Abstract: Provided are a chromium catalyst precursor, an ethylene oligomerization catalyst including the same, and a method of preparing an ethylene oligomer using the same. More particularly, a chromium catalyst precursor which may oligomerize ethylene with high activity and high selectivity in spite of not using methylaluminoxane (MAO) or modified-methylaluminoxane (MMAO), an oligomerization catalyst including the same, and a method of preparing an ethylene oligomer using the same are provided.

Abstract: The present invention relates to a fire resistant paint composition, to a production method for same and to a painting method for a fire resistant paint using same, and, one example of implementation of the present invention can provide a fire resistant paint composition comprising: between 70 and 95 wt. % of a binder; between 1 and 10 wt. % of an aerogel; between 1 and 5 wt. % of a foaming agent; and the remainder of water, and can provide a production method for same and a painting method for a fire resistant paint using same.

Abstract: Disclosed are a BS apparatus, a terminal apparatus, and a QoS control method for implementing the service flow-based QoS control without increasing complexity compared to the conventional bearer-based QoS control method.

Abstract: A locking apparatus according to an embodiment includes an approach detector, a signal transmitter, a measurer, and a controller. The approach detector detects an approach of a user carrying a terminal. The signal transmitter transmits a key authentication signal for authenticating a pre-shared digital key to the terminal using first short-range wireless communication when the approach of the user is detected. The measurer connects a session by generating a security channel with the terminal using second short-range wireless communication and measuring at least one of a distance to and an angle with respect to the terminal when the digital key is authenticated through the key authentication signal. The controller determines whether or not to unlock an access facility in accordance with at least one of the distance to and the angle with respect to the terminal.

Abstract: The present invention relates to a stopper-type charging device and a driving method thereof, wherein the stopper-type charging device includes an emergency bell unit that is provided on a main body part of a stopper for a vehicle installed on a floor of a parking lot and operates to prevent crime in a surveillance blind spot in the parking lot, a light emitting unit that guides a vehicle parked in the parking lot to be parked in a parking section for charging, and operates as a flash to illuminate a periphery of the vehicle, and a sensor unit that detects a fire by detecting a flame of the vehicle being charged.

Abstract: The present invention relates to a stopper-type charging device and a driving method thereof, wherein the stopper-type charging device includes an emergency bell unit that is provided on a main body part of a stopper for a vehicle installed on a floor of a parking lot and operates to prevent crime in a surveillance blind spot in the parking lot, a light emitting unit that guides a vehicle parked in the parking lot to be parked in a parking section for charging, and operates as a flash to illuminate a periphery of the vehicle, and a sensor unit that detects a fire by detecting a flame of the vehicle being charged.

Abstract: The disclosed computer-implemented method may include compiling a neural network, and the compiling may include organizing an interconnected set of nodes in a series of layers, and for each node in each layer, assigning an associated activation of a plurality of activations. Each activation may output a respective tensor of a plurality of tensors. The compiling may also include allocating memory for the activations by determining a respective memory size for each activation, and based on the respective memory size for each activation, assigning a memory block in the neural network to the activation. The method may also include, after the allocating the memory for the activations, accessing the memory blocks to perform the plurality of activations and thereby execute the neural network. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Methods and apparatus implementing Hardware/Software co-optimization to improve performance and energy for inter-VM communication for NFVs and other producer-consumer workloads. The apparatus include multi-core processors with multi-level cache hierarchies including and L1 and L2 cache for each core and a shared last-level cache (LLC). One or more machine-level instructions are provided for proactively demoting cachelines from lower cache levels to higher cache levels, including demoting cachelines from L1/L2 caches to an LLC. Techniques are also provided for implementing hardware/software co-optimization in multi-socket NUMA architecture system, wherein cachelines may be selectively demoted and pushed to an LLC in a remote socket. In addition, techniques are disclosure for implementing early snooping in multi-socket systems to reduce latency when accessing cachelines on remote sockets.

Abstract: The disclosed computer-implemented method may include (i) identifying a neural network that comprises an interconnected set of nodes organized in a set of layers represented by a plurality of matrices that each comprise a plurality of weights, where each weight represents a connection between a node in the interconnected set of nodes that resides in one layer in the set of layers and an additional node in the set of interconnected nodes that resides in a different layer in the set of layers, (ii) encrypting, using an encryption cipher, the plurality of weights, (iii) detecting that execution of the neural network has been initiated, and (iv) decrypting, using the encryption cipher, the plurality of weights in response to detecting that the execution of the neural network has been initiated. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method may include (1) identifying an artificial neural network comprising a set of nodes interconnected via a set of connections, and (2) training the artificial neural network by, for each connection in the set of connections, determining a quantized weight value associated with the connection. Determining the quantized weight value associated with the connection may include (1) associating a loss function with the connection, the loss function including a periodic regularization function that describes a relationship between an input value and a weight value of the connection, (2) determining a minimum of the associated loss function with respect to the weight value in accordance with the periodic regularization function, and (3) generating the quantized weight value associated with the connection based on the determined minimum of the loss function. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A locking apparatus according to an embodiment includes an approach detector, a signal transmitter, a measurer, and a controller. The approach detector detects an approach of a user carrying a terminal. The signal transmitter transmits a key authentication signal for authenticating a pre-shared digital key to the terminal using first short-range wireless communication when the approach of the user is detected. The measurer connects a session by generating a security channel with the terminal using second short-range wireless communication and measuring at least one of a distance to and an angle with respect to the terminal when the digital key is authenticated through the key authentication signal. The controller determines whether or not to unlock an access facility in accordance with at least one of the distance to and the angle with respect to the terminal.

