pwc-archive/papers-with-abstracts huggingface dataset:
Koopman Mode Decomposition of Oscillatory Temperature Field inside a Room
Authors: Naoto Hiramatsu, Yoshihiko Susuki, Atsushi Ishigame
Date: 2020-08-27 00:00:00
Tasks: Time Series, Time Series Analysis

Koopman mode decomposition (KMD) is a technique of nonlinear time-series analysis capable of decomposing data on complex spatio temporal dynamics into multiple modes oscillating with single frequencies, called the Koopman modes (KMs). We apply KMD to measurement data on oscillatory dynamics of a temperature field inside a room that is a complex phenomenon ubiquitous in our daily lives and has a clear technological motivation in energy-efficient air conditioning. To characterize not only the oscillatory field (scalar field) but also associated heat flux (vector field), we introduce the notion of a temperature gradient using the spatial gradient of a KM. By estimating the temperature gradient directly from data, we show that KMD is capable of extracting a distinct structure of the heat flux embedded in the oscillatory temperature field, relevant in terms of air conditioning.
Synchronized Attachment and the Darwinian Evolution of Coronaviruses CoV-1 and CoV-2
Authors: J. C. Phillips
Date: 2020-08-27 00:00:00

CoV2019 has evolved to be much more dangerous than CoV2003. Experiments suggest that structural rearrangements dramatically enhance CoV2019 activity. We identify a new first stage of infection which precedes structural rearrangements by using biomolecular evolutionary theory to identify sequence differences enhancing viral attachment rates. We find a small cluster of mutations which show that CoV-2 has a new feature that promotes much stronger viral attachment and enhances contagiousness. The extremely dangerous dynamics of human coronavirus infection is a dramatic example of evolutionary approach of self-organized networks to criticality. It may favor a very successful vaccine. The identified mutations can be used to test the present theory experimentally.
On Compatibility and Region of Attraction for Safe, Stabilizing Control Laws
Authors: Wenceslao Shaw Cortez, Dimos V. Dimarogonas
Date: 2020-08-27 00:00:00

A novel control method is proposed to ensure compatibility of safe, stabilizing control laws, i.e., simultaneous satisfaction of asymptotic stability and constraint satisfaction for nonlinear affine systems. The results are dependent on an asymptotically stabilizing control law and a zeroing control barrier function (ZCBF), but do not require a control Lyapunov function (CLF). Sufficient conditions for a region of attraction are defined for which the proposed control safely stabilizes the system, while preserving the decrescent Lyapunov function of the original stabilizing control law. The proposed methodology for nonlinear affine systems requires checking conditions of the system dynamics over a substantial portion of the state space, which may be computationally expensive. To facilitate the search for compatibility, we extend the results to a class of nonlinear systems including mechanical systems for which a novel controller is designed to guarantee passivity, safety, and stability. Numerical examples are used to demonstrate the proposed technique.
HoloGen: An open source toolbox for high-speed hologram generation
Authors: Peter J. Christopher, Andrew Kadis, George S. D. Gordon, Timothy D. Wilkinson
Date: 2020-08-24 00:00:00
Tasks: 3D Holography, Benchmarking, Mixed Reality, Retrieval, Vocal Bursts Intensity Prediction

