October 2022 arXiv papers — page 73

Geoffrey Mboya, Balazs Szendroi

The aim of this paper is to classify mildly singular Calabi-Yau threefolds fibred in low-degree weighted K3 surfaces and embedded as anticanonical hypersurfaces in weighted scrolls, extending results of Mullet. We also study projective degenerations, revisiting an example due to Gross and Ruan. Finally we briefly discuss the general question of embedding a p

D. Favero, C. De Santi, K. Mukherjee, M. Borga

For the development of reliable vertical GaN transistors, a detailed analysis of the robustness of the gate stack is necessary, as a function of the process parameters and material properties. To this aim, we report a detailed analysis of breakdown performance of planar GaN-on-Si MOS capacitors. The analysis is carried out on capacitors processed on differen

M. K. Volkov, K. Nurlan, A. A. Pivovarov

Decays of scalar mesons $K^*_0(800) \to K\pi$ and $K^*_0(1430) \to K\pi, K \eta, K \eta', K_1 \pi$ are described in the extended $U(3 ) \times U(3)$ Nambu - Jona-Lasinio chiral quark model. The obtained results are in satisfactory agreement with the new experimental data obtained by the BaBar collaboration, which markedly differ from the existing values in t

Natalia Garcia-Fritz, Hector Pasten, Thanases Pheidas

Except for a limited number of cases, a complete classification of the Diophantine sets of polynomial rings and fields of rational functions seems out of reach at present. We contribute to this problem by proving that several natural sets and relations over these structures are not Diophantine.

Shixuan Zhu, Qi Shen, Yiming Zhang, Zhenwei Dong

Bundle Recommendation (BR) aims at recommending bundled items on online content or e-commerce platform, such as song lists on a music platform or book lists on a reading website. Several graph based models have achieved state-of-the-art performance on BR task. But their performance is still sub-optimal, since the data sparsity problem tends to be more severe

Xinyu Li, Jianjun Xu, Haoyang Cheng

A new method for clustering functional data is proposed via information maximization. The proposed method learns a probabilistic classifier in an unsupervised manner so that mutual information (or squared loss mutual information) between data points and cluster assignments is maximized. A notable advantage of this proposed method is that it only involves con

Tymoteusz Salamon, Marcin Płodzień, Maciej Lewenstein, Katarzyna Roszak

In spin-based architectures of quantum devices, the hyperfine interaction between the electron spin qubit and the nuclear spin environment remains one of the main sources of decoherence. This paper provides a short review of the current advances in the theoretical description of the qubit decoherence dynamics. Next, we study the qubit-environment entanglemen

Yuan Liu, Lixuan Cao, Bin Wu

Eco-evolutionary dynamics is crucial to understand how individuals' behaviors and the surrounding environment interplay with each other. Typically, it is assumed that individuals update their behaviors via linear imitation function, i.e., the replicator dynamics. It has been proved that there cannot be limit circles in such eco-evolutionary dynamics. It sugg

Yotam Ashkenazi, Shlomi Dolev

This paper demonstrates and proves that the coordination of actions in a distributed swarm can be enhanced by using quantum entanglement. In particular, we focus on - Global and local simultaneous random walks, using entangled qubits that collapse into the same (or opposite) direction, either random direction or totally controlled simultaneous movements. - I

Yunxia Wang, Zhiyong Si

In this paper, we will provide the the finite element method for the electro-osmotic flow in micro-channels, in which a convection-diffusion type equation is given for the charge density $\rho^e$. A time-discrete method based on the backward Euler method is designed. The theoretical analysis shows that the numerical algorithm is unconditionally stable and ha

Antonio Paolillo, Mirko Nava, Dario Piga, Alessandro Giusti

An increasing number of nonspecialist robotic users demand easy-to-use machines. In the context of visual servoing, the removal of explicit image processing is becoming a trend, allowing an easy application of this technique. This work presents a deep learning approach for solving the perception problem within the visual servoing scheme. An artificial neural

Gábor Bíró, Bence Tankó-Bartalis, Gergely Gábor Barnaföldi

The results of a Machine Learning-based method is presented here to investigate the scaling properties of the final state charged hadron and mean jet multiplicity distributions. Deep residual neural network architectures with different complexities are utilized to predict the final state multiplicity distribution from the parton-level final state, generated

