pwc-archive/papers-with-abstracts huggingface dataset:
From Aircraft Tracking Data to Network Delay Model: A Data-Driven Approach Considering En-Route Congestion
Authors: Yu Lin, Lishuai Li, Pan Ren, Yanjun Wang, W. Y. Szeto
Date: 2020-11-11 00:00:00

En-route congestion causes delays in air traffic networks and will become more prominent as air traffic demand will continue to increase yet airspace volume cannot grow. However, most existing studies on flight delay modeling do not consider en-route congestion explicitly. In this study, we propose a new flight delay model, Multi-layer Air Traffic Network Delay (MATND) model, to capture the impact of en-route congestion on flight delays over an air traffic network. This model is developed by a data-driven approach, taking aircraft tracking data and flight schedules as inputs to characterize a national air traffic network, as well as a system-level model approach, modeling the delay process based on queueing theory. The two approaches combined make the network delay model a close representation of reality and easy-to-implement for what-if scenario analysis. The proposed MATND model includes 1) a data-driven method to learn a network composed of airports, en-route congestion points, and air corridors from aircraft tracking data, 2) a stochastic and dynamic queuing network model to calculate flight delays and track their propagation at both airports and in en-route congestion areas, in which the delays are computed via a space-time decomposition method. Using one month of historical aircraft tracking data over China's air traffic network, MATND is tested and shows to give an accurate quantification of delays of the national air traffic network. "What-if" scenario analyses are conducted to demonstrate how the proposed model can be used for the evaluation of air traffic network improvement strategies, where the manipulation of reality at such a scale is impossible. Results show that MATND is computationally efficient, well suited for evaluating the impact of policy alternatives on system-wide delay at a macroscopic level.
Optimal Synchronization Control for Heterogeneous Multi-Agent Systems: Online Adaptive Learning Solutions
Date: 2020-11-11 00:00:00

This paper presents an online adaptive learning solution to optimal
synchronization control problem of heterogeneous multi-agent systems via a
novel distributed policy iteration approach.
On Performance of Multiscale Sparse Fast Fourier Transform Algorithm
Authors: Bin Li, Zhikang Jiang, Jie Chen
Date: 2020-11-11 00:00:00

Computing the Sparse Fast Fourier Transform(sFFT) of a K-sparse signal of size N has emerged as a critical topic for a long time. The sFFT algorithms decrease the runtime and sampling complexity by taking advantage of the signal inherent characteristics that a large number of signals are sparse in the frequency domain(e.g., sensors, video data, audio, medical image, etc.). The first stage of sFFT is frequency bucketization through one of these filters: Dirichlet kernel filter, flat filter, aliasing filter, etc. Compared to other sFFT algorithms, the sFFT algorithms using the flat filter is more convenient and efficient because the filtered signal is concentrated both in the time domain and frequency domain. Up to now, three sFFT algorithms sFFT1.0, sFFT2.0, sFFT3.0 algorithm have been proposed by the Massachusetts Institute of Technology(MIT) in 2013. Still, the sFFT4.0 algorithm using the multiscale approach method has not been implemented yet. This paper will discuss this algorithm comprehensively in theory and implement it in practice. It is proved that the performance of the sFFT4.0 algorithm depends on two parameters. The runtime and sampling complexity are in direct ratio to the multiscale parameter and in inverse ratio to the extension parameter. The robustness is in direct ratio to the extension parameter and in inverse ratio to the multiscale parameter. Compared with three similar algorithms or other four types of algorithms, the sFFT4.0 algorithm has excellent runtime and sampling complexity that ten to one hundred times better than the fftw algorithm, although the robustness of the algorithm is medium.
Low-resource expressive text-to-speech using data augmentation
Authors: Goeric Huybrechts, Thomas Merritt, Giulia Comini, Bartek Perz, Raahil Shah, Jaime Lorenzo-Trueba
Date: 2020-11-11 00:00:00
Tasks: Data Augmentation, text-to-speech, Text to Speech, Voice Conversion

