Abstract: A power-assisted working machine and mower. The mower includes a body including a traveling assembly and a drive motor for driving the traveling assembly; a handle device connected to the body and including an operating member, where the operating member includes a grip for a user to hold; and a connecting rod connected to the body. The mower further includes a motor parameter detection device configured to detect at least one of the rotor position and the working current of the drive motor; an angle detection device configured to detect an angle of inclination of a workplane of the mower relative to a horizontal plane; and a controller configured to estimate thrust applied to the handle device according to the rotor position and/or the working current and the angle of inclination.

Abstract: A walk-behind electric device includes a device body, a moving wheel, a moving motor, a power interface, and a control unit. The moving wheel is connected to the device body. The moving motor is configured to drive the moving wheel. The power interface is connected to a power supply so as to supply power to the moving motor. The control unit is configured to identify, according to device parameters of the walk-behind electric device, an auxiliary operation that a user wants to perform on the walk-behind electric device, where the auxiliary operation is at least partially positively correlated to at least one output parameter of the moving motor.

Abstract: A machine receives a first set of global parameters from a global parameter server. Multiple learner processors in the machine execute an algorithm that models an entity type using the first set of global parameters and a mini-batch of data known to describe the entity type. The machine generates a consolidated set of gradients that describes a direction for the first set of global parameters in order to improve an accuracy of the algorithm in modeling the entity type when using the first set of global parameters and the mini-batch of data. The machine transmits the consolidated set of gradients to the global parameter server. The machine then receives a second set of global parameters from the global parameter server, where the second set of global parameters is a modification of the first set of global parameters based on the consolidated set of gradients.

Abstract: A walk-behind self-propelled working machine includes a main body, an operation switch, and a handle device. The main body includes a moving assembly and a drive motor. The operation switch is connected to the drive motor. The handle device is connected to the main body and includes an operation member, a connecting rod, and a sensing device. The operation member includes a gripping portion for a user to hold. The connecting rod is connected to the main body. The sensing device is configured to sense a thrust that is applied to the handle device to drive the walk-behind self-propelled working machine and further includes a pressure sensor and a pressing member. When the gripping portion receives the thrust, the pressing member applies a force along a preset straight line to the pressure sensor to drive the pressure sensor to deform.

Abstract: A machine receives a first set of global parameters from a global parameter server. The first set of global parameters includes data that weights one or more operands used in an algorithm that models an entity type. Multiple learner processors in the machine execute the algorithm using the first set of global parameters and a mini-batch of data known to describe the entity type. The machine generates a consolidated set of gradients that describes a direction for the first set of global parameters in order to improve an accuracy of the algorithm in modeling the entity type when using the first set of global parameters and the mini-batch of data. The machine transmits the consolidated set of gradients to the global parameter server. The machine then receives a second set of global parameters from the global parameter server, where the second set of global parameters is a modification of the first set of global parameters based on the consolidated set of gradients.

Abstract: Version vector-based rules are used to facilitate asynchronous execution of machine learning algorithms. The method uses version vector based rule to generate aggregated parameters and determine when to return the parameters. The method also includes coordinating the versions of aggregated parameter sets among all the parameter servers. This allows to broadcast to enforce the version consistency; generate parameter sets in an on-demand manner to facilitate version control. Furthermore the method includes enhancing the version consistency at the learner's side and resolving the inconsistent version when mismatching versions are detected.

Abstract: A rear-moving, self-propelled working machine includes a main machine and a handle device. The main machine includes a moving assembly and a motor for driving the moving assembly. The handle device is connected to the main machine and includes an operation member, a connecting rod assembly, a housing, a sensing device, and a trigger assembly. The operation member includes a grip for a user to hold. The connecting rod assembly includes a first connecting rod connected to the main machine. The sensing device is used for sensing a thrust applied to the handle device to drive the rear-moving, self-propelled working machine. The trigger assembly is capable of applying a force to the sensing device when the grip receives the thrust. The trigger assembly is connected to the connecting rod assembly, and the sensing device is connected to the operation member.

Abstract: A walk-behind self-propelled working machine includes a main body, an operation switch, and a handle device. The main body includes a moving assembly and a drive motor. The operation switch is connected to the drive motor. The handle device is connected to the main body and includes an operation member, a connecting rod, and a sensing device. The operation member includes a gripping portion for a user to hold. The connecting rod is connected to the main body. The sensing device is configured to sense a thrust that is applied to the handle device to drive the walk-behind self-propelled working machine and further includes a pressure sensor and a pressing member. When the gripping portion receives the thrust, the pressing member applies a force along a preset straight line to the pressure sensor to drive the pressure sensor to deform.

Abstract: An electric glue gun includes a housing, a motor supported by the housing, a bracket which extends from the housing for supporting a glue cartridge, a pushing mechanism, supported by the housing and connected to the motor, comprising a drive rod wherein the pushing mechanism is driven by the motor to enable the drive rod to squeeze glue out of the glue cartridge, and a controller configured to control the motor to enable the drive rod to return by a preset distance after the motor stops driving the drive rod forward or after the drive rod reaches a limited position.

