pwc-archive/papers-with-abstracts huggingface dataset:
Research and development of MolAICal for drug design via deep learning and classical programming
Date: 2020-06-17 00:00:00
Tasks: Deep Learning, Drug Design, Molecular Docking

Deep learning methods have permeated into the research area of computer-aided
drug design. The deep learning generative model and classical algorithm can be
simultaneously used for three-dimensional (3D) drug design in the 3D pocket of
the receptor. Here, three aspects of MolAICal are illustrated for drug design:
in the first part, the MolAICal uses the genetic algorithm, Vinardo score and
deep learning generative model trained by generative adversarial net (GAN) for
drug design. In the second part, the deep learning generative model is trained
by drug-like molecules from the drug database such as ZINC database. The
MolAICal invokes the deep learning generative model and molecular docking for
drug virtual screening automatically. In the third part, the useful drug tools
are added for calculating the relative properties such as Pan-assay
interference compounds (PAINS), Lipinski's rule of five, synthetic
accessibility (SA), and so on. Besides, the structural similarity search and
quantitative structure-activity relationship (QSAR), etc are also embedded for
the calculations of drug properties in the MolAICal. MolAICal will constantly
optimize and develop the current and new modules for drug design. The MolAICal
can help the scientists, pharmacists and biologists to design the rational 3D
drugs in the receptor pocket through the deep learning model and classical
programming. MolAICal is free of charge for any academic and educational
purposes, and it can be downloaded from the website https://molaical.github.io.
Analytic solution of the SEIR epidemic model via asymptotic approximant
Date: 2020-06-30 00:00:00
Tasks: Form, Math

An analytic solution is obtained to the SEIR Epidemic Model. The solution is
created by constructing a single second-order nonlinear differential equation
in $\ln S$ and analytically continuing its divergent power series solution such
that it matches the correct long-time exponential damping of the epidemic
model. This is achieved through an asymptotic approximant (Barlow et. al, 2017,
Q. Jl Mech. Appl. Math, 70 (1), 21-48) in the form of a modified symmetric
Pad\'e approximant that incorporates this damping. The utility of the
analytical form is demonstrated through its application to the COVID-19
pandemic.
Modelling the delayed nonlinear fiber response in coherent optical communications
Authors: Daniel Semrau, Eric Sillekens, Robert I. Killey, Polina Bayvel
Date: 2020-06-17 00:00:00

Fiber nonlinearities, that lead to nonlinear signal interference (NLI), are typically regarded as an instantaneous material response with respect to the optical field. However, in addition to an instantaneous part, the nonlinear fiber response consists of a delayed contribution, referred to as the Raman response. The imaginary part of its Fourier transform, referred to as the Raman gain spectrum, leads to inter-channel stimulated Raman scattering (ISRS). ISRS is a nonlinear effect that redistributes optical power from high to lower frequencies during propagation. However, as the nonlinear fiber response is causal, the Raman spectrum obeys the Kramers-Kronig relations resulting in the real part of the complex valued Raman spectrum. While the impact of the imaginary part (i.e. ISRS) is well studied, the direct implications of its associated real part on the NLI are unexplored. In this work, a theory is proposed to analytically quantify the impact of the real Raman spectrum on the nonlinear interference power. Starting from a generalized Manakov equation, an extension of the ISRS Gaussian Noise (GN) model is derived to include the real Raman spectrum and, thus, to account for the complete nonlinear Raman response. Accurate integral expressions are derived and approximations in closed-form are proposed. Different formulations for the case of single -and dual polarized signals are derived and novel analytical approximations of the real Raman spectrum are proposed. Moreover, it is analytically shown that the real Raman spectrum scales the strength of the instantaneous nonlinear distortions depending on the frequency separation of the interacting frequencies. A simple functional form is derived to assess the scaling of the NLI strength. The proposed theory is validated by numerical simulations over C-and C+L band, using experimentally measured fiber data.
An Immune-related lncRNAs Model for Prognostic of SKCM Patients Base on Cox Regression and Coexpression Analysis
Authors: Wenjie Jiang, Chang Lu, Jing Qu, Xiaoyu Mei
Date: 2020-06-17 00:00:00
Tasks: Prognosis

