pwc-archive/papers-with-abstracts huggingface dataset:
Point Spread Function Estimation for Wide Field Small Aperture Telescopes with Deep Neural Networks and Calibration Data
Authors: Peng Jia, Xuebo Wu, Zhengyang Li, Bo Li, Weihua Wang, Qiang Liu, Adam Popowicz
Date: 2020-11-20 00:00:00
Tasks: Image Restoration

The point spread function (PSF) reflects states of a telescope and plays an important role in development of data processing methods, such as PSF based astrometry, photometry and image restoration. However, for wide field small aperture telescopes (WFSATs), estimating PSF in any position of the whole field of view is hard, because aberrations induced by the optical system are quite complex and the signal to noise ratio of star images is often too low for PSF estimation. In this paper, we further develop our deep neural network (DNN) based PSF modelling method and show its applications in PSF estimation. During the telescope alignment and testing stage, our method collects system calibration data through modification of optical elements within engineering tolerances (tilting and decentering). Then we use these data to train a DNN (Tel--Net). After training, the Tel--Net can estimate PSF in any field of view from several discretely sampled star images. We use both simulated and experimental data to test performance of our method. The results show that the Tel--Net can successfully reconstruct PSFs of WFSATs of any states and in any positions of the FoV. Its results are significantly more precise than results obtained by the compared classic method - Inverse Distance Weight (IDW) interpolation. Our method provides foundations for developing of deep neural network based data processing methods for WFSATs, which require strong prior information of PSFs.
Edge Adaptive Hybrid Regularization Model For Image Deblurring
Authors: Tingting Zhang, Jie Chen, Caiying Wu, Zhifei He, Tieyong Zeng, Qiyu Jin
Date: 2020-11-20 00:00:00
Tasks: Deblurring, Image Deblurring, Image Restoration, model, SSIM

The parameter selection is crucial to regularization based image restoration methods. Generally speaking, a spatially fixed parameter for regularization item in the whole image does not perform well for both edge and smooth areas. A larger parameter of regularization item reduces noise better in smooth areas but blurs edge regions, while a small parameter sharpens edge but causes residual noise. In this paper, an automated spatially adaptive regularization model, which combines the harmonic and TV models, is proposed for reconstruction of noisy and blurred images. In the proposed model, it detects the edges and then spatially adjusts the parameters of Tikhonov and TV regularization terms for each pixel according to the edge information. Accordingly, the edge information matrix will be also dynamically updated during the iterations. Computationally, the newly-established model is convex, which can be solved by the semi-proximal alternating direction method of multipliers (sPADMM) with a linear-rate convergence rate. Numerical simulation results demonstrate that the proposed model effectively reserves the image edges and eliminates the noise and blur at the same time. In comparison to state-of-the-art algorithms, it outperforms other methods in terms of PSNR, SSIM and visual quality.
Smart obervation method with wide field small aperture telescopes for real time transient detection
Authors: Peng Jia, Qiang Liu, Yongyang Sun, Yitian Zheng, Wenbo Liu, Yifei Zhao
Date: 2020-11-20 00:00:00
Tasks: Ensemble Learning

Wide field small aperture telescopes (WFSATs) are commonly used for fast sky survey. Telescope arrays composed by several WFSATs are capable to scan sky several times per night. Huge amount of data would be obtained by them and these data need to be processed immediately. In this paper, we propose ARGUS (Astronomical taRGets detection framework for Unified telescopes) for real-time transit detection. The ARGUS uses a deep learning based astronomical detection algorithm implemented in embedded devices in each WFSATs to detect astronomical targets. The position and probability of a detection being an astronomical targets will be sent to a trained ensemble learning algorithm to output information of celestial sources. After matching these sources with star catalog, ARGUS will directly output type and positions of transient candidates. We use simulated data to test the performance of ARGUS and find that ARGUS can increase the performance of WFSATs in transient detection tasks robustly.
CSCF: a chaotic sine cosine firefly Algorithm for practical application problems
Authors: Bryar A. Hassan
Date: 2020-11-20 00:00:00

