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260 results for “Logic”
The transcriptional logic of ant odorant receptors
<p>We utilize snRNA-Seq to investigate the expression of odorant receptor genes in olfactory sensory neurons of the developing pupae of the clonal raider ant, <em>Ooceraea biroi</em>.</p>
Fuzzy Logic Automated Insulin Regulation
ClinicalTrials.gov study NCT03040414. IPD Sharing: NO. Countries: 4. Publications: 11.
LOGIC-Insulin Algorithm-guided Versus Nurse-directed Blood Glucose Control During Critical Illness
ClinicalTrials.gov study NCT01420302. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Logical inferences from visual and auditory information in ruffed lemurs and sifakas
Open the record for dataset details and reuse information.
Tomography of entangling two-qubit logic operations in exchange-coupled donor electron spin qubits
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Benchmark circuits used in the work entitled "Sequential Logic Encryption with Dynamic Keys"
<p>Benchmark circuits used in the work entitled "Sequential Logic Encryption with Dynamic Keys".<br> The original benchmarks are from LGSynth’91 (downloaded from https://ddd.fit.cvut.cz/prj/Benchmarks/).</p> <p>Contents:<br> oriCircuit: original benchmark circuits.<br> k5r5/circuit: encrypted circuits with k=5 and r=5.<br> k5r5/key: secret keys of the encrypted circuits with k=5 and r=5.<br> k5r10/circuit: encrypted circuits with k=5 and r=10.<br> k5r10/key: secret keys of the encrypted circuits with k=5 and r=10.<br> k10r5/circuit: encrypted circuits with k=10 and r=5.<br> k10r5/key: secret keys of the encrypted circuits with k=10 and r=5.<br> k10r10/circuit: encrypted circuits with k=10 and r=10.<br> k10r10/key: secret keys of the encrypted circuits with k=10 and r=10.<br> k15r5/circuit: encrypted circuits with k=15 and r=5.<br> k15r5/key: secret keys of the encrypted circuits with k=15 and r=5.<br> k15r10/circuit: encrypted circuits with k=15 and r=10.<br> k15r10/key: secret keys of the encrypted circuits with k=15 and r=10.</p>
A Unified Translation of Linear Temporal Logic to ω-Automata: Supplemental Material for the Experimental Evaluation
<p>This dataset contains the evaluated tools and the data on which the experimental evaluation section of the article "A Unified Translation of Linear Temporal Logic to ω-Automata" by Javier Esparza, Jan Křetínský, and Salomon Sickert is based on. Further, we include instructions on how to recreate the results.</p>
Domain-specific Language for Data-driven Design Time Analyses and Result Mappings for Logic Programs - Data Set
<p>This data set contains additional information to the master thesis of Sebastian Hahner. This includes the prototypical implementation of the domain-specific language (DSL) as well as sample scenarios and mapping results.</p>
Data from: Two influential primate classifications logically aligned
Classifications and phylogenies of perceived natural entities change in the light of new evidence. Taxonomic changes, translated into Code-compliant names, frequently lead to name:meaning dissociations across succeeding treatments. Classification standards such as the Mammal Species of the World (MSW) may experience significant levels of taxonomic change from one edition to the next, with potential costs to long-term, large-scale information integration. This circumstance challenges the biodiversity and phylogenetic data communities to express taxonomic congruence and incongruence in ways that both humans and machines can process, that is, to logically represent taxonomic alignments across multiple classifications. We demonstrate that such alignments are feasible for two classifications of primates corresponding to the second and third MSW editions. Our approach has three main components: (i) use of taxonomic concept labels, that is name sec. author (where sec. means according to), to assemble each concept hierarchy separately via parent/child relationships; (ii) articulation of select concepts across the two hierarchies with user-provided Region Connection Calculus (RCC-5) relationships; and (iii) the use of an Answer Set Programming toolkit to infer and visualize logically consistent alignments of these input constraints. Our use case entails the Primates sec. Groves (1993; MSW2–317 taxonomic concepts; 233 at the species level) and Primates sec. Groves (2005; MSW3–483 taxonomic concepts; 376 at the species level). Using 402 RCC-5 input articulations, the reasoning process yields a single, consistent alignment and 153,111 Maximally Informative Relations that constitute a comprehensive meaning resolution map for every concept pair in the Primates sec. MSW2/MSW3. The complete alignment, and various partitions thereof, facilitate quantitative analyses of name:meaning dissociation, revealing that nearly one in three taxonomic names are not reliable across treatments—in the sense of the same name identifying congruent taxonomic meanings. The RCC-5 alignment approach is potentially widely applicable in systematics and can achieve scalable, precise resolution of semantically evolving name usages in synthetic, next-generation biodiversity, and phylogeny data platforms.
