Miracle-dz/UniFAR-FTUs huggingface dataset:
Effective Parameter Optimization & Classification using Bat-Inspired Algorithm with Improving NSSA

Network Security is an important aspectin communication-related activities. In recent times, the advent of more sophisticated technologies changed the way the information is being sharedwith everyone in any part of the world.Concurrently, these advancements are mishandled to compromise the end-user devices intentionally to steal their personal information. The number of attacks made on targeted devices is increasing over time. Even though the security mechanisms used to defend the network is enhanced and kept updated periodically, new advanced methods are developed by the intruders to penetrate the system. In order to avoid these discrepancies, effective strategies must be applied to enhance the security measures in the network. In this paper, a machine learning-based approach is proposed to identify the pattern of different categories of attacks made in the past. KDD cup 1999 dataset is accessed to develop this predictive model. Bat optimization algorithm identifies the optimal parameter subset. Supervised machine learning algorithms were employed to train the model from the data to make predictions. The performance of the system is evaluated through evaluation metrics like accuracy, precision and so on. Four classification algorithms were used out of which, gradient boosting model outperformed the benchmarked algorithms and proved its importance on data classification based on the accuracy obtained from this model.
A modification of quasi-Newton's methods helping to avoid saddle points

We recall that if $A$ is an invertible and symmetric real $m\times m$ matrix, then it is diagonalisable. Therefore, if we denote by $\mathcal{E}^{+}(A)\subset \mathbb{R}^m$ (respectively $\mathcal{E}^{-}(A)\subset \mathbb{R}^m$) to be the vector subspace generated by eigenvectors with positive eigenvalues of $A$ (correspondingly the vector subspace generated by eigenvectors with negative eigenvalues of $A$), then we have an orthogonal decomposition $\mathbb{R}^m=\mathcal{E}^{+}(A)\oplus \mathcal{E}^{-}(A)$. Hence, every $x\in \mathbb{R}^m$ can be written uniquely as $x=pr_{A,+}(x)+pr_{A,-}(x)$ with $pr_{A,+}(x)\in \mathcal{E}^{+}(A)$ and $pr_{A,-}(x)\in \mathcal{E}^{-}(A)$. We propose the following simple new modification of quasi-Newton's methods. {\bf New Q-Newton's method. } Let $\Delta =\{\delta _0,\delta _1,\delta _2,\ldots \}$ be a countable set of real numbers which has at least $m+1$ elements. Let $f:\mathbb{R}^m\rightarrow \mathbb{R}$ be a $C^2$ function. Let $\alpha >0$. For each $x\in \mathbb{R}^m$ such that $\nabla f(x)\not=0$, let $\delta (x)=\delta _j$, where $j$ is the smallest number so that $\nabla ^2f(x)+\delta _j||\nabla f(x)||^{1+\alpha}Id$ is invertible. (If $\nabla f(x)=0$, then we choose $\delta (x)=\delta _0$. ) Let $x_0\in \mathbb{R}^m$ be an initial point. We define a sequence of $x_n\in \mathbb{R}^m$ and invertible and symmetric $m\times m$ matrices $A_n$ as follows: $A_n=\nabla ^2f(x_n)+\delta (x_n) ||\nabla f(x_n)||^{1+\alpha}Id$ and $x_{n+1}=x_n-w_n$, where $w_n=pr_{A_n,+}(v_n)-pr_{A_n,-}(v_n)$ and $v_n=A_n^{-1}\nabla f(x_n)$. The main result of this paper roughly says that if $f$ is $C^3$ and a sequence $\{x_n\}$, constructed by the New Q-Newton's method from a random initial point $x_0$, {\bf converges}, then the limit point is not a saddle point, and the convergence rate is the same as that of Newton's method.
ProPoS: A Probabilistic Proof-of-Stake Protocol

