Miracle-dz/UniFAR-FTUs huggingface dataset:
Universal ASR: Unify and Improve Streaming ASR with Full-context Modeling

Streaming automatic speech recognition (ASR) aims to emit each hypothesized word as quickly and accurately as possible, while full-context ASR waits for the completion of a full speech utterance before emitting completed hypotheses. In this work, we propose a unified framework, Universal ASR, to train a single end-to-end ASR model with shared weights for both streaming and full-context speech recognition. We show that the latency and accuracy of streaming ASR significantly benefit from weight sharing and joint training of full-context ASR, especially with inplace knowledge distillation. The Universal ASR framework can be applied to recent state-of-the-art convolution-based and transformer-based ASR networks. We present extensive experiments with two state-of-the-art ASR networks, ContextNet and Conformer, on two datasets, a widely used public dataset LibriSpeech and an internal large-scale dataset MultiDomain. Experiments and ablation studies demonstrate that Universal ASR not only simplifies the workflow of training and deploying streaming and full-context ASR models, but also significantly improves both emission latency and recognition accuracy of streaming ASR. With Universal ASR, we achieve new state-of-the-art streaming ASR results on both LibriSpeech and MultiDomain in terms of accuracy and latency.
A Catalog of Broad Morphology of Pan-STARRS Galaxies Based on Deep Learning

Autonomous digital sky surveys such as Pan-STARRS have the ability to image a very large number of galactic and extragalactic objects, and the large and complex nature of the image data reinforces the use of automation. Here we describe the design and implementation of a data analysis process for automatic broad morphology annotation of galaxies, and applied it to the data of Pan-STARRS DR1. The process is based on filters followed by a two-step convolutional neural network (CNN) classification. Training samples are generated by using an augmented and balanced set of manually classified galaxies. Results are evaluated for accuracy by comparison to the annotation of Pan-STARRS included in a previous broad morphology catalog of Sloan Digital Sky Survey galaxies. Our analysis shows that a CNN combined with several filters is an effective approach for annotating the galaxies and removing unclean images. The catalog contains morphology labels for 1,662,190 galaxies with ∼95% accuracy. The accuracy can be further improved by selecting labels above certain confidence thresholds. The catalog is publicly available.
Variable Impedance Control and Learning—A Review

Robots that physically interact with their surroundings, in order to accomplish some tasks or assist humans in their activities, require to exploit contact forces in a safe and proficient manner. Impedance control is considered as a prominent approach in robotics to avoid large impact forces while operating in unstructured environments. In such environments, the conditions under which the interaction occurs may significantly vary during the task execution. This demands robots to be endowed with online adaptation capabilities to cope with sudden and unexpected changes in the environment. In this context, variable impedance control arises as a powerful tool to modulate the robot's behavior in response to variations in its surroundings. In this survey, we present the state-of-the-art of approaches devoted to variable impedance control from control and learning perspectives (separately and jointly). Moreover, we propose a new taxonomy for mechanical impedance based on variability, learning, and control. The objective of this survey is to put together the concepts and efforts that have been done so far in this field, and to describe advantages and disadvantages of each approach. The survey concludes with open issues in the field and an envisioned framework that may potentially solve them.
Regret minimization in stochastic non-convex learning via a proximal-gradient approach

Motivated by applications in machine learning and operations research, we study regret minimization with stochastic first-order oracle feedback in online constrained, and possibly non-smooth, non-convex problems. In this setting, the minimization of external regret is beyond reach for first-order methods, so we focus on a local regret measure defined via a proximal-gradient mapping. To achieve no (local) regret in this setting, we develop a prox-grad method based on stochastic first-order feedback, and a simpler method for when access to a perfect first-order oracle is possible. Both methods are min-max order-optimal, and we also establish a bound on the number of prox-grad queries these methods require. As an important application of our results, we also obtain a link between online and offline non-convex stochastic optimization manifested as a new prox-grad scheme with complexity guarantees matching those obtained via variance reduction techniques.
MedICaT: A Dataset of Medical Images, Captions, and Textual References

Understanding the relationship between figures and text is key to scientific document understanding. Medical figures in particular are quite complex, often consisting of several subfigures (75% of figures in our dataset), with detailed text describing their content. Previous work studying figures in scientific papers focused on classifying figure content rather than understanding how images relate to the text. To address challenges in figure retrieval and figure-to-text alignment, we introduce MedICaT, a dataset of medical images in context. MedICaT consists of 217K images from 131K open access biomedical papers, and includes captions, inline references for 74% of figures, and manually annotated subfigures and subcaptions for a subset of figures. Using MedICaT, we introduce the task of subfigure to subcaption alignment in compound figures and demonstrate the utility of inline references in image-text matching. Our data and code can be accessed at https://github.com/allenai/medicat.
Thorough characterisation of the 16 Cygni system

