Pokémon maker confirms it was victim of hack The Pokémon CompanyPokémon is one of the world's best-known entertainment brands Pokémon maker Game Freak has confirmed it was the victim of a data leak after information appeared online over the weekend. The company, which has developed the Nintendo-exclusive video game series since 1996, said its servers were hacked in August this year. A statement said 2,606 items containing the names and email addresses of current, former and contract employees were accessed. The company did not comment on other information shared online claiming to show details of unreleased and upcoming projects. Game Freak said it would individually contact those affected where possible, and strengthen security measures to prevent similar hacks in future.

South Korea's military said Monday it was "fully ready" to respond after North Korea ordered troops on the border to prepare to fire, in an escalating dispute over drone flights to Pyongyang. The nuclear-armed North has accused Seoul of flying drones over its capital to drop propaganda leaflets filled with "inflammatory rumors and rubbish," and warned Sunday that if another drone was detected, it would consider it "a declaration of war." Seoul's military previously denied it was behind the flights, with local speculation centered on activist groups in the South, which have long sent propaganda and U.S. currency northward, typically by balloon.

In the past two years the French capital has been in the throes of AI fever and has launched some of Europe's most talked-about startups, including Mistral, which is currently valued at 6.2 billion ( 4.7 billion). That's partly down to the support the industry has received. President Emmanuel Macron has given French AI startups some emphatic political backing, while telecoms billionaire Xavier Niel has provided much investment and will to finance national ambition. In September 2023, Niel invested 200 million ( 212 million), splitting that money between funding for startups such as Mistral, an AI research lab called Kyutai and a cloud supercomputer powered by Nvidia. "I'm the old guy who likes entrepreneurs and the idea was always the same: how we can help this talent to stay here, creating companies," says Niel. Niel, a prolific French businessman who owns telecommunications company Iliad, believes European AI companies now have a unique opportunity to act. "If you want to create a search engine now from scratch, you cannot win because you weren't there 25 years ago.

Two years ago, Jon Fath moved with his family to Portugal from the Netherlands with the sole purpose of launching a fintech startup there. "This country is brimming with talent and ambition," Fath says. "I thank Lisbon for welcoming me, along with so many other expats and entrepreneurs, so warmly." Indeed, it's no surprise that the European Commission named Lisbon as 2023's European Capital of Innovation, while the Financial Times, in partnership with Statista, ranked two Portuguese startup hubs in Europe's top ten startup hubs--including the Unicorn Factory Lisboa, which launched in 2022 and has already supported more than 820 startups and helped raise more than 1 billion ( 1.1 billion) . "Portugal offers unique advantages, such as its climate, safety, and cost of living, which make it an attractive choice over countries in central or northern Europe," says Nuno Pereira, CEO of Paynest.

Home to fine cheese, breathtaking scenery and footballing politics (FIFA HQ overlooks Lake Zurich), Switzerland's largest city is also a financial juggernaut. The central square of Paradeplatz is its beating heart, where the Swiss banking system pumps venture capital funding into a thriving tech ecosystem--around CHF 72 million (more than 1 billion) was poured into Zurich startups alone in 2023. Fintech is a natural major player, but if the banking industry is the ecosystem's engine, innovation is its fuel. Many of the city's most exciting startups began life as student projects at its world-leading universities, which provide a steady flow of great thinkers. "Zurich is a city of ideas, people, and capital," says Frank Floessel, head of entrepreneurship at ETH Zurich, a public research university focused on science, technology, and engineering that'spins out' an average of 25 startups every year. "We have plenty of talent and know-how.

In the "Startup-up, Scale-up" review report published last year, chancellor Rachel Reeves promised to make Britain the "high growth, start-up hub of the world". Now, almost six months into the new government, entrepreneurs remain encouraged by the promises made in the Labour manifesto. "The ambition embodied in Great British Energy and the 2030 decarbonization targets is precisely what we need and deserve," says Shilpika Gautam, CEO of greentech startup Opna, about Labour's energy policies. "It's high time the UK caught up with the policy and financing innovations in other countries, such as the Inflation Reduction Act in the US." Amit Gudka, founder of Field, agrees: "We welcome Labour's plans to double onshore wind, triple solar and quadruple offshore wind by 2030. These plans are ambitious, but not unrealistic, provided the Government continues to make clear policy decisions and create a stable policy and regulatory environment."

