1,015,297 Works

Generative Modeling of Complex Data

Luca Canale, Nicolas Grislain, Grégoire Lothe & Johan Leduc
In recent years, several models have improved the capacity to generate synthetic tabular datasets. However, such models focus on synthesizing simple columnar tables and are not useable on real-life data with complex structures. This paper puts forward a generic framework to synthesize more complex data structures with composite and nested types. It then proposes one practical implementation, built with causal transformers, for struct (mappings of types) and lists (repeated instances of a type). The results...

Structure Property in Cu crystallization

Bobin Li
Phase transition is a central topic in condensed matter physics, all the time. In this paper, as a general representative of phase transition, the Cu crystallization is discussed. And some physical quantities is defined to quantificationally describe the structure property in Cu crystallization, such as diffusion property and symmetry so on. As a result, it is indicated that there are some interesting changes of structure property in Cu crystallization.

On the absence of global weak solutions for a nonlinear time-fractional Schrödinger equation

Munirah Alotaibi, Mohamed Jleli, Maria Alessandra Ragusa & Bessem Samet
In this paper, an initial value problem for a nonlinear time-fractional Schrödinger equation with a singular logarithmic potential term is investigated. The considered problem involves the left/forward Hadamard-Caputo fractional derivative with respect to the time variable. Using the test function method with a judicious choice of the test function, we obtain sufficient criteria for the absence of global weak solutions.

Objective Prediction of Tomorrow's Affect Using Multi-Modal Physiological Data and Personal Chronicles: A Study of Monitoring College Student Well-being in 2020

Salar Jafarlou, Jocelyn Lai, Zahra Mousavi, Sina Labbaf, Ramesh Jain, Nikil Dutt, Jessica Borelli & Amir Rahmani
Monitoring and understanding affective states are important aspects of healthy functioning and treatment of mood-based disorders. Recent advancements of ubiquitous wearable technologies have increased the reliability of such tools in detecting and accurately estimating mental states (e.g., mood, stress, etc.), offering comprehensive and continuous monitoring of individuals over time. Previous attempts to model an individual's mental state were limited to subjective approaches or the inclusion of only a few modalities (i.e., phone, watch). Thus, the...

Stochastic Identification-based Active Sensing Acousto-Ultrasound SHM Using Stationary Time Series Models

Shabbir Ahmed & Fotis Kopsaftopoulos
In this work, a probabilistic damage detection and identification scheme using stochastic time series models in the context of acousto-ultrasound guided wave-based SHM is proposed, and its performance is assessed experimentally. In order to simplify the damage detection and identification process, model parameters are modified based on the singular value decomposition (SVD) as well as the principal component analysis (PCA)-based truncation approach. The modified model parameters are then used to estimate a statistical characteristic quantity...

Restricted Variable Chevalley-Warning Theorems

Anurag Bishnoi & Pete L. Clark
We pursue various restricted variable generalizations of the Chevalley-Warning theorem for low degree polynomial systems over a finite field. Our first such result involves variables restricted to Cartesian products of the Vandermonde subsets of $\F_q$ defined by Gács-Weiner and Sziklai-Takáts. We then define an invariant $\uomega(X)$ of a nonempty subset of $\F_q^n$. Our second result involves $X$-restricted variables when the degrees of the polynomials are small compared to $\uomega(X)$. We end by exploring various classes...

Jointly Learning Knowledge Embedding and Neighborhood Consensus with Relational Knowledge Distillation for Entity Alignment

Xinhang Li, Yong Zhang & Chunxiao Xing
Entity alignment aims at integrating heterogeneous knowledge from different knowledge graphs. Recent studies employ embedding-based methods by first learning the representation of Knowledge Graphs and then performing entity alignment via measuring the similarity between entity embeddings. However, they failed to make good use of the relation semantic information due to the trade-off problem caused by the different objectives of learning knowledge embedding and neighborhood consensus. To address this problem, we propose Relational Knowledge Distillation for...

Neuro-Symbolic Entropy Regularization

Kareem Ahmed, Eric Wang, Kai-Wei Chang & Guy Van den Broeck
In structured prediction, the goal is to jointly predict many output variables that together encode a structured object -- a path in a graph, an entity-relation triple, or an ordering of objects. Such a large output space makes learning hard and requires vast amounts of labeled data. Different approaches leverage alternate sources of supervision. One approach -- entropy regularization -- posits that decision boundaries should lie in low-probability regions. It extracts supervision from unlabeled examples,...

Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matching

Hanchen Wang, Ying Zhang, Lu Qin, Wei Wang, Wenjie Zhang & Xuemin Lin
Subgraph matching is a fundamental problem in various fields that use graph structured data. Subgraph matching algorithms enumerate all isomorphic embeddings of a query graph q in a data graph G. An important branch of matching algorithms exploit the backtracking search approach which recursively extends intermediate results following a matching order of query vertices. It has been shown that the matching order plays a critical role in time efficiency of these backtracking based subgraph matching...

