May 2023 arXiv papers — page 95

Yingxiu Zhao, Bowen Yu, Haiyang Yu, Bowen Li

The goal of document-grounded dialogue (DocGD) is to generate a response by grounding the evidence in a supporting document in accordance with the dialogue context. This process involves four variables that are causally connected. Recently, task-specific pre-training has greatly boosted performances on many downstream tasks. Existing DocGD methods, however,

Aiden Durrant, Georgios Leontidis

Hyperbolic manifolds for visual representation learning allow for effective learning of semantic class hierarchies by naturally embedding tree-like structures with low distortion within a low-dimensional representation space. The highly separable semantic class hierarchies produced by hyperbolic learning have shown to be powerful in low-shot tasks, however,

Xiangyu Rui, Xiangyong Cao, Li Pang, Zeyu Zhu

Hyperspectral pansharpening is a process of merging a high-resolution panchromatic (PAN) image and a low-resolution hyperspectral (LRHS) image to create a single high-resolution hyperspectral (HRHS) image. Existing Bayesian-based HS pansharpening methods require designing handcraft image prior to characterize the image features, and deep learning-based HS pa

Gongfan Fang, Xinyin Ma, Xinchao Wang

Generative modeling has recently undergone remarkable advancements, primarily propelled by the transformative implications of Diffusion Probabilistic Models (DPMs). The impressive capability of these models, however, often entails significant computational overhead during both training and inference. To tackle this challenge, we present Diff-Pruning, an effi

Chuan Meng, Negar Arabzadeh, Mohammad Aliannejadi, Maarten de Rijke

Query performance prediction (QPP) is a core task in information retrieval. The QPP task is to predict the retrieval quality of a search system for a query without relevance judgments. Research has shown the effectiveness and usefulness of QPP for ad-hoc search. Recent years have witnessed considerable progress in conversational search (CS). Effective QPP co

Sergio Cabello, Panos Giannopoulos

We study the problem of $k$-means clustering in the space of straight-line segments in $\mathbb{R}^{2}$ under the Hausdorff distance. For this problem, we give a $(1+\epsilon)$-approximation algorithm that, for an input of $n$ segments, for any fixed $k$, and with constant success probability, runs in time $O(n+ \epsilon^{-O(k)} + \epsilon^{-O(k)}\cdot \log^

Eric M. Friedlander

We investigate infinite dimensional modules for an affine group scheme $\mathbb G$ of finite type over a field of positive characteristic $p$. For any subspace $X \subset \mathcal O(\mathbb G)$ of the coordinate algebra of $\mathbb G$, we consider the abelian subcategory $Mod(\mathbb G,X) \subset Mod(\mathbb G)$ of ``$X$-comodules" and the left exact functor

Ryokan Ri, Ryo Ueda, Jason Naradowsky

To develop computational agents that better communicate using their own emergent language, we endow the agents with an ability to focus their attention on particular concepts in the environment. Humans often understand an object or scene as a composite of concepts and those concepts are further mapped onto words. We implement this intuition as cross-modal at

Konstantinos Makantasis, Kosmas Pinitas, Antonios Liapis, Georgios N. Yannakakis

How can we reliably transfer affect models trained in controlled laboratory conditions (in-vitro) to uncontrolled real-world settings (in-vivo)? The information gap between in-vitro and in-vivo applications defines a core challenge of affective computing. This gap is caused by limitations related to affect sensing including intrusiveness, hardware malfunctio

Svenja Ehlers, Marco Klein, Alexander Heinlein, Mathies Wedler

Accurate short-term predictions of phase-resolved water wave conditions are crucial for decision-making in ocean engineering. However, the initialization of remote-sensing-based wave prediction models first requires a reconstruction of wave surfaces from sparse measurements like radar. Existing reconstruction methods either rely on computationally intensive

Zherui Han, Zixin Xiong, William T. Riffe, Hunter B. Schonfeld

The lattice thermal conductivity ($\kappa$) of two ceramic materials, cerium dioxide (CeO$_2$) and magnesium oxide (MgO), is computed up to 1500 K using first principles and the phonon Boltzmann Transport Equation (PBTE) and compared to time-domain thermoreflectance (TDTR) measurements up to 800 K. Phonon renormalization and the four-phonon effect, along wit

Heng-Tong Ding, Wei-Ping Huang, Swagato Mukherjee, Peter Petreczky

Macroscopic properties of the strong interaction near its chiral phase transition exhibit scaling behaviors, which are the same as those observed close to the magnetic transition in a 3-dimensional classical spin system with $O(4)$ symmetry. We show that the universal scaling properties of the chiral phase transition in Quantum Chromodynamics (QCD) at the ma

