Abstract: Systems and methods include reception of an instruction to perform a consistency check on compressed column data. In response to the instruction, a compression algorithm applied to uncompressed column data to generate the compressed column data is determined, one or more consistency checks associated with the compression algorithm are determined, wherein a first one or more consistency checks associated with a first compression algorithm are different from a second one or more consistency checks associated with a second compression algorithm, the one or more consistency checks are executed on the compressed column data, and, if the one or more consistency checks are not satisfied, a notification is transmitted to a user.

Abstract: A dual-truncated wheel Kibble balance includes: a stationary frame; a double balance wheel disposed on the stationary frame; a pair of guide members, such that one of the guide members is disposed on each opposing end of the double balance wheel; outer connector bands, such that each guide member is gravitationally suspended from the double balance wheel by at least one of the outer connector bands; inner connector bands, such that each guide member is gravitationally suspended from the double balance wheel by at least one of the inner connector bands; and a central flexural bridge in mechanical communication with the stationary frame and the double balance wheel, such that the central flexural bridge is: interposed between the stationary frame and double balance wheel, interconnects the stationary frame and the double balance wheel, and flexes in response to pivotal motion of the double balance wheel relative to the stationary frame.

Abstract: The technology described herein identifies and mitigates phishing attempts by analyzing user input received at the operating system level. Initially, a credential, such as a username or password, is registered with the threat detection system. The technology described herein intercepts user input at the operating system level, generates a hash of the input, and compares it with a hash of a credential being monitored. A credential entry is detected when a hash for the character string entered matches a hash for a credential being monitored. The technology described herein will perform a threat assessment when a secret entry is detected. The threat assessment may use the application context and the network context as inputs to the assessment. Various mitigation actions may be taken when a threat is detected.

Abstract: Various embodiments discussed herein enable the detection of malicious content. Some embodiments do this by determining a similarity score between content, computer objects, or indications (e.g., vectors, file hashes, file signatures, code, etc.) known to be malicious and other content (e.g., unknown files) or indications based on feature weighting. Over various training stages, certain feature characteristics for each labeled malicious content or indication can be learned. For example, for a first malware family of computer objects, the most prominent feature may be a particular URL, whereas other features change considerably for different iterations of the first malware family of computer objects. Consequently, the particular URL can be weighted to determine a particular output classification corresponding to malicious behavior.

Abstract: The techniques disclosed herein enable systems to train a machine learning model to classify malicious command line strings and select anomalous and uncertain samples for analysis. To train the machine learning model, a system receives a labeled data set containing command line inputs that are known to be malicious or benign. Utilizing a term embedding model, the system can generate aggregated numerical representations of the command line inputs for analysis by the machine learning model. The aggregated numerical representations can include various information such as term scores that represent a probability that an individual term of the command line string is malicious as well as numerical representations of the individual terms. The system can subsequently provide the aggregated numerical representations to the machine learning model for analysis. Based on the aggregated numerical representations, the machine learning model can learn to distinguish malicious command line inputs from benign inputs.

Abstract: A user equipment (UE) is configured to perform a prewarming process, wherein the prewarming process includes concurrently performing a face detection process and an unlock process independent of face detection, determine whether a face detection operation indicates the presence of a face within a field of view of a camera of the UE and disable prewarming for a predetermined time period when the face detection operation is not satisfied.

Abstract: Described are examples for curating threat intelligence data including receiving threat intelligence data comprising a list of entities, one or more associations between entities, a reputation score for each entity, and/or a confidence value corresponding to the one or more associations. An updated reputation score for at least one of a first type of entities can be determined based at least in part on the confidence value and/or on determining a reputation score of at least one of a second type of entities to which the at least one of the first type of entities is associated in the one or more associations. The reputation score of the at least one of the first type of entities can be updated, in the threat intelligence data, to the updated reputation score.

Abstract: The disclosed embodiments generate a plurality of anomaly detector configurations and compare results generated by these anomaly detectors to a reference result set. The reference result set is generated by a trained model. A correlation between each result generated by the anomaly detectors and the result set is compared to select an anomaly detector configuration that provides results most similar to those of the trained model. In some embodiments, data defining the selected configuration is then communicated to a product installation. The product installation instantiates the defined anomaly detector and analyzes local events using the instantiated detector. In some other embodiments, the defined anomaly detector is instantiated by the same system that selects the anomaly detector, and thus in these embodiments, the anomaly detector configuration is not transmitted from one system to another.

Abstract: Various embodiments discussed herein enable the detection of malicious content. Some embodiments do this by determining a similarity score between content, computer objects, or indications (e.g., vectors, file hashes, file signatures, code, etc.) known to be malicious and other content (e.g., unknown files) or indications based on feature weighting. Over various training stages, certain feature characteristics for each labeled malicious content or indication can be learned. For example, for a first malware family of computer objects, the most prominent feature may be a particular URL, whereas other features change considerably for different iterations of the first malware family of computer objects. Consequently, the particular URL can be weighted to determine a particular output classification corresponding to malicious behavior.

