Miracle-dz/UniFAR-FTUs huggingface dataset:
Elastically driven Kelvin–Helmholtz-like instability in straight channel flow

Significance Kelvin–Helmholtz instability (KHI) describes the growth of perturbations of the interface separating counterpropagating streams of Newtonian fluids with different velocities and densities with heavier fluid at the bottom. It is one of the most studied shear flow instabilities widespread in nature and industrial processes. The KHI mechanism is based on competition between destabilizing velocity difference and stabilizing stratification. We study KHI in channel shear flow of viscoelastic fluid, considered stable due to elastic stress generated by polymers stretched by shear strain. Contrary to generally accepted opinion, we discover the elastically driven KH-like instability with temporal dynamics similar to Newtonian KHI but driven by strikingly different elastically driven destabilizing mechanism of elastic wave interacting with wall-normal vorticity amplifying interface perturbations. Originally, Kelvin–Helmholtz instability (KHI) describes the growth of perturbations at the interface separating counterpropagating streams of Newtonian fluids of different densities with heavier fluid at the bottom. Generalized KHI is also used to describe instability of free shear layers with continuous variations of velocity and density. KHI is one of the most studied shear flow instabilities. It is widespread in nature in laminar as well as turbulent flows and acts on different spatial scales from galactic down to Saturn’s bands, oceanographic and meteorological flows, and down to laboratory and industrial scales. Here, we report the observation of elastically driven KH-like instability in straight viscoelastic channel flow, observed in elastic turbulence (ET). The present findings contradict the established opinion that interface perturbations are stable at negligible inertia. The flow reveals weakly unstable coherent structures (CSs) of velocity fluctuations, namely, streaks self-organized into a self-sustained cycling process of CSs, which is synchronized by accompanied elastic waves. During each cycle in ET, counter propagating streaks are destroyed by the elastic KH-like instability. Its dynamics remarkably recall Newtonian KHI, but despite the similarity, the instability mechanism is distinctly different. Velocity difference across the perturbed streak interface destabilizes the flow, and curvature at interface perturbation generates stabilizing hoop stress. The latter is the main stabilizing factor overcoming the destabilization by velocity difference. The suggested destabilizing mechanism is the interaction of elastic waves with wall-normal vorticity leading to interface perturbation amplification. Elastic wave energy is drawn from the main flow and pumped into wall-normal vorticity growth, which destroys the streaks.
Universal gravitational-wave signatures from heavy new physics in the electroweak sector

We calculate the gravitational-wave spectra produced by the electroweak phase transition with TeV-scale Beyond-Standard-Model physics in the early universe. Our study captures the effect of quantum and thermal fluctuations within a non-perturbative framework. We discover a universal relation between the mean bubble separation and the strength parameter of the phase transition, which holds for a wide range of new-physics contributions. The ramifications of this result are three-fold: first, they constrain the gravitational-wave spectra resulting from heavy (TeV-scale) new physics. Second, they contribute to distinguishing heavy from light new physics directly from the gravitational-wave signature. Third, they suggest that a concerted effort of gravitational-wave observations together with collider experiments could be required to distinguish between different models of heavy new physics.
Predicting the flow field in a U-bend with deep neural networks

This paper describes a study based on computational fluid dynamics (CFD) and deep neural networks that focusing on predicting the flow field in differently distorted U-shaped pipes. The main motivation of this work was to get an insight about the justification of the deep learning paradigm in hydrodynamic hull optimisation processes that heavily depend on computing turbulent flow fields and that could be accelerated with models like the one presented. The speed-up can be even several orders of magnitude by surrogating the CFD model with a deep convolutional neural network. An automated geometry creation and evaluation process was set up to generate differently shaped two-dimensional U-bends and to carry out CFD simulation on them. This process resulted in a database with different geometries and the corresponding flow fields (2-dimensional velocity distribution), both represented on 128x128 equidistant grids. This database was used to train an encoder-decoder style deep convolutional neural network to predict the velocity distribution from the geometry. The effect of two different representations of the geometry (binary image and signed distance function) on the predictions was examined, both models gave acceptable predictions with a speed-up of two orders of magnitude.
Finite Element Model to Study Calcium Signalling in Cardiac Myocytes Involving Pump, Leak and Excess Buffer

The calcium signaling plays an important role in expansion and contraction of myocytes. This calcium signaling is achieved by diffusion of calcium, out flux of calcium through pumps, in flow of calcium through leak and buffering mechanisms in cardiac myocytes. In this paper an attempt has been made to develop a model of calcium signaling in myocytes incorporating diffusion of calcium, pump, leak and excess buffers. The model has been developed for a one dimensional steady state case. Appropriate boundary conditions have been framed. The finite element method has been employed to obtain the solution. The numerical results have been used to study the effect of buffers, pump and leak on calcium signaling in myocytes.
Microbunching instability characterization via temporally modulated laser pulses

