March 2023 arXiv papers — page 5

José G. Hasbani, Paulina Sepúlveda, Ignacio Muga, Victor M. Calo

The Adaptive Stabilized Finite Element method (AS-FEM) developed in Calo et. al. combines the idea of the residual minimization method with the inf-sup stability offered by the discontinuous Galerkin (dG) frameworks. As a result, the discretizations deliver stabilized approximations and residual representatives in the dG space that can drive automatic adapti

Jinghe Yang, Mingming Gong, Girish Nair, Jung Hoon Lee

In recent years, learning-based feature detection and matching have outperformed manually-designed methods in in-air cases. However, it is challenging to learn the features in the underwater scenario due to the absence of annotated underwater datasets. This paper proposes a cross-modal knowledge distillation framework for training an underwater feature detec

Alan Fitzsimmons, Karem Meech, Luca Matrà, Susanne Pfalzner

In this chapter we review our knowledge of our galaxy's cometary population outside our Oort Cloud - exocomets and Interstellar Objects (ISOs). We start with a brief overview of planetary system formation, viewed as a general process around stars. We then take a more detailed look at the creation and structure of exocometary belts, as revealed by the unprece

Xinxin Zhang, Dike Li, Jianqin Zhu, Zhi Tao

Digital twin is a modern technology for many advanced applications. To construct a digital twin of a thermal system, it is required to make online estimations of unknown time-varying boundary conditions from sensor measured data, which needs to solve inverse heat transfer problems (IHTPs). However, a fast and accurate solution is challenging since the measur

Olivier Lerda, Ammar Mian, Guillaume Ginolhac, Jean-Philippe Ovarlez

Multi-array systems are widely used in sonar and radar applications. They can improve communication speeds, target discrimination, and imaging. In the case of a multibeam sonar system that can operate two receiving arrays, we derive new adaptive to improve detection capabilities compared to traditional sonar detection approaches. To do so, we more specifical

Francesco Chiossi, Sven Mayer

Mixed Reality (MR) allows users to interact with digital objects in a physical environment, but several limitations have hampered widespread adoption. Physiologically adaptive systems detecting user's states can drive interaction and address these limitations. Here, we highlight potential usability and interaction limitations in MR and how physiologically ad

Edoardo Marangone, Claudio Di Ciccio, Daniele Friolo, Eugenio Nerio Nemmi

Multi-party business processes rely on the collaboration of various players in a decentralized setting. Blockchain technology can facilitate the automation of these processes, even in cases where trust among participants is limited. Transactions are stored in a ledger, a replica of which is retained by every node of the blockchain network. The operations sav

Pavel V. Kostryukov, Artur Dobrowolski

In this paper, using a quasi-classical statistical approach based on the Langevin equation, we simulate the fission dynamics of selected even-even $\rm U$, $\rm Pu$, $\rm Cm$, $\rm Cf$ and $\rm Fm$ actinide nuclei. As a preparatory part of the work, before solving the Langevin equations, the determination of transport parameters such as inertia and friction

Pham Thai Hoang Tung, Nguyen Tan Viet, Ngo Tien Anh, Phan Duy Hung

The prevalence of memes on social media has created the need to sentiment analyze their underlying meanings for censoring harmful content. Meme censoring systems by machine learning raise the need for a semi-supervised learning solution to take advantage of the large number of unlabeled memes available on the internet and make the annotation process less cha

Alireza Akbari, Burkhard Schmidt, Peter Thalmeier

We investigate the dispersive paramagnetic excitons on the honeycomb lattice that originate from the crystalline-electric field (CEF) split localized f-electron states in the paramagnetic state due to intersite exchange. We start with a symmetry analysis of possible Ising-type singlet-singlet and xy-type singlet-doublet models. The former supports only symme

Nayan Man Singh Pradhan, Patrick Frank, An Mo, Alexander Badri-Spröwitz

Parallel robots are capable of high-speed manipulation and have become essential tools in the industry. The proximal placement of their motors and the low weight of their end effectors make them ideal for generating highly dynamic motion. Therefore, parallel robots can be adopted for motion platform designs, as long as end effector loads are low. Traditional

H. Kolesova, M. Laine, S. Procacci

Taking axion inflation as an example, we estimate the maximal temperature ($T_{\rm max}^{ }$) that can be reached in the post-inflationary universe, as a function of the confinement scale of a non-Abelian dark sector ($\Lambda_{\rm IR}^{ }$). Below a certain threshold $\Lambda_{\rm IR}^{ } < \Lambda_{\rm 0}^{ } \sim 2\times 10^{-8}_{ } m_{\rm pl}^{ }$, the s

