Miracle-dz/UniFAR-FTUs huggingface dataset:
USING POMDPS FOR DIALOG MANAGEMENT

This paper explains how partially observable Markov decision processes (POMDPs) can provide a principled mathematical framework for modelling the inherent uncertainty in spoken dialog systems. It briefly summarises the basic mathematics and explains why exact optimisation is intractable. It then describes a form of approximation called the Hidden Information State model which does scale and which can be used to build practical systems.
Efficient techniques for interactive texture placement

This paper describes efficient algorithms for the placement and distortion of textures. The textures include surface color maps and environment maps. Affine transformations of a texture, as well as localized warps, are used to align features in the texture with features of the model. Image-space caches are used to enable texture placement in real time.
A Fair Subcarrier Allocation Algorithm for Cooperative Multiuser OFDM Systems with Grouped Users

Dynamic resource allocation improves the performance of multiuser OFDM systems by exploiting multiuser diversity. Cooperative diversity is a technique where multiple users share their resources to realize a spatial diversity gain through cooperation. In this paper, the problem of downlink subcarrier allocation in a cooperative multiuser system is investigated. We assume a single-cell case where the base station has perfect knowledge of subchannel gains and all the mobile users are paired in cooperative groups. The mobile users in one cooperative group relay their partner data stream which is received from base station using a time division protocol. Based on the capacity contribution from the relaying terminal, a new parameter called cooperation coefficient is introduced. Considering the cooperation among users in assigning the subcarriers, a new subcarrier allocation algorithm is proposed. The performance of the proposed algorithm is then evaluated for different values of cooperation coefficients and is shown to maintain the same level of fairness but higher data rates compared with a similar algorithm which does not consider cooperation.
Dermoscopy for the in vivo Detection of Sarcoptes scabiei

We report the case of an 80-year-old patient who had intense pruritus which did not respond to a 3-month treatment with topical corticosteroids. On dermoscopy examination of the excoriations, we found the typical dermoscopic aspect of the scabies mite at a distance. Dermoscopy allows identifying a triangular structure which corresponds to the anterior section of the mite including the mouth part and the 2 pairs of front legs. This aspect has been described as resembling a jetliner with its trail, a delta glider or a spermatozoid. Traditional diagnostic methods for scabies failed in this case because the mites were at a distance from the burrows. This was due to the fact that the reaction to the mite was less pronounced and the diagnosis is frequently missed. Dermoscopy is a useful tool for the diagnosis of scabies either as a diagnostic test or to guide the traditional diagnostic tests.
NEuronMOrphological analysis tool: open-source software for quantitative morphometrics

Morphometric analysis of neurons and brain tissue is relevant to the study of neuron circuitry development during the first phases of brain growth or for probing the link between microstructural morphology and degenerative diseases. As neural imaging techniques become ever more sophisticated, so does the amount and complexity of data generated. The NEuronMOrphological analysis tool NEMO was purposely developed to handle and process large numbers of optical microscopy image files of neurons in culture or slices in order to automatically run batch routines, store data and apply multivariate classification and feature extraction using 3-way principal component analysis (PCA). Here we describe the software's main features, underlining the differences between NEMO and other commercial and non-commercial image processing tools, and show an example of how NEMO can be used to classify neurons from wild-type mice and from animal models of autism.
Effects of Protriptyline and Perphenazine in Neurotic Depressed Outpatients

A four-week comparison of protriptyline 10 mg three times a day and perphenazine 2 mg three times a day, alone and in combination, and a placebo in a group of nonpsychotic depressed outpatients showed no significant differences among any of the treatment groups after both two and four weeks, and a slight trend in favor of the placebo group. A significant positive correlation was observed between changes in hostility and changes in depression, contrary to many commonly held assumptions regarding the relationship between hostility and depression.
Selecting Features with Class Based and Importance Weighted Document Frequency in Text Classification

Document Frequency (DF), which counts how many documents a feature appears in, is reported by Yang and Pedersen [1] to be quite effective for feature selection in text classification. Features with the same DF value are likely to have different appearance distribution over categories, and demonstrate quite different discriminative powers for classification. However, the original DF metric is class independent and does not consider features' distribution over classes. On the other hand, different features play different roles in delivering the content of a document. The chosen features are expected to be the important ones, which carry the main information of a document collection. However, the traditional DF metric considers features equally important. To overcome simultaneously the above two problems of the original document frequency metric, we propose a class based and importance weighted document frequency measure. Preliminary experiments on two text classification datasets do validate the effectiveness of the proposed metric.
GPU computing with orientation maps for extracting local invariant features

