pwc-archive/papers-with-abstracts huggingface dataset:
Environmental assessment of a new generation battery: The magnesium-sulfur system
Authors: Claudia Tomasini Montenegro, Jens F. Peters, Manuel Baumann, Zhirong Zhao-Karger, Christopher Wolter, Marcel Weil
Date: 2021-04-08 00:00:00

As environmental concerns mostly drive the electrification of our economy and the corresponding increase in demand for battery storage systems, information about the potential environmental impacts of the different battery systems is required. However, this kind of information is scarce for emerging post-lithium systems such as the magnesium-sulfur (MgS) battery. Therefore, we use life cycle assessment following a cradle-to-gate perspective to quantify the cumulative energy demand and potential environmental impacts per Wh of the storage capacity of a hypothetical MgS battery (46 Wh/kg). Furthermore, we also estimate global warming potential (0.33 kg CO2 eq/Wh) , fossil depletion potential (0.09 kg oil eq / Wh), ozone depletion potential (2.5E-08 kg CFC-11/Wh) and metal depletion potential (0.044 kg Fe eq/Wh), associated with the MgS battery production. The battery is modelled based on an existing prototype MgS pouch cell and hypothetically optimised according to the current state of the art in lithium-ion batteries (LIB), exploring future improvement potentials. It turns out that the initial (non-optimised) prototype cell cannot compete with current LIB in terms of energy density or environmental performance, mainly due to the high share of non-active components, decreasing its performance substantially. Therefore, if the assumed evolutions of the MgS cell composition are achieved to overcome current design hurdles and reach a comparable lifespan, efficiency, cost and safety levels to that of existing LIB; then the MgS battery has significant potential to outperform both existing LIB, and lithium-sulfur batteries.
Fingerprint Presentation Attack Detection utilizing Time-Series, Color Fingerprint Captures
Authors: Richard Plesh, Keivan Bahmani, Ganghee Jang, David Yambay, Ken Brownlee, Timothy Swyka, Peter Johnson, Arun Ross, Stephanie Schuckers
Date: 2021-04-08 00:00:00
Tasks: Feature Engineering, General Classification, Time Series, Time Series Analysis

Fingerprint capture systems can be fooled by widely accessible methods to spoof the system using fake fingers, known as presentation attacks. As biometric recognition systems become more extensively relied upon at international borders and in consumer electronics, presentation attacks are becoming an increasingly serious issue. A robust solution is needed that can handle the increased variability and complexity of spoofing techniques. This paper demonstrates the viability of utilizing a sensor with time-series and color-sensing capabilities to improve the robustness of a traditional fingerprint sensor and introduces a comprehensive fingerprint dataset with over 36,000 image sequences and a state-of-the-art set of spoofing techniques. The specific sensor used in this research captures a traditional gray-scale static capture and a time-series color capture simultaneously. Two different methods for Presentation Attack Detection (PAD) are used to assess the benefit of a color dynamic capture. The first algorithm utilizes Static-Temporal Feature Engineering on the fingerprint capture to generate a classification decision. The second generates its classification decision using features extracted by way of the Inception V3 CNN trained on ImageNet. Classification performance is evaluated using features extracted exclusively from the static capture, exclusively from the dynamic capture, and on a fusion of the two feature sets. With both PAD approaches we find that the fusion of the dynamic and static feature-set is shown to improve performance to a level not individually achievable.
Unified Supervised-Unsupervised (SUPER) Learning for X-ray CT Image Reconstruction
Authors: Siqi Ye, Zhipeng Li, Michael T. McCann, Yong Long, Saiprasad Ravishankar
Date: 2020-10-06 00:00:00
Tasks: Computed Tomography (CT), Image Reconstruction

