Miracle-dz/UniFAR-FTUs huggingface dataset:
Local Key Estimation in Music Recordings: A Case Study Across Songs, Versions, and Annotators

While global key and chord estimation for both popular and classical music recordings have received a lot of attention, little research has been devoted to estimating the local key for classical music. Partly, this may be due to its inherent ambiguity and subjectivity, which makes annotating local keys a challenging task. In this article, we approach local key estimation with a cross-version dataset comprising nine performances (versions) of Schubert's song cycle Winterreise annotated by three different music theory experts. We consider two baseline methods that are representative for common types of signal processing algorithms: an HMM-based system and a CNN-based approach. For both models, we employ a similar training procedure including the optimization of hyperparameters on a validation split. We systematically evaluate the model predictions and provide musical explanations for key confusions. As a central contribution, we explore how different training–test splits affect the models’ efficacy. Splitting along the song axis, we find that both methods perform similarly well. Splitting along the version axis, we obtain substantially higher accuracies, especially for the CNN, which seems to effectively learn the harmonic progressions of the songs (“cover song effect”) and successfully generalizes to unseen versions. We further discuss the results for several songs in detail and assess our results from the perspective of multiple annotators. This cross-annotator study reveals that a substantial part of the systems’ errors coincides with annotator disagreement and an even larger part can be traced back to musically explainable relationships among different keys.
LiDAR Point Cloud Recognition and Visualization with Deep Learning for Overhead Contact Inspection

As overhead contact (OC) is an essential part of power supply systems in high-speed railways, it is necessary to regularly inspect and repair abnormal OC components. Relative to manual inspection, applying LiDAR (light detection and ranging) to OC inspection can improve efficiency, accuracy, and safety, but it faces challenges to efficiently and effectively segment LiDAR point cloud data and identify catenary components. Recent deep learning-based recognition methods are rarely employed to recognize OC components, because they have high computational complexity, while their accuracy needs to be improved. To track these problems, we first propose a lightweight model, RobotNet, with depthwise and pointwise convolutions and an attention module to recognize the point cloud. Second, we optimize RobotNet to accelerate its recognition speed on embedded devices using an existing compilation tool. Third, we design software to facilitate the visualization of point cloud data. Our software can not only display a large amount of point cloud data, but also visualize the details of OC components. Extensive experiments demonstrate that RobotNet recognizes OC components more accurately and efficiently than others. The inference speed of the optimized RobotNet increases by an order of magnitude. RobotNet has lower computational complexity than other studies. The visualization results also show that our recognition method is effective.
Flavonol‐mediated stabilization of PIN efflux complexes regulates polar auxin transport

The transport of auxin controls the rate, direction and localization of plant growth and development. The course of auxin transport is defined by the polar subcellular localization of the PIN proteins, a family of auxin efflux transporters. However, little is known about the composition and regulation of the PIN protein complex. Here, using blue‐native PAGE and quantitative mass spectrometry, we identify native PIN core transport units as homo‐ and heteromers assembled from PIN1, PIN2, PIN3, PIN4 and PIN7 subunits only. Furthermore, we show that endogenous flavonols stabilize PIN dimers to regulate auxin efflux in the same way as does the auxin transport inhibitor 1‐naphthylphthalamic acid (NPA). This inhibitory mechanism is counteracted both by the natural auxin indole‐3‐acetic acid and by phosphomimetic amino acids introduced into the PIN1 cytoplasmic domain. Our results lend mechanistic insights into an endogenous control mechanism which regulates PIN function and opens the way for a deeper understanding of the protein environment and regulation of the polar auxin transport complex.
A Novel Routing Algorithm for the Acceleration of Flow Scheduling in Time-Sensitive Networks

IEEE Time-Sensitive Networking (TSN) Task Group specifies a series of standards such as 802.1Qbv for enhancing the management of time-critical flows in real-time networks. Under the IEEE 802.1Qbv standard, the scheduling algorithm is employed to determine the time when a specific gate in the network entities is opened or closed so that the real-time requirements for the flows are guaranteed. The computation time of this scheduling algorithm is critical for the system where dynamic network configurations and settings are required. In addition, the network routing where the paths of the flows are determined has a significant impact on the computation time of the network scheduling. This paper presents a novel scheduling-aware routing algorithm to minimize the computation time of the scheduling algorithm in network management. The proposed routing algorithm determines the path for each time-triggered flow by including the consideration of the period of the flow. This decreases the occurrence of path-conflict during the stage of network scheduling. The detailed outline of the proposed algorithm is presented in this paper. The experimental results show that the proposed routing algorithm reduces the computation time of network scheduling by up to 30% and improves the schedulability of time-triggered flows is the network.
Techno-Economic Analysis of Deployment Options for Converged 5G Wireless-Optical Access Networks

