Abstract: In one embodiment, a method for retrieving a compressed data chunk from a source memory to a data buffer using a direct-memory access includes generating a source address indicating a location in the source memory at which a metadata corresponding to a compressed data chunk is stored, reading the metadata from the source address, where the metadata includes a data address, a size and compression options associated with the compressed data chunk, reading the compressed data chunk from the source memory based on the data address and the size within the metadata, decompressing the compressed data chunk based on the compression options within the metadata, and storing the decompressed data chunk into the data buffer.

Abstract: In one embodiment, a method includes, at each iteration i among N iterations of a first loop, reading first data corresponding to row i of a first tensor from a first source memory, reading second data from column i of the transpose buffer, writing the first data to column i of the transpose buffer, and causing the second data to be written to row i of a second tensor at a first destination memory and, at each iteration j among N iterations of a second loop, reading third data corresponding to row j of a third tensor from a second source memory, reading fourth data from row j of the transpose buffer, writing the third data to row j of the transpose buffer, and causing the fourth data to be written to row j of a fourth tensor at a second destination memory.

Abstract: Described are techniques for linking generating a skill-stored user profile, and linking same with a natural language processing (NLP) system-stored user profile. In at least some examples, a user may provide a natural language input to a NLP system. The NLP system may determine a skill is to process to perform an action responsive to the natural language input. To perform the action, the skill may require the user have a user profile stored by the skill, but the user may not have such a user profile. However, the NLP system may store a user profile for the user. The NLP system may determine the user profile stored thereby and may send, with user permission, information in the user profile to the skill. The skill may use the received information to generate and store a user profile for the user. Thereafter, the skill may provide the NLP system with a user profile identifier that the skill may use to identify the user's profile stored thereby.

Abstract: Described are techniques for linking generating a skill-stored user profile, and linking same with a natural language processing (NLP) system-stored user profile. In at least some examples, a user may provide a natural language input to a NLP system. The NLP system may determine a skill is to process to perform an action responsive to the natural language input. To perform the action, the skill may require the user have a user profile stored by the skill, but the user may not have such a user profile. However, the NLP system may store a user profile for the user. The NLP system may determine the user profile stored thereby and may send, with user permission, information in the user profile to the skill. The skill may use the received information to generate and store a user profile for the user. Thereafter, the skill may provide the NLP system with a user profile identifier that the skill may use to identify the user's profile stored thereby.

Abstract: Methods, systems, apparatuses, and computer program products are described herein that enable execution of a large AI model on a memory-constrained target device that is communicatively connected to a parameter server, which stores a master copy of the AI model. The AI model may be dissected into smaller portions (e.g., layers or sub-layers), and each portion may be executed as efficiently as possible on the target device. After execution of one portion of the AI model is finished, another portion of the AI model may be downloaded and executed at the target device. To improve efficiency, the input samples may be divided into microbatches, and a plurality of microbatches executing in sequential order may form a minibatch. The size of the group of microbatches or minibatch can be manually or automatically adjusted to reduce the communication overhead.

Abstract: Methods, systems, apparatuses, and computer program products are described herein that enable execution of a large AI model on a memory-constrained target device that is communicatively connected to a parameter server, which stores a master copy of the AI model. The AI model may be dissected into smaller portions (e.g., layers or sub-layers), and each portion may be executed as efficiently as possible on the target device. After execution of one portion of the AI model is finished, another portion of the AI model may be downloaded and executed at the target device. To improve efficiency, the input samples may be divided into microbatches, and a plurality of microbatches executing in sequential order may form a minibatch. The size of the group of microbatches or minibatch can be manually or automatically adjusted to reduce the communication overhead.

Abstract: Described are techniques for linking generating a skill-stored user profile, and linking same with a natural language processing (NLP) system-stored user profile. In at least some examples, a user may provide a natural language input to a NLP system. The NLP system may determine a skill is to process to perform an action responsive to the natural language input. To perform the action, the skill may require the user have a user profile stored by the skill, but the user may not have such a user profile. However, the NLP system may store a user profile for the user. The NLP system may determine the user profile stored thereby and may send, with user permission, information in the user profile to the skill. The skill may use the received information to generate and store a user profile for the user. Thereafter, the skill may provide the NLP system with a user profile identifier that the skill may use to identify the user's profile stored thereby.

