pinecone/core-2020-05-10-deduplication huggingface dataset:
Synthesizing and tuning chemical reaction networks with specified
  behaviours

We consider how to generate chemical reaction networks (CRNs) from functional
specifications. We propose a two-stage approach that combines synthesis by
satisfiability modulo theories and Markov chain Monte Carlo based optimisation.
First, we identify candidate CRNs that have the possibility to produce correct
computations for a given finite set of inputs. We then optimise the reaction
rates of each CRN using a combination of stochastic search techniques applied
to the chemical master equation, simultaneously improving the of correct
behaviour and ruling out spurious solutions. In addition, we use techniques
from continuous time Markov chain theory to study the expected termination time
for each CRN. We illustrate our approach by identifying CRNs for majority
decision-making and division computation, which includes the identification of
both known and unknown networks.Comment: 17 pages, 6 figures, appeared the proceedings of the 21st conference
  on DNA Computing and Molecular Programming, 201
New Geometric Algorithms for Fully Connected Staged Self-Assembly

We consider staged self-assembly systems, in which square-shaped tiles can be added to bins in several stages. Within these bins, the tiles may connect to each other, depending on the glue types of their edges. Previous work by Demaine et al. showed that a relatively small number of tile types suffices to produce arbitrary shapes in this model. However, these constructions were only based on a spanning tree of the geometric shape, so they did not produce full connectivity of the underlying grid graph in the case of shapes with holes; designing fully connected assemblies with a polylogarithmic number of stages was left as a major open problem. We resolve this challenge by presenting new systems for staged assembly that produce fully connected polyominoes in  O(log[superscript 2]n)  stages, for various scale factors and temperature  τ=2 as well as  τ=1. Our constructions work even for shapes with holes and uses only a constant number of glues and tiles. Moreover, the underlying approach is more geometric in nature, implying that it promised to be more feasible for shapes with compact geometric description
Mining and visualizing usage of educational systems using linked data

This work introduces a case study on usage of semantic context modelling and creation of Linked Data from logs in educational systems like a Personal Learning Environment (PLE) with focus on improvements and monitoring such systems, in generally, with respect to social, functional, user and activity centric level [7,15]. The case study demonstrates the application of semantic modelling of the activity context, from data collected over two years from the PLE at Graz University of Technology, using adequate domain ontologies, semantic technologies and visualization as reflection for potential technical and functional improvements. As it will be shown, this approach offers easy interfacing and extensibility on technological level and fast insight on statistical and preference trends for analytic tasks
Immersive Installation: “A Virtual St Kilda”

This paper discusses a Virtual Histories project, which developed a digital reconstruction of St Kilda. St Kilda is the most remote and western part of the United Kingdom. It was evacuated in the 1930s and lay empty for several decades. It is a world heritage site for both built and natural environment . The Virtual St Kilda acted as a focus for the collection and presentation of tangible and intangible cultural heritage. It was on show as an exhibition in the Taigh Chearsabah museum (Figure 5) located in North Uist Scotland. The exhibition is built around the OpenSimulator Open VirtualWorld server using commodity hardware. The simulation covers some 4 square km of virtual space, and models both tangible and intangible culture. It is integrated into an exhibition and articulates an interpretation of the St Kilda legacy through the prism of contemporary North Uist life.Postprin
A General Framework for Privacy-Preserving Distributed Greedy Algorithm

Increasingly more attention is paid to the privacy in online applications due
to the widespread data collection for various analysis purposes. Sensitive
information might be mined from the raw data during the analysis, and this led
to a great privacy concern among people (data providers) these days. To deal
with this privacy concerns, multitudes of privacy-preserving computation
schemes are proposed to address various computation problems, and we have found
many of them fall into a class of problems which can be solved by greedy
algorithms.
  In this paper, we propose a framework for distributed greedy algorithms in
which instances in the feasible set come from different parties. By our
framework, most generic distributed greedy algorithms can be converted to a
privacy preserving one which achieves the same result as the original greedy
algorithm while the private information associated with the instances is still
protected.Comment: This paper has been withdrawn due to personal reason
Classification on imbalanced data sets, taking advantage of errors to improve performance

Classification methods usually exhibit a poor performance when they are applied on imbalanced data sets. In order to overcome this problem, some algorithms have been proposed in the last decade. Most of them generate synthetic instances in order to balance data sets, regardless the classification algorithm. These methods work reasonably well in most cases; however, they tend to cause over-fitting. In this paper, we propose a method to face the imbalance problem. Our approach, which is very simple to implement, works in two phases; the first one detects instances that are difficult to predict correctly for classification methods. These instances are then categorized into “noisy” and “secure”, where the former refers to those instances whose most of their nearest neighbors belong to the opposite class. The second phase of our method, consists in generating a number of synthetic instances for each one of those that are difficult to predict correctly. After applying our method to data sets, the AUC area of classifiers is improved dramatically. We compare our method with others of the state-of-the-art, using more than 10 data sets
PSO-based method for svm classification on skewed data-sets

