Miracle-dz/UniFAR-FTUs huggingface dataset:
A review of knowledge graph application scenarios in cyber security

Facing the dynamic complex cyber environments, internal and external cyber threat intelligence, and the increasing risk of cyber-attack, knowledge graphs show great application potential in the cyber security area because of their capabilities in knowledge aggregation, representation, management, and reasoning. However, while most research has focused on how to develop a complete knowledge graph, it remains unclear how to apply the knowledge graph to solve industrial real challenges in cyber-attack and defense scenarios. In this review, we provide a brief overview of the basic concepts, schema, and construction approaches for the cyber security knowledge graph. To facilitate future research on cyber security knowledge graphs, we also present a curated collection of datasets and open-source libraries on the knowledge construction and information extraction task. In the major part of this article, we conduct a comparative review of the different works that elaborate on the recent progress in the application scenarios of the cyber security knowledge graph. Furthermore, a novel comprehensive classification framework is created to describe the connected works from nine primary categories and eighteen subcategories. Finally, we have a thorough outlook on several promising research directions based on the discussion of existing research flaws.
Compactly generated tensor t-structures on the derived category of a Noetherian scheme

We introduce a tensor compatibility condition for t-structures. For any Noetherian scheme $X$, we prove that there is a one-to-one correspondence between the set of filtrations of Thomason subsets and the set of aisles of compactly generated tensor compatible t-structures on the derived category of $X$. This generalizes the earlier classification of compactly generated t-structures for commutative rings to schemes. Hrbek and Nakamura have reformulated the famous telescope conjecture for t-structures. As an application of our main theorem, we prove that a tensor version of the conjecture is true for separated Noetherian schemes.
External control of a genetic toggle switch via Reinforcement Learning

We use a learning-based strategy to stabilize a synthetic toggle switch via an external control approach, adopting a sim-to-real paradigm to overcome the data efficiency problem that would render the algorithm infeasible for practical use in synthetic biology. Here, the policy is learnt via training on a simple model capturing the main dynamical features of a toggle switch and is then validated in-silico using stochastic agent-based simulations. Our in-silico experiments confirm the viability of the approach suggesting its potential use for in-vivo control implementation in microfluidics.
Resolved imaging of exoplanets with the solar gravitational lens

We discuss the feasibility of direct multipixel imaging of exoplanets with the solar gravitational lens (SGL) in the context of a realistic deep space mission. For this, we consider an optical telescope, placed in the image plane that forms in the strong interference region of the SGL. We consider an Earth-like exoplanet located in our immediate stellar neighborhood and model its characteristics using our own Earth. We estimate photon fluxes from such a compact, extended, resolved exoplanet. This light appears in the form of an Einstein ring around the Sun, seen through the solar corona. The solar corona background contributes a significant amount of stochastic noise and represents the main noise source for observations utilizing the SGL. We estimate the magnitude of this noise. We compute the resulting signal-to-noise ratios (SNRs) and related integration times that are needed to perform imaging measurements under realistic conditions. It is known that deconvolution, removing the blur due to the SGL’s spherical aberration substantially decreases the SNR. Our key finding is that this “penalty” is significantly mitigated when sampling locations in the image plane (image pixels) remain widely spaced. Consequently, we conclude that an imaging mission is challenging but feasible, using technologies that are either already available or in active development. Under realistic conditions, high-resolution imaging of Earth-like exoplanets in our galactic neighborhood requires only weeks or months of integration time, not years as previously thought: a high quality 1000 × 1000 pixel image of an Earth-like planet at Proxima Centauri could be obtained with SNR>10 using approximately 14 months of integration time.
Investigating Deep Learning Benchmarks for Electrocardiography Signal Processing

