Miracle-dz/UniFAR-FTUs huggingface dataset:
Hypoxic Conditions in Crown Galls Induce Plant Anaerobic Responses That Support Tumor Proliferation

Agrobacterium tumefaciens infection of wounded plant tissues causes the formation of crown gall tumors. Upon infection, genes encoded on the A. tumefaciens tumor inducing plasmid are integrated in the plant genome to induce the biosynthesis of auxin and cytokinin, leading to uncontrolled cell division. Additional sequences present on the bacterial T-DNA encode for opine biosynthesis genes, which induce the production of opines that act as a unique carbon and nitrogen source for Agrobacterium. Crown galls therefore become a very strong sink for photosynthate. Here we found that the increased metabolic demand in crown galls causes an increase in oxygen consumption rate, which leads to a steep drop in the internal oxygen concentration. Consistent with this, plant hypoxia-responsive genes were found to be significantly upregulated in crown galls compared to uninfected stem tissue. Following this observation, we aimed at understanding whether the low-oxygen response pathway, mediated by group VII ethylene response factor (ERF-VII) transcription factors, plays a role in the development of crown galls. We found that quintuple knock-out mutants of all ERF-VII members, which are incapable of inducing the hypoxic response, show reduced crown gall symptoms. Conversely, mutant genotypes characterized by constitutively high levels of hypoxia-associated transcripts, displayed more severe crown gall symptoms. Based on these results, we concluded that uncontrolled cell proliferation of crown galls established hypoxic conditions, thereby requiring adequate anaerobic responses of the plant tissue to support tumor growth.
The Neural Correlates of the Clock-Drawing Test in Healthy Aging

Importance: The clock-drawing test (CDT) is an important neurocognitive assessment tool, widely used as a screening test for dementia. Behavioral performance on the test has been studied extensively, but there is scant literature on the underlying neural correlates. Purpose: To administer the CDT naturalistically to a healthy older aging population in an MRI environment, and characterize the brain activity associated with test completion. Main Outcome and Measure: Blood-oxygen-level dependent (BOLD) functional MRI was conducted as participants completed the CDT using novel tablet technology. Brain activity during CDT performance was contrasted to rest periods of visual fixation. Performance on the CDT was evaluated using a standardized scoring system (Rouleau score) and time to test completion. To assess convergent validity, performance during fMRI was compared to performance on a standard paper version of the task, administered in a psychometric testing room. Results: Study findings are reported for 33 cognitively healthy older participants aged 52–85. Activation was observed in the bilateral frontal, occipital and parietal lobes as well as the supplementary motor area and precentral gyri. Increased age was significantly correlated with Rouleau scores on the clock number drawing (R2) component (rho = -0.55, p < 0.001); the clock hand drawing (R3) component (rho = -0.50, p < 0.005); and the total clock (rho = -0.62, p < 0.001). Increased age was also associated with decreased activity in the bilateral parietal and occipital lobes as well as the right temporal lobe and right motor areas. Conclusion and Relevance: This imaging study characterizes the brain activity underlying performance of the CDT in a healthy older aging population using the most naturalistic version of the task to date. The results suggest that the functions of the occipital and parietal lobe are significantly altered by the normal aging process, which may lead to performance decrements.
Smart Web Services (SmartWS) - The Future of Services on the Web

The past few years have been marked by an increased use of sensor technologies, abundant availability of mobile devices, and growing popularity of wearables, which enable the direct integration of their data as part of rich client applications. Despite the potential and added value that such aggregate applications bring, the implementations are usually custom solutions for particular use cases and do not support easy integration of further devices. To this end, the vision of the Web of Things (WoT) is to leverage Web standards in order to interconnect all types of devices and real-world objects, and thus to make them a part of the World Wide Web (WWW) and provide overall interoperability. In this context we introduce Smart Web Services (SmartWS) that not only provide remote access to resources and functionalities, by relying on standard communication protocols, but also encapsulate `intelligence'. Smartness features can include, for instance, context-based adaptation, cognition, inference and rules that implement autonomous decision logic in order to realize services that automatically perform tasks on behalf of the users, without requiring their explicit involvement. In this paper, we present the key characteristics of SmartWS, and introduce a reference implementation framework. Furthermore, we describe a specific use case for implementing SmartWS in the medical domain and specify a maturity model for determining the quality and usability of SmartWS.
Robust Environmental Sound Recognition With Sparse Key-Point Encoding and Efficient Multispike Learning

