Game Theory: Is the betting industry ruining sport? Betting is now embedded in modern sport. In Türkiye, referees are under investigation for placing thousands of bets. NBA players and coaches are facing major sanctions over gambling violations. But on the flip side, those same players, teams and leagues wear and promote gambling brands.

Harry and Meghan's photo-gate leaves Kardashian clan'upset': Sussexes demanded not to be pictured inside Kris Jenner's 70th birthday party before mystery deletion Epstein's ultimate betrayal of Trump as emails reveal billionaire's twisted plot against president: 'I am the one able to take him down' Father of cheerleader who mysteriously died on Carnival cruise speaks out on investigation... and reveals the horrific theories he's heard I tried the'magic' pill that claims to cure migraines, back pain, anxiety and insomnia. The relief was instant... and it costs just $25 a month Kim Kardashian's daughter North West, 12, shocks fans with'high-risk piercing' not suitable for kids Alex Murdaugh's housekeeper says she KNEW the lawyer killed his wife and son in bombshell new book Civil rights leader Rev. Jesse Jackson hospitalized in Chicago Donald Trump leaves Ozzy Osbourne's widow Sharon in tears after paying tribute to the late rocker Kelly Clarkson's staff'feel like s***': TV insiders reveal star's huge backstage transformation after death of ex-husband He killed his daughter, 2, in a hot car then committed suicide on day he was due to be jailed. Then she tried to have her rich husband assassinated. Epstein's mysterious falling out with Clinton is revealed in emails to Obama lawyer inviting her to his infamous NYC townhouse John Travolta's son Benjamin, 14, has grown into his spitting image as Grease star proudly shares new clip Sober Dolphins coach Mike McDaniel'indebted' to Commanders' Dan Quinn for helping him beat drinking problem Diddy has prison release date pushed BACK amid allegations of'drinking moonshine' Kill a comrade or be killed: Three winters into Putin's war, his army is devouring itself. Trump makes sordid joke about Muslim president's WIFE at the White House The Navy commander who stared down Al Qaeda on the USS Cole has a new enemy... and a chilling warning for America Swear words that were once potent are losing their sting, a new study has revealed.

Imagine an autonomous car driving along a quiet suburban road when suddenly a dog runs onto the road. The system must brake hard and decide, within a fraction of a second, whether to swerve into oncoming traffic--where the other autonomous car might make space--to steer right and hit the roadside barrier, or to continue straight and injure the dog. The first two options risk only material damage; the last harms a living creature. Each choice is justifiable and involves trade-offs between safety, property and ethical concerns. However, today's autonomous systems are not designed to explicitly take such value-laden conflicts into account.

Since September, residents on South Korea's Jeju island have been spotting small packs of what appear to be bags of Chinese tea washed ashore. Upon closer inspection, however, they were found to contain ketamine. Some 28kg (62 lbs) of the drug, wrapped in foil and labelled with the Chinese character for tea, have been found on at least eight occasions, police say. Ketamine is used as an anaesthetic in medical procedures, but its recreational use is illegal in South Korea. It can cause severe physical and mental damage, including to the heart and lungs, when misused.

The following study presents results obtained from experiments in which, before training a classification model, we added noise to the labels of the training set, so that the information contained in this set is not entirely correct. In fact, most datasets encountered in practical situations contain some degree of noise, which highlights the importance of this type of study for new techniques before implementing them in real-world applications. In this case, we are interested in measuring the impact of noise addition on BCOPS (Guan & Tib-shirani, 2022), a algorithm based on conformal prediction (V ovk et al., 2005) which, when combined with other machine learning methods, allows the construction of prediction sets for the test set observations in classification tasks. Prediction sets are sets that contain the possible values (for regression tasks) or possible classes (for classification tasks) for new observations. These sets are constructed so that the probability of the true value or class being contained within them meets a coverage guarantee. In the work developed by Guan & Tibshirani (2022), the possibility of using these prediction sets to detect outlier observations - meaning, observations whose true class was not present during training - is emphasized. Thus, we aim to measure both the classification coverage and the abstention rate on outlier observations of the BCOPS algorithm under the addition of noise, considering some of the datasets and machine learning algorithms used by Guan & Tibshirani (2022).

Large Language Models (LLMs) are increasingly used as daily recommendation systems for tasks like education planning, yet their recommendations risk perpetuating societal biases. This paper empirically examines geographic, demographic, and economic biases in university and program suggestions from three open-source LLMs: LLaMA-3.1-8B, Gemma-7B, and Mistral-7B. Using 360 simulated user profiles varying by gender, nationality, and economic status, we analyze over 25,000 recommendations. Results show strong biases: institutions in the Global North are disproportionately favored, recommendations often reinforce gender stereotypes, and institutional repetition is prevalent. While LLaMA-3.1 achieves the highest diversity, recommending 481 unique universities across 58 countries, systemic disparities persist. To quantify these issues, we propose a novel, multi-dimensional evaluation framework that goes beyond accuracy by measuring demographic and geographic representation. Our findings highlight the urgent need for bias consideration in educational LMs to ensure equitable global access to higher education.

We study the problem of learning policies that maximize cumulative reward while satisfying safety constraints, even when the real environment differs from a simulator or nominal model. We focus on robust constrained Markov decision processes (RCMDPs), where the agent must maximize reward while ensuring cumulative utility exceeds a threshold under the worst-case dynamics within an uncertainty set. While recent works have established finite-time iteration complexity guarantees for RCMDPs using policy optimization, their sample complexity guarantees remain largely unexplored. In this paper, we first show that Markovian policies may fail to be optimal even under rectangular uncertainty sets unlike the {\em unconstrained} robust MDP. To address this, we introduce an augmented state space that incorporates the remaining utility budget into the state representation. Building on this formulation, we propose a novel Robust constrained Value iteration (RCVI) algorithm with a sample complexity of $\mathcal{\tilde{O}}(|S||A|H^5/ε^2)$ achieving at most $ε$ violation using a generative model where $|S|$ and $|A|$ denote the sizes of the state and action spaces, respectively, and $H$ is the episode length. To the best of our knowledge, this is the {\em first sample complexity guarantee} for RCMDP. Empirical results further validate the effectiveness of our approach.

The increasing digitalization of education presents unprecedented opportunities for data-driven personalization, but it also introduces significant challenges to student data privacy. Conventional recommender systems rely on centralized data, a paradigm often incompatible with modern data protection regulations. A novel privacy-preserving recommender system is proposed and evaluated to address this critical issue using Federated Learning (FL). The approach utilizes a Deep Neural Network (DNN) with rich, engineered features from the large-scale ASSISTments educational dataset. A rigorous comparative analysis of federated aggregation strategies was conducted, identifying FedProx as a significantly more stable and effective method for handling heterogeneous student data than the standard FedAvg baseline. The optimized federated model achieves a high-performance F1-Score of 76.28%, corresponding to 92% of the performance of a powerful, centralized XGBoost model. These findings validate that a federated approach can provide highly effective content recommendations without centralizing sensitive student data. Consequently, our work presents a viable and robust solution to the personalization-privacy dilemma in modern educational platforms.