Idris Akinade, Jesujoba Alabi, David Adelani, Clement Odoje

This paper investigates the performance of massively multilingual neural machine translation (NMT) systems in translating Yor\`ub\'a greetings ($\varepsilon$ k\'u [MASK]), which are a big part of Yor\`ub\'a language and culture, into English. To evaluate these models, we present IkiniYor\`ub\'a, a Yor\`ub\'a-English translation dataset containing some Yor\`u

David Sychrovský, Sameer Desai, Martin Loebl

In many real world situations, like minor traffic offenses in big cities, a central authority is tasked with periodic administering punishments to a large number of individuals. Common practice is to give each individual a chance to suffer a smaller fine and be guaranteed to avoid the legal process with probable considerably larger punishment. However, thank

Lukas Anzeletti

We are interested in existence of solutions to the $d$-dimensional equation \begin{equation*} X_t=x_0+\int_0^t b(X_s)ds + B_t, \end{equation*} where $B$ is a (fractional) Brownian motion with Hurst parameter $H\leqslant 1/2$ and $b$ is an $\mathbb{R}^d$-valued measure in some Besov space. We exhibit a class of drifts $b$ such that weak existence holds. In pa

Hiroshi Kozaki, Tatsuhiko Koike, Yoshiyuki Morisawa, Hideki Ishihara

We study the classical dynamics of the Nambu-Goto strings with a null symmetry in curved spacetimes admitting a null Killing vector field. The Nambu-Goto equation is reduced to first order ordinary differential equations and is always integrable in contrast to the case of non-null symmetries where integrability requires additional spacetime symmetries. It is

Bingfan Zhu, Yanchao Yang, Xulong Wang, Youyi Zheng

We propose VDN-NeRF, a method to train neural radiance fields (NeRFs) for better geometry under non-Lambertian surface and dynamic lighting conditions that cause significant variation in the radiance of a point when viewed from different angles. Instead of explicitly modeling the underlying factors that result in the view-dependent phenomenon, which could be

Xin You, Junjun He, Jie Yang, Yun Gu

Medical image segmentation is a fundamental task for medical image analysis and surgical planning. In recent years, UNet-based networks have prevailed in the field of medical image segmentation. However, convolution-neural networks (CNNs) suffer from limited receptive fields, which fail to model the long-range dependency of organs or tumors. Besides, these m

Zhi Yang, Kang Li, Haitao Gan, Zhongwei Huang

The information diffusion performance of GCN and its variant models is limited by the adjacency matrix, which can lower their performance. Therefore, we introduce a new framework for graph convolutional networks called Hybrid Diffusion-based Graph Convolutional Network (HD-GCN) to address the limitations of information diffusion caused by the adjacency matri

Irina Vorontsova, Roman Goncharov, Sergey Kynev, Fedor Kiselev

Numerically, a theoretical analysis of the noise impact caused by spontaneous Raman scattering, four-wave mixing, and linear channel crosstalk on the measurement-device-independent continuous variable quantum key distribution systems is conducted. The analysis considers symmetry and asymmetry of system paths, as well as possible channel allocation schemes, f

Daniel Drayss, Dengyang Fang, Christoph Füllner, Grigory Lihachev

We propose and demonstrate a novel scheme for optical arbitrary waveform measurement (OAWM) that exploits chip-scale Kerr soliton combs as highly scalable multiwavelength local oscillators (LO) for ultra-broadband full-field waveform acquisition. In contrast to earlier concepts, our approach does not require any optical slicing filters and thus lends itself

Mārtiņš Kālis, Andris Locāns, Rolands Šikovs, Hassan Naseri

Boltzmann machine is a powerful machine learning model with many real-world applications, for example by constructing deep belief networks. Statistical inference on a Boltzmann machine can be carried out by sampling from its posterior distribution. However, uniform sampling from such a model is not trivial due to an extremely multi-modal distribution. Quantu

Robert Lefringhausen, Supitsana Srithasan, Armin Lederer, Sandra Hirche

As control engineering methods are applied to increasingly complex systems, data-driven approaches for system identification appear as a promising alternative to physics-based modeling. While the Bayesian approaches prevalent for safety-critical applications usually rely on the availability of state measurements, the states of a complex system are often not

Jean-Christophe Pain

In this article, an exact series expansion for the Dottie number (solution of the equation $\cos(x) = x$) is presented. Its derivation consists in combining the Kaplan representation of the Dottie number as a series in odd powers of $\pi$, with a series for $\pi^{2k+1}$ ($k\geq 1$) obtained using the Fa\`a di Bruno formula.

