Miracle-dz/UniFAR-FTUs huggingface dataset:
MCDAL: Maximum Classifier Discrepancy for Active Learning

Recent state-of-the-art active learning methods have mostly leveraged generative adversarial networks (GANs) for sample acquisition; however, GAN is usually known to suffer from instability and sensitivity to hyperparameters. In contrast to these methods, in this article, we propose a novel active learning framework that we call Maximum Classifier Discrepancy for Active Learning (MCDAL) that takes the prediction discrepancies between multiple classifiers. In particular, we utilize two auxiliary classification layers that learn tighter decision boundaries by maximizing the discrepancies among them. Intuitively, the discrepancies in the auxiliary classification layers’ predictions indicate the uncertainty in the prediction. In this regard, we propose a novel method to leverage the classifier discrepancies for the acquisition function for active learning. We also provide an interpretation of our idea in relation to existing GAN-based active learning methods and domain adaptation frameworks. Moreover, we empirically demonstrate the utility of our approach where the performance of our approach exceeds the state-of-the-art methods on several image classification and semantic segmentation datasets in active learning setups.
Linear Polytree Structural Equation Models: Structural Learning and Inverse Correlation Estimation

We are interested in the problem of learning the directed acyclic graph (DAG) when data are generated from a linear structural equation model (SEM) and the causal structure can be characterized by a polytree. Specially, under both Gaussian and sub-Gaussian models, we study the sample size conditions for the well-known Chow-Liu algorithm to exactly recover the equivalence class of the polytree, which is uniquely represented by a CPDAG. We also study the error rate for the estimation of the inverse correlation matrix under such models. Our theoretical findings are illustrated by comprehensive numerical simulations, and experiments on benchmark data also demonstrate the robustness of the method when the ground truth graphical structure can only be approximated by a polytree.
Anomalous transport in high-mobility superconducting SrTiO3 thin films

The study of subtle effects on transport in semiconductors requires high-quality epitaxial structures with low defect density. Using hybrid molecular beam epitaxy (MBE), SrTiO3 films with a low-temperature mobility exceeding 42,000 cm2 V−1 s−1 at a low carrier density of 3 × 1017 cm−3 were achieved. A sudden and sharp decrease in residual resistivity accompanied by an enhancement in the superconducting transition temperature were observed across the second Lifshitz transition where the third band becomes occupied, revealing dominant intraband scattering. These films further revealed an anomalous behavior in the Hall carrier density as a consequence of the antiferrodistortive (AFD) transition and the temperature dependence of the Hall scattering factor. Using hybrid MBE growth, phenomenological modeling, temperature-dependent transport measurements, and scanning superconducting quantum interference device imaging, we provide critical insights into the important role of inter- versus intraband scattering and of AFD domain walls on normal-state and superconducting properties of SrTiO3.
Discovery of a paired Gaussian and long-tailed distribution of potential energies in nanoglasses

It is generally believed that the intrinsic properties of glasses are intimately related to potential energy landscapes (PELs). However, little is known about the PELs of glasses below the glass transition temperature (Tg). Taking advantage of lower potential energy barriers in nano systems, we have systematically investigated the dynamics behavior of two nano glasses, Al43 and Al46. Structure transformation is identified in our pure molecular-dynamics simulation far below Tg, which manifests the existence of metabasins in PELs. Surprisingly, we find that the distribution of potential energies shows a paired-Gaussian and long-tailed distribution at temperatures below and approaching Tg, correspondingly the distribution of the α-relaxation time exhibits an exponential decay. In contrast to the Gaussian distribution of energy in typical liquids and solids, the paired-Gaussian and long-tailed distribution of potential energies, as well as the exponential distribution of the α-relaxation time, may be considered as the intrinsic feature of a glass or supercooled liquid. The current results are important not only for checking the reliability of various PEL-based models, but also for exploring the microscopic nature of glasses.
Two-fluid coexistence and phase separation in a one-dimensional model with pair hopping and density interactions

We compute the phase diagram of a one-dimensional model of spinless fermions with pair-hopping and nearest-neighbor interaction, first introduced by Ruhman and Altman, using the density-matrix renormalization group combined with various analytical approaches. Although the main phases are a Luttinger liquid of fermions and a Luttinger liquid of pairs, we also find remarkable phases in which only a fraction of the fermions are paired. In such case, two situations arise: either fermions and pairs coexist spatially in a two-fluid mixture, or they are spatially segregated leading to phase separation. These results are supported by several analytical models that describe in an accurate way various relevant cuts of the phase diagram. Last, we identify relevant microscopic observables that capture the presence of these two fluids: while originally introduced in a phenomenological way, they support a wider application of two-fluid models for describing pairing phenomena.
Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference

In this work, we propose a domain generalization (DG) approach to learn on several labeled source domains and transfer knowledge to a target domain that is inaccessible in training. Considering the inherent conditional and label shifts, we would expect the alignment of p(x|y) and p(y). However, the widely used domain invariant feature learning (IFL) methods relies on aligning the marginal concept shift w.r. t. p(x), which rests on an unrealistic assumption that p(y) is invariant across domains. We thereby propose a novel variational Bayesian inference framework to enforce the conditional distribution alignment w.r. t. p(x|y) via the prior distribution matching in a latent space, which also takes the marginal label shift w.r. t. p(y) into consideration with the posterior alignment. Extensive experiments on various benchmarks demonstrate that our framework is robust to the label shift and the cross-domain accuracy is significantly improved, thereby achieving superior performance over the conventional IFL counterparts.
How to Trust Strangers: Composition of Byzantine Quorum Systems

Trust is the basis of any distributed, fault-tolerant, or secure system. A trust assumption specifies the failures that a system, such as a blockchain network, can tolerate and determines the conditions under which it operates correctly. In systems subject to Byzantine faults, the trust assumption is usually specified through sets of processes that may fail together. Trust has traditionally been symmetric, such that all processes in the system adhere to the same, global assumption about potential faults. Recently, asymmetric trust models have also been considered, especially in the context of blockchains, where every participant is free to choose who to trust. In both cases, it is an open question how to compose trust assumptions. Consider two or more systems, run by different and possibly disjoint sets of participants, with different assumptions about faults: how can they work together? This work answers this question for the first time and offers composition rules for symmetric and for asymmetric quorum systems. These rules are static and do not require interaction or agreement on the new trust assumption among the participants. Moreover, they ensure that if the original systems allow for running a particular protocol (guaranteeing consistency and availability), then so will the joint system. At the same time, the composed system tolerates as many faults as possible, subject to the underlying consistency and availability properties. Reaching consensus with asymmetric trust in the model of personal Byzantine quorum systems (Losa et al., DISC 2019) was shown to be impossible, if the trust assumptions of the processes diverge from each other. With asymmetric quorum systems, and by applying our composition rule, we show how consensus is actually possible, even with the combination of disjoint sets of processes.
Drug loading and release kinetics in polymeric micelles: Comparing dynamic versus unimolecular sugar-based micelles for controlled release

Amphiphilic macromolecules, possessing sugar-based hydrophobic and poly(ethylene glycol) hydrophilic domains, provide tunable structures that form effective polymeric micellar drug delivery systems. In this work, we compare traditional dynamic micelles and covalently bound unimolecular amphiphilic macromolecule micelles to study the effects of amphiphilic macromolecule hydrophobic domain branching, micelle architecture, and hydrodynamic volume of two drugs (triclosan and suloctidil) to elucidate the micellar structure–property relationships that govern drug loading and release kinetics. Overall, more hydrophobic micelles with either longer amphiphilic macromolecule alkyl side chains or a higher degree of hydrophobic domain branching exhibited increased triclosan loading compared to less hydrophobic micelles with smaller amphiphilic macromolecule hydrophobic domains. However, varying levels of micelle hydrophobicity did not significantly change suloctidil loading, where only minimal loading differences were seen between micelles with highly hydrophobic and less hydrophobic domains. In both dynamic and unimolecular micelles, the loading extent was primarily drug volume-dependent, where the smaller triclosan molecules demonstrated increased loading and sustained release compared to the larger suloctidil molecules. Unimolecular micelles followed a similar trend with generally higher loading capacities compared to dynamic micelles. Release characteristics for both amphiphilic macromolecule micelle types demonstrated little correlation to the amphiphilic macromolecule chemistry or micelle architecture and were instead primarily drug-dependent, with suloctidil- and triclosan-loaded micelles following the Korsmeyer–Peppas and Weibull models, respectively. The micelle structure–property relationships identified herein allow for improved drug–micelle compatibility to optimize drug delivery systems for poorly water-soluble drugs.
A hard X-ray view of luminous and ultra-luminous infrared galaxies in GOALS – I. AGN obscuration along the merger sequence

