Miracle-dz/UniFAR-FTUs huggingface dataset:
A Single Shot Text Detector with Scale-adaptive Anchors

Currently, most top-performing text detection networks tend to employ fixed-size anchor boxes to guide the search for text instances. They usually rely on a large amount of anchors with different scales to discover texts in scene images, thus leading to high computational cost. In this paper, we propose an end-to-end box-based text detector with scale-adaptive anchors, which can dynamically adjust the scales of anchors according to the sizes of underlying texts by introducing an additional scale regression layer. The proposed scale-adaptive anchors allow us to use a few number of anchors to handle multi-scale texts and therefore significantly improve the computational efficiency. Moreover, compared to discrete scales used in previous methods, the learned continuous scales are more reliable, especially for small texts detection. Additionally, we propose Anchor convolution to better exploit necessary feature information by dynamically adjusting the sizes of receptive fields according to the learned scales. Extensive experiments demonstrate that the proposed detector is fast, taking only $0.28$ second per image, while outperforming most state-of-the-art methods in accuracy.
Active Frequency with a Positive Feedback Anti-Islanding Method Based on a Robust PLL Algorithm for Grid-Connected PV PCS

This paper proposes an active frequency with a positive feedback in the d-q frame anti-islanding method suitable for a robust phase-locked loop (PLL) algorithm using the FFT concept. In general, PLL algorithms for grid-connected PV PCS use d-q transformation and controllers to make zero an imaginary part of the transformed voltage vector. In a real grid system, the grid voltage is not ideal. It may be unbalanced, noisy and have many harmonics. For these reasons, the d-q transformed components do not have a pure DC component. The controller tuning of a PLL algorithm is difficult. The proposed PLL algorithm using the FFT concept can use the strong noise cancelation characteristics of a FFT algorithm without a PI controller. Therefore, the proposed PLL algorithm has no gain-tuning of a PI controller, and it is hardly influenced by voltage drops, phase step changes and harmonics. Islanding prediction is a necessary feature of inverter-based photovoltaic (PV) systems in order to meet the stringent standard requirements for interconnection with an electrical grid. Both passive and active anti-islanding methods exist. Typically, active methods modify a given parameter, which also affects the shape and quality of the grid injected current. In this paper, the active anti-islanding algorithm for a grid-connected PV PCS uses positive feedback control in the d-q frame. The proposed PLL and anti-islanding algorithm are implemented for a 250kW PV PCS. This system has four DC/DC converters each with a 25kW power rating. This is only one-third of the total system power. The experimental results show that the proposed PLL, anti-islanding method and topology demonstrate good performance in a 250kW PV PCS.
Homozygous CREM-IbΔC-X Overexpressing Mice Are a Reliable and Effective Disease Model for Atrial Fibrillation

Background: Atrial fibrillation (AF) is a significant cause of morbidity and mortality with foreseeably increasing prevalence. While large animal models of the disease are well established but resource intensive, transgenic AF mouse models are not yet widely used to develop or validate novel therapeutics for AF. Hemizygous mice with a cardiomyocyte-specific overexpression of the human cAMP response element modulator (CREM) isoform IbΔC-X spontaneously develop AF on grounds of an arrhythmogenic substrate consisting of alterations in structure, conduction, and calcium handling. Objective: We investigated if homozygous expression of the CREM-IbΔC-X transgene in mice alters the time course of AF development, and if homozygous CREM-IbΔC-X transgenics could be suitable as a disease model of AF. Methods: Southern Blot, quantitative real-time PCR, and immunoblotting were used to identify and verify homozygous transgenics. Cardiac gravimetry, quantitative real-time RT-PCR, histology, survival analysis, and repeated ECG recordings allowed assessment of phenotypic development and effects of antiarrhythmic drugs. Results: Homozygous animals could be identified by Southern blot and quantitative PCR, showing a strong trend to increased transgenic protein expression. In homozygous animals, atrial hypertrophy appeared earlier and more pronounced than in hemizygous animals, going along with an earlier onset of spontaneous AF, while no increased early mortality was observed. Application of a rate-controlling drug (esmolol) led to the expected result of a decreased heart rate. Application of a rhythm-controlling drug (flecainide) showed effects on heart rate variability, but did not lead to a definitive conversion to sinus rhythm. Conclusion: We suggest homozygous CREM-IbΔC-X overexpressing mice as a reliable model of early onset, rapidly progressive AF.
Broadband Proximity-Coupled Microstrip Planar Antenna Array for 5G Cellular Applications

A novel low-cost, high-gain millimeter-wave antenna has been presented. The antenna is a 6 × 5 proximity-coupled planar array suitable for 5G cellular applications. Good agreement between simulated and measured results achieved shows that the proposed antenna structure is efficient in achieving broadband characteristics and low sidelobe levels with a compact size. The antenna has a gain of 21 dBi over a bandwidth of 27.5–28.5 GHz. It also exhibits an impedance bandwidth of 9.8% from 26.04 to 28.78 GHz.
The MaryTTS entry for the Blizzard Challenge 2016

