Publications | Mohammad Moshtaghi

The Canonical Amoebot Model: Algorithms and Concurrency Control

Fri, 17 Feb 2023 00:00:00 +0000

Invited Paper: Asynchronous Deterministic Leader Election in Three-Dimensional Programmable Matter

Wed, 04 Jan 2023 00:00:00 +0000

Cutting Through the Noise to Infer Autonomous System Topology

Mon, 02 May 2022 00:00:00 +0000

Local Mutual Exclusion for Dynamic, Anonymous, Bounded Memory Message Passing Systems

Fri, 29 Apr 2022 00:00:00 +0000

Reply to de Marchi: Modeling Polarization of Political Attitudes

Thu, 21 Apr 2022 00:00:00 +0000

Labor and Delivery Unit Closures in Rural Georgia from 2012 to 2016 and the Impact on Black Women: A Mixed-Methods Investigation

Fri, 18 Feb 2022 00:00:00 +0000

Abstract

Background. Obstetric provider coverage in rural Georgia has worsened, with nine rural labor and delivery units (LDUs) closing outside the Atlanta Metropolitan Statistical Area from 2012 to 2016. Georgia consistently has one of the highest maternal mortality rates in the nation and faces increased adverse health consequences from this decline in obstetric care.

Objective. This study explores what factors may be associated with rural hospital LDU closures in Georgia from 2012 to 2016.

Methods. This study describes differences between rural Georgia hospitals based on LDU closure status through a quantitative analysis of 2011 baseline regional, hospital, and patient data, and a qualitative analysis of newspaper articles addressing the closures.

Results. LDUs that closed had higher proportions of Black female residents in their Primary Care Service Areas (PCSAs), of Black birthing patients, and of patients with Medicaid, self-pay or other government insurance; lower LDU birth volume; more women giving birth within their PCSA of residence; fewer obstetricians and obstetric provider equivalents per LDU; and fewer average annual births per obstetric provider. Qualitative results indicate financial distress primarily contributed to closures, but also suggest that low birth volume and obstetric provider shortage impacted closures.

Conclusions for Practice. Rural LDU closure in Georgia has a disproportionate impact on Black and low-income women and may be prevented through funding maternity healthcare and addressing provider shortages.

Preventing Extreme Polarization of Political Attitudes

Tue, 14 Dec 2021 00:00:00 +0000

Science-Practice Blog: How might individual interactions lead to polarization in a society? Nancy Lewis. The Morton Deutsch International Center for Cooperation and Conflict Resolution, June 2022.
Like a natural system, democracy faces collapse as polarization leads to loss of diversity. Princeton University. EurekaAlert!, December 2021.
Preventing extreme polarization of political attitudes. Santa Fe Institute, December 2021. (Also featured in EurekaAlert!).
Computing scenarios for defusing polarizing politics. Joe Kullman. ASU FullCircle, December 2021. (Also featured in ASU News).
Reducing extreme polarization is key to stabilizing democracy. Stephanie Forrest and Joshua Daymude. Brookings TechTank, January 2022. (Reposted by Bishop Jennifer Reddall).
Researchers look to technology to find out what’s increasing the country’s social and political divide. Arizona Horizon, January 2022.

Deadlock and Noise in Self-Organized Aggregation Without Computation

Tue, 09 Nov 2021 00:00:00 +0000

The Canonical Amoebot Model: Algorithms and Concurrency Control

Mon, 04 Oct 2021 00:00:00 +0000

Programming Active Cohesive Granular Matter with Mechanically Induced Phase Changes

Fri, 23 Apr 2021 00:00:00 +0000

Simple Robots, Smart Algorithms: Meet the BOBbots. Dana Randall. Georgia Tech News Center, April 2021. (Also featured in EurekaAlert!).
Greater Than the Sum of Its Parts. Gary Werner. ASU FullCircle, April 2021. (Also featured in ASU News).

Collaborating in Motion: Distributed and Stochastic Algorithms for Emergent Behavior in Programmable Matter

Mon, 15 Mar 2021 00:00:00 +0000

Abstract

The world is filled with systems of entities that collaborate in motion, both natural and engineered. These cooperative distributed systems are capable of sophisticated emergent behavior arising from the comparatively simple interactions of their members. A model system for emergent collective behavior is programmable matter, a physical substance capable of autonomously changing its properties in response to user input or environmental stimuli. This dissertation studies distributed and stochastic algorithms that control the local behaviors of individual modules of programmable matter to induce complex collective behavior at the macroscale. It consists of four parts.

In the first, the canonical amoebot model of programmable matter is proposed. A key goal of this model is to bring algorithmic theory closer to the physical realities of programmable matter hardware, especially with respect to concurrency and energy distribution. Two protocols are presented that together extend sequential, energy-agnostic algorithms to the more realistic concurrent, energy-constrained setting without sacrificing correctness, assuming the original algorithms satisfy certain conventions.

