We investigate motion planning algorithms for the assembly of shapes in the \emph{tilt model} in which unit-square tiles move in a grid world under the influence of uniform external forces and self-assemble according to certain rules. We provide several heuristics and experimental evaluation of their success rate, solution length, runtime, and memory consumption.

Before employing artificial intelligence (AI) to find job candidates, Southwest Airlines had no definitive way to track the success of the company's email and website hiring campaigns. The airline also couldn't queue up potential applicants who'd logged into a job listings page and left before an applicable position had been posted. Since it began using an AI-enabled hiring platform from tech firm Phenom, the airline now has "a warm pipeline of candidates" it can draw on whenever jobs opportunities arise, according to Kelby Tansey, manager for recruitment marketing at Southwest Airlines. Tansey said the airline can now reach out to "passive" job candidates who came to Southwest but couldn't find an open position at the time. "We'll drive them into certain pipelines within the Phenom platform so we can capture their resume, their skills, and note some of those candidates and then let them know when the job opens up," Tansey said.

The U.S. market for robotics and artificial intelligence career openings is exploding based on early 2022 trends from job postings on Robots.Jobs, the marketplace specifically for robotics and AI companies looking for talent and for jobseekers looking for the latest industry opportunities. Feb. 2, 2022 - The U.S. market for robotics and artificial intelligence career openings is exploding based on early 2022 trends from job postings on Robots.Jobs, the marketplace specifically for robotics and AI companies looking for talent and for jobseekers looking for the latest industry opportunities. In the last 90 days, open positions on Robots.Jobs have increased by more than 500 percent. Newly featured job-posters include autonomous drone hardware and sensors company GreenSight and Intrinsic AI, making industrial robotics accessible and usable for businesses. "Robotics, IoT and AI careers are in high demand across almost all industries, including industrial, healthcare, biotech, logistics, consumer and more," said Ann P. Walsh, CEO & cofounder, Robots.Jobs.

Demand for TensorFlow expertise is one of the leading indicators of machine learning and AI adoption globally. Kaggle's State of Data Science and Machine Learning 2020 Survey found that TensorFlow is the second most used machine learning framework today, with 50.5% of respondents currently using it. TensorFlow expertise continues to be one of the most marketable machine learning and AI skills in 2021, making it a reliable leading indicator of technology adoption. In 2020, there were on average 4,134 LinkedIn open positions that required TensorFlow expertise soaring to 8,414 open LinkedIn positions this year in the U.S. alone. Globally, demand for TensorFlow expertise has doubled from 12,172 open positions in 2020 to 26,958 available jobs on LinkedIn today.

The interaction patterns of employees in social and professional networks play an important role in the success of employees and organizations as a whole. However, in many fields there is a severe under-representation of minority groups; moreover, minority individuals may be segregated from the rest of the network or isolated from one another. While the problem of increasing the representation of minority groups in various fields has been well-studied, diver- sification in terms of numbers alone may not be sufficient: social relationships should also be considered. In this work, we consider the problem of assigning a set of employment candidates to positions in a social network so that diversity and overall fitness are maximized, and propose Fair Employee Assignment (FairEA), a novel algorithm for finding such a matching. The output from FairEA can be used as a benchmark by organizations wishing to evaluate their hiring and assignment practices. On real and synthetic networks, we demonstrate that FairEA does well at finding high-fitness, high-diversity matchings.

One of the major applications of generative models for drug Discovery targets the lead-optimization phase. During the optimization of a lead series, it is common to have scaffold constraints imposed on the structure of the molecules designed. Without enforcing such constraints, the probability of generating molecules with the required scaffold is extremely low and hinders the practicality of generative models for de-novo drug design. To tackle this issue, we introduce a new algorithm to perform scaffold-constrained in-silico molecular design. We build on the well-known SMILES-based Recurrent Neural Network (RNN) generative model, with a modified sampling procedure to achieve scaffold-constrained generation. We directly benefit from the associated reinforcement Learning methods, allowing to design molecules optimized for different properties while exploring only the relevant chemical space. We showcase the method's ability to perform scaffold-constrained generation on various tasks: designing novel molecules around scaffolds extracted from SureChEMBL chemical series, generating novel active molecules on the Dopamine Receptor D2 (DRD2) target, and, finally, designing predicted actives on the MMP-12 series, an industrial lead-optimization project.

What I am doing is bypassing the interface to be able to place transactions by using code. If you just need to trade, there is no need to use code at all. In fact, it is likely going to be more complicated. However, if you need to make hundreds of microtransactions per day based on indicators that change every few seconds, then you are not trading normally, you are doing quantitative trading. In this account, I will set up a simulation.

Interest in artificial intelligence is at a fever pitch, but it can be difficult for corporations to determine whether it's all hype or if the advanced tech can actually improve operations. While a healthy amount of cynicism remains around the technology, it's imperative that organizations begin to think about incorporating it now -- especially because the journey can take as long as 10 years, according to Simon Moss, the global head of AI and automation at the consulting firm Infosys. "The decisions around a cluster of separate but deeply related technology innovations are existential to whether a firm will be strategically successful or not over the next decade," he told Business Insider. Companies are already using AI in a number of different ways, including to help figure out whether store shelves need to be restocked or to cut back the number of applications that human-resources departments must review for open positions. Still, the vast amount of AI efforts fail.

The ISI Foundation is looking for excellent candidates at all level of seniority to join the Financial AI Lab, a mixed initiative between fundamental research performed at ISI Foundation and real-world business-driven challenges posed by Intesa Sanpaolo Group (the largest financial group in Italy, and 3rd largest in Europe). The appointed researchers will develop and apply data science and machine learning algorithms to solve interesting problems in the financial domain, having access to exclusive data provided by the industrial partner, while being fully embedded in a research institute with the freedom of pursuing curiosity-driven research. Mentoring will be provided by a multidisciplinary team including Francesco Bonchi, Paolo Bajardi, Andre Panisson, Gianmarco De Francisci Morales, David Garcia-Soriano, and others. Core skills you will need for this position: - PhD in Computer Science, Computer Engineering, Machine Learning, Statistics, or a related quantitative field. ISI offers an unusually rich opportunity for collegial interaction in a highly competitive environment.

The ISI Foundation is looking for exceptional candidates to join the Algorithmic Data Analytics research team, under the supervision of Dr. Francesco Bonchi . The appointed researcher will undertake fundamental research activities related to the development of methods and algorithms for data science. Talented and highly motivated postdoctoral researchers, as well as more senior researchers with internationally-recognized achievements, will be considered. Core skills you will need: - Ph.D. in Computer Science or related disciplines, with special emphasis on one of the following areas: Data Mining, Machine Learning, Algorithmic Theory, High Performance (Data-intensive, Scalable, Distributed, Streaming) Computing, Data Bases. ISI offers: - We provide an unusually rich opportunity for collegial interaction in a highly competitive and multidisciplinary environment.