November 2022 arXiv papers — page 12

Khunpob Sereesuchart

In this paper, we discuss the trace operator for homogeneous fractional Sobolev spaces over infinite strip-like domains. We determine intrinsic seminorms on the trace space that allow for a bounded right inverse. The intrinsic seminorm includes two features previously used to describe the trace of homogeneous Sobolev spaces, a relation between the two discon

Nikolaos Ellinas, Myrsini Christidou, Alexandra Vioni, June Sig Sung

In this paper, we present a novel method for phoneme-level prosody control of F0 and duration using intuitive discrete labels. We propose an unsupervised prosodic clustering process which is used to discretize phoneme-level F0 and duration features from a multispeaker speech dataset. These features are fed as an input sequence of prosodic labels to a prosody

Gakh G. I., Konchatnij M. I., Merenkov N. P., Gakh A. G

The differential cross section and some polarization observables have been calculated for the $e^+\,e^-\to \gamma\,\gamma$ reaction taking into account the contribution of the heavy excited electron and the general form $e\,e\,\gamma\,\gamma$ contact interaction. The spin correlation coefficients are calculated for the case when both beams have arbitrary pol

Tomoki Fukuma, Koki Noda, Hiroki Kumagai, Hiroki Yamamoto

In recent years, social media has been criticized for yielding polarization. Identifying emerging disagreements and growing polarization is important for journalists to create alerts and provide more balanced coverage. While recent studies have shown the existence of polarization on social media, they primarily focused on limited topics such as politics with

Mohammad Hossein Modirrousta, Parisa Forghani Arani, Mahdi Aliyari Shoorehdeli

In order to gain access to networks, different types of intrusion attacks have been designed, and the attackers are working on improving them. Computer networks have become increasingly important in daily life due to the increasing reliance on them. In light of this, it is quite evident that algorithms with high detection accuracy and reliability are needed

Zhongwei Shen

In this paper we study the homogenization of the Dirichlet problem for the Stokes equations in a perforated domain with multiple microstructures. First, under the assumption that the interface between subdomains is a union of Lipschitz surfaces, we show that the effective velocity and pressure are governed by a Darcy law, where the permeability matrix is pie

Alexandr Buryak, Emily Clader, Ran J. Tessler

In our previous two papers, we constructed an $r$-spin theory in genus zero for Riemann surfaces with boundary and fully determined the corresponding intersection numbers, providing an analogue of Witten's $r$-spin conjecture in genus zero in the open setting. In particular, we proved that the generating series of open $r$-spin intersection numbers is determ

David Bojanić, Kristijan Bartol, Josep Forest, Stefan Gumhold

Learning universal representations across different applications domain is an open research problem. In fact, finding universal architecture within the same application but across different types of datasets is still unsolved problem too, especially in applications involving processing 3D point clouds. In this work we experimentally test several state-of-the

Anilkumar Parsi, Diyou Liu, Andrea Iannelli, Roy S. Smith

Adaptive model predictive control (MPC) methods using set-membership identification to reduce parameter uncertainty are considered in this work. Strong duality is used to reformulate the set-membership equations exactly within the MPC optimization. A predicted worst-case cost is then used to enable performance-oriented exploration. The proposed approach guar

Huiyan Qi, Lechao Cheng, Jingjing Chen, Yue Yu

Transfer learning aims to improve the performance of target tasks by transferring knowledge acquired in source tasks. The standard approach is pre-training followed by fine-tuning or linear probing. Especially, selecting a proper source domain for a specific target domain under predefined tasks is crucial for improving efficiency and effectiveness. It is con

Christoph Breunig, Ruixuan Liu, Zhengfei Yu

We propose a double robust Bayesian inference procedure on the average treatment effect (ATE) under unconfoundedness. For our new Bayesian approach, we first adjust the prior distributions of the conditional mean functions, and then correct the posterior distribution of the resulting ATE. Both adjustments make use of pilot estimators motivated by the semipar

Eric J. Ching, Ryan F. Johnson, Andrew D. Kercher

In this second part of our two-part paper, we extend to multiple spatial dimensions the one-dimensional, fully conservative, positivity-preserving, and entropy-bounded discontinuous Galerkin scheme developed in the first part for the chemically reacting Euler equations. Our primary objective is to enable robust and accurate solutions to complex reacting-flow

