Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantically-oriented mutation operator in cartesian genetic programming for evolutionary circuit design

Cartesian genetic programming (CGP) represents the most efficient method for the evolution of digital circuits. Despite many successful applications, however, CGP suffers from limited scalability, especially when used for evolutionary circuit design, i.e. design of circuits from a randomly initialized population. Considering the multiplier design problem, for example, the 5×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}5-bit multiplier represents the most complex circuit designed by the evolution from scratch. The efficiency of CGP highly depends on the performance of the point mutation operator, however, this operator is purely stochastic. This contrasts with the recent developments in genetic programming (GP), where advanced informed approaches such as semantic-aware operators are incorporated to improve the search space exploration capability of GP. In this paper, we propose a semantically-oriented mutation operator (SOMOk\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm {SOMO}^k$$\end{document}) suitable for the evolutionary design of combinational circuits. In contrast to standard point mutation modifying the values of the mutated genes randomly, the proposed operator uses semantics to determine the best value for each mutated gene. Compared to the common CGP and its variants, the proposed method converges on common Boolean benchmarks substantially faster while keeping the phenotype size relatively small. The successfully evolved instances presented in this paper include 10-bit parity, 10 + 10-bit adder and 5×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}5-bit multiplier. The most complex circuits were evolved in less than one hour with a single-thread implementation running on a common CPU.
On the shock change equations

We revisit and derive the shock-change equations relating the dynamics of a shock wave with the partial derivatives describing the motion of a reactive fluid with general equation of state in a stream-tube with arbitrary area variation. We specialize these to a perfect gas, in which we obtain all shock-change equations in closed form. These are further simplified for strong shocks. We discuss the general usefulness of these equations in problems of reactive compressible flow and in the development of intrinsic evolution equations for the shock, such as the approximations made by Whitham and Sharma.
Age-of-Information With Information Source Diversity in an Energy Harvesting System

Age of information (AoI) is a key performance metric for the Internet of things (IoT). Timely status updates are essential for many IoT applications; however, they often suffer from harsh energy constraints and the unreliability of underlying information sources. To overcome these unpredictabilities, one can employ multiple sources that track the same process of interest, but with different energy costs and reliabilities. We consider an energy-harvesting (EH) monitoring node equipped with a finite-size battery and collecting status updates from multiple heterogeneous information sources. We investigate the policies that minimize the average AoI, formulating a Markov decision process (MDP) to choose the optimal actions of either updating from one of the sources or remaining idle, based on the current energy level and the AoI at the monitoring node. We analyze the structure of the optimal solution for different cost/AoI distribution combinations, and compare its performance with an aggressive policy that transmits whenever possible.
Higgs-electroweak chiral Lagrangian: One-loop renormalization group equations

Starting from the one-loop divergences we obtained previously, we work out the renormalization of the Higgs-Electroweak Chiral Lagrangian explicitly and in detail. This includes the renormalization of the lowest-order Lagrangian, as well as the decomposition of the remaining divergences into a complete basis of next-to-leading-order counterterms. We provide the list of the corresponding beta functions. We show how our results match the one-loop renormalization of some of the dimension-6 operators in SMEFT. We further point out differences with related work in the literature and discuss them. As an application of the obtained results, we evaluate the divergences of the vacuum expectation value of the Higgs field at one loop and show that they can be appropriately removed by the corresponding renormalization. We also work out the finite renormalization required to keep the no-tadpole condition on the Higgs field at one loop.
Experimental verification of a plasmonic hook in a dielectric Janus particle

We report on a very first experimental observation of a new curved plasmonic beam, a plasmonic hook (PH), for surface plasmon-polariton (SPP) waves. We demonstrate that the SPP PH effect could be realized within a trapezoidal dielectric microstructure with the relative ease of fabrication compared to the generation of the Airy SPP waves which require complex techniques to compensate the wave vectors mismatch. Using numerical analysis, we predicted the PH phenomenon, optimized the microstructure and then confirmed the SPP PH existence via amplitude and phase-resolved scattering scanning near-field optical microscopy at the telecom wavelengths of 1530-1570 nm. The experimental results are in reasonable agreement with our predictions. Importantly, the SPP PH demonstrates the smallest radius of curvature ever recorded for SPP waves compared to that for the Airy-family plasmonic beams providing many useful applications spreading from nanoparticles manipulation toward bio-sensing within a nanoscale.
Code Smells and Refactoring: A Tertiary Systematic Review of Challenges and Observations

