Miracle-dz/UniFAR-FTUs huggingface dataset:
Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions

The connection between games and no-regret algorithms has been widely studied in the literature. A fundamental result is that when all players play no-regret strategies, this produces a sequence of actions whose time-average is a coarse-correlated equilibrium of the game. However, much less is known about equilibrium selection in the case that multiple equilibria exist. In this work, we study the convergence of no-regret bidding algorithms in auctions. Besides being of theoretical interest, bidding dynamics in auctions is an important question from a practical viewpoint as well. We study the repeated game between bidders in which a single item is sold at each time step and the bidder's value is drawn from an unknown distribution. We show that if the bidders use any mean-based learning rule then the bidders converge with high probability to the truthful pure Nash Equilibrium in a second price auction, in VCG auction in the multi-slot setting and to the Bayesian Nash equilibrium in a first price auction. We note mean-based algorithms cover a wide variety of known no-regret algorithms such as Exp3, UCB, \epsilon-Greedy etc. Also, we analyze the convergence of the individual iterates produced by such learning algorithms, as opposed to the time-average of the sequence. Our experiments corroborate our theoretical findings and also find a similar convergence when we use other strategies such as Deep Q-Learning.
Biosynthesis and Gene Engineering of Plant Fatty Acids

The plant fatty acids are known to play a vital physiological role and to possess highly edible and industrial value. Pathways of fatty acid biosynthesis are complicated. A series of enzymes including acetyl_CoA carboxylase, fatty acid synthetase, fatty acid desaturase, and fatty acid elongase are involved. Recently, studies on pathways of fatty acid biosynthesis had made a great progress. Several related genes had been cloned. The mechanism of fatty acid synthesis had preliminarily been demonstrated. Moreover, manipulation of fatty acid biosynthesis using gene engineering had also been studied. This paper reviewed the recent progress of studies on biosynthesis and gene engineering of saturated fatty acids, unsaturated fatty acids, and very long chain fatty acids in plant.
Toward the Fundamental Limits of Imitation Learning

Imitation learning (IL) aims to mimic the behavior of an expert policy in a sequential decision-making problem given only demonstrations. In this paper, we focus on understanding the minimax statistical limits of IL in episodic Markov Decision Processes (MDPs). We first consider the setting where the learner is provided a dataset of $N$ expert trajectories ahead of time, and cannot interact with the MDP. Here, we show that the policy which mimics the expert whenever possible is in expectation $\lesssim \frac{|\mathcal{S}| H^2 \log (N)}{N}$ suboptimal compared to the value of the expert, even when the expert follows an arbitrary stochastic policy. Here $\mathcal{S}$ is the state space, and $H$ is the length of the episode. Furthermore, we establish a suboptimality lower bound of $\gtrsim |\mathcal{S}| H^2 / N$ which applies even if the expert is constrained to be deterministic, or if the learner is allowed to actively query the expert at visited states while interacting with the MDP for $N$ episodes. To our knowledge, this is the first algorithm with suboptimality having no dependence on the number of actions, under no additional assumptions. We then propose a novel algorithm based on minimum-distance functionals in the setting where the transition model is given and the expert is deterministic. The algorithm is suboptimal by $\lesssim \min \{ H \sqrt{|\mathcal{S}| / N} ,\ |\mathcal{S}| H^{3/2} / N \}$, showing that knowledge of transition improves the minimax rate by at least a $\sqrt{H}$ factor.
Hellinger KL-UCB based Bandit Algorithms for Markovian and i.i.d. Settings

