Deep Learning the Effects of Photon Sensors on the Event Reconstruction Performance in an Antineutrino Detector

We provide a fast approach incorporating the usage of deep learning for evaluating the effects of photon sensors in an antineutrino detector on the event reconstruction performance therein. This work is an attempt to harness the power of deep learning for detector designing and upgrade planning. Using the Daya Bay detector as a benchmark case and the vertex reconstruction performance as the objective for the deep neural network, we find that the photomultiplier tubes (PMTs) have different relative importance to the vertex reconstruction. More importantly, the vertex position resolutions for the Daya Bay detector follow approximately a multi-exponential relationship with respect to the number of PMTs and hence, the coverage. This could also assist in deciding on the merits of installing additional PMTs for future detector plans. The approach could easily be used with other objectives in place of vertex reconstruction

Pore structure characterization of Chang-7 tight sandstone using MICP combined with N2GA techniques and its geological control factors

Understanding the pore networks of unconventional tight reservoirs such as tight sandstones and shales is crucial for extracting oil/gas from such reservoirs. Mercury injection capillary pressure (MICP) and N(2) gas adsorption (N(2)GA) are performed to evaluate pore structure of Chang-7 tight sandstone. Thin section observation, scanning electron microscope, grain size analysis, mineral composition analysis, and porosity measurement are applied to investigate geological control factors of pore structure. Grain size is positively correlated with detrital mineral content and grain size standard deviation while negatively related to clay content. Detrital mineral content and grain size are positively correlated with porosity, pore throat radius and withdrawal efficiency and negatively related to capillary pressure and pore-to-throat size ratio; while interstitial material is negatively correlated with above mentioned factors. Well sorted sediments with high debris usually possess strong compaction resistance to preserve original pores. Although many inter-crystalline pores are produced in clay minerals, this type of pores is not the most important contributor to porosity. Besides this, pore shape determined by N(2)GA hysteresis loop is consistent with SEM observation on clay inter-crystalline pores while BJH pore volume is positively related with clay content, suggesting N(2)GA is suitable for describing clay inter-crystalline pores in tight sandstones

Toward 6G TKμ\mu Extreme Connectivity: Architecture, Key Technologies and Experiments

Sixth-generation (6G) networks are evolving towards new features and order-of-magnitude enhancement of systematic performance metrics compared to the current 5G. In particular, the 6G networks are expected to achieve extreme connectivity performance with Tbps-scale data rate, Kbps/Hz-scale spectral efficiency, and $\mu$s-scale latency. To this end, an original three-layer 6G network architecture is designed to realise uniform full-spectrum cell-free radio access and provide task-centric agile proximate support for diverse applications. The designed architecture is featured by super edge node (SEN) which integrates connectivity, computing, AI, data, etc. On this basis, a technological framework of pervasive multi-level (PML) AI is established in the centralised unit to enable task-centric near-real-time resource allocation and network automation. We then introduce a radio access network (RAN) architecture of full spectrum uniform cell-free networks, which is among the most attractive RAN candidates for 6G TK$\mu$ extreme connectivity. A few most promising key technologies, i.e., cell-free massive MIMO, photonics-assisted Terahertz wireless access and spatiotemporal two-dimensional channel coding are further discussed. A testbed is implemented and extensive trials are conducted to evaluate innovative technologies and methodologies. The proposed 6G network architecture and technological framework demonstrate exciting potentials for full-service and full-scenario applications.Comment: 15 pages, 12 figure

Age Is Important for the Early-Stage Detection of Breast Cancer on Both Transcriptomic and Methylomic Biomarkers

Patients at different ages have different rates of cell development and metabolisms. As a result, age should be an essential part of how a disease diagnosis model is trained and optimized. Unfortunately, most of the existing studies have not taken age into account. This study demonstrated that disease diagnosis models could be improved by merely applying individual models for patients of different age groups. Both transcriptomes and methylomes of the TCGA breast cancer dataset (TCGA-BRCA) were utilized for the analysis procedure of feature selection and classification. Our experimental data strongly suggested that disease diagnosis modeling should integrate patient age into the whole experimental design