Miracle-dz/UniFAR-FTUs huggingface dataset:
The Relationship between Perceived Cognitive Impairment and the Experience of Pain in Individuals with Multiple Sclerosis

The purpose of this study was to assess whether cognitive impairment was related to the experience of pain in individuals with multiple sclerosis. Participants included 239 women and 33 men recruited through the National Multiple Sclerosis Society and the Multiple Sclerosis World support group website. It was predicted that, as cognitive impairment and cognitive general concerns increased for individuals diagnosed with multiple sclerosis, neuropathic pain, pain intensity, and pain interference would increase as well. Cognitive concerns was the only significant predictor of neuropathic pain (β = .33, t = 2.97, p = .003). Cognitive impairment was the only significant predictor of pain intensity (β = .26, t = 2.41, p = .017). Finally, both cognitive general concerns (β = .25 t = 2.39, p = .018) and cognitive impairment (β = .26, t = 2.46, p = .015) were significant predictors of pain interference.
Knochen und Diabetes mellitus Bone and Diabetes mellitus

Diabetes mellitus is associated with an increased risk for osteoporosis and fractures. Patients with diabetes mellitus type 1 are more severely affected (relative risk for hip fracture reaching from 2.5 to 12). The relative risk for vertebral fractures is doubled. In contrast, patients with diabetes mellitus 2 exhibit only a moderately increased fracture risk, which is associated with duration of diabetic disease, vascular disease, neuropathy, and insulin treatment. The causes for the increased fracture risk are toxic effects of high glucose concentrations on osteoblasts, the glycation of bone matrix proteins leading to changes in collagen structure, endocrine alterations, and changes in bone architecture. The fracture risk may be increased, even without significant loss of measured bone mineral density (BMD). Therefore, the therapeutic intervention threshold of BMD measurements has to be increased. Bone tissue is an endocrine organ and modulates sugar metabolism. Undercarboxylated osteocalcin released from bone stimulates insulin secretion in the pancreatic gland, improves insulin sensitivity and modulates the risk of diabetes manifestation.
Internet usage and rural household income in China

This paper examines the impact of internet usage on rural household income in China and investigates its major mechanisms. In order to address the potential endogeneity of internet usage, we exploit a generated instrumental variable strategy that relies on heteroscedasticity of the error terms. Our findings indicate that internet access significantly boosts the aggregate income of rural households. Unlike the positive impact of internet usage on household nonfarm income, wage income, and property income, internet usage imposes negative effect on agricultural income. Mechanism investigation reveals that internet usage boosts rural nonfarm income via the channels of encouraging entrepreneurship and extending income sources. Internet usage increases wage income by enhancing social interaction and encouraging migration. Internet usage decreases agricultural income through the channels of labor input and land input reduction.
BEYOND WATER WARS: LESSONS ON FORMING COLLABORATIVE GOVERNANCE FROM COCHABAMBA

The now famous Water War in Cochabamba, Bolivia, provided some of the first shots against privatisation of water services heard around world, as an awakened and politicized citizenry rose up to protest private participation in their water company SEMAPA and to reclaim their publicly provided water services. Cochabamba’s resistance to neo-liberal water policies and its calls for public water service quickly resonated in the emerging transnational social movement on water. In the early months of 2000, tens of thousands of people participated in the massive popular protests. These protests forced the Bolivian government to cancel the highly contested service contract with the private water provider, Aguas Del Tunari,1 and to return the public water company SEMAPA to the state. While the state company SEMAPA was known to be inefficient, corrupt and controlled by party politics, the newly politicised and engaged public aspired to recreate their water company by reorganising it to include a new participatory democratic form of governance called “social control”. Eight years later, there are important lessons to be learned from the struggle to achieve participatory democratic water governance in Cochabamba. Because they involve reconfiguring power relationships, participatory processes can face intense resistance and possible co-optation from those who benefited from past forms of company organisation. Corruption and political elite control represent two of the greatest factors in poor performing water companies. They are also the key obstacles to creating new forms of transparency and expanding citizen involvement in decision-making. Moreover, the technical bias that traditionally exists among the professional staff of water companies often challenges greater community participation in the technical aspects of the service provision. Lastly, contentious social groups may struggle to create effective participatory institutions, develop new capacities, and form strategic relationships with company officials in order to effectively participate in collective governance.
Visual Perception Model for Rapid and Adaptive Low-light Image Enhancement

