pwc-archive/papers-with-abstracts huggingface dataset:
Decentralized Design and Plug-and-Play Distributed Control for Linear Multi-Channel Systems
Authors: Taekyoo Kim, Donggil Lee, Hyungbo Shim
Date: 2020-11-19 00:00:00

We propose a distributed control, in which many identical control agents are deployed for controlling a linear time-invariant plant that has multiple input-output channels. Each control agent can join or leave the control loop during the operation of stabilization without particular initialization over the whole networked agents. Once new control agents join the loop, they self-organize their control dynamics, which does not interfere the control by other active agents, which is achieved by local communication with the neighboring agents. The key idea enabling these features is the use of Bass' algorithm, which allows the distributed computation of stabilizing gains by solving a Lyapunov equation in a distributed manner.
Outage Constrained Robust Transmission Design for IRS-aided Secure Communications with Direct Communication Links
Authors: Sheng Hong, Cunhua Pan, Gui Zhou, Hong Ren, Kezhi Wang
Date: 2020-11-19 00:00:00

In this paper, we investigate the robust outage constrained transmission design for an intelligent reflecting surface (IRS) aided secure communication system. We assume that a single-antenna legitimate receiver (Bob) served by a multi-antenna base station (BS) is overheard by multiple single-antenna eavesdroppers (Eves), and the artificial noise (AN) is incorporated to confuse the Eves. In particular, we aim to jointly optimize the transmit beamforming, the AN spatial distribution at the BS, and the passive beamforming at the IRS, where the Eve-related channels are assumed to be imperfect due to the channel estimation errors and their concealment. Two scenarios with partial channel state information (CSI) error of only cascaded BS-IRS-Eve channel and full CSI errors of both cascaded BS-IRS-Eve channel and direct BS-Eve channel are investigated under the statistical CSI error model. An outage-constrained power minimization problem is formulated by limiting the maximum allowable data rate of Eves and ensuring the minimum data rate requirement of Bob. To solve it, the Bernstein-type inequality and alternating optimization strategy are exploited, and artful mathematical manipulations are performed to facilitate the optimization on the phase shifts of the IRS. Simulation results confirm the performance advantages of the proposed algorithm.
Non-equilibrium dynamics of adaptation in sensory systems
Authors: Daniele Conti, Thierry Mora
Date: 2020-11-19 00:00:00

Adaptation is used by biological sensory systems to respond to a wide range of environmental signals, by adapting their response properties to the statistics of the stimulus in order to maximize information transmission. We derive rules of optimal adaptation to changes in the mean and variance of a continuous stimulus in terms of Bayesian filters, and map them onto stochastic equations that couple the state of the environment to an internal variable controling the response function. We calculate numerical and exact results for the speed and accuracy of adaptation, and its impact on information transmission. We find that, in the regime of efficient adaptation, the speed of adaptation scales sublinearly with the rate of change of the environment. Finally, we exploit the mathematical equivalence between adaptation and stochastic thermodynamics to quantitatively relate adaptation to the irreversibility of the adaptation time course, defined by the rate of entropy production. Our results suggest a means to empirically quantify adaptation in a model-free and non-parametric way.
Safe model-based design of experiments using Gaussian processes
Authors: Panagiotis Petsagkourakis, Federico Galvanin
Date: 2020-11-19 00:00:00
Tasks: Experimental Design, Gaussian Processes, Safe Exploration

