Abstract: Fabricating a regrown GaN p-n junction includes depositing a n-GaN layer on a substrate including n+-GaN, etching a surface of the n-GaN layer to yield an etched surface, depositing a p-GaN layer on the etched surface, etching a portion of the n-GaN layer and a portion of the p-GaN layer to yield a mesa opposite the substrate, and passivating a portion of the p-GaN layer around an edge of the mesa. The regrown GaN p-n junction is defined at an interface between the n-GaN layer and the p-GaN layer. The regrown GaN p-n junction includes a substrate, a n-GaN layer on the substrate having an etched surface, a p-GaN layer on the etched surface, a mesa defined by an etched portion of the n-GaN layer and an etched portion of the p-GaN layer, and a passivated portion of the p-GaN layer around an edge of the mesa.

Abstract: Fabricating a vertical-channel junction field-effect transistor includes forming an unintentionally doped GaN layer on a bulk GaN layer by metalorganic chemical vapor deposition, forming a Cr/SiO2 hard mask on the unintentionally doped GaN layer, patterning a fin by electron beam lithography, defining the Cr and SiO2 hard masks by reactive ion etching, improving a regrowth surface with inductively coupled plasma etching, removing hard mask residuals, regrowing a p-GaN layer, selectively etching the p-GaN layer, forming gate electrodes by electron beam evaporation, and forming source and drain electrodes by electron beam evaporation. The resulting vertical-channel junction field-effect transistor includes a doped GaN layer, an unintentionally doped GaN layer on the doped GaN layer, and a p-GaN regrowth layer on the unintentionally doped GaN layer. Portions of the p-GaN regrowth layer are separated by a vertical channel of the unintentionally doped GaN layer.

Abstract: A control method for a reinforcement learning system includes following operations. The reinforcement learning system obtains training data relating to an interaction system. The interaction system interacts with a reinforcement learning agent. A neural network model is utilized by the reinforcement learning agent for selecting sequential actions from a set of candidate actions. The neural network model is trained to maximize cumulative rewards collected by the reinforcement learning agent in response to the sequential actions. During training of the neural network model, auxiliary rewards of the cumulative rewards are provided to the reinforcement learning agent according to a comparison between symptom inquiry actions of the sequential actions and diagnosed symptoms in the training data.

Abstract: The invention is directed to methods for selecting a treatment option for an activated B cell-like diffuse large B cell lymphoma (ABC DLBCL) subject, a germinal center B cell-like diffuse large B cell lymphoma (GCB DLBCL) subject, a primary mediastinal B cell lymphoma (PMBL) subject, a Burkitt lymphoma (BL) subject, or a mantle cell lymphoma (MCL) subject by analyzing digital gene expression data obtained from the subject, e.g., from a biopsy sample.

Abstract: Disclosed herein are methods of diagnosing and treating a subject with prostate cancer, as well as methods of monitoring the responsiveness of a subject having prostate cancer to a therapeutic agent.

Abstract: Provided is an immunoconjugate useful in inhibiting tumor growth, and a composition and/or protein mixture comprising the immunoconjugate. Also provided are methods for the production of the immunoconjugate, as well as pharmaceutical uses of the immunoconjugate in inhibiting tumor growth, including but not limited to treatment of cancers.

Abstract: A switching device including a GaN substrate; an unintentionally doped GaN layer on a first surface of the GaN substrate; a regrown unintentionally doped GaN layer on the unintentionally doped GaN layer; a regrowth interface between the unintentionally doped GaN layer and the regrown unintentionally doped GaN layer; a p-GaN layer on the regrown unintentionally doped GaN layer; a first electrode on the p-GaN layer; and a second electrode on a second surface of the GaN substrate.

Abstract: A method for preventing or treating a non-alcoholic fatty liver disease in a subject who is at risk of suffering from the non-alcoholic fatty liver disease or suffers from the non-alcoholic fatty liver disease, by administering to the subject a composition comprising a therapeutically effective amount of Antrodins.

Abstract: A computing method, suitable for computing a transformer model, include following steps. An input matrix corresponding to an input sequence of feature vectors is projected into a query matrix according to first learnable weights. The input matrix is projected into a value matrix according to second learnable weights. A factorized matrix is generated by an incomplete Cholesky factorization according to the query matrix and a transpose of the query matrix. An intermediate matrix is calculated according to a product between a transpose of the factorized matrix and the value matrix. An output matrix is calculated according to a product between the factorized matrix (H) and the intermediate matrix.

Abstract: Fabricating a vertical-channel junction field-effect transistor includes forming an unintentionally doped GaN layer on a bulk GaN layer by metalorganic chemical vapor deposition, forming a Cr/SiO2 hard mask on the unintentionally doped GaN layer, patterning a fin by electron beam lithography, defining the Cr and SiO2 hard masks by reactive ion etching, improving a regrowth surface with inductively coupled plasma etching, removing hard mask residuals, regrowing a p-GaN layer, selectively etching the p-GaN layer, forming gate electrodes by electron beam evaporation, and forming source and drain electrodes by electron beam evaporation. The resulting vertical-channel junction field-effect transistor includes a doped GaN layer, an unintentionally doped GaN layer on the doped GaN layer, and a p-GaN regrowth layer on the unintentionally doped GaN layer. Portions of the p-GaN regrowth layer are separated by a vertical channel of the unintentionally doped GaN layer.

