Miracle-dz/UniFAR-FTUs huggingface dataset:
Kernel-phase detection limits-Hypothesis testing and the example of JWST NIRISS full-pupil images

Context. The James Webb Space Telescope (JWST) will offer high angular resolution observing capability in the near-infrared with masking interferometry on the Near-Infrared Imager and Slitless Spectrograph (NIRISS), and coronagraphic imaging on the NearInfrared Camera (NIRCam) and the Mid-Infrared Instrument (MIRI). Full-aperture kernel-phase-based interferometry complements these observing modes by allowing us to probe for companions at small angular resolution while preserving the telescope throughput. Aims. Our goal is to derive both theoretical and operational contrast-detection limits for the kernel-phase analysis of JWST NIRISS full-pupil observations using tools from hypothesis testing theory. The study is immediately applied to observations of faint brown dwarfs with this instrument, but the tools and methods introduced here are applicable in a wide variety of contexts. Methods. We construct a statistically independent set of observable quantities from a collection of aberration-robust kernel phases. Three detection tests based on these observable quantities are designed and analysed, all having the property of guaranteeing a constant false-alarm rate for phase aberrations smaller than about one radian. One of these tests, the likelihood ratio or Neyman-Pearson test, provides a theoretical performance bound for any detection test. Results. The operational detection method considered here is shown to exhibit only marginal power loss with respect to the theoretical bound. In principle, for the test set to a false-alarm probability of 1%, companions at contrasts reaching 103 and separations of 200 mas around objects of magnitude 14.1 are detectable with a probability of 68%. For the brightest objects observable using the full pupil of JWST and NIRISS, contrasts of up to 104 at separations of 200 mas could ultimately be achieved, barring significant wavefront drift. We also provide a statistical analysis of the uncertainties affecting the contrasts and separations that are estimated for the detected companions. Conclusions. The proposed detection method is close to the ultimate bound and offers guarantees on the probability of making a false detection for binaries, as well as on the error bars for the estimated parameters of the binaries that will be detected by JWST NIRISS. This method is not only applicable to JWST NIRISS but to any imaging system with adequate sampling.
Reproducibility Report for ACM SIGMOD 2020 Paper: “Locality-Sensitive Hashing Scheme based on Longest Circular Co-Substring”

The work on the reproducibility of this project is praiseworthy. All required dependencies and build steps are carefully noted in the provided git repository. A series of scripts allow to automatically rerun the experiments, reproduce the results, and recreate some of the plots in the paper. The reproduced results are similar to the values reported in the paper and, importantly, all relationships between the compared methods are maintained.
Seasonality in living kidney donation in the United States from 1995-2019

For nearly two decades, the annual number of US living kidney donors has been characterized by worrying patterns of decline and no factors have been identified to explain and reverse these patterns. Evidence suggests that there is seasonality in living kidney donation; herein we investigate whether potentially modifiable social, economic, and structural issues might explain this seasonality. Using donor-registry data from the Scientific Registry of Transplant Recipients, we described this seasonality in living kidney donation and used Poisson regression stratified by both donor-recipient biological relationship and estimated household income tertile to quantify these trends. In every decade from 1980-2020, there was a summer-only surge in living kidney donations (13%-25% for biologically related donors and 10%-17% for unrelated donors). This summer-only surge was evident for the months of June, July, and August when compared with January for each given year and statistically significant in some groups (range of incidence rate ratio [IRR] for related donors: 1.05-1.34; IRR for unrelated donors: 1.08-1.19). We observed this summer-only surge across all three income tertiles ($73,544+, $52,635-$73,544, and <$52,635) and regardless of donor-recipient relationship. Seasonal variation in donation is associated with structural factors, which may serve as potential targets for interventions to increase donation.
Enhancing network functionalities for emerging mobile networks through learning

OF THE DISSERTATION Enhancing Network Functionalities for Emerging Mobile Networks through Learning By Tingting Sun Dissertation Director: Profs. Yanyong Zhang and Wade Trappe With rapid evolution of technology and growing use of wireless devices in our daily lives, mobile network is becoming one of the most promising platforms for many brand new applications. Several distinguished features make mobile network different from traditional computer networks, such as high mobility and unpredictable mobility patterns. With the networks becoming increasingly diverse and complex, it’s more and more difficult to know the properties of a network a-priori. Therefore, the need for ”learning” important network characteristics in such a dynamic knowledge setting becomes crucial. In this thesis, we show our research efforts to explore methods to learn important network properties, and improve the following three aspects of mobile networks: data management, load management, and identification services. The data management issue is most critical when there is no central infrastructure available or when the mobile-to-infrastructure communication bandwidth is limited. Since blindly uploading every piece of sensor data to a remote server is inefficient, local data aggregation is required to reduce the communication cost and improve efficiency. We propose the Geocache concept and the Boomerang anchoring protocol to address this issue, and further introduce adaptive learning methods to better deliver time-sensitive data. Our efforts in load management are focused on adaptive load-balancing schemes for wireless
A comparative analysis for deep learning model ( hyDL-CO v 1 . 0 ) and 1 Kalman Filter to predict CO in China 2 3

