Abstract: A computer-implemented method comprises generating an event from a task demonstration within a digital channel, forming a collaboration context based on the event, defining a collaboration by associating an assistive action with the collaboration context, transforming the collaboration context into a conversational model, evaluating an interaction session within the digital channel for an interaction that matches the collaboration context, and upon detection of the interaction that matches the collaboration context, providing the assistive action corresponding to collaboration context.

Abstract: Various embodiments are provided herein for performing a mathematical calculation in a computing environment. A quantization scheme is implemented, allowing at most one (1) non-zero-valued bit in the mantissa of a floating point number.

Abstract: The illustrative embodiments provide for signature discourse transformation. An embodiment includes detecting a signature discourse of a user by analyzing the user's discourse using corpus linguistics. The embodiment also includes generating computing a collection of words distinct to the user. The embodiment also includes sensing, using tone analysis and a natural language process model, a tone of the collection of words distinct to the user in the signature discourse utterance. The embodiment also includes mapping a cartesian value of the collection of words distinct to the user. The embodiment also includes deriving a signature discourse transformation (SDT) model from the signature discourse utterances, the collection of words, the tone, and the cartesian value. The embodiment also includes transforming, using the signature discourse transformation, an original discourse by the user into an anonymized text.

Abstract: An embodiment configures a software robot to operate collaboratively with a plurality of platforms within a work environment. The embodiment decomposes, via the software robot, a request to perform a task received from one of the platforms, into at least one integration-action pair. An integration in this pair represents a configuration to operate on an execution platform within the plurality of platforms where a function is to be executed; an action in the pair represents the function to be performed on the execution platform. Responsive to the determination that the at least one integration-action pair does not exist in a database of integration-action pairs, the embodiment trains the software robot to perform the function on the platform.

Abstract: An embodiment includes parsing an input dataset associated with a first node of a decision tree, where the input dataset includes a set of profile values for a set of projected usage parameters for a computing environment. The embodiment identifies a structure of the dataset using a recursive neural network that predicts a question sequence in a hierarchical tree format. The embodiment calculates a first deviation from the predicted question sequence and determines whether the deviation exceeds a threshold value. The embodiment generates a modified input dataset using a disambiguation rule and calculates a second deviation of the modified structure from the predicted question sequence and determines whether the deviation exceeds the threshold value. The embodiment assembles a customized hierarchical path using a generative model and assembles the customized hierarchical path by performing iterations of generating a series of candidate questions until a leaf node is reached.

Abstract: A method includes: receiving, by a computing device, user input to move content from an augmented reality (AR) interface to a virtual reality (VR) interface; obtaining, by the computing device, security levels of users in a VR environment associated with the VR interface; determining, by the computing device and based on the security levels, which of the users in the VR environment is permitted to see the content; and changing, by the computing device, at least one of the AR interface to the VR interface based on the determining.

Abstract: A base substrate, high-k substrate layers on the base substrate with discrete decoupling capacitors embedded, high density substrate layers on the high-k substrate layers supporting wiring and wiring spacing of less than 2 up to about 10 micron width, pitch connectivity between the upper surface of the base substrate and a lower surface of the set of high density substrate layers supports less than 50 up to about 300 micron pitch, the pitch connectivity on an upper surface of the set of high density substrate layers supports less than about 150 micron pitch. A method including attaching a set of metal posts at each contact on a lower surface of a set of high density substrate layers, attaching to a handler, attaching an interconnect layer to a base substrate, and attaching the set of high density substrate layers to the base substrate while aligning each metal post with a contact.

Abstract: A computer hardware system includes a full self-driving (FSD) assistive system for assisting a user with a trip using a FSD autonomous vehicle and hardware processing configured to initiate the following executable operations. Based upon a first user input to a computing device associated with the user, a proposed route of the trip is received. A previously-stored user profile of the user and a context of the trip is retrieved. The vehicle is searched for based upon the user profile, the proposed route, and the context. The vehicle is configured using the user profile. External sensors are polled for road conditions along the route, and the road conditions are forwarded to the vehicle. An onboard analytics device of the vehicle uses the road conditions to change a driving mode of the vehicle.

