pwc-archive/papers-with-abstracts huggingface dataset:
A Data Envelopment Analysis Approach to Benchmark the Performance of Mutual Funds in India
Authors: Adit Chopra
Date: 2020-08-25 00:00:00

As the Indian economy grows digitally and becomes more financially inclusive, more and more investors have started to invest in the Indian capital markets. The number of retail and institutional folios with Indian mutual fund schemes have continued to rise for the 74th consecutive month. This study considers 139 mutual fund schemes (98 equity schemes) and aims to ascertain the various metrics and parameters, retail and institutional investors continue to rely on to make investment recommendations. We compare these with the results from a data envelopment analysis model that generates an efficiency frontier based on an optimal risk, cost, and return trade-off. We further put forth an iteration of the DEA model, not only considering risk, cost, and return characteristics but also incorporating metrics such as the information ratio which hold significance for retail and institutional investors. We compare these results with traditional metrics and fund rankings published by established industry rating agencies.
Instability Margin Analysis for Parametrized LTI Systems with Application to Repressilator
Authors: Shinji Hara, Tetsuya Iwasaki, Yutaka Hori
Date: 2020-08-25 00:00:00

This paper is concerned with a robust instability analysis for the single-input-single-output unstable linear time-invariant (LTI) system under dynamic perturbations. The nominal system itself is possibly perturbed by the static gain of the uncertainty, which would be the case when a nonlinear uncertain system is linearized around an equilibrium point. We define the robust instability radius as the smallest $H_\infty$ norm of the stable linear perturbation that stabilizes the nominal system. There are two main theoretical results: one is on a partial characterization of unperturbed nominal systems for which the robust instability radius can be calculated exactly, and the other is a numerically tractable procedure for calculating the exact robust instability radius for nominal systems parametrized by a perturbation parameter. The results are applied to the repressilator in synthetic biology, where hyperbolic instability of a unique equilibrium guarantees the persistence of oscillation phenomena in the global sense, and the effectiveness of our linear robust instability analysis is confirmed by numerical simulations.
Data monitoring committees for clinical trials evaluating treatments of COVID-19
Authors: Tobias Mütze, Tim Friede
Date: 2020-08-20 00:00:00

The first cases of coronavirus disease 2019 (COVID-19) were reported in December 2019 and the outbreak of SARS-CoV-2 was declared a pandemic in March 2020 by the World Health Organization. This sparked a plethora of investigations into diagnostics and vaccination for SARS-CoV-2, as well as treatments for COVID-19. Since COVID-19 is a severe disease associated with a high mortality, clinical trials in this disease should be monitored by a data monitoring committee (DMC), also known as data safety monitoring board (DSMB). DMCs in this indication face a number of challenges including fast recruitment requiring an unusually high frequency of safety reviews, more frequent use of complex designs and virtually no prior experience with the disease. In this paper, we provide a perspective on the work of DMCs for clinical trials of treatments for COVID-19. More specifically, we discuss organizational aspects of setting up and running DMCs for COVID-19 trials, in particular for trials with more complex designs such as platform trials or adaptive designs. Furthermore, statistical aspects of monitoring clinical trials of treatments for COVID-19 are considered. Some recommendations are made regarding the presentation of the data, stopping rules for safety monitoring and the use of external data. The proposed stopping boundaries are assessed in a simulation study motivated by clinical trials in COVID-19.
Spectrum Sharing Strategies for UAV-to-UAV Cellular Communications
Authors: M. Mahdi Azari, Giovanni Geraci, Adrian Garcia-Rodriguez, Sofie Pollin
Date: 2020-08-18 00:00:00

In this article, we consider a cellular network deployment where UAV-to-UAV (U2U) transmit-receive pairs coexist with the uplink (UL) of cellular ground users (GUEs). Our analysis focuses on comparing two spectrum sharing mechanisms: i) overlay, where the available time-frequency resources are split into orthogonal portions for U2U and GUE communications, and ii) underlay, where the same resources may be accessed by both link types, resulting in mutual interference. We evaluate the coverage probability and rate of all links and their interplay to identify the best spectrum sharing mechanism. Among other things, our results demonstrate that, in scenarios with a large number of UAV pairs, adopting overlay spectrum sharing seems the most suitable approach for maintaining a minimum guaranteed rate for UAVs and a high GUE UL performance. We also find that increasing the density of U2U links degrades their rates in the overlay-where UAVs only receive interference from other UAVs-, but not significantly so in the underlay -- where the effect of GUE-generated interference is dominant.
Design of mechanical structure and tracking control system for 5 DOF surgical robot
Date: 2020-08-15 00:00:00

