Miracle-dz/UniFAR-FTUs huggingface dataset:
Graph and Sparse-Based Robust Nonnegative Block Value Decomposition for Clustering

In this paper, we first investigate the nonnegative block value decomposition (NBVD) approach through graph-based representation for clustering called G-NBVD. Then, we propose our three-step graph and sparse-based robust NBVD (GSR-NBVD) via robust NBVD (R-NBVD) framework. The robustness to outliers is obtained by converting the Frobenius norm of error function to the $\ell _{2,1}$-norm for NBVD structure that compensates the effect of samples that are not conforming to NBVD. To exploit the connection between the learning matrix and its corresponding coefficients through sparse representation, we enforce the sparse constraints on the middle matrix in the R-NBVD framework called SR-NBVD. To enhance the geometrical information from data space to the new space, we add a term to our objective minimization function through a regularized graph representation compact form called GSR-NBVD. Then, we prove the convergence of our proposed methods and show a visualization of the effectiveness of G-NBVD and GSR-NBVD step-by-step. Finally, we evaluate our proposed clustering methods over different kinds of data sets. The experimental results confirm that our methods outperforms several state-of-the-art methods through different metrics.
Dynamic Trustworthiness Overlapping Community Discovery in Mobile Internet of Things

In the mobile Internet of Things (IoT), dynamic community discovery has attracted increasing attention, because it can provide strong reliability and security for mobile IoT applications, such as data forwarding or fusion. However, when building community structures, most traditional global detection methods only focus on the encounter ratio between nodes and need to have holistic cognition for the whole network structure information. Thus, in IoT applications with large-scale or incomplete structures, there are deficiencies, such as lower security and higher time complexity. This paper proposes a detection scheme for dynamic trustworthiness overlapping community, called D2-TOC. This scheme first employs evidence-based data between node pairs, such as contact probability, recency, and service degree, to construct the trustworthiness relationships between devices and the network model of mobile IoT, which can provide the security guarantee for data interaction from the start. Second, the D2-TOC uses optimized random walks and seeds expansion methods to fulfill the initialization and evolution of overlapping communities, which avoids the high temporal and spatial complexity generated by global community detection. Finally, the experimental results have proven that, in mobile IoT scenarios, our D2-TOC algorithm has a better performance than the DEMON, Kim–Han, LFM, and UEOC in accuracy and efficiency.
A Sinusoidal Pulsewidth Modulation (SPWM) Technique for Capacitor Voltage Balancing of a Nested T-Type Four-Level Inverter

In this letter, a new control method based on the sinusoidal pulsewidth modulation scheme is proposed to control capacitor voltages of a T-type four-level nested neutral-point-clamped (NNPC) inverter. The T-type four-level NNPC inverter has a lower number of switches and components compared with other four-level classic and advanced inverters, which make this topology attractive for high-power medium-voltage applications. This topology has been proposed and studied with the assumption of constant dc sources instead of flying capacitors. In this letter, a simple single-phase modulator is developed to balance flying capacitor voltages. The performance and the feasibility of the proposed control technique are evaluated experimentally under a steady-state and transient conditions and for different modulation indexes and loads. The experimental results demonstrate the effectiveness of the developed control method to control the capacitors’ voltages.
Game Analysis of Electricity Retail Market Considering Customers’ Switching Behaviors and Retailers’ Contract Trading

Deregulation of electricity retail market enables competition among retailers and thereby enriches customers’ choices. In this environment, retailers compete in the retail market mainly by bidding their retail prices. As bidding price of a retailer increases, customers would reduce their demands on this retailer, or even switch their demands to other retailers with lower bidding prices. These potential consequences will inevitably impact retailers’ bidding strategies in the retail market. The impacts of consumers’ switching behaviors and contract trading on strategic behaviors of retailers in the retail market is first investigated in this paper. A Bertrand-based game model for the retail market is proposed while considering customers’ switching behaviors and retailers’ contract trading. Specifically, in the proposed model, the market share function is introduced to describe customers’ elasticity and switching behaviors. Also, the close-loop interaction of the wholesale market and retail market is considered. In addition, the existence and uniqueness of the Nash equilibrium for the game model are proved. The nonlinear complementarity approach is employed to find the Nash equilibrium outcomes. Effectiveness of the theoretical model is verified by numerical examples. Simulation results show that both customers’ switching behaviors and retailers’ contract trading can help mitigate market power abuse of retailers.
Spatial and Temporal Adaptive FIR Clutter Filtering

