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The red macroalgae Porphyra, commonly known as Nori, is widely used as food around the world due to its high nutrient content, including the significant abundance of colored phycobiliproteins (PBPs). Among these, R‐phycocyanin (R‐PC) stands out for its vibrant purple color and numerous bioactive properties, making it a valuable protein for the food industry. However, R‐PC's limited thermal stability necessitates alternative processing methods to preserve its color and bioactive properties. Our study aimed to investigate the in‐situ stability of oligomeric R‐PC under high pressure (HP) conditions (up to 4000 bar) using a combination of absorption, fluorescence, and small‐angle X‐ray scattering (SAXS) techniques. The unfolding of R‐PC is a multiphase process. Initially, low pressure induces conformational changes in the R‐PC oligomeric form (trimers). As pressure increases above 1600 bar, these trimers dissociate into monomers, and at pressures above 3000 bar, the subunits begin to unfold. When returned to atmospheric pressure, R‐PC partially refolds, retaining 50% of its original color absorbance. In contrast, heat treatment causes irreversible and detrimental effects on R‐PC color, highlighting the advantages of HP treatment in preserving both the color and bioactive properties of R‐PC compared to heat treatment. [ABSTRACT FROM AUTHOR]

The continuous internal bremsstrahlung spectrum accompanying a beta and neutrino from the 14 C isotope was measured by magnetic deflection method in the region of 10–150 keV using a 7.62 cm × 7.62 cm NaI(Tl) scintillation spectrometer. The NaI(Tl) detector was calibrated by the standard radioactive sources. The detector response functions are generated for single-energy gamma photons with the help of experimental detector parameters and the Monte Carlo method. The experimental set-up and the geometry were adjusted carefully to minimise the external bremsstrahlung effect and the undesirable gamma counts from the surroundings in the detector. The raw experimental data were unfolded from the detector response effects by applying the singular value decomposition method. The experimental spectrum was compared with both theoretical and Monte Carlo calculation spectra. [ABSTRACT FROM AUTHOR]

The distortions of measured beta spectra are addressed by means of unfolding algorithms. Two different approaches, the Maximum-Likelihood ExpectationMaximization and the Tikhonov regularization, are tested on various simulated spectra, for which the initial spectrum to retrieve is known, and on a 99Tc spectrum measured with our dedicated setup. Statistical uncertainties of distorted measured spectra are propagated by determining the covariance matrices. Both algorithms provide satisfactory results but Tikhonov performs overall better for most of the studied radionuclides. Highlight is made on the necessity to employ at least two independent methods to ensure the accuracy of the unfolded spectra and to estimate the internal bias of each algorithm. [ABSTRACT FROM AUTHOR]

This paper studies the upscaling of an elliptic problem with a highly oscillating quasilinear matrix coefficient, a quasilinear term, and a semilinear term in domains periodically perforated with holes of critical size. A Signorini boundary condition is imposed on the boundary of the holes, while a Dirichlet boundary condition is prescribed on the exterior boundary. Using the periodic unfolding method, we obtain an obstacle problem with a nonnegativity spreading effect. [ABSTRACT FROM AUTHOR]

We present a formalism for Petri nets based on polynomial-style finite-set configurations and etale maps. The formalism supports both a geometric semantics in the style of Goltz and Reisig (processes are etale maps from graphs) and an algebraic semantics in the style of Meseguer and Montanari, in terms of free coloured props, and allows the following unification: for P a Petri net, the Segal space of P-processes is shown to be the free coloured prop-in-groupoids on P. There is also an unfolding semantics à la Winskel, which bypasses the classical symmetry problems: with the new formalism, every Petri net admits a universal unfolding, which in turn has associated an event structure and a Scott domain. Since everything is encoded with explicit sets, Petri nets and their processes have elements. In particular, individual-token semantics is native. (Collectivetoken semantics emerges from rather drastic quotient constructions à la Best–Devillers, involving taking π0 of the groupoids of states [ABSTRACT FROM AUTHOR]

