Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Oracle-Efficient Online Learning

The classical algorithms for online learning and decision-making have the benefit of achieving the optimal performance guarantees, but suffer from computational complexity limitations when implemented at scale. More recent sophisticated techniques, which we refer to as oracle-efficient methods, address this problem by dispatching to an offline optimization oracle that can search through an exponentially-large (or even infinite) space of decisions and select that which performed the best on any dataset. But despite the benefits of computational feasibility, oracle-efficient algorithms exhibit one major limitation: while performing well in worst-case settings, they do not adapt well to friendly environments. In this paper we consider two such friendly scenarios, (a)"small-loss"problems and (b) IID data. We provide a new framework for designing follow-the-perturbed-leader algorithms that are oracle-efficient and adapt well to the small-loss environment, under a particular condition which we call approximability (which is spiritually related to sufficient conditions provided by Dud\'{i}k et al., [2020]). We identify a series of real-world settings, including online auctions and transductive online classification, for which approximability holds. We also extend the algorithm to an IID data setting and establish a"best-of-both-worlds"bound in the oracle-efficient setting.
Breaking Bad Degeneracies with Love: Improving gravitational-wave measurements through universal relations

The distance-inclination degeneracy limits gravitational-wave parameter estimation of compact binary mergers. Although the degeneracy can be partially broken by including higher-order modes or precession, these eﬀects are suppressed in binary neutron stars. In this work we implement a new parameterization of the tidal eﬀects in the binary neutron star waveform, exploiting the binary Love relations, that breaks the distance-inclination degeneracy. The binary Love relations prescribe the tidal deformability of a neutron star as a function of its source-frame mass in an equation-of-state insensitive way, and thus allows direct measurement of the redshift of the source. If the cosmological parameters are assumed to be known, the redshift can be converted to a luminosity distance, and the distance-inclination degeneracy can thus be broken. We implement this new approach, studying a range of binary neutron-star observing scenarios using Bayesian parameter estimation on synthetic data. In the era of the third generation detectors, for observations with signal-to-noise ratios ranging from 6 to 167, we forecast up to a ∼ 70% decrease in the 90% credible interval of the distance and inclination, and up to a ∼ 50% decrease in that of the source-frame component masses. For edge-on systems, our approach can result in moderate ( ∼ 50% ) improvement in the measurements of distance and inclination for binaries with signal-to-noise ratio as low as 10. This prescription can be used to better infer the source-frame masses, and hence reﬁne population properties of neutron stars, such as their maximum mass, impacting nuclear astrophysics. When combined with the search for electromagnetic counterpart observations, the work presented here can be used to put improved bounds on the opening angle of jets from binary neutron star mergers.
Local regularity for nonlinear elliptic and parabolic equations with anisotropic weights

Abstract The main purpose of this paper is to capture the asymptotic behaviour for solutions to a class of nonlinear elliptic and parabolic equations with the anisotropic weights consisting of two power-type weights of different dimensions near the degenerate or singular point, especially covering the weighted p-Laplace equations and weighted fast diffusion equations. As a consequence, we also establish the local Hölder estimates for their solutions in the presence of single power-type weights.
Log-concavity of level Hilbert functions and pure $O$-sequences

We investigate log-concavity in the context of level Hilbert functions and pure $O$-sequences, two classes of numerical sequences introduced by Stanley in the late Seventies whose structural properties have since been the object of a remarkable amount of interest in combinatorial commutative algebra. However, a systematic study of the log-concavity of these sequences began only recently, thanks to a paper by Iarrobino. The goal of this note is to address two general questions left open by Iarrobino's work: 1) Given the integer pair $(r,t)$, are all level Hilbert functions of codimension $r$ and type $t$ log-concave? 2) How about pure $O$-sequences with the same parameters? Iarrobino's main results consisted of a positive answer to 1) for $r=2$ and any $t$, and for $(r,t)=(3,1)$. Further, he proved that the answer to 1) is negative for $(r,t)=(4,1)$. Our chief contribution to 1) is to provide a negative answer in all remaining cases, with the exception of $(r,t)=(3,2)$, which is still open in any characteristic. We then propose a few detailed conjectures specifically on level Hilbert functions of codimension 3 and type 2. As for question 2), we show that the answer is positive for all pairs $(r,1)$; negative for $(r,t)=(3,4)$; and negative for any pair $(r,t)$ with $r\ge 4$ and $2\le t\le r+1$. Interestingly, the main case that remains open is again $(r,t)=(3,2)$. Further, we conjecture that, in analogy with the behavior of arbitrary level Hilbert functions, log-concavity fails for pure $O$-sequences of any codimension $r\ge 3$ and type $t$ large enough.
Effects of wind veer on a yawed wind turbine wake in atmospheric boundary layer flow

