Rebalance Research Articles

Terminal Wiener Index of Balanced Trees

for A Tree T, The Terminal Wiener Index TW(T) Is Defined As Half The Sum Of All Distances Of The Form D(U, V), Where The Summation Is Over All Possible Pairs Of Pendent Vertices U,V In T . We Consider A Class Of Balanced Binary Trees Called 1-Trees And Compute Their Terminal Wiener Index Values. We Also Determine 1-Trees With Minimum And Maximum Terminal Wiener Index.

Phase unwrapping method based on parallel local minimum reliability dual expanding for large-scale data

Considering data reliability, the optimized connections of dual residues for phase discontinuity reconstruction provide a more reliable scheme and produce more robust unwrapped results. However, their practical implementation usually involves a time-consuming iterative global operation, which is not suitable for application to the phase unwrapping (PU) of big blocks of interferometric synthetic aperture radar (InSAR) phase data. A parallel PU method based on local minimum reliability dual expanding is proposed. With given quality weight maps, the dual reliability is defined based on residues, and the minimum reliability residue pairs are introduced to represent possible discontinuous boundaries. We provide a dynamic expanding method for the pairs with local minimum reliability searching and dual merging. The final minimum balanced trees obtained are used for path integrating of PU with the aid of reliability maps. The calculation of reliability maps, the residue pair searching, and the dynamic expanding are designed to be carried out in parallel. We employ the interface propagation scheme based on the eikonal equation and flood-filling for the parallel implement. Two big blocks of airborne InSAR data were processed by the proposed method, the experimental results and analysis verified its robustness and efficiency for the large-scale PU problem.

Classifying Power Quality Disturbances Based on Phase Space Reconstruction and a Convolutional Neural Network

This paper presents a hybrid approach combining phase space reconstruction (PSR) with a convolutional neural network (CNN) for power quality disturbance (PQD) classification. Firstly, a PSR technique is developed to transform a 1D voltage disturbance signal into a 2D image file. Then, a CNN model is developed for the image classification. The feature maps are extracted automatically from the image file and different patterns are derived from variables in CNN. A set of synthetic signals, as well as operational measurements, are used to validate the proposed method. Moreover, the test results are also compared with existing methods, including empirical mode decomposition (EMD) with balanced neural tree (BNT), S-transform (ST) with neural network (NN) and decision tree (DT), hybrid ST with DT, adaptive linear neuron (ADALINE) with feedforward neural network (FFNN), and variational mode decomposition (VMD) with deep stochastic configuration network (DSCN). Based on deep learning algorithms, the proposed method is capable of providing more accurate results without any human intervention for PQDs. It also enables the planning of PQ remedy actions.

Neighbor Dependency-Based Dynamic Fusion Tree Generation for a Multi-radar System

This paper proposes a fusion tree generation (FTG) algorithm for a two-tier fusion process in a multi-radar system. The two-tier fusion process divides the fusion process into local and global parts. The fusion workload of a multi-radar system at the central server can be reduced by applying two-tier approach. However, the two-tier fusion process requires a balanced fusion tree to increase the number of processed tracks. This paper presents a dynamic fusion tree generation (D-FTG) algorithm based on a clustering scheme. We developed a novel clustering scheme, which can be used in generating balanced fusion trees in a multi-radar system. The developed clustering scheme is able to generate a balanced fusion tree with the neighbor dependency-based scoring method, manage the dynamic changes of the distribution of targets with the pruning-and-rejoining strategy, and cope with the failure of radar nodes by repeating initial clustering. Simulation results show that D-FTG outperforms existing clustering methods when used to generate balanced fusion trees in a dynamic multi-radar system.

