PATROL: Privacy-Oriented Pruning for Collaborative Inference Against Model Inversion Attacks

Collaborative inference has been a promising solution to enable resource-constrained edge devices to perform inference using state-of-the-art deep neural networks (DNNs). In collaborative inference, the edge device first feeds the input to a partial DNN locally and then uploads the intermediate result to the cloud to complete the inference. However, recent research indicates model inversion attacks (MIAs) can reconstruct input data from intermediate results, posing serious privacy concerns for collaborative inference. Existing perturbation and cryptography techniques are inefficient and unreliable in defending against MIAs while performing accurate inference. This paper provides a viable solution, named PATROL, which develops privacy-oriented pruning to balance privacy, efficiency, and utility of collaborative inference. PATROL takes advantage of the fact that later layers in a DNN can extract more task-specific features. Given limited local resources for collaborative inference, PATROL intends to deploy more layers at the edge based on pruning techniques to enforce task-specific features for inference and reduce task-irrelevant but sensitive features for privacy preservation. To achieve privacy-oriented pruning, PATROL introduces two key components: Lipschitz regularization and adversarial reconstruction training, which increase the reconstruction errors by reducing the stability of MIAs and enhance the target inference model by adversarial training, respectively

Optimized Path Planning for USVs under Ocean Currents

The proposed work focuses on the path planning for Unmanned Surface Vehicles (USVs) in the ocean enviroment, taking into account various spatiotemporal factors such as ocean currents and other energy consumption factors. The paper proposes the use of Gaussian Process Motion Planning (GPMP2), a Bayesian optimization method that has shown promising results in continuous and nonlinear path planning algorithms. The proposed work improves GPMP2 by incorporating a new spatiotemporal factor for tracking and predicting ocean currents using a spatiotemporal Bayesian inference. The algorithm is applied to the USV path planning and is shown to optimize for smoothness, obstacle avoidance, and ocean currents in a challenging environment. The work is relevant for practical applications in ocean scenarios where an optimal path planning for USVs is essential for minimizing costs and optimizing performance.Comment: 9 pages and 7 figures, submitted for IEEE Transactions on Man, systems ,and Cybernetic

Effects of Surface Modification of Nanotube Arrays on the Performance of CdS Quantum-Dot-Sensitized Solar Cells

CdS-sensitized TiO2 nanotube arrays have been fabricated using the method of successive ionic layer adsorption and reaction and used as a photoanode for quantum-dot-sensitized solar cells. Before being coated with CdS, the surface of TiO2 nanotube arrays was treated with TiCl4, nitric acid (HNO3), potassium hydroxide (KOH), and methyltrimethoxysilane (MTMS), respectively, for the purpose of reducing the interface transfer resistance of quantum-dot-sensitized solar cells. The surfaces of the modified samples represented the characteristics of superhydrophilic and hydrophobic which directly affect the power conversion efficiency of the solar cells. The results showed that surface modification resulted in the reduction of the surface tension, which played a significant role in the connectivity of CdS and TiO2 nanotube arrays. In addition, the solar cells based on CdS/TiO2 electrode treated by HNO3 achieved a maximum power conversion efficiency of 0.17%, which was 42% higher than the reference sample without any modification

Towards Real-World Visual Tracking with Temporal Contexts

Visual tracking has made significant improvements in the past few decades. Most existing state-of-the-art trackers 1) merely aim for performance in ideal conditions while overlooking the real-world conditions; 2) adopt the tracking-by-detection paradigm, neglecting rich temporal contexts; 3) only integrate the temporal information into the template, where temporal contexts among consecutive frames are far from being fully utilized. To handle those problems, we propose a two-level framework (TCTrack) that can exploit temporal contexts efficiently. Based on it, we propose a stronger version for real-world visual tracking, i.e., TCTrack++. It boils down to two levels: features and similarity maps. Specifically, for feature extraction, we propose an attention-based temporally adaptive convolution to enhance the spatial features using temporal information, which is achieved by dynamically calibrating the convolution weights. For similarity map refinement, we introduce an adaptive temporal transformer to encode the temporal knowledge efficiently and decode it for the accurate refinement of the similarity map. To further improve the performance, we additionally introduce a curriculum learning strategy. Also, we adopt online evaluation to measure performance in real-world conditions. Exhaustive experiments on 8 wellknown benchmarks demonstrate the superiority of TCTrack++. Real-world tests directly verify that TCTrack++ can be readily used in real-world applications.Comment: Accepted by IEEE TPAMI, Code: https://github.com/vision4robotics/TCTrac

Synthesis and Characterization of Hierarchical Structured TiO 2

Hierarchical structured TiO2 nanotubes were prepared by mechanical ball milling of highly ordered TiO2 nanotube arrays grown by electrochemical anodization of titanium foil. Scanning electron microscopy, transmission electron microscopy, X-ray diffraction, specific surface area analysis, UV-visible absorption spectroscopy, photocurrent measurement, photoluminescence spectra, electrochemical impedance spectra, and photocatalytic degradation test were applied to characterize the nanocomposites. Surface area increased as the milling time extended. After 5 h ball milling, TiO2 hierarchical nanotubes exhibited a corn-like shape and exhibited enhanced photoelectrochemical activity in comparison to commercial P25. The superior photocatalytic activity is suggested to be due to the combined advantages of high surface area of nanoparticles and rapid electron transfer as well as collection of the nanotubes in the hierarchical structure. The hierarchical structured TiO2 nanotubes could be applied into flexible applications on solar cells, sensors, and other photoelectrochemical devices

