Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolutionary Transitions of MicroRNA-Target Pairs

How newly generated microRNA (miRNA) genes are integrated into gene regulatory networks during evolution is fundamental in understanding the molecular and evolutionary bases of robustness and plasticity in gene regulation. A recent model proposed that after the birth of a miRNA, the miRNA is generally integrated into the network by decreasing the number of target genes during evolution. However, this decreasing model remains to be carefully examined by considering in vivo conditions. In this study, we therefore compared the number of target genes among miRNAs with different ages, combining experiments with bioinformatics predictions. First, we focused on three Drosophila miRNAs with different ages. As a result, we found that an older miRNA has a greater number of target genes than a younger miRNA, suggesting the increasing number of targets for each miRNA during evolution (increasing model). To further confirm our results, we also predicted all target genes for all miRNAs in D. melanogaster, considering co-expression of miRNAs and mRNAs in vivo. The results obtained also do not support the decreasing model but are reasonably consistent with the increasing model of miRNA-target pairs. Furthermore, our large-scale analyses of currently available experimental data of miRNA-target pairs also showed a weak but the same trend in humans. These results indicate that the current decreasing model of miRNA-target pairs should be reconsidered and the increasing model may be more appropriate to explain the evolutionary transitions of miRNA-target pairs in many organisms.
Robust beamformer and artificial noises for MISO wiretap channels with multiple eavesdroppers

We consider MISO wiretap channels with multiple eavesdroppers. Under the deterministic channel uncertainties, the beamformer and the covariance of the artificial noise are jointly designed to minimize the transmit power subject to SINR constraints. Our design problems are resolved by using a semidefinite program, which can be numerically solved. Simulation results are provided to see the effects of channel uncertainties on the transmit power.
A LIDA-based Model of the Attentional Blink

The attentional blink (AB) refers to the impairment in consciously perceiving the second of two targets pr esented in close temporal proximity (200 – 500ms) in a rapid s erial visual presentation paradigm. The present paper is a preliminary report describing a conceptual and part ially computational model of the AB based on the LIDA (Le arning Intelligent Distribution Agent) cognitive architect ure. The model aims to provide a biologically plausible expl anation of the AB, explaining a wide range of AB-related pheno mena, among other mental phenomena accounted for by LIDA. Computational results in a basic visual AB paradigm are presented and compared to human data. The Attentional Blink When subjects are asked to identify two targets sep arated by a short time (200-500ms) in a stream of distract ors, an Attentional Blink (AB) occurs subjects often fail to report the second target (see Fig 1). In this paper we wil l focus on the AB in rapid serial visual presentations (RSVP) of pictures (however, the AB has been shown to occur a ross a wide range of stimuli types and modalities – see (M artens &
The Laurent norm

We generalize a semi-norm for the Alexander polynomial of a connected, compact, oriented 3-manifold on its first cohomology group to a semi-norm for an arbitrary Laurent polynomial f on the dual vector space to the space of exponents of f. We determine a decomposition formula for this Laurent norm; an expression for the Laurent norm for f in terms of the Laurent norms for each of the irreducible factors of f. For an n-variable polynomial f, we introduce a space of m \leq n essential variables which determine the reduced Laurent norm unit ball; a convex polyhedron of the same dimension m as the Newton polyhedron of f. In the space spanned by the essential variables, the Laurent semi-norm for polynomials with at least two terms is shown to be a norm.
Brain Model For Developmental Robots : The Spatial Brain for Any Temporal Lengths

I present a general purpose model of the brain, called Self-Aware and Self-Effecting (SASE) model. Rooted in the biological genomic equivalence principle, our model proposes a general-purpose cell-centered in-place learning scheme to handle all levels of brain development and operation, from the cell level all the way to the brain level. It clarifies five necessary “chunks” of the brain “puzzle”: development, architecture, area, space and time. Then, I concentrate on how such a model enables a developmental robot to deal with temporal contexts. It deals with temporal context of any length without a dedicated temporal component.
Exploiting Temporal Feature Integration for Generalized Sound Recognition