The rise of mixed reality systems such as Microsoft HoloLens has prompted an increase in interest in the fields of 2D and 3D holography. Already applied in fields including telecommunications, imaging, projection, lithography, beam shaping and optical tweezing, Computer Generated Holography (CGH) offers an exciting approach to a wide range of light shaping problems. The numerical processing required to generate a hologram is high and requires significant domain expertise. This has historically slowed the adoption of holographic techniques in emerging fields. In this paper we present HoloGen, an open-source Cuda C and C ++ framework for computer generated holography. HoloGen unites, for the first time, a wide array of existing hologram generation algorithms with state of the art performance while attempting to remain intuitive and easy to use. This is enabled by a C # and Windows Presentation Framework (WPF) graphical user interface (GUI). A novel reflection based parameter hierarchy is used to ensure ease of modification. Extensive use of C ++ templates based on the Standard Template Library (STL), compile time flexibility is preserved while maintaining runtime performance. The current release of HoloGen unites implementations of well known generation algorithms including Gerchberg-Saxton (GS), Liu-Taghizadeh (LT), Direct Search (DS), Simulated Annealing (SA) and One-Step Phase-Retrieval (OSPR) with less known specialist variants including Weighted GS and Adaptive OSPR. Benchmarking results are presented for several key algorithms. The software is freely available under an MIT license.
Length- and Noise-aware Training Techniques for Short-utterance Speaker Recognition
Date: 2020-08-27 00:00:00
Tasks: Representation Learning, Speaker Recognition

Speaker recognition performance has been greatly improved with the emergence
of deep learning. Deep neural networks show the capacity to effectively deal
with impacts of noise and reverberation, making them attractive to far-field
speaker recognition systems. The x-vector framework is a popular choice for
generating speaker embeddings in recent literature due to its robust training
mechanism and excellent performance in various test sets. In this paper, we
start with early work on including invariant representation learning (IRL) to
the loss function and modify the approach with centroid alignment (CA) and
length variability cost (LVC) techniques to further improve robustness in
noisy, far-field applications. This work mainly focuses on improvements for
short-duration test utterances (1-8s). We also present improved results on
long-duration tasks. In addition, this work discusses a novel self-attention
mechanism. On the VOiCES far-field corpus, the combination of the proposed
techniques achieves relative improvements of 7.0% for extremely short and 8.2%
for full-duration test utterances on equal error rate (EER) over our baseline
system.
Market-making with reinforcement-learning (SAC)
Authors: Alexey Bakshaev
Date: 2020-08-27 00:00:00
Tasks: Position, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The paper explores the application of a continuous action space soft actor-critic (SAC) reinforcement learning model to the area of automated market-making. The reinforcement learning agent receives a simulated flow of client trades, thus accruing a position in an asset, and learns to offset this risk by either hedging at simulated "exchange" spreads or by attracting an offsetting client flow by changing offered client spreads (skewing the offered prices). The question of learning minimum spreads that compensate for the risk of taking the position is being investigated. Finally, the agent is posed with a problem of learning to hedge a blended client trade flow resulting from independent price processes (a "portfolio" position). The position penalty method is introduced to improve the convergence. An Open-AI gym-compatible hedge environment is introduced and the Open AI SAC baseline RL engine is being used as a learning baseline.
Link Budget Analysis for Reconfigurable Smart Surfaces in Aerial Platforms
Authors: Safwan Alfattani, Wael Jaafar, Yassine Hmamouche, Halim Yanikomeroglu, Abbas Yongaçoglu
Date: 2020-08-27 00:00:00

Non-terrestrial networks, including Unmanned Aerial Vehicles (UAVs), High Altitude Platform Station (HAPS) and Low Earth Orbiting (LEO) satellites, are expected to have a pivotal role in the sixth generation wireless networks. With their inherent features such as flexible placement, wide footprint, and preferred channel conditions, they can tackle several challenges in current terrestrial networks However, their successful and widespread adoption relies on energy-efficient on-board communication systems. In this context, the integration of Reconfigurable Smart Surfaces (RSS) into aerial platforms is envisioned as a key enabler of energy-efficient and cost-effective deployments of aerial platforms. Indeed, RSS consist of low-cost reflectors capable of smartly directing signals in a nearly passive way. We investigate in this paper the link budget of RSS-assisted communications under the two discussed RSS reflection paradigms in the literature, namely the specular and the scattering reflection paradigm types. Specifically, we analyze the characteristics of RSS-equipped aerial platforms and compare their communication performance with that of RSS-assisted terrestrial networks, using standardized channel models. In addition, we derive the optimal aerial platforms placements under both reflection paradigms. The obtained results provide important insights for the design of RSS-assisted communications. For instance, given that a HAPS has a large RSS surface, it provides superior link budget performance in most studied scenarios. In contrast, the limited RSS area on UAVs and the large propagation loss in LEO satellite communications make them unfavorable candidates for supporting terrestrial users. Finally, the optimal location of the RSS-equipped platform may depend on the platform's altitude, coverage footprint, and type of environment.
Structured Autocorrelation Matrix Estimation for Coprime Arrays
Authors: Dimitris G. Chachlakis, Panos P. Markopoulos
Date: 2020-08-27 00:00:00
Tasks: Direction of Arrival Estimation

