Miracle-dz/UniFAR-FTUs huggingface dataset:
Fuzzy Overclustering: Semi-Supervised Classification of Fuzzy Labels with Overclustering and Inverse Cross-Entropy

Deep learning has been successfully applied to many classification problems including underwater challenges. However, a long-standing issue with deep learning is the need for large and consistently labeled datasets. Although current approaches in semi-supervised learning can decrease the required amount of annotated data by a factor of 10 or even more, this line of research still uses distinct classes. For underwater classification, and uncurated real-world datasets in general, clean class boundaries can often not be given due to a limited information content in the images and transitional stages of the depicted objects. This leads to different experts having different opinions and thus producing fuzzy labels which could also be considered ambiguous or divergent. We propose a novel framework for handling semi-supervised classifications of such fuzzy labels. It is based on the idea of overclustering to detect substructures in these fuzzy labels. We propose a novel loss to improve the overclustering capability of our framework and show the benefit of overclustering for fuzzy labels. We show that our framework is superior to previous state-of-the-art semi-supervised methods when applied to real-world plankton data with fuzzy labels. Moreover, we acquire 5 to 10% more consistent predictions of substructures.
PR ] 2 F eb 2 02 2 One-dimensional reflected BSDEs with quadratic growth generators

In this paper, we study the existence, uniqueness and comparison theorem for solutions of one-dimensional reflected backward stochastic differential equations (RBSDEs) with one continuous obstacle. The generators of such RBSDEs have a quadratic growth in z, with the quadratic term taking the form f(y)|z|2, where the function f is defined on an open interval D and local integral. The corresponding results on BSDEs are also obtained. Our proofs mainly depend on a transformation based on f and a domination method based on RBSDEs with two obstacles. As applications, we give a probabilistic interpretation of an obstacle problem for a quadratic PDE with local integral coefficient, and study an optimal stopping problem for the payoff of American options.
ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers

We describe a Question Answering (QA) dataset that contains complex questions with conditional answers, i.e. the answers are only applicable when certain conditions apply. We call this dataset ConditionalQA. In addition to conditional answers, the dataset also features:(1) long context documents with information that is related in logically complex ways;(2) multi-hop questions that require compositional logical reasoning;(3) a combination of extractive questions, yes/no questions, questions with multiple answers, and not-answerable questions;(4) questions asked without knowing the answers.We show that ConditionalQA is challenging for many of the existing QA models, especially in selecting answer conditions. We believe that this dataset will motivate further research in answering complex questions over long documents.
Attention-guided Generative Models for Extractive Question Answering

We propose a novel method for applying Transformer models to extractive question answering (QA) tasks. Recently, pretrained generative sequence-to-sequence (seq2seq) models have achieved great success in question answering. Contributing to the success of these models are internal attention mechanisms such as cross-attention. We propose a simple strategy to obtain an extractive answer span from the generative model by leveraging the decoder cross-attention patterns. Viewing cross-attention as an architectural prior, we apply joint training to further improve QA performance. Empirical results show that on open-domain question answering datasets like NaturalQuestions and TriviaQA, our method approaches state-of-the-art performance on both generative and extractive inference, all while using much fewer parameters. Furthermore, this strategy allows us to perform hallucination-free inference while conferring significant improvements to the model's ability to rerank relevant passages.
Cardiac Electrophysiology Meshfree Modeling through the Mixed Collocation Method

We present the meshfree mixed collocation method (MCM) for cardiac electrophysiology simulation. Capitalizing on the meshfree property of MCM, we introduce an immersed grid approach for automated generation of meshfree node grids from medical image data. This approach allows us to avoid the time-consuming mesh generation and processing that mesh-based methods like the finite element method (FEM) require. We employ the MCM to solve the cardiac monodomain model considering electrical propagation in 2D tissue sheets, 3D tissue slabs, and a realistic biventricular anatomy. We demonstrate that the solutions obtained by the MCM are in good agreement with the FEM, particularly when immersed grid is used. These findings confirm the suitability of the MCM for cardiac electrophysiology simulation and make the MCM a promising alternative to the FEM for cardiac electrical investigations.
Systematic Inequalities in Language Technology Performance across the World’s Languages

Natural language processing (NLP) systems have become a central technology in communication, education, medicine, artificial intelligence, and many other domains of research and development. While the performance of NLP methods has grown enormously over the last decade, this progress has been restricted to a minuscule subset of the world’s \approx6,500 languages. We introduce a framework for estimating the global utility of language technologies as revealed in a comprehensive snapshot of recent publications in NLP. Our analyses involve the field at large, but also more in-depth studies on both user-facing technologies (machine translation, language understanding, question answering, text-to-speech synthesis) as well as foundational NLP tasks (dependency parsing, morphological inflection). In the process, we (1) quantify disparities in the current state of NLP research, (2) explore some of its associated societal and academic factors, and (3) produce tailored recommendations for evidence-based policy making aimed at promoting more global and equitable language technologies. Data and code to reproduce the findings discussed in this paper areavailable on GitHub (https://github.com/neubig/globalutility).
Molecular Graph Generation via Geometric Scattering

