February 2024 arXiv papers — page 29

Alon Albalak, Yanai Elazar, Sang Michael Xie, Shayne Longpre

A major factor in the recent success of large language models is the use of enormous and ever-growing text datasets for unsupervised pre-training. However, naively training a model on all available data may not be optimal (or feasible), as the quality of available text data can vary. Filtering out data can also decrease the carbon footprint and financial cos

Dave Epstein, Ben Poole, Ben Mildenhall, Alexei A. Efros

We introduce a method to generate 3D scenes that are disentangled into their component objects. This disentanglement is unsupervised, relying only on the knowledge of a large pretrained text-to-image model. Our key insight is that objects can be discovered by finding parts of a 3D scene that, when rearranged spatially, still produce valid configurations of t

Raimundas Vidunas

Evaluation of low degree hypergeometric polynomials to zero defines an algebraic hypersurface in the affine space of the free parameters and the argument. This article investigates the algebraic surfaces 2F1(-N,b;c;z)=0 for N=3 and N=4. As a captivating application, these surfaces parametrize certain families of genus 0 Belyi maps.

Wenzhao Zhao, Steffen Albert, Barbara D. Wichtmann, Angelika Maurer

Filter-decomposition-based group equivariant convolutional neural networks (CNNs) have shown promising stability and data efficiency for 3D image feature extraction. However, these networks, which rely on parameter sharing and discrete transformation groups, often underperform in modern deep neural network architectures for processing volumetric images with

Dolf Huybrechts, Tommaso Roscilde

The coupling of a quantum system to an environment leads generally to decoherence, and it is detrimental to quantum correlations within the system itself. Yet some forms of quantum correlations can be robust to the presence of an environment - or may even be stabilized by it. Predicting (let alone understanding) them remains arduous, given that the steady st

Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio

Various human-designed prompt engineering techniques have been proposed to improve problem solvers based on Large Language Models (LLMs), yielding many disparate code bases. We unify these approaches by describing LLM-based agents as computational graphs. The nodes implement functions to process multimodal data or query LLMs, and the edges describe the infor

Mikayel Samvelyan, Sharath Chandra Raparthy, Andrei Lupu, Eric Hambro

As large language models (LLMs) become increasingly prevalent across many real-world applications, understanding and enhancing their robustness to adversarial attacks is of paramount importance. Existing methods for identifying adversarial prompts tend to focus on specific domains, lack diversity, or require extensive human annotations. To address these limi

Xinzhe Zuo, Jiaxi Zhao, Shu Liu, Stanley Osher

We provide a numerical analysis and computation of neural network projected schemes for approximating one dimensional Wasserstein gradient flows. We approximate the Lagrangian mapping functions of gradient flows by the class of two-layer neural network functions with ReLU (rectified linear unit) activation functions. The numerical scheme is based on a projec

Christian Bourdarias, Anupam Pal Choudhury, Billel Guelmame, Stéphane Junca

In this article, we consider a class of strictly hyperbolic triangular systems involving a transport equation. Such systems are known to create measure solutions for the initial value problem. Adding a stronger transversality assumption on the fields, we are able to obtain solutions in $L^\infty$ under optimal fractional $BV$ regularity of the initial data.

Jupinder Parmar, Shrimai Prabhumoye, Joseph Jennings, Mostofa Patwary

We introduce Nemotron-4 15B, a 15-billion-parameter large multilingual language model trained on 8 trillion text tokens. Nemotron-4 15B demonstrates strong performance when assessed on English, multilingual, and coding tasks: it outperforms all existing similarly-sized open models on 4 out of 7 downstream evaluation areas and achieves competitive performance

József Balogh, Suyun Jiang, Haoran Luo

We estimate the maximum possible number of cliques of size $r$ in an $n$-vertex graph free of a fixed complete $r$-partite graph $K_{s_1, s_2, \ldots, s_r}$. By viewing every $r$-clique as a hyperedge, the upper bound on the Tur\'an number of the complete $r$-partite hypergraphs gives the upper bound $O\left(n^{r - {1}/{\prod_{i=1}^{r-1}s_i}}\right)$. We imp

Jason Phang

Gisting (Mu et al., 2023) is a simple method for training models to compress information into fewer token representations using a modified attention mask, and can serve as an economical approach to training Transformer-based hypernetworks. We introduce HyperLlama, a set of Gisting-based hypernetworks built on Llama-2 models that generates task-specific soft