Michele Fabrizio

The long search for insulating materials that possess low-energy quasiparticles carrying electron's quantum numbers except charge - inspired by the neutral spin-1/2 excitations, the so-called spinons, exhibited by Anderson's resonating-valence-bond state - seems to have reached a turning point after the discovery of several Mott insulators displaying same th

Samuel L. Krushkal

Recently the author proved that the 1977 Hummel-Scheinberg-Zalcman conjecture on coefficients of nonvanishing $H^p$ functions is true for all $p = 2m, m \in \mathbb{N}$, i.e., for the Hilbertian Hardy spaces $H^{2m}$. As a consequence, this also implies a proof of the Krzyz conjecture for bounded nonvanishing functions which originated this direction. In the

Oskar E. Ström, Jason P. Beech, Jonas O. Tegenfeldt

We observe regular patterns emerging across multiple length scales with high-concentration DNA solutions in microfluidic pillar arrays at low Reynolds numbers and high Deborah. Interacting vortices between pillars lead to long-range order in the form of large travelling waves consisting of DNA at high concentration and extension. Waves are formed in quadrati

Duyal Yolcu

This note complements the paper "One-Way Ticket to Las Vegas and the Quantum Adversary" (arxiv:2301.02003). I develop the ideas behind the adversary bound - universal algorithm duality therein in a different form, using the same perspective as Barnum-Saks-Szegedy in which query algorithms are defined as sequences of feasible reduced density matrices rather t

Takuma Matsuda, Suguru Otani

We construct a new unified panel dataset that combines route-year-level freight rates with shipping quantities for the six major routes and industry-year-level newbuilding, secondhand, and scrap prices from 1966 (the beginning of the industry) to 2009. We offer detailed instructions on how to merge various datasets and validate the data's consistency by indu

Zhaolin Ren, Yang Zheng, Maryam Fazel, Na Li

The problem of controller reduction has a rich history in control theory. Yet, many questions remain open. In particular, there exist very few results on the order reduction of general non-observer based controllers and the subsequent quantification of the closed-loop performance. Recent developments in model-free policy optimization for Linear Quadratic Gau

Chen Zhao, Yinlin Hu, Mathieu Salzmann

Object location prior is critical for the standard 6D object pose estimation setting. The prior can be used to initialize the 3D object translation and facilitate 3D object rotation estimation. Unfortunately, the object detectors that are used for this purpose do not generalize to unseen objects. Therefore, existing 6D pose estimation methods for unseen obje

Heeseung Kwon, Francisco M. Castro, Manuel J. Marin-Jimenez, Nicolas Guil

Attention operator has been widely used as a basic brick in visual understanding since it provides some flexibility through its adjustable kernels. However, this operator suffers from inherent limitations: (1) the attention kernel is not discriminative enough, resulting in high redundancy, and (2) the complexity in computation and memory is quadratic in the

Sarika C. K., Sayantan Majumdar, A. K. Sood

Under applied stress, the viscosity of many dense particulate suspensions increases drastically, a response known as discontinuous shear-thickening (DST). In some cases, the applied stress can even transform the suspension into a solid-like shear jammed state. Although shear jamming (SJ) has been probed for dense suspensions with particles having well-define

S G Whittington

We prove several rigorous results about the asymptotic behaviour of the numbers of polygons and self-avoiding walks confined to a square on the square lattice. Specifically we prove that the dominant asymptotic behaviour of polygons confined to an LxL square is identical to that of self-avoiding walks that cross an LxL square from one corner vertex to the op

Raphaël Forien, Bastian Wiederhold

In this paper, we uncover new asymptotic isolation by distance patterns occurring under long-range dispersal of offspring. We extend a recent work of the first author, in which this information was obtained from forwards-in-time dynamics using a novel stochastic partial differential equations approach for spatial $\Lambda$-Fleming-Viot models. The latter wer

Tim Schopf, Daniel Braun, Florian Matthes

Text classification of unseen classes is a challenging Natural Language Processing task and is mainly attempted using two different types of approaches. Similarity-based approaches attempt to classify instances based on similarities between text document representations and class description representations. Zero-shot text classification approaches aim to ge

Merlin Humml, Lutz Schröder

Epistemic logics typically talk about knowledge of individual agents or groups of explicitly listed agents. Often, however, one wishes to express knowledge of groups of agents specified by a given property, as in `it is common knowledge among economists'. We introduce such a logic of common knowledge, which we term abstract-group epistemic logic (AGEL). That

Ceyhun Elmacioglu, Kieran Hilmer, Christopher O'Neill, Melin Okandan

In this paper, we consider the following question: "given the multiplicity $m$ and embedding dimension $e$ of a numerical semigroup $S$, what can be said about the cardinality $\eta$ of a minimal presentation of $S$?" We approach this question from a combinatorial (poset-theoretic) perspective, utilizing the recently-introduced notion of a Kunz nilsemigroup.

