Miracle-dz/UniFAR-FTUs huggingface dataset:
Inverting multiple quantum many-body scars via disorder

The recent observations of persistent revivals in the Rydberg atom chain have revealed a weak ergodicity breaking mechanism known as quantum many-body scars, which is typically a collection of states with low entanglement embedded in otherwise thermal spectra. Here, by generalizing a generic formalism, we propose a direct evolution from the quantum many-body scars to the multiple inverted quantum many-body scars, i.e., different sets of excited states with volume-law entanglement embedded in a sea of the many-body localized spectrum. When increasing the disorder strength, a tower of exact eigenstates remain intact, acting as conventional quantum many-body scars at weak disorder, and each residing inside narrow energy windows with the emerged inverted quantum many-body scar at strong disorder. Moreover, the strong disorder also induces additional sets of inverted quantum many-body scars with their energies concentrating in the middle of the exact eigenstates. As a result, all the multiple inverted quantum many-body scars are approximately equidistant in energy. We further examine the stability of the conventional and the inverted quantum many-body scars against the external random field. Our findings expand the variety of nonthermal systems and connect the weak violation of ergodicity with the weak violation of many-body localization.
On the long range dependence of time-changed generalized mixed fractional Brownian motion

: We introduce a generalized mixed fractional Brownian motion (gmfBm) as a linear combination of two independent fractional Brownian motions with possibly diﬀerent Hurst indices and investigate conditions under which the time-changed gmfBm exhibit long range dependence when the time-change is induced by a tempered stable subordinator or a Gamma process.
Anisotropy of the Upper Critical Field in (TMTSF)2PF6

The temperature dependence of the upper critical magnetic field H{sub c2}(T) in the quasi-one-dimensional molecular superconductor (TMTSF){sub 2}PF{sub 6} was determined via resistivity, for the intrachain ({bold a}), interchain ({bold b}), and interplane ({bold c}{sup {asterisk}}) directions. For {ital H} along {bold a} and {bold b}, H{sub c2}(T) exhibits pronounced positive curvature, with no sign of saturation to 0.1K. A novel anisotropy inversion is observed, wherein H{sup b}{sub c2} grows larger than H{sup a}{sub c2} and exceeds the paramagnetic limit by more than 200{percent}. The anomalous shape of H{sub c2}(T) is consistent with recent predictions of unconventional behavior in anisotropic superconductors, including the possibility of triplet pairing and reentrant superconductivity in very high field. {copyright} {ital 1997} {ital The American Physical Society}
Sensor Scheduling Criterion for Space Surveillance Based on Relative Orbit Analysis

To exploit the observation effectiveness of the space surveillance network comprehensively and provide a mathematical explanation of a sensor scheduling result, the analytical sensor scheduling criterion is required. This paper presents an analytical sensor scheduling criterion of the space surveillance network based on the relative orbit analysis. A virtual chief orbit and the relative orbit elements, which are the set of orbit element differences of the real satellite relative to the virtual chief satellite, are used to describe the motion of the real satellite. Because the virtual chief orbit, which is selected under the constraint that the relative orbit radius is less than 0.3% of that of the chief orbit radius, is accurately known, the uncertainty of the relative motion will describe the uncertainty of orbit determination of the real satellite. Then one can obtain an analytical criterion derived from the covariance matrix of the relative orbit elements to measure the observation effectiveness of a s.
A note on the rate of convergence of integration schemes for closed surfaces

In this paper, we issue an error analysis for integration over discrete surfaces using the surface parametrization presented in Praetorius and Stenger (Arch Numer Softw 1(1):2022, 2022) as well as prove why even-degree polynomials utilized for approximating both the smooth surface and the integrand exhibit a higher convergence rate than odd-degree polynomials. Additionally, we provide some numerical examples that illustrate our findings and propose a potential approach that overcomes the problems associated with the original one.
Boundary conditions dependence of the phase transition in the quantum Newman-Moore model

