pwc-archive/papers-with-abstracts huggingface dataset:
Histogram Equalization Of The Image
Authors: Irem Doken, Melih Gokdemir, W. T Al-Shaibani, Ibraheem Shayea
Date: 2021-08-29 00:00:00
Tasks: Local Color Enhancement

The relevance and impact of probability distributions on image processing are the subject of this study.It may be characterized as a probability distribution function of brightness for a certain area, which might be a whole picture. To generate a histogram, the probability density function of the brightness is frequently calculated by counting how many times each brightness occurs in the picture region. The brightness average is defined as the sample mean of the brightness of pixels in a certain region. The frequency is shown by the histogram. The histogram has a wide range of uses in image processing. It could, for starters, be used for picture analysis. Second, the functions of an image's brightness and contrast, as well as the final two uses of equalizing and thresholding. Normalizing a histogram is one technique to convert the intensities of discrete distributions to the probability of discrete distribution functions. The technique to equalize the histogram is to control the image's contrast by altering their intensity distribution functions. The major goal of this procedure is to give the cumulative probability function a linear trend (CDF).A method of segmentation is to divide a section of the picture into constituent areas or objects.
Transmittance Multispectral Imaging for Reheated Coconut Oil Differentiation
Authors: D. Y. L. Ranasinghe, H. M. H. K. Weerasooriya, S. Herath, M. P. B. Ekanayake, H. M. V. R. Herath, G. M. R. I. Godaliyadda, T. Madhujith
Date: 2021-08-28 00:00:00
Tasks: Clustering

Oil reheating has a significant impact on global health due to its extensive consumption, especially in South Asia, and severe health risks. Nevertheless, food image analysis using multispectral imaging systems(MISs) has not been applied to oil reheating analysis despite their vast application in rapid food quality screening. To that end, the paper discusses the application of a low-cost MSI to estimate the 'reheat cycle count classes' (number of times an oil sample is recursively heated) and identify 'critical classes' at which substantial changes in the oil sample have materialized. Firstly, the reheat cycle count class is estimated with Bhattacharyya distance between the reheated and a pure oil sample as the input. The classification was performed using a support vector machine classifier that resulted in an accuracy of 83.34 % for reheat cycle count identification. Subsequently, an unsupervised clustering procedure was introduced using a modified spectral clustering (SC) algorithm to distinguish critical classes under reheating. In addition, laboratory experiments were performed to ascertain the ramifications of the reheating process with a chemical analysis. The chemical analysis of the coconut oil samples used in the experiment coincided with the chemical analysis results and was statistically significant (p < 0.05). Accordingly, the proposed work closes the gap for using multispectral imaging for oil reheating and proposes a novel algorithm for unsupervised detection of critical property changes in the oil. Hence, the proposed research work is significant in its practical implications, contribution to food image analysis, and unsupervised classification mechanisms.
Prior-Guided Deep Interference Mitigation for FMCW Radars
Authors: JianPing Wang, Runlong Li, Yuan He, Yang Yang
Date: 2021-08-30 00:00:00

A prior-guided deep learning (DL) based interference mitigation approach is proposed for frequency modulated continuous wave (FMCW) radars. In this paper, the interference mitigation problem is tackled as a regression problem. Considering the complex-valued nature of radar signals, the complex-valued convolutional neural network is utilized as an architecture for implementation, which is different from the conventional real-valued counterparts. Meanwhile, as the useful beat signals of FMCW radars and interferences exhibit different distributions in the time-frequency domain, this prior feature is exploited as a regularization term to avoid overfitting of the learned representation. The effectiveness and accuracy of our proposed complex-valued fully convolutional network (CV-FCN) based interference mitigation approach are verified and analyzed through both simulated and measured radar signals. Compared to the real-valued counterparts, the CV-FCN shows a better interference mitigation performance with a potential of half memory reduction in low Signal to Interference plus Noise Ratio (SINR) scenarios. Moreover, the CV-FCN trained using only simulated data can be directly utilized for interference mitigation in various measured radar signals and shows a superior generalization capability. Furthermore, by incorporating the prior feature, the CV-FCN trained on only 1/8 of the full data achieves comparable performance as that on the full dataset in low SINR scenarios, and the training procedure converges faster.
Energy Efficient Water-Filling Power Allocation with Cell Division for Massive MIMO Systems
Authors: Abdolrasoul Sakhaei Gharagezlou, Jafar Pourrostam, Mahdi Nangir, Mir Mahdi Safari
Date: 2021-08-27 00:00:00

