Miracle-dz/UniFAR-FTUs huggingface dataset:
Output feedback controller synthesis for piecewise-affine systems with multiple equilibria

This work builds on the stability analysis of piecewise-affine systems reported by Hassibi et al. (1998) and extends it to obtain a new synthesis tool for output feedback controllers. The proposed technique relies on formulating the search for a piecewise-quadratic Lyapunov function and a piecewise-affine controller as a bilinear matrix inequality. This can be solved iteratively as a set of two convex optimization problems involving linear matrix inequalities which can be solved numerically and very efficiently. A key point in this design technique is that it can be used to design controllers with different structures depending on the number of constraints that are added. In particular, it is shown that a controller with the structure of a regulator and estimator can be designed so that switching based on state estimates rather than on the output can be performed. It is also shown that many other desired features can be included in the design. Furthermore, the applicability of this design method to systems with multiple equilibrium points is shown in simulation examples.
Performance Effects of IT Capability and Customer Service: The Moderating Role of Service Process Innovation

The effect of information technology (IT) for firm performance is lasting paid much attention. The relationship between IT and firm performance requests intermedium through which IT may influence firm performance. However, there is limited academic investigation on how IT can create performance gains for firms through customer service. This study proposes that customer service in service delivery and service customization are significant mediators through which IT influences firm performance. We also argue that service process innovation is a critical organizational factor that moderates the relationship among IT, customer service (service delivery and service customization), and firm performance. The study examines how the IT capability of a firm should be managed and organized to enhance service delivery and service customization, in turn, whether and how service delivery and service customization influence firm performance. Furthermore, through our theorizing, we draw attention to a significant role of service process innovation as a critical moderator in contemporary firms.
Rate of Primary Refractory Disease in B and T-Cell Non-Hodgkin’s Lymphoma: Correlation with Long-Term Survival

Background Primary refractory disease is a main challenge in the management of non-Hodgkin’s Lymphoma (NHL). This survey was performed to define the rate of refractory disease to first-line therapy in B and T-cell NHL subtypes and the long-term survival of primary refractory compared to primary responsive patients. Methods Medical records were reviewed of 3,106 patients who had undergone primary treatment for NHL between 1982 and 2012, at the Hematology Centers of Torino and Bergamo, Italy. Primary treatment included CHOP or CHOP-like regimens (63.2%), intensive therapy with autograft (16.9%), or other therapies (19.9%). Among B-cell NHL, 1,356 (47.8%) received first-line chemotherapy with rituximab. Refractory disease was defined as stable/progressive disease, or transient response with disease progression within six months. Results Overall, 690 (22.2%) patients showed primary refractory disease, with a higher incidence amongst T-cell compared to B-cell NHL (41.9% vs. 20.5%, respectively, p<0.001). Several other clinico-pathological factors at presentation were variably associated with refractory disease, including histological aggressive disease, unfavorable clinical presentation, Bone Marrow involvement, low lymphocyte/monocyte ration and male gender. Amongst B-cell NHL, the addition of rituximab was associated with a marked reduction of refractory disease (13.6% vs. 26.7% for non-supplemented chemotherapy, p<0.001). Overall, primary responsive patients had a median survival of 19.8 years, compared to 1.3 yr. for refractory patients. A prolonged survival was consistently observed in all primary responsive patients regardless of the histology. The long life expectancy of primary responsive patients was documented in both series managed before and after 2.000. Response to first line therapy resulted by far the most predictive factor for long-term outcome (HR for primary refractory disease: 16.52, p<0.001). Conclusion Chemosensitivity to primary treatment is crucial for the long-term survival in NHL. This supports the necessity of studies aimed to early identify refractory disease and to develop different treatment strategies for responsive and refractory patients.
RT 1 R 1 / 2 : Report on the efficacy of Persistent Operating Systems in supporting Persistent Application Systems

