Hierarchical Variant Research Articles

Carbon Dioxide Removal from Humid Atmosphere by a Porous Hierarchical Silicoaluminophosphate/Carbon Composite Adsorbent

A chabazite-type silicoaluminophosphate (SAPO-34) was grown within the meso- and macropores of activated carbon (AC) via a confined space synthesis and functionalized via the addition of strontium(II) (i.e., Sr2+-CSAPO-34) for the selective adsorption of CO2 in the presence of humidity. The in situ growth of the SAPO phase was corroborated through SEM/EDAX, XRD, and pore size distribution profiles. About 80% of the meso- and macropores of AC were occupied by the SAPO. Sr2+-CSAPO-34 was further characterized via XRD, TGA, ICP-OES, and water contact angle measurements. A physical mixture of Sr2+-SAPO-34 and AC was also prepared to contrast against the hierarchical variant. The selectivity and capacity for trace CO2 removal were evaluated through single-component equilibrium and multicomponent fixed-bed adsorption. Bed tests (v = 200 mL min–1 and Ci = 500, 1000, or 2500 ppm) showed that the CO2 capacity remains in the presence of 90% relative humidity, with no signs of roll-up. Specifically, the uptake capacity of the Sr2+-CSAPO-34 bed for a CO2 feed content of 1000 ppm was 0.11 mmol per cm3 of bed and with a breakthrough point greater than 2000 bed volumes; this is superior compared to other adsorbents for CO2 capture under humid conditions. The Sr2+-CSAPO-34 composite bed was also subjected to various cycles upon vacuum-assisted thermal regeneration, and no decrease in adsorption capacity was observed. The adsorbent hierarchical design approach showed that a synergistic combination of hydrophobicity and enhanced adsorbate–adsorbent interactions at the physisorption level is a promising strategy for removing trace CO2 under humid conditions.

Dependency parsing with bottom-up Hierarchical Pointer Networks

Dependency parsing is a crucial step towards deep language understanding and, therefore, widely demanded by numerous Natural Language Processing applications. In particular, left-to-right and top-down transition-based algorithms that rely on Pointer Networks are among the most accurate approaches for performing dependency parsing. Additionally, it has been observed for the top-down algorithm that Pointer Networks’ sequential decoding can be improved by implementing a hierarchical variant, more adequate to model dependency structures. Considering all this, we develop a bottom-up oriented Hierarchical Pointer Network for the left-to-right parser and propose two novel transition-based alternatives: an approach that parses a sentence in right-to-left order and a variant that does so from the outside in. We empirically test the proposed neural architecture with the different algorithms on a wide variety of languages, outperforming the original approach in practically all of them and setting new state-of-the-art results on the English and Chinese Penn Treebanks for non-contextualized and BERT-based embeddings.

Can Developmental Trauma disorder be distinguished from posttraumatic stress disorder? A confirmatory factor Analytic Test of Four Structural Models.

Developmental Trauma Disorder (DTD) is a proposed child psychopathology diagnosis with emotion/somatic, attention/behavioral, and self/relational dysregulation symptoms extending beyond posttraumatic stress disorder (PTSD). Confirmatory factor analyses (CFAs) tested four structural models with structured interview data for trauma history, PTSD, and DTD with 507 children receiving mental health or pediatric care ( N = 162, 32% diagnosed with DTD; N = 176; 35% with PTSD; N = 169, 33% with neither). A unidimensional model with a single latent variable had unacceptable fit (RMSEA = 0.094; CFI = 0.844). Compared to a model with PTSD and DTD as correlated first-order latent variables, a multidimensional model with correlated latent variables corresponding to the PTSD and DTD symptom clusters (Dc 2 =105.62, Ddf = 14, p < .001) and a hierarchical variant with correlated second order DTD and PTSD latent variables (Dc 2 =48.10, Ddf = 6, p < .001) fit the data better. The non-hierarchical multidimensional model was superior to the hierarchical variant (Dc 2 =66.05, Ddf = 8, p < .001). Stronger latent variable inter-correlations within PTSD and DTD domains than across domains, suggested that DTD and PTSD are distinguishable despite their inter-correlation. Exposure to family violence was the primary correlate of both the DTD and PTSD second-order latent variables. Results indicate that children's trauma-related symptoms involve six inter-correlated domains extend beyond PTSD's symptoms (i.e., re-experiencing, avoidance, arousal) to include DTD symptoms of emotional, cognitive-behavioral, and self-relational dysregulation. The inter-relationship of the DTD and PTSD latent variables suggest that DTD may constitute a component within a complex PTSD diagnosis paralleling the new adult CPTSD diagnosis.