E. Neres Júnior, J. C. C. Felipe, A. P. Baêta Scarpelli

In this work, we study a model in nonlinear electrodynamics in the presence of a CPT-even term that violates Lorentz symmetry. The Lorentz-breaking vector, in addition to the usual background magnetic field, produces interesting effects in the dispersion relations. The consequences on the vacuum refractive index and the group velocity are studied. Vacuum bir

Davide Rigoni, Luca Parolari, Luciano Serafini, Alessandro Sperduti

Using only image-sentence pairs, weakly-supervised visual-textual grounding aims to learn region-phrase correspondences of the respective entity mentions. Compared to the supervised approach, learning is more difficult since bounding boxes and textual phrases correspondences are unavailable. In light of this, we propose the Semantic Prior Refinement Model (S

Ingmar Schubert, Jingwei Zhang, Jake Bruce, Sarah Bechtle

We investigate the use of transformer sequence models as dynamics models (TDMs) for control. We find that TDMs exhibit strong generalization capabilities to unseen environments, both in a few-shot setting, where a generalist TDM is fine-tuned with small amounts of data from the target environment, and in a zero-shot setting, where a generalist TDM is applied

Francesco Cesarone, Rosella Giacometti, Jacopo Maria Ricci

In this paper, we propose an outlier detection algorithm for multivariate data based on their projections on the directions that maximize the Cumulant Generating Function (CGF). We prove that CGF is a convex function, and we characterize the CGF maximization problem on the unit n-circle as a concave minimization problem. Then, we show that the CGF maximizati

Kristiina Rahkema, Dietmar Pfahl, Rudolf Ramler

Reusing existing solutions in the form of third-party libraries is common practice when writing software. Package managers are used to manage dependencies to third-party libraries by automating the process of installing and updating the libraries. Library dependencies themselves can have dependencies to other libraries creating a dependency network with seve

Tanvirul Islam, Anindya Banerji, Chin Jia Boon, Wang Rui

Verifying the quality of a random number generator involves performing computationally intensive statistical tests on large data sets commonly in the range of gigabytes. Limitations on computing power can restrict an end-user's ability to perform such verification. There are also applications where the user needs to publicly demonstrate that the random bits

R. Inanc Baykur, Noriyuki Hamada

We produce infinitely many distinct irreducible smooth 4-manifolds homeomorphic to #(2m+1)(CP^2 # -CP^2) and #(2n+1)(S^2 x S^2), respectively, for each m>3 and n>4. These provide the smallest exotic closed simply connected 4-manifolds with signature zero known to date, and in each one of these homeomorphism classes, we get minimal symplectic 4-manifolds. Our

Xinze Li, Yixin Cao, Muhao Chen, Aixin Sun

Goal-oriented Script Generation is a new task of generating a list of steps that can fulfill the given goal. In this paper, we propose to extend the task from the perspective of cognitive theory. Instead of a simple flat structure, the steps are typically organized hierarchically - Human often decompose a complex task into subgoals, where each subgoal can be

Xuran Li, Peng Wu, Kaixiang Dong, Zhen Zhang

Deep neural networks (DNNs) often face challenges due to their vulnerability to various adversarial perturbations, including false perturbations that undermine prediction accuracy and biased perturbations that cause biased predictions for similar inputs. This paper introduces a novel approach, RobustFair, to evaluate the accurate fairness of DNNs when subjec

Daniele Cassani, Lele Du, Zhisu Liu

In this paper we study the following nonlinear Choquard equation $$ -\Delta u+u=\left(\ln\frac{1}{|x|}\ast F(u)\right)f(u),\quad\text{ in }\,\mathbb{R}^2, $$ where $f\in C^1(\mathbb{R})$ and $F$ is the primitive of the nonlinearity $f$ vanishing at zero. We use an asymptotic approximation approach to establish the existence of positive solutions to the above

Nikolaos Kidonakis, Nodoka Yamanaka

We present theoretical results for the associated production of a single top quark and a $Z$ boson ($tqZ$ production) at LHC energies. We calculate higher-order corrections from soft-gluon emission for this process. We compute the approximate NNLO (aNNLO) cross section at LHC energies, including uncertainties from scale dependence and from parton distributio

Leo Versteegen

A graph $H$ is called strongly common if for every coloring $\phi$ of $K_n$ with two colors, the number of monochromatic copies of $H$ is at least the number of monochromatic copies of $H$ in a random coloring of $K_n$ with the same density of color classes as $\phi$. In this note we prove that if a graph has odd girth but is not a cycle, then it is not stro