In this paper, the tracking control problem for 5 DOF surgical robot which is
affected by tremor of surgeons' hand is considered. Mechanical modeling and
dynamic analysis of a robotic arm in slave subsystem of a telesurgery system
will be discussed and the reasons for selecting the appropriate materials for
different parts of robot will be explained. It would be required this robot,
which will do the main part of the surgery, be controlled based on the
uncertain properties of the tissues of patients body. Improved Lyapunov Based
control method with uncertainty observer is applied to improve the accuracy of
tracking procedure for a surgical manipulator to track a specified reference
signal in the presence of tremor that is modeled as constant bounded
disturbance. Based on the disturbance rejection scheme, tracking controllers
are constructed which are asymptotically stabilizing in the sense of Lyapunov.
The control strategy was implemented using a PC interface. Computer simulation
results demonstrate that accurate trajectory tracking can be achieved by using
the proposed controllers.
In situ Measurement of Airborne Particle Concentration in a Real Dental Office: Implications for Disease Transmission
Authors: Maryam Ravazi, Zahid Butt, Mark H. E. Lin, Helen Chen, Zhongchao Tan
Date: 2020-08-19 00:00:00

Recent guidelines by WHO recommend delaying non-essential oral health care amid COVID-19 pandemic and call for research on aerosol generated during dental procedures. Thus, this study aims to assess the mechanisms of dental aerosol dispersion in dental offices and to provide recommendations based on a quantitative study to minimize infection transmission in dental offices. The spread and removal of aerosol particles generated from dental procedures in a dental office are measured near the source and at the corner of the office. We studied the effects of air purification (on/off), door condition (open/close), and particle sizes on the temporal concentration distribution of particles. The results show that in the worst-scenario scenario it takes 95 min for 0.5 um particles to settle, and that it takes a shorter time for the larger particles. The indoor air purifier tested expedited the removal time at least 6.3 times faster than the scenario air purifier off. Airborne particles may be transported from the source to the rest of the room, even when the particle concentrations in the generation zone return to the background level. These results are expected to be valuable to related policy making and technology development for infection disease control in dental offices and similar built environments.
Millimeter Wave Channel Modeling via Generative Neural Networks
Authors: William Xia, Sundeep Rangan, Marco Mezzavilla, Angel Lozano, Giovanni Geraci, Vasilii Semkin, Giuseppe Loianno
Date: 2020-08-25 00:00:00

Statistical channel models are instrumental to design and evaluate wireless communication systems. In the millimeter wave bands, such models become acutely challenging; they must capture the delay, directions, and path gains, for each link and with high resolution. This paper presents a general modeling methodology based on training generative neural networks from data. The proposed generative model consists of a two-stage structure that first predicts the state of each link (line-of-sight, non-line-of-sight, or outage), and subsequently feeds this state into a conditional variational autoencoder that generates the path losses, delays, and angles of arrival and departure for all its propagation paths. Importantly, minimal prior assumptions are made, enabling the model to capture complex relationships within the data. The methodology is demonstrated for 28GHz air-to-ground channels in an urban environment, with training datasets produced by means of ray tracing.
Analysis of Adaptive Synchrosqueezing Transform with a Time-varying Parameter
Authors: Jian Lu, Qingtang Jiang, Lin Li
Date: 2020-08-22 00:00:00

The synchrosqueezing transform (SST) was developed recently to separate the components of non-stationary multicomponent signals. The continuous wavelet transform-based SST (WSST) reassigns the scale variable of the continuous wavelet transform of a signal to the frequency variable and sharpens the time-frequency representation. The WSST with a time-varying parameter, called the adaptive WSST, was introduced very recently in the paper "Adaptive synchrosqueezing transform with a time-varying parameter for non-stationary signal separation". The well-separated conditions of non-stationary multicomponent signals with the adaptive WSST and a method to select the time-varying parameter were proposed in that paper. In addition, simulation experiments in that paper show that the adaptive WSST is very promising in estimating the instantaneous frequency of a multicomponent signal, and in accurate component recovery. However the theoretical analysis of the adaptive WSST has not been studied. In this paper, we carry out such analysis and obtain error bounds for component recovery with the adaptive WSST and the 2nd-order adaptive WSST. These results provide a mathematical guarantee to non-stationary multicomponent signal separation with the adaptive WSST.
Dynamics of feed forward induced interference training
Authors: Shirui Tang
Date: 2020-08-24 00:00:00

