Miracle-dz/UniFAR-FTUs huggingface dataset:
Valuation and Hedging of the Ruin‐Contingent Life Annuity (Rcla)

This paper analyzes a novel type of mortality contingent-claim called a ruin-contingent life annuity (RCLA). This product fuses together a path-dependent equity put option with a "personal longevity" call option. The annuitant's (i.e. long position) payoff from a generic RCLA is \$1 of income per year for life, akin to a defined benefit pension, but deferred until a pre-specified financial diffusion process hits zero. We derive the PDE and relevant boundary conditions satisfied by the RCLA value (i.e. the hedging cost) assuming a complete market where No Arbitrage is possible. We then describe some efficient numerical techniques and provide estimates of a typical RCLA under a variety of realistic parameters. The motivation for studying the RCLA on a stand-alone basis is two-fold. First, it is implicitly embedded in approximately \$1 trillion worth of U.S. variable annuity (VA) policies; which have recently attracted scrutiny from financial analysts and regulators. Second, the U.S. administration - both Treasury and Department of Labor - have been encouraging Defined Contribution (401k) plans to offer stand-alone longevity insurance to participants, and we believe the RCLA would be an ideal and cost effective candidate for that job.
A novel Topology Aggregation Algorithm for Hierarchical Routing in Asymmetric Networks

: Routing scalability may degrade due to the increasing network scale. So the global communication network in the future will intend to a hierarchical routing architecture with multiple subnetworks and routing layers. In hierarchical routing architecture, each subnetwork compacts its internal topology by topology aggregation algorithm and propagates the aggregated routing information to outside. So the topology aggregation algorithm will affect the routing performance greatly. In this paper, a novel topology aggregation method—SHEF algorithm is proposed to compact the asymmetric directed graph. Based upon the heavy-edge-first principle, the source-oriented method is integrated with minimum spanning tree method to aggregate the directed graph. Compared with the traditional methods, the suggested algorithm can reduce the weight deviation by 75 percent. Simulation results show that SHEF has a good tradeoff between the routing information complexity and routing accuracy, so yields outstanding routing performance.
Human-Automation Interaction Research

Scientific research accesses the past to predict the future. The history of science is often best told by those who have lived it. Our purpose is to provide a brief history of human-automation interaction research, including a review of theories for describing human performance with automated systems, an accounting of automation effects on cognitive performance, a description of the origins of adaptive automation and key developments, and an identification of contemporary methods and issues in operator functional state classification. Based on this history and acknowledgements of the state of the art of human-automaton interaction, future predictions are offered.
Formação de residentes multiprofissionais em saúde: limites e contribuições para a integração ensino-serviço

Objetivo: analisar os limites e contribuições na formação dos residentes multiprofissionais em saúde para a integração ensino-serviço. Método: pesquisa qualitativa, realizada com 13 docentes vinculados a um Programa de Residência Multiprofissional em Saúde, os quais participaram do estudo por meio de entrevistas semiestruturadas. Os dados foram analisados mediante análise temática de Minayo e os princípios éticos foram respeitados. Resultados: a formação dos residentes é permeada por limitações envolvendo a falta de trabalhadores nos serviços de saúde que contemplem todos os núcleos profissionais propostos pelo Programa e o estranhamento do papel dos residentes ao serem integrantes provisórios das equipes de saúde. Para tanto, a valorização das atividades realizadas a partir da inserção dos residentes tem contribuído para a atenção à saúde e para os serviços de saúde. Conclusão: salienta-se, como contribuição, a discussão da temática de formação profissional, tendo em vista a importância de refletir sobre as transformações das práticas em saúde na perspectiva multiprofissional e de acordo com as necessidades locorregionais.
Temporal Matching Kernel with Explicit Feature Maps

This paper proposes a framework for content-based video retrieval that addresses various tasks as particular event retrieval, copy detection or video synchronization. Given a video query, the method is able to efficiently retrieve, from a large collection, similar video events or near-duplicates with temporarily consistent excerpts. As a byproduct of the representation, it provides a precise temporal alignment of the query and the detected video excerpts. Our method converts a series of frame descriptors into a single visual-temporal descriptor, called a temporal invariant match kernel. This representation takes into account the relative positions of the visual frames: the frame descriptors are jointly encoded with their timestamps. When matching two videos, the method produces a score function for all possible relative timestamps, which is maximized to obtain both the similarity score and the relative time offset. Then, we propose two complementary contributions to further improve the detection and localization performance.The first is a novel query expansion method that takes advantage of the joint descriptor/timestamp representation to automatically align the first result set and produce an enriched temporal query. In contrast to other query expansion methods proposed for videos, it preserves the localization capability. Second, we improve the localization trade-off between quality and representation size by using several complementary temporal match kernels. We evaluate our approach on benchmarks for particular event retrieval, copy detection and video synchronization. Our experiments show that our approach achieve excellent detection and localization results.
Adolescent subgenual anterior cingulate activity is related to harm avoidance

