Miracle-dz/UniFAR-FTUs huggingface dataset:
Semiparametric Efficient G-estimation with Invalid Instrumental Variables

The instrumental variable method is widely used in the health and social sciences for identification and estimation of causal effects in the presence of potential unmeasured confounding. In order to improve efficiency, multiple instruments are routinely used, leading to concerns about bias due to possible violation of the instrumental variable assumptions. To address this concern, we introduce a new class of G-estimators that are guaranteed to remain consistent and asymptotically normal for the causal effect of interest provided that a set of at least γ out of K candidate instruments are valid, for γ≤K set by the analyst ex ante, without necessarily knowing the identity of the valid and invalid instruments. We provide formal semiparametric efficiency theory supporting our results. Both simulation studies and applications to the UK Biobank data demonstrate the superior empirical performance of our estimators compared to competing methods.
Improving sandwich variance estimation for marginal Cox analysis of cluster randomized trials

Cluster randomized trials (CRTs) frequently recruit a small number of clusters, therefore necessitating the application of small‐sample corrections for valid inference. A recent systematic review indicated that CRTs reporting right‐censored, time‐to‐event outcomes are not uncommon and that the marginal Cox proportional hazards model is one of the common approaches used for primary analysis. While small‐sample corrections have been studied under marginal models with continuous, binary, and count outcomes, no prior research has been devoted to the development and evaluation of bias‐corrected sandwich variance estimators when clustered time‐to‐event outcomes are analyzed by the marginal Cox model. To improve current practice, we propose nine bias‐corrected sandwich variance estimators for the analysis of CRTs using the marginal Cox model and report on a simulation study to evaluate their small‐sample properties. Our results indicate that the optimal choice of bias‐corrected sandwich variance estimator for CRTs with survival outcomes can depend on the variability of cluster sizes and can also slightly differ whether it is evaluated according to relative bias or type I error rate. Finally, we illustrate the new variance estimators in a real‐world CRT where the conclusion about intervention effectiveness differs depending on the use of small‐sample bias corrections. The proposed sandwich variance estimators are implemented in an R package CoxBcv.
Rational Exponents Near Two

A longstanding conjecture of Erd˝os and Simonovits states that for every rational r between 1 and 2 there is a graph H such that the largest number of edges in an H-free graph on n vertices is Q(nr). Answering a question raised by Jiang, Jiang and Ma, we show that the conjecture holds for all rationals of the form 2􀀀a=b with b sufficiently large in terms of a.
RGB-D SLAM in Indoor Planar Environments With Multiple Large Dynamic Objects

This work presents a novel dense RGB-D SLAM approach for dynamic planar environments that enables simultaneous multi-object tracking, camera localisation and background reconstruction. Previous dynamic SLAM methods either rely on semantic segmentation to directly detect dynamic objects; or assume that dynamic objects occupy a smaller proportion of the camera view than the static background and can, therefore, be removed as outliers. With the aid of camera motion prior, our approach enables dense SLAM when the camera view is largely occluded by multiple dynamic objects. The dynamic planar objects are separated by their different rigid motions and tracked independently. The remaining dynamic non-planar areas are removed as outliers and not mapped into the background. The evaluation demonstrates that our approach outperforms the state-of-the-art methods in terms of localisation, mapping, dynamic segmentation and object tracking. We also demonstrate its robustness to large drift in the camera motion prior.
HEAR 2021: Holistic Evaluation of Audio Representations

What audio embedding approach generalizes best to a wide range of downstream tasks across a variety of everyday domains without ﬁne-tuning? The aim of the HEAR 2021 NeurIPS challenge is to develop a general-purpose audio representation that provides a strong basis for learning in a wide variety of tasks and scenarios. HEAR 2021 evaluates audio representations using a benchmark suite across a variety of domains, including speech, environmental sound, and music. In the spirit of shared exchange, each participant submitted an audio embedding model following a common API that is general-purpose, open-source, and freely available to use. Twenty-nine models by thirteen external teams were evaluated on nineteen diverse downstream tasks derived from sixteen datasets. Open evaluation code, submitted models and datasets are key contributions, enabling comprehensive and reproducible evaluation, as well as previously impossible longitudinal studies. It still remains an open question whether one single general-purpose audio representation can perform as holistically as the human ear.
On uniform controllability of 1D transport equations in the vanishing viscosity limit

