pwc-archive/papers-with-abstracts huggingface dataset:
Learning Explicit Prosody Models and Deep Speaker Embeddings for Atypical Voice Conversion
Authors: Disong Wang, Songxiang Liu, Lifa Sun, Xixin Wu, Xunying Liu, Helen Meng
Date: 2020-11-03 00:00:00
Tasks: speech-recognition, Speech Recognition, Voice Conversion

Though significant progress has been made for the voice conversion (VC) of typical speech, VC for atypical speech, e.g., dysarthric and second-language (L2) speech, remains a challenge, since it involves correcting for atypical prosody while maintaining speaker identity. To address this issue, we propose a VC system with explicit prosodic modelling and deep speaker embedding (DSE) learning. First, a speech-encoder strives to extract robust phoneme embeddings from atypical speech. Second, a prosody corrector takes in phoneme embeddings to infer typical phoneme duration and pitch values. Third, a conversion model takes phoneme embeddings and typical prosody features as inputs to generate the converted speech, conditioned on the target DSE that is learned via speaker encoder or speaker adaptation. Extensive experiments demonstrate that speaker adaptation can achieve higher speaker similarity, and the speaker encoder based conversion model can greatly reduce dysarthric and non-native pronunciation patterns with improved speech intelligibility. A comparison of speech recognition results between the original dysarthric speech and converted speech show that absolute reduction of 47.6% character error rate (CER) and 29.3% word error rate (WER) can be achieved.
Guided Nonlocal Means Estimation of Polarimetric Covariance for Canopy State Classification
Authors: Jørgen A. Agersborg, Stian Normann Anfinsen, Jane Uhd Jepsen
Date: 2021-06-17 00:00:00

We have developed a nonlocal algorithm for estimating polarimetric synthetic aperture radar (PolSAR) covariance matrices on single-look complex (SLC) format resolution. The algorithm is inspired by recent work with guided nonlocal means (NLM) speckle filtering, where a co-registered optical image is used to aid the filtering. Based on patch-wise dissimilarities in the SAR and optical domains we set the weights used for the nonlocal average of the outer product of the lexicographic target vectors which form the estimate. By use of this method we show that the estimated covariance matrices preserve the local structure better than previous filtering methods and improve the separation of live from defoliated and dead forest. The detail preserving nature of the algorithm also means that it can be applicable in other settings where preserving the SLC format resolution is necessary.
An Endless Optical Phase Delay for Phase Synchronization in High-Capacity DCIs
Authors: Rakesh Ashok, Sana Naaz, Shalabh Gupta
Date: 2021-06-17 00:00:00

In this work, we propose and demonstrate a module to linearly add an arbitrary amount of continuous (reset-free) phase delay to an optical signal. The proposed endless optical phase delay (EOPD) uses an optical IQ modulator and control electronics (CE) to add the desired amount of phase delay that can continuously increase with time. In order to adjust for the bias voltages and control voltage amplitudes in the EOPD, some of which may be time varying, a multivariate gradient descent algorithm is used. The EOPD has been demonstrated experimentally, and its use in a high-capacity data center interconnect (DCI) application has been outlined in this letter. The EOPD may find its use in many other applications that require precise phase/frequency adjustments in real-time.
Deep Reinforcement Learning Based Optimization for IRS Based UAV-NOMA Downlink Networks
Authors: Shiyu Jiao, Ximing Xie, Zhiguo Ding, Fellow, IEEE
Date: 2021-06-17 00:00:00
Tasks: Deep Reinforcement Learning, Position, reinforcement-learning, Reinforcement Learning (RL)