A coprime array receiver processes a collection of received-signal snapshots to estimate the autocorrelation matrix of a larger (virtual) uniform linear array, known as coarray. By the received-signal model, this matrix has to be (i) Positive-Definite, (ii) Hermitian, (iii) Toeplitz, and (iv) its noise-subspace eigenvalues have to be equal. Existing coarray autocorrelation matrix estimates satisfy a subset of the above conditions. In this work, we propose an optimization framework which offers a novel estimate satisfying all four conditions. Numerical studies illustrate that the proposed estimate outperforms standard counterparts, both in autocorrelation matrix estimation error and Direction-of-Arrival estimation.
Efficient closed-form estimation of large spatial autoregressions
Authors: Abhimanyu Gupta
Date: 2020-08-27 00:00:00
Tasks: Form

Newton-step approximations to pseudo maximum likelihood estimates of spatial autoregressive models with a large number of parameters are examined, in the sense that the parameter space grows slowly as a function of sample size. These have the same asymptotic efficiency properties as maximum likelihood under Gaussianity but are of closed form. Hence they are computationally simple and free from compactness assumptions, thereby avoiding two notorious pitfalls of implicitly defined estimates of large spatial autoregressions. For an initial least squares estimate, the Newton step can also lead to weaker regularity conditions for a central limit theorem than those extant in the literature. A simulation study demonstrates excellent finite sample gains from Newton iterations, especially in large multiparameter models for which grid search is costly. A small empirical illustration shows improvements in estimation precision with real data.
Consensus for Clusters of Agents with Cooperative and Antagonistic Relationships
Date: 2020-08-27 00:00:00

In this paper we address the consensus problem in the context of networked
agents whose communication graph can be split into a certain number of clusters
in such a way that interactions between agents in the same clusters are
cooperative, while interactions between agents belonging to different clusters
are antagonistic. This problem set-up arises in the context of social networks
and opinion dynamics, where reaching consensus means that the opinions of the
agents in the same cluster converge to the same decision. The consensus problem
is here investigated under the assumption that agents in the same cluster have
the same constant and pre-fixed amount of trust (/distrust) to be distributed
among their cooperators (/adversaries). The proposed solution establishes how
much agents in the same group must be conservative about their opinions in
order to converge to a common decision.
Design and implementation in USRP of a preamble-based synchronizer for OFDM systems
Authors: Authors, :, José Herrera-Bustamante, Vanessa Rodríguez-Ludeña, A. G. Correa-Mena, Diego Barragán-Guerrero
Date: 2020-08-27 00:00:00

The Orthogonal Frequency Division Multiplexing (OFDM) is one of the most widely adopted schemes in wireless technologies such as Wi-Fi and LTE due to its high transmission rates, and the robustness against Intersymbol Interference (ISI). However, OFDM is highly sensitive to synchronism errors, which affects the orthogonality of the carriers. We analyzed several synchronization algorithms based on the correlation of the preamble symbols through the implementation in Software-Defined Radio (SDR) using the Universal Software Radio Peripheral (USRP). Such an implementation was performed in three stages: frame detection, comparing the autocorrelation output and the average power of the received signal; time synchronism, where the cross-correlation based on the short and long preamble symbols was implemented; and the frequency synchronism, where the Carrier Frequency Offset (CFO) added by the channel was detected and corrected. The synchronizer performance was verified through the USRP implementation. The results serve as a practical guide to selecting the optimal synchronism scheme and show the versatility of the USRP to implement digital communication systems efficiently.
Text-Conditioned Transformer for Automatic Pronunciation Error Detection
Authors: Zhan Zhang, Yuehai Wang, Jianyi Yang
Date: 2020-08-28 00:00:00
Tasks: Decoder