Support Vector Machines (SVM) have shown excellent generalization power in classification problems. However, on skewed data-sets, SVM learns a biased model that affects the classifier performance, which is severely damaged when the unbalanced ratio is very large. In this paper, a new external balancing method for applying SVM on skewed data sets is developed. In the first phase of the method, the separating hyperplane is computed. Support vectors are then used to generate the initial population of PSO algorithm, which is used to improve the population of artificial instances and to eliminate noise instances. Experimental results demonstrate the ability of the proposed method to improve the performance of SVM on imbalanced data-sets.Proyecto UAEM 3771/2014/CI
Verified over-approximation of the diameter of propositionally factored transition systems

To guarantee the completeness of bounded model checking (BMC) we require a completeness threshold. The diameter of the Kripke model of the transition system is a valid completeness threshold for BMC of safety properties. The recurrence diameter gives us an upper bound on the diameter for use in practice. Transition systems are usually described using (propositionally) factored representations. Bounds for such lifted representations are calculated in a compositional way, by first identifying and bounding atomic subsystems, and then composing those results according to subsystem dependencies to arrive at a bound for the concrete system. Compositional approaches are invalid when using the diameter to bound atomic subsystems, and valid when using the recurrence diameter. We provide a novel overapproximation of the diameter, called the sublist diameter, that is tighter than the recurrence diameter. We prove that compositional approaches are valid using it to bound atomic subsystems. Those proofs are mechanised in HOL4. We also describe a novel verified compositional bounding technique which provides tighter overall bounds compared to existing bottom-up approaches.Full Tex
Formalizing Size-Optimal Sorting Networks: Extracting a Certified Proof
  Checker

Since the proof of the four color theorem in 1976, computer-generated proofs
have become a reality in mathematics and computer science. During the last
decade, we have seen formal proofs using verified proof assistants being used
to verify the validity of such proofs.
  In this paper, we describe a formalized theory of size-optimal sorting
networks. From this formalization we extract a certified checker that
successfully verifies computer-generated proofs of optimality on up to 8
inputs. The checker relies on an untrusted oracle to shortcut the search for
witnesses on more than 1.6 million NP-complete subproblems.Comment: IMADA-preprint-c
A Concrete Memory Model for CompCert

International audienceSemantics preserving compilation of low-level C programs is challenging because their semantics is implementation defined according to the C standard. This paper presents the proof of an enhanced and more concrete memory model for the CompCert C compiler which assigns a definite meaning to more C programs. In our new formally verified memory model, pointers are still abstract but are nonetheless mapped to concrete 32-bit integers. Hence, the memory is finite and it is possible to reason about the binary encoding of pointers. We prove that the existing memory model is an abstraction of our more concrete model thus validating formally the soundness of CompCert's abstract semantics of pointers. We also show how to adapt the front-end of CompCert thus demonstrating that it should be feasible to port the whole compiler to our novel memory model
Validating Dominator Trees for a Fast, Verified Dominance Test

International audienceThe problem of computing dominators in a control flow graph is central to numerous modern compiler optimizations. Many efficient algorithms have been proposed in the litterature, but mechanizing the correctness of the most sophisticated algorithms is still considered as too hard problems, and to this date, verified compilers use less optimized implementations. In contrast, production compilers, like GCC or LLVM, implement the classic, efficient Lengauer-Tarjan algorithm [12], to compute dominator trees. And subsequent optimization phases can then determine whether a CFG node dominates another node in constant time by using their respective depth-first search numbers in the dominator tree. In this work, we aim at integrating such techniques in verified compilers. We present a formally verified validator of untrusted dominator trees, on top of which we implement and prove correct a fast dominance test following these principles. We conduct our formal development in the Coq proof assistant, and integrate it in the middle-end of the CompCertSSA verified compiler. We also provide experimental results showing performance improvement over previous formalizations
Improved Sieving on Algebraic Curves

International audienceThe best algorithms for discrete logarithms in Jacobians of algebraic curves of small genus are based on index calculus methods coupled with large prime variations. For hyperelliptic curves, relations are obtained by looking for reduced divisors with smooth Mumford representation (Gaudry); for non-hyperelliptic curves it is faster to obtain relations using special linear systems of divisors (Diem, Diem and Kochinke). Recently, Sarkar and Singh have proposed a sieving technique, inspired by an earlier work of Joux and Vitse, to speed up the relation search in the hyperelliptic case. We give a new description of this technique, and show that this new formulation applies naturally to the non-hyperelliptic case with or without large prime variations. In particular, we obtain a speed-up by a factor approximately 3 for the relation search in Diem and Kochinke's methods
Depth optimized efficient homomorphic sorting

