Miracle-dz/UniFAR-FTUs huggingface dataset:
QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits

Quantum noise is the key challenge in Noisy Intermediate-Scale Quantum (NISQ) computers. Previous work for mitigating noise has primarily focused on gate-level or pulse-level noise-adaptive compilation. However, limited research has explored a higher level of optimization by making the quantum circuits themselves resilient to noise.In this paper, we propose QuantumNAS, a comprehensive framework for noise-adaptive co-search of the variational circuit and qubit mapping. Variational quantum circuits are a promising approach for constructing quantum neural networks for machine learning and variational ansatzes for quantum simulation. However, finding the best variational circuit and its optimal parameters is challenging due to the large design space and parameter training cost. We propose to decouple the circuit search from parameter training by introducing a novel SuperCircuit. The SuperCircuit is constructed with multiple layers of pre-defined parameterized gates (e.g., U3 and CU3) and trained by iteratively sampling and updating the parameter subsets (SubCircuits) of it. It provides an accurate estimation of SubCircuits performance trained from scratch. Then we perform an evolutionary co-search of SubCircuit and its qubit mapping. The SubCircuit performance is estimated with parameters inherited from SuperCircuit and simulated with real device noise models. Finally, we perform iterative gate pruning and finetuning to remove redundant gates in a fine-grained manner.Extensively evaluated with 12 quantum machine learning (QML) and variational quantum eigensolver (VQE) benchmarks on 14 quantum computers, QuantumNAS significantly outperforms noise-unaware search, human, random, and existing noise-adaptive qubit mapping baselines. For QML tasks, QuantumNAS is the first to demonstrate over 95% 2-class, 85% 4-class, and 32% 10-class classification accuracy on real quantum computers. It also achieves the lowest eigenvalue for VQE tasks on H2, H2O, LiH, CH4, BeH2 compared with UCCSD baselines. We also open-source the TorchQuantum library for fast training of parameterized quantum circuits to facilitate future research.
To Ship or Not to Ship: An Extensive Evaluation of Automatic Metrics for Machine Translation

Automatic metrics are commonly used as the exclusive tool for declaring the superiority of one machine translation system’s quality over another. The community choice of automatic metric guides research directions and industrial developments by deciding which models are deemed better. Evaluating metrics correlations with sets of human judgements has been limited by the size of these sets. In this paper, we corroborate how reliable metrics are in contrast to human judgements on – to the best of our knowledge – the largest collection of judgements reported in the literature. Arguably, pairwise rankings of two systems are the most common evaluation tasks in research or deployment scenarios. Taking human judgement as a gold standard, we investigate which metrics have the highest accuracy in predicting translation quality rankings for such system pairs. Furthermore, we evaluate the performance of various metrics across different language pairs and domains. Lastly, we show that the sole use of BLEU impeded the development of improved models leading to bad deployment decisions. We release the collection of 2.3M sentence-level human judgements for 4380 systems for further analysis and replication of our work.
When can localized spins interacting with conduction electrons in ferro- or antiferromagnets be described classically via the Landau-Lifshitz equation: Transition from quantum many-body entangled to quantum-classical nonequilibrium states

Experiments in spintronics and magnonics operate with macroscopically large number of localized spins within ferromagnetic (F) or antiferromagnetic (AF) materials, so that their nonequilibrium dynamics is standardly described by the Landau-Lifshitz (LL) equation treating localized spins as classical vectors of fixed length. However, spin is a genuine quantum degree of freedom, and even though quantum effects become progressively less important for spin value $S>1$, they exist for all $S<\infty$. While this has motivated exploration of limitations/breakdown of the LL equation, by using examples of F insulators, analogous comparison of fully quantum many-body vs. quantum (for electrons)-classical (for localized spins) dynamics in systems where nonequilibrium conduction electrons are present is lacking. Here we employ quantum Heisenberg F or AF chains of $N=4$ sites, whose localized spins interact with conduction electrons via $sd$ exchange interaction, to perform such comparison by starting from unentangled pure (at zero temperature) or mixed (at finite temperature) quantum state of localized spins as the initial condition. This reveals that quantum-classical dynamics can faithfully reproduce fully quantum dynamics in the F metallic case, but only when spin $S$, Heisenberg exchange between localized spins and $sd$ exchange are sufficiently small. Increasing any of these three parameters can lead to substantial deviations, which are explained by the dynamical buildup of entanglement between localized spins and/or between them and electrons. In the AF metallic case, substantial deviations appear even at early times, despite starting from unentangled N\'{e}el state, which therefore poses a challenge on how to rigorously justify wide usage of the LL equation in phenomenological modeling of antiferromagnetic spintronics experiments.
A Systematic Literature Review of Automated ICD Coding and Classification Systems using Discharge Summaries