Abstract: A computer aided medical method include following steps. An initial symptom is collected through an interaction interface. A representative prediction model is selected from plural candidate prediction models according to the initial symptom. The candidate prediction models are trained by a machine learning algorithm according to clinical data. A series of sequential actions is generated according to the representative prediction model and the initial symptom. The sequential actions are selected from plural candidate actions in the representative prediction model. The candidate actions include plural inquiry actions and plural disease prediction actions. Each of the sequential actions is one of the inquiry actions or the disease prediction actions. The series of sequential actions is displayed on the interaction interface.

Abstract: A medical system is able to provide a symptom query interpretation and/or a disease diagnosis interpretation. The medical system includes an interface and a processor. The interface is configured for receiving an input state. The processor is coupled with the interface. The processor is configured to execute a symptom checker to select a current action, from a plurality of candidate symptom queries and a plurality of candidate disease predictions, according to the input state. In response to the current action is a first symptom query, the processor is configured to execute an interpretable module interacted with the symptom checker to generate a diagnostic tree for simulating possible diagnosis paths, and generate a symptom query interpretation about the first symptom query according to the diagnostic tree.

Abstract: A p-n diode includes a first electrode, a n-GaN layer on the first electrode, a p-GaN layer on the n-GaN layer, and a second electrode on a first portion of the p-GaN layer. A region of the p-GaN layer surrounding the electrode is a passivated region. Treating a GaN power device having a p-GaN layer includes covering a portion of the p-GaN layer with a metal layer, exposing the p-GaN layer to a hydrogen plasma, and thermally annealing the p-GaN layer, thereby passivating a region of the p-GaN layer proximate the metal layer.

Abstract: Proteinaceous heterodimers, pharmaceutical compositions, medicaments and/or kits comprising the proteinaceous heterodimers, methods for producing the proteinaceous heterodimers, and uses thereof.

Abstract: This application discloses a method: determining, based on a length of to-be-encoded information bits and a code rate, a code length N after encoding; determining, based on N, a minimum segment code length, and a maximum segment code length, a reserved segment quantity of each type of segments in segments of b?a+1 types of segment code lengths and a reserved code length corresponding to N, where the minimum segment code length is 2{circumflex over (?)}a, and the maximum segment code length is 2{circumflex over (?)}b; determining a segment quantity of each type of segments based on N, the reserved code length, a segment code length of each type of segments, and the reserved segment quantity of each type of segments, where N corresponds to S segments, and a segment code length of an ith segment in the S segments is greater than or equal to a segment code length of an (i+1)th segment in the S segments.

Abstract: A computer aided medical method includes the following steps. An initial symptom of a patient and context information is collected through an interaction interface. Actions in a series are sequentially generated according to the candidate prediction models and the initial symptom. Each of the actions corresponds to one of the inquiry actions or one of the disease prediction actions. If the latest one of the sequential actions corresponds to one of the disease prediction actions, potential disease predictions are generated in a first ranking evaluated by the candidate prediction models. The first ranking is adjusted into a second ranking according to the context information. A result prediction corresponding to the potential disease predictions is generated in the second ranking.

Abstract: A machine learning method includes steps of: obtaining, by a processor, a model parameter from a memory, and performing, by a processor, a classification model according to the model parameter, wherein the classification model comprises a plurality of neural network structural layers; calculating, by the processor, a first loss and a second loss according to a plurality of training samples, wherein the first loss corresponds to an output layer of the plurality of neural network structural layers, and the second loss corresponds to one, which is before the output layer, of the plurality of neural network structural layers; and performing, by the processor, a plurality of updating operations for the model parameter according to the first loss and the second loss to train the classification model.

Abstract: Fabricating a vertical-channel junction field-effect transistor includes forming an unintentionally doped GaN layer on a bulk GaN layer by metalorganic chemical vapor deposition, forming a Cr/SiO2 hard mask on the unintentionally doped GaN layer, patterning a fin by electron beam lithography, defining the Cr and SiO2 hard masks by reactive ion etching, improving a regrowth surface with inductively coupled plasma etching, removing hard mask residuals, regrowing a p-GaN layer, selectively etching the p-GaN layer, forming gate electrodes by electron beam evaporation, and forming source and drain electrodes by electron beam evaporation. The resulting vertical-channel junction field-effect transistor includes a doped GaN layer, an unintentionally doped GaN layer on the doped GaN layer, and a p-GaN regrowth layer on the unintentionally doped GaN layer. Portions of the p-GaN regrowth layer are separated by a vertical channel of the unintentionally doped GaN layer.

Abstract: An antibody may be capable of specifically binding CD137 or an antigen binding fragment thereof, wherein the light chain variable region and the heavy chain variable region have one or more mutations. The antibody or antigen binding fragment thereof may be used in preparing a drug. The antibody or an antigen binding fragment thereof may specifically bind CD137, and may include a light chain variable region VL and a heavy chain variable region VH, wherein compared to a sequence as shown in SEQ ID NO: 103, the VL comprises one or more VL amino acid mutations, and the VL amino acid mutation occurs at one or more positions: V3, A10, K44, D71, and/or V77.

Abstract: A power control system with automatic balancing between dual inputs of power includes a first power supply, a second power supply, a power supply, a second power supply, a plurality of first power supply units and a plurality of second power supply units. The first power supply unit includes a first switch unit, and the second power supply unit includes a second switch unit. When the first power supply is powered off, the second power supply is switched through the first switch unit to power the first power supply unit. When the second power supply is powered off, the first power supply is switched through the second switch unit to power the second power supply unit. The power control system disclosed improves power usage and efficiency of the power supplies.