ILNet: Low-level Matters for Salient Infrared Small Target Detection

Infrared small target detection is a technique for finding small targets from infrared clutter background. Due to the dearth of high-level semantic information, small infrared target features are weakened in the deep layers of the CNN, which underachieves the CNN's representation ability. To address the above problem, in this paper, we propose an infrared low-level network (ILNet) that considers infrared small targets as salient areas with little semantic information. Unlike other SOTA methods, ILNet pays greater attention to low-level information instead of treating them equally. A new lightweight feature fusion module, named Interactive Polarized Orthogonal Fusion module (IPOF), is proposed, which integrates more important low-level features from the shallow layers into the deep layers. A Dynamic One-Dimensional Aggregation layers (DODA) are inserted into the IPOF, to dynamically adjust the aggregation of low dimensional information according to the number of input channels. In addition, the idea of ensemble learning is used to design a Representative Block (RB) to dynamically allocate weights for shallow and deep layers. Experimental results on the challenging NUAA-SIRST (78.22% nIoU and 1.33e-6 Fa) and IRSTD-1K (68.91% nIoU and 3.23e-6 Fa) dataset demonstrate that the proposed ILNet can get better performances than other SOTA methods. Moreover, ILNet can obtain a greater improvement with the increasement of data volume. Training code are available at https://github.com/Li-Haoqing/ILNet

Click on Mask: A Labor-efficient Annotation Framework with Level Set for Infrared Small Target Detection

Infrared Small Target Detection is a challenging task to separate small targets from infrared clutter background. Recently, deep learning paradigms have achieved promising results. However, these data-driven methods need plenty of manual annotation. Due to the small size of infrared targets, manual annotation consumes more resources and restricts the development of this field. This letter proposed a labor-efficient and cursory annotation framework with level set, which obtains a high-quality pseudo mask with only one cursory click. A variational level set formulation with an expectation difference energy functional is designed, in which the zero level contour is intrinsically maintained during the level set evolution. It solves the issue that zero level contour disappearing due to small target size and excessive regularization. Experiments on the NUAA-SIRST and IRSTD-1k datasets reveal that our approach achieves superior performance. Code is available at https://github.com/Li-Haoqing/COM.Comment: 4 pages, 5 figures, references adde

Effect of Bentonite Admixture Content on Effective Porosity and Hydraulic Conductivity of Clay-based Barrier Backfill Materials

Clay-based barrier wall has been diffusely employed as vertical barriers. Nevertheless, there were few project practices of these walls in China. And few research have been performed to study the impact on the permeability of the addition of domestic bentonites. To solve this problem, the influences of bentonite level on hydraulic conductivity, porosity and clay-bound water of soil-bentonite admixtures have been assessed employing a flexible-wall test and water centrifugal dewatering experiment with various bentonites. The outcomes revealed that as barrier walls are constructed by blending bentonite and Fujian standard sandy soil, there is a critical bentonite level of the smallest porosity. If the bentonite level is less than the critical bentonite content, hydraulic conductivity is reduced quickly, while if the bentonite level is greater than the critical bentonite content, hydraulic conductivity is reduced gently. Additionally, as the bentonite level grew, the clay-bound water centage of the admixtures continually improved. Supposing that the clay-bound water enclosed the clay grains, a near computation approach of the effective porosity is put forward and showed that the effective porosity decreased with bentonite content. Additionally, an exponential relationship was found between the effective porosity and the permeability

Geotechnical properties of sewage sludge solidified with Sulphoaluminate cement

The geotechnical properties of sewage sludge solidified with sulphoaluminate cement are presented. The sludge has a high water content and organic matter which is not easy to disposal. After Solidification/Stabilization (S/S), landfill disposal of sewage sludge is widely adopted in China. However, there is little research focused on the geotechnical properties of sewage sludge after S/S treatment and the impact on the landfill site is also difficult to be evaluated. To solve this problem, this paper is aimed to evaluate the basic mechanics properties of solidified materials by means of Atterberg limit, triaxial test, consolidation test and permeability test. The result showed that the strength and the hydraulic conductivity of the modified sludge was close to that of the high organic soil. By adding suitable additives, modified sludge could not only satisfy the requirement of being landfilled but also be utilized as a construction material

