Miracle-dz/UniFAR-FTUs huggingface dataset:
SpreadRank: A Novel Approach for Identifying Influential Spreaders in Complex Networks

Identifying influential spreaders in complex networks is critical for information spread and malware diffusion suppression. In this paper, we propose a novel influential spreader identification method, called SpreadRank, which considers the path reachability in information spreading and uses its quantitative index as a measure of node spread centrality to obtain the spread influence of a single node. To avoid the overlapping of the influence range of the node spread, this method establishes a dynamic influential node set selection mechanism based on the spread centrality value and the principle of minimizing the maximum connected branch after network segmentation, and it selects a group of nodes with the greatest overall spread influence. Experiments based on the SIR model demonstrate that, compared to other existing methods, the selected influential spreaders of SpreadRank can quickly diffuse or suppress information more effectively.
Unveiling the Genomic Basis of Chemosensitivity in Sarcomas of the Extremities: An Integrated Approach for an Unmet Clinical Need

Myxofibrosarcoma (MFS) and undifferentiated pleomorphic sarcoma (UPS) can be considered as a spectrum of the same disease entity, representing one of the most common adult soft tissue sarcoma (STS) of the extremities. While MFS is rarely metastasizing, it shows an extremely high rate of multiple frequent local recurrences (50–60% of cases). On the other hand, UPS is an aggressive sarcoma prone to distant recurrence, which is correlated to a poor prognosis. Differential diagnosis is challenging due to their heterogeneous morphology, with UPS remaining a diagnosis of exclusion for sarcomas with unknown differentiation lineage. Moreover, both lesions suffer from the unavailability of diagnostic and prognostic biomarkers. In this context, a genomic approach combined with pharmacological profiling could allow the identification of new predictive biomarkers that may be exploited for differential diagnosis, prognosis and targeted therapy, with the aim to improve the management of STS patients. RNA-Seq analysis identified the up-regulation of MMP13 and WNT7B in UPS and the up-regulation of AKR1C2, AKR1C3, BMP7, and SGCG in MFS, which were confirmed by in silico analyses. Moreover, we identified the down-regulation of immunoglobulin genes in patient-derived primary cultures that responded to anthracycline treatment compared to non-responder cultures. Globally, the obtained data corroborated the clinical observation of UPS as an histotype refractory to chemotherapy and the key role of the immune system in determining chemosensitivity of these lesions. Moreover, our results confirmed the validity of genomic approaches for the identification of predictive biomarkers in poorly characterized neoplasms as well as the robustness of our patient-derived primary culture models in recapitulating the chemosensitivity features of STS. Taken as a whole, this body of evidence may pave the way toward an improvement of the prognosis of these rare diseases through a treatment modulation driven by a biomarker-based patient stratification.
Collocation Method to Solve Elliptic Equations, Bivariate Poly-Sinc Approximation

The paper proposes a collocation method to solve bivariate elliptic partial differential equations. The method uses Lagrange approximation based on Sinc point collocations. The proposed approximation is collocat- ing on non-equidistant interpolation points generated by conformal maps, called Sinc points. We prove the upper bound of the error for the bivariate Lagrange approximation at these Sinc points. Then we define a colloca- tion algorithm using this approximation to solve elliptic PDEs. We verify the Poly-Sinc technique for different elliptic equations and compare the approximate solutions with exact solutions.
A Decision Making Model Where the Cell Exhibits Maximum Detection Probability: Statistical Signal Detection Theory and Molecular Experimental Data

Molecular noise and signaling abnormalities in biochemical signaling systems in cells affect signaling events and consequently may alter cellular decision making results. Since unexpected and altered cellular decisions may contribute to the development of many pathological conditions and diseases, it is of interest to develop proper models to characterize and measure molecular signal detection parameters and cellular decisions. In this paper and using the Neyman-Pearson signal detection theorem, we propose a signal detection model in which the cell maximizes its signal detection probability in the presence of noise. To evaluate the usefulness of the proposed model, we use measured molecular experimental data of the important TNF-NF- $\kappa \mathbf{B}$ cell signaling system. Our results demonstrate that the proposed model provides biologically relevant findings. The introduced Neyman-Pearson-based molecular signal detection framework allows to systematically model and quantify the signal detection behavior and failure of molecular signaling systems, and compute their key decision making parameters such as detection and false alarm probabilities. With regard to the specific TNF-NF- $\kappa\mathbf{B}$ system case study in this paper and given the high involvement of the transcription factor NF- $\kappa \mathbf{B}$ in cell survival, programmed cell death, immune signaling and stress response, the developed signal detection framework can serve as a useful tool to model the associated cell decision making processes.
A New “Stepped Particle Swarm Optimization” for Inverse Design of Nanophotonic Devices