A comparative analysis for deep learning model (hyDL-CO v1.0) and 1 Kalman Filter to predict CO in China 2 3 Weichao Han†, Tai-Long He†, Zhaojun Tang, Min Wang, Dylan Jones, Zhe Jiang* 4 5 School of Earth and Space Sciences, University of Science and Technology of China, Hefei, 6 Anhui, 230026, China. 7 Department of Physics, University of Toronto, Toronto, ON, M5S 1A7, Canada. 8 †These authors contributed equally to this work. 9 10 *Correspondence to: Zhe Jiang (zhejiang@ustc.edu.cn) 11 12 13 Abstract 14
A Self-determination Theory Perspective: How the ‘Drama in Education’ Can Boost Students’ Motivation of Learning

Improving students' intrinsic learning motivation is a topic that educators pay close attention to and constantly explore in teaching methods. In particular, China's education environment has always adopted the rules of exam-oriented education, which means paying more attention to results, just as China's primary education used to. In recent years, with the continuous advancement of quality education, it is imperative to improve students' learning enthusiasm and explore more effective teaching methods. This article attempts to put forward a drama teaching method, which is educational drama, as an idea to provide some pedagogical reflections. At the same time, the paper will be based on the perspective of self-determination theory, looking for the theoretical support of educational drama in the direction of psychology. It will cross-check how educational drama as a pedagogical method can contribute to students' motivation through specific pedagogical methods and the psychological theories embedded in them.
EVALUATION OF BONE WIDTH CHANGE USING A TWO-STAGE RIDGE SPLITTING APRROACH in NARROW POSTERIOR MANDIBULAR RIDGE (AN OBSERVATIONAL CASE-SERIES STUDY)

INTRODUCTION: Alveolar ridge splitting technique is a commonly used procedure for horizontal ridge augmentation of narrow ridges to allow placement of implants. However, the conventional one stage technique might randomly cause mal-fracture and necrosis of the fractured part in case the periosteum is detached from the distracted bone-plate. OBJECTIVE: to evaluate the effectiveness of the two-stage ridge splitting technique to preserve the blood supply and avoid necrosis of the expanded part. MATERIALS AND METHODS: Twelve patients were selected randomly presenting a posterior mandibular narrow ridge of less than 3 mm width. The sample was selected to match a list of inclusion and exclusion criteria. In the first stage, all patients underwent mandibular ridge splitting technique using piezotome. In the second stage, ridge expansion and implants were placed with only envelope flap. Assessment included measurement of bone dimensions and gain of bone width from the cone beam computed tomography. RESULTS: There was significant bone gain after the bone expansion measured at 2 weeks followed by some bone resorption at 4 months after implant placement. The bone density was increased at 4 months by average two times compared to the initial measurements in all patients. There was a significant increase between the primary implant stability at time of implant placement and 4 months after implant placement in all patients. All cases showed uneventful healing, except two cases, of which one showed soft tissue dehiscence and the other showed soft tissue infection. Both cases were managed conservatively. CONCLUSION: This study showed the staged ridge splitting approach to be safe and predictable therapy that might overcome possible complications of one-stage ridge-split procedures.
Comparison of Yo-Yo Intermittent Recovery Test Level 1 between Guards and Forwards in Basketball Players

BACKGROUND AND PURPOSE: Yo-Yo intermittent tests are frequently used in a variety of sports and research studies to determine physical fitness. Specifically, the Yo-Yo intermittent recovery test was a valid measure of fitness performance in the manner of total covered distance in meters, maximum oxygen consumption in VO2 Max and levels achieved which is set in the way of increasing speed in kilometer/hour with decreasing time in seconds to accelerate the test to measure and calculate the outcomes of the test. The purpose of the study is to compare the Aerobic endurance fitness using Yo-Yo Intermittent Recovery test level 1 between Guards and Forwards in basketball players. This study helped to measure an individual's ability to repeatedly perform intervals over a prolonged period of time. METHOD: Participants were explained the procedure and informed consent obtained. 137 participants who were basketball players were taken. Out of which 81 were male and 56 were female basketball players. YYIRT 1 test was performed on them. The test was continued till the participant could continue the test or if the participant gets 2 warning signals simultaneously for not reaching the end point till the beep rings and then it was stopped. Outcome measures used were total distance covered and VO2 max which was compared between 2 groups of guards and forwards as per their playing positions. Result: This study showed that there is no significant difference in performance i.e., total distance covered, levels achieved and VO2 max between guards and forwards basketball players in both genders. Conclusion: This study showed similar aerobic capacity between Guards and Forwards playing position with of Yo-Yo Intermittent Recovery Test Level-1. This study also found that there were significant differences between both the genders in total covered distance, VO2Max and Levels achieved of YYIRT Level-1.
Part of Speech tagger efficacy on South African English

Part-of-speech tagging is a fundemental part of natural language processing that forms the basis of a lot of research and processing in the field. It is a mature sub-field of nat-ural language processing that has reached near-perfect levels of accuracies for English Kupiec [12] and Jianchao [8]. The research in the field was focussed on American and/or British English. We wish to conduct this research with a focus on South Africa and the English that has developed there through decades of assimilating loan words from other language found in the southern regions of Africa. To this end our researched focussed on tagging articles, blogs and nov-els produced in South Africa to test how domains affected the accuracies of existing taggers. We found that taggers trained on British or American English are able to tag South African Enlish with nearly the same accuracy as the English variants they were trained on.
Euro Area Banks' Sensitivity to Changes in Carbon Price

In recent years there has been growing attention on the risks posed by climate change. One relevant question for financial stability is to which extent the materialisation of transition risks emerging from the sudden implementation of climate change mitigation policies would impact the financial system. In this paper we analyze the effects of changes in carbon price on the European banking system. We assess this climate change transition risk through a banking sector contagion model where firms are negatively impacted by an increase in carbon prices. Using a unique granular dataset we evaluate the consequences of a combination of different increases in carbon prices and firm emission reduction strategies. We find that taking early policy action, implying more gradual changes in carbon prices, is not expected to lead to adverse impacts on the banking system, especially if firms reduce their emissions efficiently. Conversely, a disorderly, abrupt transition to a low carbon economy requiring very high sudden changes in carbon prices might have disruptive effects on the financial system, especially if firms fail to reduce their emissions.
Clinical interpretation of machine learning models for prediction of diabetic complications using electronic health records

Type 2 diabetes is a massive public health issue that continues to grow. The rate of diabetic complication progression varies across individuals. Understanding factors that alter the rate of complication onset may uncover new clinical interventions and help prioritize individuals for more aggressive management. Here, we explore how various machine learning models and types of electronic health records can predict fast versus slow diabetic complication onset using only patient data prior to diabetes diagnosis. We find that optimized random forests generally perform best among the tested models and combining all data sources yields the best predictive performance. A key differentiator of our study is our model interpretation, which identifies specific patient metrics from each dataset that play a unique role in the progression of each complication. Overall, our clinical interpretation of machine learning models can identify patients at risk for poorer outcomes years in advance of their diabetic complication.
Unfolded Deep Kernel Estimation for Blind Image Super-resolution

Blind image super-resolution (BISR) aims to reconstruct a high-resolution image from its low-resolution counterpart degraded by unknown blur kernel and noise. Many deep neural network based methods have been proposed to tackle this challenging problem without considering the image degradation model. However, they largely rely on the training sets and often fail to handle images with unseen blur kernels during inference. Deep unfolding methods have also been proposed to perform BISR by utilizing the degradation model. Nonetheless, the existing deep unfolding methods cannot explicitly solve the data term of the unfolding objective function, limiting their capability in blur kernel estimation. In this work, we propose a novel unfolded deep kernel estimation (UDKE) method, which, for the first time to our best knowledge, explicitly solves the data term with high efficiency. The UDKE based BISR method can jointly learn image and kernel priors in an end-to-end manner, and it can effectively exploit the information in both training data and image degradation model. Experiments on benchmark datasets and real-world data demonstrate that the proposed UDKE method could well predict complex unseen non-Gaussian blur kernels in inference, achieving significantly better BISR performance than state-of-the-art. The source code of UDKE is available at: https://github.com/natezhenghy/UDKE.
Deep Learning the Shape of the Brain Connectome

To statistically study the variability and diﬀerences between normal and abnormal brain connectomes, a mathematical model of the neural connections is required. In this paper, we represent the brain connectome as a Riemannian manifold, which allows us to model neural connections as geodesics. We show for the ﬁrst time how one can leverage deep neural networks to estimate a Riemannian metric of the brain that can accommodate ﬁber crossings and is a natural modeling tool to infer the shape of the brain from DWMRI. Our method achieves excellent performance in geodesic-white-matter-pathway alignment and tackles the long-standing issue in previous methods: the inability to recover the crossing ﬁbers with high ﬁdelity.
Variable and Orbital-Dependent Spin-Orbit Field Orientations in an InSb Double Quantum Dot Characterized via Dispersive Gate Sensing

