Miracle-dz/UniFAR-FTUs huggingface dataset:
Primary-Ambient Source Separation for Upmixing to Surround Sound Systems

Extracting spatial information from an audio recording is a necessary step for upmixing stereo tracks to be played on surround systems. One important spatial feature is the perceived direction of the different audio sources in the recording, which determines how to remix the different sources in the surround system. The focus of this paper is the separation of two types of audio sources: primary (direct) and ambient (surrounding) sources. Several approaches have been proposed to solve the problem, based mainly on the correlation between the two channels in the stereo recording. In this paper, we propose a new approach based on training a neural network to determine and extract the two sources from a stereo track. By performing a subjective and objective evaluation between the proposed method and common methods from the literature, the proposed approach shows improvement in the separation accuracy, while being computationally attractive for real-time applications.
Robotic Cleaning Through Dirt Rearrangement Planning with Learned Transition Models

We address the problem of enabling a manipulator to move arbitrary amounts and configurations of dirt on a surface to a goal region using a cleaning tool. We represent this problem as heuristic search with a set of primitive dirt-oriented tool actions. We present dirt and action representations that allow efficient learning and prediction of future dirt states, given the current dirt state and applied action. We also present a method for sampling promising actions based on a clustering of dirt states and heuristics for planning. We demonstrate the effectiveness of our approach on challenging cleaning tasks through implementations on PR2 and Fetch robots.
Enabling external routing logic in ONOS with Intent Monitor and Reroute service

SDN intent-based networking allows programmers to specify high-level policies without worrying about the low-level configurations which are compiled by the controller. Our Intent Monitor and Reroute service (IMR) extends the capabilities of ONOS Intent Framework by allowing to both compile multiple intents together and to re-optimize their paths, according to the network state and their flow statistics, via off-platform applications (OPA). In this demo we show two examples of intent-based ONOS application whose network performance can be improved, with no modifications to their code, exploiting the IMR service.
Representation of Pose Invariant Face Images Using SIFT Descriptors

The choice of a face database should solemnly depend on the problem to be solved. In this research work, we use the Face Recognition Technology (FERET) database to address the challenge of face pose variations. The Scale Invariant Feature Transform (SIFT) is used to represent these face images in the database. SIFT has been proven to be a robust and a powerful method for general object detection in the past years. This method is now popular in the field of face recognition for purposes of extracting key points which are scale and orientation invariant from the face image. This work demonstrates that through extracting SIFT features from different face image patches and at different sigma $\sigma$ values, a face pose can be classified towards better pose invariant face recognition.
Effects of thermal stimulation on neurons and astrocytes cultured from the rat median preoptic nucleus

Warming or cooling of the median preoptic nucleus (MnPO) in-vivo evokes appropriate thermoregulatory responses. In the present study, we aimed to investigate whether single neurons (and astrocytes) of primary rat MnPO cell cultures maintain properties, which are consistent with their putative role within the central thermoregulatory structures. Using the fura-2 ratio imaging technique, we therefore measured changes of intracellular Ca2+ concentrations ([Ca2+]i) in neurons of rat MnPO primary cultures stimulated by rapid cooling from 37 to 25°C, or warming from 37 to 45°C, or glutamate, the transmitter which transfers thermal information to MnPO neurons. In the first experiment, we tested the responses to external cooling in a group of 212 neurons. Overall, 165 of these neurons were responsive to stimulation with glutamate; just four of them responded to the cold-stimulus with an increase of [Ca2+]i, and only one of these neurons was responsive to stimulation with menthol. In the second experiment, 24 of 327 neurons and 23 of 241 astrocytes responded to external warming with quick and pronounced Ca2+ signals. Another 33 (10%) neurons showed a moderate and slowly developing increase of [Ca2+]i during the warming, which reflected the temperature changes in the chamber. These data correspond to properties of MnPO neurons upon thermal stimulation obtained by other experimental approaches. Primary cultures derived from the rat MnPO can thus be used to investigate neuronal thermosensitive properties and their possible modulation by other stimuli.
Frequent, short bouts of light-intensity exercises while standing decreases systolic blood pressure: Breaking Up Sitting Time after Stroke (BUST-Stroke) trial

