Miracle-dz/UniFAR-FTUs huggingface dataset:
PRESUMPTIVE LIBERTARIANISM: CAN THE MARKET MODEL SUPPORT LIBERTARIANISM, RATHER

LIBERTARIANISM AS A MORAL and political doctrine is often the target of fierce criticism. Such criticism commonly takes as its starting point features that seem to come into conflict with widely and firmly entrenched moral intuitions. In this article, I will be concerned with a specific problem of that very sort, a problem that recently has been given a potent form by David Sobel: the conflation problem (Sobel 2012; 2013). The problem, in short, is the following: the basic libertarian tenet is full and stringent self-ownership. Transgressing self-ownership is strictly forbidden (or at least “a very big deal”). This implies that one infringement of self-ownership (say, being stabbed) is precisely as forbidden as any other such infringement (say,
Recognition of Facial Expressions using Local Binary Patterns of Important Facial Parts

Facial Expression Recognition is one of the exciting and challenging field; it has important applications in many areas such as data driven animation, human computer interaction and robotics. Extracting effective features from the human face is an important step for successful facial expression recognition. In this paper we have evaluated Local Binary Patterns of some important parts of human face, for person independent as well as person dependent facial expression recognition. Extensive experiments on JAFFE database are conducted. The experiment results show that person dependent method is highly accurate and outperform many existing methods.
POTENTIAL SCOPE AND CHALLENGES OF BEHAVIOURALLY INFORMED REGULATION IMPLICATIONS FOR MODERN REGULATORY RISK MANAGEMENT Introduction

This paper looks at how behavioural sciences and the relevant knowledge can be applied by public authorities to design, test and implement interventions that help society and consumers make choices that are both sustainable and to their well-being while remaining compatible with legitimate collective decision-making. Used in the context of comprehensive risk governance, behavioural insight (here abbreviated as BI) is valuable and useful in helping de ne a problem in the rst instance and providing an understanding of the risks and shortfalls before any designing regulation even begins. More speci cally, this paper addresses the possibility of intervening in individual or group behaviour, and discusses effectiveness and legality. The legitimacy of such interventions is derived from the overall governance process that recommends a democratic and inclusive selection rule for choosing a speci c transformation path over other alternatives. If such a legitimate decision has been made, then it is morally and politically justi ed for collective actors to shape human behaviour through interventions. However, it is essential that such approaches are fully transparent and open to public scrutiny and critique.
Towards the Extraction of Frequent Patterns in Complex Process Models

In this paper, we present WoMine, an algorithm to retrieve frequent behavioural patterns from the model. Our approach searches in process models extracting structures with sequences, selections, parallels and loops, which are frequently executed in the logs. This proposal has been validated with a set of process models, and compared with the state of the art techniques. Experiments have validated that WoMine can find all types of patterns, extracting information that cannot be mined with the state of the art techniques.
Software Design Level Vulnerability Classification Model

Classification of software security vulnerability no doubt facilitates the understanding of securityrelated information and accelerates vulnerability analysis. The lack of proper classification not only hinders its understanding but also renders the strategy of developing mitigation mechanism for clustered vulnerabilities. Now software developers and researchers are agreed on the fact that requirement and design phase of the software are the phases where security incorporation yields maximum benefits. In this paper we have attempted to design a classifier that can identify and classify design level vulnerabilities. In this classifier, first vulnerability classes are identified on the basis of well established security properties like authentication and authorization. Vulnerability training data is collected from various authentic sources like Common Weakness Enumeration (CWE), Common Vulnerabilities and Exposures (CVE) etc. From these databases only those vulnerabilities were included whose mitigation is possible at the design phase. Then this vulnerability data is pre-processed using various processes like text stemming, stop word removal, cases transformation. After pre-processing, SVM (Support Vector Machine) is used to classify vulnerabilities. Bootstrap validation is used to test and validate the classification process performed by the classifier. After training the classifier, a case study is conducted on NVD (National Vulnerability Database) design level vulnerabilities. Vulnerability analysis is done on the basis of classification result.
Context Style Explanation for Recommender Systems

