Low-energy behavior of spin-liquid electron spectral functions

We calculate the electron spectral function for a spin-liquid with a spinon Fermi surface and a Dirac spin-liquid. Calculations are based upon the slave-rotor mean-field theory. We consider the effect of gauge fluctuations using a simple model and find the behavior is not strongly modified. The results, distinct from conventional Mott insulator or band theory predictions, suggest that measuring the spectral function e.g. via ARPES could help in the experimental verification and characterization of spin liquids.Comment: 7 pages, 7 figure

The Sample Complexity of Search over Multiple Populations

This paper studies the sample complexity of searching over multiple populations. We consider a large number of populations, each corresponding to either distribution P0 or P1. The goal of the search problem studied here is to find one population corresponding to distribution P1 with as few samples as possible. The main contribution is to quantify the number of samples needed to correctly find one such population. We consider two general approaches: non-adaptive sampling methods, which sample each population a predetermined number of times until a population following P1 is found, and adaptive sampling methods, which employ sequential sampling schemes for each population. We first derive a lower bound on the number of samples required by any sampling scheme. We then consider an adaptive procedure consisting of a series of sequential probability ratio tests, and show it comes within a constant factor of the lower bound. We give explicit expressions for this constant when samples of the populations follow Gaussian and Bernoulli distributions. An alternative adaptive scheme is discussed which does not require full knowledge of P1, and comes within a constant factor of the optimal scheme. For comparison, a lower bound on the sampling requirements of any non-adaptive scheme is presented.Comment: To appear, IEEE Transactions on Information Theor

Development of a redox-free mitsunobu reaction exploiting phosphine oxides as precursors to dioxyphosphoranes

The development of the first redox-free protocol for the Mitsunobu reaction is described. This has been achieved by exploiting triphenylphosphine oxide – the unwanted by-product in the conventional Mitsunobu reaction – as the precursor to the active P(V) coupling reagent. Multinuclear NMR studies are consistent with hydroxyl activation via an alkoxyphosphonium salt

Reduced Order Model for Chemical Kinetics: A case study with Primordial Chemical Network

Chemical kinetics plays an important role in governing the thermal evolution in reactive flows problems. The possible interactions between chemical species increase drastically with the number of species considered in the system. Various ways have been proposed before to simplify chemical networks with an aim to reduce the computational complexity of the chemical network. These techniques oftentimes require domain-knowledge experts to handcraftedly identify important reaction pathways and possible simplifications. Here, we propose a combination of autoencoder and neural ordinary differential equation to model the temporal evolution of chemical kinetics in a reduced subspace. We demonstrated that our model has achieved a close-to 10-fold speed-up compared to commonly used astro-chemistry solver for a 9-species primordial network, while maintaining 1 percent accuracy across a wide-range of density and temperature.Comment: 10 pages, 8 figures, accepted to the ICML 2022 Machine Learning for Astrophysics worksho

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

The rising popularity of intelligent mobile devices and the daunting computational cost of deep learning-based models call for efficient and accurate on-device inference schemes. We propose a quantization scheme that allows inference to be carried out using integer-only arithmetic, which can be implemented more efficiently than floating point inference on commonly available integer-only hardware. We also co-design a training procedure to preserve end-to-end model accuracy post quantization. As a result, the proposed quantization scheme improves the tradeoff between accuracy and on-device latency. The improvements are significant even on MobileNets, a model family known for run-time efficiency, and are demonstrated in ImageNet classification and COCO detection on popular CPUs.Comment: 14 pages, 12 figure