Ayuki Kamada, Takumi Kuwahara, Ami Patel

Dark matter self-scattering is one of key ingredients for small-scale structure of the Universe, while dark matter annihilation is important for the indirect measurements. There is a strong correlation between the velocity-dependent self-scattering cross section and the Sommerfeld enhancement factor for the dark matter annihilation cross section. In this stu

Yusuke Tampo, Taichi Kato, Naoto Kojiguchi, Sergey Yu. Shugarov

Superoutbursts in WZ Sge-type dwarf novae (DNe) are characterized by both early superhumps and ordinary superhumps originating from the 2:1 and 3:1 resonances, respectively. However, some WZ Sge-type DNe show a superoutburst lacking early superhumps; it is not well established how these differ from superoutbursts with an early superhump phase. We report time

Daochang Liu, Qiyue Li, AnhDung Dinh, Tingting Jiang

Temporal action segmentation is crucial for understanding long-form videos. Previous works on this task commonly adopt an iterative refinement paradigm by using multi-stage models. We propose a novel framework via denoising diffusion models, which nonetheless shares the same inherent spirit of such iterative refinement. In this framework, action predictions

Feiran Zhao, Florian Dörfler, Keyou You

Policy optimization (PO), an essential approach of reinforcement learning for a broad range of system classes, requires significantly more system data than indirect (identification-followed-by-control) methods or behavioral-based direct methods even in the simplest linear quadratic regulator (LQR) problem. In this paper, we take an initial step towards bridg

Émiland Garrabé, Hozefa Jesawada, Carmen Del Vecchio, Giovanni Russo

We consider the problem of estimating the possibly non-convex cost of an agent by observing its interactions with a nonlinear, non-stationary and stochastic environment. For this inverse problem, we give a result that allows to estimate the cost by solving a convex optimization problem. To obtain this result we also tackle a forward problem. This leads to th

Leonardo Crespi, Paolo Roncaglioni, Damiano Dei, Ciro Franzese

In the medical images field, semantic segmentation is one of the most important, yet difficult and time-consuming tasks to be performed by physicians. Thanks to the recent advancement in the Deep Learning models regarding Computer Vision, the promise to automate this kind of task is getting more and more realistic. However, many problems are still to be solv

John A G Roberts, Asaki Saito, Franco Vivaldi

In rotations with a binary symbolic dynamics, a critical curve is the locus of parameters for which the boundaries of the partition that defines the symbolic dynamics are connected via a prescribed number of iterations and symbolic itinerary. We study the arithmetical and geometrical properties of these curves in parameter space.

Angelo Garofalo, Yvan Tortorella, Matteo Perotti, Luca Valente

On-chip DNN inference and training at the Extreme-Edge (TinyML) impose strict latency, throughput, accuracy and flexibility requirements. Heterogeneous clusters are promising solutions to meet the challenge, combining the flexibility of DSP-enhanced cores with the performance and energy boost of dedicated accelerators. We present DARKSIDE, a System-on-Chip w

Alex Takyung Lee, Kyungwha Park, In-Ho Lee

We theoretically propose that hexagonal silicon-based crystals, $P6/m$-Si$_6$ and $P6/m$-NaSi$_6$, are topological Dirac semimetals with superconducting critical temperatures of 12 K and 13 K, respectively, at ambient pressure. Band inversion occurs with the Fu-Kane topological invariant $\mathbb{Z}_2=1$, even in the absence of spin-orbit coupling. The Dirac

Soheil Yasini, Zahra Shaterzadeh-Yazdi, Mahmoud Mohammad Taheri

The coherent dynamics and control of spin qubits are essential requirements for quantum technology. A prominent challenge for coherent control of a spin qubit in a set of qubits is the destructive effect of the applied magnetic field on the coherent dynamics of neighbouring qubits due to its spatial extension. We propose a novel scheme to characterize the co

Mart van Baalen, Andrey Kuzmin, Suparna S Nair, Yuwei Ren

Recently, the idea of using FP8 as a number format for neural network training has been floating around the deep learning world. Given that most training is currently conducted with entire networks in FP32, or sometimes FP16 with mixed-precision, the step to having some parts of a network run in FP8 with 8-bit weights is an appealing potential speed-up for t

Irving Calderón, Michael Magee

Let $\Gamma$ be a Schottky subgroup of $\mathrm{SL} (2,\mathbb{Z})$. We establish a uniform and explicit lower bound of the second eigenvalue of the Laplace-Beltrami operator of congruence coverings of the hyperbolic surface $\Gamma \backslash \mathbb{H}^2$ provided the limit set of $\Gamma$ is thick enough.