Large-Scale Interactive Recommendation with Tree-Structured Policy Gradient

Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for long-run performance. As IRS is always with thousands of items to recommend (i.e., thousands of actions), most existing RL-based methods, however, fail to handle such a large discrete action space problem and thus become inefficient. The existing work that tries to deal with the large discrete action space problem by utilizing the deep deterministic policy gradient framework suffers from the inconsistency between the continuous action representation (the output of the actor network) and the real discrete action. To avoid such inconsistency and achieve high efficiency and recommendation effectiveness, in this paper, we propose a Tree-structured Policy Gradient Recommendation (TPGR) framework, where a balanced hierarchical clustering tree is built over the items and picking an item is formulated as seeking a path from the root to a certain leaf of the tree. Extensive experiments on carefully-designed environments based on two real-world datasets demonstrate that our model provides superior recommendation performance and significant efficiency improvement over state-of-the-art methods.

Versatile Linkage: a Family of Space-Conserving Strategies for Agglomerative Hierarchical Clustering

Agglomerative hierarchical clustering can be implemented with several strategies that differ in the way elements of a collection are grouped together to build a hierarchy of clusters. Here we introduce versatile linkage, a new infinite system of agglomerative hierarchical clustering strategies based on generalized means, which go from single linkage to complete linkage, passing through arithmetic average linkage and other clustering methods yet unexplored such as geometric linkage and harmonic linkage. We compare the different clustering strategies in terms of cophenetic correlation, mean absolute error, and also tree balance and space distortion, two new measures proposed to describe hierarchical trees. Unlike the $\beta$-flexible clustering system, we show that the versatile linkage family is space-conserving.

Querying shortest paths on time dependent road networks

For real-world time dependent road networks (TDRNs), answering shortest path-based route queries and plans in real-time is highly desirable by many industrial applications. Unfortunately, traditional ( Dijkstra - or A *-like) algorithms are computationally expensive for such tasks on TDRNs. Naturally, indexes are needed to meet the real-time constraint required by real applications. In this paper, we propose a novel height-balanced tree-structured index, called TD-G-tree, which supports fast route queries over TDRNs. The key idea is to use hierarchical graph partitioning to split a road network into hierarchical partitions. This will produce a balanced tree, where each tree node corresponds to a partition and each parent-child relationship corresponds to a partition and its sub-partition. We then compute and index time dependent shortest paths (TDSPs) only for borders ( i.e ., vertices whose edges are cut by a partition). Based on TD-G-tree, we devise efficient algorithms to support TDSP queries, as well as time-interval based route planning, for computing optimal solutions through dynamic programming and chronological divide-and-conquer. Extensive experiments on real-world datasets show that our method significantly outperforms existing approaches.

Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design.

In medicinal chemistry programs it is key to design and make compounds that are efficacious and safe. This is a long, complex, and difficult multiparameter optimization process, often including several properties with orthogonal trends. New methods for the automated design of compounds against profiles of multiple properties are thus of great value. Here we present a fragment-based reinforcement learning approach based on an actor-critic model, for the generation of novel molecules with optimal properties. The actor and the critic are both modeled with bidirectional long short-term memory (LSTM) networks. The AI method learns how to generate new compounds with desired properties by starting from an initial set of lead molecules and then improving these by replacing some of their fragments. A balanced binary tree based on the similarity of fragments is used in the generative process to bias the output toward structurally similar molecules. The method is demonstrated by a case study showing that 93% of the generated molecules are chemically valid and more than a third satisfy the targeted objectives, while there were none in the initial set.

Why Do Firms Decide To Go Public? A Case Study of Karachi Stock Exchange

This study empirically examines the motivations of going public decision. This study used pre-IPO characteristics and ex-post consequences of IPO firms to address the exceeding research objective. This study used a sample of 70 newly listed firms during 2000 to 2014 on Karachi Stock Exchange (KSE). In this study, a panel probit regression model is used to estimate the impact of fundamental factors on going public decision. The results of this analysis reveal that larger-size firms, carrying high sales growth, more profitable, industry MTB ratio, and firms related general trade industry come into sight are significant determinants. Furthermore, this study also examined ex-post consequences of IPO firms by comparing with the same pre-IPO characteristics by using ‘Wilcoxon two-sample signed rank’ test. The results of this investigation reveal that: Going public decision is used (i) to dilute ownership, risk diversification, and reduction in external monitoring (ii) to finance their future investments and expansions, and (iii) to rebalance their capital structure

