pwc-archive/papers-with-abstracts huggingface dataset:
Corona Games: Masks, Social Distancing and Mechanism Design
Authors: Balazs Pejo, Gergely Biczok
Date: 2020-06-11 00:00:00
Tasks: Decision Making

Pandemic response is a complex affair. Most governments employ a set of quasi-standard measures to fight COVID-19 including wearing masks, social distancing, virus testing and contact tracing. We argue that some non-trivial factors behind the varying effectiveness of these measures are selfish decision-making and the differing national implementations of the response mechanism. In this paper, through simple games, we show the effect of individual incentives on the decisions made with respect to wearing masks and social distancing, and how these may result in a sub-optimal outcome. We also demonstrate the responsibility of national authorities in designing these games properly regarding the chosen policies and their influence on the preferred outcome. We promote a mechanism design approach: it is in the best interest of every government to carefully balance social good and response costs when implementing their respective pandemic response mechanism.
Power System Disturbance Classification with Online Event-Driven Neuromorphic Computing
Authors: Kaveri Mahapatra, Sen Lu, Abhronil Sengupta, Nilanjan Ray Chaudhuri
Date: 2020-06-11 00:00:00
Tasks: Classification, CPU, General Classification

Accurate online classification of disturbance events in a transmission network is an important part of wide-area monitoring. Although many conventional machine learning techniques are very successful in classifying events, they rely on extracting information from PMU data at control centers and processing them through CPU/GPUs, which are highly inefficient in terms of energy consumption. To solve this challenge without compromising accuracy, this paper presents a novel methodology based on event-driven neuromorphic computing architecture for classification of power system disturbances. A Spiking Neural Network (SNN)-based computing framework is proposed, which exploits sparsity in disturbances and promotes local event driven operation for unsupervised learning and inference from incoming data. Spatio-temporal information of PMU signals is first extracted and encoded into spike trains and classification is achieved with SNN-based supervised and unsupervised learning framework. Moreover, a QR decomposition-based selection technique is proposed to identify signals participating in the low rank subspace of multiple disturbance events. Performance of the proposed method is validated on data collected from a 16-machine, 5-area New England-New York system.
Image-Based Model Predictive Control via Dynamic Mode Decomposition
Authors: Qiugang Lu, Victor M. Zavala
Date: 2020-06-11 00:00:00
Tasks: Model Predictive Control

We present a data-driven model predictive control (MPC) framework for systems with high state-space dimensionalities. This work is motivated by the need to exploit sensor data that appears in the form of images (e.g., 2D or 3D spatial fields reported by thermal cameras). We propose to use dynamic mode decomposition (DMD) to directly build a low-dimensional model from image data and we use such model to obtain a tractable MPC controller. We demonstrate the scalability of this approach (which we call DMD-MPC) by using a 2D thermal diffusion system. Here, we assume that the evolution of the thermal field is captured by 50x50 pixel images, which results in a 2500-dimensional state-space. We show that the dynamics of this high-dimensional space can be accurately predicted by using a 40-dimensional DMD model and we show that the field can be manipulated satisfactorily by using an MPC controller that embeds the low-dimensional DMD model. We also show that the DMD-MPC controller significantly outperforms a standard MPC controller that uses data from a finite set of spatial locations (proxy locations) to manipulate the high-dimensional thermal field. This comparison illustrates the value of information embedded in image data.
Functional modules from variable genes: Leveraging percolation to analyze noisy, high-dimensional data
Authors: Steffen Werner, W Mathijs Rozemuller, Annabel Ebbing, Anna Alemany, Joleen Traets, Jeroen S. van Zon, Alexander van Oudenaarden, Hendrik C. Korswagen, Greg J. Stephens, Thomas S. Shimizu
Date: 2020-06-11 00:00:00
Tasks: Clustering

