It is widely known that decoding problems for random linear codes are computationally hard in general. Surprisingly, Kopparty and Saraf proved query-efficient list-decodability of sparse random linear codes by showing a reduction from a decoding problem for sparse random linear codes to that for the Hadamard code with small number of queries even under high error rate [11]. In this paper, we show a more direct list-decoding algorithm for sparse random linear codes with small number of queries from a Fourier-analytic approach.

Secure sets and defensive alliances in graphs are studied. They are sets of vertices that are safe in some senses. In this paper, we first present a fixed-parameter algorithm for finding a small secure set, whose running time is much faster than the previously known one. We then present improved bound on the smallest sizes of defensive alliances and secure sets for hypercubes. These results settle some open problems paused recently.

This paper discusses the decidability of node query preservation problems for tree transducers. We assume a transformation given by a deterministic linear top-down data tree transducer (abbreviated as DLTV) and an n-ary query based on runs of a tree automaton. We say that a DLTV Tr strongly preserves a query Q if there is a query Q' such that for every tree t, the answer set of Q' for Tr(t) is equal to the answer set of Q for t. We also say that Tr weakly preserves Q if there is a query Q' such that for every t, the answer set of Q' for Tr(t) includes the answer set of Q for t. We show that the weak preservation problem is coNP-complete and the strong preservation problem is in 2-EXPTIME. We also show that the problems are decidable when a given transducer is a functional extended linear top-down data tree transducer with regular look-ahead, which is a more expressive transducer than DLTV.

This paper describes an architectural design and related services of a new Japanese academic backbone network, called SINET5, which will be launched in April 2016. The network will cover all 47 prefectures with 100-Gigabit Ethernet technology and connect each pair of prefectures with a minimized latency. This will enable users to leverage evolving cloud-computing powers as well as draw on a high-performance platform for data-intensive applications. The transmission layer will form a fully meshed, SDN-friendly, and reliable network. The services will evolve to be more dynamic and cloud-oriented in response to user demands. Cyber-security measures for the backbone network and tools for performance acceleration and visualization are also discussed.

In the big data era, messaging systems are required to process large volumes of message traffic with high scalability and availability. However, conventional systems have two issues regarding availability. The first issue is that failover processing itself has a risk of failure. The second issue is to find a trade-off between consistency and availability. We propose a resilient messaging system based on a distributed in-memory key-value store (KVS). Its servers are interconnected with each other and messages are distributed to multiple servers in normal processing state. This architecture can continue messaging services wherever in the messaging system server/process failures occur without using failover processing. Furthermore, we propose two methods for improved resilience: the round-robin method with a slowdown KVS exclusion and the two logical KVS counter-rotating rings to provide short-term-availability in the messaging system. Evaluation results demonstrate that the proposed system can continue service without failover processing. Compared with the conventional method, our proposed distribution method reduced 92% of error responses to clients caused by server failures.

With the rise of component-based software development, its reliability has attracted much attention from both academic and industry communities. Component-based software development focuses on architecture design, and thus it is important for reliability analysis to emphasize software architecture. Existing approaches to architecture-based software reliability analysis don't model the usage profile explicitly, and they ignore the difference between the testing profile and the practical profile of components, which limits their applicability and accuracy. In response to these issues, a new reliability modeling and prediction approach is introduced. The approach considers reliability-related architecture factors by explicitly modeling the system usage profile, and transforms the testing profile into the practical usage profile of components by representing the profile with input sub-domains. Finally, the evaluation experiment shows the potential of the approach.

Face recognition under variable illumination conditions is a challenging task. Numbers of approaches have been developed for solving the illumination problem. In this paper, we summarize and analyze some noteworthy issues in illumination processing for face recognition by reviewing various representative approaches. These issues include a principle that associates various approaches with a commonly used reflectance model and the shared considerations like contribution of basic processing methods, processing domain, feature scale, and a common problem. We also address a more essential question-what to actually normalize. Through the discussion on these issues, we also provide suggestions on potential directions for future research. In addition, we conduct evaluation experiments on 1) contribution of fundamental illumination correction to illumination insensitive face recognition and 2) comparative performance of various approaches. Experimental results show that the approaches with fundamental illumination correction methods are more insensitive to extreme illumination than without them. Tan and Triggs' method (TT) using L1 norm achieves the best results among nine tested approaches.

