Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital Genetic variation in the zebrafish Genetic variation in the zebrafish

Although zebrafish was introduced as a laboratory model organism several decades ago and now serves as a primary model for developmental biology, there is only limited data on its genetic variation. An establishment of a dense polymorphism map becomes a requirement for effective linkage analysis and cloning approaches in zebrafish. By comparing ESTs to whole-genome shotgun data, we predicted >50,000 high-quality candidate SNPs covering the zebrafish genome with average resolution of 41 kbp. We experimentally validated ∼ 65% of a randomly sampled subset by genotyping 16 samples from seven commonly used zebrafish strains. The analysis reveals very high nucleotide diversity between zebrafish isolates. Even with the limited number of samples that we genotyped, zebrafish isolates revealed considerable interstrain variation, ranging from 7% (inbred) to 37% (wild-derived) of polymorphic sites being heterozygous. The increased proportion of polymorphic over monomorphic sites results in five times more frequent observation of a three allelic variant compared with human or mouse. Phylogenetic analysis shows that comparisons between even the least divergent strains used in our analysis may provide one informative marker approximately every 500 nucleotides. Furthermore, the number of haplotypes per locus is relatively large, reflecting independent establishment of the different lines from wild isolates. Finally, our results suggest the presence of prominent C-to-U and A-to-I RNA editing events in zebrafish. Overall, the levels and organization of genetic variation between and within commonly used zebrafish strains are markedly different from other laboratory model organisms, which may affect experimental design and interpretation. and ss49785942–ss49839678. ]
Design and Evaluation of an IMU Sensor-based System for the Rehabilitation of Upper Limb Motor Dysfunction

Stroke is one of the most significant non-communicable diseases in the world with approximately 15 million people experiencing a new or recurrent stroke each year. More than half of stroke survivors have some degree of permanent sensorimotor impairment that requires specialized physical rehabilitation. Wearable technologies are a cost-effective means by which to monitor and provide feedback about sensorimotor function across the different phases of stroke recovery, with data-driven insights used to improve clinical decision-making and care experiences. In this paper, we describe the redesign of a single inertial measurement unit (IMU) sensor system (i.e., the T'ena sensor), and evaluate the ability of the sensor to accurately measure movement kinematics during the performance of common post-stroke motor task. Results indicate high to very high agreement and correlation values between the T'ena sensor and the gold-standard motion capture system, regardless of kinematic parameter. In sum, the described T'ena sensor is capable of accurately measuring upper limb movement kinematics, using only a single sensor. The adoption of portable and low-cost devices have the ability to make a substantial impact for the millions of persons who exhibit motor impairments after a stroke.
A Localisation Study of Deep Learning Models for Chest X-ray Image Classification

Deep learning models have demonstrated superhuman performance in a multitude of image classification tasks, including the classification of chest X-ray images. Despite this, medical professionals are reluctant to embrace these models in clinical settings due to a lack of interpretability, citing being able to visualise the image areas contributing most to a model's predictions as one of the best ways to establish trust. To aid the discussion of their suitability for real-world use, in this work, we attempt to address this issue by conducting a localisation study of two state-of-the-art deep learning models for chest X-ray image classification, ResNet-38-large-meta and CheXNet, on a set of 984 radiologist annotated X-ray images from the publicly available ChestX-ray14 dataset. We do this by applying and comparing several state-of-the-art visualisation methods, combined with a novel dynamic thresholding approach for generating bounding boxes, which we show to outperform the static thresholding method used by similar localisation studies in the literature. Results also seem to indicate that localisation quality is more sensitive to the choice of thresholding scheme than the visualisation method used, and that a high discriminative ability as measured by classification performance is not necessarily sufficient for models to produce useful and accurate localisations.
Income Inequality and Rising Household Debt - Evidence from Sweden