Data from: Using fuzzy logic to determine the vulnerability of marine species to climate change
Marine species are being impacted by climate change and ocean acidification, although their level of vulnerability varies due to differences in species' sensitivity, adaptive capacity and exposure to climate hazards. Due to limited data on the biological and ecological attributes of many marine species, as well as inherent uncertainties in the assessment process, climate change vulnerability assessments in the marine environment frequently focus on a limited number of taxa or geographic ranges. As climate change is already impacting marine biodiversity and fisheries, there is an urgent need to expand vulnerability assessment to cover a large number of species and areas. Here, we develop a modelling approach to synthesize data on species-specific estimates of exposure, and ecological and biological traits to undertake an assessment of vulnerability (sensitivity and adaptive capacity) and risk of impacts (combining exposure to hazards and vulnerability) of climate change (including ocean acidification) for global marine fishes and invertebrates. We use a fuzzy logic approach to accommodate the variability in data availability and uncertainties associated with inferring vulnerability levels from climate projections and species' traits. Applying the approach to estimate the relative vulnerability and risk of impacts of climate change in 1074 exploited marine species globally, we estimated their index of vulnerability and risk of impacts to be on average 52 ± 19 SD and 66 ± 11 SD, scaling from 1 to 100, with 100 being the most vulnerable and highest risk, respectively, under the 'business-as-usual' greenhouse gas emission scenario (Representative Concentration Pathway 8.5). We identified 157 species to be highly vulnerable while 294 species are identified as being at high risk of impacts. Species that are most vulnerable tend to be large-bodied endemic species. This study suggests that the fuzzy logic framework can help estimate climate vulnerabilities and risks of exploited marine species using publicly and readily available information.
Data from: Integrating fuzzy logic and statistics to improve reliabile definition of biogeographic regions and transition zones
The present study uses the amphibian species of the Mediterranean Region to develop a consistent procedure based on fuzzy sets with which biogeographic regions and biotic transition zones can be objectively detected and reliably mapped. Biogeographical regionalizations are abstractions of the geographical organization of life on Earth that provide frameworks for cataloguing species and ecosystems, for answering basic questions in biogeography, evolutionary biology and systematics, and for assessing priorities for conservation. On the other hand, limits between regions may form sharply defined boundaries along some parts of their borders, whereas elsewhere they may consist of broad transition zones. The fuzzy set approach provided a heuristic way to analyze the complexity of the biota within an area; significantly different regions were detected whose mutual limits were sometimes fuzzy, sometimes clearly crisp. Most of the regionalizations described in the literature for the Mediterranean Region present a certain degree of convergence when they are compared within the context of fuzzy interpretation, as many of the differences found between regionalizations are located in transition zones, according to our case study. Compared to other classification procedures based on fuzzy sets, the novelty of our method is that both fuzzy logic and statistics are used together in a synergy in order to avoid arbitrary decisions in the definition of biogeographic regions and transition zones.
