Deep learning algorithms trained to segment brain tumors from magnetic resonance imaging (MRI) perform well in ideal conditions supplied by curated datasets. An example is the MICCAI BraTS 2018 dataset, which provides a set of four MRI sequences (T1, T1 post-gadolinium contrast-enhanced, T2, FLAIR) on 285 samples, along with ground truth segmentations annotated by experts. In real-world settings, however, it is not uncommon for patients to undergo MRI with a limited number of image sequence… Deep learning algorithms trained to segment brain tumors from magnetic resonance imaging (MRI) perform well in ideal conditions supplied by curated datasets. An example is the MICCAI BraTS 2018 dataset, which provides a set of four MRI sequences (T1, T1 post-gadolinium contrast-enhanced, T2, FLAIR) on 285 samples, along with ground truth segmentations annotated by experts. In real-world settings, however, it is not uncommon for patients to undergo MRI with a limited number of image sequence acquisitions. We examined the effect of restricting the imaging sequence set when training a deep learning model for brain tumor segmentation. Our goal was to identify a limited subset that still achieved acceptably high performance. Docker eats 25% of your Redis throughput. We ran the same Redis 8.6 binary in three configurations: bare metal, Sandlock (our open-source process sandbox), and Docker. Same hardware, same workload: 50 concurrent clients, 256-byte payloads, 100K requests. The results: Sandlock: 141K ops/sec, 0.33 ms median latency, 0.88 ms p99 Docker: 113K ops/sec, 0.50 ms median latency, 1.46 ms p99 That is 25% higher throughput and 66% lower tail latency. The reason is structural. Docker routes every packet through iptables DNAT, a Linux bridge, and a veth pair. Sandlock confines the process with Landlock and seccomp-bpf at the syscall boundary, leaving the host network stack untouched. The fast-path syscalls (sendmsg, recvmsg) pass through a lightweight BPF filter and go straight to the kernel's TCP implementation. For bulk data transfers, the difference is zero. For workloads that process thousands of small messages per second (real-time stream processing, in-memory caching, sidecar services), it is the dominant overhead. Sandlock requires no root privileges, no container runtime, and no network namespaces. It runs on any Linux 6.7+ kernel with Landlock support, including Ubuntu 24.04, Debian 13, Fedora 40+, RHEL 10, and AWS Bottlerocket. The benchmark script is open source. We encourage you to reproduce the results on your own hardware. Full writeup with methodology and analysis: https://lnkd.in/gwTUVj_9 Most teams running LLM inference are over-provisioning GPUs “just in case.” We benchmarked what happens when you combine NVIDIA NIM with NVIDIA Run:ai with the same inference workloads on dramatically fewer GPUs — with minimal throughput loss. ✅ GPU Fractions + Bin Packing: Run multiple NIM models on shared GPUs with true memory isolation — consolidate your deployments, increase density, and reduce your compute footprint. ✅ Dynamic GPU Fractions: Let models burst into unused headroom during traffic spikes — no extra GPUs required. ✅ GPU Memory Swap: Keep infrequently used models warm in CPU memory to slash first-request latency. ✅ Inference Priority Scheduling: Prioritize user-facing inference while training flexes around it. The takeaway: You may not need more GPUs — just smarter orchestration of the ones you have. 🔗 Full benchmarks and methodology in the tech blog: https://bit.ly/407QnUs There were many reports about new e2e audio dialog systems claiming that e2e architectures might be superior to “traditional” multi model system. This study finds that AudioLM models underperform in dialog consistency metrics vs modular systems https://lnkd.in/gqdXeZ5d The paper addresses a key limitation in using large language models (LLMs) for multi-robot task planning: while LLMs enable intuitive task specification by non-experts, the plans they produce often lack kinematic feasibility and efficiency, especially for long-horizon tasks. To overcome this, the authors integrate symbolic reasoning with formal task specifications using Hierarchical Linear Temporal Logic (LTL). This neuro-symbolic framework grounds LLM reasoning into hierarchical logic structures that can be formally verified, and then solves the resulting Simultaneous Task Allocation and Planning (STAP) problem. Unlike conventional static and offline planning methods, the framework incorporates a receding horizon planning (RHP) loop, which dynamically updates task plans in response to real-time perception and environmental changes, such as moving human agents or evolving goals. Through extensive real-world experiments, the authors demonstrate that their approach significantly outperforms existing baselines in terms of task success rate, interaction fluency with humans, and planning latency. The results suggest that grounding high-level, natural language task specifications from LLMs in formal temporal logic and combining them with dynamic, perception-driven replanning enables more reliable and efficient human-aware multi-robot collaboration. Overall, this work advances the integration of LLM reasoning, formal methods, and real-time robotic planning, providing a scalable solution for complex collaborative robotics tasks. https://lnkd.in/gWaKXSEs Reo.Dev showed up at KubeCon, and it’s clear they’re building for the people who actually move infra forward. 💪 Stronger signals, sharper workflows, deeper data intelligence… this team is levelling up dev productivity from ground up ✈️ Achintya Gupta Airflow orchestration is not a prettier cron. It’s the contract for how your data stays reliable. We inherited a shop with 40 noisy DAGs, blocking sensors, and retries hiding bad data. Here’s what turned it around: - Model dependencies with Datasets/clear trigger rules; kill cross-DAG sensors. - Make every task idempotent and add data checks before writes; fail fast. - Let Airflow conduct, not perform: push heavy compute to your engine; use deferrable operators and SLAs for “stale” alerts. Incidents dropped, backfills became boring, and on-call sleep returned. How do you decide what belongs in Airflow vs in your transformation layer? #Airflow #DataEngineering