Miracle-dz/UniFAR-FTUs huggingface dataset:
Quasiparticle effects in magnetic-field-resilient 3D transmons

Recent research shows that quasiparticle-induced decoherence of superconducting qubits depends on the superconducting-gap asymmetry originating from the different thicknesses of the top and bottom films in Al/AlO$_x$/Al junctions. Magnetic field is a key tuning knob to investigate this dependence as it can change the superconducting gaps in situ. We present measurements of the parity-switching time of a field-resilient 3D transmon with in-plane field up to 0.41T. At low fields, small parity splitting requires qutrit pulse sequences for parity measurements. We measure a non-monotonic evolution of the parity lifetime with in-plane magnetic field, increasing up to 0.2T, followed by a decrease at higher fields. We demonstrate that the superconducting-gap asymmetry plays a crucial role in the observed behavior. At zero field, the qubit frequency is nearly resonant with the superconducting-gap difference, favoring the energy exchange with the quasiparticles and so enhancing the parity-switching rate. With a higher magnetic field, the qubit frequency decreases and gets detuned from the gap difference, causing the initial increase of the parity lifetime, while photon-assisted qubit transitions increase, producing the subsequent decrease at higher fields. Besides giving a deeper insight into the parity-switching mechanism in conventional transmon qubits, we establish that Al-AlO$_x$-Al JJs could be used in architectures for the parity-readout and manipulation of topological qubits based on Majorana zero modes.
Sparse convex relaxations in polynomial optimization

We present a novel, general, and unifying point of view on sparse approaches to polynomial optimization. Solving polynomial optimization problems to global optimality is a ubiquitous challenge in many areas of science and engineering. Historically, different approaches on how to solve nonconvex polynomial optimization problems based on convex relaxations have been developed in different scientific communities. Here, we introduce the concept of monomial patterns. A pattern determines what monomials are to be linked by convex constraints in a convex relaxation of a polynomial optimization problem. This concept helps to understand existing approaches from different schools of thought, to develop novel relaxation schemes, and to derive a flexible duality theory, which can be specialized to many concrete situations that have been considered in the literature. We unify different approaches to polynomial optimization including polyhedral approximations, dense semidefinite relaxations, SONC, SAGE, and TSSOS in a self-contained exposition. We also carry out computational experiments to demonstrate the practical advantages of a flexible usage of pattern-based sparse relaxations of polynomial optimization problems.
X-Shot: A Unified System to Handle Frequent, Few-shot and Zero-shot Learning Simultaneously in Classification

In recent years, few-shot and zero-shot learning, which learn to predict labels with limited annotated instances, have garnered significant attention. Traditional approaches often treat frequent-shot (freq-shot; labels with abundant instances), few-shot, and zero-shot learning as distinct challenges, optimizing systems for just one of these scenarios. Yet, in real-world settings, label occurrences vary greatly. Some of them might appear thousands of times, while others might only appear sporadically or not at all. For practical deployment, it is crucial that a system can adapt to any label occurrence. We introduce a novel classification challenge: X-shot, reflecting a real-world context where freq-shot, few-shot, and zero-shot labels co-occur without predefined limits. Here, X can span from 0 to positive infinity. The crux of X-shot centers on open-domain generalization and devising a system versatile enough to manage various label scenarios. To solve X-shot, we propose BinBin (Binary INference Based on INstruction following) that leverages the Indirect Supervision from a large collection of NLP tasks via instruction following, bolstered by Weak Supervision provided by large language models. BinBin surpasses previous state-of-the-art techniques on three benchmark datasets across multiple domains. To our knowledge, this is the first work addressing X-shot learning, where X remains variable.
Averaging Rate Scheduler for Decentralized Learning on Heterogeneous Data

State-of-the-art decentralized learning algorithms typically require the data distribution to be Independent and Identically Distributed (IID). However, in practical scenarios, the data distribution across the agents can have significant heterogeneity. In this work, we propose averaging rate scheduling as a simple yet effective way to reduce the impact of heterogeneity in decentralized learning. Our experiments illustrate the superiority of the proposed method (~3% improvement in test accuracy) compared to the conventional approach of employing a constant averaging rate.
Non-reflective traveling waves in finite thin beams: A parametric study