Background Stroke survivors sit for long periods each day. Uninterrupted sitting is associated with increased risk of cardiovascular disease. Breaking up uninterrupted sitting with frequent, short bouts of light-intensity physical activity has an immediate positive effect on blood pressure and plasma clotting factors in healthy, overweight, and type 2 diabetic populations. Aim We examined the effect of frequent, short bouts of light-intensity physical activity on blood pressure and plasma fibrinogen in stroke survivors. Methods Prespecified secondary analyses from a three-armed randomized, within-participant, crossover trial. Participants were 19 stroke survivors (nine female, aged 68 years old, 90% able to walk independently). The experimental conditions were sitting for 8 h uninterrupted, sitting with 3 min bouts of light-intensity exercise while standing every 30 min, or sitting with 3 min of walking every 30 min. Blood pressure was measured every 30 min over 8 h and plasma fibrinogen at the beginning, middle, and end of each day. Intention-to-treat analyses were performed using linear mixed models including fixed effects for condition, period, and order, and a random intercept for participant to account for repeated measures and missing data. Results Sitting with 3 min bouts of light-intensity exercise while standing every 30 min decreased systolic blood pressure by 3.5 mmHg (95% CI 1.7–5.4) compared with sitting for 8 h uninterrupted. For participants not taking antihypertensive medications, sitting with 3 min of walking every 30 min decreased systolic blood pressure by 5.0 mmHg (95% CI −7.9 to 2.0) and sitting with 3 min bouts light-intensity exercise while standing every 30 min decreased systolic blood pressure by 4.2 mmHg (95% CI −7.2 to −1.3) compared with sitting for 8 h uninterrupted. There was no effect of condition on diastolic blood pressure (p = 0.45) or plasma fibrinogen levels (p = 0.91). Conclusion Frequent, short bouts of light-intensity physical activity decreases systolic blood pressure in stroke survivors. However, before translation into clinical practice, the optimal duration and timing of physical activity bouts needs to be determined. Clinical trial registration Australian and New Zealand Clinical Trials Registry http://www.anzctr. org.au ANZTR12615001189516.
hsa-miR-24 suppresses metastasis in nasopharyngeal carcinoma by regulating the c-Myc/epithelial-mesenchymal transition axis

Distant metastasis is the major contributor to treatment failure and mortality in patients with nasopharyngeal carcinoma (NPC). The lack of effective treatment strategies for metastatic NPC is the major cause for the low survival rate. Therefore, it is crucial to understand the molecular mechanisms underlying NPC metastasis and to identify potential biomarkers for targeted therapy. MicroRNA (miRNAs or miRs) have been shown to play an important role in tumorigenesis and metastasis. In the present study, we aimed to evaluate the significance of hsa-miR-24 in NPC metastasis. Significantly lower hsa-miR-24 levels were observed in NPC metastatic tumors and higher hsa-miR-24 levels were associated with longer progression-free and metastasis-free survival durations. hsa-miR-24 overexpression inhibited cell proliferation, invasion and migration. Using bioinformatics approaches together with functional luciferase reporter assays, we demonstrated that the c-Myc 3′-UTR was a direct target of hsa-miR-24 in regulating NPC metastasis. Protein profiling analysis revealed that a high c-Myc expression was inversely associated with metastasis-free overall survival and with epithelial-mesenchymal transition (EMT). Furthermore, the overexpression of hsa-miR-24 decreased NPC cell invasive ability induced by the overexpression of c-Myc, associated with EMT epithelial marker (E-cadherin) restoration. Thus, on the whole, the findings of this study demonstrate that hsa-miR-24 suppresses metastasis in NPC by regulating the c-Myc/EMT axis, suggesting that hsa-miR-24 may be used as a prognostic factor and as a novel target for the prevention of NPC metastasis.
StitchAD-GAN for Synthesizing Apparent Diffusion Coefficient Images of Clinically Significant Prostate Cancer

