Abstract: This specification describes systems, apparatus and methods for deploying Tsetlin machine models on resource-constrained devices. According to a first aspect of this specification, there is described apparatus comprising: one or more sensors; at least one processor; and at least one memory with storing instructions that, when executed by the at least one processor, cause the apparatus at to at least: collect one or more sets of sensor data using the one or more sensors; classify the one or more sets of sensor data using an encoded Tsetlin machine. The encoded Tsetlin machine comprises a compressed representation of a trained Tsetlin machine. The compressed representation is based on a number of exclude decisions of the trained Tsetlin machine being greater than a number of include decisions of the trained Tsetlin machine.

Abstract: A three-dimensional discretized computer model of a part in a three-dimensional discretized design space comprising three-dimensional geometrical elements is obtained. A signed distance field based on a geometry of the three-dimensional discretized computer model of the part as represented in the three-dimensional geometrical elements of the three-dimensional discretized design space is produced. At least one non-branching cooling channel in a portion of the three-dimensional discretized design space is generated.

Abstract: A three-dimensional computer model of a cooling mold for a part and a specification of an initial layout of one or more cooling channels integrated into the cooling mold is obtained. Data regarding temperatures of a cavity surface of the cooling mold in contact with the part is produced. Individual portions of the one or more cooling channels are moved toward hotter portions of the cavity surface, without moving any branch junctions of the one or more cooling channels and while keeping one or more diameters of the one or more cooling channels constant.

Abstract: There is provided an apparatus which can include a first module and a second module coupled to the first module. The first module can be configured to receive at least one input signal and/or generate at least one input signal. The input signal can be associated with at least one operating parameter and/or at least one target variable, associable with a structure (e.g., a pipeline). The second module can be configured to process the input signal by manner of data cleaning, data wrangling and/or data merging, to produce at least one output signal communicable for further processing (e.g., Machine-Learning based processing).

Abstract: Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design and manufacture of physical structures using toolpaths generated by reinforcement learning for use with subtractive manufacturing systems and techniques, include: obtaining, in a computer aided design or manufacturing program, a three dimensional model of a manufacturable object; generating toolpaths that are usable by a computer-controlled manufacturing system to manufacture at least a portion of the manufacturable object by providing at least a portion of the three dimensional model to a machine learning algorithm that employs reinforcement learning, wherein the machine learning algorithm includes one or more scoring functions that include rewards that correlate with desired toolpath characteristics comprising toolpath smoothness, toolpath length, and avoiding collision with the three dimensional model; and providing the toolpaths to the computer-controlled manufacturing system to manufacture at le

Abstract: There is provided a structure which can be suitable for use with a pipeline. The pipeline can include a plurality of pipe sections. The structure can be shaped and dimensioned to couple at least two pipe sections. The structure can include a housing which can be shaped and dimensioned to carry at least one device. The device(s) can be capable of receiving at least one detection signal associated with the pipeline. The detection signal(s) can be communicated within at least a portion of the pipeline. Moreover, the detection signal(s) can be received by an analyzer for analysis to determine at least one defect associable with the pipeline.

Abstract: Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design and manufacture of physical structures using toolpaths generated by reinforcement learning for use with subtractive manufacturing systems and techniques, include: obtaining, in a computer aided design or manufacturing program, a three dimensional model of a manufacturable object; generating toolpaths that are usable by a computer-controlled manufacturing system to manufacture at least a portion of the manufacturable object by providing at least a portion of the three dimensional model to a machine learning algorithm that employs reinforcement learning, wherein the machine learning algorithm includes one or more scoring functions that include rewards that correlate with desired toolpath characteristics comprising toolpath smoothness, toolpath length, and avoiding collision with the three dimensional model; and providing the toolpaths to the computer-controlled manufacturing system to manufacture at le

Abstract: A needle assembly, such as a catheter assembly, having a needle shaft with a needle tip formed from a first rigid material for puncturing a subject. The shaft section, proximal of the needle tip can be formed from a second material different from the first material. The needle tip is attached to the needle shaft to form the needle. The first material can have higher rigidity than the second material and the needle can project through a catheter tube and used in a catheter assembly.

Abstract: Disclosed are a method of manufacturing a thin film transistor, a thin film transistor, a display panel, and a display device. The method includes forming a gate electrode, forming an oxide semiconductor layer at least partially overlapping the gate electrode, and forming a source electrode and a drain electrode electrically connected to the oxide semiconductor layer, wherein the forming of the oxide semiconductor layer includes forming a first oxide semiconductor layer, and forming a second oxide semiconductor layer on the first oxide semiconductor layer, the second oxide semiconductor layer having a higher energy bandgap than the first oxide semiconductor layer, wherein the forming of the second oxide semiconductor layer is performed by a different process from the forming of the first oxide semiconductor layer, and the forming of the second oxide semiconductor layer includes spraying a precursor solution for the second oxide semiconductor on the first oxide semiconductor layer followed by heat treatment.

Abstract: A method for growing a non-polar a-plane gallium nitride includes cleaning of r-sapphire substrate, and nitridating for initiating growth sequences. The growth sequences include growing a gallium nitride nucleation layer, growing a thick first layer of gallium nitride, growing a film stack of gallium nitride and aluminum nitride as a superlattices layer, and overgrowing of gallium nitride on superlattices layer to form a second layer. The non-polar a-plane gallium nitride is grown by inserting multiple layers of a gallium nitride and an aluminum nitride for improving lateral surface morphology of gallium nitride on r-sapphire substrate.

