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81. 发明申请

WO2021058626A1 CONTROLLING AGENTS USING CAUSALLY CORRECT ENVIRONMENT MODELS 审中-公开
公开(公告)号：WO2021058626A1
公开(公告)日：2021-04-01
申请号：PCT/EP2020/076664
申请日：2020-09-24
申请人： DEEPMIND TECHNOLOGIES LIMITED
发明人： DANIHELKA, Ivo , REZENDE, Danilo Jimenez , GREGOR, Karol , PAPAMAKARIOS, Georgios , WEBER, Theophane Guillaume
IPC分类号： G06K9/62 , G06N3/08 , G06N3/00 , G06N3/04
摘要： Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using an environment model to simulate state transitions of an environment being interacted with by an agent that is controlled using a policy neural network. One of the methods includes initializing an internal representation of a state of the environment at a current time point; repeatedly performing the following operations: receiving an action to be performed by the agent; generating, based on the internal representation, a predicted latent representation that is a prediction of a latent representation that would have been generated by the policy neural network by processing an observation characterizing the state of the environment corresponding to the internal representation; and updating the internal representation to simulate a state transition caused by the agent performing the received action by processing the predicted latent representation and the received action using the environment model.

82. 发明申请

WO2021058588A1 TRAINING ACTION SELECTION NEURAL NETWORKS USING HINDSIGHT MODELLING 审中-公开
公开(公告)号：WO2021058588A1
公开(公告)日：2021-04-01
申请号：PCT/EP2020/076604
申请日：2020-09-23
申请人： DEEPMIND TECHNOLOGIES LIMITED
发明人： GUEZ, Arthur Clement , VIOLA, Fabio , WEBER, Theophane Guillaume , BUESING, Lars , HEESS, Nicolas Manfred Otto
IPC分类号： G06N3/02 , G06K9/62
摘要： A reinforcement learning method and system that selects actions to be performed by a reinforcement learning agent interacting with an environment. A causal model is implemented by a hindsight model neural network and trained using hindsight i.e. using future environment state trajectories. As the method and system does not have access to this future information when selecting an action, the hindsight model neural network is used to train a model neural network which is conditioned on data from current observations, which learns to predict an output of the hindsight model neural network.

83. 发明申请

WO2013019177A1 ENHANCED IN-DEVICE COEXISTENCE INTERFERENCE AVOIDANCE USING PREDETERMINED DOWNLINK CHANNEL 审中-公开
标题翻译：增强的设备间干扰避免使用预定的下行通道
公开(公告)号：WO2013019177A1
公开(公告)日：2013-02-07
申请号：PCT/US2011/045874
申请日：2011-07-29
申请人： RESEARCH IN MOTION LIMITED , KOO, Changhoi , CAI, Zhijun , HEO, Youn Hyoung
发明人： KOO, Changhoi , CAI, Zhijun , HEO, Youn Hyoung
IPC分类号： H04W72/12 , H04W16/14
CPC分类号： H04W72/1215 , H04W16/14 , H04W72/082
摘要： A method, system and device are provided for avoiding in-device coexistence interference between different radio technologies deployed in adjacent bands on the same device by allocating a non-interfering downlink signaling channel for downlink reception of interference avoidance instructions at the UE device. In operation, a user equipment device detects IDC interference and sends an IDC indication message to the radio network to get an appropriate solution, but instead of waiting at the current frequency to receive the IDC response from the radio network, the user equipment device moves to a non-interfering downlink signaling channel for downlink reception of interference avoidance instructions.
摘要翻译：提供了一种方法，系统和设备，用于通过分配用于在UE设备处的干扰避免指令的下行链路接收的非干扰下行链路信令信道来避免部署在相同设备上的相邻频带中的不同无线电技术之间的设备内共存干扰。在操作中，用户设备设备检测IDC干扰并向无线网络发送IDC指示消息以获得适当的解决方案，但是不是等待当前频率从无线网络接收IDC响应，所以用户设备设备移动到用于下行链路接收干扰避免指令的非干扰下行链路信令信道。