Sergiu Hart

A "repeat voting" procedure is proposed, whereby voting is carried out in two identical rounds. Every voter can vote in each round, the results of the first round are made public before the second round, and the final result is determined by adding up all the votes in both rounds. It is argued that this simple modification of election procedures may well inc

Ivica Kostric, Krisztian Balog, Tølløv Alexander Aresvik, Nolwenn Bernard

DAGFiNN is a conversational conference assistant that can be made available for a given conference both as a chatbot on the website and as a Furhat robot physically exhibited at the conference venue. Conference participants can interact with the assistant to get advice on various questions, ranging from where to eat in the city or how to get to the airport t

Abbas Cheddad, Christian Nordahl

Amidst the outbreak of the coronavirus (COVID 19) pandemic, distance education, where the learning process is conducted online, has become the norm. Campus-based programs and courses have been redesigned in a timely manner which was a challenge for teachers not used to distance teaching. Students engagement and active participation become an issue; add to th

Juhan Kim

Given a specific collection of curves on an oriented surface with punctures, we associate a power series by counting its intersections with multicurves. This paper presents a reciprocity formula on the power series when multicurves with no component contractible to a puncture are concerned, as a generalization of the reciprocity presented in arXiv:1612.02518

Phuong Nguyen, Ben Hörz

Implementations of measurement kernels in high-level Lattice QCD frameworks enable rapid prototyping, but can leave hardware capabilities significantly underutilized. This is an acceptable tradeoff if the time spent in unoptimized routines is generally small. The computational cost of modern spectroscopy projects however can be comparable to or even exceed t

Alex Hämäläinen, Mustafa Mert Çelikok, Samuel Kaski

Probabilistic user modeling is essential for building machine learning systems in the ubiquitous cases with humans in the loop. However, modern advanced user models, often designed as cognitive behavior simulators, are incompatible with modern machine learning pipelines and computationally prohibitive for most practical applications. We address this problem

Hao Hao, Qing-Yuan Zhao, Yang-Hui Huang, Jie Deng

Deep space explorations require transferring huge amounts of data quickly from very distant targets. Laser communication is a promising technology that can offer a data rate of magnitude faster than conventional microwave communication due to the fundamentally narrow divergence of light. This study demonstrated a photon-sensitive receiver prototype with over

Harshit Kesarwani, Dheeraj Naidu Amudala, Venkatesh Tentu, Rohit Budhiraja

We consider a hardware-impaired multi-cell Rician faded massive multi-input multi-output (mMIMO) system with two-layer pilot decontamination precoding, also known as large-scale fading precoding (LSFP). Each BS is equipped with a flexible dynamic analog-to-digital converter (ADC)/digital-to-analog converter (DAC) architecture and the user equipments (UEs) ha

Etienne Boursier, Vianney Perchet

Due mostly to its application to cognitive radio networks, multiplayer bandits gained a lot of interest in the last decade. A considerable progress has been made on its theoretical aspect. However, the current algorithms are far from applicable and many obstacles remain between these theoretical results and a possible implementation of multiplayer bandits al

Lei Hsiung, Yung-Chen Tang, Pin-Yu Chen, Tsung-Yi Ho

With the advancement of deep learning technology, neural networks have demonstrated their excellent ability to provide accurate predictions in many tasks. However, a lack of consideration for neural network calibration will not gain trust from humans, even for high-accuracy models. In this regard, the gap between the confidence of the model's predictions and

Nicholas Sale, Biagio Lucini, Jeffrey Giansiracusa

Topological Data Analysis (TDA) is a field that leverages tools and ideas from algebraic topology to provide robust methods for analysing geometric and topological aspects of data. One of the principal tools of TDA, persistent homology, produces a quantitative description of how the connectivity and structure of data changes when viewed over a sequence of sc

Lisong Chen, Ayres Freitas

This paper describes a modular framework for the description of electroweak scattering and decay processes, including but not limited to Z-resonance physics. The framework consistently combines a complex-pole expansion near a s-channel resonance with a regular fixed-order perturbative description away from the resonance, in a manifestly gauge-invariant schem

