Air Quality Measurements at a Laying Hen House: Particulate Matter Concentrations and Emissions

Particulate matter (PM) was measured in the ventilation exhaust air of a caged layer house using three tapered element oscillating microbalances (TEOMs). Diurnal patterns of PM concentration and emission were observed during 6 days in June 2002. The average daily mean (±95% c.i.) concentrations and emissions were 39±8.0, 518±74, and 1887±563 .g/m3 and 1.1±0.3, 16±3.4, and 63±15 g/d-AU for PM2.5, PM10, and total suspended particulates (TSP), respectively. Daytime (lights on) PM2.5, PM10, and TSP concentrations were 151, 108, and 136% higher (P\u3c0.05) than at night. Emissions peaked during the day when birds were most active and ventilation rates were the highest. Wide diurnal variations in PM concentration and ventilation were observed. PM emission was correlated to ventilation, ambient and exhaust temperatures, and relative humidity (P\u3c0.05)

NMR Experimental Demonstration of Probabilistic Quantum Cloning

The method of quantum cloning is divided into two main categories: approximate and probabilistic quantum cloning. The former method is used to approximate an unknown quantum state deterministically, and the latter can be used to faithfully copy the state probabilistically. So far, many approximate cloning machines have been experimentally demonstrated, but probabilistic cloning remains an experimental challenge, as it requires more complicated networks and a higher level of precision control. In this work, we designed an efficient quantum network with a limited amount of resources, and performed the first experimental demonstration of probabilistic quantum cloning in an NMR quantum computer. In our experiment, the optimal cloning efficiency proposed by Duan and Guo [Phys. Rev. Lett. \textbf{80}, 4999 (1998)] is achieved.Comment: 4 pages, 5 figures; to be published in Physical Review Letter

Fast Deep Matting for Portrait Animation on Mobile Phone

Image matting plays an important role in image and video editing. However, the formulation of image matting is inherently ill-posed. Traditional methods usually employ interaction to deal with the image matting problem with trimaps and strokes, and cannot run on the mobile phone in real-time. In this paper, we propose a real-time automatic deep matting approach for mobile devices. By leveraging the densely connected blocks and the dilated convolution, a light full convolutional network is designed to predict a coarse binary mask for portrait images. And a feathering block, which is edge-preserving and matting adaptive, is further developed to learn the guided filter and transform the binary mask into alpha matte. Finally, an automatic portrait animation system based on fast deep matting is built on mobile devices, which does not need any interaction and can realize real-time matting with 15 fps. The experiments show that the proposed approach achieves comparable results with the state-of-the-art matting solvers.Comment: ACM Multimedia Conference (MM) 2017 camera-read

Ammonia Emissions from U.S. Poultry Houses: Part I—Measurement System and Techniques

A multi-state, multi-disciplinary research project is currently underway to quantify aerial ammonia (NH3) emissions from selected US poultry houses with different housing and management schemes. A series of publications will result from this study. This paper highlights the system and techniques used by the participating institutions to continuously measure NH3 and carbon dioxide concentrations and determine building ventilation rate. Specifically, a portable monitoring unit (PMU) has been developed and refined for field measurement and acquisition of NH3 level, CO2 level and building static pressure. Ammonia level is measured with electro-chemical sensors that undergo cyclic purging to avoid sensor saturation. Building ventilation rate is directly measured by calibrating the airflow rates of fans in-situ with a Fan Assessment Numeration System (FANS) device and recording of fans runtime, or indirectly calculated using the CO2 balance method based on the latest metabolic rate information for the modern birds (W-36 laying hens). Comparative tests were conducted between the PMU and a chemiluminescence NH3 analyzer in a field emission laboratory (FEL), and there were no significant differences between the two measurement methods (P=0.33)

Data from a comparative proteomic analysis of tumor-derived lung-cancer CD105+ endothelial cells

AbstractIncreasing evidence indicates that tumor-derived endothelial cells (TECs) are more relevant for the study of tumor angiogenesis and for screening antiangiogenic drugs than normal ECs (NECs). In this data article, high-purity (>98%) primary CD105+ NECs and TECs purified from a mouse Lewis lung carcinoma model bearing 0.5cm tumors were identified using 2D-PAGE and Matrix-assisted laser desorption/ionization tandem mass spectrometry (MALDI-MS/MS). All the identified proteins were categorized functionally by Gene Ontology (GO) analysis, and gene-pathway annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, protein–protein interaction networks were also built. The proteomics and bioinformatics data presented here provide novel insights into the molecular characteristics and the early modulation of the TEC proteome in the tumor microenvironment

PoisonPrompt: Backdoor Attack on Prompt-based Large Language Models

Prompts have significantly improved the performance of pretrained Large Language Models (LLMs) on various downstream tasks recently, making them increasingly indispensable for a diverse range of LLM application scenarios. However, the backdoor vulnerability, a serious security threat that can maliciously alter the victim model's normal predictions, has not been sufficiently explored for prompt-based LLMs. In this paper, we present POISONPROMPT, a novel backdoor attack capable of successfully compromising both hard and soft prompt-based LLMs. We evaluate the effectiveness, fidelity, and robustness of POISONPROMPT through extensive experiments on three popular prompt methods, using six datasets and three widely used LLMs. Our findings highlight the potential security threats posed by backdoor attacks on prompt-based LLMs and emphasize the need for further research in this area.Comment: To Appear in IEEE ICASSP 2024, code is available at: https://github.com/grasses/PoisonPromp

Super-High-Fidelity Image Compression via Hierarchical-ROI and Adaptive Quantization

Learned Image Compression (LIC) has achieved dramatic progress regarding objective and subjective metrics. MSE-based models aim to improve objective metrics while generative models are leveraged to improve visual quality measured by subjective metrics. However, they all suffer from blurring or deformation at low bit rates, especially at below $0.2bpp$. Besides, deformation on human faces and text is unacceptable for visual quality assessment, and the problem becomes more prominent on small faces and text. To solve this problem, we combine the advantage of MSE-based models and generative models by utilizing region of interest (ROI). We propose Hierarchical-ROI (H-ROI), to split images into several foreground regions and one background region to improve the reconstruction of regions containing faces, text, and complex textures. Further, we propose adaptive quantization by non-linear mapping within the channel dimension to constrain the bit rate while maintaining the visual quality. Exhaustive experiments demonstrate that our methods achieve better visual quality on small faces and text with lower bit rates, e.g., $0.7X$ bits of HiFiC and $0.5X$ bits of BPG