Miracle-dz/UniFAR-FTUs huggingface dataset:
Mapping of SPARC/BM-40/Osteonectin-binding Sites on Fibrillar Collagens*

The 33-kDa matrix protein SPARC (BM-40, osteonectin) binds several collagen types with moderate affinity. The collagen-binding site resides in helix αA of the extracellular calcium-binding domain of SPARC and is partially masked by helix αC. Previously, we found that the removal of helix αC caused a 10-fold increase in the affinity of SPARC for collagen, and we identified amino acids crucial for binding by site-directed mutagenesis. In this study, we used rotary shadowing, CNBr peptides, and synthetic peptides to map binding sites of SPARC onto collagens I, II, and III. Rotary shadowing and electron microscopy of SPARC-collagen complexes identified a major binding site ∼180 nm from the C terminus of collagen. SPARC binding was also detected with lower frequency near the matrix metalloproteinase cleavage site. These data fit well with our analysis of SPARC binding to CNBr peptides, denaturation of which abolished binding, indicating triple-helical conformation of collagen to be essential. SPARC binding was substantially decreased in two of seven α2(I) mutant procollagen I samples and after N-acetylation of Lys/Hyl side chains in wild-type collagen. Synthetic peptides of collagen III were used to locate the binding sites, and we found SPARC binding activity in a synthetic triple-helical peptide containing the sequence GPOGPSGPRGQOGVMGFOGPKGNDGAO (where O indicates 4-hydroxyproline), with affinity for SPARC comparable with that of procollagen III. This sequence is conserved among α chains of collagens I, II, III, and V. In vitro collagen fibrillogenesis was delayed in the presence of SPARC, suggesting that SPARC might modulate collagen fibril assembly in vivo.
An improved multi-bags-of-features histograms representation for ear recognition

In this paper, we present a novel approach for ear representation and recognition based on the combined of binarized statistical image feature (BSIF filter), multi-bag of feature model and the overlapping decomposition method to generate the histogram sequence. The recognition accuracy can be enhanced by the following steps. Firstly, the texture information in ear image is extracted using the Binarized Statistical Image Features. Secondly, from the training image responses, the K-means clustering algorithm is used to learn the multi bag-of-feature dictionary. Thirdly, the overlapping decomposition is applied to obtain local ear feature descriptors. Next, the histograms obtained are normalized. Then, the global representation of the ear image is obtained by concatenating all histograms calculated at each level. After that, the discriminant representation of ear image is constructed, using kernel Fisher discriminant analysis. Finally, the k-nearest neighbor and the support vector machine classifiers are used for ear identification. Experiments conducted on IIT-Delhi-1 database; show that the proposed approach provide a significant performance improvement compared to the state of the art in terms of accuracy.
Audio Compression Using Graph-based Transform

Graph-based Transform is one of the recent transform coding methods which has been used successfully in the state-of-art data decorrelation applications. In this paper, we propose a Graph-based Transform (GT) for audio compression. Hence, we introduce a proper graph structure for audio. Then the audio frames are projected onto an orthogonal matrix consisting of eigenvectors of the introduced graph matrix, leading to the sparse coefficients. The results show that the proposed method outperforms the conventional transform methods like Discrete Cosine Transform (DCT) and Walsh-Hadamard Transform (WHT) in decorrelation of the audio signals.
Vehicle density, vehicle speed and packet inter-arrival time analysis in IEEE 802.11p EDCA based VANETs

