Miracle-dz/UniFAR-FTUs huggingface dataset:
CBNet: A Composite Backbone Network Architecture for Object Detection

Modern top-performing object detectors depend heavily on backbone networks, whose advances bring consistent performance gains through exploring more effective network structures. In this paper, we propose a novel and flexible backbone framework, namely CBNet, to construct high-performance detectors using existing open-source pre-trained backbones under the pre-training fine-tuning paradigm. In particular, CBNet architecture groups multiple identical backbones, which are connected through composite connections. Specifically, it integrates the high- and low-level features of multiple identical backbone networks and gradually expands the receptive field to more effectively perform object detection. We also propose a better training strategy with auxiliary supervision for CBNet-based detectors. CBNet has strong generalization capabilities for different backbones and head designs of the detector architecture. Without additional pre-training of the composite backbone, CBNet can be adapted to various backbones (i.e., CNN-based vs. Transformer-based) and head designs of most mainstream detectors (i.e., one-stage vs. two-stage, anchor-based vs. anchor-free-based). Experiments provide strong evidence that, compared with simply increasing the depth and width of the network, CBNet introduces a more efficient, effective, and resource-friendly way to build high-performance backbone networks. Particularly, our CB-Swin-L achieves 59.4% box AP and 51.6% mask AP on COCO test-dev under the single-model and single-scale testing protocol, which are significantly better than the state-of-the-art results (i.e., 57.7% box AP and 50.2% mask AP) achieved by Swin-L, while reducing the training time by $6\times $ . With multi-scale testing, we push the current best single model result to a new record of 60.1% box AP and 52.3% mask AP without using extra training data. Code is available at https://github.com/VDIGPKU/CBNetV2.
Optimal condition to probe strong coupling of two-dimensional excitons and zero-dimensional cavity modes

The light-matter interaction associated with a two-dimensional (2D) excitonic transition coupled to a zero-dimensional (0D) photonic cavity is fundamentally different from coupling localized excitations in quantum dots or color centers, which have negligible spatial extent compared to the cavity-confined mode profile. By calculating the radiation-matter coupling of the exciton transition of a surface deposited 2D material and a 0D photonic crystal nanobeam mode, we found that there is an optimal spatial extent of the monolayer material that maximizes such an interaction strength due to the competition between minimizing the excitonic envelope function area and maximizing the total integrated field. This is counter to the intuition from the Dicke model, where the oscillator strength is expected to monotonically grow with the number of oscillators, which correlates to the monolayer area assuming the excitonic wavefunction 1 ar X iv :2 10 7. 00 07 8v 1 [ co nd -m at . m es -h al l] 3 0 Ju n 20 21 is delocalized over the entire quantum well. We also found that at near zero excitoncavity detuning, the direct transmission efficiency of a waveguide-integrated cavity can be severely suppressed, which suggests performing experiments by using a side-coupled cavity to get better performances.
Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards

The growth of online consumer health questions has led to the necessity for reliable and accurate question answering systems. A recent study showed that manual summarization of consumer health questions brings significant improvement in retrieving relevant answers. However, the automatic summarization of long questions is a challenging task due to the lack of training data and the complexity of the related subtasks, such as the question focus and type recognition. In this paper, we introduce a reinforcement learning-based framework for abstractive question summarization. We propose two novel rewards obtained from the downstream tasks of (i) question-type identification and (ii) question-focus recognition to regularize the question generation model. These rewards ensure the generation of semantically valid questions and encourage the inclusion of key medical entities/foci in the question summary. We evaluated our proposed method on two benchmark datasets and achieved higher performance over state-of-the-art models. The manual evaluation of the summaries reveals that the generated questions are more diverse and have fewer factual inconsistencies than the baseline summaries. The source code is available here: https://github.com/shwetanlp/CHQ-Summ.
Approximate Frank-Wolfe Algorithms over Graph-structured Support Sets

