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3,648 results for “induction”
Supporting Material to "SIFA: Exploiting Ineffective Fault Inductions on Symmetric Cryptography"
<p>Supplementary material to the paper "SIFA: Exploiting Ineffective Fault Inductions on Symmetric Cryptography" by Christoph Dobraunig, Maria Eichlseder, Thomas Korak, Stefan Mangard, Florian Mendel, and Robert Primas (CHES 2018, <a href="https://eprint.iacr.org/2018/071">https://eprint.iacr.org/2018/071</a>).</p> <p> </p> <p>Ineffectively faulted AES Ciphertexts for different platforms and with different fault countermeasures in place (including infection-based configurations). Files:</p> <ul> <li>*/ct_correct.txt: AES ciphertexts where a fault was induced during the encryption, but did not change the ciphertext (decimal, CSV, 1 row per ciphertext)</li> <li>*/round_keys.txt: 11 expanded AES round keys used for all ciphertexts (decimal, CSV, 1 row per round key)</li> <li>*/sei_hardware.dat: Results of the statistical key-recovery evaluation. Row i lists the SEI of the right key, the SEI of the best wrong key, and the rank of the correct key after using 4*i of the ciphertexts in ct_correct.txt.</li> </ul> <p>Target platforms and setups are described in more detail in the paper.</p>
Supplemental Data from: Association Between Mutation Clearance After Induction Therapy and Outcomes in Acute Myeloid Leukemia
<p>Supplemental Data for:</p> <p>Association Between Mutation Clearance After Induction Therapy and Outcomes in Acute Myeloid Leukemia. JAMA. 2015<br> (Paper available at: <a href="https://www.ncbi.nlm.nih.gov/pubmed/26305651">PubMed</a> <a href="https://jamanetwork.com/journals/jama/fullarticle/2429715">JAMA</a>)<br> <br> Authors: Jeffery M. Klco, M.D., Ph.D.* Christopher A. Miller, Ph.D.*, Malachi Griffith, Ph.D., Allegra Petti, Ph.D., David H. Spencer, M.D., Ph.D., Shamika Ketkar-Kulkarni, M.S., Lukas D. Wartman, M.D., Matthew Christopher, M.D., Ph.D., Tamara L. Lamprecht, B.S., Nicole M. Helton, B.S., Eric J. Duncavage, M.D., Jacqueline E. Payton, M.D., Ph.D., Jack Baty, B.A., Sharon E. Heath, Obi L. Griffith, Ph.D., Dong Shen, Ph.D., Jasreet Hundal, M.S., Gue Su Chang, Ph.D., Robert Fulton, M.S., Michelle O'Laughlin, B.S., Catrina Fronick, B.S., Vincent Magrini, Ph.D., Ryan T. Demeter, B.E., David E. Larson, Ph.D., Shashikant Kulkarni, M.S., Ph.D., Bradley A. Ozenberger, Ph.D., John S. Welch, M.D., Ph.D., Matthew J. Walter, M.D., Timothy A. Graubert, M.D., Peter Westervelt, M.D., Ph.D., Jerald P. Radich, M.D., Daniel C. Link, M.D., Elaine R. Mardis, Ph.D., John F. DiPersio, M.D., Ph.D., Richard K. Wilson, Ph.D., and Timothy J. Ley</p>
Traffic forecasting with Virtual Induction Loops - SUMO simulation dataset
<p>This repository is associated with my doctoral dissertation titled, "<strong>Smartphone based applications for Road Traffic Telematics</strong>". In particular this repository serves as the basis of Chapter 7 titled, "<strong>Traffic forecasting with Virtual Induction Loops (VIL)</strong>". The basic idea is to validate a traffic forecasting system which uses machine learning techniques on the simulation of real traffic flows on a real intersection in the City of Turin. This dataset contains simulation output from SUMO software for 56 real days between the months of October-2017 to April-2018. Details about these days are available in my thesis. For each day, 3 output files are available. Here is the description and naming convention:</p> <ol> <li>M1_100seed_100pr_dump.csv (This is the data dump file from SUMO. It contains flows of every single vehicle that was simulated. Naming convention is day_seed_vilPenetrationRate_dump.csv)</li> <li>M1_100seed_ilNorth_100pr.xml (This is the output from a simulated induction loop for Northbound traffic. Naming convention is day_seed_ilNorth_vilPenetrationRate.xml)</li> <li>M1_100seed_ilSouth_100pr.xml (This is the output from a simulated induction loop for Southbound traffic. Naming convention is day_seed_ilSouth_vilPenetrationRate.xml)</li> </ol> <p>For further details, please refer to my thesis.</p>