Vehicular ad-hoc networks (VANETs) are considered as a key component of future Intelligent Transportation Systems (ITS). The IEEE 802.11p standard modifies the IEEE 802.11a to adapt to vehicular networks. In the MAC sublayer, the IEEE 802.11p standard uses Enhanced Distributed Channel Access (EDCA) mechanism to provide differentiated media access by defining four access categories (ACs). However, VANET characteristics such as network size, high speed, fast topology changes, traffic density, can greatly affect network performance, especially for the MAC sub-layer. Therefore, important issues arise about the ability of this standard to provide reliable and efficient services for vehicular environment. Since test beds are still limited, computer simulations are very useful for the automobile industry. This paper present a simulation-based analysis of the impact of the vehicle density, vehicle speed and packet inter-arrival time on the performance of the IEEE 802.11p EDCA mechanism in VANET. Throughput, average delay and packet delivery ratio (PDR) were considered as evaluation metrics. The obtained results confirm that IEEE 802.11p EDCA provides an effective differentiation service mechanism that can be adapted to critical ITS applications. However, we noted a performance degradation in extreme cases. As in large number of communicating vehicles, very high speeds, and high transmission rates.
An adaptive video streaming framework for Scalable HEVC (SHVC) standard

This paper presents an implementation of a Media Aware Network Element (MANE) for dynamic video content adaptation in Scalable HEVC (SHVC) video streaming. The experimental results discuss the varying quality-to-playback time ratio and decoding power consumption with random access period in SHVC encoding under fluctuating and persistent network bandwidth conditions
A Comprehensive Academic Success and Professional Development (ASAP) Framework that uses Career-Steering/Shaping Projects to Train Engineering Students and Develop Critical Life/Professional Skills: Part I - Impact on Key Groups

This Research to Practice Full Paper (Part I) presents a comprehensive Academic Success and Professional Development (ASAP) mentoring-scholarship program framework – built on a community of practice (consisting of learners and faculty-industry-peer mentors) – that uses career-steering/shaping projects as a central tool to motivate and train engineering students for the many challenges/opportunities before them. Key organizational principles, themes, activities and instruments driving the Arizona State University (ASU) program are described. While currently focusing on upper-division transfer students, the program (supported by NSF/industrial funding since 2001) also serves upper-division non-transfers and some graduate students. Data for the most recent cohort of 74 scholars shows how different groups were impacted by key program activities/instruments. The data shows that projects – specifically our “interest paper” – can prepare students for many critical activities; e.g. choosing a technical area, career planning, choosing potential employers, preparing for a career fair, getting a paid project/internship, career planning, preparing for graduate school, preparing for the engineering workforce, the ongoing technological revolution, problems of national importance, and lifelong learning. While this paper (Part I) focusses on the program and how career-steering/shaping projects can be used to prepare engineering students, Part II provides a case study for 14 scholars conducting funded research projects.
A Comparative Study on Electrical Tree Growth in Silicone Rubber Containing Nanoalumina and Halloysite Nanoclay

Recently, silicone rubber (SiR) is preferably used in high-voltage cable accessories because it offers a wide range of voltage and temperature applications and excellent electrical and mechanical properties. Furthermore, the SiR exhibits flexibility and inhibits oxidation. In this paper, the electrical tree behavior of SiR nanocomposites containing nanoalumina and halloysite nanoclay was investigated; moreover, the nanofiller concentrations were predetermined at 0 vol%, 1 vol%, 2 vol%, and 3 vol%. The electrical tree growth was observed at room temperature at 8 kVrms after tree inception voltage (TIV). The TIV and electrical tree growth in SiR nanocomposites were monitored using an online digital monitoring system, which consisted of a stereomicroscope and a digital camera. The experimental results indicated that the addition of 1 vol%–2 vol% nanoalumina in SiR composite significantly improved the TIV and electrical tree growth rate. However, the addition of 3 vol% nanoalumina in the SiR composite reduced the TIV and accelerated the electric tree growth rate. In the presence of halloysite nanoclay of approximately 3 vol%, the SiR composite showed improved TIV and electrical tree growth rate.
Deep Road Scene Understanding

Road scene understanding is a difficult task in autonomous driving. In this letter, we propose a novel deep encoder–decoder architecture for road scene understanding in an end-to-end manner. This core trainable understanding engine includes an encoder network, a decoder network with two streams, and a pixel-level fusion network with classification layer. The encoder network is composed of the front-end model of the classical convolution neural network, VGGNet. The decoder network with two streams includes multi-scale skip connection modules to reduce the down-scaling effect. Finally, a fusion network fuses the two-level information from the two streams of the decoder network for precise pixel-level classification. Additionally, the convolution layer is added to each skip connection module to increase the depth of the architecture. Our architecture achieves outstanding performance on the publicly available CamVid dataset and significantly outperforms previous architectures. This deep architecture is ideal for road scene understanding.
13.5 A 512Gb 3-bit/Cell 3D Flash Memory on 128-Wordline-Layer with 132MB/s Write Performance Featuring Circuit-Under-Array Technology

