Miracle-dz/UniFAR-FTUs huggingface dataset:
An Optimal Weight Model for Single Image Super-Resolution

In this paper, a novel example-based super- resolution method is introduced. The objective is to estimate a high-resolution image from a single low- resolution image. By considering an image as a set of small image patches, our method is performed on each patch with the help of a given database of high and low-resolution image patch pairs. For each given low-resolution patch, its high-resolution version is considered as a sparse positive linear combination of the high-resolution patches from the database. The coefficients of this combination are referred to as the weights, and an optimal weight model is proposed to find this combination such that the high-resolution patch is consistent with the low- resolution patch while being similar to the best candidate high-resolution patches from the database. Experimental results show the good performance of our method over some state-of-the-art methods and confirm the efficiency of the proposed method.
Data query using short domain question in natural language

This paper presents the application of understanding of short domain question in natural language to data query. A domain dictionary can be obtained through extracting schema of all tables and short text data from database by ODBC API. The word segmentation tool, IK Analyzer, which is extended with the obtained domain dictionary, is used to segment the short text questions. From the segmentation results, the keywords are extracted to obtain query target and query requirement of the question and to generate a SQL statement for data query. The method proposed in this paper can be applied to question-answering system based on database.
Mammogram images thresholding based on between-class variance using a mixture of gamma distributions

With one million new cases in the world every year, breast cancer is the most common malignancy in women and it has been proved that an early diagnosis of the disease can help to strongly enhance the expectancy of survival. Mammography is the most effective imaging method for detecting no-palpable early-stage breast cancer. Image processing techniques has been used for processing the mammogram image. Image thresholding is an important concept, both in the area of objects segmentation and recognition. It has been widely used due to the simplicity of implementation and speed of time execution. Many thresholding techniques have been proposed in the literature. The aim of this paper is to provide formula and their implementation to threshold images using Between-Class Variance with a Mixture of Gamma Distributions. The algorithms will be described by given their steps, and applications. Experimental results are presented to show good results on segmentation of mammogram image.
A method for the quantitative analysis of the layering of HIV-related stigma

HIV-related stigma is regarded as one of the major barriers in the development of effective prevention and care programs; but the stigma associated with HIV stigma is not a singular entity. The stigma of the infection is layered with other stigmas, such as those associated with the routes of transmission (e.g., sex work and injecting drug use) and personal characteristics (e.g., race, religion, ethnicity and gender). In developing programs and policies to overcome HIV-related stigma, cognisance needs to be taken of all the sources of stigma, and how they may interact. A novel method is described for examining the layers of HIV/AIDS-related stigma, and secondary data are adapted to illustrate this. The importance of understanding the layering of stigma for the development of effective interventions is also discussed.
UWB Pulse Design Method Based on Chebyshev Function Approximation

Ultra Wideband radios will unavoidably face/cause severe interference from/to the existing narrowband systems nearby. To avoid interference, one of effective methods is to design appropriate pulse waveform. In this paper the problem of pulse design was equal to the problem of chebyshev function approximation and Remez algorithm was used to acquire the optimum pulse waveform. Matlab simulation results showed that the power spectrum density of the pulse based on chebyshev function approximation meets the power constraint of the objective spectral mask. The comparison were presented for the bit error rate performance analyses of the pulse position modulation time hopping ultra wideband impulse radio communication system employing the pulse and the second derivative Gaussian pulse in the additive white Gaussian noise channel. The performance of the pulse is better than the second derivative Gaussian pulse.
Middleware approaches for adaptivity of Kahn Process Networks on Networks-on-Chip

We investigate and propose a number of different middleware approaches, namely virtual connector, virtual connector with variable rate, and request-driven, which implement the semantics of Kahn Process Networks on Network-on-Chip architectures. All of the presented solutions allow for run-time system adaptivity. We implement the approaches on a Network-on-Chip multiprocessor platform prototyped on an FPGA. Their comparison in terms of the introduced overhead is presented on two case studies with different communication characteristics. We found out that the virtual connector mechanism outperforms other approaches in the communication-intensive application. In the other case study, which has a higher computation/communication ratio, the middleware approaches show similar performance.
Pathogen Inactivating Properties and Increased Sensitivity in Molecular Diagnostics by PAXgene, a Novel Non-Crosslinking Tissue Fixative

