Miracle-dz/UniFAR-FTUs huggingface dataset:
Discovering Threshold-based Frequent Closed Itemsets over Probabilistic Data

In recent years, many new applications, such as sensor network monitoring and moving object search, show a growing amount of importance of uncertain data management and mining. In this paper, we study the problem of discovering threshold-based frequent closed item sets over probabilistic data. Frequent item set mining over probabilistic database has attracted much attention recently. However, existing solutions may lead an exponential number of results due to the downward closure property over probabilistic data. Moreover, it is hard to directly extend the successful experiences from mining exact data to a probabilistic environment due to the inherent uncertainty of data. Thus, in order to obtain a reasonable result set with small size, we study discovering frequent closed item sets over probabilistic data. We prove that even a sub-problem of this problem, computing the frequent closed probability of an item set, is #P-Hard. Therefore, we develop an efficient mining algorithm based on depth-first search strategy to obtain all probabilistic frequent closed item sets. To reduce the search space and avoid redundant computation, we further design several probabilistic pruning and bounding techniques. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments.
Rule-Based Inference in Large Knowledge Bases

The process of inference can be conceived as the continual redescription of a given structure In the system knowledge base until a desired structure Is formed. Inference rules then represent single transformations to be used In this redescription process. If the rules are themselves Integrated into the knowledge base, the rule application mechanism can take advantage of knowledge base organization to maintain an efficient inference process, even in systems that must deal with large amounts of knowledge. This paper presents a methodology for implementing rule application, and gives two examples of its use in knowledge-based systems,
TAGUCHI APPROACH TO DESIGN OPTIMIZATION FOR QUALITY AND COST: AN OVERVIEW

Calibrations to existing cost of doing business in space indicate that to establish human presence on the Moon and Mars with the Space Exploration Initiative (SEI) will require resources, felt by many, to be more than the national budget can afford. In order for SEI to succeed, we must actually design and build space systems at lower cost this time, even with tremendous increases in quality and performance requirements, such as extremely high reliability. This implies that both government and industry must change the way they do business. Therefore, new philosophy and technology must be employed to design and produce reliable, high quality space systems at low cost. In recognizing the need to reduce cost and improve quality and productivity, Department of Defense (DoD) and National Aeronautics and Space Administration (NASA) have initiated Total Quality Management (TQM). TQM is a revolutionary management strategy in quality assurance and cost reduction. TQM requires complete management commitment, employee involvement, and use of statistical tools. The quality engineering methods of Dr. Taguchi, employing design of experiments (DOE), is one of the most important statistical tools of TQM for designing high quality systems at reduced cost. Taguchi methods provide an efficient and systematic way to optimize designs for performance, quality, and cost. Taguchi methods have been used successfully in Japan and the United States in designing reliable, high quality products at low cost in such areas as automobiles and consumer electronics. However, these methods are just beginning to see application in the aerospace industry. The purpose of this paper is to present an overview of the Taguchi methods for improving quality and reducing cost, describe the current state of applications and its role in identifying cost sensitive design parameters.
Movement‐sensitive and direction and orientation‐selective cutaneous receptive fields in the hand area of the post‐central gyrus in monkeys.

1. In the hand area of the post‐central gyrus of three alert Macaca speciosa monkeys neurones related to cutaneous receptors but not activated by simple touch on the receptive field were recorded using the transdural micro‐electrode recording technique. Thirty‐six cells were found to have complex cutaneous receptive field properties. These neurones were subdivided into the following three groups. 2. Nine neurones were not activated by punctate stimuli on the receptive fields but responded well to movement along the skin. The activity of these neurones was not affected by the direction of movement; nor was it sensitive to different textures of the moving surface. 3. Eighteen neurones responded to cutaneous movement along the skin surface in a particular direction giving no response to stimulation in the opposite direction and intermediate responses to intermediate directions. Similar responses were evoked from different subparts of the receptive field. 4. Nine neurones responded well to an edge placed on the skin in an optimal orientation or moved along the skin in a direction perpendicular to the edge. A maximal response was produced by stimuli of the same optimal orientation in different parts of the receptive field. The significance of the stimuli to the monkey had only a minor influence on the magnitude of the responses of these neurones and no influence on the receptive field properties. 5. The occurrence of the complex cutaneous cells increased from anterior to posterior within the post‐central gyrus and most of them were found in Brodmann's area 2. Thus we postulate that the complex receptive field properties arise as a consequence of cortical processing in a network in which postsynaptic one‐way lateral inhibition generates the directional properties of the neurones. 6. The complex cutaneous neurones constituted only 6% of the neurones studied in the hand area of the post‐central gyrus. Thus the prevalence of neurones with elongated and direction‐selective receptive fields is low in the primary somatosensory cortex in comparison with the visual cortex. These neurones may, however, serve the sterognostic capcity of the hand by contributing information about stimulus motion, orientation and direction of movement on the skin.
Cellular expression pattern of the protease‐activated receptor 4 in the hippocampus in naïve rats and after global ischaemia

