Miracle-dz/UniFAR-FTUs huggingface dataset:
Submersible Motor Pump Fault Diagnosis System: A Comparative Study of Classification Methods

In this paper, an artificial intelligence solution to diagnose faults before acquisition of submersible petroleum motor pump systems is presented. Proper fault identification is time consuming and demands highly trained human experts. The diagnosis system is intended to facilitate the work of the human component of this important process by replicating the decision of highly trained experts through a classifier. To perform the automatic diagnosis, firstly intermediate features are extracted as the vibration spectra. Subsequently, high level features are extracted and fed into a classifier that outputs the final diagnose. To validate our proposal and to select the best classifier (among K-Nearest-Neighbour, Random Forest, Support Vector Machine and Decision Tree) for this problem, we performed a comparative study using real data acquired in tests accomplished before acquisition of submersible motor pumps. Our dataset comprises thousands of entries of accelerometer sensors (vertically distributed along the particular system components) data labelled by an human expert to one of the considered scenarios (normal pump, faulty sensor, faulty pump with rubbing, misalignment or unbalance). Results have showed that the evaluated classifiers have equivalent performance for the given problem, and that the standardization procedure can improve the performance of some classifiers. The performance of the classifiers is sufficient to facilitate the work performed by humans and consequently reduce the time spent in the pump fault diagnosis process.
Studies in One-Handed Mobile Design: Habit, Desire and Agility

In this paper we explore single handed mobile computing. Our approach is to understand how devices are currently used (habit), how users would prefer to interact with devices (desire) and the relationship between device size and thumb freedom (agility). We use a different methodology to study each factor: a field study, survey, and empirical evaluation, respectively. We found that users are already operating mobile phones with one hand. While the majority of users would like to perform personal information management tasks with a single hand, current mobile device designs do not well support this mode of interaction. Lastly, thumb mobility tends to decrease as device size increases, but users generally can perform localized movement well, even for large devices.
Varicella-Zoster Virus Infection of Differentiated Human Neural Stem Cells

ABSTRACT Primary varicella-zoster virus (VZV) infection in humans produces varicella (chickenpox), after which the virus becomes latent in ganglionic neurons. Analysis of the physical state of viral nucleic acid and virus gene expression during latency requires postmortem acquisition of fresh human ganglia. To provide an additional way to study the VZV-host relationship in neurons, we developed an in vitro model of infected differentiated human neural stem cells (NSCs). NSCs were induced to differentiate in culture dishes coated with poly-l-lysine and mouse laminin in the presence of fibroblast growth factor 2 (FGF-2), nerve growth factor (NGF), brain-derived neurotropic factor (BDNF), dibutyryl cyclic AMP, and retinoic acid. Immunostaining with neuronal (MAP2a and β-tubulin), astrocyte (GFAP), and oligodendrocyte (CNPase) markers revealed that differentiated neurons constituted approximately 90% of the cell population. These neurons were maintained in culture for up to 8 weeks. No cytopathic effect (CPE) developed in neurons infected with cell-free VZV (Zostavax vaccine) compared to human fetal lung fibroblasts infected with VZV. Weeks later, VZV DNA virus-specific transcripts (open reading frames [ORFs] 21, 29, 62, and 63) were detected in infected neurons, and dual immunofluorescence staining revealed the presence of VZV IE63 and gE exclusively in healthy-appearing neurons, but not in astrocytes. Neither the tissue culture medium nor a homogenate prepared from VZV-infected neurons produced a CPE in fibroblasts. VZV induced apoptosis in fibroblasts, as shown by activation of caspase 3 and by terminal deoxynucleotidyltransferase-mediated dUTP-biotin nick end labeling (TUNEL) staining, but not in neurons. This model provides a unique in vitro system to study the VZV-neuronal relationship.
Generating Small Combinatorial Test Suite via LP

Generating a set of combinatorial test cases for web application and development is very important. Linear programming (LP) is one of approaches to generating small combinatorial test suite. But, for software under test (SUT), the number of combinatorial test cases is often extremely large. If using LP to optimize test suite, the optimizing efficiency is low for the enormous size of LP variables and constraints (we call macro-LP). In view of the issue, we propose a new approach to optimization of test suite by using LP. The main ideas is to divide macro-LP into microLP which has relatively small sizes of LP variables and constraints. Experimental results show that the approach can improve the efficiency of solving test suite via LP greatly.
Long-Term Abacus Training Induces Automatic Processing of Abacus Numbers in Children

