Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Framework for Inference on Markov Population Models

In this work we construct a joint Gaussian likelihood for approximate inference on Markov population models. We demonstrate that Markov population models can be approximated by a system of linear stochastic differential equations with time-varying coefficients. We show that the system of stochastic differential equations converges to a set of ordinary differential equations. We derive our proposed joint Gaussian deterministic limiting approximation (JGDLA) model from the limiting system of ordinary differential equations. The results is a method for inference on Markov population models that relies solely on the solution to a system deterministic equations. We show that our method requires no stochastic infill and exhibits improved predictive power in comparison to the Euler-Maruyama scheme on simulated susceptible-infected-recovered data sets. We use the JGDLA to fit a stochastic susceptible-exposed-infected-recovered system to the Princess Diamond COVID-19 cruise ship data set.
Model and Hybrid Polystyrenes Containing Trispentafluorophenylgermanium End Groups

Abstract The synthesis of the model functionalized polymers of various molecular weight–containing active pentafluorophenylgermanium end groups was performed through chain-transfer reaction to germanium organic compounds-tris(pentafluorophenyl)germane by a free-radical polymerization of styrene. Upon subsequent activated copolycondensation of the obtained polymers with tris(pentafluorophenyl)germane in methyl ethyl ketone environment in the presence of the activator triethylamine hybrid linear-dendritic the block copolymers of polystyrene and perfluorinated poly(phenylenegermane) were obtained. Molar mass and hydrodynamic characteristics of model and hybrid polymers were studied by the methods of size exclusion chromatography, static and dynamic light scattering, and viscometry.
StarNet: Gradient-free Training of Deep Generative Models using Determined System of Linear Equations

In this paper we present an approach for training deep generative models solely based on solving determined systems of linear equations. A network that uses this approach, called a StarNet, has the following desirable properties: 1) training requires no gradient as solution to the system of linear equations is not stochastic, 2) is highly scalable when solving the system of linear equations w.r. t the latent codes, and similarly for the parameters of the model, and 3) it gives desirable least-square bounds for the estimation of latent codes and network parameters within each layer.
Deep Reinforcement Learning-based Anti-jamming Power Allocation in a Two-cell NOMA Network

The performance of Non-orthogonal Multiple Access (NOMA) system dramatically decreases in the presence of inter-cell interference. This condition gets more challenging if a smart jammer is interacting in a network. In this paper, the NOMA power allocation of two independent Base Stations (BSs) against a smart jammer is, modeled as a sequential game. In this game, at first, each BS as a leader independently chooses its power allocation strategy. Then, the smart jammer as the follower selects its optimal strategy based on the strategies of the BSs. The solutions of this game are, derived under different conditions. Based on the game-theoretical analysis, three new schemes are proposed for anti-jamming NOMA power allocation in a two-cell scenario called a) Q-Learning based Unselfish (QLU) NOMA power allocation scheme, b) Deep Q-Learning based Unselfish (DQLU) NOMA power allocation scheme, and c) Hot Booting Deep Q-Learning based Unselfish (HBDQLU) NOMA power allocation scheme. In these methods the BSs do not coordinate with each other. But our analysis theoretically predicts that with high probability, the proposed methods will converge to the optimal strategy from the total network point of view. Simulation results show the convergence of the proposed schemes and also their outperformance with respect to the Q-Learning-based Selfish (QLS) NOMA power allocation method.
Sensei: Self-Supervised Sensor Name Segmentation

A sensor name, typically an alphanumeric string, encodes the key context (e.g., function and location) of a sensor needed for deploying smart building applications. Sensor names, however, are curated in a building vendor-specific manner using different structures and vocabularies that are often esoteric. They thus require tremendous manual effort to annotate on a per-building basis; even to just segment these sensor names into meaningful chunks. In this paper, we propose a fully automated self-supervised framework, Sensei, which can learn to segment sensor names without any human annotation. Specifically, we employ a neural language model to capture the underlying sensor naming structure and then induce self-supervision based on information from the language model to build the segmentation model. Extensive experiments on five real-world buildings comprising thousands of sensors demonstrate the superiority of Sensei over baseline methods.
DB4HLS: A Database of High-Level Synthesis Design Space Explorations

