Miracle-dz/UniFAR-FTUs huggingface dataset:
Hierarchical Dirichlet Process Hidden Markov Models for abnormality detection in robotic assembly

The Hierarchical Dirichlet Process Hidden Markov model (HDP-HMM) is a Bayesian non parametric extension of the classical Hidden Markov Model (HMM) that allows to infer posterior probability over the cardinality of the hidden space, thus avoiding the necessity of cross-validation arising in standard EM training. This paper presents the application of Hierarchical Dirichlet Process Hidden Markov Models (HDP-HMM) to error detection during a robotic assembly task. Force sensor data is recorded for successful and failed task executions and manually labeled. An HDP-HMM is then fit to a set of training trials for each task execution outcome. We show how posteriors on the learned models could be used to recognize on-line deviation from expected behavior, thus allowing the robotic system to promptly react to task execution errors.
Vision-assisted modeling for model-based video representations

The objective of this thesis is to develop algorithms and software tools for acquiring a structured scene representation from real imagery which can be used for 2-D video coding as well as for interactive 3-D presentation. A new approach will be introduced that recovers visible 3-D structure and texture from one or more partially overlapping uncalibrated views of a scene composed of straight edges. The approach will exploit CAD-like geometric properties which are invariant under perspective projection and are readily identifiable among detectable features in images of man-made scenes (i.e. scenes that contain right angles, parallel edges and planar surfaces). In certain cases, such knowledge of 3-D geometric relationships among detected 2-D features allows recovery of scene structure, intrinsic camera parameters, and camera rotation/position from even a single view. Model-based representations of video offer the promise of ultra-low bandwidths by exploiting the redundancy of information contained in sequences which visualize what is largely a static 3-D world. An algorithm will be proposed that uses a previously recovered estimate of a scene's 3-D structure and texture for the purpose of model-based video coding. Camera focal length and pose will be determined for each frame in a video sequence taken in that known scene. Unmodeled elements (e.g. actors, lighting effects, and noise) will be detected and described in separate compensating signals. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)
0 Ja n 20 07 On the zero viscosity limit

The question of whether the zero viscosity limit ν → 0 is identical to the no viscosity ν ≡ 0 case is investigated in a simple shell (GOY) model with only three shells. We find that it is possible to express two velocities in terms of Bessel functions. The third velocity function acts as a background. The relevant Bessel functions are infinitely oscillating as ν → 0 and are not analytic as functions of ν at the point ν = 0. We also mention a perturbative method which may be used to improve the model. ∗Also at the Institute for Advanced Cycling, Blegdamsvej 19, Copenhagen, Denmark
Neuroblast entry into quiescence is regulated intrinsically by the combined action of spatial Hox proteins and temporal identity factors

Neural stem cell quiescence is an important feature in invertebrate and mammalian central nervous system development, yet little is known about the mechanisms regulating entry into quiescence, maintenance of cell fate during quiescence, and exit from quiescence. Drosophila neural stem cells (called neuroblasts) provide an excellent model system for investigating these issues. Drosophila neuroblasts enter quiescence at the end of embryogenesis and resume proliferation during larval stages; however, no single neuroblast lineage has been traced from embryo into larval stages. Here, we establish a model neuroblast lineage, NB3-3, which allows us to reproducibly observe lineage development from neuroblast formation in the embryo, through quiescence, to the resumption of proliferation in larval stages. Using this new model lineage, we show a continuous sequence of temporal changes in the neuroblast, defined by known and novel temporal identity factors, running from embryonic through larval stages, and that quiescence suspends but does not alter the order of neuroblast temporal gene expression. We further show that neuroblast entry into quiescence is regulated intrinsically by two independent controls: spatial control by the Hox proteins Antp and Abd-A, and temporal control by previously identified temporal transcription factors and the transcription co-factor Nab.
Engaging Clinicians in the Visualization Design Process – Is It Possible ?

Creating and customizing visualization for electronic health record data requires a close collaboration with clinicians, to understand their tasks, needs and mental model. This process can develop into an infinite process. Taking into consideration the existence of clinicians with advanced IT knowledge, but not programmers, we focus on engaging them to create their own visualizations. This paper presents how clinicians can use uVis Studio to create three visualizations by dragging and dropping controls into the design panel, and specifying formulas for each control in the property grid.
Magnesium Levels in Drinking Water and Coronary Heart Disease Mortality Risk: A Meta-Analysis

