Miracle-dz/UniFAR-FTUs huggingface dataset:
A multiscale constitutive model for metal forming of dual phase titanium alloys by incorporating inherent deformation and failure mechanisms

Ductile metals undergo a considerable amount of plastic deformation before failure. Void nucleation, growth and coalescence is the mechanism of failure in such metals. α–β titanium alloys are ductile in nature and are widely used for their unique set of properties such as specific strength, fracture toughness, corrosion resistance and resistance to fatigue failures. Voids in these alloys have been reported to nucleate on the phase boundaries between α and β phase. Based on the findings of crystal plasticity finite element method investigations of the void growth at the interface of α and β phases, a void nucleation, growth, and coalescence model has been formulated. An existing single-phase crystal plasticity theory is extended to incorporate underlying physical mechanisms of deformation and failure in dual phase titanium alloys. Effects of various factors [stress triaxiality, Lode parameter, deformation state (equivalent stress), and phase boundary inclination] on void nucleation, growth and coalescence are used to formulate a phenomenological constitutive model while their interaction with a conventional crystal plasticity theory is established. An extensive parametric assessment of the model is carried out to quantify and understand the effects of the material parameters on the overall material response. Performance of the proposed model is then assessed and verified by comparing the results of the proposed model with the RVE study results. Application of the constitutive model for utilisation in the design and optimisation of the forming process of α–β titanium alloy components is also demonstrated using experimental data.
A census of coronal mass ejections on solar-like stars

Coronal mass ejections (CMEs) may have major importance for planetary and stellar evolution. Stellar CME parameters, such as mass and velocity, have yet not been determined statistically. So far only a handful of stellar CMEs has been detected mainly on dMe stars using spectroscopic observations. We therefore aim for a statistical determination of CMEs of solar-like stars by using spectroscopic data from the ESO phase 3 and Polarbase archives. To identify stellar CMEs, we use the Doppler signal in optical spectral lines being a signature of erupting filaments that are closely correlated to CMEs. We investigate more than 3700 h of on-source time of in total 425 dF-dK stars. We find no signatures of CMEs and only few flares. To explain this low level of activity, we derive upper limits for the non-detections of CMEs and compare those with empirically modelled CME rates. To explain the low number of detected flares, we adapt a flare power law derived from EUV data to the H α regime, yielding more realistic results for H α observations. In addition, we examine the detectability of flares from the stars by extracting Sun-as-a-star H α light curves. The extrapolated maximum numbers of observable CMEs are below the observationally determined upper limits, which indicates that the on-source times were mostly too short to detect stellar CMEs in H α. We conclude that these non-detections are related to observational biases in conjunction with a low level of activity of the investigated dF-dK stars.
Point-of-Care Diabetic Retinopathy Diagnosis: A Standalone Mobile Application Approach

Although deep learning research and applications have grown rapidly over the past decade, it has shown limitation in healthcare applications and its reachability to people in remote areas. One of the challenges of incorporating deep learning in medical data classification or prediction is the shortage of annotated training data in the healthcare industry. Medical data sharing privacy issues and limited patient population size can be stated as some of the reasons for training data insufficiency in healthcare. Methods to exploit deep learning applications in healthcare have been proposed and implemented in this dissertation. Traditional diagnosis of diabetic retinopathy requires trained ophthalmologists and expensive imaging equipment to reach healthcare centres in order to provide facilities for treatment of preventable blindness. Diabetic people residing in remote areas with shortage of healthcare services and ophthalmologists usually fail to get periodical diagnosis of diabetic retinopathy thereby facing the probability of vision loss or impairment. Deep learning and mobile application development have been integrated in this dissertation to provide an easy to use point-of-care smartphone based diagnosis of diabetic retinopathy. In order to solve the challenge of shortage of healthcare centres and trained ophthalmologists, the standalone diagnostic service was built so as to be operated by a non-expert without an internet connection. This approach could be transferred to other areas of medical image classification.
Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations

Identifying when to give treatments to patients and how to select among multiple treatments over time are important medical problems with a few existing solutions. In this paper, we introduce the Counterfactual Recurrent Network (CRN), a novel sequence-to-sequence model that leverages the increasingly available patient observational data to estimate treatment effects over time and answer such medical questions. To handle the bias from time-varying confounders, covariates affecting the treatment assignment policy in the observational data, CRN uses domain adversarial training to build balancing representations of the patient history. At each timestep, CRN constructs a treatment invariant representation which removes the association between patient history and treatment assignments and thus can be reliably used for making counterfactual predictions. On a simulated model of tumour growth, with varying degree of time-dependent confounding, we show how our model achieves lower error in estimating counterfactuals and in choosing the correct treatment and timing of treatment than current state-of-the-art methods.
Boundary Control of Vlasov--Fokker--Planck Equations