The high volume and quality of apparent diffusion coefﬁcient (ADC) data containing clinically signiﬁcant (CS) prostate cancer (PCa) are critical for automated PCa detection with a high accuracy. However, ADC data of CS PCa is scarce and costly to obtain in practice. This paper proposes a novel Generative Adversarial Network (GAN), named StitchAD-GAN, for synthesizing high-quality ADC images of CS PCa. Our StitchAD-GAN employs a StitchLayer in the generator to address the difﬁcult-to-optimize problem in most GANs. Instead of directly optimizing a complex generation from a low dimensional noise to an ADC image, we optimize n easier generations of sub-images, which are then aggregated into a full size image in the StitchLayer. Our discriminative module approximates two distances: 1) the Wasserstein distance (W-distance) between the synthetic and real ADC data of CS PCa, and 2) an auxiliary distance (AD) Jensen-Shannon divergence (JSD) between the synthetic CS PCa and real nonCS PCa data. By minimizing the W-distance and maximizing the JSD simultaneously, our StitchAD-GAN can capture CS PCa features in addition to predominant prostate gland information, and in turn synthesize more clinically meaningful ADC data of CS PCa. Visual and quantitative results demonstrate greater quality of our synthetic CS PCa data than those of the state-of-the-art methods’ and even real data.
Quasi-Optical Schottky Barrier Diode Detector for mmWave/sub-THz Wireless Communication

We report on the development of a compact quasioptical zero-bias Schottky barrier diode (QO-SBD) envelope detector for mmWave/sub-THz frequencies. The detector consists of a Schottky diode mounted on a self-complementary bowtie antenna mounted over a hyper-hemispherical silicon lens. The device is optimised to be used for multi-Gbps wireless communication systems. To increase the video bandwidth of the receiver, a wideband impedance matching network is designed and integrated. Measurements show that a 3-dB video bandwidth of 10GHz is realised for the proposed receiver, which is the highest value achieved for a wide-band QO-type detector.
The Incremental Proximal Method: A Probabilistic Perspective

In this work, we highlight a connection between the incremental proximal method and stochastic filters. We begin by showing that the proximal operators coincide, and hence can be realized with, Bayes updates. We give the explicit form of the updates for the linear regression problem and show that there is a one-to-one correspondence between the proximal operator of the least-squares regression and the Bayes update when the prior and the likelihood are Gaussian. We then carry out this observation to a general sequential setting: We consider the incremental proximal method, which is an algorithm for large-scale optimization, and show that, for a linear-quadratic cost function, it can naturally be realized by the Kalman filter. We then discuss the implications of this idea for nonlinear optimization problems where proximal operators are in general not realizable. In such settings, we argue that the extended Kalman filter can provide a systematic way for the derivation of practical procedures.
Model-Based External Force/Moment Estimation for Humanoid Robots with no Torque Measurement

The dynamics of a humanoid robot cannot be correctly described independently from the external forces acting on it. These forces have to be reconstructed to enable the robot to control them or to compensate for them. Force sensors are usually used to measure these forces, but because of their cost, they are often put only on the ankle/feet and possibly the wrists. This paper addresses the issue of the estimation of external forces and moments that apply at any part of a robot without direct force measurements and without torque measurements. The sensors used are the regular force sensors and the IMUs of the robot. The method relies on a model-based estimator able to make the fusion between these sensors and the whole body dynamics. The estimator reconstructs a single state vector containing the floating-base kinematics, a filtered measurement of contact force and an additional estimation external force that we evaluate in this paper. Validation is performed on HRP-2 in a multi-contact motion.
Frontal Face Generation from Multiple Pose-Variant Faces with CGAN in Real-World Surveillance Scene

