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As the advancement of data collection through internet platforms, biomedical tests and genetic sequencing, individual and heterogeneous datasets have been prevalent in science discovery. However, traditional homogeneous or population-wise statistical and machine learning tools are incapable of analyzing such complex datasets. In this thesis, we propose novel statistical models and machine learning models to incorporate heterogenous information and apply these methods in crowdsourcing, individualized treatment rules and effective data integration. In the first topic of the thesis, we propose a new statistical model to incorporate heterogeneity in crowdsourcing problems. In the past two decades, crowdsourcing has emerged as a solution for collecting large scale labels and has been impactful in many data collection procedures, such as the Imagenet. However, aggregating crowdsourced labels could be challenging due to the heterogeneity of task difficulty and worker ability. We propose a two-stage model to predict the true labels for multicategory classification tasks in crowdsourcing. In the first stage, we fit the observed labels with a latent factor model with subgrouping structures for both tasks and workers to accommodate their heterogeneity. Group-specific rotations are introduced to align workers with different task categories to solve multicategory crowdsourcing tasks. In the second stage, we propose a concordance-based approach to identify high-quality worker subgroups who are relied upon to assign labels to tasks. In theory, we show the estimation consistency of the latent factors and the prediction consistency of the proposed method. The simulation studies and real data application show that the proposed method outperforms the existing competitive methods. In the second topic, we propose a novel statistical model to learn individualized treatment rules with combination treatments. The individualized treatment rule (ITR), which recommends an optimal treatment ba

The emergence of novel the dummy data injection attack (DDIA) poses a severe threat to the secure and stable operation of power systems. These attacks are particularly perilous due to the minimal Euclidean spatial separation between the injected malicious data and legitimate data, rendering their precise detection challenging using conventional distance-based methods. Furthermore, existing research predominantly focuses on various machine learning techniques, often analyzing the temporal data sequences post-attack or relying solely on Euclidean spatial characteristics. Unfortunately, this approach tends to overlook the inherent topological correlations within the non-Euclidean spatial attributes of power grid data, consequently leading to diminished accuracy in attack localization. To address this issue, this study takes a comprehensive approach. Initially, it examines the underlying principles of these new DDIAs on power systems. Here, an intricate mathematical model of the DDIA is designed, accounting for incomplete topological knowledge and alternating current (AC) state estimation from an attacker's perspective. This model aims to mitigate the vulnerabilities associated with direct current (DC) flow-based attack data generation methods that can be readily detected. Subsequently, by integrating a priori knowledge of grid topology and considering the temporal correlations within measurement data and the topology-dependent attributes of the power grid, this study introduces temporal and spatial attention matrices. These matrices adaptively capture the spatio-temporal correlations within the attacks. Leveraging gated stacked causal convolution and graph wavelet sparse convolution, the study jointly extracts spatiotemporal DDIA features. This methodology significantly enhances the dynamic correlation mining capability while improving computational efficiency in the neighborhood. Finally, the research proposes a DDIA localization method based on spatio-temporal graph

Creating 3D textured meshes using generative artificial intelligence has garnered significant attention recently. While existing methods support text-based generative texture generation or editing on 3D meshes, they often struggle to precisely control pixels of texture images through more intuitive interaction. While 2D images can be edited generatively using drag interaction, applying this type of methods directly to 3D mesh textures still leads to issues such as the lack of local consistency among multiple views, error accumulation and long training times. To address these challenges, we propose a generative point-based 3D mesh texture editing method called DragTex. This method utilizes a diffusion model to blend locally inconsistent textures in the region near the deformed silhouette between different views, enabling locally consistent texture editing. Besides, we fine-tune a decoder to reduce reconstruction errors in the non-drag region, thereby mitigating overall error accumulation. Moreover, we train LoRA using multi-view images instead of training each view individually, which significantly shortens the training time. The experimental results show that our method effectively achieves dragging textures on 3D meshes and generates plausible textures that align with the desired intent of drag interaction.

Spiking neural networks (SNNs) serve as one type of efficient model to process spatio-temporal patterns in time series, such as the Address-Event Representation data collected from Dynamic Vision Sensor (DVS). Although convolutional SNNs have achieved remarkable performance on these AER datasets, benefiting from the predominant spatial feature extraction ability of convolutional structure, they ignore temporal features related to sequential time points. In this paper, we develop a recurrent spiking neural network (RSNN) model embedded with an advanced spiking convolutional block attention module (SCBAM) component to combine both spatial and temporal features of spatio-temporal patterns. It invokes the history information in spatial and temporal channels adaptively through SCBAM, which brings the advantages of efficient memory calling and history redundancy elimination. The performance of our model was evaluated in DVS128-Gesture dataset and other time-series datasets. The experimental results show that the proposed SRNN-SCBAM model makes better use of the history information in spatial and temporal dimensions with less memory space, and achieves higher accuracy compared to other models.

Multi-modal large language models have demonstrated impressive performance across various tasks in different modalities. However, existing multi-modal models primarily emphasize capturing global information within each modality while neglecting the importance of perceiving local information across modalities. Consequently, these models lack the ability to effectively understand the fine-grained details of input data, limiting their performance in tasks that require a more nuanced understanding. To address this limitation, there is a compelling need to develop models that enable fine-grained understanding across multiple modalities, thereby enhancing their applicability to a wide range of tasks. In this paper, we propose GroundingGPT, a language enhanced multi-modal grounding model. Beyond capturing global information like other multi-modal models, our proposed model excels at tasks demanding a detailed understanding of local information within the input. It demonstrates precise identification and localization of specific regions in images or moments in videos. To achieve this objective, we design a diversified dataset construction pipeline, resulting in a multi-modal, multi-granularity dataset for model training. The code, dataset, and demo of our model can be found at https: //github.com/lzw-lzw/GroundingGPT.

The rapid proliferation of Large Language Models (LLMs) has been a driving force in the growth of cloud-based LLM services, which are now integral to advancing AI applications. However, the dynamic auto-regressive nature of LLM service, along with the need to support exceptionally long context lengths, demands the flexible allocation and release of substantial resources. This presents considerable challenges in designing cloud-based LLM service systems, where inefficient management can lead to performance degradation or resource wastage. In response to these challenges, this work introduces DistAttention, a novel distributed attention algorithm that segments the KV Cache into smaller, manageable units, enabling distributed processing and storage of the attention module. Based on that, we propose DistKV-LLM, a distributed LLM serving system that dynamically manages KV Cache and effectively orchestrates all accessible GPU and CPU memories spanning across the data center. This ensures a high-performance LLM service on the cloud, adaptable to a broad range of context lengths. Validated in a cloud environment with 32 NVIDIA A100 GPUs in configurations from 2 to 32 instances, our system exhibited 1.03-2.4x end-to-end throughput improvements and supported context lengths 2-19x longer than current state-of-the-art LLM service systems, as evidenced by extensive testing across 18 datasets with context lengths up to 1,900K.