We study the triangular plaquette model (TPM, also known as the Newman-Moore model) in the presence of a transverse magnetic field on a lattice with periodic boundaries in both spatial dimensions. We consider specifically the approach to the ground state phase transition of this quantum TPM (QTPM, or quantum Newman-Moore model) as a function of the system size and type of boundary conditions. Using cellular automata methods, we obtain a full characterization of the minimum energy configurations of the TPM for arbitrary tori sizes. For the QTPM, we use these cycle patterns to obtain the symmetries of the model, which we argue determine its quantum phase transition: we find it to be a first-order phase transition, with the addition of spontaneous symmetry breaking for system sizes which have degenerate classical ground states. For sizes accessible to numerics, we also find that this classification is consistent with exact diagonalization, Matrix Product States and Quantum Monte Carlo simulations.
Cross Far- and Near-field Wireless Communications in Terahertz Ultra-large Antenna Array Systems

Terahertz (THz) band owning the abundant multi-ten-GHz bandwidth is capable to support Terabit-per-second wireless communications, which is a pillar technology for 6G and beyond systems. With sub-millimeter-long antennas, ultra-massive (UM) MIMO and intelligent surface (IS) systems with thousands of array elements are exploited to effectively combat the distance limitation and blockage problems, which compose a promising THz ultra-large antenna array (ULAA) system. As a combined effect of wavelength and array aperture, the resulting coverage of THz systems ranges from near-field to far-field, leading to a new paradigm of cross-field communications. Although channel models, communications theories, and networking strategies have been studied for far-field and near-field separately, the unified design of cross-field communications that achieve high spectral efficiency and low complexity is still missing. In this article, the challenges and features of THz ULAA cross-field communications are investigated. Furthermore, cross-field solutions in three perspectives are presented, including a hybrid spherical- and planar-wave channel model, cross-field channel estimation, and widely-spaced multi-subarray hybrid beamforming, where a subarray as a basic unit in THz ULAA systems is exploited. The approximation error of channel modeling accuracy, spectral efficiency, and estimation error of these designs are numerically evaluated. Finally, as a roadmap of THz ULAA cross-field communications, multiple open problems and potential research directions are elaborated.
OpenCon: Open-world Contrastive Learning

Machine learning models deployed in the wild naturally encounter unlabeled samples from both known and novel classes. Challenges arise in learning from both the labeled and unlabeled data, in an open-world semi-supervised manner. In this paper, we introduce a new learning framework, open-world contrastive learning (OpenCon). OpenCon tackles the challenges of learning compact representations for both known and novel classes and facilitates novelty discovery along the way. We demonstrate the effectiveness of OpenCon on challenging benchmark datasets and establish competitive performance. On the ImageNet dataset, OpenCon significantly outperforms the current best method by 11.9% and 7.4% on novel and overall classification accuracy, respectively. Theoretically, OpenCon can be rigorously interpreted from an EM algorithm perspective--minimizing our contrastive loss partially maximizes the likelihood by clustering similar samples in the embedding space. The code is available at https://github.com/deeplearning-wisc/opencon.
Advancing 3D finger knuckle recognition via deep feature learning

Contactless 3D finger knuckle patterns have emerged as an effective biometric identifier due to its discriminativeness, visibility from a distance, and convenience. Recent research has developed a deep feature collaboration network which simultaneously incorporates intermediate features from deep neural networks with multiple scales. However, this approach results in a large feature dimension, and the trained classification layer is required for comparing probe samples, which limits the introduction of new classes. This paper advances this approach by investigating the possibility of learning a discriminative feature vector with the least possible dimension for representing 3D finger knuckle images. Experimental results are presented using a publicly available 3D finger knuckle images database with comparisons to popular deep learning architectures and the state-of-the-art 3D finger knuckle recognition methods. The proposed approach offers outperforming results in classification and identification tasks under the more practical feature comparison scenario, i.e., using the extracted deep feature instead of the trained classification layer for comparing probe samples. More importantly, this approach can offer 99% reduction in the size of feature templates, which is highly attractive for deploying biometric systems in the real world. Experiments are also performed using other two public biometric databases with similar patterns to ascertain the effectiveness and generalizability of our proposed approach.
Image Denoising: The Deep Learning Revolution and Beyond - A Survey Paper -