High-level synthesis (HLS) frameworks allow to easily specify a large number of variants of the same hardware design by only acting on optimization directives. Nonetheless, the hardware synthesis of implementations for all possible combinations of directive values is impractical even for simple designs. Addressing this shortcoming, many HLS design space exploration (DSE) strategies have been proposed to devise directive settings leading to high-quality implementations while limiting the number of synthesis runs. All these works require considerable efforts to validate the proposed strategies and/or to build the knowledge base employed to tune abstract models, as both tasks mandate the syntheses of large collections of implementations. Currently, such data gathering is performed ad hoc: 1) leading to a lack of standardization, hampering comparisons between DSE alternatives; and 2) posing a very high burden to researchers willing to develop novel DSE strategies. Against this backdrop, we here introduce DB4HLS, a database of exhaustive HLS explorations comprising more than 100 000 design points collected over four years equivalent of synthesis time. The open structure of DB4HLS allows the incremental integration of new DSEs, which can be easily defined with a dedicated domain-specific language. We think that of our database, available at https://www.db4hls. inf.usi. ch/, will be a valuable tool for the research community investigating automated strategies for the optimization of HLS-based hardware designs.
Towards Modelling Coherence in Spoken Discourse

While there has been significant progress towards modelling coherence in written discourse, the work in modelling spoken discourse coherence has been quite limited. Unlike the coherence in text, coherence in spoken discourse is also dependent on the prosodic and acoustic patterns in speech. In this paper, we model coherence in spoken discourse with audio-based coherence models. We perform experiments with four coherence-related tasks with spoken discourses. In our experiments, we evaluate machine-generated speech against the speech delivered by expert human speakers. We also compare the spoken discourses generated by human language learners of varying language proficiency levels. Our results show that incorporating the audio modality along with the text benefits the coherence models in performing downstream coherence related tasks with spoken discourses.
MrGCN: Mirror Graph Convolution Network for Relation Extraction with Long-Term Dependencies

The ability to capture complex linguistic structures and long-term dependencies among words in the passage is essential for many nat-ural language understanding tasks. In relation extraction, dependency trees that contain rich syntactic clues have been widely used to help capture long-term dependencies in text. Graph neural networks (GNNs), one of the means to encode dependency graphs, has been shown effective in several prior works. However, relatively little attention has been paid to the receptive ﬁelds of GNNs, which can be crucial in tasks with extremely long text that go beyond single sentences and require discourse analysis. In this work, we leverage the idea of graph pooling and propose the Mirror Graph Convo-lution Network (MrGCN), a GNN model with pooling-unpooling structures tailored to relation extraction. The pooling branch reduces the graph size and enables the GCN to obtain larger receptive ﬁelds within less layers; the unpooling branch restores the pooled graph to its original resolution such that token-level relation extraction can be performed. Experiments on two datasets demonstrate the effectiveness of our method, showing signiﬁcant improvements over previous results.
Analysis of Filter Size Effect In Deep Learning

_________________________ Abstract Many studies are carried out on the subject that to develop the structure used more effectively and in a shorter time using eep learning in many areas , it is aimed to reduce the duration of the operation and the processing power required, except to obtain the best result with the changes made in the variables, functions and data in the models used. In this study, in the leaf classification made using Mendeley data set consisting of leaf images with a fixed background, all other variables such as layer number, iteration, number of layers in the model and pooling process were kept constant, except for the filter dimensions of the convolution layers in the determined model. Convolution layers in 3 different filter sizes and in addition to this, many results obtained in 2 different structures, increasing and decreasing, and 3 different image sizes were examined. In the literature, it is seen that different uses of pooling layers, changes due to increase or decrease in the number of layers, the difference in the size of the data used, and the results of many functions used with different parameters are evaluated. In the leaf classification of the determined data set with CNN, the change in the filter size of the convolution layer together with the change in different filter combinations and in different sized images was focused. Using the data set and data reproduction methods, it was aimed to make the differences in filter sizes and image sizes more distinct. Using the fixed number of iterations, model and data set, the effect of different filter sizes has been observed.
Style Normalization and Restitution for Domain Generalization and Adaptation