In this paper, we consider approximate Frank-Wolfe (FW) algorithms to solve convex optimization problems over graph-structured support sets where the linear minimization oracle (LMO) cannot be efﬁciently obtained in general. We ﬁrst demonstrate that two popular approximation assumptions ( additive and multiplicative gap errors) are not applicable in that no cheap gap-approximate LMO oracle exists. Thus, approximate dual maximization oracles (DMO) are proposed, which approximate the inner product rather than the gap. We prove that the standard FW method using a δ -approximate DMO converges as O ((1 − δ ) √ s/δ ) in the worst case, and as O ( L/ ( δ 2 t )) over a δ -relaxation of the constraint set. Furthermore, when the solution is on the boundary, a variant of FW converges as O (1 /t 2 ) under the quadratic growth assumption. Our empirical results suggest that even these improved bounds are pessimistic, showing fast convergence in recovering real-world images with graph-structured sparsity.
The Kaiser-Rocket effect: three decades and counting

The peculiar motion of the observer, if not accurately accounted for, is bound to induce a well-defined clustering signal in the distribution of galaxies. This signal is related to the Kaiser rocket effect. Here we examine the amplitude and form of this effect, both analytically and numerically, and discuss possible implications for the analysis and interpretation of forthcoming cosmological surveys. For an idealistic cosmic variance dominated full-sky survey with a Gaussian selection function peaked at z ∼ 1.5 it is a > 5σ effect and it can in principle bias very significantly the inference of cosmological parameters, especially for primordial non-Gaussianity. For forthcoming surveys, with realistic masks and selection functions, the Kaiser rocket is not a significant concern for cosmological parameter inference except perhaps for primordial non-Gaussianity studies. However, it is a systematic effect, whose origin, nature and imprint on galaxy maps are well known and thus should be subtracted or mitigated. We present several approaches to do so.
Project MOMO: Multiwavelength Observations and Modeling of OJ 287

Our project MOMO (Multiwavelength observations and modeling of OJ 287) consists of dedicated, dense, long-term flux and spectroscopic monitoring, and deep follow-up observations of the blazar OJ 287 at >13 frequencies from the radio to the X-ray band since late 2015. In particular, we are using Swift to obtain optical-UV-X-ray spectral energy distributions (SEDs) and the Effelsberg telescope to obtain radio measurements between 2 and 40 GHz. MOMO is the densest long-term monitoring of OJ 287 involving X-rays and broad-band SEDs. The theoretical part of the project aims at understanding jet and accretion physics of the blazar central engine in general and the supermassive binary black hole scenario in particular. Results are presented in a sequence of publications and so far included: detection and detailed analysis of the bright 2016/17 and 2020 outbursts and the long-term light curve; Swift, XMM, and NuSTAR spectroscopy of the 2020 outburst around maximum; and interpretation of selected events in the context of the binary black hole scenario of OJ 287 (papers I–IV). Here, we provide a description of the project MOMO, a summary of previous results, the latest results, and we discuss future prospects.
Long-term Dynamical Stability in the Outer Solar System. I. The Regular and Chaotic Evolution of the 34 Largest Trans-Neptunian Objects

We carried out an extensive analysis of the stability of the outer solar system, making use of the frequency analysis technique over short-term integrations of nearly 100,000 test particles, as well as a statistical analysis of 200 1 Gyr long numerical simulations, which consider the mutual perturbations of the giant planets and the 34 largest trans-Neptunian objects (we have called all 34 objects “dwarf planets,” DPs, even if probably only the largest of them are true DPs). From the frequency analysis, we produced statistical diffusion maps for a wide region of the a–e phase-space plane; we also present the average diffusion time for orbits as a function of perihelion. We later turned our attention to the 34 DPs, making an individualized analysis for each of them and producing a first approximation of their future stability. From the 200 distinct realizations of the orbital evolution of the 34 DPs, we classified the sample into three categories, including 17 stable, 11 unstable, and 6 resonant objects; we also found that, statistically, two objects from the sample will leave the trans-Neptunian region within the next gigayear, most likely being ejected from the solar system, but with a nonnegligible probability of going inside the orbit of Neptune, either to collide with a giant planet or even falling to the inner solar system, where our simulations are no longer able to resolve their continuous evolution.
Zero-pronoun Data Augmentation for Japanese-to-English Translation