Xu Yuan, Chen Xu, Qiwei Chen, Chao Li

In this era of information explosion, a personalized recommendation system is convenient for users to get information they are interested in. To deal with billions of users and items, large-scale online recommendation services usually consist of three stages: candidate generation, coarse-grained ranking, and fine-grained ranking. The success of each stage de

Andrea Secchi, Filippo Troiani

Quantum states of a few-particle system capacitively coupled to a metal gate can be discriminated by measuring the quantum capacitance, which can be identified with the second derivative of the system energy with respect to the gate voltage. This approach is here generalized to the multi-voltage case, through the introduction of the quantum capacitance matri

S Ali John Naqvi, Abdullah Tauqeer, Rohaib Bhatti, S Bazil Ali

An essential stage in computer aided diagnosis of chest X rays is automated lung segmentation. Due to rib cages and the unique modalities of each persons lungs, it is essential to construct an effective automated lung segmentation model. This paper presents a reliable model for the segmentation of lungs in chest radiographs. Our model overcomes the challenge

Nicolas Broutin, Luc Devroye, Gabor Lugosi, Roberto Imbuzeiro Oliveira

Motivated by online recommendation systems, we study a family of random forests. The vertices of the forest are labeled by integers. Each non-positive integer $i\le 0$ is the root of a tree. Vertices labeled by positive integers $n \ge 1$ are attached sequentially such that the parent of vertex $n$ is $n-Z_n$, where the $Z_n$ are i.i.d.\ random variables tak

Frank van der Velde

Recently, a number of articles have argued that deep learning models such as GPT could also capture key aspects of language processing in the human mind and brain. However, I will argue that these models are not suitable as neural models of human language. Firstly, because they fail on fundamental boundary conditions, such as the amount of learning they requ

Thomas Lucas, Fabien Baradel, Philippe Weinzaepfel, Grégory Rogez

We address the problem of action-conditioned generation of human motion sequences. Existing work falls into two categories: forecast models conditioned on observed past motions, or generative models conditioned on action labels and duration only. In contrast, we generate motion conditioned on observations of arbitrary length, including none. To solve this ge

Pierre-Alexandre Gourdain, Aidan Bachmann

Interferometry can measure the shape or the material density of a system that could not be measured otherwise by recording the difference between the phase change of a signal and a reference phase. This difference is always between $-\pi$ and $\pi$ while it is the absolute phase that is required to get a true measurement. There is a long history of methods d

Berkay Kullukcu, Levent Beker

This study presents the simulation, experimentation, and design considerations of a Poly(vinylidene fluoride co-trifluoroethylene)/ Polyethylene Terephthalate (PVDF-TrFe / PET), laser-cut, flexible piezoelectric energy harvester. It is possible to obtain energy from the environment around autonomous sensor systems, which can then be used to power various equ

Yucong Lin, Hongming Xiao, Jiani Liu, Zichao Lin

Recently, knowledge-enhanced methods leveraging auxiliary knowledge graphs have emerged in relation extraction, surpassing traditional text-based approaches. However, to our best knowledge, there is currently no public dataset available that encompasses both evidence sentences and knowledge graphs for knowledge-enhanced relation extraction. To address this g

I. S. Burmistrov

Comment on recent paper by I. Horv\'ath and P. Marko\v{s}, "Super-universality in Anderson localization", Phys. Rev. Lett. 129, 106601 (2022) [arXiv:2110.11266].

Allister Loder, Fabienne Cantner, Andrea Cadavid, Markus B. Siewert

In spring 2022, the German federal government agreed on a set of measures that aimed at reducing households' financial burden resulting from a recent price increase, especially in energy and mobility. These measures included among others, a nation-wide public transport ticket for 9EUR per month and a fuel tax cut that reduced fuel prices by more than 15%. In

Pengjin Wei, Guohang Yan, Yikang Li, Kun Fang

With the development of neural networks and the increasing popularity of automatic driving, the calibration of the LiDAR and the camera has attracted more and more attention. This calibration task is multi-modal, where the rich color and texture information captured by the camera and the accurate three-dimensional spatial information from the LiDAR is incred

Ahmed Abbes, Michel Gros

Faltings initiated in 2005 a p-adic analogue of the (complex) Simpson correspondence whose construction has been taken up by various authors, according to several approaches. Following the one we initiated previously, we present an overview of a new monograph developing new features of the p-adic Simpson correspondence, inspired by our construction of the re