This paper presents a methodology that incorporates temporal feature integration for automated generalized sound recognition. Such a system can be of great use to scene analysis and understanding based on the acoustic modality. The performance of three feature sets based on Mel filterbank, MPEG-7 audio protocol, and wavelet decomposition is assessed. Furthermore we explore the application of temporal integration using the following three different strategies: (a) short-term statistics, (b) spectral moments, and (c) autoregressive models. The experimental setup is thoroughly explained and based on the concurrent usage of professional sound effects collections. In this way we try to form a representative picture of the characteristics of ten sound classes. During the first phase of our implementation, the process of audio classification is achieved through statistical models (HMMs) while a fusion scheme that exploits the models constructed by various feature sets provided the highest average recognition rate. The proposed system not only uses diverse groups of sound parameters but also employs the advantages of temporal feature integration.
Real-Time Collision Warning and Avoidance at Intersections

Monitoring traffic intersections in real-time as well as predicting possible collisions is an important first step towards building an early collision warning system. We present the general vision methods used in a system addressing this problem and describe the practical adaptations necessary to achieve real-time performance. A novel method for three dimensional vehicle size estimation is presented. We also describe a method for target localization in real-world coordinates, which allows for sequential incorporation of measurements from multiple cameras into a single target's state vector. Additionally, a fast implementation of a false-positive reduction method for the foreground pixel masks is developed. Finally, a low-overhead collision prediction algorithm using the time-as-axis paradigm is presented. The proposed system was able to perform in real-time on videos of quarter-VGA (320x240) resolution. The errors in target position and dimension estimates in a test video sequence are quantified.
Is There a (Host) Genetic Predisposition to Progressive Multifocal Leukoencephalopathy?

Progressive multifocal leukoencephalopathy (PML) has been described in association with a variety of predisposing risk factors, including HIV/AIDS, lymphoproliferative disorders and, most recently, treatment with a range of biologics, most notably natalizumab in multiple sclerosis (MS) (1). However, while these underlying disorders appear to be (usually) necessary, they are not sufficient to predict the development of PML, since only a small fraction of such individuals will succumb, raising the question of whether host genetic factors must also play a role. Evidence is mounting that this is, indeed, the case but more work needs to be done to fully delineate these underlying genetic factors. Finally, while it is possible that an underlying genetic susceptibility for PML in general will be uncovered in the future, the current evidence argues for a collection of multiple, individually rare underlying susceptibilities (with one predominant single genetic susceptibility in each individual), as described in this review.
Automatic player detection, tracking and mapping to field model for broadcast soccer videos

We present an automatic soccer analysis framework through detecting, tracking players, and reflecting the information into the field model. First, a player detector is built on four-seed edge feature approach which extracts player regions applied in long view shots including playfield extraction, shot view classification, and player segmentation. Second, a multi-player tracker uses a high general reversible jump Markov chain Monte Carlo (RJMCMC)-based approach to associate player regions detected in each frame. Third, a fast calibration algorithm matches field model by fitting two regions: the center circle and the penalty areas so as to provide the geometry transformation enabled to map player positions in a video frame to the real-world coordinate. The experimental results on broadcast videos of the 2010 FIFA World Cup South Africa have evaluated to prove the effective design and the promise of the proposed framework.
Hardware-Software Codesign of a Fingerprint Alignment Processor

Within the biometrics field, the development of an automatic personal authentication system is nowadays an open research problem. Most of the difficulties rely on the complexity and the computational power needed to implement an algorithm reliable enough to guarantee the validity of the recognition system even when only low-quality biometric information is available from the users. Apart from the inherent complexity of the biometric algorithm, there exist many additional requirements for the application: high-security level, real-time characteristics and low-cost. All these factors originate a technical challenge. A compromise between the final application performance and the amount of resources needed to implement the system exists. Following this direction, the design of a fingerprint alignment processor developed by means of hardware-software codesign techniques is presented in this paper. The performance achieved with this processor is compared against the performance reached with an only software-oriented solution. The acceleration factor achieved by the hardware proves the feasibility of real-time applications, which is not guaranteed when developing the same algorithm under purely-software platforms.
Concerted Action of Two Formins in Gliding Motility and Host Cell Invasion by Toxoplasma gondii