It is well known that frontal face is much easier to be recognized than pose-variant face for both human and machine perception. However, it is not easy to acquire a frontal face in real-world video surveillance. This paper proposes a method to synthetize a frontal face for recognition in video surveillance scene, which is based on Conditional Generative Adversarial Networks (cGAN) with input of multiple pose-variant faces from a video. Experimental results show that the proposed approach can generate suitable frontal faces and improve face recognition by around 20% on a dataset of 43276 face images from 19 persons, collected from the real-world video surveillance scene. The effectiveness of multiple frames against single frame as input is demonstrated. Moreover, we investigate the generator with different depth for synthetizing frontal faces, in which an up-down sampling trick is designed for synthetizing higher quality frontal face images and boosts the performance of the generator.
Echo State Network with Multiple Loops Reservoir and its Application in Network Traffic Prediction

Echo state network (ESN), which was proposed as a novel recurrent neural network (RNN), has already been proved to exhibit better prediction ability than traditional neural networks in dealing with time series prediction. However, ESN's randomly generated reservoir structure is of high complexity and can not guarantee the stability of the prediction. In this paper, we propose a novel ESN with deterministic multiple loops reservoir structure (MLR) to avoid the randomness of the reservoir in the classic ESN. In addition, compared with the adjacent-feedback loop reservoir structure (ALR), the novel reservoir structure strengthens the connection of neurons in the reservoir and improves the nonlinear approximation ability of ESN. To test its performance, our MLR-based ESN is applied to network traffic prediction. Extensive simulation results regarding to different prediction steps demonstrate that MLR can achieve higher prediction accuracy, and outperform existing prediction models. Furthermore, we also analyze the influence of parameters of MLR on the prediction accuracy, such as the neuronal interval of multiple loops and the number of loops.
Scalable Decision Making with Sensor Occlusions for Autonomous Driving

Autonomous driving in urban areas requires avoiding other road users with only partial observability of the environment. Observations are only partial because obstacles can occlude the field of view of the sensors. The problem of robust and efficient navigation under uncertainty can be framed as a partially observable Markov decision process (POMDP). In order to bypass the computational cost of scaling the formulation to avoiding multiple road users, this paper demonstrates a decomposition method that leverages the optimal avoidance strategy for a single user. We evaluate the performance of two POMDP solution techniques augmented with the decomposition method for scenarios involving a pedestrian crosswalk and an intersection.
Single image resolution enhancement by patch based learning

In this paper we propose a resolution enhancement approach using patch based learning method. The patches in natural image are occurred repeatedly many time inside the image. This tendency of occurring similar patches is within the same scale, as well as across different scales. Hence the present method estimates a function to predict the pixels of a High Resolution patch using its corresponding Low Resolution pixel and their spatial neighbourhood. This method gives best results in terms of reconstruction accuracy of output High Resolution patch size when given input Low Resolution patch size is increased. The patch based single image super-resolution is more efficient than earlier bilinear and bicubic methods. The present method uses zero component analysis which is used as preprocessing step for improving perceptual element such as edges.
An Integrated Control of IP and Optical Network for Multi-grade Virtualized Networks

In the 5G era, it is expected that various services will be provided on network slices, which are virtualized network resources. A network slice has to support isolation capabilities and multi-grade performance capabilities such as ultra-low-latency and ultra-reliability due to the 5G requirements. IP and optical networks are also required to provide multi-grade property as a part of a network slice. Multi-layer SDN control is beneficial technology to realize the multi-grade network because an integrated control of IP and optical layers enables to combine network technologies across layers. It brings flexibility of service levels in terms of network performance and reliability. In this paper, we propose and demonstrate a multi-grade network which provides various types of virtualized network according to user requirements. Our platform is composed of IP and optical layers and ONOS-based SDN controller. We show on-demand provisioning of a virtualized network with a provisioning tracker that presents current status of provisioning in real-time.
Drug Screening Identifies Sigma-1-Receptor as a Target for the Therapy of VWM Leukodystrophy