Large Eddy Simulations (LES) are used to study the eﬀects of veer (the height-dependent lateral deﬂection of wind velocity due to Coriolis acceleration) on the evolution of wind turbine wakes. Speciﬁcally, this work focuses on turbines that are yawed with respect to the mean incoming wind velocity, which produces laterally deﬂected wakes that have a curled (crescent-shaped) structure. These eﬀects can be attributed to the introduction of streamwise mean vorticity and the formation of a Counter-rotating Vortex Pair (CVP) on the top and bottom of the wake. In a Truly Neutral Boundary Layer (TNBL) in which wind veer eﬀects are absent, these eﬀects can be captured well with existing analytical wake models (Bastankhah et al. J. Fluid Mech. (2022), 933, A2). However, in the more realistic case of atmospheric boundary layers subjected to Coriolis acceleration, existing models need to be re-examined and generalized to include the eﬀects of wind veer. To this end, the ﬂow in a Conventionally Neutral Atmospheric Boundary Layer (CNBL) interacting with a yawed wind turbine is investigated in this study. Results indicate that in the presence of veer the CVP’s top and bottom vortices exhibit considerable asymmetry. However, upon removing the veer component of vorticity, the resulting distribution is much more symmetric and agrees well with that observed in a TNBL. These results are used to develop a simple correction to predict the mean velocity distribution in the wake of a yawing turbine in a CNBL using analytical models. The correction includes the veer-induced sideways wake deformation, as proposed by Abkar et al. (Energies (2018), 11(7), 1838). The resulting model predictions are compared to mean velocity distributions from the LES and good agreement is obtained.
Strain‐Invariant, Highly Water Stable All‐Organic Soft Conductors Based on Ultralight Multi‐Layered Foam‐Like Framework Structures

Soft and flexible conductors are essential for the development of soft robots, wearable electronics, electronic tissue, and implants. However, conventional soft conductors are inherently characterized by a large change in conductance upon mechanical deformation or under alternating environmental conditions, e.g., humidity, drastically limiting their application potential. This work demonstrates a novel concept for the development of strain‐invariant, highly elastic and highly water stable all‐organic soft conductors, overcoming the limitations of previous strain‐invariant soft conductors. For the first time, thin film deposition technologies are combined in a three‐dimensional fashion, resulting in micro‐ and nano‐engineered, multi‐layered (<50 nm), ultra‐lightweight (< 15 mg cm−3) foam‐like framework structures based on Poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate) and Polytetrafluoroethylene (PTFE), characterized by a highly strain‐invariant conductivity (≈184 S/m) between 80% compressive and 25% tensile strain. Both the initial electrical and mechanical properties are retained during long‐term cycling, even after 2000 cycles at 50% compression. Furthermore, the PTFE thin film renders the framework structure highly hydrophobic, resulting in stable electrical properties, even when immersed in water for a month. Such innovative multi‐scaled and multi‐layered functional materials are of interest for a broad range of applications in soft electronics, energy storage and conversion, sensing, water and air purification, as well as biomedicine.
Efficient surrogate-assisted inference for patient-reported outcome measures with complex missing mechanism

