April 2024 arXiv papers — page 120

J. T. Penedo, S. T. Petcov

Recently, it was shown that modular symmetry may solve the strong CP problem without axions, by producing a vanishing QCD angle while generating a large quark CP violation phase. We extend this framework to finite modular groups, systematically identifying the allowed mass textures. We find quark fields must furnish 1D representations and scan the minimal mo

Francesco Ballerini, Pierluigi Zama Ramirez, Roberto Mirabella, Samuele Salti

Neural Radiance Fields (NeRFs) have emerged as a standard framework for representing 3D scenes and objects, introducing a novel data type for information exchange and storage. Concurrently, significant progress has been made in multimodal representation learning for text and image data. This paper explores a novel research direction that aims to connect the

Jiang Wu, Rui Li, Haofei Xu, Wenxun Zhao

Matching cost aggregation plays a fundamental role in learning-based multi-view stereo networks. However, directly aggregating adjacent costs can lead to suboptimal results due to local geometric inconsistency. Related methods either seek selective aggregation or improve aggregated depth in the 2D space, both are unable to handle geometric inconsistency in t

Jing Wen, Xiaoming Zhao, Zhongzheng Ren, Alexander G. Schwing

We introduce GoMAvatar, a novel approach for real-time, memory-efficient, high-quality animatable human modeling. GoMAvatar takes as input a single monocular video to create a digital avatar capable of re-articulation in new poses and real-time rendering from novel viewpoints, while seamlessly integrating with rasterization-based graphics pipelines. Central

Moreno D'Incà, Elia Peruzzo, Massimiliano Mancini, Dejia Xu

Text-to-image generative models are becoming increasingly popular and accessible to the general public. As these models see large-scale deployments, it is necessary to deeply investigate their safety and fairness to not disseminate and perpetuate any kind of biases. However, existing works focus on detecting closed sets of biases defined a priori, limiting t

Runtao Liu, Ashkan Khakzar, Jindong Gu, Qifeng Chen

With the ability to generate high-quality images, text-to-image (T2I) models can be exploited for creating inappropriate content. To prevent misuse, existing safety measures are either based on text blacklists, which can be easily circumvented, or harmful content classification, requiring large datasets for training and offering low flexibility. Hence, we pr

Yiwen Tang, Ray Zhang, Jiaming Liu, Zoey Guo

Large foundation models have recently emerged as a prominent focus of interest, attaining superior performance in widespread scenarios. Due to the scarcity of 3D data, many efforts have been made to adapt pre-trained transformers from vision to 3D domains. However, such 2D-to-3D approaches are still limited, due to the potential loss of spatial geometries an

Mazda Moayeri, Samyadeep Basu, Sriram Balasubramanian, Priyatham Kattakinda

Recent text-to-image generative models such as Stable Diffusion are extremely adept at mimicking and generating copyrighted content, raising concerns amongst artists that their unique styles may be improperly copied. Understanding how generative models copy "artistic style" is more complex than duplicating a single image, as style is comprised by a set of el

Xueyi Liu, Kangbo Lyu, Jieqiong Zhang, Tao Du

We explore the dexterous manipulation transfer problem by designing simulators. The task wishes to transfer human manipulations to dexterous robot hand simulations and is inherently difficult due to its intricate, highly-constrained, and discontinuous dynamics and the need to control a dexterous hand with a DoF to accurately replicate human manipulations. Pr

Ming Li, Taojiannan Yang, Huafeng Kuang, Jie Wu

To enhance the controllability of text-to-image diffusion models, existing efforts like ControlNet incorporated image-based conditional controls. In this paper, we reveal that existing methods still face significant challenges in generating images that align with the image conditional controls. To this end, we propose ControlNet++, a novel approach that impr

V. Arvind, Abhranil Chatterjee, Partha Mukhopadhyay

Let $X=X_1\sqcup X_2\sqcup\ldots\sqcup X_k$ be a partitioned set of variables such that the variables in each part $X_i$ are noncommuting but for any $i\neq j$, the variables $x\in X_i$ commute with the variables $x'\in X_j$. Given as input a square matrix $T$ whose entries are linear forms over $\mathbb{Q}\langle{X}\rangle$, we consider the problem of check

Mingyang Xie, Haiyun Guo, Brandon Y. Feng, Lingbo Jin

Imaging through scattering media is a fundamental and pervasive challenge in fields ranging from medical diagnostics to astronomy. A promising strategy to overcome this challenge is wavefront modulation, which induces measurement diversity during image acquisition. Despite its importance, designing optimal wavefront modulations to image through scattering re

