Category Archives: Phil

Phil 12.13.17

7:00 – ASRC MKT

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Phil 12.12.17

7:00 – 3:30 ASRC MKT

  • Schedule physical
  • Call Lisa Cross
  • Need to make sure that an amplified agent also has amplified influence in calculating velocity – Fixed
  • Towards the end of this video is an interview with Ian Couzin talking about how mass communication is disrupting our ability to flock ‘correctly’ due to the decoupling of distance and information
  • Write up fire stampede. Backups everywhere, one hole, antennas burn so the AI keeps trust in A* but loses awareness as the antennas burn: “The Los Angeles Police Department asked drivers to avoid navigation apps, which are steering users onto more open routes — in this case, streets in the neighborhoods that are on fire.” [LA Times] Also this slow motion version of the same thing: For the Good of Society — and Traffic! — Delete Your Map App
  • First self-driving car ‘race’ ends in a crash at the Buenos Aires Formula E ePrix; two cars enter, one car survives
  • Taking a closer look at Oscillator Models and Collective Motion (178 Citations) and Consensus and Cooperation in Networked Multi-Agent Systems (6,291 Citations)
  • Consensus and Cooperation in Networked Multi-Agent Systems
    • Reza Olfati-SaberAlex Fax, and Richard M. Murray
    • We discuss the connections between consensus problems in networked dynamic systems and diverse applications including synchronization of coupled oscillators, flocking, formation control, fast consensus in small world networks, Markov processes and gossip-based algorithms, load balancing in networks, rendezvous in space, distributed sensor fusion in sensor networks, and belief propagation. We establish direct connections between spectral and structural properties of complex networks and the speed of information diffusion of consensus algorithms (Abstract)
    • In networks of agents (or dynamic systems), “consensus” means to reach an agreement regarding a certain quantity of interest that depends on the state of all agents. A “consensus algorithm” (or protocol) is an interaction rule that specifies the information exchange between an agent and all of its (nearest) neighbors on the network (pp 215)
      • In my work, this is agreement on heading and velocity
    • Graph Laplacians are an important point of focus of this paper. It is worth mentioning that the second smallest eigenvalue of graph Laplacians called algebraic connectivity quantifies the speed of convergence of consensus algorithms. (pp 216)
    • More recently, there has been a tremendous surge of interest among researchers from various disciplines of engineering and science in problems related to multi-agent networked systems with close ties to consensus problems. This includes subjects such as consensus [26]–[32], collective behavior of flocks and swarms [19], [33]–[37], sensor fusion [38]–[40], random networks [41], [42], synchronization of coupled oscillators [42]–[46], algebraic connectivity of complex networks [47]–[49], asynchronous distributed algorithms [30], [50], formation control for multi-robot systems [51]–[59], optimization-based cooperative control [60]–[63], dynamic graphs [64]–[67], complexity of coordinated tasks [68]–[71], and consensus-based belief propagation in Bayesian networks [72], [73]. (pp 216)
      • That is a dense lit review. How did they order it thematically?
    • A byproduct of this framework is to demonstrate that seemingly different consensus algorithms in the literature [10], [12]–[15] are closely related. (pp 216)
    • To understand the role of cooperation in performing coordinated tasks, we need to distinguish between unconstrained and constrained consensus problems. An unconstrained consensus problem is simply the alignment problem in which it suffices that the state of all agents asymptotically be the same. In contrast, in distributed computation of a function f(z), the state of all agents has to asymptotically become equal to f(z), meaning that the consensus problem is constrained. We refer to this constrained consensus problem as the f-consensus problem. (pp 217)
      • Normal exploring/flocking/stampeding is unconstrained. Herding adds constraint, though it’s dynamic. The variables that have to be manipulated in the case of constraint to result in the same amount of consensus are probably what’s interesting here. Examples could be how ‘loud’ does the herder have to be? Also, how ‘primed’ does the population have to be to accept herding?
    • …cooperation can be informally interpreted as “giving consent to providing one’s state and following a common protocol that serves the group objective.” (pp 217)
    • Formal analysis of the behavior of systems that involve more than one type of agent is more complicated, particularly, in presence of adversarial agents in noncooperative games [79], [80]. (pp 217)
  • Not sure about this one. It just may be another set of algorithms to do flocking. Maybe some network implications? Flocking for Multi-Agent Dynamic Systems: Algorithms and Theory. It is one of the papers that the Consensus and Cooperation paper above leans on heavily though…
  • The Emergence of Consensus: A Primer
    • The origin of population-scale coordination has puzzled philosophers and scientists for centuries. Recently, game theory, evolutionary approaches and complex systems science have provided quantitative insights on the mechanisms of social consensus. However, the literature is vast and scattered widely across fields, making it hard for the single researcher to navigate it. This short review aims to provide a compact overview of the main dimensions over which the debate has unfolded and to discuss some representative examples. It focuses on those situations in which consensus emerges ‘spontaneously’ in absence of centralised institutions and covers topic that include the macroscopic consequences of the different microscopic rules of behavioural contagion, the role of social networks, and the mechanisms that prevent the formation of a consensus or alter it after it has emerged. Special attention is devoted to the recent wave of experiments on the emergence of consensus in social systems.
  • Critical dynamics in population vaccinating behavior
    • Complex adaptive systems exhibit characteristic dynamics near tipping points such as critical slowing down (declining resilience to perturbations). We studied Twitter and Google search data about measles from California and the United States before and after the 2014–2015 Disneyland, California measles outbreak. We find critical slowing down starting a few years before the outbreak. However, population response to the outbreak causes resilience to increase afterward. A mathematical model of measles transmission and population vaccine sentiment predicts the same patterns. Crucially, critical slowing down begins long before a system actually reaches a tipping point. Thus, it may be possible to develop analytical tools to detect populations at heightened risk of a future episode of widespread vaccine refusal.
  • For Aaron’s Social Gradient Descent Agent research (lit review)
    • On distributed search in an uncertain environment (Something like Social Gradient Descent Agents)
      • The paper investigates the case where N agents solve a complex search problem by communicating to each other their relative successes in solving the task. The problem consists in identifying a set of unknown points distributed in an n–dimensional space. The interaction rule causes the agents to organize themselves so that, asymptotically, each agent converges to a different point. The emphasis of this paper is on analyzing the collective dynamics resulting from nonlinear interactions and, in particular, to prove convergence of the search process.
    • A New Clustering Algorithm Based Upon Flocking On Complex Network (Sizing and timing for flocking systems seems to be ok?)
      • We have proposed a model based upon flocking on a complex network, and then developed two clustering algorithms on the basis of it. In the algorithms, firstly a k-nearest neighbor (knn) graph as a weighted and directed graph is produced among all data points in a dataset each of which is regarded as an agent who can move in space, and then a time-varying complex network is created by adding long-range links for each data point. Furthermore, each data point is not only acted by its k nearest neighbors but also r long-range neighbors through fields established in space by them together, so it will take a step along the direction of the vector sum of all fields. It is more important that these long-range links provides some hidden information for each data point when it moves and at the same time accelerate its speed converging to a center. As they move in space according to the proposed model, data points that belong to the same class are located at a same position gradually, whereas those that belong to different classes are away from one another. Consequently, the experimental results have demonstrated that data points in datasets are clustered reasonably and efficiently, and the rates of convergence of clustering algorithms are fast enough. Moreover, the comparison with other algorithms also provides an indication of the effectiveness of the proposed approach.
  • Done with the first draft of the white paper! And added the RFP section to the LMN productization version
  • Amazon Sage​Maker: Amazon SageMaker is a fully managed machine learning service. With Amazon SageMaker, data scientists and developers can quickly and easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment. It provides an integrated Jupyter authoring notebook instance for easy access to your data sources for exploration and analysis, so you don’t have to manage servers. It also provides common machine learning algorithms that are optimized to run efficiently against extremely large data in a distributed environment. With native support for bring-your-own-algorithms and frameworks, Amazon SageMaker offers flexible distributed training options that adjust to your specific workflows. Deploy a model into a secure and scalable environment by launching it with a single click from the Amazon SageMaker console. Training and hosting are billed by minutes of usage, with no minimum fees and no upfront commitments. (from the documentation)

