pwc-archive/papers-with-abstracts huggingface dataset:
Multivariate analysis of Brillouin imaging data by supervised and unsupervised learning
Date: 2020-09-15 00:00:00

Brillouin imaging relies on the reliable extraction of subtle spectral
information from hyperspectral datasets. To date, the mainstream practice has
been using line fitting of spectral features to retrieve the average peak shift
and linewidth parameters. Good results, however, depend heavily on sufficient
SNR and may not be applicable in complex samples that consist of spectral
mixtures. In this work, we thus propose the use of various multivariate
algorithms that can be used to perform supervised or unsupervised analysis of
the hyperspectral data, with which we explore advanced image analysis
applications, namely unmixing, classification and segmentation in a phantom and
live cells. The resulting images are shown to provide more contrast and detail,
and obtained on a timescale $10^2$ faster than fitting. The estimated spectral
parameters are consistent with those calculated from pure fitting.
On NOMA-Based mmWave Communications
Authors: Yu Tian, Gaofeng Pan, Mohamed-Slim
Date: 2020-09-15 00:00:00

Non-orthogonal multiple access (NOMA) and millimeter-wave (mmWave) communication are two promising techniques to increase the system capacity in the fifth-generation (5G) mobile network. The former can achieve high spectral efficiency by modulating the information in power domain and the latter can provide extremely large spectrum resources. Fluctuating two-ray (FTR) channel model has already been proved to accurately agree with the small-scale fading effects in mmWave communications in experiments. In this paper, the performance of NOMA-based communications over FTR channels in mmWave communication systems is investigated in terms of outage probability (OP) and ergodic capacity (EC). Specifically, we consider the scenario that one base station (BS) transmits signals to two users simultaneously under NOMA scheme. The BS and users are all equipped with a single antenna. Two power allocation strategies are considered: the first one is a general (fixed) power allocation scheme under which we derive the OP and EC of NOMA users in closed form; the other one is an optimal power allocation scheme that can achieve the maximum sum rate for the whole system. Under the second scheme, not only the closed-form OP and EC but also the upper and lower bounds of EC are derived. Furthermore, we also derive the asymptotic expression for the OP in high average SNR region to investigate the diversity order under these two schemes. Finally, we show the correctness and accuracy of our derived expressions by Monte-Carlo simulation.
A Scalable Real-Time Architecture for Neural Oscillation Detection and Phase-Specific Stimulation
Authors: Christopher Thomas, Thilo Womelsdorf
Date: 2020-09-15 00:00:00

Oscillations in the local field potential (LFP) of the brain are key signatures of neural information processing. Perturbing these oscillations at specific phases in order to alter neural information processing is an area of active research. Existing systems for phase-specific brain stimulation typically either do not offer real-time timing guarantees (desktop computer based systems) or require extensive programming of vendor-specific equipment. This work presents a real-time detection system architecture that is platform-agnostic and that scales to thousands of recording channels, validated using a proof-of-concept microcontroller-based implementation.
Bearing-Only Navigation with Field of View Constraints
Date: 2020-09-15 00:00:00

This paper addresses the problem of navigation using only relative direction
measurements (i.e., relative distances are unknown) under field of view
constraints. We present a novel navigation vector field for the bearing-based
visual homing problem with respect to static visual landmarks in 2-D and 3-D
environments. Our method employs two control fields that are tangent and normal
to ellipsoids having landmarks as their foci. The tangent field steers the
robot to a set of points where the average of observed bearings is parallel to
the average of the desired bearings, and the normal field uses the angle
between a pair of bearings as a proxy to adjust the robot's distance from
landmarks and to satisfy the field of view constraints. Both fields are blended
together to construct an almost globally stable control law. Our method is easy
to implement, as it requires only comparisons between average bearings, and
between angles of pairs of vectors. We provide simulations that demonstrate the
performance of our approach for a double integrator system and unicycles.
Analog vs. Digital Spatial Transforms: A Throughput, Power, and Area Comparison
Authors: Zephan M. Enciso, Seyed Hadi Mirfarshbafan, Oscar Castañeda, Clemens JS. Schaefer, Christoph Studer, Siddharth Joshi
Date: 2020-09-15 00:00:00
Tasks: Data Compression

