Assessing and Enhancing Robustness of Deep Learning Models with Corruption Emulation in Digital Pathology

Deep learning in digital pathology brings intelligence and automation as substantial enhancements to pathological analysis, the gold standard of clinical diagnosis. However, multiple steps from tissue preparation to slide imaging introduce various image corruptions, making it difficult for deep neural network (DNN) models to achieve stable diagnostic results for clinical use. In order to assess and further enhance the robustness of the models, we analyze the physical causes of the full-stack corruptions throughout the pathological life-cycle and propose an Omni-Corruption Emulation (OmniCE) method to reproduce 21 types of corruptions quantified with 5-level severity. We then construct three OmniCE-corrupted benchmark datasets at both patch level and slide level and assess the robustness of popular DNNs in classification and segmentation tasks. Further, we explore to use the OmniCE-corrupted datasets as augmentation data for training and experiments to verify that the generalization ability of the models has been significantly enhanced

Quasi-1D graphene superlattices formed on high index surfaces

We report preparation of large area quasi-1D monolayer graphene superlattices on a prototypical high index surface Cu(410)-O and characterization by Raman spectroscopy, Auger electron spectroscopy (AES), low energy electron diffraction (LEED), scanning tunneling microscopy (STM) and scanning tunneling spectroscopy (STS). The periodically stepped substrate gives a 1D modulation to graphene, forming a superlattice of the same super-periodicity. Consequently the moire pattern is also quasi-1D, with a different periodicity. Scanning tunneling spectroscopy measurements revealed new Dirac points formed at the superlattice Brillouin zone boundary as predicted by theories.Comment: 4 figure

Mobile phone addiction and mental health: the roles of sleep quality and perceived social support

As a global phenomenon, mobile phone addiction has become an increasingly common issue among Chinese university students. Although previous research explored the link between mobile phone addiction and mental health, the possible mechanism underlying the above association is unclear. We administered a cross-sectional survey to 585 participants from two universities in Kunming, southwest China, from October 2021 to January 2022. Our results suggested that mobile phone addiction was negatively associated with mental health, and sleep quality partially mediated the relationship between mobile phone addiction and mental health. Furthermore, perceived social support positively moderated the direct effect of sleep quality on mental health, as well as the indirect effect of mobile phone addiction on mental health. These findings provide a new insight into the underlying mechanism by which mobile phone addiction affects university students’ mental health. The results emphasize a necessary task for administrators, health workers, and family members to attach importance to the overuse of mobile phones among university students

Unified and Dynamic Graph for Temporal Character Grouping in Long Videos

Video temporal character grouping locates appearing moments of major characters within a video according to their identities. To this end, recent works have evolved from unsupervised clustering to graph-based supervised clustering. However, graph methods are built upon the premise of fixed affinity graphs, bringing many inexact connections. Besides, they extract multi-modal features with kinds of models, which are unfriendly to deployment. In this paper, we present a unified and dynamic graph (UniDG) framework for temporal character grouping. This is accomplished firstly by a unified representation network that learns representations of multiple modalities within the same space and still preserves the modality's uniqueness simultaneously. Secondly, we present a dynamic graph clustering where the neighbors of different quantities are dynamically constructed for each node via a cyclic matching strategy, leading to a more reliable affinity graph. Thirdly, a progressive association method is introduced to exploit spatial and temporal contexts among different modalities, allowing multi-modal clustering results to be well fused. As current datasets only provide pre-extracted features, we evaluate our UniDG method on a collected dataset named MTCG, which contains each character's appearing clips of face and body and speaking voice tracks. We also evaluate our key components on existing clustering and retrieval datasets to verify the generalization ability. Experimental results manifest that our method can achieve promising results and outperform several state-of-the-art approaches

Boron Nitride Nanotube Films Grown From Boron Ink Painting

The growth of nanotube films on various substrates and surfaces is vital for applications in nanoscale functional devices. We report a simple and versatile boron (B) ink painting method that enables high-density boron nitride nanotube (BNNT) films with any desired pattern to be grown on, and firmly attached to, different surfaces. In the method, special B ink is first painted, sprayed or inkjet printed at desired location with required pattern, and then the ink layer is annealed in a nitrogen-containing atmosphere to form BNNT film. The B ink is a liquid mixture of ball-milled B particles, metal nitrate and ethanol. This is the first method capable of growing BNNTs on complex non-flat surfaces, which greatly broadens the potential application of BNNTs. For example, it is demonstrated that a BNNT coated steel mesh can separate water and oil on a microliter scale; a needle given an internal BNNT coating could greatly enhance microfluidic transport; and a coated screw could be used to minimize wear at the interface.Comment: 10 pages, 6 figure

Validating quantum-supremacy experiments with exact and fast tensor network contraction

The quantum circuits that declare quantum supremacy, such as Google Sycamore [Nature \textbf{574}, 505 (2019)], raises a paradox in building reliable result references. While simulation on traditional computers seems the sole way to provide reliable verification, the required run time is doomed with an exponentially-increasing compute complexity. To find a way to validate current ``quantum-supremacy" circuits with more than $50$ qubits, we propose a simulation method that exploits the ``classical advantage" (the inherent ``store-and-compute" operation mode of von Neumann machines) of current supercomputers, and computes uncorrelated amplitudes of a random quantum circuit with an optimal reuse of the intermediate results and a minimal memory overhead throughout the process. Such a reuse strategy reduces the original linear scaling of the total compute cost against the number of amplitudes to a sublinear pattern, with greater reduction for more amplitudes. Based on a well-optimized implementation of this method on a new-generation Sunway supercomputer, we directly verify Sycamore by computing three million exact amplitudes for the experimentally generated bitstrings, obtaining an XEB fidelity of $0.191\%$ which closely matches the estimated value of $0.224\%$. Our computation scales up to $41,932,800$ cores with a sustained single-precision performance of $84.8$ Pflops, which is accomplished within $8.5$ days. Our method has a far-reaching impact in solving quantum many-body problems, statistical problems as well as combinatorial optimization problems where one often needs to contract many tensor networks which share a significant portion of tensors in common.Comment: 7 pages, 4 figures, comments are welcome

MTA3 Represses Cancer Stemness by Targeting the SOX2OT/SOX2 Axis

Cancer cell stemness (CCS) plays critical roles in both malignancy maintenance and metastasis, yet the underlying molecular mechanisms are far from complete. Although the importance of SOX2 in cancer development and CCS are well recognized, the role of MTA3 in these processes is unknown. In this study, we used esophageal squamous cell carcinoma (ESCC) as a model system to demonstrate that MTA3 can repress both CCS and metastasis in vitro and in vivo. Mechanistically, by forming a repressive complex with GATA3, MTA3 downregulates SOX2OT, subsequently suppresses the SOX2OT/SOX2 axis, and ultimately represses CCS and metastasis. More importantly, MTA

MTA3 Represses Cancer Stemness by Targeting the SOX2OT/SOX2 Axis

Cancer cell stemness (CCS) plays critical roles in both malignancy maintenance and metastasis, yet the underlying molecular mechanisms are far from complete. Although the importance of SOX2 in cancer development and CCS are well recognized, the role of MTA3 in these processes is unknown. In this study, we used esophageal squamous cell carcinoma (ESCC) as a model system to demonstrate that MTA3 can repress both CCS and metastasis in vitro and in vivo. Mechanistically, by forming a repressive complex with GATA3, MTA3 downregulates SOX2OT, subsequently suppresses the SOX2OT/SOX2 axis, and ultimately represses CCS and metastasis. More importantly, MTA