Category Archives: Uncategorized

Receive-only mode for phones to save power

Airplane mode cuts off all or non-wifi communication, which is undesirable. Receive-only mode would allow receipt of texts and recorded messages, save power and prevent detection of the phone by radio frequency methods (not by a metal detector). If the phone is stationary, then there is no need for it to send periodic keep-alive or hand-off signals to the cell phone tower. The phone’s accelerometer and GPS receiver can detect with reasonable accuracy whether it stays in the same cell tower’s range. Only when the phone moves a large enough distance will sending hand-off or check-in signals become necessary.

Location can also be detected using the radio receiver of the phone (which every phone has for calls and texts) if multiple cellphone towers are in range – just triangulate. A saved map of tower coverage areas in the phone helps position the phone and detect when the phone moves to a different tower’s area.

A software modification should be enough to create a receive-only mode: turn off sending (supplying power to the antenna) but keep receiving (measure and record the voltage and current in the antenna). Add optional deactivation of the receive-only mode based on the accelerometer and GPS detecting the phone moving out of range of the current cell tower.

Recumbent bicycle bunny hop in theory

I have not tried this, so it is just speculation. There are many claims online that a recumbent bike cannot be bunny hopped. However, lifting the front wheel should be possible while sitting on the bike, because lifting the front caster of an office chair is possible without touching the floor. Lean forward, then slam your torso back against the backrest – careful that you don’t tip over backward. Your legs may be lifted or the feet may rest on top of the “spider” at the bottom of the chair.

On a recumbent, a further boost comes from suddenly pedalling hard in low gear, which accelerates the rear wheel forward and under, rotating the front wheel up around the pivot of the rear wheel.

Lifting the rear wheel of a recumbent should be possible while seated, because popping your butt off the floor when sitting with straight legs is possible without using your leg muscles. Put your fists on the floor slightly behind and to the side of your hips. Bend your elbows, then suddenly straighten them, pushing explosively against the floor. Your butt and your fists lift a few inches. Keep your legs locked straight. Very strong people can do this with legs lifted (in boat pose: body in V-shape with only the butt and fists touching the floor).

Because lifting each wheel is possible and the movements do not directly oppose each other, a recumbent should be bunnyhoppable. Lift first the front and then the rear wheel.

Contraception increases high school graduation – questionable numbers

In Stevenson et al 2021 “The impact of contraceptive access on high school graduation” in Science Advances, some numbers do not add up. In the Supplementary Material, Table S1 lists the pre-intervention Other, non-Hispanic cohort size in the 2010 US Census and 2009 through 2017 1-year American Community Survey data as 300, but Table S2 as 290 = 100+70+30+90 (Black + Asian + American Indian + Other/Multiple Races). The post-intervention cohort size is 200 in Table S1, but 230 in Table S2, so the difference is in the other direction (S2 larger) and cannot be due to the same adjustment of one Table for both cohorts, e.g. omitting some racial group or double-counting multiracial people. The main conclusions still hold with the adjusted numbers.

It is interesting that the graduation rate for the Other race group is omitted from the main paper and the Supplementary Material Table S3, because by my calculations, in Colorado, the Other graduation rate decreased after the CFPI contraception access expansion, but in the Parallel Trends states (the main comparison group of US states that the authors use), the Other graduation rate increased significantly. The one missing row in the Table is exactly the one in which the results are the opposite to the rest of the paper and the conclusions of the authors.

Animal experiments on whether pose and expression control mood

Amy Cuddy promoted power poses which she claimed boosted confidence and success. Replication of her results failed (the effects were not found in other psychology studies), then succeeded again, so the debate continues. Similarly, adopting a smiling expression makes people happier. Measuring the psychological effects of posture and expression is complicated in humans. For example, due to experimenter demand effects. Animals are simpler and cheaper to experiment with, but I did not find any animal experiments on power poses on Google Scholar on 28.03.2021.

The idea of the experiment is to move the animal into a confident or scared pose and measure the resulting behaviour, stress hormones, dominance hormones, maybe scan the brain. Potentially mood-affecting poses differ between animals, but are well-known for common pets. Lifting a dog’s tail up its back is a confident pose. Moving the tail side to side or putting the chest close to the ground and butt up in a “play-with-me bow” is happy, excited. Putting the dog’s tail between the legs is scared. Moving the dog’s gums back to bare its teeth is angry. Arching a cat’s back is angry. Curling the cat up and half-closing its eyes is contented.

