Miracle-dz/UniFAR-FTUs huggingface dataset:
Stability of noisy quantum computing devices

Noisy, intermediate-scale quantum (NISQ) computing devices offer opportunities to test the principles of quantum computing but are prone to errors arising from various sources of noise. Fluctuations in the noise itself lead to unstable devices that undermine the reproducibility of NISQ results. Here we characterize the reliability of NISQ devices by quantifying the stability of essential performance metrics. Using the Hellinger distance, we quantify the similarity between experimental characterizations of several NISQ devices by comparing gate fidelities, duty cycles, and register addressability across temporal and spatial scales. Our observations collected over 22 months reveal large fluctuations in each metric that underscore the limited scales on which current NISQ devices may be considered reliable.
Sound attenuation derived from quenched disorder in solids

In scattering experiments, the dynamical structure factor (DSF) characterizes inter-particle correlations and their time evolution. We analytically evaluated the DSF of disordered solids with disorder in the spring constant, by averaging over quenched disorder in the values of lattice bond strength, along the acoustic branch. The width of the resulting acoustic excitation peak is treated as the effective damping constant Γ(q), which we found to grow linearly with exchanged momentum q. This is verified by numerically calculating a model system consisting of harmonic linear chains with disorder in spring constant. We also found that the quenched averaging of the vibrational density of states produces a characteristic peak at a frequency related to the average acoustic resonance. Such a peak (the excess over Debye law) may be related to the “boson peak” frequently discussed in disordered solids, in our case explicitly arising from the quenched disorder in the distribution of spring constants.
Morse Theoretic Templates for High Dimensional Homology Computation

We introduce the notion of a template for discrete Morse theory. Templates provide a memory efficient approach to the computation of homological invariants (e.g., homology, persistent homology, Conley complexes) of cell complexes. We demonstrate the effectiveness of templates in two settings: first, by computing the homology of certain cubical complexes which are homotopy equivalent to $\mathbb{S}^d$ for $1\leq d \leq 20$, and second, by computing Conley complexes and connection matrices for a collection of examples arising from a Conley-Morse theory on spaces of braids diagrams.
Entanglement entropy of non-Hermitian free fermions

We study the entanglement properties of non-Hermitian free fermionic models with translation symmetry using the correlation matrix technique. Our results show that the entanglement entropy has a logarithmic correction to the area law in both one-dimensional and two-dimensional systems. For any one-dimensional one-band system, we prove that each Fermi point of the system contributes exactly 1/2 to the coefficient c of the logarithmic correction. Moreover, this relation between c and Fermi point is verified for more general one-dimensional and two-dimensional cases by numerical calculations and finite-size scaling analysis. In addition, we also study the single-particle and density–density correlation functions.
Lossless broadband adiabatic polarizing beam splitting in a plasmonic system

The intriguing analogy between quantum physics and optics has inspired the design of unconventional integrated photonics devices. In this paper, we numerically demonstrate a broadband integrated polarization beam splitter (PBS) by implementing the stimulated Raman adiabatic passage (STIRAP) technique in a three-waveguide plasmonic system. Our proposed PBS exhibits >250 nm TM bandwidth with <-40 dB extinction and >150 nm TE bandwidth with <-20 dB extinction, covering the entire S-, Cand L-band and partly E-band. Moreover, near-lossless light transfer is achieved in our system despite the incorporation of a plasmonic hybrid waveguide because of the unique loss mitigating feature of the STIRAP scheme. Through this approach, various broadband integrated devices that were previously impossible can be realized, which will allow innovation in integrated optics.
Native casein micelles as nanocarriers for &bgr;‐carotene: pH‐and temperature‐induced opening of the micellar structure

Summary To apply native casein micelles (CM) as nanocarriers for lipophilic substances in non- or low-fat food products, they have to be conditioned before loading. In this study, an experimental set-up for the production and loading of CM was developed. Microfiltration was used to separate CM from skimmed milk. To identify optimal loading conditions temperatures (2, 20, 40 °C), pH values (6.8 and 5.5) and holding times (5, 15, 30, 60 min) were varied. The release of calcium, phosphate and protein from the micellar phase as well as static light scattering and transmission electron microscopy showed that CM were optimally primed at 2 °C and a pH of 5.5 for 5 min. Therefore, loading with β-carotene was performed under those conditions. After the back-extraction of β-carotene, the photometrical analyses revealed total recovery rates of >79% whereby 94% of it was associated with the native CM.
Alignments of Triad Phases in 1D Burgers and 3D Navier-Stokes Flows

