Abstract: A power supply for a smooth power output level transitioning includes an energy storage circuit for temporarily storing electric energy for driving a load, a semiconductor switch for pulse-width modulation (PWM) switching, and a digital PWM controller. The digital PWM controller generates a driving waveform to regulate on and off status of the semiconductor switch. The driving waveform toggles between PWM periods of a first type and PWM periods of a second type, and gradually adjusts a ratio of numbers of the PWM periods of the two types over time. The toggling driving waveform achieves one or more intermediate finer power output level that cannot be realized by a single type of PWM period with an intermediate duty cycle, due to the minimum item unit of the driving waveform limited by a clock rate of the digital PWM controller.

Abstract: Embodiments provide methods, systems and apparatus for predictive management of efficient selecting and receiving of retail electric utility service to a facility for a period, by automated selecting of a retail utility service provider corresponding to a selected least cost path of predicted rate plan choices across the period, wherein costs of all possible, viable time-bounded predicted rate plan choices are determined for predicted consumer usage where a predicted market of retail rate formulas for the period are predicted in relation to at least one variable, such as weather.

Abstract: A method and testing apparatus determine receding contact angles of liquids on surfaces by depositing a liquid in a manner whereby the volume of the drop is increased through stepwise addition of smaller drops. Each increment of volume growth causes the perimeter of the drop to advance across the surface. The incremental volume elements impart sufficient energy to the growing drop such that the drop perimeter expands beyond its equilibrium diameter for that volume. The drop perimeter tends to contract between volume additions as the excess energy is dissipated. The method and testing apparatus determine the receding contact angle between the incremental volume additions.

Abstract: The present invention relates to filter material for inclusion in a smoking article, said filter material comprising a base material comprising or made from fibers having a first diameter or mean diameter, and fine fibers having a diameter or mean diameter which is smaller than the first diameter, wherein the filter material comprises more than 10% by weight and/or by volume fine fibers or wherein the diameter or mean diameter of the fine fibers is between about 1.0 ?m and about 1.5 ?m. The invention also relates to filters or filter elements comprising the filter material, smoking articles comprising the same, and use of the filter material in smoke filtration.

Abstract: In some implementations there may be provided a system. The system may include a processor and a memory. The memory may include program code which causes operations when executed by the processor. The operations may include analyzing a series of events contained in received data. The series of events may include events that occur during the execution of a data object. The series of events may be analyzed to at least extract, from the series of events, subsequences of events. A machine learning model may determine a classification for the received data. The machine learning model may classify the received data based at least on whether the subsequences of events are malicious. The classification indicative of whether the received data is malicious may be provided. Related methods and articles of manufacture, including computer program products, are also disclosed.

Abstract: The invention relates to peptidic compounds, which peptidic compounds are compounds of formula (I)?, or a pharmaceutically acceptable salt, or solvate, or N-oxide, or stereoisomer thereof: wherein R1; R2; s; t; u; Aa78 and G1 are as defined herein. The peptidic compounds are useful in activating the Nrf2 pathway.

Abstract: A system is provided for training a machine learning model to detect malicious container files. The system may include at least one processor and at least one memory. The memory may include program code which when executed by the at least one processor provides operations including: processing a container file with a trained machine learning model, wherein the trained machine learning is trained to determine a classification for the container file indicative of whether the container file includes at least one file rendering the container file malicious; and providing, as an output by the trained machine learning model, an indication of whether the container file includes the at least one file rendering the container file malicious. Related methods and articles of manufacture, including computer program products, are also disclosed.

Abstract: A method and system is provided for controlling a display in a machine operating in a work area whereby a plurality of views are displayed on a screen with each of the plurality of views corresponding to a plurality of functions and having one or more of the views independently available on another display.

Abstract: In one respect, there is provided a system for training a neural network adapted for classifying one or more scripts. The system may include at least one processor and at least one memory. The memory may include program code which when executed by the at least one memory provides operations including: receiving a disassembled binary file that includes a plurality of instructions; processing the disassembled binary file with a convolutional neural network configured to detect a presence of one or more sequences of instructions amongst the plurality of instructions and determine a classification for the disassembled binary file based at least in part on the presence of the one or more sequences of instructions; and providing, as an output, the classification of the disassembled binary file. Related computer-implemented methods are also disclosed.

Abstract: Using a recurrent neural network (RNN) that has been trained to a satisfactory level of performance, highly discriminative features can be extracted by running a sample through the RNN, and then extracting a final hidden state hh where i is the number of instructions of the sample. This resulting feature vector may then be concatenated with the other hand-engineered features, and a larger classifier may then be trained on hand-engineered as well as automatically determined features. Related apparatus, systems, techniques and articles are also described.