The capability for environmental sound recognition (ESR) can determine the fitness of individuals in a way to avoid dangers or pursue opportunities when critical sound events occur. It still remains mysterious about the fundamental principles of biological systems that result in such a remarkable ability. Additionally, the practical importance of ESR has attracted an increasing amount of research attention, but the chaotic and nonstationary difficulties continue to make it a challenging task. In this article, we propose a spike-based framework from a more brain-like perspective for the ESR task. Our framework is a unifying system with consistent integration of three major functional parts which are sparse encoding, efficient learning, and robust readout. We first introduce a simple sparse encoding, where key points are used for feature representation, and demonstrate its generalization to both spike- and nonspike-based systems. Then, we evaluate the learning properties of different learning rules in detail with our contributions being added for improvements. Our results highlight the advantages of multispike learning, providing a selection reference for various spike-based developments. Finally, we combine the multispike readout with the other parts to form a system for ESR. Experimental results show that our framework performs the best as compared to other baseline approaches. In addition, we show that our spike-based framework has several advantageous characteristics including early decision making, small dataset acquiring, and ongoing dynamic processing. Our framework is the first attempt to apply the multispike characteristic of nervous neurons to ESR. The outstanding performance of our approach would potentially contribute to draw more research efforts to push the boundaries of spike-based paradigm to a new horizon.
An Empirical Study on Regularization of Deep Neural Networks by Local Rademacher Complexity

Regularization of Deep Neural Networks (DNNs) for the sake of improving their generalization capability is important and challenging. The development in this line benefits theoretical foundation of DNNs and promotes their usability in different areas of artificial intelligence. In this paper, we investigate the role of Rademacher complexity in improving generalization of DNNs and propose a novel regularizer rooted in Local Rademacher Complexity (LRC). While Rademacher complexity is well known as a distribution-free complexity measure of function class that help boost generalization of statistical learning methods, extensive study shows that LRC, its counterpart focusing on a restricted function class, leads to sharper convergence rates and potential better generalization given finite training sample. Our LRC based regularizer is developed by estimating the complexity of the function class centered at the minimizer of the empirical loss of DNNs. Experiments on various types of network architecture demonstrate the effectiveness of LRC regularization in improving generalization. Moreover, our method features the state-of-the-art result on the CIFAR-$10$ dataset with network architecture found by neural architecture search.
A Large-Scale Empirical Study on Industrial Fake Apps

While there have been various studies towards Android apps and their development, there is limited discussion of the broader class of apps that fall in the fake area. Fake apps and their development are distinct from official apps and belong to the mobile underground industry. Due to the lack of knowledge of the mobile underground industry, fake apps, their ecosystem and nature still remain in mystery. To fill the blank, we conduct the first systematic and comprehensive empirical study on a large-scale set of fake apps. Over 150,000 samples related to the top 50 popular apps are collected for extensive measurement. In this paper, we present discoveries from three different perspectives, namely fake sample characteristics, quantitative study on fake samples and fake authors' developing trend. Moreover, valuable domain knowledge, like fake apps' naming tendency and fake developers' evasive strategies, is then presented and confirmed with case studies, demonstrating a clear vision of fake apps and their ecosystem.
Mind-Object Identity: A Solution to the Hard Problem

Here I present a mind-object identity theory based on a straightforward hypothesis: One's experience of an object is identical with the object itself. To defend this hypothesis, I will reconsider the notion of a physical object in terms of relative and actual properties. To address cases of misperception such as dreams and hallucinations, I will also reconsider the notion of present in relative terms. Both the object and the present are recast as object-relative.
Mechanistic Insight Into the Interaction Between Helicobacter pylori Urease Subunit α and Its Molecular Chaperone Hsp60

Helicobacter pylori is the etiologic agent in a variety of gastroduodenal diseases. As its key pathogenic factors, both urease and Hsp60 play important roles in the pathogenesis of H. pylori. Previous studies have suggested that there is close relationship between urease and Hsp60, which implied that Hsp60 may act as a chaperone in urease stabilization and assembly. However, how these two proteins interact remains unclear. In this study, the impact of Hsp60 on urease activity of H. pylori lysate was first detected to confirm the interaction between urease and Hsp60. Pull-down assays further indicated that Hsp60 could bind to UreA subunit but not UreB. Then, the 3D structure of Hsp60 was modeled using I-TASSER to simulate the binding complex with UreA by molecular docking. The results showed that UreA is a perfect fit for the cavity of Hsp60. Analysis of the resulting model demonstrated that at least seven residues of UreA, located on two interfaces, participate in the interaction. Site-directed mutagenesis of these potential residues showed reduced affinity with Hsp60 than the wild type UreA through surface plasmon resonance (SPR) experiments, and D68 appears to have an important role in the affinity. Further analysis also showed that mutation of E25 and K26 caused a more rapid association and dissociation than with wild UreA, implying that they have roles in stabilizing the interaction complex. These affinity comparisons suggested that the interfaces predicted by molecular docking are credible. Our study indicated a direct interaction between Hsp60 and urease and revealed the binding interfaces and key residues involved in the interaction. These results provide further evidence for the chaperone activity of Hsp60 toward urease and lay a foundation to better understand the maturation mechanism of urease in H. pylori.
2-D Embedding of Large and High-dimensional Data with Minimal Memory and Computational Time Requirements

