Miracle-dz/UniFAR-FTUs huggingface dataset:
The NTNU System at the Interspeech 2020 Non-Native Children's Speech ASR Challenge

This paper describes the NTNU ASR system participating in the Interspeech 2020 Non-Native Children's Speech ASR Challenge supported by the SIG-CHILD group of ISCA. This ASR shared task is made much more challenging due to the coexisting diversity of non-native and children speaking characteristics. In the setting of closed-track evaluation, all participants were restricted to develop their systems merely based on the speech and text corpora provided by the organizer. To work around this under-resourced issue, we built our ASR system on top of CNN-TDNNF-based acoustic models, meanwhile harnessing the synergistic power of various data augmentation strategies, including both utterance- and word-level speed perturbation and spectrogram augmentation, alongside a simple yet effective data-cleansing approach. All variants of our ASR system employed an RNN-based language model to rescore the first-pass recognition hypotheses, which was trained solely on the text dataset released by the organizer. Our system with the best configuration came out in second place, resulting in a word error rate (WER) of 17.59 %, while those of the top-performing, second runner-up and official baseline systems are 15.67%, 18.71%, 35.09%, respectively.
SDN Enabled and OpenFlow Compatible Network Performance Monitoring System

Network performance monitoring holds a pivotal role in improving the overall network performance. It is essential to monitor the traffic statistics at Internet eXchange Points (IXPs) to optimize the traffic flows. The existing monitoring system either lacks usability or programmability. We present a Software Defined Networking (SDN) enabled and OpenFlow (OF) compatible network performance monitoring system (SDX-Manager) to improve the usability or programmability of IXP simultaneously. The proposed system integrates traditional IXP-Manager with the SDN controller and Grafana to better visualize the traffic. It provides a dashboard to the network administrator for defining rules over IXP, such as port blocking and traffic redirection. Furthermore, it facilitates the administrative tasks that include adding, modifying, and deleting the users and IXP objects (Ports, Switches). The proposed SDX-Manager is deployed and tested in a virtual and physical environment using OF enabled Allied Telesis Switch. Results indicate that SDX-Manager has improved network analysis using different statistics such as packets in/out per second, bits in/out per second, scrape duration, and query count.
Off-axis optical tweezers by an optical analog of the Magnus effect

It is shown that a circular dipole can deflect the focused laser beam that induces it, and will experience a corresponding transverse force. Quantitative expressions are derived for Gaussian and tophat beams, while the effects vanish in the plane-wave limit. The phenomena are analogous to the Magnus effect pushing a spinning ball onto a curved trajectory. The optical case originates in the coupling of spin and orbital angular momentum of the dipole and the light. In optical tweezers the force causes off-axis displacement of the trapping position of an atom by a spin-dependent amount up to $\lambda/2\pi$, set by the direction of a magnetic field. This suggests direct methods to demonstrate and explore these effects, for instance to induce spin-dependent motion.
Efficient Wait-k Models for Simultaneous Machine Translation

Simultaneous machine translation consists in starting output generation before the entire input sequence is available. Wait-k decoders offer a simple but efficient approach for this problem. They first read k source tokens, after which they alternate between producing a target token and reading another source token. We investigate the behavior of wait-k decoding in low resource settings for spoken corpora using IWSLT datasets. We improve training of these models using unidirectional encoders, and training across multiple values of k. Experiments with Transformer and 2D-convolutional architectures show that our wait-k models generalize well across a wide range of latency levels. We also show that the 2D-convolution architecture is competitive with Transformers for simultaneous translation of spoken language.
Towards Characterizing COVID-19 Awareness on Twitter

The coronavirus (COVID-19) pandemic has significantly altered our lifestyles as we resort to minimize the spread through preventive measures such as social distancing and quarantine. An increasingly worrying aspect is the gap between the exponential disease spread and the delay in adopting preventive measures. This gap is attributed to the lack of awareness about the disease and its preventive measures. Nowadays, social media platforms (ie., Twitter) are frequently used to create awareness about major events, including COVID-19. In this paper, we use Twitter to characterize public awareness regarding COVID-19 by analyzing the information flow in the most affected countries. Towards that, we collect more than 46K trends and 622 Million tweets from the top twenty most affected countries to examine 1) the temporal evolution of COVID-19 related trends, 2) the volume of tweets and recurring topics in those trends, and 3) the user sentiment towards preventive measures. Our results show that countries with a lower pandemic spread generated a higher volume of trends and tweets to expedite the information flow and contribute to public awareness. We also observed that in those countries, the COVID-19 related trends were generated before the sharp increase in the number of cases, indicating a preemptive attempt to notify users about the potential threat. Finally, we noticed that in countries with a lower spread, users had a positive sentiment towards COVID-19 preventive measures. Our measurements and analysis show that effective social media usage can influence public behavior, which can be leveraged to better combat future pandemics.
Meta-learning with Stochastic Linear Bandits

