Aston Villa boss Unai Emery had an unhappy 18-month spell in charge of Arsenal that ended in 2019, but can he get the better of his old club on Tuesday? After the abuse he took from Arsenal fans, I'd love nothing more than Emery to go back to the Emirates and win, said BBC Sport football expert Chris Sutton. He absolutely didn't deserve that. Some of those fans should take a long, hard look at themselves for the way they mocked him. I hope Villa go there and spank them, just because of that. Sutton is making predictions for all 380 Premier League games this season, against AI, BBC Sport readers and a variety of guests. For week 19 - which includes the final games of 2025 on Tuesday, 30 December and the first matches of 2026 on New Year's Day - he takes on singer-songwriter Tom A Smith, who is a Sunderland fan.

Pok\'emon Red, a classic Game Boy JRPG, presents significant challenges as a testbed for agents, including multi-tasking, long horizons of tens of thousands of steps, hard exploration, and a vast array of potential policies. We introduce a simplistic environment and a Deep Reinforcement Learning (DRL) training methodology, demonstrating a baseline agent that completes an initial segment of the game up to completing Cerulean City. Our experiments include various ablations that reveal vulnerabilities in reward shaping, where agents exploit specific reward signals. We also discuss limitations and argue that games like Pok\'emon hold strong potential for future research on Large Language Model agents, hierarchical training algorithms, and advanced exploration methods. Source Code: https://github.com/MarcoMeter/neroRL/tree/poke_red

Network-wide traffic flow, which captures dynamic traffic volume on each link of a general network, is fundamental to smart mobility applications. However, the observed traffic flow from sensors is usually limited across the entire network due to the associated high installation and maintenance costs. To address this issue, existing research uses various supplementary data sources to compensate for insufficient sensor coverage and estimate the unobserved traffic flow. Although these studies have shown promising results, the inconsistent availability and quality of supplementary data across cities make their methods typically face a trade-off challenge between accuracy and generality. In this research, we first time advocate using the Global Open Multi-Source (GOMS) data within an advanced deep learning framework to break the trade-off. The GOMS data primarily encompass geographical and demographic information, including road topology, building footprints, and population density, which can be consistently collected across cities. More importantly, these GOMS data are either causes or consequences of transportation activities, thereby creating opportunities for accurate network-wide flow estimation. Furthermore, we use map images to represent GOMS data, instead of traditional tabular formats, to capture richer and more comprehensive geographical and demographic information. To address multi-source data fusion, we develop an attention-based graph neural network that effectively extracts and synthesizes information from GOMS maps while simultaneously capturing spatiotemporal traffic dynamics from observed traffic data. A large-scale case study across 15 cities in Europe and North America was conducted. The results demonstrate stable and satisfactory estimation accuracy across these cities, which suggests that the trade-off challenge can be successfully addressed using our approach.

Ukraine has reported downing more than two dozen Russian drones over the country's capital, Kyiv, as Russia's defence ministry announced the destruction of eight Ukrainian drones near the annexed Crimean peninsula. The extent of the damage from the rival attacks early on Sunday was not immediately clear. Kyiv Mayor Vitali Klitschko said that at least one person was wounded in the city's historic Podil neighbourhood and a fire broke out near one of its parks. Debris from downed drones fell on the Darnytskyi, Solomianskyi, Shevchenkivskyi, Sviatoshynskyi and Podil districts, Klitschko and the city's military administration said. In the Shevchenkivskyi district, debris sparked a fire in an apartment, which was quickly distinguished.

The concept of walkable urban development has gained increased attention due to its public health, economic, and environmental sustainability benefits. Unfortunately, land zoning and historic under-investment have resulted in spatial inequality in walkability and social inequality among residents. We tackle the problem of Walkability Optimization through the lens of combinatorial optimization. The task is to select locations in which additional amenities (e.g., grocery stores, schools, restaurants) can be allocated to improve resident access via walking while taking into account existing amenities and providing multiple options (e.g., for restaurants). To this end, we derive Mixed-Integer Linear Programming (MILP) and Constraint Programming (CP) models. Moreover, we show that the problem's objective function is submodular in special cases, which motivates an efficient greedy heuristic. We conduct a case study on 31 underserved neighborhoods in the City of Toronto, Canada. MILP finds the best solutions in most scenarios but does not scale well with network size. The greedy algorithm scales well and finds near-optimal solutions. Our empirical evaluation shows that neighbourhoods with low walkability have a great potential for transformation into pedestrian-friendly neighbourhoods by strategically placing new amenities. Allocating 3 additional grocery stores, schools, and restaurants can improve the "WalkScore" by more than 50 points (on a scale of 100) for 4 neighbourhoods and reduce the walking distances to amenities for 75% of all residential locations to 10 minutes for all amenity types. Our code and paper appendix are available at https://github.com/khalil-research/walkability.

Three years ago, the San Francisco Board of Supervisors made history by becoming the first city in the nation to ban use of facial recognition technology by local government. Last night, the board went in a different direction, giving police the right to kill a criminal suspect with a teleoperated robot if they believe there is an imminent threat of death to police or members of the public. Assistant police chief David Lazar said ahead of the vote that killer robots might be needed in scenarios involving mass shootings or suicide bombers, citing the Mandalay Bay shooting in Las Vegas in 2017 and the killing of five police officers in Dallas, Texas, in 2016. Dallas police ultimately used explosives strapped to a Remotec F5A bomb disposal robot--a model also possessed by the San Francisco Police Department--to kill that suspect. The new administrative code requires a police chief to authorize use of deadly force involving a robot and to first consider de-escalation or an alternative use of force.

TAMPERE, Finland--(BUSINESS WIRE)--Tampere, one of Finland's largest cities, is the first in Europe to introduce the Liveable Well-Being City indicators, which Japan uses to measure well-being factors from the perspective of residents in its 27 cities. The indicators will provide important information to support knowledge management on the state of the urban environment, the quality of services and the well-being of citizens. The co-operation between Tampere and Japan will start with the application of the indicators developed in co-operation between Smart City Institute Japan and several research institutes and universities. The model utilises both objective and subjective data collected from urban residents to improve well-being and streamline everyday life. The data is an important foundation for knowledge management: it enables cities to identify their success points and development needs from the residents' perspective.

Hansen Technologies, a leading global provider of software and services to the energy, water and communications industries, is pleased to announce that it has signed a multi-year agreement with the City of New Bern, part of the State of North Carolina, as the city charts a new digital transformation journey and envisions a new technological infrastructure. Under the terms of the agreement, Hansen will provide Hansen CIS, part of the Hansen Suite for Energy and Utilities, to the historical city, delivered through a SaaS (Software as a Service) model – marking another successful progression in Hansen's Cloud and SaaS-based CIS strategy within North America. This continues to meet the evolving needs of North American utilities and municipalities as they look to migrate towards more flexible and scalable software platforms. This will modernize New Bern's existing infrastructure and enable the replacement of their existing systems. Equipped with enhanced UI configuration capabilities and an expanded integration framework, Hansen CIS empowers utilities and municipalities to manage the full customer service and revenue lifecycle for water and energy-related services.

I have never lived in Los Angeles, but I have probably spent more time thinking about L.A. than any other city that I haven't resided in. This is partly the fault of Hollywood, of Ice Cube and The White Album, of Curb Your Enthusiasm and Party Down, of the despised Lakers, but it's mostly the fault of Mike Davis. Davis, the historian and urban theorist who died on Tuesday, was probably my favorite writer about cities that I have ever read. He didn't only write about L.A., not by a long shot, but L.A. was his Beatrice, his Dark Lady. Every time I visit Los Angeles Davis' work floods through my brain, often down to specific words, phrases, and sentences.

The International Standards on Auditing require auditors to collect reasonable assurance that financial statements are free of material misstatement. At the same time, a central objective of Continuous Assurance is the'real-time' assessment of digital accounting journal entries. Recently, driven by the advances in artificial intelligence, Deep Learning techniques have emerged in financial auditing to examine vast quantities of accounting data. However, learning highly adaptive audit models in decentralised and dynamic settings remains challenging. It requires the study of data distribution shifts over multiple clients and time periods. In this work, we propose a Federated Continual Learning framework enabling auditors to learn audit models from decentral clients continuously. We evaluate the framework's ability to detect accounting anomalies in common scenarios of organizational activity. Our empirical results, using real-world datasets and combined federatedcontinual learning strategies, demonstrate the learned model's ability to detect anomalies in audit settings of data distribution shifts.