Miracle-dz/UniFAR-FTUs huggingface dataset:
Faro: a framework for measuring the scientific performance of petascale Rubin Observatory data products

The Vera C. Rubin Observatory will advance many areas of astronomy over the next decade with its unique widefast- deep multi-color imaging survey, the Legacy Survey of Space and Time (LSST). 1 The LSST will produce approximately 20TB of raw data per night, which will be automatically processed by the LSST Science Pipelines to generate science-ready data products – processed images, catalogs and alerts. To ensure that these data products enable transformative science with LSST, stringent requirements have been placed on their quality and scientific fidelity, for example on image quality and depth, astrometric and photometric performance, and object recovery completeness. In this paper we introduce faro, a framework for automatically and efficiently computing scientific performance metrics on the LSST data products for units of data of varying granularity, ranging from single-detector to full-survey summary statistics. By measuring and monitoring metrics, we are able to evaluate trends in algorithmic performance and conduct regression testing during development, compare the performance of one algorithm against another, and verify that the LSST data products will meet performance requirements by comparing to specifications. We present initial results using faro to characterize the performance of the data products produced on simulated and precursor data sets, and discuss plans to use faro to verify the performance of the LSST commissioning data products.
Are Hitting Formulas Hard for Resolution?

Hitting formulas, introduced by Iwama, are an unusual class of propositional CNF formulas. Not only is their satisfiability decidable in polynomial time, but even their models can be counted in closed form. This stands in stark contrast with other polynomial-time decidable classes, which usually have algorithms based on backtracking and resolution and for which model counting remains hard, like 2-SAT and Horn-SAT. However, those resolution-based algorithms usually easily imply an upper bound on resolution complexity, which is missing for hitting formulas. Are hitting formulas hard for resolution? In this paper we take the first steps towards answering this question. We show that the resolution complexity of hitting formulas is dominated by so-called irreducible hitting formulas, first studied by Kullmann and Zhao, that cannot be composed of smaller hitting formulas. However, by definition, large irreducible unsatisfiable hitting formulas are difficult to construct; it is not even known whether infinitely many exist. Building upon our theoretical results, we implement an efficient algorithm on top of the Nauty software package to enumerate all irreducible unsatisfiable hitting formulas with up to 14 clauses. We also determine the exact resolution complexity of the generated hitting formulas with up to 13 clauses by extending a known SAT encoding for our purposes. Our experimental results suggest that hitting formulas are indeed hard for resolution.
Earthquake Theorem for Cluster Algebras of Finite Type

We introduce a cluster algebraic generalization of Thurston’s earthquake map for the cluster algebras of finite type, which we call the cluster earthquake map. It is defined by gluing exponential maps, which is modeled after the earthquakes along ideal arcs. We prove an analogue of the earthquake theorem, which states that the cluster earthquake map gives a homeomorphism between the spaces of $\mathbb {R}^{\textrm {trop}}$- and $\mathbb {R}_{>0}$-valued points of the cluster $\mathcal {X}$-variety. For those of type $A_{n}$ and $D_{n}$, the cluster earthquake map indeed recovers the earthquake maps for marked disks and once-punctured marked disks, respectively. Moreover, we investigate certain asymptotic behaviors of the cluster earthquake map, which give rise to “continuous deformations” of the Fock–Goncharov fan.
Reflexive combinatory algebras

We introduce the notion of reflexivity for combinatory algebras. Reflexivity can be thought of as an equational counterpart of the Meyer–Scott axiom of combinatory models, which indeed allows us to characterize an equationally definable counterpart of combinatory models. This new structure, called strongly reflexive combinatory algebra, admits a finite axiomatization with seven closed equations, and the structure is shown to be exactly the retract of combinatory models. Lambda algebras can be characterized as strongly reflexive combinatory algebras that are stable. Moreover, there is a canonical construction of a lambda algebra from a strongly reflexive combinatory algebra. The resulting axiomatization of lambda algebras by the seven axioms for strong reflexivity together with those for stability is shown to correspond to the axiomatization of lambda algebras due to Selinger (2002, J. Funct. Program., 12, 549–566).
Pre-stressed Bi-stable Hair Clip Mechanism for Faster Swimming Robots

Structural instability is a hazard that leads to catastrophic failure and is generally avoided through special designs. A trend, however, has emerged over the past decades pointing to the harnessing of mechanisms with instability. Inspired by the snapping of a hair clip, we are finessing the unique characteristics of the lateral-torsional buckling of beams and the snap-through of pre-buckled dome-like thin-wall structures in a new field: the in-plane prestressed mechanism. Analyses reveal how the 2D-3D assembly of an in-plane prestressed actuator (IPA) is achieved and how the post-buckling energy landscape is pictured. Combining them with soft robotics, we show that the inclusion of a bistable IPA can enormously enhance the performance of an underwater fish robot as well as inspire a finger-like soft gripper.
SoK: Content Moderation in Social Media, from Guidelines to Enforcement, and Research to Practice

