Miracle-dz/UniFAR-FTUs huggingface dataset:
Resource Allocation for Virtual Streaming Media Server Cluster in Cloud-based Multi-version VoD

With the rapid development of mobile Internet and smart devices, VoD (video on demand) providers build media cloud to offer multi-bitrate video streaming services to users at a reduced cost, called as cloud-based multi-version VoD. In cloud-based multi-version VoD, we need to solve the problem of allocating appropriate resources for virtual streaming media server cluster with the aim of optimizing the user experience and reducing the service cost. To address this problem, a resource allocation for virtual streaming media server cluster in cloud-based multi-version VoD is proposed in this paper. We firstly analyze the user historical learning logs to mine the user behavior characteristics, including the average user request arrival rate, the video playing time distribution, and the video popularity distribution, etc. Then, based on the user behavior characteristics and the queueing theory, a resource allocation model for the virtual streaming media server cluster is introduced. It predicts the user arrival rate at first and then allocates appropriate resources dynamically to solve the resources allocation irrationality problem. Simulation results have proved the proposed method can allocate appropriate resources for virtual streaming media server cluster, which can ensure the user experience satisfaction and improve the resources utilization.
Inhibition of atypical protein kinase C-ι effectively reduces the malignancy of prostate cancer cells by downregulating the NF-κB signaling cascade

Prostate cancer (PC) is the most common type of cancer among men. Aggressive and metastatic PC results in life- threatening tumors, and represents one of the leading causes of mortality in men. Previous studies of atypical protein kinase C isoforms (aPKCs) have highlighted its role in the survival of cultured prostate cells via the nuclear factor (NF)-κB pathway. The present study showed that PKC-ι was overexpressed in PC samples collected from cancer patients but not in non-invasive prostate tissues, indicating PKC-ι as a possible prognostic biomarker for the progression of prostate carcinogenesis. Immunohistochemical staining further confirmed the association between PKC-ι and the prostate malignancy. The DU-145 and PC-3 PC cell lines, and the non-neoplastic RWPE-1 prostatic epithelial cell line were cultured and treated with aPKC inhibitors 2-acetyl-1,3-cyclopentanedione (ACPD) and 5-amino-1-(1R,2S,3S,4R)-2,3-dihydroxy-4-methylcyclopentyl)-1H-imidazole-4-carboxamide (ICA-1). Western blot data demonstrated that ICA-1 was an effective and specific inhibitor of PKC-ι and that ACPD inhibited PKC-ι and PKC-ζ. Furthermore, the two inhibitors significantly decreased malignant cell proliferation and induced apoptosis. The inhibitors showed no significant cytotoxicity towards the RWPE-1 cells, but exhibited cytostatic effects on the DU-145 and PC-3 cells prior to inducing apoptosis. The inhibition of aPKCs significantly reduced the translocation of NF-κB to the nucleus. Furthermore, this inhibition promoted apoptosis, reduced signaling for cell survival, and reduced the proliferation of PC cells, whereas the normal prostate epithelial cells were relatively unaffected. Overall, the results suggested that PKC-ι and PKC-ζ are essential for the progression of PC, and that ACPD and ICA-1 can be effectively used as potential inhibitors in targeted therapy.
Alpha-Stable Low-Rank Plus Residual Decomposition for Speech Enhancement

In this study, we propose a novel probabilistic model for separating clean speech signals from noisy mixtures by decomposing the mixture spectra into a structured speech part and a more flexible residual part. The main novelty in our model is that it uses a family of heavy-tailed distributions, so called the $\alpha$-stable distributions, for modeling the residual signal. We develop an expectation-maximization algorithm for parameter estimation and a Monte Carlo scheme for posterior estimation of the clean speech. Our experiments show that the proposed method outperforms relevant factorization-based algorithms by a significant margin.
Towards Low-Cost Image-based Plant Phenotyping using Reduced-Parameter CNN

