Selecting Inter-city Transportation Routes in Complex Terrains Using Quantitative Methods – A Case Study from Northern Yunnan, China

A variety of factors are involved in inter-city transportation route selection in the areas of complex terrain. With the help of Geographic Information System (GIS), three quantitative methods were employed to determine the transport routes between 44 cities in Northern Yunnan (China), an area with alpine valleys, Karst mountains, and plateau basins. The network analysis in GIS was used to find the routes based on the Transport Suitability Evaluation (TSE) map, which was produced from several factors, including population density, terrain slope, vertical terrain dissection, landslide and mud-flock area, land cover types, and Normalized Difference Vegetation Index (NDVI). Analytic Hierarchy Process (AHP), Grey Relational Analysis (GRA), and Delphi analysis were used to collect and calculate suitability values of these factors. Finally, all the routes connecting 44 cities of Northern Yunnan formed a network which could provide reference for route selection planning in the area.  </p

Fangchinoline alleviates cognitive impairments through enhancing autophagy and mitigating oxidative stress in Alzheimer’s disease models

Introduction: Alzheimer’s disease (AD) is a debilitating, progressive, neurodegenerative disorder characterized by the deposition of amyloid-β (Aβ) peptides and subsequent oxidative stress, resulting in a cascade of cytotoxic effects. Fangchinoline (Fan), a bisbenzylisoquinoline alkaloid isolated from traditional Chinese herb Stephania tetrandra S. Moorec, has been reported to possess multiple potent biological activities, including anti-inflammatory and antioxidant properties. However, the potential neuroprotective efficacy of Fan against AD remains unknown.Methods: N2AAPP cells, the mouse neuroblastoma N2A cells stably transfected with human Swedish mutant APP695, were served as an in vitro AD model. A mouse model of AD was constructed by microinjection of Aβ1–42 peptides into lateral ventricle of WT mice. The neuroprotective effects of Fan on AD were investigated through a combination of Western blot analysis, immunoprecipitation and behavioral assessments.Results and discussion: It was found that Fan effectively attenuated the amyloidogenic processing of APP by augmenting autophagy and subsequently fostering lysosomal degradation of BACE1 in N2AAPP cells, as reflected by the decrease in P62 levels, concomitant with the increase in Beclin-1 and LC3-II levels. More importantly, Fan significantly ameliorated cognitive impairment in an Aβ1–42-induced mouse model of AD via the induction of autophagy and the inhibition of oxidative stress, as evidenced by an increase in antioxidants including glutathione reductase (GR), total antioxidant capacity (T-AOC), nuclear factor erythroid-2-related factor 2 (Nrf2), heme oxygenase-1 (HO-1), and superoxide dismutase-1 (SOD-1) and a decrease in pro-oxidants including hydrogen peroxide (H2O2) and inducible nitric oxide synthase (i-NOS), coupled with a reduction in apoptosis marker, cleaved caspase-3. Taken together, our study demonstrate that Fan ameliorates cognitive dysfunction through promoting autophagy and mitigating oxidative stress, making it a potential therapeutic agent for AD

Statistical Knowledge Assessment for Large Language Models

Given varying prompts regarding a factoid question, can a large language model (LLM) reliably generate factually correct answers? Existing LLMs may generate distinct responses for different prompts. In this paper, we study the problem of quantifying knowledge contained in an LLM regarding a given set of facts. We propose KaRR, a statistical approach to assess factual knowledge for LLMs. The main idea is to estimate the ratio of LLM generating text corresponding to the answer entity given diverse prompts of the subject and the querying relation, versus it generating by random chances. Our assessment suite contains a comprehensive set of 994,123 entities and 600 relations, with 1,395,905 text aliases. We use our method to evaluate 20 LLMs of various sizes, including LLaMA, Alpaca, OPT, etc. Experiments show that our results have a strong correlation (0.43 Kendall's $\tau$) with the results of human assessment on LLMs. Our results reveal that the knowledge in LLMs with the same backbone architecture adheres to the scaling law, while tuning on instruction-following data sometimes compromises the model's capability to generate factually correct text reliably.Comment: Accepted by NeurIPS 202

ImageNetVC: Zero-Shot Visual Commonsense Evaluation on 1000 ImageNet Categories

Recently, Pretrained Language Models (PLMs) have been serving as general-purpose interfaces, posing a significant demand for comprehensive visual knowledge. However, it remains unclear how well current PLMs and their visually augmented counterparts (VaLMs) can master visual commonsense knowledge. To investigate this, we propose ImageNetVC, a fine-grained, human-annotated dataset specifically designed for zero-shot visual commonsense evaluation across 1,000 ImageNet categories. Utilizing ImageNetVC, we delve into the fundamental visual commonsense knowledge of both unimodal PLMs and VaLMs, uncovering the scaling law and the influence of the backbone model on VaLMs. Furthermore, we investigate the factors affecting the visual commonsense knowledge of large-scale models, providing insights into the development of language models enriched with visual commonsense knowledge. Our code and dataset are available at https://github.com/hemingkx/ImageNetVC

Fast Computation of Sliding Discrete Tchebichef Moments and Its Application in Duplicated Regions Detection

International audienceComputational load remains a major concern when processing signals by means of sliding transforms. In this paper, we present an efficient algorithm for the fast computation of one-dimensional and two-dimensional sliding discrete Tchebichef moments. To do so, we first establish the relationships that exist between the Tchebichef moments of two neighboring windows taking advantage of Tchebichef polynomials’ properties. We then propose an original way to fast compute the moments of one window by utilizing the moment values of its previous window. We further theoretically establish the complexity of our fast algorithm and illustrate its interest within the framework of digital forensics and more precisely the detection of duplicated regions in an audio signal or an image. Our algorithm is used to extract local features of such a signal tampering. Experimental results show that its complexity is independent of the window size, validating the theory. They also exhibit that our algorithm is suitable to digital forensics and beyond to any applications based on sliding Tchebichef moments

Effect of rs1344706 in the ZNF804A gene on the brain network.