Tiange Luo, Justin Johnson, Honglak Lee

Scalable annotation approaches are crucial for constructing extensive 3D-text datasets, facilitating a broader range of applications. However, existing methods sometimes lead to the generation of hallucinated captions, compromising caption quality. This paper explores the issue of hallucination in 3D object captioning, with a focus on Cap3D method, which ren

Simon Schrodi, David T. Hoffmann, Max Argus, Volker Fischer

Contrastive vision-language models (VLMs), like CLIP, have gained popularity for their versatile applicability to various downstream tasks. Despite their successes in some tasks, like zero-shot object recognition, they perform surprisingly poor on other tasks, like attribute recognition. Previous work has attributed these challenges to the modality gap, a se

Anton Schäfer, Shauli Ravfogel, Thomas Hofmann, Tiago Pimentel

Multilinguality is crucial for extending recent advancements in language modelling to diverse linguistic communities. To maintain high performance while representing multiple languages, multilingual models ideally align representations, allowing what is learned in one language to generalise to others. Prior research has emphasised the importance of parallel

Aounon Kumar, Himabindu Lakkaraju

Large language models (LLMs) are increasingly being integrated into search engines to provide natural language responses tailored to user queries. Customers and end-users are also becoming more dependent on these models for quick and easy purchase decisions. In this work, we investigate whether recommendations from LLMs can be manipulated to enhance a produc

Adrinil Paul, Chandrasekhar Bhamidipati

We study circular orbits of charged particles in spherically symmetric AdS black holes in higher derivative theories of gravity, and their limiting ISCOs (innermost stable circular orbits). The dual interpretation is in terms of heavy-light double twist conformal field theory (CFT) operators in the large spin limit, whose anomalous dimensions can be extracte

Sijun Tan, Xiuyu Li, Shishir Patil, Ziyang Wu

Processing long contexts remains a challenge for large language models (LLMs) due to the quadratic computational and memory overhead of the self-attention mechanism and the substantial KV cache sizes during generation. We propose LLoCO, a novel approach to address this problem by learning contexts offline through context compression and in-domain parameter-e

M. E. Shirokov

We describe analytical properties of the average output entropy of a quantum channel as a function of a pair (channel, input ensemble). In particular, tight semicontinuity bounds for this function with the rank/energy constraints are obtained by using the modified semicontinuity bounds for the quantum conditional entropy of quantum-classical states and a spe

Muxin Zhou, Zeyuan Yin, Shitong Shao, Zhiqiang Shen

Dataset distillation aims to compress information from a large-scale original dataset to a new compact dataset while striving to preserve the utmost degree of the original data informational essence. Previous studies have predominantly concentrated on aligning the intermediate statistics between the original and distilled data, such as weight trajectory, fea

Sthitadhi Roy

We present a scaling theory of the many-body localisation transition in terms of emergent, characteristic energyscales. The analysis is based on the decomposition of the eigenstates in the basis of trivially localised states, resolved in the energies of the latter, which we refer to as the spectral decomposition of the eigenstates. The characteristic energys

Haotian Zhang, Haoxuan You, Philipp Dufter, Bowen Zhang

While Ferret seamlessly integrates regional understanding into the Large Language Model (LLM) to facilitate its referring and grounding capability, it poses certain limitations: constrained by the pre-trained fixed visual encoder and failed to perform well on broader tasks. In this work, we unveil Ferret-v2, a significant upgrade to Ferret, with three key de

Tianbao Xie, Danyang Zhang, Jixuan Chen, Xiaochuan Li

Autonomous agents that accomplish complex computer tasks with minimal human interventions have the potential to transform human-computer interaction, significantly enhancing accessibility and productivity. However, existing benchmarks either lack an interactive environment or are limited to environments specific to certain applications or domains, failing to

Markus Jacob, Fedor Nazarov

Under certain natural sufficient conditions on the sequence of uniformly bounded closed sets $E_k\subset\mathbb{R}$ of admissible coefficients, we construct a polynomial $P_n(x)=1+\sum_{k=1}^n\varepsilon_k x^k$, $\varepsilon_k\in E_k$, with at least $c\sqrt{n}$ distinct roots in $[0,1]$, which matches the classical upper bound up to the value of the constant

Chin-Yun Yu, Christopher Mitcheltree, Alistair Carson, Stefan Bilbao

Infinite impulse response filters are an essential building block of many time-varying audio systems, such as audio effects and synthesisers. However, their recursive structure impedes end-to-end training of these systems using automatic differentiation. Although non-recursive filter approximations like frequency sampling and frame-based processing have been

