Miracle-dz/UniFAR-FTUs huggingface dataset:
Massively Parallel Graph Drawing and Representation Learning

To fully exploit the performance potential of modern multi-core processors, machine learning and data mining algorithms for big data must be parallelized in multiple ways. Today’s CPUs consist of multiple cores, each following an independent thread of control, and each equipped with multiple arithmetic units which can perform the same operation on a vector of multiple data objects. Graph embedding, i.e. converting the vertices of a graph into numerical vectors is a data mining task of high importance and is useful for graph drawing (low-dimensional vectors) and graph representation learning (high-dimensional vectors). In this paper, we propose MulticoreGEMPE (Graph Embedding by Minimizing the Predictive Entropy), an information-theoretic method which can generate low and high-dimensional vectors. MulticoreGEMPE applies MIMD (Multiple Instructions Multiple Data, using OpenMP) and SIMD (Single Instructions Multiple Data, using AVX-512) parallelism. We propose general ideas applicable in other graph-based algorithms like vectorized hashing and vectorized reduction. Our experimental evaluation demonstrates the superiority of our approach.
Dark Energy Survey year 3 results: point spread function modelling

We introduce a new software package for modelling the point spread function (PSF) of astronomical images, called piff (PSFs In the Full FOV), which we apply to the first three years (known as Y3) of the Dark Energy Survey (DES) data. We describe the relevant details about the algorithms used by piff to model the PSF, including how the PSF model varies across the field of view (FOV). Diagnostic results show that the systematic errors from the PSF modelling are very small over the range of scales that are important for the DES Y3 weak lensing analysis. In particular, the systematic errors from the PSF modelling are significantly smaller than the corresponding results from the DES year one (Y1) analysis. We also briefly describe some planned improvements to piff that we expect to further reduce the modelling errors in future analyses.
Identifying and interpreting tuning dimensions in deep networks

In neuroscience, a tuning dimension is a stimulus attribute that accounts for much of the activation variance of a group of neurons. These are commonly used to decipher the responses of such groups. While researchers have attempted to manually identify an analogue to these tuning dimensions in deep neural networks, we are unaware of an automatic way to discover them. This work contributes an unsupervised framework for identifying and interpreting "tuning dimensions" in deep networks. Our method correctly identifies the tuning dimensions of a synthetic Gabor filter bank and tuning dimensions of the first two layers of InceptionV1 trained on ImageNet.
Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness

Because of the accessibility of big data collections from consumers, products, and stores, advanced sales forecasting capabilities have drawn great attention from many businesses, especially those in retail, because of the importance of forecasting in decision making. Improvement of forecasting accuracy, even by a small percentage, may have a substantial impact on companies’ production and financial planning, marketing strategies, inventory controls, and supply chain management. Specifically, our research goal is to forecast the sales of each product in each store in the near future. Motivated by tensor factorization methodologies for context-aware recommender systems, we propose a novel approach called the advanced temporal latent factor approach to sales forecasting, or ATLAS for short, which achieves accurate and individualized predictions for sales by building a single tensor factorization model across multiple stores and products. Our contribution is a combination of a tensor framework (to leverage information across stores and products), a new regularization function (to incorporate demand dynamics), and extrapolation of the tensor into future time periods using state-of-the-art statistical (seasonal autoregressive integrated moving-average models) and machine-learning (recurrent neural networks) models. The advantages of ATLAS are demonstrated on eight product category data sets collected by Information Resources, Inc., where we analyze a total of 165 million weekly sales transactions of over 15,560 products from more than 1,500 grocery stores. Summary of Contribution: Sales forecasting has been a task of long-standing importance. Accurate sales forecasting provides critical managerial implications for companies’ decision making and operations. Improvement of forecasting accuracy may have a substantial impact on companies’ production planning, marketing strategies, inventory controls, and supply chain management, among other things. This paper proposes a novel computational (machine-learning-based) approach to sales forecasting and thus is positioned directly at the intersection of computing and business/operations research.
Statistical analysis of Wasserstein GANs with applications to time series forecasting

We provide statistical theory for conditional and unconditional Wasserstein generative adversarial networks (WGANs) in the framework of dependent observations. We prove upper bounds for the excess Bayes risk of the WGAN estimators with respect to a modified Wasserstein-type distance. Furthermore, we formalize and derive statements on the weak convergence of the estimators and use them to develop confidence intervals for new observations. The theory is applied to the special case of high-dimensional time series forecasting. We analyze the behavior of the estimators in simulations based on synthetic data and investigate a real data example with temperature data. The dependency of the data is quantified with absolutely regular beta-mixing coefficients.
Solid-Liquid Equilibria Modelling for triacylglycerols exhibiting multiple solid phases

