pwc-archive/papers-with-abstracts huggingface dataset:
Attention-based ASR with Lightweight and Dynamic Convolutions
Date: 2020-02-20 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

End-to-end (E2E) automatic speech recognition (ASR) with sequence-to-sequence
models has gained attention because of its simple model training compared with
conventional hidden Markov model based ASR. Recently, several studies report
the state-of-the-art E2E ASR results obtained by Transformer. Compared to
recurrent neural network (RNN) based E2E models, training of Transformer is
more efficient and also achieves better performance on various tasks. However,
self-attention used in Transformer requires computation quadratic in its input
length. In this paper, we propose to apply lightweight and dynamic convolution
to E2E ASR as an alternative architecture to the self-attention to make the
computational order linear. We also propose joint training with connectionist
temporal classification, convolution on the frequency axis, and combination
with self-attention. With these techniques, the proposed architectures achieve
better performance than RNN-based E2E model and performance competitive to
state-of-the-art Transformer on various ASR benchmarks including
noisy/reverberant tasks.
Homeorhesis in Waddington's Landscape by Epigenetic Feedback Regulation
Date: 2019-12-27 00:00:00

In multicellular organisms, cells differentiate into several distinct types
during early development. Determination of each cellular state, along with the
ratio of each cell type, as well as the developmental course during cell
differentiation are highly regulated processes that are robust to noise and
environmental perturbations throughout development. Waddington metaphorically
depicted this robustness as the epigenetic landscape in which the robustness of
each cellular state is represented by each valley in the landscape. This
robustness is now conceptualized as an approach toward an attractor in a
gene-expression dynamical system. However, there is still an incomplete
understanding of the origin of landscape change, which is accompanied by
branching of valleys that corresponds to the differentiation process. Recent
progress in developmental biology has unveiled the molecular processes involved
in epigenetic modification, which will be a key to understanding the nature of
slow landscape change. Nevertheless, the contribution of the interplay between
gene expression and epigenetic modification to robust landscape changes, known
as homeorhesis, remains elusive. Here, we introduce a theoretical model that
combines epigenetic modification with gene expression dynamics driven by a
regulatory network. In this model, epigenetic modification changes the
feasibility of expression, i.e., the threshold for expression dynamics, and a
slow positive-feedback process from expression to the threshold level is
introduced. Under such epigenetic feedback, several fixed-point attractors with
distinct expression patterns are generated hierarchically shaping the
epigenetic landscape with successive branching of valleys. This theory provides
a quantitative framework for explaining homeorhesis in development as
postulated by Waddington, based on dynamical-system theory with slow feedback
reinforcement.
Achieving Arbitrary Throughput-Fairness Trade-offs in the Inter Cell Interference Coordination with Fixed Transmit Power Problem
Date: 2019-12-27 00:00:00
Tasks: Fairness, Graph Matching

We study the problem of inter cell interference coordination (ICIC) with
fixed transmit power in OFDMA-based cellular networks, in which each base
station (BS) needs to decide as to which subchannel, if any, to allocate to
each of its associated mobile stations (MS) for data transmission. In general,
there exists a trade-off between the total throughput (sum of throughputs of
all the MSs) and fairness under the allocations found by resource allocation
schemes. We introduce the concept of $\tau-\alpha-$fairness by modifying the
concept of $\alpha-$fairness, which was earlier proposed in the context of
designing fair end-to-end window-based congestion control protocols for
packet-switched networks. The concept of $\tau-\alpha-$fairness allows us to
achieve arbitrary trade-offs between the total throughput and degree of
fairness by selecting an appropriate value of $\alpha$ in $[0,\infty)$. We show
that for every $\alpha \in [0,\infty)$ and every $\tau > 0$, the problem of
finding a $\tau-\alpha-$fair allocation is NP-Complete. Further, we show that
for every $\alpha \in [0, \infty)$, there exist thresholds such that if the
potential interference levels experienced by each MS on every subchannel are
above the threshold values, then the problem can be optimally solved in
polynomial time by reducing it to the bipartite graph matching problem. Also,
we propose a simple, distributed subchannel allocation algorithm for the ICIC
problem, which is flexible, requires a small amount of time to operate, and
requires information exchange among only neighboring BSs. We investigate via
simulations as to how the algorithm parameters should be selected so as to
achieve any desired trade-off between the total throughput and fairness.
Monaural Speech Enhancement Using a Multi-Branch Temporal Convolutional Network
Authors: Qiquan Zhang, Aaron Nicolson, Mingjiang Wang, Kuldip K. Paliwal, Chenxu Wang
Date: 2019-12-27 00:00:00
Tasks: Speech Enhancement