Low-light image enhancement is a promising solution to tackle the problem of insufficient sensitivity of human vision system (HVS) to perceive information in low light environments. Previous Retinex-based works always accomplish enhancement task by estimating light intensity. Unfortunately, single light intensity modelling is hard to accurately simulate visual perception information, leading to the problems of imbalanced visual photosensitivity and weak adaptivity. To solve these problems, we explore the precise relationship between light source and visual perception and then propose the visual perception (VP) model to acquire a precise mathematical description of visual perception. The core of VP model is to decompose the light source into light intensity and light spatial distribution to describe the perception process of HVS, offering refinement estimation of illumination and reflectance. To reduce complexity of the estimation process, we introduce the rapid and adaptive $\mathbf{\beta}$ and $\mathbf{\gamma}$ functions to build an illumination and reflectance estimation scheme. Finally, we present a optimal determination strategy, consisting of a \emph{cycle operation} and a \emph{comparator}. Specifically, the \emph{comparator} is responsible for determining the optimal enhancement results from multiple enhanced results through implementing the \emph{cycle operation}. By coordinating the proposed VP model, illumination and reflectance estimation scheme, and the optimal determination strategy, we propose a rapid and adaptive framework for low-light image enhancement. Extensive experiment results demenstrate that the proposed method achieves better performance in terms of visual comparison, quantitative assessment, and computational efficiency, compared with the currently state-of-the-arts.
A Deep Q-learning/genetic Algorithms Based Novel Methodology For Optimizing Covid-19 Pandemic Government Actions

Whenever countries are threatened by a pandemic, as is the case with the COVID-19 virus, governments should take the right actions to safeguard public health as well as to mitigate the negative effects on the economy. In this regard, there are two completely different approaches governments can take: a restrictive one, in which drastic measures such as self-isolation can seriously damage the economy, and a more liberal one, where more relaxed restrictions may put at risk a high percentage of the population. The optimal approach could be somewhere in between, and, in order to make the right decisions, it is necessary to accurately estimate the future effects of taking one or other measures. In this paper, we use the SEIR epidemiological model (Susceptible - Exposed - Infected - Recovered) for infectious diseases to represent the evolution of the virus COVID-19 over time in the population. To optimize the best sequences of actions governments can take, we propose a methodology with two approaches, one based on Deep Q-Learning and another one based on Genetic Algorithms. The sequences of actions (confinement, self-isolation, two-meter distance or not taking restrictions) are evaluated according to a reward system focused on meeting two objectives: firstly, getting few people infected so that hospitals are not overwhelmed with critical patients, and secondly, avoiding taking drastic measures for too long which can potentially cause serious damage to the economy. The conducted experiments prove that our methodology is a valid tool to discover actions governments can take to reduce the negative effects of a pandemic in both senses. We also prove that the approach based on Deep Q-Learning overcomes the one based on Genetic Algorithms for optimizing the sequences of actions.
Power Laws Distributions in Objective Priors

The use of objective prior in Bayesian applications has become a common practice to analyze data without subjective information. Formal rules usually obtain these priors distributions, and the data provide the dominant information in the posterior distribution. However, these priors are typically improper and may lead to improper posterior. Here, we show, for a general family of distributions, that the obtained objective priors for the parameters either follow a power-law distribution or has an asymptotic power-law behavior. As a result, we observed that the exponents of the model are between 0.5 and 1. Understand these behaviors allow us to easily verify if such priors lead to proper or improper posteriors directly from the exponent of the power-law. The general family considered in our study includes essential models such as Exponential, Gamma, Weibull, Nakagami-m, Haf-Normal, Rayleigh, Erlang, and Maxwell Boltzmann distributions, to list a few. In summary, we show that comprehending the mechanisms describing the shapes of the priors provides essential information that can be used in situations where additional complexity is presented.
How does boiling occur in lattice Boltzmann simulations?

In recent years, the lattice Boltzmann (LB) method has been widely employed to simulate boiling phenomena [A. Markus and G. Hazi, Phys. Rev. E 83, 046705 (2011); Biferale et al., Phys. Rev. Lett. 108, 104502 (2012); Li et al., Phys. Rev. E 96, 063303 (2017); Wu et al., Int. J. Heat Mass Transfer 126, 773 (2018)]. However, a very important issue still remains open, i.e., how does boiling occur in the LB simulations? For instance, the existing LB studies showed that the boiling on a hydrophobic surface begins at a lower wall superheat than that on a hydrophilic surface, which qualitatively agrees well with experimental studies, but no one has yet explained how this phenomenon appears in the LB simulations and what happened in the simulations after changing the wettability of the heating surface. In this paper, the LB boiling mechanism is revealed by analyzing boiling on a flat surface with mixed wettability and boiling on a structured surface with homogeneous wettability. Through a theoretical analysis, we demonstrate that, when the same wall superheat is applied, in the LB boiling simulations the fluid density near the heating surface decreases faster on a hydrophobic surface than that on a hydrophilic surface. Accordingly, a lower wall superheat can induce the phase transition from liquid to vapor on a hydrophobic surface than that on a hydrophilic surface. Furthermore, a similar theoretical analysis shows that the fluid density decreases fastest at concave corners in the case of a structured surface with homogeneous wettability, which explains why vapor bubbles are nucleated at concave corners in the LB simulations of boiling on structured surfaces.
DeepSoCS: A Neural Scheduler for Heterogeneous System-on-Chip (SoC) Resource Scheduling

