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This Kaggle competition provides tabular data for us to explore and my first jump back into a Kaggle competition after concentrating on business school, circuit board design, and moving into a new role at work.

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Before even looking at the data it is essential to realize the number of variables present. Over 9 million machine identifiers are in this dataset, and by the end of the competition, I had around 100 different categories for each computer.

I wasn't the only one, the competition ran into a big trap when people discovered that you if the computer has automatic updates and you compare the release date of the AVSigversion with to that date.

Many people speculated that the competition still had time series elements hidden within the data and for good reason. Many Kaggle competitions have hidden data. Several users discussed AVSignatures from the training set and compared it to Public/Private you saw a significant leftward shift for those AVSignature time variables. Chris Deotte's provides a great example above. The training set looked like it had older data than Public/Private.

2. I created additional parameters surrounding days since patching. By setting a baseline of the last known patch, I could see how far the value was from the provided output. This technique provides excellent metrics for days out of compliance. I was particularly proud of this but got rid of the function as I was not seeing improvements to different models.

Ideally, we would want to see which patches were being put out and if the system was behind in patching. If we knew how many patches out of date a system was, we would be able to create a feature for this. Then we would be able to see if there was correlation between keeping items up-to-date and detections.

With such a large dataset, memory becomes a significant problem for many people. Even loading the data was difficult because everything came in as an int64. Even bools start as int64. This bloated method for assigning memory caused some machines to crash during loading.

If the size of your array is lower than 32,768 uniques, you are safe to turn it into an int16. Unique cities number over 100,000 so we use float32 instead of Int64. IsBeta is boolean, so we can make it int8.

By splitting the builder and the learner, I can keep the code cleaner, and it allows me to run it with different options more easily. Plus experiments go faster. If something crashes in the learner, I can shave 10 minutes off loading off of the reset.

We end with a list of probability for each machine having malware. Ideally, we should see a saddle where the highest number is either on 0 or 1. If you take just a few training epochs you see this Space Invaders looking thing suggesting with more training we would get there. It was not as effective as the top public kernels which is somewhat disappointing.

More data exploration and feature work- Probably the biggest problem was not better understanding how significant some of the features were in the problem. I should have checked more for interdependence.

AUCROC- I used cross-entropy for a loss function. However, I should have looked further into using AUCROC instead. While I could print off a metric, I was not able to better explore the feasibility of it.

Embedding Sizes- It seems like some embeddings exploded outward with many examples of 1s or 2s of unique values in a test set. For example, if Chicago only has a sample size of 2, should that feature be used? It would neither help you determine if there was an infection or not.

The $900,000 funding allocation is part of the Fiscal Year 2024 Commerce, Justice, Science, and Related Agencies appropriations bill signed into law by U.S. President Joe Biden earlier this month. U.S. Congressman Robert Menendez (NJ-08) and Senators Cory Booker (D-NJ) and Bob Menendez (D-NJ) were key in the effort.

Hoboken streets flooded in 2012 during Hurricane SandyThe university currently produces sophisticated four-day advance flood prediction for regional partners such as the Port Authority of New York & New Jersey, via the Stevens Flood Advisory System, and collaborates on resiliency research and advisement with federal agencies including FEMA, NOAA, the U.S. Geological Survey and municipalities including New York City.

Stevens data has assisted the U.S. Coast Guard thousands of times, helping save hundreds of livesThe university will acquire four aerial drones packing hyperspectral imaging cameras and LiDAR systems for imaging coastal damage; two seagoing drones, both equipped with real-time GPS and multibeam sonar systems; two vehicle-mounted LiDAR systems; and additional ultrasonic sensors, cameras and data-streaming gateways for deployment in selected vulnerable coastal areas across New Jersey. The equipment will measure and disseminate observations of flood inundation in real time.