The invasive forms of apicomplexan parasites share a conserved form of gliding motility that powers parasite migration across biological barriers, host cell invasion and egress from infected cells. Previous studies have established that the duration and direction of gliding motility are determined by actin polymerization; however, regulators of actin dynamics in apicomplexans remain poorly characterized. In the absence of a complete ARP2/3 complex, the formin homology 2 domain containing proteins and the accessory protein profilin are presumed to orchestrate actin polymerization during host cell invasion. Here, we have undertaken the biochemical and functional characterization of two Toxoplasma gondii formins and established that they act in concert as actin nucleators during invasion. The importance of TgFRM1 for parasite motility has been assessed by conditional gene disruption. The contribution of each formin individually and jointly was revealed by an approach based upon the expression of dominant mutants with modified FH2 domains impaired in actin binding but still able to dimerize with their respective endogenous formin. These mutated FH2 domains were fused to the ligand-controlled destabilization domain (DD-FKBP) to achieve conditional expression. This strategy proved unique in identifying the non-redundant and critical roles of both formins in invasion. These findings provide new insights into how controlled actin polymerization drives the directional movement required for productive penetration of parasites into host cells.
Comparative Study on Robust Speech Recognition against Nonstationary Noise in the Home Environment

We focus on the problem of robust speech recognition in the presence of nonstationary sudden noise, which is very likely to happen in the home environment. As a model compensation method for this problem, we investigated the use of Parallel Model Combination (PMC) architecture developed from a clean-speech Hidden Markov Model (HMM), and a sudden-noise HMM. We analyzed three problems (1) the time structure of sudden noise, (2) the position of noise in the corrupted speech, and (3) a possible choice for the gain matching term based on the relation between the noise power and the SNR. The study was carried out based on the database recorded in home environments by a personal robot PaPeRo of NEC Corporation.
Simplified joint call and packet level teletraffic analysis of CAC strategies for VoIP traffic in wireless networks

In this paper, a simplified joint call and packet level teletraffic analysis method for performance evaluation of call admission control (CAC) strategies for voice over IP (VoIP) traffic over wireless access networks is proposed. The admission criterion of new sessions of the considered CAC strategies is based on either the total number of sessions or the number of active sessions. Arrivals and completions of VoIP sessions, on/off activity detection, and periodic and constant length packet generation of individual VoIP active sessions are modeled. Packet buffering, adaptive modulation coding, and proportional fair sharing scheduling are also considered. Our proposed analysis is based on the quantized queue and cross-level linked decomposition mechanisms that we introduce in this paper to drastically simplify the mathematical and computational procedures with no major impact on the accuracy of the results. The developed analysis allows evaluating the performance of the CAC strategies in terms of the most relevant QoS metrics of VoIP traffic at both call and packet level and, at the same time, mathematical and computational complexity are reduced with no major impact on the accuracy of numerical results.
Dimorphic Computing : Sustainable Performance Through Thick and Thin

It is known that thick and thin clients have complementary strengths and weaknesses. Thick clients offer crisp interactive response even for tightly-coupled applications with rich GUIs. However, computationally intensive tasks are limited to using only the resources locally available. Thin clients, on the other hand, are attractive in CPU-intensive and data-intensive situations because application execution can occur on powerful remote compute servers or close to large data sets. Unfortunately, the physical separation between application execution and user interaction in thin clients can lead to poor application responsiveness, especially for tightly-coupled applications with rich GUIs.
VIBRATION ANALYSIS OF VACUUM CLEANER MOTORS

Vacuum cleaner motor is a device consisting of a universal electric motor and a fan impeller that makes air flow in vacuum cleaners. In this paper a fault diagnostic system for vacuum cleaner motors is presented. The core of the system are four modules for feature extraction. The first one is based on the mathematical model of the device, the second one estimates quality of commutation while the third and fourth one rely on vibration and noise analysis respectively. Features generated by these modules from measured signals are used for fault isolation by using an approximate reasoning method. An excerpt from the diagnostic system i.e. the module for feature extraction based on vibration analysis is described in the sequel.
Health literacy profile of high school students based on knowledge, attitude and behavior to health of respiration

This research aims to describe the high school student’s health literacy profile based on their knowledge, attitude, and behavior to health of respiration. This descriptive study involved 65 participants of senior high school students of Ciamis city. Research instruments for collecting data are test and questionnaires sheets. The data were analyzed using quantitative descriptive analysis. The results showed that student’s health literacy of respiration get mean 109.94, moderate category. Based on the above results, it can be concluded that student’s health literacy of respiration is enough because student’s health literacy are in the moderate category, but it needs to be improved by the classroom learning in accompanied by changing some of the lesser habits in maintaining respiratory health.
Adaptive Wavelet Transform Method to Identify Cracks in Gears

