A dynamic neural network has ternary connection parameters and can generate various binary periodic orbits. In order to analyze the dynamics, we present two feature quantities which characterize stability and transient phenomenon of a periodic orbit. Calculating the feature quantities, we investigate influence of connection sparsity on stability of a target periodic orbit corresponding to a circuit control signal. As the sparsity increases, at first, stability of a target periodic orbit tends to be stronger. In the next, the stability tends to be weakened and various transient phenomena exist. In the most sparse case, the network has many periodic orbits without transient phenomenon.

In this letter, we consider the power allocation scheme with rate proportional fairness to maximize energy efficiency in the downlink the non-orthogonal multiple access (NOMA) systems. The optimization problem of energy efficiency is a non-convex optimization problem, and the fractional programming is used to transform the original problem into a series of optimization sub-problems. A two-layer iterative algorithm is proposed to solve these sub-problems, in which power allocation with the fixed energy efficiency is achieved in the inner layer, and the optimal energy efficiency of the system is obtained by the bisection method in the outer layer. Simulation results show the effectiveness of the proposed algorithm.

In this study, we propose a deep neural generative model for predicting daily stock price movements given news articles. Approaches involving conventional technical analysis have been investigated to identify certain patterns in past price movements, which in turn helps to predict future price movements. However, the financial market is highly sensitive to specific events, including corporate buyouts, product releases, and the like. Therefore, recent research has focused on modeling relationships between these events that appear in the news articles and future price movements; however, a very large number of news articles are published daily, each article containing rich information, which results in overfitting to past price movements used for parameter adjustment. Given the above, we propose a model based on a generative model of news articles that includes price movement as a condition, thereby avoiding excessive overfitting thanks to the nature of the generative model. We evaluate our proposed model using historical price movements of Nikkei 225 and Standard & Poor's 500 Stock Index, confirming that our model predicts future price movements better than such conventional classifiers as support vector machines and multilayer perceptrons. Further, our proposed model extracts significant words from news articles that are directly related to future stock price movements.

This paper presents a supervised method to classify a document at the sub-sentence level. Traditionally, sentiment analysis often classifies sentence polarity based on word features, syllable features, or N-gram features. A sentence, as a whole, may contain several phrases and words which carry their own specific sentiment. However, classifying a sentence based on phrases and words can sometimes be incoherent because they are ungrammatically formed. In order to overcome this problem, we need to arrange words and phrase in a dependency form to capture their semantic scope of sentiment. Thus, we transform a sentence into a dependency tree structure. A dependency tree is composed of subtrees, and each subtree allocates words and syllables in a grammatical order. Moreover, a sentence dependency tree structure can mitigate word sense ambiguity or solve the inherent polysemy of words by determining their word sense. In our experiment, we provide the details of the proposed subtree polarity classification for sub-opinion analysis. To conclude our discussion, we also elaborate on the effectiveness of the analysis result.

Spatio-temporal dependent data, such as weather observation data, are data of which the attribute values depend on both time and space. Typical methods for the visualization of such data include plotting the attribute values at each point in time on a map and displaying series of the maps in chronological order with animation, or displaying them by juxtaposing horizontally or vertically. However, these methods are problematic in that they compel readers interested in grasping the spatial changes of the attribute values to memorize the representations on the maps. The problem is exacerbated by considering that the longer the time-period covered by the data, the higher the cognitive load. In order to solve these problems, the authors propose a visualization method capable of overlaying the representations of multiple instantaneous values on a single static map. This paper explains the design of the proposed method and reports two experiments conducted by the authors to investigate the usefulness of the method. The experimental results show that the proposed method is useful in terms of the speed and accuracy with which it reads the spatial changes and its ability to present data with long time series efficiently.

In current large-scale IT systems, troubleshooting has become more complicated due to the diversification in the causes of failures, which has increased operational costs. Thus, clarifying the troubleshooting process also becomes important, though it is also time-consuming. We propose a method of automatically extracting a workflow, a graph indicating a troubleshooting process, using multiple trouble tickets. Our method extracts an operator's actions from free-format texts and aligns relative sentences between multiple trouble tickets. Our method uses a stochastic model to detect a resolution, a frequent action pattern that helps us understand how to solve a problem. We validated our method using real trouble-ticket data captured from a real network operation and showed that it can extract a workflow to identify the cause of a failure.