Codification of free-text clinical narratives have long been recognised to be beneficial for secondary uses such as funding, insurance claim processing and research. The current scenario of assigning codes is a manual process which is very expensive, time-consuming and error prone. In recent years, many researchers have studied the use of Natural Language Processing (NLP), related Machine Learning (ML) and Deep Learning (DL) methods and techniques to resolve the problem of manual coding of clinical narratives and to assist human coders to assign clinical codes more accurately and efficiently. This systematic literature review provides a comprehensive overview of automated clinical coding systems that utilises appropriate NLP, ML and DL methods and techniques to assign ICD codes to discharge summaries. We have followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA) guidelines and conducted a comprehensive search of publications from January, 2010 to December 2020 in four academic databases- PubMed, ScienceDirect, Association for Computing Machinery(ACM) Digital Library, and the Association for Computational Linguistics(ACL) Anthology. We reviewed 7,556 publications; 38 met the inclusion criteria. This review identified: datasets having discharge summaries; NLP techniques along with some other data extraction processes, different feature extraction and embedding techniques. To measure the performance of classification methods, different evaluation metrics are used. Lastly, future research directions are provided to scholars who are interested in automated ICD code assignment. Efforts are still required to improve ICD code prediction accuracy, availability of large-scale de-identified clinical corpora with the latest version of the classification system. This can be a platform to guide and share knowledge with the less experienced coders and researchers.
Time Varying Risk in U.S. Housing Sector and Real Estate Investment Trusts Equity Return

This study examines how housing sector volatilities affect real estate investment trust (REIT) equity return in the United States. I argue that unexpected changes in housing variables can be a source of aggregate housing risk, and the first principal component extracted from the volatilities of U.S. housing variables can predict the expected REIT equity returns. I propose and construct a factor-based housing risk index as an additional factor in asset price models that uses the time-varying conditional volatility of housing variables within the U.S. housing sector. The findings show that the proposed housing risk index is economically and theoretically consistent with the risk-return relationship of the conditional Intertemporal Capital Asset Pricing Model (ICAPM) of Merton (1973), which predicts an average maximum of 5.6 percent of risk premium in REIT equity return. In subsample analyses, the positive relationship is not affected by sample periods' choice but shows higher housing risk beta values for the 2009-18 sample period. The relationship remains significant after controlling for VIX, Fama-French three factors, and a broad set of macroeconomic and financial variables. Moreover, the proposed housing beta also accurately forecasts U.S. macroeconomic and financial conditions.
JS Fake Chorales: a Synthetic Dataset of Polyphonic Music with Human Annotation

High-quality datasets for learning-based modelling of polyphonic symbolic music remain less readily-accessible at scale than in other domains, such as language modelling or image classiﬁcation. Deep learning algorithms show great potential for enabling the widespread use of interactive music generation technology in consumer applications, but the lack of large-scale datasets remains a bottleneck for the development of algorithms that can consistently generate high-quality outputs. We propose that models with narrow expertise can serve as a source of high-quality scalable synthetic data, and open-source the JS Fake Chorales, a dataset of 500 pieces generated by a new learning-based algorithm, provided in MIDI form. We take consecutive outputs from the algorithm and avoid cherry-picking in order to validate the potential to further scale this dataset on-demand. We conduct an online experiment for human evaluation, designed to be as fair to the listener as possible, and ﬁnd that respondents were on average only 7% better than random guessing at distinguishing JS Fake Chorales from real chorales composed by JS Bach. Furthermore, we make anonymised data collected from experiments available along with the MIDI samples. Finally, we conduct ablation studies to demonstrate the effectiveness of using the synthetic pieces for research in polyphonic music modelling, and ﬁnd that we can improve on state-of-the-art validation set loss for the canonical JSB Chorales dataset, using a known algorithm, by simply augmenting the training set with the JS Fake Chorales.
Interpretable SincNet-based Deep Learning for Emotion Recognition from EEG brain activity

