pwc-archive/papers-with-abstracts huggingface dataset:
Convolutional Block Design for Learned Fractional Downsampling
Authors: Li-Heng Chen, Christos G. Bampis, Zhi Li, Chao Chen, Alan C. Bovik
Date: 2021-05-20 00:00:00
Tasks: SSIM, Video Compression

The layers of convolutional neural networks (CNNs) can be used to alter the resolution of their inputs, but the scaling factors are limited to integer values. However, in many image and video processing applications, the ability to resize by a fractional factor would be advantageous. One example is conversion between resolutions standardized for video compression, such as from 1080p to 720p. To solve this problem, we propose an alternative building block, formulated as a conventional convolutional layer followed by a differentiable resizer. More concretely, the convolutional layer preserves the resolution of the input, while the resizing operation is fully handled by the resizer. In this way, any CNN architecture can be adapted for non-integer resizing. As an application, we replace the resizing convolutional layer of a modern deep downsampling model by the proposed building block, and apply it to an adaptive bitrate video streaming scenario. Our experimental results show that an improvement in coding efficiency over the conventional Lanczos algorithm is attained, in terms of PSNR, SSIM, and VMAF on test videos.
Deep learning of transition probability densities for stochastic asset models with applications in option pricing
Authors: Haozhe Su, M. V. Tretyakov, David P. Newton
Date: 2021-05-21 00:00:00
Tasks: Computational Efficiency

Transition probability density functions (TPDFs) are fundamental to computational finance, including option pricing and hedging. Advancing recent work in deep learning, we develop novel neural TPDF generators through solving backward Kolmogorov equations in parametric space for cumulative probability functions. The generators are ultra-fast, very accurate and can be trained for any asset model described by stochastic differential equations. These are "single solve", so they do not require retraining when parameters of the stochastic model are changed (e.g. recalibration of volatility). Once trained, the neural TDPF generators can be transferred to less powerful computers where they can be used for e.g. option pricing at speeds as fast as if the TPDF were known in a closed form. We illustrate the computational efficiency of the proposed neural approximations of TPDFs by inserting them into numerical option pricing methods. We demonstrate a wide range of applications including the Black-Scholes-Merton model, the standard Heston model, the SABR model, and jump-diffusion models. These numerical experiments confirm the ultra-fast speed and high accuracy of the developed neural TPDF generators.
Multi-objective Optimisation of Digital Circuits based on Cell Mapping in an Industrial EDA Flow
Authors: Linan Cao, Simon J. Bale, Martin A. Trefzer
Date: 2021-05-21 00:00:00

Modern electronic design automation (EDA) tools can handle the complexity of state-of-the-art electronic systems by decomposing them into smaller blocks or cells, introducing different levels of abstraction and staged design flows. However, throughout each independent-optimised design step, overhead and inefficiency can accumulate in the resulting overall design. Performing design-specific optimisation from a more global viewpoint requires more time due to the larger search space, but has the potential to provide solutions with improved performance. In this work, a fully-automated, multi-objective (MO) EDA flow is introduced to address this issue. It specifically tunes drive strength mapping, preceding physical implementation, through multi-objective population-based search algorithms. Designs are evaluated with respect to their power, performance and area (PPA). The proposed approach is aimed at digital circuit optimisation at the block-level, where it is capable of expanding the design space and offers a set of trade-off solutions for different case-specific utilisation. We have applied the proposed MOEDA framework to ISCAS-85 and EPFL benchmark circuits using a commercial 65nm standard cell library. The experimental results demonstrate how the MOEDA flow enhances the solutions initially generated by the standard digital flow, and how simultaneously a significant improvement in PPA metrics is achieved.
ReLUSyn: Synthesizing Stealthy Attacks for Deep Neural Network Based Cyber-Physical Systems
Authors: Aarti Kashyap, Syed Mubashir Iqbal, Karthik Pattabiraman, Margo Seltzer
Date: 2021-05-21 00:00:00
Tasks: Collision Avoidance, Management

