Abstract: A charging module and a charging device. The charging module includes a three-phase rectifier module, a bus capacitor module, an inductor, and a controller. An input end of the three-phase rectifier module is electrically connected to an input power supply, and an output end of the three-phase rectifier module is electrically connected to an input end of the bus capacitor module. The bus capacitor module includes a first bus capacitor and a second bus capacitor. The first bus capacitor is connected between a first output end of a three-phase bridge arm and a midpoint of the three-phase bridge arm, and the second bus capacitor is electrically connected between a second output end of the three-phase bridge arm and the midpoint. The inductor is electrically connected to the midpoint of the three-phase bridge arm and a midpoint between the first bus capacitor and the second bus capacitor.

Abstract: A STA in a STA MLD sends an information frame on a link corresponding to the station, and the information frame is used to indicate a STA in an awake state in the STA MLD. An AP MLD receives the information frame, and sends a downlink message frame on a one or more links corresponding to a part of or all of STAs in the awake state in the STA MLD.

Abstract: Techniques for providing innocent until proven guilty (IUPG) solutions for building and using adversary resistant and false positive resistant deep learning models are disclosed. In some embodiments, a system, process, and/or computer program product includes storing a set comprising one or more innocent until proven guilty (IUPG) models for static analysis of a sample; performing a static analysis of content associated with the sample, wherein performing the static analysis includes using at least one stored IUPG model; and determining that the sample is malicious based at least in part on the static analysis of the content associated with the sample, and in response to determining that the sample is malicious, performing an action based on a security policy.

Abstract: The technology presented herein enables the use of a clustering algorithm to identify additional malicious domains based on known malicious domains. A domain identifier system identifies a first plurality of domain names associated with a malicious domain campaign and seeding a first clustering algorithm with the first plurality of domain names. After seeding the first clustering algorithm, the domain identifier system uses the first clustering algorithm to process passive domain name system (DNS) records to identify and group a second plurality of domain names associated with the malicious domain campaign.

Abstract: The current invention relates to the device and method to separate biological entities from a sample fluid by a microfluidic device. The claimed methods separate biological entities by differentiating the sizes of the biological entities with ultrasound modes. The claimed methods further utilize a multi-staged design that removes smaller size entities at earlier and wider sections and concentrates larger entities at later and narrower sections of a microfluidic channel.

Abstract: A method for performing a plurality of vehicle computing tasks includes determining the plurality of vehicle computing tasks that need to be performed and monitoring a wireless connectivity between a vehicle and a remote computing system. Monitoring the wireless connectivity between the vehicle and the remote computing system includes measuring, in real time, at least one quality of service (QoS) measurement of the wireless connectivity between the vehicle and the remote computing system. The method further includes determining whether to perform at least one of the plurality of vehicle computing tasks in at least one of the remote computing system or a vehicle controller of the vehicle based on at least one QoS measurement.

Abstract: Techniques for providing innocent until proven guilty (IUPG) solutions for building and using adversary resistant and false positive resistant deep learning models are disclosed. In some embodiments, a system, process, and/or computer program product includes storing a set comprising one or more innocent until proven guilty (IUPG) models for static analysis of a sample; performing a static analysis of content associated with the sample, wherein performing the static analysis includes using at least one stored IUPG model; and determining that the sample is malicious based at least in part on the static analysis of the content associated with the sample, and in response to determining that the sample is malicious, performing an action based on a security policy.

Abstract: To perform pattern-based detection of malicious URLs, patterns are first generated from known URLs to build a pattern repository. A URL is first normalized and parsed, and keywords are extracted and stored in an additional repository of keywords. Tokens are then determined from the parsed URL and tags are associated with the parsed substrings. Substring text may also be replaced with general identifying information. Patterns generated from known malicious and benign URLs satisfying certain criteria are published to a pattern repository of which can be accessed during subsequent detection operations. During detection, upon identifying a request which indicates an unknown URL, the URL is parsed and tokenized to generate a pattern. The repository of malicious URL patterns is queried to determine if a matching malicious URL pattern can be identified. If a matching malicious URL pattern is identified, the URL is detected as malicious.

Abstract: The technology presented herein enables the use of a clustering algorithm to identify additional malicious domains based on known malicious domains. A domain identifier system identifies a first plurality of domain names associated with a malicious domain campaign and seeding a first clustering algorithm with the first plurality of domain names. After seeding the first clustering algorithm, the domain identifier system uses the first clustering algorithm to process passive domain name system (DNS) records to identify and group a second plurality of domain names associated with the malicious domain campaign.

Abstract: A system/process/computer program product for building multi-representational learning models for static analysis of source code includes receiving training data, wherein the training data includes a set of source code files for training a multi-representational learning (MRL) model for classifying malicious source code and benign source code based on a static analysis; generating a first feature vector based on a set of characters extracted from the set of source code files; generating a second feature vector based on a set of tokens extracted from the set of source code files; and performing an ensemble of the first feature vector and the second feature vector to form a target feature vector for classifying malicious source code and benign source code based on the static analysis.