Abacus-based mental calculation (AMC) is a unique strategy for arithmetic that is based on the mental abacus. AMC experts can solve calculation problems with extraordinarily fast speed and high accuracy. Previous studies have demonstrated that abacus experts showed superior performance and special neural correlates during numerical tasks. However, most of those studies focused on the perception and cognition of Arabic numbers. It remains unclear how the abacus numbers were perceived. By applying a similar enumeration Stroop task, in which participants are presented with a visual display containing two abacus numbers and asked to compare the numerosity of beads that consisted of the abacus number, in the present study we investigated the automatic processing of the numerical value of abacus numbers in abacus-trained children. The results demonstrated a significant congruity effect in the numerosity comparison task for abacus-trained children, in both reaction time and error rate analysis. These results suggested that the numerical value of abacus numbers was perceived automatically by the abacus-trained children after long-term training.
Reweighted linearized Bregman algorithm for sparse signal recovery

In this paper, we present an efficient algorithm for sparse signal recovery with high exact recovery rate. The main idea of the algorithm is to combine two existing methods: linearized Bregman algorithm and reweighting technique. Compared with other available methods, such as reweighted Basis Pursuit (BP) and linearized Bregman, the proposed algorithm has a much lower computational complexity with higher probability of successful recovery. Numerical experiments demonstrate its efficiency and accuracy.
A novel hybrid filter to cancel the neutral harmonic current

In this paper, a novel hybrid filter topology is proposed. It consists of a novel passive filter and an active filter connected in series. The passive filter is composed of a 3/sup rd/- and a 9/sup th/- tuned filter branch and a high-pass filter branch. Each branch includes three star-connected capacitors and an inductor (and an extra resistor in the high-pass branch) connected in series. As the capacitors withstand all the nonzero sequence voltage, the voltage rating of the active filter is reduced substantially. Moreover, there are only zero-sequence fundamental and harmonic currents flowing in the inductors and the active filter, resulting in a reduction of the current ratings. Experimental results from a prototype conform the feasibility of the proposed topology.
Dynamic Copula Methods in Finance

The latest tools and techniques for pricing and risk management This book introduces readers to the use of copula functions to represent the dynamics of financial assets and risk factors, integrated temporal and cross-section applications. The first part of the book will briefly introduce the standard the theory of copula functions, before examining the link between copulas and Markov processes. It will then introduce new techniques to design Markov processes that are suited to represent the dynamics of market risk factors and their co-movement, providing techniques to both estimate and simulate such dynamics. The second part of the book will show readers how to apply these methods to the evaluation of pricing of multivariate derivative contracts in the equity and credit markets. It will then move on to explore the applications of joint temporal and cross-section aggregation to the problem of risk integration.
Studies on improving the efficiency of support vector machines

Motivation and Objective: In recent years support vector machine (SVM) has emerged as a powerful learning approach and successfully be applied in a wide variety of applications. The high generalization ability of SVMs is guaranteed by special properties of the optimal hyperplane and the use of kernel. However, SVM is considered slower than other learning approaches in both testing and training phases. In testing phase SVMs have to compare the test pattern with every support vectors included in their solutions. When the number of support vectors increases, the speed of testing phase decreases proportionally. To reduce this computational expense, reduced set methods try to replace original SVM solution by a simplified one which consists of much fewer number of vectors, called reduced vectors. However, the main drawback of former reduced set methods lies in the construction of each new reduced vector: it is required to minimize a multivariate function with local minima. Thus, in order to achieve a good simplified solution the construction must be repeated many times with different initial values. Our first objective was aiming at building a better reduced set method which overcomes the mentioned local minima problem. The second objective was to find a simple and effective way to reduce the training time in a model selection process. This objective was motivated by the fact that the selection of a good SVM for a specific application is a very time consuming task. It generally demands a series of SVM training with different parameter settings; and each SVM training solves a very expensive optimization problem. Methodology: Starting from a mechanical point of view, we proposed to simplify support vector solutions by iteratively replacing two support vectors with a newly created vector; or to substitute two member forces in an equilibrium system by an equivalent force. This approach also faces the difficulties caused by the so called pre-image problem of kernel-based methods where generally there is no exact substitution of two support vectors in a kernel-induced feature space by image of a vector in input space. However this bottom-up approach possess a big advantage that the computation of the new vector involves only two support vectors being replaced, not to involve all vectors as in the former top-down approach. The extra task of the bottom-up method is to find a heuristic to select a good pair of support vectors to substitute in each iteration. This heuristic aims at minimizing the difference between the original solution and the simplified one.
Expert systems and ICAI in tax law: killing two birds with one AI stone