$L^p$ bounds for square roots of elliptic systems on open sets

Sebastian Bechtel
We show $L^p$ estimates for square roots of second order complex elliptic systems $L$ in divergence form on open sets in $\mathbb{R}^d$ subject to mixed boundary conditions. The underlying set is supposed to be locally uniform near the Neumann boundary part, and the Dirichlet boundary part is Ahlfors-David regular. The lower endpoint for the interval where such estimates are available is characterized by $p$-boundedness properties of the semigroup generated by $-L$, and the upper endpoint...

GoSafeOpt: Scalable Safe Exploration for Global Optimization of Dynamical Systems

Bhavya Sukhija, Matteo Turchetta, David Lindner, Andreas Krause, Sebastian Trimpe & Dominik Baumann
Learning optimal control policies directly on physical systems is challenging since even a single failure can lead to costly hardware damage. Most existing model-free learning methods that guarantee safety, i.e., no failures, during exploration are limited to local optima. A notable exception is the GoSafe algorithm, which, unfortunately, cannot handle high-dimensional systems and hence cannot be applied to most real-world dynamical systems. This work proposes GoSafeOpt as the first algorithm that can safely discover globally...

Theoretical analysis of thermophoretic experimental data

J. M. Sancho
Thermophoresis is a transport phenomenon induced by a temperature gradient. Very small objects dispersed in a fluid medium and in a temperature gradient present a non homogeneous steady density. Analysing this phenomenon within the theoretical scenario of non interacting Brownian motion one can assume that those particles are driven by a spatially dependent mechanical force. This implies the existence of a potential which was derived in a previous work. From this potential the qualitative properties...

AutoSeg -- Steering the Inductive Biases for Automatic Pathology Segmentation

Felix Meissen, Georgios Kaissis & Daniel Rueckert
In medical imaging, un-, semi-, or self-supervised pathology detection is often approached with anomaly- or out-of-distribution detection methods, whose inductive biases are not intentionally directed towards detecting pathologies, and are therefore sub-optimal for this task. To tackle this problem, we propose AutoSeg, an engine that can generate diverse artificial anomalies that resemble the properties of real-world pathologies. Our method can accurately segment unseen artificial anomalies and outperforms existing methods for pathology detection on a challenging...

Exact essential norm of generalized Hilbert matrix operators on classical analytic function spaces

Mikael Lindström, Santeri Miihkinen & David Norrbo
We compute the exact value of the essential norm of a generalized Hilbert matrix operator acting on weighted Bergman spaces $A^p_v$ and weighted Banach spaces $H^\infty_v$ of analytic functions, where $v$ is a general radial weight. In particular, we obtain the exact value of the essential norm of the classical Hilbert matrix operator on standard weighted Bergman spaces $A^p_α$ for $p>2+α, \, α\ge 0,$ and on Korenblum spaces $H^\infty_α$ for $0 < α< 1.$ We...

Deformation Theory of Holomorphic Cartan Geometries, II

Indranil Biswas, Sorin Dumitrescu & Georg Schumacher
In this continuation of \cite{BDS}, we investigate the deformations of holomorphic Cartan geometries where the underlying complex manifold is allowed to move. The space of infinitesimal deformations of a flat holomorphic Cartan geometry is computed. We show that the natural forgetful map, from the infinitesimal deformations of a flat holomorphic Cartan geometry to the infinitesimal deformations of the underlying flat principal bundle on the topological manifold, is an isomorphism.

TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

Philipp Thölke & Gianni De Fabritiis
The prediction of quantum mechanical properties is historically plagued by a trade-off between accuracy and speed. Machine learning potentials have previously shown great success in this domain, reaching increasingly better accuracy while maintaining computational efficiency comparable with classical force fields. In this work we propose TorchMD-NET, a novel equivariant transformer (ET) architecture, outperforming state-of-the-art on MD17, ANI-1, and many QM9 targets in both accuracy and computational efficiency. Through an extensive attention weight analysis, we gain...

Optimization of a Real-Time Wavelet-Based Algorithm for Improving Speech Intelligibility

Tianqu Kang, Anh-Dung Dinh, Binghong Wang, Tianyuan Du, Yijia Chen & Kevin Chau
The optimization of a wavelet-based algorithm to improve speech intelligibility along with the full data set and results are reported. The discrete-time speech signal is split into frequency sub-bands via a multi-level discrete wavelet transform. Various gains are applied to the sub-band signals before they are recombined to form a modified version of the speech. The sub-band gains are adjusted while keeping the overall signal energy unchanged, and the speech intelligibility under various background interference...

