Abstract: The present disclosure relates to the technical field of video playback, and provides a multimedia information playback method and apparatus, an electronic device, and a computer readable storage medium. The method includes: during a process of an information flow being browsed, in response to that display time of a target information entry in the information flow on a display interface of the information flow reaches a first time threshold, displaying a preset prompt message on an upper layer of the target information entry, so as to prompt a user to input a corresponding gesture; and in response to that a preset gesture operation to the target information entry inputted by the user is detected, playing multimedia information corresponding to the target information entry.

Abstract: A method for classifying environmental image frames for vehicles, using a universal iterative classification model, is presented. The method combines text-based querying, active machine-learning models, and user input to form an end-to-end automatic flow for sourcing video frames captured by sensors on a vehicle. An index of frames is used and continuously populated with data for new frames, with each frame scored on its likelihood of containing a representation of a driving scenario of interest. Each iteration of the classification model produces a classification result that is predicted to belong to the scenario of interest. Binary labels can be applied to the results. Subsequent iterative training of classification models can be performed using updated training sets containing previously labeled classification results, to improve precision and accuracy in classifying image data to a driving scenario.

Abstract: Disclosed in the present invention is a nanocrystalline soft magnetic alloy with high magnetic induction and high frequency. The nanocrystalline soft magnetic alloy has a molecular formula of FeaSibBcMadCuePf, in which M includes one or more of Nb, Mo, V, Mn, and Cr, molar percent contents of elements are as follows: 6?b?15, 5?c?12, 0.5?d?3, 0.5?e?1.5, and 0.5?f?3, and the balance includes Fe and impurities. A difference between an induced anisotropy value and an average magnetocrystalline anisotropy value is 0.1-1 J/m3. The soft magnetic alloy has high magnetic permeability and low magnetic loss at high frequency. Further disclosed in the present disclosure is a method for preparing the nanocrystalline soft magnetic alloy with high magnetic induction and high frequency.

Abstract: The present disclosure provides Pyruvate Dehydrogenase Kinase 4 (PDK4) as a target for intervention of cellular senescence and a use in chemotherapy against cancer. The present disclosure reveals PDK4 as a novel target for intervention of cellular senescence and intervention in drug resistance of tumors. By targeting PDK4, drugs can be developed to inhibit drug resistance of tumors, suppress or eliminate the SASP. Additionally, drugs targeting PDK4 can be used in combination with chemotherapeutic drugs for diagnosis or prognosis of related diseases or symptoms based on this target and mechanism.

Abstract: An image registration method using a fast differential evolution sample consensus algorithm based on historical records: 1: image acquisition: acquiring a to-be-registered image and a reference image; 2: feature extraction: respectively extracting point features of the to-be-registered image and the reference image by using a SIFT algorithm; 3: feature matching: performing coarse feature matching between the to-be-registered point feature set and the reference point feature set to obtain a sample set; 4: parameter setting; 5: sorting the sample set in ascending order, and initializing a sample inlier archive AS and a model inlier archive AM; 6: first-time initialization of a population; 7: updating a fast test execution threshold uT; 8: mutation phase; 9: selection phase; 10: mutation operator parameter update; 11: resident initialization of the population; 12: repeating 7-11; 13: selecting the best individual from the population to perform spatial transformation on the to-be-registered image to obtain a regi

Abstract: The present disclosure discloses a base editor and the use thereof. The present disclosure provides a nucleic acid base editor, specifically a base editor which is not based on CRISPR technology. The base editor comprises a sequence-specific DNA binding protein, a nickase, an exonuclease and a base-specific deaminase. This base editor is single-strand-specific, and as compared with conventional base editors, the base editor of the present disclosure has wide applicability in cells and is capable of functioning in the nucleus as well as in mitochondrial DNA and/or chloroplast DNA. This base editor has the characteristics of achieving base editing products with high purity and resulting in few indel byproducts while realizing efficient base editing, which is conducive to being used as an efficient and safe gene editing tool.

Abstract: Provided are an application of a citrate metal salt in endodontic treatment, and a formulation obtained by preparation; said citrate metal salt is used for removing a tarnished layer, and the citrate metal salt is lithium citrate, sodium citrate, potassium citrate, or magnesium citrate. The citrate metal salt is co-formulated with a citrate metal salt-compatible single-chain cationic antimicrobial agent, achieving results that are currently achieved only by alternating rinsing with EDTA-containing and sodium hypochlorite preparations in endodontic treatment.

Abstract: Systems and methods for multiple object transferring using robotics. A system includes a robotic hand with a base and fingers capable of grasping objects. A robotic arm is coupled to the robotic hand and is capable of moving the robotic hand. One or more circuits are configured to operate the robotic hand and the robotic arm by identifying a pre-grasp configuration for the robotic hand and executing a transfer routine based on a Markov decision process model to operate the robotic hand and the robotic arm such that they move multiple objects from a first location to a second location.

Abstract: A heat exchanger and a heat exchange system. The heat exchanger includes two heat exchange units, wherein each of the two heat exchange units includes a plurality of heat exchange flat pipes spaced from each other and a collecting pipe in communication with the heat exchange flat pipes, and the heat exchange flat pipes of the two heat exchange units are alternately arranged; and each of the heat exchange flat pipes includes a runner reentering along a length of a corresponding heat exchange flat pipe, an inlet and an outlet are provided at the same end of the corresponding heat exchange flat pipe, the heat exchange units include two collecting pipes, the collecting pipes are configured for communicating the inlet and the outlet, and the two collecting pipes of the same heat exchange unit are disposed at two opposite sides of the heat exchanger.