Abstract: Techniques of facilitating deep learning model rescaling by computing devices. In one example, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise: a rescaling component; and a forecasting component. The rescaling component can determine a scaling ratio that maps low mesh resolution predictive data output by a partial differential equation (PDE)-based model for a sub-domain to high-resolution observational or ground-truth data for a domain comprising the sub-domain. The forecasting component can generate high mesh resolution predictive data for the domain with a machine-learning model using input data of the PDE-based model and the scaling ratio.

Abstract: Making a rechargeable Lithium energy storage device begins by forming one or more trenches in a solid silicon substrate. One or more region interface precursors are deposited in the trench followed by one or more anode materials, one or more solid polymer electrolytes (SPE), and one or more cathode materials. Electrically cycling transforms the battery structures prior to full operation of the battery. Some, or all, of the process steps can be performed while the materials are within the trench, i.e. in-situ.

Abstract: Techniques for noise and bound management for DNN training on RPU crossbar arrays using a scaling factor based on a worst-case scenario are provided. In one aspect, a method for noise and bound management includes: obtaining input vector values x for an analog crossbar array of RPU devices, wherein a weight matrix is mapped to the analog crossbar array of RPU devices; and scaling the input vector values x based on a worst-case scenario to provide scaled input vector values x? to use as input to the analog crossbar array of RPU devices, wherein the worst-case scenario includes an assumed maximal weight of the weight matrix multiplied by a sum of absolute values from the input vector values x.

Abstract: Described is a method for managing a trackable solar panel assembly and a variable reflective surface. The method can receive solar farm characteristics and location characteristics for the solar farm. The method can also receive solar panel assembly characteristics for a solar panel assembly and variable reflective surface characteristics for a variable reflective surface. The method can optimize control parameters for the solar panel assembly and the variable reflective surface, based on the solar farm characteristics, the location characteristics, the solar panel assembly characteristics, and the variable reflective surface characteristics. The method can adjust the variable reflective surface based on the control parameters.

Abstract: In a first device, a local classification and a local classification confidence score corresponding to an event input are computed. At the first device in response to a broadcast request, a remote classification and a remote classification confidence score corresponding to the event input are received, the remote classification and the remote classification confidence score being computed at a second device. At the first device, a consensus classification including the most frequent classification from a set of all received remote classifications and the local classification is formed, provided the number of classifications including the most frequent classification exceeds a threshold. In response to a consensus classification confidence score corresponding to the consensus classification exceeding a confidence threshold, a local classification model is updated. Based on the local classification and the consensus classification, the event input is assigned to a classification.

Abstract: A computer-implemented method comprises establishing a database based at least in part on network traffic data received from a network, detecting adversarial behavior within the network traffic data, identifying an adversary associated with the adversarial behavior, determining a plurality of specific indicators of compromise that are associated with the adversary that has been identified, constructing a query based on the plurality of specific indicators of compromise, submitting the query to search for the plurality of specific indicators of compromise within the network traffic data, searching for the plurality of specific indicators of compromise within the network traffic data, and generating, responsive to having located at least one potential indicator of compromise, a search report containing at least one specific indicator of compromise and displaying the search report to a user interface.

Abstract: A technique for privacy-preserving homomorphic inferencing using a neural network having an activation function, such as a non-linear high-degree polynomial. The network is trained to learn input features of an input feature vector together with their associated inverses. During inferencing, an encrypted data point is received at the network. The data point comprises an input feature vector that has been extended with a set of one or more additional feature values, the set of one or more additional feature values having been generated by applying a normalized inverse function to respective one or more features in the feature vector. Homomorphic inferencing is performed on the encrypted data point using the machine learning model to generate an encrypted result, which is then returned. By applying the normalized inverse function, the high-degree polynomial can use any value of an input feature during inferencing, whether the value is within or outside of a particular input range.

Abstract: A functionality intent is extracted from a natural language input, the functionality intent comprising an operation on a dataset. A portion of source code implementing the functionality intent is generated. Using a result of executing an executable version of the portion of source code on the dataset, a next functionality intent is recommended, the next functionality intent expressed in natural language form.

