Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward Finding The Global Optimal of Adversarial Examples

Current machine learning models are vulnerable to adversarial examples (Goodfellow et al., 2014), we noticed that current state-of-the-art methods (Kurakin et al., 2016; Cheng et al., 2018) to attack a well-trained model often stuck in local optimal values. We conduct series of experiments on both white-box and black-box settings, and find out that by different initialization, the attack algorithm will finally converge to very different local optimals, suggesting the importance of careful and thorough search in the attack space. In this paper, we propose a general boosting algorithm that can help current attack to find a more global optimal example. Specifically, we search for the adversarial examples by starting from different points/directions, and in certain interval we adopt successive halving (Jamieson & Talwalkar, 2016) to cut down the searching directions that are not promising, and use Bayesian Optimization (Pelikan et al., 1999; Bergstra et al., 2011) to resample from the search space based on the knowledge obtained from past searches. We demonstrate that by applying our methods to state-of-the-art attack algorithms in both black-and white box setting, we can further reduce the distortion between the original image and adversarial sample about 10%-20%. By adopting dynamic successive halving, we can reduce the computation cost 5-10 times without harming the final result. We conduct experiments in models trained on MNIST or ImageNet and also try on decision tree models, these experiments suggest that our method is a general way to boost the performance of current adversarial attack methods.
Doubly degenerate diffuse interface models of surface diffusion

We discuss two doubly degenerate Cahn–Hilliard (DDCH) models for isotropic surface diffusion. Degeneracy is introduced in both the mobility function and a restriction function associated to the chemical potential. Our computational results suggest that the restriction functions yield more accurate approximations of surface diffusion. We consider a slight generalization of a model that has appeared before, which is non‐variational, meaning there is no clear energy that is dissipated along the solution trajectories. We also introduce a new variational and, more precisely, energy dissipative model, which can be related to the generalized non‐variational model. For both models, we use formal matched asymptotics to show the convergence to the sharp‐interface limit of surface diffusion.
A Deep Learning Framework for Pricing Financial Instruments

We propose an integrated deep learning architecture for the stock movement prediction. Our architecture simultaneously leverages all available alpha sources. The sources include technical signals, financial news signals, and cross-sectional signals. Our architecture possesses three main properties. First, our architecture eludes overfitting issues. Although we consume a large number of technical signals but has better generalization properties than linear models. Second, our model effectively captures the interactions between signals from different categories. Third, our architecture has low computation cost. We design a graph-based component that extracts cross-sectional interactions which circumvents usage of SVD that's needed in standard models. Experimental results on the real-world stock market show that our approach outperforms the existing baselines. Meanwhile, the results from different trading simulators demonstrate that we can effectively monetize the signals.
Approximating Vertex Cover using Structural Rounding

In this work, we provide the first practical evaluation of the structural rounding framework for approximation algorithms. Structural rounding works by first editing to a well-structured class, efficiently solving the edited instance, and "lifting" the partial solution to recover an approximation on the input. We focus on the well-studied Vertex Cover problem, and edit to the class of bipartite graphs (where Vertex Cover has an exact polynomial time algorithm). In addition to the naive lifting strategy for Vertex Cover described by Demaine et al., we introduce a suite of new lifting strategies and measure their effectiveness on a large corpus of synthetic graphs. We find that in this setting, structural rounding significantly outperforms standard 2-approximations. Further, simpler lifting strategies are extremely competitive with the more sophisticated approaches. The implementations are available as an open-source Python package, and all experiments are replicable.
Improving the Interpretability of Neural Sentiment Classifiers via Data Augmentation

Sentiment analysis has been widely used by businesses for social media opinion mining, especially in the financial services industry, where customers' feedbacks are critical for companies. Recent progress of neural network models has achieved remarkable performance on sentiment classification, while the lack of classification interpretation may raise the trustworthy and many other issues in practice. In this work, we study the problem of improving the explainability of existing sentiment classifiers. We propose two data augmentation methods that create additional training examples to help improve model explainability: one method with a predefined sentiment word list as external knowledge and the other with adversarial examples. We test the proposed methods on both CNN and RNN classifiers with three benchmark sentiment datasets. The model explainability is assessed by both human evaluators and a simple automatic evaluation measurement. Experiments show the proposed data augmentation methods significantly improve the explainability of both neural classifiers.
A radio census of the massive stellar cluster Westerlund 1

