May 2023 arXiv papers — page 73

Yue Wu, Yulin Pan

We study the spectral energy transfer due to wave-triad interactions in the Garrett-Munk spectrum of internal gravity waves (IGWs) based on a numerical evaluation of the collision integral in the wave kinetic equation. Our numerical evaluation builds on the reduction of the collision integral on the resonant manifold for a horizontally isotropic spectrum. We

Marco Boggi, Andrew Putman, Nick Salter

Putman and Wieland conjectured that if $\tilde{\Sigma} \rightarrow \Sigma$ is a finite branched cover between closed oriented surfaces of sufficiently high genus, then the orbits of all nonzero elements of $H_1(\tilde{\Sigma};\mathbb{Q})$ under the action of lifts to $\tilde{\Sigma}$ of mapping classes on $\Sigma$ are infinite. We prove that this holds if $H

Mário Rocha-Neto, Gustavo Camelo-Neto, Edvaldo Nogueira-Junior, Sérgio Coutinho

We investigate the thermodynamic properties of the zero-field Blume-Capel model in the vicinity of its tricritical point (TCP). We calculate the quadrupole moment, internal energy, and entropy densities employing an exact numerical recursion procedure for the model defined on a hierarchical lattice of fractal dimension $d$. We explore the scaling behavior of

Jia Yu Tee, Oliver De Candido, Wolfgang Utschick, Philipp Geiger

Towards safe autonomous driving (AD), we consider the problem of learning models that accurately capture the diversity and tail quantiles of human driver behavior probability distributions, in interaction with an AD vehicle. Such models, which predict drivers' continuous actions from their states, are particularly relevant for closing the gap between AD agen

J. O. Button

We give necessary and sufficient conditions under which a quasi-action of any group on an arbitrary metric space can be reduced to a cobounded isometric action on some bounded valence tree, following a result of Mosher, Sageev and Whyte. Moreover if the quasi-action is metrically proper and quasi-orbits are quasi-isometric to trees then the group is virtuall

M. R. Ibrahim, D. U. Muhammad, B. Muhammad, J. O. Alaezi

This study examines the impact of Total Quality Management (TQM) practices on organizational outcomes. Results show a significant relationship between TQM practices such as top executive commitment, education and teaching, process control, and continuous progress, and how they can be leveraged to enhance performance outcomes.

Katherine Abramski, Salvatore Citraro, Luigi Lombardi, Giulio Rossetti

Large language models are becoming increasingly integrated into our lives. Hence, it is important to understand the biases present in their outputs in order to avoid perpetuating harmful stereotypes, which originate in our own flawed ways of thinking. This challenge requires developing new benchmarks and methods for quantifying affective and semantic bias, k

Oscar Morris, Russell Morris

Timely feedback is an important part of teaching and learning. Here we describe how a readily available neural network transformer (machine-learning) model (BERT) can be used to give feedback on the structure of the response to an abstracting exercise where students are asked to summarise the contents of a published article after finding it from a publicatio

Keisuke Konosu

We extend the formula for correlation functions of free scalar field theories and Dirac field theories in terms of quantum $A_{\infty}$ algebras presented in arXiv:2305.11634 to general scalar-Dirac systems. We obtain the result that the same formula as in the previous paper holds in this case. We show that correlation functions from our formula satisfy the

Sumit Soman, Ranjani H G

The landscape for building conversational interfaces (chatbots) has witnessed a paradigm shift with recent developments in generative Artificial Intelligence (AI) based Large Language Models (LLMs), such as ChatGPT by OpenAI (GPT3.5 and GPT4), Google's Bard, Large Language Model Meta AI (LLaMA), among others. In this paper, we analyze capabilities and limita

Alicia Parrish, Hannah Rose Kirk, Jessica Quaye, Charvi Rastogi

The generative AI revolution in recent years has been spurred by an expansion in compute power and data quantity, which together enable extensive pre-training of powerful text-to-image (T2I) models. With their greater capabilities to generate realistic and creative content, these T2I models like DALL-E, MidJourney, Imagen or Stable Diffusion are reaching eve

Michal Edelstein, Nestor Guillen, Justin Solomon, Mirela Ben-Chen

We propose a general convex optimization problem for computing regularized geodesic distances. We show that under mild conditions on the regularizer the problem is well posed. We propose three different regularizers and provide analytical solutions in special cases, as well as corresponding efficient optimization algorithms. Additionally, we show how to gene

