Miracle-dz/UniFAR-FTUs huggingface dataset:
TVAE: TRIPLET-BASED VARIATIONAL AUTOEN-

Deep metric learning has been demonstrated to be highly effective in learning semantic representation and encoding information that can be used to measure data similarity, by relying on the embedding learned from metric learning. At the same time, variational autoencoder (VAE) has widely been used to approximate inference and proved to have a good performance for directed probabilistic models. However, for traditional VAE, the data label or feature information are intractable. Similarly, traditional representation learning approaches fail to represent many salient aspects of the data. In this project, we propose a novel integrated framework to learn latent embedding in VAE by incorporating deep metric learning. The features are learned by optimizing a triplet loss on the mean vectors of VAE in conjunction with standard evidence lower bound (ELBO) of VAE. This approach, which we call Triplet based Variational Autoencoder (TVAE), allows us to capture more fine-grained information in the latent embedding. Our model is tested on MNIST data set and achieves a high triplet accuracy of 95.60% while the traditional VAE (Kingma & Welling, 2013) achieves triplet accuracy of 75.08%.
Risk factors and posttraumatic stress disorder: are they especially predictive following exposure to less severe stressors?

Background: The diagnosis of posttraumatic stress disorder (PTSD) requires exposure to a traumatic stressor, as defined by Criterion A in the DSM criteria for PTSD. Yet, over the course of successive revisions of the diagnostic manual, the range of qualifying stressors has expanded considerably (e.g., watching terrorist attacks on television). Moreover, stressors that fall short of qualifying for even an expanded Criterion A can produce apparent PTSD. Taken together, these findings imply that people who do satisfy symptomatic criteria for PTSD following exposure to less severe stressors carry a heavy burden of risk factors. Method: To test this hypothesis, we examined whether the association between the risk factor of lower intelligence and more severe PTSD and depression symptoms would be greater among women reporting less severe CSA (n = 15) relative to women who reported moderate (n = 54) or high (n = 31) severity CSA. Results: The evidence was consistent with this hypothesis for subjects in the low and moderate severity groups, but less so for those in the high severity group. Conclusions: Lower intelligence was a more potent risk factor for posttraumatic distress among people exposed to less severe relative to moderately severe stressors. Depression and Anxiety, 2011. © 2011 Wiley Periodicals, Inc.
Field Inhomogeneity Correction Based on Gridding Reconstruction for Magnetic Resonance Imaging

Spatial variations of the main field give rise to artifacts in magnetic resonance images if disregarded in reconstruction. With non-Cartesian k-space sampling, they often lead to unacceptable blurring. Data from such acquisitions are usually reconstructed with gridding methods and optionally restored with various correction methods. Both types of methods essentially face the same basic problem of adequately approximating an exponential function to enable an efficient processing with fast Fourier transforms. Nevertheless, they have commonly addressed it differently so far. In the present work, a unified approach is pursued. The principle behind gridding methods is first generalized to nonequispaced sampling in both domains and then applied to field inhomogeneity correction. Three new algorithms, which are compatible with a direct conjugate phase and an iterative algebraic reconstruction, are derived in this way from a straightforward embedding of the data into a higher dimensional space. Their evaluation in simulations and phantom experiments with spiral k-space sampling shows that one of them promises to provide a favorable compromise between fidelity and complexity compared with existing algorithms. Moreover, it allows a simple choice of key parameters involved in approximating an exponential function and a balance between the accuracy of reconstruction and correction
Analysis of a Quantum Error Correcting Code using Quantum Process Calculus

We describe the use of quantum process calculus to describe and analyze quantum communication protocols, following the successful field of formal methodsfrom classical computer science. The key idea is to define two systems, one modelling a protocol and one expressing a specification, and prove that they are behaviourally equivalent. We summarize the necessary theory in the process calculus CQP, including the crucial result that equivalence is a congruence, meaning that it is preserved by embedding in any context. We illustrate the approach by analyzing two versions of a quantum error correction system.
Tool use in Cebus.

This paper summarizes early anecdotal information and systematic studies of tool use in capuchin monkeys (Cebus spp.). Tool use in capuchins is neither context specific nor stereotyped. The success of capuchins in using tools and in exploiting a variety of food resources in the wild derives from several factors: their manipulative abilities, interest in external objects and a tendency to explore the environment. In using tools, capuchins are similar to apes and more proficient than other monkey species. A cognitive approach indicates, however, that (in contrast with chimpanzees) they never develop an understanding of the requirements of the tool tasks presented.
Whole genome shotgun sequencing of one Colombian clinical isolate of Mycobacterium tuberculosis reveals DosR regulon gene deletions.