A pronounced hippocampal expression of the Protease‐activated Receptor 4 (PAR4) has recently been shown. In the current study the authors define the PAR4‐associated sub‐cellular structures and the influence of global ischaemia on the expression of PAR4. For that purpose the authors performed double labelling with fluorescence immunohistochemistry on tissue from naïve and post‐ischaemic rats. In naïve animals ‐ apart from the expression in granular and pyramidal neurons ‐ there was an intensive lamellar expression of PAR4 in the CA4 region. Further analysis revealed that PAR4 was localised exclusively on mossy fibre axons in CA4 as detected by double‐labelling with calbindin D‐28k, but there was no overlap with markers of the neuronal cell body, interneurons, and post‐synaptic, pre‐synaptic and dendritic structures. Three and 14 days post ischaemia, CA1 neurons were degenerated and, consequently, there was no PAR4 signal in the CA1 band. In most other hippocampal structures no change in the PAR4 expression was detectable, with the exception of the CA3 region. Here, the fibre‐associated PAR4 signal was diminished and disintegrated post ischaemia. Additionally, a redistribution from the membrane‐bound neuronal localisation of PAR4 in control animals to a diffuse localisation all over the cell soma was revealed in the CA3 area 14 days post ischaemia. In conclusion, the current study proves for the first time that PAR4 is localised in mossy fibre axons. The altered expression in CA3 neurons after ischaemia indicates that PAR4 may be involved in post‐ischaemic adaptive mechanisms. © 2009 Wiley‐Liss, Inc.
Calibrated Structured Prediction

In user-facing applications, displaying calibrated confidence measures— probabilities that correspond to true frequency—can be as important as obtaining high accuracy. We are interested in calibration for structured prediction problems such as speech recognition, optical character recognition, and medical diagnosis. Structured prediction presents new challenges for calibration: the output space is large, and users may issue many types of probability queries (e.g., marginals) on the structured output. We extend the notion of calibration so as to handle various subtleties pertaining to the structured setting, and then provide a simple recalibration method that trains a binary classifier to predict probabilities of interest. We explore a range of features appropriate for structured recalibration, and demonstrate their efficacy on three real-world datasets.
'Sometimes, to Change the People, You've Got to Change the People': When Learning is Not Enough

This paper discusses organisational learning and change management in a major change in an Australian University. The qualitative data highlight the issues faced in the management of change, particularly in the institutionalised resistance of organisational members. Analysis suggests that the development and consolidation of shared genres of doubt about organisational information and leadership intentions accommodated entrenched resistance. In discussion, this paper revisits previous literature in change management and organisational learning that highlights the impact of the misalignment between learning and organisational imperatives. Discussion of the findings illustrates the ways in which organisational genres of doubt have the ability to undermine strategic intent in change. The findings may be used to inform change leaders about the factors that can contribute to change failure. It may also be used to alert managers to the complex relationship between change management prescriptions and organisational members’ learning and subsequent behaviour in times of change.
Complementary Discrimination Learning with Decision Lists

This paper describes the integration of a learning mechanism called complementary discrimination learning with a knowledge representation schema called decision lists. There are two main results of such an integration. One is an efficient representation for complementary concepts that is crucial for complementary discrimination style learning. The other is the first behaviorally incremental algorithm, called CDL2, for learning decision lists. Theoretical analysis and experiments in several domains have shown that CDL2 is more efficient than many existing symbolic or neural network learning algorithms, and can learn multiple concepts from noisy and inconsistent data.
Scheduled denoising autoencoders

We present a representation learning method that learns features at multiple different levels of scale. Working within the unsupervised framework of denoising autoencoders, we observe that when the input is heavily corrupted during training, the network tends to learn coarse-grained features, whereas when the input is only slightly corrupted, the network tends to learn fine-grained features. This motivates the scheduled denoising autoencoder, which starts with a high level of noise that lowers as training progresses. We find that the resulting representation yields a significant boost on a later supervised task compared to the original input, or to a standard denoising autoencoder trained at a single noise level. After supervised fine-tuning our best model achieves the lowest ever reported error on the CIFAR-10 data set among permutation-invariant methods.
Thresholds and Perception of Cold Pain, Heat Pain, and the Thermal Grill Illusion in Patients With Major Depressive Disorder

