With the advent of machine learning applications to physical layer communications problems, neural networkbased auto-encoders are considered for channel state information (CSI) feedback of closed-loop MIMO operations. An autoencoder consists of an encoder and a decoder, where the “bottleneck” connecting the encoder and the decoder can be used for CSI feedback. The quantization process is usually applied to the –bottleneck” for the CSI feedback channel. This paper proposes a new approach without an explicit quantization step for CSI coding. The new proposal, known as binary variational (BiV) CSI coding, is based on the variational autoencoder (VAE) framework, with a Bernoulli distribution assumed for the latent space. A binary sampling step provides binary samples of the latent vector for the variational encoder. The encoder and decoder of the BiV CSI coding can be trained together with the stochastic gradient descent (SGD) method. Several trained BiV models are provided to demonstrate the effectiveness of the BiV CSI coding for 3GPP-based MIMO channels.

The communication community has researched and introduced a new infrastructure under Vehicle-to-Everything (V2X), which provides smart vehicles with wireless communication capabilities. NR V2X has been actively developed in the past two years and is now mostly finished. In this paper, we analyze the design of NR V2X with a focus on Mode 2 and evaluate its performance using system-level simulations. In NR V2X, Mode 2 is the only solution for vehicles out of network coverage, where contention-based resource allocation has to be deployed. We will also explain the motivation and rationale behind the design of NR V2X.

Multiple location-based device-free activity recognition systems indicate that some activities related to specific locations can be inferred from the location system and adding location information can improve the accuracy of activity recognition. Therefore, localization technology is the basis for activity recognition and other applications. Radio-Map is an effective measure in Device-free localization (DFL). Traditional fingerprint systems that can provide such accuracy are suffering from human cost in Radio-Map construction and update. Although the human cost in update phase has been paid attention, the higher costs caused by the initially created are ignored. In addition, existing systems assume that RSS change measurements caused by different targets are fixed distribution in any region. The two drawbacks will greatly affect the practicability and robustness of Radio-Map. In this paper, we propose, DTransfer, an extremely low-cost DFL approach that localize different kinds of targets in different regions. We design an optimized low-rank matrix completion model based on singular value decomposition (SVD) to construct the sensing matrix (i.e., radio-map) of the original region, which greatly reduces the overhead. Next, we employ a rigorously designed quadratic transfer scheme to accurately locate different categories of targets in different regions. Finally, we apply the location information to the activity recognition algorithm; experiments have shown that the accuracy of the algorithm for adding location information is increased by approximately 6%. Extensive experimental results illustrate that DTransfer achieves delightful performance.

Handheld mobile photography is often affected by motion blur due to the difficulty of keeping the camera's stable. The existing processing method is usually a high-cost deblurring process of a computer, which seriously affects the user experience, and the deblurring effect is poor due to the lack of information on camera motion. Inspire by edging computing's ongoing efforts in automatically and collaboratively process more types of resources in the edge and cloud. In this paper, we present SID, a sensor-assisted image deblurring system for mobile devices. Using information about camera motion acquired from built-in sensors from smartphones (e.g. accelerometers, gyroscopes, and magnetometers), then estimating the point spread function, and combining the image's segmentation smoothing characteristics with spatial adaptation to image Deblurring. The image is preprocessed by the p-m nonlinear diffusion model, which preserves the characteristics of the image. The fuzzy image confirmation avoids the damage to the original high quality image. Wiener-Hoff optimization is used to optimize the effect of image restoration. We evaluated 400 photos with varying degrees of blur and size. Compared to traditional blind and unblind deconvolution methods, our algorithm shows significant advantages in both deblurring and processing delays.

3GPP has completed the first phase of the standardization of 5G cellular systems covering bands up to 52.6 GHz. The 3GPP Rel-15 New Radio standard provides a comprehensive framework for supporting massive MIMO in a wide variety of use cases and deployment scenarios. The NR-MIMO framework supports large scale antenna arrays having arbitrary configurations and supports digital, hybrid, and analog array architectures for both FDD and TDD deployments. With the rollout of NR soon to commence, many questions have been raised on the benefits and performance of NR-MIMO especially compared to the FD-MIMO framework in Rel-13/14 of LTE. This paper provides detailed system level simulation results showing the performance characteristics of NR-MIMO as a function of a various system and deployment parameters such as the array configuration, deployment scenario, and transmission scheme. The purpose is to answer important questions for network planners looking to roll-out NR-MIMO in the near future.