In this paper, we consider the power allocation problem for the downlink of the massive multiple-input multiple-output (MIMO) systems. We propose a new scheme by exploiting the water-filling algorithm in a cell with two zones. It is allocated more power to the farther users, and also users with better channel conditions receive more power in each zone. The users with better channel gain have a higher priority than others in the energy efficiency (EE) point of view. Combining the water-filling and cell division techniques in the MIMO systems leads to a reach EE maximization. We also use standard interference function (SIF) to propose a new iterative algorithm for solving the optimization problem and obtain an efficient power allocation scheme. Simulation results show that the proposed algorithm outperforms other similar algorithms from the EE point of view.
Dual representations of quasiconvex compositions with applications to systemic risk
Authors: Çağın Ararat, Mücahit Aygün
Date: 2021-08-29 00:00:00

Motivated by the problem of finding dual representations for quasiconvex systemic risk measures in financial mathematics, we study quasiconvex compositions in an abstract infinite-dimensional setting. We calculate an explicit formula for the penalty function of the composition in terms of the penalty functions of the ingredient functions. The proof makes use of a nonstandard minimax inequality (rather than equality as in the standard case) that is available in the literature. In the second part of the paper, we apply our results in concrete probabilistic settings for systemic risk measures, in particular, in the context of Eisenberg-Noe clearing model. We also provide novel economic interpretations of the dual representations in these settings.
On the Stability Properties and the Optimization Landscape of Training Problems with Squared Loss for Neural Networks and General Nonlinear Conic Approximation Schemes
Authors: Constantin Christof
Date: 2020-11-06 00:00:00

We study the optimization landscape and the stability properties of training problems with squared loss for neural networks and general nonlinear conic approximation schemes. It is demonstrated that, if a nonlinear conic approximation scheme is considered that is (in an appropriately defined sense) more expressive than a classical linear approximation approach and if there exist unrealizable label vectors, then a training problem with squared loss is necessarily unstable in the sense that its solution set depends discontinuously on the label vector in the training data. We further prove that the same effects that are responsible for these instability properties are also the reason for the emergence of saddle points and spurious local minima, which may be arbitrarily far away from global solutions, and that neither the instability of the training problem nor the existence of spurious local minima can, in general, be overcome by adding a regularization term to the objective function that penalizes the size of the parameters in the approximation scheme. The latter results are shown to be true regardless of whether the assumption of realizability is satisfied or not. We demonstrate that our analysis in particular applies to training problems for free-knot interpolation schemes and deep and shallow neural networks with variable widths that involve an arbitrary mixture of various activation functions (e.g., binary, sigmoid, tanh, arctan, soft-sign, ISRU, soft-clip, SQNL, ReLU, leaky ReLU, soft-plus, bent identity, SILU, ISRLU, and ELU). In summary, the findings of this paper illustrate that the improved approximation properties of neural networks and general nonlinear conic approximation instruments are linked in a direct and quantifiable way to undesirable properties of the optimization problems that have to be solved in order to train them.
Ballast water-mediated species spread risk dynamics and policy implications to reduce the invasion risk to the Mediterranean Sea
Authors: Zhaojun Wang, Mandana Saebi, Erin K. Grey, James J. Corbett
Date: 2021-08-30 00:00:00

