pwc-archive/papers-with-abstracts huggingface dataset:
Perfect Reconstruction Two-Channel Filter Banks on Arbitrary Graphs Based on an Optimization Model
Authors: Junxia You, Lihua Yang
Date: 2022-08-04 00:00:00

In this paper, we propose the construction of critically sampled perfect reconstruction two-channel filterbanks on arbitrary undirected graphs.Inspired by the design of graphQMF proposed in the literature, we propose a general ``spectral folding property'' similar to that of bipartite graphs and provide sufficient conditions for constructing perfect reconstruction filterbanks based on a general graph Fourier basis, which is not the eigenvectors of the Laplacian matrix. To obtain the desired graph Fourier basis, we need to solve a series of quadratic equality constrained quadratic optimization problems (QECQPs) which are known to be non-convex and difficult to solve. We develop an algorithm to obtain the global optimal solution within a pre-specified tolerance. Multi-resolution analysis on real-world data and synthetic data are performed to validate the effectiveness of the proposed filterbanks.
SPDE-Net: Neural Network based prediction of stabilization parameter for SUPG technique
Authors: Yadav, Sangeeta; Ganesan, Sashikumaar
Date: 2021-11-19 00:00:00
Proceeding: Asian Conference on Machine Learning 2021 11
Tasks: regression

We propose SPDE-Net, an artificial neural network (ANN) to predict the stabilization parameter for the streamline upwind/Petrov-Galerkin (SUPG) stabilization technique for solving singularly perturbed differential equations (SPDEs). The prediction task is modelled as a regression problem and is solved using ANN. Three training strategies for the ANN have been proposed i.e. supervised, $L^2$ error minimization (global) and $L^2$ error minimization (local). It has been observed that the proposed method yields accurate results and even outperforms some of the existing state-of-the-art ANN-based partial differential equation (PDE) solvers such as Physics Informed Neural Network (PINN).
Physics-inform attention temporal convolutional network for EEG-based motor imagery classification
Authors: Hamdi Altaheri, Ghulam Muhammad, and Mansour Alsulaiman
Date: 2022-08-01 00:00:00
Proceeding: IEEE Transactions on Industrial Informatics 2022 8
Tasks: Brain Computer Interface, EEG, EEG 4 classes, Eeg Decoding, Electroencephalogram (EEG), Motor Imagery

The brain-computer interface (BCI) is a cutting-edge technology that has the potential to change the world. Electroencephalogram (EEG) motor imagery (MI) signal has been used extensively in many BCI applications to assist disabled people, control devices or environments, and even augment human capabilities. However, the limited performance of brain signal decoding is restricting the broad growth of the BCI industry. In this paper, we propose an attention-based temporal convolutional network (ATCNet) for EEG-based motor imagery classification. The ATCNet model utilizes multiple techniques to boost the performance of MI classification with a relatively small number of parameters. ATCNet employs scientific machine learning to design a domain-specific DL model with interpretable and explainable features, multi-head self-attention to highlight the most valuable features in MI-EEG data, temporal convolutional network (TCN) to extract high-level temporal features, and convolutional-based sliding window to augment the MI-EEG data efficiently. The proposed model outperforms the current state-of-the-art techniques in the BCI Competition IV-2a dataset with an accuracy of 85.38% and 70.97% for the subject-dependent and subject-independent modes, respectively.
Electricity Consumption Prediction using Neural Network
Authors: S Sridevi, Kavarthapu Hari Kiran, Mandapati Hemanth Varma, K Reddy Deeraj
Date: 2021-05-29 00:00:00
Proceeding: journal 2021 5
Tasks: Management, Prediction

Human Technology is vastly and rapidly grown with the discovery of Electricity. Electricity runsthrough each and every tech object in the modern era. The Resources for producing Electrical energy arelimited. Most of the Electrical Energy is from non-renewable resources. So wasting of the Electricity iswasting of the natural fossil resources, at the same time the Electricity shouldn’t be under-produced. Theelectricity produced should be ample and should fulfil all the necessary needs of humans. To produce theelectricity precisely to fulfil the needs with the increasing demand it must be predicted accurately. This paperdiscusses the electricity consumption prediction and the price prediction for Electricity Consumed. This helpsin household electricity consumption management. 

