Estimating the impact of transport efficiency on trade costs: Evidence from Chinese agricultural traders

Using a unique survey data on agricultural traders in China in 2004, this study provides direct evidence on significance of interregional transport costs and their key determinants. Our major findings are as follows: (1) the trade barriers within China are dominated by transport-related costs but not artificial barriers, approximated by tolls and fines; (2) Labor and fuels costs are the most significant component of transport costs; (3) road quality is very important for transportation efficiency. Our results indicate that if increasing transport speed by 1 km per hour now, the fuel costs and total direct transportation costs for Chinese traders would reduce by 1.3% and 0.7% respectively.Transportation Costs, China, Agricultural Traders, Infrastructure, International Relations/Trade,

Study and Prospects: Adaptive Planning and Control of Supply Chain in One-of-a-kind Production

Based on the research project titled “Adaptive Planning and Control of Supply Chain in One-of-a-kind Production”, the research group performed a systematic review of supply chain integration, risk prediction and control and trace ability. Studies of a computer-aided and integrated production system for cost-effective OKP systemare included. Our efforts relevant to integration of supply chain in OKP, modeling &control of ripple effects in OKP supply chain and the trace ability of the OKP supply chain are introduced in this paper

Stochastic bifurcations and tipping phenomena of insect outbreak systems driven by α-stable Lévy processes

In this work, we mainly characterize stochastic bifurcations and tipping phenomena of insect outbreak dynamical systems driven by α-stable Lévy processes. In one-dimensional insect outbreak model, we find the fixed points representing refuge and outbreak from the bifurcation curves, and carry out a sensitivity analysis with respect to small changes in the model parameters, the stability index and the noise intensity. We perform the numerical simulations of dynamical trajectories using Monte Carlo methods, which contribute to looking at stochastic hysteresis phenomenon. As for two-dimensional insect outbreak system, we identify the global stability properties of fixed points and express the probability density function by the stationary solution of the nonlocal Fokker-Planck equation. Through numerical modelling, the phase portrait makes it possible to detect critical transitions among the stable states. It is then worth describing stochastic bifurcation associated with tipping points induced by noise through a careful examination of the dynamical paths of the insect outbreak system with external forcing. The results give new insight into the sensitivity of ecosystems to realistic environmental changes represented by stochastic perturbations

Pattern memory analysis based on stability theory of cellular neural networks

AbstractIn this paper, several sufficient conditions are obtained to guarantee that the n-dimensional cellular neural network can have even (⩽2n) memory patterns. In addition, the estimations of attractive domain of such stable memory patterns are obtained. These conditions, which can be directly derived from the parameters of the neural networks, are easily verified. A new design procedure for cellular neural networks is developed based on stability theory (rather than the well-known perceptron training algorithm), and the convergence in the new design procedure is guaranteed by the obtained local stability theorems. Finally, the validity and performance of the obtained results are illustrated by two examples

InferEM: Inferring the Speaker's Intention for Empathetic Dialogue Generation

Current approaches to empathetic response generation typically encode the entire dialogue history directly and put the output into a decoder to generate friendly feedback. These methods focus on modelling contextual information but neglect capturing the direct intention of the speaker. We argue that the last utterance in the dialogue empirically conveys the intention of the speaker. Consequently, we propose a novel model named InferEM for empathetic response generation. We separately encode the last utterance and fuse it with the entire dialogue through multi-head attention based intention fusion module to capture the speaker's intention. Besides, we utilize previous utterances to predict the last utterance, which simulates human's psychology to guess what the interlocutor may speak in advance. To balance the optimizing rates of the utterance prediction and response generation, a multi-task learning strategy is designed for InferEM. Experimental results demonstrate the plausibility and validity of InferEM in improving empathetic expression.Comment: 5 pages, 4 figure

CFN-ESA: A Cross-Modal Fusion Network with Emotion-Shift Awareness for Dialogue Emotion Recognition

