Miracle-dz/UniFAR-FTUs huggingface dataset:
Query-free Black-box Adversarial Attacks on Graphs

Many graph-based machine learning models are known to be vulnerable to adversarial attacks, where even limited perturbations on input data can result in dramatic performance deterioration. Most existing works focus on moderate settings in which the attacker is either aware of the model structure and parameters (white-box), or able to send queries to fetch model information. In this paper, we propose a query-free black-box adversarial attack on graphs, in which the attacker has no knowledge of the target model and no query access to the model. With the mere observation of the graph topology, the proposed attack strategy flips a limited number of links to mislead the graph models. We prove that the impact of the flipped links on the target model can be quantified by spectral changes, and thus be approximated using the eigenvalue perturbation theory. Accordingly, we model the proposed attack strategy as an optimization problem, and adopt a greedy algorithm to select the links to be flipped. Due to its simplicity and scalability, the proposed model is not only generic in various graph-based models, but can be easily extended when different knowledge levels are accessible as well. Extensive experiments demonstrate the effectiveness and efficiency of the proposed model on various downstream tasks, as well as several different graph-based learning models.
Uncertainty Estimation in Deep Neural Networks for Point Cloud Segmentation in Factory Planning

The digital factory provides undoubtedly great potential for future production systems in terms of efficiency and effectivity. A key aspect on the way to realize the digital copy of a real factory is the understanding of complex indoor environments on the basis of three-dimensional (3D) data. In order to generate an accurate factory model including the major components, i.e., building parts, product assets, and process details, the 3D data that are collected during digitalization can be processed with advanced methods of deep learning. For instance, the semantic segmentation of a point cloud enables the identification of relevant objects within the environment. In this work, we propose a fully Bayesian and an approximate Bayesian neural network for point cloud segmentation. Both of the networks are used within a workflow in order to generate an environment model on the basis of raw point clouds. The Bayesian and approximate Bayesian networks allow us to analyse how different ways of estimating uncertainty in these networks improve segmentation results on raw point clouds. We achieve superior model performance for both, the Bayesian and the approximate Bayesian model compared to the frequentist one. This performance difference becomes even more striking when incorporating the networks’ uncertainty in their predictions. For evaluation, we use the scientific data set S3DIS as well as a data set, which was collected by the authors at a German automotive production plant. The methods proposed in this work lead to more accurate segmentation results and the incorporation of uncertainty information also makes this approach especially applicable to safety critical applications aside from our factory planning use case.
A Perturbation Resilient Framework for Unsupervised Learning

Designing learning algorithms that are resistant to perturbations of the underlying data distribution is a problem of wide practical and theoretical importance. We present a general approach to this problem focusing on unsupervised learning. The key assumption is that the perturbing distribution is characterized by larger losses relative to a given class of admissible models. This is exploited by a general descent algorithm which minimizes an $L$-statistic criterion over the model class, weighting more small losses. We characterize the robustness of the method in terms of bounds on the reconstruction error for the assumed unperturbed distribution. Numerical experiments with \textsc{kmeans} clustering and principal subspace analysis demonstrate the effectiveness of our method.
Mixed-order transition in the antiferromagnetic quantum Ising chain in a field

The antiferromagnetic quantum Ising chain has a quantum critical point which belongs to the universality class of the transverse Ising model (TIM). When a longitudinal field ($h$) is switched on, the phase transition is preserved, which turns to first-order for $h/\Gamma \to \infty$, $\Gamma$ being the strength of the transverse field. Here we will re-examine the critical properties along the phase transition line. During a quantum block renormalization group calculation, the TIM fixed point for $h/\Gamma>0$ is found to be unstable. Using DMRG techniques, we calculated the entanglement entropy and the spin-spin correlation function, both of which signaled a divergent correlation length at the transition point with the TIM exponents. At the same time, the bulk correlation function has a jump and the end-to-end correlation function has a discontinuous derivative at the transition point. Consequently for finite $h/\Gamma$ the transition is of mixed-order.
Machine Learning to study the impact of gender-based violence in the news media

