Miracle-dz/UniFAR-FTUs huggingface dataset:
Tensorial Time Series Prediction via Tensor Neural Ordinary Differential Equations

In high dimensional tensorial time series prediction, it is highly desired to preserve spatial structural and underlying continuous sequential information in modelling. Existing methods either destroy the spatial structure and require a large number of parameters, such as neural ordinary differential equations (neural ODEs), or cannot capture the temporal information, especially for unevenly spaced time series, such as tensorial neural network families. Hence we propose Tensor Neural Ordinary Differential Equations (TENODEs) to address these issues. The dynamics of data is modelled by a tensorial neural network (TNN) consisting of the Tucker structure. Compared with neural ODEs, it reduces the number of parameters from $O(I^{2N})$ to $O(NI^{2})$. To further ease the computational cost, we also propose a TENODE with further dimensionality reduction which produces a low-dimensional representation of the aforementioned two pieces of information and is projected to the target space by a 1-layer TNN. In each layer of the RHS for TENODE, the weight matrices should maintain Lipschitzness according to the Picard's theorem. Hence TENODEs are consistent and converge. Multiplication of weight matrices together can affect the optimisation stability: when values of some weights are enlarged, values of the other weights are correspondingly decreased, with the loss unchanged. This may create infinite solution spaces. We thus constrain weights to be orthogonal and solve this scaling issue to enhance the optimisation process. In consequence, the proposed TEN-ODEs successfully preserve data spatial and latent continuous sequential information. Experimental results demonstrate the efficacy of our proposed methods over competitive baselines.
BER Performance Analysis for Downlink Nonorthogonal Multiple Access With Error Propagation Mitigated Method in Visible Light Communications

In order to mitigate the error propagation (EP) effect on the bit error rate (BER) performance due to the imperfect successive interference cancellation for the non-orthogonal multiple access (NOMA)-based visible light communication (VLC) downlinks, adjustable bit rate superposition coding (ABRSC) and sample-and-sum successive interference cancellation (SSSIC) decoding schemes are proposed in this paper. Based on the modified fixed-power allocation (MFPA) strategy, the closed-form expressions of BER are derived for the two-user case with on-off keying, and further extended to the multi-user scenario. The residual probability of EP is introduced and derived to theoretically assess the effectiveness of the proposed ABRSC and SSSIC schemes. Monte Carlo simulation is also conducted to validate the analytical BER results. The BER performance of the NOMA-based VLC system with multiple users is further analyzed and discussed in detail. It is found that under the total average optical transmission power constraint, the allocated average optical transmission power to current user should be higher than that to all rest of users, i.e., the power allocation factor (PAF) is supposed to be greater than 1/2, in order to avoid the error floor issue of current user, hence facilitating the successful implementation of SSSIC. Besides, increasing the bit-rate factor (BRF) could mitigate the effect of EP, and larger BRF leads to more efficient outcomes in alleviating the effect of EP.
A Reconstruction Method of Boiler Furnace Temperature Distribution Based on Acoustic Measurement

The temperature distribution in the furnace of power plant boilers is an important parameter to indicate the pulverized coal combustion state. The real-time and precise monitoring of the temperature field in the furnace is essential to ensuring the safe operation of the power plant and improving production efficiency. Acoustic thermometry is a typical noncontact temperature measurement and one of its cores is to derive the temperature distribution of the original temperature field by reconstruction algorithms. The existing temperature field reconstruction algorithms do not perform satisfactorily, and there are some problems such as incomplete reconstruction results, low reconstruction precision, and poor anti-interference ability. In order to further improve the reconstruction performance, an acoustic thermometry reconstruction algorithm based on logarithmic-quadratic (LQ) radial basis function (RBF) and singular value decomposition (LQ-SVD) is proposed in this article. This algorithm first uses the linear combination of the LQ RBFs to fit the reciprocal distribution of the acoustic velocity, and then uses the SVD method to solve the inversion model. The simulation results show that, compared with the commonly used algorithms, the proposed algorithm can obtain complete reconstruction results with significantly improved reconstruction precision, stronger robustness, and better anti-interference ability. In addition, the proposed algorithm also has good performance in the actual experiment, which verifies the feasibility and effectiveness of the algorithm in the engineering application.
Intelligent Power Control Algorithm in Heterogeneous Wireless Cellular Networks

