arXiv subjects

Christopher Rose

Publications and source records attributed to Christopher Rose.

12 recordsLinked to original sources

arXiv2025

Limits of Information Flow Between Classically Interacting Particles

Pinning down a precise understanding of information flow within physical interactions remains a central concern to fields like stochastic thermodynamics and quantum information science. In both spheres a careful accounting of bits (or qubits) enables a deeper understanding of the physical nature of information. In this work we propose a measure of information flow as a saddle-point solution of the mutual information. This approach places a lower bound on the channel capacity between a particle and an interacting environment. The measure is given by P/2E in nats/sec, with P the average power flux between the particle and its environment, and E the initial average energy of the particle, all computed in a frame where the particle has zero average momentum. We use a communication theory lens to suggest an associated channel analogy, in which this bound is interpreted as a signal-to-noise ratio. We find that this measure can also quantify early-time information flow for a particle interacting with a thermal bath.

cond-mat.stat-mech↗

arXiv2024

Quantum Analogues for Two Simple Classical Channels

We present some of the peculiar dynamics of two simple sans-entanglement quantum communication channels in a digestible form. Specifically, we contrast the classical gaussian additive channel to its quantum analogue and find that the quantum version features a capacity with interesting time dependence and counterintuitive effects of quantization. We also consider a simple two-level system and comment on the time dependence of its capacity.

quant-ph↗

arXiv2022

Evaluating evolution as a learning algorithm

We interpret the Moran model of natural selection and drift as an algorithm for learning features of a simplified fitness landscape, specifically genotype superiority. This algorithm's efficiency in extracting these characteristics is evaluated by comparing it to a novel Bayesian learning algorithm developed using information-theoretic tools. This algorithm makes use of a communication channel analogy between an environment and an evolving population. We use the associated channel-rate to determine an informative population-sampling procedure. We find that the algorithm can identify genotype superiority faster than the Moran model but at the cost of larger fluctuations in uncertainty.

q-bio.PE↗

arXiv2020

Population Susceptibility Variation and Its Effect on Contagion Dynamics

Susceptibility governs the dynamics of contagion. The classical SIR model is one of the simplest compartmental models of contagion spread, assuming a single shared susceptibility level. However, variation in susceptibility over a population can fundamentally affect the dynamics of contagion and thus the ultimate outcome of a pandemic. We develop mathematical machinery which explicitly considers susceptibility variation, illuminates how the susceptibility distribution is sculpted by contagion, and thence how such variation affects the SIR differential questions that govern contagion. Our methods allow us to derive closed form expressions for herd immunity thresholds as a function of initial susceptibility distributions and suggests an intuitively satisfying approach to inoculation when only a fraction of the population is accessible to such intervention. Of particular interest, if we assume static susceptibility of individuals in the susceptible pool, ignoring susceptibility diversity {\em always} results in overestimation of the herd immunity threshold and that difference can be dramatic. Therefore, we should develop robust measures of susceptibility variation as part of public health strategies for handling pandemics.

q-bio.PE↗

arXiv2020

Capacities and Optimal Input Distributions for Particle-Intensity Channels

This work introduces the particle-intensity channel (PIC) as a model for molecular communication systems and characterizes the capacity limits as well as properties of the optimal (capacity-achieving) input distributions for such channels. In the PIC, the transmitter encodes information, in symbols of a given duration, based on the probability of particle release, and the receiver detects and decodes the message based on the number of particles detected during the symbol interval. In this channel, the transmitter may be unable to control precisely the probability of particle release, and the receiver may not detect all the particles that arrive. We model this channel using a generalization of the binomial channel and show that the capacity-achieving input distribution for this channel always has mass points at probabilities of particle release of zero and one. To find the capacity-achieving input distributions, we develop an efficient algorithm we call dynamic assignment Blahut-Arimoto (DAB). For diffusive particle transport, we also derive the conditions under which the input with two mass points is capacity-achieving.

cs.IT↗

arXiv2020

Heterogeneity in susceptibility dictates the order of epidemiological models

The fundamental models of epidemiology describe the progression of an infectious disease through a population using compartmentalized differential equations, but do not incorporate population-level heterogeneity in infection susceptibility. We show that variation strongly influences the rate of infection, while the infection process simultaneously sculpts the susceptibility distribution. These joint dynamics influence the force of infection and are, in turn, influenced by the shape of the initial variability. Intriguingly, we find that certain susceptibility distributions (the exponential and the gamma) are unchanged through the course of the outbreak, and lead naturally to power-law behavior in the force of infection; other distributions often tend towards these "eigen-distributions" through the process of contagion. The power-law behavior fundamentally alters predictions of the long-term infection rate, and suggests that first-order epidemic models that are parameterized in the exponential-like phase may systematically and significantly over-estimate the final severity of the outbreak.

