Abstract: An air channel is utilized to ventilate a door lock for the purpose of reducing the condensation of warm, humid, air within the lock. The air channel is configured to be located between a surface mounted door lock and the door panel and vent the space between the interior and exterior locks, including the door bore. In some embodiments, the air channel is formed into the mounting bracket for the interior door lock. The air channel may be implemented to reduce condensation without requiring seals or gaskets between the interior and exterior locks.

Abstract: In one example, a system comprises a neural network array of non-volatile memory cells arranged in rows and columns; and a logical cell comprising a first plurality of non-volatile memory cells in a first row of the array and a second plurality of non-volatile memory cells in a second row adjacent to the first row; wherein the first plurality of non-volatile memory cells and the second plurality of non-volatile memory cells are configured as one or more coarse cells and one or more fine cells.

Abstract: Described herein are battery modules and methods of fabricating thereof. In some examples, a battery module comprises an enclosure, separated into two enclosure portions and a thermal portion, positioned between the two enclosure portions. Two enclosure portions are in part defined by side walls, which can be tapered. The thermal portion comprises two thermal walls, which are operable as the bottoms of the two enclosure portions and form a thermal cavity between these thermal walls. In some examples, the enclosure is a monolithically cast component. Alternatively, the enclosure can be partially cast with one thermal wall welded thereafter to a cast subassembly. The battery module also comprises two sets of batteries, each positioned into a corresponding enclosure portion. Each battery set is interconnected with an interconnecting assembly, positioned between the battery set and the corresponding cover, for this enclosure portion.

Abstract: A sensor module (8) for detecting a mechanical vibration of a component (4). The sensor module (8) includes a base (12) having a central portion (24), a first end portion (26) and a second end portion (28). The base (12) secures to the component (4). At least one spacer (14) is connected to the base (12). A circuit board (18) is connected to the base (12) by the at least one spacer (14). The central portion (24) of the base (12) is provided with a concave edge (52) and with an opposite convex edge (54) defining the width of the central portion (24). The at least one spacer (14) is connected to the central portion (24) of the base (12) closer to the concave edge (52) than the convex edge (54).

Abstract: Spatial regions potentially occupied by a robot (or other machinery) or portion thereof and a human operator during performance of all or a defined portion of a task or an application are computationally estimated. These “potential occupancy envelopes” (POEs) may be based on the states (e.g., the current and expected positions, velocities, accelerations, geometry and/or kinematics) of the robot and the human operator. Once the POEs of human operators in the workspace are established, they can be used to guide or revise motion planning for task execution.

Abstract: Disclosed herein are pyrazole pyrimidine compounds that modulate and/or inhibit hematopoietic progenitor kinase 1, as well as methods of making such compounds and therapeutic methods of using same.

Abstract: A method, apparatus and computer program product for scheduling placement of containers in association with a set of hosts. The technique utilizes metrics that characterize container-specific risks. A first metric is a host interface risk for a container that quantifies how similar or dissimilar the container is relative to other containers running on a host. Preferably, host interface risk is derived with respect to a system call interface comprising a set of system calls, and the metric is based at least in part on a measure of dissimilarity among system calls. A second metric is a data sensitivity score that quantifies a degree to which sensitive data accesses are associated to the container. Based at least in part on the host interface risk scores and the data sensitivity scores, one or more containers are automatically scheduled for placement on the set of hosts to minimize security risk for the set of hosts.

Abstract: Systems, methods, and computer-readable media for annotating process and user information for network flows. In some embodiments, a capturing agent, executing on a first device in a network, can monitor a network flow associated with the first device. The first device can be, for example, a virtual machine, a hypervisor, a server, or a network device. Next, the capturing agent can generate a control flow based on the network flow. The control flow may include metadata that describes the network flow. The capturing agent can then determine which process executing on the first device is associated with the network flow and label the control flow with this information. Finally, the capturing agent can transmit the labeled control flow to a second device, such as a collector, in the network.

Abstract: Control logic in a memory device causes a pass voltage to be applied to a plurality of wordlines of a block of a memory array of the memory device, the block comprising a plurality of sub-blocks, and the pass voltage to boost a channel potential of each of the plurality of sub-blocks to a boost voltage. The control logic further selectively discharges the boost voltage from one or more of the plurality of sub-blocks according to a data pattern representing a sequence of bits to be programmed to respective memory cells of the plurality of sub-blocks. In addition, the control logic causes a single programming pulse to be applied to a selected wordline of the plurality of wordlines of the block to program the respective memory cells of the plurality of sub-blocks according to the data pattern.

Abstract: A ring-shaped gasket for making high-purity fluid pathway connections between opposing fluid delivery apparatus elements having at least one simple flat surface in contact with the gasket. The face of at least one apparatus element typically has a circular counterbore depression to receive the gasket, but is not required. The gasket has a body, pierced through by a hole creating a fluid pathway and defining a radial inner surface, and additionally having a radial outer surface, a first axial end surface and a second axial end surface. At least one of the first and second axial end surfaces has a stress concentration feature radially adjacent to a gasket sealing region, the sealing region constructed to be in contact with a face surface of a corresponding fluid conduit port. The stress concentration feature may be a groove or a plurality of cavities disposed adjacent the gasket axial end surface sealing region.