The goal of this study is to analyze the fine structure of nonlinear modal interactions in different 1D Burgers and 3D Navier-Stokes flows. This analysis is focused on preferential alignments characterizing the phases of Fourier modes participating in triadic interactions, which are key to determining the nature of energy fluxes between different scales. We develop novel diagnostic tools designed to probe the level of coherence among triadic interactions realizing different flow scenarios. We consider extreme (in the sense of maximizing the growth of enstrophy in finite time) 1D viscous Burgers flows and 3D Navier-Stokes flows which are complemented by singularity-forming inviscid Burgers flows as well as viscous Burgers flows and Navier-Stokes flows corresponding to generic turbulent and simple unimodal initial data, such as the Taylor-Green vortex. The main finding is that while the extreme viscous Burgers and Navier-Stokes flows reveal the same relative level of enstrophy amplification by nonlinear effects, this behaviour is realized via modal interactions with vastly different levels of coherence. In the viscous Burgers flows the flux-carrying triads have phase values which saturate the nonlinearity thereby maximizing the energy flux towards small scales. On the other hand, in 3D Navier-Stokes flows with the extreme initial data the energy flux to small scales is realized by a very small subset of helical triads, with the flux-carrying triads showing again a high level of coherence, but with their phases taking a broader range of preferred values which form complex time-dependent patterns. The second main finding concerns the role of initial coherence. Comparison of the flows resulting from the extreme and generic initial conditions shows striking similarities between these two types of flows, for the 1D viscous Burgers equation as well as the 3D Navier-Stokes equation. The third main finding concerns 3D Navier-Stokes flows resulting from the Taylor-Green initial condition. On the one hand, as expected, the fluxes in this case are several times smaller than in the extreme or generic cases. On the other hand, the flux-carrying triads in this case display much more rigid patterns of phase coherence, with persistent narrow bands of preferred phase values. Finally, we find that the paradigm based on the instability assumption due to Waleffe (1992) holds true at all scales for the 3D Email address for correspondence: bprotas@mcmaster.ca 1 ar X iv :2 10 5. 09 42 5v 1 [ ph ys ic s. fl udy n] 1 9 M ay 2 02 1 Navier-Stokes flows, with some variations. The flows resulting from the extreme or generic initial conditions show a quantitative agreement with simulations of statistically stationary turbulence by Alexakis & Biferale (2018) concerning the inverse-cascade behaviour at the inertial range of homochiral triads (i.e., triads with the same helical content, or Class I in Waleffe’s notation). In contrast, in the flow resulting from the Taylor-Green initial condition, the inverse-cascade behaviour is dominated by heterochiral triads of Class II in Waleffe’s notation.
Cosmic Velocity Field Reconstruction Using AI

We develop a deep-learning technique to infer the nonlinear velocity field from the dark matter density field. The deep-learning architecture we use is a “U-net” style convolutional neural network, which consists of 15 convolution layers and 2 deconvolution layers. This setup maps the three-dimensional density field of 323 voxels to the three-dimensional velocity or momentum fields of 203 voxels. Through the analysis of the dark matter simulation with a resolution of 2h −1 Mpc, we find that the network can predict the the nonlinearity, complexity, and vorticity of the velocity and momentum fields, as well as the power spectra of their value, divergence, and vorticity and its prediction accuracy reaches the range of k ≃ 1.4 h Mpc−1 with a relative error ranging from 1% to ≲10%. A simple comparison shows that neural networks may have an overwhelming advantage over perturbation theory in the reconstruction of velocity or momentum fields.
Exploring The Limits Of Data Augmentation For Retinal Vessel Segmentation

Retinal Vessel Segmentation is important for the diagnosis of various diseases. The research on retinal vessel segmentation focuses mainly on the improvement of the segmentation model which is usually based on U-Net architecture. In our study, we use the U-Net architecture and we rely on heavy data augmentation in order to achieve better performance. The success of the data augmentation relies on successfully addressing the problem of input images. By analyzing input images and performing the augmentation accordingly we show that the performance of the U-Net model can be increased dramatically. Results are reported using the most widely used retina dataset, DRIVE.
Head-driven Phrase Structure Parsing in O($n^3$) Time Complexity

Constituent and dependency parsing, the two classic forms of syntactic parsing, have been found to benefit from joint training and decoding under a uniform formalism, Head-driven Phrase Structure Grammar (HPSG). However, decoding this unified grammar has a higher time complexity ($O(n^5)$) than decoding either form individually ($O(n^3)$) since more factors have to be considered during decoding. We thus propose an improved head scorer that helps achieve a novel performance-preserved parser in $O$($n^3$) time complexity. Furthermore, on the basis of this proposed practical HPSG parser, we investigated the strengths of HPSG-based parsing and explored the general method of training an HPSG-based parser from only a constituent or dependency annotations in a multilingual scenario. We thus present a more effective, more in-depth, and general work on HPSG parsing.
Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation

A key challenge on the path to developing agents that learn complex human-like behavior is the need to quickly and accurately quantify human-likeness. While human assessments of such behavior can be highly accurate, speed and scalability are limited. We address these limitations through a novel automated Navigation Turing Test (ANTT) that learns to predict human judgments of human-likeness. We demonstrate the effectiveness of our automated NTT on a navigation task in a complex 3D environment. We investigate six classification models to shed light on the types of architectures best suited to this task, and validate them against data collected through a human NTT. Our best models achieve high accuracy when distinguishing true human and agent behavior. At the same time, we show that predicting finer-grained human assessment of agents' progress towards human-like behavior remains unsolved. Our work takes an important step towards agents that more effectively learn complex human-like behavior.
5G Deployment Strategies for High Positioning Accuracy in Indoor Environments

Indoor positioning is currently recognized as one of the important features in emergency, commercial and industrial applications. The 5G network enhances mobility, flexibility, reliability, and security to new higher levels which greatly benefit the IoT and industrial applications. Industrial IoT (IIoT) use-cases are characterized by ambitious system requirements for positioning accuracy in many verticals. For example, on the factory floor, it is important to locate assets and moving objects such as forklifts. The deployment design for different IIoT environments has a significant impact on the positioning per-performance in terms of both accuracy and availability of the service. Indoor factory (InF) and indoor open office (IOO) are two available and standardized Third Generation Partnership Project (3GPP) scenarios for evaluation of indoor channel models and positioning performance in IIoT use cases. This paper aims to evaluate the positioning performance in terms of accuracy and availability while considering different deployment strategies. Our simulation-based evaluation shows that deployment plays a vital role when it comes to achieving high accuracy positioning performance. It is for example favorable to deploy the 5G Transmission and Reception Points (TRPs) on the walls of the factory halls than deploying them attached to the ceiling.
Low-complexity Multicast Beamforming for Multi-stream Multi-group Communications

In this paper, assuming multi-antenna transmitter and receivers, we consider a multicast beamformer design for the weighted max-min-fairness (WMMF) problem in a multi-stream multi-group communication setup. Unlike the single-stream scenario, the WMMF objective in this setup is not equivalent to maximizing the minimum weighted SINR due to the summation over the rates of multiple streams. Therefore, the non-convex problem at hand is first approximated with a convex one and then solved using Karush-Kuhn-Tucker (KKT) conditions. Then, a practically appealing closed-form solution is derived for both transmit and receive beamformers as a function of dual variables. Finally, we use an iterative solution based on the sub-gradient method to solve for the mutually coupled and interdependent dual variables. The proposed solution does not rely on generic solvers and does not require any bisection loop for finding the achievable rate of various streams. As a result, it significantly outperforms the state-of-art in terms of computational cost and convergence speed.
From Verification to Causality-based Explications

In view of the growing complexity of modern software architectures, formal models are increasingly used to understand why a system works the way it does, opposed to simply verifying that it behaves as intended. This paper surveys approaches to formally explicate the observable behavior of reactive systems. We describe how Halpern and Pearl's notion of actual causation inspired verification-oriented studies of cause-effect relationships in the evolution of a system. A second focus lies on applications of the Shapley value to responsibility ascriptions, aimed to measure the influence of an event on an observable effect. Finally, formal approaches to probabilistic causation are collected and connected, and their relevance to the understanding of probabilistic systems is discussed.
Bidirectional LSTM-CRF Attention-based Model for Chinese Word Segmentation

Chinese word segmentation (CWS) is the basic of Chinese natural language processing (NLP). The quality of word segmentation will directly affect the rest of NLP tasks. Recently, with the artificial intelligence tide rising again, Long Short-Term Memory (LSTM) neural network, as one of easily modeling in sequence, has been widely utilized in various kinds of NLP tasks, and functions well. Attention mechanism is an ingenious method to solve the memory compression problem on LSTM. Furthermore, inspired by the powerful abilities of bidirectional LSTM models for modeling sequence and CRF model for decoding, we propose a Bidirectional LSTM-CRF Attention-based Model in this paper. Experiments on PKU and MSRA benchmark datasets show that our model performs better than the baseline methods modeling by other neural networks.
A domain wall in twisted M-theory

We study a four-dimensional domain wall in twisted M-theory. The domain wall is engineered by intersecting D6 branes in the type IIA frame. We identify the classical algebra of operators on the domain wall in terms of a higher vertex operator algebra, which describes the holomorphic subsector of a 4d \mathcal{N}=1𝒩=1 supersymmetric field theory, and compute the associated mode algebra. We conjecture that the quantum deformation of the classical algebra is isomorphic to the bulk algebra of operators from which we establish twisted holography of the domain wall.
Manual Evaluation Matters: Reviewing Test Protocols of Distantly Supervised Relation Extraction

