Reviewerly/exHarmony huggingface dataset:
Performance of the Whale Optimization Algorithm in Space Steel Frame Optimization Problems

Frame optimization that contains highly non-linear and irregular functions and discrete design variables is one of the most challenging optimization problems. Therefore, gradient-based optimization techniques cannot be successful in such problems. Metaheuristic techniques, especially population-based metaheuristic techniques, perform highly effective in solving the frame optimization problem. However, stochastic processes’ performances included in metaheuristic techniques vary based on the problem. Accordingly, researches on the performance of novel metaheuristic techniques on challenging engineering problems continue. One of the novel metaheuristic techniques is the whale optimization algorithm (WOA) which is inspired by the bubble-net feeding behavior of humpback whales. The aim of this study is testing the performance of WOA for space steel frame optimization problems. For this purpose, WOA-cased frame optimization program will be developed. Benchmark frame structures are selected to compare optimum solutions with literature results.
Crossing the academic ocean? Judit Bar-Ilan’s oeuvre on search engines studies

The main objective of this work is to analyse the contributions of Judit Bar-Ilan to the search engines studies. To do this, two complementary approaches have been carried out. First, a systematic literature review of 47 publications authored and co-authored by Judit and devoted to this topic. Second, an interdisciplinarity analysis based on the cited references (publications cited by Judit) and citing documents (publications that cite Judit’s work) through Scopus. The systematic literature review unravels an immense amount of search engines studied (43) and indicators measured (especially technical precision, overlap and fluctuation over time). In addition to this, an evolution over the years is detected from descriptive statistical studies towards empirical user studies, with a mixture of quantitative and qualitative methods. Otherwise, the interdisciplinary analysis evidences that a significant portion of Judit’s oeuvre was intellectually founded on the computer sciences, achieving a significant, but not exclusively, impact on library and information sciences.
Basic Properties of a Mean Field Laser Equation

We study the nonlinear quantum master equation describing a laser under the mean field approximation. The quantum system is formed by a single mode optical cavity and two level atoms, which interact with reservoirs. Namely, we establish the existence and uniqueness of the regular solution to the nonlinear operator equation under consideration, as well as we get a probabilistic representation for this solution in terms of a mean field stochastic Schrödinger equation. To this end, we find a regular solution for the nonautonomous linear quantum master equation in Gorini–Kossakowski–Sudarshan–Lindblad form, and we prove the uniqueness of the solution to the nonautonomous linear adjoint quantum master equation in Gorini–Kossakowski–Sudarshan–Lindblad form. Moreover, we obtain rigorously the Maxwell–Bloch equations from the mean field laser equation.
Efficient simulation of so-called non-stoquastic superconducting flux circuits

There is a tremendous interest in fabricating superconducting flux circuits that are nonstoquastic---i.e., have positive off-diagonal matrix elements---in their qubit representation, as these circuits are thought to be unsimulable by classical approaches and thus could play a key role in the demonstration of speedups in quantum annealing protocols. We show however that the efficient simulation of these systems is possible by the direct simulation of the flux circuits. Our approach not only obviates the reduction to a qubit representation but also produces results that are more in the spirit of the experimental setup. We discuss the implications of our work. Specifically we argue that our results cast doubt on the conception that superconducting flux circuits represent the correct avenue for universal adiabatic quantum computers.
Fairness in Streaming Submodular Maximization: Algorithms and Hardness

Submodular maximization has become established as the method of choice for the task of selecting representative and diverse summaries of data. However, if datapoints have sensitive attributes such as gender or age, such machine learning algorithms, left unchecked, are known to exhibit bias: under- or over-representation of particular groups. This has made the design of fair machine learning algorithms increasingly important. In this work we address the question: Is it possible to create fair summaries for massive datasets? To this end, we develop the first streaming approximation algorithms for submodular maximization under fairness constraints, for both monotone and non-monotone functions. We validate our findings empirically on exemplar-based clustering, movie recommendation, DPP-based summarization, and maximum coverage in social networks, showing that fairness constraints do not significantly impact utility.
Artificial Bee Colony Algorithm

This research focused on introduction to the Artificial Bee Colony (ABC) algorithm, the foraging behavior and waggle dance of honeybees while passing information about a given food source to the rest of the bee colony. The mathematical modelling and real-life application of the bee algorithm in fast moving grocery retail outlet was presented, the result obtained from the big box store or retail facility after analysis showed that the ABC algorithm is highly effective in system modeling and prediction.
Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret

We study risk-sensitive reinforcement learning in episodic Markov decision processes with unknown transition kernels, where the goal is to optimize the total reward under the risk measure of exponential utility. We propose two provably efficient model-free algorithms, Risk-Sensitive Value Iteration (RSVI) and Risk-Sensitive Q-learning (RSQ). These algorithms implement a form of risk-sensitive optimism in the face of uncertainty, which adapts to both risk-seeking and risk-averse modes of exploration. We prove that RSVI attains an $\tilde{O}\big(\lambda(|\beta| H^2) \cdot \sqrt{H^{3} S^{2}AT} \big)$ regret, while RSQ attains an $\tilde{O}\big(\lambda(|\beta| H^2) \cdot \sqrt{H^{4} SAT} \big)$ regret, where $\lambda(u) = (e^{3u}-1)/u$ for $u>0$. In the above, $\beta$ is the risk parameter of the exponential utility function, $S$ the number of states, $A$ the number of actions, $T$ the total number of timesteps, and $H$ the episode length. On the flip side, we establish a regret lower bound showing that the exponential dependence on $|\beta|$ and $H$ is unavoidable for any algorithm with an $\tilde{O}(\sqrt{T})$ regret (even when the risk objective is on the same scale as the original reward), thus certifying the near-optimality of the proposed algorithms. Our results demonstrate that incorporating risk awareness into reinforcement learning necessitates an exponential cost in $|\beta|$ and $H$, which quantifies the fundamental tradeoff between risk sensitivity (related to aleatoric uncertainty) and sample efficiency (related to epistemic uncertainty). To the best of our knowledge, this is the first regret analysis of risk-sensitive reinforcement learning with the exponential utility.
No Quantum Ramsey Theorem for Stabilizer Codes

Recently, Nik Weaver proved a quantum analogue of the Ramsey theorem. Weaver's theorem states that for every positive integer k, there exists a positive integer nk such for any quantum channel on the nk × nk matrices, the corresponding quantum graph possesses either a k-dimensional quantum clique or a k-dimensional quantum anti-clique. Quantum anticliques coincide with error-correcting codes, while quantum cliques satisfy a dual property. In this paper we study the quantum graphs of mixed-unitary channels generated by tensor products of Pauli operators, which we call Pauli channels. We show that most quantum graphs arising from Pauli channels have non-trivial quantum cliques or quantum anti-cliques which are stabilizer codes. However, a reformulation of the quantum Ramsey theorem in terms of stabilizer codes and Pauli channels fails. Specifically, for every positive integer n, there exists an n-qubit Pauli channel for which any non-trivial quantum clique or quantum anti-clique fails to be a stabilizer code. We also show that this example is essentially unique, and hence most n-qubit Pauli channels have non-trivial quantum cliques or quantum anti-cliques which are stabilizer codes.
Steady State Analysis of Episodic Reinforcement Learning

This paper proves that the episodic learning environment of every finite-horizon decision task has a unique steady state under any behavior policy, and that the marginal distribution of the agent's input indeed converges to the steady-state distribution in essentially all episodic learning processes. This observation supports an interestingly reversed mindset against conventional wisdom: While the existence of unique steady states was often presumed in continual learning but considered less relevant in episodic learning, it turns out their existence is guaranteed for the latter. Based on this insight, the paper unifies episodic and continual RL around several important concepts that have been separately treated in these two RL formalisms. Practically, the existence of unique and approachable steady state enables a general way to collect data in episodic RL tasks, which the paper applies to policy gradient algorithms as a demonstration, based on a new steady-state policy gradient theorem. Finally, the paper also proposes and experimentally validates a perturbation method that facilitates rapid steady-state convergence in real-world RL tasks.
Agnostic learning of a single neuron with gradient descent

We consider the problem of learning the best-fitting single neuron as measured by the expected square loss $\mathbb{E}_{(x,y)\sim \mathcal{D}}[(\sigma(w^\top x)-y)^2]$ over some unknown joint distribution $\mathcal{D}$ by using gradient descent to minimize the empirical risk induced by a set of i.i.d. samples $S\sim \mathcal{D}^n$. The activation function $\sigma$ is an arbitrary Lipschitz and non-decreasing function, making the optimization problem nonconvex and nonsmooth in general, and covers typical neural network activation functions and inverse link functions in the generalized linear model setting. In the agnostic PAC learning setting, where no assumption on the relationship between the labels $y$ and the input $x$ is made, if the optimal population risk is $\mathsf{OPT}$, we show that gradient descent achieves population risk $O(\mathsf{OPT})+\epsilon$ in polynomial time and sample complexity when $\sigma$ is strictly increasing. For the ReLU activation, our population risk guarantee is $O(\mathsf{OPT}^{1/2})+\epsilon$. When labels take the form $y = \sigma(v^\top x) + \xi$ for zero-mean sub-Gaussian noise $\xi$, we show that the population risk guarantees for gradient descent improve to $\mathsf{OPT} + \epsilon$. Our sample complexity and runtime guarantees are (almost) dimension independent, and when $\sigma$ is strictly increasing, require no distributional assumptions beyond boundedness. For ReLU, we show the same results under a nondegeneracy assumption for the marginal distribution of the input.
Deep Variational Instance Segmentation

