The impact of a skilled mentor on one's life can be significant, potentially resulting in a successful career or business growth. Quantel, an AI-based mentor network platform, aims to offer mentorship opportunities to students from a diverse range of backgrounds, including government officers and corporate leaders, Lucky Rohilla, co-founder, Quantel, told FE Education. "We will bring on board mentors who possess exceptional capabilities and can effectively share their experiences with bright young minds who aspire to follow in their footsteps" Rohilla added. Incorporated in June 2020, Quantel is a recently established platform. The company ended FY22 with Rs 21.3 lakh from sales or supply of services, while net profit stood at Rs 30,324 as per regulatory filings accessed by business intelligence platform, Tofler.

The electrification of shared mobility has become popular across the globe. Many cities have their new shared e-mobility systems deployed, with continuously expanding coverage from central areas to the city edges. A key challenge in the operation of these systems is fleet rebalancing, i.e., how EVs should be repositioned to better satisfy future demand. This is particularly challenging in the context of expanding systems, because i) the range of the EVs is limited while charging time is typically long, which constrain the viable rebalancing operations; and ii) the EV stations in the system are dynamically changing, i.e., the legitimate targets for rebalancing operations can vary over time. We tackle these challenges by first investigating rich sets of data collected from a real-world shared e-mobility system for one year, analyzing the operation model, usage patterns and expansion dynamics of this new mobility mode. With the learned knowledge we design a high-fidelity simulator, which is able to abstract key operation details of EV sharing at fine granularity. Then we model the rebalancing task for shared e-mobility systems under continuous expansion as a Multi-Agent Reinforcement Learning (MARL) problem, which directly takes the range and charging properties of the EVs into account. We further propose a novel policy optimization approach with action cascading, which is able to cope with the expansion dynamics and solve the formulated MARL. We evaluate the proposed approach extensively, and experimental results show that our approach outperforms the state-of-the-art, offering significant performance gain in both satisfied demand and net revenue. A promising trend of future mobility is electric and shared. For instance, Figure 1 shows the spatial distribution of station occupancy rate (ratio and ride-sharing services [10], [11], and the de facto solution of parked vehicles to the total available space) in a real-world is to rebalance the fleet during operation.

The repository includes information that Levi's shoppers share with the company. It also houses a range of external data, derived from public and private sources, that track consumer buying patterns and behaviors, weather and climate forecasts, economic trends and more. This cache, Ms. Walsh said, is vital to implementing Levi's enterprise-wide AI capability. The application of machine learning and automation to the data has helped the company enhance personalization of consumer marketing, make informed pricing decisions, predict demand and optimize fulfillment, all of which have helped the business, she said. The Morning Download delivers daily insights and news on business technology from the CIO Journal team.

Megan serves as Publishing Operations Manager at Emerj, and is currently attending The American University in Paris, where she is pursuing degrees in global communications and international business administration. Square is a financial services company that aims to "build common business tools in unconventional ways so more people can start, run and grow their businesses." Founded in 2009 in San Francisco by Twitter Co-Founder Jack Dorsey and Jim McKelvey, Square reports total net revenue of $9.5 billion for 2020. Originally known for its card-reader dongles, Square has expanded to create a business toolkit for small business owners, including various hardware and software products and services such as Square Capital, Square Terminal, and most recently, Square Banking. We will begin by taking a closer look at how Square uses machine learning to enable its various software solutions that aim to increase fraud protection for sellers.

Personalized recommendation systems (RS) are extensively used in many services. Many of these are based on learning algorithms where the RS uses the recommendation history and the user response to learn an optimal strategy. Further, these algorithms are based on the assumption that the user interests are rigid. Specifically, they do not account for the effect of learning strategy on the evolution of the user interests. In this paper we develop influence models for a learning algorithm that is used to optimally recommend websites to web users. We adapt the model of \cite{Ioannidis10} to include an item-dependent reward to the RS from the suggestions that are accepted by the user. For this we first develop a static optimisation scheme when all the parameters are known. Next we develop a stochastic approximation based learning scheme for the RS to learn the optimal strategy when the user profiles are not known. Finally, we describe several user-influence models for the learning algorithm and analyze their effect on the steady user interests and on the steady state optimal strategy as compared to that when the users are not influenced.

Welcome to the Hackett Group First Quarter Earnings Conference Call. Your lines have been placed on listed only mode. Hosting tonight's call are Mr. Ted Fernandez, Chairman and CEO; and Mr. Rob Ramirez, Chief Financial Officer. Mr. Ramirez, you may begin. Good afternoon, everyone, and thank you for joining us to discuss The Hackett Group's First Quarter Results. Speaking on the call today and here to answer your questions are Ted Fernandez, Chairman and CEO of The Hackett Group; and myself, Rob Ramirez, Chief Financial Officer. Our press announcement was released over the wires at 4:14 p.m. Eastern Time. For a copy of the release, please visit our website at www.thehackettgroup.com. We will also place any additional financial or statistical data discussed on this call that is not contained in the release on the Investor Relations page of our website. Before we begin, I would like to remind you that in the following comments and in the Q&A session, we will be making statements about expected future results, which may be forward-looking statements for the purposes of the federal securities laws. These statements relate to our current expectations, estimates and projections and are not a guarantee of future performance.

Activision Blizzard Inc. (NASDAQ:ATVI) beat analysts' expectations Thursday with revenue of 1.46 billion, the company revealed in its earnings report for the first three months of 2016. Analysts had forecast around 1.3 billion. The beginning of the year is notoriously sleepy for video games due to the lack of major releases, but the completed acquisition of King in February added intrigue as investors saw the first returns in the 5.9 billion investment in the developer of "Candy Crush." Activision Blizzard's adjusted revenue of 908 million was up 29 percent year-over-year. The company reported King had 463 monthly active users with a net revenue of 207 million, accounting for 23 percent of Activision Blizzard's total sales.

Conference-AAAI-84, paid in 1985 (6.870)