Abstract: Devices and methods for treatment of a patient's vasculature are described. Embodiments may include a permeable implant connected to an expandable stent through a plurality of wires. The permeable shell is configured for placement in an aneurysm and the expandable stent is configured for placement in a parent vessel proximal, near, or adjacent the aneurysm.

Abstract: Software instructions are executed on a processor within a computer system to configure a streaming engine with stream parameters to define a multidimensional array. The stream parameters define a size for each dimension of the multidimensional array and a pad value indicator. Data is fetched from a memory coupled to the streaming engine responsive to the stream parameters. A stream of vectors is formed for the multidimensional array responsive to the stream parameters from the data fetched from memory. A padded stream vector is formed that includes a specified pad value without accessing the pad value from system memory.

Abstract: A streaming engine in a system receives a first set of stream parameters into a queue to define a first stream along with an indication of either a queue mode of operation or a speculative mode of operation for the first stream. Acquisition of the first stream then begins. At some point, a second set of stream parameters is received into the queue to define a second stream. When the queue mode of operation was specified for the first stream, the second set of parameters is queued and acquisition of the second stream is delayed until completion of acquisition of the first stream. When the speculative mode of operation was specified for the first stream, acquisition of the first stream is canceled upon receipt of the second set of stream parameters and acquisition of the second stream begins immediately.

Abstract: One aspect of systems and methods for data correction includes identifying a false label from among predicted labels corresponding to different parts of an input sample, wherein the predicted labels are generated by a neural network trained based on a training set comprising training samples and training labels corresponding to parts of the training samples; computing an influence of each of the training labels on the false label by approximating a change in a conditional loss for the neural network corresponding to each of the training labels; identifying a part of a training sample of the training samples and a corresponding source label from among the training labels based on the computed influence; and modifying the training set based on the identified part of the training sample and the corresponding source label to obtain a corrected training set.

Abstract: The present disclosure describes a model for large scale color prediction of objects identified in images. Embodiments of the present disclosure include an object detection network, an attention network, and a color classification network. The object detection network generates object features for an object in an image and may include a convolutional neural network (CNN), region proposal network, or a ResNet. The attention network generates an attention vector for the object based on the object features, wherein the attention network takes a query vector based on the object features, and a plurality of key vector and a plurality of value vectors corresponding to a plurality of colors as input. The color classification network generates a color attribute vector based on the attention vector, wherein the color attribute vector indicates a probability of the object including each of the plurality of colors.

Abstract: Software instructions are executed on a processor within a computer system to configure a steaming engine with stream parameters to define a multidimensional array. The stream parameters define a size for each dimension of the multidimensional array and a pad value indicator. Data is fetched from a memory coupled to the streaming engine responsive to the stream parameters. A stream of vectors is formed for the multidimensional array responsive to the stream parameters from the data fetched from memory. A padded stream vector is formed that includes a specified pad value without accessing the pad value from system memory.

Abstract: A streaming engine in a system receives a first set of stream parameters into a queue to define a first stream along with an indication of either a queue mode of operation or a speculative mode of operation for the first stream. Acquisition of the first stream then begins. At some point, a second set of stream parameters is received into the queue to define a second stream. When the queue mode of operation was specified for the first stream, the second set of parameters is queued and acquisition of the second stream is delayed until completion of acquisition of the first stream. When the speculative mode of operation was specified for the first stream, acquisition of the first stream is canceled upon receipt of the second set of stream parameters and acquisition of the second stream begins immediately.

Abstract: A group captioning system includes computing hardware, software, and/or firmware components in support of the enhanced group captioning contemplated herein. In operation, the system generates a target embedding for a group of target images, as well as a reference embedding for a group of reference images. The system identifies information in-common between the group of target images and the group of reference images and removes the joint information from the target embedding and the reference embedding. The result is a contrastive group embedding that includes a contrastive target embedding and a contrastive reference embedding with which to construct a contrastive group embedding, which is then input to a model to obtain a group caption for the target group of images.

Abstract: Systems and methods for color prediction are described. Embodiments of the present disclosure receive an image that includes an object including a color, generate a color vector based on the image using a color classification network, where the color vector includes a color value corresponding to each of a set of colors, generate a bias vector by comparing the color vector to teach of a set of center vectors, where each of the set of center vectors corresponds to a color of the set of colors, and generate an unbiased color vector based on the color vector and the bias vector, where the unbiased color vector indicates the color of the object.

Abstract: Techniques related to executing a plurality of instructions by a processor comprising receiving a first instruction configured to cause the processor to output a first data value to a first address in a first data cache, outputting, by the processor, the first data value to a second address in a second data cache, receiving a second instruction configured to cause a streaming engine associated with the processor to prefetch data from the first data cache, determining that the first data value has not been outputted from the second data cache to the first data cache, stalling execution of the second instruction, receiving an indication, from the second data cache, that the first data value has been output from the second data cache to the first data cache, and resuming execution of the second instruction based on the received indication.

