A first step to accelerating fingerprint matching based on deformable minutiae clustering

Fingerprint recognition is one of the most used biometric methods for authentication. The identification of a query fingerprint requires matching its minutiae against every minutiae of all the fingerprints of the database. The state-of-the-art matching algorithms are costly, from a computational point of view, and inefficient on large datasets. In this work, we include faster methods to accelerating DMC (the most accurate fingerprint matching algorithm based only on minutiae). In particular, we translate into C++ the functions of the algorithm which represent the most costly tasks of the code; we create a library with the new code and we link the library to the original C# code using a CLR Class Library project by means of a C++/CLI Wrapper. Our solution re-implements critical functions, e.g., the bit population count including a fast C++ PopCount library and the use of the squared Euclidean distance for calculating the minutiae neighborhood. The experimental results show a significant reduction of the execution time in the optimized functions of the matching algorithm. Finally, a novel approach to improve the matching algorithm, considering cache memory blocking and parallel data processing, is presented as future work.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Performance Study of Block ACK and Reverse Direction in IEEE 802.11n Using a Markov Chain Model

IEEE 802.11n networks are widely used in home and corporate network environments because they offer high-speed wireless Internet access at relatively low-cost. The 802.11n standard introduced several key features including Block acknowledgement (ACK) and reverse direction (RD) data transmission for enhanced system performance. An in-depth study of 802.11n system capacity for Block ACK mechanisms (both protected and unprotected) and RD data flows is required to assist optimum planning and design of such systems in view of the limited wireless channel capacity. In this paper we study the interdependencies of Block ACK and RD mechanisms using a discrete bi-directional Markov chain model under non-saturated traffic loads. We present a mathematical model to derive throughput, delay, and packet loss probability for both protected and unprotected Block ACKs under varying loads. We validate the model using MATLAB based numerical studies. Results obtained show that the combined effect of protected Block ACK and RD flows has a positive impact on system performance. However, unprotected Block ACK wastes transmission opportunity (TXOP) especially in collisions and therefore degrades the system performance. Our findings reported in this paper provide some insights into the performance of 802.11n with respect to Block ACK and RD methods. This study may help network researchers and engineers in their contribution to the development of next generation wireless LANs such as IEEE 802.11ac

Effects of furanace on Brachionus

Tiger prawn P.monodon) larvae utilize Brachionus a rotifer, as food in the Zoea 3 and mysis stages when they change from an herbivorous to an omnivorous diet. The present work aims to show the effects of furanace on the population growth of Brachionus. Cultures of Brachionus were obtained and fed with Chlorella at a density of 1-2x10 SUP-6 cells/ml. Five liters of the culture water were placed in each of 4 white, circular, 152x304 mm plastic basins. The mean initial densities of the rotifer ranged from 26 . 5 to 38 . 5 individuals/ml. The concentrations of furanace were 0, 1, 2 and 3 mg /l. The cultures were vigorously aerated. Population growth was observed after 3, 6, and 9 hours of exposure. The cultures were thoroughly mixed before samples were taken to ensure an almost equal distribution of the rotifers in the water. To facilitate the counting of the rotifer, one drop of Lugol's solution was added to each sample. This immobilizes the rotifer as well as stops further reproduction. Individuals with only the lorica left or with badly deformed lorica were considered dead. Population counts were done using a Sedgwick-Rafter counting chamber. Among the different durations of exposure, the percentage survival of the populations in the furanace baths were highest after 3 hr. There were slight increases in the control and 2 mg/l and slight decreases in 1 and 3 mg/l. The differences in the mean densities are statistically insignificant at . 01 significance level. After a 6-hr exposure, the control population reached its peak density with a survival of 89%. Populations in furanace baths decreased to 88 . 5% in both 2 and 3 mg /l followed closely by 87% in 1 mg/l. Again, no statistical differences exist among all the levels. The mean percentage survival in 1 and 2 mg/l increased (89% and 91%, respectively) after a 9-hr expsoure, while those in the control and 3 mg/l decreased to 86 . 5% and 88 . 25%, respectively. There were no marked differences in appearance noted among the individuals in furanace baths and those in the control

Realistic Gluino Axion Model Consistent with Supersymmetry Breaking at the TeV Scale

