pwc-archive/papers-with-abstracts huggingface dataset:
High-Frequency Radar Ocean Current Mapping at Rapid Scale with Autoregressive Modeling
Authors: Baptiste Domps, Dylan Dumas, Charles-Antoine Guérin, Julien Marmain
Date: 2020-06-19 00:00:00
Tasks: Time Series, Time Series Analysis

We use an Autoregressive (AR) approach combined with a Maximum Entropy Method (MEM) to estimate radial surface currents from coastal High-Frequency Radar (HFR) complex voltage time series. The performances of this combined AR-MEM model are investigated with synthetic HFR data and compared with the classical Doppler spectrum approach. It is shown that AR-MEM drastically improves the quality and the rate of success of the surface current estimation for short integration time. To confirm these numerical results, the same analysis is conducted with an experimental data set acquired with a 16.3 MHz HFR in Toulon. It is found that the AR-MEM technique is able to provide high-quality and high-coverage maps of surface currents even with very short integration time (about 1 minute) where the classical spectral approach can only fulfill the quality tests on a sparse coverage. Further useful application of the technique is found in the tracking of surface current at high-temporal resolution. Rapid variations of the surface current at the time scale of the minute are unveiled and shown consistent with a $f^{-5/3}$ decay of turbulent spectra.
The interplay between randomness and structure during learning in RNNs
Authors: Friedrich Schuessler, Francesca Mastrogiuseppe, Alexis Dubreuil, Srdjan Ostojic, Omri Barak
Date: 2020-06-19 00:00:00
Proceeding: NeurIPS 2020 12

Recurrent neural networks (RNNs) trained on low-dimensional tasks have been widely used to model functional biological networks. However, the solutions found by learning and the effect of initial connectivity are not well understood. Here, we examine RNNs trained using gradient descent on different tasks inspired by the neuroscience literature. We find that the changes in recurrent connectivity can be described by low-rank matrices, despite the unconstrained nature of the learning algorithm. To identify the origin of the low-rank structure, we turn to an analytically tractable setting: training a linear RNN on a simplified task. We show how the low-dimensional task structure leads to low-rank changes to connectivity. This low-rank structure allows us to explain and quantify the phenomenon of accelerated learning in the presence of random initial connectivity. Altogether, our study opens a new perspective to understanding trained RNNs in terms of both the learning process and the resulting network structure.
On the Time Trend of COVID-19: A Panel Data Study
Date: 2020-06-23 00:00:00

In this paper, we study the trending behaviour of COVID-19 data at country
level, and draw attention to some existing econometric tools which are
potentially helpful to understand the trend better in future studies. In our
empirical study, we find that European countries overall flatten the curves
more effectively compared to the other regions, while Asia & Oceania also
achieve some success, but the situations are not as optimistic elsewhere.
Africa and America are still facing serious challenges in terms of managing the
spread of the virus, and reducing the death rate, although in Africa the virus
spreads slower and has a lower death rate than the other regions. By comparing
the performances of different countries, our results incidentally agree with Gu
et al. (2020), though different approaches and models are considered. For
example, both works agree that countries such as USA, UK and Italy perform
relatively poorly; on the other hand, Australia, China, Japan, Korea, and
Singapore perform relatively better.
Manifold Feature Index: A novel index based on high-dimensional data simplification
Authors: Chenkai Xu, Hongwei Lin, Xuansu Fang
Date: 2020-06-19 00:00:00

In this paper, we propose a novel stock index model, namely the manifold feature(MF) index, to reflect the overall price activity of the entire stock market. Based on the theory of manifold learning, the researched stock dataset is assumed to be a low-dimensional manifold embedded in a higher-dimensional Euclidean space. After data preprocessing, its manifold structure and discrete Laplace-Beltrami operator(LBO) matrix are constructed. We propose a high-dimensional data feature detection method to detect feature points on the eigenvectors of LBO, and the stocks corresponding to these feature points are considered as the constituent stocks of the MF index. Finally, the MF index is generated by a weighted formula using the price and market capitalization of these constituents. The stock market studied in this research is the Shanghai Stock Exchange(SSE). We propose four metrics to compare the MF index series and the SSE index series (SSE 50, SSE 100, SSE 150, SSE 180 and SSE 380). From the perspective of data approximation, the results demonstrate that our indexes are closer to the stock market than the SSE index series. From the perspective of risk premium, MF indexes have higher stability and lower risk.
Reconfigurable Intelligent Surfaces and Metamaterials: The Potential of Wave Propagation Control for 6G Wireless Communications
Authors: George C. Alexandropoulos, Geoffroy Lerosey, Merouane Debbah, Mathias Fink
Date: 2020-06-19 00:00:00