Recommender systems support users by helping them choose items, and explanations for the recommendations further enhance such support. Previous explanation styles were based on information about users and items, such as the demographics of users and contents of items. Contexts, such as “usage scenarios” and “accompanying persons,” have not been used for explanations, although they influence user’s choice of items. In this paper, we propose a context style explanation method, presenting contexts suitable for consuming the recommended items. The expected impacts of context style explanations are as follows: 1) persuasiveness: recognition of a suitable context for usage motivates users to consume items, and 2) usefulness: envisioning a context helps users to make the right choices because the values of items depend on contexts. We evaluate the persuasiveness and usefulness of the context-style explanation by a crowdsourcing-based user study in a restaurant recommendation setting. The context style explanation is compared to the demographic and content style explanations. We also combine the context style and other explanation styles, confirming that hybrid styles improve the persuasiveness and usefulness of the explanation. Further, we investigate the personal preferences for explanation styles and reveal how gender and age relate to such preferences. The contributions of this paper are: the proposal of the novel context style explanation method, the demonstration of the persuasiveness and usefulness of the proposed method by a user study, and the findings of genderand age-dependence of explanation style preferences.
Examples: Nonlinear continuous-time models

This file demonstrates the utilization of dynr in fitting single-regime and regime-switching nonlinear dynamic models. As extensions of linear models, nonlinear dynamic models incorporate nonlinearities into the change processes. Such nonlinearities may take the form of interactions between components of a system, and have many useful applications across different scientific disciplines. In the study of human dynamics, for instance, many processes are characterized by changes that are dependent on interactions with other processes. Nonlinear ordinary differential equations have been used to model, among other phenomena, ovulatory regulation [2], circadian rhythms [3], cerebral development [16], substance use [1], cognitive aging [5], parent-child interactions [17], couple dynamics [4, 7]; and sudden transitions in attitudes [18]. To facilitate the specification of more complex dynamic models, especially those that involve the use of specialized mathematical functions (e.g., trigonometric, power, logistic and exponential functions), or those for which the user would rather not specify in matrix form, dynr provides users with a formula interface that can accommodate nonlinear as well as linear dynamic functions.
An Overview of the Advanced Smartcard Access Control System (ASACS)

The Advanced Smartcard Access Control System (ASACS) was developed by the National Institute of Standards and Technology in conjunction with Datakey and Trusted Information Systems. The system includes a sma.rtcard with public key capabilities and a portable reader/writer with comp utati onal capabiliti es, incl u ding· a microprocessor, programmable memory, a keypad, and an LCD display. Through the use of a layered interface. ASACS was integrated into several demonstration programs and into the TIS Privacy Enhanced Mail (TIS/PEM) system. This paper provides a brief overview of the ASACS.
Changes of occlusal plane inclination after orthodontic treatment with and without extraction in Class II patients

Introduction: The inclination of the occlusal plane (OP) is related to type of dental occlusion. The aim of this study was to evaluate and compare the inclination of occlusal plane in Class II patients treated with the upper first premolars extraction or non-extraction modalities. Materials & Methods: Totally, forty 16-25-year-old patients (15 males and 25 females) with class II malocclusion, whose active phase of orthodontic treatment was completed using MBT with 0.022-inch slot size, were selected. Twenty patients were treated with extraction of upper first premolars and the rest were treated without any extraction. Preand post-treatment cephalograms were traced and several cephalometric variables were measured. The preand post-treatment changes of angles within each group and between two groups were compared via paired t-test and independent t-test, respectively. The statistically significant level was set at p<0.05. Results: After treatment, functional occlusal plane (FOP) angle to sella-nasion (SN) plane angle increased in both groups (5.0 degree in extraction group and 0.6 degree in non-extraction group), which was not statistically significant in both groups. Bisecting occlusal plane (BOP) angle to SN plane one in the extraction group enhanced by 0.8 degree, which was not statistically significant, but it decreased by 0.8 degree in the non-extraction group, which was not statistically significant. Conclusion: Although the use of extraction and non-extraction protocols for the treatment of Class II patients did not cause significant changes in the BOP-SN and FOP-SN angles, small changes in these angles can have marked clinical changes in facial harmony and occlusal relationships. Extraction decision depending on factors such as amount of crowding, incisors protrusion, etc.
Nomogram for Individualized Prediction of Hepatocellular Carcinoma with Portal Vein Tumor Thrombosis on Conservative Treatment