Marc Theveneau, Nicolas Keriven

As interest in graph data has grown in recent years, the computation of various geometric tools has become essential. In some area such as mesh processing, they often rely on the computation of geodesics and shortest paths in discretized manifolds. A recent example of such a tool is the computation of Wasserstein barycenters (WB), a very general notion of ba

Kelsey P. Hawkins, Ali Pakniyat, Evangelos Theodorou, Panagiotis Tsiotras

We propose a new method for the numerical solution of the forward-backward stochastic differential equations (FBSDE) appearing in the Feynman-Kac representation of the value function in stochastic optimal control problems. Using Girsanov's change of probability measures, it is demonstrated how a McKean-Markov branched sampling method can be utilized for the

Marouane Tliba, Aymen Sekhri, Mohamed Amine Kerkouri, Aladine Chetouani

Predicting the quality of multimedia content is often needed in different fields. In some applications, quality metrics are crucial with a high impact, and can affect decision making such as diagnosis from medical multimedia. In this paper, we focus on such applications by proposing an efficient and shallow model for predicting the quality of medical images

Wencai Liu

For periodic graph operators, we establish criteria to determine the overlaps of spectral band functions based on Bloch varieties. One criterion states that for a large family of periodic graph operators, the irreducibility of Bloch varieties implies no non-trivial periods for spectral band functions. This particularly shows that spectral band functions of d

Moslem Mir, Saeed H. Abedinpour

Broken Galilean invariance in a spin-orbit coupled system can amplify many-body effects on its different responses. We study the anomalous Hall and spin Hall conductivities of a magnetic two-dimensional electron gas with Rashba spin-orbit coupling. We show that both of these conductivities in the intrinsic limit are fully specified in terms of the longitudin

Vinod Kumar Chauhan, Anna Ledwoch, Alexandra Brintrup, Manuel Herrera

Currently, flight delays are common and they propagate from an originating flight to connecting flights, leading to large disruptions in the overall schedule. These disruptions cause massive economic losses, affect airlines' reputations, waste passengers' time and money, and directly impact the environment. This study adopts a network science approach for so

Vinod Kumar Chauhan, Muhannad Alomari, James Arney, Ajith Kumar Parlikad

While consolidation strategies form the backbone of many supply chain optimisation problems, exploitation of multi-tier material relationships through consolidation remains an understudied area, despite being a prominent feature of industries that produce complex made-to-order products. In this paper, we propose an optimisation framework for exploiting multi

Vinod Kumar Chauhan, Soheila Molaei, Marzia Hoque Tania, Anshul Thakur

Observational studies have recently received significant attention from the machine learning community due to the increasingly available non-experimental observational data and the limitations of the experimental studies, such as considerable cost, impracticality, small and less representative sample sizes, etc. In observational studies, de-confounding is a

Hang Liu, Zhi-Peng Xing, Chang Yang

We explore the semileptonic and nonleptonic decays of doubly heavy baryons $(\Omega_{cc}^ {(*) +}, \Omega_{bb}^ {(*)0}, \Omega_{bc}^ {(*) -}, \Omega_{bc}^ {\prime 0}) $ induced by the $s\to u$ transition. Hadronic form factors are parametrized by transition matrix elements and are calculated in the light front quark model. With the form factors, we make use

Tjark Heitmann, Jonas Richter, Jacek Herbrych, Jochen Gemmer

The Lindblad master equation is one of the main approaches to open quantum systems. While it has been widely applied in the context of condensed matter systems to study properties of steady states in the limit of long times, the actual route to such steady states has attracted less attention yet. Here, we investigate the nonequilibrium dynamics of spin chain

Qile Yan, Shixiao Jiang, John Harlim

In this paper, we propose a mesh-free numerical method for solving elliptic PDEs on unknown manifolds, identified with randomly sampled point cloud data. The PDE solver is formulated as a spectral method where the test function space is the span of the leading eigenfunctions of the Laplacian operator, which are approximated from the point cloud data. While t

Georgios Rizos, Jenna Lawson, Simon Mitchell, Pranay Shah

We focus on using the predictive uncertainty signal calculated by Bayesian neural networks to guide learning in the self-same task the model is being trained on. Not opting for costly Monte Carlo sampling of weights, we propagate the approximate hidden variance in an end-to-end manner, throughout a variational Bayesian adaptation of a ResNet with attention a

Guangyao Li, Dmitry K. Efimkin

As the Earth rotates, the Coriolis force causes several oceanic and atmospheric waves to be trapped along the equator, including Kelvin, Yanai, Rossby, and Poincar\'e modes. It has been demonstrated that the mathematical origin of these waves is related to the nontrivial topology of the underlying hydrodynamic equations. Inspired by recent observations of Bo

