Miracle-dz/UniFAR-FTUs huggingface dataset:
A Wavenumber-Domain Iterative Approach for Rapid 3-D Imaging of Gravity Anomalies and Gradients

We present a wavenumber-domain iterative approach for rapid 3-D imaging of gravity anomalies and gradients data, which is based on the 3-D mesh model with a flat observational surface. The approach deconvolves the spectra of gravity anomalies or gradients by a 2-D deconvolution filter describing the spectrum of the imaging operator and then transforms the resultant spectra into the space domain to derive the density distribution. This 2-D deconvolution filtering is operated layer by layer from top to bottom in the subsurface and finally, all the results are merged to generate the 3-D density distribution. We improve previous 2-D deconvolution filters by involving a depth-scaling factor and utilize a priori constraint and the iteration algorithm for imaging, enable the presented approach to produce a density model with a considerable resolution and accuracy. The wavenumber-domain algorithm makes the imaging faster than the conventional space-domain inversions. Tests on the synthetic data and the real data from a metallic deposit area in Northwest China verified the feasibility and high efficiency of the presented approach.
Development of a heat flow code to simulate production of a functionally graded material robotic gripper using the additive manufacture process

The additive manufacture process is thermally-complex – many different material mechanisms are occurring due to the heating/cooling cycle that parts are put through when the material is deposited. This complexity is compounded when parts are made from a combination of materials, as in functionally graded material (FGM) parts. To better understand this complexity, a Python™ code has been developed to plot heat flow through the part as material is being deposited. The outputs of such code will highlight the influence of the deposition tool path and indicate to the engineer what areas of a part may require redesigning. A robotic arm gripper is used as a test bed throughout the development of the code.
Including operational aspects in the planning of power systems with large amounts of variable generation: A review of modeling approaches

In the past, power system planning was based on meeting the load duration curve at minimum cost. The increasing share of variable generation (VG) makes operational constraints more important in the planning problem, and there is more and more interest in considering aspects such as sufficient ramping capability, sufficient reserve procurement, power system stability, storage behavior, and the integration of other energy sectors often through demand response assets. In VG integration studies, several methods have been applied to combine the planning and operational timescales. We present a four‐level categorization for the modeling methods, in order of increasing complexity: (1a) investment model only, (1b) operational model only, (2) unidirectionally soft‐linked investment and operational models, (3a) bidirectionally soft‐linked investment and operational models, (3b) operational model with an investment update algorithm, and (4) co‐optimization of investments and operation. The review shows that using a low temporal resolution or only few representative days will not suffice in order to determine the optimal generation portfolio. In addition, considering operational effects proves to be important in order to get a more optimal generation portfolio and more realistic estimations of system costs. However, operational details appear to be less significant than the temporal representation. Furthermore, the benefits and impacts of more advanced modeling techniques on the resulting generation capacity mix significantly depend on the system properties. Thus, the choice of the model should depend on the purpose of the study as well as on system characteristics.
The Innovation-Institution Connection: One Simple Diagram

The purpose of this note is to introduce a simple diagram that I suggest provides an intuitive and useful way of looking at the connection between institutions and innovations, regardless of what level of institutions one is concerned with. The 'back-of-the-envelope' diagram focuses attention on the effects of a new idea on the existing ideas that some institution might represent, and the sort of innovated institution that it might help engender.
Structural analysis of Phytophthora suppressor of RNA silencing 2 (PSR2) reveals a conserved modular fold contributing to virulence

Significance Effectors are essential virulence proteins. The notorious Phytophthora pathogens use a large arsenal of effectors to manipulate cellular processes in plant hosts. An overall understanding of effector function and the mechanisms underlying their diversification and adaptation is critical to implementing durable resistance. We analyze the protein structure of Phytophthora suppressor of RNA silencing 2 (PSR2), which allows the discovery of a conserved structural and functional module that tolerates sequence flexibility. Importantly, this module, often arranged as highly organized tandem repeats that are concatenated through a conserved mechanism, is present in hundreds of Phytophthora effectors. This study provides insight into effector evolution and offers new opportunities to combat severe Phytophthora diseases. Phytophthora are eukaryotic pathogens that cause enormous losses in agriculture and forestry. Each Phytophthora species encodes hundreds of effector proteins that collectively have essential roles in manipulating host cellular processes and facilitating disease development. Here we report the crystal structure of the effector Phytophthora suppressor of RNA silencing 2 (PSR2). PSR2 produced by the soybean pathogen Phytophthora sojae (PsPSR2) consists of seven tandem repeat units, including one W-Y motif and six L-W-Y motifs. Each L-W-Y motif forms a highly conserved fold consisting of five α-helices. Adjacent units are connected through stable, directional linkages between an internal loop at the C terminus of one unit and a hydrophobic pocket at the N terminus of the following unit. This unique concatenation results in an overall stick-like structure of PsPSR2. Genome-wide analyses reveal 293 effectors from five Phytophthora species that have the PsPSR2-like arrangement, that is, containing a W-Y motif as the “start” unit, various numbers of L-W-Y motifs as the “middle” units, and a degenerate L-W-Y as the “end” unit. Residues involved in the interunit interactions show significant conservation, suggesting that these effectors also use the conserved concatenation mechanism. Furthermore, functional analysis demonstrates differential contributions of individual units to the virulence activity of PsPSR2. These findings suggest that the L-W-Y fold is a basic structural and functional module that may serve as a “building block” to accelerate effector evolution in Phytophthora.
Gestión de ancho de banda en tráfico de red en hardware reconfigurable