In the regret-based formulation of multi-armed bandit (MAB) problems, except in rare instances, much of the literature focuses on arms with i.i. d. rewards. In this paper, we consider the problem of obtaining regret guarantees for MAB problems in which the rewards of each arm form a Markov chain which may not belong to a single parameter exponential family. To achieve logarithmic regret in such problems is not difficult: a variation of standard KL-UCB does the job. However, the constants obtained from such an analysis are poor for the following reason: i.i. d. rewards are a special case of Markov rewards and it is difficult to design an algorithm that works well independent of whether the underlying model is truly Markovian or i.i. d. To overcome this issue, we introduce a novel algorithm that identifies whether the rewards from each arm are truly Markovian or i.i. d. using a Hellinger distance-based test. Our algorithm then switches from using a standard KL-UCB to a specialized version of KL-UCB when it determines that the arm reward is Markovian, thus resulting in low regret for both i.i. d. and Markovian settings.
Adaptive Methods for Short-Term Electricity Load Forecasting During COVID-19 Lockdown in France

The coronavirus disease 2019 (COVID-19) pandemic has urged many governments in the world to enforce a strict lockdown where all nonessential businesses are closed and citizens are ordered to stay at home. One of the consequences of this policy is a significant change in electricity consumption patterns. Since load forecasting models rely on calendar or meteorological information and are trained on historical data, they fail to capture the significant break caused by the lockdown and have exhibited poor performances since the beginning of the pandemic. In this paper we introduce two methods to adapt generalized additive models, alleviating the aforementioned issue. Using Kalman filters and fine-tuning allows to adapt quickly to new electricity consumption patterns without requiring exogenous information. The proposed methods are applied to forecast the electricity demand during the French lockdown period, where they demonstrate their ability to significantly reduce prediction errors compared to traditional models. Finally, expert aggregation is used to leverage the specificities of each predictions and enhance results even further.
ALMA Observations of Giant Molecular Clouds in M33. II. Triggered High-mass Star Formation by Multiple Gas Colliding Events at the NGC 604 Complex

We present the results of ALMA observations in 12CO(J = 2 − 1), 13CO(J = 2 − 1), and C18O(J = 2 − 1) lines and 1.3 mm continuum emission toward a massive (∼106 M⊙) giant molecular cloud associated with the giant H ii region NGC 604 in one of the nearest spiral galaxies, M33, at an angular resolution of 0. ″44 × 0. ″27 (1.8 pc × 1.1 pc). The 12CO and 13CO images show highly complicated molecular structures composed of a lot of filaments and shells whose lengths are 5–20 pc. We found three 1.3 mm continuum sources to be dense clumps at the edges of two shells and also at the intersection of several filaments. We examined the velocity structures of the 12CO(J = 2 − 1) emission in the shells and filaments containing dense clumps, and concluded that expansion of the H ii regions cannot explain the formation of such dense cores. Alternatively, we suggest that cloud–cloud collisions induced by an external H i gas flow and the galaxy’s rotation compressed the molecular material into dense filaments/shells which are ongoing high-mass star formation sites. We propose that multiple gas converging/colliding events with a velocity of a few tens of kilometers per second are necessary to build up NGC 604, the most significant cluster-forming complex in the Local Group of galaxies.
Towards the Quantification of Safety Risks in Deep Neural Networks

Safety concerns on the deep neural networks (DNNs) have been raised when they are applied to critical sectors. In this paper, we define safety risks by requesting the alignment of the network's decision with human perception. To enable a general methodology for quantifying safety risks, we define a generic safety property and instantiate it to express various safety risks. For the quantification of risks, we take the maximum radius of safe norm balls, in which no safety risk exists. The computation of the maximum safe radius is reduced to the computation of their respective Lipschitz metrics - the quantities to be computed. In addition to the known adversarial example, reachability example, and invariant example, in this paper we identify a new class of risk - uncertainty example - on which humans can tell easily but the network is unsure. We develop an algorithm, inspired by derivative-free optimization techniques and accelerated by tensor-based parallelization on GPUs, to support efficient computation of the metrics. We perform evaluations on several benchmark neural networks, including ACSC-Xu, MNIST, CIFAR-10, and ImageNet networks. The experiments show that, our method can achieve competitive performance on safety quantification in terms of the tightness and the efficiency of computation. Importantly, as a generic approach, our method can work with a broad class of safety risks and without restrictions on the structure of neural networks.
Short-Term X-ray Variability during Different Activity Phases of Blazars S5 0716+714 and PKS 2155-304

