Abstract: A method and system for estimating the ground state energy of a quantum Hamiltonian. The disclosed algorithm may run on any hardware and is suited for early fault tolerant quantum computers. The algorithm employs low-depth quantum circuits with one ancilla qubit with classical post-processing. The algorithm first draws samples from Hadamard tests in which the unitary is a controlled time evolution of the Hamiltonian. The samples are used for evaluating the convolution of the spectral measure and a filter function, and then inferring the ground state energy from this convolution. Quantum circuit depth is linear in the inverse spectral gap and poly-logarithmic in the inverse target accuracy and inverse initial overlap. Runtime is polynomial in the inverse spectral gap, inverse target accuracy, and inverse initial overlap. The algorithm produces a highly-accurate estimate of the ground state energy with reasonable runtime using low-depth quantum circuits.

Abstract: A method for automatically classifying emission tomographic images includes receiving original images and a plurality of class labels designating each original image as belonging to one of a plurality of possible classifications and utilizing a data generator to create generated images based on the original images. The data generator shuffles the original images. The number of generated images is greater than the number of original images. One or more geometric transformations are performed on the generated images. A binomial sub-sampling operation is applied to the transformed images to yield a plurality of sub-sampled images for each original image. A multi-layer convolutional neural network (CNN) is trained using the sub-sampled images and the class labels to classify input images as corresponding to one of the possible classifications. A plurality of weights corresponding to the trained CNN are identified and those weights are used to create a deployable version of the CNN.

Abstract: A split type bolt includes a bolt cap and a screw rod. The bolt cap is composed of a plurality of bolt cap split parts, and the screw rod is composed of a screw rod base and a plurality of screw rod split parts. A gap is formed between adjacent bolt cap split parts and is filled with a filling material, and a gap is formed between adjacent screw rod split parts and is not filled. A method for manufacturing the split type bolt includes the steps of cutting an incomplete through gap from the bolt cap to the screw rod of a high strength bolt by a machine tool, and arranging the filling material in the through gap formed in the bolt cap.

Abstract: A method for determining an abnormality score map for Single-photon Emission Computed Tomography (SPECT) gamma camera flood analysis includes extracting a plurality of image patches from an input flood image and generating a feature vector for each image patch. A per-patch abnormality score is generated for each feature vector by comparing the feature vector against a normal flood feature dictionary comprising one or more normal flood feature vectors generated using a plurality of normal flood images. Then, an abnormality score map may be generated to depict the per-patch abnormality scores for the input flood image.

Abstract: A method for automatically classifying emission tomographic images includes receiving original images and a plurality of class labels designating each original image as belonging to one of a plurality of possible classifications and utilizing a data generator to create generated images based on the original images. The data generator shuffles the original images. The number of generated images is greater than the number of original images. One or more geometric transformations are performed on the generated images. A binomial sub-sampling operation is applied to the transformed images to yield a plurality of sub-sampled images for each original image. A multi-layer convolutional neural network (CNN) is trained using the sub-sampled images and the class labels to classify input images as corresponding to one of the possible classifications. A plurality of weights corresponding to the trained CNN are identified and those weights are used to create a deployable version of the CNN.

Abstract: A method for determining an abnormality score map for Single-photon Emission Computed Tomography (SPECT) gamma camera flood analysis includes extracting a plurality of image patches from an input flood image and generating a feature vector for each image patch. A per-patch abnormality score is generated for each feature vector by comparing the feature vector against a normal flood feature dictionary comprising one or more normal flood feature vectors generated using a plurality of normal flood images. Then, an abnormality score map may be generated to depict the per-patch abnormality scores for the input flood image.

Abstract: The present invention discloses single-walled carbon nanotubes horizontal arrays with ultra-high density and the preparation method. The method comprises the following steps: loading a catalyst on a single crystal growth substrate; after annealing, introducing hydrogen into a chemical vapor deposition system to conduct a reduction reaction of the catalyst; and maintaining the introduction of the hydrogen to conduct the orientated growth of a single-walled carbon nanotube. The density of the ultra-high density single-walled carbon nanotube horizontal array obtained by this method exceeds 130 tubes/micrometer, and an electrical performance test is performed on the prepared ultra-high density single-walled carbon nanotube horizontal array shows a high on-current density of 380 ?A/?m, and the transconductance of 102.5 ?S/?m.