Nosimpler

Josh Mitchell, a consultant at the security firm Nuix, analyzed five body camera models from five different companies: Vievu, Patrol Eyes, Fire Cam, Digital Ally, and CeeSc. The companies all market their devices to law enforcement groups around the US...

In all but the Digital Ally device, the vulnerabilities would allow an attacker to download footage off a camera, edit things out or potentially make more intricate modifications, and then re-upload it, leaving no indication of the change. Or an attacker could simply delete footage they don't want law enforcement to have...

Additionally, Mitchell says that some of the more sophisticated models, which contain radios for Bluetooth or cellular data connectivity, also have vulnerabilities that can be exploited to remotely stream live footage off the cameras, or to modify, add, and delete the footage stored on the devices...

Then, when the camera connects to a PC for syncing, it could deliver all sorts of malicious code: a Windows exploit that could ultimately allow an attacker to gain remote access to the police network, ransomware to spread across the network and lock everything down, a worm that infiltrates the department's evidence servers and deletes everything, or even cryptojacking software to mine cryptocurrency using police computing resources...

"These are full-feature computers walking around on your chest, and they have all of the issues that go along with that."

The post Spiders Can Fly! appeared first on Marginal REVOLUTION.

The post Every law is violent appeared first on Marginal REVOLUTION.

JUST IN: Rep. Chris Collins (R-NY), Trump’s earliest congressional backer, arrested by the FBI on securities fraud-related charges, via @jonathan4ny

— Peter Alexander (@PeterAlexander) August 8, 2018

This paper explores the physics of the what-if question "what if the entire Earth was instantaneously replaced with an equal volume of closely packed, but uncompressed blueberries?" While the assumption may be absurd, the consequences can be explored rigorously using elementary physics. The result is not entirely dissimilar to a small ocean-world exoplanet.

Kick Control: Using the Attracting States Arising Within the Sensorimotor Loop of Self-Organized Robots as Motor Primitives.

Front Neurorobot. 2018;12:40

Authors: Sándor B, Nowak M, Koglin T, Martin L, Gros C

Abstract
Self-organized robots may develop attracting states within the sensorimotor loop, that is within the phase space of neural activity, body and environmental variables. Fixpoints, limit cycles and chaotic attractors correspond in this setting to a non-moving robot, to directed, and to irregular locomotion respectively. Short higher-order control commands may hence be used to kick the system from one self-organized attractor robustly into the basin of attraction of a different attractor, a concept termed here as kick control. The individual sensorimotor states serve in this context as highly compliant motor primitives. We study different implementations of kick control for the case of simulated and real-world wheeled robots, for which the dynamics of the distinct wheels is generated independently by local feedback loops. The feedback loops are mediated by rate-encoding neurons disposing exclusively of propriosensoric inputs in terms of projections of the actual rotational angle of the wheel. The changes of the neural activity are then transmitted into a rotational motion by a simulated transmission rod akin to the transmission rods used for steam locomotives. We find that the self-organized attractor landscape may be morphed both by higher-level control signals, in the spirit of kick control, and by interacting with the environment. Bumping against a wall destroys the limit cycle corresponding to forward motion, with the consequence that the dynamical variables are then attracted in phase space by the limit cycle corresponding to backward moving. The robot, which does not dispose of any distance or contact sensors, hence reverses direction autonomously.

PMID: 30050427 [PubMed]

In America’s less affluent neighborhoods, low-income minorities are often left to grapple with the gentrification juggernaut all on their own. But these marginalized people will be happy to know they have an ally in Ryan and Caitlin Hubbard and their son, Corey—a white, upper-middle-class family that is helping to fight the good fight by refusing to live anywhere that isn’t already 100 percent white.

We introduce an all-optical Diffractive Deep Neural Network (D2NN) architecture that can learn to implement various functions after deep learning-based design of passive diffractive layers that work collectively. We experimentally demonstrated the success of this framework by creating 3D-printed D2NNs that learned to implement handwritten digit classification and the function of an imaging lens at terahertz spectrum. With the existing plethora of 3D-printing and other lithographic fabrication methods as well as spatial-light-modulators, this all-optical deep learning framework can perform, at the speed of light, various complex functions that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that can learn to perform unique tasks using D2NNs.