Traditional model-based image reconstruction (MBIR) methods combine forward and noise models with simple object priors. Recent machine learning methods for image reconstruction typically involve supervised learning or unsupervised learning, both of which have their advantages and disadvantages. In this work, we propose a unified supervised-unsupervised (SUPER) learning framework for X-ray computed tomography (CT) image reconstruction. The proposed learning formulation combines both unsupervised learning-based priors (or even simple analytical priors) together with (supervised) deep network-based priors in a unified MBIR framework based on a fixed point iteration analysis. The proposed training algorithm is also an approximate scheme for a bilevel supervised training optimization problem, wherein the network-based regularizer in the lower-level MBIR problem is optimized using an upper-level reconstruction loss. The training problem is optimized by alternating between updating the network weights and iteratively updating the reconstructions based on those weights. We demonstrate the learned SUPER models' efficacy for low-dose CT image reconstruction, for which we use the NIH AAPM Mayo Clinic Low Dose CT Grand Challenge dataset for training and testing. In our experiments, we studied different combinations of supervised deep network priors and unsupervised learning-based or analytical priors. Both numerical and visual results show the superiority of the proposed unified SUPER methods over standalone supervised learning-based methods, iterative MBIR methods, and variations of SUPER obtained via ablation studies. We also show that the proposed algorithm converges rapidly in practice.
Sensor-Aided Beamwidth and Power Control for Next Generation Vehicular Communications
Authors: Dario Tagliaferri, Mattia Brambilla, Monica Nicoli, Umberto Spagnolini
Date: 2021-04-08 00:00:00

Ultra-reliable low-latency Vehicle-to-Everything (V2X) communications are needed to meet the extreme requirements of enhanced driving applications. Millimeter-Wave (24.25-52.6 GHz) or sub-THz (>100 GHz) V2X communications are a viable solution, provided that the highly collimated beams are kept aligned during vehicles' maneuverings. In this work, we propose a sensor-assisted dynamic Beamwidth and Power Control (BPC) system to counteract the detrimental effect of vehicle dynamics, exploiting data collected by on-board inertial and positioning sensors, mutually exchanged among vehicles over a parallel low-rate link, e.g., 5G New Radio (NR) Frequency Range 1 (FR1). The proposed BPC solution works on top of a sensor-aided Beam Alignment and Tracking (BAT) system, overcoming the limitations of fixed-beamwidth systems and optimizing the performance in challenging Vehicle-to-Vehicle (V2V) scenarios, even if extensions to Vehicle-to-Infrastructure (V2I) use-cases are feasible. We validate the sensor-assisted dynamic BPC on real trajectories and sensors' data collected by a dedicated experimental campaign. The goal is to show the advantages of the proposed BPC strategy in a high data-rate Line-Of-Sight (LOS) V2V context, and to outline the requirements in terms of sensors' sampling time and accuracy, along with the end-to-end latency on the control channel.
Map-based Channel Modeling and Generation for U2V mmWave Communication
Authors: Qiuming Zhu, Kai Mao, Maozhong Song, Xiaomin Chen, Boyu Hua, Weizhi Zhong, Xijuan Ye
Date: 2021-04-08 00:00:00

Unmanned aerial vehicle (UAV) aided millimeter wave (mmWave) technologies have a promising prospect in the future communication networks. By considering the factors of three-dimensional (3D) scattering space, 3D trajectory, and 3D antenna array, a non-stationary channel model for UAV-to-vehicle (U2V) mmWave communications is proposed. The computation and generation methods of channel parameters including interpath and intra-path are analyzed in detail. The inter-path parameters are calculated in a deterministic way, while the parameters of intra-path rays are generated in a stochastic way. The statistical properties are obtained by using a Gaussian mixture model (GMM) on the massive ray tracing (RT) data. Then, a modified method of equal areas (MMEA) is developed to generate the random intra-path variables. Meanwhile, to reduce the complexity of RT method, the 3D propagation space is reconstructed based on the user-defined digital map. The simulated and analyzed results show that the proposed model and generation method can reproduce non-stationary U2V channels in accord with U2V scenarios. The generated statistical properties are consistent with the theoretical and measured ones as well.
A Deep Ensemble-based Wireless Receiver Architecture for Mitigating Adversarial Attacks in Automatic Modulation Classification
Authors: Rajeev Sahay, Christopher G. Brinton, David J. Love
Date: 2021-04-08 00:00:00
Tasks: Classification, Deep Learning, General Classification

Deep learning-based automatic modulation classification (AMC) models are susceptible to adversarial attacks. Such attacks inject specifically crafted wireless interference into transmitted signals to induce erroneous classification predictions. Furthermore, adversarial interference is transferable in black box environments, allowing an adversary to attack multiple deep learning models with a single perturbation crafted for a particular classification model. In this work, we propose a novel wireless receiver architecture to mitigate the effects of adversarial interference in various black box attack environments. We begin by evaluating the architecture uncertainty environment, where we show that adversarial attacks crafted to fool specific AMC DL architectures are not directly transferable to different DL architectures. Next, we consider the domain uncertainty environment, where we show that adversarial attacks crafted on time domain and frequency domain features to not directly transfer to the altering domain. Using these insights, we develop our Assorted Deep Ensemble (ADE) defense, which is an ensemble of deep learning architectures trained on time and frequency domain representations of received signals. Through evaluation on two wireless signal datasets under different sources of uncertainty, we demonstrate that our ADE obtains substantial improvements in AMC classification performance compared with baseline defenses across different adversarial attacks and potencies.
Design of a Power Amplifying-RIS for Free-Space Optical Communication Systems
Authors: Alain R. Ndjiongue, Telex M. N. Ngatched, Octavia A. Dobre, Harald Haas
Date: 2021-04-08 00:00:00

The steering dynamics of re-configurable intelligent surfaces (RIS) have hoisted them to the front row of technologies that can be exploited to solve skip-zones in wireless communication systems. They can enable a programmable wireless environment, turning it into a partially deterministic space that plays an active role in determining how wireless signals propagate. However, RIS-based communication systems' practical implementation may face challenges such as noise generated by the RIS structure. Besides, the transmitted signal may face a double-fading effect over the two portions of the channel. This article tackles this double-fading problem in near-terrestrial free-space optical (nT-FSO) communication systems using a RIS module based upon liquid-crystal (LC) on silicon (LCoS). A doped LC layer can directly amplify a light when placed in an external field. Leveraging on this capacity of a doped LC, we mitigate the double-attenuation faced by the transmitted signal. We first revisit the nT-FSO power loss scenario, then discuss the direct-light amplification, and consider the system performance. Results show that at 51 degrees of the incoming light incidence angle, the proposed LCoS design has minimal RIS depth, implying less LC's material. The performance results show that the number of bit per unit bandwidth is upper-bounded and grows with the ratio of the sub-links distances. Finally, we present and discuss open issues to enable new research opportunities towards the use of RIS and amplifying-RIS in nT-FSO systems.
A simple and robust method for noise variance estimation for time-varying signals
Authors: Qin Li, Junchan Zhao
Date: 2021-04-07 00:00:00

In this brief paper, we present a simple approach to estimate the variance of measurement noise with time-varying 1-D signals. The proposed approach exploits the relationship between the noise variance and the variance of the prediction errors (or innovation sequence) of a linear estimator, the idea that was pioneered by [9] and [2]. Compared with the classic and more recent methods in the same category, the proposed method can render more robust estimates with the presence of unmodelled dynamics and outliers in the measurement.
Shake It or Shrink It: Mass Transport and Kinetics in Surface Bioassays Using Agitation and Microfluidics
Authors: Iago Pereiro, Anna Fomitcheva-Khartchenko, Govind V. Kaigala
Date: 2021-04-08 00:00:00