Currently, a lot of efforts are put in place to design and implement a high-capacity, flexible and highly efficient 5G network infrastructure. A very important consideration that should be kept in mind when designing future 5G networks is developing an adequate network architecture and high-capacity and future-proof backhaul and fronthaul links. Especially novel network concepts such as the cloud radio access network (C-RAN) set very high requirements on the backhaul and fronthaul connectivity and call for a solution that ensures high-capacity, flexibility, practicability and future readiness while providing an easy and economical migration path.This paper examines deployment options for converged optical-wireless access networks in urban areas regarding their cost efficiency and implementation flexibility. It presents results of a case study for a coordinated planning of optical fixed access and wireless backhaul solutions for 5G networks by considering following options: i) roll-out of a converged fiber-to-the-home (FTTH) network infrastructure that provides both fixed internet access and integrated cellular backhaul, ii) use of the existing fiber-to-the-curb (FTTC) network infrastructure to provide the backhaul for cellular networks, and iii) deployment of a dedicated optical fiber network to act as backhaul of cellular networks. A techno-economic study was carried out to compare the economic viability of the three deployment options under realistic assumptions.
Comparative Analysis of Machine Learning Techniques for the Classification of Knee Abnormality

Knee abnormality is a major problem in elderly people these days. It can be diagnosed by using Magnetic Resonance Imaging (MRI) or X-Ray imaging techniques. X-Ray is only used for primary evaluation, while MRI is an efficient way to diagnose knee abnormality, but it is very expensive. In this work, Surface EMG (sEMG) signals acquired from healthy and knee abnormal individuals during three different lower limb movements: Gait, Standing and Sitting, were used for classification. Hence, first Discrete Wavelet Transform (DWT) was used for denoising the input signals; then, eleven different time-domain features were extracted by using a 256 msec windowing with 25% of overlapping. After that, the features were normalized between 0 (zero) to 1 (one) and then selected by using the backward elimination method based on the p-value test. Five different machine learning classifiers: k-nearest neighbor, support vector machine, decision tree, random forest and extra tree, were studied for the classification step. Our result shows that the Extra Tree Classifier with ten cross-validations gave the highest accuracy (91%) in detecting knee abnormality from the sEMG signals under analysis.
Stateful-TCP—A New Approach to Accelerate TCP Slow-Start

The Transmission Control Protocol (TCP) is one of the pillars of the Internet. Therefore extensive research has been conducted to improve its performance, primarily via optimizing TCP’s congestion control algorithm. In this work, we show that besides congestion control, TCP’s Slow-Start algorithm is increasingly becoming a bottleneck in modern high-speed networks. To tackle the problem, we propose a different approach called Stateful-TCP, where path bandwidth estimated in a previous flow is used to instantly ramp up the transmission rate of the subsequent flow to the same destination. This eliminates the need for bandwidth probing in conventional Slow-Start, enabling TCP to efficiently utilize the available path bandwidth right from the beginning. We applied Stateful-TCP to Linux’s default TCP implementation – Cubic, to form S-Cubic and evaluated its performance via extensive emulations and Internet experiments. Results from independent Internet experiments using over 1,000 end-user clients showed that S-Cubic could reduce FCT by 37.5% and increase throughput by over 50% compared to Cubic. As opposed to an entirely new TCP design, Stateful-TCP is designed to complement congestion control and thus could potentially be applied to many of the existing TCP variants, as well as incorporated into future TCP designs.
Performance of Minkowski-type Distances in Similarity Search - A Geometrical Approach

This work is an attempt at exploring distances, in the context of Similarity Search (SS), where an approximate match for a given query q is sought from a given dataset $\mathcal{X}$. One view is that the query q itself is a noise η corrupted version of an $x\in \mathcal{X}$. Recently, François et al., [1] had studied the efficacy of Minkowski-type distances in retrieving the x given q in the presence of both white and highly coloured noise η. Noting that not all conclusions in [1] hold true, in high dimensions, in this work, we have undertaken a similar study but that which differs in the following way: Taking into account various other factors not considered in [1]. Our geometric approach to these investigations have revealed hitherto unknown impact of both the domain geometry and denseness of the data set and has led us to propose an index which aids in explaining the simulation results obtained and in understanding the impact of the 3D’s of Dimensionality, Domain geometry and Denseness of the data on the appropriateness of a Distance function in the setting of SS algorithms.
Considering Cell Proliferation to Optimize Detection of Radiation-Induced 53BP1 Positive Foci in 15 Mouse Strains Ex Vivo