4:00 – 5:00 Meeting with Aaron M. to discuss Academic RB wishlist.

 

Phil 12.11.17

7:00 – 3:00 ASRC MKT

  • Machine learning art gallery from NIPS this year: img_20171208_212755
  • I’m reading this article on the prehistory of Bitcoin, and am realizing that there are several implications for ensuring immutability of data. For example, the entire set of records could be hashed to produce a unique has that would be disrupted if any of the records were altered.
  • Continuing Schooling as a strategy for taxis in a noisy environment here. Done! Promoted to Phlog
  • Still collecting data for web access times at work. Average time to open/finish loading a page is something around 5 seconds at work, 2 seconds at home.
  • Neural correlates of causal power judgments
    • Denise Dellarosa Cummins
    • Causal inference is a fundamental component of cognition and perception. Probabilistic theories of causal judgment (most notably causal Bayes networks) derive causal judgments using metrics that integrate contingency information. But human estimates typically diverge from these normative predictions. This is because human causal power judgments are typically strongly influenced by beliefs concerning underlying causal mechanisms, and because of the way knowledge is retrieved from human memory during the judgment process. Neuroimaging studies indicate that the brain distinguishes causal events from mere covariation, and also distinguishes between perceived and inferred causality. Areas involved in error prediction are also activated, implying automatic activation of possible exception cases during causal decision-making.
  • Writing up the Academic scenario

3:00 – 4:00 Fika – end of semester shindig

4:00 – 6:00 Meeting w/Wayne

  • Basically a status report. Maybe look at computational ecology journals if CHIIR falls through in a bad way
  • Look at workshops as well – Max Plank could be fun
  • Workshopped a workshop title with Wayne and Shimei

 

Phil 12.10.17

Thinking about the map. In cases where it is impossible to project cleanly down to 2 dimensions, like you could with this Strava heatmap:

Strava

Adding elements like ‘highways’ (wormholes?) connecting two distant points might make sense. In this way, the larger dimensions are preserved, and the unusual relationships are still visible. In the case of language vs semantics, this could show the connections of ‘Java’ as a computer language, beverage, and country:

StravaWormholes

There are several ways of looking at these projections too. I would think that a map made entirely of long haul air routes would project differently than roads. It should be possible to ‘morph’ between these projections to explore the relationships.

air-canada-2-17-international-route-map

Cute thing:

dqeg3kcuqaamtt6

Phil 12/8/17

7:00 – 4:00 ASRC MKT

  • Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution
    • Judea Pearl 
    • Current machine learning systems operate, almost exclusively, in a statistical, or model-blind mode, which entails severe theoretical limits on their power and performance. Such systems cannot reason about interventions and retrospection and, therefore, cannot serve as the basis for strong AI. To achieve human level intelligence, learning machines need the guidance of a model of reality, similar to the ones used in causal inference. To demonstrate the essential role of such models, I will present a summary of seven tasks which are beyond reach of current machine learning systems and which have been accomplished using the tools of causal inference.
  • Talk with the first-ever robot politician on Facebook Messenger
  • Reddit network visualizations and a pass at a map (from 2014)
  • Continuing Schooling as a strategy for taxis in a noisy environment here
    • The models also suggest that there is a trade-off in strengthening tendencies to align with neighbours: strong alignment produces tight angular distributions, but increases the time needed to adjust course when the direction of the gradient changes. A reasonable balance seems to be achieved when individuals take roughly the same time to coalesce into a polarized group as they do to orient to the gradient in asocial taxis. (pp 518)
      • There is something about the relationship between explore and exploit in this statement that I really need to think about
  • Continuing white paper. Done with intro, background, and phase 1
  • Thinking about explore/exploit and how it’s not a good narrative. Everyone wants to be an explorer. So I’m trying to think of other framings: Nomad/Farmer?
  • More progress on the white paper. Split out just the LMN work (Phase 1&2). There will be a separate effort for mapping.

Phil 12.7.17

ASRC MKT 7:00 – 4:30

  • Association of moral values with vaccine hesitancy
  • Online extremism and the communities that sustain it: Detecting the ISIS supporting community on Twitter
  • Continuing Schooling as a strategy for taxis in a noisy environment here
  • Consensus and Cooperation in Networked Multi-Agent Systems
    • This paper provides a theoretical framework for analysis of consensus algorithms for multi-agent networked systems with an emphasis on the role of directed information flow, robustness to changes in network topology due to link/node failures, time-delays, and performance guarantees. An overview of basic concepts of information consensus in networks and methods of convergence and performance analysis for the algorithms are provided. Our analysis framework is based on tools from matrix theory, algebraic graph theory, and control theory. We discuss the connections between consensus problems in networked dynamic systems and diverse applications including synchronization of coupled oscillators, flocking, formation control, fast consensus in smallworld networks, Markov processes and gossip-based algorithms, load balancing in networks, rendezvous in space, distributed sensor fusion in sensor networks, and belief propagation. We establish direct connections between spectral and structural properties of complex networks and the speed of information diffusion of consensus algorithms. A brief introduction is provided on networked systems with nonlocal information flow that are considerably faster than distributed systems with lattice-type nearest neighbor interactions. Simulation results are presented that demonstrate the role of smallworld effects on the speed of consensus algorithms and cooperative control of multivehicle formations.
  • Found this in the citations of the above paper with terms “belief space flocking“: Spatial Coordination Games for Large-Scale Visualization
    • Dimensionality reduction (’visualization’) is a central problem in statistics. Several of the most popular solutions grew out of interaction metaphors (springs, boids, neurons, etc.) We show that the problem can be framed as a game of coordination and solved with standard game-theoretic concepts. Nodes that are close in a (high-dimensional) graph must coordinate in a (low-dimensional) screen position. We derive a game solution, a GPU-parallel implementation and report visualization experiments in several datasets. The solution is a very practical application of game-theory in an important problem, with fast and low-stress embeddings.
  • Lots of progress on the White Paper. Aaron wants to split out the WordRank work and the mapping work as two separate epochs. He thinks they may be easier to pitch than the phased approach
  • Some discussion on how explore/exploit is a bad metaphor due to the bad associations with exploit
  • Added a SIGINT use case
  • Discussed the ‘map weaving from trajectories’ concept

