Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification with Rejection Based on Cost-sensitive Classification

The goal of classification with rejection is to avoid risky misclassification in error-critical applications such as medical diagnosis and product inspection. In this paper, based on the relationship between classification with rejection and cost-sensitive classification, we propose a novel method of classification with rejection by learning an ensemble of cost-sensitive classifiers, which satisfies all the following properties for the first time: (i) it can avoid estimating class-posterior probabilities, resulting in improved classification accuracy. (ii) it allows a flexible choice of losses including non-convex ones, (iii) it does not require complicated modifications when using different losses, (iv) it is applicable to both binary and multiclass cases, and (v) it is theoretically justifiable for any classification-calibrated loss. Experimental results demonstrate the usefulness of our proposed approach in clean-labeled, noisy-labeled, and positive-unlabeled classification.
Deterministic Approximation for Submodular Maximization over a Matroid in Nearly Linear Time

We study the problem of maximizing a non-monotone, non-negative submodular function subject to a matroid constraint. The prior best-known deterministic approximation ratio for this problem is $\frac{1}{4}-\epsilon$ under $\mathcal{O}(({n^4}/{\epsilon})\log n)$ time complexity. We show that this deterministic ratio can be improved to $\frac{1}{4}$ under $\mathcal{O}(nr)$ time complexity, and then present a more practical algorithm dubbed TwinGreedyFast which achieves $\frac{1}{4}-\epsilon$ deterministic ratio in nearly-linear running time of $\mathcal{O}(\frac{n}{\epsilon}\log\frac{r}{\epsilon})$. Our approach is based on a novel algorithmic framework of simultaneously constructing two candidate solution sets through greedy search, which enables us to get improved performance bounds by fully exploiting the properties of independence systems. As a byproduct of this framework, we also show that TwinGreedyFast achieves $\frac{1}{2p+2}-\epsilon$ deterministic ratio under a $p$-set system constraint with the same time complexity. To showcase the practicality of our approach, we empirically evaluated the performance of TwinGreedyFast on two network applications, and observed that it outperforms the state-of-the-art deterministic and randomized algorithms with efficient implementations for our problem.
FUEL: Fast UAV Exploration Using Incremental Frontier Structure and Hierarchical Planning

Autonomous exploration is a fundamental problem for various applications of unmanned aerial vehicles(UAVs). Existing methods, however, were demonstrated to insufficient exploration rate, due to the lack of efficient global coverage, conservative motion plans and low decision frequencies. In this letter, we propose <bold>FUEL</bold>, a hierarchical framework that can support <italic>F</italic>ast <italic>U</italic>AV <italic>E</italic>xp<italic>L</italic>oration in complex unknown environments. We maintain crucial information in the entire space required by exploration planning by a frontier information structure (FIS), which can be updated incrementally when the space is explored. Supported by the FIS, a hierarchical planner plans exploration motions in three steps, which find efficient global coverage paths, refine a local set of viewpoints and generate minimum-time trajectories in sequence. We present extensive benchmark and real-world tests, in which our method completes the exploration tasks with unprecedented efficiency (3-8 times faster) compared to state-of-the-art approaches. Our method will be made open source to benefit the community<xref ref-type="fn" rid="fn1"><sup>1</sup></xref><fn id="fn1"><label><sup>1</sup></label><p>To be released at <uri>https://github.com/HKUST-Aerial-Robotics/FUEL</uri>. </p></fn>.
Selenium invoked antioxidant defense system in Azolla caroliniana plant

