FADE: Fusing the Assets of Decoder and Encoder for Task-Agnostic Upsampling

We consider the problem of task-agnostic feature upsampling in dense prediction where an upsampling operator is required to facilitate both region-sensitive tasks like semantic segmentation and detail-sensitive tasks such as image matting. Existing upsampling operators often can work well in either type of the tasks, but not both. In this work, we present FADE, a novel, plug-and-play, and task-agnostic upsampling operator. FADE benefits from three design choices: i) considering encoder and decoder features jointly in upsampling kernel generation; ii) an efficient semi-shift convolutional operator that enables granular control over how each feature point contributes to upsampling kernels; iii) a decoder-dependent gating mechanism for enhanced detail delineation. We first study the upsampling properties of FADE on toy data and then evaluate it on large-scale semantic segmentation and image matting. In particular, FADE reveals its effectiveness and task-agnostic characteristic by consistently outperforming recent dynamic upsampling operators in different tasks. It also generalizes well across convolutional and transformer architectures with little computational overhead. Our work additionally provides thoughtful insights on what makes for task-agnostic upsampling. Code is available at: http://lnkiy.in/fade_inComment: Accepted to ECCV 2022. Code is available at http://lnkiy.in/fade_i

Learning to Upsample by Learning to Sample

We present DySample, an ultra-lightweight and effective dynamic upsampler. While impressive performance gains have been witnessed from recent kernel-based dynamic upsamplers such as CARAFE, FADE, and SAPA, they introduce much workload, mostly due to the time-consuming dynamic convolution and the additional sub-network used to generate dynamic kernels. Further, the need for high-res feature guidance of FADE and SAPA somehow limits their application scenarios. To address these concerns, we bypass dynamic convolution and formulate upsampling from the perspective of point sampling, which is more resource-efficient and can be easily implemented with the standard built-in function in PyTorch. We first showcase a naive design, and then demonstrate how to strengthen its upsampling behavior step by step towards our new upsampler, DySample. Compared with former kernel-based dynamic upsamplers, DySample requires no customized CUDA package and has much fewer parameters, FLOPs, GPU memory, and latency. Besides the light-weight characteristics, DySample outperforms other upsamplers across five dense prediction tasks, including semantic segmentation, object detection, instance segmentation, panoptic segmentation, and monocular depth estimation. Code is available at https://github.com/tiny-smart/dysample.Comment: Accepted by ICCV 202

Resilient control of networked control systems with stochastic denial of service attacks

This paper focuses on resilient control of networked control systems (NCSs) under the denial of service (DoS) attacks characterized by a Markov process. Firstly, based on the game between attack strategies and defense strategies, the packet dropouts induced by DoS attacks are modeled as a Markov process. Secondly, an NCS under DoS attacks is modeled as a Markovian jump linear system. Then, by use of the Lyapunov theory and the derived NCS model, four theorems are given for the system stability analysis and controller design. Finally, a numerical example is used to illustrative the effectiveness of proposed method

SAPA: Similarity-Aware Point Affiliation for Feature Upsampling

We introduce point affiliation into feature upsampling, a notion that describes the affiliation of each upsampled point to a semantic cluster formed by local decoder feature points with semantic similarity. By rethinking point affiliation, we present a generic formulation for generating upsampling kernels. The kernels encourage not only semantic smoothness but also boundary sharpness in the upsampled feature maps. Such properties are particularly useful for some dense prediction tasks such as semantic segmentation. The key idea of our formulation is to generate similarity-aware kernels by comparing the similarity between each encoder feature point and the spatially associated local region of decoder features. In this way, the encoder feature point can function as a cue to inform the semantic cluster of upsampled feature points. To embody the formulation, we further instantiate a lightweight upsampling operator, termed Similarity-Aware Point Affiliation (SAPA), and investigate its variants. SAPA invites consistent performance improvements on a number of dense prediction tasks, including semantic segmentation, object detection, depth estimation, and image matting. Code is available at: https://github.com/poppinace/sapaComment: Accepted to NeurIPS 2022. Code is available at https://github.com/poppinace/sap

