sung to the accompanying melody, which is a challenging problem that has attracted some interests in different aspects of the translation process. In this paper, we propose Lyrics-Melody Translation with Adaptive Grouping (LTAG), a holistic so

Xingyu Chen, Jianhuan Cen, Qingsong Zou

In this paper, we propose a new adaptive technique, named adaptive trajectories sampling (ATS), which is used to select training points for the numerical solution of partial differential equations (PDEs) with deep learning methods. The key feature of the ATS is that all training points are adaptively selected from trajectories that are generated according to

Jin Sob Kim, Hyun Joon Park, Wooseok Shin, Sung Won Han

Sound event localization and detection (SELD) combines the identification of sound events with the corresponding directions of arrival (DOA). Recently, event-oriented track output formats have been adopted to solve this problem; however, they still have limited generalization toward real-world problems in an unknown polyphony environment. To address the issu

Oluwatosin Ogundare, Gustavo Quiros Araya

Interest in Large Language Models (LLMs) has increased drastically since the emergence of ChatGPT and the outstanding positive societal response to the ease with which it performs tasks in Natural Language Processing (NLP). The triumph of ChatGPT, however, is how it seamlessly bridges the divide between language generation and knowledge models. In some cases

Peng Fang, Arijit Khan, Siqiang Luo, Fang Wang

Graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in machine learning tasks. The increasing availability of billion-edge graphs underscores the importance of learning efficient and effective embeddings on large graphs, such as link prediction on Twitter with over one billion edges. Most existing graph embedding methods fall s

Ye Mo, Yi Zhuo, Shunlong Li

Shortwave track diseases are generally reflected in the form of local track irregularity. Such diseases will greatly impact the train-track-bridge interaction (TTBI) dynamic system, seriously affecting train safety. Therefore, a method is proposed to detect and localize local track irregularities based on multis-sensor time-frequency features of high-speed r

Alex S. Leong, Alexei T. Skvortsov

In this paper we consider the problem of estimation of scalar field distribution (e.g. pollutant, moisture, temperature) from noisy measurements collected by unmanned autonomous vehicles such as UAVs. The field is modelled as a sum of Fourier components/modes, where the number of modes retained and estimated determines in a natural way the approximation qual

Hyeonsoo Lee, Junha Kim, Eunkyung Park, Minjeong Kim

Recently, deep learning models have shown the potential to predict breast cancer risk and enable targeted screening strategies, but current models do not consider the change in the breast over time. In this paper, we present a new method, PRIME+, for breast cancer risk prediction that leverages prior mammograms using a transformer decoder, outperforming a st

Mehrdad Noori, Milad Cheraghalikhani, Ali Bahri, Gustavo A. Vargas Hakim

Standard deep learning models such as convolutional neural networks (CNNs) lack the ability of generalizing to domains which have not been seen during training. This problem is mainly due to the common but often wrong assumption of such models that the source and target data come from the same i.i.d. distribution. Recently, Vision Transformers (ViTs) have sh

Nankai Lin, Junheng He, Zhenghang Tang, Dong Zhou

Recently, more and more research has focused on addressing bias in text classification models. However, existing research mainly focuses on the fairness of monolingual text classification models, and research on fairness for multilingual text classification is still very limited. In this paper, we focus on the task of multilingual text classification and pro

Arkady L. Kholodenko

Although electrons (fermions) and photons(bosons) produce the same interference patterns in the two-slit experiments, the description of these patterns is markedly different. Photons are spin one, relativistic and massless while electrons are spin 1/2 massive particles producing the same interference patterns. In spite of these differences, already in early

Arianna I. Renzini, Alba Romero-Rodrguez, Colm Talbot, Max Lalleman

The collection of gravitational waves (GWs) that are either too weak or too numerous to be individually resolved is commonly referred to as the gravitational-wave background (GWB). A confident detection and model-driven characterization of such a signal will provide invaluable information about the evolution of the Universe and the population of GW sources w