In this paper, we present a novel scheduling solution for a class of System-on-Chip (SoC) systems where heterogeneous chip resources (DSP, FPGA, GPU, etc.) must be efficiently scheduled for continuously arriving hierarchical jobs with their tasks represented by a directed acyclic graph. Traditionally, heuristic algorithms have been widely used for many resource scheduling domains, and Heterogeneous Earliest Finish Time (HEFT) has been a dominating state-of-the-art technique across a broad range of heterogeneous resource scheduling domains over many years. Despite their long-standing popularity, HEFT-like algorithms are known to be vulnerable to a small amount of noise added to the environment. Our Deep Reinforcement Learning (DRL)-based SoC Scheduler (DeepSoCS), capable of learning the “best” task ordering under dynamic environment changes, overcomes the brittleness of rule-based schedulers such as HEFT with significantly higher performance across different types of jobs. We describe a DeepSoCS design process using a real-time heterogeneous SoC scheduling emulator, discuss major challenges, and present two novel neural network design features that lead to outperforming HEFT: (i) hierarchical job- and task-graph embedding; and (ii) efficient use of real-time task information in the state space. Furthermore, we introduce effective techniques to address two fundamental challenges present in our environment: delayed consequences and joint actions. Through an extensive simulation study, we show that our DeepSoCS exhibits the significantly higher performance of job execution time than that of HEFT with a higher level of robustness under realistic noise conditions. We conclude with a discussion of the potential improvements for our DeepSoCS neural scheduler.
Practical Traffic-space Adversarial Attacks on Learning-based NIDSs

Machine learning (ML) techniques have been increasingly used in anomaly-based network intrusion detection systems (NIDS) to detect unknown attacks. However, ML has shown to be extremely vulnerable to adversarial attacks, aggravating the potential risk of evasion attacks against learning-based NIDSs. In this situation, prior studies on evading traditional anomaly-based or signature-based NIDSs are no longer valid. Existing attacks on learning-based NIDSs mostly focused on feature-space and/or white-box attacks, leaving the study on practical gray/black-box attacks largely unexplored. To bridge this gap, we conduct the first systematic study of the practical traffic-space evasion attack on learning-based NIDSs. We outperform the previous work in the following aspects: (1) practical---instead of directly modifying features, we provide a novel framework to automatically mutate malicious traffic with extremely limited knowledge while preserving its functionality; (2) generic---the proposed attack is effective for any ML classifiers (i.e., model-agnostic) and most non-payload-based features; (3) explainable---we propose a feature-based interpretation method to measure the robustness of targeted systems against such attacks. We extensively evaluate our attack and defense scheme on Kitsune, a state-of-the-art learning-based NIDS, as well as measuring the robustness of various NIDSs using diverse features and ML classifiers. Experimental results show promising results and intriguing findings.
Initializing Perturbations in Multiple Directions for Fast Adversarial Training

Recent developments in the filed of Deep Learning have demonstrated that Deep Neural Networks(DNNs) are vulnerable to adversarial examples. Specifically, in image classification, an adversarial example can fool the well trained deep neural networks by adding barely imperceptible perturbations to clean images. Adversarial Training, one of the most direct and effective methods, minimizes the losses of perturbed-data to learn robust deep networks against adversarial attacks. It has been proven that using the fast gradient sign method (FGSM) can achieve Fast Adversarial Training. However, FGSM-based adversarial training may finally obtain a failed model because of overfitting to FGSM samples. In this paper, we proposed the Diversified Initialized Perturbations Adversarial Training (DIP-FAT) which involves seeking the initialization of the perturbation via enlarging the output distances of the target model in a random directions. Due to the diversity of random directions, the embedded fast adversarial training using FGSM increases the information from the adversary and reduces the possibility of overfitting. In addition to preventing overfitting, the extensive results show that our proposed DIP-FAT technique can also improve the accuracy of the clean data. The biggest advantage of DIP-FAT method: achieving the best banlance among clean-data, perturbed-data and efficiency.
Exotic Superconducting States in FeSe-based Materials

