A Low-Cost Microfluidic Method for Microplastics Identification: Towards Continuous Recognition

Plastic pollution has emerged as a growing concern worldwide. In particular, the most abundant plastic debris, microplastics, has necessitated the development of rapid and effective identification methods to track down the stages and evidence of the pollution. In this paper, we combine low-cost plastic staining technologies using Nile Red with the continuous feature offered by microfluidics to propose a low-cost 3D printed device for the identification of microplastics. It is observed that the microfluidic devices indicate comparable staining and identification performance compared to conventional Nile Red staining processes while offering the advantages of continuous recognition for long-term environmental monitoring. The results also show that concentration, temperature, and residency time possess strong effects on the identification performance. Finally, various microplastics have been applied to further demonstrate the effectiveness of the proposed devices. It is found that, among different types of microplastics, non-spherical microplastics show the maximal fluorescence level. Meanwhile, natural fibers indicate better staining quality when compared to synthetic ones

LDP: Language-driven Dual-Pixel Image Defocus Deblurring Network

Recovering sharp images from dual-pixel (DP) pairs with disparity-dependent blur is a challenging task.~Existing blur map-based deblurring methods have demonstrated promising results. In this paper, we propose, to the best of our knowledge, the first framework to introduce the contrastive language-image pre-training framework (CLIP) to achieve accurate blur map estimation from DP pairs unsupervisedly. To this end, we first carefully design text prompts to enable CLIP to understand blur-related geometric prior knowledge from the DP pair. Then, we propose a format to input stereo DP pair to the CLIP without any fine-tuning, where the CLIP is pre-trained on monocular images. Given the estimated blur map, we introduce a blur-prior attention block, a blur-weighting loss and a blur-aware loss to recover the all-in-focus image. Our method achieves state-of-the-art performance in extensive experiments

Event Camera Data Pre-training

This paper proposes a pre-trained neural network for handling event camera data. Our model is a self-supervised learning framework, and uses paired event camera data and natural RGB images for training. Our method contains three modules connected in a sequence: i) a family of event data augmentations, generating meaningful event images for self-supervised training; ii) a conditional masking strategy to sample informative event patches from event images, encouraging our model to capture the spatial layout of a scene and accelerating training; iii) a contrastive learning approach, enforcing the similarity of embeddings between matching event images, and between paired event and RGB images. An embedding projection loss is proposed to avoid the model collapse when enforcing the event image embedding similarities. A probability distribution alignment loss is proposed to encourage the event image to be consistent with its paired RGB image in the feature space. Transfer learning performance on downstream tasks shows the superiority of our method over state-of-the-art methods. For example, we achieve top-1 accuracy at 64.83% on the N-ImageNet dataset

Event Camera Data Dense Pre-training

This paper introduces a self-supervised learning framework designed for pre-training neural networks tailored to dense prediction tasks using event camera data. Our approach utilizes solely event data for training. Transferring achievements from dense RGB pre-training directly to event camera data yields subpar performance. This is attributed to the spatial sparsity inherent in an event image (converted from event data), where many pixels do not contain information. To mitigate this sparsity issue, we encode an event image into event patch features, automatically mine contextual similarity relationships among patches, group the patch features into distinctive contexts, and enforce context-to-context similarities to learn discriminative event features. For training our framework, we curate a synthetic event camera dataset featuring diverse scene and motion patterns. Transfer learning performance on downstream dense prediction tasks illustrates the superiority of our method over state-of-the-art approaches. Notably, our single model secured the top position in the challenging DSEC-Flow benchmark

Monitoring LSO/LYSO Crystal Based Calorimeters

Precision light monitoring is important for keeping excellent energy resolution promised by LSO/LYSO crystals in severe radiation environment. In this paper, we report an investigation on the wavelength choice for monitoring LYSO crystal based calorimeters. Gamma-ray induced absorption and light output loss were measured for 20 cm long crystals from five different vendors. Monitoring sensitivity and divergence between crystals from different vendors were investigated. The pros and cons of two monitoring approaches using emission and excitation light and their practical implementation for a LYSO/W Shashlik test beam matrix are discussed

From Analogue to Digital: Reconsidering Copyright And The Exclusive Rights of Authors In An Era Of Technological Change

After the First Industrial Revolution, a series of technologies challenged copyright law and pushed the law to accommodate, expand, and develop. Compared with analogue technologies, digital technologies present an even greater challenge to copyright law, which is under pressure to adapt to the rapid changes in the technologies. When digital technology was in its infancy, analogue copyright law was extended to the digital realm and became known as digital copyright law. ‘Digital copyright law’, however, is no more than a tailoring, tinkering and twisting of analogue copyright law, which fits poorly into the new digital environment. In colloquial terms, it is fitting the square digital copyright law into a round digital hole. The digital world is an entirely new environment and digital technology is advancing at an unprecedented rate. There is a need for a new approach to digital copyright law that could accommodate digital technologies for disseminating copyright works in a more realistic manner than the current approach of simply adapting old analogue concepts. Current digital copyright law—a phrase that broadly refers to any provision or regulation dealing with copyright issues in the digital environment—is not consistent with technological developments. Digital technologies continually expand access to digital copyright works, whereas current digital copyright law significantly restricts such access. The approach suggested in this thesis allows content users to freely access digital copyright works while ensuring copyright holders’ adequate remuneration from the works. It is inspired by an existing business model under which users can freely replicate and disseminate (or access) digital copyright works but cannot freely use the works. To accommodate this model, the thesis suggests that current digital copyright law needs to be overhauled

Heavy Metal Pollution in Surface Water of Linglong Gold Mining Area, China

AbstractThe concentrations and distribution patterns of lead, mercury, zinc, copper, chromium, arsenic, cadmium in surface water of Linglong deposit area were discussed. The result shows that the surface water of Linglong mining area is seriously polluted by mercury, zinc and cadmium, which of the concentration are higher than the III class of National Surface Water Quality Standard, and moderately polluted by chromium and arsenic, which of the concentration conforms to the III class national surface water quality standard, and light polluted by lead and copper, which of the concentration conforms to the II class national surface water quality standard. The concentration of heavy metals in the gold deposit areas depends upon the distance from the pollution source and scalar transport in rivulet flows, decreases along the flow direction. The concentration and distribution of heavy metal pollutants in surface water are dominated by the geochemical situation and the pollution source, but seriously affected by mining leachate and chemical wastewater discharge

LCCo: Lending CLIP to Co-Segmentation

This paper studies co-segmenting the common semantic object in a set of images. Existing works either rely on carefully engineered networks to mine the implicit semantic information in visual features or require extra data (i.e., classification labels) for training. In this paper, we leverage the contrastive language-image pre-training framework (CLIP) for the task. With a backbone segmentation network that independently processes each image from the set, we introduce semantics from CLIP into the backbone features, refining them in a coarse-to-fine manner with three key modules: i) an image set feature correspondence module, encoding global consistent semantic information of the image set; ii) a CLIP interaction module, using CLIP-mined common semantics of the image set to refine the backbone feature; iii) a CLIP regularization module, drawing CLIP towards this co-segmentation task, identifying the best CLIP semantic and using it to regularize the backbone feature. Experiments on four standard co-segmentation benchmark datasets show that the performance of our method outperforms state-of-the-art methods