Functional Embedding Research Articles

Regional Variationsofand Regional VariationswithinFunctional Differentiation – The Middle East and World Society

AbstractThis article highlights the territorial and functional embedding of world regions within a functionally differentiated world society, as well as the heterogeneity between different (local) practices of functional differentiation within world regions. Its argument proceeds in two steps. In a first step, it discusses the distinction between regional variationsoffunctional differentiation versus regional variationswithinfunctional differentiation as an important tool in order to characterize specific variations of structural patterns. In a second step it turns to the Middle Eastern case, arguing that while at first glance this may be a candidate for a regional variation of functional differentiation, a closer look reveals that it has been characterized by a very specific variationwithinfunctional differentiation for quite a while. The article concludes by using these observations for some thoughts on the functional differentiation of world society more generally.

Projector-Free Capped-Fragment Scheme within Density Functional Embedding Theory for Covalent and Ionic Compounds.

Quantum-mechanics-(QM)-based simulations now routinely aid in understanding and even discovering new chemistries involving molecules and materials exhibiting desired functionalities. Ab initio correlated wavefunction (CW) theories systematically improve QM methods, with many exhibiting high accuracy. However, execution of CW methods requires expensive computations that typically scale poorly with system size. Divide-and-conquer approaches partition large systems into smaller fragments; a lower level of theory treats fragment interactions while a preferred higher level of theory describes the important fragment. These methods offer ways to incorporate CWs into chemical simulations of large systems, e.g., biomolecules, surfaces, large inorganic clusters, bulk crystals, etc. Here we propose a partitioning protocol that utilizes capping atoms to saturate severed covalent bonds at fragment interfaces and density functional embedding theory (DFET) to describe fragment interactions. The capping groups in each fragment provide an ad-hoc potential that approximates the effects of the environment. An embedding potential optimized via DFET then serves as an augmentation of the capping group to simulate the effects of the environment. We concurrently use an auxiliary fragment (a separate system comprised of only the combined capping groups) to account for, and thereby correct, the electron density contributions of all the capping groups added to all of the fragments. This method depends only on the capped-subsystem and auxiliary-fragment electron densities, forgoing, as with the original DFET developed for metallic systems, orbital-based projector approaches that determine a nonlocal action of the embedding potential onto the fragment electrons. By using an auxiliary fragment, the method maintains a purely electron-density-dependent embedding potential, substantially lessening the cost and leading to simpler implementation. Here, we demonstrate the utility of our capped-DFET and ensuing capped embedded CW method in two contrasting systems, namely, an organic molecule and an ionic metal oxide cluster.

Metal-to-Ligand Charge-Transfer Spectrum of a Ru-Bipyridine-Sensitized TiO2 Cluster from Embedded Multiconfigurational Excited-State Theory.

Understanding optical properties of the dye molecule in dye-sensitized solar cells (DSSCs) from first-principles quantum mechanics can contribute to improving the efficiency of such devices. While density functional theory (DFT) and time-dependent DFT have been pivotal in simulating optoelectronic properties of photoanodes used in DSSCs at the atomic scale, questions remain regarding DFT's adequacy and accuracy to furnish critical information needed to understand the various excited-state processes involved. Here, we simulate the absorption spectra of a dye-sensitized solar cell analogue, comprised of a Ru-bipyridine (Ru-bpy) dye molecule and a small TiO2 cluster via DFT and via an accurate embedded correlated wavefunction (CW) theory. We generated CW spectra for the adsorbed Ru-bpy dye via a recently introduced capped density functional embedding theory or capped-DFET (to generate the embedding potential that accounts for the interaction of the molecule and the TiO2 cluster). We then combined capped-DFET with the accurate but expensive multiconfigurational complete active space second-order perturbation theory (CASPT2)-embedded CASPT2. Because the CW theory is conducted on only a portion of the total system in the presence of an embedding potential that describes that portion's interaction with its environment, we efficiently obtain CW-quality predictions that reflect local properties of the entire system. Specifically, for example, with capped-DFET and embedded CW theory, we can simulate accurately a plethora of metal-to-ligand charge-transfer excited properties at a manageable computational cost. Here, we predict detailed electronic spectra within the visible region, featuring the lowest three singlet and triplet excited states, along with predictions of the singlets' lifetimes. We illustrated these results using a Jablonski diagram that show the relative energy position of the singlet and longer-lived triplet excited states and analyzed and proposed relaxation paths for the excited state corresponding to the most intense but short-lived absorption (interconversion, intersystem crossing, fluorescence, and phosphorescence) that may lead to longer-lived excited states necessary for efficient charge separation required to generate current in solar cells.

