Miracle-dz/UniFAR-FTUs huggingface dataset:
Cosmic-ray Transport in Magnetohydrodynamic Turbulence

This paper studies cosmic-ray (CR) transport in magnetohydrodynamic (MHD) turbulence. CR transport is strongly dependent on the properties of the magnetic turbulence. We perform test particle simulations to study the interactions of CR with both total MHD turbulence and decomposed MHD modes. The spatial diffusion coefficients and the pitch angle scattering diffusion coefficients are calculated from the test particle trajectories in turbulence. Our results confirm that the fast modes dominate the CR propagation, whereas Alfvén and slow modes are much less efficient and have shown similar pitch-angle scattering rates. We investigate the cross field transport on large and small scales. On large/global scales, normal diffusion is observed and the diffusion coefficient is suppressed by MAζ compared to the parallel diffusion coefficients, with ζ closer to 4 in Alfvén modes than that in total turbulence, as theoretically expected. For the CR transport on scales smaller than the turbulence injection scale, both the local and global magnetic reference frames are adopted. Superdiffusion is observed on such small scales in all the cases. Particularly, CR transport in Alfvén modes show clear Richardson diffusion in the local reference frame. The diffusion transitions smoothly from the Richardson’s one with index 1.5 to normal diffusion as the particle mean free path decreases from λ ∥ ≫ L to λ ∥ ≪ L, where L is the injection/coherence length of turbulence. Our results have broad applications to CRs in various astrophysical environments.
Entanglement Dynamics Governed by Time-Dependent Quantum Generators

In the article, we investigate entanglement dynamics defined by time-dependent linear generators. We consider multilevel quantum systems coupled to an environment that induces decoherence and dissipation, such that the relaxation rates depend on time. By applying the condition of partial commutativity, one can precisely describe the dynamics of selected subsystems. More specifically, we investigate the dynamics of entangled states. The concurrence is used to quantify the amount of two-qubit entanglement in the time domain. The framework appears to be an efficient tool for investigating quantum evolution of entangled states driven by time-local generators. In particular, non-Markovian effects can be included to observe the restoration of entanglement in time.
Towards Coherent Visual Storytelling with Ordered Image Attention

We address the problem of visual storytelling, 001 i.e . , generating a story for a given sequence 002 of images. While each story sentence should 003 describe a corresponding image, a coherent 004 story also needs to be consistent and relate 005 to both future and past images. Current ap-006 proaches encode images independently, disre-007 garding relations between images. Our ap-008 proach learns to encode images with differ-009 ent interactions based on the story position 010 (i.e., past image or future image). To this 011 end, we develop a novel message-passing-like 012 algorithm for ordered image attention (OIA) 013 that collects interactions across all the im-014 ages in the sequence. Finally, to generate the 015 story’s sentences, a second attention mecha-016 nism picks the important image attention vec-017 tors with an Image-Sentence Attention (ISA). 018 The obtained results improve the METEOR 019 score on the VIST dataset by 1%. Further-020 more, a thorough human study conﬁrms im-021 provements and demonstrates that order-based 022 Interactions signiﬁcantly improve coherency 023 (64.20% vs . 28.70%). 024
Improving Distinction between ASR Errors and Speech Disfluencies with Feature Space Interpolation

Fine-tuning pretrained language models (LMs) is a popular approach to automatic speech recognition (ASR) error detection during post-processing. While error detection systems often take advantage of statistical language archetypes captured by LMs, at times the pretrained knowledge can hinder error detection performance. For instance, presence of speech disfluencies might confuse the post-processing system into tagging disfluent but accurate transcriptions as ASR errors. Such confusion occurs because both error detection and disfluency detection tasks attempt to identify tokens at statistically unlikely positions. This paper proposes a scheme to improve existing LM-based ASR error detection systems, both in terms of detection scores and resilience to such distracting auxiliary tasks. Our approach adopts the popular mixup method in text feature space and can be utilized with any black-box ASR output. To demonstrate the effectiveness of our method, we conduct post-processing experiments with both traditional and end-to-end ASR systems (both for English and Korean languages) with 5 different speech corpora. We find that our method improves both ASR error detection F 1 scores and reduces the number of correctly transcribed disfluencies wrongly detected as ASR errors. Finally, we suggest methods to utilize resulting LMs directly in semi-supervised ASR training.
Lung Sound Classification Using Co-tuning and Stochastic Normalization