Effect of Long-Term vs. Short-Term Ambient Ozone Exposure on Radial Stem Growth, Sap Flux and Xylem Morphology of O3-Sensitive Poplar Trees

High ozone (O3) pollution impairs the carbon and water balance of trees, which is of special interest in planted forests. However, the effect of long-term O3 exposure on tree growth and water use, little remains known. In this study, we analysed the relationships of intra-annual stem growth pattern, seasonal sap flow dynamics and xylem morphology to assess the effect of long term O3 exposure of mature O3-sensitive hybrid poplars (‘Oxford’ clone). Rooted cuttings were planted in autumn 2007 and drip irrigated with 2 liters of water as ambient O3 treatment, or 450 ppm ethylenediurea (N-[2-(2-oxo-1-imidazolidinyl)ethyl]-N0-phenylurea, abbreviated as EDU) solution as O3 protection treatment over all growing seasons. During 2013, point dendrometers and heat pulses were installed to monitor radial growth, stem water relations and sap flow. Ambient O3 did not affect growth rates, even if the seasonal culmination point was 20 days earlier on average than that recorded in the O3 protected trees. Under ambient O3, trees showed reduced seasonal sap flow, however, the lower water use was due to a decrease of Huber value (decrease of leaf area for sapwood unit) rather than to a change in xylem morphology or due to a direct effect of sluggish stomatal responses on transpiration. Under high evaporative demand and ambient O3 concentrations, trees showed a high use of internal stem water resources modulated by stomatal sluggishness, thus predisposing them to be more sensitive water deficit during summer. The results of this study help untangle the compensatory mechanisms involved in the acclimation processes of forest species to long-term O3 exposure in a context of global change.

Extreme Multiclass Classification Criteria

We analyze the theoretical properties of the recently proposed objective function for efficient online construction and training of multiclass classification trees in the settings where the label space is very large. We show the important properties of this objective and provide a complete proof that maximizing it simultaneously encourages balanced trees and improves the purity of the class distributions at subsequent levels in the tree. We further explore its connection to the three well-known entropy-based decision tree criteria, i.e., Shannon entropy, Gini-entropy and its modified variant, for which efficient optimization strategies are largely unknown in the extreme multiclass setting. We show theoretically that this objective can be viewed as a surrogate function for all of these entropy criteria and that maximizing it indirectly optimizes them as well. We derive boosting guarantees and obtain a closed-form expression for the number of iterations needed to reduce the considered entropy criteria below an arbitrary threshold. The obtained theorem relies on a weak hypothesis assumption that directly depends on the considered objective function. Finally, we prove that optimizing the objective directly reduces the multi-class classification error of the decision tree.

Colored operads, series on colored operads, and combinatorial generating systems

We introduce bud generating systems, which are used for combinatorial generation. They specify sets of various kinds of combinatorial objects, called languages. They can emulate context-free grammars, regular tree grammars, and synchronous grammars, allowing us to work with all these generating systems in a unified way. The theory of bud generating systems uses colored operads. Indeed, an object is generated by a bud generating system if it satisfies a certain equation in a colored operad. To compute the generating series of the languages of bud generating systems, we introduce formal power series on colored operads and several operations on these. Series on colored operads are crucial to express the languages specified by bud generating systems and allow us to enumerate combinatorial objects with respect to some statistics. Some examples of bud generating systems are constructed; in particular to specify some sorts of balanced trees and to obtain recursive formulas enumerating these.

