Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a Full Ice-cream Cone Model for Halo Coronal Mass Ejections

It is essential to determine three-dimensional parameters (e.g., radial speed, angular width, and source location) of coronal mass ejections (CMEs) for the space weather forecast. In this study, we investigate which cone type represents a halo CME morphology using 29 CMEs (12 Solar and Heliospheric Observatory (SOHO)/Large Angle and Spectrometric Coronagraph (LASCO) halo CMEs and 17 Solar Terrestrial Relations Observatory (STEREO)/Sun–Earth Connection Coronal and Heliospheric Investigation COR2 halo CMEs) from 2010 December to 2011 June. These CMEs are identified as halo CMEs by one spacecraft (SOHO or one of STEREO A and B) and limb ones by the other spacecraft (One of STEREO A and B or SOHO). From cone shape parameters of these CMEs, such as their front curvature, we find that the CME observational structures are much closer to a full ice-cream cone type than a shallow ice-cream cone type. Thus, we develop a full ice-cream cone model based on a new methodology that the full ice-cream cone consists of many flat cones with different heights and angular widths to estimate the three-dimensional parameters of the halo CMEs. This model is constructed by carrying out the following steps: (1) construct a cone for a given height and angular width, (2) project the cone onto the sky plane, (3) select points comprising the outer boundary, and (4) minimize the difference between the estimated projection speeds with the observed ones. By applying this model to 12 SOHO/LASCO halo CMEs, we find that 3D parameters from our method are similar to those from other stereoscopic methods (i.e., a triangulation method and a Graduated Cylindrical Shell model).
Quasibound states in the continuum in photonic crystal based optomechanical microcavities

We present a detailed study of mechanically compliant, photonic-crystal-based microcavities featuring a quasi-bound state in the continuum. Such systems have recently been predicted to reduce the optical loss in Fabry-Perot-type optomechanical cavities. However, they require two identical photonic-crystal slabs facing each other, which poses a considerable challenge for experimental implementation. We investigate how such an ideal system can be simplified and still exhibit a quasi-bound state in the continuum. We find that a suspended photonic-crystal slab facing a distributed Bragg reflector realizes an optomechanical system with a quasi-bound state in the continuum. In this system, the radiative cavity loss can be eliminated to the extent that the cavity loss is dominated by dissipative loss originating from material absorption only. These proposed optomechanical cavity designs are predicted to feature optical quality factors in excess of 10^5.
A Neural Separation Algorithm for the Rounded Capacity Inequalities

The cutting plane method is a key technique for successful branch-and-cut and branch-price-and-cut algorithms that find the exact optimal solutions for various vehicle routing problems (VRPs). Among various cuts, the rounded capacity inequalities (RCIs) are the most fundamental. To generate RCIs, we need to solve the separation problem, whose exact solution takes a long time to obtain; therefore, heuristic methods are widely used. We design a learning-based separation heuristic algorithm with graph coarsening that learns the solutions of the exact separation problem with a graph neural network (GNN), which is trained with small instances of 50 to 100 customers. We embed our separation algorithm within the cutting plane method to find a lower bound for the capacitated VRP (CVRP) with up to 1,000 customers. We compare the performance of our approach with CVRPSEP, a popular separation software package for various cuts used in solving VRPs. Our computational results show that our approach finds better lower bounds than CVRPSEP for large-scale problems with 400 or more customers, whereas CVRPSEP shows strong competency for problems with less than 400 customers. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) [RS-2023-00259550] and the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) [2022-0-01032, Development of Collective Collaboration Intelligence Framework for Internet of Autonomous Things]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022. 0310 .
Kernel $\epsilon$-Greedy for Contextual Bandits

We consider a kernelized version of the $\epsilon$-greedy strategy for contextual bandits. More precisely, in a setting with finitely many arms, we consider that the mean reward functions lie in a reproducing kernel Hilbert space (RKHS). We propose an online weighted kernel ridge regression estimator for the reward functions. Under some conditions on the exploration probability sequence, $\{\epsilon_t\}_t$, and choice of the regularization parameter, $\{\lambda_t\}_t$, we show that the proposed estimator is consistent. We also show that for any choice of kernel and the corresponding RKHS, we achieve a sub-linear regret rate depending on the intrinsic dimensionality of the RKHS. Furthermore, we achieve the optimal regret rate of $\sqrt{T}$ under a margin condition for finite-dimensional RKHS.
Superheavy quasistable strings and walls bounded by strings in the light of NANOGrav 15 year data