Surface assays, such as ELISA and immunofluorescence, are nothing short of ubiquitous in biotechnology and medical diagnostics today. The development and optimization of these assays generally focuses on three aspects: immobilization chemistry, ligand-receptor interaction and concentrations of ligands, buffers and sample. A fourth aspect, the transport of the analyte to the surface, is more rarely delved into during assay design and analysis. Improving transport is generally limited to the agitation of reagents, a mode of flow generation inherently difficult to control, often resulting in inconsistent reaction kinetics. However, with assay optimization reaching theoretical limits, the role of transport becomes decisive. This perspective develops an intuitive and practical understanding of transport in conventional agitation systems and in microfluidics, the latter underpinning many new life science technologies. We give rules of thumb to guide the user on system behavior, such as advection regimes and shear stress, and derive estimates for relevant quantities that delimit assay parameters. Illustrative cases with examples of experimental results are used to clarify the role of fundamental concepts such as boundary and depletion layers, mass diffusivity or surface tension.
Market Regime Detection via Realized Covariances: A Comparison between Unsupervised Learning and Nonlinear Models
Authors: Andrea Bucci, Vito Ciciretti
Date: 2021-04-08 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

There is broad empirical evidence of regime switching in financial markets. The transition between different market regimes is mirrored in correlation matrices, whose time-varying coefficients usually jump higher in highly volatile regimes, leading to the failure of common diversification methods. In this article, we aim to identify market regimes from covariance matrices and detect transitions towards highly volatile regimes, hence improving tail-risk hedging. Starting from the time series of fractionally differentiated sentiment-like future values, two models are applied on monthly realized covariance matrices to detect market regimes. Specifically, the regime detection is implemented via vector logistic smooth transition autoregressive model (VLSTAR) and through an unsupervised learning methodology, the agglomerative hierarchical clustering. Since market regime switches are unobservable processes that describe the latent change of market behaviour, the ability of correctly detecting market regimes is validated in two ways: firstly, randomly generated data are used to assess a correct classification when regimes are known; secondly, a na\"{i}ve trading strategy filtered with the detected regime switches is used to understand whether an improvement is showed when accounting for regime switches. The results point to the VLSTAR as the best performing model for labelling market regimes.
Head Network Distillation: Splitting Distilled Deep Neural Networks for Resource-Constrained Edge Computing Systems
Authors: Yoshitomo Matsubara, Davide Callegaro, Sabur Baidya, Marco Levorato, Sameer Singh
Date: 2020-11-20 00:00:00
Tasks: Edge-computing, image-classification, Image Classification, Knowledge Distillation, Model Compression, Neural Network Compression

As the complexity of Deep Neural Network (DNN) models increases, their deployment on mobile devices becomes increasingly challenging, especially in complex vision tasks such as image classification. Many of recent contributions aim either to produce compact models matching the limited computing capabilities of mobile devices or to offload the execution of such burdensome models to a compute-capable device at the network edge - the edge servers. In this paper, we propose to modify the structure and training process of DNN models for complex image classification tasks to achieve in-network compression in the early network layers. Our training process stems from knowledge distillation, a technique that has been traditionally used to build small - student - models mimicking the output of larger - teacher - models. Here, we adopt this idea to obtain aggressive compression while preserving accuracy. Our results demonstrate that our approach is effective for state-of-the-art models trained over complex datasets, and can extend the parameter region in which edge computing is a viable and advantageous option. Additionally, we demonstrate that in many settings of practical interest we reduce the inference time with respect to specialized models such as MobileNet v2 executed at the mobile device, while improving accuracy.
Distilled Split Deep Neural Networks for Edge-Assisted Real-Time Systems
Authors: Yoshitomo Matsubara, Sabur Baidya, Davide Callegaro, Marco Levorato, Sameer Singh
Date: 2019-10-01 00:00:00
Tasks: Edge-computing, Image Classification, Knowledge Distillation, Model Compression, Neural Network Compression