Enabling Efficient Batch Updating Verification for Multi‐versioned Data in Cloud Storage

Existing dynamic data possession verification schemes not only suffer from low efficiency of rebalancing its Merkle Hash tree (MHT) when executing data updating, but also lack effective mechanism to verify multi-version files. Aiming at these problems, this paper propose a new data structure called Rank-based multi-version Merkle AVL tree (RBMV-MAT) to achieve efficient batch updating verification for multi-version data. RBMV-MAT uses a special lock and relaxed balance to decrease the frequency of rebalacing operations. The experimental results show that our efficient scheme has better efficiency than those of existing methods.

Block Binary Search Tree

This paper illustrates a novel data structure created by me called Block Binary Search Tree (BBST). BBST is a data structure which extends Binary Search Tree by replacing the key-value pair domain with an array of key-value pairs in each node of Binary Search Tree. It greatly reduces the space usage cost compared to B+ tree, Binary Search Trees and all kinds of different Self-Balancing Binary Search Trees such as AVL, RBT, Splay.

Relationship between chlorosis, photosynthesis and the nutrient content of plane trees in the presence of chemical and organic fertilizers

Chlorosis disorder is a major problem affecting the growth and physiological processes of many trees including plane trees (Platanus orientalis L.). This experiment was conducted to study the relationship between leaf chlorosis disorder and the nutritional status and some important physiological characteristics of plane trees. The experiment was arranged in a randomized complete block design with six replications and four treatments including control, manure (M), manure + fertilizer (20-5-10) (MF), and manure + fertilizer + mycorrhizal fungi (MFA) ( Glomus intraradices + G. mosseae) . The results showed that although all treatments significantly improved the nutrients content, soluble carbohydrates content, photosynthesis rate and chlorophyll content in the leaves, they mostly reached their peak in the mycorrhizal inoculated plants. Nitrogen (N), phosphorus (P) and zinc (Zn) were increased in the AMF amended trees compared to the control plants. The photosynthesis rate was enhanced by all the mixtures at least by 60% compared to the control. The most Chlorosis (17.5%) to the leaves recorded on the control plants, while leaf damage dropped to less than 2.9% at mycorrhizal treatment leading to the improved nutritional balance in the plane trees. The results proved the effectiveness of including mycorrhizal inoculation to the common fertilization practices to prevent leaf chlorosis in the plane trees.

Parallel likelihood calculation for phylogenetic comparative models: The SPLITT C++ library

Abstract Phylogenetic comparative models (PCMs) have been used to study macroevolutionary patterns, to characterize adaptive phenotypic landscapes, to quantify rates of evolution, to measure trait heritability, and to test various evolutionary hypotheses. A major obstacle to applying these models has been the complexity of evaluating their likelihood function. Recent works have shown that for many PCMs, the likelihood can be obtained in time proportional to the size of the tree based on post‐order tree traversal, also known as pruning. Despite this progress, inferring complex multi‐trait PCMs on large trees remains a time‐intensive task. Here, we study parallelizing the pruning algorithm as a generic technique for speeding‐up PCM‐inference. We implement several parallel traversal algorithms in the form of a generic C++ library for Serial and Parallel LIneage Traversal of Trees (SPLITT). Based on SPLITT, we provide examples of parallel likelihood evaluation for several popular PCMs, ranging from a single‐trait Brownian motion model to complex multi‐trait Ornstein‐Uhlenbeck and mixed Gaussian phylogenetic models. Using the phylogenetic Ornstein–Uhlenbeck mixed model (POUMM) as a showcase, we run benchmarks on up to 24 CPU cores, reporting up to an order of magnitude parallel speed‐up for the likelihood calculation on simulated balanced and unbalanced trees of up to 100,000 tips with up to 16 traits. Noticing that the parallel speed‐up depends on multiple factors, the SPLITT library is capable to automatically select the fastest traversal strategy for a given hardware, tree‐topology, and data. Combining SPLITT likelihood calculation with adaptive Metropolis sampling on real data, we show that the time for Bayesian POUMM inference on a tree of 10,000 tips can be reduced from several days to less than an hour. We conclude that parallel pruning effectively accelerates the likelihood calculation and, thus, the statistical inference of Gaussian phylogenetic models. For time‐intensive Bayesian inferences, we recommend combining this technique with adaptive Metropolis sampling. Beyond Gaussian models, the parallel tree traversal can be applied to numerous other models, including discrete trait and birth–death population dynamics models. Currently, SPLITT supports multi‐core shared memory architectures, but can be extended to distributed memory architectures as well as graphical processing units.