In recent years, deep learning has witnessed its blossom in the field of Electrocardiography (ECG) processing, outperforming traditional signal processing methods in various tasks, for example, classification, QRS detection, wave delineation. Although many neural architectures have been proposed in the literature, there is a lack of systematic studies and open-source libraries for ECG deep learning. In this paper, we propose a deep learning framework, named \texttt{torch\_ecg}, which gathers a large number of neural networks, both from literature and novel, for various ECG processing tasks. It establishes a convenient and modular way for automatic building and flexible scaling of the networks, as well as a neat and uniform way of organizing the preprocessing procedures and augmentation techniques for preparing the input data for the models. Besides, \texttt{torch\_ecg} provides benchmark studies using the latest databases, illustrating the principles and pipelines for solving ECG processing tasks and reproducing results from the literature. \texttt{torch\_ecg} offers the ECG research community a powerful tool meeting the growing demand for the application of deep learning techniques.
Derivation of an Effective Rheology for Dilute Suspensions of Microswimmers

We provide a rigorous derivation of an effective rheology for dilute viscous suspensions of self-propelled particles. We derive at the limit an effective Stokes system with two additional terms. The first term corresponds to an effective viscosity that matches the famous Einstein's formula $\mu_{eff}=(1+ \frac{5}{2} \lambda)\mu$. The second additional term derived is an effective active stress $\sigma_1$ that appears as a forcing term $\lambda \, \text{div} \, \sigma_1$ in the fluid equation. We then investigate the effects of this term depending on the micro-swimmers nature and their distribution in the fluid.
ẢNH HƯỞNG CỦA LIỀU LƯỢNG BÓN KALI ĐẾN NĂNG SUẤT VÀ CHẤT LƯỢNG CÀ RỐT TẠI PHÚ THỌ

- 1, respectively, was arranged in 3 replicates according to the randomized complete block design. Basal fertilizer contained 10 tons of compost fertilizer + 120 kg N + 100 kg P 2 O 5 each ha. The results showed that potassium fertilizer doses affected on growth, development, yield, quality of carrot plants. High doses of potassium improved the tuber size and tuber weight, yield as well as the brix and carotene content of the carrot. However, up to 240 kg N/ha, the yield and quality did not increase. At the dose of 240 kg K 2 O ha - 1, carrot production has the highest economic efficiency.
When Frictions are Fractional: Rough Noise in High-Frequency Data

The analysis of high-frequency ﬁnancial data is often impeded by the presence of noise. This article is motivated by intraday transactions data in which market microstructure noise appears to be rough , that is, best captured by a continuous-time stochastic process that locally behaves as fractional Brownian motion. Assuming that the underlying eﬃcient price process follows a continuous Itô semimartingale, we derive consistent estimators and asymptotic conﬁdence intervals for the roughness parameter of the noise and the integrated price and noise volatilities, in all cases where these quantities are identiﬁable. In addition to desirable features such as serial dependence of increments, compatibility between diﬀerent sampling frequencies and diurnal eﬀects, the rough noise model can further explain divergence rates in volatility signature plots that vary considerably over time and between assets. Abstract This supplement contains the proof of Theorem 3.1 (Appendices A–D), the proof of Theorem 4.2 (Appendix E), the details of the iterative debiasing procedure of Section 4.2 (Appendix F), our choice of tuning parameters for the simulation study (Appendix G) and an empirical analysis of quote data (Appendix H).
Consistent Estimators for Nonlinear Vessel Models

In this work, the issue of obtaining consistent parameter estimators for nonlinear regression models where the regressors are second-order modulus functions is explored. It is shown that consistent instrumental variable estimators can be obtained by estimating first and second-order moments of non-additive environmental disturbances' probability distributions as nuisance parameters in parallel to the sought-after model parameters, conducting experiments with a static excitation offset of sufficient amplitude and forcing the instruments to have zero mean. The proposed method is evaluated in a simulation example with a model of a marine surface vessel.
Denoiser-based projections for 2-D super-resolution multi-reference alignment

