Pruning Mechanism Research Articles

A new framework for mining weighted periodic patterns in time series databases

Mining periodic patterns in time series databases is a daunting research task that plays a significant role at decision making in real life applications. There are many algorithms for mining periodic patterns in time series, where all patterns are considered as uniformly same. However, in real life applications, such as market basket analysis, gene analysis and network fault experiment, different types of items are found with several levels of importance. Again, the existing algorithms generate huge periodic patterns in dense databases or in low minimum support, where most of the patterns are not important enough to participate in decision making. Hence, a pruning mechanism is essential to reduce these unimportant patterns. As a purpose of mining only important patterns in a minimal time period, we propose a weight based framework by assigning different weights to different items. Moreover, we develop a novel algorithm, WPPM (Weighted Periodic Pattern Mining Algorithm), in time series databases underlying suffix trie structure. To the best of our knowledge, ours is the first proposal that can mine three types of weighted periodic pattern, (i.e. single, partial, full) in a single run. A pruning method is introduced by following downward property, with respect of the maximum weight of a given database, to discard unimportant patterns. The proposed algorithm presents flexibility to user by providing intermediate unimportant pattern skipping opportunity and setting different starting positions in the time series sequence. The performance of our proposed algorithm is evaluated on real life datasets by varying different parameters. At the same time, a comparison between the proposed and an existing algorithm is shown, where the proposed approach outperformed the existing algorithm in terms of time and pattern generation.

Exceptional contextual subgraph mining

Many relational data result from the aggregation of several individual behaviors described by some characteristics. For instance, a bike-sharing system may be modeled as a graph where vertices stand for bike-share stations and connections represent bike trips made by users from one station to another. Stations and trips are described by additional information such as the description of the geographical environment of the stations (business vs. residential area, closeness to POI, elevation, urbanization density, etc.), or properties of the bike trips (timestamp, user profile, weather, events and other special conditions about the trip). Identifying highly connected components (such as communities or quasi-cliques) in this graph provides interesting insights into global usages but does not capture mobility profiles that characterize a subpopulation. To tackle this problem we propose an approach rooted in exceptional model mining to find exceptional contextual subgraphs, i.e., subgraphs generated from a context or a description of the individual behaviors that is exceptional (behaves in a different way) compared to the whole augmented graph. The dependency between a context and an edge is assessed by a $$\chi ^2$$?2 test and the weighted relative accuracy measure is used to only retain contexts that strongly characterize connected subgraphs. We present an original algorithm that uses sophisticated pruning techniques to restrict the search space of vertices, context refinements, and edges to be considered. An experimental evaluation on synthetic data and two real-life datasets demonstrates the effectiveness of the proposed pruning mechanisms, as well as the relevance of the discovered patterns.

Mechanisms of Neurite Pruning

Abstract:The axons, dendrites and synapses of neurons are among the most intricate structures cells can build. Their morphogenesis involves growth mechanisms, but also regressive mechanisms like retraction or degeneration of supernumerary or non-specific processes. Regressive mechanisms are collectively known as pruning. Pruning can serve the specification of neuronal circuits or the removal of developmentally intermediate structures. Pruning can affect both single synapses or long stretches of neurite. Here, we introduce well-characterized models of developmental pruning in mammals and

The role of polypeptide compounds in mechanism of CNS plasticity in patients with hereditary pathology of peripheral motor neuron

Synaptic pruning is a physiological mechanism of neuroplasticity, which is regulated through synthesis of growth polypeptides, neurotrophins. The role of neurotrophins in the mechanism of synaptic pruning in patients with hereditary pathology of peripheral motor neuron was studied in a clinical experimental trial. It was found that patients had elevated levels of regulatory growth polypeptides, which led to the axon growth inhibition effect in organotypic tissue cultures. Thus, neurotrophin overexpression can be considered as a factor preventing synaptic pruning and contributing to further process of neurological degeneration in nerve tissue in patients with hereditary pathology of peripheral motor neuron.

P.1.i.019 - Simultaneous imaging of task-specific glucose metabolism and functional connectivity with hybrid PET-MR

A pruning mechanism is developed for the identification of polynomial NARX/NARMAX models. in order to systematically delete redundant terms inserted in the structure selection phase. The proposed approach is capable of overcoming somelimitations met by classical regressor deletion criteria, especially when the data available for identitication are not adequately exciting or the model family does not include all the correct regressors. The effectiveness of the algorithm is shown by comparison analysis on some simulation examples.