Construction of kinetic models has become an indispensable step in the development and scale up of processes in the industry. Model-based design of experiments (MBDoE) has been widely used for the purpose of improving parameter precision in nonlinear dynamic systems. This process needs to account for both parametric and structural uncertainty, as the feasibility constraints imposed on the system may well turn out to be violated leading to unsafe experimental conditions when an optimally designed experiment is performed. In this work, a Gaussian process is utilized in a two-fold manner: 1) to quantify the uncertainty realization of the physical system and calculate the plant-model mismatch, 2) to compute the optimal experimental design while accounting for the parametric uncertainty. This method provides a guarantee for the probabilistic satisfaction of the constraints in the context of model-based design of experiments. The method is assisted with the use of adaptive trust-regions in order to facilitate a satisfactory local approximation. The proposed method is able to allow the design of optimal experiments starting from limited preliminary knowledge of the parameter set, leading to a safe exploration of the parameter space. The performance of this method is demonstrated through illustrative case studies regarding the parameter identification of the kinetic model in flow reactors.
On binary pulsars and the force of gravity
Authors: Davor Palle
Date: 2002-11-21 00:00:00
Tasks: valid

The energy-momentum budget of the astrophysical systems can be studied by the exact local conservation equation derived by Landau and Lifshitz. We show that a similar equation is valid for the Einstein-Cartan gravity. We reanalyze a binary pulsar system using the Landau-Lifshitz conservation equation and show that the orbital period change rate can be completely understood as a curvature backreaction process. Taking into account the detailed theoretical and observational research of relativistic binary pulsar systems, especially the system of Hulse and Taylor, we conclude that general relativity and astrophysical observations rule out the existence of gravitational radiation. We comment upon the LIGO GW events and their alternative explanation, as well as the recent pulsar timing arrays data.
Design of statistical quality control procedures using genetic algorithms
Authors: Hatjimihail Aristides T., Hatjimihail Theophanes T.
Date: 2018-11-30 00:00:00

In general, we can not use algebraic or enumerative methods to optimize a
quality control (QC) procedure so as to detect the critical random and
systematic analytical errors with stated probabilities, while the probability
for false rejection is minimum. Genetic algorithms (GAs) offer an alternative,
as they do not require knowledge of the objective function to be optimized and
search through large parameter spaces quickly. To explore the application of
GAs in statistical QC, we have developed an interactive GAs based computer
program that designs a novel near optimal QC procedure, given an analytical
process. The program uses the deterministic crowding algorithm. An illustrative
application of the program suggests that it has the potential to design QC
procedures that are significantly better than 45 alternative ones that are used
in the clinical laboratories.
Cascade hash tables: a series of multilevel double hashing schemes with O(1) worst case lookup time
Authors: Li Shaohua
Date: 2015-06-25 00:00:00

In this paper, the author proposes a series of multilevel double hashing
schemes called cascade hash tables. They use several levels of hash tables. In
each table, we use the common double hashing scheme. Higher level hash tables
work as fail-safes of lower level hash tables. By this strategy, it could
effectively reduce collisions in hash insertion. Thus it gains a constant worst
case lookup time with a relatively high load factor(70%-85%) in random
experiments. Different parameters of cascade hash tables are tested.
Genetic Code as a Harmonic System: three Supplements
Authors: Miloje M. Rakocevic
Date: 2007-03-05 00:00:00
Tasks: Relation

The paper represents three supplements to the source paper, q-bio/0610044 [q-bio.OT], with three new series of harmonic structures of the genetic code, determined by Gauss arithmetical algorithm; by Table of Minimal Adding, as in (Rakocevic, 2011a: Table 4; 2011b: Table 4); all structures in relation to Binary-code tree (Rakocevic, 1998). The determination itself is realized through atom and nucleon number balancing and nuancing of molekular polarity. In the first supplement the word is about some additional harmonic structures in relation to a previous our paper (Rakocevic, 2004); in the second one about the relation that structures with the polarity of protein amino acids. In the third supplement we give new ideas about the genetic code by an inclusion of the notions cipher of the genetic code and the key of that cipher.
A Gaussian Process-based Price-Amount Curve Construction for Demand Response Provided by Internet Data Centers
Authors: Yang Liu, Hung D. Nguyen
Date: 2020-12-02 00:00:00
Tasks: regression

