November 2022 arXiv papers — page 172

Guoxia Wang, Zhihua Wu, Xiaomin Fang, Yingfei Xiang

The accuracy of AlphaFold2, a frontier end-to-end structure prediction system, is already close to that of the experimental determination techniques. Due to the complex model architecture and large memory consumption, it requires lots of computational resources and time to train AlphaFold2 from scratch. Efficient AlphaFold2 training could accelerate the deve

Federico Sabbatini, Roberta Calegari

Opaque models belonging to the machine learning world are ever more exploited in the most different application areas. These models, acting as black boxes (BB) from the human perspective, cannot be entirely trusted if the application is critical unless there exists a method to extract symbolic and human-readable knowledge out of them. In this paper we analys

Juni Kim, Zhikang Dong, Eric Guan, Judah Rosenthal

We provide a new non-invasive, easy-to-scale for large amounts of subjects and a remotely accessible method for (hidden) emotion detection from videos of human faces. Our approach combines face manifold detection for accurate location of the face in the video with local face manifold embedding to create a common domain for the measurements of muscle micro-mo

Kou Lei, Liu Chuang, Cai Guo-Wei, Zhang Zhe

Three-phase PWM rectifiers are adopted extensively in industry because of their excellent properties and potential advantages. However, while the IGBT has an open-circuit fault, the system does not crash suddenly, the performance will be reduced for instance voltages fluctuation and current harmonics. A fault diagnosis method based on deep feedforward networ

Adityanarayanan Radhakrishnan, Max Ruiz Luyten, Neha Prasad, Caroline Uhler

Transfer learning refers to the process of adapting a model trained on a source task to a target task. While kernel methods are conceptually and computationally simple machine learning models that are competitive on a variety of tasks, it has been unclear how to perform transfer learning for kernel methods. In this work, we propose a transfer learning framew

Zexin Cai, Weiqing Wang, Ming Li

The present paper proposes a waveform boundary detection system for audio spoofing attacks containing partially manipulated segments. Partially spoofed/fake audio, where part of the utterance is replaced, either with synthetic or natural audio clips, has recently been reported as one scenario of audio deepfakes. As deepfakes can be a threat to social securit

Lei Kou, Yang Li, Fangfang Zhang, Xiaodong Gong

In recent years, with the development of wind energy, the number and scale of wind farms are developing rapidly. Since offshore wind farm has the advantages of stable wind speed, clean, renewable, non-polluting and no occupation of cultivated land, which has gradually become a new trend of wind power industry all over the world. The operation and maintenance

Yuying Liang, Ryuki Hyodo

Particles of various sizes can exist around Mars. The orbits of large particles are mainly governed by Martian gravity, while those of small particles could be significantly affected by non-gravitational forces. Many of the previous studies of particle dynamics around Mars have focused on relatively small particles (radius of $r_{\rm p} \lesssim 100 \, μm$)

Raphaël Pellegrin, Blake Bullwinkel, Marios Mattheakis, Pavlos Protopapas

Physics-Informed Neural Networks (PINNs) offer a promising approach to solving differential equations and, more generally, to applying deep learning to problems in the physical sciences. We adopt a recently developed transfer learning approach for PINNs and introduce a multi-head model to efficiently obtain accurate solutions to nonlinear systems of ordinary

Nouman Khan, Mehrdad Moharrami, Vijay Subramanian

Recent studies suggested that the BitTorrent's rarest-first protocol, owing to its work-conserving nature, can become unstable in the presence of non-persistent users. Consequently, for any provably stable protocol, many peers, at some point, would have to be endogenously forced to hold off their file-download activity. In this work, we propose a tunable

Woo-Seok Jung, Young-Tak Oh

The purpose of this paper is to study induction and restriction of two-variable Hecke algebras. First, we provide the explicit form of the Mackey decomposition formula. And then, we elucidate how (anti-)involutions interact with induction product and restriction.

Xiaoshui Huang, Wentao Qu, Yifan Zuo, Yuming Fang

Rejecting correspondence outliers enables to boost the correspondence quality, which is a critical step in achieving high point cloud registration accuracy. The current state-of-the-art correspondence outlier rejection methods only utilize the structure features of the correspondences. However, texture information is critical to reject the correspondence out

Antonina M. Kosikova, Omid Sedehi, Costas Papadimitriou, Lambros S. Katafygiotis

Bayesian model updating based on Gaussian Process (GP) models has received attention in recent years, which incorporates kernel-based GPs to provide enhanced fidelity response predictions. Although most kernel functions provide high fitting accuracy in the training data set, their out-of-sample predictions can be highly inaccurate. This paper investigates th

I. Bahmani Jafarloo, C. Bocci, E. Guardo, G. Malara

In this paper we address the question if, for points $P, Q \in \mathbb{P}^{2}$, $I(P)^{m} \star I(Q)^{n}=I(P \star Q)^{m+n-1}$ and we obtain different results according to the number of zero coordinates in $P$ and $Q$. Successively, we use our results to define the so called Hadamard fat grids, which are the result of the Hadamard product of two sets of coll