In this study, a new spatial and temporal FIR clutter filter has been developed using the tissue velocity. This filter uses the PSF attenuation and the tissue velocity estimates to design a filter which is time-dependent but also a function of the position relative to the tissue. The spatial and temporal adaptive FIR filter is compared with three standard FIR clutter filters: (i) a low cutoff FIR filter, (ii) a high cutoff FIR filter, and (iii) a temporal adaptive FIR filter. In the following, this comparison was made with an in vivo carotid data set from a high frame rate acquisition using unsteered plane waves. The axial component of tissue velocities was estimated using a 1D phase-based estimator. For blood flow, 2D vector velocities were estimated using a 2D phase-based estimator after the introduction of transverse oscillation in post-acquisition. Overall, both adaptive filters outperform the non-adaptive filters. Indeed, standard filtering methods yield strong outliers in the presence of low flow velocity for the maximum filter and high flow velocity for the minimum filter. Because the spatial and temporal adaptive filter takes into account the local tissue velocity, it facilitates flow velocity estimation closer to the wall, particularly for low flow. Indeed, only the relevant (based on the PSF) tissue velocity is removed from a specific location in the lumen. Consequently, a local high tissue velocity does not influence the cutoff velocities in the entire vessel. These new adaptive filtering methods could lead to better separation between blood and tissue in recordings from pathological carotids with spatially varying tissue velocity.
DeCNT: Deep Deformable CNN for Table Detection

This paper presents a novel approach for the detection of tables present in documents, leveraging the potential of deep neural networks. Conventional approaches for table detection rely on heuristics that are error prone and specific to a dataset. In contrast, the presented approach harvests the potential of data to recognize tables of arbitrary layout. Most of the prior approaches for table detection are only applicable to PDFs, whereas, the presented approach directly works on images making it generally applicable to any format. The presented approach is based on a novel combination of deformable CNN with faster R-CNN/FPN. Conventional CNN has a fixed receptive field which is problematic for table detection since tables can be present at arbitrary scales along with arbitrary transformations (orientation). Deformable convolution conditions its receptive field on the input itself allowing it to mold its receptive field according to its input. This adaptation of the receptive field enables the network to cater for tables of arbitrary layout. We evaluated the proposed approach on two major publicly available table detection datasets: ICDAR-2013 and ICDAR-2017 POD. The presented approach was able to surpass the state-of-the-art performance on both ICDAR-2013 and ICDAR-2017 POD datasets with a F-measure of 0.994 and 0.968, respectively, indicating its effectiveness and superiority for the task of table detection.
Commutation Behavior Analysis of a Dual 3L-ANPC-VSC Phase-Leg PEBB Using 4.5-kV and 1.5-kA HV-IGBT Modules

Three-level neutral-point clamped voltage source converter (3L-NPC-VSC) is widely used in high-power and medium-voltage (MV) applications and the most used multilevel VSC topology. 3L-Active-NPC-VSC (3L-ANPC-VSC) was introduced to overcome the unequal losses distribution as main structural drawback of the 3L-NPC-VSC. During the switching event of semiconductor devices, the stray inductance takes a special role in the commutation path of the switching current. Mutual coupling inductance between the components participating in the commutation path has to be considered for a theoretical assessment of the total stray inductance of each commutation path. Besides, the measurement of the commutation path stray inductance has to be carried out during the turn-on of the semiconductor device, instead of during the turn-off, to consider the reverse blocking voltage of the opposite switching semiconductor device. This paper shows how a dual 3L-ANPC-VSC phase-leg has been carefully designed and assembled to build a ±10 MVAr five-level VSC for MV-static synchronous compensator applications. The minimization of the stray inductance, the equalization of the total switching power losses for all commutation paths and power density maximization with snubberless operation are within the main technical design requisites. On the basis of the theoretical analysis, the stray inductance of all the commutation paths has been extracted, considering mutual coupling effect, by means of three-dimensional finite-element-analysis simulations. Once the design concept is theoretically validated, the estimated values have been compared with the experimental results obtained from the performance of double-pulse tests for each commutation path. This paper presents, for the first time, all the analysis details of the 3L-ANPC-VSC commutation behavior using 4.5-kV and 1.5-kA trench high-voltage insulated-gate bipolar transistor modules.
Exploring Energy and Accuracy Tradeoff in Structure Simplification of Trained Deep Neural Networks

