Foundational text-to-image latent diffusion architectures operate with systemic geographic and demographic blind spots. Pre-trained predominantly on Western and East Asian web-scale corpuses, these models default to homogenized, inaccurate stereotypes when prompted for the Global South. This paper presents the architec...
Fahim Abdullah· Zenodo (CERN European Organi...· 0 citations
Artificial Intelligence of Things (AIoT) refers to the deliberate pairing of artificial intelligence with the sensing and connectivity that the Internet of Things (IoT) already provides, so that the data streaming in from distributed devices turns into insight and, ultimately, action [1]. A properly designed AIoT syste...
Abijai M P, Riya Jyothish, L. C. Manikandan· Zenodo (CERN European Organi...· 0 citations
Artificial Intelligence of Things (AIoT) refers to the deliberate pairing of artificial intelligence with the sensing and connectivity that the Internet of Things (IoT) already provides, so that the data streaming in from distributed devices turns into insight and, ultimately, action [1]. A properly designed AIoT syste...
Abijai M P, Riya Jyothish, L. C. Manikandan· Zenodo (CERN European Organi...· 0 citations
A frozen language model can be coupled to a frozen partner model through small trainable latent bridges, with no text at the interface: Furui [5] builds such a system around a physics partner, where the quantity being carried is a number the partner computes. This paper asks whether the same channel can carry a disposi...
Ryoji Furui· Zenodo (CERN European Organi...· 0 citations
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In the research of ultrawideband (UWB) indoor positioning, non-line-of-sight (NLOS) signals constitute the core bottleneck leading to the degradation of positioning accuracy. Existing NLOS/line-of-sight (LOS) classification methods suffer from three key limitations: they fail to fully exploit the complex-value characte...
Fang Li, Jia-Cheng Ni, Ji-Cheng Yao et al.· IEEE Sensors Journal· 0 citations
This work presents a novel standard cell-compliant static latch that can be placed alongside digital logic and investigates the use of backgate biasing to improve system stability, and draws a comparison between the 22 nm implementations from the original work and the 12 nm implementation, indicating that moving to fin...
Florian Freye, Christian Lanius, Nils Mutert et al.· Journal of Signal Processing...· 0 citations
Graph Neural Networks (GNNs) have achieved widespread success from social networks to AI-for-Science. Most existing GNN frameworks adopt scatter-first (edge-centric) or gather-first (vertex-centric) scheduling paradigms for message passing. However, these paradigms are closely tied to traditional CUDA-core execution mo...
Jin-Liang Shi, Shi-Gang Li, Rong-Tian Fu et al.· IEEE Transactions on Paralle...· 0 citations
The Internet of Things (IoT) is a revolutionary innovation that enables greater automation, efficiency, and ease of use across various domains. Cloud computing is an efficient solution for processing and analyzing the large volume of data generated by IoT components. Data flows from edge devices to cloud environments d...
Sheng-Biao Li· Journal of engineering and a...· 0 citations
This work completely closes the approximation gap between undirected and directed DS in the semi-streaming setting, matching the $(1-\varepsilon)-approximate undirected DS algorithm by Esfandiari, Hajiaghayi, and Woodruff (2016).
The increasing demand for compute power is pushing system scaling toward advanced 3D and backside-integrated architectures. While the wafer backside opens a dynamic design space with new opportunities to optimize power delivery in scaled systems, it also introduces significant challenges for failure analysis (FA). In...
K. J. P. Jacobs, D. R. Glenn, C. Hart et al.· International Symposium for...· 0 citations
Automatic engagement prediction is a significant aspect of Human-Computer Interaction (HCI) and affective computing, enabling systems to capture user interest and deliver timely interventions. In dyadic conversations, a person’s (target’s) engagement can be influenced by both their own behavioral signals and those of t...
Monisha Singh, A. Dhall· Proceedings of the 28th Inte...· 0 citations
Abstract The observed UV continua of active galactic nuclei (AGN) generally lack a strong intrinsic H I Lyman edge predicted by classical optically thick accretion disk atmosphere models. We revisit this long-standing problem using our previous sub-Eddington ( L / L Edd ∼ 0.03) geometrically thin 3D accretion disk simu...
I. K. Kaul, Yan-Fei Jiang, Omer Blaes et al.· The Astrophysical Journal Le...· 0 citations
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 2, 2026