For Japanese-to-English translation, zero pronouns in Japanese pose a challenge, since the model needs to infer and produce the corresponding pronoun in the target side of the English sentence. However, although fully resolving zero pronouns often needs discourse context, in some cases, the local context within a sentence gives clues to the inference of the zero pronoun. In this study, we propose a data augmentation method that provides additional training signals for the translation model to learn correlations between local context and zero pronouns. We show that the proposed method significantly improves the accuracy of zero pronoun translation with machine translation experiments in the conversational domain.
Goal-Conditioned Reinforcement Learning with Imagined Subgoals

Goal-conditioned reinforcement learning endows an agent with a large variety of skills, but it often struggles to solve tasks that require more temporally extended reasoning. In this work, we propose to incorporate imagined subgoals into policy learning to facilitate learning of complex tasks. Imagined subgoals are predicted by a separate high-level policy, which is trained simultaneously with the policy and its critic. This high-level policy predicts intermediate states halfway to the goal using the value function as a reachability metric. We don't require the policy to reach these subgoals explicitly. Instead, we use them to define a prior policy, and incorporate this prior into a KL-constrained policy iteration scheme to speed up and regularize learning. Imagined subgoals are used during policy learning, but not during test time, where we only apply the learned policy. We evaluate our approach on complex robotic navigation and manipulation tasks and show that it outperforms existing methods by a large margin.
Obrada zavarenih priključaka metodom mehaničkog udara visokom frekvencijom

Kod zavarenih konstrukcija, oštećenja umorom najčešće nastaju na mjestima zavara, koji predstavljaju geometrijske i materijalne diskontinuitete. Zbog toga su razvijene metode obrade zavara nakon zavarivanja. Jedna od relativno novih metoda obrade je metoda mehaničkog udara visokom frekvencijom. Ta metoda utječe na promjenu geometrije zavara, lokalno povećanje tvrdoće te uklanjanje nepovoljnih zaostalih naprezanja nastalih procesom zavarivanja. U radu se daje pregled navedene metode te se ističu područja koja zahtijevaju dodatna razmatranja.
ORR_A_252616 121..125

1Department of Medicine, King’s College London, Guy’s Campus, London SE1 1UL, UK; 2Department of Medicine, Imperial College London, London SW7 2BU, UK; 3Department of Orthopaedic Surgery, St George’s Hospital, London SW17 0QT, UK Background: An estimated one in two healthy adults in the United Kingdom suffer from low levels of 25[OH]D. Vitamin D is involved in modulating immune response, but there is less clarity over its role in orthopaedic infection. This study assesses the relationship between serum 25[OH]D concentration and orthopaedic infection. Methods: A total of 205 patients in a tertiary referral centre for orthopaedic infection were included in the study. They were divided into groups based on their infection status, matched by age and gender. Data were statistically analysed to determine presence and direction of relationship. Results: A total of 114 patients had an infection. There was no statistically significant difference in age or gender between the two groups. Mean serum 25[OH]D concentration was 39 nmol/L in the group with infection and 59 nmol/L in the group without an infection (p<0.01). Overall mean serum 25[OH]D concentration was 48 nmol/L. There was a correlation between low serum 25 [OH]D concentration and rate of infection (odds ratio, 5.94; 95% confidence interval [CI], 3.24 to 10.92) with a bivariate correlation of −0.338 (p<0.01). Conclusion: This study demonstrates an association between low levels of serum 25[OH]D and increased orthopaedic infection. Orthopaedic inpatients suffered from vitamin D insufficiency, and there was a correlation between higher levels of serum 25[OH]D and lower rates of infection. This suggests that prophylactic supplementation of 25[OH]D may improve outcomes, and provides a foundation for randomized controlled trials to assess its effectiveness in practice.
Forecast and Observation-Impact Experiments in the Navy Global Environmental Model with Assimilation of ECWMF Analysis Data in the Global Domain