While measurement advances now allow extensive surveys of gene activity (large numbers of genes across many samples), interpretation of these data is often confounded by noise -- expression counts can differ strongly across samples due to variation of both biological and experimental origin. Complimentary to perturbation approaches, we extract functionally related groups of genes by analyzing the standing variation within a sampled population. To distinguish biologically meaningful patterns from uninterpretable noise, we focus on correlated variation and develop a novel density-based clustering approach that takes advantage of a percolation transition generically arising in random, uncorrelated data. We apply our approach to two contrasting RNA sequencing data sets that sample individual variation -- across single cells of fission yeast and whole animals of C. elegans worms -- and demonstrate robust applicability and versatility in revealing correlated gene clusters of diverse biological origin, including cell cycle phase, development/reproduction, tissue-specific functions, and feeding history. Our technique exploits generic features of noisy high-dimensional data and is applicable, beyond gene expression, to feature-rich data that sample population-level variability in the presence of noise.
Delegation in Veto Bargaining
Authors: Navin Kartik, Andreas Kleiner, Richard Van Weelden
Date: 2020-06-11 00:00:00

A proposer requires the approval of a veto player to change a status quo. Preferences are single peaked. Proposer is uncertain about Vetoer's ideal point. We study Proposer's optimal mechanism without transfers. Vetoer is given a menu, or a delegation set, to choose from. The optimal delegation set balances the extent of Proposer's compromise with the risk of a veto. Under reasonable conditions, "full delegation" is optimal: Vetoer can choose any action between the status quo and Proposer's ideal action. This outcome largely nullifies Proposer's bargaining power; Vetoer frequently obtains her ideal point, and there is Pareto efficiency despite asymmetric information. More generally, we identify when "interval delegation" is optimal. Optimal interval delegation can be a Pareto improvement over cheap talk. We derive comparative statics. Vetoer receives less discretion when preferences are more likely to be aligned, by contrast to expertise-based delegation. Methodologically, our analysis handles stochastic mechanisms.
Incentives and Efficiency in Constrained Allocation Mechanisms
Authors: Joseph Root, David S. Ahn
Date: 2020-06-11 00:00:00

We study private-good allocation under general constraints. Several prominent examples are special cases, including house allocation, roommate matching, social choice, and multiple assignment. Every individually strategy-proof and Pareto efficient two-agent mechanism is an "adapted local dictatorship." Every group strategy-proof N-agent mechanism has two-agent marginal mechanisms that are adapted local dictatorships. These results yield new characterizations and unifying insights for known characterizations. We find all group strategy-proof and Pareto efficient mechanisms for the roommates problem. We give a related result for multiple assignment. We prove the Gibbard--Satterthwaite Theorem and give a partial converse.
Fibration symmetries uncover the building blocks of biological networks
Authors: Flaviano Morone, Ian Leifer, Hernan A. Makse
Date: 2020-06-10 00:00:00

A major ambition of systems science is to uncover the building blocks of any biological network to decipher how cellular function emerges from their interactions. Here, we introduce a graph representation of the information flow in these networks as a set of input trees, one for each node, which contains all pathways along which information can be transmitted in the network. In this representation, we find remarkable symmetries in the input trees that deconstruct the network into functional building blocks called fibers. Nodes in a fiber have isomorphic input trees and thus process equivalent dynamics and synchronize their activity. Each fiber can then be collapsed into a single representative base node through an information-preserving transformation called 'symmetry fibration', introduced by Grothendieck in the context of algebraic geometry. We exemplify the symmetry fibrations in gene regulatory networks and then show that they universally apply across species and domains from biology to social and infrastructure networks. The building blocks are classified into topological classes of input trees characterized by integer branching ratios and fractal golden ratios of Fibonacci sequences representing cycles of information. Thus, symmetry fibrations describe how complex networks are built from the bottom up to process information through the synchronization of their constitutive building blocks.
Nowcasting Covid-19 statistics reported withdelay: a case-study of Sweden
Date: 2020-06-11 00:00:00

