22 skills found
VITA-Group / TENAS[ICLR 2021] "Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective" by Wuyang Chen, Xinyu Gong, Zhangyang Wang
xymb-endcrystalme / LinearRegionFileFormatToolsNo description available
RealTriassic / LinearPaperA fork of Paper that implements the experimental Linear region file in the Minecraft dedicated server.
ShelvanLee / XFEM# XFEM_Fracture2D ### Description This is a Matlab program that can be used to solve fracture problems involving arbitrary multiple crack propagations in a 2D linear-elastic solid based on the principle of minimum potential energy. The extended finite element method is used to discretise the solid continuum considering cracks as discontinuities in the displacement field. To this end, a strong discontinuity enrichment and a square-root singular crack tip enrichment are used to describe each crack. Several crack growth criteria are available to determine the evolution of cracks over time; apart from the classic maximum tension (or hoop-stress) criterion, the minimum total energy criterion and the local symmetry criterion are implemented implicitly with respect to the discrete time-stepping. ### Key features * *Fast:* The stiffness matrix and the force vector (i.e. the equations' system) and the enrichment tracking data structures are updated at each time step only with respect to the changes in the fracture topology. This ultimately results in the major part of the computational expense in the solution to the linear system of equations rather than in the post-processing of the solution or in the assembly and updating of the equations. As Matlab offers fast and robust direct solvers, the computational times are reasonably fast. * *Robust.* Suitable for multiple crack propagations with intersections. Furthermore, the stress intensity factors are computed robustly via the interaction integral approach (with the inclusion of the terms to account for crack surface pressure, residual stresses or strains). The minimum total energy criterion and the principle of local symmetry are implemented implicitly in time. The energy release rates are computed based on the stiffness derivative approach using algebraic differentiation (rather than finite differencing of the potential energy). On the other hand, the crack growth direction based on the local symmetry criterion is determined such that the local mode-II stress intensity factor vanishes; the change in a crack tip kink angle is approximated using the ratio of the crack tip stress intensity factors. * *Easy to run.* Each job has its own input files which are independent form those of all other jobs. The code especially lends itself to running parametric studies. Various results can be saved relating to the fracture geometry, fracture mechanics parameters, and the elastic fields in the solid domain. Extensive visualisation library is available for plotting results. ### Instructions 1. Get started by running the demo to showcase some of the capabilities of the program and to determine if it can be useful for you. At the Matlab's command line enter: ```Matlab >> RUN_JOBS.m ``` This will execute a series of jobs located inside the *jobs directory* `./JOBS_LIBRARY/`. These jobs do not take very long to execute (around 5 minutes in total). 2. Subsequently, you can pick one of the jobs inside `./JOBS_LIBRARY/` by defining the job title: ```Matlab >> job_title = 'several_cracks/edge/vertical_tension' ``` 3. Then you can open all the relevant scripts for this job as follows: ```Matlab >> open_job ``` The following input scripts for the *job* will be open in the Matlab's editor: 1. `JOB_MAIN.m`: This is the job's main script. It is called when executing `RUN_JOB` (or `RUN_JOBS`) and acts like a wrapper. Notably, it can serve as a convenient interface to run parametric studies and to save intermediate simulation results. 2. `Input_Scope.m`: This defines the scope of the simulation. From which crack growth criteria to use, to what to compute and what results to show via plots and/or movies. To put it simply, the script is a bunch of "switches" that tell the program what the user wants to be done. 3. `Input_Material.m`: Defines the material's elastic properties in different regions or layers (called "phases") of the computational domain. Moreover, it defines the fracture toughness of the material (assumed to be constant in all material phases). 4. `Input_Crack.m`: Defines the initial crack geometry. 5. `Input_BC.m`: Defines boundary conditions, such as displacements, tractions, crack surface pressure (assumed to be constant in all cracks), body loads (e.g. gravity, pre-stress or pre-strain). 6. `Mesh_make.m`: In-house structured mesh generator for rectangular domains using either linear triangle or bilinear quadrilateral elements. It is possible to mesh horizontal layers using different mesh sizes. 