The Mediterranean Sea is one of the most heavily invaded marine regions. This work focuses on the dynamics and potential policy options for ballast water-mediated nonindigenous species to the Mediterranean. Specifically, we (1) estimated port risks in years 2012, 2015, and 2018, (2) identified hub ports that connect many clusters, and (3) evaluated four regulatory scenarios. The risk results show that Gibraltar, Suez, and Istanbul remained high-risk ports from 2012-2018, and they served as hub ports that connected several spread clusters. With policy scenario analysis, we found that regulating the high-risk hub ports can disproportionately reduce the overall risk to the Mediterranean: the average risk to all ports was reduced by 5-10% by regulating one high-risk hub port, while the average risk to all ports was only reduced by 0.2% by regulating one average-risk Mediterranean port. We also found that only regulating high-risk ports cannot reduce their risks effectively.
Creative Diversity: Patterns in the Creative Habits of ~10,000 People
Authors: Eric Berlow, Spencer Canon, Kaustuv DeBiswas, David Gurman, Shawna Jacoby, Lizbet Simmons, Andy Walshe, Rich Williams, Tiffany Yuan, Mark Runco
Date: 2021-08-29 00:00:00
Tasks: Diversity

Despite popular media interest in uncovering the creative habits of successful people, there is a surprising paucity of empirical research on the diversity of tendencies and preferences people have when engaged in creative work. We developed a simple survey that characterized 42 creative habits along 21 independent dimensions. Data from 9,633 respondents revealed seven 'Creative Species', or clusters of people with combinations of creative habits that tend to co-occur more than expected by chance. These emergent clusters where relatively stable to random subsampling of the population and to variation in model parameters. The seven Creative Species self-sorted along a primary gradient from those characterized by more 'deliberate' creative habits (e.g., Monotasker, Risk Averse, Routine Seeker, Tenacious, Make it Happen) to those characterized by more 'open' creative habits (e.g. Multitasker, Risk Friendly, Novelty Seeker, Reframer, Let it Happen). A weaker second gradient was defined by outward and rational vs inward and intuitive creators. For the subset of respondents with data about their broad professional discipline (Art, Science, and Business) and gender, some groups were more (or less) common in some Creative Species than expected by chance, but the absolute magnitude of these differences were generally small; and knowing the discipline or gender of a person was not a good single predictor of their creative preferences or tendencies. Together these results suggest that independent of discipline or gender, people vary widely in the habits, behaviors, and contexts in which they feel most creative. Understanding creative diversity is critical for improving the creative performance of both individuals and collaborative teams.
When are Deep Networks really better than Decision Forests at small sample sizes, and how?
Authors: Haoyin Xu, Kaleab A. Kinfu, Will LeVine, Sambit Panda, Jayanta Dey, Michael Ainsworth, Yu-Chung Peng, Madi Kusmanov, Florian Engert, Christopher M. White, Joshua T. Vogelstein, Carey E. Priebe
Date: 2021-08-31 00:00:00

Deep networks and decision forests (such as random forests and gradient boosted trees) are the leading machine learning methods for structured and tabular data, respectively. Many papers have empirically compared large numbers of classifiers on one or two different domains (e.g., on 100 different tabular data settings). However, a careful conceptual and empirical comparison of these two strategies using the most contemporary best practices has yet to be performed. Conceptually, we illustrate that both can be profitably viewed as "partition and vote" schemes. Specifically, the representation space that they both learn is a partitioning of feature space into a union of convex polytopes. For inference, each decides on the basis of votes from the activated nodes. This formulation allows for a unified basic understanding of the relationship between these methods. Empirically, we compare these two strategies on hundreds of tabular data settings, as well as several vision and auditory settings. Our focus is on datasets with at most 10,000 samples, which represent a large fraction of scientific and biomedical datasets. In general, we found forests to excel at tabular and structured data (vision and audition) with small sample sizes, whereas deep nets performed better on structured data with larger sample sizes. This suggests that further gains in both scenarios may be realized via further combining aspects of forests and networks. We will continue revising this technical report in the coming months with updated results.
Iterative Filter Adaptive Network for Single Image Defocus Deblurring
Authors: Junyong Lee, Hyeongseok Son, Jaesung Rim, Sunghyun Cho, Seungyong Lee
Date: 2021-08-31 00:00:00
Proceeding: CVPR 2021 1
Tasks: Deblurring, Image Defocus Deblurring

