Miracle-dz/UniFAR-FTUs huggingface dataset:
Safety by Construction: Well-behaved Scalable Systems

—This paper presents a formal framework that provides construction principles for well-behaved scalable systems , such that starting with a prototype system satisfying a desired safety property result in a scalable system satisfying a corresponding safety property, called scalable safety property . With respect to diﬀerent aspects of scalability, the focus of this work is on property preserving structural scalability . At that, we consider systems composed of a varying set of individual components where individual components of the same type behave in the same manner, which is characteristic for the type. The respective properties can rely on speciﬁc component types and a speciﬁc number of individual components but not on the speciﬁc individuality of the components. Well-behaved scalable systems are characterised by those systems, which fulﬁl such a kind of property if already one prototype system (depending on the property) fulﬁls that property. Suﬃcient conditions to specify a certain kind of basic well-behaved scalable systems are given and it is shown, how to construct more complex systems by the composition of several synchronisation conditions. Scalable safety properties can be used to express privacy policies as well as security and dependability requirements. It is demonstrated, how the parameterised problem of verifying such a property is reduced to a ﬁnite state problem for well-behaved scalable systems. The formal framework for well-behaved scalable systems is developed in terms of preﬁx closed formal languages and alphabetic language homomorphisms.
A Score Based Ranking of the Edges for the PC Algorithm

The result of applying the PC learning algorithm can depend of the order in which independence tests are carried out. Even if these tests are ordered by increasing size of conditional sets, the PC algorithm does not take into account which edges are weaker in order to be considered to be removed before the stronger edges. This paper proposes a new learning algorithm which scores the edges according to a Bayesian metric and adds them to the final graph according to this score. Then, conditional independence tests are carried out to remove edgess as in the PC algorithm. Also, this algorithm is hybridized with a variation of the PC algorithm consisting in determining minimum size cut sets between two nodes to study the deletion of an edge. Some experiments are carried out to evaluate the performance of the new proposals against the PC algorithm.
Illustrate Your Story: Enriching Text with Images

Human perception is known to be predominantly visual. As modern web infrastructure promoted the storage of media, the web-data paradigm shifted from text-only documents to those containing text and images. A multitude of blog posts, news articles, and social media posts exist on the Internet today as examples of multimodal stories. The manual alignment of images and text in a story is time-consuming and labor intensive. We present a web application for automatically selecting relevant images from an album and placing them in suitable contexts within a body of text. The application solves a global optimization problem that maximizes the coherence of text paragraphs and image descriptors, and allows for exploring the underlying image descriptors and similarity metrics. Experiments show that our method can align images with texts with high semantic fit, and to user satisfaction.
An approach to reduce data dimension in building e ̇ective Intrusion Detection Systems

The main function of the network Intrusion Detection System (IDS) is to protect the system, analyze and predict network access behavior of users. These behaviors are considered normal or an attack. Machine learning methods (ML) are used in IDSs because of the ability to learn from past attack patterns to recognize new attack patterns. These methods are effective but have relatively high computational costs. Meanwhile, the traffic of network data is growing rapidly, the computational cost issues need to be addressed. This paper addresses the use of algorithms combined with information metrics to reduce the features of the dataset to be analyzed. As the result, it helps to build IDSs with lower cost but higher performance suitable for large scale networks. The test results on the UNSW-NB15 dataset demonstrate: with the optimal set of features suitable for the attack type as well as the machine learning method, the quality of classification is improved with less training and testing time. Received on 27 December 2019; accepted on 05 January 2020; published on 10 January 2020
Spillovers of Learning-by-Exporting and Productivity Growth: Evidence from Korean Manufacturing Micro-data

The dynamism of Asian NIEs (Newly Industrializing Economies) revealed in their exportoriented growth paths has drawn substantial attention from researchers. But, empirical studies based on longitudinal micro-data in Asia are still rare, mainly due to the lack of readily available data. Based on the plant-level raw data underlying the Annual Report on Mining and Manufacturing Survey of Korea (1990-98), this study explores links between exporting and productivity. Main findings of the paper suggest that productivity gains associated with exporting tend to have strong intra-industry spillovers. JEL Classification: D24, F10, O12, O30 This version: 5 December 2004 _____________________ * This paper came out of an on-going research project with Chin-Hee Hahn at the Korea Development Institute. Useful comments from the participants of the 2004 Far Eastern Meeting of the Econometric Society and from the seminar participants at the Korea Development Institute, the National Graduate Institute for Policy Studies (Japan), and the Bank of Japan are gratefully acknowledged. Usual disclaimer applies.
Non-thermal plasma for revalorization of a complex waste substrate in open lactic acid fermentation

