Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Algorithm for Detecting Communities in a Tripartite Networks

An efficient Algorithm is proposed for community detection through combining collaborative filtering and modularity maximization. We design the algorithm with divide and conquer thinking, i.e. divide a large network into smaller sub-networks and then conquer finding communities of each sub-network. In the first part of our Algorithm, we use sparse matrix technology for storage and processing to improve filtration efficiency. In the second part, a quantity filter coefficient is defined to degrade the time complexity of finding the leading eigenvector of matrix in modularity maximization method. We demonstrate the method with applications to a tripartite network and get a relatively ideal result.
Optimal Flight Paths over Essential Visibility Graphs

This paper deals with the problem of generating optimal 2D flight paths compliant with mission constraints resulting from no-fly zones and/or obstacles, and from flight mechanics limitations as minimum coordinate turn radius and maximum climb and descent angles. The optimization problem is converted into a minimum cost path search within a so called Essential Visibility Graph whose arcs and corresponding weights are obtained via an efficient branching algorithm in view of possible real-time implementations. A possible extension to 3D paths is also presented. Finally, numerical examples are reported with a discussion on the computational aspects.
The Three Members of the Arabidopsis Glycosyltransferase Family 92 are Functional &bgr;-1,4-Galactan Synthases

Pectin is a major component of primary cell walls and performs a plethora of functions crucial for plant growth, development and plant-defense responses. Despite the importance of pectic polysaccharides their biosynthesis is poorly understood. Several genes have been implicated in pectin biosynthesis by mutant analysis, but biochemical activity has been shown for very few. We used reverse genetics and biochemical analysis to study members of Glycosyltransferase Family 92 (GT92) in Arabidopsis thaliana. Biochemical analysis gave detailed insight into the properties of GALS1 (Galactan synthase 1) and showed galactan synthase activity of GALS2 and GALS3. All proteins are responsible for adding galactose onto existing galactose residues attached to the rhamnogalacturonan-I (RG-I) backbone. Significant GALS activity was observed with galactopentaose as acceptor but longer acceptors are favored. Overexpression of the GALS proteins in Arabidopsis resulted in accumulation of unbranched β-1, 4-galactan. Plants in which all three genes were inactivated had no detectable β-1, 4-galactan, and surprisingly these plants exhibited no obvious developmental phenotypes under standard growth conditions. RG-I in the triple mutants retained branching indicating that the initial Gal substitutions on the RG-I backbone are added by enzymes different from GALS.
Taking Stock of Tobacco Control Program and Policy Science and Impact in the United States

The 60% decline in the prevalence of cigarette smoking among U.S. adults over the past 50 years represents a significant public health achievement. This decline was steered in part by national, state, and local tobacco control programs and policies, such as public education campaigns; widespread smoke-free air laws; higher cigarette prices that have been driven by large increases in federal, state, and local cigarette excise taxes; and other tobacco control policy and systems-level changes that discourage smoking. Using the MPOWER framework informed by the Centers for Disease Control and Prevention (CDC) Office on Smoking and Health and the World Health Organization (WHO), this paper reviews these accomplishments and identifies gaps in tobacco control policy implementation and additional research needed to extend these historic successes.
Feature Extraction Classification for Personal Identification using Iris

This paper presents a new approach for feature extraction of iris image for recognition purpose. Two methods are used in this work for feature extraction and classification. Firstly input image is pre-processed where iris region and pupil region are extracted from the eye image using Canny edge detection and Hough Transform. Then key features are extracted from the iris region and using these features classification is performed for iris recognition. Combination of three feature extraction algorithms and a classifier is used in each method. The combination of Gabor filter, Principally Rotated complex wavelet filter (PR-CWF), Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) is used as method1. Similarly combination of PR-CWF, Bayer’s Nearest Neighbour (BNN), Fast Fourier Transform and Artificial Neural Network (ANN) is used as method2.
Seeds of future past: climate change and the thermal memory of plant reproductive traits