Maria Schlungbaum, Benjamin Lindner

Motivated by experimental observations, we investigate a variant of the cocktail party problem: the detection of a weak periodic stimulus in the presence of fluctuations and another periodic stimulus which is stronger than the periodic signal to be detected. Specifically, we study the response of a population of stochastic leaky integrate-and-fire (LIF) neur

Andrei Poblaguev

The requirements for hadron polarimetry at the future Electron Ion Collider (EIC) include measurements of the absolute helion ($^3$He, $h$) beam polarization with systematic uncertainties better than $\sigma^\text{syst}_P/P\le1\%$. Recently, it was proposed that the Polarized Atomic Hydrogen Gas Jet Target (HJET) be utilized for the precision measurement of

Iain J. Cruickshank, Jessica Zhu, Nathaniel D. Bastian

With the rise of phenomena like `fake news' and the growth of heavily-biased media ecosystems, there has been increased attention on understanding and evaluating media bias. Of particular note in the evaluation of media bias is writing style bias, which includes lexical bias and framing bias. We propose a novel approach to evaluating writing style bias that

Jack F. Gallimore, C. M. Violette Impellizzeri

We present High Sensitivity Array observation of the water megamasers of NGC 1068. We obtain absolute astrometry with 0.3 mas precision that confirms the association of the disk masers with the nuclear radio continuum source S1. The new observations reveal two new blueshifted groups of disk masers. We also detect the 22 GHz continuum on short interferometric

Xiaowen Shi, Nimish Prashant Nazirkar, Ravi Kashikar, Dmitry Karpov

The piezoelectric response is a measure of the sensitivity of a material's polarization to stress or its strain to an applied field. Using in-operando x-ray Bragg coherent diffraction imaging, we observe that topological vortices are the source of a five-fold enhancement of the piezoelectric response near the vortex core. The vortices form where several low

Jiaming Liu, Yangqiming Wang, Tongze Zhang, Yulu Fan

Traditional semi-supervised learning tasks assume that both labeled and unlabeled data follow the same class distribution, but the realistic open-world scenarios are of more complexity with unknown novel classes mixed in the unlabeled set. Therefore, it is of great challenge to not only recognize samples from known classes but also discover the unknown numbe

Rubén Hurtado-Gutiérrez, Álvaro Tejero

In this article, we present a simple, inexpensive, and effective method for measuring the capacitor charge and discharge processes using a light-emitting diode (LED) and the light meter of a smartphone. We propose a simple circuit in which the LED's brightness is linear on the capacitor's voltage, allowing us to use the smartphone to monitor the capacitor st

Jiaxi Jiang, Christian Holz

Recent image restoration methods have produced significant advancements using deep learning. However, existing methods tend to treat the whole image as a single entity, failing to account for the distinct objects in the image that exhibit individual texture properties. Existing methods also typically generate a single result, which may not suit the preferenc

Sam Spilsbury, Pekka Marttinen, Alexander Ilin

Meta-learning and few-shot prompting are viable methods to induce certain types of compositional behaviour. However, these methods can be very sensitive to the choice of support examples used. Choosing good supports from the training data for a given test query is already a difficult problem, but in some cases solving this may not even be enough. We consider

Chenhui Shen, Liying Cheng, Xuan-Phi Nguyen, Yang You

With the recent undeniable advancement in reasoning abilities in large language models (LLMs) like ChatGPT and GPT-4, there is a growing trend for using LLMs on various tasks. One area where LLMs can be employed is as an alternative evaluation metric for complex generative tasks, which generally demands expensive human judges to complement the traditional au

Chen-How Huang, Thierry Giamarchi, Miguel A. Cazalilla

For open quantum systems, integration of the bath degrees of freedom using the second order cumulant expansion in the Keldysh path integral provides an alternative derivation of the effective action for systems coupled to general baths. The baths can be interacting and not necessarily Markovian. Using this method in the Markovian limit, we compute the partic

Pan Peng, Yuyang Wang

We revisit the relation between two fundamental property testing models for bounded-degree directed graphs: the bidirectional model in which the algorithms are allowed to query both the outgoing edges and incoming edges of a vertex, and the unidirectional model in which only queries to the outgoing edges are allowed. Czumaj, Peng and Sohler [STOC 2016] showe

Abdelrahman Zayed, Goncalo Mordido, Samira Shabanian, Sarath Chandar

The advances in natural language processing (NLP) pose both opportunities and challenges. While recent progress enables the development of high-performing models for a variety of tasks, it also poses the risk of models learning harmful biases from the data, such as gender stereotypes. In this work, we investigate the role of attention, a widely-used techniqu

