The importance of being scrambled: supercharged Quasi Monte Carlo

In many financial applications Quasi Monte Carlo (QMC) based on Sobol low-discrepancy sequences (LDS) outperforms Monte Carlo showing faster and more stable convergence. However, unlike MC QMC lacks a practical error estimate. Randomized QMC (RQMC) method combines the best of two methods. Application of scrambled LDS allow to compute confidence intervals around the estimated value, providing a practical error bound. Randomization of Sobol' LDS by two methods: Owen's scrambling and digital shift are compared considering computation of Asian options and Greeks using hyperbolic local volatility model. RQMC demonstrated the superior performance over standard QMC showing increased convergence rates and providing practical error bounds.Comment: arXiv admin note: text overlap with arXiv:2106.0842

Estimating Degradation Model Parameters from Character Images

This paper discusses the use of character images to determine the parameters of an image degradation model. The acute angles in character images provide information used to find the model parameters. Three experiments are conducted to evaluate the use of characters. In the first experiment, large quantities of corners from character images are used to investigate how their contribution affects the mean and the standard deviation of the parameter estimators. In the second experiment, we focus on the relationship between the angles of the corners used in estimation and the estimation results. In the last experiment, we examine how likely the text in a common page would offer a reasonable estimation result compared to the results from experiments 1 and 2

What is the Connection Between Issues, Bugs, and Enhancements? (Lessons Learned from 800+ Software Projects)

Agile teams juggle multiple tasks so professionals are often assigned to multiple projects, especially in service organizations that monitor and maintain a large suite of software for a large user base. If we could predict changes in project conditions changes, then managers could better adjust the staff allocated to those projects.This paper builds such a predictor using data from 832 open source and proprietary applications. Using a time series analysis of the last 4 months of issues, we can forecast how many bug reports and enhancement requests will be generated next month. The forecasts made in this way only require a frequency count of this issue reports (and do not require an historical record of bugs found in the project). That is, this kind of predictive model is very easy to deploy within a project. We hence strongly recommend this method for forecasting future issues, enhancements, and bugs in a project.Comment: Accepted to 2018 International Conference on Software Engineering, at the software engineering in practice track. 10 pages, 10 figure

Solving Linear Coupled Fractional Differential Equations by Direct Operational Method and Some Applications

A new direct operational inversion method is introduced for solving coupled linear systems of ordinary fractional differential equations. The solutions so-obtained can be expressed explicitly in terms of multivariate Mittag-Leffler functions. In the case where the multiorders are multiples of a common real positive number, the solutions can be reduced to linear combinations of Mittag-Leffler functions of a single variable. The solutions can be shown to be asymptotically oscillatory under certain conditions. This technique is illustrated in detail by two concrete examples, namely, the coupled harmonic oscillator and the fractional Wien bridge circuit. Stability conditions and simulations of the corresponding solutions are given

Cloud-Based Data Analytics on Human Factor Measurement to Improve Safer Transport

Improving safer transport includes individual and collective behavioural aspects and their interaction. A system that can monitor and evaluate the human cognitive and physical capacities based on human factor measurement is often beneficial to improve safety in driving condition. However, analysis and evaluation of human factor measurement i.e. demographics, behaviour and physiology in real-time is challenging. This paper presents a methodology for cloud-based data analysis, categorization and metrics correlation in real-time through a H2020 project called SimuSafe. Initial implementation of this methodology shows a step-by-step approach which can handle huge amount of data with variation and verity in the cloud

Deficits in temporal order memory induced by interferon-alpha (IFN-α) treatment are rescued by aerobic exercise

Patients receiving cytokine immunotherapy with IFN-α frequently present with neuropsychiatric consequences and cognitive impairments, including a profound depressive-like symptomatology. While the neurobiological substrates of the dysfunction that leads to adverse events in IFN-α-treated patients remains ill-defined, dysfunctions of the hippocampus and prefrontal cortex (PFC) are strong possibilities. To date, hippocampal deficits have been well-characterised; there does however remain a lack of insight into the nature of prefrontal participation. Here, we used a PFC-supported temporal order memory paradigm to examine if IFN-α treatment induced deficits in performance; additionally, we used an object recognition task to assess the integrity of the perirhinal cortex (PRH). Finally, the utility of exercise as an ameliorative strategy to recover temporal order deficits in rats was also explored. We found that IFN-α-treatment impaired temporal order memory discriminations, whereas recognition memory remained intact, reflecting a possible dissociation between recognition and temporal order memory processing. Further characterisation of temporal order memory impairments using a longitudinal design revealed that deficits persisted for 10 weeks following cessation of IFN-α-treatment. Finally, a 6 week forced exercise regime reversed IFN-α-induced deficits in temporal order memory. These data provide further insight into the circuitry involved in cognitive impairments arising from IFN-α-treatment. Here we suggest that PFC (or the hippocampo-prefrontal pathway) may be compromised whilst the function of the PRH is preserved. Deficits may persist after cessation of IFN-α-treatment which suggests that extended patient monitoring is required. Aerobic exercise may be restorative and could prove beneficial for patients treated with IFN-α