(PDF) Electricity Consumption Prediction using Neural Network. Available from: https://www.researchgate.net/publication/351969354_Electricity_Consumption_Prediction_using_Neural_Network [accessed Aug 05 2022].
Thread: Circuits
Authors: Nick Cammarata, Shan Carter, Gabriel Goh, Chris Olah, Michael Petrov, Ludwig Schubert, Chelsea Voss, Ben Egan, Swee Kiat Lim
Date: 2020-03-10 00:00:00
Proceeding: Distill 2020 3
Tasks: Articles

In the original narrative of deep learning, each neuron builds progressively more abstract, meaningful features by composing features in the preceding layer. In recent years, there’s been some skepticism of this view, but what happens if you take it really seriously?

InceptionV1 is a classic vision model with around 10,000 unique neurons — a large number, but still on a scale that a group effort could attack. What if you simply go through the model, neuron by neuron, trying to understand each one and the connections between them? The circuits collaboration aims to find out.

The natural unit of publication for investigating circuits seems to be short papers on individual circuits or small families of features. Compared to normal machine learning papers, this is a small and unusual topic for a paper.

To facilitate exploration of this direction, Distill is inviting a “thread” of short articles on circuits, interspersed with critical commentary by experts in adjacent fields. The thread will be a living document, with new articles added over time, organized through an open slack channel (#circuits in the Distill slack). Content in this thread should be seen as early stage exploratory research.
Visualizing Weights
Authors: Chelsea Voss, Nick Cammarata, Gabriel Goh, Michael Petrov, Ludwig Schubert, Ben Egan, Swee Kiat Lim, Chris Olah
Date: 2021-02-04 00:00:00
Proceeding: Distill 2021 2

The problem of understanding a neural network is a little bit like reverse engineering a large compiled binary of a computer program. In this analogy, the weights of the neural network are the compiled assembly instructions. At the end of the day, the weights are the fundamental thing you want to understand: how does this sequence of convolutions and matrix multiplications give rise to model behavior?

Trying to understand artificial neural networks also has a lot in common with neuroscience, which tries to understand biological neural networks. As you may know, one major endeavor in modern neuroscience is mapping the connectomes of biological neural networks: which neurons connect to which. These connections, however, will only tell neuroscientists which weights are non-zero. Getting the weights – knowing whether a connection excites or inhibits, and by how much – would be a significant further step. One imagines neuroscientists might give a great deal to have the access to weights that those of us studying artificial neural networks get for free.

And so, it’s rather surprising how little attention we actually give to looking at the weights of neural networks. There are a few exceptions to this, of course. It’s quite common for researchers to show pictures of the first layer weights in vision models (these are directly connected to RGB channels, so they’re easy to understand as images). In some work, especially historically, we see researchers reason about the weights of toy neural networks by hand. And we quite often see researchers discuss aggregate statistics of weights. But actually looking at the weights of a neural network other than the first layer is quite uncommon – to the best of our knowledge, mapping weights between hidden layers to meaningful algorithms is novel to the circuits project.
High-Low Frequency Detectors
Authors: Ludwig Schubert, Chelsea Voss, Nick Cammarata, Gabriel Goh, Chris Olah
Date: 2021-01-27 00:00:00
Proceeding: Distill 2021 1
Tasks: Vocal Bursts Intensity Prediction

Some of the neurons in vision models are features that we aren’t particularly surprised to find. Curve detectors, for example, are a pretty natural feature for a vision system to have. In fact, they had already been discovered in the animal visual cortex. It’s easy to imagine how curve detectors are built up from earlier edge detectors, and it’s easy to guess why curve detection might be useful to the rest of the neural network.

High-low frequency detectors, on the other hand, seem more surprising. They are not a feature that we would have expected a priori to find. Yet, when systematically characterizing the early layers of InceptionV1, we found a full fifteen neurons of mixed3a that appear to detect a high frequency pattern on one side, and a low frequency pattern on the other.

