Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep U-B-V imaging of the Lockman Hole with the LBT Observations and number counts

We used the large binocular camera (LBC) mounted on the large binocular telescope (LBT) to observe the Lockman Hole in the U, B, and V bands. Our observations cover an area of 925 arcmin 2 . We reached depths of 26.7, 26.3, and 26.3 mag(AB) in the three bands, respectively, in terms of 50% source detection effi ciency, making this survey the deepest U-band survey and one of the deepest B and V band surveys with respect to its covered area. We extracted a large number of sources (∼ 89000), detected in all three bands and examined their surface density, comparing it with models of galaxy evolution. We find good agreement with previous claim s of a steep faint-end slope of the luminosity functions, caused by late -type and irregular galaxies at z> 1.5. A population of dwarf star-forming galaxies at 1.5< z< 2.5 is needed to explain the U-band number counts. We also find ev idence of strong supernova feedback at high redshift. This survey is complementary to the r, i, and z Lockman Hole survey conducted with the Subaru telescope and provides the essential wavelength coverage to derive photometric redshifts and select different types of sources from the Lockman Hole for further study.
Sharing of emotional memories with a listener: differences between adolescents and emerging adults

ABSTRACT Research demonstrated a strong influence of personal narratives on emotions linked to memories. However, few studies considered shared narratives and the role of listener’s behavior, especially in adolescence and emerging adulthood. This paper aims to explore the impact of listening attitude on emotions related to memories in adolescents and emerging adults. A total of 157 participants were asked to choose a memory: in the experimental conditions (Attentive Listening, AL vs. Detached Listening, DL), they narrated it to a listener, in the Control Group, they internally reflected about it. Emotions linked to memory at the first time, to narrative/reflection, and to memory after 15 days were measured, as well as the perception of listener’s behavior. Results showed that participants perceived the differences of the listening, although adolescents perceived less listener’s detached stance. Moreover, among adolescents, positive emotions increased after narrative for both experimental conditions, instead, among emerging adults, only AL condition participants increased their positive emotions. Moreover, adults maintained the positive effects of narrative also after 15 days. Authors discussed the role of an empathic context and the adolescent egocentrism in shared narratives about personal memories.
Deformation correction in ultrasound imaging in an elastography framework

Tissue deformation in ultrasound imaging is an inevitable phenomenon and poses challenges to the development of many techniques related to ultrasound image registration, including multimodal image fusion, freehand three-dimensional ultrasound, and quantitative measurement of tissue geometry. In this thesis, a novel trajectory-based method to correct tissue deformation in ultrasound B-mode imaging and elastography is developed in the framework of elastography. To characterize the change of tissue deformation with contact force, a force sensor is used to provide contact force measurement. Correlation-based displacement estimation techniques are applied to ultrasound images acquired under different contact forces. Based on the estimation results, a two-dimensional trajectory field is constructed, where pixel coordinates in each scan are plotted against the corresponding contact force. Interpolation or extrapolation by polynomial curve fitting is then applied to each trajectory to estimate the image under a specified contact force. The performance of displacement estimation and polynomial curve fitting are analyzed in a simulation framework incorporating FEM and ultrasound simulation. Influences of parameter selection are also examined. It is found that in displacement estimation, the coarse-to-fine approach outperforms single-level template search, and correlation filtering in coarse scale provides noticeable improvement in estimation performance. The strategies of image acquisition and order selection in polynomial curve fitting are also evaluated. Additionally, a finer force resolution is found to give better performance in predicting pixel positions under zero force. Deformation correction in both B-mode imaging and elastography is demonstrated through simulation and in-vitro experiments. The performance of correction is quantified by translational offset and area estimation of the tissue inclusions. It is found that, for both B-mode and elastography images, those performance metrics are significantly improved after correction. Moreover, it is shown that a finer resolution in force control gives better performance in deformation correction, which agrees with the analysis of polynomial curve fitting. Thesis Supervisor: Brian W. Anthony Title: Research Scientist, Department of Mechanical Engineering
Low Levels of GSTA1 Expression Are Required for Caco-2 Cell Proliferation