Local invariant features have been widely used as fundamental elements for image matching and object recognition. Although dense sampling of local features is useful in achieving an improved performance in image matching and object recognition, it results in increased computational costs for feature extraction. The purpose of this paper is to develop fast computational techniques for extracting local invariant features through the use of a graphics processing unit (GPU). In particular, we consider an algorithm that uses multiresolutional orientation maps to calculate local descriptors consisting of the histograms of gradient orientations. By using multiresolutional orientation maps and applying Gaussian filters to them, we can obtain voting values for the histograms for all the pixels in a scale space pyramid. We point out that the use of orientation maps has two advantages in GPU computing. First, it improves the efficiency of parallel computing by reducing the number of memory access conflicts in the overlaps among local regions, and secondly it utilizes a fast implementation of Gaussian filters that permits the use of shared memory for the many convolution operations required for orientation maps. We conclude with experimental results that demonstrate the usefulness of multiresolutional orientation maps for fast feature extraction.
Consensus-Based Secondary Frequency and Voltage Droop Control of Virtual Synchronous Generators for Isolated AC Micro-Grids

As the penetration of renewable energy sources is increasing in the AC micro-grid, the stability of the closed-loop system has raised a major concern since conventional distributed interface converters (DICs) used in the AC micro-grid do not have a rotating mass, and hence low inertia. High penetration of DIC-based micro-grid may result in poor frequency and voltage response during large disturbance. In order to overcome this difficulty, the virtual synchronous generator (VSGs) was proposed recently in which the DIC mimics conventional synchronous generators (SGs) by designing proper parameters of the SG into each local droop control mechanism of the DIC. Meanwhile, due to the recent advances of distributed control, the concept of consensus-based control can be applied to study this droop control problem of VSGs. One important feature of this consensus-based control is that it can be implemented on each local DIC with communications among their neighboring DICs. In contrast to most existing secondary control schemes, no central controller is required. Under this framework, if DICs are redesigned as VSGs, the frequency and voltage of each DIC can be restored to their pre-specified values obtained from the steady-state analysis. In addition, the proper real and reactive power sharing still can be achieved according to the nominal rating of each DIC. The stability of the closed-loop system is ensured by the transient energy function under certain mild conditions. Numerical experiments of a 14-bus/6-DIC micro-grid system on real-time simulators are performed to validate the effectiveness of the proposed control mechanism.
Adaptive Probabilistic Learning by Collectives in Dynamic Environments

Probability Collective (PC) is a methodology for distributed optimization by sampling an explicitly parameterized probability distribution over the space of solutions. This parameterization effectively utilizes granules of probability distributions to construct computational models for solving complex systems-level optimization problems. In this paper we present a study of using this probabilistic collective learning framework for adaptive optimization in the context of dynamic environments. Two scenarios of PC in dynamic optimization tasks are compared: PC1 (original PC settings), PC2 (the probability distributions are reset to uniform when an environment changes). By allowing PC to re-explore the search space, we show that PC2 is more adaptive to environmental changes, thereby outperforming the original PC in rate of descent as well as long term extrema-tracking optimization. The study of the PC in changing environments therefore sheds light into how this probabilistic learning methodology utilizes the features of granular computation to solve complex dynamic optimization problems.
Merging SPOT-P and KVR-1000 for updating urban maps

This communication presents a method allowing the combination of images acquired on the same area with different dates for updating urban maps. This method makes use of two mathematical tools, the wavelet transform and the multiresolution analysis which are shortly introduced. An example is provided in Jeddah (Saudi Aradia) for the updating of a KVR-1000 image by a SPOT panchromatic image acquired at a more recent date. Due to the difficulty to obtain KVR-1000 images at recent dates, this method allows the combination of the high spatial resolution of this kind of images and the regular acquisition of images by the different Earth observation satellites.
Universal Refinable Trellis Coded Quantization

We introduce a novel universal refinable trellis quantization scheme (URTCQ) that is suitable for bitplane coding with many reconstruction stages. Existing refinable trellis quantizers either require excessive codebook training and are outperformed by scalar quantization for more than two stages (MS-TCQ, E-TCQ), require a huge computational burden (SR-TCQ) or achieve a good rate distortion performance in the last stage only (UTCQ). The presented quantization technique is a mixture of a scalar quantizer and an improved version of the E-TCQ. For all supported sources only one time training to an i.i. d. uniform source is required and its incremental bitrate is not more than 1 bps for each stage. The complexity is proportional to the number of stages and the number of trellis states. We compare the rate distortion performance of our work on generalized Gaussian i.i. d. sources with the quantizers deployed in JPEG2000 (USDZQ, UTCQ). It turns out that it is in no stage worse than the scalar quantizer and usually outperforms the UTCQ except for the last stage.
Adaptive observer for fault estimation in nonlinear systems described by a Takagi-Sugeno model

