Miracle-dz/UniFAR-FTUs huggingface dataset:
Audio Retrieval By Voice Imitation

Existing sound retrieval systems are mostly based on a textual query. Using text to describe a sound signal is not intuitive and is often inaccurate due to subjective impression of the user; different people may use different words to describe the same sound which makes theses system complex to design and unintuitive to use. Vocal imitation, however, is the most natural human way to describe a sound. In this paper we consider a newly rising approach for sound retrieval based on vocal imitations, where the user records himself imitating the desired sound, and the system retrieves a ranked list of the most similar sounds in the dataset. In this work we represent sound signals using histograms, obtained with respect to a Gaussian Mixture Model (GMM), representing the spectral domain. This recently proposed approach was successfully applied for word representation in a keyword spotting task. Having a fixed length representation for vocal imitation signals allows us to train a robust classifier using support vector machine (SVM). Given a test imitation signal, we apply the classifier and use the output score to rank the retrieved signals, based on a majority vote. Our simulation results show that the proposed system yields a more accurate ranking compared with other existing solutions.
EFA-An efficient algorithm for front allocation in non-dominated sorting

Evolutionary algorithms can be used to solve the multi objective optimization problems in the field of machine learning. At the heart of many MOEAs exists a non dominated sorting procedure which sorts the individuals in the population into different groups (fronts) based on their fitness values. The non dominated sorting procedure can be divided into two sub problems. The first problem deals with finding the domination count (the number of individuals that dominates a particular solution) for entire set of the individuals in the population. The second sub problem aims on allocating the front to every individual in the population. This paper discusses the relationship between the domination count of an individual and the front allocated to it. Using these relations we propose the efficient algorithms for mapping an individual to its corresponding front.
Fetal ECG Extraction Using Independent Components and Characteristics Matching

In this paper, further investigations into a simpler automated use of Independent Component Analysis (ICA) in the process of Fetal ECG (FECG) extraction are performed. Extracting FECG signals through abdominal electrodes helps clinicians in diagnosing the overall health of the fetus non-invasively. In the ICA technique, FECG signals are separated from Abdominal ECG (AECG) mixtures containing maternal and noise signals. 300,000 Data samples of three AECG recordings are obtained from PhysioNet database at 1 kHz sampling frequency. Data are pre-processed through MATLAB software by centering, whitening, and filtering techniques. Then, a simpler Fast ICA algorithm is developed and used to smoothly distinguish between AECG components through automatic signal characteristics matching. Moreover, further analysis of the extracted FECG signal is performed to determine the fetus heart rate. Results successfully show efficient automatic separation between the FECG, Maternal ECG (MECG), and noise from the AECG recordings. In addition, the developed characteristics matching algorithm automatically identified the fetus signal and smoothed it to be ready for further fetal health observations. The integration of AECG signal characteristics as a prior information into the ICA algorithm promises to assist clinicians in decision making when diagnosing fetal health conditions non-invasively.
Dynamic Analysis for the Average Shortest Path Length of Mobile Ad Hoc Networks Under Random Failure Scenarios

In general, the mobile ad hoc networks (MANETs) are built on the basis of the random distribution of the nodes, while the nodes of real networks usually have the characteristics of location preference choice. The evolving network model based on local-area choice is proposed for MANET based on the complex network theory. The proposed model of the topology not only considers the scale-free nature of the network and the actual mobility of MANET but also considers the consumption of the node energy. Random failures often occur in MANET. Most of the existing research focuses on the changes in network topology caused by random node failures. In order to describe the impact of random edge failures on the topology of MANET, we focus on the average shortest path length (ASPL) which is an important feature of the network topology and propose the formula for calculating the ASPL of the MANET after the random edge failure. The experimental simulation analyzes the change of the ASPL of MANET in the random failure scenario (after random edge deletion). By comparing with the actual scene results, the proposed estimation formula which describes this change more accurately is proved. The formula proposed in this paper provides a general framework for studying the shortest path of MANET.
Fiber-Optic Fabry–Perot Sensor for Simultaneous Measurement of Tilt Angle and Vibration Acceleration

A fiber-optic Fabry–Perot (F-P) sensor, based on a pendulum structure, for simultaneous measurement of tilt angle and vibration acceleration was proposed, which also has a high-tilt angle resolution. The real-time cavity lengths are absolutely measured using a fast demodulation algorithm. The tilt angle is estimated from the mean value of the real-time cavity lengths while the vibration acceleration derives from the alternating quantity of them. A series of experiments are conducted using a micro-tilt platform and a vibration table to simulate tilt angle chang. The experimental results show a tilt angle resolution of 0.023” in the measurement range from –2.8° to 2.6°. Meanwhile, the vibration acceleration results are obtained by using a spectral analysis to the alternating quantity and basing on a non-linear fitting curve of the frequency response of the acceleration sensibility, within the frequency range of 0 ~ 180 Hz.
Resource Allocation Algorithm With Multi-Platform Intelligent Offloading in D2D-Enabled Vehicular Networks

