Miracle-dz/UniFAR-FTUs huggingface dataset:
Migration and health right: Probabilistic estimate of the factors that impact on health right of the migrant population, Peru 2019–2021

The study aims to identify factors associated with the violation of the right to health of the regular migrant population with respect to the nonmigrant population in Peru during the period 2019–2021, based on the complaints of health services users. It is a three-year cross-sectional and retrospective study on a total population of 122,505 complainants to the National Superintendency of Health (SUSALUD). The types of health rights used were those established in Peruvian Law No. 29414. An unordered multinomial probability model was used to estimate the probability of belonging to five types of violated rights based on the regular migrant and nonmigrant population, and the exogenous variables that affect this probability. The individual significance tests of the model, the tests for combining categories and the test of independence of irrelevant alternatives by means of the Wald and Hausman-McFadden tests were previously taken. The results indicated an increase in complaints from regular migrants of 5.6% in the 2019–2021 period unlike nonmigrants who had a decrease of 12.2%. The greatest probability that health rights of regular migrants are violated refers to access to information and the right to care and recovery, where their probability of violation is 27.7% and 25.4%, respectively (p-v < 0.05, CI = 95%). Likewise, health rights are more likely to be violated if they are women; if they are adults (41 years old on average); if they do not possess any type of health insurance; if they use Peruvian Ministry of Health (MINSA) services; and if they are located in metropolitan cities, such as Lima and Callao.
Evidence and Modiﬁcation of Non-Visual Eyestalk Organs in Troglobiont Mysida and Stygiomysida (Crustacea)

: Why do the eyes remain comparatively large, whereas the cornea is reduced in most troglobiont mysids? Are there any important organ size-dependent functions served by non-visual eye rudiments? This issue was approached by measuring eye structures in ﬁve troglobiont species of Mysida and two Stygiomysida compared with 14 troglophiles and 49 trogloxene Mysida species. The Organ of Bellonci (OB) was found in all Mysida and, as ﬁrst records, also in Stygiomysida. The length of OBs increased with individual body length (BL) and eye length (EL) in four examined species: from postnauplioid larvae to adult stage in a troglophile and a trogloxene mysid species examined; and from juveniles to adults in a troglobiont mysid and a troglobiont stygiomysid. At the interspeciﬁc level, EL was, on average, 26% shorter at a given BL, while the OB was 40% longer at a given BL and 54% longer at a given EL, respectively, in adult troglobionts compared with trogloxene mysids. The OB is clearly proliferating, while the cornea is reduced in troglobionts. This points to sensory functions (possibly together with other functions). The sensory pore organ was found in all 15 Mysida species whose eyes were mounted on slides, and the ﬁrst record of this organ was also found in Stygiomysida.
Working Paper No. E2021/2 Multilateral Political Effects on Outbound Tourism

: To capture the role of politics in tourism, we propose a novel measure to quantify political relations based on text analysis of published diplomatic statements. We explain how political relations affect outbound tourist flows from China to Japan and Korea. Estimated on monthly data (1997m1-2018m12), our model shows how China-Japan disputes affect tourist flows to Korea and how China-Korea clashes influence the number of Chinese tourists going to Japan. The political effects are estimated to peak after three months, but half of the effects vanish in six months. We also observe asymmetries in the political effects—the tourists respond more to negative political shocks than to positive ones, and more to territorial disputes than to war history disputes.
Safe and sustainable by design A computer-based approach to redesign chemicals for reduced environmental hazards

chemical redesign for reduced persistency. The approach includes elements of Essential Use, Alternatives Assessment and Green and Circular Chemistry and ties into goals recently formulated in the context of the EU Green Deal. The organophosphate chemical triisobutylphosphate (TiBP) was used as a case study for exploration of the approach, as its emission to the environment was expected to be inevitable when used as a flame retardant. Over 6.3 million alternative structures were created in silico and filtered based on QSAR outputs to remove potentially non-readily biodegradable structures. With a multi-criteria analysis based on predicted properties and synthesizability a top 500 of most desirable structures was identified. The target structure (di-n -butyl (2-hydroxyethyl) phosphate) was manually selected and synthesized. The approach can be expanded and further verified to reach its full potential in the mitigation of chemical pollution and to help enable a safe circular economy.
A Domain Independent Social Media Depression Detection Model