Recent adult studies suggest that the subgenual anterior cingulate cortex (sgACC) is involved in fundamental mental operations such as affective processing and inhibitory control. However, little is known about inhibition-associated sgACC function in adolescents, and there are no published data regarding whether personality characteristics are related to inhibition-associated sgACC brain activity in adolescents. This study examined the relationship between personality and inhibition-associated sgACC response in healthy adolescents. Seventeen adolescents of 13–17 years of age underwent functional magnetic resonance imaging while performing a parametric stop-signal task. Greater harm avoidance levels were significantly associated with increased inhibition-related sgACC activity. These results establish, for the first time, a link between personality and differential sgACC activation in adolescents.
A Comparative Study of Lossless Compression Algorithm on Text Data

With increasing amount of text data being stored rapidly, efficient information retrieval and Storage in the compressed domain has become a major concern. Compression is the process of coding that will effectively reduce the total number of bits needed to represent certain information. Data compression has been one of the critical enabling technologies for the ongoing digital multimedia revolution. There are lots of data compression algorithms which are available to compress files of different formats. This paper provides a survey of different basic lossless data compression algorithms on English text files: LZW, Huffman, Fixed-length code (FLC), and Huffman after using Fixed-length code (HFLC). All the above algorithms are evaluated and tested on different text files of different sizes. To find the best algorithm among above, comparison is made in terms of compression: Size, Ratio, Time (Speed), and Entropy. The paper is concluded by the decision showing which algorithm performs best over text data.
Inducing Probabilistic Programs by Bayesian Program Merging

This report outlines an approach to learning generative models from data. We express models as probabilistic programs, which allows us to capture abstract patterns within the examples. By choosing our language for programs to be an extension of the algebraic data type of the examples, we can begin with a program that generates all and only the examples. We then introduce greater abstraction, and hence generalization, incrementally to the extent that it improves the posterior probability of the examples given the program. Motivated by previous approaches to model merging and program induction, we search for such explanatory abstractions using program transformations. We consider two types of transformation: Abstraction merges common subexpressions within a program into new functions (a form of anti-unification). Deargumentation simplifies functions by reducing the number of arguments. We demonstrate that this approach finds key patterns in the domain of nested lists, including parameterized sub-functions and stochastic recursion.
Domain distribution and intrinsic disorder in hubs in the human protein–protein interaction network

Intrinsic disorder and distributed surface charge have been previously identified as some of the characteristics that differentiate hubs (proteins with a large number of interactions) from non‐hubs in protein–protein interaction networks. In this study, we investigated the differences in the quantity, diversity, and functional nature of Pfam domains, and their relationship with intrinsic disorder, in hubs and non‐hubs. We found that proteins with a more diverse domain composition were over‐represented in hubs when compared with non‐hubs, with the number of interactions in hubs increasing with domain diversity. Conversely, the fraction of intrinsic disorder in hubs decreased with increasing number of ordered domains. The difference in the levels of disorder was more prominent in hubs and non‐hubs with fewer domains. Functional analysis showed that hubs were enriched in kinase and adaptor domains acting primarily in signal transduction and transcription regulation, whereas non‐hubs had more DNA‐binding domains and were involved in catalytic activity. Consistent with the differences in the functional nature of their domains, hubs with two or more domains were more likely to connect distinct functional modules in the interaction network when compared with single domain hubs. We conclude that the availability of greater number and diversity of ordered domains, in addition to the tendency to have promiscuous domains, differentiates hubs from non‐hubs and provides an additional means of achieving interaction promiscuity. Further, hubs with fewer domains use greater levels of intrinsic disorder to facilitate interaction promiscuity with the prevalence of disorder decreasing with increasing number of ordered domains.
The Big Data Newsvendor: Practical Insights from Machine Learning

In Ban and Rudin’s (2018) “The Big Data Newsvendor: Practical Insights from Machine Learning,” the authors take an innovative machine-learning approach to a classic problem solved by almost every company, every day, for inventory management. By allowing companies to use large amounts of data to predict the correct answers to decisions directly, they avoid intermediate questions, such as “how many customers will we get tomorrow? ” and instead can tell the company how much inventory to stock for these customers. This has implications for almost all other decision-making problems considered in operations research, which has traditionally considered data estimation separately from the decision optimization. Their proposed methods are shown to work both analytically and empirically with the latter explored in a hospital nurse staffing example in which the best one-step, feature-based newsvendor algorithm (the kernel-weights optimization method) is shown to beat the best-practice benchmark by 24% in the out-of-sample cost at a fraction of the speed.
On the time relations of mental processes: An examination of systems of processes in cascade.

