Neural networks (NNs) fail to perform well or make excessive predictions when predicting out-of-distribution or unseen datasets. In contrast, Bayesian neural networks (BNNs) can quantify the uncertainty of their inference to solve this problem. Nevertheless, BNNs have not been widely adopted owing to their increased memory and computational cost. In this study, we propose a novel approach to extend binary neural networks by introducing a probabilistic interpretation of binary weights, effectively converting them into BNNs. The proposed approach can reduce the number of weights by half compared to the conventional method. A comprehensive comparative analysis with established methods like Monte Carlo dropout and Bayes by backprop was performed to assess the performance and capabilities of our proposed technique in terms of accuracy and capturing uncertainty. Through this analysis, we aim to provide insights into the advantages of this Bayesian extension.

Cloud gaming systems allow users to play games that require high-performance computational capability on their mobile devices at any location. However, playing games through cloud gaming systems increases the Round-Trip Time (RTT) due to increased network delay. To simulate a local gaming experience for cloud users, we must minimize RTTs, which include network delays. The speculative video transmission pre-generates and encodes video frames corresponding to all possible user inputs and sends them to the user before the user’s input. The speculative video transmission mitigates the network, whereas a simple solution significantly increases the video traffic. This paper proposes tile-wise delta detection for traffic reduction of speculative video transmission. More specifically, the proposed method determines a reference video frame from the generated video frames and divides the reference video frame into multiple tiles. We calculate the similarity between each tile of the reference video frame and other video frames based on a hash function. Based on calculated similarity, we determine redundant tiles and do not transmit them to reduce traffic volume in minimal processing time without implementing a high compression ratio video compression technique. Evaluations using commercial games showed that the proposed method reduced 40-50% in traffic volume when the SSIM index was around 0.98 in certain genres, compared with the speculative video transmission method. Furthermore, to evaluate the feasibility of the proposed method, we investigated the effectiveness of network delay reduction with existing computational capability and the requirements in the future. As a result, we found that the proposed scheme may mitigate network delay by one to two frames, even with existing computational capability under limited conditions.

This paper proposes a non-linear overloaded MIMO detector that outperforms the conventional soft-input maximum likelihood detector (MLD) with less computational complexity. We propose iterative log-likelihood ratio (LLR) estimation and multi stage LLR estimation for the proposed detector to achieve such superior performance. While the iterative LLR estimation achieves better BER performance, the multi stage LLR estimation makes the detector less complex than the conventional soft-input maximum likelihood detector (MLD). The computer simulation reveals that the proposed detector achieves about 0.6dB better BER performance than the soft-input MLD with about half of the soft-input MLD's complexity in a 6×3 overloaded MIMO OFDM system.

In this study, vertically aligned carbon nanotubes (VA-CNTs) were grown from filler-added LB films with accumulated AlFe2O4 nanoparticles and palmitic acid (C16) as the filler molecule after different hydrogen reduction temperatures of 500°C and 750°C, and the grown VA-CNTs were compared and evaluated. As a result, VA-CNTs were approximately doubled in length after 500°C hydrogen reduction compared to 750°C hydrogen reduction when AlFe2O4 NPs were used. On the other hand, when the catalyst area ratio was decreased by using palmitic acid, i.e., the distance between CNTs was increased, VA-CNTs rapidly shortened after 500°C hydrogen reduction, and VA-CNTs were no longer obtained even in the range where VA-CNTs were obtained in 750°C hydrogen reduction. The inner and outer diameters of VA-CNTs decreased with decreasing catalyst area ratio at 750°C hydrogen reduction and tended to increase at 500°C hydrogen reduction. The morphology of the catalyst nanoparticles after CVD was observed to change significantly depending on the hydrogen reduction temperature and catalyst area ratio. These observations indicate that the state of the catalyst nanoparticles immediately before the CNT growth process greatly affects the physical properties of the CNTs.

A Boolean network (BN) is well known as a discrete model for analysis and control of complex networks such as gene regulatory networks. Since complex networks are large-scale in general, it is important to consider model reduction. In this paper, we consider model reduction that the information on fixed points (singleton attractors) is preserved. In model reduction studied here, the interaction graph obtained from a given BN is utilized. In the existing method, the minimum feedback vertex set (FVS) of the interaction graph is focused on. The dimension of the state is reduced to the number of elements of the minimum FVS. In the proposed method, we focus on complement and absorption laws of Boolean functions in substitution operations of a Boolean function into other one. By simplifying Boolean functions, the dimension of the state may be further reduced. Through a numerical example, we present that by the proposed method, the dimension of the state can be reduced for BNs that the dimension of the state cannot be reduced by the existing method.

