We therefore assume that the algorithm has access to an oracle that knows intuitively what it means to be fair, but cannot explicitly enunciate the fairness metric.

Thisoptimizationcan beperformedinparallelforeachs, keeping t 1 fixed.

Consideringa C-wayclassification task, our model consists of source classifier and feature extractor h = gs ϕ, which maps input spaceRI topredictionvector spaceRC,andh(x) = argmaxc h(x)[c]. Following in[25,26,27,28],wedenoteDTc astheconditional distribution (probability measure) ofDT given the ground truthy = c, and also assume that the supports ofDTi andDTj aredisjointforalli = j. Following [25, 27, 26], we study target domain relies on theexpansion property, which implies the continuity of data distributions in each class-wise subpopulations. Thus, x DS,x B(x) DS, the network predictions are consistent, i.e.RDS(h)=0. Theorem A.2. Suppose the condition of Claim 3.1 holds andDT,DS satisfies (q,γ)-constant expansion.

Herein we show that a positive encoder gap exists for signals that are (approximately)k-sparse. Furthermore, considerZ0 as the set of m random variablesz that only differs fromZ in itsith variable, z0i = (y0i,x0i). We finally get expressions for the covering number as a function of . Thus,weneedλ/m 2λ/(1+ν)2, which is satisfied as long asm 2 (1+ν)2/2, which is satisfied in all relevant scenarios. For remarkc), denote x = x0 +v, and note that the minimizer of the above optimization problem satisfies (as follows from optimality of the minimizer [Mehta and Gray,2013, Lemma 13 of Supplementary]) 1 2 kx DϕD(x)k22+λkϕD(x)k1= 1 2 kxk22 1 2 kDϕD(x)k22.

Sarah Martin investigates the virtual world of the children's online game Roblox with the profile of an eight-year-old girl with parental control settings turned on. Sarah Martin investigates the virtual world of the children's online game Roblox with the profile of an eight-year-old girl with parental control settings turned on. In seven days my young alter ego is cyberbullied and attacked while exploring clubs, casinos and horror games, all with parental controls in place. Is the platform safe for children - or an'X-rated paedophile hellscape'? Wed 5 Nov 2025 09.00 ESTLast modified on Wed 5 Nov 2025 09.01 EST I am an eight-year-old girl, standing near-naked in a room full of strangers. As the room spins and zooms upon me and people glide around me, I clock my features.

Recent years have seen a surge in research focused on leveraging graph learning techniques to detect neurodegenerative diseases. However, existing graph-based approaches typically lack the ability to localize and extract the specific brain regions driving neurodegenerative pathology within the full connectome. Additionally, recent works on multimodal brain graph models often suffer from high computational complexity, limiting their practical use in resource-constrained devices. In this study, we present BrainMAP, a novel multimodal graph learning framework designed for precise and computationally efficient identification of brain regions affected by neurodegenerative diseases. First, BrainMAP utilizes an atlas-driven filtering approach guided by the AAL atlas to pinpoint and extract critical brain subgraphs. Unlike recent state-of-the-art methods, which model the entire brain network, BrainMAP achieves more than 50% reduction in computational overhead by concentrating on disease-relevant subgraphs. Second, we employ an advanced multimodal fusion process comprising cross-node attention to align functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data, coupled with an adaptive gating mechanism to blend and integrate these modalities dynamically. Experimental results demonstrate that BrainMAP outperforms state-of-the-art methods in computational efficiency, without compromising predictive accuracy.

Accurate and robust localization is a critical enabler for emerging 5G and 6G applications, including autonomous driving, extended reality (XR), and smart manufacturing. While data-driven approaches have shown promise, most existing models require large amounts of labeled data and struggle to generalize across deployment scenarios and wireless configurations. To address these limitations, we propose a foundation-model-based solution tailored for wireless localization. We first analyze how different self-supervised learning (SSL) tasks acquire general-purpose and task-specific semantic features based on information bottleneck (IB) theory. Building on this foundation, we design a pretraining methodology for the proposed Large Wireless Localization Model (LWLM). Specifically, we propose an SSL framework that jointly optimizes three complementary objectives: (i) spatial-frequency masked channel modeling (SF-MCM), (ii) domain-transformation invariance (DTI), and (iii) position-invariant contrastive learning (PICL). These objectives jointly capture the underlying semantics of wireless channel from multiple perspectives. We further design lightweight decoders for key downstream tasks, including time-of-arrival (ToA) estimation, angle-of-arrival (AoA) estimation, single base station (BS) localization, and multiple BS localization. Comprehensive experimental results confirm that LWLM consistently surpasses both model-based and supervised learning baselines across all localization tasks. In particular, LWLM achieves 26.0%--87.5% improvement over transformer models without pretraining, and exhibits strong generalization under label-limited fine-tuning and unseen BS configurations, confirming its potential as a foundation model for wireless localization.

Multimodal neuroimaging modeling has becomes a widely used approach but confronts considerable challenges due to heterogeneity, which encompasses variability in data types, scales, and formats across modalities. This variability necessitates the deployment of advanced computational methods to integrate and interpret these diverse datasets within a cohesive analytical framework. In our research, we amalgamate functional magnetic resonance imaging, diffusion tensor imaging, and structural MRI into a cohesive framework. This integration capitalizes on the unique strengths of each modality and their inherent interconnections, aiming for a comprehensive understanding of the brain's connectivity and anatomical characteristics. Utilizing the Glasser atlas for parcellation, we integrate imaging derived features from various modalities: functional connectivity from fMRI, structural connectivity from DTI, and anatomical features from sMRI within consistent regions. Our approach incorporates a masking strategy to differentially weight neural connections, thereby facilitating a holistic amalgamation of multimodal imaging data. This technique enhances interpretability at connectivity level, transcending traditional analyses centered on singular regional attributes. The model is applied to the Human Connectome Project's Development study to elucidate the associations between multimodal imaging and cognitive functions throughout youth. The analysis demonstrates improved predictive accuracy and uncovers crucial anatomical features and essential neural connections, deepening our understanding of brain structure and function.

Optimization of human-AI teams hinges on the AI's ability to tailor its interaction to individual human teammates. A common hypothesis in adaptive AI research is that minor differences in people's predisposition to trust can significantly impact their likelihood of complying with recommendations from the AI. Predisposition to trust is often measured with self-report inventories that are administered before interactions. We benchmark a popular measure of this kind against behavioral predictors of compliance. We find that the inventory is a less effective predictor of compliance than the behavioral measures in datasets taken from three previous research projects. This suggests a general property that individual differences in initial behavior are more predictive than differences in self-reported trust attitudes. This result also shows a potential for easily accessible behavioral measures to provide an AI with more accurate models without the use of (often costly) survey instruments.

The partnership includes AI-powered XR solutions for staff and trainers at Defence Technology Institute. IRVINE, CA, December 19, 2022 – EON Reality, Inc. ("EON Reality"), a global leader in Virtual and Augmented Reality ("XR") industry and education solutions powered by Artificial Intelligence (AI), has announced a new deal with Thailand's Defence Technology Institute (DTI). The partnership provides the staff and trainers at DTI with access to EON Reality's XR tools. One of the leading research and development agencies under Thailand's Ministry of Defence, the Defence Technology Institute launched at the start of 2009 as a major part of the Thai government's program to increase domestic production of necessary weaponry. But while its primary purpose may be arms manufacturing, DTI has proven an invaluable tool for all sorts of technological advancements and creations over the years.