Abstract Refactoring and smells have been well researched by the software-engineering research community these past decades. Several secondary studies have been published on code smells, discussing their implications on software quality, their impact on maintenance and evolution, and existing tools for their detection. Other secondary studies addressed refactoring, discussing refactoring techniques, opportunities for refactoring, impact on quality, and tools support. In this paper, we present a tertiary systematic literature review of previous surveys, secondary systematic literature reviews, and systematic mappings. We identify the main observations (what we know) and challenges (what we do not know) on code smells and refactoring. We perform this tertiary review using eight scientific databases, based on a set of five research questions, identifying 40 secondary studies between 1992 and 2018. We organize the main observations and challenges about code smell and their refactoring into: smells definitions, most common code-smell detection approaches, code-smell detection tools, most common refactoring, and refactoring tools. We show that code smells and refactoring have a strong relationship with quality attributes, i.e., with understandability, maintainability, testability, complexity, functionality, and reusability. We argue that code smells and refactoring could be considered as the two faces of a same coin. Besides, we identify how refactoring affects quality attributes, more than code smells. We also discuss the implications of this work for practitioners, researchers, and instructors. We identify 13 open issues that could guide future research work. Thus, we want to highlight the gap between code smells and refactoring in the current state of software-engineering research. We wish that this work could help the software-engineering research community in collaborating on future work on code smells and refactoring.
Physics-Based Approach for a Neural Networks Enabled Design of All-Dielectric Metasurfaces

Machine learning methods have found novel application areas in various disciplines as they offer low-computational cost solutions to complex problems. Recently, metasurface design has joined among these applications, and neural networks enabled significant improvements within a short period of time. However, there are still outstanding challenges that needs to be overcome. Here, we propose a data pre-processing approach based on the governing laws of the physical problem to eliminate dimensional mismatch between high dimensional optical response and low dimensional feature space of metasurfaces. We train forward and inverse models to predict optical responses of cylindrical meta-atoms and to retrieve their geometric parameters for a desired optical response, respectively. Our approach provides accurate prediction capability even outside the training spectral range. Finally, using our inverse model, we design and demonstrate a focusing metalens as a proof-of-concept application, thus validating the capability of our proposed approach. We believe our method will pave the way towards practical learning-based models to solve more complicated photonic design problems.
Visual Commonsense Graphs: Reasoning about the Dynamic Context of a Still Image

Even from a single frame of a still image, people can reason about the dynamic story of the image before, after, and beyond the frame. For example, given an image of a man struggling to stay afloat in water, we can reason that the man fell into the water sometime in the past, the intent of that man at the moment is to stay alive, and he will need help in the near future or else he will get washed away. We propose VisualComet, the novel framework of visual commonsense reasoning tasks to predict events that might have happened before, events that might happen next, and the intents of the people at present. To support research toward visual commonsense reasoning, we introduce the first large-scale repository of Visual Commonsense Graphs that consists of over 1.4 million textual descriptions of visual commonsense inferences carefully annotated over a diverse set of 60,000 images, each paired with short video summaries of before and after. In addition, we provide person-grounding (i.e., co-reference links) between people appearing in the image and people mentioned in the textual commonsense descriptions, allowing for tighter integration between images and text. We establish strong baseline performances on this task and demonstrate that integration between visual and textual commonsense reasoning is the key and wins over non-integrative alternatives.
Tailoring ‘photonic hook’ from Janus dielectric microbar

A new type of curved subwavelength light beam, a photonic hook (PH), emerging from the diffraction of a light wave at a mesoscale dielectric object with broken internal symmetry (Janus particle), was recently reported. For efficient and technically simpler generation of PHs, we propose a new type of asymmetric Janus microparticles, the orthogonal microbars, composed from materials with different refractive indices. Based on the numerical simulations by means of the finite elements method, we investigate the physical mechanism of PH formation by analyzing the field intensity distribution and the energy fluxes near such particles. The influence of dielectric substrate is also studied for the first time. We show that by changing the refractive index contrast between two parts of Janus bar the shape and curvature of the PHs can be efficiently controlled. The criteria of PH quality for complex characterizing the photonic hook curvature is introduced and discussed as well.
Information and order of genomic sequences within chromosomes as identified by complexity theory. An integrated methodology

