Miracle-dz/UniFAR-FTUs huggingface dataset:
Unsupervised-Restricted Deconvolutional Neural Network for Very High Resolution Remote-Sensing Image Classification

As the acquisition of very high resolution (VHR) satellite images becomes easier owing to technological advancements, ever more stringent requirements are being imposed on automatic image interpretation. Moreover, per-pixel classification has become the focus of research interests in this regard. However, the efficient and effective processing and the interpretation of VHR satellite images remain a critical task. Convolutional neural networks (CNNs) have recently been applied to VHR satellite images with considerable success. However, the prevalent CNN models accept input data of fixed sizes and train the classifier using features extracted directly from the convolutional stages or the fully connected layers, which cannot yield pixel-to-pixel classifications. Moreover, training a CNN model requires large amounts of labeled reference data. These are challenging to obtain because per-pixel labeled VHR satellite images are not open access. In this paper, we propose a framework called the unsupervised-restricted deconvolutional neural network (URDNN). It can solve these problems by learning an end-to-end and pixel-to-pixel classification and handling a VHR classification using a fully convolutional network and a small number of labeled pixels. In URDNN, supervised learning is always under the restriction of unsupervised learning, which serves to constrain and aid supervised training in learning more generalized and abstract feature. To some degree, it will try to reduce the problems of overfitting and undertraining, which arise from the scarcity of labeled training data, and to gain better classification results using fewer training samples. It improves the generality of the classification model. We tested the proposed URDNN on images from the Geoeye and Quickbird sensors and obtained satisfactory results with the highest overall accuracy (OA) achieved as 0.977 and 0.989, respectively. Experiments showed that the combined effects of additional kernels and stages may have produced better results, and two-stage URDNN consistently produced a more stable result. We compared URDNN with four methods and found that with a small ratio of selected labeled data items, it yielded the highest and most stable results, whereas the accuracy values of the other methods quickly decreased. For some categories with fewer training pixels, accuracy for categories from other methods was considerably worse than that in URDNN, with the largest difference reaching almost 10%. Hence, the proposed URDNN can successfully handle the VHR image classification using a small number of labeled pixels. Furthermore, it is more effective than state-of-the-art methods.
A Clinical Screener for Compulsive Buying

Advancements in understanding problematic behaviors require the ability to identify affected or at-risk individuals. This article reports the development of a scale to identify compulsive buyers. Seven items representing specific behaviors, motivations, and feelings associated with buying significantly contributed to correctly classifying approximately 88 percent of the subjects. Evidence indicates this screening scale is undimmed and possesses good reliability. Validity is demonstrated by comparing members of a general consumer sample who are identified as compulsive buyers by the screener with self-identified compulsive buyers and noncompulsoriness consumers on several established correlates and outcomes of compulsive buying. Evidence of external validity using a separate sample is also presented. Copyright 1992 by the University of Chicago.
No Laughing Matter

Laughter matters! From an analysis of a very large corpus of naturally-occurring conversational speech we have con-ﬁrmed that approximately one in ten utterances contains laugh-ter. From among these laughing utterances, we were able to distinguish four types of laughter according to what each revealed about the speaker’s affective state, and we were able to recognise these different types automatically, by use of Hidden Markov Models trained on laugh segments, with a success rate of greater than 75%. The paper also presents a speech synthesis interface that enables the control of such emotional expression for use in an interactive conversation.
Content and Context of the Administrative Internship: How Mentoring and Sustained Activities Impact Preparation

As universities prepare educational leaders for twenty-first century schools, the quality and authenticity of the internship experience require both the strengths of university faculty and the expertise of practitioners to create authentic experiences that seek to bridge the theory-to-practice divide. One mechanism to achieve this is through quality mentorship by school- and central-office-based personnel during the internship experience. A central aspect of this professional relationship is the ability for the mentor to provide access to quality activities that will prepare the aspiring leader for his/her first administrative position. This exploratory study examined language utilized by interns in their time logs as well as information gleaned from preliminary surveys of both interns and mentors upon completion of their experience. Findings revealed the complexity of language analysis and revealed a need for understanding activities of interns and interactions with mentors in a contextual fashion.
Scalable Grid Resource Discovery through Distributed Search

This paper proposes a simple and scalable web-based model for grid resource discovery for the Internet. The resource discovery model contains the metadata and resource finder web services. The information of resource finder web services is kept in the repositories that are distributed in the application layer of Internet. The resource finder web services will be discovered by sending queries to the repositories in a similar way as the DNS protocol. The underlying technology for implementation of the two architectures of this model is introduced. These architectures are: Direct and Centralized Web-Based Grid Resource Discovery. The resource discovery time is computed after simulating each of these models in GridSim.
Assessing the Validity of Generalized Trust Questions: What Kind of Trust are we Measuring?

