nils-herrmann/clean_scirepeval huggingface dataset:
Artificial Immune Systems

The human immune system has numerous properties that make it ripe for exploitation in the computational domain, such as robustness and fault tolerance, and many different algorithms, collectively termed Artificial Immune Systems (AIS), have been inspired by it. Two generations of AIS are currently in use, with the first generation relying on simplified immune models and the second generation utilising interdisciplinary collaboration to develop a deeper understanding of the immune system and hence produce more complex models. Both generations of algorithms have been successfully applied to a variety of problems, including anomaly detection, pattern recognition, optimisation and robotics. In this chapter an overview of AIS is presented, its evolution is discussed, and it is shown that the diversification of the field is linked to the diversity of the immune system itself, leading to a number of algorithms as opposed to one archetypal system. Two case studies are also presented to help provide insight into the mechanisms of AIS; these are the idiotypic network approach and the Dendritic Cell Algorithm.
DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified Text

Existing text generation methods tend to produce repeated and “boring” expressions. To tackle this problem, we propose a new text generation model, called Diversity-Promoting Generative Adversarial Network (DP-GAN). The proposed model assigns low reward for repeatedly generated text and high reward for “novel” and fluent text, encouraging the generator to produce diverse and informative text. Moreover, we propose a novel languagemodel based discriminator, which can better distinguish novel text from repeated text without the saturation problem compared with existing classifier-based discriminators. The experimental results on review generation and dialogue generation tasks demonstrate that our model can generate substantially more diverse and informative text than existing
Efficient application of GPGPU for lava flow hazard mapping

The individuation of areas that are more likely to be impacted by new events in volcanic regions is of fundamental relevance for mitigating possible consequences, both in terms of loss of human lives and material properties. For this purpose, the lava flow hazard maps are increasingly used to evaluate, for each point of a map, the probability of being impacted by a future lava event. Typically, these maps are computed by relying on an adequate knowledge about the volcano, assessed by an accurate analysis of its past behavior, together with the explicit simulation of thousands of hypothetical events, performed by a reliable computational model. In this paper, General- Computation with Graphics Processing Units (GPGPU) is applied, in conjunction with the SCIARA lava flow Cellular Automata model, to the process of building the lava invasion maps. Using different GPGPU devices, the paper illustrates some different implementation strategies and discusses numerical results obtained for a case study at Mt. Etna (Italy), Europe’s most active volcano.
Genetic foundations of human intelligence

Individual differences in intelligence (cognitive abilities) are a prominent aspect of human psychology, and play a substantial role in influencing important life outcomes. Their phenotypic structure—as described by the science of psychometrics—is well understood and well replicated. Approximately half of the variance in a broad range of cognitive abilities is accounted by a general cognitive factor (g), small proportions of cognitive variance are caused by separable broad domains of mental function, and the substantial remainder is caused by variance that is unique to highly specific cognitive skills. The heritability of g is substantial. It increases from a low value in early childhood of about 30%, to well over 50% in adulthood, which continues into old age. Despite this, there is still almost no replicated evidence concerning the individual genes, which have variants that contribute to intelligence differences. Here, we describe the human intelligence phenotype, summarise the evidence for its heritability, provide an overview of and comment on molecular genetic studies, and comment on future progress in the field.
Nanofabrication and coloration study of artificial Morpho butterfly wings with aligned lamellae layers

The bright and iridescent blue color from Morpho butterfly wings has attracted worldwide attentions to explore its mysterious nature for long time. Although the physics of structural color by the nanophotonic structures built on the wing scales has been well established, replications of the wing structure by standard top-down lithography still remains a challenge. This paper reports a technical breakthrough to mimic the blue color of Morpho butterfly wings, by developing a novel nanofabrication process, based on electron beam lithography combined with alternate PMMA/LOR development/dissolution, for photonic structures with aligned lamellae multilayers in colorless polymers. The relationship between the coloration and geometric dimensions as well as shapes is systematically analyzed by solving Maxwell's Equations with a finite domain time difference simulator. Careful characterization of the mimicked blue by spectral measurements under both normal and oblique angles are carried out. Structural color in blue reflected by the fabricated wing scales, is demonstrated and further extended to green as an application exercise of the new technique. The effects of the regularity in the replicas on coloration are analyzed. In principle, this approach establishes a starting point for mimicking structural colors beyond the blue in Morpho butterfly wings.
A simulation as a service cloud middleware