The recently proposed model of using the dynamical phase of the gluino to solve the strong CP problem is shown to admit a specific realization in terms of fundamental singlet superfields, such that the breaking of supersymmetry occurs only at the TeV scale, despite the large axion scale of 10^{9} to 10^{12} GeV. Phenomenological implications are discussed.Comment: 12 pp, 2 fig

Genome-wide profiling of uncapped mRNA

Gene transcripts are under extensive posttranscriptional regulation, including the regulation of their stability. A major route for mRNA degradation produces uncapped mRNAs, which can be generated by decapping enzymes, endonucleases, and small RNAs. Profiling uncapped mRNA molecules is important for the understanding of the transcriptome, whose composition is determined by a balance between mRNA synthesis and degradation. In this chapter, we describe a method to profile these uncapped mRNAs at the genome scale

Users-Centric Adaptive Learning System Based on Interval Type-2 Fuzzy Logic for Massively Crowded E-Learning Platforms

Abstract Technological advancements within the educational sector and online learning promoted portable data-based adaptive techniques to influence the developments within transformative learning and enhancing the learning experience. However, many common adaptive educational systems tend to focus on adopting learning content that revolves around pre-black box learner modelling and teaching models that depend on the ideas of a few experts. Such views might be characterized by various sources of uncertainty about the learner response evaluation with adaptive educational system, linked to learner reception of instruction. High linguistic uncertainty levels in e-learning settings result in different user interpretations and responses to the same techniques, words, or terms according to their plans, cognition, pre-knowledge, and motivation levels. Hence, adaptive teaching models must be targeted to individual learners’ needs. Thus, developing a teaching model based on the knowledge of how learners interact with the learning environment in readable and interpretable white box models is critical in the guidance of the adaptation approach for learners’ needs as well as understanding the way learning is achieved. This paper presents a novel interval type-2 fuzzy logic-based system which is capable of identifying learners’ preferred learning strategies and knowledge delivery needs that revolves around characteristics of learners and the existing knowledge level in generating an adaptive learning environment. We have conducted a large scale evaluation of the proposed system via real-word experiments on 1458 students within a massively crowded e-learning platform. Such evaluations have shown the proposed interval type-2 fuzzy logic system’s capability of handling the encountered uncertainties which enabled to achieve superior performance with regard to better completion and success rates as well as enhanced learning compared to the non-adaptive systems, adaptive system versions led by the teacher, and type-1-based fuzzy based counterparts.</jats:p

Multicolor CRISPR labeling of chromosomal loci in human cells

The intranuclear location of genomic loci and the dynamics of these loci are important parameters for understanding the spatial and temporal regulation of gene expression. Recently it has proven possible to visualize endogenous genomic loci in live cells by the use of transcription activator-like effectors (TALEs), as well as modified versions of the bacterial immunity clustered regularly interspersed short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) system. Here we report the design of multicolor versions of CRISPR using catalytically inactive Cas9 endonuclease (dCas9) from three bacterial orthologs. Each pair of dCas9-fluorescent proteins and cognate single-guide RNAs (sgRNAs) efficiently labeled several target loci in live human cells. Using pairs of differently colored dCas9-sgRNAs, it was possible to determine the intranuclear distance between loci on different chromosomes. In addition, the fluorescence spatial resolution between two loci on the same chromosome could be determined and related to the linear distance between them on the chromosome\u27s physical map, thereby permitting assessment of the DNA compaction of such regions in a live cell

Are Soft Prompts Good Zero-shot Learners for Speech Recognition?

Large self-supervised pre-trained speech models require computationally expensive fine-tuning for downstream tasks. Soft prompt tuning offers a simple parameter-efficient alternative by utilizing minimal soft prompt guidance, enhancing portability while also maintaining competitive performance. However, not many people understand how and why this is so. In this study, we aim to deepen our understanding of this emerging method by investigating the role of soft prompts in automatic speech recognition (ASR). Our findings highlight their role as zero-shot learners in improving ASR performance but also make them vulnerable to malicious modifications. Soft prompts aid generalization but are not obligatory for inference. We also identify two primary roles of soft prompts: content refinement and noise information enhancement, which enhances robustness against background noise. Additionally, we propose an effective modification on noise prompts to show that they are capable of zero-shot learning on adapting to out-of-distribution noise environments