ZNF804A rs1344706 (A/C) was the first SNP that reached genome-wide significance for schizophrenia. Recent studies have linked rs1344706 to functional connectivity among specific brain regions. However, no study thus far has examined the role of this SNP in the entire functional connectome. In this study, we used degree centrality to test the role of rs1344706 in the whole-brain voxel-wise functional connectome during the resting state. 52 schizophrenia patients and 128 healthy controls were included in the final analysis. In our whole-brain analysis, we found a significant interaction effect of genotype&nbsp;×&nbsp;diagnosis at the precuneus (PCU) (cluster size&nbsp;=&nbsp;52 voxels, peak voxel MNI coordinates: x&nbsp;=&nbsp;9, y&nbsp;=&nbsp;-&nbsp;69, z&nbsp;=&nbsp;63, F&nbsp;=&nbsp;32.57, FWE corrected P&nbsp;&lt;&nbsp;0.001). When we subdivided the degree centrality network according to anatomical distance, the whole-brain analysis also found a significant interaction effect of genotype&nbsp;×&nbsp;diagnosis at the PCU with the same peak in the short-range degree centrality network (cluster size&nbsp;=&nbsp;72 voxels, F&nbsp;=&nbsp;37.29, FWE corrected P&nbsp;&lt;&nbsp;0.001). No significant result was found in the long-range degree centrality network. Our results elucidated the contribution of rs1344706 to functional connectivity within the brain network, and may have important implications for our understanding of this risk gene's role in functional dysconnectivity in schizophrenia

A Survey on In-context Learning

With the increasing ability of large language models (LLMs), in-context learning (ICL) has become a new paradigm for natural language processing (NLP), where LLMs make predictions only based on contexts augmented with a few examples. It has been a new trend to explore ICL to evaluate and extrapolate the ability of LLMs. In this paper, we aim to survey and summarize the progress and challenges of ICL. We first present a formal definition of ICL and clarify its correlation to related studies. Then, we organize and discuss advanced techniques, including training strategies, demonstration designing strategies, as well as related analysis. Finally, we discuss the challenges of ICL and provide potential directions for further research. We hope that our work can encourage more research on uncovering how ICL works and improving ICL.Comment: Papers collected until 2023/05/2

bSRWPSO-FKNN: A boosted PSO with fuzzy K-nearest neighbor classifier for predicting atopic dermatitis disease

IntroductionAtopic dermatitis (AD) is an allergic disease with extreme itching that bothers patients. However, diagnosing AD depends on clinicians’ subjective judgment, which may be missed or misdiagnosed sometimes.MethodsThis paper establishes a medical prediction model for the first time on the basis of the enhanced particle swarm optimization (SRWPSO) algorithm and the fuzzy K-nearest neighbor (FKNN), called bSRWPSO-FKNN, which is practiced on a dataset related to patients with AD. In SRWPSO, the Sobol sequence is introduced into particle swarm optimization (PSO) to make the particle distribution of the initial population more uniform, thus improving the population’s diversity and traversal. At the same time, this study also adds a random replacement strategy and adaptive weight strategy to the population updating process of PSO to overcome the shortcomings of poor convergence accuracy and easily fall into the local optimum of PSO. In bSRWPSO-FKNN, the core of which is to optimize the classification performance of FKNN through binary SRWPSO.ResultsTo prove that the study has scientific significance, this paper first successfully demonstrates the core advantages of SRWPSO in well-known algorithms through benchmark function validation experiments. Secondly, this article demonstrates that the bSRWPSO-FKNN has practical medical significance and effectiveness through nine public and medical datasets.DiscussionThe 10 times 10-fold cross-validation experiments demonstrate that bSRWPSO-FKNN can pick up the key features of AD, including the content of lymphocytes (LY), Cat dander, Milk, Dermatophagoides Pteronyssinus/Farinae, Ragweed, Cod, and Total IgE. Therefore, the established bSRWPSO-FKNN method practically aids in the diagnosis of AD

Modification of sodium dodecyl sulfate and evaluation of foaming activity

In this study, to optimize the foaming activity of sodium dodecyl sulfate (SDS), modified sodium dodecyl sulfate surfactants (MSDS-1 and MSDS-2) are prepared by using methanol and diethanol amine as modifiers by the Mannich reaction. The foaming properties and foam stability of the products are evaluated by the Ross–Miles method and the Waring blender method. The microstructures of the foams produced by three surfactants are compared. The effects of temperature, inorganic salt, methanol, and condensate oil on the foaming activity of SDS, MSDS-1, and MSDS-2 are studied. The results obtained show that the best foaming concentration of all three products is 0.5%. Compared with SDS, the temperature resistance, methanol resistance, salt resistance and anti-condensate oil performance of MSDS-1 and MSDS-2 are improved. Among them, the temperature resistance, salt resistance, and methanol resistance of the MSDS-1 solution are the best. The MSDS-2 solution has the best anti-condensate performance. Besides, the foam size becomes smaller, the foam wall thickens, and the foam stability is improved after modification. The overall performance of SDS as a foaming agent can be improved by the Mannich modification