Cyber Physical Systems (cps) are deployed in many mission-critical settings, such as medical devices, autonomous vehicular systems and aircraft control management systems. As more and more CPS adopt Deep Neural Networks (Deep Neural Network (dnns), these systems can be vulnerable to attacks. . Prior work has demonstrated the susceptibility of CPS to False Data Injection Attacks (False Data Injection Attacks (fdias), which can cause significant damage. We identify a new category of attacks on these systems. In this paper, we demonstrate that DNN based CPS are also subject to these attacks. These attacks, which we call Ripple False Data Injection Attacks (rfdia), use minimal input perturbations to stealthily change the dnn output. The input perturbations propagate as ripples through multiple dnn layers to affect the output in a targeted manner. We develop an automated technique to synthesize rfdias against DNN-based CPS. Our technique models the attack as an optimization problem using Mixed Integer Linear Programming (Mixed Integer Linear Program (milp)). We define an abstraction for dnnbased cps that allows us to automatically: 1) identify the critical inputs, and 2) find the smallest perturbations that produce output changes. We demonstrate our technique on three practical cps with two mission-critical applications: an (Artifical Pancreas System (aps)) and two aircraft control management systems (Horizontal Collision Avoidance System (hcas) and Collision Avoidance System-Xu (acas-xu)).
Optimal Estimator Design and Properties Analysis for Interconnected Systems with Asymmetric Information Structure
Authors: Yan Wang, Junlin Xiong, Zaiyue Yang, Rong Su
Date: 2021-05-21 00:00:00
Tasks: State Estimation

This paper studies the optimal state estimation problem for interconnected systems. Each subsystem can obtain its own measurement in real time, while, the measurements transmitted between the subsystems suffer from random delay. The optimal estimator is analytically designed for minimizing the conditional error covariance. The boundedness of the expected error covariance (EEC) is analyzed. In particular, a new condition that is easy to verify is established for the boundedness of EEC. Further, the properties of EEC with respect to the delay probability are studied. We found that there exists a critical probability such that the EEC is bounded if the delay probability is below the critical probability. Also, a lower and upper bound of the critical probability is derived. Finally, the proposed results are applied to a power system, and the effectiveness of the designed methods is illustrated by simulations.
Near-Optimal Design of Safe Output Feedback Controllers from Noisy Data
Authors: Luca Furieri, Baiwei Guo, Andrea Martin, Giancarlo Ferrari-Trecate
Date: 2021-05-21 00:00:00

As we transition towards the deployment of data-driven controllers for black-box cyberphysical systems, complying with hard safety constraints becomes a primary concern. Two key aspects should be addressed when input-output data are corrupted by noise: how much uncertainty can one tolerate without compromising safety, and to what extent is the control performance affected? By focusing on finite-horizon constrained linear-quadratic problems, we provide an answer to these questions in terms of the model mismatch incurred during a preliminary identification phase. We propose a control design procedure based on a quasiconvex relaxation of the original robust problem and we prove that, if the uncertainty is sufficiently small, the synthesized controller is safe and near-optimal, in the sense that the suboptimality gap increases linearly with the model mismatch level. Since the proposed method is independent of the specific identification procedure, our analysis holds in combination with state-of-the-art behavioral estimators beyond standard least-squares. The main theoretical results are validated by numerical experiments.
Predictive control barrier functions: Enhanced safety mechanisms for learning-based control
Authors: Kim P. Wabersich, Melanie N. Zeilinger
Date: 2021-05-21 00:00:00

While learning-based control techniques often outperform classical controller designs, safety requirements limit the acceptance of such methods in many applications. Recent developments address this issue through so-called predictive safety filters, which assess if a proposed learning-based control input can lead to constraint violations and modifies it if necessary to ensure safety for all future time steps. The theoretical guarantees of such predictive safety filters rely on the model assumptions and minor deviations can lead to failure of the filter putting the system at risk. This paper introduces an auxiliary soft-constrained predictive control problem that is always feasible at each time step and asymptotically stabilizes the feasible set of the original safety filter, thereby providing a recovery mechanism in safety-critical situations. This is achieved by a simple constraint tightening in combination with a terminal control barrier function. By extending discrete-time control barrier function theory, we establish that the proposed auxiliary problem provides a `predictive' control barrier function. The resulting algorithm is demonstrated using numerical examples.
Modeling the Operation of an Autonomous Profilograph as a Dynamic System When Measuring Oceanological Fields
Authors: L. A. Krasnodubets, Aleksandr N. Grekov
Date: 2021-05-21 00:00:00