Random Neural Networks with Hierarchical Committees for Improved Routing in Wireless Mesh Networks with Interference

We propose a hierarchical (nested) variant of a recurrent random neural network (RNN) with reinforced learning, introduced by Gelenbe. Each neuron (committee) in a top-level RNN represents a different bottom-level RNN (or sub-committee). The bottom-level RNNs choose the best routing and the top-level RNN chooses the currently best bottom-level RNN. Each of the bottom RNNs is trained in a different way. When they differ in their choice of the best path, several cognitive packets are routed according to the different decisions. In that case, a respective ACK packet trains individual bottom RNNs and not all bottom RNNs at once. An example presents an optimisation of a real-time routing in a dense mesh network of wireless sensors relaying small metering messages between each other, until the messages reach a common gateway. The network is experiencing a periodic electromagnetic interference. The hierarchical variant causes a small increase in the number of smart packets but allows a considerably better routing quality.

Hierarchical Hybrid Logic

We introduce HHL, a hierarchical variant of hybrid logic. We study first order correspondence results and prove a Hennessy-Milner like theorem relating (hierarchical) bisimulation and modal equivalence for HHL. Combining hierarchical transition structures with the ability to refer to specific states at different levels, this logic seems suitable to express and verify properties of hierarchical transition systems, a pervasive semantic structure in Computer Science.

Hierarchical organization in visual working memory: From global ensemble to individual object structure

When remembering a natural scene, both detailed information about specific objects and summary representations such as the gist of a scene are encoded. However, formal models of change detection that are used to estimate working memory capacity, typically assume observers simply encode and maintain memory representations that are treated independently from one another without considering the (hierarchical) object or scene structure. To overcome this limitation, we present a hierarchical variant of the change detection task that attempts to formalize the role of object structure, thus, allowing for richer, more graded memory representations. We demonstrate that detection of a global-object change precedes local-object changes of hierarchical shapes to a large extent. Moreover, when systematically varying object repetitions between individual items at a global or a local level, memory performance declines mainly for repeated global objects, but not for repeated local objects, which suggests that ensemble (i.e., summary) representations are likewise biased toward a global level. In addition, this global memory precedence effect is shown to be independent from encoding durations, and mostly cannot be attributed to differences in saliency or shape discriminability at global/local object levels. This pattern of results is suggestive of a global/local difference occurring primarily during memory maintenance. Altogether, these findings challenge visual-working-memory (vWM) models that propose that a fixed number of objects can be remembered regardless of the individual object structure. Instead, our results support a hierarchical model that emphasizes the role for structured representations among objects in vWM.

A Scalable User Fairness Model for Adaptive Video Streaming Over SDN-Assisted Future Networks

The growing demand for online distribution of high quality and high throughput content is dominating today's Internet infrastructure. This includes both production and user-generated media. Among the myriad of media distribution mechanisms, HTTP adaptive streaming (HAS) is becoming a popular choice for multi-screen and multi-bitrate media services over heterogeneous networks. HAS applications often compete for network resources without any coordination between each other. This leads to quality of experience (QoE) fluctuations on delivered content, and unfairness between end users, while new network protocols, technologies, and architectures, such as software defined networking (SDN), are being developed for the future Internet. The programmability, flexibility, and openness of these emerging developments can greatly assist the distribution of video over the Internet. This is driven by the increasing consumer demands and QoE requirements. This paper introduces a novel user-level fairness model UFair and its hierarchical variant UFair <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HA</sup> , which orchestrate HAS media streams using emerging network architectures and incorporate three fairness metrics (video quality, switching impact, and cost efficiency) to achieve user-level fairness in video distribution. UFair <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HA</sup> has also been implemented in a purpose-built SDN testbed using open technologies, including OpenFlow. Experimental results demonstrate the performance and feasibility of our design for video distribution over future networks.