The purpose of this thesis is to examines the relationship between income inequality and level of indebtedness among the Swedish population. Using a relative consumption theory framework, the effect of income inequality on debt ratio and propensity to acquire debt above one’s ability is analyzed. Further, the sample is divided into two subgroupings to check if the effect is more profound in urban than rural areas. This is done through a cross-sectional analysis using municipality level data from 2014. The empirical findings support that income inequality has a strong positive effect on debt ratio as well as the notion of acquiring debt above one’s ability. The findings do not support a greater effect in urban areas, rather implying that the effect is greater in rural areas. Based on these findings, income inequality have undesirable effects and policies, such as taxation of luxury goods and a more progressive taxation system, can act to mitigate income inequality in the society. (Less)
ViT-DR: Vision Transformers in Diabetic Retinopathy Grading Using Fundus Images

Diabetic retinopathy (DR) is a complication of diabetes caused by blood leakage into the retinal tissues. DR has become one of the primary causes of visual impairment worldwide, and it is the leading risk factor for poor vision in patients aged 25 to 74. Many symptoms cause retinal degener-ation, resulting in vision loss. Early DR detection helps avoid vision loss. Manual DR grading using fundus images is time-consuming and requires expert ophthalmologists. Therefore, we propose Vision Transformer (ViT) based DR severity classifi-cation method. In this work, the fundus images are initially divided into non-overlapping patches to retain location information. Then, the flattened patches are converted into sequences before going through a linear and positional embedding process. The generated sequence is then fed into several multi-head attention layers, which produce the final representation. The first token sequence is fed into a softmax classification layer in the classification stage, which produces the recognition output. The proposed work is tested on Kaggle, IDRiD databases. The results obtained are better than the convolutional neural networks and state-of-the-art.
Researchers' Perception of MOOCs: An Indian Case Study

Massive open online courses have gained popularity during the coronavirus induced lockdown. In India educational institutions have been working online since last fifteen months. During this time research scholar enrolled in higher educational institution have taken MOOC course to augment knowledge. This study is an attempt to understand the researchers' perception of MOOCs. It uses the qualitative approach to understand the experience of learning with MOOCs, especially during the pandemic. It outlines the ways in which researchers benefited by taking MOOCs while highlighting their grievances. This study also has a lot of implications for the researchers and facilitators of MOOCs as it points at the lacunae in the application of their products which once rectified will help them yield desired results.
Consensus Control of Multi-Agent Systems With Uncertain Communication Links

This paper is devoted to the consensus control of a leader-follower multi-agent system (MAS) modeled by a random digraph. Due to the uncertainties in the environment, the communication links between distinct agents are represented by a time-varying probability matrix. We propose a novel robust control strategy to achieve approximate agreement among agents in the presence of an asynchronous network with uncertain communication links. The uncertainties are modeled as disturbances, and the controller is designed to debilitate them. Under the proposed controller, it is guaranteed that the global consensus error satisfies the L2-gain performance in the presence of uncertainties. The functionality of the proposed controller in reducing the impact of uncertainty is proved mathematically and verified by simulations.
Effectiveness of a Passive Neck Support Mechanism for Overhead Occupational Tasks

Overhead working tasks are usually associated with a higher incidence of work-related musculoskeletal disor-ders (WRMDs) at the level of the shoulder and neck. Shoulder exoskeletons effectively reduce shoulder muscle activity during overhead work. Neck support's development and validation, on the other hand, has not equally advanced yet. Also, a limited amount of occupational neck support devices, which yield sim-ilar characteristics and limitations, are commercially available. Hence, a new neck support that possesses the characteristic to be fully adjustable and compatible with a previously developed shoulder exoskeleton has been designed and characterized. Preliminary results of a pilot study, conducted to investigate the effectiveness of the neck support during the execution of an overhead handling task, indicate that the novel neck support can reduce neck muscle activity up to 81%. Moreover, the combination of a neck support with a shoulder exoskeleton during overhead work may elicit a synergistic effect between the devices in terms of reduction of shoulder muscle activity.
Engineering Collective Adaptive Systems with CASSim