Janusz Dybizbański, Yaser Rowshan

The $p$-partite Ramsey number for quadrilateral, denoted by $r_p(C_4,k)$, is the least positive integer $n$ such that any coloring of the edges of a complete $p$-partite graph with $n$ vertices in each partition with $k$ colors will result in a monochromatic copy of $C_4$. In this paper, we present an upper bound for $r_p(C_4,k)$ and the exact values of $r_p

Alexandre Yip Gonçalves Dias, Marcelo Knörich Zuffo

The representation of parallax on virtual environment is still a problem to be studied. Common algorithms, such as Bump Mapping, Parallax Mapping and Displacement Mapping, treats this problem for small disparity between a real object and a simplified model. This work will introduce a new texture structure and one possible render algorithm able to display par

Bhavik Kumar

This work investigates the application of quantum simulation in the ongoing "second" quantum revolution, which employs various synthetic quantum matter platforms, such as ultracold atoms in optical lattices, Rydberg atoms, and superconducting qubits, to realize exotic condensed matter and particle physics phenomena with high precision and control. Gauge theo

Anne Wu, Kianté Brantley, Yoav Artzi

This study evaluates three state-of-the-art MLLMs -- GPT-4V, Gemini Pro, and the open-source model IDEFICS -- on the compositional natural language vision reasoning task NLVR. Given a human-written sentence paired with a synthetic image, this task requires the model to determine the truth value of the sentence with respect to the image. Despite the strong pe

Lucas Fabian Naumann, Jannik Irmai, Shengxian Zhao, Bjoern Andres

The lifted multicut problem is a combinatorial optimization problem whose feasible solutions relate one-to-one to the decompositions of a graph $G = (V, E)$. Given an augmentation $\widehat{G} = (V, E \cup F)$ of $G$ and given costs $c \in \mathbb{R}^{E \cup F}$, the objective is to minimize the sum of those $c_{uw}$ with $uw \in E \cup F$ for which $u$ and

Can Dalkiran, Murad Özkoç

The main purpose of this paper is to introduce the concept of $e^*$-topological ring. This class appears as a generalized form of the class of $\beta$-topological rings. In addition, we have discussed the relation between the concept of $e^*$-topological ring and some other types of topological rings existing in the literature. Also, some fundamental results

Debora Impera, Michele Rimoldi, Giona Veronelli

The ABP method for proving isoperimetric inequalities has been first employed by Cabr\'e in $\mathbb{R}^n$, then developed by Brendle, notably in the context of non-compact Riemannian manifolds of non-negative Ricci curvature and positive asymptotic volume ratio. In this paper, we expand upon their approach and prove isoperimetric inequalities (sharp in the

James T. Wilson

Bayesian optimization is a popular framework for efficiently tackling black-box search problems. As a rule, these algorithms operate by iteratively choosing what to evaluate next until some predefined budget has been exhausted. We investigate replacing this de facto stopping rule with criteria based on the probability that a point satisfies a given set of co

Fujian Jia, Xin Liu, Lixi Deng, Jiwen Gu

In the past year, there has been a growing trend in applying Large Language Models (LLMs) to the field of medicine, particularly with the advent of advanced language models such as ChatGPT developed by OpenAI. However, there is limited research on LLMs specifically addressing oncology-related queries. The primary aim of this research was to develop a special

Thawatchai Mayteevarunyoo, Boris A. Malomed

We report results of systematic investigation of dynamics featured by moving two-dimensional (2D) solitons generated by the fractional nonlinear Schroedinger equation (FNLSE) with the cubic-quintic nonlinearity. The motion of solitons is a nontrivial problem, as the fractional diffraction breaks the Galilean invariance of the underlying equation. The additio

Neelima Borade, Jonas Franzel, Johannes Girsch, Wei Yao

We determine the restriction of Weil representations of unitary groups to maximal tori. In the local case, we show that the Weil representation contains a pair of compatible characters if and only if a root number condition holds. In the global case, we show that a torus period corresponding to a maximal anisotropic torus of the global theta lift of a charac

H. N. Hausser, J. Keller, T. Nordmann, N. M. Bhatt

We present a scalable mixed-species Coulomb crystal clock based on the $^1S_0$ $\leftrightarrow$ $^3P_0$ transition in $^{115}$In$^+$. $^{172}$Yb$^+$ ions are co-trapped and used for sympathetic cooling. Reproducible interrogation conditions for mixed-species Coulomb crystals are ensured by a conditional preparation sequence with permutation control. We demo