Important end-use properties of vegetable oils based edible products are strong related to the equilibrium between a solid crystalline network and a liquid phase. This work presents the available literature development of solid-liquid equilibrium in triacylglycerol systems and highlights how it can be coupled with a Computer-Aided Mixture and Blend Design framework, for design new mixtures/blends with improved properties allowing a better use of renewable resources as vegetable oils. Stability tests were implemented as they are an essential step for powerful solid liquid equilibrium resolution and some results were presented for a four component triacylglycerol mixture in different temperatures and compositions
RealAnt: An Open-Source Low-Cost Quadruped for Research in Real-World Reinforcement Learning

Current robot platforms available for research are either very expensive or unable to handle the abuse of exploratory controls in reinforcement learning. We develop RealAnt, a minimal low-cost physical version of the popular 'Ant' benchmark used in reinforcement learning. RealAnt costs only $410 in materials and can be assembled in less than an hour. We validate the platform with reinforcement learning experiments and provide baseline results on a set of benchmark tasks. We demonstrate that the TD3 algorithm can learn to walk the RealAnt from less than 45 minutes of experience. We also provide simulator versions of the robot (with the same dimensions, state-action spaces, and delayed noisy observations) in the MuJoCo and PyBullet simulators. We open-source hardware designs, supporting software, and baseline results for ease of reproducibility.
Single- and narrow-line photoluminescence in a boron nitride-supported MoSe 2 /graphene heterostructure

Heterostructures made from van der Waals materials provide a template to investigate proximity effects at atomically sharp heterointerfaces. In particular, near-field charge and energy transfer in heterostructures made from semiconducting transition metal dichalcogenides (TMD) have attracted interest to design model 2D "donor-acceptor" systems and new optoelectronic components. Here, using of Raman scattering and photoluminescence spectroscopies, we report a comprehensive characterization of a molybedenum diselenide (MoSe$_2$) monolayer deposited onto hexagonal boron nitride (hBN) and capped by mono- and bilayer graphene. Along with the atomically flat hBN susbstrate, a single graphene epilayer is sufficient to passivate the MoSe$_2$ layer and provides a homogenous environment without the need for an extra capping layer. As a result, we do not observe photo-induced doping in our heterostructure and the MoSe$_2$ excitonic linewidth gets as narrow as 1.6~meV, hence approaching the homogeneous limit. The semi-metallic graphene layer neutralizes the 2D semiconductor and enables picosecond non-radiative energy transfer that quenches radiative recombination from long-lived states. Hence, emission from the neutral band edge exciton largely dominates the photoluminescence spectrum of the MoSe$_2$/graphene heterostructure. Since this exciton has a picosecond radiative lifetime at low temperature, comparable with the energy transfer time, its low-temperature photoluminescence is only quenched by a factor of $3.3 \pm 1$ and $4.4 \pm 1$ in the presence of mono- and bilayer graphene, respectively. Finally, we exploit the valley-contrasting properties of monolayer TMDs and show that our simple stack provides a single-line 2D chiral emitter with degrees of valley polarization and coherence up to $30\,\%$ and $45\,\%$ at low temperature under excitation 60 meV above the bright exciton line.
Effect of Short-Term Debt on Financial Growth of Non-Financial Firms Listed at Nairobi Securities Exchange

A significant number of the non-financial firms listed at Nairobi Securities Exchange (NSE) have been experiencing declining financial performance which deter investors from investing in such firms. The lenders are also not willing to lend to such firms. As such, the firms struggle to raise funds for their operations. Prudent financing decisions can lead to financial growth of the firm. The purpose of this study is to assess the effect of short-term debt on financial growth of non-financial firms listed at Nairobi Securities Exchange for a period of ten years from 2008 to 2017. Financial firms were excluded because of their specific sector characteristics and stringent regulatory framework. The study is guided by Agency Theory and Theory of Growth of the Firm. Explanatory research design was adopted. The target population of the study comprised of 45 non-financial firms listed at the NSE for a period of ten years from 2008 to 2017. The study conducted both descriptive statistics analysis and panel data analysis. The result indicates that, short term debt explains 45.99% and 25.6% of variations in financial growth as measured by growth in earnings per share and growth in market capitalization respectively. Short term debt positively and significantly influences financial growth measured using both growth in earnings per share and growth in market capitalization. The study recommends that, the management of non-financial firms listed at Nairobi Securities Exchange to employ financing means that can improve the earnings per share, market capitalization and enhance the value of the firm for the benefit of its stakeholders.
On the sensitivity of weak gravitational lensing to the cosmic expansion function