Phil 12.6.17

7:00 – 6:00 ASRC MKT

  • Best Free Alternative PDF Viewer to Adobe Reader
  • Downloaded Modeling Political Information Transmission as a Game of Telephone and gave it a skim. It  looks very much like this is an example of dimension reduction. To refine the idea, there need to be several conditions that lead to a stampeed
    • Dimension reduction and alignment need to occur across a population. It’s no good to have dimension reduction if everyone is pointing in a different direction.
    • The belief has to be ‘containing’ in some way. Either by social interaction (trust), or a lack of awareness of other ideas, it needs to be difficult to break out of.
      • This can be manipulated by external actors posing as trusted members of the group. Direction and level of uniformity can be influenced.
    • It has to be dynamic. A static belief provides the implicit ability to move away from it in any direction. A belief that is evolving fast enough maintains alignment by focusing the need for novelty (exploration?) in one direction.
  • Starting Schooling as a strategy for taxis in a noisy environment here
  • Demo
  • Chaining of matrices should be possible. Imaging an author/term matrix and a document/term matrix. Raising the weight of the author raises the weights on the associated terms. Those terms can be multiplied by the weights in the document term matrix which should result in a correct(?) re-weighting.
  • Chat with Shimei after picking up my gps
    • The Rat Park Experiment
    • Smoking and the bandit: A preliminary study of smoker and non-smoker differences in exploratory behavior measured with a multi-armed bandit task
      • Advantageous decision-making is an adaptive trade-off between exploring alternatives and exploiting the most rewarding option. This trade-off may be related to maladaptive decision-making associated with nicotine dependence; however, explore/exploit behavior has not been previously investigated in the context of addiction. The explore/exploit trade-off is captured by the multi-armed bandit task, in which different arms of a slot machine are chosen to discover the relative payoffs. The goal of this study was to preliminarily investigate whether smokers differ from non-smokers in their degree of exploratory behavior. Smokers (n = 18) and non-smokers (n = 17) completed a six-armed bandit task as well as self-report measures of behavior and personality traits. Smokers were found to exhibit less exploratory behavior (i.e. made fewer switches between slot machine arms) than non-smokers within the first 300 trials of the bandit task. The overall proportion of exploratory choices negatively correlated with self-reported measures of delay aversion and nonplanning impulsivity. These preliminary results suggest that smokers make fewer initial exploratory choices on the bandit task. The bandit task is a promising measure that could provide valuable insights into how nicotine use and dependence is associated with explore/exploit decision-making.

Phil 12.5.17

7:00 – 4:00 ASRC MKT

Phil 12.4.17

7:00 – ASRC MKT

3:00 – Campus

  • Fika
  • Meeting w/Wayne
    • Up to date. He was a bit worried that I might be going off the rails with the Neural Coupling work, but relaxed when I showed how it was being used to buttress the flocking model. And I have access to an fMRI, it seems…
    • Information Ecologies – The common rhetoric about technology falls into two extreme categories: uncritical acceptance or blanket rejection. Claiming a middle ground, Bonnie Nardi and Vicki O’Day call for responsible, informed engagement with technology in local settings, which they call information ecologies.An information ecology is a system of people, practices, technologies, and values in a local environment. Nardi and O’Day encourage the reader to become more aware of the ways people and technology are interrelated. They draw on their empirical research in offices, libraries, schools, and hospitals to show how people can engage their own values and commitments while using technology.
  • Bonus meeting with Shimei. Rambled through the following topics
    • Reinforcement learning with flocks and gradient descent
    • Flocking, herding and social engineering
    • Suspicious OS
    • She has a tall son 🙂