Composite topological structures such as superheavy"quasi-stable strings"(QSS) and"walls bounded by strings"(WBS) arise in realistic extensions of the Standard Model of high energy physics. We show that the gravitational radiation emitted in the early universe by these two unstable structures with a dimensionless string tension $G\mu\approx 10^{-6}$ is consistent with the NANOGrav evidence of low frequency gravitational background as well as the recent LIGO-VIRGO constraints, provided the superheavy strings and monopoles experience a certain amount of inflation. For the case of walls bounded by strings, the domain walls arise from the spontaneous breaking of a remnant discrete gauge symmetry around the electroweak scale. The quasi-stable strings, on the other hand, arise from a two step breaking of a local gauge symmetry. The monopoles appear from the first breaking and get connected to strings that arise from the second breaking. Both composite structures decay by emitting gravitational waves over a wide frequency range. The Bayes factors for QSS and WBS relative to the inspiraling supermassive black hole binaries are estimated to be about 60 and 30 respectively, which are comparable with that of metastable strings and cosmic superstrings.
Trinomial Planar Functions on Cubic and Quartic Extensions of Finite Fields

Planar functions, introduced by Dembowski and Ostrom, are functions from a finite field to itself that give rise to finite projective planes. They exist, however, only for finite fields of odd characteristics. They have attracted much attention in the last decade thanks to their interest in theory and those deep and various applications in many fields. This paper focuses on planar trinomials over cubic and quartic extensions of finite fields. Our achievements are obtained using connections with quadratic forms and classical algebraic tools over finite fields. Furthermore, given the generality of our approach, the methodology presented could be employed to drive more planar functions on some finite extension fields.
How Simulation Helps Autonomous Driving: A Survey of Sim2real, Digital Twins, and Parallel Intelligence

Developing autonomous driving technologies necessitates addressing safety and cost concerns. Both academic research and commercial applications of autonomous driving vehicles require extensive simulation and real-world testing. The challenge lies in effectively transferring driving knowledge from the virtual simulation world to the reality world, known as the reality gap (RG). This gap arises due to differences in lighting, textures, vehicle dynamics, and agents' behaviors between the two environments. To address this issue, researchers have explored three main approaches: sim2real, digital twins (DTs), and parallel intelligence (PI) technologies. This article reviews these solutions and examines their applications and innovations in autonomous driving. Furthermore, we delve into the state-of-the-art algorithms, models, and involved simulators, and discuss the developmental process from sim2real to DTs and PI. The presentation also sheds light on the challenges and future perspectives in the development of sim2real, DTs, and PI in the field of autonomous driving.
Bounded (O(1)) Regret Recommendation Learning via Synthetic Controls Oracle

In online exploration systems where users with fixed preferences repeatedly arrive, it has recently been shown that O(1), i.e., bounded regret, can be achieved when the system is modeled as a linear contextual bandit. This result may be of interest for recommender systems, where the popularity of their items is often short-lived, as the exploration itself may be completed quickly before potential long-run non-stationarities come into play. However, in practice, exact knowledge of the linear model is difficult to justify. Furthermore, the potential existence of unobservable covariates, uneven user arrival rates, interpretation of the necessary rank condition, and users opting out of private data tracking all need to be addressed for practical recommender system applications. In this work, we conduct a theoretical study to address all these issues while still achieving bounded regret. The key differentiating assumption we make here is the presence of effective Synthetic Control Methods (SCM), which are shown to be a practical relaxation of the exact linear model knowledge assumption. As this assumption explicitly enables domain generalization to the confidence interval of unseen cases, we observe that it can be interpreted as a pre-trained model in the few-shot transfer learning (i.e. in-context learning) literature. More generally, our result provides that domain generalization to the confidence intervals of unseen cases is what enables bounded regret. We verify our theoretical bounded regret result using a minimal simulation experiment.
Anytime-Valid Linear Models and Regression Adjusted Causal Inference in Randomized Experiments

Linear regression adjustment is commonly used to analyse randomised controlled experiments due to its efficiency and robustness against model misspecification. Current testing and interval estimation procedures leverage the asymptotic distribution of such estimators to provide Type-I error and coverage guarantees that hold only at a single sample size. Here, we develop the theory for the anytime-valid analogues of such procedures, enabling linear regression adjustment in the sequential analysis of randomised experiments. We first provide sequential $F$-tests and confidence sequences for the parametric linear model, which provide time-uniform Type-I error and coverage guarantees that hold for all sample sizes. We then relax all linear model parametric assumptions in randomised designs and provide nonparametric model-free sequential tests and confidence sequences for treatment effects. This formally allows experiments to be continuously monitored for significance, stopped early, and safeguards against statistical malpractices in data collection. A particular feature of our results is their simplicity. Our test statistics and confidence sequences all emit closed-form expressions, which are functions of statistics directly available from a standard linear regression table. We illustrate our methodology with the sequential analysis of software A/B experiments at Netflix, performing regression adjustment with pre-treatment outcomes.
An Interleaving Guided Metamorphic Testing Approach for Concurrent Programs