On the basis of the proposed model of a diving buoy with adjustable buoyancy, a model of a double-loop adaptive control system for the speed modes of an autonomous profiler has been developed, depending on the gradients of the measured parameters. The results of modeling are presented.
Rational Dynamic Price Model for Demand Response Programs in Modern Distribution Systems
Authors: Rayees A. Thokar, Nikhil Gupta, K. R. Niazi, Anil Swarnkar, Nand K. Meena
Date: 2021-05-21 00:00:00

Demand response (DR) refers to change in electricity consumption pattern of customers during on-peak hours in lieu of financial gains to reduce stress on distribution systems. Existing dynamic price models have not provided adequate success to price-based demand response (PBDR) programs. It happened as these models have raised typical socio-economic problems pertaining to cross-subsidy, free-riders, social inequity, assured profit of utilities, financial gains and comfort of customers, etc. This paper presents a new dynamic price model for PBDR in distribution systems which aims to overcome some of the above mentioned problems of the existing price models. The main aim of the developed price model is to overcome the problems of cross-subsidy and free-riders of the existing price models for widespread acceptance, deployment and efficient utilization of PBDR programs in contemporary distribution systems. Proposed price model generates demand-linked price signal that imposes different price signals to different customers during on-peak hours and remains static otherwise. This makes proposed model a class apart from other existing models. The novelty of the proposed model lies in the fact that the financial benefits and penalties pertaining to DR are self-adjusted among customers while preserving social equity and profit of the utility. Such an ideology has not been yet addressed in the literature. Detailed investigation of application results on a standard test bench reveals that the proposed model equally cares regarding the interests of both customers and utility. For economic assessment, a comparison of the proposed price model with the existing pricing models is also performed.
Discrete hybrid Izhikevich neuron model: nodal and network behaviours considering electromagnetic flux coupling
Authors: Sishu Shankar Muni, Karthikeyan Rajagopal, Anitha Karthikeyan, Sundaram Arun
Date: 2021-05-16 00:00:00

We analyse the dynamics of the improved discretised version of the well known Izhikevich neuronmodel under the action of external electromagnetic field. It is found that the three-dimensional IZHmap shows rich dynamics. With the variation of the electromagnetic field, period-doubling routeto chaos in a repeating fashion is observed from the bifurcation diagram. Even the forward andbackward continuation bifurcation diagram which do not completely overlap suggests that there is multistability in the system. The phenomenon of bistability (coexistence of periodic and chaotic attractors) is observed. The presence of periodic and chaotic attractor is aided by the maximal Lyapunov exponent diagram. The Lyapunov phase diagram of electromagnetic field and synapses current shows a large parameter region of chaotic and periodic behaviors with the presence of unbounded regions as well. The IZH map shows a plethora of spiking and bursting patterns such as mixed-mode patterns, tonic spiking, phasic spiking, steady spikes, regular spikes, spike bursting, periodic bursting, phasic bursting, chaotic firing, etc with the variation of electromagnetic coupling strength and the synapses current. We also investigate the presence of chimera states in a ring-star, ring, star network of IZH map neurons. Chimera states are found in the case of ring-star and ring network while synchronised clusters were found in the case of star network and are aided by the spatiotemporal plots, space-time plot, recurrence plots. The rich dynamics shown by the discretised IZH map makes it a promising research model to study about neurodynamics.
Novel ion-doped mesoporous glasses for bone tissue engineering: Study of their strctural characteristics influenced by the presence of phosphorous oxide
Authors: A Philippart, N Gomez-Cerezo, D Arcos, AJ Salinas, E Boccardi, M Vallet-Regi, AR Boccaccini
Date: 2021-05-21 00:00:00