Doris Arzoumanian, Sota Arakawa, Masato I. N. Kobayashi, Kazunari Iwasaki

Cylindrical molecular filaments are observed to be the main sites of Sun-like star formation, while massive stars form in dense hubs, at the junction of multiple filaments. The role of hub-filament configurations has not been discussed yet in relation to the birth environment of the solar system and to infer the origin of isotopic ratios of Short-Lived Radio

Nicolle González, José Simental, Monica Vazirani

We introduce the higher rank $(q,t)$-Catalan polynomials and prove they equal truncations of the Hikita polynomial to a finite number of variables. Using affine compositions and a certain standardization map, we define a dinv statistic on rank $r$ semistandard $(m,n)$-parking functions and prove codinv counts the dimension of an affine space in an affine pav

Masataka Kawai, Noriaki Ota, Shinsuke Yamaoka

Pretraining a deep learning model on large image datasets is a standard step before fine-tuning the model on small targeted datasets. The large dataset is usually general images (e.g. imagenet2012) while the small dataset can be specialized datasets that have different distributions from the large dataset. However, this 'large-to-small' strategy is not well-

Kenichiro Tanabe

Let $V$ be a vertex algebra of countable dimension, $G$ a subgroup of ${\rm Aut} V$ of finite order, $V^{G}$ the fixed point subalgebra of $V$ under the action of $G$, and ${\mathscr S}$ a finite $G$-stable set of inequivalent irreducible twisted weak $V$-modules associated with possibly different automorphisms in $G$. We show a Schur--Weyl type duality for

Tao Yu

In this paper, we classify Left-invariant Ricci collineations associated to Yano connections on three-dimensional Lorentzian Lie groups.

Xiangru Li, Boyu Lin

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has acquired tens of millions of low-resolution spectra of stars. This paper investigated the parameter estimation problem for these spectra. To this end, we proposed a deep learning model StarGRU network (StarGRUNet). This network was further applied to estimate the stellar atmospheric p

Jiaqi Jin, Siwei Wang, Zhibin Dong, Xinwang Liu

The success of existing multi-view clustering relies on the assumption of sample integrity across multiple views. However, in real-world scenarios, samples of multi-view are partially available due to data corruption or sensor failure, which leads to incomplete multi-view clustering study (IMVC). Although several attempts have been proposed to address IMVC,

Mukhlis Amien

This study provides an overview of the history of the development of Natural Language Processing (NLP) in the context of the Indonesian language, with a focus on the basic technologies, methods, and practical applications that have been developed. This review covers developments in basic NLP technologies such as stemming, part-of-speech tagging, and related

Tong Zhao, Andrea Tagliabue, Jonathan P. How

The deployment of agile autonomous systems in challenging, unstructured environments requires adaptation capabilities and robustness to uncertainties. Existing robust and adaptive controllers, such as those based on model predictive control (MPC), can achieve impressive performance at the cost of heavy online onboard computations. Strategies that efficiently

Trent J. Sakakini, Justin P. Koeln

Thermal Energy Storage (TES) devices, which leverage the constant-temperature thermal capacity of the latent heat of a Phase Change Material (PCM), provide benefits to a variety of thermal management systems by decoupling the absorption and rejection of thermal energy. While performing a role similar to a battery in an electrical system, it is critical to kn

Jingzhi Hu, Zhe Chen, Jun Luo

Metamaterial-based reconfigurable holographic surfaces (RHSs) have been proposed as novel cost-efficient antenna arrays, which are promising for improving the positioning and communication performance of integrated sensing and communications (ISAC) systems. However, due to the high frequency selectivity of the metamaterial elements, RHSs face challenges in s

Neil Epstein

The notion of an Egyptian domain (where the analogue of Egyptian fractions works appropriately), first explored by Guerrieri-Loper-Oman, is extended to the more general notions of generically and locally Egyptian domains. Results from the previous paper are extended, as well as reinterpreted in the new expanded context. It is shown that localizations of poly

