We prove generalization error bounds of classes of low-rank matrices with some norm constraints for collaborative filtering tasks. Our bounds are tighter, compared to known bounds using rank or the related quantity only, by taking the additional L1 and L∞ constraints into account. Also, we show that our bounds on the Rademacher complexity of the classes are optimal.

Recently, Hof et al. extended the type-2 Duman and Salehi (DS2) bound to generalized decoding, which was introduced by Forney, with decision criterion FR. From this bound, they derived two significant bounds. One is the Shulman-Feder bound for generalized decoding (GD) with the binary-input output-symmetric channel. The other is an upper bound for an ensemble of linear block codes, by applying the average complete weight distribution directly to the DS2 bound for GD. For the Shulman-Feder bound for GD, the authors derived a condition under which an upper bound is minimized at an intermediate step and show that this condition yields a new bound which is tighter than Hof et al.'s bound. In this paper, we first extend this result for non-binary linear block codes used over a class of symmetric channels called the regular channel. Next, we derive a new tighter bound for an ensemble of linear block codes, which is based on the average weight distribution.

This paper argues that a layered approach is more suitable for Information Centric Networking (ICN) than a narrow-waist approach and proposes an ICN mechanism called ZINK. In ZINK, a location-independent content name is resolved to a list of node IDs of content servers in the application layer and a node ID is mapped to a node locator in the network layer, which results in scalable locator-based routing. An ID/Locator split approach in the network layer can efficiently support client/serever mobility. Efficient content transfer is achieved by using sophisticated functions in the transport layer such as multipath transfer for bandwidth aggregation or fault tolerance. Existing well-tuned congestion control in the transport layer achieves fairness not only among ICN flows but also among ICN flows and other flows. A proof-of concept prototype of ZINK is implemented on an IPv6 stack. Evaluation results show that the time for content finding is practical, efficient content transfer is possible by using multipath transfer, and the mobility support mechanism is scalable as shown in a nationwide experiment environment in Japan.

A low-complexity signal detection approach based on the Kaczmarz algorithm (KA) is proposed to iteratively realize minimum mean square error (MMSE) detection for uplink massive multiple-input multiple-output (MIMO) systems. While KA is used for straightforward matrix inversion, the MMSE detection requires the computation of the Gram matrix with high complexity. In order to avoid the Gram matrix computation, an equivalent augmented matrix is applied to KA-based MMSE detection. Moreover, promising initial estimation and an approximate method to compute soft-output information are utilized to further accelerate the convergence rate and reduce the complexity. Simulation results demonstrate that the proposed approach outperforms the recently proposed Neumann series, conjugate gradient, and Gauss-Seidel methods in complexity and error-rate performance. Meanwhile, the FPGA implementation results confirm that our proposed method can efficiently compute the approximate inverse with low complexity.

This paper proposes a novel access control scheme with collision detection that utilizes multiple-input multiple-output (MIMO) technology. Carrier sense multiple access with collision detection (CSMA/CD) is used in Ethernet wired local area networks (LANs) for media access control (MAC). CSMA/CD can immediately abort a transmission if any collision is detected and is thus able to change to a retransmission state. In Ethernet, CSMA/CD results in a transmission efficiency of approximately 90% because the protocol makes the transmission band available for useful communication by this retransmission function. Conversely, in conventional wireless LANs (WLANs), the packet collisions due to interfering signals and the retransmission due to collisions are significant issues. Because conventional WLANs cannot detect packet collisions during signal transmission, the success of a transmission can only be determined by whether an acknowledgment (ACK) frame has been received. Consequently, the transmission efficiency is low — approximately 60%. The objective of our study is to increase the transmission efficiency of WLANs to make it at least equal to that of Ethernet. Thus, we propose a novel access control scheme with collision detection that utilizes MIMO technology. When preamble signals are transmitted before transmitting data packets from an antenna, the proposed scheme can detect packet collisions during signal transmission at another antenna; then, the affected packets are retransmitted immediately. Two fundamental technologies are utilized to realize our proposed scheme. The first technology is the access control protocol in the MAC layer in the form of the MIMO frame sequence protocol, which is used to detect signal interference. The other technology is signal processing in the physical (PHY) layer that actualizes collision detection. This paper primarily deals with the proposed MAC layer scheme, which is evaluated by theoretical analyses and computer simulations. Evaluation by computer simulations indicate that the proposed scheme in a transmission efficiency of over 90%.

