Miracle-dz/UniFAR-FTUs huggingface dataset:
COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks

As reinforcement learning (RL) has achieved near human-level performance in a variety of tasks, its robustness has raised great attention. While a vast body of research has explored test-time (evasion) attacks in RL and corresponding defenses, its robustness against training-time (poisoning) attacks remains largely unanswered. In this work, we focus on certifying the robustness of offline RL in the presence of poisoning attacks, where a subset of training trajectories could be arbitrarily manipulated. We propose the first certification framework, COPA, to certify the number of poisoning trajectories that can be tolerated regarding different certification criteria. Given the complex structure of RL, we propose two certification criteria: per-state action stability and cumulative reward bound. To further improve the certification, we propose new partition and aggregation protocols to train robust policies. We further prove that some of the proposed certification methods are theoretically tight and some are NP-Complete problems. We leverage COPA to certify three RL environments trained with different algorithms and conclude: (1) The proposed robust aggregation protocols such as temporal aggregation can significantly improve the certifications; (2) Our certification for both per-state action stability and cumulative reward bound are efficient and tight; (3) The certification for different training algorithms and environments are different, implying their intrinsic robustness properties. All experimental results are available at https://copa-leaderboard.github. io.
Attacking deep networks with surrogate-based adversarial black-box methods is easy

A recent line of work on black-box adversarial attacks has revived the use of transfer from surrogate models by integrating it into query-based search. However, we find that existing approaches of this type underperform their potential, and can be overly complicated besides. Here, we provide a short and simple algorithm which achieves state-of-the-art results through a search which uses the surrogate network's class-score gradients, with no need for other priors or heuristics. The guiding assumption of the algorithm is that the studied networks are in a fundamental sense learning similar functions, and that a transfer attack from one to the other should thus be fairly"easy". This assumption is validated by the extremely low query counts and failure rates achieved: e.g. an untargeted attack on a VGG-16 ImageNet network using a ResNet-152 as the surrogate yields a median query count of 6 at a success rate of 99.9%. Code is available at https://github.com/fiveai/GFCS.
Occlusion Fields: An Implicit Representation for Non-Line-of-Sight Surface Reconstruction

Non-line-of-sight reconstruction (NLoS) is a novel indirect imaging modality that aims to recover objects or scene parts outside the field of view from measurements of light that is indirectly scattered off a directly visible, diffuse wall. Despite recent advances in acquisition and reconstruction techniques, the well-posedness of the problem at large, and the recoverability of objects and their shapes in particular, remains an open question. The commonly employed Fermat path criterion is rather conservative with this regard, as it classifies some surfaces as unrecoverable, although they contribute to the signal. In this paper, we use a simpler necessary criterion for an opaque surface patch to be recoverable. Such piece of surface must be directly visible from some point on the wall, and it must occlude the space behind itself. Inspired by recent advances in neural implicit representations, we de-vise a new representation and reconstruction technique for NLoS scenes that unifies the treatment of recoverability with the reconstruction itself. Our approach, which we validate on various synthetic and experimental datasets, exhibits interesting properties. Unlike memory-inefficient volumetric representations, ours allows to infer adaptively tessellated surfaces from time-of-flight measurements of moderate resolution. It can further recover features beyond the Fermat path criterion, and it is robust to significant amounts of self-occlusion. We believe that this is the first time that these properties have been achieved in one system that, as an addi-tional benefit, is trainable and hence suited for data-driven approaches.
KinyaBERT: a Morphology-aware Kinyarwanda Language Model

Pre-trained language models such as BERT have been successful at tackling many natural language processing tasks. However, the unsupervised sub-word tokenization methods commonly used in these models (e.g., byte-pair encoding - BPE) are sub-optimal at handling morphologically rich languages. Even given a morphological analyzer, naive sequencing of morphemes into a standard BERT architecture is inefficient at capturing morphological compositionality and expressing word-relative syntactic regularities. We address these challenges by proposing a simple yet effective two-tier BERT architecture that leverages a morphological analyzer and explicitly represents morphological compositionality.Despite the success of BERT, most of its evaluations have been conducted on high-resource languages, obscuring its applicability on low-resource languages. We evaluate our proposed method on the low-resource morphologically rich Kinyarwanda language, naming the proposed model architecture KinyaBERT. A robust set of experimental results reveal that KinyaBERT outperforms solid baselines by 2% in F1 score on a named entity recognition task and by 4.3% in average score of a machine-translated GLUE benchmark. KinyaBERT fine-tuning has better convergence and achieves more robust results on multiple tasks even in the presence of translation noise.
Data Efficient 3D Learner via Knowledge Transferred from 2D Model