Over the past fifteen years much research effort has been expended in attempting to build systems which provide orthogonal persistence. The idea behind persistence [5] is simple: all data in a system should be able to persist (survive) for as long as that data is required. Orthogonal persistence means that all data may be persistent and that data may be manipulated in a uniform manner regardless of the length of time it persists. In this sense, persistent systems provide a uniform abstraction over storage. Orthogonal persistence is not found in contemporary operating systems, nor in most programming languages or database systems. In these systems, long-lived data is treated in a fundamentally different manner from short-lived data. Traditionally, long term data is maintained in a database or file system and short-term data is managed by a programming language. A number of experimental systems supporting orthogonal persistence have been constructed. These usually provide a large store within which concurrent processes may manipulate persistent data. In some of these systems, the stores contain all data including procedures, graphics objects, processes and their associated state. A common feature of persistent systems is that the persistent stores supporting them are both resilient and stable. Resiliency is usually achieved by making store updates non-destructive; this is generally implemented using logging [4] or some shadowing technique [31]. If persistent systems are to be anything other than research vehicles, they must be both stable and resilient. The persistent systems which exhibit these properties and that have been constructed to date, with a few exceptions, have been constructed on top of traditional operating systems. Existing operating systems do not provide an ideal platform for the development of persistent systems. This is not surprising since this was never part of their design goals. Indeed, most operating systems have files as their only abstraction over long term memory. Tanenbaum [44] has listed the four major components of an operating system as being memory management, file system, input-output and process management. The nature of these four components is different in persistent systems. In a persistent system, the functionality of the file system and memory management are replaced by the persistent store. In many operating systems, input-output is presented using the same abstractions as the file system; clearly this is not appropriate in a persistent environment. Some persistent systems require that the state of a process persists; this is not easily supported using conventional operating systems. It is therefore to be expected that an operating system designed to support persistence will have a different structure from a conventional operating system and will provide a different set of facilities. We can summarise the principal requirements of such an operating system as follows:
NIR image based pedestrian detection in night vision with cascade classification and validation

Pedestrian detection is one of the vital issues in advanced driving assistance applications. It is even more important in nighttime. This paper presents a robust algorithm for a nighttime pedestrian detection system. A NIR (Near Infrared) camera is used in this system to take images of a night scene. As there are large intra class variations in the pedestrian poses, a tree structured classifier is proposed here to handle the problem by training it with different subset of images and different sizes. This paper discusses about combination of Haar-Cascade and HOG-SVM (Histogram of Oriented Gradients-Support Vector Machine) for classification and validation. Haar-Cascade is trained such that to classify the full body of humans which eliminates most of the non-pedestrian regions. For refining the pedestrians after detection, a part based SVM classifier with HOG features is used. Upper and lower body part HOG features of the pedestrians are used for part based validation of detected bounding boxes. A full body validation scheme is also implemented using HOG-SVM when any one of the part based validation does not validate that particular part. Combination of the different types of complementary features yields better results. Experiments on test images determines that the proposed pedestrian detection system has a high detection rate and low false alarm rate since it works on part based validation process.
Weight monitoring with linear temporal logic: complexity and decidability

Many important performance and reliability measures can be formalized as the accumulated values of weight functions. In this paper, we introduce an extension of linear time logic including past (LTL) with new operators that impose constraints on the accumulated weight along path fragments. The fragments are characterized by regular conditions formalized by deterministic finite automata (monitor DFA). This new logic covers properties expressible by several recently proposed formalisms. We study the model-checking problem for weighted transition systems, Markov chains and Markov decision processes with rational weights. While the general problem is undecidable, we provide algorithms and sharp complexity bounds for several sublogics that arise by restricting the monitoring DFA.
ARTIFICIAL PARIETAL CORTEX NEURONS FOR 3D RECONSTRUCTION

A neural network is used for three-dimensional (3D) reconstruction of a point from a pair of images obtained with an active stereo system. Our active stereo system describes the position of a point with eight parameters: two pan angles, two tilt angles, and two-dimensional coordinates of the projected point in each image. Three-dimensional (3D) reconstruction consists of learning the function which maps these eight parameters to the 3D world coordinates (xw , yw , and zw). This paper outlines two possible networks for learning the mapping function. One contains simple Gaussian neurons in the hidden layer. The other is composed of neurons based on a model of the neurons in the parietal cortex of the human brain thought to be involved in 3D reconstruction from visual data. The paper compares the performance of each network. We evaluate the size of training set required and the effect of the selection method used to obtain the training examples. INTRODUCTION Three dimensional reconstruction with active stereo cameras is a difficult problem. The traditional kinematic model based approaches can involve more than forty different parameters. Finding these parameters requires a complicated calibration procedure and the collection of several different images [Knight and Reid, 2000, Guse, 1999]. Neubert, et al [2001] suggest that a neural network (NN) may provide a more accurate and robust method for 3D reconstruction. This paper further explores the ability of artificial neural networks to estimate three dimensional coordinates from a dynamic stereo head. Two neural network structures were investigated. The networks were compared using sum squared error and optimum size of the training set. Feed-forward neural networks are often used to approximate functions and have several advantages over the standard model-based approach to 3D reconstruction from stereo images. They do not require a pre-supposed camera and kinematic model, and are robust to noise in the training data. This makes them well suited to the reconstruction problem, but regression with a neural network can require a rather large set of training data to create a quality model of the function and avoid overfitting. One of the most important factors in using a neural network to approximate a function is the proper choice of the network structure and neurons [Poggio and Girosi, 1990]. Simple traditional neural networks are well understood, but may not be capable of providing a good approximation of the desired function. This paper will justify the added complexity During part of the period of this research, the first author also worked as a KTI Fellow, and received fellowship support under the NSF GK-12 Program (NSF-9979628). This research supported in part by NSF IRI-9703352.
A Robust Wireless Sensor Network Localization Algorithm in NLOS Environment