We investigate meta-learning procedures in the setting of stochastic linear bandits tasks. The goal is to select a learning algorithm which works well on average over a class of bandits tasks, that are sampled from a task-distribution. Inspired by recent work on learning-to-learn linear regression, we consider a class of bandit algorithms that implement a regularized version of the well-known OFUL algorithm, where the regularization is a square euclidean distance to a bias vector. We first study the benefit of the biased OFUL algorithm in terms of regret minimization. We then propose two strategies to estimate the bias within the learning-to-learn setting. We show both theoretically and experimentally, that when the number of tasks grows and the variance of the task-distribution is small, our strategies have a significant advantage over learning the tasks in isolation.
Bayesian Predictive Beamforming for Vehicular Networks: A Low-Overhead Joint Radar-Communication Approach

The development of dual-functional radar-communication (DFRC) systems, where vehicle localization and tracking can be combined with vehicular communication, will lead to more efficient future vehicular networks. In this paper, we develop a predictive beamforming scheme in the context of DFRC systems. We consider a system model where the road-side unit estimates and predicts the motion parameters of vehicles based on the echoes of the DFRC signal. Compared to the conventional feedback-based beam tracking approaches, the proposed method can reduce the signaling overhead and improve the accuracy of the angle estimation. To accurately estimate the motion parameters of vehicles in real-time, we propose a novel message passing algorithm based on factor graph, which yields a near optimal performance achieved by the maximum a posteriori estimation. The beamformers are then designed based on the predicted angles for establishing the communication links. With the employment of appropriate approximations, all messages on the factor graph can be derived in a closed-form, thus reduce the complexity. Simulation results show that the proposed DFRC based beamforming scheme is superior to the feedback-based approach in terms of both estimation and communication performance. Moreover, the proposed message passing algorithm achieves a similar performance of the high-complexity particle filtering-based methods.
Efficiently Reusing Old Models Across Languages via Transfer Learning

Recent progress in neural machine translation (NMT) is directed towards larger neural networks trained on an increasing amount of hardware resources. As a result, NMT models are costly to train, both financially, due to the electricity and hardware cost, and environmentally, due to the carbon footprint. It is especially true in transfer learning for its additional cost of training the “parent” model before transferring knowledge and training the desired “child” model. In this paper, we propose a simple method of re-using an already trained model for different language pairs where there is no need for modifications in model architecture. Our approach does not need a separate parent model for each investigated language pair, as it is typical in NMT transfer learning. To show the applicability of our method, we recycle a Transformer model trained by different researchers and use it to seed models for different language pairs. We achieve better translation quality and shorter convergence times than when training from random initialization.
Differentially Private ADMM for Convex Distributed Learning: Improved Accuracy via Multi-Step Approximation

Alternating Direction Method of Multipliers (ADMM) is a popular algorithm for distributed learning, where a network of nodes collaboratively solve a regularized empirical risk minimization by iterative local computation associated with distributed data and iterate exchanges. When the training data is sensitive, the exchanged iterates will cause serious privacy concern. In this paper, we aim to propose a new differentially private distributed ADMM algorithm with improved accuracy for a wide range of convex learning problems. In our proposed algorithm, we adopt the approximation of the objective function in the local computation to introduce calibrated noise into iterate updates robustly, and allow multiple primal variable updates per node in each iteration. Our theoretical results demonstrate that our approach can obtain higher utility by such multiple approximate updates, and achieve the error bounds asymptotic to the state-of-art ones for differentially private empirical risk minimization.
Deep learning for post-processing ensemble weather forecasts

Quantifying uncertainty in weather forecasts is critical, especially for predicting extreme weather events. This is typically accomplished with ensemble prediction systems, which consist of many perturbed numerical weather simulations, or trajectories, run in parallel. These systems are associated with a high computational cost and often involve statistical post-processing steps to inexpensively improve their raw prediction qualities. We propose a mixed model that uses only a subset of the original weather trajectories combined with a post-processing step using deep neural networks. These enable the model to account for non-linear relationships that are not captured by current numerical models or post-processing methods. Applied to the global data, our mixed models achieve a relative improvement in ensemble forecast skill (CRPS) of over 14%. Furthermore, we demonstrate that the improvement is larger for extreme weather events on select case studies. We also show that our post-processing can use fewer trajectories to achieve comparable results to the full ensemble. By using fewer trajectories, the computational costs of an ensemble prediction system can be reduced, allowing it to run at higher resolution and produce more accurate forecasts. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.
The katydid Spermatophore (Orthoptera: Tettigoniidae): Male nutritional investment and its fate in the mated female