J. Nemchik, B. Z. Kopeliovich

Coherent photoproduction of heavy quarkonia on nuclear targets is studied within the QCD color dipole formalism including several main phenomena: i) The correlation between impact parameter of a collision $\vec b$ and dipole orientation $\vec r$; ii) The higher-twist nuclear shadowing related to the $\bar QQ$ Fock state of the photon; iii) The leading-twist

Stefan Braun, Erik McDermott, Roger Hsiao

The neural transducer is an end-to-end model for automatic speech recognition (ASR). While the model is well-suited for streaming ASR, the training process remains challenging. During training, the memory requirements may quickly exceed the capacity of state-of-the-art GPUs, limiting batch size and sequence lengths. In this work, we analyze the time and spac

Petr Šimánek, Daniel Vašata, Pavel Kordík

Designing faster optimization algorithms is of ever-growing interest. In recent years, learning to learn methods that learn how to optimize demonstrated very encouraging results. Current approaches usually do not effectively include the dynamics of the optimization process during training. They either omit it entirely or only implicitly assume the dynamics o

Joseph D. Hart, Francesco Sorrentino, Thomas L. Carroll

Reservoir computing, a recurrent neural network paradigm in which only the output layer is trained, has demonstrated remarkable performance on tasks such as prediction and control of nonlinear systems. Recently, it was demonstrated that adding time-shifts to the signals generated by a reservoir can provide large improvements in performance accuracy. In this

Douglas F. Pinto, Marcelo S. Zanetti, Marcos L. W. Basso, Jonas Maziero

In Ref. [Phys. Rev. A 100, 062317 (2019)], the authors reported an algorithm to implement, in a circuit-based quantum computer, a general quantum measurement (GQM) of a two-level quantum system, a qubit. Even though their algorithm seems right, its application involves the solution of an intricate non-linear system of equations in order to obtain the angles

Hartmut Surmann, Marc Thurow, Dominik Slomma

This work proposes a new method for real-time dense 3d reconstruction for common 360{\deg} action cams, which can be mounted on small scouting UAVs during USAR missions. The proposed method extends a feature based Visual monocular SLAM (OpenVSLAM, based on the popular ORB-SLAM) for robust long-term localization on equirectangular video input by adding an add

Berndt Müller, Andreas Schäfer

The dual holographic description has enjoyed many successes in explaining fundamental properties of the early stages of relativistic heavy ion collisions up to the formation of a minimal-viscosity quark-gluon fluid. However, there have been few attempts to extend its application beyond this stage. Here we explore the prospects for such an extension beyond th

Zheren Fu, Zhendong Mao, Bo Hu, An-An Liu

Deep metric learning aims to learn an embedding space, where semantically similar samples are close together and dissimilar ones are repelled against. To explore more hard and informative training signals for augmentation and generalization, recent methods focus on generating synthetic samples to boost metric learning losses. However, these methods just use

Radoslaw Adamczak, Grigoris Paouris, Peter Pivovarov, Paul Simanjuntak

We establish a family of isoperimetric inequalities for sets that interpolate between intersection bodies and dual Lp centroid bodies. This provides a bridge between the Busemann intersection inequality and the Lutwak--Zhang inequality. The approach depends on new empirical versions of these inequalities.

K. Hebeler, V. Durant, J. Hoppe, M. Heinz

Three-nucleon (3N) interactions are key for an accurate solution of the nuclear many-body problem. However, fully taking into account 3N forces constitutes a computational challenge and hence approximate treatments are commonly employed. The method of normal ordering has proven to be a powerful tool that allows to systematically include 3N interactions in an

Anna-Christina Eilers, Robert A. Simcoe, Minghao Yue, Ruari Mackenzie

We present the first rest-frame optical spectrum of a high-redshift quasar observed with JWST/NIRCam in Wide Field Slitless (WFSS) mode. The observed quasar, J0100+2802, is the most luminous quasar known at $z>6$. We measure the mass of the central supermassive black hole (SMBH) by means of the rest-frame optical H$\beta$ emission line, and find consistent m

Bei-Lei Liu, Yue-Chao Wang, Yu Liu, Yuan-Ji Xu

As correlation strength has a key influence on the simulation of strongly correlated materials, many approaches have been proposed to obtain the parameter using first-principles calculations. However, the comparison of the different Coulomb strengths obtained using these approaches and an investigation of the mechanisms behind them are still needed. Taking l