Zhibin Wang, Yapeng Zhao, Yong Zhou, Yuanming Shi

The rapid advancement of artificial intelligence technologies has given rise to diversified intelligent services, which place unprecedented demands on massive connectivity and gigantic data aggregation. However, the scarce radio resources and stringent latency requirement make it challenging to meet these demands. To tackle these challenges, over-the-air com

Theodor Schnitzler, Katharina Kohls, Evangelos Bitsikas, Christina Pöpper

Mobile instant messengers such as WhatsApp use delivery status notifications in order to inform users if a sent message has successfully reached its destination. This is useful and important information for the sender due to the often asynchronous use of the messenger service. However, as we demonstrate in this paper, this standard feature opens up a timing

Manuel Wingenfelder, Marcel Sarstedt, Lutz Hofmann

This paper extends the research regarding the determination of the feasible operation region under the impact of electric vehicles. Thereby, the active and reactive power flexibility potentials of EV for alternating current and direct current bidirectional charging are limited in first instance by regulatory guidelines. In this paper German regulatory guidel

Harun Al Rashid, Garima Goyal, Alireza Akbari, Dheeraj Kumar Singh

We investigate the temperature dependence of quasiparticle interference in the high $T_c$-cuprates using an Exact-Diagonalization + Monte-Carlo based scheme to simulate the $d$-wave superconducting order parameter. The quasiparticle interference patterns have features largely resulting from the scattering vectors of the octet model at lower temperature. Our

Li-Chun Zhang

Node embedding is a central topic in graph representation learning. Computational efficiency and scalability can be challenging to any method that requires full-graph operations. We propose sampling approaches to node embedding, with or without explicit modelling of the feature vector, which aim to extract useful information from both the eigenvectors relate

Xiao Sun, Weiqing Cheng, Song Liang, Shengwei Ye

We report the first demonstration of a 1555 nm stepped-height ridge waveguide polarization mode converter monolithically integrated with a side wall grating distributed-feedback (DFB) laser using the identical epitaxial layer scheme. The device shows stable single longitudinal mode (SLM) operation with the output light converted from TE to TM polarization wi

Tiago Carvalho, Jackson Cunha, Bruno Rodrigues Freita

In this paper we obtain 32 canonical forms for 3D piecewise smooth vector fields presenting the so called cusp-fold singularity. All these canonical forms are topologically distinct and collect the main topological aspects of the singularities described as kind of the tangencies involved and positions of the sliding, escaping and crossing regions. Also, one-

Gizem Gezici, Aldo Lipani, Yucel Saygin, Emine Yilmaz

Search engines decide what we see for a given search query. Since many people are exposed to information through search engines, it is fair to expect that search engines are neutral. However, search engine results do not necessarily cover all the viewpoints of a search query topic, and they can be biased towards a specific view since search engine results ar

Chaopeng Tan, Jiarong Yao, Xuegang, Ban

Accurate traffic demand estimation is critical for the dynamic evaluation and optimization of signalized intersections. Existing studies based on connected vehicle (CV) data are designed for a single phase only and have not sufficiently studied the real-time traffic demand estimation for oversaturated traffic conditions. Therefore, this study proposes a cycl

Pouria Mehrabi, Hamid D. Taghirad

Both in terrestrial and extraterrestrial environments, the precise and informative model of the ground and the surface ahead is crucial for navigation and obstacle avoidance. The ground surface is not always flat and it may be sloped, bumpy and rough specially in off-road terrestrial scenes. In bumpy and rough scenes the functional relationship of the surfac

Stefanos Laskaridis, Stylianos I. Venieris, Alexandros Kouris, Rui Li

In the last decade, Deep Learning has rapidly infiltrated the consumer end, mainly thanks to hardware acceleration across devices. However, as we look towards the future, it is evident that isolated hardware will be insufficient. Increasingly complex AI tasks demand shared resources, cross-device collaboration, and multiple data types, all without compromisi

Sigeng Chen, Jeffrey S. Rosenthal, Aki Dote, Hirotaka Tamura

The Metropolis algorithm involves producing a Markov chain to converge to a specified target density $\pi$. In order to improve its efficiency, we can use the Rejection-Free version of the Metropolis algorithm, which avoids the inefficiency of rejections by evaluating all neighbors. Rejection-Free can be made more efficient through the use of parallelism har

