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246 results for “Latency”
IX-01 Effect on Intravaginal Ejaculatory Latency Time (IELT) and Patient Reported Outcomes in Men With Premature Ejaculation (PE)
ClinicalTrials.gov study NCT02232425. IPD Sharing: Not stated. Countries: 2. Publications: 1.
Combination Latency Reversal With High Dose Disulfiram Plus Vorinostat in HIV-infected Individuals on ART
ClinicalTrials.gov study NCT03198559. IPD Sharing: NO. Countries: 1. Publications: 1.
IX-01 Effect on Intravaginal Ejaculatory Latency Time (IELT), Patient Reported Outcomes and Safety in Men With Premature Ejaculation (PE)
ClinicalTrials.gov study NCT03055806. IPD Sharing: NO. Countries: 1. Publications: 1.
A distinct isoform of lymphoid enhancer binding factor 1 (LEF1) epigenetically restricts EBV reactivation to maintain viral latency
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Generalized LDPC codes for ultra reliable low latency communication in 5G and beyond
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Data from: Short-latency preference for faces in the primate superior colliculus
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Provisioning latency increases closer to roads and is associated with species-specific reproductive success in two urban adapters
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Position and time coordinates, performance, and latencies of delayed non-match to place trials performed by rats with silenced hippocampal inputs to RSC
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Integrator complex subunit 12 knockout overcomes a transcriptional block to HIV latency reversal
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Dataset for Neural Network 3D Body Pose Tracking and Prediction for Motion-to-Photon Latency Compensation in Distributed Virtual Reality
<p>Distributed Virtual Reality (DVR) systems enable geographically dispersed users to interact in a shared virtual environment. The realism of the interaction is crucial to increase the feeling of co-presence. Latency, produced either by hard- or software components of DVR applications, impedes reaching high realism levels of the DVR experience. For example, the time delay between the user's motion and the corresponding display rendering of the DVR system might lead to adverse effects such as a reduced sense of presence or motion sickness. One way of minimizing the latency is to predict user's motion and thus compensate for the inherent latency in the system. In order to address this problem, we propose a neural network 3D pose tracking and prediction system with latency guarantees for end-to-end avatar reconstruction. We evaluate and compare our system against multiple traditional methods and provide a thorough analysis on real-world human motion data. Datasets used in the paper experiments. Datasets used in paper experiments.</p>
DEDICAT 6G Smart warehousing latency measurements
<p>Comprises latency measurements from the Smart Warehousing piloting and testing activities with the use of two different networks. </p>
latency profiling
<p>latency profiling</p>
Egg loads and data of latency to mating and period of mating lasted in Anastatus disparis
<p>Effect of oviposition experience on egg loads and latency to mating and period of mating lasted in <em>Anastatus disparis</em></p>
Measuring Network Latency from a Wireless ISP: Variations Within and Across Subnets - Dataset
<h1>Overview</h1> <p>This archive contains the data from a network latency measurement campaign conducted inside the wireless ISP JackRabbit Wireless between 2023-10-30 and 2023-12-01. The measurement setup and an analysis of the data is provided in the paper "Measuring Network Latency from a Wireless ISP: Variations Within and Across Subnets", accepted for the ACM Internet Measurement Conference 2024. </p> <p>Code for parsing the data and reproducing the plots can be found at <a href="https://github.com/simosund/Measuring-Network-Latency-from-a-Wireless-ISP.git">https://github.com/simosund/Measuring-Network-Latency-from-a-Wireless-ISP.git</a></p> <h2>Manifest</h2> <ul> <li><strong>The raw subfolder:</strong> contains the original data captured by epping from the measurement campaign. However, note that we have merged all subnet entries from the internal network into a single entry at each timepoint to avoid revealing details about the subnet structure on the internal network. Some internal subnet entries that only carry control traffic have also been removed. It is still possible to recreate all the results from the paper with this data.