Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Universal Adversarial Perturbations

Universal Adversarial Perturbations (UAPs) are imperceptible, image-agnostic vectors that cause deep neural networks (DNNs) to misclassify inputs with high probability. In practical attack scenarios, adversarial perturbations may undergo transformations such as changes in pixel intensity, scaling, etc. before being added to DNN inputs. Existing methods do not create UAPs robust to these real-world transformations, thereby limiting their applicability in practical attack scenarios. In this work, we introduce and formulate UAPs robust against real-world transformations. We build an iterative algorithm using probabilistic robustness bounds and construct such UAPs robust to transformations generated by composing arbitrary sub-differentiable transformation functions. We perform an extensive evaluation on the popular CIFAR-10 and ILSVRC 2012 datasets measuring our UAPs' robustness under a wide range common, real-world transformations such as rotation, contrast changes, etc. We further show that by using a set of primitive transformations our method can generalize well to unseen transformations such as fog, JPEG compression, etc. Our results show that our method can generate UAPs up to 23% more robust than state-of-the-art baselines.
Double-heavy tetraquarks with strangeness in the chiral quark model

Recently, some progress has been made in the experiments on double-heavy tetraquarks, such as reported by the LHCb Collaboration and reported by the Belle Collaboration. Coming on the heels of our previous work about and , we present a study on the bound and resonance states of their companions, ( ) tetraquarks with strange flavor in the chiral quark model. Two pictures, meson-meson and diquark-antidiquark ones, and their couplings were considered in our calculations. Isospin violation was neglected herein. Our numerical analysis indicated that the states with and with are the most promising stable states against strong interactions. Besides, we found several resonance states for the double-heavy strange tetraquarks with the real scaling method.
FedBC: Calibrating Global and Local Models via Federated Learning Beyond Consensus

In this work, we quantitatively calibrate the performance of global and local models in federated learning through a multi-criterion optimization-based framework, which we cast as a constrained program. The objective of a device is its local objective, which it seeks to minimize while satisfying nonlinear constraints that quantify the proximity between the local and the global model. By considering the Lagrangian relaxation of this problem, we develop a novel primal-dual method called Federated Learning Beyond Consensus (\texttt{FedBC}). Theoretically, we establish that \texttt{FedBC} converges to a first-order stationary point at rates that matches the state of the art, up to an additional error term that depends on a tolerance parameter introduced to scalarize the multi-criterion formulation. Finally, we demonstrate that \texttt{FedBC} balances the global and local model test accuracy metrics across a suite of datasets (Synthetic, MNIST, CIFAR-10, Shakespeare), achieving competitive performance with state-of-the-art.
Resolving desorption of complex organic molecules in a hot core: Transition from non-thermal to thermal desorption or two-step thermal desorption?

Using the high angular resolution provided by the ALMA interferometre we want to resolve the COM emission in the hot molecular core Sagittarius B2(N1) and thereby shed light on the desorption process of Complex Organic Molecules (COMs) in hot cores. We use data taken as part of the 3 mm spectral line survey Re-exploring Molecular Complexity with ALMA (ReMoCA) to investigate the morphology of COM emission in Sagittarius B2(N1). Spectra of ten COMs are modelled under the assumption of LTE and population diagrams are derived for positions at various distances to the south and west from the continuum peak. Based on this analysis, resolved COM rotation temperature and COM abundance profiles are derived. Based on the morphology, a rough separation into O- and N-bearing COMs can be done. Temperature profiles are in agreement with expectations of protostellar heating of an envelope with optically thick dust. Abundance profiles reflect a similar trend as seen in the morphology and, to a great extent, agree with results of astrochemical models that, besides the co-desorption with water, predict that O-bearing COMs are mainly formed on dust grain surfaces at low temperatures while at least some N-bearing COMs and CH$_3$CHO are substantially formed in the gas phase at higher temperatures. Our observational results, in comparison with model predictions, suggest that COMs that are exclusively or to a great extent formed on dust grains desorb thermally at ~100 K from the grain surface likely alongside water. Non-zero abundance values below ~100 K suggest that another desorption process is at work at these low temperatures: either non-thermal desorption or partial thermal desorption related to lower binding energies experienced by COMs in the outer, water-poor ice layers. In either case, this is the first time that the transition between two regimes of COM desorption has been resolved in a hot core.
Linear Newman-Penrose charges as subleading BMS and dual BMS charges

In this paper, we further develop previous work on asymptotically flat spacetimes and extend subleading BMS and dual BMS charges in a large r expansion to all orders in r −1. This forms a complete account of this prescription in relation to the previously discovered Newman-Penrose charges. We provide an explanation for the origin of the infinite tower of linear Newman-Penrose charges with regards to asymptotic symmetries and justify why these charges fail to be conserved at the non-linear level as well as failing to exhibit full supertranslation invariance even at the linear level.
An Improved Sample Complexity Lower Bound for Quantum State Tomography