Ayodeji Adeniran, David Mohaisen

Cryptocurrencies, arguably the most prominent application of blockchains, have been on the rise with a wide mainstream acceptance. A central concept in cryptocurrencies is "mining pools", groups of cooperating cryptocurrency miners who agree to share block rewards in proportion to their contributed mining power. Despite many promised benefits of cryptocurren

Stuti Jain, Emilie Chouzenoux, Kriti Kumar, Angshul Majumdar

Co-administration of two or more drugs simultaneously can result in adverse drug reactions. Identifying drug-drug interactions (DDIs) is necessary, especially for drug development and for repurposing old drugs. DDI prediction can be viewed as a matrix completion task, for which matrix factorization (MF) appears as a suitable solution. This paper presents a n

Vipul Upadhyay, Poshika Gandhi, Rohit Juneja, Rahul Marathe

We investigate heat current magnification due to asymmetry in the number of spins in two-branched classical and quantum spin systems. We begin by studying the classical Ising like spin models using Q2R and CCA dynamics and show that just the difference in the number of spins is not enough and some other source of asymmetry is required to observe heat current

Nathan Berkovits, Carlos R. Mafra

The pure spinor formalism for the superstring has the advantage over the more conventional Ramond-Neveu-Schwarz formalism of being manifestly spacetime supersymmetric, which simplifies the computation of multiparticle and multiloop amplitudes and allows the description of Ramond-Ramond backgrounds. In addition to the worldsheet variables of the Green-Schwarz

Zekai Wang, Stavros Stavrakis, Bing Yao

Cardiac disease is the leading cause of death in the US. Accurate heart disease detection is of critical importance for timely medical treatment to save patients' lives. Routine use of electrocardiogram (ECG) is the most common method for physicians to assess the electrical activities of the heart and detect possible abnormal cardiac conditions. Fully utiliz

Yassine El Gantouh

This paper focuses on boundary approximate controllability under positivity constraints of a wide range of infinite-dimensional control systems. We develop frequency domain controllability criteria. Firstly, we derive a controllability result under positivity constraints on the control for such systems. Then, and more importantly, we provide a necessary and

Chaopeng Tan, Yumin Cao, Xuegang, Ban

Time-dependent fixed-time control is a cost-effective control method that is widely employed at signalized intersections in numerous countries. Existing optimization models rely on traditional delay models with specific assumptions regarding vehicle arrivals. Recent advancements in intelligent mobility have led to development of high-resolution trajectory da

Suresh Bishnoi, Skyler Badge, Jayadeva, N. M. Anoop Krishnan

Due to their disordered structure, glasses present a unique challenge in predicting the composition-property relationships. Recently, several attempts have been made to predict the glass properties using machine learning techniques. However, these techniques have the limitations, namely, (i) predictions are limited to the components that are present in the o

Zhifeng Wang, Yao Yang, Chunyan Zeng, Shuai Kong

Digital audio tampering detection can be used to verify the authenticity of digital audio. However, most current methods use standard electronic network frequency (ENF) databases for visual comparison analysis of ENF continuity of digital audio or perform feature extraction for classification by machine learning methods. ENF databases are usually tricky to o

Hongcheng Guo, Jiaheng Liu, Haoyang Huang, Jian Yang

Multimodal Machine Translation (MMT) focuses on enhancing text-only translation with visual features, which has attracted considerable attention from both natural language processing and computer vision communities. Recent advances still struggle to train a separate model for each language pair, which is costly and unaffordable when the number of languages i

Louis-Vincent Bouthier, Thomas Gibaud

Typically, in quiescent conditions, attractive colloids at low volume fractions form fractal gels structured into two length scales: the colloidal and the fractal cluster scales. However when flow interfere with gelation colloidal fractal gels may display three distinct length scales [Dag\`es, et al., Soft Matter 18, 6645 (2022)]. Following those recent expe

Jérémy Champagne, Damien Roy

Following Schmidt, Thurnheer and Bugeaud-Kristensen, we study how Dirichlet's theorem on linear forms needs to be modified when one requires that the vectors of coefficients of the linear forms make a bounded acute angle with respect to a fixed proper non-zero subspace $V$ of $\mathbb{R}^n$. Assuming that the point of $\mathbb{R}^n$ that we are approximating