Abstract: A knowledge distillation system and method trains neural networks utilizing a non-conventional replay buffer and augmented data tuples. In at least one embodiment, the knowledge distillation system and method pretrain a teacher model that implements a contextual bandit algorithm. A lightweight student model determines online contextual bandit data tuples as to context x, arm/action a, and reward/payoff r. The data tuples are stored in a replay buffer. The teacher model randomly samples data tuples from the replay buffer and augments the sampled data tuples. Augmented data tuples are stored in the replay buffer. The student model batch processes augmented data tuples to update parameters of contextual bandit data tuples.

Abstract: A method is provided. The method includes: obtaining a first sample dataset; inputting at least one first question text corresponding to at least one piece of first sample data into a dialog model separately to obtain at least one first answer prediction result; inputting each second question text into the dialog model to obtain a second answer prediction result output by the dialog model; inputting the second answer prediction result into a reward model to obtain a score of the second answer prediction result output by the reward model; determining a comprehensive loss based on the at least one first answer prediction result, a first answer text of each of the at least one piece of first sample data, and a score corresponding to each of at least one piece of second sample data; and adjusting at least one parameter of the dialog model based on the comprehensive loss.

Abstract: A circuitry, method, computer storage medium, and terminal for deskew processing include: receiving data lanes, clock signal lanes, deskew control modules, and deskew modules. The receiving data lane receives input data signals, which are in alternating standard sequence. The clock signal lane receives input clock signals. The deskew control module is connected to the receiving data lane and is set to acquire the sample data obtained by sampling the data signal of the data signal lane by the clock signal of the clock signal lane with a preset duration, and determine the delay information based on the sample data. The deskew module is connected to the deskew control module and is set to adjust the phase offset of both the clock signal and the data signal based on the delay information.

Abstract: The present application discloses a method and an apparatus for adversarial training of a machine learning (ML) model and a medium. The method includes: obtaining input information in a training sample; extracting features of a plurality of input characters in the input information; inputting the features of the plurality of input characters to the ML model, to capture an attention weight on an input character of the plurality of input characters by an attention layer of the ML model; disturbing the attention weight captured by the attention layer, so that the ML model outputs a predicted character according to the attention weight disturbed; and training the ML model according to a difference between the predicted character and a labeled character in the training sample.

Abstract: A somatosensory interaction method is provided. The electronic device may determine an exercise load of a somatosensory action of somatosensory interaction content based on a figure of a user. During somatosensory interaction, the electronic device may obtain a heart rate of the user to adjust the exercise load of the somatosensory action of the somatosensory interaction content. If an exercise capability indicated by the figure and the heart rate of the user is stronger, the exercise load of the somatosensory action of the somatosensory interaction content is higher. On the contrary, the exercise load of the somatosensory action of the somatosensory interaction content is lower.

Abstract: An image generation method, an apparatus, an electronic device and a storage medium are provided. The method includes: discretizing a target text to obtain a plurality of text tokens; obtaining a resolution sequence based on an initial resolution and a target resolution, wherein the resolution sequence comprises a plurality of resolutions, and a difference between two adjacent resolutions of the plurality of resolutions is a preset increment; generating image tokens corresponding respectively to the plurality of resolutions based on the plurality of text tokens and the resolution sequence; and fusing all the image tokens to obtain a target image corresponding to the target text.

Abstract: Disclosed is a torque sensing bionic clamping jaw. In the torque sensing bionic clamping jaw, a left clamping jaw skeleton is movably connected to a substrate, the left clamping jaw skeleton includes a first left groove and a second left groove which are in a side facing a right jaw, a left sensor quick-release connecting piece is detachably connected to the first left groove, a left torque measuring sensor is mounted on the left sensor quick-release connecting piece, a left flexible gasket quick-release connecting piece is detachably connected to the second left groove, and a left flexible gasket is mounted on the left flexible gasket quick-release connecting piece; a right torque measuring sensor is mounted on a right sensor quick-release connecting piece and opposite to the left torque measuring sensor, and a right flexible gasket is mounted on a right flexible gasket quick-release connecting piece.

Abstract: The task execution method includes: retrieving, from a storage unit, a hyperparameter of a target network layer in a target model; executing, using an operator unit, a first computational subtask in a computational task, according to the hyperparameter of the target network layer, so as to obtain a first feature output by the target network layer; executing, in response to reusing the hyperparameter of the target network layer, a second computational subtask in the computational task using the operator unit based on the first feature retrieved from the storage unit, so as to obtain a second feature output by the target network layer, where the first computational subtask and the second computational subtask are subtasks sequentially executed in the computational task; and determining a model output result of the target model using the operator unit based on the second feature retrieved from the storage unit.

Abstract: The disclosure provides a method and an apparatus for processing an image, an electronic device, and a storage medium, which relates to the field of artificial intelligence technologies, and particularly to a technical field such as computer vision, deep learning, and large-scale models. The solution includes: obtaining an input content adapted to an image processing task, in which the input content includes at least one of: a first text token sequence, a first image token sequence, or an image-text fusion sequence; obtaining a joint feature representation including multimodal semantic information by performing cross-modal semantic modeling on the input content, in which the multimodal semantic information indicates a semantic correlation relationship of the input content in different modalities; and generating an output content adapted to the image processing task based on the joint feature representation.

Abstract: A training method for a multimodal speech language large model is provided. The implementation is: obtaining first response speech data generated by the multimodal speech language large model by inputting first inquiry speech data into the multimodal speech language large model; determining an inquiry text corresponding to the first inquiry speech data and a response text corresponding to the first response speech data; determining, based on the inquiry text and the response text, a first score; determining, based on speech features of the first inquiry speech data and speech features of the first response speech data, a second score, where the speech features include at least one of speech clarity, speech rate feature, timbre feature, intonation feature, and emotion feature; and adjusting, based on the first score and the second score, parameters of the multimodal speech language large model.