Efficient image copy detection using multi-scale fingerprints

Inspired by multi-resolution histogram, we propose a multi-scale SIFT descriptor to improve the discriminability. A series of SIFT descriptions with different scale are first acquired by varying the actual size of each spatial bin. Then principle component analysis (PCA) is employed to reduce them to low dimensional vectors, which are further combined into one 128-dimension multi-scale SIFT description. Next, an entropy maximization based binarization is employed to encode the descriptions into binary codes called fingerprints for indexing the local features. Furthermore, an efficient search architecture consisting of lookup tables and inverted image ID list is designed to improve the query speed. Since the fingerprint building is of low-complexity, this method is very efficient and scalable to very large databases. In addition, the multi-scale fingerprints are very discriminative such that the copies can be effectively distinguished from similar objects, which leads to an improved performance in the detection of copies. The experimental evaluation shows that our approach outperforms the state of the art methods.Inspired by multi-resolution histogram, we propose a multi-scale SIFT descriptor to improve the discriminability. A series of SIFT descriptions with different scale are first acquired by varying the actual size of each spatial bin. Then principle component analysis (PCA) is employed to reduce them to low dimensional vectors, which are further combined into one 128-dimension multi-scale SIFT description. Next, an entropy maximization based binarization is employed to encode the descriptions into binary codes called fingerprints for indexing the local features. Furthermore, an efficient search architecture consisting of lookup tables and inverted image ID list is designed to improve the query speed. Since the fingerprint building is of low-complexity, this method is very efficient and scalable to very large databases. In addition, the multi-scale fingerprints are very discriminative such that the copies can be effectively distinguished from similar objects, which leads to an improved performance in the detection of copies. The experimental evaluation shows that our approach outperforms the state of the art methods

Model-based temporal evolution and spatial equilibrium analysis of green development in China's Yangtze River Economic Belt from 2009 to 2018

Green development is one of the important guidance and core contents of high-quality development of regional economy and society, and its spatial–temporal pattern evolution analysis is helpful to the scientific formulation of relevant policies. However, the green development evaluation is faced with multi attribute decision making difficulties. In view of this, firstly, based on discussing the connotation of green development from ontology, epistemology, axiology and methodology, a new evaluation indicator system from the four dimensions of energy conservation, emission reduction, efficiency increase and harmony was explored. Then, coupling symmetric cross entropy multi attribute decision making and Gini coefficient method, a comprehensive evaluation framework of temporal evolution and spatial equilibrium of green development was constructed and applied in 11 provinces (cities) of China's Yangtze River Economic Belt (YEB) from 2009 to 2018. The results show that during the study period, the green development level of YEB has improved year by year, but there was still a large gap. Among the 120 research samples, only 41.7% had a ranking score of more than 6, and only Chongqing in 2017 had a ranking score of more than 8. The Gini coefficient of green development in 11 provinces (cities) is about 0.2, which is balanced on the whole, but there is a significant difference in the indicators, such as proportion of new energy power generation, sulfur dioxide emission per 104 RMB yuan GDP and proportion of investment in environmental protection; The effect of energy conservation and emission reduction is obvious, while efficiency increase and harmony need to be improved. The complementary role of 11 provinces (cities) in YEB's green development has become increasingly prominent, which made that YEB (as a whole) ranked second in comparison with 11 provinces (cities) in 2018

Characterizing the Dilemma of Performance and Index Size in Billion-Scale Vector Search and Breaking It with Second-Tier Memory

Vector searches on large-scale datasets are critical to modern online services like web search and RAG, which necessity storing the datasets and their index on the secondary storage like SSD. In this paper, we are the first to characterize the trade-off of performance and index size in existing SSD-based graph and cluster indexes: to improve throughput by 5.7$\times$ and 1.7$\times$, these indexes have to pay a 5.8$\times$ storage amplification and 7.7$\times$ with respect to the dataset size, respectively. The root cause is that the coarse-grained access of SSD mismatches the fine-grained random read required by vector indexes with small amplification. This paper argues that second-tier memory, such as remote DRAM/NVM connected via RDMA or CXL, is a powerful storage for addressing the problem from a system's perspective, thanks to its fine-grained access granularity. However, putting existing indexes -- primarily designed for SSD -- directly on second-tier memory cannot fully utilize its power. Meanwhile, second-tier memory still behaves more like storage, so using it as DRAM is also inefficient. To this end, we build a graph and cluster index that centers around the performance features of second-tier memory. With careful execution engine and index layout designs, we show that vector indexes can achieve optimal performance with orders of magnitude smaller index amplification, on a variety of second-tier memory devices. Based on our improved graph and vector indexes on second-tier memory, we further conduct a systematic study between them to facilitate developers choosing the right index for their workloads. Interestingly, the findings on the second-tier memory contradict the ones on SSDs