In the present study, the authors introduce a geometrically improved model inspired by the mammalian basilar membrane's properties and special vibratory behavior while conducting a parametric investigation. The goal of this model is to mimic the broadband non-reflective traveling wave response to excitation frequencies, as observed in the basilar membrane. Simple structural elements, such as beams, springs, and dashpots are utilized for this purpose. Therefore, the beam's equation of motion is developed using Hamilton's principle, and the Galerkin method is implemented as the discretization scheme. Then, the model is verified by comparing its outcome to results presented in the literature. Finally, a parametric study is conducted to reveal the effect of different parameters, i.e., the absorber's location and coefficients' values, excitation frequency, geometric tapering, and material grading on the non-reflective traveling wave response of the beam. The results reveal that traveling waves and their quality are strongly dependent on these parameters. In addition, this study suggests that adopting a single spring-damper system within the span of the beam as the wave reflection absorber may not address the entire bandwidth.
Fabric tensors and effective properties of granular materials with application to snow

Granular materials e.g., gravel, sand, snow, and metallic powders are important to many engineering analysis and design problems. Such materials are not always randomly arranged, even in a natural environment. For example, applied strain can transform a randomly distributed assembly into a more regular arrangement. Deviations from random arrangements are described via material symmetry. A random collection exhibits textural isotropy whereas regular patterns are anisotropic. Among natural materials, snow is perhaps unique because thermal factors commonly induce microstructural changes, including material symmetry. This process temperature gradient metamorphism produces snow layers that can exhibit anisotropy. To adequately describe the behavior of such layers, mathematical models must account for potential anisotropy. This feature is absent from models speci cally developed for snow, and, in most granular models in general. Material symmetry is quanti ed with fabric tensors in the constitutive models proposed here. Fabric tensors statistically characterize directional features in the microstructure. For example, the collective orientation of intergranular bonds impacts processes like conduction and loading. Anisotropic, microstructural models are analytically developed here for the conductivity, di usivity, permeability, and sti ness of granular materials. The methodology utilizes homogenization an algorithm linking microscopic and macroscopic scales. Idealized geometries and constitutive assumptions are also applied at the microscopic scale. Fabric tensors tying the granular arrangement to a ected material properties are a natural analysis outcome. The proposed conductivity model is compared to measured data. Dry dense snow underwent temperature gradient metamorphism in a lab. Both the measured heat transfer coe cient and a developing ice structure favored the direction of the applied gradient. Periodic tomography was used to calculate microstructural variables required by the conductivity model. Through the fabric tensor, model evolution coincides with measured changes in the heat transfer coe cient. The model also predicts a di erent conductivity in directions orthogonal to the gradient due to developing anisotropy. Models that do not consider directional microstructural features cannot predict such behavior because they are strictly valid for isotropic materials. The conclusions are that anisotropy in snow can be signi cant, fabric tensors can characterize such symmetry, and constitutive models incorporating fabric tensors o er a more complete description of material behavior.
Wildest Dreams: Reproducible Research in Privacy-preserving Neural Network Training

Machine Learning (ML), addresses a multitude of complex issues in multiple disciplines, including social sciences, finance, and medical research. ML models require substantial computing power and are only as powerful as the data utilized. Due to high computational cost of ML methods, data scientists frequently use Machine Learning-as-a-Service (MLaaS) to outsource computation to external servers. However, when working with private information, like financial data or health records, outsourcing the computation might result in privacy issues. Recent advances in Privacy-Preserving Techniques (PPTs) have enabled ML training and inference over protected data through the use of Privacy-Preserving Machine Learning (PPML). However, these techniques are still at a preliminary stage and their application in real-world situations is demanding. In order to comprehend discrepancy between theoretical research suggestions and actual applications, this work examines the past and present of PPML, focusing on Homomorphic Encryption (HE) and Secure Multi-party Computation (SMPC) applied to ML. This work primarily focuses on the ML model's training phase, where maintaining user data privacy is of utmost importance. We provide a solid theoretical background that eases the understanding of current approaches and their limitations. In addition, we present a SoK of the most recent PPML frameworks for model training and provide a comprehensive comparison in terms of the unique properties and performances on standard benchmarks. Also, we reproduce the results for some of the papers and examine at what level existing works in the field provide support for open science. We believe our work serves as a valuable contribution by raising awareness about the current gap between theoretical advancements and real-world applications in PPML, specifically regarding open-source availability, reproducibility, and usability.
Introduction to the monodromy conjecture

The monodromy conjecture is a mysterious open problem in singularity theory. Its original version relates arithmetic and topological/geometric properties of a multivariate polynomial $f$ over the integers, more precisely, poles of the $p$-adic Igusa zeta function of $f$ should induce monodromy eigenvalues of $f$. The case of interest is when the zero set of $f$ has singular points. We first present some history and motivation. Then we expose a proof in the case of two variables, and partial results in higher dimension, together with geometric theorems of independent interest inspired by the conjecture. We conclude with several possible generalizations.
Observation of counterflow superfluidity in a two-component Mott insulator

