Miracle-dz/UniFAR-FTUs huggingface dataset:
The Psychedelic Renaissance in Clinical Research: A Bibliometric Analysis of Three Decades of Human Studies with Psychedelics

ABSTRACT Psychedelics were used in the treatment of psychiatric conditions prior to their prohibition in the late 1960s. In the past three decades, there is a revived research interest in the therapeutic potential of psychedelic drugs with expected FDA approvals for treatment of various conditions. Given the exponential scientific growth of this field, we sought to characterize, analyze, and visualize trends in its top-cited articles. Bibliometric analyses are quantitative approaches to characterize a scientific field, including evaluation of the impact of academic literature. The bibliometric analysis and visualizations were conducted with R-tools for comprehensive science mapping. The top-cited 100 articles were cited between 82 and 668 times (median 125; mean 158). Fifty-four percent of the T100 articles were produced in the past decade (2010–2020). Network and author impact analysis highlighted key figures and primary collaboration networks within the top 100 publications. UK, USA, Switzerland, Spain, and Brazil lead the field. Results are discussed in terms of research growth, access, diversity, and the distribution of knowledge and experience in the field. These aggregated data and insights on the second wave of psychedelic research facilitate research evaluation, data-driven funding policies, and a practical map for researchers and clinicians entering the field.
Counteracting Dark Web Text-Based CAPTCHA with Generative Adversarial Learning for Proactive Cyber Threat Intelligence

Automated monitoring of dark web (DW) platforms on a large scale is the first step toward developing proactive Cyber Threat Intelligence (CTI). While there are efficient methods for collecting data from the surface web, large-scale dark web data collection is often hindered by anti-crawling measures. In particular, text-based CAPTCHA serves as the most prevalent and prohibiting type of these measures in the dark web. Text-based CAPTCHA identifies and blocks automated crawlers by forcing the user to enter a combination of hard-to-recognize alphanumeric characters. In the dark web, CAPTCHA images are meticulously designed with additional background noise and variable character length to prevent automated CAPTCHA breaking. Existing automated CAPTCHA breaking methods have difficulties in overcoming these dark web challenges. As such, solving dark web text-based CAPTCHA has been relying heavily on human involvement, which is labor-intensive and time-consuming. In this study, we propose a novel framework for automated breaking of dark web CAPTCHA to facilitate dark web data collection. This framework encompasses a novel generative method to recognize dark web text-based CAPTCHA with noisy background and variable character length. To eliminate the need for human involvement, the proposed framework utilizes Generative Adversarial Network (GAN) to counteract dark web background noise and leverages an enhanced character segmentation algorithm to handle CAPTCHA images with variable character length. Our proposed framework, DW-GAN, was systematically evaluated on multiple dark web CAPTCHA testbeds. DW-GAN significantly outperformed the state-of-the-art benchmark methods on all datasets, achieving over 94.4% success rate on a carefully collected real-world dark web dataset. We further conducted a case study on an emergent Dark Net Marketplace (DNM) to demonstrate that DW-GAN eliminated human involvement by automatically solving CAPTCHA challenges with no more than three attempts. Our research enables the CTI community to develop advanced, large-scale dark web monitoring. We make DW-GAN code available to the community as an open-source tool in GitHub.
The upper tail problem for induced 4‐cycles in sparse random graphs

Building on the techniques from the breakthrough paper of Harel, Mousset and Samotij, which solved the upper tail problem for cliques, we compute the asymptotics of the upper tail for the number of induced copies of the 4‐cycle in the binomial random graph . We observe a new phenomenon in the theory of large deviations of subgraph counts. This phenomenon is that, in a certain (large) range of , the upper tail of the induced 4‐cycle does not admit a naive mean‐field approximation.
An Interpretable Federated Learning-based Network Intrusion Detection Framework