Objective The thermal grill illusion (TGI) in which interlacing cold and warm bars create the illusion of a painful sensation has been suggested as an experimental model for central pain states and pain processing. The aim of this study was to use this technique to gain further insights into the altered pain perception in major depressive disorder (MDD). Methods In 18 unmedicated patients with MDD, cold and heat pain thresholds (CPT/HPT) as well as the perception of the TGI were examined and compared with 18 matched controls. Results CPT and HPT were significantly increased in patients (7.9°C and 47.5°C) compared with controls (15.9°C and 45.2°C, respectively; p < .05). In the range of TGI stimuli that were perceived painful by controls, the patients did not indicate painful sensations, thereby indicating a shift of the stimulus-response curve of TGI pain perception toward higher stimulus intensities, that is, greater temperature differentials between cold and warm bars (11.5°C for controls, 16.7°C for patients). The patients rated the pain intensity perceived at the respective pain thresholds (CPT and HPT) in tendency higher than did the controls, whereas they perceived the TGI less painful despite increased stimulus intensities. Unpleasantness ratings were similar between groups. Conclusions CPT, HPT and temperature differentials for the perception of the TGI, were increased in patients with MDD as compared with controls. Pain intensity, however, was rated differently for CPT and HPT, where patients indicated higher ratings in tendency, and for the TGI stimulation, where pain was perceived less intense.
Recent advances in dynamic m6A RNA modification

The identification of m6A demethylases and high-throughput sequencing analysis of methylated transcriptome corroborated m6A RNA epigenetic modification as a dynamic regulation process, and reignited its investigation in the past few years. Many basic concepts of cytogenetics have been revolutionized by the growing understanding of the fundamental role of m6A in RNA splicing, degradation and translation. In this review, we summarize typical features of methylated transcriptome in mammals, and highlight the ‘writers’, ‘erasers’ and ‘readers’ of m6A RNA modification. Moreover, we emphasize recent advances of biological functions of m6A and conceive the possible roles of m6A in the regulation of immune response and related diseases.
Mental Tracking: A Computational Model of Spatial Development

Psychological experiments on children's development of spatial knowledge suggest experience at self-locomotion with visual tracking as important factors. Yet, the mechanism underlying development is unknown. We propose a robot that learns to mentally track a target object (i.e., maintaining a representation of an object's position when outside the field-of-view) as a model for spatial development. Mental tracking is considered as prediction of an object's position given the previous environmental state and motor commands, and the current environment state resulting from movement. Following Jordan and Rumelhart's (1992) forward modeling architecture the system consists of two components: an inverse model of sensory input to desired motor commands; and a forward model of motor commands to desired sensory input (goals). The robot was tested on the "three cups" paradigm (where children are required to select the cup containing the hidden object under various movement conditions). Consistent with child development, without the capacity for self-locomotion the robot's errors are self-center based. When given the ability of self-locomotion the robot responds allocentrically.
Model of seawater polluted by oil-in-water emulsion as a response to the increasing shipping activities

150-250 words) Intensive shipping affects marine environment in an extent degree, increasing seawater pollution by hazardous substances, including fuel oil and crude oil. Bilge water from ship power plants usually contains a mixture of dispersed oils, which form spherical droplets of diameter ranging from 0.01 to 10 m. Present methods for detection of dispersed oil require taking a water sample or putting a measuring device into seawater, which allows only to gather point data from limited locations. In order to meet the demand of remote monitoring of endangered zones, a study of optical properties of oil-in-water emulsions was conducted. Presented model of seawater polluted by oil-in-water emulsion can potentially enable remote optical detection of oil-in-water emulsion in visible bands. It is based on the fact that oil droplets become additional absorbents and attenuators in water body. Optical analyses consist of calculations of spectral absorption and scattering coefficients and scattering phase functions for oil emulsions on the basis of Lorentz-Mie theory including measurements of refractive index and determination of oil droplets size distribution. The radiative transfer theory is applied to simulate the contribution of oil emulsion to the remote sensing reflectance. Presented system for radiative transfer simulation is based on Monte Carlo code and it involves optical tracing of virtual photons.
Evaluation of nested polymerase chain reaction for rapid diagnosis of clinically suspected tuberculous pleurisy.