Background Requirements on tissue fixatives are getting more demanding as molecular analysis becomes increasingly relevant for routine diagnostics. Buffered formaldehyde in pathology laboratories for tissue fixation is known to cause chemical modifications of biomolecules which affect molecular testing. A novel non-crosslinking tissue preservation technology, PAXgene Tissue (PAXgene), was developed to preserve the integrity of nucleic acids in a comparable way to cryopreservation and also to preserve morphological features comparable to those of formalin fixed samples. Methods Because of the excellent preservation of biomolecules by PAXgene we investigated its pathogen inactivation ability and biosafety in comparison to formalin by in-vitro testing of bacteria, human relevant fungi and human cytomegalovirus (CMV). Guidelines for testing disinfectants served as reference for inactivation assays. Furthermore, we tested the properties of PAXgene for detection of pathogens by PCR based assays. Results All microorganisms tested were similarly inactivated by PAXgene and formalin except Clostridium sporogenes, which remained viable in seven out of ten assays after PAXgene treatment and in three out of ten assays after formalin fixation. The findings suggest that similar biosafety measures can be applied for PAXgene and formalin fixed samples. Detection of pathogens in PCR-based diagnostics using two CMV assays resulted in a reduction of four to ten quantification cycles of PAXgene treated samples which is a remarkable increase of sensitivity. Conclusion PAXgene fixation might be superior to formalin fixation when molecular diagnostics and highly sensitive detection of pathogens is required in parallel to morphology assessment.
Do We See It the Same Way? Event Perception in ADHD: Description and Links to Social Impairments

iii Chapter 1: Background . 1 History and Diagnosis of ADHD . 2 Evolution of Core Cognitive Features of ADHD . 4 Associated Impairments . 7 Event Perception: Empirical Evidence and Theory. 14 Rationale for Current Research. 19 Overview of Current Studies . 21 Chapter 2: Study 1: Event Segmentation Deficits in Attention-Deficit/Hyperactivity Disorder . 23 Abstract . 24 Introduction . 25 Methods . 31 Results . 35 Discussion . 37 Appendix . 41 Chapter 3: Study 2: ADHD Symptoms, Event Perception and Social Skills in Early/Late Adolescents . 51 Abstract . 52 Part A: Methods . 60 Part A: Results . 64 Part B: Methods . 67 Part B: Results . 68 Discussion . 70 Summary . 76 Appendix . 77 References . 81 Chapter 4: Conclusion . 90 Summary . 101 References . 103 EVENT PERCEPTION AND ADHD iii
CHASE: Component High Availability-Aware Scheduler in Cloud Computing Environment

Cloud computing promises flexible integration of the compute capabilities for on-demand access through the concept of virtualization. However, uncertainties are raised regarding the high availability of the cloud-hosted applications. High availability is a crucial requirement for multi-tier applications providing business services for a broad range of enterprises. This paper proposes a novel component high availability-aware scheduling technique, CHASE, which maximizes the availability of applications without violating service level agreements with the end-users. Using CHASE, prior criticality analysis is conducted on applications to schedule them based on their impact on their execution environment and business functionality. This paper presents the advantages and shortcomings of CHASE compared to an optimal solution, Open Stack Nova scheduler, high availability-agnostic, and redundancy-agnostic schedulers. The evaluation results demonstrate that the proposed solution improves the availability of the scheduled components compared to the latter schedulers. CHASE prototype is also defined for runtime scheduling in Open Stack environment.
Multi-Channel Communication for Wireless Database

Wireless networks are becoming a more and more popular means of accessing contents outside of a user’s local machine. Current wireless networking systems use only a single channel of the standard 802.11 spectrum, this leads to bottlenecks, including but not limited to bandwidth reduction, contention, congestion and collisions. In this paper we propose a new model for a more efficient use of mobile databases using multichannel communication in the current 802.11 structure. We prove using simulation that multi-channel communication reduces contention and congestion.
Some fingerprints of V1 mechanisms in the bottom up saliency for visual selection