Automatic pronunciation error detection (APED) plays an important role in the domain of language learning. As for the previous ASR-based APED methods, the decoded results need to be aligned with the target text so that the errors can be found out. However, since the decoding process and the alignment process are independent, the prior knowledge about the target text is not fully utilized. In this paper, we propose to use the target text as an extra condition for the Transformer backbone to handle the APED task. The proposed method can output the error states with consideration of the relationship between the input speech and the target text in a fully end-to-end fashion.Meanwhile, as the prior target text is used as a condition for the decoder input, the Transformer works in a feed-forward manner instead of autoregressive in the inference stage, which can significantly boost the speed in the actual deployment. We set the ASR-based Transformer as the baseline APED model and conduct several experiments on the L2-Arctic dataset. The results demonstrate that our approach can obtain 8.4\% relative improvement on the $F_1$ score metric.
Pricing and Capital Allocation for Multiline Insurance Firms With Finite Assets in an Imperfect Market
Authors: John A. Major, Stephen J. Mildenhall
Date: 2020-08-28 00:00:00

We analyze multiline pricing and capital allocation in equilibrium no-arbitrage markets. Existing theories often assume a perfect complete market, but when pricing is linear, there is no diversification benefit from risk pooling and therefore no role for insurance companies. Instead of a perfect market, we assume a non-additive distortion pricing functional and the principle of equal priority of payments in default. Under these assumptions, we derive a canonical allocation of premium and margin, with properties that merit the name the natural allocation. The natural allocation gives non-negative margins to all independent lines for default-free insurance but can exhibit negative margins for low-risk lines under limited liability. We introduce novel conditional expectation measures of relative risk within a portfolio and use them to derive simple, intuitively appealing expressions for risk margins and capital allocations. We give a unique capital allocation consistent with our law invariant pricing functional. Such allocations produce returns that vary by line, in contrast to many other approaches. Our model provides a bridge between the theoretical perspective that there should be no compensation for bearing diversifiable risk and the empirical observation that more risky lines fetch higher margins relative to subjective expected values.
Strategies to integrate multi-omics data for patient survival prediction
Authors: Lana X Garmire
Date: 2020-08-28 00:00:00
Tasks: Survival Prediction

Genomics, especially multi-omics, has made precision medicine feasible. The completion and publicly accessible multi-omics resource with clinical outcome, such as The Cancer Genome Atlas (TCGA) is a great test bed for developing computational methods that integrate multi-omics data to predict patient cancer phenotypes. We have been utilizing TCGA multi-omics data to predict cancer patient survival, using a variety of approaches, including prior-biological knowledge (such as pathways), and more recently, deep-learning methods. Over time, we have developed methods such as Cox-nnet, DeepProg, and two-stage Cox-nnet, to address the challenges due to multi-omics and multi-modality. Despite the limited sample size (hundreds to thousands) in the training datasets as well as the heterogeneity nature of human populations, these methods have shown significance and robustness at predicting patient survival in independent population cohorts. In the following, we would describe in detail these methodologies, the modeling results, and important biological insights revealed by these methods.
Layoffs, Inequity and COVID-19: A Longitudinal Study of the Journalism Jobs Crisis in Australia from 2012 to 2020
Authors: Nik Dawson, Sacha Molitorisz, Marian-Andrei Rizoiu, Peter Fray
Date: 2020-08-28 00:00:00

