Dietary Shifts in Relation to Fruit Availability among Masked Palm Civets (Paguma larvata) in Central China

The spatial and temporal distribution of food resources can profoundly affect foraging decisions and prey selection, potentially resulting in shifts in diet in response to changes in resource availability. The masked palm civet (Paguma larvata) has long been regarded as a dietary generalist that feeds primarily on fruits and small mammals. Both types of food resources may vary spatially and temporally and the diet of P. larvata is expected to change in response to variation in the availability and distribution of these resources. To address the effects of such variation on foraging by masked palm civets, we studied a population of P. larvata inhabiting a highly heterogeneous habitat in central China consisting of primary forest, selectively logged forest, logged forest, broad-leaved and coniferous forest plantations, and cultivated farmland. Available food resources included wild fruits, cultivated fruits, leaves, plant cortexes, amphibians, reptiles, birds, small mammals, molluscs, and arthropods. The abundance of these food categories varied significantly among seasons and habitats and civets altered consumption of these categories according to their temporal and spatial availability. The diversity of items consumed also varied significantly among seasons and habitats. From June to October, wild fruits were the main food of civets in forest habitats, whereas cultivated fruits were the main food in farmland. In contrast, from November to May, civets in forested habitats consumed primarily rodents and birds. Concordant with these changes was a shift from foraging in primary forest (November–May) to foraging in logged forest and farmland (June–October) that appeared to be associated with the availability of fruits. These results demonstrate the ability of civets to change their diet, both spatially and temporally, in response to changing food resources. To better understand how foraging behavior of civets varies with resource availability, similar studies should be conducted in tropical environments characterized by year-round availability of fruit.Peer reviewe

Risk Evaluation of Debris Flow Hazard Based on Asymmetric Connection Cloud Model

Risk assessment of debris flow is a complex problem involving various uncertainty factors. Herein, a novel asymmetric cloud model coupled with connection number was described here to take into account the fuzziness and conversion situation of classification boundary and interval nature of evaluation indicators for risk assessment of debris flow hazard. In the model, according to the classification standard, the interval lengths of each indicator were first specified to determine the digital characteristic of connection cloud at different levels. Then the asymmetric connection clouds in finite intervals were simulated to analyze the certainty degree of measured indicator to each evaluation standard. Next, the integrated certainty degree to each grade was calculated with corresponding indicator weight, and the risk grade of debris flow was determined by the maximum integrated certainty degree. Finally, a case study and comparison with other methods were conducted to confirm the reliability and validity of the proposed model. The result shows that this model overcomes the defect of the conventional cloud model and also converts the infinite interval of indicators distribution into finite interval, which makes the evaluation result more reasonable

CharacterGLM: Customizing Chinese Conversational AI Characters with Large Language Models

In this paper, we present CharacterGLM, a series of models built upon ChatGLM, with model sizes ranging from 6B to 66B parameters. Our CharacterGLM is designed for generating Character-based Dialogues (CharacterDial), which aims to equip a conversational AI system with character customization for satisfying people's inherent social desires and emotional needs. On top of CharacterGLM, we can customize various AI characters or social agents by configuring their attributes (identities, interests, viewpoints, experiences, achievements, social relationships, etc.) and behaviors (linguistic features, emotional expressions, interaction patterns, etc.). Our model outperforms most mainstream close-source large langauge models, including the GPT series, especially in terms of consistency, human-likeness, and engagement according to manual evaluations. We will release our 6B version of CharacterGLM and a subset of training data to facilitate further research development in the direction of character-based dialogue generation.Comment: Work in progres

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

Modelling uncertain decision boundary for text classification

Text classification is to classify documents into predefined categories by learned classifiers. Classic text classifiers cannot unambiguously describe decision boundary between relevant and irrelevant documents because of uncertainties caused by feature selection and knowledge learning. This research proposes a three-way decision model for dealing with uncertain decision boundary based on rough sets and centroid solution to improve classification performance. It partitions training samples into three regions by two main boundary vectors, and resolves the boundary region by two derived boundary vectors to generate decision rules for making 'two-way' decisions

Centroid Training to achieve effective text classification

Traditional text classification technology based on machine learning and data mining techniques has made a big progress. However, it is still a big problem on how to draw an exact decision boundary between relevant and irrelevant objects in binary classification due to much uncertainty produced in the process of the traditional algorithms. The proposed model CTTC (Centroid Training for Text Classification) aims to build an uncertainty boundary to absorb as many indeterminate objects as possible so as to elevate the certainty of the relevant and irrelevant groups through the centroid clustering and training process. The clustering starts from the two training subsets labelled as relevant or irrelevant respectively to create two principal centroid vectors by which all the training samples are further separated into three groups: POS, NEG and BND, with all the indeterminate objects absorbed into the uncertain decision boundary BND. Two pairs of centroid vectors are proposed to be trained and optimized through the subsequent iterative multi-learning process, all of which are proposed to collaboratively help predict the polarities of the incoming objects thereafter. For the assessment of the proposed model, F1 and Accuracy have been chosen as the key evaluation measures. We stress the F1 measure because it can display the overall performance improvement of the final classifier better than Accuracy. A large number of experiments have been completed using the proposed model on the Reuters Corpus Volume 1 (RCV1) which is important standard dataset in the field. The experiment results show that the proposed model has significantly improved the binary text classification performance in both F1 and Accuracy compared with three other influential baseline models

Risk Response Strategies Selection over the Life Cycle of Project Portfolio

The successful implementation of project portfolios (PP) calls for effective risk management, in which selecting optimal risk response strategies help to reduce the impact of risk. Project portfolio risks (PPRs) exhibit causality and time dependency over the life cycle, which result in cumulative effects over time. By accounting for these risk correlations, risk response could be more effective in reducing expected losses than risk independence assumption. To support effective and sustainable risk management, this study proposes a novel risk response method that integrates the dynamic Bayesian network (DBN) model and reward–risk optimization model to select risk response strategies for different stages of the PP life cycle. The proposed method supports a more comprehensive analysis of risk contagion paths by opening the black box of the risk propagation paths during the PP life cycle. In this method, the PPRs, as the DBN nodes, are first identified, considering the project’s interdependency. Second, DBN analysis is used to assess PPRs by visually modeling the causality and life cycle correlation among risks. Then, the reward–risk optimization model is built to determine risk response strategies for each stage of the life cycle under the constraints. Finally, the proposed method selects risk response strategies for different stages of the PP life cycle. The findings reveal that the risk response effects are maximized if the risks are responded to in earlier stages. Moreover, the findings contribute to helping managers choose the optimal risk response strategies consistent with the risk response budget. As the effect of the strategy depends on the actual situation of the PP, the factors affecting the response effect of the strategies are recommended for further study