We propose a non-photorealistic rendering method to automatically generate reaction-diffusion-pattern-like images from photographic images. The proposed method uses smoothing filter with a circular window, and changes the size of the circular window depending on the position in photographic images. By partially changing the size of the circular window, the size of reaction-diffusion patterns can be changed partially. To verify the effectiveness of the proposed method, experiments were conducted to apply the proposed method to various photographic images.

To achieve the extreme high data rate and extreme coverage extension requirements of 6G wireless communication, new spectrum in sub-THz (100-300GHz) and non-terrestrial network (NTN) are two of the macro trends of 6G candidate technologies, respectively. However, non-linearity of power amplifiers (PA) is a critical challenge for both sub-THz and NTN. Therefore, high power efficiency (PE) or low peak to average power ratio (PAPR) waveform design becomes one of the most significant 6G research topics. Meanwhile, high spectral efficiency (SE) and low out-of-band emission (OOBE) are still important key performance indicators (KPIs) for 6G waveform design. Single-carrier waveform discrete Fourier transform spreading orthogonal frequency division multiplexing (DFT-s-OFDM) has achieved many research interests due to its high PE, and it has been supported in 5G New Radio (NR) when uplink coverage is limited. So DFT-s-OFDM can be regarded as a candidate waveform for 6G. Many enhancement schemes based on DFT-s-OFDM have been proposed, including null cyclic prefix (NCP)/unique word (UW), frequency-domain spectral shaping (FDSS), and time-domain compression and expansion (TD-CE), etc. However, there is no unified framework to be compatible with all the enhancement schemes. This paper firstly provides a general description of the 6G candidate waveforms based on DFT-s-OFDM enhancement. Secondly, the more flexible TD-CE supporting methods for unified non-orthogonal waveform (uNOW) are proposed and discussed. Thirdly, a unified waveform framework based on DFT-s-OFDM structure is proposed. By designing the pre-processing and post-processing modules before and after DFT in the unified waveform framework, the three technical methods (NCP/UW, FDSS, and TD-CE) can be integrated to improve three KPIs of DFT-s-OFDM simultaneously with high flexibility. Then the implementation complexity of the 6G candidate waveforms are analyzed and compared. Performance of different DFT-s-OFDM enhancement schemes is investigated by link level simulation, which reveals that uNOW can achieve the best PAPR performance among all the 6G candidate waveforms. When considering PA back-off, uNOW can achieve 124% throughput gain compared to traditional DFT-s-OFDM.

The demand for low-latency transmission of large-capacity video, such as 4K and 8K, is increasing for various applications such as live-broadcast program production, sports viewing, and medical care. In the broadcast industry, low-latency video transmission is required in remote production, an emerging workflow for outside broadcasting. For ideal remote production, long-distance transmission of uncompressed 8K60p video signals, ultra-low latency less than 16.7 ms, and PTP synchronization through network are required; however, no existing video-transmission system fully satisfy these requirements. We focused on optical transport technologies capable of long-distance and large-capacity communication, which were previously used only in telecommunication-carrier networks. To fully utilize optical transport technologies, we propose the first-ever video-transmission system architecture capable of sending and receiving uncompressed 8K video directly through large-capacity optical paths. A transmission timing control in seamless protection switching is also proposed to improve the tolerance to network impairment. As a means of implementation, we focused on whitebox transponder, an emerging type of optical transponder with a disaggregation configuration. The disaggregation configuration enables flexible configuration changes, additional implementations, and cost reduction by separating various functions of optical transponders and controlling them with a standardized interface. We implemented the ultra-low-latency video-transmission system utilizing whitebox transponder Galileo. We developed a hardware plug-in unit for video transmission (VideoPIU), and software to control the VideoPIU. In the video-transmission experiments with 120-km optical fiber, we confirmed that it was capable of transmitting uncompressed 8K60p video stably in 1.3 ms latency and highly accurate PTP synchronization through the optical network, which was required in the ideal remote production. In addition, the application to immersive sports viewing is also presented. Consequently, excellent potential to support the unprecedented applications is demonstrated.

