Miracle-dz/UniFAR-FTUs huggingface dataset:
Three-Dimensional Carotid Ultrasound Plaque Texture Predicts Vascular Events

Background and Purpose— Carotid ultrasound atherosclerosis measurements, including those of the arterial wall and plaque, provide a way to monitor patients at risk of vascular events. Our objective was to examine carotid ultrasound plaque texture measurements and the change in carotid plaque texture during 1 year in patients at risk of events and to compare these with measurements of plaque volume and other risk factors as predictors of vascular events. Methods— We evaluated 298 patients with carotid atherosclerosis using 3-dimensional (3D) ultrasound at baseline and after 1 year and measured carotid plaque volume and 376 measures of plaque texture. Patients were followed up to 5 years (median [range], 3.12 [0.77–4.66]) for myocardial infarction, transient ischemic attack, and stroke. Sparse Cox regression was used to select the most predictive plaque texture measurements in independent training sets using a 10-fold cross-validation, repeated 5×, to ensure unbiased results. Results— Receiver operator curves and Kaplan–Meier analysis showed that changes in texture and total plaque volume combined provided the best predictor of vascular events. In multivariate Cox regression, changes in plaque texture (median hazard ratio, 1.4; P<0.001) and total plaque volume (median hazard ratio, 1.5 per 100 mm3; P<0.001) were both significant predictors, whereas the Framingham risk score was not. Conclusions— Changes in both plaque texture and volume are strongly predictive of vascular events. In high-risk patients, 3D ultrasound plaque measurements should be considered for vascular event risk prediction.
Serological evidence of West Nile Virus (WNV) in mammalian species in Turkey

In this study, the sera collected from a variety of mammalian species (ass-mules, cat, cattle, dog, horse, human and sheep) in 10 representative provinces of Turkey, were surveyed for the presence of neutralizing antibodies to West Nile virus (WNV). Overall, 1 of 40 (2·5%) ass-mules, 4 of 100 (4%) cattle, 43 of 114 (37·7%) dogs, 35 of 259 (13·5%) horses, 18 of 88 (20·4%) humans and 1 of 100 (1%) sheep, tested positive for WNV-neutralizing antibodies. The results indicate that a wide range of mammals are exposed to a West Nile-related virus and this could contribute to the long-term survival of this virus in the absence of overt disease.
Robust multiple comparisons against a control group with application in toxicology

The Dunnett procedure compares several treatment or dose groups with a control group, while controlling the familywise error rate. When deviations from the normal distribution and heterogeneous variances occur, the nominal $\alpha$ level may be violated, and power may be reduced. Various robust modifications are discussed, whereby the novel most likely transformation (MLT)-Dunnett version is recommended as almost always appropriate by means of a simulation study. The MLT-Dunnett is especially useful because it can jointly and comparably analyse differently scaled endpoints. Furthermore, a related multiple endpoints test is proposed using the odds ratio as a common effect size. With the statistical software R, the method is readily applicable using the CRAN libraries \verb|multcomp| and \verb|mlt|, real data can be easily analyzed.
De-Biased Graphical Lasso for High-Frequency Data

This paper develops a new statistical inference theory for the precision matrix of high-frequency data in a high-dimensional setting. The focus is not only on point estimation but also on interval estimation and hypothesis testing for entries of the precision matrix. To accomplish this purpose, we establish an abstract asymptotic theory for the weighted graphical Lasso and its de-biased version without specifying the form of the initial covariance estimator. We also extend the scope of the theory to the case that a known factor structure is present in the data. The developed theory is applied to the concrete situation where we can use the realized covariance matrix as the initial covariance estimator, and we obtain a feasible asymptotic distribution theory to construct (simultaneous) confidence intervals and (multiple) testing procedures for entries of the precision matrix.
Few-Shot Unsupervised Image-to-Image Translation

Unsupervised image-to-image translation methods learn to map images in a given class to an analogous image in a different class, drawing on unstructured (non-registered) datasets of images. While remarkably successful, current methods require access to many images in both source and destination classes at training time. We argue this greatly limits their use. Drawing inspiration from the human capability of picking up the essence of a novel object from a small number of examples and generalizing from there, we seek a few-shot, unsupervised image-to-image translation algorithm that works on previously unseen target classes that are specified, at test time, only by a few example images. Our model achieves this few-shot generation capability by coupling an adversarial training scheme with a novel network design. Through extensive experimental validation and comparisons to several baseline methods on benchmark datasets, we verify the effectiveness of the proposed framework. Our implementation and datasets are available at https://github.com/NVlabs/FUNIT
POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion

Increasing demand for fashion recommendation raises a lot of challenges for online shopping platforms and fashion communities. In particular, there exist two requirements for fashion outfit recommendation: the Compatibility of the generated fashion outfits, and the Personalization in the recommendation process. In this paper, we demonstrate these two requirements can be satisfied via building a bridge between outfit generation and recommendation. Through large data analysis, we observe that people have similar tastes in individual items and outfits. Therefore, we propose a Personalized Outfit Generation (POG) model, which connects user preferences regarding individual items and outfits with Transformer architecture. Extensive offline and online experiments provide strong quantitative evidence that our method outperforms alternative methods regarding both compatibility and personalization metrics. Furthermore, we deploy POG on a platform named Dida in Alibaba to generate personalized outfits for the users of the online application iFashion. This work represents a first step towards an industrial-scale fashion outfit generation and recommendation solution, which goes beyond generating outfits based on explicit queries, or merely recommending from existing outfit pools. As part of this work, we release a large-scale dataset consisting of 1.01 million outfits with rich context information, and 0.28 billion user click actions from 3.57 million users. To the best of our knowledge, this dataset is the largest, publicly available, fashion related dataset, and the first to provide user behaviors relating to both outfits and fashion items.
Mediation of the transition from biotrophy to necrotrophy in hemibiotrophic plant pathogens by secreted effector proteins

Hemibiotrophs, such as Phytophthora infestans, exhibit distinct phases of their life cycle: an early asymptomatic biotrophic phase and a late necrotrophic stage that is characterized by tissue degradation and disease symptoms. To date, little is known of the molecular mechanisms that promote each distinct phase, nor those that mediate the transition between the two. We hypothesized that these phytopathogens might secrete distinct classes of effector proteins that first suppress plant defense responses and associated programmed cell death (PCD), and later induce large scale necrosis. To this end, we have identified proteins that are secreted by P. infestans early or late in the infection cycle. Recently we described the characterization of SNE1, which is specifically expressed during early biotrophic growth in the host plant tomato (Solanum lycopersicum). We found that SNE1 suppresses the action of necrosis-inducing effectors (Nep1-like proteins), including PiNPP1.1 and PsojNIP, which are secreted by Phytophthora during necrotrophic growth, as well as PCD mediated by a broad spectrum of Avr-R protein interactions. This suggests that SNE1 and PiNPP1.1 act antagonistically, thereby providing a highly regulated means to control the transition from biotrophy to necrotrophy.
Unsupervised Detection of Distinctive Regions on 3D Shapes

This article presents a novel approach to learn and detect distinctive regions on 3D shapes. Unlike previous works, which require labeled data, our method is unsupervised. We conduct the analysis on point sets sampled from 3D shapes, then formulate and train a deep neural network for an unsupervised shape clustering task to learn local and global features for distinguishing shapes with respect to a given shape set. To drive the network to learn in an unsupervised manner, we design a clustering-based nonparametric softmax classifier with an iterative re-clustering of shapes, and an adapted contrastive loss for enhancing the feature embedding quality and stabilizing the learning process. By then, we encourage the network to learn the point distinctiveness on the input shapes. We extensively evaluate various aspects of our approach and present its applications for distinctiveness-guided shape retrieval, sampling, and view selection in 3D scenes.
Proximate molecular quasar absorbers

We present results from a search for strong H2 absorption systems proximate to quasars (zabs ≈ zem) in the Sloan Digital Sky Survey (SDSS) Data Release 14. The search is based on the Lyman-Werner band signature of damped H2 absorption lines without any prior on the associated metal or neutral hydrogen content. This has resulted in the detection of 81 systems with N(H2) ∼ 1019 − 1020 cm−2 located within a few thousand km s−1 from the quasar. Compared to a control sample of intervening systems, this implies an excess of proximate H2 systems by about a factor of 4 to 5. The incidence of H2 systems increases steeply with decreasing relative velocity, reaching an order of magnitude higher than expected from intervening statistics at Δv < 1000 km s−1. The most striking feature of the proximate systems compared to the intervening ones is the presence of Ly − α emission in the core of the associated damped H I absorption line in about half of the sample. This puts constraints on the relative projected sizes of the absorbing clouds to those of the quasar line emitting regions. Using the SDSS spectra, we estimate the H I, metal and dust content of the systems, which are found to have typical metallicities of one tenth Solar, albeit with a large spread among individual systems. We observe trends between the fraction of leaking Ly − α emission and the relative absorber-quasar velocity as well as with the excitation of several metal species, similar to what has been seen in metal-selected proximate DLAs. With the help of theoretical H I-H2 transition relations, we show that the presence of H2 helps to break the degeneracy between density and strength of the UV field as main sources of excitation and hence provides unique constraints on the possible origin and location of the absorbing clouds. We suggest that most of these systems originate from galaxies in the quasar group, although a small fraction of them could be located in the quasar host as well. We conclude that follow-up observations are still required to investigate the chemical and physical conditions in individual clouds and to assess the importance of AGN feedback for the formation and survival of H2 clouds.
Tight Regret Bounds for Infinite-armed Linear Contextual Bandits