Semi-Supervised Nonlinear Distance Metric Learning via Forests of Max-Margin Cluster Hierarchies

Metric learning is a key problem for many data mining and machine learning applications, and has long been dominated by Mahalanobis methods. Recent advances in nonlinear metric learning have demonstrated the potential power of non-Mahalanobis distance functions, particularly tree-based functions. We propose a novel nonlinear metric learning method that uses an iterative, hierarchical variant of semi-supervised max-margin clustering to construct a forest of cluster hierarchies, where each individual hierarchy can be interpreted as a weak metric over the data. By introducing randomness during hierarchy training and combining the output of many of the resulting semi-random weak hierarchy metrics, we can obtain a powerful and robust nonlinear metric model. This method has two primary contributions: first, it is semi-supervised, incorporating information from both constrained and unconstrained points. Second, we take a relaxed approach to constraint satisfaction, allowing the method to satisfy different subsets of the constraints at different levels of the hierarchy rather than attempting to simultaneously satisfy all of them. This leads to a more robust learning algorithm. We compare our method to a number of state-of-the-art benchmarks on $k$ -nearest neighbor classification, large-scale image retrieval and semi-supervised clustering problems, and find that our algorithm yields results comparable or superior to the state-of-the-art.

Hierarchical Parallel Genetic Optimization Fuzzy ARTMAP Ensemble

In this paper, a framework for designing optimum pattern classifiers is proposed. The fuzzy ARTMAP (FAM) is first used as a base classifier. Multiple FAM classifiers form an ensemble to improve classification accuracy. Multi-objective genetic algorithms (GAs) are then used to search for the best combinations of variables, for the FAM classifiers. Based on the population of potential solutions, another GA selects the best combination of FAM classifiers to create an ensemble. Individual decisions are combined using a probabilistic voting scheme. To increase the inter-classifier diversity, a hierarchical parallel GA variant and a negative correlation method is employed during the genetic optimization phase for the ensemble evaluation. The proposed framework is evaluated using benchmark and real-world data sets, and the results compared with literature. Results positively indicate the proposed framework is effective in undertaking data classification tasks.

High performance locks for multi-level NUMA systems

Efficient locking mechanisms are critically important for high performance computers. On highly-threaded systems with a deep memory hierarchy, the throughput of traditional queueing locks, e.g., MCS locks, falls off due to NUMA effects. Two-level cohort locks perform better on NUMA systems, but fail to deliver top performance for deep NUMA hierarchies. In this paper, we describe a hierarchical variant of the MCS lock that adapts the principles of cohort locking for architectures with deep NUMA hierarchies. We describe analytical models for throughput and fairness of Cohort-MCS (C-MCS) and Hierarchical MCS (HMCS) locks that enable us to tailor these locks for high performance on any target platform without empirical tuning. Using these models, one can select parameters such that an HMCS lock will deliver better fairness than a C-MCS lock for a given throughput, or deliver better throughput for a given fairness. Our experiments show that, under high contention, a three-level HMCS lock delivers up to 7.6x higher lock throughput than a C-MCS lock on a 128-thread IBM Power 755 and a five-level HMCS lock delivers up to 72x higher lock throughput on a 4096-thread SGI UV 1000. On the K-means clustering code from the MineBench suit, a three-level HMCS lock reduces the running time by up to 55% compared to the C-MCS lock on a IBM Power 755.