Appropriate power control algorithm can increase total network rate and reduce energy consumption through optimizing signal-to-interference-plus-noise ratio (SINR) received by users. Thus it is vital for heterogeneous cellular networks, which are composed of macro base stations (BSs) and micro BSs. However, most existing power control schemes neglect the dynamic traffic variation and backhaul bandwidth constraint due to the variability of wireless traffic and difficulty of problem solving. This results in the performance reduction and resource waste under the highly dynamic traffic. In view of this, we propose an intelligent power control algorithm based on accurate traffic prediction. First, hybrid prediction scheme, composed of long short term memory (LSTM) and historical average value (HAV), is used to predict the traffic demand of each BS. Based on the predicted results, we propose a power control scheme. It achieves a weighted tradeoff between the total network rate and energy consumption under the consideration of backhaul constraint. By using the properties of concave function and the function structure of backhaul constraint, the Marks-Wright algorithm and difference of two convex functions (DC) programming are respectively employed to solve the non-convex optimization model approximately and accurately. Simulation results demonstrate that the prediction accuracy of proposed scheme is 3.73% and 6.03% higher than that of the autoregressive integrated moving average (ARIMA) and support vector machine (SVM) algorithm, respectively. Compared with full power transmission, energy consumption of the proposed algorithm is reduced by 58.29%, while total network rate is increased by 3.26%.
Nonlinear Model Predictive Control of Mild Hybrid Powertrains With Electric Supercharging

Energy management plays a decisive role in the design of efficient 48V mild hybrid drives owing to the limited amounts of energy and power. In particular, strong nonlinearities of an electrified engine air path and the high interaction between the 48V mild hybrid powertrain and the electrical system pose a major challenge. Current research results show the fundamental potential of optimization-based energy management strategies. However there is a lack of optimization-based solutions that allow online direct control of the electrified air path and the electric motor. In this context, this study presents a nonlinear model predictive control (NMPC) approach for a 48V mild hybrid powertrain with an electric supercharger, which is able to simultaneously improve the response behavior and energy consumption. The control concept is developed on the basis of a detailed system description. The special features of optimization-based control for the degrees of freedom of the powertrain, i.e., the belt starter generator (BSG) and the electrified air path through a throttle, waste gate, and electric supercharger (eC), are addressed. After an analysis and verification of the control properties, the NMPC is evaluated in dynamic driving cycle simulations through a comparison with a state-of-the-art rule-based control strategy. The investigations show that the NMPC is able to control the system well even under transient situations with a high influence of disturbance variables. In addition, the NMPC allows a specific and fuel saving use of the 48V system while improving the driving dynamics at the same time.
Advanced Damascene integration using selective deposition of barrier metal with Self Assemble Monolayer

Selective deposition of Cu diffusion barrier metal layer on dielectric with Self-Assembled Monolayer (SAM) has been demonstrated. Via resistance is expected to decrease by eliminating the barrier at via bottom in dual- and semi-damascene structure. In this study, we report the evaluation of SAMs to enable selective ALD-barrier metal deposition and, as an example, we show Cu via prefill integration using ELD-Cu with no barrier / liner at via bottom and no seam void for metal filling.
Disk Failure Prediction with Multiple Channel Convolutional Neural Network

With the increase of data centers, the number of disks also grows rapidly. Therefore, the prediction of disk failures has become an important task for both academia and industry. Existing prediction schemes predict disk failure in the short prediction horizon or with a short time window. However, these schemes cannot achieve ideal performance for a long prediction horizon with a long time window. In this paper, we proposed a deep learning method that can effectively solve the above problems. We refine the Self-Monitoring, Analysis and Reporting Technology (SMART) attributes by using information entropy to select the most related attributes for prediction. Moreover, we proposed the Multiple Channel Convolutional Neural Network based LSTM (MCCNN-LSTM) model to predict whether disk failures will occur in a given disk in next few days. We further evaluate the MCCNN-LSTM model by comparing it with the state-of-the-art works. Extensive experiments show that our model can improve FDR (Fault Detection Rate) to 99.8% and reduce FAR (False Alarm Rate) to 0.2%.
Design of Finger Vein Recognition SOC Based on FPGA

Finger vein recognition technology is a new generation of biometric technology. The System on Chip (SOC) solution based on the ARM Cortex-M3 kernel and Xilinx XC7A75T FPGA for the portable finger vein recognition system was proposed in this paper. By the research of software and hardware division, the Region of Interest (ROI) extraction module with large amount of computation was designed as a hardware accelerator to improve the speed of finger vein recognition. The obtained system realized the whole process of finger vein recognition, and displayed every image processing results on 2.8 inch LCD. A large number of tests showed that the system had high stability and the recognition accuracy was greater than 97%. Using FPGA to accelerate ROI module can increase the overall recognition speed by more than 15%. This system has high application value in biometric field.
Assessment of Mel-Filter Bank Features on Sound Classifications Using Deep Convolutional Neural Network