With the recent explosion of geographic data generated by smartphones, sensors, and satellites, a data storage that can handle the massive volume of data and support high-computational spatial queries is becoming essential. Although key-value stores efficiently handle large-scale data, they are not equipped with effective functions for supporting geographic data. To solve this problem, in this paper, we present G-HBase, a high-performance geographical database based on HBase, a standard key-value store. To index geographic data, we first use Geohash as the rowkey in HBase. Then, we present a novel partitioning method, namely binary Geohash rectangle partitioning, to support spatial queries. Our extensive experiments on real datasets have demonstrated an improved performance with k nearest neighbors and range query in G-HBase when compared with SpatialHadoop, a state-of-the-art framework with native support for spatial data. We also observed that performance of spatial join in G-HBase is on par with SpatialHadoop and outperforms SJMR algorithm in HBase.

NAND flash memories used in solid-state drives (SSDs) will be replaced with storage-class memories (SCMs), which are comparable with NAND flash in their cost, and with DRAM in their speed. This paper describes the performance difference of the SCM/NAND flash hybrid SSD and the SCM-based SSD with between sector-unit read (512 Byte) and page-unit read (16 KByte, NAND flash page-size) using synthetic and real workload. Also, effect of the SCM read-unit size on SSD performance are analyzed. When SCM write/read latency is 0.1 us, performance difference of the SCM/NAND flash hybrid SSD with between page- and sector-unit read is about 1% and 6% at most for the write-intensive and read-intensive workloads, respectively. However, performance of the SCM-based SSD is significantly improved when sector-unit read is used because extra read latency does not occur. Especially, the SCM-based SSD IOPS is improved by 131% for proj_3 (read-hot-random), because its read request size is small but its read request ratio is large. This paper also shows IOPS of SCM-based SSD write/read with sector-unit read can be predicted by the average write/read request size of workloads.

This paper addresses the issue of modeling the discourse nature of lyrics and presented the first study aiming at capturing the two common discourse-related notions: storylines and themes. We assume that a storyline is a chain of transitions over topics of segments and a song has at least one entire theme. We then hypothesize that transitions over topics of lyric segments can be captured by a probabilistic topic model which incorporates a distribution over transitions of latent topics and that such a distribution of topic transitions is affected by the theme of lyrics. Aiming to test those hypotheses, this study conducts experiments on the word prediction and segment order prediction tasks exploiting a large-scale corpus of popular music lyrics for both English and Japanese (around 100 thousand songs). The findings we gained from these experiments can be summarized into two respects. First, the models with topic transitions significantly outperformed the model without topic transitions in word prediction. This result indicates that typical storylines included in our lyrics datasets were effectively captured as a probabilistic distribution of transitions over latent topics of segments. Second, the model incorporating a latent theme variable on top of topic transitions outperformed the models without such variables in both word prediction and segment order prediction. From this result, we can conclude that considering the notion of theme does contribute to the modeling of storylines of lyrics.

We present an 82.5GS/s over-sampling based burst-mode clock and data recovery (BM-CDR) IC chip-set comprising an 82.5GS/s over-sampling IC using 8×10.3GHz multi-phase clocks and a dual-rate data selector logic IC to realize the 10.3Gb/s and 1.25Gb/s dual-rate burst-mode fast-lock operation required for 10-Gigabit based fiber-to-the-x (FTTx) services supported by 10-Gigabit Ethernet passive optical network (10G-EPON) systems. As the key issue for designing the proposed 82.5GS/s BM-CDR, a fresh study of the optimum number of multi-phase clocks, which is equivalent to the sampling resolution, is undertaken, and details of the 10.3Gb/s cum 1.25/Gb/s dual-rate optimum phase data selection logic based on a blind phase decision algorithm, which can realize a full single-platform dual-rate BM-CDR, ate also presented. By using the power of the proposed 82.5GS/s over-sampling BM-CDR in cooperation with our dual-rate burst-mode optical receiver, we further demonstrated that a short dual-rate and burst-mode preamble of 256ns supporting receiver settling and CDR recovery times was successfully achieved, while obtaining high receiver sensitivities of -31.6dBm at 10.3Gb/s and -34.6dBm at 1.25Gb/s and a high pulse-width distortion tolerance of +/-0.53UI, which are superior to the 10G-EPON standard.