Computational methods for lung sound analysis are beneficial for computer-aided diagnosis support, storage and monitoring in critical care. In this paper, we use pre-trained ResNet models as backbone architectures for classification of adventitious lung sounds and respiratory diseases. The learned representation of the pre-trained model is transferred by using vanilla fine-tuning, co-tuning, stochastic normalization and the combination of the co-tuning and stochastic normalization techniques. Furthermore, data augmentation in both time domain and time-frequency domain is used to account for the class imbalance of the ICBHI and our multi-channel lung sound dataset. Additionally, we introduce spectrum correction to account for the variations of the recording device properties on the ICBHI dataset. Empirically, our proposed systems mostly outperform all state-of-the-art lung sound classification systems for the adventitious lung sounds and respiratory diseases of both datasets.
Dissipative dynamics in open XXZ Richardson-Gaudin models

In specific open systems with collective dissipation the Liouvillian can be mapped to a non-Hermitian Hamiltonian. We here consider such a system where the Liouvillian is mapped to an XXZ Richardson-Gaudin integrable model and detail its exact Bethe ansatz solution. While no longer Hermitian, the Hamiltonian is pseudo-Hermitian/PT-symmetric, and as the strength of the coupling to the environment is increased the spectrum in a fixed symmetry sector changes from a broken pseudo-Hermitian phase with complex conjugate eigenvalues to a pseudo-Hermitian phase with real eigenvalues, passing through a series of exceptional points and associated dissipative quantum phase transitions. The homogeneous limit supports a nontrivial steady state, and away from this limit this state gives rise to a slow logarithmic growth of the decay rate (spectral gap) with system size. Using the exact solution, it is furthermore shown how at large coupling strengths the ratio of the imaginary to the real part of the eigenvalues becomes approximately quantized in the remaining symmetry sectors.
The étale-open topology and the stable fields conjecture

For an arbitrary field $K$ and $K$-variety $V$, we introduce the \'etale-open topology on the set $V(K)$ of $K$-points of $V$. This topology agrees with the Zariski topology, Euclidean topology, or valuation topology when $K$ is separably closed, real closed, or $p$-adically closed, respectively. Topological properties of the \'etale-open topology corresponds to algebraic properties of $K$. For example, the \'etale-open topology on $\mathbb{A}^1(K)$ is not discrete if and only if $K$ is large. As an application, we show that a large stable field is separably closed.
Rheotaxis of active droplets

Rheotaxis is a well-known phenomenon among microbial organisms and artificial active colloids, wherein the swimmers respond to an imposed flow. We report the first experimental evidence of upstream rheotaxis by spherical active droplets. It is shown that the presence of a nearby wall and the resulting strong flow-gradient at the droplet level is at the root of this phenomenon. Experiments with optical cells of different heights reveal that rheotaxis is observed only for a finite range of shear rates, independent of the bulk flow-rate. We conjecture that the flow induced distortion of an otherwise isotropic distribution of filled/empty micelles around the droplet propels it against the flow. We also show that nematic droplets exhibit elastic stress-induced oscillations during their rheotactic flight. A promising potential of manipulating the rheotactic behavior to trap as well as shuttle droplets between target locations is demonstrated, paving way to potentially significant advancement in bio-medical applications.
Making Contrastive Learning Robust to Shortcuts

Contrastive learning is one of the fastest growing research areas in machine learning due to its ability to learn useful representations without labeled data. However, contrastive learning is susceptible to shortcuts – i.e., it may learn shortcut features irrelevant to the task of interest, and discard relevant information. Past work has addressed this limitation via hand-crafted data augmentations that eliminate the shortcut. But, manually crafted augmentations do not work across all datasets and tasks. Further, data augmentations fail in addressing short-cuts in multi-attribute classiﬁcation when one attribute acts as a shortcut around other attributes. In this paper, we analyze the objective function of contrastive learning and formally prove that it is vulnerable to shortcuts. We then present reconstructive contrastive learning (RCL), a framework for learning unsupervised representations that are robust to shortcuts. The key idea is to force the learned representation to reconstruct the input, which naturally counters potential shortcuts. Extensive experiments verify that RCL is highly robust to shortcuts and outperforms state-of-the-art contrastive learning methods on a variety of datasets and tasks.
Maximum likelihood thresholds via graph rigidity

