Abstract: Transistor structure including deep source and/or drain semiconductor that is contacted by metallization from both a front (e.g., top) side and a back (e.g., bottom) side of transistor structure. The deep source and/or drain semiconductor may be epitaxial, following crystallinity of a channel region that may be monocrystalline A first layer of the source and/or drain semiconductor may have lower impurity doping while a second layer of the source and/or drain semiconductor may have higher impurity doping. The deep source and/or drain semiconductor may extend below the channel region and be adjacent to a sidewall of a sub-channel region such that metallization in contact with the back side of the transistor structure may pass through a thickness of the first layer of the source and/or drain semiconductor to contact the second layer of the source and/or drain semiconductor.

Abstract: A computer-implemented surgical planning method is provided. The method includes obtaining anatomical data of an anatomical volume and path data including a tool path along which a tool will move. The method also includes obtaining tool data; merging the path data and the anatomical data; and for a point of the tool path, identifying a location of the point, loading a geometry of the tool at the location, identifying an intersection between the tool and the anatomical volume at the location, determining density values of the anatomical data within the intersection, computing a tool contact factor related to the intersection, setting a planned feed rate factor for the tool based on the density values and the tool contact factor, associating the planned feed rate factor with the point; and outputting cut plan data including the planned feed rate factor associated with the point of the tool path.

Abstract: A computing system obtains a document that includes text. The computing system identifies a fact referenced in the text of the document, where the fact includes a fact name and a fact value. The computing system determines a topic of the document based upon the text of the document. The computing system identifies a factoid stored in a data store based upon the topic and the fact name, where the factoid includes the fact name and a second fact value, and further where the factoid has been generated based upon second text of a second document. While the document is being displayed to a user, the computing system causes a message to be displayed to the user, where the message prompts the user to accept or reject replacement of the fact value in the document with the second fact value.

Abstract: An approach for tuning a data management system is provided. A processor retrieves one or more samples pairs of record data. A processor determines an error rate regarding the performance of a matching operation of a data management system based on the one or more sample pairs. In response to the error rate being above a value, a processor adjusts at least one threshold value associated with the matching operation of the data management system.

Abstract: An embedded multiprocessor system is provided that includes a multiprocessor system on a chip (SOC), a memory coupled to the multiprocessor SOC, the memory storing application software partitioned into an initial boot stage and at least one additional boot stage, and a secondary boot loader configured to boot load the initial boot stage on at least one processor of the multiprocessor SOC, wherein the initial boot stage begins executing and flow of data from the initial boot stage to the at least one additional boot stage is disabled, wherein the application software is configured to boot load a second boot stage of the at least one additional boot stage on at least one other processor of the multiprocessor SOC and to enable flow of data between the initial boot stage and the second boot stage.

Abstract: A cross domain generalization system for industrial artificial intelligence (AI) applications is disclosed. A target encoder subsystem obtains target data from a target machine product and generates lower dimensional data for obtained target data using a target artificial intelligence (AI) model. The generated lower dimensional data are corresponding to a plurality of target embeddings data. The target encoder subsystem further applies the plurality of target embeddings data into a source classifier AI model. A source classifier subsystem predicts a quality of the target machine product by generating class labels for each of the plurality of target embeddings data based on a result of the classifier AI model. The goal of the present invention is to learn features or representations such that the correlation with a label space is similar both in source and target domains while being invariant of data distributions.

Abstract: Among other things, equipment is located at an intersection of a transportation network. The equipment includes an input to receive data from a sensor oriented to monitor ground transportation entities at or near the intersection. A wireless communication device sends to a device of one of the ground transportation entities, a warning about a dangerous situation at or near the intersection, there is a processor and a storage for instructions executable by the processor to perform actions including the following. A machine learning model is stored that can predict behavior of ground transportation entities at or near the intersection at a current time. The machine learning model is based on training data about previous motion and related behavior of ground transportation entities at or near the intersection.

Abstract: The present disclosure provides systems and methods for synthesizing computer-readable code based on the receipt of input and output examples. A computing system in accordance with the disclosure can be configured to receive a given input and output, access and library of operations, and perform a search of a library of operations (e.g., transpose, slice, norm, etc.) that can be applied to the input. By applying the operations to the input and tracking the results, the computing system may identify an expression comprising one or a combination of operations that when applied to the input generates the output. In this manner, implementations of the disclosure may be used to identify one or more solutions that a user having access to the library of operations may use to generate the output from the input.

Abstract: A customer order prediction system can include a computing device configured to obtain customer order data characterizing a customer’s interaction with an electronic marketplace and to obtain environmental data characterizing at least one event expected to effect the customer’s ordering behavior. The computing device can be further configured to generate feature data based on the customer data and the environmental data wherein the feature data organizes the customer data and the environmental data based on characteristics of an order size estimation model. The computing device is also configured to determine a predicted customer order size using the order size estimation model and to adjust a delivery availability of the electronic marketplace based on the predicted order size.