Phil 12.1.17

7:00 – 4:30 ASRC MKT

ZeynepWeb1-3

  • High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs. This shows NNs filling in slots in semantic maps (which are actually semantic mattes, and not to be confused with earlier self-organizing semantic maps). How is this with other, more linear processes like sound and narrative?
  • Continuing Alignment in social interactions here.
  • People flock in computer mediated environments: Spontaneous flocking in human groups
  • Schooling as a strategy for taxis in a noisy environment
    • Daniel Grunbaum
    • Abstract
      • A common strategy to overcome this problem is taxis, a behaviour in which an animal performs a biased random walk by changing direction more rapidly when local conditions are getting worse.
        • Consider voters switching from Bush->Obama->Trump
      • Such an animal spends more time moving in right directions than wrong ones, and eventually gets to a favourable area. Taxis is ineffcient, however, when environmental gradients are weak or overlain by `noisy’ small-scale fluctuations. In this paper, I show that schooling behaviour can improve the ability of animals performing taxis to climb gradients, even under conditions when asocial taxis would be ineffective. Schooling is a social behaviour incorporating tendencies to remain close to and align with fellow members of a group. It enhances taxis because the alignment tendency produces tight angular distributions within groups, and dampens the stochastic effects of individual sampling errors. As a result, more school members orient up-gradient than in the comparable asocial case. However, overly strong schooling behaviour makes the school slow in responding to changing gradient directions. This trade-off suggests an optimal level of schooling behaviour for given spatio-temporal scales of environmental variations.
        • This has implications for everything from human social interaction to ANN design.
    • Notes
      • Because limiting resources typically have `patchy’ distributions in which concentrations may vary by orders of magnitude, success or failure in finding favourable areas often has an enormous impact on growth rates and reproductive success. To locate resource concentrations, many aquatic organisms display tactic behaviours, in which they orient with respect to local variations in chemical stimuli or other environmental properties. (pp 503)
      • Here, I propose that schooling behaviours improve the tactic capabilities of school members, and enable them to climb faint and noisy gradients which they would otherwise be unable to follow. (pp 504)
      • Schooling is thought to result from two principal behavioural components: (1) tendencies to move towards neighbours when isolated, and away from them when too close, so that the group retains a characteristic level of compactness; and (2) tendencies to align orientation with those of neighbours, so that nearby animals have similar directions of travel and the group as a whole exhibits a directional polarity. (pp 504)
        • My models indicate that attraction isn’t required, as long as there is a distance-graded awareness. In other words, you align most strongly with those agents that are closest.
      • I focus in this paper on schooling in aquatic animals, and particularly on phytoplankton as a distributed resource. However, although I do not examine them specifically, the modelling approaches and the basic results apply more generally to other environmental properties (such as temperature), to other causes of population movement (such as migration) and to other socially aggregating species which form polarized groups (such as flocks, herds and swarms). (pp 504)
      • Under these circumstances, the search of a nektonic filter-feeder for large-scale concentrations of phytoplankton is analogous to the behaviour of a bacterium performing chemotaxis. The essence of the analogy is that, while higher animals have much more sophisticated sensory and cognitive capacities, the scale at which they sample their environment is too small to identify accurately the true gradient. (pp 505)
        • And, I would contend for determining optimal social interactions in large groups.
      • Bacteria using chemotaxis usually do not directly sense the direction of the gradient. Instead, they perform random walks in which they change direction more often or by a greater amount if conditions are deteriorating than if they are improving (Keller and Segel, 1971; Alt, 1980; Tranquillo, 1990). Thus, on average, individuals spend more time moving in favourable directions than in unfavourable ones. (pp 505)
      • A bacterial analogy has been applied to a variety of behaviours in more complex organisms, such as spatially varying di€usion rates due to foraging behaviours or food-handling in copepods and larval ®sh (Davis et al., 1991), migration patterns in tuna (Mullen, 1989) and restricted area searching in ladybugs (Kareiva and Odell, 1987) and seabirds (Veit et al., 1993, 1995). The analogy provides for these higher animals a quantitative prediction of distribution patterns and abilities to locate resources at large space and time scales, based on measurable characteristics of small-scale movements. (pp 505)
      • I do not consider more sophisticated (and possibly more effective) social tactic algorithms, in which explicit information about the environment at remote points is actively or passively transmitted between individuals, or in which individual algorithms (such as slowing down when in relatively high concentrations) cause the group to function as a single sensing unit (Kils, 1986, described in Pitcher and Parrish, 1993). (pp 506)
        • This is something that could be easily added to the model. There could be a multiplier for each data cell that acts as a velocity scalar of the flock. That should have significant effects! This could also be applied to gradient descent. The flock of Gradient Descent Agents (GDAs) could have a higher speed across the fitness landscape, but slow and change direction when a better value is found by one of the GDAs. It occurs to me that this would work with a step function, as long as the baseline of the flock is sufficiently broad.
      • When the noise predominates (d <= 1), the angular distribution of individuals is nearly uniform, and the up-gradient velocity is near zero. In a range of intermediate values of d(0.3 <= d <= 3), there is measurable but slow movement up-gradient. The question I will address in the next two sections is: Can individuals in this intermediate signal-to-noise range with slow gradient-climbing rates improve their tactic ability by adopting a social behaviour (i.e. schooling)? (pp 508)
      • The key attributes of these models are: (1) a decreasing probability of detection or responsiveness to neighbours at large separation distances; (2) a social response that includes some sort of switch from attractive to repulsive interactions with neighbours, mediated by either separation distance or local density of animals*; and (3) a tendency to align with neighbours (Inagaki et al., 1976; Matuda and Sannomiya, 1980, 1985; Aoki, 1982; Huth and Wissel, 1990, 1992; Warburton and Lazarus, 1991; Grunbaum, 1994). (pp 508)
        • Though not true of belief behavior (multiple individuals can share the same belief), for a Gradient Descent Agent (GDA), the idea of attraction/repulsion may be important.
      • If the number of neighbours is within an acceptable range, then the individual does not respond to them. On the other hand, if the number is outside that range, the individual turns by a small amount, Δθ3, to the left or right according to whether it has too many or too few of them and which side has more neighbours. In addition, at each time step, each individual randomly chooses one of its visible neighbours and turns by a small amount, Δθ4, towards that neighbour’s heading. (pp 508)
