Miracle-dz/UniFAR-FTUs huggingface dataset:
Decidable and semi-decidable controller synthesis for classes of discrete time hybrid systems

In this paper, we study classes of discrete time hybrid systems for which the classical algorithm for computing the maximal controlled invariant set and the least restrictive controller is computable and guaranteed to terminate in a finite number of iterations. We show how the algorithm can be encoded using quantifier elimination, which leads to a semi-decidability result for definable hybrid systems. For discrete time linear systems with linear constraints that are either controllable or nilpotent and have bounded disturbances, we show that the controlled invariance algorithm terminates in a number of iterations which is at most the dimension of the state space. Both in the hybrid and in the linear case, our results are much more general than the corresponding ones for continuous time systems. Finally we show that for linear systems with ellipsoidal constraints, an approximated solution can be obtained using robust convex programming. We provide an example showing that our algorithm gives better estimations than other ellipsoidal methods and is more efficient than the exact method for linear constraints.
Statistical Query Algorithms for Stochastic Convex Optimization

Stochastic convex optimization, where the objective is the expectation of a random convex function, is an important and widely used method with numerous applications in machine learning, statistics, operations research and other areas. We study the complexity of stochastic convex optimization given only statistical query (SQ) access to the objective function. We show that well-known and popular methods, including first-order iterative methods and polynomial-time methods, can be implemented using only statistical queries. For many cases of interest we derive nearly matching upper and lower bounds on the estimation (sample) complexity including linear optimization in the most general setting. We then present several consequences for machine learning, differential privacy and proving concrete lower bounds on the power of convex optimization based methods. A new technical ingredient of our work is SQ algorithms for estimating the mean vector of a distribution over vectors in $\mathbb{R}^d$ with optimal estimation complexity. This is a natural problem and we show that our solutions can be used to get substantially improved SQ versions of Perceptron and other online algorithms for learning halfspaces.
Acknowledgments: We Thank

16] A. Bar-Noy and D. Dolev. A partial equivalence between shared-memory and message-passing in an asynchronous fail-stop distributed environment. 23 the number of processes in the group is unknown. In later rounds, the size of the group is bounded by the size of the view obtained in the previous round, and os-im-upscan can be used. As proved in 19], at least one process halts in each round; therefore, the number of rounds is at most k. In the rst round, im-upscan requires O(k 3) steps, while in later rounds, os-im-scan requires O(k 2) steps. This implies the next theorem: Theorem 6.1 Algorithm 6 solves the one-shot (2k ? 1)-renaming problem, with O(k 3) step complexity. Gideon Stupp for helpful discussions. References 1] K. Abrahamson. On achieving consensus using a shared memory. Algorithm 6 Adaptive (2k ? 1)-renaming (based on 19]): code for processor p i. 1. if (start-level = 0) then S := im-upscan() // sets start-level 2. else S := os-im-upscan(start-level) 3. rstSlot := NewRange(rstSlot,direction,jSj) 4. if (id i = max S id j) then return(rstSlot) // allocate self 5. else rename(rstSlot,:direction) // start again in a new range, opposite direction NewRange(slot, direction, snapSize) 6. if (direction) then return(slot + (2snapSize ? 1)) // allocate going up 7. else return(slot ? (2snapSize ? 1)) // allocate going down are employed to partition processes into groups, according to their arrival times, in order to guarantee that fast processes (acquiring names many times) will not lock out slower processes. Unfortunately, the step complexity of both algorithms 11, 20] is at least exponential in n (see 23]). Therefore, although our collect algorithm can easily be used to make both algorithms adaptive, the step complexity of the adaptive versions is exponential in k. Last, and most interesting, is the one-shot (2k ? 1)-renaming algorithm of Borowsky and Gafni 19], which can be made adaptive by using our immediate snapshot algorithm. The BG renaming algorithm proceeds in rounds; a process takes an immediate snapshot in each round, and processes are partitioned into groups according to the size of their views. The views also partition the name space into disjoint intervals; processes in each group continue the algorithm in the associated interval. The process with the maximal id in the group gets a name in the interval; other processes proceed to the next round. See the code in Algorithm 6. The BG algorithm uses a …
Understanding the Open-Source Software Development Process : a Case Study with CVSChecker

The open-source process model is emerging as a new lightweight paradigm for software development and has already produced several successful products. This process is fundamentally different from more traditional analysisand design-driven processes, which raises a set of interesting research questions: what activities are carried out in open-source projects and by whom? Are there typical or exceptional patterns? CVSChecker is a tool designed to analyze the performance of individual developers and the work-distribution patterns of teams based on historical source-code repository data. In this paper, we report on a case study conducted using CVSChecker to examine a small open-source project. We discuss the insights that the CVSChecker analysis produced regarding this project and compare them to results from previous case studies performed with senior student teams.
Random Telegraph Noise analysis as a tool to link physical device features to electrical reliability in nanoscale devices

