Miracle-dz/UniFAR-FTUs huggingface dataset:
Recognition of precursor microRNAs by the Dicer cofactor TRBP

MicroRNAs (miRNAs) are small non-coding RNAs that enable post-transcriptional gene regulation through RNA interference (RNAi). The biogenesis of miRNAs involves distinct enzymatic processing steps, each of which requires a specific set of proteins. The processing of precursor miRNAs (pre-miRNAs) is performed by the endoribonuclease Dicer. Dicer enzymes are conserved in all organisms that utilise RNAi and they always associate with a double-stranded (ds) RNA-binding protein. In mammals, Dicer associates with TAR element binding-protein (TRBP) but the precise role of TRBP in miRNA biogenesis remains unclear. Research over the last 15 years has suggested that TRBP aids Dicer substrate recognition, facilitates diffusion of Dicer along dsRNA substrates, influences the length of Dicer products, and regulates Dicer activity or stability. Human TRBP is a 366-residue protein that comprises three dsRNA-binding domains (dsRBDs) separated by long unstructured linkers. The two N-terminal Type-A dsRBDs bind dsRNA while the C-terminal Type-B domain mediates protein-protein interactions. The goal of this thesis is to explore the structure-function relationship and RNA-binding properties of TRBP. DsRBDs recognise dsRNA via three interaction surfaces: Regions 1-3. Previous research has highlighted the contribution of Region 2 and suggested that the sequence and molecular dynamics of this loop may tune the dsRNA-binding affinity of the domain. Here, nuclear magnetic resonance (NMR) spectroscopy was used to evaluate the structure and ps-ns backbone dynamics of the two Type-A dsRBDs of TRBP, with particular focus on RNA-binding region 2. Analysis of 15N NMR relaxation parameters and residue composition of this region revealed differences between the two dsRBDs. A series of single and double mutant variants were made in single and tandem dsRBD constructs, and their dsRNA-binding properties were tested using several pre-miRNAs. A previously reported difference in binding affinity between dsRBD-1 and 2 was shown to be dependent on pH, which was also shown to affect the foldedness of dsRBD-1. Single-site mutations were shown to alter the dynamics of Region 2 and to affect RNA-binding affinity. These analyses suggested that Region 2 may be required for sensing the presence of non-Watson-Crick features in a pre-miRNA. The implications of these results on Dicer activity are unclear, but the characterisation of binding properties and backbone dynamics reported here provide further insight into pre-miRNA recognition by TRBP and proteins with multiple dsRBD more generally.
High performance connected digit recognition using codebook exponents

The authors describe the latest developments by the speech research group at CRIM in speaker-independent connected digit recognition, using hidden Markov models (HMMs) trained with maximum mutual information estimation (MMIE). The work presented is a continuation of work previously described by the authors (see Proc. 1991 IEEE Inf. Conf. on Acoust. Speech and Sign. Process., pp.533-536). The main differences are: (1) use of the 20-kHz TI/NIST corpus available on CD-ROM (instead of the 10-kHz distribution tape), (2) use of word models (instead of sub-word units), (3) addition of second derivative parameters, and (4) a more elaborate training procedure for codebook exponents. The experiments described were all performed on the complete adult portion of the corpus. The baseline system, using discrete HMMs and MMIE, has a 0.67% word error rate and a 2.03% string error rate. The authors describe techniques that allowed them to improve greatly the recognition rate. <<ETX>>
Analysis of maintenance work categories through measurement

Empirical data from a two-year measurement effort in the software maintenance environment of a Canadian organization are presented. The findings reported are based on a daily data-collection process including 2152 work requests which required 11365 days to complete. This set of empirical data confirms some of the findings of the opinion surveys in terms of the stability of the overall workload distribution in the maintenance area, while the analysis of any subset of data indicates that this overall average hides significant differences. The authors include a discussion on the improved measurement program implemented, and illustrate how insights into the maintenance process are gained through various measurements. <<ETX>>
Software pipelining of loops by pipelining strongly connected components

A modified data dependency graph (DDG) for modeling loop-optimizing restrictions is proposed. By using it, pipelining, loop-carried data dependencies and folding of individual multicycle operations over iteration boundaries can be modeled. Properties of semantically correct software pipelining transformations are first defined in the acyclic DDG of the unrolled loop. The case when an operation in a loop body is dependent on that same operation in some previous iteration is analyzed in the acyclic DDG of the unrolled loop. The algorithm for finding a real machine optimal schedule is split into two independent tasks: finding an optimal acyclic data dependency graph of one loop body and then finding an optimal schedule for a real machine by implementing basic block methods. A method for loop optimizing named 'pipelining of strongly connected components' is proposed. It is proved that the method is semantically correct. The loop body optimization is converted into sub-tasks of optimizing individual strongly connected components of the modified data dependency graph of a loop. <<ETX>>
Application of Coherency Property of Generator to Analysis of Low-Frequency Oscillation in Power System

