Miracle-dz/UniFAR-FTUs huggingface dataset:
Confidence Intervals for Tracking Performance Scores

The objective evaluation of trackers quantifies the discrepancy between tracking results and a manually annotated ground truth. As generating ground truth for a video dataset is tedious and time-consuming, often only keyframes are manually annotated. The annotation between these keyframes is then obtained semi-automatically, for example with linear interpolation. This approximation has two main undesirable consequences: first, interpolated annotations may drift from the actual object, especially with moving cameras; second, trackers that use linear prediction or regularize trajectories with linear interpolation unfairly gain a higher tracking evaluation score. This problem may become even more important when semi-automatically annotated datasets are used to train machine learning modules. To account for these annotation inaccuracies for a given dataset, we identify objects whose annotations are interpolated and propose a simple method that analyzes existing annotations and produces a confidence interval to complement tracking scores. These confidence intervals quantify the uncertainty in the annotation and allow us to appropriately interpret the ranking of trackers with respect to the chosen tracking performance score.
Upper Extremity Pain and Pitching Mechanics in National Collegiate Athletic Association (NCAA) Division I Softball

Abstract The purpose of this study was to evaluate pitching mechanics between female softball pitchers with upper extremity pain and those without upper extremity pain. Specifically, the trunk, shoulder and elbow kinematics and shoulder kinetics during the change-up softball pitch were examined. Fifty-five collegiate softball pitchers participated, divided into those with upper extremity pain (20.0±1.3 yrs.; 174.4±6.9 cm; 82.9±12.4 kg; 11.1±2.6 yrs. of experience; n=23) and those who were pain-free (19.9±1.4 yrs.; 173.8±6.9 cm; 81.4±12.5 kg; 10.0±2.5 yrs. of experience; n=32). Pitching mechanics were obtained via the trakSTAR™ electromagnetic tracking system (Ascension Technologies, Inc., Burlington, VT, USA). Mann-Whitney U tests revealed significant differences in shoulder horizontal abduction at foot contact (p=0.014, U=153, Z=2.450) and trunk lateral flexion at ball release (p=0.012, U=150, Z=–2.515); and between shoulder distraction force at ball release (p=0.034, U=168, Z=–2.124). The pain group illustrated greater shoulder horizontal abduction at foot contact, less trunk lateral flexion towards the throwing side at ball release, and greater shoulder distraction at ball release than the pain-free group. The differences in trunk and shoulder kinematics, and shoulder kinetics between groups allows for insight into further studies examining injury pervasiveness in softball pitching.
A Deep Learning Approach to Assessing Non-native Pronunciation of English Using Phone Distances

© 2018 International Speech Communication Association. All rights reserved. The way a non-native speaker pronounces the phones of a language is an important predictor of their proficiency. In grading spontaneous speech, the pairwise distances between generative statistical models trained on each phone have been shown to be powerful features. This paper presents a deep learning alternative to model-based phone distances in the form of a tunable Siamese network feature extractor to extract distance metrics directly from the audio frame sequence. Features are extracted at the phone instance level and combined to phone-level representations using an attention mechanism. Pair-wise distances between phone features are then projected through a feed-forward layer to predict score. The extraction stage is initialised on either a binary phone instance-pair classification task, or to mimic the model-based features, then the whole system is fine-tuned end-to-end, optimising the learning of the distance metric to the score prediction task. This method is therefore more adaptable and more sensitive to phone instance level phenomena. Its performance is compared against a DNN trained on Gaussian phone model distance features.
Role of ecology and phylogeny in determining tapeworm assemblages in skates (Rajiformes)

Abstract An understanding of the mechanisms that determine host and parasite relationships is a central aim in parasitology. Association of a parasite species with a host species may be influenced primarily by phylogenetic constraints that cause parasite species to co-speciate with their host species, or predominantly by ecological parameters that influence all other co-evolutionary scenarios. This study aimed to investigate the role of co-speciation as well as other co-evolutionary scenarios in influencing the assemblages of tapeworm parasites (marine cestodes) in skate hosts (Rajiformes) using a modification of the PACo (Procrustean Approach to Cophylogeny) method. The study found that phylogeny and host ecology are both significant predictors of skate–tapeworm relationships, implying that co-speciation as well as other co-evolutionary scenarios are shaping these associations. The study also investigated the key ecological parameters influencing host-switching and found that host diet, distribution depth, average body size and geographical location have a combined effect. Given the importance of parasites in ensuring healthy and stable marine ecosystems, the findings of this study have implications for conservation management worldwide.
Recurrent Global Convolutional Network for Scene Text Detection