Spatial linear transforms that process multiple parallel analog signals to simplify downstream signal processing find widespread use in multi-antenna communication systems, machine learning inference, data compression, audio and ultrasound applications, among many others. In the past, a wide range of mixed-signal as well as digital spatial transform circuits have been proposed---it is, however, a longstanding question whether analog or digital transforms are superior in terms of throughput, power, and area. In this paper, we focus on Hadamard transforms and perform a systematic comparison of state-of-the-art analog and digital circuits implementing spatial transforms in the same 65\,nm CMOS technology. We analyze the trade-offs between throughput, power, and area, and we identify regimes in which mixed-signal or digital Hadamard transforms are preferable. Our comparison reveals that (i) there is no clear winner and (ii) analog-to-digital conversion is often dominating area and energy efficiency---and not the spatial transform.
Encompassing Tests for Value at Risk and Expected Shortfall Multi-Step Forecasts based on Inference on the Boundary
Authors: Timo Dimitriadis, Xiaochun Liu, Julie Schnaitmann
Date: 2020-09-15 00:00:00

We propose forecast encompassing tests for the Expected Shortfall (ES) jointly with the Value at Risk (VaR) based on flexible link (or combination) functions. Our setup allows testing encompassing for convex forecast combinations and for link functions which preclude crossings of the combined VaR and ES forecasts. As the tests based on these link functions involve parameters which are on the boundary of the parameter space under the null hypothesis, we derive and base our tests on nonstandard asymptotic theory on the boundary. Our simulation study shows that the encompassing tests based on our new link functions outperform tests based on unrestricted linear link functions for one-step and multi-step forecasts. We further illustrate the potential of the proposed tests in a real data analysis for forecasting VaR and ES of the S&P 500 index.
Q-space quantitative diffusion MRI measures using a stretched-exponential representation
Authors: Tomasz Pieciak, Maryam Afzali, Fabian Bogusz, Aja-Fernández, Derek K. Jones
Date: 2020-09-15 00:00:00
Tasks: Diffusion  MRI, Prognosis

Diffusion magnetic resonance imaging (dMRI) is a relatively modern technique used to study tissue microstructure in a non-invasive way. Non-Gaussian diffusion representation is related to the restricted diffusion and can provide information about the underlying tissue properties. In this paper, we analytically derive $n$-th order statistics of the signal considering a stretched-exponential representation of the diffusion. Then, we retrieve the Q-space quantitative measures such as the Return-To-the-Origin Probability (RTOP), Q-space mean square displacement (QMSD), Q-space mean fourth-order displacement (QMFD). The stretched-exponential representation enables the handling of the diffusion contributions from a higher $b$-value regime under a non-Gaussian assumption, which can be useful in diagnosing or prognosis of neurodegenerative diseases in the early stages. Numerical implementation of the method is freely available at https://github.com/TPieciak/Stretched.
Adversarial Attacks on Co-Occurrence Features for GAN Detection
Date: 2020-09-16 00:00:00
Tasks: Deep Learning

Improvements in Generative Adversarial Networks (GANs) have greatly reduced
the difficulty of producing new, photo-realistic images with unique semantic
meaning. With this rise in ability to generate fake images comes demand to
detect them. While numerous methods have been developed for this task, the
majority of them remain vulnerable to adversarial attacks. In this paper,
develop two novel adversarial attacks on co-occurrence based GAN detectors.
These are the first attacks to be presented against such a detector. We show
that our method can reduce accuracy from over 98% to less than 4%, with no
knowledge of the deep learning model or weights. Furthermore, accuracy can be
reduced to 0% with full knowledge of the deep learning model details.
The role of hydrophobic interactions in folding of $β$-sheets
Authors: Jiacheng Li, Xiaoliang Ma, Hongchi Zhang, Chengyu Hou, Liping Shi, Shuai Guo, Chenchen Liao, Bing Zheng, Lin Ye, Lin Yang, Xiaodong He
Date: 2020-09-16 00:00:00
Tasks: Protein Folding