We explored the statistical properties of short-term X-ray variability using long-exposure XMM-Newton data during high X-ray variability phases of blazars S5 0716+714 and PKS 2155-304. In general, the hardness ratio shows correlated variations with the source flux state (count rate), but in a few cases, mainly the bright phases, the trend is complex with both correlation and anti-correlation, indicating spectral evolution. Stationarity tests suggest the time series are non-stationarity or have trend stationarity. Except for one, none of the histogram fits resulted in a reduced-χ2∼1 for a normal and log-normal profile but a normal profile is favored in general. On the contrary, the Anderson–Darling test favors log-normal with a test-statistic value lower for log-normal over normal for all the observations, even if out of significance limits. None of the IDs show linear RMS-flux relation. The contrary inferences from different widely used statistical methods indicate that a careful analysis is needed while the complex behavior of count rate with hardness ratio suggests spectral evolution over a few 10 s of kilo-seconds during bright phases of the sources. In these cases, the spectrum extracted from whole observation may not be meaningful for spectral studies and certainly not a true representation of the spectral state of the source.
Energy Efficient Power Allocation in Massive MIMO NOMA Systems Based on SIF Using Cell Division Technique

In this paper, we investigate energy-efficient power allocation for the downlink of the massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) systems. In our proposed scheme, we divide a cell into two zones. The first area is for users whose distance from the base station (BS) is less than half of the radius of cell and the second area is for users whose distance from BS is more than half of the radius of cell. Based on distance of users from BS and the number of users in each area, we dedicate an amount of power for each user. We also use standard interference function (SIF) to propose a new iterative algorithm to solve the optimization problem and obtain the optimal power allocation scheme. Simulation results show that the proposed algorithm outperforms other algorithms from the energy efficiency (EE) point of view.
On the Use of Grey Literature: A Survey with the Brazilian Software Engineering Research Community

Background: The use of Grey Literature (GL) has been investigated in diverse research areas. In Software Engineering (SE), this topic has an increasing interest over the last years. Problem: Even with the increase of GL published in diverse sources, the understanding of their use on the SE research community is still controversial. Objective: To understand how Brazilian SE researchers use GL, we aimed to become aware of the criteria to assess the credibility of their use, as well as the benefits and challenges. Method: We surveyed 76 active SE researchers participants of a flagship SE conference in Brazil, using a questionnaire with 11 questions to share their views on the use of GL in the context of SE research. We followed a qualitative approach to analyze open questions. Results: We found that most surveyed researchers use GL mainly to understand new topics. Our work identified new findings, including: 1) GL sources used by SE researchers (e.g., blogs, community website); 2) motivations to use (e.g., to understand problems and to complement research findings) or reasons to avoid GL (e.g., lack of reliability, lack of scientific value); 3) the benefit that is easy to access and read GL and the challenge of GL to have its scientific value recognized; and 4) criteria to assess GL credibility, showing the importance of the content owner to be renowned (e.g., renowned author and institutions). Conclusions: Our findings contribute to form a body of knowledge on the use of GL by SE researchers, by discussing novel (some contradictory) results and providing a set of lessons learned to both SE researchers and practitioners.
The polytope algebra of generalized permutahedra

The polytope algebra of a zonotope can be endowed with the structure of a module over the Tits algebra of the corresponding hyperplane arrangement. We explore this structure and find relations between statistics on (signed) permutations and the composition series of the module associated with the (signed) braid arrangement. We prove that the relations defining the polytope algebra are compatible with the Hopf monoid structure of generalized permutahedra.
Neurodynamic TDOA Localization with NLOS Mitigation via Maximum Correntropy Criterion