Jonas Kühne, Michele Magno, Luca Benini

Optical Flow (OF) is the movement pattern of pixels or edges that is caused in a visual scene by the relative motion between an agent and a scene. OF is used in a wide range of computer vision algorithms and robotics applications. While the calculation of OF is a resource-demanding task in terms of computational load and memory footprint, it needs to be exec

Ruochen Xu, Song Wang, Yang Liu, Shuohang Wang

Query-focused summarization (QFS) aims to extract or generate a summary of an input document that directly answers or is relevant to a given query. The lack of large-scale datasets in the form of documents, queries, and summaries has hindered model development in this area. In contrast, multiple large-scale high-quality datasets for generic summarization exi

Ratish Puduppully, Anoop Kunchukuttan, Raj Dabre, Ai Ti Aw

This study investigates machine translation between related languages i.e., languages within the same family that share linguistic characteristics such as word order and lexical similarity. Machine translation through few-shot prompting leverages a small set of translation pair examples to generate translations for test sentences. This procedure requires the

Sohir Maskey, Raffaele Paolino, Aras Bacho, Gitta Kutyniok

Graph neural networks (GNNs) have shown state-of-the-art performances in various applications. However, GNNs often struggle to capture long-range dependencies in graphs due to oversmoothing. In this paper, we generalize the concept of oversmoothing from undirected to directed graphs. To this aim, we extend the notion of Dirichlet energy by considering a dire

Yichong Xu, Ruochen Xu, Dan Iter, Yang Liu

While large models such as GPT-3 demonstrate exceptional performance in zeroshot and fewshot summarization tasks, their extensive serving and fine-tuning costs hinder their utilization in various applications. Conversely, previous studies have found that although automatic metrics tend to favor smaller fine-tuned models, the quality of the summaries they gen

Slavomír Hanzely

In this paper, we propose the first sketch-and-project Newton method with fast $\mathcal O(k^{-2})$ global convergence rate for self-concordant functions. Our method, SGN, can be viewed in three ways: i) as a sketch-and-project algorithm projecting updates of Newton method, ii) as a cubically regularized Newton ethod in sketched subspaces, and iii) as a damp

Mohit Pundir, David S. Kammer, Ueli Angst

Understanding fracture in cementitious materials caused by the deposition and growth of corrosion products requires scale-bridging approaches due to the large length-scale difference between the micro-pores, where deposition occurs, and the structure, where deterioration manifests. Cementitious materials bear a highly heterogeneous micro-structure owing to t

Ruan van der Merwe, Herman Kamper

We consider the problem of few-shot spoken word classification in a setting where a model is incrementally introduced to new word classes. This would occur in a user-defined keyword system where new words can be added as the system is used. In such a continual learning scenario, a model might start to misclassify earlier words as newer classes are added, i.e

Md Umar Hashmi, Dirk Van Hertem

With growing intermittency and uncertainty in distribution networks around the world, ensuring operational integrity is becoming challenging. Recent use cases of dynamic operating envelopes (DOEs) indicate that they can be utilized for network awareness for autonomous operation of flexibility, maximizing distributed generation integration, coordinating flexi

Dario Izzo, Emmanuel Blazquez, Robin Ferede, Sebastien Origer

Spacecraft and drones aimed at exploring our solar system are designed to operate in conditions where the smart use of onboard resources is vital to the success or failure of the mission. Sensorimotor actions are thus often derived from high-level, quantifiable, optimality principles assigned to each task, utilizing consolidated tools in optimal control theo

Yabo Zhang, Yuxiang Wei, Dongsheng Jiang, Xiaopeng Zhang

Text-driven diffusion models have unlocked unprecedented abilities in image generation, whereas their video counterpart still lags behind due to the excessive training cost of temporal modeling. Besides the training burden, the generated videos also suffer from appearance inconsistency and structural flickers, especially in long video synthesis. To address t

Kiyong Lee, Nikhil Krishnaswamy, James Pustejovsky

VoxML is a modeling language used to map natural language expressions into real-time visualizations using commonsense semantic knowledge of objects and events. Its utility has been demonstrated in embodied simulation environments and in agent-object interactions in situated multimodal human-agent collaboration and communication. It introduces the notion of o

Mireya Jurado, Ramon G. Gonze, Mário S. Alvim, Catuscia Palamidessi

Local differential privacy (LDP) is a variant of differential privacy (DP) that avoids the need for a trusted central curator, at the cost of a worse trade-off between privacy and utility. The shuffle model is a way to provide greater anonymity to users by randomly permuting their messages, so that the link between users and their reported values is lost to