By “high frequency” and “low frequency” here, we mean spatial frequency — just like when we take the Fourier transform of an image.

One worry we might have about the circuits approach to studying neural networks is that we might only be able to understand a limited set of highly-intuitive features. High-low frequency detectors demonstrate that it’s possible to understand at least somewhat unintuitive features.
Weight Banding
Authors: Michael Petrov, Chelsea Voss, Ludwig Schubert, Nick Cammarata, Gabriel Goh, Chris Olah
Date: 2021-04-08 00:00:00
Proceeding: Distill 2021 4

Open up any ImageNet conv net and look at the weights in the last layer. You’ll find a uniform spatial pattern to them, dramatically unlike anything we see elsewhere in the network. No individual weight is unusual, but the uniformity is so striking that when we first discovered it we thought it must be a bug. Just as different biological tissue types jump out as distinct under a microscope, the weights in this final layer jump out as distinct when visualized with NMF. We call this phenomenon weight banding.
Curve Detectors
Authors: Nick Cammarata, Gabriel Goh, Shan Carter, Ludwig Schubert, Michael Petrov, Chris Olah
Date: 2020-06-17 00:00:00
Proceeding: Distill 2020 6

Every vision model we've explored in detail contains neurons which detect curves. Curve detectors in vision models have been hinted at in the literature as far back as 2013 (see figures in Zeiler & Fergus ), and similar neurons have been studied carefully in neuroscience . We briefly discussed curve in our earlier overview of early vision, but wanted to examine them in more depth. This article is the first part of a three article deep dive into curve detectors: their behavior, how they're built from earlier neurons, and their prevalence across models.
Spotting micro-movements in image sequence by introducing intelligent cubic-LBP
Authors: Vida Esmaeili, Mahmood Mohassel Feghhi, Seyed Omid Shahdi
Date: 2022-07-25 00:00:00
Proceeding: IET Image Processing 2022 7

Invisible to naked human eyes, micro-movements are barely noticeable. There are wide ranges of micro-movement applications from spotting subtle changes in a volcano, vascular pulse, and blood vessel to micro-expression detection. In the latter, some evil thoughts can be unveiled, resulting in identifying crooks and lawbreakers and it arises from involuntary subtle and short-duration of facial muscles movements. Precise spotting of these tiny movements is possible only when multiple aspects of temporal images are scrutinized. Meanwhile, since motions often happen in one or two directions, it is rudimentary to extract complete feature sets in textural-based approaches such as cubic Local Binary Pattern (cubic-LBP). Approaches like cubic-LBP also have imposed an unnecessary computation burden. Hence, in this research, a novel method named intelligent cubic-LBP is proposed, which incorporates Convolutional Neural Network (CNN) model. This model learns to select the useful plane(s) automatically. Apex is then detected by applying Partial Autocorrelation Coefficient (PACF) on the selected plane(s). The experimental results show significant improvement in micro-movements identification. The accuracy of the apex frame detection has elevated to 10% and 17% in the Chinese Academy of Sciences Micro-Expressions (CASME) and the CASME II databases, respectively.
A comprehensive survey on facial micro-expression: approaches and databases
Authors: Vida Esmaeili, Mahmood Mohassel Feghhi, Seyed Omid Shahdi
Date: 2022-05-06 00:00:00
Proceeding: Multimedia Tools and Applications 2022 5
Tasks: Survey

Over the past few years, the importance of the facial micro-expression (FME) has garnered increasing attention from experts because of its potential applications from the judgment court to the psychology research centers. A real challenge for developing an extensive system of the FME analysis is to select a suitable method and a database. In this manuscript, we have conducted a comprehensive and comparative survey to address the aforementioned challenge, and to give clear guidelines to alleviate further researches. To come up with this task, we have justified each method in terms of its pros and cons, which are meant to be beneficial for researchers choosing a method or a database, which suits their context application. Also, we have exhaustively analyzed the whole framework of the FME system by decomposing its pipeline into the pre-processing, the feature extraction, and the classification.
Autonomous Apex Detection and Micro-Expression Recognition using Proposed Diagonal Planes
Authors: Vida Esmaeili, Mahmood Mohassel Feghhi, Seyed Omid Shahdi
Date: 2020-04-01 00:00:00
Proceeding: Int. J. Nonlinear Anal. Appl. 2020 4
Tasks: Micro Expression Recognition, Micro-Expression Recognition, Motion Magnification, Optical Flow Estimation