In this paper, an optimal method is proposed to design sparse-coefficient notch filters with principal basic vectors in the column space of a matrix constituted with frequency samples. The proposed scheme can perform in two stages. At the first stage, the principal vectors can be determined in the least-squares sense. At the second stage, with some components of the principal vectors, the notch filter design is formulated as a linear optimization problem according to the desired specifications. Optimal results can form sparse coefficients of the notch filter by solving the linear optimization problem. The simulation results show that the proposed scheme can achieve better performance in designing a sparse-coefficient notch filter of small order compared with other methods such as the equiripple method, the orthogonal matching pursuit based scheme and the L1-norm based method.

Knowledge management is important for government agencies in improving service delivery to their customers and data inter-operation within and across organizations. Building organizational knowledge repository for government agency has unique challenges. In this paper, we propose that enterprise ontology can provide support for government agencies in capturing organizational taxonomy, best practices and global data schema. A case study of a large-scale adoption for the Thailand's Excise Department is elaborated. A modular design approach of the enterprise ontology for the excise tax domain is discussed. Two forms of organizational knowledge: global schema and standard practices were captured in form of ontology and rule-based knowledge. The organizational knowledge was deployed to support two KM systems: excise recommender service and linked open data. Finally, we discuss some lessons learned in adopting the framework in the government agency.

Capital expenditure (CAPEX) reduction and efficient wavelength allocation are critical for the future access networks. Elastic lambda aggregation network (EλAN) based on WDM and OFDM technologies is expected to realize efficient wavelength allocation. In this paper, we propose adaptive bandwidth allocation (ABA) algorithm for EλAN under the conditions of crowded networks, in which modulation format, symbol rate and the number of sub-carriers are adaptively decided based on the distance of PON-section, QoS and bandwidth demand of each ONU. Network simulation results show that the proposed algorithm can effectively reduce the total bandwidth and achieve steady high spectrum efficiency and contribute to the further reduction of CAPEX of future optical access networks.

The widespread application of mobile electronic devices has triggered a boom in energy consumption, especially in user equipment (UE). In this paper, we investigate the energy-efficiency (EE) of a UE experiencing the worst channel conditions, which is termed worst-EE. Due to the limited battery of the mobile equipment, worst-EE is a suitable metric for EE fairness optimization in the uplink transmissions of orthogonal frequency division multiple access (OFDMA) networks. More specifically, we determine the optimal power and sub-carrier allocation to maximize the worst-EE with respect to UEs' transmit power, sub-carriers and statistical quality-of-service (QoS). In order to maximize the worst-EE, we formulate a max-min power and sub-carrier allocation problem, which involves nonconvex fractional mixed integer nonlinear programming, i.e., NP-hard to solve. To solve the problem, we first relax the allocation of sub-carriers, formulate the upper bound problem on the original one and prove the quasi-concave property of objective function. With the aid of the Powell-Hestenes-Rockfellar (PHR) approach, we propose a fairness EE sub-carrier and power allocation algorithm. Finally, simulation results demonstrate the advantages of the proposed algorithm.

This paper proposes a transmission control protocol (TCP) for long distance high-speed wireless communications, including free-space optical communications (FSOC). Extreme high frequency of wireless communications enables high-speed bit rate, but frequent signal error, including burst error, can be a quite severe problem for ordinary high-speed TCPs. To achieve 10Gbps or higher data transfer throughput on FSOC, the proposed TCP (designated “TCP-FSO”) has improved and new features including multi-layer congestion control, retransmission control with packet loss point estimation, delay-based ACK congestion control, and ACK retransmission control. We evaluated data transfer throughput of TCP-FSO and the other TCPs, by throughput model analysis and experiment on real implementation. Obtained results show that TCP-FSO achieves far higher data transfer throughput than other high-speed TCPs. For example, it achieved a thousand times higher throughput than the other high-speed TCPs in a real FSOC environment.