Reliable location estimation is an essential and fundamental issue for wireless sensor network. In order to find a localization algorithm robust to non-line-of-sight (NLOS) propagation errors in sensor network system, we analyzed some latest localization algorithms. Considering the application environments of the network, the energy consumption of nodes, the amount of computation and the location accuracy, we present a novel localization algorithm with the method of select residual weighting (SRwgh). SRwgh algorithm can reject the range measurements with large errors to improve the localization performance without identifying the NLOS error(s) or knowing channel characters. When the number of range measurements is larger than 4, this algorithm needs less amount of computation than that of conventional residual weighting algorithm and gets almost the same performance. The algorithm is compared with three other generic algorithms. The performance of the algorithms is test by simulation in both indoor and outdoor environments. Simulation results show that it is robust to the NLOS error(s) and provides quite well location accuracy.
1 THRESHOLDS FOR RATINGS ’ FORECAST DEFAULT PROBABILITIES : A MEAN SQUARED ERROR BASED APPROACH

Given the increased importance rating agencies have assumed in the determination of credit institutions’ capital requirements according to the Standardised Approach in the Basel II framework, the Basel Committee for Banking Supervision has proposed a procedure to “map” the ratings of different rating agencies into the risk weights that determine credit institutions’ capital requirements. This procedure is built upon Cumulative Default Rates (CDRs) and is based for each rating class on reference values and upper admissible thresholds for observed CDRs. In fact, whenever the upper thresholds are exceeded, supervisors are encouraged to enquiry over the rating agencies’ rating assignment standards. Thresholds are calculated on the basis of Monte Carlo simulations and assigned using the 99 and the 99.9 percentile of the distribution so obtained. Some observers notice that this procedure seems to lead to very broad ranges, and worry that this could prevent the prompt detection of on-going weaknesses in the rating assignment standards followed by some rating agencies. In this paper, an approach to determine thresholds for ratings’ forecast default probabilities using a test statistic based on the Mean Squared Error (MSE) over realized CDRs is presented. Such approach could represent a potential improvement of the “mapping” procedure suggested by the Basel Committee as the MSE can take directly into account the reference value suggested by the Basel Committee for each rating class, and it can also test the hypothesis of that reference value being correct. † European Investment Bank. * European Commission. Corresponding Author: C. Zucca: email: zucca@eib.org Tel. : +35224856033 The views expressed in the paper are those of the authors and do not necessarily correspond to the views of the European Investment Bank or of the European Commission.
Time-analysable non-partitioned shared caches for real-time multicore systems

Shared caches in multicores challenge Worst-Case Execution Time (WCET) estimation due to inter-task interferences. Hardware and software cache partitioning address this issue although they complicate data sharing among tasks and the Operating System (OS) task scheduling and migration. In the context of Probabilistic Timing Analysis (PTA) time-randomised caches are used. We propose a new hardware mechanism to control inter-task interferences in shared time-randomised caches without the need of any hardware or software partitioning. Our proposed mechanism effectively bounds inter-task interferences by limiting the cache eviction frequency of each task, while providing tighter WCET estimates than cache partitioning algorithms. In a 4-core multicore processor setup our proposal improves cache partitioning by 56% in terms of guaranteed performance and 16% in terms of average performance.
Resource Co-Allocation based on Application Profiling : A Case Study in Multi-Objective Evolutionary Computations

