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《榆林新明楼魏忠贤铜像考》

作　　者：徐涛 刘合心
出　　处：文博 2002年03期
发表时间：2002-03-01
文　　摘：
在榆林城内南大街中央,有一大型木构楼阁建筑——新明楼(又称星明楼) ，内有一尊大型铜造像。
1998年,故宫博物院朱家清氵晋等先生在观看造像后，提出了质疑，认为并非佛教或道教造像,有可能是明代延绥镇某位监军太监造像。2000 年及2001 年,陕西省文物鉴定组赴榆林进行馆藏文物鉴定时，我们两次对这尊铜造像进行了细致的考察,认为它其实并非一尊普通造像,而是明末大宦官魏忠贤的造像。

参考链接：
　　· http://scholar.ilib.cn/Abstract.aspx?A=wenb200203016
关 键 词：新明楼 魏忠贤
正　　文：
　　塞上名城榆林,秦汉时为上郡,唐代则属银州。明正统二年筑榆林城,成化九年(1473年) 延绥巡抚余子俊将延绥镇署自绥德迁至榆林,随后在成化、弘治、正德年间三拓其城,逐步形成现在的城市布局。康熙元年九月,清政府裁撤延绥巡抚,雍正九年设榆林府 ……
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参考文献：
注　释
①见赵立瀛主编《陕西古建筑》第六章第一节“榆林城”。陕西人民出版社1992 年11 月。
②以上参见《陕西省志·文物志》第三章古建筑第三节。陕西省地方志编纂委员会编,三秦出版社1995 年8 月:《中国文地图集·陕西分册》下册榆林地区榆林市。国家文物局主编,西安地图出版社1998 年12 月;《陕西名胜古迹》榆林地区榆林县历史文物概况。陕西省文物管理委员会编,陕西人民出版社1986 年12 月。
③《榆林府志·卷五》城池。清·李熙龄等撰,道光二十一年版,陕西省考古研究所藏所书。
④《延绥镇志》清·谭吉璁等纂修,康熙十二年抄本。《中国方志丛书·华北地方·第三○五号》成文出版有限公司印行。
⑤在新明楼底层,有数件记载新明楼维修状况的木匾形题记。除上世纪80 年代及光绪元年维修纪录外,还有嘉庆二十四年、光绪二十七年维修“磨洗阁”等处的记载,似与新明楼无关,故此处不再论述。
⑥见《榆林市志》卷二十三文物志,第一章第二节。榆林市志编纂委员会编,三秦出版社1996 年12 月。
⑦《图开胜迹》6 册,存榆林市档案馆。笔者存其复印本。
⑧《陕西古建筑》、《陕西省志·文物志》未提及;《陕西名胜古迹》称造像;《中国文地图集·陕西分册》、《榆林市志》、《榆林文史揽胜》称佛像。
⑨《榆林府志·卷四十四》艺文志。
[10]见《中国文地图集·陕西分册》下册,榆林地区榆林市。
[11]《榆林市志》卷十一城乡建设志第一章第二节。
[12]《明史·卷六十八》志第44 ,舆服四,中华书局1977 年第一版,以下同。
[13]韩城禹王殿原址在村镇,1980 年迁至芝川镇司马坡。该殿有“元元统三年(1355 年) 乙亥岁创建”墨书题记。见《陕西古建筑》第六章第二节。
[14]见陈娟娟《明代丝绸艺术》图八。《故宫博物院院刊》1992年1 期。
[15][16]见《陕西省志·文物志》第三章古建筑第三节慧照寺塔、福昌寺千佛铁塔。
[17]此像原在韩城城隍庙内,现已搬至韩城市博物馆陈列。
[18]穆青、汤伟健《明代民窑青花》河北人民出版社2000 年11月。
[19]明·沈德符《万历野获编》补遗卷二,阁臣赐蟒之始。元明史料笔记从刊,中华书局1997 年。
[20][21]《明史·卷六十七》志第43 ,舆服三。
[22]以上参见《明史》各人传纪。
[23]《明史·卷六十四》志第40 ,仪卫。
[24]见《中国美术全集·绘画编13·寺观壁画》文物出版社,1988年。
[25]徐达与李文忠像均见周锡保《中国古代服饰史》第一章图二十、二十一,中国戏剧出版社1984 年。
[26]参见郑景胜、郑艳萍《北镇庙壁画人物是谁?》中的常遇春像。《文物天地》1994 年6 期。
[27]《明史·卷七十四》志第50 ,职官三。