The new corona virus disease -- COVID-2019 -- is rapidly spreading through
the world. The availability of unbiased timely statistics of trends in disease
events are a key to effective responses. But due to reporting delays, the most
recently reported numbers are frequently underestimating of the total number of
infections, hospitalizations and deaths creating an illusion of a downward
trend. Here we describe a statistical methodology for predicting true daily
quantities and their uncertainty, estimated using historical reporting delays.
The methodology takes into account the observed distribution pattern of the
lag. It is derived from the removal method, a well-established estimation
framework in the field of ecology.
Injecting Reliable Radio Frequency Fingerprints Using Metasurface for The Internet of Things
Authors: Sekhar Rajendran, Zhi Sun, Feng Lin, Kui Ren
Date: 2020-06-12 00:00:00

In Internet of Things, where billions of devices with limited resources are communicating with each other, security has become a major stumbling block affecting the progress of this technology. Existing authentication schemes-based on digital signatures have overhead costs associated with them in terms of computation time, battery power, bandwidth, memory, and related hardware costs. Radio frequency fingerprint (RFF), utilizing the unique device-based information, can be a promising solution for IoT. However, traditional RFFs have become obsolete because of low reliability and reduced user capability. Our proposed solution, Metasurface RF-Fingerprinting Injection (MeRFFI), is to inject a carefully-designed radio frequency fingerprint into the wireless physical layer that can increase the security of a stationary IoT device with minimal overhead. The injection of fingerprint is implemented using a low cost metasurface developed and fabricated in our lab, which is designed to make small but detectable perturbations in the specific frequency band in which the IoT devices are communicating. We have conducted comprehensive system evaluations including distance, orientation, multiple channels where the feasibility, effectiveness, and reliability of these fingerprints are validated. The proposed MeRFFI system can be easily integrated into the existing authentication schemes. The security vulnerabilities are analyzed for some of the most threatening wireless physical layer-based attacks.
Synchronization Stability Analysis and Enhancement of Grid-Tied Multi-Converter Systems
Date: 2020-06-12 00:00:00

Synchronization instability of grid-tied converters during grid faults is a
serious concern. For phase-locked loop (PLL) synchronized converters,
synchronization stability during grid faults requires that PLL can
resynchronize with the power grid. It is of great significance for meeting grid
code specifications such as reactive current support. However, the
synchronization in the context of high-impedance weak grid is not always
readily achieved. On one hand, the terminal voltage of converters is changeable
due to being susceptible to the output current. One the other hand, there is
interaction between multiple converters interconnected to the grid. Therefore,
loss of synchronism (LOS) of converters during grid faults likely occurs.
Previous research has investigated the LOS issue of single-converter
infinite-bus (SCIB) system, but mostly overlooked the interaction between
multiple converters. Previous efforts to address the issue of LOS are also in
doubt for multi-converter systems. This paper develops a multi-converter
infinite-bus (MCIB) system model and analyzes the root cause of LOS with
considering multi-converter interaction during grid faults. Moreover, a
feedforward compensated PLL (FFC-PLL) method applicable for multi-converter
systems is developed to address the LOS issue. Simulations results verify the
effectiveness of the method.
Generic Indic Text-to-speech Synthesisers with Rapid Adaptation in an End-to-end Framework
Date: 2020-06-12 00:00:00
Tasks: text-to-speech, Text to Speech

Building text-to-speech (TTS) synthesisers for Indian languages is a
difficult task owing to a large number of active languages. Indian languages
can be classified into a finite set of families, prominent among them,
Indo-Aryan and Dravidian. The proposed work exploits this property to build a
generic TTS system using multiple languages from the same family in an
end-to-end framework. Generic systems are quite robust as they are capable of
capturing a variety of phonotactics across languages. These systems are then
adapted to a new language in the same family using small amounts of adaptation
data. Experiments indicate that good quality TTS systems can be built using
only 7 minutes of adaptation data. An average degradation mean opinion score of
3.98 is obtained for the adapted TTSes.
  Extensive analysis of systematic interactions between languages in the
generic TTSes is carried out. x-vectors are included as speaker embedding to
synthesise text in a particular speaker's voice. An interesting observation is
that the prosody of the target speaker's voice is preserved. These results are
quite promising as they indicate the capability of generic TTSes to handle
speaker and language switching seamlessly, along with the ease of adaptation to
a new language.
On the Impact of Finite-Length Probabilistic Shaping on Fiber Nonlinear Interference
Authors: Tobias Fehenberger
Date: 2020-06-12 00:00:00

