Miracle-dz/UniFAR-FTUs huggingface dataset:
Renormalization group in quantum critical theories with Harris-marginal disorder

We develop a renormalization group for weak Harris-marginal disorder in otherwise strongly interacting quantum critical theories, focusing on systems which have emergent conformal invariance. Using conformal perturbation theory, we argue that previously proposed random lines of fixed points with Lifshitz scaling in fact flow towards other universal fixed points, and this flow is captured by a “one-loop” analysis. Our approach appears best controlled in theories with only a few operators with low scaling dimension. In this regime, we compare our predictions for the flow of disorder to holographic models, and find complete agreement.
Feasibility of Remote Landmark Identification for Cricothyrotomy Using Robotic Palpation

Cricothyrotomy is a life-saving emergency intervention that secures an alternate airway route after a neck injury or obstruction. The procedure starts with identifying the correct location (the cricothyroid membrane) for creating an incision to insert an endotracheal tube. This location is determined using a combination of visual and palpation cues. Enabling robot-assisted remote cricothyrotomy may extend this life-saving procedure to injured soldiers or patients who may not be readily accessible for on-site intervention during search-and-rescue scenarios. As a first step towards achieving this goal, this paper explores the feasibility of palpation-assisted landmark identification for cricothyrotomy. Using a cricothyrotomy training simulator, we explore several alternatives for in-situ remote localization of the cricothyroid membrane. These alternatives include a) unaided telemanipulation, b) telemanipulation with direct force feedback, c) telemanipulation with superimposed motion excitation for on-line stiffness estimation and display, and d) fully autonomous palpation scan initialized based on the user’s understanding of key anatomical landmarks. Using the manually digitized cricothyroid membrane location as ground truth, we compare these four methods for accuracy and repeatability of identifying the landmark for cricothyrotomy, time of completion, and ease of use. These preliminary results suggest that the accuracy of remote cricothyrotomy landmark identification is improved when the user is aided with visual and force cues. They also show that, with proper user initialization, landmark identification using remote palpation is feasible - therefore satisfying a key prerequisite for future robotic solutions for remote cricothyrotomy.
Symplectic embeddings of 4-manifolds via Lefschetz fibrations

In this article we study proper symplectic and iso-symplectic embeddings of 4–manifolds in 6–manifolds. We show that a closed orientable smooth 4–manifold admitting a Lefschetz fibration over CP 1 admits a symplectic embedding in the symplectic manifold (CP 1 × CP 1 × CP , ωpr), where ωpr is the product symplectic form on CP 1 ×CP 1 ×CP . We also show that there exists a sub-critical Weinstein 6–manifold in which all finite type Weinstein 4–manifolds admit iso-symplectic embeddings.
Wolf: A Modular Estimation Framework for Robotics Based on Factor Graphs

This letter introduces Wolf, a C++ estimation framework based on factor graphs and targeted at mobile robotics. Wolf can be used beyond SLAM to handle self-calibration, model identification, or the observation of dynamic quantities other than localization. The architecture of Wolf allows for a modular yet tightly-coupled estimator. Modularity is enhanced via reusable plugins that are loaded at runtime depending on application setup. This setup is achieved conveniently through YAML files, allowing users to configure a wide range of applications without the need of writing or compiling code. Most procedures are coded as abstract algorithms in base classes with varying levels of specialization. Overall, all these assets allow for coherent processing and favor code re-usability and scalability. Wolf can be used with ROS, and is made publicly available and open to collaboration.
DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks

Machine learning models have been criticized for reflecting unfair biases in the training data. Instead of solving for this by introducing fair learning algorithms directly, we focus on generating fair synthetic data, such that any downstream learner is fair. Generating fair synthetic data from unfair data - while remaining truthful to the underlying data-generating process (DGP) - is non-trivial. In this paper, we introduce DECAF: a GAN-based fair synthetic data generator for tabular data. With DECAF we embed the DGP explicitly as a structural causal model in the input layers of the generator, allowing each variable to be reconstructed conditioned on its causal parents. This procedure enables inference time debiasing, where biased edges can be strategically removed for satisfying user-defined fairness requirements. The DECAF framework is versatile and compatible with several popular definitions of fairness. In our experiments, we show that DECAF successfully removes undesired bias and - in contrast to existing methods - is capable of generating high-quality synthetic data. Furthermore, we provide theoretical guarantees on the generator's convergence and the fairness of downstream models.
Uncertainty Quantification For Low-Rank Matrix Completion With Heterogeneous and Sub-Exponential Noise

