In the news today:
“Governments, IOC and UN hit by massive cyber attack” (BBC)

How did the attack work? In a mind-numbingly ordinary way:

“An email would be sent to an individual with the right level of access within the system; attached to the message was a piece of malware which would then execute and open a channel to a remote website giving them access…they sometimes embedded themselves in the network and [tried to] spread across different systems within an organisation.”

In short:

• A person with broad authority ran a bit of code.
• The code, operating with this broad authority, wreaked havoc.

Quiz questions:

1. Why did the code inherit the person’s authority?
2. Is there a good reason for allowing this?
3. In the current model of computation, is it easy and natural to grant limited authority to individual computational objects?
4. What alternative model of computation (not an added security layer!) makes it natural to grant limited authority? What is it called? (Links, please.)

Questions for thought and discussion:

1. Why does the current computational model grant authority in this indiscriminate way? How does this lead to “sandboxing”?
2. What would be the main costs and benefits of moving computation toward the alternative model? How would this model play with the existing software base?
3. What are the leading implementations of this model today, at the language and operating system levels? In your opinion, should they be promoted more vigorously?

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A paper in Science reports a design method that substantially advances the macromolecular technology base for building atomically precise nanosystems.

Background: foldamer engineering

As many readers know, biology shows an effective way build large, intricate, atomically precise systems: Use covalent chemistry to build chains of small building blocks, and design these chains to fold into nanoscale building blocks that undergo spontaneous assembly driven by Brownian motion and selective binding. This is a key step in climbing a ladder of fabrication technologies that leads to broader, more powerful capabilities.

The covalent synthesis of suitable chains of building blocks* was mastered decades ago, using programmable nanoscale machines that operate in biological systems. Designing structures that fold into compact nanoscale objects has become increasingly routine. Designing these building blocks to assemble, however, has lagged.

The approach

This highlights the importance of the paper in Science.

The authors (from the Baker lab, and I’m tempted to add “of course”) used RosettaDesign-based protein engineering tools to design proteins with surface structures that bind to a natural protein at a particular location, and with a particular orientation. Finding a protein that binds isn’t too hard — screening and evolutionary methods applied to antibodies (among other proteins) can do this — but achieving high affinity (tight binding) in a specific geometry is new.

They achieved this by designing binders with the correct geometry but mediocre binding, and then using selection (the equivalent of antibody affinity maturation) to refine the interfaces to achieve high affinity. The refinement process retains the initial alignment with good fidelity.

The binding target was a conserved region of the influenza hemagglutinin molecule, hinting at an approach to developing a subtype-independent anti-influenza therapy.

Solving a harder problem than necessary

Note, however, that authors didn’t address the problem of designing building-block interfaces, as an engineer would understand the task: They did something harder. Only side of the interface was designed to bind, while the other was a naturally occurring structure that normally binds nothing.
An engineer designing building-block assemblies, by contrast, would design the interface as a unit, not just one side of it.

It’s easy to see the advantages of being free to tweak both sides to achieve a good fit, to balance solubility and costs of desolvation, and to introduce specific binding interactions (hydrogen bonds, salt bridges, hydrophobic pockets on one side that match hydrophobic side chains on the other, etc.). Freedom to design both sides together also means that protein engineers — when pursuing engineering objectives — can exploit the best-understood motifs, rather than deliberately plunging into the unknown.

In conventional engineering, no one designing a system would freeze the design of one component, and then attempt to mate another to it at a location not designed for the purpose. Interfaces aren’t afterthoughts.

A companion perspective piece for the paper observes that

Although Fleishman et al. have produced a landmark result, it is evident that computational protein interface design is not a solved problem.

For the more symmetric engineering design problem, however, the methods described in the paper can be expected to provide a basis for reliable design tools.

I look forward to seeing the methods and the lab results. This should be low-hanging fruit.

* In other words, peptide foldamers (commonly called “proteins”) which include a range of high-performance engineering polymers.