Event structures are a well-known modeling formalism for concurrent systems with causality and conflict relations. The flow event structure (FES) is a variant of event structures, which is a generalization of the prime event structure. In an FES, two events may be in conflict even though they are not syntactically in conflict; this is called a semantic conflict. The existence of semantic conflict in an FES motivates reducing conflict relations (i.e., conflict reduction) to obtain a simpler structure. In this paper, we study conflict reduction in acyclic FESs. A necessary and sufficient condition for conflict reduction is given; algorithms to compute semantic conflict, local configurations, and conflict reduction are proposed. A great time reduction was observed in computational experiments when comparing the proposed with the naive method.

To achieve object recognition, it is necessary to find the unique features of the objects to be recognized. Results in prior research suggest that methods that use multiple modalities information are effective to find the unique features. In this paper, the overview of the system that can extract the features of the objects to be recognized by integrating visual, tactile, and auditory information as multimodal sensor information with VRAE is shown. Furthermore, a discussion about changing the combination of modalities information is also shown.

High gain antennas with narrow-beamforming are required to compensate for the high propagation loss expected in high frequency bands such as the millimeter wave and sub-terahertz wave bands, which are promising for achieving extremely high speeds and capacity. However using narrow-beamforming for initial access (IA) beam search in all directions incurs an excessive overhead. Using wide-beamforming can reduce the overhead for IA but it also shrinks the coverage area due to the lower beamforming gain. Here, it is assumed that there are some situations in which the required coverage distance differs depending on the direction from the antenna. For example, the distance to an floor for a ceiling-mounted antenna varies depending on the direction, and the distance to the obstruction becomes the required coverage distance for an antenna installation design that assumes line-of-sight. In this paper, we propose a novel IA beam search scheme with adaptive beam width control based on the distance to shield obstacles in each direction. Simulations and experiments show that the proposed method reduces the overhead by 20%-50% without shrinking the coverage area in shield environments compared to exhaustive beam search with narrow-beamforming.

Annealing machines such as quantum annealing machines and semiconductor-based annealing machines have been attracting attention as an efficient computing alternative for solving combinatorial optimization problems. They solve original combinatorial optimization problems by transforming them into a data structure called an Ising model. At that time, the bit-widths of the coefficients of the Ising model have to be kept within the range that an annealing machine can deal with. However, by reducing the Ising-model bit-widths, its minimum energy state, or ground state, may become different from that of the original one, and hence the targeted combinatorial optimization problem cannot be well solved. This paper proposes an effective method for reducing Ising model's bit-widths. The proposed method is composed of two processes: First, given an Ising model with large coefficient bit-widths, the shift method is applied to reduce its bit-widths roughly. Second, the spin-adding method is applied to further reduce its bit-widths to those that annealing machines can deal with. Without adding too many extra spins, we efficiently reduce the coefficient bit-widths of the original Ising model. Furthermore, the ground state before and after reducing the coefficient bit-widths is not much changed in most of the practical cases. Experimental evaluations demonstrate the effectiveness of the proposed method, compared to existing methods.

We propose a short signature scheme under the ring-SIS assumption in the standard model. Specifically, by revisiting an existing construction [Ducas and Micciancio, CRYPTO 2014], we demonstrate lattice-based signatures with improved reduction loss. As far as we know, there are no ways to use multiple tags in the signature simulation of security proof in the lattice tag-based signatures. We address the tag-collision possibility in the lattice setting, which improves reduction loss. Our scheme generates tags from messages by constructing a scheme under a mild security condition that is existentially unforgeable against random message attack with auxiliary information. Thus our scheme can reduce the signature size since it does not need to send tags with the signatures. Our scheme has short signature sizes of O(1) and achieves tighter reduction loss than that of Ducas et al.'s scheme. Our proposed scheme has two variants. Our scheme with one property has tighter reduction and the same verification key size of O(log n) as that of Ducas et al.'s scheme, where n is the security parameter. Our scheme with the other property achieves much tighter reduction loss of O(Q/n) and verification key size of O(n), where Q is the number of signing queries.