SKCM is the most dangerous one of skin cancer, its high degree of malignant, is the leading cause of skin cancer. And the level of radiation treatment and chemical treatment is minimal, so the mortality is high. Because of its complex molecular and cellular heterogeneity, the existing prediction model of skin cancer risk is not ideal. In this study, we developed an immune-related lncRNAs model to predict the prognosis of patients with SKCM. Screening for SKCM-related differential expression of lncRNA from TCGA. Identified immune-related lncRNAs and lncRNA-related mRNA based on the co-expression method. Through univariate and multivariate analysis, an immune-related lncRNA model is established to analyze the prognosis of SKCM patients. A 4-lncRNA skin cancer prediction model was constructed, including MIR155HG, AL137003.2, AC011374.2, and AC009495.2. According to the model, SKCM samples were divided into a high-risk group and low-risk group, and predict the survival of the two groups in 30 years. The area under the ROC curve is 0.749, which shows that the model has excellent performance. We constructed a 4-lncRNA model to predict the prognosis of patients with SKCM, indicating that these lncRNAs may play a unique role in the carcinogenesis of SKCM.
Compressed-Domain Detection and Estimation for Colocated MIMO Radar
Authors: Ehsan Tohidi, Alireza Hariri, Hamid Behroozi, Mohammad Mahdi Nayebi, Geert Leus, Athina Petropulu
Date: 2020-06-17 00:00:00
Tasks: compressed sensing, Compressive Sensing, Data Compression

This paper proposes compressed domain signal processing (CSP) multiple input multiple output (MIMO) radar, a MIMO radar approach that achieves substantial sample complexity reduction by exploiting the idea of CSP. CSP MIMO radar involves two levels of data compression followed by target detection at the compressed domain. First, compressive sensing is applied at the receive antennas, followed by a Capon beamformer which is designed to suppress clutter. Exploiting the sparse nature of the beamformer output, a second compression is applied to the filtered data. Target detection is subsequently conducted by formulating and solving a hypothesis testing problem at each grid point of the discretized angle space. The proposed approach enables an 8-fold reduction of the sample complexity in some settings as compared to a conventional compressed sensing (CS) MIMO radar thus enabling faster target detection. Receiver operating characteristic (ROC) curves of the proposed detector are provided. Simulation results show that the proposed approach outperforms recovery-based compressed sensing algorithms.
Power Efficient LED Placement Algorithm for Indoor Visible Light Communication
Authors: Yang Yang, Zhiyu Zhu, Caili Guo, Chunyan Feng
Date: 2020-06-17 00:00:00

This paper proposes a novel power-efficient light-emitting diode (LED) placement algorithm for indoor visible light communication (VLC). In the considered model, the LEDs can be designedly placed for high power efficiency while satisfying the indoor communication and illumination requirements. This design problem is formulated as a power minimization problem under both communication and illumination level constraints. Due to the interactions among LEDs and the illumination uniformity constraint, the formulated problem is complex and non-convex. To solve the problem, we first transform the complex uniformity constraint into a series of linear constraints. Then an iterative algorithm is proposed to decouple the interactions among LEDs and transforms the original problem into a series of convex sub-problems. Then, we use Lagrange dual method to solve the sub-problem and obtain a convergent solution of the original problem. Simulation results show that the proposed LED placement algorithm can harvest 22.86% power consumption gain when compared with the baseline scheme with centrally placed LEDs.
Submodularity in Action: From Machine Learning to Signal Processing Applications
Authors: Ehsan Tohidi, Rouhollah Amiri, Mario Coutino, David Gesbert, Geert Leus, Amin Karbasi
Date: 2020-06-17 00:00:00
Tasks: BIG-bench Machine Learning