High-brightness electron bunches, such as those generated and accelerated in free-electron lasers (FELs), can develop small-scale structure in the longitudinal phase space. This causes variations in the slice energy spread and current profile of the bunch which then undergo amplification, in an effect known as the microbunching instability. By imposing energy spread modulations on the bunch in the low-energy section of an accelerator, using an undulator and a modulated laser pulse in the centre of a dispersive chicane, it is possible tomanipulate the bunch longitudinal phase space. This allows for the control and study of the instability in unprecedented detail. We report measurements and analysis of such modulated electron bunches in the 2Dspectro-temporal domain at the FERMI FEL, for three different bunch compression schemes. We also perform corresponding simulations of these experiments and show that the codes are indeed able to reproduce the measurements across a wide spectral range. This detailed experimental verification of the ability of codes to capture the essential beam dynamics of the microbunching instability will benefit the design and performance of future FELs.
Deep learning for time series classification

Time series analysis is a field of data science which is interested in analyzing sequences of numerical values ordered in time. Time series are particularly interesting because they allow us to visualize and understand the evolution of a process over time. Their analysis can reveal trends, relationships and similarities across the data. There exists numerous fields containing data in the form of time series: health care (electrocardiogram, blood sugar, etc.), activity recognition, remote sensing, finance (stock market price), industry (sensors), etc. Time series classification consists of constructing algorithms dedicated to automatically label time series data. The sequential aspect of time series data requires the development of algorithms that are able to harness this temporal property, thus making the existing off-the-shelf machine learning models for traditional tabular data suboptimal for solving the underlying task. In this context, deep learning has emerged in recent years as one of the most effective methods for tackling the supervised classification task, particularly in the field of computer vision. The main objective of this thesis was to study and develop deep neural networks specifically constructed for the classification of time series data. We thus carried out the first large scale experimental study allowing us to compare the existing deep methods and to position them compared other non-deep learning based state-of-the-art methods. Subsequently, we made numerous contributions in this area, notably in the context of transfer learning, data augmentation, ensembling and adversarial attacks. Finally, we have also proposed a novel architecture, based on the famous Inception network (Google), which ranks among the most efficient to date.
Understanding the hardness of approximate query processing with joins

We study the hardness of Approximate Query Processing (AQP) of various types of queries involving joins over multiple tables of possibly different sizes. In the case where the query result is a single value (e.g., COUNT, SUM, and COUNT(DISTINCT)), we prove worst-case information-theoretic lower bounds for AQP problems that are given parameters $\epsilon$ and $\delta$, and return estimated results within a factor of 1+$\epsilon$ of the true results with error probability at most $\delta$. In particular, the lower bounds for cardinality estimation over joins under various settings are contained in our results. Informally, our results show that for various database queries with joins, unless restricted to the set of queries whose results are always guaranteed to be above a very large threshold, the amount of information an AQP algorithm needs for returning an accurate approximation is at least linear in the number of rows in the largest table. Similar lower bounds even hold for some special cases where additional information such as top-K heavy hitters and all frequency vectors are available. In the case of GROUP-BY where the query result is not a single number, we study the lower bound for the amount of information used by any approximation algorithm that does not report any non-existing group and does not miss groups of large total size. Our work extends the work of Alon, Gibbons, Matias, and Szegedy [AGMS99]. We compare our lower bounds with the amount of information required by Bernoulli sampling to give an accurate approximation. For COUNT queries with joins over multiple tables of the same size, the upper bound matches the lower bound, unless the problem setting is restricted to the set of queries whose results are always guaranteed to be above a very large threshold.
Review: The endless nutritional and pharmaceutical benefits of the Himalayan gold, Cordyceps; Current knowledge and prospective potentials

Abstract. Elkhateeb WA, Daba GM. 2020. Review: The endless nutritional and pharmaceutical benefits of the Himalayan gold, Cordyceps; Current knowledge and prospective potentials. Biofarmasi J Nat Prod Biochem 18: 70-77. As a traditional medicine, Cordyceps has long been used in Asian nations for maintaining vivacity and boosting immunity. Numerous publications on various bioactivities of Cordyceps have been investigated in both in-vitro as well as in vivo studies. Nevertheless, the role of Cordyceps is still arguable whether it acts as food supplement for health benefits or a real healing drug that can be prescribed in medicine. The Cordyceps industry has developed greatly and offers thousands of products, commonly available in a global marketplace. In this review, focus will be on introducing the ecology of Cordyceps and their classification. Moreover, elucidation of the richness of extracts originated from this mushroom in nutritional components was presented, with description of the chemical compounds of Cordyceps and its well-known compounds such as cordycepin, and cordycepic acid. Furthermore, highlights on natural growth and artificial cultivation of famous Cordyceps species were presented. The health benefits and reported bioactivities of Cordyceps species as promising antimicrobial, anticancer, hypocholesterolemic, antioxidant, antiviral, anti-inflammatory, organ protective agent, and enhancer for organ function were presented.
Distribution modelling of Eleonora’s Falcon Falco eleonorae Géné, 1839 occurrence in its wintering grounds: a niche-based approach with satellite telemetry data

