pwc-archive/papers-with-abstracts huggingface dataset:
Decoding-Energy-Rate-Distortion Optimization for Video Coding
Authors: Christian Herglotz, Andreas Heindel, André Kaup
Date: 2022-03-02 00:00:00

This paper presents a method for generating coded video bit streams requiring less decoding energy than conventionally coded bit streams. To this end, we propose extending the standard rate-distortion optimization approach to also consider the decoding energy. In the encoder, the decoding energy is estimated during runtime using a feature-based energy model. These energy estimates are then used to calculate decoding-energy-rate-distortion costs that are minimized by the encoder. This ultimately leads to optimal trade-offs between these three parameters. Therefore, we introduce the mathematical theory for describing decoding-energy-rate-distortion optimization and the proposed encoder algorithm is explained in detail. For rate-energy control, a new encoder parameter is introduced. Finally, measurements of the software decoding process for HEVC-coded bit streams are performed. Results show that this approach can lead to up to 30% of decoding energy reduction at a constant visual objective quality when accepting a bitrate increase at the same order of magnitude.
Time-Domain Analysis for Resonant Beam Charging and Communications With Delay-Divide Demodulation
Authors: Mingliang Xiong, Shun Han, Qingwen Liu, Shengli Zhou
Date: 2022-03-02 00:00:00

Laser has unique advantages such as abundant spectrum resources and low propagation divergence in wireless charging and wireless communications, compared with radio frequency. Resonant beams, as a kind of intra-cavity laser beams, have been proposed as the carrier of wireless charging and communication, as it has unique features including high power, intrinsic safety, and self-aligned mobility. However, this system has problems such as intra-cavity echo interference and power fluctuation. To study the time-domain behavior of the resonant beam system, we create a simulation algorithm by discretizing the laser rate equations which model the dynamics of the excited atom density in the gain medium and the photon density in the cavity. The simulation results are in good agreement with theoretical calculation. We also propose a delay-divide demodulation method to address the echo interference issue, and use the simulation algorithm to verify its feasibility. The results show that the resonant beam charging and communication system with the proposed demodulator is feasible and performs well. The analysis in this work also helps researchers to deeply understand the behavior of the resonant beam system.
Framework for Network-Constrained Tracking of Cyclists and Pedestrians
Authors: Alphonse Vial, Gustaf Hendeby, Winnie Daamen, Bart van Arem, Serge Hoogendoorn
Date: 2022-03-02 00:00:00

The increase in perception capabilities of connected mobile sensor platforms (e.g., self-driving vehicles, drones, and robots) leads to an extensive surge of sensed features at various temporal and spatial scales. Beyond their traditional use for safe operation, available observations could enable to see how and where people move on sidewalks and cycle paths, to eventually obtain a complete microscopic and macroscopic picture of the traffic flows in a larger area. This paper proposes a new method for advanced traffic applications, tracking an unknown and varying number of moving targets (e.g., pedestrians or cyclists) constrained by a road network, using mobile (e.g., vehicles) spatially distributed sensor platforms. The key contribution in this paper is to introduce the concept of network bound targets into the multi-target tracking problem, and hence to derive a network-constrained multi-hypotheses tracker (NC-MHT) to fully utilize the available road information. This is done by introducing a target representation, comprising a traditional target tracking representation and a discrete component placing the target on a given segment in the network. A simulation study shows that the method performs well in comparison to the standard MHT filter in free space. Results particularly highlight network-constraint effects for more efficient target predictions over extended periods of time, and in the simplification of the measurement association process, as compared to not utilizing a network structure. This theoretical work also directs attention to latent privacy concerns for potential applications.
Machine Learning Methods for Inferring the Number of UAV Emitters via Massive MIMO Receive Array
Authors: YiFan Li, Feng Shu, Jinsong Hu, Shihao Yan, Haiwei Song, Weiqiang Zhu, Da Tian, Yaoliang Song, Jiangzhou Wang
Date: 2022-03-02 00:00:00
Tasks: Classification, MME

