pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning for Network Traffic Classification
Authors: Niloofar Bayat, Weston Jackson, Derrick Liu
Date: 2021-06-02 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Deep Learning, Traffic Classification

Monitoring network traffic to identify content, services, and applications is an active research topic in network traffic control systems. While modern firewalls provide the capability to decrypt packets, this is not appealing for privacy advocates. Hence, identifying any information from encrypted traffic is a challenging task. Nonetheless, previous work has identified machine learning methods that may enable application and service identification. The process involves high level feature extraction from network packet data then training a robust machine learning classifier for traffic identification. We propose a classification technique using an ensemble of deep learning architectures on packet, payload, and inter-arrival time sequences. To our knowledge, this is the first time such deep learning architectures have been applied to the Server Name Indication (SNI) classification problem. Our ensemble model beats the state of the art machine learning methods and our up-to-date model can be found on github: \url{https://github.com/niloofarbayat/NetworkClassification}
Comparison between safety methods control barrier function vs. reachability analysis
Authors: Zhichao Li
Date: 2021-06-24 00:00:00

This report aims to compare two safety methods: control barrier function and Hamilton-Jacobi reachability analysis. We will consider the difference with a focus on the following aspects: generality of system dynamics, difficulty of construction and computation cost. A standard Dubins car model will be evaluated numerically to make the comparison more concrete.
rSoccer: A Framework for Studying Reinforcement Learning in Small and Very Small Size Robot Soccer
Authors: Felipe B. Martins, Mateus G. Machado, Hansenclever F. Bassani, Pedro H. M. Braga, Edna S. Barros
Date: 2021-06-15 00:00:00
Tasks: OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Reinforcement learning is an active research area with a vast number of applications in robotics, and the RoboCup competition is an interesting environment for studying and evaluating reinforcement learning methods. A known difficulty in applying reinforcement learning to robotics is the high number of experience samples required, being the use of simulated environments for training the agents followed by transfer learning to real-world (sim-to-real) a viable path. This article introduces an open-source simulator for the IEEE Very Small Size Soccer and the Small Size League optimized for reinforcement learning experiments. We also propose a framework for creating OpenAI Gym environments with a set of benchmarks tasks for evaluating single-agent and multi-agent robot soccer skills. We then demonstrate the learning capabilities of two state-of-the-art reinforcement learning methods as well as their limitations in certain scenarios introduced in this framework. We believe this will make it easier for more teams to compete in these categories using end-to-end reinforcement learning approaches and further develop this research area.
SRIB-LEAP submission to Far-field Multi-Channel Speech Enhancement Challenge for Video Conferencing
Authors: R G Prithvi Raj, Rohit Kumar, M K Jayesh, Anurenjan Purushothaman, Sriram Ganapathy, M A Basha Shaik
Date: 2021-06-24 00:00:00
Tasks: Speech Enhancement

This paper presents the details of the SRIB-LEAP submission to the ConferencingSpeech challenge 2021. The challenge involved the task of multi-channel speech enhancement to improve the quality of far field speech from microphone arrays in a video conferencing room. We propose a two stage method involving a beamformer followed by single channel enhancement. For the beamformer, we incorporated self-attention mechanism as inter-channel processing layer in the filter-and-sum network (FaSNet), an end-to-end time-domain beamforming system. The single channel speech enhancement is done in log spectral domain using convolution neural network (CNN)-long short term memory (LSTM) based architecture. We achieved improvements in objective quality metrics - perceptual evaluation of speech quality (PESQ) of 0.5 on the noisy data. On subjective quality evaluation, the proposed approach improved the mean opinion score (MOS) by an absolute measure of 0.9 over the noisy audio.
Augmented Synchronization of Power Systems
Authors: Peng Yang, Feng Liu, Tao Liu, David J. Hill
Date: 2021-06-24 00:00:00