J. C. Leiner, S. J. Kuhn, S. McKay, J. K. Jochum

Intra-particle entanglement of individual particles such as neutrons could enable another class of scattering probes that are sensitive to entanglement in quantum systems and materials. In this work, we present experimental results demonstrating quantum contextuality as a result of entanglement between the spin and energy modes (i.e., degrees of freedom) of

Zhenghao Lin, Zhibin Gou, Yeyun Gong, Xiao Liu

Previous language model pre-training methods have uniformly applied a next-token prediction loss to all training tokens. Challenging this norm, we posit that "9l training". Our initial analysis examines token-level training dynamics of language model, revealing distinct loss patterns for different tokens. Leveraging these insights, we introduce a new languag

Konstantin L. Metlov

Magnetic hopfions are three-dimensional localized topological solitons in the volume of a magnet. In this work, starting with a classical free energy density of a helimagnet, an approximate variational model of hopfions is studied. The hopfion stability regions on the uniaxial anisotropy-external magnetic field phase diagram are computed and their evolution

Lucas R. Timmerman, Shashikant Kumar, Phanish Suryanarayana, Andrew J. Medford

We develop a framework for on-the-fly machine learned force field molecular dynamics simulations based on the multipole featurization scheme that overcomes the bottleneck with the number of chemical elements. Considering bulk systems with up to 6 elements, we demonstrate that the number of density functional theory calls remains approximately independent of

Jun Yan

In this paper, we mainly study two notions of pattern avoidance in parking functions. First, for any collection of length 3 patterns, we compute the number of parking functions of size $n$ that avoid them under the first notion. This is motivated by the recent work of Adeniran and Pudwell, who obtained analogous results using a second notion of pattern avoid

Lujie Yang, Hongkai Dai, Zhouxing Shi, Cho-Jui Hsieh

Learning-based neural network (NN) control policies have shown impressive empirical performance in a wide range of tasks in robotics and control. However, formal (Lyapunov) stability guarantees over the region-of-attraction (ROA) for NN controllers with nonlinear dynamical systems are challenging to obtain, and most existing approaches rely on expensive solv

Emiko Saso, Houston Schuerger, Xin Shi

The landscapes of a polyhedron are subsets of its nets one must consider to identify all shortest paths. Landscapes of cubes and tetrahedra have been used to identify coordinate based formulas for the lengths of the shortest paths between points on these surfaces. We extend these results to develop formulas for the lengths of the shortest paths between point

Cheng Zhang, Qianyi Wu, Camilo Cruz Gambardella, Xiaoshui Huang

Generative models, e.g., Stable Diffusion, have enabled the creation of photorealistic images from text prompts. Yet, the generation of 360-degree panorama images from text remains a challenge, particularly due to the dearth of paired text-panorama data and the domain gap between panorama and perspective images. In this paper, we introduce a novel dual-branc

Haokun Zhao, Haixia Han, Jie Shi, Chengyu Du

As world knowledge advances and new task schemas emerge, Continual Learning (CL) becomes essential for keeping Large Language Models (LLMs) current and addressing their shortcomings. This process typically involves continual instruction tuning (CIT) and continual pre-training (CPT) to enable these models to adapt to novel tasks and acquire critical knowledge

Asaf Cohen, Chuhao Sun

In this paper, we examine the stationary relaxed singular control problem within a multi-dimensional framework for a single agent, as well as its mean field game equivalent. We demonstrate that optimal relaxed controls exist for two problem classes: one driven by queueing control and the other by harvesting models. These relaxed controls are defined by rando

Antonio Del Donno, Emanuele Latini, Thomas Weber

We revisit and extend the Durdevic theory of complete calculi on quantum principal bundles. In this setting one naturally obtains a graded Hopf-Galois extension of the higher order calculus and an intrinsic decomposition of degree 1-forms into horizontal and vertical forms. This proposal is appealing, since it is consistently equipped with a canonical braidi

Xinyu Chen, Lin Li, Rui Zhang, Peng Liang

Public Code Review (PCR) can be implemented through a Software Question Answering (SQA) community, which facilitates high knowledge dissemination. Current methods mainly focus on the reviewer's perspective, including finding a capable reviewer, predicting comment quality, and recommending/generating review comments. Our intuition is that satisfying review ne