Coherency is used to characterize the degree of confusion between generators which oscillate against each other, so it can judge the process which generators oscillate from local mode to inter-area mode. The coherency is the value of the direction cosine by using angle of the generators. when the direction cosines of two generator is 1 that indicates the oscillation direction between the two generators is inter-area oscillation mode; when the direction cosines of two generator is-1 that indicates the oscillation direction between the two generators is local oscillation mode. Coherency is affected by speed governing system and excitation system of generator.Using two area systems verifies the theoretical analysis is correct.
Risk evaluation using evolvable discriminate function

This essay proposes a new approach to risk evaluation using disease mathematical modeling. The mathematical model is an algebraic equation of the available database attributes and is used to evaluate the patient condition. If its value is greater than zero it means that the patient is ill (or in risk condition), otherwise healthy. In practice risk evaluation has been a very difficult problem mainly due its sporadic behavior (suddenly, the patient has a stroke, etc as a condition aggravation) and its database representation. The database contains, under the label of risk patient data, information of the patient condition that sometimes is in risk condition and sometimes is not, introducing errors in the algorithm training. The study was applied to Atheroscleros is database from Discovery Challenge 2003 - ECML/PKDD 2003 workshop.
Fast and robust iris recognition

Abstract Iris recognition is a reliable biometric identification technology. In this study, the one-to-many identification and one-to-one verification using iris recognition are both examined. Our method consists of three phases: pupil and iris location, iris feature image extraction and iris matching. The CASIA iris database was used in this study to evaluate the performance of the proposed method. The database includes 108 iris classes, each including seven images. The identification accuracy of our method is 99.646%; while the proposed method can achieve an equal error rate of 1.085%. On the other hand, feature extraction of an iris image in our method is only 16.78 ms. Each verification test of an iris images takes 1.4 ms and each identification test over 432 database images takes 151.2 ms. Note the experimental accuracies were reported without manual rejection of poor-quality images or invalid iris-location images, thus the efficiency and robustness of the proposed method can be proven.
The Twin Peaks of Requirements and Architecture

Quality concerns, often referred to as nonfunctional requirements, service-level agreements, quality attributes, performance constraints, or architecturally significant requirements, describe system-level attributes such as security, performance, reliability, and maintainability. In conjunction with functional requirements, these quality concerns drive and constrain a system's architectural design and often introduce significant trade-offs that must be carefully considered and balanced. The dependencies that exist between requirements and architecture have been referred to as the twin peaks of requirements and architecture. The guest editors of this special issue describe this unique situation.
Fire and Smoke Identification Algorithms based on Attention Mechanism

This paper presents several fire and smoke identification algorithms based on attention mechanism, the proposed algorithms add the attention mechanism into traditional algorithms for the fire and smoke identification, which can get the possible regions of fire and smoke quickly by saliency images, and highly accelerate the computing process. Firstly, the attention mechanism is used to get the saliency images. Secondly, the Otsu method is used to segment the saliency images. Finally, ROIs (Regions of Interest) are analyzed by the fire or smoke features, in order to judge whether the fire and smoke exists or not. To judge the fire, the fire color and intensity features are used. S imilarly, the color and circularity features are be used to judge the smoke. Keywords-fire; smoke; attention mechanism; otsu; region of interest
Fast image compression using distance function on curved space

In this paper, a new fast image compression method utilizing an improved version of the Distance Function On Curved Space (DTOCS), is presented. The maximas of distances are used directly to select control points. Also a new concept, a varying structuring element, a curvature constant, is applied to the calculation of distances. Its influence on compression results is studied.
A Conversation with Murray Rosenblatt

On an exquisite March day in 2006, David Brillinger and Richard Davis sat down with Murray and Ady Rosenblatt at their home in La Jolla, California for an enjoyable day of reminiscences and conversation. Our mentor, Murray Rosenblatt, was born on September 7, 1926 in New York City and attended City College of New York before entering graduate school at Cornell University in 1946. After completing his Ph.D. in 1949 under the direction of the renowned probabilist Mark Kac, the Rosenblatts' moved to Chicago where Murray became an instructor/assistant professor in the Committee of Statistics at the University of Chicago. Murray's academic career then took him to the University of Indiana and Brown University before his joining the University of California at San Diego in 1964. Along the way, Murray established himself as one of the most celebrated and leading figures in probability and statistics with particular emphasis on time series and Markov processes. In addition to being a fellow of the Institute of Mathematical Statistics and American Association for the Advancement of Science, he was a Guggenheim fellow (1965--1966, 1971--1972) and was elected to the National Academy of Sciences in 1984. Among his many contributions, Murray conducted seminal work on density estimation, central limit theorems under strong mixing, spectral domain methods and long memory processes. Murray and Ady Rosenblatt were married in 1949 and have two children, Karin and Daniel.
Infeed Control Algorithm of Sorting System Using Modified Trapezoidal Velocity Profiles