Freeway Inductive Loop Detector Dataset for Network-wide Traffic Speed Prediction
<p>The data is collected by the inductive loop detectors deployed on freeways in Seattle area. The freeways contain I-5, I-405, I-90, and SR-520. This data set contains spatiotemporal speed information of the freeway system. At each milepost, the speed information collected from main lane loop detectors in the same direction are averaged and integrated into 5 minutes interval speed data. The raw data is provided by Washington Start Department of Transportation (WSDOT) and processed by the <a href="http://www.uwstarlab.org/">STAR Lab</a> in the University of Washington according to data quality control and data imputation procedures [1][2]. </p> <p>The data file is a pickle file that can be easily read using the read_pickle() function in the Pandas package. The data forms as a matrix and each cell of the matrix is speed value for the specific milepost and time period. The horizontal header of the data set denotes the milepost and the vertical header indicates the timestamps. For more information on the definition of milepost, please refer to this <a href="http://data.wsdot.wa.gov/traffic/">website</a>.</p> <p>This data set been used for traffic prediction tasks in several research studies [3][4]. For more detailed information about the data set, you can also refer to this <a href="https://github.com/zhiyongc/Seattle-Loop-Data">link</a>.</p> <p><strong>References</strong>:</p> <p>[1]. Henrickson, K., Zou, Y., & Wang, Y. (2015). Flexible and robust method for missing loop detector data imputation. <em>Transportation Research Record</em>, <em>2527</em>(1), 29-36.</p> <p>[2]. Wang, Y., Zhang, W., Henrickson, K., Ke, R., & Cui, Z. (2016). <em>Digital roadway interactive visualization and evaluation network applications to WSDOT operational data usage</em> (No. WA-RD 854.1). Washington (State). Dept. of Transportation.</p> <p>[3]. Cui, Z., Ke, R., & Wang, Y. (2018). Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction. <em>arXiv preprint arXiv:1801.02143</em>.</p> <p>[4]. Cui, Z., Henrickson, K., Ke, R., & Wang, Y. (2018). Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting. <em>arXiv preprint arXiv:1802.07007</em>.</p>
Figure 4 in Induction of histopathological lesions in renal tissue of the fish Labeo rohita upon exposure to municipal wastewater of Tung Dhab Drain, Amritsar, India
Figure 4. Light micrographs of histological sections of kidney taken from fish L. rohita exposed to municipal wastewater concentrations for exposure durations of 15 (A; 35.4%), 30 (B; 17.7%, C; 26.6%), 60 (D; 26.6%, E; 35.4%) days, and recovery experiments of 60 days (F; 35.4%). The histopathological alterations were marked by () occlusion of tubular lumen; () hyaline droplet degeneration; () dilation of glomerular capillaries; () reduction of Bowman's space; () melanomacrophage centers; () nuclear hypertrophy; () cellular hypertrophy; () cytoplasmic vacuolation in the interrenal cells; () necrosis. Magnification 100×.
