Snow identification from unattended automatic weather stations images using DANet

Identifying snow phenomena in images from automatic weather station (AWS) is crucial for live weather monitoring. In this paper, we propose a convolutional neural network (CNN) based model for snow identification using images from AWS cameras. The model combines the attention mechanism of the DANet model with the classical residual network ResNet-34 to better extract the features of snow cover in camera images. To improve the generalizability of the model, we also use images from public datasets in addition to images taken by cameras from unmanned weather stations. Our results show that the proposed model achieved a POD of 91.65%, a FAR of 7.34% and a TS score of 85.45%, demonstrating its effectiveness in snow identification. This study has the potential to facilitate more efficient and effective weather monitoring in a variety of locations

Overcoming the Circular Problem for \gamma-ray Bursts in Cosmological Global Fitting Analysis

Due to the lack of low redshift long Gamma-Ray Bursts (GRBs), the circular problem has been a severe obstacle for using GRBs as cosmological candles. In this paper, we present a new method to deal with such a problem in MCMC global fitting analysis. Assuming that a certain type of correlations between different observables exists in a subsample of GRBs, for the parameters involved in the correlation relation, we treat them as free parameters and determine them simultaneously with cosmological parameters through MCMC analysis on GRB data together with other observational data. Then the circular problem is naturally eliminated in this procedure. We take the Ghirlanda relation as an example while keeping in mind the debate about its physical validity. Together with SNe Ia, WMAP and SDSS data, we include 27 GRBs with the reported Ghirlanda relation in our study, and perform MCMC global fitting. We consider the $\Lambda$CDM model and dynamical dark energy models. In each case, in addition to the constraints on the relevant cosmological parameters, we obtain the best fit values as well as the distributions of the correlation parameters $A$ and $C$. We find that the observational data sets other than GRBs can affect $A$ and $C$ considerably through their degeneracies with the cosmological parameters. The results on $A$ and $C$ for different cosmological models are in well agreement within $1\sigma$ range. The best fit value of $A$ in all models being analyzed is $A\sim 1.53$ with $\sigma \sim 0.08$. For $C$, we have the best value in the range of $0.94-0.98$ with $\sigma\sim 0.1$. It is also noted that the distributions of $A$ and $C$ are generally broader than the priors used in many studies in literature. (Abriged)Comment: 9 pages, 2 figures, 2 tables, Accepted for publication in Ap

Probing Primordial Gravitational Waves: Ali CMB Polarization Telescope

In this paper, we will give a general introduction to the project of Ali CMB Polarization Telescope (AliCPT), which is a Sino-US joint project led by the Institute of High Energy Physics (IHEP) and has involved many different institutes in China. It is the first ground-based Cosmic Microwave Background (CMB) polarization experiment in China and an integral part of China's Gravitational Waves Program. The main scientific goal of AliCPT project is to probe the primordial gravitational waves (PGWs) originated from the very early Universe. The AliCPT project includes two stages. The first stage referred to as AliCPT-1, is to build a telescope in the Ali region of Tibet with an altitude of 5,250 meters. Once completed, it will be the worldwide highest ground-based CMB observatory and open a new window for probing PGWs in northern hemisphere. AliCPT-1 telescope is designed to have about 7,000 TES detectors at 90GHz and 150GHz. The second stage is to have a more sensitive telescope (AliCPT-2) with the number of detectors more than 20,000. Our simulations show that AliCPT will improve the current constraint on the tensor-to-scalar ratio $r$ by one order of magnitude with 3 years' observation. Besides the PGWs, the AliCPT will also enable a precise measurement on the CMB rotation angle and provide a precise test on the CPT symmetry. We show 3 years' observation will improve the current limit by two order of magnitude.Comment: 11 pages, 7 figures, 2 table

CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model

Code Large Language Models (Code LLMs) have gained significant attention in the industry due to their wide applications in the full lifecycle of software engineering. However, the effectiveness of existing models in understanding non-English inputs for multi-lingual code-related tasks is still far from well studied. This paper introduces CodeFuse-13B, an open-sourced pre-trained code LLM. It is specifically designed for code-related tasks with both English and Chinese prompts and supports over 40 programming languages. CodeFuse achieves its effectiveness by utilizing a high quality pre-training dataset that is carefully filtered by program analyzers and optimized during the training process. Extensive experiments are conducted using real-world usage scenarios, the industry-standard benchmark HumanEval-x, and the specially designed CodeFuseEval for Chinese prompts. To assess the effectiveness of CodeFuse, we actively collected valuable human feedback from the AntGroup's software development process where CodeFuse has been successfully deployed. The results demonstrate that CodeFuse-13B achieves a HumanEval pass@1 score of 37.10%, positioning it as one of the top multi-lingual code LLMs with similar parameter sizes. In practical scenarios, such as code generation, code translation, code comments, and testcase generation, CodeFuse performs better than other models when confronted with Chinese prompts.Comment: 10 pages with 2 pages for reference

Genomic Mutation Profile of Primary Gastrointestinal Diffuse Large B-Cell Lymphoma.

Primary gastrointestinal diffuse large B-cell lymphoma (GI-DLBCL) is the most common gastrointestinal lymphoma, but its genetic features are poorly understood. We performed whole-exome sequencing of 25 primary tumor samples from patients with GI-DLBCL and 23 matched normal tissue samples. Oncogenic mutations were screened, and the correlations between genetic mutations and clinicopathological characteristics were analyzed. Twenty-five patients with GI-DLBCL were enrolled in the genetic mutation analysis with a median of 184 (range 79-382) protein-altering variants per patient. We identified recurrent oncogenic mutations in GI-DLBCL, including those in TP53, MUC16, B2M, CCND3, HIST1H1C, NEB, and ID3. Compared with nodal DLBCL, GI-DLBCL exhibited an increased mutation frequency of TP53 and reduced mutation frequencies of PIM1, CREBBP, BCL2, KMT2D, and EZH2. Moreover, GI-DLBCL exhibited fewer MYD88 and CD79B mutations than DLBCL in the testis and central nervous system. GI-DLBCLs with HLA-B, MEF2A, RHOA, and NAV3 mutations exhibited a tendency toward a high proliferation index. MUC16 and ETV6 mutations often occurred in tumors with early clinical staging. Our data provide a comprehensive understanding of the landscape of mutations in a small subset of GI-DLBCLs. The genetic mutation profiles of GI-DLBCL differ from those of nodal DLBCL and DLBCL in immune-privileged sites. The different mutated genes are related to the NF-κB and JAK-STAT pathways, and the different pathogenetic mechanisms leading to the development of DLBCL may be influenced by the tissue microenvironment. Differences in genetic alterations might influence the clinicopathological characteristics of GI-DLBCL

Genomic mosaicism due to homoeologous exchange generates extensive phenotypic diversity in nascent allopolyploids

Allopolyploidy is an important process in plant speciation, yet newly formed allopolyploid species typically suffer from extreme genetic bottlenecks. One escape from this impasse might be homoeologous meiotic pairing, during which homoeologous exchanges (HEs) generate phenotypically variable progeny. However, the immediate genome-wide patterns and resulting phenotypic diversity generated by HEs remain largely unknown. Here, we analyzed the genome composition of 202 phenotyped euploid segmental allopolyploid individuals from the 4th selfed generation following chromosomal doubling of reciprocal F1 hybrids of crosses between rice subspecies, using whole genome sequencing. We describe rampant occurrence of HEs that, by overcoming incompatibility or conferring superiority of hetero-cytonuclear interactions, generate extensive and individualized genomic mosaicism across the analyzed tetraploids. We show that the resulting homoeolog copy number alteration in tetraploids affects known-function genes and their complex genetic interactions, in the process creating extraordinary phenotypic diversity at the population level following a single initial hybridization. Our results illuminate the immediate genomic landscapes possible in a tetraploid genomic environment, and underscore HE as an important mechanism that fuels rapid phenotypic diversification accompanying the initial stages of allopolyploid evolution