This paper presents a structure simplification procedure that allows efficient energy and accuracy tradeoffs in implementation of trained deep neural networks (DNNs). This structure simplification procedure identifies and eliminates redundant neurons in any layer of the DNN based on the output values of these neurons over the training samples. This procedure may be applied to all layers of a DNN. Pareto-optimal points in terms of accuracy and energy consumption can be easily identified from the configurations across different layers. After redundant neurons are discarded, the weights connect to the remaining neurons will be updated using matrix multiplication without retraining. Yet, retraining may still be applied if desired to further fine tune the performance. In our experiments, we show energy-accuracy tradeoff that provides clear guidance to achieve efficient realization of trained DNNs. We also observe significant implementation cost reductions with up to 11.50 $\times$ in energy and 12.30 $\times$ in memory while the relative performance (accuracy) loss is negligible. We also discuss and validate strategies to apply our procedure to multiple layers in a DNN in order to maximize the compression ratio of the network after simplification subject to an overall budget in degradation of accuracy across all the simplified layers.
A Hierarchical Codebook Descriptor Approach for Online Writer Identification

This work proposes a technique to identify the authorship of an online handwritten document. The strategy focuses on encoding a set of feature vectors obtained from sample points of the online trace with a descriptor, by employing a pair of codebooks. The derived descriptors correspond to scoring each attribute of the feature vector on the basis of their proximity to the respective values of the assigned codevectors. The two codebooks consist of codevectors pre-learnt by a two level k-means clustering applied on the feature vectors derived from a subset of writers. The descriptors are constructed from features that are specified using a 'gap parameter', that capture the neighborhood information of the trace. Moreover, prior to classification with a SVM, we propose a weighting scheme for the descriptors corresponding to the codevectors generated after the second level of clustering. The weights, as such, are computed on the basis of entropy values obtained over a set of generated histograms. Experiments conducted on IAM Online Handwriting Database demonstrate the efficacy of the proposed descriptor
High Integrity Lane Level Localization Using Multiple Lane Markings Detection and Horizontal Protection Levels

For autonomous driving, lane level accurate localization is a necessity for complex driving maneuvers. Classical GNSS based methods are usually not accurate enough to have an unambiguous lane level localization. Having camera measurements such as lane marking detections along with high definition maps can enhance localization performance. In this paper, we are interested in high integrity localization, meaning being robust with low risk level. We propose a novel geometrical approach using horizontal protection levels on localization to propagate uncertainties and use lane markings to have an unambiguous map-matching. We demonstrate on real data that the algorithm can cope with high levels of noise on both localization and detection.
NOMA Testbed on Wi-Fi

Non-Orthogonal Multiple Access (NOMA) is currently considering as a promising technology for increasing the network efficiency in heterogeneous scenarios with different types of traffic and interference conditions. In spite of numerous theoretical works, there are few experimental studies of NOMA, most of which consider cellular networks. In this short paper, we present our pioneering NOMA testbed for Wi-Fi networks. Because the Wi-Fi technology is much simpler and cheaper than the cellular ones, we expect that the testbed can be used by the research community to validate various solutions designed for NOMA in real equipment.
Adaptive Triplet Model for Fine-Grained Visual Categorization

Fine-grained visual categorization aims at differentiating subcategories, such as different species of birds, models of cars, and variants of aircraft. It often suffers from small inter-variance and large intra-variance. To keep dissimilar images far apart and preserve large intra-variance simultaneously, we propose an adaptive triplet model. At first, images are batched as triplets and input to a general convolutional network, which extracts convolutional image features. Then, we combine adaptive triplet loss and classification loss for multi-task training. Adaptive triplet loss pulls the same-class embeddings together and pushes examples from different subcategories apart. It allocates different weights to hard and easy examples in an adaptive way in the training process. Unlike previous hard mining mechanisms that discard all non-hard triplets, it can benefit from all possible informative examples. Moreover, a second-order distance function is put forward to capture local pairwise interactions of embeddings, which is more discriminative in distance measure. Classification loss is used to provide more direct supervision for training embeddings with category specific concepts. Furthermore, it makes the prediction of category more convenient and more efficient in testing. Experiments demonstrate the state-of-the-art results on three popular fine-grained datasets, including CUB-200-2011, Stanford Cars, and FGVC-Aircraft. In addition, our network structure is relatively simple compared with previous methods, which often suffer from multiple sub-networks and complex training mechanisms. It is also applicable for most up-to-date backbone networks, while others might be restricted to specific convolutional networks.
Characterizing the Rate-Memory Tradeoff in Cache Networks Within a Factor of 2

We consider a basic caching system, where a single server with a database of <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> files (e.g., movies) is connected to a set of <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> users through a shared bottleneck link. Each user has a local cache memory with a size of <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> files. The system operates in two phases: a placement phase, where each cache memory is populated up to its size from the database, and a following delivery phase, where each user requests a file from the database, and the server is responsible for delivering the requested contents. The objective is to design the two phases to minimize the load (peak or average) of the bottleneck link. We characterize the rate-memory tradeoff of the above caching system within a factor of 2.00884 for both the peak rate and the average rate (under uniform file popularity), improving the state of the arts that are within a factor of 4 and 4.7, respectively. Moreover, in a practically important case where the number of files (<inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>) is large, we exactly characterize the tradeoff for systems with no more than five users and characterize the tradeoff within a factor of 2 otherwise. To establish these results, we develop two new converse bounds that improve over the state of the art.
Defining Customer Export Limits in PV-Rich Low Voltage Networks

