pwc-archive/papers-with-abstracts huggingface dataset:
Tandem duplications and the limits of natural selection in Drosophila yakuba and Drosophila simulans
Date: 2014-05-02 00:00:00

Tandem duplications are an essential source of genetic novelty, and their variation in natural populations is expected to influence adaptive walks. Here, we describe evolutionary impacts of recently-derived, segregating tandem duplications in Drosophila yakuba and Drosophila simulans. We observe an excess of duplicated genes involved in defense against pathogens, insecticide resistance, chorion development, cuticular peptides, and lipases or endopeptidases associated with the accessory glands, suggesting that duplications function in Red Queen dynamics and rapid evolution. We document evidence of widespread selection on the D. simulans X, suggesting adaptation through duplication is common on the X. Despite the evidence for positive selection, duplicates display an excess of low frequency variants consistent with largely detrimental impacts, limiting the variation that can effectively facilitate adaptation. Although we observe hundreds of gene duplications, we show that segregating variation is insufficient to provide duplicate copies of the entire genome, and the number of duplications in the population spans 13.4\% of major chromosome arms in D. yakuba and 9.7\% in D. simulans. Whole gene duplication rates are low at $1.17\times10^{-9}$ per gene per generation in D. yakuba and $6.03\times10^{-10}$ per gene per generation in D. simulans, suggesting long wait times for new mutations on the order of thousands of years for the establishment of sweeps. Hence, in cases where adaption depends on individual tandem duplications, evolution will be severely limited by mutation. We observe low levels of parallel recruitment of the same duplicated gene in different species, suggesting that the span of standing variation will define evolutionary outcomes in spite of convergence across gene ontologies consistent with rapidly evolving phenotypes.} }
Under Frequency Load Shedding based on PMU Estimates of Frequency and ROCOF
Date: 2018-05-02 00:00:00

The paper describes a distributed under-frequency load shedding and load restoration scheme, that exploits frequency and Rate-of-Change-of-Frequency (ROCOF) measurements produced by Phasor Measurement Units (PMUs) as detectors of a large contingency. The paper further discusses the appropriateness of using the synchrophasor model for estimating ROCOF during fast transients following a severe generation-load imbalance. The performance of the proposed relaying scheme is compared with a frequency-only based strategy, by means of a real-time digital simulator implementing the time-domain full-replica model of the IEEE 39 bus system.
DLPAlign: A Deep Learning based Progressive Alignment Method for Multiple Protein Sequences
Authors: Mengmeng Kuang, Yong liu, Lufei Gao
Date: 2020-11-21 00:00:00
Tasks: Decision Making, Multiple Sequence Alignment, Protein Secondary Structure Prediction

This paper proposed a novel and straightforward approach to improve the accuracy of progressive multiple protein sequence alignment method. We trained a decision-making model based on the convolutional neural networks and bi-directional long short term memory networks, and progressively aligned the input protein sequences by calculating different posterior probability matrices.

To evaluate this method, we have implemented a multiple sequence alignment tool called DLPAlign and compared its performance with eleven leading alignment methods on three empirical alignment benchmarks (BAliBASE, OXBench and SABMark). Our results show that DLPAlign can get the best total-column scores on the three benchmarks. When evaluated against the 711 low similarity families with average PID ≤ 30%, DLPAlign improved about 2.8% over the second-best MSA software. Besides, we compared the performance of DLPAlign and other alignment tools on a real-life application, namely protein secondary structure prediction on four protein sequences related to SARS-COV-2, and DLPAlign provides the best result in all cases.
Mediated Persuasion
Authors: Andrew Kosenko
Date: 2020-11-30 00:00:00