The problem of low-rank matrix completion with heterogeneous and sub-exponential (as opposed to homogeneous and Gaussian) noise is particularly relevant to a number of applications in modern commerce. Examples include panel sales data and data collected from web-commerce systems such as recommendation engines. An important unresolved question for this problem is characterizing the distribution of estimated matrix entries under common low-rank estimators. Such a characterization is essential to any application that requires quantification of uncertainty in these estimates and has heretofore only been available under the assumption of homogenous Gaussian noise. Here we characterize the distribution of estimated matrix entries when the observation noise is heterogeneous sub-exponential and provide, as an application, explicit formulas for this distribution when observed entries are Poisson or Binary distributed.
The Countable-armed Bandit with Vanishing Arms

We consider a bandit problem with countably many arms, partitioned into finitely many types, each characterized by a unique mean reward. A non-stationary distribution governs the relative abundance of each arm-type in the population of arms, aka the arm-reservoir. This non-stationarity is attributable to a probabilistic leakage of “optimal” arms from the reservoir over time, which we refer to as the vanishing arms phenomenon; this induces a time-varying (potentially “endogenous,” policy-dependent) distribution over the reservoir. The objective is minimization of the expected cumulative regret. We characterize necessary and sufficient conditions for achievability of sub-linear regret in terms of a critical vanishing rate of optimal arms. We also discuss two reservoir distribution-oblivious algorithms that are long-run-average optimal whenever sub-linear regret is statistically achievable. Numerical experiments highlight a distinctive characteristic of this problem related to ex ante knowledge of the “gap” parameter (the difference between the top two mean rewards): in contrast to the stationary bandit formulation, regret in our setting may suffer substantial inflation under adaptive exploration-based (gap-oblivious) algorithms such as UCB vis-à-vis their non-adaptive forced exploration-based (gap-aware) counterparts like ETC.
A Closer Look at Few-Shot Video Classification: A New Baseline and Benchmark

The existing few-shot video classification methods often employ a meta-learning paradigm by designing customized temporal alignment module for similarity calculation. While significant progress has been made, these methods fail to focus on learning effective representations, and heavily rely on the ImageNet pre-training, which might be unreasonable for the few-shot recognition setting due to semantics overlap. In this paper, we aim to present an in-depth study on few-shot video classification by making three contributions. First, we perform a consistent comparative study on the existing metric-based methods to figure out their limitations in representation learning. Accordingly, we propose a simple classifier-based baseline without any temporal alignment that surprisingly outperforms the state-of-the-art meta-learning based methods. Second, we discover that there is a high correlation between the novel action class and the ImageNet object class, which is problematic in the few-shot recognition setting. Our results show that the performance of training from scratch drops significantly, which implies that the existing benchmarks cannot provide enough base data. Finally, we present a new benchmark with more base data to facilitate future few-shot video classification without pre-training. The code will be made available at https://github.com/MCG-NJU/FSL-Video.
Linear Contextual Bandits with Adversarial Corruptions

We study the linear contextual bandit problem in the presence of adversarial corruption, where the interaction between the player and a possibly infinite decision set is contaminated by an adversary that can corrupt the reward up to a corruption level $C$ measured by the sum of the largest alteration on rewards in each round. We present a variance-aware algorithm that is adaptive to the level of adversarial contamination $C$. The key algorithmic design includes (1) a multi-level partition scheme of the observed data, (2) a cascade of confidence sets that are adaptive to the level of the corruption, and (3) a variance-aware confidence set construction that can take advantage of low-variance reward. We further prove that the regret of the proposed algorithm is $\tilde{O}(C^2d\sqrt{\sum_{t = 1}^T \sigma_t^2} + C^2R\sqrt{dT})$, where $d$ is the dimension of context vectors, $T$ is the number of rounds, $R$ is the range of noise and $\sigma_t^2,t=1\ldots,T$ are the variances of instantaneous reward. We also prove a gap-dependent regret bound for the proposed algorithm, which is instance-dependent and thus leads to better performance on good practical instances. To the best of our knowledge, this is the first variance-aware corruption-robust algorithm for contextual bandits. Experiments on synthetic data corroborate our theory.
Learning-Augmented Dynamic Power Management with Multiple States via New Ski Rental Bounds

We study the online problem of minimizing power consumption in systems with multiple power-saving states. During idle periods of unknown lengths, an algorithm has to choose between power-saving states of different energy consumption and wake-up costs. We develop a learning-augmented online algorithm that makes decisions based on (potentially inaccurate) predicted lengths of the idle periods. The algorithm's performance is near-optimal when predictions are accurate and degrades gracefully with increasing prediction error, with a worst-case guarantee almost identical to the optimal classical online algorithm for the problem. A key ingredient in our approach is a new algorithm for the online ski rental problem in the learning augmented setting with tight dependence on the prediction error. We support our theoretical findings with experiments.
Mathematical Modeling for Holistic Convex Optimization of Hybrid Trains