Software for control of autonomous robots using fuzzy logic controllers tuned by genetic algorithms
<p>This software implements the autonomous control of a robot by using a fuzzy logic controller tuned by a genetic algorithm. The software was written in C programming language for Windows (SDK). A description of the software can be found in the research publication "Arsene, C.T.C., & Zalzala, A.M.S., "Control of autonomous robots using fuzzy logic controllers tuned by genetic algorithms", In Proc Congress on Evolutionary Computation, Vol. 1, pp. 428-35, Washington DC, 1999, IEEE Computer Science Press, ISBN 0-7803-5536-9". Possibly the software to be used also for simulation of Nano-robots.</p>
How Bit-Vector Logic Can Help Improve the Verification of First-Order LTL Specifications
<p>Experimental evaluation for the encoding presented in the paper "How Bit-Vector Logic Can Help Improve the Verification of First-Order LTL Specifications".</p> <p>The encoding is implemented as a zot plugin entitled ae2bvzot.</p>
Data from: Comparison of priority rules, machine allocation, and stage allocation strategies for hybrid flow shop instances using combinatorial logic and a standard trace format
<p>datsets.zip contains benchmark data by Ruiz et al. (2008), Naderi et al.(2010), and Wittwock (1988). The according publications are listed in the related works section. The instances are publicly available under unclear license.</p><p>results.zip contains computational results for the aforementioned benchmarks using the algorithms described in "Comparison of priority rules, machine allocation, and stage allocation strategies for hybrid flow shop instances using combinatorial logic and a standard trace format". The data is licensed under Creative Commons Attribution 4.0 International license.</p>
Logic-Induced-Long-Tail (LINT) anonymous
<p>Revised dataset for ICLR 2024 submission, Paper ID 6959. Paper title: IN SEARCH OF THE LONG-TAIL: SYSTEMATIC GENERATION OF LONG-TAIL KNOWLEDGE VIA LOGICAL RULE GUIDED SEARCH.</p>
Comparison between Parma Polyhedra Library and ParetoLib for computing the validity domain of a parametric Signal Temporal Logic (STL) expression
<p>Comparison between Parma Polyhedra Library and ParetoLib for computing the validity domain of a parametric Signal Temporal Logic (STL) expression. The code and dataset in this folder is related to the example 5 in section 2 of paper "Mining of Extended Signal Temporal Logic Specifications with ParetoLib 2.0" in the journal Formal Methods and System Design.</p>
Computation Tree Logic guided Reactive Program Repair
<p>This is the supporting data for OOPSLA24 submission: Computation Tree Logic guided Reactive Program Repair. </p> <p>It includes the source code and benchmarks presented in the paper. </p>
Logical coherence in 2D compass codes Supplementary Material
<h1>Logical coherence in 2D compass codes</h1> <p>This repo contains the tools to reproduce and share the data for the paper "Logical Coherence in 2D Compass Codes" by Balint Pato, Will Judd Staples, and Kenneth R. Brown (2025) as well as data for B. Pato, Q. Miao and K. R. Brown, "Optimal Decoding of 2D Compass Codes Under Coherent Noise," 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), Montreal, QC, Canada, 2024, pp. 448-449, doi: 10.1109/QCE60285.2024.10349.</p> <h1>Setup</h1> <p>Python 3.x is required. The instructions for a Unix/Linux system:</p> <pre><code>cd coherence-in-compass-codes-paper python3 -m venv ~/.virtualenvs/cicc . ~/.virtualenvs/cicc/bin/activate pip install -r requirements.txt -r msim/requirements.txt -r msim/requirements.dev.txt </code></pre> <p>Before running any Python file, make sure the source folder is in PYTHONPATH, for example:</p> <pre><code>export PYTHONPATH="." </code></pre> <p>A quick end-to-end test that everything works:</p> <pre><code>check/all </code></pre> <h1>Downloading data</h1> <p>Pregenerated data is available in <code>data/codes.db</code> for the Logical Coherence in 2D Compass Codes paper.