Abstract: The disclosed embodiments generate a plurality of anomaly detector configurations and compare results generated by these anomaly detectors to a reference result set. The reference result set is generated by a trained model. A correlation between each result generated by the anomaly detectors and the result set is compared to select an anomaly detector configuration that provides results most similar to those of the trained model. In some embodiments, data defining the selected configuration is then communicated to a product installation. The product installation instantiates the defined anomaly detector and analyzes local events using the instantiated detector. In some other embodiments, the defined anomaly detector is instantiated by the same system that selects the anomaly detector, and thus in these embodiments, the anomaly detector configuration is not transmitted from one system to another.

Abstract: A power semiconductor device is disclosed. In one example, the device comprises a semiconductor body coupled to a first load terminal and a second load terminal and comprising a drift region configured to conduct a load current between said terminals. The drift region comprises dopants of a first conductivity type. A source region is arranged in electrical contact with the first load terminal and comprises dopants of the first conductivity type. A channel region comprises dopants of a second conductivity. At least one power unit cell that includes at least one first type trench. The at least one power unit cell further includes a first mesa zone and a second mesa zone of the semiconductor body.

Abstract: A power semiconductor device includes a semiconductor body having a front side coupled to a first load terminal structure and a backside coupled to a second load terminal structure. A front side structure arranged at the front side is at least partially included in the semiconductor body and defines a front side active region configured to conduct a load current between the load terminal structures. The front side structure includes first and second lateral edge portions and a first corner portion that forms a transition between the lateral edge portions. A drift region included in the semiconductor body is configured to carry the load current. A backside emitter region arranged in the semiconductor body in contact with the second load terminal has a net dopant concentration higher than a net dopant concentration of the drift region.

Abstract: A power semiconductor device includes a semiconductor body having a front side coupled to a first load terminal structure and a backside coupled to a second load terminal structure. A front side structure arranged at the front side is at least partially included in the semiconductor body and defines a front side active region configured to conduct a load current between the load terminal structures. The front side structure includes first and second lateral edge portions and a first corner portion that forms a transition between the lateral edge portions. A drift region included in the semiconductor body is configured to carry the load current. A backside emitter region arranged in the semiconductor body in contact with the second load terminal has a net dopant concentration higher than a net dopant concentration of the drift region.

Abstract: Described are examples for curating threat intelligence data including receiving threat intelligence data comprising a list of entities, one or more associations between entities, a reputation score for each entity, and/or a confidence value corresponding to the one or more associations. An updated reputation score for at least one of a first type of entities can be determined based at least in part on the confidence value and/or on determining a reputation score of at least one of a second type of entities to which the at least one of the first type of entities is associated in the one or more associations. The reputation score of the at least one of the first type of entities can be updated, in the threat intelligence data, to the updated reputation score.

Abstract: A power semiconductor device is disclosed. In one example, the device comprises a semiconductor body coupled to a first load terminal and a second load terminal and comprising a drift region configured to conduct a load current between said terminals. The drift region comprises dopants of a first conductivity type. A source region is arranged in electrical contact with the first load terminal and comprises dopants of the first conductivity type. A channel region comprises dopants of a second conductivity. At least one power unit cell that includes at least one first type trench. The at least one power unit cell further includes a first mesa zone and a second mesa zone of the semiconductor body.

Abstract: A power semiconductor device is disclosed. In one example, the device comprises a semiconductor body coupled to a first load terminal and a second load terminal and comprising a drift region configured to conduct a load current between said terminals. The drift region comprises dopants of a first conductivity type. A source region is arranged in electrical contact with the first load terminal and comprises dopants of the first conductivity type. A channel region comprises dopants of a second conductivity. At least one power unit cell that includes at least one first type trench. The at least one power unit cell further includes a first mesa zone and a second mesa zone of the semiconductor body.

Abstract: A power semiconductor device is disclosed. In one example, the device comprises a semiconductor body coupled to a first load terminal and a second load terminal and comprising a drift region configured to conduct a load current between said terminals. The drift region comprises dopants of a first conductivity type. A source region is arranged in electrical contact with the first load terminal and comprises dopants of the first conductivity type. A channel region comprises dopants of a second conductivity. At least one power unit cell that includes at least one first type trench. The at least one power unit cell further includes a first mesa zone and a second mesa zone of the semiconductor body.

Abstract: Identify a set or session of processes as having certain characteristics. A method obtains a known set or session of processes, wherein the known set or session of processes has the certain characteristics. A set or session of processes to be evaluated is obtained. A weighted similarity measure is performed between the known set or session of processes and the set or session of processes to be evaluated. The weighted similarity measure is performed element wise, where a comparison is performed for each defined element in the set or session of processes to be evaluated against elements in the known set or session of processes.

Abstract: Electrode tips for arc lamps for use in, for instance, a millisecond anneal system are provided. In one example implementation, an electrode for an arc lamp can have an electrode tip. The surface of the electrode tip can have one or more grooves to reduce the transportation of molten material across the surface of the electrode tip. The electrode can include an interface between the electrode tip and a heat sink. The interface can have a shape designed to have a desired lateral temperature distribution across the surface of the electrode tip.

Abstract: Identify a set or session of processes as having certain characteristics. A method obtains a known set or session of processes, wherein the known set or session of processes has the certain characteristics. A set or session of processes to be evaluated is obtained. A weighted similarity measure is performed between the known set or session of processes and the set or session of processes to be evaluated. The weighted similarity measure is performed element wise, where a comparison is performed for each defined element in the set or session of processes to be evaluated against elements in the known set or session of processes.