Miracle-dz/UniFAR-FTUs huggingface dataset:
A new complex wavelet transform by using RI-spline wavelet

We propose a new complex wavelet, the RI-spline wavelet, which is constructed using spline wavelets for dual-tree DWT. In the RI-spline wavelet, the real and imaginary components become an approximate Hilbert pair to each other. Then we propose a new dual-tree algorithm which uses an interpolation method for providing a half-sample-delay between the two filters of the trees. Finally, we experimentally show that the translation invariance, which can not be obtained by the ordinary DWT, is obtained by RI-spline wavelet.
Benefits of geosynthetic reinforcement in widening of embankments subjected to foundation differential settlement

ABSTRACT: In the present study, physical model tests were conducted to investigate the effect of the geogrid reinforcement on the reduction of the differential settlement in widening of a highway embankment. A water bag filled with water and placed beneath an embankment was used to simulate the soft foundation of the widened portion of the embankment in the physical model test. Water from the water bag was drained to simulate the development of differential settlements between the existing and widened foundations of the embankment. The geogrid layers were installed with one side fixed in the existing embankment to provide enough anchorage in the physical model tests. Settlements of the embankment, strains in the geogrids and earth pressures were monitored during the test. The test results show that the geogrid layers stabilised the soil arching in the embankment, reduced the differential settlement on the embankment surface, and enhanced the serviceability of the widened embankment. The differential settl.
Developing accessible TV applications

The development of TV applications nowadays excludes users with certain impairments from interacting with and accessing the same type of contents as other users do. Developers are also not interested in developing new or different versions of applications targeting different user characteristics. In this paper we describe a novel adaptive accessibility approach on how to develop accessible TV applications, without requiring too much additional effort from the developers. Integrating multimodal interaction, adaptation techniques and the use of simulators in the design process, we show how to adapt User Interfaces to the individual needs and limitations of elderly users. For this, we rely on the identification of the most relevant impairment configurations among users in practical user-trials, and we draw a relation with user specific characteristics. We provide guidelines for more accessible and centered TV application development.
Automated Deduction in Arithmetic with the Omega Rule

An important technique for investigating derivability in formal systems of arithmetic has been to embed such systems into semiformal systems with the w-rule. This paper exploits this notion within the domain of automated theorem-proving and discusses the implementation of such a proof environment. This involves providing an appropriate representation for infinite proofs, and a means of verifying properties of such objects.
Teachers’ cognitions and handling strategies regarding bully-victims

ABSTRACT This study aimed at exploring secondary school teachers’ cognitions regarding bully-victims, including what handling strategies those teachers often adopt to tackle the dual problem inherent to this population. In this study, 13 secondary school teachers were invited to participate in interviews, and the results indicated that most of the teachers initially identified bully-victims as merely victims. However, with increased interactions involving bully-victims and more complaints from peers, teachers began to adjust their cognitions, and they recognised that these students could not be categorised into any specific role. Moreover, the results of this study also indicated that the teachers’ cognitions regarding bully-victims influenced the handling strategies employed with these students. Teachers often gave reprimands and assistance in parallel, regardless of the involved students’ roles, and they improved bully-victims’ situations by cooperating with schools, peers, and parents. The results suggest that teachers can utilise both punishment and assistance in parallel to effectively handle both the problems and needs of bully-victims versus relying on either one or the other.
Patch ordering based SAR image despeckling via SSC and wavelet thresholding

Processing of synthetic aperture radar (SAR) images has led to the development of SAR image despeckling approaches. These approaches help to suppress the Speckle in SAR Image. In this paper, we propose a Synthetic Aperture Radar (SAR) image despeckling method based on patch ordering and transform domain filtering. The proposed method consists of two-stage filtering strategy. The first stage is coarse filtering. In this stage, denoising is done by simultaneous Sparse Coding (SSC). The second stage is refined filtering which can eliminate small artifacts generated by the coarse filtering. In this stage, filtered image is obtained by Wavelet Hard thresholding. Experimental results with both denoised images and SAR images demonstrate that the proposed method achieves state-of-art despeckling performance in terms of Structural Similarity Index Measure (SSIM), Peak Signalto-Noise Ratio (PSNR).
LOW FALSE POSITIVE RATE OF KEPLER CANDIDATES ESTIMATED FROM A COMBINATION OF SPITZER AND FOLLOW-UP OBSERVATIONS

