An Adversarial Interpretation of Information-Theoretic Bounded Rationality

Recently, there has been a growing interest in modeling planning with information constraints. Accordingly, an agent maximizes a regularized expected utility known as the free energy, where the regularizer is given by the information divergence from a prior to a posterior policy. While this approach can be justified in various ways, including from statistical mechanics and information theory, it is still unclear how it relates to decision-making against adversarial environments. This connection has previously been suggested in work relating the free energy to risk-sensitive control and to extensive form games. Here, we show that a single-agent free energy optimization is equivalent to a game between the agent and an imaginary adversary. The adversary can, by paying an exponential penalty, generate costs that diminish the decision maker's payoffs. It turns out that the optimal strategy of the adversary consists in choosing costs so as to render the decision maker indifferent among its choices, which is a definining property of a Nash equilibrium, thus tightening the connection between free energy optimization and game theory.Comment: 7 pages, 4 figures. Proceedings of AAAI-1

Maximizing Activity in Ising Networks via the TAP Approximation

A wide array of complex biological, social, and physical systems have recently been shown to be quantitatively described by Ising models, which lie at the intersection of statistical physics and machine learning. Here, we study the fundamental question of how to optimize the state of a networked Ising system given a budget of external influence. In the continuous setting where one can tune the influence applied to each node, we propose a series of approximate gradient ascent algorithms based on the Plefka expansion, which generalizes the na\"{i}ve mean field and TAP approximations. In the discrete setting where one chooses a small set of influential nodes, the problem is equivalent to the famous influence maximization problem in social networks with an additional stochastic noise term. In this case, we provide sufficient conditions for when the objective is submodular, allowing a greedy algorithm to achieve an approximation ratio of $1-1/e$. Additionally, we compare the Ising-based algorithms with traditional influence maximization algorithms, demonstrating the practical importance of accurately modeling stochastic fluctuations in the system

Classification and Geometry of General Perceptual Manifolds

Perceptual manifolds arise when a neural population responds to an ensemble of sensory signals associated with different physical features (e.g., orientation, pose, scale, location, and intensity) of the same perceptual object. Object recognition and discrimination requires classifying the manifolds in a manner that is insensitive to variability within a manifold. How neuronal systems give rise to invariant object classification and recognition is a fundamental problem in brain theory as well as in machine learning. Here we study the ability of a readout network to classify objects from their perceptual manifold representations. We develop a statistical mechanical theory for the linear classification of manifolds with arbitrary geometry revealing a remarkable relation to the mathematics of conic decomposition. Novel geometrical measures of manifold radius and manifold dimension are introduced which can explain the classification capacity for manifolds of various geometries. The general theory is demonstrated on a number of representative manifolds, including L2 ellipsoids prototypical of strictly convex manifolds, L1 balls representing polytopes consisting of finite sample points, and orientation manifolds which arise from neurons tuned to respond to a continuous angle variable, such as object orientation. The effects of label sparsity on the classification capacity of manifolds are elucidated, revealing a scaling relation between label sparsity and manifold radius. Theoretical predictions are corroborated by numerical simulations using recently developed algorithms to compute maximum margin solutions for manifold dichotomies. Our theory and its extensions provide a powerful and rich framework for applying statistical mechanics of linear classification to data arising from neuronal responses to object stimuli, as well as to artificial deep networks trained for object recognition tasks.Comment: 24 pages, 12 figures, Supplementary Material

Aminoacyl tRNA synthetase complex interacting multifunctional protein 1 simultaneously binds Glutamyl-Prolyl-tRNA synthetase and scaffold protein aminoacyl tRNA synthetase complex interacting multifunctional protein 3 of the multi-tRNA synthetase complex

Higher eukaryotes have developed extensive compartmentalization of amino acid (aa) - tRNA coupling through the formation of a multi-synthetase complex (MSC) that is composed of eight aa-tRNA synthetases (ARS) and three scaffold proteins: aminoacyl tRNA synthetase complex interacting multifunctional proteins (AIMP1, 2 and 3). Lower eukaryotes have a much smaller complex while yeast MSC consists of only two ARS (MetRS and GluRS) and one ARS cofactor 1 protein, Arc1p (Simos et al., 1996), the homolog of the mammalian AIMP1. Arc1p is reported to form a tripartite complex with GluRS and MetRS through association of the N-terminus GST-like domains (GST-L) of the three proteins (Koehler et al., 2013). Mammalian AIMP1 has no GST-L domain corresponding to Arc1p N-terminus. Instead, AIMP3, another scaffold protein of 18 kDa composed entirely of a GST-L domain, interacts with Methionyl-tRNA synthetase (MARS) (Quevillon et al., 1999) and Glutamyl-Prolyl-tRNA Synthetase (EPRS) (Cho et al., 2015). Here we report two new interactions between MSC members: AIMP1 binds to EPRS and AIMP1 binds to AIMP3. Interestingly, the interaction between AIMP1 and AIMP3 complex makes it the functional equivalent of a single Arc1p polypeptide in yeast. This interaction is not mapped to AIMP1 N-terminal coiled-coil domain, but rather requires an intact tertiary structure of the entire protein. Since AIMP1 also interacts with AIMP2, all three proteins appear to compose a core docking structure for the eight ARS in the MSC complex

Noncontact temperature pattern measuring device

Laser pyrometer techniques are utilized to accurately image a true temperature distribution on a given target without touching the target and without knowing the localized emissivity of the target. The pyrometer utilizes a very high definition laser beam and photodetector, both having a very narrow focus. The pyrometer is mounted in a mechanism designed to permit the pyrometer to be aimed and focused at precise localized points on the target surface. The pyrometer is swept over the surface area to be imaged, temperature measurements being taken at each point of focus