Exploring the protein-folding problem has been a long-standing challenge in molecular biology. Protein folding is highly dependent on folding of secondary structures as the way to pave a native folding pathway. Here, we demonstrate that a feature of a large hydrophobic surface area covering most side-chains on one side or the other side of adjacent $\beta$-strands of a $\beta$-sheet is prevail in almost all experimentally determined $\beta$-sheets, indicating that folding of $\beta$-sheets is most likely triggered by multistage hydrophobic interactions among neighbored side-chains of unfolded polypeptides, enable $\beta$-sheets fold reproducibly following explicit physical folding codes in aqueous environments. $\beta$-turns often contain five types of residues characterized with relatively small exposed hydrophobic proportions of their side-chains, that is explained as these residues can block hydrophobic effect among neighbored side-chains in sequence. Temperature dependence of the folding of $\beta$-sheet is thus attributed to temperature dependence of the strength of the hydrophobicity. The hydrophobic-effect-based mechanism responsible for $\beta$-sheets folding is verified by bioinformatics analyses of thousands of results available from experiments. The folding codes in amino acid sequence that dictate formation of a $\beta$-hairpin can be deciphered through evaluating hydrophobic interaction among side-chains of an unfolded polypeptide from a $\beta$-strand-like thermodynamic metastable state.
Location-aware Predictive Beamforming for UAV Communications: A Deep Learning Approach
Authors: Chang Liu, Weijie Yuan, Zhiqiang Wei, Xuemeng Liu, Derrick Wing Kwan Ng
Date: 2020-09-16 00:00:00

Unmanned aerial vehicle (UAV)-assisted communication becomes a promising technique to realize the beyond fifth generation (5G) wireless networks, due to the high mobility and maneuverability of UAVs which can adapt to heterogeneous requirements of different applications. However, the movement of UAVs impose challenge for accurate beam alignment between the UAV and the ground user equipment (UE). In this letter, we propose a deep learning-based location-aware predictive beamforming scheme to track the beam for UAV communications in a dynamic scenario. Specifically, a long short-term memory (LSTM)-based recurrent neural network (LRNet) is designed for UAV location prediction. Based on the predicted location, a predicted angle between the UAV and the UE can be determined for effective and fast beam alignment in the next time slot, which enables reliable communications between the UAV and the UE. Simulation results demonstrate that the proposed scheme can achieve a satisfactory UAV-to-UE communication rate, which is close to the upper bound of communication rate obtained by the perfect genie-aided alignment scheme.
Quasi-distributed fiber sensing via perfect periodic Legendre codes
Authors: Nadav Arbel, Lihi Shiloh, Nadav Levanon, Avishay Eyal
Date: 2020-09-16 00:00:00
Tasks: Sensitivity

Long-range Rayleigh-based Distributed Acoustic Sensing (DAS) systems are often limited in their sensitivity and bandwidth. The former limitation is a result of the low backscattered power and poor \textit{dynamic-strain to optical-phase} transduction efficiency. The latter constraint results from the trade-off between range and scan-rate which limits the sampling interval to the longest delay in the sensing fiber. Quasi-DAS (Q-DAS) can yield enhanced sensitivity but may still suffer from low backscattered power and low scan-rate for long-haul, many-sensor, systems. In this work we study the use of Perfect Periodic Correlation codes for interrogating a long-haul Q-DAS system. It is shown that judicious choice of the code parameters allows order of magnitude increase in detection bandwidths and in the power reflected from each sensor.
Joint Waveform and Beamforming Optimization for MIMO Wireless Power Transfer
Authors: Shanpu Shen, Bruno Clerckx
Date: 2020-09-13 00:00:00