NASA’s Kepler mission has provided several thousand transiting planet candidates during the 4 yr of its nominal mission, yet only a small subset of these candidates have been confirmed as true planets. Therefore, the most fundamental question about these candidates is the fraction of bona fide planets. Estimating the rate of false positives of the overall Kepler sample is necessary to derive the planet occurrence rate. We present the results from two large observational campaigns that were conducted with the Spitzer Space Telescope during the the Kepler mission. These observations are dedicated to estimating the false positive rate (FPR) among the Kepler candidates. We select a sub-sample of 51 candidates, spanning wide ranges in stellar, orbital, and planetary parameter space, and we observe their transits with Spitzer at 4.5 μm. We use these observations to measures the candidate’s transit depths and infrared magnitudes. An authentic planet produces an achromatic transit depth (neglecting the modest effect of limb darkening). Conversely a bandpass-dependent depth alerts us to the potential presence of a blending star that could be the source of the observed eclipse: a false positive scenario. For most of the candidates (85%), the transit depths measured with Kepler are consistent with the transit depths measured with Spitzer as expected for planetary objects, while we find that the most discrepant measurements are due to the presence of unresolved stars that dilute the photometry. The Spitzer constraints on their own yield FPRs between 5% and depending on the Kepler Objects of Interest. By considering the population of the Kepler field stars, and by combining follow-up observations (imaging) when available, we find that the overall FPR of our sample is low. The measured upper limit on the FPR of our sample is 8.8% at a confidence level of 3σ. This observational result, which uses the achromatic property of planetary transit signals that is not investigated by the Kepler observations, provides an independent indication that Kepler’s FPR is low.
Model predictive control of continuous-time nonlinear systems with piecewise constant control

A new model predictive control (MPC) algorithm for nonlinear systems is presented. The plant under control, the state and control constraints, and the performance index to be minimized are described in continuous time, while the manipulated variables are allowed to change at fixed and uniformly distributed sampling times. In so doing, the optimization is performed with respect to sequences, as in discrete-time nonlinear MPC, but the continuous-time evolution of the system is considered as in continuous-time nonlinear MPC.
FPSAC: fast phylogenetic scaffolding of ancient contigs

MOTIVATIONS Recent progress in ancient DNA sequencing technologies and protocols has lead to the sequencing of whole ancient bacterial genomes, as illustrated by the recent sequence of the Yersinia pestis strain that caused the Black Death pandemic. However, sequencing ancient genomes raises specific problems, because of the decay and fragmentation of ancient DNA among others, making the scaffolding of ancient contigs challenging. RESULTS We show that computational paleogenomics methods aimed at reconstructing the organization of ancestral genomes from the comparison of extant genomes can be adapted to correct, order and orient ancient bacterial contigs. We describe the method FPSAC (fast phylogenetic scaffolding of ancient contigs) and apply it on a set of 2134 ancient contigs assembled from the recently sequenced Black Death agent genome. We obtain a unique scaffold for the whole chromosome of this ancient genome that allows to gain precise insights into the structural evolution of the Yersinia clade.
An approach towards intrusion detection using PCA feature subsets and SVM

Presently many intrusion detection approaches are available but have drawbacks like training overhead as well as their performance factor. Increased detection rate with less false alarms can enhanced the efficiency of an intrusion detection system. One of the main limitations is the processing of raw features for classification which increases the architecture complexity and decreases the accuracy of detecting intrusions. Because of the limitations in earlier approaches, this PCA based subsets has been proposed. An SVM based IDS mechanism with Principal Component Analysis (PCA) feature subsets has been presented. Support Vector Machines (SVM) used as classifier to test and train the subsets of extracted features with Radial Basis Function (RBF) kernel.
Their approach views every female chromosome as niche in population and formation lays its foundations on the Euclidean distance between the male and female chromosomes

Variety and diversity of population are essential for convergence to global optimal in genetic algorithm. In this study, the concepts of fitness distribution, expected and cumulative fitness distribution, reproduction rate and loss of diversity are defined for a sexual selection mechanism, and their performance of this type of selection mechanism is studied theoretically. Then a genetic algorithm based on this selection mechanism and penalty function are utilized for solving multidimensional 0/1 knapsack problems. Computational experiments are conducted on the proposed technique compared with some commonly used selection mechanisms for solving multidimensional 0/1 knapsack problems from the literature.
Thinking through the non-native language: The role of private speech in mediating cognitive functioning in problem solving among proficient non-native speakers

Extending Vygotsky's theory and research to non-native language speakers, the literature available to date has provided inconclusive findings on whether L2 speakers use the non-native language to mediate thinking in problem solving. Avoiding limitations in previous studies, the present study investigated private speech produced by non-native speakers with the objective of discovering whether the non-native language can be effectively used as a medium for thinking just as the L1. Non-technical highly challenging multi-level problem-solving activity was utilized to collect private speech from four native speakers and seven L2 speakers of English. Data analysis focused on examining the characteristics of private speech in order to assess cognitive mediation in the L2. The results provided strong evidence that proficient speakers - with extensive formal and informal language acquisition experiences - used the non-native language to mediate their thinking and, unlike in previous research, there was no reversion to the L1.
Antiproliferative Effect of Indole Phytoalexins