The nation's top voting machine maker has admitted in a letter to a federal lawmaker that the company installed remote-access software on election-management systems it sold over a period of six years, raising questions about the security of those systems and the integrity of elections that were conducted with them.

In a letter sent to Sen. Ron Wyden (D-OR) in April and obtained recently by Motherboard, Election Systems and Software acknowledged that it had "provided pcAnywhere remote connection software … to a small number of customers between 2000 and 2006," which was installed on the election-management system ES&S sold them.

The statement contradicts what the company told me and fact checkers for a story I wrote for the New York Times in February. At that time, a spokesperson said ES&S had never installed pcAnywhere on any election system it sold. "None of the employees, … including long-tenured employees, has any knowledge that our voting systems have ever been sold with remote-access software," the spokesperson said.

ES&S did not respond on Monday to questions from Motherboard, and it’s not clear why the company changed its response between February and April. Lawmakers, however, have subpoena powers that can compel a company to hand over documents or provide sworn testimony on a matter lawmakers are investigating, and a statement made to lawmakers that is later proven false can have greater consequence for a company than one made to reporters...

ES&S is the top voting machine maker in the country, a position it held in the years 2000-2006 when it was installing pcAnywhere on its systems. The company's machines were used statewide in a number of states, and at least 60 percent of ballots cast in the US in 2006 were tabulated on ES&S election-management systems...

Election-management systems are not the voting terminals that voters use to cast their ballots, but are just as critical: they sit in county election offices and contain software that in some counties is used to program all the voting machines used in the county; the systems also tabulate final results aggregated from voting machines...

But election-management systems and voting machines are supposed to be air-gapped for security reasons—that is, disconnected from the internet and from any other systems that are connected to the internet. ES&S customers who had pcAnywhere installed also had modems on their election-management systems so ES&S technicians could dial into the systems and use the software to troubleshoot, thereby creating a potential port of entry for hackers as well...

Wyden told Motherboard that installing remote-access software and modems on election equipment “is the worst decision for security short of leaving ballot boxes on a Moscow street corner.”
In 2006, the same period when ES&S says it was still installing pcAnywhere on election systems, hackers stole the source code for the pcAnyhere software, though the public didn’t learn of this until years later in 2012 when a hacker posted some of the source code online, forcing Symantec, the distributor of pcAnywhere, to admit that it had been stolen years earlier...

He notes that election officials who purchased the systems likely were not aware of the potential risks they were taking in allowing this and didn’t understand the threat landscape to make intelligent decisions about installing such software.

All of this raises questions about how many counties across the US had remote-access software installed—in addition to ES&S customers—and whether intruders had ever leveraged it to subvert elections...

Wyden says he’s still waiting for ES&S to respond to the outstanding questions he sent the company in March. “ES&S needs to stop stonewalling and provide a full, honest accounting of equipment that could be vulnerable to remote attacks,” he told Motherboard. “When a corporation that makes half of America’s voting machines refuses to answer the most basic cyber security questions, you have to ask what it is hiding.”

Plan your experiment, gather your data, do your inference for all effects and interactions of interest. When all is said and done, accept some level of uncertainty in your conclusions: you might not be 97.5% sure that the treatment effect is positive, but that’s fine. For one thing, decisions need to be made. You were already going to make some decision with much less information—that is, with much more uncertainty. Now that you have more information, you can make a more informed decision. The other thing is, even if you did have a super-clean experiment with excellent measurements and a large and stable effect, so that you had that 95% interval excluding zero for your quantity of interest . . . so what? Whatever you care about is in the future, so even if your treatment was so great compared to the alternative in your sample being studied, there’s no saying what it will be in future populations under different conditions.

That’s not to say that you can’t learn from data; I’m not saying that at all. You can learn a lot from data. But forget about 95%. Just do your best, live your life, and be open about your uncertainties. You might get run over by a bus tomorrow anyway.