Power system transient stability has been translated into a Lyapunov stability problem of the post-disturbance equilibrium for decades. Despite substantial results, conventional theories suffer from the stringent requirement of knowing the post-disturbance equilibrium a priori. In contrast, the wisdom from practice, which certificates stability by only the observation of converging frequencies and voltages, seems to provide an equilibrium-independent approach. Here, we formulate the empirical wisdom by the concept of augmented synchronization and aim to bridge such a theory-practice gap. First, we derive conditions under which the convergence to augmented synchronization implies the convergence to the equilibrium set, laying the first theoretical foundation for the empirical wisdom. Then, we reveal from what initial values the power system can achieve augmented synchronization. Our results open the possibility of an equilibrium-independent power system stability analytic that re-defines the nominal motion as augmented synchronization rather than certain equilibrium. Single-machine examples and the IEEE 9-bus system well verify our results and illustrate promising implications.
Generalized Nash Equilibrium Seeking Algorithm Design for Distributed Constrained Multi-Cluster Games
Authors: Zhenhua Deng, Yan Zhao
Date: 2021-06-24 00:00:00

The multi-cluster games are addressed in this paper, where all players team up with the players in the cluster that they belong to, and compete against the players in other clusters to minimize the cost function of their own cluster. The decision of every player is constrained by coupling inequality constraints, local inequality constraints and local convex set constraints. Our problem extends well-known noncooperative game problems and resource allocation problems by considering the competition between clusters and the cooperation within clusters at the same time. Besides, without involving the resource allocation within clusters, the noncooperative game between clusters, and the aforementioned constraints, existing game algorithms as well as resource allocation algorithms cannot solve the problem. In order to seek the variational generalized Nash equilibrium (GNE) of the multi-cluster games, we design a distributed algorithm via gradient descent and projections. Moreover, we analyze the convergence of the algorithm with the help of variational analysis and Lyapunov stability theory. Under the algorithm, all players asymptotically converge to the variational GNE of the multi-cluster game. Simulation examples are presented to verify the effectiveness of the algorithm.
On Angular Speed Estimation of Rigid Bodies
Authors: Francesco Ferrante, Gildas Besançon
Date: 2021-03-04 00:00:00

The problem of estimating the angular speed of a solid body from attitude measurements is addressed. To solve this problem, we propose an observer whose dynamics are not constrained to evolve on any specific manifold. This drastically simplifies the analysis of the proposed observer. Using Lyapunov analysis, sufficient conditions for global asymptotic stability of a set wherein the estimation error is equal to zero are established. In addition, the proposed methodology is adapted to deal with angular speed estimation for systems evolving on the unit circle. The approach is illustrated through several numerical simulations.
Characterizing Bipartite Consensus on Signed Matrix-Weighted Networks via Balancing Set
Authors: Chongzhi Wang, Lulu Pan, Haibin Shao, Dewei Li, Yugeng Xi
Date: 2020-11-28 00:00:00
Tasks: Negation

In contrast with the scalar-weighted networks, where bipartite consensus can be achieved if and only if the underlying signed network is structurally balanced, the structural balance property is no longer a graph-theoretic equivalence to the bipartite consensus in the case of signed matrix-weighted networks. To re-establish the relationship between the network structure and the bipartite consensus solution, the non-trivial balancing set is introduced which is a set of edges whose sign negation can transform a structurally imbalanced network into a structurally balanced one and the weight matrices associated with edges in this set have a non-trivial intersection of null spaces. We show that necessary and/or sufficient conditions for bipartite consensus on matrix-weighted networks can be characterized by the uniqueness of the non-trivial balancing set, while the contribution of the associated non-trivial intersection of null spaces to the steady-state of the matrix-weighted network is examined. Moreover, for matrix-weighted networks with a positive-negative spanning tree, necessary and sufficient condition for bipartite consensus using the non-trivial balancing set is obtained. Simulation examples are provided to demonstrate the theoretical results.
Control of a Mixed Autonomy Signalised Urban Intersection: An Action-Delayed Reinforcement Learning Approach
Authors: Erica Salvato, Arnob Ghosh, Gianfranco Fenu, Thomas Parisini
Date: 2021-06-24 00:00:00
Tasks: Reinforcement Learning (RL)