The emergence of deep and large-scale spiking neural networks (SNNs) exhibiting high performance across diverse complex datasets has led to a need for compressing network models due to the presence of a significant number of redundant structural units, aiming to more effectively leverage their low-power consumption and biological interpretability advantages. Currently, most model compression techniques for SNNs are based on unstructured pruning of individual connections, which requires specific hardware support. Hence, we propose a structured pruning approach based on the activity levels of convolutional kernels named Spiking Channel Activity-based (SCA) network pruning framework. Inspired by synaptic plasticity mechanisms, our method dynamically adjusts the network's structure by pruning and regenerating convolutional kernels during training, enhancing the model's adaptation to the current target task. While maintaining model performance, this approach refines the network architecture, ultimately reducing computational load and accelerating the inference process. This indicates that structured dynamic sparse learning methods can better facilitate the application of deep SNNs in low-power and high-efficiency scenarios.

Humans learn multiple tasks in succession with minimal mutual interference, through the context gating mechanism in the prefrontal cortex (PFC). The brain-inspired models of spiking neural networks (SNN) have drawn massive attention for their energy efficiency and biological plausibility. To overcome catastrophic forgetting when learning multiple tasks in sequence, current SNN models for lifelong learning focus on memory reserving or regularization-based modification, while lacking SNN to replicate human experimental behavior. Inspired by biological context-dependent gating mechanisms found in PFC, we propose SNN with context gating trained by the local plasticity rule (CG-SNN) for lifelong learning. The iterative training between global and local plasticity for task units is designed to strengthen the connections between task neurons and hidden neurons and preserve the multi-task relevant information. The experiments show that the proposed model is effective in maintaining the past learning experience and has better task-selectivity than other methods during lifelong learning. Our results provide new insights that the CG-SNN model can extend context gating with good scalability on different SNN architectures with different spike-firing mechanisms. Thus, our models have good potential for parallel implementation on neuromorphic hardware and model human's behavior.

Deploying large language models (LLMs) poses challenges in terms of resource limitations and inference efficiency. To address these challenges, recent research has focused on using smaller task-specific language models, which are enhanced by distilling the knowledge rationales generated by LLMs. However, previous works mostly emphasize the effectiveness of positive knowledge, while overlooking the knowledge noise and the exploration of negative knowledge. In this paper, we first propose a general approach called quality-guided contrastive rationale distillation for reasoning capacity learning, considering contrastive learning perspectives. For the learning of positive knowledge, we collect positive rationales through self-consistency to denoise the LLM rationales generated by temperature sampling. For the negative knowledge distillation, we generate negative rationales using temperature sampling for the iteration-before smaller language models themselves. Finally, a contrastive loss is designed to better distill the positive and negative rationales into the smaller language model, where an online-update discriminator is used to judge the qualities of rationales and assign weights for better optimizing the training process. Through extensive experiments on multiple reasoning tasks, we demonstrate that our method consistently outperforms the previous distillation methods and produces higher-quality rationales.

Magnetic levitation based on the flux pinning nature of type II superconductors has the merit of self-stability, making it appealing for applications such as high speed bearings, maglev trains, space generators, etc. However, such levitation systems physically rely on the superconductor pre-capturing magnetic flux (i.e. field cooling process) before establishing the levitation state which is nonadjustable afterwards. Moreover, practical type II superconductors in the levitation system inevitably suffer from various sources of energy losses, leading to continuous levitation force decay. These intrinsic drawbacks make superconducting maglev inflexible and impractical for long term operation. Here we propose and demonstrate a new form of superconducting maglev which is tunable and with self-stability. The maglev system uses a closed-loop type II superconducting coil to lock flux of a magnet, establishing self-stable levitation between the two objects. A flux pump is used to modulate the total magnetic flux of the coil without breaking its superconductivity, thus flexibly tuning levitation force and height meanwhile maintaining self-stability. For the first time, we experimentally demonstrate a self-stable type II superconducting maglev system which is able to: counteract long term levitation force decay, adjust levitation force and equilibrium position, and establish levitation under zero field cooling condition. These breakthroughs may bridge the gap between demonstrations and practical applications of type II superconducting maglevs., Comment: 15pages,5 figures

Spiking Neural Networks (SNNs) aim to bridge the gap between neuroscience and machine learning by emulating the structure of the human nervous system. However, like convolutional neural networks, SNNs are vulnerable to adversarial attacks. To tackle the challenge, we propose a biologically inspired methodology to enhance the robustness of SNNs, drawing insights from the visual masking effect and filtering theory. First, an end-to-end SNN-based image purification model is proposed to defend against adversarial attacks, including a noise extraction network and a non-blind denoising network. The former network extracts noise features from noisy images, while the latter component employs a residual U-Net structure to reconstruct high-quality noisy images and generate clean images. Simultaneously, a multi-level firing SNN based on Squeeze-and-Excitation Network is introduced to improve the robustness of the classifier. Crucially, the proposed image purification network serves as a pre-processing module, avoiding modifications to classifiers. Unlike adversarial training, our method is highly flexible and can be seamlessly integrated with other defense strategies. Experimental results on various datasets demonstrate that the proposed methodology outperforms state-of-the-art baselines in terms of defense effectiveness, training time, and resource consumption., Comment: 8 pages, 5 figures, ECAI2024 under review

The cotton bollworm, Helicoverpa armigera, is set to become the most economically devastating crop pest in the world, threatening food security and bio-safety as its range expands across the globe. Key to understanding the eco-evolutionary dynamics of H. armigera, and thus its management, is an understanding of population connectivity and the adaptations that allow the pest to establish in unique environments. We assembled a chromosome-scale reference genome and re-sequenced 503 individuals spanning the species range to delineate global patterns of connectivity, uncovering previously cryptic population structure. Using GWAS and cell line expression of major effect loci, we show that adaptive changes in a temperature- and light-sensitive developmental pathway enable facultative diapause, and that adaptation of trehalose synthesis and transport underlies cold tolerance in extreme environments. Incorporating extensive pesticide resistance monitoring, we also characterize a suite of novel pesticide and Bt resistance alleles under selection in East China. These findings offer avenues for more effective management strategies and provide insight into how insects adapt to variable climatic conditions and newly colonized environments.