Segmentation is the core of most plant phenotyping applications. Current state-of-the-art plant phenotyping applications rely on deep Convolutional Neural Networks (CNNs). However, these networks have many layers and parameters, increasing training and test times. Phenotyping applications relying on these deep CNNs are also often difficult if not impossible to deploy on limited-resource devices. We present our work which investigates parameter reduction in deep neural networks, a first step to moving plant phenotyping applications in-field and on low-cost devices with limited resources. We re-architect four baseline deep neural networks (creating what we term "Lite CNNs") by reducing their parameters whilst making them deeper to avoid the problem of overfitting. We achieve state-of-the-art, comparable performance on our "Lite" CNNs versus the baselines. We also introduce a simple global hyper-parameter (alpha) that provides an efficient trade-off between parameter-size and accuracy.
Pretensioned Structures as Multi Axis Force Sensors

Multi axis force sensors have a great range of applications from micro scale force sensing at micro-surgical task to measuring large forces in aerospace and ship industries. Using kinematic structures for the design of the force sensors, the movement of the applied force can be confined in certain direction, thus providing the possibility of measuring specific components of the force/torque. By calculating the stiffness matrix of the kinematic structure the applied force on the payload platform is obtained through measuring the displacement of the joints of the mechanism under the external load. In this paper we investigate application of pretensioned closed kinematic structure for multi axis force sensors applications. Based on the concept provided here a planar sensor is introduced and a suitable design is found based on the mathematical formulation.
Intermediate Deep Feature Compression: the Next Battlefield of Intelligent Sensing

The recent advances of hardware technology have made the intelligent analysis equipped at the front-end with deep learning more prevailing and practical. To better enable the intelligent sensing at the front-end, instead of compressing and transmitting visual signals or the ultimately utilized top-layer deep learning features, we propose to compactly represent and convey the intermediate-layer deep learning features of high generalization capability, to facilitate the collaborating approach between front and cloud ends. This strategy enables a good balance among the computational load, transmission load and the generalization ability for cloud servers when deploying the deep neural networks for large scale cloud based visual analysis. Moreover, the presented strategy also makes the standardization of deep feature coding more feasible and promising, as a series of tasks can simultaneously benefit from the transmitted intermediate layers. We also present the results for evaluation of lossless deep feature compression with four benchmark data compression methods, which provides meaningful investigations and baselines for future research and standardization activities.
Introducing UN Salient360! Benchmark: A platform for evaluating visual attention models for 360° contents

Virtual Reality (VR) provides the users with new immersive media experiences, offering the possibility to freely explore 360° content. Understanding these new exploration behaviors is crucial for the development of efficient techniques for processing, coding, delivering and rendering omnidirectional content to offer the highest possible Quality of Experience (QoE). Progress has already been made on visual attention (VA) modeling for 360° content. In this paper we briefly review the current status of research on this topic that led us to propose a benchmarking platform for evaluating and comparing the performance of models for saliency and scanpath prediction for 360° content. This paper introduces the ‘UN Salient360! benchmark” platform featuring a dataset, a toolbox and a framework for evaluation of different class of models. This online platform can be found in httns://salient360.ls2n. fr/.
Upregulation of macrophage migration inhibitory factor promotes tumor metastasis and correlates with poor prognosis of pancreatic ductal adenocarcinoma

Macrophage migration inhibitory factor (MIF) is a pro-inflammatory cytokine that serves important roles in cancer. MIF overexpression is frequently observed in numerous human cancer types, including pancreatic carcinoma. However, the prognostic value and function of MIF in pancreatic ductal adenocarcinoma (PDAC) have not been fully elucidated. In the present study, upregulation of MIF expression in PDAC tissue compared with adjacent normal tissue was observed. Furthermore, MIF overexpression was identified to be significantly associated with poor survival rates in patients with PDAC. Multivariate Cox regression analysis confirmed that MIF was an independent risk factor for poor survival. Functional analyses demonstrated that MIF knockdown significantly inhibited the proliferation and invasion of pancreatic cancer cells in vitro compared with control cells. IN addition, mechanistic investigations revealed that silencing MIF leads to inhibition of AKT serine/threonine kinase and extracellular-signal-regulated kinase activation, and suppression of cyclin D1 and matrix metalloproteinase-2 expression, which may suppress tumor proliferation and invasion. These results highlight the importance of MIF overexpression in PDAC aggressiveness, and indicate that MIF may be a potential therapeutic target for pancreatic cancer.
Cyclostationary Technique for Spectrum Sensing in Cognitive Radio

