Miracle-dz/UniFAR-FTUs huggingface dataset:
From Model-Based Optimization Algorithms to Deep Learning Models for Clustering Hyperspectral Images

: Hyperspectral images (HSIs), captured by different Earth observation airborne and space-borne systems, provide rich spectral information in hundreds of bands, enabling far better discrimination between ground materials that are often indistinguishable in visible and multi-spectral images. Clustering of HSIs, which aims to unveil class patterns in an unsupervised way, is highly important in the interpretation of HSI, especially when labelled data is not available. A number of HSI clustering methods have been proposed. Among them, model-based optimization algorithms, which learn cluster structure of data by solving convex/non-convex optimization problems, have achieved the current state-of-the-art performance. Recent works extend the model-based algorithms to deep versions with deep neural networks, obtaining huge breakthroughs of clustering performance. However, a systematic survey on the topic is absent. This article provides a comprehensive overview of clustering methods of HSI and tracks the latest techniques and breakthroughs in the domain, including the traditional model-based optimization algorithms and the emerging deep learning based clustering methods. With a new taxonomy, we elaborate on the main ideas, technical details, advantages and disadvantages of different types of clustering methods of HSIs. We provide a systematic performance comparison between different clustering methods by conducting extensive experiments on real HSIs. Unsolved problems and future research trends in the domain are pointed out. Moreover, we provide a toolbox that contains implementations of representative clustering algorithms to help researchers to develop their own models.
Testosterone eliminates strategic prosocial behavior in healthy males

49 Humans are strategically more prosocial when their actions are being watched by others than when 50 they act alone. Using a psychopharmacogenetic approach, we investigated the computational and 51 endocrinological mechanisms of such audience-driven prosociality. 187 male participants received 52 either a single dose of testosterone or a placebo and performed a prosocial and self-benefitting 53 reinforcement learning task. Crucially, the task was performed either in private or when being 54 watched. Rival theories suggest that the hormone might either diminish or strengthen audience- 55 dependent prosociality. We show that exogenous testosterone fully eliminated strategic, i.e., feigned, 56 prosociality and thus decreased submission to audience expectations. We next performed 57 reinforcement-learning drift-diffusion computational modeling to elucidate which latent aspects of 58 decision-making testosterone acted on. The modeling revealed that testosterone compared to placebo 59 did not deteriorate reinforcement learning per se. Rather, when being watched, the hormone 60 altered the degree to which the learned information on choice value translated to action selection. 61 These results indicate that when being watched, the hormone was influencing participants towards 62 choices that conform less with audience expectations, and thus lower prosociality. Exploratory 63 analysis of personal values related to conformity and tradition corroborates this view, as it suggests 64 that the shift from socially conforming to nonconforming decisions co-varies with individual 65 differences in these values. Together, these findings indicate that instead of deceptively increasing 66 socially desirable behavior to manage reputation and status, testosterone, by acting on implicit 67 decision-making processes, counteracts conformity and submission to audience expectations.
Wheat protein fractions in relation to grain quality characters of the cultivars registered in the Czech Republic 2004-2006

Recently, the directions of wheat grain utilisation are diversifying not only in area of bakery and animal feeding but in other industrial branches such as starch and bio-ethanol production. Consequently, wider spectra of wheat grain quality parameters need to be evaluated. In case of feeding quality, content of protein fractions, essential amino acids, starch composition or viscosity are such important parameters. In situations where the wheat is unsuitable for bread making (C category) the identification of cultivars for livestock feeding would be desirable. The ability to assess wheat lines suitable for both or more directions would be valuable.
Comparison of sexually dimorphic patterns in the postcrania of South Africans and North Americans

Recently, a multivariate approach to sex estimation using the postcrania achieved higher accuracies than the commonly used cranium, recommending postcraniometric sex estimation. In South Africa (SA), sex estimation techniques using long bones exist, but they only offer univariate or bone-by-bone models, which do not acknowledge sexual dimorphism in other elements and, as expected, do not utilize robust statistical analyses. The purpose of this study was to evaluate accuracies of sex estimation in the postcrania of modern South Africans using multivariate statistics and to compare pattern expression of sexual dimorphism in blacks, whites and coloureds. The study included analysing the skeletons of a total of 360 SA black, white and coloured individuals and the data of 240 black and white North Americans (NA) (equal sex and ancestry). Sympercents expressed sexual dimorphism and were compared in the three SA groups and with the NA individuals. Three classification methods assessed the thirty-nine standard measurements taken from 11 postcranial bones. The creation of different bone models and a variety of multivariate models revealed the potential of a multivariate technique. Comparisons of linear discriminant analysis (LDA), flexible discriminant analysis (FDA) and logistic regression indicated which model provided the greatest discriminatory power between sex and sex-ancestry
Learning Nonlinear Causal Effect via Kernel Anchor Regression

