Abstract: A data classification method, for classifying unlabeled images into an inlier data set or an outlier data set, include following steps. The unlabeled images are obtained. An assigned inlier image is selected among the unlabeled images. A similarity matrix is computed and the similarity matrix includes first similarity scores of the unlabeled images relative to the assigned inlier image. Each of the unlabeled images is classified into an inlier data set or an outlier data set according to the similarity matrix, so as to generate inlier-outlier predictions of the unlabeled images.

Abstract: A data classification method includes following steps. Text samples are obtained from a dataset. The text samples are converted into text embeddings in a semantic space. An outlier-inlier ranking of the text samples is generated based on an outlier detection algorithm according to distances between the text embeddings in the semantic space. Partial samples are selected from the text samples according to the outlier-inlier ranking. A manual input command is received to assign manual-input labels on the partial samples. A prompt message is generated according to the partial samples with the manual-input labels and unlabeled samples of the text samples. The prompt message is provided to a generative pre-trained transformer model for generating inlier-outlier prediction labels about the unlabeled samples.

Abstract: A data classification method, for classifying unlabeled images into an inlier data set or an outlier data set, include following steps. The unlabeled images are obtained. An assigned inlier image is selected among the unlabeled images. A similarity matrix is computed and the similarity matrix includes first similarity scores of the unlabeled images relative to the assigned inlier image. Each of the unlabeled images is classified into an inlier data set or an outlier data set according to the similarity matrix, so as to generate inlier-outlier predictions of the unlabeled images.

Abstract: The present invention provides a clock buffer, wherein the clock buffer receives an input signal at a first node and generate an output signal at a second node. The clock buffer includes a P-type transistor, a first N-type transistor, a resistor, a transistor and a switch. A source electrode, a gate electrode and a drain electrode of the P-type transistor are coupled to a supply voltage, the first node, and the second node, respectively. A gate electrode, a drain electrode and a source electrode of the first N-type transistor are coupled to the first node, the second node and a third node, respectively. The resistor is coupled between the first node and the second node. The transistor is coupled between the first N-type transistor and a ground voltage. The switch is configured to selectively connect the third node to the ground voltage.

Abstract: A medical image detection system includes a memory and a processor. The processor is configured to execute the neural network model stored in the memory. The neural network model includes a feature extractor, a feature pyramid network, a first output head and a second output head. The feature extractor is configured for extracting intermediate tensors from a medical image. The feature pyramid network is associated with the feature extractor. The feature pyramid network is configured for generating multi-resolution feature maps according to the intermediate tensors. The first output head is configured for generating a global prediction according to the multi-resolution feature maps. The second output head is configured for generating local predictions according to the multi-resolution feature maps. The processor is configured to generate output information based on the medical image, the global prediction and the local predictions.

Abstract: An example computing device may include a housing having an outer panel, the outer panel comprising a first array of openings through the outer panel, an inner panel opposite the outer panel, the inner panel comprising a second array of openings and a third array of openings different than the second array of openings and an actuator to move the inner panel relative to the outer panel between (1) a first position in which the second array of openings are at least partially aligned with the first array of openings and the third array of openings are out of alignment with the first array of openings, and (2) a second position in which the third array of openings are at least partially aligned with the first array of openings and the second array of openings are out of alignment with the first array of openings.

Abstract: A datum rebuilding method is applied to a bio-sensor module equipped in a wearable electronic device with the datum rebuilding function. The datum rebuilding method includes continuously receiving a plurality of physical signals, identifying if one specific physical signal meets a predefined condition, and outputting a warning signal to show a reminder when the specific physical signal is identified.

Abstract: A datum warning method is applied to a bio-sensor module equipped in a wearable electronic device with the datum warning function. The datum warning method includes continuously receiving a plurality of physical signals, identifying if one specific physical signal meets a predefined condition, and outputting a warning signal to show a reminder when the specific physical signal is identified.

Abstract: A datum warning method and a datum rebuilding method are applied to a bio-sensor module equipped in a wearable electronic device. The wearable electronic device includes a warning interface and a bio-sensor module coupled with the warning interface. The bio-sensor module includes a sensor and a processor electrically connected with the sensor. The sensor is adapted to continuously receive a plurality of physical signal. The processor is adapted to identify if one specific physical signal meets a predefined condition, and output a warning signal to show a reminder via the warning interface when the specific physical signal is identified. Further, the processor is adapted to display each of the physical signals via the warning interface, and replace at least one of the physical signals and then store the replaced physical signal with non-replaced physical signals for rebuilding a statistic curve.

Abstract: A datum warning method and a datum rebuilding method are applied to a bio-sensor module equipped in a wearable electronic device. The wearable electronic device includes a warning interface and a bio-sensor module coupled with the warning interface. The bio-sensor module includes a sensor and a processor electrically connected with the sensor. The sensor is adapted to continuously receive a plurality of physical signal. The processor is adapted to identify if one specific physical signal meets a predefined condition, and output a warning signal to show a reminder via the warning interface when the specific physical signal is identified. Further, the processor is adapted to display each of the physical signals via the warning interface, and replace at least one of the physical signals and then store the replaced physical signal with non-replaced physical signals for rebuilding a statistic curve.

Abstract: A method for fabricating a resistor for a resistance random access memory (RRAM) includes: (a) forming a first electrode over a substrate; (b) forming a variable resistance layer of zirconium oxide on the first electrode under a working temperature, which ranges from 175° C. to 225° C.; and (c) forming a second electrode of Ti on the variable resistance layer.

Abstract: A method for fabricating a resistor for a resistance random access memory (RRAM) includes: (a) forming a first electrode over a substrate; (b) forming a variable resistance layer of zirconium oxide on the first electrode under a working temperature, which ranges from 175° C. to 225° C.; and (c) forming a second electrode of Ti on the variable resistance layer.