Electrospun Polyvinyl Alcohol/Cellulose Nanocrystals Composite Nanofibrous Filter: Investigation of Fabrication and Application

Particulate matter (PM) pollution has become a global environmental issue because it poses threat to public health. To protect individuals from PM exposure, one common method is using air filters for indoor air purification. However, conventional air filters have various drawbacks, such as high air resistance, the filters are not fabricated with environmentally friendly technology, and they cannot be easily regenerated. In this dissertation, a new electrospun poly(vinyl alcohol) (PVA)/cellulose nanocrystals (CNCs) composite nanofibrous filter was successfully developed. This PVA/CNCs composite material was demonstrated as air filter for the first time. The CNCs improved the filtration performance by increasing the surface charge density of the electrospinning suspension and thereby reducing diameter of fibers. High PM2.5 removal efficiency was achieved (99.1%) with low pressure drop (91 Pa) at a relatively high airflow velocity (0.2 m s-1), under extremely polluted condition (PM2.5 mass concentration \u3e500 μg m-3). The integral effect of various electrospinning suspension properties on filtration performance was also investigated using response surface methodology. With a face-centered central composite design, the operating parameters for fabricating PVA/CNCs air filters were optimized, and the optimum conditions were a suspension concentration of 7.34% and a CNCs percentage of 20%. Additionally, the water-soluble PVA/CNCs composite was converted to be completely water-resistant when the electrospun material was heated at 140 oC for only 5 min. The mechanism of the change of water solubility of the fibers was investigated systematically. Our results revealed that increased crystallinity is the key factor for improving the aqueous stability, and CNCs provided additional nucleation sites for PVA crystallization during both electrospinning and heating process. The heated filters were effectively regenerated by water washing and the filtration performance was satisfactorily maintained. Because both PVA and CNCs are nontoxic and biodegradable, no organic solvents or crosslinking agents were used in the whole fabrication process, and the heating process is facile, the method proposed in this dissertation for fabricating electrospun PVA/CNCs nanofibrous filters is environmentally friendly and cost-effectively. This new cellulose-based air filter, which possesses high removal efficiency for PM, low pressure drop, and long lifetime, is very promising

Dynamic Patch-aware Enrichment Transformer for Occluded Person Re-Identification

Person re-identification (re-ID) continues to pose a significant challenge, particularly in scenarios involving occlusions. Prior approaches aimed at tackling occlusions have predominantly focused on aligning physical body features through the utilization of external semantic cues. However, these methods tend to be intricate and susceptible to noise. To address the aforementioned challenges, we present an innovative end-to-end solution known as the Dynamic Patch-aware Enrichment Transformer (DPEFormer). This model effectively distinguishes human body information from occlusions automatically and dynamically, eliminating the need for external detectors or precise image alignment. Specifically, we introduce a dynamic patch token selection module (DPSM). DPSM utilizes a label-guided proxy token as an intermediary to identify informative occlusion-free tokens. These tokens are then selected for deriving subsequent local part features. To facilitate the seamless integration of global classification features with the finely detailed local features selected by DPSM, we introduce a novel feature blending module (FBM). FBM enhances feature representation through the complementary nature of information and the exploitation of part diversity. Furthermore, to ensure that DPSM and the entire DPEFormer can effectively learn with only identity labels, we also propose a Realistic Occlusion Augmentation (ROA) strategy. This strategy leverages the recent advances in the Segment Anything Model (SAM). As a result, it generates occlusion images that closely resemble real-world occlusions, greatly enhancing the subsequent contrastive learning process. Experiments on occluded and holistic re-ID benchmarks signify a substantial advancement of DPEFormer over existing state-of-the-art approaches. The code will be made publicly available.Comment: 12 pages, 6 figure

Di-μ-methanolato-κ4 O:O-bis[tri­chlorido(dimethyl­formamide-κO)tin(IV)]

The title compound, [Sn2(CH3O)2Cl6(C3H7NO)2], contains two hexa­coordinated SnIV atoms symmetrically bridged by two deprotonated methanol ligands, with an inversion center in the middle of the planar Sn2O2 ring. The other sites of the distorted octa­hedral coordination geometry of the SnIV atom are occupied by three Cl atoms and one O atom from a dimethyl­formamide mol­ecule. The complex mol­ecules are connected by weak C—H⋯Cl hydrogen bonds into a two-dimensional supra­molecular network parallel to (10)

Optimal Distributed Controller Design for Nonlinear Coupled Dynamical Networks

This paper is concerned with the optimal distributed impulsive controller design for globally exponential synchronization of nonlinear dynamical networks with coupling delay. By the Lyapunov-Razumikhin method, a novel criterion is proposed to guarantee the global exponential synchronization of the coupled delayed network with distributed impulsive control in terms of matrix inequalities. The sum of coupling strengths of the distributed impulsive control is minimized to save the control effort. Finally, the effectiveness of the proposed method has been demonstrated by some simulations

