Abstract: A context-based input interface from a first interface can be presented to increase a rate and accuracy of providing input to the first interface. A computing device may receive user input via a first interface. In response, the computing device may define a context using one or more characteristics of the first input interface. The computing device may then generate a second input interface configured to execute a function that modify the first input interface. The function can be selected based on the context. The computing device may receive a selection of a particular function via the second interface and implement the function to modify the first input interface. The computing device may terminate the second interface upon executing the function and return to the first interface.

Abstract: A method for the detection of multi-killchain alerts is disclosed. The method includes receiving, by a computer system, a plurality of alerts indicative of activity within a computer network, wherein a given alert specifies one or more events having attributes, and extracting attributes from events included in the plurality of alerts. The method further includes determining attribute similarity for pairs of events based on whether a given pair of events has common values for one or more attributes and whether attribute values of the given pair of events indicates lateral movement within computers of the computer network. Linked pairs are then identified based on the determined attribute similarity and added to a graph data structure. The method further includes the computer system analyzing the graph data structure to find clusters of events relating to a security attack.

Abstract: Systems and methods are provided for distributed video storage and search over edge computing devices having a short-term memory and a long-term memory. The method may comprise caching a first portion of data on a first device. The method may further comprise determining, at a second device, whether the first device has the first portion of data. The determining may be based on whether the first piece of data satisfies a specified criterion. The method may further comprise sending the data, or a portion of the data, and/or a representation of the data from the first device to a third device.

Abstract: A method for training a machine learning image segmentation algorithm to segment structural features of a blood vessel in a computed tomography (CT) image is described herein. The method comprises receiving a labelled training set for the machine learning image segmentation algorithm. The labelled training set comprising a plurality of CT images, each CT image of the plurality of CT images showing a targeted region of a subject, the targeted region including at least one blood vessel. The labelled training set further comprises a corresponding plurality of segmentation masks, each segmentation mask labelling at least one structural feature of a blood vessel in a corresponding CT image of the plurality of CT images.

Abstract: Systems and methods for pulse shaping of baseband signals in a communication network using Electro Quasistatic signal is disclosed. The system up-samples baseband symbols in digital domain for generating an up-sampled baseband symbols. Further, the system pulse shapes the up sampled baseband symbols in the digital domain to generate pulse shaped symbols to be transmitted as a bandlimited waveform based on spectral mask requirements. The system up-converts a frequency of the pulse shaped symbols in digital domain to a desired frequency band for generating a frequency up converted pulse shaped waveform based on a sampling frequency and a symbol rate. The system further generates a reference analog signal (St) by converting the frequency up converted pulse shaped waveform in digital domain to analog domain. Furthermore, the system communicates the generated reference analog signal to a receiver system using a human body.

Abstract: A method and system for training physics-informed operators with dual hypernetwork module and low-rank domain decomposition. The method includes identifying availability of information related to system behavior and geometry complexity. The method further includes dynamically determining training strategy for the PINO based on the identification. The training strategy includes at least one of a soft domain decomposition process or a hard domain decomposition process. The method includes determining whether a size of a hypernetwork output layer exceeds a first predefined threshold. The method includes generating a set of training points if the size does not exceed the first predefined threshold. The method includes iteratively training the PINO using the determined strategy based on the set of training points until an error falls below a second predefined threshold.

Abstract: An anomaly detection system using machine learning to generate predicted survey scores for a given duration and a given metric based on historic survey score data. The system compares a predicted survey score to the actual survey score and identifies anomalous actual survey scores. The anomaly detection system trains a plurality of survey score prediction models using historic survey score data. Each survey score prediction model is based on a specific survey score metric and a specific duration. The survey score prediction models generate expected survey score results for the given duration and the given metric. Based on the user-determined filtering and tolerances, the system determines if the actual survey score result is anomalous. The system generates reports for the detected anomalies and continually updates the survey score prediction models with newly obtained actual survey results, thereby improving the anomaly detection accuracy over time.

Abstract: A device for monitoring an implant comprises: an array of ultrasound transducer elements configured to receive a localization ultrasound signal from the implant; and a signal processor configured to, for each ultrasound transducer element of at least a subset of ultrasound transducer elements, identify a time point corresponding to a peak intensity of the received localization ultrasound signal at the ultrasound transducer element, wherein relative time delays between the time points identified for the ultrasound transducer elements form information relating to a location of the implant.

Abstract: Described herein are methods for the synthesis of DNA polynucleotides and polynucleotides, as well as methods for their deprotection and methods for the use of said compounds and compositions comprising said compounds. In particular, such compounds and compositions comprising them are used in methods for light-directed synthesis of DNA microarrays.

Abstract: A multi-layer cybersecurity system and method for protecting digital wallets holding blockchain-based tokens traded on a financial platform. Specifically, the invention employs random temporal windowing in relation to: (1) reshuffling designated machines running a multi-party computation protocol; and (2) rekeying of private keys held in both hot and cold environments. Furthermore, environmental based signature weighting is applied to the keys. As a result, cold signers can do anything hot signers can but hot signers cannot do what cold signers can such as taking control, recovering control, or reconstitute the holdings of the wallet in real time, if necessary.