In this work, we report a detailed discussion on the techniques and the requirements needed to enable Random Telegraph Noise (RTN) analysis as a tool to investigate device reliability. Starting with the understanding of the RTN signal properties, a set of best practices to perform measurements and data analysis is established to guarantee reliable results and a correct ensuing physical interpretation. It will be shown that combining dedicated and careful experiments with refined data analysis and comprehensive physics simulations is hence required to enable RTN analysis as a safe and innovative investigation tool for electron devices. The effectiveness of RTN analysis as an investigation tool is demonstrated on both FinFET and resistive memory devices: the parameters of RTN as observed in the experiments performed on FinFETs allow understanding the details of the defects generation during stress in such devices; RTN analysis on RRAM allows understanding the physical origin of RTN in these devices and to estimate the physical properties of defects involved in the phenomenon.
Topology Evolution Based on the Complex Networks of Heterogeneous Wireless Sensor Network

For heterogeneous wireless sensor networks energy costs, network data transmission, network life cycle and other issues, the introduction of basic research methods of complex network theory. In the modeling process, fully taking into account the degree and the current node wireless sensor network topology transformation and the residual energy of the nodes are closely related, the node with the node-link dynamic change increases and decreases have optimized network behavior, the use of mean-field theory to derive this model has been such a scale-free network model is characterized by a complex network of random failures in the network nodes with high robustness. The model algorithm by MATLAB simulation results show that the lower the more energy to get smaller heterogeneous wireless sensor networks average path length, ie, a data transmission needs of hops, the more energy savings. This network model can effectively improve the energy balance of performance and corrective network nodes.
An efficient fusion algorithm for large scale face verification based on KISSME and cosine similarity

Although the KISSME approach can effectively reduce the correlation between feature vectors of samples, it can't restrain the multimodal distribution of vectors in the global level. This drawback brings negative impact on classification performance. Inspired by KISSME, we propose our fusion algorithm of Likelihood Ratio Test and cosine similarity for large scale face verification (CS-KISSME). In our algorithm, we obtain an approximate optimum Mahalanobis distance matrix and use it as a projection matrix. After that, we measure the dissimilarities by cosine similarity in the linear transformed subspace. In this paper, we expound merits of CS-KISSME theoretically, test it on two challenging face verification datasets and achieve higher accuracy and better robustness with little time consumption.
Dynamic lightpath establishment for achieving low power all-optical networks

This paper proposes a dynamic lightpath establishment scheme considering power consumption of multifiber all-optical networks. In all-optical networks, optical amplifiers consume much power to amplify a group of optical signals with different wavelengths. The proposed scheme provides an ingenious routing and wavelength assignment strategy which preferentially uses active optical amplifiers and avoids using inactive optical amplifiers. Furthermore, the proposed scheme avoids blocking of lightpath establishment due to depletion of wavelength resources. Through simulation experiments, we show that the proposed scheme efficiently reduces both blocking probability of lightpath establishment and power consumption of optical networks.
Persuasive Dialogue : Changing User Behaviour with a Computer

Dialogue is not new to computational linguistics but, up to now, it has been applied only to very restricted domains. The computer is often either too task oriented and logic or quite mindless: no solution has been found to this problem yet. By applying human computer dialogue to persuasive dialogue, this thesis hopes to take the research in domains that have barely been studied in conjunction with human computer dialogue. First of all the research will examine if, by using the long studied theories of rhetoric in philosophy and linguistics, it will be possible to render automated dialogue systems more natural. Actually, the aspect of argumentation in dialogue has been taken into account only in specialised areas such as law where logic is mostly used. One of the interests of this project is therefore to study how rhetoric could be applied in some less restricted type of dialogue, where other aspects of rhetoric, like emotions will be useful. Another goal of this project is to study the theories of persuasive communication. These theories and the models they propose could be used in conjunction with the theories of argumentation to control the persuasiveness of the dialogue.
Nonlinear control synthesis by convex optimization

A stability criterion for nonlinear systems, recently derived by the third author, can be viewed as a dual to Lyapunov's second theorem. The criterion is stated in terms of a function which can be interpreted as the stationary density of a substance that is generated all over the state-space and flows along the system trajectories toward the equilibrium. The new criterion has a remarkable convexity property, which in this note is used for controller synthesis via convex optimization. Recent numerical methods for verification of positivity of multivariate polynomials based on sum of squares decompositions are used.
A Fresh Look at Initial and Diagnostic Assessment in Community Education Provision

