Abstract: In analog-to-digital conversion, a plurality of stages configured in a sequence to sequentially decide a plurality of bits in successive-approximation, each of the plurality of stages configured to operate in response to a corresponding clock among a plurality of clocks, and decide a corresponding bit among the plurality of bits from a corresponding positive pulse among a plurality of positive pulses and a corresponding negative pulse among a plurality of negative pulses; and a plurality of clock generating circuits respectively corresponding to a plurality of first stages among the plurality of stages, each of the plurality of clock generating circuit configured to generate the corresponding clock of a corresponding stage among the plurality of first stages based on an operation of a previous stage among the plurality of stages, the previous stage being before the corresponding stage in the sequence.

Abstract: The present invention relates to a quantum federated learning system that performs federated learning on the basis of at least one observation value input from a single-hop offloading environment, and the system includes: a global server for initializing parameters of a quantum slimmable neural network (QSNN) model and transmitting the initialized quantum slimmable neural network model to at least one local device; and the at least one local device for inputting the at least one observation value into the initialized quantum slimmable neural network model to train the quantum slimmable neural network model, and transmitting the parameters of the trained quantum slimmable neural network model to the global server side. Through the system, the environmental epidemiology problems of the federated learning performed in conventional computing, such as communication channel conditions and energy limitations over time can be solved.

Abstract: Disclosed is a method for repairing a null pointer exception performed by a computing device including a processor, which includes: obtaining an original program; and obtaining, by using a classification model, a patch program from the original program by repairing an error of the original program; and, in which the classification model may be trained in advance by using a null processing pattern dataset obtained from at least one program.

Abstract: The present invention relates to a fiber-nanowire composite-based sheet having super-amphiphilic characteristics. In the present invention, fibers including metal nanoparticles or metal oxide nanoparticles embedded in the fibers or located on the surface of the fibers are synthesized, and a sheet based on a composite in which metal nanowires or metal oxide nanowires have been grown from the above fibers is provided. A sheet of the present invention has super-amphiphilic characteristics and can be used in various fields such as the antibacterial filter field, the antibacterial film field, the antiviral filter field, the antiviral film field, the antifouling coating field, the drug delivery vehicle field, or the water treatment filter field.

Abstract: Disclosed is a network hypervisor apparatus for providing a software defined networking (SDN)-based virtual network, the network hypervisor apparatus including a data collector configured to collect control traffic data and network topology information for each virtual switch; a control traffic predictor configured to predict future control traffic based on the control traffic data and the network topology information; and a translator configured to translate a control message corresponding to a virtual switch based on a prediction result.

Abstract: A photobioreactor fabrication system is disclosed. According to a first embodiment, the photobioreactor fabrication system includes a first unwinder 10 continuously unwinding a first transparent film 1, a second unwinder 20 arranged parallel to the first unwinder 10 and continuously unwinding a second transparent film 2 such that the second film 2 faces the first film 1, a heat sealer 30 pressurizing and heat sealing the first 1 and second films 2 to a baffle 3 arranged between the first 1 and second films 2 facing each other, and a rewinder 40 simultaneously and continuously winding the first 1 and second films 2 having passed through the heat sealer 30.

Abstract: The method for solving an N-queen problem includes: an initialization step of generating a quantum substitution matrix state which is a superposition of qubit states satisfying a row condition and a column condition of the N-queen problem; an Oracle step of performing an operation of inverting a phase of at least one entire correct answer state that satisfies a diagonal condition of the N-queen problem in the quantum substitution matrix state; an amplification step of amplifying an amplitude of at least one entire correct answer state of which the phase is inverted; amplifying the amplitude of at least one entire correct answer state to correspond to a predetermined value by performing the Oracle step and the amplification step repeatedly at a predetermined number of times; and acquiring one first correct answer state among at least one entire correct answer state based on observation of a qubit state.

Abstract: Disclosed is a profiling-based distributed deep learning job ordering method and apparatus. The ordering method refers to a distributed deep learning job ordering method performed by a computing device including at least a processor and includes profiling each of a plurality of distributed deep learning jobs; and selecting distributed deep learning jobs to concurrently run based on profiling results.

Abstract: The present invention relates to a metabolome sampling and analysis method for analyzing metabolome during synthetic gas fermentation of a synthetic gas fermentation microorganisms, the method establishing an optimal condition for metabolome sampling and enabling a glucose culture and a synthetic gas culture of the synthetic gas fermentation microorganisms to be distinguished by using a selected metabolomic biomarker.

Abstract: An extrinsic calibration method of multiple 3D LiDAR sensors for an autonomous navigation system is proposed, the method including collecting point clouds by each of the multiple 3D LiDAR sensors; extracting multiple target planes corresponding to a plane from the point clouds of each of the 3D LiDAR sensors; by using the target plane of any one of the multiple 3D LiDAR sensors as a reference plane, detecting a corresponding plane from the target planes of each of the remaining of the 3D LiDAR sensors on the basis of a similarity with the reference plane; calculating initial extrinsic parameters for matching between the reference plane and the corresponding plane based on plane parameters of the reference plane and corresponding plane corresponding to each other; and calculating final extrinsic parameters that minimize variance of measurement points for the reference plane and corresponding plane, on the basis of the initial extrinsic parameters.