Epidemiological studies have demonstrated inconsistent associations between drinking water magnesium levels and risk of mortality from coronary heart disease (CHD); thus, a meta-analysis was performed to assess the association between them. Relevant studies were searched by the databases of Cochrane, EMBASE, PubMed and Web of Knowledge. Pooled relative risks (RR) with their 95% CI were calculated to assess this association using a random-effects model. Finally, nine articles with 10 studies involving 77,821 CHD cases were used in this study. Our results revealed an inverse association between drinking water magnesium level and CHD mortality (RR = 0.89, 95% CI = 0.79–0.99, I2 = 70.6). Nine of the 10 studies came from Europe, and the association was significant between drinking water magnesium level and the risk of CHD mortality (RR = 0.83, 95% CI = 0.69–0.98). In conclusion, drinking water magnesium level was significantly inversely associated with CHD mortality.
Defeating pharming attacks at the client-side

With the deployment of “always-connected” broadband Internet access, personal networks are a privileged target for attackers and DNS-based corruption. Pharming attacks - an enhanced version of phishing attacks - aim to steal users' credentials by redirecting them to a fraudulent login website, using DNS-based techniques that make the attack imperceptible to the end-user. In this paper, we define an advanced approach to alert the end-user in case of pharming attacks at the client-side. With a success rate over 95%, we validate a solution that can help differentiating legitimate from fraudulent login websites, based on a dual-step analysis (IP address check and webpage content comparison) performed using multiple DNS servers information.
A generic synthetic image generator package for the evaluation of 3D Digital Image Correlation and other computer vision-based measurement techniques

Stereo digital image correlation (also called 3D DIC) is a common measurement technique in experimental mechanics for measuring 3D shapes or 3D displacement/strain fields, in research laboratories as well as in industry. Nevertheless, like most of the optical full-field measurement techniques, 3D DIC suffers from a lack of information about its metrological performances. For the 3D DIC technique to be fully accepted as a standard measurement technique it is of key importance to assess its measurement uncertainties.
MOTION DETECTION BY THE RHT METHOD: PROBABILITY MECHANISMS AND EXTENSIONS

A new method proposed by the author and his coworkers has been earlier developed to calculate 2-D motion in a sequence of time-varying images. The method, called Motion Detection using Randomized Hough Transform (MDRHT), is based on the Randomized Hough Transform (RHT). The RHT decreases considerably the time consumption and memory requirements of the Hough Transform. The basic idea of the MDRHT is to pick randomly point pairs from two images and calculate the translation with them. The points can be e.g. edge points of the original images. This approach can avoid the di(cid:14)cul-ties of some other motion estimation methods, e.g. overlapping and covering, and has the advantages provided by the RHT. The method can be generalized by picking more than two points. The extended algorithm to calculate both the translation and the rotation is represented in this paper. Probability mechanisms of the MDRHT are also considered and new possible extensions are introduced. Algorithms are tested with both real and synthetic images and their behaviour is examined via probability formulas de(cid:12)ned in this paper.
Noise reduction and feature extraction based on low-rank representation and pairwise constraint preserving for hyperspectral images

ABSTRACT Feature extraction (FE) methods play a central role in the classification of hyperspectral images (HSIs). However, all traditional FE methods work in original feature space (OFS), OFS may suffer from noise, outliers and poorly discriminative features. This paper presents a feature space enriching technique to address the problems of noise, outliers and poorly discriminative features which may exist in OFS. The proposed method is based on low-rank representation (LRR) with the capability of pairwise constraint preserving (PCP) termed LRR-PCP. LRR-PCP does not change the dimension of OFS and only can be used as an appropriate preprocessing procedure for any classification algorithm or DR methods. The proposed LRR-PCP aims to enrich the OFS and obtain extracted feature space (EFS) which results in features richer than OFS. The problems of noise and outliers can be decreased using LRR. But, LRR cannot preserve the intrinsic local structure of the original data and only capture the global structure of data. Therefore, two additional penalty terms are added into the objective function of LRR to keep the local discriminative ability and also preserve the data diversity. LRR-PCP method not only can be used in supervised learning but also in unsupervised and semi-supervised learning frameworks. The effectiveness of LRR-PCP is investigated on three HSI data sets using some existing DR methods and as a denoising procedure before the classification task. All experimental results and quantitative analysis demonstrate that applying LRR-PCP on OFS improves the performance of the classification and DR methods in supervised, unsupervised, and semi-supervised conditions.
Pathobiology of visceral pain: molecular mechanisms and therapeutic implications. II. Genetic approaches to pain therapy.