</p> <p>The number of samples per code family:</p> <table> <tbody><tr> <th>code family</th> <th>samples</th> </tr> </tbody><tbody> <tr> <td>qshor[0.166667]_13x13_0</td> <td>2,178,400</td> </tr> <tr> <td>qshor[0.166667]_17x17_0</td> <td>2,185,800</td> </tr> <tr> <td>qshor[0.166667]_21x21_0</td> <td>2,210,800</td> </tr> <tr> <td>qshor[0.166667]_9x9_0</td> <td>2,176,600</td> </tr> <tr> <td>qshor[0.333333]_13x13_0</td> <td>3,310,800</td> </tr> <tr> <td>qshor[0.333333]_17x17_0</td> <td>3,317,870</td> </tr> <tr> <td>qshor[0.333333]_21x21_0</td> <td>3,344,800</td> </tr> <tr> <td>qshor[0.333333]_9x9_0</td> <td>3,308,000</td> </tr> <tr> <td>qshor[0.5]_13x13_0</td> <td>2,381,400</td> </tr> <tr> <td>qshor[0.5]_17x17_0</td> <td>2,388,000</td> </tr> <tr> <td>qshor[0.5]_21x21_0</td> <td>2,398,600</td> </tr> <tr> <td>qshor[0.5]_9x9_0</td> <td>2,379,400</td> </tr> <tr> <td>qshor[0.666667]_13x13_0</td> <td>2,808,400</td> </tr> <tr> <td>qshor[0.666667]_17x17_0</td> <td>2,827,000</td> </tr> <tr> <td>qshor[0.666667]_21x21_0</td> <td>2,876,600</td> </tr> <tr> <td>qshor[0.666667]_9x9_0</td> <td>2,805,600</td> </tr> <tr> <td>qshor[0.833333]_13x13_0</td> <td>3,869,600</td> </tr> <tr> <td>qshor[0.833333]_17x17_0</td> <td>3,883,200</td> </tr> <tr> <td>qshor[0.833333]_21x21_0</td> <td>3,913,400</td> </tr> <tr> <td>qshor[0.833333]_9x9_0</td> <td>3,861,400</td> </tr> <tr> <td>surface_13x13</td> <td>2,004,800</td> </tr> <tr> <td>surface_17x17</td> <td>2,004,800</td> </tr> <tr> <td>surface_21x21</td> <td>2,004,800</td> </tr> <tr> <td>surface_9x9</td> <td>2,004,800</td> </tr> <tr> <td>zstackedshor[h11]_11x11_0</td> <td>612,250</td> </tr> <tr> <td>zstackedshor[h1]_11x11_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_1x11_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_1x5_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_1x7_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_1x9_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_5x5_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_7x7_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h1]_9x9_0</td> <td>200,000</td> </tr> <tr> <td>zstackedshor[h2]_11x11_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h2]_5x5_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h2]_7x7_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h2]_9x9_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h3]_11x11_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h3]_5x5_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h3]_7x7_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h3]_9x9_0</td> <td>200,200</td> </tr> <tr> <td>zstackedshor[h5]_5x5_0</td> <td>700,000</td> </tr> <tr> <td>zstackedshor[h7]_7x7_0</td> <td>700,000</td> </tr> </tbody> </table> <p>For the surface code ML database:</p> <table> <tbody><tr> <th>code family</th> <th>samples</th> </tr> </tbody><tbody> <tr> <td>surface_101x101</td> <td>40,000</td> </tr> <tr> <td>surface_13x13</td> <td>1,599,200</td> </tr> <tr> <td>surface_151x151</td> <td>40,000</td> </tr> <tr> <td>surface_17x17</td> <td>1,599,200</td> </tr> <tr> <td>surface_201x201</td> <td>40,000</td> </tr> <tr> <td>surface_21x21</td> <td>1,599,200</td> </tr> <tr> <td>surface_23x23</td> <td>1,600,000</td> </tr> <tr> <td>surface_251x251</td> <td>40,000</td> </tr> <tr> <td>surface_25x25</td> <td>1,600,000</td> </tr> <tr> <td>surface_27x27</td> <td>1,600,000</td> </tr> <tr> <td>surface_29x29</td> <td>1,600,000</td> </tr> <tr> <td>surface_33x33</td> <td>1,600,000</td> </tr> <tr> <td>surface_37x37</td> <td>1,600,000</td> </tr> <tr> <td>surface_45x45</td> <td>2,000,000</td> </tr> <tr> <td>surface_53x53</td> <td>2,000,000</td> </tr> <tr> <td>surface_61x61</td> <td>2,000,000</td> </tr> <tr> <td>surface_69x69</td> <td>2,000,000</td> </tr> <tr> <td>surface_9x9</td> <td>1,599,380</td> </tr> </tbody> </table> <p>Sample queries:</p> <ul> <li>to find the number of samples above:</li> </ul> <pre><code><span>select</span> code_id, theta_phys, <span>count</span>(<span>*</span>) <span>from</span> logical_angle_samples <span>group</span> <span>by</span> code_id, theta_phys <span>order</span> <span>by</span> theta_phys <span>asc</span>; </code></pre> <ul> <li>to find the number of samples per datapoint for surface codes</li> </ul> <pre><code><span>select</span> code_id, theta_phys, <span>count</span>(<span>*</span>) <span>from</span> logical_angle_samples <span>group</span> <span>by</span> code_id, theta_phys <span>having</span> code_id <span>like</span> <span>'surface%'</span> <span>order</span> <span>by</span> theta_phys <span>asc</span> ; </code></pre> <ul> <li>to query data underlying the plots for infidelity metrics for the d=9 qshor=5/6 compass codes:</li> </ul> <pre><code><span>select</span> <span>*</span> <span>from</span> qshor_loaf <span>where</span> code_id <span>like</span> <span>'qshor%0.83%9x9%'</span>; </code></pre> <h2>Database schema</h2> <pre><code><span>-- code families are the roots of parametrized code hierarchies, e.g. surface, qshor. The convention is to also use the code_family as table names describing the parametrized members. </span> <span>CREATE</span> <span>TABLE</span> CODE_FAMILIES(CODE_FAMILY, <span>UNIQUE</span>(CODE_FAMILY)); <span>-- surface code members. Technically possible to create non-square surface codes, but in this paper we only explored square ones. </span> <span>CREATE</span> <span>TABLE</span> SURFACE(CODE_ID, DX, DZ, <span>UNIQUE</span>(CODE_ID)); <span>-- families of qshor (x check density) parametrized random compass codes. </span> <span>CREATE</span> <span>TABLE</span> qshor(CODE_ID, QSHOR, DX, DZ, <span>UNIQUE</span>(CODE_ID)); <span>--the main samples table, for a given code, and physical rotation angle the logical rotation angle is recorded</span> <span>CREATE</span> <span>TABLE</span> LOGICAL_ANGLE_SAMPLES(CODE_ID,THETA_PHYS TEXT, THETA_LOGICAL TEXT); <span>-- diamond distance metrics aggregated from the logical angle samples table for qshor</span> <span>CREATE</span> <span>TABLE</span> qshor_dd(code_id, theta_phys, mean, std, num_records); <span>-- loss of average fidelity metrics aggregated from the logical angle samples table for qshor</span> <span>CREATE</span> <span>TABLE</span> qshor_loaf(code_id, theta_phys, mean, std, num_records); <span>-- diamond distance metrics aggregated from the logical angle samples table for surface codes</span> <span>CREATE</span> <span>TABLE</span> surface_dd(code_id, theta_phys, mean, std, num_records); <span>-- loss of average fidelity metrics aggregated from the logical angle samples table for surface codes</span> <span>CREATE</span> <span>TABLE</span> surface_loaf(code_id, theta_phys, mean, std, num_records); <span>CREATE</span> <span>TABLE</span> pub.zstackedshor(CODE_ID, ZSHOR_HEIGHT, DX, DZ, <span>UNIQUE</span>(CODE_ID)); <span>CREATE</span> <span>TABLE</span> pub.zstackedshor_loaf(code_id, theta_phys, mean, std, num_records); <span>CREATE</span> <span>TABLE</span> pub.zstackedshor_loaf_ml(code_id, theta_phys, mean, std, num_records); <span>-- ML decoder version </span> <span>CREATE</span> <span>TABLE</span> qshor_dd_ml(code_id, theta_phys, mean, std, num_records); <span>CREATE</span> <span>TABLE</span> qshor_loaf_ml(code_id, theta_phys, mean, std, num_records); <span>CREATE</span> <span>TABLE</span> surface_dd_ml(code_id, theta_phys, mean, std, num_records); <span>CREATE</span> <span>TABLE</span> surface_loaf_ml(code_id, theta_phys, mean, std, num_records); </code></pre> <h1>Plots</h1> <p>Make sure that you have the data under <code>data/codes.db</code> - this needs to be a SQLite database. See the previous section on how to create it from the published data.