Miracle-dz/UniFAR-FTUs huggingface dataset:
Unifying Local and Global Methods for Harmonic-Percussive Source Separation

This paper addresses the separation of drums from music recordings, a task closely related to harmonic-percussive source separation (HPSS). In previous works, two families of algorithms have been prominently applied to this problem. They are based either on local filtering and diffusion schemes, or on global low-rank models. In this paper, we propose to combine the advantages of both paradigms. To this end, we use a local approach based on Kernel Additive Modeling (KAM) to extract an initial guess for the percussive and harmonic parts. Subsequently, we use Non-Negative Matrix Factorization (NMF) with soft activation constraints as a global approach to jointly enhance both estimates. As an additional contribution, we introduce a novel constraint for enhancing percussive activations and a scheme for estimating the percussive weight of NMF components. Throughout the paper, we use a real-world music example to illustrate the ideas behind our proposed method. Finally, we report promising BSS Eval results achieved with the publicly available test corpora ENST-Drums and QUASI, which contain isolated drum and accompaniment tracks.
GLRT Particle Filter for Tracking Nlos Target in Around-the-Corner Radar

This article examines the problem of tracking NLOS targets by exploiting multipath information measured by an urban around-the-corner radar. Due to the non linearity relationship between target position and multipath delays and the complexity of the multipath measurement model, a particle filter is considered. It samples separately the target state (po-sition and velocities) and the multipath amplitudes thanks to an appropriate importance density function. GLRT particle filter, a special case of the proposed particle filter, is shown to provide the most efficient localization results. Simulations demonstrate the ability of the particle filter to correctly estimate the target location and the improvement of localization performance compared to a multipath localization algorithm without tracking.
Learning Robust Manipulation Skills with Guided Policy Search via Generative Motor Reflexes

Guided Policy Search enables robots to learn control policies for complex manipulation tasks efficiently. Therein, the control policies are represented as high-dimensional neural networks which derive robot actions based on states. However, due to the small number of real-world trajectory samples in Guided Policy Search, the resulting neural networks are only robust in the neighbourhood of the trajectory distribution explored by real-world interactions. In this paper, we present a new policy representation called Generative Motor Reflexes, which is able to generate robust actions over a broader state space compared to previous methods. In contrast to prior state-action policies, Generative Motor Reflexes map states to parameters for a state-dependent motor reflex, which is then used to derive actions. Robustness is achieved by generating similar motor reflexes for many states. We evaluate the presented method in simulated and real-world manipulation tasks, including contact-rich peg-in-hole tasks. Using these evaluation tasks, we show that policies represented as Generative Motor Reflexes lead to robust manipulation skills also outside the explored trajectory distribution with less training needs compared to previous methods.
Structure Adaptive Total Variation Minimization-Based Image Decomposition

Structure-preserving image decomposition separates a given image into structure and texture by smoothing the image, simultaneously preserving or enhancing image edges. The well-studied problem of image decomposition is applied to various areas, such as image smoothing, detail enhancement, non-photorealistic rendering, image artistic rendering, and high-dynamic-range compression. In this paper, we propose a fast algorithm for structure-preserving image decomposition that adopts total variation (TV) minimization to the moving least squares (MLS) method with non-local weights, called structure adaptive TV (SATV) minimization. MLS with non-local weights provides high accuracy approximation that is robust to noise, and allows a fast convergence with TV regularization term. As a result, our proposed SATV preserves the dominant structure while flattening fine-scale details. The experimental results show that the SATV minimization algorithm provides faster and more robust image decomposition than the well-known previous approaches. We demonstrate the usefulness of our algorithm by presenting successful applications in image smoothing and detail enhancement.
Handwritten Signature Verification Using Hidden Markov Model with Hybrid Wavelet Transform

Handwritten online signature offers x, y, z coordinates along with pressure, azimuth and altitude of the pen tip. Hybrid wavelet transform was applied on the first 128 samples of the pressure parameter and 1- 16 and 33- 64 samples of the output were used as feature vector for signature verification. Using Hidden Markov Model (HMM) classifier with ergodic model, the performance was compared. For 1-16 samples DHT KEKRE offers best performance of FRR 1 % and FAR 1 %. For 33-64 samples, KEKRE 128 and HADAMARD HAAR offers best performance with FRR 1 % and FAR 1 %. Feature vector with 33-64 samples offers better FRR-FAR than 1–16 samples. For 1–16 samples DCT HAAR offers best performance of AAR 9% and ARR 3%. For 33-64 samples, DHT 128 offers best performance with AAR 34% and ARR 23%. 33-64 samples offers better AAR - ARR than 1–16 samples.
Multiplayer Bandits: A Trekking Approach