A High Quartet Distance Construction

Given two binary trees on $N$ labeled leaves, the quartet distance between the trees is the number of disagreeing quartets. By permuting the leaves at random, the expected quartets distance between the two trees is $\frac{2}{3}\binom{N}{4}$. However, no strongly explicit construction reaching this bound asymptotically was known. We consider complete, balanced binary trees on $N=2^n$ leaves, labeled by $n$ long bit sequences. Ordering the leaves in one tree by the prefix order, and in the other tree by the suffix order, we show that the resulting quartet distance is $\left(\frac{2}{3} + o(1)\right)\binom{N}{4}$, and it always exceeds the $\frac{2}{3}\binom{N}{4}$ bound.

A wavelet video coding algorithm with balanced significance probability tree based on energy weighting

This work presents a 3-D wavelet video coding algorithm. By analyzing the contribution of each biorthogonal wavelet basis to reconstructed signal’s energy, we weight each wavelet subband according to its basis energy. Based on distribution of weighted coefficients, we further discuss a 3-D wavelet tree structure named balanced significance probability tree, which places the coefficients with similar probabilities of being significant on the same layer. It is implemented by using hybrid spatial orientation tree and temporal-domain block tree. Subsequently, a novel 3-D wavelet video coding algorithm is proposed based on the energy-weighted balanced significance probability tree. Experimental results illustrate that our algorithm always achieves good reconstruction quality for different classes of video sequences. Compared with asymmetric 3-D orientation tree, the average peak signal-to-noise ratio (PSNR) gain of our algorithm are 1.24dB, 2.54dB and 2.57dB for luminance (Y) and chrominance (U,V) components, respectively. Compared with temporal-spatial orientation tree algorithm, our algorithm gains 0.38dB, 2.92dB and 2.39dB higher PSNR separately for Y, U, and V components. In addition, the proposed algorithm requires lower computation cost than those of the above two algorithms.

Rebalancing board is 'not actively considering' supervision, says chair

Rebalancing board is 'not actively considering' supervision, says chair

Fragmenting Cybersecurity Norms Through the Language(s) of Subalternity: India in “the East” and the Global Community

The global cybersecurity discourse has never proved more fragmented than in the aftermath of the failure of the last United Nations Group of Governmental Experts on Developments in the Field of Information and Telecommunications in the Context of International Security. This discourse stands trapped in long-lasting and seemingly crystallized normative stances between “the West” and “the East,” yet it also calls upon the international community to regulate a wide spectrum of phenomena, ranging from thefts of digitally-stored trade secrets to large-scale pervasive attacks, which may soon reach the threshold of armed attacks. If one situates the major cybersecurity players on a sliding scale between freedom and control over cyber content and infrastructure, the mainstream stance would place the United States, the European Union, the United Kingdom, Brazil, India, China, and Russia in that order. Nonetheless, this scale is in practice more complex, in part due to the influence of the Shanghai Cooperation Organisation, a regional security forum which has witnessed major rebalancing after the membership expansion in June 2017. This paper scrutinizes India’s contribution towards a possible fragmentation of the “Eastern” cybersecurity discourse based on hard laws and state assertiveness, and the consequent disruption of the constructivist East-West binary dialectic about cyberwarfare, cyberterrorism, cyber espionage, and online data protection. By simultaneously negotiating its sub-alterity and rejecting its subalternity, India holds the potential to reshape an otherwise almost-coherent “cyber East.”