Concurrent programs are normally composed of multiple concurrent threads sharing memory space. These threads are often interleaved, which may lead to some non-determinism in execution results, even for the same program input. This poses huge challenges to the testing of concurrent programs, especially on the test result verification, that is, the prevalent existence of the oracle problem. In this paper, we investigate the application of metamorphic testing, a mainstream technique to address the oracle problem, into the testing of concurrent programs. Based on the unique features of interleaved executions in concurrent programming, we propose an extended notion of metamorphic relations, the core part of metamorphic testing, which are particularly designed for the testing of concurrent programs. A comprehensive testing approach, namely ConMT, is thus developed and a tool is built to automate its implementation on concurrent programs written in Java. Empirical studies have been conducted to evaluate the performance of ConMT, and the experimental results show that in addition to addressing the oracle problem, ConMT outperforms the baseline traditional testing techniques with respect to higher degree of automation, better bug-detection capability, and shorter testing time. It is clear that ConMT can significantly improve the cost-effectiveness for the testing of concurrent programs and thus advances the state of the art in the field. The study also brings novelty into metamorphic testing, hence promoting the fundamental research of software testing.
From ELIZA to ChatGPT: The Evolution of Natural Language Processing and Financial Applications

Natural language processing (NLP) has revolutionized the financial industry, providing advanced techniques for the processing, analyzing, and understanding of unstructured financial text. The authors provide a comprehensive overview of the historical development of NLP, starting from early rules-based approaches to recent advances in deep learning–based NLP models. They also discuss applications of NLP in finance along with its challenges, including data scarcity and adversarial examples, and speculate about the future of NLP in the financial industry. To illustrate the capability of current NLP models, a state-of-the-art chatbot is employed as a co-author of this article.
The repayment of financial debt: some mathematical considerations

This paper focuses on the mathematics behind the repayment of financial debt. The availability of credit, the inevitable accompaniment of which is debt, is an essential component of a monetised economy. Without it most people would not be able to purchase large items like homes, cars and other expensive consumer durables. Businesses would not expand and prosper without access to credit. However, the debt incurred by the availability of credit can also bring huge stress, and indeed distress, from personal and commercial insolvencies to the inhumanities of debt bondage associated with modern-day slavery and human trafficking. The historical origins of debt lie in antiquity and even pre-date the existence of monetised economies [1].
Determination of the Physician’s Experience of the Practices, Antecedents and Outcomes of Defensive Medicine Practices

This study was conducted to determine the practices, antecedents and outcomes of defensive medicine practices. For this purpose, this study was designed as a phenomenological research study using qualitative research methods. In-depth interviews were conducted with physicians who were practitioners and, thus, the experiencers of defensive medicine. The target population of the research consisted of 1,153 physicians working in the Sakarya city centre. The in-depth interviews were conducted with 21 physicians who agreed to participate. The resulting data were encoded and analysed using the NVIVO 12 software. As a result of this research, definitions of defensive medicine practices, positive defensive medicine and negative defensive medicine have been constructed using the expressions used by physicians. Besides, we obtained results indicating the practices of defensive medicine, the antecedents of defensive medicine and the outcomes of defensive medicine. The results of this study contribute to the literature in terms of providing comprehensive knowledge of defensive medicine.
Perceptions of Patient Safety Among Middle Managers at an Operating Suite

In 2008, the World Health Organization launched the Surgical Safety Checklist (SSC) to reduce the number of adverse events occurring at operating suites. The objective of this study was to explore middle managers’ experiences of patient safety culture, the meanings they attach to this culture, and their perceptions of one of its best-known strategies, the SSC. This study used a qualitative design involving structured interviews with the head of department, quality managers, and nursing supervisors at an operating suite. Eleven middle managers acknowledged that the SSC supports memory and instils accountability. Work overload was highlighted as the main barrier in SSC implementation, while feedback was identified as the main facilitator. We found significant differences in levels of understanding of patient safety culture among different types of middle managers and professions. Key aspects for promoting an effective, long-lasting patient safety culture were also identified.
Prediction tendency, eye movements, and attention in a unified framework of neural speech tracking

Auditory speech comprehension is a multi-faceted process in which attention, prediction, and sensorimotor integration (via active sensing) interact with or complement each other. Although different conceptual models that focus on one of these aspects exist, we still lack a unified understanding of their role in speech processing. Here, we replicated and extended two recently published studies from our lab to investigate the influence of prediction tendency on ocular and neural tracking of attended speech. We propose that selective attention guides ocular speech tracking, which in turn mediates neural encoding of speech. In contrast, individual prediction tendency and its relation to neural speech tracking seem to be largely independent of attention. Importantly, prediction tendency and ocular speech tracking seem to be unrelated. With the current work, we propose a framework that aims to bridge the gaps between attention, prediction, and active (ocular) sensing in order to contribute to a holistic understanding of neural speech processing.
GIFT – an R package to access the Global Inventory of Floras and Traits