      • The results of simulations based on these rules show that schooling individuals, on average, move more directly in an up-gradient direction than asocial searchers with the same tactic parameters. Figure 4 shows the distribution of individuals in simulations of asocial and social taxis in a periodic domain (i.e. animals crossing the right boundary re-enter the left boundary, etc.). (pp 509)
      • Gradient Schooling
      • As predicted by Equation (5), asocial taxis results in a broad distribution of orientations, with a peak in the up-gradient (positive x-axis) direction but with a large fraction of individuals moving the wrong way at any given time (Fig. 5a,b). By comparison, schooling individuals tend to align with one another, forming a group with a tightened angular distribution. There is stochasticity in the average velocity of both asocial and social searchers (Fig. 5c). On average, however, schooling individuals move up-gradient faster and more directly than asocial ones. These simulation results demonstrate that it is theoretically possible to devise tactic search strategies utilizing social behaviours that are superior to asocial algorithms. That is, one of the advantages of schooling is that, potentially, it allows more successful search strategies under `noisy’ environmental conditions, where variations on the micro-scales at which animals sense their environment obscure the macro-scale gradients between ecologically favourable and unfavourable regions. (pp 510)
      • School-size effects must depend to some extent on the tactic and schooling algorithms, and the choices of parameters. However, underlying social taxis are the statistics of pooling outcomes of independent decisions, so the numerical dependence on school size may operate in a similar manner for many comparable behavioural schemes. For example, it seems reasonable to expect that, in many alternative schooling and tactic algorithms, decisions made collectively by less than 10 individuals would show some improvement over the asocial case but also retain much of the variability. Similarly, in most scenarios, group statistics probably vary only slowly with group size once it reaches sizes of 50-100. (pp 514)
      • when group size becomes large, the behaviour of model schools changes in character. With numerous individuals, stochasticity in the behaviour of each member has a relatively weaker effect on group motion. The behaviour of the group as a whole becomes more consistent and predictable, for longer time periods. (pp 514)
        • I think that this should be true in belief spaces as well. It may be difficult to track one person’s trajectory, but a group in aggregate, particularly a polarized group may be very detectable.
      • An example of group response to changing gradient direction shows that there can be a cost to strong alignment tendency. In this example, the gradient is initially pointed in the negative y-direction (Fig. 9). After an initial period of 5 time units, during which the gradient orients perpendicularly to the x-axis, the gradient reverts to the usual x-direction orientation. The school must then adjust to its new surroundings by shifting to climb the new gradient. This example shows that alignment works against course adjustment: the stronger the tendency to align, the slower is the group’s reorientation to the new gradient direction. This is apparently due to a non-linear interaction between alignment and taxis: asymmetries in the angular distribution during the transition create a net alignment flux away from the gradient direction. Thus, individuals that pay too much attention to neighbours, and allow alignment to overwhelm their tactic tendencies, may travel rapidly and persistently in the wrong direction. (pp 516)
        • So, if alignment (and velocity matching) are strong enough, the conditions for a stampede (group behavior with negative outcomes – in this case, less food) emerge
      • The models also suggest that there is a trade-off in strengthening tendencies to align with neighbours: strong alignment produces tight angular distributions, but increases the time needed to adjust course when the direction of the gradient changes. A reasonable balance seems to be achieved when individuals take roughly the same time to coalesce into a polarized group as they do to orient to the gradient in asocial taxis. (pp 518)
        • There is something about the relationship between explore and exploit in this statement that I really need to think about.
      • Social taxis is potentially effective in animals whose resources vary substantially over large length scales and for whom movements over these scales are possible. (pp 518)
        • Surviving as a social animal requires staying in the group. Since belief can cover wide ranges (e.g. religion), does there need to be a mechanism where individuals can harmonize their beliefs? From Social Norms and Other Minds The Evolutionary Roots of Higher Cognition :  Field research on primate societies in the wild and in captivity clearly shows that the capacity for (at least) implicit appreciation of permission, prohibition, and obligation social norms is directly related to survival rates and reproductive success. Without at least a rudimentary capacity to recognize and respond appropriately to these structures, remaining within a social group characterized by a dominance hierarchy would be all but impossible.
      • Interestingly, krill have been reported to school until a food patch has been discovered, whereupon they disperse to feed, consistent with a searching function for schooling. The apparent effectiveness of schooling as a strategy for taxis suggests that these schooling animals may be better able to climb obscure large-scale gradients than they would were they asocial. Interactive effects of taxis and sociality may affect the evolutionary value of larger groups both directly, by improving foraging ability with group size, and indirectly, by constraining alignment rates. (pp 518)
      • An example where sociality directly affects foraging strategy is forage area copying, in which unsuccessful fish move to the vicinity of neighbours that are observed to be foraging successfully (Pitcher et al., 1982; Ranta and Kaitala, 1991; Pitcher and Parrish, 1993). Pitcher and House (1987) interpreted area copying in goldfish as the result of a two-stage decision process: (1) a decision to stay put or move depending on whether feeding rate is high or low; and (2) a decision to join neighbours or not based upon whether or not further solitary searching is successful. Similar group dynamics have been observed in foraging seabirds (Porter and Seally, 1982; Haney et al., 1992).
      • Synchrokinesis depends upon the school having a relatively large spatial extent: part of a migrating school encounters an especially favourable or unfavourable area. The response of that section of the school is propagated throughout the school by alignment and grouping behaviours, with the result that the school as a whole is more effective at route-finding than isolated individuals. Forage area copying and synchrokinesis are distinct from social taxis in that an individual discovers and reacts to an environmental feature or resource, and fellow group members exploit that discovery. In social taxis, no individual need ever have greater knowledge about the environment than any other — social taxis is essentially bound up in the statistics of pooling the outcomes of many unreliable decisions. Synchrokinesis and social taxis are complementary mechanisms and may be expected to co-occur in migrating and gradient-climbing schools. (pp 519)
      • For example, in the comparisons of taxis among groups of various sizes, the most successful individuals were in the asocial simulation, even though as a fraction of the entire population they were vanishingly small. (pp 519)
        • Explorers have the highest payoff for the highest risks
  • Continuing white paper. Done with intro, background, and phase 1
  • Intel-powered AI Helps Fight Fraud