Abstract: A method to detect, identify and determine the position of laser points from infrared laser emitters in a firearm training simulation system. The method consists of two steps. This first step combines some common tools such as a camera with a filter switcher and edge and corner detecting algorithms in image processing to provide a fast automatic screen calibrating method. The second step is to detect laser point objects in each frame continuously captured by the camera, and then further process these objects to identify laser shots, filter out noisy objects, and eventually detect simulated shots from different laser emitters and determine the final mapped position of the shot.

Abstract: A method includes performing data processing operations using multiple functional modules. Each functional module is configured to perform one or more data processing operations in order to process input data and generate output data. The method also includes, for each functional module, generating a confidence measure associated with the output data generated by the functional module. At least two of the functional modules are configured to operate logically sequentially such that (i) a first of the functional modules provides the output data generated by the first functional module to a second of the functional modules and (ii) the confidence measure associated with the output data generated by the second functional module is based at least partially on the confidence measure associated with the output data generated by the first functional module. The multiple functional modules include heterogeneous functional modules configured to generate different types of output data.

Abstract: Numerous examples for performing tuning of a page or a word of non-volatile memory cells in an analog neural memory are disclosed. In one example, an analog neural memory system comprises an array of non-volatile memory cells arranged into rows and columns, each non-volatile memory cell comprising a word line terminal, a bit line terminal, and an erase gate terminal; a plurality of word lines, each word line coupled to word line terminals of a row of non-volatile memory cells; a plurality of bit lines, each bit line coupled to bit line terminals of a column of non-volatile memory cells; and a plurality of erase gate enable transistors, each erase gate enable transistor coupled to erase gate terminals of a word of non-volatile memory cells.

Abstract: An exemplary system, method, and computer-accessible medium for providing a financial account recommendation to person(s) can include receiving a transaction history for the person(s) associated with a first financial account, determining first rewards information based on the transaction history, accessing database(s) containing rewards rates for a plurality of second financial accounts, determining second rewards information for a particular second financial account of the plurality of second financial accounts based on the transaction history, and providing the financial account recommendation to the person(s) to one of (i) keep the first financial account or (ii) switch to the second financial account based on a comparison of the first rewards information to the second rewards information.

Abstract: Techniques are disclosed herein for using named entity recognition to resolve entity expression while transforming natural language to a meaning representation language. In one aspect, a method includes accessing natural language text, predicting, by a first machine learning model, a class label for a token in the natural language text, predicting, by a second machine-learning model, operators for a meaning representation language and a value or value span for each attribute of the operators, in response to determining that the value or value span for a particular attribute matches the class label, converting a portion of the natural language text for the value or value span into a resolved format, and outputting syntax for the meaning representation language. The syntax comprises the operators with the portion of the natural language text for the value or value span in the resolved format.

Abstract: Techniques are disclosed herein for adaptive training data augmentation to facilitate training named entity recognition (NER) models. Adaptive augmentation techniques are disclosed herein that take into consideration the distribution of different entity types within training data. The adaptive augmentation techniques generate adaptive numbers of augmented examples (e.g., utterances) based on the distribution of entities to make sure enough numbers of examples for minority class entities are generated during augmentation of the training data.

Abstract: Techniques are disclosed for augmenting training data for training a machine learning model to generate database queries. Training data comprising a first training example comprising a first natural language utterance, a logical form for the first natural language utterance, and associated first metadata is obtained. From the first training example, a template utterance is generated. A second natural language utterance is generated by filling slots in the template utterance based on a database schema and database values. Updated metadata is produced based on the first metadata and the second natural language utterance. A second training example is generated, comprising the second natural language utterance, the logical form for the first natural language utterance, and the updated metadata. The training data is augmented by adding the second training example. A machine learning model is trained to generate a database query comprising the database operation using the augmented training data set.

Abstract: Provided are compounds of Formula (I) having activity as inhibitors of G12C mutant KRAS protein, pharmaceutical compositions comprising the compounds, and uses and methods of treating certain disorders, such as cancer, including but not limited to lung, pancreatic and colorectal cancers.

Abstract: Techniques are disclosed herein for calibrating confidence scores of a machine learning model trained to translate natural language to a meaning representation language. The techniques include obtaining one or more raw beam scores generated from one or more beam levels of a decoder of a machine learning model trained to translate natural language to a logical form, where each of the one or more raw beam scores is a conditional probability of a sub-tree determined by a heuristic search algorithm of the decoder at one of the one or more beam levels, classifying, by a calibration model, a logical form output by the machine learning model as correct or incorrect based on the one or more raw beam scores, and providing the logical form with a confidence score that is determined based on the classifying of the logical form.

Abstract: Techniques are disclosed herein for addressing catastrophic forgetting and over-generalization while training a model to transform natural language to a logical form such as a meaning representation language. The techniques include accessing training data comprising natural language examples, augmenting the training data to generate expanded training data, training a machine learning model on the expanded training data, and providing the trained machine learning model. The augmenting includes (i) generating contrastive examples by revising natural language of examples identified to have caused regression during training of a machine learning model with the training data, (ii) generating alternative examples by modifying operators of examples identified within the training data that belong to a concept that exhibits bias, or (iii) a combination of (i) and (ii).