Introducing pronunciation models into decoding has been proven to be benefit to LVCSR. In this paper, a discriminative pronunciation modeling method is presented, within the framework of the Minimum Phone Error (MPE) training for HMM/GMM. In order to bring the pronunciation models into the MPE training, the auxiliary function is rewritten at word level and decomposes into two parts. One is for co-training the acoustic models, and the other is for discriminatively training the pronunciation models. On Mandarin conversational telephone speech recognition task, compared to the baseline using a canonical lexicon, the discriminative pronunciation models reduced the absolute Character Error Rate (CER) by 0.7% on LDC test set, and with the acoustic model co-training, 0.8% additional CER decrease had been achieved.

Triggered by the explosion of mobile traffic, 5G (5th Generation) cellular network requires evolution to increase the system rate 1000 times higher than the current systems in 10 years. Motivated by this common problem, there are several studies to integrate mm-wave access into current cellular networks as multi-band heterogeneous networks to exploit the ultra-wideband aspect of the mm-wave band. The authors of this paper have proposed comprehensive architecture of cellular networks with mm-wave access, where mm-wave small cell basestations and a conventional macro basestation are connected to Centralized-RAN (C-RAN) to effectively operate the system by enabling power efficient seamless handover as well as centralized resource control including dynamic cell structuring to match the limited coverage of mm-wave access with high traffic user locations via user-plane/control-plane splitting. In this paper, to prove the effectiveness of the proposed 5G cellular networks with mm-wave access, system level simulation is conducted by introducing an expected future traffic model, a measurement based mm-wave propagation model, and a centralized cell association algorithm by exploiting the C-RAN architecture. The numerical results show the effectiveness of the proposed network to realize 1000 times higher system rate than the current network in 10 years which is not achieved by the small cells using commonly considered 3.5GHz band. Furthermore, the paper also gives latest status of mm-wave devices and regulations to show the feasibility of using mm-wave in the 5G systems.

The short-channel effect (SCE) in a MOSFET with an atomically thin MoS$_{2}$ channel was studied using a TCAD simulator. We derived the surface potential roll-up, drain-induced barrier lowering (DIBL), threshold voltage, and subthreshold swing (SS) as indexes of the SCE and analyzed their dependency on the channel thickness (number of atomic layers) and channel length. The minimum scalable channel length for a one-atomic-layer-thick MoS$_{2}$ MOSFET was determined from the threshold voltage roll-off to be 7.6,nm. The one-layer-thick device showed a small DIBL of 87,mV/V at a 20 nm gate length. By using high-k gate insulator, an SS lower than 70,mV/dec is achievable in sub-10-nm-scale devices.

In this letter, we propose a novel Uniformity-Approximated Histogram Equalization (UAHE) algorithm to enhance the image as well as to preserve the image features. First, the UAHE algorithm generates the image histogram and computes the average value of all bins as the histogram threshold. In order to approximate the uniform histogram, the bins of image histograms greater than the above threshold are clipped, and the subtracted counts are averaged and uniformly assigned to the remaining bins lower than the threshold. The approximated uniform histogram is then applied to generate the intensity transformation function for image contrast enhancement. Experimental results show that our algorithm achieves the maximum entropy as well as the feature similarity values for image contrast enhancement.

One of the fast approximate similarity search techniques is a binary hashing method that transforms a real-valued vector into a binary code. The similarity between two binary codes is measured by their Hamming distance. In this method, a hash table is often used when undertaking a constant-time similarity search. The number of accesses to the hash table, however, increases when the number of bits lengthens. In this paper, we consider a method that does not access data with a long Hamming radius by using multiple binary codes. Further, we attempt to integrate the proposed approach and the existing multi-index hashing (MIH) method to accelerate the performance of the similarity search in the Hamming space. Then, we propose a learning method of the binary hash functions for multiple binary codes. We conduct an experiment on similarity search utilizing a dataset of up to 50 million items and show that our proposed method achieves a faster similarity search than that possible with the conventional linear scan and hash table search.