Dong Wang, Tao Xiao, Christoph Treude, Raula Gaikovina Kula

Images are increasingly being shared by software developers in diverse channels including question-and-answer forums like Stack Overflow. Although prior work has pointed out that these images are meaningful and provide complementary information compared to their associated text, how images are used to support questions is empirically unknown. To address this

Qi-Ling Chen, Pei-Jin Hu, Jing-Jing Su, Ming-Ming Kang

The observation of short gamma ray bursts (SGRBs) in the TeV energy range plays an important role in understanding the radiation mechanism and probing new areas of physics such as Lorentz invariance violation. However, no SGRB has been observed in this energy range due to the short duration of SGRBs and the weakness of current experiments. New experiments wi

Sanxing Chen, Hao Cheng, Xiaodong Liu, Jian Jiao

Learning transferable representation of knowledge graphs (KGs) is challenging due to the heterogeneous, multi-relational nature of graph structures. Inspired by Transformer-based pretrained language models' success on learning transferable representation for texts, we introduce a novel inductive KG representation model (iHT) for KG completion by large-scale

Rini Jasmine Gladstone, Helia Rahmani, Vishvas Suryakumar, Hadi Meidani

Physics-based deep learning frameworks have shown to be effective in accurately modeling the dynamics of complex physical systems with generalization capability across problem inputs. However, time-independent problems pose the challenge of requiring long-range exchange of information across the computational domain for obtaining accurate predictions. In the

William E. Lewis, Eric C. Harding, David A. Yager-Elorriaga, Jeffrey R. Fein

A variety of spherical crystal x-ray imager (SCXI) diagnostics have been developed and fielded on Magnetized Liner Inertial Fusion (MagLIF) experiments at the Sandia National Laboratories Z-facility. These different imaging modalities provide detailed insight into different physical phenomena such as mix of liner material into the hot fuel, cold liner emissi

Qiuchen Zhai, Gregery T. Buzzard, Kevin Mertes, Brendt Wohlberg

Multi-Agent Consensus Equilibrium (MACE) formulates an inverse imaging problem as a balance among multiple update agents such as data-fitting terms and denoisers. However, each such agent operates on a separate copy of the full image, leading to redundant memory use and slow convergence when each agent affects only a small subset of the full image. In this p

Peijie Dong, Lujun Li, Zimian Wei

Knowledge distillation (KD) is an effective training strategy to improve the lightweight student models under the guidance of cumbersome teachers. However, the large architecture difference across the teacher-student pairs limits the distillation gains. In contrast to previous adaptive distillation methods to reduce the teacher-student gap, we explore a nove

Eric Schippers, Mohammad Shirazi

Consider a compact surface $\mathscr{R}$ with distinguished points $z_1,\ldots,z_n$ and conformal maps $f_k$ from the unit disk into non-overlapping quasidisks on $\mathscr{R}$ taking $0$ to $z_k$. Let $\Sigma$ be the Riemann surface obtained by removing the closures of the images of $f_k$ from $\mathscr{R}$. We define forms which are meromorphic on $\mathsc

Niluthpol Chowdhury Mithun, Kshitij Minhas, Han-Pang Chiu, Taragay Oskiper

Precise estimation of global orientation and location is critical to ensure a compelling outdoor Augmented Reality (AR) experience. We address the problem of geo-pose estimation by cross-view matching of query ground images to a geo-referenced aerial satellite image database. Recently, neural network-based methods have shown state-of-the-art performance in c

Timmy N. Delage, Matías Gárate, Satoshi Okuzumi, Chao-Chin Yang

[Abridged] Aims. We provide an important step toward a better understanding of the magnetorotational instability (MRI)-dust coevolution in protoplanetary disks by presenting a proof of concept that dust evolution ultimately plays a crucial role in the MRI activity. Methods. First, we study how a fixed power-law dust size distribution with varying parameters

Inderpreet Singh, Vikrant Khullar

Efficient storage of solar thermal energy is still one of the major bottlenecks in realizing dispatchable solar thermal systems. Present work is a significant step in this direction, wherein, we propose, thermochromism assisted photon transport based optical charging for efficient latent heat storage. Seeding thermochromic nanoparticles into the phase change