While it remains a taboo topic, gender-based violence (GBV) undermines the health, dignity, security and autonomy of its victims. Many factors have been studied to generate or maintain this kind of violence, however, the influence of the media is still uncertain. Here, we use Machine Learning tools to extrapolate the effect of the news in GBV. By feeding neural networks with news, the topic information associated with each article can be recovered. Our findings show a relationship between GBV news and public awareness, the effect of mediatic GBV cases, and the intrinsic thematic relationship of GBV news. Because the used neural model can be easily adjusted, this also allows us to extend our approach to other media sources or topics. INTRODUCTION Gender-based violence (GBV) is directly related at an individual based on her or his biological sex or gender identity ​1,2​. It includes verbal, physical, sexual, and psychological abuse, whether occurring in public or private life. GBV is an issue faced by people all over the world, but women are disproportionately harmed ​3,4​. Despite being so prevalent, gender-based violence is largely under-reported because of stigma and lack of access to resources and support systems. Only recently, the voices of victims, survivors and/or activists, have put the spotlight on the issue and raised public awareness by the media through movements such as #MeToo, #TimesUp, #Niunamenos, #NotOneMore, #BalanceTonPorc, and others. News media are key to understanding how society and the general population react to topic ​5​. This is prevalent with GBV, where news media are one of the main sources of information. Moreover, news can influence public perceptions and consequently, social policies ​6,7​. Not only news media convey what happens, but also what could be done about it. They may report available help resources added to the piece of news. In the particular case of Spain, where
Barriers to Women’s Advancement in the Malaysian Private Enterprises

As of late, participation of women in the Malaysian workplaces is given high priority. Compared to other South East Asian countries, Malaysia still has low numbers of female participation at the workplace despite various initiatives by the government. In addition, retention of women is another issue which can be related to the issue of women’s advancement in the workplace. Though research has shown that gender bias practices are reducing, challenges in different shapes and forms still remain. This study was carried out among two groups of women; one group that is still actively employed and another group which has left the workplace, after some years of being actively employed. Both groups of women held middle management positions. The findings show that many barriers at the workplace still exist. Explicit barriers such as the glass ceiling and lack of mentoring are still hindering women from progressing in the workplaces. In addition, gender stereotyping and discrimination still exists. The respondents were confident that women have what is needed to be successful at the workplace; however, they were victimized by both implicit and explicit barriers
Efficient Online Trajectory Planning for Integrator Chain Dynamics Using Polynomial Elimination

Providing smooth reference trajectories can effectively increase performance and accuracy of tracking control applications while overshoot and unwanted vibrations are reduced. Trajectory planning computations can often be simplified significantly by transforming the system dynamics into decoupled integrator chains using methods such as feedback linearization, differential flatness or the Brunovsky normal form. We present an efficient method to plan trajectories for integrator chains subject to derivative bound constraints. Therefore, an algebraic precomputation algorithm formulates the necessary conditions for time optimality in form of a set of polynomial systems, followed by a symbolic polynomial elimination using Gröbner bases. A fast online algorithm then plans the trajectories by calculating the roots of the decomposed polynomial systems. These roots describe the switching time instants of the input signal and the full trajectory simply follows by multiple integration. This method presents a systematic way to compute fast, for low system orders even time optimal trajectories exactly via algebraic calculations without numerical approximation iterations. It is applied to various trajectory types with different continuity order, asymmetric derivative bounds and non-rest initial and final states.
Ground States of Quantum Many Body Lattice Models via Reinforcement Learning

We introduce reinforcement learning (RL) formulations of the problem of finding the ground state of a many-body quantum mechanical model defined on a lattice. We show that stoquastic Hamiltonians -- those without a sign problem -- have a natural decomposition into stochastic dynamics and a potential representing a reward function. The mapping to RL is developed for both continuous and discrete time, based on a generalized Feynman--Kac formula in the former case and a stochastic representation of the Schrodinger equation in the latter. We discuss the application of this mapping to the neural representation of quantum states, spelling out the advantages over approaches based on direct representation of the wavefunction of the system.
Determinants of Hospital Efficiency: insights from the literature

Background Achieving Universal Health Coverage and other health and health-related targets of the sustainable development goals entails curbing waste in health spending due to inefficiency. Inefficiency is a pervasive problem in health systems. The World Health Organization estimates that on average, 20-40% of the global total health expenditure is wasted. The proportion of total health expenditure attributed to hospitals is high, which implies that improving the efficiency of hospitals will lead to more efficient health systems. This study aims to synthetize the major determinants of hospital inefficiency and to develop a framework to identify causes of inefficiency and develop multi-factor interventions to address inefficiencies. Methods The study is based on survey of the literature on hospital efficiency and its determinants. The studies include those that employ ratio methods of efficiency analysis, data envelopment analysis and stochastic frontier models and econometric models such as the tobit regression to assess determinants of technical efficiency. Data was extracted in a table format categorized as those that are within the hospital, outside the hospital but within the health system and those that are outside the hospital and health system in the broader macroeconomic system and analyzed. Results Hospital efficiency is influenced by factors that may be internal to the hospital or external and thus could be wholly or partially out of the control of the hospital. Hospital-level characteristics that influence efficiency include ownership, size, specialization/scope economies, teaching status, membership of multihospital system and other factors such as case-mix and ratio of outpatients to inpatients. However, the effects of these variables are not definitive and consistent; all depends on the context. Factors out of the direct control of the hospital include geographic location, competition and reimbursement systems. The findings further elucidate that no single factor is effective in addressing hospital inefficiencies in isolation from others. Conclusion There is no one single magic formula or intervention that can be adopted by different hospitals and can be effective in improving hospital efficiencies. Multiple factors influence the efficiency of hospitals and to address hospital inefficiency multi-intervention packages focusing on the hospital and its environment should be developed.
Semi-supervised Segmentation via Uncertainty Rectified Pyramid Consistency and Its Application to Gross Target Volume of Nasopharyngeal Carcinoma

