Since 1999, the ACM Special Interest Group on Economics and Computation (SIGecom) has sponsored the leading scientific conference on advances in theory, empirics, and applications at the interface of economics and computation. The 21st ACM Conference featured invited speakers, posters, workshops, and tutorials. This year, Data Science Nigeria presented its award-winning poster, "Shared trust to End Poverty and Promote Financial Inclusion", alongside leading institutions like Stanford University, Harvard University, University of Oxford, Imperial College London and many institutions in Africa. The poster explores how real-time crowdsourcing of financial fraud data can proactively prevent fraud based on the established theories of social trust path design, network-enabled social regularization and truthful mechanisms. Data Science Nigeria has established itself as a world-class Artificial Intelligence community with proven track record in the application of cutting-edge solution-oriented Artificial Intelligence, outstanding research excellence, development of business use cases, consulting services delivery, capacity building and AI for good solutions.

Determinantal point processes (DPPs) are popular probabilistic models of diversity. In this paper, we investigate DPPs from a new perspective: property testing of distributions. Given sample access to an unknown distribution $q$ over the subsets of a ground set, we aim to distinguish whether $q$ is a DPP distribution, or $\epsilon$-far from all DPP distributions in $\ell_1$-distance. In this work, we propose the first algorithm for testing DPPs. Furthermore, we establish a matching lower bound on the sample complexity of DPP testing. This lower bound also extends to showing a new hardness result for the problem of testing the more general class of log-submodular distributions.

In this paper, we propose a faster stochastic alternating direction method of multipliers (ADMM) for nonconvex optimization by using a new stochastic path-integrated differential estimator (SPIDER), called as SPIDER-ADMM. Moreover, we prove that the SPIDER-ADMM achieves a record-breaking incremental first-order oracle (IFO) complexity of $\mathcal{O}(n+n^{1/2}\epsilon^{-1})$ for finding an $\epsilon$-approximate stationary point, which improves the deterministic ADMM by a factor $\mathcal{O}(n^{1/2})$, where $n$ denotes the sample size. As one of major contribution of this paper, we provide a new theoretical analysis framework for nonconvex stochastic ADMM methods with providing the optimal IFO complexity. Based on this new analysis framework, we study the unsolved optimal IFO complexity of the existing non-convex SVRG-ADMM and SAGA-ADMM methods, and prove they have the optimal IFO complexity of $\mathcal{O}(n+n^{2/3}\epsilon^{-1})$. Thus, the SPIDER-ADMM improves the existing stochastic ADMM methods by a factor of $\mathcal{O}(n^{1/6})$. Moreover, we extend SPIDER-ADMM to the online setting, and propose a faster online SPIDER-ADMM. Our theoretical analysis shows that the online SPIDER-ADMM has the IFO complexity of $\mathcal{O}(\epsilon^{-\frac{3}{2}})$, which improves the existing best results by a factor of $\mathcal{O}(\epsilon^{-\frac{1}{2}})$. Finally, the experimental results on benchmark datasets validate that the proposed algorithms have faster convergence rate than the existing ADMM algorithms for nonconvex optimization.

In this paper, we deal with the study of the impact of nationwide measures COVID-19 anti-pandemic. We drive two processes to analyze COVID-19 data considering measures. We associate level of nationwide measure with value of parameters related to the contact rate of the model. Then a parametric solve, with respect to those parameters of measures, shows different possibilities of the evolution of the pandemic. Two machine learning tools are used to forecast the evolution of the pandemic. Finally, we show comparison between deterministic and two machine learning tools.

According to reports, India has witnessed an exponential growth in interest and awareness in the solar energy space, and this has led to growth in the number of solar plant installations and a decrease in the cost of solar power. With the aim of using AI and cloud computing into solar energy platforms, Noida-based Skilancer Solar manufactures centrally controlled, self-powered, robotic arms for automatic cleaning of solar modules. In this week's feature, Analytics India Magazine caught up with the founders of Skilancer Solar to gain more insights into the clean-tech platform. Founded in 2018 by Manish Das and Neeraj Kumar, Skilancer Solar is a clean-tech startup that manufactures robots to clean solar panels. A lot of industries in India utilise traditional/manual methods of cleaning which are less optimal, use water inefficiently, require manpower and the cleaning frequency is also less, this often results in decreased power output by the solar plants.