Abstract: The present invention relates to a composition for inducing dedifferentiation form somatic cells to induced pluripotent stem cells (iPSs) and method of inducing dedifferentiation using same, wherein the composition for inducing dedifferentiation and the method of inducing dedifferentiation increases the efficiency of dedifferentiation from somatic cells to iPSs by stimulating CXC chemokine receptor 2 (CXCR2), which is a receptor on human somatic cells, and thus may be effectively used for inducing the dedifferentiation to iPSs.

Abstract: Disclosed herein is a method of generating a similarity determination model of programming codes based on a cross-validation ensemble and filtering strategy. The method of generating the similarity determination model is performed by a computing device including at least a processor, the method includes: performing preprocessing on raw data written in any one language; performing filtering on the preprocessed data; generating positive pairs and negative pairs for training; and training a pre-trained language model using the generated positive pairs and negative pairs.

Abstract: The present disclosure relates to a cancer cell-specific complex for targeting cancer. The complex for targeting cancer according to the present disclosure includes EGF, but is affected more by lysosomal activity rather than the EGFR signaling pathway, and thus can overcome the shortcomings of the existing EGF therapy-based diagnostic and therapeutic compositions and provide successfully personalized and improved effects of treating, preventing and alleviating cancer. A diagnostic composition according to the present disclosure enables the prediction of the anticancer performance of a therapeutic composition of the present disclosure in a subject, and thus enables anticancer treatment to be designed stably.

Abstract: Disclosed are a low-light image improvement apparatus and method. The low-light image improvement apparatus includes: an image component decomposition network module that analyzes a light image, a low-light image, and a mid-light image to decompose reflectance and illumination, respectively, wherein the mid-light image is generated using the light image and the low-light image; and a component improvement network module configured to include a mid-teacher network model that extracts a first feature map with improved reflectance and illumination of the mid-light image using the reflectance and illumination of the light image and a student network module that distills the extracted first feature map and then extracts a second feature map for the reflectance and illumination of the low-light image based on the distilled first feature map and acquires an image with improved light by reflecting a structural component of a multi-band near-infrared image in the second feature map.

Abstract: A method and apparatus with object detector training is provided. The method includes obtaining first input data and second input data from a target object; obtaining second additional input data by performing data augmentation on the second input data; extracting a first feature to a shared embedding space by inputting the first input data to a first encoder; extracting a second feature to the shared embedding space by inputting the second input data to a second encoder; extracting a second additional feature to the shared embedding space by inputting thesecond additional input data to the second encoder; identifying a first loss function based on the first feature, the second feature, and the second additional feature; identifying a second loss function based on the second feature and the second additional feature; and updating a weight of the second encoder based on the first loss function and the second loss function.

Abstract: Disclosed are a nanobarcode for controlling adhesion and polarization of macrophages and a method of controlling adhesion and polarization of macrophages by using nanobarcodes. The method of controlling adhesion and polarization of macrophages of the present invention may efficiently control adhesion and phenotypic polarization of macrophages in vivo or in vitro by tuning periodicity and sequences of ligand peptide (RGD) of a nanobarcode.

Abstract: Provided are an enzyme complex for decomposing polyethylene terephthalate (PET), a method for decomposing waste plastic using the enzyme complex, and a manufacturing method of the enzyme complex. According to the present disclosure, since the enzyme complex is a complex form of Ideonella sakaiensis-derived PETase and Candida Antarctica-derived lipase (CALB) by dockerin-cohesin binding and is simultaneously applicable to a substrate to be decomposed, it is possible to exhibit a synergistic effect on the decomposition of polyethylene terephthalate. In addition, it is possible to provide a stable enzyme complex of decomposing polyethylene terephthalate by providing a mini-scaffolding protein obtained by miniaturizing cellulosome as a scaffolding protein.

Abstract: Disclosed is a sulfur-doped micro zero-valent metal reducing agent containing a zero-valent metal, wherein a metal sulfide layer synthesized using a ball milling process may be formed on a surface of the zero-valent metal. In addition, disclosed is a method for preparing a sulfur-doped micro zero-valent metal reducing agent including, in a ball milling process using a ball milling apparatus composed of a jar, balls, and a body, a first step of preparing an inorganic mixture by mixing a zero-valent metal and sulfur with each other, and a second step of forming a metal sulfide layer synthesized on a surface of the zero-valent metal by putting the inorganic mixture into the jar together with the balls and performing ball milling.

Abstract: Disclosed are non-contiguous sample fractionating and concatenating device and a dual online multidimensional liquid chromatography system having the same. The non-contiguous sample fractionating and concatenating device according to an embodiment of the present disclosure includes a sample supply module which supplies a sample to be analyzed, and a sample fractionation module connected to the sample supply module, and which is continuously supplied with the sample, sets a plurality of unit sample supply times obtained by equally dividing a total sample supply time during which the sample is supplied from the sample supply module, sets a plurality of unit fractionation intervals obtained by equally dividing each of the plurality of unit sample supply times, and concatenates and stores the sample supplied during corresponding unit fractionation intervals within each unit sample supply time to acquire a plurality of fractions.

Abstract: Disclosed is a prediction model generation method for predicting training time and resource consumption required for distributed deep learning training and a prediction method using the prediction model. The prediction model generation method is performed by a computing device including at least one processor and includes constructing a training dataset; and generating a prediction model by training a graph neural network (GNN). The training dataset includes input data and result data, the construction of the training dataset includes converting a distributed deep learning training code (distributed training (DT) code) to a graph; and extracting an adjacency matrix and a feature matrix from the graph.