Gross Target Volume (GTV) segmentation plays an irreplaceable role in radiotherapy planning for Nasopharyngeal Carcinoma (NPC). Despite that convolutional neural networks (CNN) have achieved good performance for this task, they rely on a large set of labeled images for training, which is expensive and time-consuming to acquire. Recently, semi-supervised methods that learn from a small set of labeled images with a large set of unlabeled images have shown potential for dealing with this problem, but it is still challenging to train a high-performance model with the limited number of labeled data. In this paper, we propose a novel framework with Uncertainty Rectified Pyramid Consistency (URPC) regularization for semi-supervised NPC GTV segmentation. Concretely, we extend a backbone segmentation network to produce pyramid predictions at different scales, the pyramid predictions network (PPNet) was supervised by the ground truth of labeled images and a multi-scale consistency loss for unlabeled images, motivated by the fact that prediction at different scales for the same input should be similar and consistent. However, due to the different resolution of these predictions, encouraging them to be consistent at each pixel directly is not robust and may bring much noise and lead to a performance drop. To deal with this dilemma, we further design a novel uncertainty rectifying module to enable the framework to gradually learn from meaningful and reliable consensual regions at different scales. Extensive experiments on our collected NPC dataset with 258 volumes show that our method can largely improve performance by incorporating the unlabeled data, and this framework achieves a promising result compared with existing semi-supervised methods, which achieves 81.22% of mean DSC and 1.88 voxels of mean ASD on the test set, where the only 20% of the training set were annotated.
Changes in Soil Properties under Two Different Management Systems in the Western Amazon

Upland soils in the Amazon basin are often highly weathered and therefore possess low plant-available nutrient contents. Soil fertility is principally maintained by geochemical, biochemical, and biogeochemical processes. Within these processes, the soil microbial biomass is responsible for many of the cycles and transformations of nutrients in soils. The aim of this work was to evaluate the changes in soil fertility, in the form of nitrogen (N) and microbial activity, as indicators of the dynamic of carbon (C) with two extractants [irradiation extraction (IE) and IRGA methods], N, and phosphorus (P) in an upland soil area containing a dystrophic Yellow Latosol (Xanthic Ferralsol) in the western Amazon (Brazil) with succession of two plant covers (citrus or pasture) and management. The study was carried out in two chronosequences: primary forest followed by citrus plantations and primary forest followed by pasture. The results showed that pasture has greater capacity to accumulate organic C and total N than either primary forest or citrus plantation. Removing forest to introduce pasture or citrus plantations influences the soil fertility and microbial biomass of C, N, and P in the soil. Under the edaphoclimatic conditions, the irradiation extraction and IRGA methods all proved efficient in determining the soil microbial C activity. In addition, regardless of the depth of soil, the predominant N form is ammonium (NH4 +).
Chemical catalysis by colloids and clusters

This review covers catalysis by metal clusters and colloids. Any system that is a continuous phase is considered. Specifically not covered will be supported catalysts or heterogeneous catalysts except as they serve as comparative examples. Polymer-stabilized clusters and colloids are considered if they are soluble. The review will roughly cover the period from 1980 to mid-1992. Essential to this review is the establishment of definitions for the terms cluster and colloid. There are certain intuitive definitions for cluster and colloid. Recent work discussed below shows that the terms cluster and colloid are not clearly distinguishable. This review uses cluster and colloid interchangeably. However, several studies describe methods for distinguishing homogeneous from heterogeneous catalysts where heterogeneous catalysts may include colloids. For purposes of this review, a heterogeneous catalyst is defined as one where the catalyst is a separate phase, usually a filterable solid from a liquid phase. The distinction between mononuclear or low nuclearity clusters and large clusters or colloids does not necessarily distinguish homogeneous from heterogeneous catalysts. Methods for distinguishing whether a reaction is catalyzed by a mononuclear species or one where metal-metal bonds are present are described. The paper discusses the following: structure of large clusters and small colloids;more » onset to metallic properties; mononuclear vs. cluster catalysis; hydrogenation; CO activation; hydroformylation; H-H and C-H activation; hydrosilylation; isomerization; oligomerization; redox reactions; photocatalysis; water activation; oxidation; electrochemistry and electroless metal deposition; and ammonia synthesis and related nitrogen chemistry. 402 refs.« less
Heterogeneous diffusion of water at protein surfaces: application to BPTI

