Miracle-dz/UniFAR-FTUs huggingface dataset:
Mixture separability loss in a deep convolutional network for image classification

In machine learning, the cost function is crucial because it measures how good or bad a system is. In image classification, well-known networks only consider modifying the network structures and applying cross-entropy loss at the end of the network. However, using only cross-entropy loss causes a network to stop updating weights when all training images are correctly classified. This is the problem of early saturation. This study proposes a novel cost function, called mixture separability loss (MSL), which updates the weights of the network even when most of the training images are accurately predicted. MSL consists of between-class and within-class loss. Between-class loss maximises the differences between inter-class images, whereas within-class loss minimises the similarities between intra-class images. They designed the proposed loss function to attach to different convolutional layers in the network in order to utilise intermediate feature maps. Experiments show that a network with MSL deepens the learning process and obtains promising results with some public datasets, such as Street View House Number, Canadian Institute for Advanced Research, and the authors' self-collected Inha Computer Vision Lab gender dataset.
Multi-frame super-resolution algorithm using common vector approach

Super-resolution (SR) applications aim to use information within one or more low-resolution (LR) image(s) to obtain high-resolution (HR) image(s). LR images might well be the consecutive frames of a video sequence. When multiple LR images are used as input, motion estimation (ME) between portions of images is an important step of the solution for this ill-posed problem. In this study, the authors employed translational optical flow for ME, followed by common vector approach (CVA) for HR image reconstruction from multiple sources. CVA provides a way to reduce outliers caused by noise, occlusion, shadows and incorrect ME. HR image blocks are obtained by combining common and difference vectors of the blocks' class that are handled separately. Noise in difference vectors is reduced by a known noise reduction method before combining. Separate handling of common and difference parts guarantees better results and greatly reduce artefacts. Compared to the state-of-the-art, experimental results confirm the authors' achievement by visual, peak signal-to-noise ratio and structural similarity index measures criteria.
A Human in the Loop Approach to Capture Bias and Support Media Scientists in News Video Analysis (short paper)

Bias is inevitable and inherent in any form of communication. News often appear biased to citizens with dierent political orientations, and understood dierently by news media scholars and the broader public. In this paper we advocate the need for accurate methods for bias identication in video news item, to enable rich analytics capabilities in order to assist humanities media scholars and social political scientists. We propose to analyze biases that are typical in video news (including framing, gender and racial biases) by means of a human-in-the-loop approach that combines text and image analysis with human computation techniques.
Hierarchical CNN-based real-time fatigue detection system by visual-based technologies using MSP model

Visual-based technologies are very useful and meaningful to driver's fatigue detection. In this study, the authors present a multi-task hierarchical CNN scheme for fatigue detection system and propose a convolutional neural network (CNN) model with multi-scale pooling (MSP-Net). `Multi-task' includes three tasks: face detection, eye and mouth state detection and fatigue detection. First, they use a pre-trained network - multi-task CNN for face detection extracting eye and mouth regions. Then, the main work of this study, eye and mouth state detection is processed by MSP-Net, which can fit multi-resolution input images captured from variant cameras excellently. For the third step, the percentage of eyelid closure over the pupil over time (PERCLOS) parameters and the frequency of open mouth (FOM) parameters are used to detect fatigue, and the FOM parameters are proposed by ourselves. Besides, they successfully port the system to the embedded platform (the NVIDIA JETSON TX2 development board) and test on real driving scene. The results show that their system performs well and is robust to complex environments and is in line with the demand of real-time system.
A parallel algorithm of ICSYM for complex symmetric linear systems in quantum chemistry

Computational effort is a common issue for solving large-scale complex symmetric linear systems, particularly in quantum chemistry applications. In order to alleviate this problem, we propose a parallel algorithm of improved conjugate gradient-type iterative (CSYM). Using three-term recurrence relation and orthogonal properties of residual vectors to replace the tridiagonalization process of classical CSYM, which allows to decrease the degree of the reduce-operator from two to one communication at each iteration and to reduce the amount of vector updates and vector multiplications. Several numerical examples are implemented to show that high performance of proposed improved version is obtained both in convergent rate and in parallel efficiency.
StarStar Models: Using Events at Database Level for Process Analysis

Much time in process mining projects is spent on finding and understanding data sources and extracting the event data needed. As a result, only a fraction of time is spent actually applying techniques to discover, control and predict the business process. Moreover, there is a lack of techniques to display relationships on top of databases without the need to express a complex query to get the required information. In this paper, a novel modeling technique that works on top of databases is presented. This technique is able to show a multigraph representing activities inferred from database events, connected with edges that are annotated with frequency and performance information. The representation may be the entry point to apply advanced process mining techniques that work on classic event logs, as the model provides a simple way to retrieve a classic event log from a specified piece of model. Comparison with similar techniques and an empirical evaluation are provided.
Neurologist Standard Classification of Facial Nerve Paralysis with Deep Neural Networks

