#lowthreshold
LightNews LightNews
pablogranell.bsky.social
ADAVIN Tall Garden Fence Wide Door Low Threshold 54 inch High 4 Panels Total 8.8 Ft(L) Black Heavy Duty Metal Outdoor Dog Fence for Yard or Camping Garden Fencing Animal Barrier #tallfence #lowthreshold #dogfence #outdoorfence
ADAVIN Tall Garden Fence Wide Door Low Threshold 54 inch High 4 Panels Total 8.8 Ft(L) Black Heavy Duty Metal Outdoor Dog Fence for Yard or Camping Garden Fencing Animal Barrier
www.amazon.com
November 6, 2026 at 9:09 AM
pablogranell.bsky.social
ADAVIN Tall Garden Fence Wide Door Low Threshold 54 inch High 10 Panels Total 22.1 Ft(L) Black Heavy Duty Metal Dog Fencing for Yard or Camping Suitable for Indoor Pet Fence or Outdoor Dog Fencing #gardenfence #yardfence #lowthreshold #tallfence #heavyduty
ADAVIN Tall Garden Fence Wide Door Low Threshold 54 inch High 10 Panels Total 22.1 Ft(L) Black Heavy Duty Metal Dog Fencing for Yard or Camping Suitable for Indoor Pet Fence or Outdoor Dog Fencing
www.amazon.com
November 6, 2026 at 2:12 PM
epsteinweb.bsky.social
Parts 1-2/2 — EFTA00974589.jpg
#epsteinweb #efta00974589
https://epsteinweb.org
Available in the iOS app store now!
https://apps.apple.com/us/app/epstein-web/id6758880661
EFTA00974587 | EFTA00974589 7. Goto step 1 Instead, the state of the art is to use fairly simple data-mining to flag suspicious events with a lowthreshold - yielding a set of candidate events much smaller than the initial haystack but containing a relatively large number of false positives. In the above example, we use clustering that looks at caller ID data for calls to the bank, card transactions, payment methods, IP address data for access to the website, and account details. Seemingly unrelated accounts that are linked are scored by aggregate credit risk and queued up for human analysis in a rich, interactive analytic environment. This is how the final determination of fraud is made. The magic of our software is not magic at all - we integrate the data across multiple, disparate sources of data into a human-conceptual model based on a constrained ontology of the problem at hand. The interface is interactive (sometimes requiring some pretty serious engineering to pull off) and speaks a language that is familiar to the human experts on the problem, enabling them to move through large volumes of data very quickly. (For a simple but demonstrative workflow, take a look at this cybersecurity demo) Our newest innovation is letting the human analysts specify the pattern matching algorithms in this ontologically-typed language (Conceptually: "two credit card accounts used at the same store to buy the same item within three minutes of each other") that is then translated into search jobs on the map-reduce cluster. This allows for a tight feedback loop and hopefully stay ahead of the fraudsters. What I've been noodling on is using a mix of MDF (these institutions fighting fraud are often combing through terabytes of data produced per day) and deep learning to see if you could train classifiers that would not only spot fraud but be able to see it as their tactics change. It's sort of an interesting twist on the Turing Test: can an AI detect the signature of a human that's actively trying to evade detection? *********************************************************** The information contained in this communication is confidential, may be attorney-client privileged, may constitute inside information, and is intended only for the use of the addressee. It is the property of Jeffrey Epstein Unauthorized use, disclosure or copying of this communication or any part thereof is strictly prohibited and may be unlawful. If you have received this communication in error, please notify us immediately by return e-mail or by e-mail to jeevacation@gmail.com, and destroy this communication and all copies thereof, including all attachments. copyright -all rights reserved ******************* ********************** ****************** The information contained in this communication is confidential, may be attorney-client privileged, may constitute inside information, and is intended only for the use of the addressee. It is the property of Jeffrey Epstein EFTA00974589
May 8, 2026 at 12:55 PM
epsteinweb.bsky.social
Parts 1-2/2 — EFTA00974585.jpg
#epsteinweb #efta00974585
https://epsteinweb.org
Available in the iOS app store now!
https://apps.apple.com/us/app/epstein-web/id6758880661
EFTA00974583 | EFTA00974585 2. Use accounts, pay balances, drive up credit lines 3. Hit magic number, max out all cards 4. Write fake checks to zero balances 5. Max cards a second time before the checks bounce 6. abandon the fake identities 7. Goto step 1 Instead, the state of the art is to use fairly simple data-mining to flag suspicious events with a lowthreshold - yielding a set of candidate events much smaller than the initial haystack but containing a relatively large number of false positives. In the above example, we use clustering that looks at caller ID data for calls to the bank, card transactions, payment methods, IP address data for access to the website, and account details. Seemingly unrelated accounts that are linked are scored by aggregate credit risk and queued up for human analysis in a rich, interactive analytic environment. This is how the final determination of fraud is made. The magic of our software is not magic at all - we integrate the data across multiple, disparate sources of data into a human-conceptual model based on a constrained ontology of the problem at hand. The interface is interactive (sometimes requiring some pretty serious engineering to pull off) and speaks a language that is familiar to the human experts on the problem, enabling them to move through large volumes of data very quickly. (For a simple but demonstrative workflow, take a look at this cybersecurity demo) Our newest innovation is letting the human analysts specify the pattern matching algorithms in this ontologically-typed language (Conceptually: "two credit card accounts used at the same store to buy the same item within three minutes of each other") that is then translated into search jobs on the map-reduce cluster. This allows for a tight feedback loop and hopefully stay ahead of the fraudsters. What I've been noodling on is using a mix of MDF (these institutions fighting fraud are often combing through terabytes of data produced per day) and deep learning to see if you could train classifiers that would not only spot fraud but be able to see it as their tactics change. It's sort of an interesting twist on the Turing Test: can an Al detect the signature of a human that's actively trying to evade detection? *********************************************************** The information contained in this communication is confidential, may be attorney-client privileged, may constitute inside information, and is intended only for the use of the addressee. It is the property of Jeffrey Epstein Unauthorized use, disclosure or copying of this communication or any part thereof is strictly prohibited and may be unlawful. If you have received this communication in error, please notify us immediately by return e-mail or by e-mail to jeevacation@gmail.com, and destroy this communication and all copies thereof, including all attachments. copyright -all rights reserved *********************************************************** EFTA00974585
May 8, 2026 at 12:54 PM