In traditional quantitative trading practice, navigating the complicated and dynamic financial market presents a persistent challenge. Former machine learning approaches have struggled to fully capture various market variables, often ignore long-term information and fail to catch up with essential signals that may lead the profit. This paper introduces an enhanced transformer architecture and designs a novel factor based on the model. By transfer learning from sentiment analysis, the proposed model not only exploits its original inherent advantages in capturing long-range dependencies and modelling complex data relationships but is also able to solve tasks with numerical inputs and accurately forecast future returns over a period. This work collects more than 5,000,000 rolling data of 4,601 stocks in the Chinese capital market from 2010 to 2019. The results of this study demonstrated the model's superior performance in predicting stock trends compared with other 100 factor-based quantitative strategies with lower turnover rates and a more robust half-life period. Notably, the model's innovative use transformer to establish factors, in conjunction with market sentiment information, has been shown to enhance the accuracy of trading signals significantly, thereby offering promising implications for the future of quantitative trading strategies.

One of the more permanent changes of the past year relates to technology in the workplace. The pandemic-induced lockdowns accelerated the digital transformation of business that was already underway, and real estate is no exception, especially when it comes to the incorporation of artificial intelligence. To gain a better idea of the perception of AI in property management, AppFolio conducted a study on the crossover of these two disciplines. When asked "I believe I have a basic understanding of artificial intelligence," 85% of the property management executives, decision makers, and generalist property managers surveyed answered in the affirmative. But when asked "Have you ever used or interacted with AI-based technology," only 32% said yes. 49% said no and 19% were unsure.

Nowadays, human resource is an important part of various resources of enterprises. For enterprises, high-loyalty and high-quality talented persons are often the core competitiveness of enterprises. Therefore, it is of great practical significance to predict whether employees leave and reduce the turnover rate of employees. First, this paper established a multi-layer perceptron predictive model of employee turnover rate. A model based on Sarsa which is a kind of reinforcement learning algorithm is proposed to automatically generate a set of strategies to reduce the employee turnover rate. These strategies are a collection of strategies that can reduce the employee turnover rate the most and cost less from the perspective of the enterprise, and can be used as a reference plan for the enterprise to optimize the employee system. The experimental results show that the algorithm can indeed improve the efficiency and accuracy of the specific strategy.

Voluntary employee turnover can have a direct financial impact on organisations. And, at the time of this pandemic outbreak where the majority of the organisations are looking to cut down their employee costs, voluntary employee turnover can create a big concern for companies. And thus, the ability to predict this turnover rate of employees can not only help in making informed hiring decisions but can also help in saving a substantial financial crisis in this uncertain time. Acknowledging that, researchers and data scientists from PredictiveHire, a AI recruiting startup, built a language model that can analyse the open-ended interview questions of the candidate to infer the likelihood of a candidate's job-hopping. The study -- led by Madhura Jayaratne, Buddhi Jayatilleke -- was done on the responses of 45,000 job applicants, who used a chatbot to give an interview and also self-rated themselves on their possibility of hopping jobs.

Driver turnover has been above 90 percent for more than nine consecutive quarters and shows little sign of slowing down. What's more is that issues related to management and workplace policies and communication have caused 30 percent of drivers to leave their job. Keeping your drivers safe, recognizing them for doing the right thing, and offering a solid feedback loop are key to increasing driver engagement -- and retention. If the people who drive your products from one place to another do not feel like you have taken extra measures to keep them safe, then they will leave for jobs that do. Where does this leave you? The more drivers you lose, the more difficult it will become to transport your products.

Driver turnover has been above 90 percent for more than nine consecutive quarters and shows little sign of slowing down. What's more, is that issues related to management and workplace policies and communication have caused 30 percent of drivers to leave their job. Keeping your drivers safe, recognizing them for doing the right thing, and offering a solid feedback loop is key to increasing driver engagement -- and retention. If the people who drive your products from one place to another do not feel like you have taken extra measures to keep them safe, then they will leave for jobs that do. Where does this leave you?

Driver turnover has been above 90 percent for more than nine consecutive quarters and shows little sign of slowing down. What's more is that issues related to management and workplace policies and communication have caused 30 percent of drivers to leave their job. Keeping your drivers safe, recognizing them for doing the right thing, and offering a solid feedback loop are key to increasing driver engagement -- and retention. If the people who drive your products from one place to another do not feel like you have taken extra measures to keep them safe, then they will leave for jobs that do. Where does this leave you? The more drivers you lose, the more difficult it will become to transport your products.

One of the fundamental questions of cultural evolutionary research is how individual-level processes scale up to generate population-level patterns. Previous studies in music have revealed that frequency-based bias (e.g. conformity and novelty) drives large-scale cultural diversity in different ways across domains and levels of analysis. Music sampling is an ideal research model for this process because samples are known to be culturally transmitted between collaborating artists, and sampling events are reliably documented in online databases. The aim of the current study was to determine whether frequency-based bias has played a role in the cultural transmission of music sampling traditions, using a longitudinal dataset of sampling events across three decades. Firstly, we assessed whether turn-over rates of popular samples differ from those expected under neutral evolution. Next, we used agent-based simulations in an approximate Bayesian computation framework to infer what level of frequency-based bias likely generated the observed data. Despite anecdotal evidence of novelty bias, we found that sampling patterns at the population-level are most consistent with conformity bias.

"To AI or not to AI", a play on Shakespeare age-old adage, is making waves into today's' teched-up world as many industries are looking into AI solutions for their businesses, especially when it comes to human resources. Due to the'Hollywood' driven concept of AI, many organizations are scared of letting a non-human entity handle certain procedures of business, but the day when AI robots could possibly take over the world is far from today. Having an untold potential in the increase of efficiency, partnered with a cost-effective solution, does it not only make sense to adapt to the modern world, but to use the benefits of AI for your businesses recruitment needs, or your needs in general? Is it the right choice for your business? Here is what you need to know in order to answer that question.

Within buildings 10 and 30 of the Siemens complex on the outskirts of Munich, the next generation of German workers are toiling over a range of test projects. The assignments are carefully chosen to impart the skills needed to continue the German miracle in automated manufacturing. In one room, a group of young men train to be automotive mechatronic engineers. They've just spent the past week feverishly programming a diminutive working model of an automated production line--complete with sensors, conveyor belts, and tools that work without human input. They're able to discuss their work in surprisingly good English, but what sets them apart from their peers in the US is that none of them attend a university.