Due to the limitations of cloud computing on latency, bandwidth and data confidentiality, edge computing has emerged as a novel location-aware paradigm to provide them with more processing capacity to improve the computing performance and quality of service (QoS) in several typical domains of human activity in smart society, such as social networks, medical diagnosis, telecommunications, recommendation systems, internal threat detection, transports, Internet of Things (IoT), etc. These application domains often handle a vast collection of entities with various relationships, which can be naturally represented by the graph data structure. Graph processing is a powerful tool to model and optimize complex problems in which the graph-based data is involved. In view of the relatively insufficient resource provisioning of the portable terminals, in this paper, for the first time to our knowledge, we propose an interactive and reductive graph processing library (GPL) for edge computing in smart society at low overhead. Experimental evaluation is conducted to indicate that the proposed GPL is more user-friendly and highly competitive compared with other established systems, such as igraph, NetworKit and NetworkX, based on different graph datasets over a variety of popular algorithms.

This paper proposes a novel approach to low complexity soft input decoding for lattice reduction-aided MIMO receivers. The proposed approach feeds a soft input decoder with soft signals made from hard decision signals generated by using a lattice reduction-aided linear detector. The soft signal is a weighted-sum of some candidate vectors that are near by the hard decision signal coming out from the lattice reduction-aided linear detector. This paper proposes a technique to adjust the weight adapt to the channel for the higher transmission performance. Furthermore, we propose to introduce a coefficient that is used for the weights in order to enhance the transmission performance. The transmission performance is evaluated in a 4×4 MIMO channel. When a linear MMSE filter or a serial interference canceller is used as the linear detector, the proposed technique achieves about 1.0dB better transmission performance at the BER of 10-5 than the decoder fed with the hard decision signals. In addition, the low computational complexity of the proposed technique is quantitatively evaluated.

Non-orthogonal multiple access (NOMA) allows several users to multiplex in the power-domain to improve spectral efficiency. To further improve its performance, it is desirable to reduce inter-user interference (IUI). In this paper, we propose a downlink asynchronous NOMA (ANOMA) scheme applicable to frequency-selective channels. The proposed scheme introduces an intentional symbol offset between the multiplexed signals to reduce IUI, and it employs cyclic-prefixed single-carrier transmission with frequency-domain equalization (FDE) to reduce inter-symbol interference. We show that the mean square error for the FDE of the proposed ANOMA scheme is smaller than that of a conventional NOMA scheme. Simulation results show that the proposed ANOMA with appropriate power allocation achieves a better sum rate compared to the conventional NOMA.

Graph layouts reveal global or local structures of graph data. However, there are few studies on assisting readers in better reconstructing a graph from a layout. This paper attempts to generate a layout whose edges can be reestablished. We reformulate the graph layout problem as an edge classification problem. The inputs are the vertex pairs, and the outputs are the edge existences. The trainable parameters are the laid-out coordinates of the vertices. We propose a binary classification-based graph layout (BCGL) framework in this paper. This layout aims to preserve the local structure of the graph and does not require the total similarity relationships of the vertices. We implement two concrete algorithms under the BCGL framework, evaluate our approach on a wide variety of datasets, and draw comparisons with several other methods. The evaluations verify the ability of the BCGL in local neighborhood preservation and its visual quality with some classic metrics.

Environmental power generation devices such as solar cells are used as power sources for IoT devices. Due to the large internal resistance of such power source, LSIs in the IoT devices may malfunction when the LSI operates at high speed, a large current flows, and the voltage drops. In this paper, a standard cell library of stacked structured cells is proposed to increase the delay of logic circuits within the range not exceeding the clock cycle, thereby reducing the maximum current of the LSIs. We show that the maximum power consumption of LSIs can be reduced without increasing the energy consumption of the LSIs.

In this study, we propose a mechanism called adaptive failsoft control to address peak traffic in mobile live streaming, using a chasing playback function. Although a cache system is avaliable to support the chasing playback function for live streaming in a base station and device-to-device communication, the request concentration by highlight scenes influences the traffic load owing to data unavailability. To avoid data unavailability, we adapted two live streaming features: (1) streaming data while switching the video quality, and (2) time variability of the number of requests. The second feature enables a fallback mechanism for the cache system by prioritizing cache eviction and terminating the transfer of cache-missed requests. This paper discusses the simulation results of the proposed mechanism, which adopts a request model appropriate for (a) avoiding peak traffic and (b) maintaining continuity of service.