In the advent of big data era, interactive visualization of large data sets consisting of M*10^5+ high-dimensional feature vectors of length N (N ~ 10^3+), is an indispensable tool for data exploratory analysis. The state-of-the-art data embedding (DE) methods of N-D data into 2-D (3-D) visually perceptible space (e.g., based on t-SNE concept) are too demanding computationally to be efficiently employed for interactive data analytics of large and high-dimensional datasets. Herein we present a simple method, ivhd (interactive visualization of high-dimensional data tool), which radically outperforms the modern data-embedding algorithms in both computational and memory loads, while retaining high quality of N-D data embedding in 2-D (3-D). We show that DE problem is equivalent to the nearest neighbor nn-graph visualization, where only indices of a few nearest neighbors of each data sample has to be known, and binary distance between data samples -- 0 to the nearest and 1 to the other samples -- is defined. These improvements reduce the time-complexity and memory load from O(M log M) to O(M), and ensure minimal O(M) proportionality coefficient as well. We demonstrate high efficiency, quality and robustness of ivhd on popular benchmark datasets such as MNIST, 20NG, NORB and RCV1.
Big Data and Social/Medical Sciences: State of the Art and Future Trends

The explosion of data on the internet is a direct corollary of the social media platform. With petabytes of data being generated by end users, the researchers have access to unprecedented amount of data (Big Data). Such data provides an insight into user mental state and hence can be utilized to produce clinical evidence. This lofty goal requires a thorough understanding of not only the mental health issues but also the technology trends underlying the Big Data and how they can be leveraged effectively. The paper looks at various such concepts, provides an overview and enumerates the work that has been done in this realm. Furthermore, we provide guidelines for future work that will help in streamlining the Big Data use in social/medical sciences.
Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches

We study the sample complexity of model-based reinforcement learning (henceforth RL) in general contextual decision processes that require strategic exploration to find a near-optimal policy. We design new algorithms for RL with a generic model class and analyze their statistical properties. Our algorithms have sample complexity governed by a new structural parameter called the witness rank, which we show to be small in several settings of interest, including factored MDPs. We also show that the witness rank is never larger than the recently proposed Bellman rank parameter governing the sample complexity of the model-free algorithm OLIVE (Jiang et al., 2017), the only other provably sample-efficient algorithm for global exploration at this level of generality. Focusing on the special case of factored MDPs, we prove an exponential lower bound for a general class of model-free approaches, including OLIVE, which, when combined with our algorithmic results, demonstrates exponential separation between model-based and model-free RL in some rich-observation settings.
Robust Hidden Topology Identification in Distribution Systems

With more distributed energy resources (DERs) connected to distribution grids, better monitoring and control are needed, where identifying the topology accurately is the prerequisite. However, due to frequent re-configurations, operators usually cannot know a complete structure in distribution grids. Luckily, the growing data from smart sensors, restricted by Ohm law, provides the possibility of topology inference. In this paper, we show how line parameters of Ohm equation can be estimated for topology identification even when there are hidden nodes. Specifically, the introduced learning method recursively conducts hidden-node detection and impedance calculation. However, the assumptions on uncorrelated data, availability of phasor measurements, and a balanced system, are not met in practices, causing large errors. To resolve these problems, we employ Cholesky whitening first with a proof for measurement decorrelations. For increasing robustness further, we show how to handle practical scenarios when only measurement magnitudes are available or when the grid is three-phase unbalanced. Numerical performance is verified on multi-size distribution grids with both simulation and real-world data.
FDI: Quantifying Feature-based Data Inferability

Motivated by many existing security and privacy applications, e.g., network traffic attribution, linkage attacks, private web search, and feature-based data de-anonymization, in this paper, we study the Feature-based Data Inferability (FDI) quantification problem. First, we conduct the FDI quantification under both naive and general data models from both a feature distance perspective and a feature distribution perspective. Our quantification explicitly shows the conditions to have a desired fraction of the target users to be Top-K inferable (K is an integer parameter). Then, based on our quantification, we evaluate the user inferability in two cases: network traffic attribution in network forensics and feature-based data de-anonymization. Finally, based on the quantification and evaluation, we discuss the implications of this research for existing feature-based inference systems.
Research on Clothing Image Classification by Convolutional Neural Networks

In recent years, clothing image classification methods based on convolutional neural networks (CNNs) have attracted plenty of attention with the increasing demand for high accuracy clothing image classification in many fields. In this paper, five different CNNs are designed to implement clothing image classification, which are the conventional CNN, the CNN containing inception module, the CNN containing inception module and residual block, two transfer learned CNNs. The experimental results show that all the networks are capable of achieving good classification, among which the transfer learned CNN have higher classification accuracy.
COMPLEX SIMULATION EXPERIMENTS MADE EASY