Abstract: Embodiments disclosed herein include transistor devices and methods of forming such transistor devices. In an embodiment a transistor comprises a substrate, and a fin that extends up from the substrate. In an embodiment, the fin comprises a source region, a drain region, and a channel region between the source region and the drain region. In an embodiment, the transistor further comprises and a cavity in the fin, where the cavity is below the channel region. In an embodiment, the transistor further comprises a gate stack over the fin.

Abstract: A loyalty delivered sales estimation system uses a database with customer and retail store data including customer transactional and demographics data together with store retail and promotional data. The system estimates loyalty sales information related to one or more loyalty programs implemented by the retail store. A loyalty delivered sales (LDS) estimation module analyzes the customer and store data to identify at least two statistically similar customer groups: a first group with customers that have redeemed benefits of a loyalty programs and a second group with customers that are not enrolled with the loyalty program. The LDS estimation module computes a divergence value between the first and second customer groups over a period of time and estimates a loyalty delivered sales metrics corresponding to the one or more loyalty programs using the at least one computed divergence value.

Abstract: In one embodiment, a system for managing a virtualization environment comprises a plurality of host machines, one or more virtual disks comprising a plurality of storage devices, a virtualized file server (VFS) comprising a plurality of file server virtual machines (FSVMs), wherein each of the FSVMs is running on one of the host machines and conducts I/O transactions with the one or more virtual disks, and a virtualized file server self-healing system configured to identify one or more corrupt units of stored data at one or more levels of a storage hierarchy associated with the storage devices, wherein the levels comprise one or more of file level, filesystem level, and storage level, and when data corruption is detected, cause each FSVM on which at least a portion of the unit of stored data is located to recover the unit of stored data.

Abstract: One or more associated identifiers are determined based on one or more associated tag types of an interactable element of web content. The determined one or more associated identifiers are associated with the interactable element. Based on the association of the determined one or more associated identifiers with the interactable element, one of the interactable element is matched to a received speech input. An action is performed with respect to the interactable element based on the matching.

Abstract: A serverless computing system is configured to provide access to a machine learning model by at least associating an endpoint, comprising code that accesses the machine learning model, with an extension that interfaces between a serverless compute architecture and the endpoint. A request to perform an inference is received by the system and processed by using the serverless compute architecture to execute a compute function. The compute function cases the extension to interface with the endpoint to cause the machine learning model to perform the inference.

Abstract: A system is configured to provide access to a machine learning model by using a hybrid configuration comprising a dedicate server on which an instance of a model server is installed, and a serverless compute architecture that interfaces with an instance of the model server using an extension. A first portion of requests directed to the model server are processed by the dedicated server, and a second portion of the requests is processed by the serverless compute architecture.

Abstract: Embodiments disclosed herein include a semiconductor device. In an embodiment, the semiconductor device comprises a first transistor strata. The first transistor strata comprises a first backbone, a first transistor adjacent to a first edge of the first backbone, and a second transistor adjacent to a second edge of the first backbone. In an embodiment, the semiconductor device further comprises a second transistor strata over the first transistor strata. The second transistor strata comprises a second backbone, a third transistor adjacent to a first edge of the second backbone, and a fourth transistor adjacent to a second edge of the second backbone.

Abstract: Embodiments herein describe techniques for an integrated circuit (IC). The IC may include a lower device layer that includes a first transistor structure, an upper device layer above the lower device layer including a second transistor structure, and an isolation wall that extends between the upper device layer and the lower device layer. The isolation wall may be in contact with an edge of a first gate structure of the first transistor structure and an edge of a second gate structure of the second transistor structure, and may have a first width to the edge of the first gate structure at the lower device layer, and a second width to the edge of the second gate structure at the upper device layer. The first width may be different from the second width. Other embodiments may be described and/or claimed.

Abstract: Metallization structures under a semiconductor device layer. A metallization structure in alignment with semiconductor fin may be on a side of the fin opposite a gate stack. Backside and/or frontside substrate processing techniques may be employed to form such metallization structures on a bottom of a semiconductor fin or between bottom portions of two adjacent fins. Such metallization structures may accompany interconnect metallization layers that are over a gate stack, for example to increase metallization layer density for a given number of semiconductor device layers.

Abstract: Implementations are described herein for using machine learning to perform various tasks related to migrating source code based on relatively few (“few shots”) demonstrations. In various implementations, an autoregressive language model may be conditioned based on demonstration tuple(s). In some implementations, a demonstration tuple may include a pre-migration version of a first source code snippet and a post-migration version of the first source code snippet. In other implementations, demonstration tuples may include other data, such as intermediate forms (e.g., natural language descriptions or pseudocode), input-output pairs demonstrating intended behavior, etc. The autoregressive language model may be trained on corpora of source code and natural language documentation on the subject of computer programming.

Abstract: An apparatus is provided which comprises: a fin; a layer formed on the fin, the layer dividing the fin in a first section and a second section; a first device formed on the first section of the fin; and a second device formed on the second section of the fin.