This article examines the possibility that the components of an informationprocessing system all operate continuously, passing information from one to the next as it becomes available. A model called the cascade model is presented and it is shown to be compatible with the general form of the relation between time and accuracy in speed-accuracy trade-off experiments. In the model , experimentlLlmanipulations may have either or both of two effects on a processing level: They may alter the rate of response or the asymptotic quality pf the output. The effects of such manipulations on the output of a system of proessesare described. The model is then used to reexamine, the subtraction and additive factors methods for analyzing the composition of systems of processes. The examination of the additive factors method yields particularly interesting results. Among them is the finding that factors that affect the rates of two different processes would be expected to have additive effects on reaction times under the cascade model, whereas factors that both affect the rate of the same process would tend to interact, just as in the case in which the manipulations affect the durations of discrete stages. On the other hand, factors that affect asymptotic output tend to interact whether they affect the same or different processes. In light of this observation, the conclusions drawn from several studies about the locus of perceptual and attentional effects on processing are reexamined. Finally, an outline is presented of a new method for analyzing processes in cascade. The method extends the additive factors method to an analysis of the parameters of the function relating response time and accuracy.
in Source Code using Latent Dirichlet Allocation

Acquiring general understanding of large software systems and components from which they are built can be a time consuming task, but having such an understanding is an important prerequisite to adding features or fixing bugs. In this paper we propose the tool, namely TopicXP, to support developers during such software maintenance tasks by extracting and analyzing unstructured information in source code identifier names and comments using Latent Dirichlet Allocation. TopicXP enables developers to gain an overview of a software system under analysis by extracting and visualizing natural language topics, which generally correspond to concepts or features implemented in software classes. TopicXP is implemented as an open-source Eclipse plug-in, which proposes interactive visualization of topics along with structural dependencies between underlying classes implementing these topics. The paper also presents the results of a preliminary user study aimed at evaluating TopicXP.
On the Instability and Folding Deformation of a layered Viscoelastic Medium in Compression

When a layer of material embedded in an infinite medium is subject to a comfiression parallel with the layer an instability tends to develofi which manifests itself in the folding of the layer. This phenomenon is examined here for the general case where the layer and the surrounding medium are both viscoelastic. This problem which was examined in preliminary form in an earlier publication [6]t is treated here with particular attention to the effect of interfacial adherence of the layer and the medium, and to an evaluation of the amfilitude of the folding. In general there is a lower and upfier-critical value of the compressive load between which folding occurs with a finite rate of deformation. There appears also a dominant wave length, for which the rate of folding is maximum under a given load. The dominant wave length may or may not depend on the load. The effect of interfacial adherence while not negligible is not generally significant. The rate of folo%g increases very rapidly beyond a certain value of the wiscosity ratio of the two media. A brief discussion is also included of the thermodynamic imfilications of incremental stress-strain relations in prestressed media. c ONSIDER an inhomogeneous elastic medium of infinite extent containing a layer of material of higher rigidity. It is well known that such a system when subject to a compression parallel with the layer will buckle beyond a certain critical value of the load. This buckling instability manifests itself through a folding of the layer which appears suddenly with a characteristic wa.ve length at the critical load.2 The nature of the phenomenon is typified in the elementary treatment of the buckling of a beam or an elastic foundation. It is clear that this phenomenon is a particular case of the much more general one where the layer and the medium are viscoelastic. In this case we shall not in general observe a sharp buckling, but a folding of the layer will appear with a rate of folding and a wave-length content which are dependent on the physical properties, the compressive load, and the initial irregularities of the layer. The present paper is one in a series presenting the results of an investigation of these phenomena carried out at the Shell Development Company during the past 10 years. The subject is of course of primary interest to the geologist since it furnishes for the first time a quantitative basis for the evaluation of folding and other tectonic features of stratified rock formations. There are also interesting technological applications connected with the buckling failure of inhomogeneous structural material such as sandwich panels under conditions of creep and the deformation due to instability of materials in general in the viscoelastic temperature range when there is a high-temperature gradient. The ‘Numbers in brackets designate References at end of paper. * The buckling of layered elastic media was investigated by Bijlaard
Prefrontal dopamine and behavioral flexibility: shifting from an “inverted-U” toward a family of functions