Research on Functional Interdependence Patterns of “Village-Urban” Spatial Structure of Shenzhen: A Living and Activity Space Perspective

The urban village is a special product of the rapid urbanization process in China. To accurately understand its functional embedding mode in the urban organism is an important decision-making prerequisite for optimizing the governance effectiveness of urban renewal. Taking residents’ living and activity space as the breakthrough point, this paper analyzes the spatial functional patterns of villages in cities by measuring the “village-urban” interdependence relationship based on cellphone big data. It is founded that urban villages in Shenzhen are closely related to other parts of the city, and are not self-isolated communities. At the same time, due to the extensive construction and management mode, the function homogenization of urban village space is obvious, and its location is the main determinant of its participating way in urban activities. The research holds that from the perspective of providing citizens with low-cost opportunity to participate in urban public life and ensuring the resilient development capacity, it is necessary to coordinate and reserve a certain proportion of urban village residential land within the sub-community of residents’ living and activity space to promote the benign and healthy development of urban space organisms.

Formulation and Implementation of Density Functional Embedding Theory Using Products of Basis Functions.

The representation of embedding potential using products of atomic orbital basis functions has been developed in the context of density functional embedding theory. The formalism allows to treat pseudopotential and all-electron calculations on the same footing and enables simple transfer of the embedding potential in a compact matrix form. In addition, a cost-reduction procedure for the basis set and potential reduction based on population analysis has been proposed. Implemented for the condensed-phase and molecular systems within Gaussian and plane-waves and Gaussian and augmented plane-waves formalisms, the scheme has been tested for proton-transfer reactions in the cluster and the condensed phase and projected density of states of carbon monoxide adsorbed on platinum surface. With the computational scaling of the embedding potential optimization similar to that of hybrid density functional theory with a significantly reduced prefactor, the method allows for large-scale applications to extended systems.

Codee: A Tensor Embedding Scheme for Binary Code Search

Given a target binary function, the binary code search retrieves top-K similar functions in the repository, and similar functions represent that they are compiled from the same source codes. Searching binary code is particularly challenging due to large variations of compiler tool-chains and options and CPU architectures, as well as thousands of binary codes. Furthermore, there are some pivotal issues in current binary code search schemes, including inaccurate text-based or token-based analysis, slow graph matching, or complex deep learning processes. In this paper, we present an unsupervised tensor embedding scheme, Codee, to carry out code search efficiently and accurately at the binary function level. First, we use an NLP-based neural network to generate the semantic-aware token embedding. Second, we propose an efficient basic block embedding generation algorithm based on the network representation learning model. We learn both the semantic information of instructions and the control flow structural information to generate the basic block embedding. Then we use all basic block embeddings in a function to obtain a variable-length function feature vector. Third, we build a tensor to generate function embeddings based on the tensor singular value decomposition, which compresses the variable-length vectors into short fixed-length vectors to facilitate efficient search afterward. We further propose a dynamic tensor compression algorithm to incrementally update the function embedding database. Finally, we use the local sensitive hash method to find the top- <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> similar matching functions in the repository. Compared with state-of-the-art cross-optimization-level code search schemes, such as Asm2Vec and DeepBinDiff, our scheme achieves higher average search accuracy, shorter feature vectors, and faster feature generation performance using four datasets, OpenSSL, Coreutils, libgmp and libcurl. Compared with other cross-platform and cross-optimization-level code search schemes, such as Gemini, Safe, the average recall of our method also outperforms others.