The latest research of network and computing contributes greatly to the development of vehicular networks. However, in existing works, these two important enabling technologies are studied separately. To reduce the delay, in this paper, we propose a multi-platform intelligent offloading and resource allocation algorithm which can dynamically organize the computing resources to improve the performance of the next-generation vehicular networks. Considering the task calculation problem, the K-nearest neighbor algorithm is used to select the task offloading platform (i.e., cloud computing, mobile edge computing, or local computing). For the computational resource allocation problem and system complexity in non-local computing, reinforcement learning is used to solve the optimization problem of resource allocation. The simulation results show that compared with the baseline algorithm that all tasks are offloaded to the local or mobile edge computing server, the resource allocation scheme achieves a significant reduction in latency cost, and the average system cost can be saved by 80%.
Network Modeling of Liver Metabolism to Predict Plasma Metabolite Changes During Short-Term Fasting in the Laboratory Rat

The liver—a central metabolic organ that integrates whole-body metabolism to maintain glucose and fatty-acid regulation, and detoxify ammonia—is susceptible to injuries induced by drugs and toxic substances. Although plasma metabolite profiles are increasingly investigated for their potential to detect liver injury earlier than current clinical markers, their utility may be compromised because such profiles are affected by the nutritional state and the physiological state of the animal, and by contributions from extrahepatic sources. To tease apart the contributions of liver and non-liver sources to alterations in plasma metabolite profiles, here we sought to computationally isolate the plasma metabolite changes originating in the liver during short-term fasting. We used a constraint-based metabolic modeling approach to integrate central carbon fluxes measured in our study, and physiological flux boundary conditions gathered from the literature, into a genome-scale model of rat liver metabolism. We then measured plasma metabolite profiles in rats fasted for 5–7 or 10–13 h to test our model predictions. Our computational model accounted for two-thirds of the observed directions of change (an increase or decrease) in plasma metabolites, indicating their origin in the liver. Specifically, our work suggests that changes in plasma lipid metabolites, which are reliably predicted by our liver metabolism model, are key features of short-term fasting. Our approach provides a mechanistic model for identifying plasma metabolite changes originating in the liver.
Transmitter and Dispersion Eye Closure Quaternary (TDECQ) and Its Sensitivity to Impairments in PAM4 Waveforms

This paper presents the principles behind the transmitter and dispersion eye closure quaternary (TDECQ) test. A novel method to decompose TDECQ values is proposed where the result is separated into penalties from signal impairments that are either equalizable or nonequalizable by the reference receiver, enabling a more comprehensive study of signal quality. Moreover, the impact of selected impairments is studied in terms of their TDECQ penalty and its composition. This is achieved by producing generic test signals, where isolated impairments are applied to a high-quality reference signal. The impairments include low-pass filtering, amplitude noise, compression and eye skew. Low-pass filtering is shown to contribute mainly to increase the equalizable penalty while noise, compression and eye skew are shown to affect mainly the nonequalizable part. The results serve as a generic guideline to understand the engineering rules and parameter limits that are needed to design links using TDECQ.
Comparison of Electromagnetic Performance of SCPM Wind Power Generators With Different Topologies

This paper focuses on the comparison of electromagnetic performance of the superconducting permanent magnet (SCPM) generators with two different topologies. The torque capabilities of the two generators are first investigated. The peak torque is largely restricted by the material characteristics of the superconducting (SC) and the permanent magnet. The SCPM generators with iron-cored rotor and iron-cored stator topology (IRIST) is superior to the one with iron-cored rotor and air-cored stator topology in terms of torque capability. Furthermore, the flux density, line electromotive force, torque and its torque ripple, and the efficiency of the designed generators are evaluated by using numerical model. The simulation results confirm that IRIST has higher output torque and efficiency with the penalty of higher harmonics and torque ripples.
Resolvin D2 Induces Resolution of Periapical Inflammation and Promotes Healing of Periapical Lesions in Rat Periapical Periodontitis