Sound classification has been widely used but some aspects such as best features for classification, presence of background noise, and short duration with different characteristics make sound classification based on audio to be a challenging task. This study aims to assess the Mel-filter bank features on sound classifications using a deep convolutional neural network. We collected the DCASE 2018 task 2 challenge training data, which contained music and non-music sound classes, and then we picked six categories with distinct frequencies, wave speeds, and durations. The audio noise was then filtered using a pre-emphasis filter. The 6,500 samples were considered to generate 3-second audio data using a random sampling approach with replacement and transform them into Mel-filter bank features to attain feature vectors. These features were further input to the deep convolutional neural network for classification. The model performance was measured using seven metrics. The results showed that the log-Mel (pow-dB) produced the highest accuracy of 95.37% followed by 92.82% of log-Mel (amp-dB). The least accuracy of 82.34% was found in Mel-spectrogram (amp-freq). Overall, the log-Mel (pow-dB) had an impressive performance in contrast with other features. All features were subjected to a human hearing in Mel-scale.
Vehicle Trajectory Clustering in Urban Road Network Environment Based on Doc2Vec Model

Trajectory clustering is an important task in trajectory data mining. Grouping a trajectory dataset into clusters based on the similarity between vehicle trajectories is conducive to revealing the movement pattern of the vehicles. For trajectory clustering in a real urban road network environment, the existing methods have some deficiencies, including high time complexity in measuring the distance between trajectory sequences and poor clustering performance. This paper proposes a method for clustering vehicle traj ectories in the urban road network environment based on a word vector model. First, the original Global Positioning System (GPS) trajectory data of vehicles are converted into road segment sequences by using the urban road segment information contained in the GPS trajectory data (after map-matching). Then, the spatial properties, such as the geographical location, time and moving direction of vehicles in the original trajectory sequence, are expressed by road segments and their order. Next, the road segment sequences are converted into traj ectory segment eigenvectors with fixed dimensions using the doc2vec model, in this way, the calculation efficiency of similarity between traj ectory segments of different lengths is improved. Finally, the vehicle tracks are clustered according to the distances between the traj ectory segment eigenvectors using the hierarchical clustering method. The result of a simulation based on taxi trajectory data gathered in a real urban road network shows that the proposed method was superior to the traditional clustering methods based on trajectory space-time distance, improving the silhouette index by 10%-25%, and reducing the clustering time by two orders of magnitude.
A Blockwise Embedding for Multi-Day-Ahead Prediction of Energy Time Series by Randomized Deep Neural Networks

Nowadays, deep learning is gaining attraction as one of the most successful paradigm for a plethora of machine learning applications. While its benefits are undoubted, the high computational burden associated with its training algorithms and cross-validation procedures is stimulating new lines of research. To this end, randomized deep neural networks are one of the best alternatives in terms of efficiency-to-accuracy balance. In this paper, we present a deep neural architecture that uses randomization of some parameters in a complex structure whose novelty is twofold: it embeds past samples of the time series by using daily blocks in the input frame of a convolutional layer; it predicts a day on the whole by solving a suitable regression problem. The proposed randomized approach is compared with state-of-the-art prediction algorithms on the challenging context related to energy time series, where the day-ahead prediction is usual, obtaining comparable or even better results in terms of forecasting accuracy and training time.
MulCoB-MulFaV: Multimodal Content Based Multilingual Fact Verification

Verifying fact of multimodal (text, image and/or videos) reports is emerging as an important challenge to prevent circulation of fake news reports in various online platform. Moreover, such reports often being posted in local languages penetrate even faster, making the problem more complex. To tackle such problem many fact verifying web sites have emerged, which deploys humans to manually find the truthfulness of such reports with the help of multimodal contents that are being used in such report. But in recent times, due to diversity of the problem owing to various political, and malicious motives, the existing (manual) system fails to handle the enormity. Existing content based approaches rely only on textual content of such reports and fail to handle the multimodal nature. On the other hand, existing multimodal based approaches rely only on the report based and user based features, and fail to interpret the fake contents with evidences. In this work, we propose a novel end-to-end automated multimodal content based multilingual fact verification system, which automates the task of fact verifying web sites, and provides evidences for every judgment. Evaluation results on three benchmark datasets show the robustness and effectiveness of our approach.
Large-scale singer recognition using deep metric learning: an experimental study

Singer recognition aims to automatically recognize the singer of a given recording. Compared to spoken voices, singing voice is characterized by a much higher degree of vocal style. The task becomes more challenging when it operates on numerous singers. This paper explores different strategies in a deep metric learning framework, with special focus on their performance in a large-scale dataset consisting of audio samples from 5057 singers. We conduct thorough experiments to compare loss functions, including triplet loss, generalized end-to-end (GE2E) loss, and prototypical network (PN) loss. Effects of vocal source separation is also investigated. Using audio inputs with separated vocals, our model trained with PN loss outperforms other evaluated methods in the identification task. While in the verification task with one-on-one comparison of two single embeddings, triplet loss achieves the best results. However, verification using PN loss shows superior performance to methods with triplet loss when using the centroid of 5 embed dings to represent the singer embedding. Using longer segments for a singer representation consistently improves the performance for all evaluated tasks.
Annealing Attention Networks for User Feature-Based Rumor Early Detection on Weibo