Marcin Kurpas

We study spin-orbit proximity effects in a hybrid heterostructure build of a one-dimensional (1D) armchair carbon nanotube and two-dimensional (2D) buckled monolayer bismuthene. We show, by performing first-principles calculations, that Dirac electrons in the nanotube exhibit large spin-orbit coupling due to a close vicinity of bismuthene. The calculated low

Giacomo Falcucci, Giorgio Amati, Pierluigi Fanelli, Vesselin K. Krastev

Since its discovery, the deep-sea glass sponge Euplectella aspergillum has attracted interest in its mechanical properties and beauty. Its skeletal system is composed of amorphous hydrated silica and is arranged in a highly regular and hierarchical cylindrical lattice that begets exceptional flexibility and resilience to damage. Structural analyses dominate

Günter Rote

We survey a few strengthenings and generalizations of the Combinatorial Nullstellensatz of Alon and the Schwartz-Zippel Lemma. These lemmas guarantee the existence of (a certain number of) nonzeros of a multivariate polynomial when the variables run independently through sufficiently large ranges.

Deyi Ji, Feng Zhao, Hongtao Lu, Mingyuan Tao

With the increasing interest and rapid development of methods for Ultra-High Resolution (UHR) segmentation, a large-scale benchmark covering a wide range of scenes with full fine-grained dense annotations is urgently needed to facilitate the field. To this end, the URUR dataset is introduced, in the meaning of Ultra-High Resolution dataset with Ultra-Rich Co

Heiner Kremer, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu

Moment restrictions and their conditional counterparts emerge in many areas of machine learning and statistics ranging from causal inference to reinforcement learning. Estimators for these tasks, generally called methods of moments, include the prominent generalized method of moments (GMM) which has recently gained attention in causal inference. GMM is a spe

Conrad B. Tabi, Camus G. Latchio Tiofack, Hippolyte Tagwo, Timoléon C. Kofané

This paper analyzes the behaviors of solitons in even higher-order dispersive media and explores the modulational instability phenomenon in optical media. The analysis considers quadratic, quartic, and sextic dispersions with weakly nonlocal Kerr nonlinearity. The results show that nonlocality enhances the MI gain and leads to rogue waves in response to diff

Aitor Garcia-Ruiz, Vladimir Enaldiev, Andrew McEllistrim, Vladimir I. Fal'ko

Ferroelectricity (Valasek, J. Phys. Rev. 1921, 17, 475) - a spontaneous formation of electric polarisation - is a solid state phenomenon, usually, associated with ionic compounds or complex materials. Here we show that, atypically for elemental solids, few-layer graphenes can host an equilibrium out-of-plane electric polarisation, switchable by sliding the c

Hang Chen, Yaru Wang

In this paper, we study the rigidity of $k(\ge 1)$-extremal submanifolds in a sphere and prove various pinching theorems under different curvature conditions, including sectional and Ricci curvatures in pointwise and integral sense.

ATLAS Collaboration

A search for physics beyond the Standard Model inducing periodic signals in the dielectron and diphoton invariant mass spectra is presented using 139 fb$^{-1}$ of $\sqrt{s}=13$ TeV $pp$ collision data collected by the ATLAS experiment at the LHC. Novel search techniques based on continuous wavelet transforms are used to infer the frequency of periodic signal

Mengyang Yuan, Bo Lang, Fengnan Quan

In knowledge distillation, the knowledge from the teacher model is often too complex for the student model to thoroughly process. However, good teachers in real life always simplify complex material before teaching it to students. Inspired by this fact, we propose student-friendly knowledge distillation (SKD) to simplify teacher output into new knowledge rep

Marco Rovera

This paper introduces EventNet-ITA, a large, multi-domain corpus annotated full-text with event frames for Italian. Moreover, we present and thoroughly evaluate an efficient multi-label sequence labeling approach for Frame Parsing. Covering a wide range of individual, social and historical phenomena, with more than 53,000 annotated sentences and over 200 mod

Yusheng Tian, Wei Liu, Tan Lee

Creating synthetic voices with found data is challenging, as real-world recordings often contain various types of audio degradation. One way to address this problem is to pre-enhance the speech with an enhancement model and then use the enhanced data for text-to-speech (TTS) model training. This paper investigates the use of conditional diffusion models for

Simon Salleh Atri, Wei Cao, Bar Alon, Nirmal Roy

A crystalline solid is a periodic sequence of identical cells, each containing one or more atoms. If the constituting unit cell is not centrosymmetric, charge may distribute unevenly between the atoms, resulting in internal electric polarization. This effect serves as the basis for numerous ferroelectric, piezoelectric, and pyroelectric phenomena. In nearly