The increasing attention to the interpretability of machine learning models has led to the development of methods to explain the behavior of black-box models in a post-hoc manner. However, such post-hoc approaches generate a new explanation for every new input, and these explanations cannot be checked by humans in advance. A method that selects decision rules from a finite ruleset as explanation for neural networks has been proposed, but it cannot be used for other models. In this paper, we propose a model-agnostic explanation method to find a pre-verifiable finite ruleset from which a decision rule is selected to support every prediction made by a given black-box model. First, we define an explanation model that selects the rule, from a ruleset, that gives the closest prediction; this rule works as an alternative explanation or supportive evidence for the prediction of a black-box model. The ruleset should have high coverage to give close predictions for future inputs, but it should also be small enough to be checkable by humans in advance. However, minimizing the ruleset while keeping high coverage leads to a computationally hard combinatorial problem. Hence, we show that this problem can be reduced to a weighted MaxSAT problem composed only of Horn clauses, which can be efficiently solved with modern solvers. Experimental results showed that our method found small rulesets such that the rules selected from them can achieve higher accuracy for structured data as compared to the existing method using rulesets of almost the same size. We also experimentally compared the proposed method with two purely rule-based models, CORELS and defragTrees. Furthermore, we examine rulesets constructed for real datasets and discuss the characteristics of the proposed method from different viewpoints including interpretability, limitation, and possible use cases.

Neuromorphic computing with a spiking neural network (SNN) is expected to provide a complement or alternative to deep learning in the future. The challenge is to develop optimal SNN models, algorithms, and engineering technologies for real use cases. As a potential use cases for neuromorphic computing, we have investigated a person monitoring and worker support with a video surveillance system, given its status as a proven deep neural network (DNN) use case. In the future, to increase the number of cameras in such a system, we will need a scalable approach that embeds only a few neuromorphic devices in a camera. Specifically, this will require a shallow SNN model that can be implemented in a few neuromorphic devices while providing a high recognition accuracy comparable to a DNN with the same configuration. A shallow SNN was built by converting ResNet, a proven DNN for image recognition, and a new configuration of the shallow SNN model was developed to improve its accuracy. The proposed shallow SNN model was evaluated with a few neuromorphic devices, and it achieved a recognition accuracy of more than 80% with about 1/130 less energy consumption than that of a GPU with the same configuration of DNN as that of SNN.

We discuss the spectral efficiency of orthogonal frequency-division multiplexing (OFDM) signals widely adopted in practical systems from a viewpoint of their power spectral density property. Since the conventional OFDM does not make use of pulse shaping filter, its out-of-band (OOB) spectrum may not be negligible especially when the number of subcarriers is small. Thus, in practice, windowing is applied to mitigate OOB emission by smoothing the transition of consecutive OFDM symbols, but its effectiveness has not been well investigated. Furthermore, OFDM signal suffers from nonlinear distortion associated with its high signal peak-to-average power ratio (PAPR), which also leads to OOB radiation. We examine how power amplifier nonlinearity affects the spectral efficiency based on the theoretical results developed in the literature.

To simultaneously enhance data rate and physical layer security (PLS) for low-luminance smartphone screen to camera uplink communication, space division multiplexing using high-luminance cell-size reduction arrangement is numerically analyzed and experimentally verified. The uplink consists of a low-luminance smartphone screen and an indoor telephoto camera at a long distance of 3.5 meters. The high-luminance cell-size reduction arrangement avoids the influence of spatial inter-symbol interference (ISI) and ambient light to obtain a stable low-luminance screen. To reduce the screen luminance without decreasing the screen pixel value, the arrangement reduces only the high-luminance cell area while keeping the cell spacing. In this study, two technical issues related to high-luminance cell-size reduction arrangement are solved. First, a numerical analysis and experimental results show that the high-luminance cell-size reduction arrangement is more effective in reducing the spatial ISI at low luminance than the conventional low-luminance cell arrangement. Second, in view point of PLS enhancement at wide angles, symbol error rate should be low in front of the screen and high at wide angles. A numerical analysis and experimental results show that the high-luminance cell-size reduction arrangement is more suitable for enhancing PLS at wide angles than the conventional low-luminance cell arrangement.

In quasi-synchronous FH multiple-access (QS-FHMA) systems, no-hit-zone frequency-hopping sequences (NHZ-FHSs) can offer interference-free FHMA performance. But, outside the no-hit-zone (NHZ), the Hamming correlation of traditional NHZ-FHZs maybe so large that the performance becomes not good. And in high-speed mobile environment, Doppler shift phenomenon will appear. In order to ensure the performance of FHMA, it is necessary to study the NHZ-FHSs in the presence of transmission delay and frequency offset. In this paper, We derive a lower bound on the maximum time-frequency two-dimensional Hamming correlation outside of the NHZ of NHZ-FHSs. The Zeng-Zhou-Liu-Liu bound is a particular situation of the new bound for frequency shift is zero.