Figure 3 in Induction of histopathological lesions in renal tissue of the fish Labeo rohita upon exposure to municipal wastewater of Tung Dhab Drain, Amritsar, India
Figure 3. Kidney mean DTC values in fish L. rohita exposed to municipal wastewater for durations of 15 days (a), 30 days (b), 60 days (c) when compared to control, and (d) subjected to recovery experiments for 60 days and compared with treated group. Values are mean ± SE (vertical bars); means followed by different letters are significantly different from each other (Tukey's post-hoc test, P ≤ 0.01).
Figure 2 in Induction of histopathological lesions in renal tissue of the fish Labeo rohita upon exposure to municipal wastewater of Tung Dhab Drain, Amritsar, India
Figure 2. Histological sections of kidney of fish L. rohita taken as control. A. Posterior kidney showing renal corpuscle formed by Bowman's capsule (BC), Bowman's space (BS), and Glomerulus (G). PT: proximal tubule; distal tubule. B. Anterior kidney showing chromaffin cells (CC) and interrenal cells (IC). Magnification 100×.
Figure 1 in Induction of histopathological lesions in renal tissue of the fish Labeo rohita upon exposure to municipal wastewater of Tung Dhab Drain, Amritsar, India
Figure 1. Map showing Tung Dhab Drain and Hudiara Drain. (a) The sampling site is marked by a star (); origin of drains is shown by (); confluence of Tung Dhab Drain and Hudiara Drain is marked by (). (b) Map showing main industries and sewer outfalls along Tung Dhab Drain. () indicates sewer outfalls; () metal foundries; () paper mill; () food; () leather; () chemical industries. Source: Adapted from Google Earth Maps, accessed August 2014.
Figure 1 in From leaves to inflorescences: Gall induction of Iatrophobia brasiliensis Rübsaamen, 1915 on inflorescences of Manihot caerulescens Pohl (Euphorbiaceae) during the dry season
Figure 1 Galls induced by I. brasiliensis on M.caerulescens (a) galls on the stem; (b) galls on the leaves and (c-d) galls on the inflorescences found in the Serra da Bandeira, Barreiras, Bahia, Brazil.
Figure 1 in Induction of abnormal sperm heads in small mammals under chronic ionizing radiation
Figure 1. Radiation pollution effects after the Chernobyl NPS disaster on the abnormal sperm heads frequency in tundra voles (Microtus oeconomus Pall., n = 56) (A), bank voles (Myodes glareolus Schreb., n = 51) (B), and field mice (Apodemus agrarius Pall., n = 117) (C).
Figure 6 in Osmotic induction of stress proteins in nemerteans
Figure 6. (a) SP70 and (b) SP90 concentrations in Paranemertes peregrina during exposure to 34‰ (control), and 29 and 24‰ (hyposmotic) conditions for 0, 2, 4, 6, 12, 18 and 24 h. Values (pg Mg21) are expressed as means¡standard error.
Figure 4 in Osmotic induction of stress proteins in nemerteans
Figure 4. (a) SP70 and (b) SP90 concentrations in Paranemertes peregrina during exposure to 34‰ (control), and 39 and 44‰ (hyperosmotic) conditions for 0, 2, 4, 6, 12, 18 and 24 h. Values (pg Mg21) are expressed as means¡standard error.
Figure 1 in Osmotic induction of stress proteins in nemerteans
Figure 1. Weight changes in Paranemertes peregrina during exposure to 34‰ (control), and 39 and 44‰ (hyperosmotic) conditions for 0, 2, 4, 6, 12, 18 and 24 h. Values (%) are expressed as means¡standard error.
Figure 5 in Osmotic induction of stress proteins in nemerteans
Figure 5. Western immunoblot of SP70 and SP90 in Paranemertes peregrina during exposure to 34‰ (control), and 29 and 24‰ (hyposmotic) conditions for 2 and 18 h.
Figure 2 in Osmotic induction of stress proteins in nemerteans
Figure 2. Weight changes in Paranemertes peregrina during exposure to 34‰ (control), and 29 and 24‰ (hyposmotic) conditions for 0, 2, 4, 6, 12, 18 and 24 h. Values (%) are expressed as means¡standard error.