In this paper, we study a multi-sine multiple-input multiple-output (MIMO) wireless power transfer (WPT) system with the objective to increase the output DC power. We jointly optimize the multi-sine waveform and beamforming accounting for the rectenna nonlinearity, and consider two combining schemes for the rectennas at the receiver, namely DC and RF combinings. For DC combining, the waveform and transmit beamforming are optimized, as a function of the channel state information (CSI). For RF combining, the optimal transmit and receive beamformings are provided in closed form and the waveform is optimized. We also consider a practical RF combining circuit using phase shifter and RF power combiner and optimize the waveform, transmit beamforming, and analog receive beamforming adaptive to the CSI. Two types of performance evaluations, based on the nonlinear rectenna model and accurate and realistic circuit simulations, are provided. The evaluations demonstrate that the joint waveform and beamforming design can increase the output DC power by leveraging the beamforming gain, the frequency diversity gain, and the rectenna nonlinearity. It also shows that the joint waveform and beamforming design provides a higher output DC power than the beamforming-only design with a relative gain of 180% in a two-transmit antenna sixteen-sinewave two-receive antenna setup.
Manipulation-Robust Regression Discontinuity Designs
Authors: Takuya Ishihara, Masayuki Sawada
Date: 2020-09-16 00:00:00
Tasks: Diagnostic, regression

We present simple low-level conditions for identification in regression discontinuity designs using a potential outcome framework for the manipulation of the running variable. Using this framework, we replace the existing identification statement with two restrictions on manipulation. Our framework highlights the critical role of the continuous density of the running variable in identification. In particular, we establish the low-level auxiliary assumption of the diagnostic density test under which the design may detect manipulation against identification and hence is manipulation-robust.
Exploiting Linear Substructure In LRKFs (Extended)
Date: 2020-09-16 00:00:00
Tasks: regression

We exploit knowledge of linear substructure in the linear-regression Kalman
filters (LRKFs) to simplify the problem of moment matching. The theoretical
results yield quantifiable and significant computational speedups at no cost of
estimation accuracy, assuming partially linear estimation models. The results
apply to any symmetrical LRKF, and reductions in computational complexity are
stated as a function of the cubature rule, the number of linear and nonlinear
states in the estimation model respectively. The implications for the filtering
problem are illustrated by numerical examples.
6G Vision: An Ultra-Flexible Perspective
Authors: Ahmet Yazar, Seda Doğan-Tusha, Hüseyin Arslan
Date: 2020-09-16 00:00:00

The upcoming sixth generation (6G) communications systems are expected to support an unprecedented variety of applications, pervading every aspect of human life. It is clearly not possible to fulfill the service requirements without actualizing a plethora of flexible options pertaining to the key enabler technologies themselves. At that point, this work presents an overview of the potential 6G key enablers from the flexibility perspective, categorizes them, and provides a general framework to incorporate them in the future networks. Furthermore, the role of artificial intelligence and integrated sensing and communications as key enablers of the presented framework is also discussed.
Distributed formation maneuver control by manipulating the complex Laplacian
Authors: Hector Garcia de Marina
Date: 2020-09-16 00:00:00

This paper proposes a novel maneuvering technique for the complex-Laplacian-based formation control. We show how to modify the original weights that build the Laplacian such that a designed steady-state motion of the desired shape emerges from the local interactions among the agents. These collective motions can be exploited to solve problems such as the shaped consensus (the rendezvous with a particular shape), the enclosing of a target, or translations with controlled speed and heading to assist mobile robots in area coverage, escorting, and traveling missions, respectively. The designed steady-state collective motions correspond to rotations around the centroid, translations, and scalings of a reference shape. The proposed modification of the weights relocates one of the Laplacian's zero eigenvalues while preserving its associated eigenvector that constructs the desired shape. For example, such relocation on the imaginary or real axis induces rotational and scaling motions, respectively. We will show how to satisfy a sufficient condition to guarantee the global convergence to the desired shape and motions. Finally, we provide simulations and comparisons with other maneuvering techniques.
The direct and indirect effect of CAP support on farm income enhancement:a farm-based econometric analysis
Authors: Simone Severini, Luigi Biagini
Date: 2020-09-16 00:00:00
Tasks: regression