Bowen Li,* Jinghang Liu,* Liangzhi Xu, Qi Xu, Zhaohui Liu, Tiande Liu Department of General Surgery, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, 330000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Tiande Liu, Departments of General Surgery, Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, 330000, People’s Republic of China, Tel +8613479101447, Email liutd123@163.comBackground: The DNA binding protein NABP2 (nucleic acid binding protein 2) is a member of the SSB (single-stranded DNA-binding) protein family, which is involved in DNA damage repair. Its prognostic significance and relationship with immune infiltration in hepatocellular carcinoma (HCC), however, remain unknown.Methods: The purpose of this study was to estimate the prognostic value of NABP2 and to investigate its possible immune function in HCC. By applying multiple bioinformatics methods, we gathered and analysed data from The Cancer Genome Atlas (TCGA), Cancer Cell Lineage Encyclopedia (CCLE), and Gene Expression Omnibus (GEO) to investigate the potential oncogenic and cancer-promoting role of NABP2, including the differential expression, prognostic value, immune cell infiltration association, and drug sensitivity of NABP2 in HCC. Immunohistochemistry and Western blotting were used to validate the expression of NABP2 in HCC. The knockdown of NABP2 expression by siRNA was further used to validate its role in hepatocellular carcinoma.Results: Our findings indicated that NABP2 was overexpressed in HCC samples and was related to poor survival, clinical stage, and tumour grade in HCC patients. Analysis of functional enrichment indicated that NABP2 was potentially involved in the cell cycle, DNA replication, G2M checkpoint, E2F targets, apoptosis, P53 signalling, TGFA signalling via NF-κB, and so on. NABP2 was shown to be significantly linked to immune cell infiltration an

Hua-Xi Zou,1,2,* Tie Hu,1,2,* Jia-Yi Zhao,2,3,* Bai-Quan Qiu,2,4 Chen-Chao Zou,2,4 Qi-Rong Xu,1 Ji-Chun Liu,1,2 Song-Qing Lai,1,2 Huang Huang1,2 1Department of Cardiovascular Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China; 2Institute of Cardiovascular Diseases, Jiangxi Academy of Clinical Medical Sciences, The First Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China; 3Medical Innovation Experimental Program, Huan Kui College, Nanchang University, Nanchang, People’s Republic of China; 4Department of Cardiovascular Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Huang Huang; Song-Qing Lai, Email jack19871212@hotmail.com; ndyfy03743@ncu.edu.cnIntroduction: Sepsis is currently a common condition in emergency and intensive care units, and is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Cardiac dysfunction caused by septic myocardial injury (SMI) is associated with adverse prognosis and has significant economic and human costs. The pathophysiological mechanisms underlying SMI have long been a subject of interest. Recent studies have identified ferroptosis, a form of programmed cell death associated with iron accumulation and lipid peroxidation, as a pathological factor in the development of SMI. However, the current understanding of how ferroptosis functions and regulates in SMI remains limited, particularly in the absence of direct evidence from human heart.Methods: We performed a sequential comprehensive bioinformatics analysis of human sepsis cardiac transcriptome data obtained through the GEO database. The lipopolysaccharide-induced mouse SMI model was used to validate the ferroptosis features and transcriptional expression of key genes.Results: We identified widespread dysregulati

Dengming Lai,1,* Kun Zhu,2,* Sisi Li,3,* Yi Xiao,1 Qi Xu,4 Yisheng Sun,5 Pingping Yao,5 Daqing Ma,6,7 Qiang Shu7 1Department of Neonatal Surgery, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, People’s Republic of China; 2Department of Pathology, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, People’s Republic of China; 3School of Medicine, Hangzhou City University, Hangzhou, People’s Republic of China; 4School of Laboratory Medicine and Bioengineering, Hangzhou Medical College, Hangzhou, People’s Republic of China; 5Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, People’s Republic of China; 6Division of Anaesthetics, Pain Medicine & Intensive Care, Department of Surgery & Cancer, Faculty of Medicine, Imperial College London, Chelsea & Westminster Hospital, London, UK; 7Department of Thoracic and Cardiovascular Surgery, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qiang Shu, Department of Thoracic and Cardiovascular Surgery, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310052, People’s Republic of China, Tel +86-13906500193, Email shuqiang@zju.edu.cn Dengming Lai, Department of Neonatal Surgery, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310052, People’s Republic of China, Tel +86-13567164728, Email dengming_lai@zju.edu.cnBackground: SARS-CoV-2-induced acute lung injury but its nucleocapsid (N) and/or Spike (S) protein involvements in the disease pathology remain elusive.Methods: In vitro, t

The superradiant instabilities of Kerr-Newman black holes with charged or uncharged massive spin-0 fields are calculated analytically to the next-to-leading order in the limit of $\alpha\sim r_g \mu \ll 1$. A missing factor of $1/2$ in the previous leading-order result is identified. The next-to-leading order result has a compact form and is in good agreement with existing numerical calculations. The percentage error increases with $\alpha$, from a few percent for $\alpha\sim 0.1$ to about $50\%$ for $\alpha\sim 0.4$. Massive neutral scalars too heavy to be produced with Kerr black hole superradiance may exist in the superradiant region of Kerr-Newman black holes., Comment: 11 pages, 5 figures, 2 tables. Correction: The third paragraph of Sec. III A contained errors and has been replaced, along with Eq. (27). Matches Physical Review D version

Unlike PIWI-interacting RNA (piRNA) in other species that mostly target transposable elements (TEs), >80% of piRNAs in adult mammalian testes lack obvious targets. However, mammalian piRNA sequences and piRNA-producing loci evolve more rapidly than the rest of the genome for unknown reasons. Here, through comparative studies of chickens, ducks, mice, and humans, as well as long-read nanopore sequencing on diverse chicken breeds, we find that piRNA loci across amniotes experience: (1) a high local mutation rate of structural variations (SVs, mutations ≥ 50 bp in size); (2) positive selection to suppress young and actively mobilizing TEs commencing at the pachytene stage of meiosis during germ cell development; and (3) negative selection to purge deleterious SV hotspots. Our results indicate that genetic instability at pachytene piRNA loci, while producing certain pathogenic SVs, also protects genome integrity against TE mobilization by driving the formation of rapid-evolving piRNA sequences.

Spiking neural networks (SNNs) have manifested remarkable advantages in power consumption and event-driven property during the inference process. To take full advantage of low power consumption and improve the efficiency of these models further, the pruning methods have been explored to find sparse SNNs without redundancy connections after training. However, parameter redundancy still hinders the efficiency of SNNs during training. In the human brain, the rewiring process of neural networks is highly dynamic, while synaptic connections maintain relatively sparse during brain development. Inspired by this, here we propose an efficient evolutionary structure learning (ESL) framework for SNNs, named ESL-SNNs, to implement the sparse SNN training from scratch. The pruning and regeneration of synaptic connections in SNNs evolve dynamically during learning, yet keep the structural sparsity at a certain level. As a result, the ESL-SNNs can search for optimal sparse connectivity by exploring all possible parameters across time. Our experiments show that the proposed ESL-SNNs framework is able to learn SNNs with sparse structures effectively while reducing the limited accuracy. The ESL-SNNs achieve merely 0.28% accuracy loss with 10% connection density on the DVS-Cifar10 dataset. Our work presents a brand-new approach for sparse training of SNNs from scratch with biologically plausible evolutionary mechanisms, closing the gap in the expressibility between sparse training and dense training. Hence, it has great potential for SNN lightweight training and inference with low power consumption and small memory usage.