Facial nerve paralysis (FNP) is the most common form of facial nerve damage, which leads to significant physical pain and abnormal function in patients. Traditional FNP detection methods are based on visual diagnosis, which relies solely on the physician’s assessment. The use of objective measurements can reduce the frequency of errors which are caused by subjective methods. Hence, a fast, accurate, and objective computer method for FNP classification is proposed that uses a single Convolutional neural network (CNN), trained end-to-end directly from images, with only pixels and disease labels as inputs. We trained the CNN using a dataset of 1049 clinical images and divided the dataset into 7 categories based on classification standards with the help of neurologists. We tested its performance against the neurologists’ ground truth, and our results matched the neurologists’ level with 97.5% accuracy.
Differing Contributions of Classical Risk Factors to Type 2 Diabetes in Multi-Ethnic Malaysian Populations

The prevalence of type 2 diabetes is escalating rapidly in Asian countries, with the rapid increase likely attributable to a combination of genetic and lifestyle factors. Recent research suggests that common genetic risk variants contribute minimally to the rapidly rising prevalence. Rather, recent changes in dietary patterns and physical activity may be more important. This nested case-control study assessed the association and predictive utility of type 2 diabetes lifestyle risk factors in participants from Malaysia, an understudied Asian population with comparatively high disease prevalence. The study sample comprised 4077 participants from The Malaysian Cohort project and included sub-samples from the three major ancestral groups: Malay (n = 1323), Chinese (n = 1344) and Indian (n = 1410). Association of lifestyle factors with type 2 diabetes was assessed within and across ancestral groups using logistic regression. Predictive utility was quantified and compared between groups using the Area Under the Receiver-Operating Characteristic Curve (AUC). In predictive models including age, gender, waist-to-hip ratio, physical activity, location, family history of diabetes and average sleep duration, the AUC ranged from 0.76 to 0.85 across groups and was significantly higher in Chinese than Malays or Indians, likely reflecting anthropometric differences. This study suggests that obesity, advancing age, a family history of diabetes and living in a rural area are important drivers of the escalating prevalence of type 2 diabetes in Malaysia.
Performance modeling and analysis of hierarchical wireless communications networks with overflow and take-back traffic

Hierarchical structuring of cellular systems is an effective way to address the problem of continuing growth in traffic volume from users with varying degrees of mobility in wireless networks. Overflow and take-back strategies integrate the two tiers (microcell and macrocell) smoothly, within which both microcells and macrocells serve as a secondary resource to each other, taking into account the different mobility classes the subscribers belong to. We develop an analytical model based on MMPP (Markov modulated Poisson processes) to evaluate the performance of such hierarchical systems. We obtain the probability of call loss and forced termination for calls in progress and we contrast them to those from systems with incremental traffic handling capabilities in overflow and take-back.
Hibernation Leads to Altered Gut Communities in Bumblebee Queens (Bombus terrestris)

Many reptiles, amphibians, mammals, and insects practice some form of hibernation during which their metabolic rate is drastically reduced. This allows them to conserve energy and survive the harsh winter conditions with little or no food. While it can be expected that a reduction in host metabolism has a substantial influence on the gut microbial community, little is known about the effects of hibernation on the composition of the microbial gut community, especially for insects. In this study, we assessed and compared the bacterial gut community composition within the midgut and ileum of indoor-reared queens of Bombus terrestris before and after an artificial hibernation period of 16 weeks. Deep sequencing of 16S ribosomal RNA gene amplicons and clustering of sequence reads into operational taxonomic units (OTUs) at a similarity threshold of 97% revealed several bacterial taxa that are known to be strongly associated with corbiculate bees. Bacterial community composition after hibernation compared to before hibernation was characterized by higher OTU richness and evenness, with decreased levels of the core bacteria Gilliamella (Proteobacteria, Orbaceae) and Snodgrassella (Proteobacteria, Neisseriaceae), and increased relative abundance of non-core bacteria, including several psychrophilic and psychrotrophic taxa.
On the implementation of multilevel Monte Carlo simulation of the stochastic volatility and interest rate model using multi-GPU clusters