Mustafa Ozcan, Hamza Ergezer, Mustafa Ayazaoglu

Low-light image enhancement (LLIE) is an ill-posed inverse problem due to the lack of knowledge of the desired image which is obtained under ideal illumination conditions. Low-light conditions give rise to two main issues: a suppressed image histogram and inconsistent relative color distributions with low signal-to-noise ratio. In order to address these prob

Rosalie Iemhoff, Raheleh Jalali

These lecture notes survey the emerging area of Universal Proof Theory, which investigates general questions about the existence, equivalence, and characterization of good proof systems for broad classes of logics. In particular, the notes concentrate on the existence problem: for which logics do there exist proof systems satisfying desirable meta-properties

Priyanka Maity, Kunwar Pritiraj Rajput, Suraj Srivastava, Naveen K. D. Venkategowda

Hybrid transceivers are designed for linear decentralized estimation (LDE) in a mmWave multiple-input multiple-output (MIMO) IoT network (IoTNe). For a noiseless fusion center (FC), it is demonstrated that the MSE performance is determined by the number of RF chains used at each IoT node (IoTNo). Next, the minimum-MSE RF transmit precoders (TPCs) and receive

Zeyu Sun, Dogyoon Song, Alfred Hero

Recalibrating probabilistic classifiers is vital for enhancing the reliability and accuracy of predictive models. Despite the development of numerous recalibration algorithms, there is still a lack of a comprehensive theory that integrates calibration and sharpness (which is essential for maintaining predictive power). In this paper, we introduce the concept

Yun Liao, Ruihui Xu

This study employs the annual unbalanced panel data of 42 listed banks in China from 2006 to 2021, adopts the non-radial and non-oriented super-efficiency Data envelopment analysis (Super-SBM-UND-VRS based DEA) model considering NPL as undesired output. Our results show that the profitability super-efficiency of State-owned banks and Rural/City Commercial Ba

Bangrui Jiang, Zhenhua Guo, Yujiu Yang

Real-world image manipulation has achieved fantastic progress in recent years. GAN inversion, which aims to map the real image to the latent code faithfully, is the first step in this pipeline. However, existing GAN inversion methods fail to achieve high reconstruction quality and fast inference at the same time. In addition, existing methods are built on 2D

Guankun Wang, Tian-Ao Ren, Jiewen Lai, Long Bai

Video-assisted transoral tracheal intubation (TI) necessitates using an endoscope that helps the physician insert a tracheal tube into the glottis instead of the esophagus. The growing trend of robotic-assisted TI would require a medical robot to distinguish anatomical features like an experienced physician which can be imitated by utilizing supervised deep-

Luigi Sigillo, Eleonora Grassucci, Danilo Comminiello

This paper addresses the problem of translating night-time thermal infrared images, which are the most adopted image modalities to analyze night-time scenes, to daytime color images (NTIT2DC), which provide better perceptions of objects. We introduce a novel model that focuses on enhancing the quality of the target generation without merely colorizing it. Th

Abheek Ghosh, Paul W. Goldberg

We study the convergence of best-response dynamics in lottery contests. We show that best-response dynamics rapidly converges to the (unique) equilibrium for homogeneous agents but may not converge for non-homogeneous agents, even for two non-homogeneous agents. For $2$ homogeneous agents, we show convergence to an $\epsilon$-approximate equilibrium in $\The

Xinyu Li, Jianjun Xu, Wenquan Cui, Haoyang Cheng

Considering the case where the response variable is a categorical variable and the predictor is a random function, two novel functional sufficient dimensional reduction (FSDR) methods are proposed based on mutual information and square loss mutual information. Compared to the classical FSDR methods, such as functional sliced inverse regression and functional

Arjun Berera

Warm inflation has normalized two ideas in cosmology, that in the early universe the initial primordial density perturbations generally could be of classical rather than quantum origin and that during inflation, particle production from interactions amongst quantum field, and its backreaction effects, can occur concurrent with inflationary expansion. When we

Rakesh Arora, Sergey Shmarev

We consider the homogeneous Dirichlet problem for the parabolic equation \[ u_t- \operatorname{div} \left(|\nabla u|^{p(x,t)-2} \nabla u\right)= f(x,t) + F(x,t, u, \nabla u) \] in the cylinder $Q_T:=\Omega\times (0,T)$, where $\Omega\subset \mathbb{R}^N$, $N\geq 2$, is a $C^{2}$-smooth or convex bounded domain. It is assumed that $p\in C^{0,1}(\overline{Q}_T

