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 storing on 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 stored 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 received sample, and in response to determining that the sample is malicious, performing an action based on a security policy.

Abstract: The current invention relates to the method and apparatus to magnetically separate biological entities with magnetic labels from a fluid sample. The claimed magnetic separation device removes biological entities with magnetic labels from its fluidic solution by using a soft-magnetic center pole with two soft-magnetic side poles. The claimed device further includes processes to dissociate entities conglomerate after magnetic separation.

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: Web sites are crawled using multiple browser profiles to avoid malicious cloaking. Based on web page content returned from HTTP requests using the multiple browser profiles, web sites returning substantively different content to HTTP requests for different browser profiles are identified. Web sites are further filtered by common cloaking behavior, and redirect scripts are extracted from web page content that performed cloaking. Signatures comprising tokenized versions of the redirect scripts are generated and compared to a database of known cloaking signatures. URLs corresponding to signatures having approximate matches with signatures in the database are flagged for recrawling. Recrawled URLs are verified for malicious cloaking again using HTTP requests from multiple browser profiles.

Abstract: A system automatically frames locations by detecting a user's presence within a virtual detection space. The system detects sound in the detection space and converts the sound into electrical signals. The electrical signals are converted into a digital signals at common or periodic sampling rates. The system identifies speech segments in the digital signals and attenuates noise like components within or adjacent to them. The system identifies the physical locations of a speech source generating the speech segments and automatically adjusts a camera framing based on the estimated location of the active speech source generating the speech segments.

Abstract: A system automatically controls an electronic device's audio by detecting an active sound source presence within an auditory detection space. The system transitions the electronic device to selectively output a desired sound when the active sound source presence is detected and detects sound in the auditory detection space. The system enhances sound and transforms it into electrical signals. The system converts the electrical signals into a digital signal and identifies active sound segments in the digital signals. The system attenuates noise components in the digital signals and locates the physical location of the active sound source. It adjusts an output automatically by muting a second sound source in a second detection space.

Abstract: Web sites are crawled using multiple browser profiles to avoid malicious cloaking. Based on web page content returned from HTTP requests using the multiple browser profiles, web sites returning substantively different content to HTTP requests for different browser profiles are identified. Web sites are further filtered by common cloaking behavior, and redirect scripts are extracted from web page content that performed cloaking. Signatures comprising tokenized versions of the redirect scripts are generated and compared to a database of known cloaking signatures. URLs corresponding to signatures having approximate matches with signatures in the database are flagged for recrawling. Recrawled URLs are verified for malicious cloaking again using HTTP requests from multiple browser profiles.

Abstract: A method for separating biological entities in a fluid sample, which contains small, medium, and large biological entities, includes the steps introducing the fluid sample into a first microfluidic device as two streams along two sidewalls thereof; applying a first power to the first microfluidic device to exert a first acoustic radiation pressure to produce a first output fluid having a higher relative fraction of the large biological entities than the fluid sample and a second output fluid having a lower relative fraction of the large biological entities than the fluid sample; introducing the second output fluid into a second microfluidic device as two streams along two sidewalls thereof; and applying a second power, which is higher than the first power, to the second microfluidic device to exert a second acoustic radiation pressure to produce a third output fluid having a higher relative fraction of the medium biological entities than the fluid sample.

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: An obstacle recognition method and apparatus, a computer device, and a storage medium are provided. The method comprises: acquiring point cloud data scanned by LiDAR and time-sequence pose information of a vehicle; determining a spliced image of an eye bird view according to the point cloud data, the time-sequence pose information, and a historical frame embedded image; inputting the spliced image into a preset first CNN model to obtain a current frame embedded image and pixel-level information of the eye bird view; determining recognition information of at least one obstacle according to the current frame embedded image and pixel-level information.

Abstract: A microfluidic system for separating biological entities includes a cooling device including a thermoelectric heat pump, a first fan, and a first heat exchanger disposed between the first fan and the thermoelectric heat pump; a first housing structure having a first shell that encases the first fan and the first heat exchanger; a microfluidic device and one or more piezoelectric transducers attached thereto; and a second housing structure reversibly attached to the first housing structure and having a second shell that encloses therein the microfluidic device and the one or more piezoelectric transducers. When the first and second housing structures are coupled, a first air passage is formed between a side of the first heat exchanger and an end of the microfluidic device, a second air passage is formed between the first fan and the piezoelectric transducers, thereby allowing air to circulate between the first and second air passages.

Abstract: The invention describes a family of sensors for assaying macro-molecules and/or biological cells in solution. The invention also describes methods of making and using the sensors. Each sensor has the form of a well (a hollow cylinder having a floor but no lid) or a trench whose walls comprise a plurality of GMR or TMR devices. Suitably shaped magnets located below each well's floor pull labeled particles into the well/trench and up against the inner wall where a field gradient orients them for optimum detection. Any unattached labels that happen to also be in the well/trench are removed through suitably sized holes in the floor.

Abstract: A microfluidic device for sorting biological objects includes a microfluidic chip including a planar substrate having first and second planar surfaces, the planar substrate including first and second networks of channels recessed respectively from the first and second planar surfaces and fluidically connected by way of at least a through-hole in the planar substrate; a first lid attached to the first planar surface of the planar substrate and substantially covering the first network of channels; and a second lid attached to the second planar surface of the planar substrate and substantially covering the second network of channels; and one or more piezoelectric transducers attached to the first lid and/or the second lid and configured to generate first and second acoustic standing waves in a first linear channel of the first network of channels and a second linear channel of the second network of channels, respectively.

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: 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: A system automatically controls an electronic device's audio by detecting an active sound source presence within an auditory detection space. The system transitions the electronic device to selectively output a desired sound when the active sound source presence is detected and detects sound in the auditory detection space. The system enhances sound and transforms it into electrical signals. The system converts the electrical signals into a digital signal and identifies active sound segments in the digital signals. The system attenuates noise components in the digital signals and locates the physical location of the active sound source. It adjusts an output automatically by muting a second sound source in a second detection space.

Abstract: A system automatically frames locations by detecting a user's presence within a virtual detection space. The system detects sound in the detection space and converts the sound into electrical signals. The electrical signals are converted into a digital signals at common or periodic sampling rates. The system identifies speech segments in the digital signals and attenuates noise like components within or adjacent to them. The system identifies the physical locations of a speech source generating the speech segments and automatically adjusts a camera framing based on the estimated location of the active speech source generating the speech segments.

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 storing on 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 stored 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 received sample, and in response to determining that the sample is malicious, perform an action based on a security policy.

Abstract: Techniques for building multi-representational learning models for static analysis of source code are disclosed. In some embodiments, 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: The current invention describes methods, techniques, devices, and apparatuses for graphics and display processing for virtual and augmented reality. In some examples, this disclosure describes a projection display system capable of rendering and displaying multiple virtual objects or virtual objects utilizing an estimated distance between a real world object and a user and adjusting a focus depth of said display system. In some examples, this disclosure proposes techniques for rendering and displaying multiple virtual objects or virtual objects at the same time at multiple different distances to a user.