Plant persistence and migration in face of climate change depends on successful reproduction by seed, a central aspect of plant life that drives population dynamics, community assembly and species distributions. Plant reproduction by seed is a chain of physiological processes, the rates of which are a function of temperature, and can be modelled using thermal time models. Importantly, while seed reproduction responds to its instantaneous thermal environment, there is also evidence of phenotypic plasticity in response to the thermal history experienced by the plant's recent ancestors, by the reproducing plant since seedling establishment, and by its seeds both before and after their release. This phenotypic plasticity enables a thermal memory of plant reproduction, which allows individuals to acclimatise to their surroundings. This review synthesises current knowledge on the thermal memory of plant reproduction by seed, and highlights its importance for modelling approaches based on physiological thermal time. We performed a comprehensive search in the Web of Science and analysed 533 relevant articles, of which 81 provided material for a meta‐analysis of thermal memory in reproductive functional traits based on the effect size Zr. The articles encompassed the topics of seed development, seed yield (mass and number), seed dormancy (physiological, morphological and physical), germination, and seedling establishment. The results of the meta‐analysis provide evidence for a thermal memory of seed yield, physiological dormancy and germination. Seed mass and physiological dormancy appear to be the central hubs of this memory. We argue for integrating thermal memory into a predictive framework based on physiological time modelling. This will provide a quantitative assessment of plant reproduction, a complex system that integrates past and present thermal inputs to achieve successful reproduction in changing environments. The effects of a warming environment on plant reproduction cannot be reduced to a qualitative interpretation of absolute positives and negatives. Rather, these effects need to be understood in terms of changing rates and thresholds for the physiological process that underlie reproduction by seed.
Adaptive Malicious URL Detection: Learning in the Presence of Concept Drifts

Hackers can implant malwares (e.g. Trojans, Worms, etc.) in the web pages to steal user information and acquire money illegally. In fact, it is noted that close to one-third of all websites are potentially malicious in nature. Therefore, It makes sense to quickly detect malicious URLs on the Internet. Different from most of previous methods, in this paper, we propose a method for online malicious URL detection based on adaptive learning. By collecting the network traffic from backbone networks, we train machine learning models to detect malicious URLs. But there is a serious problem in dynamically changing environments where the statistical properties of target variable change over time, which is known as concept drift. To address this problem, we apply a nonparametric test to correctly detect concept drifts in adaptive learning. Extensive experiments with different types of concept drifts are performed to demonstrate the feasibility of our proposed method on both artificial and real datasets. Our empirical study shows that this approach has good performance in detecting malicious URLs and concept drifts.
Hybrid algorithm for few-views computed tomography of strongly absorbing media: algebraic reconstruction, TV-regularization, and adaptive segmentation

Abstract. The paper presents an original hybrid image reconstruction algorithm ART-TVS for few-views computed tomography of strongly absorbing media. It is based on the well-known algebraic reconstruction technique (ART), regularization of interim results through minimization of the total variation norm (TV-regularization), and a method of adaptive segmentation, which is a modernization of the known region growing algorithm. It is shown that the ART-TVS algorithm does not give stripe artifacts even if the number of views is very small (eight or less). ART-TVS reconstruction results for two numerical models of metal shells are compared with those obtained with the ART-TV algorithm (ART with TV-regularization and without adaptive segmentation), the iterative Potts minimization algorithm (IPMA), and our MART-AP algorithm (multiplicative ART with a priori information) we developed earlier for few-views discrete tomography. It is shown that ART-TVS outperforms ART-TV and IPMA and is comparable with MART-AP in reconstruction accuracy. Also, ART-TVS converges markedly faster than IRMA in cases where strongly underdetermined systems are treated. The algorithm we propose also demonstrates quite satisfactory resistance to projection data noise that is inherent in tomography of strongly absorbing media.
On the General Relation of Wave Field Synthesis and Spectral Division Method for Linear Arrays

Sound field synthesis aims at the reproduction of an arbitrary target sound field over an extended listening area applying a densely spaced loudspeaker ensemble. Two basic analytic methodologies—the explicit and the implicit—exist in order to derive the required loudspeaker driving functions. The explicit solution aims at the direct solution of the involved integral equation describing the general sound field synthesis problem, resulting in driving functions in the form of a spectral integral. The implicit solution extracts the driving function from an appropriate boundary integral representation of the target sound field. So far the relationship between two approaches was investigated for target field specific synthesis scenarios. For linear arrays this paper introduces a high-frequency approximation for the explicit solution resulting in a novel, purely spatial domain formulation of the direct approach. The presented driving functions allow the synthesis of an arbitrary virtual sound field, optimizing the reproduction on an arbitrary reference line. It is furthermore shown that for an arbitrary virtual sound field, the implicit solution constitutes a high-frequency approximation of the explicit method.
Radiation Pattern Optimization for QFN Packages With On-Chip Antennas at 160 GHz