Christian Santoni, Dichang Zhang, Zexia Zhang, Dimitris Samaras

This study proposes a novel machine learning (ML) methodology for the efficient and cost-effective prediction of high-fidelity three-dimensional velocity fields in the wake of utility-scale turbines. The model consists of an auto-encoder convolutional neural network with U-Net skipped connections, fine-tuned using high-fidelity data from large-eddy simulatio

Charis Stamouli, Ingvar Ziemann, George J. Pappas

We study the quadratic prediction error method -- i.e., nonlinear least squares -- for a class of time-varying parametric predictor models satisfying a certain identifiability condition. While this method is known to asymptotically achieve the optimal rate for a wide range of problems, there have been no non-asymptotic results matching these optimal rates ou

Tuhin Malik, Veronica Dexheimer, Constança Providência

We investigate how vector-isoscalar and vector-isovector interactions can be determined within the density regime of neutron stars (NSs), while fulfilling nuclear and astrophysics constrains. We make use of the Chiral Mean Field (CMF) model, a SU(3) nonlinear realization of the sigma model within the mean-field approximation, for the first time within a Baye

José Moran, Massimo Riccaboni

We review models of compositional growth, which were introduced to explain the growth statistics of various quantities ranging from firm sizes to GDP. In these models, entities are decomposed into units that grow independently. Thus, the growth rate of the entity is the addition of the growth rates of the composing units, with possibly heterogeneous weights.

Felipe Meneguzzi, Luísa R. de A. Santos, Ramon Fraga Pereira, André G. Pereira

Goal Recognition is the task by which an observer aims to discern the goals that correspond to plans that comply with the perceived behavior of subject agents given as a sequence of observations. Research on Goal Recognition as Planning encompasses reasoning about the model of a planning task, the observations, and the goals using planning techniques, result

Keonhee Han, Dominik Muhle, Felix Wimbauer, Daniel Cremers

Inferring scene geometry from images via Structure from Motion is a long-standing and fundamental problem in computer vision. While classical approaches and, more recently, depth map predictions only focus on the visible parts of a scene, the task of scene completion aims to reason about geometry even in occluded regions. With the popularity of neural radian

Siran Peng, Xiangyu Zhu, Haoyu Deng, Liang-Jian Deng

Remote sensing image fusion aims to generate a high-resolution multi/hyper-spectral image by combining a high-resolution image with limited spectral data and a low-resolution image rich in spectral information. Current deep learning (DL) methods typically employ convolutional neural networks (CNNs) or Transformers for feature extraction and information integ

Pelle Steffens

We study moduli spaces of solutions of nonlinear Partial Differential Equations on manifolds in the framework of derived $C^{\infty}$-geometry. For an arbitrary smooth stack $S$, we define $S$-families of nonlinear PDEs acting between $S$-families of submersions over an $S$-family of manifolds and show that in case the family of PDEs is elliptic and the base

Zeng YU, Yunxiao Shi

Visible-infrared person re-identification (VI-reID) aims at matching cross-modality pedestrian images captured by disjoint visible or infrared cameras. Existing methods alleviate the cross-modality discrepancies via designing different kinds of network architectures. Different from available methods, in this paper, we propose a novel parameter optimizing par

Sebastian von Hausegger

Recent measurements using catalogues of quasars and radio galaxies have shown that the dipole anisotropy in the large-scale distribution of matter is about twice as large as is expected in the standard $\Lambda$CDM model, indeed in any cosmology based on the Friedman-Lema\^itre-Robertson-Walker (FLRW) metric. This expectation is based on the kinematic interp

B. M. Latacz, M. Fleck, J. I. Jaeger, G. Umbrazunas

We demonstrate efficient sub-thermal cooling of the modified cyclotron mode of a single trapped antiproton and reach particle temperatures $T_+=E_+/k_\text{B}$ below $200\,$mK in preparation times shorter than $500\,$s. This corresponds to the fastest resistive single-particle cyclotron cooling to sub-thermal temperatures ever demonstrated. By cooling trappe

Kamal Aghazade, Ali Gholami, Hossein S. Aghamiry, Hamid Reza Siahkoohi

Elastic full-waveform inversion (EFWI) is a process used to estimate subsurface properties by fitting seismic data while satisfying wave propagation physics. The problem is formulated as a least-squares data fitting minimization problem with two sets of constraints: Partial-differential equation (PDE) constraints governing elastic wave propagation and physic

Jinkyung Park, Pamela Wisniewski, Vivek Singh

In this position paper, we discuss the potential for leveraging LLMs as interactive research tools to facilitate collaboration between human coders and AI to effectively annotate online risk data at scale. Collaborative human-AI labeling is a promising approach to annotating large-scale and complex data for various tasks. Yet, tools and methods to support ef