Linear contextual bandit is a class of sequential decision making problems with important applications in recommendation systems, online advertising, healthcare, and other machine learning related tasks. While there is much prior research, tight regret bounds of linear contextual bandit with infinite action sets remain open. In this paper, we prove regret upper bound of $O(\sqrt{d^2T\log T})\times \mathrm{poly}(\log\log T)$ where $d$ is the domain dimension and $T$ is the time horizon. Our upper bound matches the previous lower bound of $\Omega(\sqrt{d^2 T\log T})$ up to iterated logarithmic terms.
Classification-Based Financial Markets Prediction Using Deep Neural Networks

Deep neural networks (DNNs) are powerful types of artificial neural networks (ANNs) that use several hidden layers. They have recently gained considerable attention in the speech transcription and image recognition community for their superior predictive properties including robustness to over fitting. However their application to algorithmic trading has not been previously researched, partly because of their computational complexity. This paper describes the application of DNNs to predicting financial market movement directions. In particular we describe the configuration and training approach and then demonstrate their application to back testing a simple trading strategy over 43 different Commodity and FX future mid-prices at 5-minute intervals. All results in this paper are generated using a C implementation on the Intel Xeon Phi co-processor which is 11.4x faster than the serial version and a Python strategy back testing environment both of which are available as open source code written by the authors.
Osteoprotegerin Gene (OPG) Polymorphisms Associated with Peri-Implantitis Susceptibility in a Chinese Han Population

Background The aim of this study was to investigate the association between T950C (rs2073617) and G1181C (rs2073618) polymorphisms of the osteoprotegerin gene (OPG) and the susceptibility of peri-implantitis in the Chinese Han population. Material/Methods 110 patients with peri-implantitis and 116 healthy persons from the Chinese Han population were included in this study using a case-control design; rs2073617 and rs2073618 in OPG were genotyped by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). The linkage disequilibrium (LD) and haplotype analysis were performed with Haploview software. Hardy-Weinberg equilibrium (HWE) was assessed in the control group based on the genotype distributions of OPG polymorphisms. The genotype, allele, and haplotype distribution differences between the case and control groups were analyzed by chi-square test, and the relative risk of PD was expressed by odds ratio (OR) and 95% confidence interval (CI). Results The study results showed that people carrying the CC genotype of rs2073618 were more likely to have peri-implantitis than GG genotype carriers (OR=2.18, 95% CI=1.03–4.62, p=0.04). In addition, patients with the C allele had 1.47 times the risk of suffering from peri-implantitis (OR=1.47, 95% CI=1.01–2.13, p=0.04), but not rs2073617 polymorphism. The G-C haplotype frequency of rs2073618-rs2073617 in OPG was significantly correlated to the increased susceptibility of peri-implantitis (OR=2.27, 95% CI=1.20–4.30). Conclusions OPG rs2073618 polymorphism may be related to the risk of peri-implantitis, but not rs2073617. Moreover, haplotype is also a non-ignorable risk factor.
Spectral and steady-state properties of random Liouvillians

We study generic open quantum systems with Markovian dissipation, focusing on a class of stochastic Liouvillian operators of Lindblad form with independent random dissipation channels (jump operators) and a random Hamiltonian. We establish that the global spectral features, the spectral gap, and the steady-state properties follow three different regimes as a function of the dissipation strength, whose boundaries depend on the particular quantity. Within each regime, we determine the scaling exponents with the dissipation strength and system size. We find that, for two or more dissipation channels, the spectral gap increases with the system size. The spectral distribution of the steady state is Poissonian at low dissipation strength and conforms to that of a random matrix once the dissipation is sufficiently strong. Our results can help to understand the long-time dynamics and steady-state properties of generic dissipative systems.
What Influences Patient Participation in an Online Forum for Weight Loss Surgery? A Qualitative Case Study