Rumor propagation is becoming easier and leads to severe consequences for society in several minutes or hours due to the rapid development of social networks. Thus, detecting rumors in early time is necessary and urgent for the community. In recent studies, machine learning approaches are widely applied in detecting rumors based on various features extracted from content, user characteristics, and propagation structure. Some studies have shown that features extracted from users are more valuable for rumor early detection. Whereas existing studies utterly utilize various user features, and all of them are deemed as equally, which ignore the inter-feature and temporal difference. Therefore, in this paper, we analyze the effectiveness of six frequently used user features with several representative deep learning models to learn more about such difference. And we propose a novel annealing attention model based on the analysis. The proposed model learns feature-attention and temporal-attention with multi-layer perceptron and parameterized annealing function to capture the difference and enhance the original user features in rumor early detection. Experimental results on the real-world dataset demonstrate that the proposed model detects rumors with an accuracy of 93.6% on Weibo in 15 minutes, which outperforms the state-of-art methods.
An Adaptive Hybrid Beamforming Approach for 5G-MIMO mmWave Wireless Cellular Networks

Hardware complexity reduction is a key concept towards the design and implementation of next generation broadband wireless networks. To this end, the goal of the study presented in this paper is to evaluate the performance of an adaptive hybrid analog-digital beamforming approach in fifth-generation (5G) massive multiple input multiple output (MIMO) millimeter wave (mmWave) wireless cellular orientations. In this context, generated beams are formed dynamically according to traffic demands, via an on-off analog activation of radiating elements per vertical antenna array, in order to serve active users requesting high data rate services without requiring any expensive and mechanical complex steering antenna system. Each vertical array, which constitutes a radiating element of a circular array configuration, has a dedicated radio frequency chain (digital part). The performance of our proposed approach is evaluated statistically, by executing a sufficient number of independent Monte Carlo simulations per MIMO configuration, via a developed system-level simulator incorporating the latest 5G-3GPP channel model. According to the presented results, the adaptive beamforming approach can improve various key performance indicators (KPIs) of the wireless orientation, such as total downlink transmission power and blocking probability. In particular, when studying/analyzing a MIMO configuration with 15 vertical antenna arrays and 10 radiating elements per array, then, depending on the tolerable amount of transmission overhead, the proposed adaptive algorithm can significantly reduce the number of active radiating antenna elements compared to the static grid of beams case. In the same context, when keeping the number of radiating elements constant, then the total downlink transmission power as well as the blocking probability can be significantly reduced. It is important to note that all the KPIs have been extracted when deploying the developed array configuration in complex cellular orientations (two tiers of cells around the central cell).
Multistage Model Predictive Control Co-Risk Dispatch for Coupled Electricity and Heat System

The energy transition can facilitate the rapid development of the coupled electricity and heat system (CEHS), accommodating the higher share of renewable energy. However, uncertain renewable energy sources (RESs) and overlimit risk can bring significant challenges for the safe operation of the CEHS. Considering uncertain RESs integration and overlimit risk within the upper and lower bounds jointly with high probability, the multistage model predictive control (MPC) co-risk dispatch model for the CEHS can be proposed in this paper. The model can hedge against the operational risk for the multistage rolling dispatch. In the context of the multistage real-time risk dispatch, the MPC framework is presented, which contains three processes, autoregressive moving average (ARMA) model prediction process, risk-based optimization process, and penalty-based feedback correction process. In the prediction process, the ARMA method can be employed to accurately predict the RESs power, which is characterized as the merit of the short-term forecast. In the optimization process, the violation risk of overlimit chance constraints caused by the output power fluctuation can be mitigated using the distributionally robust chance-constrained (DRCC) under the Wasserstein ambiguity set. The tractable approximation reformulation can be obtained by adopting the Worst-Case Conditional Value-at-Risk (WC-CVaR) approximation method. In the feedback correction process, penalty-based load shedding and RESs spillage can lower the risk level by minimizing the risk penalty cost. In the numerical case, the risk performance analysis based on the modified Barry Island system is implemented. The analysis demonstrates that the daily average violation probability <inline-formula> <tex-math notation="LaTeX">$V_{p}$ </tex-math></inline-formula> can be mainly affected by the Wasserstein radius <inline-formula> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> while the total cost <inline-formula> <tex-math notation="LaTeX">$C_{obj}$ </tex-math></inline-formula> can be affected by the violation probability <inline-formula> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula>. The dispatch energy for the CEHS is obtained using the proposed approach, which outperforms the existing robust optimization (RO) approach.