The counterflow superfluidity (CSF) was predicted two decades ago. Counterintuitively, while both components in the CSF have fluidity, their correlated counterflow currents cancel out leading the overall system to an incompressible Mott insulator. However, realizing and identifying the CSF remain challenging due to the request on extreme experimental capabilities in a single setup. Here, we observe the CSF in a binary Bose mixture in optical lattices. We prepare a low-entropy spin-Mott state by conveying and merging two spin-1/2 bosonic atoms at every site and drive it adiabatically to the CSF at $\sim$ 1 nK. Antipair correlations of the CSF are probed though a site- and spin-resolved quantum gas microscope in both real and momentum spaces. These techniques and observations provide accessibility to the symmetry-protected topological quantum matters.
Quantum phase transition between topologically distinct quantum critical points

By constructing an exactly solvable spin model, we investigate the critical behaviors of transverse field Ising chains interpolated with cluster interactions, which exhibit various types of topologically distinct Ising critical points. Using fidelity susceptibility as an indicator, we establish the global phase diagram, including ferromagnetic, trivial paramagnetic, and symmetry-protected topological phases. Different types of critical points exist between these phases, encompassing both topologically trivial and non-trivial Ising critical points, as well as Gaussian critical points. Importantly, we demonstrate the existence of a Lifshitz transition between these topologically distinct Ising critical points, with central charge and critical exponents determined through finite-size scaling. This work serves as a valuable reference for further research on phase transitions within the gapless quantum phase of matter.
Inverse-Free Fast Natural Gradient Descent Method for Deep Learning

Second-order methods can converge much faster than first-order methods by incorporating second-order derivates or statistics, but they are far less prevalent in deep learning due to their computational inefficiency. To handle this, many of the existing solutions focus on reducing the size of the matrix to be inverted. However, it is still needed to perform the inverse operator in each iteration. In this paper, we present a fast natural gradient descent (FNGD) method, which only requires computing the inverse during the first epoch. Firstly, we reformulate the gradient preconditioning formula in the natural gradient descent (NGD) as a weighted sum of per-sample gradients using the Sherman-Morrison-Woodbury formula. Building upon this, to avoid the iterative inverse operation involved in computing coefficients, the weighted coefficients are shared across epochs without affecting the empirical performance. FNGD approximates the NGD as a fixed-coefficient weighted sum, akin to the average sum in first-order methods. Consequently, the computational complexity of FNGD can approach that of first-order methods. To demonstrate the efficiency of the proposed FNGD, we perform empirical evaluations on image classification and machine translation tasks. For training ResNet-18 on the CIFAR-100 dataset, FNGD can achieve a speedup of 2.05$\times$ compared with KFAC. For training Transformer on Multi30K, FNGD outperforms AdamW by 24 BLEU score while requiring almost the same training time.
Did Translation Models Get More Robust Without Anyone Even Noticing?

Neural machine translation (MT) models achieve strong results across a variety of settings, but it is widely believed that they are highly sensitive to"noisy"inputs, such as spelling errors, abbreviations, and other formatting issues. In this paper, we revisit this insight in light of recent multilingual MT models and large language models (LLMs) applied to machine translation. Somewhat surprisingly, we show through controlled experiments that these models are far more robust to many kinds of noise than previous models, even when they perform similarly on clean data. This is notable because, even though LLMs have more parameters and more complex training processes than past models, none of the open ones we consider use any techniques specifically designed to encourage robustness. Next, we show that similar trends hold for social media translation experiments -- LLMs are more robust to social media text. We include an analysis of the circumstances in which source correction techniques can be used to mitigate the effects of noise. Altogether, we show that robustness to many types of noise has increased.
Systematic Improvement of Quantum Monte Carlo Calculations in Transition Metal Oxides: sCI-Driven Wavefunction Optimization for Reliable Band Gap prediction