Context: Being part of the brightest solar-like stars, and close solar analogues, the 16 Cygni system is of great interest to the scientific community and may provide insight into the past and future evolution of our Sun. It has been observed thoroughly by the Kepler satellite, which provided us with data of an unprecedented quality. Aims: This paper is the first of a series aiming to extensively characterise the system. We test several choices of micro- and macro-physics to highlight their effects on optimal stellar parameters and provide realistic stellar parameter ranges. Methods: We used a recently developed method, WhoSGlAd, that takes the utmost advantage of the whole oscillation spectrum of solar-like stars by simultaneously adjusting the acoustic glitches and the smoothly varying trend. For each choice of input physics, we computed models which account, at best, for a set of seismic indicators that are representative of the stellar structure and are as uncorrelated as possible. The search for optimal models was carried out through a Levenberg-Marquardt minimisation. First, we found individual optimal models for both stars. We then selected the best candidates to fit both stars while imposing a common age and composition. Results: We computed realistic ranges of stellar parameters for individual stars. We also provide two models of the system regarded as a whole. We were not able to build binary models with the whole set of choices of input physics considered for individual stars as our constraints seem too stringent. We may need to include additional parameters to the optimal model search or invoke non-standard physical processes.
Multi-layer Residual Sparsifying Transform (MARS) Model for Low-dose CT Image Reconstruction

PURPOSE Signal models based on sparse representations have received considerable attention in recent years. On the other hand, deep models consisting of a cascade of functional layers, commonly known as deep neural networks, have been highly successful for the task of object classification and have been recently introduced to image reconstruction. In this work, we develop a new image reconstruction approach based on a novel multilayer model learned in an unsupervised manner by combining both sparse representations and deep models. The proposed framework extends the classical sparsifying transform model for images to a Multilayer residual sparsifying transform (MARS) model, wherein the transform domain data are jointly sparsified over layers. We investigate the application of MARS models learned from limited regular-dose images for low-dose CT reconstruction using penalized weighted least squares (PWLS) optimization. METHODS We propose new formulations for multilayer transform learning and image reconstruction. We derive an efficient block coordinate descent algorithm to learn the transforms across layers, in an unsupervised manner from limited regular-dose images. The learned model is then incorporated into the low-dose image reconstruction phase. RESULTS Low-dose CT experimental results with both the XCAT phantom and Mayo Clinic data show that the MARS model outperforms conventional methods such as filtered back-projection and PWLS methods based on the edge-preserving (EP) regularizer in terms of two numerical metrics (RMSE and SSIM) and noise suppression. Compared with the single-layer learned transform (ST) model, the MARS model performs better in maintaining some subtle details. CONCLUSIONS This work presents a novel data-driven regularization framework for CT image reconstruction that exploits learned multilayer or cascaded residual sparsifying transforms. The image model is learned in an unsupervised manner from limited images. Our experimental results demonstrate the promising performance of the proposed multilayer scheme over single-layer learned sparsifying transforms. Learned MARS models also offer better image quality than typical nonadaptive PWLS methods.
Mathematical Word Problem Generation from Commonsense Knowledge Graph and Equations

There is an increasing interest in the use of mathematical word problem (MWP) generation in educational assessment. Different from standard natural question generation, MWP generation needs to maintain the underlying mathematical operations between quantities and variables, while at the same time ensuring the relevance between the output and the given topic. To address above problem, we develop an end-to-end neural model to generate diverse MWPs in real-world scenarios from commonsense knowledge graph and equations. The proposed model (1) learns both representations from edge-enhanced Levi graphs of symbolic equations and commonsense knowledge; (2) automatically fuses equation and commonsense knowledge information via a self-planning module when generating the MWPs. Experiments on an educational gold-standard set and a large-scale generated MWP set show that our approach is superior on the MWP generation task, and it outperforms the SOTA models in terms of both automatic evaluation metrics, i.e., BLEU-4, ROUGE-L, Self-BLEU, and human evaluation metrics, i.e., equation relevance, topic relevance, and language coherence. To encourage reproducible results, we make our code and MWP dataset public available at https://github.com/tal-ai/MaKE_EMNLP2021.
On the parallel I/O optimality of linear algebra kernels: near-optimal LU factorization

Dense linear algebra kernels are fundamental components of many scientific computing applications. In this work we present a novel method of deriving parallel I/O lower bounds for this broad family of programs. Based on the X-Partitioning abstraction, our method explicitly captures inter-statement dependencies. Applying our analysis to LU factorization, we derive COnfLUX, an LU algorithm with the parallel I/O cost of N3/([EQUATION]) communicated elements per processor - only 1/3× over our established lower bound. We evaluate COnfLUX on various problem sizes, demonstrating empirical results that match our theoretical analysis, communicating less than Cray ScaLAPACK, SLATE, and the asymptotically-optimal CANDMC library. Running on 1,024 nodes of Piz Daint, COnfLUX communicates 1.6× less than the second-best implementation and is expected to communicate 2.1× less on a full-scale run on Summit.
CADET: A Systematic Method For Debugging Misconfigurations using Counterfactual Reasoning