Fig. 1 in Anesthetic induction and recovery of Hippocampus reidi exposed to the essential oil of Lippia alba
Fig. 1. Blood glucose levels of seahorses transported in plastic bags (one seahorse per bag) for 4 or 24 h. N= 10. BT= Before transport; Control= only water; EO = essential oil of L. alba previously diluted in ethanol (1:10) 15 µL L-1. * significantly different from before transport using two-way ANOVA and Tukey's test (P <0.05). + significantly different from control group at the same time of transport using twoway ANOVA and Tukey's test (P <0.05).
Data for: Induction of C4 genes during de-etiolation of Gynandropsis gynandra evolved through changes in cis allowing integration into ancestral C3 gene regulatory networks
<p>C4 photosynthesis has evolved repeatedly and in doing so repurposed existing enzymes to drive a carbon pump that limits the oxygenation reaction of RuBisCO. C4 proteins accumulate to levels matching those of the photosynthetic apparatus, and to allow this gene expression must be modified over evolutionary time. To better understand this rewiring of gene expression we undertook RNA-SEQ and <span>DNaseI</span>-SEQ on de-etiolating seedlings of C4 <em>Gynandropsis gynandra</em> which is evolutionarily proximate to C3 <em>A. thaliana</em>. Changes in chloroplast ultrastructure and C4 gene expression in <em>G. gynandra</em> were coordinated and rapid. C3 and C4 photosynthesis genes showed similar induction patterns, but C4 genes from <em>G. gynandra</em> were more strongly induced than orthologs from <em>A. thaliana</em>. The cistrome of <em>G. gynandra</em> was enriched in TGA, TCP and homeodomain binding sites. Furthermore,<em> in vivo</em> binding data in <em>G. gynandra</em> highlighted TGA and homeodomain as well as light responsive elements such as G- and I-box motifs as being associated with the rapid increase in transcripts derived from C4 genes. Although promoters of <em>PPDK</em> and <em>ASP1</em> from <em>G. gynandra</em> contained distinct light responsive elements, promoters from both <em>A. thaliana</em> and <em>G. gynandra</em> allowed high expression. Deletion analysis of the <em>Ppa6</em> gene from <em>G. gynandra</em> showed that regions containing G- and I-boxes were necessary for high expression. The data support a model in which accumulation of transcripts derived from C4 genes in leaves of <em>G. gynandra</em> is enhanced compared with homologs in <em>A. thaliana</em> because a variety of modifications in <em>cis</em> allowed integration into ancestral transcriptional networks.</p>
SMT-Solving Induction Proofs of Inequalities Benchmarking Repository
<p>This repository contains the full list of files and the benchmarking results that were used in the benchmarking processes described in the paper:<br> A.K. Uncu, J.H. Davenport and M. England. "SMT-Solving Induction Proofs of Inequalities". Proceedings of the 7th International Workshop on Satisfiability Checking and Symbolic Computation (SC^2 2022). </p> <p>The files are split in three branches. The Mathematica and Maple files include the calls that were made to the respective computer algebra systems, and the smt2 files are the ones used by the considered SMT solvers: Z3, CVC5 and Yices.</p> <p>The Benchmarking Results cvc has the results. The columns record the file names, the satisfiability outcome of the calls, then the times (in seconds) of the respective programmes. Any empty box (which the Maple:-RegularChains column has) would mean that the implementation does not accept that sort of input (this is due to rational functions - see the paper for details). Any time over 1200 seconds would mean that the program times out and the outcome of the question was not found in the given time.</p> <p> </p> <p> </p>
Induction of Sis1 promotes fitness but not feedback in the heat shock response