Background Portal vein tumor thrombosis (PVTT) is one of the major predictive factors for patients with hepatocellular carcinoma (HCC). The objective of this study was to establish a prognostic nomogram for identifying individual survival outcomes in patients with HCC and PVTT on conservative treatment based on specific factors. Methods Two hundred and ten patients with HCC and PVTT on conservative treatment in Beijing Ditan Hospital between June 2008 and May 2017 were studied retrospectively as a derivation cohort. We built a nomogram based on independent risk factors for survival prediction. The concordance index (c-index) and a calibration curve were used to evaluate the predictive accuracy. During the study, 102 patients were included at the Putuo Hospital and Third People's Hospital of Changzhou as a validation cohort. Results In the derivation cohort, the independent factors for overall survival were identified by multivariate analysis, namely, aspartate aminotransferase ≥119 IU/L, gamma-glutamyl transferase ≥115 IU/L, Child–Pugh class C liver function, creatinine ≥91 μmoI/L, α-fetoprotein ≥400 ng/ml, and largest tumor diameter ≥5 cm. The nomogram had a c-index of 0.737 (95% confidence interval, 0.692–0.782) and the calibration curves fitted well. The median survival time was 4.2 months in the derivation cohort, with an MST of 5 months for BCLC C stage and 1.8 months for BCLC D stage patients. Kaplan–Meier analysis showed significant statistical differences in the 6-month overall survival rates of the primary and validation cohorts after the total scores were divided into three quartiles (low risk: 0–85; intermediate risk: 86–210; high risk: ≥211; p < 0.0001 in both cohorts). Conclusions The nomogram can be a more accurate and individualized prediction for 6-month overall survival of patients with HCC and PVTT on conservative treatment, and it is possible to consider further active interventions for patients in the low-risk group (0–85 scores) to achieve the aim of prolonging survival.
A seroepidemiological study of bovine cysticercosis in Bali and Nusa Tenggara, Indonesia

Background and Aim: Taenia saginata hazardously affects human and animal health. The distribution of this disease is found almost all over the world. The study aimed to obtain epidemiological information concerning prevalence and the distribution of bovine cysticercosis in Bali and Nusa Tenggara, Indonesia. Materials and Methods: A total of 267 community-owned Bali cattle serum samples from the provinces of Bali, West Nusa Tenggara, and East Nusa Tenggara were examined. The study was conducted by examining the serum of Bali cattle using enzyme-linked immunosorbent assay technique. Risk factors related to cysticercosis that analyzed were sex, breeding type, age, physical condition, source of drinking water, pen condition, and latrine availability. Results: Seven of 91 Bali cattle sera from all regencies/cities in Bali showed a positive result. Those positive sera were originated from Buleleng (1), Gianyar (2), Denpasar (2), and Klungkung (2). Meanwhile, four of 92 Bali cattle sera from West Nusa Tenggara and seven of 84 from East Nusa Tenggara occurred antibodies against T. saginata. We identified that two risk factors that influence the incidence of T. saginata infection in Bali cattle in Bali were the sex and the cattle breeding type. Conclusion: Through this research can be made a map of bovine cysticercosis in Bali cattle in Bali and Nusa Tenggara region. By mapping the disease, it is recommended that the animal health officers should be more accurate when conducting postmortem examination, especially on cattle from a positive region.
Adaptive Stabilization of a Fractional-Order System with Unknown Disturbance and Nonlinear Input via a Backstepping Control Technique