Complexity metrics and machine learning (ML) models have been utilized to analyze the lengths of segmental genomic entities like: exons, introns, intergenic and repeat/unique DNA sequences, in each of the 22 human chromosomes. The purpose of the study was to assess information and order that may be concealed within the size distribution of these sequences. For this purpose, we developed an innovative integrated methodology. Our analysis is based upon the reconstructed phase space theorem, the non-extensive statistical theory of Tsallis, ML techniques and a new technical index, integrating the generated information, which we introduce and named it Complexity Factor (COFA). The low-dimensional deterministic nonlinear chaotic and non-extensive statistical character of the DNA sequences was verified with strong multifractal characteristics and long-range correlations with significant variations per genomic entity and per chromosome. The results of the analysis reveal changes in complexity behavior per genomic entity and chromosome regarding the size distribution of individual genomic segment. The lengths of intron regions show greater complexity behavior in all metrics than the exonic ones, with longer range correlations, and stronger memory effects, for all chromosomes. We conclude from our analysis, that the size distribution of the genomic regions within chromosomes, are not random, but follow a specific pattern with characteristic features, that have been seen here through its complexity character, and it is part of the dynamics of the whole genome according to complexity theory. This picture of dynamics of the redundancy of information in DNA recognized from ML tools for clustering, classification and prediction.
TensorOpt: Exploring the Tradeoffs in Distributed DNN Training with Auto-Parallelism

Effective parallelization strategies are crucial for the performance of distributed deep neural network (DNN) training. Recently, several methods have been proposed to search parallelization strategies but they all optimize a single objective (e.g., execution time, memory consumption) and produce only one strategy. We propose FT, an efficient algorithm that finds a set of Pareto-optimal parallelization strategies to explore the best trade-off among different objectives. FT can minimize the memory consumption when the number of devices is limited and fully utilize additional resources to reduce the execution time. Based on FT, we develop a user-friendly system, called TensorOpt, which allows users to run their distributed DNN training jobs without caring the details about searching and coding parallelization strategies. Experimental results show that TensorOpt is more flexible in adapting to resource availability compared with existing frameworks.
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond

The class of random features is one of the most popular techniques to speed up kernel methods in large-scale problems. Related works have been recognized by the NeurIPS Test-of-Time award in 2017 and the ICML Best Paper Finalist in 2019. The body of work on random features has grown rapidly, and hence it is desirable to have a comprehensive overview on this topic explaining the connections among various algorithms and theoretical results. In this survey, we systematically review the work on random features from the past ten years. First, the motivations, characteristics and contributions of representative random features based algorithms are summarized according to their sampling schemes, learning procedures, variance reduction properties and how they exploit training data. Second, we review theoretical results that center around the following key question: how many random features are needed to ensure a high approximation quality or no loss in the empirical/expected risks of the learned estimator. Third, we provide a comprehensive evaluation of popular random features based algorithms on several large-scale benchmark datasets and discuss their approximation quality and prediction performance for classification. Last, we discuss the relationship between random features and modern over-parameterized deep neural networks (DNNs), including the use of high dimensional random features in the analysis of DNNs as well as the gaps between current theoretical and empirical results. This survey may serve as a gentle introduction to this topic, and as a users’ guide for practitioners interested in applying the representative algorithms and understanding theoretical results under various technical assumptions. We hope that this survey will facilitate discussion on the open problems in this topic, and more importantly, shed light on future research directions. Due to the page limit, we suggest the readers refer to the full version of this survey https://arxiv.org/abs/2004.11154.
Observations of Disintegrating Long-period Comet C/2019 Y4 (ATLAS): A Sibling of C/1844 Y1 (Great Comet)

We present a study of C/2019 Y4 (ATLAS) using Sloan $gri$ observations from mid-January to early April 2020. During this timespan, the comet brightened with a growth in the effective cross-section of $\left(2.0 \pm 0.1 \right) \times 10^{2}$ m$^{2}$ s$^{-1}$ from the beginning to $\sim$70 d preperihelion in late March 2020, followed by a brightness fade and the comet gradually losing the central condensation. Meanwhile, the comet became progressively bluer, and was even bluer than the Sun ($g - r \approx 0.2$) when the brightness peaked, likely due to activation of subterranean fresh volatiles exposed to sunlight. With the tailward-bias corrected astrometry we found an enormous radial nongravitational parameter, $A_{1} = \left(+2.25 \pm 0.13\right) \times 10^{-7}$ au d$^{-2}$ in the heliocentric motion of the comet. Taking all of these finds into consideration, we conclude that the comet has disintegrated since mid-March 2020. By no means was the split new to the comet, as we quantified that the comet had undergone another split event around last perihelion $\sim$5 kyr ago, during which its sibling C/1844 Y1 (Great Comet) was produced, with the in-plane component of the separation velocity $\gtrsim$1 m s$^{-1}$. We constrained that the nucleus of C/2019 Y4 before disintegration was $\gtrsim$60 m in radius, and has been protractedly ejecting dust grains of $\sim$10-40 $\mu$m (assuming dust bulk density 0.5 g cm$^{-3}$) with ejection speed $\sim$30 m s$^{-1}$ in early March 2020 and increased to $\sim$80 m s$^{-1}$ towards the end of the month for grains of $\sim$10 $\mu$m.
Simulating anisoplanatic turbulence by sampling intermodal and spatially correlated Zernike coefficients