The maximum likelihood threshold (MLT) of a graph $G$ is the minimum number of samples to almost surely guarantee existence of the maximum likelihood estimate in the corresponding Gaussian graphical model. We give a new characterization of the MLT in terms of rigidity-theoretic properties of $G$ and use this characterization to give new combinatorial lower bounds on the MLT of any graph. We use the new lower bounds to give high-probability guarantees on the maximum likelihood thresholds of sparse Erd{\"o}s-R\'enyi random graphs in terms of their average density. These examples show that the new lower bounds are within a polylog factor of tight, where, on the same graph families, all known lower bounds are trivial. Based on computational experiments made possible by our methods, we conjecture that the MLT of an Erd{\"o}s-R\'enyi random graph is equal to its generic completion rank with high probability. Using structural results on rigid graphs in low dimension, we can prove the conjecture for graphs with MLT at most $4$ and describe the threshold probability for the MLT to switch from $3$ to $4$. We also give a geometric characterization of the MLT of a graph in terms of a new"lifting"problem for frameworks that is interesting in its own right. The lifting perspective yields a new connection between the weak MLT (where the maximum likelihood estimate exists only with positive probability) and the classical Hadwiger-Nelson problem.
Lattice flexibility in 
Ca3Ru2O7
: Control of electrical transport via anisotropic magnetostriction

The power of flexible lattice in Ca3Ru2O7: Exquisite control of the electrical transport via anisotropic magnetostriction Hengdi Zhao1, Hao Zheng2, Jasminka Terzic3, Wenhai Song4, Yifei Ni1, Yu Zhang1, Pedro Schlottmann5 and Gang Cao1* 1Department of Physics, University of Colorado at Boulder, Boulder, CO 80309, USA 2 Argonne National Laboratory, Lemont, IL 60439, USA 3 Department of Physics, Western Kentucky University, Bowling Green, KY 42101, USA 4 Institute of Solid State Physics, Chinese Academy of Sciences, Hefei, China 5 Department of Physics, Florida State University, Tallahassee, FL 32306, USA
Maximal $L^p$-regularity and $H^{\infty}$-calculus for block operator matrices and applications

Many coupled evolution equations can be described via $2\times2$-block operator matrices of the form $\mathcal{A}=\begin{bmatrix} A&B \\ C&D \end{bmatrix}$ in a product space $X=X_1\times X_2$ with possibly unbounded entries. Here, the case of diagonally dominant block operator matrices is considered, that is, the case where the full operator $\mathcal{A}$ can be seen as a relatively bounded perturbation of its diagonal part with $\mathsf{D}(\mathcal{A})=\mathsf{D}(A)\times \mathsf{D}(D)$ though with possibly large relative bound. For such operators the properties of sectoriality, $\mathcal{R}$-sectoriality and the boundedness of the $H^\infty$-calculus are studied, and for these properties perturbation results for possibly large but structured perturbations are derived. Thereby, the time dependent parabolic problem associated with $\mathcal{A}$ can be analyzed in maximal $L^p_t$-regularity spaces, and this is applied to a wide range of problems such as different theories for liquid crystals, an artificial Stokes system, strongly damped wave and plate equations, and a Keller-Segel model.
Synthetic Controls for Experimental Design

This article studies experimental design in settings where the experimental units are large aggregate entities (e.g., markets), and only one or a small number of units can be exposed to the treatment. In such settings, randomization of the treatment may result in treated and control groups with very different characteristics at baseline, inducing biases. We propose a variety of synthetic control designs (Abadie, Diamond and Hainmueller, 2010, Abadie and Gardeazabal, 2003) as experimental designs to select treated units in non-randomized experiments with large aggregate units, as well as the untreated units to be used as a control group. Average potential outcomes are estimated as weighted averages of treated units, for potential outcomes with treatment -- and control units, for potential outcomes without treatment. We analyze the properties of estimators based on synthetic control designs and propose new inferential techniques. We show that in experimental settings with aggregate units, synthetic control designs can substantially reduce estimation biases in comparison to randomization of the treatment.
Log-based Anomaly Detection Without Log Parsing