Collecting and labeling the registered 3D point cloud is costly. As a result, 3D resources for training are typically limited in quantity compared to the 2D images counterpart. In this work, we deal with the data scarcity challenge of 3D tasks by transferring knowledge from strong 2D models via RGB-D images. Specifically, we utilize a strong and well-trained semantic segmentation model for 2D images to augment RGB-D images with pseudo-label. The augmented dataset can then be used to pre-train 3D models. Finally, by simply fine-tuning on a few labeled 3D instances, our method already outperforms existing state-of-the-art that is tailored for 3D label efficiency. We also show that the results of mean-teacher and entropy minimization can be improved by our pre-training, suggesting that the transferred knowledge is helpful in semi-supervised setting. We verify the effectiveness of our approach on two popular 3D models and three different tasks. On ScanNet official evaluation, we establish new state-of-the-art semantic segmentation results on the data-efficient track.
Undersmoothing Causal Estimators With Generative Trees

Average causal effects are averages of (heterogeneous) individual treatment effects (ITEs) taken over the entire target population. The estimation of average causal effects has been studied in depth, but averages are insufficient for more individualised decision-making where ITEs are more appropriate. However, estimating ITEs for every population member is challenging, particularly when estimation must be based on observational data rather than data from randomised experiments. One potential problem with observational data arises when there are large differences between the sample distributions of the input features of the treated and control units. This problem is known as covariate shift. It can lead to model misspecification the harmful effects of which can be severe for ITE estimation because point estimation is highly sensitive to regions of the common support of the input space in which the number of treated or control units is very small. Moreover, common solutions are often based on reweighing schemes involving propensity scores which were originally designed for average effects and not ITEs. In this paper, we propose Debiasing Generative Trees, a novel data augmentation method based on generative trees that debiases and undersmooths causal estimators trained on augmented data. It encourages higher modelling complexity that reduces misspecification and improves estimation of ITEs. We show empirically that our proposed approach yields models of higher complexity and more accurate predictions of ITEs, and is competitive with traditional methods for estimating average treatment effects. Our results confirm that reweighing methods can struggle with ITE estimation and that the choice of model class can significantly impact prediction performance.
Panini-Net: GAN Prior Based Degradation-Aware Feature Interpolation for Face Restoration

Emerging high-quality face restoration (FR) methods often utilize pre-trained GAN models (i.e., StyleGAN2) as GAN Prior. However, these methods usually struggle to balance realness and fidelity when facing various degradation levels. Besides, there is still a noticeable visual quality gap compared with pre-trained GAN models. In this paper, we propose a novel GAN Prior based degradation-aware feature interpolation network, dubbed Panini-Net, for FR tasks by explicitly learning the abstract representations to distinguish various degradations. Specifically, an unsupervised degradation representation learning (UDRL) strategy is first developed to extract degradation representations (DR) of the input degraded images. Then, a degradation-aware feature interpolation (DAFI) module is proposed to dynamically fuse the two types of informative features (i.e., features from input images and features from GAN Prior) with flexible adaption to various degradations based on DR. Ablation studies reveal the working mechanism of DAFI and its potential for editable FR. Extensive experiments demonstrate that our Panini-Net achieves state-of-the-art performance for multi-degradation face restoration and face super-resolution. The source code is available at https://github.com/jianzhangcs/panini.
E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning

The ability to recognize analogies is fundamental to human cognition. Existing benchmarks to test word analogy do not reveal the underneath process of analogical reasoning of neural models. Holding the belief that models capable of reasoning should be right for the right reasons, we propose a first-of-its-kind Explainable Knowledge-intensive Analogical Reasoning benchmark (E-KAR). Our benchmark consists of 1,655 (in Chinese) and 1,251 (in English) problems sourced from the Civil Service Exams, which require intensive background knowledge to solve. More importantly, we design a free-text explanation scheme to explain whether an analogy should be drawn, and manually annotate them for each and every question and candidate answer. Empirical results suggest that this benchmark is very challenging for some state-of-the-art models for both explanation generation and analogical question answering tasks, which invites further research in this area.
Absorption Line Search through Three Local Group Dwarf Galaxy Halos

Dwarf galaxies are missing nearly all of their baryons and metals from the stellar disk, which are presumed to be in a bound halo or expelled beyond the virial radius. The virial temperature for galaxies with M h ∼ 109–1010 M ⊙ is similar to the collisional ionization equilibrium temperature for the C iv ion. We search for UV absorption from C iv in six sightlines toward three dwarf galaxies in the anti-M31 direction and at the periphery of the Local Group (D ≈ 1.3 Mpc; Sextans A, Sextans B, and NGC 3109). The C iv doublet is detected in only one of six sightlines, toward Sextans A, with logN(CIV)=13.06±0.08 . This is consistent with our gaseous halo models, where the halo gas mass is determined by the cooling rate, feedback, and the star formation rate; the inclusion of photoionization is an essential ingredient. This model can also reproduce the higher detection rate of O vi absorption in other dwarf samples (beyond the Local Group), with C iv only detectable within ∼0.5R vir.
DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation

This paper proposes a simple baseline framework for video-based 2D/3D human pose estimation that can achieve 10 times efficiency improvement over existing works without any performance degradation, named DeciWatch. Unlike current solutions that estimate each frame in a video, DeciWatch introduces a simple yet effective sample-denoise-recover framework that only watches sparsely sampled frames, taking advantage of the continuity of human motions and the lightweight pose representation. Specifically, DeciWatch uniformly samples less than 10% video frames for detailed estimation, denoises the estimated 2D/3D poses with an efficient Transformer architecture, and then accurately recovers the rest of the frames using another Transformer-based network. Comprehensive experimental results on three video-based human pose estimation and body mesh recovery tasks with four datasets validate the efficiency and effectiveness of DeciWatch. Code is available at https://github.com/cure-lab/DeciWatch.
Snowmass 2021 White Paper: The Selena Neutrino Experiment

Imaging devices made from an ionization target layer of amorphous selenium (aSe) coupled to a silicon complementary metal-oxide-semiconductor (CMOS) active pixel array for charge readout are a promising technology for neutrino physics. The high spatial resolution in a solid-state target provides unparalleled rejection of backgrounds from natural radioactivity in the search for neutrinoless $\beta\beta$ decay and for electron neutrino ($\nu_e$) spectroscopy with $^{82}$Se. In this white paper, we summarize the broad scientific program of a large imaging detector with a 10-ton target of $^{82}$Se. We review the detector technology, and outline the ongoing research program to realize this experiment.
Data Contamination: From Memorization to Exploitation

Pretrained language models are typically trained on massive web-based datasets, which are often “contaminated” with downstream test sets. It is not clear to what extent models exploit the contaminated data for downstream tasks. We present a principled method to study this question. We pretrain BERT models on joint corpora of Wikipedia and labeled downstream datasets, and fine-tune them on the relevant task. Comparing performance between samples seen and unseen during pretraining enables us to define and quantify levels of memorization and exploitation.Experiments with two models and three downstream tasks show that exploitation exists in some cases, but in others the models memorize the contaminated data, but do not exploit it. We show that these two measures are affected by different factors such as the number of duplications of the contaminated data and the model size. Our results highlight the importance of analyzing massive web-scale datasets to verify that progress in NLP is obtained by better language understanding and not better data exploitation.
Spin-Optical Dynamics and Quantum Efficiency of a Single V1 Center in Silicon Carbide

Color centers in silicon carbide are emerging candidates for distributed spin-based quantum applications due to the scalability of host materials and the demonstration of integration into nanophotonic resonators. Recently, silicon vacancy centers in silicon carbide have been identified as a promising system with excellent spin and optical properties. Here, we in-depth study the spin-optical dynamics of single silicon vacancy center at hexagonal lattice sites, namely V1, in 4H-polytype silicon carbide. By utilizing resonant and above-resonant sub-lifetime pulsed excitation, we determine spin-dependent excited-state lifetimes and intersystem-crossing rates. Our approach to inferring the intersystem-crossing rates is based on all-optical pulsed initialization and readout scheme, and is applicable to spin-active color centers with similar dynamics models. In addition, the optical transition dipole strength and the quantum efficiency of V1 defect are evaluated based on coherent optical Rabi measurement and local-field calibration employing electric-field simulation. The measured rates well explain the results of spin-state polarization dynamics, and we further discuss the altered photoemission dynamics in resonant enhancement structures such as radiative lifetime shortening and Purcell enhancement. By providing a thorough description of V1 center's spin-optical dynamics, our work provides deep understanding of the system which guides implementations of scalable quantum applications based on silicon vacancy centers in silicon carbide.
Cutoff in the Bernoulli-Laplace model with $O(n)$ swaps

This paper considers the $(n,k)$-Bernoulli--Laplace model in the case when there are two urns, the total number of red and white balls is the same, and the number of selections $k$ at each step is on the same asymptotic order as the number of balls $n$ in each urn. Our main focus is on the large-time behavior of the corresponding Markov chain tracking the number of red balls in a given urn. Under reasonable assumptions on the asymptotic behavior of the ratio $k/n$ as $n\rightarrow \infty$, cutoff in the total variation distance is established. A cutoff window is also provided. These results, in particular, partially resolve an open problem posed by Eskenazis and Nestoridi.
A Binary Origin for the First Isolated Stellar-mass Black Hole Detected with Astrometric Microlensing