We present ProPoS, a Proof-of-Stake protocol dedicated, but not limited, to cryptocurrencies. ProPoS is a chain-based protocol that minimizes interactions between nodes through lightweight committee voting, resulting in a more simple, robust, and scalable proposal than competing systems. It also mitigates other drawbacks of previous systems, such as high reward variance and long confirmation times. ProPoS can support large node numbers by design, and provides probabilistic safety guarantees whereby a client makes commit decisions by calculating the probability that a transaction is reverted based on its blockchain view. We present a thorough analysis of ProPoS and report on its implementation and evaluation. Furthermore, our new technique of proving safety can be applied more broadly to other Proof-of-Stake protocols.
Analyzing programming languages by community characteristics on Github and StackOverflow

The choice of programming language is a very important decision as it not only affects the performance and maintainability of the software but also dictates the talent pool and community support available. To better understand the trade-offs involved in making such a decision, we define and compute popularity, demand, availability and community engagement of programming languages through online collaboration platforms. We perform our analysis using data from Github and StackOverflow, two of the most popular programming communities. We get data related projects, languages and developer engagement from Github and programming questions with answers along with language tags from StackOverflow. We compute metrics separately for the two data sources and then combine the metrics to provide a holistic and robust picture of the communities for the most popular programming languages.
A Unified Dual-view Model for Review Summarization and Sentiment Classification with Inconsistency Loss

Acquiring accurate summarization and sentiment from user reviews is an essential component of modern e-commerce platforms. Review summarization aims at generating a concise summary that describes the key opinions and sentiment of a review, while sentiment classification aims to predict a sentiment label indicating the sentiment attitude of a review. To effectively leverage the shared sentiment information in both review summarization and sentiment classification tasks, we propose a novel dual-view model that jointly improves the performance of these two tasks. In our model, an encoder first learns a context representation for the review, then a summary decoder generates a review summary word by word. After that, a source-view sentiment classifier uses the encoded context representation to predict a sentiment label for the review, while a summary-view sentiment classifier uses the decoder hidden states to predict a sentiment label for the generated summary. During training, we introduce an inconsistency loss to penalize the disagreement between these two classifiers. It helps the decoder to generate a summary to have a consistent sentiment tendency with the review and also helps the two sentiment classifiers learn from each other. Experiment results on four real-world datasets from different domains demonstrate the effectiveness of our model.
An Empirical Methodology for Detecting and Prioritizing Needs during Crisis Events

In times of crisis, identifying essential needs is crucial to providing appropriate resources and services to affected entities. Social media platforms such as Twitter contain a vast amount of information about the general public’s needs. However, the sparsity of information and the amount of noisy content present a challenge for practitioners to effectively identify relevant information on these platforms. This study proposes two novel methods for two needs detection tasks: 1) extracting a list of needed resources, such as masks and ventilators, and 2) detecting sentences that specify who-needs-what resources (e.g., we need testing). We evaluate our methods on a set of tweets about the COVID-19 crisis. For extracting a list of needs, we compare our results against two official lists of resources, achieving 0.64 precision. For detecting who-needs-what sentences, we compared our results against a set of 1,000 annotated tweets and achieved a 0.68 F1-score.
Mapping reactor neutrino spectra from TAO to JUNO

The Jiangmen Underground Neutrino Observatory (JUNO) project aims at probing, at the same time, the two main frequencies of three-flavor neutrino oscillations, as well as their interference related to the mass ordering (normal or inverted), at a distance of ~53 km from two powerful reactor complexes in China, at Yangjiang and Taishan. In the latter complex, the unoscillated spectrum from one reactor core is planned to be closely monitored by the Taishan Antineutrino Observatory (TAO), expected to have better resolution (x 1/2) and higher statistics (x 30) than JUNO. In the context of neutrino energy spectra endowed with fine-structure features from summation calculations, we analyze in detail the effects of energy resolution and nucleon recoil on observable event spectra. We show that a model spectrum in TAO can be mapped into a corresponding spectrum in JUNO through appropriate convolutions. The mapping is exact in the hypothetical case without oscillations, and holds to a very good accuracy in the real case with oscillations. We then analyze the sensitivity to mass ordering of JUNO (and its precision oscillometry capabilities) assuming a single reference spectrum, as well as bundles of variant spectra, as obtained by changing nuclear input uncertainties in summation calculations from a publicly available toolkit. We show through a chi-squared analysis that variant spectra induce little reduction of the sensitivity in JUNO, especially when TAO constraints are included. Subtle aspects of the statistical analysis of variant spectra are also discussed.
Identification of hydrodynamic instability by convolutional neural networks