We consider a one dimensional transport equation with varying vector field and a small viscosity coefficient, controlled by one endpoint of the interval. We give upper and lower bounds on the minimal time needed to control to zero, uniformly in the vanishing viscosity limit. We assume that the vector field varies on the whole interval except at one point. The upper/lower estimates we obtain depend on geometric quantities such as an Agmon distance and the spectral gap of an associated semiclassical Schr{\"o}dinger operator. They improve, in this particular situation, the results obtained in the companion paper [LL21]. The proofs rely on a reformulation of the problem as a uniform observability question for the semiclassical heat equation together with a fine analysis of localization of eigenfunctions both in the semiclassically allowed and forbidden regions [LL22], together with estimates on the spectral gap [HS84, All98]. Along the proofs, we provide with a construction of biorthogonal families with fine explicit bounds, which we believe is of independent interest.
Compartmental Models for COVID-19 and Control via Policy Interventions

We demonstrate an approach to replicate and forecast the spread of the SARS-CoV-2 (COVID-19) pandemic using the toolkit of probabilistic programming languages (PPLs). Our goal is to study the impact of various modeling assumptions and motivate policy interventions enacted to limit the spread of infectious diseases. Using existing compartmental models we show how to use inference in PPLs to obtain posterior estimates for disease parameters. We improve popular existing models to reflect practical considerations such as the under-reporting of the true number of COVID-19 cases and motivate the need to model policy interventions for real-world data. We design an SEI3RD model as a reusable template and demonstrate its flexibility in comparison to other models. We also provide a greedy algorithm that selects the optimal series of policy interventions that are likely to control the infected population subject to provided constraints. We work within a simple, modular, and reproducible framework to enable immediate cross-domain access to the state-of-the-art in probabilistic inference with emphasis on policy interventions. We are not epidemiologists;the sole aim of this study is to serve as an exposition of methods, not to directly infer the real-world impact of policy-making for COVID-19.
Approximation Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II)

Recent theoretical works have shown that the NSGA-II efficiently computes the full Pareto front when the population size is large enough. In this work, we study how well it approximates the Pareto front when the population size is smaller. For the OneMinMax benchmark, we point out situations in which the parents and offspring cover well the Pareto front, but the next population has large gaps on the Pareto front. Our mathematical proofs suggest as reason for this undesirable behavior that the NSGA-II in the selection stage computes the crowding distance once and then removes individuals with smallest crowding distance without considering that a removal increases the crowding distance of some individuals. We then analyze two variants not prone to this problem. For the NSGA-II that updates the crowding distance after each removal (Kukkonen and Deb (2006)) and the steady-state NSGA-II (Nebro and Durillo (2009)), we prove that the gaps in the Pareto front are never more than a small constant factor larger than the theoretical minimum. This is the first mathematical work on the approximation ability of the NSGA-II and the first runtime analysis for the steady-state NSGA-II. Experiments also show the superior approximation ability of the two NSGA-II variants.
Maximizing Conditional Independence for Unsupervised Domain Adaptation