Patient-reported outcome (PRO) measures are increasingly collected as a means of measuring healthcare quality and value. The capability to predict such measures enables patient-provider shared decision making and the delivery of patient-centered care. However, due to their voluntary nature, PRO measures often suffer from a high missing rate, and the missingness may depend on many patient factors. Under such a complex missing mechanism, statistical inference of the parameters in prediction models for PRO measures is challenging, especially when flexible imputation models such as machine learning or nonparametric methods are used. Specifically, the slow convergence rate of the flexible imputation model may lead to non-negligible bias, and the traditional missing propensity, capable of removing such a bias, is hard to estimate due to the complex missing mechanism. To efficiently infer the parameters of interest, we propose to use an informative surrogate that can lead to a flexible imputation model lying in a low-dimensional subspace. To remove the bias due to the flexible imputation model, we identify a class of weighting functions as alternatives to the traditional propensity score and estimate the low-dimensional one within the identified function class. Based on the estimated low-dimensional weighting function, we construct a one-step debiased estimator without using any information of the true missing propensity. We establish the asymptotic normality of the one-step debiased estimator. Simulation and an application to real-world data demonstrate the superiority of the proposed method.
You Can't See Me: Physical Removal Attacks on LiDAR-based Autonomous Vehicles Driving Frameworks

Autonomous Vehicles (AVs) increasingly use LiDAR-based object detection systems to perceive other vehicles and pedestrians on the road. While existing attacks on LiDAR-based autonomous driving architectures focus on lowering the confidence score of AV object detection models to induce obstacle misdetection, our research discovers how to leverage laser-based spoofing techniques to selectively remove the LiDAR point cloud data of genuine obstacles at the sensor level before being used as input to the AV perception. The ablation of this critical LiDAR information causes autonomous driving obstacle detectors to fail to identify and locate obstacles and, consequently, induces AVs to make dangerous automatic driving decisions. In this paper, we present a method invisible to the human eye that hides objects and deceives autonomous vehicles' obstacle detectors by exploiting inherent automatic transformation and filtering processes of LiDAR sensor data integrated with autonomous driving frameworks. We call such attacks Physical Removal Attacks (PRA), and we demonstrate their effectiveness against three popular AV obstacle detectors (Apollo, Autoware, PointPillars), and we achieve 45{\deg} attack capability. We evaluate the attack impact on three fusion models (Frustum-ConvNet, AVOD, and Integrated-Semantic Level Fusion) and the consequences on the driving decision using LGSVL, an industry-grade simulator. In our moving vehicle scenarios, we achieve a 92.7% success rate removing 90\% of a target obstacle's cloud points. Finally, we demonstrate the attack's success against two popular defenses against spoofing and object hiding attacks and discuss two enhanced defense strategies to mitigate our attack.
Unsupervised visualization of image datasets using contrastive learning

Visualization methods based on the nearest neighbor graph, such as t-SNE or UMAP, are widely used for visualizing high-dimensional data. Yet, these approaches only produce meaningful results if the nearest neighbors themselves are meaningful. For images represented in pixel space this is not the case, as distances in pixel space are often not capturing our sense of similarity and therefore neighbors are not semantically close. This problem can be circumvented by self-supervised approaches based on contrastive learning, such as SimCLR, relying on data augmentation to generate implicit neighbors, but these methods do not produce two-dimensional embeddings suitable for visualization. Here, we present a new method, called t-SimCNE, for unsupervised visualization of image data. T-SimCNE combines ideas from contrastive learning and neighbor embeddings, and trains a parametric mapping from the high-dimensional pixel space into two dimensions. We show that the resulting 2D embeddings achieve classification accuracy comparable to the state-of-the-art high-dimensional SimCLR representations, thus faithfully capturing semantic relationships. Using t-SimCNE, we obtain informative visualizations of the CIFAR-10 and CIFAR-100 datasets, showing rich cluster structure and highlighting artifacts and outliers.
Affective Idiosyncratic Responses to Music

