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The present disclosure is generally directed to a modular busbar assembly that can be adjusted to fit varying rack power targets and connector interfaces, accommodating multiple connector geometries along one cost-effective assembly. The modular busbar assembly enables high power densities at powershelf regions by accommodating single- and double-pole connectors and connector contacts at various positions relative to the modular busbar assembly. The modular busbar assembly is customizable because the auxiliary busbar geometry may be altered independently from the primary busbar geometry.

The invention relates to a computer-implemented method to facilitate verified service requests by a user computing device, comprising receiving application program code distributed by an application service system, wherein the application program code comprises identifying information; extracting the identifying information from the application program code resident on the user computing device; transmitting an access request to the application service system, wherein the access request comprises the identifying information extracted from the application program code resident on the user computing device; receiving, from the application service system, an access token comprising the identifying information extracted from the application program code resident on the user computing device; and transmitting a service request including the access token to the application service system.

A slidable fiber optic adaptor slidable relative to a mounting support configured to be mounted to a fiber optic tray in a panel assembly is provided for fiber optic interconnection. The slidable adaptor may be slidable between a non-extended position and an extended position. Thus, when the fiber optic connectors having different polarities are connected to the slidable adaptors, the slidable fiber optic adaptor may be pulled out in the extended position to allow the fiber optic connectors with different polarities to be in a staggered configuration, thus, providing sufficient space therebetween for finger access for operators or technicians for ease of cable inspection and management.

An example computing system retrieves information from one or more applications, and receives an indication of a natural language input. The computing system applies a machine learning model to at least a portion of the information and at least a portion of the indication of the natural language input to generate instructions for generating at least one graphical component, e.g., a custom widget. The instructions may include a temporal parameter for the at least one graphical component, in which the computing system may update, based on the temporal parameter, the instructions for generating the at least one graphical component to update the at least one graphical component.

The present application relates to systems and methods for regulating power for one or more electronic devices. In accordance with aspects of the disclosure, one or more micro-controllers are configured to regulate both peak and average power being consumed by devices within the system. The power being consumed within the system can be determined over different durations of time and multiple independent feedback loops may be used to regulate power within the system. In addition, the system can include a feedback loop that is configured to allow the system to enter into a low-power mode.

Generally disclosed herein is an approach for an optimized data computation for machine learning (ML) operations using a plurality of small-sized ML accelerators integrated into a general-purpose compute system-on-chip (SoC). One or more processors of the general-purpose compute SoC may be configured to receive a workload and divide the workload into a plurality of sub-workloads. The plurality of sub-workloads may be distributed among the plurality of small-sized ML accelerators. Each of the plurality of the ML accelerators can be configured to output a partial outcome. The outputs from each ML accelerator can be combined using atomic store operations using a dedicated opcode and a specified memory address.

The technology is generally directed to the continued use of a link between nodes of a network in which at least one of the lanes in the link has failed. Rather than identify the link as a failure in response to a lane failure, the traffic may be redistributed amongst the remaining lanes in the link. The redistribution of traffic may result in the link operating in a degraded mode. To mitigate the reduced traffic capacity of the degraded mode, the remaining links may be overclocked, such that the bandwidth of each link is increased to return the link to a traffic capacity corresponding to the initial capacity. In some examples, the traffic may be diverted to a redundant, otherwise inactive lane, thereby allowing for the traffic to be transmitted and received via the link, without any degradation in the performance of the link.

A method (400) includes receiving a time series anomaly detection query (20) from a user (12) and training one or more models (212) using a set of time series data values (152). For each respective time series data value in the set, the method includes determining, using the trained models, an expected data value for the respective time series data value and determining a difference between the expected data value and the respective time series data value. The method also includes determining that the difference between the expected data value and the respective time series data value satisfies a threshold (314). In response to determining that the difference between the expected data value and the respective time series data value satisfies the threshold, the method includes determining that the respective time series data value is anomalous and reporting the anomalous respective time series data value (152A) to the user.

Example embodiments of the present disclosure provide for an example method including obtaining data associated with media channels. The example method includes obtaining, by a computing device of a first platform, first channel data associated with a first media channel of an entity, the first media channel being on a first platform. Additionally, the method can include obtaining second channel data associated with a second media channel of the entity, the second media channel being on a second platform. Moreover, the method can include processing, using a machine-learning model, the first channel data and the second channel data to determine an allocation of resources to the first media channel and the second media channel. Furthermore, the method can include generating and presenting a recommendation based on the determination.

A computer-implemented method comprising: receiving, by an application (108) executing on a user device (102) that is not an application store, a single content response from a content server (120), wherein the single content response comprises both digital content (112) referencing a second application that is not installed on the user device and application store data (116) for the second application; in response to receiving the single content response and when the application is an active application, providing, by the active application and in a user interface (110) of the active application, the digital content concurrently with an application store user interface (114) that includes (i) the application store data for the second application and (ii) an install element (118) for triggering installation of the second application on the user device from within the active application; detecting user interaction with the install element provided within the user interface of the active application; and in response to detecting the user interaction with the install element, triggering a download of the second application independent of launching an application for the application store.