Micro-expression as the main way of non-verbal communication occurs quickly and subtle in highrisk situations. Since it cannot be misleading, it discloses the real human aim. Nonetheless, feature extraction is an arduous task due to its two particular features. To resolve this problem in this paper, we propose Local Binary Pattern on Four Diagonal Planes. In fact, we utilize it after motion magnification for feature extraction in apex detection task. Also, we combine it and optical flow for micro-expression recognition. Simulation results show that automatic micro-expression apex frame detection and micro-expression recognition is promising using our method on CASME2 and CASME in comparison with other methods.
Early COVID-19 Diagnosis from Lung Ultrasound Images Combining RIULBP-TP and 3D-DenseNet
Authors: Vida Esmaeili, Mahmood Mohassel Feghhi, Seyed Omid Shahdi
Date: 2022-04-19 00:00:00
Proceeding: 9th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS) 2022 4
Tasks: COVID-19 Diagnosis

The pandemic of COVID-19 has affected the world with the high deaths rate. Early diagnosis of this disease is the bottleneck to the patient's health recovery. Its symptoms appear through the wide range of experiments especially accompany with the severe lung lesions. These lesions could be spotted on the lung ultrasound data. Being non-intrusive, low cost, portable, and accurate enough are among the main pros of ultrasound imaging. However, this imaging modality most often contain variety of noises. In order to overcome this challenge, we propose a novel approach combining Rotation Invariant Uniform LBP on 3 Planes (RIULBP-TP) and 3D-DenseNet. These methods are proved to be robust against various noises. Accordingly, our method reaches outstanding results comparing to related most state-of-the-art methods.
Micro-Expression Recognition Using Histogram of Image Gradient Orientation on Diagonal Planes
Authors: Vida Esmaeili, Mahmood Mohassel Feghhi, Seyed Omid Shahdi
Date: 2021-07-26 00:00:00
Proceeding: 5th International Conference on Pattern Recognition and Image Analysis (IPRIA) 2021 7
Tasks: Micro Expression Recognition, Micro-Expression Recognition

Micro-expression conveys a person's state-of-mind; therefore, it can be helpful in many cases. However, its micro-momentary and subtle characteristics make its recognition hard. This challenging task is too arduous in the conditions, where illumination changes. Thus, it is better to use the methods, which are insensitive to shadows and illumination change. Hence, in this paper, we propose the histogram of image gradient orientation on the diagonal planes for the feature extraction. Then, the obtained histogram is fed to the linear support vector machine for the micro-expression recognition. According to the results, the micro-expression recognition rate has been improved using our proposed method in comparison with the state-of-the-art methods.
A real-time and high-precision method for small traffic-signs recognition
Authors: Junzhou Chen, Kunkun Jia, Wenquan Chen, Zhihan Lv, Ronghui Zhang
Date: 2021-09-25 00:00:00
Proceeding: Neural Computing and Applications 2021 9
Tasks: Data Augmentation, object-detection, Traffic Sign Detection, Traffic Sign Recognition