Faster plan generation through consideration of functional dependencies and keys

It has been a recognized fact for many years that query execution can benefit from pushing group-by operators down in the operator tree and applying them before a join. This so-called eager aggregation reduces the size(s) of the join argument(s), making join evaluation faster. Lately, the idea enjoyed a revival when it was applied to outer joins for the first time and incorporated in a state-of-the-art plan generator. However, this recent approach is highly dependent on the use of heuristics because of the exponential growth of the search space that goes along with eager aggregation. Finding an optimal solution for larger queries calls for effective optimality preserving pruning mechanisms to reduce the search space size as far as possible. By a more thorough investigation of functional dependencies and keys, we provide a set of new pruning criteria and evaluate their effectiveness with respect to the runtime and memory consumption of the resulting plan generator.

Online Learning Algorithms for Double-Weighted Least Squares Twin Bounded Support Vector Machines

Twin support vector machine with two nonparallel classifying hyperplanes and its extensions have attracted much attention in machine learning and data mining. However, the prediction accuracy may be highly influenced when noise is involved. In particular, for the least squares case, the intractable computational burden may be incurred for large scale data. To address the above problems, we propose the double-weighted least squares twin bounded support vector machines and develop the online learning algorithms. By introducing the double-weighted mechanism, the linear and nonlinear double-weighted learning models are proposed to reduce the influence of noise. The online learning algorithms for solving the two models are developed, which can avoid computing the inverse of the large scale matrices. Furthermore, a new pruning mechanism which can avoid updating the kernel matrices in every iteration step for solving nonlinear model is also developed. Simulation results on three UCI data with noise demonstrate that the online learning algorithm for the linear double-weighted learning model can get least computation time as well considerable classification accuracy. Simulation results on UCI data and two-moons data with noise demonstrate that the nonlinear double-weighted learning model can be effectively solved by the online learning algorithm with the pruning mechanism.

Non-revisiting genetic algorithm with adaptive mutation using constant memory

The continuous non-revisiting genetic algorithm (cNrGA) uses the entire search history and parameter-less adaptive mutation to significantly enhance search performance. Storing the entire search history is natural and costs little when the number of fitness evaluations is small or moderate. However, if the number of evaluations required is substantial, some memory management is desirable. In this paper, we propose two pruning mechanisms to keep the memory used constant. They are least recently used pruning and random pruning. The basic idea is to prune a unit of memory when the memory threshold is reached and some new search information is required to be stored, thus keeping the overall memory used constant. Meanwhile, both pruning strategies naturally form parameter-less adaptive mutation operators. A study is carried out to evaluate the impact on performance caused by loss of search history information. Experimental results show that (1) both strategies can maintain the performance of cNrGA, up to the empirical limit when 90 % of the search history is not recorded, (2) cNrGA and its variants with constant memory outperform the real-coded genetic algorithm and the standard particle swarm optimization. By pre-extracting all the current prune-able history information and storing them into a list, namely, to-prune-list, the overhead of both pruning strategies becomes small. This suggests that cNrGA can be extended to use in situations when the number of fitness evaluations is much larger than before with no significant effect on statistical performance. This widens the applicability of cNrGA to include more practical problems that require larger number of fitness evaluations before converging to the global optima.

Sculpting neural circuits by axon and dendrite pruning.

The assembly of functional neural circuits requires the combined action of progressive and regressive events. Regressive events encompass a variety of inhibitory developmental processes, including axon and dendrite pruning, which facilitate the removal of exuberant neuronal connections. Most axon pruning involves the removal of axons that had already made synaptic connections; thus, axon pruning is tightly associated with synapse elimination. In many instances, these developmental processes are regulated by the interplay between neurons and glial cells that act instructively during neural remodeling. Owing to the importance of axon and dendritic pruning, these remodeling events require precise spatial and temporal control, and this is achieved by a range of distinct molecular mechanisms. Disruption of these mechanisms results in abnormal pruning, which has been linked to brain dysfunction. Therefore, understanding the mechanisms of axon and dendritic pruning will be instrumental in advancing our knowledge of neural disease and mental disorders.