The post On this 4th of July, let’s declare independence from “95%” appeared first on Statistical Modeling, Causal Inference, and Social Science.

joint post with Jonathan Lorand, Blake Pollard, and Maru Sarazola

In the cell, chemical reactions are often ‘coupled’ so that reactions that release energy drive reactions that are biologically useful but involve an increase in energy. But how, exactly, does coupling work?

Much is known about this question, but the literature is also full of vague explanations and oversimplifications. Coupling cannot occur in equilibrium; it arises in open systems, where the concentrations of certain chemicals are held out of equilibrium due to flows in and out. One might thus suspect that the simplest mathematical treatment of this phenomenon would involve non-equilibrium steady states of open systems. However, Bazhin has shown that some crucial aspects of coupling arise in an even simpler framework:

• Nicolai Bazhin, The essence of ATP coupling, ISRN Biochemistry 2012 (2012), article 827604.

He considers ‘quasi-equilibrium’ states, where fast reactions have come into equilibrium and slow ones are neglected. He shows that coupling occurs already in this simple approximation.

In this series of blog articles we’ll do two things. First, we’ll review Bazhin’s work in a way that readers with no training in biology or chemistry should be able to follow. (But if you get stuck, ask questions!) Second, we’ll explain a fact that seems to have received insufficient attention: in many cases, coupling relies on emergent conservation laws.

Conservation laws are important throughout science. Besides those that are built into the fabric of physics, such as conservation of energy and momentum, there are also many ’emergent’ conservation laws that hold approximately in certain circumstances. Often these arise when processes that change a given quantity happen very slowly. For example, the most common isotope of uranium decays into lead with a half-life of about 4 billion years—but for the purposes of chemical experiments in the laboratory, it is useful to treat the amount of uranium as a conserved quantity.

The emergent conservation laws involved in biochemical coupling are of a different nature. Instead of making the processes that violate these laws happen more slowly, the cell uses enzymes to make other processes happen more quickly. At the time scales relevant to cellular metabolism, the fast processes dominate, while slowly changing quantities are effectively conserved. By a suitable choice of these emergent conserved quantities, the cell ensures that certain reactions that release energy can only occur when other ‘desired’ reactions occur. To be sure, this is only approximately true, on sufficiently short time scales. But this approximation is enlightening!

Following Bazhin, our main example involves ATP hydrolysis. We consider this following schema for a whole family of reactions:

Some concrete examples of this schema include:

• The synthesis of glutamine (XY) from glutamate (X) and ammonium (Y). This is part of the important glutamate-glutamine cycle in the central nervous system.

• The synthesis of sucrose (XY) from glucose (X) and fructose (Y). This is one of many processes whereby plants synthesize more complex sugars and starches from simpler building-blocks.

In these and other examples, the two reactions, taken together, have the effect of synthesizing a larger molecule XY out of two parts X and Y while ATP is broken down to ADP and the phosphate ion P_i Thus, they have the same net effect as this other pair of reactions:

The first reaction here is just the synthesis of XY from X and Y. The second is a deliberately simplified version of ATP hydrolysis. The first involves an increase of energy, while the second releases energy. But in the schema used in biology, these processes are ‘coupled’ so that ATP can only break down to ADP + P_i if X and Y combine to form XY.

As we shall see, this coupling crucially relies on a conserved quantity: the total number of Y molecules plus the total number of P_i ions is left unchanged by reactions (1) and (2). This fact is not a fundamental law of physics, nor even a general law of chemistry (such as conservation of phosphorus atoms). It is an emergent conservation law that holds approximately in special situations. Its approximate validity relies on the fact that the cell has enzymes that make reactions (1) and (2) occur more rapidly than reactions that violate this law, such as (3) and (4).

In the series to come, we’ll start by providing the tiny amount of chemistry and thermodynamics needed to understand what’s going on. Then we’ll raise the question “what is coupling?” Then we’ll study the reactions required for coupling ATP hydrolysis to the synthesis of XY from components X and Y, and explain why these reactions are not yet enough for coupling. Then we’ll show that coupling occurs in a ‘quasiequilibrium’ state where reactions (1) and (2), assumed much faster than the rest, have reached equilibrium, while the rest are neglected. And then we’ll explain the role of emergent conservation laws!

