Faithful Representation Research Articles

Xenogenic Neural Stem Cell-Derived Extracellular Nanovesicles Modulate Human Mesenchymal Stem Cell Fate and Reconstruct Metabolomic Structure.

Extracellular nanovesicles, particularly exosomes, can deliver their diverse bioactive biomolecular content, including miRNAs, proteins, and lipids, thus providing a context for investigating the capability of exosomes to induce stem cells toward lineage-specific cells and tissue regeneration. In this study, it is demonstrated that rat subventricular zone neural stem cell-derived exosomes (rSVZ-NSCExo) can control neural-lineage specification of human mesenchymal stem cells (hMSCs). Microarray analysis shows that the miRNA content of rSVZ-NSCExo is a faithful representation of rSVZ tissue. Through immunocytochemistry, gene expression, and multi-omics analyses, the capability to use rSVZ-NSCExo to induce hMSCs into a neuroglial or neural stem cell phenotype and genotype in a temporal and dose-dependent manner via multiple signaling pathways is demonstrated. The current study presents a new and innovative strategy to modulate hMSCs fate by harnessing the molecular content of exosomes, thus suggesting future opportunities for rSVZ-NSCExo in nerve tissue regeneration.

Super-resolution of sea surface temperature with convolutional neural network- and generative adversarial network-based methods

Abstract In this paper, we perform the super-resolution of sea surface temperature data with the enhanced super-resolution generative adversarial network (ESRGAN), which is a deep neural network-based single-image super-resolution (SISR) method that uses a generative adversarial network (GAN). We generate high-quality super-resolution data with ESRGAN and with the super-resolution convolutional neural network (SRCNN) and residual-in-residual dense block network (RRDBNet) methods, which are based on convolutional neural networks (CNNs). The images generated with these methods are compared with high-resolution optimum interpolation sea surface temperature (OISST) data using root mean square error (RMSE), learned perceptual image patch similarity (LPIPS), and perceptual index (PI) evaluation methods. RRDBNet has a better RMSE than SRCNN and ESRGAN. However, CNN-based SISR methods do not provide a faithful representation of the ocean currents of OISST. ESRGAN has a better LPIPS and PI than CNN-based methods and can represent the complex distribution of ocean currents.

Corporate social responsibility and earnings quality in family firms

PurposeThe purpose of this paper is to investigate the relationship between corporate social responsibility and earnings quality, specifically in family firms.Design/methodology/approachBased on a sample of French-listed firms from the period 2005 to 2016, the authors use the instrumental variable approach based on a two-stage least-squares (2SLS) estimator.FindingsThe results show that Corporate Social Responsibility (CSR) performance is positively associated with the relevance and faithful representation of earnings. This means that companies that commit to CSR activities are more likely to provide high earnings quality. The results also show that the positive association between CSR performance and earnings quality is more prevalent in family firms suggesting that socially responsible family firms are willing to preserve their socio-emotional wealth by disclosing high quality earnings.Research limitations/implicationsThe results suggest that French firms commit to CSR to satisfy the interests of their stakeholders by disclosing high-quality information supporting the conflict resolution view of CSR. The findings also support the socio-emotional wealth perspective and suggest that family firms that engage in CSR activities provide a rich informational environment through high earnings quality.Practical implicationsThis study’s findings can be thus useful to investors for their portfolio management decisions by enabling them to identify the profile of companies with high earnings quality. These results may also help standard-setters and capital-market regulators improve market transparency by introducing new requirements to encourage investing in CSR.Originality/valueThis study extends the research on the relationship between CSR and earnings quality by focusing on two fundamental characteristics including relevance and faithful representation. This paper focuses on the effect of CSR on earnings quality in the specific context of family firms. This study offers then a better understanding of whether socially responsible family firms communicate stronger or weaker earnings quality than non-family firms based on the agency and socio-emotional wealth perspectives.

Large violations in Kochen Specker contextuality and their applications

It is of interest to study how contextual quantum mechanics is, in terms of the violation of Kochen Specker state-independent and state-dependent non-contextuality inequalities. We present state-independent non-contextuality inequalities with large violations, in particular, we exploit a connection between Kochen–Specker proofs and pseudo-telepathy games to show KS proofs in Hilbert spaces of dimension d ⩾ 217 with the ratio of quantum value to classical bias being . We study the properties of this KS set and show applications of the large violation. It has been recently shown that Kochen–Specker proofs always consist of substructures of state-dependent contextuality proofs called 01-gadgets. We show a one-to-one connection between 01-gadgets in and Hardy paradoxes for the maximally entangled state in . We use this connection to construct large violation 01-gadgets between arbitrary vectors in , as well as novel Hardy paradoxes for the maximally entangled state in , and give applications of these constructions. As a technical result, we show that the minimum dimension of the faithful orthogonal representation of a graph in is not a graph monotone, a result that may be of independent interest.