Diverse methods for complex simulation experiments can contribute to developing and gaining insights into simulation models, for example simulation-based optimization, sensitivity analysis, or statistical model-checking. An effective tool for conducting simulation experiments must be highly flexible to support such a broad range of experimental methods. Furthermore, to facilitate reproducibility and communication of simulation experiments, an effective tool for simulation experimentation must yield experiment descriptions that are easily portable, executable, and human-readable. In this tutorial we introduce SESSL, a domain-specific language for setting up simulation experiments. SESSL is flexible and extensible, and experiment descriptions are executable, often succinct, and can be executed reproducibly across machines and operating systems. Based on a few examples, we demonstrate how SESSL can be leveraged to easily conduct complex simulation experiments while reusing existing software and methods, and how SESSL’s capabilities can be extended and combined with arbitrary simulation software via bindings.
Guideline Design of an Active Gene Annotation Corpus for the Purpose of Drug Repurposing

In order to develop a gold corpus for Biomedical Natural Language Processing community for the sake of knowledge discovery in drug repurposing, an active gene annotation corpus (AGAC) was developed in this research. Five semantic trigger labels and three root regulatory trigger labels were designed as molecular- and cell- level biological entity annotations, which focused on the information of function change in biological processes resulted from mutated genes. In addition, predicates ‘ThemeOf’ and ‘CauseOf’ were as well annotated manually for the semantic knowledge extraction. Eventually, roles of gene mutation including gain of function (GOF) and loss of function (LOF) were curated through the AGAC annotation guideline. The information from AGAC annotation effectively bridge the association between mutation, gene, drug and disease, and make it possible to predict new drug direction in a large scale. AGAC corpus availability: The corpus is available in PubAn-notation platform11http://pubannotation.org/projects/HZAU_Active_Gene_Corpus.
ACTOR-ORIENTED OPTIMIZATION MODEL FOR MAINTENANCE TASKS

This paper introduces an extension of the well-established Resource-Constrained Project Scheduling Problem (RCPSP) to apply it to maintenance problems of highly frequented infrastructure. A major complication of the observed scheduling problem is that the infrastructure is only temporarily available for maintenance and repair work during the shift of the workforce. A multi-criteria evolutionary algorithm with a novel problem representation is introduced which is capable of revising technician-task allocations whereas the duration of the task may be stochastic. The main objective is to develop shift plans which maximize the utilization rate of technicians due to a minimization of waiting times caused by the use of the infrastructure through other actors. The results of the already implemented core algorithm for an actor-oriented model illustrate a fast convergence towards an optimal work allocation within a team as well as an efficient sequence of tasks.
Exploring Resource-Aware Deep Neural Network Accelerator and Architecture Design

Due to the ever-increasing number of neural networks(NNs) connections and parameters, computation on neural networks is becoming both power hankering and memory intensive. In this paper, we propose a sparse neural networks accelerator to improve memory resource utilization and improve power efficiency. In contrast to prior works, we introduce a highly integrated software and hardware co-design technique that combines resource-aware software compression algorithms and specialized hardware inference engine in the accelerator. Compared with other designs, our design can compress parameters by 90× and substantially improve storage resource utilization, performance (6.9×) and power (1.2×) for NN accelerators.
On-Line Monitoring of Partial Discharge of Less-Oil Immersed Electric Equipment Based on Pressure and UHF

For partial discharge (PD) detection of less-oil immersed electric equipment (CT, PT, bushing), frequent sampling of oil chromatographic is likely to cause negative pressure inside the equipment, and by modifying the terminal tap to access the detection impedance, it will endanger the reliability of the long-term grounding of the terminal tap. Based on this, in this paper, a combined on-line monitoring method for PD of less-oil immersed electric equipment based on pressure and UHF is proposed. UHF method is used to detect the real-time features of PD defect, and the pressure is used to detect the accumulative degree of insulation deterioration. An oil-immersed vertical CT PD detection scheme based on pressure and UHF is designed. The feasibility of the scheme is verified by experiments. And in the case of bubble insulation defects, the PRPS spectrum of leaked electromagnetic wave from non-metallic insulating gap is obtained in the UHF method, and the relationship between the accumulated severity of the defect and the pressure was also studied. The experimental results show that the pressure has a certain fluctuation during the initial period of PD. Under the condition of a certain discharge quantity, the oil pressure changes linearly with the increase of time, and the pressure growth rate varies with different discharge quantity. When the internal pressure reaches the pressure value required for expansion of the expander, the internal pressure of the device shows a non-linear growth trend. The method proposed in this paper provides a new idea for the PD insulation detection of oil-less equipment.