Li Zhaoping, Keith A. May, Ansgar Koene Department of Computer Science, University College London, UK To appear as Chapter 7 in Computational modelling in behavioural neuroscience: closing the gap between neurophysiology and behaviour Editted by Dietmar Heinke and Eirini Mavritsaki, Psychology Press, 2009. Abstract: A unique vertical bar among horizontal bars is salient and pops out perceptually regardless of the observer’s goals. Physiological data have suggested that mechanisms in the primary visual cortex (V1) contribute to the high saliency of such a unique basic feature, but fail to indicate whether V1 plays an essential or peripheral role in inputdriven or bottom up saliency. Meanwhile a biologically based V1 model has suggested that V1 mechanisms can also explain bottom up saliencies beyond the pop out of basic features (Li 1999a, 2002). For instance the low saliency of a unique conjunction feature like a red-vertical bar among red-horizontal and green-vertical bars is explained, under the hypothesis that the bottom up saliency at any location is signalled by the activity of the most active cell responding to it regardless of the cell’s preferred features such as color and orientation. While some recent experimental data have provided support for this V1 saliency hypothesis, higher visual areas such as V2 and V4 also contain neurons tuned to similar basic features that can pop out in the bottom up manner. Furthermore, previous saliency models can capture much of the visual selection behavior using generic rather than V1 specific neural mechanisms. It is therefore important to ascertain V1’s role in saliency by identifying visual selection behavior that show specific identifying characteristics, i.e., fingerprints, of V1 or other cortical areas. In this paper, we present our recent findings on bottom-up saliency based behavior of visual search and segmentation that directly implicate V1 mechanisms. The three specific fingerprints are: (1) ocular singleton captures attention despite being elusive to awareness, (2) V1’s collinear facilitation manifested in texture segmentation, and (3) a match between the redundancy gains in double feature singleton search and V1’s conjunctive cells.
The Predictive Power of Social Media: On the Predictability of U.S. Presidential Elections using Twitter

Twitter as a new form of social media potentially contains useful information that opens new opportunities for content analysis on tweets. This paper examines the predictive power of Twitter regarding the US presidential election of 2012. For this study, we analyzed 32 million tweets regarding the US presidential election by employing a combination of machine learning techniques. We devised an advanced classifier for sentiment analysis in order to increase the accuracy of Twitter content analysis. We carried out our analysis by comparing Twitter results with traditional opinion polls. In addition, we used the Latent Dirichlet Allocation model to extract the underlying topical structure from the selected tweets. Our results show that we can determine the popularity of candidates by running sentiment analysis. We can also uncover candidates popularities in the US states by running the sentiment analysis algorithm on geo-tagged tweets. To the best of our knowledge, no previous work in the field has presented a systematic analysis of a considerable number of tweets employing a combination of analysis techniques by which we conducted this study. Thus, our results aptly suggest that Twitter as a well-known social medium is a valid source in predicting future events such as elections. This implies that understanding public opinions and trends via social media in turn allows us to propose a cost- and time-effective way not only for spreading and sharing information, but also for predicting future events.
Prediction of ventricular tachycardia by a neural network using parameters of heart rate variability

In this paper, we propose a classifier that can predict ventricular tachycardia (VT) events using artificial neural networks (ANNs) trained with parameters from heart rate variability (HRV) analysis. The Spontaneous Ventricular Tachyarrhythmia Database (Medtronic Version 1.0), comprising 106 pre-VT records and 126 control data, was used. Each data set was subjected to preprocessing and parameter extraction. After correcting the ectopic beats, data in the 5 minute window prior to the 10 second duration of each event was cropped for parameter extraction. Extraction of the time domain and non-linear parameters was performed subsequently. Two-thirds of the database of extracted parameters was used to train the ANN, and the remainder was used to verify the performance. ANNs for classifying the VT events was developed, and the sensitivities of the ANN was 82.9% (71.4% specificity). The normalized area under the receiver operating characteristic (ROC) curve of the ANN was 0.75.
Cross-Document Non-Fiction Narrative Alignment

This paper describes a new method for narrative frame alignment that extends and supplements models reliant on graph theory from the domain of fiction to the domain of nonfiction news articles. Preliminary tests of this method against a corpus of 24 articles related to private security firms operating in Iraq and the Blackwater shooting of 2007 show that prior methods utilizing a graph similarity approach can work but require a narrower entity set than commonly occurs in non-fiction texts. They also show that alignment procedures sensitive to abstracted event sequences can accurately highlight similar narratological moments across documents despite syntactic and lexical differences. Evaluation against LDA for both the event sequence lists and source sentences is provided for performance comparison. Next steps include merging these semantic and graph analytic approaches and expanding the test corpus.
Depth image-based rendering from multiple cameras with 3D propagation algorithm

This paper proposes a new 3D propagation algorithm for the depth image-based rendering problem with multiple color and range cameras at arbitrary positions. The proposed algorithm efficiently renders novel images at arbitrary virtual views by propagating all available depth information from range cameras to color cameras, and then all available depth and color information from color cameras to the virtual views. Furthermore, the algorithm significantly enhances the propagated depth images by applying a new occlusion removal method and a new depth-color bilateral filtering. The paper also describes the parallelism structure of our algorithm and outlines a mapping onto massively parallel architectures such as general-purpose graphics processing units (GPGPUs). Experimental results show that the proposed algorithm provides good rendering quality while staying within computational bounds for real-time applications.
Confronting Dark Energy Models with Astrophysical Data

