Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution

Event cameras sense the intensity changes asynchronously and produce event streams with high dynamic range and low latency. This has inspired research endeavors utilizing events to guide the challenging video superresolution (VSR) task. In this paper, we make the first attempt to address a novel problem of achieving VSR at random scales by taking advantages of the high temporal resolution property of events. This is hampered by the difficulties of representing the spatial-temporal information of events when guiding VSR. To this end, we propose a novel framework that incorporates the spatial-temporal interpolation of events to VSR in a unified framework. Our key idea is to learn implicit neural representations from queried spatial-temporal coordinates and features from both RGB frames and events. Our method contains three parts. Specifically, the Spatial-Temporal Fusion (STF) module first learns the 3D features from events and RGB frames. Then, the Temporal Filter (TF) module unlocks more explicit motion information from the events near the queried timestamp and generates the 2D features. Lastly, the SpatialTemporal Implicit Representation (STIR) module recovers the SR frame in arbitrary resolutions from the outputs of these two modules. In addition, we collect a real-world dataset with spatially aligned events and RGB frames. Extensive experiments show that our method significantly surpasses the prior-arts and achieves VSR with random scales, e.g., 6.5. Code and dataset are available at https: //vlis2022.github.io/cvpr23/egvsr.Comment: Accepted by CVPR202

Energy Optimization in Multi-UAV-Assisted Edge Data Collection System

In the IoT (Internet of Things) system, the introduction of UAV (Unmanned Aerial Vehicle) as a new data collection platform can solve the problem that IoT devices are unable to transmit data over long distances due to the limitation of their battery energy. However, the unreasonable distribution of UAVs will still lead to the problem of the high total energy consumption of the system. In this work, to deal with the problem, a deployment model of a mobile edge computing (MEC) system based on multi-UAV is proposed. The goal of the model is to minimize the energy consumption of the system in the process of data transmission by optimizing the deployment of UAVs. The DEVIPSK (differential evolution algorithm with variable population size based on a mutation strategy pool initialized by K-Means) is proposed to solve the model. In DEVIPSK, the population is initialized by K-Means to obtain better initial positions of UAVs. Besides, considering the limitation of the fixed mutation strategy in the traditional evolutionary algorithm, a mutation strategy pool is used to update the positions of UAVs. The experimental results show the superiority of the DEVIPSK and provide guidance for the deployment of UAVs in the field of edge data collection in the IoT system

Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification

Recent progress in large language models (LLMs) like GPT-4 and PaLM-2 has brought significant advancements in addressing math reasoning problems. In particular, OpenAI's latest version of GPT-4, known as GPT-4 Code Interpreter, shows remarkable performance on challenging math datasets. In this paper, we explore the effect of code on enhancing LLMs' reasoning capability by introducing different constraints on the \textit{Code Usage Frequency} of GPT-4 Code Interpreter. We found that its success can be largely attributed to its powerful skills in generating and executing code, evaluating the output of code execution, and rectifying its solution when receiving unreasonable outputs. Based on this insight, we propose a novel and effective prompting method, explicit \uline{c}ode-based \uline{s}elf-\uline{v}erification~(CSV), to further boost the mathematical reasoning potential of GPT-4 Code Interpreter. This method employs a zero-shot prompt on GPT-4 Code Interpreter to encourage it to use code to self-verify its answers. In instances where the verification state registers as ``False'', the model shall automatically amend its solution, analogous to our approach of rectifying errors during a mathematics examination. Furthermore, we recognize that the states of the verification result indicate the confidence of a solution, which can improve the effectiveness of majority voting. With GPT-4 Code Interpreter and CSV, we achieve an impressive zero-shot accuracy on MATH dataset \textbf{(53.9\% $\to$ 84.3\%)}.Comment: Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verificatio

Herb-Drug Interaction: Effects of Relinqing® Granule on the Pharmacokinetics of Ciprofloxacin, Sulfamethoxazole, and Trimethoprim in Rats

