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A new Monte Carlo method has been implemented to describe the angular and polarization distributions of anisotropic liquids, like water and linear alkylbenzene, by considering orientational fluctuations of polarizability tensors. The scattered light of anisotropic liquids is depolarized with an angular distribution of $1+(1-\rho_v)/(1+3\rho_v)\cos^2\theta$, which is modified by the depolarization ratio $\rho_v$. A standalone experiment has validated the simulation results of LAB. The new method can provide more accurate knowledge on light propagation in large liquid detectors, which is beneficial to the developments of reconstruction for detectors., Comment: 13 pages, 5 figures

Uncertainty and disturbance are two most fundamental properties of a quantum measurement and they are usually separately studied in terms of the preparation and the measurement uncertainty relations. Here we shall establish an intimate connection between them that goes beyond the above mentioned two kinds of uncertainty relations. Our basic observation is that the disturbance of one measurement to a subsequent measurement, which can be quantified based on observed data, sets lower-bounds to uncertainty. This idea can be universally applied to various measures of uncertainty and disturbance, with the help of data processing inequality. The obtained relations, referred to as disturbance enhanced uncertainty relations, immediately find various applications in the field of quantum information. They ensure preparation uncertainty relation such as novel entropic uncertainty relations independent of the Maassen and Uffink relation. And they also result in a simple protocol to estimate coherence. We anticipate that this new twist on uncertainty principle may shed new light on quantum foundations and may also inspire further applications in the field of quantum information.

This paper is a short report about our work for the primal task in the Machine Learning for Combinatorial Optimization NeurIPS 2021 Competition. For each dataset of our interest in the competition, we propose customized primal heuristic methods to efficiently identify high-quality feasible solutions. The computational studies demonstrate the superiority of our proposed approaches over the competitors'., Comment: This work will be published on the ML4CO NeurIPS 2021 Competition website (https://www.ecole.ai/2021/ml4co-competition/) in the proceedings section. A succinct version will appear in a special Proceedings of Machine Learning Research (PMLR) volume dedicated to the NeurIPS 2021 competitions

The origin of water on the Earth is a long-standing mystery, requiring a comprehensive search for hydrous compounds, stable at conditions of the deep Earth and made of Earth-abundant elements. Previous studies usually focused on the current range of pressure-temperature conditions in the Earth's mantle and ignored a possible difference in the past, such as the stage of the core-mantle separation. Here, using ab initio evolutionary structure prediction, we find that only two magnesium hydrosilicate phases are stable at megabar pressures, $\alpha$-Mg$_2$SiO$_5$H$_2$ and $\beta$-Mg$_2$SiO$_5$H$_2$, stable at 262-338 GPa and >338 GPa,respectively (all these pressures now lie within the Earth's iron core). Both are superionic conductors with quasi-one-dimensional proton diffusion at relevant conditions. In the first 30 million years of Earth's history, before the Earth's core was formed, these must have existed in the Earth, hosting much of Earth's water. As dense iron alloys segregated to form the Earth's core, Mg$_2$SiO$_5$H$_2$ phases decomposed and released water. Thus, now-extinct Mg$_2$SiO$_5$H$_2$ phases have likely contributed in a major way to the evolution of our planet.

Biosensors are an essential tool for medical diagnostics, environmental monitoring and food safety. Typically, biosensors are designed to detect specific analytes through functionalization with the appropriate capture agents. However, the use of capture agents limits the number of analytes that can be simultaneously detected and reduces the robustness of the biosensor. In this work, we report a versatile, capture agent free biosensor platform based on an array of porous silicon (PSi) thin films, which has the potential to robustly detect a wide variety of analytes based on their physical and chemical properties in the nanoscale porous media. The ability of this system to reproducibly classify, quantify, and discriminate three proteins is demonstrated to concentrations down to at least 0.02g/L (between 300nM and 450nM) by utilizing PSi array elements with a unique combination of pore size and buffer pH, employing linear discriminant analysis for dimensionality reduction, and using support vector machines as a classifier. This approach represents a significant step towards a low cost, simple and robust biosensor platform that is able to detect a vast range of biomolecules., Comment: 15 pages, 3 figures, 2 tables