Many seemingly simple questions that individual users face in their daily lives may actually require substantial number of computing resources to identify the right answers. For example, a user may want to determine the right thermostat settings for different rooms of a house based on a tolerance range such that the energy consumption and costs can be maximally reduced while still offering comfortable temperatures in the house. Such answers can be determined through simulations. However, some simulation models as in this example are stochastic, which require the execution of a large number of simulation tasks and aggregation of results to ascertain if the outcomes lie within specified confidence intervals. Some other simulation models, such as the study of traffic conditions using simulations may need multiple instances to be executed for a number of different parameters. Cloud computing has opened up new avenues for individuals and organizations Shashank Shekhar shashank.shekhar@vanderbilt.edu Hamzah Abdel-Aziz hamzah.abdelaziz@vanderbilt.edu Michael Walker michael.a.walker.1@vanderbilt.edu Faruk Caglar faruk.caglar@vanderbilt.edu Aniruddha Gokhale a.gokhale@vanderbilt.edu Xenofon Koutsoukos xenonfon.koutsoukos@vanderbilt.edu 1 Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA with limited resources to obtain answers to problems that hitherto required expensive and computationally-intensive resources. This paper presents SIMaaS, which is a cloudbased Simulation-as-a-Service to address these challenges. We demonstrate how lightweight solutions using Linux containers (e.g., Docker) are better suited to support such services instead of heavyweight hypervisor-based solutions, which are shown to incur substantial overhead in provisioning virtual machines on-demand. Empirical results validating our claims are presented in the context of two
A Decentralized Model for Information Flow Control

This paper presents a new model for controlling information flow in systems with mutual distrust and decentralized authority. The model allows users to share information with distrusted code (e.g., downloaded applets), yet still control how that code disseminates the shared information to others. The model improves on existing multilevel security models by allowing users to declassify information in a decentralized way, and by improving support for fine-grained data sharing. The paper also shows how static program analysis can be used to certify proper information flows in this model and to avoid most run-time information flow checks.
Treatment of Refractory and Super-refractory Status Epilepticus

Refractory and super-refractory status epilepticus (SE) are serious illnesses with a high risk of morbidity and even fatality. In the setting of refractory generalized convulsive SE (GCSE), there is ample justification to use continuous infusions of highly sedating medications—usually midazolam, pentobarbital, or propofol. Each of these medications has advantages and disadvantages, and the particulars of their use remain controversial. Continuous EEG monitoring is crucial in guiding the management of these critically ill patients: in diagnosis, in detecting relapse, and in adjusting medications. Forms of SE other than GCSE (and its continuation in a “subtle” or nonconvulsive form) should usually be treated far less aggressively, often with nonsedating anti-seizure drugs (ASDs). Management of “non-classic” NCSE in ICUs is very complicated and controversial, and some cases may require aggressive treatment. One of the largest problems in refractory SE (RSE) treatment is withdrawing coma-inducing drugs, as the prolonged ICU courses they prompt often lead to additional complications. In drug withdrawal after control of convulsive SE, nonsedating ASDs can assist; medical management is crucial; and some brief seizures may have to be tolerated. For the most refractory of cases, immunotherapy, ketamine, ketogenic diet, and focal surgery are among several newer or less standard treatments that can be considered. The morbidity and mortality of RSE is substantial, but many patients survive and even return to normal function, so RSE should be treated promptly and as aggressively as the individual patient and type of SE indicate.
Psychopathic predators? Getting specific about the relation between psychopathy and violence.

The psychopathy checklist-revised (PCL-R; Hare, 1991, 2003) is often used to assess risk of violence, perhaps based on the assumption that it captures emotionally detached individuals who are driven to prey upon others. This study is designed to assess the relation between (a) core interpersonal and affective traits of psychopathy and impulsive antisociality on the one hand and (b) the risk of future violence and patterns of motivation for past violence on the other.



A research team reliably assessed a sample of 158 male offenders for psychopathy, using both the interview-based PCL-R and the self-report psychopathic personality inventory (PPI: Lilienfeld & Andrews, 1996). Then, a second independent research team assessed offenders' lifetime patterns of violence and their motivation. After these baseline assessments, offenders were followed in prison or the community for up to 1 year to assess their involvement in 3 different forms of violence. Baseline and follow-up assessments included both interviews and reviews of official records.



First, the PPI manifested incremental validity in predicting future violence over the PCL-R (but not vice versa)-and most of its predictive power derived solely from impulsive antisociality. Second, impulsive antisociality-not interpersonal and affective traits specific to psychopathy-were uniquely associated with instrumental lifetime patterns of past violence. The latter psychopathic traits are narrowly associated with deficits in motivation for violence (e.g., lack of fear or lack of provocation).