We study the 2-D super-resolution multi-reference alignment (SR-MRA) problem: estimating an image from its down-sampled, circularly-translated, and noisy copies. The SR-MRA problem serves as a mathematical abstraction of the structure determination problem for biological molecules. Since the SR-MRA problem is ill-posed without prior knowledge, accurate image estimation relies on designing priors that well-describe the statistics of the images of interest. In this work, we build on recent advances in image processing, and harness the power of denoisers as priors of images. In particular, we suggest to use denoisers as projections, and design two computational frameworks to estimate the image: projected expectation-maximization and projected method of moments. We provide an efficient GPU implementation, and demonstrate the effectiveness of these algorithms by extensive numerical experiments on a wide range of parameters and images.
Local convergence rates of the nonparametric least squares estimator with applications to transfer learning

Convergence properties of empirical risk minimizers can be conveniently expressed in terms of the associated population risk. To derive bounds for the performance of the estimator under covariate shift, however, pointwise convergence rates are required. Under weak assumptions on the design distribution, it is shown that least squares estimators (LSE) over 1-Lipschitz functions are also minimax rate optimal with respect to a weighted uniform norm, where the weighting accounts in a natural way for the non-uniformity of the design distribution. This implies that although least squares is a global criterion, the LSE adapts locally to the size of the design density. We develop a new indirect proof technique that establishes the local convergence behavior based on a carefully chosen local perturbation of the LSE. The obtained local rates are then applied to analyze the LSE for transfer learning under covariate shift.
Information-theoretic Online Memory Selection for Continual Learning

A challenging problem in task-free continual learning is the online selection of a representative replay memory from data streams. In this work, we investigate the online memory selection problem from an information-theoretic perspective. To gather the most information, we propose the \textit{surprise} and the \textit{learnability} criteria to pick informative points and to avoid outliers. We present a Bayesian model to compute the criteria efficiently by exploiting rank-one matrix structures. We demonstrate that these criteria encourage selecting informative points in a greedy algorithm for online memory selection. Furthermore, by identifying the importance of \textit{the timing to update the memory}, we introduce a stochastic information-theoretic reservoir sampler (InfoRS), which conducts sampling among selective points with high information. Compared to reservoir sampling, InfoRS demonstrates improved robustness against data imbalance. Finally, empirical performances over continual learning benchmarks manifest its efficiency and efficacy.
A simple catch: thermal fluctuations enable hydrodynamic trapping of microrollers by obstacles

Ernest B. van der Wee, 5, ∗ Brendan C. Blackwell, Florencio Balboa Usabiaga, Andrey Sokolov, Isaiah T. Katz, Blaise Delmotte, † and Michelle M. Driscoll ‡ Department of Physics & Astronomy, Northwestern University, Evanston, IL 60208, USA LadHyX, CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, 91120, France BCAM Basque Center for Applied Mathematics, Mazarredo 14, E48009 Bilbao, Basque Country Spain Materials Science Division, Argonne National Laboratory, Lemont, IL 60439, USA Current address: Department of Imaging Physics, Delft University of Technology, Lorentzweg 1, 2628 CJ Delft, the Netherlands (Dated: May 19, 2022)
Trajectory Optimization Using Neural Network Gradients of Learned Dynamics

—Trajectory optimization methods have achieved an exceptional level of performance on real-world robots in recent years. These methods heavily rely on accurate physics simulators, yet some aspects of the physical world, such as friction, can only be captured to a limited extent by most simulators. The goal of this paper is to leverage trajectory optimization for performing highly dynamic and complex tasks with robotic systems in absence of an accurate physics simulator. This is achieved by applying machine learning techniques to learn a differentiable dynamics model of the system from data. On the example of a Radio-controlled (RC) car, we show that from data collected in only 15 minutes of human-operated interactions with the car, a neural network is able to model highly nonlinear behaviors such as loss of traction and drifting. Furthermore, we use the analytical gradients of the neural network to perform gradient-based trajectory optimization, both in an ofﬂine and online setting. We ﬁnd that our learned model is able to represent complex physical behavior, like drifting and gives unprecedented performance in combination with trajectory optimization methods.
Towards Evaluation of Autonomously Generated Musical Compositions: A Comprehensive Survey