Periapical periodontitis results from pulpal infection leading to pulpal necrosis and resorption of periapical bone. The current treatment is root canal therapy, which attempts to eliminate infection and necrotic tissue. But, in some cases periapical inflammation doesn't resolve even after treatment. Resolvins belongs to a large family of specialized pro-resolving lipid mediators that actively resolves inflammation signaling via specific receptors. Resolvin D2 (RvD2), a metabolite of docosahexaenoic acid (DHA), was tested as an intracanal medicament in rats in vivo. Mechanism was evaluated in rat primary dental pulp cells (DPCs) in vitro. The results demonstrate that RvD2 reduces inflammatory cell infiltrate, periapical lesion size, and fosters pulp like tissue regeneration and healing of periapical lesion. RvD2 enhanced expression of its receptor, GPR18, dentin matrix acidic phosphoprotein 1 (DMP1) and mineralization in vivo and in vitro. Moreover, RvD2 induces phosphorylation of Stat3 transcription factor in dental pulp cells. We conclude that intracanal treatment with RvD2 resolves inflammation and promoting calcification around root apex and healing of periapical bone lesions. The data suggest that RvD2 induces active resolution of inflammation with pulp-like tissue regeneration after root canal infection and thus maybe suitable for treating periapical lesions.
Deep Learning-Based Hazardous Sound Classification for the Hard of Hearing and Deaf

The hard of hearing or deaf can only access limited auditory information in dangerous situations. Therefore, development of a system for sensing hazardous auditory information may be of great help to them. However, such systems have focused on effective signal transduction when a hazardous sound is detected, and the classification of hazardous sounds has been less investigated. The present study was conducted to classify sounds by using Recurrent Neural Network (RNN)-based models, Convolutional Neural Network (CNN)-based models, the combination of the two models, and ensemble models prepared by combining various models. The experimental results showed that the accuracy of the 3-layer Long Short-Term Memory (LSTM) model was 97.63% and that of the ensemble model was 98.00%. As an attempt at real-life application of the developed model, a warning system was prepared by using Raspberry Pi and a vibrator.
Implications of Distributed Link Polarization-Dependent Loss on Bitwise Achievable Information Rates for Probabilistically Shaped and Uniform DP 64-QAM

The statistical behaviors of bitwise achievable information rates (BW AIRs) and effective signal-to-noise ratios (SNRs) due to polarization-dependent loss (PDL) are experimentally characterized for probabilistically shaped and uniform dual-polarization 64-ary quadrature amplitude modulation with a bit rate of 250 Gb/s. A single-span recirculating loop is used to emulate distributed link PDL. Histograms for the BW AIR and effective SNR following offline signal processing resemble shifted, mirror-image Maxwell probability density functions for both constellations and two different per-loop PDL values. For a transmission distance of 1200 km and a per-loop PDL of 1.3 dB, the outage probabilities for the shaped and uniform constellations are estimated to be <inline-formula><tex-math notation="LaTeX">$1.1\times 10^{-3}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$4.4\times 10^{-1}$</tex-math></inline-formula>, respectively. Moreover, for an outage probability <inline-formula><tex-math notation="LaTeX">$\leq$</tex-math></inline-formula> <inline-formula><tex-math notation="LaTeX">$4.0\times 10^{-3}$</tex-math></inline-formula>, the increase in transmission distance due to shaping is reduced by 50% compared to that for PDL free transmission.
Stop Crying Over Your Cache Miss Rate: Handling Efficiently Thousands of Outstanding Misses in FPGAs

FPGAs rely on massive datapath parallelism to accelerate applications even with a low clock frequency. However, applications such as sparse linear algebra and graph analytics have their throughput limited by irregular accesses to external memory for which typical caches provides little benefit because of very frequent misses. Non-blocking caches are widely used on CPUs to reduce the negative impact of misses and thus increase performance of applications with low cache hit rate; however, they rely on associative lookup for handling multiple outstanding misses, which limits their scalability, especially on FPGAs. This results in frequent stalls whenever the application has a very low hit rate. In this paper, we show that by handling thousands of outstanding misses without stalling we can achieve a massive increase of memory-level parallelism, which can significantly speed up irregular memory-bound latency-insensitive applications. By storing miss information in cuckoo hash tables in block RAM instead of associative memory, we show how a non-blocking cache can be modified to support up to three orders of magnitude more misses. The resulting miss-optimized architecture provides new Pareto-optimal and even Pareto-dominant design points in the area-delay space for twelve large sparse matrix-vector multiplication benchmarks, providing up to 25% speedup with 24x area reduction or to 2x speedup with similar area compared to traditional hit-optimized architectures.
An Investigation of the Psychometric Properties of the Chinese Trait Emotional Intelligence Questionnaire Short Form (Chinese TEIQue-SF)

The present study examined the psychometric properties of the Chinese version of the Trait Emotional Intelligence Questionnaire Short Form (TEIQue-SF). Analyses were performed using a sample of undergraduates (N = 585) recruited from four universities across China. Confirmatory factor analysis of the Chinese TEIQue-SF supported the one-factor structure of trait emotional intelligence. Measurement invariance analyses were conducted across the Chinese sample and a sample of Canadian undergraduate students (N = 638). Although the two samples demonstrated configural and partial metric invariance, scalar invariance was not found. Cross-cultural implications and explanations of the present findings, as well as suggestions for future research are discussed.
Bio-Inspired Terrestrial Motion of Magnetic Soft Millirobots