Accurate determination of electronic properties of correlated oxides remains a significant challenge for computational theory. Traditional Hubbard-corrected density functional theory (DFT+U) frequently encounters limitations in precisely capturing electron correlation, particularly when predicting band gaps. We introduce a systematic methodology to enhance the accuracy of diffusion Monte Carlo (DMC) simulations for both ground and excited states, focusing on LiCoO$_2$ as a case study. By employing a selected CI (sCI) approach, we demonstrate the capability to optimize wavefunctions beyond the constraints of single-reference DFT+U trial wavefunctions. We show that the sCI framework enables accurate prediction of band gaps in LiCoO$_2$, closely aligning with experimental values and substantially improving upon traditional computational methods. The study uncovers a nuanced mixed state of $t_{2g}$ a $e_g$ orbitals at the band edges that is not captured by conventional single-reference methods, further elucidating the limitations of PBE+U in describing $d$-$d$ excitations. Our findings advocate for the adoption of beyond-DFT methodologies, such as sCI, to capture the essential physics of excited state wavefunctions in strongly correlated materials. The improved accuracy in band gap predictions and the ability to generate more reliable trial wavefunctions for DMC calculations underscore the potential of this approach for broader applications in the study of correlated oxides. This work not only provides a pathway for more accurate simulations of electronic structures in complex materials but also suggests a framework for future investigations into the excited states of other challenging systems.
Precise upper deviation estimates for the maximum of a branching random walk

We consider the precise upper large deviations estimates for the maximal displacement of a branching random walk. In addition, we obtain a description of the extremal process of the branching random walk conditioned on this large deviations event. This introduces a family of point measure playing a role similar to the decoration measures introduced in [9] for branching Brownian motion.
Hubble tension may indicate time-dependent dark matter energy density

The values of Hubble constant H0 by direct measurements with standard distance ladder are typically larger than those obtained from the observation of cosmic microwave background and the galaxy survey with inverse distance ladder. On the other hand, although the errors are still large, various determinations of the value of matter density parameter Omega_m are consistent with each other. Therefore, it is possible that the difference in Hubble constant is translated to the difference of physical matter energy density omega_m = Omega_m h^2, where h = H0 / (100 Km/s/Mpc). In this article it is examined the possibility of an increase of the physical dark matter energy density by a fast transition at a certain value of redshift as a possible resolution of the Hubble tension. A phenomenological fluid model of the dark sector, which is the modification of a so-called unified dark matter model, is introduced to concretely realize such a fast transition in the physical dark matter energy density.
Spectral Algorithms on Manifolds through Diffusion

The existing research on spectral algorithms, applied within a Reproducing Kernel Hilbert Space (RKHS), has primarily focused on general kernel functions, often neglecting the inherent structure of the input feature space. Our paper introduces a new perspective, asserting that input data are situated within a low-dimensional manifold embedded in a higher-dimensional Euclidean space. We study the convergence performance of spectral algorithms in the RKHSs, specifically those generated by the heat kernels, known as diffusion spaces. Incorporating the manifold structure of the input, we employ integral operator techniques to derive tight convergence upper bounds concerning generalized norms, which indicates that the estimators converge to the target function in strong sense, entailing the simultaneous convergence of the function itself and its derivatives. These bounds offer two significant advantages: firstly, they are exclusively contingent on the intrinsic dimension of the input manifolds, thereby providing a more focused analysis. Secondly, they enable the efficient derivation of convergence rates for derivatives of any k-th order, all of which can be accomplished within the ambit of the same spectral algorithms. Furthermore, we establish minimax lower bounds to demonstrate the asymptotic optimality of these conclusions in specific contexts. Our study confirms that the spectral algorithms are practically significant in the broader context of high-dimensional approximation.
Automatic Bi-modal Question Title Generation for Stack Overflow with Prompt Learning

When drafting question posts for Stack Overflow, developers may not accurately summarize the core problems in the question titles, which can cause these questions to not get timely help. Therefore, improving the quality of question titles has attracted the wide attention of researchers. An initial study aimed to automatically generate the titles by only analyzing the code snippets in the question body. However, this study ignored the helpful information in their corresponding problem descriptions. Therefore, we propose an approach SOTitle+ by considering bi-modal information (i.e., the code snippets and the problem descriptions) in the question body. Then we formalize the title generation for different programming languages as separate but related tasks and utilize multi-task learning to solve these tasks. Later we fine-tune the pre-trained language model CodeT5 to automatically generate the titles. Unfortunately, the inconsistent inputs and optimization objectives between the pre-training task and our investigated task may make fine-tuning hard to fully explore the knowledge of the pre-trained model. To solve this issue, SOTitle+ further prompt-tunes CodeT5 with hybrid prompts (i.e., mixture of hard and soft prompts). To verify the effectiveness of SOTitle+, we construct a large-scale high-quality corpus from recent data dumps shared by Stack Overflow. Our corpus includes 179,119 high-quality question posts for six popular programming languages. Experimental results show that SOTitle+ can significantly outperform four state-of-the-art baselines in both automatic evaluation and human evaluation. Our work indicates that considering bi-modal information and prompt learning in Stack Overflow title generation is a promising exploration direction.