The interplay of shaped signaling and fiber nonlinearities is reviewed in the asymptotic and finite-length regime. We present explanations and discuss implications of an optimum shaping length of just a few hundred symbols.
Channel covariance estimation in multiuser massive MIMO systems with an approach based on infinite dimensional Hilbert spaces
Authors: Renato Luis Garrido Cavalcante, Slawomir Stanczak
Date: 2020-06-12 00:00:00

We propose a novel algorithm to estimate the channel covariance matrix of a desired user in multiuser massive MIMO systems. The algorithm uses only knowledge of the array response and rough knowledge of the angular support of the incoming signals, which are assumed to be separated in a well-defined sense. To derive the algorithm, we study interference patterns with realistic models that treat signals as continuous functions in infinite dimensional Hilbert spaces. By doing so, we can avoid common and unnatural simplifications such as the presence of discrete signals, ideal isotropic antennas, and infinitely large antenna arrays. An additional advantage of the proposed algorithm is its computational simplicity: it only requires a single matrix-vector multiplication. In some scenarios, simulations show that the estimates obtained with the proposed algorithm are close to those obtained with standard estimation techniques operating in interference-free and noiseless systems.
Analysis of Nonlinear Fiber Interactions for Finite-Length Constant-Composition Sequences
Authors: Tobias Fehenberger, David S. Millar, Toshiaki Koike-Akino, Keisuke Kojima, Kieran Parsons, Helmut Griesser
Date: 2020-06-12 00:00:00

In order to realize probabilistically shaped signaling within the probabilistic amplitude shaping (PAS) framework, a shaping device outputs sequences that follow a certain nonuniform distribution. In case of constant-composition (CC) distribution matching (CCDM), the sequences differ only in the ordering of their constituent symbols, whereas the number of occurrences of each symbol is constant in every output block. Recent results by Amari \textit{et al.} have shown that the CCDM block length can have a considerable impact on the effective signal-to-noise ratio (SNR) after fiber transmission. So far, no explanation for this behavior has been presented. Furthermore, the block-length dependence of the SNR seems not to be fully aligned with previous results in the literature. This paper is devoted to a detailed analysis of the nonlinear fiber interactions for CC sequences. We confirm in fiber simulations the inverse proportionality of SNR with CCDM block length and present two explanations. The first one, which only holds in the short-length regime, is based on how two-dimensional symbols are generated from shaped amplitudes in the PAS framework. The second, more general explanation relates to an induced shuffling within a sequence, or equivalently a limited concentration of identical symbols, that is an inherent property for short CC blocks, yet not necessarily present for long blocks. This temporal property results in weaker nonlinear interactions, and thus higher SNR, for short CC sequences. For a typical multi-span fiber setup, the SNR difference is numerically demonstrated to be up to 0.7dB. Finally, we evaluate a heuristic figure of merit that captures the number of runs of identical symbols in a concatenation of several CC sequences. For moderate block lengths up to approximately 100 symbols, this metric suggests that limiting the number of identical-symbol runs can be beneficial.
Huffman-coded Sphere Shaping and Distribution Matching Algorithms via Lookup Tables
Authors: Tobias Fehenberger, David S. Millar, Toshiaki Koike-Akino, Keisuke Kojima, Kieran Parsons, Helmut Griesser
Date: 2020-06-12 00:00:00