Magnetic soft robots have the combined advantages of contactless actuation, requiring no on-board power source, and having flexible bodies that can adapt to unstructured environments. In this study, four milli-scale soft robots are designed (Inchworm, Turtle, Quadruped, and Millipede) and their actuation under external magnetic fields is investigated with the objective of reproducing multi-limbed motion patterns observed in nature. Magnetic properties are incorporated into a silicone polymer by mixing in ferromagnetic microparticles before curing. The magnet-polymer composite is used to fabricate soft magnetic parts, with pre-determined magnetization profiles achieved using a 1 T field. The resulting soft robots are actuated under external magnetic fields of 10–35 mT which are controlled using an array of six electromagnetic coils. The achieved motion patterns are analyzed over five iterations and the motions are quantified in terms of body lengths traversed per actuation cycle and speed of displacement. The speed of the specimens is calculated to be in the range of 0.15–0.37 mm/s for the actuation field used here. The ability of the soft robots to traverse uneven terrain is also tested, with the Turtle and the Millipede demonstrating successful motion.
Development of a Radial Pulse Monitoring System Based on a Graphene-Coated Fiber

The radial pulse is considered as a fundamental indicator for the diagnosis of cardiovascular diseases, which can guide therapeutic decisions in complex clinical situations. We designed and fabricated a real-time low-cost wearable pulse monitoring system based on a graphene-coated fiber to detect the radial pulse. The graphene-coated fiber, acting as sensing component, had excellent conductance property and high sensitivity. However, the amplitude of the pulse is subtle, always masked by noise. As a consequence, a diminutive signal conditioning unit integrated with a low-noise instrument amplifier was developed to enhance the signals. Furtherly, we proposed a newly multistage filter method to attenuate the noise synchronously. The signal noise ratio was improved by at least 27 dB, compared with a traditional measurement method. Meanwhile, a graphical user interface was developed to monitor the pulses in real time, making the monitor convenient and timely. The heart rate, which is of clinical significance, was measured accurately. The data were being stored in the PC terminal simultaneously to obtain an overall heart health and fitness level. The superior sensing performance and economic fabrication process of the pulse monitoring system strengthen our confidence in the application of the smart fabrics in wearable sensing and home health care fields.
User Controllable Content Retargeting and Depth Adaptation for Stereoscopic Display

This paper presents a content retargeting and depth adaptation method for the stereoscopic image. Our method allows the user to specify the retargeting scenarios to enhance the user’s visual experience. Toward this end, we proposed a warping framework that takes content retargeting, depth adaptation, and interactive editing into account simultaneously for stereoscopic images. From the viewpoint of accuracy, comfortability, and controllability, we exploit the complementary relationship among image quality, depth quality, and important content and propose a grid optimization framework to fuse the three indictors. The experimental results demonstrate that our method achieves a preferable tradeoff among accuracy, comfortability, and controllability of information presentation in retargeting, obtaining satisfactory visual experience for users.
Modality-Invariant Image-Text Embedding for Image-Sentence Matching

Performing direct matching among different modalities (like image and text) can benefit many tasks in computer vision, multimedia, information retrieval, and information fusion. Most of existing works focus on class-level image-text matching, called cross-modal retrieval, which attempts to propose a uniform model for matching images with all types of texts, for example, tags, sentences, and articles (long texts). Although cross-model retrieval alleviates the heterogeneous gap among visual and textual information, it can provide only a rough correspondence between two modalities. In this article, we propose a more precise image-text embedding method, image-sentence matching, which can provide heterogeneous matching in the instance level. The key issue for image-text embedding is how to make the distributions of the two modalities consistent in the embedding space. To address this problem, some previous works on the cross-model retrieval task have attempted to pull close their distributions by employing adversarial learning. However, the effectiveness of adversarial learning on image-sentence matching has not been proved and there is still not an effective method. Inspired by previous works, we propose to learn a modality-invariant image-text embedding for image-sentence matching by involving adversarial learning. On top of the triplet loss--based baseline, we design a modality classification network with an adversarial loss, which classifies an embedding into either the image or text modality. In addition, the multi-stage training procedure is carefully designed so that the proposed network not only imposes the image-text similarity constraints by ground-truth labels, but also enforces the image and text embedding distributions to be similar by adversarial learning. Experiments on two public datasets (Flickr30k and MSCOCO) demonstrate that our method yields stable accuracy improvement over the baseline model and that our results compare favorably to the state-of-the-art methods.