This article outlines findings from a collaborative project with two partner organisations that focused on the initial and diagnostic assessment practice used by an Adult and Community Learning provider in the north-east of England. The findings highlight the need for change with regard to how initial assessment is conducted, and, as a result of this, we make suggestions as to how providers can ensure assessment is appropriate. The researchers found that the assessment tool used for diagnostic purposes is less important than the process of assessment. This action research explored learners’ experience of assessment and makes recommendations for further changes to the assessment process.
Dual output polarimeter devoted to the study of the Cosmic Microwave Background

We have developed a correlation radiometer at 33 GHz devoted to the search for the residual polarization of the Cosmic Microwave Background (CMB). The two instrument's outputs are a linear combination of two Stokes parameters. The instrument is therefore directly sensitive to the polarized component of the radiation (rispectively linear and circular). The radiometer has a beamwidth of 7 or 14 degree, but it can be coupled to a telescope increasing the resolution. The expected CMB polarization is at most a part per million. The polarimeter has been designed to be sensitive to this faint signal, and it has been optimized to improve its long term stability, observing from the ground. In this contribution the performances of the instrument are presented, together with the preliminary tests and observations.
A Proposal of the AdaBoost-Based Detection of Phishing Sites

In this paper, we propose an approach which improves the accuracy of detecting phishing sites by employing the AdaBoost algorithm. Although there are heuristics to detect phishing sites, existing anti-phishing tools still do not achieve high accuracy in detection. We hypothesize that the inaccuracy is caused by anti-phishing tools that can not use these heuristics appropriately. Our attempt is to improve accuracy by applying the AdaBoost algorithm, the most typical of the machine learning algorithms. We also evaluate the AdaBoost-based combination method by comparing with CANTINA [1], and almost of all the results show that our proposed method provides higher accuracy to detect phishing sites.
Efficient query-driven biclustering of gene expression data using Probabilistic Relational Models

Biclustering is an increasingly popular technique to identify gene regulatory modules that are linked to biological processes. We describe a novel method, called ProBic, that was developed within the framework of Probabilistic Relational Models (PRMs). ProBic is an efficient biclustering algorithm that simultaneously identifies a set of potentially overlapping biclusters in a gene expression dataset and which can be used both in a query-driven and a global setting. The model naturally deals with missing values. Robust sets of biclusters are obtained due to the explicit modeling of noise. The maximum likelihood solution is approximated using an Expectation-Maximization strategy. ProBic was applied to various synthetic gene expression datasets and the results for synthetic data confirmed that ProBic can successfully identify biclusters under various noise levels, overlap and missing values in both the query-driven and global setting. Additional expert knowledge can be introduced through a number of prior distribution parameters. Default settings were shown to be applicable for a wide range of different datasets. Our results on synthetic data show that PRMs can be used to identify overlapping biclusters in an efficient and robust manner. keywords: biclustering, probabilistic relational model, gene expression, regulatory module, expectation-maximization. ∗corresponding author
Optimization through coupling learning capability and imitation strategy in a Multi-Robot System

In this paper, we present the interest of coupling learning capability and imitation strategy on individual and population levels in the field of Multi-Robot System. Particularly, we show that in an unknown environment adding a simple imitation capability to our bio-inspired architecture leads to a positive effect in the improvement the overall performance of the whole system. Indeed, our motivations is to optimize the robots goals discovery time and to improve the survival rate of agents. To analyze and validate our hypothesis, a series of experiments has been performed with and without a low level imitation strategy in a simulated multi-robot system. We will conclude with robotics' experiments which will feature how our approach applies accurately to real life environments.
An Improved Learning Algorithm Based On the Conjugate Gradient Method for Back Propagation Neural Networks

The conjugate gradient optimization algorithm usual ly used for nonlinear least squares is presented and i s combined with the modified back propagation algorithm yielding a new fast training multilayer perceptron (MLP) algorithm (CGFR/AG). The approaches presented in the paper consist of three steps: (1) Modification on standard back propagation algorithm by introducing gain variation term of the activation f unction, (2) Calculating the gradient descent on error with respe ct to the weights and gains values and (3) the determination of the new search direction by exploiting the information calc ulated by gradient descent in step (2) as well as the previou s search direction. The proposed method improved the trainin g efficiency of back propagation algorithm by adaptively modifyi ng the initial search direction. Performance of the proposed method is demonstrated by comparing to the conjugate gradient algorithm from neural network toolbox for the chosen benchmark. The results show that the number of iterations required by the proposed method to converge is less than 20% of what is required by the standard conjugate gradient and neural netwo rk toolbox algorithm.
GREAT-CEO: larGe scale distRibuted dEcision mAking Techniques for Wireless Chief Executive Officer Problems