Context. Massive stars and their stellar winds are important for a number of feedback processes. The mass lost in the stellar wind can help determine the end-point of the star as a neutron star (NS) or a black hole (BH). However, the impact of mass loss on the post-main sequence evolutionary stage of massive stars is not well understood. Westerlund 1 is an ideal astrophysical laboratory in which to study massive stars and their winds in great detail over a large range of different evolutionary phases. Aims. We aim to study the radio emission from Westerlund 1, in order to measure radio fluxes from the population of massive stars, and determine mass-loss rates and spectral indices where possible. Methods. Observations were carried out in 2015 and 2016 with the Australia Telescope Compact Array (ATCA) at 5.5 and 9 GHz using multiple configurations, with maximum baselines ranging from 750 m to 6 km. Results. Thirty stars are detected in the radio from the fully concatenated dataset, ten of which are Wolf-Rayet stars (WRs) (predominantly late type WN stars), five yellow hypergiants (YHGs), four red supergiants (RSGs), one luminous blue variable (LBV), the sgB[e] star W9, and several OB supergiants. New source detections in the radio are found for five WR stars, and five OB supergiants. These detections lead to evidence for three new OB supergiant binary candidates, which is inferred from derived spectral index limits. Conclusions. Spectral indices and index limits were determined for massive stars in Westerlund 1. For cluster members found to have partially optically thick emission, mass-loss rates were calculated. Under the approximation of a thermally emitting stellar wind and a steady mass-loss rate, clumping ratios were then estimated for eight WRs. Diffuse radio emission was detected throughout the cluster. Detections of knots of radio emission with no known stellar counterparts indicate the highly clumped structure of this intra-cluster medium, likely shaped by a dense cluster wind.
First and Complementary Neighborhood Combination of Adjacency Matrix for Graph Learning

A variety of effective frameworks for representation learning on graphs have been recently developed, such as the graph neural networks and the WL-kernel based models. These frameworks rely on complex modules with heavy computation and involve lots of parameters. In contrast, the transformation of adjacency matrix method is computationally simple and could improve performance with a shorter learning process. In this paper, we introduce the notion of complementary neighborhood, and propose to combine the ﬁrst and the complementary neighborhood of the adjacency matrix for graph learning. The proposed transformation can be applied to several state-of-the-art graph models. Based on a series of experiments conducted on several benchmark datasets, we show that the proposed approach improves the state-of-the-art performance for four kinds of graph tasks, including supervised and semi-supervised graph classiﬁcation, graph link prediction, graph edge generation and classiﬁcation. The implementation codes are available online at https://github.com/CODE-SUBMIT/Graph_Neighborhood_1 .
On the spectrum of an oscillator in a magnetic field

We consider the Hamiltonian for a charged particle in a harmonic potential in the presence of a magnetic field. The most symmetric case depends on one parameter, the variation of which leads from a spectrum bounded from below to an unbounded spectrum. At the transition point the spectrum is bounded from below but each eigenvalue has infinite multiplicity. The algebraic method proves to be a remarkable tool for the analysis of this quadratic Hamiltonian.
Select and Attend: Towards Controllable Content Selection in Text Generation

Many text generation tasks naturally contain two steps: content selection and surface realization. Current neural encoder-decoder models conflate both steps into a black-box architecture. As a result, the content to be described in the text cannot be explicitly controlled. This paper tackles this problem by decoupling content selection from the decoder. The decoupled content selection is human interpretable, whose value can be manually manipulated to control the content of generated text. The model can be trained end-to-end without human annotations by maximizing a lower bound of the marginal likelihood. We further propose an effective way to trade-off between performance and controllability with a single adjustable hyperparameter. In both data-to-text and headline generation tasks, our model achieves promising results, paving the way for controllable content selection in text generation.
UPC: Learning Universal Physical Camouflage Attacks on Object Detectors

In this paper, we study physical adversarial attacks on object detectors in the wild. Prior arts on this matter mostly craft instance-dependent perturbations only for rigid and planar objects. To this end, we propose to learn an adversarial pattern to effectively attack all instances belonging to the same object category (e.g., person, car), referred to as Universal Physical Camouflage Attack (UPC). Concretely, UPC crafts camouflage by jointly fooling the region proposal network, as well as misleading the classifier and the regressor to output errors. In order to make UPC effective for articulated non-rigid or non-planar objects, we introduce a set of transformations for the generated camouflage patterns to mimic their deformable properties. We additionally impose optimization constraint to make generated patterns look natural for human observers. To fairly evaluate the effectiveness of different physical-world attacks on object detectors, we present the first standardized virtual database, AttackScenes, which simulates the real 3D world in a controllable and reproducible environment. Extensive experiments suggest the superiority of our proposed UPC compared with existing physical adversarial attackers not only in virtual environments (AttackScenes), but also in real-world physical environments. Codes, models, and demos are publicly available at this https URL.
On the origin of wide-orbit ALMA planets: giant protoplanets disrupted by their cores