[28][29][52][57]明·刘若愚《酌中志》卷之十九,内臣服佩记略,北京古籍出版社1994 年5 月,以下同。
[30]《酌中志》卷之五,三朝典礼之臣记略。田义,西安人,曾任南京守备,万历中任司礼监掌印太监。
[31]《酌中志》卷之九,正监蒙难记略。杜茂,陕西人,天启初任司礼监秉笔太监,曾任承天监守备。咸阳市秦都区北杜镇福昌寺千佛铁塔,即由他出资修建,从姓氏地望看,应该为本地人。
[32][36][37]清·谷应泰《明史记事本末·卷之七十一》魏忠贤乱政中华书局1977 年2 月。
[33]《明史·卷三百五》列传193 ,宦官传二,魏忠贤。
[34][38][39]《明史·卷三百六》列传194 ,阉党,阎鸣泰传。
[35]明·郑仲夔《耳新》卷七,转引自王春瑜、杜婉言《明代宦官与经济史料初探》附录:“明代宦官与江南经济”。中国社会科学出版社1986 年9 月。
[40]明·朱长祚《玉镜新谭》卷七建祠,元明史料笔记丛刊,中华书局1989 年9 月。
[41][47]《明实录·熹宗》第70 册,卷八六、卷八四。
[42]见吴廷燮《明督抚年表》卷三延绥,所引《万历庚戌进士题名录》,中华书局1982 年6 月。
[43]见《明史·卷二百四十三》列传131 邹元标、冯从吾传。
[44]见《明史·卷二百四十四》列传132 周起元传。
[45]见《明督抚年表》卷三延绥所引《剥复录》。
[46]《玉镜新谭》卷四彳复赉。
[48][49]《明□宗□皇帝实录》七年十月、十一月《明实录》附录之一,中央研究院历史语言研究所校印本。
[50]参见清·夏燮《明通鉴》卷八十一,中华书局1959 年第一版。
[51]明·韩纩等撰《钦定逆案》,选自清钞《明季野史汇编》,中国野史集成续编编委会、四川大学图书馆编
《中国野史大观·续编》第20 册,巴蜀书社2000 年第一版。
[53]《酌中志》卷之四,恭纪今上瑞征。
[54]《酌中志》卷之十四,客魏始末纪略。
[55]《榆林府志·卷八》建置志,祠祀。
[56]《玉镜新谭》卷七,建祠。
[58][59][61]《明史·卷三百九》列传第197 ,李自成。
[60]参见《明史·卷二十二》本纪第22 ,熹宗及列传第148 杨鹤传。
[62]参见《清史稿·卷四》本纪4 ,世祖本纪一及卷四百七十四,列传261 ,吴三桂传,中华书局1977 年第一版。
[63]见《清史稿·卷二百五十七》列传44 ,许占魁传。

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Understanding The Anavar Cycle: Duration, Goals, And Results


How AI and Machine_Learning Models Can Assist in Parkinson_s
Disease (PD) Diagnosis


Below is an overview that explains the concepts and approaches researchers and clinicians
use to support PD diagnosis with AI. It covers
the most common data types, typical machine_learning pipelines, illustrative studies, and practical considerations
for real_world deployment.



> NOTE _ This guide is informational only. Clinical decisions should
always be made by qualified health professionals using validated diagnostic
tools and guidelines (e.g., Movement Disorder Society criteria, MDS-UPDRS).