Modern computing platforms are highly-configurable with thousands of interacting configurations. However, configuring these systems is challenging. Erroneous configurations can cause unexpected non-functional faults. This paper proposes CADET (short for Causal Debugging Toolkit) that enables users to identify, explain, and fix the root cause of non-functional faults early and in a principled fashion. CADET builds a causal model by observing the performance of the system under different configurations. Then, it uses casual path extraction followed by counterfactual reasoning over the causal model to: (a) identify the root causes of non-functional faults, (b) estimate the effects of various configurable parameters on the performance objective(s), and (c) prescribe candidate repairs to the relevant configuration options to fix the non-functional fault. We evaluated CADET on 5 highly-configurable systems deployed on 3 NVIDIA Jetson systems-on-chip. We compare CADET with state-of-the-art configuration optimization and ML-based debugging approaches. The experimental results indicate that CADET can find effective repairs for faults in multiple non-functional properties with (at most) 17% more accuracy, 28% higher gain, and $40\times$ speed-up than other ML-based performance debugging methods. Compared to multi-objective optimization approaches, CADET can find fixes (at most) $9\times$ faster with comparable or better performance gain. Our case study of non-functional faults reported in NVIDIA's forum show that CADET can find $14%$ better repairs than the experts' advice in less than 30 minutes.
COVID-19 Imaging Data Privacy by Federated Learning Design: A Theoretical Framework

To address COVID-19 healthcare challenges, we need frequent sharing of health data, knowledge and resources at a global scale. However, in this digital age, data privacy is a big concern that requires the secure embedding of privacy assurance into the design of all technological solutions that use health data. In this paper, we introduce differential privacy by design (dPbD) framework and discuss its embedding into the federated machine learning system. To limit the scope of our paper, we focus on the problem scenario of COVID-19 imaging data privacy for disease diagnosis by computer vision and deep learning approaches. We discuss the evaluation of the proposed design of federated machine learning systems and discuss how differential privacy by design (dPbD) framework can enhance data privacy in federated learning systems with scalability and robustness. We argue that scalable differentially private federated learning design is a promising solution for building a secure, private and collaborative machine learning model such as required to combat COVID19 challenge.
LM-Reloc: Levenberg-Marquardt Based Direct Visual Relocalization

We present LM-Reloc–a novel approach for visual relocalization based on direct image alignment. In contrast to prior works that tackle the problem with a feature-based formulation, the proposed method does not rely on feature matching and RANSAC. Hence, the method can utilize not only corners but any region of the image with gradients. In particular, we propose a loss formulation inspired by the classical Levenberg-Marquardt algorithm to train LM-Net. The learned features significantly improve the robustness of direct image alignment, especially for relocalization across different conditions. To further improve the robustness of LM-Net against large image baselines, we propose a pose estimation network, CorrPoseNet, which regresses the relative pose to bootstrap the direct image alignment. Evaluations on the CARLA and Oxford RobotCar relocalization tracking benchmark show that our approach delivers more accurate results than previous state-of-the-art methods while being comparable in terms of robustness.
The Zero Resource Speech Challenge 2020: Discovering discrete subword and word units

We present the Zero Resource Speech Challenge 2020, which aims at learning speech representations from raw audio signals without any labels. It combines the data sets and metrics from two previous benchmarks (2017 and 2019) and features two tasks which tap into two levels of speech representation. The first task is to discover low bit-rate subword representations that optimize the quality of speech synthesis; the second one is to discover word-like units from unsegmented raw speech. We present the results of the twenty submitted models and discuss the implications of the main findings for unsupervised speech learning.
Hunter observations of moose Alces alces as a management tool

Pre- and post-harvest population size and the structural composition of the moose Alces alces population in the Vefsn valley in northern Norway estimated by the use of cohort analysis were compared to population density indices derived from hunter moose observations during 1968–1993. The moose observation index (moose seen per hunter day) was able to predict the same trend in the post-harvest population size in 84% of the cases, and was significantly linearly related both to pre- and post-harvest population size. The moose observation index was more closely related to the post-than to the pre-harvest population size, probably because the main part of the harvest took place during the first week of the hunting season, whereas the observation index was based on aggregated values collected over two weeks. Moreover, the observation index tended to overestimate population size when hunting success was high, indicating that factors influencing hunting success may also affect the probability of detecting moose. The recruitment rate (calves per female) recorded by the hunters was also closely related to the recruitment rate estimated by the cohort analysis, suggesting that the observed recruitment rate may provide a useful index of the recruitment to the population. We suggest that further improvements of moose recordings as a management tool can be obtained if the data are analysed separately for shorter periods (e.g. week, day) of the hunting season because this will probably reduce the impact of annual variation in harvesting rates on the estimates.
The mechanism of the plant roots’ soil-reinforcement based on generalized equivalent confining pressure