We assess the correlation between CAP support provided to farmers and their income and use of capital and labour in the first year of the new CAP regime. This is done applying three regression models on the Italian FADN farms controlling for other farm characteristics. CAP annual payments are positively correlated with farm income and capital but are negatively correlated with labour use. Farm investment support provided by RDP measures is positively correlated to the amount of capital. Results suggest that CAP is positively affecting farm income directly but also indirectly by supporting the substitution of labour with capital
Probabilistic Value-Deviation-Bounded Source-Dependent Bit-Level Channel Adaptation for Approximate Communication
Authors: Bilgesu Arif Bilgin, Phillip Stanley-Marbell
Date: 2020-09-16 00:00:00

Computing systems that can tolerate effects of errors in their communicated data values can trade this tolerance for improved resource efficiency. Many important applications of computing, such as embedded sensor systems, can tolerate errors that are bounded in their distribution of deviation from correctness (distortion). We present a channel adaptation technique which modulates properties of I/O channels typical in embedded sensor systems, to provide a tradeoff between I/O power dissipation and distortion of communicated data. We provide an efficient-to-compute formulation for the distribution of integer distortion accounting for the distribution of transmitted values. Using this formulation we implement our value-deviation-bounded (VDB) channel adaptation. We experimentally quantify the achieved reduction in power dissipation on a hardware prototype integrated with the required programmable channel modulation circuitry. We augment these experimental measurements with an analysis of the distributions of distortions. We show that our probabilistic VDB channel adaptation can provide up to a 2$\times$ reduction in I/O power dissipation. When synthesized for a miniature low-power FPGA intended for use in sensor interfaces, a register transfer level implementation of the channel adaptation control logic requires only 106 flip-flops and 224 4-input LUTs for implementing per-bit channel adaptation on serialized streams of 8-bit sensor data.
Trajectory planning with a dynamic obstacle clustering strategy using Mixed-Integer Linear Programming
Date: 2020-09-16 00:00:00
Tasks: Clustering, Collision Avoidance, Model Predictive Control, Trajectory Planning

In this paper we propose a technique that assigns obstacles to clusters used
for collision avoidance via Mixed-Integer Programming. This strategy enables a
reduction in the number of binary variables used for collision avoidance, thus
entailing a decrease in computational cost, which has been a hindrance to the
application of Model Predictive Control approaches with Mixed-Integer
Programming formulations in real-time. Moreover, the assignment of obstacles to
clusters and the sizes of the clusters are decided within the same optimization
problem that performs the trajectory planning, thus yielding optimal cluster
choices. Simulation results are presented to illustrate an application of the
proposal.
Energy and Flow Effects of Optimal Automated Driving in Mixed Traffic: Vehicle-in-the-Loop Experimental Results
Date: 2020-09-16 00:00:00
Tasks: Model Predictive Control

This paper experimentally demonstrates the effectiveness of an anticipative
car-following algorithm in reducing energy use of gasoline engine and electric
Connected and Automated Vehicles (CAV), without sacrificing safety and traffic
flow. We propose a Vehicle-in-the-Loop (VIL) testing environment in which
experimental CAVs driven on a track interact with surrounding virtual traffic
in real-time. We explore the energy savings when following city and highway
drive cycles, as well as in emergent highway traffic created from
microsimulations. Model predictive control handles high level velocity planning
and benefits from communicated intentions of a preceding CAV or estimated
probable motion of a preceding human driven vehicle. A combination of classical
feedback control and data-driven nonlinear feedforward control of pedals
achieve acceleration tracking at the low level. The controllers are implemented
in ROS and energy is measured via calibrated OBD-II readings. We report up to
30% improved energy economy compared to realistically calibrated human driver
car-following without sacrificing following headway.