Miracle-dz/UniFAR-FTUs huggingface dataset:
Coupled-channel meson-meson scattering in the diabatic framework

We apply the diabatic framework, a QCD-based formalism for the unified study of quarkoniumlike systems in terms of heavy quark-antiquark and open-flavor meson-meson components, to the description of coupled-channel meson-meson scattering. For this purpose, we first introduce a numerical scheme to find the solutions of the diabatic Schrödinger equation for energies in the continuum, then we derive a general formula for calculating the meson-meson scattering amplitudes from these solutions. We thus obtain a completely nonperturbative procedure for the calculation of open-flavor meson-meson scattering cross sections from the diabatic potential, which is directly connected to lattice QCD calculations. A comprehensive analysis of various elastic cross sections for open-charm and open-bottom meson-meson pairs is performed in a wide range of the centerof-mass energies. The relevant structures are identified, showing a spectrum of quasi-conventional and unconventional quarkoniumlike states. In addition to the customary Breit-Wigner peaks we obtain nontrivial structures such as threshold cusps and minimums. Finally, our results are compared with existing data and with results from our previous bound-state–based analysis, finding full compatibility with both.
ReconVAT: A Semi-Supervised Automatic Music Transcription Framework for Low-Resource Real-World Data

Most of the current supervised automatic music transcription (AMT) models lack the ability to generalize. This means that they have trouble transcribing real-world music recordings from diverse musical genres that are not presented in the labelled training data. In this paper, we propose a semi-supervised framework, ReconVAT, which solves this issue by leveraging the huge amount of available unlabelled music recordings. The proposed ReconVAT uses reconstruction loss and virtual adversarial training. When combined with existing U-net models for AMT, ReconVAT achieves competitive results on common benchmark datasets such as MAPS and MusicNet. For example, in the few-shot setting for the string part version of MusicNet, ReconVAT achieves F1-scores of 61.0% and 41.6% for the note-wise and note-with-offset-wise metrics respectively, which translates into an improvement of 22.2% and 62.5% compared to the supervised baseline model. Our proposed framework also demonstrates the potential of continual learning on new data, which could be useful in real-world applications whereby new data is constantly available.
TeliNet, a simple and shallow Convolution Neural Network (CNN) to Classify CT Scans of COVID-19 patients

Hundreds of millions of cases and millions of deaths have occurred worldwide due to COVID-19. The fight against this pandemic is on-going on multiple fronts. While vaccinations are picking up speed, there are still billions of unvaccinated people. In this fight diagnosis of the disease and isolation of the patients to prevent any spreads play a huge role. Machine Learning approaches have assisted the diagnosis of COVID-19 cases by analyzing chest X-ray and CT-scan images of patients. In this research we present a simple and shallow Convolutional Neural Network based approach, TeliNet, to classify CT-scan images of COVID-19 patients. Our results outperform the F1 score of VGGNet and the benchmark approaches. Our proposed solution is also more lightweight in comparison to the other methods.
1st Place Solution for ICDAR 2021 Competition on Mathematical Formula Detection

In this technical report, we present our 1st place solution for the ICDAR 2021 competition on mathematical formula detection (MFD). The MFD task has three key challenges including a large scale span, large variation of the ratio between height and width, and rich character set and mathematical expressions. Considering these challenges, we used Generalized Focal Loss (GFL), an anchor-free method, instead of the anchor-based method, and prove the Adaptive Training Sampling Strategy (ATSS) and proper Feature Pyramid Network (FPN) can well solve the important issue of scale variation. Meanwhile, we also found some tricks, e.g., Deformable Convolution Network (DCN), SyncBN, and Weighted Box Fusion (WBF), were effective in MFD task. Our proposed method ranked 1st in the final 15 teams.
NEUTROPHIL ACTIVATION IN TRANSITIONAL GRAVITY

The release of myeloperoxidase (MPO) from neutrophils represents one possible pathway for increasing the damage associated with space flight and recovery. Oxidative damage that occurs because of space flight might arise from many sources including radiation and the gravitational changes defined as transitional gravity (TG). The relationship existing between TG, oxidative stress and tissue damage is unclear. In particular, neutrophils generate reactive oxygen species in respond to stressful challenges such as those presented by TG. The purpose of this study was to determine whether TG would activate human neutrophils and thereby induce increased levels of oxidative stress. TG experiments were conducted aboard the NASA C-9 aircraft during parabolic flights. A flight-certified apparatus was used that consisted of a cell module (holding 5 cell cartridges) chamber and a multi-channeled peristaltic pump (N=5). Human neutrophils were isolated preflight from blood drawn from the faculty and student investigators (N=5). Neutrophils were counted, loaded into the cell cartridges and maintained at 37°C prior to flight. Half of the loaded cartridges were flown while the remainder served as the ground controls. Lipid peroxidation levels were significantly higher (248.1±50.6%) in the flight group than in the ground controls. However, MPO levels in flight neutrophils (6.7±5.8%) and that released into the media (19.6±11.3%) were both significantly less than levels observed in ground controls. Although the data only half supported our hypothesis-increased levels of cellular oxidative stress, it supported the literature with respect to decreased neutrophil function following space flight. This work was supported, in part, by NASA Grants NCC9-112 and NAG3-2611, NCRR 5P20RR11104, and MBRS Grant 506GM08248.
CoBERL: Contrastive BERT for Reinforcement Learning