The author describes five separate projects he has undertaken in the intersection of computer science and Canadian income tax law. They are:A computer-assisted instruction (CAI) course for teaching income tax, programmed using conventional CAI techniques; A “document modeling” computer program for generating the documentation for a tax-based transaction and advising the lawyer-user as to what decisions should be made and what the tax effects will be, programmed in a conventional language; A prototype expert system for determining the income tax effects of transactions and tax-defined relationships, based on a PROLOG representation of the rules of the Income Tax Act; An intelligent CAI (ICAI) system for generating infinite numbers of randomized quiz questions for students, computing the answers, and matching wrong answers to particular student errors, based on a PROLOG representation of the rules of the Income Tax Act; and A Hypercard stack for providing information about income tax, enabling both education and practical research to follow the user's needs path. The author shows that non-AI approaches are a way to produce packages quickly and efficiently. Their primary disadvantage is the massive rewriting required when the tax law changes. AI approaches based on PROLOG, on the other hand, are harder to develop to a practical level but will be easier to audit and maintain. The relationship between expert systems and CAI is discussed.
Is Price Stability Enough?

No one in the industrial countries should now question the substantial economic benefits associated with reducing inflation from earlier, high levels. At the same time, history also teaches that the stability of consumer prices might not be sufficient to ensure macroeconomic stability. Past experience is replete with examples of major economic and financial crises that were not preceded by inflationary pressures. Conversely, history shows that many periods of deflation, based on rising productivity, were simultaneously characterised by rapid growth. Recent structural changes in the global economy imply that this history might have more contemporaneous relevance than is commonly thought. If so, the implication is that policies directed to the pursuit of price stability might have to be applied more flexibly and with a longer-run focus than has recently been the case.
Decompressed video enhancement via accurate regression prior

There is an increasing need for high-quality multimedia applications based on block-based hybrid video coding. Inevitably, the frame will degrade during the process of block-wise intra/inter prediction, transformation, and quantization, especially when the bit rate is low. In this paper, we propose an efficient decompressed video enhancement algorithm based on the adjusted anchored neighborhood regression (A+) method. In our work, first, we learn offline linear regressors, i.e. projection matrices from the decompressed to original video frames in the training phase. For grouping anchored neighborhoods more accurately, we adopt MI-KSVD rather than KSVD to learn the dictionary. Moreover, we exploit the mutual coherence between dictionary atoms and training samples to find the nearest neighbors. Second, in the enhancement phase, we boost the quality of input decompressed videos offline by learned regression priors. To verify the robustness of our enhancement method, extensive experiments are conducted. As shown in our experimental results, the proposed enhancement method yields superior performance both objectively and subjectively.
Appendix : 1 Hierarchical Cluster Analysis Diagrams

1. Note that the maximum number of errors for the classification networks is 504. 1. Note that the maximum number of errors for 'Balance' and 'Depth' is 540, and 504 for the other simulations (since the balanced trees are not included then) especially hard for constituents that appear at deeper levels of the tree, and is also true even if the RAAM can successfully decode the representations, which means that detail is lost for preservation of structure. The fact that it is harder to extract information about the last input (i.e., the main feature for clustering in the HCA) than the structure of the expressions, should be taken as support for Sharkey and Jackson's claims about the danger of relying on Euclidean distance as the measurement for success, and the advantages of using 'whole net' representations. What appears to be needed for successful holistic process is both information about the tree-structure and the ability to process the representations in a series of transformations, where each process step involves constituent sensitive operations (e.g., decoding of a certain constituent) and structure sensitive operations (e.g., reference to another representation). But since it is possible for a network to learn both these tasks-we see no reason for dismissing connectionist networks as incapable of solving these, what normally are considered to be, symbolic tasks. Acknowledgements We would like to thank Stuart Jackson, Noel Sharkey and Ajit Narayanan for helpful comments on an early draft of this paper. constituent sensitive experiments. These were identical to the second-last and initial state experiments above, except that they discriminated between left-and right-balanced trees. For example, having an 'A' in the left side is different from having an 'A' in the right side of the tree even if they appear at the same depth, resulting in twelve different output classes. The results of the experiments showed no significant improvement and can be studied in Appendix 3. These results are interesting since they indicate that holistic constituent sensitive computation on unit level representations, i.e., the reduced descriptions, is impossible , at least in the sense of processing the whole tree structure in one processing step. But it is, as we have shown, possible to apply a structure sensitive computation on the representation for the complete tree structure and then constituent sensitive processes to one level of the tree structure at a time, and decode the representations successfully. The holistically unrecoverable patterns …
A practical and fast iterative algorithm for φ-function computation using DJ graphs