With the widespread use of deep neural networks(DNNs) in intelligent systems, DNN accelerators with high performance and energy efficiency are greatly demanded. As one of the feasible processing-in-memory(PIM) architectures, 3D-stacked-DRAM-based PIM(DRAM-PIM) architecture enables large-capacity memory and low-cost memory access, which is a promising solution for DNN accelerators with better performance and energy efficiency. However, the low-cost characteristics of stacked DRAM and the distributed manner of memory access and data storing require us to rebalance the hardware design and DNN mapping. In this paper, we propose NicePIM to efficiently explore the design space of hardware architecture and DNN mapping of DRAM-PIM accelerators, which consists of three key components: PIM-Tuner, PIM-Mapper and Data-Scheduler. PIM-Tuner optimizes the hardware configurations leveraging a DNN model for classifying area-compliant architectures and a deep kernel learning model for identifying better hardware parameters. PIM-Mapper explores a variety of DNN mapping configurations, including parallelism between branches of DNN, DNN layer partitioning, DRAM capacity allocation and data layout pattern in DRAM to generate high-hardware-utilization DNN mapping schemes for various hardware configurations. The Data-Scheduler employs an integer-linear-programming-based data scheduling algorithm to alleviate the inter-PIM-node communication overhead of data-sharing brought by DNN layer partitioning. Experimental results demonstrate that NicePIM can optimize hardware configurations for DRAM-PIM systems effectively and can generate high-quality DNN mapping schemes with latency and energy cost reduced by 37% and 28% on average respectively compared to the baseline method., Comment: 13 pages

Spiking neural networks (SNNs) have superb characteristics in sensory information recognition tasks due to their biological plausibility. However, the performance of some current spiking-based models is limited by their structures which means either fully connected or too-deep structures bring too much redundancy. This redundancy from both connection and neurons is one of the key factors hindering the practical application of SNNs. Although Some pruning methods were proposed to tackle this problem, they normally ignored the fact the neural topology in the human brain could be adjusted dynamically. Inspired by this, this paper proposed an evolutionary-based structure construction method for constructing more reasonable SNNs. By integrating the knowledge distillation and connection pruning method, the synaptic connections in SNNs can be optimized dynamically to reach an optimal state. As a result, the structure of SNNs could not only absorb knowledge from the teacher model but also search for deep but sparse network topology. Experimental results on CIFAR100 and DVS-Gesture show that the proposed structure learning method can get pretty well performance while reducing the connection redundancy. The proposed method explores a novel dynamical way for structure learning from scratch in SNNs which could build a bridge to close the gap between deep learning and bio-inspired neural dynamics.

Spiking neural networks (SNNs) are well known as the brain-inspired models with high computing efficiency, due to a key component that they utilize spikes as information units, close to the biological neural systems. Although spiking based models are energy efficient by taking advantage of discrete spike signals, their performance is limited by current network structures and their training methods. As discrete signals, typical SNNs cannot apply the gradient descent rules directly into parameters adjustment as artificial neural networks (ANNs). Aiming at this limitation, here we propose a novel method of constructing deep SNN models with knowledge distillation (KD) that uses ANN as teacher model and SNN as student model. Through ANN-SNN joint training algorithm, the student SNN model can learn rich feature information from the teacher ANN model through the KD method, yet it avoids training SNN from scratch when communicating with non-differentiable spikes. Our method can not only build a more efficient deep spiking structure feasibly and reasonably, but use few time steps to train whole model compared to direct training or ANN to SNN methods. More importantly, it has a superb ability of noise immunity for various types of artificial noises and natural signals. The proposed novel method provides efficient ways to improve the performance of SNN through constructing deeper structures in a high-throughput fashion, with potential usage for light and efficient brain-inspired computing of practical scenarios.

Driven by the term "the arrow of time" as a general topic, the article develops a musical discussion by referring to the etymological origin of the term: philosophy (epistemology) and physics (thermodynamics). In particular, the article explores two specific conditions: distinguishability and unique orientability, from which the article derives respective musical propositions and case studies. For the distinguishability condition, the article focuses on the "recurrence" in music and tries to interpret Bach's Christmas Oratorio from the perspective of "birth/resurrection". For the unique orientability condition, the article discusses the process of delaying the climax, thereby proposing "AB-AAB left-replication" model, implying an organicist view by treating the temporal structure of music (e.g. form) as the product of a dynamic process: organic growth.

In recent years, analog circuits have received extensive attention and are widely used in many emerging applications. The high demand for analog circuits necessitates shorter circuit design cycles. To achieve the desired performance and specifications, various geometrical symmetry constraints must be carefully considered during the analog layout process. However, the manual labeling of these constraints by experienced analog engineers is a laborious and time-consuming process. To handle the costly runtime issue, we propose a graph-based learning framework to automatically extract symmetric constraints in analog circuit layout. The proposed framework leverages the connection characteristics of circuits and the devices' information to learn the general rules of symmetric constraints, which effectively facilitates the extraction of device-level constraints on circuit netlists. The experimental results demonstrate that compared to state-of-the-art symmetric constraint detection approaches, our framework achieves higher accuracy and F1-score., Comment: 11 pages,10 figures, 6 tables, 1 algorithm

There are many biological molecules in cells, including DNA, RNA and protein. Their expression levels can reflect the current statuses of cells and the changes in their expression patterns are associated with developmental as well as disease progression pathways. More importantly, these molecules can function as valuable biomarkers in the clinical and laboratory setting. To detect these molecules, a variety of methods have been developed. To assay large quantities and types of mRNA transcripts, RNA-sequencing is often used where dissociated cells or tissue are extracted in bulk and their contents are processed and sequenced via NGS platforms. Unfortunately, these tools only show expression changes on average. In addition, these bulk analyses methods do not provide important spatial information which can elucidate processes where expression patterns are closely related to its location, e.g. cell-cell communication. Therefore, tools which can spatially profile expression level of genes within the original context of a cell are continued to harbor interest in the clinical and research setting. For many years, fluorescence in situ hybridization (FISH) has been widely used for this application. In situ hybridization (ISH) is based on the specific binding between the target and its probes. The application of fluorescence probes helps to label the biomarker of interest and allows them to be observed and analyzed under a fluorescence microscope. Currently, many commercial RNA-FISH platforms exist but costs thousands of dollars to profile a small panel of genes for a small set of samples. Few pipelines tools which utilizes alternative cost-effective labeling strategies exist. The objective of this work is to develop a well-defined RNA-FISH platform which provides many guidelines for design of probe structure, blocking buffer, reagents, etc. We first constructed HEK293T cells that stably express mNeonGreen protein and validated the hybridization efficiency of the designed pro

We introduce a novel weighted least squares approach to estimate daily realized covariation and microstructure noise variance using high-frequency data. We provide an asymptotic theory and conduct a comprehensive Monte Carlo simulation to demonstrate the desirable statistical properties of the new estimator, compared with existing estimators in the literature. Using high-frequency data of 27 DJIA constituting stocks over a period from 2014 to 2020, we confirm that the new estimator performs well in comparison with existing estimators. We also show that the noise variance extracted based on our method can be used to improve volatility forecasting and asset allocation performance.