Applying Software Defined Network (SDN) technology to wireless networks are attracting much attention. Our previous study proposed a channel utilization method based on SDN/OpenFlow technology to improve the channel utilization efficiency of the multi-channel wireless backhaul network (WBN). However, since control messages are inherently transmitted with data traffic on a same channel in WBN, it inevitably degrades the network capacity. Specifically, the amount of control messages for collecting statistical information of each flow (FlowStats) linearly increases with the number of ongoing flows, thereby being the dominant overhead for backhaul networks. In this paper, we propose a new method that prevents the increase of control traffic while retaining the network performance of the previous method. Our proposed method uses statistical information of each interface (PortStats) instead of per-flow information (FlowStats), and handles multiple flows on the interface together if possible. Otherwise, to handle individual flow, we propose a way to estimate per-flow information without introducing extra control messages. Finally, we show that the proposed method offers the same performance with the previous method, while greatly reducing the amount of control traffic.

In this paper, we posit that, in future mobile network, network softwarization will be prevalent, and it becomes important to utilize deep machine learning within network to classify mobile traffic into fine grained slices, by identifying application types and devices so that we can apply Quality-of-Service (QoS) control, mobile edge/multi-access computing, and various network function per application and per device. This paper reports our initial attempt to apply deep machine learning for identifying application types from actual mobile network traffic captured from an MVNO, mobile virtual network operator and to design the system for classifying it to application specific slices.

Existing trajectory prediction methods suffer from the “data sparsity” and neglect “time awareness”, which leads to low accuracy. Aiming to the problem, we propose a fast time-aware sparse trajectories prediction with tensor factorization method (TSTP-TF). Firstly, we do trajectory synthesis based on trajectory entropy and put synthesized trajectories into the original trajectory space. It resolves the sparse problem of trajectory data and makes the new trajectory space more reliable. Then, we introduce multidimensional tensor modeling into Markov model to add the time dimension. Tensor factorization is adopted to infer the missing regions transition probabilities to further solve the problem of data sparsity. Due to the scale of the tensor, we design a divide and conquer tensor factorization model to reduce memory consumption and speed up decomposition. Experiments with real dataset show that TSTP-TF improves prediction accuracy generally by as much as 9% and 2% compared to the Baseline algorithm and ESTP-MF algorithm, respectively.

Behaviors of an object-oriented system can be visualized as reverse-engineered sequence diagrams from execution traces. This approach is a valuable tool for program comprehension tasks. However, owing to the massiveness of information contained in an execution trace, a reverse-engineered sequence diagram is often afflicted by a scalability issue. To address this issue, many trace summarization techniques have been proposed. Most of the previous techniques focused on reducing the vertical size of the diagram. To cope with the scalability issue, decreasing the horizontal size of the diagram is also very important. Nonetheless, few studies have addressed this point; thus, there is a lot of needs for further development of horizontal summarization techniques. We present in this paper a method for identifying core objects for trace summarization by analyzing reference relations and dynamic properties. Visualizing only interactions related to core objects, we can obtain a horizontally compactified reverse-engineered sequence diagram that contains system's key behaviors. To identify core objects, first, we detect and eliminate temporary objects that are trivial for a system by analyzing reference relations and lifetimes of objects. Then, estimating the importance of each non-trivial object based on their dynamic properties, we identify highly important ones (i.e., core objects). We implemented our technique in our tool and evaluated it by using traces from various open-source software systems. The results showed that our technique was much more effective in terms of the horizontal reduction of a reverse-engineered sequence diagram, compared with the state-of-the-art trace summarization technique. The horizontal compression ratio of our technique was 134.6 on average, whereas that of the state-of-the-art technique was 11.5. The runtime overhead imposed by our technique was 167.6% on average. This overhead is relatively small compared with recent scalable dynamic analysis techniques, which shows the practicality of our technique. Overall, our technique can achieve a significant reduction of the horizontal size of a reverse-engineered sequence diagram with a small overhead and is expected to be a valuable tool for program comprehension.