The commonly applied approaches for localization in the Internet of Things context using time-of-arrival (TOA) or time-difference-of-arrival (TDOA) measurements usually suffer significant performance degradation due to the presence of non-line-of-sight (NLOS) propagation. Unlike the majority of existing efforts made under the framework of convex relaxation, in this paper we devise a computationally simpler neurodynamic optimization method for robust TDOA-based localization with the use of the maximum correntropy criterion. To be specific, the outlier-insensitive correntropy-induced loss function is utilized as the measure for the fitting error after TDOA-to-TOA model transformation, whereupon we design a hardware implementable recurrent neural network to solve the derived nonlinear and nonconvex constrained optimization problem, based on the redefined augmented Lagrangian and projection theorem. The local stability of equilibrium for the established dynamical system is examined, and numerically realizing the presented projection-type neural network leads to merely quadratic complexity in the number of sensors. Simulation investigations show that our TDOA-based localization solution outperforms several state-of-the-art schemes in terms of localization accuracy, especially when the NLOS paths and errors tend to exhibit sparsity and severeness, respectively.
Synthesizing brain tumor images and annotations by combining progressive growing GAN and SPADE

Training segmentation networks requires large annotated datasets, but manual annotation is time consuming and costly. We here investigate if the combination of a noise-to-image GAN and an image-to-image GAN can be used to synthesize realistic brain tumor images as well as the corresponding tumor annotations (labels), to substantially increase the number of training images. The noise-to-image GAN is used to synthesize new label images, while the image-to-image GAN generates the corresponding MR image from the label image. Our results indicate that the two GANs can synthesize label images and MR images that look realistic, and that adding synthetic images improves the segmentation performance, although the effect is small.
Early Mortality and Survival of Self-Sown Seedlings in Glenfeshie, Inverness-Shire

On most upland range in Scotland the tree cover was destroyed centuries ago and persistent burning and grazing have deflected the subsequent development of the vegetation into relatively stable types of plagioclimax with a probable loss of species diversity over large areas (Gimingham 1964; McVean & Ratcliffe 1962; McVean & Lockie 1969). To develop wildlife and grazing values, it is now often desirable to introduce additional species to many sites, including trees and shrubs as well as herbs, and to encourage their regeneration and spread. However, the establishment of new plants from seed appears to be rather uncommon in many closed herbaceous and dwarf shrub communities (Miles 1972) and little is known on the subject. The aim of this study was to examine the early mortality and survival of self-sown seedlings in a Highland glen used primarily as a range for red deer (Cervus elaphus L.). It was hoped that the results would indicate the amount of seedling establishment in such an area and the levels, and perhaps the causes, of mortality.
A Wideband Polarization Study of Cygnus A with the Jansky Very Large Array. I. The Observations and Data

We present results from deep, wideband, high spatial and spectral resolution observations of the nearby luminous radio galaxy Cygnus A with the Jansky Very Large Array. The high surface brightness of this source enables detailed polarimetric imaging, providing images at , spanning 2–18 GHz, and at 0. ″30 (6–18 GHz). The fractional polarization from 2000 independent lines of sight across the lobes decreases strongly with decreasing frequency, with the eastern lobe depolarizing at higher frequencies than the western lobe. The depolarization shows a considerable structure, varying from monotonic to strongly oscillatory. The fractional polarization in general increases with increasing resolution at a given frequency, as expected. However, there are numerous lines of sight with more complicated behavior. We have fitted the images with a simple model incorporating random, unresolved fluctuations in the cluster magnetic field to determine the high-resolution, high-frequency properties of the source and the cluster. From these derived properties, we generated predicted polarization images of the source at lower frequencies, convolved to 0. ″75. These predictions are remarkably consistent with the observed emission. The observations are consistent with the lower-frequency depolarization being due to unresolved fluctuations on scales ≳ 300–700 pc in the magnetic field and/or electron density superposed on a partially ordered field component. There is no indication in our data of the location of the depolarizing screen or the large-scale field, either or both of which could be located throughout the cluster or in a boundary region between the lobes and the cluster.
Topology of Parametrized Motion Planning Algorithms