Differentiable programming allows for derivatives of functions implemented via computer code to be calculated automatically. These derivatives are calculated using automatic differentiation (AD). This thesis explores two applications of differentiable programming to computational plasma physics. First, we consider how differentiable programming can be used to simplify and improve stellarator optimization. We introduce a stellarator coil design code (FOCUSADD) that uses gradient-based optimization to produce stellarator coils with finite build. Because we use reverse mode AD, which can compute gradients of scalar functions with the same computational complexity as the function, FOCUSADD is simple, flexible, and efficient. We then discuss two additional applications of AD in stellarator optimization. Second, we explore how machine learning (ML) can be used to improve or replace the numerical methods used to solve partial differential equations (PDEs), focusing on time-dependent PDEs in fluid mechanics relevant to plasma physics. Differentiable programming allows neural networks and other techniques from ML to be embedded within numerical methods. This is a promising, but relatively new, research area. We focus on two basic questions. First, can we design ML-based PDE solvers that have the same guarantees of conservation, stability, and positivity that standard numerical methods do? The answer is yes; we introduce error-correcting algorithms that preserve invariants of time-dependent PDEs. Second, which types of ML-based solvers work best at solving PDEs? We perform a systematic review of the scientific literature on solving PDEs with ML. Unfortunately we discover two issues, weak baselines and reporting biases, that affect the interpretation reproducibility of a significant majority of published research. We conclude that using ML to solve PDEs is not as promising as we initially believed.

We propose Model Swarms, a collaborative search algorithm to adapt LLMs via swarm intelligence, the collective behavior guiding individual systems. Specifically, Model Swarms starts with a pool of LLM experts and a utility function. Guided by the best-found checkpoints across models, diverse LLM experts collaboratively move in the weight space and optimize a utility function representing model adaptation objectives. Compared to existing model composition approaches, Model Swarms offers tuning-free model adaptation, works in low-data regimes with as few as 200 examples, and does not require assumptions about specific experts in the swarm or how they should be composed. Extensive experiments demonstrate that Model Swarms could flexibly adapt LLM experts to a single task, multi-task domains, reward models, as well as diverse human interests, improving over 12 model composition baselines by up to 21.0% across tasks and contexts. Further analysis reveals that LLM experts discover previously unseen capabilities in initial checkpoints and that Model Swarms enable the weak-to-strong transition of experts through the collaborative search process.

Monitoring networks increasingly aim to assimilate data from a large number of diverse sensors covering many sensing modalities. Bayesian optimal experimental design (OED) seeks to identify data, sensor configurations, or experiments which can optimally reduce uncertainty and hence increase the performance of a monitoring network. Information theory guides OED by formulating the choice of experiment or sensor placement as an optimization problem that maximizes the expected information gain (EIG) about quantities of interest given prior knowledge and models of expected observation data. Therefore, within the context of seismo-acoustic monitoring, we can use Bayesian OED to configure sensor networks by choosing sensor locations, types, and fidelity in order to improve our ability to identify and locate seismic sources. In this work, we develop the framework necessary to use Bayesian OED to optimize a sensor network's ability to locate seismic events from arrival time data of detected seismic phases at the regional-scale. Bayesian OED requires four elements: 1) A likelihood function that describes the distribution of detection and travel time data from the sensor network, 2) A Bayesian solver that uses a prior and likelihood to identify the posterior distribution of seismic events given the data, 3) An algorithm to compute EIG about seismic events over a dataset of hypothetical prior events, 4) An optimizer that finds a sensor network which maximizes EIG. Once we have developed this framework, we explore many relevant questions to monitoring such as: how to trade off sensor fidelity and earth model uncertainty; how sensor types, number, and locations influence uncertainty; and how prior models and constraints influence sensor placement.

Recent breakthroughs in preference alignment have significantly improved Large Language Models' ability to generate texts that align with human preferences and values. However, current alignment metrics typically emphasize the post-hoc overall improvement, while overlooking a critical aspect: regression, which refers to the backsliding on previously correctly-handled data after updates. This potential pitfall may arise from excessive fine-tuning on already well-aligned data, which subsequently leads to over-alignment and degeneration. To address this challenge, we propose FlipGuard, a constrained optimization approach to detect and mitigate update regression with focal attention. Specifically, FlipGuard identifies performance degradation using a customized reward characterization and strategically enforces a constraint to encourage conditional congruence with the pre-aligned model during training. Comprehensive experiments demonstrate that FlipGuard effectively alleviates update regression while demonstrating excellent overall performance, with the added benefit of knowledge preservation while aligning preferences.