Social media platforms have been establishing content moderation guidelines and employing various moderation policies to counter hate speech and misinformation. The goal of this paper is to study these community guidelines and moderation practices, as well as the relevant research publications, to identify the research gaps, differences in moderation techniques, and challenges that should be tackled by the social media platforms and the research community. To this end, we study and analyze fourteen most popular social media content moderation guidelines and practices, and consolidate them. We then introduce three taxonomies drawn from this analysis as well as covering over two hundred interdisciplinary research papers about moderation strategies. We identify the differences between the content moderation employed in mainstream and fringe social media platforms. Finally, we have in-depth applied discussions on both research and practical challenges and solutions.
Denoised MDPs: Learning World Models Better Than the World Itself

The ability to separate signal from noise, and reason with clean abstractions, is critical to intelligence. With this ability, humans can efficiently perform real world tasks without considering all possible nuisance factors.How can artificial agents do the same? What kind of information can agents safely discard as noises? In this work, we categorize information out in the wild into four types based on controllability and relation with reward, and formulate useful information as that which is both controllable and reward-relevant. This framework clarifies the kinds information removed by various prior work on representation learning in reinforcement learning (RL), and leads to our proposed approach of learning a Denoised MDP that explicitly factors out certain noise distractors. Extensive experiments on variants of DeepMind Control Suite and RoboDesk demonstrate superior performance of our denoised world model over using raw observations alone, and over prior works, across policy optimization control tasks as well as the non-control task of joint position regression.
Predicting Economic Welfare with Images on Wealth

Using images containing information on wealth, this research investigates that pictures are capable of reliably predicting the economic prosperity of households. Without surveys on wealth-related information and human-made standard of wealth quality that the traditional wealth-based approach relied on, this novel approach makes use of only images posted on Dollar Street as input data on household wealth across 66 countries and predicts the consumption or income level of each household using the Convolutional Neural Network (CNN) method. The best result predicts the log of consumption level with root mean squared error of 0.66 and R-squared of 0.80 in CNN regression problem. In addition, this simple model also performs well in classifying extreme poverty with an accuracy of 0.87 and F-beta score of 0.86. Since the model shows a higher performance in the extreme poverty classification when I applied the different threshold of poverty lines to countries by their income group, it is suggested that the decision of the World Bank to define poverty lines differently by income group was valid.
Revisiting Competitive Coding Approach for Palmprint Recognition: A Linear Discriminant Analysis Perspective

The competitive Coding approach (CompCode) is one of the most promising methods for palmprint recognition. Due to its high performance and simple formulation, it has been continuously studied for many years. However, although numerous variations of CompCode have been proposed, a detailed analysis of the method is still absent. In this paper, we provide a detailed analysis of CompCode from the perspective of linear discriminant analysis (LDA) for the first time. A non-trivial sufficient condition under which the CompCode is optimal in the sense of Fisher's criterion is presented. Based on our analysis, we examined the statistics of palmprints and concluded that CompCode deviates from the optimal condition. To mitigate the deviation, we propose a new method called Class-Specific CompCode that improves CompCode by excluding non-palm-line areas from matching. A nonlinear mapping of the competitive code is also applied in this method to further enhance accuracy. Experiments on two public databases demonstrate the effectiveness of the proposed method.
A Medical Image Fusion Method based on MDLatLRRv2

Since MDLatLRR only considers detailed parts (salient features) of input images extracted by latent low-rank representation (LatLRR), it doesn't use base parts (principal features) extracted by LatLRR effectively. Therefore, we proposed an improved multi-level decomposition method called MDLatLRRv2 which effectively analyzes and utilizes all the image features obtained by LatLRR. Then we apply MDLatLRRv2 to medical image fusion. The base parts are fused by average strategy and the detail parts are fused by nuclear-norm operation. The comparison with the existing methods demonstrates that the proposed method can achieve state-of-the-art fusion performance in objective and subjective assessment.
On the Parallelism of Extremal Processes and Subordinators

We study the extremal processes through Feller semigroups theory from which it is possible to observe some parallelism with subordinators. Consequently, we observe that an extremal process possesses concepts analogous to those of Laplace exponent and L\'evy measure of a subordinator. This approach also yields a short proof of the invariance principle of the extremal processes.
Watch and Match: Supercharging Imitation with Regularized Optimal Transport