To provide important prior knowledge for the DOA estimation of UAV emitters in future wireless networks, we present a complete DOA preprocessing system for inferring the number of emitters via massive MIMO receive array. Firstly, in order to eliminate the noise signals, two high-precision signal detectors, square root of maximum eigenvalue times minimum eigenvalue (SR-MME) and geometric mean (GM), are proposed. Compared to other detectors, SR-MME and GM can achieve a high detection probability while maintaining extremely low false alarm probability. Secondly, if the existence of emitters is determined by detectors, we need to further confirm their number. Therefore, we perform feature extraction on the the eigenvalue sequence of sample covariance matrix to construct feature vector and innovatively propose a multi-layer neural network (ML-NN). Additionally, the support vector machine (SVM), and naive Bayesian classifier (NBC) are also designed. The simulation results show that the machine learning-based methods can achieve good results in signal classification, especially neural networks, which can always maintain the classification accuracy above 70\% with massive MIMO receive array. Finally, we analyze the classical signal classification methods, Akaike (AIC) and Minimum description length (MDL). It is concluded that the two methods are not suitable for scenarios with massive MIMO arrays, and they also have much worse performance than machine learning-based classifiers.
Joint Localization and Orientation Estimation in Millimeter-Wave MIMO OFDM Systems via Atomic Norm Minimization
Authors: Jianxiu Li, Maxime Ferreira Da Costa, Urbashi Mitra
Date: 2022-03-02 00:00:00

Herein, an atomic norm based method for accurately estimating the location and orientation of a target from millimeter-wave multi-input-multi-output (MIMO) orthogonal frequency-division multiplexing (OFDM) signals is presented. A novel virtual channel matrix is introduced and an algorithm to extract localization-relevant channel parameters from its atomic norm decomposition is designed. Then, based on the extended invariance principle, a weighted least squares problem is proposed to accurately recover the location and orientation using both line-of-sight and non-line-of-sight channel information. The conditions for the optimality and uniqueness of the estimate and theoretical guarantees for the estimation error are characterized for the noiseless and the noisy scenarios. Theoretical results are confirmed via simulation. Numerical results investigate the robustness of the proposed algorithm to incorrect model order selection or synchronization error, and highlight performance improvements over a prior method. The resultant performance nearly achieves the Cramer-Rao lower bound on the estimation error.
To further understand graph signals
Authors: Feng Ji, Wee Peng Tay
Date: 2022-03-02 00:00:00

Graph signal processing (GSP) is a framework to analyze and process graph-structured data. Many research works focus on developing tools such as Graph Fourier transforms (GFT), filters, and neural network models to handle graph signals. Such approaches have successfully taken care of ``signal processing'' in many circumstances. In this paper, we want to put emphasis on ``graph signals'' themselves. Although there are characterizations of graph signals using the notion of bandwidth derived from GFT, we want to argue here that graph signals may contain hidden geometric information of the network, independent of (graph) Fourier theories. We shall provide a framework to understand such information, and demonstrate how new knowledge on ``graph signals'' can help with ``signal processing''.
An Onsager-Machlup approach to the most probable transition pathway for a genetic regulatory network
Authors: Jianyu Hu, Xiaoli Chen, Jinqiao Duan
Date: 2022-03-02 00:00:00
Tasks: BIG-bench Machine Learning

We investigate a quantitative network of gene expression dynamics describing the competence development in Bacillus subtilis. First, we introduce an Onsager-Machlup approach to quantify the most probable transition pathway for both excitable and bistable dynamics. Then, we apply a machine learning method to calculate the most probable transition pathway via the Euler-Lagrangian equation. Finally, we analyze how the noise intensity affects the transition phenomena.
A Reproducing Kernel Hilbert Space approach to singular local stochastic volatility McKean-Vlasov models
Authors: Christian Bayer, Denis Belomestny, Oleg Butkovsky, John Schoenmakers
Date: 2022-03-02 00:00:00