In this paper, we reformulate the issue related to wireless mesh networks (WMNs) from the Chief Executive Officer (CEO) problem viewpoint, and provide a practical solution to a simple case of the problem. It is well known that the CEO problem is a theoretical basis for sensor networks. The problem investigated in this paper is described as follows: an originator broadcasts its binary information sequence to several forwarding nodes (relays) over Binary Symmetric Channels (BSC); the originator’s information sequence suffers from independent random binary errors; at the forwarding nodes, they just further interleave, encode the received bit sequence, and then forward it, without making heavy efforts for correcting errors that may occur in the originator-relay links, to the final destination (FD) over Additive White Gaussian Noise (AWGN) channels. Hence, this strategy reduces the complexity of the relay significantly. A joint iterative decoding technique at the FD is proposed by utilizing the knowledge of the correlation due to the errors occurring in the link between the originator and forwarding nodes (referred to as intra-link). The bit-error-rate (BER) performances show that the originator’s information can be reconstructed at the FD even by using a very simple coding scheme. We provide BER performance comparison between joint decoding and separate decoding strategies. The simulation results show that excellent performance can be achieved by the proposed system. Furthermore, extrinsic information transfer (EXIT) chart analysis is performed to investigate convergence property of the proposed technique, with the aim of, in part, optimizing the code rate at the originator. key words: CEO problem, Slepian-Wolf theorem, wireless mesh network, wireless sensor network, extract and forward, iterative decoding, EXIT chart analysis
MAnGO: an interactive R-based tool for two-colour microarray analysis

UNLABELLED MAnGO (Microarray Analysis at the Gif/Orsay platform) is an interactive R-based tool for the analysis of two-colour microarray experiments. It is a compilation of various methods, which allows the user (1) to control data quality by detecting biases with a large number of visual representations, (2) to pre-process data (filtering and normalization) and (3) to carry out differential analyses. MAnGO is not only a 'turn-key' tool, oriented towards biologists but also a flexible and adaptable R script oriented towards bioinformaticians. AVAILABILITY http://bioinfome.cgm. cnrs-gif.fr/.
Connectivity in inhomogeneous random key graphs

We consider a new random key predistribution scheme for securing heterogeneous wireless sensor networks. Each of the n sensors in the network is classified into r classes according to a probability distribution μ = {μ1,. μr} Before deployment, a class-i sensor is assigned Ki cryptographic keys that are selected uniformly at random from a pool of P keys. Once deployed, a pair of sensors can communicate securely if and only if they have a key in common. The communication topology of this network is modeled by an inhomogeneous random key graph. We establish scaling conditions on the parameters P and {K1,. ,Kr} so that this graph is connected with high probability. The result is given in the form of a zero-one law with the number of sensors n growing unboundedly large. Our result is shown to complement and improve those given by Godehardt et al. and Zhao et al. for the same model, therein referred to as the general random intersection graph.
Metacognitive Evaluations During Science Simulations: How Do Ratings of Confidence and Understanding Relate to Science Assessment Inquiry Processes?

Science simulations are interactive tasks that allow students to explore a science phenomenon, collect data, and test hypotheses and allow researchers to investigate the science inquiry processes. Though simulations are increasingly being used in assessment, little is known about how students’ confidence and understanding relate to their within-simulation assessment behaviors. To explore the relationship between metacognition and science inquiry in a simulation assessment, 309 adult participants answered a series of questions about the simulated phenomenon of concentration. For example, in the concentration simulation, participants interacted with a concentration of solutions simulation to answer questions about saturation. Participants could manipulate variables in the simulation, such as the amount of solute and solvents added to a container, and create and modify data tables. The variables available for hypothesis testing (e.g., continuous vs. categorical input variables) were manipulated between participants and interactive inquiry behaviors, test performance, ratings of understanding of concentration, and confidence in test responses were measured. Results showed that overconfidence was related to reductions in adaptive inquiry behaviors and poorer assessment performance. S C I E N T I F I C A B S T R A C T Science simulations are interactive tasks that allow students to explore a science phenomenon and engage in science inquiry by collecting data and testing hypotheses. Though simulations are increasingly being used in assessment, little is known about how students’ metacognition, such as confidence in a response and ratings of understanding of a simulated concept, relate to within-simulation assessment behaviors. The study explored the relationship between students’ metacognition and their science inquiry behaviors in a simulation assessment. During the simulation participants interacted with a concentration of solutions simulation to answer questions about saturation. Participants could manipulate variables in the simulation, such as the amount of solute and solvents added to a container, and create and modify data tables. Interactive inquiry behaviors, test performance, ratings of understanding of concentration, and confidence in test responses were measured. Results showed that overconfidence was related to reduced inquiry and poorer assessment performance.