Anbai Jiang, Wei-Qiang Zhang, Yufeng Deng, Pingyi Fan

Automatic detection of machine anomaly remains challenging for machine learning. We believe the capability of generative adversarial network (GAN) suits the need of machine audio anomaly detection, yet rarely has this been investigated by previous work. In this paper, we propose AEGAN-AD, a totally unsupervised approach in which the generator (also an autoen

Ming Yan, Xin Wang, Yudi Dai, Siqi Shen

Motion capture is a long-standing research problem. Although it has been studied for decades, the majority of research focus on ground-based movements such as walking, sitting, dancing, etc. Off-grounded actions such as climbing are largely overlooked. As an important type of action in sports and firefighting field, the climbing movements is challenging to c

Anirban Das, Subinoy Das, Shiv K. Sethi

Nontrivial dark sector physics continues to be an interesting avenue in our quest to the nature of dark matter. In this paper, we study the cosmological signatures of mass-varying dark matter where its mass changes from zero to a nonzero value in the early Universe. We compute the changes in various observables, such as, the linear matter power spectrum and

Sara Bardi, Mauro Conti, Luca Pajola, Pier Paolo Tricomi

Social Honeypots are tools deployed in Online Social Networks (OSN) to attract malevolent activities performed by spammers and bots. To this end, their content is designed to be of maximum interest to malicious users. However, by choosing an appropriate content topic, this attractive mechanism could be extended to any OSN users, rather than only luring malic

Jesus Redondo, Michele Reticcioli, Vit Gabriel, Dominik Wrana

In polarizable materials, electronic charge carriers interact with the surrounding ions, leading to quasiparticle behaviour. The resulting polarons play a central role in many materials properties including electrical transport, optical properties, surface reactivity and magnetoresistance, and polaron properties are typically investigated indirectly through

V. Gorelov, Y. Yang, M. Ruggeri, D. M. Ceperley

We present a method of calculating the energy gap of a charge-neutral excitation using only ground-state calculations. We report Quantum Monte Carlo calculations of $\Gamma\rightarrow\Gamma$ and $\Gamma\rightarrow X$ particle-hole excitation energies in diamond carbon. We analyze the finite-size effect and find the same $1/L$ decay rate as that in a charged

Abel Rojo-Francàs, Felipe Isaule, Bruno Juliá-Díaz

We present a comprehensive study of the static properties of a mobile impurity interacting with a bath with a few particles trapped in a one-dimensional harmonic trap. We consider baths with either identical bosons or distinguishable particles and we focus on the limiting case where the bath is non-interacting. We provide numerical results for the energy spe

Danil Provodin, Jérémie Joudioux, Eduard Duryev

This paper presents a bidding system for sponsored search auctions under an unknown valuation model. This formulation assumes that the bidder's value is unknown, evolving arbitrarily, and observed only upon winning an auction. Unlike previous studies, we do not impose any assumptions on the nature of feedback and consider the problem of bidding in sponsored

Bruno Casella, Roberto Esposito, Carlo Cavazzoni, Marco Aldinucci

Classic Machine Learning techniques require training on data available in a single data lake. However, aggregating data from different owners is not always convenient for different reasons, including security, privacy and secrecy. Data carry a value that might vanish when shared with others; the ability to avoid sharing the data enables industrial applicatio

Leonardo Crespi, Mattia Portanti, Daniele Loiacono

Organ at Risk (OAR) segmentation from CT scans is a key component of the radiotherapy treatment workflow. In recent years, deep learning techniques have shown remarkable potential in automating this process. In this paper, we investigate the performance of Generative Adversarial Networks (GANs) compared to supervised learning approaches for segmenting OARs f

Xuran Meng, Yuan Cao, Difan Zou

Gradient regularization, as described in \citet{barrett2021implicit}, is a highly effective technique for promoting flat minima during gradient descent. Empirical evidence suggests that this regularization technique can significantly enhance the robustness of deep learning models against noisy perturbations, while also reducing test error. In this paper, we

Chotipan Boonkongkird, Guilhem Lavaux, Sebastien Peirani, Yohan Dubois

The inference of cosmological quantities requires accurate and large hydrodynamical cosmological simulations. Unfortunately, their computational time can take millions of CPU hours for a modest coverage in cosmological scales ($\approx (100 {h^{-1}}\,\text{Mpc})^3)$). The possibility to generate large quantities of mock Lyman-$\alpha$ observations opens up t