Alexey Koshelev, Anna Tokareva

Although General Relativity predicts the presence of a singularity inside of a Black Hole, it is not a complete theory of gravity. A real structure of a Black Hole interior near an expected singularity depends on the UV completion of gravity. In this paper, we establish that the question whether singular spherically symmetric solutions are absent is governed

Sudan Pokharel, Tirthankar Roy

Significant strides have been made in advancing streamflow predictions, notably with the introduction of cutting-edge machine-learning models. Predominantly, Long Short-Term Memories (LSTMs) and Convolution Neural Networks (CNNs) have been widely employed in this domain. While LSTMs are applicable in both rainfall-runoff and time series settings, CNN-LSTMs h

Dehua Bi, Yuan Ji

We consider a Bayesian framework for estimating the sample size of a clinical trial. The new approach, called BESS, is built upon three pillars: Sample size of the trial, Evidence from the observed data, and Confidence of the final decision in the posterior inference. It uses a simple logic of "given the evidence from data, a specific sample size can achieve

Chi Tran, Huong Le Thanh

Large Language Models (LLMs) and Multimodal Large language models (MLLMs) have taken the world by storm with impressive abilities in complex reasoning and linguistic comprehension. Meanwhile there are plethora of works related to Vietnamese Large Language Models, the lack of high-quality resources in multimodality limits the progress of Vietnamese MLLMs. In

Zeyi Liao, Huan Sun

As large language models (LLMs) become increasingly prevalent and integrated into autonomous systems, ensuring their safety is imperative. Despite significant strides toward safety alignment, recent work GCG~\citep{zou2023universal} proposes a discrete token optimization algorithm and selects the single suffix with the lowest loss to successfully jailbreak a

K. Angelique Kahle, Friedrich Wyrowski, Carsten König, Ivalu Barlach Christensen

The Lagoon Nebula (M8) is host to multiple regions with recent and ongoing massive star formation. With M8-Main and M8 East, two prominent regions of massive star formation have been studied in detail over the past years, while large parts of the nebula have received little attention. These largely unexplored regions comprise a large sample of molecular clum

Weilin Ruan, Wei Chen, Xilin Dang, Jianxiang Zhou

Spatio-temporal forecasting is essential for understanding future dynamics within real-world systems by leveraging historical data from multiple locations. Existing methods often prioritize the development of intricate neural networks to capture the complex dependencies of the data. These methods neglect node-level heterogeneity and face over-parameterizatio

Yuhao Zhang, Shiqi Wang, Haifeng Qian, Zijian Wang

Code generation models are not robust to small perturbations, which often lead to incorrect generations and significantly degrade the performance of these models. Although improving the robustness of code generation models is crucial to enhancing user experience in real-world applications, existing research efforts do not address this issue. To fill this gap

Ritu Nehra, Alessandro Romito, Dganit Meidan

We study the evolution of a quantum many-body system driven by two competing measurements, which induces a topological entanglement transition between two distinct area law phases. We employ a positive operator-valued measurement with variable coupling between the system and detector within free fermion dynamics. This approach allows us to continuously track

Anna C. Doris, Daniele Grandi, Ryan Tomich, Md Ferdous Alam

This research introduces DesignQA, a novel benchmark aimed at evaluating the proficiency of multimodal large language models (MLLMs) in comprehending and applying engineering requirements in technical documentation. Developed with a focus on real-world engineering challenges, DesignQA uniquely combines multimodal data-including textual design requirements, C

Siavash Tavana, Sepideh Faghihi, Anton de Ruiter, Krishna Dev Kumar

The collision avoidance constraints are prominent as non-convex, non-differentiable, and challenging when defined in optimization-based motion planning problems. To overcome these issues, this paper presents a novel non-conservative collision avoidance technique using the notion of convex optimization to establish the distance between robotic spacecraft and

A. Hernández-Mínguez, A. V. Poshakinskiy, M. Hollenbach, P. V. Santos

The long-lived and optically addressable high-spin state of the negatively charged silicon vacancy ($\mathrm{V_{Si}}$) in silicon carbide makes it a promising system for applications in quantum technologies. Most studies of its spin dynamics have been performed in external magnetic fields applied along the symmetry axis. Here, we find that the application of