Due to high metabolic activity, proliferating cells continuously generate free radicals, which induce DNA double-strand breaks (DSB). Fluorescently tagged nuclear foci of DNA repair protein 53 binding protein-1 (53BP1) are used as a standard metric for measuring DSB formation at baseline and in response to environmental insults such as radiation. Here we demonstrate that the background level of spontaneous 53BP1+ foci formation can be modeled mathematically as a function of cell confluence, which is a metric of their proliferation rate. This model was validated using spontaneous 53BP1+ foci data from 72 cultures of primary skin fibroblasts derived from 15 different strains of mice, showing a ∼10-fold decrease from low to full confluence that is independent of mouse strain. On the other hand, the baseline level of spontaneous 53BP1+ foci in a fully confluent cell population was strain-dependent, suggesting genomic associations, and correlated with radiation sensitivity based on previous measurements in the same cell lines. Finally, we have developed an online open-access tool to correct for the effect of cell confluence on 53BP1+ foci-based quantification of DSB. This tool provides guidelines for the number of cells required to reach statistical significance for the detection of DSB induced by low doses of ionizing radiation as a function of confluence and time postirradiation.
A Multi-objective Simulated Annealing Algorithm-based Coal Blending Optimization Approach in Coking Process

The quality and cost of coke are directly affected by the proportion of different categories of coal, while the coal blending in coking process is a complex one with multiple objectives and constraints. In this study, a multi-objective simulated annealing algorithm (MOSA)-based coal blending optimization approach in coking process is proposed. The objective function which considers the coke quality indexes and its cost for coal blending is constructed, along with the corresponding constraints. Then, a modified MOSA based on the decision space search strategy is presented to calculate the optimal solution, in which the value of the variable in intermediate solution will mutate with a certain probability during the search process, thereby expanding the search range. The validation experiments using actual coal blending data are carried out. The results indicate that the strategy proposed in this paper is capable of searching the Pareto-optimal (PO) solutions accurately and comprehensively, which exhibits better performance than the existing ones, and can provide guidance for coking production.
Adaptive Weighted Robust Principal Component Analysis1

Robust principal component analysis (RPCA) via the nuclear norm minimization (NNM) is a powerful tool for image processing problems. However, most of NNM methods only consider the number of non-zero singular values of the observation matrix, and ignore the different proportions of data information in different singular values, which are related to the exact rank of clean data and should be treated differently. In this paper, we propose an adaptive weighted RPCA to simultaneously preserve low-rank structure and restore the corrupted parts. In our method, the sum of weighted singular values is included in the objective function of minimization. We first estimate the rank of the clean data contained in the observation data by Gerschgorin disks method. Then the weights are adaptively updated by considering some singular values based on the estimated rank, thus both the number and size of the singular values are considered to recover the low-rank matrix with correct information. Experimental results show that the proposed adaptive weighted RPCA algorithm can achieve better performance under various conditions compared to the existing algorithms.
Comparative Performance Analysis of LQR and MPC for Active Suspension System

This research presents the comparative analysis of two very famous controllers that are now being used to design an active suspension system (ASS). The Linear Quadratic Regulator (LQR) and Model Predictive Controller (MPC) are two controller are used here. Our main aim is to track the system’s actions with its controllers and determine which ones perform better in the same circumstances. For the designing of the ASS a quarter-car model (QCM) is used. The entire experiment is conducted on QUANSER ASS (QASS) connected to MATLAB software. The research is to minimize turbulences by applying various controls and to maximize the ride comfort of the QCM. For this purpose a LQR and MPC is used in the same framework. The outcomes are then compared to the controller that performs more effectively. The outcome reveal that the MPC performs better than the other controller. This fact is confirmed by using various performance indexes like integral absolute error (IAE), integral time absolute error (ITAE) and integral square error (ISE).
Second Order Excess Attenuation Statistics in Athens, Greece at 19.701 GHz using ALPHASAT

Satellite links operating at Ka-band and above are susceptible to propagation phenomena and require the use of advanced Fading Mitigation Techniques (FMTs) in order to minimize downtime and outages. Nonetheless both the design and the application of FMTs require highly accurate channel modeling which in turn asks for actual experimental data to test against. In this paper, the first and mainly the second order attenuation statistics (long term excess attenuation, fade and inter-fade duration and fade slope, time diversity) obtained using the ALPHASAT beacon at 19.701 GHz in Athens, Greece are presented and evaluated for a 2-year duration. Significant observations and conclusions can be drawn based on the presented results.
Expanded Optical Frequency Comb Generation Using a Gain Switched Self-Seeded Passive Feedback Laser