Plants of Azolla caroliniana were treated with differentselenium concentrations (0, 1, 2, 5, 7, 10 ppm) for seven days.Selenium (Se) content in Azolla plants increased significantly withincreasing Se concentrations in the culture media up to 5 ppm. Thisindicated that Azolla plants were a good accumulator for Se. Seleniumaccumulation determined changes in Azolla biomass, doublingtime and relative growth rates. Treatment of Azolla plants with lowconcentrations of Se (1 ppm) resulted in a significant increase in biomass.This was accompanied by a reduction in hydrogen peroxide andmalondialdehyde (MDA) contents; the decrease percentages were78% and 60%, respectively at 1 ppm Se in comparison with the control.At higher Se concentrations (>5 ppm), there was a significantincrease in H2O2 and MDA contents, these increases were 3.2- and2.8-fold at 10 ppm Se in comparison to controls, respectively. Comparedto that in controls, total ascorbate as well as total glutathionecontents, were significantly increased. The activity of the GR enzymewas significantly increased in Azolla plants with addition of differentconcentrations of Se. The increase was 2.2- and 3.4-fold at 2 and7 ppm Se, respectively. The addition of high concentrations of Se(>5 ppm) to the growth media resulted in a significant increase inthe GSH-PX and APX activities in Azolla plants. Thus, addition ofSe affects Azolla plants, and these effects change from beneficial totoxic, as reflected in the metabolism and growth of the plants.
Modified Grover operator for amplitude estimation

In this paper, we propose a quantum amplitude estimation method that uses a modified Grover operator and quadratically improves the estimation accuracy in the ideal case, as in the conventional one using the standard Grover operator. Under the depolarizing noise, the proposed method can outperform the conventional one in the sense that it can in principle achieve the ultimate estimation accuracy characterized by the quantum Fisher information in the limit of a large number of qubits, while the conventional one cannot achieve the same value of ultimate accuracy. In general this superiority requires a sophisticated adaptive measurement, but we numerically demonstrate that the proposed method can outperform the conventional one and approach to the ultimate accuracy, even with a simple non-adaptive measurement strategy.
Complete mitochondrial genome of a latent wild oak tasar silkworm, Antheraea frithi (Lepidoptera: Saturniidae)

Abstract Antheraea frithi mitogenome was sequenced to understand its phylogenetic and evolutionary relationship. It comprises 22 tRNAs, 13 protein-coding genes, two rRNAs and a AT rich region. The arrangement of mitogenome gene follows the pattern of dytrysian lepidoptera. rrnL gene consists of two unique long consensus repeats copy. tRNAs like structure of trnL and trnP are present in rrnL gene and AT rich region, respectively. The phylogenetic analysis by maximum-likelihood method showed that A. frithi was clustered with A. pernyi and A. yamamai, the commercialized wild silkworms.
Untargeted Metabolomics Reveals the Protective Effect of a Traditional Chinese Herbal Decoction on Cisplatin-Induced Acute Kidney Injury

Our previous studies have demonstrated that Jian-Pi-Yi-Shen formula (JPYSF), a traditional Chinese herbal decoction, has a renoprotective effect in 5/6 nephrectomy-induced chronic kidney injury. However, the role and potential mechanisms of JPYSF in the treatment of acute kidney injury (AKI) remain unknown. This study was designed to test the beneficial effect of JPYSF in an AKI mouse model and to investigate the underlying mechanism by using metabolomics analysis. The AKI mouse model was induced by a single intraperitoneal injection of cisplatin at a dose of 20 mg/kg. The mice in the treatment group were pretreated orally with JPYSF (18.35 g/kg/d) for 5 days before cisplatin injection. Seventy-two hours after cisplatin injection, serum and kidney samples were collected for biochemical and histological examination. Ultra-high-performance liquid chromatography coupled with quadrupole time-of-ﬂight mass spectrometry (UHPLC-QTOF/MS) was applied to analyze metabolic profiling variations in the kidney. The results showed that pretreatment with JPYSF obviously reduced the levels of serum creatinine and blood urea nitrogen and alleviated renal pathological injury in AKI mice. Orthogonal partial least-squares discriminant analysis (OPLS-DA) score plot revealed a clear separation between the AKI and AKI + JPYSF group. A total of 68 and 87 significantly differentially expressed metabolites were identified in the kidney of AKI mice responding to JPYSF treatment in negative and positive ion mode, respectively. The pivotal pathways affected by JPYSF included vitamin B6 metabolism, alanine, aspartate and glutamate metabolism, lysine biosynthesis, and butanoate metabolism. In conclusion, JPYSF can protect the kidney from cisplatin-induced AKI, which may be associated with regulating renal metabolic disorders.
Brain-Inspired Learning on Neuromorphic Substrates