Deep learning has achieved substantial improvement on single-channel speech enhancement tasks. However, the performance of multi-layer perceptions (MLPs)-based methods is limited by the ability to capture the long-term effective history information. The recurrent neural networks (RNNs), e.g., long short-term memory (LSTM) model, are able to capture the long-term temporal dependencies, but come with the issues of the high latency and the complexity of training.To address these issues, the temporal convolutional network (TCN) was proposed to replace the RNNs in various sequence modeling tasks. In this paper we propose a novel TCN model that employs multi-branch structure, called multi-branch TCN (MB-TCN), for monaural speech enhancement.The MB-TCN exploits split-transform-aggregate design, which is expected to obtain strong representational power at a low computational complexity.Inspired by the TCN, the MB-TCN model incorporates one dimensional causal dilated CNN and residual learning to expand receptive fields for capturing long-term temporal contextual information.Our extensive experimental investigation suggests that the MB-TCNs outperform the residual long short-term memory networks (ResLSTMs), temporal convolutional networks (TCNs), and the CNN networks that employ dense aggregations in terms of speech intelligibility and quality, while providing superior parameter efficiency. Furthermore, our experimental results demonstrate that our proposed MB-TCN model is able to outperform multiple state-of-the-art deep learning-based speech enhancement methods in terms of five widely used objective metrics.
Information Flow in Biological Networks for Color Vision
Authors: Jesus Malo
Date: 2019-12-27 00:00:00

Color Appearance Models are biological networks that consist of a cascade of linear+nonlinear layers that modify the linear measurements at the retinal photo-receptors leading to an internal (nonlinear) representation of color that correlates with psychophysical experience. The basic layers of these networks include: (1) chromatic adaptation (normalization of the mean and covariance of the color manifold), (2) change to opponent color channels (PCA-like rotation in the color space), and (3) saturating nonlinearities to get perceptually Euclidean color representations (similar to dimensionwise equalization). The Efficient Coding Hypothesis argues that these transforms should emerge from information-theoretic goals. In case this hypothesis holds in color vision, the question is, what is the coding gain due to the different layers of the color appearance networks? In this work, a representative family of Color Appearance Models is analyzed in terms of how the redundancy among the chromatic components is modified along the network and how much information is transferred from the input data to the noisy response. The proposed analysis is done using data and methods that were not available before: (1) new colorimetrically calibrated scenes in different CIE illuminations for proper evaluation of chromatic adaptation, and (2) new statistical tools to estimate (multivariate) information-theoretic quantities between multidimensional sets based on Gaussianization. Results confirm that the Efficient Coding Hypothesis holds for current color vision models, and identify the psychophysical mechanisms critically responsible for gains in information transference: opponent channels and their nonlinear nature are more important than chromatic adaptation at the retina.
Model reduction for a power grid model
Date: 2019-12-19 00:00:00
Tasks: model

We apply model reduction techniques to the DeMarco power grid model. The
DeMarco model, when augmented by an appropriate line failure mechanism, can be
used to study cascade failures. Here we examine the DeMarco model without the
line failure mechanism and we investigate how to construct reduced order models
for subsets of the state variables. We show that due to the oscillating nature
of the solutions and the absence of timescale separation between resolved and
unresolved variables, the construction of accurate reduced models becomes
highly non-trivial since one has to account for long memory effects. In
addition, we show that a reduced model which includes even a short memory is
drastically better than a memoryless model.
Brain-to-Brain Communication Based on Wireless Technologies: Actual and Future Perspectives
Date: 2019-12-23 00:00:00