84. 发明申请

WO2006121489A2 COMPACT MODULE SYSTEM AND METHOD 审中-公开
标题翻译：紧凑型模块系统和方法
公开(公告)号：WO2006121489A2
公开(公告)日：2006-11-16
申请号：PCT/US2006/007193
申请日：2006-02-28
申请人： STAKTEK GROUP L.P. , CADY, James, W. , WEHRLY, James, Douglas, Jr. , GOODWIN, Paul
发明人： CADY, James, W. , WEHRLY, James, Douglas, Jr. , GOODWIN, Paul
IPC分类号： G06F11/00
CPC分类号： H05K1/189 , H05K2201/056 , H05K2201/10159 , H05K2201/10189
摘要： A flexible circuit is populated on one or both sides and disposed about a substrate to create a circuit module. Along one of its edges, the flex circuit is connected to a connective facility such as a multiple pin connector while the flex circuit is disposed about a thermally-conductive form that provides structure to create a module with plural layers of circuitry in a single module. In preferred embodiments, the form is metallic and, in alternative preferred embodiments, the module circuitry is disposed within a housing. Preferred embodiments may be devised that present a compact flash module within a housing that may be connected to or into a system or product through a connective facility that is preferably a male or female socket connector while the housing is configured to mechanically adapt to an application environment.
摘要翻译：柔性电路填充在一侧或两侧并且设置在基板周围以形成电路模块。沿着其边缘之一，柔性电路连接到诸如多针连接器的连接设备，而柔性电路围绕导热形式设置，其提供结构以在单个模块中创建具有多层电路的模块。在优选实施例中，该形式是金属的，并且在替代优选实施例中，模块电路设置在壳体内。可以设计出优选实施例，其在壳体内呈现紧凑型闪光模块，其可以通过连接设备连接到系统或产品，该连接设备优选地是公插座或母插座连接器，而壳体被配置成机械地适应应用环境。

85. 发明申请

WO2005064464A2 VIRTUAL NETWORK INTERFACE 审中-公开
标题翻译：虚拟网络接口
公开(公告)号：WO2005064464A2
公开(公告)日：2005-07-14
申请号：PCT/US2004/040144
申请日：2004-12-01
申请人： INTEL CORPORATION , LEWITES, Saul
发明人： LEWITES, Saul
IPC分类号： G06F9/46
CPC分类号： G06F13/385 , G06F9/45537
摘要： A system includes an interface device that executes a driver and a processing device that executes instructions to implement a virtual machine, and to implement a virtual network interface card that is configurable to enable communication between the driver and the virtual machine. The processor executes instructions to configure the virtual network interface card.
摘要翻译：系统包括执行驱动程序的接口设备和执行指令以实现虚拟机的处理设备，以及实现可配置为实现驱动程序与虚拟机之间的通信的虚拟网络接口卡。处理器执行指令来配置虚拟网络接口卡。

86. 发明申请

WO2023088952A1 DEVICES, SYSTEMS, AND METHODS FOR NUCLEIC ACID AMPLIFICATION 审中-公开
公开(公告)号：WO2023088952A1
公开(公告)日：2023-05-25
申请号：PCT/EP2022/082111
申请日：2022-11-16
申请人： LEX DIAGNOSTICS LTD.
发明人： ARAYA-WILLIAMS, Amaru Daniel , CONSTABLE, Charlie Edward , BUCKLAND, Justin Rorke
IPC分类号： B01L3/00 , B01F33/05 , B01L7/00
摘要： A reaction cartridge system is described herein. In some embodiments, a reaction cartridge, includes a cap that contains a lyophilized reagent cake within the cap, and a stirrer element encapsulated within the lyophilized reagent cake.