We consider a mixed autonomy scenario where the traffic intersection controller decides whether the traffic light will be green or red at each lane for multiple traffic-light blocks. The objective of the traffic intersection controller is to minimize the queue length at each lane and maximize the outflow of vehicles over each block. We consider that the traffic intersection controller informs the autonomous vehicle (AV) whether the traffic light will be green or red for the future traffic-light block. Thus, the AV can adapt its dynamics by solving an optimal control problem. We model the decision process of the traffic intersection controller as a deterministic delay Markov decision process owing to the delayed action by the traffic controller. We propose Reinforcement-learning based algorithm to obtain the optimal policy. We show - empirically - that our algorithm converges and reduces the energy costs of AVs drastically as the traffic controller communicates with the AVs.
Real Time State Estimation of Power Grids Using Convolutional Neural Networks and State Forecasting Via Recurrent Neural Networks
Authors: Sahil Vohra
Date: 2021-06-19 00:00:00
Tasks: State Estimation

Power grids play a very important role in delivering electrical energy to homes, industries and other places that require it. Because of this increased demand they are facing a great challenge of voltage variations. This happens due to varied use of energy-consuming devices and appliances like electric vehicles, industrial consumption, occasional peak in energy demands etc. For these fluctuations in demands, it becomes extremely important to monitor the conditions at which the power grid operates. Once these conditions are known, the energy production can be manipulated to meet the demand. It has been found that the existing Power System State Estimation (PSSE) techniques may not be good in producing optimal Performance. Moreover, they are also expensive in terms of computational processing. To address this problem, this research proposes a state estimation method for power grids using Convolutional Neural Networks (CNN). It was found that the model produced an RMSE of 2.57 x 10-4, which was comparatively accurate than one of previous studies involved in making the estimation using a Prox Linear Model (2.97 x 10-4). Furthermore, the research also proposes Power System State Forecasting for improving system awareness and resilience. The forecasting is carried out using a model of Recurrent Neural Network (RNN). This model helps in accounting for long-term nonlinear aspects present in data and based on that it does the forecasting. The proposed model forecasted with a RMSE of 2.53 x 10-3, which is comparatively equal to the previous study mentioned above (2.59 x 10-3).
Membraneless organelles formed by liquid-liquid phase separation increase bacterial fitness
Authors: Xin Jin, Ji-Eun Lee, Charley Schaefer, Xinwei Luo, Adam J. M. Wollman, Alex L. Payne-Dwyer, Tian Tian, Xiaowei Zhang, Xiao Chen, Yingxing Li, Tom C. B. McLeish, Mark C. Leake, Fan Bai
Date: 2021-06-24 00:00:00

Liquid-liquid phase separation is emerging as a crucial phenomenon in several fundamental cell processes. A range of eukaryotic systems exhibit liquid condensates. However, their function in bacteria, which in general lack membrane-bound compartments, remains less clear. Here, we used high-resolution optical microscopy to observe single bacterial aggresomes, nanostructured intracellular assemblies of proteins, to undercover their role in cell stress. We find that proteins inside aggresomes are mobile and undergo dynamic turnover, consistent with a liquid state. Our observations are in quantitative agreement with phase-separated liquid droplet formation driven by interacting proteins under thermal equilibrium that nucleate following diffusive collisions in the cytoplasm. We have discovered aggresomes in multiple species of bacteria, and show that these emergent, metastable liquid-structured protein assemblies increase bacterial fitness by enabling cells to tolerate environmental stresses.
The gig economy in Poland: evidence based on mobile big data
Authors: Beręsewicz Maciej, Nikulin Dagmara, Szymkowiak Marcin, Wilak Kamil
Date: 2021-06-24 00:00:00
Tasks: Marketing