<div class="page"> <div class="layoutArea"> <div class="column"> <p>The heat shock response (HSR) controls expression of molecular chaperones to maintain protein homeostasis. Previously, we proposed a feedback loop model of the HSR in which heat-denatured proteins sequester the chaperone Hsp70 to activate the HSR, and subsequent induction of Hsp70 deactivates the HSR. However, recent work has implicated newly synthesized proteins (NSPs) – rather than unfolded mature proteins – and the Hsp70 co-chaperone Sis1 in HSR regulation, yet their contributions to HSR dynamics have not been determined. Here we generate a new mathematical model that incorporates NSPs and Sis1 into the HSR activation mechanism, and we perform genetic decoupling and pulse-labeling experiments to demonstrate that Sis1 induction is dispensable for HSR deactivation. Rather than providing negative feedback to the HSR, transcriptional regulation of Sis1 by Hsf1 promotes fitness by coordinating stress granules and carbon metabolism. These results support an overall model in which NSPs signal the HSR by sequestering Sis1 and Hsp70, while induction of Hsp70 – but not Sis1 – attenuates the response.</p> </div> </div> </div>
The source data for "Inductively shunted transmon: A superconducting qubit with flux noise insensitive plasmon states and a protected fluxon decay exceeding 3 hours"
<p>The following folder contains all the raw data, analysis Mathematica notebook and ScQubits python codes used to generate the results in “Inductively shunted transmon: A superconducting qubit with flux noise insensitive plasmon states and a protected fluxon decay exceeding 3 hours” in nature communications. Please follow the instruction below for proper navigation through the data:</p> <p>Fig. 1 folder:</p> <ol> <li>Run “Color map of Matrix element Vs energy parameters” to generate “x.dat”, “y.dat”, ”M.dat”(respectively EJ/EL, EJ/EC and the matrix element of the first flux transition). <strong>Make sure to correct the address where these file should be saved</strong>.</li> <li>The mathematica notebook plots the dispersion in IST limit and the matrix element gray scale color map contours separately and the full image was constructed in illustrator later. The green dots on the dispersion plot represent the position of other qubits on the color map. </li> </ol> <p>Fig. 2&3 folder:</p> <ol> <li>The python file “paper figures” uses ScQubits to generate different studies in IST limit presented in Fig. 2&3 and generates the following files:</li> </ol> <p>Fig. 2a:</p> <p>“fluxonium.hdf5”: The spectrum of a typical fluxonium</p> <p>“fluxoniumME.hdf5”: The matrix element of all transition in “fluxonium.hdf5”</p> <p> </p> <p>Fig. 2d:</p> <p>“case1.hdf5”: The spectrum of fluxonium with EJ/EC=6.6</p> <p>“ME1.hdf5”: The matrix element of transition in “case1.hdf5”</p> <p>“case2.hdf5”: The spectrum of fluxonium with EJ/EC=13</p> <p>“ME2.hdf5”: The matrix element of transition in “case2.hdf5”</p> <p>.</p> <p>.</p> <p>“case6.hdf5”: The spectrum of fluxonium with EJ/EC=200</p> <p>“ME6.hdf5”: The matrix element of transition in “case6.hdf5”</p> <p>“IST.hdf5”: The spectrum of the IST qubit</p> <p>“ISTME.hdf5”: The matrix element of transition in “IST.hdf5”</p> <p>“transmon.hdf5”: The spectrum of a transmon with the same EJ and EC as IST qubit</p> <p>Fig. 3a</p> <p>“Waveamp.hdf5”: The wave functions and eigenenergies of the IST qubit</p> <p>“WaveampT.hdf5”: The wave functions and eigenenergies of the transmon</p> <p> </p> <p>Fig. 3b:</p> <p>“ELcase1.hdf5”: The spectrum of IST qubit with EL=2 GHz</p> <p>“ELME1.hdf5”: The matrix element of transition in “ELcase1.hdf5”</p> <p>“ELcase2.hdf5”: The spectrum of IST qubit with EL=1.5 GHz</p> <p>“ELME2.hdf5”: The matrix element of transition in “ELcase2.hdf5”</p> <p>.</p> <p>.