We show that Ω( rd/ǫ ) copies of an unknown rank- r , dimension- d quantum mixed state are necessary in order to learn a classical description with 1 − ǫ ﬁdelity. This improves upon the tomography lower bounds obtained by Haah, et al. and Wright.
Social Network Community Detection Based on Textual Content Similarity and Sentimental Tendency

Shared travel has gradually become one of the hot topics discussed on social networking platforms such as Micro Blog. In a timely manner, deeper network community detection on the evaluation content of shared travel in social networks can effectively conduct research and analysis on the public opinion orientation related to shared travel, which has great application prospects. The existing community detection algorithms generally measure the similarity of nodes in the network from the perspective of spatial distance. This paper proposes a Community detection algorithm based on Textual content Similarity and sentimental Tendency (CTST), considering the network structure and node attributes at the same time. The content similarity and sentimental tendency of network community users are taken as node attributes, and on this basis, an undirected weighted network is constructed for community detection. This paper conducts experiments with actual data and analyzes the experimental results. It is found that the modularity of the community detection results is high and the effect is good.
Remotely Controlled Entanglement Generation

We consider a system of multiple qubits without any quantum control. We show that one can mediate entanglement between different subsystems in a controlled way by adding a (locally) controlled auxiliary system of the same size that couples via an always-on, distant dependent interaction to the system qubits. Solely by changing the internal state of the control system, one can selectively couple it to selected qubits, and ultimately generate different kinds of entanglement within the system. This provides an alternative way for quantum control and quantum gates that does not rely on the ability to switch interactions on and off at will, and can serve as a locally controlled quantum switch where all entanglement patterns can be created. We demonstrate that such an approach also offers an increased error tolerance w.r. t. position fluctuations.
Angular-momentum Transport in Proto-neutron Stars and the Fate of Neutron Star Merger Remnants

Both the core collapse of rotating massive stars, and the coalescence of neutron star (NS) binaries result in the formation of a hot, differentially rotating NS remnant. The timescales over which differential rotation is removed by internal angular-momentum transport processes (viscosity) have key implications for the remnant’s long-term stability and the NS equation of state (EOS). Guided by a nonrotating model of a cooling proto-NS, we estimate the dominant sources of viscosity using an externally imposed angular-velocity profile Ω(r). Although the magneto-rotational instability provides the dominant source of effective viscosity at large radii, convection and/or the Tayler–Spruit dynamo dominate in the core of merger remnants where dΩ/dr ≥ 0. Furthermore, the viscous timescale in the remnant core is sufficiently short that solid-body rotation will be enforced faster than matter is accreted from rotationally supported outer layers. Guided by these results, we develop a toy model for how the merger remnant core grows in mass and angular momentum due to accretion. We find that merger remnants with sufficiently massive and slowly rotating initial cores may collapse to black holes via envelope accretion, even when the total remnant mass is less than the usually considered threshold ≈1.2 M TOV for forming a stable solid-body rotating NS remnant (where M TOV is the maximum nonrotating NS mass supported by the EOS). This qualitatively new picture of the post-merger remnant evolution and stability criterion has important implications for the expected electromagnetic counterparts from binary NS mergers and for multimessenger constraints on the NS EOS.
Modeling Emergent Lexicon Formation with a Self-Reinforcing Stochastic Process

We introduce FiLex, a self-reinforcing stochastic process which models finite lexicons in emergent language experiments. The central property of FiLex is that it is a self-reinforcing process, parallel to the intuition that the more a word is used in a language, the more its use will continue. As a theoretical model, FiLex serves as a way to both explain and predict the behavior of the emergent language system. We empirically test FiLex's ability to capture the relationship between the emergent language's hyperparameters and the lexicon's Shannon entropy.
Blow-up of the critical norm for a supercritical semilinear heat equation

We consider the scaling critical Lebesgue norm of blow-up solutions to the semilinear heat equation $u_t=\Delta u+|u|^{p-1}u$ in an arbitrary smooth domain of $\mathbf{R}^n$. In the range $p>p_S:=(n+2)/(n-2)$, we show that the critical norm must be unbounded near the blow-up time, where the type I blow-up condition is not imposed. The range $p>p_S$ is optimal in view of the existence of type II blow-up solutions with bounded critical norm for $p=p_S$.
An approach to computing spectral shifts for black holes beyond Kerr