Bayesian Hierarchical Models for aerospace gas turbine engine prognostics

Improved prognostics is an emerging requirement for modern health monitoring that aims to increase the fidelity of failure-time predictions by the appropriate use of sensory and reliability information. In the aerospace industry, it is a key technology to maximise aircraft availability, offering a route to increase time in-service and to reduce operational disruption through improved asset management.An aircraft engine is a complex system comprising multiple subsystems that have dependent interactions so it is difficult to construct a model of its degradation dynamics based on physical principles. This complexity suggests that a statistically robust methodology for handling large quantities of real-time data would be more appropriate. In this work, therefore, a Bayesian approach is taken to exploit fleet-wide data from multiple assets to perform probabilistic estimation of remaining useful life for civil aerospace gas turbine engines.The paper establishes a Bayesian Hierarchical Model to perform inference and inform a probabilistic model of remaining useful life. Its performance is compared with that of an existing Bayesian non-Hierarchical Model and is found to be superior in typical (heterogeneous) scenarios. The techniques use Bayesian methods to combine two sources of information: historical in-service data across the engine fleet and once per-flight transmitted performance measurement from the engine(s) under prognosis. The proposed technique provides predictive results within well defined uncertainty bounds and demonstrates several advantages of the hierarchical variant’s ability to integrate multiple unit data to address realistic prognostic challenges. This is illustrated by an example from a civil aerospace gas turbine fleet data.

Método de elementos de borde jerárquico basado en el árbol de Barnes-Hut aplicado a flujo reptante exterior

In this work, a hierarchical variant of a boundary element method and its use in Stokes flow around three-dimensional rigid bodies in steady regime is presented. The proposal is based on the descending hierarchical low-order and self-adaptive algorithm of Barnes-Hut, and it is used in conjunction with an indirect boundary integral formulation of second class, whose source term is a function of the undisturbed velocity. The solution field is the double layer surface density, which is modified in order to complete the eigenvalue spectrum of the integral operator. In this way, the rigid modes are eliminated and both a non-zero force and a non-null torque on the body could be calculated. The elements are low order flat triangles, and an iterative solution by generalized minimal residual (GMRES) is used. Numerical examples include cases with analytical solutions, bodies with edges and vertices, or with intricate shapes. The main advantage of the presented technique is the possibility of considering a greater number of degrees of freedom regarding traditional collocation methods, due to the decreased demand of main memory and the reduction in the computation times.

Similarity Measures for Enhancing Interactive Streamline Seeding

Streamline seeding rakes are widely used in vector field visualization. We present new approaches for calculating similarity between integral curves (streamlines and pathlines). While others have used similarity distance measures, the computational expense involved with existing techniques is relatively high due to the vast number of euclidean distance tests, restricting interactivity and their use for streamline seeding rakes. We introduce the novel idea of computing streamline signatures based on a set of curve-based attributes. A signature produces a compact representation for describing a streamline. Similarity comparisons are performed by using a popular statistical measure on the derived signatures. We demonstrate that this novel scheme, including a hierarchical variant, produces good clustering results and is computed over two orders of magnitude faster than previous methods. Similarity-based clustering enables filtering of the streamlines to provide a nonuniform seeding distribution along the seeding object. We show that this method preserves the overall flow behavior while using only a small subset of the original streamline set. We apply focus + context rendering using the clusters which allows for faster and easier analysis in cases of high visual complexity and occlusion. The method provides a high level of interactivity and allows the user to easily fine tune the clustering results at runtime while avoiding any time-consuming recomputation. Our method maintains interactive rates even when hundreds of streamlines are used.

K13CoSn17–x (x = 0.1): A New Ternary Phase Containing ­Cobalt Centered [Sn9] Cluster Synthesized via High‐Temperature Reaction

AbstractA new ternary potassium cobalt stannide, K13CoSn17–x (x = 0.1), was obtained by reacting the mixture of the corresponding pure elements at high temperature, and structurally characterized by single‐crystal X‐ray diffraction study. K13CoSn17–x (x = 0.1) crystallizes in the orthorhombic space group Pbca (No. 61) with a = 26.2799(7) Å, b = 24.1541(6) Å, c = 29.8839(6) Å, V = 18969.3(8) Å3, and Z = 16. Its structure contains isolated [CoSn9] monocapped square antiprism and [Sn4] tetrahedron in the ratio 1:2, forming a hierarchical variant of Laves phase MgZn2. The structural relation between the title compound with MgZn2 as well as other binary stannides is also discussed.