The colonic epithelium continuously regenerates with transitions through various cellular phases including proliferation, differentiation and cell death via apoptosis. Human colonic adenocarcinoma (Caco-2) cells in culture undergo spontaneous differentiation into mature enterocytes in association with progressive increases in expression of glutathione S-transferase alpha-1 (GSTA1). We hypothesize that GSTA1 plays a functional role in controlling proliferation, differentiation and apoptosis in Caco-2 cells. We demonstrate increased GSTA1 levels associated with decreased proliferation and increased expression of differentiation markers alkaline phosphatase, villin, dipeptidyl peptidase-4 and E-cadherin in postconfluent Caco-2 cells. Results of MTS assays, BrdU incorporation and flow cytometry indicate that forced expression of GSTA1 significantly reduces cellular proliferation and siRNA-mediated down-regulation of GSTA1 significantly increases cells in S-phase and associated cell proliferation. Sodium butyrate (NaB) at a concentration of 1 mM reduces Caco-2 cell proliferation, increases differentiation and increases GSTA1 activity 4-fold by 72 hours. In contrast, 10 mM NaB causes significant toxicity in preconfluent cells via apoptosis through caspase-3 activation with reduced GSTA1 activity. However, GSTA1 down-regulation by siRNA does not alter NaB-induced differentiation or apoptosis in Caco-2 cells. While 10 mM NaB causes GSTA1-JNK complex dissociation, phosphorylation of JNK is not altered. These findings suggest that GSTA1 levels may play a role in modulating enterocyte proliferation but do not influence differentiation or apoptosis.
Semi-Supervised Pattern Based Algorithm for Arabic Relation Extraction

While several relation extraction algorithms have been developed in the past decade, mainly in the English language, only few researchers target the Arabic language owing to its complexity and rich morphology. This paper proposes a semi-supervised pattern-based bootstrapping technique to extract Arabic semantic relation that lies between entities. In order to enhance the performance to suit the morphologically rich Arabic language, stemming, semantic expansion using synonyms, and an automatic scoring technique to measure the reliability of the generated patterns and extracted relations were used. To further improve performance, a dependency parser was then used to omit negative relations. The proposed system was tested by applying it to two corpora, which differ in both size and genre, scoring a highest F-measure of 75.06%. Furthermore, the effect of adding stemming and synonyms was also experimentally tested. The results show that this bootstrapping methodology achieves higher performance than existing state-of-the-art methods, and can be expanded to include more relations for use in various NLP tasks.
Development a zooplankton recognition method for dark field image

In this paper, an automated method for detection and recognition of marine plankton in the dark field images is proposed and evaluated. The features extracted with shape, invariant moment and texture information are used for the support vector machine (SVM) classifier. Three species of planktons were used for the performance evaluation and 86% classification accuracy was achieved. It is shown that the recognition accuracy based on watershed algorithm is better than that method based on adaptive threshold segmentation algorithm. The result shows this is a promising method for real-time application.
Discovering internal fraud models in a stream of banking transactions

Internal frauds in the banking industry represent a huge cost and this problem is particularly difficult to solve because, by construction, swindlers being very imaginative persons, the fraud schemata evolves continuously. Fraud detection systems must then learn from the continuously new fraud schematas, making them difficult to design. This paper proposes a new theoretical and practical approach to detect internal frauds and to model fraud schematas. This approach is based on a particular method of abstraction that reduces the complexity of the problem from O(n2) to O(n) making its implementation in a an Java program that detects and models the frauds in real time and online with a simple professional personal computer. The results of this program are presented with its application on a real-world fraud provided by a world wide French bank.
A Context-based Model of Semantic Similarity

Lexical co-occurrence counts from large corpora have been used to construct highdimensional vector-space models of language. Distances between word vectors extracted from these models are generally considered to reflect semantic similarity. Implicit in this assumption is that ‘semantic distance’ measurements correspond to human intuitions. This paper investigates the validity of one such measure, contextual similarity, calculated from the spoken part of the British National Corpus. In Experiment 1, a moderate correlation is found between human judgements of the semantic similarity between pairs of nouns and the model’s measure of contextual similarity. The correlation between the two measures is confirmed in two additional experiments, using a new set of elicited ratings. The semantic similarity of same-category word pairs (Experiment 2A) and the similarity between words differing in syntactic category (Experiment 2B) is found to be predictable from contextual similarity. The results from the three experiments provide support for the role of lexical co-occurrence information in modelling semantic similarity.
Metagenomic Taxonomic Classification Using Extreme Learning Machines

Next-generation sequencing technologies have allowed researchers to determine the collective genomes of microbial communities co-existing within diverse ecological environments. Varying species abundance, length and complexities within different communities, coupled with discovery of new species makes the problem of taxonomic assignment to short DNA sequence reads extremely challenging. We have developed a new sequence composition-based taxonomic classifier using extreme learning machines referred to as TAC-ELM for metagenomic analysis. TAC-ELM uses the framework of extreme learning machines to quickly and accurately learn the weights for a neural network model. The input features consist of GC content and oligonucleotides. TAC-ELM is evaluated on two metagenomic benchmarks with sequence read lengths reflecting the traditional and current sequencing technologies. Our empirical results indicate the strength of the developed approach, which outperforms state-of-the-art taxonomic classifiers in terms of accuracy and implementation complexity. We also perform experiments that evaluate the pervasive case within metagenome analysis, where a species may not have been previously sequenced or discovered and will not exist in the reference genome databases. TAC-ELM was also combined with BLAST to show improved classification results. Code and Supplementary Results: http://www.cs. gmu.edu/~mlbio/TAC-ELM (BSD License).
Model-Based Feature Enhancement for Reverberant Speech Recognition