We propose a novel end-to-end learning-based approach for single image defocus deblurring. The proposed approach is equipped with a novel Iterative Filter Adaptive Network (IFAN) that is specifically designed to handle spatially-varying and large defocus blur. For adaptively handling spatially-varying blur, IFAN predicts pixel-wise deblurring filters, which are applied to defocused features of an input image to generate deblurred features. For effectively managing large blur, IFAN models deblurring filters as stacks of small-sized separable filters. Predicted separable deblurring filters are applied to defocused features using a novel Iterative Adaptive Convolution (IAC) layer. We also propose a training scheme based on defocus disparity estimation and reblurring, which significantly boosts the deblurring quality. We demonstrate that our method achieves state-of-the-art performance both quantitatively and qualitatively on real-world images.
Self-Calibrating Neural Radiance Fields
Authors: Yoonwoo Jeong, Seokjun Ahn, Christopher Choy, Animashree Anandkumar, Minsu Cho, Jaesik Park
Date: 2021-08-31 00:00:00
Proceeding: ICCV 2021 10
Tasks: NeRF

In this work, we propose a camera self-calibration algorithm for generic cameras with arbitrary non-linear distortions. We jointly learn the geometry of the scene and the accurate camera parameters without any calibration objects. Our camera model consists of a pinhole model, a fourth order radial distortion, and a generic noise model that can learn arbitrary non-linear camera distortions. While traditional self-calibration algorithms mostly rely on geometric constraints, we additionally incorporate photometric consistency. This requires learning the geometry of the scene, and we use Neural Radiance Fields (NeRF). We also propose a new geometric loss function, viz., projected ray distance loss, to incorporate geometric consistency for complex non-linear camera models. We validate our approach on standard real image datasets and demonstrate that our model can learn the camera intrinsics and extrinsics (pose) from scratch without COLMAP initialization. Also, we show that learning accurate camera models in a differentiable manner allows us to improve PSNR over baselines. Our module is an easy-to-use plugin that can be applied to NeRF variants to improve performance. The code and data are currently available at https://github.com/POSTECH-CVLab/SCNeRF.
Quantization of Generative Adversarial Networks for Efficient Inference: a Methodological Study
Authors: Pavel Andreev, Alexander Fritzler, Dmitry Vetrov
Date: 2021-08-31 00:00:00
Tasks: Neural Network Compression, Quantization

Generative adversarial networks (GANs) have an enormous potential impact on digital content creation, e.g., photo-realistic digital avatars, semantic content editing, and quality enhancement of speech and images. However, the performance of modern GANs comes together with massive amounts of computations performed during the inference and high energy consumption. That complicates, or even makes impossible, their deployment on edge devices. The problem can be reduced with quantization -- a neural network compression technique that facilitates hardware-friendly inference by replacing floating-point computations with low-bit integer ones. While quantization is well established for discriminative models, the performance of modern quantization techniques in application to GANs remains unclear. GANs generate content of a more complex structure than discriminative models, and thus quantization of GANs is significantly more challenging. To tackle this problem, we perform an extensive experimental study of state-of-art quantization techniques on three diverse GAN architectures, namely StyleGAN, Self-Attention GAN, and CycleGAN. As a result, we discovered practical recipes that allowed us to successfully quantize these models for inference with 4/8-bit weights and 8-bit activations while preserving the quality of the original full-precision models.
Effective Sequence-to-Sequence Dialogue State Tracking
Authors: Jeffrey Zhao, Mahdis Mahdieh, Ye Zhang, Yuan Cao, Yonghui Wu
Date: 2021-08-31 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Dialogue State Tracking, Language Modeling, Language Modelling, Multi-domain Dialogue State Tracking, Text Summarization

Sequence-to-sequence models have been applied to a wide variety of NLP tasks, but how to properly use them for dialogue state tracking has not been systematically investigated. In this paper, we study this problem from the perspectives of pre-training objectives as well as the formats of context representations. We demonstrate that the choice of pre-training objective makes a significant difference to the state tracking quality. In particular, we find that masked span prediction is more effective than auto-regressive language modeling. We also explore using Pegasus, a span prediction-based pre-training objective for text summarization, for the state tracking model. We found that pre-training for the seemingly distant summarization task works surprisingly well for dialogue state tracking. In addition, we found that while recurrent state context representation works also reasonably well, the model may have a hard time recovering from earlier mistakes. We conducted experiments on the MultiWOZ 2.1-2.4, WOZ 2.0, and DSTC2 datasets with consistent observations.
DeepTaskAPT: Insider APT detection using Task-tree based Deep Learning
Authors: Mohammad Mamun, Kevin Shi
Date: 2021-08-31 00:00:00
Tasks: Deep Learning

