Miracle-dz/UniFAR-FTUs huggingface dataset:
On Unique Ergodicity Of Coupled AIMD Flows

The AIMD algorithm, which underpins the Transmission Control Protocol (TCP) for transporting data packets in communication networks, is perhaps the most successful control algorithm ever deployed. Recently, its use has been extended beyond communication networks, and successful applications of the AIMD algorithm have been reported in transportation, energy, and mathematical biology. A very recent development in the use of AIMD is its application in solving large-scale optimization and distributed control problems without the need for inter-agent communication. In this context, an interesting problem arises when multiple AIMD networks that are coupled in some sense (usually through a nonlinearity). The purpose of this note is to prove that such systems in certain settings inherit the ergodic properties of individual AIMD networks. This result has important consequences for the convergence of the aforementioned optimization algorithms. The arguments in the paper also correct conceptual and technical errors in [1].
Static Knowledge vs. Dynamic Argumentation: A Dual Theory Based on Kripke Semantics

This paper establishes a dual theory about knowledge and argumentation. Our idea is rooted at both epistemic logic and argumentation theory, and we aim to merge these two fields, not just in a superficial way but to thoroughly disclose the intrinsic relevance between knowledge and argumentation. Specifically, we define epistemic Kripke models and argument Kripke models as a dual pair, and then work out a two-way generation method between these two types of Kripke models. Such generation is rigorously justified by a duality theorem on modal formulae's invariance. We also provide realistic examples to demonstrate our generation, through which our framework's practical utility gets strongly advocated. We finally propose a philosophical thesis that knowledge is essentially dynamic, and we draw certain connection to Maxwell's demon as well as the well-known proverb"knowledge is power".
Bayes goes fast: Uncertainty quantification for a covariant energy density functional emulated by the reduced basis method

A covariant energy density functional is calibrated using a principled Bayesian statistical framework informed by experimental binding energies and charge radii of several magic and semi-magic nuclei. The Bayesian sampling required for the calibration is enabled by the emulation of the high-fidelity model through the implementation of a reduced basis method (RBM)—a set of dimensionality reduction techniques that can speed up demanding calculations involving partial differential equations by several orders of magnitude. The RBM emulator we build—using only 100 evaluations of the high-fidelity model—is able to accurately reproduce the model calculations in tens of milliseconds on a personal computer, an increase in speed of nearly a factor of 3,300 when compared to the original solver. Besides the analysis of the posterior distribution of parameters, we present model calculations for masses and radii with properly estimated uncertainties. We also analyze the model correlation between the slope of the symmetry energy L and the neutron skin of 48Ca and 208Pb. The straightforward implementation and outstanding performance of the RBM makes it an ideal tool for assisting the nuclear theory community in providing reliable estimates with properly quantified uncertainties of physical observables. Such uncertainty quantification tools will become essential given the expected abundance of data from the recently inaugurated and future experimental and observational facilities.
Im2Oil: Stroke-Based Oil Painting Rendering with Linearly Controllable Fineness Via Adaptive Sampling

This paper proposes a novel stroke-based rendering (SBR) method that translates images into vivid oil paintings. Previous SBR techniques usually formulate the oil painting problem as pixel-wise approximation. Different from this technique route, we treat oil painting creation as an adaptive sampling problem. Firstly, we compute a probability density map based on the texture complexity of the input image. Then we use the Voronoi algorithm to sample a set of pixels as the stroke anchors. Next, we search and generate an individual oil stroke at each anchor. Finally, we place all the strokes on the canvas to obtain the oil painting. By adjusting the hyper-parameter maximum sampling probability, we can control the oil painting fineness in a linear manner. Comparison with existing state-of-the-art oil painting techniques shows that our results have higher fidelity and more realistic textures. A user opinion test demonstrates that people behave more preference toward our oil paintings than the results of other methods. More interesting results and the code are in https://github.com/TZYSJTU/Im2Oil.
Watch What You Pretrain For: Targeted, Transferable Adversarial Examples on Self-Supervised Speech Recognition models