Zeyu Wang, Zhenzhen Weng, Serena Yeung-Levy

Conventional approaches to human mesh recovery predominantly employ a region-based strategy. This involves initially cropping out a human-centered region as a preprocessing step, with subsequent modeling focused on this zoomed-in image. While effective for single figures, this pipeline poses challenges when dealing with images featuring multiple individuals,

Dennis Kriventsov, María Soria-Carro

We study a two-phase parabolic free boundary problem motivated by the jump of conductivity in composite materials that undergo a phase transition. Each phase is governed by a heat equation with distinct thermal conductivity, and a transmission-type condition is imposed on the free interface. We establish strong regularity properties of the free boundary: fir

Pierre Godfard

We prove that $\mathrm{SO}(3)$ modular functors in genus $0$ have geometric origin and support integral variations of Hodge structures for any odd level $r$ and $r$-th root of unity $\zeta_r\in\mathbb{C}$. We identify the TQFT intersection forms and integral structures with the geometric ones. Moreover, the gluing property of the modular functors is recovere

Isabella Carla Gonnella, Moaad Khamlich, Federico Pichi, Gianluigi Rozza

Incorporating probabilistic terms in mathematical models is crucial for capturing and quantifying uncertainties in real-world systems, especially when the solution is not unique or exhibits sudden qualitative changes as parameters vary. However, stochastic models typically require large computational resources to produce meaningful statistics. In this work,

L. Piscarreta, K. Mužić, V. Almendros-Abad, A. Scholz

We aim to provide a detailed characterization of near-infrared spectra for young LT brown dwarfs, including robust spectral typing, calibrating spectral indices, identifying possible binaries, and selecting suitable spectral standards. We processed and analyzed archival spectra from VLT/X-shooter for a sample of 56 dwarfs with ages between 10 and 600 Myr and

Jacob A. Barandes

It is difficult to extract reliable criteria for causal locality from the limited ingredients found in textbook quantum theory. In the end, Bell humbly warned that his eponymous theorem was based on criteria that "should be viewed with the utmost suspicion." Remarkably, by stepping outside the wave-function paradigm, one can reformulate quantum theory in ter

Michael Matthews, Michael Beukman, Benjamin Ellis, Mikayel Samvelyan

Benchmarks play a crucial role in the development and analysis of reinforcement learning (RL) algorithms. We identify that existing benchmarks used for research into open-ended learning fall into one of two categories. Either they are too slow for meaningful research to be performed without enormous computational resources, like Crafter, NetHack and Minecraf

M. Zoccali, A. Rojas-Arriagada, E. Valenti, R. Contreras Ramos

The nuclear region of the Milky Way is believed to host a nuclear stellar disk, co-spatial with the gaseous central molecular zone. Previous kinematical studies detected faster rotation for the stars belonging to the nuclear stellar disk, compared to the surrounding regions. We analyze the rotation velocity of stars at the nuclear stellar disk, and compare t

Klaus Deckelnick, Robert Nürnberg

We introduce novel finite element schemes for curve diffusion and elastic flow in arbitrary codimension. The schemes are based on a variational form of a system that includes a specifically chosen tangential motion. We derive optimal $L^2$- and $H^1$-error bounds for continuous-in-time semidiscrete finite element approximations that use piecewise linear elem

Arijit Manna, Sabyasachi Pal

The search for the simplest amino acid, glycine (NH$_{2}$CH$_{2}$COOH), in the interstellar medium (ISM), has become a never-ending story for astrochemistry and astrophysics researchers because that molecule plays a possible connection between the Universe and the origin of life. In the last forty years, all searches for NH$_{2}$CH$_{2}$COOH in the ISM at mi

Bowen Zhao, Zander Brumbaugh, Yizhong Wang, Hannaneh Hajishirzi

Language models (LMs) are trained on web text originating from many points in time and, in general, without any explicit temporal grounding. This work investigates the temporal chaos of pretrained LMs and explores various methods to align their internal knowledge to a target time, which we call "temporal alignment." To do this, we first automatically constru

Xuxin Cheng, Yandong Ji, Junming Chen, Ruihan Yang

Can we enable humanoid robots to generate rich, diverse, and expressive motions in the real world? We propose to learn a whole-body control policy on a human-sized robot to mimic human motions as realistic as possible. To train such a policy, we leverage the large-scale human motion capture data from the graphics community in a Reinforcement Learning framewo

Zeyu He, Chieh-Yang Huang, Chien-Kuang Cornelia Ding, Shaurya Rohatgi

Recent studies indicated GPT-4 outperforms online crowd workers in data labeling accuracy, notably workers from Amazon Mechanical Turk (MTurk). However, these studies were criticized for deviating from standard crowdsourcing practices and emphasizing individual workers' performances over the whole data-annotation process. This paper compared GPT-4 and an eth