Interest in autonomous systems has only grown over the past decade. Several platforms and tools have been introduced over that time to assist with advancing this field of research. These technologies are necessary to support the engineering and evaluation of autonomous systems, such as drones and vehicles. These systems are also deployed as collective adaptive systems (CASs), which can be especially difficult to evaluate since their behavior can have a different impact on individual and collective performance. Evaluating these systems requires extensive experimentation and simulation. However, there remains a lack of open-source applications that can simulate the wide variety of CASs that are being developed. In this paper, we introduce the collective adaptive system simulation (CASSim) to assist researchers in engineering and evaluating CASs. We describe the implementation of the simulator and provide an example of a simulation with results that can be compared to prior work.
Spin 1/2 Fermions in the Unitary Regime at Finite Temperature

We have performed a fully non-perturbative calculation of the thermal properties of a system of spin 1/2 fermions in 3D in the unitary regime. We have determined the critical temperature for the superfluid-normal phase transition. The thermodynamic behavior of this system presents a number of unexpected features, and we conclude that spin 1/2 fermions in the BCS-BEC crossover should be classified as a new type of superfluid.
GOLDRUSH - II. Clustering of galaxies at z ∼ 4–6 revealed with the half-million dropouts over the 100 deg2 area corresponding to 1 Gpc3

We present clustering properties from 579,492 Lyman break galaxies (LBGs) at z~4-6 over the 100 deg^2 sky (corresponding to a 1.4 Gpc^3 volume) identified in early data of the Hyper Suprime-Cam (HSC) Subaru strategic program survey. We derive angular correlation functions (ACFs) of the HSC LBGs with unprecedentedly high statistical accuracies at z~4-6, and compare them with the halo occupation distribution (HOD) models. We clearly identify significant ACF excesses in 10"<$\theta$<90", the transition scale between 1- and 2-halo terms, suggestive of the existence of the non-linear halo bias effect. Combining the HOD models and previous clustering measurements of faint LBGs at z~4-7, we investigate dark-matter halo mass (Mh) of the z~4-7 LBGs and its correlation with various physical properties including the star-formation rate (SFR), the stellar-to-halo mass ratio (SHMR), and the dark matter accretion rate (dotMh) over a wide-mass range of Mh/M$_\odot$=4x10^10-4x10^12. We find that the SHMR increases from z~4 to 7 by a factor of ~4 at Mh~1x10^11 M$_\odot$, while the SHMR shows no strong evolution in the similar redshift range at Mh~1x10^12 M$_\odot$. Interestingly, we identify a tight relation of SFR/dotMh-Mh showing no significant evolution beyond 0.15 dex in this wide-mass range over z~4-7. This weak evolution suggests that the SFR/dotMh-Mh relation is a fundamental relation in high-redshift galaxy formation whose star formation activities are regulated by the dark matter mass assembly. Assuming this fundamental relation, we calculate the cosmic SFR densities (SFRDs) over z=0-10 (a.k.a. Madau-Lilly plot). The cosmic SFRD evolution based on the fundamental relation agrees with the one obtained by observations, suggesting that the cosmic SFRD increase from z~10 to 4-2 (decrease from z~4-2 to 0) is mainly driven by the increase of the halo abundance (the decrease of the accretion rate).
The progenitor of SN 2005cs in the Whirlpool Galaxy