Indole phytoalexins from crucifers have been shown to exhibit significant anti-cancer, chemopreventive, and antiproliferative activity. Phytoalexins are natural low molecular antimicrobial compounds that are synthesized and accumulated in plants after their exposure to pathogenic microorganisms. Most interestingly, crucifers appear to be the only plant family producing sulfur-containing indole phytoalexins. The mechanisms underlying its anti-cancer properties are unknown. Isolation from cruciferous plants does not provide sufficient quantities of indole phytoalexins and, for biological screening, they are usually obtainable through synthesis. Understanding the molecular mechanism of the action of these substances and their structure-activity relationships is quite important in the development of new analogs with a more favorable profile of biological activities. In this review, we present the key features of indole phytoalexins, mainly their antiproliferative ativities.
Indigenous Perspectives on Operation RESET: An Initiative to Improve the Identification and Prosecution of Child Sexual Abuse Incidents in Remote Indigenous Communities

Objective Child sexual abuse in remote Indigenous communities is prevalent and damaging, and finding solutions has been challenging. This study is an evaluation of Indigenous stakeholders’ experiences of an intervention aimed at addressing child sexual abuse in remote Indigenous communities in Western Australia. Method Nine interviews were analysed using a thematic analysis methodology. Interviewees included family members of children who had been sexually abused, and Indigenous service providers who lived in the communities. Results Three broad themes emerged from the analysis: communication, action and presence, and working together to address child sexual abuse. Stakeholders described how the engagement with the team was respectful and collaborative, communication and service provision were excellent and stakeholders felt that they had a strong part to play in the process. Engagement, trust, warm relationships, and excellent outcomes were strong features of this initiative. Conclusions The operation was seen as a new way of working together, and was strongly endorsed by the Indigenous participants interviewed in this study.
A Comparative Study of 470 Cases of Early-Onset and Late-Onset Schizophrenia

The presence or absence of 22 schizophrenic symptoms was recorded with the age at onset of illness in 470 patients with non-affective, non-organic psychoses. Positive and negative formal thought disorder, affective symptoms, inappropriate affect, delusions of grandiosity or passivity, primary delusions other than delusional perception, and thought insertion and withdrawal were all more common in early-onset cases (age at onset 44 years or less; n = 336). Persecutory delusions with and without hallucinations, organised delusions, and third-person, running commentary and accusatory or abusive auditory hallucinations were all more common in late-onset cases (age at onset 45 years or more; n = 134). There was no difference between cases of early and late onset in the prevalence of delusions of reference, bizarre delusions, delusional perception, or lack of insight. We conclude that although there are clinical similarities between cases of schizophrenia with early and late onset, there are sufficient differences between them to suggest that they are not phenotypically homogeneous.
Building Detection in Aerial Images Based on Watershed and Visual Attention Feature Descriptors

This paper investigates a novel solution for the recognition of objects of interest in aerial images. The solution builds on a combination of algorithms inspired from the human visual system with classical and modern algorithms. The goal is to achieve intelligent and powerful approaches that allow for fast and automatic treatment of complex images. The methodology that is proposed innovatively combines a variation of the classical watershed segmentation algorithm with a series of feature descriptors derived from a computational model of visual attention. The feature descriptors are tuned with a machine learning approach for the task of detecting buildings in aerial images. The experimental evaluation that is conducted demonstrates that objects recognition with features derived from human visual attention performs better than when only traditional features, such as statistical texture descriptors and shape descriptors, are used. As well, the proposed solution obtains better classification rates than those reported on image processing-based recognition of buildings in the remote sensing literature.
A novel statistical learning-based framework for automatic anomaly detection and localization in crowds

We propose a novel framework for fast and robust video anomaly detection and localization in complicated crowd scenes. Images of each video are split into cells for extracting local motion features represented by optical flow. In the train videos, most background cells are subtracted by ViBe model. Feature vectors are extracted from each cell by integrating the value of optical flow in 8 different direction intervals. Then we apply Principal Component Analysis (PCA) to transform the feature vectors. The normal activity patterns in the train videos are learnt by constructing a Gaussian Mixture Model (GMM) upon the feature vectors. For any new feature vector extracted from the test video clips, we use the learnt model to calculate a probability value to represent normal level of each cell. Considering the continuity of the motion, we also use abnormal information obtained from previous frames as a supplementary for anomaly prediction in the current frame. At last, we determine whether an activity pattern of a cell is normal or abnormal by using mean shift to cluster the probability values of the frame. Qualitative experiments on real-life surveillance videos, the recently published UCSD anomaly detection datasets, validate the effectiveness of the proposed approach.
IRELESS S ENSOR N ETWORK