For a Demand Response (DR) program with internet data centers (IDC), the Price-Amount curve that estimates how the potential DR amount depends on the DR price determined by power systems is crucial. Constructing this curve is challenging mainly due to the uncertainty in IDCs' operation. A novel Gaussian Process Regression-based estimation method is thus proposed. The variance of resulting curve reflecting the IDC operational uncertainty is also calculated.
Gaussian Process-based Approach for Bilevel Optimization in the Power System -- A Critical Load Restoration Case
Authors: Yang Liu, Yu Weng, Rufan Yang, Quoc-Tuan Tran, Hung D. Nguyen
Date: 2020-12-02 00:00:00
Tasks: Bilevel Optimization, Computational Efficiency, Decision Making

Bilevel optimization problems can be used to represent the collaborative interaction between a power system and grid-connected entities, called the followers, such as data centers. Most existing approaches assume that such followers' response behaviors are made available to the power system in the operation decision-making, which may be untenable in reality. This work presents a novel idea of solving bilevel optimization problems without assuming power systems' omniscience. The followers' responses will be represented by a function of the power system's decisions using Gaussian Process Regression. Then the two layers in the bilevel problem can be solved separately by the power system and its followers. This not only avoids the omniscience assumption, but also significantly increases the computational efficiency without compromising accuracy, especially for the problems with a complex lower layer. Moreover, a bilevel critical load restoration model is developed to test the proposed technique. Compared to the conventional methods, the proposed restoration model considers the load-side operation and the varying load marginal value, and can accurately estimate load-side loss and achieve better restoration solutions. Two case studies validate the advantages of the proposed approaches from different perspectives.
Persuasion Produces the (Diamond) Paradox
Authors: Mark Whitmeyer
Date: 2020-11-27 00:00:00

This paper extends the sequential search model of Wolinsky (1986) by allowing firms to choose how much match value information to disclose to visiting consumers. This restores the Diamond paradox (Diamond 1971): there exist no symmetric equilibria in which consumers engage in active search, so consumers obtain zero surplus and firms obtain monopoly profits. Modifying the scenario to one in which prices are advertised, we discover that the no-active-search result persists, although the resulting symmetric equilibria are ones in which firms price at marginal cost.
Transient Stability Assessment of Networked Microgrids Using Neural Lyapunov Methods
Authors: Tong Huang, Sicun Gao, Le Xie
Date: 2020-12-02 00:00:00

This paper proposes a novel transient stability assessment tool for networked microgrids based on neural Lyapunov methods. Assessing transient stability is formulated as a problem of estimating the dynamic security region of networked microgrids. We leverage neural networks to learn a local Lyapunov function in the state space. The largest security region is estimated based on the learned neural Lyapunov function, and it is used for characterizing disturbances that the networked microgrids can tolerate. The proposed method is tested and validated in a grid-connected microgrid, three networked microgrids with mixed interface dynamics, and the IEEE 123-node feeder. Case studies suggest that the proposed method can address networked microgrids with heterogeneous interface dynamics, and in comparison with conventional methods that are based on quadratic Lyapunov functions, can characterize the security regions with much less conservativeness.
Accountability and Motivation
Authors: Liqun Liu
Date: 2020-12-02 00:00:00

We build a formal model that examines how different policymaking environments shape career-concerned officials' reform decisions and implementation. When career concerns are strong, officials will inefficiently initiate reforms to signal to the central government that they are congruent. To improve the quality of reform policymaking, the central government must hold officials accountable to policy outcomes. We demonstrate that the central government can exercise this accountability by requiring officials to publicize policy outcomes while maintain secrecy on implementation details. In this situation, officials can signal their congruence only through a desirable policy outcome, so they are highly motivated to carry out a reform well. We also demonstrate that the accountability on policy outcomes is infeasible under alternative policymaking environments. We apply the results to China's recent practice in decentralized reform policymaking.
Hippocampal representations emerge when training recurrent neural networks on a memory dependent maze navigation task
Authors: Justin Jude, Matthias H. Hennig
Date: 2020-12-02 00:00:00
Tasks: Hippocampus, Q-Learning

