Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a Questionnaire to Measure Immersion in Video Media: The Film IEQ

Researchers and practitioners are keen to understand how new video viewing practices driven by technological developments impact viewers’ experiences. We detail the development of the Immersive Experience Questionnaire for Film and TV (Film IEQ). An exploratory factor analysis based on responses from 414 participants revealed a four-factor structure of (1) captivation, (2) real-world dissociation, (3) comprehension, and (4) transportation. We validated the Film IEQ in an experiment that replicated prior research into the effect of viewing on screens of varying size. Responses captured by the Film IEQ indicate that watching on a small phone screen reduces the viewer’s level of comprehension, and that this negatively impacts the viewing experience, compared to watching on a larger screen. The Film IEQ allows researchers and practitioners to assess video viewing experiences using a questionnaire that is easy to administer, and that has been empirically validated.
Adaptive Convolution for Text Classification

In this paper, we present an adaptive convolution for text classification to give flexibility to convolutional neural networks (CNNs). Unlike traditional convolutions which utilize the same set of filters regardless of different inputs, the adaptive convolution employs adaptively generated convolutional filters conditioned on inputs. We achieve this by attaching filter-generating networks, which are carefully designed to generate input-specific filters, to convolution blocks in existing CNNs. We show the efficacy of our approach in existing CNNs based on the performance evaluation. Our evaluation indicates that all of our baselines achieve performance improvements with adaptive convolutions as much as up to 2.6 percentage point in seven benchmark text classification datasets.
Post-Disaster Recovery Assessment with Machine Learning-Derived Land Cover and Land Use Information

Post-disaster recovery (PDR) is a complex, long-lasting, resource intensive, and poorly understood process. PDR goes beyond physical reconstruction (physical recovery) and includes relevant processes such as economic and social (functional recovery) processes. Knowing the size and location of the places that positively or negatively recovered is important to effectively support policymakers to help readjust planning and resource allocation to rebuild better. Disasters and the subsequent recovery are mainly expressed through unique land cover and land use changes (LCLUCs). Although LCLUCs have been widely studied in remote sensing, their value for recovery assessment has not yet been explored, which is the focus of this paper. An RS-based methodology was created for PDR assessment based on multi-temporal, very high-resolution satellite images. Different trajectories of change were analyzed and evaluated, i.e., transition patterns (TPs) that signal positive or negative recovery. Experimental analysis was carried out on three WorldView-2 images acquired over Tacloban city, Philippines, which was heavily affected by Typhoon Haiyan in 2013. Support vector machine, a robust machine learning algorithm, was employed with texture features extracted from the grey level co-occurrence matrix and local binary patterns. Although classification results for the images before and four years after the typhoon show high accuracy, substantial uncertainties mark the results for the immediate post-event image. All land cover (LC) and land use (LU) classified maps were stacked, and only changes related to TPs were extracted. The final products are LC and LU recovery maps that quantify the PDR process at the pixel level. It was found that physical and functional recovery can be mainly explained through the LCLUC information. In addition, LC and LU-based recovery maps support a general and a detailed recovery understanding, respectively. It is therefore suggested to use the LC and LU-based recovery maps to monitor and support the short and the long-term recovery, respectively.
Lane Accurate Detection of Map Changes based on Low Cost Smartphone Data

Self-driving vehicles rely on High Definition Street Maps (HD Maps) to ensure the safety and comfort of their driving capabilities. However, the road network infrastructure is subject to constant changes (e.g. through constructions works, accidents, .). Such changes have to be quickly identified to avoid dangerous driving situations, for example through a reduction of driving speed or the safe handover of driving control back to the human. To address this issue we propose a road hazard detection algorithm that identifies and marks the extent of such changes based on crowdsourced GNSS data. To increase the detection speed of our proposed algorithm, we only rely on sensor information in the collection process, that is not only available through vehicles, but as well by cheap and ubiquitous devices carried on by the passengers such as smartphones. To deal with the limited accuracy of the collected data, we enhance existing algorithmic clustering approaches by leveraging additional meta-data such as the quality of the collected GNSS points and the vehicle’s current lane position. Our concept is evaluated with real world measurements in a highway construction site scenario showing improved performance in comparison to the Kernel Density Estimation reference algorithm, used versatile in Related Work.
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers

The success of lottery ticket initializations (Frankle and Carbin, 2019) suggests that small, sparsified networks can be trained so long as the network is initialized appropriately. Unfortunately, finding these "winning ticket" initializations is computationally expensive. One potential solution is to reuse the same winning tickets across a variety of datasets and optimizers. However, the generality of winning ticket initializations remains unclear. Here, we attempt to answer this question by generating winning tickets for one training configuration (optimizer and dataset) and evaluating their performance on another configuration. Perhaps surprisingly, we found that, within the natural images domain, winning ticket initializations generalized across a variety of datasets, including Fashion MNIST, SVHN, CIFAR-10/100, ImageNet, and Places365, often achieving performance close to that of winning tickets generated on the same dataset. Moreover, winning tickets generated using larger datasets consistently transferred better than those generated using smaller datasets. We also found that winning ticket initializations generalize across optimizers with high performance. These results suggest that winning ticket initializations generated by sufficiently large datasets contain inductive biases generic to neural networks more broadly which improve training across many settings and provide hope for the development of better initialization methods.
The impact of the fiducial cosmology assumption on BAO distance scale measurements

Standard analysis pipelines for measurements of Baryon Acoustic Oscillations (BAO) in galaxy surveys make use of a fiducial cosmological model to guide the data compression required to transform from observed redshifts and angles to the measured angular and radial BAO peak positions. In order to remove any dependence on the fiducial cosmology from the results, all models compared to the data should mimic the compression and its dependence on the fiducial model. In practice, approximations are made when testing models: (1) There is assumed to be no residual dependence on the fiducial cosmology after reconstruction, (2) differences in the distance–redshift relationship are assumed to match a linear scaling, and (3) differences in clustering between true and fiducial models are assumed to be removed by the free parameters used to null the non-BAO signal. We test these approximations using the current standard measurement procedure with a set of halo catalogues from the aemulus suite of N-body simulations, which span a range of wCDM cosmological models. We focus on reconstruction of the primordial BAO and locating the BAO. For the range of wCDM cosmologies covered by the aemulus suite, we find no evidence for systematic errors in the measured BAO shift parameters α∥ and α⊥ to $\lt 0.1\%$. However, the measured errors $\sigma _{\alpha _{\parallel }}$ and $\sigma _{\alpha _{\bot }}$ show a notable absolute increase by up to +0.001 and +0.002, respectively, in the case that the fiducial cosmology does not match the truth. These effects on the inferred BAO scale will be important, given the precision of measurements expected from future surveys including DESI, Euclid, and WFIRST.
MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments

The Arcade Learning Environment (ALE) is a popular platform for evaluating reinforcement learning agents. Much of the appeal comes from the fact that Atari games demonstrate aspects of competency we expect from an intelligent agent and are not biased toward any particular solution approach. The challenge of the ALE includes (1) the representation learning problem of extracting pertinent information from raw pixels, and (2) the behavioural learning problem of leveraging complex, delayed associations between actions and rewards. Often, the research questions we are interested in pertain more to the latter, but the representation learning problem adds significant computational expense. We introduce MinAtar, short for miniature Atari, a new set of environments that capture the general mechanics of specific Atari games while simplifying the representational complexity to focus more on the behavioural challenges. MinAtar consists of analogues of five Atari games: Seaquest, Breakout, Asterix, Freeway and Space Invaders. Each MinAtar environment provides the agent with a 10x10xn binary state representation. Each game plays out on a 10x10 grid with n channels corresponding to game-specific objects, such as ball, paddle and brick in the game Breakout. To investigate the behavioural challenges posed by MinAtar, we evaluated a smaller version of the DQN architecture as well as online actor-critic with eligibility traces. With the representation learning problem simplified, we can perform experiments with significantly less computational expense. In our experiments, we use the saved compute time to perform step-size parameter sweeps and more runs than is typical for the ALE. Experiments like this improve reproducibility, and allow us to draw more confident conclusions. We hope that MinAtar can allow researchers to thoroughly investigate behavioural challenges similar to those inherent in the ALE.
New stability estimates for an unfitted finite element method for two-phase Stokes problem

The paper addresses stability and finite element analysis of the stationary two-phase Stokes problem with a piecewise constant viscosity coefficient experiencing a jump across the interface between two fluid phases. We first prove a priori estimates for the individual terms of the Cauchy stress tensor with stability constants independent of the viscosity coefficient. Next, this stability result is extended to the approximation of the two-phase Stokes problem by a finite element method. In the method considered, the interface between the phases does not respect the underlying triangulation, which put the finite element method into the class of unfitted discretizations. The finite element error estimates are proved with constants independent of viscosity. Numerical experiments supporting the theoretical results are provided.
Strategy for enhanced thermoelectric performance of Bi2S3 nanorods by Bi nanoinclusions.

