Year 2022 What they find is a new type of physics generated by their artificial intelligence.
The determination of state variables to describe physical systems is a challenging task. A data-driven approach is proposed to automatically identify state variables for unknown systems from high-dimensional observational data.
Year 2022 o.o!!!
Artificial intelligence approaches inspired by human cognitive function have usually single learned ability. The authors propose a multimodal foundation model that demonstrates the cross-domain learning and adaptation for broad range of downstream cognitive tasks.
While shedding and symptom may not be closely linked in general, we found total shedding and symptom severity to be highly correlated (Pearson 0.81, Supplementary Fig. S1). Furthermore, with one exception, low shedding implied low symptom severity and vice versa. Thus associations found between shedding and pre-inoculation biomarkers like the CPV are also present in symptom severity, although to a lesser degree. Therefore in the rest of this section we report associations for the less noisy shedding measurements. The total variance explained (\(R^2\) ) by a linear model relating CPV score to shedding titers is \(R^2=0.77\) (ratio of residual variance of linear regression to variance of titers). Furthermore, a logistic regression of total shedding onto the CPV score yielded a perfect discriminant between high and low shedders, respectively defined as those whose total shedding is below versus above the population median.
The correlation between shedding titers and CPV scores is robust to reductions in the number of NCPT variables composing the score. In fact the correlation between shedding and CPV increases to greater than 0.9 when only 6 NCPT measures are incorporated: digSym-time, digSym-correct, reaction-time, posner-tutorialTime, trail-time and trail-tutorialTime. Furthermore, the CVP score incorporating only the three basic NCPT measures digSym-time, digSym-correct, trail-time achieves a correlation level of approximately 0.7 (Fig. S2). We find that adding a fourth basic NCPT variable reaction time to the CPV score computation does not appreciably affect this level of correlation. On the other hand, replacing replacing either digSym-time or digSym-correct with posner-tutorialTime produces an increase in correlation to a level greater than 0.85.
To illustrate the role of the 18 individual NCPT variables in the CPV, we plot in Fig. 1e the univariate CPV scores for the two lowest shedding and the two highest shedding participants. This figure is extracted from Fig. S3 in the Supplementary that shows the sequence of univariate CPV scores for all 18 study participants. Superimposed on the plot of these variables is a boxplot indicating score sensitivity to session perturbation, determined by leave-one-out analysis where the univariate CPV was recomputed after successively leaving a single NCPT session out of each participant’s sequence (sans screening session). Figure 1e clearly shows that certain NCPT variables have significantly higher variability for the high shedders (lower two panels) than for the low shedders (top two panels). Note that the NCPT variable with highest variability (variable achieving peak score in each panel of Fig. 1e) differs across study participants.
The ability to transmit and manipulate, with minimal loss, the smallest unit of light—the photon—plays a pivotal role in optical communications as well as designs for quantum computers that would use light rather than electric charges to store and carry information.
Now, researchers at the National Institute of Standards and Technology (NIST) and their colleagues have connected, on a single microchip, quantum dots—artificial atoms that generate individual photons rapidly and on-demand when illuminated by a laser—with miniature circuits that can guide the light without significant loss of intensity.
To create the ultra-low-loss circuits, the researchers fabricated silicon-nitride waveguides—the channels through which the photons traveled—and buried them in silicon dioxide. The channels were wide but shallow, a geometry that reduced the likelihood that photons would scatter out of the waveguides. Encapsulating the waveguides in silicon dioxide also helped to reduce scattering.
Summary: Findings reveal how a person’s cognitive performance prior to viral exposure can predict the severity of symptoms once the virus develops.
Source: University of Michigan.
If your alertness and reaction time is see-sawing more than usual, you may be more at risk of a viral illness.
New research indicates that tens to hundreds of planet-sized nomadic worlds may populate the spherical volume centered on Earth and circumscribed by Proxima Centauri, and thus…
Year 2020 300 million earth like planets in our galaxy 💜
Scientists can finally say how many “potentially habitable” planets there may be in the Milky Way.
Year 2017 19,000,000,000,000,000,000,000 possible earth like planets in the universe ✨️ 💖 🙏 ❤️
How do scientists know that there are billions of other solar systems like us in the Universe? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world.
Answer by Robert Frost, Instructor and Flight Controller at NASA, on Quora:
Continue reading “The Number Of Earth-Like Planets In The Universe Is Staggering” »
On nights and weekends, Justin Gilmer attacked an old question in pure math using the tools of information theory.
While “protein” often evokes pictures of chicken breasts, these molecules are more similar to an intricate Lego puzzle. Building a protein starts with a string of amino acids—think a myriad of Christmas lights on a string— which then fold into 3D structures (like rumpling them up for storage).
DeepMind and Baker both made waves when they each developed algorithms to predict the structure of any protein based on their amino acid sequence. It was no simple endeavor; the predictions were mapped at the atomic level.
Designing new proteins raises the complexity to another level. This year Baker’s lab took a stab at it, with one effort using good old screening techniques and another relying on deep learning hallucinations. Both algorithms are extremely powerful for demystifying natural proteins and generating new ones, but they were hard to scale up.