Scene text detection is an important as well as challenging problem in computer vision. Text information plays an important role in scene understanding, image indexing, and indoor navigation. Deep neural networks are being widely used due to their capability to learn strong text features. However, the state-of-the-art scene text detection methods capture weak text features in the early layers with no scope to improve the captured features. In this paper we propose a novel recurrent architecture to improve the learnings of a feature map at a given time, by using global and local information from the same feature map at the previous time, for detecting occluded texts and long words. We design the recurrent text convolutional layer for seamless integration of recurrent architecture with local-global map. The experimental results on publicly available scene text datasets show the efficiency of our framework. We also create our own scene text dataset.
Sketchpointnet: A Compact Network for Robust Sketch Recognition

Sketch recognition is a challenging image processing task. In this paper, we propose a novel point-based network with a compact architecture, named SketchPointNet, for robust sketch recognition. Sketch features are hierarchically learned from three mini PointNets, by successively sampling and grouping 2D points in a bottom-up fashion. SketchPointNet exploits both temporal and spatial context in strokes during point sampling and grouping. By directly consuming the sparse points, SketchPointN et is very compact and efficient. Compared with state-of-the-art techniques, SketchPointNet achieves comparable performance on the challenging TU-Berlin dataset while it significantly reduces the network size.
Topological Brain Network Distances

Existing brain network distances are often based on matrix norms. The element-wise differences in the existing matrix norms may fail to capture underlying topological differences. Further, matrix norms are sensitive to outliers. A major disadvantage to element-wise distance calculations is that it could be severely affected even by a small number of extreme edge weights. Thus it is necessary to develop network distances that recognize topology. In this paper, we provide a survey of bottleneck, Gromov-Hausdorff (GH) and Kolmogorov-Smirnov (KS) distances that are adapted for brain networks, and compare them against matrix-norm based network distances. Bottleneck and GH-distances are often used in persistent homology. However, they were rarely utilized to measure similarity between brain networks. KS-distance is recently introduced to measure the similarity between networks across different filtration values. The performance analysis was conducted using the random network simulations with the ground truths. Using a twin imaging study, which provides biological ground truth, we demonstrate that the KS distance has the ability to determine heritability.
Dense Optical Flow Variation Based 3D Face Reconstruction from Monocular Video

This paper presents a method for reconstructing 3D face expressions from monocular video sequences. Unlike previous approaches we don't require any prior face models, nor a large collection of images with diverse variation of poses and illuminations. Instead, we leverage a monocular video sequence without any restrictions. We formulate the 3D face reconstruction as an energy minimization problem integrated with dense optical flow variation, as rigid as possible(ARAP) constraint, spatial and temporal constraints. This paper offers the first dense optical flow variational approach to the problem of 3D reconstruction of non-rigid face expressions from a monocular video. Dense optical flow variation cost substitutes for photo consistency cost to enhance the reconstruction of exaggerated expressions. A generic 3D face template mesh and a simple 3D warping algorithm allow us to reconstruct a true 3D face mesh, relax the constraints of diverse views or illuminations and also avoid the dependency of the quality of prior face models, such as the facial expressions or face races varieties limitation. Finally, we use a per-pixel shape-from-shading(SFS) algorithm to estimate the fine-scale geometry details such as wrinkles to further improve the reconstruction fidelity. Given unconstrained monocular RGB videos, our method reconstructs wrinkle-level 3D face model, without the need for any prior models or diverse capture conditions.
Dense Deconvolutional Network for Semantic Segmentation

Recently, exploring multiple feature maps from different layers in fully convolutional networks (FCNs) has gained substantial attention to capture context information for semantic segmentation. This paper presents a novel encoder-decoder architecture, called dense deconvolutional network (DDN), for semantic segmentation, where the feature maps of deeper convolutional layers are densely upsampled for the shallow deconvolutional layers. The proposed DDN is trainable end-to-end, and allows us to fully investigate multiple scale context cues embedded in images. The experimental results show that our DDN outperforms previous FCNs and encoder-decoder networks (EDNs) on PASCAL VOC 2012 dataset.
Densely Connected Large Kernel Convolutional Network for Semantic Membrane Segmentation in Microscopy Images

Structural analysis of neurons can provide valuable insights of brain function. Semantic segmentation of neurons thus becomes an important technique in bioinformatics. Deep learning approaches have shown promising performance in various semantic segmentation problems. However, segmentation of neurons in Electron Microscopy (EM) images has some differences compared with typical segmentation tasks due to the image noise and the disturbance of the intracellular structures. In our work, we propose a network with a ResNet encoder and densely connected decoder with large kernels, and then refinement with simple morphological post-possessing. Two main advantages of our method are: 1) the network can prevent the loss of high-resolution information and enlarge the reception field; 2) the post-processing method is simple and can be directly applied to the probability map from the network to enhance the unconfident area. Evaluated on the ISBI2012 EM membrane segmentation challenge, the proposed method achieves competitive performance.
Fusion of Template Matching and Foreground Detection for Robust Visual Tracking