Abstract We explore different methods of solving systems of stochastic differential equations by first implementing the Euler–Maruyama and Milstein methods with a Monte Carlo simulation on a CPU. The performance of the methods is significantly improved through the recently developed antithetic multilevel Monte Carlo estimator, which yields a computation complexity of 𝒪 ⁢ ( ϵ - 2 ) {\mathcal{O}(\epsilon^{-2})} root-mean-square error and does so without the approximation of Lévy areas. Further improvements in performance are gained by moving the algorithms to a GPU - first on a single device and then on a multi-GPU cluster. Our GPU implementation of the antithetic multilevel Monte Carlo displays a major speedup in computation when compared with many commonly used approaches in the literature. While our work is focused on the simulation of the stochastic volatility and interest rate model, it is easily extendable to other stochastic systems, and it is of particular interest to those with non-diagonal, non-commutative noise.
LimitBias! Measuring Worker Biases in the Crowdsourced Collection of Subjective Judgments (short paper)

Crowdsourcing results acquired for tasks that comprise a subjective component (e.g. opinion detection, sentiment analysis) are affected by the inherent bias of the crowd workers. This leads to weaker and noisy ground-truth data. In this work we propose an approach for measuring crowd worker bias. We explore worker bias through the example task of bias detection where we compare the worker’s opinions with their annotations for speciﬁc topics. This is a ﬁrst important step towards mitigating crowd worker bias in subjective tasks.
pfla: A Python Package for Dental Facial Analysis using Computer Vision and Statistical Shape Analysis

One of the main advantages of using this software for clinical image analysis is that it automatically places landmarks, standardizing and expediting measurement procedures. This task was previously conducted manually which was very time consuming and prone to operator error and bias. With this novel approach we are able to circumvent these issues allowing us to have an efficient and objective way of conducting facial analysis.
Image segmentation framework based on optimal multi-method fusion

This study presents a multi-method fusion and optimisation framework that can optimally combine different existing methods to further enhance the segmentation performance. The framework, in which the original accumulating process is improved and a new combination process is added, is the extension of the previously developed `accumulated local fuzzy c-means with spatial information' method. In the improved accumulating process, different segmentation methods are utilised in local windows to judge whether each pixel belongs to the object. In the new combination process, the accumulated results of different segmentation methods are weighted combined, where the weights of different methods are optimised by the genetic algorithm with the objective of minimising standard deviations of both the object and the background pixels. Typical images and all images in the Weizmann's Segmentation Evaluation Database are tested in the experiments. The results show that the authors' method can perform better than some state-of-the-art methods, and combining more methods in the framework can bring better performance. Moreover, the proposed multi-method combination framework is parameterless, which increases its adaptability in various applications.
Crowdsourced Measure of News Articles Bias: Assessing Contributors' Reliability

We tackle the challenge of building a corpus of articles labelled for their political bias by relying on assessments provided by a crowd of contributors. The definition of ‘bias’ can be ambiguous to participants and both the targets of the ratings (articles) and the source of ratings (contributors) can be biased in some ways. In this paper, we explore techniques to mitigate this subjectivity and learn about the bias of both articles and contributors from the agreements and disagreements among their assessments. We report on the effectiveness of using a set of gold-standard articles to evaluate the reliability of contributors and discuss work in progress to evaluate the bias of contributors from their relative assessments of articles’ bias.
Incorporating Surface Elevation Information in UAV Multispectral Images for Mapping Weed Patches

Accurate mapping of weed distribution within a field is a first step towards effective weed management. The aim of this work was to improve the mapping of milk thistle (Silybum marianum) weed patches through unmanned aerial vehicle (UAV) images using auxiliary layers of information, such as spatial texture and estimated vegetation height from the UAV digital surface model. UAV multispectral images acquired in the visible and near-infrared parts of the spectrum were used as the main source of data, together with texture that was estimated for the image bands using a local variance filter. The digital surface model was created from structure from motion algorithms using the UAV image stereopairs. From this layer, the terrain elevation was estimated using a focal minimum filter followed by a low-pass filter. The plant height was computed by subtracting the terrain elevation from the digital surface model. Three classification algorithms (maximum likelihood, minimum distance and an object-based image classifier) were used to identify S. marianum from other vegetation using various combinations of inputs: image bands, texture and plant height. The resulting weed distribution maps were evaluated for their accuracy using field-surveyed data. Both texture and plant height have helped improve the accuracy of classification of S. marianum weed, increasing the overall accuracy of classification from 70% to 87% in 2015, and from 82% to 95% in 2016. Thus, as texture is easier to compute than plant height from a digital surface model, it may be preferable to be used in future weed mapping applications.
Tmem30a deficiency leads to retinal rod bipolar cell degeneration