A novel retransmission scheme, considering both transmission rate and frame error rate, is proposed to alleviate the inefficiencies caused by head-of-line blocking and null padding problems during retransmission in IEEE 802.11be synchronous multi-link wireless local area networks. Simulation results show that the proposed scheme improves throughput by up to 200% over the legacy scheme by reallocating lost subframes and adding effective duplicate subframes to multiple links.

The problem caused by fake news continues to worsen in today's online social networks. Intuitively, it seems effective to issue corrections as a countermeasure. However, corrections can, ironically, strengthen attention to fake news, which worsens the situation. This paper proposes a model for describing the interaction between fake news and the corrections as a reaction-diffusion system; this yields the mechanism by which corrections increase attention to fake news. In this model, the emergence of groups of users who believe in fake news is understood as a Turing pattern that appears in the activator-inhibitor model. Numerical calculations show that even if the network structure has no spatial bias, the interaction between fake news and the corrections creates groups that are strongly interested in discussing fake news. Also, we propose and evaluate a basic strategy to counter fake news.

Automatic continuous tracking of objects involved in a construction project is required for such tasks as productivity assessment, unsafe behavior recognition, and progress monitoring. Many computer-vision-based tracking approaches have been investigated and successfully tested on construction sites; however, their practical applications are hindered by the tracking accuracy limited by the dynamic, complex nature of construction sites (i.e. clutter with background, occlusion, varying scale and pose). To achieve better tracking performance, a novel deep-learning-based tracking approach called the Multi-Domain Convolutional Neural Networks (MD-CNN) is proposed and investigated. The proposed approach consists of two key stages: 1) multi-domain representation of learning; and 2) online visual tracking. To evaluate the effectiveness and feasibility of this approach, it is applied to a metro project in Wuhan China, and the results demonstrate good tracking performance in construction scenarios with complex background. The average distance error and F-measure for the MDNet are 7.64 pixels and 67, respectively. The results demonstrate that the proposed approach can be used by site managers to monitor and track workers for hazard prevention in construction sites.

Recent studies in protein structure prediction such as AlphaFold have enabled deep learning to achieve great attention on the Drug-Target Affinity (DTA) task. Most works are dedicated to embed single molecular property and homogeneous information, ignoring the diverse heterogeneous information gains that are contained in the molecules and interactions. Motivated by this, we propose an end-to-end deep learning framework to perform Molecular Heterogeneous features Fusion (MolHF) for DTA prediction on heterogeneity. To address the challenges that biochemical attributes locates in different heterogeneous spaces, we design a Molecular Heterogeneous Information Learning module with multi-strategy learning. Especially, Molecular Heterogeneous Attention Fusion module is present to obtain the gains of molecular heterogeneous features. With these, the diversity of molecular structure information for drugs can be extracted. Extensive experiments on two benchmark datasets show that our method outperforms the baselines in all four metrics. Ablation studies validate the effect of attentive fusion and multi-group of drug heterogeneous features. Visual presentations demonstrate the impact of protein embedding level and the model ability of fitting data. In summary, the diverse gains brought by heterogeneous information contribute to drug-target affinity prediction.

Visual object tracking is always a challenging task in computer vision. During the tracking, the shape and appearance of the target may change greatly, and because of the lack of sufficient training samples, most of the online learning tracking algorithms will have performance bottlenecks. In this paper, an improved real-time algorithm based on deep learning features is proposed, which combines multi-feature fusion, multi-scale estimation, adaptive updating of target model and re-detection after target loss. The effectiveness and advantages of the proposed algorithm are proved by a large number of comparative experiments with other excellent algorithms on large benchmark datasets.