We study damping signatures at the Jiangmen Underground Neutrino Observatory (JUNO), a medium-baseline reactor neutrino oscillation experiment. These damping signatures are motivated by various new physics models, including quantum decoherence, $\nu_3$ decay, neutrino absorption, and wave packet decoherence. The phenomenological effects of these models can be characterized by exponential damping factors at the probability level. We assess how well JUNO can constrain these damping parameters and how to disentangle these different damping signatures at JUNO. Compared to current experimental limits, JUNO can significantly improve the limits on $\tau_3/m_3$ in the $\nu_3$ decay model, the width of the neutrino wave packet $\sigma_x$, and the intrinsic relative dispersion of neutrino momentum $\sigma_{\rm rel}$., Comment: 17 pages, 2 figures, 4 tables. Version published in JHEP

We consider the problem of abnormality localization for clinical applications. While deep learning has driven much recent progress in medical imaging, many clinical challenges are not fully addressed, limiting its broader usage. While recent methods report high diagnostic accuracies, physicians have concerns trusting these algorithm results for diagnostic decision-making purposes because of a general lack of algorithm decision reasoning and interpretability. One potential way to address this problem is to further train these models to localize abnormalities in addition to just classifying them. However, doing this accurately will require a large amount of disease localization annotations by clinical experts, a task that is prohibitively expensive to accomplish for most applications. In this work, we take a step towards addressing these issues by means of a new attention-driven weakly supervised algorithm comprising a hierarchical attention mining framework that unifies activation- and gradient-based visual attention in a holistic manner. Our key algorithmic innovations include the design of explicit ordinal attention constraints, enabling principled model training in a weakly-supervised fashion, while also facilitating the generation of visual-attention-driven model explanations by means of localization cues. On two large-scale chest X-ray datasets (NIH ChestX-ray14 and CheXpert), we demonstrate significant localization performance improvements over the current state of the art while also achieving competitive classification performance. Our code is available on https://github.com/oyxhust/HAM.

Non-Hermiticity alters topology with the presence of non-Hermitian factors in topological systems. Most existing non-Hermitian topological systems derive their topological phases from Hermitian components, that is, the gain and loss of the system are considered simultaneously. In this work, we reveal two-dimensional non-Hermitian high-order topological insulator based on one-dimensional SSH chain, the nontrivial topology of which induced by total-loss. By introducing the loss of a specific configuration, we get a band gap with corner and edge states whose topology is characterized by the gapped wannier band. In addition, we demonstrate the existence of 'real-energy' edge states in pseudo-PT symmetric domain wall system. These results can be easily implemented in experiments, and promote the research of topological transmission of lossy systems in the real world., Comment: 8 pages, 11 figures

In the research of topological phases of matter, valley pseudospins have been introduced into photonic systems. Here, we construct a split-ring photonic crystal (SPC) in which the spilt rings are distributed according to the Kagome model. By rotating three split rings as a whole under the condition of ensuring the existence of C3v symmetry, we obtain a traditional two-band-inversion valley topology (2IVT) driven by opening twofold Dirac degeneracy point. When three split rings are rotated as a whole without ensuring the existence of C3v symmetry, a valley-like topology driven by opening twofold degeneracy point will exist. In particular, when three split rings are rotated separately, three-band-inversion valley-like topology (3IVT) will exist which is also driven by opening twofold degeneracy point. Valley topology and valley-like topology can be described by non-trivial Wannier band (WB) and bulk polarization (BP), and they both have the positive and negative refraction along the Zigzag domain-wall. Our research can be extended to other models, using controllable geometry to construct a variety of topological structures, so as to provide ideas for the research of new topological states., Comment: 7 pages, 7 figures

The 8-Pmmn borophene, a boron analogue of graphene, hosts tilted and anisotropic massless Dirac fermion quasiparticles owing to the presence of the distorted graphene-like sublattice. First-principles calculations show that the stacked 8-Pmmn borophene is transformed into the fused three-dimensional borophene under pressure, being accompanied by the partially bond-breaking and bond-reforming. Strikingly, the fused 8-Pmmn borophene inherits the Dirac band dispersion resulting in an unusual semimetal-semimetal transition. A simple tight-binding model derived from graphene qualitatively reveals the underlying physics due to the maximum preservation of graphene-like substructure after the phase transition, which contrasts greatly to the transformation of graphite into diamond associated with the semimetal-insulator transition., Comment: 6 pages, 4 figures