Due to negative social stigmas surrounding mental health disorders, a large portion of the affected population are reluctant to seek help. In consequence, many of those in need remain undiagnosed and uneducated about their condition. Language contains information on the author’s mental state and demographic, and has been leveraged by NLP models that learn to predict each one independently or jointly. With the advent of social media, we have access to an unprecedented amount of natural language data. However, these models require large labeled data sets that can be very expensive and time consuming to create. In addition, these data sets are typically derived from a single domain such as Twitter, Facebook, Reddit, Instagram, etc. As such, they are unable to generalize well to other mediums outside of their domain. Focusing on the diagnosis of depression disorder, the present contribution aims to create a domain independent depression detection model that uses a novel combination of deep learning, NLP, and machine learning techniques while using datasets much smaller than those found in the literature.
Introduction of Self-Healing Concrete and Different Methods of Its Scientific Implementation

 Abstract — Concrete, with its unique properties and advantages, has gained widespread and increasing use in the construction industry, particularly in a country's infrastructure. However, concrete exhibits certain defects, most notably the presence of micro-cracks that occur after the setting process, leading to increased costs for infrastructure repair and maintenance. As a result, self-healing concretes have garnered attention in various countries in recent years. These concretes employ different mechanisms for repair, including physical, chemical, biological, and combined approaches, each with its own subsets and implementation methods. Certain mechanisms hold significant importance, leading to specialized production methods. Given the novelty of this subject in Iran, there is limited knowledge or, in some cases, a complete lack of understanding. This paper presents various self-healing concrete mechanisms and the advantages, disadvantages, and application scope of each method.
Finding Interpretable Class-Specific Patterns through Efficient Neural Search

Discovering patterns in data that best describe the differences between classes allows to hypothesize and reason about class-specific mechanisms. In molecular biology, for example, these bear the promise of advancing the understanding of cellular processes differing between tissues or diseases, which could lead to novel treatments. To be useful in practice, methods that tackle the problem of finding such differential patterns have to be readily interpretable by domain experts, and scalable to the extremely high-dimensional data. In this work, we propose a novel, inherently interpretable binary neural network architecture Diffnaps that extracts differential patterns from data. Diffnaps is scalable to hundreds of thousands of features and robust to noise, thus overcoming the limitations of current state-of-the-art methods in large-scale applications such as in biology. We show on synthetic and real world data, including three biological applications, that unlike its competitors, Diffnaps consistently yields accurate, succinct, and interpretable class descriptions.
Spectral engineering of photonic filters using mode splitting in silicon nanowire integrated standing-wave resonators

Mode splitting induced by coherent optical mode interference in coupled resonant cavities is a key phenomenon in photonic resonators that can lead to powerful and versatile filtering functions, in close analogy to electromagnetically-inducedtransparency, Autler-Townes splitting, Fano resonances, and dark states. It can not only break the dependence between quality factor, free spectral range, and physical cavity length, but can also lead to group delay response and mode interactions that are useful for enhancing light-material interaction and dispersion engineering in nonlinear optics. In this work, we investigate mode splitting in standing-wave (SW) resonators implemented by cascaded Sagnac loop reflectors (CSLRs) and demonstrate its use for engineering the spectral profile of integrated photonic filters. By changing the reflectivity of the Sagnac loop reflectors (SLRs) and the phase shifts along the connecting waveguides, we tailor mode splitting in the CSLR resonators to achieve a wide range of filter shapes for diverse applications including enhanced light trapping, flat-top filtering, Q factor enhancement, and signal reshaping. We present the theoretical designs and compare the performance of CSLR resonators with three, four, and eight SLRs fabricated in silicon-on-insulator nanowires. We achieve high performance and versatile filter shapes via diverse mode splitting that agree well with theory. The experimental results confirm the effectiveness of our approach towards realizing integrated multi-functional SW filters for flexible spectral engineering.
Collaboration or Corporate Capture? Quantifying NLP's Reliance on Industry Artifacts and Contributions