See also:

• A High-Performance Polymer for Nanosytems Engineering
• Macromolecular Modeling for Molecular Systems Engineering
• The Molecular Machine Path to Molecular Manufacturing (1):
  Foldamers and Brownian Assembly
• The Antiparallel Structures of Science and Engineering

Image and atomic reconstruction

From the abstract:

Although atomic-resolution electron microscopy has been feasible for nearly four decades, neither electron tomography nor any other experimental technique has yet demonstrated atomic resolution in three dimensions. Here we report the 3D reconstruction of a complex crystalline nanoparticle at atomic resolution. To achieve this, we combined aberration-corrected scanning transmission electron microscopy, statistical parameter estimation theory, and discrete tomography, Unlike conventional electron tomography, only two images of the target—a silver nanoparticle embedded in an aluminium matrix—are sufficient for the reconstruction when combined with available knowledge about the particle’s crystallographic structure. Additional projections confirm the reliability of the result. The results we present help close the gap between the atomic resolution achievable in two-dimensional electron micrographs and the coarser resolution that has hitherto been obtained by conventional electron tomography. [Links added]

This computational reconstruction technique (imaging aided by inference) requires only two images, but it relies on prior knowledge (or assumptions) about the structure: for example, that the crystal is face-centered cubic structure, has no holes, and no deep grooves in its surface. The data analysis uses a stochastic method (simulated annealing), and 16 independent reconstruction runs gave a difference in the positioning of 41 (out of 784) atoms in the specimen, so the result isn’t fully determined.

When a structure results from what should be an atomically precise process, knowledge (or reasonable expectations) will typically be extensive and detailed, leaving only narrow questions to be answered by imaging — the orientation of parts across an interface, for example, or the nature of a defect in a mostly-correct structure. Imaging aided by inference should again be quite powerful, and helpful in debugging fabrication processes.

The specimen in the current study is a silver nanoparticle in an aluminum matrix, that is, an array of electron-dense atoms in a radiation-tolerant structure. Studies of biomolecular structures still face the problems of radiation damage to delicate structures that provide lower contrasts in electron density, but cryogenic electron tomography has been advancing in this domain, too. Here, covalent structures with local conformational constraints are typically known at the outset, and this has been used to provide a bridge from low-resolution images to inferred (almost) atomically precise structures.

• Electron cryomicroscopy reaches landmark molecular resolution

The 7^th Peptoid Summit highlighted progress in design technology for one of the most promising toolkits in modular molecular systems engineering.

I’ve outlined the submonomer method for peptoid synthesis as a powerful and convenient way to assemble diverse molecular components, and the recent development of crystalline peptoid nanosheets as a platform for extended atomically-precise structures. The Summit reported further progress in structure prediction and in design for predictability: for example, selection of sidechains to control cis and trans amide conformations (Aaron Crapster University of Wisconsin), selection of rigid monomers and backbone interactions to restrict rotation (Bishwajit Paul, New York University), molecular mechanics and RosettaDesign-related methods for conformational prediction and engineering (Richard Bonneau,* New York University), molecular dynamics simulations (Vincent Voelz, Simprota Corporation), and systematic peptoid force-field development (Dina Mirijanian, LBNL).

Ron Zuckermann of LBNL (peptoid pioneer, ongoing leader, great guy) reported results at the biomolecular/inorganic materials interface — peptoids that direct the rate and form of crystal growth in calcium carbonate — and progress in understanding the mechanisms that enable the formation of large peptoid nanosheets.

The sheets form when a solution of suitably-engineered peptoids is shaken (not stirred). The molecules are a peculiar kind of surfactant, and they assemble into a monolayer at the air-water interface. Bubble formation and destruction creates and destroys interfacial area: this drives the formation of monomolecular surface films, then forces them to enter the bulk fluid as bilayers. Next steps will explore more controlled methods using a Langmuir-Blodgett trough.