Predicting gene phenotype by multi-label multi-class model based on essential functional features.

Phenotype is one of the most significant concepts in genetics, which is used to describe all the characteristics of a research object that can be observed. Considering that phenotype reflects the integrated features of genotype and environment factors, it is hard to define phenotype characteristics, even difficult to predict unknown phenotypes. Restricted by current biological techniques, it is still quite expensive and time-consuming to obtain sufficient structural information of large-scale phenotype-associated genes/proteins. Various bioinformatics methods have been presented to solve such problem, and researchers have confirmed the efficacy and prediction accuracy of functional network-based prediction. But general functional descriptions have highly complicated inner structures for phenotype prediction. To further address this issue and improve the efficacy of phenotype prediction on more than ten kinds of phenotypes, we first extract functional enrichment features from GO and KEGG, and then use node2vec to learn functional embedding features of genes from a gene-gene network. All these features are analyzed by some feature selection methods (Boruta, minimum redundancy maximum relevance) to generate a feature list. Such list is fed into the incremental feature selection, incorporating some multi-label classifiers built by RAkEL and some classic base classifiers, to build an optimum multi-label multi-class classification model for phenotype prediction. According to recent researches, our method has indeed identified many literature-supported genes/proteins and their associated phenotypes, and even some candidate genes with re-assigned new phenotypes, which provide a new computational tool for the accurate and effective phenotypic prediction.

Local Flexibility for Open Partial Differential Relations

AbstractWe show that local deformations, near closed subsets, of solutions to open partial differential relations can be extended to global deformations, provided all but the highest derivatives stay constant along the subset. The applicability of this general result is illustrated by a number of examples, dealing with convex embeddings of hypersurfaces, differential forms, and lapse functions in Lorentzian geometry.The main application is a general approximation result by sections that have very restrictive local properties on open dense subsets. This shows, for instance, that given any K ∈ ℝ every manifold of dimension at least 2 carries a complete C1, 1‐metric which, on a dense open subset, is smooth with constant sectional curvature K. Of course, this is impossible for C2‐metrics in general. © 2021 The Authors. Communications on Pure and Applied Mathematics published by Wiley Periodicals LLC.

Topographic gradients of intrinsic dynamics across neocortex.

The intrinsic dynamics of neuronal populations are shaped by both microscale attributes and macroscale connectome architecture. Here we comprehensively characterize the rich temporal patterns of neural activity throughout the human brain. Applying massive temporal feature extraction to regional haemodynamic activity, we systematically estimate over 6000 statistical properties of individual brain regions' time-series across the neocortex. We identify two robust spatial gradients of intrinsic dynamics, one spanning a ventromedial-dorsolateral axis and dominated by measures of signal autocorrelation, and the other spanning a unimodal-transmodal axis and dominated by measures of dynamic range. These gradients reflect spatial patterns of gene expression, intracortical myelin and cortical thickness, as well as structural and functional network embedding. Importantly, these gradients are correlated with patterns of meta-analytic functional activation, differentiating cognitive versus affective processing and sensory versus higher-order cognitive processing. Altogether, these findings demonstrate a link between microscale and macroscale architecture, intrinsic dynamics, and cognition.

Vulnerability Rating of Source Code with Token Embedding and Combinatorial Algorithms

We present an empirical analysis of the source code of the Fluoride Bluetooth module, which is a part of standard Android OS distribution, by exhibiting a novel approach for classifying and scoring source code and vulnerability rating. Our workflow combines deep learning, combinatorial optimization, heuristics and machine learning. A combination of heuristics and deep learning is used to embed function (and method) labels into a low-dimensional Euclidean space. Because the corpus of the Fluoride source code is rather limited (containing approximately 12,000 functions), a straightforward embedding (using, e.g. code2vec) is untenable. To overcome the challenge of dearth of data, it is necessary to go through an intermediate step of Byte-Pair Encoding. Subsequently, we embed the tokens from which we assemble an embedding of function/method labels. Long short-term memory network (LSTM) is used to embed tokens. The next step is to form a distance matrix consisting of the cosines between every pairs of vectors (function embedding) which in turn is interpreted as a (combinatorial) graph whose vertices represent functions, and edges correspond to entries whose value exceed some given threshold. Cluster-Editing is then applied to partition the vertex set of the graph into subsets representing “dense graphs,” that are nearly complete subgraphs. Finally, the vectors representing the components, plus additional heuristic-based features are used as features to model the components for vulnerability risk.