Neuromorphic hardware strives to emulate brain-like neural networks and thus holds the promise for scalable, low-power information processing on temporal data streams. Yet, to solve real-world problems, these networks need to be trained. However, training on neuromorphic substrates creates significant challenges due to the offline character and the required nonlocal computations of gradient-based learning algorithms. This article provides a mathematical framework for the design of practical online learning algorithms for neuromorphic substrates. Specifically, we show a direct connection between real-time recurrent learning (RTRL), an online algorithm for computing gradients in conventional recurrent neural networks (RNNs), and biologically plausible learning rules for training spiking neural networks (SNNs). Furthermore, we motivate a sparse approximation based on block-diagonal Jacobians, which reduces the algorithm’s computational complexity, diminishes the nonlocal information requirements, and empirically leads to good learning performance, thereby improving its applicability to neuromorphic substrates. In summary, our framework bridges the gap between synaptic plasticity and gradient-based approaches from deep learning and lays the foundations for powerful information processing on future neuromorphic hardware systems.
Mica Can Alleviate TNBS-Induced Colitis in Mice by Reducing Angiotensin II and IL-17A and Increasing Angiotensin-Converting Enzyme 2, Angiotensin 1-7, and IL-10

Aim To explore the treatment effect of mica on 2,4,6-trinitrobenzenesulfonic acid solution- (TNBS-) induced colitis in mice. Materials and Methods Thirty male BALB/C mice were randomly divided into the control group, the TNBS group, and the mica group. Control mice were treated with saline solution. Experimental colitis was induced by TNBS (250 mg/kg/d) in the TNBS group and the mica group. After modeling, the mica group was treated with mica (180 mg/kg/d) for 3 days, while the TNBS group continued the treatment with TNBS. All solutions were injected intrarectally. During treatment, body weight and mice activity were monitored daily. After treatment, the colon tissues of mice were collected; angiotensin II (Ang II), angiotensin-converting enzyme 2 (ACE2), angiotensin 1-7 (Ang (1-7)), IL-17A, and IL-10 expression was analyzed by ELISA and immunohistochemistry. Results Food intake, activity, and body weight gradually decreased in the TNBS group compared to the control group and the mica group (all P < 0.05). Also, black stool adhesion in the anus and thin and bloody stool were observed in the TNBS group, but not in the other two groups. Moreover, the expression of Ang II, ACE2, Ang (1-7), IL-17A, and IL-10 in the TNBS group increased compared to that in the control group. Compared to the TNBS group, ACE2, Ang (1-7), and IL-10 in the mica group increased, while Ang II and IL-17A decreased (all P < 0.05). Conclusion Mica can alleviate TNBS-induced colitis in mice by regulating the inflammation process; it reduces Ang II and IL-17A and increases ACE2, IL-10, and Ang (1-7).
Coccoloba alnifolia Leaf Extract as a Potential Antioxidant Molecule Using In Vitro and In Vivo Assays

The genus Coccoloba is widely used in traditional folk medicine, but few scientific data exist for this genus. The goal of this study was to characterise the chemical composition and antioxidant activities of C. alnifolia leaf extracts using in vitro and in vivo assays. Six extracts were obtained: hexane (HE), chloroform (CE), ethanol (EE), methanol (ME), water end extract (WEE), and water extract (WE). Thin-layer chromatography (TLC) analysis showed the presence of phenols, saponins, terpenes, and flavonoids. In vitro assays demonstrated substantial antioxidant potential, especially for polar extracts (EE, ME, WEE, and WE). Moreover, no toxic effects were observed on mammalian cell lines for most of the extracts at the concentrations evaluated. The nematode Caenorhabditis elegans was also used as an in vivo model for testing antioxidant potential. The EE and WE were chosen, based on previously obtained results. It was observed that neither the EE nor the WE had any toxic effect on C. elegans development. Additionally, the antioxidant potential was evaluated using tert-butyl hydroperoxide as a stressor agent. The EE increased the life span of C. elegans by 28% compared to that of the control, and the WE increased the range to 39.2-41.3%. High-performance liquid chromatography (HPLC-DAD) showed the presence of gallic acid, p-coumaric acid, and vitexin in the WE. Therefore, in vitro and in vivo data demonstrated the antioxidant potential of C. alnifolia extracts and their possible biotechnological applications.
Survey of Attitudes on Personal Protection Interventions Against COVID-19 Including MMR Vaccination and Future Anti-COVID Vaccines