New analgesic drugs are necessary because a number of pain states are untreatable. Genetic approaches to the identification of analgesic drug targets include mapping genes involved in human pain perception (e.g., trkA involved in hereditary neuropathies), identifying regulators of sensory neuron function in simple multicellular organisms and then investigating the activity of their mammalian homologs (e.g., POU domain transcription factors that specify sensory cell fate), as well as difference, expression, and homology cloning of receptors, ion channels, and transcription factors present in sensory neurons. After target validation through the construction of null mutant mice, high-throughput cell-based screens can be used to identify potential drug candidates. As a result of these approaches, a number of receptors and ion channels present in sensory neurons such as voltage-gated sodium channels [sensory neuron specific (SNS) and Na channel novel] and ATP-gated (P2X3), capsaicin-gated [vanilloid receptor 1(VR1)], and proton-gated [acid-sensing ion channel (ASIC)] channels are now under investigation as potential new analgesic drug targets.
A statistical method for detecting cyber/physical attacks on SCADA systems

This paper addresses the problem of detecting cyber/physical attacks on Supervisory Control And Data Acquisition (SCADA) systems. The detection of cyber/physical attacks is formulated as the problem of detecting transient changes in stochastic-dynamical systems in the presence of unknown system states (often regarded as the nuisance parameter). The Variable Threshold Window Limited CUmulative SUM (VTWL CUSUM) test is adapted to the detection of transient changes of known profiles in the presence of nuisance parameter. Taking into account the performance criterion of the transient change detection problem, which minimizes the worst-case probability of missed detection for a given value of the worst-case probability of false alarm, the thresholds are tuned for optimizing the VTWL CUSUM algorithm. The optimal choice of thresholds leads to the simple Finite Moving Average (FMA) algorithm. The proposed algorithms are utilized for detecting the covert attack on a simple water distribution system, targeting at stealing water from the reservoir without being detected.
A subset of precise UML for model-based testing

This paper presents an original model-based testing approach that takes a UML behavioural view of the system under test and automatically generates test cases and executable test scripts according to model coverage criteria. This approach is embedded in the LEIRIOS Test Designer tool and is currently deployed in domains such as Enterprise IT and electronic transaction applications. This model-based testing approach makes it possible to automatically produce the traceability matrix from requirements to test cases as part of the test generation process. This paper defines the subset of UML used for model-based testing and illustrates it using a small example.
Network applications in energy management systems

Four steady-state network applications are described: state estimation, security analysis, power flow, and optimal power flow. Unlike other energy management system (EMS) applications, they depend on power system network models. They may execute in EMS real-time and study environments, and also operator training simulators. The intent is to provide guide to current network application practice and implementation. <<ETX>>
Supporting Groupware in Mobile Environments

This technical report describes a replicated object store designed to support typically asynchronous collabo-rative activities in mobile environments. The system allows the definition of type-specific data management solutions using an object framework that includes awareness management. High data availability is provided in mobile environments relying on server replication and client caching and exploring ad hoc networks established among clients. Partial caching allows operation on mobile devices with reduced resources. Blind invocation allows users to produce new contributions on data that is not locally available, during disconnected operation. The system also supports the integration of synchronous sessions in the overall asynchro-nous activity.
Aggregating Regression Procedures for a Better Performance

Methods have been proposed to linearly combine candidate regression procedures to improve estimation accuraccy. Applications of these methods in many examples are very succeesful, pointing to the great potential of combining procedures. A fundamental question regarding combining procedure is: What is the potential gain and how much one needs to pay for it? A partial answer to this question is obtained by Juditsky and Nemirovski (1996) for the case when a large number of procedures are to be combined. We attempt to give a more general solution. Under a l 1 constrain on the linear coe cients, we show that for pursuing the best linear combination over n procedures, in terms of rate of convergence under the squared L 2 loss, one can pay a price of order O logn=n 1 when 0 < < 1=2 and a price of order O (logn=n) 1=2 when 1=2 <1. These rates can not be improved or essentially improved in a uniform sense. This result suggests that one should be cautious in pursuing the best linear combination, because one may end up with paying a high price for nothing when linear combination in fact does not help. We show that with care in aggregation, the nal procedure can automatically avoid paying the high price for such a case and then behaves as well as the best candidate procedure in terms of rate of convergence.
Research and Implement of Classification Rule Mining Algorithm Based on Attribute Reduction

This paper brings up a new classification algorithm of data mining (CRMA) in any scale relation database. Based on rough set theory it divides relation table into several equivalence class based on attribute values, calculates information capacity in decision factor of the every condition attribution, eliminates redundancy attributions, and erases repeat units. Then classification rules can be obtained through strong equivalence class which relation table was reduced. It overcomes the redundancy nature, complicated nature and unfit nature to big capacity data or increment data of some classification algorithm at present. It has higher efficiency and widespread application perspective in large and incremental databases. The mining algorithm and an example are discussed in details.
Improved multi-criteria distribution network reconfiguration with information fusion