In the last few years, the world’s stock markets have evolved a lot thanks to introduction of high frequency trading systems that are usually run of High Performance Computers. This kind of systems require a quality of service in terms of latency that current software solutions cannot provide. This project approaches this problem from the hardware perspective, developing a hardware design solution that actually gives an optimal performance. This design focuses on an acceleration of the token bucket algorithm taking advantage of a hardware programmable device. This kind of device allows us to achieve a faster and cheaper implementation.
Vemurafenib Inhibits Active PTK6 in PTEN-null Prostate Tumor Cells

Protein tyrosine kinase 6 (PTK6, also called BRK) is overexpressed and activated in human prostate cancer. Loss of the tumor suppressor PTEN, a frequent event in prostate cancer, leads to PTK6 activation at the plasma membrane and its oncogenic signaling. The small molecule inhibitor vemurafenib, also known as PLX4032, and its tool analog PLX4720 were designed to inhibit constitutively active BRAF V600E, yet they also have potent effects against PTK6. Vemurafenib is used in the treatment of metastatic melanoma, but its efficacy in prostate cancer has not been assessed. When activated at the plasma membrane, PTK6 promotes signaling through FAK, EGFR, and ERK1/2, and we show this can be blocked by vemurafenib. In addition, PTK6-mediated cell growth, migration, and invasion are inhibited upon vemurafenib administration. Using a flank xenograft model, vemurafenib treatment reduced tumor burden. Using saturation transfer difference NMR and molecular docking, we demonstrate that vemurafenib binds in the active site of PTK6, inhibiting its activation. These structural studies provide insight into the PTK6–vemurafenib complex, which can be utilized for further refinement chemistry, whereas functional studies demonstrate that active PTK6 is a viable drug target in prostate cancer.
Drimux Dynamic Rumor Influence Minimization with User Experience in Social Networks

With the taking off improvement of huge scale online social networks, online data sharing is getting to be omnipresent regular. Different data is engendering through online social networks including both the positive and negative. In this paper, we center around the negative data issues, for example, the online rumors. Rumor blocking is a difficult issue in expansive scale social networks. Vindictive rumors could cause mayhem in the public arena and henceforth should be obstructed as quickly as time permits in the wake of being recognized. In this paper, we propose a model of dynamic rumor impact minimization with client encounter (DRIMUX). We will probably limit the impact of the rumor (i.e., the quantity of clients that have acknowledged and sent the rumor) by blocking a specific subset of hubs.
UWB Propagation Measurements and Modelling in Large Indoor Environments

In this paper, we present the results of the measurements and modeling of the propagation channel for ultra wide band (UWB) communications in large indoor environments. The selected environment is a sports center of large dimensions ( $51 \times 26$ m, 7.4m height) without intermediate obstacles so that there is always direct path (DP) between TX and RX and UWB technology can be used for critical communications of sensors. On this environment, narrow band and ultra wide band measurements have been made with a high resolution pulse channel sounder (8ns) at the nominal frequency of 3.9 GHz. Narrow band measurements have been used to model path loss for the DP, while for UWB measurements, the power delay profile have been used to obtain the statistics of the multipath of the channel. Combining both models has been possible to obtain a detailed characterization of the environment. The results constitute a complete characterization of this type of indoor environments for UWB signals and can be easily extrapolated to other environments with similar characteristics, allowing the use of UWB technology for applications of critical communications for sensors and other devices.
Cuckoo Search Algorithm Optimization Approaches for Solving Economic Load Dispatch: A Review

Cuckoo search algorithm (CSA) is an effective and highly reliable swarm intelligence based optimization approach. It is a technique of determining the most efficient, low cost and reliable operation of a power system by dispatching the available electricity generation resources to supply the load on the system. This paper presents a comprehensive review of CSA application in Economic Load Dispatch (ELD) problem. This review will assist power system engineers with a view to enhancing the optimal operation of available thermal plants in electrical power systems.
Usiigaci: Instance-aware cell tracking in stain-free phase contrast microscopy enabled by machine learning