Abstract: An electronic survey was conducted in October, 2020 among individuals primarily age 60 and older regarding their degree of confidence of deriving personal protection from 8 different antiCOVID interventions: social isolation, lockdowns, avoiding restaurants, taking MMR vaccine, wearing masks when indoors with others, avoiding hotels, avoiding commercial air travel, and using the first future specific antiCOVID vaccine. Responses were received from 135 persons from many different U.S. regions and 5 foreign countries. Respondents were generally individuals with very high levels of education and personal achievement. Results demonstrated wide diversity of responses regarding each of these interventions. None were strongly supported by a majority of respondents, but those receiving the largest proportions of strong support were social isolation (41%), wearing masks indoors (41%), and using the first antiCOVID vaccine (41%). MMR (measles, mumps, rubella) vaccination was viewed much more positively than negatively but had the highest proportion of individuals who felt they had insufficient data to formulate an opinion. The largest number of strong negative assessments were toward lockdowns (37%). We speculate that the wide variation in perception of possible benefits from the surveyed interventions, most of which have been widely practiced by or imposed upon millions of individuals, in this highly accomplished older population at increased personal risk from COVID19 reflects the current absence of rigorous scientific proof of the efficacy of any these interventions, and the continuation of the epidemic despite the widespread utilization of most of them.
MAnycast2: Using Anycast to Measure Anycast

Anycast addressing - assigning the same IP address to multiple, distributed devices - has become a fundamental approach to improving the resilience and performance of Internet services, but its conventional deployment model makes it impossible to infer from the address itself that it is anycast. Existing methods to detect anycast IPv4 prefixes present accuracy challenges stemming from routing and latency dynamics, and efficiency and scalability challenges related to measurement load. We review these challenges and introduce a new technique we call "MAnycast2" that can help overcome them. Our technique uses a distributed measurement platform of anycast vantage points as sources to probe potential anycast destinations. This approach eliminates any sensitivity to latency dynamics, and greatly improves efficiency and scalability. We discuss alternatives to overcome remaining challenges relating to routing dynamics, suggesting a path toward establishing the capability to complete, in under 3 hours, a full census of which IPv4 prefixes in the ISI hitlist are anycast.
On the Relation between Code Elements and Accessibility Issues in Android Apps

Mobile apps have gone mainstream and become part of our daily lives. Currently, many efforts have been made to make apps more accessible to people with disabilities. However, little is still known on how to implement more accessible apps. In the Android API, there are (code) elements that may be employed to (in)directly improve the app's accessibility. This paper aims to investigate the prevalence of accessibility code elements and their relation to potential accessibility issues. First, we identified code elements of the native Android API that may be related to accessibility features, and mapped them to principles and success criteria of the Web Content Accessibility Guidelines (WCAG) 2.1. Using a sample of 111 open source mobile apps available in Google Play, we conducted a characterization study to examine the prevalence of accessibility code elements. We also analyzed how these code elements are related to issues detected by the static analyzer Android Lint and the accessibility testing tool MATE. Our results indicate that code elements are not widely used; the ones directly related to accessibility are present in only a few apps. Additionally, our results would seem to suggest that apps that adopt accessibility code elements, tend to have less accessibility issues. By analyzing our results from the standpoint of the WCAG principles, we conclude that there is room for improvement in terms of how both the Android API and automated testing tools deal with accessibility-related issues.
Measures of Juvenile Fibromyalgia