Studies on prefrontal cortex (PFC) dopamine (DA) function have revealed its essential role in mediating a variety of cognitive and executive functions. A general principle that has emerged (primarily from studies on working memory) is that PFC DA, acting on D1 receptors, regulates cognition in accordance to an “inverted-U” shaped function, so that too little or too much activity has detrimental effects on performance. However, contemporary studies have indicated that the receptor mechanisms through which mesocortical DA regulates different aspects of behavioral flexibility can vary considerably across different DA receptors and cognitive operations. This article will review psychopharmacological and neurochemical data comparing and contrasting the cognitive effects of antagonism and stimulation of different DA receptors in the medial PFC. Thus, set-shifting is dependent on a co-operative interaction between PFC D1 and D2 receptors, yet, supranormal stimulation of these receptors does not appear to have detrimental effects on this function. On the other hand, modification of cost/benefit decision biases in situations involving reward uncertainty is regulated in complex and sometimes opposing ways by PFC D1 vs. D2 receptors. When viewed collectively, these findings suggest that the “inverted-U” shaped dose-response curve underlying D1 receptor modulation of working memory is not a one-size-fits-all function. Rather, it appears that mesocortical DA exerts its effects via a family of functions, wherein reduced or excessive DA activity can have a variety of effects across different cognitive domains.
Building a Repository for Online Laboratory Learning Scenarios

In the framework of our work carried out with the LORNET Research Network, we present a model for online laboratory repositories of learning scenarios based on IMS-Learning Design. A number of considerations should be taken into account to achieve a composition of services related to a given scenario for the required ad hoc synchronous and collaborative learning environment built on the fly. This paper will also show how the concept was implemented within a telepresence learning environment with a group of students using remotely and collaboratively laboratory instruments
MPLS Edge Nodes with Ability of Multiple LSPs Routing : Novel Adaptive Schemes and Performance Analysis

Multiprotocol label switching (MPLS) provides a way to establish one or several paths for traffic demands in the form of label switched paths (LSP). In this paper we propose some schemes for the multiple LSPs operation of edge nodes in MPLS networks. The proposals comprise the mechanisms of load-dependent path-decision, intervention and path selection policy to facilitate efficient multiple LSPs routing. We develop a performance model based on an extended queue MMCPP/GE/m/K. The proposed schemes are evaluated via an example having input parameters determined from a real traffic trace.
R1SVM: A Randomised Nonlinear Approach to Large-Scale Anomaly Detection

The problem of unsupervised anomaly detection arises in awide variety of practical applications. While one-class sup-port vector machines have demonstrated their effectiveness asan anomaly detection technique, their ability to model largedatasets is limited due to their memory and time complexityfor training. To address this issue for supervised learning ofkernel machines, there has been growing interest in randomprojection methods as an alternative to the computationallyexpensive problems of kernel matrix construction and sup-port vector optimisation. In this paper we leverage the theoryof nonlinear random projections and propose the RandomisedOne-class SVM (R1SVM), which is an efficient and scalableanomaly detection technique that can be trained on large-scale datasets. Our empirical analysis on several real-life andsynthetic datasets shows that our randomised 1SVM algo-rithm achieves comparable or better accuracy to deep autoen-coder and traditional kernelised approaches for anomaly de-tection, while being approximately 100 times faster in train-ing and testing
From hyperplanes to large-margin classifiers: applications of SAR ATR

In this paper, the structural risk minimization (SRM) criterion is employed to train a large margin classifier, the support vector machine (SVM). Its relative performance is compared with traditional classifiers employing hyperplanes against a realistic difficult problem, the synthetic aperture radar (SAR) automatic target recognition (ATR). In most pattern recognition applications, the task is to perform classification into a fixed number of classes. However, in some practical cases, such as ATR, one also needs to carry out a reliable pattern rejection. Experimental results showed that the SVM with the Gaussian kernels performs well in target recognition. Moreover, the SVM is able to form a local or 'bounded' decision region that presents better rejection to confusers.
Stop procrastinating: TILT, time is life time, a persuasive application

As smartphone is spreading all over the world, deep in our daily life, new problems emerge like smartphone addiction and procrastination. We propose TILT (Time Is Life Time), a persuasive application that fights procrastination and helps people to decrease phone usage. TILT is based on self-monitoring, a design principle for persuasive technology. Persuasive messages are adapted to the user context to bring diversity and relevance to persuasion. A six-weeks trial is reported showing that persuasion adaptation strengthens persuasive power of self-monitoring and limits its attrition.