Abstract: In an embodiment, an apparatus for synthetic data generation is presented. The apparatus includes a processor and a memory communicatively connected to the processor. The memory contains instructions configured to the processor to receive data. The processor is configured to input the data into a generative framework. The generative framework includes a first category of synthetic data generation and a second category of synthetic data generation. The generative framework is configured to input data an output synthetic data through at least a category of synthetic data generation. The processor is configured to generate, based on the generative framework, synthetic data from the received data.

Abstract: In implementations of systems for generating indications of relationships between electronic documents, a processing device implements a relationship system to segment text of electronic documents included in a document corpus into segments. The relationship system determines a subset of the electronic documents that includes electronic document pairs having a number of similar segments that is greater than a threshold number. The similar segments are identified using locality sensitive hashing. The electronic document pairs are classified as related documents or unrelated documents using a machine learning model that receives a pair of electronic documents as an input and generates an indication of a classification for the pair of electronic documents as an output. Indications of relationships between particular electronic documents included in the subset are generated based at least partially on the electronic document pairs that are classified as related documents.

Abstract: A method, system, and non-transitory computer-readable medium for characterizing oxide defects in semiconductor devices are described. Based on time-resolved SILC data, a total number of active oxide defects and each defect's characteristics can be determined for a deeply-scaled FET device. The discrete changes in leakage current correspond to switching of a single defect. A Bayesian-inspired algorithm is utilized to extract distinct current levels and experimental data is quantized into these extracted current levels by filtering noise. The evolution of current levels corresponds to a Markov chain. The defect currents are extracted by clustering transition probabilities and absolute differences in current levels using an affinity propagation algorithm. A maximum likelihood estimator is developed to extract a base leakage current.

Abstract: A system includes at least one processing circuit including at least one memory and one or more processors configured to: obtain, via a user interface, a non-bounded query relating to a travel trip, the non-bounded query excluding at least one parameter associated with the travel trip; identify, via a first machine learning model, the at least one excluded parameter based on the non-bounded query; identify, via a second machine learning model and from among a first set of travel results that match the non-bounded query and the excluded parameter, a second set of the travel results that meet a presentation threshold of customer popularity based on at least one of a location selected by one or more users or a time selected by the one or more users; and present, via the user interface, one or more travel results of the second set, and the presentation threshold.

Abstract: Described herein are systems and techniques to facilitate the efficient performance of operations and determination of operation states and related data. A set of operations and operation dependencies may be configured at an interactive nonlinear operation set navigator that may generate interface elements representing the operations and operation data. Operation interface elements may further include controls that, when activated, generate additional operation data and/or facilitate the initiation and completion of the represented operation. The duration and other properties associated with the execution of an operation may be tracked and used to generate alerts and perform other actions.

Abstract: Various embodiments of the present technology may provide a susceptor and a sensing system coupled to the susceptor and configured to generate a plurality of sensor output signals indicating air flow across the susceptor and sensing system. A controller is connected to the sensing system and configured to detect a flow pattern on the susceptor based on the sensor output signals.

Abstract: A method including, at an autonomous vehicle: autonomously navigating across an outdoor terrain to locate a robotic arm and a set of objects proximal an install location; accessing an image from an optical sensor; detecting a set of install features at the install location based on the image; calculating a gross install pose of the object that locates an object proximal the install location and offset from the set of install features; defining a keep-in boundary proximal the install location and encompassing the gross install pose; autonomously navigating the robotic arm to retrieve the object from the set of objects and to locate the object in the gross install pose; detecting a series of forces applied to a distal end of the robotic arm; and navigating the object in directions of the series of forces while maintaining the object fully within the keep-in boundary.

Abstract: Techniques are described for a machine learning system configured to generate respective sample embeddings for a plurality of sample statements. The machine learning system may further be configured to generate a statement embedding for a statement. The machine learning system may further be configured to determine, based on the sample embedding and the statement embedding, respective similarity scores for the sample embeddings. The machine learning system may further be configured to select, based on the respective similarity scores for the sample embeddings, one or more sample statements from the plurality of sample statements. The machine learning system may further be configured to generate a prompt including the one or more sample statements, the statement, and at least one of respective ground-truth information or respective paraphrases for the selected one or more sample statements. The machine learning system may further be configured to provide the prompt to a machine learning model.

Abstract: Systems and methods that support the creation and timely electronic scheduling and delivery of course materials for training of individuals in an organization, in which course recommendations, scheduling, and rollout are based upon a number of factors including, for example, specific individual and/or business day-to-day operational performance measures, sales performance, and seasonal weather conditions by geographical region.

Abstract: Malformed VLAN packets can be detected by programming suitable rules in a TCAM in the packet processing pipeline. In some deployments, for example, the TCAM rule(s) can match on the parsed EtherType metadata. More specifically, the match can be based on the EtherType metadata being set to a value equal to known VLAN TPIDs, such as 0x8100, 0x88a8, rather than being set to a standard EtherType.