Advancements in 3D-Flash memory-layer-stacking technology has enabled density scaling that circumvents the lithography limitations which have prevented 2D-NAND Flash memory from scaling [1]. Bit densities as high as 5.95Gb/mm2 on a single die were recently reported [2], where a 512Gb NAND Flash was built on 96 layers of memory. As memory density increases, with memory layers increasing from 96 layers to 128 layers, higher bit density can be achieved by adopting larger capacity die; however, NAND performance per bit density is reducing with the 2-plane architecture. In this work, we propose a 512Gb 3b/cell 128WL-layer NAND Flash, with a bit density of 7.80Gb/mm2: a 31% improvement over the previously reported. Three key performance improving technologies have been implemented. (1) A 4-plane architecture with circuit under array (CUA) technology to improve performance per bit density. (2) A multi-die peak-power management (PPM) system to manage peak-power consumption in the system, via the ZQ pin. (3) A 4KB-page-read mode to reduce power consumption. Figure 13.5. 1(a) summarized the key features and Fig. 13.5. 1(b) shows the die photograph and the floorplan for this work. Figure 13.5. 2 shows a table comparing this work to previous work.
A Bus-Oriented Mobile FCNs Infrastructure and Intra-Cluster BSM Transmission Mechanism

With the concept of edge computing being put forward, the edge node should play an important role in resource allocation and local computation. However, there are two basic problems to confront in this arena: the performance of basic safety message (BSM) exchange and the frequency of service migration, both of which are related to the vehicle’s mobility feature. To decrease the frequency of service migration and enhance the efficiency of the BSM exchange, in this paper, a bus-oriented mobile fog computing nodes infrastructure is proposed, while a mobile similarity-based clustering method is given. In the proposed method, the bus node, which is considered as a fog-computing node (FCN), is used as a sink node, which fulfills communication resource allocation and convergence computing tasks. MATLAB is used to simulate the path loss feature, while a network simulator (NS-3) is employed to investigate the performance of the proposed scheme. The simulation results show that the proposed scheme significantly decreases the frequency of service migration and perform high communication efficiency for intra-cluster BSMs exchange.
FinFET With Improved Subthreshold Swing and Drain Current Using 3-nm Ferroelectric Hf0.5Zr0.5O2

High-performance negative capacitance p-type FinFETs (p-FinFETs) with a 3-nm-thick ferroelectric (FE) hafnium zirconium oxides (Hf<sub>0.5</sub>Zr<sub>0.5</sub>O<sub>2</sub>) layer are fabricated based on a conventional high-<inline-formula> <tex-math notation="LaTeX">${\kappa }$ </tex-math></inline-formula> metal gate FinFETs fabrication flow. The devices show improved subthreshold swing values [34.5 mV/dec for 500-nm gate length (L<sub>G</sub>) and 53 mV/dec for 20-nm-L<sub>G</sub> devices] and slight hysteresis voltages (~9 mV for <inline-formula> <tex-math notation="LaTeX">$\textsf {L}_{\textsf {G}} = \textsf {500}$ </tex-math></inline-formula> nm and ~40 mV for <inline-formula> <tex-math notation="LaTeX">$\textsf {L}_{\textsf {G}} = \textsf {20}$ </tex-math></inline-formula>-nm transistors). With the integrated FE film, a strong driving current enhancement (up to 260%) is also obtained compared with that of conventional FinFETs. The inherent reasons for the improved characteristics contribute to the low-interface state density (<inline-formula> <tex-math notation="LaTeX">${D}_{\sf it}$ </tex-math></inline-formula>) and the perfect channel electrostatic integrity.
Fast Minimization of Fixed Polarity Reed-Muller Expressions