Affective responses to music are highly personal. Despite consensus that idiosyncratic factors play a key role in regulating how listeners emotionally respond to music, precisely measuring the marginal effects of these variables has proved challenging. To address this gap, we develop computational methods to measure affective responses to music from over 403M listener comments on a Chinese social music platform. Building on studies from music psychology in systematic and quasi-causal analyses, we test for musical, lyrical, contextual, demographic, and mental health effects that drive listener affective responses. Finally, motivated by the social phenomenon known as 网抑云 (wǎng-yì-yún), we identify influencing factors of platform user self-disclosures, the social support they receive, and notable differences in discloser user activity.
The Sparse(st) Optimization Problem: Reformulations, Optimality, Stationarity, and Numerical Results

We consider the sparse optimization problem with nonlinear constraints and an objective function, which is given by the sum of a general smooth mapping and an additional term defined by the $ \ell_0 $-quasi-norm. This term is used to obtain sparse solutions, but difficult to handle due to its nonconvexity and nonsmoothness (the sparsity-improving term is even discontinuous). The aim of this paper is to present two reformulations of this program as a smooth nonlinear program with complementarity-type constraints. We show that these programs are equivalent in terms of local and global minima and introduce a problem-tailored stationarity concept, which turns out to coincide with the standard KKT conditions of the two reformulated problems. In addition, a suitable constraint qualification as well as second-order conditions for the sparse optimization problem are investigated. These are then used to show that three Lagrange-Newton-type methods are locally fast convergent. Numerical results on different classes of test problems indicate that these methods can be used to drastically improve sparse solutions obtained by some other (globally convergent) methods for sparse optimization problems.
Gluon parton distribution of the nucleon from (
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mn>2</mml:mn><mml:mo>+</mml:mo><mml:mn>1</mml:mn><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:math>
)-flavor lattice QCD in the physical-continuum limit

We present the first physical-continuum limit $x$-dependent nucleon gluon distribution from lattice QCD using the pseudo-PDF approach, on lattice ensembles with $2+1+1$ flavors of highly improved staggered quarks (HISQ), generated by MILC Collaboration. We use clover fermions for the valence action on three lattice spacings $a \approx 0.9$, 0.12 and 0.15~fm and three pion masses $M_\pi \approx 220$, 310 and 690~MeV, with nucleon two-point measurements numbering up to $O(10^6)$ and nucleon boost momenta up to 3~GeV. We study the lattice-spacing and pion-mass dependence of the reduced pseudo-ITD matrix elements obtained from the lattice calculation, then extrapolate them to the continuum-physical limit before extracting $xg(x)/\langle x \rangle_g$. We use the gluon momentum fraction $\langle x \rangle_g$ calculated from the same ensembles to determine the nucleon gluon unpolarized PDF $xg(x)$ for the first time entirely through lattice-QCD simulation. We compare our results with previous single-ensemble lattice calculations, as well as selected global fits.
1st Place Solutions for the UVO Challenge 2022

This paper describes the approach we have taken in the challenge. We still adopted the two-stage scheme same as the last cham-pion, that is, detection first and segmentation followed. We trained more powerful detector and segmentor separately. Besides, we also perform pseudo-label training on the test set, based on student-teacher framework and end-to-end transformer based object detection. The method ranks first on the 2nd Unidentified Video Objects (UVO) challenge, achieving AR@100 of 46.8, 64.7 and 32.2 in the limited data frame track, unlimited data frame track and video track respectively.
Swinv2-Imagen: Hierarchical Vision Transformer Diffusion Models for Text-to-Image Generation

Recently, diffusion models have been proven to perform remarkably well in text-to-image synthesis tasks in a number of studies, immediately presenting new study opportunities for image generation. Google’s Imagen follows this research trend and outperforms DALLE2 as the best model for text-to-image generation. However, Imagen merely uses a T5 language model for text processing, which cannot ensure learning the semantic information of the text. Furthermore, the Efficient UNet leveraged by Imagen is not the best choice in image processing. To address these issues, we propose the Swinv2-Imagen, a novel text-to-image diffusion model based on a Hierarchical Visual Transformer and a Scene Graph incorporating a semantic layout. In the proposed model, the feature vectors of entities and relationships are extracted and involved in the diffusion model, effectively improving the quality of generated images. On top of that, we also introduce a Swin-Transformer-based UNet architecture, called Swinv2-Unet, which can address the problems stemming from the CNN convolution operations. Extensive experiments are conducted to evaluate the performance of the proposed model by using three real-world datasets, i.e. MSCOCO, CUB and MM-CelebA-HQ. The experimental results show that the proposed Swinv2-Imagen model outperforms several popular state-of-the-art methods.
HUMANISE: Language-conditioned Human Motion Generation in 3D Scenes