Xinxin Hu, Haotian Chen, Hongchang Chen, Shuxin Liu

With the rapid development of mobile networks, the people's social contacts have been considerably facilitated. However, the rise of mobile social network fraud upon those networks, has caused a great deal of distress, in case of depleting personal and social wealth, then potentially doing significant economic harm. To detect fraudulent users, call detail re

Gururaj Saileshwar, Moinuddin Qureshi

The HPCA-2023 paper "Are Randomized Caches Truly Random?" makes the claim that "MIRAGE is broken" while relying on two faulty assumptions: (1) starting from a severely compromised initial state where some sets are already full, and (2) a buggy cipher that does not provide uniformity of randomizing addresses over the cache sets. When we fixed these two shortc

Guanghui Lan, Alexander Shapiro

Optimization problems involving sequential decisions in a stochastic environment were studied in Stochastic Programming (SP), Stochastic Optimal Control (SOC) and Markov Decision Processes (MDP). In this paper we mainly concentrate on SP and SOC modelling approaches. In these frameworks there are natural situations when the considered problems are convex. Cl

Qingzhong Chen, Shilun Cai, Crystal Cai, Zefang Yu

Colonoscopic video retrieval, which is a critical part of polyp treatment, has great clinical significance for the prevention and treatment of colorectal cancer. However, retrieval models trained on action recognition datasets usually produce unsatisfactory retrieval results on colonoscopic datasets due to the large domain gap between them. To seek a solutio

Heikki Mäntysaari

We discuss recent developments towards next-to-leading order (NLO) accuracy in the dipole picture. We review recent NLO results for exclusive vector meson production and diffractive structure functions, and discuss how it is becoming possible to describe both inclusive and exclusive DIS data simultaneously within the Color Glass Condensate framework at NLO a

Seongyeon Park, Myungseo Song, Bohyung Kim, Tae-Hyun Oh

Neural text-to-speech (TTS) models can synthesize natural human speech when trained on large amounts of transcribed speech. However, collecting such large-scale transcribed data is expensive. This paper proposes an unsupervised pre-training method for a sequence-to-sequence TTS model by leveraging large untranscribed speech data. With our pre-training, we ca

Li-Ting Wu, Xin-Zhe Zhang, Ru-Zhi Luo, Jing Chen

We analyze the guided modes in coupled waveguides made of negative-index materials without gain or loss. We show that it supports non-Hermitian phenomenon on the existence of guided mode versus geometric parameters of the structure. The non-Hermitian effect is different from parity-time (PT) symmetry, and can be explained by a simple coupled-mode theory with

Shota Kanasugi, Shoichiro Tsutsui, Yuya O. Nakagawa, Kazunori Maruyama

Computation of the Green's function is crucial to study the properties of quantum many-body systems such as strongly correlated systems. Although the high-precision calculation of the Green's function is a notoriously challenging task on classical computers, the development of quantum computers may enable us to compute the Green's function with high accuracy

Phillip Guan, Eric Penner, Joel Hegland, Benjamin Letham

Stereoscopic, head-tracked display systems can show users realistic, world-locked virtual objects and environments. However, discrepancies between the rendering pipeline and physical viewing conditions can lead to perceived instability in the rendered content resulting in reduced immersion and, potentially, visually-induced motion sickness. Precise requireme

Hyeokjea Kwon, Hojun Lee, Joonwoo Bae

The kernel trick in supervised learning signifies transformations of an inner product by a feature map, which then restructures training data in a larger Hilbert space according to an endowed inner product. A quantum feature map corresponds to an instance with a Hilbert space of quantum states by fueling quantum resources to machine learning algorithms. In t