We analyse the functional derivative of the cosmic-shear power spectrum $C_\ell ^\gamma$ with respect to the cosmic expansion function. Our interest in doing so is two-fold: (i) In view of attempts to detect minor changes of the cosmic expansion function which may be due to a possibly time-dependent dark-energy density, we wish to know how sensitive the weak-lensing power spectrum is to changes in the expansion function. (ii) In view of recent empirical determinations of the cosmic expansion function from distance measurements, independent of specific cosmological models, we wish to find out how uncertainties in the expansion function translate to uncertainties in the cosmic-shear power spectrum. We find the following answers: Relative changes of the expansion function are amplified by the cosmic-shear power spectrum by a factor ≈2 − 6, weakly depending on the scale factor where the change is applied, and the current uncertainty of one example for an empirically determined expansion function translates to a relative uncertainty of the cosmic-shear power spectrum of $\approx 10~{{\%}}$.
A convergent approximation of the linear quadratic optimal control problem for Reinforcement Learning.

In this paper, we will deal with a Linear Quadratic Optimal Control problem with unknown dynamics. As a modeling assumption, we will suppose that the knowledge that an agent has on the current system is represented by a probability distribution $\pi$ on the space of matrices. Furthermore, we will assume that such a probability measure is opportunely updated to take into account the increased experience that the agent obtains while exploring the environment, approximating with increasing accuracy the underlying dynamics. Under these assumptions, we will show that the optimal control obtained by solving the "average" Linear Quadratic Optimal Control problem with respect to a certain $\pi$ converges to the optimal control driven related to the Linear Quadratic Optimal Control problem governed by the actual, underlying dynamics. This approach is closely related to Reinforcement Learning algorithms where prior and posterior probability distributions describing the knowledge on the uncertain system are recursively updated. In the last section, we will show a numerical test that confirms the theoretical results.
Customer Segmentation With Machine Learning: New Strategy For Targeted Actions

Customers Segmentation has been a topic of interest for a lot of industry, academics, and marketing leaders. The potential value of a customer to a company can be a core ingredient in decision-making. One of the big challenges in customer-based organizations is customer cognition, understanding the difference between them, and scoring them. But now with all capabilities we have, using new technologies like machine learning algorithm and data treatment we can create a very powerful framework that allow us to best understand customers needs and behaviors, and act appropriately to satisfy their needs. In the present paper, we propose a new model based on RFM model Recency, Frequency, and Monetary and k-mean algorithm to resolve those challenges. This model will allow us to use clustering, scoring, and distribution to have a clear idea about what action we should take to improve customer satisfaction.
Neural network for learning and analyzing preferences for Multi-Criteria services

In the latest years, service selection is becoming more and more important due to the significant effect of internet based services in the telecom industry. When it comes to selecting the best service, different candidate services with similar settings are proposed by different service providers. The selection should take into consideration the respect of the constraints of consumers in terms of Service Level Agreement contracts, what makes the modelling of the preferences of decision-makers for choice problems the main focus of this work. In order to model these preferences, we propose contextual preference functions based on machine learning techniques from neural networks. It will therefore be possible to further explain and decode preferences in order to facilitate negotiation and thus decision-making, thereby improving the quality of service providers while being on customer preferences.
Language representation learning models: A comparative study

Recently, Natural Language Processing has shown significant development, especially in text mining and analysis. An important task in this area is learning vector-space representations of text. Since various machine learning algorithms require representing their inputs in a vector format. In this paper, we highlight the most important language representation learning models used in the literature, ranging from the free contextual approaches like word2vec and Glove until the appearance of recent modern contextualized approaches such as ELMo, BERT, and XLNet. We show and discuss their main architectures and their main strengths and limits.
RCAR Framework: Building a Regularized Class Association Rules Model in a Categorical Data Space

Regularized Class Association Rules (RCAR) is an algorithm which produces rules based classifier in a categorical data space. The main goal of RCAR algorithm is to build classifiers which are as accurate as the state of the art algorithms, while improving the interpretability and allowing end-users to maintain and understand its outcome easily and without statistical modeling background. In this work, first, we introduce the RCAR framework, second, we provide the main functions which extract Class Association Rules (CARs), prune irrelevant rules, and rank the conserved CARs according to a set of weights calculated for each CAR. The RCAR framework also consists of multiple visualization techniques that traces the steps of the model building according to its parameters, which facilitates the model elaboration and tuning parameter for simple users. Eventually, we implemented the RCAR algorithm in the RCAR's R package.
Towards Mining Semantically Enriched Configurable Process Models