Hartmut Führ, Reihaneh Raisi Tousi

We investigate the invariance properties of general wavelet coorbit spaces and Besov-type decomposition spaces under dilations by matrices. We show that these matrices can be characterized by quasi-isometry properties with respect to a certain metric in frequency domain. We formulate versions of this phenomenon both for the decomposition and coorbit space se

Yijin Chen, Xun Xu, Yongyi Su, Kui Jia

Domain adaptation helps generalizing object detection models to target domain data with distribution shift. It is often achieved by adapting with access to the whole target domain data. In a more realistic scenario, target distribution is often unpredictable until inference stage. This motivates us to explore adapting an object detection model at test-time,

Stanislav Opanasenko, Evgeny Ferapontov

It is well known that every modular form~$f$ on a discrete subgroup $\Gamma\leqslant \textrm{SL}(2, \mathbb R)$ satisfies a third-order nonlinear ODE that expresses algebraic dependence of the functions~$f$, $f'$, $f''$ and~$f'''$. These ODEs are automatically invariant under the Lie group $\textrm{SL}(2, \mathbb R)$, which acts on the solution spaces thereo

Sandra Q. Liu, Leonardo Zamora Yañez, Edward H. Adelson

We describe a novel three-finger robot hand that has high resolution tactile sensing along the entire length of each finger. The fingers are compliant, constructed with a soft shell supported with a flexible endoskeleton. Each finger contains two cameras, allowing tactile data to be gathered along the front and side surfaces of the fingers. The gripper can p

Sathvik Kolli

Data-driven offline model-based optimization (MBO) is an established practical approach to black-box computational design problems for which the true objective function is unknown and expensive to query. However, the standard approach which optimizes designs against a learned proxy model of the ground truth objective can suffer from distributional shift. Spe

Agapius Bou Ghosn, Philip Polack, Arnaud de La Fortelle

The knowledge of the states of a vehicle is a necessity to perform proper planning and control. These quantities are usually accessible through measurements. Control theory brings extremely useful methods -- observers -- to deal with quantities that cannot be directly measured or with noisy measurements. Classical observers are mathematically derived from mo

Frederic Blum, Johann-Mattis List

Sound correspondence patterns form the basis of cognate detection and phonological reconstruction in historical language comparison. Methods for the automatic inference of correspondence patterns from phonetically aligned cognate sets have been proposed, but their application to multilingual wordlists requires extremely well annotated datasets. Since annotat

Anders Claesson, Henning Ulfarsson

An adjacent $q$-cycle is a natural generalization of an adjacent transposition. We show that the number of adjacent $q$-cycles in a permutation maps to the sum of occurrences of two mesh patterns under Foata's fundamental transformation. As a corollary we resolve Conjecture 3.14 in the paper "From Hertzprung's problem to pattern-rewriting systems" by the fir

Shiyao Wu

Due to the increasing number of graduates, many applicants experience the situation about finding a job, and employers experience difficulty filtering job applicants, which might negatively impact their effectiveness. However, most job-hunting websites lack job recommendation and CV filtering or ranking functionality, which are not integrated into the system

Matteo Costantini, Martin Möller, Johannes Schwab

We provide formulas for the Chern classes of linear submanifolds of the moduli spaces of Abelian differentials and hence for their Euler characteristic. This includes as special case the moduli spaces of k-differentials, for which we set up the full intersection theory package and implement it in the sage-program diffstrata. As an application, we give an alg

Vitaly Bergelson, Andrew Best

The polynomial Szemer\'{e}di theorem implies that, for any $\delta \in (0,1)$, any family $\{P_1,\ldots, P_m\} \subset \mathbb{Z}[y]$ of nonconstant polynomials with constant term zero, and any sufficiently large $N$, every subset of $\{1,\ldots, N\}$ of cardinality at least $\delta N$ contains a nontrivial configuration of the form $\{x,x+P_1(y),\ldots, x+P

Li Zhou, Laura Cabello, Yong Cao, Daniel Hershcovich

Detecting offensive language is a challenging task. Generalizing across different cultures and languages becomes even more challenging: besides lexical, syntactic and semantic differences, pragmatic aspects such as cultural norms and sensitivities, which are particularly relevant in this context, vary greatly. In this paper, we target Chinese offensive langu

W. M. Linhart, M. Rybak, M. Birowska, K. Mosina

Here, we investigate the role of the interlayer magnetic ordering of CrSBr in the framework of $\textit{ab initio}$ calculations and by using optical spectroscopy techniques. These combined studies allow us to unambiguously determine the nature of the optical transitions. In particular, photoreflectance measurements, sensitive to the direct transitions, have