Learning-based Network Intrusion Detection Systems (NIDSs) are widely deployed for defending various cyberattacks. Existing learning-based NIDS mainly uses Neural Network (NN) as a classifier that relies on the quality and quantity of cyberattack data. Such NN-based approaches are also hard to interpret for improving efficiency and scalability. In this paper, we design a new local-global computation paradigm, FEDFOREST, a novel learning-based NIDS by combining the interpretable Gradient Boosting Decision Tree (GBDT) and Federated Learning (FL) framework. Specifically, FEDFOREST is composed of multiple clients that extract local cyberattack data features for the server to train models and detect intrusions. A privacy-enhanced technology is also proposed in FEDFOREST to further defeat the privacy of the FL systems. Extensive experiments on 4 cyberattack datasets of different tasks demonstrate that FEDFOREST is effective, efficient, interpretable, and extendable. FEDFOREST ranks first in the collaborative learning and cybersecurity competition 2021 for Chinese college students.
Bayesian Changepoint Estimation for Spatially Indexed Functional Time Series (preprint)/ en

We propose a Bayesian hierarchical model to simultaneously estimate mean based changepoints in spatially correlated functional time series. Unlike previous methods that assume a shared changepoint at all spatial locations or ignore spatial correlation, our method treats changepoints as a spatial process. This allows our model to respect spatial heterogeneity and exploit spatial correlations to improve estimation. Our method is derived from the ubiquitous cumulative sum (CUSUM) statistic that dominates changepoint detection in functional time series. However, instead of directly searching for the maximum of the CUSUM based processes, we build spatially correlated two-piece linear models with appropriate variance structure to locate all changepoints at once. The proposed linear model approach increases the robustness of our method to variability in the CUSUM process, which, combined with our spatial correlation model, improves changepoint estimation near the edges. We demonstrate through extensive simulation studies that our method outperforms existing functional changepoint estimators in terms of both estimation accuracy and uncertainty quantification, under either weak and strong spatial correlation, and weak and strong change signals. Finally, we demonstrate our method using a temperature data set and a coronavirus disease 2019 (COVID-19) study.
Low-Rank Constraints for Fast Inference in Structured Models

Structured distributions, i.e. distributions over combinatorial spaces, are commonly used to learn latent probabilistic representations from observed data. However, scaling these models is bottlenecked by the high computational and memory complexity with respect to the size of the latent representations. Common models such as Hidden Markov Models (HMMs) and Probabilistic Context-Free Grammars (PCFGs) require time and space quadratic and cubic in the number of hidden states respectively. This work demonstrates a simple approach to reduce the computational and memory complexity of a large class of structured models. We show that by viewing the central inference step as a matrix-vector product and using a low-rank constraint, we can trade off model expressivity and speed via the rank. Experiments with neural parameterized structured models for language modeling, polyphonic music modeling, unsupervised grammar induction, and video modeling show that our approach matches the accuracy of standard models at large state spaces while providing practical speedups.
Language-driven Semantic Segmentation

We present LSeg, a novel model for language-driven semantic image segmentation. LSeg uses a text encoder to compute embeddings of descriptive input labels (e.g.,"grass"or"building") together with a transformer-based image encoder that computes dense per-pixel embeddings of the input image. The image encoder is trained with a contrastive objective to align pixel embeddings to the text embedding of the corresponding semantic class. The text embeddings provide a flexible label representation in which semantically similar labels map to similar regions in the embedding space (e.g.,"cat"and"furry"). This allows LSeg to generalize to previously unseen categories at test time, without retraining or even requiring a single additional training sample. We demonstrate that our approach achieves highly competitive zero-shot performance compared to existing zero- and few-shot semantic segmentation methods, and even matches the accuracy of traditional segmentation algorithms when a fixed label set is provided. Code and demo are available at https://github.com/isl-org/lang-seg.
Routing in an Uncertain World: Adaptivity, Efficiency, and Equilibrium

