On small Mixed Pattern Ramsey numbers

We call the minimum order of any complete graph so that for any coloring of the edges by $k$ colors it is impossible to avoid a monochromatic or rainbow triangle, a Mixed Ramsey number. For any graph $H$ with edges colored from the above set of $k$ colors, if we consider the condition of excluding $H$ in the above definition, we produce a \emph{Mixed Pattern Ramsey number}, denoted $M_k(H)$. We determine this function in terms of $k$ for all colored $4$-cycles and all colored $4$-cliques. We also find bounds for $M_k(H)$ when $H$ is a monochromatic odd cycles, or a star for sufficiently large $k$. We state several open questions.Comment: 16 page

Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem.

Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer's segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation) to 0.978 (for SegAdapter-corrected segmentation) for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by <0.01. These results suggest that the combination of automated segmentation and corrective learning provides a valuable method for accurate and efficient segmentation of the cerebellum and brainstem, particularly in large-scale neuroimaging studies, and potentially for segmenting other neural regions as well

A quantitative study of spin-flip co-tunneling transport in a quantum dot

We report detailed transport measurements in a quantum dot in a spin-flip co-tunneling regime, and a quantitative comparison of the data to microscopic theory. The quantum dot is fabricated by lateral gating of a GaAs/AlGaAs heterostructure, and the conductance is measured in the presence of an in-plane Zeeman field. We focus on the ratio of the nonlinear conductance values at bias voltages exceeding the Zeeman threshold, a regime that permits a spin flip on the dot, to those below the Zeeman threshold, when the spin flip on the dot is energetically forbidden. The data obtained in three different odd-occupation dot states show good quantitative agreement with the theory with no adjustable parameters. We also compare the theoretical results to the predictions of a phenomenological form used previously for the analysis of non-linear co-tunneling conductance, specifically the determination of the heterostructure g-factor, and find good agreement between the two.Comment: 5 pages, 5 figure

Can job turnover improve technical efficiency? : a study of state-owned enterprises in Shanghai

This paper studies the relationship between job turnover and technical efficiency of state-owned enterprise (SOEs) in Shanghai\u27s manufacturing sector during the period of 1989-1992. Data Envelopment Analysis (DEA) is used to compute measure of technical efficiency for each enterprise. Our findings indicate that, for non-expanding SOEs, the relationship between job turnover (i.e., downsizing) and technical efficiency is a U-shaped one such that efficiency declines at low levels of turnover,but after a certain level, it starts to increase. In addition, we show that small non-expanding SOEs (i.e., with employment size less than 100) start to increase their efficiency at a lower level of turnover than other medium and large SOEs. We also find that for medium and large expanding SOEs, the turnover-efficiency relationship is a positive and linear one

Knowledge and attitudes of men to prostate cancer

Objective: To ascertain the current level of understanding about prostate cancer (PCa), including treatment options and potential side effects of treatment, among older men. Design and Setting: Questionnaires administered by general practitioners (GPs) in 5 general practices in the Perth metropolitan and regional areas of Western Australia. Participants: Convenience sample of men aged 40-80 years (n=503) with or without prostate cancer presenting for routine consultations. Main outcome measures: Knowledge and attitudes of men to prostate cancer Results: Eighty percent of men did not know the function of the prostate and 48% failed to identify PCa as the most common internal cancer in men. Thirty-five percent had no knowledge of the treatments for PCa and 53% had no knowledge of the side effects of treatments. Asked how they would arrive at a decision about treatment, 70% stated they would ask the GP/specialist for all their options and then decide themselves. Conclusion: This study confirms a deficit in knowledge of the disease among men in the at risk age group. Lack of knowledge encompassed areas which could delay diagnosis and hence treatment. Overall the population preferred some GP/specialist involvement in treatment decision making

What Matters for Meta-Learning Vision Regression Tasks?

Meta-learning is widely used in few-shot classification and function regression due to its ability to quickly adapt to unseen tasks. However, it has not yet been well explored on regression tasks with high dimensional inputs such as images. This paper makes two main contributions that help understand this barely explored area. \emph{First}, we design two new types of cross-category level vision regression tasks, namely object discovery and pose estimation of unprecedented complexity in the meta-learning domain for computer vision. To this end, we (i) exhaustively evaluate common meta-learning techniques on these tasks, and (ii) quantitatively analyze the effect of various deep learning techniques commonly used in recent meta-learning algorithms in order to strengthen the generalization capability: data augmentation, domain randomization, task augmentation and meta-regularization. Finally, we (iii) provide some insights and practical recommendations for training meta-learning algorithms on vision regression tasks. \emph{Second}, we propose the addition of functional contrastive learning (FCL) over the task representations in Conditional Neural Processes (CNPs) and train in an end-to-end fashion. The experimental results show that the results of prior work are misleading as a consequence of a poor choice of the loss function as well as too small meta-training sets. Specifically, we find that CNPs outperform MAML on most tasks without fine-tuning. Furthermore, we observe that naive task augmentation without a tailored design results in underfitting.Comment: Accepted at CVPR 202