Image denoising (removal of additive white Gaussian noise from an image) is one of the oldest and most studied problems in image processing. An extensive work over several decades has led to thousands of papers on this subject, and to many well-performing algorithms for this task. Indeed, 10 years ago, these achievements have led some researchers to suspect that"Denoising is Dead", in the sense that all that can be achieved in this domain has already been obtained. However, this turned out to be far from the truth, with the penetration of deep learning (DL) into image processing. The era of DL brought a revolution to image denoising, both by taking the lead in today's ability for noise removal in images, and by broadening the scope of denoising problems being treated. Our paper starts by describing this evolution, highlighting in particular the tension and synergy that exist between classical approaches and modern DL-based alternatives in design of image denoisers. The recent transitions in the field of image denoising go far beyond the ability to design better denoisers. In the 2nd part of this paper we focus on recently discovered abilities and prospects of image denoisers. We expose the possibility of using denoisers to serve other problems, such as regularizing general inverse problems and serving as the prime engine in diffusion-based image synthesis. We also unveil the idea that denoising and other inverse problems might not have a unique solution as common algorithms would have us believe. Instead, we describe constructive ways to produce randomized and diverse high quality results for inverse problems, all fueled by the progress that DL brought to image denoising. This survey paper aims to provide a broad view of the history of image denoising and closely related topics. Our aim is to give a better context to recent discoveries, and to the influence of DL in our domain.
Multi-point Padè for the study of phase transitions: from the Ising model to lattice QCD.

The Bielefeld Parma collaboration has recently put forward a method to investigate the QCD phase diagram based on the computation of Taylor series coefficients at both zero and imaginary values of the baryonic chemical potential. The method is based on the computation of multi-point Pad\`e approximants. We review the methodological aspects of the computation and, in order to gain confidence in the approach, we report on the application of the method to the two-dimensional Ising model (probably the most popular arena for testing tools in the study of phase transitions). Besides showing the effectiveness of the multi-point Pad\`e approach, we discuss what these results can suggest in view of further progress in the study of the QCD phase diagram. We finally report on very preliminary results in which we look for Pad\`e approximants at different temperatures and fixed values of the (imaginary) baryonic chemical potential.
Active Fault Isolation for Discrete Event Systems

In practice, we can not only disable some events, but also enforce the occurrence of some events prior to the occurrence of other events by external control. In this paper, we combine these two control mechanisms to synthesize a more powerful supervisor. Here our control goal is to design an isolation supervisor which ensures in the closed-loop system, faults are isolatable in the sense that after a fault occurs, we can determine which type the fault belongs to by observing the output of the closed-loop system. The isolation supervisor starts to work when the occurrence of faults is detected. We then solve the isolation supervisor synthesis problem as follows. For a given discrete event system, we firstly construct a bipartite transition system which includes all feasible isolation supervisors. An isolation supervisor is feasible if it enforces only events that are physically possible. We then develop an algorithm to check whether the synthesis problem is solvable or not. The algorithm can also be used to find a valid isolation supervisor if the synthesis problem is solvable. The method of combining two control mechanisms can be used to synthesize more powerful supervisors for other supervisory control problems of discrete event systems as well.
Complex critical points in Lorentzian spinfoam quantum gravity: Four-simplex amplitude and effective dynamics on a double-
Δ3
 complex