In this paper, we propose a new “stepped particle swarm optimization”(SPSO) for the reverse design of nanophotonic devices. Compared with the traditional “particle swarm optimization”(PSO), this method has a fast-rising speed, is easy to jump out of local extrema, and has a wide range of application prospects. It can break through the limitations brought by the traditional PSO and obtain better device performance. Also, the SPSO can be used in the structural design of a large number of optoelectronic devices, such as grating couplers, wavelength division multiplexers, mode multiplexers, mode converters, etc. The feasibility and advantages of SPSO are verified by the design of a subwavelength convergence grating that is monolithically integrated with the detector. The SPSO is of great significance in the design of nanophotonic devices with large parameter space.
Simulative Analysis of InP-based Dual Polarization IQ Mach-Zehnder Modulators

A realistic model of an Indium-Phosphide Dual Polarization IQ Mach-Zehnder modulator supported by experimental measurements is introduced within an accurate time-domain simulator. BER vs OSNR results show the effect of model’s non-linearities increasing the modulation format complexity at different symbol rates. The intrinsic modulator SNR is estimated.
Tuning Properties of External Cavity Violet Semiconductor Laser

A tunable grating-coupled external cavity (EC) laser is realized by employing a GaN-based laser diode as the gain device. A tuning range of 4.47 nm from 403.82 to 408.29 nm is achieved. Detailed investigations reveal that the injection current strongly influences the performance of the EC laser. Below the free-running lasing threshold, EC laser works stably. While above the free-running lasing threshold, a Fabry—Perot (F-P) resonance peak in the emission spectrum and a smooth kink in the output power-injection current characteristic curve are observed, suggesting the competition between the inner F-P cavity resonance and EC resonance. Furthermore, the tuning range is found to be asymmetric and occurs predominantly on the longer wavelength side. This is interpreted in terms of the asymmetric gain distribution of GaN-based quantum well material.
Review of Wafer Surface Defect Detection Methods

Wafer surface defect detection plays an important role in controlling product quality in semiconductor manufacturing, which has become a research hotspot in computer vision. However, the induction and summary of wafer defect detection methods in the existing review literature are not thorough enough and lack an objective analysis and evaluation of the advantages and disadvantages of various techniques, which is not conducive to the development of this research field. This paper systematically analyzes the research progress of domestic and foreign scholars in the field of wafer surface defect detection in recent years. Firstly, we introduce the classification of wafer surface defect patterns and their causes. According to the different methods of feature extraction, the current mainstream methods are divided into three categories: the methods based on image signal processing, the methods based on machine learning, and the methods based on deep learning. Moreover, the core ideas of representative algorithms are briefly introduced. Then, the innovations of each method are compared and analyzed, and their limitations are discussed. Finally, we summarize the problems and challenges in the current wafer surface defect detection task, the future research trends in this field, and the new research ideas.
Research on Indoor Target Positioning System Based on Image Feature Extraction and Recognition

In view of the complexity of indoor environment and the limitations of existing positioning methods on indoor target positioning, this paper uses improved ORB algorithm to replace the target detection and recognition system on the market based on SURF or SIFT algorithm, relying on the target detection data in the real indoor scene. The system takes the image characteristics of the target as the main basis for recognition, and optimizes the indoor positioning accuracy through image recognition and positioning technology, hardware verification and evaluation. The system can effectively reduce the amount of image processing, optimize the target feature extraction, and improve the recognition accuracy. The research on this system in this paper is expected to be applied to a variety of practical scenes, such as improving the indoor positioning accuracy of UAVs, and providing useful reference for improving the positioning accuracy of a variety of large indoor scenes.
Optical Frequency Comb Generation from a Bismuth-Based Mode-Locked Fiber Laser

Highly stable and widely wavelength-tunable optical frequency comb generation from an active and harmonic mode-locked short-cavity laser employing a bismuth-oxide- based nonlinear erbium-doped fiber is demonstrated. Flat 10- GHz-spaced optical frequency comb with a frequency bandwidth of 240GHz is produced.
Empowering Network Security With Programmable Switches: A Comprehensive Survey

With the growth of network applications such as 5G and artificial intelligence, network security techniques, i.e., the techniques that detect various attacks (e.g., well-known denial-of-service (DDoS) attacks) and prevent production networks (e.g., data center networks) from being attacked, become increasingly essential for network management and have gained great popularity in the networking community. Generally, these techniques are built on proprietary hardware appliances, i.e., middleboxes, or the paradigm that combines both software-defined networking (SDN) and network function virtualization (NFV) to implement security functions. However, the techniques built on middleboxes are proven to be hard-to-manage, costly, and inflexible, thereby making them an out-of-date choice in network security. For the techniques built on SDN and NFV, they virtualize and softwarize security functions on commodity servers, leading to non-trivial performance degradation. Fortunately, the recent emergence of programmable switches brings new opportunities of empowering network security techniques with the characteristics of easy-to-manage, low cost, high flexibility, and Tbps-level performance. In this survey, we focus on this promising trend in network security. More precisely, this survey first presents the preliminaries of programmable switches, which are the primary driver of next-generation network security techniques. Next, we comprehensively review existing techniques built on programmable switches, classify these techniques, and discuss their background, motivation, design, implementation, and limitations case-by-case. Finally, we summarize open issues and future research directions in this promising research topic of network security.
A proposal for communication of intraoperative contextual information via DICOM Unified Procedure Steps

