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What is the relationship with environmental factors of phytoplankton community dynamics in saline lakes? We analyzed a data set of phytoplankton community and environmental parameters in seasonal periods (i.e., summer and autumn) in order to describe seasonal variations within the phytoplankton community and its relationship with environmental variables in Chagan Nur Lake on the Ordos Plateau in China. The lake's phytoplankton species was not abundant, with a mean density of 222.43×104 cells/L (ranging from 62.35×104 cells/L in autumn to 382.5×104 cells/L in summer 2014). However, the groups with highest densities were essentially limited to Bacillariophyta, Chlorophyta, and Cyanophyta. There were considerable seasonal variations in phytoplankton composition. Eleven environmental variables were selected to explore the relationship between phytoplankton structure and environmental factors by canonical correspondence analysis (CCA). The results of the CCA applied to the environmental factors indicated that total phosphorus (TP), pH, total ion degrees (Tds), salinity (Sal), and electrical conductivity (Con) significantly influenced the phytoplankton community (p<0.01; Monte Carlo test of first constrained axis). [ABSTRACT FROM AUTHOR]

Nervous systems are probability machines and, as such, modeling their activities should incorporate stochastic processes. In this review, we present two examples of optimal stochastic control problems with an analytic methodology on how to find optimal signals. The first example deals with neuronal activity and the second example is concerned with a higher level task: arm movement. In both cases we find optimal signals for particular tasks and find our results in agreement with the experimental Fitts Law. [ABSTRACT FROM AUTHOR]

Active learning (AL) attempts to maximize a model's performance gain while annotating the fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize a massive number of parameters if the model is to learn how to extract high-quality features. In recent years, due to the rapid development of internet technology, we have entered an era of information abundance characterized by massive amounts of available data. As a result, DL has attracted signiicant attention from researchers and has been rapidly developed. Compared with DL, however, researchers have a relatively low interest in AL. This is mainly because before the rise of DL, traditional machine learning requires relatively few labeled samples, meaning that early AL is rarely according the value it deserves. Although DL has made breakthroughs in various ields, most of this success is due to a large number of publicly available annotated datasets. However, the acquisition of a large number of high-quality annotated datasets consumes a lot of manpower, making it unfeasible in ields that require high levels of expertise (such as speech recognition, information extraction, medical images, etc.). Therefore, AL is gradually coming to receive the attention it is due. It is therefore natural to investigate whether AL can be used to reduce the cost of sample annotation while retaining the powerful learning capabilities of DL. As a result of such investigations, deep active learning (DeepAL) has emerged. Although research on this topic is quite abundant, there has not yet been a comprehensive survey of DeepAL-related works; accordingly, this article aims to ill this gap. We provide a formal classiicationmethod for the existing work, along with a comprehensive and systematic overview. In addition, we also analyze and summarize the development of DeepAL from an application perspective. Finally, we discuss the confusion and problems associated with DeepAL and provide some possible development directions. [ABSTRACT FROM AUTHOR]

Deep learning has made substantial breakthroughs in many fields due to its powerful automatic representation capabilities. It has been proven that neural architecture design is crucial to the feature representation of data and the final performance. However, the design of the neural architecture heavily relies on the researchers' prior knowledge and experience. And due to the limitations of humans' inherent knowledge, it is difficult for people to jump out of their original thinking paradigm and design an optimal model. Therefore, an intuitive idea would be to reduce human intervention as much as possible and let the algorithm automatically design the neural architecture. Neural Architecture Search (NAS) is just such a revolutionary algorithm, and the related research work is complicated and rich. Therefore, a comprehensive and systematic survey on the NAS is essential. Previously related surveys have begun to classify existing work mainly based on the key components of NAS: search space, search strategy, and evaluation strategy. While this classification method is more intuitive, it is difficult for readers to grasp the challenges and the landmark work involved. Therefore, in this survey, we provide a new perspective: beginning with an overview of the characteristics of the earliest NAS algorithms, summarizing the problems in these early NAS algorithms, and then providing solutions for subsequent related research work. In addition, we conduct a detailed and comprehensive analysis, comparison, and summary of these works. Finally, we provide some possible future research directions. [ABSTRACT FROM AUTHOR]

The low-cost crystal oscillators in wireless sensor networks are prone to be affected by their working conditions such as voltage, temperature, and humidity. Such effect is often ignored by existing time synchronization solutions that typically assume the frequency error of a given node to be constant and hence adopt frequent timestamp exchanges, resulting in high energy consumptions. We propose a novel voltage-aware time synchronization (VATS) scheme that is inspired by the fact that the clock skew is highly correlated to voltage supplies. VATS features a two-phase process: (i) it first estimates the clock skew and updates the frequency error autonomously based on the local voltage level; (ii) it then adjusts the resynchronization intervals dynamically according to a given synchronization error controlling factor and the synchronization error accumulating rate to balance the calibration accuracy and cost. Since VATS leverages voltage measurements to assist clock skew estimation, it does not require frequent timestamp exchanges as in traditional schemes. Extensive simulation results illustrate the superior performance of the proposed method in terms of calculation accuracy, robustness, and reduced timestamp exchanges for energy saving. [ABSTRACT FROM AUTHOR]