Abstract: A method for growing a semi-polar gallium nitride epitaxial layer by inserting aluminum nitride and gallium nitride multi-layers includes the steps of cleaning m-sapphire substrates and activating the m-sapphire substrates by utilizing a combination of precursors and carrier gas. The method of growing a layer of semi-polar gallium nitride epitaxial layer on m-sapphire substrates further includes nitridating for initiating growth sequence and depositing a nucleation layer. The film stack of aluminum nitride and gallium nitride multi-layers is grown to initiate growth of a super lattice layer on m-plane sapphire substrates. Subsequently, a layer of the undoped gallium nitride is deposited on the m-plane sapphire substrate.

Abstract: Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design and manufacture of physical structures using subtractive manufacturing systems and techniques include, in one aspect, a method including obtaining information regarding a geometry of a part to be machined by a computer-controlled manufacturing system from a workpiece; based on the information regarding the geometry, identifying machine components to be used by the computer-controlled manufacturing system during machining the part; determining a position for the machining of the part with respect to at least one of the machine components, to even out wear on the machine components, based on data indicating previous positions, movements and wear of components associated with the computer-controlled manufacturing system; and providing instructions usable by the computer-controlled manufacturing system, wherein the instructions are configured to cause the computer-controlled manufacturing system to use t

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predictive machining for forging distortions. In one aspect, a method includes obtaining a mesh model of a machined part to be machined from a forged part and residual stress field data for the forged part, where the mesh model includes multiple nodes corresponding to at least one boundary of the machined part, updating the mesh model by applying the residual stress field data to the multiple nodes corresponding to the at least one boundary to change positions of the multiple nodes in the mesh model, repeating the updating until an amount of change to each of the positions of the multiple nodes in the mesh model satisfies a correction criteria, and providing the compensated mesh model for use in machining the machined part from the forged part.

Abstract: The present invention relates to a composition for the treatment of colon disorder comprising oil palm polyphenolics (OPP) and fibres extracted from the vegetation liquor generated during the milling and extraction of oil from the oil palm fruit administered to a patient in an effective concentration of 50 mg gallic acid equivalents (GAE). The present invention further relates to a method of treatment and prevention of colon disorders and maintenance of gut health.

Abstract: Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design and manufacture of physical structures using toolpaths generated by reinforcement learning for use with subtractive manufacturing systems and techniques, include: obtaining, in a computer aided design or manufacturing program, a three dimensional model of a manufacturable object; generating toolpaths that are usable by a computer-controlled manufacturing system to manufacture at least a portion of the manufacturable object by providing at least a portion of the three dimensional model to a machine learning algorithm that employs reinforcement learning, wherein the machine learning algorithm includes one or more scoring functions that include rewards that correlate with desired toolpath characteristics comprising toolpath smoothness, toolpath length, and avoiding collision with the three dimensional model; and providing the toolpaths to the computer-controlled manufacturing system to manufacture at le

Abstract: A method of depositing a coating layer comprising gallium nitride on a substrate comprising the steps of: (a) providing the substrate having a plurality of side walls and valleys; (b) forming a first layer of gallium nitride deposited on the substrate, by reacting gaseous trimethylgallium and ammonia at a temperature ranging from 400 to 500° C., such that the first layer is formed on the side walls and the valleys; and (c) forming a second layer of gallium nitride deposited on top of the first layer, by reacting gaseous trimethylgallium and ammonia at a temperature ranging from 1000 to 1200° C., to obtain the coating layer comprising the first layer of gallium nitride and the second layer of gallium nitride at a thickness ranging from 3.0 to 4.5 ?m.

Abstract: A method for extracting S (-)-Oleocanthal from olive oil comprising mixing a first volume of water with a second volume of olive oil to form an olive oil/water mixture; letting the olive oil/water mixture stand; removing an aqueous fraction from the olive oil/water mixture leaving a reduced S (-)-Oleocanthal containing olive oil; and filtering the aqueous fraction to create aqueous S (-)-Oleocanthal.

Abstract: A method of depositing a coating layer comprising gallium nitride on a substrate comprising the steps of: (a) providing the substrate having a plurality of side walls and valleys; (b) forming a first layer of gallium nitride deposited on the substrate, by reacting gaseous trimethylgallium and ammonia at a temperature ranging from 400 to 500° C., such that the first layer is formed on the side walls and the valleys; and (c) forming a second layer of gallium nitride deposited on top of the first layer, by reacting gaseous trimethylgallium and ammonia at a temperature ranging from 1000 to 1200° C., to obtain the coating layer comprising the first layer of gallium nitride and the second layer of gallium nitride at a thickness ranging from 3.0 to 4.5 ?m.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predictive machining for forging distortions. In one aspect, a method includes obtaining a mesh model of a machined part to be machined from a forged part and residual stress field data for the forged part, where the mesh model includes multiple nodes corresponding to at least one boundary of the machined part, updating the mesh model by applying the residual stress field data to the multiple nodes corresponding to the at least one boundary to change positions of the multiple nodes in the mesh model, repeating the updating until an amount of change to each of the positions of the multiple nodes in the mesh model satisfies a correction criteria, and providing the compensated mesh model for use in machining the machined part from the forged part.

Abstract: A method for extracting S (-)-Oleocanthal from olive oil comprising mixing a first volume of water with a second volume of olive oil to form an olive oil/water mixture; letting the olive oil/water mixture stand; removing an aqueous fraction from the olive oil/water mixture leaving a reduced S (-)-Oleocanthal containing olive oil; and filtering the aqueous fraction to create aqueous S (-)-Oleocanthal.