</p> <p>“ELcase6.hdf5”: The spectrum of IST qubit with EL=0.25 GHz</p> <p>“ELME6.hdf5”: The matrix element of transition in “ELcase6.hdf5”</p> <p> </p> <p>Fig. 3b inset:</p> <p>“WaveampEL.hdf5”contains the wave functions for El={2,1.5,1,0.75,0.5,0.25}GHz.</p> <p> </p> <p>Fig. 3c:</p> <p>“EC.hdf5” contains numerical simulation of an IST qubit with fixed EJ and Ec while EL is changing to calculate anharmonicity.</p> <ol> <li>The Mathematica notebook “Theory_figures” runs based on the files above and plot the result presented in the paper.</li> </ol> <p>Fig. 5 folder:</p> <ol> <li>Fig. 5a&b folder contains the raw data of spectroscopy of the IST qubit with different temperature and the Mathematica notebook “Tempsweeps_figa&b” simply plots the data. In the data set the I and Q quadrature as well as the amplitude and power of the signal coming back from cavity is provided.</li> <li>Fig. 5c folder contains several sweeps of both spectroscopy and resonator performed at fridge base temperature (7mK) labeled as “specge#.txt” and “Res_VNA_*.txt” respectively. The ScQubits python code “IST_Device” provides a fit for the data using the fit procedure explained in Supplementary Note 4 and generates the bare spectrum of the device saved in “Fit.h5”. The Mathematica notebook “spec_analysis” uses all spectroscopy data and the fit file to plot Fig. 5c.</li> </ol> <p>Fig. 6 folder: Contains all the raw data of T1 and T2 experiment at different flux positions across a flux quantum. The Mathematica notebook “T1&2” performs all the analysis presented in Fig. 6 for devices A, B and C.</p> <p>Fig. 7 folder:</p> <ol> <li>Fig. 7a: In this folder the we provide the raw data for fidelity experiment. The data is in the “*.mat” format and contains 40000 single shot I&Q bins collected with measurement band width of 2MHz and integration time of 500ns. The files names indicate whether the data was taken with qubit prepared in ground/excited state by having “_g_”/”_e_”. Following the state preparation condition, the measurement power at which the data was taken is indicated. The Mathematica notebook “fidelity_sweep” takes the data and extract the fidelities shown in Fig. 7a and the 2D histogram plots presented in Supplementary Figure 5d.</li> <li>Fig. 7b: The raw data for QND-ness experiment is presented in this folder. Each file contains 500 time traces of the two consecutive pulses applied to the resonator to study the non-QND effects of the IST qubit in high power. The Qubit preparation condition is apparent in the file name along with the power at which the measurement was performed. The Mathematica notebook “QND_ness” extracts the QND_ness and plots the results shown in Fig. 7b</li> </ol> <p> </p> <p>Fig. 8 folder:</p> <ol> <li>Fig. 8a: This folder contains the spectroscopy sweeps conditions by the fluxon state using a strong microwave pulse applied to the resonator. The Mathematica notebook “sweeps” plots the data.</li> <li>Fig. 8c: This folder contains the raw data for long fluxon decays collected using quantum machines (QM). In this experiment the fluxon excitation pulse was applied and repeated until a successful fluxon state is detected. Afterwards, the experiment enters monitoring stage where every 30s we check the fluxon state until a tunneling to fluxon ground state is detected. This event is logged and the QM repeats the fluxon excitation immediately followed by a monitoring stage and logging the time it took for tunneling to occur. The raw data of every 30 second monitoring stage is saved in files with “_raw_” in their labels. The files containing “_taus_” in their names have only the logged tunneling time events. The Mathematica notebook “qubit analysis” takes the data for three external flux bias and, by loading the “_taus_” files, reconstructs the quasi quantum jump traces and finally the decay traces presented in Fig. 8c.</li> </ol>
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