George Kour, Samuel Ackerman, Orna Raz, Eitan Farchi

The ability to compare the semantic similarity between text corpora is important in a variety of natural language processing applications. However, standard methods for evaluating these metrics have yet to be established. We propose a set of automatic and interpretable measures for assessing the characteristics of corpus-level semantic similarity metrics, al

Brenden W. Hamilton, Alejandro Strachan

Mechanophores, stimuli-responsive molecules that respond chromatically to mechanochemical reactions, are important for understanding the coupling between mechanics and chemistry as well as in engineering applications. However, the atomic-level understanding of their activation originates from gas phase studies or under simple linear elongation forces directl

Jiao He, Pei Su

We consider the evolution of a small rigid body in an incompressible viscous fluid filling the whole space $\rline^3$. When the small rigid body shrinks to a "massless" point in the sense that its density is constant, we prove that the solution of the fluid-rigid body system converges to a solution of the Navier-Stokes equations in the full space. Based on s

P. Acharya, M. Fritts, D. -M. Mei, V. Mandic

For the first time, time-dependent internal charge amplification through impact ionization has been observed in a planar germanium (Ge) detector operated at cryogenic temperature. In a time period of 30 and 45 minutes after applying a bias voltage, the charge energy corresponding to a baseline of the 59.54 keV $\gamma$ rays from a $^{241}$Am source is amplif

Zhipeng Lu

We introduce a homogeneous method to deal with summations with homogeneous factors. Then we use it to compute main terms in the asymptotics of distance energy of square lattices in circles, which relates to the conjecture of distinct distances by Erdos.

Eric J. Ching, Ryan F. Johnson, Andrew D. Kercher

In this paper, we develop a fully conservative, positivity-preserving, and entropy-bounded discontinuous Galerkin scheme for simulating the chemically reacting, compressible Euler equations with complex thermodynamics. The proposed formulation is an extension of the conservative, high-order numerical method previously developed by Johnson and Kercher [J. Com

Inderjeet Singh, Kazuya Kakizaki, Toshinori Araki

Deep Metric Learning (DML) is a prominent field in machine learning with extensive practical applications that concentrate on learning visual similarities. It is known that inputs such as Adversarial Examples (AXs), which follow a distribution different from that of clean data, result in false predictions from DML systems. This paper proposes MDProp, a frame

Yige Yao, Xiaotian Bao, Yunke Zhu, Xinyu Sui

Charge carrier dynamics essentially determine the performance of various optoelectronic applications of colloidal semiconductor nanocrystals. Among them, two-dimensional nanoplatelets provide new adjustment freedom for their unique core/crown heterostructure. Herein, we demonstrate that by fine-tuning the core size and the lateral quantum confinement, the ch

Hongtao Lv, Zhilin Zhang, Zhenzhe Zheng, Jinghan Liu

Digital advertising constitutes one of the main revenue sources for online platforms. In recent years, some advertisers tend to adopt auto-bidding tools to facilitate advertising performance optimization, making the classical \emph{utility maximizer} model in auction theory not fit well. Some recent studies proposed a new model, called \emph{value maximizer}

Charles Poussot-Vassal, Denis Matignon, Ghilslain Haine, Pierre Vuillemin

In this work, we detail a procedure to construct a reduced order model on the basis of frequency-domain data, that preserves the non-strictly passive property and the port-Hamiltonian structure. The proposed scheme is based on Benner et al. (2020) contribution, which has been adapted (i) to handle non-strictly passive model, and (ii) to handle numerical issu

Dharshana Nalatamby, Florence Gibouin, Javier Ordoñez-Hernández, Julien Renaudeau

In numerous industrial processes involving fluids, viscosity is a determinant factor for reaction rates, flows, drying, mixing, etc. Its importance is even more determinant for phenomena observed are at the micro- and nano- scales as in nanopores or in micro and nanochannels for instance. However, despite notable progresses of the techniques used in microrhe

Richard Einsele, Joscha Hoche, Roland Mitric

Herein, we present a new method to efficiently calculate electronically excited states in large molecular assemblies, consisting of hundreds of molecules. For this purpose, we combine the long-range corrected tight-binding density-functional fragment molecular orbital method (FMO-LC-DFTB) with an excitonic Hamiltonian, which is constructed in the basis of lo

Kui Zhang, Hang Zhou, Jie Zhang, Qidong Huang

Deep 3D point cloud models are sensitive to adversarial attacks, which poses threats to safety-critical applications such as autonomous driving. Robust training and defend-by-denoising are typical strategies for defending adversarial perturbations. However, they either induce massive computational overhead or rely heavily upon specified priors, limiting gene