There are many applications that aim to create a complete model for an autonomously generated composition; systems are able to generate muzak songs, assist singers in transcribing songs or can imitate long-dead authors. Subjective understanding of creativity or aesthetics differs not only within preferences (popular authors or genres), but also differs on the basis of experienced experience or socio-cultural environment. So, what do we want to achieve with such an adaptation? What is the benefit of the resulting work for the author, who can no longer evaluate this composition? And in what ways should we evaluate such a composition at all?
ACuTE: Automatic Curriculum Transfer from Simple to Complex Environments

Despite recent advances in Reinforcement Learning (RL), many problems, especially real-world tasks, remain prohibitively expensive to learn. To address this issue, several lines of research have explored how tasks, or data samples themselves, can be sequenced into a curriculum to learn a problem that may otherwise be too difficult to learn from scratch. However, generating and optimizing a curriculum in a realistic scenario still requires extensive interactions with the environment. To address this challenge, we formulate the curriculum transfer problem, in which the schema of a curriculum optimized in a simpler, easy-to-solve environment (e.g., a grid world) is transferred to a complex, realistic scenario (e.g., a physics-based robotics simulation or the real world). We present"ACuTE", Automatic Curriculum Transfer from Simple to Complex Environments, a novel framework to solve this problem, and evaluate our proposed method by comparing it to other baseline approaches (e.g., domain adaptation) designed to speed up learning. We observe that our approach produces improved jumpstart and time-to-threshold performance even when adding task elements that further increase the difficulty of the realistic scenario. Finally, we demonstrate that our approach is independent of the learning algorithm used for curriculum generation, and is Sim2Real transferable to a real world scenario using a physical robot.
Automatic adjoint differentiation for special functions involving expectations

We explain how to compute gradients of functions of the form $G = \frac{1}{2} \sum_{i=1}^{m} (E y_i - C_i)^2$, which often appear in the calibration of stochastic models, using Automatic Adjoint Differentiation and parallelization. We expand on the work of arXiv:1901.04200 and give faster and easier to implement approaches. We also provide an implementation of our methods and apply the technique to calibrate European options.
Weight Matrix Dimensionality Reduction in Deep Learning via Kronecker Multi-layer Architectures

Deep learning using neural networks is an effective technique for generating models of complex data. However, training such models can be expensive when networks have large model capacity resulting from a large number of layers and nodes. For training in such a computationally prohibitive regime, dimensionality reduction techniques ease the computational burden, and allow implementations of more robust networks. We propose a novel type of such dimensionality reduction via a new deep learning architecture based on fast matrix multiplication of a Kronecker product decomposition; in particular our network construction can be viewed as a Kronecker product-induced sparsification of an"extended"fully connected network. Analysis and practical examples show that this architecture allows a neural network to be trained and implemented with a significant reduction in computational time and resources, while achieving a similar error level compared to a traditional feedforward neural network.
Information in probability: Another information-theoretic proof of a finite de Finetti theorem

We recall some of the history of the information-theoretic approach to deriving core results in probability theory and indicate parts of the recent resurgence of interest in this area with current progress along several interesting directions. Then we give a new information-theoretic proof of a ﬁnite version of de Finetti’s classical representation theorem for ﬁnite-valued random variables. We derive an upper bound on the relative entropy between the distribution of the ﬁrst 𝑘 in a sequence of 𝑛 exchangeable random variables, and an appropriate mixture over product distributions. The mixing measure is characterised as the law of the empirical measure of the original sequence, and de Finetti’s result is recovered as a corollary. The proof is nicely motivated by the Gibbs conditioning principle in connection with statistical mechanics, and it follows along an appealing sequence of steps. The technical estimates required for these steps are obtained via the use of a collection of combinatorial tools known within information theory as ‘the method of types. ’
A parallel iterative procedure for weak Galerkin methods for second order elliptic problems

