Miracle-dz/UniFAR-FTUs huggingface dataset:
Lack of Follow-Up for Celiac Disease During Childhood Not Associated With Poor Health Outcomes: A Regional Swedish Cohort Study

Objectives: The objective of the study is to examine the association between the lack of follow-up for celiac disease (CD) during childhood and dietary adherence, disease remission, and health-related quality of life (HRQoL). Methods: We invited 243 randomly selected children diagnosed with CD in 2013–2018 in Gothenburg, Sweden, and 162 consented to participate (67%). We retrieved information on clinical follow-up and current wellbeing using medical and laboratory records data, as well as validated questionnaires on symptoms of CD, dietary adherence, and HRQoL. We analyzed tissue-transglutaminase antibodies (tTGA) as a measure of disease remission. We defined lack of follow-up as no CD-related physician/dietician-led visit or measurement of tTGA over the past 24 months of study enrollment. Results: The mean age at study enrolment was 12.7 (range 7.8–18.2) years. Out of 162 children with an average disease duration of 5.3 (range 2.3–8.8) years, 23 (14%) lacked follow-up. tTGA had normalized in 94% [95% confidence interval (CI) = 71%–100%] of children without follow-up versus 91% (95% CI: 85%–95%) of children with continued follow-up. Of children without follow-up, 65% (95% CI: 38%–86%) reported a dietary adherence score indicating very good adherence, versus 72% (95% CI: 63%–80%) of those with continued follow-up. Also, lack of follow-up was not significantly associated with growth, symptom scores, or HRQoL. Conclusions: In this regional cohort study of mostly older children and adolescents, lack of follow-up for CD was not significantly linked to dietary adherence, disease remission, or HRQoL. How these results hold in larger, unselected samples with longer follow-up, including transition to adult care, warrants further study.
An efficient sieving based secant method for sparse optimization problems with least-squares constraints

In this paper, we propose an efficient sieving based secant method to address the computational challenges of solving sparse optimization problems with least-squares constraints. A level-set method has been introduced in [X. Li, D.F. Sun, and K. -C. Toh, SIAM J. Optim. , 28 (2018), pp. 1842--1866] that solves these problems by using the bisection method to find a root of a univariate nonsmooth equation $\varphi(\lambda) = \varrho$ for some $\varrho>0$, where $\varphi(\cdot)$ is the value function computed by a solution of the corresponding regularized least-squares optimization problem. When the objective function in the constrained problem is a polyhedral gauge function, we prove that (i) for any positive integer $k$, $\varphi(\cdot)$ is piecewise $C^k$ in an open interval containing the solution $\lambda^*$ to the equation $\varphi(\lambda) = \varrho$; (ii) the Clarke Jacobian of $\varphi(\cdot)$ is always positive. These results allow us to establish the essential ingredients of the fast convergence rates of the secant method. Moreover, an adaptive sieving technique is incorporated into the secant method to effectively reduce the dimension of the level-set subproblems for computing the value of $\varphi(\cdot)$. The high efficiency of the proposed algorithm is demonstrated by extensive numerical results.
Polyhedral Geometry in OSCAR

OSCAR is an innovative new computer algebra system which combines and extends the power of its four cornerstone systems - GAP (group theory), Singular (algebra and algebraic geometry), Polymake (polyhedral geometry), and Antic (number theory). Here, we give an introduction to polyhedral geometry computations in OSCAR, as a chapter of the upcoming OSCAR book. In particular, we define polytopes, polyhedra, and polyhedral fans, and we give a brief overview about computing convex hulls and solving linear programs. Three detailed case studies are concerned with face numbers of random polytopes, constructions and properties of Gelfand-Tsetlin polytopes, and secondary polytopes.
Benchmarking Scalable Epistemic Uncertainty Quantification in Organ Segmentation

Deep learning based methods for automatic organ segmentation have shown promise in aiding diagnosis and treatment planning. However, quantifying and understanding the uncertainty associated with model predictions is crucial in critical clinical applications. While many techniques have been proposed for epistemic or model-based uncertainty estimation, it is unclear which method is preferred in the medical image analysis setting. This paper presents a comprehensive benchmarking study that evaluates epistemic uncertainty quantification methods in organ segmentation in terms of accuracy, uncertainty calibration, and scalability. We provide a comprehensive discussion of the strengths, weaknesses, and out-of-distribution detection capabilities of each method as well as recommendations for future improvements. These findings contribute to the development of reliable and robust models that yield accurate segmentations while effectively quantifying epistemic uncertainty.
ICAFusion: Iterative Cross-Attention Guided Feature Fusion for Multispectral Object Detection