This paper investigates the application of deep deterministic policy gradient (DDPG) to intelligent reflecting surface (IRS) based unmanned aerial vehicles (UAV) assisted non-orthogonal multiple access (NOMA) downlink networks. The deployment of the UAV equipped with an IRS is important, as the UAV increases the flexibility of the IRS significantly, especially for the case of users who have no line of sight (LoS) path to the base station (BS). Therefore, the aim of this letter is to maximize the sum rate by jointly optimizing the power allocation of the BS, the phase shifting of the IRS and the horizontal position of the UAV. Because the formulated problem is not convex, the DDPG algorithm is utilized to solve it. The computer simulation results are provided to show the superior performance of the proposed DDPG based algorithm.
A Reinforcement Learning Approach for an IRS-assisted NOMA Network
Authors: Ximing Xie, Shiyu Jiao, Zhiguo Ding
Date: 2021-06-17 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This letter investigates a sum rate maximizationproblem in an intelligent reflective surface (IRS) assisted non-orthogonal multiple access (NOMA) downlink network. Specif-ically, the sum rate of all the users is maximized by jointlyoptimizing the beams at the base station and the phase shiftat the IRS. The deep reinforcement learning (DRL), which hasachieved massive successes, is applied to solve this sum ratemaximization problem. In particular, an algorithm based on thedeep deterministic policy gradient (DDPG) is proposed. Both therandom channel case and the fixed channel case are studied inthis letter. The simulation result illustrates that the DDPG basedalgorithm has the competitive performance on both case.
A factor graph EM algorithm for inference of kinetic microstates from patch clamp measurements
Authors: Alexander S. Moffett, Guiying Cui, Peter J. Thomas, William D. Hunt, Nael A. McCarty, Ryan S. Westafer, Andrew W. Eckford
Date: 2021-06-17 00:00:00
Tasks: parameter estimation

We derive a factor graph EM (FGEM) algorithm, a technique that permits combined parameter estimation and statistical inference, to determine hidden kinetic microstates from patch clamp measurements. Using the cystic fibrosis transmembrane conductance regulator (CFTR) and nicotinic acetylcholine receptor (nAChR) as examples, we perform {\em Monte Carlo} simulations to demonstrate the performance of the algorithm. We show that the performance, measured in terms of the probability of estimation error, approaches the theoretical performance limit of maximum {\em a posteriori} estimation. Moreover, the algorithm provides a reliability score for its estimates, and we demonstrate that the score can be used to further improve the performance of estimation. We use the algorithm to estimate hidden kinetic states in lab-obtained CFTR single channel patch clamp traces.
Precoding Design for Multi-user MIMO Systems with Delay-Constrained and -Tolerant Users
Authors: Minsu Kim, Jeonghun Park, Jemin Lee
Date: 2021-06-17 00:00:00

In both academia and industry, multi-user multiple-input multiple-output (MU-MIMO) techniques have shown enormous gains in spectral efficiency by exploiting spatial degrees of freedom. So far, an underlying assumption in most of the existing MU-MIMO design has been that all the users use infinite blocklength, so that they can achieve the Shannon capacity. This setup, however, is not suitable considering delay-constrained users whose blocklength tends to be finite. In this paper, we consider a heterogeneous setting in MU-MIMO systems where delay-constrained users and delay-tolerant users coexist, called a DCTU-MIMO network. To maximize the sum spectral efficiency in this system, we present the spectral efficiency for delay-tolerant users and provide a lower bound of the spectral efficiency for delay-constrained users. We consider an optimization problem that maximizes the sum spectral efficiency of delay-tolerant users while satisfying the latency constraint of delay-constrained users, and propose a generalized power iteration (GPI) precoding algorithm that finds a principal precoding vector. Furthermore, we extend a DCTU-MIMO network to the multiple time slots scenario and propose a recursive generalized power iteration precoding algorithm. In simulation results, we prove proposed methods outperform baseline schemes and present the effect of network parameters on the ergodic sum spectral efficiency.
Trading with the Crowd
Authors: Eyal Neuman, Moritz Voß
Date: 2021-06-17 00:00:00