On-chip antennas are frequently used as radiating elements for monolithic microwave integrated circuits (MMICs) operating above 100 GHz. Their radiation pattern is wide since the radiator extension is typically about half wavelength. In a package, the wide radiation pattern is easily disturbed by interference from parasitic radiation of the package. In this paper, an approach to minimize and adjust the interference of the package is proposed and applied to a quad flat no leads (QFN) package housing. A structured metal sheet positioned between chip and exposed pad is used to generate enough degrees of freedom for the optimization of the 3-D-radiation pattern in open-cavity and molded QFN packages. For the open-cavity QFN package, a U-shaped slot is introduced, which reduces the propagation of surface waves to the sides and creates a uniform radiation pattern. For the molded QFN package with opening around the on-chip antenna, a corrugated metal sheet is introduced. This leads to a robust packaged radar MMIC with an on-chip antenna at 160 GHz, which provides a directed radiation pattern to boresight. Both optimized radiation patterns are well suited for the illumination of dielectric lenses, which results in a higher lens gain and a lower sidelobe level of the lens pattern compared to the standard QFN packaging. The proposed concepts are verified by measurements for the open-cavity and molded QFN packages at 160 GHz.
Privacy Preserving Distributed Deep Learning and Its Application in Credit Card Fraud Detection

Preserving privacy in machine learning on multi-party data is of importance to many domains. In practice, existing solutions suffer from several critical limitations, such as significantly reduced utility under privacy constraints or excessive communication burden between the information fusion center and local data providers. In this paper, we propose and implement a new distributed deep learning framework that addresses these shortcomings and preserves privacy more efficiently than previous methods. During the stochastic gradient descent training of a deep neural network, we focus on the parameters with large absolute gradients in order to save privacy budget consumption. We adopt a generalization of the Report-Noisy-Max algorithm in differential privacy to select these gradients and prove its privacy guarantee rigorously. Inspired by the recent novel idea of Terngrad, we also quantize the released gradients to ternary levels {-B, 0, B}, where B is the bound of gradient clipping. Applying Terngrad can significantly reduce the communication cost without incurring severe accuracy loss. Furthermore, we evaluate the performance of our method on a real-world credit card fraud detection data set consisting of millions of transactions.
Rapid Assembly of Carbon Nanoparticles Into Electrical Elements by Optically-Induced Electroosmotic Flow

We report the rapid assembly of carbon nanoparticles (CNPs) into functional electrical elements utilizing optically induced electroosmotic flow (OIEF) in a specialized microfluidics chip. Numerical simulations of various optically induced electrokinetics forces exerted on CNPs were initially performed to ascertain the viable forces that could overcome the Brownian motion and produce sufficient force to manipulate and assemble the CNPs. The results confirmed the theoretical prediction that only the force induced by OIEF could manipulate the CNPs. Subsequently, a series of experiments for assembling CNPs were conducted. This was followed by electrical characterization of the assembled three-dimensional CNP microstructures. The results proved that OIEF could effectively and rapidly assemble CNPs in about 45 s. We used measurements of the current–voltage relationship to validate that the CNP-based microstructures are resistive elements, and their resistance could be controlled by the width and length of the microstructures. We believe that, by using the OIEF technique, different nanoparticles of varying electrical properties could be assembled rapidly in a specialized microfluidics chip to create micro-electrical elements in the future, including integrating different nanoparticle elements into functional electrical devices.
Automated Consolidation of Virtual Machines

Virtualization of OS is the technology driving all modern data centers. Along with benefits such as reduced costs and easy implementation, virtualization provides the capability to migrate live Virtual Machines (VM) across nodes of a cluster. The existing technology requires manual selection of VMs for live migration which involves redundant migration and hence, not a feasible and efficient solution for large data centers. In this paper, we introduce an automated consolidation technique to reduce the number of active nodes in a cluster. This proposed technique consists of two modules namely consolidation and load balancing which help in achieving the least number of active nodes on the cluster where each VM satisfies Software Licensing Agreement (SLA). This facilitates in achieving quick live migration and node setup while reducing useless migrations which adversely impacts cluster performance. The implementation of the proposed technique has resulted in successful live migration on cluster and consolidation of VMs to minimum number of nodes in the cluster at any given time.
UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification

The Fact Extraction and VERification (FEVER) shared task was launched to support the development of systems able to verify claims by extracting supporting or refuting facts from raw text. The shared task organizers provide a large-scale dataset for the consecutive steps involved in claim verification, in particular, document retrieval, fact extraction, and claim classification. In this paper, we present our claim verification pipeline approach, which, according to the preliminary results, scored third in the shared task, out of 23 competing systems. For the document retrieval, we implemented a new entity linking approach. In order to be able to rank candidate facts and classify a claim on the basis of several selected facts, we introduce two extensions to the Enhanced LSTM (ESIM).
CAOS: A Context Acquisition and Offloading System