Copper Benzene Tricarboxylate Metal–Organic Framework with Wide Permanent Mesopores Stabilized by Keggin Polyoxometallate Ions

Porous solids with organized multiple porosity are of scientific and technological importance for broadening the application range from traditional areas of catalysis and adsorption/separation to drug release and biomedical imaging. Synthesis of crystalline porous materials offering a network of uniform micro- and mesopores remains a major scientific challenge. One strategy is based on variation of synthesis parameters of microporous networks, such as, for example, zeolites or metal-organic frameworks (MOFs). Here, we show the rational development of an hierarchical variant of the microporous cubic Cu(3)(BTC)(2) (BTC = 1,3,5-benzenetricarboxylate) HKUST-1 MOF having strictly repetitive 5 nm wide mesopores separated by uniform microporous walls in a single crystal structure. This new material coined COK-15 (COK = Centrum voor Oppervlaktechemie en Katalyse) was synthesized via a dual-templating approach. Stability was enhanced by Keggin type phosphotungstate (HPW) systematically occluded in the cavities constituting the walls between the mesopores.

Towards automated debugging in software evolution: Evaluating delta debugging on real regression bugs from the developers’ perspectives

Delta debugging has been proposed to isolate failure-inducing changes when regressions occur. In this work, we focus on evaluating delta debugging in practical settings from developers’ perspectives. A collection of real regressions taken from medium-sized open source programs is used in our evaluation. Towards automated debugging in software evolution, a tool based on delta debugging is created and both the limitations and costs are discussed.We have evaluated two variants of delta debugging. Different from successful isolation in Zeller's initial studies, the results in our experiments vary wildly. Two thirds of isolated changes in studied programs provide direct or indirect clues in locating regression bugs. The remaining results are superfluous changes or even wrong isolations. In the case of wrong isolations, the isolated changes cause the same behaviour of the regression but are failure-irrelevant. Moreover, the hierarchical variant does not yield definite improvements in terms of the efficiency and accuracy.

Hierarchical Image‐Space Radiosity for Interactive Global Illumination

AbstractWe introduce image‐space radiosity and a hierarchical variant as a method for interactively approximating diffuse indirect illumination in fully dynamic scenes. As oft observed, diffuse indirect illumination contains mainly low‐frequency details that do not require independent computations at every pixel. Prior work leverages this to reduce computation costs by clustering and caching samples in world or object space. This often involves scene preprocessing, complex data structures for caching, or wasted computations outside the view frustum. We instead propose clustering computations in image space, allowing the use of cheap hardware mipmapping and implicit quadtrees to allow coarser illumination computations. We build on a recently introduced multiresolution splatting technique combined with an image‐space lightcut algorithm to intelligently choose virtual point lights for an interactive, one‐bounce instant radiosity solution. Intelligently selecting point lights from our reflective shadow map enables temporally coherent illumination similar to results using more than 4096 regularly‐sampled VPLs.

Boosting multi-label hierarchical text categorization

Hierarchical Text Categorization (HTC) is the task of generating (usually by means of supervised learning algorithms) text classifiers that operate on hierarchically structured classification schemes. Notwithstanding the fact that most large-sized classification schemes for text have a hierarchical structure, so far the attention of text classification researchers has mostly focused on algorithms for “flat” classification, i.e. algorithms that operate on non-hierarchical classification schemes. These algorithms, once applied to a hierarchical classification problem, are not capable of taking advantage of the information inherent in the class hierarchy, and may thus be suboptimal, in terms of efficiency and/or effectiveness. In this paper we propose TreeBoost.MH, a multi-label HTC algorithm consisting of a hierarchical variant of AdaBoost.MH, a very well-known member of the family of “boosting” learning algorithms. TreeBoost.MH embodies several intuitions that had arisen before within HTC: e.g. the intuitions that both feature selection and the selection of negative training examples should be performed “locally”, i.e. by paying attention to the topology of the classification scheme. It also embodies the novel intuition that the weight distribution that boosting algorithms update at every boosting round should likewise be updated “locally”. All these intuitions are embodied within TreeBoost.MH in an elegant and simple way, i.e. by defining TreeBoost.MH as a recursive algorithm that uses AdaBoost.MH as its base step, and that recurs over the tree structure. We present the results of experimenting TreeBoost.MH on three HTC benchmarks, and discuss analytically its computational cost.