Unsupervised domain adaptation studies how to transfer a learner from a labeled source domain to an unlabeled target domain with different distributions. Existing methods mainly focus on matching the marginal distributions of the source and target domains, which probably lead a misalignment of samples from the same class but different domains. In this paper, we deal with this misalignment by achieving the class-conditioned transferring from a new perspective. We aim to maximize the conditional independence of feature and domain given class in the reproducing kernel Hilbert space. The optimization of the conditional independence measure can be viewed as minimizing a surrogate of a certain mutual information between feature and domain. An interpretable empirical estimation of the conditional dependence is deduced and connected with the unconditional case. Besides, we provide an upper bound on the target error by taking the class-conditional distribution into account, which provides a new theoretical insight for most class-conditioned transferring methods. In addition to unsupervised domain adaptation, we extend our method to the multi-source scenario in a natural and elegant way. Extensive experiments on four benchmarks validate the effectiveness of the proposed models in both unsupervised domain adaptation and multiple source domain adaptation.
Reducing impacts of hopper dredging on marine turtles in the northwestern Gulf of Mexico

NORTHWESTERN GULF OF MEXICO by GARY W. SUNDIN (Under the Direction of Sara H. Schweitzer) ABSTRACT Three species of threatened or endangered marine turtles are sometimes harmed or killed by hopper dredges in U.S. shipping channels: loggerhead (Caretta caretta), green (Chelonia mydas), and Kemp’s ridley (Lepidochelys kempii). The U.S. Army Corps of Engineers manages dredging in these channels and works to monitor and mitigate negative impacts on turtles. I analyzed Corps of Engineers data to examine turtle behavior and relative abundance in shipping channels in the northwestern Gulf of Mexico, and provide information to reduce dredge-turtle interactions in the region. Turtles were taken by dredges and captured by trawls more frequently in southern channels relative to northern channels. Dredge take and trawl capture rates were greatest during March-June relative to other periods. Projects using relocation experienced fewer takes, on average, than projects without relocation trawling. Dredge hopper size and drag arm configuration, sea surface temperature, and period of day also affected rates of take and capture.
Deep Partial Multiplex Network Embedding

Network embedding is an effective technique to learn the low-dimensional representations of nodes in networks. Real-world networks are usually with multiplex or having multi-view representations from different relations. Recently, there has been increasing interest in network embedding on multiplex data. However, most existing multiplex approaches assume that the data is complete in all views. But in real applications, it is often the case that each view suffers from the missing of some data and therefore results in partial multiplex data. In this paper, we present a novel Deep Partial Multiplex Network Embedding approach to deal with incomplete data. In particular, the network embeddings are learned by simultaneously minimizing the deep reconstruction loss with the autoencoder neural network, enforcing the data consistency across views via common latent subspace learning, and preserving the data topological structure within the same network through graph Laplacian. We further prove the orthogonal invariant property of the learned embeddings and connect our approach with the binary embedding techniques. Experiments on four multiplex benchmarks demonstrate the superior performance of the proposed approach over several state-of-the-art methods on node classification, link prediction and clustering tasks.
Sequential Gaussian approximation for nonstationary time series in high dimensions

Gaussian couplings of partial sum processes are derived for the high-dimensional regime $d=o(n^{1/3})$. The coupling is derived for sums of independent random vectors and subsequently extended to nonstationary time series. Our inequalities depend explicitly on the dimension and on a measure of nonstationarity, and are thus also applicable to arrays of random vectors. To enable high-dimensional statistical inference, a feasible Gaussian approximation scheme is proposed. Applications to sequential testing and change-point detection are described.
Reliably Re-Acting to Partner's Actions with the Social Intrinsic Motivation of Transfer Empowerment

We consider multi-agent reinforcement learning (MARL) for cooperative communication and coordination tasks. MARL agents can be brittle because they can overfit their training partners' policies. This overfitting can produce agents that adopt policies that act under the expectation that other agents will act in a certain way rather than react to their actions. Our objective is to bias the learning process towards finding reactive strategies towards other agents' behaviors. Our method, transfer empowerment, measures the potential influence between agents' actions. Results from three simulated cooperation scenarios support our hypothesis that transfer empowerment improves MARL performance. We discuss how transfer empowerment could be a useful principle to guide multi-agent coordination by ensuring reactiveness to one's partner.
The Lingering Death of Periodic Near-Sun Comet 323P/SOHO