Purpose: Stillage is the main by-product of bioethanol production and cost of its treatment significantly affects the economy of bioethanol production. Utilization of stillage for fermentative lactic acid (LA) and biomass production improves sustainability of bioethanol production. Standard process of thermal sterilization of stillage for biorefinery purposes is energy demanding and is causing deterioration of valuable compounds present in stillage. Modern biorefinery processes require energy efficient recovery of nutrients from stillage at different scales. Methods: We replaced standard sterilization with ultrasound (UT) and plasma (PT) treatments and observed a significant reduction in number of viable microorganisms in the stillage after PT and UT. After applied treatment, we initiated lactic acid fermentation (LAF) by Lactobacillus rhamnosus ATCC 7469. Concentration of LA is used to quantify the efficiency of stillage revalorization. Results: We find that the highest LA productivity of 1.21 g/Lh and yield of 0.82 g/g are obtained after PT. While, UT of 10 min provides productivity of 1.02 g/Lh and LA yield of 0.69 g/g. The results are benchmarked against closed LAF. We achieved 20% better revalorization of stillage by PT when compared with conventional sterilization. An excellent L (+) LA stereoselectivity of 95.5% is achieved after PT LAF. From the aspect of energy efficiency, PT was three times lower than UT and almost ten times lower than thermal sterilization. Conclusions: This way we achieved a simpler and energy efficient process for LA production on stillage in „open” fermentation. These are new findings in fermentative treatment of stillage or similar byproducts and wastes for the benefit of industry, academia and policy making.
Multi-source domain adaptation with guarantees

This paper addresses the problem of unsupervised multi-source domain adaptation and in particular the question of how to combine the source data in an optimal way. We prove a generalisation bound that characterises how the amount of transfer from different source tasks influences the final performance of a trained predictor. In contrast to most previous works, which provide guarantees for fixed transfer weights but do not allow choosing them in a data-dependent way, our bound yields a principled algorithm for weighting the source tasks effectively.
10th International IFAC Symposium on Computer Applications in Biotechnology SIMPLE 3D VASCULARIZATION MODELS FOR PERFUSION BIOREACTORS

Growing cells in a perfusion bioreactor for tissue engineering is challenging. Cell viability and distribution is determined by complex interactions between a medium flowing through a porous scaffold and cell growth. Using a mathematical model that includes oxygen transport by diffusion/convection and cell growth mechanism, the effect of simple vascularization within the scaffold is investigated. As an initial approximation, straight channels through the length of the scaffold are considered, with performance assessed using a systematic set of criteria. Results indicate the approach can inform the choice of suitable channel size and spacing, and should enable enhanced scaffold design. Copyright © 2007 IFAC
Design of Microstrip-Line-Fed Rotman-lens Beamforming Network at 274 GHz

Rotman-lens is widely used in multi-beam antenna as beamforming networks which give several beam directions. Rotman-lens provides discrete beam-scanning by switching input ports. In this paper, we designed a microstrip-line-fed Rotman-lens phase shifter at 274 GHz. The Rotman-lens in this paper is designed using single layer of the multi-layer substrate used in the terahertz frequency band. The results of the simulation confirmed that the output power distributes between −10 to −15 dB. Based on these transmission characteristics obtained from the analysis, it is confirmed that directional scanning in five directions is possible with a size of approximately 5 mm square.
NBER WORKING PAPER SERIES ARE CONSUMERS POORLY INFORMED ABOUT FUEL ECONOMY? EVIDENCE FROM TWO EXPERIMENTS Hunt Allcott

It is often asserted that consumers are poorly informed about and inattentive to fuel economy, causing them to buy low-fuel economy vehicles despite their own best interest. This paper presents evidence on this assertion through two experiments providing fuel economy information to new vehicle shoppers. Results show zero statistical or economic effect on average fuel economy of vehicles purchased. In the context of a simple optimal policy model, the estimates suggest that current and proposed U.S. fuel economy standards are significantly more stringent than needed to address the classes of imperfect information and inattention addressed by our interventions. Hunt Allcott Department of Economics New York University 19 W. 4th Street, 6th Floor New York, NY 10012 and NBER hunt.allcott@nyu.edu Christopher Knittel MIT Sloan School of Management 100 Main Street, E62-513 Cambridge, MA 02142 and NBER knittel@mit.edu Are Consumers Poorly Informed about Fuel Economy? Evidence from Two Experiments Hunt Allcott and Christopher Knittel∗
Leveraging One-Sided Communication for Sparse Triangular Solvers