Submodularity is a discrete domain functional property that can be interpreted as mimicking the role of the well-known convexity/concavity properties in the continuous domain. Submodular functions exhibit strong structure that lead to efficient optimization algorithms with provable near-optimality guarantees. These characteristics, namely, efficiency and provable performance bounds, are of particular interest for signal processing (SP) and machine learning (ML) practitioners as a variety of discrete optimization problems are encountered in a wide range of applications. Conventionally, two general approaches exist to solve discrete problems: $(i)$ relaxation into the continuous domain to obtain an approximate solution, or $(ii)$ development of a tailored algorithm that applies directly in the discrete domain. In both approaches, worst-case performance guarantees are often hard to establish. Furthermore, they are often complex, thus not practical for large-scale problems. In this paper, we show how certain scenarios lend themselves to exploiting submodularity so as to construct scalable solutions with provable worst-case performance guarantees. We introduce a variety of submodular-friendly applications, and elucidate the relation of submodularity to convexity and concavity which enables efficient optimization. With a mixture of theory and practice, we present different flavors of submodularity accompanying illustrative real-world case studies from modern SP and ML. In all cases, optimization algorithms are presented, along with hints on how optimality guarantees can be established.
Functional connectome fingerprinting: Identifying individuals and predicting cognitive function via deep learning
Date: 2020-06-17 00:00:00
Tasks: Dictionary Learning, Functional Connectivity

The dynamic characteristics of functional network connectivity have been
widely acknowledged and studied. Both shared and unique information has been
shown to be present in the connectomes. However, very little has been known
about whether and how this common pattern can predict the individual
variability of the brain, i.e. "brain fingerprinting", which attempts to
reliably identify a particular individual from a pool of subjects. In this
paper, we propose to enhance the individual uniqueness based on an autoencoder
network. More specifically, we rely on the hypothesis that the common neural
activities shared across individuals may lessen individual discrimination. By
reducing contributions from shared activities, inter-subject variability can be
enhanced. Results show that that refined connectomes utilizing an autoencoder
with sparse dictionary learning can successfully distinguish one individual
from the remaining participants with reasonably high accuracy (up to 99:5% for
the rest-rest pair). Furthermore, high-level cognitive behavior (e.g., fluid
intelligence, executive function, and language comprehension) can also be
better predicted using refined functional connectivity profiles. As expected,
the high-order association cortices contributed more to both individual
discrimination and behavior prediction. The proposed approach provides a
promising way to enhance and leverage the individualized characteristics of
brain networks.
Deep learning Profit & Loss
Authors: Pietro Rossi, Flavio Cocco, Giacomo Bormetti
Date: 2020-06-17 00:00:00
Tasks: Deep Learning

Building the future profit and loss (P&L) distribution of a portfolio holding, among other assets, highly non-linear and path-dependent derivatives is a challenging task. We provide a simple machinery where more and more assets could be accounted for in a simple and semi-automatic fashion. We resort to a variation of the Least Square Monte Carlo algorithm where interpolation of the continuation value of the portfolio is done with a feed forward neural network. This approach has several appealing features not all of them will be fully discussed in the paper. Neural networks are extremely flexible regressors. We do not need to worry about the fact that for multi assets payoff, the exercise surface could be non connected. Neither we have to search for smart regressors. The idea is to use, regardless of the complexity of the payoff, only the underlying processes. Neural networks with many outputs can interpolate every single assets in the portfolio generated by a single Monte Carlo simulation. This is an essential feature to account for the P&L distribution of the whole portfolio when the dependence structure between the different assets is very strong like the case where one has contingent claims written on the same underlying.
Guaranteed phase synchronization of hybrid oscillators using symbolic Euler's method: The Brusselator and biped examples
Date: 2020-06-17 00:00:00

The phenomenon of phase synchronization was evidenced in the 17th century by
Huygens while observing two pendulums of clocks leaning against the same wall.
This phenomenon has more recently appeared as a widespread phenomenon in
nature, and turns out to have multiple industrial applications. The exact
parameter values of the system for which the phenomenon manifests itself are
however delicate to obtain in general, and it is interesting to find formal
sufficient conditions to guarantee phase synchronization. Using the notion of
reachability, we give here such a formal method. More precisely, our method
selects a portion $S$ of the state space, and shows that any solution starting
at $S$ returns to $S$ within a fixed number of periods $k$. Besides, our method
shows that the components of the solution are then (almost) in phase. We
explain how the method applies on the Brusselator reaction-diffusion and the
biped walker examples.
Hybrid Beamforming Structure for Massive MIMO System: Full-connection v.s. Partial-connection
Authors: Zheda Li, Shengqian Han, Andreas F. Molisch
Date: 2020-06-18 00:00:00