Ion-doped binary SiO2-CaO and ternary SiO2-CaO-P2O5 mesoporous bioactive glasses were synthesised and characterised to evaluate the influence of P2O5 in the glass network structure. Strontium, copper and cobalt oxides in a proportion of 0.8 mol% were selected as dopants because the osteogenic and angiogenic properties reported for these elements. Although the four glass compositions investigated presented analogous textural properties, TEM analysis revealed that the structure of those containing P2O5 exhibited an increased ordered mesoporosity. Furthermore, 29Si NMR revealed that the incorporation of P2O5 increased the network connectivity and that this compound captured the Sr2+, Cu2+ and Co2+ ions preventing them to behave as modifiers of the silica network. In addition, 31P NMR results revealed that the nature of the cation directly influences the characteristics of the phosphate clusters. In this study, we have proven that phosphorous oxide entraps doping-metallic ions, granting these glasses with a greater mesopores order.
Dyadic aggregated autoregressive (DASAR) model for time-frequency representation of biomedical signals
Authors: Marco A. Pinto-Orellana, Habib Sherkat, Peyman Mirtaheri, Hugo L. Hammer
Date: 2021-05-13 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

This paper introduces a new time-frequency representation method for biomedical signals: the dyadic aggregated autoregressive (DASAR) model. Signals, such as electroencephalograms (EEGs) and functional near-infrared spectroscopy (fNIRS), exhibit physiological information through time-evolving spectrum components at specific frequency intervals: 0-50 Hz (EEG) or 0-150 mHz (fNIRS). Spectrotemporal features in signals are conventionally estimated using short-time Fourier transform (STFT) and wavelet transform (WT). However, both methods may not offer the most robust or compact representation despite their widespread use in biomedical contexts. The presented method, DASAR, improves precise frequency identification and tracking of interpretable frequency components with a parsimonious set of parameters. DASAR achieves these characteristics by assuming that the biomedical time-varying spectrum comprises several independent stochastic oscillators with (piecewise) time-varying frequencies. Local stationarity can be assumed within dyadic subdivisions of the recordings, while the stochastic oscillators can be modeled with an aggregation of second-order autoregressive models (ASAR). DASAR can provide a more accurate representation of the (highly contrasted) EEG and fNIRS frequency ranges by increasing the estimation accuracy in user-defined spectrum region of interest (SROI). A mental arithmetic experiment on a hybrid EEG-fNIRS was conducted to assess the efficiency of the method. Our proposed technique, STFT, and WT were applied on both biomedical signals to discover potential oscillators that improve the discrimination between the task condition and its baseline. The results show that DASAR provided the highest spectrum differentiation and it was the only method that could identify Mayer waves as narrow-band artifacts at 97.4-97.5 mHz.
Identification and Estimation of a Partially Linear Regression Model using Network Data: Inference and an Application to Network Peer Effects
Authors: Eric Auerbach
Date: 2021-05-20 00:00:00

This paper provides additional results relevant to the setting, model, and estimators of Auerbach (2019a). Section 1 contains results about the large sample properties of the estimators from Section 2 of Auerbach (2019a). Section 2 considers some extensions to the model. Section 3 provides an application to estimating network peer effects. Section 4 shows the results from some simulations.
The impact of Over The Top service providers on the Global Mobile Telecom Industry: A quantified analysis and recommendations for recovery
Authors: Ahmed Awwad
Date: 2021-05-21 00:00:00

Telecom industry is significantly evolving all over the globe than ever. Mobile users number is increasing remarkably. Telecom operators are investing to get more users connected and to improve user experience, however, they are facing various challenges. Decrease of main revenue streams of voice calls, SMS (Short Message Service) and LDC (Long distance calls) with a significant increase in data traffic. In contrary, with free cost, OTT (Over the top) providers such as WhatsApp and Facebook communication services rendered over networks that built and owned by MNOs. Recently, OTT services gradually substituting the traditional MNOs` services and became ubiquitous with the help of the underlying data services provided by MNOs. The OTTs` services massive penetration into telecom industry is driving the MNOs to reconsider their strategies and revenue sources.
Learning from zero: how to make consumption-saving decisions in a stochastic environment with an AI algorithm
Authors: Rui, Shi
Date: 2021-05-21 00:00:00
Tasks: AI Agent, Decision Making