In the second part, stateful distributed algorithms using amoebot memory and communication are presented for leader election, object coating, convex hull formation, and hexagon formation. The first three algorithms are proven to have linear runtimes when assuming a simplified sequential setting. The final algorithm for hexagon formation is instead proven to be correct under unfair asynchronous adversarial activation, the most general of all adversarial activation models.

In the third part, distributed algorithms are combined with ideas from statistical physics and Markov chain design to replace algorithm reliance on memory and communication with biased random decisions, gaining inherent self-stabilizing and fault-tolerant properties. Using this stochastic approach, algorithms for compression, shortcut bridging, and separation are designed and analyzed.

Finally, a two-pronged approach to "programming" physical ensembles is presented. This approach leverages the physics of local interactions to pair theoretical abstractions of self-organizing particle systems with experimental robot systems of active granular matter that intentionally lack digital computation and communication. By physically embodying the salient features of an algorithm in robot design, the algorithm’s theoretical analysis can predict the robot ensemble’s behavior. This approach is applied to phototaxing, aggregation, dispersion, and object transport.

Bio-Inspired Energy Distribution for Programmable Matter

Tue, 05 Jan 2021 00:00:00 +0000

Summary

In short, individual bacterium that are metabolically stressed (lacking in nutrients) catalyze a wave of chemical signaling that inhibits the rest of the biofilm’s nutrient uptake. This allows more nutrients to flow to the stressed bacteria, effectively solving the energy distribution problem. We model energy constraints in the amoebot model by giving each particle a battery for storing energy that it can transfer to its neighbors or spend to perform actions. We assume at least one particle in the system has access to an external energy source from which it can harvest energy. The goal of our energy distribution algorithm is to coordinate the transfer of energy between particles so that all particles meet their actions’ energy demands and at least one particle can actually perform its action within a bounded amount of time. After some initial setup, our Energy-Sharing algorithm works in the following phases repeated by each particle individually:

Communication. Particles propagate “stress” and “inhibition” signals to communicate the energy states of starving particles throughout the system.
Sharing. Particles harvest energy they lack from an external source (if they have access to one) and transfer energy to neighbors that need it.
Usage. Particles that are not inhibited spend their stored energy to perform actions, participating in the collective behavior.

We prove that the Energy-Sharing algorithm allows the particle system to meet its energy demands every ${\cal O}(n)$ asynchronous rounds, where $n$ is the number of particles in the system. A simulation of the Energy-Sharing algorithm can be seen below, corresponding to Figure 2 in the paper. All particles are initially idle, with the exception of the root (with external energy access) shown with a gray/black ring. The setup phase establishes the spanning forest (or tree, in this case) rooted at particle(s) with energy access; a particle’s parent direction is shown as an arc. Since all particles start with empty batteries, stress flags (shown as red rings) quickly propagate throughout the system and inhibit flags soon follow. As energy is harvested by the root and shared throughout the system, some particles (shown with yellow rings) receive sufficient energy to meet the demand for their next action but remain inhibited from using it. This inhibition remains until all stressed particles in the system receive sufficient energy to meet their demands, at which point particles (shown with green rings) can reset their inhibit flags and use their energy. After using energy, these particles may again become stressed and trigger another stage of inhibition.

When communication of the stress/inhibit flags is disabled, the energy distribution strategy in Energy-Sharing fails (just as the bacterial biofilms fail to feed all bacteria when their signal relays are disabled). A simulation of this setting is shown below, corresponding to Figure 3 in the paper. Without the communication phase to inhibit particles from using energy while those that are stressed recharge, particles continuously share any energy they have with their descendants in the spanning forest. Thus, while the leaves of the spanning forest occasionally meet their energy demands(seen as the flickering darker green particles), even after 1000 rounds most particles have still not met their energy demand even once.

As an independent but supporting result, we present the Forest-Prune-Repair algorithm that enables a spanning forest of particles to self-repair in the presence of crash failures so long as the set of non-faulty particles remains connected and there is at least one non-crashed root. We prove that this algorithm stabilizes in a spanning forest rooted at root particles in at most ${\cal O}(m^2)$ asynchronous rounds, where $m$ is the number of particles that are severed from the spanning forest by crash failures.

The Energy-Sharing algorithm relies on an underlying spanning forest structure to communicate its stress/inhibition signals. However, if particles move as they often do in collective behaviors, this disrupts the communication structure. Thus, the Forest-Prune-Repair algorithm can be used as an underlying primitive in Energy-Sharing so that Energy-Sharing can be composed with existing amoebot model algorithms, extending them to consider energy constraints. Below is an example of Energy-Sharing composed with Basic Shape Formation, corresponding to Figure 5 in the paper.