We study a game of strategic information design between a sender, who chooses state-dependent information structures, a mediator who can then garble the signals generated from these structures, and a receiver who takes an action after observing the signal generated by the first two players. We characterize sufficient conditions for information revelation, compare outcomes with and without a mediator and provide comparative statics with regard to the preferences of the sender and the mediator. We also provide novel conceptual and computational insights about the set of feasible posterior beliefs that the sender can induce, and use these results to obtain insights about equilibrium outcomes. The sender never benefits from mediation, while the receiver might. Strikingly, the receiver benefits when the mediator's preferences are not perfectly aligned with hers; rather the mediator should prefer more information revelation than the sender, but less than perfect revelation.
Energy-Efficient Design of Broad Beams for Massive MIMO Systems
Authors: Sven O. Petersson, Maksym A. Girnyk
Date: 2020-12-04 00:00:00
Tasks: Diversity

Massive MIMO is a promising air interface technique for 5G wireless communications. Such antennas offers capabilities to utilize channel correlations to create beams suitable for user specific transmissions. Most of the prior research is devoted to user-specific transmission in such systems, where a narrow beam is formed towards a user to improve its reception. Meanwhile, public-channel transmission to multiple users at once by means of broad beams has been understudied. In this paper, we introduce the concept of array-size invariant (ASI) beamforming, enabling generation of broad beams from very large antenna arrays where all elements are transmitting at maximum power. The ASI technique offers the possibility to achieve a perfectly flat array factor by exploiting the additional degree of freedom coming from polarization diversity. The technique is applicable to uniform linear and rectangular arrays and is characterized by low-complexity beam synthesis. The usefulness of the ASI beamforming is illustrated by means of a numerical example showing the design of a cell-specific broad beam for publicchannel transmission in a realistic network deployment.
Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging
Authors: Zachary J. Lee, George Lee, Ted Lee, Cheng Jin, Rand Lee, Zhi Low, Daniel Chang, Christine Ortega, Steven H. Low
Date: 2020-12-04 00:00:00
Tasks: Model Predictive Control, Scheduling

We describe the architecture and algorithms of the Adaptive Charging Network (ACN), which was first deployed on the Caltech campus in early 2016 and is currently operating at over 100 other sites in the United States. The architecture enables real-time monitoring and control and supports electric vehicle (EV) charging at scale. The ACN adopts a flexible Adaptive Scheduling Algorithm based on convex optimization and model predictive control and allows for significant over-subscription of electrical infrastructure. We describe some of the practical challenges in real-world charging systems, including unbalanced three-phase infrastructure, non-ideal battery charging behavior, and quantized control signals. We demonstrate how the Adaptive Scheduling Algorithm handles these challenges, and compare its performance against baseline algorithms from the deadline scheduling literature using real workloads recorded from the Caltech ACN and accurate system models. We find that in these realistic settings, our scheduling algorithm can improve operator profit by 3.4 times over uncontrolled charging and consistently outperforms baseline algorithms when delivering energy in highly congested systems.
Model-plant mismatch learning offset-free model predictive control
Authors: Sang Hwan Son, Jong Woo Kim, Tae Hoon Oh, Jong Min Lee
Date: 2020-12-04 00:00:00
Tasks: model, Model Predictive Control

We propose model-plant mismatch learning offset-free model predictive control (MPC), which learns and applies the intrinsic model-plant mismatch, to effectively exploit the advantages of model-based and data-driven control strategies and overcome the limitations of each approach. In this study, the model-plant mismatch map on steady-state manifold in the controlled variable space is approximated via a general regression neural network from the steady-state data for each setpoint. Though the learned model-plant mismatch map can provide the information at the equilibrium point (i.e., setpoint), it cannot provide model-plant mismatch information during the transient state. Moreover, the intrinsic model-plant mismatch can vary due to system characteristics changes during operation. Therefore, we additionally apply a supplementary disturbance variable which is updated from the disturbance estimator based on the nominal offset-free MPC scheme. Then, the combined disturbance signal is applied to the target problem and finite-horizon optimal control problem of offset-free MPC to improve the prediction accuracy and closed-loop performance of the controller. By this, we can exploit both the learned model-plant mismatch information and the stabilizing property of the nominal disturbance estimator approach. The closed-loop simulation results demonstrate that the developed scheme can properly learn the intrinsic model-plant mismatch and efficiently improve the model-plant mismatch compensating performance in offset-free MPC. Moreover, we examine the robust asymptotic stability of the developed offset-free MPC scheme, which is known to be difficult to analyze in nominal offset-free MPC, by exploiting the learned model-plant mismatch information.
Universal constraints on selection strength in lineage trees
Authors: Arthur Genthon, David Lacoste
Date: 2020-12-04 00:00:00