Tommaso Boccato, Matteo Ferrante, Andrea Duggento, Nicola Toschi

In this study, we explore the impact of network topology on the approximation capabilities of artificial neural networks (ANNs), with a particular focus on complex topologies. We propose a novel methodology for constructing complex ANNs based on various topologies, including Barab\'asi-Albert, Erd\H{o}s-R\'enyi, Watts-Strogatz, and multilayer perceptrons (ML

Claus Kiefer, Hamid Mohaddes

We first present a consistent canonical formulation of the general (non-marginal) Oppenheimer-Snyder model. The switching between comoving and stationary observer is achieved by promoting coordinate transformations between dust proper time and Schwarzschild-Killing time to canonical ones. This leads to a multivalued Hamiltonian which is deparameterizable. We

Tommaso Toso, Alain Y. Kibangou, Paolo Frasca

Navigation apps have become pervasive in providing real-time route recommendations to travelers willing to minimize their travel times. However, such technologies introduce new complexities, raising concerns about their overall impact on traffic networks. This paper focuses on evaluating the effect of navigation apps on traffic flows, particularly examining

Sofia B. S. D. Castro, Alexander Lohse

We consider heteroclinic networks between $n \in \mathbb{N}$ nodes where the only connections are those linking each node to its two subsequent neighbouring ones. Using a construction method where all nodes are placed in a single one-dimensional space and the connections lie in coordinate planes, we show that it is possible to robustly realise these networks

Bas van Stein, Fu Xing Long, Moritz Frenzel, Peter Krause

We propose DoE2Vec, a variational autoencoder (VAE)-based methodology to learn optimization landscape characteristics for downstream meta-learning tasks, e.g., automated selection of optimization algorithms. Principally, using large training data sets generated with a random function generator, DoE2Vec self-learns an informative latent representation for any

Hu Haotian, Wang Fanyi, Su Jingwen, Gao Shiyu

3D object detection is one of the most important tasks in autonomous driving and robotics. Our research focuses on tackling low efficiency issue of point-based methods on large-scale point clouds. Existing point-based methods adopt farthest point sampling (FPS) strategy for downsampling, which is computationally expensive in terms of inference time and memor

Alexandre Anahory Simoes, Leonardo Colombo

In this paper we derive Hamel equations for the motion of nonholonomic systems subject to inequality constraints in quasivelocities. As examples, the vertical rolling disk hitting a wall and the Chaplygin sleigh with a knife edge constraint hitting a circular table are shown to illustrate the theoretical results.

Qian Luo, Yunfei Li, Yi Wu

Grounded understanding of natural language in physical scenes can greatly benefit robots that follow human instructions. In object manipulation scenarios, existing end-to-end models are proficient at understanding semantic concepts, but typically cannot handle complex instructions involving spatial relations among multiple objects. which require both reasoni

Seong Jun Park, M. Y. Choi

Birth-death processes take place ubiquitously throughout the universe. In general, birth and death rates depend on the system size (corresponding to the number of products or customers undergoing the birth-death process) and thus vary every time birth or death occurs, which makes fluctuations in the rates inevitable. The differential-difference equation gove

Alexandre Anahory Simoes, Maria Barbero Liñán, Leonardo Colombo, David Martín de Diego

Retractions maps are used to define a discretization of the tangent bundle of the configuration manifold as two copies of the configuration manifold where the dynamics take place. Such discretization maps can be conveniently lifted to a higher-order tangent bundle to construct geometric integrators for the higher-order Euler-Lagrange equations. Given a cost

Rahul Devendra, Ribhu Chopra, Kumar Appaiah

Most of the metrics used for detecting a causal relationship among multiple time series ignore the effects of practical measurement impairments, such as finite sample effects, undersampling and measurement noise. It has been shown that these effects significantly impair the performance of the underlying causality test. In this paper, we consider the problem

Debayan Bhattacharya, Finn Behrendt, Benjamin Tobias Becker, Dirk Beyersdorff

Paranasal anomalies are commonly discovered during routine radiological screenings and can present with a wide range of morphological features. This diversity can make it difficult for convolutional neural networks (CNNs) to accurately classify these anomalies, especially when working with limited datasets. Additionally, current approaches to paranasal anoma

Reshmi TR, Murugan K

In IPv6 based MANETs, the neighbor discovery enables nodes to self-configure and communicate with neighbor nodes through autoconfiguration. The Stateless address autoconfiguration (SLAAC) has proven to face several security issues. Even though the Secure Neighbor Discovery (SeND) uses Cryptographically Generated Addresses (CGA) to address these issues, it cr

Łukasz Stettner

In the paper adapting Krein Rutman theory we show the existence of solutions to the long run risk sensitive control problem for controlled discrete time Markov processes over locally compact separable metric spaces.