 
The paper:

• John Baez, Jonathan Lorand, Blake S. Pollard and Maru Sarazola,
Biochemical coupling through emergent conservation laws.

The blog series:

• Part 1 – Introduction.

• Part 2 – Review of reaction networks and equilibrium thermodynamics.

• Part 3 – What is coupling?

• Part 4 – Interactions.

• Part 5 – Coupling in quasiequilibrium states.

• Part 6 – Emergent conservation laws.

• Part 7 – The urea cycle.

• Part 8 – The citric acid cycle.

Pryor unhooks the deer’s skull from the wall above his still-curled-up companion. Examines it. Not a good specimen –the back half of the lower jaw’s missing, a gap that, with the open cranial cavity, makes room enough for Pryor’s head.

He puts it on. – Will Eaves, Murmur

So as we roll into the last dying embers of the flaming glory that is (North American) Pride Month (Europe you’ve still got some fun ahead), I’ve doing my best to make both good and questionable decisions.

The good decision was to read an absolutely fabulous book by a British author named Will Eaves (on a run of absolutely stunning books) who fairly recently released a book called Murmur.  Murmur is an odd beast, I guess you could say it’s a novel about Alan Turing but that would be moderately inaccurate and probably unfair.

Firstly, because even from one of my favourite authors, I am basically allergic to people writing about maths and mathematicians. It’s always so stodgy and wooden, as if they are writing about a world they don’t understand but also can’t convincingly fake. (Pride month analogy: I mostly avoid straight people writing queer stories for the same reason.) And Turing is a particular disaster for cultural portrayals: he intersects with too many complicated things (world war 2, cryptography, computers, pre-1967 British homosexuality) for him to ever be anything but an avatar.

So this is not a book about Alan Turing being a tortured genius. It’s a book about a guy called Alec Pryor who just happens to share a bunch of biographical details with Turing (Bletchley, Cambridge, ex-Fiance, arrest and chemical castration, Jungian therapist). And it’s not a book about him being sad, wronged, gay genius who kills himself. It’s a story about him living and him processes the changes in his internal life due to his punishment and his interactions with the outside world and his past and his musings on consciousness and computation.

All of which is to say Murmur is a complex, wonderfully written book that over its hundred and seventy something pages sketches out a fully realized world that doesn’t make me want to hide under the sofa in despair. And it does that rare thing for people telling this story: it doesn’t flatten out the story by focusing on the punchline but rather the person and the life behind it.

(As an aside, I’d strongly recommend Hannah Gadsby’s Netflix special Nanette, which talks about the damage we do by truncating our stories to make other people happy. It’s the only cultural document so far of 2018 worth going out of your way to see.)

I would recommend you find yourself a copy. It’s published by a small press, so order it from them or, if you’re in London, pop into Gays The Word and get yourself a copy.

And now that you’ve made your way through the unexpected book review, let’s get to the point

But  the point of writing this post wasn’t to do a short review of a wonderful book (it was to annoy Aki who is waiting for me to finish something). But Murmur is a book that spends some time (as you inevitably do when considering an ersatz Turing) considering the philosophical implications of artificial intelligence.  (Really selling it there Daniel.) And this parallels some discussion that I’ve been seeing around the traps about what we talk about when we talk about neural networks.

Also because the quote that I ripped from an absolutely wonderful run in the novel to unceremoniously shove at the top of this post made me think of how we use methods that we don’t fully understand.

The first paper I fell into (and incidentally reading papers on neural nets is my aforementioned questionable decision) has the direct title Polynomial Regression As an Alternative to Neural Nets, where Cheng, Khomtchouk, and Matloff argue that we might as well use polynomial regression as it’s easier to interpret than a NN and basically gives the same answer.

The main mathematical argument in the paper is that if you build a NN with a polynomial activation function, each layer gives a higher-order polynomial. They argue that the Stone-Weierstrass approximation theorem suggests that any activation function will lead to a NN that can be well approximated by a high-order polynomial.