Multiplexed mRNA profiling in the spatial context provides new information enabling basic research and clinical applications. Unfortunately, existing spatial transcriptomics methods are limited due to either low multiplexing or complexity. Here, we introduce a spatialomics technology, termed Multi Omic Single-scan Assay with Integrated Combinatorial Analysis (MOSAICA), that integrates in situ labeling of mRNA and protein markers in cells or tissues with combinatorial fluorescence spectral and lifetime encoded probes, spectral and time-resolved fluorescence imaging, and machine learning-based decoding. We demonstrate MOSAICA's multiplexing scalability in detecting 10-plex targets in fixed colorectal cancer cells using combinatorial labeling of five fluorophores with facile error-detection and removal of autofluorescence. MOSAICA's analysis is strongly correlated with sequencing data (Pearson's r = 0.96) and was further benchmarked using RNAscopeTM and LGC StellarisTM. We further apply MOSAICA for multiplexed analysis of clinical melanoma Formalin-Fixed Paraffin-Embedded (FFPE) tissues. We finally demonstrate simultaneous co-detection of protein and mRNA in cancer cells.

We found a mistake in the widely-used black hole superradiance solution proposed by Detweiler. After the correction and adding the NLO contribution, an improved analytic solution is obtained, which has a compact form and an excellent agreement with the numerical results. This improved solution could be important in the quantitative study of superradiance and axion-like particles., Comment: 5 pages, 3 figures, comments are welcome

During the main COVID-19 global pandemic lockdown period of 2020 an impromptu set of pollination ecologists came together via social media and personal contacts to carry out standardised surveys of the flower visits and plants in gardens. The surveys involved 67 rural, suburban and urban gardens, of various sizes, ranging from 61.18° North in Norway to 37.96° South in Australia, resulting in a data set of 25,174 rows, with each row being a unique interaction record for that date/site/plant species, and comprising almost 47,000 visits to flowers, as well as records of flowers that were not visited by pollinators, for over 1,000 species and varieties belonging to more than 460 genera and 96 plant families. The more than 650 species of flower visitors belong to 12 orders of invertebrates and four of vertebrates. In this first publication from the project, we present a brief description of the data and make it freely available for any researchers to use in the future, the only restriction being that they cite this paper in the first instance. The data generated from these global surveys will provide scientific evidence to help us understand the role that private gardens (in urban, rural and suburban areas) can play in conserving insect pollinators and identify management actions to enhance their potential.

Modern field programmable gate array(FPGA) can be partially dynamically reconfigurable with heterogeneous resources distributed on the chip. And FPGA-based partially dynamically reconfigurable system(FPGA-PDRS) can be used to accelerate computing and improve computing flexibility. However, the traditional design of FPGA-PDRS is based on manual design. Implementing the automation of FPGA-PDRS needs to solve the problems of task modules partitioning, scheduling, and floorplanning on heterogeneous resources. Existing works only partly solve problems for the automation process of FPGA-PDRS or model homogeneous resource for FPGA-PDRS. To better solve the problems in the automation process of FPGA-PDRS and narrow the gap between algorithm and application, in this paper, we propose a complete workflow including three parts, pre-processing to generate the list of task modules candidate shapes according to the resources requirements, exploration process to search the solution of task modules partitioning, scheduling, and floorplanning, and post-optimization to improve the success rate of floorplan. Experimental results show that, compared with state-of-the-art work, the proposed complete workflow can improve performance by 18.7\%, reduce communication cost by 8.6\%, on average, with improving the resources reuse rate of the heterogeneous resources on the chip. And based on the solution generated by the exploration process, the post-optimization can improve the success rate of the floorplan by 14\%.

Adopting a performer-centric perspective, we frequently encounter two statements: "music flows", and "music is life-like". This dissertation builds on top of the two statements above, resulting in an exploration of the role of temporal asymmetry in music (generalizing "music flows") and its relation to the idea of organicism (generalizing "music is life-like"). We focus on two aspects of temporal asymmetry. The first aspect concerns the vastly different epistemic mechanisms with which we obtain knowledge of the past and the future. A particular musical consequence follows: recurrence. The epistemic difference between the past and the future shapes our experience and interpretation of recurring events in music. The second aspect concerns the arrow of time: the unambiguous ordering imposed on temporal events gives rise to the a priori pointedness of time, rendering time asymmetrical and irreversible. A discussion on thermodynamics informs us musically: the arrow of time effectuates itself in musical forms by delaying the placement of the climax. Organicism serves as a mediating topic, engaging with the concept of life as in organisms. On the one hand, organicism is related to temporal asymmetry in science via a thermodynamical interpretation of life as entropy-reducing entities. On the other hand, organicism is a topic native to music via the universally acknowledged artistic idea that music should be interpreted as a vital force possessing volitional power. With organicism as a mediator, we better understand the role of temporal asymmetry in music. In particular, we view musical form as a process of expansion and elaboration analogous to organic growth. Finally, we present an organicist interpretation of delaying the climax: viewing musical form as the result of organic growth, the arrow of time translates to a preference for prepending structure over appending structure.

During the main COVID-19 global pandemic lockdown period of 2020 an impromptu set of pollination ecologists came together via social media and personal contacts to carry out standardised surveys of the flower visits and plants in gardens. The surveys involved 67 rural, suburban and urban gardens, of various sizes, ranging from 61.18° North in Norway to 37.96° South in Australia, resulting in a data set of 25,174 rows, with each row being a unique interaction record for that date/site/plant species, and comprising almost 47,000 visits to flowers, as well as records of flowers that were not visited by pollinators, for over 1,000 species and varieties belonging to more than 460 genera and 96 plant families. The more than 650 species of flower visitors belong to 12 orders of invertebrates and four of vertebrates. In this first publication from the project, we present a brief description of the data and make it freely available for any researchers to use in the future, the only restriction being that they cite this paper in the first instance. The data generated from these global surveys will provide scientific evidence to help us understand the role that private gardens (in urban, rural and suburban areas) can play in conserving insect pollinators and identify management actions to enhance their potential.