This paper deals with the problem of fault estimation for linear and nonlinear systems. An adaptive proportional integral observer is designed to estimate both the system state and sensor and actuator faults which can affect the system. The model of the system is first augmented in such a manner that the original sensor faults appear as actuator faults in this new model. The faults are then considered as unknown inputs and are estimated using a classical proportional-integral observer. The proposed method is first developed for linear systems and is then extended to nonlinear ones that can be represented by a Takagi-Sugeno model. In the two cases, examples of low dimensions illustrate the effectiveness of the proposed method.
A framework for anomaly detection of robot behaviors

Autonomous mobile robots are designed to behave appropriately in changing real-world environments without human intervention. In order to satisfy the requirements of autonomy, the robots have to cope with unknown settings and issues of uncertainties in dynamic and complex environments. A first step is to provide a robot with cognitive capabilities and the ability of self-examination to detect behavioral abnormalities. Unfortunately, most existing anomaly recognition systems are neither suitable for the domain of robotic behavior nor well generalizable. In this work a novel spatial-temporal anomaly detection framework for robotic behaviors is introduced which is characterized by its high level of generalization, the semi-unsupervised manner and its high flexibility in application.
Efficient Tree-based Approximation for Entailment Graph Learning

Learning entailment rules is fundamental in many semantic-inference applications and has been an active field of research in recent years. In this paper we address the problem of learning transitive graphs that describe entailment rules between predicates (termed entailment graphs). We first identify that entailment graphs exhibit a "tree-like" property and are very similar to a novel type of graph termed forest-reducible graph. We utilize this property to develop an iterative efficient approximation algorithm for learning the graph edges, where each iteration takes linear time. We compare our approximation algorithm to a recently-proposed state-of-the-art exact algorithm and show that it is more efficient and scalable both theoretically and empirically, while its output quality is close to that given by the optimal solution of the exact algorithm.
E Protein Silencing by the Leukemogenic AML1-ETO Fusion Protein

The AML1-ETO fusion protein, generated by the t(8;21) chromosomal translocation, is causally involved in nearly 15% of acute myeloid leukemia (AML) cases. This study shows that AML1-ETO, as well as ETO, inhibits transcriptional activation by E proteins through stable interactions that preclude recruitment of p300/CREB-binding protein (CBP) coactivators. These interactions are mediated by a conserved ETO TAF4 homology domain and a 17–amino acid p300/CBP and ETO target motif within AD1 activation domains of E proteins. In t(8;21) leukemic cells, very stable interactions between AML1-ETO and E proteins underlie a t(8;21) translocation-specific silencing of E protein function through an aberrant cofactor exchange mechanism. These studies identify E proteins as AML1-ETO targets whose dysregulation may be important for t(8;21) leukemogenesis, as well as an E protein silencing mechanism that is distinct from that associated with differentiation-inhibitory proteins.
ANN-based Control Method Implemented in a Voltage Source Converter for Industrial Micro-grid

With more and more attention on the grid current harmonic in recent years, many control schemes of the Pulse Width Modulation Voltage Source Converter (PWM-VSC) have been investigated. Conventional PI controller has shown limitations such as sensitivity to load and system parameter variation. Even the stability of the system can be threatened under a large and sudden load change. In this paper, the practical situation of a VSC for industrial Micro Grid (MG) is considered and an Artificial neural network (ANN) based control method is employed to solve the problem. Meanwhile, an on-line parameter tuning algorithm is introduced for its advantage of self-tuning and system character identification. The proposed control scheme is verified through simulation based on SABER software. The simulation results have shown the advantage of the proposed method and the performance of the parameter tuning session.
Crew Resource Management Application in Commercial Aviation

The purpose of this study was to extend previous examinations of commercial multi-crew airplane accidents and incidents to evaluate the Crew Resource Management (CRM) application as it relates to error management during the final approach and landing phase of flight. With data obtained from the Federal Aviation Administration (FAA) and the National Transportation Safety Board (NTSB), a x 2 test of independence was performed to examine if there would be a statistically significant relationship between airline management practices and CRM-related causes of accidents/incidents. Between 2002 and 2012, 113 accidents and incidents occurred in the researched segments of flight. In total, 57 (50 percent) accidents/incidents listed a CRM-related casual factor or included a similar commentary within the analysis section of the investigation report. No statistically significant relationship existed between CRM-related accidents/incidents
Reducing I/O complexity by simulating coarse grained parallel algorithms

