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2,653 papers
These quantitative findings demonstrate that tightly coupled hybrid co-processing, physically adjacent to the control electronics, is critical for extending the computational bound of Noisy Intermediate-Scale Quantum (NISQ) devices.
This paper’s key contribution is a module extension for an existing ROS-focused container framework that leverages Kubernetes’ capabilities—controlled rolling updates, ROS-adapted readiness checks, distributed workload management, and embedded observability—to bridge cloud-native practices with robotics development.
The deployment of large-scale 32-bit neural networks is hindered by the escalating parameter sizes and inherent susceptibility to noise. Here, we present a non-volatile memory (NVM)-based probabilistic computing architecture, which harnesses both the stochastic nature and in-memory computing capabilities of NVM to enha...
Quantum Reinforcement Learning (QRL) represents a novel approach to reinforcement learning that leverages the unique capabilities of quantum computing to enhance learning efficiency and enable the exploration of complex behavioral rules. This paper explores the theoretical foundations, design principles, and potential...
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The current paradigm of frontier AI continues to yield significant value, but as compute scaling faces potentially diminishing returns, future AI progress is unlikely to rely on a single, isolated breakthrough. The next wave of capability improvements will most likely emerge from a complex mix of extensions, hybrids an...
Prunus spinosa L. (blackthorn) is a widely distributed wild fruit species in Romania with considerable, yet underexplored, potential as a functional food ingredient and natural source of bioactive compounds. This study aimed to characterize the phytochemical composition, mineral and contaminant profiles, and antioxidan...
The retail industry is fundamentally changing due to emerging technologies (Morenza-Cinos et al., 2019; Ta et al., 2023). Machine learning (ML) and artificial intelligence (AI) for example, can help retailers improve demand forecasting accuracy (Modgil et al., 2021; Chou et al., 2023) and analyze data in real time, ena...
A probabilistic graphical computing paradigm based on memristors is reported, which not only unifies the framework of Bayesian inference-based baseband signal processing algorithms, but also achieves a substantial improvement in energy efficiency via memristor-based in-memory computing.
DECISION: REJECT ELEMENT: No element survives — pool density ≥90 filter is a lagging, regime-dependent proxy, not a geometric timing edge. It does not align with E8-node/Gods-Clock rhythm logic; it filters by market state, not by entry timing. EXPECTED IMPACT: +1.6pp win rate is within noise for n=1347 (σ≈1.3pp), and t...
Since the establishment of von Neumann and Nash's concept of equilibrium, game theory has become a universal language in economics and social sciences. However, classical game theory is deeply rooted in the mechanistic worldview, and its deterministic assumptions, static equilibrium, and individualistic methodology are...
Quantum state simulation is a cornerstone of quantum computing, aiming to accurately represent and manipulate the behavior of quantum systems. However, traditional methods of quantum state simulation, such as the Density Matrix Renormalization Group (DMRG) method and the Variational Quantum Eigensolver (VQE), often suf...
DECISION: REJECT ELEMENT: No element survives — pool density ≥90 filter is a lagging, regime-dependent proxy, not a geometric timing edge. It does not align with E8-node/Gods-Clock rhythm logic; it filters by market state, not by entry timing. EXPECTED IMPACT: +1.6pp win rate is within noise for n=1347 (σ≈1.3pp), and t...
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
An ongoing survey tracks the latest AI accelerator systems to keep hardware relevant for Lincoln Laboratory staff and sponsors.
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.
Associate Professor Cathy Wu uses reinforcement learning to help map out improvements to transportation and other multifaceted systems.
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