Phil 11.30.17

7:00 – 4:30 ASRC MKT

  • Need to get this book: Simulating Social Complexity
  • Continuing Alignment in social interactions here. This looks much better. Found this book on game theory for groups. It took me a while to determine if ‘joint action’ is the coordinated ‘joint’ action of two people, or it it was the coordinated action of two people’s joints. It’s neuroscience after all.
  • Continuing on the Research Browser white paper. Based on the HHS requests, I added default performance logging
  • In a lot of meetings as an Aaron Proxey
  • Good progress on the white paper. Just about finished the background section. Need to add pix of the current prototype

Phil 11.29.17

7:00 – 4:30 ASRC MKT

Pattern is a web mining module for the Python programming language.

  • It has tools for data mining (Google, Twitter and Wikipedia API, a web crawler, a HTML DOM parser), natural language processing (part-of-speech taggers, n-gram search, sentiment analysis, WordNet), machine learning (vector space model, clustering, SVM), network analysis and visualization.
  • Promoted Speaker–listener neural coupling underlies successful communication notes to Phlog
  • Added some bits to the JCSCW Flocking and herding article

  • Alignment in social interactions
    • According to the prevailing paradigm in social-cognitive neuroscience, the mental states of individuals become shared when they adapt to each other in the pursuit of a shared goal. We challenge this view by proposing an alternative approach to the cognitive foundations of social interactions. The central claim of this paper is that social cognition concerns the graded and dynamic process of alignment of individual minds, even in the absence of a shared goal. When individuals reciprocally exchange information about each other’s minds processes of alignment unfold over time and across space, creating a social interaction. Not all cases of joint action involve such reciprocal exchange of information. To understand the nature of social interactions, then, we propose that attention should be focused on the manner in which people align words and thoughts, bodily postures and movements, in order to take one another into account and to make full use of socially relevant information.
    • The concept of alignment has since evolved and is used to describe the multi-level, dynamic, and interactive mechanisms that underpin the sharing of people’s mental attitudes and representations in all kinds of social interactions (Dale, Fusaroli, & Duran, 2013). (pp 253)
    • The underlying justification for subsuming all these cases under the same mechanism is that cognition and action cannot be separated. The sharing of minds and bodies can then be conceptualized in terms of an integrated system of alignment, defined as the dynamic coupling of behavioural and/or cognitive states of two people (Dumas, Laroche, & Lehmann, 2014). (pp 253)
    • we are interested in the explanatory significance of alignment for a more general theory of social interaction, not in instrumental behaviour and/or alignment per se. (pp 254)
    • The central claim of this paper is that the alignment of minds, which emerges in social interactions, involves the reciprocal exchange of information whereby individuals adjust minds and bodies in a graded and dynamic manner. As these processes of alignment unfold, interacting partners will exchange information about each other’s minds and therefore act socially, whether or not a shared goal is in place. (pp 254)
    • In particular, in recent theoretical and empirical work on social cognition, reciprocity is increasingly recognized as a useful resource to capture the ‘‘jointness” of a joint action. Interpersonal understanding can be achieved by reading into one another’s mind reciprocally (Butterfill, 2013), and an explanation of the processes whereby the alignment of minds and bodies unfolds in space and time should involve an account of reciprocity (Zahavi & Rochat, 2015). In the process of a reciprocal exchange of information, individuals may adapt to varying degrees to one another. This is certainly the case in instances of temporal synchronisation and coordination in which physical alignment in time and space has been theorized to depend on cognitive models of adaptation (Elliott, Chua, & Wing, 2016; Hayashi & Kondo, 2013; Repp & Su, 2013) and thus on reciprocal interactions (D’Ausilio, Novembre, Fadiga, & Keller, 2015; Keller, Novembre, & Hove, 2014; Tognoli & Kelso, 2015). The behaviour of one player results in a change in behaviour of the other in a reciprocal way so as to achieve temporal synchrony. Interestingly, though not surprisingly, this reciprocal exchange of information results in physical alignment, which in turn has also been shown to result in greater degrees of affiliation and greater mental alignment (Hove & Risen, 2009; Rabinowitch & Knafo-Noam, 2015; Wiltermuth & Heath, 2009). Specifically, we suggest that, rather than a focus on the sharedness of the intended goal, we should attend to the graded exchange of information that creates alignment. The most social of interactions, in our formulation, are those in which ‘‘live” (‘‘online”, see Schilbach, 2014) information is exchanged dynamically (i.e. over time, across multiple points) bidirectionally and used to adapt behaviour and align with another (Jasmin et al., 2016). (pp 255)
    • Indeed, it is possible to have reciprocity and thus social interaction without cooperation. This would be the case, for example, in a competitive scenario in which the minds of the subjects are aligned at the appropriate level of description, and the sharing is essential to solve social dilemmas involving antagonistic behaviour (Bratman, 2014). In these exchanges, what is needed for the minds of the agents to attune to one another is that they adapt thoughts, bodily postures and movements, to take one another into account and reason as a team, even though the team might consist of competitive actors where none is aware that they are acting from the perspective of the same group and in the pursuit of some common goal (Bacharach, 2006). (pp 255)
    • fundamentally social nature has to do with the process whereby systems reciprocate thoughts and experiences, rather than with the endpoint i.e. the goal. It turns out that two features are often taken to be central to the process whereby interacting agents align minds and bodies. First, the interacting agents must be aware that they are doing something together with others. Second, the success of their joint performance is taken as a measure of how shared the participants’ goals are. (pp 255)
    • our suggestion is that what matters for the relevant alignment of minds and bodies to occur is the reciprocal exchange of information, not awareness of the reciprocal exchange of information. (pp 255)
      • This is all that is needed for flocking to happen. It is the range of that exchange that determines the phase change from independent to flock to stampede. Trust is involved in the reciprocity too, I think
    • Becoming mutually aware that we are sharing attitudes, dispositions, bodily postures, perhaps goals, does not mean that the ‘jointness’ of our actions has become available to each of us for conscious report. Reciprocity of awareness is emphatically not the same as awareness of reciprocity. The process of reciprocally exchanging information and mutually adapting to one another need not necessarily result in any degree of shared awareness. (pp 256)
    • In animals, a signal, for example about the source of food, that is too weak for an individual fish to follow can be followed by a group through the simple rules of bodily alignment that create shoaling behaviour (Grunbaum, 1998). Shoaling behaviour can also be observed in humans (Belz, Pyritz, & Boos, 2013), who can achieve group advantage through more complex forms of adjustment than just bodily alignment. Pairs of participants trying to detect a weak visual signal can achieve a greater group advantage when they align the terms they use to report their confidence in what they saw (Fusaroli et al., 2012). Indeed, linguistic alignment at many levels can be observed in dialogue (Pickering & Garrod, 2004) and can improve comprehension (Adank, Hagoort, & Bekkering, 2010; Fusaroli et al., 2012). (pp 256)
    • Much research has been driven, so far, by the implicit goal of identifying optimal group performance as a proxy for mental alignment (Fusaroli et al., 2012), however, there is conceptual room and empirical evidence for arguing that optimal task performance is not a good index of mental alignment or ‘optimal sociality’. In other words, taking achievement of a shared goal as the paradigm of a social interaction leads to the binary conception of sociality according to which an interaction is either (optimally) social, or it is not. (pp 256)
      • This is a problem that I have with opinion dynamics models. Convergence on a particular opinion isn’t the only issue. There is a dynamic process where opinions fall in and out of favor. This is the difference between the contagion model, which is one way (uninfected->infected) and motion through belief space. The goal really doesn’t matter, except in a subset of cases (Though these may be very important)
    • Two systems can interact when they have access to information relating to each other (Bilek et al., 2015). There are different ways of exchanging information between systems and hence different types of interaction (Liu & Pelowski, 2014), but in every case some kind of alignment occurs (Coey, Varlet, & Richardson, 2012; Huygens, 1673). (pp 257)
    • Such offline interaction can be contrasted with the case of online social interactions, where both participants act. The distinction between offline and online social interaction tasks is now acknowledged as crucial for advancing our understanding of the cognition processes underlying social interaction (Schilbach, 2014). (pp 257)
    • In contrast to salsa, consider the case of tango in which movements are improvised and as such require constant, mutual adaptation (Koehne et al., 2015; Tateo, 2014). Tango dancers have access to information relating to each other and, by virtue of the task, they exchange information with one another across time in a reciprocal and bidirectional fashion. The juxtaposition of tango with salsa highlights a spectrum of degrees of mutual reciprocity, with a richer form of interaction and greater need for alignment in tango compared with salsa.
    • we will take reciprocity to be the primary requirement for social interactions. We suggest that reciprocity can be identified with a special kind of alignment, mutual alignment, involving adjustment in both parties to the interaction. However, not all cases of joint action lead to mutual alignment. It is important to distinguish this mutual alignment from other types of alignment, which do not involve a reciprocal exchange of information between the agents. (pp 257)
    • In contrast to salsa, consider the case of tango in which movements are improvised and as such require constant, mutual adaptation (Koehne et al., 2015; Tateo, 2014). Tango dancers have access to information relating to each other and, by virtue of the task, they exchange information with one another across time in a reciprocal and bidirectional fashion. The juxtaposition of tango with salsa highlights a spectrum of degrees of mutual reciprocity, with a richer form of interaction and greater need for alignment in tango compared with salsa. (pp 257)
    • AlignmentInSocialInteractions(pp 258)
    • The biggest challenge currently facing philosophers and scientists of social cognition is to understand social interactions. We suggest that this problem is best approached at the level of processes of mental alignment rather than through joint action tasks based on shared goals, and we propose that the key process is one of reciprocal, dynamic and graded adaptation between the participants in the interaction. Defining social interactions in terms of reciprocal patterns of alignment shows that not all joint actions involve reciprocity and also that social interactions can occur in the absence of shared goals. This approach has two particular advantages. First, it emphasises the key point that interactions can only be fully understood at the level of the group, rather than the individual. The pooling together of individual mental resources generates results that exceed the sum of the individual contributions. But, second, our approach points towards the mechanisms of adaptation that must be occurring within each individual in order to create the interaction (Friston & Frith, 2015). (pp 259)
    • This picture of social interaction in terms of mental alignment suggests two important theoretical developments. One is about a possible way to characterize the idea that types of social interaction lie on a continuum of possible solutions. If we focus on the task or the shared goal being pursued by agents jointly, as the current literature suggests, then only limited subdivisions of types of interaction will emerge. If, however, our focus extends so as to integrate the nature of the interaction, conceived of in terms of information exchange, then we can arrive at a higher degree of resolution of the space in which social interaction lie. This will define a spectrum of types of interaction (not just offline versus online social cognition), suggesting a dimensional rather than a discrete picture. After all, alignment comes in degrees and a spectrum-like definition of sociality implies that there is a variety of forms of alignment and hence of interactions. (pp 269)
      • My work would indicate that meaningful transitions occur for Unaligned (pure explore), Complex (flocking), and Total (stampede).
  • Continuing to work on The Socio-Temporal Brain: Connecting People in Time here
    • Not as good as I thought it would be. Some useful items, but there is no brain analysis of chorusing animals, just the co-mention
  • Continuing Research Browser white paper. Added note to work through linking multiple tags to the same item with visibility controls. Kindle has a feature like this.
  • Reading section 16.7 on personalized web services  (pp 372 – 375) for words and concepts for Augmented Data Discovery. Then Where to Add Actions in Human-in-the-Loop Reinforcement LearningPolicy Shaping: Integrating Human Feedback with Reinforcement Learning, and AXIS: Generating Explanations at Scale with Learner sourcing and Machine Learning

