Results 121 to 130 of about 28,952 (292)

Mono‐ and Bilayer MoS2 Photodetectors: High‐Performance Broadband AC Readout With Color‐Selective Noise Suppression

open access: yesAdvanced Optical Materials, EarlyView.
Mono‐ and bilayer MoS2 photodetectors enable wavelength‐selective AC photoresponse and optically driven capacitance modulation under visible illumination. Green excitation produces the strongest cumulative capacitive response, consistent with trap‐mediated charge accumulation at mono/bilayer and metal–MoS2 interfaces.
Pegah Zandi   +5 more
wiley   +1 more source

Information Transmission Strategies for Self‐Organized Robotic Aggregation

open access: yesAdvanced Robotics Research, EarlyView.
In this review, we discuss how information transmission influences the neighbor‐based self‐organized aggregation of swarm robots. We focus specifically on local interactions regarding information transfer and categorize previous studies based on the functions of the information exchanged.
Shu Leng   +5 more
wiley   +1 more source

Deteksi Penipuan Pada Transaksi Kartu Kredit Menggunakan Metode Stochastic Gradient Descent Dengan Momentum

open access: yes, 2023
Penipuan kartu kredit memberikan kerugian yang sangat besar bagi pemilik kartu, pemberi kredit, dan pihak terkait. Permasalahan ini terus meningkat setiap tahunnya dan telah menjadi permasalahan yang serius.
Mandalla, Achmad Zaki
core  

Identifying Physical Interactions in Contact‐Based Robot Manipulation for Learning from Demonstration

open access: yesAdvanced Robotics Research, EarlyView.
Robots can learn manipulation tasks from human demonstrations. This work proposes a versatile method to identify the physical interactions that occur in a demonstration, such as sequences of different contacts and interactions with mechanical constraints.
Alex Harm Gert‐Jan Overbeek   +3 more
wiley   +1 more source

Stochastic gradient descent with differentially private updates

open access: yes, 2013
—Differential privacy is a recent framework for com-putation on sensitive data, which has shown considerable promise in the regime of large datasets. Stochastic gradient methods are a popular approach for learning in the data-rich regime because they are
Anand D. Sarwate   +2 more
core   +1 more source

TRMT6‐Mediated m1A Modification of CDK9 mRNA Is a Dual‐Pronged Pathogenic Driver for HBV‐Related Hepatocellular Carcinoma

open access: yesAdvanced Science, EarlyView.
TRMT6‐mediated m1A modification in CDK9 mRNA enhances its mRNA stability and translation efficiency, thereby increasing the protein levels of CDK9. Upregulated CDK9 promotes the progression of HCC by elevating the levels of oncogenic factors including p‐STAT3, MCL1, and BCL‐2. On the other hand, CDK9 phosphorylates TARDBP at Ser254 to activate HBV core
Rui Zhang   +12 more
wiley   +1 more source

Stochastic Gradient Descent with Adaptive Data

open access: yesOperations Research
Stochastic Gradient Descent with Adaptive Data Stochastic gradient descent (SGD) is a central tool in modern optimization, but its classical theory relies on the assumption that data are independent of the decisions being optimized. In many operations research settings, this assumption fails: policies influence system dynamics, and ...
Ethan Che, Jing Dong, Xin T. Tong
openaire   +2 more sources

Icariin Enhances the Enzymatic Activity of N‐acetylgalactosaminidase to Augment Akkermansia Abundance in Gut Microbiota for Improved PD‐1 Blockade Efficacy in Tumor Suppression

open access: yesAdvanced Science, EarlyView.
Icariin promoted the growth of Akk by enhancing the activity of N‐acetylgalactosaminidase (Amuc_0920), which enhanced mucin utilization and provided a favorable nutrient environment for bacterial growth. This icariin‐mediated enrichment of Akk further reshaped the tumor microenvironment and promoted CD8+ T cell infiltration, ultimately synergizing with
Shuangying Qiao   +12 more
wiley   +1 more source

Scaling of hardware-compatible perturbative training algorithms

open access: yesAPL Machine Learning
In this work, we explore the capabilities of multiplexed gradient descent (MGD), a scalable and efficient perturbative zeroth-order training method for estimating the gradient of a loss function in hardware and training it via stochastic gradient descent.
B. G. Oripov   +3 more
doaj   +1 more source

Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks

open access: yes
Despite the non-convex optimization landscape, over-parametrized shallow networks are able to achieve global convergence under gradient descent. The picture can be radically different for narrow net-works, which tend to get stuck in badly-generalizing ...
Stephan, Ludovic   +4 more
core  

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