This paper applies acceleration/deceleration control–based velocity profiles to an infeed control algorithm for a cross‐belt‐type sorting system to improve the accuracy and performance of the system's infeed. The velocity profiles are of a trapezoidal shape and often have to be modified to ensure that parcels correctly synchronize with their intended carriers. Under the proposed method, an infeed line can handle up to 5,600 items/h, which indicates a 40% increase in performance in comparison with its existing handling rate of 4,000 items/h. This improvement in performance may lead to a reduction in the number of infeed lines required in a sorting system. The proposed infeed control algorithm is applied to a cross‐belt‐type sorting system (model name: SCS 1500) manufactured by Vanderlande Industries.
Spatial Analysis for Crime Pattern of Metropolis in Transition Using Police Records and GIS: a Case Study of Shanghai, China

Crime is a long plagued issue of human society. Especially, with the expansion of cities and population aggregation in recent years, crime problems have become a great challenge in urbanization process. Both urban policy-makers and police departments have realized the importance of a better understanding of the dynamics of crime. This study explores the spatial characteristic of crime in Shanghai using Geographical Information System (GIS) and police call data of the metropolis in 2006. In the research, the combination of both applied and theoretical methods is used to analyze the characteristics of Shanghai crime. To find out the crime pattern of the city, three spatial levels (i.e. macrolevel, mesolevel and microlevel) are adopted. In this paper the crime is divided into two categories: violent crime and property crime. The spatial distribution of the two kinds of crimes is analyzed on a comparative basis of different properties in terms of land use type and population features, and the relevant factors of the crime pattern are investigated. The results reveal that there are some hotspots in downtown, transportation hubs, where population density is relatively high and the crime density decreases gradually away from the center. The analytical and theoretical result will undoubtedly lead to enhanced crime prevention strategies of Shanghai in the future.
A T-model neural network with learning ability

The authors introduce a novel neural network called the T-model with learning capability. They utilized the least mean square algorithm to train the T-model and exploit the learning capability of the introduced network. The architecture and the algorithm of the T-model network are described. Some typical examples to illustrate the effectiveness of the algorithm are included and compared with the Hopfield model and a multilayer model. The simulations and experiments showed that the algorithm is quite effective in training the T-model network for many problems. <<ETX>>
Fuzzy clustering and interpretation of fully polarimetric SAR data

Classification of Earth terrain components using fully polarimetric synthetic aperture radar (SAR) data sets is an important application of radar remote sensing. For the operational application some demands, besides the accuracy requirements, must be fulfilled. In order to make the handling of the classification easy for users, the algorithms have to be data set independent and the handling must be possible without a priori knowledge. The ultimate aim is an unsupervised algorithm which is suitable for automation. In this treatment we propose an approach applying an unsupervised automatic clustering of the H/A//spl alpha///spl lambda//sub 1/ space. From the resulting clusters rules are derived for a fuzzy rule based classification. The resulting clusters can then be assigned to the desired object classes by the user. The approach enables us to combine the wide range of information contained in polarimetric SAR data with the robust and still flexible strategy of fuzzy rule based classifiers and with a high degree of automation. The effectiveness of this approach is demonstrated using fully-polarimetric L-band airborne SAR data acquired with the E-SAR system of the DLR at the well know test site of Oberpfaffenhofen, Germany.
An Improved Framework for Managing Requirements Changes

The management of requirements changes is an important task in requirement engineering, and the request for changing requirements occurs after the establishment of the requirements specification should be handled more charily. The logic-based techniques have attracted great attention, Ke-Dian Mu and others adopted the negotiation-style revision proposed by Booth to manage the requirements changes effectively. Based on Mu’s work, this paper improves the method of equivalence classes division, and proposes the “4 equivalence classes” division method, which improved the negotiation efficiency to some extent, perfected the original framework.
Learning from labeled and unlabeled data

Due to the considerable time and expense required in labeling data, a challenge is to propose learning algorithms that can learn from a small amount of labeled data and a much larger amount of unlabeled data. In this paper, we propose one such algorithm which uses an evolutionary strategy to iteratively adjust the class membership of the patterns in the unlabeled sample. The iterative adjustment is done so that the class conditional distribution obtained from such a labeling allows a maximum a posteriori classification with minimum classification error on the labeled patterns. We detail the algorithm and provide results obtained by the proposed algorithm on 5 different datasets.
Architectures for Developing Multiuser, Immersive Learning Scenarios