Monotonic Random Forest with an Ensemble Pruning Mechanism based on the Degree of Monotonicity

In classification problems, it is very common to have ordinal data in the variables, in both explanatory and class variables. When the class variable should increase according to a subset of explanatory variables, the problem must satisfy the monotonicity constraint. It is well known that standard classification tree algorithms, such as CART or C4.5, are not guaranteed to produce monotonic trees, even if the data set is completely monotone. Recently, some classifiers have been designed to handle these kinds of problems. In decision trees, growing and pruning mechanisms have been updated to improve the monotonicity of the trees. In this paper, we study the suitability of using these mechanisms in the generation of Random Forests. For this, we propose a simple ensemble pruning mechanism based on the degree of monotonicity of the resulting trees. The performance of several decision trees are evaluated through experimental studies on monotonic data sets. We deduce that the trees produced by the Random Forest also hold the monotonicity restriction but achieve a slightly better predictive performance than standard algorithms.

Modulation of GluN3A expression in Huntington disease: a new n-methyl-D-aspartate receptor-based therapeutic approach?

Huntington disease (HD) is an inherited neurodegenerative disorder with no cure or effective palliative treatment. An ideal therapy would arrest pathogenesis at early stages before neuronal damage occurs. However, although the genetic mutation that causes HD is known, the molecular chain of events that leads from the mutation to disease is not well understood. Accumulating evidence suggests that synaptic dysregulation may be involved, and the earliest known deficit is hyperfunction of glutamate-type N-methyl-d-aspartate receptors (NMDARs) in the selectively vulnerable medium spiny neurons of the striatum. A previous study found that the mutant Htt protein interferes with downregulation of juvenile NMDAR subtypes that contain GluN3A subunits by sequestering the endocytic adaptor PACSIN1 and preventing their removal from the cell surface. Loss of PACSIN1 and consequent gain of GluN3A function reactivate a synapse pruning mechanism that is important during development but harmful when active at later stages. Suppressing the GluN3A reactivation corrected the NMDAR hyperfunction and prevented the full range of HD signs and symptoms in mouse models, encouraging efforts to develop GluN3A-selective antagonists and/or explore alternative therapeutic approaches to block GluN3A expression.

Improved Differential and Linear Active S‐Boxes Search Techniques for Feistel Type Ciphers

We propose improved differential and linear active S-boxes search techniques for Feistel type ciphers. We give a uniform representation of Feistel type structures which can benefit the analysis of differential propagation. By analyzing the properties of Feistel type environment, we present some important observations of differential propagation and propose a notion of equivalent state set which can narrow down the search space noticeably. We present a practical algorithm to improve the search of active S-boxes for Feistel type ciphers. It is basically a Viterbi search operating on equivalent state set and also adopts the pruning mechanism. Our experimental results show that the improved algorithm have advantages in respect to memory and time complexities, and it can be applied efficiently to Feistel cipher with large blocks. The search program can be implemented in normal PC, which will be more practical and useful for the designers and cryptanalysts.

Mechanisms of developmental neurite pruning.

The precise wiring of the nervous system is a combined outcome of progressive and regressive events during development. Axon guidance and synapse formation intertwined with cell death and neurite pruning sculpt the mature circuitry. It is now well recognized that pruning of dendrites and axons as means to refine neuronal networks, is a wide spread phenomena required for the normal development of vertebrate and invertebrate nervous systems. Here we will review the arising principles of cellular and molecular mechanisms of neurite pruning. We will discuss these principles in light of studies in multiple neuronal systems, and speculate on potential explanations for the emergence of neurite pruning as a mechanism to sculpt the nervous system.