In this paper we introduce and study a new concept of parametrised topological complexity, a topological invariant motivated by the motion planning problem of robotics. In the parametrised setting, a motion planning algorithm has high degree of universality and flexibility, it can function under a variety of external conditions (such as positions of the obstacles etc). We explicitly compute the parameterised topological complexity of obstacle-avoiding collision-free motion of many particles (robots) in 3-dimensional space. Our results show that the parameterised topological complexity can be significantly higher than the standard (nonparametrised) invariant.
Joint Optimization of Beamforming, Phase-Shifting and Power Allocation in a Multi-Cluster IRS-NOMA Network

The combination of non-orthogonal multiple access (NOMA) and intelligent reflecting surface (IRS) is an efficient solution to significantly enhance the energy efficiency of the wireless communication system. In this paper, a downlink multi-cluster NOMA network is considered, where each cluster is supported by one IRS. This paper aims to minimize the transmit power by jointly optimizing the beamforming, the power allocation and the phase shift of each IRS. The formulated problem is non-convex and challenging to be solved due to the coupled variables, i.e., the beamforming vector, the power allocation coefficient and the phase shift matrix. To address this non-convex problem, an alternating optimization based algorithm is proposed. Specifically, the primal problem is divided into two subproblems for beamforming optimization and phase shifting feasiblity, where the two subproblems are solved iteratively. Moreover, to guarantee the feasibility of the beamforming optimization problem, an iterative algorithm is proposed to search the feasible initial points. To reduce the complexity, a simplified algorithm based on partial exhaustive search for this system model is also proposed. Simulation results demonstrate that the proposed alternating algorithm can yield a better performance gain than the partial exhaustive search algorithm, NOMA with random IRS phase shift scheme and OMA-IRS scheme.
DAICS: A Deep Learning Solution for Anomaly Detection in Industrial Control Systems

Deep Learning is emerging as an effective technique to detect sophisticated cyber-attacks targeting Industrial Control Systems (ICSs). The conventional detection approach is to learn the “normal” behaviour of the system, to be then able to label noteworthy deviations from it as anomalies. However, the normal behaviour of ICSs continuously evolves over time for multiple reasons, such as update/replacement of devices, workflow modifications or others. As a consequence, the accuracy of the anomaly detection process may be dramatically affected with a considerable amount of false alarms being generated. This article presents DAICS, a novel deep learning framework with a modular design to fit in large ICSs. The key component of the framework is a 2-branch neural network that learns the changes in the ICS behaviour with a small number of data samples and a few gradient updates. This is supported by an automatic tuning mechanism of the detection threshold that takes into account the changes in the prediction error under normal operating conditions. In this regard, no specialised human intervention is needed to update the other parameters of the system. DAICS has been evaluated using publicly available datasets and shows an increased detection rate and accuracy compared to state-of-the-art approaches, as well as higher robustness to additive noise.
Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations

Many real-world applications involve black-box optimization of multiple objectives using continuous function approximations that trade-off accuracy and resource cost of evaluation. For example, in rocket launching research, we need to find designs that trade-off return-time and angular distance using continuous-fidelity simulators (e.g., varying tolerance parameter to trade-off simulation time and accuracy) for design evaluations. The goal is to approximate the optimal Pareto set by minimizing the cost for evaluations. In this paper, we propose a novel approach referred to as information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations (iMOCA)} to solve this problem. The key idea is to select the sequence of input and function approximations for multiple objectives which maximize the information gain per unit cost for the optimal Pareto front. Our experiments on diverse synthetic and real-world benchmarks show that iMOCA significantly improves over existing single-fidelity methods.