Block-wise access to data is a central theme in the design of efficient external memory (EM) algorithms. A second important issue, when more than one disk is present, is fully parallel disk I/O. In this paper we present a deterministic simulation technique which transforms parallel algorithms into (parallel) external memory algorithm. Specifically; we present a deterministic simulation technique which transforms coarse grained multicomputer (CGM) algorithms into external memory algorithms for the parallel disk model. Our technique optimizes block-wise data access and parallel disk I/O and, at the same time, utilizes multiple processors connected via a communication network or shared memory. We obtain new improved parallel external memory algorithms for a large number of problems including sorting, permutation, matrix transpose, several geometric and GIS problems including 3D convex hulls (2D Voronoi diagrams), and various graph problems. All of the (parallel) external memory algorithms obtained via simulation are analyzed with respect to the computation time, communication time and the number of I/Os. Our results answer to the challenge posed by the ACM working group on storage I/O for large-scale computing.
Gone but not Forgotten

Recently we published data showing that under some circumstances the effect of protein kinase M zeta (PKMζ) inhibition in the amygdala on the expression of fear-potentiated startle (FPS) is a function of when the drug was applied relative to when testing occurred (Parsons and Davis, 2011). We showed in three separate experiments that if memory is tested anywhere from 10 to 15 days after infusion of the PKMζ inhibitor zeta-pseudosubstrate inhibitory peptide (ZIP) there was no evidence of memory impairment. However, there was a reliable impairment if testing took place a few hours or a few days after infusion of ZIP. A commentary was published by Nader (2011) critiquing several aspects of our findings and the conclusions drawn. Here, we respond to the criticism.
A feasibility study on age-related factors of wrist pulse using principal component analysis

Various analysis methods for examining wrist pulse characteristics are needed for accurate pulse diagnosis. In this feasibility study, principal component analysis (PCA) was performed to observe age-related factors of wrist pulse from various analysis parameters. Forty subjects in the age group of 20s and 40s were participated, and their wrist pulse signal and respiration signal were acquired with the pulse tonometric device. After pre-processing of the signals, twenty analysis parameters which have been regarded as values reflecting pulse characteristics were calculated and PCA was performed. As a results, we could reduce complex parameters to lower dimension and age-related factors of wrist pulse were observed by combining-new analysis parameter derived from PCA. These results demonstrate that PCA can be useful tool for analyzing wrist pulse signal.
Thermodynamic Properties of Aqueous Species Calculated Using the HKF Model: How Do Different Thermodynamic and Electrostatic Models for Solvent Water Affect Calculated Aqueous Properties?

Thermodynamic properties of aqueous species are essential for modeling of fluid-rock interaction processes. The Helgeson-Kirkham-Flowers (HKF) model is widely used for calculating standard state thermodynamic properties of ions and complexes over a wide range of temperatures and pressures. To do this, the HKF model requires thermodynamic and electrostatic models of water solvent. In this study, we investigate and quantify the impact of choosing different models for calculating water solvent volumetric and dielectric properties, on the properties of aqueous species calculated using the HKF model. We identify temperature and pressure conditions at which the choice of different models can have a considerable effect on the properties of aqueous species and on fluid mineral equilibrium calculations. The investigated temperature and pressure intervals are 25–1000°C and 1–5 kbar, representative of upper to middle crustal levels, and of interest for modeling ore-forming processes. The thermodynamic and electrostatic models for water solvent considered are: Haar, Gallagher and Kell (1984), Wagner and Pruß (2002), and Zhang and Duan (2005), to calculate water volumetric properties, and Johnson and Norton (1991), Fernandez and others (1997), and Sverjensky and others (2014), to calculate water dielectric properties. We observe only small discrepancies in the calculated standard partial molal properties of aqueous species resulting from using different water thermodynamic models. However, large differences in the properties of charged species can be observed at higher temperatures (above 500°C) as a result of using different electrostatic models. Depending on the aqueous speciation and the reactions that control the chemical composition, the observed differences can vary. The discrepancy between various electrostatic models is attributed to the scarcity of experimental data at high temperatures. These discrepancies restrict the reliability of the geochemical modeling of hydrothermal and ore formation processes, and the retrieval of thermodynamic parameters from experimental data at elevated temperatures and pressures.
Beyond Disagreement-Based Agnostic Active Learning

We study agnostic active learning, where the goal is to learn a classifier in a pre-specified hypothesis class interactively with as few label queries as possible, while making no assumptions on the true function generating the labels. The main algorithms for this problem are {\em{disagreement-based active learning}}, which has a high label requirement, and {\em{margin-based active learning}}, which only applies to fairly restricted settings. A major challenge is to find an algorithm which achieves better label complexity, is consistent in an agnostic setting, and applies to general classification problems. In this paper, we provide such an algorithm. Our solution is based on two novel contributions -- a reduction from consistent active learning to confidence-rated prediction with guaranteed error, and a novel confidence-rated predictor.