LFG parse disambiguation for Wolof

This paper presents several techniques for managing ambiguity in LFG parsing of Wolof, a less-resourced Niger-Congo language. Ambiguity is pervasive in Wolof and This raises a number of theoretical and practical issues for managing ambiguity associated with different objectives. From a theoretical perspective, the main aim is to design a large-scale grammar for Wolof that is able to make linguistically motivated disambiguation decisions, and to find appropriate ways of controlling ambiguity at important interface representations. The practical aim is to develop disambiguation strategies to improve the performance of the grammar in terms of efficiency, robustness and coverage.To achieve these goals, different avenues are explored to manage ambiguity in the Wolof grammar, including the formal encoding of noun class indeterminacy, lexical specifications, the use of Constraint Grammar models (Karlsson 1990) for morphological disambiguation, the application of the c-structure pruning mechanism (Cahill et al. 2007, 2008; Crouch et al. 2013), and the use of optimality marks for preferences (Frank et al. 1998, 2001). The parsing system is further controlled by packing ambiguities. In addition, discriminant-based techniques for parse disambiguation (Rosén et al. 2007) are applied for treebanking purposes.

An efficient method for mining non-redundant sequential rules using attributed prefix-trees

Mining sequential generator patterns and mining closed sequential patterns are important to sequence mining. They address difficulties in the mining of sequential patterns by reducing the number of sequential rules, and the results are often used to generate non-redundant sequential rules. This paper proposes an efficient method for mining non-redundant sequential rules from an attributed prefix-tree. The proposed method has two phases. In the first phase, it builds a prefix-tree that stores all the sequential patterns from a given sequence database. Then in the second phase, it mines non-redundant sequential rules from this prefix-tree. In the prefix-tree building process, each node on the prefix-tree has a field that indicates whether this node is a minimal sequential generator pattern, and another field that indicates whether this node is a closed sequential pattern. By traversing the prefix-tree, non-redundant sequential rules can be easily mined from a minimal sequential generator pattern X to a closed sequential pattern Y such that X is a prefix of Y. In addition, a good pruning mechanism is proposed to reduce the search space and the execution time in the mining process. Experimental results show that the proposed method is more efficient than existing methods in mining non-redundant sequential rules.

Regional security arrangements as a filter for norm diffusion: the African Union, the European Union and the responsibility to protect

Regional security arrangements play a central role in modifying emerging norms as they travel from the global to the local level. This process of norm localization is shaped by various factors such as the characteristics of regional security cultures, corresponding resonance with the emerging norm, institutional voice opportunities, and mechanisms of framing and pruning as they are utilized by norm entrepreneurs. The article applies this analytical framework to the localization of the responsibility to protect (R2P) norm by the African Union and the European Union. Subsequently, the paper examines how localization of the R2P norm in both regions affected their reactions to the Libyan crisis in 2011. It also examines the likely ramifications that the intervention may have on the future reception of the R2P by African and European actors.

A highly efficient RPF-based broadcast protocol using a new two-level pruning mechanism

Reverse path forwarding (RPF) based data communication is one of the widely used data communication protocols in networks. The most attractive feature of RPF is that reasonably low amount of memory is needed per router for storing the routing information (DVR table). However, it generates quite a large number of duplicate packets resulting in poor bandwidth utilization. We have observed that if the location of the broadcast source is considered while forwarding a packet, considerable improvement of the bandwidth utilization can be achieved from the viewpoint of less number of duplicate packets generated. In this paper we have used this location aspect in the design of a novel hierarchical pruning mechanism to improve bandwidth utilization for broadcasting based on RPF mechanism for data communication. Experimental results support our theoretical findings and hence guarantee improvement of bandwidth utilization.

Caspase-mediated cleavage of actin and tubulin is a common feature and sensitive marker of axonal degeneration in neural development and injury

BackgroundAxon degeneration is a characteristic feature of multiple neuropathologic states and is also a mechanism of physiological neurodevelopmental pruning. The vast majority of in vivo studies looking at axon degeneration have relied on the use of classical silver degeneration stains, which have many limitations including lack of molecular specificity and incompatibility with immunolabeling methods. Because Wallerian degeneration is well known to involve cytoskeletal disassembly and because caspases are recently implicated in aspects of this process, we asked whether antibodies directed at caspase-generated neoepitopes of beta-actin and alpha-tubulin would be useful immunohistochemical markers of pathological and developmental axon degeneration.ResultsHere we demonstrate that several forms of axon degeneration involve caspase-mediated cleavage of these cytoskeletal elements and are well-visualized using this approach. We demonstrate the generation of caspase-induced neoepitopes in a) an in vitro neuronal culture model using nerve growth factor-deprivation-induced degeneration and b) an in vivo model using ethanol-induced neuronal apoptosis, and c) during normal developmental pruning and physiological turnover of neurons.ConclusionsOur findings support recent experimental data that suggests caspase-3 and caspase-6 have specific non-redundant roles in developmental pruning. Finally, these findings may have clinical utility, as these markers highlight degenerating neurites in human hypoxic-ischemic injury. Our work not only confirms a common downstream mechanism involved in axon degeneration, but also illuminates the potential utility of caspase-cleavage-neoepitope antibodies as markers of neurodegeneration.