Ouriel Blœdé

In a precedent article, we computed the set $\textbf{C}(K)$ of central elements of an unstable algebra $K$ over the Steenrod algebra, in the sense of Dwyer and Wilkerson, when $K$ is noetherian and $nil_1$-closed. For $K$ noetherian and $k$ a positive integer, we define $\textbf{C}_k(K)$, the set of so-called central elements of $K$ away from $\mathcal{N}il_

Ziru Chen, Shijie Chen, Michael White, Raymond Mooney

Despite recent progress in text-to-SQL parsing, current semantic parsers are still not accurate enough for practical use. In this paper, we investigate how to build automatic text-to-SQL error correction models. Noticing that token-level edits are out of context and sometimes ambiguous, we propose building clause-level edit models instead. Besides, while mos

Arlind Kadra, Sebastian Pineda Arango, Josif Grabocka

Even though neural networks have been long deployed in applications involving tabular data, still existing neural architectures are not explainable by design. In this paper, we propose a new class of interpretable neural networks for tabular data that are both deep and linear at the same time (i.e. mesomorphic). We optimize deep hypernetworks to generate exp

Yaobo Liang, Quanzhi Zhu, Junhe Zhao, Nan Duan

There are two primary approaches to addressing cross-lingual transfer: multilingual pre-training, which implicitly aligns the hidden representations of various languages, and translate-test, which explicitly translates different languages into an intermediate language, such as English. Translate-test offers better interpretability compared to multilingual pr

Oleg Mushkarov, Nikolai Nikolov

We study the relationship between the areas of the consecutive quadrilaterals cut from a convex quadrilateral in the plane by means of a finite or infinite number of straight lines intersecting two of its opposite sides. Moreover, we obtain a geometric description of all possible areas obtained in this way given the ratios of the lengths of consecutive segme

Ryota Keyaki, Susumu Fukatsu

One-time readout temporal ghost imaging is attempted by utilizing optoelectronic devices that are not originally intended for signal photon detection purposes and as such slow by design. A visible light-emitting diode having a response time $\tau$=0.036 ms and a solar cell with $\tau$=3.1ms are used to retrieve a rectangular pulse train, which is otherwise r

Elena Rozas, Evgeny Sedov, Yannik Brune, Sven Höfling

We take advantage of the polariton bistability in semiconductor microcavities to estimate the interaction strength between lower exciton-polariton and dark exciton states. We combine the quasiresonant excitation of polaritons and the nominally forbidden two-photon excitation (TPE) of dark excitons in a GaAs microcavity. To this end, we use an ultranarrow lin

Shuofei Qiao, Honghao Gui, Chengfei Lv, Qianghuai Jia

Tools serve as pivotal interfaces that enable humans to understand and reshape the environment. With the advent of foundation models, AI systems can utilize tools to expand their capabilities and interact with the real world. Existing tool learning methodologies, encompassing supervised fine-tuning and prompt engineering approaches, often induce large langua

Joe Stacey, Marek Rei

Knowledge distillation optimises a smaller student model to behave similarly to a larger teacher model, retaining some of the performance benefits. While this method can improve results on in-distribution examples, it does not necessarily generalise to out-of-distribution (OOD) settings. We investigate two complementary methods for improving the robustness o

Zihao Fu, Yixuan Su, Zaiqiao Meng, Nigel Collier

Biomedical named entity recognition is one of the core tasks in biomedical natural language processing (BioNLP). To tackle this task, numerous supervised/distantly supervised approaches have been proposed. Despite their remarkable success, these approaches inescapably demand laborious human effort. To alleviate the need of human effort, dictionary-based appr

Maria Han Veiga, Lorenzo Micalizzi, Davide Torlo

The (modern) arbitrary derivative (ADER) approach is a popular technique for the numerical solution of differential problems based on iteratively solving an implicit discretization of their weak formulation. In this work, focusing on an ODE context, we investigate several strategies to improve this approach. Our initial emphasis is on the order of accuracy o

Itai Kreisler, Mor Shpigel Nacson, Daniel Soudry, Yair Carmon

Recent research shows that when Gradient Descent (GD) is applied to neural networks, the loss almost never decreases monotonically. Instead, the loss oscillates as gradient descent converges to its ''Edge of Stability'' (EoS). Here, we find a quantity that does decrease monotonically throughout GD training: the sharpness attained by the gradient flow solutio