Machine learning methods, such as deep learning, show promising results in the medical domain. However, the lack of interpretability of these algorithms may hinder their applicability to medical decision support systems. This paper studies an interpretable deep learning technique, called SincNet. SincNet is a convolutional neural network that efficiently learns customized band-pass filters through trainable sinc-functions. In this study, we use SincNet to analyze the neural activity of individuals with Autism Spectrum Disorder (ASD), who experience characteristic differences in neural oscillatory activity. In particular, we propose a novel SincNet-based neural network for detecting emotions in ASD patients using EEG signals. The learned filters can be easily inspected to detect which part of the EEG spectrum is used for predicting emotions. We found that our system automatically learns the high-α (9-13 Hz) and β (13-30 Hz) band suppression often present in individuals with ASD. This result is consistent with recent neuroscience studies on emotion recognition, which found an association between these band suppressions and the behavioral deficits observed in individuals with ASD. The improved interpretability of SincNet is achieved without sacrificing performance in emotion recognition.
An In Situ Study of Turbulence near Stellar Bow Shocks

Stellar bow shocks are observed in a variety of interstellar environments and shaped by the conditions of gas in the interstellar medium (ISM). In situ measurements of turbulent density fluctuations near stellar bow shocks are only achievable with a few observational probes, including Hα-emitting bow shocks and the Voyager Interstellar Mission (VIM). In this paper, we examine density variations around the Guitar Nebula, an Hα bow shock associated with PSR B2224+65, in tandem with density variations probed by VIM near the boundary of the solar wind and ISM. High-resolution Hubble Space Telescope observations of the Guitar Nebula taken between 1994 and 2006 trace density variations over scales from hundreds to thousands of au, while VIM density measurements made with the Voyager 1 Plasma Wave System constrain variations from thousands of meters to tens of au. The power spectrum of density fluctuations constrains the amplitude of the turbulence wavenumber spectrum near the Guitar Nebula to log10Cn2=−0.8±0.2 m−20/3 and for the very local ISM probed by Voyager to log10Cn2=−1.57±0.02 m−20/3. Spectral amplitudes obtained from multiepoch observations of four other Hα bow shocks also show significant enhancements from values that are considered typical for the diffuse, warm ionized medium, suggesting that density fluctuations near these bow shocks may be amplified by shock interactions with the surrounding medium or selection effects that favor Hα emission from bow shocks embedded in denser media.
CCAT-prime Collaboration: Science Goals and Forecasts with Prime-Cam on the Fred Young Submillimeter Telescope

We present a detailed overview of the science goals and predictions for the Prime-Cam direct-detection camera–spectrometer being constructed by the CCAT-prime collaboration for dedicated use on the Fred Young Submillimeter Telescope (FYST). The FYST is a wide-field, 6 m aperture submillimeter telescope being built (first light in late 2023) by an international consortium of institutions led by Cornell University and sited at more than 5600 m on Cerro Chajnantor in northern Chile. Prime-Cam is one of two instruments planned for FYST and will provide unprecedented spectroscopic and broadband measurement capabilities to address important astrophysical questions ranging from Big Bang cosmology through reionization and the formation of the first galaxies to star formation within our own Milky Way. Prime-Cam on the FYST will have a mapping speed that is over 10 times greater than existing and near-term facilities for high-redshift science and broadband polarimetric imaging at frequencies above 300 GHz. We describe details of the science program enabled by this system and our preliminary survey strategies.
The Stationary Prophet Inequality Problem

We study a continuous and infinite time horizon counterpart to the classic prophet inequality, which we term the stationary prophet inequality problem. Here, copies of a good arrive and perish according to Poisson point processes. Buyers arrive similarly and make take-it-or-leave-it offers for unsold items. The objective is to maximize the (infinite) time average revenue of the seller. Our main results are pricing-based policies which (i) achieve a 1/2-approximation of the optimal offline policy, which is best possible, and (ii) achieve a better than (1-1/e)-approximation of the optimal online policy. Result (i) improves upon bounds implied by recent work of Collina et al. (WINE'20), and is the first optimal prophet inequality for a stationary problem. Result (ii) improves upon a 1-1/e bound implied by recent work of Aouad and Sarita (EC'20), and shows that this prevalent bound in online algorithms is not optimal for this problem.
The Fluid Dynamics of the One-Body Stationary States of Quantum Mechanics with Real Valued Wavefunctions

