arXiv:2602.13299v1 Announce Type: new Abstract: Pediatric congenital hydronephrosis (CH) is a common urinary tract disorder, primarily caused by obstruction at the renal pelvis-ureter junction. Magnetic resonance urography (MRU) can visualize hydronephrosis, including renal pelvis and calyces, by utilizing the natural contrast provided by water. Existing voxel-based segmentation approaches can extract CH regions from MRU, facilitating disease diagnosis and prognosis. However, these segmentation methods predominantly focus on morphological features, such as size, shape, and structure. To enable functional assessments, […]

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arXiv:2603.03454v1 Announce Type: new Abstract: Offline Reinforcement Learning (RL) is an emerging field of RL in which policies are learned solely from demonstrations. Within offline RL, some environments involve balancing multiple objectives, but existing multi-objective offline RL algorithms do not provide an efficient way to find a fair compromise. FairDICE (see arXiv:2506.08062v2) seeks to fill this gap by adapting OptiDICE (an offline RL algorithm) to automatically learn weights for multiple objectives to e.g. incentivise fairness among objectives. As […]

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Identity and Access Management (IAM) increasingly relies on adaptive controls—step-up challenges, recovery verification, device and behavior signals, and continuous authorization—to reduce account takeover and misuse. At the same time, IAM systems must prepare for post-quantum cryptography (PQC) transitions that affect credentials, signing, and verification paths. These shifts create a practical governance problem: when an identity action is allowed, challenged, denied, or escalated (e.g., passwordless enrollment, recovery credential release, privileged step-up, or machine key rotation), teams must be able […]

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OpenAI announced on Thursday something that power users have been asking for: a $100/month plan. Previously subscriptions jumped from $20 to $200 per month.

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Reinforcement learning (RL) is increasingly used to personalize instruction in intelligent tutoring systems, yet the field lacks a formal framework for defining and evaluating pedagogical safety. We introduce a four-layer model of pedagogical safety for educational RL comprising structural, progress, behavioral, and alignment safety and propose the Reward Hacking Severity Index (RHSI) to quantify misalignment between proxy rewards and genuine learning. We evaluate the framework in a controlled simulation of an AI tutoring environment with 120 sessions across […]

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Federated reinforcement learning typically aggregates value functions or policies by parameter averaging, which emphasizes expected return and can obscure statistical multimodality and tail behavior that matter in safety-critical settings. We formalize federated distributional reinforcement learning (FedDistRL), where clients parametrize quantile value function critics and federate these networks only. We also propose TR-FedDistRL, which builds a per client, risk-aware Wasserstein barycenter over a temporal buffer. This local barycenter provides a reference region to constrain the parameter averaged critic, ensuring […]

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Hi guys, I am a junior data scientist working in the internal audit department of a non banking financial institution. I have been hired for the role of a model risk auditor. Prior to this I have experience only in developing and evaluation logistic probability of default models. Now i audit the model validation team(mrm) at my current company.so i basically am stuck on a issue as there is no one in my team with a technical background, […]

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Federated Learning (FL) enables distributed Artificial Intelligence (AI) across cloud-edge environments by allowing collaborative model training without centralizing data. In cross-device deployments, FL systems face strict communication and participation constraints, as well as strong non-independent and identically distributed (non-IID) data that degrades convergence and model quality. Since only a subset of devices (a.k.a clients) can participate per training round, intelligent client selection becomes a key systems challenge. This paper proposes FedLECC (Federated Learning with Enhanced Cluster Choice), a […]

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arXiv:2602.23404v1 Announce Type: new Abstract: Teleoperated quadruped robots are increasingly deployed in safety-critical missions — industrial inspection, military reconnaissance, and emergency response — yet the security of their communication and control infrastructure remains insufficiently characterized. Quadrupeds present distinct security challenges arising from dynamic stability constraints, gait-dependent vulnerability windows, substantial kinetic energy, and elevated operator cognitive load. This survey synthesizes peer-reviewed literature and vulnerability disclosures (2019–2025) to provide comprehensive analysis of cybersecurity threats, consequences, and countermeasures for teleoperated quadruped […]

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