For many computer vision applications, the learned models usually have high performance on the training datasets but suffer from significant performance degradation when deployed in new environments, where there are usually style differences between the training images and the testing images. For high-level vision tasks, an effective domain generalizable model is expected to be able to learn feature representations that are both generalizable and discriminative. In this paper, we design a novel Style Normalization and Restitution module (SNR) to simultaneously ensure high generalization and discrimination capability of the networks. In SNR, particularly, we filter out the style variations (e.g., illumination, color contrast) by performing Instance Normalization (IN) to obtain style normalized features, where the discrepancy among different samples/domains is reduced. However, such a process is task-ignorant and inevitably removes some task-relevant discriminative information, which may hurt the performance. To remedy this, we propose to distill task-relevant discriminative features from the residual (i.e., the difference between the original feature and the style normalized feature) and add them back to the network to ensure high discrimination. Moreover, for better disentanglement, we enforce a dual restitution loss constraint to encourage the better separation of task-relevant and task-irrelevant features. We validate the effectiveness of our SNR on different vision tasks, including classification, semantic segmentation, and object detection. Experiments demonstrate that our SNR is capable of improving the performance of networks for domain generalization (DG) and unsupervised domain adaptation (UDA).
On $m$--fold Holomorphic Differentials and Modular Forms

Let Γ be the Fuchsian group of the first kind [7, Section 1.7, page 28]. Examples of such groups are the important modular groups such as SL2(Z) and its congruence subgroups Γ0(N), Γ1(N), and Γ(N) [7, Section 4.2]. Let H be the complex upper half-plane. The quotient Γ\H can be compactified by adding a finite number of Γ-orbits of points in R∪{∞} called cusps of Γ and we obtain a compact Riemann surface which will be denoted by RΓ. For l ≥ 1, let H l (RΓ) be the space of all holomorphic differentials on RΓ (see [5], or Section 3) in this paper). Let m ≥ 2 be an even integer. Let Sm(Γ) be the space of (holomorphic) cusp forms of weight m (see Section 2). It is well–known that S2(Γ) is naturally isomorphic to the vector space H (RΓ) (see [7, Theorem 2.3.2]). This is employed on many instances in studying various properties of modular curves (see for example [19, Chapter 6]). In this paper we study the generalization of this concept to the holomorphic differentials of higher order. For an even integer m ≥ 4, in general, the space Sm(Γ) is too big to be isomorphic to H (RΓ) due to presence of cusps and elliptic points. So, in general we define a subspace
On relation between generalized diffusion equations and subordination schemes

Generalized (non-Markovian) diffusion equations with different memory kernels and subordination schemes based on random time change in the Brownian diffusion process are popular mathematical tools for description of a variety of non-Fickian diffusion processes in physics, biology and earth sciences. Some of such processes (notably, the fluid limits of continuous time random walks) allow for either kind of description, but other ones do not. In the present work we discuss the conditions under which a generalized diffusion equation does correspond to a subordination scheme, and the conditions under which a subordination scheme does possess the corresponding generalized diffusion equation. Moreover, we discuss examples of random processes for which only one, or both kinds of description are applicable.
Active Learning Under Malicious Mislabeling and Poisoning Attacks

Deep neural networks usually require large labeled datasets for training to achieve state-of-the-art performance in many tasks, such as image classification and natural language processing. Although a lot of data is created each day by active Internet users, most of these data are unlabeled and are vulnerable to data poisoning attacks. In this paper, we develop an efficient active learning method that requires fewer labeled instances and incorporates the technique of adversarial retraining in which additional labeled artificial data are generated without increasing the budget of the labeling. The generated adversarial examples also provide a way to measure the vulnerability of the model. To check the performance of the proposed method under an adversarial setting, i.e., malicious mislabeling and data poisoning attacks, we perform an extensive evaluation on the reduced CIFAR-10 dataset, which contains only two classes: airplane and frog. Our experimental results demonstrate that the proposed active learning method is efficient for defending against malicious mislabeling and data poisoning attacks. Specifically, whereas the baseline active learning method based on the random sampling strategy performs poorly (about 50%) under a malicious misla-beling attack, the proposed active learning method can achieve the desired accuracy of 89% using only one-third of the dataset on average.
An iterative K-FAC algorithm for Deep Learning

