Projecting Ising Model Parameters for Fast Mixing

Inference in general Ising models is difficult, due to high treewidth making tree-based algorithms intractable. Moreover, when interactions are strong, Gibbs sampling may take exponential time to converge to the stationary distribution. We present an algorithm to project Ising model parameters onto a parameter set that is guaranteed to be fast mixing, under several divergences. We find that Gibbs sampling using the projected parameters is more accurate than with the original parameters when interaction strengths are strong and when limited time is available for sampling.Comment: Advances in Neural Information Processing Systems 201

Rotational hysteresis of the exchange anisotropy direction in Co/FeMn thin films

We have investigated the effects of rotating an applied field on the exchange anisotropy in Co/FeMn thin films. When the applied field is initially along the cooling field direction, the longitudinal hysteresis loop has a maximum coercivity and the transverse hysteresis loop is flat, indicating that the exchange field is along the cooling field direction. When the applied field angle is rotated away and then restored to the original field cooling direction, the exchange anisotropy direction has changed. The rotation of the exchange field direction trails the applied field and is hysteretic. The rotational hysteresis of the exchange field direction is due to the weak anisotropy in thin FeMn layers, and decreases with increasing FeMn thickness.Comment: 13 pages, 3 figures, to appear in J. Appl. Phy

Development Strategies and Regional Income Disparities in China

Since the economic reforms began in 1978, China has achieved remarkable economic results. Real GDP per capita grew at an average annual rate of 8.1% in the period of 1978-2001. Maintaining such a high growth rate over such a long period of time with a population of more than one billion truly is a miracle in world economy history (Lin et. al. 1994 and 1999).China, Regional income disparities, Income Distribution

Development Strategies and Regional Income Disparities in China

economic development strategy, regional income disparities, viability, China, economy

Economic Development Strategy, Openness and Rural Poverty: A Framework and China's Experiences

economic development strategy, income distribution, globalization, poverty

Equilibrium problems for Raney densities

The Raney numbers are a class of combinatorial numbers generalising the Fuss--Catalan numbers. They are indexed by a pair of positive real numbers $(p,r)$ with $p>1$ and $0 < r \le p$, and form the moments of a probability density function. For certain $(p,r)$ the latter has the interpretation as the density of squared singular values for certain random matrix ensembles, and in this context equilibrium problems characterising the Raney densities for $(p,r) = (\theta +1,1)$ and $(\theta/2+1,1/2)$ have recently been proposed. Using two different techniques --- one based on the Wiener--Hopf method for the solution of integral equations and the other on an analysis of the algebraic equation satisfied by the Green's function --- we establish the validity of the equilibrium problems for general $\theta > 0$ and similarly use both methods to identify the equilibrium problem for $(p,r) = (\theta/q+1,1/q)$, $\theta > 0$ and $q \in \mathbb Z^+$. The Wiener--Hopf method is used to extend the latter to parameters $(p,r) = (\theta/q + 1, m+ 1/q)$ for $m$ a non-negative integer, and also to identify the equilibrium problem for a family of densities with moments given by certain binomial coefficients.Comment: 13 page

A Novel Implementation of Machine Learning for the Efficient, Explainable Diagnosis of COVID-19 from Chest CT

In a worldwide health crisis as exigent as COVID-19, there has become a pressing need for rapid, reliable diagnostics. Currently, popular testing methods such as reverse transcription polymerase chain reaction (RT-PCR) can have high false negative rates. Consequently, COVID-19 patients are not accurately identified nor treated quickly enough to prevent transmission of the virus. However, the recent rise of medical CT data has presented promising avenues, since CT manifestations contain key characteristics indicative of COVID-19. This study aimed to take a novel approach in the machine learning-based detection of COVID-19 from chest CT scans. First, the dataset utilized in this study was derived from three major sources, comprising a total of 17,698 chest CT slices across 923 patient cases. Image preprocessing algorithms were then developed to reduce noise by excluding irrelevant features. Transfer learning was also implemented with the EfficientNetB7 pre-trained model to provide a backbone architecture and save computational resources. Lastly, several explainability techniques were leveraged to qualitatively validate model performance by localizing infected regions and highlighting fine-grained pixel details. The proposed model attained an overall accuracy of 0.927 and a sensitivity of 0.958. Explainability measures showed that the model correctly distinguished between relevant, critical features pertaining to COVID-19 chest CT images and normal controls. Deep learning frameworks provide efficient, human-interpretable COVID-19 diagnostics that could complement radiologist decisions or serve as an alternative screening tool. Future endeavors may provide insight into infection severity, patient risk stratification, and prognosis.Comment: 19 page