We present a new and practical method of computing φ-function for all variables in a function for Static Single Assignment (SSA) form. The new algorithm is based on computing the Merge set of each node in the control flow graph of a function (a node here represents a basic block and the terms will be used interchangeably). Merge set of a node n is the set of nodes N, where φ-functions may need to be placed if variables are defined in n. It is not necessary for n to have a definition of a variable in it. Thus, the merge set of n is dictated by the underlying structure of the CFG. The new method presented here precomputes the merge set of every node in the CFG using an iterative approach. Later, these merge sets are used to carry out the actual φ-function placement. The advantages are in examples where dense definitions of variables are present (i.e., original definitions of variables---user defined or otherwise, in a majority of basic blocks). Our experience with SSA in the High Level Optimizer (optimization levels +O3/+O4) shows that most examples from the Spec2000 benchmark suite require a high percentage of basic blocks to have their φ points computed. Previous methods of computing the same relied on the dominance frontier (DF) concept, first introduced by Cytron et al. The method presented in this paper gives a new effective iterative solution to the problem. Also, in cases, where the control flow graph does not change, our method does not require any additional computation for new definitions introduced as part of optimizations. We present implementation details with results from Spec2000 benchmarks. Our algorithm runs faster than the existing methods used.
MicroRNA-132/212 family enhances arteriogenesis after hindlimb ischaemia through modulation of the Ras-MAPK pathway

Arteriogenesis is a complicated process induced by increased local shear‐and radial wall‐stress, leading to an increase in arterial diameter. This process is enhanced by growth factors secreted by both inflammatory and endothelial cells in response to physical stress. Although therapeutic promotion of arteriogenesis is of great interest for ischaemic diseases, little is known about the modulation of the signalling cascades via microRNAs. We observed that miR‐132/212 expression was significantly upregulated after occlusion of the femoral artery. miR‐132/212 knockout (KO) mice display a slower perfusion recovery after hind‐limb ischaemia compared to wildtype (WT) mice. Immunohistochemical analysis demonstrates a clear trend towards smaller collateral arteries in KO mice. Although Ex vivo aortic ring assays score similar number of branches in miR‐132/212 KO mice compared to WT, it can be stimulated with exogenous miR‐132, a dominant member of the miR‐132/212 family. Moreover, in in vitro pericyte‐endothelial co‐culture cell assays, overexpression of miR‐132 and mir‐212 in endothelial cells results in enhanced vascularization, as shown by an increase in tubular structures and junctions. Our results suggested that miR‐132/212 may exert their effects by enhancing the Ras‐Mitogen‐activated protein kinases MAPK signalling pathway through direct inhibition of Rasa1, and Spred1. The miR‐132/212 cluster promotes arteriogenesis by modulating Ras‐MAPK signalling via direct targeting of its inhibitors Rasa1 and Spred1.
Spatial Sound Synthesis in Computer-Aided Composition*

In this article we describe our ongoing research and development efforts towards integrating the control of sound spatialisation in computer-aided composition. Most commonly, the process of sound spatialisation is separated from the world of symbolic computation. We propose a model in which spatial sound rendering is regarded as a subset of sound synthesis, and spatial parameters are treated as abstract musical materials within a global compositional framework. The library OMPrisma is presented, which implements a generic system for the control of spatial sound synthesis in the computer-aided composition environment OpenMusic.
The Determinants of Teacher Attrition in Upstate New York

Policy makers and scholars have long been interested in teacher attrition, particularly in poor, urban schools. We investigate the determinants of teacher attrition in five large metropolitan areas in upstate New York. We focus on a teacher's decision to leave a school district or to leave teaching using the Prentice-Gloeckler-Meyer technique for proportional hazards with unobserved heterogeneity. We find that teachers in districts with higher salaries relative to nonteaching salaries in the same county are less likely to leave teaching and that a teacher is less likely to change districts when he or she teaches in a district near the top of the teacher salary distribution in that county. We also find, however, that the impact of salary on the probability of leaving teaching is small and that very large salary increases would be required to offset the impact of concentrated student disadvantage on the attrition of female teachers.