Background Many patients who undergo weight loss (bariatric) surgery seek information and social support in online discussion forums, but the vast amount of available information raises concerns about the impact of such information. A secure online discussion forum was developed and offered to bariatric surgery patients. The forum was moderated and allowed contact with peers and health care professionals. Objective The purposes of this study were to explore how individuals undergoing bariatric surgery used the moderated discussion forum and to better understand what influenced their participation in the forum. Methods The study was designed as an explorative case study. We conducted participant observation of the discussion forum over a time period of approximately six months. For further insight, we carried out in-depth semistructured interviews with seven patients who had access to the forum. We analyzed the material inductively, using content and thematic analysis. Results The patients used the forum as an arena in which to interact with peers and providers, as well as to provide and achieve informational and social support. The analysis suggests that there are three major themes that influenced participation in the online discussion forum: (1) the participant’s motivation to seek information, advice, and guidance, (2) the need for social support and networking among peers, and (3) concerns regarding self-disclosure. Conclusions The findings of this study imply that a moderated discussion forum for bariatric surgery patients has potential for use in a therapeutic context. The discussion forum fulfilled the informational and support needs of the bariatric surgery patients and was particularly useful for those who excluded themselves from the traditional program and experienced barriers to expressing their own needs. Even though our findings imply that the patients benefitted from using the forum regardless of their active or passive participation, restraining factors, such as considerations regarding self-disclosure, must be further investigated to prevent certain users from being precluded from participation.
Collaborative Learning in a Wiki Environment: Experiences from a software engineering course

The post-graduate course, Software Requirements for Business Systems, in the Department of Computing of the Open University involves teaching systematic elicitation and documentation of requirements for software systems. On a software development project, team members often work remotely from one another and increasingly use wikis to collaboratively develop the requirements specification. In order to emulate requirements engineering practice, the course has been enhanced to include group collaboration using a wiki. In this paper, we describe the wiki-based collaborative activities and the evaluation of the pedagogical effectiveness of a wiki for collaborative learning. Our evaluations have confirmed that the strength of a wiki, as a collaborative authoring tool, can facilitate the learning of course concepts and students’ appreciation of the distributed nature of the RE process context. However, there is a need to support the discussion aspects of collaborative activities with more appropriate tools. We have also found that there are certain usability aspects of wikis that can mar a positive student experience. This paper will be of interest to academics aspiring to employ wikis on their courses and to practitioners who wish to realize the potential of wikis in facilitating information sharing, knowledge management, and in fostering collaboration within and between organizations.
The design space of opinion measurement interfaces: exploring recall support for rating and ranking

Rating interfaces are widely used on the Internet to elicit people's opinions. Little is known, however, about the effectiveness of these interfaces and their design space is relatively unexplored. We provide a taxonomy for the design space by identifying two axes: Measurement Scale for absolute rating vs. relative ranking, and Recall Support for the amount of information provided about previously recorded opinions. We present an exploration of the design space through iterative prototyping of three alternative interfaces and their evaluation. Among many findings, the study showed that users do take advantage of recall support in interfaces, preferring those that provide it. Moreover, we found that designing ranking systems is challenging; there may be a mismatch between a ranking interface that forces people to specify a total ordering for a set of items, and their mental model that some items are not directly comparable to each other.
Label-Noise Robust Multi-Domain Image-to-Image Translation

Multi-domain image-to-image translation is a problem where the goal is to learn mappings among multiple domains. This problem is challenging in terms of scalability because it requires the learning of numerous mappings, the number of which increases proportional to the number of domains. However, generative adversarial networks (GANs) have emerged recently as a powerful framework for this problem. In particular, label-conditional extensions (e.g., StarGAN) have become a promising solution owing to their ability to address this problem using only a single unified model. Nonetheless, a limitation is that they rely on the availability of large-scale clean-labeled data, which are often laborious or impractical to collect in a real-world scenario. To overcome this limitation, we propose a novel model called the label-noise robust image-to-image translation model (RMIT) that can learn a clean label conditional generator even when noisy labeled data are only available. In particular, we propose a novel loss called the virtual cycle consistency loss that is able to regularize cyclic reconstruction independently of noisy labeled data, as well as we introduce advanced techniques to boost the performance in practice. Our experimental results demonstrate that RMIT is useful for obtaining label-noise robustness in various settings including synthetic and real-world noise.
A marriage of category theory and set theory: a finitely axiomatized nonstandard first-order theory implying ZF

It is well known that ZFC, despite its usefulness as a foundational theory for mathematics, has two unwanted features: it cannot be written down explicitly due to its infinitely many axioms, and it has a countable model due to the L\"{o}wenheim-Skolem theorem. This paper presents the axioms one has to accept to get rid of these two features. For that matter, some twenty axioms are formulated in a nonstandard first-order language with countable many constants: to this collection of axioms is associated a universe of discourse consisting of a class of objects, each of which is a set, and a class of arrows, each of which is a function. The axioms of ZF are derived from this finite axiom scheme, and it is shown that it does not have a countable model--if it has a model at all, that is. Furthermore, the axioms of category theory are proven to hold: the present universe may therefore serve as an ontological basis for category theory. However, it has not been investigated whether any of the soundness and completeness properties hold for the present theory: the inevitable conclusion is therefore that only further research can establish whether the present results indeed constitute an advancement in the foundations of mathematics.