Several scientific experiments require coordinated resource allocation in multi-cluster environments. Rescheduling these experiments can reduce applications’ response time and increase system utilization. However, existing allocation models, which are based on user run time estimates, limit rescheduling due to environment heterogeneity. This paper introduces a resource co-allocation model based on application profiling to enable automatic rescheduling in multi-cluster environments. Using a multi-objective evolutionary application as a case study, we demonstrate how to generate run time predictions and their impact on rescheduling. The evaluation is on Grid’5000; a largescale platform comprising clusters with heterogeneous capabilities. From the evolutionary research field, this paper presents and compares the synchronous and asynchronous models for the target application. Our main findings are: it is possible to generate run time predictions to enable rescheduling by using a simple and practical approach with 7% error; and the asynchronous model for multi-objective evolutionary computations produces better optimization results and is faster than its synchronous counterpart.
Corner Detection Based on Eigenvalues product of Covariance Matrices over Edges

In this paper we present a corner detection which makes use of eigenvalues of covariance matrices of different support regions over edge points. Edges are first extracted through the use of Canny edge detection, and then determine corners according to eigenvalues product of covariance matrices of the edge at various regions of support. Experimental results show that the proposed method has more robustness for noise and various geometrical transform. Index Terms—Corner detection, Covariance matrix, Eigenvalues product, Region of support
A hybrid recommendation algorithm based on Hadoop

Recommender system has been widely used and collaborative filtering algorithm is the most widely used algorithm in recommender system. As scale of recommender system continues to expand, the number of users and items of recommender system is growing exponentially. As a result, the single-node machine implementing these algorithms is time-consuming and unable to meet the computing needs of large data sets. To improve the performance, we proposed a distributed collaborative filtering recommendation algorithm combining k-means and slope one on Hadoop. Apache Hadoop is an open-source organization's distributed computing framework. In this paper, the former hybrid recommendation algorithm was designed to parallel on MapReduce framework. The experiments were applied to the MovieLens dataset to exploit the benefits of our parallel algorithm. The experimental results present that our algorithm improves the performance.
Analysing Software Systems by Using Combinations of Metrics

Reengineering an object oriented software system requires some analysis of the existing system. This paper describes, how a combination of complexity and coupling metrics can be used to make such an analysis more focused and thus more e ective: the metrics are used to identify the key classes of a system, i.e. classes that structure the system and implement its most important concepts | understanding them and their collaborations is a good basis for every reengineering task. The paper is intended to be particularly helpful for practitioners, therefore it focuses on practical experiences that have been made with various case studies.
Using Decision Trees and Support Vector Machines to Classify Genes by Names

In this paper we report an application of machine learning methods to classify gene names into two categories: known and unknown ones. We acquired a data set of 1,624 genes by letting a human expert classify them manually. To capture the knowledge of classification, we also asked the expert to derive a set of rules. In parallel, we trained two machine learners to capture the same knowledge. Both decision trees (CART) and Support Vector Machines (SVMs) outperform the expert rules; the cross-validated error rates are below 1%, and the area under the curve of Receiver Operating Characteristic (ROC) curves reach higher than 0.99. In summary, CART and SVMs reduced the overall error rate of prediction by 40% and 88%, respectively, compared with classification using expert rule sets. In addition, the machine classifiers are able to find some errors made by the human expert himself. Finally, we used the expert system to classify 7,447 genes on the Affymetrix U74A microarray chip. Results show 70% of the genes on this chip are known ones. In conclusion, we successfully demonstrate that the machine-derived classifiers are more capable of handling the job efficiently than the expertderived classifier. It further supports the idea that in many application domains, experts can perform the task, but cannot tell how; whereas expert systems are able to capture the knowledge from the experts.
High speed nano-scale positioning using a piezoelectric tube actuator with active shunt control

Piezoelectric tube scanners are the actuators of choice in scanning probe microscopy. These nanopositioners exhibit a dominant first resonant mode that is excited due to harmonics of the input scan signal. This introduces errors in the scan obtained. The presence of this resonant mode limits the upper bound of a triangular scan rate to around 1/100th of the first mechanical resonance frequency. Passive and active shunts have shown to damp this resonant mode substan- tially and improve scan performance. Sensorless active shunts optimised using H2 and H1 tech- niques, is designed. These shunts reduce the amplitude of the first resonant peak of a prototype tube nanopositioner by 24 dB. A triangle wave input is used to test the improvement in scan per- formance due to the damping achieved by these active shunts. Analysis shows that damping the resonant mode in such fashion reduces the scan error by five times.