Background To quantitatively evaluate the contribution of plant roots to soil shear strength, the generalized equivalent confining pressure (GECP), which is the difference in confining pressure between the reinforced and un-reinforced soil specimens at the same shear strength, was proposed and considered in terms of the function of plant roots in soil reinforcement. Methods In this paper, silt loam soil was selected as the test soil, and the roots of Indigofera amblyantha were chosen as the reinforcing material. Different drainage conditions (consolidation drained (CD), consolidation undrained (CU), and unconsolidated undrained (UU)) were used to analyse the influences of different root distribution patterns (horizontal root (HR), vertical root (VR), and complex root (CR)) and root contents (0.25%, 0.50%, and 0.75%) on the shear strength of soil-root composites. Results The cohesion (c) values of the soil-root composites varied under different drainage conditions and root contents, while the internal friction angle (φ ) values remain basically stable under different drainage conditions. Under the same root content and drainage conditions, the shear strength indexes ranked in order of lower to higher were HR, VR and CR. The GECP of the soil-root composites with a 0.75% root content was 1.5–2.0 times that with a 0.50% root content and more than 5 times that with a 0.25% root content under the CD and CU conditions. The GECP in reinforced soil followed the sequence of CD > CU > UU. The GECP of the plant roots increased as confining pressure increased under CD and CU conditions while showed a complex change to the confining pressure under the UU condition. Conclusion It was concluded that the evaluation of plant root reinforcing soil based on GECP can be used to measure effectively the influences of roots on soil under different drainage conditions and root distribution patterns.
Cannabinoid receptor‐mediated regulation of neuronal activity in the main olfactory bulb

Neurons in the main olfactory bulb (MOB) express cannabinoid receptor CB1R. In mouse brain slices we used patch‐clamping to study the role of CB1R in regulating mitral and periglomerular cells. CB1R agonists evoked depolarization and increased firing; CB1R antagonists reduced firing of mitral cells. The CB1R effects on mitral cells were absent in subglomerular slices in which the olfactory nerve layer and glomerular layer were removed suggesting the glomerular layer as the site of action of CB1R. Periglomerular cells showed the inverse response pattern to CB1R activation. A CB1R agonist inhibited periglomerular cells whereas a CB1R antagonist activated periglomerular cells. Block of presynaptic inhibition in olfactory nerve afferents through blockade of GABAB and dopamine D2 receptors eliminated the CB1R effects on mitral cells. These findings suggest that CB1R indirectly regulates glutamate release from olfactory nerve terminals which in turn activates metabotropic glutamate receptors on mitral cells. Indeed, blockade of metabotropic glutamate receptor mGluR1 prevented CB1R‐mediated firing increases and CB1R‐mediated inhibition of mitral cells. We hypothesize that CB1R directly regulates periglomerular cells which in turn controls glutamate release by olfactory nerve afferents to regulate mitral cells. Support: HU‐HS Fac. Seed Grant Program, Whitehall Found. , NIH (S06GM08016, U54NS039407).
Radiographic and Clinical Findings of Single-Visit Root Canal Treatments with Apical Enlargement in Necrotic Teeth: A Retrospective Cohort Study

This study evaluated the long-term clinical outcomes of single-visit root canal treatments with apical enlargement on patients with necrotic pulp tissue retrospectively. A total of 137 teeth with necrotic pulp tissue which underwent single-visit root canal treatments were included. The root canals were shaped up until the apical constriction, which was determined by an apex locator. The outcomes were evaluated by two independent and calibrated endodontists clinically and radiographically. Teeth were dichotomized into healed (PAI ≤ 2, no signs or symptoms) and nonhealed (PAI > 2, with/without signs or symptoms) groups. Each patients' preoperative PAI and lesion size were recorded to evaluate the preoperative periapical status as well as several other prognostic factors. Statistical analyses were performed (p = 0.05) on ninety teeth. The mean observation time was 60 months. Out of ninety teeth, 87 (96.7%) were healed and 3 (3.3%) were nonhealed. No correlations were found between the prognostic factors and the outcomes (p > 0.05). Cohen's kappa and Gwet's agreement coefficient scores between the preoperative PAI scores and preoperative lesion sizes showed good agreements, with values of 0.834 and 0.898, respectively. Apical enlargement is a viable treatment option for single-visit root canal treatments.