Effective feature fusion of multispectral images plays a crucial role in multi-spectral object detection. Previous studies have demonstrated the effectiveness of feature fusion using convolutional neural networks, but these methods are sensitive to image misalignment due to the inherent deffciency in local-range feature interaction resulting in the performance degradation. To address this issue, a novel feature fusion framework of dual cross-attention transformers is proposed to model global feature interaction and capture complementary information across modalities simultaneously. This framework enhances the discriminability of object features through the query-guided cross-attention mechanism, leading to improved performance. However, stacking multiple transformer blocks for feature enhancement incurs a large number of parameters and high spatial complexity. To handle this, inspired by the human process of reviewing knowledge, an iterative interaction mechanism is proposed to share parameters among block-wise multimodal transformers, reducing model complexity and computation cost. The proposed method is general and effective to be integrated into different detection frameworks and used with different backbones. Experimental results on KAIST, FLIR, and VEDAI datasets show that the proposed method achieves superior performance and faster inference, making it suitable for various practical scenarios. Code will be available at https://github.com/chanchanchan97/ICAFusion.
Notation3 as an Existential Rule Language

Notation3 Logic (\nthree) is an extension of RDF that allows the user to write rules introducing new blank nodes to RDF graphs. Many applications (e.g., ontology mapping) rely on this feature as blank nodes -- used directly or in auxiliary constructs -- are omnipresent on the Web. However, the number of fast \nthree reasoners covering this very important feature of the logic is rather limited. On the other hand, there are engines like VLog or Nemo which do not directly support Semantic Web rule formats but which are developed and optimized for very similar constructs: existential rules. In this paper, we investigate the relation between \nthree rules with blank nodes in their heads and existential rules. We identify a subset of \nthree which can be mapped directly to existential rules and define such a mapping preserving the equivalence of \nthree formulae. In order to also illustrate that in some cases \nthree reasoning could benefit from our translation, we then employ this mapping in an implementation to compare the performance of the \nthree reasoners EYE and cwm to VLog and Nemo on \nthree rules and their mapped counterparts. Our tests show that the existential rule reasoners perform particularly well for use cases containing many facts while especially the EYE reasoner is very fast when dealing with a high number of dependent rules. We thus provide a tool enabling the Semantic Web community to directly use existing and future existential rule reasoners and benefit from the findings of this active community.
Counterfactual time series analysis for the air pollution during the outbreak of COVID-19 in Wuhan (preprint)

Environmental issues are becoming one of the main topics of concern for society, and the quality of air is closely linked to people's lives. Previous studies have examined the effects of abrupt interventions on changes in air pollution. For example, researchers used an interrupted time series design to quantify the impact of the 1990 Dublin coal ban; and a regression discontinuity to determine the arbitrary spatial impact of the Huaihe River policy in China. An important feature of each of these studies is that they investigated abrupt and localized changes over relatively short time spans (the Dublin coal ban) and spatial scales (the Huaihe policy). Due to the abrupt nature of these interventions, defining a hypothetical experiment in these studies is straightforward. In response to the novel coronavirus outbreak, China implemented 'the largest quarantine in human history' in Wuhan on January 23, 2020. Similar measures were implemented in other Chinese cities. Since then, the movement of people and associated production and consumption activities have been significantly reduced. This provides us with an unprecedented opportunity to estimate the changes in air pollution brought about by this sudden "silent" move. We speculate that the initiative will lead to a significant reduction in regional air pollution. Thus, we performed counterfactual time series analysis on Wuhan air quality data from 2017-2022 based on three models, SARIMA, LSTM and XGBOOST, and compared the excellence of different models. Finally, we conclude that 'silent' measures will significantly reduce air pollution. Using this conclusion to further investigate the extent of air pollution reduction will help the country to better designate environmental policies.
Green's function in general relativity

This report provides Green's functions (classical propagators) of gravitational fields of vierbein and spin-connection in general relativity. The existence of Green's function of the Laplace operator in curved space with an indefinite metric is ensured owing to the Hodge harmonic analysis. The analyticity of Green's function is naturally determined intrinsically, keeping a causality. This report proposed a novel definition of the momentum space in curved space-time and the linearisation of the Einstein equation as a free field consistent with that for the Yang-Mills gauge field. The proposed linearisation does not utilize the weak-field approximation; thus, the method applies to highly caved space-time. We gave two examples of Green's function of gravitational fields, the plane wave solution and the Schwarzschild solution.
Higher-order topological phase diagram revealed by anomalous Nernst effect in a Janus ScClI monolayer