</li> <li><strong>The processed subfolder: </strong>This contains processed versions of the data that have been merged in a few different ways, allowing the results from the paper to be recreated quickly without having to parse all of the original data. <ul> <li><strong>subnets_stats_merged.json:</strong> The data from the entire measurement period have been merged into a single entry per subnet</li> <li><strong>{lan,wan}_per10s_stats_merged.json:</strong> All the internal or external data merged into a single entry per timepoint (10s).</li> <li><strong>top_100asn_stats_10min_json:</strong> The external data merged per ASN (for the 100 ASNs with the highest amount of downlink traffic during the measurement period) at a 10 minute resolution.</li> <li><strong>protocol_counters.hdf5: </strong>Counters for number of packets of each <a href="https://www.iana.org/assignments/protocol-numbers/protocol-numbers.xhtml">IP-protocol</a>. The most common protocols, such as TCP, UDP and ICMP are named, where less common ones are just numbered.</li> <li><strong>ecn_counters.hdf5:</strong> Counters of the four ECN-marks (Not-ECT, ECT(1), ECT(0) and CE).</li> </ul> </li> <li><strong>The overhead_measurements subfolder: </strong>CPU and network load measurements from a later period used to estimate the overhead of the epping tool. In the covered period, epping was running between the 19th of April and the 3rd of May. <ul> <li><strong>InfluxDB_CPU_20240413-20240513.csv:</strong> CSV with hourly CPU utilization per core on the DuT.</li> <li><strong>jackrabbit-shaper-per\_day-20240421-20240513.csv:</strong> CSV with network load metrics (throughput in bytes and packets) every 48 minutes.</li> <li><strong>proglistoutput_after_enabling_bpf_stats_24hr.txt:</strong> Output from <code>bpftool prog list</code> after enabling <code>sysctl -w kernel.bpf_stats_enabled=1</code>, containing run counts and cumulative runtime for all eBPF programs on the system.</li> </ul> </li> <li><strong>rrc14_20231115_ipasn.dat: </strong>Data from RIPE´s route collector RRC14 in the middle of the measurement campaign (2023-11-15), used to map subnets into ASNs.</li> <li><strong>asn.txt:</strong> RIPE's list of AS names (from <a href="https://ftp.ripe.net/ripe/asnames/asn.txt">https://ftp.ripe.net/ripe/asnames/asn.txt</a>), used to translate ASNs into AS names.</li> </ul> <h2>Data format for raw data</h2> <p>The raw data is organized into one subfolder per day, with each subfolder containing one compressed json-file per minute. All files have been included in a tar-archive.</p> <p>The JSON format in each file consists of a single list of entries. There are three types of entries.</p> <h3>Configuration entry</h3> <p>Each file starts with one configuration entry, containing the following fields:<br><br></p> <ul> <li><strong> timestamp:</strong> Unix timestamp in nanoseconds when entry was created</li> <li><strong>bins: </strong>The maximum number of bins in the histogram of a subnet entry. Always 250 in this dataset. Note that histograms may contain less bins as unused bins in the upper range are truncated.</li> <li><strong>bin_width_ns:</strong> The width of the bins in nanoseconds. Always 4000000 (4ms) in this dataset.</li> <li><strong>aggregation_interval_ns: </strong>How often the aggregated stats are reported in nanoseconds. Always 10000000000 (10s) in this dataset.</li> <li><strong>timeout_interval_ns: </strong>How long epping will keep a subnet entry around since it last saw traffic for it. Always 30000000000 (30s) in this dataset. Note that a subnet entry may only be deleted after its stats are reported, so no stats will be lost regardless of value.</li> <li><strong>ipv4_prefix_len:</strong> Subnet size to aggregate IPv4 traffic. Always 24 (/24) in this dataset.</li> <li><strong>ipv6_prefix_len: </strong>Subnet size to aggregate IPv6 traffic. Always /48 in this dataset.</li> </ul> <p>Example:</p> <pre><code>{<span>"timestamp"</span>: <span>1698661062033443066</span>, <span>"bins"</span>: <span>250</span>, <span>"bin_width_ns"</span>: <span>4000000</span>, <span>"aggregation_interval_ns"</span>: <span>10000000000</span>, <span>"timeout_interval_ns"</span>: <span>30000000000</span>, <span>"ipv4_prefix_len"</span>: <span>24</span>, <span>"ipv6_prefix_len"</span>: <span>48</span>}</code></pre> <h3><br>Subnet stats entry</h3> <p>The most common entry will be the subnet stats entry, which contains the aggregated stats for one /24 subnet (or /48 if IPv6) over a 10 second period. Each entry always contains the following fields:</p> <ul> <li><strong>timestamp:</strong> A unix timestamp in nanoseconds of when the entry were reported. The entry will thus contain the stats for the period [timestamp - 10s, timestamp].