Exploiting fluid interfaces to direct self-assembly

To generalize, this points to the utility of fluid interfaces for preorganizing molecular components (not necessarily sheets) on the way to forming soluble structures. An amphiphilic molecule or aggregate at an interface is constrained in three degrees of freedom (one translational, two rotational); this squeezes out considerable entropy and thereby facilitates molecular assembly. Compressing a surface layer of these components then can squeeze entropy from the remaining two degrees of translational freedom, and can even provide appreciable activation energy to overcome long-range intermolecular repulsions (e.g., charge-charge interactions among hydrophilic groups). Finally, as the peptoid nanosheet example shows, compression can drive a further assembly or folding process based on solvophobic forces — the same forces that are responsible for most of the stabilization energy of large peptide foldamers, a high-performance polymer for atomically precise nanosytems engineering.

It’s particularly nice that the surface-area cycling that drives the process can be provided by simply shaking the container.

* Richard Bonneau was a founding Rosetta developer.

I think that, when it can, scientific research should answer important practical questions, and I think it’s reasonable to ask that this be done with some degree of rationality and thoroughness.

The scientific literature on diet and health shows how large the failures can be. I think we can do much better.

Diet, heath, and the void

Here’s an important question:

How can someone choose a diet that lowers the risk of early, slow, and horrible death?

This general, practical question leads to many specific, scientific questions about messy real-world causality, and some of these can be answered.

The answers are almost irrelevant to basic science (they fit in the lower left quadrant of the table in my previous post), but they are vitally important to human health.

Here’s what I find striking and disturbing:

1. Standard research methods can answer many of these questions.
2. No one did the work that would have answered these questions.
3. The system hasn’t changed, and I see no real effort to change it.

The scandal of the science of dietary fats

For example, consider the question of cardiovascular disease and isocaloric substitutions among saturated fats, polyunsaturated fats, and carbohydrates.

And the answers? Saturated fats have long been known to be deadly, so surely there is unequivocal science that shows… What’s that? — Shows nothing of the kind?

Here’s the title and abstract of a representative 2010 analysis of the literature to date:

Effects on Coronary Heart Disease of Increasing Polyunsaturated Fat in Place of Saturated Fat: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Reduced saturated fat (SFA) consumption is recommended to reduce coronary heart disease (CHD), but there is an absence of strong supporting evidence from randomized controlled trials (RCTs) of clinical CHD events and few guidelines focus on any specific replacement nutrient….

This study finds that replacing substantial amounts of SFAs with polyunsaturated fatty acids — but not carbohydrates — reduces cardiovascular disease mortality, but that this is almost precisely offset by increases in non-cardiovascular-disease mortality. Another meta-analysis suggests that switching from SFAs to carbohydrates increases the risk of coronary deaths. [17 June: Updated to correct confused references.]

These evaluations of the scientific literature are not outliers.

A huge warning flag about the way we do science

For decades, questions of diet and health have been among the most prominent scientific questions in the world, with millions of lives at stake. For decades, billions of dollars have been spent by foundations and public agencies on health-branded research, presumably in the public interest. And for decades, basic questions regarding diet and health have been unanswered, or worse, answered by studies so poorly conceived that they led to the wrong conclusions.

More billions of dollars — and priceless public attention and life-changing effort — were poured into promoting and responding to advice based on this needlessly inadequate science.

Obvious questions, obvious importance, accessible answers, a failure to respond in a remotely rational way.

For better results, repair institutions

What might be done to help correct this? Here’s a broad brush-stroke suggestion for avoiding huge gaps in science done in the public interest:

1. Obligate science funding agencies to establish institutional mechanisms that are responsible for registering (allegedly) open scientific questions, and from diverse, external sources.
2. Make it embarrassing for the responsible parties to ignore questions that obviously shouldn’t be ignored.
3. For each question worth attention, require that they state an explicit estimate of the importance and difficulty of answering it.
4. Obligate the agency to either fund research on the most important and answerable questions, or to explicitly state reasons for neglecting them.

It think that institutions along these lines would cause a perceptible shift toward rational priorities. There are many potential implementations. The financial cost would be negligible. It would be great sport

I urge reformers to ask a simple question: If practices along these lines had been in place for the last 20 years, wouldn’t we be better off? Looking forward, how much would this be worth?