The merger of two or more galaxies can enhance the inflow of material from galactic scales into the close environments of active galactic nuclei (AGNs), obscuring and feeding the supermassive black hole (SMBH). Both recent simulations and observations of AGN in mergers have confirmed that mergers are related to strong nuclear obscuration. However, it is still unclear how AGN obscuration evolves in the last phases of the merger process. We study a sample of 60 luminous and ultra-luminous IR galaxies (U/LIRGs) from the GOALS sample observed by NuSTAR. We find that the fraction of AGNs that are Compton thick (CT; $N_{\rm H}\ge 10^{24}\rm \, cm^{-2}$) peaks at $74_{-19}^{+14}{{\ \rm per\ cent}}$ at a late merger stage, prior to coalescence, when the nuclei have projected separations (dsep) of 0.4–6 kpc. A similar peak is also observed in the median NH [$(1.6\pm 0.5)\times 10^{24}\rm \, cm^{-2}$]. The vast majority ($85^{+7}_{-9}{{\ \rm per\ cent}}$) of the AGNs in the final merger stages (dsep ≲ 10 kpc) are heavily obscured ($N_{\rm H}\ge 10^{23}\rm \, cm^{-2}$), and the median NH of the accreting SMBHs in our sample is systematically higher than that of local hard X-ray-selected AGN, regardless of the merger stage. This implies that these objects have very obscured nuclear environments, with the $N_{\rm H}\ge 10^{23}\rm \, cm^{-2}$ gas almost completely covering the AGN in late mergers. CT AGNs tend to have systematically higher absorption-corrected X-ray luminosities than less obscured sources. This could either be due to an evolutionary effect, with more obscured sources accreting more rapidly because they have more gas available in their surroundings, or to a selection bias. The latter scenario would imply that we are still missing a large fraction of heavily obscured, lower luminosity ($L_{2-10}\lesssim 10^{43}\rm \, erg\, s^{-1}$) AGNs in U/LIRGs.
Safe-screening rules for atomic-norm regularization

Safe-screening rules are algorithmic techniques meant to detect and safely discard unneeded variables during the solution process with the aim of accelerating computation. These techniques have been shown to be eﬀective for one-norm regularized problems. This paper generalizes the safe-screening rule proposed by Ndiaye et al. [J. Mach. Learn. Res., 2017] to various optimization problem formulations with atomic-norm regularization. For nuclear-norm regularized problems in particular, it is shown that the proposed generalized safe-screening rule cannot safely reduce the problem size. In that case, approximation guarantees are developed that allows for reducing the problem size.
No indication of chiral flux current in the topological kagome metal CsV$_{3}$Sb$_{5}$

Huazhou Li, Siyuan Wan, Han Li, Qing Li, Qiangqiang Gu, Huan Yang, Yongkai Li, Zhiwei Wang, Yugui Yao, and Hai-Hu Wen 1 National Laboratory of Solid State Microstructures and Department of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China 2 Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing 100081, China and 3 Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems, Beijing Institute of Technology, Beijing 100081, China
Mining Data Impressions From Deep Models as Substitute for the Unavailable Training Data

Pretrained deep models hold their learnt knowledge in the form of model parameters. These parameters act as “memory” for the trained models and help them generalize well on unseen data. However, in absence of training data, the utility of a trained model is merely limited to either inference or better initialization towards a target task. In this paper, we go further and extract synthetic data by leveraging the learnt model parameters. We dub them Data Impressions, which act as proxy to the training data and can be used to realize a variety of tasks. These are useful in scenarios where only the pretrained models are available and the training data is not shared (e.g., due to privacy or sensitivity concerns). We show the applicability of data impressions in solving several computer vision tasks such as unsupervised domain adaptation, continual learning as well as knowledge distillation. We also study the adversarial robustness of lightweight models trained via knowledge distillation using these data impressions. Further, we demonstrate the efficacy of data impressions in generating data-free Universal Adversarial Perturbations (UAPs) with better fooling rates. Extensive experiments performed on benchmark datasets demonstrate competitive performance achieved using data impressions in absence of original training data.
A Flexible Exoskeleton for Collision Resilience

With inspiration from arthropods' exoskeletons, we designed a simple, easily manufactured, semi-rigid structure with flexible joints that can passively damp impact energy. This exoskeleton fuses the protective shell to the main robot structure, thereby minimizing its loss in payload capacity. Our design is simple to build and customize using cheap components and consumer-grade 3D printers. Our results show we can build a sub-250g, autonomous quadcopter with visual navigation that can survive multiple collisions, shows a five-fold increase in the passive energy absorption, that is also suitable for automated battery swapping, and with enough computing power to run deep neural network models. This structure makes for an ideal platform for high-risk activities (such as flying in a cluttered environment or reinforcement learning training) without damage to the hardware or the environment.
Fine dimensional properties of spectral measures

Operators with zero dimensional spectral measures appear naturally in the theory of ergodic Schr\"odinger operators. We develop the concept of a complete family of Hausdorff measure functions in order to analyze and distinguish between these measures with any desired precision. We prove that the dimension of spectral measures of half-line operators with positive upper Lyapunov exponent are at most logarithmic for every possible boundary phase. We show that this is sharp by constructing an explicit operator whose spectral measure obtains this dimension. We also extend and improve some basic results from the theory of rank one perturbations and quantum dynamics to encompass generalized Hausdorff dimensions.
Fully discrete best approximation type estimates in $L^{\infty}(I;L^2(Ω)^d)$ for finite element discretizations of the transient Stokes equations