We consider the traffic assignment problem in nonatomic routing games where the players’ cost functions may be subject to random fluctuations (e.g., weather disturbances, perturbations in the underlying network, etc.). We tackle this problem from the viewpoint of a control interface that makes routing recommendations based solely on observed costs and without any further knowledge of the system’s governing dynamics – such as the network’s cost functions, the distribution of any random events affecting the network, etc. In this online setting, learning methods based on the popular exponential weights algorithm converge to equilibrium at an O(1/ √ T ) rate: this rate is known to be order-optimal in stochastic networks, but it is otherwise suboptimal in static networks. In the latter case, it is possible to achieve an O(1/T 2) equilibrium convergence rate via the use of finely tuned accelerated algorithms; on the other hand, these accelerated algorithms fail to converge altogether in the presence of persistent randomness, so it is not clear how to achieve the “best of both worlds” in terms of convergence speed. Our paper seeks to fill this gap by proposing an adaptive routing algortihm with the following desirable properties: (i) it seamlessly interpolates between the O(1/T 2) and O(1/ √ T ) rates for static and stochastic environments respectively; (ii) its convergence speed is polylogarithmic in the number of paths in the network; (iii) the method’s per-iteration complexity and memory requirements are both linear in the number of nodes and edges in the network; and (iv) it does not require any prior knowledge of the problem’s parameters.
Edge and vertex flippings in regular and bipartite graphs

This paper studies two related Markov chains on a given graph, edge flipping and its dual, vertex flipping. The edge flipping is defined by Chung and Graham in [CG12]. Its eigenvalues and stationary distributions for some cases are identified in the same paper. We further study its spectral properties to show a lower bound for the rate of convergence in the case of regular graphs. In particular, we show a cutoff of order n 4 log n for the edge flipping on the complete graph. Besides, some recursive formulas and explicit expressions are obtained for stationary distributions of both processes on bipartite graphs.
Configuration spaces of commuting elements

In this article we introduce the space of configurations of commuting elements in a topological group and show that it satisfies rational homological stability for the sequences of unitary, special unitary and symplectic groups. We also prove that it satisfies cohomological rational representation stability with respect to the number of elements in the tuple for finite products of such groups, in particular cohomological rational stability for the space of unordered configurations of commuting elements. Finally we present some computations of cohomology in the unstable range.
Communication-Efficient Federated Learning with Acceleration of Global Momentum

Federated learning often suffers from unstable and slow convergence due to heterogeneous characteristics of participating clients. Such tendency is aggravated when the client participation ratio is low since the information collected from the clients at each round is prone to be more inconsistent. To tackle the challenge, we propose a novel federated learning framework, which improves the stability of the server-side aggregation step, which is achieved by sending the clients an accelerated model estimated with the global gradient to guide the local gradient updates. Our algorithm naturally aggregates and conveys the global update information to participants with no additional communication cost and does not require to store the past models in the clients. We also regularize local update to further reduce the bias and improve the stability of local updates. We perform comprehensive empirical studies on real data under various settings and demonstrate the remarkable performance of the proposed method in terms of accuracy and communication-efficiency compared to the state-of-the-art methods, especially with low client participation rates. Our code is available at https://github.com/ ninigapa0/FedAGM
Efficiency of isothermal active matter engines: Strong driving beats weak driving.

We study microscopic engines that use a single active particle as their "working medium. " Part of the energy required to drive the directed motion of the particle can be recovered as work, even at a constant temperature. A wide class of synthetic active particles can be captured by schematically accounting for the chemical degrees of freedom that power the directed motion without having to resolve the exact microscopic mechanism. We derive analytical results for the quasistatic thermodynamic efficiency, i.e., the fraction of available chemical energy that can be recovered as mechanical work. While this efficiency is vanishingly small for colloidal particles, it increases as the dissipation is increased beyond the linear-response regime and goes through a maximum at large propulsion speeds. Our results demonstrate that driving beyond the linear-response regime has nontrivial consequences for the efficiency of active engines.
Permuted and Unlinked Monotone Regression in $\mathbb{R}^d$: an approach based on mixture modeling and optimal transport