Debo Cheng, Ziqi Xu, Jiuyong Li, Lin Liu

The instrumental variable (IV) approach is a widely used way to estimate the causal effects of a treatment on an outcome of interest from observational data with latent confounders. A standard IV is expected to be related to the treatment variable and independent of all other variables in the system. However, it is challenging to search for a standard IV fro

Collin Mark Joseph, Ralf Meyer

We describe explicit generators for the "real" K-theory of "real" spheres in van Daele's picture. Pulling these generators back along suitable maps from tori to spheres produces a family of Hamiltonians used in the physics literature on topological insulators. We compute their K-theory classes geometrically, based on wrong-way functoriality of K-theory and t

Sumit Roy

Hitchin in [Duke Math. J. 54 (1), 91-114 (1987)] introduced a proper morphism from the moduli space of stable $G$-Higgs bundles ($G=\mathrm{GL}(n,\mathbb{C}),\mathrm{Sp}(2m,\mathbb{C})$ and $\mathrm{SO}(n,\mathbb{C})$) over a curve to a vector space of invariant polynomials and he described the generic fibers of that morphism. In this paper, we first describ

Leonardo A. Dos Santos

We have recently hit the milestone of 5,000 exoplanets discovered. In stark contrast with the Solar System, most of the exoplanets we know to date orbit extremely close to their host stars, causing them to lose copious amounts of gas through atmospheric escape at some stage in their lives. In some planets, this process can be so dramatic that they shrink in

Wen Zhou

From the perspective of constructing the classification model, this paper uses the weight coefficient (influencing factors) in the model to analyze the sales impact on different brands of electric vehicles, and optimizes the existing sales strategy.

Maximilian Rimbach-Russ, Stephan G. J. Philips, Xiao Xue, Lieven M. K. Vandersypen

Semiconductor spin qubits demonstrated single-qubit gates with fidelities up to $99.9\%$ benchmarked in the single-qubit subspace. However, tomographic characterizations reveals non-negligible crosstalk errors in a larger space. Additionally, it was long thought that the two-qubit gate performance is limited by charge noise which couples to the qubits via th

C. Malyshev, N. M. Bogoliubov

Relations between the mean values of distributions of flipped spins on periodic Heisenberg XX chain and some aspects of enumerative combinatorics are discussed. The Bethe vectors, which are the state-vectors of the model, are considered both as on- and off-shell. It is this approach that makes it possible to represent and to study the correlation functions i

Camille Bergez-Casalou, Bertram Bitsch, Sean N. Raymond

Several planetary systems are known to host multiple giant planets. However, when two giant planets are accreting from the same disk, it is unclear what effect the presence of the second planet has on the gas accretion process of both planets. In this paper we perform long-term 2D isothermal hydrodynamical simulations (over more than 0.5 Myrs) with the FARGO

Harris Papadopoulos

Unlike the typical classification setting where each instance is associated with a single class, in multi-label learning each instance is associated with multiple classes simultaneously. Therefore the learning task in this setting is to predict the subset of classes to which each instance belongs. This work examines the application of a recently developed fr

Arsenii Mustafin, Alex Olshevsky, Ioannis Ch. Paschalidis

Temporal difference (TD) learning is a policy evaluation in reinforcement learning whose performance can be enhanced by variance reduction methods. Recently, multiple works have sought to fuse TD learning with Stochastic Variance Reduced Gradient (SVRG) method to achieve a geometric rate of convergence. However, the resulting convergence rate is significantl

Hongyi Li, Zhen Peng, Chengwei Pan, Di Zhao

Projected gradient descent and its Riemannian variant belong to a typical class of methods for low-rank matrix estimation. This paper proposes a new Nesterov's Accelerated Riemannian Gradient algorithm by efficient orthographic retraction and tangent space projection. The subspace relationship between iterative and extrapolated sequences on the low-rank matr

Wen Zhou

Context: The IoT system infrastructure platform facility vulnerability attack has become the main battlefield of network security attacks. Most of the traditional vulnerability mining methods rely on vulnerability detection tools to realize vulnerability discovery. However, due to the inflexibility of tools and the limitation of file size, its scalability It

Motasem Alfarra, Zhipeng Cai, Adel Bibi, Bernard Ghanem

Continual Learning is a step towards lifelong intelligence where models continuously learn from recently collected data without forgetting previous knowledge. Existing continual learning approaches mostly focus on image classification in the class-incremental setup with clear task boundaries and unlimited computational budget. This work explores the problem

