A rollable display device includes a rollable structure including a plurality of unit structures, a display panel structure attached to the rollable structure, and a plurality of magnetic objects on a bezel region of the rollable structure, wherein respective widths of the unit structures increase in a direction from a first side of the rollable structure to a second side of the rollable structure, wherein the unit structures collectively form first through (n)th rolling cycles, as the rollable structure is rolled, and a (k)th rolling cycle encircles a (k−1)th rolling cycle, and wherein the plurality of magnetic objects aligns the (k)th rolling cycle with the (k−1)th rolling cycle by causing a magnetic attraction between the (k)th rolling cycle and the (k−1)th rolling cycle.

A compute device may include one or more processors operable at variable performance levels depending upon power supplied from a compute device power supply. A baseboard management controller of the compute device may periodically calculate an adjustment value for the power supply to adjust the power delivered to the one or more processors. The adjustment value may be calculated as a function of a thermal margin between the temperature of the one or more processors over time and a thermal operating limit of the one or more processors.

A device implementing adaptive memory performance control by thread group may include a memory and at least one processor. The at least one processor may be configured to execute a group of threads on one or more cores. The at least one processor may be configured to monitor a plurality of metrics corresponding to the group of threads executing on one or more cores. The metrics may include, for example, a core stall ratio and/or a power metric. The at least one processor may be configured to determine, based at least in part on the plurality of metrics, a memory bandwidth constraint with respect to the group of threads executing on the one or more cores. The at least one processor may be configured to, in response to determining the memory bandwidth constraint, increase a memory performance corresponding to the group of threads executing on the one or more cores.

A method of identifying a cause of an anomalous feature measured from system circuitry on an integrated circuit (IC) chip, the IC chip comprising the system circuitry and monitoring circuitry for monitoring the system circuitry by measuring features of the system circuitry in each window of a series of windows, the method comprising: (i) from a set of windows prior to the anomalous window comprising the anomalous feature, identifying a candidate window set in which to search for the cause of the anomalous feature; (ii) for each of the measured features of the system circuitry: (a) calculating a first feature probability distribution of that measured feature for the candidate window set; (b) calculating a second feature probability distribution of that measured feature for window(s) not in the candidate window set; (c) comparing the first and second feature probability distributions; and (d) identifying that measured feature in the timeframe of the candidate window set as a cause of the anomalous feature if the first and second feature probability distributions differ by more than a threshold value; (iii) iterating steps (i) and (ii) for further candidate window sets from the set of windows prior to the anomalous window; and (iv) outputting a signal indicating those measured feature(s) of step (ii)(d) identified as a cause of the anomalous feature.

A system and methods for sandboxed malware analysis and automated patch development, deployment and validation, comprising a business operating system, vulnerability scoring engine, binary translation engine, sandbox simulation engine, at least one network endpoint, at least one database, a network, and a combination of machine learning and vulnerability probing techniques, to analyze software, locate any vulnerabilities or malicious behavior, and attempt to patch and prevent undesired behavior from occurring, autonomously.

A read-disturb-based physical storage read temperature information identification system includes a global read temperature identification subsystem coupled to at least one storage device. Each at least one storage device reads valid data and obsolete data from at least one physical block in that storage device and, based on the reading of the valid data and the obsolete data, generates read disturb information associated with each row provided by the at least one physical block in that storage device. Each at least one storage devices then uses the read disturb information associated with each row provided by the at least one physical block in that storage device to generate a local logical storage element read temperature map for that storage device that it provides to the global read temperature identification subsystem.

In one aspect, an example methodology implementing the disclosed techniques can include, by a baseboard management controller (BMC), responsive to a virtual console of a server being launched on a client device via the BMC, collecting one or more environment parameters of the client device and collecting one or more environmental parameters of the server. The method can also include, by the BMC, determining an appropriate display configuration for the server based on an analysis of the one or more environment parameters of the client device and the one or more environmental parameters of the server and configuring a display configuration of the server in accordance with the determined appropriate display configuration.

A vehicle can capture data that can be converted into a synthetic scenario for use in a simulator. Objects can be identified in the data and attributes associated with the objects can be determined. The data can be used to generate a synthetic scenario of a simulated environment. The scenarios can include simulated objects that traverse the simulated environment and perform actions based on the attributes associated with the objects, the captured data, and/or interactions within the simulated environment. In some instances, the simulated objects can be filtered from the scenario based on attributes associated with the simulated objects and can be instantiated and/or destroyed based on triggers within the simulated environment. The scenarios can be used for testing and validating interactions and responses of a vehicle controller within the simulated environment.

A system includes a processing system and a memory system. The memory system stores instructions for identifying a cloud application in a cloud environment as a non-disposable application and monitoring a plurality of instances of the non-disposable application running in the cloud environment. The instructions when executed by the processing system further result in determining that a number of the instances of the non-disposable application should be modified based on one or more demand predictions by an artificial intelligence scaler, adjusting the number of the instances of the non-disposable application running in the cloud environment based on the one or more demand predictions, and modifying an allocation of one or more resources of the cloud environment associated with adjusting the number of the instances of the non-disposable application.

A method is provided for adding a sensor monitoring feature of a newly-added sensor to a system monitoring feature provided by a baseboard management controller (BMC). The BMC stores a BMC firmware that contains a main program, a sensor library and a sensor data record. The BMC updates the sensor library to a target sensor library that includes identification information of the additional sensor, and functions used to execute the sensor monitoring feature of the additional sensor. By executing the main program, the BMC loads the target sensor library, and adds the identification information of the additional sensor to the sensor data record.