Now as a general rule, anytime someone whips out Stone-Weierstrass I feel a little skeptical. Because the bit of me that remembers my approximation theory remembers that the construction in this theorem is very slow to converge. I’m also alarmed by the use of high-degree polynomial regression using the natural basis and no regularization. Both of these things are a very bad idea.

But the whole story–that neural networks can also be understood as being quite like polynomial regression and that analogy can allow us to improve our NN techniques–is the sort of story that we need to tell to understand how to make these methods better in a principled way.

(Honestly, I’m not a giant fan of a lot of the paper–it does polynomial regression in exactly the way people have been telling applied people they should never do it. But hey, there’s some interesting stuff in there.)

The other paper I read was a whole lot more interesting. Rather than trying to characterize a neural network as something else, it instead tries to argue that NNs should be used to process electronic health records. And it gets good results compared to standard methods, which is always a good sign. But that’s not what’s interesting.

The interesting thing comes via Twitter. Uri Shalit, who’s a prof at Technion, noticed something very interesting in the appendix. Table 1 in the appendix showed that regularized logistic regression performs almost exactly as well as the complicated deep net.

Once again, the Goliath of  AI is slain by the David of “really boring statistical methods”.

But again, there’s more to this than that. Firstly, the logistic regression required some light “feature engineering” (ie people who knew what they were doing had to do something to the covariates). In particular, they had to separate them into time bins to allow the model to do a good job at modelling time. The Deep Net didn’t need that.

This particular case of feature engineering is trivial, but in a lot of cases it’s careful understanding of the structure of the problem that lets us use simple statistical techniques instead of something weird and complex.  My favourite example of this is Bin Yu’s work where she (read: her and a lot of collaborators) basically reconstructed movies a person was watching from an fMRI scan! The way they did it was to understand how certain patterns excite certain pathway (basically using existing science) and putting those activation patterns in as covariates in a LASSO regression. So the simplest modern technique + feature engineering (aka science) gave fabulous results.

The argument for all of these complex AI and deep learning methods is that they allow us to be a little more sloppy with the science. And it seems to work quite well for images and movies, which are extremely structured. But electronic health records are not particularly structured and can have some really weird missingness problems, so it’s not clear that the same methods will have as much room to move. In this case a pretty boring regularized logistic regression does almost exactly as well, which suggests that the deep net is not able to really fly.

The path forward now is to start understanding these cases, working out how things work, when things work, and what techniques we can beg borrow and steal from other areas of stats and machine learning. Because deep learning is not a panacea, it’s just a boy standing in front of a girl asking her to love him.

The post In my role as professional singer and ham appeared first on Statistical Modeling, Causal Inference, and Social Science.

Most social, technological and biological networks are embedded in a finite dimensional space, and the distance between two nodes influences the likelihood that they link to each other. Indeed, in social systems, the chance that two individuals know each other drops rapidly with the distance between them; in the cell, proteins predominantly interact with proteins in the same cellular compartment; in the brain, neurons mainly link to nearby neurons. Most modeling frameworks that aim to capture the empirically observed degree distributions tend to ignore these spatial constraints. In contrast, models that account for the role of the physical distance often predict bounded degree distributions, in disagreement with the empirical data. Here we address a long-standing gap in the spatial network literature by deriving several key network characteristics of spatial networks, from the analytical form of the degree distribution to path lengths and local clustering. The mathematically exact results predict the existence of two distinct phases, each governed by a different dynamical equation, with distinct testable predictions. We use empirical data to offer direct evidence for the practical relevance of each of these phases in real networks, helping better characterize the properties of spatial networks.

We provide a vivid demonstration of the mechanical effect of transverse spin momentum in an optical beam in free space. This component of the Poynting momentum was previously thought to be virtual, and unmeasurable. Here, its effect is revealed in the inertial motion of a probe particle in a circularly polarized Gaussian trap, in vacuum. Transverse spin forces combine with thermal fluctuations to induce a striking range of non-equilibrium phenomena. With increasing beam power we observe (i) growing departures from energy equipartition, (ii) the formation of coherent, thermally excited orbits and, ultimately, (iii) the ejection of the particle from the trap. Our results complement and corroborate recent measurements of spin momentum in evanescent waves, and extend them to a new geometry, in free space. In doing so, we exhibit fundamental, generic features of the mechanical interaction of circularly polarized light with matter. The work also shows how observations of the under-damped motion of probe particles can provide detailed information about the nature and morphology of momentum flows in arbitrarily structured light fields as well as providing a test bed for elementary non-equilibrium statistical mechanics.