Recent measurements of gravitational-wave ringdown following the merger of binary black holes raise the prospect of precision black hole spectroscopy in the near future. To perform the most sensitive tests of the nature of black holes using ringdown measurements, it is critical to compute the deviations to the spectrum of black holes in particular extensions of relativity. These spectral shifts are also needed to interpret any violations of the predictions of relativity that may be de-tected during ringdown. Here we present a ﬁrst step towards computing the shifts to the spectrum of Kerr black holes with arbitrary spins, by deriving a modiﬁed Teukolsky equation governing the perturbations of black holes in theories beyond GR. Our approach applies to a class of theories which includes dynamical Chern-Simons gravity and shift-symmetric scalar Gauss-Bonnet gravity, in the case where the deviations from relativity are small. This allows for a perturbative approach to solving the equations of motion. Further, we show how to use the modiﬁed equation to compute the leading-order spectral shifts of Kerr black holes, using eigenvalue perturbation methods. Our formalism provides a practical approach to predicting ringdown for black holes in a range of promising extensions to relativity, enabling future precision searches for their signatures in black hole ringdown.
ICC++: Explainable Image Retrieval for Art Historical Corpora using Image Composition Canvas

Image compositions are helpful in the study of image structures and assist in discovering semantics of the underlying scene portrayed across art forms and styles. With the digitization of artworks in recent years, thousands of images of a particular scene or narrative could potentially be linked together. However, manually linking this data with consistent objectiveness can be a highly challenging and time-consuming task. In this work, we present a novel approach called Image Composition Canvas (ICC++) to compare and retrieve images having similar compositional elements. ICC++ is an improvement over ICC specializing in generating low and high-level features (compositional elements) motivated by Max Imdahl’s work. To this end, we present a rigorous quantitative and qualitative comparison of our approach with traditional and state-of-the-art (SOTA) methods showing that our proposed method outperforms all of them. In combination with deep features, our method outperforms the best deep learning-based method, opening the research direction for explainable machine learning for digital humanities. We will release the code and the data post publication.
Is [Y/Mg] a Reliable Age Diagnostic for FGK Stars?

Current spectroscopic surveys are producing large catalogs of chemical abundances for stars of all types. The yttrium-to-magnesium ratio, [Y/Mg], has emerged as a candidate age indicator for solar twins in the local stellar neighborhood. However, it is unclear whether it is a viable age diagnostic for more diverse stellar types, so we investigate [Y/Mg] as an age indicator for the FGK-type planet host stars observed by Kepler. We find that the [Y/Mg] “Clock” is most precise for solar twins, with a [Y/Mg]/age slope of m = −0.0370 ±0.0071 dex Gyr−1 and σ Age = 2.6 Gyr. We attribute the lower precision compared to literature results to nonsolar twins contaminating our solar twin sample and recommend a 1.5 Gyr systematic uncertainty for stellar ages derived with any [Y/Mg]–Age relation. We also analyzed the [Y/Mg] Clock as a function of T eff, logg , and metallicity individually and find no strong trends, but we compute statistically significant [Y/Mg]–Age relations for subsamples defined by ranges in T eff, logg , and metallicity. Finally, we compare [Y/Mg] and rotation ages and find statistically similar trends as for isochrone ages, although we find that rotation ages perform better for GK dwarfs while isochrones perform better for FG subgiants. We conclude that the [Y/Mg] Clock is most precise for solar twins and analogs but is also a useful age diagnostic for FGK stars.
Multi-View Clustering for Open Knowledge Base Canonicalization

Open information extraction (OIE) methods extract plenty of OIE triples from unstructured text, which compose large open knowledge bases (OKBs). Noun phrases and relation phrases in such OKBs are not canonicalized, which leads to scattered and redundant facts. It is found that two views of knowledge (i.e., a fact view based on the fact triple and a context view based on the fact triple's source context) provide complementary information that is vital to the task of OKB canonicalization, which clusters synonymous noun phrases and relation phrases into the same group and assigns them unique identifiers. However, these two views of knowledge have so far been leveraged in isolation by existing works. In this paper, we propose CMVC, a novel unsupervised framework that leverages these two views of knowledge jointly for canonicalizing OKBs without the need of manually annotated labels. To achieve this goal, we pro- pose a multi-view CH K-Means clustering algorithm to mutually reinforce the clustering of view-specific embeddings learned from each view by considering their different clustering qualities. In order to further enhance the canonicalization performance, we propose a training data optimization strategy in terms of data quantity and data quality respectively in each particular view to refine the learned view-specific embeddings in an iterative manner. Additionally, we propose a Log-Jump algorithm to predict the optimal number of clusters in a data-driven way without requiring any labels. We demonstrate the superiority of our framework through extensive experiments on multiple real-world OKB data sets against state-of-the-art methods.
A Multimodal Perceived Stress Classification Framework using Wearable Physiological Sensors