Weihao Shen, Yalei Huang, Xinyu Yao, Fangyi Qi

We report the discovery of a giant out-of-plane coercivity in the Fe-doped Mn3Sn single crystals. The compound of Mn2.42Fe0.58Sn exhibits a series of magnetic transitions accompanying with large magnetic anisotropy and electric transport properties. Compared with the ab-plane easy axis in Mn3Sn, it switches to the c-axis in Mn2.42Fe0.58Sn, producing a suffic

Natalia E. Kopteva, Dmitri R. Yakovlev, Eyüp Yalcin, Ilya A. Akimov

Optical orientation of carrier spins by circularly polarized light is the basis of spin physics in semiconductors. Here, we demonstrate strong optical orientation of 85\%, approaching the ultimate limit of unity, for excitons in FA$_{0.9}$Cs$_{0.1}$PbI$_{2.8}$Br$_{0.2}$ lead halide perovskite bulk crystals. Time-resolved photoluminescence allows us to distin

Wenjing Wang, Huan Yang, Zixi Tuo, Huiguo He

With the explosive popularity of AI-generated content (AIGC), video generation has recently received a lot of attention. Generating videos guided by text instructions poses significant challenges, such as modeling the complex relationship between space and time, and the lack of large-scale text-video paired data. Existing text-video datasets suffer from limi

Xiaopeng Si, Dong Huang, Yulin Sun, Dong Ming

Emotion recognition plays a crucial role in human-computer interaction, and electroencephalography (EEG) is advantageous for reflecting human emotional states. In this study, we propose MACTN, a hierarchical hybrid model for jointly modeling local and global temporal information. The model is inspired by neuroscience research on the temporal dynamics of emot

Chao Liu

Nobel Prize laureate P.J.E. Peebles [24] has emphasized the importance and difficulties of studying the large scale clustering of matter in cosmology. Nonlinear gravitational instability plays a central role in understanding the clustering of matter and the formation of nonlinear structures in the universe and stellar systems. However, there is no rigorous r

Wojciech G. Stark, Julia Westermayr, Oscar A. Douglas-Gallardo, James Gardner

Reactive chemistry of molecular hydrogen at surfaces, notably dissociative sticking and hydrogen evolution, plays a crucial role in energy storage and fuel cells. Theoretical studies can help to decipher underlying mechanisms and reaction design, but studying dynamics at surfaces is computationally challenging due to the complex electronic structure at inter

Vitaly Aksenov, Dmitry Ivanov, Ravil Galiev

In this work, we present a new benchmarking suite with new real-life inspired skewed workloads to test the performance of concurrent index data structures. We started this project to prepare workloads specifically for self-adjusting data structures, i.e., they handle more frequent requests faster, and, thus, should perform better than their standard counterp

D. Bricalli, F. F. Favale, G. P. Pirola

In this paper, we analyze the Hessian locus associated to a general cubic hypersurface, by describing for every $n$ its singular locus and its desingularization. The strategy is based on strong connections between the Hessian and the quadrics defined as partial derivatives of the cubic polynomial. In particular, we focus our attention on the singularities of

Nimesh Agrawal, Nikita Malik, Sandeep Kumar

Learning on graphs is becoming prevalent in a wide range of applications including social networks, robotics, communication, medicine, etc. These datasets belonging to entities often contain critical private information. The utilization of data for graph learning applications is hampered by the growing privacy concerns from users on data sharing. Existing pr

Yuan Zhou, Xin Chen, Yanrong Guo, Shijie Hao

Incremental few-shot semantic segmentation (IFSS) aims to incrementally extend a semantic segmentation model to novel classes according to only a few pixel-level annotated data, while preserving its segmentation capability on previously learned base categories. This task faces a severe semantic-aliasing issue between base and novel classes due to data imbala

Borja Balle, James Bell, Adrià Gascón

Motivated by recent developments in the shuffle model of differential privacy, we propose a new approximate shuffling functionality called Alternating Shuffle, and provide a protocol implementing alternating shuffling in a single-server threat model where the adversary observes all communication. Unlike previous shuffling protocols in this threat model, the

Wei Xiang, Chao Liang, Bang Wang

Implicit Discourse Relation Recognition (IDRR) aims at classifying the relation sense between two arguments without an explicit connective. Recently, the ConnPrompt~\cite{Wei.X:et.al:2022:COLING} has leveraged the powerful prompt learning for IDRR based on the fusion of multi-prompt decisions from three different yet much similar connective prediction templa

Wenhao Li, Dan Qiao, Baoxiang Wang, Xiangfeng Wang

The difficulty of appropriately assigning credit is particularly heightened in cooperative MARL with sparse reward, due to the concurrent time and structural scales involved. Automatic subgoal generation (ASG) has recently emerged as a viable MARL approach inspired by utilizing subgoals in intrinsically motivated reinforcement learning. However, end-to-end l