Distantly supervised (DS) relation extraction (RE) has attracted much attention in the past few years as it can utilize large-scale auto-labeled data. However, its evaluation has long been a problem: previous works either took costly and inconsistent methods to manually examine a small sample of model predictions, or directly test models on auto-labeled data -- which, by our check, produce as much as 53% wrong labels at the entity pair level in the popular NYT10 dataset. This problem has not only led to inaccurate evaluation, but also made it hard to understand where we are and what's left to improve in the research of DS-RE. To evaluate DS-RE models in a more credible way, we build manually-annotated test sets for two DS-RE datasets, NYT10 and Wiki20, and thoroughly evaluate several competitive models, especially the latest pre-trained ones. The experimental results show that the manual evaluation can indicate very different conclusions from automatic ones, especially some unexpected observations, e.g., pre-trained models can achieve dominating performance while being more susceptible to false-positives compared to previous methods. We hope that both our manual test sets and novel observations can help advance future DS-RE research.
Lookahead Acquisition Functions for Finite-Horizon Time-Dependent Bayesian Optimization and Application to Quantum Optimal Control

We propose a novel Bayesian method to solve the maximization of a time-dependent expensive-to-evaluate stochastic oracle. We are interested in the decision that maximizes the oracle at a finite time horizon, given a limited budget of noisy evaluations of the oracle that can be performed before the horizon. Our recursive two-step lookahead acquisition function for Bayesian optimization makes nonmyopic decisions at every stage by maximizing the expected utility at the specified time horizon. Specifically, we propose a generalized two-step lookahead framework with a customizable \emph{value} function that allows users to define the utility. We illustrate how lookahead versions of classic acquisition functions such as the expected improvement, probability of improvement, and upper confidence bound can be obtained with this framework. We demonstrate the utility of our proposed approach on several carefully constructed synthetic cases and a real-world quantum optimal control problem.
Will Betelguese Explode?

Since October 2019, Betelgeuse began to dim noticeably and by January 2020 its brightness had dropped by a factor of approximately 2.5, demoting it from the position of the top (apparent) brightest 11 th star to the 21 st! Astronomers were excited and thought of it as the lull before the storm, Betelguese was ready to go supernova! This article is aimed more as a case study where we show how this question was answered using scientific arguments and data. It will also highlight the importance of supernovae to human existence and give a brief discussion on the evolution of massive stars. And also, answer the question!
An Attractor-Guided Neural Networks for Skeleton-Based Human Motion Prediction

Joint relation modeling is a curial component in human motion prediction. Most existing methods tend to design skeletal-based graphs to build the relations among joints, where local interactions between joint pairs are well learned. However, the global coordination of all joints, which reflects human motion's balance property, is usually weakened because it is learned from part to whole progressively and asynchronously. Thus, the final predicted motions are sometimes unnatural. To tackle this issue, we learn a medium, called balance attractor (BA), from the spatiotemporal features of motion to characterize the global motion features, which is subsequently used to build new joint relations. Through the BA, all joints are related synchronously, and thus the global coordination of all joints can be better learned. Based on the BA, we propose our framework, referred to Attractor-Guided Neural Network, mainly including Attractor-Based Joint Relation Extractor (AJRE) and Multi-timescale Dynamics Extractor (MTDE). The AJRE mainly includes Global Coordination Extractor (GCE) and Local Interaction Extractor (LIE). The former presents the global coordination of all joints, and the latter encodes local interactions between joint pairs. The MTDE is designed to extract dynamic information from raw position information for effective prediction. Extensive experiments show that the proposed framework outperforms state-of-the-art methods in both short and long-term predictions in H3.6M, CMU-Mocap, and 3DPW.
A Stochastic Composite Augmented Lagrangian Method For Reinforcement Learning

In this paper, we consider the linear programming (LP) formulation for deep reinforcement learning. The number of the constraints depends on the size of state and action spaces, which makes the problem intractable in large or continuous environments. The general augmented Lagrangian method suffers the double-sampling obstacle in solving the LP. Namely, the conditional expectations originated from the constraint functions and the quadratic penalties in the augmented Lagrangian function impose difficulties in sampling and evaluation. Motivated from the updates of the multipliers, we overcome the obstacles in minimizing the augmented Lagrangian function by replacing the intractable conditional expectations with the multipliers. Therefore, a deep parameterized augment Lagrangian method is proposed. Furthermore, the replacement provides a promising breakthrough to integrate the two steps in the augmented Lagrangian method into a single constrained problem. A general theoretical analysis shows that the solutions generated from a sequence of the constrained optimizations converge to the optimal solution of the LP if the error is controlled properly. A theoretical analysis on the quadratic penalty algorithm under neural tangent kernel setting shows the residual can be arbitrarily small if the parameter in network and optimization algorithm is chosen suitably. Preliminary experiments illustrate that our method is competitive to other state-of-the-art algorithms.