Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-time Attacks Against Deep Reinforcement Learning Policies

Recent work has discovered that deep reinforcement learning (DRL) policies are vulnerable to adversarial examples. These attacks mislead the policy of DRL agents by perturbing the state of the environment observed by agents. They are feasible in principle but too slow to fool DRL policies in real time. We propose a new attack to fool DRL policies that is both effective and efﬁcient enough to be mounted in real time. We utilize the Universal Adversarial Perturbation (UAP) method to compute effective perturbations independent of the individual inputs to which they are applied. Via an extensive evaluation using Atari 2600 games, we show that our technique is effective, as it fully degrades the performance of both deterministic and stochastic policies (up to 100%, even when the l ∞ bound on the perturbation is as small as 0.005). We also show that our attack is efﬁ-cient, incurring an online computational cost of 0.027ms on average. It is faster compared to the response time (0.6ms on average) of agents with different DRL policies, and considerably faster than prior attacks (2.7ms on average). Furthermore, we demonstrate that known defenses are ineffective against universal perturbations. We propose an effective detection technique which can form the basis for robust defenses against attacks based on universal perturbations.
Constraining Spatial Densities of Early Ice Formation in Small Dense Molecular Cores from Extinction Maps

Tracing dust in small dense molecular cores is a powerful tool to study the conditions required for ices to form during the prestellar phase. To study these environments, five molecular cores were observed: three with ongoing low-mass star formation (B59, B335, and L483), and two starless collapsing cores (L63 and L694-2). Deep images were taken in the infrared JHK bands with the United Kingdom Infrared Telescope WFCAM (Wide Field Camera) instrument and IRAC channels 1 and 2 on the Spitzer Space Telescope. These five photometric bands were used to calculate extinction along the line of sight toward background stars. After smoothing the data, we produced high spatial resolution extinction maps (∼13″–29″). The maps were then projected into the third dimension using the AVIATOR algorithm implementing the inverse Abel transform. The volume densities of the total hydrogen were measured along lines of sight where ices (H2O, CO, and CH3OH) have previously been detected. We find that lines of sight with pure CH3OH or a mixture of CH3OH with CO have maximum volume densities above 1.0 × 105 cm−3. These densities are only reached within a small fraction of each of the cores (∼0.3%–2.1%). CH3OH presence may indicate the onset of complex organic molecule formation within dense cores, and thus we can constrain the region where this onset can begin. The maximum volume densities toward star-forming cores in our sample (∼(1.2–1.7) × 106 cm−3) are higher than those toward starless cores (∼(3.5–9.5) × 105 cm−3).
Characteristic formulation of the Regge-Wheeler and Zerilli Green functions

We present a characteristic initial value approach to calculating the Green function of the ReggeWheeler and Zerilli equations. We combine well-known numerical methods with newly derived initial data to obtain a scheme which can in principle be generalised to any desired order of convergence. We demonstrate the approach with implementations up to sixth-order in the grid spacing. By combining the results of our numerical code with late-time tail expansions and methods of subtracting the direct part of the Green function, we show that the scalar self-force in Schwarzschild spacetime can be computed to better accuracy than previous Green function based approaches. We also demonstrate agreement with frequency domain methods for computing the Green function in the gravitational case. Finally, we apply the Regge-Wheeler and Zerilli Green functions to the computation of the gravitational energy flux.
An Exquisitely Deep View of Quenching Galaxies through the Gravitational Lens: Stellar Population, Morphology, and Ionized Gas