This exercise proposes a learning mechanism to model economic agent's decision-making process using an actor-critic structure in the literature of artificial intelligence. It is motivated by the psychology literature of learning through reinforcing good or bad decisions. In a model of an environment, to learn to make decisions, this AI agent needs to interact with its environment and make explorative actions. Each action in a given state brings a reward signal to the agent. These interactive experience is saved in the agent's memory, which is then used to update its subjective belief of the world. The agent's decision-making strategy is formed and adjusted based on this evolving subjective belief. This agent does not only take an action that it knows would bring a high reward, it also explores other possibilities. This is the process of taking explorative actions, and it ensures that the agent notices changes in its environment and adapt its subjective belief and decisions accordingly. Through a model of stochastic optimal growth, I illustrate that the economic agent under this proposed learning structure is adaptive to changes in an underlying stochastic process of the economy. AI agents can differ in their levels of exploration, which leads to different experience in the same environment. This reflects on to their different learning behaviours and welfare obtained. The chosen economic structure possesses the fundamental decision making problems of macroeconomic models, i.e., how to make consumption-saving decisions in a lifetime, and it can be generalised to other decision-making processes and economic models.
Fixed-k Tail Regression: New Evidence on Tax and Wealth Inequality from Forbes 400
Authors: Ji Hyung Lee, Yuya Sasaki, Alexis Akira Toda, Yulong Wang
Date: 2021-05-20 00:00:00
Tasks: regression, Time Series Analysis

We develop a novel fixed-k tail regression method that accommodates the unique feature in the Forbes 400 data that observations are truncated from below at the 400th largest order statistic. Applying this method, we find that higher maximum marginal income tax rates induce higher wealth Pareto exponents. Setting the maximum tax rate to 30-40% (as in U.S. currently) leads to a Pareto exponent of 1.5-1.8, while counterfactually setting it to 80% (as suggested by Piketty, 2014) would lead to a Pareto exponent of 2.6. We present a simple economic model that explains these findings and discuss the welfare implications of taxation.
Latent-space scalability for multi-task collaborative intelligence
Authors: Hyomin Choi, Ivan V. Bajic
Date: 2021-05-21 00:00:00
Tasks: Object, object-detection, Object Detection

We investigate latent-space scalability for multi-task collaborative intelligence, where one of the tasks is object detection and the other is input reconstruction. In our proposed approach, part of the latent space can be selectively decoded to support object detection while the remainder can be decoded when input reconstruction is needed. Such an approach allows reduced computational resources when only object detection is required, and this can be achieved without reconstructing input pixels. By varying the scaling factors of various terms in the training loss function, the system can be trained to achieve various trade-offs between object detection accuracy and input reconstruction quality. Experiments are conducted to demonstrate the adjustable system performance on the two tasks compared to the relevant benchmarks.
Robust heartbeat detection using multimodal recordings and ECG quality assessment with signal amplitudes dispersion
Authors: Zahra Rezaei Khavas, Babak Mohammadzadeh Asl
Date: 2021-05-20 00:00:00

Method: In this study, a new method is introduced for distinguishing noise-free segments of ECG from noisy segments that use sample amplitude dispersion with an adoptive threshold for variance of samples amplitude and a method which uses compatibility of detected beats in ECG and some of the other signals which are related to the heart activity such as BP, arterial pressure (ART) and pulmonary artery pressure (PAP). A prioritization is applied in other pulsatile signals based on the amplitude and clarity of the peaks on them, and a fusion strategy is employed for segments on which ECG is noisy and other available signals in the data, which contain peaks corresponding to R peak of the ECG, are scored in three steps scoring function.