Recent ALMA observations may indicate a surprising abundance of sub-Jovian planets on very wide orbits in protoplanetary discs that are only a few million years old. These planets are too young and distant to have been formed via the core accretion (CA) scenario, and are much less massive than the gas clumps born in the classical gravitational instability (GI) theory. It was recently suggested that such planets may form by the partial destruction of GI protoplanets: energy output due to the growth of a massive core may unbind all or most of the surrounding pre-collapse protoplanet. Here we present the first 3D global disc simulations that simultaneously resolve grain dynamics in the disc and within the protoplanet. We confirm that massive GI protoplanets may self-destruct at arbitrarily large separations from the host star provided that solid cores of mass ∼10–20 M⊕ are able to grow inside them during their pre-collapse phase. In addition, we find that the heating force recently analysed by Masset & Velasco Romero (2017) perturbs these cores away from the centre of their gaseous protoplanets. This leads to very complicated dust dynamics in the protoplanet centre, potentially resulting in the formation of multiple cores, planetary satellites, and other debris such as planetesimals within the same protoplanet. A unique prediction of this planet formation scenario is the presence of sub-Jovian planets at wide orbits in Class 0/I protoplanetary discs.
Modeling Psychotherapy Dialogues with Kernelized Hashcode Representations: A Nonparametric Information-Theoretic Approach.

We propose a novel dialogue modeling framework, the first-ever nonparametric kernel functions based approach for dialogue modeling, which learns kernelized hashcodes as compressed text representations; unlike traditional deep learning models, it handles well relatively small datasets, while also scaling to large ones. We also derive a novel lower bound on mutual information, used as a model-selection criterion favoring representations with better alignment between the utterances of participants in a collaborative dialogue setting, as well as higher predictability of the generated responses. As demonstrated on three real-life datasets, including prominently psychotherapy sessions, the proposed approach significantly outperforms several state-of-art neural network based dialogue systems, both in terms of computational efficiency, reducing training time from days or weeks to hours, and the response quality, achieving an order of magnitude improvement over competitors in frequency of being chosen as the best model by human evaluators.
The connection between merging double compact objects and the Ultraluminous X-ray Sources

We explore the different formation channels of merging double compact objects (DCOs: BH-BH/BH-NS/NS-NS) that went through a ultraluminous X-ray phase (ULX: X-ray sources with apparent luminosity exceeding $10^{39}\rm {\ erg\ s^{-1}}$). There are many evolutionary scenarios which can naturally explain the formation of merging DCO systems: isolated binary evolution, dynamical evolution inside dense clusters and chemically homogeneous evolution of field binaries. It is not clear which scenario is responsible for the majority of LIGO/Virgo sources. Finding connections between ULXs and DCOs can potentially point to the origin of merging DCOs as more and more ULXs are discovered. We use the StarTrack population synthesis code to show how many ULXs will form merging DCOs in the framework of isolated binary evolution. Our merger rate calculation shows that in the local Universe typically $50\%$ of merging BH-BH progenitor binaries have evolved through a ULX phase. This indicates that ULXs can be used to study the origin of LIGO/Virgo sources. We have also estimated that the fraction of observed ULXs that will form merging DCOs in future varies between $5\%$ to $40\%$ depending on common envelope model and metallicity.
One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases

Different texts shall by nature correspond to different number of keyphrases. This desideratum is largely missing from existing neural keyphrase generation models. In this study, we address this problem from both modeling and evaluation perspectives. We first propose a recurrent generative model that generates multiple keyphrases as delimiter-separated sequences. Generation diversity is further enhanced with two novel techniques by manipulating decoder hidden states. In contrast to previous approaches, our model is capable of generating diverse keyphrases and controlling number of outputs. We further propose two evaluation metrics tailored towards the variable-number generation. We also introduce a new dataset StackEx that expands beyond the only existing genre (i.e., academic writing) in keyphrase generation tasks. With both previous and new evaluation metrics, our model outperforms strong baselines on all datasets.
Cosmic birefringence test of the Hubble tension

A cosmic pseudo-Nambu-Goldstone boson has been proposed to resolve the four-sigma discrepancy between precision measurements of the expansion rate of the universe. We show this solution may also produce a cosmic birefringence (CB) signal in the polarization of the cosmic microwave background (CMB); observations of the CB power spectrum and cross-correlation with the CMB can independently confirm the presence of this new boson. We find significant new contributions to the CB signal, and show that future CMB experiments will greatly improve sensitivity to CB and Hubble tension scenarios.
Multi-Step Greedy and Approximate Real Time Dynamic Programming