Several genomes of different Mycobacterium tuberculosis isolates have been completely sequenced around the world. The genomic information obtained have shown higher diversity than originally thought and specific adaptations to different human populations. Within this work, we sequenced the genome of one Colombian M. tuberculosis virulent isolate. Genomic comparison against the reference genome of H37Rv and other strains showed multiple deletion and insertions that ranged between a few bases to thousands. Excluding PPE and PG-PGRS genes, 430 proteins present changes in at least 1 amino acid. Also, novel positions of the IS6110 mobile element were identified. This isolate is also characterized by a large genomic deletion of 3.6 kb, leading to the loss and modification of the dosR regulon genes, Rv1996 and Rv1997. To our knowledge, this is the first report of the genome sequence of a Latin American M. tuberculosis clinical isolate.
About the attractor phenomenon in decomposed reinforcement learning

We consider tackling a single-agent RL problem by decomposing it to n learners. These learners are generally trained egocentrically: they are greedy with respect to their own local focus. In this extended abstract, we show theoretically and empirically that this leads to the presence of attractors: states attracting and detaining the agent, against what the global objective function would advise.
Identifying Anastrepha (Diptera;Tephritidae) Species Using DNA Barcodes

Abstract Molecular identification of fruit flies in the genus Anastrepha (Diptera;Tephritidae) is important to support plant pest exclusion, suppression, and outbreak eradication. Morphological methods of identification of this economically important genus are often not sufficient to identify species when detected as immature life stages. DNA barcoding a segment of the mitochondrial cytochrome oxidase I gene has been proposed as a method to identify pests in the genus.The identification process for these fruit flies, however, has not been explained in prior DNA barcode studies. DNA barcode methods assume that available DNA sequence records are biologically meaningful. These records, however, can be limited to the most common species or lack population-level measurements of diversity for pests. In such cases, the available data used as a reference are insufficient for completing an accurate identification. Using 539 DNA sequence records from 74 species of Anastrepha, we demonstrate that our barcoding data can distinguish four plant pests: Anastrepha grandis (Macquart) (Diptera; Tephritidae), Anastrepha ludens (Loew), Anastrepha serpentina (Wiedemann), and Anastrepha striata Schiner.This is based on genetic distances of barcode records for the pests and expert evaluation of species and population representation in the data set. DNA barcoding of the cytochrome oxidase I gene alone cannot reliably diagnose the pests Anastrepha fraterculus (Wiedemann), Anastrepha obliqua (Macquart), and Anastrepha suspensa (Loew).
Tin‐containing layered double hydroxides: Synthesis and application in poly(vinyl chloride) cable formulations

Layered double hydroxides (LDHs) and Sn‐containing LDHs have been synthesized using a co‐precipitation method, and the resulting products have been characterized by X‐ray diffraction (XRD), transmission electron microscopy (TEM), Fourier‐transform infrared spectroscopy (FTIR) and Brunauer‐Emmett‐Teller(BET) surface area measurement, clearly showing that Sn‐containing LDH hybrids have been successfully prepared. TEM shows the ‘house‐of‐cards’ structure of the Sn‐LDHs produced, resulting from the edge‐to‐face interaction of the LDH layers. Higher tin levels lead to an additional magnesium hydroxystannate, MgSn(OH)6, (‘MHS’) phase that is present in the form of approximately 40‐nm cubic particles in an LDH/MHS hybrid structure. FTIR and XRD suggest that, at low levels of Sn, the Sn4+ may exist in the form of amorphous hydrated tin(IV) oxide rather than being incorporated into the LDH lattice. These powders have been compounded into poly(vinyl chloride) (PVC), and their fire performance has been evaluated using limited oxygen index and cone calorimeter techniques. Peak rate of heat release and smoke parameter can be reduced by 64% and 81%, respectively, when replacing 10 wt% of the primary ATH fire‐retardant filler by the synthesized Sn‐LDHs, while keeping the total fire‐retardant loading at 100 phr. Thermogravimetric analysis indicates that Sn‐LDH is an effective char promoter for PVC. Copyright © 2011 John Wiley & Sons, Ltd.
No Safe Base: An Attachment Theory Perspective on the Mediational Effects of Reappraisal and Resilience on the Well-being of Homeless Women in the Skid Row District of Los Angeles

This study aims to test the relationship between attachment style and well-being by examining the possible mediating roles of emotion regulation (conceptualized as reappraisal and suppression) and resilience (Karreman & Vingerhoets, 2012). One hundred homeless women living in homeless shelters in the Skid Row district of Los Angeles were sampled to test Karreman and Vingerhoets’ model of attachment and well-being (2012). Dismissive attachment style comprised the largest group among the four measured attachment styles (n=39). Both dismissive and secure attachment positively correlated with well-being in this sample. Fearful attachment was the only attachment style negatively related to well-being. Preoccupied attachment was not related to well-being. Emotion regulation failed to function as a mediator in this study. Higher resilience mediated the relationship between secure and dismissive attachment styles and well-being, while lower resilience decreased well-being and mediated the relationship between well-being and fearful attachment. Results as well as research and clinical implications of attachment style and well-being in a homeless population are discussed.
Latent Constrained Correlation Filter