Motivated by the challenges related to the calibration of financial models, we consider the problem of numerically solving a singular McKean-Vlasov equation $$ d X_t= \sigma(t,X_t) X_t \frac{\sqrt v_t}{\sqrt {E[v_t|X_t]}}dW_t, $$ where $W$ is a Brownian motion and $v$ is an adapted diffusion process. This equation can be considered as a singular local stochastic volatility model. Whilst such models are quite popular among practitioners, unfortunately, its well-posedness has not been fully understood yet and, in general, is possibly not guaranteed at all. We develop a novel regularization approach based on the reproducing kernel Hilbert space (RKHS) technique and show that the regularized model is well-posed. Furthermore, we prove propagation of chaos. We demonstrate numerically that a thus regularized model is able to perfectly replicate option prices due to typical local volatility models. Our results are also applicable to more general McKean--Vlasov equations.
A mathematical modelling portrait of Wnt signalling in early vertebrate embryogenesis
Authors: Claudiu V. Giuraniuc, Shabana Zain, Shahmama Ghafoor, Stefan Hoppler
Date: 2022-03-01 00:00:00

There are two phases of Wnt signalling in early vertebrate embryogenesis: very early, maternal Wnt signalling promotes dorsal development, and slightly later, zygotic Wnt signalling promotes ventral and lateral mesoderm induction. However, recent molecular biology analysis has revealed more complexity among the direct Wnt target genes, with at least five classes. Here in order to test the logic and the dynamics of a new Gene Regulatory Network model suggested by these discoveries we use mathematical modelling based on ordinary differential equations (ODEs). Our mathematical modelling of this Gene Regulatory Network reveals that a simplified model, with one "super-gene" for each class is sufficient to a large extent to describe the regulatory behaviour previously observed experimentally.
Advanced Methods for Connectome-Based Predictive Modeling of Human Intelligence: A Novel Approach Based on Individual Differences in Cortical Topography
Authors: Evan D. Anderson, Ramsey Wilcox, Anuj Nayak, Christopher Zwilling, Pablo Robles-Granda, Been Kim, Lav R. Varshney, Aron K. Barbey
Date: 2022-03-01 00:00:00
Proceeding: NeurIPS Workshop AI4Scien 2021 12
Tasks: feature selection

Individual differences in human intelligence can be modeled and predicted from in vivo neurobiological connectivity. Many established modeling frameworks for predicting intelligence, however, discard higher-order information about individual differences in brain network topology, and show only moderate performance when generalized to make predictions in out-of-sample subjects. In this paper, we propose that connectome-based predictive modeling, a common predictive modeling framework for neuroscience data, can be productively modified to incorporate information about brain network topology and individual differences via the incorporation of bagged decision trees and the network based statistic. These modifications produce a novel predictive modeling framework that leverages individual differences in cortical tractography to generate accurate regression predictions of intelligence scores. Network topology-based feature selection provides for natively interpretable networks as input features, increasing the model's explainability. Investigating the proposed modeling framework's efficacy, we find that advanced connectome-based predictive modeling generates neuroscience predictions that account for a significantly greater proportion of variance in general intelligence scores than previously established methods, advancing our scientific understanding of the network architecture that underlies human intelligence.
Uncovering the dynamic effects of DEX treatment on lung cancer by integrating bioinformatic inference and multiscale modeling of scRNA-seq and proteomics data
Authors: Minghan Chen, Chunrui Xu, Ziang Xu, wei he, Haorui Zhang, Jing Su, Qianqian Song
Date: 2022-03-01 00:00:00
Tasks: Prognosis, Time Series Analysis

Motivation: Lung cancer is one of the leading causes for cancer-related death, with a five-year survival rate of 18%. It is a priority for us to understand the underlying mechanisms that affect the implementation and effectiveness of lung cancer therapeutics. In this study, we combine the power of Bioinformatics and Systems Biology to comprehensively uncover functional and signaling pathways of drug treatment using bioinformatics inference and multiscale modeling of both scRNA-seq data and proteomics data. The innovative and cross-disciplinary approach can be further applied to other computational studies in tumorigenesis and oncotherapy. Results: A time series of lung adenocarcinoma-derived A549 cells after DEX treatment were analysed. (1) We first discovered the differentially expressed genes in those lung cancer cells. Then through the interrogation of their regulatory network, we identified key hub genes including TGF-\b{eta}, MYC, and SMAD3 varied underlie DEX treatment. Further enrichment analysis revealed the TGF-\b{eta} signaling pathway as the top enriched term. Those genes involved in the TGF-\b{eta} pathway and their crosstalk with the ERBB pathway presented a strong survival prognosis in clinical lung cancer samples. (2) Based on biological validation and further curation, a multiscale model of tumor regulation centered on both TGF-\b{eta}-induced and ERBB-amplified signaling pathways was developed to characterize the dynamics effects of DEX therapy on lung cancer cells. Our simulation results were well matched to available data of SMAD2, FOXO3, TGF\b{eta}1, and TGF\b{eta}R1 over the time course. Moreover, we provided predictions of different doses to illustrate the trend and therapeutic potential of DEX treatment.
Applying multi-angled parallelism to Spanish topographical maps
Authors: Josep-Maria Cusco, Marcos Faundez-Zanuy
Date: 2022-02-24 00:00:00