In this article, we study stochastic multiarmed bandits with many players. The players do not know the number of players, cannot communicate with each other, and if multiple players select a common arm, they collide and none of them receive any reward. We consider the static scenario, where the number of players remains fixed, and the dynamic scenario, where the players enter and leave at any time. We provide algorithms based on a novel “trekking approach” that guarantees constant regret for the static case and sublinear regret for the dynamic case with high probability. The trekking approach eliminates the need to estimate the number of players resulting in fewer collisions and improved regret performance compared to state-of-the-art algorithms. We also develop an epoch-less algorithm that eliminates any requirement of time synchronization across the players provided each player can detect the presence of other players on an arm. We validate our theoretical guarantees using simulation-based and real testbed-based experiments.
Satellite cells delivered in their niche efficiently generate functional myotubes in three-dimensional cell culture

Biophysical/biochemical cues from the environment contribute to regulation of the regenerative capacity of resident skeletal muscle stem cells called satellites cells. This can be observed in vitro, where muscle cell behaviour is influenced by the particular culture substrates and whether culture is performed in a 2D or 3D environment, with changes including morphology, nuclear shape and cytoskeletal organization. To create a 3D skeletal muscle model we compared collagen I, Fibrin or PEG-Fibrinogen with different sources of murine and human myogenic cells. To generate tension in the 3D scaffold, biomaterials were polymerised between two flexible silicone posts to mimic tendons. This 3D culture system has multiple advantages including being simple, fast to set up and inexpensive, so providing an accessible tool to investigate myogenesis in a 3D environment. Immortalised human and murine myoblast lines, and primary murine satellite cells showed varying degrees of myogenic differentiation when cultured in these biomaterials, with C2 myoblasts in particular forming large multinucleated myotubes in collagen I or Fibrin. However, murine satellite cells retained in their niche on a muscle fibre and embedded in 3D collagen I or Fibrin gels generated aligned, multinucleated and contractile myotubes.
Fast Detection of Abnormal Events in Videos with Binary Features

Millions of surveillance cameras are currently installed in public places around the world, making it necessary to intelligently analyse the acquired data to detect the occurrence of abnormal events. A vast number of methods to detect such events have been recently proposed; unfortunately, there is a lack of methods capable of detecting these events as frames are acquired, also known as online processing. In this paper, we present an online framework for video anomaly detection that employs binary features to encode motion information, and low-complexity probabilistic models for detection. Evaluation results on the popular UCSD dataset and on a recently introduced real-event video surveillance dataset show that our framework outperforms non-online and online methods.
Asymmetric Spatio-Temporal Embeddings for Large-Scale Image-to-Video Retrieval

We address the problem of image-to-video retrieval. Given a query image, the aim is to identify the frame or scene within a collection of videos that best matches the visual input. Matching images to videos is an asymmetric task in which specific features for capturing the visual information in images and, at the same time, compacting the temporal correlation from videos are needed. Methods proposed so far are based on the temporal aggregation of hand-crafted features. In this work, we propose a deep learning architecture for learning specific asymmetric spatio-temporal embeddings for image-tovideo retrieval. Our method learns non-linear projections from training data for both images and videos and projects their visual content into a common latent space, where they can be easily compared with a standard similarity function. Experiments conducted here show that our proposed asymmetric spatio-temporal embeddings outperform stateof-the-art in standard image-to-video retrieval datasets.
Semantic Mapping with Omnidirectional Vision

This paper presents a purely visual semantic mapping framework using omnidirectional images. The approach rests upon the robust segmentation of the robot's local free space, replacing conventional range sensors for the generation of occupancy grid maps. The perceptions are mapped into a bird's eye view allowing an inverse sensor model directly by removing the non-linear distortions of the omnidirectional camera mirror. The system relies on a place category classifier to label the navigation relevant categories: room, corridor, doorway, and open room. Each place class maintains a separated grid map that are fused with the range-based occupancy grid for building a dense semantic map.
Mind the Gap: Mapping Mass Spectral Databases in Genome-Scale Metabolic Networks Reveals Poorly Covered Areas

The use of mass spectrometry-based metabolomics to study human, plant and microbial biochemistry and their interactions with the environment largely depends on the ability to annotate metabolite structures by matching mass spectral features of the measured metabolites to curated spectra of reference standards. While reference databases for metabolomics now provide information for hundreds of thousands of compounds, barely 5% of these known small molecules have experimental data from pure standards. Remarkably, it is still unknown how well existing mass spectral libraries cover the biochemical landscape of prokaryotic and eukaryotic organisms. To address this issue, we have investigated the coverage of 38 genome-scale metabolic networks by public and commercial mass spectral databases, and found that on average only 40% of nodes in metabolic networks could be mapped by mass spectral information from standards. Next, we deciphered computationally which parts of the human metabolic network are poorly covered by mass spectral libraries, revealing gaps in the eicosanoids, vitamins and bile acid metabolism. Finally, our network topology analysis based on the betweenness centrality of metabolites revealed the top 20 most important metabolites that, if added to MS databases, may facilitate human metabolome characterization in the future.
Random Matrix Asymptotics of Inner Product Kernel Spectral Clustering