This study examines analysis and forecast impacts in the Navy Global Environmental Model (NAVGEM) from direct assimilation of temperature and wind “pseudo-raob” profiles derived from analysis fields of the European Centre for Medium-Range Weather Forecasting-Integrated Forecast System (ECWMF-IFS). The pseudo-raob profiles are provided on eight vertical levels from 250 hPa to 1000 hPa on a 1° × 1° resolution rectilinear grid and are assimilated as synthetic observation data by NAVGEM at 0000 UTC and 1200 UTC for an experimental time period of 48 days. The pseudo-raob observations are assumed in these experiments to have observation errors identical to temperature and wind data provided by conventional radiosonde observations. In this diagnostic context, the assimilation of pseudo-raob profiles significantly reduces temperature and height biases in the NAVGEM analysis and provides general improvements to forecast skill when verified against both self-analysis and rawinsondes. Reduction of NAVGEM temperature bias is most evident in southern hemisphere high latitudes, where the assimilation of pseudo-raob information mitigates NAVGEM temperature bias and indicates sub-optimal bias correction of radiance data in the NAVGEM Control analysis. Despite the revisiting of assimilated observation information when assimilating pseudo-raobs from the IFS analysis into the NAVGEM analysis, improvement to the NAVGEM analyses and forecasts is both statistically significant and consistent across several verification techniques. This suggests that there are likely small effects from any correlations between pseudo-raob data and the NAVGEM background. The assimilation of pseudo-raob data also reduces the total observation impact in NAVGEM as estimated by the adjoint model, which is an indicator of general improvement to analysis and forecast quality.
End-to-End Rotation Averaging with Multi-Source Propagation

This paper presents an end-to-end neural network for multiple rotation averaging in SfM. Due to the manifold constraint of rotations, conventional methods usually take two separate steps involving spanning tree based initialization and iterative nonlinear optimization respectively. These methods can suffer from bad initializations due to the noisy spanning tree or outliers in input relative rotations. To handle these problems, we propose to integrate initialization and optimization together in an unified graph neural network via a novel differentiable multi-source propagation module. Specifically, our network utilizes the image context and geometric cues in feature correspondences to reduce the impact of outliers. Furthermore, unlike the methods that utilize the spanning tree to initialize orientations according to a single reference node in a top-down manner, our net-work initializes orientations according to multiple sources while utilizing information from all neighbors in a differentiable way. More importantly, our end-to-end formulation also enables iterative re-weighting of input relative orientations at test time to improve the accuracy of the final estimation by minimizing the impact of outliers. We demonstrate the effectiveness of our method on two real-world datasets, achieving state-of-the-art performance.
Reply to comment: Conventional manual small‐incision cataract surgery

1. Chew MC, Tan CS. Conventional manual small‐incision cataract surgery. Indian J Ophthalmol 2015;63:293‐4. 2. Yang J, Lai P, Wu D, Long Z. Manual cataract extraction via a subconjunctival limbus oblique incision for mature cataracts. Indian J Ophthalmol 2014;62:274‐8. 3. Venkatesh R, Tan CS, Sengupta S, Ravindran RD, Krishnan KT, Chang DF. Phacoemulsification versus manual small‐incision cataract surgery for white cataract. J Cataract Refract Surg 2010;36:1849‐54. 4. Ruit S, Tabin G, Chang D, Bajracharya L, Kline DC, Richheimer W, et al. A prospective randomized clinical trial of phacoemulsification vs manual sutureless small‐incision extracapsular cataract surgery in Nepal. Am J Ophthalmol 2007;143:32‐38. 5. Deng JW, Yang YT, Zeng Y, Tang ZM, Liu XJ, Fu XY. Two‐hook technique for nucleus extraction in manual sutureless extracapsular cataract extraction. J Cataract Refract Surg 2013;39:497‐500. 6. Zeng Y, Deng JW, Gao JH. A novel nucleus extraction technique using a vectis in sutureless, manual, small‐incision cataract surgery. Nepal J Ophthalmol 2014;6:140‐4. Letters to the Editor
The biological responses and mechanisms of endothelial cells to magnesium alloy