Plug-and-Play (PnP) and Regularization-by-Denoising (RED) are recent paradigms for image reconstruction that leverage the power of modern denoisers for image regularization. In particular, they have been shown to deliver state-of-the-art reconstructions with CNN denoisers. Since the regularization is performed in an ad-hoc manner, understanding the convergence of PnP and RED has been an active research area. It was shown in recent works that iterate convergence can be guaranteed if the denoiser is averaged or nonexpansive. However, integrating nonexpansivity with gradient-based learning is challenging, the core issue being that testing nonexpansivity is intractable. Using numerical examples, we show that existing CNN denoisers tend to violate the nonexpansive property, which can cause PnP or RED to diverge. In fact, algorithms for training nonexpansive denoisers either cannot guarantee nonexpansivity or are computationally intensive. In this work, we construct contractive and averaged image denoisers by unfolding splitting-based optimization algorithms applied to wavelet denoising and demonstrate that their regularization capacity for PnP and RED can be matched with CNN denoisers. To our knowledge, this is the first work to propose a simple framework for training contractive denoisers using network unfolding. [ABSTRACT FROM AUTHOR]

Fluorinated graphene, a two-dimensional nanomaterial composed of three atomic layers, a central carbon layer sandwiched between two layers of fluorine atoms, has attracted considerable attention across various fields, particularly for its potential use in biomedical applications. Nonetheless, scant effort has been devoted to assessing the potential toxicological implications of this nanomaterial. In this study, we scrutinize the potential impact of fluorinated graphene on a protein model, HP35 by utilizing extensive molecular dynamics (MD) simulation methods. Our MD results elucidate that upon adsorption to the nanomaterial, HP35 undergoes a denaturation process initiated by the unraveling of the second helix of the protein and the loss of the proteins hydrophobic core. In detail, substantial alterations in various structural features of HP35 ensue, including alterations in hydrogen bonding, Q value, and RMSD. Subsequent analyses underscore that hydrophobic and van der Waals interactions (predominant), alongside electrostatic energy (subordinate), exert influence over the adsorption of HP35 on the fluorinated graphene surface. Mechanistic scrutiny attests that the unrestrained lateral mobility of HP35 on the fluorinated graphene nanomaterial primarily causes the exposure of HP35's hydrophobic core, resulting in the eventual structural denaturation of HP35. A trend in the features of 2D nanostructures is proposed that may facilitate the denaturation process. Our findings not only substantiate the potential toxicity of fluorinated graphene but also unveil the underlying molecular mechanism, which thereby holds significance for the prospective utilization of such nanomaterials in the field of biomedicine. [ABSTRACT FROM AUTHOR]

High hydrostatic pressure (HHP) has gained prominence in the food processing industry over the last decade. In addition to the effectiveness of microbial and enzymatic inactivation, HHP directly impacts protein structures and properties. Accordingly, this review article aims to consolidate relevant research findings elucidating the effects of HHP on protein structure, allergenicity, bioactivities, and functional properties across diverse protein sources. They encompass cereals, legumes, nuts, meat, poultry products, milk, eggs, seafood, algae, insects, seeds, and vegetables. This review provides insights into the consistent trends of HHP effects on each protein source. In conclusion, HHP induces alterations in non-covalent bonds within protein structures, leading to the unfolding of their interior regions and consequential changes in their properties. Remarkably, the allergenicity of cereals, legumes, and nuts decreases while their bioactivities and digestibility escalate. The disruption of non-covalent bonds during HHP results in the exposure of the interior hydrophobic regions to the surface microenvironment, thereby enhancing the surface hydrophobicity of proteins, particularly those derived from seeds and vegetables. HHP weakens the allergenicity and elevates the foaming properties of proteins from dairy products, including improving the gelling properties and antioxidant activities of egg proteins. Texture profiles of meat and poultry, particularly hardness, are enhanced. Furthermore, HHP demonstrates the potential to diminish the allergenicity of seafood proteins and augment insect protein bioactivities. Lastly, HHP enhances the extraction of algal bioactive components, improving their nutritional quality. [ABSTRACT FROM AUTHOR]

Bavinck's critical appropriation of modern grammar in discussing Christian doctrine has led to his being described as a thinker who is "modern yet orthodox." His appropriation of modern philosophy and psychology is clearly seen in his use of 'personality' in his doctrine of God and anthropology. This article argues that his use in these two areas demonstrates the organic ontology of creatures created in the image of their Creator. Thus, in understanding divine personality, human personality comes into focus, both theologically and psychologically. The article concludes with reflections on how the study of personality in Bavinck's writings helps one to understand his entire project. [ABSTRACT FROM AUTHOR]