The progenitor of SN 2005cs, in the galaxy M51, is identified i n pre-explosion HST ACS WFC imaging. Differential astrometry, with post-explosion ACS HRC F330W images, permitted the identification of the progenitor with an accuracy of 0.01 ′′ . The progenitor was detected in the F814W pre-explosion image withI = 23.3± 0.2, but was below the detection thresholds of the F435W and F555W images, withB < 24.8 and V < 25 at 5�. Limits were placed on the U and R band fluxes of the progenitor from pre-exp losion HST WFPC2 F336W and F675W images. Deep images in the infra-red from NIRI on the the Gemini telescope were taken 2 months prior to explosion, but the progenitor is not clearly detected on these. The upper limits for the JHK magnitudes of the progenitor were J < 21.9,H < 21.1 and K < 20.7. Despite having a detection on only one band, a restrictive spectral energy distribution of the progenitor star can be constructed and a robust case is made that the progenitor was a red supergiant with spectral type between mid K-type to late M-type type. The spectral energy distribution allows a region in the theoretical HR diagram to be determined which must contain the progenitor star. The initial mass of the star is cons trained to be MZAMS = 9 +32 M⊙, which is very similar to the identified progenitor of the type IIP SN 2003gd, and also consistent with upper mass limits placed on five other similar SN e. The upper limit in the deep K-band image is significant in that it allows us to rule out the possibility that the progenitor was a significantly higher mass object enshrouded in a dus t cocoon before core-collapse. This is further evidence that the trend for type II-P SNe to ar ise in low to moderate mass red supergiants is real.
Hardness of Linear Index Coding on Perturbed Instances

The index coding problem is concerned with the amount of information that a sender has to transmit to multiple receivers in a way that enables each of them to retrieve its requested data relying on prior side information. For linear index coding, the problem is characterized by the minrank parameter of a graph that represents the side information map of the receivers. Previous work has shown that it is N P-hard to determine the minrank parameter of graphs. In this work, we study the computational complexity of the minrank parameter on perturbed instances, obtained from worst-case instances by a random extension of the side information available to the receivers. This setting is motivated by applications of index coding, in which the side information is accumulated via repeated transmissions that suffer from loss of data due to noisy communication or storage capacity. We prove that determining the minrank parameter remains computationally hard on perturbed instances. Our contribution includes an extension of several hardness results of the minrank parameter to the perturbed setting as well as a general technique for deriving the hardness of the minrank parameter on perturbed instances from its hardness on worst-case instances.
Ecological Validation of Machine Learning Models for Spatiotemporal Gait Analysis in Free-living Environments Using Instrumented Insoles

Continuous gait monitoring may aid the early diagnosis of neurological and musculoskeletal conditions and help validate the effectiveness of new treatments. However, consumer-grade activity trackers can only capture summary gait metrics, whereas most of the research-grade devices capable of estimating fine-grained gait parameters are too cumbersome for extended-time use in real-life environments. Instrumented footwear may offer a promising alternative tool owing to their good accuracy and relatively small form factor, but their ability to detect stride-by-stride spatial gait parameters in free-living conditions has not been well explored to date. This work describes machine learning (ML) inference models for an insole system capable of accurately estimating stride time (ST), length (SL), and velocity (SV) in real-life environments. Functional validity was assessed through unstructured tests including straight-line walking, curve walking, and turns. Ecological validity was examined in free-living conditions. The ML models demonstrated better accuracy than conventional data processing methods (mean absolute errors in unstructured conditions were 3.55% for SL and 3.59% for SV). Real-life gait parameters estimated with the ML models showed stronger associations with a standardized walking test compared with the same parameters obtained with conventional methods. These results indicate proof-of-concept feasibility of using instrumented insoles and ML inference models for free-living spatiotemporal gait analysis.
Class-aware data augmentation by GAN specialisation to improve endoscopic images classification

An expert eye is often needed to correctly identify mucosal lesions within endoscopic images. Hence, computer-aided diagnosis systems could decrease the need for highly specialized senior endoscopists and the effect of medical desertification. Moreover, they can significantly impact the latest endoscopic classification challenges such as the Inflammatory Bowel Disease (IBD) gradation. Most of the existing methods are based on deep learning algorithms. However, it is well known that these techniques suffer from the lack of data and/or class imbalance which can be lowered by using augmentation strategies thanks to synthetic generations. Late GAN framework progress made available accurate and production-ready artificial image generation that can be harnessed to extend training sets. It requires, however, to deal with the unsupervised nature of those networks to produce class-aware artificial images. In this article, we present our work to extend two datasets through a class-aware GAN-based augmentation strategy with the help of the state-of-the-art framework StyleGAN2-ADA and fine-tuning. We especially focused our efforts on endoscopic and IBD datasets to improve the classification and gradation of these images.
The Stroop Competition: A Social-Evaluative Stroop Test for Acute Stress Induction

