Oracle has released version 27 of the Java programming language and virtual machine. As the second non-LTS release since JDK 25, the final feature set includes nine JEPs, five of which are still progressing through the preview and incubator stages. This release focuses on strengthening security, future language innovation, and projects under the auspices of the Java Verified Portfolio.

By Michael Redlich

If you clicked on a fake HBO Max ad on Reddit in the past week, you might have fallen victim to a rising "ClickFix" security threat.

arXiv:2609.12610v1 Announce Type: cross Abstract: Deep learning has been widely applied across many areas of experimental high-energy physics, yet existing models address only event-level classification or object tagging and therefore still require reconstruction algorithms tailored to each decay channel. We present the first application of segmentation to resonance tagging at BESIII and introduce ResoSeg, a deep learning model that jointly performs particle-level segmentation and event-level classification, enabling a one-pass analysis of resonance to anything decays while precisely reconstructing the relevant resonance properties. We demonstrate the reconstruction of $\eta_c$ with $e^+e^-\to\pi^+\pi^-h_c$, $h_c\to\gamma\eta_c$, $\eta_c\to\text{anything}$. The model is trained on BESIII-$\eta_c$ dataset, which is constructed with per-track true labels obtained via a Truth-Matching Algorithm. Experimental results show that the average combined efficiency of ResoSeg is more than double that of the conventional 16-channel approach across energy points from 4.19 to 4.60\,GeV. The model generalizes to unseen energy points, adapts to other $\eta_c$ production modes through transfer learning, and remains robust against variations in the $\eta_c$ mass, width, and branching fractions, providing a general, resonance-aware model applicable beyond $\eta_c$ and BESIII. The source code is available at https://github.com/oashen/ResoSeg.

Netflix has reworked its Conductor workflow orchestration engine to handle larger workloads, increasing supported workflow size from about 2,500 to 30,000 tasks and reducing p99 workflow evaluation latency by about 40%. Conductor 4.0 separates workflow metadata from task data, moves evaluation to asynchronous processing, and introduces dynamic worker allocation and concurrency controls.

By Leela Kumili

The investigation also comes as the DOJ has faced criticism over how it handles major mergers, including questions about political influence.

CERN engineers announced a shift from Red Hat-based distributions to Debian for its accelerator control systems. This decision stems from Red Hat's tightening compiler mandates, which threatened legacy hardware. The transition, focused on 2,200 specialized control machines, is set for completion in late 2026, while CERN's other systems will remain with Red Hat and AlmaLinux.

By Olimpiu Pop

SpaceX's recent IPO and Starship rocket test flight delivered two big data points that offer a realistic vision for the coming years — and one that may disappoint both the company's boosters and its critics.

American Airlines said Tuesday it plans to install Starlink on more than 500 Airbus aircraft, the latest carrier win for IPO-bound SpaceX.

Although visitors to an event like TechEx North America will always want to see the cutting edge front and centre stage, the nuance and detail brought to the show by the speakers and exhibitors mean that it’s sometimes the smaller considerations that need to play big – at least, in the minds of enterprise decision-makers.

Across the different tracks of Edge Computing, IoT, Data Centre Congress, and Cyber Security, the question was about what needs to be built around AI before it takes its place in the physical, business-oriented world?

The Edge Computing track, with its roots in traditional industries, looked at latency, deployment discipline, and cybersecurity for IIoT/IT amalgams. The day-one programme positioned edge computing as a place where companies can reassess the value of their data assets, look at how decisions are made by autonomous equipment, and the required speed of processing.

Sessions looked at scaling edge deployments (in multi-site businesses, for example), agentic network operations, distributed inference – on-prem, in-cloud or hybrid – immutable edge infrastructure, and how zero-trust cybersecurity lessons can be applied to control systems.

Ed Doran of the Edge AI Foundation chaired a programme that had as its starting point that the edge is a demanding place in which to operate. The track included reps from Akamai, Spectro Cloud, Scylos, TÜV Rheinland, the OPC Foundation, and Germany’s Schneider Electric. Discussions covered issues in manufacturing and IoT, and delved into industrial automation and connected control and attenuation devices.

Moving intelligence closer to the machine changes risk profiles (in which direction was a matter for debate), and faster local decisions may reduce latency and dependence on central cloud services, but where do observability and control in decision-makers’ minds?

The IoT Tech Expo day-one track on Industrial IoT and Digital Twins looked at manufacturing, with sessions covering smart factory trends, AI beyond Industry 4.0, asset management, practical road-maps for escaping pilot purgatory (more on that below), physical AI in everyday ops, and digital twins.