Stain-free, single-cell segmentation and tracking is tantamount to the holy grail of microscopic cell migration analysis. Phase contrast microscopy (PCM) images with cells at high density are notoriously difficult to segment accurately; thus, manual segmentation remains the de facto standard practice. In this work, we introduce Usiigaci, an all-in-one, semi-automated pipeline to segment, track, and visualize cell movement and morphological changes in PCM. Stain-free, instance-aware segmentation is accomplished using a mask regional convolutional neural network (Mask R-CNN). A Trackpy-based cell tracker with a graphical user interface is developed for cell tracking and data verification. The performance of Usiigaci is validated with electrotaxis of NIH/3T3 fibroblasts. Usiigaci provides highly accurate cell movement and morphological information for quantitative cell migration analysis.
Design and Implementation of WeChat Mini Program for University Dormitory Based on SOA

According to the actual demand of management on university dormitory, a WeChat mini program for university dormitory based on SOA which makes teachers and students manage dormitory conveniently and efficiently is proposed. The Web-Service is adopted to encapsulate back-end functions into a series of services for front-end calls. The WeChat mini program is used as a front-end development carrier to realize the management functions of university dormitory. Experiments show that the proposed system is stable and the working efficiency is greatly increased. The results show that the application system can capture the requirements of student dormitory management, with good scalability.
Global optimization for sparse solution of least squares problems

Finding solutions to least-squares problems with low cardinality has found many applications, including portfolio optimization, subset selection in statistics, and inverse problems in signal processing. Although most works consider local approaches that scale with high-dimensional problems, some others address its global optimization via mixed integer programming (MIP) reformulations. We propose dedicated branch-and-bound methods for the exact resolution of moderate-size, yet difficult, sparse optimization problems, through three possible formulations: cardinality-constrained and cardinality-penalized least-squares, and cardinality minimization under quadratic constraints. A specific tree exploration strategy is built. Continuous relaxation problems involved at each node are reformulated as -norm-based optimization problems, for which a dedicated algorithm is designed. The obtained certified solutions are shown to better estimate sparsity patterns than standard methods on simulated variable selection problems involving highly correlated variables. Problem instances selecting up to 24 components among 100 variables, and up to 15 components among 1000 variables, can be solved in less than 1000 s. Unguaranteed solutions obtained by limiting the computing time to 1s are also shown to provide competitive estimates. Our algorithms strongly outperform the CPLEX MIP solver as the dimension increases, especially for quadratically-constrained problems. The source codes are made freely available online.
An Efficient Method for Object Segmentation in Video Scenes

The system present a coherent, frame work for tracking multiple objects. A video locales is a sequences of image features that share similar features in the spatialtemporal domain videos. The main objective of the paper to track region of interest using segmentation. MRF&SLIC algorithm for the effective segmentation. (i.e) introduce new super pixel & MRF structures. By improving the segmentation accuracy, the accuracy of the objects tracking improved. It embeds the looks as auxiliary nodes and edges in the MRF structure can enhance the segmentation in one graph cut. In the addition experimental evaluation validate the superiority of the proposed approach over the state-ofthe art methods in both efficient and effectiveness.
Optimized Detection and Isolating Mobile Attacks Through SADEC Lite Protocol In Ad Hoc Networks

Four attacks namely data direct to the wrong p lace, energy control, identity group of representative, a nd colluding can be easily begin campaign against mult i hop wireless ad hoc networks. There is also possibi lity of Wormhole attack that may be launched by the aggressive elements class from multiple ends of a wireless sensor network against a set of target sen sor nodes. Moreover, a legitimate node comes under mistrust. As a result of the attack, the incapacita ted data-generating genuine sensor nodes are replaced w ith malicious nodes that will involve in further malevo lent activity against sensory resources. To better utili ze protocol for local monitoring by considerably increasing the number of nodes in a neighborhood th at can do monitoring. The results signify the need for distributed pattern recognition for detecting malic ious attacks in a timely and accurate manner
The perceived importance and objective measurement of walkability in the built environment rating

The built environment plays an important role in shaping physical activities and furthering a healthy lifestyle. An obesogenic environment, which promotes obesity through uninviting neighborhood design, can cause sedentary living and environmentally induced inactivity, particularly by reducing walking. Existing measures of walkability consider the distribution and features of potential destinations, but fail to account for key aspects of the built environment design, pedestrian preferences, or various reasons for walking. In this paper, we propose a new assessment method, the Perceived importance and Objective measurement of Walkability in the built Environment Rating (POWER), incorporating pedestrian preferences derived from a walking preference survey. By conducting a university campus-based case study, we developed a quantitative method, the customized analytic hierarchy process, to analyze data from the survey. The customized analytic hierarchy process was used to calculate the perceived importance of various factors. In addition, objective measurements were collected and processed from Geographic Information System (GIS) data and fieldwork. Using both perceived importance and objective measurements, we created a POWER map showing the most walkable and unwalkable places on campus. The outcome of this case study reveals the nuanced spatial variations with regard to walkability. The proposed integral measure creates an improved method for rating the walkability of the built environment. Future applications and limitations are also discussed.
Software Engineering Projects Analysis using Interactive Multimodal Graph Explorer - IMiGEr