We formulate and solve a multi-player stochastic differential game between financial agents who seek to cost-efficiently liquidate their position in a risky asset in the presence of jointly aggregated transient price impact, along with taking into account a common general price predicting signal. The unique Nash-equilibrium strategies reveal how each agent's liquidation policy adjusts the predictive trading signal to the aggregated transient price impact induced by all other agents. This unfolds a quantitative relation between trading signals and the order flow in crowded markets. We also formulate and solve the corresponding mean field game in the limit of infinitely many agents. We prove that the equilibrium trading speed and the value function of an agent in the finite $N$-player game converges to the corresponding trading speed and value function in the mean field game at rate $O(N^{-2})$. In addition, we prove that the mean field optimal strategy provides an approximate Nash-equilibrium for the finite-player game.
Implementing Permutations in the Brain and SVO Frequencies of Languages
Authors: Denis Turcu, Christos Papadimitriou
Date: 2021-06-17 00:00:00
Tasks: Sentence

The subject-verb-object (SVO) word order prevalent in English is shared by about $42\%$ of world languages. Another $45\%$ of all languages follow the SOV order, $9\%$ the VSO order, and fewer languages use the three remaining permutations. None of the many extant explanations of this phenomenon take into account the difficulty of implementing these permutations in the brain. We propose a plausible model of sentence generation inspired by the recently proposed Assembly Calculus framework of brain function. Our model results in a natural explanation of the uneven frequencies. Estimating the parameters of this model yields predictions of the relative difficulty of dis-inhibiting one brain area from another. Our model is based on the standard syntax tree, a simple binary tree with three leaves. Each leaf corresponds to one of the three parts of a basic sentence. The leaves can be activated through lock and unlock operations and the sequence of activation of the leaves implements a specific word order. More generally, we also formulate and algorithmically solve the problems of implementing a permutation of the leaves of any binary tree, and of selecting the permutation that is easiest to implement on a given binary tree.
Unsupervised classification of cell imaging data using the quantization error in a Self Organizing Map
Authors: Birgitta Dresp-Langley, JM Wandeto
Date: 2021-06-17 00:00:00
Tasks: Quantization, Sensitivity

This study exploits previously demonstrated properties such as sensitivity to the spatial extent and the intensity of local image contrast of the quantization error in the output of a Self Organizing Map (SOM QE). Here, the SOM QE is applied to double color staining based cell viability data in 96 image simulations. The results show that the SOM QE consistently and in only a few seconds detects fine regular spatial increases in relative amounts of RED or GREEN pixel staining across the test images, reflecting small, systematic increases or decreases in the percentage of theoretical cell viability below the critical threshold. Such small changes may carry clinical significance, but are almost impossible to detect by human vision. Moreover, we demonstrate a clear sensitivity of the SOM QE to differences in the relative physical luminance (Y) of the colors, which here translates into a RED GREEN color selectivity. Across differences in relative luminance, the SOM QE exhibits consistently greater sensitivity to the smallest spatial increases in RED image pixels compared with smallest increases of identical spatial extents in GREEN image pixels. Further selective color contrast studies on simulations of biological imaging data will allow generating increasingly larger benchmark datasets and, ultimately, unravel the full potential of fast, economic, and unprecedentedly precise biological data analysis using the SOM QE.
Market Complete Option Valuation using a Jarrow-Rudd Pricing Tree with Skewness and Kurtosis
Authors: Yuan Hu, Abootaleb Shirvani, W. Brent Lindquist, Frank J. Fabozzi, Svetlozar T. Rachev
Date: 2021-06-16 00:00:00

Applying the Cherny-Shiryaev-Yor invariance principle, we introduce a generalized Jarrow-Rudd (GJR) option pricing model with uncertainty driven by a skew random walk. The GJR pricing tree exhibits skewness and kurtosis in both the natural and risk-neutral world. We construct implied surfaces for the parameters determining the GJR tree. Motivated by Merton's pricing tree incorporating transaction costs, we extend the GJR pricing model to include a hedging cost. We demonstrate ways to fit the GJR pricing model to a market driver that influences the price dynamics of the underlying asset. We supplement our findings with numerical examples.
Diversified reward-risk parity in portfolio construction
Authors: Jaehyung Choi, Hyangju Kim, Young Shin Kim
Date: 2021-06-16 00:00:00