Janko Latschev, Alexandru Oancea

We derive the notions of BV unital infinitesimal bialgebra and BV Frobenius algebra from the topology of suitable compactifications of moduli spaces of decorated genus 0 curves. We construct these structures respectively on reduced symplectic homology and Rabinowitz Floer homology. As an application, we construct these structures in nonequivariant string top

Pratik Patil, Yuchen Wu, Ryan J. Tibshirani

We analyze the statistical properties of generalized cross-validation (GCV) and leave-one-out cross-validation (LOOCV) applied to early-stopped gradient descent (GD) in high-dimensional least squares regression. We prove that GCV is generically inconsistent as an estimator of the prediction risk of early-stopped GD, even for a well-specified linear model wit

Anna Gori, Giulia Sarfatti, Fabio Vlacci

We introduce a non-commutative resultant, for slice regular polynomials in two quaternionic variables, defined in terms of a suitable Dieudonn\'e determinant.We use this tool to investigate the existence of common zeros of slice regular polynomials.

Shirong Xu, Will Wei Sun, Guang Cheng

In various real-world scenarios, such as recommender systems and political surveys, pairwise rankings are commonly collected and utilized for rank aggregation to derive an overall ranking of items. However, preference rankings can reveal individuals' personal preferences, highlighting the need to protect them from exposure in downstream analysis. In this pap

Recai Erdem

The effect of gravitational particle production of scalar particles on the total effective cosmic energy density (in the era after photon decoupling till the present) is considered. The effect is significant for heavy particles. It is found that gravitational particle production results in an effective increase in the directly measured value of the Hubble co

Jiri Gesi, Iftekhar Ahmed

Transformer-based models have demonstrated considerable potential for source code modeling tasks in software engineering. However, they are limited by their dependence solely on automatic self-attention weight learning mechanisms. Previous studies have shown that these models overemphasize delimiters added by tokenizers (e.g., [CLS], [SEP]), which may lead t

Lucas B. Vieira, Huan-Yu Ku, Costantino Budroni

Entanglement-breaking channels (equivalently, measure-and-prepare channels) are an important class of quantum operations noted for their ability to destroy multipartite spatial quantum correlations. Inspired by this property, they have also been employed in defining notions of "classical memory", under the assumption that such channels effectively act as a c

Yushun Zhang, Congliang Chen, Tian Ding, Ziniu Li

SGD performs worse than Adam by a significant margin on Transformers, but the reason remains unclear. In this work, we provide an explanation through the lens of Hessian: (i) Transformers are "heterogeneous": the Hessian spectrum across parameter blocks vary dramatically, a phenomenon we call "block heterogeneity"; (ii) Heterogeneity hampers SGD: SGD perform

Juan Paucar

In this article we extend the notion of $L^p$-measure subgroups couplings, a quantitative asymmetric version of measure equivalence that was introduced by Delabie, Koivisto, Le Ma\^itre and Tessera for finitely generated groups, to the setting of locally compact compactly generated unimodular groups. As an example of these couplings; using ideas from Bader a

Paul Röttger, Valentin Hofmann, Valentina Pyatkin, Musashi Hinck

Much recent work seeks to evaluate values and opinions in large language models (LLMs) using multiple-choice surveys and questionnaires. Most of this work is motivated by concerns around real-world LLM applications. For example, politically-biased LLMs may subtly influence society when they are used by millions of people. Such real-world concerns, however, s

Myung Jun Kim, Léo Grinsztajn, Gaël Varoquaux

Pretrained deep-learning models are the go-to solution for images or text. However, for tabular data the standard is still to train tree-based models. Indeed, transfer learning on tables hits the challenge of data integration: finding correspondences, correspondences in the entries (entity matching) where different words may denote the same entity, correspon

Anna Gori, Giulia Sarfatti, Fabio Vlacci

In this paper we prove a strong version of the Hilbert Nullstellensatz in the ring $\mathbb H[q_1,\ldots,q_n]$ of slice regular polynomials in several quaternionic variables. Our proof deeply depends on a detailed analysis of the common zeros of slice regular polynomials which belong to an ideal in $\mathbb H[q_1,\ldots,q_n]$. This study motivates the introd

Evans Nyanney, Megha Pandey, Mrinal Kanti Roychowdhury

Bucklew and Wise (1982) showed that the quantization dimension of an absolutely continuous probability measure on a given Euclidean space is constant and equals the Euclidean dimension of the space, and the quantization coefficient exists as a finite positive number. By giving different examples, in this paper, we have shown that the quantization coefficient