This paper describes a visualization technique designed to help work with complex diagrams containing multiple types of nodes and edges, by using a combination of visual clutter reduction and graph exploration techniques. We show its application, including preliminary evaluation, on software engineering projects data gathered from various tools and repositories used for software development. An online tool implementing the technique and plans for its extension by a connected view of time perspective data are briefly presented.
Matroids, Matchings, and Fairness

The need for fairness in machine learning algorithms is increasingly critical. A recent focus has been on developing fair versions of classical algorithms, such as those for bandit learning, regression, and clustering. In this work we extend this line of work to include algorithms for optimization subject to one or multiple matroid constraints. We map out a series of results, showing optimal solutions, approximation algorithms, and hardness proofs depending on the specific flavor of the problem. Our algorithms are efficient and empirical experiments demonstrate that fairness can be achieved with a modest compromise to the overall objective value.
StreoScenNet: surgical stereo robotic scene segmentation

Surgical robot technology has revolutionized surgery toward a safer laparoscopic surgery and ideally been suited for surgeries requiring minimal invasiveness. Sematic segmentation from robot-assisted surgery videos is an essential task in many computer-assisted robotic surgical systems. Some of the applications include instrument detection, tracking and pose estimation. Usually, the left and right frames from the stereoscopic surgical instrument are used for semantic segmentation independently from each other. However, this approach is prone to poor segmentation since the stereo frames are not integrated for accurate estimation of the surgical scene. To cope with this problem, we proposed a multi encoder and single decoder convolutional neural network named StreoScenNet which exploits the left and right frames of the stereoscopic surgical system. The proposed architecture consists of multiple ResNet encoder blocks and a stacked convolutional decoder network connected with a novel sum-skip connection. The input to the network is a set of left and right frames and the output is a mask of the segmented regions for the left frame. It is trained end-to-end and the segmentation is achieved without the need of any pre- or post-processing. We compare the proposed architectures against state-of-the-art fully convolutional networks. We validate our methods using existing benchmark datasets that includes robotic instruments as well as anatomical objects and non-robotic surgical instruments. Compared with the previous instrument segmentation methods, our approach achieves a significant improved Dice similarity coefficient.
AS-EKF: A Delay Aware State Estimation Technique for Telepresence Robot Navigation

State estimation in dynamical telepresence systemsis very important in real-world applications as the true state of the robot is unspecified and sensors provide only a sequence of noisy measurements. In this research, we proposed a new technique of state estimation using delayed sensor measurements of a Telepresence robot for real-time navigation. An Augmented State Extended Kalman Filter (AS-EKF) is introduced to estimate the true position of the robot. The proposed algorithm was successfully tested in a real-environment experimental frameworkusing a state-of-the-art differential-drive telepresence robot. Our results show improvements of an average of more than 34% when compared to traditional EKF.
Learning Task-specific Activation Functions using Genetic Programming

Deep Neural Networks have been shown to be beneficial for a variety of tasks, in particular allowing for end-toend learning and reducing the requirement for manual design decisions. However, still many parameters have to be chosen manually in advance, also raising the need to optimize them. One important, but often ignored parameter is the selection of a proper activation function. In this paper, we tackle this problem by learning taskspecific activation functions by using ideas from genetic programming. We propose to construct piece-wise activation functions (for the negative and the positive part) and introduce new genetic operators to combine functions in a more efficient way. The experimental results for multi-class classification demonstrate that for different tasks specific activation functions are learned, also outperforming widely used generic baselines.
Deep 3D convolutional neural network for automatic cancer tissue detection using multispectral photoacoustic imaging

Multispectral photoacoustic (MPA) specimen imaging modality is proven successful in differentiating photoacoustic (PA) signal characteristics from a cancer and normal region. The oxy and de-oxy hemoglobin content in a human tissue captured in the MPA data are the key features for cancer detection. In this study, we propose to use deep 3D convolution neural network trained on the thyroid MPA dataset and tested on the prostate MPA dataset to evaluate this potential. The proposed algorithm first extracts the spatial, spectral, and temporal features from the thyroid MPA image data using 3D convolutional layers and detects cancer tissue using the logistic function, the last layer of the network. The model achieved an AUC (area under the curve) of the ROC (receiver operating characteristic) curve of 0.72 on the prostate MPA dataset.