While recent neural text-to-speech (TTS) systems perform remarkably well, they typically require a substantial amount of recordings from the target speaker reading in the desired speaking style. In this work, we present a novel 3-step methodology to circumvent the costly operation of recording large amounts of target data in order to build expressive style voices with as little as 15 minutes of such recordings. First, we augment data via voice conversion by leveraging recordings in the desired speaking style from other speakers. Next, we use that synthetic data on top of the available recordings to train a TTS model. Finally, we fine-tune that model to further increase quality. Our evaluations show that the proposed changes bring significant improvements over non-augmented models across many perceived aspects of synthesised speech. We demonstrate the proposed approach on 2 styles (newscaster and conversational), on various speakers, and on both single and multi-speaker models, illustrating the robustness of our approach.
FastSVC: Fast Cross-Domain Singing Voice Conversion with Feature-wise Linear Modulation
Authors: Songxiang Liu, Yuewen Cao, Na Hu, Dan Su, Helen Meng
Date: 2020-11-11 00:00:00
Tasks: Voice Conversion

This paper presents FastSVC, a light-weight cross-domain singing voice conversion (SVC) system, which can achieve high conversion performance, with inference speed 4x faster than real-time on CPUs. FastSVC uses Conformer-based phoneme recognizer to extract singer-agnostic linguistic features from singing signals. A feature-wise linear modulation based generator is used to synthesize waveform directly from linguistic features, leveraging information from sine-excitation signals and loudness features. The waveform generator can be trained conveniently using a multi-resolution spectral loss and an adversarial loss. Experimental results show that the proposed FastSVC system, compared with a computationally heavy baseline system, can achieve comparable conversion performance in some scenarios and significantly better conversion performance in other scenarios. Moreover, the proposed FastSVC system achieves desirable cross-lingual singing conversion performance. The inference speed of the FastSVC system is 3x and 70x faster than the baseline system on GPUs and CPUs, respectively.
On Performance of Sparse Fast Fourier Transform Algorithms Using the Aliasing Filter
Authors: Bin Li, Zhikang Jiang, Jie Chen
Date: 2020-11-11 00:00:00
Tasks: compressed sensing

Computing the Sparse Fast Fourier Transform(sFFT) of a K-sparse signal of size N has emerged as a critical topic for a long time. There are mainly two stages in the sFFT: frequency bucketization and spectrum reconstruction. Frequency bucketization is equivalent to hashing the frequency coefficients into B buckets through one of these filters: Dirichlet kernel filter, flat filter, aliasing filter, etc. The spectrum reconstruction is equivalent to identifying frequencies that are isolated in their buckets. More than forty different sFFT algorithms compute Discrete Fourier Transform(DFT) by their unique methods so far. In order to use them properly, the urgent topic of great concern is how to analyze and evaluate the performance of these algorithms in theory and practice. The paper mainly discusses the sFFT Algorithms using the aliasing filter. In the first part, the paper introduces the technique of three frameworks: the one-shot framework based on the compressed sensing(CS) solver, the peeling framework based on the bipartite graph and the iterative framework based on the binary tree search. Then, we get the conclusion of the performance of six corresponding algorithms: sFFT-DT1.0, sFFT-DT2.0, sFFT-DT3.0, FFAST, R-FFAST and DSFFT algorithm in theory. In the second part, we make two categories of experiments for computing the signals of different SNR, different N, different K by a standard testing platform and record the run time, percentage of the signal sampled and L0, L1, L2 error both in the exactly sparse case and general sparse case. The result of experiments satisfies the inferences obtained in theory.
Cryo-RALib -- a modular library for accelerating alignment in cryo-EM
Authors: Szu-Chi Chung, Cheng-Yu Hung, Huei-Lun Siao, Hung-Yi Wu, Wei-Hau Chang, I-Ping Tu
Date: 2020-11-11 00:00:00
Tasks: Benchmarking, GPU, ISAC

