IC-FPS: Instance-Centroid Faster Point Sampling Module for 3D Point-base Object Detection

3D object detection is one of the most important tasks in autonomous driving and robotics. Our research focuses on tackling low efficiency issue of point-based methods on large-scale point clouds. Existing point-based methods adopt farthest point sampling (FPS) strategy for downsampling, which is computationally expensive in terms of inference time and memory consumption when the number of point cloud increases. In order to improve efficiency, we propose a novel Instance-Centroid Faster Point Sampling Module (IC-FPS) , which effectively replaces the first Set Abstraction (SA) layer that is extremely tedious. IC-FPS module is comprised of two methods, local feature diffusion based background point filter (LFDBF) and Centroid-Instance Sampling Strategy (CISS). LFDBF is constructed to exclude most invalid background points, while CISS substitutes FPS strategy by fast sampling centroids and instance points. IC-FPS module can be inserted to almost every point-based models. Extensive experiments on multiple public benchmarks have demonstrated the superiority of IC-FPS. On Waymo dataset, the proposed module significantly improves performance of baseline model and accelerates inference speed by 3.8 times. For the first time, real-time detection of point-based models in large-scale point cloud scenario is realized

Higher atmospheric CO2 levels favour C3 plants over C4 plants in utilizing ammonium as a nitrogen source

Photosynthesis of wheat and maize declined when grown with NH4+ as a nitrogen (N) source at ambient CO2 concentration compared to those grown with a mixture of NO3– and NH4+, or NO3– as the sole N source. Interestingly, these N nutritional physiological responses changed when the atmospheric CO2 concentration increases. We studied the photosynthetic responses of wheat and maize growing with various N forms at three levels of growth CO2 levels. Hydroponic experiments were carried out using a C3 plant (wheat, Triticum aestivum L. cv. Chuanmai 58) and a C4 plant (maize, Zea mays L. cv. Zhongdan 808) given three types of N nutrition: sole NO3– (NN), sole NH4+ (AN) and a mixture of both NO3– and NH4+ (Mix-N). The test plants were grown using custom-built chambers where a continuous and desired atmospheric CO2 (Ca) concentration could be maintained: 280 μmol mol–1 (representing the pre-Industrial Revolution CO2 concentration of the 18th century), 400 μmol mol–1 (present level) and 550 μmol mol–1 (representing the anticipated futuristic concentration in 2050). Under AN, the decrease in net photosynthetic rate (Pn) was attributed to a reduction in the maximum RuBP-regeneration rate, which then caused reductions in the maximum Rubisco-carboxylation rates for both species. Decreases in electron transport rate, reduction of electron flux to the photosynthetic carbon [Je(PCR)] and electron flux for photorespiratory carbon oxidation [Je(PCO)] were also observed under AN for both species. However, the intercellular (Ci) and chloroplast (Cc) CO2 concentration increased with increasing atmospheric CO2 in C3 wheat but not in C4 maize, leading to a higher Je(PCR)/ Je(PCO) ratio. Interestingly, the reduction of Pn under AN was relieved in wheat through higher CO2 levels, but that was not the case in maize. In conclusion, elevating atmospheric CO2 concentration increased Ci and Cc in wheat, but not in maize, with enhanced electron fluxes towards photosynthesis, rather than photorespiration, thereby relieving the inhibition of photosynthesis under AN. Our results contributed to a better understanding of NH4+ involvement in N nutrition of crops growing under different levels of CO2

Efficient Adaptive Activation Rounding for Post-Training Quantization

Post-training quantization attracts increasing attention due to its convenience in deploying quantized neural networks. Although rounding-to-nearest remains the prevailing method for DNN quantization, prior research has demonstrated its suboptimal nature when applied to weight quantization. They propose optimizing weight rounding schemes by leveraging output error rather than the traditional weight quantization error. Our study reveals that similar rounding challenges also extend to activation quantization. Despite the easy generalization, the challenges lie in the dynamic nature of activation. Adaptive rounding is expected for varying activations and the method is subjected to runtime overhead. To tackle this, we propose the AQuant quantization framework with a novel perspective to reduce output error by adjusting rounding schemes of activations. Instead of using the constant rounding border 0.5 of the rounding-to-nearest operation, we make the border become a function w.r.t. the activation value to change the activation rounding by the adaptive border. To deal with the runtime overhead, we use a coarse-grained version of the border function. Finally, we introduce our framework to optimize the border function. Extensive experiments show that AQuant achieves notable improvements compared to state-of-the-art works and pushes the accuracy of ResNet-18 up to 60.31% under the 2-bit weight and activation quantization