Utilizing dispersive gate sensing (DGS), we investigate the spin-orbit field ($\textbf{B}_{SO}$) orientation in a many-electron double quantum dot (DQD) defined in an InSb nanowire. While characterizing the inter-dot tunnel couplings, the measured dispersive signal depends on the electron charge occupancy, as well as on the amplitude and orientation of the external magnetic field. The dispersive signal is mostly insensitive to the external field orientation when a DQD is occupied by a total odd number of electrons. For a DQD occupied by a total even number of electrons, the dispersive signal is reduced when the finite external magnetic field aligns with the effective $\textbf{B}_{SO}$ orientation. This fact enables the identification of $\textbf{B}_{SO}$ orientations for different DQD electron occupancies. The $\textbf{B}_{SO}$ orientation varies drastically between charge transitions, and is generally neither perpendicular to the nanowire nor in the chip plane. Moreover, $\textbf{B}_{SO}$ is similar for pairs of transitions involving the same valence orbital, and varies between such pairs. Our work demonstrates the practicality of DGS in characterizing spin-orbit interactions in quantum dot systems, without requiring any current flow through the device.
An Interpretable Neuro-Symbolic Reasoning Framework for Task-Oriented Dialogue Generation

We study the interpretability issue of task-oriented dialogue systems in this paper. Previously, most neural-based task-oriented dialogue systems employ an implicit reasoning strategy that makes the model predictions uninterpretable to humans. To obtain a transparent reasoning process, we introduce neuro-symbolic to perform explicit reasoning that justifies model decisions by reasoning chains. Since deriving reasoning chains requires multi-hop reasoning for task-oriented dialogues, existing neuro-symbolic approaches would induce error propagation due to the one-phase design. To overcome this, we propose a two-phase approach that consists of a hypothesis generator and a reasoner. We first obtain multiple hypotheses, i.e., potential operations to perform the desired task, through the hypothesis generator. Each hypothesis is then verified by the reasoner, and the valid one is selected to conduct the final prediction. The whole system is trained by exploiting raw textual dialogues without using any reasoning chain annotations. Experimental studies on two public benchmark datasets demonstrate that the proposed approach not only achieves better results, but also introduces an interpretable decision process.
Operadic approach to HNN-extensions of Leibniz algebras

We construct HNN-extensions of Lie di-algebras in the variety of di-algebras and provide a presentation for the replicated HNN-extension of a Lie di-algebras. Then, by applying the method of Gröbner-Shirshov bases for replicated algebras, we obtain a linear basis. As an application of HNN-extensions, we prove that Lie di-algebras are embedded in their HNNextension.
The Long Arc of Fairness: Formalisations and Ethical Discourse

In recent years, the idea of formalising and modelling fairness for algorithmic decision making (ADM) has advanced to a point of sophisticated specialisation. However, the relations between technical (formalised) and ethical discourse on fairness are not always clear and productive. Arguing for an alternative perspective, we review existing fairness metrics and discuss some common issues. For instance, the fairness of procedures and distributions is often formalised and discussed statically, disregarding both structural preconditions of the status quo and downstream effects of a given intervention. We then introduce dynamic fairness modelling, a more comprehensive approach that realigns formal fairness metrics with arguments from the ethical discourse. A dynamic fairness model incorporates (1) ethical goals, (2) formal metrics to quantify decision procedures and outcomes and (3) mid-term or long-term downstream effects. By contextualising these elements of fairness-related processes, dynamic fairness modelling explicates formerly latent ethical aspects and thereby provides a helpful tool to navigate trade-offs between different fairness interventions. To illustrate the framework, we discuss an example application – the current European efforts to increase the number of women on company boards, e . g. via quota solutions – and present early technical work that fits within our framework.
City-wide Street-to-Satellite Image Geolocalization of a Mobile Ground Agent

Cross-view image geolocalization provides an estimate of an agent's global position by matching a local ground image to an overhead satellite image without the need for GPS. It is challenging to reliably match a ground image to the correct satellite image since the images have significant viewpoint differences. Existing works have demonstrated localization in constrained scenarios over small areas but have not demonstrated wider-scale localization. Our approach, called Wide-Area Geolocalization (WAG), combines a neural network with a particle filter to achieve global position estimates for agents moving in GPS-denied environments, scaling efficiently to city-scale regions. WAG introduces a trinomial loss function for a Siamese network to robustly match non-centered image pairs and thus enables the generation of a smaller satellite image database by coarsely discretizing the search area. A modified particle filter weighting scheme is also presented to improve localization accuracy and convergence. Taken together, WAG's network training and particle filter weighting approach achieves city-scale position estimation accuracies on the order of 20 meters, a 98% reduction compared to a baseline training and weighting approach. Applied to a smaller-scale testing area, WAG reduces the final position estimation error by 64% compared to a state-of-the-art baseline from the literature. WAG's search space discretization additionally significantly reduces storage and processing requirements. We include in our submission a video demonstrating particle filter convergence results for WAG compared to the baseline for the Chicago test area.