Abstract: The disclosure relates to a method for automatically controlling a biochip and an automatic control system, wherein the method comprises the operations of carrying a suction member through a pipette mechanism to suck a reagent, and carrying the suction member that has sucked the reagent through the pipette mechanism to add the reagent to the biochip, and also provides an automatic control system of a biochip using the method for automatically controlling a biochip. The method and system of a biochip may automatically schedule and control a plurality of biochips, so as to complete the labeling and capturing of biological information in tissues, and perform overall collection of the captured products, which meet the requirements of high utilization rate and high degree of automation for a biochip.

Abstract: A laminated glass includes an outer glass sheet, an inner glass sheet, an adhesive layer, and a non-metal reflective film that are stacked with each other. The outer glass sheet has a first surface and a second surface. The inner glass sheet has a third surface and a fourth surface. The adhesive layer is located between the second surface and the third surface. The non-metal reflective film is located between the second surface and the adhesive layer, or is located between the adhesive layer and the third surface, or is located on the fourth surface, or is located inside the adhesive layer. A solar direct reflectance RE of the laminated glass measured from a first surface side satisfies RE?12%. A vehicle includes the laminated glass as described above.

Abstract: Techniques are provided for learning static bound management parameters for an analog resistive processing unit system which is configured for neuromorphic computing. For example, a system comprises one or more processors which are configured to: perform a first training process to train a first artificial neural network model; perform a second training process to retrain the first artificial neural network model using matrix-vector compute operations which are a function of bound management parameters of an analog resistive processing unit system, to thereby generate a second artificial neural network model with learned static bound management parameters; and configure the resistive processing unit system to implement the second artificial neural network model and the learned static bound management parameters.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for machine learning. In an example method, a first plurality of quantization scales for a set of machine learning model parameters is accessed, and a shared quantization scale for the set of machine learning model parameters is accessed. A second plurality of quantization scales is generated based on the shared quantization scale and the first plurality of quantization scales. A dequantized set of machine learning model parameters is generated based on the shared quantization scale and the second plurality of quantization scales. A machine learning model output is generated based on the dequantized set of machine learning model parameters.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for machine learning. In an example method, a weight tensor for a layer of a machine learning model is determined, where the weight tensor comprises per-block values in a first precision encoding. The weight tensor is upscaled to a second precision encoding having a higher precision than the first precision encoding to generate an upscaled weight tensor, and an input tensor for the layer of the machine learning model is accessed. An output tensor for the layer of the machine learning model is generated based on multiplying the upscaled weight tensor with the input tensor.

Abstract: The present disclosure provides an information identification-based power Internet of Things protection method, and the method includes that: the safety identification errors of a source network, a channel network and a storage network of a system and the safety identification errors of a place name, a user and a node corresponding to each network are respectively determined by using a statistical analysis method; an information loss value and an information gain value are calculated according to the safety identification errors, the trust degree is calculated according to the information loss value and the information gain value, and a target function is obtained by maximizing the credibility and minimizing the safety identification error to obtain target control input by using a baseline learning machine method, so that the electric power Internet of Things system is controlled according to the target control input.

Abstract: A vehicle pillar assembly and a vehicle are provided. The vehicle pillar assembly includes a glass panel and an optical sensor. The glass panel defines a signal transmission window, the optical sensor corresponds to the signal transmission window and is disposed on one side of the glass panel, and the optical sensor is configured to receive an optical signal transmitted through the signal transmission window. The glass panel has a first curvature radius in a first direction and a second curvature radius in a second direction. The first direction is perpendicular to the second direction, where a ratio of the first curvature radius to the second curvature radius is a bending coefficient of the glass panel, and the bending coefficient ranges from 0 to 0.3.

Abstract: Systems and techniques are described for processing media data and for providing haptic feedback that is customized based on the media data. A media system receives media content to be output using an output device according to media output settings associated with a media output software application. The media content can be associated with an environment that is at least partially generated by the software application, as in a video game. The output device is configured to output different portions of the media content at different times across a period of time. The media system detects a condition associated with the media content at a first time along the period of time. The media system actuates at least one haptic feedback actuator according to a haptic feedback pattern corresponding to the condition.

Abstract: Disclosed herein are apparatuses and methods for performing joint balancing procedures. The apparatus may have femoral paddle and a tibial paddle attached to a housing. The housing may include a distraction mechanism to vary the space between the femoral paddle and the tibial paddle. The tibial paddle may lie entirely within the femoral paddle in a closed position. A load sensor may be placed in the femoral paddle to measure ligament tension. The apparatus may be inserted into a knee joint and positioned to remain within the knee joint during flexion and extension of the knee without everting a patella.

Abstract: Methods, systems, and apparatuses for image segmentation are provided. For example, a computing device may obtain an image, and may apply a process to the image to generate input image feature data and input image segmentation data. Further, the computing device may obtain reference image feature data and reference image classification data for a plurality of reference images. The computing device may generate reference image segmentation data based on the reference image feature data, the reference image classification data, and the input image feature data. The computing device may further blend the input image segmentation data and the reference image segmentation data to generate blended image segmentation data. The computing device may store the blended image segmentation data within a data repository. In some examples, the computing device provides the blended image segmentation data for display.