We look into modeling fuel cell hybrid trains for the purpose of optimizing their operation using convex optimization. Models and constraints necessary to form a physically feasible yet convex problem are reviewed. This effort is described as holistic due to the broad consideration of train speed, energy management system, and battery thermals. The minimized objective is hydrogen fuel consumption for a given target journey time. A novel battery thermal model is proposed to aid with battery thermal management and thus preserve battery lifetime. All models are derived in the space-domain which along constraint relaxations guarantee a convex optimization problem. First-principle knowledge and real-world data justify the suitableness of the proposed models for the intended optimization problem.
Which Model To Trust: Assessing the Influence of Models on the Performance of Reinforcement Learning Algorithms for Continuous Control Tasks

The need for algorithms able to solve Reinforcement Learning (RL) problems with few trials has motivated the advent of model-based RL methods. The reported performance of model-based algorithms has dramatically increased within recent years. However, it is not clear how much of the recent progress is due to improved algorithms or due to improved models. While different modeling options are available to choose from when applying a model-based approach, the distinguishing traits and particular strengths of different models are not clear. The main contribution of this work lies precisely in assessing the model influence on the performance of RL algorithms. A set of commonly adopted models is established for the purpose of model comparison. These include Neural Networks (NNs), ensembles of NNs, two different approximations of Bayesian NNs (BNNs), that is, the Concrete Dropout NN and the Anchored Ensembling, and Gaussian Processes (GPs). The model comparison is evaluated on a suite of continuous control benchmarking tasks. Our results reveal that significant differences in model performance do exist. The Concrete Dropout NN reports persistently superior performance. We summarize these differences for the benefit of the modeler and suggest that the model choice is tailored to the standards required by each specific application.
Jet launching from merging magnetized binary neutron stars with realistic equations of state

We perform general relativistic, magnetohydrodynamic (GRMHD) simulations of binary neutron stars in quasi-circular orbit that merge and undergo delayed or prompt collapse to a black hole (BH). The stars are irrotational and modeled using an SLy or an H4 nuclear equation of state. To assess the impact of the initial magnetic field configuration on jet launching, we endow the stars with a purely poloidal magnetic field that is initially unimportant dynamically and is either confined to the stellar interior or extends from the interior into the exterior as in typical pulsars. Consistent with our previous results, we find that only the BH + disk remnants originating from binaries that form hypermassive neutron stars (HMNSs) and undergo delayed collapse can drive magneticallypowered jets. We find that the closer the total mass of the binary is to the threshold value for prompt collapse, the shorter is the time delay between the gravitational wave peak amplitude and jet launching. This time delay also strongly depends on the initial magnetic field configuration. We also find that seed magnetic fields confined to the stellar interior can launch a jet over ∼ 25 ms later than those with pulsar-like magnetic fields. The lifetime of the jet [∆t . 150 ms] and its outgoing Poynting luminosity [LEM ∼ 1052±1erg/s] are consistent with typical short gamma-ray burst central engine lifetimes, as well as with the Blandford–Znajek mechanism for launching jets and their associated Poynting luminosities. Our numerical results also suggest that the dynamical ejection of matter can be enhanced by the magnetic field. Therefore, GRMHD studies are required to fully understand kilonova signals from GW170818-like events.
Heterogeneous coefficients, control variables, and identification of multiple treatment effects

Multidimensional heterogeneity and endogeneity are important features of models with multiple treatments. We consider a heterogeneous coefficients model where the outcome is a linear combination of dummy treatment variables, with each variable representing a different kind of treatment. We use control variables to give necessary and sufficient conditions for identification of average treatment effects. With mutually exclusive treatments we find that, provided the heterogeneous coefficients are mean independent from treatments given the controls, a simple identification condition is that the generalized propensity scores (Imbens, 2000) be bounded away from zero and that their sum be bounded away from one, with probability one. Our analysis extends to distributional and quantile treatment effects, as well as corresponding treatment effects on the treated. These results generalize the classical identification result of Rosenbaum and Rubin (1983) for binary treatments.
AxoNN: An asynchronous, message-driven parallel framework for extreme-scale deep learning

In the last few years, the memory requirements to train state-of-the-art neural networks have far exceeded the DRAM capacities of modern hardware accelerators. This has necessitated the development of efficient algorithms to train these neural networks in parallel on large-scale GPU-based clusters. Since computation is relatively inexpensive on modern GPUs, designing and implementing extremely efficient communication in these parallel training algorithms is critical for extracting the maximum performance. This paper presents AxoNN, a parallel deep learning framework that exploits asynchrony and message-driven execution to schedule neural network operations on each GPU, thereby reducing GPU idle time and maximizing hardware efficiency. By using the CPU memory as a scratch space for offloading data periodically during training, AxoNN is able to reduce GPU memory consumption by four times. This allows us to increase the number of parameters per GPU by four times, thus reducing the amount of communication and increasing performance by over 13%. When tested against large transformer models with 12–100 billion parameters on 48–384 NVIDIA Tesla V100 GPUs, AxoNN achieves a per-GPU throughput of 49.4–54.78% of theoretical peak and reduces the training time by 22-37 days (15–25% speedup) as compared to the state-of-the-art.
Gender Systematics in the NRAO Proposal Review System