This is the first report on the enhanced thermoelectric (TE) properties of novel Bi2S3-Bi nanocomposites synthesized using a one-step polyol method at different reaction temperatures (TRe) and time. They are well-characterized as nanorod-composites, coexistent with orthorhombic Bi2S3 and rhombohedral Bi phases together in which the latter coats the former forming Bi2S3-Bi core-shell type structures along with independent Bi nanoparticles (NPs). There is a very significant observation of systematic reduction in electri-cal resistivity \r{ho} with reaction temperature and time duration increase, revealing a promising approach for reduction of \r{ho} in this highly resistive Bi2S3 and hence resolving the earlier obstacles for its thermoelectric application potentials for the past few decades. Most astonishingly, TE power factor at 300 K of highest Bi content nanocomposite pellet, made at 27 oC using ~900 MPa pressure, is 3 orders of magnitude greater than that of hot-pressed Bi2S3, or even 23% better than that of spark plasma-sintered core-shell Bi2S3@Bi sample reported earlier (Tarachand et al. Nano Res. 2016, 9, 3291; Ge et al. ACS Appl. Mater. Interfaces 2017, 9, 4828). Considering the probable greatly reduced thermal conductivity due to their complex nanostructures, the significantly improved TE performance potential near 300 K is highly anticipated for these toxic- and rare earth element-free TE nanocomposites, making the present synthesis method as a pioneering approach for developing enhanced thermoelectric properties of Bi2S3-based materials without using extra sintering steps.
Floorplan repair using dynamic whitespace management

We describe an efficient, top-down strategy for overlap removal and floorplan repair which repairs overlaps in floorplans produced by placement algorithms or rough floorplanning methodologies. The algorithmic framework that we propose incorporates a novel geometric shifting technique within a top-down flow. The effect of our algorithm is quantified across a broad range of floorplans produced by multiple tools. Our method succeeds in producing valid placements in almost all cases; moreover, compared to leading methods, it requires only one fifth the run-time and produces placements with 4 to 13% less HPWL and up to 43% less cell movement.
Multivariate conditional transformation models

Regression models describing the joint distribution of multivariate responses conditional on covariate information have become an important aspect of contemporary regression analysis. However, a limitation of such models are the rather simplistic assumptions often made, for example, a constant dependence structure not varying with covariates or the restriction to linear dependence between the responses. We propose a general framework for multivariate conditional transformation models that overcomes these limitations and describes the entire distribution in a tractable and interpretable yet flexible way conditional on nonlinear effects of covariates. The framework can be embedded into likelihood‐based inference, including results on asymptotic normality, and allows the dependence structure to vary with covariates. In addition, it scales well‐beyond bivariate response situations, which were the main focus of most earlier investigations. We illustrate the benefits in a trivariate analysis of childhood undernutrition and demonstrate empirically that complex truly multivariate data‐generating processes can be inferred from observations.
Active Deep Decoding of Linear Codes

High quality data is essential in deep learning to train a robust model. While in other fields data is sparse and costly to collect, in error decoding it is free to query and label thus allowing potential data exploitation. Utilizing this fact and inspired by active learning, two novel methods are introduced to improve Weighted Belief Propagation (WBP) decoding. These methods incorporate machine-learning concepts with error decoding measures. For BCH(63,36), (63,45) and (127,64) codes, with cycle-reduced parity-check matrices, improvement of up to 0.4dB at the waterfall region, and of up to 1.5dB at the error-floor region in FER, over the original WBP, is demonstrated by smartly sampling the data, without increasing inference (decoding) complexity. The proposed methods constitutes an example guidelines for model enhancement by incorporation of domain knowledge from error-correcting field into a deep learning model. These guidelines can be adapted to any other deep learning based communication block.
Target-oriented Opinion Words Extraction with Target-fused Neural Sequence Labeling

Opinion target extraction and opinion words extraction are two fundamental subtasks in Aspect Based Sentiment Analysis (ABSA). Recently, many methods have made progress on these two tasks. However, few works aim at extracting opinion targets and opinion words as pairs. In this paper, we propose a novel sequence labeling subtask for ABSA named TOWE (Target-oriented Opinion Words Extraction), which aims at extracting the corresponding opinion words for a given opinion target. A target-fused sequence labeling neural network model is designed to perform this task. The opinion target information is well encoded into context by an Inward-Outward LSTM. Then left and right contexts of the opinion target and the global context are combined to find the corresponding opinion words. We build four datasets for TOWE based on several popular ABSA benchmarks from laptop and restaurant reviews. The experimental results show that our proposed model outperforms the other compared methods significantly. We believe that our work may not only be helpful for downstream sentiment analysis task, but can also be used for pair-wise opinion summarization.
Ranking and synchronization from pairwise measurements via SVD