Bardia Nadimi, Hao Zheng

Recently, there has been a surging interest in using large language models (LLMs) for Verilog code generation. However, the existing approaches are limited in terms of the quality of the generated Verilog code. To address such limitations, this paper introduces an innovative multi-expert LLM architecture for Verilog code generation (MEV-LLM). Our architectur

Georgios D. Kolezas, George Fikioris, John A. Roumeliotis

The method of fundamental solutions (MFS), also known as the method of auxiliary sources (MAS), is a well-known computational method for the solution of boundary-value problems. The final solution ("MAS solution") is obtained once we have found the amplitudes of $N$ auxiliary "MAS sources." Past studies have demonstrated that it is possible for the MAS solut

Andrei Agrachev, Bettina Kazandjian

We study L 1 -optimal stabilization of linear systems with finite and infinite horizons. Main results concern the existence, uniqueness and structure of optimal solutions, and the robustness of optimal cost.

Wenyang Qian, Bin Wu

Thermal properties of quantum fields at finite temperature are crucial to understanding strongly interacting matter and recent development in quantum computing has provided an alternative and promising avenue of study. In this work, we study thermal field theories involving only fermions using quantum algorithms. We first delve into the presentations of ferm

Maximilian Bergbauer, Peter Munch, Wolfgang A. Wall, Martin Kronbichler

Unfitted finite element methods, like CutFEM, have traditionally been implemented in a matrix-based fashion, where a sparse matrix is assembled and later applied to vectors while solving the resulting linear system. With the goal of increasing performance and enabling algorithms with polynomial spaces of higher degrees, this contribution chooses a more abstr

Khoa Bang Pham

For a polynomial function $f \colon \mathbb{C}^n \longrightarrow \mathbb{C}$, it is well-known in singularity theory (after Thom, Pham, Verdier,...) that outside a finite subset of $\mathbb{C}$, the function is a locally trivial $C^{\infty}$-fibration. The minimal such finite set is called the bifurcation set associated with $f$ and determining the bifurcati

C. -J. Yang, K. M. Spohr, M. Cernaianu, D. Doria

We propose a novel scheme for the population and depletion of nuclear isomers. The scheme combines the $\gamma$-photons with energies $\gtrsim 10$ keV emitted during the interaction of a contemporary high-intensity laser pulse with a plasma and one or multiple photon beams supplied by intense lasers. Due to nonlinear effects, two- or multi-photon absorption

Dennis Maseizik, Günter Sigl

We apply current analytical knowledge on the characteristic mass and linear evolution of miniclusters down to redshift $z=0$ to the hypothetical minicluster distribution of the Milky Way. Using the mass-radius relation and a core-halo relation for stable soliton solutions composed of axion-like particles (ALPs), we connect the galactic minicluster mass distr

Martyna Górska, Mariusz Lemańczyk, Thierry de la Rue

We solve Boshernitzan's problem of characterization (in terms of so called Furstenberg systems) of bounded sequences that are orthogonal to all uniquely ergodic systems. Some variations of Boshernitzan's problem involving characteristic classes are considered. As an application, we characterize sequences orthogonal to all uniquely ergodic systems whose (uniq

Miha Rot, Mitja Jančič, Gregor Kosec

In this paper, we address a way to reduce the total computational cost of meshless approximation by reducing the required stencil size through spatially varying computational node regularity. Rather than covering the entire domain with scattered nodes, only regions with geometric details are covered with scattered nodes, while the rest of the domain is discr

Olga Demler, Franco Giulianini, Yanyan Liu, Malte Londschien

We developed a method called batch and drift correction method by White Noise Normalization (WiNNbeta) to correct individual metabolites for batch effects and drifts. This method tests for white noise properties to identify metabolites in need of correction and corrects them by using fine-tuned splines. To test the method performance we applied WiNNbeta to L

Jeremy R. Harper

This study introduces a novel software tool leveraging large language model (LLM) prompts, designed to automate the generation of academic articles from Python code a significant advancement in the fields of biomedical informatics and computer science. Selected for its widespread adoption and analytical versatility, Python served as a foundational proof of c

Ian Chi, Martin Fraas, Tina Tan

Single-mode squeezed states exhibit a direct correspondence with points on the Poincar\'e disk. In this study, we delve into this correspondence and describe the motions of the disk generated by a quadratic Hamiltonian. This provides a geometric representation of squeezed states and their evolution. We discuss applications in bang-bang and adiabatic control

