Conservation and tourism:the importance of local communities in the governance of protected areas

This study examines the intricate relationship between conservation and tourism in protected areas, with a specific focus on achieving sustainable coexistence. Using the Wulingyuan World Heritage Site as a case study, it dissects this complex interplay across three key dimensions: governance, socio-cultural factors, and political-economic dynamics. The research reveals that governance at Wulingyuan is fragmented, leading to coordination challenges, and highlights the diverse and evolving sense of place among local residents impacted by tourism-related changes. Furthermore, it delves into tourism-induced green land acquisition, which has significant economic implications. To strike a balance between conservation and tourism interests, the study advocates for a balanced governance paradigm that involves systematically integrating local stakeholders' perspectives, engaging local communities' sense of place, empowering vulnerable groups, and implementing innovative funding mechanisms dedicated to conservation. This approach has the potential to generate socio-economic benefits while preserving environmental integrity and community well-being in protected areas

Robust Non-Rigid Registration with Reweighted Position and Transformation Sparsity

Non-rigid registration is challenging because it is ill-posed with high degrees of freedom and is thus sensitive to noise and outliers. We propose a robust non-rigid registration method using reweighted sparsities on position and transformation to estimate the deformations between 3-D shapes. We formulate the energy function with position and transformation sparsity on both the data term and the smoothness term, and define the smoothness constraint using local rigidity. The double sparsity based non-rigid registration model is enhanced with a reweighting scheme, and solved by transferring the model into four alternately-optimized subproblems which have exact solutions and guaranteed convergence. Experimental results on both public datasets and real scanned datasets show that our method outperforms the state-of-the-art methods and is more robust to noise and outliers than conventional non-rigid registration methods.Comment: IEEE Transactions on Visualization and Computer Graphic

Evidence from Tax-Exempt Firms on Motives for Participating in Sale-Leaseback Agreements

Previous research finds evidence that tax factors motivate the participants in leasing transactions. Tax-arbitrage arguments predict that leasing participants gain when the lessor’s tax rate exceeds that of the lessee. The research employs a sample of effectively tax-exempt REIT lessors to explore alternative leasing motives. Changes in REIT qualification rules are examined to develop an Agency-Cost, and competing Income-Retention Hypothesis for lessors. The rules and changes suggest that REIT management has the incentive, motive and opportunity to make real-estate investments quickly. The evidence developed is consistent with agency-costs arising from the possibility that they may overpay for properties.

Impact of International Sport Events in Promoting International Attention of Cities：Evidence from Google Trends

In order to raise the international attention and gain more competitive advantages, many Chinese cities are aiming at holding international sport events to promote urban development. However, previous studies indicated that international sport events have attracted international attention for hosting cities, but only stay at theoretical level of interpretation, empirical studies are scarce and needed. Therefore, the major purpose of this study was to investigate the impact of international sport events on urban development from the perspective of cities’ international attention, and to provide new dimensions and ideas for the relationship between sport events and urban development. This study used Google Trends corpus as the data source and used the Google Trends Index to measure five cities’ (Beijing, Shanghai, Nanjing, Chengdu, Wuhan) international attention in order to analyze the impact of international sport events on hosting cities in China. Through data mining, this study collected weekly data of international attention (time range is from January 1st, 2014 to December 31th, 2018) of five cities and compared with major media events in the corresponding period when highest international attention appeared in each city. Using Pearson Correlation, correlation analysis of international attention (all categories) and international attention (sports categories) of five cities was tested. Valuable information was obtained through the relevant keyword search function of the search volume ranking to provide more comprehensive evidence for the empirical results. There was a significant correlation between the international attention (all categories) and international attention (sports categories) in these five cities, and Nanjing\u27s data were the strongest (P=0.987). Through empirical analysis, the peak time of international attention in Beijing, Shanghai, Nanjing, Chengdu and Wuhan in recent five years was highly consistent with the major international sport events (Beijing IAAF World Championships, 2015; ATP1000 Shanghai Masters, 2018; Nanjing Summer Youth Olympics, 2014; ATP250 Chengdu Open ,2018; Wuhan Open ,2018), but the effect was negligible as the events ended. International sport events are an important dynamic attraction for hosting cities to gain more international attention, which can produce intense impulsive impact on each city\u27s international attention. The effect of international sport events on hosting city\u27s international attention is relatively short and concentrated, and there is no obvious long term effect. Finally, the international attention of cities has a relatively stable level from a long time perspective

Efficient Black-Box Speaker Verification Model Adaptation with Reprogramming and Backend Learning

The development of deep neural networks (DNN) has significantly enhanced the performance of speaker verification (SV) systems in recent years. However, a critical issue that persists when applying DNN-based SV systems in practical applications is domain mismatch. To mitigate the performance degradation caused by the mismatch, domain adaptation becomes necessary. This paper introduces an approach to adapt DNN-based SV models by manipulating the learnable model inputs, inspired by the concept of adversarial reprogramming. The pre-trained SV model remains fixed and functions solely in the forward process, resembling a black-box model. A lightweight network is utilized to estimate the gradients for the learnable parameters at the input, which bypasses the gradient backpropagation through the black-box model. The reprogrammed output is processed by a two-layer backend learning module as the final adapted speaker embedding. The number of parameters involved in the gradient calculation is small in our design. With few additional parameters, the proposed method achieves both memory and parameter efficiency. The experiments are conducted in language mismatch scenarios. Using much less computation cost, the proposed method obtains close or superior performance to the fully finetuned models in our experiments, which demonstrates its effectiveness