Suppose that we have a regression problem with response variable Y in $\mathbb{R}^d$ and predictor X in $\mathbb{R}^d$, for $d \geq 1$. In permuted or unlinked regression we have access to separate unordered data on X and Y, as opposed to data on (X,Y)-pairs in usual regression. So far in the literature the case $d=1$ has received attention, see e.g., the recent papers by Rigollet and Weed [Information&Inference, 8, 619--717] and Balabdaoui et al. [J. Mach. Learn. Res., 22(172), 1--60]. In this paper, we consider the general multivariate setting with $d \geq 1$. We show that the notion of cyclical monotonicity of the regression function is sufficient for identification and estimation in the permuted/unlinked regression model. We study permutation recovery in the permuted regression setting and develop a computationally efficient and easy-to-use algorithm for denoising based on the Kiefer-Wolfowitz [Ann. Math. Statist. , 27, 887--906] nonparametric maximum likelihood estimator and techniques from the theory of optimal transport. We provide explicit upper bounds on the associated mean squared denoising error for Gaussian noise. As in previous work on the case $d = 1$, the permuted/unlinked setting involves slow (logarithmic) rates of convergence rooting in the underlying deconvolution problem. Numerical studies corroborate our theoretical analysis and show that the proposed approach performs at least on par with the methods in the aforementioned prior work in the case $d = 1$ while achieving substantial reductions in terms of computational complexity.
A Novel Endo-Polygalacturonase from Penicillium oxalicum: Gene Cloning, Heterologous Expression and Its Use in Acidic Fruit Juice Extraction

An endo-polygalacturonase (endo-PGase) exhibiting excellent performance during acidic fruit juice production would be highly attractive to the fruit juice industry. However, candidate endo-PGases for this purpose have rarely been reported. In this study, we expressed a gene from Penicillium oxalicum in Pichia pastoris. The recombinant enzyme PoxaEnPG28C had an optimal enzyme activity at pH 4.5 and 45°C and was stable at pH 3.0–6.5 and < 45°C. The enzyme had a specific activity of 4,377.65 ± 55.37 U/mg towards polygalacturonic acid, and the Km and Vmax values of PoxaEnPG28C were calculated as 1.64 g/l and 6127.45 U/mg, respectively. PoxaEnPG28C increased the light transmittance of orange, lemon, strawberry and hawthorn juice by 13.9 ± 0.3%, 29.4 ± 3.8%, 95.7 ± 10.2% and 79.8 ± 1.7%, respectively; it reduced the viscosity of the same juices by 25.7 ± 1.6%, 52.0 ± 4.5%, 48.2 ± 0.7% and 80.5 ± 2.3%, respectively, and it increased the yield of the juices by 24.5 ± 0.7%, 12.7 ± 2.2%, 48.5 ± 4.2% and 104.5 ± 6.4%, respectively. Thus, PoxaEnPG28C could be considered an excellent candidate enzyme for acidic fruit juice production. Remarkably, fruit juice production using hawthorn as an material was reported for the first time.
Recommendations for COPD management in Central and Eastern Europe

ABSTRACT Introduction The Global Initiative for Chronic Obstructive Lung Disease (GOLD) strategy report provides guidance on effective management of chronic obstructive pulmonary disease (COPD) according to local healthcare systems. However, COPD is a heterogenous disease and certain aspects, including prevalence, disease–time course and phenotype distribution, can differ between countries. Moreover, features of clinical practice and healthcare systems for patients with COPD can vary widely, even in geographically close and economically similar countries. Areas covered Based on an initial workshop of respiratory physicians from eleven countries across Central and Eastern Europe (CEE) in December 2018 and subsequent discussions, this article offers region-specific insights from clinical practice and healthcare systems in CEE. Taking recommendations from the GOLD 2022 report into account, we suggest approaches to adapt these into national clinical guidelines for COPD management in CEE. Expert opinion Several factors should be considered when optimizing management of COPD in CEE compared with other regions, including differences in smoking status, vaccination uptake, prevalence of tuberculosis and nontuberculous mycobacteria, and variations in healthcare systems. We provide guidance and algorithms for pharmacologic and non-pharmacologic management of COPD for the following scenarios: initial and follow-up treatment, treatment of patients with frequent exacerbations, and withdrawal of inhaled corticosteroids where appropriate. PLAIN LANGUAGE SUMMARY Chronic obstructive pulmonary disease (COPD) is a common disease of the lungs. It causes symptoms such as breathlessness, cough, and production of phlegm. In people with COPD, these symptoms often reduce the quality of their lives. From time to time, symptoms may get worse in people with the disease. This worsening is known as ‘exacerbation’. Exacerbations of COPD can be so bad that they lead to hospital admissions. The Global Initiative for Chronic Obstructive Lung Disease (GOLD) regularly gives advice to doctors around the world. This can help them to provide their patients with the best possible treatment for COPD. However, people with the disease and healthcare systems vary from country to country. This means that the guidance may need to be adjusted to the needs and available resources of different regions. This review looks at how COPD is treated in Central and Eastern Europe. We suggest how to adapt the GOLD recommendations to best suit the Central and Eastern European region. Graphical Abstract
Effects of Reproductive Factors on Lauren Intestinal-Type Gastric Cancers in Females: A Multicenter Retrospective Study in South Korea