In this article we compare the performance of two typical hyrbid beamforming structures for multiuser massive MIMO systems, i.e., the full- and partial-connection structures. Under the assumption of small angular spread for mmWave channels, given the analog precoder formed towards users and the zero-forcing digital precoder, we develop an explicit upper bound for the analog-and-digital-precoded channel gain of users, based on which the relationship between the two structures is investigated. The analysis results show that the full-connection structure is not always better than the partial-connection structure, and the regimes suitable for each structure are revealed. Simulations are conducted to validate the analysis results.
Ecological notes on the Annulated Treeboa (Corallus annulatus) from a Costa Rican Lowland Tropical Wet Forest
Date: 2020-06-17 00:00:00

The Annulated Treeboa (Corallus annulatus) is one of nine currently
recognized species in the boid genus Corallus. Its disjunct range extends from
eastern Guatemala into northern Honduras, southeastern Nicaragua, northeastern
Costa Rica, and southwestern Panama to northern Colombia west of the Andes. It
is the only species of Corallus found on the Caribbean versant of Costa Rica,
where it occurs at elevations to at least 650m and perhaps as high as 1,000m.
Corallus annulatus occurs mostly in primary and secondary lowland tropical wet
and moist rainforest and it appears to be genuinely rare. Besides C. cropanii
and C. blombergi (the latter closely related to C. annulatus), it is the rarest
member of the genus. Aside from information on habitat and activity, little is
known regarding its natural history.
Nash SIR: An Economic-Epidemiological Model of Strategic Behavior During a Viral Epidemic
Date: 2020-06-17 00:00:00
Tasks: Epidemiology

This paper develops a Nash-equilibrium extension of the classic SIR model of
infectious-disease epidemiology ("Nash SIR"), endogenizing people's decisions
whether to engage in economic activity during a viral epidemic and allowing for
complementarity in social-economic activity. An equilibrium epidemic is one in
which Nash equilibrium behavior during the epidemic generates the epidemic.
There may be multiple equilibrium epidemics, in which case the epidemic
trajectory can be shaped through the coordination of expectations, in addition
to other sorts of interventions such as stay-at-home orders and accelerated
vaccine development. An algorithm is provided to compute all equilibrium
epidemics.
Learning-Based Real-Time Event Identification Using Rich Real PMU Data
Authors: Yuxuan Yuan, Yifei Guo, Kaveh Dehghanpour, Zhaoyu Wang, Yanchao Wang
Date: 2020-06-17 00:00:00
Tasks: Time Series, Time Series Analysis

A large-scale deployment of phasor measurement units (PMUs) that reveal the inherent physical laws of power systems from a data perspective enables an enhanced awareness of power system operation. However, the high-granularity and non-stationary nature of PMU time series and imperfect data quality could bring great technical challenges to real-time system event identification. To address these issues, this paper proposes a two-stage learning-based framework. At the first stage, a Markov transition field (MTF) algorithm is exploited to extract the latent data features by encoding temporal dependency and transition statistics of PMU data in graphs. Then, a spatial pyramid pooling (SPP)-aided convolutional neural network (CNN) is established to efficiently and accurately identify operation events. The proposed method fully builds on and is also tested on a large real dataset from several tens of PMU sources (and the corresponding event logs), located across the U.S., with a time span of two consecutive years. The numerical results validate that our method has high identification accuracy while showing good robustness against poor data quality.
Neuromechanical Mechanisms of Gait Adaptation in C. elegans: Relative Roles of Neural and Mechanical Coupling
Authors: Carter L. Johnson, Timothy J. Lewis, Robert D. Guy
Date: 2020-06-17 00:00:00