Muhammad Al-Zafar Khan, Riven Narain

We derive the Lie point symmetries for the MIT Bag Model for quark stars in relativistic astrophysics. Four cases of reduction arise; three cases of specific values of the measure of the anisotropy variation, and one general case, which we postulate as a specific relationship between the two gravitational potentials. We demonstrate the applicability of the m

Zeyuan Tan, Xiulong Yuan, Congjie He, Man-Kit Sit

Systems for serving inference requests on graph neural networks (GNN) must combine low latency with high throughout, but they face irregular computation due to skew in the number of sampled graph nodes and aggregated GNN features. This makes it challenging to exploit GPUs effectively: using GPUs to sample only a few graph nodes yields lower performance than

Joana C. Costa, Tiago Roxo, Hugo Proença, Pedro R. M. Inácio

Deep Learning is currently used to perform multiple tasks, such as object recognition, face recognition, and natural language processing. However, Deep Neural Networks (DNNs) are vulnerable to perturbations that alter the network prediction (adversarial examples), raising concerns regarding its usage in critical areas, such as self-driving vehicles, malware

Soham Gokhale, Utpal Manna

We consider the stochastic Landau-Lifshitz-Bloch equation in dimensions 1,2,3, perturbed by a real-valued Wiener process. We consider a Suslin space-valued control process with a general control operator, which can depend on both the control and the corresponding solution. We reduce the equation to a more general (relaxed) form, where the concept of Young me

Y. Jiménez-Teja, R. A. Dupke, P. A. A. Lopes, J. M. Vílchez

The intracluster light (ICL) fraction, measured at certain specific wavelengths, has been shown to provide a good marker for determining the dynamical stage of galaxy clusters, i.e., merging versus relaxed, for small to intermediate redshifts. Here, we apply it for the first time to a high-redshift system, SPT-CLJ0615-5746 at z=0.97, using its RELICS (Reioni

Arthur Pander Maat

Hilbert modules over a $C^*$-category were first defined by Mitchener, who also proved that they form a $C^*$-category. An Eilenberg-Watts theorem for Hilbert modules over $C^*$-algebras was proved by Blecher. We follow a similar path to prove an Eilenberg-Watts theorem for Hilbert modules over $C^*$-categories and characterize equivalences of categories of

Jelena Gajic, Milos Arsenovic, Miodrag Mateljevic

We prove existence and uniqueness of a solution of the Dirichlet problem for separately $(\alpha, \beta)$ - harmonic functions on the unit polydisc $\mathbb D^n$ with boundary data in $C(\mathbb T^n)$ using $(\alpha, \beta)$ - Poisson kernel. A characterization by hypergeometric functions of such functions which are also m - homogeneous is given, this charac

Yixuan Huang, Nichols Crawford Taylor, Adam Conkey, Weiyu Liu

Objects rarely sit in isolation in everyday human environments. If we want robots to operate and perform tasks in our human environments, they must understand how the objects they manipulate will interact with structural elements of the environment for all but the simplest of tasks. As such, we'd like our robots to reason about how multiple objects and envir

Chao Wang, Shuren Qi, Zhiqiu Huang, Yushu Zhang

The vulnerability of deep neural networks to adversarial perturbations has been widely perceived in the computer vision community. From a security perspective, it poses a critical risk for modern vision systems, e.g., the popular Deep Learning as a Service (DLaaS) frameworks. For protecting deep models while not modifying them, current algorithms typically d

Jingye Chen, Yupan Huang, Tengchao Lv, Lei Cui

Diffusion models have gained increasing attention for their impressive generation abilities but currently struggle with rendering accurate and coherent text. To address this issue, we introduce TextDiffuser, focusing on generating images with visually appealing text that is coherent with backgrounds. TextDiffuser consists of two stages: first, a Transformer

Xiyu Zhang, Jiaqi Yang, Shikun Zhang, Yanning Zhang

As a fundamental problem in computer vision, 3D point cloud registration (PCR) aims to seek the optimal pose to align a point cloud pair. In this paper, we present a 3D registration method with maximal cliques (MAC). The key insight is to loosen the previous maximum clique constraint, and mine more local consensus information in a graph for accurate pose hyp

Gabriela Ben Melech Stan, Diana Wofk, Scottie Fox, Alex Redden

This research paper proposes a Latent Diffusion Model for 3D (LDM3D) that generates both image and depth map data from a given text prompt, allowing users to generate RGBD images from text prompts. The LDM3D model is fine-tuned on a dataset of tuples containing an RGB image, depth map and caption, and validated through extensive experiments. We also develop

