Miracle-dz/UniFAR-FTUs huggingface dataset:
Inferring comparative advantage via entropy maximization

We revise the procedure proposed by Balassa to infer comparative advantage, which is a standard tool in Economics to analyze specialization (of countries, regions, etc). Balassa’s approach compares a country’s export of a given product with what would be expected from a benchmark based on the total volumes of countries and product flows. Based on results in the literature, we show that implementing Balassa’s idea leads to conditions for estimating parameters conflicting with the information content of the model itself. Moreover, Balassa’s approach does not implement any statistical validation. Hence, we propose an alternative procedure to overcome such a limitation, based upon the framework of entropy maximization and implementing a proper test of hypothesis: the ‘key products’ of a country are, now, the ones whose production is significantly larger than expected, under a null-model constraining the same amount of information defining Balassa’s approach. What we found is that country diversification is always observed, regardless of the strictness of the validation procedure. Besides, the ranking of countries’ fitnesses is only partially affected by the details of the validation scheme employed for the analysis while large differences are found to affect the rankings of product complexities. The routine for implementing the entropy-based filtering procedures employed here is freely available through the official Python Package Index PyPI.
ESTIMATE FAILURE PROBABILITY WITH NEURAL OPERATOR

The evaluation of failure probability for complex engineering system is a computationally intensive task. While the Monte Carlo method is easy to implement, it converges slowly and therefore requires numerous repeated simulations of the failure model to generate suﬃcient samples. To improve the eﬃciency, methods based on surrogate models are proposed to approximate the limit state function. In this work, we reframe the approximation of the limit state function as an operator learning problem and utilize the DeepONet framework with a hybrid approach to estimate the failure probability. Numerical results show that our proposed method outperforms the prior neural hybrid method.
Hyper-Laplacian Regularized Concept Factorization in Low-rank Tensor Space for Multi-view Clustering

Tensor-oriented multi-view subspace clustering has achieved significant strides in assessing high-order correlations and improving clustering analysis of multi-view data. Nevertheless, most of existing investigations are typically hampered by the two flaws. First, self-representation based tensor subspace learning usually induces high time and space complexity, and is limited in perceiving nonlinear local structure in the embedding space. Second, the tensor singular value decomposition (t-SVD) model redistributes each singular value equally without considering the diverse importance among them. To well cope with the issues, we propose a hyper-Laplacian regularized concept factorization (HLRCF) in low-rank tensor space for multi-view clustering. Specifically, we adopt the concept factorization to explore the latent cluster-wise representation of each view. Further, the hypergraph Laplacian regularization endows the model with the capability of extracting the nonlinear local structures in the latent space. Considering that different tensor singular values associate structural information with unequal importance, we develop a self-weighted tensor Schatten p-norm to constrain the tensor comprised of all cluster-wise representations. Notably, the tensor with smaller size greatly decreases the time and space complexity in the low-rank optimization. Finally, experimental results on eight benchmark datasets exhibit that HLRCF outperforms other multi-view methods, showingcasing its superior performance.
L OCALFORMER : A L OCALITY -P RESERVING V ISION T RANSFORMER

Zigzag flattening (ZF) is commonly used in computer vision as a default option to unfold matrices, e.g., in patch slicing for Vision Transformer (ViT). However, when decomposing multi-scale-object web images, ZF cannot preserve the smoothness of local information well. To address this, we draw inspiration from Space-Filling Curves (SFC) and investigate Hilbert flattening (HF) as an alternative for visual models. We provide a comprehensive theoretical discussion and practical analysis, demonstrating the superiority of HF over other SFC in locality and multi-scale robustness. We leverage HF to alleviate the problem of the lack of locality bias in the shallow layers of ViT, which formulates our Localformer. Extensive experiments demonstrate that Localformer consistently improves performance for several common visual tasks. Additionally, upon inspection, we find that Localformer enhances representation learning and length extrapolation abilities of ViT.
TSGCNeXt: Dynamic-Static Multi-Graph Convolution for Efficient Skeleton-Based Action Recognition with Long-term Learning Potential