We introduce diversified risk parity embedded with various reward-risk measures and more generic allocation rules for portfolio construction. We empirically test the proposed reward-risk parity strategies and compare their performance with an equally-weighted risk portfolio in various asset universes. The reward-risk parity strategies we tested exhibit consistent outperformance evidenced by higher average returns, Sharpe ratios, and Calmar ratios. The alternative allocations also reflect less downside risks in Value-at-Risk, conditional Value-at-Risk, and maximum drawdown. In addition to the enhanced performance and reward-risk profile, transaction costs can be reduced by lowering turnover rates. The diversified reward-risk parity allocations gain superior performance in the Carhart four-factor analysis.
Topic Modeling and Progression of American Digital News Media During the Onset of the COVID-19 Pandemic
Authors: Xiangpeng Wan, Michael C. Lucic, Hakim Ghazzai, Yehia Massoud
Date: 2021-05-25 00:00:00
Tasks: Articles, Community Detection

Currently, the world is in the midst of a severe global pandemic, which has affected all aspects of people's lives. As a result, there is a deluge of COVID-related digital media articles published in the United States, due to the disparate effects of the pandemic. This large volume of information is difficult to consume by the audience in a reasonable amount of time. In this paper, we develop a Natural Language Processing (NLP) pipeline that is capable of automatically distilling various digital articles into manageable pieces of information, while also modelling the progression topics discussed over time in order to aid readers in rapidly gaining holistic perspectives on pressing issues (i.e., the COVID-19 pandemic) from a diverse array of sources. We achieve these goals by first collecting a large corpus of COVID-related articles during the onset of the pandemic. After, we apply unsupervised and semi-supervised learning procedures to summarize articles, then cluster them based on their similarities using the community detection methods. Next, we identify the topic of each cluster of articles using the BART algorithm. Finally, we provide a detailed digital media analysis based on the NLP-pipeline outputs and show how the conversation surrounding COVID-19 evolved over time.
Conference proceedings KI4Industry AI for SMEs -- The online congress for practical entry into AI for SMEs
Authors: Michael Arnemann, Per Olof Beckemeier, Thomas Bertram, Michael Eder, Maximilian Erschig, Matthias Feiner, Francisco Javier Fernandez Garcia, Frederic Foerster, Ruediger Haas, Martin Kipfmueller, Jan Kotschenreuther, Bernd Langer, Ivan Lozada Rodriguez, Thomas Meibert, Simon Ottenhaus, Stefan Paschek, Lars Pfotzer, Michael M. Roth, Tim Schanz, Philip Scherer, Janine Schwienke, Martin Simon, Robin Tenscher-Philipp
Date: 2021-06-14 00:00:00

The Institute of Materials and Processes, IMP, of the University of Applied Sciences in Karlsruhe, Germany in cooperation with VDI Verein Deutscher Ingenieure e.V, AEN Automotive Engineering Network and their cooperation partners present their competences of AI-based solution approaches in the production engineering field. The online congress KI 4 Industry on November 12 and 13, 2020, showed what opportunities the use of artificial intelligence offers for medium-sized manufacturing companies, SMEs, and where potential fields of application lie. The main purpose of KI 4 Industry is to increase the transfer of knowledge, research and technology from universities to small and medium-sized enterprises, to demystify the term AI and to encourage companies to use AI-based solutions in their own value chain or in their products.
Causal inference using the algorithmic Markov condition
Authors: Dominik Janzing, Bernhard Schoelkopf
Date: 2008-04-23 00:00:00
Tasks: Causal Inference