We introduce a novel Lyapunov function for stabilization of linear Vlasov--Fokker--Planck type equations with stiff source term. Contrary to existing results relying on transport properties to obtain stabilization, we present results based on hypocoercivity analysis for the Fokker--Planck operator. The existing estimates are extended to derive suitable feedback boundary control to guarantee the exponential stabilization. Further, we study the associated macroscopic limit and derive conditions on the feedback boundary control such that in the formal limit no boundary layer exists.
Outlier Guided Optimization of Abdominal Segmentation

Abdominal multi-organ segmentation of computed tomography (CT) images has been the subject of extensive research interest. It presents a substantial challenge in medical image processing, as the shape and distribution of abdominal organs can vary greatly among the population and within an individual over time. While continuous integration of novel datasets into the training set provides potential for better segmentation performance, collection of data at scale is not only costly, but also impractical in some contexts. Moreover, it remains unclear what marginal value additional data have to offer. Herein, we propose a single-pass active learning method through human quality assurance (QA). We built on a pre-trained 3D U-Net model for abdominal multi-organ segmentation and augmented the dataset either with outlier data (e.g., exemplars for which the baseline algorithm failed) or inliers (e.g., exemplars for which the baseline algorithm worked). The new models were trained using the augmented datasets with 5-fold cross-validation (for outlier data) and withheld outlier samples (for inlier data). Manual labeling of outliers increased Dice scores with outliers by 0.130, compared to an increase of 0.067 with inliers (p<0.001, two-tailed paired t-test). By adding 5 to 37 inliers or outliers to training, we find that the marginal value of adding outliers is higher than that of adding inliers. In summary, improvement on single-organ performance was obtained without diminishing multi-organ performance or significantly increasing training time. Hence, identification and correction of baseline failure cases present an effective and efficient method of selecting training data to improve algorithm performance.
Analytic theory for Bragg atom interferometry based on the adiabatic theorem

High-fidelity Bragg pulses are an indispensable tool for state-of-the-art atom interferometry experiments. In this paper, we introduce an analytic theory for such pulses. Our theory is based on the pivotal insight that the physics of Bragg pulses can be accurately described by the adiabatic theorem. We show that efficient Bragg diffraction is possible with any smooth and adiabatic pulse shape and that high-fidelity Gaussian pulses are exclusively adiabatic. Our results give strong evidence that adiabaticity according to the adiabatic theorem is a necessary requirement for high-performance Bragg pulses. Our model provides an intuitive understanding of the Bragg condition, also referred to as the condition on the "pulse area". It includes corrections to the adiabatic evolution due to Landau-Zener processes as well as the effects of a finite atomic velocity distribution. We verify our model by comparing it to an exact numerical integration of the Schr\"odinger equation for Gaussian pulses diffracting four, six, eight and ten photon recoils. Our formalism provides an analytic framework to study systematic effects as well as limitations to the accuracy of atom interferometers employing Bragg optics that arise due to the diffraction process.
Quantitative understanding of negative thermal expansion in scandium trifluoride from neutron total scattering measurements

Negative thermal expansion (NTE) - the phenomenon where some materials shrink rather than expand when heated - is an intriguing and useful phenomenon, but it remains poorly understood. Current understanding hinges on the role of specific vibrational modes, but in fact thermal expansion is a weighted sum of contributions from every possible mode. Here we overcome this difficulty by deriving a real-space model of atomic motion in the prototypical NTE material scandium trifluoride, ScF3, from total neutron scattering data. We show that NTE in this material depends not only on rigid unit modes - the vibrations in which the scandium coordination octahedra remain undistorted - but also on modes that distort these octahedra. Furthermore, in contrast with previous predictions, we show that the quasiharmonic approximation coupled with renormalisation through anharmonic interactions describes this behaviour well. Our results point the way towards a new understanding of how NTE is manifested in real materials.
Meta-Learning across Meta-Tasks for Few-Shot Learning

Existing meta-learning based few-shot learning (FSL) methods typically adopt an episodic training strategy whereby each episode contains a meta-task. Across episodes, these tasks are sampled randomly and their relationships are ignored. In this paper, we argue that the inter-meta-task relationships should be exploited and those tasks are sampled strategically to assist in meta-learning. Specifically, we consider the relationships defined over two types of meta-task pairs and propose different strategies to exploit them. (1) Two meta-tasks with disjoint sets of classes: this pair is interesting because it is reminiscent of the relationship between the source seen classes and target unseen classes, featured with domain gap caused by class differences. A novel learning objective termed meta-domain adaptation (MDA) is proposed to make the meta-learned model more robust to the domain gap. (2) Two meta-tasks with identical sets of classes: this pair is useful because it can be employed to learn models that are robust against poorly sampled few-shots. To that end, a novel meta-knowledge distillation (MKD) objective is formulated. There are some mistakes in the experiments. We thus choose to withdraw this paper.
Elementary Methods for Persistent Homotopy Groups