Thanks to automated cryo-EM and GPU-accelerated processing, single-particle cryo-EM has become a rapid structure determination method that permits capture of dynamical structures of molecules in solution, which has been recently demonstrated by the determination of COVID-19 spike protein in March, shortly after its breakout in late January 2020. This rapidity is critical for vaccine development in response to emerging pandemic. This explains why a 2D classification approach based on multi-reference alignment (MRA) is not as popular as the Bayesian-based approach despite that the former has advantage in differentiating structural variations under low signal-to-noise ratio. This is perhaps because that MRA is a time-consuming process and a modular GPU-acceleration library for MRA is lacking. Here, we introduce a library called Cryo-RALib that expands the functionality of CUDA library used by GPU ISAC. It contains a GPU-accelerated MRA routine for accelerating MRA-based classification algorithms. In addition, we connect the cryo-EM image analysis with the python data science stack so as to make it easier for users to perform data analysis and visualization. Benchmarking on the TaiWan Computing Cloud (TWCC) container shows that our implementation can accelerate the computation by one order of magnitude. The library is available at https://github.com/phonchi/Cryo-RAlib.
Power Injection Attacks in Smart Distribution Grids with Photovoltaics
Authors: Martin Lindström, Hampei Sasahara, Xingkang He, Henrik Sandberg, Karl Henrik Johansson
Date: 2020-11-11 00:00:00

In order to protect smart distribution grids from intrusions, it is important to understand possible risks and impacts of attacks. We study the worst-case attack strategy of a power injection attack against the physical layer of a smart distribution grid with a high penetration of photovoltaic resources. We derive both the worst attack signal and worst attack location: The worst attack signal is a step function which switches its sign at the final stage, and the worst attack location is the node with the largest impedance to the grid substation. Numerical examples on a European benchmark model verify the developed results. Finally, both theoretical and numerical results are used to discuss feasible defense strategies against power injection attacks.
Social Diversity and Spread of Pandemic: Evidence from India
Authors: Upasak Das, Udayan Rathore, Prasenjit Sarkhel
Date: 2020-11-11 00:00:00
Tasks: Diversity

Compliance with the public health guidelines during a pandemic requires coordinated community actions which might be undermined in socially diverse areas. In this paper, we assess the relationship between caste-group diversity and the spread of COVID-19 infection during the nationwide lockdown and unlocking period in India. On the extensive margin, we find that caste-homogeneous districts systematically took more days to cross the concentration thresholds of 50 to 500 cases. Estimates on the intensive margin, using daily cases, further show that caste-homogeneous districts experienced slower growth in infection. Overall, the effects of caste-group homogeneity remained positive and statistically significant for 2.5 months (about 76 days) after the beginning of the lockdown and weakened with subsequent phases of the lockdown. The results hold even after accounting for the emergence of initial hotspots before lockdown, broader diffusion patterns through daily fixed effects, region fixed effects, and dynamic administrative response through time-variant lagged COVID-19 fatalities at the district level. These effects are not found to be confounded by differential levels of testing and underreporting of cases in some states. Consistent estimates from bias-adjusted treatment effects also ensure that our findings remain robust even after accounting for other unobservables. We find suggestive evidence of higher engagement of community health workers in caste-homogenous localities, which further increased after the lockdown. We posit this as one potential channel that can explain our results. Our findings reveal how caste-group diversity can be used to identify potential hotspots during public health emergencies and emphasize the importance of community health workers and decentralized policy response.
Selling two complementary goods
Authors: Komal Malik, Kolagani Paramahamsa
Date: 2020-11-11 00:00:00
Tasks: Vocal Bursts Valence Prediction

A seller is selling a pair of divisible complementary goods to an agent. The agent consumes the goods only in a specific ratio and freely disposes of excess in either goods. The value of the bundle and the ratio are private information of the agent. In this two-dimensional type space model, we characterize the incentive constraints and show that the optimal (expected revenue-maximizing) mechanism is a ratio-dependent posted price or a posted price mechanism for a class of distributions. We also show that the optimal mechanism is a posted price mechanism when the value and the ratio are independently distributed.
Reel Stock Analysis for an Integrated Paper Packaging Company
Authors: Constantine Goulimis, Gaston Simone
Date: 2020-11-08 00:00:00