Noncontextual coloring of orthogonality hypergraphs

We discuss representations and colorings of orthogonality hypergraphs in terms of their two-valued states interpretable as classical truth assignments. Such hypergraphs, if they allow for a faithful orthogonal representation, have quantum mechanical realizations in terms of intertwined contexts or maximal observables that are widely discussed as empirically testable criteria for contextuality. A reconstruction is possible for the class of perfectly separable hypergraphs. Colorings can be constructed from a minimal set of two-valued states. Some examples from exempt categories are presented, which either cannot be reconstructed by two-valued states or whose two-valued states cannot yield a chromatic number that is equal to the maximal clique number.

The Korean Vernacular Story: Telling Tales of Contemporary Chosŏn in Sinographic Writing

The Korean Vernacular Story: Telling Tales of Contemporary Chosŏn in Sinographic Writing

Environment Effects on X-Ray Absorption Spectra With Quantum Embedded Real-Time Time-Dependent Density Functional Theory Approaches.

In this work we implement the real-time time-dependent block-orthogonalized Manby-Miller embedding (rt-BOMME) approach alongside our previously developed real-time frozen density embedding time-dependent density functional theory (rt-TDDFT-in-DFT FDE) code, and investigate these methods’ performance in reproducing X-ray absorption spectra (XAS) obtained with standard rt-TDDFT simulations, for model systems comprised of solvated fluoride and chloride ions ([X@, X = F, Cl). We observe that for ground-state quantities such as core orbital energies, the BOMME approach shows significantly better agreement with supermolecular results than FDE for the strongly interacting fluoride system, while for chloride the two embedding approaches show more similar results. For the excited states, we see that while FDE (constrained not to have the environment densities relaxed in the ground state) is in good agreement with the reference calculations for the region around the K and L1 edges, and is capable of reproducing the splitting of the 1s1 (n + 1)p1 final states (n + 1 being the lowest virtual p orbital of the halides), it by and large fails to properly reproduce the 1s1 (n + 2)p1 states and misses the electronic states arising from excitation to orbitals with important contributions from the solvent. The BOMME results, on the other hand, provide a faithful qualitative representation of the spectra in all energy regions considered, though its intrinsic approximation of employing a lower-accuracy exchange-correlation functional for the environment induces non-negligible shifts in peak positions for the excitations from the halide to the environment. Our results thus confirm that QM/QM embedding approaches are viable alternatives to standard real-time simulations of X-ray absorption spectra of species in complex or confined environments.

Subgraph Complementation and Minimum Rank

Any finite simple graph $G = (V,E)$ can be represented by a collection $\mathscr{C}$ of subsets of $V$ such that $uv\in E$ if and only if $u$ and $v$ appear together in an odd number of sets in $\mathscr{C}$. Let $c_2(G)$ denote the minimum cardinality of such a collection. This invariant is equivalent to the minimum dimension of a faithful orthogonal representation of $G$ over $\mathbb{F}_2$ and is closely connected to the minimum rank of $G$. We show that $c_2 (G) = \operatorname{mr}(G,\mathbb{F}_2)$ when $\operatorname{mr}(G,\mathbb{F}_2)$ is odd, or when $G$ is a forest. Otherwise, $\operatorname{mr}(G,\mathbb{F}_2)\leqslant c_2 (G)\leqslant \operatorname{mr}(G,\mathbb{F}_2)+1$. Furthermore, we show that the following are equivalent for any graph $G$ with at least one edge: i. $c_2(G)=\operatorname{mr}(G,\mathbb{F}_2)+1$; ii. the adjacency matrix of $G$ is the unique matrix of rank $\operatorname{mr}(G,\mathbb{F}_2)$ which fits $G$ over $\mathbb{F}_2$; iii. there is a minimum collection $\mathscr{C}$ as described in which every vertex appears an even number of times; and iv. for every component $G'$ of $G$, $c_2(G') = \operatorname{mr}(G',\mathbb{F}_2) + 1$. We also show that, for these graphs, $\operatorname{mr}(G,\mathbb{F}_2)$ is twice the minimum number of tricliques whose symmetric difference of edge sets is $E$. Additionally, we provide a set of upper bounds on $c_2(G)$ in terms of the order, size, and vertex cover number of $G$. Finally, we show that the class of graphs with $c_2(G)\leqslant k$ is hereditary and finitely defined. For odd $k$, the sets of minimal forbidden induced subgraphs are the same as those for the property $\operatorname{mr}(G,\mathbb{F}_2)\leq k$, and we exhibit this set for $c_2(G) \leqslant 2$.