Abstract: In an approach to improve internet protocol (IP) classification, embodiments of the present invention classify, by an IP classifier, a set of samples from a given IP range. Further, embodiments utilize the IP classifier to predict whether an IP address is dynamic or static and apply, by a client computer, an error-correction tree to the set of samples. Additionally, embodiments split, by the client computer, the range into two or more sub-ranges of a smaller size in response to determining a classification certainty does not exceed or is not within a predetermined threshold.

Abstract: Nuclear Magnetic Resonant Imaging (also called Magnetic Resonant Imaging or “MRI”) devices which are implantable, internal or insertable are provided. The disclosure describes ways to miniaturize, simplify, calibrate, cool, and increase the utility of MRI systems for structural investigative purposes, and for biological investigation and potential treatment. It teaches use of target objects of fixed size, shape and position for calibration and comparison to obtain accurate images. It further teaches cooling of objects under test by electrically conductive leads or electrically isolated leads; varying the magnetic field of the probe to move chemicals or ferrous metallic objects within the subject. The invention also teaches comparison of objects using review of the frequency components of a received signal rather than by a pictorial representation.

Abstract: In response to encountering a function entry instruction while running an application, a processor computes an entry hash value using a hash of three hash input parameters, wherein one of the input parameters is a secret key stored in the special purpose register. In response to encountering a function exit instruction, the processor computes an exit hash value using the same three input parameters and the same hash used when computing the entry hash value. The processor determines if the entry hash value is the same as the exit hash value. In response to the entry hash value being the same as the exit hash value the processor exits the function and in response to the entry hash value not being the same as the exit hash value the processor executes a trap interrupt.

Abstract: One or more systems, computer-implemented methods and/or computer program products to facilitate a process to monitor and/or facilitate a modification to a manufacturing process. A system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an initialization component that identifies values of inflow data of one or more inflows of a set of inflows to a manufacturing process as control variables, and a computation optimization component that optimizes one or more intermediate flows, outflows or flow qualities of the manufacturing process using, for mode-specific regression models, decision variables that are based on a set of joint-levels of the control variables. An operation mode determination component can determine operation modes of the manufacturing process that are together defined by a set of joint-levels of the control variables.

Abstract: In an approach for transforming workflows based on goals and constraints, a processor receives a workflow definition and one or more events for a workflow output by a workflow orchestrator, wherein the one or more events are at least one of a parsing event and a scheduling event. A processor determines, using one or more transformers, one or more transformations to the workflow based on a set of goals and constraints, wherein the one or more transformations is at least one of addition of a new node, addition of a new graph, modification of at least one existing node, modification of at least one existing graph, removal of at least one existing node, and removal of at least one existing graph. A processor dynamically applies, using one or more enforcers, the one or more transformations to the workflow based on the set of goals and constraints.

Abstract: A compatibility is ascertained between a configuration of a quantum processor (q-processor) of a quantum cloud compute node (QCCN) in a quantum cloud environment (QCE) and an operation requested in a first instruction in a portion (q-portion) of a job submitted to the QCE, the QCE including the QCCN and a conventional compute node (CCN), the CCN including a conventional processor configured for binary computations. In response to the ascertaining, a quantum instruction (q-instruction) is constructed corresponding to the first instruction. The q-instruction is executed using the q-processor of the QCCN to produce a quantum output signal (q-signal). The q-signal is transformed into a corresponding quantum computing result (q-result). A final result is returned to a submitting system that submitted the job, wherein the final result comprises the q-result.

Abstract: A machine learning system that uses a split net configuration to incorporate arbitrary constraints receives a set of input data and a set of functional constraints. The machine learning system jointly optimizes a deep learning model by using the set of input data and a wide learning model by using the set of constraints. The deep learning model includes an input layer, an output layer, and an intermediate layer between the input layer and the output layer. The wide learning model includes an input layer and an output layer but no intermediate layer. The machine learning system provides a machine learning model comprising the optimized deep learning model and the optimized wide learning model.

Abstract: A computer-implemented method for monitoring and auditing secure software builds is disclosed. The computer-implemented includes receiving a build file containing a sequence of build instructions. The computer-implemented method further includes, for each build instruction being executed: determining an indicator value corresponding to a monitoring level associated with a build instruction; and modifying logging of trace information associated with a computing system executing the build instruction based, at least in part, on the indicator value corresponding to the monitoring level.