Network Service Chaining and Embedding With Provable Bounds

Network function virtualization (NFV) is introduced to effectively deliver end-to-end network services for the emerging Internet of Things (IoT), multiaccess edge computing, and 5G communication techniques. In NFV, the network service request can be accommodated in the form of a service function chain (SFC). The SFC will have to reserve abundant resources, such as link bandwidth, service functions, and computation in the physical network to meet the demands of customers. Minimizing the cost from the resource reservation in NFV remains challenging, even though a few works in the literature proposed cost-optimization methodologies with assumptions to guarantee their correctness. In this article, we comprehensively investigate how to minimize the cost when delivering network services as SFCs with provable bounds and fewer assumptions. We formally define the problem of minimum cost service function chaining and embedding (MC-SFCE) and propose an algorithm, namely, cost factor-based SFCE optimization with shortcut (COFO-SC), for MC-SFCE. Novel mathematical analysis is provided to demonstrate the correctness of our approaches and related bounds. Our extensive simulations and analysis also show that the proposed COFO-SC outperforms the schemes directly extended from the existing work.

Learning features from enhanced function call graphs for Android malware detection

Analyzing the runtime behaviors of Android apps is crucial for malware detection. In this paper, we attempt to learn the behavior level features of an app from function calls. The challenges of this task are twofold. First, the absence of function attributes hinders the understanding of app behaviors. Second, the graphical representation of function calls cannot be directly processed by classical machine learning algorithms. In this paper, we develop two methods to overcome these challenges. Based on function embedding, we first propose the concept of enhanced function call graphs (E-FCGs) to characterize app runtime behaviors. We then develop a Graph Convolutional Network (GCN) based algorithm to obtain vector representations of E-FCGs. Extensive experiments show that the features learned by our method can achieve surprisingly high detection performance on a variety of classifiers (e.g., LR, DT, SVM, KNN, RF, MLP and CNN), significantly outperforming the traditional static features.

Benchmarking an Embedded Adaptive Sampling Configuration Interaction Method for Surface Reactions: H2 Desorption from and CH4 Dissociation on Cu(111).

Embedded (emb-) correlated wavefunction (CW) theory enables accurate assessments of both ground- and excited-state reaction mechanisms involved in heterogeneous catalysis. Embedded multireference second-order perturbation theory (emb-MRPT2) based on reference wavefunctions generated via embedded complete active space self-consistent field (emb-CASSCF) theory is currently state-of-the-art. However, the factorial scaling of CASSCF limits the size of active space and the complexity of systems that can be studied. Here, we assess the efficacy of an alternative CW method, adaptive sampling configuration interaction (ASCI)-which enables large active spaces to be used-for studying surface reactions. We couple ASCI with density functional embedding theory (DFET) and benchmark its performance for two reactions: H2 desorption from and CH4 dissociation on the Cu(111) surface. Unlike embedded complete active space second-order perturbation theory (emb-CASPT2) that accurately reproduces a measured H2 desorption barrier, embedded ASCI, using a very large active space (though one that still comprises a small portion of the full set of orbitals) fails to do so. Adding an extra correlation term from embedded Møller-Plesset second-order perturbation theory (emb-MP2) improves the desorption barrier and endothermicity predictions. Thus, the inaccuracy of embedded ASCI comes from the missing dynamic correlation from the many other electrons and orbitals not included in the active space. For CH4 dissociation, again embedded ASCI overestimates the dissociation barrier compared to emb-CASPT2 predictions. Adding dynamic correlation from emb-MP2 helps correct the barrier. However, this composite approach suffers from double counting of correlation within embedded ASCI followed by emb-MP2 calculations. We therefore conclude that the state-of-the-art emb-MRPT2 based on reference wavefunctions generated via emb-CASSCF remains the method of choice for studying surface reactions. emb-ASCI is useful when large active spaces beyond the limit of emb-CASSCF are essential, such as to study complex surface reactions with significant multiconfigurational character (static correlation) but weak dynamic correlation.