Felicia Lucke, Felix Mann

We consider the following problem: for a given graph $G$ and two integers $k$ and $d$, can we apply a fixed graph operation at most $k$ times in order to reduce a given graph parameter $\pi$ by at least $d$? We show that this problem is NP-hard when the parameter is the independence number and the graph operation is vertex deletion or edge contraction, even

Ismael Ben-Yelun, Miguel Diaz-Lago, Luis Saucedo-Mora, Miguel Angel Sanz

Applications of Structural Health Monitoring (SHM) combined with Machine Learning (ML) techniques enhance real-time performance tracking and increase structural integrity awareness of civil, aerospace and automotive infrastructures. This SHM-ML synergy has gained popularity in the last years thanks to the anticipation of maintenance provided by arising ML al

Luca Barbieri, Osvaldo Simeone, Monica Nicoli

Bayesian Federated Learning (FL) offers a principled framework to account for the uncertainty caused by limitations in the data available at the nodes implementing collaborative training. In Bayesian FL, nodes exchange information about local posterior distributions over the model parameters space. This paper focuses on Bayesian FL implemented in a device-to

D. O. Akat'ev, A. V. Vasiliev, N. M. Shafeev, F. M. Ablayev

A new version of quantum hashing technique is developed wherein a quantum hash is constructed as a sequence of single-photon high-dimensional states (qudits). A proof-of-principle implementation of the high-dimensional quantum hashing protocol using orbital-angular momentum encoding of single photons is implemented. It is shown that the number of qudits decr

Henry Best, Joshua Fagin, Georgios Vernardos, Matthew O'Dowd

In the near future, wide field surveys will discover 1000's of new strongly lensed quasars, and these will be monitored with unprecedented cadence by the Legacy Survey of Space and Time (LSST). Many of these will undergo caustic-crossing microlensing events over the 10-year LSST survey, in which a sharp caustic feature from a stellar body in the lensing gala

Yunlong Zheng, Haomin Rao

Two-field mimetic gravity was recently realized by looking at the singular limit of the conformal transformation between the auxiliary metric and the physical metric with two scalar fields involved. In this paper, we reanalyze the singular conformal limit and find a more general solution for the conformal factor A, which greatly broadens the form of two-fiel

Elena Mäder-Baumdicker, Melanie Rothe

We give a topological classification of Lawson's bipolar minimal surfaces corresponding to his $\xi$- and $\eta$-family. Therefrom we deduce upper as well as lower bounds on the area of these surfaces, and find that they are not embedded.

A. Ronan

We derive double coset formulae for the genus and extended genus of a finitely generated nilpotent group G, using the notions of bounded and bounded above automorphisms of $\prod G_S$, which are defined relative to a fixed fracture square for G.

Harish Kumar, M. Köpf, A. Ullrich, M. Klinger

Spin-orbit coupling in heavy 5$d$ metal oxides, in particular, iridates have received tremendous interest in recent years due to the realization of exotic electronic and magnetic phases. Here, we report the synthesis, structural, magnetic, thermodynamic, and optical properties of the ternary iridate Pr$_3$IrO$_7$. Single crystals of Pr$_3$IrO$_7$ have been g

Peng Xing, Hao Tang, Jinhui Tang, Zechao Li

Knowledge Distillation-based Anomaly Detection (KDAD) methods rely on the teacher-student paradigm to detect and segment anomalous regions by contrasting the unique features extracted by both networks. However, existing KDAD methods suffer from two main limitations: 1) the student network can effortlessly replicate the teacher network's representations, and

Dongchen Huang, Junde Liu, Tian Qian, Yi-feng Yang

De-noising plays a crucial role in the post-processing of spectra. Machine learning-based methods show good performance in extracting intrinsic information from noisy data, but often require a high-quality training set that is typically inaccessible in real experimental measurements. Here, using spectra in angle-resolved photoemission spectroscopy (ARPES) as

Marcelo Botta Cantcheff

An asymptotically AdS geometry connecting two or more boundaries is given by a entangled state, that can be expanded in the product basis of the Hilbert spaces of each CFT living on the boundaries. We derive a prescription to compute this expansion for states describing spacetimes with general spatial topology in arbitrary dimension. To large N, the expansio

Thomas F Burns, Irwansyah

Artificial and biological neural networks (ANNs and BNNs) can encode inputs in the form of combinations of individual neurons' activities. These combinatorial neural codes present a computational challenge for direct and efficient analysis due to their high dimensionality and often large volumes of data. Here we improve the computational complexity -- from f

