Person Re-identification with Correspondence Structure Learning

This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification. We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair. The learned correspondence structure can not only capture the spatial correspondence pattern between cameras but also handle the viewpoint or human-pose variation in individual images. We further introduce a global-based matching process. It integrates a global matching constraint over the learned correspondence structure to exclude cross-view misalignments during the image patch matching process, hence achieving a more reliable matching score between images. Experimental results on various datasets demonstrate the effectiveness of our approach

Remuneration Level on Teachers Turnover in Secondary Schools in Uganda

There is always a challenge in remuneration on employee’s turnover, thus good remuneration strategies results into improved organizational performance. This paper examines the level of remuneration on teacher turnover in Secondary Schools in Rubaga Division, Kampala Uganda. It examined the effects of both monetary and none monetary rewards. This study mainly relied on the Expectancy theory. The research helps practicing manager to create an effective, balanced and efficient operating reward structure for the organization. The study included school proprietors, teachers, head teachers and government officials (ministry of Education). For the purpose of this research, we used quantitative design and collected data from 120 personnel, selecting 100 teachers and 20 head teachers. The study finds that absence of proper allowances, would directly force teachers out of the teaching profession. The results indicated that there is a significant relationship between remuneration level and teacher turnover in secondary schools in Rubaga Division, Kampala City. Keywords: Remuneration, Monetary rewards, Turnover

Learning Correspondence Structures for Person Re-identification

This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification. We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair. The learned correspondence structure can not only capture the spatial correspondence pattern between cameras but also handle the viewpoint or human-pose variation in individual images. We further introduce a global constraint-based matching process. It integrates a global matching constraint over the learned correspondence structure to exclude cross-view misalignments during the image patch matching process, hence achieving a more reliable matching score between images. Finally, we also extend our approach by introducing a multi-structure scheme, which learns a set of local correspondence structures to capture the spatial correspondence sub-patterns between a camera pair, so as to handle the spatial misalignments between individual images in a more precise way. Experimental results on various datasets demonstrate the effectiveness of our approach.Comment: IEEE Trans. Image Processing, vol. 26, no. 5, pp. 2438-2453, 2017. The project page for this paper is available at http://min.sjtu.edu.cn/lwydemo/personReID.htm arXiv admin note: text overlap with arXiv:1504.0624

High-Tech Service Platform Ecosystem Evolution: A Simulation Analysis using Lotka-Volterra Model

Technical service platform exerts a strong effect on supporting the innovation of the high-tech industry as a critical constituent of the modern service industry, and it can effectively enhance the development potential of technological innovation, but the degree of separation from technical service chain to high-tech industry chain is currently high. To explore how to improve the utilization efficiency of scientific and technological resources and facilitate the sustainable development of the high-tech industry by relying on technical service platform,a high-tech service platform was constructed by using Lotka-Volterra (L-V) model on the basis of ecosystem theory, the evolution path and stability conditions of high-tech service platform were analyzed followed by numerical simulation by Matlab computing. Results show that the development of hightech service platform follows the evolution path of "bilateral platform → core platform → platform ecosystem"; population evolution pattern in high-tech service platform ecosystem is decided by interdependence coefficient between populations; populations inside high-tech service platform ecosystem generate natural selection and synergistic effect and realize ecological balance among populations through evolution. Evolution of high-tech service platform system in this study provides a new theoretical framework for effective fusion and collaboration of science and technology service and industry, which is significant for elevating scientific and technological innovation level and improving technical service system construction

Mapping EEG Signals to Visual Stimuli: A Deep Learning Approach to Match vs. Mismatch Classification

Existing approaches to modeling associations between visual stimuli and brain responses are facing difficulties in handling between-subject variance and model generalization. Inspired by the recent progress in modeling speech-brain response, we propose in this work a ``match-vs-mismatch'' deep learning model to classify whether a video clip induces excitatory responses in recorded EEG signals and learn associations between the visual content and corresponding neural recordings. Using an exclusive experimental dataset, we demonstrate that the proposed model is able to achieve the highest accuracy on unseen subjects as compared to other baseline models. Furthermore, we analyze the inter-subject noise using a subject-level silhouette score in the embedding space and show that the developed model is able to mitigate inter-subject noise and significantly reduce the silhouette score. Moreover, we examine the Grad-CAM activation score and show that the brain regions associated with language processing contribute most to the model predictions, followed by regions associated with visual processing. These results have the potential to facilitate the development of neural recording-based video reconstruction and its related applications

Effects of Root-Zone Temperature and N, P, and K Supplies on Nutrient Uptake of Cucumber (Cucumis sativus L.) Seedlings in Hydroponics