Logic minimization has recently attracted significant attention because in many applications it is important to have a compact representation as possible. In this paper, we propose a fast minimization algorithm (FMA) of fixed polarity Reed–Muller expressions (FPRMs). The main idea behind the FMA is to search the minimum FPRM with the fewest products by using the proposed binary differential evolution algorithm (BDE). The BDE can efficiently maintain population diversity and achieve a better tradeoff between the exploration and exploitation capabilities by use of proposed binary random mutation operator and improved selection operator. The experimental results on 24 MCNC benchmark circuits demonstrate that the FMA outperforms the genetic algorithm-based and simulated annealing genetic algorithm-based FPRMs minimization algorithms in terms of accuracy of solutions and solving efficiency. To the best of our knowledge, we are the first to use differential evolution algorithm to minimize FPRMs. The FMA can be extended to derive a minimum mixed polarity Reed–Muller expression.
Symmetric Bilinear Regression for Signal Subgraph Estimation

There is an increasing interest in learning a set of small outcome-relevant subgraphs in network-predictor regression. The extracted signal subgraphs can greatly improve the interpretation of the association between the network predictor and the response. In brain connectomics, the brain network for an individual corresponds to a set of interconnections among brain regions and there is a strong interest in linking the brain connectome to human cognitive traits. Modern neuroimaging technology allows a very fine segmentation of the brain, producing very large structural brain networks. Therefore, accurate and efficient methods for identifying a set of small predictive subgraphs become crucial, leading to discovery of key interconnected brain regions related to the trait and important insights on the mechanism of variation in human cognitive traits. We propose a symmetric bilinear model with $L_1$ penalty to search for small clique subgraphs that contain useful information about the response. A coordinate descent algorithm is developed to estimate the model where we derive analytical solutions for a sequence of conditional convex optimizations. Application of this method on human connectome and language comprehension data shows interesting discovery of relevant interconnections among several small sets of brain regions and better predictive performance than competitors.
2'-Fluoro RNA shows increased Watson-Crick H-bonding strength and stacking relative to RNA: evidence from NMR and thermodynamic data.

Oligonucleotides containing 2'-deoxy-2'-fluoro-ribonucleotides (2'-F RNA) have found numerous beneficial applications in ribozyme,[1] antisense,[2,3] siRNA,[4,5] miRNA,[6] and aptamer[7] based nucleic acid therapeutics. Moreover, antisense oligonucleotides bearing 2'-F ribonucleotides were recently found to exhibit favorable properties for modulating splicing relative to other 2'-modifications. [8] At the ribose 2'-position fluorine preorganizes the sugar for a C3'-endo conformation that matches the preferred structure of RNA duplexes. We found that siRNAs extensively modified with 2'-F modified pyrimidines showed increased nuclease stability, reduced immune stimulation and in some cases exhibited favorable activity in vitro and in vivo relative to unmodified control RNA. [5] The stabilizing effect of 2'-F modification as measured by thermal melting of RNA duplexes amounts to ca. 1.8°C per nucleotide.
Video Generation and Synthesis Network for Long-term Video Interpolation

In this paper, we propose a bidirectional synthesis video interpolation technique based on deep learning, using a forward and a backward video generation network and a synthesis network. The forward generation network first extrapolates a video sequence, given the past video frames, and then the backward generation network generates the same video sequence, given the future video frames. Next, a synthesis network fuses the results of the two generation networks to create an intermediate video sequence. To jointly train the video generation and synthesis networks, we define a cost function to approximate the visual quality and the motion of the interpolated video as close as possible to those of the original video. Experimental results show that the proposed technique outperforms the state-of-the art long-term video interpolation model based on deep learning.
Adaptation Content in Robotic Systems: A Systematic Mapping Study