The onset of hydrodynamic instabilities is of great importance in both industry and daily life, due to the dramatic mechanical and thermodynamic changes for different types of flow motions. In this paper, modern machine learning techniques, especially the convolutional neural networks (CNN), are applied to identify the transition between different flow motions raised by hydrodynamic instability, as well as critical non-dimensionalized parameters for characterizing this transit. CNN not only correctly predicts the critical transition values for both Taylor-Couette (TC) flow and Rayleigh- Benard (RB) convection under various setups and conditions, but also shows an outstanding performance on robustness and noise-tolerance. In addition, key spatial features used for classifying different flow patterns are revealed by the principal component analysis.
Serverless End Game: Disaggregation enabling Transparency

For many years, the distributed systems community has struggled to smooth the transition from local to remote computing. Transparency means concealing the complexities of distributed programming like remote locations, failures or scaling. For us, full transparency implies that we can compile, debug and run unmodified single-machine code over effectively unlimited compute, storage, and memory resources. We elaborate in this article why resource disaggregation in serverless computing is the definitive catalyst to enable full transparency in the Cloud. We demonstrate with two experiments that we can achieve transparency today over disaggregated serverless resources and obtain comparable performance to local executions. We also show that locality cannot be neglected for many problems and we present five open research challenges: granular middleware and locality, memory disaggregation, virtualization, elastic programming models, and optimized deployment. If full transparency is possible, who needs explicit use of middleware if you can treat remote entities as local ones? Can we close the curtains of distributed systems complexity for the majority of users?
Time- and energy-resolved effects in the boron-10 based multi-grid and helium-3 based thermal neutron detectors

The boron-10 based multi-grid detector is being developed as an alternative to helium-3 based neutron detectors. At the European Spallation Source, the detector will be used for time-of-flight neutron spectroscopy at cold to thermal neutron energies. The objective of this work is to investigate fine time- and energy-resolved effects of the Multi-Grid detector, down to a few µeV, while comparing it to the performance of a typical helium-3 tube. Furthermore, it is to characterize differences between the detector technologies in terms of internal scattering, as well as the time reconstruction of ∼ µs short neutron pulses. The data were taken at the Helmholtz Zentrum Berlin, where the Multi-Grid detector and a helium-3 tube were installed at the ESS test beamline, V20. Using a Fermi-chopper, the neutron beam of the reactor was chopped into a few tens of µs wide pulses before reaching the detector, located a few tens of cm downstream. The data of the measurements show an agreement between the derived and calculated neutron detection efficiency curve. The data also provide fine details on the effect of internal scattering, and how it can be reduced. For the first time, the chopper resolution was comparable to the timing resolution of the Multi-Grid detector. This allowed a detailed study of time- and energy resolved effects, as well as a comparison with a typical helium-3 tube.
DNAzymes to β1 and β3 mRNA Down-regulate Expression of the Targeted Integrins and Inhibit Endothelial Cell Capillary Tube Formation in Fibrin and Matrigel*