A parallelizable iterative procedure based on domain decomposition is presented and analyzed for weak Galerkin finite element methods for second order elliptic equations. The convergence analysis is established for the decomposition of the domain into individual elements associated to the weak Galerkin methods or into larger subdomains. A series of numerical tests are illustrated to verify the theory developed in this paper.
Modeling and mechanical perturbations reveal how spatially regulated anchorage gives rise to spatially distinct mechanics across the mammalian spindle

During cell division, the spindle generates force to move chromosomes. In mammals, microtubule bundles called kinetochore-fibers (k-fibers) attach to and segregate chromosomes. To do so, k-fibers must be robustly anchored to the dynamic spindle. We previously developed microneedle manipulation to mechanically challenge k-fiber anchorage, and observed spatially distinct response features revealing the presence of heterogeneous anchorage (Suresh et al. 2020). How anchorage is precisely spatially regulated, and what forces are necessary and sufficient to recapitulate the k-fiber’s response to force remain unclear. Here, we develop a coarse-grained k-fiber model and combine with manipulation experiments to infer underlying anchorage using shape analysis. By systematically testing different anchorage schemes, we find that forces solely at k-fiber ends are sufficient to recapitulate unmanipulated k-fiber shapes, but not manipulated ones for which lateral anchorage over a 3 μm length scale near chromosomes is also essential. Such anchorage robustly preserves k-fiber orientation near chromosomes while allowing pivoting around poles. Anchorage over a shorter length scale cannot robustly restrict pivoting near chromosomes, while anchorage throughout the spindle obstructs pivoting at poles. Together, this work reveals how spatially regulated anchorage gives rise to spatially distinct mechanics in the mammalian spindle, which we propose are key for function.
Classification of Natural Language Processing Techniques for Requirements Engineering

Research in applying natural language processing (NLP) techniques to requirements engineering (RE) tasks spans more than 40 years, from initial efforts carried out in the 1980s to more recent attempts with machine learning (ML) and deep learning (DL) techniques. However, in spite of the progress, our recent survey shows that there is still a lack of systematic understanding and organization of commonly used NLP techniques in RE. We believe one hurdle facing the industry is lack of shared knowledge of NLP techniques and their usage in RE tasks. In this paper, we present our effort to synthesize and organize 57 most frequently used NLP techniques in RE. We classify these NLP techniques in two ways: first, by their NLP tasks in typical pipelines and second, by their linguist analysis levels. We believe these two ways of classification are complementary, contributing to a better understanding of the NLP techniques in RE and such understanding is crucial to the development of better NLP tools for RE.
MA-Dreamer: Coordination and communication through shared imagination

Multi-agent RL is rendered difficult due to the non-stationary nature of environment perceived by individual agents. Theoretically sound methods using the REINFORCE estimator are impeded by its high-variance, whereas value-function based methods are affected by issues stemming from their ad-hoc handling of situations like inter-agent communication. Methods like MADDPG are further constrained due to their requirement of centralized critics etc. In order to address these issues, we present MA-Dreamer, a model-based method that uses both agent-centric and global differentiable models of the environment in order to train decentralized agents' policies and critics using model-rollouts a.k. a `imagination'. Since only the model-training is done off-policy, inter-agent communication/coordination and `language emergence' can be handled in a straight-forward manner. We compare the performance of MA-Dreamer with other methods on two soccer-based games. Our experiments show that in long-term speaker-listener tasks and in cooperative games with strong partial-observability, MA-Dreamer finds a solution that makes effective use of coordination, whereas competing methods obtain marginal scores and fail outright, respectively. By effectively achieving coordination and communication under more relaxed and general conditions, out method opens the door to the study of more complex problems and population-based training.