—Mental stress is a largely prevalent condition known to affect many people and could be a serious health concern. The quality of human life can be signiﬁcantly im- proved if mental health is properly managed. Towards this, we propose a robust method for perceived stress classiﬁ- cation, which is based on using multimodal data, acquired from forty subjects, including three (electroencephalog- raphy (EEG), galvanic skin response (GSR), and photoplethysmography (PPG)) physiological modalities. The data is acquired for three minutes duration in an open eyes condition. A perceived stress scale (PSS) questionnaire is used to record the stress of participants, which is then used to assign stress labels (two- and three classes). Time (four from GSR and PPG signals) and frequency (four from EEG signal) domain features are extracted. Among EEG- based features, using a frequency band selection algorithm for selecting the optimum EEG frequency subband, the theta band was selected. Further, a wrapper-based method is used for optimal feature selection. Human stress level classiﬁcation is performed using three different classiﬁers, which are fed with a fusion of the selected set of features from three modalities. A signiﬁcant accuracy (95% for two classes, and 77.5% for three classes) was achieved using the multilayer perceptron classiﬁer.
History effects in the creep of a disordered brittle material

We study the creep behavior of a disordered brittle material (concrete) under successive loading steps, using acoustic emission and ultrasonic sensing to track internal damage. The primary creep rate is observed to follow a (Omori-type) power-law decay in the strain rate, the number of acoustic emission events as well as the amplitudes of the ultrasonic beams, supporting a brittle-creep mechanism. The main outcome is however the discovery of unexpected history effects that make the material less prone to creep when it has been previously deformed and damaged under primary creep at a lower applied stress. With the help of a progressive damage model implementing thermal activation, we interpret this as an aging-under-stress phenomenon: during an initial creep step at relatively low applied stress, the easy-to-damage sites are exhausted first, depleting the excitation spectrum at low stress gap values. Consequently, upon reloading under a larger applied stress, although previously damaged, the material creeps (and damages) less than it would under the same stress but without pre-creeping. Besides shedding a new light on the fundamental physics of creep of disordered brittle materials, this has important practical consequences in the interpretation of some experimental procedures, such as stress-stepping experiments.
An Updated Metal-dependent Theoretical Scenario for Classical Cepheids

To properly quantify the possible residual systematic errors affecting the classical Cepheid distance scale, a detailed theoretical scenario is recommended. By extending the set of nonlinear, convective pulsation models published for Z = 0.02 to Z = 0.004, Z = 0.008, and Z = 0.03, we provide a detailed homogeneous, nonlinear model grid taking into account simultaneous variations of the mass–luminosity relation, the efficiency of superadiabatic convection, and the chemical composition. The dependence of the inferred period–radius, period–mass–radius, and period–mass–luminosity–temperature relations on the input parameters is discussed for both the fundamental and first overtone modes. The trend of the instability strip getting redder as the metallicity increases is confirmed for the additional mass–luminosity assumptions and mixing length values. From the obtained multifilter light curves, we derive the mean magnitudes and colors, and in turn the period–luminosity–color and period–Wesenheit relations, for each assumed chemical composition, mass–luminosity relation, and efficiency of superadiabatic convection. Application to a well-studied sample of Cepheids in the Large Magellanic Cloud allows us to constrain the dependence of the inferred distance modulus on the assumed mass–luminosity relation, and the inclusion of the metallicity term in the derivation of the period–Wesenheit relations allows us, for each assumed mass–luminosity relation, to predict the metallicity dependence of the Cepheid distance scale. The obtained metal-dependent, period–Wesenheit relations are compared with recent results in the literature and applied to a sample of Gaia Early Data Release 3 Galactic Cepheids with known metal abundances to derive individual parallaxes. The comparison of these predictions with Gaia results is finally discussed.
Generative Pretraining for Black-Box Optimization

Many problems in science and engineering involve optimizing an expensive black-box function over a high-dimensional space. For such black-box optimization (BBO) problems, we typically assume a small budget for online function evaluations, but also often have access to a fixed, offline dataset for pretraining. Prior approaches seek to utilize the offline data to approximate the function or its inverse but are not sufficiently accurate far from the data distribution. We propose BONET, a generative framework for pretraining a novel black-box optimizer using offline datasets. In BONET, we train an autoregressive model on fixed-length trajectories derived from an offline dataset. We design a sampling strategy to synthesize trajectories from offline data using a simple heuristic of rolling out monotonic transitions from low-fidelity to high-fidelity samples. Empirically, we instantiate BONET using a causally masked Transformer and evaluate it on Design-Bench, where we rank the best on average, outperforming state-of-the-art baselines.