The influence of the protein surface on the dynamics of water is investigated by a computer simulation of the dynamics of divine pancreatic trypsin inhibitor (BPTI) in water for 500 ps. The diffusion constant for water moving away from the protein surface has been determined by comparing computer simulation data with those of an analytical model. For 75% of the surface waters, the diffusion off the protein surface is a factor of 4 slower, but for the other 25%, the diffusion is a factor of 15 faster than in bulk water
Maturity-Onset Diabetes of the Young Identified Among Algerian Probands with Early-Onset Diabetes

Aim To investigate the prevalence of variants within selected maturity-onset diabetes of the young (MODY)-genes among Algerian patients initially diagnosed with type 1 diabetes (T1D) or type 2 diabetes (T2D), yet presenting with a MODY-like phenotype. Methods Eight unrelated patients with early-onset diabetes (before 30 years) and six relatives with diabetes were examined by targeted re-sequencing for variants in genes known to be involved in MODY (HNF1A, GCK, HNF4A, HNF1B, INS, ABCC8, KCNJ1). Clinical data for probands were retrieved from hospital records. Results A total of 12 variants were identified, of which three were classified as pathogenic and one as a variant of uncertain clinical significance (VUS). Two of the pathogenic variants were found in GCK (p.Gly261Arg and p.Met210Lys, respectively) in one proband each and the remaining pathogenic variant was found in HNF1B (p.Gly76Cys) in a proband also carrying the VUS in HNF1A (p.Thr156Met). Conclusion Variants in known MODY-genes can be the cause of early-onset diabetes in Algerians diagnosed with T1D or T2D among patients presenting with a MODY-like phenotype; thus, genetic screening should be considered.
Preoperative Combined Prediction Models Have Superior Capability in Predicting Survival as the Child-Pugh Grade in Patients with HCC after Interventional Embolotherapy

Background It is of important clinical significance for hepatocellular carcinoma (HCC) patients to evaluate prognosis before interventional embolotherapy. Methods A total of 106 patients with HCC after interventional embolotherapy who had complete data with follow-up information until September 2019 were included in this study. These data were analyzed using SPSS Version 22.0 and R (version 3.6.1) statistical software. Results 1) The diameter of the tumor, ascites, FIT, AFP, ALT, AST, GGT, and Child–Pugh score had the ability to predict the prognosis and survival of patients with HCC. Among these molecules, the predictive effectiveness (or the area under the receiver operating characteristic [ROC] curve) of GGT was the highest, although it was slightly lower than the predictive effectiveness of the Child–Pugh score, which is the gold standard for survival analysis. 2) Among survival analyses combining five molecular indicators, the predictive postoperative viability for combination 1 was the strongest with an area under the ROC curve (AUC) of 0.856 (0.779, 0.932), similar to the all-molecular combination (combination 16) with an AUC of 0.872 (0.798, 0.945), but much higher than that of the Child–Pugh score of 0.720 (0.616, 0.823) for HCC patients (all p<0.05). 3) Kaplan–Meier analyses showed that the 3-year cumulative survival rates were 55.3% for low-risk patients and 2.6% for high-risk patients. Conclusion A combined prediction model can determine the optimal combination of preoperative routine detection indices in patients with HCC intervention, and ROC curve analysis can quantify the efficacy of these indices in the survival and prognosis of HCC. Interestingly, combination 1 showed stronger predictive capability than the Child–Pugh score in predicting death risks for postoperative patients with HCC. When combination 1 has several missing clinical data, these combination prediction models (12, 3, 7, 13, 16) are also a replaceable choice. These findings may have important clinical significance in the formulation of individualized medical programs.
Setting prices for second-hand video games

In the transition to the purchase and sale of video games in its digital format, the second-hand market has grown significantly given the importance that the consumer provides to physical video games. Given the uncertainty that exists about the quality of a video game, videogame stores have become intermediaries that facilitate the purchase/sale and guarantee the quality of these. In this document, we analyse the interaction between a store and a video player, where each agent initially owns a video game used to determine the price of the object. The agents interact through a three-stage game with incomplete information where the store sets the purchase and sale prices of the used video games without knowing the valuation that the video player has on them. It is shown that there is a unique subgame perfect Nash equilibrium when the valuations are independent and follow a uniform continuous distribution. The comparative statics shows a positive relationship between the equilibrium prices and how the video player values the video games; Also, it is observed that, in equilibrium, the video player does not sell his good.