In this study, we consider algorithm unfolding for the multiple measurement vector (MMV) problem in the case where only few training samples are available. Algorithm unfolding has been shown to empirically speed-up in a data-driven way the convergence of various classical iterative algorithms, but for supervised learning, it is important to achieve this with minimal training data. For this, we consider learned block iterative shrinkage thresholding algorithm (LBISTA) under different training strategies. To approach almost data-free optimization at minimal training overhead, the number of trainable parameters for algorithm unfolding has to be substantially reduced. We therefore explicitly propose a reduced-size network architecture based on the Kronecker structure imposed by the MMV observation model and present the corresponding theory in this context. To ensure proper generalization, we then extend the analytic weight approach by Liu and Chen to LBISTA and the MMV setting. Rigorous theoretical guarantees and convergence results are stated for this case. We show that the network weights can be computed by solving an explicit equation at the reduced MMV dimensions which also admits a closed-form solution. Toward more practical problems, we then considered convolutional observation models and show that the proposed architecture and the analytical weight computation can be further simplified and thus open new directions for convolutional neural networks. Finally, we evaluate the unfolded algorithms in numerical experiments and discuss connections to other sparse recovering algorithms. [ABSTRACT FROM AUTHOR]

Biometrical sciences and disease diagnosis in particular, are often concerned with the analysis of associations for cross‐classified data, for which distance association models give us a graphical interpretation for non‐sparse matrices with a low number of categories. In this framework, usually binary exploratory and response variables are present, with analysis based on individual profiles being of great interest. For saturated models, we show the usual linear relationship for log‐linear models is preserved in full dimension for the distance association parameterization. This enables a two‐step procedure to facilitate the analysis and the interpretation of associations in terms of unfolding after the overall and main effects are removed. The proposed procedure can deal with cross‐classified data for profiles by binary variables, and it is easy to implement using traditional statistical software. For disease diagnosis, the problems of a degenerate solution in the unfolding representation, and that of determining significant differences between the profile locations are addressed. A hypothesis test of independence based on odds ratio is considered. Furthermore, a procedure is proposed to determine the causes of the significance of the test, avoiding the problem of error propagation. The equivalence between a test for equality of odds ratio pairs and the test for equality of location for two profiles in the unfolding representation in the disease diagnosis is shown. The results have been applied to a real example on the diagnosis of coronary disease, relating the odds ratios with performance parameters of the diagnostic test. [ABSTRACT FROM AUTHOR]

In production areas dedicated to the manufacturing of small series or unique products, i.e. in sectors which require frequent calculations for geometric determinations (such as the case of obtaining the unfolded shape of oblique cones frustums from the joints of different tubes (pipes) of varying diameters and/or orientations it is useful to achieve their development within a short timeframe, accurately and with minimal effort. Regarding this, an AutoLisp function was created for the rapid development of an oblique cone frustum, capable of being used in the current working session of the drawing executed in AutoCAD, without requiring additional calculations or drawings [ABSTRACT FROM AUTHOR]

We consider the non-semisimple 0:1 resonance (i.e. the unperturbed equilibrium has two purely imaginary eigenvalues ± i α ( α ∈ R and α > 0) and a non-semisimple double-zero one) Hamiltonian bifurcation with one distinguished parameter, which corresponds to the supercritical Hamiltonian pitchfork bifurcation. Based on BCKV singularity theory established by [H.W. Broer, S. -N. Chow, Y. Kim, and G. Vegter, A normally elliptic Hamiltonian bifurcation, Z. Angew. Math. Phys. 44 (3) (1993), pp. 389–432], this bifurcation essentially triggered by the reversible universal unfolding M = 1 2 p 2 + 1 4 q 4 + (λ + I 1) q 2 with respect to BCKV-restricted morphisms of the planar non-semisimple singularity 1 2 p 2 + 1 4 q 4 (the I 1 is regarded as distinguished parameter with respect to the external parameter λ). We first give the plane bifurcation diagram of the integrable Hamiltonian on each level of integral in detail, which is related to the usual supercritical Hamiltonian pitchfork bifurcation. Then, we use the S 1 -symmetry generated by the additional pair of imaginary eigenvalues ± i α to reconstruct the above plane bifurcation phenomenon caused by the zero eigenvalue pair into the case with two degrees of freedom. Finally, we prove the persistence of typical bifurcation scenarios (e.g. 2-dimensional invariant tori and the symmetric homoclinic orbit) under the small Hamiltonian perturbations, as proposed by [H.W. Broer, S. -N. Chow, Y. Kim, and G. Vegter, A normally elliptic Hamiltonian bifurcation, Z. Angew. Math. Phys. 44 (3) (1993), pp. 389–432]. An example system (the coupled Duffing oscillator) with strong linear coupling and weak local nonlinearity is given for this bifurcation. [ABSTRACT FROM AUTHOR]