Linearity of graph products

In this work we prove that, given a simplicial graph $\Gamma$ and a family $\mathcal{G}$ of linear groups over a domain $R$, the graph product $\Gamma\mathcal{G}$ is linear over $R[\underline t]$, where $\underline t$ is a tuple of finitely many linearly independent variables. As a consequence we obtain that any graph product of finitely many groups linear over the complex numbers is again a linear group over the complex numbers. This solves an open problem of Hsu and Wise in the case of faithful representations over $\mathbb C$.

Representation of real solvable Lie algebras having 2-dimensional derived ideal and geometry of coadjoint orbits of corresponding Lie groups

Let Lie ([Formula: see text]) be the class of all [Formula: see text]-dimensional real solvable Lie algebras having [Formula: see text]-dimensional derived ideals. In 2020, Le et al. gave a classification of all non-2-step nilpotent Lie algebras of Lie ([Formula: see text], 2). We propose in this paper to study representations of these Lie algebras as well as their corresponding connected and simply connected Lie groups. That is, for each algebra, we give an upper bound of the minimal degree of a faithful representation. Then, we give a geometrical description of coadjoint orbits of corresponding groups. Moreover, we show that the characteristic property of the family of maximal dimensional coadjoint orbits of an MD-group studied by Shum et al. is still true for the Lie groups considered here. Namely, we prove that, for each considered group, the family of the maximal dimensional coadjoint orbits forms a measurable foliation in the sense of Connes. The topological classification of these foliations is also provided.

T-copula and Wasserstein distance-based stochastic neighbor embedding

The aim of dimensionality reduction is to obtain the faithful low-dimensional representations of high-dimensional data by preserving the data quality. It is beneficial to better visualize the high-dimensional data and improve the classification or clustering performance. Many dimensionality reduction methods based on the framework of stochastic neighbor embedding have been developed. However, most of them use the Euclidean distance to describe the dissimilarity of data points in high-dimensional space, which is not suitable for high-dimensional data with non-linear manifold structure. In addition, they usually use the family of normal distributions as their embedding distributions in low-dimensional space. This will incur that they are only suitable to deal with the spherical data. In order to deal with these issues, we present a novel dimensionality reduction method by integrating the Wasserstein distance and t-copula function into the stochastic neighbor embedding model. We first employ the Gaussian distribution equipped with the Wasserstein distance to describe the pairwise similarity in the high-dimensional space. Then, the t-copula function is used to generate a general heavy-tailed distribution for the description of low-dimensional pairwise similarity, which can process different shapes of data and avoid the crowding problem. Furthermore, Kullback–Leibler divergence is employed to measure the difference between the high-dimensional and low-dimensional similarities. Finally, a gradient descent algorithm with adaptive moment estimation is developed to solve the proposed objective function. Extensive experiments are conducted on eight real-world datasets to demonstrate the effectiveness of the proposed method in terms of the dimensional reduction quality, classification and clustering evaluation metrics.

A Compactification of the Universal Moduli Space of Principal G-Bundles

Let G be a semisimple linear algebraic group, rho :Ghookrightarrow text {SL}(V) a finite-dimensional faithful representation, gge 2 a natural number, and delta a positive rational number. We prove the existence of a compactification of the universal moduli space of semistable principal G-bundles over overline{text {M}}_{g}, provided that delta is sufficiently large, having the following property: the fibers over singular curves are the moduli spaces of delta -semistable singular principal G-bundles.

Self-healing codes: How stable neural populations can track continually reconfiguring neural representations

As an adaptive system, the brain must retain a faithful representation of the world while continuously integrating new information. Recent experiments have measured population activity in cortical and hippocampal circuits over many days and found that patterns of neural activity associated with fixed behavioral variables and percepts change dramatically over time. Such "representational drift" raises the question of how malleable population codes can interact coherently with stable long-term representations that are found in other circuits and with relatively rigid topographic mappings of peripheral sensory and motor signals. We explore how known plasticity mechanisms can allow single neurons to reliably read out an evolving population code without external error feedback. We find that interactions between Hebbian learning and single-cell homeostasis can exploit redundancy in a distributed population code to compensate for gradual changes in tuning. Recurrent feedback of partially stabilized readouts could allow a pool of readout cells to further correct inconsistencies introduced by representational drift. This shows how relatively simple, known mechanisms can stabilize neural tuning in the short term and provides a plausible explanation for how plastic neural codes remain integrated with consolidated, long-term representations.

