Miracle-dz/UniFAR-FTUs huggingface dataset:
8-bit Numerical Formats for Deep Neural Networks

Given the current trend of increasing size and complexity of machine learning architectures, it has become of critical importance to identify new approaches to improve the computational efﬁciency of model training. In this context, we address the advantages of ﬂoating-point over ﬁxed-point representation, and present an in-depth study on the use of 8-bit ﬂoating-point number formats for activations, weights, and gradients for both training and inference. We explore the effect of different bit-widths for exponents and signiﬁcands and different exponent biases. The experimental results demonstrate that a suitable choice of these low-precision formats enables faster training and reduced power consumption without any degradation in accuracy for a range of deep learning models for image classiﬁcation and language processing. computational efﬁciency and communication efﬁciency in the exchange of data processing units. This the recent advances in the of low-precision numerical formats to enable reli-able training of modern deep architectures with power efﬁcient computations, and provides a com-prehensive study of the effect of the use of low-precision formats on the training and generalization performance of different model architectures. We also address the trade-off of ﬂoating-point formats versus integer formats, and the effect of low-precision arithmetic on non-convex optimization and generalization.
Exploring the combination of deep-learning based direct segmentation and deformable image registration for cone-beam CT based auto-segmentation for adaptive radiotherapy

Purpose: CBCT-based online adaptive radiotherapy (ART) calls for accurate auto-segmentation models to reduce the time cost for physicians to edit contours, since the patient is immobilized on the treatment table waiting for treatment to start. However, auto-segmentation of CBCT images is a difficult task, majorly due to low image quality and lack of true labels for training a deep learning (DL) model. Meanwhile CBCT auto-segmentation in ART is a unique task compared to other segmentation problems, where manual contours on planning CT (pCT) are available. Methods: To make use of this prior knowledge, we propose to combine deformable image registration (DIR) and direct segmentation (DS) on CBCT for head and neck patients. First, we use deformed pCT contours derived from multiple DIR methods between pCT and CBCT as pseudo labels for training. Second, we use deformed pCT contours as bounding box to constrain the region of interest for DS. Meanwhile deformed pCT contours are used as pseudo labels for training, but are generated from different DIR algorithms from bounding box. Third, we fine-tune the model with bounding box on true labels. We try to compete against DIR-only auto-segmentation. Results: We found that DS on CBCT trained with pseudo labels and without utilizing any prior knowledge for training has very poor segmentation performance compared to DIR-only segmentation. However, adding deformed pCT contours as bounding box in the DS network can dramatically improve segmentation performance, comparable to DIR-only segmentation. The DS model with bounding box can be further improved by fine-tuning it with some real labels instead of using pseudo labels for training. Experiments showed that 7 out of 19 structures have at least 0.2 dice similarity coefficient increase compared to DIR-only segmentation. Conclusion: Utilizing deformed pCT contours as pseudo labels for training and as bounding box for shape and location feature extraction in a DS model is a good way to combine DIR and DS.
Dependency Structures in Cryptocurrency Market from High to Low Frequency

We investigate logarithmic price returns cross-correlations at different time horizons for a set of 25 liquid cryptocurrencies traded on the FTX digital currency exchange. We study how the structure of the Minimum Spanning Tree (MST) and the Triangulated Maximally Filtered Graph (TMFG) evolve from high (15 s) to low (1 day) frequency time resolutions. For each horizon, we test the stability, statistical significance and economic meaningfulness of the networks. Results give a deep insight into the evolutionary process of the time dependent hierarchical organization of the system under analysis. A decrease in correlation between pairs of cryptocurrencies is observed for finer time sampling resolutions. A growing structure emerges for coarser ones, highlighting multiple changes in the hierarchical reference role played by mainstream cryptocurrencies. This effect is studied both in its pairwise realizations and intra-sector ones.
HM-LDM: A Hybrid-Membership Latent Distance Model

A central aim of modeling complex networks is to accurately embed networks in order to detect structures and predict link and node properties. The latent space models (LSM) have become prominent frameworks for embedding networks and include the latent distance (LDM) and eigenmodel (LEM) as the most widely used LSM specifications. For latent community detection, the embedding space in LDMs has been endowed with a clustering model whereas LEMs have been constrained to part-based non-negative matrix factorization (NMF) inspired representations promoting community discovery. We presently reconcile LSMs with latent community detection by constraining the LDM representation to the D-simplex forming the hybrid-membership latent distance model (HM-LDM). We show that for sufficiently large simplex volumes this can be achieved without loss of expressive power whereas by extending the model to squared Euclidean distances, we recover the LEM formulation with constraints promoting part-based representations akin to NMF. Importantly, by systematically reducing the volume of the simplex, the model becomes unique and ultimately leads to hard assignments of nodes to simplex corners. We demonstrate experimentally how the proposed HM-LDM admits accurate node representations in regimes ensuring identifiability and valid community extraction. Importantly, HM-LDM naturally reconciles soft and hard community detection with network embeddings exploring a simple continuous optimization procedure on a volume constrained simplex that admits the systematic investigation of trade-offs between hard and mixed membership community detection.
Magnetar Engines in Fast Blue Optical Transients and Their Connections with SLSNe, SNe Ic-BL, and lGRBs