Abstract: A computer-implemented method for remote management of hardware security modules (HSMs) includes receiving a command request from a mobile device. The command request includes an encrypted key part and an encrypted signing key. The HSM decrypts the command request using a key associated with a security zone of the mobile device. The HSM decrypts the encrypted key part and the encrypted signing key. Decrypting the encrypted key part and the encrypted signing key includes using the key associated with the security zone of the mobile device and a key associated with a remote administrator associated with the mobile device. A command is generated for a domain with a target HSM. The command is generated using the decrypted key part and the decrypted signing key. The command is transmitted to the domain for execution by the target HSM. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Techniques regarding generating and/or training one or more symbolic models are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a training component that can train a symbolic model via active machine learning. The symbolic model can characterize a formal planning language for a planning domain as a plurality of digital image sequences.

Abstract: An example operation may include one or more of receiving a request for storage at a blockchain network, attaching, via a blockchain node, a verifiable credential created by a self-sovereign identity (SSI) network to a blockchain transaction associated with the request, where the verifiable credential includes a claim of the blockchain node and a proof of the SSI network that created the verifiable credential, transmitting the blockchain transaction and the attached verifiable credential to one or more other blockchain nodes, and storing the blockchain transaction and the attached verifiable credential via a data block on the blockchain.

Abstract: A method and computer program product for obtaining values are run using a neural network according to a machine learning algorithm. One embodiment may comprise accessing one or more datafiles of input data, where the input data is representable in a d-dimensional space, with d>1. The method may explore N distinct paths of the input data in the d-dimensional space, where N?1, and collects data along the N distinct paths explored to respectively form N sequences of M objects each, with M?2. For one or more sequences of the N sequences formed, values obtained from the M objects of each sequence may be coupled into one or more input nodes of a neural network, which is then run according to the machine learning algorithm to obtain L output values from, L?1.

Abstract: Memory structures including an MTJ-containing pillar that is void of re-sputtered bottom electrode metal particles is provided by first forming the MTJ-containing pillar on a sacrificial material-containing structure, and thereafter replacing the sacrificial material-containing structure with at least a replacement bottom electrode structure. In some embodiments, the sacrificial material-containing structure is replaced with both a bottom electrode diffusion barrier liner and a replacement bottom electrode structure.

Abstract: An electronic circuit includes word lines; bit lines intersecting the word lines at a plurality of grid points; and resistive processing units located at the grid points. Baseline stochastic pulse input units are coupled to the word lines; differential stochastic pulse input units are coupled to the word lines; and bitline stochastic pulse input units are coupled to the bit lines. Control circuitry coupled to the pulse input units is configured to cause each of the baseline stochastic pulse input units to generate a baseline pulse train using base input data, each of the differential stochastic pulse input units to generate a differential pulse train using differential input data defining differences from the base input data, and each of the bitline stochastic pulse input units to generate a bitline pulse train using bit line input data. Neural network weights can thus be stored in the resistive processing units.

Abstract: An apparatus for injecting solder material in via holes located in a top surface of a wafer is provided. The apparatus includes an injection head having a contact surface for contacting the top surface of the wafer, and at least one aperture for injecting the solder material though the injection head into the via holes. The apparatus further includes an evacuating device connected to the injection head for evacuating gas from the via holes. The injection head has a chamfer part on an edge of a contact surface contacting the top surface of the wafer.

Abstract: Networks for distributing parameters and data to neural network compute cores. In various embodiments, a neural inference chip comprises a plurality of neural cores and at least one network interconnecting the plurality of neural cores. Each of the plurality of neural cores is adapted to apply a plurality of synaptic weights to a plurality of input activations to produce a plurality of output activations. The at least one network is adapted to simultaneously deliver synaptic weights and/or input activations to the plurality of neural cores.

Abstract: A memory device includes a semiconductor device on a wafer. The semiconductor device includes a gate structure, a first source/drain region, and a second source/drain region. The gate structure is on the first side of the wafer. The first source/drain region is also on the first side of the wafer, and contacts a first end of the gate structure. The second source/drain region is on the second side of the wafer and extends into the first side to contact a second end of the gate structure. The memory device further includes a memory storage element on the second side of the wafer. The memory storage element contacts the second source/drain region.