Relinqing granule (RLQ) is the best-selling Chinese patent drug for treatment of urinary system diseases. In this study, the effects of RLQ on the pharmacokinetics of ciprofloxacin, sulfamethoxazole, and trimethoprim in SD rats were investigated. Rats were randomly divided into control group 1, control group 2, RLQ group 1, and RLQ group 2. RLQ group 1 and RLQ group 2 were treated orally with RLQ for 7 days, and rats were treated with the same volume of water in control group 1 and control group 2. Then, RLQ group 1 and control group 1 were given intragastrically ciprofloxacin on day 8, while RLQ group 2 and control group 2 were given intragastrically sulfamethoxazole and trimethoprim on day 8. Blood samples were collected and determined. There was no significant influence of pharmacokinetic parameters of trimethoprim on two groups. But some pharmacokinetic parameters of ciprofloxacin and sulfamethoxazole in RLQ pretreated rats were evidently altered (P < 0.05), which indicated that absorption of ciprofloxacin and sulfamethoxazole in RLQ pretreated rats was significantly affected. It indicated the coadministration of RLQ would have an influence on the efficacy of ciprofloxacin and sulfamethoxazole, and the doses of ciprofloxacin tablet and compound sulfamethoxazole tablet need adjustment

Analisis Strategi Penerapan Sistem Manajemen Keamanan Pangan HACCP (Hazard Analysis and Critical Control Points) Di PT. Sierad Produce Tbk. Parung

Quality and safety food products problem was usually after thought in the food industry development issues, accordance with the consumer\u27s desirability that understand the importance of product quality and food safety. Hazard Analysis and Critical Control Points (HACCP) certification is one way for company to implementing food safety. Sierad Produce Corp. at this moment has obtained HACCP certificate to produce chicken carcasses.But the implementation need to be controlled, as the case of foodborne illness and foodborne disease can occur easily if not properly controlled. The main objective of this research is to develop the best strategy to implement HACCP and to maintain the food safety quality system at Sierad Produce Corp. The information and data that has been collected within this research were covering both the primary and secondary data based on the date of September 2012 to December 2012. The methods used in this research are descriptive analysis, Internal Factor Evaluation (IFE), External Factor Evaluation (EFE), Internal External (IE), Strength Weakness Opportunity Threat (SWOT) and Analysis Hierarchy Process (AHP). Based on this research, the best strategy for implementing HACCP and sustain the system on Sierad Produce are Critical Control Points (CCP) evaluation and improvement of production room

Fibroblasts from Distinct Pancreatic Pathologies Exhibit Disease-Specific Properties.

Although fibrotic stroma forms an integral component of pancreatic diseases, whether fibroblasts programmed by different types of pancreatic diseases are phenotypically distinct remains unknown. Here, we show that fibroblasts isolated from patients with pancreatic ductal adenocarcinoma (PDAC), chronic pancreatitis (CP), periampullary tumors, and adjacent normal (NA) tissue (N = 34) have distinct mRNA and miRNA profiles. Compared with NA fibroblasts, PDAC-associated fibroblasts were generally less sensitive to an antifibrotic stimulus (NPPB) and more responsive to positive regulators of activation such as TGFβ1 and WNT. Of the disease-associated fibroblasts examined, PDAC- and CP-derived fibroblasts shared greatest similarity, yet PDAC-associated fibroblasts expressed higher levels of tenascin C (TNC), a finding attributable to miR-137, a novel regulator of TNC. TNC protein and transcript levels were higher in PDAC tissue versus CP tissue and were associated with greater levels of stromal activation, and conditioned media from TNC-depleted PDAC-associated fibroblasts modestly increased both PDAC cell proliferation and PDAC cell migration, indicating that stromal TNC may have inhibitory effects on PDAC cells. Finally, circulating TNC levels were higher in patients with PDAC compared with CP. Our characterization of pancreatic fibroblast programming as disease-specific has consequences for therapeutic targeting and for the manner in which fibroblasts are used in research. SIGNIFICANCE: Primary fibroblasts derived from various types of pancreatic diseases possess and retain distinct molecular and functional characteristics in culture, providing a series of cellular models for treatment development and disease-specific research