[Motivation] In order to enable context-aware behavior of surgical assistance systems, the acquisition of various information about the current intraoperative situation is crucial. To achieve this, the complex task of situation recognition can be delegated to a specialized system. Consequently, a standardized interface is required for the seamless transfer of the recognized contextual information to the assistance systems, enabling them to adapt accordingly. [Methods] Our group analyzed four medical interface standards to determine their suitability for exchanging intraoperative contextual information. The assessment was based on a harmonized data and service model derived from the requirements of expected context-aware use cases. The Digital Imaging and Communications in Medicine (DICOM) and IEEE 11073 for Service-oriented Device Connectivity (SDC) were identified as the most appropriate standards. [Results] We specified how DICOM Unified Procedure Steps (UPS), can be used to effectively communicate contextual information. We proposed the inclusion of attributes to formalize different granularity levels of the surgical workflow. [Conclusions] DICOM UPS SOP classes can be used for the exchange of intraoperative contextual information between a situation recognition system and surgical assistance systems. This can pave the way for vendor-independent context awareness in the OR, leading to targeted assistance of the surgical team and an improvement of the surgical workflow.
A Two-stage Raman Imaging Denoising Algorithm Based on Deep Learning

For the problem of low Signal-to-Noise Ratio (SNR) of the image reconstructed from Raman spectra, this paper proposes a two-stage denoising algorithm based on deep learning, including spectrum denoising and image denoising. Because spectra and images of the same sample are scarce, the spectrum denoising algorithm and the image denoising one are trained on two irrelevant dataset. Denoising and baseline correction are performed on the raw Raman spectra using 1D-ResUNet. The low SNR images are denoised by Reversed Convolutional Block Attention Module UNet (RCBAM-UNet). Experimental results showed that the Mean Squared Error (MSE) between the denoised spectrum processed by our method and the ground truth decreased by 103-104, when compared to the raw data. The quantitative results of image denoising on Structural Similarity (SSIM) and Peak Signal-to-Noise Ratio (PSNR) are 98.4% and 39.7, respectively. The performance of our two-stage algorithm on the testing dataset (an independent dataset) showed increased SNR and Contrast to Noise Ratio (CNR) in Raman imaging denoising. Our study provides an efficient method for the improvement of Raman image quality.
Centralized Virtual Mapping Algorithm in Virtual Network

Virtual Network (VN) is an essential approach to reduce the complexity of the internet-based communication and computing environment. Network virtualization concept to overcome the existing robust routing, decentralization, and authentication. In this paper, we propose a collaborative and centralized algorithm which consist of three algorithms to do mapping of VN into substrate network such as, Capacity Node Sorting (CNS) algorithm, shortest path Tree (SPT) algorithm and Main Mapping (MM) algorithm. It also responsible for load balancing with respect to node capacity. AVN mapping protocol has been proposed to communicate and exchange message between substrate nodes to achieve mapping. Implementation and experimental of VN mapping algorithm are performed using Mininet simulation with RYU and POX controller.
Preference Propagation in Temporal/Capacity Constraint Graphs

Abstract : Scheduling can be formalized as a constraint satisfaction problem. Within this framework activities in a plan are interconnected via temporal relation constraints a la Allen, thereby defining a temporal constraint graph (TCG). Additionally there are capacity constraints restricting the use of each resource to only one activity at a time. Together these constraints form a temporal/capacity constraint graph (T/CCG). Preferences such as meeting due dates, reducing order flowtime, or selecting accurate machines are modeled as utility functions over the domain of possible start times and durations of activities and over the sets of possible resources activities can use. These preferences interact via the TCG and via the resource capacity constraints. Hence, in general, they cannot be simultaneously optimized. The objective of preference propagation techniques is to transform such local a priori preferences so as to account for their interactions. This paper describes a probabilistic framework in which start time, duration and resource preferences are propagated across T/CCGs in order to focus attention in an incremental scheduler.
Image Retrieval Based on Color, Shape, and Texture for Ornamental Leaf with Medicinal Functionality Images