The growing adoption of residential photovoltaic (PV) systems around the world is presenting distribution network operators (DNOs) with technical challenges, particularly on low voltage (LV) networks. The need to mitigate these issues with simple yet effective measures in countries with high PV penetrations is likely to drive the adoption of limits on the very exports that affect this infrastructure. Defining the most adequate limit, however, requires understanding the tradeoffs between the technical benefits and the effects on PV owners. This paper proposes two methodologies: an optimal power flow (OPF) based technique to define the export limit that solves technical problems with minimal curtailment, and a Monte Carlo based analysis to investigate the spectrum of such tradeoffs considering different PV penetrations and export limits. A real U.K. residential LV network with 180 customers is analyzed using realistic 1-min resolution daily load and PV generation profiles across seasons. Results demonstrate that, for DNOs, the OPF-based approach is effective in determining the most technically adequate export limit. However, for policy makers, the spectrum of tradeoffs provided by the Monte Carlo approach can help defining export limits that reduce curtailment at the expense of partially mitigating technical issues.
Assessment of Diabetic Kidney Disease Using Ultrasound Localization Microscopy: An in Vivo Feasibility Study in Rats

Diseases such as diabetes mellitus can lead to chronic kidney disease (CKD). The early diagnosis of CKD is of significant importance to prevent it from rapid deterioration, while the current methods used in the clinic is susceptible. As one of the major complications of diabetes, diabetic kidney disease (DKD) leads to the significant changes of renal microvasculature and perfusion in the early stage. However, they cannot be observed by conventional imaging methods. Ultrasound localization microscopy (ULM) has been developed in recent years to significantly improve the spatial resolution of ultrasound imaging by localizing the microbubbles (MBs) flowing in microvasculature. This technique can not only obtain the microvasculature in deep tissue, but also provide an accurate blood velocity map. In this study, ULM was performed on an acute ischemic-reperfused rat kidney model and a normal rat kidney to investigate its in vivo feasibility in evaluation of DKD. The microvascular morphology and velocity maps of the kidneys were obtained. Results show that under current experimental conditions, a resolution of 36 μ m is reached. Much less MB events (70 k vs 160 k from 6, 000 frames) and much lower mean blood flow speed (typically < 10 mm/s vs ~30 mm/s) were detected in the ischemic-reperfused kidney than in the normal kidney. This study shows the potential of ULM in the assessment of DKD/CKD.
Subspace Change-Point Detection: A New Model and Solution

Change-point detection has a long history of research in statistical signal processing and remains a fundamental problem in many real-world applications involving information extraction from streaming data. Exploiting low-dimensional structures (subspace in particular) of high-dimensional signals is another research topic of significance, as it improves not only efficiency in computation, storage, and communication, but also interpretability and understanding of data. In this paper, we formulate the problem of subspace change-point detection, where a stream of high-dimensional data points lie on a low-dimensional subspace that abruptly changes at a certain time instant. Then, we propose a cumulative-sum-based algorithm for the task of detection in this problem setup. From the algorithmic perspective, the proposed method is computationally efficient, as it proceeds in an online and recursive manner, and each iteration involves simple matrix computation; it is also practical and flexible, as it works under mild conditions on the distribution of data points after change point. From the theoretical perspective, we derive rigorous performance bounds of the proposed algorithm in terms of false alarm rate and detection delay. We also conduct extensive numerical experiments on both synthetic and real-world data to validate effectiveness of our algorithm and correctness of theoretical analysis.
Low-Complexity Resource Allocation for Downlink Multicarrier NOMA Systems

In this paper, we investigate resource allocation for a downlink multicarrier non-orthogonal multiple access (NOMA) system with a base station and an even number of users, where every two users are grouped together to share some subcarriers for data transmission. We formulate an optimization problem in terms of subcarrier and power allocation to maximize the system capacity under a specific proportional user fairness constraint. For a given subcarrier allocation, we first derive an optimal power allocation factor for two users on a subcarrier subject to maximizing the minimum user capacity on the subcarrier. With such optimal power allocation factors for all subcarriers, a low-complexity suboptimal algorithm is then proposed for joint subcarrier and power allocation to meet the proportional user fairness constraint. Simulation results demonstrate that the proposed scheme provides better system capacity than the conventional orthogonal frequency division multiple access (OFDMA) scheme.