How groups think
and how to do it better

A review of Infotopia

I’ve been discussing problems with public information and ways to improve it with Michael Nielsen, and on this topic, he recommended Infotopia: how many minds produce knowledge by Cass Sunstein. Having just finished reading it, I recommend it too.

With a solid grounding in experiments and studies of group behavior (and enlightened common sense), Sunstein explores how groups and societies succeed and fail in what is arguably their most vital task: drawing out and assembling pieces of knowledge that are scattered among many minds. When this process of knowledge integration succeeds, groups can understand, decide, and act with knowledge and wisdom that exceeds that of any of their members.

When knowledge integration fails or goes astray, however, groups can perform worse than even their average members, and sometimes worse than any member.

The results of Suntein’s exploration are sobering, but the opportunities for improvement are staggering. If the knowledge and recommendations that Sunstein offers us were widely known and applied, blunders in group decision making would become substantially less common. These blunders range in size from small to large, and by “large blunders” I mean disastrously wrong decisions at the trillion-dollar, fate-of-nations level.

Infotopia is broad, describing, comparing, and analyzing a range of processes that draw on the power of many minds:

• Group deliberation
• Polling
• Conventional markets
• Prediction markets
• Open source development
• Blogging
• Wikis and Wikipedia

Sunstein helps us understand how these process operate and why they work and don’t work under various circumstances. Perhaps the most disturbing result is that deliberative discussion by groups often doesn’t work — that it fails to elicit information from its members, and that, under typical conditions, deliberation is more likely to amplify errors than to correct them.

In addition to diagnosis, Sunstein offers recommendations for improvement, some that a reader can apply next afternoon at work, and others that would involve changing how organizations operate.

Knowledge matters. Decisions matter. If your work or interests involve either, I think you’d enjoy reading Infotopia, and forever after, be glad that you did.

(I recently reviewed another book by Cass Sunstein, Nudge, and with similar enthusiasm.)

• A Map of Science
• How to Learn About Everything
• How to Understand Everything (and Why)
• The Antiparallel Structures of Science and Engineering
• Science and Engineering: A Layer-Cake of Inquiry and Design
• A Telescope Aimed at the Future
• Exploratory Engineering:
  Applying the predictive power of science
  to future technologies

Perimeter defense
considered harmful

William Wulf and Anita Jones have written a brief, tantalizing, and important article in Science: “Reflections on Cybersecurity”. They point the way out of a tangle of security problems (not all, of course) that costs billions of dollars in losses billions in countermeasures, and billions more in opportunity costs — some known and some not even imagined.

With current countermeasures, we’ve been losing the war. Wulf and Jones argue that the fundamental approach is wrong:

The current model for most cybersecurity is “perimeter defense”….Hackers try to “break in,” “firewalls” protect the system, “intrusion” must be detected, etc. But is perimeter defense the right underlying model?

They note that

[1] perimeter defense does not protect against the compromised insider….

[2] once the perimeter has been breached, the attacker has free access….

and [3] …it has never worked. It did not work for ancient walled cities or for the French in World War II (at 20 to 25 km deep, the Maginot Line was the most formidable military defense ever built, yet France was overrun in 35 days). And it has not worked for cybersecurity. To our knowledge no one has ever built a secure, nontrivial computer system based on this model.

The authors note the reason for the success and scalability of protocols that form the foundation of the internet:

Dave Parnas, one of the early software engineers, made a provocative and, we think, deeply important observation that helps to explain the success of the TCP/IP protocols. He pointed out that, when doing a design, the hardest decision to change is the one you make first, because all the subsequent ones to some extent depend on it. The decision for the TCP/IP protocols to do so little never had to be reconsidered, because it precluded so little.

They recommend that we deploy a cryptographic protocol of similar generality, one that does little, but does it well, and thereby provides a foundation for systems as complex, diverse, and specialized as the vast superstructure built on TCP/IP:

Is such a minimal mechanism feasible? We think so. In particular, at the network level, an application can use any computable function to decide whether or not to provide its service to a client if it can be absolutely certain who is requesting it. There is a class of algorithms known as “cryptographic protocols” for doing this that require knowing the public key of an object—so we conjecture that by providing just a way of accessing the public key of an object, one could build an arbitrary end-to-end security policy.