The Stroop test is one of the most widely used protocols to induce cognitive stress and reliably activates the sympathetic nervous system (SNS). However, it only moderately activates the hypothalamic-pituitary-adrenal (HPA) axis, the stress axis responsible for cortisol secretion. In other well-known stress protocols, such as the cold pressor test, adding social-evaluative elements to the regular procedure has proven to cause increased HPA axis activation. For this reason, we introduce the “Stroop Competition”, a novel stress protocol based on the established Stroop test that adds social-evaluative feedback by conducting the test against a fake opponent with subsequent performance evaluation. We investigated the stress response of 22 participants performing either the “Stroop Competition” (Competition group) or the regular Stroop test (Control group) three consecutive times. Stress responses were assessed using ECG recordings to extract heart rate (HR) and heart rate variability (HRV) and saliva samples to extract salivary alpha-amylase (sAA) and cortisol. In the Competition group, participants experienced higher SNS activation indicated by significantly higher HR and lower HRV levels as well as higher sAA response to the stressor compared to the Control group. Additionally, overall cortisol levels were significantly higher in the Competition group supporting higher HPA axis activity. The findings of our pilot study confirm our hypothesis that adding social-evaluative elements to the Stroop test causes a more effective activation of both the SNS and HPA axis. We are convinced that our novel “Stroop Competition” protocol will provide a valuable addition to the already existing stress protocols in biopsychological research.
Transport properties of cascading gauge theories

Cascading gauge theories of Klebanov et al. provide a model within a framework of gauge theory/string theory duality for a four-dimensional nonconformal gauge theory with a spontaneously generated mass scale. Using the dual supergravity description, we study sound wave propagation in strongly coupled cascading gauge theory plasma. We analytically compute the speed of sound and the bulk viscosity of cascading gauge theory plasma at a temperature much larger than the strong coupling scale of the theory. The sound wave dispersion relation is obtained from the hydrodynamic pole in the stress-energy tensor two-point correlation function. The speed of sound extracted from the pole of the correlation function agrees with its value computed in Phys. Rev. D 72, 066003 (2005), using the equation of state. We find that the bulk viscosity of the hot cascading gauge theory plasma is nonzero at the leading order in the deviation from conformality.
Motor Performance Index for Evaluation of Distributed Pattern in Multi-channel EEG

Recently, motor imagery (MI) is widely used for brain-computer interface (BCI) for rehabilitation, but it is an ambiguous task and difficult to perform for patients with stroke. To overcome this limitation, MI guidance systems were proposed in previous studies. However, the performance index to evaluate motor performance enhancement of MI guidance system and the motor ability for different motor tasks was focused on magnitude even though they used many kinds of the feature vector. The present study proposes the motor performance index considering the distributed pattern in multi-channel EEG by using the direction for multi-dimensional feature vectors. For validation of the proposed motor performance index, eleven healthy participants' data from our previous study were analyzed through cross-validation. Offline analysis results showed proposed direction-based indexation can be applicable for the evaluation of motor performance to compare different motor tasks. In addition, the present study showed that the MI guidance system can induce a similar activation pattern to motor execution, compared to pure MI.
A Differential Measure of the Strength of Causation

The ability to quantify the strength of an interaction between events represented by random variables is important in many applications such as medicine and environmental science. We present the problem of measuring the strength of a causal interaction, starting from the linear perspective and generalizing to a nonlinear measure of causal influence, using a differential calculus approach. The proposed measure of causal strength is interpretable and may be estimated efficiently using Gaussian process regression. We validate our estimation approach on several synthesized data sets, considering both static variables and time series.