Experimental investigation on permeability and mechanical deformation of coal containing gas under load

Coalbed effective permeability is widely used as a primary index to evaluate gas-drainage effect in CBM exploitation field. However, it seems to be difficult to obtain by the reason of dynamic change in close relationship with crustal stress, methane pressure, porosity, and adsorption. Due to their dissimilar adsorption properties and tectonic deformation degrees, different types of coal containing gas have various stress-strain and gas seepage curves. The paper presents the experimental investigations of the dynamic relationship between coal permeability and deformation under load. In this work, stress-strain and permeability investigations were performed using anthracite lump with a vitrinite reflectance of about 3.24% at various pressures and temperatures. The permeability (including the initial, minimum, and maximum) decreased with increasing temperature. At a constant confining pressure, the strains in different directions almost all increased with increasing axial stress and decreased with increasing pore methane pressure during the prefracture stage. At a constant pore pressure, the compression strength of the coal specimens increased approximately linearly during the prefracture stage and sharply decreased during the postfracture stage, while the permeability decreased rapidly and then increased slowly during the prefracture and remained stable during the postfracture stage. The permeability of the coal specimens mainly depended on the inner fissures. The permeability was greater during the postfracture than that during the prefracture stage. At the same temperature, the gas seepage curve of each coal specimen could be divided into three sections: decreasing, increasing, and constant sections. The necessary time for the permeability to reach a steady state increased as the confining and pore pressures increased. At high confining pressures (i.e., 6 MPa and 8 MPa), no significant differences between the methane seepage velocities of the specimens were evident, and their seepage curves were similar to prefracture. However, clear differences were observable at the postfracture stage. The seepage abilities of the coal specimens were more sensitive to stress than temperature in the same condition

Bridging the Domain Gap for Multi-Agent Perception

Existing multi-agent perception algorithms usually select to share deep neural features extracted from raw sensing data between agents, achieving a trade-off between accuracy and communication bandwidth limit. However, these methods assume all agents have identical neural networks, which might not be practical in the real world. The transmitted features can have a large domain gap when the models differ, leading to a dramatic performance drop in multi-agent perception. In this paper, we propose the first lightweight framework to bridge such domain gaps for multi-agent perception, which can be a plug-in module for most existing systems while maintaining confidentiality. Our framework consists of a learnable feature resizer to align features in multiple dimensions and a sparse cross-domain transformer for domain adaption. Extensive experiments on the public multi-agent perception dataset V2XSet have demonstrated that our method can effectively bridge the gap for features from different domains and outperform other baseline methods significantly by at least 8% for point-cloud-based 3D object detection.Comment: Accepted by ICRA2023.Code: https://github.com/DerrickXuNu/MPD

Domain Adaptation based Enhanced Detection for Autonomous Driving in Foggy and Rainy Weather

Typically, object detection methods for autonomous driving that rely on supervised learning make the assumption of a consistent feature distribution between the training and testing data, however such assumption may fail in different weather conditions. Due to the domain gap, a detection model trained under clear weather may not perform well in foggy and rainy conditions. Overcoming detection bottlenecks in foggy and rainy weather is a real challenge for autonomous vehicles deployed in the wild. To bridge the domain gap and improve the performance of object detectionin foggy and rainy weather, this paper presents a novel framework for domain-adaptive object detection. The adaptations at both the image-level and object-level are intended to minimize the differences in image style and object appearance between domains. Furthermore, in order to improve the model's performance on challenging examples, we introduce a novel adversarial gradient reversal layer that conducts adversarial mining on difficult instances in addition to domain adaptation. Additionally, we suggest generating an auxiliary domain through data augmentation to enforce a new domain-level metric regularization. Experimental findings on public V2V benchmark exhibit a substantial enhancement in object detection specifically for foggy and rainy driving scenarios.Comment: only change the title of this pape

In vivo analysis of Caenorhabditis elegans noncoding RNA promoter motifs

<p>Abstract</p> <p>Background</p> <p>Noncoding RNAs (ncRNAs) play important roles in a variety of cellular processes. Characterizing the transcriptional activity of ncRNA promoters is therefore a critical step toward understanding the complex cellular roles of ncRNAs.</p> <p>Results</p> <p>Here we present an <it>in vivo </it>transcriptional analysis of three <it>C. elegans </it>ncRNA upstream motifs (UM1-3). Transcriptional activity of all three motifs has been demonstrated, and mutational analysis revealed differential contributions of different parts of each motif. We showed that upstream motif 1 (UM1) can drive the expression of green fluorescent protein (GFP), and utilized this for detailed analysis of temporal and spatial expression patterns of 5 SL2 RNAs. Upstream motifs 2 and 3 do not drive GFP expression, and termination at consecutive T runs suggests transcription by RNA polymerase III. The UM2 sequence resembles the tRNA promoter, and is actually embedded within its own short-lived, primary transcript. This is a structure which is also found at a few plant and yeast loci, and may indicate an evolutionarily very old dicistronic transcription pattern in which a tRNA serves as a promoter for an adjacent snoRNA.</p> <p>Conclusion</p> <p>The study has demonstrated that the three upstream motifs UM1-3 have promoter activity. The UM1 sequence can drive expression of GFP, which allows for the use of UM1::GFP fusion constructs to study temporal-spatial expression patterns of UM1 ncRNA loci. The UM1 loci appear to act in concert with other upstream sequences, whereas the transcriptional activities of the UM2 and UM3 are confined to the motifs themselves.</p

Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather

Most object detection methods for autonomous driving usually assume a consistent feature distribution between training and testing data, which is not always the case when weathers differ significantly. The object detection model trained under clear weather might not be effective enough in foggy weather because of the domain gap. This paper proposes a novel domain adaptive object detection framework for autonomous driving under foggy weather. Our method leverages both image-level and object-level adaptation to diminish the domain discrepancy in image style and object appearance. To further enhance the model's capabilities under challenging samples, we also come up with a new adversarial gradient reversal layer to perform adversarial mining for the hard examples together with domain adaptation. Moreover, we propose to generate an auxiliary domain by data augmentation to enforce a new domain-level metric regularization. Experimental results on public benchmarks show the effectiveness and accuracy of the proposed method. The code is available at https://github.com/jinlong17/DA-Detect.Comment: Accepted by WACV2023. Code is available at https://github.com/jinlong17/DA-Detec

Differential expression of miRNAs related to caste differentiation in the honey bee, Apis mellifera

International audienceAbstractHoney bees are very important eusocial insects and are involved in the pollination of many plants. Queen bees and worker bees can develop from the same fertilized eggs and are thus genetically identical despite their substantial behavioral and physiological differences. The mechanism governing developmental differences between worker and queen bees has always attracted much interest. While there are several reports on messenger RNA (mRNA) expression related to caste differentiation or microRNA (miRNA) expression in one time point of caste differentiation, no systematic investigation of the dynamic expression of small RNAs along with these two caste development has, thus far, been carried out. In this study, we present the dynamic expression profiles of queen and worker bee small RNAs and show caste-specific miRNA expression patterns between them, indicating that miRNAs may be related to the differential development of worker and queen bee larvae. Results presented here will make a valuable contribution to understanding of the caste switch between worker and queen bees

MPCViT: Searching for MPC-friendly Vision Transformer with Heterogeneous Attention

Secure multi-party computation (MPC) enables computation directly on encrypted data on non-colluding untrusted servers and protects both data and model privacy in deep learning inference. However, existing neural network (NN) architectures, including Vision Transformers (ViTs), are not designed or optimized for MPC protocols and incur significant latency overhead due to the Softmax function in the multi-head attention (MHA). In this paper, we propose an MPC-friendly ViT, dubbed MPCViT, to enable accurate yet efficient ViT inference in MPC. We systematically compare different attention variants in MPC and propose a heterogeneous attention search space, which combines the high-accuracy and MPC-efficient attentions with diverse structure granularities. We further propose a simple yet effective differentiable neural architecture search (NAS) algorithm for fast ViT optimization. MPCViT significantly outperforms prior-art ViT variants in MPC. With the proposed NAS algorithm, our extensive experiments demonstrate that MPCViT achieves 7.9x and 2.8x latency reduction with better accuracy compared to Linformer and MPCFormer on the Tiny-ImageNet dataset, respectively. Further, with proper knowledge distillation (KD), MPCViT even achieves 1.9% better accuracy compared to the baseline ViT with 9.9x latency reduction on the Tiny-ImageNet dataset.Comment: 6 pages, 6 figure

Anomalous fractionation of mercury isotopes in the Late Archean atmosphere

This work was funded by a Natural Environment Research Council (NERC) Fellowship NE/H016805/2 and Standard Grant NE/J023485/2 (to A.L.Z.). R.Y. was funded by the Chinese Academy of Sciences through the Hundred Talent Plan. G.J.I. recognizes continued support from R. Summons under the auspices of the Simons Collaboration on the Origin of Life. We thank J. Kirschvink, J. Grotzinger, A. Knoll, and the Agouron Institute for organizing and funding the Agouron Drilling Project, and the Council for Geoscience in South Africa, specifically those at the National Core Library in Donkerhoek, for facilitating access to the core materials.Earth’s surface underwent a dramatic transition ~2.3 billion years ago when atmospheric oxygen first accumulated during the Great Oxidation Event, but the detailed composition of the reducing early atmosphere is not well known. Here we develop mercury (Hg) stable isotopes as a proxy for paleoatmospheric chemistry and use Hg isotope data from 2.5 billion-year-old sedimentary rocks to examine changes in the Late Archean atmosphere immediately prior to the Great Oxidation Event. These sediments preserve evidence of strong photochemical transformations of mercury in the absence of molecular oxygen. In addition, these geochemical records combined with previously published multi-proxy data support a vital role for methane in Earth’s early atmosphere.Publisher PDFPeer reviewe