We observed near-Sun comet 323P/SOHO for the first time using ground and space telescopes. In late 2020 December, the object was recovered at Subaru showing no cometary features on its way to perihelion. However, in our postperihelion observations, it developed a long narrow tail mimicking a disintegrated comet. The ejecta, composed of at least millimeter-sized dust with power-law size distribution index 3.2 ± 0.2, was impulsively produced shortly after the perihelion passage, during which ≳0.1%–10% of the nucleus mass was shed due to excessive thermal stress and rotational disruption. Two fragments of ∼20 m in radius (assuming a geometric albedo of 0.15) were seen in Hubble Space Telescope observations from early 2021 March. The nucleus, with an effective radius of 86 ± 3 m (the same albedo assumed) and an aspect ratio of ∼0.7, has a rotation period of 0.522 hr, which is the shortest for known comets in the solar system and implies cohesive strength ≳10–100 Pa in the interior. The color of the object was freakish and changed temporally in a never-before-seen manner. Using our astrometry, we found a strong nongravitational effect following a heliocentric dependency of rH−8.5 in the transverse motion of the object. Our N-body integration reveals that 323P has a likelihood of 99.7% to collide with the Sun in the next two millennia driven by the ν 6 secular resonance.
Generalization Through The Lens Of Leave-One-Out Error

Despite the tremendous empirical success of deep learning models to solve various learning tasks, our theoretical understanding of their generalization ability is very limited. Classical generalization bounds based on tools such as the VC dimension or Rademacher complexity, are so far unsuitable for deep models and it is doubtful that these techniques can yield tight bounds even in the most idealistic settings (Nagarajan&Kolter, 2019). In this work, we instead revisit the concept of leave-one-out (LOO) error to measure the generalization ability of deep models in the so-called kernel regime. While popular in statistics, the LOO error has been largely overlooked in the context of deep learning. By building upon the recently established connection between neural networks and kernel learning, we leverage the closed-form expression for the leave-one-out error, giving us access to an efficient proxy for the test error. We show both theoretically and empirically that the leave-one-out error is capable of capturing various phenomena in generalization theory, such as double descent, random labels or transfer learning. Our work therefore demonstrates that the leave-one-out error provides a tractable way to estimate the generalization ability of deep neural networks in the kernel regime, opening the door to potential, new research directions in the field of generalization.
Screentone-Preserved Manga Retargeting

As a popular comic style, manga offers a unique impression by utilizing a rich set of bitonal patterns, or screentones, for illustration. However, screentones can easily be contaminated with visual-unpleasant aliasing and/or blurriness after resampling, which harms its visualization on displays of diverse resolutions. To address this problem, we propose the first manga retargeting method that synthesizes a rescaled manga image while retaining the screentone in each screened region. This is a non-trivial task as accurate region-wise segmentation remains challenging. Fortunately, the rescaled manga shares the same region-wise screentone correspondences with the original manga, which enables us to simplify the screentone synthesis problem as an anchor-based proposals selection and rearrangement problem. Specifically, we design a novel manga sampling strategy to generate aliasing-free screentone proposals, based on hierarchical grid-based anchors that connect the correspondences between the original and the target rescaled manga. Furthermore, a Recurrent Proposal Selection Module (RPSM) is proposed to adaptively integrate these proposals for target screentone synthesis. Besides, to deal with the translation insensitivity nature of screentones, we propose a translation-invariant screentone loss to facilitate the training convergence. Extensive qualitative and quantitative experiments are conducted to verify the effectiveness of our method, and notably compelling results are achieved compared to existing alternative techniques.
Continual and Sliding Window Release for Private Empirical Risk Minimization