A generalized Bohr-Rogosinski phenomenon

Kamaljeet Gangania & S. Sivaprasad Kumar
In this paper, we generalize the Bohr-Rogosinski sum for the Ma-Minda classes of starlike and convex functions. Also the phenomenon is studied for the classes of starlike functions with respect to symmetric points and conjugate points along with their convex cases. Further, the connection between the derived results and the known ones are established with the suitable examples.

Turbulence in the Sub-Alfvénic Solar Wind

G. P. Zank, L. -L. Zhao, L. Adhikri, D. Telloni, J. C. Kasper, M. Stevens, A. Rahmati & S. D. Bale
Parker Solar Probe (PSP) entered a region of the sub-Alfvenic solar wind during encounter 8 and we present the first detailed analysis of low-frequency turbulence properties in this novel region. The magnetic field and flow velocity vectors were highly aligned during this interval. By constructing spectrograms of the normalized magnetic helicity, cross helicity, and residual energy, we find that PSP observed primarily Alfvenic fluctuations, a consequence of the highly field-aligned flow that renders quasi-2D fluctuations...

LST: Lexicon-Guided Self-Training for Few-Shot Text Classification

Hazel Kim, Jaeman Son & Yo-Sub Han
Self-training provides an effective means of using an extremely small amount of labeled data to create pseudo-labels for unlabeled data. Many state-of-the-art self-training approaches hinge on different regularization methods to prevent overfitting and improve generalization. Yet they still rely heavily on predictions initially trained with the limited labeled data as pseudo-labels and are likely to put overconfident label belief on erroneous classes depending on the first prediction. To tackle this issue in text classification, we...

AssistMe: Policy iteration for the longitudinal control of a non-holonomic vehicle

Catalin Stefan Teodorescu & Tom Carlson
In this article we design a physically-inspired model-based assist-as-needed semi-autonomous control (ASC) algorithm to address the problem of safely driving a vehicle (a power wheelchair) in an environment with static obstacles. Once implemented online, the proposed algorithm requires limited computing power and relies on pre-computed (offline) maps (look-up tables). These are readily available by implementing policy iteration that minimizes the expected time to termination (safely stopping near an obstacle), by taking into account: (i) the...

Rate Splitting for General Multicast

Lingzhi Zhao, Ying Cui, Sheng Yang, Shlomo Shamai, Yunbo Han & Yunfei Zhang
Immersive video, such as virtual reality (VR) and multi-view videos, is growing in popularity. Its wireless streaming is an instance of general multicast, extending conventional unicast and multicast, whose effective design is still open. This paper investigates the optimization of general rate splitting with linear beamforming for general multicast. Specifically, we consider a multi-carrier single-cell wireless network where a multi-antenna base station (BS) communicates to multiple single-antenna users via general multicast. Linear beamforming is adopted...

The R package $\texttt{ebmstate}$ for disease progression analysis under empirical Bayes Cox models

Rui J. Costa & Moritz Gerstung
The software package $\texttt{mstate}$, in articulation with the package $\texttt{survival}$, provides not only a well-established multi-state survival analysis framework in R, but also one of the most complete, as it includes point and interval estimation of relative transition hazards, cumulative transition hazards and state occupation probabilities, both under clock-forward and clock-reset models; personalised estimates, i.e. estimates for an individual with specific covariate measurements, can also be obtained with $\texttt{mstate}$ by fitting a Cox regression model....

The Asymptotic Structure of Gravity in Higher Even Dimensions

Chandramouli Chowdhury, Ruchira Mishra & Siddharth G. Prabhu
We investigate the notion of asymptotic symmetries in classical gravity in higher even dimensions, with $D = 6$ space-time dimensions as the prototype. Unlike in four dimensions, certain non-linearities persist which necessitates the complete non-linear analysis we undertake. We show that the free data is parametrized by a pair of symmetric trace-free tensors at future (past) null infinity. This involves a redefinition of the radiative field. We define a symplectic structure generating the radiative phase...

Wideband Multi-User MIMO Communications with Frequency Selective RISs: Element Response Modeling and Sum-Rate Maximization

Konstantinos D. Katsanos, Nir Shlezinger, Mohammadreza F. Imani & George C. Alexandropoulos
Reconfigurable Intelligent Surfaces (RISs) are an emerging technology for future wireless communication systems, enabling improved coverage in an energy efficient manner. RISs are usually metasurfaces, constituting of two-dimensional arrangements of metamaterial elements, whose individual response is commonly modeled in the literature as an adjustable phase shifter. However, this model holds only for narrowband communications, and when wideband transmissions are utilized, one has to account for the frequency selectivity of metamaterials, whose response usually follows a...

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