Adders are the primary components in the data-path logic of a microprocessor, and thus, adder design has been always a critical issue in the very large-scale integration (VLSI) industry. However, it is infeasible for designers to obtain optimal adder architecture by exhaustively running EDA flow due to the extremely large design space. Previous arts have proposed the machine learning-based framework to explore the design space. Nevertheless, they fall into suboptimality due to a two-stage flow of the learning process and less efficient nor effective feature representations of prefix adder structures. In this article, we first integrate a variational graph autoencoder and a neural process (NP) into an end-to-end, multibranch framework, which is termed the graph neural process . The former performs automatic feature learning of prefix adder structures, whilst the latter one is designed as an alternative to the Gaussian process. Then, we propose a sequential optimization framework with the graph NP as the surrogate model to explore the Pareto-optimal prefix adder structures with tradeoff among Quality-of-Result (QoR) metrics, such as power, area, and delay. The experimental results show that compared with state-of-the-art methodologies, our framework can achieve a much better Pareto frontier in multiple QoR metric spaces with fewer design-flow evaluations.

East Asia has experienced long-standing territorial and maritime conflicts. Due to the existence of numerous maritime States, we have witnessed trilateral or quadrilateral overlaps where the presence of third States in maritime disputes emerges. So as to address the rights and interests of third States in a multistate-disputed maritime zone, one can observe that different approaches are adopted against the background of international law and international relations. As the research shows, international law is inadequate to fully protect and safeguard the rights and interests of third States. To address such underlying legal dilemmas, the thesis articulates that regime theory becomes more pragmatic and inclusive for addressing rights and interests that are commonly shared by involved States in a multistate-disputed zone. Since each model in the mainstream international relations theories focuses on one variable and fails to cover all elements of regime, the multivariate maritime regime should be set up to handle the maritime rights and interests of relevant stakeholders that are commonly shared in the same disputed maritime zone. In addition, rather than contested maritime boundaries being a source of conflict, this thesis argues that a regime theory approach can present a mechanism through which contested claims can be transformed into a driver of cooperation among states in the region. In East Asia, the establishment of a multivariate maritime regime helps disputing States develop transboundary marine cooperation. It also serves to promote current regime settings in NEA and the SCS, in order to deal with shared regional concerns.

To complement our previous discovery of the young snake-like structure in the solar neighborhood and reveal the structure's full extent, we build two samples of stars within the Snake and its surrounding territory from {\tt Gaia EDR3}. With the friends-of-friends algorithm, we identify 2694 and 9615 Snake member candidates from the two samples. Thirteen open clusters are embedded in these member candidates. By combining the spectroscopic data from multiple surveys, we investigate the comprehensive properties of the candidates and find that they \thj{are very likely to} belong to one sizable structure, since most of the components are well bridged in their spatial distributions, and follow a single stellar population with an age of $30-40$\,Myr and solar metallicity. This sizable structure is best explained as hierarchically primordial, and probably formed from a filamentary giant molecular cloud with unique formation history in localized regions. To analyze the dynamics of the Snake, we divide the structure into five groups according to their tangential velocities; we find that the groups are expanding at a coherent rate ($\kappa_X\sim3.0\,\times10^{-2}\,\rm km\,s^{-1}\,pc^{-1}$) along the length of the structure ($X$-direction). \thj{The corresponding expansion age ($\tau\sim33$\,Myr) is highly consistent with the age of the Snake}. With over ten thousand member stars, the Snake is an ideal laboratory to study nearby coeval stellar formation, stellar physics, and environmental evolution over a large spatial extent., Comment: 18 pages, 13 figures, 1 table, submitted to MNRAS (revised according to the referee's comments)

Jie Mei,1,2,&ast; Xu-Qi Sun,3,&ast; Wen-Ping Lin,1,2,&ast; Shao-Hua Li,1,2 Liang-He Lu,1,2 Jing-Wen Zou,1,2 Wei Wei,1,2 Rong-Ping Guo1,2 1Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, People’s Republic of China; 2State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People’s Republic of China; 3Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Rong-Ping Guo; Wei Wei Email guorp@sysucc.org.cn; weiwei@sysucc.org.cnBackground: Inflammatory response is related to cancer progression and patient survival. However, the value in predicting survival in hepatocellular carcinoma (HCC) patients who received anti-PD-1 therapy has not been elucidated. This study aimed to compare the predictive ability of inflammation-based scores for the prognosis of HCC patients after anti-PD-1 therapy.Methods: A total of 442 patients who received anti-PD-1 therapy were included in the study. Representative inflammation-based prognostic scores, including the platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-C-reactive protein (CRP) ratio (LCR), lymphocyte-to-monocyte ratio (LMR), systemic immune inflammation index (SII), CRP-to-albumin ratio (CAR), prognostic nutritional index (PNI), Glasgow Prognostic Score (GPS), modified Glasgow Prognostic Score (mGPS), and prognostic index (PI), were assessed for prediction accuracy using Kaplan–Meier survival curves, time-dependent receiver operating characteristic (ROC) and Harrell’s concordance index (C-index) analyses.Results: All the inflammation-based prognostic scores exhibited good discriminatory ability in overall survival (OS) (all P < 0.01), while the PNI score was a unique independent predictor for OS in multivariate a

Artificial neural network (ANN) is a versatile tool to study the neural representation in the ventral visual stream, and the knowledge in neuroscience in return inspires ANN models to improve performance in the task. However, it is still unclear how to merge these two directions into a unified framework. In this study, we propose an integrated framework called Deep Autoencoder with Neural Response (DAE-NR), which incorporates information from ANN and the visual cortex to achieve better image reconstruction performance and higher neural representation similarity between biological and artificial neurons. The same visual stimuli (i.e., natural images) are input to both the mice brain and DAE-NR. The encoder of DAE-NR jointly learns the dependencies from neural spike encoding and image reconstruction. For the neural spike encoding task, the features derived from a specific hidden layer of the encoder are transformed by a mapping function to predict the ground-truth neural response under the constraint of image reconstruction. Simultaneously, for the image reconstruction task, the latent representation obtained by the encoder is assigned to a decoder to restore the original image under the guidance of neural information. In DAE-NR, the learning process of encoder, mapping function and decoder are all implicitly constrained by these two tasks. Our experiments demonstrate that if and only if with the joint learning, DAE-NRs can improve the performance of visual image reconstruction and increase the representation similarity between biological neurons and artificial neurons. The DAE-NR offers a new perspective on the integration of computer vision and neuroscience.