We obtain general inequalities constraining the difference between the average of an arbitrary function of a phenotypic trait, which includes the fitness landscape of the trait itself, in the presence or in the absence of natural selection. These inequalities imply bounds on the strength of selection, which can be measured from the statistics of trait values and divisions along lineages. The upper bound is related to recent generalizations of linear response relations in Stochastic Thermodynamics, and shares common features with Fisher's fundamental theorem of natural selection, and with its generalization by Price, although they define different measures of selection. The lower bound follows from recent improvements on Jensen's inequality, and both bounds depend on the variability of the fitness landscape. We illustrate our results using numerical simulations of growing cell colonies and with experimental data of time-lapse microscopy experiments of bacteria cell colonies.
In silico study on the contribution of the follicular route to dermal permeability of small molecules
Authors: Daniel Sebastia-Saez, Guoping Lian, Tao Chen
Date: 2020-12-04 00:00:00

Purpose. This study investigates in silico the contribution of the hair follicle to the overall dermal permeability of small molecules, as published experimental work provides inconclusive information on whether the follicular route favours the permeation of hydrophobic or hydrophilic permeants. Method. A study is conducted varying physico-chemical parameters of permeants such as lipophilicity, molecular weight and protein binding. The simulated data is compared to published experimental data to discuss how those properties can modulate the contribution of the hair follicle to the overall dermal permeation. Results. The results indicate that the contribution of the follicular route to dermal permeation can range from negligible to notable depending on the combination of lipophilic/hydrophilic properties of the substance filling the follicular route and the permeant. Conclusion. Characterisation of the substance filling the follicular route is required for analysing the experimental data of dermal permeation of small molecules, as changes between in vivo and in vitro due to handling of samples and cessation of vital functions can modify the contribution of the follicular route to overall dermal permeation, hence hindering data interpretation.
A Multivariate Realized GARCH Model
Authors: Ilya Archakov, Peter Reinhard Hansen, Asger Lunde
Date: 2020-12-04 00:00:00
Tasks: model

We propose a novel class of multivariate GARCH models that incorporate realized measures of volatility and correlations. The key innovation is an unconstrained vector parametrization of the conditional correlation matrix, which enables the use of factor models for correlations. This approach elegantly addresses the main challenge faced by multivariate GARCH models in high-dimensional settings. As an illustration, we explore block correlation matrices that naturally simplify to linear factor models for the conditional correlations. The model is applied to the returns of nine assets, and its in-sample and out-of-sample performance compares favorably against several popular benchmarks.
A Canonical Representation of Block Matrices with Applications to Covariance and Correlation Matrices
Authors: Ilya Archakov, Peter Reinhard Hansen
Date: 2020-12-04 00:00:00

We obtain a canonical representation for block matrices. The representation facilitates simple computation of the determinant, the matrix inverse, and other powers of a block matrix, as well as the matrix logarithm and the matrix exponential. These results are particularly useful for block covariance and block correlation matrices, where evaluation of the Gaussian log-likelihood and estimation are greatly simplified. We illustrate this with an empirical application using a large panel of daily asset returns. Moreover, the representation paves new ways to regularizing large covariance/correlation matrices, test block structures in matrices, and estimate regressions with many variables.
An Approach to State Signal Shaping by Limit Cycle Model Predictive Control
Authors: Carlos Cateriano Yáñez, Gerwald Lichtenberg, Georg Pangalos, Javier Sanchis Sáez
Date: 2020-12-04 00:00:00
Tasks: Model Predictive Control