Instance Segmentation, which seeks to obtain both class and instance labels for each pixel in the input image, is a challenging task in computer vision. State-of-the-art algorithms often employ two separate stages, the first one generating object proposals and the second one recognizing and refining the boundaries. Further, proposals are usually based on detectors such as faster R-CNN which search for boxes in the entire image exhaustively. In this paper, we propose a novel algorithm that directly utilizes a fully convolutional network (FCN) to predict instance labels. Specifically, we propose a variational relaxation of instance segmentation as minimizing an optimization functional for a piecewise-constant segmentation problem, which can be used to train an FCN end-to-end. It extends the classical Mumford-Shah variational segmentation problem to be able to handle permutation-invariant labels in the ground truth of instance segmentation. Experiments on PASCAL VOC 2012, Semantic Boundaries dataset(SBD), and the MSCOCO 2017 dataset show that the proposed approach efficiently tackle the instance segmentation task. The source code and trained models will be released with the paper.
Construction Worker Posture Estimation Using OpenPose

Visual field observations are critical in construction project management. Cost reduction and increases in data usability have made RGB cameras more prevalent on construction sites, expanding capabilities of the modern construction manager. The eyes of a construction manager are irreplaceable tools which monitor and subjectively evaluate construction activities. The variety of “right” and “wrong” ways to complete a construction task make the automation of visual field operations a difficult task. Existing vision-based methods are limited to RGB-depth cameras which are rarely used on construction sites. This paper presents a method for using deep learning techniques to aid the automatic recognition of construction worker activities from RGB camera footage and to what extent can computer vision accurately detect construction worker activity. OpenPose human estimation algorithm was used to create 2D human pose samples of construction activities using RGB cameras. The samples are used to train and test a time distributed feed forward neural network with LSTM Keras model classifier. The results of this work in progress set the stage for improving the usefulness of visual data for project management in a variety of construction scenarios and other labor-intensive sectors. Monitoring the safety and productivity of construction workers will become more efficient allowing much-needed increases in our capacity to develop and maintain infrastructure.
Implicit Distributional Reinforcement Learning

To improve the sample efficiency of policy-gradient based reinforcement learning algorithms, we propose implicit distributional actor critic (IDAC) that consists of a distributional critic, built on two deep generator networks (DGNs), and a semi-implicit actor (SIA), powered by a flexible policy distribution. We adopt a distributional perspective on the discounted cumulative return and model it with a state-action-dependent implicit distribution, which is approximated by the DGNs that take state-action pairs and random noises as their input. Moreover, we use the SIA to provide a semi-implicit policy distribution, which mixes the policy parameters with a reparameterizable distribution that is not constrained by an analytic density function. In this way, the policy's marginal distribution is implicit, providing the potential to model complex properties such as covariance structure and skewness, but its parameter and entropy can still be estimated. We incorporate these features with an off-policy algorithm framework to solve problems with continuous action space, and compare IDAC with the state-of-art algorithms on representative OpenAI Gym environments. We observe that IDAC outperforms these baselines for most tasks.
Methods for Extracting Relational Data from Unstructured Texts Prior to Network Visualization in Humanities Research

Network modelling methodologies in the digital humanities have been be used to advance inquiry in a variety of areas—most commonly those having to do with correspondence, citation, and social media networks. While new technologies have made generating high-quality and even dynamic network visualizations relatively easy, key challenges remain for humanities researchers. Many common objects of humanistic inquiry, such as literary, historiographic, and biographical texts are often not easily transformed into the kinds of data structures necessary for network visualization. The Transparency to Visibility (T2V) Project was initiated to develop new methods and toolkits that can support humanistic researchers who need to extract relationship data from unstructured texts to support network visualization. The T2V team used bioethics accountability statements to pilot and evaluate different methods for extracting relationship data. The resulting machine-learning-enhanced natural language processing (NLP) and metadata-assisted approaches offer promising potential pathways for contemporary digital humanities and future toolkit development. Funding statement: Funding was provided by a National Endowment for the Humanities Digital Humanities Advancement grant (HAA-261070).
Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample

We consider the task of generating diverse and novel videos from a single video sample. Recently, new hierarchical patch-GAN based approaches were proposed for generating diverse images, given only a single sample at training time. Moving to videos, these approaches fail to generate diverse samples, and often collapse into generating samples similar to the training video. We introduce a novel patch-based variational autoencoder (VAE) which allows for a much greater diversity in generation. Using this tool, a new hierarchical video generation scheme is constructed: at coarse scales, our patch-VAE is employed, ensuring samples are of high diversity. Subsequently, at finer scales, a patch-GAN renders the fine details, resulting in high quality videos. Our experiments show that the proposed method produces diverse samples in both the image domain, and the more challenging video domain.
Few-Cost Salient Object Detection with Adversarial-Paced Learning

Detecting and segmenting salient objects from given image scenes has received great attention in recent years. A fundamental challenge in training the existing deep saliency detection models is the requirement of large amounts of annotated data. While gathering large quantities of training data becomes cheap and easy, annotating the data is an expensive process in terms of time, labor and human expertise. To address this problem, this paper proposes to learn the effective salient object detection model based on the manual annotation on a few training images only, thus dramatically alleviating human labor in training models. To this end, we name this task as the few-cost salient object detection and propose an adversarial-paced learning (APL)-based framework to facilitate the few-cost learning scenario. Essentially, APL is derived from the self-paced learning (SPL) regime but it infers the robust learning pace through the data-driven adversarial learning mechanism rather than the heuristic design of the learning regularizer. Comprehensive experiments on four widely-used benchmark datasets demonstrate that the proposed method can effectively approach to the existing supervised deep salient object detection models with only 1k human-annotated training images. The project page is available at this https URL.
Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding

It is a truth universally acknowledged that an observed association without known mechanism must be in want of a causal estimate. However, causal estimation from observational data often relies on the (untestable) assumption of `no unobserved confounding'. Violations of this assumption can induce bias in effect estimates. In principle, such bias could invalidate or reverse the conclusions of a study. However, in some cases, we might hope that the influence of unobserved confounders is weak relative to a `large' estimated effect, so the qualitative conclusions are robust to bias from unobserved confounding. The purpose of this paper is to develop \emph{Austen plots}, a sensitivity analysis tool to aid such judgments by making it easier to reason about potential bias induced by unobserved confounding. We formalize confounding strength in terms of how strongly the confounder influences treatment assignment and outcome. For a target level of bias, an Austen plot shows the minimum values of treatment and outcome influence required to induce that level of bias. Domain experts can then make subjective judgments about whether such strong confounders are plausible. To aid this judgment, the Austen plot additionally displays the estimated influence strength of (groups of) the observed covariates. Austen plots generalize the classic sensitivity analysis approach of Imbens [Imb03]. Critically, Austen plots allow any approach for modeling the observed data and producing the initial estimate. We illustrate the tool by assessing biases for several real causal inference problems, using a variety of machine learning approaches for the initial data analysis. Code is available at this https URL
Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher

Knowledge distillation is a strategy of training a student network with guide of the soft output from a teacher network. It has been a successful method of model compression and knowledge transfer. However, currently knowledge distillation lacks a convincing theoretical understanding. On the other hand, recent finding on neural tangent kernel enables us to approximate a wide neural network with a linear model of the network's random features. In this paper, we theoretically analyze the knowledge distillation of a wide neural network. First we provide a transfer risk bound for the linearized model of the network. Then we propose a metric of the task's training difficulty, called data inefficiency. Based on this metric, we show that for a perfect teacher, a high ratio of teacher's soft labels can be beneficial. Finally, for the case of imperfect teacher, we find that hard labels can correct teacher's wrong prediction, which explains the practice of mixing hard and soft labels.
On the convergence of an improved discrete simulated annealing via landscape modification.

In this paper, we propose new Metropolis-Hastings and simulated annealing algorithms on finite state space via modifying the energy landscape. The core idea of landscape modification relies on introducing a parameter $c$, in which the landscape is modified once the algorithm is above this threshold parameter. We illustrate the power and benefits of landscape modification by investigating its effect on the classical Curie-Weiss model with Glauber dynamics and external magnetic field in the subcritical regime. This leads to a landscape-modified mean-field equation, and with appropriate choice of $c$ the free energy landscape can be transformed from a double-well into a single-well, while the location of the global minimum is preserved on the modified landscape. Consequently, running algorithms on the modified landscape can improve the convergence to the ground-state in the Curie-Weiss model. In the setting of simulated annealing, we demonstrate that landscape modification can yield improved mean tunneling time between global minima, and give convergence guarantee using an improved logarithmic cooling schedule with reduced critical height. Finally, we discuss connections between landscape modification and other acceleration techniques such as Catoni's energy transformation algorithm, preconditioning, importance sampling and quantum annealing. We stress that the technique developed in this paper is applicable to any difference-based discrete optimization algorithm.