Assessment of the vulnerability to contamination of fractured aquifers based on DRASTIC method

Vulnerability map is an effective tool to assess and identify the areas that are most susceptible to groundwater contamination, and can aid in the planning and management of municipal groundwater. These maps can help decision-making regarding land use and cover, pointing out proper sites to rural and/or urban sprawling but also highlighting restrictions for groundwater withdrawal. This paper aims at evaluating the influence of the lineament density (DRASTIC-LD) and the influence of the land use and cover, along with the lineament density, (DRASTIC-LDLU) in the classification of the vulnerability of the Serra Geral Aquifer System. The DRASTIC approach was modified by adding the lineament density. The DRASTIC-LD method showed high (70.73%), moderate (17.04%) and very high (12.17%) vulnerability. The DRASTIC-LDLU method presented high (55.35%), extreme (19.65%), very high (14.66%) and moderate (10.31%) vulnerability. Lineament density and land use and cover had a significant influence on the vulnerability classification. The spatial distribution of vulnerable zones was quite similar to those obtained by applying the original DRASTIC model. However, a significant increase in vulnerability was observed across the study area when the lineament density parameter was added, followed by an even greater vulnerability when the lineaments were inserted together with land use land cover. Inserting these two parameters together provides a more accurate result of vulnerability in fractured aquifers, as it is a more faithful representation of the reality of this system.

Relevance of supplementary fair value disclosures under market uncertainty: effects on audit fees and investors’ pricing

PurposeConcerns relating to the representational faithfulness and, consequently, the relevance of fair value (FV) estimates are likely to be heightened in the wake of market uncertainty caused by the COVID pandemic. Therefore, this paper aims to study the relevance of supplementary disclosures intended to improve the representational faithfulness of FV estimates by examining their impacts on audit fees and investors’ valuation of FV adjustments in the uncertain market condition of 2020.Design/methodology/approachThe sample is comprising Australian real estate firms. The authors develop both weighted and unweighted disclosure indices based on supplementary disclosures related to Level 3 FVs under IFRS 13 Fair Value Measurement. The authors measure the levels of disclosure by the sample firms based on these indices from 2018 to 2020 and ascertain their effects on audit fees and the market value of FV adjustments on investment properties.FindingsThe authors find that real estate firms increased supplementary FV disclosures during 2020. The authors document a negative association between supplementary disclosures and audit fees, although the authors find no incremental impact of disclosures on audit fees during the pandemic. Additionally, the authors find that investors’ pricing of FV adjustments increased with the increase in disclosures during the market uncertainty of 2020, while in the pre-uncertainty period, their pricing influence was not significant.Originality/valueThe findings extend the understanding of the role of supplementary disclosures on Level 3 investment properties in mitigating the perceived audit risk for auditors and the faithful representation concerns for investors in a distressed market environment.

The autobiographical narrative as a damage assessment means: injuries to profound subjective sensitivities - personalization and objectivization of medicolegal relevance.

The article aims to outline and expound upon the impairing aspects of an acoustic lesion involving deafness, analyzing the disabling effects on all aspects of the daily life of the injured person, even in the absence of permanent psychic damage that can be nosographically categorized. Such a form of additional damage, which can hardly be quantified, is certainly immaterial and perhaps cannot even be qualified as biological; although it still falls within the realm of psychic functioning, it must be adequately framed by the forensic doctor, in order to enable the Judge to issue a coherently motivated ruling, fully contextualized within the clinical reality of the patient. This task is strictly medicolegal in nature, and requires the professional to carry out in-depth work thoroughly based on listening for the purpose of providing the courts with a faithful representation.

RT-qPCR Detection of SARS-CoV-2: No Need for a Dedicated Reverse Transcription Step.

Reverse transcription of RNA coupled to amplification of the resulting cDNA by the polymerase chain reaction (RT-PCR) is one of the principal molecular technologies in use today, with applications across all areas of science and medicine. In its real-time, fluorescence-based usage (RT-qPCR), it has long been a core technology driving the accurate, rapid and sensitive laboratory diagnosis of infectious diseases. However, RT-qPCR protocols have changed little over the past 30 years, with the RT step constituting a significant percentage of the time taken to complete a typical RT-qPCR assay. When applied to research investigations, reverse transcription has been evaluated by criteria such as maximum yield, length of transcription, fidelity, and faithful representation of an RNA pool. Crucially, however, these are of less relevance in a diagnostic RT-PCR test, where speed and sensitivity are the prime RT imperatives, with specificity contributed by the PCR component. We propose a paradigm shift that omits the requirement for a separate high-temperature RT step at the beginning of an RT-qPCR assay. This is achieved by means of an innovative protocol that incorporates suitable reagents with a revised primer and amplicon design and we demonstrate a proof of principle that incorporates the RT step as part of the PCR assay setup at room temperature. Use of this modification as part of a diagnostic assay will of course require additional characterisation, validation and optimisation of the PCR step. Combining this revision with our previous development of fast qPCR protocols allows completion of a 40 cycle RT-qPCR run on a suitable commercial instrument in approximately 15 min. Even faster times, in combination with extreme PCR procedures, can be achieved.