In this paper, a new backstepping-based adaptive stabilization of a fractional-order system with unknown parameters is investigated. We assume that the controlled system is perturbed by external disturbance, the bound of external disturbance to be unknown in advance. Moreover, the effects of sector and dead-zone nonlinear inputs both are taken into account. A fractional-order auxiliary system is established to generate the necessary signals for compensation the nonlinear inputs. Meantime, in order to deal with these unknown parameters, some fractional-order adaption laws are given. The frequency-distributed model is used so that the indirect Lyapunov theory is available in designing controllers. Finally, simulation results are presented to verify the effectiveness and robustness of the proposed control strategy.
Does Our Universe Prefer Exotic Smoothness?

Various experimentally verified values of physical parameters indicate that the universe evolves close to the topological phase of exotic smoothness structures on R 4 and K3 surface. The structures determine the α parameter of the Starobinski model, the number of e-folds, the spectral tilt, the scalar-to-tensor ratio and the GUT and electroweak energy scales, as topologically supported quantities. Neglecting exotic R 4 and K3 leaves these free parameters undetermined. We present general physical and mathematical reasons for such preference of exotic smoothness. It appears that the spacetime should be formed on open domains of smooth K 3 # C P 2 ¯ at extra-large scales possibly exceeding our direct observational capacities. Such potent explanatory power of the formalism is not that surprising since there exist natural physical conditions, which we state explicitly, that allow for the unique determination of a spacetime within the exotic K3.
AMF-IDBSCAN: Incremental Density Based Clustering Algorithm Using Adaptive Median Filtering Technique

Density-based spatial clustering of applications with noise (DBSCAN) is a fundament algorithm for density-based clustering. It can discover clusters of arbitrary shapes and sizes from a large amount of data, which is containing noise and outliers. However, it fails to treat large datasets, to attend to outperforming when new data objects are inserted into the existing database, to remove totally a noise points and outliers and to handle the local density variation that exists within the cluster. So, a good clustering method should allow a significant density modification within the cluster and should learn a dynamics and large databases. In this paper, an enhancement of the DBSCAN algorithm is proposed based on incremental clustering called AMF-IDBSCAN which builds incrementally the clusters of different shapes and sizes in large datasets and eliminates the presence of noise and outliers. The proposed AMF-IDBSCAN algorithm uses a canopy clustering algorithm to pre-clustering datasets to decrease the volume of data, applies an incremental DBSCAN for clustering the data points and Adaptive Median Filtering (AMF) technique for post-clustering to reduce the number of outliers by replacing noises by chosen medians. Experimental results are obtained from the University California Irvine (UCI) repository UCI data sets. The final results show that our algorithm get good results with respect to the famous DBSCAN, IDBSCAN, and DMDBSCAN
Craft beer waste as substrate for pyocyanin synthesis

Pyocyanin is a fluorescent phenazine pigment of a bright blue color, synthesized exclusively by about 95% of Pseudomonas aeruginosa strains. Laboratory acquisition of the pigment is achieved by employing simple protocols. The aim of this work was to increase the production of pyocyanin by adding different concentrations of craft beer waste (w/v) to King A broth, incubated by shaking at 150 rpm, at 29±1oC for 96 hours. Compared to the control group, a positive correlation was observed between the production of pyocyanin and the percentage of malt bagasse added. This contributed to the increase of pigment concentration up to approximately 70%, over the control, with values ranging from 21 to 58 μg/ml. The craft beer waste proved to be an environmentally friendly option as an adjuvant in the process of pyocyanin production in a synthetic medium.
Demand for Safety, Risky Loans: A Model of Securitization

We build a competitive equilibrium model of securitization in the presence of demand for safety by some investors. Securitization allows to create safe assets by pooling idiosyncratic risks from loan originators, leading to higher aggregate loan issuance. Yet, the distribution of loan risks out of their originators creates a moral hazard problem. An increase in the demand for safety leads to a securitization boom and riskier originated loans. When demand for safety is high, welfare is Pareto higher than in an economy with no securitization despite the origination of riskier loans. Aggregate lending expansions driven by demand for safety may, paradoxically, lead to riskier loan issuance than expansions driven by standard credit supply shocks.
Building Momentum evidence table: effects of implemented restrictions on food advertising and other forms of commercial promotion