The future 6G of wireless communication networks will have to meet multiple requirements in increasingly demanding levels, either individually or in combinations in small groups. This trend has spurred recent research activities on transceiver hardware architectures and novel wireless connectivity concepts. Among the emerging wireless hardware architectures belong the Reconfigurable Intelligent Surfaces (RISs), which are artificial planar structures with integrated electronic circuits that can be programmed to manipulate an incoming ElectroMagnetic (EM) field in a wide variety of functionalities. Incorporating RISs in wireless networks has been recently advocated as a revolutionary means to transform any naturally passive wireless communication environment to an active one. This can be accomplished by deploying cost-effective and easy to coat RISs to the environment's objects (e.g., building facades and indoor walls/ceilings), thus, offering increased environmental intelligence for the scope of diverse wireless networking objectives. In this paper, we first provide a brief history on wave propagation control for optics and acoustics, and overview two representative indoor wireless trials at 2.47GHz for spatial EM modulation with a passive discrete RIS. The first trial dating back to 2014 showcases the feasibility of highly accurate spatiotemporal focusing and nulling, while the second very recent one demonstrates that passive RISs can enrich multipath scattering, thus, enabling throughput boosted communication links. Motivated by the late research excitement on the RIS potential for intelligent EM wave propagation modulation, we describe the status on RIS hardware architectures and present key open challenges and future research directions for RIS design and RIS-empowered 6G wireless communications.
Spectral-Energy Efficiency Trade-off-based Beamforming Design for MISO Non-Orthogonal Multiple Access Systems
Authors: Haitham Al-Obiedollah, Kanapathippillai Cumanan, Jeyarajan Thiyagalingam, Jie Tang, Alister G. Burr, Zhiguo Ding, Octavia A. Dobre
Date: 2020-06-19 00:00:00

Energy efficiency (EE) and spectral efficiency (SE) are two of the key performance metrics in future wireless networks, covering both design and operational requirements. For previous conventional resource allocation techniques, these two performance metrics have been considered in isolation, resulting in severe performance degradation in either of these metrics. Motivated by this problem, in this paper, we propose a novel beamforming design that jointly considers the trade-off between the two performance metrics in a multiple-input single-output non-orthogonal multiple access system. In particular, we formulate a joint SE-EE based design as a multi-objective optimization (MOO) problem to achieve a good tradeoff between the two performance metrics. However, this MOO problem is not mathematically tractable and, thus, it is difficult to determine a feasible solution due to the conflicting objectives, where both need to be simultaneously optimized. To overcome this issue, we exploit a priori articulation scheme combined with the weighted sum approach. Using this, we reformulate the original MOO problem as a conventional single objective optimization (SOO) problem. In doing so, we develop an iterative algorithm to solve this non-convex SOO problem using the sequential convex approximation technique. Simulation results are provided to demonstrate the advantages and effectiveness of the proposed approach over the available beamforming designs.
Why Stake When You Can Borrow?
Authors: Tarun Chitra, Alex Evans
Date: 2020-06-16 00:00:00
Tasks: POS

As smart contract platforms autonomously manage billions of dollars of capital, quantifying the portfolio risk that investors engender in these systems is increasingly important. Recent work illustrates that Proof of Stake (PoS) is vulnerable to financial attacks arising from on-chain lending and has worse capital efficiency than Proof of Work (PoW) \cite{fanti_pos_econ}. Numerous methods for improving capital efficiency have been proposed that allow stakers to create fungible derivative claims on their staked assets. In this paper, we construct a unifying model for studying the security risks of these proposals. This model combines birth-death P\'olya processes and risk models adapted from the credit derivatives literature to assess token inequality and return profiles. We find that there is a sharp transition between 'safe' and 'unsafe' derivative usage. Surprisingly, we find that contrary to \cite{fanti2019compounding} there exist conditions where derivatives can \emph{reduce} concentration of wealth in these networks. This model also applies to Decentralized Finance (DeFi) protocols where staked assets are used as insurance. Our theoretical results are validated using agent-based simulation.
Modeling and Optimal Control of Hybrid UAVs with Wind Disturbance
Date: 2020-06-19 00:00:00