Although existing deep learning models perform remarkably well at predicting physicochemical properties and binding affinities, the generation of new molecules with optimized properties remains challenging. Inherently, most GNNs perform poorly in whole-graph representation due to the limitations of the message-passing paradigm. Furthermore, step-by-step graph generation frameworks that use reinforcement learning or other sequential processing can be slow and result in a high proportion of invalid molecules with substantial post-processing needed in order to generate valid molecules. To address these issues, we propose a representation-first approach to molecular graph generation. We guide the latent representation of an autoencoder by capturing graph structure information with the geometric scattering transform and apply penalties that organize the representation by molecular properties. We show that this highly structured latent space can be directly used for molecular graph generation by the use of a GAN. We demonstrate that our architecture learns meaningful representations of drug datasets and provides a platform for drug synthesis using publicly available ZINC and BindingDB datasets.
Fractional Spin Excitations in the Infinite-Layer Cuprate 
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We use resonant inelastic x-ray scattering (RIXS) to investigate the magnetic dynamics of the infinite-layer cuprate CaCuO2. We find that close to the (1/2,0) point the single magnon decays into a broad continuum of excitations accounting for 80% of the total magnetic spectral weight. Polarization resolved RIXS spectra reveal the overwhelming dominance of spin-flip ($\Delta S = 1$) characterof this continuum with respect to the $\Delta S= 0$ multimagnon contributions. Moreover, its incident energy dependence is identical to that of the magnon, supporting a common physical origin. We propose that the continuum originates from the decay of the magnon into spinon pairs, and we relate it to the exceptionally high ring exchange $J_\mathrm{c} \sim J_1$ of CaCuO2. In the infinite layer cuprates long-range and multi-site hopping integrals are pivotal and amplify the 2D quantum magnetism effects in spite the 3D antiferromagnetic N\'eel order.
CasSeqGCN: Combining Network Structure and Temporal Sequence to Predict Information Cascades

One important task in the study of information cascade is to predict the future recipients of a message given its past spreading trajectory. While the network structure serves as the backbone of the spreading, an accurate prediction can hardly be made without the knowledge of the dynamics on the network. The temporal information in the spreading sequence captures many hidden features, but predictions based on sequence alone have their limitations. Recent efforts start to explore the possibility of combining both the network structure and the temporal feature. Here, we propose a new end-to-end prediction method CasSeqGCN in which the structure and temporal feature are simultaneously taken into account. A cascade is divided into multiple snapshots which record the network topology and the state of nodes. The graph convolutional network (GCN) is used to learn the representation of a snapshot. A novel aggregation method based on dynamic routing is proposed to aggregate node representation and the long short-term memory (LSTM) model is used to extract temporal information. CasSeqGCN predicts the future cascade size more accurately compared with other state-of-art baseline methods. The ablation study demonstrates that the improvement mainly comes from the design of the input and the GCN layer. We explicitly design an experiment to show the quality of the cascade representation learned by our approach is better than other methods. Our work proposes a new approach to combine the structural and temporal features, which not only gives a useful baseline model for future studies of cascade prediction, but also brings new insights on a wide collection of problems related with dynamics on and of the network.
The Spitzer/IRAC Legacy over the GOODS Fields: Full-depth 3.6, 4.5, 5.8, and 8.0 μm Mosaics and Photometry for >9000 Galaxies at z ∼ 3.5–10 from the GOODS Reionization Era Wide-area Treasury from Spitzer (GREATS)

We present the deepest Spitzer/InfraRed Array Camera (IRAC) 3.6, 4.5, 5.8, and 8.0 μm wide-area mosaics yet over the Great Observatories Origins Deep Survey (GOODS)-N and GOODS-S fields as part of the GOODS Reionization Era wide-Area Treasury from Spitzer (GREATS) project. We reduced and mosaicked in a self-consistent way observations taken by the 11 different Spitzer/IRAC programs over the two GOODS fields from 12 yr of Spitzer cryogenic and warm-mission data. The cumulative depth in the 3.6 μm and 4.5 μm bands amounts to ∼4260 hr, ∼1220 hr of which are new very deep observations from the GREATS program itself. In the deepest area, the full-depth mosaics reach ≳200 hr over an area of ∼100 arcmin2, corresponding to a sensitivity of ∼29 AB magnitude at 3.6 μm (1σ for point sources). Archival cryogenic 5.8 μm and 8.0 μm band data (a cumulative 976 hr) are also included in the release. The mosaics are projected onto the tangential plane of the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey/GOODS at a 0. ″3 pixel−1 scale. This paper describes the methodology enabling, and the characteristics of, the public release of the mosaic science images, the corresponding coverage maps in the four IRAC bands, and the empirical point-spread functions (PSFs). These PSFs enable mitigation of the source blending effects by taking into account the complex position-dependent variation in the IRAC images. The GREATS data products are in the Infrared Science Archive. We also release the deblended 3.6–8.0 μm photometry 9192 Lyman-break galaxies at z ∼ 3.5–10. GREATS will be the deepest mid-infrared imaging until the James Webb Space Telescope and, as such, constitutes a major resource for characterizing early galaxy assembly.
Scotogenic Majorana neutrino masses in a predictive orbifold theory of flavor