Inferring the causal structure that links n observables is usually based upon detecting statistical dependences and choosing simple graphs that make the joint measure Markovian. Here we argue why causal inference is also possible when only single observations are present. We develop a theory how to generate causal graphs explaining similarities between single objects. To this end, we replace the notion of conditional stochastic independence in the causal Markov condition with the vanishing of conditional algorithmic mutual information and describe the corresponding causal inference rules. We explain why a consistent reformulation of causal inference in terms of algorithmic complexity implies a new inference principle that takes into account also the complexity of conditional probability densities, making it possible to select among Markov equivalent causal graphs. This insight provides a theoretical foundation of a heuristic principle proposed in earlier work. We also discuss how to replace Kolmogorov complexity with decidable complexity criteria. This can be seen as an algorithmic analog of replacing the empirically undecidable question of statistical independence with practical independence tests that are based on implicit or explicit assumptions on the underlying distribution.
Multi-Image Super-Resolution for Remote Sensing using Deep Recurrent Networks
Authors: Md Rifat Arefin, Vincent Michalski, Pierre-Luc St-Charles, Alfredo Kalaitzis, Sookyung Kim, Samira E. Kahou, Yoshua Bengio
Date: 2020-07-28 00:00:00
Proceeding: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2020 7
Tasks: Decoder, Earth Observation, Image Super-Resolution, Super-Resolution

High-resolution satellite imagery is critical for various earth observation applications related to environment monitoring, geoscience, forecasting, and land use analysis. However, the acquisition cost of such high-quality imagery due to the scarcity of providers and needs for high-frequency revisits restricts its accessibility in many fields. In this work, we present a data-driven, multi-image super resolution approach to alleviate these problems. Our approach is based on an end-to-end deep neural network that consists of an encoder, a fusion module, and a decoder. The encoder extracts co-registered highly efficient feature representations from low-resolution images of a scene. A Gated Re-current Unit (GRU)-based module acts as the fusion module, aggregating features into a combined representation. Finally, a decoder reconstructs the super-resolved image. The proposed model is evaluated on the PROBA-V dataset released in a recent competition held by the European Space Agency. Our results show that it performs among the top contenders and offers a new practical solution for real-world applications.
A Modified Wind Driven Optimization Model for Global Continuous Optimization
Authors: Abdennour Boulesnane, Souham Meshoul
Date: 2015-05-29 00:00:00
Proceeding: 2015 2015 5

Metaheuristics have been proposed as an alternative to mathematical optimization methods to address non convex problems involving large search spaces. Within this context a new promising metaheuristic inspired from earth atmosphere phenomena and termed as Wind Driven Optimization (WDO) has been developed by Bayraktar. WDO has been successfully applied to solve continuous optimization problems. However it requires tuning several parameters and it may lead to premature convergence. In this paper the basic WDO is modified in a way to improve the search capabilities of the algorithm and to reduce the number of tunable parameters. In the proposed variant of WDO, the original model equation is modified by introducing a pressure based term to replace the rank based term. Furthermore, the value of the gravitational term is automatically and adaptively set. The performance of the proposed modified WDO has been assessed using several benchmarks in numerical optimization. The obtained results show that the modified WDO outperforms the original WDO in most test problems from both accuracy and robustness.
Separating the Early Universe from the Late Universe: cosmological parameter estimation beyond the black box
Authors: Max Tegmark, Matias Zaldarriaga
Date: 2002-07-02 00:00:00
Tasks: Clustering, parameter estimation

We present a method for measuring the cosmic matter budget without assumptions about speculative Early Universe physics, and for measuring the primordial power spectrum P*(k) non-parametrically, either by combining CMB and LSS information or by using CMB polarization. Our method complements currently fashionable ``black box'' cosmological parameter analysis, constraining cosmological models in a more physically intuitive fashion by mapping measurements of CMB, weak lensing and cluster abundance into k-space, where they can be directly compared with each other and with galaxy and Lyman alpha forest clustering. Including the new CBI results, we find that CMB measurements of P(k) overlap with those from 2dF galaxy clustering by over an order of magnitude in scale, and even overlap with weak lensing measurements. We describe how our approach can be used to raise the ambition level beyond cosmological parameter fitting as data improves, testing rather than assuming the underlying physics.