Abstract: The disclosed method may include (1) determining whether a next operation of a plurality of operations of an artificial neural network (ANN) is dependent upon a Boolean predication value based on a representative value for a weight or an input of a node of the ANN, (2) based on the next operation not being dependent on the Boolean predication value, allowing the next operation to update a state of the ANN, and (3) based on the next operation being dependent on the Boolean predication value, performing at least one of (a) allowing, based on the Boolean predication value being a first value, the next operation to update the state of the ANN, and (b) preventing, based on the Boolean predication value being a second value different from the first value, the next operation from updating the state of the ANN. Various other methods and systems are also disclosed.

Abstract: A polyolefin thermoplastic elastomer composition for an airbag chute, includes a polypropylene, an olefin block copolymer, and a dendrimer as a material applied to a raw material of a passenger airbag chute. An olefin elastomer composite resin composition for an airbag chute includes 30-80 parts by weight of a polypropylene resin, 30-70 parts by weight of an olefin block copolymer, and 0.1-5 parts by weight of a dendrimer based on 100 parts by weight of the olefin elastomer composite resin composition. The olefin elastomer composite resin composition for an airbag chute, improves the dispersion of the elastomer by applying a high-flow elastomer and a low-flow polypropylene, and improves flowability and meltability characteristics without deteriorating physical properties by applying the dendrimer.

Abstract: A disclosed computing system may include a special-purpose hardware device having an input subsystem, a linearization subsystem, and a matrix multiplication unit. The input subsystem may facilitate on-the-fly convolution lowering within a neural network convolution layer by directing input volume patches to logical unit(s) of the device. The linearization subsystem may be configured to receive a patch from the input subsystem and to linearize the patch by arranging elements of the patch as a portion of a data matrix row. The matrix multiplication unit of device may be configured to receive the data matrix from the linearization subsystem and to apply a filter matrix to the data matrix via a matrix multiplication operation. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The present invention relates to a golf cart system including a cart body; a control module having a GPS sensor, a receiver for receiving position information of a terminal, golf course information on a cart driving area and a cart driving limit area of the golf club from a server, and information on whether the cart body can enter the golf course with grass, and a driving mode selector for selecting any mode of a cart road driving mode and a cart road and golf course entry driving mode, a server for transmitting the golf course information to the receiver; and the terminal having a GPS sensor for providing the position information thereof to the receiver in real time.

Abstract: In an embodiment, a processor includes a sparse access buffer having a plurality of entries each to store for a memory access instruction to a particular address, address information and count information; and a memory controller to issue read requests to a memory, the memory controller including a locality controller to receive a memory access instruction having a no-locality hint and override the no-locality hint based at least in part on the count information stored in an entry of the sparse access buffer. Other embodiments are described and claimed.

Abstract: The disclosed computer-implemented method may include receiving an input value and a floating-point scaling factor and determining (1) an integer scaling factor based on the floating-point scaling factor, (2) a pre-scaling adjustment value representative of a number of places by which to shift a binary representation of the input value prior to a scaling operation, and (3) a post-scaling adjustment value representative of a number of places by which to shift the binary representation of the input value following the scaling operation. The method may further include calculating a scaled result value by (1) shifting rightwards the binary representation of the input value by the pre-scaling adjustment value, (2) scaling the shifted binary representation of the input value by the integer scaling factor, and (3) shifting rightwards the shifted and scaled binary value by the post-scaling adjustment value. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method may include (i) identifying an artificial neural network that processes each input to the artificial neural network in a fixed number of operations, (ii) performing an analysis on the artificial neural network to determine an execution metric that represents the fixed number of operations performed by the artificial neural network to process each input, (iii) determining a quality-of-service metric for an executing system that executes the artificial neural network, and (iv) optimizing power consumption of the executing system by configuring, based on the execution metric and the quality-of-service metric, a processing throughput of at least one physical processor of the executing system, thereby causing the executing system to execute the artificial neural network at a rate that satisfies the quality-of-service metric while limiting the power consumption of the executing system. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Methods and apparatus implementing Hardware/Software co-optimization to improve performance and energy for inter-VM communication for NFVs and other producer-consumer workloads. The apparatus include multi-core processors with multi-level cache hierarchies including and L1 and L2 cache for each core and a shared last-level cache (LLC). One or more machine-level instructions are provided for proactively demoting cachelines from lower cache levels to higher cache levels, including demoting cachelines from L1/L2 caches to an LLC. Techniques are also provided for implementing hardware/software co-optimization in multi-socket NUMA architecture system, wherein cachelines may be selectively demoted and pushed to an LLC in a remote socket. In addition, techniques are disclosure for implementing early snooping in multi-socket systems to reduce latency when accessing cachelines on remote sockets.

Abstract: The disclosed computer-implemented method for efficiently updating neural networks may include (i) identifying a neural network that comprises sets of interconnected nodes represented at least in part by a plurality of matrices and that is trained on a training computing device and executes on at least one endpoint device, (ii) constraining a training session for the neural network to reduce the size in memory of the difference between the previous values of the matrices prior to the training session and the new values of the matrices after the training session, (iii) creating a delta update for the neural network that describes the difference between the previous values and the new values, and (iv) updating the neural network on the endpoint device to the new state by sending the delta update from the training computing device to the endpoint computing device. Various other methods, systems, and computer-readable media are also disclosed.