Understanding principles of neurolocomotion requires the synthesis of neural activity, sensory feedback, and biomechanics. The nematode \textit{C. elegans} is an ideal model organism for studying locomotion in an integrated neuromechanical setting because its neural circuit has a well-characterized modular structure and its undulatory forward swimming gait adapts to the surrounding fluid with a shorter wavelength in higher viscosity environments. This adaptive behavior emerges from the neural modules interacting through a combination of mechanical forces, neuronal coupling, and sensory feedback mechanisms. However, the relative contributions of these coupling modes to gait adaptation are not understood. The model consists of repeated neuromechanical modules that are coupled through the mechanics of the body, short-range proprioception, and gap-junctions. The model captures the experimentally observed gait adaptation over a wide range of mechanical parameters, provided that the muscle response to input from the nervous system is faster than the body response to changes in internal and external forces. The modularity of the model allows the use of the theory of weakly coupled oscillators to identify the relative roles of body mechanics, gap-junctional coupling, and proprioceptive coupling in coordinating the undulatory gait. The analysis shows that the wavelength of body undulations is set by the relative strengths of these three coupling forms. In a low-viscosity fluid environment, the competition between gap-junctions and proprioception produces a long wavelength undulation, which is only achieved in the model with sufficiently strong gap-junctional coupling.The experimentally observed decrease in wavelength in response to increasing fluid viscosity is the result of an increase in the relative strength of mechanical coupling, which promotes a short wavelength.
Approximate Maximum Likelihood for Complex Structural Models
Authors: Veronika Czellar, David T. Frazier, Eric Renault
Date: 2020-06-18 00:00:00

Indirect Inference (I-I) is a popular technique for estimating complex parametric models whose likelihood function is intractable, however, the statistical efficiency of I-I estimation is questionable. While the efficient method of moments, Gallant and Tauchen (1996), promises efficiency, the price to pay for this efficiency is a loss of parsimony and thereby a potential lack of robustness to model misspecification. This stands in contrast to simpler I-I estimation strategies, which are known to display less sensitivity to model misspecification precisely due to their focus on specific elements of the underlying structural model. In this research, we propose a new simulation-based approach that maintains the parsimony of I-I estimation, which is often critical in empirical applications, but can also deliver estimators that are nearly as efficient as maximum likelihood. This new approach is based on using a constrained approximation to the structural model, which ensures identification and can deliver estimators that are nearly efficient. We demonstrate this approach through several examples, and show that this approach can deliver estimators that are nearly as efficient as maximum likelihood, when feasible, but can be employed in many situations where maximum likelihood is infeasible.
Hyperspectral Super-Resolution via Interpretable Block-Term Tensor Modeling
Authors: Meng Ding, Xiao Fu, Ting-Zhu Huang, Jun Wang, Xi-Le Zhao
Date: 2020-06-18 00:00:00
Tasks: Super-Resolution, Tensor Decomposition

This work revisits coupled tensor decomposition (CTD)-based hyperspectral super-resolution (HSR). HSR aims at fusing a pair of hyperspectral and multispectral images to recover a super-resolution image (SRI). The vast majority of the HSR approaches take a low-rank matrix recovery perspective. The challenge is that theoretical guarantees for recovering the SRI using low-rank matrix models are either elusive or derived under stringent conditions. A couple of recent CTD-based methods ensure recoverability for the SRI under relatively mild conditions, leveraging on algebraic properties of the canonical polyadic decomposition (CPD) and the Tucker decomposition models, respectively. However, the latent factors of both the CPD and Tucker models have no physical interpretations in the context of spectral image analysis, which makes incorporating prior information challenging---but using priors is often essential for enhancing performance in noisy environments. This work employs an idea that models spectral images as tensors following the block-term decomposition model with multilinear rank-$(L_r, L_r, 1)$ terms (i.e., the LL1 model) and formulates the HSR problem as a coupled LL1 tensor decomposition problem. Similar to the existing CTD approaches, recoverability of the SRI is shown under mild conditions. More importantly, the latent factors of the LL1 model can be interpreted as the key constituents of spectral images, i.e., the endmembers' spectral signatures and abundance maps. This connection allows us to easily incorporate prior information for performance enhancement. A flexible algorithmic framework that can work with a series of structural information is proposed to take advantage of the model interpretability. The effectiveness is showcased using simulated and real data.