In this article we obtain an optimal best approximation type result for fully discrete approximations of the transient Stokes problem. For the time discretization we use the discontinuous Galerkin method and for the spatial discretization we use standard finite elements for the Stokes problem satisfying the discrete inf-sup condition. The analysis uses the technique of discrete maximal parabolic regularity. The results require only natural assumptions on the data and do not assume any additional smoothness of the solutions.
A Deep Signed Directional Distance Function for Object Shape Representation

Neural networks that map 3D coordinates to signed distance function (SDF) or occupancy values have enabled high-fidelity implicit representations of object shape. This paper develops a new shape model that allows synthesizing novel distance views by optimizing a continuous signed directional distance function (SDDF). Similar to deep SDF models, our SDDF formulation can represent whole categories of shapes and complete or interpolate across shapes from partial input data. Unlike an SDF, which measures distance to the nearest surface in any direction, an SDDF measures distance in a given direction. This allows training an SDDF model without 3D shape supervision, using only distance measurements, readily available from depth camera or Lidar sensors. Our model also removes post-processing steps like surface extraction or rendering by directly predicting distance at arbitrary locations and viewing directions. Unlike deep view-synthesis techniques, such as Neural Radiance Fields, which train high-capacity black-box models, our model encodes by construction the property that SDDF values decrease linearly along the viewing direction. This structure constraint not only results in dimensionality reduction but also provides analytical confidence about the accuracy of SDDF predictions, regardless of the distance to the object surface.
Calibration Technique for Suppressing Residual Etalon Artifacts in Slit-Averaged Raman Spectroscopy

Back-illuminated charged-coupled device (BI-CCD) arrays increase quantum efficiency but also amplify etaloning, a multiplicative, wavelength-dependent fixed-pattern effect. When spectral data from hundreds of BI-CCD rows are combined, the averaged spectrum will generally appear etalon-free. This can mask substantial etaloning at the row level, even if the BI-CCD has been treated to suppress the effect. This paper compares two methods of etalon correction, one with simple averaging and one with row-by-row calibration using a fluorescence standard. Two BI-CCD arrays, both roughened by the supplier to reduce etaloning, were used to acquire Raman spectra of murine bone specimens. For one array, etaloning was the dominant source of noise under the exposure conditions chosen, even for the averaged spectrum across all rows; near-infrared-excited Raman peaks were noticeably affected. In this case, row-by-row calibration improved the spectral quality of the average spectrum. The other CCD’s performance was shot-noise limited and therefore received no benefit from the extra calibration. The different results highlight the importance of checking for and correcting row-level fixed pattern when measuring weak Raman signals in the presence of a large fluorescence background. Graphical Abstract
Griddings of permutations and hardness of pattern matching

We study the complexity of the decision problem known as Permutation Pattern Matching, or PPM. The input of PPM consists of a pair of permutations $\tau$ (the `text') and $\pi$ (the `pattern'), and the goal is to decide whether $\tau$ contains $\pi$ as a subpermutation. On general inputs, PPM is known to be NP-complete by a result of Bose, Buss and Lubiw. In this paper, we focus on restricted instances of PPM where the text is assumed to avoid a fixed (small) pattern $\sigma$; this restriction is known as Av($\sigma$)-PPM. It has been previously shown that Av($\sigma$)-PPM is polynomial for any $\sigma$ of size at most 3, while it is NP-hard for any $\sigma$ containing a monotone subsequence of length four. In this paper, we present a new hardness reduction which allows us to show, in a uniform way, that Av($\sigma$)-PPM is hard for every $\sigma$ of size at least 6, for every $\sigma$ of size 5 except the symmetry class of $41352$, as well as for every $\sigma$ symmetric to one of the three permutations $4321$, $4312$ and $4231$. Moreover, assuming the exponential time hypothesis, none of these hard cases of Av($\sigma$)-PPM can be solved in time $2^{o(n/\log n)}$. Previously, such conditional lower bound was not known even for the unconstrained PPM problem. On the tractability side, we combine the CSP approach of Guillemot and Marx with the structural results of Huczynska and Vatter to show that for any monotone-griddable permutation class C, PPM is polynomial when the text is restricted to a permutation from C.
Graph-Based Learning for Stock Movement Prediction with Textual and Relational Data

Predicting stock prices from textual information is a challenging task due to the uncertainty of the market and the difficulty in understanding the natural language from a machine’s perspective. Previous research has mostly focused on sentiment extraction based on news associated with a single stock; however, the stocks on the financial market can be highly correlated, as news regarding one stock can quickly impact the prices of other stocks. To take this effect into account, the authors propose a new stock movement prediction framework: multigraph recurrent network for stock forecasting (MGRN). This architecture allows combining the textual sentiment from financial news and multiple relational information extracted from other types of financial data. Through an accuracy test and a trading simulation on the stocks of the STOXX Europe 600 index, the authors demonstrate a better performance of their model compared with other benchmarks.