Because this minimalist foundation precludes so little, its generality seems assured.

In addition to its other virtues, it would be smoothly compatible with computational methods that can solve crucial vulnerability problems at the level of applications on individual machines (with very lightweight implementations, by the way). Wulf himself led pioneering work in this area, and he is no lightweight in computer science or Federal technology policy.

This proposal deserves more than ordinary attention.

(And here, more than ordinary emphasis.)

Not quite the same

In the science press, Big News often turns out to be hyped trivia, but the current Big News in quantum computing is something else — a self-hyping mutant of genuine big news, the discovery of an algorithm that promises exponential speedup in a class of problems where the result depends on the solution to a large system of linear equations. Contrary to the mutant news, however, the algorithm does not provide solutions to systems of linear equations: It outputs scalars, not vectors, and this is not at all the same thing.

The paper, published in Phys. Rev. Letters, reports work done at University of Bristol and MIT (Seth Lloyd is a coauthor). The mutation appears in the press release from the MIT News Office, which headlines “A quantum algorithm that solves systems of linear equation”, and says that

The quantum algorithm…can solve systems with a trillion equations and a trillion variables.

Well, no, it can’t — the output is a trillion times smaller. [In the comments, Larry notes that the MIT News Office article later quotes Lloyd directly, explaining the key caveat: that the algorithm delivers a scalar measurement on a solution vector, which can be a function of the entire vector or, as a special case, any one of the trillion vector components.]

Science News, reasonably enough, follows the not-quite-right formulation in the MIT lead, speaking of the algorithm “quickly solving monster linear equations”, while a brief news item in Science reports that “Solving a set of linear equations is a generic and important problem…[and the authors have developed] a quantum algorithm that will solve the problem much more rapidly”.

The synopsis of the paper provided by the journal’s publisher, the APS, says that it describes “a quantum algorithm for solving a set of linear equations”, and then, to clarify, notes that the algorithm “does not find the solution to the linear equations”. (It actually does clarify, though it’s written a bit sideways.)

The paper, “Quantum algorithm for linear systems of equations” is available as a preprint [pdf]. Here’s the abstract, highlighting the part that tends to drop out of the press reports:

Quantum algorithm for linear systems of equations

Solving linear systems of equations is a common problem that arises
both on its own and as a subroutine in more complex problems: given a
matrix A and a vector b, and a vector x such that Ax = b. We consider the case where one doesn’t need to know the solution x itself, but rather an approximation of the expectation value of some operator associated with x, e.g., x^†Mx for some matrix M. In this case, when A is sparse and well-conditioned, with largest dimension N, the best classical algorithms can find x and estimate x^†Mx in O(N poly log(N)) time. Here, we exhibit a quantum algorithm for this task that runs in poly(log N) time, an exponential improvement over the best classical algorithm.

If providing an input vector of size N were itself a problem of size N, of course, then an algorithm that included the input operations wouldn’t scale so well.

This observation highlights an important difference between quantum algorithms of the present kind and algorithms in the other two known classes that enable exponential speedup on interesting problems, Shor’s factoring algorithm (and its relatives) and quantum simulations of of quantum systems themselves: Those algorithms provide speedups that could bring problems down from otherwise inaccessible exponential realms (or thereabout); this one would push a tractable problem down into log-land.

Afterthought, 11 November: I suppose the reason I’m intrigued by this sort of process (subtle-but-enormous distortions creeping into descriptions) is that I’ve seen it happen in second-, third-, and fourth-hand descriptions of studies of prospective advanced nanotechnologies.

A vivid example is the simplistic equation of atomically precise fabrication (for example, by using positional constraints to localize familiar kinds of chemical reactions) with grabbing and placing individual atoms (which would typically require something more like magic). Unfortunately, in this case, subsequent discussions centered on the impossibility of magic, rather than the implementation of technology.

• How Nanotubes Grow: A theory that has nothing to do with reality