Trump's Epstein crisis explodes as lewd birthday letter showing president's signature is revealed Judge's'promise' let career criminal walk free to butcher Ukrainian refugee after his MOM said he should be locked up'She was so f***ed up': Carolyn Bessette's friends tell MAUREEN CALLAHAN of her secret Daddy issue, JFK Jr's murder brag that drove her mad... and why everything we know about her is a lie The chaos behind when Meghan Markle was told not to be at Queen Elizabeth II's deathbed They were locked in a dungeon inside a house of horrors. But incredible footage shows five kids' daring acts while their parents were out... and it left neighbors speechless Turn back the clock with the K-beauty retinol cream Amazon shoppers say leaves their skin'silky smooth' - and it's now $10 Scientists crack the ultimate answer to the meaning of life... and it's hidden among 38M obituaries CBS News hires a CONSERVATIVE to police interviews after Trump and Noem'deceptive' editing fury Scientist claims life on Earth was not random... but engineered Supreme Court LIFTS restrictions on Trump's immigration raids despite claims agents targeted people by race I was 52 with a collapsed'turkey neck'. Here's how I turned back the clock 10 years Plastic surgeons weigh in on Jessica Simpson's dramatic new look at VMAs as fans declare her'unrecognizable' Billionaire turns his back on Trump as he blasts President's'risky' financial move that could cost Americans their savings Trump loses appeal and must pay $83 million to E. Jean Carroll AMANDA PLATELL: Harry is'desperate' to come back to Britain and reclaim his royal role - but this fresh snub from William makes it clear why it will never happen... and why he'll never forgive his brother Scientists crack the ultimate answer to the meaning of life... and it's hidden among 38million obituaries Scientists on a mission to uncover what constitutes a life well lived found the answer after analyzing 38 million obituaries from the US spanning 30 years. Using automated text analysis tools, the team found that the most commonly celebrated values were tradition and benevolence. Nearly 80 percent of obituaries highlighted respect for customs or religion, while 76 percent emphasized caring, reliability and trustworthiness.

Markowitz laid the foundation of portfolio theory through the mean-variance optimization (MVO) framework. However, the effectiveness of MVO is contingent on the precise estimation of expected returns, variances, and covariances of asset returns, which are typically uncertain. Machine learning models are becoming useful in estimating uncertain parameters, and such models are trained to minimize prediction errors, such as mean squared errors (MSE), which treat prediction errors uniformly across assets. Recent studies have pointed out that this approach would lead to suboptimal decisions and proposed Decision-Focused Learning (DFL) as a solution, integrating prediction and optimization to improve decision-making outcomes. While studies have shown DFL's potential to enhance portfolio performance, the detailed mechanisms of how DFL modifies prediction models for MVO remain unexplored. This study aims to investigate how DFL adjusts stock return prediction models to optimize decisions in MVO, addressing the question: "MSE treats the errors of all assets equally, but how does DFL reduce errors of different assets differently?" Answering this will provide crucial insights into optimal stock return prediction for constructing efficient portfolios.

This paper evaluated the effectiveness of using generative AI to simplify science communication and enhance the public's understanding of science. By comparing lay summaries of journal articles from PNAS, yoked to those generated by AI, this work first assessed linguistic simplicity across such summaries and public perceptions. Study 1a analyzed simplicity features of PNAS abstracts (scientific summaries) and significance statements (lay summaries), observing that lay summaries were indeed linguistically simpler, but effect size differences were small. Study 1b used a large language model, GPT-4, to create significance statements based on paper abstracts and this more than doubled the average effect size without fine-tuning. Study 2 experimentally demonstrated that simply-written GPT summaries facilitated more favorable perceptions of scientists (they were perceived as more credible and trustworthy, but less intelligent) than more complexly-written human PNAS summaries. Crucially, Study 3 experimentally demonstrated that participants comprehended scientific writing better after reading simple GPT summaries compared to complex PNAS summaries. In their own words, participants also summarized scientific papers in a more detailed and concrete manner after reading GPT summaries compared to PNAS summaries of the same article. AI has the potential to engage scientific communities and the public via a simple language heuristic, advocating for its integration into scientific dissemination for a more informed society.

Asset allocation is an investment strategy that aims to balance risk and reward by constantly redistributing the portfolio's assets according to certain goals, risk tolerance, and investment horizon. Unfortunately, there is no simple formula that can find the right allocation for every individual. As a result, investors may use different asset allocations' strategy to try to fulfil their financial objectives. In this work, we conduct an extensive benchmark study to determine the efficacy and reliability of a number of optimization techniques. In particular, we focus on traditional approaches based on Modern Portfolio Theory, and on machine-learning approaches based on deep reinforcement learning. We assess the model's performance under different market tendency, i.e., both bullish and bearish markets. For reproducibility, we provide the code implementation code in this repository.