Paulina Karczmarek, Gergely Hajdu, Grzegorz Pietrzyński, Wolfgang Gieren

Because of their period-luminosity relation (PLR), classical Cepheids play a key role in the calibration of the extragalactic distance scale and the determination of the Hubble-Lema\^{i}tre constant $H_0$. Recent findings show that the majority of classical Cepheids should be in binary or multiple systems, which might undermine their accuracy, as the extra -

S. Imajo, H. Akatsuka, K. Hatanaka, T. Higuchi

Metal tubes plated with nickel-phosphorus are used in many fundamental physics experiments using ultracold neutrons (UCN) because of their ease of fabrication. These tubes are usually polished to a average roughness of 25-150 nm. However, there is no scattering model that accurately describes UCN scattering on such a rough guide surface with a mean-square ro

Ankita Agarwal, Tanvi Banerjee, Joy Gockel, Saniya LeBlanc

An additive manufacturing (AM) process, like laser powder bed fusion, allows for the fabrication of objects by spreading and melting powder in layers until a freeform part shape is created. In order to improve the properties of the material involved in the AM process, it is important to predict the material characterization property as a function of the proc

Pittawat Taveekitworachai, Febri Abdullah, Mury F. Dewantoro, Ruck Thawonmas

This paper presents the first ChatGPT4PCG Competition at the 2023 IEEE Conference on Games. The objective of this competition is for participants to create effective prompts for ChatGPT--enabling it to generate Science Birds levels with high stability and character-like qualities--fully using their creativity as well as prompt engineering skills. ChatGPT is

Ling Chen, Hongbin Zhang, Baocheng Zhang

We demonstrate an optical realization for the rotating BTZ black hole using the recent popular photon fluid model in an optical vortex but with a new proposed expression for the optical phase. We also give the numerical realization for the optical vortex to ensure that it can be generated experimentally. Different from the earlier suggestions for the analogu

Yui Hin Arvin Leung

We prove that the graphs $T\boxslash P$ have unbounded stack number and queue number $3$, where $T$ is a tree and $P$ is a path, and $\boxslash$ denotes the graph strong product but with one of the directions removed. The previous best known results is that graphs with queue number $4$ can have unbounded stack number.

Jingwei Sun, Jun Li, Yonghong Hao, Cuiting Qi

In this paper, the authors propose a new approach to solving the groundwater flow equation in the Toth basin of arbitrary top and bottom topographies using deep learning. Instead of using traditional numerical solvers, they use a DeepONet to produce the boundary-to-solution mapping. This mapping takes the geometry of the physical domain along with the bounda

Gaston Giribet, Nicholas Labranche, Joan La Madrid

The two-point string amplitude at tree level in flat spacetime reproduces the expected expression for free particles. This has been proven by Erbin, Maldacena and Skliros in [JHEP 07 (2019) 139] by two methods. Here, we provide an alternative proof of this result. Our method consists in considering a timelike Liouville direction as a regularization parameter

Michael T. Lacey, Ji Li, Brett D. Wick

Let $ \Pi _{b}$ be a bounded $n$ parameter paraproduct with symbol $b$. We demonstrate that this operator is in the Schatten class $S^p$, $0<p<\infty$, if the symbol is in the $n$ parameter Besov space $B_p$. Our result covers both the dyadic and continuous version of the paraproducts in the multiparameter setting.

Jingyang Lin, Junyu Chen, Hanjia Lyu, Igor Khodak

Diagnosis of adverse neonatal outcomes is crucial for preterm survival since it enables doctors to provide timely treatment. Machine learning (ML) algorithms have been demonstrated to be effective in predicting adverse neonatal outcomes. However, most previous ML-based methods have only focused on predicting a single outcome, ignoring the potential correlati

Jin Liu, Jianye Chen, Chongfeng Fan, Fengyu Zhou

The link prediction task aims to predict missing entities or relations in the knowledge graph and is essential for the downstream application. Existing well-known models deal with this task by mainly focusing on representing knowledge graph triplets in the distance space or semantic space. However, they can not fully capture the information of head and tail

