Tourism: an alternative to development?: reconsidering farming, tourism and conservation incentives in Northwest Yunnan mountainous communities

In the last decade, tourism has developed rapidly in the mountainous areas of northwest Yunnan. This growth has led to substantial economic and social changes, with resulting environmental consequences. This article uses a case study to illustrate how local farmers involved in tourism changed their agricultural practices as a result of the transformations that took place in the area. The aim was to examine tourism's expected benefits of poverty alleviation and conservation incentives. Tourism investments were found to have been adopted only by households with available cash and labor, whereas they remained inaccessible for the poor, small landowners who most needed a new source of income and used their land more exhaustively. Relatively rich, large landowners did not take the opportunity to reduce their agricultural activities. Instead, they used supplementary incomes earned from tourism to hire external labor to cultivate their land more intensely. Tourism development failed to generate real incentives for mountain farmers to adopt more conservation measures and prevent soil erosion and nonpoint source agricultural water pollution, which currently constitute serious environmental problems for mountain environments in Yunnan. This article presents recommendations based on the conclusions of the study

A GIS approach towards estimating tourist's off-road use in a mountainous protected area of Northwest Yunnan, China

To address the environmental impacts of tourism in protected areas, park managers need to understand the spatial distribution of tourist use. Standard monitoring measures (tourist surveys and counting and tracking techniques) are not sufficient to accomplish this task, in particular for off-road travel. This article predicts tourists' spatial use patterns through an alternative approach: park accessibility measurement. Naismith's rule and geographical information system's anisotropic cost analysis are integrated into the modeling process, which results in a more realistic measure of off-road accessibility than that provided by other measures. The method is applied to a mountainous United Nations Educational, Scientific and Cultural Organization (UNESCO) World Heritage Site in northwest Yunnan Province, China, where there is increasing concern about potential impacts of unregulated tourist use. Based on the assumption that accessibility tends to attract more tourists, a spatial pattern of predicted off-road use by tourists is derived. This pattern provides information that can help park managers develop strategies that are effective for both tourism management and species conservation

Intelligent ZHENG Classification of Hypertension Depending on ML-kNN and Information Fusion

Hypertension is one of the major causes of heart cerebrovascular diseases. With a good accumulation of hypertension clinical data on hand, research on hypertension's ZHENG differentiation is an important and attractive topic, as Traditional Chinese Medicine (TCM) lies primarily in “treatment based on ZHENG differentiation.” From the view of data mining, ZHENG differentiation is modeled as a classification problem. In this paper, ML-kNN—a multilabel learning model—is used as the classification model for hypertension. Feature-level information fusion is also used for further utilization of all information. Experiment results show that ML-kNN can model the hypertension's ZHENG differentiation well. Information fusion helps improve models' performance

Cortical Network Response to Acupuncture and the Effect of the Hegu Point:An FNIRS study

Acupuncture is a practice of treatment based on influencing specific points on the body by inserting needles. According to traditional Chinese medicine, the aim of acupuncture treatment for pain management is to use specific acupoints to relieve excess, activate qi (or vital energy), and improve blood circulation. In this context, the Hegu point is one of the most widely-used acupoints for this purpose, and it has been linked to having an analgesic effect. However, there exists considerable debate as to its scientific validity. In this pilot study, we aim to identify the functional connectivity related to the three main types of acupuncture manipulations and also identify an analgesic effect based on the hemodynamic response as measured by functional near-infrared spectroscopy (fNIRS). The cortical response of eleven healthy subjects was obtained using fNIRS during an acupuncture procedure. A multiscale analysis based on wavelet transform coherence was employed to assess the functional connectivity of corresponding channel pairs within the left and right somatosensory region. The wavelet analysis was focused on the very-low frequency oscillations (VLFO, 0.01–0.08 Hz) and the low frequency oscillations (LFO, 0.08–0.15 Hz). A mixed model analysis of variance was used to appraise statistical differences in the wavelet domain for the different acupuncture stimuli. The hemodynamic response after the acupuncture manipulations exhibited strong activations and distinctive cortical networks in each stimulus. The results of the statistical analysis showed significant differences ( p < 0.05 ) between the tasks in both frequency bands. These results suggest the existence of different stimuli-specific cortical networks in both frequency bands and the anaesthetic effect of the Hegu point as measured by fNIRS

Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance

As large language models (LLMs) are continuously being developed, their evaluation becomes increasingly important yet challenging. This work proposes Chain-of-Thought Hub, an open-source evaluation suite on the multi-step reasoning capabilities of large language models. We are interested in this setting for two reasons: (1) from the behavior of GPT and PaLM model family, we observe that complex reasoning is likely to be a key differentiator between weaker and stronger LLMs; (2) we envisage large language models to become the next-generation computational platform and foster an ecosystem of LLM-based new applications, this naturally requires the foundation models to perform complex tasks that often involve the composition of linguistic and logical operations. Our approach is to compile a suite of challenging reasoning benchmarks to track the progress of LLMs. Our current results show that: (1) model scale clearly correlates with reasoning capabilities; (2) As of May 2023, Claude-v1.3 and PaLM-2 are the only two models that are comparable with GPT-4, while open-sourced models still lag behind; (3) LLaMA-65B performs closely to code-davinci-002, indicating that with successful further development such as reinforcement learning from human feedback (RLHF), it has great potential to be close to GPT-3.5-Turbo. Our results also suggest that for the open-source efforts to catch up, the community may focus more on building better base models and exploring RLHF.Comment: Preprint. Code at https://github.com/FranxYao/chain-of-thought-hu

Interstellar: Using Halide's Scheduling Language to Analyze DNN Accelerators

We show that DNN accelerator micro-architectures and their program mappings represent specific choices of loop order and hardware parallelism for computing the seven nested loops of DNNs, which enables us to create a formal taxonomy of all existing dense DNN accelerators. Surprisingly, the loop transformations needed to create these hardware variants can be precisely and concisely represented by Halide's scheduling language. By modifying the Halide compiler to generate hardware, we create a system that can fairly compare these prior accelerators. As long as proper loop blocking schemes are used, and the hardware can support mapping replicated loops, many different hardware dataflows yield similar energy efficiency with good performance. This is because the loop blocking can ensure that most data references stay on-chip with good locality and the processing units have high resource utilization. How resources are allocated, especially in the memory system, has a large impact on energy and performance. By optimizing hardware resource allocation while keeping throughput constant, we achieve up to 4.2X energy improvement for Convolutional Neural Networks (CNNs), 1.6X and 1.8X improvement for Long Short-Term Memories (LSTMs) and multi-layer perceptrons (MLPs), respectively.Comment: Published as a conference paper at ASPLOS 202

Research on dynamic robust planning method for active distribution network considering correlation

The universality of load subjects in distribution network brings challenges to the reliability of distribution network planning results. In this paper, a two-stage dynamic robust distribution network planning method considering correlation is proposed. The method evaluates the correlation between random variables using the Spearman rank correlation coefficient, and converts the correlated random variables into mutually independent random variables by Cholesky decomposition and independent transformation; expresses the source-load uncertainty by a bounded interval without distribution, and describes the active distribution network planning as a dynamic zero-sum game problem by combining with the two-phase dynamic robust planning; use the Benders decomposition approach to tackle the issue; mathematical simulation is used to confirm the accuracy and efficacy of the method. The results show that the dynamic robustness planning method of active distribution network taking into account the correlation can accurately simulate the operation of active distribution network with uncertain boundaries, which enhances the reliability and economy of the active distribution network planning results

Insight-HXMT observations of Swift J0243.6+6124 during its 2017-2018 outburst

The recently discovered neutron star transient Swift J0243.6+6124 has been monitored by {\it the Hard X-ray Modulation Telescope} ({\it Insight-\rm HXMT). Based on the obtained data, we investigate the broadband spectrum of the source throughout the outburst. We estimate the broadband flux of the source and search for possible cyclotron line in the broadband spectrum. No evidence of line-like features is, however, found up to $\rm 150~keV$. In the absence of any cyclotron line in its energy spectrum, we estimate the magnetic field of the source based on the observed spin evolution of the neutron star by applying two accretion torque models. In both cases, we get consistent results with $B\rm \sim 10^{13}~G$, $D\rm \sim 6~kpc$ and peak luminosity of $\rm >10^{39}~erg~s^{-1}$ which makes the source the first Galactic ultraluminous X-ray source hosting a neutron star.Comment: publishe

Overview to the Hard X-ray Modulation Telescope (Insight-HXMT) Satellite

As China's first X-ray astronomical satellite, the Hard X-ray Modulation Telescope (HXMT), which was dubbed as Insight-HXMT after the launch on June 15, 2017, is a wide-band (1-250 keV) slat-collimator-based X-ray astronomy satellite with the capability of all-sky monitoring in 0.2-3 MeV. It was designed to perform pointing, scanning and gamma-ray burst (GRB) observations and, based on the Direct Demodulation Method (DDM), the image of the scanned sky region can be reconstructed. Here we give an overview of the mission and its progresses, including payload, core sciences, ground calibration/facility, ground segment, data archive, software, in-orbit performance, calibration, background model, observations and some preliminary results.Comment: 29 pages, 40 figures, 6 tables, to appear in Sci. China-Phys. Mech. Astron. arXiv admin note: text overlap with arXiv:1910.0443