Ali Tourani, Hriday Bavle, Jose Luis Sanchez-Lopez, Holger Voos

Vision-based sensors have shown significant performance, accuracy, and efficiency gain in Simultaneous Localization and Mapping (SLAM) systems in recent years. In this regard, Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map generation. We can see many research works tha

Diogo Boito, Attilio Cucchieri, Cristiane Y. London, Tereza Mendes

We employ Pad\'e approximants in the study of the analytic structure of the four-dimensional $SU(2)$ Landau-gauge gluon and ghost propagators in the infrared regime. The approximants, which are model independent, serve as fitting functions for the lattice data. We carefully propagate the uncertainties due to the fitting procedure, taking into account all pos

Bahareh Ghari, Ali Tourani, Asadollah Shahbahrami

Nowadays, utilizing Advanced Driver-Assistance Systems (ADAS) has absorbed a huge interest as a potential solution for reducing road traffic issues. Despite recent technological advances in such systems, there are still many inquiries that need to be overcome. For instance, ADAS requires accurate and real-time detection of pedestrians in various driving scen

Adaku Uchendu, Thai Le, Dongwon Lee

Two interlocking research questions of growing interest and importance in privacy research are Authorship Attribution (AA) and Authorship Obfuscation (AO). Given an artifact, especially a text t in question, an AA solution aims to accurately attribute t to its true author out of many candidate authors while an AO solution aims to modify t to hide its true au

Lorenzo Perini, Paul Buerkner, Arto Klami

Anomaly detection methods identify examples that do not follow the expected behaviour, typically in an unsupervised fashion, by assigning real-valued anomaly scores to the examples based on various heuristics. These scores need to be transformed into actual predictions by thresholding, so that the proportion of examples marked as anomalies equals the expecte

Abhra Chaudhuri, Massimiliano Mancini, Yanbei Chen, Zeynep Akata

Representation learning for sketch-based image retrieval has mostly been tackled by learning embeddings that discard modality-specific information. As instances from different modalities can often provide complementary information describing the underlying concept, we propose a cross-attention framework for Vision Transformers (XModalViT) that fuses modality

Minseon Kim, Hyeonjeong Ha, Dong Bok Lee, Sung Ju Hwang

Despite the success on few-shot learning problems, most meta-learned models only focus on achieving good performance on clean examples and thus easily break down when given adversarially perturbed samples. While some recent works have shown that a combination of adversarial learning and meta-learning could enhance the robustness of a meta-learner against adv

Maciej Wołoszyn, Krzysztof Kułakowski

A new model of an evolution of ranks of employees due to staff turnover in an organization is designed. If the rank is determined only by the performance, the rank shift of incumbents due to the turnover is proportional to the initial rank: the status of high staff is reduced only slightly. This effect that has been observed in the literature. However, if th

Minseon Kim, Hyeonjeong Ha, Sooel Son, Sung Ju Hwang

Recently, unsupervised adversarial training (AT) has been highlighted as a means of achieving robustness in models without any label information. Previous studies in unsupervised AT have mostly focused on implementing self-supervised learning (SSL) frameworks, which maximize the instance-wise classification loss to generate adversarial examples. However, we

Tiziano Dalmonte, Marianna Girlando

We introduce a family of comparative plausibility logics over neighbourhood models, generalising Lewis' comparative plausibility operator over sphere models. We provide axiom systems for the logics, and prove their soundness and completeness with respect to the semantics. Then, we introduce two kinds of analytic proof systems for several logics in the family

Michael Bertolacci, Andrew Zammit-Mangion, Andrew Schuh, Beata Bukosa

The natural cycles of the surface-to-atmosphere fluxes of carbon dioxide (CO$_2$) and other important greenhouse gases are changing in response to human influences. These changes need to be quantified to understand climate change and its impacts, but this is difficult to do because natural fluxes occur over large spatial and temporal scales. To infer trends

Katarzyna Berent, Julyan H. E. Cartwright, Antonio G. Checa, Carlos Pimentel

We demonstrate that nature has produced a close-packed helical twisted filamentous material in the biomineralization of the mollusc. In liquid crystals, twist-bend nematics have been predicted and observed. We present and analyse evidence that the helical biomineral microstructure of mollusc shells may be formed from such a liquid-crystal precursor.