A targeted adversarial attack produces audio samples that can force an Automatic Speech Recognition (ASR) system to output attacker-chosen text. To exploit ASR models in real-world, black-box settings, an adversary can leverage the transferability property, i.e. that an adversarial sample produced for a proxy ASR can also fool a different remote ASR. However recent work has shown that transferability against large ASR models is very difficult. In this work, we show that modern ASR architectures, specifically ones based on Self-Supervised Learning, are in fact vulnerable to transferability. We successfully demonstrate this phenomenon by evaluating state-of-the-art self-supervised ASR models like Wav2Vec2, HuBERT, Data2Vec and WavLM. We show that with low-level additive noise achieving a 30dB Signal-Noise Ratio, we can achieve target transferability with up to 80% accuracy. Next, we 1) use an ablation study to show that Self-Supervised learning is the main cause of that phenomenon, and 2) we provide an explanation for this phenomenon. Through this we show that modern ASR architectures are uniquely vulnerable to adversarial security threats.
Fluid Batching: Exit-Aware Preemptive Serving of Early-Exit Neural Networks on Edge NPUs

With deep neural networks (DNNs) emerging as the backbone in a multitude of computer vision tasks, their adoption in real-world applications broadens continuously. Given the abundance and omnipresence of smart devices in the consumer landscape,"smart ecosystems'' are being formed where sensing happens concurrently rather than standalone. This is shifting the on-device inference paradigm towards deploying centralised neural processing units (NPUs) at the edge, where multiple devices (e.g. in smart homes or autonomous vehicles) can stream their data for processing with dynamic rates. While this provides enhanced potential for input batching, naive solutions can lead to subpar performance and quality of experience, especially under spiking loads. At the same time, the deployment of dynamic DNNs, comprising stochastic computation graphs (e.g. early-exit (EE) models), introduces a new dimension of dynamic behaviour in such systems. In this work, we propose a novel early-exit-aware scheduling algorithm that allows sample preemption at run time, to account for the dynamicity introduced both by the arrival and early-exiting processes. At the same time, we introduce two novel dimensions to the design space of the NPU hardware architecture, namely Fluid Batching and Stackable Processing Elements, that enable run-time adaptability to different batch sizes and significantly improve the NPU utilisation even at small batches. Our evaluation shows that the proposed system achieves an average 1.97x and 6.7x improvement over state-of-the-art DNN streaming systems in terms of average latency and tail latency service-level objective (SLO) satisfaction, respectively.
Cosmological Simulation of Galaxy Groups and Clusters. II. Studying Different Modes of Feedback through X-Ray Observations

The impact of feedback from active galactic nuclei (AGNs) on the cosmological evolution of the large-scale structure is a long-studied problem. However, it is still not well understood how the feedback energy couples to the ambient medium to influence the properties of AGN host galaxies and dark matter halos. In this work we investigate different modes of AGN feedback and their effect on the surrounding medium by probing the diffuse X-ray emission from the hot gas inside galaxy groups and clusters. For this purpose, we use the cosmological hydrodynamic simulation SIMBA to theoretically calculate the X-ray emission from simulated galaxy clusters/groups with the help of the Astrophysical Plasma Emission Code. We also perform synthetic observations of these systems with the Chandra X-ray telescope using the ray-tracing simulator Model of AXAF Response to X-rays. Our results show that in addition to the radiative wind mode of feedback from the AGNs, jet and X-ray modes of feedback play significant roles in suppressing the X-ray emission from the diffuse gas in the vicinity of the black hole. Our mock observational maps suggest that the signatures of AGN feedback from high-redshift objects may not be detected with the instrumental resolution of current X-ray telescopes like Chandra, but provide promising prospects for detection of these features with potential X-ray missions such as Lynx.
Joint horizontal‐vertical anisotropic scaling, isobaric and isoheight wind statistics from aircraft data