Background Mycoplasma genitalium (MG) causes urogenital tract infections and is associated with reproductive morbidity. Although MG has been reported across many regions and population groups, it is not yet routinely tested for in China. Our study contributes to current research by reporting the prevalence and correlates of MG infection in patients attending a sexually transmitted infection (STI) clinic in Guangdong from Jan 2017-May 2018. Methods Urethral (from 489 men) and endo-cervical (from 189 women) samples, blood samples, and patient histories (via questionnaires) were collected. Doctors clinically diagnosed anogenital warts (GW) during the examination (n = 678). The presence of MG was evaluated using an in-house via polymerase chain reaction protocol. We also tested all participants for herpes simplex virus-2 (HSV-2), Neisseria gonorrhoeae (NG), Chlamydia trachomatis (CT), syphilis and HIV. Univariate and multivariate logistic regression were used to evaluate factors associated with MG. Results MG was detected in 7.2% (49/678) of the patients (men, 7.4%; women, 6.9%). The MG positivity rate was 14.2% among symptomatic patients, and 5.6% for asymptomatic patients, respectively. Only 36.7% (18/49) Mg positive patients were symptomatic. Among the MG-infected patients, 10.2% were co-infected with CT, 6.1% with NG, 8.2% with HSV-2, 4.1% with syphilis and 22.4% with GW. Presentation with clinical symptoms was significantly associated with MG infection [OR = 2.52 (2.03–3.13)]. In our analysis, MG was not associated with other STIs. Conclusions MG is a relatively common infection among individuals attending an STI clinic in Guangdong Province. Routine testing of symptomatic patients may be necessary, and more epidemiological studies are needed to provide evidence for future testing guidelines.

Brain network is a large-scale complex network with scale-free, small-world, and modularity properties, which largely supports this high-efficiency massive system. In this paper, we propose to synthesize brain-network-inspired interconnections for large-scale network-on-chips. Firstly, we propose a method to generate brain-network-inspired topologies with limited scale-free and power-law small-world properties, which have a low total link length and extremely low average hop count approximately proportional to the logarithm of the network size. In addition, given the large-scale applications and the modular topology, we present an application mapping method, including task mapping and deterministic deadlock-free routing, to minimize the power consumption and hop count. Finally, a cycle-accurate simulator BookSim2 is used to validate the architecture performance with different synthetic traffic patterns and large-scale test cases, including real-world communication networks for the graph processing application. Experiments show that, compared with other topologies and methods, the NoC design generated by the proposed method presents significantly lower average hop count and lower average latency. Especially in graph processing applications with a power-law and tightly coupled inter-core communication, the brain-network-inspired NoC has up to 70% lower average hop count and 75% lower average latency than mesh-based NoCs., Comment: 19 pages, 15 figures, 8 tables, accepted by ACM TODAES

In view of the problems of high energy consumption, low resource utilisation and serious environmental pollution in the traditional single-slot electrolytic manganese or manganese dioxide, this work proposed a double-membrane three-chamber electrolysis method to achieve cathode electrodeposition of manganese while the anode was co-produced with electrolytic manganese dioxide, and the intermediate compartment electrochemically regenerated sulphuric acid. By analysing the changes of ion concentration in each compartment, the feasibility of double-membrane three-chamber electrolysis was explored, and the effects of three ion exchange membranes on electrodeposition were compared. The results show that the double-membrane three-chamber electrolysis method can realise the electrodeposition of manganese metal and the simultaneous production of electrolytic manganese dioxide, and the cathode current efficiency can reach more than 80%, the anode current efficiency can reach more than 60%, and the acid recovery rate in the middle compartment is higher than 65%. Compared with Ionsep-HC and LANRAN-AM, the TRJM-10W homogeneous membrane has the lowest cell voltage. After 12 hours of electrolysis, the cathode current efficiency is 86.25%, the anode current efficiency is 70.6%, and the intermediate compartment acid recovery rate is 73.08%. However, the pH value of the catholyte increases the most, and the concentration of Mn2+ in the middle compartment is the highest., In view of the problems of high energy consumption, low resource utilisation and serious environmental pollution in the traditional single-slot electrolytic manganese or manganese dioxide, this work proposed a double-membrane three-chamber electrolysis method to achieve cathode electrodeposition of manganese while the anode was co-produced with electrolytic manganese dioxide, and the intermediate compartment electrochemically regenerated sulphuric acid. By analysing the changes of ion concentration in each compartment, the feasibility of double-membrane three-chamber electrolysis was explored, and the effects of three ion exchange membranes on electrodeposition were compared. The results show that the double-membrane three-chamber electrolysis method can realise the electrodeposition of manganese metal and the simultaneous production of electrolytic manganese dioxide, and the cathode current efficiency can reach more than 80%, the anode current efficiency can reach more than 60%, and the acid recovery rate in the middle compartment is higher than 65%. Compared with Ionsep-HC and LANRAN-AM, the TRJM-10W homogeneous membrane has the lowest cell voltage. After 12 hours of electrolysis, the cathode current efficiency is 86.25%, the anode current efficiency is 70.6%, and the intermediate compartment acid recovery rate is 73.08%. However, the pH value of the catholyte increases the most, and the concentration of Mn2+ in the middle compartment is the highest.

Introduction Access to epilepsy surgery is improving across Canada, but the province of Manitoba, with a population of approximately 1.37 million, has - until recently - lacked the local infrastructure to deliver surgical epilepsy care. This thesis aims to describe the change in care provided to pediatric epilepsy patients in Manitoba since the formation of a comprehensive Pediatric Epilepsy Program, provide an early analysis of local outcomes, and present a framework for program development. Methods Data was collected retrospectively from medical records on patients who previously had epilepsy surgery in Manitoba, and prospectively on new, incoming patients. Caregivers were asked to complete questionnaires on quality of life and satisfaction with the Program. An online database was created to capture demographic information, seizure and neuropsychological outcomes, patient and caregiver quality of life, and caregiver satisfaction. Descriptive statistics were used to describe these outcomes. Results Prior to the formation of the Pediatric Epilepsy Program, 16 patients underwent vagal nerve stimulator (VNS) insertions. Mean time to surgery was 5.5 +/- 2.92 years. At a follow-up of 1-5 years, 1 patient (6.25%) achieved Engel class I outcome, 12 patients (75%) achieved Engel class III outcome, and 3 patients (12.5%) achieved Engel class IV outcome. After formation of the Pediatric Epilepsy Program, 14 patients underwent a variety of epilepsy surgeries, including 11 resective procedures and 3 VNS insertions. Mean time to surgery was 5.49 +/- 2.99 years. At a follow-up of 3-12 months, 11 patients (78.6%) achieved Engel class I outcome, 2 patients (14.3%) achieved Engel class III outcome, and 1 (7.1%) patient achieved Engel class IV outcome. Neuropsychological testing remained stable in the 2 patients it was completed on. The average QOLCE-55 score measuring patient quality of life was (59.7 +/- 23.2)/100. The average CarerQol-7D score measuring caregiver quality of life

