Abstract: In an example embodiment, machine learning is utilized to make recommendations for next actions by users of an online network. These next actions are called “next best actions.” The machine learning may be performed to train a multitask deep machine learning model to make recommendations based on a series of inputs, including, for example, contextual information that relies upon action sequences of the user and historical users, and user intent. The use of a multitask deep machine learning model allows for the model to generate action recommendations that are personalized, contextual, and coordinate across various different aspects of the online network, rather than being limited to only a single aspect. Likewise, the multi-task deep machine learning model can also be tailored to optimized different use-case specific objectives while at the same time being easy to scale and maintain.

Abstract: In an example embodiment, a deep machine learning model ranks cohorts of users as well as cohorts of products in a single ranking. When utilized to determine which cohort members to display to a user, the system selects one user cohort and one product cohort as the “best” (e.g., the top ranked user cohort and the top ranked product cohort). This ranking may be based on a number of contextual and non-contextual features, including viewer features (characteristics of the user operating the user interface), viewee features (characteristics of or related to the litem that the user is viewing, such as the characteristics of another user whose profile the user is viewing), and viewer-viewee relationship features (indications about how the viewer and viewee are related, such as common schools, locations, places of employment, etc.).

Abstract: An integrated real-time personal safety system, described herein, is a holistic solution designed to enhance user safety through proactive hazard detection and alerts. This system comprises a data collection unit that features a sensor-embedded wearable device and a set of environmental and physiological sensors, providing comprehensive coverage of the user's surroundings and health status. Aided by wireless communication modules, the system ensures seamless transmission of real-time data to cloud-based services for analysis. The system's processing unit leverages this data to predict potential threats and initiates the signal alert unit to promptly notify the user through visual, auditory, or haptic cues. By bridging the gap between data collection, analysis, and user alert, the system ensures a proactive response to potential hazards.

Abstract: An integrated die ejector for separating a die from a wafer, the die ejector includes an ejector mechanism that includes an ejector pin. There is an ejector base coupled to the ejector pin and configured to couple the integrated die ejector to a die attach ejector machine. There is a pepper pot at least partially surrounding the ejector mechanism and ejector base and a sleeve coupled thereto and positioned between the ejector mechanism and pepper pot. The sleeve and pepper pot define an upper and lower motion limit of the ejector base and retain the ejector mechanism within the pepper pot such that the integrated die ejector may be coupled to and decoupled from the die attach ejector machine as a single unit to reduce installation time.

Abstract: Ethylene-based polymers having a melt index from 0.8 to 8 g/10 min, a density from 0.94 to 0.96 g/cm3, and a ratio of Mw/Mn from 6 to 20 are disclosed. These polymers can have one or more of an environmental stress crack resistance (ESCR) of at least 5,000 hr (condition A, 100% Igepal), an ESCR of at least 2,500 hr (condition A, 10% Igepal), a CY-a parameter from 0.35 to 0.53, a PSP2 value from 5 to 8.5, a tan ? at 0.1 sec?1 from 8 to 24 degrees, a Mz from 275,000 to 420,000 g/mol, an IVc from 2 to 3 dL/g, a strain hardening modulus (SHM) from 19 to 47 MPa, and/or an amount of polymer eluting between 93 and 95° C. in an ATREF profile of from 7 to 30 wt. %.

Abstract: The disclosure relates to a novel class of compounds that exhibit activity inhibitory activity toward arginase, and pharmaceutical compositions comprising the compounds of the disclosure. Also provided herein are methods of treating cancer with the arginase inhibitors of the disclosure.

Abstract: An impact tool, including a casing, a drive disposed in the casing, a main shaft and a gear assembly connected to the drive, a hammer assembly connected to the gear assembly and main shaft, and an anvil, the main shaft defining a rotation axis around which the hammer assembly and anvil can rotate, the anvil having a head and a stem part, the stem part being substantially cylindrical, and at least one lug extending radially from that end of the stem part which is remote from the head, forming a bottom face substantially perpendicular to the rotation axis R; the anvil further includes a guide part extending axially from that end of the stem part which is remote from the head, and a front end of the main shaft is accommodated in the guide part.

Abstract: Embodiments relate to a super-resolution engine that converts a lower resolution input image into a higher resolution output image. The super-resolution engine includes a directional scaler, an enhancement processor, a feature detection processor, a blending logic circuit, and a neural network. The directional scaler generates directionally scaled image data by upscaling the input image. The enhancement processor generates enhanced image data by applying an example-based enhancement, a peaking filter, or some other type of non-neural network image processing scheme to the directionally scaled image data. The feature detection processor determines features indicating properties of portions of the directionally scaled image data. The neural network generates residual values defining differences between a target result of the super-resolution enhancement and the directionally scaled image data. The blending logic circuit blends the enhanced image data with the residual values according to the features.

Abstract: A socket holding device for a power tool includes an anvil with a drive square head, a holding bolt, and a stop pin. The holding bolt locks a tool element that is clipped on the drive square head and the holding bolt is supportable elastically in a recessed seat of the drive square head such that the holding bolt is moveable perpendicularly to a rotation axis of the anvil. The stop pin is disposable in a mounting hole of the drive square head and the holding bolt is lockable by the stop pin when the holding bolt is supported elastically in the recessed seat of the drive square head and the stop pin is disposed in the mounting hole. The stop pin has a first portion partially clippable on the holding bolt and a diameter of the first portion is larger than an inner diameter of the mounting hole.