A longitudinal resource for population neuroscience of school-age children and adolescents in China

During the past decade, cognitive neuroscience has been calling for population diversity to address the challenge of validity and generalizability, ushering in a new era of population neuroscience. The developing Chinese Color Nest Project (devCCNP, 2013–2022), the first ten-year stage of the lifespan CCNP (2013–2032), is a two-stages project focusing on brain-mind development. The project aims to create and share a large-scale, longitudinal and multimodal dataset of typically developing children and adolescents (ages 6.0–17.9 at enrolment) in the Chinese population. The devCCNP houses not only phenotypes measured by demographic, biophysical, psychological and behavioural, cognitive, affective, and ocular-tracking assessments but also neurotypes measured with magnetic resonance imaging (MRI) of brain morphometry, resting-state function, naturalistic viewing function and diffusion structure. This Data Descriptor introduces the first data release of devCCNP including a total of 864 visits from 479 participants. Herein, we provided details of the experimental design, sampling strategies, and technical validation of the devCCNP resource. We demonstrate and discuss the potential of a multicohort longitudinal design to depict normative brain growth curves from the perspective of developmental population neuroscience. The devCCNP resource is shared as part of the “Chinese Data-sharing Warehouse for In-vivo Imaging Brain” in the Chinese Color Nest Project (CCNP) – Lifespan Brain-Mind Development Data Community (https://ccnp.scidb.cn) at the Science Data Bank

Calibration of the Timing Performance of GECAM-C

As a new member of the Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM) after GECAM-A and GECAM-B, GECAM-C (originally called HEBS), which was launched on board the SATech-01 satellite on July 27, 2022, aims to monitor and localize X-ray and gamma-ray transients from $\sim$ 6 keV to 6 MeV. GECAM-C utilizes a similar design to GECAM but operates in a more complex orbital environment. In this work, we utilize the secondary particles simultaneously produced by the cosmic-ray events on orbit and recorded by multiple detectors, to calibrate the relative timing accuracy between all detectors of GECAM-C. We find the result is 0.1 $\mu \rm s$, which is the highest time resolution among all GRB detectors ever flown and very helpful in timing analyses such as minimum variable timescale and spectral lags, as well as in time delay localization. Besides, we calibrate the absolute time accuracy using the one-year Crab pulsar data observed by GECAM-C and Fermi/GBM, as well as GECAM-C and GECAM-B. The results are $2.02\pm 2.26\ \mu \rm s$ and $5.82\pm 3.59\ \mu \rm s$, respectively. Finally, we investigate the spectral lag between the different energy bands of Crab pulsar observed by GECAM and GBM, which is $\sim -0.2\ {\rm \mu s\ keV^{-1}}$.Comment: submitte

Ground calibration of Gamma-Ray Detectors of GECAM-C

As a new member of GECAM mission, GECAM-C (also named High Energy Burst Searcher, HEBS) was launched onboard the SATech-01 satellite on July 27th, 2022, which is capable to monitor gamma-ray transients from $\sim$ 6 keV to 6 MeV. As the main detector, there are 12 gamma-ray detectors (GRDs) equipped for GECAM-C. In order to verify the GECAM-C GRD detector performance and to validate the Monte Carlo simulations of detector response, comprehensive on-ground calibration experiments have been performed using X-ray beam and radioactive sources, including Energy-Channel relation, energy resolution, detection efficiency, SiPM voltage-gain relation and the non-uniformity of positional response. In this paper, the detailed calibration campaigns and data analysis results for GECAM-C GRDs are presented, demonstrating the excellent performance of GECAM-C GRD detectors.Comment: third versio