Abstract--We develop an energy management system (EMS) for artificial intelligence (AI) data centers with colocate d renewable generation. Under a cost-minimizing framework, th e EMS of renewable-colocated data center (RCDC) co-optimize s AI workload scheduling, on-site renewable utilization, an d electricity market participation. Within both wholesale and re tail market participation models, the economic benefit of the RCD C operation is maximized. Empirical evaluations using real-world traces of electricity prices, data center power consumptio n, and renewable generation demonstrate significant electric ity cost reduction from renewable and AI data center colocations. Index T erms --AI data center power system, energy management system, flexible demand, large load colocation, worklo ad scheduling.

Embedding-based similarity metrics between text sequences can be influenced not just by the content dimensions we most care about, but can also be biased by spurious attributes like the text's source or language. These document confounders cause problems for many applications, but especially those that need to pool texts from different corpora. This paper shows that a debiasing algorithm that removes information about observed confounders from the encoder representations substantially reduces these biases at a minimal computational cost. Document similarity and clustering metrics improve across every embedding variant and task we evaluate -- often dramatically. Interestingly, performance on out-of-distribution benchmarks is not impacted, indicating that the embeddings are not otherwise degraded.

Retrieval-Augmented Generation (RAG) systems are emerging as a key approach for grounding Large Language Models (LLMs) in external knowledge, addressing limitations in factual accuracy and contextual relevance. However, there is a lack of empirical studies that report on the development of RAG-based implementations grounded in real-world use cases, evaluated through general user involvement, and accompanied by systematic documentation of lessons learned. This paper presents five domain-specific RAG applications developed for real-world scenarios across governance, cybersecurity, agriculture, industrial research, and medical diagnostics. Each system incorporates multilingual OCR, semantic retrieval via vector embeddings, and domain-adapted LLMs, deployed through local servers or cloud APIs to meet distinct user needs. A web-based evaluation involving a total of 100 participants assessed the systems across six dimensions: (i) Ease of Use, (ii) Relevance, (iii) Transparency, (iv) Responsiveness, (v) Accuracy, and (vi) Likelihood of Recommendation. Based on user feedback and our development experience, we documented twelve key lessons learned, highlighting technical, operational, and ethical challenges affecting the reliability and usability of RAG systems in practice.

A key value proposition of machine learning is generalizability: the same methods and model architecture should be able to work across different domains and different contexts. While powerful, this generalization can sometimes go too far, and miss the importance of the specifics. In this work, we look at how fair machine learning has often treated as interchangeable the identity axis along which discrimination occurs. In other words, racism is measured and mitigated the same way as sexism, as ableism, as ageism. Disciplines outside of computer science have pointed out both the similarities and differences between these different forms of oppression, and in this work we draw out the implications for fair machine learning. While certainly not all aspects of fair machine learning need to be tailored to the specific form of oppression, there is a pressing need for greater attention to such specificity than is currently evident. Ultimately, context specificity can deepen our understanding of how to build more fair systems, widen our scope to include currently overlooked harms, and, almost paradoxically, also help to narrow our scope and counter the fear of an infinite number of group-specific methods of analysis.

Is recognising Palestine a way to'save face' for Western leaders? This is the moment a drone launched by Yemen's Houthis exploded in the Israeli city of Eilat. Footage shows it over the southern port city before bursting into flames in a residential neighbourhood, injuring at least 22 people, after Israeli military failed to intercept it. Finland's president hails rise of global south at UNGA Estonia calls Russian jets violating its airspace a'hostile act'

Is recognising Palestine a way to'save face' for Western leaders? A Spanish naval vessel will join the Global Sumud Flotilla for Gaza to provide assistance and, if necessary, conduct rescues, Spanish Prime Minister Pedro Sanchez has said. Italy earlier announced it was dispatching a frigate after a night of drone attacks on the humanitarian flotilla opposed by the Israeli government. Finland's president hails rise of global south at UNGA Estonia calls Russian jets violating its airspace a'hostile act'

The process learns generalized Casimirs for energy and entropy guaranteed to be conserved and nondecreasing, respectively.

Vladimir Putin will keep driving the war forward wider and deeper if he is not stopped, Ukraine's President Zelensky has warned. Speaking at the UN's General Assembly in New York, Zelensky said more countries would be met with Russian aggression unless allies displayed a united front and ramped up support. He said all nations were threatened by a global arms race, as military technology advances, adding that weapons decide who survives and called for global rules on AI. His comments come after US President Donald Trump shifted his position on the Russia-Ukraine war, saying for the first time that Ukraine could win back all of its land. Zelensky criticised international institutions, suggesting they are too weak to offer Ukraine safety guarantees, adding - in apparent reference to Nato - that being part of a long-standing military alliance doesn't automatically mean you are safe. We are now living through the most destructive arms race in human history, he said.