Pierre Le Pelletier de Woillemont, Rémi Labory, Vincent Corruble

The increasing complexity of gameplay mechanisms in modern video games is leading to the emergence of a wider range of ways to play games. The variety of possible play-styles needs to be anticipated by designers, through automated tests. Reinforcement Learning is a promising answer to the need of automating video game testing. To that effect one needs to tra

Junpeng Liu, Miaomiao Zhao, Yun-Tong Yang, Hong-Gang Luo

The ground state of quantum Rabi model (QRM) exhibits rich nonclassical states including squeezed state, cat state, and entangled state in different parameter regimes. In this paper, we firstly use the polaron picture to figure out the process of superradiant phase transition (SPT) in view of the nonclassical states. According to the polaron wave functions,

Frithjof Karsch, Anirban Lahiri, Marius Neumann, Christian Schmidt

Normalizing flows are generative machine learning models which can efficiently approximate probability distributions, using only given samples of a distribution. This architecture is used to interpolate the chiral condensate obtained from QCD simulations with five degenerate quark flavors in the HISQ action. From this a model for the probability distribution

Zheng Li, Xiang Li, Lingfeng Yang, Borui Zhao

Most existing distillation methods ignore the flexible role of the temperature in the loss function and fix it as a hyper-parameter that can be decided by an inefficient grid search. In general, the temperature controls the discrepancy between two distributions and can faithfully determine the difficulty level of the distillation task. Keeping a constant tem

S. Bhuvaneswari, R. Radha, R. Muthuganesan

In this article, we consider the heterodinuclear complex [Ni(dpt)(H2O)Cu(pba)].2H2O [pba =1,3-propylenebis(oxamato) and dpt = bis-(3-aminopropyl)amine] realized through the theoretical model of mixed spin-(1/2,1) coupled via Heisenberg interaction. We study the behaviors of thermal quantum correlations of the above material via Measurement-Induced Nonlocalit

Yifan Huang, Clayton Barham, Eric Page, PK Douglas

Temporal exponential random graph models (TERGM) are powerful statistical models that can be used to infer the temporal pattern of edge formation and elimination in complex networks (e.g., social networks). TERGMs can also be used in a generative capacity to predict longitudinal time series data in these evolving graphs. However, parameter estimation within

Christopher J. Holder, Majid Khonji, Jorge Dias, Muhammad Shafique

A major challenge in machine learning is resilience to out-of-distribution data, that is data that exists outside of the distribution of a model's training data. Training is often performed using limited, carefully curated datasets and so when a model is deployed there is often a significant distribution shift as edge cases and anomalies not included in the

Haochuan Cui, Junjie Sheng, Bo Jin, Yiqiu Hu

With the rapid development of cloud computing, virtual machine scheduling has become one of the most important but challenging issues for the cloud computing community, especially for practical heterogeneous request sequences. By analyzing the impact of request heterogeneity on some popular heuristic schedulers, it can be found that existing scheduling algor

Katsuya Hashino, Shinya Kanemura, Tomo Takahashi, Masanori Tanaka

We investigate production of primordial black holes from first-order electroweak phase transition in the framework of the nearly aligned Higgs effective field theory, in which non-decoupling quantum effects are properly described. Since the mass of such primordial black holes is evaluated to be about $10^{-5}$ of the solar mass, current and future microlensi

Cibrán López, Agustí Emperador, Edgardo Saucedo, Riccardo Rurali

Solid-state electrolytes (SSE) with high ion conductivity are pivotal for the development and large-scale adoption of green-energy conversion and storage technologies such as fuel cells, electrocatalysts and solid-state batteries. Yet, SSE are extremely complex materials for which general rational design principles remain indeterminate. Here, we unite first-

Sung-Soo Byun, Peter J. Forrester

The Ginibre unitary ensemble (GinUE) consists of $N \times N$ random matrices with independent complex standard Gaussian entries. This was introduced in 1965 by Ginbre, who showed that the eigenvalues form a determinantal point process with an explicit correlation kernel, and after scaling they are supported on the unit disk with constant density. For some t

Brenda B. Malabarba, K. P. Khemchandani, A. Martinez Torres, E. Oset

In this work we present an attempt to describe the $X_1(2900)$ found by the LHCb collaboration, in the experimental data on the invariant mass spectrum of $ D^-K^+$, as a three-meson molecular state of the $K\rho\bar D$ system. We discuss that the interactions in all the subsystems are attractive in nature, with the $\rho \bar D$ interaction generating $\bar

