Abstract: A mask includes a reflective layer, an absorption layer, a buffer layer and an absorption part. The absorption layer is disposed over the reflective layer. The buffer layer is disposed between the reflective layer and the absorption layer. The absorption part is disposed in the reflective layer, the buffer layer and the absorption layer.

Abstract: An embodiment of the invention provides a data authentication device. The data authentication device may include a main memory, a backup memory, a platform control hub (PCH) and an embedded controller (EC). The main memory may be configured to store data. The backup memory may be configured to back up the data stored in the main memory. The PCH is coupled to the main memory and generates a write command to write a first data image to the main memory, wherein the first data image comprises updated data and a digital signature. The EC is coupled to the main memory, the backup memory and the PCH and obtains the first data image from the PCH. When the EC detects a write command, the EC may perform an authentication for the updated data based on the first data image or a second data image corresponding to the first data image.

Abstract: This invention pertains to compounds of the general formula (I) and their applications. The invention introduces a novel class of compounds exhibiting androgen receptor (AR) antagonistic activity. In particular, these compounds target a binding pocket located in the dimer interface of AR ligand binding domain (AR-LBD). These compounds effectively disrupt AR dimerization, thereby suppressing AR signaling. The high AR antagonistic activity and favorable safety profile of the compounds provided by the present invention have been substantiated through biochemical assays and animal models. Therefore, these compounds can be utilized in the formulation of medications to treat diseases characterized by abnormal expression of AR, including but not limited to prostate cancer, metastatic prostate cancer, castration-resistant prostate cancer, breast cancer and ovarian cancer.

Abstract: The system and method may assess the merchant risk level on a more continuous scale rather than a binary categorization. It may produce a continuous risk score proportional to the likelihood of a merchant being risky, effectively addressing the issue of shades of gray encountered by the traditional blacklisting approach. The continuous risk score feature provides greater flexibility as it allows the payment network to make dynamic pricing decisions (known as interchange optimization) based on the merchant risk level. Using collective intelligence from transactions across the payment network, the system and method may be able to assess the merchant risk level with high accuracy. The system and method may be particularly beneficial to small merchants with low transaction volume as even a few fraudulent transactions can easily put them in the high-risk merchant category. Further, the system and method may help payment processing networks make better decision on cross-border transactions.

Abstract: A method for automatic optical inspection includes (i) receiving an image of an object, (ii) classifying the image of the object as one of a plurality of categories by a classification model, (iii) determining a label for the image of the object as being qualified if the category obtained by the classification model is a first category, and (iv) performing defect measurement for the image by a segmentation model and determining a label for the image as being qualified or unqualified based on the defect measurement obtained by the segmentation model if the category obtained by the classification model is a second category.

Abstract: Systems, methods, and computer program products are provided for predicting a specified geographic area of a user. An example system includes a processor configured to determine a verified geographic area associated with each user, and determine a feature vector associated with an account of each user. The processor is also configured to receive transaction data and determine a value of each parameter of the feature vector for each user based on the transaction data to produce a training matrix. The processor is further configured to train and validate a geographic area prediction model based on the training matrix. The processor is further configured to repeatedly generate a prediction that a user will conduct a transaction in a geographic area, communicate an offer to the user based on the prediction, receive new training data, and update the geographic area prediction model based on the new training data.

Abstract: The present invention relates to novel antimicrobial peptides and compositions comprising the same. The present invention also provides a method for for treating microbial infections, including bacterial infections and fungal infections.

Abstract: Provided are computer-implemented methods for generating embeddings for objects which may include receiving heterogeneous network data associated with a plurality of objects in a heterogeneous network; selecting at least one pattern of objects; determining instances of each pattern of objects based on the heterogeneous network data; generating a pattern matrix for each pattern of objects based on the instances of the pattern of objects; generating pattern sequence data associated with a portion of each pattern matrix; generating network sequence data associated with a portion of the heterogeneous network data; and combining the pattern sequence data and the network sequence data into combined sequence data. In some non-limiting embodiments or aspects, methods may include generating a vector for each object of the plurality of objects based on the combined sequence data. Systems and computer program products are also provided.

Abstract: A semiconductor device is provided, which includes a semiconductor stack and a first contact structure. The semiconductor stack includes an active layer and has a first surface and a second surface. The first contact structure is located on the first surface and includes a first semiconductor layer, a first metal element-containing structure and a first p-type or n-type layer. The first metal element-containing structure includes a first metal element. The first p-type or n-type layer physically contacts the first semiconductor layer and the first metal element-containing structure. The first p-type or n-type layer includes an oxygen element (O) and a second metal element and has a thickness less than or equal to 20 nm, and the first semiconductor layer includes a phosphide compound or an arsenide compound.

Abstract: Provided are systems for determining adversarial examples that include at least one processor to determine a first additional input from a plurality of additional inputs based on a proximity of the first additional input to an initial input, determine a second additional input from the plurality of additional inputs based on a proximity of the second additional input to the first additional input, generate a first vector embedding, a second vector embedding and a third vector embedding based on the second additional input, generate a first relational embedding, a second relational embedding, and a third relational embedding based on the third vector embedding and the first vector embedding, concatenate the first relational embedding, the second relational embedding, and the third relational embedding to provide a concatenated version, and determine whether the first input is an adversarial example based on the concatenated version. Methods and computer program products are also provided.