An aeromagnetic denoising-decomposition-3D inversion approach for mineral exploration

Reduction of aeromagnetic noise and extraction of mineralization-related residual anomalies are critical for aeromagnetic data processing in mineral exploration. This study introduced a multifractal singular value decomposition (MSVD) method to remove the noise and improved the bi-dimensional empirical mode decomposition (BEMD) algorithm to extract residual magnetic anomalies. It is shown that MSVD and improved BEMD could effectively reduce the noise and extract residual magnetic anomalies. Then, a wavenumber–domain iterative approach is applied in 3D imaging of magnetic anomalies and gradients with depth constraints, which is a rapid tool for qualitative and quantitative interpretation of magnetic data and is suitable for rapidly imaging large-scale data. The 3D inversion result is verified by four geological sections along the regional tectonic directions and some drilling holes on the deposits. It is revealed that this proposed approach is practical and effective in dealing with aeromagnetic data interpretation and inversion for mineral exploration

Transcription on a Hydrophobic Unnatural Base Pair by T7 RNA Polymerase

Bacteriophage T7 RNA polymerase (T7 RNAP) is frequently used for RNA synthesis with unnatural base pairs (UBPs) and specific synthetic alterations in a wide range of biotechnological and medicinal applications. As a knowledge gap in the area, however, the molecular basis for recognition and processing of UBPs by T7 RNAP during transcription remains poorly understood. We explored how the hydrophobic Ds–Pa pair is recognized and processed as a third base pair by T7 RNAP during transcription elongation. T7 RNAP integrates DsTP opposite Pa with great efficiency, equivalent to that of natural nucleotides, although the kinetics of PaTP incorporation opposite Ds is significantly slower. Using structural biology approach, we discovered that T7 RNAP recognizes unnatural substrates differently than its natural substrates. We found distinct unnatural nucleoside triphosphate binding sites for PaTP and DsTP at the pre-insertion state. We identified several separate-of-function mutants of T7 RNAP that affect UBP transcription selectively but not normal nucleic acid transcription. These results provide molecular insights into the recognition of UBP transcription by T7 RNAP. Our investigations also provide important information for the creation of the next generation of UBPs for efficient transcription and other applications

An End-to-End Mutually Interactive Emotion–Cause Pair Extractor via Soft Sharing

Emotion–cause pair extraction (ECPE), i.e., extracting pairs of emotions and corresponding causes from text, has recently attracted a lot of research interest. However, current ECPE models face two problems: (1) The common two-stage pipeline causes the error to be accumulated. (2) Ignoring the mutual connection between the extraction and pairing of emotion and cause limits the performance. In this paper, we propose a novel end-to-end mutually interactive emotion–cause pair extractor (Emiece) that is able to effectively extract emotion–cause pairs from all potential clause pairs. Specifically, we design two soft-shared clause-level encoders in an end-to-end deep model to measure the weighted probability of being a potential emotion–cause pair. Experiments on standard ECPE datasets show that Emiece achieves drastic improvements over the original two-step ECPE model and other end-to-end models in the extraction of major emotional cause pairs. The effectiveness of soft sharing and the applicability of the Emiece framework are further demonstrated by ablation experiments

Instability Analysis of a Low-Angle Low-Expansive Soil Slope under Seasonal Wet-Dry Cycles and River-Level Variations

There were a small amount of obvious offsets at the bearing of bridge piers built on an artificial gentle canal bank terrace and many tensile cracks visible at the surface of the mortar block stones covering the terrace soil in several years following construction. To determine these reasons, a comprehensive site investigation and a wide variety of tests were implemented, which included geophysical tests, in situ tests, laboratory tests, pile integrity detection, and numerical analysis with the finite element method (FEM). The results revealed that the soil of the low-angle slope was the potentially low-expansive clay soil. The reduction in soil shear strength deriving from seasonal wet-dry cycles and river-level variations led to the instability and failure of the low-angle low-expansive soil slope, which triggered the collapses of the soil slope and lots of fractures in the piles of the bridge foundation. The typical characteristics of the instability and failure of the low-angle low-expansive soil slope were tractional detachment and slow sliding