The production of corrugated paper boxes accounts for roughly one third of the world's total paper production and, as a result of both COVID-19 and the rise of e-commerce, is a growing market. We provide a fresh approach to determining near-optimal stock policies for integrated paper companies. The new approach shows that existing policies can be improved by a significant margin. In a case study we saw a reduction in total waste by 9%, with a simultaneous decrease in logistics costs.
Multi-Frequency Canonical Correlation Analysis (MFCCA): A Generalised Decoding Algorithm for Multi-Frequency SSVEP
Authors: Jing Mu, Ying Tan, David B. Grayden, Denny Oetomo
Date: 2020-10-27 00:00:00
Tasks: Decoder, SSVEP

Stimulation methods that utilise more than one stimulation frequency have been developed for steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs) with the purpose of increasing the number of targets that can be presented simultaneously. However, there is no unified decoding algorithm that can be used without training for each individual users or cases, and applied to a large class of multi-frequency stimulated SSVEP settings. This paper extends the widely used canonical correlation analysis (CCA) decoder to explicitly accommodate multi-frequency SSVEP by exploiting the interactions between the multiple stimulation frequencies. A concept of order, defined as the sum of absolute value of the coefficients in the linear combination of the input frequencies, was introduced to assist the design of Multi-Frequency CCA (MFCCA). The probability distribution of the order in the resulting SSVEP response was then used to improve decoding accuracy. Results show that, compared to the standard CCA formulation, the proposed MFCCA has a 20% improvement in decoding accuracy on average at order 2, while keeping its generality and training-free characteristics.
Optimal Signal Processing for Common Randomness Generation over MIMO Gaussian Channels with Applications in Identification
Authors: Rami Ezzine, Wafa Labidi, Christian Deppe, Holger Boche
Date: 2020-11-11 00:00:00

Common randomness (CR), as a resource, is not commonly exploited in existing practical communication systems. In the CR generation framework, both the sender and receiver aim to generate a common random variable observable to both, ideally with low error probability. The availability of this CR allows us to implement correlated random protocols that can lead to faster and more efficient algorithms. Previous work focused on CR generation over perfect channels with limited capacity. In our work, we consider the problem of CR generation from independent and identically distributed (i.i.d.) samples of a correlated finite source with one-way communication over a Gaussian channel. We first derive the CR capacity for single-input single-output (SISO) Gaussian channels. This result is then used for the derivation of the CR capacity in the multiple-input multiple-output (MIMO) case. CR plays a key role in the identification scheme since it may allow a significant increase in the identification capacity of channels. In the identification framework, the decoder is interested in knowing \emph{whether} a specific message of special interest to him has been sent or not, rather than knowing \emph{what} the received message is. In many new applications, such as several machine-to-machine and human-to-machine systems and the tactile internet, this post-Shannon scheme is more efficient than classical transmission. In our work, we also consider a CR-assisted secure identification scheme and develop a lower bound on the corresponding secure identification capacity.
Dynamics of market states and risk assessment
Authors: Hirdesh K. Pharasi, Eduard Seligman, Suchetana Sadhukhan, Parisa Majari, Thomas H. Seligman
Date: 2020-11-10 00:00:00
Tasks: Clustering

Previous research explored various conditions of financial markets based on the similarity of correlation structures and classified as market states. We introduce modifications to previous selection criteria for these market states, mainly due to increased attention to the transition matrix between the states. Clustering and thus market states are fixed by the optimization of two parameters -- number of clusters and noise suppression, but in similar conditions, we give preference to the clustering which avoids large jumps in the transition matrix. We found statistically significant results applying this model to the SP 500 and Nikkei 225 markets for the pre-COVID-19 pandemic era (2006-2019). Retaining the epoch length of 20 trading days but reducing the shift of the epoch to a single trading day we are led to the concept of a trajectory of the market in the space of correlation matrices. We may visualize these states after dimensional scaling to two or three dimensions. This approach, using dynamics, improves the options of risk assessment, opens the door to dynamical treatments of markets (e.g. hedging), and shows noise suppression in a new light.
A WLAV-based Robust Hybrid State Estimation using Circuit-theoretic Approach
Date: 2020-11-11 00:00:00
Tasks: State Estimation