Spectrum is main part of wireless communication. Today spectrum security is a big problem due to advanced growth of users. Spectrum is basically of two types. The user that doesn't need any license to operate is called as Secondary user. In contrast, the user that needs license to operate for wireless communication is called primary user. It operates in fix geographical area for fixed time. Spectrum frequencies cannot be increased because it has limited range. We can try to improve spectrum efficiency with the help of various technologies or methods like Cognitive radio, Software defined Radio and Spectrum sharing technique. Spectrum sensing is a method to obtain the spectrum use characteristics across multiple dimensions such as time, space, frequency, and code and finding which type of signals are present in the spectrum. Here, Spectrum Sensing method namely Cyclo-stationary is thought to be implemented.
Automated Irrigation and Crop Security System in Agriculture Using Internet of Things

Regardless of high technology advancements and industrialization, approximately 70 percent of the Indian population is connected with agriculture and its related activities. The most important agricultural process, which should be controlled and adapted for better growth of the plants is irrigation. Due to changing climate and degraded quality of soil, farmers are getting less productions in farms. Currently, cultivation consumes around 70 percent of the fresh water. This percentage should be decreased by performing efficient water management. Agriculture production decreases due to intrusion of birds, animals, etc. on farmland. It can be controlled by securing the farmland with sensors, buzzers and scarecrow (automated toy). The proposed solution is to collect, analyse and monitor the real time sensor data like temperature, humidity, soil moisture, PIR (Passive Infrared Sensor), etc. with the help of Internet of Things (IoT) technology in every 10 seconds from soil and environment. Such that irrigation and crop security operations will be monitored automatically based on pre-calculated threshold values. This venture applies the water effectively, in the right place, at the right time and in the proper sum. It brings notable benefits such as water savings, money savings, auto watering to plants, security to the grown crops without hand of human and 24/7 surveillance into soil and crop. We have successfully achieved 92.24 percent accuracy while testing a prototype.
Speech Bandwidth Extension Using Generative Adversarial Networks

Speech blind bandwidth extension technologies have been available for some time, but until now have not seen widespread deployment, partly because the added bandwidth has been accompanied by added artifacts. In this paper, we present three generations of blind bandwidth extension technologies, from Vector Quantization mapping through Gaussian Mixture Models, to our latest architecture based on deep neural networks using Generative Adversarial Networks. This latest approach shows a sharp jump in quality, and demonstrates that machine-learning based blind bandwidth extension algorithms can achieve quality equal to wideband codecs, both objectively and subjectively. We believe that blind bandwidth extension can now achieve sufficiently high quality to warrant deployment in the existing telecommunication networks.
Learning Finer-class Networks for Universal Representations

Many real-world visual recognition use-cases can not directly benefit from state-of-the-art CNN-based approaches because of the lack of many annotated data. The usual approach to deal with this is to transfer a representation pre-learned on a large annotated source-task onto a target-task of interest. This raises the question of how well the original representation is "universal", that is to say directly adapted to many different target-tasks. To improve such universality, the state-of-the-art consists in training networks on a diversified source problem, that is modified either by adding generic or specific categories to the initial set of categories. In this vein, we proposed a method that exploits finer-classes than the most specific ones existing, for which no annotation is available. We rely on unsupervised learning and a bottom-up split and merge strategy. We show that our method learns more universal representations than state-of-the-art, leading to significantly better results on 10 target-tasks from multiple domains, using several network architectures, either alone or combined with networks learned at a coarser semantic level.
UAV Communications Based on Non-Orthogonal Multiple Access