This research is focusing on ornamental leaf with dual functionalities, which are ornamental and medicinal functionalities. However, only few people know about the medicinal functionality of this plant. In Indonesia, this plant is also easy to find because mostly cultivates in front of the house. If its medicinal function and that easiness are taken into consideration, this leaf should be an option towards the full chemical-based medicines. This image retrieval system utilizes color, shape, and texture features from leaf images. HSV-based color histogram, Zernike complex moments, and Dyadic wavelet transformation are the color, shape, and texture features extractor methods, respectively. We also implement the Bayesian automatic weighting formula instead of assignment of static weighting factor. From the results, this proposed method is very powerful from any rotation, lighting, and perspective changes.
The effect of angular resolution and 3D rendering on the perceived quality of the industrial use cases of light field visualization

Light field displays enable glasses-free 3D visualization without major constraints regarding the number of simultaneous viewers. There are numerous potential future use case contexts for such devices. Industrial contexts include prototype review and other types of modeling use cases, where the visualized content may vary a lot in terms of complexity and dimensions. However, the perception of 3D models with higher structural fidelity may be severely affected by the ray density of visualization. Furthermore, as the visualized content is always the sharpest in the plane of the screen of the light field display, rendering with greater depth may already result in blurred regions even if the angular resolution is high. In this paper, the effect of angular resolution and 3D rendering on the perceived quality of the industrial use cases of light field visualization is explored via a subjective study. The structurally-diverse source contents are rendered with different depth values, and the resulting test stimuli are created in seven extents of angular resolution, ranging between 0.5 degrees and 2 degrees. The obtained results not only exhibit the combined effect of the investigated features, but also highlight methodological considerations.
Regulation of Autophagy via Carbohydrate and Lipid Metabolism in Cancer

Simple Summary The metabolism of carbohydrates and lipids is essential to meet the diverse needs of cells. The autophagy cellular pathway is crucial for normal cellular function through its ability to target and degrade damaged organelles and misfolded proteins. Autophagy also provides cells with necessary energy as an end product. Cancer cells have a high rate of proliferation and, thus, require more energy compared to normal cells. Not surprisingly, cancer cells are highly dependent on these metabolic pathways. This review provides an in-depth review of recent research findings on how carbohydrate and lipid metabolism regulate autophagy and how this impacts cancer cells. Abstract Metabolic changes are an important component of tumor cell progression. Tumor cells adapt to environmental stresses via changes to carbohydrate and lipid metabolism. Autophagy, a physiological process in mammalian cells that digests damaged organelles and misfolded proteins via lysosomal degradation, is closely associated with metabolism in mammalian cells, acting as a meter of cellular ATP levels. In this review, we discuss the changes in glycolytic and lipid biosynthetic pathways in mammalian cells and their impact on carcinogenesis via the autophagy pathway. In addition, we discuss the impact of these metabolic pathways on autophagy in lung cancer.
Penalized Maximum Likelihood Method to a Class of Skewness Data Analysis

An extension of some standard likelihood and variable selection criteria based on procedures of linear regression models under the skew-normal distribution or the skew- distribution is developed. This novel class of models provides a useful generalization of symmetrical linear regression models, since the random term distributions cover both symmetric as well as asymmetric and heavy-tailed distributions. A generalized expectation-maximization algorithm is developed for computing the penalized estimator. Efficacy of the proposed methodology and algorithm is demonstrated by simulated data.
Ultrasonic camera automatic image depth and stitching modifications for monitoring aerospace composites

Two modifications to an ultrasonic camera system have been performed in an effort to reduce setup time and post inspection image processing. Current production ultrasonic cameras have image gates that are adjusted manually. The process to adjust them prior to each inspection consumes large amounts of time and requires a skilled operator. The authors have overcome this by integrating the A-Scan and image together such that the image gating is automatically adjusted using the A-Scan data. The system monitors the A-scan signal which is in the center of the camera's field of view (FOV) and adjusts the image gating accordingly. This integration will allow for defect detection at any depth of the inspected area. Ultrasonic camera operation requires the inspector to scan the surface manually while observing the cameras FOV in the monitor. If the monitor image indicates a defect the operator then stores that image manually and marks an index on the surface as to where the image has been acquired. The second modification automates this effort by employing a digital encoder and image capture card. The encoder is used to track movement of the camera on the structures surface, record positions, and trigger the image capture device. The images are stored real time in the buffer memory rather than on the hard drive. The storing of images in the buffer enables for a more rapid acquisition time compared to storing the images individually to the hard drive. Once the images are stored, an algorithm tracks the movement of the camera through the encoder and accordingly displays the image to the inspector. Upon completion of the scan, an algorithm digitally stitches all the images to create a single full field image. The modifications were tested on a aerospace composite laminate with known defects and the results are discussed.
Global stability proofs for continuous-time indirect adaptive control schemes

This note presents a general stability proof for continuous-time adaptive control, with very general assumptions on the identifier and controller. We show that if the exogenous input signal is rich enough, then both the identifier and the controller converge to their "true" values. Applications of the proof to pole placement design and design based on the factorization approach are discussed.