In Australia and beyond, journalism is reportedly an industry in crisis, a crisis exacerbated by COVID-19. However, the evidence revealing the crisis is often anecdotal or limited in scope. In this unprecedented longitudinal research, we draw on data from the Australian journalism jobs market from January 2012 until March 2020. Using Data Science and Machine Learning techniques, we analyse two distinct data sets: job advertisements (ads) data comprising 3,698 journalist job ads from a corpus of over 8 million Australian job ads; and official employment data from the Australian Bureau of Statistics. Having matched and analysed both sources, we address both the demand for and supply of journalists in Australia over this critical period. The data show that the crisis is real, but there are also surprises. Counter-intuitively, the number of journalism job ads in Australia rose from 2012 until 2016, before falling into decline. Less surprisingly, for the entire period studied the figures reveal extreme volatility, characterised by large and erratic fluctuations. The data also clearly show that COVID-19 has significantly worsened the crisis. We then tease out more granular findings, including: that there are now more women than men journalists in Australia, but that gender inequity is worsening, with women journalists getting younger and worse-paid just as men journalists are, on average, getting older and better-paid; that, despite the crisis besetting the industry, the demand for journalism skills has increased; and that, perhaps concerningly, the skills sought by journalism job ads increasingly include social media and generalist communications.
Classification of Imagined Speech Using Siamese Neural Network
Authors: Dong-Yeon Lee, Minji Lee, Seong-Whan Lee
Date: 2020-08-28 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), General Classification

Imagined speech is spotlighted as a new trend in the brain-machine interface due to its application as an intuitive communication tool. However, previous studies have shown low classification performance, therefore its use in real-life is not feasible. In addition, no suitable method to analyze it has been found. Recently, deep learning algorithms have been applied to this paradigm. However, due to the small amount of data, the increase in classification performance is limited. To tackle these issues, in this study, we proposed an end-to-end framework using Siamese neural network encoder, which learns the discriminant features by considering the distance between classes. The imagined words (e.g., arriba (up), abajo (down), derecha (right), izquierda (left), adelante (forward), and atr\'as (backward)) were classified using the raw electroencephalography (EEG) signals. We obtained a 6-class classification accuracy of 31.40% for imagined speech, which significantly outperformed other methods. This was possible because the Siamese neural network, which increases the distance between dissimilar samples while decreasing the distance between similar samples, was used. In this regard, our method can learn discriminant features from a small dataset. The proposed framework would help to increase the classification performance of imagined speech for a small amount of data and implement an intuitive communication system.
Decoding Visual Recognition of Objects from EEG Signals based on Attention-Driven Convolutional Neural Network
Authors: Jenifer Kalafatovich, Minji Lee, Seong-Whan Lee
Date: 2020-08-28 00:00:00
Tasks: EEG, Electroencephalogram (EEG), General Classification

The ability to perceive and recognize objects is fundamental for the interaction with the external environment. Studies that investigate them and their relationship with brain activity changes have been increasing due to the possible application in an intuitive brain-machine interface (BMI). In addition, the distinctive patterns when presenting different visual stimuli that make data differentiable enough to be classified have been studied. However, reported classification accuracy still low or employed techniques for obtaining brain signals are impractical to use in real environments. In this study, we aim to decode electroencephalography (EEG) signals depending on the provided visual stimulus. Subjects were presented with 72 photographs belonging to 6 different semantic categories. We classified 6 categories and 72 exemplars according to visual stimuli using EEG signals. In order to achieve a high classification accuracy, we proposed an attention driven convolutional neural network and compared our results with conventional methods used for classifying EEG signals. We reported an accuracy of 50.37% and 26.75% for 6-class and 72-class, respectively. These results statistically outperformed other conventional methods. This was possible because of the application of the attention network using human visual pathways. Our findings showed that EEG signals are possible to differentiate when subjects are presented with visual stimulus of different semantic categories and at an exemplar-level with a high classification accuracy; this demonstrates its viability to be applied it in a real-world BMI.