Higher-order topological properties of two-dimensional(2D) magnetic materials have recently been proposed. In 2D ferromagnetic Janus materials, we find that ScClI is a second-order topological insulator (SOTI). By means of a multi-orbital tight-binding model, we analyze the orbital contributions of higher-order topologies. Further, we give the complete high-order topological phase diagram of ScClI, based on the external field modulation of the magneto-valley coupling and energy levels. 2D ScClI has a pronounced valley polarization, which causes different insulating phases to exhibit completely different anomalous Nernst conductance. As a result, we use the matched anomalous Nernst effect to reveal the topological phase transition process of ScClI. We utilize the characteristics of valley electronics to link higher-order topological materials with the anomalous Nernst effect, which has potential implications for high-order topological insulators and valley electronics.
SerIOS: Enhancing Hardware Security in Integrated Optoelectronic Systems

Silicon photonics (SiPh) has different applications, from enabling fast and high-bandwidth communication for high-performance computing systems to realizing energy-efficient optical computation for AI hardware accelerators. However, integrating SiPh with electronic sub-systems can introduce new security vulnerabilities that cannot be adequately addressed using existing hardware security solutions for electronic systems. This paper introduces SerIOS, the first framework aimed at enhancing hardware security in optoelectronic systems by leveraging the unique properties of optical lithography. SerIOS employs cryptographic keys generated based on imperfections in the optical lithography process and an online detection mechanism to detect attacks. Simulation and synthesis results demonstrate SerIOS's effectiveness in detecting and preventing attacks, with a small area footprint of less than 15% and a 100% detection rate across various attack scenarios and optoelectronic architectures, including photonic AI accelerators.
Artificial Intelligence for Smart Transportation

There are more than 7,000 public transit agencies in the U.S. (and many more private agencies), and together, they are responsible for serving 60 billion passenger miles each year. A well-functioning transit system fosters the growth and expansion of businesses, distributes social and economic benefits, and links the capabilities of community members, thereby enhancing what they can accomplish as a society. Since affordable public transit services are the backbones of many communities, this work investigates ways in which Artificial Intelligence (AI) can improve efficiency and increase utilization from the perspective of transit agencies. This book chapter discusses the primary requirements, objectives, and challenges related to the design of AI-driven smart transportation systems. We focus on three major topics. First, we discuss data sources and data. Second, we provide an overview of how AI can aid decision-making with a focus on transportation. Lastly, we discuss computational problems in the transportation domain and AI approaches to these problems.
A Survey on Point-of-Interest Recommendations Leveraging Heterogeneous Data

Tourism is an important application domain for recommender systems. In this domain, recommender systems are for example tasked with providing personalized recommendations for transportation, accommodation, points-of-interest (POIs), etc. Among these tasks, in particular the problem of recommending POIs that are of likely interest to individual tourists has gained growing attention in recent years. Providing POI recommendations to tourists can however be especially challenging due to the variability of the user's context. With the rapid development of the Web and today's multitude of online services, vast amounts of data from various sources have become available, and these heterogeneous data represent a huge potential to better address the challenges of POI recommendation problems. In this work, we provide a survey of published research on the problem of POI recommendation between 2021 and 2023. The literature was surveyed to identify the information types, techniques and evaluation methods employed. Based on the analysis, it was observed that the current research tends to focus on a relatively narrow range of information types and there is a significant potential in improving POI recommendation by leveraging heterogeneous data. As the first information-centric survey on POI recommendation research, this study serves as a reference for researchers aiming to develop increasingly accurate, personalized and context-aware POI recommender systems.
Tracing snowlines and C/O ratio in a planet-hosting disk. ALMA molecular line observations towards the HD 169142 disk

The composition of a forming planet is set by the material it accretes from its parent protoplanetary disk. Therefore, it is crucial to map the chemical make-up of the gas in disks to understand the chemical environment of planet formation. This paper presents molecular line observations taken with the Atacama Large Millimeter/submillimeter Array of the planet-hosting disk around the young star HD 169142. We detect N2H+, CH3OH, [CI], DCN, CS, C34S, 13CS, H2CS, H2CO, HC3N and c-C3H2 in this system for the first time. Combining these data with the recent detection of SO and previously published DCO+ data, we estimate the location of H2O and CO snowlines and investigate radial variations in the gas phase C/O ratio. We find that the HD 169142 disk has a relatively low N2H+ flux compared to the disks around Herbig stars HD 163296 and MWC 480 indicating less CO freeze-out and place the CO snowline beyond the millimetre disk at ~150 au. The detection of CH3OH from the inner disk is consistent with the H2O snowline being located at the edge of the central dust cavity at ~20 au. The radially varying CS/SO ratio across the proposed H2O snowline location is consistent with this interpretation. Additionally, the detection of CH3OH in such a warm disk adds to the growing evidence supporting the inheritance of complex ices in disks from the earlier, colder stages of star formation. Finally, we propose that the giant HD 169142 b located at 37 au is forming between the CO2 and H2O snowlines where the local elemental make of the gas is expected to have C/O=1.0.
SPM: Structured Pretraining and Matching Architectures for Relevance Modeling in Meituan Search