Quintino Francesco Lotito, Alberto Montresor, Federico Battiston

A wide variety of complex systems are characterized by interactions of different types involving varying numbers of units. Multiplex hypergraphs serve as a tool to describe such structures, capturing distinct types of higher-order interactions among a collection of units. In this work, we introduce a comprehensive set of measures to describe structural conne

Tianhang Zheng, Yanlu Li, Bohan Deng, Baochun Li

Federated learning has emerged as a decentralized approach for training high-performance models without accessing user data. Despite its effectiveness, it is vulnerable to poisoning attacks, where malicious users manipulate the global model by uploading poisoned updates. In this paper, we propose FedReview, a review-based mechanism to identify and dispose th

Zu-Cheng Chen, Lang Liu

Understanding the nature of primordial fluctuations is pivotal to unraveling the Universe's early evolution. While these fluctuations are observed to be nearly scale-invariant, quasi-adiabatic, and Gaussian on large scales, their small-scale behavior remains poorly constrained, offering a potential window into new physics. Recent detections of a stochastic g

Marcela Carena, Henry Lamm, Ying-Ying Li, Wanqiang Liu

In the quantum simulation of lattice gauge theories, gauge symmetry can be either fixed or encoded as a redundancy of the Hilbert space. While gauge-fixing reduces the number of qubits, keeping the gauge redundancy can provide code space to mitigate and correct quantum errors by checking and restoring Gauss's law. In this work, we consider the correctable er

Fridrik Freyr Gautason, Jesse van Muiden

In this note we analyze the semi-classical quantization of D3 branes in three different holographic backgrounds in type IIB string theory. The first background is Euclidean AdS$_5$ with $S^1\times S^3$ boundary accompanied with a twist to preserve supersymmetry. We work out the spectrum of fluctuations around the classical D3-brane configuration, compute its

Daniil Dmitriev, Kristóf Szabó, Amartya Sanyal

In this paper, we provide lower bounds for Differentially Private (DP) Online Learning algorithms. Our result shows that, for a broad class of $(\varepsilon,\delta)$-DP online algorithms, for number of rounds $T$ such that $\log T\leq O(1 / \delta)$, the expected number of mistakes incurred by the algorithm grows as $\Omega(\log \frac{T}{\delta})$. This matc

Benedikt Peterseim

In recent times, there has been a growing interest in a structuralist understanding of probability, measure and integration theory. The present thesis contributes to this programme in three ways. First, we construct a commutative probability monad on the cartesian closed category of hk-spaces (also known as CGWH spaces, or weak Hausdorff k-spaces in the lite

Saketh Reddy Karra, Theja Tulabandhula

Weblogs, comprised of records detailing user activities on any website, offer valuable insights into user preferences, behavior, and interests. Numerous recommendation algorithms, employing strategies such as collaborative filtering, content-based filtering, and hybrid methods, leverage the data mined through these weblogs to provide personalized recommendat

Hamid Beigy, Mohammad Mahini, Salman Qadami, Morteza Saghafian

Simplets, constituting elementary units within simplicial complexes (SCs), serve as foundational elements for the structural analysis of SCs. Previous efforts have focused on the exact count or approximation of simplet count rather than their frequencies, with the latter being more practical in large-scale SCs. This paper enables simplet frequency analysis o

Yangyang Cheng, Peter Keevash

Thomass\'{e} conjectured the following strengthening of the well-known Caccetta-Haggkvist Conjecture: any digraph with minimum out-degree $\delta$ and girth $g$ contains a directed path of length $\delta(g-1)$. Bai and Manoussakis \cite{Bai} gave counterexamples to Thomass\'{e}'s conjecture for every even $g\geq 4$. In this note, we first generalize their co

Renren Jin, Jiangcun Du, Wuwei Huang, Wei Liu

Increasing the number of parameters in large language models (LLMs) usually improves performance in downstream tasks but raises compute and memory costs, making deployment difficult in resource-limited settings. Quantization techniques, which reduce the bits needed for model weights or activations with minimal performance loss, have become popular due to the

M. Serna-Aguilera, X. B. Nguyen, A. Singh, L. Rockers

Individuals with Autism Spectrum Disorder (ASD) often experience challenges in health, communication, and sensory processing; therefore, early diagnosis is necessary for proper treatment and care. In this work, we consider the problem of detecting or classifying ASD children to aid medical professionals in early diagnosis. We develop a deep learning model th