 

Phil 11.28.17

7:00 – 8:00 Research, 8:30 – 4:30 ASRC MKT

  • Continuing Speaker–listener neural coupling underlies successful communication here. Done! Will promote to Phlog later.
  • Collective cognition in animal groups
    • Iain Couzin
    • The remarkable collective action of organisms such as swarming ants, schooling fish and flocking birds has long captivated the attention of artists, naturalists, philosophers and scientists. Despite a long history of scientific investigation, only now are we beginning to decipher the relationship between individuals and group-level properties. This interdisciplinary effort is beginning to reveal the underlying principles of collective decision-making in animal groups, demonstrating how social interactions, individual state, environmental modification and processes of informational amplification and decay can all play a part in tuning adaptive response. It is proposed that important commonalities exist with the understanding of neuronal processes and that much could be learned by considering collective animal behavior in the framework of cognitive science.
  • The Socio-Temporal Brain: Connecting People in Timethis looks like it might explicitly link human neural coupling and flocking.
    • Temporal and social processing are intricately linked. The temporal extent and organization of interactional behaviors both within and between individuals critically determine interaction success. Conversely, social signals and social context influence time perception by, for example, altering subjective duration and making an event seem ‘out of sync’. An ‘internal clock’ involving subcortically orchestrated cortical oscillations that represent temporal information, such as duration and rhythm, as well as insular projections linking temporal information with internal and external experiences is proposed as the core of these reciprocal interactions. The timing of social relative to non-social stimuli augments right insular activity and recruits right superior temporal cortex. Together, these reciprocal pathways may enable the exchange and respective modulation of temporal and social computations.
    • timing is not encapsulated but interacts closely with the social processes that emerge from interpersonal interactions [1–3]. As interactional behaviors play out in time, their temporal signatures carry important information. They guide attention, convey a message, and mold bonds between individuals. Moreover, interactional behaviors in turn give meaning to time and influence its perception and representation. A range of disorders that jointly compromise temporal and social processes attest to this relation (pp 760)
    • We review here the many ways in which timing intersects with social processing. We explore this intersection for the communicative behavior of an individual as well as for the behavioral coordination between communicating agents. We detail neuroimaging evidence on how temporal and social information are represented in the brain and identify points of structural and functional convergence (pp 760)
    • The temporal coordination of behavior in animals is referred to as chorusing. Chorusing describes sporadic behaviors such those of flying birds (Figure I), which may change direction and speed in a manner resembling a single superorganism [87]. In addition, it describes behaviors that occur repeatedly and with some amount of temporal regularity, as in the mating calls of male frogs. Although less frequent than sporadic chorusing, rhythmic chorusing is displayed by a range of taxa including insects, reptiles, birds, and mammals [88, 89]. (pp 761)
    • Research into the functionality of chorusing suggests species differences. For some, it seems to be a mere byproduct of competitive interactions. For others, it reduces the risk of predation [98]. By analogy with the selfish-herd principle, overlapping with others in time makes it harder for predators to single out individual prey. Last, there are species in which chorusing serves as a fitness display in the context of sexual selection [99] and as a means to foster social bonds [100]. Because of its pervasiveness and social context, some suggest chorusing to be the driving evolutionary force for a species’ timing sense [88]. (pp 761)
    • The degree of temporal coordination between interaction partners relates to interaction success. For example, it produces affective consequences [21]. This was revealed by a study in which pairs of strangers discussed four topics and completed an affective state questionnaire before and after each topic. Results provided evidence for emerging synchrony between discussion partners (Figure 1) and for its causal effect on ensuing positive affect. Related research showed that individuals more readily empathize with a synchronous as compared to a non-synchronous partner [22] and that synchronous dyads are more creative [23] and trusting [24] than nonsynchronous dyads. (pp 762)
  • Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes
    • Word embeddings use vectors to represent words such that the geometry between vectors captures semantic relationship between the words. In this paper, we develop a framework to demonstrate how the temporal dynamics of the embedding can be leveraged to quantify changes in stereotypes and attitudes toward women and ethnic minorities in the 20th and 21st centuries in the United States. We integrate word embeddings trained on 100 years of text data with the U.S. Census to show that changes in the embedding track closely with demographic and occupation shifts over time. The embedding captures global social shifts – e.g., the women’s movement in the 1960s and Asian immigration into the U.S – and also illuminates how specific adjectives and occupations became more closely associated with certain populations over time. Our framework for temporal analysis of word embedding opens up a powerful new intersection between machine learning and quantitative social science.
    • bias
    • I think this is really close to the belief trajectories I’m trying to tease out. In the figures above, note that it is possible to extract both trajectories and normative terms. Plus, the paper has a really good writeup of methods in the appendices.
  • Continuing to write up use cases/RB proposal
  • Also still doing stuff for the HHS RFI
  • Relevant to the Research Browser:
    • Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples <- Agent generation is a thing!
      • We address the problem of extracting an automaton from a trained recurrent neural network (RNN). We present a novel algorithm that uses exact learning and abstract interpretation to perform efficient extraction of a minimal automaton describing the state dynamics of a given RNN. We use Angluin’s L* algorithm as a learner and the given RNN as an oracle, employing abstract interpretation of the RNN for answering equivalence queries. Our technique allows automaton-extraction from the RNN while avoiding state explosion, even when the state vectors are large and fine differentiation is required between RNN states. 
        We experiment with automata extraction from multi-layer GRU and LSTM based RNNs, with state-vector dimensions and underlying automata and alphabet sizes which are significantly larger than in previous automata-extraction work. In some cases, the underlying target language can be described with a succinct automata, yet the extracted automata is large and complex. These are cases in which the RNN failed to learn the intended generalization, and our extraction procedure highlights words which are misclassified by the seemingly “perfect” RNN.
    • Policy Shaping: Integrating Human Feedback with Reinforcement Learning
      • A long term goal of Interactive Reinforcement Learning is to incorporate nonexpert human feedback to solve complex tasks. Some state-of-the-art methods have approached this problem by mapping human information to rewards and values and iterating over them to compute better control policies. In this paper we argue for an alternate, more effective characterization of human feedback: Policy Shaping. We introduce Advise, a Bayesian approach that attempts to maximize the information gained from human feedback by utilizing it as direct policy labels. We compare Advise to state-of-the-art approaches and show that it can outperform them and is robust to infrequent and inconsistent human feedback.
    • AXIS: Generating Explanations at Scale with Learnersourcing and Machine Learning
      • While explanations may help people learn by providing information about why an answer is correct, many problems on online platforms lack high-quality explanations. This paper presents AXIS (Adaptive eXplanation Improvement System), a system for obtaining explanations. AXIS asks learners to generate, revise, and evaluate explanations as they solve a problem, and then uses machine learning to dynamically determine which explanation to present to a future learner, based on previous learners’ collective input. Results from a case study deployment and a randomized experiment demonstrate that AXIS elicits and identifies explanations that learners find helpful. Providing explanations from AXIS also objectively enhanced learning, when compared to the default practice where learners solved problems and received answers without explanations. The rated quality and learning benefit of AXIS explanations did not differ from explanations generated by an experienced instructor.