In this paper, we study amplitude shaping schemes for the probabilistic amplitude shaping (PAS) framework as well as algorithms for constant-composition distribution matching (CCDM). Huffman-coded sphere shaping (HCSS) is discussed in detail, which internally uses Huffman coding to determine the composition to be used and relies on conventional CCDM algorithms for mapping and demapping. Numerical simulations show that HCSS closes the performance gap between distribution matching schemes and sphere shaping techniques such as enumerative sphere shaping (ESS). HCSS is based on an architecture that is different from the trellis-based setup of ESS. It allows to tailor the used HCSS compositions to the transmission channel and to take into account complexity constraints. We further discuss in detail multiset ranking (MR) and subset ranking (SR) as alternatives to arithmetic-coding (AC) CCDM. The advantage of MR over AC is that it requires less sequential operations for mapping. SR operates on binary alphabets only, which can introduce some additional rate loss when a nonbinary-to-binary transformation is required. However, the binomial coefficients required for SR can be precomputed and stored in a lookup table (LUT). We perform an analysis of rate loss and decoding performance for the proposed techniques and compare them to other prominent amplitude shaping schemes. For medium to long block lengths, MR-HCSS and SR-HCSS are shown to have similar performance to ESS. SR-HCSS and uniform 64QAM are compared in additive white Gaussian noise simulations and shaping gains of 0.5 dB and 1 dB are demonstrated with 1 kbit and 100 kbit LUT size, respectively.
Privacy Against Adversarial Classification in Cyber-Physical Systems
Date: 2020-06-12 00:00:00
Tasks: Classification

For a class of Cyber-Physical Systems (CPSs), we address the problem of
performing computations over the cloud without revealing private information
about the structure and operation of the system. We model CPSs as a collection
of input-output dynamical systems (the system operation modes). Depending on
the mode the system is operating on, the output trajectory is generated by one
of these systems in response to driving inputs. Output measurements and driving
inputs are sent to the cloud for processing purposes. We capture this
"processing" through some function (of the input-output trajectory) that we
require the cloud to compute accurately - referred here as the trajectory
utility. However, for privacy reasons, we would like to keep the mode private,
i.e., we do not want the cloud to correctly identify what mode of the CPS
produced a given trajectory. To this end, we distort trajectories before
transmission and send the corrupted data to the cloud. We provide mathematical
tools (based on output-regulation techniques) to properly design distorting
mechanisms so that: 1) the original and distorted trajectories lead to the same
utility; and the distorted data leads the cloud to misclassify the mode.
Model Predictive Control of a Food Production Unit: A Case Study for Lettuce Production
Date: 2020-06-12 00:00:00
Tasks: Model Predictive Control

Plant factories with artificial light are widely researched for food
production in a controlled environment. For such control tasks, models of the
energy and resource exchange in the production unit as well as those of the
plant's growth process may be used. To achieve minimal operation cost, optimal
control strategies can be applied to the system, taking into account the
availability of resources by control reference specification. A particular
advantage of model predictive control (MPC) is the incorporation of constraints
that comply with actuator limitations and general plant growth conditions. In
this work, a model of a production unit is derived including a description of
the relation between the actuators' electrical signals and the input values to
the model. Furthermore, a preliminary model based state tracking control is
evaluated for production unit containing Lettuce. It could be observed that the
controller is capable to track the reference while satisfying the constraint
under changing weather conditions and resource availability.
Monolingual Data Selection Analysis for English-Mandarin Hybrid Code-switching Speech Recognition
Date: 2020-09-14 00:00:00
Tasks: speech-recognition, Speech Recognition

In this paper, we conduct data selection analysis in building an
English-Mandarin code-switching (CS) speech recognition (CSSR) system, which is
aimed for a real CSSR contest in China. The overall training sets have three
subsets, i.e., a code-switching data set, an English (LibriSpeech) and a
Mandarin data set respectively. The code-switching data are Mandarin dominated.
First of all, it is found using the overall data yields worse results, and
hence data selection study is necessary. Then to exploit monolingual data, we
find data matching is crucial. Mandarin data is closely matched with the
Mandarin part in the code-switching data, while English data is not. However,
Mandarin data only helps on those utterances that are significantly
Mandarin-dominated. Besides, there is a balance point, over which more
monolingual data will divert the CSSR system, degrading results. Finally, we
analyze the effectiveness of combining monolingual data to train a CSSR system
with the HMM-DNN hybrid framework. The CSSR system can perform within-utterance
code-switch recognition, but it still has a margin with the one trained on
code-switching data.