Russian mercenary groups have enabled renegade military commander Khalifa Hafter, who is based in eastern Libya, to blockade the country's oil exports, starving the country of much-needed money. Moscow's backing of Haftar, a former CIA asset, has increased tensions with the United States. Russian private military contractors are active in 16 African nations. How is the country paying for its overseas wars? Also on Counting the Cost: Currency crisis, debt default, hyperinflation and poverty - Lebanon was in economic and political paralysis long before the devastating explosion in Beirut.

The global Artificial Intelligence (AI) in Fintech market focuses on encompassing major statistical evidence for the Artificial Intelligence (AI) in Fintech industry as it offers our readers a value addition on guiding them in encountering the obstacles surrounding the market. A comprehensive addition of several factors such as global distribution, manufacturers, market size, and market factors that affect the global contributions are reported in the study. In addition the Artificial Intelligence (AI) in Fintech study also shifts its attention with an in-depth competitive landscape, defined growth opportunities, market share coupled with product type and applications, key companies responsible for the production, and utilized strategies are also marked. This intelligence and 2026 forecasts Artificial Intelligence (AI) in Fintech industry report further exhibits a pattern of analyzing previous data sources gathered from reliable sources and sets a precedented growth trajectory for the Artificial Intelligence (AI) in Fintech market. The report also focuses on a comprehensive market revenue streams along with growth patterns, analytics focused on market trends, and the overall volume of the market. Moreover, the Artificial Intelligence (AI) in Fintech report describes the market division based on various parameters and attributes that are based on geographical distribution, product types, applications, etc.

Much has been said about the fusion of bio-inspired optimization algorithms and Deep Learning models for several purposes: from the discovery of network topologies and hyper-parametric configurations with improved performance for a given task, to the optimization of the model's parameters as a replacement for gradient-based solvers. Indeed, the literature is rich in proposals showcasing the application of assorted nature-inspired approaches for these tasks. In this work we comprehensively review and critically examine contributions made so far based on three axes, each addressing a fundamental question in this research avenue: a) optimization and taxonomy (Why?), including a historical perspective, definitions of optimization problems in Deep Learning, and a taxonomy associated with an in-depth analysis of the literature, b) critical methodological analysis (How?), which together with two case studies, allows us to address learned lessons and recommendations for good practices following the analysis of the literature, and c) challenges and new directions of research (What can be done, and what for?). In summary, three axes - optimization and taxonomy, critical analysis, and challenges - which outline a complete vision of a merger of two technologies drawing up an exciting future for this area of fusion research.

Every millimeter of Beijing is monitored by state-of-the-art surveillance cameras, according to the Beijing Public Safety Bureau. Facial recognition algorithms matched with images filed away in a secret database could see you in legal trouble for something you did near your front door. A semi-political post made in a private chat could lead to the loss of your job. Yet it is only the tip of the iceberg -- and the very beginning -- of the rise of technologically-advanced China and its dystopian dreams. It might be a deterrent to crime, as the Chinese Communist Party (CCP) leadership advocates, but it also means a substantial loss of privacy and saps any semblance of free expression.

Missing value problem in spatiotemporal traffic data has long been a challenging topic, in particular for large-scale and high-dimensional data with complex missing mechanisms and diverse degrees of missingness. Recent studies based on tensor nuclear norm have demonstrated the superiority of tensor learning in imputation tasks by effectively characterizing the complex correlations/dependencies in spatiotemporal data. However, despite the promising results, these approaches do not scale well to large tensors. In this paper, we focus on addressing the missing data imputation problem for large-scale spatiotemporal traffic data. To achieve both high accuracy and efficiency, we develop a scalable autoregressive tensor learning model---Low-Tubal-Rank Autoregressive Tensor Completion (LATC-Tubal)---based on the existing framework of Low-Rank Autoregressive Tensor Completion (LATC), which is well-suited for spatiotemporal traffic data that characterized by multidimensional structure of location$\times$ time of day $\times$ day. In particular, the proposed LATC-Tubal model involves a scalable tensor nuclear norm minimization scheme by integrating linear unitary transformation. Therefore, the tensor nuclear norm minimization can be solved by singular value thresholding on the transformed matrix of each day while the day-to-day correlation can be effectively preserved by the unitary transform matrix. Before setting up the experiment, we consider two large-scale 5-minute traffic speed data sets collected by the California PeMS system with 11160 sensors. We compare LATC-Tubal with state-of-the-art baseline models, and find that LATC-Tubal can achieve competitively accuracy with a significantly lower computational cost. In addition, the LATC-Tubal will also benefit other tasks in modeling large-scale spatiotemporal traffic data, such as network-level traffic forecasting.