Abstract: Described are techniques for linking generating a skill-stored user profile, and linking same with a natural language processing (NLP) system-stored user profile. In at least some examples, a user may provide a natural language input to a NLP system. The NLP system may determine a skill is to process to perform an action responsive to the natural language input. To perform the action, the skill may require the user have a user profile stored by the skill, but the user may not have such a user profile. However, the NLP system may store a user profile for the user. The NLP system may determine the user profile stored thereby and may send, with user permission, information in the user profile to the skill. The skill may use the received information to generate and store a user profile for the user. Thereafter, the skill may provide the NLP system with a user profile identifier that the skill may use to identify the user's profile stored thereby.

Abstract: Methods, systems, apparatuses, and computer program products are described herein that enable execution of a large AI model on a memory-constrained target device that is communicatively connected to a parameter server, which stores a master copy of the AI model. The AI model may be dissected into smaller portions (e.g., layers or sub-layers), and each portion may be executed as efficiently as possible on the target device. After execution of one portion of the AI model is finished, another portion of the AI model may be downloaded and executed at the target device. To improve efficiency, the input samples may be divided into microbatches, and a plurality of microbatches executing in sequential order may form a minibatch. The size of the group of microbatches or minibatch can be manually or automatically adjusted to reduce the communication overhead.

Abstract: Described herein is a light emitting diode (LED) luminaire comprising a cylindrical plastic housing having a first open end and a second open end. At the first open end, an externally threaded formation terminating with a flange is formed to mate with an internally threaded mounting unit, and at the second open end, an internal ridge is formed with a circumferential groove at its base. Inside the cylindrical plastic housing, an aluminum heat sink is insert molded. The aluminum heat sink has a cylindrical profile matching with the internal profile of the plastic housing. The aluminum heat sink has an open end towards the first open end of the plastic housing and a closed end resting on the internal ridge at the second open end of the plastic housing. With the interior surface of the closed end of the aluminum heat sink, a printed circuit board (PCB) holder plate locks a PCB driver.

Abstract: Described herein is a light emitting diode (LED) luminaire comprising a cylindrical plastic housing having a first open end and a second open end. At the first open end, an externally threaded formation terminating with a flange is formed to mate with an internally threaded mounting unit, and at the second open end, an internal ridge is formed with a circumferential groove at its base. Inside the cylindrical plastic housing, an aluminum heat sink is insert molded. The aluminum heat sink has a cylindrical profile matching with the internal profile of the plastic housing. The aluminum heat sink has an open end towards the first open end of the plastic housing and a closed end resting on the internal ridge at the second open end of the plastic housing. With the interior surface of the closed end of the aluminum heat sink, a printed circuit board (PCB) holder plate locks a PCB driver.

Abstract: The present invention relates to a dual-chamber pack with a first chamber comprising a container; and a second chamber comprising a reservoir, a biphasic connector, a plunger, and a plug with a breakable polymeric membrane. The container of the first chamber is prefilled with a pharmaceutically acceptable vehicle and the reservoir of the second chamber is prefilled with a solid composition of an active ingredient, wherein the solid composition of the active ingredient is mixed with the pharmaceutically acceptable vehicle to form a liquid pharmaceutical composition upon activation of the dual-chamber pack.

Abstract: The present invention relates to a dual-chamber pack with a first chamber comprising a container; and a second chamber comprising a reservoir, a biphasic connector, a plunger, and a plug with a breakable polymeric membrane. The container of the first chamber is prefilled with a pharmaceutically acceptable vehicle and the reservoir of the second chamber is prefilled with a solid composition of an active ingredient, wherein the solid composition of the active ingredient is mixed with the pharmaceutically acceptable vehicle to form a liquid pharmaceutical composition upon activation of the dual-chamber pack.

Abstract: An invention is provided for parallel ECC error location in a memory. The invention includes partitioning a set of field elements into w partitions. Then, for each of the w partitions of field elements, i) providing a set of r different field elements of the partition to r parallel search element. Next, in operation ii), each parallel search element computes a sum that is based on a set of coefficients of an error locator polynomial and the field element provided to the particular parallel search element. The set of field elements is advanced r field elements in GF(2m), and operations i) through iii) are repeated using the next r different field elements of the partition.

Abstract: The present invention relates to a packaging kit used for the stabilization of sirolimus particles wherein the packaging kit is in the form of a vacuum sealed impermeable container. It further relates to a method for stabilization of sirolimus particles as well as composition comprising the particles.

Abstract: An invention is provided for parallel ECC error location in a memory. The invention includes partitioning a set of field elements into w partitions. Then, for each of the w partitions of field elements, i) providing a set of r different field elements of the partition to r parallel search element. Next, in operation ii), each parallel search element computes a sum that is based on a set of coefficients of an error locator polynomial and the field element provided to the particular parallel search element. The set of field elements is advanced r field elements in GF(2m), and operations i) through iii) are repeated using the next r different field elements of the partition.