q-bio.PE↗

arXiv2019

Principles of Information Storage in Small-Molecule Mixtures

Molecular data systems have the potential to store information at dramatically higher density than existing electronic media. Some of the first experimental demonstrations of this idea have used DNA, but nature also uses a wide diversity of smaller non-polymeric molecules to preserve, process, and transmit information. In this paper, we present a general framework for quantifying chemical memory, which is not limited to polymers and extends to mixtures of molecules of all types. We show that the theoretical limit for molecular information is two orders of magnitude denser by mass than DNA, although this comes with different practical constraints on total capacity. We experimentally demonstrate kilobyte-scale information storage in mixtures of small synthetic molecules, and we consider some of the new perspectives that will be necessary to harness the information capacity available from the vast non-genomic chemical space.

cs.ET↗

arXiv2018

Parallelized Linear Classification with Volumetric Chemical Perceptrons

In this work, we introduce a new type of linear classifier that is implemented in a chemical form. We propose a novel encoding technique which simultaneously represents multiple datasets in an array of microliter-scale chemical mixtures. Parallel computations on these datasets are performed as robotic liquid handling sequences, whose outputs are analyzed by high-performance liquid chromatography. As a proof of concept, we chemically encode several MNIST images of handwritten digits and demonstrate successful chemical-domain classification of the digits using volumetric perceptrons. We additionally quantify the performance of our method with a larger dataset of binary vectors and compare the experimental measurements against predicted results. Paired with appropriate chemical analysis tools, our approach can work on increasingly parallel datasets. We anticipate that related approaches will be scalable to multilayer neural networks and other more complex algorithms. Much like recent demonstrations of archival data storage in DNA, this work blurs the line between chemical and electrical information systems, and offers early insight into the computational efficiency and massive parallelism which may come with computing in chemical domains.

cs.ET↗

arXiv2017

Capacity of Molecular Channels with Imperfect Particle-Intensity Modulation and Detection

This work introduces the particle-intensity channel (PIC) as a model for molecular communication systems and characterizes the properties of the optimal input distribution and the capacity limits for this system. In the PIC, the transmitter encodes information, in symbols of a given duration, based on the number of particles released, and the receiver detects and decodes the message based on the number of particles detected during the symbol interval. In this channel, the transmitter may be unable to control precisely the number of particles released, and the receiver may not detect all the particles that arrive. We demonstrate that the optimal input distribution for this channel always has mass points at zero and the maximum number of particles that can be released. We then consider diffusive particle transport, derive the capacity expression when the input distribution is binary, and show conditions under which the binary input is capacity-achieving. In particular, we demonstrate that when the transmitter cannot generate particles at a high rate, the optimal input distribution is binary.

cs.IT↗

arXiv2016

Inscribed Matter Communication: Part I

We provide a fundamental treatment of the molecular communication channel wherein "inscribed matter" is transmitted across a spatial gap to provide reliable signaling between a sender and receiver. Inscribed matter is defined as an ensemble of "tokens" (molecules, objects, and so on) and is inspired, at least partially, by biological systems where groups of individually constructed discrete particles ranging from molecules through membrane-bound structures containing molecules to viruses and organisms are released by a source and travel to a target -- for example, morphogens or semiochemicals diffuse from one cell, tissue or organism diffuse to another. For identical tokens that are neither lost nor modified, we consider messages encoded using three candidate communication schemes: a) token timing (timed release), b) token payload (composition), and c) token timing plus payload. We provide capacity bounds for each scheme and discuss their relative utility. We find that under not unreasonable assumptions, megabit per second rates could be supported at femtoWatt transmitter powers. Since quantities such as token concentration or bin-counting are derivatives of token arrival timing, individual token timing undergirds all molecular communication techniques. Thus, our modeling and results about the physics of efficient token-based information transfer can inform investigations of diverse theoretical and practical problems in engineering and biology. This work, Part I, focuses on the information theoretic bounds on capacity. Part II develops some of the mathematical and information-theoretic ideas that support the bounds presented here.

cs.ET↗

arXiv2016

Inscribed Matter Communication: Part II

This paper is Part II of a two-paper set which develops a finest-grain per-molecule timing treatment of molecular communication. We first consider a simple one-molecule timing channel with a molecule launch deadline, similar to but different from previous work ("Bits Through Queues") where the constraint was mean launch time. We also derive a number of results related to the {\em ordering entropy}, a key quantity which undergirds the capacity bounds for the molecular timing channel, both with and without token data payloads. We then conclude with an upper bound on molecular timing-channel capacity.

cs.ET↗

arXiv2012

Timing Channels with Multiple Identical Quanta

We consider mutual information between release times and capture times for a set of M identical quanta traveling independently from a source to a target. The quanta are immediately captured upon arrival, first-passage times are assumed independent and identically distributed and the quantum emission times are constrained by a deadline. The primary application area is intended to be inter/intracellular molecular signaling in biological systems whereby an organelle, cell or group of cells must deliver some message (such as transcription or developmental instructions) over distance with reasonable certainty to another organelles, cells or group of cells. However, the model can also be applied to communications systems wherein indistinguishable signals have random transit latencies.

cs.IT↗