Viral suppression is measured as a viral load test result of <200 copies/mL at the most recent viral load test during measurement year. Black women are HIV priority population in the Austin TGA who have higher disparities than others with HIV.

How to find small area data and the problems that will encounter trying to do so.

The Deep Noise Suppression (DNS) challenge is designed to foster innovation in the area of noise suppression to achieve superior perceptual speech quality.

This challenge has two two tracks:

Track 1: Real-Time Denoising track for wide band scenario

The noise suppressor must take less than the stride time Ts (in ms) to process a frame of size T (in ms) on an Intel Core i5 quad-core machine clocked at 2.4 GHz or equivalent processor. For example, Ts = T/2 for 50% overlap between frames. The total algorithmic latency allowed including the frame size T, stride time Ts, and any look ahead must be less than or equal to 40ms. For example, for a real-time system that receives 20ms audio chunks, if you use a frame length of 20ms with a stride of 10ms resulting in an algorithmic latency of 30ms, then you satisfy the latency requirements. If you use a frame of size 32ms with a stride of 16ms resulting in an algorithmic latency of 48ms, then your method does not satisfy the latency requirements as the total algorithmic latency exceeds 40ms. If your frame size plus stride T1=T+Ts is less than 40ms, then you can use up to (40-T1) ms future information.

Track 2: Real-Time Denoising track for full band scenario

Satisfy Track 1 requirements but at 48 kHz.

The global data center fire detection and suppression market size was worth around USD 904 million in 2023 and is predicted to grow to around USD 1719 million by 2032

Wildfire Suppression Difficulty Index (SDI) 90th Percentile is a rating of relative difficulty in performing fire control work under regionally appropriate fuel moisture and 15 mph uphill winds (@ 20 ft). SDI factors in topography, fuels, expected fire behavior under prevailing conditions, fireline production rates in various fuel types with and without heavy equipment, and access via roads, trails, or cross-country travel. SDI does not account for standing snags or other overhead hazards to firefighters, so it is not a firefighter hazard map. It is only showing in relative terms where it is harder or easier to perform suppression work.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

Dataset for Quieting the Static: A Study of Static Analysis Alert Suppressions This is the dataset for our empirical study on the practices of software bug suppression in open source projects. Directory Structure

./categorization: Contains the categorization spreadsheet data of sampled suppressions in csv format, as well as the raw JSON sample. ./categorization/html_files: Contains the annotated code fragments of the sampling process in HTML format. ./data: Contains the datasets of canonicalized configuration and annotation warning suppressions in JSON format.

The data center fire detection and suppression market size was valued at USD 1.14 billion in 2024 and is set to exceed USD 2.88 billion by 2037, expanding at over 7.4% CAGR during the forecast period i.e., between 2025-2037. North America industry is estimated to account for largest revenue share of 35% by 2037, attributed to availability of a large number of data centers in the United States.

The Latin America data center fire detection and suppression market is expected to grow at a CAGR of over 7% from 2022 to 2027, and is expected to cross USD 1 billion by 2027.

United States Data Center Fire Detection and Suppression Market has valued at USD 1.6 billion in 2023 and is anticipated to project robust growth in the forecast period with a CAGR of 7.5% through 2029.

Data replication files

These images, videos, and tables show experimental data, where single lines of viscoelastic inks were extruded into moving viscoelastic support baths. Lines were printed at varying angles relative to the camera, such that videos and images captured the side of horizontal lines, cross-sections of horizontal lines, and the side of vertical lines. Metadata including pressure graphs, programmed speeds, toolpaths, and rheology data are also included.

Mating disruption is a commercially available management tactic for pyralid moths that are pests of stored products. However, evaluations of efficacy have had limited replication which limits the ability to draw conclusions about effectiveness or impact of different variables on efficacy. We evaluated mating disruption of Plodia interpunctella in 33 retail pet supply stores, and the impact of factors such as insect density and application rate on efficacy. The objective of the project reported here was to evaluate how well mating disruption can suppress moth populations in retail pet stores, by assessing treatments under ‘real world’ conditions found in commercial operations. Data set included the characteristics of the stores including volume and geographic location, the mating disruption treatment types including the number and location of dispensers, and the captures of moths in pheromone baited traps over time before applying the mating disruption treatment and during the mating disruption treatment.

This dataset contains spiking activity from 5 utah-arrays in human LOC in response to a flash suppression paradigm.