Structure and Bonding of the Hexameric Platinum(II) Dichloride, Pt6Cl12 (β‐PtCl2)

AbstractThe crystal structure of Pt6Cl12 (β‐PtCl2) was redetermined ($R \bar 3 m$ ah = 13.126Å, ch = 8.666Å, Z = 3; arh = 8.110Å, α = 108.04°; 367 hkl, R = 0.032). As has been shown earlier, the structure is in principle a hierarchical variant of the cubic structure type of tungsten (bcc), which atoms are replaced by the hexameric Pt6Cl12 molecules. Due to the 60° rotation of the cuboctahedral clusters about one of the trigonal axes, the symmetry is reduced from $Im \bar 3 m$ to $R \bar 3 m$ ($I \bar 3 m$). The molecule Pt6Cl12 shows the (trigonally elongated) structure of the classic M6X12 cluster compounds with (distorted) square‐planar PtCl4 fragments, however without metal‐metal bonds. The Pt atoms are shifted outside the Cl12 cuboctahedron by Δ = +0.046Å ($\bar {\rm d}$(Pt—Cl) = 2.315Å; $\bar {\rm d}$(Pt—Pt) = 3.339Å). The scalar relativistic DFT calculations results in the full $m \bar 3 m$ symmetry for the optimized structure of the isolated molecule with d(Pt—Cl) = 2.381Å, d(Pt—Pt) = 3.468Å and Δ = +0.072Å. The electron distribution of the Pt‐Pt antibonding HOMO exhibits an outwards‐directed asymmetry perpendicular to the PtCl4 fragments, that plays the decisive role for the cluster packing in the crystal. A comparative study of the Electron Localization Function with the hypothetical trans‐(Nb2Zr4)Cl12 molecule shows the distinct differences between Pt6Cl12 and clusters with metal‐metal bonding. Due to the characteristic electronic structure, the crystal structure of Pt6Cl12 in space group $R \bar 3 m$ is an optimal one, which results from comparison with rhombohedral Zr6I12 and a cubic bcc arrangement.

Suitability of Molecular Descriptors for Database Mining. A Comparative Analysis

Database mining methods rely on the molecular descriptors used to characterize a structural database. In the present investigation, five different types of descriptors (log P, UNITY fingerprints, ISIS keys, VolSurf, and GRIND) are applied to characterize various databases (n = 1007, 100, and 229) comprising drugs almost exclusively. The validity of the descriptors is comparatively analyzed via principal component analysis and its hierarchical variant, consensus principal component analysis. Both pharmacodynamic and pharmacokinetic aspects of database mining are treated. For pharmacodynamic aspects, clustering behavior achieved with the different descriptors is tested on the chemically homogeneous beta-blockers, benzodiazepines, and penicillins and on the chemically more diverse class I antiarrhythmics. The following ranking is observed: UNITY fingerprints > ISIS keys and GRIND > VolSurf > log P. Regarding information content, the CPCA superweight plot indicates similarity between fingerprints and ISIS keys as well as between VolSurf and log P, while GRIND differs from all the remaining descriptors. Solubility data and blood/brain barrier penetrating behavior serve as test cases for pharmacokinetic aspects. Comparison of the descriptors applied to these data reveals that VolSurf has the most realistic and consistent behavior, GRIND shows intermediate behavior, while UNITY fingerprints and ISIS keys are not well suited for pharmacokinetic profiling. From this comparative analysis, we conclude that VolSurf descriptors exhibit particular advantages in treating pharmacokinetic aspects; UNITY fingerprints, ISIS keys, and GRIND descriptors are of special value for tackling pharmacodynamic aspects of database mining. The parameter log P is of limited applicability in database mining because of rather poor reliability and lack of completeness of data.