Phospholipids are asymmetrically distributed across the mammalian plasma membrane, with phosphatidylserine (PS) and phosphatidylethanolamine concentrated in the cytoplasmic leaflet of the membrane bilayer and phosphatidylcholine in the exoplasmic leaflet. This asymmetric distribution is dependent on a group of P4 ATPases called PS flippases. The proper transport and function of PS flippases require a β‐subunit transmembrane protein 30A (TMEM30A). Disruption of PS flippases leads to several human diseases. Tmem30a is essential for photoreceptor survival. However, the roles of Tmem30a in the retinal rod bipolar cells (RBC) remain elusive. To investigate the role of Tmem30a in the RBCs, we generated a RBC‐specific Tmem30a knockout (cKO) mouse model using PCP2‐Cre line. The Tmem30a cKO mice exhibited defect in RBC function and progressive RBC death. PKCα staining of retinal cryosections from cKO mice revealed a remarkable dendritic sprouting of rod bipolar cells during the early degenerative process. Immunostaining analysis of PSD95 and mGluT6 expression demonstrated that rod bipolar cells in Tmem30a cKO retinas exhibited aberrant dendritic sprouting as a result of impaired synaptic efficacy, which implied a crucial role for Tmem30a in synaptic transmission in the retina. In addition, loss of Tmem30a led to reactive gliosis with increased expression of glial fibrillary acidic protein and CD68. TUNEL staining suggested that apoptotic cell death occurred in the retinal inner nuclear layer (INL). Our data show that loss of Tmem30a in RBCs results in dendritic sprouting of rod bipolar cells, increased astrogliosis and RBC death. Taken together, our studies demonstrate an essential role for Tmem30a in the retinal bipolar cells.
ssdtools: An R package to fit Species Sensitivity Distributions

Species sensitivity distributions (SSDs) are cumulative probability distributions that are used to estimate the percent of species that are affected by a given concentration of a chemical. The concentration that affects 5% of the species is referred to as the 5% Hazard Concentration (HC). Hazard concentrations are used for the derivation of environmental quality criteria and ecological risk assessment for contaminated ecosystems (Posthuma, Suter II, & Traas, 2001).
Scarplet: A Python package for topographic template matching and diffusion dating

Detection and analysis of landforms is a major problem in geoscience, including identifying and measuring the relative ages of earthquake fault scarps. Inverse methods using the diffusion equation have been applied to relatively date a wide range of landforms, including scarps (Hanks, 2000; Hilley, DeLong, Prentice, Blisniuk, & Arrowsmith, 2010), terraces (Avouac & Peltzer, 1993), and impact craters (Fassett & Thomson, 2014). Size (height) and relative age estimates from these techniques provide important constraints where instrumental or historic data may be sparse: for example, in a fault zone with a limited record of historic seismicity. Similar wavelet-based methods are widely used in geophysics and channel network analysis as feature extraction techniques (Lashermes, Foufoula-Georgiou, & Dietrich, 2007; Passalacqua, Tarolli, & Foufoula-Georgiou, 2010).
Orange is the new black: Kinases are the new master regulators of tumor suppression

For many decades, kinases have predominantly been characterized as oncogenes and drivers of tumorigenesis, because activating mutations in kinases occur in cancer with high frequency. The oncogenic functions of kinases relate to their roles as growth factor receptors and as critical mediators of mitogen‐activated pathways. Indeed, some of the most promising cancer therapeutic agents are kinase inhibitors. However, cancer genomics studies, especially screens that utilize high‐throughput identification of loss‐of‐function somatic mutations, are beginning to shed light on a widespread role for kinases as tumor suppressors. The initial characterization of tumor‐suppressing kinases— in particular members of the protein kinase C (PKC) family, MKK4 of the mitogen‐activated protein kinase kinase family, and DAPK3 of the death‐associated protein kinase family— laid the foundation for bioinformatic approaches that enable the identification of other tumor‐suppressing kinases. In this review, we discuss the important role that kinases play as tumor suppressors, using several examples to illustrate the history of their discovery and highlight the modern approaches that presently aid in the identification of tumor‐suppressing kinases. © 2018 IUBMB Life, 71(6):738–748, 2019
Valence matters: An electrophysiological study on how emotions influence cognitive performance in children

Emotional stimuli have been found to influence cognitive performance in children, but it is not clear whether this effect varies with the cognitive demands of the task. In this study, we examined how emotional expressions influenced cognitive performance and event-related potentials (ERPs) in early and middle childhood under varying cognitive control demands. Two groups of children (4.5-6.0 and 7.0-8.5 years) completed a modified flanker task where the stimuli were faces displaying task-irrelevant emotional expressions. Emotional influence varied depending on emotional valence: Accuracy was greater for happy targets, while response time and N2 latency were longer for angry targets. In younger children only, angry targets elicited a larger late frontal negativity. Cognitive control demands did not modulate the effect of emotions on behavioral performance or ERPs, contrasting with findings in adults. Findings are discussed in relation to the dual competition model and previous work demonstrating a positivity bias in children.