Compact, low-power optoelectronic devices that integrate sensing, memory, and computing have shown substantial potential for realizing software-hardware co-design in artificial intelligence systems. However, existing optoelectronic devices integrating multiple functions typically suffer from fixed carrier dynamics that...
Dong-Yue Li, Wen-Bo Zhang, Jie Xing et al.· ACS Applied Materials and In...· 0 citations
An Anatomy-Preserving Contrastive Unpaired Translation framework with a CT-source Sobel edge-consistency term for CT → US translation is evaluated, finding that it improves one relative structural proxy over CUT, but absolute structural scores remained low.
En-Hui He, Yi-Ran Wang, Zhan-Xiong Yi et al.· Biomedizinische Technik. Bio...· 0 citations
Pervasive intelligent applications are increasingly deployed on mobile and Internet of Things (IoT) edge devices. Consequently, Large Language Models (LLMs) are increasingly used to support these applications. Yet, due to their high resource demands, LLMs are mostly deployed in the cloud. Layer-wise edge-cloud inferenc...
Jing-Po Xu, Paul Joe Maliakel, Ivona Brandić et al.· 0 citations
Training large language models (LLMs) is resource-intensive, and adapting them for diverse deployment scenarios with varying computational constraints remains challenging. While elastic architectures enable flexible model deployment and sparsely activated models allow input-adaptive computation, existing approaches tre...
Arnav Kundu, Zhao-Yang Xu, Bai-Ru Hou et al.· 0 citations
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It is shown that for every weakly sparse graph class $\mathscr C$, the class $\mathscr C_Z$ is monadically dependent for $\textsf{FOscon}$ if and only if $\mathscr C$ excludes a fixed minor.
Nikolas Mählmann, Patrice Ossona de Mendez, Nicole Schirrmacher et al.· 0 citations
The exponential, pervasive rise of artificial intelligence (AI), hosted in large data centres, will test the limits of the underpinning infrastructure, including electricity and water supplies, which are becoming increasingly precious resources. Rather than performing all AI computations in centralized servers, for man...
Robert L. Z. Hoye, Markus Hellenbrand, Nuno Estrócio et al.· 0 citations
Numerical studies suggest that quantum reservoir computing performs optimally near the edge of chaos, a behavior commonly attributed to a balance between memory retention and nonlinear information processing. Here we develop an explicit quantum reservoir model and rigorously analyze its chaotic regime and integrable po...
We develop an equivariant K-theoretic vertex formalism for a colored, abelianized higher-rank sector on smooth projective toric threefolds $X$. Nonzero first Chern classes are encoded as magnetic fluxes and absorbed into line-bundle twists, after which the virtual character is redistributed into finite perturbative, ve...
N. Piazzalunga, A. Sheshmani, Shing-Tung Yau· 0 citations
A Green-function Evidence Network (EN), a regression-based variant of Evidence Networks and, to the authors' knowledge, the first Evidence Network applied to neutron-star EOS inference, is introduced.
Accurate modelling of the band-edge electronic structure of transition metal dichalcogenides (TMDs), such as MoS$_2$, is essential for understanding their optical, electronic and spintronic properties and enabling future applications. Here, we supplement the standard PBE exchange-correlation functional with on-metal ($...
Xue Li, Suad Alshammari, I. Rozhansky et al.· 0 citations
This work describes four representative VLA models on an edge GPU server and two onboard SoCs, using single-inference profiling and 43,200 closed-loop episodes, and guides joint design of VLA model architectures, hardware, and runtime policies.
Seonghun Jung, Sieun Moon, Jiyoung Jeong et al.· 0 citations
The first polynomial improvements over the textbook algorithms for 3-SUM and All-Pairs Shortest Paths are given, and the Exact Triangle hypothesis is refuted, and the All-Edges Sparse Triangle problem is solved in truly subquadratic time on sparse lopsided tripartite graphs.
Josh Alman, Virginia Vassilevska Williams· 0 citations
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.
MIT News · Artificial Intelligence· news.mit.eduOct 2, 2026