We discuss fits of cosmological dark energy models to the available data on high-redshift supernovae and baryon oscillations. We consider a conventional model with Cold Dark Matter and a cosmological constant (ΛCDM), a model invoking super-horizon perturbations (SHDM) and models based on Liouville strings in which dark energy is provided by a rolling dilaton field (Q-cosmology). The two main high-redshift supernova data sets give compatible constraints on these models. We find that ΛCDM fits very well the combined supernova data sets. These are also compatible with the data on baryon acoustic oscillations, yielding together a determination of the matter density in the ΛCDM (assuming a flat Universe) which is comparable with that provided by the three-year WMAP data. The supernova data are also fit quite well by the super-horizon model. A naive version of the Q-cosmology model does not fit the supernova data, but a simple parametrization of the full version of the model that includes off-shell effects fits the data very well. A detailed discussion of the model-dependent off-shell corrections to the Q-cosmology model is given in the Appendix.
Inference Network-based Indonesian Spoken Query Information Retrieval

Query term misrecognition caused by the speech recognizer is one of the important issues in the spoken query information retrieval. The misrecognized term in the transcribed query leads to the retrieval of irrelevant documents. To raise the correct ranking of the retrieved documents, we use a speech recognition confidence score based on word posterior probability to weight the term in the inference network-based (IN-based) Indonesian information retrieval system. Our result shows that this technique can improve the mean reciprocal rank (MRR) score of the retrieved documents.
An Adaptive Replication Algorithm in P2P File Systems with Unreliable Nodes

The paper1 focuses on distributed file systems in P2P networks. We introduce a novel file replication scheme which is adaptive, reacting to changes in the patterns of access to the file system by dynamically creating or deleting replicas. Replication is used to increase data availability in the presence of site or communication failures and to decrease retrieval costs by local access if possible. Our system is completely decentralized and nodes can be removed/added dynamically. We also propose an overlay architecture for file search. This architecture is structured, but also based on random walk. Our system has a mobile agent which performs dynamic load-balancing. This agent is event driven and circulates in the network to find and “destroy” the least important files and thus limit the proliferation of superfluous replicas. We have implemented our method at TCP/IP sockets level.
Design of Time and Frequency Domain Pilots for Generalized Multicarrier Systems

By the generalized multi-carrier (GMC) principle a unified framework to describe various multi-carrier as well as single carrier approaches is established. In this paper the GMC principle is extended to pilot design and channel estimation, so that a unified description of pilot aided channel estimation (PACE) by interpolation in time and frequency is established. This applies to frequency domain pilots which are embedded in the GMC signal, as well as pilots sequences time multiplexed with data-bearing GMC blocks. A comparative performance evaluation of the two different GMC variants OFDM and single carrier with various pilot allocation schemes is carried out, also taking into account practical constraints such as the peak to average power ratio (PAPR) and required power amplifier back-off. It is found that a serial modem with time or frequency multiplexed pilots could be designed with a power amplifier with about 2 dB lower maximum power rating than that of a corresponding OFDM modem.
Object-Oriented Application Framework on Manufacturing Domain

The problematic issues in the development of manufacturing software systems are related to the various nature of manufacturing systems, which are wide, dynamic and complex. The purpose of this paper is to provide a solution by using framework-based software engineering to solve these problems. Framework-based software engineering is the idea of constructing software systems based on the integration of reusable components rather than developing software from scratch. The manufacturing application framework proposed in this paper is to be developed as a set of integrated reusable components, which can be adapted to suit specific manufacturing applications. This paper focuses on two subdomains of the manufacturing domain, i.e., Production Management (PM) and Statistical Quality Control (SQC). A generic model is defined based on the structure and behavior of each of the PM and SQC subdomains, from which the design infrastructure of the application framework is derived, based on the concept of design pattern. The details of the application framework development by integrating objectoriented technology and component-based development to achieve large-scale software reuse for manufacturing application develop-ment projects are also discussed.
0 INPUT FREQUENCY AND THE ACQUISITION OF SYLLABLE STRUCTURE IN POLISH Draft December 2014

This paper examines the relationship between the language input and phonological development. Using novel data from a longitudinal corpus of spontaneous child speech in Polish, we evaluate and compare the predictions of a variety of input-based phonotactic models for syllable structure acquisition. We find that many commonly examined input statistics can make dramatically different predictions, as do different assumptions about the representational units over which statistics are calculated. Several models perform surprisingly well in predicting major effects in production accuracy, but results vary systematically depending on the representational level examined. The most successful models reference multiple abstract units of phonological representation. We also show there are departures between the predictions of the best phonotactic models and the children’s production patterns that suggest input frequency alone is not sufficient to explain the developmental patterns.