In the social capital literature a distinction is made between trust expressed in people in general, and trust in people who are known to us personally. In this article we investigate the frames of reference respondents make use of when answering two commonly used interpersonal trust questions. Half of our sample was administered a version which asks respondents whether "most people" can be trusted. The other half of the sample was administered an alternative version of the question, in which the object of trust is restricted to "people in your local area. " Immediately after answering the trust question all respondents were asked to report, in their own words, who came to mind when formulating their response. Counter to the widespread assumption that these questions measure generalized trust, we find that a substantial number of respondents report having thought about people who are known to them personally. Furthermore, respondents who report having thought about individuals who are known to them also report substantially higher levels of trust than people who say they thought about abstract categories such as "people in general. " Our results suggest that apparent differences in trust across question formats and groups within the general public derive, at least in part, from heterogeneity in question interpretation.
Imaging the spatiotemporal dynamics of cognition with high‐resolution evoked potential methods

Subsecond temporal resolution is necessary to resolve the changes in brain activity that are associated with task‐related cognitive processes. Evoked potentials (EPs) provide the requisite millisecondrange temporal resolution, and do so with nonencumbering recordings at a reasonably low cost. These features would seem to make EPs the ideal complement to structural and functiona magnetic resonance images (MRIs) and positron emission tomography (PET). However, until recently, the utility of EPs has been limited by their poor spatial resolution. Sufficient spatial correspondence has not existed between EPs and other brain imaging modalities to determine unambiguously the relationship between a sequence of EP components and structures visualized by MRI or PET. We describe progress that has been made toward overcoming this obstacle by registering electrophysiological data with anatomical information from each subject's MRI, by improving the spatial resolution of the EP, and by using analytic methods for measuring the spatiotemporal dynamics of distributed neurocognitive processes. The use of these techniques is illustrated by reviewing several experiments in which sequences of component neural processes were measured during cognitive tasks. © 1994 Wiley‐Liss, Inc.
Towards an online voice-based gender and internal state detection model

In human-robot interaction, gender and internal state detection play an important role in making the robot reacting in an appropriate manner. This research focuses on the important features to extract from a voice signal in order to construct successful gender and internal state detection systems, and shows the benefits of combining both systems together on the total average recognition score. Moreover, it consists a foundation on an ongoing approach to estimate the human internal state online via unsupervised clustering algorithms.
A Bayesian Phylogenetic Model for Counting Recombination Events

We describe a Bayesian method for counting recombination events in the evolutionary history of aligned viral sequences when recombination is rare. Previous recombination detection methods have focused on testing whether particular sequences are recombinant. Our method, in constrast, examines multiple recombinant sequences with the same recombinant structure and seeks to place a lower bound on the number of recombination events. Distinguishing the number of recombination events in the history of sequences with similar structure will be necessary to find recombination hotspots.
Spatiotemporal prediction for log‐Gaussian Cox processes

Space–time point pattern data have become more widely available as a result of technological developments in areas such as geographic information systems. We describe a flexible class of space–time point processes. Our models are Cox processes whose stochastic intensity is a space–time Ornstein–Uhlenbeck process. We develop moment‐based methods of parameter estimation, show how to predict the underlying intensity by using a Markov chain Monte Carlo approach and illustrate the performance of our methods on a synthetic data set.
Social support as a factor inhibiting teenage risk-taking: views of students, parents and professionals

A large-scale study conducted in Tasmania, Australia, of teenage risk-taking across 26 potentially harmful risk activities has examined a range of factors that encourage or inhibit risk-taking. Among these factors, the degree of social and professional support the teenage students say they would access for personal, study or health problems has been examined and correlated with the respondents’ risk-taking profiles. Findings reveal that the wider the range of social support, including parents, family and friends, the less likely are teenagers to participate in risk-taking activities, as measured by the Personal Risk Score Category Index developed for the research. Respondents who relied only on friends’ support or had no-one to access for support had higher risk-taking profiles. Comparative analysis of parents’ and pre-service professionals’ expectations differed from those of the students in overestimating the extent to which students would access professionals for advice and help with personal, study and health problems, and their degree of trust in professional help. Parents also overestimated the extent to which the students would rely on their parents for support and advice compared with the students' views. The implications of this intergenerational mismatch for risk prevention and intervention programmes are discussed.
The First Data Release of the Sloan Digital Sky Survey