In e-commerce search, relevance between query and documents is an essential requirement for satisfying user experience. Different from traditional e-commerce platforms that offer products, users search on life service platforms such as Meituan mainly for product providers, which usually have abundant structured information, e.g. name, address, category, thousands of products. Modeling search relevance with these rich structured contents is challenging due to the following issues: (1) there is language distribution discrepancy among different fields of structured document, making it difficult to directly adopt off-the-shelf pretrained language model based methods like BERT. (2) different fields usually have different importance and their length vary greatly, making it difficult to extract document information helpful for relevance matching. To tackle these issues, in this paper we propose a novel two-stage pretraining and matching architecture for relevance matching with rich structured documents. At pretraining stage, we propose an effective pretraining method that employs both query and multiple fields of document as inputs, including an effective information compression method for lengthy fields. At relevance matching stage, a novel matching method is proposed by leveraging domain knowledge in search query to generate more effective document representations for relevance scoring. Extensive offline experiments and online A/B tests on millions of users verify that the proposed architectures effectively improve the performance of relevance modeling. The model has already been deployed online, serving the search traffic of Meituan for over a year.
Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Models

Medical image segmentation with deep learning is an important and widely studied topic because segmentation enables quantifying target structure size and shape that can help in disease diagnosis, prognosis, surgery planning, and understanding. Recent advances in the foundation VLMs and their adaptation to segmentation tasks in natural images with VLSMs have opened up a unique opportunity to build potentially powerful segmentation models for medical images that enable providing helpful information via language prompt as input, leverage the extensive range of other medical imaging datasets by pooled dataset training, adapt to new classes, and be robust against out-of-distribution data with human-in-the-loop prompting during inference. Although transfer learning from natural to medical images for image-only segmentation models has been studied, no studies have analyzed how the joint representation of vision-language transfers to medical images in segmentation problems and understand gaps in leveraging their full potential. We present the first benchmark study on transfer learning of VLSMs to 2D medical images with thoughtfully collected 11 existing 2D medical image datasets of diverse modalities with carefully presented 9 types of language prompts from 14 attributes. Our results indicate that VLSMs trained in natural image-text pairs transfer reasonably to the medical domain in zero-shot settings when prompted appropriately for non-radiology photographic modalities; when finetuned, they obtain comparable performance to conventional architectures, even in X-rays and ultrasound modalities. However, the additional benefit of language prompts during finetuning may be limited, with image features playing a more dominant role; they can better handle training on pooled datasets combining diverse modalities and are potentially more robust to domain shift than the conventional segmentation models.
Text Injection for Capitalization and Turn-Taking Prediction in Speech Models

Text injection for automatic speech recognition (ASR), wherein unpaired text-only data is used to supplement paired audio-text data, has shown promising improvements for word error rate. This study examines the use of text injection for auxiliary tasks, which are the non-ASR tasks often performed by an E2E model. In this work, we use joint end-to-end and internal language model training (JEIT) as our text injection algorithm to train an ASR model which performs two auxiliary tasks. The first is capitalization, which is a de-normalization task. The second is turn-taking prediction, which attempts to identify whether a user has completed their conversation turn in a digital assistant interaction. We show results demonstrating that our text injection method boosts capitalization performance for long-tail data, and improves turn-taking detection recall.
BSED: Baseline Shapley-Based Explainable Detector

Explainable artificial intelligence (XAI) has witnessed significant advances in the field of object recognition, with saliency maps being used to highlight image features relevant to the predictions of learned models. Although these advances have made AI-based technology more interpretable to humans, several issues have come to light. Some approaches present explanations irrelevant to predictions, and cannot guarantee the validity of XAI (axioms). In this study, we propose the Baseline Shapley-based Explainable Detector (BSED), which extends the Shapley value to object detection, thereby enhancing the validity of interpretation. The Shapley value can attribute the prediction of a learned model to a baseline feature while satisfying the explainability axioms. The processing cost for the BSED is within the reasonable range, while the original Shapley value is prohibitively computationally expensive. Furthermore, BSED is a generalizable method that can be applied to various detectors in a model-agnostic manner, and interpret various detection targets without fine-grained parameter tuning. These strengths can enable the practical applicability of XAI. We present quantitative and qualitative comparisons with existing methods to demonstrate the superior performance of our method in terms of explanation validity. Moreover, we present some applications, such as correcting detection based on explanations from our method.