Adrian Dawid

Let $L_0,L_1,L_2 \subset M$ be exact Lagrangian spheres in a Liouville domain $M$ with $2c_1(M)=0$. If $L_0,L_1,L_2$ form an $A_3$-configuration, we show that $\mathscr{L}(L_0)$ and $\mathscr{L}(L_2)$ endowed with the Hofer metric contain quasi-isometric embeddings of $(\mathbb{R}^\infty, \|\cdot\|_\infty)$, i.e. infinite-dimensional quasi-flats. A corollary

Lakshmi Pullasseri, Luiz H. Santos

In two-dimensional electronic lattices, changes in the topology of the Fermi surface (Lifshitz transitions) lead to Van Hove singularities characterized by a divergence in the electronic density of states. Van Hove singularities can enhance the effect of electronic interactions, providing a platform to explore novel correlated electronic states. In this work

Kenny Peng, Nikhil Garg

We consider a many-to-one matching market where colleges share true preferences over students but make decisions using only independent noisy rankings. Each student has a true value $v$, but each college $c$ ranks the student according to an independently drawn estimated value $v + X_c$ for $X_c\sim \mathcal{D}.$ We ask a basic question about the resulting s

Albert J. Wakhloo, Will Slatton, SueYeon Chung

Humans and animals can recognize latent structures in their environment and apply this information to efficiently navigate the world. However, it remains unclear what aspects of neural activity contribute to these computational capabilities. Here, we develop an analytical theory linking the geometry of a neural population's activity to the generalization per

Haowei Liu, Yaya Shi, Haiyang Xu, Chunfeng Yuan

In video-text retrieval, most existing methods adopt the dual-encoder architecture for fast retrieval, which employs two individual encoders to extract global latent representations for videos and texts. However, they face challenges in capturing fine-grained semantic concepts. In this work, we propose the UNIFY framework, which learns lexicon representation

Ashiwini Balodhi, Brianna Billingsley, Tai Kong, Min Gyu Kim

We investigated the effect of Ni doping in a recently proposed quantum spin liquid (QSL) candidate Li$_4$CuTeO$_6$. We performed a comprehensive study on the structural and magnetic properties. We find that the anti-site disorder between Li$^+$ and Cu$^{2+}$ persists until 50\% Ni doping in which Ni and Cu occupy different crystallographic sites. As a result

Jiafeng Guo, Changjiang Zhou, Ruqing Zhang, Jiangui Chen

Knowledge-intensive language tasks (KILTs) typically require retrieving relevant documents from trustworthy corpora, e.g., Wikipedia, to produce specific answers. Very recently, a pre-trained generative retrieval model for KILTs, named CorpusBrain, was proposed and reached new state-of-the-art retrieval performance. However, most existing research on KILTs,

Srinjoy Mitra, Jean-Pierre Raskin

This paper discusses the importance of reflective and socially conscious education in engineering schools, particularly within the EE/CS sector. While most engineering disciplines have historically aligned themselves with the demands of the technology industry, the lack of critical examination of industry practices and their impact on justice, equality, and

Yan Jiang, Hancheng Min, Baosen Zhang

Frequency security assessment following major disturbances has long been one of the central tasks in power system operations. The standard approach is to study the center of inertia frequency, an aggregate signal for an entire system, to avoid analyzing the frequency signal at individual buses. However, as the amount of low-inertia renewable resources in a g

Pedro Abdalla, Gil Kur

Quantifying uncertainty in high-dimensional sparse linear regression is a fundamental task in statistics that arises in various applications. One of the most successful methods for quantifying uncertainty is the debiased LASSO, which has a solid theoretical foundation but is restricted to settings where the noise is purely additive. Motivated by real-world a

Laura Kriener, Kristin Völk, Ben von Hünerbein, Federico Benitez

Behavior can be described as a temporal sequence of actions driven by neural activity. To learn complex sequential patterns in neural networks, memories of past activities need to persist on significantly longer timescales than the relaxation times of single-neuron activity. While recurrent networks can produce such long transients, training these networks i

Xi-Yuan Yin, Wesley Agoua, Tong Wu, Wouter J. T. Bos

We investigate the role of the correlation between a scalar quantity and the vorticity in two-dimensional mixing at infinite P\'eclet number. We assess, using a diffusivity independent mixing-norm, the dynamics of both Galerkin-truncated ensembles and freely evolving two-dimensional scalar mixing. Both statistical mechanics and numerical experiments show how