We fit the multiband lightcurves of 40 fast blue optical transients (FBOTs) with the magnetar engine model. The mass of the FBOT ejecta, the initial spin period, and the polar magnetic field of the FBOT magnetars are respectively constrained to Mej=0.11−0.09+0.22M⊙ , Pi=9.1−4.4+9.3ms , and Bp=11−7+18×1014G . The wide distribution of the value of B p spreads the parameter ranges of the magnetars from superluminous supernovae (SLSNe) to broad-line Type Ic supernovae (SNe Ic-BL; some are observed to be associated with long-duration gamma-ray bursts), which are also suggested to be driven by magnetars. Combining FBOTs with the other transients, we find a strong universal anticorrelation of Pi∝Mej−0.41 , indicating they could share a common origin. To be specific, it is suspected that all of these transients originate from the collapse of extremely stripped stars in close binary systems, but with different progenitor masses. As a result, FBOTs distinguish themselves by their small ejecta masses with an upper limit of ∼1 M ⊙, which leads to an observational separation in the rise time of the lightcurves of ∼10 days. In addition, FBOTs together with SLSNe can be separated from SNe Ic-BL by an empirical line in the M peak–t rise plane corresponding to an energy requirement of the mass of 56Ni of ∼0.3M ej, where M peak is the peak absolute magnitude of the transients and t rise is the rise time.
Study of three-flavored heavy dibaryons using lattice QCD

We present results of the first lattice QCD calculation of three-flavored heavy dibaryons both in the flavor-symmetric and antisymmetric channels. These dibaryons have spin zero, and are constructed using various possible combinations of quark flavors with at least one of them as the charm or the bottom quark, i.e., namely, $H_c(cudcud), H_b(budbud), H_{bcs}(bcsbcs)$, $H_{csl}(cslcsl), H_{bsl}(bslbsl)$ and $H_{bcl}(bclbcl)$; $l\in u,d$. We compute the ground state masses of these dibaryons and the calculations are performed on three $N_f=2+1+1$ HISQ gauge ensembles of the MILC collaboration, with lattice spacings $a =$ 0.1207, 0.0888 and 0.0582 fm. A relativistic overlap action is employed for the valence light to charm quarks while a non-relativistic-QCD Hamiltonian with improved coefficients is used for the bottom quarks. Unlike the doubly heavy tetraquarks, one and two-flavored heavy dibaryons, for which lattice QCD calculations have predicted deeply bound strong-interactions-stable states, for these $H_c, H_b, H_{csl},H_{bsl}$ dibaryons we do not find any such deeply bound state. However, for $H_{bcs}$, our results indicate the presence of an energy level $29\pm 24$ MeV below the lowest two-baryon threshold, which could be relevant for its future experimental searches. Moreover, we find that the energy difference between the ground state of $H_{bcl}$ and its lowest threshold increases when $m_l>m_s$. Taken together, our findings indicate the possibility of the existence of the $H_{bcs}$ dibaryon while all other physical three-flavored dibaryons are much closer to their thresholds suggesting either they are weakly bound or unbound, resolving which requires further detail study. Our results also point that the binding of a dibaryon configuration becomes stronger with the increase of its valence quark masses which suggests an interesting aspect of strong interactions at multiple scales.
Combinatorially minimal Mori dream surfaces of general type

In this paper, we suggest a new approach to study minimal sur- faces of general type with p g = 0 via their Cox rings, especially using the notion of combinatorially minimal Mori dream space introduced by Hausen in [22]. First, we study general properties of combinatorially minimal Mori dream surfaces. Then we discuss how to apply these ideas to the study of minimal surfaces of general type with p g = 0 which are very important but still mysterious objects. In our previous paper [31], we provided several examples of Mori dream surfaces of general type with p g = 0 and computed their eﬀective cones explicitly. In this paper, we study their ﬁbrations, explicit combinatorially minimal models and discuss singularities of the combinatorially minimal models. We also show that many minimal surfaces of general type with p g = 0 arise from the minimal resolutions of combinatorially minimal Mori dream surfaces.
Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks

We consider the off-policy evaluation problem of reinforcement learning using deep convolutional neural networks. We analyze the deep fitted Q-evaluation method for estimating the expected cumulative reward of a target policy, when the data are generated from an unknown behavior policy. We show that, by choosing network size appropriately, one can leverage any low-dimensional manifold structure in the Markov decision process and obtain a sample-efficient estimator without suffering from the curse of high data ambient dimensionality. Specifically, we establish a sharp error bound for fitted Q-evaluation, which depends on the intrinsic dimension of the state-action space, the smoothness of Bellman operator, and a function class-restricted $\chi^2$-divergence. It is noteworthy that the restricted $\chi^2$-divergence measures the behavior and target policies' {\it mismatch in the function space}, which can be small even if the two policies are not close to each other in their tabular forms. We also develop a novel approximation result for convolutional neural networks in Q-function estimation. Numerical experiments are provided to support our theoretical analysis.
Self-Knowledge Distillation based Self-Supervised Learning for Covid-19 Detection from Chest X-Ray Images

The global outbreak of the Coronavirus 2019 (COVID-19) has overloaded worldwide healthcare systems. Computer-aided diagnosis for COVID-19 fast detection and patient triage is becoming critical. This paper proposes a novel self-knowledge distillation based self-supervised learning method for COVID-19 detection from chest X-ray images. Our method can use self-knowledge of images based on similarities of their visual features for self-supervised learning. Experimental results show that our method achieved an HM score of 0.988, an AUC of 0.999, and an accuracy of 0.957 on the largest open COVID-19 chest X-ray dataset.
SC-Transformer++: Structured Context Transformer for Generic Event Boundary Detection

This report presents the algorithm used in the submission of Generic Event Boundary Detection (GEBD) Challenge at CVPR 2022. In this work, we improve the existing Structured Context Transformer (SC-Transformer) method for GEBD. Specifically, a transformer decoder module is added after transformer encoders to extract high quality frame features. The final classification is performed jointly on the results of the original binary classifier and a newly introduced multi-class classifier branch. To enrich motion information, optical flow is introduced as a new modality. Finally, model ensemble is used to further boost performance. The proposed method achieves 86.49% F1 score on Kinetics-GEBD test set. which improves 2.86% F1 score compared to the previous SOTA method.
Learning to Generate Artistic Character Line Drawing

—Character line drawing synthesis can be formulated as a special case of image-to-image translation problem that automatically manipulates the photo-to-line drawing style transformation. In this paper, we present the ﬁrst generative adversarial network based end-to-end trainable translation architecture, dubbed P2LDGAN, for automatic generation of high-quality character drawings from input photos/images. The core component of our approach is the joint geometric-semantic-driven generator, which uses our well-designed cross-scale dense skip connections framework to embed learned geometric and semantic information for generating delicate line drawings. In order to support the evaluation of our model, we release a new dataset including 1,532 well-matched pairs of freehand character line drawings as well as corresponding character images/photos, where these line drawings with diverse styles are manually drawn by skilled artists. Extensive experiments on our introduced dataset demonstrate the superior performance of our proposed models against the state-of-the-art approaches in terms of quantitative, qualitative and human evaluations. Our code, models and dataset is available at https://github.com/cnyvfang/P2LDGAN.
A Compressed Sensing Faraday Depth Reconstruction Framework for the MeerKAT MIGHTEE-POL Survey

In this work we present a novel compute framework for reconstructing Faraday depth signals from noisy and incomplete spectro-polarimetric radio datasets. This framework is based on a compressed-sensing approach that addresses a number of outstanding issues in Faraday depth reconstruction in a systematic and scaleable manner. We apply this framework to early-release data from the MeerKAT MIGHTEE polarisation survey.
How Far I'll Go: Offline Goal-Conditioned Reinforcement Learning via f-Advantage Regression

Offline goal-conditioned reinforcement learning (GCRL) promises general-purpose skill learning in the form of reaching diverse goals from purely offline datasets. We propose $\textbf{Go}$al-conditioned $f$-$\textbf{A}$dvantage $\textbf{R}$egression (GoFAR), a novel regression-based offline GCRL algorithm derived from a state-occupancy matching perspective; the key intuition is that the goal-reaching task can be formulated as a state-occupancy matching problem between a dynamics-abiding imitator agent and an expert agent that directly teleports to the goal. In contrast to prior approaches, GoFAR does not require any hindsight relabeling and enjoys uninterleaved optimization for its value and policy networks. These distinct features confer GoFAR with much better offline performance and stability as well as statistical performance guarantee that is unattainable for prior methods. Furthermore, we demonstrate that GoFAR's training objectives can be re-purposed to learn an agent-independent goal-conditioned planner from purely offline source-domain data, which enables zero-shot transfer to new target domains. Through extensive experiments, we validate GoFAR's effectiveness in various problem settings and tasks, significantly outperforming prior state-of-art. Notably, on a real robotic dexterous manipulation task, while no other method makes meaningful progress, GoFAR acquires complex manipulation behavior that successfully accomplishes diverse goals.
On the Nature of Ultracool White Dwarfs: Not so Cool after All