This paper proposes a semi-supervised source separation method for stereophonic music signals containing multiple recorded or processed signals, where synthesized music is focused on the stereophonic music. As the synthesized music signals are often generated as linear combinations of many individual source signals and their respective mixing gains, phase or phase difference information between inter-channel signals, which represent spatial characteristics of recording environments, cannot be utilized as acoustic clues for source separation. Non-negative Tensor Factorization (NTF) is an effective technique which can be used to resolve this problem by decomposing amplitude spectrograms of stereo channel music signals into basis vectors and activations of individual music source signals, along with their corresponding mixing gains. However, it is difficult to achieve sufficient separation performance using this method alone, as the acoustic clues available for separation are limited. To address this issue, this paper proposes a Cepstral Distance Regularization (CDR) method for NTF-based stereo channel separation, which involves making the cepstrum of the separated source signals follow Gaussian Mixture Models (GMMs) of the corresponding the music source signal. These GMMs are trained in advance using available samples. Experimental evaluations separating three and four sound sources are conducted to investigate the effectiveness of the proposed method in both supervised and semi-supervised separation frameworks, and performance is also compared with that of a conventional NTF method. Experimental results demonstrate that the proposed method yields significant improvements within both separation frameworks, and that cepstral distance regularization provides better separation parameters.

Code smells are indicators of design flaws or problems in the source code. Various tools and techniques have been proposed for detecting code smells. These tools generally detect a large number of code smells, so approaches have also been developed for prioritizing and filtering code smells. However, lack of empirical data detailing how developers filter and prioritize code smells hinders improvements to these approaches. In this study, we investigated ten professional developers to determine the factors they use for filtering and prioritizing code smells in an open source project under the condition that they complete a list of five tasks. In total, we obtained 69 responses for code smell filtration and 50 responses for code smell prioritization from the ten professional developers. We found that Task relevance and Smell severity were most commonly considered during code smell filtration, while Module importance and Task relevance were employed most often for code smell prioritization. These results may facilitate further research into code smell detection, prioritization, and filtration to better focus on the actual needs of developers.

A plasmonic black pole (PBP) consisting of a series of touching spherical metal surfaces is analyzed using the finite-difference time-domain (FDTD) method with the periodic boundary condition. First, the wavelength characteristics of the PBP are studied under the assumption that the PBP is omnidirectionally illuminated. It is found that partial truncation of each metal sphere reduces the reflectivity over a wide wavelength range. Next, we consider the case where the PBP is illuminated with a cylindrical wave from a specific direction. It is shown that an absorptivity of more than 80% is obtained over a wavelength range of λ=500 nm to 1000 nm. Calculation regarding the Poynting vector distribution also shows that the incident wave is bent and absorbed towards the center axis of the PBP.

Bug report summarization has been explored in past research to help developers comprehend important information for bug resolution process. As text mining technology advances, many summarization approaches have been proposed to provide substantial summaries on bug reports. In this paper, we propose an enhanced summarization approach called TSM by first extending a semantic model used in AUSUM with the anthropogenic and procedural information in bug reports and then integrating the extended semantic model with the shallow textual information used in BRC. We have conducted experiments with a dataset of realistic software projects. Compared with the baseline approaches BRC and AUSUM, TSM demonstrates the enhanced performance in achieving relative improvements of 34.3% and 7.4% in the F1 measure, respectively. The experimental results show that TSM can effectively improve the performance.

A multi-channel cognitive radio is a powerful solution for recovering the exhaustion of frequency spectrum resources. In a cognitive radio, although master and slave terminals (which construct a communication link) have the freedom to access arbitrary channels, access channel mismatch is caused. A rendezvous scheme based on frequency hopping can compensate for this mismatch by exchanging control signals through a selected channel in accordance with a certain rule. However, conventional frequency hopping schemes do not consider an access protocol of both control signals in the rendezvous scheme and the signal caused by channel access from other systems. Further, they do not consider an information sharing method to reach a consensus between the master and slave terminals. This paper proposes a modified rendezvous scheme based on learning-based channel occupancy rate (COR) estimation and describes a specific channel-access rule in the slave terminal. On the basis of this rule, the master estimates a channel selected by the slave by considering the average COR of the other systems. Since the master can narrow down the number of channels, a fast rendezvous scheme with a few control signals is established.