Baicalin-modified polyethylenimine for miR-34a efficient and safe delivery

The security and efficiency of gene delivery vectors are inseparable for the successful construction of a gene delivery vector. This work provides a practical method to construct a charge-regulated, hydrophobic-modified, and functionally modified polyethylenimine (PEI) with effective gene delivery and perfect transfection performance through a condensation reaction, named BA-PEI. The carrier was shown to possess a favorable compaction of miRNAs into positively charged nanoparticles with a hydrodynamic size of approximately 100 nm. Additionally, BA-PEI possesses perfect degradability, which benefits the release of miR-34a from the complexes. In A549 cells, the expression level of the miR-34a gene was checked by Western blotting, which reflects the transfection efficiency of BA-PEI/miR-34a. When miR-34a is delivered to the cell, the perfect anti-tumor ability of the BA-PEI/miR-34a complex was systematically evaluated with the suppressor tumor gene miR-34a system in vitro and in vivo. BA-PEI-mediated miR-34a gene transfection is more secure and effective than the commercial transfection reagent, thus providing a novel approach for miR-34a-based gene therapy

Slumping Dynamics of Tilled Sandy Soils in North-East Thailand

International audienceRecompaction of tilled layers, under the effect of rainfall or irrigation only (i.e. without any external loading), was called slumping by Mullins et al. (1990). It has been observed in various soil types with negative effects on plant production. Our objective was to characterise the dynamics of slumping at the ploughed layer scale in a sandy soil of North East Thailand. An experimental field was tilled to two depths (20 and 40 cm) with or without ridges and furrows and was submitted i) to natural rainfall during two months (214mm in June and July 2007) or ii) to experimental flood irrigation (100 or 200 mm over some hours). Changes in bulk density with time were observed, particularly under flooding and after heavy daily rainfall. Final bulk density of 1.60 Mg m 3 has been measured over 20 cm depth while initial bulk density after tillage was 1.25 Mg m 3. Bulk density profiles were often characterised with two maximum values, either in the top layer (0-5 cm) or at the bottom of the ploughed layer (15-20 or 35-40 cm). We demonstrated that several processes occurred simultaneously: i) a redistribution of sand particles from the top of ridges to the bottom of furrows that decreased soil roughness, ii) a 2 to 5 cm topsoil collapse when water infiltrated, iii) a soil collapse at greater depths due to overburden pressure. These phenomena agree with the theory of granular material and the decrease in capillary forces between sand grains during wetting. The specific changes in bulk density profiles induced by rainfall should allow the occurrence of slumping to be predicted or identified as a function of soil, climate and tillage conditions

Slumping dynamics in tilled sandy soils under natural rainfall and experimental flooding

International audienceCompaction of tilled layers under the single effect of rainfall or irrigation was called slumping. Slumping affects strongly root development and plant biomass production. It has been observed in different soil types, but sandy soils appear particularly prone to this physical degradation. Our objectives in this study were (i) to measure in the field the changes in soil structure and water status simultaneously, (ii) to study the effects of rainfall and management practices on slumping, and (iii) to propose a conceptual model for sandy soil slumping. An experimental site was selected in Northeast Thailand and we studied the effect of tillage depth and initial water content on slumping dynamic. Plots (9 m × 15 m each) were tilled at (i) two depths (20 and 40 cm, called S and previous termDnext term respectively) in dry conditions, (ii) at 20 cm depth in dry or wet conditions (called Y and W respectively). These plots were submitted to natural rainfall for 20 or 61 days to get different total rainfall amounts (114 and 212 mm respectively). In addition, smaller plots (0.24 m2 each) were used for experimental flooding irrigation (similar to measured rainfalls, i.e. 100 and 200 mm). Soil bulk density, soil surface elevation, soil water content and matric potential were measured. A decrease in soil elevation was observed in all treatments. In the absence of erosion it was interpreted as a loss of porosity which resulted from slumping. Bulk density increased in all layers of the tilled profile (from 1.38 to 1.57 g cm−3). In the surface layer (0-5 cm) this increase was systematically higher compared to deeper layer. No significant difference in final bulk density was found between the S and W treatments, and between the Y and W treatments. Bulk density increased more rapidly in the Y and W treatments than in the S and previous termDnext term treatments, even though the cumulative rainfall was lower. After the flooding experime