Recently, numerous meta-heuristic based approaches are deliberated to reduce the computational complexities of several existing approaches that include tricky derivations, very large memory space requirement, initial value sensitivity etc. However, several optimization algorithms namely firefly algorithm, sine cosine algorithm, particle swarm optimization algorithm have few drawbacks such as computational complexity, convergence speed etc. So to overcome such shortcomings, this paper aims in developing a novel Chaotic Sine Cosine Firefly (CSCF) algorithm with numerous variants to solve optimization problems. Here, the chaotic form of two algorithms namely the sine cosine algorithm (SCA) and the Firefly (FF) algorithms are integrated to improve the convergence speed and efficiency thus minimizing several complexity issues. Moreover, the proposed CSCF approach is operated under various chaotic phases and the optimal chaotic variants containing the best chaotic mapping is selected. Then numerous chaotic benchmark functions are utilized to examine the system performance of the CSCF algorithm. Finally, the simulation results for the problems based on engineering design are demonstrated to prove the efficiency, robustness and effectiveness of the proposed algorithm.
Rethinking Weight Decay For Efficient Neural Network Pruning
Authors: Hugo Tessier, Vincent Gripon, Mathieu Léonardon, Matthieu Arzel, Thomas Hannagan, David Bertrand
Date: 2020-11-20 00:00:00
Tasks: Efficient Neural Network, Network Pruning

Introduced in the late 1980s for generalization purposes, pruning has now become a staple for compressing deep neural networks. Despite many innovations in recent decades, pruning approaches still face core issues that hinder their performance or scalability. Drawing inspiration from early work in the field, and especially the use of weight decay to achieve sparsity, we introduce Selective Weight Decay (SWD), which carries out efficient, continuous pruning throughout training. Our approach, theoretically grounded on Lagrangian smoothing, is versatile and can be applied to multiple tasks, networks, and pruning structures. We show that SWD compares favorably to state-of-the-art approaches, in terms of performance-to-parameters ratio, on the CIFAR-10, Cora, and ImageNet ILSVRC2012 datasets.
Exploring Global Information for Session-based Recommendation
Authors: Ziyang Wang, Wei Wei, Gao Cong, Xiao-Li Li, Xian-Ling Mao, Minghui Qiu, Shanshan Feng
Date: 2020-11-20 00:00:00
Tasks: Session-Based Recommendations

Session-based recommendation (SBR) is a challenging task, which aims at recommending items based on anonymous behavior sequences. Most existing SBR studies model the user preferences based only on the current session while neglecting the item-transition information from the other sessions, which suffer from the inability of modeling the complicated item-transition pattern. To address the limitations, we introduce global item-transition information to strength the modeling of the dynamic item-transition. For fully exploiting the global item-transition information, two ways of exploring global information for SBR are studied in this work. Specifically, we first propose a basic GNN-based framework (BGNN), which solely uses session-level item-transition information on session graph. Based on BGNN, we propose a novel approach, called Session-based Recommendation with Global Information (SRGI), which infers the user preferences via fully exploring global item-transitions over all sessions from two different perspectives: (i) Fusion-based Model (SRGI-FM), which recursively incorporates the neighbor embeddings of each node on global graph into the learning process of session level item representation; and (ii) Constrained-based Model (SRGI-CM), which treats the global-level item-transition information as a constraint to ensure the learned item embeddings are consistent with the global item-transition. Extensive experiments conducted on three popular benchmark datasets demonstrate that both SRGI-FM and SRGI-CM outperform the state-of-the-art methods consistently.
A Deep Language-independent Network to analyze the impact of COVID-19 on the World via Sentiment Analysis
Authors: Ashima Yadav, Dinesh Kumar Vishwakarma
Date: 2020-11-20 00:00:00
Tasks: Sentence, Sentiment Analysis