Counterexamples for multi-parameter weighted paraproducts

We build the plethora of counterexamples to bi-parameter two weight embedding theorems. Two weight one parameter embedding results (which is the same as results of boundedness of two weight classical paraproducts, or two weight Carleson embedding theorems) are well known since the works of Sawyer in the 80’s. Bi-parameter case was considered by S. Y. A. Chang and R. Fefferman but only when underlying measure is Lebesgue measure. The embedding of holomorphic functions on bi-disc requires general input measure. In [9] we classified such embeddings if the output measure has tensor structure. In this note we give examples that without tensor structure requirement all results break down.

Alliance of Commerce and Financial Intermediation with Social Networks: Problems and Prospects

The embedding of trading and banking functions in a social network offers promising prospects for trade, financial intermediation, and social networks, as well as for network users. Social sites are becoming more sophisticated due to the variety of applications and interaction channels that provide not only social contacts (P2P), but also business-to-business (B2B), business-to-consumer (B2C), and consumer-to-consumer systems (C2C).

Poset embeddings of Hilbert functions for two hypersurface rings

Let R=𝕂[a,b,c,d]∕(ad−bc) or 𝕂[a,b,c]∕(ac−b2), where 𝕂 is a field of arbitrary characteristic and a,b,c,d are indeterminates. In this paper we will show that there is an order preserving embedding from the poset of Hilbert functions of homogeneous ideals of R to the poset of homogeneous ideals of R. We also obtain related results for Betti numbers when the characteristic of 𝕂 is 0.

Hierarchical Epidemic Risk Modeling of Spreading New COVID-19 Coronavirus

The subject of the research are a multi-level study of the territorial characteristics of emergency situations, analysis of operational information, and risk management of various origins. A topic model of the current COVID-19 coronavirus epidemic process have been developed in terms of reliability theory. The model is based on the methods of parametric hierarchical and variational modeling, which takes into account the similarity of exponential functions of the relationship between the parameters of the situation at different organizational levels.The objective of the work is to identify the characteristics of the reaction of the population of different countries to anti-epidemic measures of public risk management.The research methods are based on information technology for designing and approximating curves and surfaces with functions of various complexity, which is implemented by means of fractal parametric modeling of spatial objects and allows you to create new graphical confi gurations by varying the parameters and functions of their connections. Calculations are performed in variables that are biased relative to the environmental values, which makes it possible to take into account the territorial specifi cs of managing the parameters of the failure rate (risk of disease). In comparative statistical analysis, the advantage of this modeling is related to the relativity of reliability indicators, which makes it possible to eliminate country-specific features of epidemiological data collection. The complexity of hierarchical models is expressed in the multiplicity of embedding (superposition) of exponential functions. The model of the epidemic does not regulate the intensity of infection (disease risk), but reduces the acceptable risk of infection by organizational pressure on the magnitude of this risk. The effectiveness of the impact is assessed by the values of stable manageability indicators, which is individual for each territory. The Chinese authorities demonstrated high manageability of the population's behavior in the conditions of the beginning of the epidemic. The average manageability level is typical for Western countries with similar trends in the development of the pandemic. Russian society has shown low manageability with a high degree of state readiness to fight the pandemic.

Revisiting Competing Paths in Electrochemical CO2 Reduction on Copper via Embedded Correlated Wavefunction Theory.

We re-evaluate two key steps in the mechanism of CO2 reduction on copper at a higher level of theory capable of correcting inherent errors in density functional theory (DFT) approximations, namely, embedded correlated wavefunction (ECW) theory. Here, we consider the CO reduction step on Cu(111), which is critical to understanding reaction selectivity. We optimize embedding potentials at the periodic plane-wave DFT level using density functional embedding theory (DFET). All possible adsorption sites (adsites) for each adsorbate then are screened with ECW theory at the catalytically active site to refine the local electronic structure. Unsurprisingly, DFT and ECW theory predict different adsite preferences, largely because of DFT's inability to properly situate the CO 2π* level. Differing preferred adsites suggest that different reaction pathways could emerge from DFT versus ECW theory. Starting from these preferred ECW theory adsites, we then obtain reaction pathways at the plane-wave DFT level using the climbing-image nudged elastic band method to determine minimum energy paths. Thereafter, we perform ECW calculations at the catalytically active site to correct the energetics at each interpolated structure (image) along the reaction pathways. Via this approach, we confirm that the first step in CO reduction via hydrogen transfer on Cu(111) is to form hydroxymethylidyne (*COH) instead of formyl (*CHO). Although the prediction to preferentially form *COH is consistent with that of DFT, the two theories predict quite different structural and mechanistic behaviors, suggesting that verification is needed for other parts of the mechanism of CO2 reduction, which is the subject of ongoing work.

Localized Molecular Orbital-Based Embedding Scheme for Correlated Methods.

Embedding strategies currently provide the best compromise between accuracy and computational cost in modeling chemical properties and processes of large and complex systems. In this framework, different methods have been proposed all over the years, from the very popular QM/MM approaches to the more recent and very promising density matrix and density functional embedding techniques. Here, we present a further development of the quantum mechanics/extremely localized molecular orbital technique (QM/ELMO) method, a recently proposed multiscale embedding strategy in which the chemically active region of the investigated system is treated at a fully quantum mechanical level, while the rest is described by frozen extremely localized molecular orbitals previously transferred from proper libraries or tailor-made model molecules. In particular, in this work we discuss and assess in detail the extension of the QM/ELMO approach to density functional theory and post-Hartree-Fock techniques by evaluating its performances when it is used to describe chemical reactions, bond dissociations, and intermolecular interactions. The preliminary test calculations have shown that, in the investigated cases, the new embedding strategy enables the results of the corresponding fully quantum mechanical computations to be reproduced within chemical accuracy in almost all the cases but with a significantly reduced computational cost, especially when correlated post-Hartree-Fock strategies are used to describe the quantum mechanical subsystem. In light of the obtained results, we already envisage the future application of the new correlated QM/ELMO techniques to the investigation of more challenging problems, such as the modeling of enzyme catalysis, the study of excited states of biomolecules, and the refinement of macromolecular X-ray crystal structures.

Myeloarchitecture gradients in the human insula: Histological underpinnings and association to intrinsic functional connectivity

Insular cortex is a core hub involved in multiple cognitive and socio-affective processes. Yet, the anatomical mechanisms that explain how it is involved in such a diverse array of functions remain incompletely understood. Here, we tested the hypothesis that changes in myeloarchitecture across the insular cortex explain how it can be involved in many different facets of cognitive function. Detailed intracortical profiling, performed across hundreds of insular locations on the basis of myelin-sensitive magnetic resonance imaging (MRI), was compressed into a lower-dimensional space uncovering principal axes of myeloarchitectonic variation. Leveraging two datasets with different high-resolution MRI contrasts, we obtained robust support for two principal dimensions of insular myeloarchitectonic differentiation in vivo, one running from ventral anterior to posterior banks and one radiating from dorsal anterior towards both ventral anterior and posterior subregions. Analyses of post mortem 3D histological data showed that the antero-posterior axis was mirrored in cytoarchitectural markers, even when controlling for sulco-gyral folding. Resting-state functional connectomics in the same individuals and ad hoc meta-analyses showed that myelin gradients in the insula relate to diverse affiliation to macroscale intrinsic functional systems, showing differential shifts in functional network embedding across each myelin-derived gradient. Collectively, our findings offer a novel approach to capture structure-function interactions of a key node of the limbic system, and suggest a multidimensional structural basis underlying the diverse functional roles of the insula.