Davidson Lab researchers Jon Miller, Laura Kerr, Marouane Temimi, Philip Orton, Raju Datla, Mahmoud Ayyad, and David Runnels were integral in the effort to secure the funding and in building and maintaining the observation and warning systems, added Hajj.

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Background:  Chronic kidney disease (CKD) measures (estimated glomerular filtration rate [eGFR] and albuminuria) are frequently assessed in clinical practice and improve the prediction of incident cardiovascular disease (CVD), yet most major clinical guidelines do not have a standardized approach for incorporating these measures into CVD risk prediction. "CKD Patch" is a validated method to calibrate and improve the predicted risk from established equations according to CKD measures.

Methods:  Utilizing data from 4,143,535 adults from 35 datasets, we developed several "CKD Patches" incorporating eGFR and albuminuria, to enhance prediction of risk of atherosclerotic CVD (ASCVD) by the Pooled Cohort Equation (PCE) and CVD mortality by Systematic COronary Risk Evaluation (SCORE). The risk enhancement by CKD Patch was determined by the deviation between individual CKD measures and the values expected from their traditional CVD risk factors and the hazard ratios for eGFR and albuminuria. We then validated this approach among 4,932,824 adults from 37 independent datasets, comparing the original PCE and SCORE equations (recalibrated in each dataset) to those with addition of CKD Patch.

Interpretation:  The "CKD Patch" can be used to quantitatively enhance ASCVD and CVD mortality risk prediction equations recommended in major US and European guidelines according to CKD measures, when available.

Background:  Atrial fibrillation (AF) is associated with increased risks of stroke and heart failure. Electronic health record (EHR)-based AF risk prediction may facilitate efficient deployment of interventions to diagnose or prevent AF altogether.

Conclusions:  EHR-AF demonstrates predictive accuracy for incident AF using readily ascertained EHR data. AF risk is associated with incident stroke and heart failure. Use of such risk scores may facilitate decision support and population health management efforts focused on minimizing AF-related morbidity.

In 2011, the first wave of the baby boom generation turned 65. Since that time, over 100,000 North Carolinians have turned 65 every year and this trend will continue well into the future. As a result, the older adult population will grow at double the rate of the population as a whole. By 2029, one in five North Carolinians will be at least 65 years old and by 2031 there will be more older adults than children. The median age is predicted to rise from 39 in 2021 to 42 by 2050 (compared to 35 in 2000).

The childhood population will grow much slower than in the past. Between 2021 and 2050, the under 18 population is predicted to grow by about half a million from 2.3 million children in 2021 to 2.8 million children by 2050.

In the past, most North Carolinians were non-Hispanic White. African Americans and American Indians were the second and third largest racial/ethnic groups in North Carolina. But since the 1990s, there has been rapid growth in the Hispanic and Asian & Pacific Islander populations. By 2050, our latest projections show that 14% of North Carolinians will be Hispanic/Latino, 52% non-Hispanic White, and 34% all other groups. This compares to 11% Hispanic/Latino, 62% non-Hispanic White, and 28% all other groups in 2020.

These population projections use historical censuses and the latest population estimates to produce population projections assuming a continuation of historical trends into the future. The State Demographer prepares these population projections annually. You can access summary tables or the several datasets that provide population projections by sex, age, race and Hispanic origin for the state, regions, and counties.

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We conducted a GWAS of dominance deviations from the additive model (Supplementary Note) by meta-analyzing summary statistics from association analyses conducted in 23andMe and UKB (N = 2,574,253). Theory and evidence from the quantitative genetics literature, including findings from two recent papers13,14 that estimated dominance SNP heritability across dozens of phenotypes (but not EA), suggest that dominance effects explain at most a very small share of the variance in polygenic phenotypes15. Nevertheless, in the behavior genetics literature, when the phenotypic correlation between monozygotic twins is more than twice as large as the phenotypic correlation between dizygotic twins, it remains common practice to attribute the violation of the additive model to dominance variance. 152ee80cbc

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