In this paper, we present a new technique for automatic speech recognition (ASR) in reverberant environments. Our approach is aimed at the enhancement of the logarithmic Mel power spectrum, which is computed at an intermediate stage to obtain the widely used Mel frequency cepstral coefficients (MFCCs). Given the reverberant logarithmic Mel power spectral coefficients (LMPSCs), a minimum mean square error estimate of the clean LMPSCs is computed by carrying out Bayesian inference. We employ switching linear dynamical models as an a priori model for the dynamics of the clean LMPSCs. Further, we derive a stochastic observation model which relates the clean to the reverberant LMPSCs through a simplified model of the room impulse response (RIR). This model requires only two parameters, namely RIR energy and reverberation time, which can be estimated from the captured microphone signal. The performance of the proposed enhancement technique is studied on the AURORA5 database and compared to that of constrained maximum-likelihood linear regression (CMLLR). It is shown by experimental results that our approach significantly outperforms CMLLR and that up to 80% of the errors caused by the reverberation are recovered. In addition to the fact that the approach is compatible with the standard MFCC feature vectors, it leaves the ASR back-end unchanged. It is of moderate computational complexity and suitable for real time applications.
Design of near perfect reconstruction prototype filter with FFT interpolation

For higher-order and high complexity in designing perfect reconstruction prototype filter, a novel approach base on interpolation in FFT domain is proposed to design the near perfect reconstruction prototype filter. In our proposed scheme, we employ unconstrained iterative algorithm to obtain low-order model filter with high stopband attenuation, then exploits the FFT transform of model filter to get the discrete spectrum, and interpolates zeros at high-frequency position. Finally, the IFFT transform is performed to get the prototype filter which is suitable for near perfect reconstruction. Moreover, the afore mentioned approach use interpolation of FFT domain in combination with iterative optimizing, which avoids a large number of parameters to be optimized when the prototype filter is designed with a high stopband attenuation. Besides, the image spectrum is saved, which avoids the filter design of image suppressor, and results in the order reduction of the prototype filter and relieves the complexity of design. Simulation results reveal that our proposed method embraces favorable performance improvements compared with the conventional methods.
An animation-and-chirplet based approach to intruder classification using PIR sensing

The development of a Passive Infra-Red (PIR) sensing based intrusion detection system is presented here having the ability to reject vegetative clutter and distinguish between human and animal intrusions. This has potential application to reducing human-animal conflicts in the vicinity of a wildlife park. The system takes on the form of a sensor-tower platform (STP) and was developed in-house. It employs a sensor array that endows the platform with a spatial-resolution capability. Given the difficulty of collecting data involving animal motion, a simulation tool was created with the aid of Blender and OpenGL software that is capable of quickly generating streams of human and animal-intrusion data. The generated data was then examined to identify a suitable collection of features that are useful in classification. The features selected corresponded to parameters that model the received signal as the super-imposition of a fixed number of chirplets, an energy signature and a cross-correlation parameter. The resultant feature vector was then passed on to a Support Vector Machine (SVM) for classification. This approach to classification was validated by making use of real-world data collected by the STP which showed both STP design as well as classification technique employed to be quite effective. The average classification accuracy with both real and simulated data was in excess of 94%.
Use of a Generalized Additive Model to Investigate Key Abiotic Factors Affecting Microcystin Cellular Quotas in Heavy Bloom Areas of Lake Taihu

Lake Taihu is the third largest freshwater lake in China and is suffering from serious cyanobacterial blooms with the associated drinking water contamination by microcystin (MC) for millions of citizens. So far, most studies on MCs have been limited to two small bays, while systematic research on the whole lake is lacking. To explain the variations in MC concentrations during cyanobacterial bloom, a large-scale survey at 30 sites across the lake was conducted monthly in 2008. The health risks of MC exposure were high, especially in the northern area. Both Microcystis abundance and MC cellular quotas presented positive correlations with MC concentration in the bloom seasons, suggesting that the toxic risks during Microcystis proliferations were affected by variations in both Microcystis density and MC production per Microcystis cell. Use of a powerful predictive modeling tool named generalized additive model (GAM) helped visualize significant effects of abiotic factors related to carbon fixation and proliferation of Microcystis (conductivity, dissolved inorganic carbon (DIC), water temperature and pH) on MC cellular quotas from recruitment period of Microcystis to the bloom seasons, suggesting the possible use of these factors, in addition to Microcystis abundance, as warning signs to predict toxic events in the future. The interesting relationship between macrophytes and MC cellular quotas of Microcystis (i.e., high MC cellular quotas in the presence of macrophytes) needs further investigation.
Visual enhancement of service maps in logistics clouds