The main problem is that the animal may resist being moved into these poses or get stressed by the unfamiliar treatment. A period of habituation training is needed, but if the pose has an effect, then part of this effect realises during the habituation. In this case, the measured effect size is attenuated, i.e. the pre- and post-treatment mood and behaviour look similar.

A similar experiment in people is to have a person or a robot move the limbs of the participants of the experiment into power poses instead of asking them to assume the pose. The excuse or distraction from the true purpose of the experiment may be light physical exercise, physical therapy or massage. This includes a facial massage, which may stretch the face into a smile or compress into a frown. The usual questionnaires and measurements may be administered after moving the body or face into these poses or expressions.

Improving the accuracy of waist circumference measurement

Waistline needs to be measured for clothes size determination and health evaluation, but the instructions to do it are vague: keep the stomach neutral, neither sucked in nor bulged out, hold the measuring tape not too loosely nor too tight. A person’s waist expands and contracts with the breath, so at which point of the breath cycle should the measurement be taken? How much tension should the measuring tape have?

Averaging several measurements would be more accurate than a single vaguely defined one, both in the sense of replication across different measurers and in the sense of corresponding to the physical quantity of interest (health status, tightness of the clothes). One simple way is to average the measured perimeter of a maximally sucked-in gut and a maximally pushed-out gut.

A better measure, but quite difficult to take, is to average the waist circumference across the whole cycle of breathing in and out, for several cycles. A device to do this would be a loop of flexible measuring tape pulled into a roll by a spring, similarly to a construction measuring tape made of metal. The roll would contain electronics which continuously record how much tape is in the roll, therefore the length of the loop outside the roll. The length data could be averaged to obtain the average waistline.

A more modern method is to use two cameras pointed at the person from two sides at waist height, recording a 3D video of the waist over the breath cycle. The video could then be averaged on a computer to find the mean volume. Whole-body volume could be determined similarly – no need for Archimedes’ Eureka method of submerging the body in a bathtub with a volume scale on it. The volume and the weight determine the density of the body, which gives partial information about its fat percentage.

Paying pharmaceutical firms for capacity is problematic

Castillo et al. 2021 (doi:10.1126/science.abg0889) make many valid points, e.g., vaccine production should be greatly expanded using taxpayer money because the quicker recovery from the pandemic more than pays for the expansion. Castillo et al. also suggest paying pharmaceutical manufacturers for the capacity they install instead of the quantity they produce. The reasoning of the authors is that producers are delaying installing capacity and the delivery of their promised vaccine quantities to save costs and to supply higher-paying buyers first, because the penalties for delaying are small. Producers refuse to sign contracts with larger penalties.

What the authors do not mention is that the same problems occur when paying for capacity. In addition, the capacity needs to be monitored, which is more difficult than checking the delivered quantity. Before large-scale production, how to detect the „Potemkin capacity” of installing cheap production lines unsuitable for large quantities? The manufacturer may later simply claim technical glitches when the production line does not work. Effective penalties are needed, which in turn requires motivating the producer to sign a contract containing these, just like for a quantity contract.

Paying in advance for capacity before the vaccine is proven to work insures firms against the risk of failure, as Castillo et al. say. The problem is that such advance payment also attracts swindlers who promise a miracle cure and then run with the money – there is adverse selection in who enters the government’s capacity contract scheme. Thus capacity contracts should be restricted to firms with a good established reputation. However, vaccines from innovative entrants may also be needed, which suggests continuing to use quantity contracts at least for some firms. If the law requires treating firms equally, then they should all be offered a similar contract.

Identifying unmeasurable effort in contests

To distinguish unmeasurable effort from unmeasurable exogenous factors like talent or environmental interference in contests, assumptions are needed, even for partial identification when overall performance can be objectively measured (e.g., chess move quality evaluated by a computer). Combining one of the following assumptions with the additive separability of effort and the exogenous factors provides sign restrictions on coefficient estimates. Additive separability means that talent or the environment changes performance the same way at any effort level.

One such identifying assumption is that effort is greatest when it makes the most difference – against an equal opponent. By contrast, effort is lower against much better and much worse opponents.