The advent of transformers, higher computational budgets, and big data has engendered remarkable progress in Natural Language Processing (NLP). Impressive performance of industry pre-trained models has garnered public attention in recent years and made news headlines. That these are industry models is noteworthy. Rarely, if ever, are academic institutes producing exciting new NLP models. Using these models is critical for competing on NLP benchmarks and correspondingly to stay relevant in NLP research. We surveyed 100 papers published at EMNLP 2022 to determine whether this phenomenon constitutes a reliance on industry for NLP publications. We find that there is indeed a substantial reliance. Citations of industry artifacts and contributions across categories is at least three times greater than industry publication rates per year. Quantifying this reliance does not settle how we ought to interpret the results. We discuss two possible perspectives in our discussion: 1) Is collaboration with industry still collaboration in the absence of an alternative? Or 2) has free NLP inquiry been captured by the motivations and research direction of private corporations?
Precision Analysis on Simulational Finite Element Model of Dam and Batholith

Displacement model, element shape and boundary condition are critical factors affecting the precision of the simulational finite element model. It analyzes the causation which causes the error from the basic formulas, at the same time studies the methods to improve the precision in calculating of finite element. At the end, it gets some significant results by using this method in an practice.
Cerebral Ischemia: Patterns and Pitfalls Evaluation of CT Perfusion in the Setting of

V. Spektor SUMMARY: CTP has a growing role in evaluating stroke. It can be performed immediately following NCCT and has advantages of accessibility and speed. Differentiation of salvageable ischemic penum-bra from unsalvageable core infarct may help identify patients most likely to benefit from thrombectomy or thrombolysis. Still, CTP interpretation can be complex. We review normal and ischemic perfusion patterns followed by an illustrative series of technical/diagnostic challenges of CTP interpretation in the setting of acute stroke syndromes.
Artificial intelligence-assisted quantification and assessment of whole slide images for pediatric kidney disease diagnosis

Abstract Motivation Pediatric kidney disease is a widespread, progressive condition that severely impacts growth and development of children. Chronic kidney disease is often more insidious in children than in adults, usually requiring a renal biopsy for diagnosis. Biopsy evaluation requires copious examination by trained pathologists, which can be tedious and prone to human error. In this study, we propose an artificial intelligence (AI) method to assist pathologists in accurate segmentation and classification of pediatric kidney structures, named as AI-based Pediatric Kidney Diagnosis (APKD). Results We collected 2935 pediatric patients diagnosed with kidney disease for the development of APKD. The dataset comprised 93 932 histological structures annotated manually by three skilled nephropathologists. APKD scored an average accuracy of 94% for each kidney structure category, including 99% in the glomerulus. We found strong correlation between the model and manual detection in detected glomeruli (Spearman correlation coefficient r = 0.98, P < .001; intraclass correlation coefficient ICC = 0.98, 95% CI = 0.96–0.98). Compared to manual detection, APKD was approximately 5.5 times faster in segmenting glomeruli. Finally, we show how the pathological features extracted by APKD can identify focal abnormalities of the glomerular capillary wall to aid in the early diagnosis of pediatric kidney disease. Availability and implementation https://github.com/ChunyueFeng/Kidney-DataSet.
Influence of cardiometabolic medications on abdominal aortic aneurysm growth in the UK Aneurysm Growth Study: metformin and angiotensin-converting enzyme inhibitors associated with slower aneurysm growth