Fatigue Detection for Ship OOWs Based on Input Data Features, from The Perspective of Comparison with Vehicle Drivers: A Review

Ninety percent of the world’s cargo is transported by sea, and the fatigue of ship officers of the watch (OOWs) contributes significantly to maritime accidents. The fatigue detection of ship OOWs is more difficult than that of vehicles drivers owing to an increase in the automation degree. In this study, research progress pertaining to fatigue detection in OOWs is comprehensively analysed based on a comparison with that in vehicle drivers. Fatigue detection techniques for OOWs are organised based on input sources, which include the physiological/behavioural features of OOWs, vehicle/ship features, and their comprehensive features. Prerequisites for detecting fatigue in OOWs are summarised. Subsequently, various input features applicable and existing applications to the fatigue detection of OOWs are proposed, and their limitations are analysed. The results show that the reliability of the acquired feature data is insufficient for detecting fatigue in OOWs, as well as a non-negligible invasive effect on OOWs. Hence, low-invasive physiological information pertaining to the OOWs, behaviour videos, and multisource feature data of ship characteristics should be used as inputs in future studies to realise quantitative, accurate, and real-time fatigue detections in OOWs on actual ships

Immune-mediated inflammatory diseases and risk of venous thromboembolism: A Mendelian randomization study

IntroductionImmune-mediated inflammatory diseases (IMIDs) have been associated with an increased risk of venous thromboembolism (VTE) in multiple observational studies. However, a direct causally relation between IMIDs and VTE remains unclear to date. Here, we used Mendelian randomization (MR) analysis to investigate causal associations between IMIDs and VTE.MethodsWe collected genetic data from published genome-wide association studies (GWAS) for six common IMIDs, specifically inflammatory bowel disease (IBD), Crohn’s disease (CD), ulcerative colitis (UC), rheumatoid arthritis (RA), psoriasis (PSO), and systemic lupus erythematosus (SLE); and summary-level data for VTE, pulmonary embolism (PE), and deep vein thrombosis (DVT) from the FinnGen database. Two-sample MR analysis using inverse variance weighting (IVW) was performed to identify causal associations between IMIDs and VTE/DVT/PE, and sensitivity analyses were implemented for robustness.ResultsIVW analysis showed a causal relationship between genetically predicted UC (one type of IBD) and the risk of VTE (OR = 1.043, 95% CI: 1.013-1.073, p = 0.004) and DVT (OR = 1.088, 95% CI: 1.043-1.136, p < 0.001), but we found no evidence of causality between UC and PE (OR = 1.029, 95% CI: 0.986-1.074, p = 0.19). In addition, no associations were observed between total IBD, CD, RA, SLE, or PSO and VTE/DVT/PE. Sensitivity analysis found no evidence for horizontal pleiotropy.ConclusionThis MR study provides new genetic evidence for the causal relationship between IMIDs and the risk of VTE. Our findings highlight the importance of active intervention and monitoring to mitigate VTE risk in patients with IBD, in particular those presenting with UC

Connecting Dense Gas Tracers of Star Formation in our Galaxy to High-z Star Formation

Observations have revealed prodigious amounts of star formation in starburst galaxies as traced by dust and molecular emission, even at large redshifts. Recent work shows that for both nearby spiral galaxies and distant starbursts, the global star formation rate, as indicated by the infrared luminosity, has a tight and almost linear correlation with the amount of dense gas as traced by the luminosity of HCN. Our surveys of Galactic dense cores in HCN 1-0 emission show that this correlation continues to a much smaller scale, with nearly the same ratio of infrared luminosity to HCN luminosity found over 7-8 orders of magnitude in L_IR, with a lower cutoff around 10^{4.5} L_sun of infrared luminosity. The linear correlation suggests that we may understand distant star formation in terms of the known properties of local star-forming regions. Both the correlation and the luminosity cutoff can be explained if the basic unit of star formation in galaxies is a dense core, similar to those studied in our Galaxy.Comment: 10 pages, 2 figures. In press for ApJ Letter