In this paper, an expanded optical frequency comb (OFC) using a gain-switched self-seeded passive feedback laser (PFL), is presented. The device consists of two sections comprising an active distributed feedback (DFB) section and a passive integrated feedback (IFB) section, providing an on-chip injection to the active DFB section. Optimising the feedback from the IFB section results in an expansion of the number of generated OFC lines. As each comb line can be used as a distinct data carrier, the ability to boost the number of tones results in an increase in the overall data rate that can be transmitted using a single OFC. The paper presents a characterisation of important parameters of the laser, such as phase noise and linewidth, both in continuous wave (CW) and gain-switched mode operations. The effect of varying the phase shift of the feedback signal, through varying the bias applied to the IFB section, is also examined. The results show that by optimising the feedback signal, an expanded OFC can be generated.
Research on Visual Performance Evaluation Model of E-commerce Websites

With the growth of users' personalized demand for e-commerce visual design and the rapid development of machine learning technology, a large number of Internet pages are automatically designed and generated by computers. After being generated, effective evaluation mechanisms are needed for verification and feedback. At present, the evaluation model of visual design generally takes traditional web pages as the research object, and there is little research on the complexity and marketing characteristics of e-commerce information. It is necessary to consider how visual design can promote the effectiveness of communication between users and web pages from the perspectives of psychology, behavior and content. This paper proposes a "multi-dimensional visual performance evaluation model" for the evaluation after the generation of contemporary e-commerce web page design. This paper first constructs the visual marketing system and discusses the circulation mechanism of user demand import, e-commerce web page design, multi-dimensional visual performance evaluation and visual precision marketing. Under the system, a relational model of how "visual performance indicators (aesthetic performance, search behavior performance, information usefulness performance)" based on "scene factors" affect "overall evaluation" is proposed, defined as a multidimensional visual performance evaluation model, and verified by quantitative experiments. The model provides visual design basis for designers and recyclable evaluation data for machine learning generated pages.
Control of Inertia in Hybrid Microgrids from a Regulated DC Microgrid System

A methodology for controlling the inertia of the interlinking converter of a hybrid AC-DC microgrid working in both grid connected and isolated modes, is presented in this paper. The DC microgrid system is a Photovoltaic Generator (PVG) and a battery based energy storage (BES) based system, with constant impedance loads. The purpose of the DC microgrid is to manage the power within the DC system and to provide the necessary inertial support to the AC system. For the control of the interlinking voltage source inverter, a DC link voltage based virtual inertia strategy is formulated, which is formed as a lead-lag compensator with the change in the DC link voltage as the input, and the frequency as the output. The proposed method serves a two-fold advantage as a unified controller, i.e., the requisite inertial response is provided, as the compensator is the outcome of the swing equation, and the same controller can act as a bus signalling control, therefore managing the power flow to the utility in the grid connected mode of operation. Time domain analysis of the microgrid system verifies the efficacy of the proposed approach for both power flow based on utility command, as well as inertial support in the isolated mode. The compensator tuning is achieved with the help of a small signal analysis of the cascaded inertial controller deployed.
CRLH-TL Unit Cell for the Design of a Wideband Dual Circularly Polarized Antenna

A Wideband dual circularly polarized antenna by using composited right/left-handed transmission line (CRLH-TL) unit cell is presented in this paper. The CPLH-TL unit cell is composed of a right-hand element with the negative phase shift and a left-hand element with the positive phase shift. With these different phase shift characteristics, a feed network combined with the phase shift element are elaborately designed to feed the crossed dipoles for DCP radiation. The designed DCP antenna was also fabricated and measured for verification. It shows the presented antenna has wide impedance bandwidth and low axial ratio for DCP radiation.
Cost-Benefit Analysis of Battery Energy Storage in Electric Power Grids: Research and Practices

This paper provides an overview of methods for including Battery Energy Storage Systems (BESS) into electric power grid planning. The general approach to grid planning is the same with and without BESS, but when BESS is included as an alternative, other methods are necessary, which adds significant complexity to the planning problem. Although recent research literature proposes a wide range of methods and models for Cost-Benefit Analysis (CBA) of BESS for grid applications, these are to a little extent applied in practice. For the research-based methods to be suitable for grid planning, they should handle timing of installations as well as sizing and siting of BESS. Moreover, they must capture long-term developments in load and generation. Finally, the CBA methods need realistic modelling of the operational benefits of BESS, taking into account multi-period AC power flow, battery degradation, and utilization for multiple grid services.