Abstract: A walk-behind self-propelled working machine includes a main body, an operation switch, and a handle device. The main body includes a moving assembly and a drive motor. The operation switch is connected to the drive motor. The handle device is connected to the main body and includes an operation member, a connecting rod, and a sensing device. The operation member includes a gripping portion for a user to hold. The connecting rod is connected to the main body. The sensing device is configured to sense a thrust that is applied to the handle device to drive the walk-behind self-propelled working machine and further includes a pressure sensor and a pressing member. When the gripping portion receives the thrust, the pressing member applies a force along a preset straight line to the pressure sensor to drive the pressure sensor to deform.

Abstract: A storage controller of a machine receives training data associated with a neural network model. The neural network model includes a plurality of layers, and the machine further including at least one graphics processing unit. The storage controller trains at least one layer of the plurality of layers of the neural network model using the training data to generate processed training data. A size of the processed data is less than a size of the training data. Training of the at least one layer includes adjusting one or more weights of the at least one layer using the training data. The storage controller sends the processed training data to at least one graphics processing unit of the machine. The at least one graphics processing unit is configured to store the processed training data and train one or more remaining layers of the plurality of layers using the processed training data.

Abstract: A method for providing a graphical visualization of a neural network to a user is provided. The method includes generating the graphical visualization of the neural network at least in part by: representing layers of the neural network as respective three-dimensional blocks, wherein at least a first dimension of a given block is proportional to a computational complexity of a layer of the neural network represented by the given block; and representing data flows between the layers of the neural network as respective three-dimensional structures connecting blocks representing the layers of the neural network, wherein a first dimension of a given structure is proportional to each of a first dimension and a second dimension of a data flow represented by the given structure. The method also includes displaying the graphical visualization of the neural network to the user.

Abstract: A computer-implemented topology-aware all-reduce method for an environment including a plurality of systems is provided. Each system of the systems includes a plurality of computing modules. The computer-implemented topology-aware all-reduce method according to aspects of the invention includes locally partitioning and scattering data slices among the computing modules of each system to produce local summation results. The local summation results are copied from the computing modules to corresponding host memories of the f systems. A cross system all-reduce operation is executed among the systems to cause an exchange of the local summation results across the host memories and a determination of final summation partitions from the local summation results. The final summation partitions are copied from the host memories to the corresponding computing modules of each system.

Abstract: An electric glue gun includes a housing, a motor supported by the housing, a bracket which extends from the housing for supporting a glue cartridge, a pushing mechanism, supported by the housing and connected to the motor, comprising a drive rod wherein the pushing mechanism is driven by the motor to enable the drive rod to squeeze glue out of the glue cartridge, and a controller configured to control the motor to enable the drive rod to return by a preset distance after the motor stops driving the drive rod forward or after the drive rod reaches a limited position.

Abstract: A processing unit topology of a neural network including a plurality of processing units is determined. The neural network includes at least one machine in which each machine includes a plurality of nodes, and wherein each node includes at least one of the plurality of processing units. One or more of the processing units are grouped into a first group according to a first affinity. The first group is configured, using a processor and a memory, to use a first aggregation procedure for exchanging model parameters of a model of the neural network between the processing units of the first group. One or more of the processing units are grouped into a second group according to a second affinity. The second group is configured to use a second aggregation procedure for exchanging the model parameters between the processing units of the second group.

Abstract: One or more parameter changes for one or more parameters are computed at one or more worker nodes. The one or more parameters on a remote server are updated based on the computed one or more parameter changes. The updating is performed via one or more remote direct memory access atomic operations with the remote server.

Abstract: A cache management system performs cache management in a Remote Direct Memory Access (RDMA) key value data store. The cache management system receives a request from at least one client configured to access a data item stored in a data location of a remote server, and determines a popularity of the data item based on a frequency at which the data location is accessed by the at least one client. The system is further configured to determine a lease period of the data item based on the frequency and assigning the lease period to the data location.

Abstract: Version vector-based rules are used to facilitate asynchronous execution of machine learning algorithms. The method uses version vector based rule to generate aggregated parameters and determine when to return the parameters. The method also includes coordinating the versions of aggregated parameter sets among all the parameter servers. This allows to broadcast to enforce the version consistency; generate parameter sets in an on-demand manner to facilitate version control. Furthermore the method includes enhancing the version consistency at the learner's side and resolving the inconsistent version when mismatching versions are detected.

Abstract: Version vector-based rules are used to facilitate asynchronous execution of machine learning algorithms. The method uses version vector based rule to generate aggregated parameters and determine when to return the parameters. The method also includes coordinating the versions of aggregated parameter sets among all the parameter servers. This allows to broadcast to enforce the version consistency; generate parameter sets in an on-demand manner to facilitate version control. Furthermore the method includes enhancing the version consistency at the learner's side and resolving the inconsistent version when mismatching versions are detected.

Abstract: A method includes storing parameter versions utilized by learner instances in each of two or more epochs in a parameter receiving buffer of a parameter server, the learner instances performing distributed execution of workload computations of a machine learning algorithm. The method also includes creating a parameter roster in the parameter server comprising parameter version vectors specifying the parameter versions used by each of the learner instances during each of the two or more epochs. The method further includes generating one or more aggregated parameter sets for storage in an aggregated parameters buffer by aggregating parameter versions from the parameter receiving buffer based on the parameter version vectors in the parameter roster and providing aggregated parameter sets from the aggregated parameters buffer to the learner instances for deterministic replay of the distributed execution of the workload computations of the machine learning algorithm.