Recall that we define the population-level risk for the regression stage E(H), population-level risk with regularization E ξ (H), and the empirical risk Ê γ,m ξ (H) with E p X and E p Y being replaced by E n1 α1,X and E2 α2,Y , respectively. Denote the optimal operator to E ξ (H) as H γ ξ = argminH E γ ξ (H). We now define the empirical risk E γ,m(H) with true E p X and E p Y , and the corresponding optimal operator.
First Impressions Matter: A Model of Confirmatory Bias

Psychological research indicates that people have a cognitive bias that leads them to misinterpret new information as supporting previously held hypotheses. We model such confirmatory bias in a symmetric model in which exactly one of two hypotheses is true. We show that the confirmatory bias induces overconfidence: Given any probabilistic assessment by an agent that one of the hypotheses is probably true, the appropriate beliefs should deem it less likely to be true. When the agent believes relativ ely weakly in a hypothesis after receiving extensive information, the hypothesis he believes in may be more likely to be wrong than right. If the confirmatory bias is strong enough, with positive probability the agent may eventually come to believe with near certainty in a false hypothesis even after receiving fan infinite amount of information.
Metaheuristics in Combinatorial Optimization

The emergence of metaheuristics for solving diÆcult combinatorial optimization problems is one of the most notable achievements of the last two decades in operations research. This paper provides an account of the most recent developments in the eld and identi es some common issues and trends. Examples of applications are also reported for vehicle routing and scheduling problems.
Measurement of exhaled alveolar nitrogen oxide in patients with lung cancer: a friend from the past still precious today

: Nitric oxide (NO) is a marker of airway inflammation and indirectly a general indicator of inflammation and oxidative stress. NO is a contributing factor in lung cancer at an early stage and also after chemotherapy treatment of lung cancer. We studied whether exhaled NO levels were altered by three cycles of chemotherapy at diagnosis and after chemotherapy, and whether, directly or indirectly, these changes were related to the course of disease. Also, a correlation of NO levels with other markers of inflammation was performed. We studied 42 patients diagnosed early: 26 men and 16 women with lung cancer. We analyzed blood tests for control of inflammatory markers, functional pulmonary tests, and alveolar exhaled NO. We recorded a decrease in exhaled NO after three cycles of chemotherapy in all patients, regardless of histological type and stage: there were 42 patients with mean 9.8 NO after three cycles (average 7.7). Also, a strong correlation appeared between NO measurements before and after chemotherapy and C-reactive protein ( P , 0.05, r = 0.42, before) and ( P , 0.045, r = 0.64, after). NO alveolar measurement as an indicator of airway inflammation indicates response to chemotherapy in lung cancer. Also, the inflammatory process in lung cancer was confirmed and indicated response to chemotherapy through an index that is sensitive to inflammatory disease of the airways.
Neutralizing the Adverse Industry Impacts of CO 2 Abatement Policies : What Does It Cost ?

The most cost-effective policies for achieving CO2 abatement (e.g., carbon taxes) are considered politically unacceptable because of distributional consequences. This paper explores policies designed to address distributional concerns. Using an intertemporal, numerical general equilibrium model of the United States, we examine how efficiency costs change when CO2 abatement policies include elements that neutralize adverse impacts on energy industries. We find that desirable distributional outcomes can be achieved at relatively low cost in terms of efficiency. Without substantial added cost to the overall economy, the government can implement carbon abatement policies that protect profits and equity values in fossil-fuel industries. The key to this conclusion is that CO2 abatement policies have the potential to generate rents that are very large in relation to the potential loss of profit. By enabling firms to retain only a very small fraction of these potential rents, the government can protect firms’ profits and equity values. Consequently, the government needs to grandfather only a small percentage of CO2 emissions permits or, similarly, must exempt only a small fraction of emissions from the base of a carbon tax. Each of these government policies involves only a small sacrifice of potential government revenue. Such revenue has an efficiency value because it can be used to finance cuts in pre-existing distortionary taxes. Because these policies give up little of this potential revenue, they involve only a small sacrifice in terms of efficiency. We also find that there is a very large difference between preserving firms’ profits and preserving their tax payments. Allowing firms to enjoy a dollar-for-dollar offset to their payments of carbon taxes— for example, through industry-specific cuts in corporate tax rates—substantially overcompensates firms, raising profits and equity values significantly relative to the unregulated situation. This reflects the fact that producers can shift onto consumers most of the burden from a carbon tax. The efficiency costs of such policies are far greater than the costs of policies that do not overcompensate firms.
Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions

Abstract. The evaluation of modelling diagnostics with appropriate observations is an important task that establishes the capabilities and reliability of models. In this study we compare aerosol and cloud properties obtained from three different climate models (ECHAM-HAM, ECHAM-HAM-SALSA, and NorESM) with satellite observations using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The simulator MODIS-COSP version 1.4 was implemented into the climate models to obtain MODIS-like cloud diagnostics, thus enabling model-to-model and model-to-satellite comparisons. Cloud droplet number concentrations (CDNCs) are derived identically from MODIS-COSP-simulated and MODIS-retrieved values of cloud optical depth and effective radius. For CDNC, the models capture the observed spatial distribution of higher values typically found near the coasts, downwind of the major continents, and lower values over the remote ocean and land areas. However, the COSP-simulated CDNC values are higher than those observed, whilst the direct model CDNC output is significantly lower than the MODIS-COSP diagnostics. NorESM produces large spatial biases for ice cloud properties and thick clouds over land. Despite having identical cloud modules, ECHAM-HAM and ECHAM-HAM-SALSA diverge in their representation of spatial and vertical distributions of clouds. From the spatial distributions of aerosol optical depth (AOD) and aerosol index (AI), we find that NorESM shows large biases for AOD over bright land surfaces, while discrepancies between ECHAM-HAM and ECHAM-HAM-SALSA can be observed mainly over oceans. Overall, the AIs from the different models are in good agreement globally, with higher negative biases in the Northern Hemisphere. We evaluate the aerosol–cloud interactions by computing the sensitivity parameter ACICDNC=dln⁡(CDNC)/dln⁡(AI) on a global scale. However, 1 year of data may be considered not enough to assess the similarity or dissimilarities of the models due to large temporal variability in cloud properties. This study shows how simulators facilitate the evaluation of cloud properties and expose model deficiencies, which are necessary steps to further improve the parameterisation in climate models.
Statistical and Computational Limits for Tensor-on-Tensor Association Detection

In this paper, we consider the tensor-on-tensor association detection problem, where the goal is to detect whether there is an association between the tensor responses to tensor covariates linked via a low-rank tensor parameter. We first develop tight bounds on the signal-to-noise ratio (SNR) such that the detection problem is statistically possible. We then provide testing procedures that succeed when the SNR is above the threshold. On the other hand, the statistical optimal tests often require computing the largest singular value of a given tensor, which can be NP-hard in general. To complement that, we develop efficient polynomial-time testing procedures with provable guarantees. We also develop matching lower bounds under the Statistical Query model and show that the SNRs required by the proposed polynomial-time algorithms are essential for computational efficiency. We identify a gap that appears between the SNR requirements of the optimal unconstrained-time tests and polynomial-time tests if and only if the sum of the tensor response order and the tensor covariate order is no less than three. To our best knowledge, this is the first complete characterization of the statistical and computational limits for the general tensor-on-tensor association detection problem. Our findings significantly generalize the results in the literature on signal detection in linear regression and low-rank matrix trace regression. Finally, the connection on the computational hardness of the detection problem and the corresponding estimation problem is discussed.
Geotourism: the tourism of geology and landscape

Setting an agenda for geotourism The significance of aesthetic landscape appreciation to modern geotourism provision Australia's geological heritage: a national inventory for future geoparks and geotourism Centralized data management approaches in geotourism: a view from Finland Promoting geotourism: a case study from northeast Iceland The Australian Alps: opportunities and challenges for geotourism Reconsidering the boundaries and applications of Geotourism - lessons learnt from tourism at Mount Vesuvius Management of geotourism stakeholders - experiences from the Network History of the Earth Geotourism in the Hawaiian Islands Lisa M. King, James Cook The Cretaceous fossil sites of South Korea: identifying geosites, science and geotourism Geotourism and geotourist education in Poland Geotourism product interpretation: Rangitoto Island,Auckland, New Zealand Geotourism potential in North Carolina: perspectives from interpretation at state parks Interpretation rocks! Designing signs for geotourism sites The Jurassic Coast World Heritage Site: understanding the nature of geotourism USA scenic byways - connecting people to places The UNESCO global network of national geoparks The future of geotourism: where to from here? Index
Corrigendum: TOPAS/Geant4 configuration for ionization chamber calculations in proton beams (2018 Phys. Med. Biol. 63 115013)