Pierre Le Pelletier de Woillemont, Rémi Labory, Vincent Corruble

Modern video games are becoming richer and more complex in terms of game mechanics. This complexity allows for the emergence of a wide variety of ways to play the game across the players. From the point of view of the game designer, this means that one needs to anticipate a lot of different ways the game could be played. Machine Learning (ML) could help addr

Kazutoshi Shinoda, Saku Sugawara, Akiko Aizawa

Question answering (QA) models for reading comprehension tend to learn shortcut solutions rather than the solutions intended by QA datasets. QA models that have learned shortcut solutions can achieve human-level performance in shortcut examples where shortcuts are valid, but these same behaviors degrade generalization potential on anti-shortcut examples wher

Fuxin Fan, Yangkong Wang, Ludwig Ritschl, Ramyar Biniazan

The existence of metallic implants in projection images for cone-beam computed tomography (CBCT) introduces undesired artifacts which degrade the quality of reconstructed images. In order to reduce metal artifacts, projection inpainting is an essential step in many metal artifact reduction algorithms. In this work, a hybrid network combining the shift window

Paul Bach, Nadja Klein

We introduce a highly efficient fully Bayesian approach for anisotropic multidimensional smoothing. The main challenge in this context is the Markov chain Monte Carlo update of the smoothing parameters as their full conditional posterior comprises a pseudo-determinant that appears to be intractable at first sight. As a consequence, most existing implementati

Herma M. Cuppen, Jennifer A. Noble, Stephane Coussan, Britta Redlich

Interstellar and cometary ices play an important role in the formation of planetary systems around young stars. Their main constituent is amorphous solid water (ASW). Although ASW is widely studied, vibrational energy dissipation and structural changes due to vibrational excitation are less well understood. The hydrogen-bonding network is likely a crucial co

Ravishankar Krishnaswamy, Shi Li, Varun Suriyanarayana

We consider the online unrelated-machine load balancing problem with recourse, where the algorithm is allowed to re-assign prior jobs. We give a $(2+\epsilon)$-competitive algorithm for the problem with $O_\epsilon(\log n)$ amortized recourse per job. This is the first $O(1)$-competitive algorithm for the problem with reasonable recourse, and the competitive

Tobias Barker, Christophe Prange

In this short survey paper, we focus on some new developments in the study of the regularity or potential singularity formation for solutions of the 3D Navier-Stokes equations. Some of the motivating questions are: Are certain norms accumulating/concentrating on small scales near potential blow-up times? At what speed do certain scale-invariant norms blow-up

Jennifer Gerasimov, Deyu Tu, Vivek Hitaishi, Padinhare Cholakkal Harikesh

Future brain-computer interfaces will require local and highly individualized signal processing of fully integrated electronic circuits within the nervous system and other living tissue. New devices will need to be developed that can receive data from a sensor array, process data into meaningful information, and translate that information into a format that

Louise Guillon, Joël Chavas, Audrey Bénézit, Marie-Laure Moutard

Like fingerprints, cortical folding patterns are unique to each brain even though they follow a general species-specific organization. Some folding patterns have been linked with neurodevelopmental disorders. However, due to the high inter-individual variability, the identification of rare folding patterns that could become biomarkers remains a very complex

Jiaxin Wen, Yeshuang Zhu, Jinchao Zhang, Jie Zhou

Recent studies have shown the impressive efficacy of counterfactually augmented data (CAD) for reducing NLU models' reliance on spurious features and improving their generalizability. However, current methods still heavily rely on human efforts or task-specific designs to generate counterfactuals, thereby impeding CAD's applicability to a broad range of NLU

Chunlin Yu, Ye Shi, Zimo Liu, Shenghua Gao

Lifelong person re-identification (LReID) is in significant demand for real-world development as a large amount of ReID data is captured from diverse locations over time and cannot be accessed at once inherently. However, a key challenge for LReID is how to incrementally preserve old knowledge and gradually add new capabilities to the system. Unlike most exi

Edward Danquah Donkor, Alessandro Laio, Ali Hassanali

Machine-learning (ML) has become a key workhorse in molecular simulations. Building an ML model in this context, involves encoding the information of chemical environments using local atomic descriptors. In this work, we focus on the Smooth Overlap of Atomic Positions (SOAP) and their application in studying the properties of liquid water both in the bulk an