Canyu Zhang, Zhenyao Wu, Xinyi Wu, Ziyu Zhao

3D point cloud semantic segmentation aims to group all points into different semantic categories, which benefits important applications such as point cloud scene reconstruction and understanding. Existing supervised point cloud semantic segmentation methods usually require large-scale annotated point clouds for training and cannot handle new categories. Whil

Isaac Buckland, Douglas Bergman

The reconstruction of cosmic-ray-induced extensive air showers with a non-imaging Cherenkov detector array requires knowledge of the Cherenkov yield of any given air shower for a given set of shower parameters. Although air showers develop in a stochastic cascade, certain characteristics of the particles in the shower have been shown to come from universal p

Rashmi Ranjan Bhuyan, Adel Javanmard, Sungchul Kim, Gourab Mukherjee

We consider dynamic pricing strategies in a streamed longitudinal data set-up where the objective is to maximize, over time, the cumulative profit across a large number of customer segments. We consider a dynamic model with the consumers' preferences as well as price sensitivity varying over time. Building on the well-known finding that consumers sharing sim

Minghan Zhu, Shizhong Han, Hong Cai, Shubhankar Borse

In this paper, we develop rotation-equivariant neural networks for 4D panoptic segmentation. 4D panoptic segmentation is a benchmark task for autonomous driving that requires recognizing semantic classes and object instances on the road based on LiDAR scans, as well as assigning temporally consistent IDs to instances across time. We observe that the driving

Andrew Ducharme

A knot K is the resultant of a knot H if there exists a minimal crossing diagram D of K such that some crossings of D can be altered to produce H. K is fertile if every prime knot H with crossing number less than c(K) is a resultant of K. K is n-fertile if every prime knot H with crossing number less than n is a resultant of K. We classify the fertility and

Senmao Li, Joost van de Weijer, Taihang Hu, Fahad Shahbaz Khan

A significant research effort is focused on exploiting the amazing capacities of pretrained diffusion models for the editing of images.They either finetune the model, or invert the image in the latent space of the pretrained model. However, they suffer from two problems: (1) Unsatisfying results for selected regions and unexpected changes in non-selected reg

C. M. Wood, J. C. A. Miller-Jones, A. Bahramian, S. J. Tingay

Tracking the motions of transient jets launched by low-mass X-ray binaries (LMXBs) is critical for determining the moment of jet ejection, and identifying any corresponding signatures in the accretion flow. However, these jets are often highly variable and can travel across the resolution element of an image within a single observation, violating a fundament

Vladislav Lialin, Vijeta Deshpande, Xiaowei Yao, Anna Rumshisky

This paper presents a systematic overview of parameter-efficient fine-tuning methods, covering over 50 papers published between early 2019 and mid-2024. These methods aim to address the challenges of fine-tuning large language models by training only a small subset of parameters. We provide a taxonomy that covers a broad range of methods and present a detail

Yibo Xu, Vijay Gopal Thirupakuzi Vangipuram, Vishank Telasara, Junao Cheng

Device based on GaN have great potential for high power switching applications due to its high breakdown field and high electron mobility. In this work, we present the device design of a vertical GaN-on-GaN PN power diode using high dielectric constant (high-k) dielectrics for electrical field management and high breakdown voltages, in together with guard-ri

Per Berglund, Andrew Geraci, Tristan Hübsch, David Mattingly

Recently we have discussed a new approach to the problem of quantum gravity in which the quantum mechanical structures that are traditionally fixed, such as the Fubini-Study metric in the Hilbert space of states, become dynamical and so implement the idea of gravitizing the quantum. In this paper we elaborate on a specific test of this new approach to quantu

Hanamichi Kawamura, Takumi Maesaka, Masataka Ono

We prove some weighted sum formulas for half multiple zeta values, half finite multiple zeta values, and half symmetric multiple zeta values. The key point of our proof is Dougall's identity for the generalized hypergeometric function ${}_{5}F_{4}$. Similar results for interpolated refined symmetric multiple zeta values and half refined symmetric multiple ze