In this work, we study two approaches for the problem of RNA-Protein Interaction (RPI). In the first approach, we use a feature-based technique by combining extracted features from both sequences and secondary structures. The feature-based approach enhanced the prediction accuracy as it included much more available information about the RNA-protein pairs. In the second approach, we apply search algorithms and data structures to extract effective string patterns for prediction of RPI, using both sequence information (protein and RNA sequences), and structure information (protein and RNA secondary structures). This led to different string-based models for predicting interacting RNA-protein pairs. We show results that demonstrate the effectiveness of the proposed approaches, including comparative results against leading state-of-the-art methods.

Researchers have been trying to develop polar IR-HARQ schemes in the past years, and incremental freezing is introduced as a scalable IR-HARQ scheme. However, its finite-length block performance is not ideal and sometimes is even worse than Chase combining. We had a key observation that there should be a final combining stage for all transmissions, with which the polar IR-HARQ performance could be significantly improved in the finite block-length regime. In this paper, we discuss models based on this observation and show the conditions for them to achieve the capacity of the underlying channels.

A flower pollination algorithm is proposed based on the hormone modulation mechanism (HMM-FPA) to solve the no-wait flow shop scheduling problem (NWFSP). This algorithm minimizes the maximum accomplished time. Random keys are encoded based on an ascending sequence of components to make the flower pollination algorithm (FPA) suitable for the no-wait flow shop scheduling problem. The hormone modulation factor is introduced to strengthen information sharing among the flowers and improve FPA cross-pollination to enhance the algorithm global search performance. A variable neighborhood search strategy based on dynamic self-adaptive variable work piece blocks is constructed to improve the local search quality. Three common benchmark instances are applied to test the proposed algorithm. The result verifies that this algorithm is effective.

The rapid increase in available protein structure datasets requires new techniques for fast, yet, effective analysis of protein 3D structures. In this work, we propose a structure-based signature for protein families, suitable for rapid analysis of multidomain protein structures. Our method is alignment-free, using protein strings as the basic representation. A key novelty is the two-stage approach, whereby an initial list of candidate protein superfamilies are rapidly identified using the protein family signature, and then information retrieval methods are applied only to the members of the candidate superfamilies. This approach is the key to both improved speed, and improved structure retrieval accuracy. Experimental results, including comparative results with state-of-the-art methods, demonstrate the performance of the proposed protein family signature on queries with multidomain protein structures.

In this paper, the discrete-time quaternion-valued neural network with linear threshold activation functions is investigated. The sufficient conditions to the boundedness and global exponential periodicity of the neural network are obtained by using characteristic equation, Lyapunov functional and M-matrix. Simulation results illustrative the effectiveness of the conclusions obtained in this paper.

Based on the collection and study of the extensive literature, this paper concludes and classifies the detection and forecasting technologies and its current application status in the micro-blog hot topic. Furthermore, combined with research characteristics of the detection and prediction of micro-blog hot topic and including the domestic characteristics, we draw out the limitations of the current related research, and point out the direction for further improvements. Finally, it has carried on the forecast on the future prospect.

With the development of the social network, posting announcements, image ads and other fresh events with pictures play an important role. Therefor, taking clear digital pictures is an urgent need to be solved right now. However, the movement or instability of lens will cause the picture faint blur, which greatly reduces the user experience. The current solutions are to put the virtual dummy photo in the computer of a higher configuration for processing. This process seriously affected the timeliness and universality of information dissemination. In this paper, we proposed portable deblurring system for mobile terminal of fuzzy pictures based on two classic image restoration algorithm of Wiener filter and blind restoration. Proposed mobile deblurring system adaptively evaluates the degree of picture blurs, and the point diffusion function is resolved by the existing fuzzy picture feature. At the same time, the optimization of the ambiguity is made to ensure the lightweight and low latency of the Mobile-Deblur algorithm. Finally, the validity and superiority in the Mobile-Deblur System is proved by simulation and real environment experiment respectively in the picture to deblur on the mobile phone.

Digital pictures captured by mobile device for information sharing play an important role in this rapidly growing social network. Posting of image ads, events and other announcements are also important. However, photos are subject to blurring caused by motion or virtual focus. The current solution, only in the computer desktop with complex system to deal with these fuzzy photos. With the development of camera-embedded mobile devices, timely release of clear pictures has been used in various fields. In this paper, we present Just - a forthright system for improving the quality of picture on the mobile device. The tags are automatically classified for the digital pictures according to the degree of blurring and deblurring the original images based on Wiener filter. Initial deployment and experimentation prove the effectiveness of Just.

One recent and important advancement in information and coding theory is polar code, which is selected as the coding scheme for 5G eMBB control channels. In this paper, we propose a variant of polar codes that provides significant coding gain in the regime of short blocks and enables early termination of decoding processes. The overall scheme depends on distributing CRC bits in the whole information block, and a single interleaving/deinterleaving pattern can be defined to implement CRC distribution. The design can reduce the decoding latency and energy consumption of hardware, which is crucial for mobile applications like 5G.

