Search for: All records

Fully Homomorphic Encryption (FHE) is a cryptographic technique that enables privacy-preserving computation. State-of-the-art Boolean FHE implementations provide a very low-level interface, usually exposing a limited set of Boolean gates that programmers must use to write their FHE applications. This programming model is unnecessarily restrictive: many Boolean FHE schemes supportprogrammable bootstrapping, an operation that allows evaluation of an arbitrary fixed-size lookup table. However, most modern FHE compilers are only capable of reasoning about traditional Boolean circuits, and therefore struggle to take full advantage of programmable bootstrapping. We present COATL, an FHE compiler that makes use of programmable bootstrapping to produce circuits that are smaller and more efficient than their traditional Boolean counterparts. COATL generates circuits usingarithmetic lookup tables, a novel abstraction we introduce for reasoning about computations in Boolean FHE programs. We demonstrate on a variety of benchmarks that COATL can generate circuits that run up to 1.5× faster than those generated by other state-of-the-art compilation strategies. 

Model experiments reveal that tire tread wears during driving to generate a population of aerosolized nanoparticles and larger microplastic particles. Formation of nano- and micro-particulate pollutants appear to arise from independent processes. 

Abstract General relativistic magnetohydrodynamics (GRMHD) simulations are an indispensable tool in studying accretion onto compact objects. The Event Horizon Telescope (EHT) frequently uses libraries of ideal GRMHD simulations to interpret polarimetric, event-horizon-scale observations of supermassive black holes at the centers of galaxies. In this work, we present a library of 10 nonradiative, ideal GRMHD simulations that were utilized by the EHT Collaboration in their analysis of Sagittarius A*. The parameter survey explores both low (SANE) and high (MAD) magnetization states across five black hole spinsa_* = −15/16, −1/2, 0, +1/2, +15/16 where each simulation was run out to 30,000GM/c⁻³. We find the angular momentum and energy flux in SANE simulations closely matches the thin-disk value, with minor deviations in prograde models due to fluid forces. This leads to spin equilibrium arounda_* ∼ 0.94, consistent with previous studies. We study the flow of conserved quantities in our simulations and find mass, angular momentum, and energy transport in SANE accretion flows to be primarily inward and fluid dominated. MAD models produce powerful jets with outflow efficiency >1 fora_* = + 0.94, leading to black hole spin-down in prograde cases. We observe outward directed energy and angular momentum fluxes on the horizon, as expected for the Blandford–Znajek mechanism. MAD accretion flows are sub-Keplerian and exhibit greater variability than their SANE counterpart. They are also hotter than SANE disks withinr ≲ 10GM/c⁻². This study is accompanied by a public release of simulation data athttp://thz.astro.illinois.edu/. 

Abstract With advances in materials and manufacturing techniques, recent years have seen a number of conductive composite materials that exhibit pronounced strain-dependent electrical resistivity, allowing them to be used for embedded, cost-effective strain sensing in various applications. The strain-resistivity relationship of these materials, however, is often highly nonlinear and dynamic, posing challenges for effective use of such strain sensors. In this paper, a computationally efficient scheme is proposed for compensating the nonlinear, dynamic strain-resistance behavior of a soft conductive rubber using a time delay neural network. The accuracy and feasibility of the technique is evaluated with a soft robotic arm incorporating three strain sensors for proprioception. Experimental results show that the sensing scheme is able to predict both the tip position and the shape of the robotic manipulator, achieving an average tip positional error of less than 4% relative to the total length of the manipulator. 

Abstract Our understanding of the assembly timeline of the Milky Way has been transforming along with the dramatic increase in astrometric and spectroscopic data available over the past several years. Many substructures in chemo-dynamical space have been discovered and identified as the remnants of various galactic mergers. To investigate the timeline of these mergers, we select main-sequence turnoff and subgiant stars (MSTOs) from the H3 survey, finding members in seven metal-poor components of the halo: Gaia-Sausage/Enceladus (GSE), the Helmi Streams, Thamnos, Sequoia, Wukong/LMS-1, Arjuna, and I’itoi. We also select out a metal-poor in situ population to facilitate comparison to the evolution of the Milky Way itself at these early epochs. We fit individual isochrone ages to the MSTOs in each of these substructures and use the resulting age distributions to infer simple star formation histories (SFHs). For GSE, we resolve an extended SFH that truncates ≈10 Gyr ago, as well as a clear age–metallicity relation. From this age distribution and measured SFH we infer that GSE merged with the Milky Way at a time 9.5–10.2 Gyr ago, in agreement with previous estimates. We infer that the other mergers occurred at various times ranging from 9 to 13 Gyr ago, and that the metal-poor in situ Galaxy built up within only a few billion years. These results reinforce the emerging picture that both the disk and halo of the Milky Way experienced a rapid assembly. 

Lifelong learning, which refers to an agent's ability to continuously learn and enhance its performance over its lifespan, is a significant challenge in artificial intelligence (AI), that biological systems tackle efficiently. This challenge is further exacerbated when AI is deployed in untethered environments with strict energy and latency constraints. We take inspiration from neural plasticity and investigate how to leverage and build energy-efficient lifelong learning machines. Specifically, we study how a combination of neural plasticity mechanisms, namely neuromodulation, synaptic consolidation, and metaplasticity, enhance the continual learning capabilities of AI models. We further co-design architectures that leverage compute-in-memory topologies and sparse spike-based communication with quantization for the edge. Aspects of this co-design can be transferred to federated lifelong learning scenarios.