Unraveling the origin of higher success probabilities in quantum annealing versus semi-classical annealing

Quantum annealing aims at finding optimal solutions to complex optimization problems using a suitable quantum many body Hamiltonian encoding the solution in its ground state. To find the solution one typically evolves the ground state of a soluble, simple initial Hamiltonian adiabatically to the ground state of the designated final Hamiltonian. Here we explore whether and when a full quantum representation of the dynamics leads to higher probability to end up in the desired ground when compared to a classical mean field approximation. As simple but nontrivial example we target the ground state of interacting bosons trapped in a tight binding lattice with small local defect by turning on long range interactions. Already two atoms in four sites interacting via two cavity modes prove complex enough to exhibit significant differences between the full quantum model and a mean field approximation for the cavity fields mediating the interactions. We find a large parameter region of highly successful quantum annealing, where the semi-classical approach largely fails. Here we see strong evidence for the importance of entanglement to end close to the optimal solution. The quantum model also reduces the minimal time for a high target occupation probability. Surprisingly, in contrast to naive expectations that enlarging the Hilbert space is beneficial, different numerical cut-offs of the Hilbert space reveal an improved performance for lower cut-offs, i.e. an nonphysical reduced Hilbert space, for short simulation times. Hence a less faithful representation of the full quantum dynamics sometimes creates a higher numerical success probability in even shorter time. However, a sufficiently high cut-off proves relevant to obtain near perfect fidelity for long simulations times in a single run. Overall our results exhibit a clear improvement to find the optimal solution based on a quantum model versus simulations based on a classical field approximation.

Point cloud quality assessment based on geometry-aware texture descriptors

In recent years, 3D imaging technologies have advanced tremendously, allowing more faithful representations of the physical world. More specifically, imaging technology advancements have driven the production of plenoptic devices that can capture and display visual contents. These devices represent the visual data using an approximation of the plenoptic illumination function, which can describe visible objects in any position and from any point-of-view of the 3D space. Depending on the capturing device, this approximation can correspond to holograms, light fields, or Point Clouds (PCs) imaging formats. Among these formats, PCs have become very popular for a wide range of applications, such as immersive virtual reality scenarios. As a consequence, in the last couple of years, there has been a great effort to develop novel acquisition, representation, compression, and transmission solutions for PC contents in the research community. In particular, the development of objective quality assessment methods that are able to predict the perceptual quality of PCs has attracted a lot of attention. In this paper, we present an effective framework for assessing the quality of PCs, which is based on descriptors that extract geometry-aware texture information of PC contents. In this framework, the statistics of the extracted information are used to model the PC visual quality. We also present the research and experiments carried to evaluate the most appropriate distance metrics and regression methods to be used together with the proposed descriptors. Experimental results show that the proposed framework exhibit good and robust performance when compared with several state-of-the-art Point Cloud Quality Assessment (PCQA) methods. A C++ implementation of the metrics described in this paper can be found at https://gitlab.com/gpds-unb/pc_metric.

Variational Optimization of Continuous Matrix Product States.

Just as matrix product states represent ground states of one-dimensional quantum spin systems faithfully, continuous matrix product states (cMPS) provide faithful representations of the vacuum of interacting field theories in one spatial dimension. Unlike the quantum spin case, however, for which the density matrix renormalization group and related matrix product state algorithms provide robust algorithms for optimizing the variational states, the optimization of cMPS for systems with inhomogeneous external potentials has been problematic. We resolve this problem by constructing a piecewise linear parameterization of the underlying matrix-valued functions, which enables the calculation of the exact reduced density matrices everywhere in the system by high-order Taylor expansions. This turns the variational cMPS problem into a variational algorithm from which both the energy and its backwards derivative can be calculated exactly and at a cost that scales as the cube of the bond dimension. We illustrate this by finding ground states of interacting bosons in external potentials and by calculating boundary or Casimir energy corrections of continuous many-body systems with open boundary conditions.