This paper addresses modeling and control of a six-degree-of-freedom unmanned
aerial vehicle capable of vertical take-off and landing in the presence of wind
disturbances. We design a hybrid vehicle that combines the benefits of both the
fixed-wing and the rotary-wing UAVs. A non-linear model for the hybrid vehicle
is rapidly built, combining rigid body dynamics, aerodynamics of wing, and
dynamics of the motor and propeller. Further, we design an H2 optimal
controller to make the UAV robust to wind disturbances. We compare its results
against that of PID and LQR-based control. Our proposed controller results in
better performance in terms of root mean squared errors and time responses
during two scenarios: hover and level-flight.
Valuing the quality option in agricultural commodity futures: a Monte Carlo simulation based approach
Authors: Sanjay Mansabdar, Hussain C Yaganti
Date: 2020-06-19 00:00:00

Agricultural commodity futures are often settled by delivery. Quality options that allow the futures short to deliver one of several underlying assets are commonly used in such contracts to prevent manipulation. Inclusion of these options reduces the price of the futures contract and leads to degraded contract hedging performance. Valuation of these options is a first step in assessing the impact of the quality options embedded into a futures contract. This paper demonstrates a Monte Carlo simulation based approach to estimate the value of a quality option. In order to improve simulation efficiency, the technique of antithetic variables is used. This approach can help in the assessment of the impact of embedded quality options.
Distributionally Robust Profit Opportunities
Authors: Derek Singh, Shuzhong Zhang
Date: 2020-06-18 00:00:00

This paper expands the notion of robust profit opportunities in financial markets to incorporate distributional uncertainty using Wasserstein distance as the ambiguity measure. Financial markets with risky and risk-free assets are considered. The infinite dimensional primal problems are formulated, leading to their simpler finite dimensional dual problems. A principal motivating question is how does distributional uncertainty help or hurt the robustness of the profit opportunity. Towards answering this question, some theory is developed and computational experiments are conducted. Finally some open questions and suggestions for future research are discussed.
Pycro-manager: open-source software for integrated microscopy hardware control and image processing
Date: 2020-06-19 00:00:00

{\mu}Manager, an open-source microscopy acquisition software, has been an
essential tool for many microscopy experiments over the past 15 years, but is
not easy to use for experiments in which image acquisition and analysis are
closely coupled. This is because {\mu}Manager libraries are written in C++ and
Java, whereas image processing is increasingly carried out with data science
and machine learning tools most easily accessible through the Python
programming language. We present Pycro-Manager, a tool that enables rapid
development of such experiments, while also providing access to the wealth of
existing tools within {\mu}Manager through Python.
Neural-network-assisted in situ processing monitoring by speckle pattern observation
Authors: Shuntaro Tani, Yutsuki Aoyagi, Yohei Kobayashi
Date: 2020-06-19 00:00:00

We propose a method to monitor the progress of laser processing using laser speckle patterns. Laser grooving and percussion drilling were performed using femtosecond laser pulses. The speckle patterns from a processing point were monitored with a high-speed camera and analyzed with a deep neural network. The deep neural network enabled us to extract multiple information from the speckle pattern without a need for analytical formulation. The trained neural network was able to predict the ablation depth with an uncertainty of 2 \micron, as well as the material under processing, which will be useful for composite material processing.
CDM Based Virtual FMCW MIMO Radar Imaging at 79GHz
Authors: Shahrokh Hamidi, Safieddin Safavi-Naeini
Date: 2020-06-19 00:00:00

