Learning Discriminative Features with Class Encoder

Deep neural networks usually benefit from unsupervised pre-training, e.g. auto-encoders. However, the classifier further needs supervised fine-tuning methods for good discrimination. Besides, due to the limits of full-connection, the application of auto-encoders is usually limited to small, well aligned images. In this paper, we incorporate the supervised information to propose a novel formulation, namely class-encoder, whose training objective is to reconstruct a sample from another one of which the labels are identical. Class-encoder aims to minimize the intra-class variations in the feature space, and to learn a good discriminative manifolds on a class scale. We impose the class-encoder as a constraint into the softmax for better supervised training, and extend the reconstruction on feature-level to tackle the parameter size issue and translation issue. The experiments show that the class-encoder helps to improve the performance on benchmarks of classification and face recognition. This could also be a promising direction for fast training of face recognition models.Comment: Accepted by CVPR2016 Workshop of Robust Features for Computer Visio

Thermodynamic Analysis Of Steam Ejector Refrigeration Cycle

Steam ejectors are capable of drawing large volumes of vapor within a relatively small space and at a low cost. In this study, the compressor is replaced by a constant-area mixing ejector to reduce the energy consumption in refrigeration cycle. The influence of various parameters on the performance of the system is obtained by an iterative program and reasons are analyzed in this paper. The effect of pressure difference, the difference of evaporation pressure and primary nozzle outlet pressure, on the COP and the exergy loss of every component in system is considered. Finally the key points to optimize the ejector cycle and the minimum exergy loss location to optimize the ejector design are obtained by theoretical research. A better understanding for the real industrial application is provided by this theoretical analysis on the steam ejector refrigeration system and a foundation for the simulation and experimental reach is laid

S3^3FD: Single Shot Scale-invariant Face Detector

This paper presents a real-time face detector, named Single Shot Scale-invariant Face Detector (S$^3$FD), which performs superiorly on various scales of faces with a single deep neural network, especially for small faces. Specifically, we try to solve the common problem that anchor-based detectors deteriorate dramatically as the objects become smaller. We make contributions in the following three aspects: 1) proposing a scale-equitable face detection framework to handle different scales of faces well. We tile anchors on a wide range of layers to ensure that all scales of faces have enough features for detection. Besides, we design anchor scales based on the effective receptive field and a proposed equal proportion interval principle; 2) improving the recall rate of small faces by a scale compensation anchor matching strategy; 3) reducing the false positive rate of small faces via a max-out background label. As a consequence, our method achieves state-of-the-art detection performance on all the common face detection benchmarks, including the AFW, PASCAL face, FDDB and WIDER FACE datasets, and can run at 36 FPS on a Nvidia Titan X (Pascal) for VGA-resolution images.Comment: Accepted by ICCV 2017 + its supplementary materials; Updated the latest results on WIDER FAC

Rapid progression may indicate pathological under-diagnosis in a case of spinal cord astrocytoma

Introduction: Most intramedullary spinal cord tumors are low-grade gliomas and are usually characterized by slow progression.  This is a case of a patient histologically diagnosed as low grade intramedullary astrocytomas but with fast growing behavior.Presentation of Case: A 67-year-old man who was diagnosed with a low-grade but-fast-growing intramedullary astrocytoma. He lost his ability to walk within 1 month after symptom onset.  Preoperative spinal MRI showed an intramedullary lesion from T2 to T4.  Decompression surgery was performed at the T2–T4 level and the tumor was partially removed, followed by standard radiotherapy and TMZ chemotherapy.  Histological examination showed a low-grade astrocytoma (WHO grade II).  However, the tumor rapidly progressed and the patient eventually developed disability in all four limbs.  MRI then showed the tumor to extend from C2 to T7.  The patient died of respiratory failure 17 months after his surgery.Conclusions: This case indicated that for patient with low-grade spinal cord astrocytoma, if the clinical progression does not match the pathological diagnosis，the treatment plan should be reconsidered

The perceived value of local knowledge tourism: dimension identification and scale development

IntroductionLocal knowledge tourism encompasses the rich cultural heritage, historical narratives, and traditional practices of a specific destination. Despite its significance in enhancing the tourist experience, there is a dearth of research examining the subjective perceptions and values of visitors engaging in local knowledge tourism. Consequently, there is a pressing need to explore the composition of perceived tourist values in this unique context.MethodsDue to the exploratory nature of this research, a constructivist grounded theory and content analysis are applied to analyze the data.ResultsThis study identifies and conceptualizes five distinct dimensions of perceived values in local knowledge tourism: functional value, emotional value, social value, cognitive value, and self-actualization value. Furthermore, an 18-item scale is developed to measure these dimensions quantitatively.DiscussionThis research makes several significant contributions: (1) it expands the scope of perceived value research within the tourism domain and enhances our understanding of the tourist experience in local knowledge tourism; (2) it provides a reliable instrument for future quantitative investigations into the behavior and mindset of local knowledge tourists; and (3) it offers theoretical foundations and practical insights for destination managers seeking to develop tourism products tailored to the preferences and expectations of local knowledge tourists