Edupreneurs

The for-profit sector is an active, viable and financially successful piece of the landscape of education and assumed to continue growing (Breneman, 2005). “Edupreneurs” or private, for-profit education companies provide desirable and affordable educational products and services for students, or better, customers. At the tertiary level, for-profit higher education is defined “private institution[s] in which the individual(s) or agency in control receives compensation other than wages, rent, or other expenses for the assumption of risk” (NCES, 2003). In other words, public higher education and private not-for-profit colleges and universities on the one hand are not entitled to benefit private interests and net earnings cannot be distributed to owners or shareholders (IRS, 2003; Quoted after Kinser, K. & Levy, D.C., 2005, p.6). On the other hand, for-profit institutions set their goal to make a profit for their owners or shareholders (Kinser, 2005). According to John Sperling (1997), For-profit universities offer several advantages over non-profit institutions, among which are the for-profit’s accountability for educational effectiveness, operational efficiency, cost benefits, and the time it takes them to respond to changes in the education needs. Fueled by the trends of internationalization, globalization, commercialization, and privatization in the education sector, for-profit education expands worldwide. This research intends to feature the Chinese echoes to the trend of For-profit education. The purpose of the study is three-fold. To begin with, the author aims to portray the scope and size of Chinese for-profit education sector, and make a tentative classification for “Edupreneurs” operating in Chinese education and training market. Next, the author aims to show the panorama of Chinese for-profit education, looking into the yesterday (causes of the emergence), today (strengths and weaknesses of the operation), and tomorrow (conceptualization of the optimal “Edupreneur”) of Chinese “Edupreneurs”. Last but not least, the researcher proposes to promote educational cooperation between Germany and China. Germany is blessed with excellent educational resources and services, and among one of the most popular destinations for international student mobility. Nevertheless, Germany has been avoiding the private surge, and thus a for-profit surge so far, even when faced with severe budget cuts and funding problems. Is this a voluntary or reluctant rejection, under the current educational system lacking self-management and autonomy? A quest for combining educational provision and consumption between Germany and China will then be incorporated in this study. Qualitative research methods are used to collect data. The primary source of data comes from semi-structured interviews with middle or senior administrators of selected for-profit educational companies. Other sources include direct observation made by the researcher during the periods of visiting the interviewees and companies; official documents (archival records, legislation, ministerial publications); internal documents; company fact book; company website; journalism (newspapers, periodicals), and others

Spatial-Assistant Encoder-Decoder Network for Real Time Semantic Segmentation

Semantic segmentation is an essential technology for self-driving cars to comprehend their surroundings. Currently, real-time semantic segmentation networks commonly employ either encoder-decoder architecture or two-pathway architecture. Generally speaking, encoder-decoder models tend to be quicker,whereas two-pathway models exhibit higher accuracy. To leverage both strengths, we present the Spatial-Assistant Encoder-Decoder Network (SANet) to fuse the two architectures. In the overall architecture, we uphold the encoder-decoder design while maintaining the feature maps in the middle section of the encoder and utilizing atrous convolution branches for same-resolution feature extraction. Toward the end of the encoder, we integrate the asymmetric pooling pyramid pooling module (APPPM) to optimize the semantic extraction of the feature maps. This module incorporates asymmetric pooling layers that extract features at multiple resolutions. In the decoder, we present a hybrid attention module, SAD, that integrates horizontal and vertical attention to facilitate the combination of various branches. To ascertain the effectiveness of our approach, our SANet model achieved competitive results on the real-time CamVid and cityscape datasets. By employing a single 2080Ti GPU, SANet achieved a 78.4 % mIOU at 65.1 FPS on the Cityscape test dataset and 78.8 % mIOU at 147 FPS on the CamVid test dataset. The training code and model for SANet are available at https://github.com/CuZaoo/SANet-mai

Transceiver design and multi-hop D2D for UAV IoT coverage in disasters

When natural disasters strike, the coverage for Internet of Things (IoT) may be severely destroyed, due to the damaged communications infrastructure. Unmanned aerial vehicles (UAVs) can be exploited as flying base stations to provide emergency coverage for IoT, due to its mobility and flexibility. In this paper, we propose multi-antenna transceiver design and multi-hop device-to-device (D2D) communication to guarantee the reliable transmission and extend the UAV coverage for IoT in disasters. Firstly, multi-hop D2D links are established to extend the coverage of UAV emergency networks due to the constrained transmit power of the UAV. In particular, a shortest-path-routing algorithm is proposed to establish the D2D links rapidly with minimum nodes. The closed-form solutions for the number of hops and the outage probability are derived for the uplink and downlink. Secondly, the transceiver designs for the UAV uplink and downlink are studied to optimize the performance of UAV transmission. Due to the non-convexity of the problem, they are first transformed into convex ones and then, low-complexity algorithms are proposed to solve them efficiently. Simulation results show the performance improvement in the throughput and outage probability by the proposed schemes for UAV wireless coverage of IoT in disasters

CSI-PPPNet: A One-Sided One-for-All Deep Learning Framework for Massive MIMO CSI Feedback

To reduce multiuser interference and maximize the spectrum efficiency in orthogonal frequency division duplexing massive multiple-input multiple-output (MIMO) systems, the downlink channel state information (CSI) estimated at the user equipment (UE) is required at the base station (BS). This paper presents a novel method for massive MIMO CSI feedback via a one-sided one-for-all deep learning framework. The CSI is compressed via linear projections at the UE, and is recovered at the BS using deep learning (DL) with plug-and-play priors (PPP). Instead of using handcrafted regularizers for the wireless channel responses, the proposed approach, namely CSI-PPPNet, exploits a DL based denoisor in place of the proximal operator of the prior in an alternating optimization scheme. In this way, a DL model trained once for denoising can be repurposed for CSI recovery tasks with arbitrary compression ratio. The one-sided one-for-all framework reduces model storage space, relieves the burden of joint model training and model delivery, and could be applied at UEs with limited device memories and computation power. Extensive experiments over the open indoor and urban macro scenarios show the effectiveness and advantages of the proposed method

Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation

Federated learning (FL) enables multiple client medical institutes collaboratively train a deep learning (DL) model with privacy protection. However, the performance of FL can be constrained by the limited availability of labeled data in small institutes and the heterogeneous (i.e., non-i.i.d.) data distribution across institutes. Though data augmentation has been a proven technique to boost the generalization capabilities of conventional centralized DL as a "free lunch", its application in FL is largely underexplored. Notably, constrained by costly labeling, 3D medical segmentation generally relies on data augmentation. In this work, we aim to develop a vicinal feature-level data augmentation (VFDA) scheme to efficiently alleviate the local feature shift and facilitate collaborative training for privacy-aware FL segmentation. We take both the inner- and inter-institute divergence into consideration, without the need for cross-institute transfer of raw data or their mixup. Specifically, we exploit the batch-wise feature statistics (e.g., mean and standard deviation) in each institute to abstractly represent the discrepancy of data, and model each feature statistic probabilistically via a Gaussian prototype, with the mean corresponding to the original statistic and the variance quantifying the augmentation scope. From the vicinal risk minimization perspective, novel feature statistics can be drawn from the Gaussian distribution to fulfill augmentation. The variance is explicitly derived by the data bias in each individual institute and the underlying feature statistics characterized by all participating institutes. The added-on VFDA consistently yielded marked improvements over six advanced FL methods on both 3D brain tumor and cardiac segmentation.Comment: 28th biennial international conference on Information Processing in Medical Imaging (IPMI 2023): Oral Pape

Evaluation of Hybrid VMAT Advantages and Robustness Considering Setup Errors Using Surface Guided Dose Accumulation for Internal Lymph Mammary Nodes Irradiation of Postmastectomy Radiotherapy

ObjectivesSetup error is a key factor affecting postmastectomy radiotherapy (PMRT) and irradiation of the internal mammary lymph nodes is the most investigated aspect for PMRT patients. In this study, we evaluated the robustness, radiobiological, and dosimetric benefits of the hybrid volumetric modulated arc therapy (H-VMAT) planning technique based on the setup error in dose accumulation using a surface-guided system for radiation therapy.MethodsWe retrospectively selected 32 patients treated by a radiation oncologist and evaluated the clinical target volume (CTV), including internal lymph node irradiation (IMNIs), and considered the planning target volume (PTV) margin to be 5 mm. Three different planning techniques were evaluated: tangential-VMAT (T-VMAT), intensity-modulated radiation therapy (IMRT), and H-VMAT. The interfraction and intrafraction setup errors were analyzed in each field and the accumulated dose was evaluated as the patients underwent daily surface-guided monitoring. These parameters were included while evaluating CTV coverage, the dose required for the left anterior descending artery (LAD) and the left ventricle (LV), the normal tissue complication probability (NTCP) for the heart and lungs, and the second cancer complication probability (SCCP) for contralateral breast (CB).ResultsWhen the setup error was accounted for dose accumulation, T-VMAT (95.51%) and H-VMAT (95.48%) had a higher CTV coverage than IMRT (91.25%). In the NTCP for the heart, H-VMAT (0.04%) was higher than T-VMAT (0.01%) and lower than IMRT (0.2%). However, the SCCP (1.05%) of CB using H-VMAT was lower than that using T-VMAT (2%) as well as delivery efficiency. And T-VMAT (3.72) and IMRT (10.5).had higher plan complexity than H-VMAT (3.71).ConclusionsIn this study, based on the dose accumulation of setup error for patients with left-sided PMRT with IMNI, we found that the H-VMAT technique was superior for achieving an optimum balance between target coverage, OAR dose, complication probability, plan robustness, and complexity

Patterns of de novo metastasis and survival outcomes by age in breast cancer patients: a SEER population-based study

BackgroundThe role of age in metastatic disease, including breast cancer, remains obscure. This study was conducted to determine the role of age in patients with de novo metastatic breast cancer.MethodsBreast cancer patients diagnosed with distant metastases between 2010 and 2019 were retrieved from the Surveillance, Epidemiology, and End Results database. Comparisons were performed between young (aged ≤ 40 years), middle-aged (41–60 years), older (61–80 years), and the oldest old (> 80 years) patients. Adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) were estimated using multivariate Cox proportional hazard models. Survival analysis was performed by the Kaplan–Meier method.ResultsThis study included 24155 (4.4% of all patients) de novo metastatic breast cancer patients. The number of young, middle-aged, older, and the oldest old patients were 195 (8.3%), 9397 (38.9%), 10224 (42.3%), and 2539 (10.5%), respectively. The 5-year OS rate was highest in the young (42.1%), followed by middle-aged (34.8%), older (28.3%), and the oldest old patients (11.8%). Multivariable Cox regression analysis showed that middle-aged (aHR, 1.18; 95% CI, 1.10–1.27), older (aHR, 1.42; 95% CI, 1.32–1.52), and the oldest old patients (aHR, 2.15; 95% CI, 1.98–2.33) had worse OS than young patients. Consistently, middle-aged (aHR, 1.16; 95% CI, 1.08–1.25), older (aHR, 1.32; 95% CI, 1.23–1.43), and the oldest old patients (aHR, 1.86; 95% CI, 1.71–2.03) had worse BCSS than young patients.ConclusionThis study provided clear evidence that de novo metastatic breast cancer had an age-specific pattern. Age was an independent risk factor for mortality in patients with de novo metastatic breast cancer