Abstract: An active learning framework is provided that employs a plurality of machine learning components that operate over iterations of a training phase followed by an active learning phase. In each iteration of the training phase, the machine learning components are trained from a pool of labeled observations. In the active learning phase, the machine learning components are configured to generate metrics used to control sampling of unlabeled observations for labeling such that newly labeled observations are added to a pool of labeled observations for the next iteration of the training phase. The machine learning components can include an inspection (or primary) learning component that generates a predicted label and uncertainty score for an unlabeled observation, and at least one additional component that generates a quality metric related to the unlabeled observation or the predicted label. The uncertainty score and quality metric(s) can be combined for efficient sampling of observations for labeling.

Abstract: Provided are a thermoplastic resin composition and a molded product using same, wherein the thermoplastic resin composition includes a base resin including (A-1) 10 wt % to 30 wt % of a first acrylate-based graft copolymer having an acrylate-based rubbery polymer with an average particle diameter of 300 nm to 400 nm; (A-2) 10 wt % to 35 wt % of a second acrylate-based graft copolymer having an acrylate-based rubbery polymer with an average particle diameter of 100 nm to 200 nm; (B) 10 wt % to 15 wt % of an aromatic vinyl compound-vinyl cyanide compound copolymer; and (C) 35 wt % to 55 wt % of ?-methylstyrene-based copolymer, and (D) 1 to 5 parts by weight of an ultrahigh molecular weight styrene-acrylonitrile copolymer having a weight average molecular weight of greater than or equal to 5,000,000 g/mol, based on 100 parts by weight of the base resin.

Abstract: A neural processing unit may comprise a first circuitry including a plurality of processing elements (PEs) configured to perform operations of an artificial neural network model, the plurality of PEs including an adder, a multiplier, and an accumulator, and a clock signal supply circuitry configured to output one or more clock signals. When the plurality of PEs include a first group of PEs and a second group of PEs, a first clock signal among the one or more clock signals, may be supplied to the first group of PEs and a second clock signal among the one or more clock signals, may be supplied to the second group of PEs. At least one of the first and second clock signals may have a preset phase based on a phase of an original clock signal.

Abstract: A glove that is detectable by a metal detector. The glove is prepared from latex formulation including a metallic additive. The metallic additive includes a pigment, a surfactant and a solvent. The metallic additive is used in an amount of at least 10 phr based on an amount of a latex formulation.

Abstract: Some embodiments of the invention provide a method for implementing a temporal convolution network (TCN) that includes several layers of machine-trained processing nodes. While processing one set of inputs that is provided to the TCN at a particular time, some of the processing nodes of the TCN use intermediate values computed by the processing nodes for other sets of inputs that were provided to the TCN at earlier times. To speed up the operation of the TCN and improve its efficiency, the method of some embodiments stores intermediate values computed by the TCN processing nodes for earlier sets of TCN inputs, so that these values can later be used for processing later set of TCN inputs.

Abstract: A recyclable nano composite, a preparation method, and an application thereof are provided. The preparation method includes providing a reinforcement material including a conductive material, or, a combination of the conductive material and an insulating material; directly mixing the reinforcement material with a matrix material, or, molding the reinforcement material to form a film, fiber or three-dimensional network structure formed by the reinforcement material, and then compounding the film, fiber or three-dimensional network structure with the matrix material to obtain the recyclable nano composite. The present disclosure further discloses a recycling method of a reinforcement material. The recyclable nano composite provided by the present disclosure has high strength, high toughness, conductivity, electromagnetic shielding and other properties; furthermore, by simple treatment, the reinforcement material can be recycled.