Abstract: Disclosed is a method for training a graphics processing neural network with a patch-based approach, which involves calculating an overlapping size and an invalid size of an output of each of at least one of multiple feature extraction layers of the graphic processing neural network according to a predetermined cropping scheme, dividing an input image into first patches in the forward pass and the first gradients into second patches in the backward pass to run streamline operation of the first and second patches. Before training, each first patch overlaps neighboring first patches at adjacent edges. In the forward pass, an invalid portion of the output of each of the at least one of the feature extraction layers cropped out based on the predetermined cropping scheme and a corresponding invalid size. Such method secures streamline operation in favor of enhanced memory utilization in training and accurate model prediction.

Abstract: Disclosed is a method for enhancing memory utilization and throughput of a computing platform in training a deep neural network (DNN). The critical features of the method includes: calculating a memory size for every operation in a computational graph, storing the operations in the computational graph in multiple groups with the operations in each group being executable in parallel and a total memory size less than a memory threshold of a computational device, sequentially selecting a group and updating a prefetched group buffer, and simultaneously executing the group and prefetching data for a group in the prefetched group buffer to the corresponding computational device when the prefetched group buffer is update. Because of group execution and data prefetch, the memory utilization is optimized and the throughput is significantly increased to eliminate issues of out-of-memory and thrashing.

Abstract: Disclosed is a method for training a graphics processing neural network with a patch-based approach, which involves calculating an overlapping size and an invalid size of an output of each of at least one of multiple feature extraction layers of the graphic processing neural network according to a predetermined cropping scheme, dividing an input image into first patches in the forward pass and the first gradients into second patches in the backward pass to run streamline operation of the first and second patches. Before training, each first patch overlaps neighboring first patches at adjacent edges. In the forward pass, an invalid portion of the output of each of the at least one of the feature extraction layers cropped out based on the predetermined cropping scheme and a corresponding invalid size. Such method secures streamline operation in favor of enhanced memory utilization in training and accurate model prediction.

Abstract: Disclosed is a method for enhancing memory utilization and throughput of a computing platform in training a deep neural network (DNN). The critical features of the method includes: calculating a memory size for every operation in a computational graph, storing the operations in the computational graph in multiple groups with the operations in each group being executable in parallel and a total memory size less than a memory threshold of a computational device, sequentially selecting a group and updating a prefetched group buffer, and simultaneously executing the group and prefetching data for a group in the prefetched group buffer to the corresponding computational device when the prefetched group buffer is update. Because of group execution and data prefetch, the memory utilization is optimized and the throughput is significantly increased to eliminate issues of out-of-memory and thrashing.

Abstract: A method for predicting epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma is provided. The method utilizes a lung adenocarcinoma EGFR mutation classification model based on a deep learning model, and performs back-propagation training on the deep learning model by using whole-slide pathological images and corresponding pathological data. The trained lung adenocarcinoma EGFR mutation classification model can determine whether a to-be-classified slide-level image with lung adenocarcinoma features have EGFR mutations.

Abstract: A pod is used to contain a photomask formed with two parallel faces. The pod includes two lens units respectively connected to two portions of the pod corresponding to the faces of the photomask. Each of the lens units includes a lens of a transmittance equal to or larger than 90%. The lens of each of the lens units includes a visible portion equal to or larger than a pattern formed on the photomask. Light is allowed to reach or leave the photomask via the visible portion of the lens of each of the lens units when the photomask is contained in the pod.

Abstract: A lithographic method includes the step of providing a photomask with a pattern, the step of using a transparent pod to contain the photomask, the step of inserting the transparent pod in a lithographic machine, the step of using the lithograph machine to cast light onto the photomask via the transparent pod, and the step of transferring the pattern to a wafer.

Abstract: A method is provided for inspecting a photomask. The photomask is formed with upper and lower faces and four lateral faces. The method includes using a transparent pod to contain the photomask, inserting the transparent pod in an inspecting machine, using a light source to cast light over one of the upper and lower faces of the photomask, using an image sensor to receive at least some of the light from the upper or lower face of the photomask via the transparent pod, and determining whether there is contamination on the upper or lower face of the photomask based on the light received by the image sensor.

Abstract: Provided are a composition for an organic electronic device and an organic electronic device using the same. The composition includes a first host compound represented by the following Formula (I) and a second host compound represented by the following Formula (II), wherein Y is a single bond, O or S; Z1 to Z3 are each CH or adjacent two of Z1 to Z3 are joined together to form an aryl ring or a heteroaryl ring; two of X1 to X3 are each a nitrogen atom, the other of X1 to X3 is CH or a nitrogen atom; Q1 and Q2 are each CH or Q1 and Q2 are joined together to form an aryl ring.