It is difficult to continually update private machine learning models with new data while maintaining privacy. Data incur increasing privacy loss -- as measured by differential privacy -- when they are used in repeated computations. In this paper, we describe regularized empirical risk minimization algorithms that continually release models for a recent window of data. One version of the algorithm uses the entire data history to improve the model for the recent window. The second version uses a sliding window of constant size to improve the model, ensuring more relevant models in case of evolving data. The algorithms operate in the framework of stochastic gradient descent. We prove that even with releasing a model at each time-step over an infinite time horizon, the privacy cost of any data point is bounded by a constant $\epsilon$ differential privacy, and the accuracy of the output models are close to optimal. Experiments on MNIST and Arxiv publications data show results consistent with the theory.
Fast and Data Efficient Reinforcement Learning from Pixels via Non-Parametric Value Approximation

We present Nonparametric Approximation of Inter-Trace returns (NAIT), a Reinforcement Learning algorithm for discrete action, pixel-based environments that is both highly sample and computation efficient. NAIT is a lazy-learning approach with an update that is equivalent to episodic Monte-Carlo on episode completion, but that allows the stable incorporation of rewards while an episode is ongoing. We make use of a fixed domain-agnostic representation, simple distance based exploration and a proximity graph-based lookup to facilitate extremely fast execution. We empirically evaluate NAIT on both the 26 and 57 game variants of ATARI100k where, despite its simplicity, it achieves competitive performance in the online setting with greater than 100x speedup in wall-time.
The Two Hot Corinos of the SVS13-A Protostellar Binary System: Counterposed Siblings

We present ALMA high-angular-resolution (∼50 au) observations of the Class I binary system SVS13-A. We report images of SVS13-A in numerous interstellar complex organic molecules: CH3OH, 13CH3OH, CH3CHO, CH3OCH3, and NH2CHO. Two hot corinos at different velocities are imaged in VLA4A (V sys = +7.7 km s−1) and VLA4B (V sys = +8.5 km s−1). From a non-LTE analysis of methanol lines, we derive a gas density of 3 × 108 cm−3 and gas temperatures of 140 and 170 K for VLA4A and VLA4B, respectively. For the other species, the column densities are derived from an LTE analysis. Formamide, which is the only N-bearing species detected in our observations, is more prominent around VLA4A, while dimethyl ether, methanol, and acetaldehyde are associated with both VLA4A and VLA4B. We derive in the two hot corinos abundance ratios of ∼1 for CH3OH, 13CH3OH, and CH3OCH3; ∼2 for CH3CHO; and ∼4 for NH2CHO. The present data set supports chemical segregation between the different species inside the binary system. The emerging picture is that of an onion-like structure of the two SVS13-A hot corinos, caused by the different binding energies of the species, also supported by ad hoc quantum chemistry calculations. In addition, the comparison between molecular and dust maps suggests that the interstellar complex organic molecules emission originates from slow shocks produced by accretion streamers impacting the VLA4A and VLA4B disks and enriching the gas-phase component.
A Survey for Solving Mixed Integer Programming via Machine Learning

This paper surveys the trend of leveraging machine learning to solve mixed integer programming (MIP) problems. Theoretically, MIP is an NP-hard problem, and most of the combinatorial optimization (CO) problems can be formulated as the MIP. Like other CO problems, the human-designed heuristic algorithms for MIP rely on good initial solutions and cost a lot of computational resources. Therefore, we consider applying machine learning methods to solve MIP, since ML-enhanced approaches can provide the solution based on the typical patterns from the historical data. In this paper, we first introduce the formulation and preliminaries of MIP and several traditional algorithms to solve MIP. Then, we advocate further promoting the different integration of machine learning and MIP and introducing related learning-based methods, which can be classified into exact algorithms and heuristic algorithms. Finally, we propose the outlook for learning-based MIP solvers, direction towards more combinatorial optimization problems beyond MIP, and also the mutual embrace of traditional solvers and machine learning components.
Curvature in the balance: the Weyl functional and scalar curvature of $4$-manifolds

The infimum of the Weyl functional is shown to be surprisingly small on many compact 4-manifolds that admit positive-scalar-curvature metrics. Results are also proved that systematically compare the scalar and self-dual Weyl curvatures of certain almost-Kaehler 4-manifolds.