Abstract: A method of improving at least one of quality and yield of a physical process comprises: obtaining values, from respective performances of the physical process, for a plurality of variables associated with the physical process; determining at least one Gaussian mixture model (GMM) representing the values for the variables for the performances of the physical process; based at least in part on the at least one GMM, computing at least one anomaly score for at least one of the variables for at least one of the performances of the physical process; based on the at least one anomaly score, identifying the at least one of the performances of the physical process as an outlier; and, based at least in part on the outlier identification, modifying the at least one of the variables for one or more subsequent performances of the physical process.

Abstract: A method, apparatus, computer system, and computer program product for parallelizing quantum processes for processing a problem. A computer system identifies subproblems in the problem based on a structure of the problem. The computer system identifies quantum circuits in a set of quantum computers to process the subproblems. The computer system executes jobs on the quantum circuits to solve the subproblems.

Abstract: A computer implemented method of administering a complex system includes receiving multivariate data from a plurality of sensors of the system in an ambient state. Event sequences in the received multivariate data are identified. The multivariate event sequences are projected to a lower stochastic latent embedding. A temporal structure of the sequences is learned in a lower latent space. A probabilistic prediction in the lower latent space is provided. The probabilistic prediction in the lower stochastic latent space is decoded to an event prediction in the ambient state.

Abstract: In some examples, a power adapter comprises a first connector having multiple power setting keys, the first connector to couple to a first device; a second connector to couple to a second device; and a processor coupled to the first connector and the second connector, the processor to determine if the second device is configured to supply power in accordance with a power setting of the first device.

Abstract: An example electronic device includes a controller and a connector coupled to the controller. The connector includes a first set of pins and a second set of pins. The controller concurrently is to communicate with a first module of a modular computing system via the first set of pins and to communicate with a second module of the modular computing system, coupled to the first module, via the second set of pins.

Abstract: An example of an apparatus is provided. The apparatus includes a power supply to connect to a power source. The power supply is to receive and to distribute a total power from the power source. The apparatus further includes a first device to receive a first portion of the total power from the power supply. The apparatus further includes a second device to receive a second portion of the total power from the power supply. A sum of the first portion and the second portion is the total power. In addition, the apparatus includes a controller to control the second device, wherein the controller is to determine the total power demanded by the first device and the second device. The controller is to reduce the second portion of the total power and to restore the second portion of the total power.

Abstract: An example system may include a processor and a non-transitory machine-readable storage medium storing instructions executable by the processer to determine, by referencing a firmware configuration setting, a universal serial bus (USB) port role to be assigned to a USB port when a condition is detected by the firmware; and send, to a power delivery (PD) controller of the USB port, a command to assign the USB port role to the USB port responsive to a detection of the condition.

Abstract: Anomaly detection and remedial recommendation techniques for improving the quality and yield of microelectronic products are provided. In one aspect, a method for quality and yield improvement via anomaly detection includes: collecting time series sensor data during individual steps of a semiconductor manufacturing process; calculating anomaly scores for each of the individual steps using a predictive model; and implementing changes to the semiconductor manufacturing process based on the anomaly scores. A system for quality and yield improvement via anomaly detection is also provided.

Abstract: In one example in accordance with the present disclosure, a modular computing component is described. The modular computing component includes a first terminal to connect the modular computing component to at least one of a host computing device and another modular computing component. Controller memory of the modular computing component stores information relating to at least one of build level information, revision level information, and generation level information. A controller of the modular computing component transmits the at least one of build level information, revision level information, and generation level information to the host computing device.

Abstract: A computing device for time series modeling and forecasting includes a processor, and a memory coupled to the processor. The memory stores instructions to cause the processor to perform acts including encoding an input of a multivariate time series data, and performing a non-linear mapping of the encoded multivariate time series data to a lower-dimensional latent space. The next values in time of the encoded multivariate time series data in the lower dimensional latent space are predicted. The predicted next values and a random noise are mapped back to an input space to provide a predictive distribution sample for a next time points of the multivariate time series data. One or more time series forecasts based on the predictive distribution sample are output.

Abstract: An example intrusion detection system for a computer includes: an ambient light sensor to detect an increase in ambient light indicative of a housing of the computer being opened; and a super input/output integrated circuit (SIO) to receive a signal from the ambient light sensor indicating that the housing of the computer has been opened.

Abstract: Provided is an apparatus for classifying a medical image. The apparatus includes a database configured to store a first image, a generator configured to generate a second image on the basis of a latent vector which is a concatenation of noise information having a certain size and random uniform class labels of a plurality of diseases, a discriminator configured to receive the first image and the second image and attempt to recognize the first image and the second image as a real image and a fake image, and a classifier configured to classify the first image and the second image.

Abstract: An example of an apparatus is provided. The apparatus includes a power supply to connect to a power source. The power supply is to receive and to distribute a total power from the power source. The apparatus further includes a first device to receive a first portion of the total power from the power supply. The apparatus further includes a second device to receive a second portion of the total power from the power supply. A sum of the first portion and the second portion is the total power. In addition, the apparatus includes a controller to control the second device, wherein the controller is to determine the total power demanded by the first device and the second device. The controller is to reduce the second portion of the total power and to restore the second portion of the total power.

Abstract: A display device may include a universal serial bus (USB) hub formed within a housing of the display device; a plurality of USB ports, each of a plurality of USB ports communicatively coupled to the USB hub via a ribbon; a guiderail formed on an outer surface of the housing display with each of the plurality of USB ports coupled thereto to allow the movement of the USB ports along the guiderail.

Abstract: An apparatus may include a basic input/output system (BIOS) coupled to a controller. A communication port may be coupled to the controller. The controller may determine that the communication port has entered a locked state, send a first signal to the communication port to power off a bus associated with the communication port for a threshold period of time, and send a second signal to the communication port to power on the bus associated with the communication port in response to expiration of the threshold period of time.

Abstract: Example implementations relate to an input/output (I/O) hub. An example I/O hub can include a registered jack (RJ) 12 I/O retail port and a Universal Serial Bus (USB) type-C port to deliver power and communication data to the RJ12 I/O retail port.

Abstract: An example electronic device includes a controller and a connector coupled to the controller. The connector includes a first set of pins and a second set of pins. The controller concurrently is to communicate with a first module of a modular computing system via the first set of pins and to communicate with a second module of the modular computing system, coupled to the first module, via the second set of pins.

Abstract: Based on at least one manufacturing process characteristics associated with a manufacturing process, a prediction time at which to execute a selected machine learning model selected from multiple trained machine learning models is determined, and at the prediction time, the selected machine learning model is executed. Executing the selected machine learning model predicts a control set point for future values of state variables of the manufacturing process, for controlling the manufacturing process. Based on at least one of the manufacturing process characteristics, a learning time at which to train a machine learning model is determined, and at the learning time, the machine learning model is trained based on historical process data associated with the manufacturing process.