Can neural networks learn goal-directed behaviour using similar strategies to the brain, by combining the relationships between the current state of the organism and the consequences of future actions? Recent work has shown that recurrent neural networks trained on goal based tasks can develop representations resembling those found in the brain, entorhinal cortex grid cells, for instance. Here we explore the evolution of the dynamics of their internal representations and compare this with experimental data. We observe that once a recurrent network is trained to learn the structure of its environment solely based on sensory prediction, an attractor based landscape forms in the network's representation, which parallels hippocampal place cells in structure and function. Next, we extend the predictive objective to include Q-learning for a reward task, where rewarding actions are dependent on delayed cue modulation. Mirroring experimental findings in hippocampus recordings in rodents performing the same task, this training paradigm causes nonlocal neural activity to sweep forward in space at decision points, anticipating the future path to a rewarded location. Moreover, prevalent choice and cue-selective neurons form in this network, again recapitulating experimental findings. Together, these results indicate that combining predictive, unsupervised learning of the structure of an environment with reinforcement learning can help understand the formation of hippocampus-like representations containing both spatial and task-relevant information.
Holographic Communication using Intelligent Surfaces
Authors: Davide Dardari, Nicoló Decarli
Date: 2020-12-02 00:00:00
Tasks: valid

Holographic communication is intended as an holistic way to manipulate with unprecedented flexibility the electromagnetic field generated or sensed by an antenna. This is of particular interest when using large antennas at high frequency (e.g., the millimeter wave or terahertz), whose operating condition may easily fall in the Fresnel propagation region (radiating near-field), where the classical plane wave propagation assumption is no longer valid. This paper analyzes the optimal communication involving large intelligent surfaces, realized for example with metamaterials as possible enabling technology for holographic communication. It is shown that traditional propagation models must be revised and that, when exploiting spherical wave propagation in the Fresnel region with large surfaces, new opportunities are opened, for example, in terms of the number of orthogonal communication channels.
On Extending NLP Techniques from the Categorical to the Latent Space: KL Divergence, Zipf's Law, and Similarity Search
Authors: Adam Hare, Yu Chen, Yinan Liu, Zhenming Liu, Christopher G. Brinton
Date: 2020-12-02 00:00:00
Tasks: BIG-bench Machine Learning, Sentence, Word Embeddings

Despite the recent successes of deep learning in natural language processing (NLP), there remains widespread usage of and demand for techniques that do not rely on machine learning. The advantage of these techniques is their interpretability and low cost when compared to frequently opaque and expensive machine learning models. Although they may not be be as performant in all cases, they are often sufficient for common and relatively simple problems. In this paper, we aim to modernize these older methods while retaining their advantages by extending approaches from categorical or bag-of-words representations to word embeddings representations in the latent space. First, we show that entropy and Kullback-Leibler divergence can be efficiently estimated using word embeddings and use this estimation to compare text across several categories. Next, we recast the heavy-tailed distribution known as Zipf's law that is frequently observed in the categorical space to the latent space. Finally, we look to improve the Jaccard similarity measure for sentence suggestion by introducing a new method of identifying similar sentences based on the set cover problem. We compare the performance of this algorithm against several baselines including Word Mover's Distance and the Levenshtein distance.
Scalable Transfer Evolutionary Optimization: Coping with Big Task Instances
Authors: Mojtaba Shakeri, Erfan Miahi, Abhishek Gupta, Yew-Soon Ong
Date: 2020-12-03 00:00:00