Myeonghun Lee, Kyoungmin Min

As protein therapeutics play an important role in almost all medical fields, numerous studies have been conducted on proteins using artificial intelligence. Artificial intelligence has enabled data driven predictions without the need for expensive experiments. Nevertheless, unlike the various molecular fingerprint algorithms that have been developed, protein

Yasuharu Okamoto

This study investigates a new hybrid method for solving the combinatorial problem of optimizing fractional functions with 0-1 binary variables. The method combines density matrix minimization (DMM), tabu search (TS), and the Dinkelbach algorithm to optimize fractional functions. Although DMM alone did not provide a sufficiently accurate solution, combining D

Guy Blanc, Jane Lange, Ali Malik, Li-Yang Tan

We show how any PAC learning algorithm that works under the uniform distribution can be transformed, in a blackbox fashion, into one that works under an arbitrary and unknown distribution $\mathcal{D}$. The efficiency of our transformation scales with the inherent complexity of $\mathcal{D}$, running in $\mathrm{poly}(n, (md)^d)$ time for distributions over

Edvard K. Holden, Konstantin Korovin

Premise selection is crucial for large theory reasoning as the sheer size of the problems quickly leads to resource starvation. This paper proposes a premise selection approach inspired by the domain of image captioning, where language models automatically generate a suitable caption for a given image. Likewise, we attempt to generate the sequence of axioms

Kenichiro Tanabe

Let $V_{L}$ be the vertex algebra associated to a non-degenerate even lattice $L$, $\theta$ the automorphism of $V_{L}$ induced from the $-1$ symmetry of $L$, and $V_{L}^{+}$ the fixed point subalgebra of $V_{L}$ under the action of $\theta$. In this series of papers, we classify the irreducible weak $V_{L}^{+}$-modules and show that any irreducible weak $V_

Stanley Gudder

We begin with a study of operations and the effects they measure. We define the probability that an effect $a$ occurs when the system is in a state $\rho$ by $P_\rho (a)= tr(\rho a)$. If $P_\rho (a)\ne 0$ and $\mathcal{I}$ is an operation that measures $a$, we define the conditional probability of an effect $b$ given $a$ relative to $\mathcal{I}$ by \begin{e

Eli Zoghlin, Matthew B. Stone, Stephen D. Wilson

Here we report a study of the spin dynamics in the ferromagnetic chain compound $Li_{2}CuO_{2}$. Inelastic neutron scattering measurements allow for the spin Hamiltonian to be determined using a $J_{1}-J_{2}$ $XXZ$-Heisenberg spin chain model with weak interchain interactions. The primary exchange parameters determined from our data are qualitatively consist

Max Emerick, Bassam Bamieh

We consider a setting in which one swarm of agents is to service or track a second swarm, and formulate an optimal control problem which trades off between the competing objectives of servicing and motion costs. We consider the continuum limit where large-scale swarms are modeled in terms of their time-varying densities, and where the Wasserstein distance be

Bruce D. Lee, Ingvar Ziemann, Anastasios Tsiamis, Henrik Sandberg

We present a local minimax lower bound on the excess cost of designing a linear-quadratic controller from offline data. The bound is valid for any offline exploration policy that consists of a stabilizing controller and an energy bounded exploratory input. The derivation leverages a relaxation of the minimax estimation problem to Bayesian estimation, and an

Georgiana Fasolă, Marius Tărnăuceanu

Given a construction $f$ on groups, we say that a group $G$ is \textit{$f$-realisable} if there is a group $H$ such that $G\cong f(H)$, and \textit{completely $f$-realisable} if there is a group $H$ such that $G\cong f(H)$ and every subgroup of $G$ is isomorphic to $f(H_1)$ for some subgroup $H_1$ of $H$ and vice versa. In this paper, we determine completely

Dheer Noal Desai

Let $C_{2k_1, 2k_2, \ldots, 2k_t}$ denote the graph obtained by intersecting $t$ distinct even cycles $C_{2k_1}, C_{2k_2}, \ldots, C_{2k_t}$ at a unique vertex. In this paper, we determine the unique graphs with maximum adjacency spectral radius among all graphs on $n$ vertices that do not contain any $C_{2k_1, 2k_2, \ldots, 2k_t}$ as a subgraph, for $n$ suf