The Sloan Digital Sky Survey (SDSS) has validated and made publicly available its First Data Release. This consists of 2099 deg2 of five-band (u, g, r, i, z) imaging data, 186,240 spectra of galaxies, quasars, stars and calibrating blank sky patches selected over 1360 deg2 of this area, and tables of measured parameters from these data. The imaging data go to a depth of r ≈ 22.6 and are photometrically and astrometrically calibrated to 2% rms and 100 mas rms per coordinate, respectively. The spectra cover the range 3800–9200 Å, with a resolution of 1800–2100. This paper describes the characteristics of the data with emphasis on improvements since the release of commissioning data (the SDSS Early Data Release) and serves as a pointer to extensive published and on-line documentation of the survey.
Fruit and vegetable consumption in the prevention of oesophageal and cardia cancers

The incidence of adenocarcinoma of the oesophagus has increased rapidly in recent decades. In order to appreciate the potential for prevention by means of dietary modification, we estimated the aetiological fractions and the increments in absolute risk attributable to low intake of fruit and vegetables for adenocarcinoma and squamous cell carcinoma of the oesophagus and for adenocarcinoma of the gastroesophageal junction. We conducted a nationwide population‐based case–control study in Sweden, with participation of 608 cases and 815 controls. We used unconditional logistic regression to estimate relative risks, from which we calculated aetiological fractions. Individuals in the highest exposure quartile (median 4.8 servings/day) versus the lowest (median 1.5 servings/day) showed approximately 50% lower risk of oesophageal adenocarcinoma and 40% lower risk of oesophageal squamous cell carcinoma, but no risk reduction for gastric cardia adenocarcinoma. Approximately 20% of oesophageal adenocarcinoma, and likewise squamous cell carcinoma, in Sweden was attributed to consuming less than three servings of fruit and vegetables per day. A very large number of individuals (over 25 000) would need to increase their fruit and vegetable consumption moderately in order to prevent one oesophageal cancer per year. Moderate relative risk reductions translate into weak absolute risk reductions for oesophageal cancers in Sweden.
MSSV: Tree-Based Mapping of Algorithms to Prede ned Structures (Extended Version)

Due to the need for fast design cycles and low production cost, programmable circuits like FPGAs and DSP processors (henceforth called target structures) are becoming increasingly popular. Design planning, detailed design as well as updating such designs requires mapping existing algorithms onto these circuits. Instead of writing target-speci c mappers, we propose using retargetable mappers. The technique reported is this paper is based on pattern matching. Binary code is generated as a result of this matching process. This paper describes the essential techniques of our mapper MSSV and identi es areas for improvements. As a result, it shows that e cient handling of alternative mappings is crucial for an acceptable performance. This report is also intended as a reference for new developments.
A Novel Data-Driven Fast Capacity Estimation of Spent Electric Vehicle Lithium-ion Batteries

Fast capacity estimation is a key enabling technique for second-life of lithium-ion batteries due to the hard work involved in determining the capacity of a large number of used electric vehicle (EV) batteries. This paper tries to make three contributions to the existing literature through a robust and advanced algorithm: (1) a three layer back propagation artificial neural network (BP ANN) model is developed to estimate the battery capacity. The model employs internal resistance expressing the battery’s kinetics as the model input, which can realize fast capacity estimation; (2) an estimation error model is established to investigate the relationship between the robustness coefficient and regression coefficient. It is revealed that commonly used ANN capacity estimation algorithm is flawed in providing robustness of parameter measurement uncertainties; (3) the law of large numbers is used as the basis for a proposed robust estimation approach, which optimally balances the relationship between estimation accuracy and disturbance rejection. An optimal range of the threshold for robustness coefficient is also discussed and proposed. Experimental results demonstrate the efficacy and the robustness of the BP ANN model together with the proposed identification approach, which can provide an important basis for large scale applications of second-life of batteries.
Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation

Segmentation of 3D images is a fundamental problem in biomedical image analysis. Deep learning (DL) approaches have achieved state-of-the-art segmentation perfor- mance. To exploit the 3D contexts using neural networks, known DL segmentation methods, including 3D convolution, 2D convolution on planes orthogonal to 2D image slices, and LSTM in multiple directions, all suffer incompatibility with the highly anisotropic dimensions in common 3D biomedical images. In this paper, we propose a new DL framework for 3D image segmentation, based on a com- bination of a fully convolutional network (FCN) and a recurrent neural network (RNN), which are responsible for exploiting the intra-slice and inter-slice contexts, respectively. To our best knowledge, this is the first DL framework for 3D image segmentation that explicitly leverages 3D image anisotropism. Evaluating using a dataset from the ISBI Neuronal Structure Segmentation Challenge and in-house image stacks for 3D fungus segmentation, our approach achieves promising results comparing to the known DL-based 3D segmentation approaches.
A Multigrid-Fourier Method for the Computation of Elastic Fields with Application to Heteroepitaxy