Several recent investigations indicate the existence of gender-related systematic trends in the peer review of proposals for observations on astronomical facilities. This includes the National Radio Astronomy Observatory (NRAO) where there is evidence of a gender imbalance in the rank of proposals with male principal investigators (PIs) favored over female PIs. Since semester 2017A (17A), the NRAO has taken the following steps: (1) inform science review panels (SRPs) and the telescope time allocation committee (TAC) about the gender imbalance; and (2) increase the female representation on SRPs and the TAC to reflect the community demographics. Here we analyze SRP normalized rank-ordered scores, or linear ranks, by PI gender for NRAO observing proposals from semesters 12A–21A. We use bootstrap resampling to generate modeled distributions and the Anderson–Darling (AD) test to evaluate the probability that the linear rank distributions for male and female PIs are drawn from the same parent sample. We find that between semesters 12A–17A that male PIs are favored over female PIs (AD p-value 0.0084), whereas between semesters 17B–21A female PIs are favored over male PIs, but at a lower significance (AD p-value 0.11). Therefore the gender imbalance is currently being ameliorated, but this imbalance may have been reversed. Regardless, we plan to adopt a dual-anonymous approach to proposal review to reduce the possibility of bias to occur.
Gaussian Process Sampling and Optimization with Approximate Upper and Lower Bounds

Many functions have approximately-known upper and/or lower bounds, potentially aiding the modeling of such functions. In this paper, we introduce Gaussian process models for functions where such bounds are (approximately) known. More specifically, we propose the first use of such bounds to improve Gaussian process (GP) posterior sampling and Bayesian optimization (BO). That is, we transform a GP model satisfying the given bounds, and then sample and weight functions from its posterior. To further exploit these bounds in BO settings, we present bounded entropy search (BES) to select the point gaining the most information about the underlying function, estimated by the GP samples, while satisfying the output constraints. We characterize the sample variance bounds and show that the decision made by BES is explainable. Our proposed approach is conceptually straightforward and can be used as a plug in extension to existing methods for GP posterior sampling and Bayesian optimization.
The SSA22 H i Tomography Survey (SSA22-HIT). I. Data Set and Compiled Redshift Catalog

We conducted a deep spectroscopic survey, named SSA22-HIT, in the SSA22 field with the DEep Imaging MultiObject Spectrograph (DEIMOS) on the Keck telescope, designed to tomographically map high-z H i gas through analysis of Lyα absorption in background galaxies’ spectra. In total, 198 galaxies were spectroscopically confirmed at 2.5 < z < 6 with a few low-z exceptions in the 26 × 15 arcmin2 area, of which 148 were newly determined in this study. Our redshift measurements were merged with previously confirmed redshifts available in the 34 × 27 arcmin2 area of the SSA22 field. This compiled catalog containing 730 galaxies of various types at z > 2 is useful for various applications, and it is made publicly available. Our SSA22-HIT survey has increased by approximately twice the number of spectroscopic redshifts of sources at z > 3.2 in the observed field. From a comparison with publicly available redshift catalogs, we show that our compiled redshift catalog in the SSA22 field is comparable to those among major extragalactic survey fields in terms of a combination of wide area and high surface number density of objects at z > 2. About 40% of the spectroscopically confirmed objects in SSA22-HIT show reasonable quality of spectra in the wavelengths shorter than Lyα when a sufficient amount of smoothing is adopted. Our data set enables us to make the H i tomographic map at z ≳ 3, which we present in a parallel study.
Highly Efficient Natural Image Matting

Over the last few years, deep learning based approaches have achieved outstanding improvements in natural image matting. However, there are still two drawbacks that impede the widespread application of image matting: the reliance on user-provided trimaps and the heavy model sizes. In this paper, we propose a trimap-free natural image matting method with a lightweight model. With a lightweight basic convolution block, we build a two-stages framework: Segmentation Network (SN) is designed to capture sufficient semantics and classify the pixels into unknown, foreground and background regions; Matting Refine Network (MRN) aims at capturing detailed texture information and regressing accurate alpha values. With the proposed cross-level fusion Module (CFM), SN can efficiently utilize multi-scale features with less computational cost. Efficient non-local attention module (ENA) in MRN can efficiently model the relevance between different pixels and help regress high-quality alpha values. Utilizing these techniques, we construct an extremely light-weighted model, which achieves comparable performance with ~1\% parameters (344k) of large models on popular natural image matting benchmarks.