Eleonora’s Falcon is a long-distance migrant of the Palearctic region. In recent years, the advent of satellite telemetry has enabled a more detailed investigation of the species’s migratory and wintering periods. In this study, we model the distribution pattern of four Eleonora’s Falcons originating from Greece within their wintering grounds in Madagascar with the use of satellite telemetry data and a niche-based technique, Maxent. The model predicted few highly suitable areas for the occurrence of the species, restricted to elevated areas receiving large amounts of precipitation during the wintering period, containing patches of primary and degraded humid submontane forests as well as cultivation. Most of these areas occurred within the previously estimated home ranges of the four falcons, as well as of three falcons from another independent study. Taking into account the ongoing alterations in landscape structure that occur within the eastern rainforest region of Madagascar, we believe that it is imperative to better understand the ecological requirements of Eleonora’s Falcon. To this end, we recommend the application of Maxent in the study of habitat selection of the species that could be further refined with the inclusion of biotic interactions and seasonal resource availability.
How macroeconomists lost control of stabilization policy: towards dark ages

Abstract This paper is a study of the history of the transplant of mathematical tools using negative feedback for macroeconomic stabilisation policy from 1948 to 1975 and the subsequent break of the use of control for stabilisation policy which occurred from 1975 to 1993. New-classical macroeconomists selected a subset of the tools of control that favoured their support of rules against discretionary stabilisation policy. The Lucas critique and Kydland and Prescott’s time-inconsistency were over-statements that led to the “dark ages” of the prevalence of the stabilisation-policy-ineffectiveness idea. These over-statements were later revised following the success of the Taylor rule.
PL DENSITY INVARIANT FOR TYPE II DEGENERATING K3 SURFACES, MODULI COMPACTIFICATION AND HYPER-KÄHLER METRIC

Abstract A protagonist here is a new-type invariant for type II degenerations of K3 surfaces, which is explicit piecewise linear convex function from the interval with at most $18$ nonlinear points. Forgetting its actual function behavior, it also classifies the type II degenerations into several combinatorial types, depending on the type of root lattices as appeared in classical examples. From differential geometric viewpoint, the function is obtained as the density function of the limit measure on the collapsing hyper-Kähler metrics to conjectural segments, as in [HSZ19]. On the way, we also reconstruct a moduli compactification of elliptic K3 surfaces by [AB19], [ABE20], [Brun15] in a more elementary manner, and analyze the cusps more explicitly. We also interpret the glued hyper-Kähler fibration of [HSVZ18] as a special case from our viewpoint, and discuss other cases, and possible relations with Landau–Ginzburg models in the mirror symmetry context.
Student-Initiated Action Advising via Advice Novelty

Action advising is a budget-constrained knowledge exchange mechanism between teacher–student peers that can help tackle exploration and sample inefficiency problems in deep reinforcement learning (RL). Most recently, student-initiated techniques that utilize state novelty and uncertainty estimations have obtained promising results. However, the approaches built on these estimations have some potential weaknesses. First, they assume that the convergence of the student’s RL model implies less need for advice. This can be misleading in scenarios with teacher’s absence early on where the student is likely to learn suboptimally by itself; yet also ignore the teacher’s assistance later. Second, the delays between encountering states and having them to take effect in the RL model updates in the presence of the experience replay dynamics cause a feedback lag in what the student actually needs advice for. We propose a student-initiated algorithm that alleviates these by employing random network distillation (RND) to measure the novelty of a piece of advice. Furthermore, we perform RND updates only for the advised states to ensure that the student’s own learning does not impair its ability to leverage the teacher. Experiments in GridWorld and MinAtar show that our approach performs on par with the state of the art and demonstrates significant advantages in the scenarios where the existing methods are prone to fail.
Learning to be safe, in finite time