Since Energy-Sharing meets the system’s energy demands every ${\cal O}(n)$ asynchronous rounds and Forest-Prune-Repair repairs any disruptions to the communication structure as particles move, a composed algorithm will not be impeded by the underlying energy distribution primitive, but may add significant overhead to its runtime. However, we observe reasonable performance in practice: for example, since hexagon formation terminates in ${\cal O}(n)$ rounds, our proven bounds suggest that the composed algorithm could terminate in time ${\cal O}(n^2)$ or worse but the graph below demonstrates an overhead that appears asymptotically sublinear. With the addition of more energy roots, the composed algorithm is dramatically faster, approaching the runtime achieved without energy constraints.

As a final, informal experiment, we performed a simulation where energy is used for reproduction, mimicking the bacterial biofilms we were inspired by. In our preliminary experiments, we obtain behavior that is qualitatively similar to the biofilm growth patterns observed by Liu and Prindle et al.; in particular, the use of communication and inhibition leads to an oscillatory growth rate. The simulation below corresponds to Figure 7 in the paper.

Convex Hull Formation for Programmable Matter

Sat, 04 Jan 2020 00:00:00 +0000

A Local Stochastic Algorithm for Separation in Heterogeneous Self-Organizing Particle Systems

Fri, 20 Sep 2019 00:00:00 +0000

Simulation of Programmable Matter Systems Using Active Tile-Based Self-Assembly

Wed, 24 Jul 2019 00:00:00 +0000

Computing by Programmable Particles

Sun, 13 Jan 2019 00:00:00 +0000

A Stochastic Approach to Shortcut Bridging in Programmable Matter

Fri, 28 Sep 2018 00:00:00 +0000

Phototactic Supersmarticles

Tue, 25 Sep 2018 00:00:00 +0000

Smart Swarms Seek New Ways to Cooperate. Kevin Hartnett. Quanta Magazine, February 2018. (Also featured in ACM TechNews).

Brief Announcement: A Local Stochastic Algorithm for Separation in Heterogeneous Self-Organizing Particle Systems

Mon, 23 Jul 2018 00:00:00 +0000

Improved Leader Election for Self-Organizing Programmable Matter

Sun, 31 Dec 2017 00:00:00 +0000

On the Runtime of Universal Coating for Programmable Matter

Tue, 28 Nov 2017 00:00:00 +0000

Opening Paths to Progress with Programmable Materials. Joe Kullman. ASU Now, March 2017. (Also featured in ACM TechNews).

A Stochastic Approach to Shortcut Bridging in Programmable Matter

Thu, 24 Aug 2017 00:00:00 +0000

Image of Eciton army ants forming a shortcut bridge is reproduced with permission from Reid et al. (PNAS, 2015).

A Markov Chain Algorithm for Compression in Self-Organizing Particle Systems

Mon, 25 Jul 2016 00:00:00 +0000

SSS 2020 Report 2: Compression of programmable matter. Laurent Feuilloley. Discrete Notes, November 2020.
Opening Paths to Progress with Programmable Materials. Joe Kullman. ASU Now, March 2017. (Also featured in ACM TechNews).
A Creeping Model of Computation. Dick Lipton and Ken Regan. Gödel’s Lost Letter and P=NP, September 2016.

Compression in Self-Organizing Particle Systems

Tue, 31 May 2016 00:00:00 +0000

Publications | Mohammad Moshtaghi

The Canonical Amoebot Model: Algorithms and Concurrency Control

Invited Paper: Asynchronous Deterministic Leader Election in Three-Dimensional Programmable Matter

Cutting Through the Noise to Infer Autonomous System Topology

Local Mutual Exclusion for Dynamic, Anonymous, Bounded Memory Message Passing Systems

Reply to de Marchi: Modeling Polarization of Political Attitudes

Labor and Delivery Unit Closures in Rural Georgia from 2012 to 2016 and the Impact on Black Women: A Mixed-Methods Investigation

Abstract

Preventing Extreme Polarization of Political Attitudes

Deadlock and Noise in Self-Organized Aggregation Without Computation

The Canonical Amoebot Model: Algorithms and Concurrency Control

Programming Active Cohesive Granular Matter with Mechanically Induced Phase Changes

Related Press

Collaborating in Motion: Distributed and Stochastic Algorithms for Emergent Behavior in Programmable Matter

Abstract

Bio-Inspired Energy Distribution for Programmable Matter

Summary

Convex Hull Formation for Programmable Matter

A Local Stochastic Algorithm for Separation in Heterogeneous Self-Organizing Particle Systems

Simulation of Programmable Matter Systems Using Active Tile-Based Self-Assembly

Computing by Programmable Particles

A Stochastic Approach to Shortcut Bridging in Programmable Matter

Phototactic Supersmarticles

Related Press

Brief Announcement: A Local Stochastic Algorithm for Separation in Heterogeneous Self-Organizing Particle Systems

Improved Leader Election for Self-Organizing Programmable Matter

On the Runtime of Universal Coating for Programmable Matter

Related Press

A Stochastic Approach to Shortcut Bridging in Programmable Matter

A Markov Chain Algorithm for Compression in Self-Organizing Particle Systems

Related Press

Compression in Self-Organizing Particle Systems