Background/Aims Gastric cancers (GCs), particularly the Lauren intestinal type, show a male predominance. The aim of this study was to investigate the effects of reproductive factors on GCs in females, according to Lauren classification. Methods Medical records of 1,849 males and 424 females who underwent radical gastrectomy or endoscopic resection for GCs between 2010 and 2018 were reviewed. The incidences of intestinal-type GCs were compared between males and groups of females stratified according to postmenopausal period. Associations between reproductive factors in females and intestinal-type GCs were analyzed using multivariate models. Results The proportions of intestinal-type GCs were significantly lower in premenopausal (19%), less than 10 years postmenopausal (30.4%), and 10 to 19 years postmenopausal females (44.1%) than in males (61.0%) (p<0.05 for all). Females ≥20 years postmenopause had a proportion of intestinal-type GCs similar to that in males (60.6% vs 61.0%; p=0.948). Multivariate analysis revealed that age (odds ratio [OR], 1.075; 95% confidence interval [CI], 1.039 to 1.113; p<0.001) and parity ≥3 (OR, 1.775; 95% CI, 1.012 to 3.114; p=0.045) were positively associated with an increased risk of intestinal-type GCs in postmenopausal females, while long fertility duration (OR, 1.147; 95% CI, 1.043 to 1.261; p=0.005) was positively associated with an increased risk of intestinal-type GCs in premenopausal females. Conclusions There were no significant differences in the proportions of intestinal-type GCs between males and females ≥20 years postmenopause, suggesting that female reproductive factors play a role in the prevention of intestinal-type GC.
QuadTree Attention for Vision Transformers

Transformers have been successful in many vision tasks, thanks to their capability of capturing long-range dependency. However, their quadratic computational complexity poses a major obstacle for applying them to vision tasks requiring dense predictions, such as object detection, feature matching, stereo, etc. We introduce QuadTree Attention, which reduces the computational complexity from quadratic to linear. Our quadtree transformer builds token pyramids and computes attention in a coarse-to-fine manner. At each level, the top K patches with the highest attention scores are selected, such that at the next level, attention is only evaluated within the relevant regions corresponding to these top K patches. We demonstrate that quadtree attention achieves state-of-the-art performance in various vision tasks, e.g. with 4.0% improvement in feature matching on ScanNet, about 50% flops reduction in stereo matching, 0.4-1.5% improvement in top-1 accuracy on ImageNet classification, 1.2-1.8% improvement on COCO object detection, and 0.7-2.4% improvement on semantic segmentation over previous state-of-the-art transformers. The codes are available at https://github.com/Tangshitao/QuadtreeAttention.
Atomic disorder and Berry phase driven anomalous Hall effect in a 
Co2FeAl
 Heusler compound

Gaurav K. Shukla, Ajit K. Jena, Nisha Shahi, K. K. Dubey, Indu Rajput, Sonali Baral, Kavita Yadav, K. Mukherjee, Archana Lakhani, Karel Carva, Seung-Cheol Lee, Satadeep Bhattacharjee, and Sanjay Singh School of Materials Science and Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi 221005, India Indo-Korea Science and Technology Center (IKST), Bangalore 560065, India UGC-DAE Consortium for Scientific Research, Indore, India School of Basic Sciences,Indian Institute of Technology, Mandi, India Department of Condensed Matter Physics, Charles University, Ke Karlovu 5, CZ-12116 Praha, Czech Republic