Imitation learning holds tremendous promise in learning policies efficiently for complex decision making problems. Current state-of-the-art algorithms often use inverse reinforcement learning (IRL), where given a set of expert demonstrations, an agent alternatively infers a reward function and the associated optimal policy. However, such IRL approaches often require substantial online interactions for complex control problems. In this work, we present Regularized Optimal Transport (ROT), a new imitation learning algorithm that builds on recent advances in optimal transport based trajectory-matching. Our key technical insight is that adaptively combining trajectory-matching rewards with behavior cloning can significantly accelerate imitation even with only a few demonstrations. Our experiments on 20 visual control tasks across the DeepMind Control Suite, the OpenAI Robotics Suite, and the Meta-World Benchmark demonstrate an average of 7.8X faster imitation to reach 90% of expert performance compared to prior state-of-the-art methods. On real-world robotic manipulation, with just one demonstration and an hour of online training, ROT achieves an average success rate of 90.1% across 14 tasks.
Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions

We consider the task of meta-analysis in high-dimensional settings in which the data sources are similar but non-identical. To borrow strength across such heterogeneous datasets, we introduce a global parameter that emphasizes interpretability and statistical efficiency in the presence of heterogeneity. We also propose a one-shot estimator of the global parameter that preserves the anonymity of the data sources and converges at a rate that depends on the size of the combined dataset. For high-dimensional linear model settings, we demonstrate the superiority of our identification restrictions in adapting to a previously seen data distribution as well as predicting for a new/unseen data distribution. Finally, we demonstrate the benefits of our approach on a large-scale drug treatment dataset involving several different cancer cell-lines.
Association of empty nest with depressive symptom among Chinese elderly population: The China Longitudinal Aging Social Survey

Currently, empty nest has become one type of the family pattern among the old population and it influences the old population’s mental health. The current study aimed to explore the association between the empty nest and depressive symptoms (DSs) among the elderly population in China. Data were obtained from baseline of the China Longitudinal Aging Social Survey. Logistic regression was used to examine the association between the empty nest and DS among Chinese older adults. In the current study, 48% of the subjects were empty-nest elderly. The prevalence of DS was 43.7% among the empty-nest old population, which was higher than the nonempty nesters. A positive association was found between the empty nest and DS; the odds ratio (95% confidence interval) was 1.28 (1.16–1.42). No matter living with a spouse or living alone, empty nesters were more likely to have DS than nonempty nesters. Empty nest is a risk factor for having DS among the old population in China, especially among those who live without a spouse. Our result is valuable for the development of special family support DS prevention programs for those who were empty nest.
Severe and refractory hypocalcaemia secondary to osteoblastic bone metastases in bladder signet ring carcinoma: A case report and literature review

Rationale: Symptomatic hypocalcaemia is uncommon, occurring in <2% of patients with malignancy. Osteoblastic bone metastasis as a cause of hypocalcaemia is rare and not reported in bladder cancer. Patient concerns: We report a case of refractory hypocalcaemia in a patient with bladder cancer with extensive osteoblastic bone metastases. A 64-year-old male with a history of signet ring bladder carcinoma with osteoblastic bone metastases presented with severe hypocalcaemia with corrected calcium of 1.64 (2.09–2.46) mmol/L as well as hypomagnesemia and hypophosphatemia. He was previously treated with chemotherapy and immunotherapy. Denosumab was also initiated for the prevention of skeletal-related events. Diagnoses: Additional investigations showed significantly elevated bone formation markers N-terminal propeptide of type I procollagen and alkaline phosphatase. Chest radiography and computed tomography scan also demonstrated extensive areas of sclerotic bone lesions suggestive of osteoblastic bone metastases. He was diagnosed with severe hypocalcaemia secondary to osteoblastic bone metastases and partly to denosumab, vitamin D deficiency, and hypomagnesemia. Interventions: He was treated aggressively with calcium and vitamin D replacement. Outcomes: Despite prolonged intravenous calcium replacement and high doses of oral calcium, cholecalciferol, and calcitriol replacement, he had persistent hypocalcaemia with calcium levels ranging from 1.8 to 1.9 mmol/L. He died 4 months after his admission. Lessons: Osteoblastic bone metastases lead to an increased influx of calcium and phosphate into the bone leading to hypocalcaemia and should be considered as a differential in severe and refractory hypocalcaemia. It is rare and has not been described in bladder cancer. Precaution should be taken upon the initiation of antiresorptive in patients with osteoblastic bone metastases.
The Spatial Distribution and Genetic Diversity of the Soybean Cyst Nematode, Heterodera glycines, in China: It Is Time to Take Measures to Control Soybean Cyst Nematode