It is demonstrated that the probability density function—given by the square of a quantum mechanical wavefunction that is a real-valued eigenvector of a time-independent, one-body Schrödinger equation—satisfies a compressible-flow generalization of the Bernoulli equation, where the mass density is the probability density times the mass of the system; the pressure and velocity fields are defined by functions depending on the probability density, and the gradient and the Laplacian of the probability density. There are two possible directions of the velocity on a streamline, called uphill and downhill flow, where the fluid particles move in the direction of increasing and decreasing density, respectively. The “Bernoullian” equation, mentioned above, is not a statement of the conservation of energy; instead, it is a statement of local (spatial) conservation of specific energy, an intensive, uniform scalar-field that is a constant of motion for each fluid element. Relationships between the mathematical objects from the one-body quantum states considered, called Q1 probability states, and the corresponding objects from the steady flow states, called Q1 flow states, are discovered. For example, the integrand of the expectation value of the kinetic energy for a Q1 probability state is the sum of the kinetic-energy per volume and the pressure of the corresponding Q1 flow state. The velocity definition mentioned above implies a generalization of the steady-flow continuity equation where mass is not locally conserved, and where the pressure is proportional to the mass creation rate per volume. However, over all space, both mass and energy is conserved. The gradient of the Bernoullian equation, mentioned above, is demonstrated to be equivalent to the steady flow Euler equation for variable mass and irrotational flow. This implies that Q1 flow is a flow with variable mass, that is steady, irrotational, inviscid, and compressible. Speed of sound equations are obtained from a spherical, acoustic wave-pulse propagating along a streamline of an ambient state of Q1 flow with a spherical flow geometry and a uniform speed for both the fluid velocity and the wave pulse velocities (relative to the fluid velocity) restricted to a streamline. One of the solutions of the derived quadratic equation is that the wave-pulse velocity on a streamlime is equal in magnitude but opposite in direction of the fluid velocity on the streamlime. Possible interpretations for a flow with this solution are considered, including that it simply the solution for the case of steady flow with no wave pulse. The other solution is given by explicit formulas which depends on scalar fields of the ambient state, and this solution is the focus of attention from that point on. It is proven that the extremums of the momentum per volume on a streamline occur at points that are Mach 1 speed. The developed formalism is applied to a particle in a one-
TMD observables in unpolarised Semi-Inclusive DIS at COMPASS

In 2016 and 2017, the COMPASS Collaboration at CERN collected a large sample of DIS events with a longitudinally polarised 160 GeV/c muon beam scattering off a liquid hydrogen target. Part of the collected data has been analysed to extract preliminary results for the amplitudes of the modulations in the azimuthal angle of the charged hadrons and for their transverse-momentum distributions. These observables give relevant information for the study of the transverse momentum and spin structure of the nucleon. Similarly to the results obtained in COMPASS with a deuteron target, the azimuthal asymmetries exhibit interesting kinematic dependences, while the squared-transverse-momentum distributions can be described by the sum of two exponential functions.
Query2Label: A Simple Transformer Way to Multi-Label Classification

This paper presents a simple and effective approach to solving the multi-label classification problem. The proposed approach leverages Transformer decoders to query the existence of a class label. The use of Transformer is rooted in the need of extracting local discriminative features adaptively for different labels, which is a strongly desired property due to the existence of multiple objects in one image. The built-in cross-attention module in the Transformer decoder offers an effective way to use label embeddings as queries to probe and pool class-related features from a feature map computed by a vision backbone for subsequent binary classifications. Compared with prior works, the new framework is simple, using standard Transformers and vision backbones, and effective, consistently outperforming all previous works on five multi-label classification data sets, including MS-COCO, PASCAL VOC, NUS-WIDE, and Visual Genome. Particularly, we establish $91.3\%$ mAP on MS-COCO. We hope its compact structure, simple implementation, and superior performance serve as a strong baseline for multi-label classification tasks and future studies. The code will be available soon at https://github.com/SlongLiu/query2labels.
Linear causality and stability of third-order relativistic dissipative fluid dynamics

We analyze the linear causality and stability of third-order fluid dynamics considering perturbations around a global equilibrium state. We investigate the formulation derived from kinetic theory, using the Chapman-Enskog expansion, in PRC 88, 021903, which was shown to be in excellent agreement with solutions of the microscopic theory. From this analysis, we demonstrate that this theory is linearly acausal and unstable and that such instabilities cannot be corrected by tuning the transport coefficients. We then propose a modification of this theory, valid only in the linear regime, that can be constructed to be linearly causal and stable and obtain the conditions the transport coefficients must satisfy in order for this to be the case.
Groupoids and singular foliations

This thesis has two objectives. The first objective is to introduce a notion of equivalence for singular foliations that preserves their transverse geometry and is compatible with the notions of Morita equivalence of the holonomy groupoids and the transverse equivalence for regular foliations that appeared in the 1980’s. The second one is to describe the structures behind quotients of singular foliations and to connect these results with their associated holonomy groupoids. We also want to give an introduction to the notion of singular foliations as given in [AS09], as well as to their relation with Lie groupoids and Lie algebroids.