Learning to generate diverse scene-aware and goal-oriented human motions in 3D scenes remains challenging due to the mediocre characteristics of the existing datasets on Human-Scene Interaction (HSI); they only have limited scale/quality and lack semantics. To fill in the gap, we propose a large-scale and semantic-rich synthetic HSI dataset, denoted as HUMANISE, by aligning the captured human motion sequences with various 3D indoor scenes. We automatically annotate the aligned motions with language descriptions that depict the action and the unique interacting objects in the scene; e.g., sit on the armchair near the desk. HUMANISE thus enables a new generation task, language-conditioned human motion generation in 3D scenes. The proposed task is challenging as it requires joint modeling of the 3D scene, human motion, and natural language. To tackle this task, we present a novel scene-and-language conditioned generative model that can produce 3D human motions of the desirable action interacting with the specified objects. Our experiments demonstrate that our model generates diverse and semantically consistent human motions in 3D scenes.
Privacy Explanations - A Means to End-User Trust

Software systems are ubiquitous, and their use is ingrained in our everyday lives. They enable us to get in touch with people quickly and easily, support us in gathering information, and help us perform our daily tasks. In return, we provide these systems with a large amount of personal information, often unaware that this is jeopardizing our privacy. End users are typically unaware of what data is collected, for what purpose, who has access to it, and where and how it is stored. To address this issue, we looked into how explainability might help to tackle this problem. We created privacy explanations that aim to help to clarify to end users why and for what purposes speciﬁc data is required. We asked end users about privacy explanations in a survey and found that the majority of respondents (91.6 %) are generally interested in receiving privacy explanations. Our ﬁndings reveal that privacy explanations can be an important step towards increasing trust in software systems and can increase the privacy awareness of end users. These ﬁndings are a signiﬁcant step in developing privacy-aware systems and incorporating usable privacy features into them, assisting users in protecting their privacy.
Studies in gauge/string dualities

Stony Brook University Libraries, Department of Physics and Astronomy. Lawrence Martin (Dean of Graduate School), Martin Rocek (Professor C.N. Yang Institute for Theoretical Physics, Stony Brook University Dissertation Director), Peter van Nieuwenhuizen (Professor C.N. Yang Institute for Theoretical Physics, Stony Brook University, Chairman of Dissertation), Warren Siegal (Professor C.N. Yang Institute for Theoretical Physics, Stony Brook University), Axel Drees (Professor Department of Physics, Stony Brook University), Micheal Anderson (Professor Department of Mathematics, Stony Brook University, Outside Member).
ULN: Towards Underspecified Vision-and-Language Navigation

Vision-and-Language Navigation (VLN) is a task to guide an embodied agent moving to a target position using language instructions. Despite the significant performance improvement, the wide use of fine-grained instructions fails to characterize more practical linguistic variations in reality. To fill in this gap, we introduce a new setting, namely Underspecified vision-and-Language Navigation (ULN), and associated evaluation datasets. ULN evaluates agents using multi-level underspecified instructions instead of purely fine-grained or coarse-grained, which is a more realistic and general setting. As a primary step toward ULN, we propose a VLN framework that consists of a classification module, a navigation agent, and an Exploitation-to-Exploration (E2E) module. Specifically, we propose to learn Granularity Specific Sub-networks (GSS) for the agent to ground multi-level instructions with minimal additional parameters. Then, our E2E module estimates grounding uncertainty and conducts multi-step lookahead exploration to improve the success rate further. Experimental results show that existing VLN models are still brittle to multi-level language underspecification. Our framework is more robust and outperforms the baselines on ULN by ~10% relative success rate across all levels.