During the last few years, intensive research efforts are being done in the
field of brain interfaces to extract neuro-information from the signals
representing neuronal activities in the human brain. Recent development of
brain-to-computer interfaces support direct communication between animals'
brains, enabling direct brain-to-brain communication. Although these results
are based on binary communication with relaxed requirements of latency and
throughput, the fast development in neuro-science technologies indicates
potential new scenarios for wireless communications between brains. In this
paper we highlight technologies that are being used today to enable
brain-to-brain communication and propose potential wireless communication
architectures and requirements for future scenarios.
EdgeDRNN: Enabling Low-latency Recurrent Neural Network Edge Inference
Authors: Chang Gao, Antonio Rios-Navarro, Xi Chen, Tobi Delbruck, Shih-Chii Liu
Date: 2019-12-22 00:00:00
Tasks: Edge-computing, GPU

This paper presents a Gated Recurrent Unit (GRU) based recurrent neural network (RNN) accelerator called EdgeDRNN designed for portable edge computing. EdgeDRNN adopts the spiking neural network inspired delta network algorithm to exploit temporal sparsity in RNNs. It reduces off-chip memory access by a factor of up to 10x with tolerable accuracy loss. Experimental results on a 10 million parameter 2-layer GRU-RNN, with weights stored in DRAM, show that EdgeDRNN computes them in under 0.5 ms. With 2.42 W wall plug power on an entry level USB powered FPGA board, it achieves latency comparable with a 92 W Nvidia 1080 GPU. It outperforms NVIDIA Jetson Nano, Jetson TX2 and Intel Neural Compute Stick 2 in latency by 6X. For a batch size of 1, EdgeDRNN achieves a mean effective throughput of 20.2 GOp/s and a wall plug power efficiency that is over 4X higher than all other platforms.
Multivariate Systemic Optimal Risk Transfer Equilibrium
Authors: Alessandro Doldi, Marco Frittelli
Date: 2019-12-27 00:00:00

A Systemic Optimal Risk Transfer Equilibrium (SORTE) was introduced in: "Systemic optimal risk transfer equilibrium", Mathematics and Financial Economics (2021), for the analysis of the equilibrium among financial institutions or in insurance-reinsurance markets. A SORTE conjugates the classical B\"{u}hlmann's notion of a risk exchange equilibrium with a capital allocation principle based on systemic expected utility optimization. In this paper we extend such a notion to the case when the value function to be optimized is multivariate in a general sense, and it is not simply given by the sum of univariate utility functions. This takes into account the fact that preferences of single agents might depend on the actions of other participants in the game. Technically, the extension of SORTE to the new setup requires developing a theory for multivariate utility functions and selecting at the same time a suitable framework for the duality theory. Conceptually, this more general framework allows us to introduce and study a Nash Equilibrium property of the optimizer. We prove existence, uniqueness, and the Nash Equilibrium property of the newly defined Multivariate Systemic Optimal Risk Transfer Equilibrium.
Adaptive-CS-Net: FastMRI with Adaptive Intelligence
Date: 2019-12-13 00:00:00
Tasks: compressed sensing

Adaptive intelligence aims at empowering machine learning techniques with the
extensive use of domain knowledge. In this work, we present the application of
adaptive intelligence to accelerate MR acquisition. Starting from undersampled
k-space data, an iterative learning-based reconstruction scheme inspired by
compressed sensing theory is used to reconstruct the images. We adopt deep
neural networks to refine and correct prior reconstruction assumptions given
the training data. Our results show that an adaptive intelligence approach
performs better than traditional methods as well as deep learning methods that
do not take prior knowledge into account.
Conditional Correlations and Principal Regression Analysis for Futures
Authors: Armine Karami, Raphael Benichou, Michael Benzaquen, Jean-Philippe Bouchaud
Date: 2019-12-27 00:00:00
Tasks: regression