Reza Hadi Mogavi, Jennifer Hoffman, Chao Deng, Yiwei Du

The emergence of the metaverse is being widely viewed as a revolutionary technology owing to a myriad of factors, particularly the potential to increase the accessibility of learning for students with disabilities. However, not much is yet known about the views and expectations of disabled students in this regard. The fact that the metaverse is still in its

Christopher Mattern

In this work we consider a new family of algorithms for sequential prediction, Hierarchical Partitioning Forecasters (HPFs). Our goal is to provide appealing theoretical - regret guarantees on a powerful model class - and practical - empirical performance comparable to deep networks - properties at the same time. We built upon three principles: hierarchicall

Yuan Sui, Mengyu Zhou, Mingjie Zhou, Shi Han

Large language models (LLMs) are becoming attractive as few-shot reasoners to solve Natural Language (NL)-related tasks. However, the understanding of their capability to process structured data like tables remains an under-explored area. While tables can be serialized as input for LLMs, there is a lack of comprehensive studies on whether LLMs genuinely comp

T. G. Blackburn, B. King, S. Tang

Accurate modelling is necessary to support precision experiments investigating strong-field QED phenomena. This modelling is particularly challenging in the transition between the perturbative and nonperturbative regimes, where the normalized laser amplitude $a_0$ is comparable to unity and wavelength-scale interference is significant. Here we describe how t

Yu Zheng, Hongyuan Su, Jingtao Ding, Depeng Jin

Millions of slum dwellers suffer from poor accessibility to urban services due to inadequate road infrastructure within slums, and road planning for slums is critical to the sustainable development of cities. Existing re-blocking or heuristic methods are either time-consuming which cannot generalize to different slums, or yield sub-optimal road plans in term

Richard S. J. Tol

I propose the Dominicy-Hill-Worton estimator to estimate the current climate niche of Homo Sapiens and our croplands. I use this to extrapolate the degree of unprecedentedness of future climates. Worton's peeled hull is a non-parametric, N-dimensional generalization of order statistics. Dominicy and colleagues show that Hill's estimator of the tail-index can

Adrian Kochsiek, Apoorv Saxena, Inderjeet Nair, Rainer Gemulla

We propose KGT5-context, a simple sequence-to-sequence model for link prediction (LP) in knowledge graphs (KG). Our work expands on KGT5, a recent LP model that exploits textual features of the KG, has small model size, and is scalable. To reach good predictive performance, however, KGT5 relies on an ensemble with a knowledge graph embedding model, which its

Ilias Chalkidis, Yova Kementchedjhieva

Multi-label text classification (MLC) is a challenging task in settings of large label sets, where label support follows a Zipfian distribution. In this paper, we address this problem through retrieval augmentation, aiming to improve the sample efficiency of classification models. Our approach closely follows the standard MLC architecture of a Transformer-ba

Zhenlan Ji, Pingchuan Ma, Shuai Wang, Yanhui Li

There has been an increasing interest in enhancing the fairness of machine learning (ML). Despite the growing number of fairness-improving methods, we lack a systematic understanding of the trade-offs among factors considered in the ML pipeline when fairness-improving methods are applied. This understanding is essential for developers to make informed decisi

Yuhei Suzuki

Associated to a family of $G$-$\ast$-endomorphisms on a $G$-C*-algebra $A$ satisfying certain minimality conditions, we give a $G$-C*-correspondence $\mathcal{E}$ over $A$ whose Cuntz--Pimsner algebra $\mathcal{O}_\mathcal{E}$ is simple. For certain quasi-free flows $\gamma$ (commuting with the $G$-action) on $\mathcal{O}_\mathcal{E}$, we further prove the s

Jonas Kühne, Michele Magno, Luca Benini

Optical flow estimation is crucial for autonomous navigation and localization of unmanned aerial vehicles (UAV). On micro and nano UAVs, real-time calculation of the optical flow is run on low power and resource-constrained microcontroller units (MCUs). Thus, lightweight algorithms for optical flow have been proposed targeting real-time execution on traditio

Diego Goldsztajn, Sem C. Borst, Johan S. H. van Leeuwaarden

Consider a network of $n$ single-server queues where tasks arrive independently at each server at rate $\lambda_n$. The servers are connected by a graph that is resampled at rate $\mu_n$ in a way that is symmetric with respect to the servers, and each task is dispatched to the shortest queue in the graph neighborhood where it appears. We aim to gain insight

Mariia Savchenko, Igor Skrypnik, Yevgeniia Yevgenieva

In the case $q> p\dfrac{n+2}{n}$, we give a proof of the weak Harnack inequality for non-negative super-solutions of degenerate double-phase parabolic equations under the additional assumption that $u\in L^{s}_{loc}(\Omega_{T})$ with some $s >p\dfrac{n+2}{n}$.