Abstract: Embodiments relate to a super-resolution engine that converts a lower resolution input image into a higher resolution output image. The super-resolution engine includes a directional scaler, an enhancement processor, a feature detection processor, a blending logic circuit, and a neural network. The directional scaler generates directionally scaled image data by upscaling the input image. The enhancement processor generates enhanced image data by applying an example-based enhancement, a peaking filter, or some other type of non-neural network image processing scheme to the directionally scaled image data. The feature detection processor determines features indicating properties of portions of the directionally scaled image data. The neural network generates residual values defining differences between a target result of the super-resolution enhancement and the directionally scaled image data. The blending logic circuit blends the enhanced image data with the residual values according to the features.

Abstract: Embodiments relate to a super-resolution engine that converts a lower resolution input image into a higher resolution output image. The super-resolution engine includes a directional scaler, an enhancement processor, a feature detection processor, a blending logic circuit, and a neural network. The directional scaler generates directionally scaled image data by upscaling the input image. The enhancement processor generates enhanced image data by applying an example-based enhancement, a peaking filter, or some other type of non-neural network image processing scheme to the directionally scaled image data. The feature detection processor determines features indicating properties of portions of the directionally scaled image data. The neural network generates residual values defining differences between a target result of the super-resolution enhancement and the directionally scaled image data. The blending logic circuit blends the enhanced image data with the residual values according to the features.

Abstract: A system and method for translating and matching attributes in data records that describe travel items is provided. In an embodiment, a plurality of records is received from a plurality of data sources. Record parsing logic is used divide strings in the records into individual words and match single words in the plurality of records to attributes. Using the matched attributes, record comparison logic creates a confidence score that describes the likelihood that two records describe the same listing or inventory item. If the confidence score exceeds a given threshold, the records are determined to match. A consolidated record is then created from the two matched records.

Abstract: A system and method for actively updating a data cache is provided. In an embodiment, a plurality of travel listings is received from a plurality of data sources and stored in a data cache. Invalidity prediction instructions are used to determine that a travel listing in the data cache contains inaccurate information. Listing relationship instructions are used to identify listings that are related to the travel listing that contains inaccurate information. Data update requests are then sent to the plurality of data sources for the related listings.

Abstract: An apparatus includes a first database, a memory, and first and second processors. The first database stores a list including a first identifier assigned to the first processor and a second identifier assigned to the second processor. The processors each randomly shuffle a copy of the list and place the first element of their shuffled copy in a third list. Each processor further determines that the first identifier appears a first number of times and the second identifier appears a second number of times in the third list, the first number greater than the second number. In response to determining that the first number is greater than the second number, the first processor copies data stored in a second database into the memory and sets a flag to true, while the second processor determines that the flag is set to true and accesses the data copy stored in the memory.

Abstract: Embodiments relate to a super-resolution engine that converts a lower resolution input image into a higher resolution output image. The super-resolution engine includes a directional scaler, an enhancement processor, a feature detection processor, a blending logic circuit, and a neural network. The directional scaler generates directionally scaled image data by upscaling the input image. The enhancement processor generates enhanced image data by applying an example-based enhancement, a peaking filter, or some other type of non-neural network image processing scheme to the directionally scaled image data. The feature detection processor determines features indicating properties of portions of the directionally scaled image data. The neural network generates residual values defining differences between a target result of the super-resolution enhancement and the directionally scaled image data. The blending logic circuit blends the enhanced image data with the residual values according to the features.

Abstract: Embodiments relate to a super-resolution engine that converts a lower resolution input image into a higher resolution output image. The super-resolution engine includes a directional scaler, an enhancement processor, a feature detection processor, a blending logic circuit, and a neural network. The directional scaler generates directionally scaled image data by upscaling the input image. The enhancement processor generates enhanced image data by applying an example-based enhancement, a peaking filter, or some other type of non-neural network image processing scheme to the directionally scaled image data. The feature detection processor determines features indicating properties of portions of the directionally scaled image data. The neural network generates residual values defining differences between a target result of the super-resolution enhancement and the directionally scaled image data. The blending logic circuit blends the enhanced image data with the residual values according to the features.

Abstract: Embodiments relate to a super-resolution engine that converts a lower resolution input image into a higher resolution output image. The super-resolution engine includes a directional scaler, an enhancement processor, a feature detection processor, a blending logic circuit, and a neural network. The directional scaler generates directionally scaled image data by upscaling the input image. The enhancement processor generates enhanced image data by applying an example-based enhancement, a peaking filter, or some other type of non-neural network image processing scheme to the directionally scaled image data. The feature detection processor determines features indicating properties of portions of the directionally scaled image data. The neural network generates residual values defining differences between a target result of the super-resolution enhancement and the directionally scaled image data. The blending logic circuit blends the enhanced image data with the residual values according to the features.

Abstract: A system and method for translating and matching attributes in data records that describe travel items is provided. In an embodiment, a plurality of records is received from a plurality of data sources. Record parsing logic is used divide strings in the records into individual words and match single words in the plurality of records to attributes. Using the matched attributes, record comparison logic creates a confidence score that describes the likelihood that two records describe the same listing or inventory item. If the confidence score exceeds a given threshold, the records are determined to match. A consolidated record is then created from the two matched records.

Abstract: A system and method for translating and matching attributes in data records that describe travel items is provided. In an embodiment, a plurality of records is received from a plurality of data sources. Record parsing logic is used divide strings in the records into individual words and match single words in the plurality of records to attributes. Using the matched attributes, record comparison logic creates a confidence score that describes the likelihood that two records describe the same listing or inventory item. If the confidence score exceeds a given threshold, the records are determined to match. A consolidated record is then created from the two matched records.