Multi-Angled Parallelism (MAP) is a method to recognize lines in binary images. It is suitable to be implemented in parallel processing and image processing hardware. The binary image is transformed into directional planes, upon which, directional operators of erosion-dilation are iteratively applyed. From a set of basic operators, more complex ones are created, which let to extract the several types of lines. Each type is extracted with a different set of operations and so the lines are identified when extracted. In this paper, an overview of MAP is made, and it is adapted to line recognition in Spanish topographical maps, with the double purpose of testing the method in a real case and studying the process of adapting it to a custom application.
StaSiS-Net: a stacked and siamese stereo network for depth reconstruction in modern 3D laparoscopy.
Authors: Francesco Bardozzo, Toby Collins, Antonello Forgione, Alexandre Hostettler, Roberto Tagliaferri
Date: 2022-04-06 00:00:00
Proceeding: Medical Image Analysis 2022 4
Tasks: Decoder, Depth Estimation, GPU, Stereo Depth Estimation

Accurate and real-time methodologies for a non-invasive three-dimensional representa-tion and reconstruction of internal patient structures is one of the main research fieldsin computer-assisted surgery and endoscopy. Mono and stereo endoscopic images ofsoft tissues are converted into a three-dimensional representation by the estimation ofdepth maps. However, automatic, detailed, accurate and robust depth map estimationis a challenging problem which, moreover, is strictly dependent on a robust estimateof the disparity map. Many traditional algorithms are often inefficient or not accu-rate. In this work, novel self-supervised stacked and Siamese encoder/decoder neuralnetworks are proposed to compute accurate disparity maps for 3D laparoscopy depthreconstructions. These networks produce disparities in real-time on standard GPU-equipped desktop computers and after, with a minimal parameter configuration theirdepth reconstruction. We compare their performance on three different public datasetsand on a new challenging simulated dataset and they outperform state-of-the-art monoand stereo depth estimation methods. Extensive robustness and sensitivity analyses onmore than 30 000 frames has been performed. This work leads to important improve-ments in mono and stereo real-time depth estimations of soft tissues and organs with avery low average mean absolute disparity reconstruction error with respect to groundtruth.
Sugeno integral generalization applied to improve adaptive image binarization
Authors: Bardozzo Francesco, De La Osa Borja, Horansk{\'a}, L'ubom{\'\i}ra, Fumanal-Idocin Javier, delli Priscoli  Mattia, Troiano  Luigi, Tagliaferri Roberto, Fernandez Javier, Bustince Humberto
Date: 2020-10-20 00:00:00
Proceeding: Information Fusion 2020 10
Tasks: Binarization

Classic adaptive binarization methodologies threshold pixels intensity with re-spect to adjacent pixels exploiting integral images. In turn, integral imagesare generally computed optimally by using the summed-area-table algorithm(SAT). This document presents a new adaptive binarization technique basedon fuzzy integral images. Which, in turn, this technique is supported by anefficient design of a modified SAT for generalized Sugeno fuzzy integrals. Wedefine this methodology as FLAT (Fuzzy Local Adaptive Thresholding). Exper-imental results show that the proposed methodology produced a better imagequality thresholding than well-known global and local thresholding algorithms.We proposed new generalizations of different fuzzy integrals to improve existingresults and reaching an accuracy≈0.94 on a wide dataset. Moreover, due tohigh performances, these new generalized Sugeno fuzzy integrals created ad hocfor adaptive binarization, can be used as tools for grayscale processing and morecomplex real-time thresholding applications.
Multi-Tailed Vision Transformer for Efficient Inference
Authors: Yunke Wang, Bo Du, Wenyuan Wang, Chang Xu
Date: 2022-03-03 00:00:00