Abstract Background There is a clinical need for treatments that can slow or prevent the growth of an abdominal aortic aneurysm, not only to reduce the need for surgery, but to provide a means to treat those who cannot undergo surgery. Methods Analysis of the UK Aneurysm Growth Study (UKAGS) prospective cohort was conducted to test for an association between cardiometabolic medications and the growth of an abdominal aortic aneurysm above 30 mm in diameter, using linear mixed-effect models. Results A total of 3670 male participants with data available on abdominal aortic aneurysm growth, smoking status, co-morbidities, and medication history were included. The mean age at recruitment was 69.5 years, the median number of surveillance scans was 6, and the mean(s.e.) unadjusted abdominal aortic aneurysm growth rate was 1.75(0.03) mm/year. In a multivariate linear mixed-effect model, smoking (mean(s.e.) +0.305(0.07) mm/year, P = 0.00003) and antiplatelet use (mean(s.e.) +0.235(0.06) mm/year, P = 0.00018) were found to be associated with more rapid abdominal aortic aneurysm growth, whilst metformin was strongly associated with slower abdominal aortic aneurysm growth (mean(s.e.) −0.38(0.1) mm/year, P = 0.00019), as were angiotensin-converting enzyme inhibitors (mean(s.e.) −0.243(0.07) mm/year, P = 0.0004), angiotensin II receptor antagonists (mean(s.e.) −0.253(0.08) mm/year, P = 0.00255), and thiazides/related diuretics (mean(s.e.) −0.307(0.09) mm/year, P = 0.00078). Conclusion The strong association of metformin with slower abdominal aortic aneurysm growth highlights the importance of the ongoing clinical trials assessing the effectiveness of metformin with regard to the prevention of abdominal aortic aneurysm growth and/or rupture. The association of angiotensin-converting enzyme inhibitors, angiotensin II receptor antagonists, and diuretics with slower abdominal aortic aneurysm growth points to the possibility that optimization of cardiovascular risk management as part of abdominal aortic aneurysm surveillance may have the secondary benefit of also reducing abdominal aortic aneurysm growth rates.
AniRes2D: Anisotropic Residual-enhanced Diffusion for 2D MR Super-Resolution

Anisotropic low-resolution (LR) magnetic resonance (MR) images are fast to obtain but hinder automated processing. We propose to use denoising diffusion probabilistic models (DDPMs) to super-resolve these 2D-acquired LR MR slices. This paper introduces AniRes2D, a novel approach combining DDPM with a residual prediction for 2D super-resolution (SR). Results demonstrate that AniRes2D outperforms several other DDPM-based models in quantitative metrics, visual quality, and out-of-domain evaluation. We use a trained AniRes2D to super-resolve 3D volumes slice by slice, where comparative quantitative results and reduced skull aliasing are achieved compared to a recent state-of-the-art self-supervised 3D super-resolution method. Furthermore, we explored the use of noise conditioning augmentation (NCA) as an alternative augmentation technique for DDPM-based SR models, but it was found to reduce performance. Our findings contribute valuable insights to the application of DDPMs for SR of anisotropic MR images.
CE: Reducing Bias Against People with Substance Use Disorders

ABSTRACT Addiction is a highly misunderstood and stigmatized chronic illness frequently encountered by health care providers during routine medical care. People with substance use disorders, in particular, face extraordinary stigma and bias when interacting with health care providers, including nurses. Stigma associated with addiction contributes to health inequities and is recognized as a significant barrier to people seeking and receiving necessary health care. Since patients often spend the most time with nurses in the clinical setting, nurses are ideally positioned to address addiction stigma. However, many nurses lack knowledge about addiction, stigma, and the impact of the words they use, whether in conversation or in clinical documentation. This article reviews the consequences of addiction stigma (labeling, stereotyping, or discrimination) and the steps nurses can take to reduce biases related to substance use. A case scenario based on our experience will be used to guide a discussion of opportunities for nurses to intervene and improve care. The authors outline the consequences of addiction stigma (labeling, stereotyping, or discrimination) and the steps nurses can take to reduce biases related to substance use.
Memory-Efficient Optical Flow via Radius-Distribution Orthogonal Cost Volume