Software systems often record important runtime information in system logs for troubleshooting purposes. There have been many studies that use log data to construct machine learning models for detecting system anomalies. Through our empirical study, we find that existing log-based anomaly detection approaches are significantly affected by log parsing errors that are introduced by 1) OOV (out-of-vocabulary) words, and 2) semantic misunderstandings. The log parsing errors could cause the loss of important information for anomaly detection. To address the limitations of existing methods, we propose NeuralLog, a novel log-based anomaly detection approach that does not require log parsing. NeuralLog extracts the semantic meaning of raw log messages and represents them as semantic vectors. These representation vectors are then used to detect anomalies through a Transformer-based classification model, which can capture the contextual information from log sequences. Our experimental results show that the proposed approach can effectively understand the semantic meaning of log messages and achieve accurate anomaly detection results. Overall, NeuralLog achieves F1-scores greater than 0.95 on four public datasets, outperforming the existing approaches.
Stability of Associated Primes and Depth of Integral Closures of Powers of Edge Ideals

It is very hard to compute the integral closure I from I except for monomial ideals (see e.g. [19]). However, the integral closures of large powers of an ideal have very nice behavior. Ratliff [15, 16] showed that the sequence {Ass(R/In)}n>1 is increasing and it stabilizes for large n, i.e. there exists n0 > 1 such that Ass(R/In) = Ass(R/In0). Although McAdam [10] gave a characterization of the stable set Ass(R/In0). It is still mysterious about the set Ass(R/In) for each n. If we would like to know about how big of n0, according to Herzog, Rauf and Vladiou [6] we define the index of stability of integral closures of powers of I by
Revisiting the Okubo-Marshak Argument

Modular localization and the theory of string-localized fields have revolutionized several key aspects of quantum field theory. They reinforce the contention that local symmetry emerges directly from quantum theory, but global gauge invariance remains in general an unwarranted assumption to be examined case by case. Armed with those modern tools, we reconsider here the classical Okubo–Marshak argument on the non-existence of a “strong CP problem” in quantum chromodynamics.
Probing the Nucleosynthetic Contribution of Low-metallicity, Low-mass Star Companions of CEMP Stars

The observed abundance diversities among CEMP stars can shed light on the formation and evolution of elements in the early Galaxy. In this work, we present results obtained from a detailed abundance analysis of a sample of seven extrinsic carbon stars. The analysis is based on high-resolution spectra obtained with HCT/HESP (R∼60,000) and SUBARU/HDS (R∼50,000). We present, for the first time, the elemental abundance results for the objects BD−19 132, BD−19 290, HE 1304−2111, HE 1354−2257, and BD+19 3109. Abundances of a few elements are available in the literature for HE 1157−0518 and HD 202851; we present an update on the abundances of these elements along with new abundance estimates for several other elements. Our analysis confirms the object HD 202851 to be a CH star. While BD−19 132, HE 1354−2257, and BD+19 3109 are found to be CEMP-s stars, the objects BD−19 290, HE 1157−0518, and HE 1304−2111 are found to belong to CEMP-r/s group. The observed abundance patterns of the three CEMP-r/s stars are well reproduced with the i-process model predictions. While the objects BD+19 3109 and HD 202851 are confirmed binaries, the binary status of the remaining objects is not known. Analysis based on different elemental abundance ratios confirms low-mass former AGB companions for all the objects. Kinematic analysis shows that BD−19 290, HE 1157−0518, HE 1354−2257, and BD+19 3109 belong to the Galactic halo, whereas BD−19 132, HE 1304−2111, and HD 202851 are members of Galactic thin disk.
Training-Based Equivalence Relations in Large-Scale Quantized Communication Systems