Abstract: Provided are a novel compound and an organic electronic device using the same. The novel compound is represented by the following Formula (I): wherein n1, n2, m1, m2, m3, and g1 are each independently an integral, and the sum of n1 and n2 is 2 or 3; Ant is L1, L2 and L3 are each independently an arylene group; G1 and G2 are each independently selected from the group consisting of: a hydrogen atom, a deuterium atom, an alkyl group, an alkenyl group, an alkynyl group, a cycloalkyl group, a heterocycloalkyl group, an aryl group, and a heteroaryl group; and R1 and R2 are each independently selected from the group consisting of: a hydrogen atom, a deuterium atom, an alkyl group, and an aryl group.

Abstract: Provided are a novel compound and an organic electronic device using the same. The novel compound is represented by Formula (I): wherein n1, n2, m1, m2 and m3 are each an integer, the sum of n1 and n2 is 2 or 3; L1, L2 and L3 are each an arylene group; R1 and R2 are each selected from the group consisting of: H, D, an alkyl group, and an aryl group; G is selected from the group consisting of: H, D, —N(Z3)(Z4) group, an alkyl group, an alkenyl group, an alkynyl group, a cycloalkyl group, a heterocycloalkyl group, an aryl group, and a heteroaryl group; and Z1 to Z4 are each selected from the group consisting of: an alkyl group, an alkenyl group, an alkynyl group, a cycloalkyl group, a heterocycloalkyl group, an aryl group, and a heteroaryl group.

Abstract: Provided are a novel compound and an organic electronic device using the same. The novel compound is represented by the following Formula (I): wherein *b is bonded to one of *a1 and a2*, and *c is bonded to the other of *a1 and a2*; L1 to L2 are each independently an arylene group having 6 to 60 ring carbon atoms; Y1 is selected from the group consisting of: a hydrogen atom, a deuterium atom, an alkyl group having 1 to 12 carbon atoms, and an aryl group having 6 to 30 ring carbon atoms; and m1 to m2 are each independently an integer 0 or 1.

Abstract: Provided are a novel compound and an organic electronic device using the same. The novel corn pound is represented by the following Formula (I): wherein Y is an oxygen atom, a sulfur atom, or a sulfur dioxide group; X1 and X2 are each independently C(Ra), multiple (Ra)s are the same or different, and the two (Ra)s are joined together to form a first aryl ring; X3 and X4 are each independently C(Rb), multiple (Rb)s are the same or different, and the two (Rb)s are joined to form a second aryl ring or a heteroaryl ring.

Abstract: Provided are a novel compound and an organic electronic device using the same. The novel compound is represented by the following Formula (I): wherein X1 and X2 are each independently C(Ra), multiple (Ra)s are the same or different, and the two (Ra)s are joined together to form an aryl ring; wherein X3 and X4 are each independently C(Rb), multiple (Rb)s are the same or different, and the two (Rb)s are joined together to form a polycyclic aromatic ring.

Abstract: Provided are a novel compound and an organic electronic device using the same. The novel compound is represented by the following Formula (I): wherein X1 and X2 are each independently C(Ra), and the two (Ra)s are the same or different; X3 and X4 are each independently C(Rb), and the two (Rb)s are the same or different; the two (Ra)s are joined together to form an aryl ring and the two (Rb)s are joined together to form an oxygen-containing heteroaryl ring, a sulfur-containing heteroaryl ring, or a polycyclic aromatic ring; wherein Y1 and Y2 are the same or different; Y1 and Y2 are each represented by NR?R?; R? and R? are the same or different; at least one of R? and R? is an aryl group.

Abstract: The present specification relates to a novel compound and an organic electronic device using the same.

Abstract: Provided are a novel compound and an organic electronic device using the same. The novel compound is represented by the following Formula (I): wherein A1 and A2 each represent a carbon atom; *1 is bonded to one of A1 and A2, and *2 is bonded to the other of A1 and A2; a is an integer from 1 to 4; b is an integer from 0 to 3.

Abstract: Provided are a novel compound and an organic electronic device using the same. The novel compound is represented by the following Formula (I): G1 and G2 in Formula (I) are each independently selected from the group consisting of: and G11 and G12 are each a specific aryl group or heteroaryl group. The organic electronic device comprising the novel compound has the beneficial effects of improved luminous efficacy and external quantum efficiency.

Abstract: Provided are a composition for an organic electronic device and an organic electronic device using the same. The composition includes a first host compound represented by the following Formula (I) and a second host compound represented by the following Formula (II). wherein Y is a single bond, O or S; Z1 to Z3 are each CH or adjacent two of Z1 to Z3 are joined together to form an aryl ring or a heteroaryl ring; two of X1 to X3 are each a nitrogen atom, the other of X1 to X3 is CH or a nitrogen atom; Q1 and Q2 are each CH or Q1 and Q2 are joined together to form an aryl ring.

Abstract: Provided are a novel compound and an organic electronic device using the same. The novel compound is represented by the following Formula (I): wherein G1 and G2 are each selected from the group consisting of: one of G11 and G12 is a specific aryl group or heteroaryl group. The organic electronic device comprising the novel compound has the beneficial effect of prolonged lifespan.