Juvenile fibromyalgia (JFM) is a chronic and debilitating noninflammatory musculoskeletal pain condition that is typically diagnosed in adolescence. JFM is characterized by diffuse widespread pain, sleep difficulty, fatigue, and other associated symptoms (1). This condition often involves impairment in physical, social, and emotional functioning (2–9). Currently, there are no specific medical tests or disease markers to diagnose the condition. Diagnosis is based on subjective patient report of pain and other symptoms, after ruling out underlying inflammatory disorders or other medical conditions. A multidimensional assessment of fibromyalgia syndrome that includes measures of pain, fatigue, sleep, overall functioning, and quality of life (QOL) is recommended to gain a complete picture of JFM symptoms and their impact on patients’ ability to function in their daily lives (10). Information presented in this review reflects several recent and important developments since the last review of measures used in JFM (11) that have enhanced evidencebased assessment in youth with JFM. In the following sections, we discuss a new measure for the proper classification of fibromyalgia in pediatric patients derived from the 2010 American College of Rheumatology (ACR) criteria for fibromyalgia, the Pain and Symptom Assessment Tool (PSAT) for JFM. Specifically, we present results from the initial validation of the PSAT, a measure developed to reliably identify youth with JFM and distinguish JFM from other chronic localized pain conditions. Next, we review the psychometric properties and utility of the recently developed pediatric PatientReported Outcomes Measurement Information System (PROMIS) scales: the Pediatric Pain Interference Scale, the Anxiety Scale, and Depression Scale. Finally, we present updated information on widely used instruments discussed in our previous review for measuring difficulties associated with JFM, including the Functional Disability Inventory (FDI) and the Pediatric Quality of Life (PedsQL) 3.0 Rheumatology Module. See Tables 1 and 2 for summaries of practical applications and psychometric properties of each of the reviewed measures. PAIN AND SYMPTOM ASSESSMENT TOOL
An analysis framework of research frontiers based on the large‐scale open academic graph

As a high‐quality and well‐structured dataset, the large‐scale open academic graph formed under the influence of the open science movement has created new research conditions for research frontier analysis. Constructing the analysis framework of research frontiers based on the large‐scale open academic graph can effectively promote the realization of data‐driven knowledge discovery and the analysis and decision‐making of sci‐tech intelligence. The definition and analysis methods of research frontiers were summarized through related studies, and the data structure of the specific open academic graph was investigated. The thoughts and steps of research frontier analysis based on the open academic graph were put forward, and an available analysis framework of research frontiers based on the large‐scale open academic graph was constructed. The proposed framework can achieve deep, relevant and dynamic analysis of research frontiers in various disciplines based on the emerging large‐scale open academic graph. It will provide a novel perspective for performing dynamic analysis across time and space, multidimensional analysis under multiple factors, and multiscale evolution analysis of research frontiers.
Health‐Related Quality of Life Measures in Adult Systemic Lupus Erythematosus

Systemic lupus erythematosus (SLE) is a chronic inflammatory autoimmune disorder with variable multisystem involvement, an unpredictable relapsingremitting course, an early onset, and a significant impact on healthrelated quality of life (HRQoL) (1). Previous research has shown poor correlation between HRQoL and physician assessments of disease activity and damage, highlighting the distinct contribution of HRQoL data to understanding patient trajectories and supporting the need for its assessment in SLE (2). Furthermore, HRQoL has been found to be an important determinant of adherence and health care use in patients with SLE and has been advocated by regulatory agencies for use in clinical trials (3). Patientreported HRQoL measures, in the form of questionnaires, have been either developed exclusively for use in SLE (diseasespecific measures) or have been used in patients with SLE but developed for any disease state or healthy individuals (generic measures). Patientreported outcome evaluation has been incorporated in drug clinical trials in SLE; however, it has not been a consistent practice (4), and it is not clear whether sensitivity to change over time has been observed (5,6). Knowledge of acceptable measurement properties, practical usability, generalizability, and cultural adaptability would help determine the adequacy of the HRQoL measure for clinical practice and in clinical research. Seven measures validated for the assessment of HRQoL in SLE will be reviewed. These include the diseasespecific measures Quality of Life in Systemic Lupus Erythematosus (LQoL), Systemic Lupus Erythematosus Quality of Life (SLEQoL), Lupus Impact Tracker (LIT), Lupus Quality of Life Tool (LupusQoL), and Lupus PatientReported Outcome (LupusPRO) and the generic measures 36Item Medical Outcomes Study Short Form (SF36) and European Quality of Life–5D (EQ-5D). The PatientReported Outcomes Measurement Information System (PROMIS) scales will not be included in this review because they are included in the relevant domain reviews (eg, fatigue, physical function) of this edition. The goal of this review is to provide a comprehensive review of these outcome measures to inform future selection of these tools in clinical practice or for research purposes (Tables 1 and 2).