Epileptic seizure prediction is an important research topic in the clinical epilepsy treatment, which can provide opportunities to take precautionary measures for epilepsy patients and medical staff. EEG is an commonly used tool for studying brain activity, which records the electrical discharge of brain. Many studies based on machine learning algorithms have been proposed to solve the task using EEG signal. In this study, we propose a novel seizure prediction models based on convolutional neural networks and scalp EEG for a binary classification between preictal and interictal states. The short-time Fourier transform has been used to translate raw EEG signals into STFT sepctrums, which is applied as input of the models. The fusion features have been obtained through the side-output constructions and used to train and test our models. The test results show that our models can achieve comparable results in both sensitivity and FPR upon fusion features. The proposed patient-specific model can be used in seizure prediction system for EEG classification.

In recent years, the applications of deep learning have facilitated the development of green intelligent transportation system (ITS), and carbon dioxide estimation has been one of important issues in green ITS. Furthermore, the carbon dioxide estimation could be modelled as the fuel consumption estimation. Therefore, a clustering-based neural network is proposed to analyze clusters in accordance with fuel consumption behaviors and obtains the estimated fuel consumption and the estimated carbon dioxide. In experiments, the mean absolute percentage error (MAPE) of the proposed method is only 5.61%, and the performance of the proposed method is higher than other methods.

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.

Online social networks have increased their impact on the real world, which motivates information senders to control the propagation process of information to promote particular actions of online users. However, the existing works on information provisioning seem to oversimplify the users' decision-making process that involves information reception, internal actions of social networks, and external actions of social networks. In particular, characterizing the best practices of information provisioning that promotes the users' external actions is a complex task due to the complexity of the propagation process in OSNs, even when the variation of information is limited. Therefore, we propose a new information diffusion model that distinguishes user behaviors inside and outside of OSNs, and formulate an optimization problem to maximize the number of users who take the external actions by providing information over multiple rounds. Also, we define a robust provisioning policy for the problem, which selects a message sequence to maximize the expected number of desired users under the probabilistic uncertainty of OSN settings. Our experiment results infer that there could exist an information provisioning policy that achieves nearly-optimal solutions in different types of OSNs. Furthermore, we empirically demonstrate that the proposed robust policy can be such a universally optimal solution.

Let us consider a regular expression r of length m and a text string T of length n over an alphabet Σ. Then, the RE minimal substring search problem is to find all minimal substrings of T matching r. Yamamoto proposed O(mn) time and O(m) space algorithm using a Thompson automaton. In this paper, we improve Yamamoto's algorithm by introducing parallelism. The proposed algorithm runs in O(mn) time in the worst case and in O(mn/p) time in the best case, where p denotes the number of processors. Besides, we show a parameter related to the parallel time of the proposed algorithm. We evaluate the algorithm experimentally.

Many modern regular expression engines employ various extensions to give more expressive support for real-world usages. Among the major extensions employed by many of the modern regular expression engines are backreferences and lookaheads. A question of interest about these extended regular expressions is their expressive power. Previous works have shown that (i) the extension by lookaheads does not enhance the expressive power, i.e., the expressive power of regular expressions with lookaheads is still regular, and that (ii) the extension by backreferences enhances the expressive power, i.e., the expressive power of regular expressions with backreferences (abbreviated as rewb) is no longer regular. This raises the following natural question: Does the extension of regular expressions with backreferences by lookaheads enhance the expressive power of regular expressions with backreferences? This paper answers the question positively by proving that adding either positive lookaheads or negative lookaheads increases the expressive power of rewb (the former abbreviated as rewblp and the latter as rewbln). A consequence of our result is that neither the class of finite state automata nor that of memory automata (MFA) of Schmid[2] (which corresponds to regular expressions with backreferenes but without lookaheads) corresponds to rewblp or rewbln. To fill the void, as a first step toward building such automata, we propose a new class of automata called memory automata with positive lookaheads (PLMFA) that corresponds to rewblp. The key idea of PLMFA is to extend MFA with a new kind of memories, called positive-lookahead memory, that is used to simulate the backtracking behavior of positive lookaheads. Interestingly, our positive-lookahead memories are almost perfectly symmetric to the capturing-group memories of MFA. Therefore, our PLMFA can be seen as a natural extension of MFA that can be obtained independently of its original intended purpose of simulating rewblp.

The literature presents a digitized pension system based on a consortium blockchain, with the aim of overcoming existing pension system challenges such as multiparty collaboration, manual intervention, high turnaround time, cost transparency, auditability, etc. In addition, the adoption of hyperledger fabric and the introduction of smart contracts aim to transform multi-organizational workflow into a synchronized, automated, modular, and error-free procedure.