The complex critical points are analyzed in the 4-dimensional Lorentzian Engle-Pereira-Rovelli-Livine (EPRL) spinfoam model in the large-$j$ regime. For the 4-simplex amplitude, taking into account the complex critical point generalizes the large-$j$ asymptotics to the situation with non-Regge boundary data and relates to the twisted geometry. For generic simplicial complexes, we present a general procedure to derive the effective theory of Regge geometries from the spinfoam amplitude in the large-$j$ regime by using the complex critical points. The effective theory is analyzed in detail for the spinfoam amplitude on the double-$\Delta_3$ simplicial complex. We numerically compute the effective action and the solution of the effective equation of motion on the double-$\Delta_3$ complex. The effective theory reproduces the classical Regge gravity when the Barbero-Immirzi parameter $\gamma$ is small.
Scaling laws for fully developed turbulent shear flows. Part 2. Processing of experimental data

In Part 1 of this work (Barenblatt 1993) a non-universal scaling law (depending on the Reynolds number) for the mean velocity distribution in fully developed turbulent shear flow was proposed, together with the corresponding skin friction law. The universal logarithmic law was also discussed and it was shown that it can be understood, in fact, as an asymptotic branch of the envelope of the curves corresponding to the scaling law. Here in Part 2 the comparisons with experimental data are presented in detail. The whole set of classic Nikuradze (1932) data, concerning both velocity distribution and skin friction, was chosen for comparison. The instructive coincidence of predictions with experimental data suggests the conclusion that the influence of molecular viscosity within the main body of fully developed turbulent shear flows remains essential, even at very large Reynolds numbers. Meanwhile, some incompleteness of the experimental data presented in the work of Nikuradze (1932) is noticed, namely the lack of data in the range of parameters where the difference between scaling law and universal logarithmic law predictions should be the largest.
Rheological and Sensory Evaluation of Wheat Flour Tortillas During Storage

ABSTRACT Texture of wheat flour tortillas over 15 days at room temperature was evaluated using an expert sensory panel, consumer panels, subjective rollability test, large deformation rheological methods (i.e., bending, extensibility [1-D and 2-D], and puncture tests), and stress relaxation method. Most of the changes in texture occurred during the initial 8 days of storage, while texture of tortillas changed slowly thereafter. Differences in texture between fresh and 1-day-old tortillas were detected by many objective rheological methods but not by either sensory panel. The expert sensory panel observed a rapid decrease in tortilla extensibility and an increase in staleness between 1 and 8 days of storage and smaller changes in sensory scores after 8 days of storage. Most objective rheological parameters changed rapidly between 0 and 5 days, and slowly after 5 days of storage. Significant correlations and factor analysis reveal that changes occurring in flour tortillas during staling are estimated better.
Calculation of the hyperfine structure of Dy, Ho, Cf, and Es

A recently developed version of the configuration interaction (CI) method for open shells with a large number of valence electrons has been used to study two heavy atoms, californium (Cf, Z= 98) and einsteinium (Es, Z= 99). Motivated by experimental work to measure the hyperfine structure (HFS) for these atoms, we perform the calculations of the magnetic dipole HFS constants $A$ and electric quadrupole HFS constant $B$ for the sake of interpretation of the measurements in terms of nuclear magnetic moment $\mu$ and electric quadrupole moment $Q$. For verification of our computations, we have also carried out similar calculations for the lighter homologs dysprosium (Dy, Z= 66) and holmium (Ho, Z= 67), whose electronic structures are similar to Cf and Es, respectively. We have conducted a revision of the nuclear moments of some isotopes of Es leading to an improved value of the magnetic moment of $^{253}$Es [$\mu$($^{253}$Es) = 4.20(13)$\mu_N$].
InPars-Light: Cost-Effective Unsupervised Training of Efficient Rankers