Electrical grids are no more isolated infrastructures but they provide services towards other infrastructures and meanwhile water networks, telecommunications, gas pipelines and transport systems are mandatory in order to produce and deliver electricity. The reconfiguration algorithm determines the optimal tree configuration of the grid, after overloads or permanent faults. In literature, the reconfiguration algorithm takes into account feasibility, radiality, load balancing and energy losses.The aim of this paper is to consider the effects of interconnected infrastructures on the reconfiguration algorithm. In order to realise this aim, we must collect information coming from heterogeneous infrastructures and normalize it. CISIApro is an agent-based simulator which gathers data on equipment operability and evaluates the cascading effects of faults, cyber attacks and natural disasters. The short-term forecast provided by CISIApro is the input of the decision support system for electrical reconfiguration purpose. This decision support system is made of two parts: an off-line tool able to generate a large number of possible configurations and a multi-criteria decision making able to evaluate several criteria. The criteria are availability of the telecommunication network for closing the needed switches, availability of the generators to supply the downstream loads, load balancing and, eventually, the blackouts or the unfulfilled loads, in terms of population and importance. The algorithm has been tested on a scenario made of three interconnected infrastructures: distribution grid, gas pipeline and telecommunication network. The electrical grid is the IEEE 14 busbar system, where one generator is a gas turbine, i.e., a load in the gas pipeline. Some results are explained for understanding how information fusion can improve decision support systems.
Choice Based Revenue Management for Parallel Flights

This paper describes a revenue management project with a major airline that operates in a fiercely competitive market involving two major hubs and having more than 30 parallel daily flights. The market has a number of unusual characteristics including (1) almost half of customers choose not to purchase the tickets after booking; (2) about half of customers purchase their tickets within 3 days of departure; and (3) a significant number of customers no-show or go-show. We formulate choice based stochastic optimization problems to maximize the expected revenue for the airline. The inputs of the stochastic models include booking arrival rates, competitor assortment selection, booking choice probabilities for the airline’s own flights as well as competitors’ flights, booking-to-ticketing conversion probabilities, and go-show and no-show probabilities. We build a number of booking choice models, including multinomial logit models, nested logit models, and mixed logit models. The latter two types of models are aimed at incorporating unobserved heterogeneous customer preferences for different departure times. We formulate corresponding deterministic (fluid) optimization problems under each of the three booking choice models. We designed a column generation algorithm to compute optimal or nearoptimal solutions for the deterministic problems, and the solutions are used to make assortment selections for the stochastic problem. The models used as input for the optimization problems are calibrated using 2011 data or 2012 data. Simulation studies using 2012 data show that the fluid based booking policies generate significantly more revenue than the airline’s existing policy, and that policies based on the simpler multinomial logit models perform better than policies based on nested logit and mixed logit models, even when the simulation is based on the latter models, and even though the latter models seem to be more realistic.
Visual up Gradation of Mammogram Image Using Grey Scale Extending Techniques

Breast cancer is the most common of all cancers affecting women in the developed countries. More middle-age women die of breast cancer than of any other single cause. This paper gives the idea about the Early Detection of Breast Cancer with some preprocessing steps. Grey scale Extending is basically improving the Interpretability or perception of information in an image for human viewers and providing better input for other automated image processing techniques. The principal objective of gray scale extending of a Mammogram Image is to modify the image to make it more suitable for finding the tumor cell location in the Breast. In grey scales extending Where Considerable background noise or variation in contrast and illumination exists, Local thresholding technique is more suitable than that of Global one. In this paper we are discussed about Local and Global thresholding to improve the visual up gradation of a Mammogram Image and Java as an image processing tool was used. With this technique we conclude that the proposed method is will performs more effectively for visual up gradation of mammogram image for further image processing steps . After further image processing steps like Triangulation we can find the location of different contracts cells in an image. This giving extra aid to radiologists to detect and classify the mammograms of breast cancer.
Entrepreneurial engagement in a depleted small town: Legitimacy and embeddedness

Abstract Our study of entrepreneurial engagement within a depleted New Zealand community contributes to understanding the role of place and legitimacy in entrepreneurial practice. General decline characterised Stanton until a newcomer entrepreneur started several businesses that rejuvenated the town. She became a local economic hero to many stakeholders; but others deployed social narratives of place that drew on different values. Our analysis of interactions shows how perceptions of place legitimise or vilify and we demonstrate how place intercedes upon economically generated legitimacy of entrepreneurial practice. Social constructions of place and notions of embeddedness influenced this entrepreneurial enactment. Conceptually, we challenge ideas about universal legitimacy ensuing from economic entrepreneurial benefits. Our theoretical contribution offers socio-spatial propositions for understanding entrepreneurial legitimacy through place.