Ofir Ben Shoham, Nadav Rappoport

Electronic Health Records (EHR) data contains medical records such as diagnoses, medications, procedures, and treatments of patients. This data is often considered sensitive medical information. Therefore, the EHR data from the medical centers often cannot be shared, making it difficult to create prediction models using multi-center EHR data, which is essent

Jia Huang, Alvika Gautam, Srikanth Saripalli

To ensure safe autonomous driving in urban environments with complex vehicle-pedestrian interactions, it is critical for Autonomous Vehicles (AVs) to have the ability to predict pedestrians' short-term and immediate actions in real-time. In recent years, various methods have been developed to study estimating pedestrian behaviors for autonomous driving scena

Guy Yariv, Itai Gat, Lior Wolf, Yossi Adi

In recent years, image generation has shown a great leap in performance, where diffusion models play a central role. Although generating high-quality images, such models are mainly conditioned on textual descriptions. This begs the question: "how can we adopt such models to be conditioned on other modalities?". In this paper, we propose a novel method utiliz

María Camarasa-Gómez, Ashwin Ramasubramaniam, Jeffrey B. Neaton, Leeor Kronik

The accurate description of electronic properties and optical absorption spectra is a long-standing challenge for density functional theory. Recently, the introduction of screened range-separated hybrid (SRSH) functionals for solid-state materials has allowed for the calculation of fundamental band gaps and optical absorption spectra that are in very good ag

Lucas Potin, Vincent Labatut, Pierre-Henri Morand, Christine Largeron

Public Procurement refers to governments' purchasing activities of goods, services, and construction of public works. In the European Union (EU), it is an essential sector, corresponding to 15% of the GDP. EU public procurement generates large amounts of data, because award notices related to contracts exceeding a predefined threshold must be published on th

Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak

Transformers have revolutionized almost all natural language processing (NLP) tasks but suffer from memory and computational complexity that scales quadratically with sequence length. In contrast, recurrent neural networks (RNNs) exhibit linear scaling in memory and computational requirements but struggle to match the same performance as Transformers due to

Mark Mets, Andres Karjus, Indrek Ibrus, Maximilian Schich

Automated stance detection and related machine learning methods can provide useful insights for media monitoring and academic research. Many of these approaches require annotated training datasets, which limits their applicability for languages where these may not be readily available. This paper explores the applicability of large language models for automa

Sang-Yeong Jo, Sung Whan Yoon

Handling out-of-distribution samples is a long-lasting challenge for deep visual models. In particular, domain generalization (DG) is one of the most relevant tasks that aims to train a model with a generalization capability on novel domains. Most existing DG approaches share the same philosophy to minimize the discrepancy between domains by finding the doma

Mihai Marciu, Dana Maria Ioan

In the present manuscript the basic Einstein--Hilbert cosmological model is extended, by adding a new functional $F(G, T_{\mu\nu}T^{\mu\nu})$ in the fundamental action, encoding specific geometrical effects due to a nontrivial coupling with the Gauss-Bonnet invariant ($G$), and the energy--momentum squared term ($T_{\mu\nu}T^{\mu\nu}$). After obtaining the c

Sofía Llavayol, Juliana Xavier

In this work we show that every quotient of a torus endomorphism has a parabolic orbifold. This answers a question of Mario Bonk and Daniel Meyer posed in their book "Expanding Thurston maps".

Zaixi Zhang, Qi Liu

Generating molecules with high binding affinities to target proteins (a.k.a. structure-based drug design) is a fundamental and challenging task in drug discovery. Recently, deep generative models have achieved remarkable success in generating 3D molecules conditioned on the protein pocket. However, most existing methods consider molecular generation for prot

Lana Sinapayen

This paper reports on patterns exhibiting self-replication with spontaneous, inheritable mutations and exponential genetic drift in Neural Cellular Automata. Despite the models not being explicitly trained for mutation or inheritability, the descendant patterns exponentially drift away from ancestral patterns, even when the automaton is deterministic. While

Zhuojun Tian, Zhaoyang Zhang, Zhaohui Yang, Richeng Jin

In conventional distributed learning over a network, multiple agents collaboratively build a common machine learning model. However, due to the underlying non-i.i.d. data distribution among agents, the unified learning model becomes inefficient for each agent to process its locally accessible data. To address this problem, we propose a graph-attention-based

