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This capsule presents a demonstration of shared, rerunnable and reproducible research results in STATA. It captures the code and data published by Olivier Deschênes and Michael Greenstone in 2011, with the article "Climate Change, Mortality, and Adaptation: Evidence from Annual Fluctuations in Weather in the US." in American Economic Journal: Applied Economics. The research material (data and code) were copied from the American Economic Journal website at https://www.aeaweb.org/articles?id=10.1257/app.3.4.152
Data Types:
  • Software/Code
This software provide a simple way to extract traditional features of fingerprint image as show in the following list. [1] Segmentation Mask [2] Orientation Field [3] Singular Area (Core, Delta) [4] Enhanced Image [5] Skeleton Image [6] Minutiae Location and Direction This software may not have as good performance as you can find in Commercial off-the-shelf (COTS) software or in a state-of-the-art algorithm. However, I would like to provide newcomers with a basic tool and playground to enjoy testing their new ideas. Thank you very much for your visit and for playing with this capsule.
Data Types:
  • Software/Code
CAPSULE: Code for the TMI special Issue article: PAPER: 'Deep Learning Computed Tomography: Learning Projection-Domain Weights from Image Domain in Limited Angle Problems' ABSTRACT: 'In this paper, we present a new deep learning framework for 3-D tomographic reconstruction. To this end, we map filtered back-projection-type algorithms to neural networks.However, the back-projection cannot be implemented as a fully connected layer due to its memory requirements. To overcome this problem, we propose a new type of cone-beam back-projection layer, efficiently calculating the forward pass. We derive this layer’s backward pass as a projection operation.Unlike most deep learning approaches for reconstruction, our new layer permits joint optimization of correction steps in volume and projection domain. Evaluation is performed numerically on a public dataset in a limited angle setting showing a consistent improvement over analytical algorithms while keeping the same computational test-time complexity by design. In the region of interest, the peak signal-to-noise ratio has increased by23%. In addition, we show that the learned algorithm can be interpreted using known concepts from cone beam reconstruction:the network is able to automatically learn strategies such as compensation weights and apodization windows.' MODEL & DATA: Pretrained model is made available. Training data is available under https://www.aapm.org/grandchallenge/lowdosect/#trainingData IMPLEMENTATION: This capsule is based on the open-sourced code under [1] which belongs to the TMI special Issue paper. We are using the compiling capability of PYRO-NN [2]. The Tensorflow wheele which is stored within the capsule, is a built of Tensorflow 1.12 including the Kernel implementations from [1] built within the Tensorflow wheele using PYRO-NN_Layers [3]. The Tensorflow wheele is compiled for Cuda 9.2 and CuDNN 7.1 with a minimum compute capability starting from 3.7 . [1] https://github.com/ma0ho/Deep-Learning-Cone-Beam-CT/ [2] https://github.com/csyben/PYRO-NN [3] https://github.com/csyben/PYRO-NN-Layers
Data Types:
  • Software/Code
This work addresses the problem of coherently detecting turbo coded orthogonal frequency division multiplexed (OFDM) signals, transmitted through frequency selective Rayleigh fading channels. A single transmit and receive antenna is assumed. The channel output is distorted by a carrier frequency and phase offset, besides additive white Gaussian noise (AWGN). A new frame structure for OFDM, consisting of a known preamble, cyclic prefix and data is proposed. A filter that is matched to the preamble is used for start-of-frame (SoF) detection. A two-step ML detector for frequency-offset estimation is proposed, which has a much lower complexity compared to the single step ML detector. Turbo decoding and data interleaving is used to significantly enhance the bit-error-rate (BER) performance of the coherent receiver. Simulation results show that the BER performance of the practical coherent receiver is close to the ideal coherent receiver, for data length equal to the preamble length, and attains a BER of about 4x10 ^{-5} at an SNR of just 8 dB. It is also shown that the probability of erasure is less than 10 ^{-6} for a preamble length of 512 QPSK symbols. The proposed algorithms are well suited for implementation on a DSP-platform.
