SSC-RS: Elevate LiDAR Semantic Scene Completion with Representation Separation and BEV Fusion

Semantic scene completion (SSC) jointly predicts the semantics and geometry of the entire 3D scene, which plays an essential role in 3D scene understanding for autonomous driving systems. SSC has achieved rapid progress with the help of semantic context in segmentation. However, how to effectively exploit the relationships between the semantic context in semantic segmentation and geometric structure in scene completion remains under exploration. In this paper, we propose to solve outdoor SSC from the perspective of representation separation and BEV fusion. Specifically, we present the network, named SSC-RS, which uses separate branches with deep supervision to explicitly disentangle the learning procedure of the semantic and geometric representations. And a BEV fusion network equipped with the proposed Adaptive Representation Fusion (ARF) module is presented to aggregate the multi-scale features effectively and efficiently. Due to the low computational burden and powerful representation ability, our model has good generality while running in real-time. Extensive experiments on SemanticKITTI demonstrate our SSC-RS achieves state-of-the-art performance.Comment: 8 pages, 5 figures, IROS202

PANet: LiDAR Panoptic Segmentation with Sparse Instance Proposal and Aggregation

Reliable LiDAR panoptic segmentation (LPS), including both semantic and instance segmentation, is vital for many robotic applications, such as autonomous driving. This work proposes a new LPS framework named PANet to eliminate the dependency on the offset branch and improve the performance on large objects, which are always over-segmented by clustering algorithms. Firstly, we propose a non-learning Sparse Instance Proposal (SIP) module with the ``sampling-shifting-grouping" scheme to directly group thing points into instances from the raw point cloud efficiently. More specifically, balanced point sampling is introduced to generate sparse seed points with more uniform point distribution over the distance range. And a shift module, termed bubble shifting, is proposed to shrink the seed points to the clustered centers. Then we utilize the connected component label algorithm to generate instance proposals. Furthermore, an instance aggregation module is devised to integrate potentially fragmented instances, improving the performance of the SIP module on large objects. Extensive experiments show that PANet achieves state-of-the-art performance among published works on the SemanticKITII validation and nuScenes validation for the panoptic segmentation task.Comment: 8 pages, 3 figures, IROS202

Transcranial Direct Current Stimulation of the Right Lateral Prefrontal Cortex Changes a priori Normative Beliefs in Voluntary Cooperation

A priori normative beliefs, the precondition of social norm compliance that reflects culture and values, are considered unique to human social behavior. Previous studies related to the ultimatum game revealed that right lateral prefrontal cortex (rLPFC) has no stimulation effects on normative beliefs. However, no research has focused on the effects of a priori belief on the rLPFC in voluntary cooperation attached to the public good (PG) game. In this study, we used a linear asymmetric PG to confirm the influence of the rLPFC on a priori normative beliefs without threats of external punishment through transcranial direct current stimulation (tDCS). Participants engaged via computer terminals in groups of four (i.e., two high-endowment players with 35G$and two low-endowment players with 23G$). They were anonymous and had no communication during the entire process. They were randomly assigned to receive 15 min of either anodal, cathodal, or sham stimulation and then asked to answer questions concerning a priori normative beliefs (norm.belief and pg.belief). Results suggested that anodal/cathodal tDCS significantly (P < 0.001) shifted the participants’ a priori normative beliefs in opposite directions compared to the shift in the sham group. In addition, different identities exhibited varying degrees of change (28.80–54.43%). These outcomes provide neural evidence of the rLPFC mechanism’s effect on the normative beliefs in voluntary cooperation based on the PG framework

Experiment on separated layer rock failure technology for stress reduction of entry under coal pillar in mining conditions