Qi Xu, Fen Pan, Yan Sun, Chun Wang, Yingying Shi, Tiandong Zhang, Fangyuan Yu, Hong Zhang Department of Clinical Laboratory, Shanghai Children’s Hospital, Shanghai Jiaotong University, Shanghai, People’s Republic of ChinaCorrespondence: Hong Zhang Department of Clinical LaboratoryShanghai Children’s Hospital, Shanghai Jiaotong University, No. 24, Lane 1400, West Beijing Road, Shanghai 200040, People’s Republic of ChinaTel/ Fax +86 21 6247 4880 Ext 82301Email schjyk2015@126.comPurpose: To determine the epidemiology characteristics of intestinal colonization of carbapenem-resistant Enterobacteriaceae (CRE) among inpatients in a pediatric hospital in China.Methods: A retrospective study was conducted from April to December 2019. Medical records were reviewed to extract the clinical information. Antimicrobial susceptibility was performed by broth microdilution method. Drug resistance determinants and plasmid types were analyzed using polymerase chain reaction (PCR) assays. Multilocus sequence typing (MLST) and Enterobacterial repetitive intergenic consensus sequences PCR (ERIC-PCR) were employed to determine the genetic relationships between strains.Results: A total of 90 CRE strains were isolated, with a fecal carriage rate of 8.6% (90/1052), and mainly distributed in E. aerogenes (n=30), K. pneumoniae (n=25) and E. coli (n=23). More than 50% of CRE colonizers had a history of invasive procedures and antibiotic exposures. As high as 91.1% (82/90) of CRE isolates carried carbapenemase genes, with blaNDM-5 (n=56) being the most common, and mainly found in E. aerogenes (51.8%, 29/56) and E. coli (32.1%, 18/56) isolates, which primarily belonged to ST4 (100%, 29/29) and ST692 (55.6%, 10/18), respectively. Followed by blaKPC-2 (n=12), and all found in K. pneumoniae ST11 isolates. Other carbapenemase genes including blaNDM-1, blaIMP-4 and blaIMP-26. Meanwhile, ESBL genes (blaCTX-M, blaTEM-1 and blaSHV) and AmpC genes (blaDHA-1 and blaE

This journal is The Royal Society of Chemistry. Tin-based anode materials have aroused interest due to their high capacities. Nevertheless, the volume expansion problem during lithium insertion/extraction processes has severely hindered their practical application. In particular, nano-micro hierarchical structure is attractive with the integrated advantages of nano-effect and high thermal stability of the microstructure. Herein, hierarchical Sn/SnO nanosheets assembled by carbon-coated hollow nanospheres were successfully synthesized by a facile glucose-assisted hydrothermal method, in which the glucose served as both morphology-control agent and carbon source. The hierarchical Sn/SnO nanosheets exhibit excellent electrochemical performances owing to the unique configuration and carbon coating. Specifically, a reversible high capacity of 2072.2 mA h g-1 was observed at 100 mA g-1. Further, 964.1 mA h g-1 after 100 cycles at 100 mA g-1 and 820.4 mA h g-1 at 1000 mA g-1 after 300 cycles could be obtained. Encouragingly, the Sn/SnO also presents certain sodium ion storage properties. This facile synthetic strategy may provide new insight into fabricating high-performance Sn-based anode materials combining the advantages of both structure and carbon coating.

Variational autoencoders (VAEs) are influential generative models with rich representation capabilities from the deep neural network architecture and Bayesian method. However, VAE models have a weakness that assign a higher likelihood to out-of-distribution (OOD) inputs than in-distribution (ID) inputs. To address this problem, a reliable uncertainty estimation is considered to be critical for in-depth understanding of OOD inputs. In this study, we propose an improved noise contrastive prior (INCP) to be able to integrate into the encoder of VAEs, called INCPVAE. INCP is scalable, trainable and compatible with VAEs, and it also adopts the merits from the INCP for uncertainty estimation. Experiments on various datasets demonstrate that compared to the standard VAEs, our model is superior in uncertainty estimation for the OOD data and is robust in anomaly detection tasks. The INCPVAE model obtains reliable uncertainty estimation for OOD inputs and solves the OOD problem in VAE models.

We introduce the first method for automatic image generation from scene-level freehand sketches. Our model allows for controllable image generation by specifying the synthesis goal via freehand sketches. The key contribution is an attribute vector bridged Generative Adversarial Network called EdgeGAN, which supports high visual-quality object-level image content generation without using freehand sketches as training data. We have built a large-scale composite dataset called SketchyCOCO to support and evaluate the solution. We validate our approach on the tasks of both object-level and scene-level image generation on SketchyCOCO. Through quantitative, qualitative results, human evaluation and ablation studies, we demonstrate the method's capacity to generate realistic complex scene-level images from various freehand sketches.

Variational auto-encoders (VAEs) are an influential and generally-used class of likelihood-based generative models in unsupervised learning. The likelihood-based generative models have been reported to be highly robust to the out-of-distribution (OOD) inputs and can be a detector by assuming that the model assigns higher likelihoods to the samples from the in-distribution (ID) dataset than an OOD dataset. However, recent works reported a phenomenon that VAE recognizes some OOD samples as ID by assigning a higher likelihood to the OOD inputs compared to the one from ID. In this work, we introduce a new model, namely Bigeminal Priors Variational auto-encoder (BPVAE), to address this phenomenon. The BPVAE aims to enhance the robustness of the VAEs by combing the power of VAE with the two independent priors that belong to the training dataset and simple dataset, which complexity is lower than the training dataset, respectively. BPVAE learns two datasets'features, assigning a higher likelihood for the training dataset than the simple dataset. In this way, we can use BPVAE's density estimate for detecting the OOD samples. Quantitative experimental results suggest that our model has better generalization capability and stronger robustness than the standard VAEs, proving the effectiveness of the proposed approach of hybrid learning by collaborative priors. Overall, this work paves a new avenue to potentially overcome the OOD problem via multiple latent priors modeling.

We propose a least squares regression framework for the estimation of the realized covariation matrix using high frequency data. The new estimator is robust to market microstructure noise (MMS) and non-synchronous trading. Comprehensive simulation and empirical analysis show that our estimator performs as well as a set of popular estimators in the literature. More importantly, our framework allows for the unique identification of MMS noise moments. We find that these noise moments are related to measures of liquidity and contain predictive information that helps to significantly improve out-of-sample asset allocation.

The development of airport alliances has undergone hardships, although airport alliances have emerged simultaneously with other airline alliances in an attempt to improve competitiveness. Technology spillover is a major benefit of airport alliances, but the effects of such spillover are not as obvious as might be expected. This paper proposes a three-stage DEA-based methodological framework to uncover the existence and characteristics of technology spillover among allied airports, and identify whether the technology spillover of airport alliances outperforms the self-development of airports. A case study that concerns multi-airport companies and non-allied airports in China, where airports and airlines are rapidly expanding, is included. The results show that the technology spillover of airport alliances does not become wide spread among all of the allied airports, which is most likely because civil aviation in China is still in a stage of rapid development. However, such spillover does appear among small- and mid-sized allied airports in cities with fast urban economic development. Finally, the related implications of our methodological framework are discussed along with our findings.