In this paper, we study the basic properties of persistent homotopy groups. We show that the persistent fundamental group benefits from the Van Kampen theorem, and the interleaving distance between total spaces is less than or equal to the maximum of the interleaving distances between subspaces. We also prove excision and Hurewicz theorems for persistent homotopy groups. As an application, we analyze the sublevelset persistent homotopy groups of the energy landscape of alkane molecules.
Synergistic information in a dynamical model implemented on the human structural connectome reveals spatially distinct associations with age

Abstract We implement the dynamical Ising model on the large-scale architecture of white matter connections of healthy subjects in the age range 4–85 years, and analyze the dynamics in terms of the synergy, a quantity measuring the extent to which the joint state of pairs of variables is projected onto the dynamics of a target one. We find that the amount of synergy in explaining the dynamics of the hubs of the structural connectivity (in terms of degree strength) peaks before the critical temperature, and can thus be considered as a precursor of a critical transition. Conversely, the greatest amount of synergy goes into explaining the dynamics of more central nodes. We also find that the aging of structural connectivity is associated with significant changes in the simulated dynamics: There are brain regions whose synergy decreases with age, in particular the frontal pole, the subcallosal area, and the supplementary motor area; these areas could then be more likely to show a decline in terms of the capability to perform higher order computation (if structural connectivity was the sole variable). On the other hand, several regions in the temporal cortex show a positive correlation with age in the first 30 years of life, that is, during brain maturation.
Personalized acute stress classification from physiological signals with neural processes

Objective: A person's affective state has known relationships to physiological processes which can be measured by wearable sensors. However, while there are general trends those relationships can be person-specific. This work proposes using neural processes as a way to address individual differences. Methods: Stress classifiers built from classic machine learning models and from neural processes are compared on two datasets using leave-one-participant-out cross-validation. The neural processes models are contextualized on data from a brief period of a particular person's recording. Results: The neural processes models outperformed the standard machine learning models, and had the best performance when using periods of stress and baseline as context. Contextual points chosen from other participants led to lower performance. Conclusion: Neural processes can learn to adapt to person-specific physiological sensor data. There are a wide range of affective and medical applications for which this model could prove useful.
epiCOLOC: Integrating Large-Scale and Context-Dependent Epigenomics Features for Comprehensive Colocalization Analysis

High-throughput genome-wide epigenomic assays, such as ChIP-seq, DNase-seq and ATAC-seq, have profiled a huge number of functional elements across numerous human tissues/cell types, which provide an unprecedented opportunity to interpret human genome and disease in context-dependent manner. Colocalization analysis determines whether genomic features are functionally related to a given search and will facilitate identifying the underlying biological functions characterizing intricate relationships with queries for genomic regions. Existing colocalization methods leveraged diverse assumptions and background models to assess the significance of enrichment, however, they only provided limited and predefined sets of epigenomic features. Here, we comprehensively collected and integrated over 44,385 bulk or single-cell epigenomic assays across 53 human tissues/cell types, such as transcription factor binding, histone modification, open chromatin and transcriptional event. By classifying these profiles into hierarchy of tissue/cell type, we developed a web portal, epiCOLOC (http://mulinlab.org/epicoloc or http://mulinlab.tmu.edu.cn/epicoloc), for users to perform context-dependent colocalization analysis in a convenient way.
Cardiovascular Mortality Can Be Predicted by Heart Rate Turbulence in Hemodialysis Patients

Background Excess mortality in hemodialysis patients is mostly of cardiovascular origin. We examined the association of heart rate turbulence (HRT), a marker of baroreflex sensitivity, with cardiovascular mortality in hemodialysis patients. Methods A population of 290 prevalent hemodialysis patients was followed up for a median of 3 years. HRT categories 0 (both turbulence onset [TO] and slope [TS] normal), 1 (TO or TS abnormal), and 2 (both TO and TS abnormal) were obtained from 24 h Holter recordings. The primary end-point was cardiovascular mortality. Associations of HRT categories with the endpoints were analyzed by multivariable Cox regression models including HRT, age, albumin, and the improved Charlson Comorbidity Index for hemodialysis patients. Multivariable linear regression analysis identified factors associated with TO and TS. Results During the follow-up period, 20 patients died from cardiovascular causes. In patients with HRT categories 0, 1 and 2, cardiovascular mortality was 1, 10, and 22%, respectively. HRT category 2 showed the strongest independent association with cardiovascular mortality with a hazard ratio of 19.3 (95% confidence interval: 3.69–92.03; P < 0.001). Age, calcium phosphate product, and smoking status were associated with TO and TS. Diabetes mellitus and diastolic blood pressure were only associated with TS. Conclusion Independent of known risk factors, HRT assessment allows identification of hemodialysis patients with low, intermediate, and high risk of cardiovascular mortality. Future prospective studies are needed to translate risk prediction into risk reduction in hemodialysis patients.
Fat Wasting Is Damaging: Role of Adipose Tissue in Cancer-Associated Cachexia