Preceptron model updating with back propagation has become the routine of deep learning. Continuous feed forward procedure is required in order for backward propagate to function properly. Doubting the underlying physical interpretation on transformer based models such as GPT brought about by the routine explaination, a new method of training is proposed in order to keep self-consistency of the physics. By treating the GPT model as a space-time diagram, and then trace the worldlines of signals, identifing the possible paths of signals in order fot a self-attention event to occure. With a slight modification, self-attention can be viewed as an ising model interaction, which enables the goal to be designed as energy of system. Target is treated as an external magnetic field inducing signals modeled as magnetic dipoles. A probability network is designed to pilot input signals travelling for different durations through different routes. A rule of updating the probabilities is designed in order to form constructive interference at target locations so that instantaneous energy can be maximised. Experiment was conducted on a 4-class classification problem extracted from MNIST. The results exhibit interesting but expected behavours, which do not exist in a bp updated network, but more like learning in a real human, especially in the few-shot scenario.
Estimation of energy consumption of electric vehicles using Deep Convolutional Neural Network to reduce driver's range anxiety
Authors: Shatrughan Modi, Jhilik Bhattacharya, Prasenjit Basak
Date: 2020-08-24 00:00:00

The goal of this work is to reduce driver's range anxiety by estimating the real-time energy consumption of electric vehicles using deep convolutional neural network. The real-time estimate can be used to accurately predict the remaining range for the vehicle and hence, can reduce driver's range anxiety. In contrast to existing techniques, the non-linearity and complexity induced by the combination of influencing factors make the problem more suitable for a deep learning approach. The proposed approach requires three parameters namely, vehicle speed, tractive effort and road elevation. Multiple experiments with different variants are performed to explore the impact of number of layers and input feature descriptors. The comparison of proposed approach and five of the existing techniques show that the proposed model performed consistently better than existing techniques with lowest error.
Use of adaptive filtering techniques and deconvolution to obtain low range sidelobe samples
Authors: Mohit Kumar, V. Chandrasekar
Date: 2020-08-10 00:00:00
Tasks: Sensitivity

In this paper the use of adaptive filtering techniques to obtain better peak sidelobe suppression and integrated sidelobe energy will be discussed with regard to weather radars and obtaining better sensitivity with this technique. The performance of these new coefficient sets obtained with adaptive filter (using RLS optimization) will be discussed and presented. They will also be compared with the existing techniques and their peak sidelobe levels.
Inference for parameters identified by conditional moment restrictions using a generalized Bierens maximum statistic
Authors: Xiaohong Chen, Sokbae Lee, Myung Hwan Seo, Myunghyun Song
Date: 2020-08-25 00:00:00
Tasks: Model Selection

Many economic panel and dynamic models, such as rational behavior and Euler equations, imply that the parameters of interest are identified by conditional moment restrictions. We introduce a novel inference method without any prior information about which conditioning instruments are weak or irrelevant. Building on Bierens (1990), we propose penalized maximum statistics and combine bootstrap inference with model selection. Our method optimizes asymptotic power by solving a data-dependent max-min problem for tuning parameter selection. Extensive Monte Carlo experiments, based on an empirical example, demonstrate the extent to which our inference procedure is superior to those available in the literature.
Smart Healthcare for Diabetes: A COVID-19 Perspective
Authors: Amit M. Joshi, Urvashi P. Shukla, Saraju P. Mohanty
Date: 2020-07-29 00:00:00

Diabetes is considered as an critical comorbidity linked with the latest coronavirus disease 2019 (COVID-19) which spreads through Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2). The diabetic patients have higher threat of infection from novel corona virus. Depending on the region in the globe, 20% to 50% of patients infected with COVID-19 pandemic had diabetes. The current article discussed the risk associated with diabetic patients and also recommendation for controlling diabetes during this pandemic situation. The article also discusses the case study of COVID-19 at various regions around the globe and the preventive actions taken by various countries to control the effect from the virus. The article presents several smart healthcare solutions for the diabetes patients to have glucose insulin control for the protection against COVID-19.
Anticipated synchronization in human EEG data: unidirectional causality with negative phase-lag
Date: 2020-08-25 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Functional Connectivity

Understanding the functional connectivity of the brain has become a major
goal of neuroscience. In many situatons, the relative phase difference,
together with coherence patterns, have been employed to infer the direction of
the information flow. However, it has been recently shown in local field
potential data from monkeys the existence of a synchronized regime in which
unidirectionally coupled areas can present both positive and negative phase
differences. During the counterintuitive regime, called anticipated
synchronization (AS), the phase difference does not reflect the causality. Here
we investigate coherence and causality at the alpha frequency band (10 Hz)
between pairs of electroencephalogram (EEG) electrodes in humans during a
GO/NO-GO task. We show that human EEG signals can exhibit anticipated
synchronization, which is characterized by a unidirectional influence from an
electrode A to an electrode B, but the electrode B leads the electrode A in
time. To the best of our knowledge, this is the first verification of AS in EEG
signals and in the human brain. The usual delayed synchronization (DS) regime
is also present between many pairs. DS is characterized by a unidirectional
influence from an electrode A to an electrode B and a positive phase difference
between A and B which indicates that the electrode A leads the electrode B in
time. Moreover, we show that EEG signals exhibit diversity in phase relations:
the pairs of electrodes can present in-phase, anti-phase, or out-of-phase
synchronization with a similar distribution of positive and negative phase
differences.
Extending fibre nonlinear interference power modelling to account for general dual-polarisation 4D modulation formats
Authors: Gabriele Liga, Astrid Barreiro, Hami Rabbani, Alex Alvarado
Date: 2020-08-25 00:00:00

