If you shine light of high enough energy on to a metal, electrons will be emitted from the metal.

So where are we at with this virus people?

In continuum mechanics, stress is a physical quantity that expresses the internal forces that neighbouring particles of a continuous material exert on each other, while strain is the measure of the deformation of the material.

For example, when a solid vertical bar is supporting an overhead weight, each particle in the bar pushes on the particles immediately below it. When a liquid is in a closed container under pressure, each particle gets pushed against by all the surrounding particles.

The container walls and the pressure-inducing surface (such as a piston) push against them in (Newtonian) reaction.

These macroscopic forces are actually the net result of a very large number of intermolecular forces and collisions between the particles in those molecules. Stress is frequently represented by a lowercase Greek letter sigma (σ).

Residual stresses inside a plastic protractor are revealed by the polarized light.

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2020 marks the start of the quantum dot revolution.

Completely ignoring the fact that "photocatalytic generation of reactive oxygen species (ROS) from quantum dots (QDs) has been widely reported."

Results: It was found that whereas singlet oxygen (1O2) was not produced by photoexcited QDs, superoxide anion(O2− )and hydroxyl radicals(OH) were generated by QDs, especially in the presence of a physiological concentration of electron donating agent including NADH.

Using cell metabolic activity assays and various probes of ROS generation, the formation of ROS in cellular environments was demonstrated.

Conclusion: Illumination of QD-treated cells and bacteria with light did affect viability. The above results, together with those of assays in the presence of various scavengers of specific ROS, indicate that the formation of ROS by QDs mainly proceeded via electron transfer.

(THREAD CONTINUED)
As mentioned in the above videos human cloning has been confirmed and many of the elites you are now seeing are in fact clones or doubles standing in for themselves. The reason for these clones is because humanity has not yet awakened enough to handle knowing

all the crimes these sick disgusting Deep State Freemasonic Illuminati Cabal Globalist Satanists are truly involved in. The majority of society would literally go mentally insane if they learned all of this information at once.

The Nature versus Nurture is a debate about scientific doctrine. It is a debate about the correct way of explanation for cognition.

The Representation versus Non-Representation shares the same debate. It is also about the correct way of explanation for cognition.

It is also the difference between thinking in nouns versus thinking in verbs. Nature and Representation have deep roots in Western thinking. It is a functionalist and reductionist way of thinking that meets its limits when analyzing complex adaptive systems.

It is perhaps due to this Western bias that concepts from the study of complex systems have had little traction in modern society. It is of course, more than a Western bias, but a human bias to seek out simple causal explanations of this world.

But the world is complex and messy. Spiral dynamics which is an evolutionary model of individuals and society explains how models of the world shape ourselves and our civilization.

Next up at #enigma2021, Sanghyun Hong will be speaking about "A SOUND MIND IN A VULNERABLE BODY: PRACTICAL HARDWARE ATTACKS ON DEEP LEARNING"

(Hint: speaker is on the

In recent years ML models have worked from research labs to production, which makes ML security important. Adversarial ML research studies how to mess with ML

For example by messing with the training data (c.f. Tay which became super-racist super-fast) or by foiling ML models by changing inputs in ways humans can't see.

Prior work considers ML models in a standalone, mathematical way
* looks at the robustness in an isolated manner
* doesn't look at the whole ecosystem and how the model is used -- ML models are running in real hardware with real software which has real vulns!

This talk focuses on hardware-level vulnerabilities. This is particularly interesting because these can break cryptographic guarantees (because those are outside of their threat models)
e.g. fault injection attacks, side-channel attacks

Ct values can be used to estimate epidemic dynamics UPDATE! Ct values are expected to change depending on whether the epidemic is growing or declining, and we harness this to estimate the epidemic trajectory. Lots of cool new analyses and methods!

Highlights:
- Cts from symptom-based surveillance change over time, but the effect is weaker
- Methods to infer incidence using single cross-sections of Cts
- Unbiased by changing testing coverage
- Gaussian process (wiggly line) model for incidence tracking using Ct values
2/12

This work is *JOINTLY led* with @LeekShaffer and PI’d by @michaelmina_lab. Thank you also to the ever insightful @mlipsitch and to coauthors @SanjatKanjilal @gabriel_stacey and @nialljlennon. 3/12

Premise: times since infection depend on the epidemic trajectory. Distributions of randomly sampled viral loads proxy times since infection. With calibration, Ct values can estimate growth rate. We focus on qPCR in SARS-CoV-2, but the principle applies to any outbreak. 4/12

Result 1: viral loads are shifted higher (Cts lower) during epidemic growth and lower (Cts higher) during decline when individuals are sampled *based on the onset of symptoms*. We simulated linelist data under symptom-based surveillance and looked at TSI and Cts over time. 5/12

1/

Get a cup of coffee.

In this thread, I'll help you understand the basics of Binomial Thinking.

The future is always uncertain. There are many different ways it can unfold -- some more likely than others. Binomial thinking helps us embrace this view.

2/

The S&P 500 index is at ~3768 today.

Suppose we want to predict where it will be 10 years from now.

Historically, we know that this index has returned ~10% per year.

If we simply extrapolate this, we get an estimate of ~9773 for the index 10 years from now:

3/

What we just did is called a "point estimate" -- a prediction about the future that's a single number (9773).

But of course, we know the future is uncertain. It's impossible to predict it so precisely.

So, there's a sense of *false precision* in point estimates like this.

4/

To emphasize the uncertainty inherent in such predictions, a better approach is to predict a *range* of values rather than a single number.

For example, we may say the index will return somewhere in the *range* of 8% to 12% (instead of a fixed 10%) per year.

5/

10 years from now, this implies an index value in the *range* [8135, 11703]: