Depth Of Hierarchy Research Articles

Modeling of excitation dynamics in large-size molecular systems: Hierarchical equations with compartmentalization.

We describe the new method that can be useful for calculation of the excitation dynamics in large molecular arrays that can be split into compartments with weak exciton coupling between them. In this method, the dynamics within each compartment is evaluated nonperturbatively using hierarchical equations of motion (HEOM), whereas transfers between the exciton states belonging to different compartments are treated by the generalized Förster (gF) theory. In a combined HEOM-gF approach, the number of equations increases linearly when adding new compartments as opposed to pure HEOM, where a depth of hierarchy exhibits strong non-linear grows when scaling the total number of molecules. Comparing the combined HEOM-gF method with an exact HEOM solution enabled us to estimate the parameters corresponding to a validity range of the proposed theory. The possibility of using the method for modeling of energy transfers in photosynthetic antenna supercomplexes is discussed.

Event Graph Guided Compositional Spatial-Temporal Reasoning for Video Question Answering.

Video question answering (VideoQA) is challenging since it requires the model to extract and combine multi-level visual concepts from local objects to global actions from complex events for compositional reasoning. Existing works represent the video with fixed-duration clip features that make the model struggle in capturing the crucial concepts in multiple granularities. To overcome this shortcoming, we propose to represent the video with an Event Graph in a hierarchical structure whose nodes correspond to visual concepts of different levels (object, relation, scene and action) and edges indicate their spatial-temporal relationships. We further propose a H ierarchical S patial- T emporal T ransformer (HSTT) which takes nodes from the graph as visual input to realize compositional reasoning guided by the event graph. To fully exploit the spatial-temporal context delivered from the graph structure, on the one hand, we encode the nodes in the order of their semantic hierarchy (depth) and occurrence time (breadth) with our improved graph search algorithm; On the other hand, we introduce edge-guided attention to combine the spatial-temporal context among nodes according to their edge connections. HSTT then performs QA by cross-modal interactions guaranteed by the hierarchical correspondence between the multi-level event graph and the cross-level question. Experiments on the recent challenging AGQA and STAR datasets show that the proposed method clearly outperforms the existing VideoQA models by a large margin, including those pre-trained with large-scale external data. Our code is available at https://github.com/ByZ0e/HSTT.

Spike-timing dependent plasticity partially compensates for neural delays in a multi-layered network of motion-sensitive neurons.

The ability of the brain to represent the external world in real-time is impacted by the fact that neural processing takes time. Because neural delays accumulate as information progresses through the visual system, representations encoded at each hierarchical level are based upon input that is progressively outdated with respect to the external world. This 'representational lag' is particularly relevant to the task of localizing a moving object-because the object's location changes with time, neural representations of its location potentially lag behind its true location. Converging evidence suggests that the brain has evolved mechanisms that allow it to compensate for its inherent delays by extrapolating the position of moving objects along their trajectory. We have previously shown how spike-timing dependent plasticity (STDP) can achieve motion extrapolation in a two-layer, feedforward network of velocity-tuned neurons, by shifting the receptive fields of second layer neurons in the opposite direction to a moving stimulus. The current study extends this work by implementing two important changes to the network to bring it more into line with biology: we expanded the network to multiple layers to reflect the depth of the visual hierarchy, and we implemented more realistic synaptic time-courses. We investigate the accumulation of STDP-driven receptive field shifts across several layers, observing a velocity-dependent reduction in representational lag. These results highlight the role of STDP, operating purely along the feedforward pathway, as a developmental strategy for delay compensation.

Adaptive geometric multigrid for the mixed finite cell formulation of Stokes and Navier–Stokes equations

AbstractThe unfitted finite element methods have emerged as a popular alternative to classical finite element methods for the solution of partial differential equations and allow modeling arbitrary geometries without the need for a boundary‐conforming mesh. On the other hand, the efficient solution of the resultant system is a challenging task because of the numerical ill‐conditioning that typically entails from the formulation of such methods. We use an adaptive geometric multigrid solver for the solution of the mixed finite cell formulation of saddle‐point problems and investigate its convergence in the context of the Stokes and Navier–Stokes equations. We present two smoothers for the treatment of cutcells in the finite cell method and analyze their effectiveness for the model problems using a numerical benchmark. Results indicate that the presented multigrid method is capable of solving the model problems independently of the problem size and is robust with respect to the depth of the grid hierarchy.

"I see salt everywhere": A qualitative examination of the utility of arts-based participatory workshops to study noncommunicable diseases in Tanzania and Malawi.

The burden of noncommunicable diseases (NCDs) including hypertension, diabetes, and cancer, is rising in Sub-Saharan African countries like Tanzania and Malawi. This increase reflects complex interactions between diverse social, environmental, biological, and political factors. To intervene successfully, new approaches are therefore needed to understand how local knowledges and attitudes towards common NCDs influence health behaviours. This study compares the utility of using a novel arts-based participatory method and more traditional focus groups to generate new understandings of local knowledges, attitudes, and behaviours towards NCDs and their risk factors. Single-gender arts-based participatory workshops and focus group discussions were conducted with local communities in Tanzania and Malawi. Thematic analysis compared workshop and focus group transcripts for depth of content and researcher-participant hierarchies. In addition, semiotic analysis examined the contribution of photographs of workshop activities to understanding participants' experiences and beliefs about NCD risk factors. The arts-based participatory workshops produced in-depth, vivid, emotive narratives of participants' beliefs about NCDs and their impact (e.g., "… it spreads all over your body and kills you-snake's poison is similar to diabetes poison"), while the focus groups provided more basic accounts (e.g., "diabetes is a fast killer"). The workshops also empowered participants to navigate activities with autonomy, revealing their almost overwhelmingly negative beliefs about NCDs. However, enabling participants to direct the focus of workshop activities led to challenges, including the perpetuation of stigma (e.g., comparing smells associated with diabetes symptoms with sewage). Semiotic analysis of workshop photographs provided little additional insight beyond that gained from the transcripts. Arts-based participatory workshops are promising as a novel method to inform development of culturally relevant approaches to NCD prevention in Tanzania and Malawi. Future research should incorporate more structured opportunities for participant reflection during the workshops to minimise harm from any emerging stigma.

Hierarchy Depth in Directed Networks.

In this study, we explore the depth measures for flow hierarchy in directed networks. Two simple measures are defined—rooted depth and relative depth—and their properties are discussed. The method of loop collapse is introduced, allowing investigation of networks containing directed cycles. The behavior of the two depth measures is investigated in Erdös-Rényi random graphs, directed Barabási-Albert networks, and in Gnutella p2p share network. A clear distinction in the behavior between non-hierarchical and hierarchical networks is found, with random graphs featuring unimodal distribution of depths dependent on arc density, while for hierarchical systems the distributions are similar for different network densities. Relative depth shows the same behavior as existing trophic level measure for tree-like networks, but is only statistically correlated for more complex topologies, including acyclic directed graphs.

COVID19 Outbreak: A Hierarchical Framework for User Sentiment Analysis

Social networking sites in the most modernized world are flooded with large data volumes. Extracting the sentiment polarity of important aspects is necessary; as it helps to determine people’s opinions through what they write. The Coronavirus pandemic has invaded the world and been given a mention in the social media on a large scale. In a very short period of time, tweets indicate unpredicted increase of coronavirus. They reflect people’s opinions and thoughts with regard to coronavirus and its impact on society. The research community has been interested in discovering the hidden relationships from short texts such as Twitter and Weiboa; due to their shortness and sparsity. In this paper, a hierarchical twitter sentiment model (HTSM) is proposed to show people’s opinions in short texts. The proposed HTSM has two main features as follows: constructing a hierarchical tree of important aspects from short texts without a predefined hierarchy depth and width, as well as analyzing the extracted opinions to discover the sentiment polarity on those important aspects by applying a valence aware dictionary for sentiment reasoner (VADER) sentiment analysis. The tweets for each extracted important aspect can be categorized as follows: strongly positive, positive, neutral, strongly negative, or negative. The quality of the proposed model is validated by applying it to a popular product and a widespread topic. The results show that the proposed model outperforms the state-of-the-art methods used in analyzing people’s opinions in short text effectively.

Depth-two P systems can simulate Turing machines with NP oracles

Among the computational features that determine the computing power of polarizationless P systems with active membranes, the depth of the membrane hierarchy is one of the least explored. It is known that this model of P systems can solve ▪-complete problems when no constraints are given on the depth of the membrane hierarchy, whereas the complexity class P∥#P is characterized by monodirectional shallow P systems with minimal cooperation, whose depth is 1. No similar result is currently known for polarizationless systems without cooperation or other additional features. In this paper we show that these P systems, using a membrane hierarchy of depth 2, are able to solve at least all decision problems that are in the complexity class ▪, the class of problems solved in polynomial time by deterministic Turing machines that are given the possibility to make a polynomial number of parallel queries to oracles for ▪ problems.

Conditional Deep Gaussian Processes: Empirical Bayes Hyperdata Learning.

It is desirable to combine the expressive power of deep learning with Gaussian Process (GP) in one expressive Bayesian learning model. Deep kernel learning showed success as a deep network used for feature extraction. Then, a GP was used as the function model. Recently, it was suggested that, albeit training with marginal likelihood, the deterministic nature of a feature extractor might lead to overfitting, and replacement with a Bayesian network seemed to cure it. Here, we propose the conditional deep Gaussian process (DGP) in which the intermediate GPs in hierarchical composition are supported by the hyperdata and the exposed GP remains zero mean. Motivated by the inducing points in sparse GP, the hyperdata also play the role of function supports, but are hyperparameters rather than random variables. It follows our previous moment matching approach to approximate the marginal prior for conditional DGP with a GP carrying an effective kernel. Thus, as in empirical Bayes, the hyperdata are learned by optimizing the approximate marginal likelihood which implicitly depends on the hyperdata via the kernel. We show the equivalence with the deep kernel learning in the limit of dense hyperdata in latent space. However, the conditional DGP and the corresponding approximate inference enjoy the benefit of being more Bayesian than deep kernel learning. Preliminary extrapolation results demonstrate expressive power from the depth of hierarchy by exploiting the exact covariance and hyperdata learning, in comparison with GP kernel composition, DGP variational inference and deep kernel learning. We also address the non-Gaussian aspect of our model as well as way of upgrading to a full Bayes inference.

Hierarchical identity-based inner product functional encryption

Inner product functional encryption (IPFE), as a novel cryptographic primitive, can be classified into public-key IPFE (PK-IPFE) and secret-key IPFE (SK-IPFE). PK-IPFE has the following functions: an encryptor generates the ciphertext by encrypting a vector x→, and a decryptor decrypts the ciphertext and obtains the inner product 〈x→,y→〉 if it holds the secret key related to vector y→. The general IPFE cannot describe the hierarchical structure. The superior cannot use the general IPFE to issue the delegating keys for the subordinates, which complicates key management. We first propose a new primitive, hierarchical identity-based PK-IPFE (HID-PK-IPFE). HID-PK-IPFE has the following features. First, the identity of the decryptor can be specified when encrypting. Second, the identity of the recipient has a hierarchical structure. In the hierarchical tree, the upper-level users can generate the private key of the lower-level users; that is, our scheme has a delegation function. We formalize the selective chosen plaintext attack model of HID-PK-IPFE, propose the scheme in the standard model, and prove the security of the scheme on the basis of d-DBDHE assumption. We conducted two sets of simulations. We set the maximum depth of the identity hierarchy to 10, and increased the vector length from 10 to 15. We then listed the time spent by each algorithm in these two ranges. The simulation results show that the running time of each algorithm of our HID-PK-IPFE is completely acceptable in actual application scenarios.

Corporate Hierarchies Under Employee Representation

This paper analyzes whether workplace employee representation (ER) affects the design of firm hierarchies. We rationalize the role of ER within a knowledge-based model of hierarchies, where the firm's choice of hierarchical layers depends on the trade-off between communication and knowledge acquisition costs. Using a sample of more than 20000 private-sector workplaces in 32 countries, we document conditional correlations between ER and the depth of hierarchy that are consistent with our framework. The presence of ER is positively associated with the number of organizational layers, though the relationship is tempered by firm size. Additional instrumental variable estimates reinforce our main result. ER positively correlates with job training and skill development, suggesting that the effect of ER (if any) is to reduce knowledge costs. Moreover, ER is associated with enhanced internal communication via staff meetings. The analysis of managers' perceptions suggests the higher frequency of meetings in firms with ER does not lead to more delays in the implementation of organizational changes. Taken together, our findings suggest that ER increases the depth of firm hierarchy by facilitating the flow of information to top decision makers, possibly through skip-level reporting, and hence reducing communication costs.

Leakage-Resilient Hierarchical Identity-Based Encryption with Recipient Anonymity

As a promising public key cryptographic primitive, hierarchical identity-based encryption (HIBE) introduces key delegation mechanisms into identity-based encryption. However, key leakage and recipient anonymity issues have not been adequately addressed in HIBE. Hence, direct applications of traditional HIBE schemes will violate data security and abuse users’ privacy in practice. In this paper, we propose an anonymous unbounded hierarchical identity-based encryption scheme, which achieves bounded leakage resilience and the hierarchy depth is not limited. Our security proofs based on the dual system encryption technique show that the proposed scheme is capable of resisting key leakage and it realizes recipient anonymity in the standard model. In addition, leakage resilience analysis indicates that our scheme allows the leakage rate of approximate 1/3 no matter the hierarchy depth of identities. Finally, performance comparisons show the practicability of our scheme. In particular, the secret key of our construction is of a fixed-length.

Syntactic Hierarchy Depth: Distribution, Interrelation and Cross-Linguistic Properties

This paper, with data from five languages, focuses on discussing cross-linguistic syntactic hierarchy depth, including its probability distribution, interrelation with position and sentence length, and linguistic and cognitive properties regarding mean depth of syntactic hierarchy. It is found that: (1) syntactic hierarchy depth abides by the negative binomial distribution, (2) syntactic hierarchy depth is a power function of position and sentence length and the co-increase interrelation indicates that all languages have the universality of a depth saving principle but not necessarily based on the right-branching preference, (3) mean depth of syntactic hierarchy is an index of global syntactic complexity and a reflection of the capacity limit of working memory; syntactic hierarchy may be the principal but not the only way to reduce memory load. Mean depth may correlate as well with language typology.

Discriminatively Learned Hierarchical Rank Pooling Networks

Rank pooling is a temporal encoding method that summarizes the dynamics of a video sequence to a single vector which has shown good results in human action recognition in prior work. In this work, we present novel temporal encoding methods for action and activity classification by extending the unsupervised rank pooling temporal encoding method in two ways. First, we present discriminative rank pooling in which the shared weights of our video representation and the parameters of the action classifiers are estimated jointly for a given training dataset of labelled vector sequences using a bilevel optimization formulation of the learning problem. When the frame level features vectors are obtained from a convolutional neural network (CNN), we rank pool the network activations and jointly estimate all parameters of the model, including CNN filters and fully-connected weights, in an end-to-end manner which we coined as end-to-end trainable rank pooled CNN. Importantly, this model can make use of any existing convolutional neural network architecture (e.g., AlexNet or VGG) without modification or introduction of additional parameters. Then, we extend rank pooling to a high capacity video representation, called hierarchical rank pooling. Hierarchical rank pooling consists of a network of rank pooling functions, which encode temporal semantics over arbitrary long video clips based on rich frame level features. By stacking non-linear feature functions and temporal sub-sequence encoders one on top of the other, we build a high capacity encoding network of the dynamic behaviour of the video. The resulting video representation is a fixed-length feature vector describing the entire video clip that can be used as input to standard machine learning classifiers. We demonstrate our approach on the task of action and activity recognition. We present a detailed analysis of our approach against competing methods and explore variants such as hierarchy depth and choice of non-linear feature function. Obtained results are comparable to state-of-the-art methods on three important activity recognition benchmarks with classification performance of 76.7% mAP on Hollywood2, 69.4% on HMDB51, and 93.6% on UCF101.

Non-Perturbative Calculation of Two-Dimensional Spectra Using the Stochastic Hierarchy of Pure States.

Two-dimensional electronic spectroscopy has become an important experimental technique to obtain information on, for example, electronic coherences in large molecular complexes or vibronic couplings. For the correct interpretation of two-dimensional spectra, however, detailed theoretical calculations are required. Reliable theoretical calculations are impeded by large system sizes and large numbers of vibrational degrees of freedom that need to be explicitly taken into account. Here, we demonstrate that a numerical approach based on a stochastic hierarchy of pure states (HOPS) does allow the calculation of two-dimensional spectra, notwithstanding the stochasticity of the method. The number of coupled equations as well as the hierarchy depth shows a superior scaling with system size as compared to the previously developed hierarchical equations of motion (HEOM). Large systems thus become accessible for numerical calculation of two-dimensional spectra.

Emergence of heterogeneity in acute leukemias.

BackgroundLeukemias are malignant proliferative disorders of the blood forming system. Sequencing studies demonstrate that the leukemic cell population consists of multiple clones. The genetic relationship between the different clones, referred to as the clonal hierarchy, shows high interindividual variability. So far, the source of this heterogeneity and its clinical relevance remain unknown. We propose a mathematical model to study the emergence and evolution of clonal heterogeneity in acute leukemias. The model allows linking properties of leukemic clones in terms of self-renewal and proliferation rates to the structure of the clonal hierarchy.ResultsComputer simulations imply that the self-renewal potential of the first emerging leukemic clone has a major impact on the total number of leukemic clones and on the structure of their hierarchy. With increasing depth of the clonal hierarchy the self-renewal of leukemic clones increases, whereas the proliferation rates do not change significantly. The emergence of deep clonal hierarchies is a complex process that is facilitated by a cooperativity of different mutations.ConclusionComparison of patient data and simulation results suggests that the self-renewal of leukemic clones increases with the emergence of clonal heterogeneity. The structure of the clonal hierarchy may serve as a marker for patient prognosis.ReviewersThis article was reviewed by Marek Kimmel, Tommaso Lorenzi and Tomasz Lipniacki.

Differentiating Technique: Constructing Efficient HIBE with Constant Size Ciphertext and Authorized Delegation

As Hierarchical Identity Based Encryption (HIBE) system usually maps the true institutional structure of an organization or entity relationship between objects in real world, It is important that computation & communication complexity of private key, ciphertext, cryptographic computations and so on related to an entity in the hierarchy is independent to the hierarchy depth of the entity. Moreover, key escrow problem that any non-leaf entity in a hierarchical identity based cryptosystem can derive private keys for its descendants with use of its private key should be resolved, in order to prevent any entity from behaving on behalf of its descendants. In this paper, a new technique is introduced for composing a private key for each individual entity in HIBE system by differentiating between non-local identifiers and local identifiers of the identity of the entity. That we call Identifier Discrimination. With the technique, A selective identity secure HIBE system is constructed under Decisional Bilinear Diffie-Hellman (DBDH) assumption without using random oracles, where the private key and the ciphertext consist of constant number of group elements, and decryption requires only three bilinear map computations, regardless of the identity hierarchy depth. Moreover, in contrast to previous HIBE constructions, where private key for an entity can be derived by its ancestors with direct use of their private keys, key escrow problem inherent in identity based cryptosystems is resolved in our HIBE construction. Privilege of deriving private keys for an entity can be delegated to any of its ancestors through authorization by distributing specifically crafted values to the ancestor in our HIBE system, that we call Authorized Delegation.

Unbounded Anonymous Hierarchical Identity-Based Encryption in the Standard Model

In this work, we present a new anonymous HIBE scheme which is unbounded in the sense that the public parameters do not impose additional limitations on the functionality of the systems. In most of previous constructions of anonymous HIBE in the standard model, a maximum hierarchy depth had to be fixed at setup. Our constructions avoid these limitations. We use a nested dual system encryption argument to prove full security for our anonymous HIBE scheme, in the standard model and relying on static assumptions.

A multicompartment mathematical model of cancer stem cell-driven tumor growth dynamics.

Tumors are appreciated to be an intrinsically heterogeneous population of cells with varying proliferation capacities and tumorigenic potentials. As a central tenet of the so-called cancer stem cell hypothesis, most cancer cells have only a limited lifespan, and thus cannot initiate or reinitiate tumors. Longevity and clonogenicity are properties unique to the subpopulation of cancer stem cells. To understand the implications of the population structure suggested by this hypothesis--a hierarchy consisting of cancer stem cells and progeny non-stem cancer cells which experience a reduction in their remaining proliferation capacity per division--we set out to develop a mathematical model for the development of the aggregate population. We show that overall tumor progression rate during the exponential growth phase is identical to the growth rate of the cancer stem cell compartment. Tumors with identical stem cell proportions, however, can have different growth rates, dependent on the proliferation kinetics of all participating cell populations. Analysis of the model revealed that the proliferation potential of non-stem cancer cells is likely to be small to reproduce biologic observations. Furthermore, a single compartment of non-stem cancer cell population may adequately represent population growth dynamics only when the compartment proliferation rate is scaled with the generational hierarchy depth.

Measurement and analysis of P2P IPTV program resource.

With the rapid development of P2P technology, P2P IPTV applications have received more and more attention. And program resource distribution is very important to P2P IPTV applications. In order to collect IPTV program resources, a distributed multi-protocol crawler is proposed. And the crawler has collected more than 13 million pieces of information of IPTV programs from 2009 to 2012. In addition, the distribution of IPTV programs is independent and incompact, resulting in chaos of program names, which obstructs searching and organizing programs. Thus, we focus on characteristic analysis of program resources, including the distributions of length of program names, the entropy of the character types, and hierarchy depth of programs. These analyses reveal the disorderly naming conventions of P2P IPTV programs. The analysis results can help to purify and extract useful information from chaotic names for better retrieval and accelerate automatic sorting of program and establishment of IPTV repository. In order to represent popularity of programs and to predict user behavior and popularity of hot programs over a period, we also put forward an analytical model of hot programs.