This article proposes a novel framework for UAV networks with massive access capability supported by NOMA. In order to better understand NOMA-enabled UAV networks, three case studies are carried out. We first provide performance evaluation of NOMA-enabled UAV networks by adopting stochastic geometry to model the positions of UAVs and ground users. Then we investigate the joint trajectory design and power allocation for static NOMA users based on a simplified 2D model of UAV flying around at fixed height. As a further advance, we demonstrate the UAV placement issue with the aid of machine learning techniques when the ground users are roaming and the UAVs are capable of adjusting their positions in three dimensions accordingly. With these case studies, we can comprehensively understand the UAV systems from fundamental theory to practical implementation.
Applying Multitask Learning to Acoustic-Phonemic Model for Mispronunciation Detection and Diagnosis in L2 English Speech

For mispronunciation detection and diagnosis (MDD), nowadays approaches generally treat the phonemes in correct and mispronunciations as the same despite the fact they may actually carry different characteristics. Furthermore, serious data imbalance issue between correct and mispronunciation in dataset further influences the performances. To address these problems, this paper investigates the use of multi-task (MT) learning technique to enhance the acoustic-phonemic model (APM) for MDD. The phonemes in correct and mispronunciations are processed separately but in multi-task manner considering both correct and mispronunciation recognition tasks. A feature representation module is further proposed to improve performance. Compared with baseline APM, the proposed MT-APM, R-MT-APM achieve better performance not only in Precision, Recall and F-Measure, but also in mispronunciation detection and diagnosis accuracies. With feature representation module, R-MT-APM achieves the highest mispronunciation detection accuracy.
Noninvasive Blockade of Action Potential by Electromagnetic Induction

Conventional anesthesia methods such as injective anesthetic agents may cause various side effects such as injuries, allergies, and infections. We aim to investigate a noninvasive scheme of an electromagnetic radiator system to block action potential (AP) in neuron fibers. We achieved a high-gradient and unipolar tangential electric field by designing circular geometric coils on an electric rectifier filter layer. An asymmetric sawtooth pulse shape supplied the coils in order to create an effective blockage. The entire setup was placed 5 cm above 50 motor and sensory neurons of the spinal cord. A validated time-domain full-wave analysis code Based on cable model of the neurons and the electric and magnetic potentials is used to simulate and investigate the proposed scheme. We observed action potential blockage on both motor and sensory neurons. In addition, the introduced approach shows promising potential for AP manipulation in the spinal cord.
Particle Flow Particle Filter for Gaussian Mixture Noise Models

Particle filters has become a standard tool for state estimation in nonlinear systems. However, their performance usually deteriorates if the dimension of state space is high or the measurements are highly informative. A major challenge is to construct a proposal density that is well matched to the posterior distribution. Particle flow methods are a promising option for addressing this task. In this paper, we develop a particle flow particle filter algorithm to address the case where both the process noise and the measurement noise are distributed as mixtures of Gaussians. Numerical experiments are performed to explore when the proposed method offers advantages compared to existing techniques.
VCO Verilog AMS Model for Fast Simulation in VCO-Based ADC

This work presents an event-driven all-digital VCO model to be used into a VCO-based ADC for reduced computational time behavioral simulations. The proposed model is explained and compared with standard models in terms of both simulation speed and accuracy. It takes into account important analog behaviors such as non-linearity and phase noise. Results highlight the fact that the proposed model allows to reduce the simulation time up to 92% while used in a feedback system: a VCO-based ADC.
Phosphatidylethanolamine Induces an Antifibrotic Phenotype in Normal Human Lung Fibroblasts and Ameliorates Bleomycin-Induced Lung Fibrosis in Mice