A similar identifying assumption is that if there is personal conflict between some contest participants but not others, then effort is likely higher against a hated opponent than a neutral one.

The performance of a given contestant against an equal opponent compared to against an unequal one is a lower bound on how much effort affects performance. Similarly, the performance against a hated rival compared to against a neutral contestant is a lower bound on the effect of effort. The lower bound is not the total influence of effort, because even against an unequal neutral opponent, effort is still positive.

Computer vision training sets of photos are endogenous

In principle, every pixel could be independent of any other, so the set of possible photos is the number of pixels times the number of colours – billions at least. No training data set is large enough to cover these photo possibilities many times over, as required for statistical analysis, of which machine learning is a subfield. The problem is solved by restricting attention to a small subset of possible photos. In this case, there is a reasonable number of possible photos, which can be covered by a reasonably large training data set.

Useful photos on any topic usually contain just one main object, such as a face, with less than 100 secondary objects (furniture, clothes, equipment). There is a long right tail – some useful photos have dozens of the main object, like a group photo full of faces, but I do not know of a photo with a thousand distinguishable faces. Photos of mass events may have ten thousand people, but lack the resolution to make any face in these useful.

Only selected photos are worth analysing. Only photos sufficiently similar to these are worth putting in a computer vision training dataset. The sample selection occurs both on the input and the output side: few of the billions of pixel arrangements actually occur as photos to be classified by machine vision and most of the training photos are similar to those. There are thus fewer outputs to predict than would be generated from a uniform random distribution and more inputs close to those outputs than would occur if input data was uniform random. Both speed learning.

When photo resolution improves, more objects of interest may appear in photos without losing usefulness to blur. Then such photos become available in large numbers and are added to the datasets.

Moon phase and sleep correlation is not quite a sine wave

Casiraghi et al. (2021) in Science Advances (DOI: 10.1126/sciadv.abe0465) show that human sleep duration and onset depends on the phase of the moon. Their interpretation is that light availability during the night caused humans to adapt their sleep over evolutionary time. Casiraghi et al. fit a sine curve to both sleep duration and onset as functions of the day in the monthly lunar cycle, but their Figure 1 A, B for the full sample and the blue and orange curves for the rural groups in Figure 1 C, D show a statistically significant deviation from a sine function. Instead of same-sized symmetric peaks and troughs, sleep duration has two peaks with a small trough between, then a large sharp trough which falls more steeply than rises, then two peaks again. Sleep onset has a vertically reflected version of this pattern. These features are statistically significant, based on the confidence bands Casiraghi and coauthors have drawn in Figure 1.

The significant departure of sleep patterns from a sine wave calls into question the interpretation that light availability over evolutionary time caused these patterns. What fits the interpretation of Casiraghi et al. is that sleep duration is shortest right before full moon, but what does not fit is that the duration is longest right after full and new moons, but shorter during a waning crescent moon between these.

It would better summarise the data to use the first four terms of a Fourier series instead of just the first term. There seems little danger of overfitting, given N=69 and t>60.

A questionable choice of the authors is to plot the sleep duration and onset of only the 35 best-fitting participants in Figure 2. A more honest choice yielding the same number of plots would pick every other participant in the ranking from the best fit to the worst.

In the section Materials and Methods, Casiraghi et al. fitted both a 15-day and a 30-day cycle to test for the effect of the Moon’s gravitational pull on sleep. The 15-day component was weaker in urban communities than rural, but any effect of gravity should be the same in both. By contrast, the effect of moonlight should be weaker in urban communities, but the urban community data (Figure 1 C, D green curve) fits a simple sine curve better than rural. It seems strange that sleep in urban communities would correlate more strongly with the amount of moonlight, like Figure 1 shows.

Clinical trials of other drugs in other species to predict a drug’s effect in humans

Suppose we want to know whether a drug is safe or effective for humans, but do not have data on what it does in humans, only on its effects in mice, rats, rhesus macaques and chimpanzees. In general, we can predict the effect of the drug on humans better with the animal data than without it. Information on “nearby” realisations of a random variable (effect of the drug) helps predict the realisation we are interested in. The method should weight nearby observations more than observations further away when predicting. For example, if the drug has a positive effect in animals, then predicts a positive effect in humans, and the larger the effect in animals, the greater the predicted effect in humans.