</p> <p>Plots are generated under the folder <code>figures</code>. There are separate entry points for each figure in the paper:</p> <ul> <li>combined thresholds in the appendix resulting in <code>figures/threshold_plot-combined_full.pdf</code> and <code>figures/threshold_plot-combined_zoom.pdf</code>:</li> </ul> <pre><code>python cicc/plot/combined_threshold.py </code></pre> <ul> <li>the main plot containing the manually extracted thresholds for the random compass codes and the surface code:</li> </ul> <pre><code>python cicc/plot/qshor_family.py </code></pre> <ul> <li>the QCE2024 poster / extended abstract plots</li> </ul> <pre><code>python cicc/plot/qce2024_figures.py </code></pre> <ul> <li>the repetition code and Z-stacked Shor code plots matching the formulae plots</li> </ul> <pre><code>python cicc/plot/zstack_zshor_thresholds.py </code></pre> <h1>A note on angle conventions</h1> <p>All rotations in this project are around the Z-axis. There are two ways one can interpret the rotation angle based on the unitary rotation:</p> <ul> <li>spin angles: Rz(theta_spin) = exp(-i theta_spin/2 Z)</li> <li>direct angles: Rz(theta_direct) = exp(i theta_direct Z)</li> </ul> <p>Thus, for the same rotation unitary, theta_spin = - 2 * theta_direct. The paper by Bravyi, Engelbrecht, Konig and Peard [^bravyi2018] for the rotated surface code uses the direct angles convention, and similarly, msim uses direct angles. However, this paper uses spin angles closer to the physics convention.</p> <p>[^bravyi2018]:Bravyi, Sergey, Matthias Englbrecht, Robert König, and Nolan Peard, ‘Correcting Coherent Errors with Surface Codes’, Npj Quantum Information, 4.1 (2018), 55 <a href="https://doi.org/10.1038/s41534-018-0106-y">https://doi.org/10.1038/s41534-018-0106-y</a></p> <p>The table below summarizes the angle conventions in different parts of the codebase to avoid confusion:</p> <table> <tbody><tr> <th>Place</th> <th>convention</th> </tr> </tbody><tbody> <tr> <td>physical angles for the samplers in this project</td> <td>direct angles / pi</td> </tr> <tr> <td>msim simulator</td> <td>direct angles</td> </tr> <tr> <td>msim coeffs framework</td> <td>spin angles</td> </tr> <tr> <td>database logical angles</td> <td>direct angles</td> </tr> <tr> <td>database physical angles</td> <td>direct angles</td> </tr> <tr> <td>plots</td> <td>spin angles / pi</td> </tr> </tbody> </table> <h1>Generating your own data</h1> <h2>Random Compass Codes</h2> <p>Generating data for random compass codes for a given set of qshor values, distances and physical theta values:</p> <pre><code> python cicc/sample_angles/qshor_angles.py --help usage: qshor_angles.py [-h] [--db DB] --dz DZ --q Q --theta_phys THETA_PHYS [--num_runs NUM_RUNS] options: -h, --help show this help message and exit --db DB database file --dz DZ code Z distance --q Q Qshor probability metric --theta_phys THETA_PHYS physical rotation (direct angles) divided by pi --num_runs NUM_RUNS number of iterations: the total number of samples, which is divided across sqrt(num_runs) randomly generated codes </code></pre> <p>For example:</p> <pre><code>python cicc/sample_angles/qshor_angles.py --dz "[9, 13, 17, 21]" --theta_phys="np.linspace(0.01,0.25, 10)" --qshor="[1/6, 2/6, 3/6, 4/6, 5/6]" </code></pre> <h2>Surface code</h2> <p>Generating data for the rotated surface code:</p> <pre><code>python cicc/sample_angles/rsc_angles.py --help usage: rsc_angles.py [-h] [--db DB] --dz DZ --theta_phys THETA_PHYS [--batch_size BATCH_SIZE] [--num_workers NUM_WORKERS] [--num_runs NUM_RUNS] options: -h, --help show this help message and exit --db DB database file --dz DZ code Z distance --theta_phys THETA_PHYS physical rotation (direct angles) divided by pi --batch_size BATCH_SIZE batch size for writing to the db --num_workers NUM_WORKERS parallelism --num_runs NUM_RUNS number of iterations: the total number of samples </code></pre> <p>For example</p> <pre><code>python cicc/sample_angles/rsc_angles.py --dz "[5, 9, 13, 17, 21, 25, 29]" --theta_phys="np.linspace(0.01,0.25, 10)" </code></pre> <h2>Repetition codes</h2> <p>Generating data for the repetition codes:</p> <pre><code>python cicc/sample_angles/repcodes_angles.py --help usage: repcodes_angles.py [-h] [--db DB] --dz DZ --theta_phys THETA_PHYS [--num_runs NUM_RUNS] [--batch_size BATCH_SIZE] options: -h, --help show this help message and exit --db DB database file --dz DZ code Z distance --theta_phys THETA_PHYS physical rotation (direct angles) divided by pi --num_runs NUM_RUNS number of iterations: the total number of samples --batch_size BATCH_SIZE batch size </code></pre> <p>For example</p> <pre><code>python cicc/sample_angles/repcodes_angles.py --dz "[5,7,9,11]" --theta_phys "list(reversed(list(np.linspace(0.04,0.18,20))))" --num_runs 10000 </code></pre> <h2>Z-stacked Shor codes</h2> <p>Generating data for the repetition codes:</p> <pre><code>python cicc/sample_angles/repcodes_angles.py --help usage: zstacked_shor_angles.py [-h] [--db DB] --dz DZ [--height HEIGHT] [--zshor ZSHOR] --theta_phys THETA_PHYS [--num_runs NUM_RUNS] [--batch_size BATCH_SIZE] Sample angles for ZStackedShor codes. Example usage: Run 10,000 samples for height-2 and height-3 ZStackedShor codes with Z distance 3, 5, and 7, and angles 0.1, 0.15, and 0.17: python -m cicc.sample_angles.zstacked_shor_angles --dz '[3,5,7]' --height [2,3] --theta_phys '[0.1,0.15,0.17]' --num_runs 10000 --batch_size 10 Run 10,000 samples for Z-Shor codes with Z distance 3, 5, and 7, and angles 0.1, 0.15, and 0.17: python -m cicc.sample_angles.zstacked_shor_angles --dz '[3,5,7]' --zshor --theta_phys '[0.1,0.15,0.17]' --num_runs 10000 --batch_size 10 Run 10,000 samples for X-Shor codes with Z distance 3, 5, and 7, and angles 0.1, 0.15, and 0.17: python -m cicc.sample_angles.zstacked_shor_angles --dz '[3,5,7]' --height 1 --theta_phys '[0.1,0.15,0.17]' --num_runs 10000 --batch_size 10 options: -h, --help show this help message and exit --db DB database file --dz DZ code Z distance --height HEIGHT height of the Z-Shor code block --zshor ZSHOR Z-shor code - Z-Shor height equals to dz --theta_phys THETA_PHYS physical rotation (direct angles) divided by pi --num_runs NUM_RUNS number of iterations: the total number of samples --batch_size BATCH_SIZE batch size </code></pre> <h2>Regenerating aggregated statistics</h2> <p>In the combined plot (<code>cicc/plot/combined_threshold.py</code>), by default, the already computed statistics are plotted. Hence, when new data is generated, these need to be recalculated. In <code>cicc/plot/combined_threshold.py</code> there is a section that can be modified to trigger the required recalculation (slow):</p> <pre><code> recompute( conn, <span># set to True to recompute qshor statistics </span> recompute_qshor=<span>False</span>, <span># set to True to recompute surface code statistics </span> recompute_rsc=<span>False</span>, <span># set to True to recompute qshor statistics with ML decoding </span> recompute_ml_qshor=<span>False</span>, <span># set to True to recompute surface code statistics with ML decoding</span> recompute_ml_rsc=<span>False</span>, )</code></pre>
Logic Shrinkage: Learned FPGA Netlist Sparsity for Efficient Neural Network Inference [Artefact Evaluation]
<p>Source code of paper "Logic Shrinkage: Learned FPGA Netlist Sparsity for Efficient Neural Network Inference" submitted to FPGA'22 for artefact evaluation.</p>
Miscellanies [IO Islamic 1552] Anthology of treatises on logic and grammar.
