Abstract: Disclosed are a calculation method and an installation mechanism for compressor unit single meter alignment pad iron adjustment. The installation mechanism includes: a bracket and a connecting piece installed on the bracket; and an installation piece arranged on an end portion of the bracket. The bracket is provided with an installation box, the installation box is internally and rotatably provided with a rolling rod, the rolling rod is winded with a connecting rope, one end, away from the rolling rod, of the connecting rope is provided with a fixing piece, one end of the rolling rod extends to an outer side of the installation box and is connected with a cam, the cam is provided with a groove, the installation box is provided with a limiting piece, one side, towards the cam, of the limiting piece is provided with an open slot.

Abstract: A transistor with a fin structure and a nanosheet includes a fin structure. A first gate device is disposed on the fin structure. A first source/drain layer is disposed at one side of the first gate device. A first source/drain layer is on the fin structure and extends into the fin structure. A second source/drain layer is disposed at another side of the first gate device. The second source/drain layer is on the fin structure and extends into the fin structure. A nanosheet is disposed above the first gate device, between the first source/drain layer and the second source/drain layer, and contacts the first source/drain layer and the second source/drain layer. A second gate device surrounds the nanosheet.

Abstract: A projection system and a control method thereof are provided. The projection system includes a projector. The projector comprises a projection module and a processor. The processor is electrically coupled to the projection module. The projector confirms whether a first triggering event is detected and turns on a sleep aid mode in response to the first triggering event. The sleep aid mode corresponds to at least one control parameter. In the sleep aid mode, the projection module plays at least one multimedia file according to the at least one control parameter. The processor adjusts at least one parameter value of the at least one control parameter to adjust the at least one multimedia file correspondingly. The projector confirms whether a second triggering event is detected and the projection module stops playing the at least one multimedia file and turns off the sleep aid mode in response to the second triggering event.

Abstract: A thin film transistor includes a gate electrode embedded in an insulating layer that overlies a substrate, a gate dielectric overlying the gate electrode, an active layer comprising a compound semiconductor material and overlying the gate dielectric, and a source electrode and drain electrode contacting end portions of the active layer. The gate dielectric may have thicker portions over interfaces with the insulating layer to suppress hydrogen diffusion therethrough. Additionally or alternatively, a passivation capping dielectric including a dielectric metal oxide material may be interposed between the active layer and a dielectric layer overlying the active layer to suppress hydrogen diffusion therethrough.

Abstract: A device includes a semiconductor fin, and a gate stack on sidewalls and a top surface of the semiconductor fin. The gate stack includes a high-k dielectric layer, a work-function layer overlapping a bottom portion of the high-k dielectric layer, and a blocking layer overlapping a second bottom portion of the work-function layer. A low-resistance metal layer overlaps and contacts the work-function layer and the blocking layer. The low-resistance metal layer has a resistivity value lower than second resistivity values of both of the work-function layer and the blocking layer. A gate spacer contacts a sidewall of the gate stack.

Abstract: In various examples, sensor configuration for autonomous or semi-autonomous systems and applications is described. Systems and methods are disclosed that may use image feature correspondences between camera images along with an assumption that image features are locally planar to determine parameters for calibrating an image sensor with a LiDAR sensor and/or another image sensor. In some examples, an optimization problem is constructed that attempts to minimize a geometric loss function, where the geometric loss function encodes the notion that corresponding image features are views of a same point on a locally planar surface (e.g., a surfel or mesh) that is constructed from LiDAR data generated using a LiDAR sensor. In some examples, performing such processes to determine the calibration parameters may remove structure estimation from the optimization problem.

Abstract: In one embodiment, a system generates an image using either a first or a second image signal processing (ISP) algorithm, where the first or second ISP algorithm is applied to raw image data of a camera of an autonomous driving vehicle (ADV) to generate the image. The system applies a machine learning model to the image to identify a representation of an obstacle, where the machine learning model is generated by a few shots learning algorithm that contrasts labeled data of a positive training sample from images corresponding to the first and second ISP algorithms to labeled data of a negative training sample from images corresponding to the first and second ISP algorithm. The system determines a classification and a location of the obstacle based on the representation of the obstacle. The system plans a motion control of the ADV based on the classification and location of the detected object.

Abstract: A humanoid finger mechanism includes: a finger seat; at least one proximal knuckle, located at an outer side of the finger seat; at least one middle knuckle, located at an outer side of the at least one proximal knuckle; a distal knuckle, located at an outer side of the at least one middle knuckle; a driver, located at an inner side of the finger seat; a driving cable, wherein a first end of the driving cable is connected with the driver, while a second end of the driving cable is connected with the distal knuckle after sequentially penetrating through the finger seat, the at least one proximal knuckle and the at least one middle knuckle; and a tension band, a first end of the tension band is connected with the finger seat, while a second end of the tension band is connected with the distal knuckle.

Abstract: There are provided method, apparatus, device, and medium for determining update gradient for contrastive learning model. In the method, a gradient factor of a first type for the contrastive learning model is determined based on a first group of training data and a second group of training data for training the contrastive learning model. The gradient factor of the first type is not used for backpropagation during a training process. In a first stage of the training process, a gradient factor of a second type associated with the first group of training data is determined based on the contrastive learning model. The gradient factor of the second type is used for backpropagation during the training process. Gradient is obtained for updating the contrastive learning model based on the gradient factor of the first type and the gradient factor of the second type associated with the first group of training data.