Zhen Qin, Songlin Yang, Weixuan Sun, Xuyang Shen

Hierarchically gated linear RNN (HGRN, \citealt{HGRN}) has demonstrated competitive training speed and performance in language modeling while offering efficient inference. However, the recurrent state size of HGRN remains relatively small, limiting its expressiveness. To address this issue, we introduce a simple outer product-based state expansion mechanism,

Ivailo Hartarsky, Augusto Teixeira

Metastability thresholds lie at the heart of bootstrap percolation theory. Yet proving precise lower bounds is notoriously hard. We show that for two of the most classical models, two-neighbour and Frob\"ose, upper bounds are sharp to essentially arbitrary precision, by linking them to their local counterparts. In Frob\"ose bootstrap percolation, iteratively

Glen Neville, Jiazhen Liu, Sonia Chernova, Harish Ravichandar

Complex multi-objective missions require the coordination of heterogeneous robots at multiple inter-connected levels, such as coalition formation, scheduling, and motion planning. The associated challenges are exacerbated when solutions to these interconnected problems need to simultaneously maximize task performance and respect practical constraints on time

Daijin Yang, Erica Kleinman, Giovanni Maria Troiano, Elina Tochilnikova

Mixed-initiative co-creative storytelling games have existed for some time as a way to merge storytelling with play. However, modern mixed-initiative co-creative storytelling games predominantly prioritize story creation over gameplay mechanics, which might not resonate with all players. As such, there is untapped potential for creating mixed-initiative game

Samuel Cahyawijaya, Delong Chen, Yejin Bang, Leila Khalatbari

The widespread application of LLMs across various tasks and fields has necessitated the alignment of these models with human values and preferences. Given various approaches of human value alignment, there is an urgent need to understand the scope and nature of human values injected into these LLMs before their deployment and adoption. We propose UniVaR, a h

Daisuke Fujii, Akihiro Iwanaka, Masakiyo Kitazawa, Daiki Suenaga

We investigate the thermodynamics and phase structure of $SU(3)$ Yang-Mills theory on $\mathbb{T}^2\times\mathbb{R}^2$ in Euclidean spacetime in an effective-model approach. The model incorporates two Polyakov loops along two compactified directions as dynamical variables, and is constructed to reproduce thermodynamics on $\mathbb{T}^2\times\mathbb{R}^2$ mea

SangWoo Park, Amritanshu Pandey

An important tool grid operators use to safeguard against failures, whether naturally occurring or malicious, involves detecting anomalies in the power system SCADA data. In this paper, we aim to solve a real-time anomaly detection problem. Given time-series measurement values coming from a fixed set of sensors on the grid, can we identify anomalies in the n

Darius Jurčiukonis, Luís Lavoura

We consider the addition of a single $SU(2)$ multiplet of complex scalar fields to the Standard Model (SM). We explicitly consider the various possible values of the weak isospin $J$ of that multiplet, up to and including $J = 7/2$. We allow the multiplet to have arbitrary weak hypercharge. The scalar fields of the multiplet are assumed to have no vacuum exp

Mohammed Lahsaini, Mohamed Lechiakh, Alexandre Maurer

Recommendation algorithms (RS) used by social media, like YouTube, significantly shape our information consumption across various domains, especially in healthcare. Hence, algorithmic auditing becomes crucial to uncover their potential bias and misinformation, particularly in the context of controversial topics like abortion. We introduce a simple yet effect

Jose M. G. Vilar, Leonor Saiz

Continuous adaptation to variable environments is crucial for the survival of living organisms. Here, we analyze how adaptation, forecasting, and resource mobilization towards a target state, termed actionability, interact to determine biological function. We develop a general theory and show that it is possible for organisms to continuously track their opti

Chih-Whi Chen, Yongjie Wang

We investigate representations of the $\ell$-th Takiff superalgebras $\widetilde{\mathfrak g}_\ell := \widetilde{\mathfrak g}\otimes \mathbb C[\theta]/(\theta^{\ell+1})$, for $\ell>0$, associated with a basic classical and a periplectic Lie superalgebras $\widetilde{\mathfrak g}$. We introduce the odd reflections and formulate a general notion of typical rep

Elia Buono, Gabriel G. Katul, Davide Vettori, Davide Poggi

Turbulent flows over a large surface area (S) covered by n obstacles experience an overall drag due to the presence of the ground and the protruding obstacles into the flow. The drag partition between the roughness obstacles and the ground is analyzed using an analytical model proposed by Raupach (1992) and is hereafter referred to as R92. The R92 is based o

Rob Jansen, Ryan Wails, Aaron Johnson

Website fingerprinting (WF) is a dangerous attack on web privacy because it enables an adversary to predict the website a user is visiting, despite the use of encryption, VPNs, or anonymizing networks such as Tor. Previous WF work almost exclusively uses synthetic datasets to evaluate the performance and estimate the feasibility of WF attacks despite evidenc

Jordi Hernández

We prove that a general cubic in the Hassett divisor $\mathcal{C}_{14}$ of special cubic fourfolds of discriminant $14$ contains a non-minimal K3 surface of degree $10$ containing two skew $(-1)$-lines and contained in a smooth quadric hypersurface $Q^4\subseteq \mathbb{P}^5$, but not contained in any other (possibly of lower rank) quadric hypersurface.

