A new type of bubble solutions for a critical fractional Schr\"{o}dinger equation

We consider the following critical fractional Schr\"{o}dinger equation \begin{equation*} (-\Delta)^s u+V(|y'|,y'')u=u^{2_s^*-1},\quad u>0,\quad y =(y',y'') \in \mathbb{R}^3\times\mathbb{R}^{N-3}, \end{equation*} where $N\geq 3,s\in(0,1)$, $2_s^*=\frac{2N}{N-2s}$ is the fractional critical Sobolev exponent and $V(|y'|,y'')$ is a bounded non-negative function in $\mathbb{R}^3\times\mathbb{R}^{N-3}$. If $r^{2s}V(r,y'')$ has a stable critical point $(r_0,y_0'')$ with $r_0>0$ and $V(r_0,y_0'')>0$, by using a modified finite-dimensional reduction method and various local Pohozaev identities, we prove that the problem above has a new type of infinitely many solutions which concentrate at points lying on the top and the bottom of a cylinder. And the concentration points of the bubble solutions include saddle points of the function $r^{2s}V(r,y'')$. We choose cleverly one of the reduced parameters $\bar{h}$ which depends on the scaling parameter $\lambda$ and avoid to compute the first partial derivative of the reduced functional with respect to $\bar{h}$ directly. Also we have to overcome some difficulties caused by the fractional Laplacian.Comment: 47 page

Global budget of black carbon aerosol and implications for climate forcing

This thesis explores the factors controlling the distribution of black carbon (BC) in the atmosphere/troposphere and its implications for climate forcing. BC is of great climate interest because of its warming potential. Estimates of BC climate forcing have large uncertainty, in part due to poor knowledge of the distribution of BC in the atmosphere. This dissertation first examines the factors controlling the sources of BC in the Arctic in winter and spring using a global chemical transport model (GEOS-Chem). Emission inventories of BC and wet scavenging of aerosols in the model are updated to reproduce observed atmospheric concentrations of BC as well as observed snow BC content in the Arctic in winter-spring. The simulation shows a dominant contribution of fuel (fossil fuel and biofuel) combustion to BC in Arctic spring. Arctic snow BC content is dominated by fuel combustion sources in winter, but has equal contributions from open fires and fuel combustion in spring. The estimated decrease in Arctic snow albedo due to BC deposition in spring is 0.6%, resulting in a regional surface radiative forcing of 1.2 W m-2. The dissertation then extends the evaluation of the BC simulation to the global scale using aircraft observations over source regions, continental outflow and remote regions and ground-based measurements. The observed low BC concentrations over the remote oceans imply more efficient BC removal than is currently implemented in models. The simulation that has total BC emissions of 6.5 Tg C a-1 and a mean tropospheric lifetime of 4.2 days for 2009 (vs. 6.8 &plusmn 1.8 days for the AeroCom models) captures the principal features of observed BC. The simulation estimates a global mean BC absorbing aerosol optical depth of 0.0017 and a top-of-atmosphere direct radiative forcing (DRF) of 0.19 W m-2, with a range of 0.17-0.31 W m-2 based on uncertainties in the BC atmospheric distribution. The DRF is lower than previous estimates, which could be biased high because of excessive BC concentrations over the oceans and in the free troposphere.Engineering and Applied Science

Integrating Second Life into an EFL Program: Students’ Perspectives

Second Life (SL) is a three dimension virtual world imagined and created by its users. To explore various facets of language learning within SL, faculty members of an American university and a Chinese university took an evaluation research approach to search for appropriate ways to integrate SL into an EFL (English as a Foreign Language) program. This paper reports a part of the research efforts with a focus on the Chinese students’ perspectives of an EFL Program in SL. Specifically included in this paper are (a) the Chinese students’ perceived technology readiness to use SL for EFL learning, (b) their perceptions of SL, and (c) the EFL Program implemented in SL. The paper reviews related literature and theoretical support, describes the study’s context and its implementation procedures, and discusses the evaluation results and implications. Finally, the paper shares with the audience some considerations for integrating SL into an EFL progra

Probing Single DNA Molecule Transport Using Fabricated Nanopores

Nanopores can serve as high throughput, single-molecule sensing devices that provide insight into the distribution of static and dynamic molecular activities, properties, or interactions. We have studied double stranded DNA electrophoretic transport dynamics through fabricated nanopores in silicon nitride. A fabricated pore enables us to interrogate a broader range of molecules with a wider range of conditions than can be investigated in a self-assembled protein pore in a lipid membrane.Molecular and Cellular Biolog

Measurement of Interphase Forces based on Dual-modality ERT/DP Sensor in Horizontal Two-phase Flow Gas-water

In order to better understand the mechanisms of two-phase flow and the prevailing flow regimes in horizontal pipelines, the evaluation of interphase forces is paramount. This study develops a method to quantitatively estimate the interphase force in two-phase gas-water flow in horizontal pipeline. The electrical resistance tomography technology is used to measure the void fraction, while the differential pressure perpendicular to the horizontal pipe is measured in different flow patterns via a Differential Pressure sensor. The inner pipe diameter is 50 mm, the water flow range from 3.26 m3/h to 7.36 m3/h, the gas flowrate range from 1 to 60 l/min, which covered a range of flow patterns, the absolute pressure range from0.07 MPa to 0.12 MPa. The relationship between the differential pressure drop and interphase force is established, and the effects of these forces on the flow are analyzed. Experimental results indicate that the dual-modality measurement system was successfully provided a quantitative evaluation of inter-phase forces in two-phase horizontal gas-water flow

Automatic recognition of white blood cell images with memory efficient superpixel metric GNN: SMGNN

An automatic recognizing system of white blood cells can assist hematologists in the diagnosis of many diseases, where accuracy and efficiency are paramount for computer-based systems. In this paper, we presented a new image processing system to recognize the five types of white blood cells in peripheral blood with marked improvement in efficiency when juxtaposed against mainstream methods. The prevailing deep learning segmentation solutions often utilize millions of parameters to extract high-level image features and neglect the incorporation of prior domain knowledge, which consequently consumes substantial computational resources and increases the risk of overfitting, especially when limited medical image samples are available for training. To address these challenges, we proposed a novel memory-efficient strategy that exploits graph structures derived from the images. Specifically, we introduced a lightweight superpixel-based graph neural network (GNN) and broke new ground by introducing superpixel metric learning to segment nucleus and cytoplasm. Remarkably, our proposed segmentation model superpixel metric graph neural network (SMGNN) achieved state of the art segmentation performance while utilizing at most 10000X less than the parameters compared to existing approaches. The subsequent segmentation-based cell type classification processes showed satisfactory results that such automatic recognizing algorithms are accurate and efficient to execeute in hematological laboratories. Our code is publicly available at https://github.com/jyh6681/SPXL-GNN

RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking

In various natural language processing tasks, passage retrieval and passage re-ranking are two key procedures in finding and ranking relevant information. Since both the two procedures contribute to the final performance, it is important to jointly optimize them in order to achieve mutual improvement. In this paper, we propose a novel joint training approach for dense passage retrieval and passage re-ranking. A major contribution is that we introduce the dynamic listwise distillation, where we design a unified listwise training approach for both the retriever and the re-ranker. During the dynamic distillation, the retriever and the re-ranker can be adaptively improved according to each other's relevance information. We also propose a hybrid data augmentation strategy to construct diverse training instances for listwise training approach. Extensive experiments show the effectiveness of our approach on both MSMARCO and Natural Questions datasets. Our code is available at https://github.com/PaddlePaddle/RocketQA.Comment: EMNLP 202