These findings and their consistency with some past research led us to advise against making broad generalizations about the relation between psychopathy and violence.
Optimal Inference in Crowdsourced Classification via Belief Propagation

Crowdsourcing systems are popular for solving large-scale labeling tasks with low-paid workers. We study the problem of recovering the true labels from the possibly erroneous crowdsourced labels under the popular Dawid–Skene model. To address this inference problem, several algorithms have recently been proposed, but the best known guarantee is still significantly larger than the fundamental limit. We close this gap by introducing a tighter lower bound on the fundamental limit and proving that the belief propagation (BP) exactly matches the lower bound. The guaranteed optimality of BP is the strongest in the sense that it is information-theoretically impossible for any other algorithm to correctly label a larger fraction of the tasks. Experimental results suggest that the BP is close to optimal for all regimes considered and improves upon competing the state-of-the-art algorithms.
Global existence of solutions to fuzzy partial hyperbolic functional differential equations with generalized Hukuhara derivatives

We combine two aspects, fuzzy mathematics and partial functional differential equations to get fuzzy partial hyperbolic functional differential equations in the sense of generalized Hukuhara-differentiability. In this setting, the global existence of four solutions, each one corresponding to a different type of differentiability, is investigated in the whole infinite domain [a,∞) × [b,∞). 7 8 9 10
Efficient Hybrid Proxy Re-Encryption for Practical Revocation and Key Rotation

We consider the problems of i) using public-key encryption to enforce dynamic access control on clouds; and ii) key rotation of data stored on clouds. Historically, proxy re-encryption, ciphertext delegation, and related technologies have been advocated as tools that allow for revocation and the ability to cryptographically enforce dynamic access control on the cloud, and more recently they have suggested for key rotation of data stored on clouds. Current literature frequently assumes that data is encrypted directly with public-key encryption primitives. However, for efficiency reasons systems would need to deploy with hybrid encryption. Unfortunately, we show that if hybrid encryption is used, then schemes are susceptible to a key-scraping attack. Given a proxy re-encryption or delegation primitive, we show how to construct a new hybrid scheme that is resistant to this attack and highly efficient. The scheme only requires the modification of a small fraction of the bits of the original ciphertext. The number of modifications scales linearly with the security parameter and logarithmically with the file length: it does not require the entire symmetric-key ciphertext to be re-encrypted! Beyond the construction, we introduce new security definitions for the problem at hand, prove our construction secure, discuss use cases, and provide quantitative data showing its practical benefits and efficiency. We show the construction extends to identity-based proxy re-encryption and revocable-storage attribute-based encryption, and thus that the construction is robust, supporting most primitives of interest.
An Industrial Application of Mutation Testing: Lessons, Challenges, and Research Directions

Mutation analysis evaluates a testing or debugging technique by measuring how well it detects mutants, which are systematically seeded, artificial faults. Mutation analysis is inherently expensive due to the large number of mutants it generates and due to the fact that many of these generated mutants are not effective; they are redundant, equivalent, or simply uninteresting and waste computational resources. A large body of research has focused on improving the scalability of mutation analysis and proposed numerous optimizations to, e.g., select effective mutants or efficiently execute a large number of tests against a large number of mutants. However, comparatively little research has focused on the costs and benefits of mutation testing, in which mutants are presented as testing goals to a developer, in the context of an industrial-scale software development process. This paper draws on an industrial application of mutation testing, involving 30,000+ developers and 1.9 million change sets, written in 4 programming languages. It shows that mutation testing with productive mutants does not add a significant overhead to the software development process and reports on mutation testing benefits perceived by developers. This paper also quantifies the costs of unproductive mutants, and the results suggest that achieving mutation adequacy is neither practical nor desirable. Finally, this paper describes lessons learned from these studies, highlights the current challenges of efficiently and effectively applying mutation testing in an industrial-scale software development process, and outlines research directions.
BIOEFFICACY OF PLANT EXTRACTS ON FUSARIUM OXYSPORUM F . SP . CUBENSE CAUSING PANAMA WILT OF BANANA

Wilt is an important disease of banana causing significant reduction in yield. In presentstudy, the pathogenic fungus was isolated frompseudo stem of infected plants of banana.The in vitro efficacy of different plant extracts viz.,Azardiachta indica, Artemessia annua, Eucalyptus globulus, and Ocimum sanctum were tested to managepanama wilt of banana. Different concentrations 5, 10, 15 and 20% of plant extracts were used in the study. All the plant extracts showed significant reduction in the growth ofpathogen. Among the different extracts 20% of Azardiachta indica was found most effective followed by Eucalyptus globulus, Artemessia annua and Ocimum sanctum.
When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs

Remote sensing image scene classification is an active and challenging task driven by many applications. More recently, with the advances of deep learning models especially convolutional neural networks (CNNs), the performance of remote sensing image scene classification has been significantly improved due to the powerful feature representations learnt through CNNs. Although great success has been obtained so far, the problems of within-class diversity and between-class similarity are still two big challenges. To address these problems, in this paper, we propose a simple but effective method to learn discriminative CNNs (D-CNNs) to boost the performance of remote sensing image scene classification. Different from the traditional CNN models that minimize only the cross entropy loss, our proposed D-CNN models are trained by optimizing a new discriminative objective function. To this end, apart from minimizing the classification error, we also explicitly impose a metric learning regularization term on the CNN features. The metric learning regularization enforces the D-CNN models to be more discriminative so that, in the new D-CNN feature spaces, the images from the same scene class are mapped closely to each other and the images of different classes are mapped as farther apart as possible. In the experiments, we comprehensively evaluate the proposed method on three publicly available benchmark data sets using three off-the-shelf CNN models. Experimental results demonstrate that our proposed D-CNN methods outperform the existing baseline methods and achieve state-of-the-art results on all three data sets.
A Novel Approximation to Dynamic Time Warping allows Anytime Clustering of Massive Time Series Datasets

Given the ubiquity of time series data, the data mining community has spent significant time investigating the best time series similarity measure to use for various tasks and domains. After more than a decade of extensive efforts, there is increasing evidence that Dynamic Time Warping (DTW) is very difficult to beat. Given that, recent efforts have focused on making the intrinsically slow DTW algorithm faster. For the similarity-search task, an important subroutine in many data mining algorithms, significant progress has been made by replacing the vast majority of expensive DTW calculations with cheap-to-compute lower bound calculations. However, these lower bound based optimizations do not directly apply to clustering, and thus for some realistic problems, clustering with DTW can take days or weeks. In this work, we show that we can mitigate this untenable lethargy by casting DTW clustering as an anytime algorithm. At the heart of our algorithm is a novel data-adaptive approximation to DTW which can be quickly computed, and which produces approximations to DTW that are much better than the best currently known linear-time approximations. We demonstrate our ideas on real world problems showing that we can get virtually all the accuracy of a batch DTW clustering algorithm in a fraction of the time.
The microbiome in early life: implications for health outcomes

Recent studies have characterized how host genetics, prenatal environment and delivery mode can shape the newborn microbiome at birth. Following this, postnatal factors, such as antibiotic treatment, diet or environmental exposure, further modulate the development of the infant's microbiome and immune system, and exposure to a variety of microbial organisms during early life has long been hypothesized to exert a protective effect in the newborn. Furthermore, epidemiological studies have shown that factors that alter bacterial communities in infants during childhood increase the risk for several diseases, highlighting the importance of understanding early-life microbiome composition. In this review, we describe how prenatal and postnatal factors shape the development of both the microbiome and the immune system. We also discuss the prospects of microbiome-mediated therapeutics and the need for more effective approaches that can reconfigure bacterial communities from pathogenic to homeostatic configurations.
New IBD genetics: common pathways with other diseases.

Complex disease genetics has been revolutionised in recent years by the advent of genome-wide association (GWA) studies. The chronic inflammatory bowel diseases (IBDs), Crohn's disease and ulcerative colitis have seen notable successes culminating in the discovery of 99 published susceptibility loci/genes (71 Crohn's disease; 47 ulcerative colitis) to date. Approximately one-third of loci described confer susceptibility to both Crohn's disease and ulcerative colitis. Amongst these are multiple genes involved in IL23/Th17 signalling (IL23R, IL12B, JAK2, TYK2 and STAT3), IL10, IL1R2, REL, CARD9, NKX2.3, ICOSLG, PRDM1, SMAD3 and ORMDL3. The evolving genetic architecture of IBD has furthered our understanding of disease pathogenesis. For Crohn's disease, defective processing of intracellular bacteria has become a central theme, following gene discoveries in autophagy and innate immunity (associations with NOD2, IRGM, ATG16L1 are specific to Crohn's disease). Genetic evidence has also demonstrated the importance of barrier function to the development of ulcerative colitis (HNF4A, LAMB1, CDH1 and GNA12). However, when the data are analysed in more detail, deeper themes emerge including the shared susceptibility seen with other diseases. Many immune-mediated diseases overlap in this respect, paralleling the reported epidemiological evidence. However, in several cases the reported shared susceptibility appears at odds with the clinical picture. Examples include both type 1 and type 2 diabetes mellitus. In this review we will detail the presently available data on the genetic overlap between IBD and other diseases. The discussion will be informed by the epidemiological data in the published literature and the implications for pathogenesis and therapy will be outlined. This arena will move forwards very quickly in the next few years. Ultimately, we anticipate that these genetic insights will transform the landscape of common complex diseases such as IBD.