Abstract: A semiconductor device including a substrate; a continuous buried oxide layer (BOX) formed on the substrate; and a plurality of nanosheet gate-all-round (GAA) device structures on the BOX, wherein a first plurality of stacked gates of the nanosheet GAA device structures are disposed in a logic portion of the substrate and have a first nanosheet width, wherein a second plurality of stacked gates of the nanosheet GAA device structures are disposed in a high density region of the substrate and have a second nanosheet width less than the first nanosheet width, wherein the nanosheet GAA device structures are disposed directly on the continuous buried oxide layer, and wherein a bottom layer of the nanosheet GAA device structures is a bottom gate formed directly on the BOX.

Abstract: A method, a system, and a non-transitory computer readable medium for measuring a local critical dimension uniformity of an array of two-dimensional structural elements, the method may include obtaining an acquired optical spectrometry spectrum of the array; feeding the acquired optical spectrometry spectrum of the array to a trained machine learning process, wherein the trained machine learning process is trained to map an optical spectrometry spectrum to an average critical dimension (CD) and a local critical dimension uniformity (LCDU); and outputting, by the trained machine learning process, the average CD and the LCDU of the array.

Abstract: Techniques that facilitate machine learning using multi-dimensional time series data are provided. In one example, a system includes a snapshot component and a machine learning component. The snapshot component generates a first sequence of multi-dimensional time series data and a second sequence of multi-dimensional time series data from multi-dimensional time series data associated with at least two different data types generated by a data system over a consecutive period of time. The machine learning component that analyzes the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data using a convolutional neural network system to predict an event associated with the multi-dimensional time series data.

Abstract: A plurality of web pages in a web browser are presented to a user. One or more web pages of the plurality of web pages are navigated in the web browser by following at least one hyperlink. A browser history of the user is stored as a chronological list or recently presented web pages. A determination is made whether a dwell time of the user on a first web page has exceeded a threshold time. In response to determining that the dwell time exceeded the threshold, an of-user-interest tag is added to the first web page stored to the browsing history.

Abstract: Provided are a computer program product, device, system, and method for write data management. In one embodiment, a write management controller calculates an order for writing data and parity blocks of write data code words on tape media in a variable interleaved fashion which ensures a minimum physical longitudinal separation along the length of tape between data and parity blocks of the same code word. The calculated write order information may be provided back to a host or other data source so that the write data is eventually transmitted to the tape drive for writing on tape media in accordance with the calculated write order. In this manner, a minimum physical longitudinal separation along the length of tape between data and parity blocks of the same code word may be achieved to increase the reliability of the data storage.

Abstract: A field effect transistor (FET) includes a substrate; a channel material located on the substrate, the channel material comprising one of graphene or a nanostructure; a gate located on a first portion of the channel material; and a contact aligned to the gate, the contact comprising one of a metal silicide, a metal carbide, and a metal, the contact being located over a source region and a drain region of the FET, the source region and the drain region comprising a second portion of the channel material.

Abstract: Techniques regarding an embedded microstrip transmission line implemented in one more superconducting microwave electronic devices are provided. For example, one or more embodiments described herein can comprise an apparatus, which can include a superconducting material layer positioned on a raised portion of a dielectric substrate. The raised portion can extend from a surface of the dielectric substrate. The apparatus can also comprise a dielectric film that covers at least a portion of the superconducting material layer and the raised portion of the dielectric substrate.

Abstract: From an input image comprising a portion of text, the portion of text is extracted. The portion of text comprises text in a first natural language. The input image is classified into a candidate scene within a set of predefined scenes. The candidate scene is scored according to a quality measure. Using a predefined scene parameter of the scored candidate scene, the portion of text is translated. In the input image, the portion of text is replaced. with the translated portion of text.

Abstract: Using a failure probability model, a probability of a failure in a first system within a specified time period is predicted. Using an impact model, an impact of the failure on a second system is predicted. Using a resilience model, an impact reduction of the failure is predicted. The probability, the impact, and the impact reduction are mapped into a recommended service level agreement category for the first system, the mapping performed using a classifier model.