Providing configurable process model with high quality is a primary objective to derive process variants with better accuracy and facilitate process model reuse. For this purpose, many research works have been interested in configurable process mining techniques to discover and configure processes from event logs. Moreover, to use the knowledge captured by event logs when mining processes, the concept of semantic process mining is introduced. It allows for combining semantic technologies with process mining. Despite the diversity of works in mining and customizing configurable process models, the application of these techniques is still limited to use semantics in minimizing the complexity of discovered processes. However, it seems to be pertinent to discover semantically enriched configurable process models directly from event logs. Consequently, this can facilitate using semantic in configuring, verifying conformance or enhancing discovered configurable processes. In this paper, we present a comparative study of existing works that focus on mining configurable process models with respect to semantic technologies. Our aim is to propose a new framework to automatically discover semantically enriched configurable processes.
Comparison between the morphological analyzers based on graph and the one based on surface patterns

In this article, we will present the different approaches that we have developed in recent years for the morphological analysis of Arabic words: the surface pattern-based approach and the graph-based approach. To make these analyzers, we have developed several databases either to learn these approaches or test them. The obtained results from the approaches that we have developed are very interesting and show the importance of these approaches in Arabic morphological analysis.
State of the art of Fairness, Interpretability and Explainability in Machine Learning: Case of PRIM

The adoption of complex machine learning (ML) models in recent years has brought along a new challenge related to how to interpret, understand, and explain the reasoning behind these complex models' predictions. Treating complex ML systems as trustworthy black boxes without domain knowledge checking has led to some disastrous outcomes. In this context, interpretability and explainability are often used unintelligibly, and fairness, on the other hand, has become lately popular due to some discrimination problems in ML. While closely related, interpretability and explainability denote different features of prediction. In this sight, the aim of this paper is to give an overview of the interpretability, explainability and the fairness concepts in the literature and to evaluate the performance of the Patient Rule Induction Method (PRIM) concerning these aspects.
Tunable Monopole Antenna in the Sub-6 GHz Spectrum for Picocell Base Stations

A tunable monopole antenna in the European Union (EU) norms for the Sub-6 GHz band in picocell base stations application is presented in this paper. The antenna has a simple design with a bandpass filter incorporated in its ground plane. The frequency tunability capability of the antenna is achieved by varying varactor diodes capacitance, which leads to changing the effective length of the bandpass filter slots, as a result the operating frequency continuously tuned from 3.4 to 3.8 GHz. The designed antenna is modeled in CST, then verified using HFSS and ADS. The simulated antenna performance is analyzed in detail including the return loss, radiation patterns, and gain. The obtained results show that the developed antenna is very suitable for picocell base stations, especially for the 5G/Sub-6 GHz band.
RIVER 2.0: an open-source testing framework using AI techniques

This paper presents the latest updates to the RIVER open-source testing platform for x86 programs, focusing on how artificial intelligence (AI) techniques can be used to improve the automated testing processes. It is also important to mention that RIVER is the first open-source platform that offers a concolic execution engine with reinforcement learning capabilities. On the industry side, this can allow security software engineers to test their applications with fewer costs, while for the research community, it can help prototyping new ideas faster. As a secondary contribution, our work makes a summary of the AI techniques that were used for testing processes either in our previous work or other existing work in the field. The presentation describes technical aspects, challenges, and future work.
X-ray Phase Contrast Tomography Serves Preclinical Investigation of Neurodegenerative Diseases

We report a qualitative study on central nervous system (CNS) damage that demonstrates the ability of X-ray phase contrast tomography (XPCT) to confirm data obtained with standard 2D methodology and permits the description of additional features that are not detected with 2D or other 3D techniques. In contrast to magnetic resonance or computed tomography, XPCT makes possible the high-resolution 3D imaging of soft tissues classically considered “invisible” to X-rays without the use of additional contrast agents, or without the need for intense processing of the tissue required by 2D techniques. Most importantly for studies of CNS diseases, XPCT enables a concomitant multi-scale 3D biomedical imaging of neuronal and vascular networks ranging from cells through to the CNS as a whole. In the last years, we have used XPCT to investigate neurodegenerative diseases, such as Alzheimer’s disease (AD) and multiple sclerosis (MS), to shed light on brain damage and extend the observations obtained with standard techniques. Here, we show the cutting-edge ability of XPCT to highlight in 3D, concomitantly, vascular occlusions and damages, close associations between plaques and damaged vessels, as well as dramatic changes induced at neuropathological level by treatment in AD mice. We corroborate data on the well-known blood-brain barrier dysfunction in the animal model of MS, experimental autoimmune encephalomyelitis, and further show its extent throughout the CNS axis and at the level of the single vessel/capillary.