Fragilities of Liquids Predicted from the Random First Order Transition Theory of Glasses

A microscopically motivated theory of glassy dynamics based on an underlying random first order transition is developed to explain the magnitude of free energy barriers for glassy relaxation. A variety of empirical correlations embodied in the concept of liquid "fragility" are shown to be quantitatively explained by such a model. The near universality of a Lindemann ratio characterizing the maximal amplitude of thermal vibrations within an amorphous minimum explains the variation of fragility with a liquid's configurational heat capacity density. Furthermore the numerical prefactor of this correlation is well approximated by the microscopic calculation. The size of heterogeneous reconfiguring regions in a viscous liquid is inferred and the correlation of nonexponentiality of relaxation with fragility is qualitatively explained. Thus the wide variety of kinetic behavior in liquids of quite disparate chemical nature reflects quantitative rather than qualitative differences in their energy landscapes.Comment: 10 pages including 4 eps figure

An End-to-End Framework For Universal Lesion Detection With Missing Annotations

Fully annotated large-scale medical image datasets are highly valuable. However, because labeling medical images is tedious and requires specialized knowledge, the large-scale datasets available often have missing annotation issues. For instance, DeepLesion, a large-scale CT image dataset with labels for various kinds of lesions, is reported to have a missing annotation rate of 50\%. Directly training a lesion detector on it would suffer from false negative supervision caused by unannotated lesions. To address this issue, previous works have used sophisticated multi-stage strategies to switch between lesion mining and detector training. In this work, we present a novel end-to-end framework for mining unlabeled lesions while simultaneously training the detector. Our framework follows the teacher-student paradigm. In each iteration, the teacher model infers the input data and creates a set of predictions. High-confidence predictions are combined with partially-labeled ground truth for training the student model. On the DeepLesion dataset, using the original partially labeled training set, our model can outperform all other more complicated methods and surpass the previous best method by 2.3\% on average sensitivity and 2.7\% on average precision, achieving state-of-the-art universal lesion detection results

Bulk Strong Matter: the Trinity

Our world is wonderful because of the normal but negligibly small baryonic part (i.e., atoms) although unknown dark matter and dark energy dominate the Universe. A stable atomic nucleus could be simply termed as ``strong matter'' since its nature is dominated by the fundamental strong interaction. Is there any other form of strong matter? Although nuclei are composed of 2-flavoured (i.e., up and down flavours of valence quarks) nucleons, it is conjectured that bulk strong matter could be 3-flavoured (with additional strange quarks) if the baryon number exceeds the critical value, $A_{\rm c}$, in which case quarks could be either free (so-called strange quark matter) or localized (in strangeons, coined by combining ``strange nucleon''). Bulk strong matter could be manifested in the form of compact stars, cosmic rays, and even dark matter. This trinity will be explained in this brief review, that may impact dramatically on today's physics, particularly in the era of multi-messenger astronomy after the discovery of gravitational wave.Comment: 12 pages, 2 figures. Accepted by Advances in Physics:

A Service Restoration Method for Active Distribution Network

AbstractFor a large scale of distributed generations being connected to the power distribution network, the traditional service restoration methods cannot meet the demand of the distributed generation's large access which facing significant challenges. Service restoration of active distribution network (ADN) is a multi-objective, multiple-constraint, and complex optimization problem. Considering the user priority level, the load amounts restored, the counts of switch operation, the network loss after the power restoration, and the operation of power sources, this article establishes a restoration model based on grid actual situation, which is more realistic for the ADN. As a different dimension of different objective, this article proposes the generalized model in order to compare those solutions conveniently, the paper uses genetic algorithm to get recovery scheme. Results of case study show that the proposed model is effective

Granular-ball computing: an efficient, robust, and interpretable adaptive multi-granularity representation and computation method