Kronecker-factored Approximate Curvature (K-FAC) method is a high efficiency second order optimizer for the deep learning. Its training time is less than SGD(or other first-order method) with same accuracy in many large-scale problems. The key of K-FAC is to approximates Fisher information matrix (FIM) as a block-diagonal matrix where each block is an inverse of tiny Kronecker factors. In this short note, we present CG-FAC -- an new iterative K-FAC algorithm. It uses conjugate gradient method to approximate the nature gradient. This CG-FAC method is matrix-free, that is, no need to generate the FIM matrix, also no need to generate the Kronecker factors A and G. We prove that the time and memory complexity of iterative CG-FAC is much less than that of standard K-FAC algorithm.
Effect of Calcium on the in vitro Growth of Aechmea blanchetiana (Baker) L. B. Smith Plantlets

ABSTRACT This work investigated the influence of different concentrations of calcium on the growth of plantlets of the bromeliad Aechmea blanchetiana cultured in vitro. Seedlings of A. blanchetiana were axenically cultured in liquid Murashige and Skoog basal medium supplemented with different concentrations of calcium (Ca; 1.5, 3, 4.5, 6, or 12 mM) without growth regulators. The resulting plantlets were cultured under 93 μmol m−2 s−1 illumination, 12 hour photoperiod regime and 25°C ± 1 for 120 days with subculture to fresh identical media every 30 days. The addition of calcium at 9.38 mM to MS modified medium increased the production of fresh and dry mass of plantlets, whilst chlorine from calcium chloride dehydrate (CaCl2 2 H2O) in excess (3.35 mM) decreased both the fresh and dry mass of plantlets.
NCQ: Code Reuse Support for Node.js Developers

Code reuse is an important part of software development. The adoption of code reuse practices is especially common among Node.js developers. The Node.js package manager, NPM, indexes over 1 Million packages and developers often seek out packages to solve programming tasks. Due to the vast number of packages, selecting the right package is difficult and time consuming. With the goal of improving productivity of developers that heavily reuse code through third-party packages, we present Node Code Query (NCQ), a Read-Eval-Print-Loop environment that allows developers to 1) search for NPM packages using natural language queries, 2) search for code snippets related to those packages, 3) automatically correct errors in these code snippets, 4) quickly setup new environments for testing those snippets, and 5) transition between search and editing modes. In two user studies with a total of 20 participants, we find that participants begin programming faster and conclude tasks faster with NCQ than with baseline approaches, and that they like, among other features, the search for code snippets and packages. Our results suggest that NCQ makes Node.js developers more efficient in reusing code.
Estimation of the size and structure of the broad line region using Bayesian approach

Understanding the geometry and kinematics of the broad line region (BLR) of active galactic nuclei (AGN) is important to estimate black hole masses in AGN and study the accretion process. The technique of reverberation mapping (RM) has provided estimates of BLR size for more than 100 AGN now, however, the structure of the BLR has been studied for only a handful number of objects. Towards this, we investigated the geometry of the BLR for a large sample of 57 AGN using archival RM data. We performed systematic modeling of the continuum and emission line light curves using a Markov Chain Monte Carlo method based on Bayesian statistics implemented in PBMAP (Parallel Bayesian code for reverberation− MAPping data) code to constrain BLR geometrical parameters and recover velocity integrated transfer function. We found that the recovered transfer functions have various shapes such as single-peaked, double-peaked and top-hat suggesting that AGN have very different BLR geometries. Our model lags are in general consistent with that estimated using the conventional cross-correlation methods. The BLR sizes obtained from our modeling approach is related to the luminosity with a slope of 0.583 ± 0.026 and 0.471 ± 0.084 based on Hβ and Hα lines, respectively. We found a non-linear response of emission line fluxes to the ionizing optical continuum for 93% objects. The estimated virial factors for the AGN studied in this work range from 0.79 to 4.94 having a mean at 1.78 ± 1.77 consistent with the values found in the literature.
The estimation of approximation error using inverse problem and a set of numerical solutions

In this paper, we consider the inverse problem for the estimation of a point-wise approximation error occurring at the discretization of the system of partial differential equations. We analyse the set of the solutions, obtained by the numerical algorithms of the dissimilar structures on the same grid. The differences between the numerical solutions are used as the input data for the inverse problem, which is posed in the variational statement with the zero-order Tikhonov regularization. The numerical tests, performed for the two-dimensional inviscid compressible flows corresponding to Edney-I and Edney-VI shock wave interference modes, are provided. The comparison of the estimated error and the exact error, obtained by subtraction of numerical and analytic solutions, is presented.