Real Time Dynamic Programming (RTDP) is a well-known Dynamic Programming (DP) based algorithm that combines planning and learning to find an optimal policy for an MDP. It is a planning algorithm because it uses the MDP's model (reward and transition functions) to calculate a 1-step greedy policy w.r. t. ~an optimistic value function, by which it acts. It is a learning algorithm because it updates its value function only at the states it visits while interacting with the environment. As a result, unlike DP, RTDP does not require uniform access to the state space in each iteration, which makes it particularly appealing when the state space is large and simultaneously updating all the states is not computationally feasible. In this paper, we study a generalized multi-step greedy version of RTDP, which we call $h$-RTDP, in its exact form, as well as in three approximate settings: approximate model, approximate value updates, and approximate state abstraction. We analyze the sample, computation, and space complexities of $h$-RTDP and establish that increasing $h$ improves sample and space complexity, with the cost of additional offline computational operations. For the approximate cases, we prove that the asymptotic performance of $h$-RTDP is the same as that of a corresponding approximate DP -- the best one can hope for without further assumptions on the approximation errors. $h$-RTDP is the first algorithm with a provably improved sample complexity when increasing the lookahead horizon.
An Average Curvature Accelerated Composite Gradient Method for Nonconvex Smooth Composite Optimization Problems

This paper presents an accelerated composite gradient (ACG) variant, referred to as the AC-ACG method, for solving nonconvex smooth composite minimization problems. As opposed to well-known ACG variants that are either based on a known Lipschitz gradient constant or a sequence of maximum observed curvatures, the current one is based on the average of all past observed curvatures. More specifically, AC-ACG uses a positive multiple of the average of all observed curvatures until the previous iteration as a way to estimate the "function curvature" at the current point and then two resolvent evaluations to compute the next iterate. In contrast to other variable Lipschitz estimation variants, e.g., the ones based on the maximum curvature, AC-ACG always accepts the aforementioned iterate regardless how poor the Lipschitz estimation turns out to be. Finally, computational results are presented to illustrate the efficiency of AC-ACG on both randomly generated and real-world problem instances.
Age Determination of Galactic B-type Stars in Double-lined Eclipsing Binaries

We present the results of age determination for galactic B-type main-sequence stars that are components of double-lined eclipsing binaries. Only detached systems are considered. We analyze 38 binary systems that meet such criteria. The analysis is based on evolutionary computations, and we consider that the age is determined if there is a common value from the radius–age diagrams and the agreement in the position of both components in the Hertzsprung–Russell diagram. In some cases, to meet these two conditions, it was necessary to adjust the value of the metallicity, Z, or/and the parameter of overshooting from the convective core, αov. We determine a consistent age for 33 of the 38 binaries. Additionally, we completed extensive computations, and for each system, we give the range of αov and Z for which a consistent solution exists. The age of the studied B-type main-sequence stars ranges, as counted from the Zero Age Main Sequence, from about 2.5 Myr to about 200 Myr.
Discrete time crystals in many-body quantum chaos

Discrete time crystals (DTCs) are new phases of matter characterized by the presence of an observable evolving with $nT$ periodicity under a $T$-periodic Hamiltonian, where $n>1$ is an integer insensitive to small parameter variations. In particular, DTCs with $n=2$ have been extensively studied in periodically quenched and kicked spin systems in recent years. In this paper, we study the emergence of DTCs from the many-body quantum chaos perspective, using a rather simple model depicting a harmonically driven spin chain. We advocate to first employ a semiclassical approximation to arrive at a mean-field Hamiltonian and then identify the parameter regime at which DTCs exist, with standard tools borrowed from studies of classical chaos. Specifically, we seek symmetric-breaking solutions by examining the stable islands on the Poincare surface of section of the mean-field Hamiltonian. We then turn to the actual many-body quantum system, evaluate the stroboscopic dynamics of the total magnetization in the full quantum limit, and verify the existence of DTCs. Our effective and straightforward approach indicates that in general DTCs are one natural aspect of many-body quantum chaos with mixed classical phase space structure. Our approach can also be applied to general time-periodic systems, which is thus promising for finding DTCs with $n>2$ and opening possibilities for exploring DTCs properties beyond their time-translational breaking features.
Privacy-Preserving Bandits

Contextual bandit algorithms~(CBAs) often rely on personal data to provide recommendations. Centralized CBA agents utilize potentially sensitive data from recent interactions to provide personalization to end-users. Keeping the sensitive data locally, by running a local agent on the user's device, protects the user's privacy, however, the agent requires longer to produce useful recommendations, as it does not leverage feedback from other users. This paper proposes a technique we call Privacy-Preserving Bandits (P2B); a system that updates local agents by collecting feedback from other local agents in a differentially-private manner. Comparisons of our proposed approach with a non-private, as well as a fully-private (local) system, show competitive performance on both synthetic benchmarks and real-world data. Specifically, we observed only a decrease of 2.6% and 3.6% in multi-label classification accuracy, and a CTR increase of 0.0025 in online advertising for a privacy budget $\epsilon \approx 0.693$. These results suggest P2B is an effective approach to challenges arising in on-device privacy-preserving personalization.