Accuracy-Complexity Tradeoff Analysis and Complexity Reduction Methods for Non-Stationary IMT-A MIMO Channel Models

open access journalHigh-mobility wireless communication systems have attracted growing interests in recent years. For the deployment of these systems, one fundamental work is to build accurate and efficient channel models. In high-mobility scenarios, it has been shown that the standardized channel models, e.g., IMT-Advanced (IMT-A) multiple-input multiple-output (MIMO) channel model, provide noticeable longer stationary intervals than measured results and the wide-sense stationary (WSS) assumption may be violated. Thus, the non-stationarity should be introduced to the IMT-A MIMO channel model to mimic the channel characteristics more accurately without losing too much efficiency. In this paper, we analyze and compare the computational complexity of the original WSS and non-stationary IMT-A MIMO channel models. Both the number of real operations and simulation time are used as complexity metrics. Since introducing the nonstationarity to the IMT-A MIMO channel model causes extra computational complexity, some computation reduction methods are proposed to simplify the non-stationary IMT-A MIMO channel model while retaining an acceptable accuracy. Statistical properties including the temporal autocorrelation function, spatial cross-correlation function, and stationary interval are chosen as the accuracy metrics for verifications. It is shown that the tradeoff between the computational complexity and modeling accuracy can be achieved by using these proposed complexity reduction methods

Self-Supervised Learning of Whole and Component-Based Semantic Representations for Person Re-Identification

Person Re-Identification (ReID) is a challenging problem, focusing on identifying individuals across diverse settings. However, previous ReID methods primarily concentrated on a single domain or modality, such as Clothes-Changing ReID (CC-ReID) and video ReID. Real-world ReID is not constrained by factors like clothes or input types. Recent approaches emphasize on learning semantics through pre-training to enhance ReID performance but are hindered by coarse granularity, on-clothes focus and pre-defined areas. To address these limitations, we propose a Local Semantic Extraction (LSE) module inspired by Interactive Segmentation Models. The LSE module captures fine-grained, biometric, and flexible local semantics, enhancing ReID accuracy. Additionally, we introduce Semantic ReID (SemReID), a pre-training method that leverages LSE to learn effective semantics for seamless transfer across various ReID domains and modalities. Extensive evaluations across nine ReID datasets demonstrates SemReID's robust performance across multiple domains, including clothes-changing ReID, video ReID, unconstrained ReID, and short-term ReID. Our findings highlight the importance of effective semantics in ReID, as SemReID can achieve great performances without domain-specific designs

A longitudinal study of summertime occupant behaviour and thermal comfort in office buildings in northern China

The adaptive behaviour and thermal responses of building occupants can be responsible for significant uncertainties when comparing monitored and modelled building energy performance. A better understanding of the interaction of occupants and their buildings is necessary for managing this uncertainty and reducing discrepancies between predicted and actual energy use (commonly known as ‘the performance gap’). This paper presents the results from a longitudinal study during a summer season of ten mixed-mode offices located in Harbin, a city in northern China, which experiences severe winters and warm summers. The study collected data from on-line daily surveys, field measurements of the local environment, occupants' experiences and adaptive control behaviours. Occupant-building interactions were analysed through observing adaptive behaviour, perceived thermal sensations in the physical environment, architectural geometric variables and personnel characteristics. The driving mechanisms for behaviours and feelings were also studied. The results showed a high probability of window opening for both day and night, and a high frequency of the use of a mix of cooling options, including fans and air conditioning, accompanied by natural ventilation in the summer season. The active interaction of the offices' internal environments with the outdoor environment motivated more connections of occupant thermal feelings with the outdoor physical variables. Relative humidity levels were potential key predictors for window opening, and the geometric parameters of offices, occupants' fan use and perceived thermal feelings also showed a level of predictive ability. Evaluating the nature of occupant feelings and behaviours interactions may inform and improve results from building performance-based design

Thermal comfort, occupant control behaviour and performance gap – a study of office buildings in north-east China using data mining

Simulation techniques have been increasingly applied to building performance evaluation and building environmental design. However, uncertain and random factors, such as occupant behaviour, can generate a performance gap between the results from computer simulations and real buildings. This study involved a longitudinal questionnaire survey conducted for one year, along with a continuous recording of environmental parameters and behaviour state changes, in ten offices located in the severe cold region of north-east China. The offices varied from private rooms to open-plan spaces. The thermal comfort experiences of the office workers and their environmental control behaviours were tracked and analysed during summer and winter seasons. The interaction of the thermal comfort experiences of the occupants and behaviour changes were analysed, and window-opening behaviour patterns were defined by applying data mining techniques. The results also generated window-opening behaviour working profiles to link to building performance simulation software. The aim was to apply these profiles to further study the discrepancies between simulation and monitored results that arise from real-world occupant behaviour patterns

Research on quantitative inversion of ion adsorption type rare earth ore based on convolutional neural network

Rare earth resource is a national strategic resource, which plays an essential role in the field of high technology research and development. In this paper, we aim to use remote sensing quantitative inversion prospecting technology, use surface-to-surface mode, and model inversion and evaluation through convolutional neural network model to achieve a new research method for large-scale, low-cost, rapid and efficient exploration of ion-adsorbed rare earth ore. The results show that the RE2O3 content of samples has significant negative correlation with the second, third and fourth band of GF-2 image, but has no significant correlation with the first band of GF-2 image; the convolution neural network model can be used to reconstruct the RE2O3 content. The content distribution map of RE2O3 obtained by inversion is similar to that of geochemical map, which indicates that the convolution neural network model can be used to invert the RE2O3 content in the sampling area. The quantitative inversion results show that the content distribution characteristics of ion adsorption rare earth ore in the study area are basically consistent with the actual situation; there are two main high anomaly areas in the study area. The high anomaly area I is a known mining area, and the high anomaly area II can be a prospective area of ion adsorption type rare earth deposit. It shows that the remote sensing quantitative inversion prospecting method of ion adsorption type rare earth deposit based on Convolutional Neural Networks (CNN) model is feasible