The energy consumption of network virtualization environments (NVEs) has become a critical issue. In this paper, we focus on reducing the data switching energy consumption of NVE. We first analyze the data switching energy of NVE. Then, we propose a dynamic energy efficient virtual link resource reallocation (eEVLRR) approach for NVE. eEVLRR dynamically reallocates the energy efficient substrate resources (s-resources) for virtual links with dynamic changes of embeddable s-resources to save the data switching energy. In order to avoid traffic interruptions while reallocating, we design a cross layer application-session-based forwarding model for eEVLRR that can identify and forward each data transmission flow along the initial specified substrate data transport path until end without traffic interruptions. The results of performance evaluations show that eEVLRR not only guarantees the allocated s-resources of virtual links are continuously energy efficient to save data switching energy but also has positive impacts on virtual network acceptance rate, revenues and s-resources utilization.

This paper presents low-noise amplifier (LNA)-less 300-GHz CMOS receivers that operate above the NMOS unity-power-gain frequency, fmax. The receivers consist of a down-conversion mixer with a doubler- or tripler-last multiplier chain that upconverts an LO1/n signal into 300 GHz. The conversion gain of the receiver with the doubler-last multiplier is -19.5 dB and its noise figure, 3-dB bandwidth, and power consumption are 27 dB, 27 GHz, and 0.65 W, respectively. The conversion gain of the receiver with the tripler-last multiplier is -18 dB and its noise figure, 3-dB bandwidth, and power consumption are 25.5 dB, 33 GHz, and 0.41 W, respectively. The receivers achieve a wireless data rate of 32 Gb/s with 16QAM. This shows the potential of the moderate-fmax CMOS technology for ultrahigh-speed THz wireless communications.

To meet the demand for continuous increase in data traffic, full usage of polarization freedom of light is becoming inevitable in the next-generation optical communication and datacenter networks. In particular, Stokes-vector modulation direct-detection (SVM-DD) formats are expected as potentially cost-effective method to transmit multi-level signals without using costly coherent transceivers in the short-reach links. For the SVM-DD formats to be practical, both the transmitter and receiver need to be substantially simpler, smaller, and lower-cost as compared to coherent counterparts. To this end, we have recently proposed and demonstrated novel SV modulator and receiver circuits realized on monolithic InP platforms. With compact non-interferometric configurations, relatively simple fabrication procedures, and compatibility with other active photonic components, the proposed devices should be attractive candidate in realizing low-cost monolithic transceivers for SVM formats. In this paper, we review our approaches as well as recent progresses and provide future prospects.

This paper focuses mainly on issues related to the pricing of American options under a fuzzy environment by taking into account the clustering of the underlying asset price volatility, leverage effect and stochastic jumps. By treating the volatility as a parabolic fuzzy number, we constructed a Levy-GJR-GARCH model based on an infinite pure jump process and combined the model with fuzzy simulation technology to perform numerical simulations based on the least squares Monte Carlo approach and the fuzzy binomial tree method. An empirical study was performed using American put option data from the Standard & Poor's 100 index. The findings are as follows: under a fuzzy environment, the result of the option valuation is more precise than the result under a clear environment, pricing simulations of short-term options have higher precision than those of medium- and long-term options, the least squares Monte Carlo approach yields more accurate valuation than the fuzzy binomial tree method, and the simulation effects of different Levy processes indicate that the NIG and CGMY models are superior to the VG model. Moreover, the option price increases as the time to expiration of options is extended and the exercise price increases, the membership function curve is asymmetric with an inclined left tendency, and the fuzzy interval narrows as the level set α and the exponent of membership function n increase. In addition, the results demonstrate that the quasi-random number and Brownian Bridge approaches can improve the convergence speed of the least squares Monte Carlo approach.

In this letter, we explored the usage of spatio-temporal information in one unified framework to improve the performance of multichannel speech recognition. Generalized cross correlation (GCC) is served as spatial feature compensation, and an attention mechanism across time is embedded within long short-term memory (LSTM) neural networks. Experiments on the AMI meeting corpus show that the proposed method provides a 8.2% relative improvement in word error rate (WER) over the model trained directly on the concatenation of multiple microphone outputs.

We have proposed and developed new electro-optic modulators for the pre-equalization of signal distortion caused by the optical fiber chromatic dispersion effect. We found that the synthesis of an almost arbitrary impulse response function is obtainable by utilizing an electro-optic modulator composed of a Mach-Zehnder waveguide and travelling-wave electrodes on a ferro-electric material substrate with polarization-reversed structures. In this paper, the operational principle, design and simulation results of the pre-equalization modulator are presented. Some preliminary experimental results are also shown with future prospects.