The continuous evolution and spread of virulent forms of the soybean cyst nematode (SCN) driven by the environment and anthropogenic intervention is a serious threat to the soybean production worldwide, including China. Especially in China, the implemented measures to control SCN are insufficient for sustainable agricultural development yet. We summarized our knowledge about the spread and spatial distribution of SCN in China and the virulence diversity in the main soybean growing areas. To reveal the genetic relatedness and diversity of SCN populations, we re-sequenced 53 SCN genomes from the Huang-Huai Valleys, one of the two main soybean growing areas in China. We identified spreading patterns linked to the local agroecosystems and topographies. Moreover, we disclosed the first evidence for the selection of complex virulence in the field even under low selection pressure in an example from North Shanxi. SCN is present in all soybean growing areas in China but SCN susceptible cultivars are still largely grown indicating that SCN-related damage and financial loss have not received the attention they deserve yet. To prevent increasing yield losses and to improve the acceptance of resistant cultivars by the growers, we emphasized that it is time to accelerate SCN resistance breeding, planting resistant cultivars to a larger extent, and to support farmers to implement a wider crop rotation for sustainable development of the soybean production in China.
Editorial: Petal Development: From Cell Biology to EvoDevo

The petal is an excellent model system for studying plant organogenesis because it is simple in structure and dispensable for plant viability (Irish, 2008). The petal is also a useful system for investigating plant-animal interactions, as it is the major plant part that attracts pollinators in many outcrossing species (Fenster et al., 2004; Stuurman et al., 2004). The nine articles in this Research Topic focus on several important aspects of research associated with petal development and function. We organize this collection of studies into several groups based on common themes as described below.
Features and Outcomes of Confirmed COVID-19 Patients Presenting to the Emergency Department

Objective This study is aimed to determine whether there is a correlation between demographic characteristics, symptoms, initial vital signs, laboratory findings, and clinical outcome(s) of patients with coronavirus disease 2019 (COVID-19). Methods This descriptive, single-center study retrospectively reviewed data from the medical records of patients confirmed with COVID-19 in a tertiary academic center in Jeddah, Saudi Arabia, between March and June 2020. Results The present study enrolled 1039 patients (mean age ± SD, 45.16 ± 19.33 years) suffering from COVID-19, of whom 60.9% were not known to have any medical illnesses. The most common comorbidity was cardiovascular disease (27.8%). Patients with advanced age (p < 0.001), cardiovascular disease (p < 0.001), diabetes mellitus (p = 0.003), asthma (p = 0.008), renal disease (p = 0.020), fever (p = 0.002), dyspnea (p < 0.001), tachypnea (p < 0.001), low albumin (p < 0.001), low alkaline phosphatase levels (p = 0.008), high C-reactive protein (p = 0.003), high fibrinogen (p = 0.047), and high lactate levels (p = 0.015) were more likely to be admitted. Conclusions Patients with increased age, multiple comorbidities, and unstable initial vital signs at emergency department presentation experienced a more severe course of COVID-19 and required admission.
The Lifecycle of a Clinical Cadaver: A Practice-Based Ethnography

Abstract Phenomenon Cadavers have long played an important and complex role in medical education. While research on cadaver-based simulation has largely focused on exploring student attitudes and reactions or measuring improvements in procedural performance, the ethical, philosophical, and experiential aspects of teaching and learning with cadavers are rarely discussed. In this paper, we shed new light on the fascinating philosophical moves in which people engage each and every time they find themselves face to face with a cadaver. Approach Over a two-year period (2018/19–2019/20), we applied ethnographic methods (137 hours of observation, 24 interviews, and the analysis of 22 documents) to shadow the educational cadaver through the practical stages involved in cadaver-based simulation: 1. cadaver preparation, 2. cadaver-based skill practice with physicians and residents, and 3. interment and memorial services. We used Deleuze and Guattari’s concepts of becoming and acts of creation to trace the ontological “lifecycle” of an educational cadaver as embedded within everyday work practices. Findings We delineated six sub-phases of the lifecycle, through which the cadaver transformed ontologically from person to donor, body, cadaver, educational cadaver, teacher, and loved one/legacy. These shifts involved a network of bureaucratic, technical, educational, and humanistic practices that shaped the way the cadaver was perceived and acted upon at different moments in the lifecycle. By highlighting, at each phase, 1) the ontological transitions of the cadaver, itself, and 2) the practices, events, settings, and people involved in each of these transitions, we explored questions of “being” as it related to the ontological ambiguity of the cadaver: its conceptualization as both person and tool, simultaneously representing life and death. Insights Engaging deeply with the philosophical questions of cadaver-based simulation (CBS) helped us conceptualize the lifecycle as a series of meaningful and purposeful acts of becoming. Following the cadaver from program entry to interment allowed us to contemplate how its ontological ambiguity shapes every aspect of cadaver-based simulation. We found that in discussions of fidelity in medical simulation, beyond both the physical and functional, it is possible to conceive of a third type: ontological . The humanness of the cadaver makes CBS a unique, irreplaceable, and inherently philosophical, practice.