</li> <li><strong>ip_prefix:</strong> The subnet in standard IPv4 or IPv6 CIDR notation.</li> <li><strong>rx_stats</strong> and <strong>tx_stats:</strong> Packet and byte counts for packets received from/transmitted to the subnet. Note that <em>rx</em> and <em>tx</em> is from the perspective of the epping node, not the perspective of the subnet, so <em>rx</em> is packets epping has seen sent from the subnet, and <em>tx</em> is packets epping has seen sent towards the subnet. <ul> <li><strong>TCP_TS</strong>, <strong>TCP_noTS</strong> and <strong>other:</strong> Entries for TCP traffic that has TCP timestamps enabled, TCP traffic with TCP timestamps disabled, and non-TCP traffic, respectively. The entry will be omitted if there is no traffic of the relevant type. <ul> <li><strong>packets:</strong> Number of packets of the specific type</li> <li><strong>bytes:</strong> Sum of bytes in the observed packets, including headers (up to and including the 14-byte Ethernet header).</li> </ul> </li> </ul> </li> </ul> <p>Additionally, the following fields will be present if any RTT has been observed for the subnet. Note that the subnet will include RTTs for traffic going towards the subnet, i.e. SRC -> subnet -> SRC.</p> <ul> <li><strong>min_rtt:</strong> The minimum observed RTT in nanoseconds.</li> <li><strong>max_rtt:</strong> The maximum observed RTT in nanoseconds.</li> <li><strong>histogram:</strong>: A histogram with all observed RTTs. The bin width and maximum number of bins is specified by the configuration entry (always 4ms wide bins and max 250 bins in this dataset). Note that the histogram will truncate 0-bins after the last non-zero bin (e.g. if the maximum RTT is 7 ms, the histogram will only contain 2 bins).</li> <li><strong>count_rtt:</strong> The total number of RTTs observed (sum of histogram bin counts)</li> <li><strong>mean_rtt:</strong> The approximate mean RTT in nanoseconds.</li> <li><strong>median_rtt:</strong> The approximate median RTT in nanoseconds.</li> <li><strong>p95_rtt:</strong> The approximate 95th percentile of the RTTs in nanoseconds</li> </ul> <p>Note that <em>mean_rtt</em>, <em>median_rtt</em> and <em>p95_rtt</em> are all calculated from the histogram, assuming all RTTs are in the middle of the histogram bins, and will thus be +/- 2ms of the real value in this dataset.</p> <p>Example:</p> <div> <pre><code>{<span>"timestamp"</span>: <span>1698661072800129847</span>, <span>"ip_prefix"</span>: <span><span>"</span>100.64.0.0/24<span>"</span></span>, <span>"rx_stats"</span>: {<span>"TCP_TS"</span>: {<span>"packets"</span>: <span>64816</span>, <span>"bytes"</span>: <span>23063341</span>}, <span>"other"</span>: {<span>"packets"</span>: <span>39783</span>, <span>"bytes"</span>: <span>16074723</span>}, <span>"TCP_noTS"</span>: {<span>"packets"</span>: <span>8505</span>, <span>"bytes"</span>: <span>7403649</span>}}, <span>"tx_stats"</span>: {<span>"TCP_TS"</span>: {<span>"packets"</span>: <span>134424</span>, <span>"bytes"</span>: <span>175370347</span>}, <span>"other"</span>: {<span>"packets"</span>: <span>76267</span>, <span>"bytes"</span>: <span>86104300</span>}, <span>"TCP_noTS"</span>: {<span>"packets"</span>: <span>7946</span>, <span>"bytes"</span>: <span>5174210</span>}}, <span>"min_rtt"</span>: <span>50766</span>, <span>"max_rtt"</span>: <span>440315940</span>, <span>"histogram"</span>: [<span>622</span>, <span>596</span>, <span>835</span>, <span>1106</span>, <span>932</span>, <span>410</span>, <span>228</span>, <span>139</span>, <span>108</span>, <span>68</span>, <span>78</span>, <span>79</span>, <span>73</span>, <span>63</span>, <span>49</span>, <span>35</span>, <span>22</span>, <span>33</span>, <span>31</span>, <span>24</span>, <span>21</span>, <span>18</span>, <span>23</span>, <span>15</span>, <span>20</span>, <span>20</span>, <span>37</span>, <span>35</span>, <span>96</span>, <span>113</span>, <span>95</span>, <span>80</span>, <span>36</span>, <span>22</span>, <span>24</span>, <span>27</span>, <span>17</span>, <span>31</span>, <span>9</span>, <span>9</span>, <span>14</span>, <span>18</span>, <span>10</span>, <span>21</span>, <span>22</span>, <span>21</span>, <span>22</span>, <span>25</span>, <span>8</span>, <span>9</span>, <span>16</span>, <span>8</span>, <span>12</span>, <span>9</span>, <span>7</span>, <span>5</span>, <span>6</span>, <span>6</span>, <span>3</span>, <span>4</span>, <span>4</span>, <span>2</span>, <span>2</span>, <span>1</span>, <span>1</span>, <span>3</span>, <span>3</span>, <span>1</span>, <span>0</span>, <span>3</span>, <span>2</span>, <span>0</span>, <span>1</span>, <span>1</span>, <span>1</span>, <span>1</span>, <span>2</span>, <span>2</span>, <span>1</span>, <span>2</span>, <span>1</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>1</span>, <span>2</span>, <span>0</span>, <span>1</span>, <span>1</span>, <span>0</span>, <span>0</span>, <span>1</span>, <span>0</span>, <span>1</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>1</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>1</span>], <span>"count_rtt"</span>: <span>6568</span>, <span>"mean_rtt"</span>: <span>37529232.64311815</span>, <span>"median_rtt"</span>: <span>18000000.0</span>, <span>"p95_rtt"</span>: <span>150000000.0</span>}</code></pre> </div> <div> <h3>Global counters entry</h3> <p>Each report interval will also contain one entry with global counters, with the following fields:</p> <ul> <li><strong>timestamp:</strong> Unix timestamp in nanoseconds of when the entry was reported. The entry will thus contain stats for the period [timestamp - 10s, timestamp].</li> <li><strong>protocol_counters:</strong> A set of fields with packet and byte counters for the <a href="https://www.iana.org/assignments/protocol-numbers/protocol-numbers.xhtml" rel="nofollow">protocols indicated in the IP-header</a>. The most common protocols will be named, specifically <strong>TCP</strong>, <strong>UDP</strong>, <strong>ICMP</strong>, <strong>ICMPv6</strong> and the special <strong>non-IP</strong> for non-IP packets. All other protocols will simply use the protocol number. Protocols for which no traffic has been observed are omitted. <ul> <li><strong>packets:</strong> The number of packets of the specific type</li> <li><strong>bytes:</strong> Sum of bytes in the observed packets, including headers (up to and including the 14-byte Ethernet header). Only the list of named protocols above will include this byte counter.</li> </ul> </li> <li><strong>ecn_counters:</strong> Counters for the four distinct ECN code points in the IP header. May contain the fields <strong>no_ECT</strong> (00), <strong>ECT1</strong> (01), <strong>ECT0</strong> (10) and <strong>CE</strong> (11).</li> </ul> </div> <p>Example:</p> <div> <pre><code>{<span>"timestamp"</span>: <span>1698661072848798575</span>, <span>"protocol_counters"</span>: {<span>"TCP"</span>: {<span>"packets"</span>: <span>249032</span>, <span>"bytes"</span>: <span>242282230</span>}, <span>"UDP"</span>: {<span>"packets"</span>: <span>119778</span>, <span>"bytes"</span>: <span>108251262</span>}, <span>"ICMP"</span>: {<span>"packets"</span>: <span>1523</span>, <span>"bytes"</span>: <span>445347</span>}, <span>"ICMPv6"</span>: {<span>"packets"</span>: <span>87</span>, <span>"bytes"</span>: <span>25031</span>}, <span>"41"</span>: {<span>"packets"</span>: <span>1</span>}, <span>"47"</span>: {<span>"packets"</span>: <span>251</span>}, <span>"89"</span>: {<span>"packets"</span>: <span>22</span>}}, <span>"ecn_counters"</span>: {<span>"no_ECT"</span>: <span>347980</span>, <span>"ECT1"</span>: <span>2</span>, <span>"ECT0"</span>: <span>22709</span>, <span>"CE"</span>: <span>3</span>}, <span>"errors"</span>: {}}</code></pre> </div>
Reduced latency in manual interception with anticipatory smooth eye movements
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Unexpected Latency of Z-Stereoretentive Ruthenium Olefin Metathesis Catalysts Bearing Unsymmetrical N-heterocyclic Carbene or Cyclic(alkyl)(amino)carbene Ligands
<p>Data confirming the structure of the new compounds obtained within the project, published in <em>Organometallics</em> <strong>2023</strong>, <em>42</em>, 2453–2459; <a href="https://doi.org/10.1021/acs.organomet.2c00428">doi.org/10.1021/acs.organomet.2c00428</a></p> <p>The research was supported by the National Science Centre, Poland (OPUS grant 2019/33/B/ST4/00874).</p>
Starlink Latency and Downlink Throughput Measurement Dataset
<p>Please refer to `README.txt` for a description of this dataset.</p>
Latency in Pulmonary Tuberculosis
ClinicalTrials.gov study NCT01154959. IPD Sharing: Not stated. Countries: 1. Publications: 20.
Effect of Respiratory Exercises On The Intravaginal Ejaculation Latency Time
ClinicalTrials.gov study NCT05517694. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Latency and Early Neonatal Provision of Antiretroviral Drugs Clinical Trial
ClinicalTrials.gov study NCT02431975. IPD Sharing: YES. Countries: 1. Publications: 2.
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International Brain Laboratory public data
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