---




1. Why AI Helps



Traditional Diagnosis Limitations How AI
Adds Value


Neurological exam + history + UPDRS Subjective,
variable inter_rater reliability Quantifies subtle
patterns, reduces observer bias


Imaging (MRI/CT) Expensive, limited sensitivity for early disease Detects micro_structural changes via advanced analysis



Wearable data (accelerometry) Requires manual feature extraction Learns features directly from raw signals


---




2. Typical Data Sources



Modality Common Measures Example Feature


Accelerometer (wrist/ankle) Raw tri_axial acceleration, gyroscope
RMS velocity amplitude


Gyroscope Angular velocity Mean tilt angle


Pressure sensor Footfall force Peak pressure distribution


Video Pose keypoints (OpenPose) Joint angle trajectories


EEG/EMG Raw voltage traces Muscle activation timing


---




3. Feature Engineering Workflow




Pre_processing


- Resample to a common frequency.
- Apply low_pass filter (e.g., 4th order Butterworth) to remove noise.


- Remove gravity component via high_pass filtering
or sensor fusion.





Segmentation


- Detect gait events (heel strike, toe off) using thresholding
on acceleration magnitude.
- Window the data into strides (~0.5_1_s).





Feature Extraction



Domain Feature Type Example


Time_domain Statistical moments mean, std, skewness, kurtosis of each axis


Peak detection max/min acceleration peaks per stride


Zero_crossing rate number of times signal crosses zero


Frequency_domain Spectral energy sum of power spectral density in gait frequency band (0.5_3_Hz)


Dominant frequency frequency with maximum PSD


Time_frequency Wavelet coefficients detail coefficients at scales corresponding to stride period



Implementation Tip: Use `scipy.signal.welch` for PSD estimation; apply a Hamming window to
reduce spectral leakage.




4.2 Feature Selection and Dimensionality Reduction


With dozens of extracted features, selecting the most informative subset improves classifier performance and reduces overfitting.






Correlation Analysis: Compute Pearson correlation coefficients between features
and target labels. Remove highly correlated pairs (e.g., correlation > 0.9) to
avoid redundancy.



Recursive Feature Elimination (RFE): Employ scikit-learn_s `RFE` with a lightweight
estimator (e.g., Logistic Regression or Random Forest) to rank
features iteratively.



Principal Component Analysis (PCA): Reduce dimensionality while preserving variance.
PCA components can serve as inputs to the classifier if interpretability is not critical.




Illustrative Code Snippet


from sklearn.feature_selection import RFE, SelectKBest, f_classif


Assuming X_features and y_labels are prepared

estimator = RandomForestClassifier(n_estimators=100)
selector = RFE(estimator, n_features_to_select=10, step=1).fit(X_features, y_labels)

X_selected = selector.transform(X_features)
Reduced feature set







3. Classification Model: Support Vector Machine (SVM)



3.1 Algorithmic Overview


A Support Vector Machine constructs a hyperplane in the feature space that maximally separates classes.
For two_class problems, the optimization seeks to minimize (|w|) subject to correct classification constraints:





( y_i (langle w, x_i rangle + b) ge 1 - xi_i ), with
slack variables (xi_i ge 0) allowing misclassification.


Regularization parameter (C) balances margin width and
training error.



The dual formulation introduces Lagrange multipliers (alpha_i):

[
max_alpha sum_i=1^Nalpha_i - frac12sum_i,jalpha_ialpha_j y_iy_j K(x_i, x_j)
]
subject to (0 le alpha_i le C) and (sum_i alpha_i y_i = 0), where (K) is
the kernel.



Training complexity scales with number of support vectors (SVs).
In high dimensions, the dot product computation (x_i^top
x_j) becomes expensive; however, since each vector has at most
10 non-zero entries, sparse matrix operations can be employed to accelerate this step.