In the katydid Requena verticalis Walker. radioactively labelled protein hydrolysate fed to males is incorporated into various parts of the body, but is more concentrated in the reproductive accessory glands that produce the large spermatophore. The spermatophore consists of two parts, only one of which contains sperm. Both parts are eaten by the female after mating. Females mated to these males had high concentrations of label in their spermathecae; this was most probably derived from label in the storedejaculate. Label in other parts of the body is acquired mainly from nutrients in the spermatophore eaten by the female. Ovaries and immature eggs of females at 3 days post-mating had higher concentrations of male-donated proteins than had somatic tissues, but females at 9-13 days post-mating did not show this difference. However, the older females had developed mature ovarian (and thus unfertilized) eggs which did have higher concentrations of label.
SemEval-2020 Task 5: Detecting Counterfactuals by Disambiguation

In this paper, we explore strategies to detect and evaluate counterfactual sentences. Since causal insight is an inherent characteristic of a counterfactual, is it possible to use this information in order to locate antecedent and consequent fragments in counterfactual statements? We thus propose to compare and evaluate models to correctly identify and chunk counterfactual sentences. In our experiments, we attempt to answer the following questions: First, can a learned model discern counterfactual statements reasonably well? Second, is it possible to clearly identify antecedent and consequent parts of counterfactual sentences?
Low-Complexity Vector Quantized Compressed Sensing via Deep Neural Networks

Sparse signals, encountered in many wireless and signal acquisition applications, can be acquired via compressed sensing (CS) to reduce computations and transmissions, crucial for resource-limited devices, e.g., wireless sensors. Since the information signals are often continuous-valued, digital communication of compressive measurements requires quantization. In such a quantized compressed sensing (QCS) context, we address remote acquisition of a sparse source through vector quantized noisy compressive measurements. We propose a deep encoder-decoder architecture, consisting of an encoder deep neural network (DNN), a quantizer, and a decoder DNN, that realizes low-complexity vector quantization aiming at minimizing the mean-square error of the signal reconstruction for a given quantization rate. We devise a supervised learning method using stochastic gradient descent and backpropagation to train the system blocks. Strategies to overcome the vanishing gradient problem are proposed. Simulation results show that the proposed non-iterative DNN-based QCS method achieves higher rate-distortion performance with lower algorithm complexity as compared to standard QCS methods, conducive to delay-sensitive applications with large-scale signals.
The effect of initial planting density on branching characteristics of Eucalyptus pilularis and E. grandis

Summary Branch-related defects can significantly decrease the quality of plantation eucalypt logs grown for solid-wood products. In this study we examined the effect of initial spacing on branching characteristics of 5-y-old plantation-grown Eucalyptus grandis and E. pilularis on the North Coast of New South Wales. For each sample tree all branches on the lower stem (the butt log, 0.3–6 m) were assessed for diameter, angle and condition. Trees were sampled in plots grown at four initial planting densities: 838 (3 m x 4 m), 1111 (3 m x 3 m), 1667 (3 x 2 m) and 3333 (3 m x 1 m) trees ha-1. The butt log of both species contained 55–70 branches, regardless of the initial density. Initial density did not affect branch formation, but it did affect the subsequent growth and persistence of branches. Lower initial density increased both the mean branch diameter and the number of large branches (> 2.5 cm diameter) per tree in both species. Branch size also increased with height up the stem, but the rate of increase was not different between densities. A major difference between species was that E. pilularis had more small dead branches that had not been shed. A single spacing prescription is possible for both species because there was no difference between species in the relationship between large branches and density.
ASPECTOS BIOLÓGICOS DO REDUZIDO NÚMERO DE SEMENTES DA TANGERINA 'SUNKI'

Estudaram-se as causas biologicas do reduzido numero de sementes da tangerina `Sunki' (Citrus sunki Hort. ex. Tan.), promissor porta-enxerto para a citricultura brasileira. As seguintes hipoteses foram investigadas: (1) existencia de uma limitacao na anatomia do fruto, decorrente do pequeno numero de ovulos por ovario; (2) ocorrencia de esterilidade gametica em funcao do desenvolvimento anormal do saco embrionario e/ou dos graos de polen; (3) presenca de auto-incompatibilidade. Realizaram-se cortes histologicos de ovarios e de sacos embrionarios, observacoes detalhadas de frutos e suas sementes, bem como polinizacoes com outras especies e cultivares. Concluiu-se que apesar de a tangerina `Sunki' produzir normalmente apenas duas a tres sementes viaveis, possui potencial biologico para originar ate dezessete sementes por fruto. A formacao de gametas tanto masculinos como femininos e normal. Quanto ao reduzido numero de sementes por fruto, pode-se explica-lo em razao do acentuado grau de auto-incompatibilidade. Quando polinizada com especies compativeis, ha um aumento significativo no numero de sementes por fruto, cuja magnitude depende do polinizador especifico utilizado. Em termos biologicos, e possivel e praticamente exequivel aumentar o numero de sementes da tangerina `Sunki' por meio de polinizadores adequados, no entanto, esse aumento e acompanhado pelo maior numero de plantas zigoticas e nao de nucelares como seria de interesse comercial.
Fragmented monopole crystal, dimer entropy, and Coulomb interactions in 
Dy2Ir2O7