Abstract Due to its good biocompatibility and degradability, magnesium alloy (Mg alloy) has shown great promise in cardiovascular stent applications. Rapid stent re-endothelialization is derived from migrated and adhered endothelial cells (ECs), which is an effective way to reduce late thrombosis and inhibit hyperplasia. However, fundamental questions regarding Mg alloy affecting migration and adhesion of ECs are not fully understood. Here, we evaluated the effects of Mg alloy on the ECs proliferation, adhesion and migration. A global gene expression profiling of ECs co-culturing with Mg alloy was conducted, and the adhesion- and migration-related genes were examined. We found that Mg alloy had no adverse effects on ECs viability but significantly affected ECs migration and adhesion. Co-cultured with Mg alloy extract, ECs showed contractive adhesion morphology and decreased motility, which was supported by the down-regulation of adhesion-related genes (Paxillin and Vinculin) and migration-related genes (RAC 1, Rho A and CDC 42). Accordingly, the re-endothelialization of Mg alloy stent was inhibited in vivo. Our results may provide new inspiration for improving the broad application of Mg alloy stents.
Biomarkers for interstitial lung disease and acute-onset diffuse interstitial lung disease in rheumatoid arthritis

Interstitial lung disease (ILD) is frequently a complication of rheumatoid arthritis (RA) as an extra-articular manifestation which has a poor prognosis. Acute-onset diffuse ILD (AoDILD) occasionally occurs in RA and includes acute exacerbation of ILD, drug-induced ILD, and Pneumocystis pneumonia. AoDILD also confers a poor prognosis in RA. Previously-established biomarkers for ILD include Krebs von den lungen-6 and surfactant protein-D originally defined in patients with idiopathic pulmonary fibrosis; the sensitivity of these markers for RA-associated ILD (RA-ILD) is low. Although many studies on ILD markers have been performed in idiopathic pulmonary fibrosis, only a few validation studies in RA-ILD or AoDILD have been reported. Biomarkers for RA-ILD and AoDILD are thus still required. Recently, genomic, cytokine, antibody, and metabolomic profiles of RA-ILD or AoDILD have been investigated with the aim of improving biomarkers. In this review, we summarize current preliminary data on these potential biomarkers for RA-ILD or AoDILD. The development of biomarkers on RA-ILD has only just begun. When validated, such candidate biomarkers will provide valuable information on pathogenesis, prognosis, and drug responses in RA-ILD in future.
Parent-Child Attachment: A Principle-Based Concept Analysis

Introduction Extensive evidence indicates that the quality of parent-child attachment is related to later socio-emotional and physical health outcomes. Yet, despite its clinical relevance, the parent-child attachment concept has been inconsistently applied across the disciplines of nursing, medicine and psychology and is often conflated with parent-child bonding in nursing literature. Objectives To provide readers with a critical analysis of the concept of parent-child attachment. Using a principle-based concept analysis, we clarify how parent-child attachment is understood from a multidisciplinary perspective to advance the use of this concept in nursing practice. Concept Description: Attachment is an affectionate, mutually satisfying relationship between a child and a caregiver that serves the purpose of making the child feel safe, secure, and protected. Discussion In this principle-based concept analysis, each definitional (i.e., epistemological, pragmatic, linguistic, and logical) principle contributes to an understanding of the strengths and limitations of the state of science about this concept. The discussion highlights how applying the concept of parent-child attachment security may offer exciting and promising opportunities for nursing clinical work with families. Conclusion The understanding of the concept of parent-child attachment differs among disciplines of nursing, medicine and psychology and offers exciting and promising opportunities for clarity and collaborative, multi-disciplinary work.