Towards the end of 2019, Wuhan experienced an outbreak of novel coronavirus, which soon spread all over the world, resulting in a deadly pandemic that infected millions of people around the globe. The government and public health agencies followed many strategies to counter the fatal virus. However, the virus severely affected the social and economic lives of the people. In this paper, we extract and study the opinion of people from the top five worst affected countries by the virus, namely USA, Brazil, India, Russia, and South Africa. We propose a deep language-independent Multilevel Attention-based Conv-BiGRU network (MACBiG-Net), which includes embedding layer, word-level encoded attention, and sentence-level encoded attention mechanism to extract the positive, negative, and neutral sentiments. The embedding layer encodes the sentence sequence into a real-valued vector. The word-level and sentence-level encoding is performed by a 1D Conv-BiGRU based mechanism, followed by word-level and sentence-level attention, respectively. We further develop a COVID-19 Sentiment Dataset by crawling the tweets from Twitter. Extensive experiments on our proposed dataset demonstrate the effectiveness of the proposed MACBiG-Net. Also, attention-weights visualization and in-depth results analysis shows that the proposed network has effectively captured the sentiments of the people.
Are Chess Discussions Racist? An Adversarial Hate Speech Data Set
Authors: Rupak Sarkar, Ashiqur R. KhudaBukhsh
Date: 2020-11-20 00:00:00

On June 28, 2020, while presenting a chess podcast on Grandmaster Hikaru Nakamura, Antonio Radi\'c's YouTube handle got blocked because it contained "harmful and dangerous" content. YouTube did not give further specific reason, and the channel got reinstated within 24 hours. However, Radi\'c speculated that given the current political situation, a referral to "black against white", albeit in the context of chess, earned him this temporary ban. In this paper, via a substantial corpus of 681,995 comments, on 8,818 YouTube videos hosted by five highly popular chess-focused YouTube channels, we ask the following research question: \emph{how robust are off-the-shelf hate-speech classifiers to out-of-domain adversarial examples?} We release a data set of 1,000 annotated comments where existing hate speech classifiers misclassified benign chess discussions as hate speech. We conclude with an intriguing analogy result on racial bias with our findings pointing out to the broader challenge of color polysemy.
1st AfricaNLP Workshop Proceedings, 2020
Authors: Kathleen Siminyu, Laura Martinus, Vukosi Marivate
Date: 2020-11-20 00:00:00

Proceedings of the 1st AfricaNLP Workshop held on 26th April alongside ICLR 2020, Virtual Conference, Formerly Addis Ababa Ethiopia.
ONION: A Simple and Effective Defense Against Textual Backdoor Attacks
Authors: Fanchao Qi, Yangyi Chen, Mukai Li, Yuan YAO, Zhiyuan Liu, Maosong Sun
Date: 2020-11-20 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Backdoor Attack, backdoor defense

Backdoor attacks are a kind of emergent training-time threat to deep neural networks (DNNs). They can manipulate the output of DNNs and possess high insidiousness. In the field of natural language processing, some attack methods have been proposed and achieve very high attack success rates on multiple popular models. Nevertheless, there are few studies on defending against textual backdoor attacks. In this paper, we propose a simple and effective textual backdoor defense named ONION, which is based on outlier word detection and, to the best of our knowledge, is the first method that can handle all the textual backdoor attack situations. Experiments demonstrate the effectiveness of our model in defending BiLSTM and BERT against five different backdoor attacks. All the code and data of this paper can be obtained at https://github.com/thunlp/ONION.
Topic modelling discourse dynamics in historical newspapers
Authors: Jani Marjanen, Elaine Zosa, Simon Hengchen, Lidia Pivovarova, Mikko Tolonen
Date: 2020-11-20 00:00:00
Tasks: Topic Models

This paper addresses methodological issues in diachronic data analysis for historical research. We apply two families of topic models (LDA and DTM) on a relatively large set of historical newspapers, with the aim of capturing and understanding discourse dynamics. Our case study focuses on newspapers and periodicals published in Finland between 1854 and 1917, but our method can easily be transposed to any diachronic data. Our main contributions are a) a combined sampling, training and inference procedure for applying topic models to huge and imbalanced diachronic text collections; b) a discussion on the differences between two topic models for this type of data; c) quantifying topic prominence for a period and thus a generalization of document-wise topic assignment to a discourse level; and d) a discussion of the role of humanistic interpretation with regard to analysing discourse dynamics through topic models.
On Random Matrices Arising in Deep Neural Networks. Gaussian Case
Authors: Leonid Pastur
Date: 2020-01-17 00:00:00