Abstract. Simulating atmospheric turbulence is an essential task for evaluating turbulence mitigation algorithms and training learning-based methods. Advanced numerical simulators for atmospheric turbulence are available, but they require evaluating wave propagation, which is computationally expensive. We present a propagation-free method for simulating imaging through turbulence with applications in ground-to-ground imaging. The key idea behind our work is a method to draw intermodal and spatially correlated Zernike coefficients. By establishing the equivalence of the angle-of-arrival correlation by Basu, McCrae, and Fiorino (2015) with the multiaperture correlation by Chanan (1992), we show that the Zernike coefficients can be drawn according to a covariance matrix defining the correlations. We propose fast and scalable sampling strategies to draw these samples. The method allows us to compress the wave propagation problem into a sampling problem, hence making the simulator significantly faster than existing ones. Experimental results show that the simulator has an excellent match with the theory and real turbulence data. We anticipate the simulator, which balances speed and accuracy, will be a useful tool for various computational imaging applications.
Weakly Supervised Learning Guided by Activation Mapping Applied to a Novel Citrus Pest Benchmark

Pests and diseases are relevant factors for production losses in agriculture and, therefore, promote a huge investment in the prevention and detection of its causative agents. In many countries, Integrated Pest Management is the most widely used process to prevent and mitigate the damages caused by pests and diseases in citrus crops. However, its results are credited by humans who visually inspect the orchards in order to identify the disease symptoms, insects and mite pests. In this context, we design a weakly supervised learning process guided by saliency maps to automatically select regions of interest in the images, significantly reducing the annotation task. In addition, we create a large citrus pest benchmark composed of positive samples (six classes of mite species) and negative samples. Experiments conducted on two large datasets demonstrate that our results are very promising for the problem of pest and disease classification in the agriculture field.
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning

Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications, due to non-interactivity between agents, curse of dimensionality and computation complexity. Hence, several decentralized MARL algorithms are motivated. However, existing decentralized methods only handle the fully cooperative setting where massive information needs to be transmitted in training. The block coordinate gradient descent scheme they used for successive independent actor and critic steps can simplify the calculation, but it causes serious bias. In this paper, we propose a flexible fully decentralized actor-critic MARL framework, which can combine most of actor-critic methods, and handle large-scale general cooperative multi-agent setting. A primal-dual hybrid gradient descent type algorithm framework is designed to learn individual agents separately for decentralization. From the perspective of each agent, policy improvement and value evaluation are jointly optimized, which can stabilize multi-agent policy learning. Furthermore, our framework can achieve scalability and stability for large-scale environment and reduce information transmission, by the parameter sharing mechanism and a novel modeling-other-agents methods based on theory-of-mind and online supervised learning. Sufficient experiments in cooperative Multi-agent Particle Environment and StarCraft II show that our decentralized MARL instantiation algorithms perform competitively against conventional centralized and decentralized methods.
Multi-level Binarized LSTM in EEG Classification for Wearable Devices

Long Short-Term Memory (LSTM) is widely used in various sequential applications. Complex LSTMs could be hardly deployed on wearable and resourced-limited devices due to the huge amount of computations and memory requirements. Binary LSTMs are introduced to cope with this problem, however, they lead to significant accuracy loss in some applications such as EEG classification which is essential to be deployed in wearable devices. In this paper, we propose an efficient multi-level binarized LSTM which has significantly reduced computations whereas ensuring an accuracy pretty close to full precision LSTM. By deploying 5-level binarized weights and inputs, our method reduces area and delay of MAC operation about $31\times and 27\times$ in 65nm technology, respectively with less than 0.01% accuracy loss. In contrast to many compute-intensive deep-learning approaches, the proposed algorithm is lightweight, and therefore, brings performance efficiency with accurate LSTM-based EEG classification to realtime wearable devices.
Numerical Analysis for Nematic Electrolytes

We consider a system of nonlinear PDEs modeling nematic electrolytes, and construct a dissipative solution with the help of its implementable, structure-inheriting and space–time discretization. Computational studies are performed to study the mutual effects of electric, elastic and viscous effects onto the molecules in a nematic electrolyte.