How to retrieve the closest content from an in-network cache is one of the most important issues in Information-Centric Networking (ICN). This paper proposes a novel content discovery scheme called Local Tree Hunting (LTH). By adding branch-cast functionality to a local tree for content requests to a Content-Centric Network (CCN) response node, the discovery area for caching nodes expands. Since the location of such a branch-casting node moves closer to the request node when the content is more widely cached, the discovery range, i.e. the branch size of the local tree, becomes smaller. Thus, the discovery area is autonomously adjusted depending on the content dissemination. With this feature, LTH is able to find the “almost true closest” caching node without checking all the caching nodes in the in-network cache. The performance analysis employed in Zipf's law content distribution model and which uses the Least Recently Used eviction rule shows the superiority of LTH with respect to identifying the almost exact closest cache.

In this letter, we propose a simple framework for accelerating a state-of-the-art histogram-based weighted median filter at no expense. It is based on a process of determining the filter processing direction. The determination is achieved by measuring the local feature variation of input images. Through experiments with natural images, it is verified that, depending on input images, the filtering speed can be substantially increased by changing the filtering direction.

Superconducting tunnel junction (STJ) array detectors can exhibit excellent performance with respect to energy resolution, detection efficiency, and counting rate in the soft X-ray energy range, by which those excellent properties STJ array detectors are well suited for detecting X-rays at synchrotron radiation facilities. However, in order to achieve a high throughput analysis for trace impurity elements such as dopants in structural or functional materials, the sensitive area of STJ array detectors should be further enlarged up to more than 10 times larger by increasing the pixel number in array detectors. In this work, for a large STJ-pixel number of up to 1000 within a 10,mm- square compact chip, we have introduced three-dimensional (3D) structure by embedding a wiring layer in a SiO$_{2}$ isolation layer underneath a base electrode layer of STJs. The 3D structure is necessary for close-packed STJ arrangement, avoiding overlay of lead wiring, which is common in conventional two-dimensional layout. The fabricated STJ showed excellent current-voltage characteristics having low subgap currents less than 2,nA, which are the same as those of conventional STJs. An STJ pixel has an energy resolution of 31,eV (FWHM) for C-K$alpha $ (277,eV).

Recently, a next-generation heterodyne mixer detector---a hot electron bolometer (HEB) mixer employing a superconducting microbridge---has gradually opened up terahertz-band astronomy. The surrounding state-of-the-art technologies including fabrication processes, 4 K cryostats, cryogenic low-noise amplifiers, local oscillator sources, micromachining techniques, and spectrometers, as well as the HEB mixers, have played a valuable role in the development of super-low-noise heterodyne spectroscopy systems for the terahertz band. The current developmental status of terahertz-band HEB mixer receivers and their applications for spectroscopy and astronomy with ground-based, airborne, and satellite telescopes are presented.

Content caching networks like Information-Centric Networking (ICN) are beneficial to reduce the network traffic by storing content data on routers near to users. In ICN, it becomes an important issue to manage system resources, such as storage and network bandwidth, which are influenced by cache characteristics of each cache node. Meanwhile, cache aging techniques based on Time-To-Live (TTL) of content facilitate analyzing cache characteristics and can realize appropriate resource management by setting efficient TTLs. However, it is difficult to search for the efficient TTLs in a distributed cache system connected by multiple cache nodes. Therefore, we propose an adaptive control mechanism of the TTL value of content in distributed cache systems by using predictive models which can estimate the impact of the TTL values on network resources and cache performance. Furthermore, we show the effectiveness of the proposed mechanism.

Detecting traffic anomalies is an indispensable component of overall security architecture. As Internet and traffic data with more sophisticated attacks grow exponentially, preserving security with signature-based traffic analyzers or analyzers that do not support massive traffic are not sufficient. In this paper, we propose a novel method based on combined sketch technique and S-transform analysis for detecting anomalies in massive traffic streams. The method does not require any prior knowledge such as attack patterns and models representing normal traffic behavior. To detect anomalies, we summarize the entropy of traffic data over time and maintain the summarized data in sketches. The entropy fluctuation of the traffic data aggregated to the same bucket is observed by S-transform to detect spectral changes referred to as anomalies in this work. We evaluated the performance of the method with real-world backbone traffic collected at the United States and Japan transit link in terms of both accuracy and false positive rates. We also explored the method parameters' influence on detection performance. Furthermore, we compared the performance of our method to S-transform-based and Wavelet-based methods. The results demonstrated that our method was capable of detecting anomalies and overcame both methods. We also found that our method was not sensitive to its parameter settings.