Correlation filters are special classifiers designed for shift-invariant object recognition, which are robust to pattern distortions. The recent literature shows that combining a set of sub-filters trained based on a single or a small group of images obtains the best performance. The idea is equivalent to estimating variable distribution based on the data sampling (bagging), which can be interpreted as finding solutions (variable distribution approximation) directly from sampled data space. However, this methodology fails to account for the variations existed in the data. In this paper, we introduce an intermediate step—solution sampling—after the data sampling step to form a subspace, in which an optimal solution can be estimated. More specifically, we propose a new method, named latent constrained correlation filters (LCCF), by mapping the correlation filters to a given latent subspace, and develop a new learning framework in the latent subspace that embeds distribution-related constraints into the original problem. To solve the optimization problem, we introduce a subspace-based alternating direction method of multipliers, which is proven to converge at the saddle point. Our approach is successfully applied to three different tasks, including eye localization, car detection, and object tracking. Extensive experiments demonstrate that LCCF outperforms the state-of-the-art methods.1 1The source code will be publicly available. https://github.com/bczhangbczhang/
The immunoproteasome‐specific inhibitor ONX 0914 reverses susceptibility to acute viral myocarditis

Severe heart pathology upon virus infection is closely associated with the immunological equipment of the host. Since there is no specific treatment available, current research focuses on identifying new drug targets to positively modulate predisposing immune factors. Utilizing a murine model with high susceptibility to coxsackievirus B3‐induced myocarditis, this study describes ONX 0914—an immunoproteasome‐specific inhibitor—as highly protective during severe heart disease. Represented by reduced heart infiltration of monocytes/macrophages and diminished organ damage, ONX 0914 treatment reversed fulminant pathology. Virus‐induced immune response features like overwhelming pro‐inflammatory cytokine and chemokine production as well as a progressive loss of lymphocytes all being reminiscent of a sepsis‐like disease course were prevented by ONX 0914. Although the viral burden was only minimally affected in highly susceptible mice, resulting maintenance of immune homeostasis improved the cardiac output, and saved animals from severe illness as well as high mortality. Altogether, this could make ONX 0914 a potent drug for the treatment of severe virus‐mediated inflammation of the heart and might rank immunoproteasome inhibitors among drugs for preventing pathogen‐induced immunopathology.
Questioni di Economia e Finanza

This paper develops a methodology for identifying systemically important financial institutions based on that developed by the Basel Committee on Banking Supervision (2011) and used by the Financial Stability Board in its yearly G-SIBs identification. The methodology uses publicly available data to provide fully transparent results with a G-SIBs list that helps to bridge the gap between market knowledge and supervisory decisions. Moreover, the results include a complete ranking of the banks in the sample, according to their systemic importance scores. The methodology is then applied to EU and Eurozone samples of banks to obtain their systemic importance ranking and SIFI lists. This is one of the first methodologies capable of identifying systemically relevant banks at the European level. A statistical analysis and some geographical and historical evidence provide further insight into the notion of systemic importance, its policy implications and the future applications of this methodology.
Evaluation of a new simple collection device for sampling of microparticles in exhaled breath

The microparticle fraction of exhaled breath is of interest for developing clinical biomarkers. Exhaled particles may contain non-volatile components from all parts of the airway system, formed during normal breathing. This study aimed to evaluate a new, simple sampling device, based on impaction, for collecting microparticles from exhaled breath. Performance of the new device was compared with that of the existing SensAbues membrane filter device. The analytical work used liquid chromatography-tandem mass spectrometry methods. The new device collected three subsamples and these were separately analysed from eight individuals. No difference was observed between the centre position (0.91 ng/sample) and the side positions (1.01 ng/sample) using major phosphatidylcholine (PC) 16:0/16:0 as the analyte. Exhaled breath was collected from eight patients on methadone maintenance treatment. The intra-individual variability in measured methadone concentration between the three collectors was 8.7%. In another experiment using patients on methadone maintenance treatment, the sampling efficiency was compared with an established filter device. Compared to the existing device, the efficiency of the new device was 121% greater for methadone and 1450% greater for DPPC. The data from lipid analysis also indicated that a larger fraction of the collected material was from the distal parts. Finally, a study using an optical particle counter indicated that the device preferentially collects the larger particle fraction. In conclusion, this study demonstrates the usefulness of the new device for collecting non-volatile components from exhaled breath. The performance of the device was superior to the filter device in several aspects.
Non-destructive examination of epoxy glass composites using time domain reflection setup

