Oracle Solaris 9开发者指南：J2EE特性与服务

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"Oracle Solaris 9 - Developer’s Guide to J2EE Features and Services Sun ONE Application Server-96" 本文档是Oracle Solaris 9开发人员针对J2EE特性和服务的指南，专注于Sun ONE应用服务器版本7。该文档发布于2003年3月，由Sun Microsystems公司出版，涵盖了关于J2EE开发的关键信息。 一、J2EE（Java 2 Platform, Enterprise Edition） J2EE是Java平台的企业版，提供了一个标准的框架来构建、部署和管理多层企业应用程序。它包括一系列组件模型，如Java Servlets、JavaServer Pages (JSP)、Enterprise JavaBeans (EJB)等，以及用于事务处理、安全、资源管理的服务。 1. **JavaServlets**：Servlets是Java编写的服务器端程序，用于扩展Web服务器的功能，处理HTTP请求并生成响应。 2. **JavaServer Pages (JSP)**：JSP是一种动态网页技术，允许将HTML代码与Java代码混合编写，简化了Web应用的视图层开发。 3. **Enterprise JavaBeans (EJB)**：EJB是J2EE的核心组件，提供了面向对象的业务逻辑容器，支持实体Bean（代表数据库中的持久化对象）、会话Bean（处理短暂的业务逻辑）和消息驱动Bean（用于处理消息队列）。 二、Sun ONE Application Server Sun ONE Application Server是Sun Microsystems提供的一个基于J2EE的服务器平台，用于托管和管理企业级Java应用。它提供以下关键特性： 1. **部署和管理**：支持应用程序的部署、更新和卸载，以及服务器的管理和监控。 2. **安全性**：通过集成的身份验证、授权和加密机制确保应用的安全运行。 3. **集群和负载均衡**：支持多服务器集群，实现故障转移和负载均衡，提高系统可用性和性能。 4. **事务处理**：提供JTA（Java Transaction API）支持，保证跨多个资源的事务一致性。 5. **Web服务**：支持SOAP和WSDL，便于创建和消费Web服务。 三、操作系统集成 作为Oracle Solaris的一部分，这个J2EE开发指南还涵盖了Solaris操作系统与J2EE应用服务器的集成。Oracle Solaris 9是当时先进的Unix操作系统，提供了以下优势： 1. **性能优化**：Solaris的优化对Java应用特别有利，如其ZFS文件系统和DTrace诊断工具。 2. **稳定性**：Solaris以其强大的内核和健壮的安全性闻名，适合运行关键业务应用。 3. **硬件兼容性**：支持多种硬件架构，包括SPARC和x86，提供广泛的硬件选择。 4. **网络服务**：Solaris提供了丰富的网络服务，如DNS、NFS、FTP等，方便企业网络集成。 四、法规遵从性与出口控制 文档提到了产品可能受美国出口控制法律和其他国家的进口法规约束，意味着在国际间分发和使用时需要遵守相关法律法规。 总结，Oracle Solaris 9的J2EE开发指南详细介绍了如何在Sun ONE Application Server上开发、部署和管理J2EE应用，同时提供了对Solaris操作系统的集成指导，旨在帮助开发者构建稳定、高性能的多层企业系统。

Oracle9i J2EE 应用开发指南.rar

2010-07-19 上传
Oracle9i J2EE 应用开发指南.rar

修改下列模块代码，使其能够对三维模型的直线特征进行提取：class FaceKernelCorrelation(nn.Module): def __init__(self, num_kernel=64, sigma=0.2): super(FaceKernelCorrelation, self).__init__() self.num_kernel = num_kernel self.sigma = sigma self.weight_alpha = Parameter(torch.rand(1, num_kernel, 4) * np.pi) self.weight_beta = Parameter(torch.rand(1, num_kernel, 4) * 2 * np.pi) self.bn = nn.BatchNorm1d(num_kernel) self.relu = nn.ReLU() def forward(self, normals, neighbor_index): b, _, n = normals.size() center = normals.unsqueeze(2).expand(-1, -1, self.num_kernel, -1).unsqueeze(4) neighbor = torch.gather(normals.unsqueeze(3).expand(-1, -1, -1, 3), 2, neighbor_index.unsqueeze(1).expand(-1, 3, -1, -1)) neighbor = neighbor.unsqueeze(2).expand(-1, -1, self.num_kernel, -1, -1) fea = torch.cat([center, neighbor], 4) fea = fea.unsqueeze(5).expand(-1, -1, -1, -1, -1, 4) weight = torch.cat([torch.sin(self.weight_alpha) * torch.cos(self.weight_beta), torch.sin(self.weight_alpha) * torch.sin(self.weight_beta), torch.cos(self.weight_alpha)], 0) weight = weight.unsqueeze(0).expand(b, -1, -1, -1) weight = weight.unsqueeze(3).expand(-1, -1, -1, n, -1) weight = weight.unsqueeze(4).expand(-1, -1, -1, -1, 4, -1) dist = torch.sum((fea - weight)**2, 1) fea = torch.sum(torch.sum(np.e**(dist / (-2 * self.sigma**2)), 4), 3) / 16 return self.relu(self.bn(fea))