Multiple Input Multiple Output (MIMO) Frequency Modulated Continuous Wave (FMCW) radars operating at 79GHz are compact, light and cost effective devices with low peak-to-average power ratio that have applications in different areas such as automotive industry and Unmanned Arial Vehicle (UAV) based radar imaging. In order to keep the structure small and simple, these radars come with small number of transmitters and receivers. The number of elements can be virtually increased using techniques such as Time Division Multiplexing (TDM), Frequency Division Multiplexing (FDM) or Code Division Multiplexing (CDM) and as a result higher angular resolution can be achieved. Both TDM and FDM based virtual FMCW MIMO radar imaging process have been reported in literature. However, to the best of our knowledge CDM based virtual FMCW MIMO radar has not received any attention. In this paper we will be using an 79GHz FMCW MIMO radar and apply the idea of the CDM method to increase the number of elements virtually which in turn enhances the angular resolution.
Learning Tversky Similarity
Authors: Rahnama Javad, Hüllermeier Eyke
Date: 2020-05-27 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

In this paper, we advocate Tversky's ratio model as an appropriate basis for
computational approaches to semantic similarity, that is, the comparison of
objects such as images in a semantically meaningful way. We consider the
problem of learning Tversky similarity measures from suitable training data
indicating whether two objects tend to be similar or dissimilar.
Experimentally, we evaluate our approach to similarity learning on two image
datasets, showing that is performs very well compared to existing methods.
StressGAN: A Generative Deep Learning Model for 2D Stress Distribution Prediction
Authors: Jiang Haoliang, Nie Zhenguo, Yeo Roselyn, Farimani Amir Barati, Kara Levent Burak
Date: 2020-05-30 00:00:00
Tasks: Generative Adversarial Network

Using deep learning to analyze mechanical stress distributions has been
gaining interest with the demand for fast stress analysis methods. Deep
learning approaches have achieved excellent outcomes when utilized to speed up
stress computation and learn the physics without prior knowledge of underlying
equations. However, most studies restrict the variation of geometry or boundary
conditions, making these methods difficult to be generalized to unseen
configurations. We propose a conditional generative adversarial network (cGAN)
model for predicting 2D von Mises stress distributions in solid structures. The
cGAN learns to generate stress distributions conditioned by geometries, load,
and boundary conditions through a two-player minimax game between two neural
networks with no prior knowledge. By evaluating the generative network on two
stress distribution datasets under multiple metrics, we demonstrate that our
model can predict more accurate high-resolution stress distributions than a
baseline convolutional neural network model, given various and complex cases of
geometry, load and boundary conditions.
Unifying Few- and Zero-Shot Egocentric Action Recognition
Authors: Scott Tyler R., Shvartsman Michael, Ridgeway Karl
Date: 2020-05-27 00:00:00
Tasks: Action Recognition, Benchmarking, Classification, General Classification, Metric Learning, open-set classification, zero-shot-classification, Zero-shot Generalization, Zero-Shot Learning

Although there has been significant research in egocentric action
recognition, most methods and tasks, including EPIC-KITCHENS, suppose a fixed
set of action classes. Fixed-set classification is useful for benchmarking
methods, but is often unrealistic in practical settings due to the
compositionality of actions, resulting in a functionally infinite-cardinality
label set. In this work, we explore generalization with an open set of classes
by unifying two popular approaches: few- and zero-shot generalization (the
latter which we reframe as cross-modal few-shot generalization). We propose a
new set of splits derived from the EPIC-KITCHENS dataset that allow evaluation
of open-set classification, and use these splits to show that adding a
metric-learning loss to the conventional direct-alignment baseline can improve
zero-shot classification by as much as 10%, while not sacrificing few-shot
performance.
Pedestrian Tracking with Gated Recurrent Units and Attention Mechanisms
Authors: Elhousni Mahdi, Huang Xinming
Date: 2020-05-31 00:00:00
Tasks: Deep Learning