Z. Y. Li, H. Z. Shen

We study the non-Markovian dynamics of a two-level giant atom interacting with a one-dimensional semi-infinite waveguide through multiple coupling points, where a perfect mirror is located at the endpoint of the waveguide. The system enters a non-Markovian process when the travel time of the photon between adjacent coupling points is sufficiently large compa

Faisal Ahmed, Myungjin Lee, Suresh Subramaniam, Motoharu Matsuura

Federated Learning (FL) has garnered significant interest recently due to its potential as an effective solution for tackling many challenges in diverse application scenarios, for example, data privacy in network edge traffic classification. Despite its recognized advantages, FL encounters obstacles linked to statistical data heterogeneity and labeled data s

Jee-eun Lee, Andrew Bylard, Robert Sun, Luis Sentis

Jerk-constrained trajectories offer a wide range of advantages that collectively improve the performance of robotic systems, including increased energy efficiency, durability, and safety. In this paper, we present a novel approach to jerk-constrained time-optimal trajectory planning (TOTP), which follows a specified path while satisfying up to third-order co

Yi-Ran Liu, Jing-Rui Zhang, Yun-Long Zhang

The Weyl double copy builds the relation between gauge theory and gravity theory, especially the correspondence between gauge solutions and gravity solutions. In this paper, we obtain the slowly rotating charge solutions from Weyl double copy for the Kerr black hole with small Chern-Simons correction. Based on the Weyl double copy relation, for the Petrov ty

Zhengye Yang, Richard Radke

Video anomaly detection research is generally evaluated on short, isolated benchmark videos only a few minutes long. However, in real-world environments, security cameras observe the same scene for months or years at a time, and the notion of anomalous behavior critically depends on context, such as the time of day, day of week, or schedule of events. Here,

Guozhi Dong, Moritz Flaschel, Michael Hintermüller, Kostas Papafitsoros

In the field of quantitative imaging, the image information at a pixel or voxel in an underlying domain entails crucial information about the imaged matter. This is particularly important in medical imaging applications, such as quantitative Magnetic Resonance Imaging (qMRI), where quantitative maps of biophysical parameters can characterize the imaged tissu

Daniel Bath, Uli Walther

Given a matroid or flag of matroids we introduce several broad classes of polynomials satisfying Deletion-Contraction identities, and study their singularities. There are three main families of polynomials captured by our approach: matroidal polynomials on a matroid (including matroid basis polynomials, configuration polynomials, Tutte polynomials); flag mat

Hanzhi Jiang, Márk Mezei

A fruitful avenue in investigating out-of-equilibrium quantum many-body systems is to abruptly change their Hamiltonian and study the subsequent evolution of their quantum state. If this is done once, the setup is called a quench, while if it is done periodically, it is called Floquet driving. We consider the solvable setup of a two-dimensional CFT driven by

Glen Smith, Adit Gupta, Christopher MacLellan

Intelligent tutoring systems (ITS) are effective for improving students' learning outcomes. However, their development is often complex, time-consuming, and requires specialized programming and tutor design knowledge, thus hindering their widespread application and personalization. We present the Apprentice Tutor Builder (ATB) , a platform that simplifies tu

Wenjie Wu, Yiquan Wang, Ge Yan, Yuming Zhao

Quantum computing has gained considerable attention, especially after the arrival of the Noisy Intermediate-Scale Quantum (NISQ) era. Quantum processors and cloud services have been made world-wide increasingly available. Unfortunately, jobs on existing quantum processors are often executed in series, and the workload could be heavy to the processor. Typical

Chris Jones, Lucas Pesenti

We study a general class of nonlinear iterative algorithms which includes power iteration, belief propagation and approximate message passing, and many forms of gradient descent. When the input is a random matrix with i.i.d. entries, we use Boolean Fourier analysis to analyze these algorithms as low-degree polynomials in the entries of the input matrix. Each