We study in this article the asymptotic performance of spectral clustering with inner product kernel for Gaussian mixture models of high dimension with numerous samples. As is now classical in large dimensional spectral analysis, we establish a phase transition phenomenon by which a minimum distance between the class means and covariances is required for clustering to be possible from the dominant eigenvectors. Beyond this phase transition, we evaluate the asymptotic content of the dominant eigenvectors thus allowing for a full characterization of clustering performance. However, a surprising finding is that in some particular scenarios, the phase transition does not occur and clustering can be achieved irrespective of the class means and covariances. This is evidenced here in the case of the mixture of two Gaussian datasets having the same means and arbitrary difference between covariances.
3D Exterior Soundfield Reproduction Using a Planar Loudspeaker Array

In this paper, we propose a planar array of dipole (or first-order) loudspeakers to reproduce a full three dimensional (3D) exterior soundfield over a desired spatial region. The proposed method is inspired by the spherical harmonics based solution for 3D soundfield reproduction. When decomposed in terms of spherical harmonics, we separate the corresponding soundfield coefficients into two sets (even and odd) and exploit the inherent properties of Legendre polynomials to control each set using a planar array of dipole (or first-order) speakers. We provide simulation examples to demonstrate the performance of the proposed method.
Frequency-Domain Model of Voltage-Source Inverters with Closed-Loop Current Control

The goal of this paper is to derive a modeling approach in the frequency domain for pulse-width modulated voltage-source inverters with closed-loop control. The feedback loop of a closed-loop control introduces a harmonic content into the input signal of the modulator. Based on models for pulsewidth modulation with multiple-frequency input signals presented in previous publications, the incorporation of the feedback loop became feasible. However, the pulse-width modulator models are unidirectional and do not cover the reaction of the plant through the feedback control loop. This paper develops an analytical nonlinear model for voltage-source inverters with closed-loop control for steady-state operation, based on Fourier coefficients. The model of a digital control system including the feedback of the dc-link voltage and the feedback of the ac-side current is created, incorporating the influence of the analog-to-digital conversion and the discrete-time control. The nonlinear equation system is numerically and simultaneously solved and validated with time-domain simulations and measurements on a converter prototype.
Gated-GAN: Adversarial Gated Networks for Multi-Collection Style Transfer

Style transfer describes the rendering of an image’s semantic content as different artistic styles. Recently, generative adversarial networks (GANs) have emerged as an effective approach in style transfer by adversarially training the generator to synthesize convincing counterfeits. However, traditional GAN suffers from the mode collapse issue, resulting in unstable training and making style transfer quality difficult to guarantee. In addition, the GAN generator is only compatible with one style, so a series of GANs must be trained to provide users with choices to transfer more than one kind of style. In this paper, we focus on tackling these challenges and limitations to improve style transfer. We propose adversarial gated networks (Gated-GAN) to transfer multiple styles in a single model. The generative networks have three modules: an encoder, a gated transformer, and a decoder. Different styles can be achieved by passing input images through different branches of the gated transformer. To stabilize training, the encoder and decoder are combined as an auto-encoder to reconstruct the input images. The discriminative networks are used to distinguish whether the input image is a stylized or genuine image. An auxiliary classifier is used to recognize the style categories of transferred images, thereby helping the generative networks generate images in multiple styles. In addition, Gated-GAN makes it possible to explore a new style by investigating styles learned from artists or genres. Our extensive experiments demonstrate the stability and effectiveness of the proposed model for multi-style transfer.
Analysis of Biomolecules Based on the Surface Enhanced Raman Spectroscopy

Analyzing biomolecules is essential for disease diagnostics, food safety inspection, environmental monitoring and pharmaceutical development. Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for detecting biomolecules due to its high sensitivity, rapidness and specificity in identifying molecular structures. This review focuses on the SERS analysis of biomolecules originated from humans, animals, plants and microorganisms, combined with nanomaterials as SERS substrates and nanotags. Recent advances in SERS detection of target molecules were summarized with different detection strategies including label-free and label-mediated types. This comprehensive and critical summary of SERS analysis of biomolecules might help researchers from different scientific backgrounds spark new ideas and proposals.
Partial Resurfacing of the Knee with the BioPoly Implant