Shuzheng Si, Wentao Ma, Haoyu Gao, Yuchuan Wu

Task-oriented dialogue (TOD) models have made significant progress in recent years. However, previous studies primarily focus on datasets written by annotators, which has resulted in a gap between academic research and real-world spoken conversation scenarios. While several small-scale spoken TOD datasets are proposed to address robustness issues such as ASR

Mohamed Gaidi, Mounir Bedhiafi

In Dunkl theory on $\mathbb{R}^{n}$ which generalizes classical Fourier analysis, we study the solution of the Klein-Gordon-equation defined by: \begin{eqnarray} \nonumber \partial_{t}^{2}u-\Delta_{k}u=-m^{2}u \ , \ \ \ u (x,0)=g(x) \ , \ \ \ \partial_{t}u(x,0)=f(x) \end{eqnarray} with \ $m > 0$ \ and \ $\partial_{t}^{2}u$ \ is the second derivative of the s

Kathrin Bringmann, Ben Kane, Srimathi Varadharajan

In this paper, we construct generalized $L$-functions associated to meromorphic modular forms of weight $\frac12$ for the theta group with a single simple pole in the fundamental domain. We then consider their behaviour towards $i\infty$ and relate this to the Riemann zeta function.

Pablo A. Ferrari, Stefano Olla

We study the fluctuations in equilibrium for a dynamics of rods with random length. This includes the classical hard rod elastic collisions, when rod lengths are constant and equal to a positive value. We prove that in the diffusive space-time scaling, an initial fluctuation of density of particles of velocity $v$, after recentering on its Euler evolution, e

Jinliang Deng, Xiusi Chen, Renhe Jiang, Du Yin

Multivariate time-series (MTS) forecasting is a paramount and fundamental problem in many real-world applications. The core issue in MTS forecasting is how to effectively model complex spatial-temporal patterns. In this paper, we develop a adaptive, interpretable and scalable forecasting framework, which seeks to individually model each component of the spat

Ibrahim Alabdulmohsin, Xiaohua Zhai, Alexander Kolesnikov, Lucas Beyer

Scaling laws have been recently employed to derive compute-optimal model size (number of parameters) for a given compute duration. We advance and refine such methods to infer compute-optimal model shapes, such as width and depth, and successfully implement this in vision transformers. Our shape-optimized vision transformer, SoViT, achieves results competitiv

Ruize Gao, Zhirui Zhang, Yichao Du, Lemao Liu

Nearest Neighbor Machine Translation ($k$NN-MT) has achieved great success in domain adaptation tasks by integrating pre-trained Neural Machine Translation (NMT) models with domain-specific token-level retrieval. However, the reasons underlying its success have not been thoroughly investigated. In this paper, we comprehensively analyze $k$NN-MT through theor

Konstantin Gasenzer, Moritz Wolter

Today's generative neural networks allow the creation of high-quality synthetic speech at scale. While we welcome the creative use of this new technology, we must also recognize the risks. As synthetic speech is abused for monetary and identity theft, we require a broad set of deepfake identification tools. Furthermore, previous work reported a limited abili

Jirka Poropudas, Topi Halme

This paper studies the relationship between basketball teams' four factors and efficiency ratings as defined by Oliver (2004). The paper introduces an equation showing how a team's four factors in conjunction with its field goal and free throw percentages can be used to calculate its offensive rating. Moreover, the substitution of defensive four factors into

Jian Ding, Nan Xue, Gui-Song Xia, Bernt Schiele

Current semantic segmentation models have achieved great success under the independent and identically distributed (i.i.d.) condition. However, in real-world applications, test data might come from a different domain than training data. Therefore, it is important to improve model robustness against domain differences. This work studies semantic segmentation

Keisuke Fujii, Kazushi Tsutsui, Atom Scott, Hiroshi Nakahara

Modeling of real-world biological multi-agents is a fundamental problem in various scientific and engineering fields. Reinforcement learning (RL) is a powerful framework to generate flexible and diverse behaviors in cyberspace; however, when modeling real-world biological multi-agents, there is a domain gap between behaviors in the source (i.e., real-world d

Micheal W Reeks

Nationally approved emission factors of mass versus particle size for particulate emissions from UK MSW incinerators when converted to particle number versus size, indicate that nearly all (>90\%) of the emitted particles are ultra fine particles (ufps) < .1 micron in size. A similar result is true also of US MSW incinerators emissions. This would imply that

A. Ingram, M. Ewing, A. Marinucci, D. Tagliacozzo

We present an X-ray spectro-polarimetric analysis of the bright Seyfert galaxy IC 4329A. The Imaging X-ray Polarimetry Explorer (IXPE) observed the source for ~500 ks, supported by XMM-Newton (~60 ks) and NuSTAR (~80 ks) exposures. We detect polarisation in the 2-8 keV band with 2.97 sigma confidence. We report a polarisation degree of $3.3\pm1.1$ per cent a

Alexander L. Gavrilyuk, Sho Suda

It follows from Delsarte theory that the Witt $4$-$(11,5,1)$ design gives rise to a $Q$-polynomial association scheme $\mathcal{W}$ defined on the set of its blocks. In this note we show that $\mathcal{W}$ is unique, i.e., defined up to isomorphism by its parameters.