87. 发明申请

WO2023057651A1 RATING TASKS AND POLICIES USING CONDITIONAL PROBABILITY DISTRIBUTIONS DERIVED FROM EQUILIBRIUM-BASED SOLUTIONS OF GAMES 审中-公开
公开(公告)号：WO2023057651A1
公开(公告)日：2023-04-13
申请号：PCT/EP2022/078056
申请日：2022-10-10
申请人： DEEPMIND TECHNOLOGIES LIMITED
发明人： MARRIS, Luke Christopher
IPC分类号： G06N3/00 , G06N5/00 , G06N3/08 , G06N7/00 , G06N3/006 , G06N3/084 , G06N3/086 , G06N5/01 , G06N7/01
摘要： Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for rating tasks and policies using conditional probability distributions derived from equilibrium-based solutions of games. One of the methods includes: determining, for each action selection policy in a pool of action selection policies, a respective performance measure of the action selection policy on each task in a pool of tasks, processing the performance measures of the action selection policies on the tasks to generate data defining a joint probability distribution over a set of action selection policy - task pairs, and processing the joint probability distribution over the set of action selection policy - task pairs to generate a respective rating for each action selection policy in the pool of action selection policies, where the respective rating for each action selection policy characterizes a utility of the action selection policy in performing tasks from the pool of tasks.

88. 发明申请

WO2023057565A2 STEP-UNROLLED DENOISING NEURAL NETWORKS 审中-公开
公开(公告)号：WO2023057565A2
公开(公告)日：2023-04-13
申请号：PCT/EP2022/077806
申请日：2022-10-06
申请人： DEEPMIND TECHNOLOGIES LIMITED
发明人： SAVINOV, Nikolay , CHUNG, Junyoung , BINKOWSKI, Mikolaj , VAN DEN OORD, Aaron Gerard Antonius , ELSEN, Erich Konrad
IPC分类号： G06N3/088 , G06N3/0455 , G06N3/047 , G06F40/47 , G06N3/084 , G06V10/82 , G06V20/70 , G10L13/02 , G10L15/16
摘要： Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating output sequences using a non-auto-regressive neural network.

89. 发明申请

WO2023036631A1 CHARACTERIZING AND IMPROVING OF IMAGE PROCESSING 审中-公开
公开(公告)号：WO2023036631A1
公开(公告)日：2023-03-16
申请号：PCT/EP2022/073852
申请日：2022-08-26
申请人： INAIT SA
发明人： LÜTGEHETMANN, Daniel Milan , ZAGANIDIS, Dimitri , BERGER, Nicolas Alain , SCHÜRMANN, Felix
IPC分类号： G06V10/44 , G06V20/56 , G06V20/64 , G06T7/20 , G05D1/02 , G06V10/98
摘要： A method for characterizing correctness of image processing includes receiving images of an instance of an object, wherein the images were acquired at different relative poses, identifying positions of corresponding landmarks on the object in each of the received images, receiving information characterizing a difference in position or a difference in orientation of at least one of the instance of the object and one or more imaging devices when the images were acquired, transferring the positions of landmarks identified in a first of the images based on the difference in position or the difference in orientation, and comparing the positions of the transferred landmarks with the positions of the corresponding of the landmarks identified in a second of the received images, and characterizing a correctness of the identification of the positions of the landmarks in at least one of the received images.

90. 发明申请

WO2023012234A1 CONTROLLING AGENTS BY SWITCHING BETWEEN CONTROL POLICIES DURING TASK EPISODES 审中-公开
公开(公告)号：WO2023012234A1
公开(公告)日：2023-02-09
申请号：PCT/EP2022/071864
申请日：2022-08-03
申请人： DEEPMIND TECHNOLOGIES LIMITED
发明人： SCHAUL, Tom , PÎSLAR, Miruna
IPC分类号： G06N3/04 , G06N3/08 , G06N3/00
摘要： Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling agents. In particular, an agent can be controlled to perform a task episode by switching the control policy that is used to control the agent at one or more time steps during the task episode.

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