Nicolò Dal Fabbro, Michele Rossi, Luca Schenato, Subhrakanti Dey

Edge networks call for communication efficient (low overhead) and robust distributed optimization (DO) algorithms. These are, in fact, desirable qualities for DO frameworks, such as federated edge learning techniques, in the presence of data and system heterogeneity, and in scenarios where internode communication is the main bottleneck. Although computationa

Si-Qi Liu, Zhe Wang, Youjin Zhang

For an integrable hierarchy which possesses a bihamiltonian structure with semisimple hydrodynamic limit, we prove that the linear reciprocal transformation with respect to any of its symmetry transforms it to another bihamiltonian integrable hierarchy. Moreover, we show that the central invariants of the bihamiltonian structure are preserved under such a li

Xuerao He, Pedro A. Vázquez, Mengqi Zhang

We present numerical analyses of 2-D electrohydrodynamic (EHD) flows of a dielectric liquid between a wire electrode and two plate electrodes with a Poiseuille flow, using direct numerical simulation and global stability analysis. Both conduction and injection mechanisms for charge generation are considered. In this work, we focused on the intensity of the c

Alexander Gairat, Vadim Shcherbakov

This paper concerns a local volatility model in which volatility takes two possible values, and the specific value depends on whether the underlying price is above or below a given threshold value. The model is known, and a number of results have been obtained for it. In particular, option pricing formulas and a power law behaviour of the implied volatility

Dmitriy Kalugin-Balashov

In this paper, we unveil a groundbreaking method to amplify full-text search lemmatization, utilizing the OpenCorpora dataset and a bespoke paradigm retrieval algorithm. Our primary aim is to streamline the extraction of a word's primary form or lemma - a crucial factor in full-text search. Additionally, we propose a compact dictionary storage strategy, sign

Ning Lu, Shengcai Liu, Rui He, Qi Wang

Large language models (LLMs) have shown remarkable performance in various tasks and have been extensively utilized by the public. However, the increasing concerns regarding the misuse of LLMs, such as plagiarism and spamming, have led to the development of multiple detectors, including fine-tuned classifiers and statistical methods. In this study, we equip L

Jesse Heyninck, Bart Bogaerts

Approximation fixpoint theory (AFT) is an abstract and general algebraic framework for studying the semantics of non-monotonic logics. In recent work, AFT was generalized to non-deterministic operators, i.e.\ operators whose range are sets of elements rather than single elements. In this paper, we make three further contributions to non-deterministic AFT: (1

Patrick Kahardipraja, Brielen Madureira, David Schlangen

Language is by its very nature incremental in how it is produced and processed. This property can be exploited by NLP systems to produce fast responses, which has been shown to be beneficial for real-time interactive applications. Recent neural network-based approaches for incremental processing mainly use RNNs or Transformers. RNNs are fast but monotonic (c

Ghislaine Flore Kabadiang Ngon, Conrad Bertrand Tabi, Timoléon Crépin Kofané

The letter introduces an extended (3+1)-dimensional [(3+1)D] nonlocal cubic complex Ginzburg-Landau equation describing the dynamics of dissipative light bullets in optical fiber amplifiers under the interplay between dopants and a spatially nonlocal nonlinear response. The model equation includes the effects of fiber dispersion, linear gain, nonlinear loss,

Yixiong Chen, Li Liu, Chris Ding

This paper introduces a novel explainable image quality evaluation approach called X-IQE, which leverages visual large language models (LLMs) to evaluate text-to-image generation methods by generating textual explanations. X-IQE utilizes a hierarchical Chain of Thought (CoT) to enable MiniGPT-4 to produce self-consistent, unbiased texts that are highly corre

Luc Jaulin

This paper proposes a minimal contractor and a minimal separator for an ellipse in the plane. The task is facilitated using actions induced by the hyperoctahedral group of symmetries. An application related to the localization of an object using multiple sonars is proposed.

Ang Lv, Xu Tan, Peiling Lu, Wei Ye

Symbolic music generation aims to create musical notes, which can help users compose music, such as generating target instrument tracks based on provided source tracks. In practical scenarios where there's a predefined ensemble of tracks and various composition needs, an efficient and effective generative model that can generate any target tracks based on th

Luigi Sbailò, Luca M. Ghiringhelli

Uncertainty-quantification methods are applied to estimate the confidence of deep-neural-networks classifiers over their predictions. However, most widely used methods are known to be overconfident. We address this problem by developing an algorithm that exploits the latent-space representation of data points fed into the network, to assess the accuracy of t