PENGARUH PELATIHAN DAN MOTIVASI TERHADAP KINERJA KARYAWAN KASUS PERUSAHAAN DISTRIBUTOR ALAT BERAT

Tantangan untuk menghadapi persaingan pada bisnis alat-alat berat mengakibatkan PT ABC Tbkharus dapat meningkatkan kinerja divisi yang berhubungan dengan kegiatan purna jual (divisi service). Pelatihandan motivasi karyawan dapat meningkatkan kinerja karyawan. Tujuan dari penelitian ini adalah (1) menganalisistingkat persepsi karyawan terhadap pelatihan, motivasi, dan kinerja karyawan; (2) menganalisis pengaruh pelatihanterhadap kinerja karyawan; (3) menganalisis pengaruh motivasi terhadap kinerja karyawan; and (4) menganalisispengaruh pelatihan dan motivasi terhadap kinerja karyawan. Penelitian ini menggunakan metode deskriptifmelalui kuesioner. Jumlah sampel dalam penelitian ini adalah 71 karyawan divisi service pada PT ABC Tbk.Data diperoleh melalui penyebaran kuesioner, kemudian dianalisa menggunakan analisis regresi linier berganda.Hasil penelitian menunjukkan bahwa persepsi karyawan terhadap pelatihan, motivasi, dan kinerja tergolong baik.Hasil penelitian menggunakan analisis regresi linear berganda menunjukkan bahwa motivasi berpengaruh nyataterhadap kinerja, namun pelatihan tidak berpengaruh nyata terhadap kinerja. Berdasarkan hasil penelitian tersebutdan untuk meningkatkan kinerja karyawan, saran untuk manajemen adalah : (1) menyelenggarakan pelatihan yangsesuai dengan tugas dan tanggung jawab karyawan; (2) memerhatikan lingkungan kerja untuk menjaga motivasikaryawan; (3) menjaga semangat kerja karyawan.Kata kunci : kinerja, motivasi, pelatihan, regresi linier berganda

Space and time-related firing in a model of hippocampo-cortical interactions

International audienceIn a previous model [3], a spectral timing neural network [4] was used to account for the role of the Hs in the acquisition of classical conditioning. The ability to estimate the timing between separate events was then used to learn and predict transitions between places in the environment. We propose a neural architecture based on this work and explaining the out-of-field activities in the Hs along with their temporal prediction capabilities. The model uses the hippocampo-cortical pathway as a means to spread reward signals to entorhinal neurons. Secondary predictions of the reward signal are then learned, based on transition learning, by pyramidal neurons of the CA region

Regulation of Yersina pestis Virulence by AI-2 Mediated Quorum Sensing

The proposed research was motivated by an interest in understanding Y. pestis virulence mechanisms and bacteria cell-cell communication. It is expected that a greater understanding of virulence mechanisms will ultimately lead to biothreat countermeasures and novel therapeutics. Y. pestis is the etiological agent of plague, the most devastating disease in human history. Y. pestis infection has a high mortality rate and a short incubation before mortality. There is no widely available and effective vaccine for Y. pestis and multi-drug resistant strains are emerging. Y. pestis is a recognized biothreat agent based on the wide distribution of the bacteria in research laboratories around the world and on the knowledge that methods exist to produce and aerosolize large amounts of bacteria. We hypothesized that cell-cell communication via signaling molecules, or quorum sensing, by Y. pestis is important for the regulation of virulence factor gene expression during host invasion, though a causative link had never been established. Quorum sensing is a mode of intercellular communication which enables orchestration of gene expression for many bacteria as a function of population density and available evidence suggests there may be a link between quorum sensing and regulation of Y. pesits virulence. Several pathogenic bacteria have been shown to regulate expression of virulence factor genes, including genes encoding type III secretion, via quorum sensing. The Y. pestis genome encodes several cell-cell signaling pathways and the interaction of at least three of these are thought to be involved in one or more modes of host invasion. Furthermore, Y. pestis gene expression array studies carried out at LLNL have established a correlation between expression of known virulence factors and genes involved in processing of the AI-2 quorum sensing signal. This was a basic research project that was intended to provide new insights into bacterial intercellular communication and how it is used to regulate virulence in Y. pestis. It is known that many bacteria use intercellular signaling molecules to orchestrate gene expression and cellular function. A fair amount is known about production and uptake of signaling molecules, but very little is known about how intercellular signaling regulates other pathways. Although several studies demonstrate that intercellular signaling plays a role in regulating virulence in other pathogens, the link between signaling and regulation of virulence has not been established. Very little work had been done directly with Y. pestis intercellular signaling apart from the work carried out at LLNL. The research we proposed was intended to both establish a causative link between AI-2 intercellular signaling and regulation of virulence in Y. pestis and elucidate the fate of the AI-2 signaling molecule after it is taken up and processed by Y. pestis. Elucidating the fate of AI-2 was expected to lead directly to the understanding of how AI-2 signal processing regulates other pathways as well as provide new insights in this direction