Gadha Ramesh, Ved Mahajan, Debasish Koner, Rakesh S. Singh

Much attention has been devoted to understanding the microscopic pathways of phase transition between two equilibrium condensed phases (such as liquids and solids). However, the microscopic pathways between non-equilibrium, non-diffusive amorphous (glassy) phases still remain poorly understood. In this work, we have employed computer simulations, persistence

Frank Lewis, Julita Vassileva

The problem of ``Dark Patterns" in user interface/user experience (UI/UX) design has proven a difficult issue to tackle. Malicious and explotitative design has expanded to multiple domains in the past 10 years and which has in turn led to multiple taxonomies attempting to describe them. While these taxonomies holds their own merit, and constitute unique cont

Kyle DuFrene, Luke Strohbehn, Keegan Nave, Ravi Balasubramanian

Robotic manipulation in human environments is a challenging problem for researchers and industry alike. In particular, opening doors/drawers can be challenging for robots, as the size, shape, actuation and required force is variable. Because of this, it can be difficult to collect large real-world datasets and to benchmark different control algorithms on the

Wesley G. Lautenschlaeger, Thaísa Tamusiunas

We provide a necessary and sufficient condition to the existence of an ordered globalization of a partial ordered action of an ordered groupoid on a ring and we also present criteria to obtain uniqueness. Furthermore, we apply those results to obtain a Morita context and to show that an inverse semigroup partial action has a globalization (unique up to isomo

Jasper Kirton-Wingate, Shafique Ahmed, Adeel Hussain, Mandar Gogate

Since the advent of Deep Learning (DL), Speech Enhancement (SE) models have performed well under a variety of noise conditions. However, such systems may still introduce sonic artefacts, sound unnatural, and restrict the ability for a user to hear ambient sound which may be of importance. Hearing Aid (HA) users may wish to customise their SE systems to suit

Jake Daniels, Nghiem V. Nguyen

In this manuscript, consideration is given to the existence of periodic traveling-wave solutions to the $abcd$-system. This system was derived by Bona, Saut, and Chen to describe small amplitude, long wavelength gravity waves on the surface of water. These exact solutions are formulated in terms of the Jacobi elliptic function cnoidal. The existence of expli

Navneet Agrawal, Ehsan Tohidi, Renato L. G. Cavalcante, Sławomir Stańczak

The "near-field" propagation modeling of wireless channels is necessary to support sixth-generation (6G) technologies, such as intelligent reflecting surface (IRS), that are enabled by large aperture antennas and higher frequency carriers. As the conventional far-field model proves inadequate in this context, there is a pressing need to explore and bridge th

Nicki Barari, Xin Lian, Christopher J. MacLellan

Deep neural networks have excelled in machine learning, particularly in vision tasks, however, they often suffer from catastrophic forgetting when learning new tasks sequentially. In this work, we introduce Cobweb4V, an alternative to traditional neural network approaches. Cobweb4V is a novel visual classification method that builds on Cobweb, a human like l

Zahra Esmaeilbeig, Arindam Bose, Mojtaba Soltanalian

Frequency-modulated continuous wave (FMCW) radar with inter-chirp coding produces high side-lobes in the Doppler and range dimensions of the radar's ambiguity function. The high side-lobes may cause miss-detection due to masking between targets that are at similar range and have large received power difference, as is often the case in automotive scenarios. I

O. Pirahmad, H. Pottmann, M. Skopenkov

Motivated by the design of flexible nets, we classify all nets of arbitrary size m x n that admit a continuous family of area-preserving Combescure transformations. There are just two different classes. The nets in the first class are special cases of cone nets that have been recently studied by Kilian, Mueller, and Tervooren. The second class consists of Ko

Orsolya Kálmán, Aurél Gábris, Igor Jex, Tamás Kiss

The ability of preparing perfect Bell pairs with a practical scheme is of great relevance for quantum communication as well as distributed quantum computing. We propose a scheme which probabilistically, but unambiguously produces the $\left|\Phi_{+}\right>$ Bell pair from four copies of qubit pairs initially in the same arbitrary pure quantum state. The same

Enrico Liscio, Luciano C. Siebert, Catholijn M. Jonker, Pradeep K. Murukannaiah

Understanding citizens' values in participatory systems is crucial for citizen-centric policy-making. We envision a hybrid participatory system where participants make choices and provide motivations for those choices, and AI agents estimate their value preferences by interacting with them. We focus on situations where a conflict is detected between particip

P. Bevington, J. Nicholson, J. D. Zipfel, W. Chalupczak

Collective spins in thermal gases are at the core of a multitude of science and technology applications. In most of them, the random thermal motion of the particles is considered detrimental as it is responsible for decoherence and noise. In conditions of diffusive propagation, thermal atoms can potentially occupy various stable spatial modes in a glass cell