Currently, mobile applications have become increasingly complex. One trend is the usage of contextual information gathered from device sensors and external data sources, which are used to improve the user experience when using mobile applications. As the amount of sensing data increases as well as the complexity of context inference procedures, it leads to an undesirable situation where the mobile device has not enough resources (e.g., memory capacity, processing speed, and battery power) to perform some computation tasks. This paper addresses this issue by proposing CAOS, a software platform that employs the offloading approach to enhance mobile context-aware applications. CAOS allows mobile application developers to offload rocesses and data into cloud and cloudlets infrastructures, aiming at improving the overall application performance and saving battery power. In order to assess our proposal, a reference implementation was built, and experiments were performed in order to evaluate performance and energy consumption of our proposal. Results showed gains in both aspects in most of scenarios, which denote the feasibility of the proposed solution.
Nerve growth factor and substance P may be involved in moist exposed burn ointment-mediated chronic refractory wound healing

Moist exposed burn ointment (MEBO) is becoming increasingly popular in China as it shortens wound-healing time and reduces scar formation. However, its exact mechanism in mediating the wound-healing process is not yet clear. In the present study a total of 90 healthy adult male Wistar rats of specific-pathogen-free grade were divided equally into a control group, wound group, MEBO group, recombinant bovine basic fibroblast growth factor (rb-bFGF) group and sham operation group. Wound healing was observed from the extracted granulation tissues and recorded at three time points on 3, 7 and 14 days. Different levels of tumor necrosis factor α (TNF-α) and interleukin-6 (IL-6) in tissue homogenate were detected using ELISA. Western blot analysis and quantitative PCR were used to detect the expression of nerve growth factor (NGF), substance P (SP) as well as tyrosine kinase A (TrkA) receptor protein and the corresponding mRNA levels in granulation tissue. It was observed that the wound healing progressed faster in the MEBO and rb-bFGF groups compared with the wound group (P<0.01). TNF-α and IL-6 had an upward-downward trend at three time points, with the wound group demonstrating the most obvious increase (P<0.01). NGF and SP mRNA and protein levels in granulation tissue in MEBO, rb-bFGF and sham operation groups reached their highest levels on day 7 and then decreased on day 14. The expression level of TrkA was also measured simultaneously and its expression pattern was similar to that of NGF and SP. These results suggested that MEBO may promote nerve repair and accelerate wound healing through mediating the expression levels of NGF and SP, as well as TrkA.
Amnesia and the Multiple Memory Systems of the Brain

The character Dory from Finding Nemo has a memory problem. Poor Dory has a hard time remembering anything. She forgets who her family is and what has happened in her life. In just a couple of minutes, she forgets the things that her friends say to her. Movie makers are trying to show what amnesia, a type of memory loss, is like. However, real-life amnesia looks very different from how it appears in movies. We will talk about how hurting the hippocampus, a special, small part of the brain, can cause real-life amnesia. We will talk about how amnesia makes it extremely hard to learn and remember some kinds of information (facts, e.g., the rules of a video game), but not other kinds of information (skills, e.g., how to use a joystick). Finally, we will talk about the special people throughout history who have taught scientists the most about what amnesia is, what causes amnesia, and what can be done to help people who have amnesia.
Approximated Model Matching Control for Running Robots with Series Elastic Actuators

Realization of the robot's running gaits has been studied in many ways. Spring loaded inverted pendulum (SLIP) model has been popularly used to realize stable running gaits. For the robot with series elastic actuators (SEAs), conventional control methods can't be applied directly because of the SEA mechanism. This paper explains the reason of difficulties of the SEA mechanism to apply conventional control methods, and a systematic method to realize the stable running gaits for the robots with SEAs is proposed. The validity of the proposed method is shown by numerical simulation results.
Efficient DOA Estimation Algorithm for Noncircular Sources Under Unknown Mutual Coupling

Mutual coupling effects may seriously degrade the direction-of-arrival (DOA) estimation performance in practical sensor arrays. In this article, a computationally efficient DOA estimation algorithm is proposed for noncircular sources in the presence of mutual coupling. Utilizing the noncircularity of the sources, we first construct an extended covariance matrix. Then, based on the banded symmetric Toeplitz structure of the mutual coupling matrix, the extended covariance matrix is divided into two submatrices. By simultaneous singular-value decomposition (SVD) of the two submatrices, the DOA parameter is obtained in a closed form. The proposed algorithm can improve the DOA estimation performance. Moreover, it is computationally efficient since it does not require any spectral search and avoids high-order statistics. Simulation results demonstrate both the efficiency and the effectiveness of our proposed algorithm under unknown mutual coupling.
A new alignment method for OWL-Lite ontologies using propagation of similarity over the graph

In this paper, we introduce a new approach for aligning OWL-Lite ontologies. The main originality of the alignment method stands in the fact that it palliates the main drawbacks appearing in the literature approaches, i.e., problem of user-parameter settings. Carried out experimental results pointed out a sharp improvement in the precision and recall evaluation metrics.