Dive Deeper into Rectifying Homography for Stereo Camera Online Self-Calibration

Accurate estimation of stereo camera extrinsic parameters is the key to guarantee the performance of stereo matching algorithms. In prior arts, the online self-calibration of stereo cameras has commonly been formulated as a specialized visual odometry problem, without taking into account the principles of stereo rectification. In this paper, we first delve deeply into the concept of rectifying homography, which serves as the cornerstone for the development of our novel stereo camera online self-calibration algorithm, for cases where only a single pair of images is available. Furthermore, we introduce a simple yet effective solution for global optimum extrinsic parameter estimation in the presence of stereo video sequences. Additionally, we emphasize the impracticality of using three Euler angles and three components in the translation vectors for performance quantification. Instead, we introduce four new evaluation metrics to quantify the robustness and accuracy of extrinsic parameter estimation, applicable to both single-pair and multi-pair cases. Extensive experiments conducted across indoor and outdoor environments using various experimental setups validate the effectiveness of our proposed algorithm. The comprehensive evaluation results demonstrate its superior performance in comparison to the baseline algorithm. Our source code, demo video, and supplement are publicly available at mias.group/StereoCalibrator

SpecLLM: Exploring Generation and Review of VLSI Design Specification with Large Language Model

The development of architecture specifications is an initial and fundamental stage of the integrated circuit (IC) design process. Traditionally, architecture specifications are crafted by experienced chip architects, a process that is not only time-consuming but also error-prone. Mistakes in these specifications may significantly affect subsequent stages of chip design. Despite the presence of advanced electronic design automation (EDA) tools, effective solutions to these specification-related challenges remain scarce. Since writing architecture specifications is naturally a natural language processing (NLP) task, this paper pioneers the automation of architecture specification development with the advanced capabilities of large language models (LLMs). Leveraging our definition and dataset, we explore the application of LLMs in two key aspects of architecture specification development: (1) Generating architecture specifications, which includes both writing specifications from scratch and converting RTL code into detailed specifications. (2) Reviewing existing architecture specifications. We got promising results indicating that LLMs may revolutionize how these critical specification documents are developed in IC design nowadays. By reducing the effort required, LLMs open up new possibilities for efficiency and accuracy in this crucial aspect of chip design

RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Model

Inspired by the recent success of large language models (LLMs) like ChatGPT, researchers start to explore the adoption of LLMs for agile hardware design, such as generating design RTL based on natural-language instructions. However, in existing works, their target designs are all relatively simple and in a small scale, and proposed by the authors themselves, making a fair comparison among different LLM solutions challenging. In addition, many prior works only focus on the design correctness, without evaluating the design qualities of generated design RTL. In this work, we propose an open-source benchmark named RTLLM, for generating design RTL with natural language instructions. To systematically evaluate the auto-generated design RTL, we summarized three progressive goals, named syntax goal, functionality goal, and design quality goal. This benchmark can automatically provide a quantitative evaluation of any given LLM-based solution. Furthermore, we propose an easy-to-use yet surprisingly effective prompt engineering technique named self-planning, which proves to significantly boost the performance of GPT-3.5 in our proposed benchmark

High genetic abundance of Rpi-blb2/Mi-1.2/Cami gene family in Solanaceae

Relative genomic positions of genes among potato (upper), pepper (middle) and tomato (lower) along chromosome 6. (DOCX 282 kb

RTLCoder: Outperforming GPT-3.5 in Design RTL Generation with Our Open-Source Dataset and Lightweight Solution

The automatic generation of RTL code (e.g., Verilog) using natural language instructions and large language models (LLMs) has attracted significant research interest recently. However, most existing approaches heavily rely on commercial LLMs such as ChatGPT, while open-source LLMs tailored for this specific design generation task exhibit notably inferior performance. The absence of high-quality open-source solutions restricts the flexibility and data privacy of this emerging technique. In this study, we present a new customized LLM solution with a modest parameter count of only 7B, achieving better performance than GPT-3.5 on two representative benchmarks for RTL code generation. This remarkable balance between accuracy and efficiency is made possible by leveraging our new RTL code dataset and a customized LLM algorithm, both of which will be made fully open-source. Furthermore, we have successfully quantized our LLM to 4-bit with a total size of 4GB, enabling it to function on a single laptop with only slight performance degradation. This efficiency allows the RTL generator to serve as a local assistant for engineers, ensuring all design privacy concerns are addressed