VisorGPT: Learning Visual Prior via Generative Pre-Training

Various stuff and things in visual data possess specific traits, which can be learned by deep neural networks and are implicitly represented as the visual prior, e.g., object location and shape, in the model. Such prior potentially impacts many vision tasks. For example, in conditional image synthesis, spatial conditions failing to adhere to the prior can result in visually inaccurate synthetic results. This work aims to explicitly learn the visual prior and enable the customization of sampling. Inspired by advances in language modeling, we propose to learn Visual prior via Generative Pre-Training, dubbed VisorGPT. By discretizing visual locations of objects, e.g., bounding boxes, human pose, and instance masks, into sequences, VisorGPT can model visual prior through likelihood maximization. Besides, prompt engineering is investigated to unify various visual locations and enable customized sampling of sequential outputs from the learned prior. Experimental results demonstrate that VisorGPT can effectively model the visual prior, which can be employed for many vision tasks, such as customizing accurate human pose for conditional image synthesis models like ControlNet. Code will be released at https://github.com/Sierkinhane/VisorGPT.Comment: Project web-page: https://sierkinhane.github.io/visor-gpt

Physical Trajectory Inference Attack and Defense in Decentralized POI Recommendation

As an indispensable personalized service within Location-Based Social Networks (LBSNs), the Point-of-Interest (POI) recommendation aims to assist individuals in discovering attractive and engaging places. However, the accurate recommendation capability relies on the powerful server collecting a vast amount of users' historical check-in data, posing significant risks of privacy breaches. Although several collaborative learning (CL) frameworks for POI recommendation enhance recommendation resilience and allow users to keep personal data on-device, they still share personal knowledge to improve recommendation performance, thus leaving vulnerabilities for potential attackers. Given this, we design a new Physical Trajectory Inference Attack (PTIA) to expose users' historical trajectories. Specifically, for each user, we identify the set of interacted POIs by analyzing the aggregated information from the target POIs and their correlated POIs. We evaluate the effectiveness of PTIA on two real-world datasets across two types of decentralized CL frameworks for POI recommendation. Empirical results demonstrate that PTIA poses a significant threat to users' historical trajectories. Furthermore, Local Differential Privacy (LDP), the traditional privacy-preserving method for CL frameworks, has also been proven ineffective against PTIA. In light of this, we propose a novel defense mechanism (AGD) against PTIA based on an adversarial game to eliminate sensitive POIs and their information in correlated POIs. After conducting intensive experiments, AGD has been proven precise and practical, with minimal impact on recommendation performance

Analysis of Density Wave Oscillations in Helically Coiled Tube Once-Through Steam Generator

Helically coiled tube Once-Through Steam Generator (H-OTSG) is one of the key equipment types for small modular reactors. The flow instability of the secondary side of the H-OTSG is particularly serious, because the working condition is in the range of low and medium pressure. This paper presents research on density wave oscillations (DWO) in a typical countercurrent H-OTSG. Based on the steady-state calculation, the mathematical model of single-channel system was established, and the transfer function was derived. Using Nyquist stability criterion of the single variable, the stability cases were studied with an in-house computer program. According to the analyses, the impact law of the geometrical parameters to the system stability was obtained. RELAP5/MOD3.2 code was also used to simulate DWO in H-OTSG. The theoretical analyses of the in-house program were compared to the simulation results of RELAP5. A correction factor was introduced to reduce the error of RELAP5 when modeling helical geometry. The comparison results agreed well which showed that the correction is effective

Self‐potential ambient noise and spectral relationship with urbanization, seismicity, and strain rate revealed via the Taiwan Geoelectric Monitoring Network

AbstractGeoelectric self‐potential (SP) signals are sensitive to natural and anthropogenic factors. The SP spectral characteristics under the different factors in Taiwan were investigated, and the SP spectral scalings were correlated with urbanization level, seismicity, and crustal deformation. The ambient SP noise models were first established by estimating the probability density functions of the spectrograms at each frequency. The effects of the natural and anthropogenic factors on the SP signals are understood by comparing the SP noise models under various conditions, such as precipitation, urbanization, and electric trains. Results show that the SP signals in areas of high industrialization and human activity and areas close to train stations behave as white noises and exhibit a distinct spectral ripple at frequencies around 1 Hz. On the other hand, the SP spectral power law parameters, Gutenberg‐Richter b values, and dilation strain rates were estimated by using the SP, earthquake catalog, and GPS data, respectively, during 2012–2017. By investigating the correlations of the SP spectral parameters with the Gutenberg‐Richter b value, dilation strain rates, and urbanization level, the SP optimal frequency band is found between 0.006 and 1 Hz due to the high correlation between the SP and seismicity data and between the SP and dilation data and the low correlation between the SP and urbanization data. Hence, this study may help the filtering and screening of the SP data and facilitate the understanding of the mechano‐electric behavior in the crust

Microbial Community and Functional Structure Significantly Varied among Distinct Types of Paddy Soils But Responded Differently along Gradients of Soil Depth Layers

Paddy rice fields occupy broad agricultural area in China and cover diverse soil types. Microbial community in paddy soils is of great interest since many microorganisms are involved in soil functional processes. In the present study, Illumina Mi-Seq sequencing and functional gene array (GeoChip 4.2) techniques were combined to investigate soil microbial communities and functional gene patterns across the three soil types including an Inceptisol (Binhai), an Oxisol (Leizhou), and an Ultisol (Taoyuan) along four profile depths (up to 70 cm in depth) in mesocosm incubation columns. Detrended correspondence analysis revealed that distinctly differentiation in microbial community existed among soil types and profile depths, while the manifest variance in functional structure was only observed among soil types and two rice growth stages, but not across profile depths. Along the profile depth within each soil type, Acidobacteria, Chloroflexi, and Firmicutes increased whereas Cyanobacteria, β-proteobacteria, and Verrucomicrobia declined, suggesting their specific ecophysiological properties. Compared to bacterial community, the archaeal community showed a more contrasting pattern with the predominant groups within phyla Euryarchaeota, Thaumarchaeota, and Crenarchaeota largely varying among soil types and depths. Phylogenetic molecular ecological network (pMEN) analysis further indicated that the pattern of bacterial and archaeal communities interactions changed with soil depth and the highest modularity of microbial community occurred in top soils, implying a relatively higher system resistance to environmental change compared to communities in deeper soil layers. Meanwhile, microbial communities had higher connectivity in deeper soils in comparison with upper soils, suggesting less microbial interaction in surface soils. Structure equation models were developed and the models indicated that pH was the most representative characteristics of soil type and identified as the key driver in shaping both bacterial and archaeal community structure, but did not directly affect microbial functional structure. The distinctive pattern of microbial taxonomic and functional composition along soil profiles implied functional redundancy within these paddy soils