A novel nonlinear model predictive control approach for state signal shaping is proposed. The control strategy introduces a residual shape cost kernel based on the dynamics of circular limit cycles from a supercritical Neimark-Sacker bifurcation normal form. This allows the controller to impose a fundamental harmonic state signal shape with a specific frequency and amplitude. An application example for harmonic compensation in distribution grids integrated with renewable energies is presented. The controller is tasked with the calculation of the reference current for an active power filter used for load compensation. The results achieved are successful, reducing the harmonic distortion to satisfactory levels while ensuring the correct frequency and amplitude.
Imperfect Credibility versus No Credibility of Optimal Monetary Policy
Authors: Jean-Bernard Chatelain, Kirsten Ralf
Date: 2020-12-04 00:00:00

A minimal central bank credibility, with a non-zero probability of not renegning his commitment ("quasi-commitment"), is a necessary condition for anchoring inflation expectations and stabilizing inflation dynamics. By contrast, a complete lack of credibility, with the certainty that the policy maker will renege his commitment ("optimal discretion"), leads to the local instability of inflation dynamics. In the textbook example of the new-Keynesian Phillips curve, the response of the policy instrument to inflation gaps for optimal policy under quasi-commitment has an opposite sign than in optimal discretion, which explains this bifurcation.
Discussion on a Class of Model-Free Adaptive Control for Multivariable Systems
Authors: Feilong Zhang
Date: 2020-10-28 00:00:00

The model-free adaptive control (MFAC) law is a promising method in applications. We analyzed model-free adaptive control (MFAC) law through closed-loop function to widen its application range.
New Class K_infinity Function-Based Adaptive Sliding Mode Control Design
Authors: J. Song, Z. Zuo, M. Basin
Date: 2020-12-04 00:00:00

To reduce the chattering and overestimation phenomena existing in classical adaptive sliding mode control, this paper presents a new class K_infinity function-based adaptive sliding mode control scheme. Two controllers are proposed in terms of concave and convex barrier functions to implement this kind of control methodology. To avoid large initial control magnitudes, two modified control schemes are provided, which extend the proposed methodology to different scenarios. It is proven that the proposed controllers yield finite-time convergence to a real sliding mode. Finally, simulations and discussions are presented to show the advantages and effectiveness of the proposed control methodology.
Tail-risk protection: Machine Learning meets modern Econometrics
Authors: Bruno Spilak, Wolfgang Karl Härdle
Date: 2020-10-07 00:00:00
Tasks: BIG-bench Machine Learning, Econometrics

Tail risk protection is in the focus of the financial industry and requires solid mathematical and statistical tools, especially when a trading strategy is derived. Recent hype driven by machine learning (ML) mechanisms has raised the necessity to display and understand the functionality of ML tools. In this paper, we present a dynamic tail risk protection strategy that targets a maximum predefined level of risk measured by Value-At-Risk while controlling for participation in bull market regimes. We propose different weak classifiers, parametric and non-parametric, that estimate the exceedance probability of the risk level from which we derive trading signals in order to hedge tail events. We then compare the different approaches both with statistical and trading strategy performance, finally we propose an ensemble classifier that produces a meta tail risk protection strategy improving both generalization and trading performance.
Asymmetric uncertainty : Nowcasting using skewness in real-time data
Authors: Paul Labonne
Date: 2020-12-04 00:00:00
Tasks: regression

This paper presents a new way to account for downside and upside risks when producing density nowcasts of GDP growth. The approach relies on modelling location, scale and shape common factors in real-time macroeconomic data. While movements in the location generate shifts in the central part of the predictive density, the scale controls its dispersion (akin to general uncertainty) and the shape its asymmetry, or skewness (akin to downside and upside risks). The empirical application is centred on US GDP growth and the real-time data come from Fred-MD. The results show that there is more to real-time data than their levels or means: their dispersion and asymmetry provide valuable information for nowcasting economic activity. Scale and shape common factors (i) yield more reliable measures of uncertainty and (ii) improve precision when macroeconomic uncertainty is at its peak.
Therapeutic target discovery using Boolean network attractors: improvements of kali
Date: 2016-11-10 00:00:00
Tasks: Drug Discovery

