Findings from the AI Climate Hoax: What is the real climate impact of data centres? - Finextra Research
Findings from the AI Climate Hoax: What is the real climate impact of data centres? Finextra Research
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Beyond Metadata: Multimodal, Policy-Aware Detection of YouTube Scam Videos
arXiv:2509.23418v2 Announce Type: replace Abstract: YouTube is a major platform for information and entertainment, but its wide accessibility also makes it attractive for scammers to upload deceptive or malicious content. Prior detection approaches rely largely on textual or statistical metadata, such as titles, descriptions, view counts, or likes, which are effective in many cases but can be evaded through benign-looking text, manipulated statistics, or other obfuscation strategies (e.g., 'Leetspeak'), while ignoring visual cues. In this study, we systematically investigate multimodal approaches for detecting YouTube scams. Our dataset consolidates established scam categories and augments them with full-length videos and policy-grounded reasoning annotations. Experiments show that a text-
Online Flow Time Minimization: Tight Bounds for Non-Preemptive Algorithms
arXiv:2511.03485v3 Announce Type: replace Abstract: This paper studies the online scheduling problem of minimizing total flow time for $n$ jobs on $m$ identical machines. A classical $\Omega(n)$ lower bound shows that no deterministic single-machine algorithm can beat the trivial greedy, even when $n$ is known in advance. However, this barrier is specific to deterministic algorithms on a single machine, leaving open what randomization, multiple machines, or the kill-and-restart capability can achieve. We give a nearly complete answer. For randomized non-preemptive algorithms, we establish a tight $\Theta(\sqrt{n/m})$ competitive ratio, which also improves the best offline approximation to $O(\sqrt{n/m})$. For deterministic non-preemptive algorithms on multiple machines, we prove an $O(n/m^
On the average-case complexity landscape for Tensor-Isomorphism-complete problems over finite fields
arXiv:2604.00591v1 Announce Type: cross Abstract: In Grochow and Qiao (SIAM J. Comput., 2021), the complexity class Tensor Isomorphism (TI) was introduced and isomorphism problems for groups, algebras, and polynomials were shown to be TI-complete. In this paper, we study average-case algorithms for several TI-complete problems over finite fields, including algebra isomorphism, matrix code conjugacy, and $4$-tensor isomorphism. Our main results are as follows. Over the finite field of order $q$, we devise (1) average-case polynomial-time algorithms for algebra isomorphism and matrix code conjugacy that succeed in a $1/\Theta(q)$ fraction of inputs and (2) an average-case polynomial-time algorithm for the $4$-tensor isomorphism that succeeds in a $1/q^{\Theta(1)}$ fraction of inputs. Prior t
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