Skeleton-based action recognition has achieved remarkable results in human action recognition with the development of graph convolutional networks (GCNs). However, the recent works tend to construct complex learning mechanisms with redundant training and exist a bottleneck for long time-series. To solve these problems, we propose the Temporal-Spatio Graph ConvNeXt (TSGCNeXt) to explore efficient learning mechanism of long temporal skeleton sequences. Firstly, a new graph learning mechanism with simple structure, Dynamic-Static Separate Multi-graph Convolution (DS-SMG) is proposed to aggregate features of multiple independent topological graphs and avoid the node information being ignored during dynamic convolution. Next, we construct a graph convolution training acceleration mechanism to optimize the back-propagation computing of dynamic graph learning with 55.08\% speed-up. Finally, the TSGCNeXt restructure the overall structure of GCN with three Spatio-temporal learning modules,efficiently modeling long temporal features. In comparison with existing previous methods on large-scale datasets NTU RGB+D 60 and 120, TSGCNeXt outperforms on single-stream networks. In addition, with the ema model introduced into the multi-stream fusion, TSGCNeXt achieves SOTA levels. On the cross-subject and cross-set of the NTU 120, accuracies reach 90.22% and 91.74%.
Forecasting small-scale dynamics of fluid turbulence using deep neural networks

Significance In recent decades, direct numerical simulations (DNS) of the Navier–Stokes equations have become a prominent tool for studying turbulent flows ubiquitously encountered in nature and technology. However, both practical and theoretical requirements for increasing the Reynolds number present a challenge for the DNS: As the Reynolds number increases, cutting-edge simulations need to be larger and longer, rendering them prohibitively expensive and unfeasible. Alternatively, can one leverage deep neural networks to advantageously learn from existing simulations at lower Reynolds numbers to cheaply and accurately predict small-scale properties at higher unseen Reynolds numbers? This study represents a significant step forward in addressing this fundamental and crucial question at hand.
Right orthogonal class of pure projective modules over pure hereditary rings

We denote by $\mathcal{W}$ the class of all pure projective modules. Present article we investigate $\mathcal{W}$-injective modules and these modules are defined via the vanishing of cohomology of pure projective modules. First we prove that every module has a $\mathcal{W}$-injective preenvelope and then every module has a $\mathcal{W}$-injective coresolution over an arbitrary ring. Further, we show that the class of all $\mathcal{W}$-injective modules is coresolving (injectively resolving) over a pure-hereditary ring. Moreover, we analyze the dimension of $\mathcal{W}$-injective coresolution over a pure-hereditary ring. It is shown that $\sup\{ \cores_{\mathcal{W}^{\bot}}(M) \colon M \mbox{is an }R\mbox{-module }\} = \Fcor_{\mathcal{W}^{\bot}}(R) = \sup\{\pd(G) \colon G \mbox{ is a pure projective } R\mbox{-module}\}$ and we give some equivalent conditions of $\mathcal{W}$-injective envelope with the unique mapping property. In the last section, we proved the desirable properties of the dimension when the ring is semisimple artinian.
Posterior Sampling from the Spiked Models via Diffusion Processes

Sampling from the posterior is a key technical problem in Bayesian statistics. Rigorous guarantees are difficult to obtain for Markov Chain Monte Carlo algorithms of common use. In this paper, we study an alternative class of algorithms based on diffusion processes. The diffusion is constructed in such a way that, at its final time, it approximates the target posterior distribution. The stochastic differential equation that defines this process is discretized (using a Euler scheme) to provide an efficient sampling algorithm. Our construction of the diffusion is based on the notion of observation process and the related idea of stochastic localization. Namely, the diffusion process describes a sample that is conditioned on increasing information. An overlapping family of processes was derived in the machine learning literature via time-reversal. We apply this method to posterior sampling in the high-dimensional symmetric spiked model. We observe a rank-one matrix ${\boldsymbol \theta}{\boldsymbol \theta}^{\sf T}$ corrupted by Gaussian noise, and want to sample ${\boldsymbol \theta}$ from the posterior. Our sampling algorithm makes use of an oracle that computes the posterior expectation of ${\boldsymbol \theta}$ given the data and the additional observation process. We provide an efficient implementation of this oracle using approximate message passing. We thus develop the first sampling algorithm for this problem with approximation guarantees.
Deep Convolutional Tables: Deep Learning without Convolutions