Abstract: Data to is analyzed using feature hashing to detect malware. A plurality of features in a feature set is hashed. The feature set is generated from a sample. The sample includes at least a portion of a file. Based on the hashing, one or more hashed features are indexed to generate an index vector. Each hashed feature corresponds to an index in the index vector. Using the index vector, a training dataset is generated. Using the training dataset, a machine learning model for identifying at least one file having a malicious code is trained.

Abstract: In some implementations there may be provided a system. The system may include a processor and a memory. The memory may include program code which causes operations when executed by the processor. The operations may include analyzing a series of events contained in received data. The series of events may include events that occur during the execution of a data object. The series of events may be analyzed to at least extract, from the series of events, subsequences of events. A machine learning model may determine a classification for the received data. The machine learning model may classify the received data based at least on whether the subsequences of events are malicious. The classification indicative of whether the received data is malicious may be provided. Related methods and articles of manufacture, including computer program products, are also disclosed.

Abstract: The present invention provides processes for the preparation of 3, 5-Dihydroxy-4-isopropyl-trans-stilbene or a salt or solvate thereof and novel intermediates used therein. In some embodiments the 3, 5-Dihydroxy-4-isopropyl-trans-stilbene is prepared from (E)-2-chloro-2-isopropyl-5-styrylcyclohexane-1,3-dione. Also disclosed are crystal forms of 3, 5-Dihydroxy-4-isopropyl-trans-stilbene or a salt or solvate thereof and pharmaceutical compositions comprising same.

Abstract: In one respect, there is provided a system for training a machine learning model to detect malicious container files. The system may include at least one processor and at least one memory. The memory may include program code which when executed by the at least one processor provides operations including: processing a container file with a trained machine learning model, wherein the trained machine learning is trained to determine a classification for the container file indicative of whether the container file includes at least one file rendering the container file malicious; and providing, as an output by the trained machine learning model, an indication of whether the container file includes the at least one file rendering the container file malicious. Related methods and articles of manufacture, including computer program products, are also disclosed.

Abstract: In one respect, there is provided a system for training a neural network adapted for classifying one or more scripts. The system may include at least one processor and at least one memory. The memory may include program code which when executed by the at least one memory provides operations including: receiving a disassembled binary file that includes a plurality of instructions; processing the disassembled binary file with a convolutional neural network configured to detect a presence of one or more sequences of instructions amongst the plurality of instructions and determine a classification for the disassembled binary file based at least in part on the presence of the one or more sequences of instructions; and providing, as an output, the classification of the disassembled binary file. Related computer-implemented methods are also disclosed.

Abstract: Data is analyzed using feature hashing to detect malware. A plurality of features in a feature set is hashed. The feature set is generated from a sample. The sample includes at least a portion of a file. Based on the hashing, one or more hashed features are indexed to generate an index vector. Each hashed feature corresponds to an index in the index vector. Using the index vector, a training dataset is generated. Using the training dataset, a machine learning model for identifying at least one file having a malicious code is trained.

Abstract: In one respect, there is provided a system for training a machine learning model to detect malicious container files. The system may include at least one processor and at least one memory. The at least one memory may include program code that provides operations when executed by the at least one processor. The operations may include: training, based on a training data, a machine learning model to enable the machine learning model to determine whether at least one container file includes at least one file rendering the at least one container file malicious; and providing the trained machine learning model to enable the determination of whether the at least one container file includes at least one file rendering the at least one container file malicious. Related methods and articles of manufacture, including computer program products, are also disclosed.

Abstract: The present invention relates to filter material for inclusion in a smoking article, said filter material comprising a base material comprising or made from fibres having a first diameter or mean diameter, and fine fibres having a diameter or mean diameter which is smaller than the first diameter, wherein the filter material comprises more than 10% by weight and/or by volume fine fibres or wherein the diameter or mean diameter of the fine fibres is between about 1.0 ?m and about 1.5 ?m. The invention also relates to filters or filter elements comprising the filter material, smoking articles comprising the same, and use of the filter material in smoke filtration.

Abstract: Using a recurrent neural network (RNN) that has been trained to a satisfactory level of performance, highly discriminative features can be extracted by running a sample through the RNN, and then extracting a final hidden state hi, where i is the number of instructions of the sample. This resulting feature vector may then be concatenated with the other hand-engineered features, and a larger classifier may then be trained on hand-engineered as well as automatically determined features. Related apparatus, systems, techniques and articles are also described.