In this paper, we present an end-to-end framework for visual tracking that contains fully convolutional template matching network and fully convolutional foreground detection network. It fuses the response maps of foreground detection and template matching for robust tracking and it can inherits all the merits of them. Besides, our network don't need additional datasets to train and only object information in the first frame is needed in training stage. We conduct extensive experiments on OTB2013 and OTB2015 and our tracker achieve state-of-the-art performance in both efficiency and accuracy.
Neural Speech Turn Segmentation and Affinity Propagation for Speaker Diarization

Speaker diarization is the task of determining "who speaks when" in an audio stream. Most diarization systems rely on statistical models to address four sub-tasks: speech activity detection (SAD), speaker change detection (SCD), speech turn clustering, and re-segmentation. First, following the recent success of recurrent neural networks (RNN) for SAD and SCD, we propose to address re-segmentation with Long-Short Term Memory (LSTM) networks. Then, we propose to use affinity propagation on top of neural speaker embeddings for speech turn clustering, outperforming regular Hierarchical Agglomerative Clustering (HAC). Finally, all these modules are combined and jointly optimized to form a speaker diarization pipeline in which all but the clustering step are based on RNNs. We provide experimental results on the French Broadcast dataset ETAPE where we reach state-of-the-art performance.
Algometer Precision for Quantifying Mechanical Nociceptive Threshold When Applied to the Udder of Lactating Dairy Cows

Objectives of this study were to: (1) quantify the reliability of an algometer for measuring mechanical nociceptive thresholds when applied to the udder of dairy cows; and (2) evaluate whether covariates, such as cow characteristics or time of the day, would influence algometer measurements. This prospective study was performed in a university herd of 37 lactating cows during five consecutive days, involving two raters. Two types of measurement were obtained: one qualitative binary measure (i.e., reaction vs. no reaction) and one quantitative measure presented in kilograms (i.e., mechanical nociceptive threshold, MNT) for the cows that reacted. Kappa statistics were used to investigate test-retest and inter-rater reliability for the qualitative measure, while concordance correlation coefficient (CCC) and limits of agreement plot were used for the quantitative measure. Whether algometer measurements were influenced by several covariates (i.e., time of the day, level of milk production, days in milk, and parity) was then evaluated using logistic or linear regression models, depending on the outcome. The algometer was moderately reliable; there was moderate test-retest reliability (Kappa = 0.53; CCC = 0.58) and inter-rater reliability (Kappa = 0.42; CCC = 0.54). The MNT varied substantially as a function of time of the day and parity. This is the first study reporting reliability of a pressure algometer for quantifying MNT and investigating covariates possibly affecting this measurement when applied to the udder of dairy cows. It is concluded that the use of an algometer for quantifying MNT on the udder is only moderately repeatable and is influenced by extraneous covariates. Its usage in research setting to quantify changes in sensitivity at the udder level should, therefore, be considered very cautiously or it should be further developed.
Effects of thermoforming on the physical and mechanical properties of thermoplastic materials for transparent orthodontic aligners

Objective The aim of this systematic multiscale analysis was to evaluate the effects of thermoforming on the physical and mechanical properties of thermoplastic materials used to fabricate transparent orthodontic aligners (TOAs). Methods Specimens were fabricated using four types of thermoplastic materials with different thicknesses under a thermal vacuum. Transparency, water absorption and solubility, surface hardness, and the results of three-point bending and tensile tests were evaluated before and after thermoforming. Data were analyzed using one-way analysis of variance and Student's t-test. Results After thermoforming, the transparency of Duran and Essix A+ decreased, while the water absorption ability of all materials; the water solubility of Duran, Essix A+, and Essix ACE; and the surface hardness of Duran and Essix A+ increased. The flexure modulus for the 0.5-mm-thick Duran, Essix A+, and eCligner specimens increased, whereas that for the 0.75-/1.0-mm-thick Duran and eClginer specimens decreased. In addition, the elastic modulus increased for the 0.5-mm-thick Essix A+ specimens and decreased for the 0.75-mm-thick Duran and Essix ACE and the 1.0-mm-thick Essix ACE specimens. Conclusions Our findings suggest that the physical and mechanical properties of thermoplastic materials used for the fabrication of TOAs should be evaluated after thermoforming in order to characterize their properties for clinical application.
Benchmarking Binarisation Schemes for Deep Face Templates