3.2 Random Forests


Random forests build an ensemble of decision trees on bootstrap
samples and random subsets of features at splits. The number of trees (T), depth,
and feature subset size are key hyperparameters.





Computational Complexity



Training: For each tree, we sample (n_s approx 0.632 n)
instances (bootstrap). At each node, we evaluate a random subset of features (m_f) (commonly
(sqrtd)) to find the best split. The cost per split is proportional to (O(m_f log
n_s)) if sorted splits are used; otherwise,
(O(n_s m_f)). The total cost per tree thus depends on depth (D):
roughly (O(T D m_f n_s)).


Prediction: Each instance traverses one path per tree (depth (D)), checking a single feature
at each node. Thus (O(T D)) operations.



In practice, with shallow trees ((D approx 3-5)) and small number of trees ((T approx 10-30)), both training and inference are inexpensive.



2.2 Random Forests


Random forests extend decision trees by introducing bagging (bootstrap
aggregating) and random feature selection at splits:





For each tree, sample with replacement (n_s) instances from the training set.



At each node, select a random subset of features (size (m))
and find the best split among them.



The key differences in complexity are:



Training: The bootstrap sampling adds negligible overhead; the feature
subsampling reduces the cost per node because fewer candidate splits must be evaluated.



Inference: Each tree still requires traversing from root to leaf, so the cost is similar to
that of a single decision tree. However, since we now have many trees, inference scales linearly with
the number of trees.



Because each tree is shallow and the feature subsampling
dramatically reduces the time per node, random forests can be trained quickly even for large numbers of trees.



3.4 Empirical Observations


When measuring training times on real datasets (e.g., image
classification tasks), we observe:





Training Time: Random forest training time scales roughly linearly with the
number of trees and logarithmically with the depth of each tree, but remains
far below that required for deep convolutional neural networks.



Inference Speed: Once trained, random forests provide extremely fast predictions.
For a test set of 10,000 images, inference may complete in seconds on a standard CPU.




These observations confirm that random forests can be
deployed efficiently in production environments where computational
resources are limited or latency is critical.






5. Implications for Practical Deployment



5.1 Use Cases Where Random Forests Excel




Small to Medium-Dataset Scenarios: When the available labeled data is insufficient to train deep models effectively, random forests can still achieve reasonable
performance.


Feature-Rich Applications: If handcrafted features (e.g., color histograms, texture descriptors)
capture enough discriminative information, random
forests can exploit them efficiently.


Resource-Constrained Environments: Devices with limited processing power or memory
(mobile phones, embedded systems) can run random forest classifiers without the
overhead of deep networks.


Rapid Prototyping: Random forests require fewer hyperparameters to tune and are less sensitive to training data distribution, making them suitable for quick experimentation.




5.2 When Deep Learning May Be Preferable


Despite their computational demands, CNNs can surpass random forests
when:





Large Datasets: With sufficient labeled examples (e.g.,
ImageNet), CNNs learn rich feature hierarchies that generalize well.




Complex Visual Tasks: Fine-grained recognition, object detection, or segmentation often benefit from end-to-end learning and multi-scale representations.



Transfer Learning: Pretrained CNNs can be fine-tuned on smaller datasets with relatively little data, leveraging learned low-level filters.








6. Conclusion


The choice between Random Forest classifiers and Convolutional Neural Networks for image recognition hinges on a
nuanced assessment of dataset size, computational resources, model complexity,
and performance requirements. Random Forests offer simplicity, interpretability, and robustness with modest datasets, while CNNs excel
when ample data and computing power are available to harness their powerful hierarchical feature learning capabilities.
By systematically evaluating these factors_data volume,
training time, inference speed, and desired accuracy_a practitioner can make an informed decision tailored to
the specific constraints and objectives of their application. This analytical framework serves as a guide for navigating the trade-offs inherent in selecting an appropriate machine learning model for visual recognition tasks.


References:


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