Lung surfactant is a complex mixture of phospholipids and specific proteins but its role in the pathogenesis of interstitial lung diseases is not established. Herein, we analyzed the effects of three representative phospholipid components, that is, dipalmitoilphosphatidylcoline (DPPC), phosphatidylglycerol (PG) and phosphatidylethanolamine (PE), on collagen expression, apoptosis and Ca2+ signaling in normal human lung fibroblasts (NHLF) and probed their effect in an experimental model of lung fibrosis. Collagen expression was measured with RT-PCR, apoptosis was measured by using either the APOPercentage assay kit (Biocolor Ltd., Northern Ireland, UK) or the Caspase-Glo 3/7 assay (Promega, Madison, WI, USA) and Ca2+ signaling by conventional epifluorescence imaging. The effect in vivo was tested in bleomycin-induced lung fibrosis in mice. DPPC and PG did not affect collagen expression, which was downregulated by PE. Furthermore, PE promoted apoptosis and induced a dose-dependent Ca2+ signal. PE-induced Ca2+ signal and apoptosis were both blocked by phospholipase C, endoplasmic reticulum pump and store-operated Ca2+ entry inhibition. PE-induced decrease in collagen expression was attenuated by blocking phospholipase C. Finally, surfactant enriched with PE and PE itself attenuated bleomycin-induced lung fibrosis and decreased the soluble collagen concentration in mice lungs. This study demonstrates that PE strongly contributes to the surfactant-induced inhibition of collagen expression in NHLF through a Ca2+ signal and that early administration of Beractant enriched with PE diminishes lung fibrosis in vivo.
A Temperature Variation Tolerant CMOS-Only Voltage Reference for RFID Applications

In this paper a reference voltage circuit is presented for outdoor RFID applications. The circuit consists of a Dickson charge pump and a series of stacked diode connected CMOS devices. With this configuration we have introduced a new approach to produce a 1.515V reference voltage from a Dickson charge pump which shows robust behavior against temperature variation. Taking advantage of a harvested RFID signal rendered the circuit suitable for a wide range of applications in which energy and area constraints are of great concern. The circuit has been designed with conventional CMOS devices using a commercial 40nm technology and simulated with cadence. The proposed circuit consumes 235nW power with 88 PPM/○C temperature coefficient in temperature range of −10°C to 125°C. The total active area of the circuit is 0.00036mm2. The circuit shows +10% −12% variation from the nominal value due to process corner analysis and its PSRR(dB) is −47©915MHz.
Identification of Bilinear Forms with the Kalman Filter

In this paper, we develop the Kalman filter for the identification of bilinear forms. In this framework, the bilinear term is defined with respect to the impulse responses of a spatiotemporal model, which resembles a multiple-input/single-output system. Recently, the identification of such bilinear forms was addressed in terms of the Wiener filter and conventional adaptive algorithms, i.e., least-mean-square and recursive least-squares. In this work, apart from the derivation of the Kalman filter tailored for the identification of bilinear forms, a simplified (i.e., low complexity) version of the algorithm is also presented. Simulation results support the theoretical findings and indicate the good performance of the proposed solutions.
A Parallel Fusion Approach to Piano Music Transcription Based on Convolutional Neural Network

In this paper, a supervised approach based on Convolutional Neural Networks (CNN) for polyphonic piano transcription is presented. The system consists of pitch detection model, onset/offset detection model, and note search model. The pitch detection model is a single-channel CNN predicting the probabilities of pitches contained in one frame of the audio. The onset/offset model based on dual-channel CNN is used for estimating the probabilities of each pitch's onset or offset in a frame. The note search model is rule-based; it integrates the outputs of the pitch model and onset/offset model to determine the final onset, offset and pitch of notes in audio. Two experiments with different dataset conditions are accomplished to compare with state-of-the-art approaches on the same datasets. Experimental results reveal that the proposed approach preforms better in both frame- and note-based metrics.