In this paper, we implement and evaluate a one-sided communication-based distributed-memory sparse triangular solve (SpTRSV). SpTRSV is used in conjunction with Sparse LU to affect preconditioning in linear solvers. One-sided communication paradigms enjoy higher effective network bandwidth and lower synchronization costs compared to their two-sided counterparts. We use a passive target mode in one-sided communication to implement a synchronizationfree task queue to manage the messaging between producerconsumer pairs. Whereas some numerical methods lend themselves to simple performance analysis, the DAG-based computational graph of SpTRSV demands we construct a critical path performance model in order to assess our observed performance relative to machine capabilities. In alignment with our model, our foMPI-based one-sided implementation of SpTRSV reduces communication time by 1.5× to 2.5× and improves SpTRSV solver performance by up to 2.4× compared to the SuperLU DIST’s two-sided MPI implementation running on 64 to 4,096 processes on Cray
Controllable Level Blending between Games using Variational Autoencoders

Previous work explored blending levels from existing games to create levels for a new game that mixes properties of the original games. In this paper, we use Variational Autoencoders (VAEs) for improving upon such techniques. VAEs are artificial neural networks that learn and use latent representations of datasets to generate novel outputs. We train a VAE on level data from Super Mario Bros. and Kid Icarus, enabling it to capture the latent space spanning both games. We then use this space to generate level segments that combine properties of levels from both games. Moreover, by applying evolutionary search in the latent space, we evolve level segments satisfying specific constraints. We argue that these affordances make the VAE-based approach especially suitable for co-creative level design and compare its performance with similar generative models like the GAN and the VAE-GAN.
Inhibitory Effects of Magnesium Ascorbyl Phosphate on Melanogenesis

The inhibitory effects of magnesium ascorbyl phosphate (VC-PMG) on melanogenesis were investigated in vitro using purified tyrosinase, B16F10 murine melanoma cell extract, and KHm-1/4 human melanoma cells. VC-PMG inhibited tyrosinase activity at a concentration of more than 0.001% for the purified tyrosinase, at a concentration of more than 0.01% for the B16F10 murine melanoma cells extract, and at a concentration of 0.1% for the KHm-1/4 human melanoma cells.Percutaneous absorption of 14C-labeled VC-PMG was examined by applying creams containing 3% VC-PMG onto dermatomed human cadaver skin. After 48 hours, VC-PMG was retained in the skin at levels of less than 2% of the applied dose.The lightening effects of VC-PMG on hyperpigmentation disorders, such as ephelides, chloasma, and senile freckle, in human skin in vivo were also studied. Cream containing 10% VC-PMG was applied to the pigmented area of 34 patients' faces twice a day for three months. Measurement by a color difference meter showed that the color of those pigmentations for 26 patients was lightened due to the topical application of VC-PMG cream. VC-PMG cream was also applied to non-pigmented area of 27 out of 34 patients. VC-PMG cream was also effective in lightening on 11 out of 27 healthy skin.These results suggested that VC-PMG was absorbed by topical application, and stayed in the skin, and inhibited tyrosinase activity of melanocytes. In vivo results clearly showed that topical application of VC-PMG can be effective in lightening human skin pigmentations.
A Low-Overhead Encoding Scheme to Extend the Lifetime of Nonvolatile Memories

Emerging nonvolatile memories (NVMs) are promising to replace DRAM as main memory. However, NVMs suffer from limited write endurance and high write energy. Encoding method reduces the bit flips of NVMs by exploiting additional tag bits to encode the data. The effect of the encoding method is limited by the capacity overhead of the tag bits. In this article, we propose to exploit the space saved by compression to store the tag bits of the encoding method. We observe that the saved space size of each compressed cache line varies, and different encoding methods have different tradeoffs between capacity overhead and effect. To fully exploit the space saved by compression for improving lifetime, we select the proper encoding method according to the saved space size. To improve the compression coverage and compression ratio, we select an efficient compression scheme from two compression algorithms and provide more space for data encoding. Still, some data patterns cannot be compressed by any compression technique. We use the Flip-N-Write with 3.1% capacity overhead to encode uncompressible cache lines. The experimental results show that our scheme reduces the bit flips by 32.5%, decreases the energy consumption by 22.6% and improves the lifetime by 69.9% with 3.5% capacity overhead.
Poisson Spatial Autoregression Modelling of Poverty Count Data in the Philippines

Count data with skewed distribution and possible spatial autoregression (SAR) often causes diffi culty in modelling. Violations on the assumptions in ordinary least squares (OLS) may occur. While Poisson regression can offer some remedy in modelling count data, it still does not take into account the spatial dependencies of the data. This paper uses general linear estimation via backfi tting algorithm in Poisson-SAR of poverty count in the Philippines for 2000. The model is assessed based on comparison from other models and the actual poverty count (MAPE and poverty map). MAPE was lowest in Poisson-SAR compared to other models.
A Survey of Several Advanced Mathematical Concepts Implemented in Students’ Computer Science Projects∗