Note to readers this table is not a systematic review of the literature. The integrity, methodology or funding source of each study included in the table has not been examined by the authors. Inclusion criteria: a study evaluating an implemented marketing restriction in a country, whether it be a mandatory government-led policy; a co-regulatory policy; or an industry self-regulatory system. Systematic reviews of all regulatory mechanisms are listed at the end of the table.
Tumor neoantigenicity assessment with CSiN score incorporates clonality and immunogenicity to predict immunotherapy outcomes

CSiN score is an improved tool to assess the overall quality of tumor neoantigens that predicts response to immunotherapy. Neoantigen Number Crunching Immunotherapy with anti–PD-1 and other checkpoint inhibitors is an important cancer treatment modality, but improved biomarkers are needed to better predict which patients will respond. Current computational approaches that assess tumor immunogenicity by deep sequencing of tumor samples to count mutations and predict neoantigen epitopes are unable to factor in clonal variation within tumors. Lu et al. developed an algorithm to calculate CSiN score, a metric that also integrates the distribution of mutations among tumor clones. Testing of CSiN score against other indices of tumor neoantigen burden revealed improved correlations with outcome and prognosis in cohorts of patients with tumor types known to be immunogenic. Calculation of CSiN scores from tumor genomics data may assist in selection of patients most likely to benefit from cancer immunotherapy. Lack of responsiveness to checkpoint inhibitors is a central problem in the modern era of cancer immunotherapy. Tumor neoantigens are critical targets of the host antitumor immune response, and their presence correlates with the efficacy of immunotherapy treatment. Many studies involving assessment of tumor neoantigens principally focus on total neoantigen load, which simplistically treats all neoantigens equally. Neoantigen load has been linked with treatment response and prognosis in some studies but not others. We developed a Cauchy-Schwarz index of Neoantigens (CSiN) score to better account for the degree of concentration of immunogenic neoantigens in truncal mutations. Unlike total neoantigen load determinations, CSiN incorporates the effect of both clonality and MHC binding affinity of neoantigens when characterizing tumor neoantigen profiles. By analyzing the clinical responses in 501 treated patients with cancer (with most receiving checkpoint inhibitors) and the overall survival of 1978 patients with cancer at baseline, we showed that CSiN scores predict treatment response to checkpoint inhibitors and prognosis in patients with melanoma, lung cancer, and kidney cancer. CSiN score substantially outperformed prior genetics-based prediction methods of responsiveness and fills an important gap in research involving assessment of tumor neoantigen burden.
Near-IR emissive rare-earth nanoparticles for guided surgery

Intraoperative image-guided surgery (IGS) has attracted extensive research interests in determination of tumor margins from surrounding normal tissues. Introduction of near infrared (NIR) fluorophores into IGS could significantly improve the in vivo imaging quality thus benefit IGS. Among the reported NIR fluorophores, rare-earth nanoparticles exhibit unparalleled advantages in disease theranostics by taking advantages such as large Stokes shift, sharp emission spectra, and high chemical/photochemical stability. The recent advances in elements doping and morphologies controlling endow the rare-earth nanoparticles with intriguing optical properties, including emission span to NIR-II region and long life-time photoluminescence. Particularly, NIR emissive rare earth nanoparticles hold advantages in reduction of light scattering, photon absorption and autofluorescence, largely improve the performance of nanoparticles in biological and pre-clinical applications. In this review, we systematically compared the benefits of RE nanoparticles with other NIR probes, and summarized the recent advances of NIR emissive RE nanoparticles in bioimaging, photodynamic therapy, drug delivery and NIR fluorescent IGS. The future challenges and promises of NIR emissive RE nanoparticles for IGS were also discussed.