A novel approach based on DNA-cleaving deoxyribozymes (DNAzymes) was developed to control expression of β1 and β3 integrins in endothelial cells. To engineer a specific cleavage site in mRNA, the flanking domains of DNAzymes were derived from oligodeoxynucleotides complementary to sequences corresponding to 1053–1070 and 1243–1267 in β1 and β3 mRNA, respectively. Phosphorothioate analogues of these antisense oligodeoxynucleotides, designated β1-1053 and β3-1243, significantly inhibited expression of β1 and β3 integrin subunits in endothelial and K562 cells at the level of mRNA and protein synthesis. They also specifically decreased the cell surface expression of corresponding subunits in endothelial cells and K562 cells, as measured by flow cytometry. In functional tests, β1-1053 and β3-1243 markedly reduced adhesion of cells to fibronectin and vitronectin, respectively. We designed DNAzymes to β1 and β3 mRNAs containing a 15-deoxynucleotide catalytic domain that was flanked by two substrate recognition segments of 8 and 10 deoxynucleotides for β1 and β3 DNAzymes, respectively. Both DNAzymes in the presence of Mg2+specifically cleaved their substrates, synthetic β1 and β3 mRNA fragments. Although DNAzymes were partially modified with phosphorothioate and with 2′-O-methyl groups at both the 5′ and 3′ ends indicated similar kinetic parameters, they were significantly more potent than the unmodified DNAzymes because of their much higher resistance to nuclease degradation. Similar to the antisense oligonucleotides, DNAzymes abolished microvascular endothelial cell capillary tube formation in fibrin and Matrigel. In conclusion, DNAzymes to β1 and β3 mRNAs with 2′-O-methyl modifications are potentially useful as gene-inactivating agents and may ultimately provide a therapeutic means to inhibit angiogenesisin vivo.
On numerical semigroups with at most 12 left elements

Abstract For a numerical semigroup with embedding dimension e, conductor c and left part set In 1978 Wilf asked, in equivalent terms, whether always holds, a question known since as Wilf’s conjecture. Using a closely related lower bound we show that if then thereby settling Wilf’s conjecture in this case. This is best possible, since cases are known where and Wilf’s conjecture remains open for
Light-in-the-loop: using a photonics co-processor for scalable training of neural networks

As neural networks grow larger and more complex and data-hungry, training costs are skyrocketing. Especially when lifelong learning is necessary, such as in recommender systems or self-driving cars, this might soon become unsustainable. In this study, we present the first optical co-processor able to accelerate the training phase of digitally-implemented neural networks. We rely on direct feedback alignment as an alternative to backpropagation, and perform the error projection step optically. Leveraging the optical random projections delivered by our co-processor, we demonstrate its use to train a neural network for handwritten digits recognition.
Scalable Gaussian Processes, with Guarantees: Kernel Approximations and Deep Feature Extraction

We provide approximation guarantees for a linear-time inferential framework for Gaussian processes, using two low-rank kernel approximations based on random Fourier features and truncation of Mercer expansions. In particular, we bound the Kullback-Leibler divergence between the idealized Gaussian process and the one resulting from a low-rank approximation to its kernel. Additionally, we present strong evidence that these two approximations, enhanced by an initial automatic feature extraction through deep neural networks, outperform a broad range of state-of-the-art methods in terms of time efficiency, negative log-predictive density, and root mean squared error.
Birth sites of young stellar associations and recent star formation in a flocculent corrugated disc

With backwards orbit integration we estimate birth locations of young stellar associations and moving groups identified in the solar neighborhood that are younger than 70 Myr. The birth locations of most of these stellar associations are at smaller galactocentric radius than the Sun, implying that their stars moved radially outwards after birth. Exceptions to this rule include are the Argus and Octans associations which formed outside the Sun's Galactocentric radius. Variations in birth heights of the stellar associations suggest that they were born in a corrugated disk of molecular clouds, similar to that inferred from the current filamentary molecular cloud distribution and dust extinction maps. Multiple spiral arm features with different but near corotation pattern speeds and at different heights could account for the stellar association birth sites. We find that the young stellar associations are located in between peaks in the UV stellar velocity distribution for stars in the solar neighborhood. This would be expected if they were born in a spiral arm which perturbs stellar orbits that cross it. In contrast, stellar associations seem to be located near peaks in the vertical phase space distribution, suggesting that the gas in which stellar associations are born moves vertically together with the low velocity dispersion disk stars.
New approaches to the general relativistic Poynting-Robertson effect