In optical communications, four-dimensional (4D) modulation formats encode information onto the quadrature components of two arbitrary orthogonal states of polarisation of the optical field. These formats have recently regained attention due their potential power efficiency, nonlinearity tolerance, and ultimately to their still unexplored shaping gains. As in the fibre-optic channel the shaping gain is closely related to the nonlinearity tolerance of a given modulation format, predicting the effect of nonlinearity is key to effectively optimise the transmitted constellation. Many analytical models available in the optical communication literature allow, within a first-order perturbation framework, the computation of the average power of the nonlinear interference (NLI) accumulated in coherent fibre-optic transmission systems. However, all current models only operate under the assumption of a transmitted polarisation-multiplexed, two-dimensional (PM-2D) modulation format. PM-2D formats represent a limited subset of the possible dual-polarisation 4D formats, namely, only those where data transmitted on each polarisation channel are mutually independent and identically distributed. This document presents a step-by-step mathematical derivation of the extension of existing NLI models to the class of arbitrary dual-polarisation 4D modulation formats. In particular, the methodology adopted follows the one of the popular enhanced Gaussian noise model, albeit dropping most assumptions on the geometry and statistic of the transmitted 4D modulation format. The resulting expressions show that, whilst in the PM-2D case the NLI power depends only on different statistical high-order moments of each polarisation component, for a general 4D constellation also several others cross-polarisation correlations need to be taken into account.
On Structured-Closed-Loop versus Structured-Controller Design: the Case of Relative Measurement Feedback
Authors: Emily Jensen, Bassam Bamieh
Date: 2020-08-25 00:00:00

We consider the optimal distributed controller design problem subject to two structural requirements: locality, i.e. available measurements and sub-controllers' interactions are governed by a graph structure, and relative feedback, i.e. only differences of measurements are available to the controller. We formalize controller locality in terms of the controller's transfer function, state-space realization, or resulting closed-loop mapping. We demonstrate that the relative feedback requirement can be written as a convex constraint on the controller and (in special cases) on the resulting closed-loop, and we characterize the allowable structures of relative feedback controllers. We prove that sparse closed-loop design is a convex relaxation of structured controller state-space design, even in the continuous time IIR setting. This formalizes and extends results of the recently developed System Level Synthesis framework. We take a first step toward quantifying the performance gap associated with this convex relaxation by constructing a class of examples (based on relative feedback requirements) for which the difference in performance, measured by an H2 norm, is infinite. The results presented are used to contrast several issues of structural constraints in distributed control design that remain as open problems.
Untangling the complexity of market competition in consumer goods -A complex Hilbert PCA analysis
Authors: Makoto Mizuno, Hideaki Aoyama, Yoshi Fujiwara
Date: 2020-08-26 00:00:00
Tasks: Management

Today's consumer goods markets are rapidly evolving with significant growth in the number of information media as well as the number of competitive products. In this environment, obtaining a quantitative grasp of heterogeneous interactions of firms and customers, which have attracted interest of management scientists and economists, requires the analysis of extremely high-dimensional data. Existing approaches in quantitative research could not handle such data without any reliable prior knowledge nor strong assumptions. Alternatively, we propose a novel method called complex Hilbert principal component analysis (CHPCA) and construct a synchronization network using Hodge decomposition. CHPCA enables us to extract significant comovements with a time lead/delay in the data, and Hodge decomposition is useful for identifying the time-structure of correlations. We apply this method to the Japanese beer market data and reveal comovement of variables related to the consumer choice process across multiple products. Furthermore, we find remarkable customer heterogeneity by calculating the coordinates of each customer in the space derived from the results of CHPCA. Lastly, we discuss the policy and managerial implications, limitations, and further development of the proposed method.
Proof for frequency response analysis using chirp signals
Authors: Resmi Suresh, Raghunathan Rengaswamy
Date: 2020-08-26 00:00:00

Hundreds of applications utilize frequency response characterization of a system. Identification of frequency response requires long experimentation time, use of transformation techniques and other difficulties associated with isolating the system behavior precisely at individual frequencies. In this work, we report a hitherto unknown result that can be leveraged to mitigate these difficulties resulting in a tremendous reduction in the experimentation time. This result has the possibility of revolutionizing how frequency response studies are performed.