Longwall entrance is especially vulnerable to the combined mining of nearby coal seams because of the substantial deformation disaster loaded by the abutment stress caused by the mining disturbance. Changes to the fracture characteristics, movement behavior, and structural morphology of the bearing structure above the coal pillar are recommended using the separated layer rock failure technology (SLRFT) to safeguard the entry beneath the coal pillar from high abutment stress. To simulate the impacts of the SLRFT on the decrease of the abutment stress surrounding the entry under the coal pillar under the plane–stress circumstances, two experimental models were created. Abutment stress revolution, roof movement laws, and fracture features were all tracked using three identical monitoring systems in each experimental model. The experimental results indicate that SLRFT generates the shorter caving step length, more layered collapse, and higher caving height of the immediate roof, which improves the dilatancy of caving rock mass, the filling rate, and the compaction degree of the worked-out area. In the ceiling above the worked-out area, the fracture progresses from a non-penetrating horizontal and oblique gaping fracture to stepped closed fractures and piercing fractures. The main roof’s subsidence shifts from a linear, slow tendency to a stepped, fast one. The bearing structure changes from two-side cantilever structure with a T type into one-side cantilever structure with a basin type. Because the compacted worked-out region has a bigger support area, more of the overburden load is transferred there, weakening the abutment stress around the longwall entry from 12.5 kPa to 3.7 kPa. The stress reduction degree increases with the reduction of the cantilever length of the bearing structure and the increasing of the support coefficient of the compacted worked-out area. These findings illustrate the effectiveness of SLRFT in lowering entrance stress. With the established experimental model, it is possible to evaluate the viability, efficiency, and design of SLRFT under various engineering and geological circumstances

Review and development of surrounding rock control technology for gob-side entry retaining in China

The research and application of gob-side entry retaining (GER) technology in Chinese coal mines has been over 60 years. Two major technical types including GER with filling and GER with roof cutting were developed. However, due to the complex mining and geological conditions of the mined-out coal seam in different mining areas, as well as the strong mining pressure behaviours in the retained roadway, the promotion and application of GER technology presented ups and downs. Firstly, the achievements and key technological advances in the main GER types in China, the principle of surrounding rock stability, road-in support technology, roadside support technology, adaptability evaluation and surrounding rock stability monitoring are summarized, and the application applicability of different technologies at this stage is analyzed. Then, the difficulties and challenges faced by current GER technology are summarized: there is still no systematic theory for GER with strong mining pressure; there are still shortcomings in the theoretical understanding of the interaction mechanism between the surrounding rock and the support body for GER with filling; the mechanical and deformation characteristics of the filling materials for the roadside support body are not yet suitable for GER in deep working faces or with strong mining pressure; the mechanism and control technology of floor heave for GER are not yet perfect; the research on stability control of filling body for GER under strong dynamic load or rock burst is still in a blank. Finally, concerned with such difficulties and challenges, several reserve technologies have been proposed: the coordinated control of controlled roof cutting and filling for GER in fully mechanized caving/ full-seam mining in the thick coal seam, and the GER technology with additive modified high-water materials in working faces with strong mining pressure; finally, a set of intelligent inversion workflow for rock mechanical parameters used in GER numerical simulations for GER is established, and an intelligent optimization design method for GER support parameters is proposed

PRL-3, a Metastasis Associated Tyrosine Phosphatase, Is Involved in FLT3-ITD Signaling and Implicated in Anti-AML Therapy

Combination with other small molecule drugs represents a promising strategy to improve therapeutic efficacy of FLT3 inhibitors in the clinic. We demonstrated that combining ABT-869, a FLT3 inhibitor, with SAHA, a HDAC inhibitor, led to synergistic killing of the AML cells with FLT3 mutations and suppression of colony formation. We identified a core gene signature that is uniquely induced by the combination treatment in 2 different leukemia cell lines. Among these, we showed that downregulation of PTP4A3 (PRL-3) played a role in this synergism. PRL-3 is downstream of FLT3 signaling and ectopic expression of PRL-3 conferred therapeutic resistance through upregulation of STAT (signal transducers and activators of transcription) pathway activity and anti-apoptotic Mcl-1 protein. PRL-3 interacts with HDAC4 and SAHA downregulates PRL-3 via a proteasome dependent pathway. In addition, PRL-3 protein was identified in 47% of AML cases, but was absent in myeloid cells in normal bone marrows. Our results suggest such combination therapies may significantly improve the therapeutic efficacy of FLT3 inhibitors. PRL-3 plays a potential pathological role in AML and it might be a useful therapeutic target in AML, and warrant clinical investigation