According to European regulations, if the amount of regenerative braking is determined by the travel of the brake pedal, more stringent standards must be applied, otherwise it may adversely affect the existing vehicle safety system. The use of engine or vehicle speed to derive regenerative braking is one way to avoid strict design standards, but this introduces discontinuity in powertrain torque when the driver releases the acceleration pedal or applies the brake pedal. This is shown to cause oscillations in the pedal input and powertrain torque when a conventional driver model is adopted. Look-ahead information, together with other predicted vehicle states, are adopted to control the vehicle speed, in particular, during deceleration, and to improve the driver model so that oscillations can be avoided. The improved driver model makes analysis and validation of the control strategy for an integrated starter generator (ISG) hybrid powertrain possible. [ABSTRACT FROM AUTHOR]

Infection with oncogenic human papillomaviruses (HPVs), typified by HPV type 16 (HPV16), is a necessary cause of cervical cancer. Prophylactic vaccination with HPV16 L1 virus-like particles (VLPs) provides immunity. HPV16 VLPs activate dendritic cells and a potent neutralizing immunoglobulin G (IgG) response, yet many cervical cancer patients fail to generate detectable VLP-specific IgG. Therefore, we examined the role of the innate recognition of HPV16 L1 in VLP-induced immune responses and its evasion during carcinogenesis. Nonconservative mutations within HPV16 L1 have been described in isolates from cervical cancer and its precursor, high-grade cervical intraepithelial neoplasia (CIN). We determined the effect of mutations in L1 upon in vitro self-assembly into VLPs and their influence upon the induction of innate and adaptive immune responses in mice. Several nonconservative mutations in HPV16 L1 isolated from high-grade CIN or cervical carcinoma prevent self. assembly of L1 VLPs. Intact VLPs, but not assembly-defective L1, activate dendritic cells to produce proinflammatory factors, such as alpha interferon, that play a critical role in inducing adaptive immunity. Indeed, effective induction of L1-specific IgG1 and IgG2a was dependent upon intact VLP structure. Dendritic cell activation and production of virus-specific neutralizing IgG by VLPs requires MyD88-dependent signaling, although the L1 structure that initiates MyD88-mediated signaling is distinct from the neutralizing epitopes. We conclude that innate recognition of the intact L1 VLP structure via MyD88 is critical in the induction of high-titer neutralizing IgG. Tumor progression is associated with genetic instability and L1 mutants. Selection for assembly-deficient L1 mutations suggests the evasion of MyD88-dependent immune control during cervical carcinogenesis. [ABSTRACT FROM AUTHOR]

In neurons, dendrites form the major sites of information receipt and integration. It is thus vital that, during development, the dendritic arbor is adequately formed to enable proper neural circuit formation and function. While several known processes shape the arbor, little is known of those that govern dendrite branching versus extension. Here, we report a new mechanism instructing dendrites to branch versus extend. In it, glutamate signaling activates mGluR5 receptors to promote Ckd5- mediated phosphorylation of the C-terminal PDZ-binding motif of delta-catenin. The phosphorylation state of this motif determines delta-catenin's ability to bind either Pdlim5 or Magi1. Whereas the delta:Pdlim5 complex enhances dendrite branching at the expense of elongation, the delta:Magi1 complex instead promotes lengthening. Our data suggest that these complexes affect dendrite development by differentially regulating the small-GTPase RhoA and actin-associated protein Cortactin. We thus reveal a "phospho-switch" within delta-catenin, subject to a glutamate-mediated signaling pathway, that assists in balancing the branching versus extension of dendrites during neural development. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), Radio Signal Strength Indicator (RSSI)-based localization techniques have been widely used in various applications, such as intrusion detection, battlefield surveillance, and animal monitoring. One fundamental performance measure in those applications is the sensing coverage of WSNs. Insufficient coverage will significantly reduce the effectiveness of the applications. However, most existing studies on coverage assume that the sensing range of a sensor node is a disk, and the disk coverage model is too simplistic for many localization techniques. Moreover, there are some localization techniques of WSNs whose coverage model is non-disk, such as RSSI-based localization techniques. In this paper, we focus on detecting and recovering coverage holes of WSNs to enhance RSSI-based localization techniques whose coverage model is an ellipse. We propose an algorithm inspired by Voronoi tessellation and Delaunay triangulation to detect and recover coverage holes. Simulation results show that our algorithm can recover all holes and can reach any set coverage rate, up to 100% coverage. [ABSTRACT FROM AUTHOR]