Logistics and its involved parties are nowadays faced with demanding challenges, in order to fulfill their customers' needs. Hence logistics service providers are constrained to cooperate with each other, which leads to the challenge of `understanding' each other's service descriptions and integrating the strongly differing IT-systems. An emerging approach to solve this problem is the operation of cloud platforms. Main tasks are service retrieval and composition. However, a suitable visualization is needed to generate a high user acceptance. With the service map concept a first step is taken, that certainly needs further improvement. This paper briefly gives an introduction to general information visualization and analyzes the suitability of several approaches for improving the service map concept with regards to different scales of measurement. After their comparison a general guideline for fostering the visualization concept is derived. Objective is the increase of information content while keeping an intuitive usability.
Generalised Skilling–Bryan Minimisation for Micro-Rotation Imaging in Light Microscopy

The original Skilling–Bryan method, first introduced in the field of astronomy, minimises the statistical cost function assuming Gaussian noise and the entropy prior functional. In contrast to the conventional trust region methods, where the solution search in a high‐dimensional space may be very expensive, the Skilling–Bryan scheme minimises the cost function in a subspace instead, yielding much more efficient computation. However, in low‐photon‐count processes, such as image formation in confocal microscopy, Poisson noise assumption is more suitable. In addition, it would be desirable to use the 2nd order, Skilling–Bryan optimisation framework in applications where general prior functionals with the positivity constraint is required. In this work, we hence generalise the Skilling–Bryan method to be used with the Poisson noise model as well as with various kinds of prior functions including the total variation prior. The proposed method is generic whereas here it is applied in the micro‐rotation imaging,.
Performance evaluation of a situation aware multipath routing solution

The dynamics of modern communication networks, especially regarding the user's behavior, determine the necessity of an effective routing protocol that can ensure high performance. In this paper we present the implementation and performance evaluation of a new dynamic multipath routing solution, called Situation Aware Multipath (SAMP), using the OPNET Modeler. The main features of our solution are load balancing and congestion avoidance. To increase the scalability, the network is divided into multipath routing domains. We defined two types of routers: AR (Adaptive Router) and AMR (Adaptive Multipath Router), with different capabilities depending on their location inside the domain. SAMP offers network robustness and reliability by taking advantage of the path diversity. The performance of the proposed solution is evaluated and compared, in terms of throughput and network resource utilization, with three standard routing protocols: OSPF, ECMP and EIGRP. The simulation results demonstrate that the new proposed multipath routing algorithm can assure increased network resource utilization and also a high transmission quality, even in case of congestion.
SWAPit: a multiple views paradigm for exploring associations of texts and structured data

Visualization interfaces that offer multiple coordinated views on a particular set of data items are useful for navigating and exploring complex information spaces. In this paper we address the problem of mining text information which is associated with structured data from relational data sources. We present a multi-view paradigm that closely integrates the analysis of unstructured text data with related structured data sets. Our concept brings together views on text similarity, text categories, and associated relational attributes for application fields like customer relationship management or business intelligence. A prototype is presented that exemplarily implements our multi-view framework.
Application of an on-line medical monitoring system

Wireless technologies which are the most important development areas in the twenty-first century and Wireless Body Sensor Networks which are developed for use in health care continues to be the focus on attention of researchers. Wireless Body Sensor Networks are used for monitoring health signals such as body temperature, heart pulse, EEG, and ECG of the people who are old, chronic illness or weak. In this paper, a health monitoring system is monitored via the Internet is presented. Implemented system consists of two parts: hardware and software. In the hardware part, body temperature and heart pulses of the patient are collected by the designed embedded system. And the system sends the data to health center over wireless sensor networks. And software part is a web based monitoring interface which accomplish to visualization and analysis of the collected data from the patient using MATLAB Builder NE and Web Figure.
Mining for unambiguous instances to adapt part-of-speech taggers to new domains

We present a simple, yet effective approach to adapt part-of-speech (POS) taggers to new domains. Our approach only requires a dictionary and large amounts of unlabeled target data. The idea is to use the dictionary to mine the unlabeled target data for unambiguous word sequences, thus effectively collecting labeled target data. We add the mined instances to available labeled newswire data to train a POS tagger for the target domain. The induced models significantly improve tagging accuracy on held-out test sets across three domains (Twitter, spoken language, and search queries). We also present results for Dutch, Spanish and Portuguese Twitter data, and provide two novel manually-annotated test sets.