We propose a novel formulation of deep networks that do not use dot-product neurons and rely on a hierarchy of voting tables instead, denoted as convolutional tables (CTs), to enable accelerated CPU-based inference. Convolutional layers are the most time-consuming bottleneck in contemporary deep learning techniques, severely limiting their use in the Internet of Things and CPU-based devices. The proposed CT performs a fern operation at each image location: it encodes the location environment into a binary index and uses the index to retrieve the desired local output from a table. The results of multiple tables are combined to derive the final output. The computational complexity of a CT transformation is independent of the patch (filter) size and grows gracefully with the number of channels, outperforming comparable convolutional layers. It is shown to have a better capacity:compute ratio than dot-product neurons, and that deep CT networks exhibit a universal approximation property similar to neural networks. As the transformation involves computing discrete indices, we derive a soft relaxation and gradient-based approach for training the CT hierarchy. Deep CT networks have been experimentally shown to have accuracy comparable to that of CNNs of similar architectures. In the low-compute regime, they enable an error:speed tradeoff superior to alternative efficient CNN architectures.
CHEAT: A Large-scale Dataset for Detecting ChatGPT-writtEn AbsTracts

The powerful ability of ChatGPT has caused widespread concern in the academic community. Malicious users could synthesize dummy academic content through ChatGPT, which is extremely harmful to academic rigor and originality. The need to develop ChatGPT-written content detection algorithms call for large-scale datasets. In this paper, we initially investigate the possible negative impact of ChatGPT on academia,and present a large-scale CHatGPT-writtEn AbsTract dataset (CHEAT) to support the development of detection algorithms. In particular, the ChatGPT-written abstract dataset contains 35,304 synthetic abstracts, with Generation, Polish, and Mix as prominent representatives. Based on these data, we perform a thorough analysis of the existing text synthesis detection algorithms. We show that ChatGPT-written abstracts are detectable, while the detection difficulty increases with human involvement.Our dataset is available in https://github.com/botianzhe/CHEAT.
Knowledge Distillation from 3D to Bird’s-Eye-View for LiDAR Semantic Segmentation

LiDAR point cloud segmentation is one of the most fundamental tasks for autonomous driving scene understanding. However, it is difficult for existing models to achieve both high inference speed and accuracy simultaneously. For example, voxel-based methods perform well in accuracy, while Bird’s-Eye-View (BEV)-based methods can achieve real-time inference. To overcome this issue, we develop an effective 3D-to-BEV knowledge distillation method that transfers rich knowledge from 3D voxel-based models to BEV-based models. Our framework mainly consists of two modules: the voxel-to-pillar distillation module and the label-weight distillation module. Voxel-to-pillar distillation distills sparse 3D features to BEV features for middle layers to make the BEV-based model aware of more structural and geometric information. Label-weight distillation helps the model pay more attention to regions with more height information. Finally, we conduct experiments on the SemanticKITTI dataset and Paris-Lille-3D. The results on SemanticKITTI show more than 5% improvement on the test set, especially for classes such as motorcycle and person, with more than 15% improvement. The code can be accessed at https://github.com/fengjiang5/Knowledge-Distillation-from-Cylinder3D-to-PolarNet.
Use of a Translating Cowl on A SSBJ for Improved Takeoff Performance

With balanced field length being one of the critical market reference points for a business jet, engine performance at takeoff plays an important role in aircraft design. The optimized supersonic shape is very often not suitable for low speed performance; adaptations and extra mechanisms become necessary to meet market and performance requirements but usually come with a price: extra weight. The simplicity of the system is often reflected in weight and its efficiency in thrust and runway length. Considering several factors including mass flow, engine recovery, and engine distortion, several configurations for a translating cowl were analyzed for a Mach 1.5 optimized supersonic business jet in order to achieve maximum thrust at takeoff conditions. Five different freestream velocities were considered, and the analysis was performed by running full Navier Stokes CFD on the proposed configurations, using NASA’s FUN3d unstructured Navier Stokes code. At the end, it was shown that performance from a translating cowl is a viable solution as engine pressure recovery and mass flow requirements were met, which could not be achieved with the supersonic inlet geometry.
3D MRI PD‐SPACE‐COR Predicting Safety Margin for Coracoid Transfer