We explore the effect of past market movements on the instantaneous correlations between assets within the futures market. Quantifying this effect is of interest to estimate and manage the risk associated to portfolios of futures in a non-stationary context. We apply and extend a previously reported method called the Principal Regression Analysis (PRA) to a universe of $84$ futures contracts between $2009$ and $2019$. We show that the past up (resp. down) 10 day trends of a novel predictor -- the eigen-factor -- tend to reduce (resp. increase) instantaneous correlations. We then carry out a multifactor PRA on sectorial predictors corresponding to the four futures sectors (indexes, commodities, bonds and currencies), and show that the effect of past market movements on the future variations of the instantaneous correlations can be decomposed into two significant components. The first component is due to the market movements within the index sector, while the second component is due to the market movements within the bonds sector.
A Common Gene Expression Signature Analysis Method for Multiple Types of Cancer
Authors: Yingcheng Sun, Xiangru Liang, Kenneth Loparo
Date: 2019-12-28 00:00:00
Tasks: Clustering, Diagnostic

Mining gene expression profiles has proven valuable for identifying signatures serving as surrogates of cancer phenotypes. However, the similarities of such signatures across different cancer types have not been strong enough to conclude that they represent a universal biological mechanism shared among multiple cancer types. Here we describe a network-based approach that explores gene-to-gene connections in multiple cancer datasets while maximizing the overall association of the subnetwork with clinical outcomes. With the dataset of The Cancer Genome Atlas (TCGA), we studied the characteristics of common gene expression of three types of cancers: Rectum adenocarcinoma (READ), Breast invasive carcinoma (BRCA) and Colon adenocarcinoma (COAD). By analyzing several pairs of highly correlated genes after filtering and clustering work, we found that the co-expressed genes across multiple types of cancers point to particular biological mechanisms related to cancer cell progression , suggesting that they represent important attributes of cancer in need of being elucidated for potential applications in diagnostic, prognostic and therapeutic products applicable to multiple cancer types.
Vehicle Communication using Hash Chain-based Secure Cluster
Date: 2019-12-28 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Clustering

We introduce a hash chain-based secure cluster. Here, secure cluster refers
to a set of vehicles having vehicular secrecy capacity of more than a reference
value. Since vehicle communication is performed in such a secure cluster,
basically secure vehicle communication can be expected. Secure hash clusters
can also be expected by sharing hash chains derived from vehicle identification
numbers. We are also convinced that our paper is essential for future
autonomous vehicles by providing secure clustering services using MEC. In the
near term, autonomous driving, our paper makes it possible to expect strong and
practically safe vehicle communications.
OpenRadar: A Toolkit for Prototyping mmWave Radar Applications
Date: 2019-12-28 00:00:00

Millimeter-Wave (mmWave) radar sensors are gaining popularity for their
robust sensing and increasing imaging capabilities. However, current radar
signal processing is hardware specific, which makes it impossible to build
sensor agnostic solutions. OpenRadar serves as an interface to prototype,
research, and benchmark solutions in a modular manner. This enables creating
software processing stacks in a way that has not yet been extensively explored.
In the wake of increased AI adoption, OpenRadar can accelerate the growth of
the combined fields of radar and AI. The OpenRadar API was released on Oct 2,
2019 as an open-source package under the Apache 2.0 license. The codebase
exists at https://github.com/presenseradar/openradar.
Bayesian estimation of large dimensional time varying VARs using copulas
Authors: Mike Tsionas, Marwan Izzeldin, Lorenzo Trapani
Date: 2019-12-28 00:00:00

This paper provides a simple, yet reliable, alternative to the (Bayesian) estimation of large multivariate VARs with time variation in the conditional mean equations and/or in the covariance structure. With our new methodology, the original multivariate, n dimensional model is treated as a set of n univariate estimation problems, and cross-dependence is handled through the use of a copula. Thus, only univariate distribution functions are needed when estimating the individual equations, which are often available in closed form, and easy to handle with MCMC (or other techniques). Estimation is carried out in parallel for the individual equations. Thereafter, the individual posteriors are combined with the copula, so obtaining a joint posterior which can be easily resampled. We illustrate our approach by applying it to a large time-varying parameter VAR with 25 macroeconomic variables.
Aligning Statistical Dynamics Captures Biological Network Functioning
Date: 2019-12-29 00:00:00
Tasks: Specificity