Abstract: A cold plate apparatus that has an outlet plenum leading to an outlet opening includes an outlet transition that connects the outlet opening to the outlet plenum. The outlet transition defines a smoothly curving flow path from a direction along a long dimension of the outlet plenum, which is parallel to a plane defined by the outlet opening, to a direction along a centerline of the outlet opening, which is at an angle from the plane defined by the outlet opening. The outlet transition provides a smooth variation of cross-sectional area from the outlet plenum to the outlet opening.

Abstract: A method, a computer program product, and a computer system render a plurality of applications and tabs thereof on a plurality of displays. The method includes determining the applications and the tabs thereof that are open across the displays and keywords associated with the tabs. The method includes generating a data structure corresponding to a current view across the displays comprising information associated with the displays, the applications, the tabs, and summarized contents based on the keywords. The method includes monitoring a user operation context provided by a user indicative of requested information in a selected tab among the tabs. The method includes updating the data structure with the user operation context indicative of the selected tab, a corresponding application, and a corresponding display. The method includes rendering an updated view across the displays in which the selected tab is positioned to be viewed by the user.

Abstract: Techniques for managing and compressing quantum output data (QOD) associated with quantum computing are presented. In response to receiving QOD from a quantum computer, a compressor component can compress QOD at first compression level to generate first compressed QOD, and can compress QOD at second compression level to generate second compressed QOD, the second compressed QOD can be less compressed than the first compressed QOD. Compressor management component (CMC) can determine whether first QOD includes sufficient data to enable it to be suitably processed by quantum logic. If so, CMC can allow first compressed QOD to continue to be sent to quantum logic and can discard second compressed QOD. If not sufficient, CMC can determine that second compressed QOD is to be processed by quantum logic. If CMC determines second compressed QOD does not include sufficient data, CMC can determine that the QOD is to be processed by quantum logic.

Abstract: A method, computer system, and a computer program product for optimizing social collaboration is provided. The present invention may include identifying content within a social collaboration history with a high level of engagement. The present invention may include generating one or more groups of unique topic keywords based on the identified content. The present invention may include receiving a potential post from a user. The present invention may include identifying an overlap between the potential post and at least one of the one or more groups of unique topic keywords. The present invention may include providing one or more recommendations to the user.

Abstract: Techniques and a system to facilitate estimation of a quantum phase, and more specifically, to facilitate estimation of an expectation value of a quantum state, by utilizing a hybrid of quantum and classical methods are provided. In one example, a system is provided. The system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include an encoding component and a learning component. The encoding component can encode an expectation value associated with a quantum state. The learning component can utilize stochastic inference to determine the expectation value based on an uncollapsed eigenvalue pair.

Abstract: An example operation may include one or more of receiving, by a document processor node, a document and a signed digital receipt identifying an owner of a version of the document associated with a blockchain transaction, verifying, by a document processor node, the version of the document based on the signed digital receipt, and executing a smart contract to enable a conflict-free document versioning based on a hash of the document recorded on a ledger of the blockchain.

Abstract: A processor may automatically select a cloud service provider. A processor may receive one or more parameters associated with an entity and a cloud service dataset associated with a provider. A processor may generate a digital twin of the entity using the one or more parameters. A processor may simulate the digital twin of the entity and the cloud service dataset. A processor may identify, responsive to simulating the digital twin of the entity and the cloud service dataset, one or more predicted conditions of cloud service dataset on the entity. A processor may select the provider based, at least in part, on the one or more predicted conditions.

Abstract: In example aspects of this disclosure, a method includes generating, by one or more computing devices, a parametric model that expresses condition states for each of a plurality of assets, and the probability of the assets transitioning between the condition states; generating, by the one or more computing devices, stochastic degradation predictions of a group of the assets, based on the condition states and the probability of transitioning between the condition states for at least some of the assets; and generating, by the one or more computing devices, a maintenance schedule based on: the stochastic degradation predictions of the group of the assets, costs of corrective maintenance for assets in a failed state, and costs of scheduled maintenance for the assets.