Monte Carlo (MC) calculations are a fundamental tool for the investigation of ionization chambers (ICs) in radiation fields, and for calculations in the scope of IC reference dosimetry. Geant4, as used for the toolkit TOPAS, is a major general purpose code, generally suitable for investigating ICs in primary proton beams. To provide reliable results, the impact of parameter settings and the limitations of the underlying condensed history (CH) algorithm need to be known. A Fano cavity test was implemented in Geant4 (10.03.p1) for protons, based on the existing version for electrons distributed with the Geant4 release. This self-consistent test allows the calculation to be compared et al for k Q , Q 0 with IAEA-TRS398 values within the given uncertainties. An additional uncertainty for the MC-calculated k Q , Q 0 of ∼ 0.3% by hadronic interaction models should be considered.
Metabolic Engineering of Microalgae for Enhanced Lipid Production

Increasing awareness in recombinant protein technologies for human and animal health applications has focused on microalgae as a platform with the potential to meet a large impending demand. Here we describe a microalgae protein expression system and compare the advantages and disadvantages to other platforms currently operating on a commercial level. High-value recombinant proteins that have been produced in microalgae are presented, and strategies for developing production strains with improved commercial properties are discussed
Analgesic efficacy of oral gabapentin added to standard epidural corticosteroids in patients with failed back surgery

Objective: Failed back surgery syndrome is characterized by the presence of intractable pain and varying degrees of functional incapacity after lumbar spine surgery. Because the mechanisms that cause pain are variable, treatment of this syndrome is quite difficult, and one of the most common methods that is used for treatment nowadays is epidural injection. This research evaluates the analgesic efficacy of addition of oral gabapentin treatment to epidural corticosteroid application in patients with failed back surgery syndromes. Methods: Forty-two patients, including 23 females and 19 males, with failed back surgery syndrome who had been previously operated on at least twice due to lumbar disc herniation were randomly divided into two groups. Following epidural application of a single dose of methylprednisolone in the first group of patients (Group K), an oral medical treatment containing naproxen sodium, tizanidine, and vitamin B and C complex, was devised to be applied for one month. For the second group, oral gabapentin was added to the same treatment regime (Group G). Pain levels were evaluated by a visual analog scale for straight leg raise before, during, and after treatment, as well as in the first and third months. Results: There was no demographically significant difference between the patients ( P . 0.05). After the beginning of treatment, it was observed that the pain level in Group G patients regressed earlier and that it progressed at a significantly lower level (both in the first and third month controls). Conclusion: It was concluded that addition of oral gabapentin to epidural corticosteroid application in patients with failed back surgery syndromes was effective in ameliorating pain at an early stage without significant side effects.
Generated ECG Signal Feasibility Evaluation for Classification

Nowadays, Electrocardiogram (ECG) signals can be measured using wearable devices, such as smart watches. In this study, 12-lead ECG signals were generated from lead I and their feasibility was tested to obtain more details. The 12-lead ECG signals were generated using a U-net-based generative adversarial network (GAN) that was trained on ECG data obtained from the Asan Medical Center. Subsequently, unseen PTB-XL PhysioNet data were used to produce real 12-lead ECG signals for classification. The generated and real 12-lead ECG signals were then compared using a ResNet classification model; and the normal, atrial fibrillation (A-fib), left bundle branch block (LBBB), right bundle branch block (RBBB), left ventricular hypertrophy (LVH), and right ventricular hypertrophy (RVH) were classified. The mean precision, recall, and f1-score for the real 12-lead ECG signals are 0.70, 0.72, and 0.70, and that for the generated 12-lead ECG signals are 0.82, 0.80, and 0.81, respectively. The generated 12-lead ECG signals perform better than real 12-lead ECG signals. In this study, the 12-lead generative model was evaluated by classifying the 6 diagnostic classes, and the results showed that generated 12-lead ECG signals can be used to diagnose cardiac diseases.
A Framework for Security Requirements Engineering

Although security requirements engineering has recently attracted increasing attention, it has lacked a context in which to operate. A number of papers have described how security requirements may be violated, but apart from a few hints in the general literature, none have described satisfactorily what security requirements are. This paper proposes a framework which unifies the concepts of the two disciplines of requirements engineering and security engineering. From requirements engineering it takes the concept of functional goals, which are operationalised into functional requirements, with appropriate constraints. From security engineering it takes the concept of assets, together with threats of harm to those assets. Security goals aim to protect from those threats, and are operationalised into security requirements, which take the form of constraints on the functional requirements. In addition we explore the consequences of the fact that security is concerned with the protection of assets, while computers only provide interfaces. We show how to specify the relationship between security requirements and the specification of software behaviour, using Jackson's Problem Frames approach.