The nutrient uptake and allocation of cucumber (Cucumis sativus L.) seedlings at different root-zone temperatures (RZT) and different concentrations of nitrogen (N), phosphorus (P), and potassium (K) nutrients were examined. Plants were grown in a nutrient solution for 30 d at two root-zone temperatures (a diurnally fluctuating ambient 10°C-RZT and a constant 20° C-RZT) with the aerial parts of the plants maintained at ambient temperature (10°C -30°C). Based on a Hoagland nutrient solution, seven N, P, and K nutrient concentrations were supplied to the plants at each RZT. Results showed that total plant and shoot dry weights under each nutrient treatment were significantly lower at low root-zone temperature (10°C-RZT) than at elevated root-zone temperature (20°C-RZT). But higher root dry weights were obtained at 10°C-RZT than those at 20°C-RZT. Total plant dry weights at both 10°C-RZT and 20°C-RZT were increased with increased solution N concentration, but showed different responses under P and K treatments. All estimated nutrient concentrations (N, P, and K) and uptake by the plant were obviously influenced by RZT. Low root temperature (10°C-RZT) caused a remarkable reduction in total N, P, and K uptake of shoots in all nutrient treatments, and more nutrients were accumulated in roots at 10 degrees C-RZT than those at 20°C-RZT. N, P, and K uptakes and distribution ratios in shoots were both improved at elevated root-zone temperature (20° C-RZT). N supplies were favorable to P and K uptake at both 10°C-RZT and 20°C-RZT, with no significantly positive correlation between N and P, or N and K uptake. In conclusion, higher RZT was more beneficial to increase of plant biomass and mineral nutrient absorption than was increase of nutrient concentration. Among the three element nutrients, increasing N nutrient concentration in solution promoted better tolerance to low RZT in cucumber seedlings than increasing P and K. In addition, appropriately decreased P concentration favors plant growth

Analysing National Innovation System of Pakistan

This paper evaluates the national innovation system of Pakistan. Our analytical framework develops upon the OECD framework to analyze the national innovation systems. The broader frame of analysis includes the dimensions of institutional pattern, institutional functions, and institutional interactions. We discuss the system of innovation in Pakistan under the yardsticks of policy formulation, research and development activities, research and development funding, development of human resource, transfer of technology, and technology entrepreneurship, research and development collaboration among institutions, and technological adoption. It has been found that the Pakistan’s system of innovation is yet at the initial stages of its development as compared to many developing countries. Efforts are being made on many fronts to improve the efficiency of the system it is recommended that policy formulation process should include all the stakeholders. More efforts are needed for technological diffusion. Key words: National Innovation system, technological entrepreneurship, technology diffusion, Pakista

Broadband energy-efficient optical modulation by hybrid integration of silicon nanophotonics and organic electro-optic polymer

Silicon-organic hybrid integrated devices have emerging applications ranging from high-speed optical interconnects to photonic electromagnetic-field sensors. Silicon slot photonic crystal waveguides (PCWs) filled with electro-optic (EO) polymers combine the slow-light effect in PCWs with the high polarizability of EO polymers, which promises the realization of high-performance optical modulators. In this paper, a broadband, power-efficient, low-dispersion, and compact optical modulator based on an EO polymer filled silicon slot PCW is presented. A small voltage-length product of V{\pi}*L=0.282Vmm is achieved, corresponding to an unprecedented record-high effective in-device EO coefficient (r33) of 1230pm/V. Assisted by a backside gate voltage, the modulation response up to 50GHz is observed, with a 3-dB bandwidth of 15GHz, and the estimated energy consumption is 94.4fJ/bit at 10Gbit/s. Furthermore, lattice-shifted PCWs are utilized to enhance the optical bandwidth by a factor of ~10X over other modulators based on non-band-engineered PCWs and ring-resonators.Comment: 12 pages, 4 figures, SPIE Photonics West Conference 201

Rethinking Cross-Domain Pedestrian Detection: A Background-Focused Distribution Alignment Framework for Instance-Free One-Stage Detectors

Cross-domain pedestrian detection aims to generalize pedestrian detectors from one label-rich domain to another label-scarce domain, which is crucial for various real-world applications. Most recent works focus on domain alignment to train domain-adaptive detectors either at the instance level or image level. From a practical point of view, one-stage detectors are faster. Therefore, we concentrate on designing a cross-domain algorithm for rapid one-stage detectors that lacks instance-level proposals and can only perform image-level feature alignment. However, pure image-level feature alignment causes the foreground-background misalignment issue to arise, i.e., the foreground features in the source domain image are falsely aligned with background features in the target domain image. To address this issue, we systematically analyze the importance of foreground and background in image-level cross-domain alignment, and learn that background plays a more critical role in image-level cross-domain alignment. Therefore, we focus on cross-domain background feature alignment while minimizing the influence of foreground features on the cross-domain alignment stage. This paper proposes a novel framework, namely, background-focused distribution alignment (BFDA), to train domain adaptive onestage pedestrian detectors. Specifically, BFDA first decouples the background features from the whole image feature maps and then aligns them via a novel long-short-range discriminator.Comment: This paper published on IEEE Transactions on Image Processing on August 2023.See https://ieeexplore.ieee.org/document/1023112