The use of extra space-time dimensions provides a promising approach to the ﬂavour problem. The chosen compactiﬁcation of a 6-dimensional orbifold implies a remnant family symmetry A 4 . This makes interesting predictions for quark and lepton masses, for neutrino oscillations and neutrinoless double beta decay, providing also a very good global description of all ﬂavour observables. Due to an auxiliary Z 4 symmetry, we implement a scotogenic Majorana neutrino mass generation mechanism with a viable WIMP dark matter candidate.
Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design

Molecular design and synthesis planning are two critical steps in the process of molecular discovery that we propose to formulate as a single shared task of conditional synthetic pathway generation. We report an amortized approach to generate synthetic pathways as a Markov decision process conditioned on a target molecular embedding. This approach allows us to conduct synthesis planning in a bottom-up manner and design synthesizable molecules by decoding from optimized conditional codes, demonstrating the potential to solve both problems of design and synthesis simultaneously. The approach leverages neural networks to probabilistically model the synthetic trees, one reaction step at a time, according to reactivity rules encoded in a discrete action space of reaction templates. We train these networks on hundreds of thousands of artificial pathways generated from a pool of purchasable compounds and a list of expert-curated templates. We validate our method with (a) the recovery of molecules using conditional generation, (b) the identification of synthesizable structural analogs, and (c) the optimization of molecular structures given oracle functions relevant to drug discovery.
MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction

Keyphrase extraction (KPE) automatically extracts phrases in a document that provide a concise summary of the core content, which benefits downstream information retrieval and NLP tasks. Previous state-of-the-art methods select candidate keyphrases based on the similarity between learned representations of the candidates and the document. They suffer performance degradation on long documents due to discrepancy between sequence lengths which causes mismatch between representations of keyphrase candidates and the document. In this work, we propose a novel unsupervised embedding-based KPE approach, Masked Document Embedding Rank (MDERank), to address this problem by leveraging a mask strategy and ranking candidates by the similarity between embeddings of the source document and the masked document. We further develop a KPE-oriented BERT (KPEBERT) model by proposing a novel self-supervised contrastive learning method, which is more compatible to MDERank than vanilla BERT. Comprehensive evaluations on six KPE benchmarks demonstrate that the proposed MDERank outperforms state-of-the-art unsupervised KPE approach by average 1.80 F1@15 improvement. MDERank further benefits from KPEBERT and overall achieves average 3.53 F1@15 improvement over SIFRank.
NoisyActions2M: A Multimedia Dataset for Video Understanding from Noisy Labels

Deep learning has shown remarkable progress in a wide range of problems. However, efficient training of such models requires large-scale datasets, and getting annotations for such datasets can be challenging and costly. In this work, we explore user-generated freely available labels from web videos for video understanding. We create a benchmark dataset consisting of around 2 million videos with associated user-generated annotations and other meta information. We utilize the collected dataset for action classification and demonstrate its usefulness with existing small-scale annotated datasets, UCF101 and HMDB51. We study different loss functions and two pretraining strategies, simple and self-supervised learning. We also show how a network pretrained on the proposed dataset can help against video corruption and label noise in downstream datasets. We present this as a benchmark dataset in noisy learning for video understanding. The dataset, code, and trained models are publicly available here for future research. A longer version of our paper is also available here.
Coherence of high-dimensional random matrices in a Gaussian case : application of the Chen-Stein method

This paper studies the $\tau$-coherence of a (n x p)-observation matrix in a Gaussian framework. The $\tau$-coherence is defined as the largest magnitude outside a diagonal bandwith of size $\tau$ of the empirical correlation coefficients associated to our observations. Using the Chen-Stein method we derive the limiting law of the normalized coherence and show the convergence towards a Gumbel distribution. We generalize here the results of Cai and Jiang [CJ11a]. We assume that the covariance matrix of the model is bandwise. Moreover, we provide numerical considerations highlighting issues from the high dimension hypotheses. We numerically illustrate the asymptotic behaviour of the coherence with Monte-Carlo experiment using a HPC splitting strategy for high dimensional correlation matrices.
Multistage linguistic conditioning of convolutional layers for speech emotion recognition