Recently, Vision Transformer (ViT) has achieved promising performance in image recognition and gradually serves as a powerful backbone in various vision tasks. To satisfy the sequential input of Transformer, the tail of ViT first splits each image into a sequence of visual tokens with a fixed length. Then the following self-attention layers constructs the global relationship between tokens to produce useful representation for the downstream tasks. Empirically, representing the image with more tokens leads to better performance, yet the quadratic computational complexity of self-attention layer to the number of tokens could seriously influence the efficiency of ViT's inference. For computational reduction, a few pruning methods progressively prune uninformative tokens in the Transformer encoder, while leaving the number of tokens before the Transformer untouched. In fact, fewer tokens as the input for the Transformer encoder can directly reduce the following computational cost. In this spirit, we propose a Multi-Tailed Vision Transformer (MT-ViT) in the paper. MT-ViT adopts multiple tails to produce visual sequences of different lengths for the following Transformer encoder. A tail predictor is introduced to decide which tail is the most efficient for the image to produce accurate prediction. Both modules are optimized in an end-to-end fashion, with the Gumbel-Softmax trick. Experiments on ImageNet-1K demonstrate that MT-ViT can achieve a significant reduction on FLOPs with no degradation of the accuracy and outperform other compared methods in both accuracy and FLOPs.
LGT-Net: Indoor Panoramic Room Layout Estimation with Geometry-Aware Transformer Network
Authors: Zhigang Jiang, Zhongzheng Xiang, Jinhua Xu, Ming Zhao
Date: 2022-03-03 00:00:00
Proceeding: CVPR 2022 1
Tasks: Room Layout Estimation

3D room layout estimation by a single panorama using deep neural networks has made great progress. However, previous approaches can not obtain efficient geometry awareness of room layout with the only latitude of boundaries or horizon-depth. We present that using horizon-depth along with room height can obtain omnidirectional-geometry awareness of room layout in both horizontal and vertical directions. In addition, we propose a planar-geometry aware loss function with normals and gradients of normals to supervise the planeness of walls and turning of corners. We propose an efficient network, LGT-Net, for room layout estimation, which contains a novel Transformer architecture called SWG-Transformer to model geometry relations. SWG-Transformer consists of (Shifted) Window Blocks and Global Blocks to combine the local and global geometry relations. Moreover, we design a novel relative position embedding of Transformer to enhance the spatial identification ability for the panorama. Experiments show that the proposed LGT-Net achieves better performance than current state-of-the-arts (SOTA) on benchmark datasets.
Random Quantum Neural Networks (RQNN) for Noisy Image Recognition
Authors: Debanjan Konar, Erol Gelenbe, Soham Bhandary, Aditya Das Sarma, Attila Cangi
Date: 2022-03-03 00:00:00
Tasks: Decision Making, image-classification, Image Classification

Classical Random Neural Networks (RNNs) have demonstrated effective applications in decision making, signal processing, and image recognition tasks. However, their implementation has been limited to deterministic digital systems that output probability distributions in lieu of stochastic behaviors of random spiking signals. We introduce the novel class of supervised Random Quantum Neural Networks (RQNNs) with a robust training strategy to better exploit the random nature of the spiking RNN. The proposed RQNN employs hybrid classical-quantum algorithms with superposition state and amplitude encoding features, inspired by quantum information theory and the brain's spatial-temporal stochastic spiking property of neuron information encoding. We have extensively validated our proposed RQNN model, relying on hybrid classical-quantum algorithms via the PennyLane Quantum simulator with a limited number of \emph{qubits}. Experiments on the MNIST, FashionMNIST, and KMNIST datasets demonstrate that the proposed RQNN model achieves an average classification accuracy of $94.9\%$. Additionally, the experimental findings illustrate the proposed RQNN's effectiveness and resilience in noisy settings, with enhanced image classification accuracy when compared to the classical counterparts (RNNs), classical Spiking Neural Networks (SNNs), and the classical convolutional neural network (AlexNet). Furthermore, the RQNN can deal with noise, which is useful for various applications, including computer vision in NISQ devices. The PyTorch code (https://github.com/darthsimpus/RQN) is made available on GitHub to reproduce the results reported in this manuscript.