This paper aims to put forward the concept that learning to take safe actions in unknown environments, even with probability one guarantees, can be achieved without the need for an unbounded number of exploratory trials, provided that one is willing to relax its optimality requirements mildly. We focus on the canonical multi-armed bandit problem and seek to study the exploration-preservation trade-off intrinsic within safe learning. More precisely, by defining a handicap metric that counts the number of unsafe actions, we provide an algorithm for discarding unsafe machines (or actions), with probability one, that achieves constant handicap. Our algorithm is rooted in the classical sequential probability ratio test, redefined here for continuing tasks. Under standard assumptions on sufficient exploration, our rule provably detects all unsafe machines in an (expected) finite number of rounds. The analysis also unveils a trade-off between the number of rounds needed to secure the environment and the probability of discarding safe machines. Our decision rule can wrap around any other algorithm to optimize a specific auxiliary goal since it provides a safe environment to search for (approximately) optimal policies. Simulations corroborate our theoretical findings and further illustrate the aforementioned trade-offs.
Entropy Regularization for Mean Field Games with Learning

Entropy regularization has been extensively adopted to improve the efficiency, the stability, and the convergence of algorithms in reinforcement learning. This paper analyzes both quantitatively and qualitatively the impact of entropy regularization for mean field games (MFGs) with learning in a finite time horizon. Our study provides a theoretical justification that entropy regularization yields time-dependent policies and, furthermore, helps stabilizing and accelerating convergence to the game equilibrium. In addition, this study leads to a policy-gradient algorithm with exploration in MFG. With this algorithm, agents are able to learn the optimal exploration scheduling, with stable and fast convergence to the game equilibrium.
Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learning

While neural sequence learning methods have made significant progress in single-document summarization (SDS), they produce unsatisfactory results on multi-document summarization (MDS). We observe two major challenges when adapting SDS advances to MDS: (1) MDS involves larger search space and yet more limited training data, setting obstacles for neural methods to learn adequate representations; (2) MDS needs to resolve higher information redundancy among the source documents, which SDS methods are less effective to handle. To close the gap, we present RL-MMR, Maximal Margin Relevance-guided Reinforcement Learning for MDS, which unifies advanced neural SDS methods and statistical measures used in classical MDS. RL-MMR casts MMR guidance on fewer promising candidates, which restrains the search space and thus leads to better representation learning. Additionally, the explicit redundancy measure in MMR helps the neural representation of the summary to better capture redundancy. Extensive experiments demonstrate that RL-MMR achieves state-of-the-art performance on benchmark MDS datasets. In particular, we show the benefits of incorporating MMR into end-to-end learning when adapting SDS to MDS in terms of both learning effectiveness and efficiency.
Efficacy of plant essential oils to control post-harvest decay of sweet cherry (Prunus avium L.) fruit

Summary The development of natural crop protection products as alternatives to the use of synthetic fungicides is currently popular. The aim of this study was to evaluate the anti-fungal effects of several essential oils against the fungal pathogen, Botrytis cinerea, the causal agent of grey mould disease on sweet cherry (Prunus avium L.) under in vitro and in vivo conditions. Three essential oils (from fennel, black caraway, and peppermint) were each tested at five concentrations (0, 200, 400, 600, or 800 µl l–1). In vitro results showed that the essential oil of black caraway had the highest fungicidal effect. The growth of grey mould was completely inhibited by the essential oil of black caraway at 400 µl l–1. In vivo, the essential oils of black caraway, fennel, and peppermint, at all concentrations, inhibited the growth of grey mould on sweet cherry fruit compared with the untreated controls. The application of each essential oil decreased the percentage loss in fresh weight significantly, and increased the storage-life of the fruit. Black caraway oil, at 800 µl l–1, maintained significantly higher soluble solids contents, titrable acidity values, and anthocyanin and carbohydrate contents than all other treatments. These results show that plant essential oils can have a strong effect on reducing post-harvest decay and improving the quality of sweet cherry fruit.These plant essential oils could provide an alternative to synthetic chemicals to control post-harvest phytopathogenic fungi on sweet cherry fruit.
Recoverable Systems

Motivated by the established notion of storage codes, we consider sets of infinite sequences over a finite alphabet such that every <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-tuple of consecutive entries is uniquely recoverable from its <inline-formula> <tex-math notation="LaTeX">$l$ </tex-math></inline-formula>-neighborhood in the sequence. We address the problem of finding the maximum growth rate of the set, which we term capacity, as well as constructions of explicit families that approach the optimal rate. The techniques that we employ rely on the connection of this problem with constrained systems. In the second part of the paper we consider a modification of the problem wherein the entries in the sequence are viewed as random variables over a finite alphabet that follow some joint distribution, and the recovery condition requires that the Shannon entropy of the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-tuple conditioned on its <inline-formula> <tex-math notation="LaTeX">$l$ </tex-math></inline-formula>-neighborhood be bounded above by some <inline-formula> <tex-math notation="LaTeX">$\epsilon >0$ </tex-math></inline-formula>. We study properties of measures on infinite sequences that maximize the metric entropy under the recoverability condition. Drawing on tools from ergodic theory, we prove some properties of entropy-maximizing measures. We also suggest a procedure of constructing an <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula>-recoverable measure from a corresponding deterministic system.