The maximum bone length available for coracoid process transfer varies among individuals, while no preoperative guideline has been developed for predicting the safety margin (SM) in Latarjet shoulder reconstruction. The aim of the study was to evaluate the 3D MRI proton density (PD)‐weighted sampling perfection with application‐optimized contrasts using different flip‐angle evolution (SPACE) sequence in preoperatively predicting SM for coracoid transfer.
Gabor-Zernike Features based Face Recognition Scheme

A Facial recognition (FR) system in still images is an important application in computer vision and image processing. Extraction of invariant features is the core of FR systems. In this paper, we propose a novel and efficient facial image representation based on Gabor energy filters (GFs) and Complex Zernike moments (ZMs), where GFs is used for texture feature extraction and ZMs extracts shape features, Almost all existing methods use only magnitude component of the ZMs (respectively GFs) as features in recognition task. Recently it is well known that the phase component of moments (respectively Gabor Filters) also captures useful information for image representation, in other hand, a simple Genetic Algorithm (GA) is applied to select the moment features that better discriminate human faces and facial expressions, under several pose and illumination conditions. Next, the extracted feature vectors are projected onto a low-dimensional subspace using Random Projection (RP) technique. The (GF+ZM+RP) feature vectors are then applied to a powerful face support vector machine (SVM) classifier, employing the Gaussian radial basis function as kernel function (RBF kernel). Comprehensive performance evaluation of our proposed algorithm is based on Libor Spacek’s Facial Images Databases and Color FERET Database. It can be concluded from the experimental results that the performance of the proposed FR system outperforms other related approaches in terms of recognition rate.
MiR-2b-2-5p regulates lipid metabolism and reproduction by targeting CREB in Bactrocera dorsalis

ABSTRACT In female animals, metabolic homoeostasis and reproductive fitness are critical to population expansion. The trade-off between lipid storage and reproduction inevitably occurs. However, most studies have focused on the complex network of relationships between reproductive and metabolic physiology at the transcriptional level. In this study, we identified a microRNA, miR-2b-2-5p, in a highly invasive quarantine pest, Bactrocera dorsalis. Knockdown of miR-2b-2-5p by antagomiR microinjection impaired ovarian development, reduced fecundity, and decreased triglyceride (TAG) storage in the fat body, whereas overexpression of miR-2b-2-5p by injection of its mimic caused reproductive defects similar to knockdown but increased TAG. Bioinformatics analysis and dual luciferase assay indicated that cyclic AMP response element (CRE)-binding protein (CREB) was the target gene of miR-2b-2-5p. RNAi-mediated knockdown of CREB led to excessive lipid storage and reproductive defects. Further starvation treatment revealed that miR-2b-2-5p functions by fine-tuning CREB expression in response to dietary stimuli. These results suggest that miR-2b-2-5p acts as a monitor to regulate CREB mRNA levels in the fat body, maintaining lipid homoeostasis and keeping the reproductive system on track. Thus, our study not only provides new insights into the interaction between metabolism and reproduction at the posttranscriptional level in B. dorsalis, but also providing a potential eco-friendly control strategy (RNAi-based biopesticides targeting essential miRNAs) for this notorious agricultural pest.
Effects of intravenous thrombolysis with and without salvianolic acids for injection on the functional recovery of patients with acute ischemic stroke: A systematic review, meta‐analysis, and trial sequential analysis