Human cognition operates on a "Global-first" cognitive mechanism, prioritizing information processing based on coarse-grained details. This mechanism inherently possesses an adaptive multi-granularity description capacity, resulting in computational traits such as efficiency, robustness, and interpretability. The analysis pattern reliance on the finest granularity and single-granularity makes most existing computational methods less efficient, robust, and interpretable, which is an important reason for the current lack of interpretability in neural networks. Multi-granularity granular-ball computing employs granular-balls of varying sizes to daptively represent and envelop the sample space, facilitating learning based on these granular-balls. Given that the number of coarse-grained "granular-balls" is fewer than sample points, granular-ball computing proves more efficient. Moreover, the inherent coarse-grained nature of granular-balls reduces susceptibility to fine-grained sample disturbances, enhancing robustness. The multi-granularity construct of granular-balls generates topological structures and coarse-grained descriptions, naturally augmenting interpretability. Granular-ball computing has successfully ventured into diverse AI domains, fostering the development of innovative theoretical methods, including granular-ball classifiers, clustering techniques, neural networks, rough sets, and evolutionary computing. This has notably ameliorated the efficiency, noise robustness, and interpretability of traditional methods. Overall, granular-ball computing is a rare and innovative theoretical approach in AI that can adaptively and simultaneously enhance efficiency, robustness, and interpretability. This article delves into the main application landscapes for granular-ball computing, aiming to equip future researchers with references and insights to refine and expand this promising theory

Tackling the Incomplete Annotation Issue in Universal Lesion Detection Task By Exploratory Training

Universal lesion detection has great value for clinical practice as it aims to detect various types of lesions in multiple organs on medical images. Deep learning methods have shown promising results, but demanding large volumes of annotated data for training. However, annotating medical images is costly and requires specialized knowledge. The diverse forms and contrasts of objects in medical images make fully annotation even more challenging, resulting in incomplete annotations. Directly training ULD detectors on such datasets can yield suboptimal results. Pseudo-label-based methods examine the training data and mine unlabelled objects for retraining, which have shown to be effective to tackle this issue. Presently, top-performing methods rely on a dynamic label-mining mechanism, operating at the mini-batch level. However, the model's performance varies at different iterations, leading to inconsistencies in the quality of the mined labels and limits their performance enhancement. Inspired by the observation that deep models learn concepts with increasing complexity, we introduce an innovative exploratory training to assess the reliability of mined lesions over time. Specifically, we introduce a teacher-student detection model as basis, where the teacher's predictions are combined with incomplete annotations to train the student. Additionally, we design a prediction bank to record high-confidence predictions. Each sample is trained several times, allowing us to get a sequence of records for each sample. If a prediction consistently appears in the record sequence, it is likely to be a true object, otherwise it may just a noise. This serves as a crucial criterion for selecting reliable mined lesions for retraining. Our experimental results substantiate that the proposed framework surpasses state-of-the-art methods on two medical image datasets, demonstrating its superior performance

A Survey on UAV-enabled Edge Computing: Resource Management Perspective

Edge computing facilitates low-latency services at the network's edge by distributing computation, communication, and storage resources within the geographic proximity of mobile and Internet-of-Things (IoT) devices. The recent advancement in Unmanned Aerial Vehicles (UAVs) technologies has opened new opportunities for edge computing in military operations, disaster response, or remote areas where traditional terrestrial networks are limited or unavailable. In such environments, UAVs can be deployed as aerial edge servers or relays to facilitate edge computing services. This form of computing is also known as UAV-enabled Edge Computing (UEC), which offers several unique benefits such as mobility, line-of-sight, flexibility, computational capability, and cost-efficiency. However, the resources on UAVs, edge servers, and IoT devices are typically very limited in the context of UEC. Efficient resource management is, therefore, a critical research challenge in UEC. In this article, we present a survey on the existing research in UEC from the resource management perspective. We identify a conceptual architecture, different types of collaborations, wireless communication models, research directions, key techniques and performance indicators for resource management in UEC. We also present a taxonomy of resource management in UEC. Finally, we identify and discuss some open research challenges that can stimulate future research directions for resource management in UEC.Comment: 36 pages, Accepted to ACM CSU

Heterogeneous firm responses to increases in high-skilled workers: Evidence from China's college enrollment expansion

Over the past several decades, the returns to college education have steadily increased in many countries of the world despite an increased supply of college graduates. In this paper, using local-labor market data on the composition of the labor force combined with detailed firm-level data covering the period of a large-scale expansion of college enrollment in China, we seek to identify within-firm adjustments to labor market changes. The empirical work is guided by a model in which there are two types of production technologies, characterized by two different types of capitals, one skill-biased and the other labor-biased. The empirical results, consistent with the model and the observed trends in schooling and rates of return, indicate that there were significant adjustments in capital and R\&D within-firms in response to an enlarged college-educated labor force