Aircraft measurements of the horizontal wind have consistently found transitions from roughly k−5/3 to k−2.4 spectra at scales Δxc ranging from about 100–500 km. Since drop sondes find k−2.4spectra in the vertical, the simplest explanation is that the aircraft follow gently sloping trajectories (such as isobars) so that at large scales, they estimate vertical rather than horizontal spectra. In order to directly test this hypothesis, we used over 14500 flight segments from GPS and TAMDAR sensor equipped commercial aircraft. We directly estimate the joint horizontal‐vertical (Δx, Δz) wind structure function finding ‐ for both longitudinal and transverse components ‐ that the ratio of horizontal to vertical scaling exponents isHz ≈ 0.57 ± 0.02, close to the theoretical prediction of the 23/9D turbulence model which predicts Hz = 5/9 = 0.555…. This model also predicts that isobars and isoheight statistics will diverge after Δxc; using the observed fractal dimension of the isobars (≈1.79 ± 0.02), we find that the isobaric scaling exponents are almost exactly as predicted theoretically and Δxc ≈ 160, 125 km, (transverse, longitudinal). These results thus give strong direct support to the 23/9D scaling stratification model.
GOALS-JWST: Mid-infrared Spectroscopy of the Nucleus of NGC 7469

We present mid-infrared spectroscopic observations of the nucleus of the nearby Seyfert galaxy NGC 7469 taken with the MIRI instrument on the James Webb Space Telescope (JWST) as part of Directors Discretionary Time Early Release Science program 1328. The high-resolution nuclear spectrum contains 19 emission lines covering a wide range of ionization. The high-ionization lines show broad, blueshifted emission reaching velocities up to 1700 km s−1 and FWHM ranging from ∼500 to 1100 km s−1. The width of the broad emission and the broad-to-narrow line flux ratios correlate with ionization potential. The results suggest a decelerating, stratified, AGN-driven outflow emerging from the nucleus. The estimated mass outflow rate is 1–2 orders of magnitude larger than the current black hole accretion rate needed to power the AGN. Eight pure rotational H2 emission lines are detected with intrinsic widths ranging from FWHM ∼125 to 330 km s−1. We estimate a total mass of warm H2 gas of ∼1.2 × 107 M ⊙ in the central 100 pc. The PAH features are extremely weak in the nuclear spectrum, but a 6.2 μm PAH feature with an equivalent width of ∼0.07 μm and a flux of 2.7 × 10−17 W m−2 is detected. The spectrum is steeply rising in the mid-infrared, with a silicate strength of ∼0.02, significantly smaller than seen in most PG QSOs but comparable to other Seyfert 1s. These early MIRI mid-infrared IFU data highlight the power of JWST to probe the multiphase interstellar media surrounding actively accreting supermassive black holes.
Analyzing Prospects for Quantum Advantage in Topological Data Analysis

Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances.
Reduced submodules of finite dimensional polynomial modules

Let $k$ be a field with characteristic zero, $R$ be the ring $k[x_1, \cdots, x_n]$ and $I$ be a monomial ideal of $R$. We study the Artinian local algebra $R/I$ when considered as an $R$-module $M$. We show that the largest reduced submodule of $M$ coincides with both the socle of $M$ and the $k$-submodule of $M$ generated by all outside corner elements of the Young diagram associated with $M$. Interpretations of different reduced modules is given in terms of Macaulay inverse systems. It is further shown that these reduced submodules are examples of modules in a torsion-torsionfree class, together with their duals; coreduced modules, exhibit symmetries in regard to Matlis duality and torsion theories. Lastly, we show that any $R$-module $M$ of the kind described here satisfies the radical formula.
Early JWST Imaging Reveals Strong Optical and NIR Color Gradients in Galaxies at z ∼ 2 Driven Mostly by Dust