2023-05-25 上传
fea = fea.unsqueeze(5).expand(-1, -1, -1, -1, -1, 6) weight = torch.cat([torch.sin(self.weight_alpha) * torch.cos(self.weight_beta), torch.sin(self.weight_alpha) * torch.sin(self.weight_beta), ...

详细解释一下这段话The direct summation causes that the response maximum of the feature group is positively related to the number of fea- tures in the group. Next, the summation is normalized to relieve this relativity

2023-06-04 上传
这段话是在讨论特征组的响应最大值与组中特征数量之间的关系。其中，“direct summation”指的是直接将特征组中各个特征的值相加得到的结果，这种方法会导致特征组的响应最大值与组中特征数量呈正相关关系，也就是说...

Two individual features achieved log-rank P values of 0.003 and P value <0.001 respectively in the validation data set. The other four individual features failed to stratify patients into high-risk and low-risk groups in the val- idation data set. The multi-feature signature was successful to predict the OS of patients in the validation data set and performed better than any individual feature. From the statistical perspective, nonsignificant association with survival does not mean less importance. On the other hand, multivariate model is statistically robust in survival analysis26. Moreover, the intra-tumor genetic heterogeneity suggests that tumor subregions could be genetically different and may comprise multiple subclones. This could be better reflected by multiple high-order deep fea- tures extracted from multi-subregions in multi-modalities rather than individual feature. Similar to the genomic studies of exploring biomarkers from high-throughput genomic data, it is also regarded as a common“-omics” approach to construct a multi-factor radiomics signature for outcome prediction. 解释

2023-06-06 上传
这段话介绍了一项研究，该研究使用了多个特征来预测患者的生存期，其中有两个特征在验证数据集中获得了显著的统计学结果，但其他四个特征则没有。然而，使用多个特征的组合可以更准确地预测患者的生存期，比单个特征...

Based on the selected 150 features, we aimed to construct a radiomics signature using multivariate Cox regression model for prediction of survival in GBM patients. Because there were more image features than patients, strong feature selection and shrinkage were still required to prevent overfitting as well as increase interpretation. To address this problem, the least absolute shrinkage and selection operator (LASSO) Cox regression model was used on the discovery data set for signature construction25. Depending on the regulation weight λ, LASSO shrinks all regression coefficients towards zero and sets the coefficients of many irrelevant fea- tures exactly to zero. To find an optimal λ, 10-fold cross validation with minimum criteria was employed, where the final value of λ yielded minimum cross validation error. The retained features with nonzero coefficients were used for regression model fitting and combined into a radiomics signature. Subsequently, we obtained a radiom- ics score for each patient by a linear combination of retained features weighed by their model coefficients. The R package glmnet was used for LASSO Cox regression modeling. 解释

2023-06-01 上传
通过10倍交叉验证来寻找最优的λ值，最终的λ值是使交叉验证误差最小的值。保留具有非零系数的特征进行回归模型拟合，并将它们组合成一个放射组学签名。然后，通过保留特征的系数进行线性组合，得到每个患者的放射组...

逐行解释代码： def forward_Boosting(self, x, weight_mat=None): out = self.gru_features(x) fea = out[0] if self.use_bottleneck: fea_bottleneck = self.bottleneck(fea[:, -1, :]) fc_out = self.fc(fea_bottleneck).squeeze() else: fc_out = self.fc_out(fea[:, -1, :]).squeeze() out_list_all = out[1] out_list_s, out_list_t = self.get_features(out_list_all) loss_transfer = torch.zeros((1,)).cuda() if weight_mat is None: weight = (1.0 / self.len_seq * torch.ones(self.num_layers, self.len_seq)).cuda() else: weight = weight_mat dist_mat = torch.zeros(self.num_layers, self.len_seq).cuda() for i in range(len(out_list_s)): criterion_transder = TransferLoss( loss_type=self.trans_loss, input_dim=out_list_s[i].shape[2]) for j in range(self.len_seq): loss_trans = criterion_transder.compute( out_list_s[i][:, j, :], out_list_t[i][:, j, :]) loss_transfer = loss_transfer + weight[i, j] * loss_trans dist_mat[i, j] = loss_trans return fc_out, loss_transfer, dist_mat, weight