Given the rapidly increasing quantity of genomic and proteomic data that is now easily available even to a casual observer, the new challenge is in making sense out of the vast quantities of data. Efficient and reliable analysis of protein 3D structures is identified as a major challenge in this post genomic era. Whether the objective of the analysis is for protein classification, protein similarity search, protein structure prediction, discovery of protein structural motifs, or assignment of a functional class to a newly discovered protein, a key aspect in the analysis is the representation used to encode the protein 3D structural information. In this work, we introduce a family of string encodings as an effective descriptor for protein 3D structures. We show how the choice of parameters affects the performance and compare the result with other related research.

Given the rapidly increasing quantity of available genomic and proteomic data, efficient and reliable analysis of protein 3D structures has become a major challenge in the post genomic era. In this work, we introduce the sorted protein shape context, and its encoding into a protein shape string as an effective descriptor for protein 3D structures. Based on the new encoding, we present a method for predicting the functional family for a given protein 3D structure. Applying the proposed method on a dataset of known protein families from Pfam resulted in an average Type I error rate of 10% and Type II error rate of 0.1%.

Architectures are described which allow a Java-based front-end to run R code on a server. The front-end described here is a Java application called JavaStat (http://javastat.stat.wvu.edu). JavaStat is a highly-interactive program for data analysis and dynamic visualization with data management capabilities. The objective is to bring the high-level functions of R to JavaStat, without excessive duplicative development work. Results returned from R are wrapped and then displayed using linked, dynamic plots in JavaStat. The principal idea is to use Remote Method Invocation to communicate with a Java server program (JRIServer), which in turn communicates with R using Java/R Interface (JRI). Two versions have been implemented. The first (basic) architecture maintains a connection between the client and server in order to return the results from R. This is suitable for small to moderate data sets in which relatively simple models are run. The second (enhanced) architecture queues the requests and uses polling to fetch the results. It is suitable for large data sets and complex models, e. g., those encountered in genomic studies. JavaStat supports the basic architecture out of the box, but a user account is required to enable the enhanced architectures. The enhanced architecture supports workflows, e. g., genomic and modeling workflows.

Statistical methods have been intensively applied in genomic signal processing (Dougherty et al. 2005). For budding yeast Saccharomyces cerevisiae with around 6000 proteins, genome-wide protein-protein-interaction (PPI) (Fromont-Racine et al. 2000, Ito et al. 2001, Newman et al. 2000, and Uetz et al. 2000 among others) and protein subcellular localization (PSL) (Huh et al. 2003) data recently became available and for the latter the presence of 4152 proteins is experimentally tested in each of the 22 subcellular compartments. Recent work shows that multiple biological sources are helpful for both PSL and PPI predictions, and this paper studies statistical feasibility of modeling PPI from PSL since PSLs may play different marginal or joint roles in the complex regulatory network. However, our results indicate that PSL may be controversial for this purpose as an independent source.

Changes in the configurational entropies of molecules make important contributions to the free energies of reaction for processes such as protein‐folding, noncovalent association, and conformational change. However, obtaining entropy from molecular simulations represents a long‐standing computational challenge. Here, two recently introduced approaches, the nearest‐neighbor (NN) method and the mutual‐information expansion (MIE), are combined to furnish an efficient and accurate method of extracting the configurational entropy from a molecular simulation to a given order of correlations among the internal degrees of freedom. The resulting method takes advantage of the strengths of each approach. The NN method is entirely nonparametric (i.e., it makes no assumptions about the underlying probability distribution), its estimates are asymptotically unbiased and consistent, and it makes optimum use of a limited number of available data samples. The MIE, a systematic expansion of entropy in mutual information terms of increasing order, provides a well‐characterized approximation for lowering the dimensionality of the numerical problem of calculating the entropy of a high‐dimensional system. The combination of these two methods enables obtaining well‐converged estimations of the configurational entropy that capture many‐body correlations of higher order than is possible with the simple histogramming that was used in the MIE method originally. The combined method is tested here on two simple systems: an idealized system represented by an analytical distribution of six circular variables, where the full joint entropy and all the MIE terms are exactly known, and the R,S stereoisomer of tartaric acid, a molecule with seven internal‐rotation degrees of freedom for which the full entropy of internal rotation has been already estimated by the NN method. For these two systems, all the expansion terms of the full MIE of the entropy are estimated by the NN method and, for comparison, the MIE approximations up to third order are also estimated by simple histogramming. The results indicate that the truncation of the MIE at the two‐body level can be an accurate, computationally nondemanding approximation to the configurational entropy of anharmonic internal degrees of freedom. If needed, higher‐order correlations can be estimated reliably by the NN method without excessive demands on the molecular‐simulation sample size and computing time. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2008