APT, known as Advanced Persistent Threat, is a difficult challenge for cyber defence. These threats make many traditional defences ineffective as the vulnerabilities exploited by these threats are insiders who have access to and are within the network. This paper proposes DeepTaskAPT, a heterogeneous task-tree based deep learning method to construct a baseline model based on sequences of tasks using a Long Short-Term Memory (LSTM) neural network that can be applied across different users to identify anomalous behaviour. Rather than applying the model to sequential log entries directly, as most current approaches do, DeepTaskAPT applies a process tree based task generation method to generate sequential log entries for the deep learning model. To assess the performance of DeepTaskAPT, we use a recently released synthetic dataset, DARPA Operationally Transparent Computing (OpTC) dataset and a real-world dataset, Los Alamos National Laboratory (LANL) dataset. Both of them are composed of host-based data collected from sensors. Our results show that DeepTaskAPT outperforms similar approaches e.g. DeepLog and the DeepTaskAPT baseline model demonstrate its capability to detect malicious traces in various attack scenarios while having high accuracy and low false-positive rates. To the best of knowledge this is the very first attempt of using recently introduced OpTC dataset for cyber threat detection.
A Subsampling-Based Method for Causal Discovery on Discrete Data
Authors: Austin Goddard, Yu Xiang
Date: 2021-08-31 00:00:00
Tasks: Causal Discovery

Inferring causal directions on discrete and categorical data is an important yet challenging problem. Even though the additive noise models (ANMs) approach can be adapted to the discrete data, the functional structure assumptions make it not applicable on categorical data. Inspired by the principle that the cause and mechanism are independent, various methods have been developed, leveraging independence tests such as the distance correlation measure. In this work, we take an alternative perspective and propose a subsampling-based method to test the independence between the generating schemes of the cause and that of the mechanism. Our methodology works for both discrete and categorical data and does not imply any functional model on the data, making it a more flexible approach. To demonstrate the efficacy of our methodology, we compare it with existing baselines over various synthetic data and real data experiments.
Detecting Mitosis against Domain Shift using a Fused Detector and Deep Ensemble Classification Model for MIDOG Challenge
Authors: Jingtang Liang, Cheng Wang, Yujie Cheng, Zheng Wang, Fang Wang, Liyu Huang, Zhibin Yu, YuBo Wang
Date: 2021-08-31 00:00:00
Tasks: Data Augmentation, Prognosis

Mitotic figure count is an important marker of tumor proliferation and has been shown to be associated with patients' prognosis. Deep learning based mitotic figure detection methods have been utilized to automatically locate the cell in mitosis using hematoxylin \& eosin (H\&E) stained images. However, the model performance deteriorates due to the large variation of color tone and intensity in H\&E images. In this work, we proposed a two stage mitotic figure detection framework by fusing a detector and a deep ensemble classification model. To alleviate the impact of color variation in H\&E images, we utilize both stain normalization and data augmentation, aiding model to learn color irrelevant features. The proposed model obtains an F1 score of 0.7550 on the preliminary testing set released by the MIDOG challenge.
Incorporating Deception into CyberBattleSim for Autonomous Defense
Authors: Erich Walter, Kimberly Ferguson-Walter, Ahmad Ridley
Date: 2021-08-31 00:00:00
Tasks: CyberBattleSim, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deceptive elements, including honeypots and decoys, were incorporated into the Microsoft CyberBattleSim experimentation and research platform. The defensive capabilities of the deceptive elements were tested using reinforcement learning based attackers in the provided capture the flag environment. The attacker's progress was found to be dependent on the number and location of the deceptive elements. This is a promising step toward reproducibly testing attack and defense algorithms in a simulated enterprise network with deceptive defensive elements.