Recent studies have shown that galaxies at cosmic noon are redder in the center and bluer in the outskirts, mirroring results in the local universe. These color gradients could be caused by gradients in either the stellar age or dust opacity; however, distinguishing between these two causes is impossible with rest-frame optical photometry alone. Here we investigate the underlying causes of the gradients from spatially resolved rest-frame U − V versus V − J color–color diagrams measured from early observations with the James Webb Space Telescope. We use 1–4 μm NIRCam photometry from the CEERS survey of a sample of 54 galaxies with logM*/M⊙>10 at redshifts 1.7 < z < 2.3 selected from the 3D-HST catalog. We model the light profiles in the F115W, F200W, and F356W NIRCam bands using imcascade, a Bayesian implementation of the multi-Gaussian expansion technique that flexibly represents galaxy profiles using a series of Gaussians. We construct resolved rest-frame U − V and V − J color profiles. The majority of star-forming galaxies have negative gradients (i.e., redder in the center, bluer in the outskirts) in both U − V and V − J colors consistent with radially decreasing dust attenuation. A smaller population (roughly 15%) of star-forming galaxies have positive U − V but negative V − J gradients implying centrally concentrated star formation. For quiescent galaxies, we find a diversity of UVJ color profiles, with roughly one-third showing star formation in their center. This study showcases the potential of JWST to study the resolved stellar populations of galaxies at cosmic noon.
Long-term Optical and γ-Ray Variability of the Blazar PKS 1222+216

The γ-ray emission from flat-spectrum radio quasars (FSRQs) is thought to be dominated by the inverse Compton scattering of the external sources of photon fields, e.g., accretion disk, broad-line region (BLR), and torus. FSRQs show strong optical emission lines and hence can be a useful probe of the variability in BLR output, which is the reprocessed disk emission. We study the connection between the optical continuum, Hγ line, and γ-ray emissions from the FSRQ PKS 1222+216, using long-term (∼2011–2018) optical spectroscopic data from Steward Observatory and γ-ray observations from Fermi Large Area Telescope (LAT). We measured the continuum (F C,opt) and Hγ (F Hγ ) fluxes by performing a systematic analysis of the 6029–6452 Å optical spectra. We observed stronger variability in F C,opt than F Hγ , an inverse correlation between the Hγ equivalent width and F C,opt, and a redder-when-brighter trend. Using discrete cross-correlation analysis, we found a positive correlation (DCF ∼ 0.5) between the F γ‐ray>100 MeV and F C,opt (6024–6092 Å) light curves with a time lag consistent with zero at the 2σ level. We found no correlation between the F γ‐ray>100 MeV and F Hγ light curves, probably dismissing the disk contribution to the optical and γ-ray variability. The observed strong variability in the Fermi-LAT flux and F γ‐ray>100 MeV − F C,opt correlation could be due to the changes in the particle acceleration at various epochs. We derived the optical-to-γ-ray spectral energy distributions during the γ-ray flaring and quiescent epochs that show a dominant disk component with no variability. Our study suggests that the γ-ray emission zone is likely located at the edge of the BLR or in the radiation field of the torus.
Controllability of a Class of Swarm Signaling Networks

In this paper, we propose closed-form analytical expressions to determine the minimum number of driver nodes that is needed to control a specific class of networks. We consider swarm signaling networks with regular out-degree distribution where a fraction $p$ of the links is unavailable. We further apply our method to networks with bi-modal out-degree distributions. Our approximations are validated through intensive simulations. Results show that our approximations have high accuracy when compared with simulation results for both types of out-degree distribution.
Shallow shadows: Expectation estimation using low-depth random Clifford circuits