The paper deals with distribution of singular values of product of random matrices arising in the analysis of deep neural networks. The matrices resemble the product analogs of the sample covariance matrices, however, an important difference is that the population covariance matrices, which are assumed to be non-random in the standard setting of statistics and random matrix theory, are now random, moreover, are certain functions of random data matrices. The problem has been considered in recent work [21] by using the techniques of free probability theory. Since, however, free probability theory deals with population matrices which are independent of the data matrices, its applicability in this case requires an additional justification. We present this justification by using a version of the standard techniques of random matrix theory under the assumption that the entries of data matrices are independent Gaussian random variables. In the subsequent paper [18] we extend our results to the case where the entries of data matrices are just independent identically distributed random variables with several finite moments. This, in particular, extends the property of the so-called macroscopic universality on the considered random matrices.
Deep Multi-Frame MVDR Filtering for Single-Microphone Speech Enhancement
Authors: Marvin Tammen, Simon Doclo
Date: 2020-11-20 00:00:00
Tasks: Speech Enhancement

Multi-frame algorithms for single-microphone speech enhancement, e.g., the multi-frame minimum variance distortionless response (MFMVDR) filter, are able to exploit speech correlation across adjacent time frames in the short-time Fourier transform (STFT) domain. Provided that accurate estimates of the required speech interframe correlation vector and the noise correlation matrix are available, it has been shown that the MFMVDR filter yields a substantial noise reduction while hardly introducing any speech distortion. Aiming at merging the speech enhancement potential of the MFMVDR filter and the estimation capability of temporal convolutional networks (TCNs), in this paper we propose to embed the MFMVDR filter within a deep learning framework. The TCNs are trained to map the noisy speech STFT coefficients to the required quantities by minimizing the scale-invariant signal-to-distortion ratio loss function at the MFMVDR filter output. Experimental results show that the proposed deep MFMVDR filter achieves a competitive speech enhancement performance on the Deep Noise Suppression Challenge dataset. In particular, the results show that estimating the parameters of an MFMVDR filter yields a higher performance in terms of PESQ and STOI than directly estimating the multi-frame filter or single-frame masks and than Conv-TasNet.
Neural Network Compression Using Higher-Order Statistics and AuxiliaryReconstruction Losses
Authors: Christos Chatzikonstantinou, Georgios Th. Papadopoulos, Kosmas Dimitropoulos, Petros Daras
Date: 2020-06-15 00:00:00
Tasks: Neural Network Compression

In this paper, the problem of pruning and compressingthe weights of various layers of deep neural networks is in-vestigated. The proposed method aims to remove redundantfilters from the network to reduce computational complex-ity and storage requirements, while improving the perfor-mance of the original network. More specifically, a novelfilter selection criterion is introduced based on the fact thatfilters whose weights follow a Gaussian distribution corre-spond to hidden units that do not capture important aspectsof data.  To this end, Higher Order Statistics (HOS) areused and filters with low cumulant values that do not de-viate significantly from Gaussian distribution are identifiedand removed from the network. In addition, a novel prun-ing strategy is proposed aiming to decide on the pruningratio of each layer using the Shapiro-Wilk normality test.The use of auxiliary MSE losses (intermediate and afterthe softmax layer) during the fine-tuning phase further im-proves the overall performance of the compressed network.Extensive experiments with different network architecturesand comparison with state-of-the-art approaches on well-known public datasets, such as CIFAR-10, CIFAR-100 andILSCVR-12, demonstrate the great potential of the proposedapproach.
Brain-inspired replay for continual learning with artificial neural networks
Authors: Gido M. van de Ven, Hava T. Siegelmann & Andreas S. Tolias
Date: 2020-08-13 00:00:00
Tasks: class-incremental learning, Class Incremental Learning, Continual Learning, Incremental Learning