Empirical studies of graphs have contributed enormously to our understanding
of complex systems. Known today as network science, what was originally a
theoretical study of graphs has grown into a more scientific exploration of
communities spanning the physical, biological, and social. However, as the
quantity and types of networks have grown so has their heterogeneity in quality
and specificity. This has hampered efforts to develop general network theory
capable of inferring functioning and predicting dynamics across study systems.
We have successfully approached this challenge by aligning networks to each
other rather than comparing parameter estimates from individually fitted models
or properties of edge topologies. By comparing the predictability of
statistical dynamics originating from each network's constituent nodes we were
able to build a functional classifier that distinguished underlying processes
in both synthetic and real-world network data spanning the entire biological
scale from cellular machinery to ecosystems.
Highly fluorescent copper nanoclusters for sensing and bioimaging
Authors: Yu An, Ying Ren, Jing Tang, Jun Chen, Baisong Chang
Date: 2019-12-29 00:00:00

Metal nanoclusters (NCs), typically consisting of a few to tens of metal atoms, bridge the gap between organometallic compounds and crystalline metal nanoparticles. As their size approaches the Fermi wavelength of electrons, metal NCs exhibit discrete energy levels, which in turn results in the emergence of intriguing physical and chemical (or physicochemical) properties, especially strong fluorescence. In the past few decades, dramatic growth has been witnessed in the development of different types of noble metal NCs (mainly AuNCs and AgNCs). However, compared with noble metals, copper is a relatively earth-abundant and cost-effective metal. Theoretical and experimental studies have shown that copper NCs (CuNCs) possess unique catalytic and photoluminescent properties. In this context, CuNCs are emerging as a new class of nontoxic, economic, and effective phosphors and catalysts, drawing significant interest across the life and medical sciences. To highlight these achievements, this review begins by providing an overview of a multitude of factors that play central roles in the fluorescence of CuNCs. Additionally, a critical perspective of how the aggregation of CuNCs can efficiently improve the florescent stability, tunability, and intensity is also discussed. Following, we present representative applications of CuNCs in detection and bioimaging. Finally, we outline current challenges and our perspective on the development of CuNCs.
Credit Risk: Simple Closed Form Approximate Maximum Likelihood Estimator
Authors: Anand Deo, Sandeep Juneja
Date: 2019-12-29 00:00:00
Tasks: Form

We consider discrete default intensity based and logit type reduced form models for conditional default probabilities for corporate loans where we develop simple closed form approximations to the maximum likelihood estimator (MLE) when the underlying covariates follow a stationary Gaussian process. In a practically reasonable asymptotic regime where the default probabilities are small, say 1-3% annually, the number of firms and the time period of data available is reasonably large, we rigorously show that the proposed estimator behaves similarly or slightly worse than the MLE when the underlying model is correctly specified. For more realistic case of model misspecification, both estimators are seen to be equally good, or equally bad. Further, beyond a point, both are more-or-less insensitive to increase in data. These conclusions are validated on empirical and simulated data. The proposed approximations should also have applications outside finance, where logit-type models are used and probabilities of interest are small.
Sequential Learning of CSI for MmWave Initial Alignment
Date: 2019-12-29 00:00:00

MmWave communications aim to meet the demand for higher data rates by using
highly directional beams with access to larger bandwidth. An inherent challenge
is acquiring channel state information (CSI) necessary for mmWave transmission.
We consider the problem of adaptive and sequential learning of the CSI during
the mmWave initial alignment phase of communication. We focus on the
single-user with a single dominant path scenario where the problem is
equivalent to acquiring an optimal beamforming vector, where ideally, the
resulting beams point in the direction of the angle of arrival with the desired
resolution. We extend our prior by proposing two algorithms for adaptively and
sequentially selecting beamforming vectors for learning of the CSI, and that
formulate a Bayesian update to account for the time-varying fading model.
Numerically, we analyze the outage probability and expected spectral efficiency
of our proposed algorithms and demonstrate improvements over strategies that
utilize a practical hierarchical codebook.