Multimodal Emotion Recognition in Conversation (ERC) has garnered growing attention from research communities in various fields. In this paper, we propose a cross-modal fusion network with emotion-shift awareness (CFN-ESA) for ERC. Extant approaches employ each modality equally without distinguishing the amount of emotional information, rendering it hard to adequately extract complementary and associative information from multimodal data. To cope with this problem, in CFN-ESA, textual modalities are treated as the primary source of emotional information, while visual and acoustic modalities are taken as the secondary sources. Besides, most multimodal ERC models ignore emotion-shift information and overfocus on contextual information, leading to the failure of emotion recognition under emotion-shift scenario. We elaborate an emotion-shift module to address this challenge. CFN-ESA mainly consists of the unimodal encoder (RUME), cross-modal encoder (ACME), and emotion-shift module (LESM). RUME is applied to extract conversation-level contextual emotional cues while pulling together the data distributions between modalities; ACME is utilized to perform multimodal interaction centered on textual modality; LESM is used to model emotion shift and capture related information, thereby guide the learning of the main task. Experimental results demonstrate that CFN-ESA can effectively promote performance for ERC and remarkably outperform the state-of-the-art models.Comment: 13 pages, 10 figure

GraphCFC: A Directed Graph Based Cross-Modal Feature Complementation Approach for Multimodal Conversational Emotion Recognition

Emotion Recognition in Conversation (ERC) plays a significant part in Human-Computer Interaction (HCI) systems since it can provide empathetic services. Multimodal ERC can mitigate the drawbacks of uni-modal approaches. Recently, Graph Neural Networks (GNNs) have been widely used in a variety of fields due to their superior performance in relation modeling. In multimodal ERC, GNNs are capable of extracting both long-distance contextual information and inter-modal interactive information. Unfortunately, since existing methods such as MMGCN directly fuse multiple modalities, redundant information may be generated and diverse information may be lost. In this work, we present a directed Graph based Cross-modal Feature Complementation (GraphCFC) module that can efficiently model contextual and interactive information. GraphCFC alleviates the problem of heterogeneity gap in multimodal fusion by utilizing multiple subspace extractors and Pair-wise Cross-modal Complementary (PairCC) strategy. We extract various types of edges from the constructed graph for encoding, thus enabling GNNs to extract crucial contextual and interactive information more accurately when performing message passing. Furthermore, we design a GNN structure called GAT-MLP, which can provide a new unified network framework for multimodal learning. The experimental results on two benchmark datasets show that our GraphCFC outperforms the state-of-the-art (SOTA) approaches.Comment: 13 page

Multiple Factors Drive Variation of Forest Root Biomass in Southwestern China

The roots linking the above-ground organs and soil are key components for estimating net primary productivity and carbon sequestration of forests. The patterns and drivers of root biomass in forest have not been examined well at the regional scale, especially for the widely distributed forest ecosystems in southwestern China. We attempted to determine the spatial patterns of root biomass (RB, Mg/ha), annual increment root biomass (AIRB, Mg/ha/year), ratio of root and above-ground (RRA), and the relative contributions of abiotic and biotic factors that drive the variation of root biomass. Forest biomass and multiple factors (climate, soil, forest types, and stand characteristics) of 318 plots in this region (790,000 km2) were analyzed in this research. The AB (the mean values for forest aboveground biomass per ha, Mg/ha), RB, AIRB, and RRA were 126 Mg/ha, 28 Mg/ha, 0.69 Mg/ha and 0.22, respectively. AB, RB, AIRB, and RRA varied across all the plots and forest types. Both RB and AIRB showed significant spatial patterns of distribution, while RRA did not show any spatial patterns of distribution. Up to 28.4% of variation in total of RB, AIRB, and RRA can be attributed to the climate, soil, and stand characteristics. The explained or contribution rates of climate, soil, and stand characteristics for variation of whole forest root biomass were 6.7%, 16.9%, and 10.9%, respectively. Path analysis in structural equation model (SEM) indicated the direct influence of stand age on RB. AIRB was greater than that of the other factors. Climate, soil and stand characteristics in different forest types could explain 9.7%–96.1%, 15.4%–96.4%, and 36.7%–99.4% of variations in RB, AIRB, and RRA, respectively, which suggests that the multiple factors may be important in explaining the variations in forest root biomass. The results of the analysis of root biomass per ha, annual increment of root biomass per ha, and ratio of root and above-ground in the seven forest types categorized by climate, soil, and stand characteristics may be used for accurately determining C sequestration by the forest root and estimating forest biomass in this region