This work presents an in-depth analysis of four gravitationally lensed red galaxies at z = 1.6–3.2. The sources are magnified by factors of 2.7–30 by foreground clusters, enabling spectral and morphological measurements that are otherwise challenging. Our sample extends below the characteristic mass of the stellar mass function and is thus more representative of the quiescent galaxy population at z > 1 than previous spectroscopic studies. We analyze deep VLT/X-SHOOTER spectra and multiband Hubble Space Telescope photometry that cover the rest-frame UV-to-optical regime. The entire sample resembles stellar disks as inferred from lensing-reconstructed images. Through stellar population synthesis analysis, we infer that the targets are young (median age = 0.1–1.2 Gyr) and formed 80% of their stellar masses within 0.07–0.47 Gyr. Mg ii λλ 2796, 2803 absorption is detected across the sample. Blueshifted absorption and/or redshifted emission of Mg ii are found in the two youngest sources, indicative of a galactic-scale outflow of warm (T ∼ 104 K) gas. The [O iii] λ5007 luminosity is higher for the two young sources (median age less than 0.4 Gyr) than the two older ones, perhaps suggesting a decline in nuclear activity as quenching proceeds. Despite high-velocity (v ≈ 1500 km s−1) galactic-scale outflows seen in the most recently quenched galaxies, warm gas is still present to some extent long after quenching. Altogether, our results indicate that star formation quenching at high redshift must have been a rapid process (<1 Gyr) that does not synchronize with bulge formation or complete gas removal. Substantial bulge growth is required if they are to evolve into the metal-rich cores of present-day slow rotators.
Shuffle Transformer with Feature Alignment for Video Face Parsing

This is a short technical report introducing the solution of the Team TCParser for Short-video Face Parsing Track of The 3rd Person in Context (PIC) Workshop and Challenge at CVPR 2021. In this paper, we introduce a strong backbone which is cross-window based Shuffle Transformer for presenting accurate face parsing representation. To further obtain the finer segmentation results, especially on the edges, we introduce a Feature Alignment Aggregation (FAA) module. It can effectively relieve the feature misalignment issue caused by multi-resolution feature aggregation. Benefiting from the stronger backbone and better feature aggregation, the proposed method achieves 86.9519% score in the Short-video Face Parsing track of the 3rd Person in Context (PIC) Workshop and Challenge, ranked the first place.
Detection of Consonant Errors in Disordered Speech Based on Consonant-vowel Segment Embedding

Speech sound disorder (SSD) refers to a type of developmental disorder in young children who encounter persistent difficulties in producing certain speech sounds at the expected age. Consonant errors are the major indicator of SSD in clinical assessment. Previous studies on automatic assessment of SSD revealed that detection of speech errors concerning short and transitory consonants is less satisfactory. This paper investigates a neural network based approach to detecting consonant errors in disordered speech using consonant-vowel (CV) diphone segment in comparison to using consonant monophone segment. The underlying assumption is that the vowel part of a CV segment carries important information of co-articulation from the consonant. Speech embeddings are extracted from CV segments by a recurrent neural network model. The similarity scores between the embeddings of the test segment and the reference segments are computed to determine if the test segment is the expected consonant or not. Experimental results show that using CV segments achieves improved performance on detecting speech errors concerning those"difficult"consonants reported in the previous studies.
Assessment of Subjective and Objective Quality of Live Streaming Sports Videos

Video live streaming is gaining prevalence among video streaming services, especially for the delivery of popular sporting events. Many objective Video Quality Assessment (VQA) models have been developed to predict the perceptual quality of videos. Appropriate databases that exemplify the distortions encountered in live streaming videos are important to designing and learning objective VQA models. Towards making progress in this direction, we built a video quality database specifically designed for live streaming VQA research. The new video database is called the Laboratory for Image and Video Engineering (LIVE) Live stream Database. The LIVE Livestream Database includes 315 videos of 45 contents impaired by 6 types of distortions. We also performed a subjective quality study using the new database, whereby more than 12,000 human opinions were gathered from 40 subjects. We demonstrate the usefulness of the new resource by performing a holistic evaluation of the performance of current state-of-the-art (SOTA) VQA models. The LIVE Livestream database is being made publicly available for these purposes at https://live.ece. utexas.edu/research/LIVE_APV_Study/apv_index. html.
Robust Training in High Dimensions via Block Coordinate Geometric Median Descent