 

Phil 11.27.17

There are new versions of dom4j!!!

I had to do two things this morning that required 2FA. Instead of being another irritating step to do anything, it now feels like a comforting barrier to bad things

Today’s big thought. The Contagion metaphor of information cascade (Bikhchandani) is a bad one (These folks agree). The flocking/stampeding metaphor is more complete. For example, it provides the concept of position, alignment, and velocity in a belief space. Two equally ‘viral’ (belief space position?)  memes may be released into the wild, but the one closest to the current alignment will be the one that will succeed more. Flocking effects result in sub-stampede cascades. The ‘refractory’ period is the time it takes for the population to get into alignment again. There is also something in the notion that a flock or a stampede is a group navigating with incomplete information that is fundamentally different from disease spread, which is more like percolation. It also fits better with what’s being done in reinforcement learning.

A nice description of the contagion model, from Information Epidemics and Synchronized Viral Social Contagion

  • In network science the processes of information contagion can be studied using epidemic models. If we consider information as a disease, three different models can be used to model its proliferation through the network: SIS, SIRS and SIR (Ball 1997; Newman 2002).
In the first model an element of the population (a node in a network) can be either susceptible (S) or infected (I) and goes through these stage one after the other: susceptible – infected – susceptible. 
According to the second model, the node also has a refractory stage (R) before it becomes susceptible again, so the states are: susceptible – infected – refractory – susceptible. 
Finally, in the third model once the node is infected once it cannot be infected again, so for each node the sequence is: susceptible – infected – removed (R). Depending on the nature of information studied, a certain model can be more suitable than another (Fig 2). 



7:00 – 3:00 ASRC MKT

  • Renewed Tajour.com for a year. Will probably switch over to the UMBC server for the next version though
  • Starting Speaker–listener neural coupling underlies successful communication here
  • The next paper will be Mirroring and beyond: coupled dynamics as a generalized framework for modelling social interactions
    • Uri Hasson (HassonLab at Princeton)
    • Chris Frith (Scholar)
    • When people observe one another, behavioural alignment can be detected at many levels, from the physical to the mental. Likewise, when people process the same highly complex stimulus sequences, such as films and stories, alignment is detected in the elicited brain activity. In early sensory areas, shared neural patterns are coupled to the low-level properties of the stimulus (shape, motion, volume, etc.), while in high-order brain areas, shared neural patterns are coupled to high-levels aspects of the stimulus, such as meaning. Successful social interactions require such alignments (both behavioural and neural), as communication cannot occur without shared understanding. However, we need to go beyond simple, symmetric (mirror) alignment once we start interacting. Interactions are dynamic processes, which involve continuous mutual adaptation, development of complementary behaviour and division of labour such as leader– follower roles. Here, we argue that interacting individuals are dynamically coupled rather than simply aligned. This broader framework for understanding interactions can encompass both processes by which behaviour and brain activity mirror each other (neural alignment), and situations in which behaviour and brain activity in one participant are coupled (but not mirrored) to the dynamics in the other participant. To apply these more sophisticated accounts of social interactions to the study of the underlying neural processes we need to develop new experimental paradigms and novel methods of data analysis
  • Started on the use cases
  • Added sections in an HHS RFI that looks a *lot* like the research browser, with some more attention to data provenance. Which is a thing that should be added to the RB anyway.

3:00 – 4:00 Campus

  • Julie wants an ‘append’ option to the ignore.xml. Done!
  • Relaxed Fika. Learned about Stacy’s research, Julie’s CHI DC, and Andrea’s affective computing. Intimate and nice!
  • No Wayne?

Phil 11.26.17

User experience design for APIs

  • Writing code is rarely just a private affair between you and your computer. Code is not just meant for machines; it has human users. It is meant to be read by people, used by other developers, maintained and built upon. Developers who produce better code, in greater quantity, when they are kept happy and productive, working with tools they love. Developers who unfortunately are often being let down by their tools, and left cursing at obscure error messages, wondering why that stupid library doesn’t do what they thought it would. Our tools have great potential to cause us pain, especially in a field as complex as software engineering.