Joao Pedro Araujo, Jiaman Li, Karthik Vetrivel, Rishi Agarwal

Synthesizing 3D human motion in a contextual, ecological environment is important for simulating realistic activities people perform in the real world. However, conventional optics-based motion capture systems are not suited for simultaneously capturing human movements and complex scenes. The lack of rich contextual 3D human motion datasets presents a roadbl

Carl Christian Kjelgaard Mikkelsen, Lorién López-Villellas, Pablo García-Risueño

We analyze the convergence of quasi-Newton methods in exact and finite precision arithmetic. In particular, we derive an upper bound for the stagnation level and we show that any sufficiently exact quasi-Newton method will converge quadratically until stagnation. In the absence of sufficient accuracy, we are likely to retain rapid linear convergence. We conf

Min Liu, Yu Bao, Chengqi Zhao, Shujian Huang

Benefiting from the sequence-level knowledge distillation, the Non-Autoregressive Transformer (NAT) achieves great success in neural machine translation tasks. However, existing knowledge distillation has side effects, such as propagating errors from the teacher to NAT students, which may limit further improvements of NAT models and are rarely discussed in e

Sheng-Hong Lai, Jen-Chi Lee, Yi Yang

We discover a stringy scaling behavior for a class of n-point Regge string scattering amplitudes (RSSA). The number of independent kinematics variables is found to be reduced by dim M.

Mischa Dombrowski, Hadrien Reynaud, Johanna P. Müller, Matthew Baugh

Recent advancements in diffusion models have significantly impacted the trajectory of generative machine learning research, with many adopting the strategy of fine-tuning pre-trained models using domain-specific text-to-image datasets. Notably, this method has been readily employed for medical applications, such as X-ray image synthesis, leveraging the pleth

Filip Lemic, Jakob Struye, Thomas Van Onsem, Jeroen Famaey

The advancement of Virtual Reality (VR) technology is focused on improving its immersiveness, supporting multiuser Virtual Experiences (VEs), and enabling users to move freely within their VEs while remaining confined to specialized VR setups through Redirected Walking (RDW). To meet their extreme data-rate and latency requirements, future VR systems will re

Harvey L. Devereux, Matthew S. Turner

Inspired by the swarming or flocking of animal systems we study groups of agents moving in unbounded 2D space. Individual trajectories derive from a ``bottom-up'' principle: individuals reorient to maximise their future path entropy over environmental states. This can be seen as a proxy for keeping options open, a principle that may confer evolutionary fitne

Jianfeng Xiang, Jiaolong Yang, Binbin Huang, Xin Tong

In this paper, we introduce a novel 3D-aware image generation method that leverages 2D diffusion models. We formulate the 3D-aware image generation task as multiview 2D image set generation, and further to a sequential unconditional-conditional multiview image generation process. This allows us to utilize 2D diffusion models to boost the generative modeling

Masaki Imagawa, Daisuke Kawagoe

We consider a boundary value problem of a stationary advection equation with the homogeneous inflow boundary condition in a bounded domain with Lipschitz boundary, and consider its perturbation by $\epsilon \Delta$, where $\epsilon$ is a positive parameter and $\Delta$ is the Laplacian. In this article, we show the $L^2$ strong convergence of solutions as th

Mario Klisse

By employing the external Kasparov product, Hawkins, Skalski, White and Zacharias constructed spectral triples on crossed product C$^\ast$-algebras by equicontinuous actions of discrete groups. They further raised the question for whether their construction turns the respective crossed product into a compact quantum metric space in the sense of Rieffel. By i

Nimisha Dutta, Reeta Devi, Arindam Boruah, Saumen Acharjee

In this work, we investigate the physical and electronic properties of $\beta_{12}$-borophene FET-based gas sensor using a theoretical quantum capacitance model based on tight-binding approach. We study the impact of adsorbed NH$_3$, NO, NO$_2$ and CO gas molecule on its density of states, carrier concentration, quantum capacitance and I-V characteristics. W

Alejandro Crema

We present an standard constraints generation algorithm to find an explicit set whose robustness is equal to the robustness of the feasible solution set of a combinatorial optimization problem with cost uncertainty. Computational experience shows that for problems with moderate dimensions the running time of the algorithm may be tolerable and in many cases t