We show that a quantized large-scale system with unknown parameters and training signals can be analyzed by examining an equivalent system with known parameters by modifying the signal power and noise variance in a prescribed manner. Applications to training in wireless communications and signal processing are shown. In wireless communications, we show that the optimal number of training signals can be significantly smaller than the number of transmitting elements. Similar conclusions can be drawn when considering the symbol error rate in signal processing applications, as long as the number of receiving elements is large enough. We show that a linear analysis of training in a quantized system can be accurate when the thermal noise is high or the system is operating near its saturation rate.
Prevalence of Human Papillomavirus and Genotype Distribution in Pregnant and Non-Pregnant Women in China

Purpose The status of human papillomavirus (HPV) infection in pregnant and non-pregnant women in China remains unclear. This study aimed to compare the prevalence and genotype distributions of HPV between pregnant and non-pregnant women in China. Patients and Methods A case-control study was conducted of pregnant women during the second trimester and age-matched non-pregnant women attending the Fujian Maternity and Child Health Hospital between January 1, 2017 and December 31, 2018. Participants underwent cervical cytology testing and HPV genotyping. The genotyping test was able to identify 14 high-risk HPV (HR-HPV), four possible HR-HPV, and five low-risk HPV (LR-HPV) types. Further colposcopy and a cervical biopsy were performed if indicated. The primary outcomes were HPV prevalence and genotype distribution. Results In total, 1077 pregnant and 1077 non-pregnant women were enrolled. Compared with non-pregnant women, pregnant women had a higher prevalence of HPV (24.2% vs 14.8%), HR-HPV (20.2% vs 11.7%), and LR-HPV (8% vs 4.5%) infection. In pregnant women, the most prevalent HPV genotypes were HPV-52 (6.0%), -16 (3.5%), -58 (2.6%), -53 (2.5%), and -51 (2.5%), while in non-pregnant women the most prevalent genotypes were HPV-52 (3.6%), -81 (1.9%), -51 (1.8%), -68 (1.4%), and -16 (1.3%). In women aged ≥35 years, HR-HPV (P=0.002) and LR-HPV (P=0.001) prevalence were significantly higher in pregnant women. However, in women aged <35 years, only HR-HPV prevalence was higher in pregnant women. Pregnant and non-pregnant women with HPV-16 and HPV-58 infection had a high prevalence of high-grade squamous intra-epithelial lesions (HSIL) (HPV-16: P<0.001 and P=0.005, HPV-58: P=0.043 and P=0.005); but with other HR-HPV genotypes, only non-pregnant women had an increased HSIL prevalence. Conclusion In China, the HPV prevalence is higher in pregnant women than that in non-pregnant women and is also age- and genotype-dependent. HPV-infected pregnant women aged ≥35 years and those with HPV-16 should be closely monitored to enable rapid clinical intervention.
Degranulation enhances presynaptic membrane packing, which protects NK cells from perforin-mediated autolysis

Natural killer (NK) cells kill a target cell by secreting perforin into the lytic immunological synapse, a specialized interface formed between the NK cell and its target. Perforin creates pores in target cell membranes allowing delivery of proapoptotic enzymes. Despite the fact that secreted perforin is in close range to both the NK and target cell membranes, the NK cell typically survives while the target cell does not. How NK cells preferentially avoid death during the secretion of perforin via the degranulation of their perforin-containing organelles (lytic granules) is perplexing. Here, we demonstrate that NK cells are protected from perforin-mediated autolysis by densely packed and highly ordered presynaptic lipid membranes, which increase packing upon synapse formation. When treated with 7-ketocholesterol, lipid packing is reduced in NK cells making them susceptible to perforin-mediated lysis after degranulation. Using high-resolution imaging and lipidomics, we identified lytic granules themselves as having endogenously densely packed lipid membranes. During degranulation, lytic granule–cell membrane fusion thereby further augments presynaptic membrane packing, enhancing membrane protection at the specific sites where NK cells would face maximum concentrations of secreted perforin. Additionally, we found that an aggressive breast cancer cell line is perforin resistant and evades NK cell–mediated killing owing to a densely packed postsynaptic membrane. By disrupting membrane packing, these cells were switched to an NK-susceptible state, which could suggest strategies for improving cytotoxic cell-based cancer therapies. Thus, lipid membranes serve an unexpected role in NK cell functionality protecting them from autolysis, while degranulation allows for the inherent lytic granule membrane properties to create local ordered lipid “shields” against self-destruction.