For reliable and secure power grid operation, AC state-estimation (ACSE) must
provide certain guarantees of convergence while being resilient against
bad-data. This paper develops a circuit-theoretic weighted least absolute value
(WLAV) based hybrid ACSE that satisfies these needs to overcome some of the
limitations of existing ACSE methods. Hybrid refers to the inclusion of RTU and
PMU measurement data, and the use of the LAV objective function enables
automatic rejection of bad data while providing clear identification of
suspicious measurements from the sparse residual vector. Taking advantage of
linear construction of the measurement models in circuit-theoretic approach,
the proposed hybrid SE is formulated as a LP problem with guaranteed
convergence. To address efficiency, we further develop problem-specific
heuristics for fast convergence. To validate the efficacy of the proposed
approach, we run ACSE on large cases and compare the results against WLS-based
algorithms. We further demonstrate the advantages of our solution methodology
over standard commercial LP solvers through comparison of runtime and
convergence performance.
A Two-Stage Radar Sensing Approach based on MIMO-OFDM Technology
Authors: Liang Liu, Shuowen Zhang
Date: 2020-11-12 00:00:00
Tasks: compressed sensing, Vocal Bursts Valence Prediction

Recently, integrating the communication and sensing functions into a common network has attracted a great amount of attention. This paper considers the advanced signal processing techniques for enabling the radar to sense the environment via the communication signals. Since the technologies of orthogonal frequency division multiplexing (OFDM) and multiple-input multiple-output (MIMO) are widely used in the legacy cellular systems, this paper proposes a two-stage signal processing approach for radar sensing in an MIMO-OFDM system, where the scattered channels caused by various targets are estimated in the first stage, and the location information of the targets is then extracted from their scattered channels in the second stage. Specifically, based on the observations that radar sensing is similar to multi-path communication in the sense that different targets scatter the signal sent by the radar transmitter to the radar receiver with various delay, and that the number of scatters is limited, we show that the OFDM-based channel training approach together with the compressed sensing technique can be utilized to estimate the scattered channels efficiently in Stage I. Moreover, to tackle the challenge arising from range resolution for sensing the location of closely spaced targets, we show that the MIMO radar technique can be leveraged in Stage II such that the radar has sufficient spatial samples to even detect the targets in close proximity based on their scattered channels. Last, numerical examples are provided to show the effectiveness of our proposed sensing approach which merely relies on the existing MIMO-OFDM communication techniques.
Hamiltonian Dynamics of Saturated Elongation in Amyloid Fiber Formation
Authors: Liu Hong, Xizhou Liu, Thomas C. T. Michaels, Tuomas P. J. Knowles
Date: 2020-11-12 00:00:00
Tasks: valid

Elongation is a fundament process in amyloid fiber growth, which is normally characterized by a linear relationship between the fiber elongation rate and the monomer concentration. However, in high concentration regions, a sub-linear dependence was often observed, which could be explained by a universal saturation mechanism. In this paper, we modeled the saturated elongation process through a Michaelis-Menten like mechanism, which is constituted by two sub-steps -- unspecific association and dissociation of a monomer with the fibril end, and subsequent conformational change of the associated monomer to fit itself to the fibrillar structure. Typical saturation concentrations were found to be $7-70\mu M$ for A$\beta$40, $\alpha$-synuclein and etc. Furthermore, by using a novel Hamiltonian formulation, analytical solutions valid for both weak and strong saturated conditions were constructed and applied to the fibrillation kinetics of $\alpha$-synuclein and silk fibroin.