Loss of body weight, especially loss of adipose tissue and skeletal muscle weight, characterizes cancer-associated cachexia (CAC). Clinically, therapeutic options for CAC are limited due to the complicated signaling between cancer and other organs. Recent research advances show that adipose tissues play a critical role during thermogenesis, glucose homeostasis, insulin sensitivity, and lipid metabolism. Understanding the adipocyte lipolysis, the formation of beige adipocytes, and the activation of brown adipocytes is vital for novel therapies for metabolic syndromes like CAC. The system-level crosstalk between adipose tissue and other organs involves adipocyte lipolysis, white adipose tissue browning, and secreted factors and metabolites. Novel CAC animal models and accumulating molecular signaling knowledge have provided mechanisms that may ultimately be translated into future therapeutic possibilities that benefit CAC patients. This mini review discusses the role of adipose tissue in CAC development, mechanism, and therapy.
Identification of Immune Responses to Japanese Encephalitis Virus Specific T Cell Epitopes

Background: Due to the similarity between the dengue (DENV) and the Japanese encephalitis virus (JEV) there is potential for immune cross-reaction. We sought to identify T cell epitopes that are specific to JEV and do not cross react with DENV. Methodology: 20mer peptides were synthesized from regions which showed >90% conservation. Using IFNγ cultured ELISpot assays, we investigated JEV-specific T cell responses in DENV− and JEV− non-immune individuals (DENV−JEV− = 21), JEV seronegative and had not received the JE vaccine, but who were DENV seropositive (DENV+JEV− = 22), JEV+(seropositive for JEV and had received the JE vaccine), but seronegative for DENV (DENV−JEV+ = 23). We further assessed the responses to these peptides by undertaking ex vivo IFNγ assays and flow cytometry. Results: None of DENV−JEV− individuals responded to any of the 20 JEV-specific peptides. High frequency of responses was seen to 6/20 peptides by individuals who were JEV+ but DENV−, where over 75% of the individuals responded to at least one peptide. P34 was the most immunogenic peptide, recognized by 20/23 (86.9%) individuals who were DENV−JEV+, followed by peptide 3 and peptide 7 recognized by 19/23 (82.6%). Peptide 34 from the NS2a region, showed <25% homology with any flaviviruses, and <20% homology with any DENV serotype. Peptide 20 and 32, which were also from the non-structural protein regions, showed <25% homology with DENV. Ex vivo responses to these peptides were less frequent, with only 40% of individuals responding to peptide 34 and 16–28% to other peptides, probably as 5/6 peptides were recognized by CD4+ T cells. Discussion: We identified six highly conserved, T cell epitopes which are highly specific for JEV, in the Sri Lankan population. Since both JEV and DENV co-circulate in the same regions and since both JE and dengue vaccines are likely to be co-administered in the same geographical regions in future, these JEV-specific T cell epitopes would be useful to study JEV-specific T cell responses, in order to further understand how DENV and JEV-specific cellular immune responses influence each other.
Cognitive Bias Under Adverse and Rewarding Conditions: A Systematic Review of Rodent Studies

Background: Cognitive bias refers to emotional influences on cognition and provides a cognitive measure of negativity- or positivity-bias through assessment of the behavioral responses to ambiguous stimuli. Thus, under negative conditions an animal is more likely to judge ambiguous stimuli as negative, and under positive conditions as positive. The transfer of past experiences to novel but similar situations is highly adaptive, as it allows the animal to anticipate on the most likely outcome of the ambiguous cues. Methods: We conducted a systematic review to summarize the current state of evidence on cognitive bias in rodents under adverse and rewarding or supportive conditions. Results: In total 20 studies were identified, in which auditory, spatial, tactile, or visual tasks were used. Stressed rodents generally made fewer positive responses than their non-stressed conspecifics. Housing enrichment made rodents more positive in anticipation of ambiguous cues. Ethanol seeking rats generalized the ambiguous cues to sucrose and less to ethanol if sucrose was available. Amphetamine, fluoxetine, and ketamine shifted the bias toward positivity, while reboxetine elevated negative bias. Conclusion: The auditory tasks have been most extensively validated, followed by the tactile and spatial tasks, and finally the visual tasks. The tactile and spatial tasks use latency as readout, which is sensitive to confounding factors. It is yet uncertain whether spatial tasks measure cognitive bias. Across all tasks, with some exceptions, rodents exposed to stress show less positivity-bias when exposed to ambiguous cues, whereas rodents exposed to rewarding substances or treated with antidepressant drugs are biased toward reward. Considering the methodological heterogeneity and risk of bias, the present data should be interpreted with caution.