2023-07-20 上传
- `fea_bottleneck = self.bottleneck(fea[:, -1, :])`：将`fea[:, -1, :]`传递给瓶颈层`self.bottleneck`进行处理，得到瓶颈层的输出张量，赋值给变量`fea_bottleneck`。 - `fc_out = self.fc(fea_bottleneck)....

cost = Variable(torch.FloatTensor(refimg_fea.size()[0], refimg_fea.size()[1]*2, self.maxdisp//4, refimg_fea.size()[2], refimg_fea.size()[3]).zero_()).cuda() for i in range(self.maxdisp//4): if i > 0 : cost[:, :refimg_fea.size()[1], i, :,i:] = refimg_fea[:,:,:,i:] cost[:, refimg_fea.size()[1]:, i, :,i:] = targetimg_fea[:,:,:,:-i] else: cost[:, :refimg_fea.size()[1], i, :,:] = refimg_fea cost[:, refimg_fea.size()[1]:, i, :,:] = targetimg_fea cost = cost.contiguous()翻译一下这段代码

2023-06-11 上传
这段代码主要是为了计算代价图（cost volume），其中refimg_fea和targetimg_fea是经过卷积得到的特征图。具体来说，代码首先创建一个大小为[batch_size, channel*2, maxdisp/4, height, width]的0张量cost，并将其...

如何用python打开.fea文件

2023-06-13 上传
.fea 文件通常是指字体描述文件，包含了字体的轮廓、字形、度量信息等等。如果您想在 Python 中读取 .fea 文件，可以使用 FontTools 库。FontTools 是一个强大的字体处理工具包，它可以读取和写入多种字体格式，包括...

Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple/ Collecting paddle Using cached https://pypi.tuna.tsinghua.edu.cn/packages/55/cf/e4b6b9a54d2f072e4491e34317bf5f5fea260da8a3072e641832dc9ce770/paddle-1.0.2.tar.gz (579 kB) Preparing metadata (setup.py): started Preparing metadata (setup.py): finished with status 'error' error: subprocess-exited-with-error python setup.py egg_info did not run successfully. exit code: 1 [8 lines of output] Traceback (most recent call last): File "<string>", line 36, in <module> File "<pip-setuptools-caller>", line 34, in <module> File "C:\Users\91761\AppData\Local\Temp\pip-install-38tf9p6a\paddle_f25427c3911343f7bbb1b1ab8eef3961\setup.py", line 3, in <module> import paddle File "C:\Users\91761\AppData\Local\Temp\pip-install-38tf9p6a\paddle_f25427c3911343f7bbb1b1ab8eef3961\paddle\__init__.py", line 5, in <module> import common, dual, tight, data, prox ModuleNotFoundError: No module named 'common' [end of output] note: This error originates from a subprocess, and is likely not a problem with pip. error: metadata-generation-failed Encountered error while generating package metadata. See above for output. note: This is an issue with the package mentioned above, not pip. hint: See above for details. [notice] A new release of pip available: 22.3.1 -> 23.1.2 [notice] To update, run: python.exe -m pip install --upgrade pip如何解决

2023-06-10 上传
这个错误信息看起来像是安装PaddlePaddle时出现了问题。可能是由于一些依赖项缺失或版本不兼容导致的。建议您尝试以下解决方法： 1. 确保您使用的是最新版本的pip，可以运行以下命令进行升级： ...

5月 29 09:03:52 linaro-alip kernel: rockchip_canfd fea60000.can can0: rockchip_canfd_get_berr_counter RX_ERR_CNT=0x00000000, TX_ERR_CNT=0x00000000 5月 29 09:04:28 linaro-alip kernel: dwhdmi-rockchip fdea0000.hdmi: use tmds mode 5月 29 09:06:15 linaro-alip systemd[1]: isc-dhcp-server.service: Found left-over process 1911 (dhcpd) in control group while starting unit. Ignoring. 5月 29 09:06:15 linaro-alip systemd[1]: This usually indicates unclean termination of a previous run, or service implementation deficiencies. 5月 29 09:06:15 linaro-alip isc-dhcp-server[1944]: Launching IPv4 server only. 5月 29 09:06:15 linaro-alip isc-dhcp-server[1944]: Starting ISC DHCPv4 server: dhcpddhcpd service already running (pid file /var/run/dhcpd.pid currenty exists) ... 5月 29 09:06:15 linaro-alip isc-dhcp-server[1954]: failed! 5月 29 09:06:15 linaro-alip systemd[1]: isc-dhcp-server.service: Failed with result 'exit-code'.

2023-05-30 上传
根据你提供的日志信息，可以看到 isc-dhcp-server 启动失败，错误信息是 "dhcpd service already running (pid file /var/run/dhcpd.pid currenty exists) ... failed!"。 这个错误提示表明 dhcpd 服务已经在运行中...
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