We introduce a sorting scheme which is capable of efficiently sorting encrypted data without the secret key. The technique is obtained by focusing on the multiplicative depth of the sorting circuit alongside the more traditional metrics such as number of comparisons and number of iterations. The reduced depth allows much reduced noise growth and thereby makes it possible to select smaller parameter sizes in somewhat homomorphic encryption instantiations resulting in greater efficiency savings. We first consider a number of well known comparison based sorting algorithms as well as some sorting networks, and analyze their circuit implementations with respect to multiplicative depth. In what follows, we introduce a new ranking based sorting scheme and rigorously analyze the multiplicative depth complexity as O(log(N) + log(l)), where N is the size of the array to be sorted and l is the bit size of the array elements. Finally, we simulate our sorting scheme using a leveled/batched instantiation of a SWHE library. Our sorting scheme performs favorably over the analyzed classical sorting algorithms
Incremental complexity of a bi-objective hypergraph transversal problem

International audienceThe hypergraph transversal problem has been intensively studied, from both a theoretical and a practical point of view. In particular , its incremental complexity is known to be quasi-polynomial in general and polynomial for bounded hypergraphs. Recent applications in computational biology however require to solve a generalization of this problem, that we call bi-objective transversal problem. The instance is in this case composed of a pair of hypergraphs (A, B), and the aim is to find minimal sets which hit all the hyperedges of A while intersecting a minimal set of hyperedges of B. In this paper, we formalize this problem, link it to a problem on monotone boolean ∧ − ∨ formulae of depth 3 and study its incremental complexity
Minimal disconnected cuts in planar graphs.

The problem of finding a disconnected cut in a graph is NP-hard in general but polynomial-time solvable on planar graphs. The problem of finding a minimal disconnected cut is also NP-hard but its computational complexity is not known for planar graphs. We show that it is polynomial-time solvable on 3-connected planar graphs but NP-hard for 2-connected planar graphs. Our technique for the first result is based on a structural characterization of minimal disconnected cuts in 3-connected K 3,3  -free-minor graphs and on solving a topological minor problem in the dual. We show that the latter problem can be solved in polynomial-time even on general graphs. In addition we show that the problem of finding a minimal connected cut of size at least 3 is NP-hard for 2-connected apex graphs
On the mathematics of data centre network topologies.

In a recent paper, combinatorial designs were used to construct switch-centric data centre networks that compare favourably with the ubiquitous (enhanced) fat-tree data centre networks in terms of the number of servers within (given a fixed server-to-server diameter). Unfortunately there were flaws in some of the proofs in that paper. We correct these flaws here and extend the results so as to prove that the core combinatorial construction, namely the 3-step construction, results in data centre networks with optimal path diversity
Reasoning with global assumptions in arithmetic modal logics

We establish a generic upper bound ExpTime for reasoning with global assumptions in coalgebraic modal logics. Unlike earlier results of this kind, we do not require a tractable set of tableau rules for the in- stance logics, so that the result applies to wider classes of logics. Examples are Presburger modal logic, which extends graded modal logic with linear inequalities over numbers of successors, and probabilistic modal logic with polynomial inequalities over probabilities. We establish the theoretical upper bound using a type elimination algorithm. We also provide a global caching algorithm that offers potential for practical reasoning
Color characterization comparison for machine vision-based fruit recognition

In this paper we present a comparison between three color characterizations methods applied for fruit recognition, two of them are selected from two related works and the third is the authors’ proposal; in the three works, color is represented in the RGB space. The related works characterize the colors considering their intensity data; but employing the intensity data of colors in the RGB space may lead to obtain imprecise models of colors, because, in this space, despite two colors with the same chromaticity if they have different intensities then they represent different colors. Hence, we introduce a method to characterize the color of objects by extracting the chromaticity of colors; so, the intensity of colors does not influence significantly the color extraction. The color characterizations of these two methods and our proposal are implemented and tested to extract the color features of different fruit classes. The color features are concatenated with the shape characteristics, obtained using Fourier descriptors, Hu moments and four basic geometric features, to form a feature vector. A feed-forward neural network is employed as classifier; the performance of each method is evaluated using an image database with 12 fruit classes
Evolutionary nonnegative matrix factorization for data compression