Abstract: Systems and methods for intent discovery and video summarization are described. Embodiments of the present disclosure receive a video and a transcript of the video, encode the video to obtain a sequence of video encodings, encode the transcript to obtain a sequence of text encodings, apply a visual gate to the sequence of text encodings based on the sequence of video encodings to obtain gated text encodings, and generate an intent label for the transcript based on the gated text encodings.

Abstract: A system includes a memory controller, multiple memories coupled to the memory controller, and multiple controlling components coupled to the memory controller. The memory controller calculates an error correction code (ECC) syndrome of a first type for a segment of data; stores the segment of data and the ECC syndrome of the first type in a first memory of the multiple memories; receives a request from a controlling component of the multiple controlling components directed to the segment of data, the controlling component implementing an ECC syndrome of a second type; transforms the ECC syndrome of the first type for the segment of data to the ECC syndrome of the second type; detects a number of errors, if any, present in the segment of data; and takes further action depending on how many errors are detected.

Abstract: A group captioning system includes computing hardware, software, and/or firmware components in support of the enhanced group captioning contemplated herein. In operation, the system generates a target embedding for a group of target images, as well as a reference embedding for a group of reference images. The system identifies information in-common between the group of target images and the group of reference images and removes the joint information from the target embedding and the reference embedding. The result is a contrastive group embedding that includes a contrastive target embedding and a contrastive reference embedding with which to construct a contrastive group embedding, which is then input to a model to obtain a group caption for the target group of images.

Abstract: Techniques related to executing a plurality of instructions by a processor comprising receiving a first instruction configured to cause the processor to output a first data value to a first address in a first data cache, outputting, by the processor, the first data value to a second address in a second data cache, receiving a second instruction configured to cause a streaming engine associated with the processor to prefetch data from the first data cache, determining that the first data value has not been outputted from the second data cache to the first data cache, stalling execution of the second instruction, receiving an indication, from the second data cache, that the first data value has been output from the second data cache to the first data cache, and resuming execution of the second instruction based on the received indication.

Abstract: The present description relates to systems, methods, and non-transitory computer readable media for generating digital responses to digital queries by utilizing a classification model and query-specific analysis models. For example, the described systems can train a classification model to generate query classifications corresponding to product queries, conversational queries, and/or recommendation/purchase queries. Moreover, the described systems can apply the classification model to select pertinent models for particular queries. For example, upon classifying a product query, the described systems can utilize a neural ranking model (trained based on a set of training product specifications and training queries) to generate relevance scores for product specifications associated with a digital query.

Abstract: An apparatus includes a central processing unit (CPU) core and a cache subsystem coupled to the CPU core. The cache subsystem includes a memory configured to store a line of data and an error correcting code (ECC) syndrome associated with the line of data, where the ECC syndrome is calculated based on the line of data and the ECC syndrome is a first type ECC. The cache subsystem also includes a controller configured to, in response to a request from a master configured to implement a second type ECC, the request being directed to the line of data, transform the first type ECC syndrome for the line of data to a second type ECC syndrome send a response to the master. The response includes the line of data and the second type ECC syndrome associated with the line of data.

Abstract: Described herein is an Autonomous Conversational AI system, which does not require any human configuration or annotation, and is used to have multi-tum dialogs with a user. A typical Conversational AI system consists of three main models: Natural Language Understanding (NLU), Dialog Manager (DM) and Natural Language Generation (NLG), which requires human provided data and configuration. The system proposed herein leverages novel Conversational AI methods which automatically generates conversational AI configuration from any historical conversation logs. The automatically generated configuration contains Auto-Topics, Auto-Subtopics, Auto-Intents, Auto-Responses and Auto-Flows which are used to automatically train NLU, DM and NLG models. Once these models are trained for given conversation logs, the system can be used to have dialog with any user.

Abstract: Systems and methods for key-phrase extraction are described. The systems and methods include receiving a transcript including a text paragraph and generating key-phrase data for the text paragraph using a key-phrase extraction network. The key-phrase extraction network is trained to identify domain-relevant key-phrase data based on domain data obtained using a domain discriminator network. The systems and methods further include generating meta-data for the transcript based on the key-phrase data.

Abstract: A system and method for natural language communication between a user and a multifunction peripheral incorporates a chatbot. The chatbot response to verbal user commands or inquiries with corresponding device reports or operation. The chatbot operates in conjunction monitored multifunction peripheral states and document processing jobs, and tenders a verbal reply accordingly, supplying information to inform the user of additional matters related to the inquiry and of likely usefulness to the user.

Abstract: A temperature detection system (10) comprising a layer of spin cross-over material (11) in thermal contact with a target surface (12); at least one first light source (13) configured to provide a first and a second illumination (13a, 33a) of at least a first portion (12a) of the layer of spin cross-over material (11); at least one first light receiver (14) configured to capture first and second return light (14b, 34b) coming from the layer of spin cross-over material (11) and resulting respectively from the first and second illuminations; generate a first signal (S1) based on the first return light (14b); and generate a second signal (S2) based on the second return light (34b); a computation circuit (15, 17) configured to determine, based at least on a correlation between the first and second signals (S1, S2), a temperature of the layer of spin cross-over material (11).