We report on analysis of epoxy composite reinforced with a glass fiber mat by means of Time Domain Spectroscopy (TDS) technique in reflection way. The samples consisted of 2 or 3 layers with the beveling angle of the layers equal to 90° and thickness up to 2 mm. Two samples with artificially induced air-bubbles (~1-2 mm in diameter) and a sample which consisted of the 2 layers of epoxy and a layer of an air-bubble foil between them were also investigated. The measurements shown that all layers as well as defects can be identified.
2D-QSAR study, molecular docking, and molecular dynamics simulation studies of interaction mechanism between inhibitors and transforming growth factor-beta receptor I (ALK5)

Transforming growth factor type 1 receptor (ALK5) is kinase associated with a wide variety of pathological processes, and inhibition of ALK5 is a good strategy to treat many kinds of cancer and fibrotic diseases. Recently, a series of compounds have been synthesized as ALK5 inhibitors. However, the study of their selectivity against other potential targets remains elusive. In this research, a data-set of ALK5 inhibitors were collected and studied based on the combination of 2D-QSAR, molecular docking and molecular dynamics simulation. The quality of QSAR models were assessed statistically by F, R2, and R2ADJ, proved to be credible. The cross-validations for the models (q2LOO = 0.571 and 0.629, respectively) showed their robustness, while the external validations (r2test = 0.703 and 0.764, respectively) showed their predictive power. Besides, the predicted binding free energy results calculated by MM/GBSA method were in accordance with the experimental data, and the van der Waals energy term was the factor that had the most significant impact on ligand binding. What is more, several important residues were found to significantly affect the binding affinity. Finally, based on our analyses above, a proposed series of molecules were designed.
Advanced Characterization Techniques of Organic Matter in Aqueous Solutions

Water is the most precious resource to human being, but it is polluted by different organic compounds. Organic matter (OM) in aqeous solutions is one of the important parameters of concern for human and environmental impact, and thus, it is essential to better characterize specifically targeted organic matter in aggregated and individual level of concentrations. This review presents different analytical tools and protocols to investigate detailed properties and characterization. Physical, chemical and biological aspects of OM are envisaged in terms of traditional and advanced measurement methods.
Patient-Identified Priorities Leading to Attempted Suicide: Life Is Lived in Interpersonal Relationships

Background: Attempted suicide is a major public health problem. Aim: The aim of this study was to identify patient-identified problems and triggers typically leading to attempted suicide. Method: A representative sample of 66 adult patients was recruited from all clinical sites and psychiatrists who treat patients after attempted suicide in the Canton of Basel-City (Switzerland). Patients were diagnosed using the Structured Clinical Interview for DSM-IV (SCID) and interviewed with a local adaptation of the Explanatory Model Interview Catalogue (EMIC) to study underlying problems and triggers of attempted suicide. Results: Of the patients, 92.4% had at least one DSM-IV disorder, with depressive disorders being the most prevalent disorder. Although half (50.0%) of the patients identified a health problem, 71.2% identified an interpersonal conflict as underlying problem leading to the suicide attempt. Furthermore, an interpersonal conflict was identified as the trigger of the suicide attempt by more than half of the patients (54.5%). Limitations: The study included German-speaking patients only. Conclusion: According to patients, interpersonal problems often amplify underlying psychiatric problems, leading to suicide attempts. Social and interpersonal stressors should be acknowledged with integrated clinical and social interventions to prevent suicidal behavior in patients and populations.
Two-dimensional Model of Ciliwung River Flood in DKI Jakarta for Development of the Regional Flood Index Map

The objective of this study was to present a sophisticated method of developing supporting material for flood control implementation in DKI Jakarta. High flow rates in the Ciliwung River flowing through Jakarta regularly causes extensive flooding in the rainy season. The affected area comprises highly densely populated villages. For developing an efficient early warning system in view of decreasing the vulnerability of the locations a flood index map has to be available. This study analyses the development of a flood risk map of the inundation area based on a two-dimensional modeling using FESWMS. The reference event used for the model was the most recent significant flood in 2007. The resulting solution represents flood characteristics such as inundation area, inundation depth and flow velocity. Model verification was performed by confrontation of the results with survey data. The model solution was overlaid with a street map of Jakarta. Finally, alternatives for flood mitigation measures are discussed.
Learning Invariances for Policy Generalization

While recent progress has spawned very powerful machine learning systems, those agents remain extremely specialized and fail to transfer the knowledge they gain to similar yet unseen tasks. In this paper, we study a simple reinforcement learning problem and focus on learning policies that encode the proper invariances for generalization to different settings. We evaluate three potential methods for policy generalization: data augmentation, meta-learning and adversarial training. We find our data augmentation method to be effective, and study the potential of meta-learning and adversarial learning as alternative task-agnostic approaches. Keywords: reinforcement learning, generalization, data augmentation, meta-learning, adversarial learning.