BACKGROUND Early diagnosis of tuberculosis is important in its control. The conventional techniques like smear microscopy and culture suffer from low sensitivity for diagnosis of extra-pulmonary tuberculosis like Pleural Tuberculosis (PTB) due to paucibacillary nature of the fluid. Polymerase Chain Reaction (PCR) is presently seen as a promising alternative to conventional techniques. In this study we have evaluated IS6110 sequence based nested PCR (nPCR) for the detection of Mycobacterium tuberculosis (MTB) DNA directly from clinical samples. The results of PCR were compared with the results of conventional methods like smear, culture and Adenosine Deaminase (ADA) activity. MATERIAL AND METHODS A total of 50 pleural fluid samples from the patients with history suggestive of tuberculosis were taken. All the samples were processed for Ziehl-Neelsan (ZN) staining for Acid Fast Bacilli (AFB), culture ADA activity and PCR with primers targeting 123bp fragment of IS6110 of MTB complex. RESULTS A significant difference was seen in the sensitivities of conventional methods and PCR (p<0.05). Out of these 50 samples 3 were positive by smear, culture was positive in 5 samples, 21 samples showed high ADA activity and 29 were positive by PCR with overall 100% sensitivity of PCR using culture on LJ media as gold standard. CONCLUSIONS The combined analysis of nPCR, ADA activity and other lab investigations can be very useful in the rapid diagnosis in cases of PTB.
A Real-Time Novelty Detector for a Mobile Robot

Recognising new or unusual features of an environment is an ability which is potentially very useful to a robot. This paper demonstrates an algorithm which achieves this task by learning an internal representation of `normality' from sonar scans taken as a robot explores the environment. This model of the environment is used to evaluate the novelty of each sonar scan presented to it with relation to the model. Stimuli which have not been seen before, and therefore have more novelty, are highlighted by the filter. The filter has the ability to forget about features which have been learned, so that stimuli which are seen only rarely recover their response over time. A number of robot experiments are presented which demonstrate the operation of the filter.
Unsupervised speaker adaptation of deep neural network based on the combination of speaker codes and singular value decomposition for speech recognition

Recently, we have proposed a general adaptation scheme for deep neural network based on discriminant condition codes and applied it to supervised speaker adaptation in speech recognition based on either frame-level cross-entropy or sequence-level maximum mutual information training criterion [1, 2, 3, 4]. In this case, each condition code is associated with one speaker in data, which is thus called speaker code for convenience. Our previous work has shown that speaker code based methods are quite effective in adapting DNNs even when only a very small amount of adaptation data is available. However, we have to use a large speaker code size and complex processes to obtain the best ASR performance since good initializations of speaker codes and connection weights are very important. In this paper, we propose a method using singular value decomposition (SVD) as in [5] to initialize speaker codes and connection weights to obtain a comparable ASR performance as before but with a smaller speaker code size and much less computation complexity. Meanwhile, we have evaluated unsupervised speaker adaptation with the proposed method in large vocabulary speech recognition in the Switchboard task. Experimental results have shown that it is effective for providing well initializations and suitable in adapting large DNN models.
An Improved Algorithm For Minimum-area Retiming

The concept of improving the timing behavior of a circuit by relocatingflip-flops is called retiming and was first presented by Leisersonand Saxe. The ASTRA algorithm proposed an alternativeview of retiming using the equivalence between retiming and clockskew optimization. This work defines the relationship betweenthe Leiserson-Saxe and the ASTRA approaches and utilizes it tosolve the problem of retiming for minimum area. The new algorithm,Minaret, uses the linear programming formulation of theLeiserson-Saxe approach. The underlying philosophy of the ASTRAapproach is incorporated to reduce the number of variablesand constraints in the linear program. This reduction in the sizeof the linear program makes Minaret space and time efficient, enablingminimum area retiming of circuits with over 56,000 gates inunder 15 minutes.
Comparative Study and Mathematical Modeling of Machining Parameters in Ultrasonic-Assisted EDM of AISI H13 Tool Steel by the Application of Workpiece Vibration

The correct selection of the machining parameters is one of the most significant issues to take into consideration in Ultrasonic-assisted Electrical Discharge Machining (US-EDM) and EDM processes. In the present work, a study has been made to develop and extract statistical models to show the relationship between important machining performance data (material removal rate (MRR), tool wear ratio (TWR) and surface roughness Ra) and the input machining parameters (pulse current, and pulse-on time) in the EDM and US-EDM of AISI H13. The models obtained were used to analyze the effects of input parameters on machining performance. In addition, a comparative study was carried out to investigate the effect of ultrasonic vibration of the workpiece on machining performance. The results show that Ultrasonic vibration of the workpiece can significantly reduce the inactive pulses and improves the stability of process. Also US-EDM is effective in attaining a high material removal rate (MRR) in finishing regime in comparison with conventional EDM. The results of Analysis of Variance (ANOVA) indicate that the proposed mathematical models can adequately explain the performance within the limits of the factors being studied.