We provide practical and powerful schemes for learning many properties of an unknown n-qubit quantum state using a sparing number of copies of the state. Specifically, we present a depth-modulated randomized measurement scheme that interpolates between two known classical shadows schemes based on random Pauli measurements and random Clifford measurements. These can be seen within our scheme as the special cases of zero and infinite depth, respectively. We focus on the regime where depth scales logarithmically in n and provide evidence that this retains the desirable properties of both extremal schemes whilst, in contrast to the random Clifford scheme, also being experimentally feasible. We present methods for two key tasks; estimating expectation values of certain observables from generated classical shadows and, computing upper bounds on the depth-modulated shadow norm, thus providing rigorous guarantees on the accuracy of the output estimates. We consider observables that can be written as a linear combination of poly(n) Paulis and observables that can be written as a low bond dimension matrix product operator. For the former class of observables both tasks are solved efficiently in n. For the latter class, we do not guarantee efficiency but present a method that works in practice; by variationally computing a heralded approximate inverses of a tensor network that can then be used for efficiently executing both these tasks.
Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels

Recent studies on contrastive learning have achieved remarkable performance solely by leveraging few labels in the context of medical image segmentation. Existing methods mainly focus on instance discrimination and invariant mapping. However, they face three common pitfalls: (1) tailness: medical image data usually follows an implicit long-tail class distribution. Blindly leveraging all pixels in training hence can lead to the data imbalance issues, and cause deteriorated performance; (2) consistency: it remains unclear whether a segmentation model has learned meaningful and yet consistent anatomical features due to the intra-class variations between different anatomical features; and (3) diversity: the intra-slice correlations within the entire dataset have received significantly less attention. This motivates us to seek a principled approach for strategically making use of the dataset itself to discover similar yet distinct samples from different anatomical views. In this paper, we introduce a novel semi-supervised 2D medical image segmentation framework termed Mine yOur owN Anatomy (MONA), and make three contributions. First, prior work argues that every pixel equally matters to the model training; we observe empirically that this alone is unlikely to define meaningful anatomical features, mainly due to lacking the supervision signal. We show two simple solutions towards learning invariances - through the use of stronger data augmentations and nearest neighbors. Second, we construct a set of objectives that encourage the model to be capable of decomposing medical images into a collection of anatomical features in an unsupervised manner. Lastly, we both empirically and theoretically, demonstrate the efficacy of our MONA on three benchmark datasets with different labeled settings, achieving new state-of-the-art under different labeled semi-supervised settings
Evolution of entanglement entropy in strongly correlated bosons in an optical lattice

We investigate the time evolution of the second-order R\'enyi entropy (RE) for bosons in a one-dimensional optical lattice following a sudden quench of the hopping amplitude $J$. Specifically, we examine systems that are quenched into the strongly correlated Mott-insulating (MI) regime with $J/U\ll 1$ ($U$ denotes the strength of the on-site repulsive interaction) from the MI limit with $J=0$. In this regime, the low-energy excited states can be effectively described by fermionic quasiparticles known as doublons and holons. They are excited in entangled pairs through the quench dynamics. By developing an effective theory, we derive a direct relation between the RE and correlation functions associated with doublons and holons. This relation allows us to analytically calculate the RE and obtain a physical picture for the RE, both in the ground state and during time evolution through the quench dynamics, in terms of doublon holon pairs. In particular, we show that the RE is proportional to the population of doublon-holon pairs that span the boundary of the subsystem. Our quasiparticle picture introduces some remarkable features that are absent in previous studies on the dynamics of entanglement entropy in free-fermion models. It provides with valuable insights into the dynamics of entanglement entropy in strongly-correlated systems.
A constrained mean curvature flow and Alexandrov-Fenchel inequalities

In this article, we study a locally constrained mean curvature flow for star-shaped hypersurfaces with capillary boundary in the half-space. We prove its long-time existence and the global convergence to a spherical cap. Furthermore, the capillary quermassintegrals defined in \cite{WWX2022} evolve monotonically along the flow, and hence we establish a class of new Alexandrov-Fenchel inequalities for convex hypersurfaces with capillary boundary in the half-space.