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★ Do you get excited or upset about AWS SCPs, or GCP Org Policies?
★ Do you have experience developing software to solve cloud security challenges?
★ Do you downplay your cloud security knowledge but actually you know a lot of niche oddities of cloud IAM?
★ Do you like working in diverse security teams that care about your wellbeing?
★ Do you want to get paid to work on cloud security for one of the most sophisticated AWS environments in the world?
I'm hiring a…

Security Software Engineer (L5), Cloud Infrastructure Security | USA - Remote | Netflix
Netflix is one of the world's leading entertainment services, with over 300 million paid memberships in over 190 countries enjoying TV series, films and games across a wide variety of genres and languages. Members can play, pause and resume watching as much as they want, anytime, anywhere, and can change their plans at any time. Netflix operates our streaming service control plane on Amazon Web Services, which has led to one of the largest and most sophisticated AWS environments in the world. C…

Google launches Gemini 3 Pro Image, aka Nano Banana Pro, with more control, improved text rendering, and enhanced world knowledge, for free in the Gemini app (Abner Li/9to5Google)
https://9to5google.com/2025/11/20/gemini-3-nano-banana-pro/

Google rolling out Gemini 3-powered ‘Nano Banana Pro’ image gen, editing
Google is following the launch of Gemini 3 Pro with Nano Banana Pro. The image generation and editing model is officially Gemini 3 Pro Image.

Memory Retrieval and Consolidation in Large Language Models through Function Tokens
Shaohua Zhang, Yuan Lin, Hang Li
https://arxiv.org/abs/2510.08203 https://

Memory Retrieval and Consolidation in Large Language Models through Function Tokens
The remarkable success of large language models (LLMs) stems from their ability to consolidate vast amounts of knowledge into the memory during pre-training and to retrieve it from the memory during inference, enabling advanced capabilities such as knowledge memorization, instruction-following and reasoning. However, the mechanisms of memory retrieval and consolidation in LLMs remain poorly understood. In this paper, we propose the function token hypothesis to explain the workings of LLMs: Duri…

Sunspot Observations in 1684-1702: John Flamsteed and Philippe de La Hire
Nadezhda Zolotova, Mikhail Vokhmyanin
https://arxiv.org/abs/2510.11375 https://ar…

Sunspot Observations in 1684-1702: John Flamsteed and Philippe de La Hire
In this work, we present an extensive review and detailed analysis of sunspot measurements, drawings, and engravings made by John Flamsteed and, mainly, by Philippe de La Hire during the Maunder minimum. All available information and contemporary knowledge about the sunspot nature are shown. The coordinates, areas, and numbers of sunspots and sunspot groups are reconstructed. Based on these observations, La Hire, Jean-Dominique Cassini, and his son Jacques Cassini regularly published results th…

We distinguish "personal unfairness",
-- the view that one’s own economic situation is unfair,
from "social unfairness",
-- the view that the economic situation of others in society is unfair.
Uncertainties associated with the transition to a globalized knowledge economy heighten people’s feelings of personal unfairness
Feelings of personal unfairness increase support for the "populist right"
and feelings about social unfairn…

Left-Wing Populism vs. Right-Wing Populism - What's the Difference? | This vs. That
What's the difference between Left-Wing Populism and Right-Wing Populism? Left-wing populism and right-wing populism are both political movements that aim to...

Chronologically Consistent Generative AI
Songrun He, Linying Lv, Asaf Manela, Jimmy Wu
https://arxiv.org/abs/2510.11677 https://arxiv.org/pdf/2510.11677

Chronologically Consistent Generative AI
We introduce a family of chronologically consistent, instruction-following large language models to eliminate lookahead bias. Each model is trained only on data available before a clearly defined knowledge-cutoff date, ensuring strict temporal separation from any post-cutoff data. The resulting framework offers (i) a simple, conversational chat interface, (ii) fully open, fixed model weights that guarantee replicability, and (iii) a conservative lower bound on forecast accuracy, isolating the s…

Observation Matrix Design for Densifying MIMO Channel Estimation via 2D Ice Filling
Zijian Zhang, Mingyao Cui
https://arxiv.org/abs/2510.08887 https://arxi…

Observation Matrix Design for Densifying MIMO Channel Estimation via 2D Ice Filling
In recent years, densifying multiple-input multiple-output (MIMO) has attracted much attention from the communication community. Thanks to the subwavelength antenna spacing, the strong correlations among densifying antennas provide sufficient prior knowledge about channel state information (CSI). This inspires the careful design of observation matrices (e.g., transmit precoders and receive combiners), that exploits the CSI prior knowledge, to boost channel estimation performance. Aligned with t…

Experimental overview on the charmed baryon decays
Pei-Rong Li, Xiao-Rui Lyu, Yangheng Zheng
https://arxiv.org/abs/2509.19141 https://arxiv.org/pdf/2509.19…

Experimental overview on the charmed baryon decays
The charmed baryon was first observed experimentally in 1975, one year after the charm quark's confirmation via the discovery of the $J/ψ$ particle. Studying charmed baryon decays provides a pathway to investigate both strong and weak interactions, leveraging the weak decays of the embedded charm quark. However, for approximately three decades following its discovery, experimental knowledge of charmed baryons remained significantly limited compared to those of the hidden-charm $ψ$ mesons and …

Desiderata for a biomedical knowledge network: opportunities, challenges and future Directions
Chunlei Wu, Hongfang Liu, Jason Flannick, Mark A. Musen, Andrew I. Su, Lawrence Hunter, Thomas M. Powers, Cathy H. Wu
https://arxiv.org/abs/2509.22424

Desiderata for a biomedical knowledge network: opportunities, challenges and future Directions
Knowledge graphs, collectively as a knowledge network, have become critical tools for knowledge discovery in computable and explainable knowledge systems. Due to the semantic and structural complexities of biomedical data, these knowledge graphs need to enable dynamic reasoning over large evolving graphs and support fit-for-purpose abstraction, while establishing standards, preserving provenance and enforcing policy constraints for actionable discovery. A recent meeting of leading scientists di…

TF-DWGNet: A Directed Weighted Graph Neural Network with Tensor Fusion for Multi-Omics Cancer Subtype Classification
Tiantian Yang, Zhiqian Chen
https://arxiv.org/abs/2509.16301

TF-DWGNet: A Directed Weighted Graph Neural Network with Tensor Fusion for Multi-Omics Cancer Subtype Classification
Integration and analysis of multi-omics data provide valuable insights for cancer subtype classification. However, such data are inherently heterogeneous, high-dimensional, and exhibit complex intra- and inter-modality dependencies. Recent advances in graph neural networks (GNNs) offer powerful tools for modeling such structure. Yet, most existing methods rely on prior knowledge or predefined similarity networks to construct graphs, which are often undirected or unweighted, failing to capture t…