Abstract: A feature quantity based on the strength of a correlation between each of a plurality of different visual stimulus patterns corresponding to a plurality of different sound sources and a pupil diameter change amount of a user is obtained, and a destination to which the user pays auditory attention for a sound from the sound source is estimated using the feature quantity.

Abstract: This disclosure concerns a method of forming a porous material, comprising impregnating a porous polymer with a pore-forming agent in order to form an impregnated polymer, and at least partially carbonising the impregnated polymer at a temperature of about 150° C. to about 500° C. in order to form the porous material. The porous material is characterised by a crystallinity of about 10% to about 70% relative to the porous polymer. This disclosure also concerns the porous material thereof.

Abstract: For transformer fault detection, a method generates a transition region that separates a health region and a fault region in a two-dimensional feature space of two feature indicators for a plurality of operation conditions using a fault detection model for a power transformer type. The method determines the feature indicators of a given power transformer of the power transformer type. The method determines whether the feature indicators in the transition region satisfy a fault condition. The method predicts an inter-turn short fault for the given power transformer in response to satisfying the fault condition or the feature indicators being in the fault region.

Abstract: An additive method for preparing a large die blank for isothermal forging comprising preparing a plurality of titanium-zirconium-molybdenum alloy plate-shaped elements of a preset shape; preparing a plurality of foil-shaped intermediate layers of pure tantalum, a niobium-tungsten alloy and a tantalum-tungsten alloy of a preset shape; forming an assembly of a preset configuration, such that the foil-shaped intermediate layers are sandwiched between the titanium-zirconium-molybdenum alloy plate-shaped elements; applying an axial pressure to the assembly under high-temperature vacuum to perform diffusion connections to obtain a primary blank; subjecting the primary blank to a homogenization treatment under a high temperature, vacuum or inert gas protection to homogenize the structure and components at a connection interface in the primary blank; and cooling the homogenized primary blank to obtain a die blank.

Abstract: Provided is a method for detecting group activities in a network. The method may include receiving interaction data associated with a plurality of interactions. For each account identifier associated with at least one interaction, a value may be determined for each of a first set of categories, and a vector may be generated based on the value for each category. The length of each vector may be determined. At least one relational graph may be generated based on the interaction data. Each relational graph may be associated with a respective category of a second set of categories. At least one cluster of nodes may be determined based on the relational graph(s). A score for each cluster may be determined based on the length of the vector associated with the account identifier of each node of the cluster of nodes. A system and computer program product are also disclosed.

Abstract: A computer system can perform a semi-supervised machine learning processes to cluster a plurality of entities within a population based on their features and associated labels. The computer system can generate visualization data representing the clusters of entities and associated labels for displaying on a user interface. A user can review the clustering of entities and use the user interface to add or modify the labels associated with a particular entity or set of entities. The computer system can use the user's feedback to update the labels and then re-determine the clustering of entities using the semi-supervised machine learning process with the updated labels as input. As such, the computer system can use the user's feedback to improve the accuracy of the machine learning model without requiring a larger amount of labeled input data.

Abstract: A plastic mold steel plate and a method for producing the same. The method includes: heating a billet, the temperature of a soaking section being 1210˜1250° C.; rolling into a steel plate, an initial rolling temperature being 1060˜1140° C., and a final rolling temperature being 980˜1050° C.; moving the steel plate to a cooling bed for air cooling to 200° C. or lower; then normalizing, a normalizing temperature being Ac3+60° C. to Ac3+90° C.; after normalizing, moving the steel plate to the cooling bed for air cooling to Bf?50° C. to Bf?20° C.; finally, cross-stacking the steel plate with a ferritic pearlitic steel plate with a temperature of 450˜550° C.; during the stacking period, self-tempering the steel plate until the temperature of the steel plate is reduced to Bf?50° C. to Bf?20° C.; and after that, unstacking and air cooling.

Abstract: An example operation includes one or more of receiving traffic data uploaded from vehicles that are currently operating within a geographic area via a computer network, generating a plurality of macroscopic fundamental diagrams (MFDs) for a plurality of sub-areas within the geographic area, respectively, based on the received traffic data, identifying a sub-area among the plurality of sub-areas which is congested based on the plurality of MFDs, and transmitting routing instructions to the vehicles within the geographic area to divert the vehicles away from the sub-area.

Abstract: The present application discloses a method for preventing a fall of an operator, a controller, a safety belt and a computer-readable storage medium. The method for preventing a fall of an operator includes obtaining magnetic field information of the safety belt body worn by the operator; querying a preset fixed state mapping table based on the magnetic field information of the safety belt body to obtain a fixed state of the safety belt, the fixed state mapping table is configured to save a mapping relationship between the magnetic field information and the fixed state; and outputting a warning signal based the fixed state.

Abstract: A system, method, and computer program product for incorporating knowledge from more complex models in simpler models. A method may include obtaining first training data associated with a first set of features and second training data associated with a second set of features different than the first set of features; training a first model based on the first training data and the second training data; and training a second model, using a loss function that depends on an output of an intermediate layer of the first model and an output of the second model, based on the second training data.