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1.发明公开公开(公告)号：US20240321401A1
公开(公告)日：2024-09-26
申请号：US18324199
申请日：2023-05-26
IPC分类号： G16C10/00
CPC分类号： G16C10/00
摘要： A method for performing a molecular dynamics simulation includes inputting an initial condition into an evolution model to predict a first condition at a next time step and inputting the initial condition into a molecular dynamics model to predict a second condition at the next time step. It is determined whether to use the first condition or the second condition as a prediction in the molecular dynamics simulation based on an estimated uncertainty associated with the evolution model.
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公开(公告)号：US20240296347A1
公开(公告)日：2024-09-05
申请号：US18272627
申请日：2021-05-14
发明人： Julia GASTINGER , Timo SZTYLER
IPC分类号： G06N5/02
CPC分类号： G06N5/02
摘要： A method for decision-making regarding a decision in an environment by a data processing system in view of multiple different objectives includes: collecting information within the environment, describing the information in at least one temporal knowledge graph (TKG), forecasting a future development of one future state or more future states at a future time or more future points in time, under different decisions by the at least one TKG. The method further describes each resulting future state/decision combination by a corresponding temporal knowledge graph, rates an adherence of each forecasted future state to each objective of the multiple different objectives, considers a trade-off between the objectives for decision-making in a time-aware manner; and provides the decision.
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3.发明公开公开(公告)号：US20240249848A1
公开(公告)日：2024-07-25
申请号：US18288828
申请日：2021-05-17
摘要： A method for monitoring of a proneness of a physical environment to infectious disease transmission includes a training phase in which unlabeled sensor data is obtained from sensors of the physical environment in order to provide a set of sensor features. A labeling matrix that is fed to a generative model is generated by applying situation labeling functions, wherein the generative model feeds a discriminative classifier model with probabilistic labels for the sensor features, wherein the probabilistic labels of the generative model are used for training the discriminative classifier model. A subset of the sensor features is determined based on an optimization procedure by a feature selection optimizer entity. In an operational phase, the discriminative classifier model uses the subset of sensor features for detecting predefined situations which make the physical environment prone to infectious disease transmission.
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公开(公告)号：US20240187255A1
公开(公告)日：2024-06-06
申请号：US18277232
申请日：2021-04-28
发明人： Claudio SORIENTE , Dario FIORE
CPC分类号： H04L9/3255 , H04L9/0861
摘要： A method to enhance an anonymous signature scheme with user-controlled linkability includes generating, by a signer of a ring signature scheme or a group signature scheme, a signer-specific secret (x) and generating a secret key based on the generated secret (x). The signer augments a message to be signed with a message-unique value that is related to the signer-specific secret (x) thereby generating an augmented message. The signer signs the augmented message with the secret key of the signer and produces a proof that an arbitrary set of signed messages embed the signer-specific secret (x). The signer anonymously publishes the produced proof for verification by a third-party verifier.
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5.发明公开公开(公告)号：US20240169189A1
公开(公告)日：2024-05-23
申请号：US18177213
申请日：2023-03-02
发明人： Zhao Xu , Sascha Saralajew , Ammar Shaker
IPC分类号： G06N3/048 , G06F16/901 , G06N3/0895 , G16H50/20
CPC分类号： G06N3/048 , G06F16/9024 , G06N3/0895 , G16H50/20
摘要： A method for generating a self-explaining decision in an artificial intelligence (AI) system includes receiving or defining a graph for a task in the AI system, the graph including a plurality of nodes connected by edges. Message passing is performed among the nodes of the graph, wherein a discrete attention mechanism is implemented during the message passing, whereby features of each node are transformed into a discrete representation, which varies depending on which neighboring node a message is passed to. The self-explaining decision is generated for one of the nodes based on the message passing.
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公开(公告)号：US20240104387A1
公开(公告)日：2024-03-28
申请号：US18161107
申请日：2023-01-30
IPC分类号： G06N3/092
CPC分类号： G06N3/092
摘要： A method for learning logical rules over graph structured data to generate a prediction in a machine learning system includes obtaining graph structured data from a technical application domain of the machine learning system. A graph neural network is trained to learn logical rules using message passing. The prediction is generated in the machine learning system based on the learned logical rules.
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公开(公告)号：US20240045959A1
公开(公告)日：2024-02-08
申请号：US18055849
申请日：2022-11-16
IPC分类号： G06F21/56
CPC分类号： G06F21/566 , G06F2221/033
摘要： A method for thwarting attacks on a machine-learning (ML) model is provided. The method includes determining, by the ML model, a classification vector based on an input. The method further includes evaluation the classification vector based on a threshold parameter to determine a threshold result. The method also includes outputting a classification prediction based on the threshold result.
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公开(公告)号：US20240036908A1
公开(公告)日：2024-02-01
申请号：US18271499
申请日：2021-04-21
IPC分类号： G06F9/455
CPC分类号： G06F9/45558 , G06F2009/45583
摘要： A method for supporting memory deduplication for unikernel images includes aligning, by a memory aligner entity, memory pages of unikernel images such that a consistent memory alignment is generated across the unikernel images. A memory deduplication identifier entity generates a unique page identifier for a plurality of memory pages of the unikernel images. The memory deduplication identifier entity matches page identifiers of memory pages for a unikernel image, which is to be loaded into a physical memory, with page identifiers of memory pages that have already been loaded into the physical memory and providing matching information about the matching to a page merger entity. The page merger entity performs page merging based on the matching information provided by the memory deduplication identifier entity.
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9.发明公开公开(公告)号：US20240028802A1
公开(公告)日：2024-01-25
申请号：US17964085
申请日：2022-10-12
发明人： Nicolas Weber
IPC分类号： G06F30/323 , G06F30/327
CPC分类号： G06F30/323 , G06F30/327
摘要： A method is provided for transforming a high-level language representation of a tensor computation graph into a low level language. The method includes assigning, for each input edge of each node in the tensor computation graph, a tensor shape, assigning, for each dimension of the input and output of each layer of the tensor computation graph, a loop primitive, and generating, from the tensor computation graph and the assigned loop primitives, an initial loop structure. The method further includes positioning the layers of the tensor computation graph within a nested loop structure to provide a final loop structure, collapsing loops in the final loop structure, and mapping the collapsed loops to hardware components configured to execute the collapsed loops.
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公开(公告)号：US20230412390A1
公开(公告)日：2023-12-21
申请号：US18032365
申请日：2020-11-23
发明人： Sergey FEDOROV
IPC分类号： H04L9/32
CPC分类号： H04L9/3239
摘要： A rollback protection method for preventing message equivocation in a consensus system is provided. The consensus system includes distributed computational nodes connected by a network and configured to run a TEE-based consensus protocol. The method includes: executing, within a trusted execution environment of a node of distributed computational nodes, a trusted component instance, that includes volatile protected memory with protected data stored therein and a protected piece of code implementing at least a part of a consensus algorithm, generating identity data comprising a unique ephemeral identity, and storing the identity data in the volatile protected memory of the trusted component instance, and certifying a message of the consensus algorithm, wherein a certified consensus algorithm message is generated by cryptographically binding parts of the consensus algorithm message to the unique ephemeral identity of the trusted component instance and at least parts of the protected data of the trusted component instance.
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