Many damages and faults can cause problems in gear unit operation. A crack in the tooth root is probably the least desirable among them. It often leads to failure of gear unit operation. By monitoring vibrations, it is possible to determine the presence of a crack. Signals are, however, very noisy. This makes it difficult to define properties of individual components. Wavelet analysis is an effective tool for analysing signals and for defining properties. In this paper, a denoising method based on wavelet analysis, which takes prior information about impulse probability density into consideration, is used to identify transient information from vibration signals of a gear unit with a fatigue crack in the tooth root.
An Approach for Modeling and Analysis of Security System Architectures

Security system architecture governs the composition of components in security systems and interactions between them. It plays a central role in the design of software security systems that ensure secure access to distributed resources in networked environment. In particular, the composition of the systems must consistently assure security policies that it is supposed to enforce. However, there is currently no rigorous and systematic way to predict and assure such critical properties in security system design. A systematic approach is introduced to address the problem. We present a methodology for modeling security system architecture and for verifying whether required security constraints are assured by the composition of the components. We introduce the concept of security constraint patterns, which formally specify the generic form of security policies that all implementations of the system architecture must enforce. The analysis of the architecture is driven by the propagation of the global security constraints onto the components in an incremental process. We show that our methodology is both flexible and scalable. It is argued that such a methodology not only ensures the integrity of critical early design decisions, but also provides a framework to guide correct implementations of the design. We demonstrate the methodology through a case study in which we model and analyze the architecture of the Resource Access Decision (RAD) Facility, an OMG standard for application-level authorization service.
Blocking-Aware Partitioning for Multiprocessors

In recent years multiprocessor architectures have become mainstream, and multi-core processors are found in products ranging from small portable cell phones to large computer servers. In parallel, research on real-time systems has mainly focused on traditional single-core processors. Hence, in order for real-time systems to fully leverage on the extra capacity offered by new multi-core processors, new design techniques, scheduling approaches, and real-time analysis methods have to be developed. In the multi-core and multiprocessor domain there are mainly two scheduling approaches, global and partitioned scheduling. Under global scheduling each task can execute on any processor at any time while under partitioned scheduling tasks are statically allocated to processors and migration of tasks among processors is not allowed. Besides simplicity and efficiency of partitioned scheduling protocols, existing scheduling and synchronization methods developed for single-core processor platforms can more easily be extended to partitioned scheduling. This also simplifies migration of existing systems to multi-cores. An important issue related to partitioned scheduling is distribution of tasks among processors which is a bin-packing problem. In this thesis we propose a partitioning framework for distributing tasks on the processors of multi-core platforms. Depending on the type of performance we desire to achieve, the framework may distribute a task set differently, e.g., in an application in which tasks process huge amounts of data the goal of the framework may be to decrease cache misses.Furthermore, we propose a blocking-aware partitioning heuristic algorithm to distribute tasks onto the processors of a multi-core architecture. The objective of the proposed algorithm is to decrease blocking overhead of tasks which reduces the total utilization and has the potential to reduce the number of required processors.Finally, we have implemented a tool to facilitate evaluation and comparison of different multiprocessor scheduling and synchronization approaches, as well as different partitioning heuristics. We have applied the tool in the evaluation of several partitioning heuristic algorithms, and the tool is flexible to which any new scheduling or synchronization protocol as well as any new partitioning heuristic can easily be added.
EFFORT ESTIMATION IN COMPONENT BASED SOFTWARE ENGINEERING

Component-based software engineering (CBSE) represents an exciting and promising paradigm for software development. Software components are one of the key issues in CBSE. The software development community is continuously seeking new methods for improving software quality and enhancing development productivity. There is an increasing need for componentbased metrics to help manage and foster quality in component-based software development. The traditional software product and process metrics are neither suitable nor sufficient in measuring the complexity of software components, which ultimately is necessary for quality and productivity improvement within organizations adopting CBSE. In the software development process, scheduling and predictability are important components to delivering a product on time and within budget. Effort estimation artifacts offer a rich data set for improving scheduling accuracy and understanding the development process. In this paper, we survey few existing component-based reusability metrics. These metrics give a border view of component’s understandability, adaptability, and portability. This paper is split into four sections. First section of this paper emphasize s on need of effort estimation in component based software engineering . Second section of this paper elabor ates concept of deriving a metrics using this concept of COCOMO Model. Third section emphasize on how to calculate adjustment factor for the software might be partly developed from software already existing. Fourth section elaborates conclusion and future work. Paper proposes metrics to evaluate effort and development time by conventional and component based software engineering approach and shows the result comparison.