We carried out a reproducibility study of InPars, which is a method for unsupervised training of neural rankers (Bonifacio et al., 2022). As a by-product, we developed InPars-light, which is a simple-yet-effective modification of InPars. Unlike InPars, InPars-light uses 7x-100x smaller ranking models and only a freely available language model BLOOM, which -- as we found out -- produced more accurate rankers compared to a proprietary GPT-3 model. On all five English retrieval collections (used in the original InPars study) we obtained substantial (7%-30%) and statistically significant improvements over BM25 (in nDCG and MRR) using only a 30M parameter six-layer MiniLM-30M ranker and a single three-shot prompt. In contrast, in the InPars study only a 100x larger monoT5-3B model consistently outperformed BM25, whereas their smaller monoT5-220M model (which is still 7x larger than our MiniLM ranker) outperformed BM25 only on MS MARCO and TREC DL 2020. In the same three-shot prompting scenario, our 435M parameter DeBERTA v3 ranker was at par with the 7x larger monoT5-3B (average gain over BM25 of 1.3 vs 1.32): In fact, on three out of five datasets, DeBERTA slightly outperformed monoT5-3B. Finally, these good results were achieved by re-ranking only 100 candidate documents compared to 1000 used by Bonifacio et al. (2022). We believe that InPars-light is the first truly cost-effective prompt-based unsupervised recipe to train and deploy neural ranking models that outperform BM25. Our code and data is publicly available. https://github.com/searchivarius/inpars_light/
Quantization of the Bateman damping system with conformable derivative

In this work, the conformable Bateman Lagrangian for the damped harmonic oscillator system is proposed using the conformable derivative concept. In other words, the integer derivatives are replaced by conformable derivatives of order $\alpha$ with $0<\alpha\leq 1$. The corresponding conformable Euler-Lagrange equations of motion and fractional Hamiltonian are then obtained. The system is then canonically quantized and the conformable Schrodinger equation is constructed. The fractional-order dependence of the energy eigenvalues $E_n ^\alpha$ and eigenfunctions $\psi_n ^\alpha$ are obtained using using suitable transformations and the extended fractional Nikiforov-Uvarov method. The corresponding conformable continuity equation is also derived and the probability density and probability current are thus suitably defined. The probability density evolution as well as its dependence on $\alpha$ is plotted and analyzed for various situations. It is found that the energy eigenvalues are real and there are sort of gradual ordering in the behavior of the probability densities.
Quantum State Reduction of General Initial States through Spontaneous Unitarity Violation

The inability of Schrödinger’s unitary time evolution to describe the measurement of a quantum state remains a central foundational problem. It was recently suggested that the unitarity of Schrödinger dynamics can be spontaneously broken, resulting in measurement as an emergent phenomenon in the thermodynamic limit. Here, we introduce a family of models for spontaneous unitarity violation that apply to generic initial superpositions over arbitrarily many states, using either single or multiple state-independent stochastic components. Crucially, we show that Born’s probability rule emerges spontaneously in all cases.
Sharper Analysis for Minibatch Stochastic Proximal Point Methods: Stability, Smoothness, and Deviation

The stochastic proximal point (SPP) methods have gained recent attention for stochastic optimization, with strong convergence guarantees and superior robustness to the classic stochastic gradient descent (SGD) methods showcased at little to no cost of computational overhead added. In this article, we study a minibatch variant of SPP, namely M-SPP, for solving convex composite risk minimization problems. The core contribution is a set of novel excess risk bounds of M-SPP derived through the lens of algorithmic stability theory. Particularly under smoothness and quadratic growth conditions, we show that M-SPP with minibatch-size n and iteration count T enjoys an in-expectation fast rate of convergence consisting of an O ( 1 T 2 ) bias decaying term and an O ( 1 nT ) variance decaying term. In the small-n-large-T setting, this result substantially improves the best known results of SPP-type approaches by revealing the impact of noise level of model on convergence rate. In the complementary small-T -large-n regime, we provide a two-phase extension of M-SPP to achieve comparable convergence rates. Moreover, we derive a near-tight high probability (over the randomness of data) bound on the parameter estimation error of a sampling-without-replacement variant of M-SPP. Numerical evidences are provided to support our theoretical predictions when substantialized to Lasso and logistic regression models.