Search for new high-temperature superconductors and insight into their superconducting mechanism are of fundamental importance in condensed matter physics. The discovery of near-room temperature superconductivity at more than a million atmospheres ushers in a new era for superconductors. However, the critical task of identifying materials with comparable superconductivity at near or ambient pressure remains. Carbon materials can always lead to intriguing surprises due to their structural diversity and electronic adjustability. Insulating diamond upon doping or external stimuli has achieved superconducting state. Thus, it still has a great opportunity to find superconducting ones with higher transition temperature (Tc). Here, we report an intrinsic superconducting graphite-diamond hybrid through first-principles calculations, whose atomic-resolution structural characteristics have been experimentally determined recently. The predicted Tc is approximated at 39 K at ambient pressure, and strain energizing can further boost Tc to 42 K. The strong electron-phonon coupling associated with the out-of-plane vibration of carbon atoms at the junction plays a dominant role in the superconducting transition. Our work demonstrates the great potential of such carbon materials as high-Tc superconductors, which will definitely attract extensive research., Comment: 11 pages, 3 figures

We propose a reinforcement learning (RL) approach to compute the expression of quasi-stationary distribution. Based on the fixed-point formulation of quasi-stationary distribution, we minimize the KL-divergence of two Markovian path distributions induced by the candidate distribution and the true target distribution. To solve this challenging minimization problem by gradient descent, we apply the reinforcement learning technique by introducing the reward and value functions. We derive the corresponding policy gradient theorem and design an actor-critic algorithm to learn the optimal solution and the value function. The numerical examples of finite state Markov chain are tested to demonstrate the new method., Comment: 18 pages, 5 figures

Rare electromagnetic decays of charmed mesons are useful laboratories to explore the structure of hadronic states and the interactions between photon and charmed mesons, to test the chiral perturbation theory in flavor sector and to search for new physics including dark photons. In this paper, we calculate the relative branching ratios of electromagnetic Dalitz decays $D_{(s)}^\ast\to D_{(s)}\ell^+\ell^-$ to their corresponding radiative decays $D_{(s)}^\ast\to D_{(s)}\gamma$, the dileptonic invariant mass spectra and the leptonic angular distributions with transition form factor in Vector-Meson Dominance model, where $D_{(s)}^\ast$ represents $D^\ast(2007)^0$, $D^\ast(2010)^\pm$, $D^\ast(2640)^\pm$, $D_s^{\ast\pm}$, $D_{s1}^\ast(2700)^\pm$ and $D_{s1}^\ast(2860)^\pm$., Comment: 9 pages, 12 figures

We address the propagation of vortex beams with the circular Airy-Gaussian shape in a (2+1)-dimensional optical waveguide modeled by the fractional nonlinear Schrodinger equation. Systematic analysis of autofocusing of the beams reveals a strongly non-monotonous dependence of the peak intensity in the focal plane on the corresponding Levy index, with a strong maximum at alpha =1.4. Effects of the nonlinearity strength, the ratio of widths of the Airy and Gaussian factors in the input, as well as the beam vorticity, on the autofocusing dynamics are explored. In particular, multiple autofocusing events occur if the nonlinearity is strong enough. Under the action of the azimuthal modulational instability, an axisymmetric beam may split into a set of separating bright spots. In the case of strong fractality (for alpha close to 1), the nonlinear beam self-traps, after the first instance of autofocusing, into a breathing vortical quasi-soliton. Radiation forces induced by the beam field are considered too, and a capture position for a probe nanoparticle is thus identified., Comment: 17pages,11figures,to be published in J. Opt. Soc. Am. B

Video question answering (VideoQA) is challenging given its multimodal combination of visual understanding and natural language understanding. While existing approaches seldom leverage the appearance-motion information in the video at multiple temporal scales, the interaction between the question and the visual information for textual semantics extraction is frequently ignored. Targeting these issues, this paper proposes a novel Temporal Pyramid Transformer (TPT) model with multimodal interaction for VideoQA. The TPT model comprises two modules, namely Question-specific Transformer (QT) and Visual Inference (VI). Given the temporal pyramid constructed from a video, QT builds the question semantics from the coarse-to-fine multimodal co-occurrence between each word and the visual content. Under the guidance of such question-specific semantics, VI infers the visual clues from the local-to-global multi-level interactions between the question and the video. Within each module, we introduce a multimodal attention mechanism to aid the extraction of question-video interactions, with residual connections adopted for the information passing across different levels. Through extensive experiments on three VideoQA datasets, we demonstrate better performances of the proposed method in comparison with the state-of-the-arts., Comment: Submitted to AAAI'22