To cope with complicated interference scenarios in realistic acoustic environment, supervised deep neural networks (DNNs) are investigated to estimate different user-defined targets. Such techniques can be broadly categorized into magnitude estimation and time-frequency mask estimation techniques. Further, the mask such as the Wiener gain can be estimated directly or derived by the estimated interference power spectral density (PSD) or the estimated signal-to-interference ratio (SIR). In this paper, we propose to incorporate the multi-task learning in DNN-based single-channel speech enhancement by using the speech presence probability (SPP) as a secondary target to assist the target estimation in the main task. The domain-specific information is shared between two tasks to learn a more generalizable representation. Since the performance of multi-task network is sensitive to the weight parameters of loss function, the homoscedastic uncertainty is introduced to adaptively learn the weights, which is proven to outperform the fixed weighting method. Simulation results show the proposed multi-task scheme improves the speech enhancement performance overall compared to the conventional single-task methods. And the joint direct mask and SPP estimation yields the best performance among all the considered techniques.

Recent IoT devices require extremely low standby power consumption, while a certain performance is needed during the active time, and Coarse-Grained Reconfigurable Arrays (CGRAs) have received attention because of their high energy efficiency. For further reduction of the standby energy consumption of CGRAs, the leakage power for their configuration memory must be reduced. Although the power gating is a common technique, the lost data in flip-flops and memory must be retrieved after the wake-up. Recovering everything requires numerous state transitions and considerable overhead both on its execution time and energy. To address the problem, Non-volatile Cool Mega Array (NVCMA), a CGRA providing non-volatile flip-flops (NVFFs) with spin transfer torque type non-volatile memory (NVM) technology has been developed. However, in general, non-volatile memory technologies have problems with reliability. Some NVFFs are stacked-at-0/1, and cannot store the data in a certain possibility. To improve the chip yield, we propose a mapping algorithm to avoid faulty processing elements of the CGRA caused by the erroneous configuration data. Next, we also propose a method to add an error-correcting code (ECC) mechanism to NVFFs for the configuration and constant memory. The proposed method was applied to NVCMA to evaluate the availability rate and reduction of write time. By using both methods, the average availability ratio of 94.2% was achieved, while the average availability ratio of the nine applications was 0.056% when the probability of failure of the FF was 0.01. The energy for storing data becomes about 2.3 times because of the hardware overhead of ECC but the proposed method can save 8.6% of the writing power on average.

The power consumption of optical network units (ONUs) is a major issue in optical access networks. The downstream buffer is one of the largest power consumers among the functional blocks of an ONU. A cyclic sleep scheme for reducing power has been reported, which periodically powers off not only the downstream buffer but also other components, such as optical transceivers, when the idle period is long. However, when the idle period is short, it cannot power off those components even if the input data rate is low. Therefore, as continuous traffic, such as video, increases, the power-reduction effect decreases. To resolve this issue, we propose another sleep scheme in which the downstream buffer can be partially powered off by cooperative operation with an optical line terminal. Simulation and experimental results indicate that the proposed scheme reduces ONU power consumption without causing frame loss even while the ONU continuously receives traffic and the idle period is short.

This paper proposes a parallel peak cancellation (PC) process for the computational complexity-efficient algorithm called PC with a channel-null constraint (PCCNC) in the adaptive peak-to-average power ratio (PAPR) reduction method using the null space in a multiple-input multiple-output (MIMO) channel for MIMO-orthogonal frequency division multiplexing (OFDM) signals. By simultaneously adding multiple PC signals to the time-domain transmission signal vector, the required number of iterations of the iterative algorithm is effectively reduced along with the PAPR. We implement a constraint in which the PC signal is transmitted only to the null space in the MIMO channel by beamforming (BF). By doing so the data streams do not experience interference from the PC signal on the receiver side. Since the fast Fourier transform (FFT) and inverse FFT (IFFT) operations at each iteration are not required unlike the previous algorithm and thanks to the newly introduced parallel processing approach, the enhanced PCCNC algorithm reduces the required total computational complexity and number of iterations compared to the previous algorithms while achieving the same throughput-vs.-PAPR performance.