Objectives: A systematic study on the general relativistic Poynting-Robertson effect has been developed so far by introducing different complementary approaches, which can be mainly divided in two kinds: (1) improving the theoretical assessments and model in its simple aspects, and (2) extracting mathematical and physical information from such system with the aim to extend methods or results to other similar physical systems of analogue structure. Methods/Analysis: We use these theoretical approaches: relativity of observer splitting formalism; Lagrangian formalism and Rayleigh potential with a new integration method; Lyapunov theory os stability. Findings: We determined the three-dimensional formulation of the general relativistic Poynting-Robertson effect model. We determine the analytical form of the Rayleigh potential and discuss its implications. We prove that the critical hypersurfaces (regions where there is a balance between gravitational and radiation forces) are stable configurations. Novelty /Improvement: Our new contributions are: to have introduced the three-dimensional description; to have determined the general relativistic Rayleigh potential for the first time in the General Relativity literature; to have provided an alternative, general and more elegant proof of the stability of the critical hypersurfaces.
Kernel based regression with robust loss function via iteratively reweighted least squares

Least squares kernel based methods have been widely used in regression problems due to the simple implementation and good generalization performance. Among them, least squares support vector regression (LS-SVR) and extreme learning machine (ELM) are popular techniques. However, the noise sensitivity is a major bottleneck. To address this issue, a generalized loss function, called $\ell_s$-loss, is proposed in this paper. With the support of novel loss function, two kernel based regressors are constructed by replacing the $\ell_2$-loss in LS-SVR and ELM with the proposed $\ell_s$-loss for better noise robustness. Important properties of $\ell_s$-loss, including robustness, asymmetry and asymptotic approximation behaviors, are verified theoretically. Moreover, iteratively reweighted least squares (IRLS) is utilized to optimize and interpret the proposed methods from a weighted viewpoint. The convergence of the proposal are proved, and detailed analyses of robustness are given. Experiments on both artificial and benchmark datasets confirm the validity of the proposed methods.
COVID-Pro in Italy: a dashboard for a province-based analysis

This paper presents an dashboard developed to analyse the outbreak of the Covid-19 infection in Italy considering daily NUTS-3 data on positive cases provided by the Italian Ministry of Health and on deaths derived from Italian regional authorities' official press conferences. Descriptive time series plots are provided together with a map describing the spatial distribution of province cumulative cases and rates. A section on a proposed time-dependent adjusted SIRD model for NUTS-3 regions is also provided in the dashboard.
Further observations on bornological covering properties and selection principles

This article is a continuation of the study of bornological open covers and related selection principles in metric spaces done in (Chandra et al. 2020) using the idea of strong uniform convergence (Beer and Levi, 2009) on bornology. Here we explore further ramifications, presenting characterizations of various selection principles related to certain classes of bornological covers using the Ramseyan partition relations, interactive results between the cardinalities of bornological bases and certain selection principles involving bornological covers, producing new observations on the $\mathfrak{B}^s$-Hurewicz property introduced in (Chandra et al. 2020) and several results on the $\mathfrak{B}^s$-Gerlits-Nagy property of $X$ which is introduced here following the seminal work of (Gerlits and Nagy, 1982). In addition, in the finite power $X^n$ with the product bornology $\mathfrak{B}^n$, the ${\mathfrak{B}^n}^s$-Hurewicz property as well as the ${\mathfrak{B}^n}^s$-Gerlits-Nagy property of $X^n$ are characterized in terms of properties of $(C(X),\tau^s_\mathfrak{B})$ like countable fan tightness, countable strong fan tightness along with the Reznichenko's property.
Constructive description of Hardy-Sobolev spaces in strictly pseudoconvex domains with minimal smoothness

Let $\Omega\subset\mathbb{C}^n$ be a strictly pseudoconvex Runge domain with $C^2$-smooth defining function, $l\in\mathbb{N},$ $p\in(1,\infty). $ We prove that the holomorphic function $f$ has derivatives of order $l$ in $H^p(\Omega)$ if and only if there exists a sequence on polynomials $P_n$ of degree $n$ such that $\sum\limits_{k=1}^{\infty}2^{2lk}\left\lvert f(z)-P_{2^k}(z) \right\rvert^2\in L^p(\partial\Omega). $