Background: Current treatments for focal chondral and osteochondral lesions of the femoral condyle have been associated with variable outcomes. We conducted a clinical trial of the BioPoly RS Partial Resurfacing Knee Implant to address this unmet need. Methods: We performed a single-arm, prospective study in which 33 patients with focal cartilage lesions affecting the femoral condyle were managed with the BioPoly RS Partial Resurfacing Knee Implant. Knee injury and Osteoarthritis Outcome Score (KOOS) scores, a visual analog scale (VAS) for pain, the Short Form-36 (SF-36) physical component score , and the Tegner activity score were used to assess outcomes preoperatively and at 6 months, 1 year, and 2 years postoperatively. The KOOS outcomes at 2 years were compared with historical outcomes following microfracture treatment. Results: We found significant and clinically meaningful improvements in the KOOS scores, VAS pain score, and SF-36 physical component score (p < 0.025) when the values at all 3 postoperative time points were compared with the preoperative scores, and we also found significant improvements when the Tegner activity score at 2 years was compared with the preoperative score (p < 0.025). More than half of the cohort of patients had had a previous failure of cartilage-repair procedures. No significant differences were detected between younger patients (⩽40 years) and older patients (>40 years). When compared with historical microfracture data, the BioPoly RS Implant demonstrated significantly superior KOOS scores for quality of life and sports. Conclusions: The present study indicated that the BioPoly RS Partial Resurfacing Knee Implant is safe, that it resulted in significantly improved knee function by 6 months, and that this improvement was sustained for 2 years regardless of patient age. The BioPoly RS Knee Implant allows return to a higher level of sporting activity than microfracture. Additional long-term follow-up is needed to determine the long-term effects of the device. Level of Evidence: Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence.
Listening to Each Speaker One by One with Recurrent Selective Hearing Networks

Deep learning-based single-channel source separation algorithms are currently being actively investigated. Among them, Deep Clustering (DC) and Deep Attractor Networks (DANs) have made it possible to separate an arbitrary number of speakers. In particular, they cleverly combine a neural network and a K-means clustering algorithm to obtain source separation masks with the assumption that the correct number of speakers at the test time is known in advance. Unlike DC and DAN, Permutation Invariant Training (PIT) was proposed as a purely neural network-based mask estimator. Essentially, however, PIT can deal with only a fixed number of speakers, given the strong relationship between the dimensions of the output nodes and the assumed number of sources. Considering these limitations and merits of such conventional methods, this paper proposes a purely neural-network based mask estimator that can handle an arbitrary number of sources, and simultaneously estimate the number of sources in the test signal. To accomplish this, while the conventional methods deal with the source separation problem as a one-pass problem, we cast the problem as a recursive multi-pass source extraction problem based on a recurrent neural network (RNN) that can learn and determine how many computational steps/iterations have to be performed depending on the input signals. In this paper, we describe our proposed method in detail, and experimentally show its efficacy in terms of source separation and source counting performance.
DVFS Binning Using Machine-Learning Techniques

This paper presents a framework which can avoid the lengthy system test by utilizing machine-learning techniques to classify parts into different DVFS bins based on the results collected at CP and FT test only. The core machine-learning techniques in use are Bayesian linear regression for model fitting and stepwise regression for feature selection. Another method, called the incremental F_max-model search, is also presented to reduce the test time of collecting the required data for each training sample. The experiments are conducted based on 249 test chips of an industrial SoC. The experimental results demonstrate that our proposed framework can achieve a high accuracy ratio of placing a part into correct DVFS bin without placing any slower part into a faster DVFS bin. The experimental results also demonstrate that the incremental F_max-model search can save 45.1% and 52.6% of applications of the system-level test compared to the conventional median linear search and binary search, respectively.
On Selecting Antenna Placements in Indoor Radio Environments

In this work, we introduce an antenna placement algorithm for indoor radio networks. The algorithm aims to minimize the number of antennas required to provide sufficient coverage in an area of interest, minimizing the cost of equipment and installation work. The optimization algorithm exploits a semi-deterministic model for the most dominant radio paths. Each path is in turn determined with the A⋆ path finding algorithm. Both the proposed antenna placement algorithm and the used indoor radio propagation model are evaluated using real measurements, confirming the efficiency of the method.
A Feature Fusion Method Based on Extreme Learning Machine for Speech Emotion Recognition

Speech emotion recognition is important to understand users' intention in human-computer interaction. However, it is a challenging task partly because we cannot clearly know which feature and model are effective to distinguish emotions. Previous studies utilize convolutional neural network (CNN) directly on spectrograms to extract features, and bidirectional long short term memory (BLSTM) is the state-of-the-art model. However, there are two problems of CNN-BLSTM. Firstly, it doesn't utilize heuristic features based on priori knowledge. Secondly, BLSTM has a complex structure and high complexity in training. To address the first problem, we propose a feature fusion method that combines CNN-based features and heuristic-based discriminative features which are extracted from heuristic features using deep neural network (DNN). In addition, we utilize extreme learning machine (ELM) instead of BLSTM to solve the second problem. The experiments conducted on EmoDB and our method leads to 40% relative error reduction in Fl-score compared to CNN-BLSTM.