A limitation of weighting is that it does not take into account the slope of the effect when moving from further observations to nearer. For example, a very large effect of the drug in mice and rats but a small effect in macaques and chimpanzees predicts the same effect in humans as a small effect in rodents and a large one in monkeys and apes, if the weighted average effect across animals is the same in both cases. However, intuitively, the first case should have a smaller predicted effect in humans than the second, because moving to animals more similar to humans, the effect becomes smaller in the first case but larger in the second. The idea is similar to a proportional integral-derivative (PID) controller in engineering.

The slope of the effect of the drug is extra information that increases the predictive power of the method if the assumption that the similarity of effects decreases in genetic distance holds. Of course, if this assumption fails in the data, then imposing it may result in bias.

Assumptions may be imposed on the method using constrained estimation. One constraint is the monotonicity of the effect in some measure of distance between observations. The method may allow for varying weights by adding interaction terms (e.g., the effect of the drug times genetic similarity). The interaction terms unfortunately require more data to estimate.

Extraneous information about the slope of the effect helps justify the constraints and reduces the need for adding interaction terms, thus decreases the data requirement. An example of such extra information is whether the effects of other drugs that have been tested in these animals as well as humans were monotone in genetic distance. Using information about these other drugs imposes the assumption that the slopes of the effects of different drugs are similar. The similarity of the slopes should intuitively depend on the chemical similarity of the drugs, with more distant drugs having more different profiles of effects across animals.

The similarity of species in terms of the effects drugs have on them need not correspond to genetic similarity or the closeness of any other observable characteristic of these organisms, although often these similarities are similar. The similarity of interest is how similar the effects of the drug are across these species. Estimating this similarity based on the similarity of other drugs across these animals may also be done by a weighted regression, perhaps with constraints or added interaction terms. More power for the estimation may be obtained from simultaneous estimation of the drug-effect-similarity of the species and the effect of the drug in humans. An analogy is demand and supply estimation in industrial organisation where observations about each side of the market give information about the other side. Another analogy is duality in mathematics, in this case between the drug-effect-similarity of the species and the given drug’s similarity of effects across these species.

The similarity of drugs in terms of their effects on each species need not correspond to chemical similarity, although it often does. The similarity of interest for the drugs is how similar their effects are in humans, and also in other species.

The inputs into the joint estimation of drug similarity, species similarity and the effect of the given drug in humans are the genetic similarity of the species, the chemical similarity of the drugs and the effects for all drug-species pairs that have been tested. In the matrix where the rows are the drugs and the columns the species, we are interested in filling in the cell in the row “drug of interest” and the column “human”. The values in all the other cells are informative about this cell. In other words, there is a benefit from filling in these other cells of the matrix.

Given the duality of drugs and species in the drug effect matrix, there is information to be gained from running clinical trials of chemically similar human-use-approved drugs in species in which the drug of interest has been tested but the chemically similar ones have not. The information is directly about the drug-effect-similarity of these species to humans, which indirectly helps predict the effect of the drug of interest in humans from the effects of it in other species. In summary, testing other drugs in other species is informative about what a given drug does in humans. Adapting methods from supply and demand estimation, or otherwise combining all the data in a principled theoretical framework, may increase the information gain from these other clinical trials.

Extending the reasoning, each (species, drug) pair has some unknown similarity to the (human, drug of interest) pair. A weighted method to predict the effect in the (human, drug of interest) pair may gain power from constraints that the similarity of different (species, drug) pairs increases in the genetic closeness of the species and the chemical closeness of the drugs.

Define Y_{sd} as the effect of drug d in species s. Define X_{si} as the observable characteristic (gene) i of species s. Define X_{dj} as the observable characteristic (chemical property) j of drug d. The simplest method is to regress Y_{sd} on all the X_{si} and X_{dj} and use the coefficients to predict the Y_{sd} of the (human, drug of interest) pair. If there are many characteristics i and j and few observations Y_{sd}, then variable selection or regularisation is needed. Constraints may be imposed, like X_{si}=X_i for all s and X_{dj}=X_j for all d.

Fused LASSO (least absolute shrinkage and selection operator), clustered LASSO and prior LASSO seem related to the above method.