This full paper of research category presents a Systematic Mapping Study of the state-of-the-art of research on adaptation content in Robotic Systems, in the fields of cognitive profile, learning style and student model. In general, teachers develop the material that is to be taught and organize pedagogical activities for the students. As a consequence, students have a learning environment that does not take into account activities that have already been completed, actions taken, learning preferences and cognitive profile. Activities and teaching materials in robotic environments are the same for all students, and may end up being insufficient for the effective learning of each one. The initial search of four main academic databases resulted in 137 papers out of which 15 papers were included in the final analysis. Our article presents different researches, which involve adapting content for learning in robotic environments.
Migration of Multiplatform Legacy Network to Single Software-Defined-Network (SDN)

Software-Defined-Network now a days prove to be a paradigm for Network industry. A legacy network is ruling the computer networks from decades. Legacy network's technologies and techniques are combined to secure network and making it more efficient for users. The SDN now a days emerging as replacing entity for traditional networks. SDN facilitates in terms of security to its attached appliance, traffic, policies, traffic flows and mainly its architecture. SDN provides a central management for its manageability. Security challenges in SDN Architecture attracts various stakeholders for migrating to it. Legacy network have been used with multiplatform (hardware, software) for attaining desired performance, task, security. The multiplatform hardware and software needs to be managed quite often for stability. This research work will explains how to migrate from Legacy Network to Software-Defined-Network (SDN) considering all its security perspective and how to eliminate Multiplatform of legacy network to SDN based architecture.
Fuzzy Logic Inverter Controller in Photovoltaic Applications: Issues and Recommendations

Fossil fuels and other conventional energy sources used to generate electricity are finite. Therefore, alternative energy sources should be pursued to meet present and future energy demands. The photovoltaic (PV) is a promising renewable energy source, especially for the remote areas. The PV is a DC power source that needs to be converted into usable AC power using an inverter. However, its nonlinearity and output fluctuation pose challenges in the design of PV based inverter. In this paper, a PV inverter controller system with the fundamentals of a fuzzy logic controller (FLC) and its applications and execution are reviewed. The different fuzzy controllers, inverter control algorithms, and switching techniques are studied. The findings indicate that the fuzzy logic controls have been gaining attention in the area of power control engineering, especially in inverter controller design for PV applications and generation. The FLC has a flexible and intelligent design, expedient user interface, easy computation and learning system, and combinations of different control algorithms. The FLC is also verifiable for completeness, redundancy, and consistency. However, finding the boundaries of membership functions and other rules of FLC requires manual tuning, long computation time, and considerable effort. This paper comprehensively reviews the FLC-based inverter control system to minimize PV output fluctuations, which cause inverter issues related to output harmonics, power factor, switching schemes, losses, and system implementation. The inverter system and its control strategy for future PV applications and generation require further research and development. Consequently, this review focuses on many factors and challenges and provides recommendations for designing capable and efficient inverter control systems for converting PV power to usable AC power. All the highlighted insights of this review will hopefully lead to increased efforts toward the development of the advanced inverter control systems for PV applications for AC loads and the utility grid.
Image Saliency Detection By Residual And Inception-like CNNs

Saliency detection is an important task in image processing as it can solve many problems and it usually is the first step in other processes. Saliency detection is traditionally carried out by using hand-crafted features obtained from principles in neuroscience. On the other hand, introduction of convolutional neural networks was to some degree a paradigm shift in the field of object detection and it impacted other areas of image processing, including salient regions detection, as a whole. In this paper various methods of saliency detection systems are reviewed. Then, two new methods based on deep learning and convolutional neural networks have been introduced for pixel level detection. These methods are fast and accurate making them desirable for real-time implementation.
Deep Learning Models for Melody Perception: An Investigation on Symbolic Music Data

We investigate the deep learning approaches on the melody extraction problem on symbolic music data. Specifically, we compare two different approaches: the first one employs recurrent neural networks (RNN) by considering melody extraction as a sequence prediction problem, while the second employs fully convolutional networks (FCN) by considering it as a image semantic segmentation problem. Both methods are tested against a MIDI dataset with melody tracks acting as ground truth. A more challenging case that the melodies are shifted by one octave is also considered. Evaluation results show the advantage of the semantic segmentation approach in terms of the accuracy.