Geometric median (\textsc{Gm}) is a classical method in statistics for achieving a robust estimation of the uncorrupted data; under gross corruption, it achieves the optimal breakdown point of 0.5. However, its computational complexity makes it infeasible for robustifying stochastic gradient descent (SGD) for high-dimensional optimization problems. In this paper, we show that by applying \textsc{Gm} to only a judiciously chosen block of coordinates at a time and using a memory mechanism, one can retain the breakdown point of 0.5 for smooth non-convex problems, with non-asymptotic convergence rates comparable to the SGD with \textsc{Gm}.
Three-dimensional geometry and topology effects in viscous streaming

Abstract Recent studies on viscous streaming flows in two dimensions have elucidated the impact of body curvature variations on resulting flow topology and dynamics, with opportunities for microfluidic applications. Following that, we present here a three-dimensional characterization of streaming flows as functions of changes in body geometry and topology, starting from the well-known case of a sphere to progressively arrive at toroidal shapes. We leverage direct numerical simulations and dynamical systems theory to systematically analyse the reorganization of streaming flows into a dynamically rich set of regimes, the origins of which are explained using bifurcation theory.
Mobile Augmented Reality: User Interfaces, Frameworks, and Intelligence

Mobile Augmented Reality (MAR) integrates computer-generated virtual objects with physical environments for mobile devices. MAR systems enable users to interact with MAR devices, such as smartphones and head-worn wearables, and perform seamless transitions from the physical world to a mixed world with digital entities. These MAR systems support user experiences using MAR devices to provide universal access to digital content. Over the past 20 years, several MAR systems have been developed, however, the studies and design of MAR frameworks have not yet been systematically reviewed from the perspective of user-centric design. This article presents the first effort of surveying existing MAR frameworks (count: 37) and further discusses the latest studies on MAR through a top-down approach: (1) MAR applications; (2) MAR visualisation techniques adaptive to user mobility and contexts; (3) systematic evaluation of MAR frameworks, including supported platforms and corresponding features such as tracking, feature extraction, and sensing capabilities; (4) and underlying machine learning approaches supporting intelligent operations within MAR systems. Finally, we summarise the development of emerging research fields and the current state-of-the-art and discuss the important open challenges and possible theoretical and technical directions. This survey aims to benefit both researchers and MAR system developers alike.
Euclid preparation

The Euclid Space Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across ∼ 15000deg 2 of the sky. Euclid is expected to detect ∼ 12 billion astronomical sources, facilitating new insights into cosmology, galaxy evolution, and various other topics. In order to optimally exploit the expected very large dataset, appropriate methods and software tools need to be developed. Here we present a novel machine-learning-based methodology for the selection of quiescent galaxies using broadband Euclid I E , Y E , J E , and H E photometry, in combination with multi-wavelength photometry from other large surveys (e.g. the Rubin LSST). The ARIADNE pipeline uses meta-learning to fuse decision-tree ensembles, nearest-neighbours, and deep-learning methods into a single classifier that yields significantly higher accuracy than any of the individual learning methods
Understanding and Evaluating Racial Biases in Image Captioning

Image captioning is an important task for benchmarking visual reasoning and for enabling accessibility for people with vision impairments. However, as in many machine learning settings, social biases can influence image captioning in undesirable ways. In this work, we study bias propagation pathways within image captioning, focusing specifically on the COCO dataset. Prior work has analyzed gender bias in captions using automatically-derived gender labels; here we examine racial and intersectional biases using manual annotations. Our first contribution is in annotating the perceived gender and skin color of 28,315 of the depicted people after obtaining IRB approval. Using these annotations, we compare racial biases present in both manual and automatically-generated image captions. We demonstrate differences in caption performance, sentiment, and word choice between images of lighter versus darker-skinned people. Further, we find the magnitude of these differences to be greater in modern captioning systems compared to older ones, thus leading to concerns that without proper consideration and mitigation these differences will only become increasingly prevalent. Code and data is available at https://princetonvisualai.github. io/imagecaptioning-bias/.
TSO: Curriculum Generation using continuous optimization