Abstract: A method is disclosed herein. The method includes receiving, from a server, first data that is indicative of a first likelihood of the UE successfully performing at least one of a position fix or a transmission of sensor data based on a first set of characteristics associated with the UE and at least one additional UE. The method includes computing, based on the first data and a second set of characteristics associated with the UE, a second likelihood of the UE successfully performing at least one of the position fix or the transmission of the sensor data. The method includes scheduling, based on the second likelihood, a wake-up time instance or a sleep time instance. The method includes transitioning the UE (1) from a sleep state to an active state at the wake-up time instance or (2) from the active state to the sleep state at the sleep time instance.

Abstract: Disclosed are systems, apparatuses, processes, and computer-readable media to provide haptic feedback in electronic devices based on contextual awareness. According to some aspects, a method of controlling haptic feedback includes obtaining environmental information indicative of an environment of a computing device from at least one sensor and determining a context associated with the mobile device based on the environmental information. The method may include determining haptic feedback for output by the mobile device based on the context.

Abstract: A processor-implemented method for quantization-aware federated training includes quantizing, by a server, a global model. The global model is quantized at multiple different quantization levels for each of one or more subnetwork models to generate one or more quantized subnetwork models. The one or more subnetwork models are assigned to one or more of multiple devices according to device processing capabilities. The server distributes to one or more of the multiple devices, a quantized subnetwork model. The server receives a model update from the one or more devices based on local data. The server generates an updated global model according to an aggregation function based on the model update from each of the one or more devices.

Abstract: A method for managing model updates by a first zone server, associated with a first zone model of a plurality of zone models, includes receiving a global model from a global server associated with the global model. The method also includes transmitting the global model to user equipment (UEs) in a first group of UEs associated with the first zone model. The method further includes receiving, from one or more UEs in the first group, model updates associated with the global model based on transmitting the global model. The method further includes transmitting, to the global server, an average of the model updates received from the one or more UEs. The method also includes updating the global model to generate the first zone model based on the model updates. The method further includes transmitting the first zone model to one or more UEs in the first group.

Abstract: Disclosed herein are apparatuses and methods for performing joint balancing procedures. The apparatus may have femoral paddle and a tibial paddle attached to a housing. The housing may include a distraction mechanism to vary the space between the femoral paddle and the tibial paddle. The tibial paddle may lie entirely within the femoral paddle in a closed position. A load sensor may be placed in the femoral paddle to measure ligament tension. The apparatus may be inserted into a knee joint and positioned to remain within the knee joint during flexion and extension of the knee without everting a patella.

Abstract: Disclosed are systems, apparatuses, processes, and computer-readable media to provide haptic feedback in electronic devices based on contextual awareness. According to some aspects, a method of controlling haptic feedback includes obtaining environmental information indicative of an environment of a computing device from at least one sensor and determining a context associated with the mobile device based on the environmental information. The method may include determining haptic feedback for output by the mobile device based on the context.

Abstract: Various aspects generally relate to routing web traffic over multiple internet protocol (IP) interfaces. For example, a user equipment (UE) may receive a request to open, in parallel, multiple transmission control protocol (TCP) socket connections associated with hypertext transfer protocol (HTTP) traffic, distribute the multiple TCP socket connections among multiple available IP interfaces, and route the HTTP traffic associated with the multiple TCP socket connections over the multiple IP interfaces. Additionally, or alternatively, the UE may receive a request to open a user datagram protocol (UDP) socket associated with QUIC traffic, select, from multiple available IP interfaces, a current IP interface that has a best quality of service (QOS) metric, and route the QUIC traffic associated with the UDP socket over the current IP interface. Numerous other aspects are described.

Abstract: Vehicle window glass is provided. The vehicle window glass includes outer glass, inner glass, and an interlayer connected between the outer glass and the inner glass. The outer glass has a first tempered region and a second tempered region. The first tempered region is connected to the second tempered region. A surface stress of the first tempered region is greater than a surface stress of the second tempered region. A vehicle is further provided.

Abstract: Systems and techniques are described herein for modifying the scale and/or position of objects in images. For instance, a system can obtain a two-dimensional (2D) input image from a camera and a three-dimensional (3D) representation of the 2D input image. The system can further determine a first portion of the 3D representation of the 2D input image corresponding to a target object in the 2D input image. The system can adjust a pose of the first portion of the 3D representation of the 2D input image corresponding to the target object. The system can further generate a 2D output image having a modified version of the target object based on the adjusted pose of the first portion of the 3D representation of the 2D input image corresponding to the target object to be output on a display.

Abstract: A processor-implemented method includes receiving machine learning model updates from clients in a federated learning system. The method also includes determining a fixed local zone associated with each of the clients, the fixed local zone having a first fixed boundary. The method includes updating model weights of a central machine learning model based on local machine learning updates for a local subset of the clients corresponding to the fixed local zone. The method includes updating the model weights of the central machine learning model based on neighbor machine learning updates for a neighbor subset of the clients. The neighbor subset corresponds to a fixed neighbor zone that neighbors the fixed local zone and has a second fixed boundary. The neighbor machine learning updates have a different weight than the local machine learning updates when updating model weights. A value of the different weight corresponds to a similarity parameter.