The present study investigated whether adaptive suppression mechanisms can be applied to stimuli with a history of threat association. In the experiment, a threat-conditioning task was first used to associate one of two colors—green or cyan—with an electric shock, thereby establishing conditions of threat-history and no-threat-history. Subsequently, in a visual search task, 30 participants reported the orientation of the line inside the target diamond while occasionally being either undistracted or distracted by threat-history or no-threat-history distractors, which appeared across various spatial locations. The results showed that distractors appearing at high-probability locations were effectively suppressed, with suppression being stronger for threat-history distractors than for no-threat-history distractors. These findings indicate that threat history may facilitate visual search through an adaptive attentional suppression mechanism.

Competition theory is founded on the premise that individuals benefit from harming their competitors, which helps them secure resources and prevent inhibition by neighbours. When multiple individuals compete, however, competition has complex indirect effects that reverberate through competitive neighbourhoods. The consequences of such “diffuse” competition are poorly understood. For example, competitive effects may dilute as they propagate through a neighbourhood, weakening benefits of neighbour suppression. Another possibility is that competitive effects may rebound on strong competitors, as their inhibitory effects on their neighbours benefit other competitors in the community. Diffuse competition is unintuitive in part because we lack a clear conceptual framework for understanding how individual interactions manifest in communities of multiple competitors. Here, I use mathematical and agent-based models to illustrate that diffuse interactions—as opposed to direct pairwise interactions—are likely the dominant mode of interaction among multiple competitors. Consequently, competitive effects may regularly rebound, incurring fitness costs under certain conditions, especially when kin-kin interactions are common. These models provide a powerful framework for investigating competitive ability and its evolution and produce clear predictions in ecologically realistic scenarios. Methods The data are based on mathematical and computational simulations, executed and analyzed in R.

An .xlsx formatted table of summary data for all neurons analyzed. Data is the average firing rate 75 to 375 ms post image onset. Each cell is the average of 4 trials for the given condition, presented in pseudorandom order during a recording session. Rows are neurons, columns are conditions.

Data Organization:

Row: rows 1-32 are monkey1, neurons 1-32, tetrad1 rows 33-111 are monkey2, neurons 1-79, tetrad1 rows 112-143 are monkey1, neurons 1-32, tetrad2 rows 144-222 are monkey2, neurons 1-79, tetrad2

Column: 1: prime response, neither match condition, shape1 color1 2: prime response, neither match condition, shape1 color2 3: prime response, neither match condition, shape2 color1 4: prime response, neither match condition, shape2 color2 5: prime response, shape match condition, shape1 color1 6: prime response, shape match condition, shape1 color2 7: prime response, shape match condition, shape2 color1 8: prime response, shape match condition, shape2 color2 9: prime response, color match condition, shape1 color1 10: prime response, color match condition, shape1 color2 11: prime response, color match condition, shape2 color1 12: prime response, color match condition, shape2 color2 13: prime response, both match condition, shape1 color1 14: prime response, both match condition, shape1 color2 15: prime response, both match condition, shape2 color1 16: prime response, both match condition, shape2 color2 17: probe response, neither match condition, shape1 color1 18: probe response, neither match condition, shape1 color2 19: probe response, neither match condition, shape2 color1 20: probe response, neither match condition, shape2 color2 21: probe response, shape match condition, shape1 color1 22: probe response, shape match condition, shape1 color2 23: probe response, shape match condition, shape2 color1 24: probe response, shape match condition, shape2 color2 25: probe response, color match condition, shape1 color1 26: probe response, color match condition, shape1 color2 27: probe response, color match condition, shape2 color1 28: probe response, color match condition, shape2 color2 29: probe response, both match condition, shape1 color1 30: probe response, both match condition, shape1 color2 31: probe response, both match condition, shape2 color1 32: probe response, both match condition, shape2 color2

Saudi Arabia Data Center Fire Detection and Suppression Market was valued at USD 56 million in 2023 and is anticipated to project robust growth in the forecast period with a CAGR of 11.4% through 2029.

Global Data Center Fire Detection and Suppression Market was valued at USD 1.2 Billion in 2023 and is anticipated to project robust growth in the forecast period with a CAGR of 7.8% through 2029

Wildfire Suppression Difficulty Index (SDI) 90th Percentile is a rating of relative difficulty in performing fire control work under regionally appropriate fuel moisture and 15 mph uphill winds (@ 20 ft).

Get key insights from Market Research Intellect's Data Center Fire Detection And Suppression Market Report, valued at USD 5.2 billion in 2024, and forecast to grow to USD 8.9 billion by 2033, with a CAGR of 7.5% (2026-2033).