Feature vectors extracted from biometric characteristics are often represented using floating point values. It is, however, more appealing to store and compare feature vectors in a binary representation, since it generally requires less storage and facilitates efficient comparators which utilise intrinsic bit operations. Furthermore, the binary representations are very often necessary for some specific application scenarios, e.g. template protection and indexing. In recent years, usage of deep neural networks for facial recognition has vastly improved the biometric performance of said systems. In this paper, various binarisation schemes are applied to such feature vectors and benchmarked for biometric performance. It is shown that with only a negligible drop in biometric performance, the storage space and computational requirements can be vastly decreased.
Building Large-vocabulary Speaker-independent Lipreading Systems

Constructing a viable lipreading system is a challenge because it is claimed that only 30% of information of speech production is visible on the lips. Nevertheless, in small vocabulary tasks, there have been several reports of high accuracies. However, investigation of larger vocabulary tasks is much rarer. This work examines constructing a large vocabulary lipreading system using an approach based-on Deep Neural Network Hidden Markov Models (DNN-HMMs). We tackle the problem of lipreading an unseen speaker. We investigate the effect of employing several steps to pre-process visual features. Moreover, we examine the contribution of language modelling in a lipreading system where we use longer n -grams to recognise visual speech. Our lipreading system is constructed on the 6000-word vocabulary TCD-TIMIT audiovisual speech corpus. The results show that visual speech recognition can definitely reach 50% word accuracy on large vocabularies. We actually achieved a mean of 53.83% measured via three-fold cross-validation on the speaker independent setting of the TCD-TIMIT corpus using bigrams.
Food Image Recognition by Personalized Classifier

Since the development of food diaries could enable people to develop healthy eating habits, food image recognition is in high demand to reduce the effort in food recording. Previous studies have worked on this challenging domain with datasets having fixed numbers of samples and classes. However, in the real-world setting, it is impossible to include all of the foods in the database because the number of classes of foods is large and increases continually. In addition to that, inter-class similarity and intra-class diversity also bring difficulties to the recognition. In this paper, we attempted to solve these problems by using deep convolutional neural network features to build a personalized classifier which incrementally learns the user's data and adapts to the user's eating habit. As a result, we achieved the state-of-the-art accuracy of food image recognition by the personalization of 300 food records per user.
Global for Coarse and Part for Fine: A Hierarchical Action Recognition Framework

Action recognition is one significant yet challenging task in computer vision. Recent methods mainly model an end-to-end one-stage non-deep or deep learning networks to distinguish different action categories. In this paper we introduce one novel hierarchical action classification framework: Unlike existing one-stage recognition models, the proposed work improves the recognition accuracy by: 1) developing a hierarchical coarse-to-fine action classification framework by dividing the recognition processing into two stages: coarse- grained classification and fine-grained classification, and 2) representing actions in different stages with different granularity features representation: global features are utilized for coarse classifiers while more body parts patterns for fine-grained classifiers are aggregated. Experiments on two widely-tested benchmark datasets show that our method can achieve state-of-the-art or competitive performance compared with existing results using one-stage models, with advantages regarding the recognition accuracy and robustness.
Bit-Metric Decoding of Non-Binary LDPC Codes With Probabilistic Amplitude Shaping

A new approach for combining non-binary (NB) low-density parity-check codes with higher order modulation and probabilistic amplitude shaping (PAS) is presented. Instead of symbol-metric decoding, a bit-metric decoder is used so that matching the field order of the NB code to the constellation size is not needed, which increases the flexibility of the coding scheme. Information rates, density evolution thresholds, and finite-length simulations show that the flexibility comes at no loss of performance if PAS is used.
Hierarchical Recurrent Neural Networks for Acoustic Modeling

Recurrent neural network (RNN)-based acoustic models are widely used in speech recognition, and end-to-end training with CTC (connectionist temporal classiﬁcation) shows good performance. In order to improve the ability to keep temporarily distant information, we employ hierarchical recurrent neural networks (HRNNs) to the acoustic modeling in speech recognition. HRNN consists of multiple RNN layers that operate on different time-scales, and the frequency of operation at each layer is controlled by learned gates from training data. We employ gate activation regularization techniques to control the operation of the hierarchical layers. When tested with the WSJ eval92, our best model obtained the word error rate of 5.19% with beam search decoding using RNN based character-level language models. Compared to an LSTM based acoustic model with a similar parameter size, we achieved a relative word error rate improvement of 10.5%. Even though this model employs uni-directional RNN models, it showed the performance improvements over the previous bi-directional RNN based acoustic models.