The MaryTTS system is a modular architecture text-to-speech (TTS) system whose development started around 15 years ago. This paper presents the MaryTTS entry for the Blizzard Challenge 2016. For this entry, we used the default configuration of MaryTTS based on the unit selection paradigm. However, the architecture is currently undergoing a massive refactoring process in order to provide a more fully modular system. This will allow researchers to focus only on some part of the synthesis process. The current participation objective includes assessing the current baseline quality in order to evaluate any future improvements. These can be achieved more easily thanks to a more flexible and robust architecture. The results obtained in this challenge prove that our system is not obsolete, but improvements need to be made to maintain it in the state of the art in the future.
Low cost high gain triple band mmWave Sierpinski antenna loaded with uniplanar EBG for 5G applications

A triple band mmWave microstrip antenna on low-cost FR4 substrate is proposed in this paper. The triple band is achieved using Sierpinski fractal structure with an optimized ground plane. The resonances observed are at 29.05 GHz, 35.75 GHz and 41 GHz with 10 dB impedance bandwidths of 4 GHz, 2.06 GHz and 2 GHz respectively. These bands fall into the candidate bands for upcoming 5G applications. The proposed unloaded triple band antenna has low gain throughout these bands, hence a uniplanar optimized EBG structure is proposed for gain enhancement across the intended bands of operation. The reflection-phase of various structures of EBG unit cells were characterized to obtain the optimum design. The optimum unit cell was loaded onto the triple band antenna to obtain Peak Gain of 2.37 dBi and 17.37 dBi in the 28 and 38 GHz bands, respectively. The proposed EBG loaded antenna is fabricated and characterized.
Development of Reliable and Stable QL1-NMF Algorithm for Analyzing Environmental ELF Magnetic Signals

We previously developed two NMF algorithms (QL1-NMF1 and QL1-NMF2) using the quasi-L1 norm for analyzing environmental ELF magnetic field measurements. When the data included many outliers, the QL1-NMF algorithms returned better results than other BSS algorithms using the L1 norm. However, the derivative of the cost function in QL1-NMF1 was not based on a monotonically increasing function. This problem decreased the validity of the algorithm. QL1-NMF2 had serious problems with stability though it was based on a monotonically increasing derivative. The method therefore required an improvement of validity and stability. In the work described in this paper, we introduced new update functions that were based on a monotonically increasing derivative. Computer simulation results and real data results confirmed the new algorithm worked more stability than the previous one. Moreover, we showed the new algorithm was fast and accurate.
An Integrated Algorithm Method to Optimize Resource Allocation with a Case Study of Production Line

There is a continuous flow performed one product unit that is routed from process to process in a production line. It is an important issue to figure out how to allocate the optimal resource and buffers in a production line for increasing efficiency and reducing costs. However, using Simplified Swarm Optimization (SSO) to solve this problem is easy to be inflected by initial solutions. This study tries to find the optimal configuration in a production line. This study utilizes the integrated method, combing SSO with Simulated Annealing (SA) to find the best scheme efficiently. Then, compare the results by using SSO and SA to the results by using SSO only. The estimate result is the integrated method is better than using SSO only. The results will be verified by simulation in the end. The production line is consisting of four workstations, three finite-size buffers (queues), and an infinite supply of blank parts. This study wants to find the best scheme of numbers of machines and buffers for maximizing the profits. According to the analysis results, this study has shown that integrated method combining SSO with SA is better than SSO with this case study. It is expected that it could find the optimal solution more efficiently.
Learning Personalized Representation for Inverse Problems in Medical Imaging Using Deep Neural Network

Recently deep neural networks have been widely and successfully applied in computer vision tasks and attracted growing interests in medical imaging. One barrier for the application of deep neural networks to medical imaging is the need of large amounts of prior training pairs, which is not always feasible in clinical practice. In this work we propose a personalized representation learning framework where no prior training pairs are needed, but only the patient's own prior images. The representation is expressed using a deep neural network with the patient's prior images as network input. We then applied this novel image representation to inverse problems in medical imaging in which the original inverse problem was formulated as a constraint optimization problem and solved using the alternating direction method of multipliers (ADMM) algorithm. Anatomically guided brain positron emission tomography (PET) image reconstruction and image denoising were employed as examples to demonstrate the effectiveness of the proposed framework. Quantification results based on simulation and real datasets show that the proposed personalized representation framework outperform other widely adopted methods.
Dynamic thermal modeling of buildings and application to a hospital

The study presents an original implementation of an algorithm capable to describe the thermal behavior of buildings. The envelope structure and the climatic data are used as input data. The algorithm identifies shares of heating and cooling loads for walls, windows and fresh air. The results were obtained for a real hospital based on hourly calculations. Based on the provided results, energy efficient solutions, including shielding technologies, can be hierarchized considering the thermal losses and gains corresponding to each component.
Selective Harmonic Elimination With Optimal DC Sources in Multilevel Inverters Using Generalized Pattern Search

This paper presents the computation of optimal dc sources and switching angles using the generalized pattern search (GPS) optimization method for harmonic elimination in a cascaded multilevel inverter. The goal is to solve nonlinear equations for eliminating low-order harmonics and reaching desired fundamental component. For case study, 5- and 7-level inverters are chosen. The 7-level inverter with equal dc sources and 5- and 7-level inverters with an optimal dc source are applied to eliminate fifth and seventh harmonics. To obtain this goal, MATLAB software is used. Considering simulation results, cost, and simpler implementation, 5-level inverter with an optimized dc source is more effective in all modulation indexes in comparison with 7-level inverter. Also, results demonstrate the superiority of GPS over genetic algorithm (GA) in attaining accurate global minima. For better evaluation of optimizing more dc sources and GPS performance in comparison with GA, 7-level inverter with two optimal dc sources is applied to eliminate fifth, seventh, 11th, and 13th harmonics. Finally, to validate the accuracy of the 5-level inverter with an optimal dc source and GPS performance, the experimental results are provided for a 5-level cascaded H-bridge inverter.
Type Synthesis of Three-DOF Translational Parallel Mechanisms

This paper presents a simple but effective type synthesis method for spatial parallel mechanisms with three translational degrees of freedom based on the screw theory. Firstly all possible connecting-chain structures of three-DOF parallel mechanisms are enumerated. According to the reciprocal relationship between screw constraint forces and the motion screw, a novel synthesis method is presented. By using this method, type synthesis for three-DOF translational parallel mechanisms has been made in a systematic and detailed way. As a result, some novel parallel mechanisms generating spatial translation have been obtained. To verify the significance of type synthesis for this kind of mechanism, the paper also gives a concrete application instance, which is used for a micromanipulator for manipulating the bio-cells.Copyright © 2003 by ASME
Hierarchical control system for energy savings in wastewater treatment plant

The primary goal of sustainable wastewater companies is to improve overall unit efficiency by reducing the required materials, electrical energy consumption savings and improving in the process technologies. The purpose of this paper is to explore the design and implementation of a hierarchical control system for operational optimization of the entire wastewater treatment plant. Two-layer hierarchical control has been designed and implemented: process control as a lower layer and a higher layer of optimization. The goal is a lower energy consumption of the plant while maintaining the quality parameters of the effluent water. Because the aeration system is responsible of up to 70% of the total energy consumption of the plant, the higher-level control layer deals with the optimization of the aeration process. The results indicate a considerable energy saving compared to the initial plant control system.
Security and Clustering Of Big Data in Map Reduce Framework: A Survey

For maintaining the authenticity, privacy and confidentiality of larger dataset are outsourced to the cloud in the encrypted format. Cloud storage provides data management and reduces the costs. Various clustering methods like K mean, K nearest neighbouring, DBSCAN clustering methods are implemented to cluster massive data that are related to each other using the map-reduce framework in big data analytics. Clustering was done on encrypted partitioned data in order to protect the information from the third party access. Various approaches have been used for securing and maintaining the efficiency and performance of millions of dataset with variety, velocity, and volume.
Collaborative Artificial Intelligence (AI) for User-Cell Association in Ultra-Dense Cellular Systems

In this paper, the problem of cell association between small base stations (SBSs) and users in dense wireless networks is studied using artificial intelligence (AI) techniques. The problem is formulated as a mean-field game in which the users' goal is to maximize their data rate by exploiting local data and the data available at neighboring users via an imitation process. Such a collaborative learning process prevents the users from exchanging their data directly via the cellular network's limited backhaul links and, thus, allows them to improve their cell association policy collaboratively with minimum computing. To solve this problem, a neural Q-learning learning algorithm is proposed that enables the users to predict their reward function using a neural network whose input is the SBSs selected by neighboring users and the local data of the considered user. Simulation results show that the proposed imitation-based mechanism for cell association converges faster to the optimal solution, compared with conventional cell association mechanisms without imitation.
Image-guided surgery using near-infrared Turn-ON fluorescent nanoprobes for precise detection of tumor margins