In this article we address the question of how to measure the size and characteristics of the platform economy. We propose a~different, to sample surveys, approach based on smartphone data, which are passively collected through programmatic systems as part of online marketing. In particular, in our study we focus on two types of services: food delivery (Bolt Courier, Takeaway, Glover, Wolt and transport services (Bolt Driver, Free Now, iTaxi and Uber). Our results show that the platform economy in Poland is growing. In particular, with respect to food delivery and transportation services performed by means of applications, we observed a growing trend between January 2018 and December 2020. Taking into account the demographic structure of apps users, our results confirm findings from past studies: the majority of platform workers are young men but the age structure of app users is different for each of the two categories of services. Another surprising finding is that foreigners do not account for the majority of gig workers in Poland. When the number of platform workers is compared with corresponding working populations, the estimated share of active app users accounts for about 0.5-2% of working populations in 9 largest Polish cities.
Unique Stable Matchings
Authors: Gregory Z. Gutin, Philip R. Neary, Anders Yeo
Date: 2021-06-24 00:00:00

In this paper we consider the issue of a unique prediction in one to one two sided matching markets, as defined by Gale and Shapley (1962), and we prove the following. Theorem. Let P be a one-to-one two-sided matching market and let P be its associated normal form, a (weakly) smaller matching market with the same set of stable matchings, that can be obtained using procedures introduced in Irving and Leather (1986) and Balinski and Ratier (1997). The following three statements are equivalent (a) P has a unique stable matching. (b) Preferences on P* are acyclic, as defined by Chung (2000). (c) In P* every market participant's preference list is a singleton.
Robust Misspecified Models and Paradigm Shifts
Authors: Cuimin Ba
Date: 2021-06-24 00:00:00

Individuals use models to guide decisions, but many models are wrong. This paper studies which misspecified models are likely to persist when individuals also entertain alternative models. Consider an agent who uses her model to learn the relationship between action choices and outcomes. The agent exhibits sticky model switching, captured by a threshold rule such that she switches to an alternative model when it is a sufficiently better fit for the data she observes. The main result provides a characterization of whether a model persists based on two key features that are straightforward to derive from the primitives of the learning environment, namely, the model's asymptotic accuracy in predicting the equilibrium pattern of observed outcomes and the 'tightness' of the prior around this equilibrium. I show that misspecified models can be robust in that they persist against a wide range of competing models -- including the correct model -- despite individuals observing an infinite amount of data. Moreover, simple misspecified models with entrenched priors can be even more robust than correctly specified models. I use this characterization to provide a learning foundation for the persistence of systemic biases in two applications. First, in an effort-choice problem, I show that overconfidence in one's ability is more robust than underconfidence. Second, a simplistic binary view of politics is more robust than the more complex correct view when individuals consume media without fully recognizing the reporting bias.
Modeling Magnetic Particle Imaging for Dynamic Tracer Distributions
Authors: Christina Brandt, Christiane Schmidt
Date: 2021-06-24 00:00:00
Tasks: Image Reconstruction

Magnetic Particle Imaging (MPI) is a promising tracer-based, functional medical imaging technique which measures the non-linear magnetization response of magnetic nanoparticles to a dynamic magnetic field. For image reconstruction, system matrices from time-consuming calibration scans are used predominantly. Finding modeled forward operators for magnetic particle imaging, which are able to compete with measured matrices in practice, is an ongoing topic of research. The existing models for magnetic particle imaging are by design not suitable for arbitrary dynamic tracer concentrations. Neither modeled nor measured system matrices account for changes in the concentration during a single scanning cycle. In this paper we present a new MPI forward model for dynamic concentrations. A static model will be introduced briefly, followed by the changes due to the dynamic behavior of the tracer concentration. Furthermore, the relevance of this new extended model is examined by investigating the influence of the extension and example reconstructions with the new and the standard model.
Transform-Based Feature Map Compression for CNN Inference
Authors: Yubo Shi, Meiqi Wang, Siyi Chen, Jinghe Wei, Zhongfeng Wang
Date: 2021-06-24 00:00:00
Tasks: Quantization