Data Types:
  • Software/Code
Recent advances in sensor technologies have led to an extraordinary growth of data sources and streaming applications. Usually, these applications require data elements, arriving into the system, to be processed within a time threshold (deadline). Moreover, the processing may involve the execution of complex simulations or control algorithms that are typically computationally intensive and that are often executed as batch processes. For instance, a smart electric grid application for charging of electric vehicle batteries of an electric area requires to gather charging requests in order to subsequently compute an optimized scheduling algorithm that preserves a number of constraints and satisfies users’ preferences. That scheduling has to be done within a time threshold that is typically around 15 minutes. Understanding how such streaming content can be processed within some time threshold remains a non-trivial and important research topic. In our paper [1], we investigated how a cloud-based computational infrastructure can autonomically respond to such streaming content, offering Quality of Service guarantees. We proposed an autonomic controller (based on feedback control and queueing theory) to elastically provision virtual machines to meet performance targets associated with a particular data stream. In this capsule, we provide the code for our autonomic controller. [1] R. Tolosana-Calasanz, J. Diaz-Montes, O. F. Rana and M. Parashar, "Feedback-Control & Queueing Theory-Based Resource Management for Streaming Applications," in IEEE Transactions on Parallel and Distributed Systems, vol. 28, no. 4, pp. 1061-1075, 1 April 2017.
Data Types:
  • Software/Code
This matlab file can be used to calculate the working distance of the miniaturized OCT probes presented in "Etching-enabled extreme miniaturization of optical coherence tomography probes with controlled focusing capabilities" by A. Abid et al. submitted in Journal of Biomedical Optics (2019). The various fiber constants are provided along with the used reference. Users can set accordingly the spacer fiber length (CL), GRIN fiber length (Lg), and protective fiber length (CLp) to reproduce our results. They can also use the code to design their own probes.
Data Types:
  • Software/Code
Chemical creativity in the design of synthetic chemical entities (NCEs) with druglike properties has been the domain of medicinal chemists. Here, we explore the capability of a chemistry-savvy machine intelligence to generate synthetically accessible molecules. DINGOS (Design of Innovative NCEs Generated by Optimization Strategies) is a virtual assembly method that combines a rule-based approach with a machine learning model trained on successful synthetic routes described in chemical patent literature. This unique combination enables a balance between ligand-similarity based generation of innovative compounds by scaffold hopping and forward-synthetic feasibility of the designs. In a prospective proof-of-concept application, DINGOS successfully produced sets of de novo designs for four approved drugs that were in agreement with the desired structural and physicochemical properties. Target prediction indicated more than 50% of the designs as biologically active. Four selected computer-generated compounds were successfully synthesized in accordance with the synthetic route proposed by DINGOS. The results of this study demonstrate the capability of machine learning models to capture implicit chemical knowledge from chemical reaction data and suggest feasible syntheses of new chemical matter.
Data Types:
  • Software/Code
Codes for the following paper: "A Generalized Method for Binary Optimization: Convergence Analysis and Applications". To reproduce the results of our ABMO-S method on the dataset CIFAR-10, please run 'demo_ABMO'. Then the MAP, Precision@500 and Precision-Recall curve with code lengths of 32 to 96 will be presented.
Data Types:
  • Software/Code
This capsule provides the analytical equations for current and transconductances of a long-channel double gate (DG) Junction FET (JFET) in OCTAVE ( Matlab like ) code. The mobile charges are calculated using a Lambert-W approximate function as described in the paper. The model and TCAD simulation data are also plotted demonstrating good agreement and the continuity of the model in all regions of operation (using the same equations from linear to saturation and sub- to above-threshold).
Data Types:
  • Software/Code
Simultaneous mesoscopic and two-photon imaging of neuronal activity in cortical circuits Daniel Barson1,2,3,*, Ali S. Hamodi1,*, Xilin Shen4, Gyorgy Lur 1,5, R. Todd Constable2,4,6, Jessica A. Cardin1,7, Michael C. Crair1,7,8,^, Michael J. Higley1,7,9,^ Spontaneous and sensory-evoked activity propagates across varying spatial scales in the mammalian cortex, but technical challenges have limited conceptual links between the function of local neuronal circuits and brain-wide network dynamics. We present a method for simultaneous cellular-resolution two-photon calcium imaging of a local microcircuit and mesoscopic widefield calcium imaging of the entire cortical mantle in awake mice. Our multi-scale approach involves a microscope with an orthogonal axis design where the mesoscopic objective is oriented above the brain and the two-photon objective is oriented horizontally, with imaging performed through a microprism. We also introduce a viral transduction method for robust and widespread gene delivery in the mouse brain. These approaches allow us to identify the behavioral state-dependent functional connectivity of pyramidal neurons and vasoactive intestinal peptide (VIP)-expressing interneurons with long-range cortical networks. Our imaging system provides a powerful strategy for investigating cortical architecture across a wide range of spatial scales.
Data Types:
  • Software/Code
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