Residents’ Social Relationships in Everyday Life in a Tourism Ancient Town

This study uses several theories to explain the daily social relationship among various groups of actors residing in a tourism destination. Data were collected through in-depth interviews, surveys, non-participant observation, and secondary documents. The study investigates the social relationships between residents and tourists, within and between locals and migrants, and within and between tourism and non-tourism practitioners. Results reveal that each group has its own communication circles, where communication happens mainly within the same group based on residents’ original social networks and only partly between different groups due to new business-related social networks. Inner-group communications and different degrees of inter-group isolation exist among the resident groups. The causes were discussed. The findings are significant for the sustainability of a tourism ancient town

A Generalized Packing Server for Scheduling Task Graphs on Multiple Resources

This paper presents the generalized packing server. It reduces the problem of scheduling tasks with precedence constraints on multiple processing units to the problem of scheduling independent tasks. The work generalizes our previous contribution made in the specific context of scheduling Map/Reduce workflows. The results apply to the generalized parallel task model, introduced in recent literature to denote tasks described by workflow graphs, where some subtasks may be executed in parallel subject to precedence constraints. Recent literature developed schedulability bounds for the generalized parallel tasks on multiprocessors. The generalized packing server, described in this paper, is a run-time mechanism that packs tasks into server budgets (in a manner that respects precedence constraints) allowing the budgets to be viewed as independent tasks by the underlying scheduler. Consequently, any schedulability results derived for the independent task model on multiprocessors become applicable to generalized parallel tasks. The catch is that the sum of capacities of server budgets exceeds by a certain ratio the sum of execution times of the original generalized parallel tasks. Hence, a scaling factor is derived that converts bounds for independent tasks into corresponding bounds for generalized parallel tasks. The factor applies to any work-conserving scheduling policy in both the global and partitioned multiprocessor scheduling models. We show that the new schedulability bounds obtained for the generalized parallel task model, using the aforementioned conversion, improve in several cases upon the best known bounds in current literature. Hence, the packing server is shown to improve the schedulability of generalized parallel tasks. Evaluation results confirm this observation.Ope

Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels

Recent studies on contrastive learning have achieved remarkable performance solely by leveraging few labels in the context of medical image segmentation. Existing methods mainly focus on instance discrimination and invariant mapping. However, they face three common pitfalls: (1) tailness: medical image data usually follows an implicit long-tail class distribution. Blindly leveraging all pixels in training hence can lead to the data imbalance issues, and cause deteriorated performance; (2) consistency: it remains unclear whether a segmentation model has learned meaningful and yet consistent anatomical features due to the intra-class variations between different anatomical features; and (3) diversity: the intra-slice correlations within the entire dataset have received significantly less attention. This motivates us to seek a principled approach for strategically making use of the dataset itself to discover similar yet distinct samples from different anatomical views. In this paper, we introduce a novel semi-supervised 2D medical image segmentation framework termed Mine yOur owN Anatomy (MONA), and make three contributions. First, prior work argues that every pixel equally matters to the model training; we observe empirically that this alone is unlikely to define meaningful anatomical features, mainly due to lacking the supervision signal. We show two simple solutions towards learning invariances - through the use of stronger data augmentations and nearest neighbors. Second, we construct a set of objectives that encourage the model to be capable of decomposing medical images into a collection of anatomical features in an unsupervised manner. Lastly, our extensive results on three benchmark datasets with different labeled settings validate the effectiveness of our proposed MONA which achieves new state-of-the-art under different labeled settings

Enhanced Global-Brain Functional Connectivity in the Left Superior Frontal Gyrus as a Possible Endophenotype for Schizophrenia

The notion of dysconnectivity in schizophrenia has been put forward for many years and results in substantial attempts to explore altered functional connectivity (FC) within different networks with inconsistent results. Clinical, demographical, and methodological heterogeneity may contribute to the inconsistency. Forty-four patients with first-episode, drug-naive schizophrenia, 42 unaffected siblings of schizophrenia patients and 44 healthy controls took part in this study. Global-brain FC (GFC) was employed to analyze the imaging data. Compared with healthy controls, patients with schizophrenia and unaffected siblings shared enhanced GFC in the left superior frontal gyrus (SFG). In addition, patients had increased GFC mainly in the thalamo-cortical network, including the bilateral thalamus, bilateral posterior cingulate cortex (PCC)/precuneus, left superior medial prefrontal cortex (MPFC), right angular gyrus, and right SFG/middle frontal gyrus and decreased GFC in the left ITG/cerebellum Crus I. No other altered GFC values were observed in the siblings group relative to the control group. Further ROC analysis showed that increased GFC in the left SFG could separate the patients or the siblings from the controls with acceptable sensitivities. Our findings suggest that increased GFC in the left SFG may serve as a potential endophenotype for schizophrenia

TELEX HEBDOMADAIRE NR 95 DU 17.09.82 DESTINE A L'ENSEMBLE DES DELEGATIONS EXTERIEURES ET BUREAUX DE PRESS ET D'INFORMATION INDEPENDANTS DANS LES PAYS TIERS = WEEKLY MEMO NO. 95 FOR 17.09.82 TO FOREIGN DELEGATIONS AND PRESS BUREAUS OF THIRD COUNTRIES

<p>High-performance liquid chromatography (HPLC) results of (A) commercial surfactin sample, and (B) our extract surfactin of <i>B</i>. <i>subtilis</i> HH2 in LB medium. There were three main peaks (Peak A-C) of the extract and the surfactin standard in the same location.</p

Large-Scale Fabrication of Boron Nitride Nanotubes via a Facile Chemical Vapor Reaction Route and Their Cathodoluminescence Properties

Cylinder- and bamboo-shaped boron nitride nanotubes (BNNTs) have been synthesized in large scale via a facile chemical vapor reaction route using ammonia borane as a precursor. The structure and chemical composition of the as-synthesized BNNTs are extensively characterized by X-ray diffraction, scanning electron microscopy, high-resolution transmission electron microscopy, and selected-area electron diffraction. The cylinder-shaped BNNTs have an average diameter of about 100 nm and length of hundreds of microns, while the bamboo-shaped BNNTs are 100–500 nm in diameter with length up to tens of microns. The formation mechanism of the BNNTs has been explored on the basis of our experimental observations and a growth model has been proposed accordingly. Ultraviolet–visible and cathodoluminescence spectroscopic analyses are performed on the BNNTs. Strong ultraviolet emissions are detected on both morphologies of BNNTs. The band gap of the BNNTs are around 5.82 eV and nearly unaffected by tube morphology. There exist two intermediate bands in the band gap of BNNTs, which could be distinguishably assigned to structural defects and chemical impurities