Similar to debates on AI deployment in the knowledge sector, it was the gap between demo and deployment that was the area subject to most scrutiny. Industrial and back office AI both might work well in a presentation, but can stall when they meet old machines (or legacy software).

The alliterative pilot purgatory held considerable weight in several sessions on the various presentation stages and on the show floor, day one. The Rockwell Automation and Ford session on physical AI and connected asset intelligence looked especially hard at scaling projects that seem to work well in concept, but may fail when hitting the real world. How does intelligence enter daily operations without becoming another dashboard that nobody owns?

Digital twins received similar appraisal. The better version of the digital twin isn’t a visual replica used for demons – although they do have their uses. Instead, several speakers called for, and presented, operational models that can actually help a factory, city, or municipal facility. In addition to pre-testing decisions and improving maintenance, what should the modern digital twin be designed to achieve?

The TechEx programme linked ideas between speakers from Siemens, Korea’s LG CNS, Boston Dynamics, and others across the different show strands. The takehome everywhere was that smart systems, be they deeply embedded in engineering sites or the back office, need to be designed in concord with the people or machines that they’re designed to benefit.

Day-one sessions at the Data Centre Congress track looked at the big issues facing the sector today: construction, power, procurement, cooling, water, and the network spine needed for AI DCs. Keynote speakers and round-table discussion guests talked about construction chaos and power issues, with the event’s early visitors hearing from TechEx’s host city, Santa Clara, about its own data centre journey.

The DC issue remains central to the wider AI debate. As a technology, AI depends on compute, and dense compute at that. This in turn depends on power, cooling, land, and permits. A recurring theme in the infrastructure-focused talks was how AI economics affects the infrastructure stack, with the former rapidly changing, the latter taking years to mature.

In many ways, the TechEx event is unique, in that it brings the issues affecting a whole industry under one roof; a place where the bigger picture can be visualised. In the Data Center Congress, we learned that water and power constraints can cut through the rhetoric around the scale of AI. Sessions under the AI and Big Data roof helped temper the idea of a ‘stampede’ to AI productivity, citing their own reasons why unplanned and disorganised implementations of technology don’t fit the modern enterprise. The data centre is now one of the places where AI strategy becomes physical; the enterprise board room’s considerations are practical.

The Cyber Security and Cloud Expo track put its own take on deployment forward. Here, the day-one programme dealt with security culture, compliance, speed, ransomware, shadow AI, data exfiltration, legacy systems, open-source dependency issues, and the CISO relationship with the C-suite. There was a general consensus around AI adoption increasing a company’s attack surface, and a much-repeated message that existing security weaknesses don’t diminish when the business wants faster, smarter tools.

Sessions on shadow AI and data exfiltration were especially relevant to the wider event. Many companies’ staff use AI services inside business workflows, sometimes without approval, and usually with no facility for logging their activities. That makes data governance and cyber governance effectively the same conversation.

The benefits of one conference playing host to complementary tracks were manifest in several cases. For instance, the cybersecurity track’s concerns around legacy systems were echoed on the IoT and Edge stages, where issues were raised about modern, smart intelligence meeting older plant systems. Security in any context can sometimes become an afterthought, but critical infrastructure in the form of transport or energy means that cybersecurity has to play a central role.

The TechEx North America day-one tracks that were concerned with infrastructure gave the conference a dose of reality, at least in some respects. AI may be discussed in terms of agentic automation, but deployments depend on networks, data centre capacity, and cybersecurity. Edge and IoT sessions showed how intelligence reaches machines, and how carefully and considerately it needs to be applied. The data centre-focused sessions showed the material limits of physical construction, while the cybersecurity sessions showed how a desire for speed can be the enemy.

The day showed the thousands of attendees that putting AI in production isn’t a case of switching the software on. There’s a reliance on the mundane matters of buildings and grids, networks, and security. Companies that understand these issues are more likely to deploy the latest in technology successfully. Getting the bigger picture is what this event is all about.

(Image source: TechForge)

 

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information.

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For most of its life, Airwallex and Stripe have mostly operated in different geographies, selling to different buyers. That's changing.

X is cutting back on payments to accounts that are “flooding the timeline” with clickbait and rapid-fire news aggregation, according to its head of product Nikita Bier

France's move to ditch Windows for Linux is its latest effort to reduce its reliance on American tech giants.

TigerFS is a new experimental filesystem that mounts a database as a directory and stores files directly in PostgreSQL. The open source project exposes database data through a standard filesystem interface, allowing developers and AI agents to interact with it using common Unix tools such as ls, cat, find, and grep, rather than via APIs or SDKs.