High-temperature superconductivity and a wide variety of exotic superconducting states discovered in FeSe-based materials have been at the frontier of research on condensed matter physics over the past decade. Unique properties originating from the multiband electronic structure, strongly orbital-dependent phenomena, extremely small Fermi energy, electronic nematicity, and topological aspects give rise to many distinct and fascinating superconducting states. Here, we provide an overview of our current understanding of the superconductivity of {\it bulk} FeSe-based materials, focusing on FeSe and the isovalent substituted FeSe$_{1-x}$S$_{x}$ and FeSe$_{1-x}$Te$_{x}$. We discuss the highly nontrivial superconducting properties in FeSe, including extremely anisotropic pairing states, crossover phenomena from Bardeen--Cooper--Schrieffer (BCS) to Bose--Einstein condensation (BEC) states, a novel field-induced superconducting phase, and broken time-reversal symmetry. We also discuss the evolution of the superconducting gap function with sulfur and tellurium doping, paying particular attention to the impact of quantum critical nematic fluctuations and the topological superconductivity. FeSe-based materials provide an excellent playground to study various exotic superconducting states.
Characterizing information leaders in Twitter during COVID-19 crisis

Information is key during a crisis such as the current COVID-19 pandemic as it greatly shapes people opinion, behaviour and even their psychological state. It has been acknowledged from the Secretary-General of the United Nations that the infodemic of misinformation is an important secondary crisis produced by the pandemic. Infodemics can amplify the real negative consequences of the pandemic in different dimensions: social, economic and even sanitary. For instance, infodemics can lead to hatred between population groups that fragment the society influencing its response or result in negative habits that help the pandemic propagate. On the contrary, reliable and trustful information along with messages of hope and solidarity can be used to control the pandemic, build safety nets and help promote resilience and antifragility. We propose a framework to characterize leaders in Twitter based on the analysis of the social graph derived from the activity in this social network. Centrality metrics are used to identify relevant nodes that are further characterized in terms of users parameters managed by Twitter. We then assess the resulting topology of clusters of leaders. Although this tool may be used for surveillance of individuals, we propose it as the basis for a constructive application to empower users with a positive influence in the collective behaviour of the network and the propagation of information.
Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures

When learning to ride a bike, a child falls down a number of times before achieving the first success. As falling down usually has only mild consequences, it can be seen as a tolerable failure in exchange for a faster learning process, as it provides rich information about an undesired behavior. In the context of Bayesian optimization under unknown constraints (BOC), typical strategies for safe learning explore conservatively and avoid failures by all means. On the other side of the spectrum, non conservative BOC algorithms that allow failing may fail an unbounded number of times before reaching the optimum. In this work, we propose a novel decision maker grounded in control theory that controls the amount of risk we allow in the search as a function of a given budget of failures. Empirical validation shows that our algorithm uses the failures budget more efficiently in a variety of optimization experiments, and generally achieves lower regret, than state-of-the-art methods. In addition, we propose an original algorithm for unconstrained Bayesian optimization inspired by the notion of excursion sets in stochastic processes, upon which the failures-aware algorithm is built.
Reverberation Modeling for Source-Filter-based Neural Vocoder

This paper presents a reverberation module for source-filter-based neural vocoders that improves the performance of reverberant effect modeling. This module uses the output waveform of neural vocoders as an input and produces a reverberant waveform by convolving the input with a room impulse response (RIR). We propose two approaches to parameterizing and estimating the RIR. The first approach assumes a global time-invariant (GTI) RIR and directly learns the values of the RIR on a training dataset. The second approach assumes an utterance-level time-variant (UTV) RIR, which is invariant within one utterance but varies across utterances, and uses another neural network to predict the RIR values. We add the proposed reverberation module to the phase spectrum predictor (PSP) of a HiNet vocoder and jointly train the model. Experimental results demonstrate that the proposed module was helpful for modeling the reverberation effect and improving the perceived quality of generated reverberant speech. The UTV-RIR was shown to be more robust than the GTI-RIR to unknown reverberation conditions and achieved a perceptually better reverberation effect.
Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A Gene Expression Programming Approach

Efficient identification of people and objects, segmentation of regions of interest and extraction of relevant data in images, texts, audios and videos are evolving considerably in these past years, which deep learning methods, combined with recent improvements in computational resources, contributed greatly for this achievement. Although its outstanding potential, development of efficient architectures and modules requires expert knowledge and amount of resource time available. In this paper, we propose an evolutionary-based neural architecture search approach for efficient discovery of convolutional models in a dynamic search space, within only 24 GPU hours. With its efficient search environment and phenotype representation, Gene Expression Programming is adapted for network’s cell generation. Despite having limited GPU resource time and broad search space, our proposal achieved similar state-of-the-art to manually-designed convolutional networks and also NAS-generated ones, even beating similar constrained evolutionary-based NAS works. The best cells in different runs achieved stable results, with a mean error of 2.82 % in CIFAR-10 dataset (which the best model achieved an error of 2.67 %) and 18.83 % for CIFAR-100 (best model with 18.16 %). For ImageNet in the mobile setting, our best model achieved top-l and top-5 errors of 29.51 % and 10.37 %, respectively. Although evolutionary-based NAS works were reported to require a considerable amount of GPU time for architecture search, our approach obtained promising results in little time, encouraging further experiments in evolutionary-based NAS, for search and network representation improvements.