Many reinforcement learning (RL) agents require a large amount of experience to solve tasks. We propose Contrastive BERT for RL (CoBERL), an agent that combines a new contrastive loss and a hybrid LSTM-transformer architecture to tackle the challenge of improving data efficiency. CoBERL enables efficient, robust learning from pixels across a wide range of domains. We use bidirectional masked prediction in combination with a generalization of recent contrastive methods to learn better representations for transformers in RL, without the need of hand engineered data augmentations. We find that CoBERL consistently improves performance across the full Atari suite, a set of control tasks and a challenging 3D environment.
Acceleration statistics of tracer and light particles in compressible homogeneous isotropic turbulence

Abstract The accelerations of tracer and light particles (bubbles) in compressible homogeneous isotropic turbulence are investigated by using data from direct numerical simulations up to turbulent Mach number $M_t =1$. For tracer particles, the flatness factor of acceleration components, $F_a$, increases gradually for $M_t \in [0.3, 1]$. On the contrary, $F_a$ for bubbles develops a maximum around $M_t \sim 0.6$. The probability density function of longitudinal acceleration of tracers is increasingly skewed towards the negative value as $M_t$ increases. By contrast, for light particles, the skewness factor of longitudinal acceleration, $S_a$, first becomes more negative with the increase of $M_t$, and then goes back to $0$ when $M_t$ is larger than $0.6$. Similarly, differences among tracers and bubbles appear also in the zero-crossing time of acceleration correlation. It is argued that all these phenomena are intimately linked to the flow structures in the compression regions close to shocklets.
Dual Optimization for Kolmogorov Model Learning Using Enhanced Gradient Descent

Data representation techniques have made a substantial contribution to advancing data processing and machine learning (ML). Improving predictive power was the focus of previous representation techniques, which unfortunately perform rather poorly on the interpretability in terms of extracting underlying insights of the data. Recently, the Kolmogorov model (KM) was studied, which is an interpretable and predictable representation approach to learning the underlying probabilistic structure of a set of random variables. The existing KM learning algorithms using semi-definite relaxation with randomization (SDRwR) or discrete monotonic optimization (DMO) have, however, limited utility to big data applications because they do not scale well computationally.In this paper, we propose a computationally scalable KM learning algorithm, based on the regularized dual optimization combined with enhanced gradient descent (GD) method. To make our method more scalable to large-dimensional problems, we propose two acceleration schemes, namely, the eigenvalue decomposition (EVD) elimination strategy and an approximate EVD algorithm. Furthermore, a thresholding technique by exploiting the error bound analysis and leveraging the normalized Minkowski $\ell _1$-norm, is provided for the selection of the number of iterations of the approximate EVD algorithm. When applied to big data applications, it is demonstrated that the proposed method can achieve compatible training/prediction performance with significantly reduced computational complexity; roughly two orders of magnitude improvement in terms of the time overhead, compared to the existing KM learning algorithms. Furthermore, it is shown that the accuracy of logical relation mining for interpretability by using the proposed KM learning algorithm exceeds 80%.
Stabilizing Neural Control Using Self-Learned Almost Lyapunov Critics

The lack of stability guarantee restricts the practical use of learning-based methods in core control problems in robotics. We develop new methods for learning neural control policies and neural Lyapunov critic functions in the modelfree reinforcement learning (RL) setting. We use sample-based approaches and the Almost Lyapunov function conditions to estimate the region of attraction and invariance properties through the learned Lyapunov critic functions. The methods enhance stability of neural controllers for various nonlinear systems including automobile and quadrotor control.
Modelling and Characterisation of Flexibility From Distributed Energy Resources