Over the decades, the Markowitz framework has been used extensively in portfolio analysis though it puts too much emphasis on the analysis of the market uncertainty rather than on the trend prediction. While generative adversarial network (GAN), conditional GAN (CGAN), and autoencoding CGAN (ACGAN) have been explored to generate financial time series and extract features that can help portfolio analysis. The limitation of the CGAN or ACGAN framework stands in putting too much emphasis on generating series and finding the internal trends of the series rather than predicting the future trends. In this paper, we introduce a hybrid approach on conditional GAN based on deep generative models that learns the internal trend of historical data while modeling market uncertainty and future trends. We evaluate the model on several real-world datasets from both the US and Europe markets, and show that the proposed HybridCGAN and HybridACGAN models lead to better portfolio allocation compared to the existing Markowitz, CGAN, and ACGAN approaches.

Reinforcement learning is arguably the coolest branch of artificial intelligence. It has already proven its prowess: stunning the world, beating the world champions in games of Chess, Go, and even DotA 2. Using RL for stock trading has always been a holy grail among data scientists. Stock trading has drawn our imaginations because of its ease of access and to misquote Cardi B, we like diamond and we like dollars . There are several ways of using Machine Learning for stock trading. One approach is to use forecasting techniques to predict the movement of the stock and build some heuristic based bot that uses the prediction to make decisions.

Whether you realize it or not, artificial intelligence is a driving force in the beauty industry at the moment. Utilizing machine learning, brands are able to not only recommend products to customers but also track how those products are working. However, nowhere is the tech more viable than at the dermatologist's office, where it could prove to be a powerful diagnostic tool in the hands of experts (and okay, maybe on a Snapchat filter that turns you into an 85-year-old version of yourself, too). Before we dive into the possibilities that artificial intelligence could have for derms, it's first important to understand what it is. For some time, algorithms--the most basic form of A.I.--have made it possible for brands to offer "customized" products for your skin's needs.

As the cornerstone of modern portfolio theory, Markowitz's mean-variance optimization is considered a major model adopted in portfolio management. However, due to the difficulty of estimating its parameters, it cannot be applied to all periods. In some cases, naive strategies such as Equally-weighted and Value-weighted portfolios can even get better performance. Under these circumstances, we can use multiple classic strategies as multiple strategic arms in multi-armed bandit to naturally establish a connection with the portfolio selection problem. This can also help to maximize the rewards in the bandit algorithm by the trade-off between exploration and exploitation. In this paper, we present a portfolio bandit strategy through Thompson sampling which aims to make online portfolio choices by effectively exploiting the performances among multiple arms. Also, by constructing multiple strategic arms, we can obtain the optimal investment portfolio to adapt different investment periods. Moreover, we devise a novel reward function based on users' different investment risk preferences, which can be adaptive to various investment styles. Our experimental results demonstrate that our proposed portfolio strategy has marked superiority across representative real-world market datasets in terms of extensive evaluation criteria.

Financial portfolio management is one of the problems that are most frequently encountered in the investment industry. Nevertheless, it is not widely recognized that both Kelly Criterion and Risk Parity collapse into Mean Variance under some conditions, which implies that a universal solution to the portfolio optimization problem could potentially exist. In fact, the process of sequential computation of optimal component weights that maximize the portfolio's expected return subject to a certain risk budget can be reformulated as a discrete-time Markov Decision Process (MDP) and hence as a stochastic optimal control, where the system being controlled is a portfolio consisting of multiple investment components, and the control is its component weights. Consequently, the problem could be solved using model-free Reinforcement Learning (RL) without knowing specific component dynamics. By examining existing methods of both value-based and policy-based model-free RL for the portfolio optimization problem, we identify some of the key unresolved questions and difficulties facing today's portfolio managers of applying model-free RL to their investment portfolios.

A new study from Stanford University has discovered the lies most commonly told on dating apps. Researchers found that lies to appear more interesting and dateable, such as falsifying one's availability, are most likely used to deceive potential partners. Of the many types of lies observed in these scenarios, the study found that polite'butler lies' used to thwart unwanted interactions were the most common, accounting for nearly 30 percent. A study from Stanford University analyzed more than 3,000 messages on mobile dating apps from over 200 participants. 'Most of these lies were about relationships – or not starting relationships – rather than lying to hook up,' said Jeffrey Hancock, a professor of communication in the Stanford School of Humanities and Sciences.