A recent analysis of the 100 pc white dwarf sample in the SDSS footprint demonstrated for the first time the existence of a well-defined ultracool—or IR-faint—white dwarf sequence in the Hertzsprung–Russell diagram. Here we take advantage of this discovery to enlarge the IR-faint white dwarf sample threefold. We expand our selection to the entire Pan-STARRS survey footprint as well as the Montreal White Dwarf Database 100 pc sample and identify 37 candidates with strong flux deficits in the optical. We present follow-up Gemini optical spectroscopy of 30 of these systems and confirm all of them as IR-faint white dwarfs. We identify an additional set of 33 objects as candidates based on their colors and magnitudes. We present a detailed model atmosphere analysis of all 70 newly identified IR-faint white dwarfs together with 35 previously known objects reported in the literature. We discuss the physics of model atmospheres and show that the key physical ingredient missing in our previous generation of model atmospheres was the high-density correction to the He− free–free absorption coefficient. With new model atmospheres calculated for the purpose of this analysis, we now obtain significantly higher effective temperatures and larger stellar masses for these IR-faint white dwarfs than the T eff and M values reported in previous analyses, thus solving a two-decade-old problem. In particular, we identify in our sample a group of ultramassive white dwarfs in the Debye cooling phase with stellar parameters never measured before.
Impossibility of Collective Intelligence

Democratization of AI involves training and deploying machine learning models across heterogeneous and potentially massive environments. Diversity of data opens up a number of possibilities to advance AI systems, but also introduces pressing concerns such as privacy, security, and equity that require special attention. This work shows that it is theoretically impossible to design a rational learning algorithm that has the ability to successfully learn across heterogeneous environments, which we decoratively call collective intelligence (CI). By representing learning algorithms as choice correspondences over a hypothesis space, we are able to axiomatize them with essential properties. Un-fortunately, the only feasible algorithm compatible with all of the axioms is the standard empirical risk minimization (ERM) which learns arbitrarily from a single environment. Our impossibility result reveals informational incomparability between environments as one of the foremost obstacles for researchers who design novel algorithms that learn from multiple environments, which sheds light on prerequisites for success in critical areas of machine learning such as out-of-distribution generalization, federated learning, algorithmic fairness, and multi-modal learning.
Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoising

Recent advances in differentiable rendering have enabled high-quality reconstruction of 3D scenes from multi-view images. Most methods rely on simple rendering algorithms: pre-filtered direct lighting or learned representations of irradiance. We show that a more realistic shading model, incorporating ray tracing and Monte Carlo integration, substantially improves decomposition into shape, materials&lighting. Unfortunately, Monte Carlo integration provides estimates with significant noise, even at large sample counts, which makes gradient-based inverse rendering very challenging. To address this, we incorporate multiple importance sampling and denoising in a novel inverse rendering pipeline. This substantially improves convergence and enables gradient-based optimization at low sample counts. We present an efficient method to jointly reconstruct geometry (explicit triangle meshes), materials, and lighting, which substantially improves material and light separation compared to previous work. We argue that denoising can become an integral part of high quality inverse rendering pipelines.
RAAT: Relation-Augmented Attention Transformer for Relation Modeling in Document-Level Event Extraction

In document-level event extraction (DEE) task, event arguments always scatter across sentences (across-sentence issue) and multipleevents may lie in one document (multi-event issue). In this paper, we argue that the relation information of event arguments is of greatsignificance for addressing the above two issues, and propose a new DEE framework which can model the relation dependencies, calledRelation-augmented Document-level Event Extraction (ReDEE). More specifically, this framework features a novel and tailored transformer,named as Relation-augmented Attention Transformer (RAAT). RAAT is scalable to capture multi-scale and multi-amount argument relations. To further leverage relation information, we introduce a separate event relation prediction task and adopt multi-task learning method to explicitly enhance event extraction performance. Extensive experiments demonstrate the effectiveness of the proposed method, which can achieve state-of-the-art performance on two public datasets.Our code is available at https://github.com/TencentYoutuResearch/RAAT.
Building Robust Ensembles via Margin Boosting

In the context of adversarial robustness, a single model does not usually have enough power to defend against all possible adversarial attacks, and as a result, has sub-optimal robustness. Consequently, an emerging line of work has focused on learning an ensemble of neural networks to defend against adversarial attacks. In this work, we take a principled approach towards building robust ensembles. We view this problem from the perspective of margin-boosting and develop an algorithm for learning an ensemble with maximum margin. Through extensive empirical evaluation on benchmark datasets, we show that our algorithm not only outperforms existing ensembling techniques, but also large models trained in an end-to-end fashion. An important byproduct of our work is a margin-maximizing cross-entropy (MCE) loss, which is a better alternative to the standard cross-entropy (CE) loss. Empirically, we show that replacing the CE loss in state-of-the-art adversarial training techniques with our MCE loss leads to significant performance improvement.