Artificial neural networks suffer from catastrophic forgetting. Unlike humans, when these networks are trained on something new, they rapidly forget what was learned before. In the brain, a mechanism thought to be important for protecting memories is the reactivation of neuronal activity patterns representing those memories. In artificial neural networks, such memory replay can be implemented as ‘generative replay’, which can successfully – and surprisingly efficiently – prevent catastrophic forgetting on toy examples even in a class-incremental learning scenario. However, scaling up generative replay to complicated problems with many tasks or complex inputs is challenging. We propose a new, brain-inspired variant of replay in which internal or hidden representations are replayed that are generated by the network’s own, context-modulated feedback connections. Our method achieves state-of-the-art performance on challenging continual learning benchmarks (e.g., class-incremental learning on CIFAR-100) without storing data, and it provides a novel model for replay in the brain.
Distinctive Interest Point Selection for Efficient Near-duplicate Image Retrieval
Authors: Burak Yildiz, M. Fatih Demirci
Date: 2016-04-28 00:00:00
Tasks: Image Retrieval, Retrieval

Distinctive subset of the interest points creation for near-duplicate image retrieval is significant in two terms. The former is that the query time decreases reasonably. The latter is that using the distinctive subsets performs better than the ordinary subsets. In this paper, we focus on the creation of such subsets for effective near-duplicate retrieval and propose a novel interest point selection method. In this method, the distinctive subset is created with a ranking according to a density map calculated from the interest points. We examined a number of experiments to show the performance of the proposed method and we got a convincing result of 95.46% recall while the precision is still 96.04%.
Unsupervised domain adaptation based COVID-19 CT infection segmentation network
Authors: Han Chen, Yifan Jiang, Murray Loew, Hanseok Ko
Date: 2020-11-23 00:00:00
Tasks: Computed Tomography (CT), Domain Adaptation, Segmentation, Unsupervised Domain Adaptation

Automatic segmentation of infection areas in computed tomography (CT) images has proven to be an effective diagnosis approach for COVID-19. However, due to the limited number of pixel-level annotated medical images, accurate segmentation remains a major challenge. In this paper, we propose an unsupervised domain adaptation based segmentation network to improve the segmentation performance of the infection areas in COVID-19 CT images. In particular, we propose to utilize the synthetic data and limited unlabeled real COVID-19 CT images to jointly train the segmentation network. Furthermore, we develop a novel domain adaptation module, which is used to align the two domains and effectively improve segmentation network's generalization capability to the real domain. Besides, we propose an unsupervised adversarial training scheme, which encourages the segmentation network to learn the domain-invariant feature, so that the robust feature can be used for segmentation. Experimental results demonstrate that our method can achieve state-of-the-art segmentation performance on COVID-19 CT images.
Elementary Effects Analysis of factors controlling COVID-19 infections in computational simulation reveals the importance of Social Distancing and Mask Usage
Authors: Kelvin K. F. Li, Stephen A. Jarvis, Fayyaz Minhas
Date: 2020-11-20 00:00:00

COVID-19 was declared a pandemic by the World Health Organization (WHO) on March 11th, 2020. With half of the world's countries in lockdown as of April due to this pandemic, monitoring and understanding the spread of the virus and infection rates and how these factors relate to behavioural and societal parameters is crucial for effective policy making. This paper aims to investigate the effectiveness of masks, social distancing, lockdown and self-isolation for reducing the spread of SARS-CoV-2 infections. Our findings based on agent-based simulation modelling show that whilst requiring a lockdown is widely believed to be the most efficient method to quickly reduce infection numbers, the practice of social distancing and the usage of surgical masks can potentially be more effective than requiring a lockdown. Our multivariate analysis of simulation results using the Morris Elementary Effects Method suggests that if a sufficient proportion of the population wore surgical masks and followed social distancing regulations, then SARS-CoV-2 infections can be controlled without requiring a lockdown.