This paper aims at improving non-negative matrix factor- ization (NMF) to facilitate data compression. An evolutionary updat- ing strategy is proposed to solve the NMF problem iteratively based on three sets of updating rules including multiplicative, firefly and sur- vival of the fittest rules. For data compression application, the quality of the factorized matrices can be evaluated by measurements such as spar- sity, orthogonality and factorization error to assess compression quality in terms of storage space consumption, redundancy in data matrix and data approximation accuracy. Thus, the fitness score function that drives the evolving procedure is designed as a composite score that takes into account all these measurements. A hybrid initialization scheme is per- formed to improve the rate of convergence, allowing multiple initial can- didates generated by different types of NMF initialization approaches. Effectiveness of the proposed method is demonstrated using Yale and ORL image datasets
The STUDENTSCALE: Measuring Students’Motivation, Interest, Learning Resources and Styles

It becomes important to consider the role of Information Technologies (IT) in society and at school,
including its impact on the teaching-learning process transformation. The use of IT should be done in an
integrated and inclusive way, it is critical to teach how to use, consume and interact with technology. This
study intends to contribute to a more depth understanding of the IT impact in the teaching-learning
process. Our main goal is to create a scale to measure the Subjects’ Interest and Motivation, Motivation
and Involvement with Learning Resources and Learning Styles. Those are important factors that impact on
students’ Learning Performance. Insights from an empirical study of 357middle education students
indicate that this multi-dimensional scale incorporates the following constructs: a) Interest and Motivation,
b) Motivation and Involvement with IT’s Learning Resources, c) Motivation and Involvement with
Teachers’ Learning Resources, and d) Non Literary Learning Styles. Discussion centers on this scale
implications for theory development andmanagement decisions. Teachers’ and schools’ managers may
better understand the learning resources and styles preferred by students, and thus to create more
motivational learning programs. Directions for future research are also presented
Contributing to computing services to benchmarking asset management knowledge management

Asset management has broadened from a focus on maintenance management to whole of life cycle asset management requiring a suite of new competencies from asset procurement to management and disposal. Well developed skills and competencies as well as practical experience are a prerequisite to maintain capability, to manage demand as well to plan and set priorities and ensure on-going asset sustainability. This paper has as its focus to establish critical understandings of data, information and knowledge for asset management along with the way in which benchmarking these attributes through computer-aided design may aid a strategic approach to asset management. The paper provides suggestions to improve sharing, integration and creation of asset-related knowledge through the application of codification and personalization approaches
On the ratio of consecutive gaps between primes

In the present work we prove a common generalization of Maynard- Tao’s recent result about consecutive bounded gaps between primes and of the Erdős-Rankin bound about large gaps between consecutive primes. The work answers in a strong form a 60-year-old problem of Erdős, which asked whether the ratio of two consecutive primegaps can be infinitely often arbitrarily small, and arbitrarily large, respectively. This is proved in the paper in a stronger form that not only dn = pn+1 - pn can be arbitrarily large compared to dn+1 but this remains true if dnC1 is replaced by the maximum of the k differences dn+1,…, dn+k for arbitrary fix k. The ratio can reach c(k) times the size of the classical Erdős-Rankin function with a constant c(k) depending only on k. © Springer International Publishing Switzerland 2015
Mindfulness for Cultivating Self-Esteem

Self-esteem is said to be a fundamental and pervasive human need. Humans are motivated to maintain high self-esteem and engage in efforts to protect their self-esteem when it comes under threat. High self-esteem is a positive personal resource and is generally related to a wide range of positive life outcomes. It is therefore important to consider factors that may enhance self-esteem. Here, we argue that mindfulness may be useful to cultivate self-esteem and, in particular, secure self-esteem as opposed to fragile high self-esteem. We begin with an overview of the benefits of healthy self-esteem and costs of low self-esteem. We then argue that individuals high in dispositional mindfulness may have greater capacity for secure high self-esteem. Evidence from clinical and experimental studies examining associations between mindfulness and self-esteem is reviewed next. We conclude with a discussion of the use of mindfulness-based interventions for the cultivation of healthy self-esteem and an outline of directions for future research.Full Tex
U-Check: Model Checking and Parameter Synthesis Under Uncertainty

Novel applications of formal modelling such as systems biology have highlighted the need to extend formal analysis techniques to domains with pervasive parametric uncertainty. Consequently, machine learning methods for parameter synthesis and uncertainty quantification are playing an increasingly significant role in quantitative formal modelling. In this paper, we introduce a toolbox for parameter synthesis and model checking in uncertain systems based on Gaussian Process emulation and optimisation. The toolbox implements in a user friendly way the techniques described in a series of recent papers at QEST and other primary venues, and it interfaces easily with widely used modelling languages such as PRISM and Bio-PEPA. We describe in detail the architecture and use of the software, demonstrating its application on a case study