To achieve higher accuracy in machine learning tasks, very deep convolutional neural networks (CNNs) are designed recently. However, the large memory access of deep CNNs will lead to high power consumption. A variety of hardware-friendly compression methods have been proposed to reduce the data transfer bandwidth by exploiting the sparsity of feature maps. Most of them focus on designing a specialized encoding format to increase the compression ratio. Differently, we observe and exploit the sparsity distinction between activations in earlier and later layers to improve the compression ratio. We propose a novel hardware-friendly transform-based method named 1D-Discrete Cosine Transform on Channel dimension with Masks (DCT-CM), which intelligently combines DCT, masks, and a coding format to compress activations. The proposed algorithm achieves an average compression ratio of 2.9x (53% higher than the state-of-the-art transform-based feature map compression works) during inference on ResNet-50 with an 8-bit quantization scheme.
NN2CAM: Automated Neural Network Mapping for Multi-Precision Edge Processing on FPGA-Based Cameras
Authors: Petar Jokic, Stephane Emery, Luca Benini
Date: 2021-06-24 00:00:00
Tasks: image-classification, Image Classification

The record-breaking achievements of deep neural networks (DNNs) in image classification and detection tasks resulted in a surge of new computer vision applications during the past years. However, their computational complexity is restricting their deployment to powerful stationary or complex dedicated processing hardware, limiting their use in smart edge processing applications. We propose an automated deployment framework for DNN acceleration at the edge on field-programmable gate array (FPGA)-based cameras. The framework automatically converts an arbitrary-sized and quantized trained network into an efficient streaming-processing IP block that is instantiated within a generic adapter block in the FPGA. In contrast to prior work, the accelerator is purely logic and thus supports end-to-end processing on FPGAs without on-chip microprocessors. Our mapping tool features automatic translation from a trained Caffe network, arbitrary layer-wise fixed-point precision for both weights and activations, an efficient XNOR implementation for fully binary layers as well as a balancing mechanism for effective allocation of computational resources in the streaming dataflow. To present the performance of the system we employ this tool to implement two CNN edge processing networks on an FPGA-based high-speed camera with various precision settings showing computational throughputs of up to 337GOPS in low-latency streaming mode (no batching), running entirely on the camera.
Can Perceptual Guidance Lead to Semantically Explainable Adversarial Perturbations?
Authors: P Charantej Reddy, Aditya Siripuram, Sumohana S. Channappayya
Date: 2021-06-24 00:00:00
Tasks: SSIM

It is well known that carefully crafted imperceptible perturbations can cause state-of-the-art deep learning classification models to misclassify. Understanding and analyzing these adversarial perturbations play a crucial role in the design of robust convolutional neural networks. However, their mechanics are not well understood. In this work, we attempt to understand the mechanics by systematically answering the following question: do imperceptible adversarial perturbations focus on changing the regions of the image that are important for classification? In other words, are imperceptible adversarial perturbations semantically explainable? Most current methods use $l_p$ distance to generate and characterize the imperceptibility of the adversarial perturbations. However, since $l_p$ distances only measure the pixel to pixel distances and do not consider the structure in the image, these methods do not provide a satisfactory answer to the above question. To address this issue, we propose a novel framework for generating adversarial perturbations by explicitly incorporating a "perceptual quality ball" constraint in our formulation. Specifically, we pose the adversarial example generation problem as a tractable convex optimization problem, with constraints taken from a mathematically amenable variant of the popular SSIM index. We use the MobileNetV2 network trained on the ImageNet dataset for our experiments. By comparing the SSIM maps generated by our method with class activation maps, we show that the perceptually guided perturbations introduce changes specifically in the regions that contribute to classification decisions i.e., these perturbations are semantically explainable.
Power and Modulation Format Transfer Learning for Neural Network Equalizers in Coherent Optical Transmission Systems
Authors: Pedro J. Freire, Daniel Abode, Jaroslaw E. Prilepsky, Sergei K. Turitsyn
Date: 2021-06-24 00:00:00
Tasks: Transfer Learning

Transfer learning is proposed to adapt an NN-based nonlinear equalizer across different launch powers and modulation formats using a 450km TWC-fiber transmission. The result shows up to 92% reduction in epochs or 90% in the training dataset.