The training of deep learning models poses vast challenges of including parameter tuning and ordering of training data. Signiﬁcant research has been done in Curriculum learning for optimizing the sequence of training data. Recent works have focused on using complex reinforcement learning techniques to ﬁnd the optimal data ordering strategy to maximize learning for a given network. In this paper, we present a simple and efﬁcient technique based on continuous optimization. We call this new approach Training Sequence Optimization (TSO). There are three critical components in our proposed approach: (a) An encoder network maps/embeds training sequence into continuous space. (b) A predictor network uses the continuous representation of a strategy as input and predicts the accuracy for ﬁxed network architecture. (c) A decoder further maps a continuous representation of a strategy to the ordered training dataset. The performance predictor and encoder enable us to perform gradient-based optimization in the continuous space to ﬁnd the embedding of optimal training data ordering with potentially better accuracy. Experiments show that we can gain 2AP with our generated optimal curriculum strategy over the random strategy using the CIFAR-100 dataset and have better boosts than the state of the art CL algorithms. We do an ablation study varying the architecture, dataset and sample sizes showcasing our approach’s robustness.
Minimizing Communication while Maximizing Performance in Multi-Agent Reinforcement Learning

Inter-agent communication can significantly increase performance in multi-agent tasks that require co-ordination to achieve a shared goal. Prior work has shown that it is possible to learn inter-agent communication protocols using multi-agent reinforcement learning and message-passing network architectures. However, these models use an unconstrained broadcast communication model, in which an agent communicates with all other agents at every step, even when the task does not require it. In real-world applications, where communication may be limited by system constraints like bandwidth, power and network capacity, one might need to reduce the number of messages that are sent. In this work, we explore a simple method of minimizing communication while maximizing performance in multi-task learning: simultaneously optimizing a task-specific objective and a communication penalty. We show that the objectives can be optimized using Reinforce and the Gumbel-Softmax reparameterization. We introduce two techniques to stabilize training: 50% training and message forwarding. Training with the communication penalty on only 50% of the episodes prevents our models from turning off their outgoing messages. Second, repeating messages received previously helps models retain information, and further improves performance. With these techniques, we show that we can reduce communication by 75% with no loss of performance.
Seeing Through Clouds in Satellite Images

This article presents a neural network-based solution to recover pixels occluded by clouds in satellite images. We leverage radio frequency (RF) signals in the ultrahigh-/superhigh-frequency band that penetrates clouds to help reconstruct the occluded regions in multispectral images. We introduce the first multimodal multitemporal method for cloud removal. Our model uses publicly available satellite observations and produces daily cloud-free images. Experimental results show that our system outperforms several baselines on multiple metrics. We also demonstrate use cases of our system in digital agriculture, flood monitoring, and wildfire detection.
Machamp: A Generalized Entity Matching Benchmark

Entity Matching (EM) refers to the problem of determining whether two different data representations refer to the same real-world entity. It has been a long-standing interest of the data management community. Many efforts have been paid in creating benchmark tasks as well as in developing advanced matching techniques for EM. However, existing benchmark tasks for EM are limited to the case where the two data collections of entities are structured tables with the same schema. Meanwhile, the tables in data collections for matching could be structured, semi-structured, or unstructured in real-world scenarios of data science. In this paper, we come up with a new research problem - Generalized Entity Matching to satisfy this requirement and create a benchmark Machamp for it. Machamp consists of seven tasks having diverse characteristics and thus provides good coverage of use cases in real applications. We summarize existing EM benchmark tasks for structured tables and conduct a series of processing and cleaning efforts to transform them into matching tasks between tables with different structures. Based on that, we further conduct comprehensive profiling of the proposed tasks and evaluate several popular entity matching approaches on them. With the help of Machamp, it is the first time that researchers can evaluate EM techniques between data collections with different structures. It is public available via link: https://github.com/megagonlabs/machamp.
Cascading Modular Network (CAM-Net) for Multimodal Image Synthesis