Chunyi Li, Guo Lu, Donghui Feng, Haoning Wu

With the evolution of storage and communication protocols, ultra-low bitrate image compression has become a highly demanding topic. However, existing compression algorithms must sacrifice either consistency with the ground truth or perceptual quality at ultra-low bitrate. In recent years, the rapid development of the Large Multimodal Model (LMM) has made it

Zhenzhang Ye, Gabriel Peyré, Daniel Cremers, Pierre Ablin

Bilevel optimization aims to optimize an outer objective function that depends on the solution to an inner optimization problem. It is routinely used in Machine Learning, notably for hyperparameter tuning. The conventional method to compute the so-called hypergradient of the outer problem is to use the Implicit Function Theorem (IFT). As a function of the er

Chen Li

Animals and robots must self-right on the ground after overturning. Biology research described various strategies and motor patterns in many species. Robotics research devised many strategies. However, we do not well understand how the physical principles of how the need to generate mechanical energy to overcome the potential energy barrier governs behaviora

Chinmay Patwardhan, Martin Frank, Jonas Kusch

The thermal radiative transfer equations model temperature evolution through a background medium as a result of radiation. When a large number of particles are absorbed in a short time scale, the dynamics tend to a non-linear diffusion-type equation called the Rosseland approximation. The main challenges for constructing numerical schemes that exhibit the co

Nicola Garofalo

We study the gradient flow of an energy with mixed homogeneity which is at the interface of Finsler and sub-Riemannian geometry

Arieh Iserles

The contention of this paper is that a spectral method for time-dependent PDEs is basically no more than a choice of an orthonormal basis of the underlying Hilbert space. This choice is governed by a long list of considerations: stability, speed of convergence, geometric numerical integration, fast approximation and efficient linear algebra. We subject diffe

A. Mariani

We consider the problem of the explicit description of the gauge-invariant subspace of pure lattice gauge theories in the Hamiltonian formulation, where the gauge group is either a compact Lie group or a finite group. The latter case is particularly interesting for quantum simulation. A basis of states where configurations are grouped according to their holo

Nitesh Chauhan, Christopher Caron, Jiawei Wang, Andrei Isichenko

Integrating precise, stable, ultra-low noise visible light lasers into atomic systems is critical for advancing quantum information sciences and improving scalability and portability. Trapped ions are a leading approach for high-fidelity quantum computing, high-accuracy optical clocks, and precision quantum sensors. However, current ion-based systems rely on

Olga Dye, Justin Heo, Ebru Celikel Cankaya

As demand for more storage and processing power increases rapidly, cloud services in general are becoming more ubiquitous and popular. This, in turn, is increasing the need for developing highly sophisticated mechanisms and governance to reduce data breach risks in cloud-based infrastructures. Our research focuses on cloud governance by harmoniously combinin

Dorje C. Brody, Lane P. Hughston

It is shown that if the wave function of a quantum system undergoes an arbitrary random transformation such that the diagonal elements of the density matrix in the decoherence basis associated with a preferred observable remain constant, then (i) the off-diagonal elements of the density matrix become smaller in magnitude, and (ii) the state of the system gai

Farhad G. Zanjani, Hong Cai, Yinhao Zhu, Leyla Mirvakhabova

This paper presents Neural Mesh Fusion (NMF), an efficient approach for joint optimization of polygon mesh from multi-view image observations and unsupervised 3D planar-surface parsing of the scene. In contrast to implicit neural representations, NMF directly learns to deform surface triangle mesh and generate an embedding for unsupervised 3D planar segmenta

Alexander Strohmaier

This short letter considers the case of acoustic scattering by several obstacles in $\mathbb{R}^{d+r}$ for $r,d \geq 1$ of the form $\Omega \times \mathbb{R}^r$, where $\Omega$ is a smooth bounded domain in $\mathbb{R}^d$. As a main result a von-Neumann-trace formula for the relative trace is obtained in this setting. As a special case we obtain a dimensiona

Rusa Mandal, Praveen S Patil, Ipsita Ray

We investigate the inverse moment of the $B_s$-meson light-cone distribution amplitude (LCDA), denoted as $\lambda_{B_s}$ and defined within the heavy quark effective theory, through the calculation of $B_s \to \eta_s$ form factors. The presence of the $s$-quark inside the $B_s$-meson dictates a notable departure of approximately $20\%$ in the $\lambda_{B_s}