Probing New Physics with High-Redshift Quasars: Axions and Non-standard Cosmology

The Hubble diagram of quasars, as candidates to ``standardizable" candles, has been used to measure the expansion history of the Universe at late times, up to very high redshifts ($z \sim 7$). It has been shown that this history, as inferred from the quasar dataset, deviates at $\gtrsim 3 \sigma$ level from the concordance ($\Lambda$CDM) cosmology model preferred by the cosmic microwave background (CMB) and other datasets. In this article, we investigate whether new physics beyond $\Lambda$CDM (B$\Lambda$CDM) or beyond the Standard Model (BSM) could make the quasar data consistent with the concordance model. We first show that an effective redshift-dependent relation between the quasar UV and X-ray luminosities, complementing previous phenomenological work in the literature, can potentially remedy the discrepancy. Such a redshift dependence can be realized in a BSM model with axion-photon conversion in the intergalactic medium (IGM), although the preferred parameter space could be in mild tension with various other astrophysical constraints on axions, depending on the specific assumptions made regarding the IGM magnetic field. We briefly discuss a variation of the axion model that could evade these astrophysical constraints. On the other hand, we show that models beyond $\Lambda$CDM such as one with a varying dark energy equation of state ($w$CDM) or the phenomenological cosmographic model with a polynomial expansion of the luminosity distance, cannot alleviate the tension. The code for our analysis, based on emcee and corner.py, is publicly available at https://github.com/ChenSun-Phys/high_z_candles.Comment: 10+3 pages, 4 figures, 4 tables, 3 appendices; comments are welcom

Low Rank Directed Acyclic Graphs and Causal Structure Learning

Despite several important advances in recent years, learning causal structures represented by directed acyclic graphs (DAGs) remains a challenging task in high dimensional settings when the graphs to be learned are not sparse. In particular, the recent formulation of structure learning as a continuous optimization problem proved to have considerable advantages over the traditional combinatorial formulation, but the performance of the resulting algorithms is still wanting when the target graph is relatively large and dense. In this paper we propose a novel approach to mitigate this problem, by exploiting a low rank assumption regarding the (weighted) adjacency matrix of a DAG causal model. We establish several useful results relating interpretable graphical conditions to the low rank assumption, and show how to adapt existing methods for causal structure learning to take advantage of this assumption. We also provide empirical evidence for the utility of our low rank algorithms, especially on graphs that are not sparse. Not only do they outperform state-of-the-art algorithms when the low rank condition is satisfied, the performance on randomly generated scale-free graphs is also very competitive even though the true ranks may not be as low as is assumed

Ultra-fast self-assembly and stabilization of reactive nanoparticles in reduced graphene oxide films.

Nanoparticles hosted in conductive matrices are ubiquitous in electrochemical energy storage, catalysis and energetic devices. However, agglomeration and surface oxidation remain as two major challenges towards their ultimate utility, especially for highly reactive materials. Here we report uniformly distributed nanoparticles with diameters around 10 nm can be self-assembled within a reduced graphene oxide matrix in 10 ms. Microsized particles in reduced graphene oxide are Joule heated to high temperature (∼1,700 K) and rapidly quenched to preserve the resultant nano-architecture. A possible formation mechanism is that microsized particles melt under high temperature, are separated by defects in reduced graphene oxide and self-assemble into nanoparticles on cooling. The ultra-fast manufacturing approach can be applied to a wide range of materials, including aluminium, silicon, tin and so on. One unique application of this technique is the stabilization of aluminium nanoparticles in reduced graphene oxide film, which we demonstrate to have excellent performance as a switchable energetic material

Pathway to Future Symbiotic Creativity

This report presents a comprehensive view of our vision on the development path of the human-machine symbiotic art creation. We propose a classification of the creative system with a hierarchy of 5 classes, showing the pathway of creativity evolving from a mimic-human artist (Turing Artists) to a Machine artist in its own right. We begin with an overview of the limitations of the Turing Artists then focus on the top two-level systems, Machine Artists, emphasizing machine-human communication in art creation. In art creation, it is necessary for machines to understand humans' mental states, including desires, appreciation, and emotions, humans also need to understand machines' creative capabilities and limitations. The rapid development of immersive environment and further evolution into the new concept of metaverse enable symbiotic art creation through unprecedented flexibility of bi-directional communication between artists and art manifestation environments. By examining the latest sensor and XR technologies, we illustrate the novel way for art data collection to constitute the base of a new form of human-machine bidirectional communication and understanding in art creation. Based on such communication and understanding mechanisms, we propose a novel framework for building future Machine artists, which comes with the philosophy that a human-compatible AI system should be based on the "human-in-the-loop" principle rather than the traditional "end-to-end" dogma. By proposing a new form of inverse reinforcement learning model, we outline the platform design of machine artists, demonstrate its functions and showcase some examples of technologies we have developed. We also provide a systematic exposition of the ecosystem for AI-based symbiotic art form and community with an economic model built on NFT technology. Ethical issues for the development of machine artists are also discussed

Reflective imaging improves spatiotemporal resolution and collection efficiency in light sheet microscopy

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Nature Communications 8 (2017): 1452, doi:10.1038/s41467-017-01250-8.Light-sheet fluorescence microscopy (LSFM) enables high-speed, high-resolution, and gentle imaging of live specimens over extended periods. Here we describe a technique that improves the spatiotemporal resolution and collection efficiency of LSFM without modifying the underlying microscope. By imaging samples on reflective coverslips, we enable simultaneous collection of four complementary views in 250 ms, doubling speed and improving information content relative to symmetric dual-view LSFM. We also report a modified deconvolution algorithm that removes associated epifluorescence contamination and fuses all views for resolution recovery. Furthermore, we enhance spatial resolution (to <300 nm in all three dimensions) by applying our method to single-view LSFM, permitting simultaneous acquisition of two high-resolution views otherwise difficult to obtain due to steric constraints at high numerical aperture. We demonstrate the broad applicability of our method in a variety of samples, studying mitochondrial, membrane, Golgi, and microtubule dynamics in cells and calcium activity in nematode embryos.This work was supported by the Intramural Research Program of the National Institute of Biomedical Imaging and Bioengineering at the National Institutes of Health. P.L. and H.S. acknowledge summer support from the Marine Biological Laboratory at Woods Hole, through the Whitman- and Fellows- program. P.L. acknowledges support from NIH National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health (NIH) under grant number R01EB017293. C.S. acknowledges funding from the National Institute of General Medical Sciences of NIH under Award Number R25GM109439 (Project Title: University of Chicago Initiative for Maximizing Student Development [IMSD]) and NIBIB under grant number T32 EB002103. Partial funding for the computation in this work was provided by NIH grant numbers S10 RRO21039 and P30 CA14599. A.U. and I.R.-S. were supported by the NSF grant number 1607645