Abstract: A process for magnetically sorting biological objects includes the steps of applying a magnetic field generated by a magnetic assembly to a flexible conduit; flowing a sample fluid containing magnetically labeled biological objects through the flexible conduit to collect the magnetically labeled biological objects on a conduit wall; removing the magnetic field from the flexible conduit; and mechanically deforming the flexible conduit to loosen the magnetically labeled biological objects collected on the conduit wall.

Abstract: Techniques for multi-representational learning models for static analysis of source code are disclosed. In some embodiments, a system/process/computer program product for multi-representational learning models for static analysis of source code includes receiving at a networked device a set comprising one or more multi-representation learning (MRL) models for static analysis of source code; performing a static analysis of source code associated with a sample received at the network device, wherein performing the static analysis includes using at least one MRL model; and determining that the sample is malicious based at least in part on the static analysis of the source code associated with the sample and without performing dynamic analysis of the sample, and in response to determining that the sample is malicious, performing an action based on a security policy.

Abstract: The present invention is directed to a method for sorting biological objects including the steps of providing a magnetic device that includes a conduit or channel having upstream and downstream sections and a magnetic means for generating first and second magnetic fields in the upstream and downstream sections, respectively; flowing a sample fluid that includes magnetically labeled biological objects and unlabeled biological objects through the upstream section to magnetically saturate the magnetically labeled biological objects by the first magnetic field; and flowing the sample fluid from the upstream section continuously to the downstream section to collect the magnetically labeled biological objects on a wall of the downstream section by the second magnetic field, wherein the first magnetic field in the upstream section has a higher average field strength than the second magnetic field in the downstream section.

Abstract: A system/process/computer program product for building multi-representational learning models for static analysis of source code includes receiving training data, wherein the training data includes a set of source code files for training a multi-representational learning (MRL) model for classifying malicious source code and benign source code based on a static analysis; generating a first feature vector based on a set of characters extracted from the set of source code files; generating a second feature vector based on a set of tokens extracted from the set of source code files; and performing an ensemble of the first feature vector and the second feature vector to form a target feature vector for classifying malicious source code and benign source code based on the static analysis.

Abstract: A spin-transfer torque magnetic random access memory (STTMRAM) element employed to store a state based on the magnetic orientation of a free layer, the STTMRAM element is made of a first perpendicular free layer (PFL) including a first perpendicular enhancement layer (PEL). The first PFL is formed on top of a seed layer. The STTMRAM element further includes a barrier layer formed on top of the first PFL and a second perpendicular reference layer (PRL) that has a second PEL. The second PRL is formed on top of the barrier layer. The STTMRAM element further includes a capping layer that is formed on top of the second PRL.

Abstract: A magnetic device for processing biological objects including a soft magnetic center pole having a bottom end and a tapered tip end; first and second soft magnetic side poles disposed on opposite sides of the soft magnetic center pole and respectively having first and second bottom ends, the first and second soft magnetic side poles respectively having first and second top ends that bend inward toward the soft magnetic center pole with a first outward side of the first top end and a second outward side of the second top end being substantially coplanar; a magnetic flux source generating magnetic flux in the soft magnetic center pole and the first and second soft magnetic side poles; and a channel plate having a channel embedded therein and a first planar surface that is operable to be in contact with or in close proximity to the first and second outward sides.

Abstract: A system/process/computer program product for building multi-representational learning models for static analysis of source code includes receiving training data, wherein the training data includes a set of source code files for training a multi-representational learning (MRL) model for classifying malicious source code and benign source code based on a static analysis; generating a first feature vector based on a set of characters extracted from the set of source code files; generating a second feature vector based on a set of tokens extracted from the set of source code files; and performing an ensemble of the first feature vector and the second feature vector to form a target feature vector for classifying malicious source code and benign source code based on the static analysis.

Abstract: Techniques for multi-representational learning models for static analysis of source code are disclosed. In some embodiments, a system/process/computer program product for multi-representational learning models for static analysis of source code includes receiving at a networked device a set comprising one or more multi-representation learning (MRL) models for static analysis of source code; performing a static analysis of source code associated with a sample received at the network device, wherein performing the static analysis includes using at least one MRL model; and determining that the sample is malicious based at least in part on the static analysis of the source code associated with the sample and without performing dynamic analysis of the sample, and in response to determining that the sample is malicious, performing an action based on a security policy.

Abstract: An author of a malicious websites campaign (scam or phishing) likely uses a legitimate third-party service to facilitate the malicious campaign. An example includes legitimate CAPTCHA (Completely Automated Public Turing Test to Tell Computers and Humans Apart) services to conceal the malicious campaign from automated security scanners. A security service/platform can employ a detection pipeline that leverages use of CAPTCHA keys across websites of a malicious websites campaign. Websites that use CAPTCHA keys found in known malicious websites can at least be identified as suspect and communicated to firewalls.

Abstract: The current invention generally relates to apparatus and method to analyze and separate biological entities, including cells, bacteria and molecules from human blood, body tissue, body fluid and other human related biological samples. The claimed apparatus and method analyze, or detect, biological entities based on optical signals received from said entities by using optical detectors. The claimed apparatus and method further separate biological entities with using micro-actuator activated sorting devices.