Construction of Optimal Tubular Networks in Arbitrary Regions in R^3

In this thesis, we describe various algorithms for the construction of tubular networks in an arbitrary three-dimensional region that possesses a principal direction along which the cross-section varies. The region can be digitized into a series of blocks B_i, each of which exhibits no variation in the principle direction. Tubular networks with one inlet and one outlet are constructed by connecting the series of blocks packed with parallel cylindrical tubes. Packing tubes into a block B_i can be simplified to packing circles into its cross section C_i which is approximated by a polygon. A set of novel circle-packing algorithms are developed to possess the following desirable features. Firstly, circles are first packed into an interior region which is common to several or all blocks and the remainder of the unpacked region are packed afterwards. Secondly, larger circles are placed primarily in the central part of the region. Lastly, at a certain stage of circle-packing, all the packed circles are moved towards the center of mass by a fictitious force so that as much as possible empty space is left along the boundary. Three fundamental connections are used to construct a tubular network with one inlet and one outlet: (i) endcap connection--two 90-degree bends that connect two adjacent tubes at their ends, (ii) a “merge” operation in which a tube flows into an adjacent tube, and (iii) a “shift” operation in which a tube is shifted into an adjacent position. Tubes at the extreme ends of a network must be connected by endcaps and the other tubes could be connected via any one of the above connections. A set of algorithms are developed to generate all the possible solutions of constructing tubular networks and to check the feasibility of solutions to make sure there is no “dead end” or “isolated loop”. Among all the feasible networks, an optimal network should be chosen with respect to a certain measure. Obviously, enumerating all the possible solutions of constructing feasible networks is extremely time-consuming and sometimes it can take millions of years. Therefore, genetic algorithm with only mutation is applied here to randomly search for the best network. In this genetic algorithm, every fundamental connection could mutate to any other fundamental connection. Thus every network could mutate to any other one and genetic algorithm could find the globally best network

FAST reveals new evidence for M94 as a merger

We report the first high-sensitivity HI observation toward the spiral galaxy M94 with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). From these observations, we discovered that M94 has a very extended HI disk, twice larger than that observed by THINGS, which is accompanied by an HI filament and seven HVCs (high velocity clouds) at different distances. The projected distances of these clouds and filament are less than 50 kpc from the galactic center. We measured a total integrated flux (including all clouds/filament) of 127.3 ($\pm$1) Jy km s$^{-1}$, corresponding to a H I mass of (6.51$\pm$0.06)$\times$10$^{8}$M$_{\odot}$, which is 63.0% more than that observed by THINGS. By comparing numerical simulations with the HI maps and the optical morphology of M94, we suggest that M94 is likely a remnant of a major merger of two galaxies, and the HVCs and HI filament could be the tidal features originated from the first collision of the merger happened about 5 Gyr ago. Furthermore, we found a seemingly isolated HI cloud at a projection distance of 109 kpc without any optical counterpart detected. We discussed the possibilities of the origin of this cloud, such as dark dwarf galaxy and RELHIC (REionization-Limited HI Cloud). Our results demonstrate that high-sensitivity and wide-field HI imaging is important in revealing the diffuse cold gas structures and tidal debris which is crucial to understanding the dynamical evolution of galaxies.Comment: 14 pages, 8 figure

Seeing What You Miss: Vision-Language Pre-training with Semantic Completion Learning

Cross-modal alignment is essential for vision-language pre-training (VLP) models to learn the correct corresponding information across different modalities. For this purpose, inspired by the success of masked language modeling (MLM) tasks in the NLP pre-training area, numerous masked modeling tasks have been proposed for VLP to further promote cross-modal interactions. The core idea of previous masked modeling tasks is to focus on reconstructing the masked tokens based on visible context for learning local-to-local alignment. However, most of them pay little attention to the global semantic features generated for the masked data, resulting in the limited cross-modal alignment ability of global representations. Therefore, in this paper, we propose a novel Semantic Completion Learning (SCL) task, complementary to existing masked modeling tasks, to facilitate global-to-local alignment. Specifically, the SCL task complements the missing semantics of masked data by capturing the corresponding information from the other modality, promoting learning more representative global features which have a great impact on the performance of downstream tasks. Moreover, we present a flexible vision encoder, which enables our model to perform image-text and video-text multimodal tasks simultaneously. Experimental results show that our proposed method obtains state-of-the-art performance on various vision-language benchmarks, such as visual question answering, image-text retrieval, and video-text retrieval

Multiplexed Serum Biomarkers for the Detection of Lung Cancer

AbstractCurrently, there is no available biomarker for lung cancer diagnosis. Here we recruited 844 lung cancer patients and 620 healthy participants from six hospitals. A total of four serum proteins was identified and subsequently assessed in the training and validation cohorts. The concentrations of four serum proteins were found to be significantly higher in lung cancer patients compared with healthy participants. The area under the curve (AUC) for the 4-biomarker were 0.86 in the training cohort, and 0.87 in the validation cohort. The classification improved to a corrected AUC of 0.90 and 0.89 respectively following addition of sex, age and smoking status. Similar results were observed for early-stage lung cancer. Remarkably, in a blinded test with a suspicious pulmonary nodule, the adjusted prediction model correctly discriminated the patients with 86.96% sensitivity and 98.25% specificity. These results demonstrated the 4-biomarker panel improved lung cancer prediction beyond that of known risk factors. Moreover, the biomarkers were valuable in differentiating benign nodules which will remain indolent from those that are likely to progress and therefore might serve as an adjuvant diagnosis tool for LDCT scanning

Hydrogen peroxide detection with quartz-enhanced photoacoustic spectroscopy using a distributed-feedback quantum cascade laser

A quartz-enhanced photoacoustic spectroscopy sensor system was developed for the sensitive detection of hydrogen peroxide (H2O2) using its absorption transitions in the v6 fundamental band at ∼7.73 μm. The recent availability of distributed-feedback quantum cascade lasers provides convenient access to a strong H2O2 absorption line located at 1295.55 cm−1. Sensor calibration was performed by means of a water bubbler that generated titrated average H2O2vapor concentrations. A minimum detection limit of 12 parts per billion (ppb) corresponding to a normalized noise equivalent absorption coefficient of 4.6 × 10−9 cm−1W/Hz1/2 was achieved with an averaging time of 100 s

Modulation of Excited State Property Based on Benzo[a, c]phenazine Acceptor: Three Typical Excited States and Electroluminescence Performance

Throwing light upon the structure-property relationship of the excited state properties for next-generation fluorescent materials is crucial for the organic light emitting diode (OLED) field. Herein, we designed and synthesized three donor-acceptor (D-A) structure compounds based on a strong spin orbit coupling (SOC) acceptor benzo[a, c]phenazine (DPPZ) to research on the three typical types of excited states, namely, the locally-excited (LE) dominated excited state (CZP-DPPZ), the hybridized local and charge-transfer (HLCT) state (TPA-DPPZ), and the charge-transfer (CT) dominated state with TADF characteristics (PXZ-DPPZ). A theoretical combined experimental research was adopted for the excited state properties and their regulation methods of the three compounds. Benefiting from the HLCT character, TPA-DPPZ achieves the best non-doped device performance with maximum brightness of 61,951 cd m−2 and maximum external quantum efficiency of 3.42%, with both high photoluminescence quantum efficiency of 40.2% and high exciton utilization of 42.8%. Additionally, for the doped OLED, PXZ-DPPZ can achieve a max EQE of 9.35%, due to a suppressed triplet quenching and an enhanced SOC