Matthew Fahrbach, Adel Javanmard, Vahab Mirrokni, Pratik Worah

We design learning rate schedules that minimize regret for SGD-based online learning in the presence of a changing data distribution. We fully characterize the optimal learning rate schedule for online linear regression via a novel analysis with stochastic differential equations. For general convex loss functions, we propose new learning rate schedules that

Vladislav A. Chistyakov, Alex Krasnok

The field of thermal emission engineering shows great potential for various applications, such as lighting, energy harvesting, and imaging, using natural or artificial structures. However, existing structures face challenges in fabrication or do not provide the necessary degree of control over key parameters such as emission intensity, spectral composition,

Vikram V. Ramaswamy, Sunnie S. Y. Kim, Ruth Fong, Olga Russakovsky

Concept-based explanations for convolutional neural networks (CNNs) aim to explain model behavior and outputs using a pre-defined set of semantic concepts (e.g., the model recognizes scene class ``bedroom'' based on the presence of concepts ``bed'' and ``pillow''). However, they often do not faithfully (i.e., accurately) characterize the model's behavior and

A. C. Bekar, E. Haghighat, E. Madenci

This study proposes a novel framework for learning the underlying physics of phenomena with moving boundaries. The proposed approach combines Ensemble SINDy and Peridynamic Differential Operator (PDDO) and imposes an inductive bias assuming the moving boundary physics evolve in its own corotational coordinate system. The robustness of the approach is demonst

Olivier Bernardi, Éric Fusy, Shizhe Liang

We define a far-reaching generalization of Schnyder woods which encompasses many classical combinatorial structures on planar graphs. Schnyder woods are defined for planar triangulations as certain triples of spanning trees covering the triangulation and crossing each other in an orderly fashion. They are of theoretical and practical importance, as they are

Moh Hosseinioun, Frank Neffke, Letian Zhang, Hyejin Youn

Modern economies require increasingly diverse and specialized skills, many of which depend on the acquisition of other skills first. Here we analyse US survey data to reveal a nested structure within skill portfolios, where the direction of dependency is inferred from asymmetrical conditional probabilities-occupations require one skill conditional on another

Yifan Jing, Chieu-Minh Tran

Suppose $G$ is a compact semisimple Lie group, $\mu$ is the normalized Haar measure on $G$, and $A, A^2 \subseteq G$ are measurable. We show that $$\mu(A^2)\geq \min\{1, 2\mu(A)+\eta\mu(A)(1-2\mu(A))\}$$ with the absolute constant $\eta>0$ (independent from the choice of $G$) quantitatively determined. We also show a more general result for connected compact

Arun Jambulapati, Kevin Tian

We investigate different aspects of area convexity [Sherman '17], a mysterious tool introduced to tackle optimization problems under the challenging $\ell_\infty$ geometry. We develop a deeper understanding of its relationship with more conventional analyses of extragradient methods [Nemirovski '04, Nesterov '07]. We also give improved solvers for the subpro

Mohamed Hibat-Allah, Marta Mauri, Juan Carrasquilla, Alejandro Perdomo-Ortiz

Generative modeling has seen a rising interest in both classical and quantum machine learning, and it represents a promising candidate to obtain a practical quantum advantage in the near term. In this study, we build over a proposed framework for evaluating the generalization performance of generative models, and we establish the first quantitative comparati

Revanth Koduru, Imtiaz Ahmed, Atanu K Saha, Xiao Lyu

In this work, we investigate the device-to-device variations in remanent polarization of Hafnium-Zirconium-Oxide based Metal-Ferroelectric-Insulator-Metal (MFIM) stacks. We consider the effects of polycrystallinity in conjunction with multi-domain effects in HZO to understand the dependencies of variations on static and dynamic voltage stimuli using our 3D d