As a fundamental element of the traffic system, traffic signs reduce the risk of accidents by providing essential information about the road condition to drivers, pedestrians, etc. With the rapid progress of computer vision and artificial intelligence, traffic-signs recognition systems have been applied for the advanced driver assistance system and auto driving system, to help drivers and self-driving vehicles capture the important road information precisely. However, in real applications, small traffic-signs recognition is still challenging. In this article, we propose an efficient method for small-size traffic-signs recognition, named traffic-signs recognition small-aware, with the inspiration of the state-of-the-art object detection framework YOLOv4 and YOLOv5. In general, there are four contributions in our work: (1) for the Backbone of the model, we introduce high-level features to construct a better detector head; (2) for the Neck of the model, receptive field blockcross is utilized for capturing the contextual information of feature map; (3) for the Head of the model, we refine the detector head grid to achieve more accurate detection of small traffic signs; (4) for the input, we propose a data augmentation method named Random Erasing-Attention, which can increase difficult samples and enhance the robustness of the model. Real experiments on the challenging dataset TT100K demonstrate that our method can achieve significant performance improvement compared with the state of the art. Moreover, it is a real-time method and shows huge potential applications in advanced driver assistance system and auto driving system.
Self-Supervised Learning Based on Spatial Awareness for Medical Image Analysis
Authors: Xuan-Bac Nguyen, Guee Sang Lee, Soo Hyung Kim, Hyung Jeong Yang
Date: 2020-08-30 00:00:00
Proceeding: IEEE Access 2020 8
Tasks: Medical Image Analysis, Organ Segmentation, Self-Supervised Learning

Medical image analysis is one of the research fields that had huge benefits from deep learning in recent years. To earn a good performance, the learning model requires large scale data with full annotation. However, it is a big burden to collect a sufficient number of labeled data for the training. Since there are more unlabeled data than labeled ones in most of medical applications, self-supervised learning has been utilized to improve the performance. However, most of current methods for self-supervised learning try to understand only semantic features of the data, but have not fully utilized properties inherent in medical images. Specifically, in CT or MR images, the spatial or structural information contained in the dataset has not been fully considered. In this paper, we propose a novel method for self-supervised learning in medical image analysis that can exploit both semantic and spatial features at the same time. The proposed method is experimented in the problems of organ segmentation, intracranial hemorrhage detection and the results show the effectiveness of the method.
Adaptation Strategy for a Distributed Autonomous UAV Formation in Case of Aircraft Loss
Authors: Tagir Muslimov
Date: 2022-08-04 00:00:00

Controlling a distributed autonomous unmanned aerial vehicle (UAV) formation is usually considered in the context of recovering the connectivity graph should a single UAV agent be lost. At the same time, little focus is made on how such loss affects the dynamics of the formation as a system. To compensate for the negative effects, we propose an adaptation algorithm that reduces the increasing interaction between the UAV agents that remain in the formation. This algorithm enables the autonomous system to adjust to the new equilibrium state. The algorithm has been tested by computer simulation on full nonlinear UAV models. Simulation results prove the negative effect (the increased final cruising speed of the formation) to be completely eliminated.
Attention and DCT based Global Context Modeling for Text-independent Speaker Recognition
Authors: Wei Xia, John H. L. Hansen
Date: 2022-08-04 00:00:00
Tasks: Speaker Recognition, Speaker Verification, Text-Independent Speaker Recognition

Learning an effective speaker representation is crucial for achieving reliable performance in speaker verification tasks. Speech signals are high-dimensional, long, and variable-length sequences containing diverse information at each time-frequency (TF) location. The standard convolutional layer that operates on neighboring local regions often fails to capture the complex TF global information. Our motivation is to alleviate these challenges by increasing the modeling capacity, emphasizing significant information, and suppressing possible redundancies. We aim to design a more robust and efficient speaker recognition system by incorporating the benefits of attention mechanisms and Discrete Cosine Transform (DCT) based signal processing techniques, to effectively represent the global information in speech signals. To achieve this, we propose a general global time-frequency context modeling block for speaker modeling. First, an attention-based context model is introduced to capture the long-range and non-local relationship across different time-frequency locations. Second, a 2D-DCT based context model is proposed to improve model efficiency and examine the benefits of signal modeling. A multi-DCT attention mechanism is presented to improve modeling power with alternate DCT base forms. Finally, the global context information is used to recalibrate salient time-frequency locations by computing the similarity between the global context and local features. This effectively improves the speaker verification performance compared to the standard ResNet model and Squeeze & Excitation block by a large margin. Our experimental results show that the proposed global context modeling method can efficiently improve the learned speaker representations by achieving channel-wise and time-frequency feature recalibration.