Offloading the execution of complex Deep Neural Networks (DNNs) models to compute-capable devices at the network edge, that is, edge servers, can significantly reduce capture-to-output delay. However, the communication link between the mobile devices and edge servers can become the bottleneck when channel conditions are poor. We propose a framework to split DNNs for image processing and minimize capture-to-output delay in a wide range of network conditions and computing parameters. The core idea is to split the DNN models into head and tail models, where the two sections are deployed at the mobile device and edge server, respectively. Different from prior literature presenting DNN splitting frameworks, we distill the architecture of the head DNN to reduce its computational complexity and introduce a bottleneck, thus minimizing processing load at the mobile device as well as the amount of wirelessly transferred data. Our results show 98% reduction in used bandwidth and 85% in computation load compared to straightforward splitting.
Ising Models for Inferring Network Structure From Spike Data
Authors: John Hertz, Yasser Roudi, Joanna Tyrcha
Date: 2011-06-09 00:00:00

Now that spike trains from many neurons can be recorded simultaneously, there is a need for methods to decode these data to learn about the networks that these neurons are part of. One approach to this problem is to adjust the parameters of a simple model network to make its spike trains resemble the data as much as possible. The connections in the model network can then give us an idea of how the real neurons that generated the data are connected and how they influence each other. In this chapter we describe how to do this for the simplest kind of model: an Ising network. We derive algorithms for finding the best model connection strengths for fitting a given data set, as well as faster approximate algorithms based on mean field theory. We test the performance of these algorithms on data from model networks and experiments.
厉害了，吃塑料的细菌！解决白色垃圾污染或毁灭地球？
Authors: 科学博士
Date: 2021-04-10 00:00:00

厉害了，吃塑料的细菌！解决白色垃圾污染或毁灭地球？ 
#塑料​ #细菌​ #垃圾污染​ #塑料污染​ #吃塑料的细菌​ #科学博士​

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Behavior-Guided Actor-Critic: Improving Exploration via Learning Policy Behavior Representation for Deep Reinforcement Learning
Authors: Ammar Fayad, Majd Ibrahim
Date: 2021-04-09 00:00:00
Tasks: Deep Reinforcement Learning, Efficient Exploration

In this work, we propose Behavior-Guided Actor-Critic (BAC), an off-policy actor-critic deep RL algorithm. BAC mathematically formulates the behavior of the policy through autoencoders by providing an accurate estimation of how frequently each state-action pair was visited while taking into consideration state dynamics that play a crucial role in determining the trajectories produced by the policy. The agent is encouraged to change its behavior consistently towards less-visited state-action pairs while attaining good performance by maximizing the expected discounted sum of rewards, resulting in an efficient exploration of the environment and good exploitation of all high reward regions. One prominent aspect of our approach is that it is applicable to both stochastic and deterministic actors in contrast to maximum entropy deep reinforcement learning algorithms. Results show considerably better performances of BAC when compared to several cutting-edge learning algorithms.
Incentive-based demand response for smart grid with reinforcement learning and deep neural network
Authors: Renzhi Lu, Seung Ho Hong
Date: 2018-12-18 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Balancing electricity generation and consumption is essential for smoothing the power grids. Any mismatch
between energy supply and demand would increase costs to both the service provider and customers and may
even cripple the entire grid. This paper proposes a novel real-time incentive-based demand response algorithm
for smart grid systems with reinforcement learning and deep neural network, aiming to help the service provider
to purchase energy resources from its subscribed customers to balance energy fluctuations and enhance grid
reliability. In particular, to overcome the future uncertainties, deep neural network is used to predict the un-
known prices and energy demands. After that, reinforcement learning is adopted to obtain the optimal incentive
rates for different customers considering the profits of both service provider and customers. Simulation results
show that this proposed incentive-based demand response algorithm induces demand side participation, pro-
motes service provider and customers profitabilities, and improves system reliability by balancing energy re-
sources, which can be regarded as a win-win strategy for both service provider and customers.