7. `Mesh_read.m`: Gmsh based mesh reader for version-1 mesh files. Of course you can use your own mesh reader provided the output variables are of the correct format (see later). 8. `Mesh_file.m`: Specifies the mesh input file (.msh). At the moment, only Gmsh mesh files of version-1 are allowed. ### Mesh_file.m A mesh file needs to be able to output the following data or variables: * `mNdCrd`: Node coordinates, size = `[nNdStd, 2]` * `mLNodS`: Element connectivities, size = `[nElemn,nLNodS]` * `vElPhz`: Element material phase (or region) ID's, size = `[nElemn,1]` * `cBCNod`: cell of boundary nodes, cell size = `{nBound,1}`, cell element size = `[nBnNod,2]` Example mesh files are located in `./JOBS_LIBRARY/`. Gmsh version-1 file format is described [here](http://www.manpagez.com/info/gmsh/gmsh-2.4.0/gmsh_60.php). ### Additional notes * global variables are defined in `.\Routines_AuxInput\Declare_Global.m` * External libraries are `.\Other_Libs\distmesh` and `.\Other_Libs\mesh2d` ### References Two external meshing libraries are used for the local mesh refinement and remeshing at the crack tip during crack propagation or prior to a crack intersection with another crack or with a boundary of the domain. Specifically, these libraries, which are located in `.\Other_Libs\`, are the following: * [*mesh2d*](https://people.sc.fsu.edu/~jburkardt/m_src/mesh2d/mesh2d.html) by Darren Engwirda * [*distmesh*](http://persson.berkeley.edu/distmesh/) by Per-Olof Persson and Gilbert Strang. ### Issues and Support For support or questions please email [sutula.danas@gmail.com](mailto:sutula.danas@gmail.com). ### Authors Danas Sutula, University of Luxembourg, Luxembourg. If you find this code useful, we kindly ask that you consider citing us. * [Minimum energy multiple crack propagation](http://hdl.handle.net/10993/29414)
benjaminirving / MaskSLICSimple linear iterative clustering (SLIC) in a region of interest (ROI)
xymb-endcrystalme / LinearPaperPaper with Linear region file format
max-andr / Provable Robustness Max Linear RegionsProvable Robustness of ReLU networks via Maximization of Linear Regions [AISTATS 2019]
VITA-Group / TEGNAS"Understanding and Accelerating Neural Architecture Search with Training-Free and Theory-Grounded Metrics" by Wuyang Chen, Xinyu Gong, Yunchao Wei, Humphrey Shi, Zhicheng Yan, Yi Yang, and Zhangyang Wang
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JohannesBuchner / Condor OptimizationCONDOR (COnstrained, Non-linear, Direct, parallel Optimization using trust Region method for high-computing load function) allows continuous parameter optimization
jonasrauber / Linear Region AttackA powerful white-box adversarial attack that exploits knowledge about the geometry of neural networks to find minimal adversarial perturbations without doing gradient descent
DanIsraelMalta / Optimal FIR Filter DesignerGiven cutoff frequency, sampling frequency and filter order application will return an all-pole, near-linear phase low pass filter with optimized magnitude response in the pass-band region.
JWatawang / MDLSR RFMNew Insights into Multi-focus Image Fusion: A Fusion Method Based on Multi-dictionary Linear Sparse Representation and Region Fusion Model
ironhide23586 / CPU Connected Component LabellingThis is a fast custom algorithm having O(n) linear time & O(n) memory complexity implemented on the CPU for solving the famous connected component labelling problem. The algorithm implemented here, takes the image and the label of the connected region and spits out the number of such regions in the image.
atifferoz / Snowfall Prediction Using Machine LearningIn this research, Machine learning algorithms like Long-Short Term Model (LSTM), Decision tree, Random Forest and XG Boost were used as a classifier to improve the accuracy of Snowfall prediction for the region of Boston. The geographical parameters like Humidity, Temperature, Wind-speed, Precipitation, Sea-level, Dew-point and Visibility were used as independent variables. Before the modeling phase, Data lagging was performed for 2 step followed by Exploratory Data Analysis was using techniques like Multiple Linear Regression, Correlation Plot and variable importance plot. Feature Selection was also executed using Logistic Regression and Boruta algorithm. Experimental evaluations resulted in the highest accuracy shown by LSTM with an accuracy of 89.98%. In terms of sensitivity, Random Forest outperformed other classifier models. Whereas, Decision tree and XG Boost resulted well in the overall performance of prediction with respect to other evaluation metrics. The results of this research added to the contribution of the knowledge in weather prediction in the domain of Snowfall for the machine learning industry.