Harnessing flexibility from distributed energy resources (DER) to participate in various markets while accounting for relevant technical and commercial constraints is essential for the development of low-carbon grids. However, there is no clear definition or even description of the salient features of aggregated DER flexibility, including its steady-state and dynamic features and how these are impacted by network constraints and market requirements. This paper proposes a comprehensive DER flexibility modelling and characterisation framework that is based on the concept of nodal operating envelope (NOE). In particular, capacity, ramp, duration and cost are identified as key flexibility metrics and associated with different but consistent NOEs describing capability, feasibility, ramp, duration, economic, technical and commercial flexibility features. These NOEs, which conceptually arise from a Venn diagram, can be built via optimal power flow (OPF) analysis, visualised in the active-reactive power space, and used by different stakeholders. Results from a canonical test system and a real distribution system illustrate the value and applicability of the proposed framework to model and characterise provision of flexibility and market services from DER for different use cases.
Review of Video Predictive Understanding: Early Action Recognition and Future Action Prediction

Video predictive understanding encompasses a wide range of eﬀorts that are concerned with the anticipation of the unobserved future from the current as well as historical video observations. Action prediction is a major sub-area of video predictive understanding and is the focus of this review. This sub-area has two major subdivisions: early action recognition and future action prediction. Early action recognition is concerned with recognizing an ongoing action as soon as possible. Future action prediction is concerned with the anticipation of actions that follow those previously observed. In either case, the causal relationship between the past, current and potential future information is the main focus. Various mathematical tools such as Markov Chains, Gaussian Processes, Auto-Regressive modeling and Bayesian recursive ﬁltering are widely adopted jointly with computer vision techniques for these two tasks. However, these approaches face challenges such as the curse of dimensionality, poor generalization and constraints from domain speciﬁc knowledge. Recently, structures that rely on deep convolutional neural networks and recurrent neural networks have been extensively proposed for improving performance of existing vision tasks, in general, and action prediction tasks, in particular. However, they have their own shortcomings, e.g., reliance on massive training data and lack of strong theoretical underpinnings. In this survey, we start by introducing the major sub-areas of the broad area of video predictive understanding, which recently have received intensive attention and proven to have practical value. Next, a thorough review of various early action recognition and future action prediction algorithms are provided with suitably organized divisions. Finally, we conclude our discussion with future research directions.
Interpretable Deep Feature Propagation for Early Action Recognition

Early action recognition (action prediction) from limited preliminary observations plays a critical role for streaming vision systems that demand real-time inference, as video actions often possess elongated temporal spans which cause undesired latency. In this study, we address action prediction by investigating how action patterns evolve over time in a spatial feature space. There are three key components to our system. First, we work with intermediate-layer ConvNet features, which allow for abstraction from raw data, while retaining spatial layout. Second, instead of propagating features per se, we propagate their residuals across time, which allows for a compact representation that reduces redundancy. Third, we employ a Kalman filter to combat error build-up and unify across prediction start times. Extensive experimental results on multiple benchmarks show that our approach leads to competitive performance in action prediction. Notably, we investigate the learned components of our system to shed light on their otherwise opaque natures in two ways. First, we document that our learned feature propagation module works as a spatial shifting mechanism under convolution to propagate current observations into the future. Thus, it captures flow-based image motion information. Second, the learned Kalman filter adaptively updates prior estimation to aid the sequence learning process.
Targeting FHL2-E-cadherin axis by miR-340-5p attenuates colon cancer cell migration and invasion

Convincing data has suggested that four and a half LIM domain 2 protein (FHL2) serves a key function in cancer cell metastasis and that microRNA (miR)-340-5p can regulate cancer cell migration. The current study hypothesized that targeting FHL2 expression by miR-340-5p in colon cancer may attenuate colon cancer cell migration and invasion. FHL2 expression was therefore assessed in colon cancer microarray datasets using Qlucore omics explorer as well as in HT-29 and AZ-97 colon cancer cell lines via reverse transcription-quantitative PCR (RT-qPCR). Colon cancer cell migration and invasion were evaluated in the presence of miR-340-5p mimic, mimic control or mimic with a target site blocker. Confocal microscopy and RT-qPCR were subsequently performed to assess FHL2, E-cadherin (E-cad) protein and mRNA expression in colon cancer cells. Microarray dataset analysis revealed that FHL2 expression was lower in primary colon cancer cells compared with normal colonic mucosa. It was revealed that the expression of miR-340-5p and FHL2 were inversely related in serum-grown and low-serum conditions in HT-29 and AZ-97 cells. Short-time serum exposure to low-serum grown cells induced FHL2 expression. Transfection of HT-29 cells with miR-340-5p mimic not only decreased serum-induced expression of FHL2 but also decreased cancer cell migration and invasion. Bioinformatics analysis revealed that FHL2 mRNA had one putative binding site for miR-340-5p at the 3-untranslated region. Blocking of the target site using a specific blocker reverted miR-340-5p mimic-induced inhibition of FHL2 expression and cancer cell migration and invasion. Confocal microscopy confirmed that the reduction of FHL2 expression by miR-340-5p mimic also reversed serum-induced E-cad disruption and that the target site blocker abrogated the effect of miR-340-5p. The current results suggested that miR-340-5p could be used to antagonize colon cancer cell metastasis by targeting the FHL2-E-cad axis.
Impact of climate change on some grapevine varieties grown in Peru for Pisco production