In conventional multiview video systems using progressive download, a user downloads videos of all viewpoints of one content to realize smooth view switching. This, however, increases the video traffic, and if the available download rate is low, the video quality suffers. Downloading only the desired viewpoint is one approach for reducing the traffic. However, in this case, playback stalls will occur after view switching. These stalls degrade the user's satisfaction for the application. In this paper, we aim at two objectives: 1) to achieve reduction in video traffic and 2) to minimize the number of playback stalls. To this end, we propose a new multiview video delivery scheme for progressive download. The main idea of the proposed scheme is that the user downloads a part of viewpoints only, which will be played back by the user with a high probability, to realize both traffic reduction and smooth view switching. In addition, we propose two download-scheduling algorithms to prevent playback stalls even at low download rates. The first algorithm prevents stalls in the cases with frequent view switching, such as zapping, while the second prevents stalls in gazing cases. Evaluations using a Joint Multiview Video Coding (JMVC) encoder and multiview video sequences show that our scheme achieves not only reduced video traffic but also decreased number of playback stalls, regardless of the user's view-switching model or download rate. In addition, we demonstrate that the proposed method does not cause playback stalls irrespective of high and low motion video contents.

A 7GS/s complete-DDFS-solution featuring a two-times interleaved RDAC with 1.2Vpp-diff output swing was fabricated in 65nm CMOS. The frequency tuning and amplitude resolutions are 24-bits and 10-bits respectively. The RDAC includes a mixed-signal, high-speed architecture for random swapping thermometer coding dynamic element matching that improves the narrowband SFDR up to 8dB for output frequencies below 1.85GHz. The proposed techniques enable a 7 GS/s operation with a spurious-free dynamic range better than 32dBc over the full Nyquist bandwidth. The worst case narrowband SFDR is 42dBc. This system consumes 87.9mW/(GS/s) from a 1.2V power supply when the RSTC-DEM method is enabled, resulting in a FoM of 458.9GS/s·2(SFDR/6)/W. A proof-of-concept chip with an active area of only 0.22mm2 was measured in prototypes encapsulated in a 144-pins low profile quad flat package.

We propose a radiation-hardened Flip-Flop (FF) with stacked transistors based on the Adaptive Coupling Flip-Flop (ACFF) with low power consumption in a 65 nm FDSOI process. The slave latch in ACFF is much weaker against soft errors than the master latch. We design several FFs with stacked transistors in the master or slave latches to mitigate soft errors. We investigate radiation hardness of the proposed FFs by α particle and neutron irradiation tests. The proposed FFs have higher radiation hardness than a conventional DFF and ACFF. Neutron irradiation and α particle tests revealed no error in the proposed AC Slave-Stacked FF (AC_SS FF) which has stacked transistors only in the slave latch. We also investigate radiation hardness of the proposed FFs by heavy ion irradiation. The proposed FFs maintain higher radiation hardness up to 40 MeV-cm2/mg than the conventional DFF. Stacked inverters become more sensitive to soft errors by increasing tilt angles. AC_SS FF achieves higher radiation hardness than ACFF with the performance equivalent to that of ACFF.

Vehicular Ad hoc Network (VANET) is an important part of the Intelligent Transportation System (ITS). VANETs can realize communication between moving vehicles, infrastructures and other intelligent mobile terminals, which can greatly improve the road safety and traffic efficiency effectively. Existing studies of vehicular ad hoc network usually consider only one data transmission model, while the increasing density of traffic data sources means that the vehicular ad hoc network is evolving into Heterogeneous Vehicular Network (HetVNET) which needs hybrid data transmission scheme. Considering the Heterogeneous Vehicular Network, this paper presents a hybrid transmission MAC protocol including vehicle to vehicle communication (V2V) and vehicle to infrastructure communication (V2I/I2V). In this protocol, the data are identified according to timeliness, on the base of the traditional V2V and V2I/I2V communication. If the time-sensitive data (V2V data) fail in transmission, the node transmits the data to the base station and let the base station cooperatively transmit the data with higher priority. This transmission scheme uses the large transmission range of base station in an effective manner. In this paper, the queueing models of the vehicles and base station are analyzed respectively by one-dimensional and two-dimensional Markov Chain, and the expressions of throughput, packet drop rate and delay are also derived. The simulation results show that this MAC protocol can improve the transmission efficiency of V2V communication and reduce the delay of V2V data without losing the system performance.

A novel and efficient coding method is proposed to improve person re-identification in the XQDA subspace. Traditional CRC (Collaborative Representation based Classification) conducts independent dictionary coding for each image and can not guarantee improved results over conventional euclidian distance. In this letter, however, a specific model is separately constructed for each probe image and each gallery image, i.e. in probe-galley pairwise manner. The proposed pairwise-specific CRC method can excavate extra discriminative information by enforcing a similarity item to pull similar sample-pairs closer. The approach has been evaluated against current methods on two benchmark datasets, achieving considerable improvement and outstanding performance.