The Milky Way is believed to host hundreds of millions of quiescent stellar-mass black holes (BHs). In the last decade, some of these objects have been potentially uncovered via gravitational microlensing events. All these detections resulted in a degeneracy between the velocity and the mass of the lens. This degeneracy has been lifted, for the first time, with the recent astrometric microlensing detection of OB110462. However, two independent studies reported very different lens masses for this event. Sahu et al. inferred a lens mass of 7.1 ± 1.3 M ⊙, consistent with a BH, while Lam et al. inferred 1.6–4.2 M ⊙, consistent with either a neutron star or a BH. Here, we study the landscape of isolated BHs formed in the field. In particular, we focus on the mass and center-of-mass speed of four subpopulations: isolated BHs from single-star origin, disrupted BHs of binary-star origin, main-sequence stars with a compact object companion, and double compact object mergers. Our model predicts that most (≳70%) isolated BHs in the Milky Way are of binary origin. However, noninteractions lead to most massive BHs (≳15–20 M ⊙) being predominantly of single origin. Under the assumption that OB110462 is a free-floating compact object, we conclude that it is more likely to be a BH originally belonging to a binary system. Our results suggest that low-mass BH microlensing events can be useful to understand binary evolution of massive stars in the Milky Way, while high-mass BH lenses can be useful to probe single stellar evolution.
Using the Hills Mechanism to Generate Repeating Partial Tidal Disruption Events and ASASSN-14ko

Periodic nuclear transients have been detected with increasing frequency, with one such system—ASASSN-14ko—exhibiting highly regular outbursts on a timescale of 114 ± 1 days. It has been postulated that the outbursts from this source are generated by the repeated partial disruption of a star, but how the star was placed onto such a tightly bound orbit about the supermassive black hole remains unclear. Here we use analytic arguments and three-body integrations to demonstrate that the Hills mechanism, where a binary system is destroyed by the tides of the black hole, can lead to the capture of a star on a ∼114 days orbit and with a pericenter distance that is comparable to the tidal radius of one of the stars within the binary. Thus, Hills capture can produce stars on tightly bound orbits that undergo repeated partial disruption, leading to a viable mechanism for generating not only the outbursts detected from ASASSN-14ko but periodic nuclear transients in general. We also show that the rate of change of the period of the captured star due to gravitational-wave emission is likely too small to produce the observed value for ASASSN-14ko, indicating that in this system there must be additional effects that contribute to the decay of the orbit. In general, however, gravitational-wave emission can be important for limiting the lifetimes of these systems and could produce observable period decay rates in future events.
DiFT: Differentiable Differential Feature Transform for Multi-View Stereo

We present a novel framework to automatically learn to transform the differential cues from a stack of images densely captured with a rotational motion into spatially discriminative and view-invariant per-pixel features at each view. These low-level features can be directly fed to any existing multi-view stereo technique for enhanced 3D reconstruction. The lighting condition during acquisition can also be jointly optimized in a differentiable fashion. We sample from a dozen of pre-scanned objects with a wide variety of geometry and reflectance to synthesize a large amount of high-quality training data. The effectiveness of our features is demonstrated on a number of challenging objects acquired with a lightstage, comparing favorably with state-of-the-art techniques. Finally, we explore additional applications of geometric detail visualization and computational stylization of complex appearance.
Wall stabilization of the rigid ballooning m = 1 mode in a paraxial mirror trap

The prospect of stabilization of the m = 1 “rigid” ballooning regime in an open axially symmetric trap with the help of a conducting lateral wall surrounding a column of isotropic plasma is studied. It is found that for effective wall stabilization, the beta parameter must exceed 70%. The dependence of the critical value of beta on the mirror ratio, the radial pressure profile, and the axial profile of the vacuum magnet has been studied. It is shown that when a conductive lateral wall is combined with conductive ends simulating the attachment of end MHD stabilizers to the central cell of an open trap, there are two critical beta values and two stability zones that can merge, making stable the entire range of allowable beta values 0 < β < 1.
Fisher forecasts for primordial non-Gaussianity from persistent homology

We study the information content of summary statistics built from the multi-scale topology of large-scale structures on primordial non-Gaussianity of the local and equilateral type. We use halo catalogs generated from numerical N-body simulations of the Universe on large scales as a proxy for observed galaxies. Besides calculating the Fisher matrix for halos in real space, we also check more realistic scenarios in redshift space. Without needing to take a distant observer approximation, we place the observer on a corner of the box. We also add redshift errors mimicking spectroscopic and photometric samples. We perform several tests to assess the reliability of our Fisher matrix, including the Gaussianity of our summary statistics and convergence. We find that the marginalized 1-σ uncertainties in redshift space are Δf NL loc ∼ 16 and Δf NL equi ∼ 41 on a survey volume of 1 (Gpc/h)3. These constraints are weakly affected by redshift errors. We close by speculating as to how this approach can be made robust against small-scale uncertainties by exploiting (non)locality.