By Renato Losio

arXiv:2604.02259v1 Announce Type: cross Abstract: To harness the power of Language Models in answering domain specific specialized technical questions, Retrieval Augmented Generation (RAG) is been used widely. In this work, we have developed a Q\&A application inspired by the Retrieval Augmented Generation (RAG), which is comprised of an in-house database indexed on the arXiv articles related to the Electron-Ion Collider (EIC) experiment - one of the largest international scientific collaboration and incorporated an open-source LLaMA model for answer generation. This is an extension to it's proceeding application built on proprietary model and Cloud-hosted external knowledge-base for the EIC experiment. This locally-deployed RAG-system offers a cost-effective, resource-constraint alternative solution to build a RAG-assisted Q\&A application on answering domain-specific queries in the field of experimental nuclear physics. This set-up facilitates data-privacy, avoids sending any pre-publication scientific data and information to public domain. Future improvement will expand the knowledge base to encompass heterogeneous EIC-related publications and reports and upgrade the application pipeline orchestration to the LangGraph framework.

Next time around, the pressure will be on SpaceX and Blue Origin.

AI is at war. Anthropic and the Pentagon feuded over how to weaponize Anthropic’s AI model Claude; then OpenAI swept the Pentagon off its feet with an “opportunistic and sloppy” deal. Users quit ChatGPT in droves. People marched through London in the biggest protest against AI to date. If you’re keeping score, Anthropic—the company founded to be ethical—is now turbocharging US strikes on Iran. 

On the lighter side, AI agents are now going viral online. OpenAI hired the creator of OpenClaw, a popular AI agent. Meta snapped up Moltbook, where AI agents seem to ponder their own existence and invent new religions like Crustafarianism. And on RentAHuman, bots are hiring people to deliver CBD gummies. The future isn’t AI taking your job. It’s AI becoming your boss and finding God.

arXiv:2603.23356v1 Announce Type: cross Abstract: We propose a novel clustering approach for point-cloud segmentation based on supervised contrastive metric learning (CML). Rather than predicting cluster assignments or object-centric variables, the method learns a latent representation in which points belonging to the same object are embedded nearby while unrelated points are separated. Clusters are then reconstructed using a density-based readout in the learned metric space, decoupling representation learning from cluster formation and enabling flexible inference. The approach is evaluated on simulated data from a highly granular calorimeter, where the task is to separate highly overlapping particle showers represented as sets of calorimeter hits. A direct comparison with object condensation (OC) is performed using identical graph neural network backbones and equal latent dimensionality, isolating the effect of the learning objective. The CML method produces a more stable and separable embedding geometry for both electromagnetic and hadronic particle showers, leading to improved local neighbourhood consistency, a more reliable separation of overlapping showers, and better generalization when extrapolating to unseen multiplicities and energies. This translates directly into higher reconstruction efficiency and purity, particularly in high-multiplicity regimes, as well as improved energy resolution. In mixed-particle environments, CML maintains strong performance, suggesting robust learning of the shower topology, while OC exhibits significant degradation. These results demonstrate that similarity-based representation learning combined with density-based aggregation is a promising alternative to object-centric approaches for point cloud segmentation in highly granular detectors.

The move could help Apple better compete with Adobe Premiere Pro and the Adobe Creative Cloud suite.

“Frankenstein” won for Best Production Design, Best Costume Design, and Best Makeup and Hairstyling, while “KPop Demon Hunters” won for Best Animated Feature and Best Original Song.

arXiv:2603.07940v1 Announce Type: cross Abstract: Modern science is reaching a critical inflection point. Instruments across disciplines, from particle physics and astronomy to genomics and climate modeling, now produce data of such scale, diversity, and interdependence that traditional analytical methods can no longer keep pace. This growing imbalance between data generation and data understanding signals the need for a new scientific paradigm. We propose that intelligent, human-supervised AI agents operating over deep-learning algorithms, represent the next evolution of the scientific method. Built upon large language models and multimodal learning, these agents can interpret scientific intent, design and execute analytical workflows, and ensure traceability through domain-specific languages that preserve human oversight and accountability. Particle physics, a historic incubator of computational innovation, offers the ideal testbed for this transition. At the Institute of High Energy Physics of the Chinese Academy of Sciences, the Dr. Sai system embodies this vision, a multi-agent reasoning framework deployed within collider research at the CEPC. This emerging approach does not replace human scientists but extends their cognitive reach, enabling discovery to scale with complexity and redefining how knowledge itself is produced in the age of intelligent machines. The significance of this paradigm transcends particle physics, offering a blueprint for all data-driven sciences facing the same complexity ceiling.