Credit Risk Migration Analysis of Illinois Farm Business: Possible Impacts of Farm Business Cycle

This study uses the cohort approach to estimate the credit risk migration probability of farm business. Using data from the Farm Business and Farm Management, this study rates the credit risk into 10 risk levels plus a default level, defines a farm business cycle with peak, normal and trough periods and evaluates the effect on farm financial performance of the farm business booms and slumps. The results show that the farms with low credit risk are more likely to stay in the same risk level but the farms with high credit risk have the trend to improve their risk situation and move upwards. The results also show that the credit risk ratings are more likely to move upgrade during farm business cycle peaks.Agricultural Finance,

Patronage Refunds Paying Decision of Farm Credit System Associations: A Logit Model

Financial Economics,

Cloud fragmentation and chemical evolution of the high-mass star-forming region G327.3-0.6

In the struggle to understand how stars form in a cluster, it is important to study the morphology, kinematics and chemistry of the star-forming clouds. This thesis focuses on the high-mass star-forming region G327.3-0.6, which is a 3 pc filament at a distance of 3.3 kpc, hosting one hot molecular core and a set of cold dense cores. It was observed with the Atacama Large Millimetre/Sub-millimetre Array (ALMA) at 1.3 mm with high resolution 2". The data were self-calibrated to improve the signal to noise ratio by a factor of 2. The dendrogram algorithm together with the background subtraction were adopted to determine 66 compact cores. Minimum spanning tree determined a median core separation at 0.15pc and possible hierarchical fragmentation, which was supported by the two-point correlation function. Core mass function (CMF) was fitted with an index of -0.83, which is a hint of high-mass star-forming regions. The fragmentation in the filament was dominated by thermal support in small scale (~0.15pc) and by turbulence in large scale (~0.4pc). With toolbox XCLASS, 26 molecules and 39 isotopes were identified in the hot core spectrum, and a temperature of 270K was derived. The temperature error is around 60%. The moment maps were derived for 42 molecular transitions and analyzed by the Histogram of Oriented Gradient (HOG), indicating correlations between DCN and continuum, SiO and H2CO/CH3OH. Principal component analysis (PCA) and clustering algorithm were applied to the average spectra of each core to classify the evolutionary stages. Four groups are found with chemical and physical distinctions, suggesting the excitation temperature of CH3OH to be a good evolutionary indicator. The infrared environment is complex and may associated with photon-dissociation regions (PDRs)

Mean almost periodicity and moment exponential stability of discrete-time stochastic shunting inhibitory cellular neural networks with time delays

summary:By using the semi-discrete method of differential equations, a new version of discrete analogue of stochastic shunting inhibitory cellular neural networks (SICNNs) is formulated, which gives a more accurate characterization for continuous-time stochastic SICNNs than that by Euler scheme. Firstly, the existence of the 2th mean almost periodic sequence solution of the discrete-time stochastic SICNNs is investigated with the help of Minkowski inequality, Hölder inequality and Krasnoselskii's fixed point theorem. Secondly, the moment global exponential stability of the discrete-time stochastic SICNNs is also studied by using some analytical skills and the proof of contradiction. Finally, two examples are given to demonstrate that our results are feasible. By numerical simulations, we discuss the effect of stochastic perturbation on the almost periodicity and global exponential stability of the discrete-time stochastic SICNNs

On the Existence of Solutions for Impulsive Duffing Dynamic Equations on Time Scales with Dirichlet Boundary Conditions

By using critical point theory, some new sufficient conditions for the existence of solutions of impulsive Duffing dynamic equations on time scales with Dirichlet boundary conditions are obtained. Some examples are also given to illustrate our results

Existence and global attractivity of positive almost periodic solutions for a kind of fishing model with pure-delay

summary:By using some analytical techniques, modified inequalities and Mawhin's continuation theorem of coincidence degree theory, some sufficient conditions for the existence of at least one positive almost periodic solution of a kind of fishing model with delay are obtained. Further, the global attractivity of the positive almost periodic solution of this model is also considered. Finally, three examples are given to illustrate the main results of this paper

A Benchmark of Long-tailed Instance Segmentation with Noisy Labels (Short Version)

In this paper, we consider the instance segmentation task on a long-tailed dataset, which contains label noise, i.e., some of the annotations are incorrect. There are two main reasons making this case realistic. First, datasets collected from real world usually obey a long-tailed distribution. Second, for instance segmentation datasets, as there are many instances in one image and some of them are tiny, it is easier to introduce noise into the annotations. Specifically, we propose a new dataset, which is a large vocabulary long-tailed dataset containing label noise for instance segmentation. Furthermore, we evaluate previous proposed instance segmentation algorithms on this dataset. The results indicate that the noise in the training dataset will hamper the model in learning rare categories and decrease the overall performance, and inspire us to explore more effective approaches to address this practical challenge. The code and dataset are available in https://github.com/GuanlinLee/Noisy-LVIS