Zarif Ikram, Golam Md Muktadir, Jim Whitehead

High-definition roads are an essential component of realistic driving scenario simulation for autonomous vehicle testing. Roundabouts are one of the key road segments that have not been thoroughly investigated. Based on the geometric constraints of the nearby road structure, this work presents a novel method for procedurally building roundabouts. The suggest

A. J. Cridland, S. Facchini, E. F. van Dishoeck, M. Benisty

Understanding the chemical link between protoplanetary disks and planetary atmospheres is complicated by the fact that the popular targets in the study of disks and planets are widely separated both in space and time. The 5 Myr PDS 70 systems offers a unique opportunity to directly compare the chemistry of a giant planet's atmosphere to the chemistry of its

François Rottenberg

Reduction of wireless network energy consumption is becoming increasingly important to reduce environmental footprint and operational costs. A key concept to achieve it is the use of lean transmission techniques that dynamically (de)activate hardware resources as a function of the load. In this paper, we propose a pioneering information-theoretic study of ti

Iztok Fister, Iztok Fister

The main deficiency of the algorithms running on digital computers nowadays is their inability to change themselves during the execution. In line with this, the paper introduces the so-called replicated algorithms, inspired by the concept of developing a human brain. Similar to the human brain, where the process of thinking is strongly parallel, replicated a

Gustavo Bergantinos, Juan D. Moreno-Ternero

We study the problem of sharing the revenues from broadcasting sports leagues axiomatically. Our key axiom is anonymity, the classical impartiality axiom. Other impartiality axioms already studied in these problems are equal treatment of equals, weak equal treatment of equals and symmetry. We study the relationship between all impartiality axioms. Besides we

Nikolas Adaloglou, Felix Michels, Hamza Kalisch, Markus Kollmann

We present a general methodology that learns to classify images without labels by leveraging pretrained feature extractors. Our approach involves self-distillation training of clustering heads based on the fact that nearest neighbours in the pretrained feature space are likely to share the same label. We propose a novel objective that learns associations bet

Haotian Hu, Fanyi Wang, Jingwen Su, Yaonong Wang

In recent years, great progress has been made in the Lift-Splat-Shot-based (LSS-based) 3D object detection method. However, inaccurate depth estimation remains an important constraint to the accuracy of camera-only and multi-model 3D object detection models, especially in regions where the depth changes significantly (i.e., the "depth jump" problem). In this

Cerstin Mahlow

Research on writing tools started with the increased availability of computers in the 1970s. After a first phase addressing the needs of programmers and data scientists, research in the late 1980s started to focus on writing-specific needs. Several projects aimed at supporting writers and letting them concentrate on the creative aspects of writing by having

Skander Kazdaghli, Iordanis Kerenidis, Jens Kieckbusch, Philip Teare

Imputing data is a critical issue for machine learning practitioners, including in the life sciences domain, where missing clinical data is a typical situation and the reliability of the imputation is of great importance. Currently, there is no canonical approach for imputation of clinical data and widely used algorithms introduce variance in the downstream

Samy Badreddine, Gianluca Apriceno, Andrea Passerini, Luciano Serafini

In this paper, we introduce Interval Real Logic (IRL), a two-sorted logic that interprets knowledge such as sequential properties (traces) and event properties using sequences of real-featured data. We interpret connectives using fuzzy logic, event durations using trapezoidal fuzzy intervals, and fuzzy temporal relations using relationships between the inter

T. Mansuryan, A. Tonello, K. Krupa, A. Desmouliere

We demonstrate how a narrowband pump and a broadband spectrum can be spatially multiplexed by selective coupling them in two distinct modes of a few-mode microstructure fiber. The first mode carries most of the input pump energy, and experiences spectral broadening. Whereas the second mode preserves the narrow bandwidth of the remaining part of the pump. Bim

Zhuoxiao Li, Zhihang Zhong, Shohei Nobuhara, Ko Nishino

Polarization is a fundamental property of light that encodes abundant information regarding surface shape, material, illumination and viewing geometry. The computer vision community has witnessed a blossom of polarization-based vision applications, such as reflection removal, shape-from-polarization, transparent object segmentation and color constancy, parti

Zishuo Li, Bo Yang, Jiayun Li, Jiaqi Yan

In this paper, we consider a Model Predictive Control (MPC) problem of a continuous-time linear time-invariant system subject to continuous-time path constraints on the states and the inputs. By leveraging the concept of differential flatness, we can replace the differential equations governing the system with linear mapping between the states, inputs, and f