In recent years, unsupervised two-dimensional (2D) dimensionality reduction methods for unlabeled large-scale data have made progress. However, performance of these degrades when the learning of similarity matrix is at the beginning of the dimensionality reduction process.Asimilarity matrix is used to reveal the underlying geometry structure of data in unsupervised dimensionality reduction methods. Because of noise data, it is difficult to learn the optimal similarity matrix. In this letter, we propose a new dimensionality reduction model for 2D image matrices: unsupervised 2D dimensionality reductionwith adaptive structure learning (DRASL). Instead of using a predetermined similarity matrix to characterize the underlying geometry structure of the original2Dimage space, our proposed approach involves the learning of a similarity matrix in the procedure of dimensionality reduction. To realize a desirable neighbors assignment after dimensionality reduction, we add a constraint to our model such that there are exact c connected components in the final subspace. To accomplish these goals, we propose a unified objective function to integrate dimensionality reduction, the learning of the similarity matrix, and the adaptive learning of neighbors assignment into it. An iterative optimization algorithm is proposed to solve the objective function. We compare the proposed method with several 2D unsupervised dimensionality methods. K-means is used to evaluate the clustering performance. We conduct extensive experiments on Coil20, AT&T, FERET, USPS, and Yale data sets to verify the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

An understanding of the changes of the rammed earth temperature of earthen ruins is important for protection of such ruins. To predict the rammed earth temperature pattern using the air temperature pattern of the monitoring data of earthen ruins, a pattern prediction method based on interesting pattern mining and correlation, called PPER, is proposed in this paper. PPER first finds the interesting patterns in the air temperature sequence and the rammed earth temperature sequence. To reduce the processing time, two pruning rules and a new data structure based on an R-tree are also proposed. Correlation rules between the air temperature patterns and the rammed earth temperature patterns are then mined. The correlation rules are merged into predictive rules for the rammed earth temperature pattern. Experiments were conducted to show the accuracy of the presented method and the power of the pruning rules. Moreover, the Ming Dynasty Great Wall dataset was used to examine the algorithm, and six predictive rules from the air temperature to rammed earth temperature based on the interesting patterns were obtained, with the average hit rate reaching 89.8%. The PPER and predictive rules will be useful for rammed earth temperature prediction in protection of earthen ruins. [ABSTRACT FROM AUTHOR]

When we encounter a malicious rumor or an infectious disease outbreak, immunizing k nodes of the relevant network with limited resources is always treated as an extremely effective method. The key challenge is how we can insulate limited nodes to minimize the propagation of those contagious things. In previous works, the best k immunised nodes are selected by learning the initial status of nodes and their strategies even if there is no feedback in the propagation process, which eventually leads to ineffective performance of their solutions. In this paper, we design a novel vaccines placement strategy for protecting much more healthy nodes from being infected by infectious nodes. The main idea of our solution is that we are not only utilizing the status of changing nodes as auxiliary knowledge to adjust our scheme, but also comparing the performance of vaccines in various transmission slots. Thus, our solution has a better chance to get more benefit from these limited vaccines. Extensive experiments have been conducted on several real-world data sets and the results have shown that our algorithm has a better performance than previous works. [ABSTRACT FROM AUTHOR]

For ecologists, understanding the reaction of animals to environmental changes is critical. Using networked sensor technology to measure wildlife and environmental parameters can provide accurate, real-time and comprehensive data for monitoring, research and conservation of wildlife. This paper reviews: (i) conventional detection technology; (ii) concepts and applications of the Internet of Things (IoT) in animal ecology; and (iii) the advantages and disadvantages of IoT. The current theoretical limits of IoT in animal ecology are also discussed. Although IoT offers a new direction in animal ecological research, it still needs to be further explored and developed as a theoretical system and applied to the appropriate scientific frameworks for understanding animal ecology. [ABSTRACT FROM AUTHOR]

The passive object localization (POL) problem in wireless sensor networks aims to determine the location of a target without any device attached for receiving or transmitting signal. This problem is challenging as there is very limited information available for deriving the target location. By combining the diffraction and scattering models, we propose a link sensing adaptive approach to POL, which first decides the target position attribute based on the signal strength and then localizes the target in different modes. We conduct rigorous localizability analyses and design a unit localization area scheme to achieve a higher level of localization accuracy. The efficacy of the proposed method is evaluated through comprehensive experiments in real life network environments. [ABSTRACT FROM AUTHOR]

Mammalian oxygen homeostasis is dependent on the HIF family of transcription factors. The CSN subunit, CSN5, binds both the CODD of HIF-1α and the pVHL tumor suppresser. High CSN5 expression generates a pVHL-independent form of CSN5 that stabilizes HIF-1α aerobically by inhibiting HIF-1α prolyl-564 hydroxylation. Aerobic CSN5 association with HIF-1α occurs independently of the CSN holocomplex, leading to HIF-1α stabilization independent of Cullin 2 deneddylation. CSN5 weakly associates with HIF-1α under hypoxic, but is required for optimal hypoxic-mediated HIF-1α stabilization. These results indicate that CSN5 regulates aerobic as well as hypoxic HIF-1α stability by different mechanisms during oncogenesis. [ABSTRACT FROM AUTHOR]