Kai Ren, Chuanping Hu

The problem of multi-object tracking is a fundamental computer vision research focus, widely used in public safety, transport, autonomous vehicles, robotics, and other regions involving artificial intelligence. Because of the complexity of natural scenes, object occlusion and semi-occlusion usually occur in fundamental tracking tasks. These can easily lead t

Baker Shalak, Christophe Delerue, Yann-Michel Niquet

Spin qubits in semiconductor quantum dots are one of the promizing devices to realize a quantum processor. A better knowledge of the noise sources affecting the coherence of such a qubit is therefore of prime importance. In this work, we study the effect of telegraphic noise induced by the fluctuation of a single electric charge. We simulate as realistically

Gurpreet Singh, Hridis K. Pal

De Haas-van Alphen (dHvA) oscillations are oscillations in the magnetization as a function of the inverse magnetic field. These oscillations are usually considered to be a property of the Fermi surface and, hence, a metallic property. Recently, however, such oscillations have been shown to arise, both experimentally and theoretically, in certain insulators w

Germán Mora Martín, Stirling Scholes, Alice Ruget, Robert K. Henderson

3D Time-of-Flight (ToF) image sensors are used widely in applications such as self-driving cars, Augmented Reality (AR) and robotics. When implemented with Single-Photon Avalanche Diodes (SPADs), compact, array format sensors can be made that offer accurate depth maps over long distances, without the need for mechanical scanning. However, array sizes tend to

Felix Rosberg, Eren Erdal Aksoy, Fernando Alonso-Fernandez, Cristofer Englund

In this work, we present a new single-stage method for subject agnostic face swapping and identity transfer, named FaceDancer. We have two major contributions: Adaptive Feature Fusion Attention (AFFA) and Interpreted Feature Similarity Regularization (IFSR). The AFFA module is embedded in the decoder and adaptively learns to fuse attribute features and featu

Sara Abdelrady Hassan, Asmaa. A. A, Sherif Yehia, M. M. Ahmed

In the current work, We investigated the production of neutral Higgs boson and two charged Charginos owing to electron-positron annihilation via different propagators

Edward Kissin, Victor S. Shulman, Yurii V. Turovskii

We consider weakly closed transitive algebras of operators containing non-zero compact operators in real Banach spaces (Lomonosov algebras). It is shown that they are naturally divided in three classes: the algebras of real, complex and quaternion classes. The properties and characterizations of algebras in each class as well as some useful examples are pres

Sagar Silva Pratapsi, Patrick H. Huber, Patrick Barthel, Sougato Bose

Reversible computation has been proposed as a future paradigm for energy efficient computation, but so far few implementations have been realised in practice. Quantum circuits, running on quantum computers, are one construct known to be reversible. In this work, we provide a proof-of-principle of classical logical gates running on quantum technologies. In pa

Chengqian Gao, Ke Xu, Liu Liu, Deheng Ye

A promising paradigm for offline reinforcement learning (RL) is to constrain the learned policy to stay close to the dataset behaviors, known as policy constraint offline RL. However, existing works heavily rely on the purity of the data, exhibiting performance degradation or even catastrophic failure when learning from contaminated datasets containing impur

Ian Vernon, Jonathan Owen, Jonathan Carter

Computer models are widely used across a range of scientific disciplines to describe various complex physical systems, however to perform full uncertainty quantification we often need to employ emulators. An emulator is a fast statistical construct that mimics the slow to evaluate computer model, and greatly aids the vastly more computationally intensive unc

Takeyoshi Kogiso, Hideto Nakashima

In this paper, we construct a new series of prehomogeneous vector spaces from figures made up of triangles, called triangle arrangements. Our main theorem states that, under suitable assumptions, we are able to construct a prehomogeneous vector space obtained from a triangle arrangement by attaching two triangle arrangements corresponding to prehomogeneous v

Hussein Ayad, Maryam Samadi, Shahram Abbassi

We investigate the dynamics of clumps that coexisted with/in advection-dominated accretion flows by considering thermal conductivity. Thermal conduction can be one of the effective factors in the energy transportation of ADAFs; hence it may indirectly affect the dynamics of clumps by means of a contact force between them and their host medium. We first study

Bin Yang, Yuming Qin

In this paper, under some appropriate assumptions, we prove the existence of the minimal time-dependent pullback $\mathcal D_{\sigma}^{\mathcal{H}_{t}}$-attractors ${\mathcal{A}}_{\mathcal D_{\sigma}^{\mathcal{H}_{t}}}$ for the non-autonomous nonlocal diffusion equations in time-dependent space $\mathcal{H}_{t}(\Omega)$. Next, in same phase space, using a pr