In patients with acute ischemic stroke (AIS), the effect of salvianolic acids for injection (SAFI) as the secondary treatment after intravenous thrombolysis (IVT) is unclear. We aimed to evaluate the efficacy of SAFI for patients with AIS undergoing IVT. We searched seven electronic databases and two registries from inception to July 24, 2022, for randomized controlled trials (RCTs) assessing the effect of SAFI plus recombinant tissue plasminogen activator (rt‐PA) on the functional recovery compared to rt‐PA alone in patients with AIS. Two independent authors selected RCTs, extracted data, and assessed the risk of bias. A meta‐analysis was conducted. Eight RCTs involving 682 patients with AIS were included. Compared to patients receiving intravenous rt‐PA alone, those receiving intravenous rt‐PA combined with SAFI had a higher likelihood of achieving favorable functional outcomes at 3 months. In addition, the use of SAFI for 2 weeks was associated with better neurological recovery. The evidence of benefit was confirmed by trial sequential analysis (TSA). The incidence of intracranial hemorrhage did not differ between the two groups. In patients with AIS, intravenous rt‐PA combined with SAFI might achieve better functional outcomes. However, further high‐quality studies are needed to firmly establish the clinical efficacy of SAFI.
Treatment of achilles insertional tendinopathy: our surgical procedure and medium-term results

Background and aim: Achilles insertional tendinopathy (AIT) is a common injury and its pathogenesis is still not entirely clear. It manifests with worsening pain and functional limitations. When conservative treatment fails, surgical treatment is indicated. The purpose of our research is to evaluate the outcome after one year in patients affected by AIT (calcific and non-calcific AIT) who were treated at our centre. Methods: Between 2014 and 2021, 42 patients suffering from AIT – a total of 47 feet – underwent surgery at our centre. The patients filled in the VISA-A and AOFAS questionnaires at the pre-operative consultation and at the clinical check-up after one year of follow up. Results: Of the 47 feet treated, 28 were calcific AITs treated by medial access and tendon reinsertion using a knotless double suture anchor system (Achilles Suture Bridge™) and 19 were non-calcific tendinopathies treated using a lateral paratendinous approach. The one-year clinical results show an increase in VISA-A scores of 48.6 and AOFAS scores of 44.1 and the absence of complications. Only one patient reported a recurrence of certain symptoms and none of the patients were hospitalized for recurrence. Conclusions: The literature is unable to establish a gold standard of treatment for AIT. The method we have used has shown excellent short- and medium-term results without any complications. Further studies are needed to prove its effectiveness in the long term. (www.actabiomedica.it)
Impact of the COVID‐19 pandemic on exercise tolerance and patient outcomes with Phase III cardiac rehabilitation in Japan: A multicenter study

This study aimed to determine how behavioural restrictions due to the emergency declaration following the coronavirus disease 2019 (COVID‐19) pandemic affect exercise tolerance and its outcomes in patients in Phase III cardiac rehabilitation programme. This is a multicenter retrospective cohort study. Participants in outpatient cardiac rehabilitation programmes and cardiopulmonary exercise testing before and after the emergency declarations were included. A total of 90 participants were included (median age 75.0 years, 69% male), and the changes in physical function and exercise tolerance were compared before and after the emergency declaration. Patients were divided into a decline‐in‐peak oxygen uptake (VO2) group and a nondecline‐in‐peak VO2 group. Comparison before and after the emergency declaration showed that the anaerobic threshold declined significantly and peak VO2 exhibited a downward trend. The decline‐in‐peak VO2 group consisted of 16 patients (17%) with better exercise tolerance, multiple comorbidities, and declined lower extremity muscle strength. These patients also had a higher rate of subsequent composite events (hazard ratio, 5.2; 95% confidence interval, 1.4–18.8, p = 0.01). Before and after the emergency declaration, the patient's exercise tolerance may decline, leading to a poor prognosis. This study suggests the importance of maintaining exercise tolerance during the COVID‐19 pandemic.
THE INTERACTION OF AN INTERPLANETARY SHOCK WITH THE EARTH'S BOW SHOCK AND MAGNETOPAUSE

We compare numerical results of a global magnetospheric MHD code with results of a 3-D MHD magnetosheath model for the interaction of an interplanetary shock with the bow shock and magnetopause. We estimate the accuracy of the modeling using the Rankine-Hugoniot conditions. We demonstrate that the numerical solutions are rather accurate, and the errors are caused mainly by a low grid resolution.