Wietse de Vries, Martijn Wieling, Malvina Nissim

We introduce the Dutch Model Benchmark: DUMB. The benchmark includes a diverse set of datasets for low-, medium- and high-resource tasks. The total set of nine tasks includes four tasks that were previously not available in Dutch. Instead of relying on a mean score across tasks, we propose Relative Error Reduction (RER), which compares the DUMB performance o

Tokuhiro Eto, Yoshikazu Giga

We introduce a capillary Chambolle type scheme for mean curvature flow with prescribed contact angle. Our scheme includes a capillary functional instead of just the total variation. We show that the scheme is well-defined and has consistency with the energy minimizing scheme of Almgren-Taylor-Wang type. Moreover, for a planar motion in a strip, we give sever

Fabienne Prosmans, François Renaville

Over the last few years, the interlibrary loan (ILL) service of the University of Li\`ege Library has evolved considerably, both in terms of habits and workflows. In this article, we will explain the main stages of this evolution: (1) first reduction in the number of ILL units (from eight to five) and involved operators (from 15 to 10) within the homemade IL

Kentaro Kasai, Masahiro Kawasaki, Naoya Kitajima, Kai Murai

We study the clustering of primordial black holes (PBHs) and axion miniclusters produced in the model proposed to explain the LIGO/Virgo events or the seeds of the supermassive black holes (SMBHs) in arXiv:2006.13137. It is found that this model predicts large isocurvature perturbations due to the clustering of PBHs and axion miniclusters, from which we obta

Hongyan Liu, Oscar van der Heide, Edwin Versteeg, Martijn Froeling

Magnetic Resonance Spin Tomography in Time-Domain (MR-STAT) is a multiparametric quantitative MR framework, which allows for simultaneously acquiring quantitative tissue parameters such as T1, T2 and proton density from one single short scan. A typical 2D MR-STAT acquisition uses a gradient-spoiled, gradient-echo sequence with a slowly varying RF flip-angle

Ambre Davat, Véronique Aubergé, Gang Feng

When human listeners try to guess the spatial position of a speech source, they are influenced by the speaker's production level, regardless of the intensity level reaching their ears. Because the perception of distance is a very difficult task, they rely on their own experience, which tells them that a whispering talker is close to them, and that a shouting

Barry C. Arnold, Sachin Sachdeva, B. G. Manjunath

It is known that all the proportional reversed hazard (PRH) processes can be de?rived by a marginal transformation applied to a power function distribution (PFD) process. Kundu [8] investigated PRH processes that can be viewed as being ob?tained by marginal transformations applied to a particular PFD process that will be described and investigated and will b

Zihao Zhang, Susan L. Epstein, Casey Breen, Sophia Xia

This paper introduces ELUA, the Ecological Laboratory for Urban Agriculture, a collaboration among landscape architects, architects and computer scientists who specialize in artificial intelligence, robotics and computer vision. ELUA has two gantry robots, one indoors and the other outside on the rooftop of a 6-story campus building. Each robot can seed, wat

Moritz A. Goerzen, Stephan von Malottki, Sebastian Meyer, Pavel F. Bessarab

Magnetic skyrmions have raised high hopes for future spintronic devices. For many applications it would be of great advantage to have more than one metastable particle-like texture available. The coexistence of skyrmions and antiskyrmions has been proposed in inversion symmetric magnets with exchange frustration. However, so far only model systems have been

Dorota I. Walicka, Robin Lefevre, Olivier Blacque, Sara A. Lopez-Paz

We report on the structural and superconducting properties of the intermetallic compound BaGaGe. We find that this material undergoes a structural second-order phase transition from the distorted AlB$_2$-type structure (1H, $a$ = 4.3254(2) \r{A}, $c$ = 5.1078(3) \r{A}, P6/mmm) into the CaIn$_2$-type structure (2H, $a$ = 4.3087(3) \r{A}, $c$ = 10.2117(6) \r{A