A multigrid-Fourier method is formulated for the computation of elastic fields with applications to heteroepitaxy. A discrete ball and spring model with an underlying cubic structure is considered, where the natural lattice spacing of the atoms comprising the deposited film is different than those of the substrate. This system is linearized resulting in a large linear system for the displacement field. An efficient method based on combining Fourier and multigrid formulations to solve this system is presented. In this algorithm, the atoms in the deposited film and the substrate atoms are handled differently. The equations for the elastic displacement of atoms in the film are extended to a rectangular region by the use of fictitious atoms and a connectivity matrix, allowing the application of standard multigrid ideas. Except for the top layer, the atoms in the substrate are completely removed and replaced by equivalent forces which can be efficiently evaluated using a fast Fourier transform. This formulatio.
Semi-unsupervised approach toward learning meaning

In this report, we implement the recursive auto encoder (RAE) method for learning meaning of sentences. We present the missing details for calculating the gradients and for back-propagation techniques. We implement a preliminary experiment to verify the correctness of our derivation for gradients. We get 64.72% accuracy on test set using the dimension of meaning d = 40.
An Analysis of the Handover Latency Components in Mobile IPv6

This article analyses the latency components in Mobile IPv6 handovers. It provides real-implementation results for significant parts of the handover process through measurements in a real MIPv6 implementation on a wireless testbed based on IEEE 802.11b. Our work aims to extract information on the actions taken by network entities during the movement of a mobile node and to correctly measure and characterize all handover delay components, something not readily achieved by simulation studies. Particular attention is given to the period leading to the L3 registration part of the handover, since this has been identified by many as the “choking point” of the whole process. The Experimental results help in understanding the effect of Duplicate Address Detection, Movement Direction, Router Advertisement Intervals, Router Solicitations, and Wireless Beacon Intervals on the overall handover delay. This work can be of benefit to those trying to build, modify or optimize mobility management protocols, since it can help them utilize real-life values in their simulations, emulations, or equations. The outcome of this work can also be utilized in recognizing items for future research.
Assessment of sexual interest using a choice reaction time task and priming: A feasibility study

Purpose. We investigated the feasibility of assessing sexual interest in hetero- and homosexual men using two information-processing methods, namely a choice reaction time task and priming. The participants were expected to have longer reaction times for sexually explicit when compared with non-explicit pictures due to sexual content-induced delay. In addition, the reaction times of the heterosexual (N=15) and homosexual (N=11) men for pictures corresponding with their sexual interest were compared to pictures not corresponding with their sexual interest. Heterosexual men were expected to have longer reaction times during the presentation of sexually explicit female as opposed to male pictures, whereas homosexual men were expected to have the opposite pattern. Method. The participants were presented either sexually explicit or non-explicit male and female target pictures (and primes that preceded the targets in random combinations) while simultaneously performing a choice reaction time task in three phases each containing a total of 160 prime–target pairs. Results. Both expectations were confirmed in phase 1 of the study. In phase 2, the means differed in the expected way, but the effects were not significant. In phase 3, the expected effect was moderated by a complex priming effect. Conclusions. The results suggest that the choice reaction time task is a promising way of measuring sexual interest but that questions of habituation should be given more attention in future studies.
On the complexity of design in imaging software

Recent evidence raised the possibility that algorithm rich software, such as in imaging processing, is more complex than other kinds (Sangwan, 2005). To further investigate this contention a set of complexity metrics were computed for two sets of open source Java software systems. The results suggest that there is little or no difference in software complexity between an imaging application, and "conventional" software, such as a simple database querying system. The tests suggest that the overall complexity of the system depends more on the software design knowledge of the engineers than the kind of system that is constructed
A compact programmable LDO regulator for ultra-low voltage SoC

This paper proposes a compact programmable low dropout (LDO) regulator for an ultra-low voltage system-on-a-chip (SoC) using the voltage scaling technique. Two innovative design concepts were proposed: a programmable multi-level resistor array that can precisely tune the ratio of the feedback resistor divider; and a current limiter that limits a small static current flowing through the resistor network while reducing the occupied area. Experimental results show that the monotonic 50-step programmable output ranging from 0.3V to 0.8V is achieved, with an input of 1V and a maximum load current of 100mA. The occupied area is only 0.017mm2.