Chong-En Lin, Kuan-Yu Chen

Non-autoregressive automatic speech recognition (ASR) has become a mainstream of ASR modeling because of its fast decoding speed and satisfactory result. To further boost the performance, relaxing the conditional independence assumption and cascading large-scaled pre-trained models are two active research directions. In addition to these strategies, we propo

Yunsheng Bai, Atefeh Sohrabizadeh, Zongyue Qin, Ziniu Hu

Recent years have witnessed the growing popularity of domain-specific accelerators (DSAs), such as Google's TPUs, for accelerating various applications such as deep learning, search, autonomous driving, etc. To facilitate DSA designs, high-level synthesis (HLS) is used, which allows a developer to compile a high-level description in the form of software code

Yangqin Jiang, Chao Huang, Lianghao Xia

Graph neural networks (GNNs) have recently emerged as an effective collaborative filtering (CF) approaches for recommender systems. The key idea of GNN-based recommender systems is to recursively perform message passing along user-item interaction edges to refine encoded embeddings, relying on sufficient and high-quality training data. However, user behavior

Jong-Chul Park, Gaurav Tomar

Neutrino-electron scattering experiments play a crucial role in investigating the non-standard interactions of neutrinos. In certain models, these interactions can include interference terms that may affect measurements. Next-generation direct detection experiments, designed primarily for dark-matter searches, are also getting sensitive to probe the neutrino

Daniil Gavrilov, Nikita Balagansky

In this paper, we propose Ahead-of-Time (AoT) P-Tuning, a novel parameter-efficient fine-tuning method for pre-trained Language Models (LMs) that adds input-dependent bias before each Transformer layer. We evaluate AoT P-Tuning on GLUE and SuperGLUE benchmarking datasets using RoBERTa and DeBERTa models, showing that it outperforms BitFit and is comparable o

Scott Rettberg, Talan Memmott, Jill Walker Rettberg, Jason Nelson

During 2022, both transformer-based AI text generation sys-tems such as GPT-3 and AI text-to-image generation systems such as DALL-E 2 and Stable Diffusion made exponential leaps forward and are unquestionably altering the fields of digital art and electronic literature. In this panel a group of electronic literature authors and theorists consider new oppor-

Amal Haddad Haddad, Damith Premasiri, Tharindu Ranasinghe, Ruslan Mitkov

The domain of Botany is rich with metaphorical terms. Those terms play an important role in the description and identification of flowers and plants. However, the identification of such terms in discourse is an arduous task. This leads in some cases to committing errors during translation processes and lexicographic tasks. The process is even more challengin

Thomaz F. S. Bastiaanssen, Thomas P. Quinn, John F. Cryan

Motivation: Studies including more than one type of 'omics data sets are becoming more prevalent. Integrating these data sets can be a way to solidify findings and even to make new discoveries. However, integrating multi-omics data sets is challenging. Typically, data sets are integrated by performing an all-vs-all correlation analysis, where each feature of

Chunchao Wen, Jianfa Zhang, Shiqiao Qin, Zhihong Zhu

Studies into scatterings of photonic structures have been so far overwhelmingly focused on their dependencies on the spatial and spectral morphologies of the incident waves. In contrast, the evolution of scattering properties through another parameter space of incident directions (momentum space) has attracted comparably little attention, though of profound

Lufeng Wang, Jiepeng Liu, Guozhong Cheng, En Liu

Shear wall structures are widely used in high-rise residential buildings, and the layout of shear walls requires many years of design experience and iterative trial and error. Currently, there are methods based on heuristic algorithms, but they generate results too slowly. Those based on Generative Adversarial Networks (GANs) or Graph Neural Networks (GNNs)

Clémence Ligneul, Chloé Najac, André Döring, Christian Beaulieu

Brain cell structure and function reflect neurodevelopment, plasticity and ageing, and changes can help flag pathological processes such as neurodegeneration and neuroinflammation. Accurate and quantitative methods to non-invasively disentangle cellular structural features are needed and are a substantial focus of brain research. Diffusion-weighted MR spectr

Joseph Slote, Alexander Volberg, Haonan Zhang

Consider $f:\Omega^n_K \to \mathbf{C}$ a function from the $n$-fold product of multiplicative cyclic groups of order $K$. Any such $f$ may be extended via its Fourier expansion to an analytic polynomial on the polytorus $\mathbf{T}^n$, and the set of such polynomials coincides with the set of all analytic polynomials on $\mathbf{T}^n$ of individual degree at

Leena Mathur, Maja J Matarić, Louis-Philippe Morency

In recent decades, the field of affective computing has made substantial progress in advancing the ability of AI systems to recognize and express affective phenomena, such as affect and emotions, during human-human and human-machine interactions. This paper describes our examination of research at the intersection of multimodal interaction and affective comp