Vanishing white matter (VWM) disease is an autosomal genetic leukodystrophy caused by mutations in subunits of eukaryotic translation initiation factor 2B (eIF2B). The clinical symptoms exhibit progressive loss of white matter in both hemispheres of the brain, accompanied by motor functions deterioration, neurological deficits, and early death. To date there is no treatment for VWM disease. The aim of this work was to expedite rational development of a therapeutic opportunity. Our approach was to design a computer-aided strategy for an efficient and reliable screening of drug-like molecules; and to use primary cultures of fibroblasts isolated from the Eif2b5R132H/R132H VWM mouse model for screening. The abnormal mitochondria content phenotype of the mutant cells was chosen as a read-out for a simple cell-based fluorescent assay to assess the effect of the tested compounds. We obtained a hit rate of 0.04% (20 hits out of 50,000 compounds from the selected library). All primary hits decreased mitochondria content and brought it closer to WT levels. Structural similarities between our primary hits and other compounds with known targets allowed the identification of three putative cellular pathways/targets: 11β-hydroxysteroid dehydrogenase type 1, Sonic hedgehog (Shh), and Sigma-1-Receptor (S1R). In addition to initial experimental indication of Shh pathway impairment in VWM mouse brains, the current study provides evidence that S1R is a relevant target for pharmaceutical intervention for potential treatment of the disease. Specifically, we found lower expression level of S1R protein in fibroblasts, astrocytes, and whole brains isolated from Eif2b5R132H/R132H compared to WT mice, and confirmed that one of the hits is a direct binder of S1R, acting as agonist. Furthermore, we provide evidence that treatment of mutant mouse fibroblasts and astrocytes with various S1R agonists corrects the functional impairments of their mitochondria and prevents their need to increase their mitochondria content for compensation purposes. Moreover, S1R activation enhances the survival rate of mutant cells under ER stress conditions, bringing it to WT levels. This study marks S1R as a target for drug development toward treatment of VWM disease. Moreover, it further establishes the important connection between white matter well-being and S1R-mediated proper mitochondria/ER function.
On SDW-MWF and Variable Span Linear Filter with Application to Speech Recognition in Noisy Environments

Neural network based spectral mask estimation for acoustic beamforming, which consists of linear filtering and mask estimation, has shown to be a promising approach for robust speech recognition in noisy environments. Nevertheless, few improvements are made on the linear filtering. In this paper, we investigate the Speech Distortion Weighted Multichannel Wiener Filter (SDW-MWF) and the variable span linear filter, and prove that they can be linked by Generalized Eigenvalue Decomposition (GEVD) of the speech covariance matrix. The resulting GEVD based SDW-MWF largely reduces the word error rate and even achieves competitive recognition performance with the state-of-the-art generalized eigenvalue beamformer. Furthermore, we found that the recent signal approximation is no better than mask approximation when combined in calculating the linear filter coefficients.
Comparison of the Drug-Drug Interaction Potential of Daptomycin in Combination with Rifampin in Healthy Adult Volunteers

We evaluated the effects of rifampin coadministration and MDR1 single nucleotide polymorphisms on the disposition of daptomycin in twelve healthy adults. There were no significant changes from baseline in the clearance (0.53 versus 0.55 liters/h, P = 1.00), volume of distribution (7.0 versus 7.2 liter, P = 0.62), or half-life (9.7 versus 9.6 h, P = 0.89) of daptomycin after exposure to rifampin. ABSTRACT We evaluated the effects of rifampin coadministration and MDR1 single nucleotide polymorphisms on the disposition of daptomycin in twelve healthy adults. There were no significant changes from baseline in the clearance (0.53 versus 0.55 liters/h, P = 1.00), volume of distribution (7.0 versus 7.2 liter, P = 0.62), or half-life (9.7 versus 9.6 h, P = 0.89) of daptomycin after exposure to rifampin. The tested MDR1 polymorphisms were not associated with significant differences in daptomycin disposition.