Advancing knowledge of biodiversity requires open-access global databases and workflows. This appears particularly crucial for plants, as ongoing changes pose major threats to this central group of organisms. Having large-scale information on plant distributions, functional traits and evolutionary history will enable the scientific community to improve its understanding of the patterns and drivers of plant diversity on a global scale. The Global Inventory of Floras and Traits (GIFT) is a global database of regional plant checklists that has proven successful in documenting biogeographical and geographical patterns of plants. Since the release of the first version of GIFT, the database kept on expanding. We introduce GIFT version 3.0, which contains 5,169 checklists referring to 3,400 regions. These checklists include a total of 371,148 land plant species, mostly vascular plants, of which 354,848 are accepted species names, and 109 functional traits. This new version uses new resources for taxonomic name standardization, is matched to a new plant phylogeny, comes with a new trait aggregation workflow, and includes additional environmental variables. We also present the GIFT R-package, which contains all necessary functions to retrieve distributional, functional, phylogenetic, and environmental data from the GIFT database. The package comes with a dedicated website, https://biogeomacro.github. io/GIFT/, which includes three rich vignettes to guide users in retrieving data from GIFT. The recent development of GIFT and its associated R-package provide ecologists with access to one of the largest plant databases. This will foster research into regional to global patterns of plant diversity and their underlying mechanisms. Proper versioning of the database and the ability to retrieve and cite data from any previous and current instance of the GIFT database will ensure the reproducibility of studies that utilize it.
Memory-specific encoding activities of the ventral tegmental area dopamine and GABA neurons

Although the midbrain dopamine (DA) system plays a crucial role in higher cognitive functions, including updating and maintaining short-term memory, the encoding properties of the somatic spiking activity of ventral tegmental area (VTA) DA neurons for short-term memory computations have not yet been identified. Here, we probed and analyzed the activity of optogenetically identified DA and GABA neurons while mice engaged in short-term memory-dependent behavior in a T-maze task. Single-neuron analysis revealed that significant subpopulations of DA and GABA neurons responded differently between left and right trials in the memory delay. With a series of control behavioral tasks and regression analysis tools, we show that firing rate differences are linked to short-term memory-dependent decisions and cannot be explained by reward-related processes, motivated behavior, or motor-related activities. This evidence provides novel insights into the mnemonic encoding activities of midbrain DA and GABA neurons.
MDSCs in breast cancer: an important enabler of tumor progression and an emerging therapeutic target

Women worldwide are more likely to develop breast cancer (BC) than any other type of cancer. The treatment of BC depends on the subtype and stage of the cancer, such as surgery, radiotherapy, chemotherapy, and immunotherapy. Although significant progress has been made in recent years, advanced or metastatic BC presents a poor prognosis, due to drug resistance and recurrences. During embryonic development, myeloid-derived suppressor cells (MDSCs) develop that suppress the immune system. By inhibiting anti-immune effects and promoting non-immune mechanisms such as tumor cell stemness, epithelial-mesenchymal transformation (EMT) and angiogenesis, MDSCs effectively promote tumor growth and metastasis. In various BC models, peripheral tissues, and tumor microenvironments (TME), MDSCs have been found to amplification. Clinical progression or poor prognosis are strongly associated with increased MDSCs. In this review, we describe the activation, recruitment, and differentiation of MDSCs production in BC, the involvement of MDSCs in BC progression, and the clinical characteristics of MDSCs as a potential BC therapy target.
Loss of ctnnd2b affects neuronal differentiation and behavior in zebrafish

Delta-catenin (CTNND2) is an adhesive junction associated protein belonging to the family of p120 catenins. The human gene is located on the short arm of chromosome 5, the region deleted in Cri-du-chat syndrome (OMIM #123450). Heterozygous loss of CTNND2 has been linked to a wide spectrum of neurodevelopmental disorders such as autism, schizophrenia, and intellectual disability. Here we studied how heterozygous loss of ctnnd2b affects zebrafish embryonic development, and larvae and adult behavior. First, we observed a disorganization of neuronal subtypes in the developing forebrain, namely the presence of ectopic isl1-expressing cells and a local reduction of GABA-positive neurons in the optic recess region. Next, using time-lapse analysis, we found that the disorganized distribution of is1l-expressing forebrain neurons resulted from an increased specification of Isl1:GFP neurons. Finally, we studied the swimming patterns of both larval and adult heterozygous zebrafish and observed an increased activity compared to wildtype animals. Overall, this data suggests a role for ctnnd2b in the differentiation cascade of neuronal subtypes in specific regions of the vertebrate brain, with repercussions in the animal’s behavior.