Introduction The effective fusion of text and audio information for categorical and dimensional speech emotion recognition (SER) remains an open issue, especially given the vast potential of deep neural networks (DNNs) to provide a tighter integration of the two. Methods In this contribution, we investigate the effectiveness of deep fusion of text and audio features for categorical and dimensional SER. We propose a novel, multistage fusion method where the two information streams are integrated in several layers of a DNN, and contrast it with a single-stage one where the streams are merged in a single point. Both methods depend on extracting summary linguistic embeddings from a pre-trained BERT model, and conditioning one or more intermediate representations of a convolutional model operating on log-Mel spectrograms. Results Experiments on the MSP-Podcast and IEMOCAP datasets demonstrate that the two fusion methods clearly outperform a shallow (late) fusion baseline and their unimodal constituents, both in terms of quantitative performance and qualitative behavior. Discussion Overall, our multistage fusion shows better quantitative performance, surpassing alternatives on most of our evaluations. This illustrates the potential of multistage fusion in better assimilating text and audio information.
HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization

To capture the semantic graph structure from raw text, most existing summarization approaches are built on GNNs with a pre-trained model. However, these methods suffer from cumbersome procedures and inefficient computations for long-text documents. To mitigate these issues, this paper proposes HetFormer, a Transformer-based pre-trained model with multi-granularity sparse attentions for long-text extractive summarization. Specifically, we model different types of semantic nodes in raw text as a potential heterogeneous graph and directly learn heterogeneous relationships (edges) among nodes by Transformer. Extensive experiments on both single- and multi-document summarization tasks show that HetFormer achieves state-of-the-art performance in Rouge F1 while using less memory and fewer parameters.
Stellar Rotation of T Tauri Stars in the Orion Star-forming Complex

We present a large-scale study of stellar rotation for T Tauri stars in the Orion star-forming complex. We use the projected rotational velocity ( vsin(i) ) estimations reported by the APOGEE-2 collaboration as well as individual masses and ages derived from the position of the stars in the HR diagram, considering Gaia-EDR3 parallaxes and photometry plus diverse evolutionary models. We find an empirical trend for vsin(i) decreasing with age for low-mass stars (0.4M ⊙ < M * < 1.2M ⊙). Our results support the existence of a mechanism linking vsin(i) to the presence of accreting protoplanetary disks, responsible for regulating stellar rotation on timescales of about 6 Myr, which is the timescale in which most of the T Tauri stars lose their inner disk. Our results provide important constraints to models of rotation in the early phases of evolution of young stars and their disks.
The Narrow Capture Problem with Partially Absorbing Targets and Stochastic Resetting

We consider a particle undergoing diffusion with stochastic resetting in a bounded domain $\calU\subset \R^d$ for $d=2,3$. The domain is perforated by a set of partially absorbing targets within which the particle may be absorbed at a rate $\kappa$. Each target is assumed to be much smaller than $|\calU|$, which allows us to use asymptotic and Green's function methods to solve the diffusion equation in Laplace space. In particular, we construct an inner solution within the interior and local exterior of each target, and match it with an outer solution in the bulk of $\calU$. This yields an asymptotic expansion of the Laplace transformed flux into each target in powers of $\nu=-1/\ln \epsilon$ ($d=2$) and $\epsilon$ ($d=3$), respectively, where $\epsilon$ is the non-dimensionalized target size. The fluxes determine how the mean first-passage time to absorption depends on the reaction rate $\kappa$ and the resetting rate $r$. For a range of parameter values, the MFPT is a unimodal function of $r$, with a minimum at an optimal resetting rate $r_{\rm opt}$ that depends on $\kappa$ and the target configuration.
Internal Feedback in Biological Control: Locality and System Level Synthesis

The presence of internal feedback pathways (IFPs) is a prevalent yet unexplained phenomenon in the brain. Motivated by experimental observations on 1) motor-related signals in visual areas, and 2) massively distributed processing in the brain, we approach this problem from a sensorimotor standpoint and make use of distributed optimal controllers to explain IFPs. We use the System Level Synthesis (SLS) controller to model neural phenomena such as signaling delay, local processing, and local reaction. Based on the SLS controller, we make qualitative predictions about IFPs that strongly align with existing experimental observations. We introduce a ‘mesocircuit’ for optimal performance with distributed and local processing, and local disturbance rejection; this mesocircuit requires extreme amounts of IFPs and memory for proper function. This is the first theory that replicates the massive amounts of IFPs in the brain purely from a priori principles, providing a new theoretical basis upon which we can build to better understand the inner workings of the brain.