Given a measurement graph $G= (V,E)$ and an unknown signal $r \in \mathbb{R}^n$, we investigate algorithms for recovering $r$ from pairwise measurements of the form $r_i - r_j$; $\{i,j\} \in E$. This problem arises in a variety of applications, such as ranking teams in sports data and time synchronization of distributed networks. Framed in the context of ranking, the task is to recover the ranking of $n$ teams (induced by $r$) given a small subset of noisy pairwise rank offsets. We propose a simple SVD-based algorithmic pipeline for both the problem of time synchronization and ranking. We provide a detailed theoretical analysis in terms of robustness against both sampling sparsity and noise perturbations with outliers, using results from matrix perturbation and random matrix theory. Our theoretical findings are complemented by a detailed set of numerical experiments on both synthetic and real data, showcasing the competitiveness of our proposed algorithms with other state-of-the-art methods.
A Closer Look at Structured Pruning for Neural Network Compression

Structured pruning is a popular method for compressing a neural network: given a large trained network, one alternates between removing channel connections and fine-tuning; reducing the overall width of the network. However, the efficacy of structured pruning has largely evaded scrutiny. In this paper, we examine ResNets and DenseNets obtained through structured pruning-and-tuning and make two interesting observations: (i) reduced networks---smaller versions of the original network trained from scratch---consistently outperform pruned networks; (ii) if one takes the architecture of a pruned network and then trains it from scratch it is significantly more competitive. Furthermore, these architectures are easy to approximate: we can prune once and obtain a family of new, scalable network architectures that can simply be trained from scratch. Finally, we compare the inference speed of reduced and pruned networks on hardware, and show that reduced networks are significantly faster. Code is available at this https URL.
Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs

Spectral clustering is one of the most popular, yet still incompletely understood, methods for community detection on graphs. This article studies spectral clustering based on the Bethe-Hessian matrix $H_r = (r^2-1)I_n + D-rA$ for sparse heterogeneous graphs (following the degree-corrected stochastic block model) in a two-class setting. For a specific value $r = \zeta$, clustering is shown to be insensitive to the degree heterogeneity. We then study the behavior of the informative eigenvector of $H_{\zeta}$ and, as a result, predict the clustering accuracy. The article concludes with an overview of the generalization to more than two classes along with extensive simulations on synthetic and real networks corroborating our findings.
Persistence of Rabies Antibody 5 Years after Postexposure Prophylaxis with Vero Cell Antirabies Vaccine and Antibody Response to a Single Booster Dose

ABSTRACT This study was done to investigate the antibody response to a Vero cell antirabies vaccine, the persistence of antibody for 5 years, and the effect of a booster dose after this interval. From August 2005 to February 2011, a total of 195 patients were enrolled into our study due to an animal bite. The Essen intramuscular (i.m.) regimen, which is recommended by the WHO for modern vaccines used in postexposure treatment, was adopted in this study. Blood samples were obtained on day 0, day 7, day 14, day 45, year 1, year 2, year 3, year 4, year 5, and year 5 plus 14 days. Immunogenicity was evaluated by the titration of neutralizing antibodies with a rapid fluorescent focus inhibition test (RFFIT). Seroconversion was expressed as the seroconversion rate (SCR). A secondary quantitative evaluation criterion, other than the seroconversion level, was the geometric mean titer (GMT). Of the 195 enrolled patients, 168 (86.4%) of them completed the whole study. No serious adverse reactions to the vaccine were reported during vaccination, the 5-year follow-up period, or revaccination. On day 14, the rabies antibody GMT value was 8.87 IU/ml in the vaccinees. During the next 5 years, the SCR in the ChengDa vaccine group gradually decreased to 34.0% at year 5, down from 90.5% at year 1. There was a significant booster effect: the GMT was 15.22 IU/ml on year 5 plus 14 days. Our findings demonstrate that the ChengDa rabies vaccine offers an alternative with a high degree of efficacy and yet limited side effects and ensures that the exposed patient will be on the safe side of the risk of rabies by the 14th day. Moreover, when followed by a booster dose 5 years later, it could boost the immunity. A further booster is effective in inducing a good neutralizing antibody response even after an interval of 5 years.
Audio tagging with noisy labels and minimal supervision

This paper introduces Task 2 of the DCASE2019 Challenge, titled "Audio tagging with noisy labels and minimal supervision". This task was hosted on the Kaggle platform as "Freesound Audio Tagging 2019". The task evaluates systems for multi-label audio tagging using a large set of noisy-labeled data, and a much smaller set of manually-labeled data, under a large vocabulary setting of 80 everyday sound classes. In addition, the proposed dataset poses an acoustic mismatch problem between the noisy train set and the test set due to the fact that they come from different web audio sources. This can correspond to a realistic scenario given by the difficulty in gathering large amounts of manually labeled data. We present the task setup, the FSDKaggle2019 dataset prepared for this scientific evaluation, and a baseline system consisting of a convolutional neural network. All these resources are freely available.