Pedestrian tracking has long been considered an important problem, especially
in security applications. Previously,many approaches have been proposed with
various types of sensors. One popular method is Pedestrian Dead Reckoning(PDR)
[1] which is based on the inertial measurement unit(IMU) sensor. However PDR is
an integration and threshold based method, which suffers from accumulation
errors and low accuracy. In this paper, we propose a novel method in which the
sensor data is fed into a deep learning model to predict the displacements and
orientations of the pedestrian. We also devise a new apparatus to collect and
construct databases containing synchronized IMU sensor data and precise
locations measured by a LIDAR. The preliminary results are promising, and we
plan to push this forward by collecting more data and adapting the deep
learning model for all general pedestrian motions.
Quasi-conformal Geometry based Local Deformation Analysis of Lateral Cephalogram for Childhood OSA Classification
Authors: Chan Hei-Long, Yuen Hoi-Man, Au Chun-Ting, Chan Kate Ching-Ching, Li Albert Martin, Lui Lok-Ming
Date: 2020-05-31 00:00:00
Tasks: General Classification

Craniofacial profile is one of the anatomical causes of obstructive sleep
apnea(OSA). By medical research, cephalometry provides information on patients'
skeletal structures and soft tissues. In this work, a novel approach to
cephalometric analysis using quasi-conformal geometry based local deformation
information was proposed for OSA classification. Our study was a retrospective
analysis based on 60 case-control pairs with accessible lateral cephalometry
and polysomnography (PSG) data. By using the quasi-conformal geometry to study
the local deformation around 15 landmark points, and combining the results with
three linear distances between landmark points, a total of 1218 information
features were obtained per subject. A L2 norm based classification model was
built. Under experiments, our proposed model achieves 92.5% testing accuracy.
A note on Almgren-Chriss optimal execution problem with geometric Brownian motion
Authors: Bastien Baldacci, Jerome Benveniste
Date: 2020-06-19 00:00:00

We solve explicitly the Almgren-Chriss optimal liquidation problem where the stock price process follows a geometric Brownian motion. Our technique is to work in terms of cash and to use functional analysis tools. We show that this framework extends readily to the case of a stochastic drift for the price process and the liquidation of a portfolio.
PCA-based Antenna Impedance Estimation in Rayleigh Fading Channels
Authors: Shaohan Wu, Brian L. Hughes
Date: 2020-06-20 00:00:00

Impedance matching between receive antenna and front-end significantly impacts channel capacity in wireless channels. To implement capacity-optimal matching, the receiver must know the antenna impedance. But oftentimes this impedance varies with loading conditions in the antenna near-field. To mitigate this variation, several authors have proposed antenna impedance estimation techniques. However, the optimality of these techniques remains unclear. In this paper, we consider antenna impedance estimation at MISO receivers over correlated Rayleigh fading channels. We derive in closed-form the optimal ML estimator under i.i.d. fading and then show it can be found via a scalar optimization in generally correlated fading channels. Numerical results suggest a computationally efficient, principal-components approach that estimates antenna impedance in real-time for all Rayleigh fading channels. Furthermore, ergodic capacity can be significantly boosted at originally poorly matched receivers with adaptive matching using our proposed approach.
Transporting Robotic Swarms via Mean-Field Feedback Control
Authors: Tongjia Zheng, Qing Han, Hai Lin
Date: 2020-06-20 00:00:00
Tasks: Density Estimation, Philosophy

With the rapid development of AI and robotics, transporting a large swarm of networked robots has foreseeable applications in the near future. Existing research in swarm robotics has mainly followed a bottom-up philosophy with predefined local coordination and control rules. However, it is arduous to verify the global requirements and analyze their performance. This motivates us to pursue a top-down approach, and develop a provable control strategy for deploying a robotic swarm to achieve a desired global configuration. Specifically, we use mean-field partial differential equations (PDEs) to model the swarm and control its mean-field density (i.e., probability density) over a bounded spatial domain using mean-field feedback. The presented control law uses density estimates as feedback signals and generates corresponding velocity fields that, by acting locally on individual robots, guide their global distribution to a target profile. The design of the velocity field is therefore centralized, but the implementation of the controller can be fully distributed -- individual robots sense the velocity field and derive their own velocity control signals accordingly. The key contribution lies in applying the concept of input-to-state stability (ISS) to show that the perturbed closed-loop system (a nonlinear and time-varying PDE) is locally ISS with respect to density estimation errors. The effectiveness of the proposed control laws is verified using agent-based simulations.