Complete tumor removal during surgery has a great impact on patient survival. To that end, the surgeon should detect the tumor, remove it and validate that there are no residual cancer cells left behind. Residual cells at the incision margin of the tissue removed during surgery are associated with tumor recurrence and poor prognosis for the patient. In order to remove the tumor tissue completely with minimal collateral damage to healthy tissue, there is a need for diagnostic tools that will differentiate between the tumor and its normal surroundings. Methods: We designed, synthesized and characterized three novel polymeric Turn-ON probes that will be activated at the tumor site by cysteine cathepsins that are highly expressed in multiple tumor types. Utilizing orthotopic breast cancer and melanoma models, which spontaneously metastasize to the brain, we studied the kinetics of our polymeric Turn-ON nano-probes. Results: To date, numerous low molecular weight cathepsin-sensitive substrates have been reported, however, most of them suffer from rapid clearance and reduced signal shortly after administration. Here, we show an improved tumor-to-background ratio upon activation of our Turn-ON probes by cathepsins. The signal obtained from the tumor was stable and delineated the tumor boundaries during the whole surgical procedure, enabling accurate resection. Conclusions: Our findings show that the control groups of tumor-bearing mice, which underwent either standard surgery under white light only or under the fluorescence guidance of the commercially-available imaging agents ProSense® 680 or 5-aminolevulinic acid (5-ALA), survived for less time and suffered from tumor recurrence earlier than the group that underwent image-guided surgery (IGS) using our Turn-ON probes. Our "smart" polymeric probes can potentially assist surgeons' decision in real-time during surgery regarding the tumor margins needed to be removed, leading to improved patient outcome.
Preparation and Evaluation of Chloramphenicol as Thermosensitive Ocular in- situ Gel

The purpose of this study was to develop poloxamer-based in-situ gel of chloramphenicol aiming to increase bioavailability and prolong corneal contact time, controlling drug release, and enhancing ocular bioavailability. The in-situ gel was prepared using different concentrations of poloxamer 407 combined with hydroxypropyl methyl cellulose (HPMC) or carbapol 940 to achieve gelation temperature about physiological temperature and improve rheological behavior and gelling properties of poloxamer gel. The prepared formulations were evaluated for their appearance, pH, and sol-gel transition temperature. The formulations F2, F3, and F5 have a gelation temperature within the accepted range 35-370C and were evaluated for their isotonicity, rheological studies, ocular irritation test, sterility and release studies. The selected formulations (F2,F3, and F5) isotonic, pseudoplastic, non irritant, pass sterility test, and the in vitro release demonstrated a diffusion-erosion controlled release of chloramphenicol over a period of 4 hr. , 6 hr. , and 6 hr. respectively. Key words: Chloramphenicol, in-situ gel, ocular dosage form, poloxamer.
A morphological operation-based approach for Sub-pleural lung nodule detection from CT images

Detection of pulmonary nodules is a frequent circumstance in daily radiology practice. Their extraction depends on their localization. Sub-pleural nodules are directly connected to the border of the pleura and have consequently open contours. Their extraction is challenging and a computer-aided diagnosis system is, hence, indispensable. In this paper, we propose an automatic segmentation approach of sub-pleural lung nodules from Computed Tomography (CT) scans based on morphological operations. This method is divided into three steps: pre-processing, initial detection of sub-pleural lung nodule and post-processing. First, a region of interest containing the nodule is extracted and converted using an adaptive thresholding algorithm. Second, morphological operations are used to create a mask for the lung lobe and segment sub-plural nodule. Finally, small structures connected to the border of the segmented image are removed and final nodule regions are detected. The proposed method is evaluated on 40 CT scan images (17 in axial acquisition and 23 in coronal reconstruction) and gives a good rate of accuracy that proves the effectiveness of our approach.
Facilitating Identification of Lactose-Fermenting Enterobacteriaceae on MacConkey Agar

Retrospective (904 isolates) and prospective (over 500 stock and current clinical isolates) data on common lactose-fermenting Gram-negative bacilli were used to develop a guide for interpretation of growth on MacConkey agar. Use of this guide facilitated correct conjecture on the identification of lactose-fermenting Enterobacteriaceae. This approach is useful as a teaching aid and for quality control when initial conjecture is compared with final identification and antimicrobial susceptibility testing.
Lightweight and Compact High-Gain Filtering Aperture Antenna Fabricated by Three-Dimensional Printing Technology

In this letter, a novel high-gain filtering aperture antenna is proposed. It consists of five resonators, four of which have radiation apertures in the end and are coupled to the fifth one in the center by dislocation. Such a mechanism gets rid of conventional irises and, hence, saves volume to some extent. The proposed antenna is characterized by a second-order Chebyshev bandpass response. Simultaneously, high gain is achieved through four radiation apertures. In order to minimize its size and weight, three-dimensional printing technology is adopted for fabrication. Finally, an antenna prototype with a center frequency of 34 GHz is designed, fabricated, and measured. The measured results show that the fractional bandwidth is 2.94% covering from 33.5 to 34.5 GHz, with the gain of 12.51 dBi. Besides, it only weighs 2.63 g for the adopted polymer material.