RLIPv2: Fast Scaling of Relational Language-Image Pre-training

Relational Language-Image Pre-training (RLIP) aims to align vision representations with relational texts, thereby advancing the capability of relational reasoning in computer vision tasks. However, hindered by the slow convergence of RLIPv1 architecture and the limited availability of existing scene graph data, scaling RLIPv1 is challenging. In this paper, we propose RLIPv2, a fast converging model that enables the scaling of relational pre-training to large-scale pseudo-labelled scene graph data. To enable fast scaling, RLIPv2 introduces Asymmetric Language-Image Fusion (ALIF), a mechanism that facilitates earlier and deeper gated cross-modal fusion with sparsified language encoding layers. ALIF leads to comparable or better performance than RLIPv1 in a fraction of the time for pre-training and fine-tuning. To obtain scene graph data at scale, we extend object detection datasets with free-form relation labels by introducing a captioner (e.g., BLIP) and a designed Relation Tagger. The Relation Tagger assigns BLIP-generated relation texts to region pairs, thus enabling larger-scale relational pre-training. Through extensive experiments conducted on Human-Object Interaction Detection and Scene Graph Generation, RLIPv2 shows state-of-the-art performance on three benchmarks under fully-finetuning, few-shot and zero-shot settings. Notably, the largest RLIPv2 achieves 23.29mAP on HICO-DET without any fine-tuning, yields 32.22mAP with just 1% data and yields 45.09mAP with 100% data. Code and models are publicly available at https://github.com/JacobYuan7/RLIPv2.Comment: Accepted to ICCV 2023. Code and models: https://github.com/JacobYuan7/RLIPv

TNFRSF10C methylation is a new epigenetic biomarker for colorectal cancer

Background Abnormal methylation of TNFRSF10C was found to be associated with different types of cancers, excluding colorectal cancer (CRC). In this paper, the performance of TNFRSF10C methylation in CRC was studied in two stages. Method The discovery stage was involved with 38 pairs of CRC tumor and paired adjacent non-tumor tissues, and 69 pairs of CRC tumor and paired adjacent non-tumor tissues were used for the validation stage. Quantitative methylation specific PCR (qMSP) method and percentage of methylated reference (PMR) were used to test and represent the methylation level of TNFRSF10C, respectively. A dual-luciferase reporter gene experiment was conducted to evaluate the promoter activity of TNFRSF10C fragment. Results A significant association of TNFRSF10C promoter hypermethylation with CRC was found and validated (discovery stage: 24.67 ± 7.52 vs. 3.36 ± 0.89; P = 0.003; validation stage: 31.21 ± 12.48 vs. 4.52 ± 1.47; P = 0.0005). Subsequent analyses of TCGA data among 46 pairs of CRC samples further confirmed our findings (cg23965061: P = 4E − 6; cg14015044: P = 1E − 7). Dual-luciferase reporter gene assay revealed that TNFRSF10C fragment was able to significantly promote gene expression (Fold change = 2.375, P = 0.013). Our data confirmed that TNFRSF10C promoter hypermethylation can predict shorter overall survival of CRC patients (P = 0.032). Additionally, bioinformatics analyses indicated that TNFRSF10C hypermethylation was significantly associated with lower TNFRSF10C expression. Conclusion Our work suggested that TNFRSF10C hypermethylation was significantly associated with the risk of CRC

Land use change and climate variation in the Three Gorges Reservoir Catchment from 2000 to 2015 based on the Google Earth Engine

Possible environmental change and ecosystem degradation have received increasing attention since the construction of Three Gorges Reservoir Catchment (TGRC) in China. The advanced Google Earth Engine (GEE) cloud-based platform and the large number of Geosciences and Remote Sensing datasets archived in GEE were used to analyze the land use and land cover change (LULCC) and climate variation in TGRC. GlobeLand30 data were used to evaluate the spatial land dynamics from 2000 to 2010 and Landsat 8 Operational Land Imager (OLI) images were applied for land use in 2015. The interannual variations in the Land Surface Temperature (LST) and seasonally integrated normalized difference vegetation index (SINDVI) were estimated using Moderate Resolution Imaging Spectroradiometer (MODIS) products. The climate factors including air temperature, precipitation and evapotranspiration were investigated based on the data from the Global Land Data Assimilation System (GLDAS). The results indicated that from 2000 to 2015, the cultivated land and grassland decreased by 2.05% and 6.02%, while the forest, wetland, artificial surface, shrub land and waterbody increased by 3.64%, 0.94%, 0.87%, 1.17% and 1.45%, respectively. The SINDVI increased by 3.209 in the period of 2000-2015, while the LST decreased by 0.253 °C from 2001 to 2015. The LST showed an increasing trend primarily in urbanized area, with a decreasing trend mainly in forest area. In particular, Chongqing City had the highest LST during the research period. A marked decrease in SINDVI occurred primarily in urbanized areas. Good vegetation areas were primarily located in the eastern part of the TGRC, such as Wuxi County, Wushan County, and Xingshan County. During the 2000–2015 period, the air temperature, precipitation and evapotranspiration rose by 0.0678 °C/a, 1.0844 mm/a, and 0.4105 mm/a, respectively. The climate change in the TGRC was influenced by LULCC, but the effect was limited. What is more, the climate change was affected by regional climate change in Southwest China. Marked changes in land use have occurred in the TGRC, and they have resulted in changes in the LST and SINDVI. There was a significantly negative relationship between LST and SINDVI in most parts of the TGRC, especially in expanding urban areas and growing forest areas. Our study highlighted the importance of environmental protection, particularly proper management of land use, for sustainable development in the catchment