A multi-subgroup predictive model based on clinical parameters and laboratory biomarkers to predict in-hospital outcomes of plasma exchange-centered artificial liver treatment in patients with hepatitis B virus-related acute-on-chronic liver failure

BackgroundPostoperative risk stratification is challenging in patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) who undergo artificial liver treatment. This study characterizes patients’ clinical parameters and laboratory biomarkers with different in-hospital outcomes. The purpose was to establish a multi-subgroup combined predictive model and analyze its predictive capability.MethodsWe enrolled HBV-ACLF patients who received plasma exchange (PE)-centered artificial liver support system (ALSS) therapy from May 6, 2017, to April 6, 2022. There were 110 patients who died (the death group) and 110 propensity score-matched patients who achieved satisfactory outcomes (the survivor group). We compared baseline, before ALSS, after ALSS, and change ratios of laboratory biomarkers. Outcome prediction models were established by generalized estimating equations (GEE). The discrimination was assessed using receiver operating characteristic analyses. Calibration plots compared the mean predicted probability and the mean observed outcome.ResultsWe built a multi-subgroup predictive model (at admission; before ALSS; after ALSS; change ratio) to predict in-hospital outcomes of HBV-ACLF patients who received PE-centered ALSS. There were 110 patients with 363 ALSS sessions who survived and 110 who did not, and 363 ALSS sessions were analyzed. The univariate GEE models revealed that several parameters were independent risk factors. Clinical parameters and laboratory biomarkers were entered into the multivariate GEE model. The discriminative power of the multivariate GEE models was excellent, and calibration showed better agreement between the predicted and observed probabilities than the univariate models.ConclusionsThe multi-subgroup combined predictive model generated accurate prognostic information for patients undergoing HBV-ACLF patients who received PE-centered ALSS

CNID: Research of Network Intrusion Detection Based on Convolutional Neural Network

Network intrusion detection system can effectively detect network attack behaviour, which is very important to network security. In this paper, a multiclassification network intrusion detection model based on convolutional neural network is proposed, and the algorithm is optimized. First, the data is preprocessed, the original one-dimensional network intrusion data is converted into two-dimensional data, and then the effective features are learned using optimized convolutional neural networks, and, finally, the final test results are produced in conjunction with the Softmax classifier. In this paper, KDD-CUP 99 and NSL-KDD standard network intrusion detection dataset were used to carry out the multiclassification network intrusion detection experiment; the experimental results show that the multiclassification network intrusion detection model proposed in this paper improves the accuracy and check rate, reduces the false positive rate, and also obtains better test results for the detection of unknown attacks

Key Parameters of Gob-Side Entry Retaining in A Gassy and Thin Coal Seam with Hard Roof

Gob-side entry retaining (GER) employed in a thin coal seam (TCS) can increase economic benefits and coal recovery, as well as mitigate gas concentration in the gob. In accordance with the caving style of a limestone roof, the gas concentration and air pressure in the gob were analyzed, and a roof-cutting mechanical model of GER with a roadside backfill body (RBB) was proposed, to determine the key parameters of the GER-TCS, including the roof-cutting resistance and the width of the RBB. The results show that if the immediate roof height is greater than the seam height, the roof-cutting resistance and width of the RBB should meet the requirement of the immediate roof being totally cut along the gob, for which the optimal roof-cutting resistance and width of RBB were determined by analytical and numerical methods. The greater the RBB width, the greater its roof-cutting resistance. The relationship between the supporting strength of the RBB and the width of the RBB can be derived as a composite curve. The floor heave of GER increases with increasing RBB width. When the width of the RBB increased from 0.8 m to 1.2 m, the floor heave increased two-fold to 146.2 mm. GER was applied in a TCS with a limestone roof of 5 m thickness; the field-measured data verified the conclusions of the numerical model