catproof / Voxel Region Growing Approach For Detecting Support Surfaces And Supported Objects In Point CloudsDeveloped an algorithm which uses voxels and a region-growing technique for detecting support surfaces in point clouds. It is linear both in the number of voxels used and the number of points in the cloud. It uses the results from the support surface detection to detect supported objects using a k-nearest neighbors region-growing algorithm.
johannesbrust / LTR LECLarge-Scale Trust-Region Methods For Linear Equality Constrained Optimization
magamba / Linear RegionsDiscover and enumerate linear regions and compute model function variation for image-based Deep Networks.
manu-prakash-choudhary / House PricepredictionThis is a ML model made upon Linear Regression to predict the possible value of house buying in banglore providing its region, area, and number of bedrooms. It is really fun project💙, I gained lots of knowledge while going over this.
iftekarpatel / Credit Consumption Prediction ChallengeProblem Statement Understanding the consumption pattern for credit cards at an individual consumer level is important for customer relationship management. This understanding allows banks to customize for consumers and make strategic marketing plans. Thus it is imperative to study the relationship between the characteristics of the consumers and their consumption patterns. Here the dataset is of some XYZ Bank that has given a sample of their customers, along with their details like age, gender and other demographics. Also shared are information on liabilities, assets and history of transactions with the bank for each customer. In addition to the above, data has been provided for a particular set of customers' credit card spend in the previous 3 months (April, May & June) and their expected average spend in the coming 3 months (July, August & September). The average spend for different set of customers needs to be predicted in the test set for the coming 3 months. Data Dictionary id Unique ID for every Customer account_type Account Type – current or saving gender Gender of customer-M or F age Age of customer region_code Code assigned to region of residence (has order) cc_cons_apr Credit card spend in April dc_cons_apr Debit card spend in April cc_cons_may Credit card spend in May dc_cons_may Debit card spend in May cc_cons_jun Credit card spend in June dc_cons_jun Debit card spend in June cc_count_apr Number of credit card transactions in April cc_count_may Number of credit card transactions in May cc_count_jun Number of credit card transactions in June dc_count_apr Number of debit card transactions in April dc_count_may Number of debit card transactions in May dc_count_jun Number of debit card transactions in June card_lim Maximum Credit Card Limit allocated personal_loan_active Active personal loan with other bank vehicle_loan_active Active Vehicle loan with other bank personal_loan_closed Closed personal loan in last 12 months vehicle_loan_closed Closed vehicle loan in last 12 months investment_1 DEMAT investment in june investment_2 fixed deposit investment in june investment_3 Life Insurance investment in June investment_4 General Insurance Investment in June debit_amount_apr Total amount debited for April credit_amount_apr Total amount credited for April debit_count_apr Total number of times amount debited in april credit_count_apr Total number of times amount credited in april max_credit_amount_apr Maximum amount credited in April debit_amount_may Total amount debited for May credit_amount_may Total amount credited for May credit_count_may Total number of times amount credited in May debit_count_may Total number of times amount debited in May max_credit_amount_may Maximum amount credited in May debit_amount_jun Total amount debited for June credit_amount_jun Total amount credited for June credit_count_jun Total number of times amount credited in June debit_count_jun Total number of times amount debited in June max_credit_amount_jun Maximum amount credited in June loan_enq Loan enquiry in last 3 months (Y or N) emi_active Monthly EMI paid to other bank for active loans cc_cons (Target) Average Credit Card Spend in next three months Evaluation Metric Submissions are evaluated on Root Mean Squared Logarithmic Error(RMSLE) between the predicted credit card consumption and the observed target. Approach At first, I conducted exploratory data analysis of the dataset to gain a deeper understanding of the data. Next, I did feature engineering to create new variables.Then I tried some scikit-learn models out of which XGBoost and Random Forest gave good RMSLE. In the end I created a stacked model of those two with Linear Regression and it has been selected as the final model. RMSLE: 115.02