In a previous article, an algorithm for identifying therapeutic targets in Boolean networks modeling pathological mechanisms was introduced. In the present article, the improvements made on this algorithm, named kali, are described. These improvements are i) the possibility to work on asynchronous Boolean networks, ii) a finer assessment of therapeutic targets and iii) the possibility to use multivalued logic. kali assumes that the attractors of a dynamical system, such as a Boolean network, are associated with the phenotypes of the modeled biological system. Given a logic-based model of pathological mechanisms, kali searches for therapeutic targets able to reduce the reachability of the attractors associated with pathological phenotypes, thus reducing their likeliness. kali is illustrated on an example network and used on a biological case study. The case study is a published logic-based model of bladder tumorigenesis from which kali returns consistent results. However, like any computational tool, kali can predict but can not replace human expertise: it is a supporting tool for coping with the complexity of biological systems in the field of drug discovery.
LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain
Authors: Tixiao Shan, Brendan Englot
Date: 2018-10-01 00:00:00
Tasks: Point Cloud Segmentation, Pose Estimation

We propose a lightweight and ground-optimized lidar odometry and mapping method, LeGO-LOAM, for realtime six degree-of-freedom pose estimation with ground vehicles. LeGO-LOAM is lightweight, as it can achieve real-time pose estimation on a low-power embedded system. LeGOLOAM is ground-optimized, as it leverages the presence of a ground plane in its segmentation and optimization steps. We first apply point cloud segmentation to filter out noise, and feature extraction to obtain distinctive planar and edge features. A two-step Levenberg-Marquardt optimization method then uses the planar and edge features to solve different components of the six degree-of-freedom transformation across consecutive scans. We compare the performance of LeGO-LOAM with a state-of-the-art method, LOAM, using datasets gathered from variable-terrain environments with ground vehicles, and show
that LeGO-LOAM achieves similar or better accuracy with reduced computational expense. We also integrate LeGO-LOAM into a SLAM framework to eliminate the pose estimation error caused by drift, which is tested using the KITTI dataset.
Near-Optimal Procedures for Model Discrimination with Non-Disclosure Properties
Authors: Dmitrii M. Ostrovskii, Mohamed Ndaoud, Adel Javanmard, Meisam Razaviyayn
Date: 2020-12-04 00:00:00

Let $\theta_0,\theta_1 \in \mathbb{R}^d$ be the population risk minimizers associated to some loss $\ell:\mathbb{R}^d\times \mathcal{Z}\to\mathbb{R}$ and two distributions $\mathbb{P}_0,\mathbb{P}_1$ on $\mathcal{Z}$. The models $\theta_0,\theta_1$ are unknown, and $\mathbb{P}_0,\mathbb{P}_1$ can be accessed by drawing i.i.d samples from them. Our work is motivated by the following model discrimination question: "What sizes of the samples from $\mathbb{P}_0$ and $\mathbb{P}_1$ allow to distinguish between the two hypotheses $\theta^*=\theta_0$ and $\theta^*=\theta_1$ for given $\theta^*\in\{\theta_0,\theta_1\}$?" Making the first steps towards answering it in full generality, we first consider the case of a well-specified linear model with squared loss. Here we provide matching upper and lower bounds on the sample complexity as given by $\min\{1/\Delta^2,\sqrt{r}/\Delta\}$ up to a constant factor; here $\Delta$ is a measure of separation between $\mathbb{P}_0$ and $\mathbb{P}_1$ and $r$ is the rank of the design covariance matrix. We then extend this result in two directions: (i) for general parametric models in asymptotic regime; (ii) for generalized linear models in small samples ($n\le r$) under weak moment assumptions. In both cases we derive sample complexity bounds of a similar form while allowing for model misspecification. In fact, our testing procedures only access $\theta^*$ via a certain functional of empirical risk. In addition, the number of observations that allows us to reach statistical confidence does not allow to "resolve" the two models $-$ that is, recover $\theta_0,\theta_1$ up to $O(\Delta)$ prediction accuracy. These two properties allow to use our framework in applied tasks where one would like to $\textit{identify}$ a prediction model, which can be proprietary, while guaranteeing that the model cannot be actually $\textit{inferred}$ by the identifying agent.