The full 4D cost volume in Recurrent All-Pairs Field Transforms (RAFT) or global matching by Transformer achieves impressive performance for optical flow estimation. However, their memory consumption increases quadratically with input resolution, rendering them impractical for high-resolution images. In this paper, we present MeFlow, a novel memory-efficient method for high-resolution optical flow estimation. The key of MeFlow is a recurrent local orthogonal cost volume representation, which decomposes the 2D search space dynamically into two 1D orthogonal spaces, enabling our method to scale effectively to very high-resolution inputs. To preserve essential information in the orthogonal space, we utilize self attention to propagate feature information from the 2D space to the orthogonal space. We further propose a radius-distribution multi-scale lookup strategy to model the correspondences of large displacements at a negligible cost. We verify the efficiency and effectiveness of our method on the challenging Sintel and KITTI benchmarks, and real-world 4K ($2160\! \times\! 3840$) images. Our method achieves competitive performance on both Sintel and KITTI benchmarks, while maintaining the highest memory efficiency on high-resolution inputs.
Computing the Volume of a Restricted Independent Set Polytope Deterministically

We construct a quasi-polynomial time deterministic approximation algorithm for computing the volume of an independent set polytope with restrictions. Randomized polynomial time approximation algorithms for computing the volume of a convex body have been known now for several decades, but the corresponding deterministic counterparts are not available, and our algorithm is the first of this kind. The class of polytopes for which our algorithm applies arises as linear programming relaxation of the independent set problem with the additional restriction that each variable takes value in the interval $[0,1-\alpha]$ for some $\alpha<1/2$. (We note that the $\alpha\ge 1/2$ case is trivial). We use the correlation decay method for this problem applied to its appropriate and natural discretization. The method works provided $\alpha>1/2-O(1/\Delta^2)$, where $\Delta$ is the maximum degree of the graph. When $\Delta=3$ (the sparsest non-trivial case), our method works provided $0.488<\alpha<0.5$. Interestingly, the interpolation method, which is based on analyzing complex roots of the associated partition functions, fails even in the trivial case when the underlying graph is a singleton.
Data-Driven Robust Reinforcement Learning Control of Uncertain Nonlinear Systems: Towards a Fully-Automated, Insulin-Based Artificial Pancreas

In this paper, a novel robust tracking control scheme for a general class of discrete-time nonlinear systems affected by unknown bounded uncertainty is presented. By solving a parameterized optimal tracking control problem subject to the unknown nominal system and a suitable cost function, the resulting optimal tracking control policy can ensure closed-loop stability by achieving a sufficiently small tracking error for the original uncertain nonlinear system. The computation of the optimal tracking controller is accomplished through the derivation of a novel Q-function-based $\lambda$-Policy Iteration algorithm. The proposed algorithm not only enjoys rigorous theoretical guarantees, but also avoids technical weaknesses of conventional reinforcement learning methods. By employing a data-driven, critic-only least squares implementation, the performance of the proposed algorithm is evaluated to the problem of fully-automated, insulin-based, closed-loop glucose control for patients diagnosed with Type 1 and Type 2 Diabetes Mellitus. The U.S. FDA-accepted DMMS.R simulator from the Epsilon Group is used to conduct a comprehensive in silico clinical campaign on a rich set of virtual subjects under completely unannounced meal and exercise settings. Simulation results underline the superior glycaemic behavior achieved by the derived approach, as well as its overall maturity for the design of highly-effective, closed-loop drug delivery systems for personalized medicine.
Similarity-based Knowledge Transfer for Cross-Domain Reinforcement Learning

Transferring knowledge in cross-domain reinforcement learning is a challenging setting in which learning is accelerated by reusing knowledge from a task with different observation and/or action space. However, it is often necessary to carefully select the source of knowledge for the receiving end to benefit from the transfer process. In this article, we study how to measure the similarity between cross-domain reinforcement learning tasks to select a source of knowledge that will improve the performance of the learning agent. We developed a semi-supervised alignment loss to match different spaces with a set of encoder-decoders, and use them to measure similarity and transfer policies across tasks. In comparison to prior works, our method does not require data to be aligned, paired or collected by expert policies. Experimental results, on a set of varied Mujoco control tasks, show the robustness of our method in effectively selecting and transferring knowledge, without the supervision of a tailored set of source tasks.