In today's digital world, we are faced with an explosion of data and models produced and manipulated by numerous large-scale cloud-based applications. Under such settings, existing transfer evolutionary optimization frameworks grapple with simultaneously satisfying two important quality attributes, namely (1) scalability against a growing number of source tasks and (2) online learning agility against sparsity of relevant sources to the target task of interest. Satisfying these attributes shall facilitate practical deployment of transfer optimization to scenarios with big task-instances, while curbing the threat of negative transfer. While applications of existing algorithms are limited to tens of source tasks, in this paper, we take a quantum leap forward in enabling more than two orders of magnitude scale-up in the number of tasks; i.e., we efficiently handle scenarios beyond 1000 source task-instances. We devise a novel transfer evolutionary optimization framework comprising two co-evolving species for joint evolutions in the space of source knowledge and in the search space of solutions to the target problem. In particular, co-evolution enables the learned knowledge to be orchestrated on the fly, expediting convergence in the target optimization task. We have conducted an extensive series of experiments across a set of practically motivated discrete and continuous optimization examples comprising a large number of source task-instances, of which only a small fraction indicate source-target relatedness. The experimental results show that not only does our proposed framework scale efficiently with a growing number of source tasks but is also effective in capturing relevant knowledge against sparsity of related sources, fulfilling the two salient features of scalability and online learning agility.
Attributes Aware Face Generation with Generative Adversarial Networks
Authors: Zheng Yuan, Jie Zhang, Shiguang Shan, Xilin Chen
Date: 2020-12-03 00:00:00
Tasks: Attribute, Face Generation

Recent studies have shown remarkable success in face image generations. However, most of the existing methods only generate face images from random noise, and cannot generate face images according to the specific attributes. In this paper, we focus on the problem of face synthesis from attributes, which aims at generating faces with specific characteristics corresponding to the given attributes. To this end, we propose a novel attributes aware face image generator method with generative adversarial networks called AFGAN. Specifically, we firstly propose a two-path embedding layer and self-attention mechanism to convert binary attribute vector to rich attribute features. Then three stacked generators generate $64 \times 64$, $128 \times 128$ and $256 \times 256$ resolution face images respectively by taking the attribute features as input. In addition, an image-attribute matching loss is proposed to enhance the correlation between the generated images and input attributes. Extensive experiments on CelebA demonstrate the superiority of our AFGAN in terms of both qualitative and quantitative evaluations.
Dr-COVID: Graph Neural Networks for SARS-CoV-2 Drug Repurposing
Authors: Siddhant Doshi, Sundeep Prabhakar Chepuri
Date: 2020-12-03 00:00:00
Tasks: Drug Design, Graph Neural Network, Link Prediction

The 2019 novel coronavirus (SARS-CoV-2) pandemic has resulted in more than a million deaths, high morbidities, and economic distress worldwide. There is an urgent need to identify medications that would treat and prevent novel diseases like the 2019 coronavirus disease (COVID-19). Drug repurposing is a promising strategy to discover new medical indications of the existing approved drugs due to several advantages in terms of the costs, safety factors, and quick results compared to new drug design and discovery. In this work, we explore computational data-driven methods for drug repurposing and propose a dedicated graph neural network (GNN) based drug repurposing model, called Dr-COVID. Although we analyze the predicted drugs in detail for COVID-19, the model is generic and can be used for any novel diseases. We construct a four-layered heterogeneous graph to model the complex interactions between drugs, diseases, genes, and anatomies. We pose drug repurposing as a link prediction problem. Specifically, we design an encoder based on the scalable inceptive graph neural network (SIGN) to generate embeddings for all the nodes in the four-layered graph and propose a quadratic norm scorer as a decoder to predict treatment for a disease. We provide a detailed analysis of the 150 potential drugs (such as Dexamethasone, Ivermectin) predicted by Dr-COVID for COVID-19 from different pharmacological classes (e.g., corticosteroids, antivirals, antiparasitic). Out of these 150 drugs, 46 drugs are currently in clinical trials. Dr-COVID is evaluated in terms of its prediction performance and its ability to rank the known treatment drugs for diseases as high as possible. For a majority of the diseases, Dr-COVID ranks the actual treatment drug in the top 15.