Developmental expression profiles of axon guidance signaling and the immune system in the marmoset cortex: Potential molecular mechanisms of pruning of dendritic spines during primate synapse formation in late infancy and prepuberty (I)

The synapse number and the related dendritic spine number in the cerebral cortex of primates shows a rapid increase after birth. Depending on the brain region and species, the number of synapses reaches a peak before adulthood, and pruning takes place after this peak (overshoot-type synaptic formation). Human mental disorders, such as autism and schizophrenia, are hypothesized to be a result of either too weak or excessive pruning after the peak is reached. Thus, it is important to study the molecular mechanisms underlying overshoot-type synaptic formation, particularly the pruning phase.To examine the molecular mechanisms, we used common marmosets (Callithrix jacchus). Microarray analysis of the marmoset cortex was performed in the ventrolateral prefrontal, inferior temporal, and primary visual cortices, where changes in the number of dendritic spines have been observed. The spine number of all the brain regions above showed a peak at 3months (3 M) after birth and gradually decreased (e.g., at 6 M and in adults). In this study, we focused on genes that showed differential expression between ages of 3 M and 6 M and on the differences whose fold change (FC) was greater than 1.2. The selected genes were subjected to canonical pathway analysis, and in this study, we describe axon guidance signaling, which had high plausibility. The results showed a large number of genes belonging to subsystems within the axon guidance signaling pathway, macrophages/immune system, glutamate system, and others. We divided the data and discussion of these results into 2 papers, and this is the first paper, which deals with the axon guidance signaling and macrophage/immune system. Other systems will be described in the next paper. Many components of subsystems within the axon guidance signaling underwent changes in gene expression from 3 M to 6 M so that the synapse/dendritic spine number would decrease at 6 M. Thus, axon guidance signaling probably contributes to the decrease in synapse/dendritic spine number at 6 M, the phenomenon that fits the overshoot-type synaptic formation in primates.Microglial activity (evaluated by quantifying AIF1 expression) and gene expression of molecules that modulate microglia, decreased at 6 M, just like the synapse/dendritic spine number. Thus, although microglial activity is believed to be related to phagocytosis of synapses/dendritic spines, microglial activity alone cannot explain how pruning was accelerated in the pruning phase. On the other hand, expression of molecules that tag synapses/dendritic spines as a target of phagocytosis by microglia (e.g., complement components) increased at 6 M, suggesting that these tagging proteins may be involved in the acceleration of pruning during the pruning phase.

Microtubule Severing at Crossover Sites by Katanin Generates Ordered Cortical Microtubule Arrays in Arabidopsis

The noncentrosomal cortical microtubules (CMTs) of land plants form highly ordered parallel arrays that mediate cell morphogenesis by orienting cellulose deposition. Since new CMTs initiate from dispersed cortical sites at random orientations, parallel array organization is hypothesized to require selective pruning of CMTs that are not in the dominant orientation. Severing of CMTs at crossover sites is proposed to be a potential pruning mechanism; however, the parameters and molecular mechanisms underlying this activity are unknown. Here, using live-cell imaging, we show that severing preferentially targets the overlying CMTs at crossover sites and leads to their depolymerization about 85% of the time. In addition, the probability of severing has a sigmoidal relationship to the crossover dwell time, indicating a strong bias for longer-lived crossover sites to be severed. We found that severing at CMT crossover sites was completely abolished in the Arabidopsis katanin mutant. Consistent with this finding, GFP-tagged katanin driven by its native promoter localizes at sites of CMT crossover prior to severing. Furthermore, array recovery experiments showed that CMTs fail to become ordered in the katanin mutant. We conclude that katanin is solely responsible for severing at CMT crossover sites and that this activity is essential to generate ordered CMT arrays.