An Energy Profile Model for Fused Deposition Modeling 3D Printing Process

This project develops a strategy to monitor and estimate the energy consumption of fused deposition modeling (FDM) additive manufacturing, which will benefit manufacturers and designers seeking to design and manufacture products with minimal energy consumption

Node and Edge Differential Privacy for Graph Laplacian Spectra: Mechanisms and Scaling Laws

This paper develops a framework for privatizing the spectrum of the graph Laplacian of an undirected graph using differential privacy. We consider two privacy formulations. The first obfuscates the presence of edges in the graph and the second obfuscates the presence of nodes. We compare these two privacy formulations and show that the privacy formulation that considers edges is better suited to most engineering applications. We use the bounded Laplace mechanism to provide differential privacy to the eigenvalues of a graph Laplacian, and we pay special attention to the algebraic connectivity, which is the Laplacian's second smallest eigenvalue. Analytical bounds are presented on the the accuracy of the mechanisms and on certain graph properties computed with private spectra. A suite of numerical examples confirms the accuracy of private spectra in practice.Comment: arXiv admin note: text overlap with arXiv:2104.0065

Differential Privacy in Cooperative Multiagent Planning

Privacy-aware multiagent systems must protect agents' sensitive data while simultaneously ensuring that agents accomplish their shared objectives. Towards this goal, we propose a framework to privatize inter-agent communications in cooperative multiagent decision-making problems. We study sequential decision-making problems formulated as cooperative Markov games with reach-avoid objectives. We apply a differential privacy mechanism to privatize agents' communicated symbolic state trajectories, and then we analyze tradeoffs between the strength of privacy and the team's performance. For a given level of privacy, this tradeoff is shown to depend critically upon the total correlation among agents' state-action processes. We synthesize policies that are robust to privacy by reducing the value of the total correlation. Numerical experiments demonstrate that the team's performance under these policies decreases by only 3 percent when comparing private versus non-private implementations of communication. By contrast, the team's performance decreases by roughly 86 percent when using baseline policies that ignore total correlation and only optimize team performance

Differentially Private Reward Functions for Multi-Agent Markov Decision Processes

Reward functions encode desired behavior in multi-agent Markov decision processes, but onlookers may learn reward functions by observing agents, which can reveal sensitive information. Therefore, in this paper we introduce and compare two methods for privatizing reward functions in policy synthesis for multi-agent Markov decision processes. Reward functions are privatized using differential privacy, a statistical framework for protecting sensitive data. Both methods we develop rely on the Gaussian mechanism, which is a method of randomization we use to perturb (i) each agent's individual reward function or (ii) the joint reward function shared by all agents. We prove that both of these methods are differentially private and compare the abilities of each to provide accurate reward values for policy synthesis. We then develop an algorithm for the numerical computation of the performance loss due to privacy on a case-by-case basis. We also exactly compute the computational complexity of this algorithm in terms of system parameters and show that it is inherently tractable. Numerical simulations are performed on a gridworld example and in waypoint guidance of an autonomous vehicle, and both examples show that privacy induces only negligible performance losses in practice.Comment: 11 Pages, 7 figure

Asset Allocation with Swarm/Human Blended Intelligence

PSO has been used to demonstrate the near-real-time optimization of frequency allocations and spatial positions for receiver assets in highly complex Electronic Warfare (EW) environments. The PSO algorithm computes optimal or near-optimal solutions so rapidly that multiple assets can be exploited in real-time and re-optimized on the fly as the situation changes. The allocation of assets in 3D space requires a blend of human intelligence and computational optimization. This paper advances the research on the tough problem of how humans interface to the swarm for directing the solution. The human intelligence places new pheromone-inspired spheres of influence to direct the final solution. The swarm can then react to the new input from the human intelligence. Our results indicate that this method can maintain the speed goal of less than 1 second, even with multiple spheres of pheromone influence in the solution space

HSD3B1 genotype identifies glucocorticoid responsiveness in severe asthma

Asthma resistance to glucocorticoid treatment is a major health problem with unclear etiology. Glucocorticoids inhibit adrenal androgen production. However, androgens have potential benefits in asthma. HSD3B1 encodes for 3β-hydroxysteroid dehydrogenase-1 (3β-HSD1), which catalyzes peripheral conversion from adrenal dehydroepiandrosterone (DHEA) to potent androgens and has a germline missense-encoding polymorphism. The adrenal restrictive HSD3B1(1245A) allele limits conversion, whereas the adrenal permissive HSD3B1(1245C) allele increases DHEA metabolism to potent androgens. In the Severe Asthma Research Program (SARP) III cohort, we determined the association between DHEA-sulfate and percentage predicted forced expiratory volume in 1 s (FEV1PP). HSD3B1(1245) genotypes were assessed, and association between adrenal restrictive and adrenal permissive alleles and FEV1PP in patients with (GC) and without (noGC) daily oral glucocorticoid treatment was determined (n = 318). Validation was performed in a second cohort (SARP I&II; n = 184). DHEA-sulfate is associated with FEV1PP and is suppressed with GC treatment. GC patients homozygous for the adrenal restrictive genotype have lower FEV1PP compared with noGC patients (54.3% vs. 75.1%; P < 0.001). In patients with the homozygous adrenal permissive genotype, there was no FEV1PP difference in GC vs. noGC patients (73.4% vs. 78.9%; P = 0.39). Results were independently confirmed: FEV1PP for homozygous adrenal restrictive genotype in GC vs. noGC is 49.8 vs. 63.4 (P < 0.001), and for homozygous adrenal permissive genotype, it is 66.7 vs. 67.7 (P = 0.92). The adrenal restrictive HSD3B1(1245) genotype is associated with GC resistance. This effect appears to be driven by GC suppression of 3β-HSD1 substrate. Our results suggest opportunities for prediction of GC resistance and pharmacologic intervention

A framework for highly multiplexed dextramer mapping and prediction of T cell receptor sequences to antigen specificity.

T cell receptor (TCR) antigen-specific recognition is essential for the adaptive immune system. However, building a TCR-antigen interaction map has been challenging due to the staggering diversity of TCRs and antigens. Accordingly, highly multiplexed dextramer-TCR binding assays have been recently developed, but the utility of the ensuing large datasets is limited by the lack of robust computational methods for normalization and interpretation. Here, we present a computational framework comprising a novel method, ICON (Integrative COntext-specific Normalization), for identifying reliable TCR-pMHC (peptide-major histocompatibility complex) interactions and a neural network-based classifier TCRAI that outperforms other state-of-the-art methods for TCR-antigen specificity prediction. We further demonstrated that by combining ICON and TCRAI, we are able to discover novel subgroups of TCRs that bind to a given pMHC via different mechanisms. Our framework facilitates the identification and understanding of TCR-antigen-specific interactions for basic immunological research and clinical immune monitoring

The James Webb Space Telescope Mission

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figure