In a wireless sensor network Quality of Information (QoI), Energy Efficiency, Redundant data avoidance, congestion control are the important metrics that affect the performance of wireless sensor network. As many approaches were proposed to increase the performance of a wireless sensor network among them clustering is one of the efficient approaches in sensor network. Many clustering algorithms concentrate mainly on power Optimization like FSCH, LEACH, and EELBCRP. There is necessity of the above metrics in wireless sensor network where nodes are densely deployed in a given network area. As the nodes are deployed densely there is maximum possibility of nodes appear in the sensing region of other nodes. So there exists an option that nodes have to send the information that is already reached the base station by its own cluster members or by members of other clusters. This mechanism will affect the QoI, Energy factor and congestion control of the wireless sensor networks. Even though clustering uses TDMA (Time Division Multiple Access) for avoiding congestion control for intra clustering data transmission, but it may fail in some critical situation. This paper proposed a energy efficient clustering which avoid data redundancy in a dense sensor network until the network becomes sparse and hence uses the TDMA efficiently during high density of the nodes.
Can one trust quantum simulators?

Various fundamental phenomena of strongly correlated quantum systems such as high-Tc superconductivity, the fractional quantum-Hall effect and quark confinement are still awaiting a universally accepted explanation. The main obstacle is the computational complexity of solving even the most simplified theoretical models which are designed to capture the relevant quantum correlations of the many-body system of interest. In his seminal 1982 paper (Feynman 1982 Int. J. Theor. Phys. 21 467), Richard Feynman suggested that such models might be solved by ‘simulation’ with a new type of computer whose constituent parts are effectively governed by a desired quantum many-body dynamics. Measurements on this engineered machine, now known as a ‘quantum simulator,’ would reveal some unknown or difficult to compute properties of a model of interest. We argue that a useful quantum simulator must satisfy four conditions: relevance, controllability, reliability and efficiency. We review the current state of the art of digital and analog quantum simulators. Whereas so far the majority of the focus, both theoretically and experimentally, has been on controllability of relevant models, we emphasize here the need for a careful analysis of reliability and efficiency in the presence of imperfections. We discuss how disorder and noise can impact these conditions, and illustrate our concerns with novel numerical simulations of a paradigmatic example: a disordered quantum spin chain governed by the Ising model in a transverse magnetic field. We find that disorder can decrease the reliability of an analog quantum simulator of this model, although large errors in local observables are introduced only for strong levels of disorder. We conclude that the answer to the question ‘Can we trust quantum simulators? ’ is … to some extent.
Design and Implementation of a Centralized Sensor Protocol for Information via Negotiation

Recent advances in wireless communications and electronics have promoted the development of wireless sensor networks (WSN) for a wide variety of applications. The size and power constraints of sensor nodes are the main concerns in the design of robust and efficient routing protocols. Consequently, various approaches, such as data-centric approach, hierarchical approach and location-based approach, are available. Each may be designed for a particular application with specific requirements. In this paper, we design and implement a data-centric routing protocol for WSNs, namely Centralized Sensor Protocol for Information via Negotiation (CSPIN), which combines the advertise-request-transfer process and a routing distribution mechanism. Implementation of the protocol was completed with the Crossbow MicaZ hardware using nesC/TinyOS.
Cognitive Deficits and their Underlying Structural and Functional Alterations in Mice Selectively Bred for High Stress Reactivity

Cognitive deficits are a common feature of major depression (MD), with largely unknown biological underpinnings. In addition to the affective and cognitive symptoms of MD, a dysregulation of the hypo- thalamic–pituitary–adrenal (HPA) axis is commonly observed in these patients. Increased plasma gluco- corticoid levels are known to render the hippocampus susceptible to neuronal damage. This structure is important for learning and memory, creating a potential link between HPA axis dysregulation and cog- nitive deficits in depression. In order to further elucidate how altered stress responsiveness may contrib- ute to the etiology of MD, three mouse lines with high (HR), intermediate (IR), or low (LR) stress reactivity were generated by selective breeding. The aim of the present study was to investigate whether increased stress reactivity is associated with deficits in hippocampus-dependent memory tests. To this end, we subjected mice from the HR, IR, and LR breeding lines to tests of recognition memory, spatial memory, and depression-like behavior. In addition, measurements of brain-derived neurotrophic factor (BDNF) in the hippocampus and plasma of these ani- mals were conducted. Our results demonstrate that HR mice exhibit hippocampus-dependent memory deficits along with decreased hippocampal, but not plasma, BDNF levels. Thus, the stress reactivity mouse lines are a prom- ising animal model of the cognitive deficits in MD with the unique feature of a genetic predisposition for an altered HPA axis reactivity, which provides the opportunity to explore the progression of the symp- toms of MD, predisposing genetic factors as well as new treatment strategies. 2009 Elsevier Ltd. All rights reserved.