Aim: The Peruvian region of Ica is an important area of grapevine cultivation, mainly for the production of pisco, the flagship hard drink of Peru. The effects of a changing climate have been assessed using the recorded temperatures of a weather station together with projected climates for the 21st century generated under the A1B SRES scenario.Methods and results: The bioclimatic indices commonly used in grapevine studies have increased in recent years and will continue to rise along the 21st century in relation to increasing temperature. In parallel, the phenology of four pisco cultivars (Quebranta, Torontel, Moscatel and Italia) has been experimentally assessed during four consecutive years, first to determine their cumulative growing degree-days and then to project them in past and future climates.Conclusion: It appears that the cycle lengths of these cultivars have been shortened in recent years and that this tendency will continue all along the 21st century.Significance and impact of the study: Assessing the immediate and future impact of climate change makes it possible to identify potential crop production problems and provides information on adaptation strategies.
Personality change in the Nottingham Study of Neurotic Disorder: 30-Year cohort study

Background: Persistence is said to be a feature of personality disorder, but there are few long-term prospective studies of the condition. A total of 200 patients with anxiety and depressive disorders involved in a randomised controlled trial initiated in 1983 had full personality status assessed at baseline. We repeated assessment of personality status on three subsequent occasions over 30 years. Methods: Personality status was recorded using methods derived from the Personality Assessment Schedule, which has algorithms for allocating Diagnostic and Statistical Manual of Mental Disorders (DSM) and the 11th International Classification of Diseases (ICD-11) categories. The category and severity of personality diagnosis were recorded at baseline in the randomised patients with DSM-III anxiety and depressive diagnoses. The same methods of assessing personality status was repeated at 2, 12 and 30 years after baseline. Results: Using the ICD-11 system, 47% of patients, mainly those with no personality disturbance at baseline, retained their personality status; of the others 16.8% improved and 20.4% worsened to more severe disorder. In DSM-III diagnosed patients, those diagnosed as Cluster A and Cluster C increased in frequency (from 14% to 40%, p < 0.001, and 21.5% to 36%, p < 0.001, respectively) over follow-up, while those with Cluster B showed little change in frequency (22% to 18%, p = 0.197). Conclusion: In this population of patients with common mental disorders, personality status showed many changes over time, inconsistent with the view that personality disorder is a persistent or stable condition. The increase in diagnoses within the Cluster A and C groups suggests personality disorder generally increases in frequency as people age.
The effects of proprioceptive neuromuscular facilitation in treating chronic low back pain: A systematic review and meta-analysis.

BACKGROUND Proprioceptive neuromuscular facilitation (PNF) has been widely used in the clinical treatment of chronic low back pain (LBP) in recent years. However, its role remains controversial and it is unclear whether PNF offers more advantages for patients with chronic LBP. OBJECTIVE The purpose of this systematic review is to evaluate the evidence on the effect of PNF on pain, waist function, pulmonary function and dynamic balance in patients with chronic LBP. METHODS Seven English and Chinese electronic databases were searched to identify articles published from 1970 to February 2020. Relevant randomized controlled trials (RCTs) were selected by two independent reviewers to investigate PNF in treatment of chronic LBP. Data extraction was performed by the same reviewers. RESULTS Twelve eligible trials involving 410 participants were included in this meta-analysis. Compared with the control group, the aggregated results suggested that PNF showed beneficial effects in relieving pain (SMD =-1.17; 95% CI: -1.50 to -0.84; p< 0.00001) and improving waist functional disability (MD =-1.63; 95% CI: -1.89 to -1.37; p< 0.00001). In addition, PNF was shown to have a significant effect on pulmonary function (MD = 0.65; 95% CI: 0.26 to 1.03; p= 0.001). However, the results of the study show that PNF could not significantly improve dynamic balance in patients with chronic LBP compared with the control group (MD =-0.04; 95% CI: -2.16 to 2.08; p= 0.97). A high risk of bias occurred in the areas of blinding (i.e., participants/personnel and outcome assessment). CONCLUSIONS PNF showed more beneficial effects in pain relief and waist function improvement in patients with chronic LBP in the short term (4 to 8 weeks of intervention) or at 12-week follow-up and also played a positive role in pulmonary function. However, no significant effect of PNF on dynamic balance was found compared with the control group. However, these results have certain limitations, and these conclusions were supported by low-quality data. Therefore, articles that are methodologically reasonable and more authoritative are required to verify the effects. In addition, articles with long-term follow-up and other outcomes are needed to confirm additional findings.