Bazhin has analyzed ATP coupling in terms of quasiequilibrium states where fast reactions have reached an approximate steady state while slow reactions have not yet reached equilibrium. After an expository introduction to the relevant aspects of reaction network theory, we review his work and explain the role of emergent conserved quantities in coupling. These are quantities, left unchanged by fast reactions, whose conservation forces exergonic processes such as ATP hydrolysis to drive desired endergonic processes.

Shocking reports have revealed that immigrant children were subdued and incapacitated with powerful psychiatric drugs at a detention center in South Texas. Legal filings show that children held at Shiloh Treatment Center in southern Houston have been “forcibly injected with medications that make them dizzy, listless, obese and even incapacitated,” according to reports by Reveal. Meanwhile, according to another Reveal investigation, taxpayers have paid more than $1.5 billion over the past four years to companies operating immigration youth facilities despite facing accusations of rampant sexual and physical abuse. For more, we speak with the reporter who broke these stories: Aura Bogado. She is an immigration reporter with Reveal from the Center for Investigative Reporting. Her latest stories are “Immigrant children forcibly injected with drugs, lawsuit claims” and “Migrant children sent to shelters with histories of abuse allegations.”

The post The origins of WEIRD psychology appeared first on Marginal REVOLUTION.

I just happened to come across this quote from Dan Simpson:

When the signal-to-noise ratio is high, modern machine learning methods trounce classical statistical methods when it comes to prediction. The role of statistics in this case is really to boost the signal-to-noise ratio through the understanding of things like experimental design.

The post What is the role of statistics in a machine-learning world? appeared first on Statistical Modeling, Causal Inference, and Social Science.

Author(s): F. Cagnetta, M. R. Evans, and D. Marenduzzo

A new statistical model predicts the evolving shape of a cellular membrane by accounting for the active feedback between the membrane and attached proteins.

[Phys. Rev. Lett. 120, 258001] Published Mon Jun 18, 2018

Symmetry is a fundamental concept in modern physics and other related sciences. Being such a powerful tool, almost all physical theories can be derived from symmetry, and the effectiveness of such an approach is astonishing. Since many physicists do not actually believe that symmetry is a fundamental feature of nature, it seems more likely it is a fundamental feature of human cognition. According to evolutionary psychologists, humans have a sensory bias for symmetry. The unconscious quest for symmetrical patterns has developed as a solution to specific adaptive problems related to survival and reproduction. Therefore, it comes as no surprise that some fundamental concepts in psychology and behavioral economics necessarily involve symmetry. The purpose of this paper is to draw attention to the role of symmetry in decision-making and to illustrate how it can be algebraically operationalized through the use of mathematical group theory.

Theoretical models of critical mass have shown how minority groups can initiate social change dynamics in the emergence of new social conventions. Here, we study an artificial system of social conventions in which human subjects interact to establish a new coordination equilibrium. The findings provide direct empirical demonstration of the existence of a tipping point in the dynamics of changing social conventions. When minority groups reached the critical mass—that is, the critical group size for initiating social change—they were consistently able to overturn the established behavior. The size of the required critical mass is expected to vary based on theoretically identifiable features of a social setting. Our results show that the theoretically predicted dynamics of critical mass do in fact emerge as expected within an empirical system of social coordination.

Some vertebrates demonstrate complex numerosity concepts—including addition, sequential ordering of numbers, or even the concept of zero—but whether an insect can develop an understanding for such concepts remains unknown. We trained individual honey bees to the numerical concepts of "greater than" or "less than" using stimuli containing one to six elemental features. Bees could subsequently extrapolate the concept of less than to order zero numerosity at the lower end of the numerical continuum. Bees demonstrated an understanding that parallels animals such as the African grey parrot, nonhuman primates, and even preschool children.