Deep generative models such as GANs have driven impressive advances in conditional image synthesis in recent years. A persistent challenge has been to generate diverse versions of output images from the same input image, due to the problem of mode collapse: because only one ground truth output image is given per input image, only one mode of the conditional distribution is modelled. In this paper, we focus on this problem of multimodal conditional image synthesis and build on the recently proposed technique of Implicit Maximum Likelihood Estimation (IMLE). Prior IMLE-based methods required different architectures for different tasks, which limit their applicability, and were lacking in fine details in the generated images. We propose CAM-Net, a unified architecture that can be applied to a broad range of tasks. Additionally, it is capable of generating convincing high frequency details, achieving a reduction of the Frechet Inception Distance (FID) by up to 45.3% compared to the baseline.
Clustering mixture models in almost-linear time via list-decodable mean estimation

We study the problem of list-decodable mean estimation, where an adversary can corrupt a majority of the dataset. Specifically, we are given a set T of n points in ℝd and a parameter 0< α <1/2 such that an α-fraction of the points in T are i.i. d. samples from a well-behaved distribution D and the remaining (1−α)-fraction are arbitrary. The goal is to output a small list of vectors, at least one of which is close to the mean of D. We develop new algorithms for this problem achieving nearly-optimal statistical guarantees, with runtime O(n1 + є0 d), for any fixed є0 > 0. All prior algorithms for this problem had additional polynomial factors in 1/α. We leverage this result, together with additional techniques, to obtain the first almost-linear time algorithms for clustering mixtures of k separated well-behaved distributions, nearly-matching the statistical guarantees of spectral methods. Prior clustering algorithms inherently relied on an application of k-PCA, thereby incurring runtimes of Ω(n d k). This marks the first runtime improvement for this basic statistical problem in nearly two decades. The starting point of our approach is a novel and simpler near-linear time robust mean estimation algorithm in the α → 1 regime, based on a one-shot matrix multiplicative weights-inspired potential decrease. We crucially leverage this new algorithmic framework in the context of the iterative multi-filtering technique of Diakonikolas et al. ’18, Diakonikolas et al. ’20, providing a method to simultaneously cluster and downsample points using one-dimensional projections — thus, bypassing the k-PCA subroutines required by prior algorithms.
Fluctuation theorems with retrodiction rather than reverse processes

Fluctuation Theorems with Retrodiction rather than Reverse Processes Clive Cenxin Aw,1 Francesco Buscemi,2 and Valerio Scarani1, 3 1)Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, Singapore 117543, Singapore 2)Graduate School of Informatics, Nagoya University, Chikusa-ku, 464-8601 Nagoya, Japan 3)Department of Physics, National University of Singapore, 2 Science Drive 3, Singapore 117542, Singapore
Mass gap for a monopole interacting with a nonlinear spinor field

Vladimir Dzhunushaliev, 2, 3, ∗ Nassurlla Burtebayev, Vladimir Folomeev, 3, 4, † Jutta Kunz, ‡ Albina Serikbolova, § and Abylaikhan Tlemisov ¶ Institute of Nuclear Physics, Almaty 050032, Kazakhstan Department of Theoretical and Nuclear Physics, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan Academician J. Jeenbaev Institute of Physics of the NAS of the Kyrgyz Republic, 265 a, Chui Street, Bishkek 720071, Kyrgyzstan International Laboratory for Theoretical Cosmology, Tomsk State University of Control Systems and Radioelectronics (TUSUR), Tomsk 634050, Russia Institut für Physik, Universität Oldenburg, Postfach 2503 D-26111 Oldenburg, Germany