Neutron scattering, specific heat and magnetisation measurements on both powders and single crystals reveal that Dy$_2$Ir$_2$O$_7$ realizes the fragmented monopole crystal state in which antiferromagnetic order and a Coulomb phase spin liquid co-inhabit. The measured residual entropy is that of a hard core dimer liquid, as predicted. Inclusion of Coulomb interactions allows for a quantitative description of both the thermodynamic data and the magnetisation dynamics, with the energy scale given by deconfined defects in the emergent ionic crystal. Our data reveal low energy excitations, as well as a large distribution of energy barriers down to low temperatures, while the magnetic response to an applied field suggests that domain wall pinning is important; results that call for further theoretical modelling.
Treatment of IFN-γ Transgenic Mice With Anti–IFN-γ Reveals the Remodeling Capacity of the Adult Pancreas

Pancreatic expression of γ-interferon (IFN-γ) initiates a cascade of pathogenic changes that include pancreatic inflammation, islet cell destruction, hyperglycemia, and islet regeneration. In this study, we explore the developmental plasticity of the adult pancreas and particularly its ability to return to normoglycemia and to remodel itself from an advanced pathogenic state. This was approached by treating adult transgenic mice with a pulse of anti–IFN-γ antibody and determining the functional and morphological status of the pancreas. We demonstrated that anti–IFN-γ antibody administration led to the reduction of hyperglycemic blood glucose levels in transgenic mice. We also observed that the pancreas returned from a profoundly perturbed state toward normality. Analysis of the mitotic index indicated that cell proliferation previously associated with islet cell regeneration was greatly reduced after anti–IFN-γ administration. Our results highlight the ability of the adult pancreas to remodel itself and return from a complex pathological state to normalcy once the trophic signal inducing this pathology is removed. These data also suggest that anti–IFN-γ administration may have important clinical implications for treatment of chronic pancreatitis in humans.
Fractional quantum Hall effect in CVD-grown graphene

We show the emergence of fractional quantum Hall states in graphene grown by chemical vapor deposition (CVD) for magnetic fields from below 3 T to 35 T where the CVD-graphene was dry-transferred. Effective composite-fermion filling factors up to ν * = 4 are visible and higher order composite-fermion states (with four flux quanta attached) start to emerge at the highest fields. Our results show that the quantum mobility of CVD-grown graphene is comparable to that of exfoliated graphene and, more specifically, that the p/3 fractional quantum Hall states have energy gaps of up to 30 K, well comparable to those observed in other silicon-gated devices based on exfoliated graphene.
Measuring and Characterizing Hate Speech on News Websites

The Web has become the main source for news acquisition. At the same time, news discussion has become more social: users can post comments on news articles or discuss news articles on other platforms like Reddit. These features empower and enable discussions among the users; however, they also act as the medium for the dissemination of toxic discourse and hate speech. The research community lacks a general understanding on what type of content attracts hateful discourse and the possible effects of social networks on the commenting activity on news articles. In this work, we perform a large-scale quantitative analysis of 125M comments posted on 412K news articles over the course of 19 months. We analyze the content of the collected articles and their comments using temporal analysis, user-based analysis, and linguistic analysis, to shed light on what elements attract hateful comments on news articles. We also investigate commenting activity when an article is posted on either 4chan’s Politically Incorrect board (/pol/) or six selected subreddits. We find statistically significant increases in hateful commenting activity around real-world divisive events like the “Unite the Right” rally in Charlottesville and political events like the second and third 2016 US presidential debates. Also, we find that articles that attract a substantial number of hateful comments have different linguistic characteristics when compared to articles that do not attract hateful comments. Furthermore, we observe that the post of a news articles on either /pol/ or the six subreddits is correlated with an increase of (hateful) commenting activity on the news articles.
An Effective and Efficient Training Algorithm for Multi-layer Feedforward Neural Networks

Network initialization is the first and critical step for training neural networks. In this paper, we propose a novel network initialization scheme based on the celebrated Stein's identity. By viewing multi-layer feedforward neural networks as cascades of multi-index models, the projection weights to the first hidden layer are initialized using eigenvectors of the cross-moment matrix between the input's second-order score function and the response. The input data is then forward propagated to the next layer and such a procedure can be repeated until all the hidden layers are initialized. Finally, the weights for the output layer are initialized by generalized linear modeling. Such a proposed SteinGLM method is shown through extensive numerical results to be much faster and more accurate than other popular methods commonly used for training neural networks.