Mathematics has a vital role in the development of computer science, electronic systems, and numerous practical applications. The objective of this poster is to review several advanced mathematical concepts and methods (modular arithmetic; Galois fields; graph theory; singular differential equations; strange attractors; fuzzy logic, and projective geometry) that contribute into the development of applications in cryptography, numerical methods, code complexity reduction, atmospheric dynamics, expert systems, computational visualization, and other areas. The mathematical concepts, algorithms, and codes are examined by undergraduate and graduate students in various courses taught by the author. These concepts have paved the roads for students’ research projects on various applications. Each student works on a selected project analyzing algorithms, creating computer codes (in Python, MATLAB, C/C++ or Java), running them at various parameters, comparing numerical results with known data, and presenting the findings to classmates and the research community. Many students published project summaries in the Rivier Academic Journal [1] and conference proceedings available from the web [2]. The opinions on why computer science students need general knowledge of mathematical concepts have been widely discussed in academia [3]. Several scholars [8] even recommended long lists of mathematical methods and formulas (ironically named as “Computer Science Cheat Sheets”) that every computer science student should be familiar with. These “Cheat Sheets” cover mostly basic mathematical concepts (e.g., series, function-value order definitions, permutations, combinations, identities, recurrences, geometry, matrices, special
Supporting Scientific Model Thinking through Curriculum-based Making

Our work investigates how electronics Making may support the development of scientific model thinking in elementary school students in formal learning contexts. This paper presents initial themes uncovered through video-based analysis of fourth-grade students engaging in Making activities to create simulation, conceptrelation, and illustrative models in the science classroom. Key themes were found in how the Making activities supported the students’ articulation of the science concepts, as well as their sense of ownership towards the projects. Our findings indicate that there are numerous factors, including the role of Making, allowance for creative freedom, and the level of Making complexity, that need to be taken into account in the design of curriculum-based Making kits and activities for model thinking. We conclude that Making has the potential to support the development of model thinking in the elementary science classroom, but much further research is needed.
Mission Design for Service Robots

This paper considers the mission design problem for a service robot which is operating in a partly known indoor environment. A service robot is requested to serve a set of work stations in the environment providing transport and delivery tasks while avoiding collisions with the obstacles during its travel. The objective is to determine the optimum sequence of the work-stations visited by the service robot exactly once assuring that the robot’s path through the work-station is collision free. The problem addressed in this paper combines two computationally hard problems: the task scheduling problem and the motion planning between the successive work stations.
Does selection by ability in secondary schooling affect future health and well-being of those who do not make the cut?

This paper explores the effect of selection by ability in secondary schooling at age 11 on adult health and well-being, adding a timely piece of evidence to the current policy debate on the expansion of selective schools in England. We estimate two separate treatment effects: that of going to a high-ability selective school (grammar), and that of going to a low-ability school (secondary modern), compared to attending a mixed-ability school (comprehensive). The data, from the National Child Development Study cohort, is first pre-processed through matching, and then analysed via parametric regressions, in order to address selection bias. Our models are estimated via both ordinary least squares (OLS) and instrumental variable (IV) approaches, although the former is preferred as we do not find robust evidence against exogeneity of school type. Outcomes include adult self-assessed physical and mental health; BMI; biomarkers for risk of cardiovascular disease; life aspirations in adolescence; adult life satisfaction, self-worth, crime and drug use. Attendance to grammar or secondary modern school does not affect most of our adult health and well-being outcomes, when compared to attending comprehensive. Childhood cognitive and non-cognitive abilities, conversely, are significant and positive predictors of health and well-being in adulthood.
Modelling Rice Yield in the Philippines using Dynamic Spatio-Temporal Models

Recent unpredictable and extreme weather episodes and infestation are some of the realistic occurrences of structural change in the agricultural system which produce outliers and extreme values in our data, and consequently pose problems when building statistical models. An estimation procedure which is robust to structural change is therefore necessary. Three spatial-temporal models with varying dynamic characteristics of the parameters are postulated each with a different estimation procedure for the agricultural yield in irrigated areas of the Philippines. One of which is a robust estimation procedure using forward search algorithm with bootstrap in a backfitting algorithm. The other two algorithms also used the backfitting algorithm but infused with the CochranneOrcutt procedure. The robust estimation procedure and the other one which considers varying parameter across space gave competitive predictive abilities and are better than the ordinary linear model. Simulation studies show the superiority of the robust estimation procedure over the Cochranne-Orcutt procedure and ordinary linear model in the presence of structural change.