<ul> <li><strong>Miscellanies.</strong></li> <li><strong>This manuscript is now IO Islamic 1552 </strong><strong>in the India Office collections.</strong></li> <li><strong>[metadata:</strong><a href="https://de.wikipedia.org/wiki/Otto_Loth"> <strong>Otto Loth, </strong></a><strong><em><a href="http://doi.org/10.5281/zenodo.3923636">A Catalogue of the Arabic Manuscripts in the Library of the India Office</a></em>, (volume 1), no. 1042 here with further notations and hyperlinks]</strong>.</li> </ul> <p>1042.</p> <p>1552. Size 9<sup>1/4</sup> in. by 5<sup>1/2</sup> in.; foll. 100.</p> <p><strong>I. Foll. 1-18</strong>. <a href="https://archive.org/details/fresh_soul2030_yahoo_748/mode/2up">شرح مائة عامل</a>. A Commentary on <a href="http://worldcat.org/identities/lccn-n82231560/"><em>‘Abd al-</em></a><em><a href="http://worldcat.org/identities/lccn-n82231560/">ḳâhir Jurjânî</a>’s</em> (d. A.H. 474) Hundred Grammatical Regents, published under the same title by <a href="https://www.worldcat.org/identities/viaf-4963152637773520220008/">Baillie</a> (<a href="https://www.worldcat.org/title/entire-and-correct-edition-of-the-five-books-upon-arabic-grammar/oclc/41126561">Calcutta, 1802</a>) and <a href="http://worldcat.org/identities/lccn-n85382046/">[Abraham] Lockett</a> (<a href="https://www.worldcat.org/title/miut-amil-and-shurhoo-miut-amil/oclc/987284027"><em>ib. </em>1814</a>).</p> <p>Ends:</p> <p>تم الرسالة المسمی بشرح مایة العوامل</p> <p><strong>II. Foll. 21-24</strong>. A short syntactical treatise, called التتمّة, probably also by <a href="https://www.worldcat.org/identities/lccn-n82231560/">JURJÂNÎ</a>. See no. <a href="https://doi.org/10.5281/zenodo.5827634">984</a>, iv.</p> <p><strong>III. Foll. 25-78</strong>. <a href="https://www.worldcat.org/identities/np-mutarrizi,%20nasir%20ibn%20abd%20as%20saiyid%20al/">MUṬARRIZÎ</a>’S Grammar المصباح. See no. <a href="https://doi.org/10.5281/zenodo.5777485">890</a>.</p> <p>Colophon:</p> <p>قد تمّت کتاب المصباح فی شهر محرم الحرام یوم الاحد منه سنه یکهزار و دوصد و هشت هجری فی عسکر الانکریز بمقام چناده <sup>ط</sup> کان من شهورهم عند اتمامها اثنی عشرین اگست سنه یکهزار و هفتصد و نود و سه</p> <p>These three treatises are written in a bold Nasta’lîḳ hand.</p> <p><strong>IV. Foll. 80-87</strong>. <a href="http://worldcat.org/identities/lccn-nr91019719/">ABHARÎ</a>’S ایساغوجی, on Logic. See no. <a href="https://doi.org/10.5281/zenodo.5291087">497</a>, i.</p> <p>Well written in Nasta’lîḳ.</p> <p><strong>V. Foll. 88-100</strong>. میزان المنطق, a treatise on Logic. See no. <a href="https://doi.org/10.5281/zenodo.5504356">573</a>.</p> <p>Well written in Nasta’lîḳ.</p> <p>These two treatises are dated Cawnpore, A.H. 1209. They were written for the purpose of being read by Major Mackenzie (میجر صاحب میجر مکنزی) with Maulawî ‘Abd al-razzâḳ.</p> <p>[Warehouse]</p> <p> </p> <p> </p>
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