Abstract: The invention provides a material surface treatment equipment, which is applied to a material substrate. The material surface treatment equipment includes a surface treatment device and at least one waveguide device. The surface treatment device is used to carry the material substrate to perform a surface treatment process. Each waveguide device is used for introducing electromagnetic waves to the material substrate to assist in performing the surface treatment process. Through the introduction of electromagnetic waves, the surface treatment process of the material substrate is easy to perform and can achieve the strengthening effect.

Abstract: In various examples, sensor configuration for autonomous or semi-autonomous systems and applications is described. Systems and methods are disclosed that may use image feature correspondences between camera images along with an assumption that image features are locally planar to determine parameters for calibrating an image sensor with a LiDAR sensor and/or another image sensor. In some examples, an optimization problem is constructed that attempts to minimize a geometric loss function, where the geometric loss function encodes the notion that corresponding image features are views of a same point on a locally planar surface (e.g., a surfel or mesh) that is constructed from LiDAR data generated using a LiDAR sensor. In some examples, performing such processes to determine the calibration parameters may remove structure estimation from the optimization problem.

Abstract: An optical element driving mechanism is provided and includes a fixed assembly, a movable assembly, a driving assembly and a stopping assembly. The fixed assembly has a main axis. The movable assembly is configured to connect an optical element, and the movable assembly is movable relative to the fixed assembly. The driving assembly is configured to drive the movable assembly to move relative to the fixed assembly. The stopping assembly is configured to limit the movement of the movable assembly relative to the fixed assembly within a range of motion.

Abstract: The present disclosure relates to a CMOS image sensor having a multiple deep trench isolation (MDTI) structure, and an associated method of formation. In some embodiments, the image sensor comprises a boundary deep trench isolation (BDTI) structure disposed at boundary regions of a pixel region surrounding a photodiode. The BDTI structure has a ring shape from a top view and two columns surrounding the photodiode with the first depth from a cross-sectional view. A multiple deep trench isolation (MDTI) structure is disposed at inner regions of the pixel region overlying the photodiode, the MDTI structure extending from the back-side of the substrate to a second depth within the substrate smaller than the first depth. The MDTI structure has three columns with the second depth between the two columns of the BDTI structure from the cross-sectional view. The MDTI structure is a continuous integral unit having a ring shape.

Abstract: The present invention provides a smart wearable device, which is held on an upper body of a wearer by a plurality of contact pad sets, and has a connection unit, a first sensing module, a second sensing module, and an extension unit.

Abstract: The present application provides tricyclic urea compounds that modulate the activity of the V617F variant of JAK2, which are useful in the treatment of various diseases, including cancer.

Abstract: A solution for generating a negative sample pair of a contrastive learning model is provided. In one method, a first data segment is obtained from a first data sequence in a plurality of data sequences for training the contrastive learning model, and a second data segment is obtained from a second data sequence in the plurality of data sequences. A data frame is selected from a further data sequence than the second data sequence in the plurality of data sequences. A third data segment is generated based on the second data segment and the data frame. A negative sample pair for training the contrastive learning model is determined based on the first data segment and the third data segment. Therefore, richer semantic information can be introduced into negative sample pairs in terms of appearance, and further the accuracy of the contrastive learning model can be improved.

Abstract: A method of training and applying contrastive learning model. The method includes obtaining a sample set and label information for training contrastive learning model, the sample set including a plurality of first samples of a first modality and a plurality of second samples of a second modality, the label information indicating a correlation between samples of the plurality of first samples and samples of the plurality of second samples; determining whether sample mixing is to be performed on the first modality or the second modality; in accordance with a determination that sample mixing is to be performed on the first modality, generating at least one first mixed sample of the first modality by mixing at least one pair of first samples among the plurality of first samples; and training the contrastive learning model at least based on the at least one first mixed sample and first mixed label information.

Abstract: Systems and methods are directed to training and utilizing a generative language model that is constrained by a predetermined template that is used to train the generative language model. Once trained, customer data is accessed and transmitted to an evaluation component associated with the generative language model. The generative language model generates one or more sentences based on a feedback input of the plurality of feedback inputs, whereby the one or more sentences each include a sentiment, a target, and a reason for the sentiment in a format defined by the predetermined template. The evaluation component then identifies the sentiment, the target, and the reason from a sentence of the one or more sentences. A communication is then presented, on a device of a user, based on at least the sentiment and the reason identified from the sentence. The communication can be an alert or a report.

Abstract: A solution for generating a positive sample pair of a contrastive learning model is provided. In one method, a first data segment and a second data segment are respectively obtained from a first data sequence in plurality of data sequences for training the contrastive learning model, the first data sequence comprising a plurality of data frames. A data frame is selected from a second data sequence in the plurality of data sequences. A third data segment is generated based on the second data segment and the data frame. A positive sample pair for training the contrastive learning model is determined using the first data segment and the third data segment. In this way, positive samples that are more difficult to distinguish can be provided, thereby increasing the accuracy of the contrastive learning model and improving the training efficiency and accuracy of downstream models.