Multiuser immersive learning scenarios hold strong potential for lifelong learning as they can support the acquisition of higher order skills in an effective, efficient, and attractive way. Existing virtual worlds, game development platforms, and game engines only partly cater for the proliferation of such learning scenarios as they are often inadequately tuned for learning. First, this study aims to identify architectures that more effectively support the development of multiuser immersive learning scenarios. Second, this study takes up the challenge to define and assemble more flexible architectures that cater for fast and easy development, which will become important in the current period of economic breakdown. Third, this study describes how such architectures should enable research into guidelines for multiuser immersive learning scenario design and development. This study outlines a method for defining and setting up such architectures by using experts and existing literature.
Note‐taking English as a simplified register

Empirical investigation of the variety of English used in note‐taking reveals this form of language to be a simplified register characterized by many of the same simplifying processes that are found in baby talk (BT) and foreigner talk (FT). The English note‐taking register (NT) is, however, distinguished from BT and FT both by having a number of reduction processes unique to it and by lacking entirely the clarifying, upgrading, and expressive processes in them. A study of the (semi‐) sentences in notes of university lectures by seven subjects identified the following 10 features: abbreviation/symbolization; omission of articles; omission of pronouns; omission of interrogative‐auxiliary do; omission of finite copulas; omission of phrases or other multiword groups; amalgamation of two or more sentences into a single topic + comment structure; participialization of relative clauses; nominalization; and passivization. The motivation for this combination of simplificational properties in NT derives from the d.
Analisis Market Basket Dengan Algoritma Apriori dan FP-Growth

Algoritma yang umum digunakan dalam proses pencarian frequent itemsets (data yang paling sering muncul) adalah Apriori. Tetapi algoritma Apriori memiliki kekurangan yaitu membutuhkan waktu yang lama dalam proses pencarian frequent itemsets. Untuk mengatasi hal tersebut maka digunakanlah algoritma FP-Growth. Dalam makalah ini akan dibahas penerapan Apriori dan FP-Growth dalam proses pencarian frequent itemsets. Penggunaan FP-Tree yangdigunakan bersamaan dengan algoritma FP-growth untuk menentukan frequent itemset dari sebuah database, berbeda dengan paradigma Apriori yang memerlukan langkah candidate generation, yaitu dengan melakukan scanning database secara berulang-ulang untuk menentukan frequent itemset. Makalah ini juga menyajikan pembahasan mengenai perbandingan kompleksitas waktu antara algoritma FP-growth dengan Apriori dan hasil dari perbandingan algoritma tersebut.
Towards Evolutional Authoring Support Systems.

The ultimate aim of this research is to specify and implement a general authoring framework for content and knowledge engineering for Intelligent Educational Systems (IES). In this context we attempt to develop an authoring tool supporting this framework that is powerful in its functionality, generic in its support of instructional strategies and user-friendly in its interaction with the author. The framework that we offer is an ontology-based authoring environment, since we see the use of ontologies as effective means to have structured content and knowledge engineering. The key contribution of our research is the meta-level Authoring Task Ontology (ATO) specifying authoring tasks, goals and activities, and knowledge about the process of engineering IES. It enables us to enable the specification of "evolutional" authoring support system, as a meta-authoring tool that defines and controls the configuration and tuning process of an authoring tool for a specific authoring process. The role of ATO is to help the IES authoring system to "evolve" by defining such a meta-tool, which "knows" the system's ontological structure. In our approach it works analogously to an authoring tool when it generates a concrete learning support system. The ATO also provides a shared vocabulary between all system components, and allows for better interoperability in a modularised architecture. In this way we have the benefit to monitor and assess the authoring process, and to prevent and solve inconsistencies and conflicting situations. Through ontological engineering, a computational formalization of the intelligent systems authoring, we also give our scientific contribution to the in-depth understanding of what the authoring process is.
Accelerating 3D convolution using graphics hardware (case study)

Many volume filtering operations used for image enhancement, data processing or feature detection can be written in terms of three-dimensional convolutions. It is not possible to yield interactive frame rates on todays hardware when applying such convolutions on volume data using software filter routines. As modern graphics workstations have the ability to render two-dimensional convoluted images to the frame buffer, this feature can be used to accelerate the process significantly. This way generic 3D convolution can be added as a powerful tool in interactive volume visualization toolkits.
A low complexity statistical voice activity detector with performance comparisons to ITU-T/ETSI voice activity detectors

Traditionally voice activity detection algorithms are based on any combination of general speech properties such as temporal energy variations, periodicity, and spectrum. This paper describes a statistical method for voice activity detection using a signal-to-noise ratio measure. The method employs a low-variance spectrum estimate and an adaptive threshold to make a statistical voice activity decision. An added advantage of the method is that it only requires low resources such as memory and computational time. Furthermore the method has been compared to modern standard voice activity detection algorithms with results indicating good performance in babble, white and vehicle noise.