In Petri net unfolding, according to the strategies of breadth first and depth first, the biggest problem lies in the potential explosion of the state space. Unfolding generates either accessible trees or branch processes. Making marking reduction or branch cutting accessible proves to be an effective approach to mitigating the state space expansion. In this paper, we propose three reduction rules based on similarity equivalence, conduct state space reduction, present three theorems supported by a case study, and propose a new unfolding algorithm for the unfolding process. In both the new case and the experiments, the completeness, optimality, completeness, and memory and time consumption are reduced by about 60%. [ABSTRACT FROM AUTHOR]

Integrin, as a mechanotransducer, establishes the mechanical reciprocity between the extracellular matrix (ECM) and cells at integrin-mediated adhesion sites. This study used steered molecular dynamics (SMD) simulations to investigate the mechanical responses of integrin αvβ3 with and without 10th type III fibronectin (FnIII10) binding for tensile, bending and torsional loading conditions. The ligand-binding integrin confirmed the integrin activation during equilibration and altered the integrin dynamics by changing the interface interaction between β-tail, hybrid and epidermal growth factor domains during initial tensile loading. The tensile deformation in integrin molecules indicated that fibronectin ligand binding modulates its mechanical responses in the folded and unfolded conformation states. The bending deformation responses of extended integrin models reveal the change in behaviour of integrin molecules in the presence of Mn2+ ion and ligand based on the application of force in the folding and unfolding directions of integrin. Furthermore, these SMD simulation results were used to predict the mechanical properties of integrin underlying the mechanism of integrin-based adhesion. The evaluation of integrin mechanics provides new insights into understanding the mechanotransmission (force transmission) between cells and ECM and contributes to developing an accurate model for integrin-mediated adhesion. This article is part of a discussion meeting issue 'Supercomputing simulations of advanced materials'. [ABSTRACT FROM AUTHOR]

Protein unfolding is a ubiquitous process responsible for the loss of protein functionality (denaturation), which, in turn, can be accompanied by the death of cells and organisms. The nature of enthalpy–entropy compensation (EEC) in the kinetics of protein unfolding is a subject of debate. In order to investigate the nature of EEC, the "completely loose" transition state (TS) model has been applied to calculate the Arrhenius parameters for the unfolding of polyglycine dimers as a model process. The calculated Arrhenius parameters increase with increasing dimer length and demonstrate enthalpy–entropy compensation. It is shown that EEC results from the linear correlations of enthalpy and entropy of activation with dimer length, which are derived directly from the properties of the transition state. It is shown that EEC in solvated (hydrated, etc.) proteins is a direct consequence of EEC in proteins themselves. The suggested model allows us also to reproduce and explain "exotic" very high values of the pre-exponential factor measured for the proteins unfolding, which are drastically higher than those known for unimolecular reactions of organic molecules. A similar approach can be applied to analyzing the nature of EEC phenomena observed in other areas of chemistry. [ABSTRACT FROM AUTHOR]

Bremsstrahlung photons measurement show elevated energy emission beyond critical energy in 28-GHz ECR and its nature is not revealed. Thus, the aim of this study is to unveil the nature of high end-point energies at the three measurement locations. Bremsstrahlung photons from a 28-GHz ECR ion source were measured at Busan center of Korean Basic Science Institute (KBSI). The gamma-ray detection system consists of three round type NaI(Tl) scintillation detectors placed 62 cm radially from the beam axis at the three measurement locations namely injection, centre and extraction sides of an ECR ion source, and a NaI(Tl) scintillation detector placed at the view port for monitoring photon intensity along the beam axis. Such design of the experiment was based on the inner structure of the ECR ion source and the shape of the ECR plasma. Bremsstrahlung photons energy spectra were measured at RF power of 1 kW to extract O16 beam with a dominant fraction of O3+ and O4+. Also, bremsstrahlung photons were measured by exchanging the detector positions for studying systematic uncertainties. Monte Carlo simulation based on the Geant4 package was performed to take geometrical acceptance and the energydependent detection efficiency into account due to large non-uniformity in the material budget. True bremsstrahlung energy spectra from the 28-GHz ECR ion source were extracted using an inverse matrix unfolding method. The measured high end-point energies at the three measurement locations were 1.68 MeV, 1.72 MeV and 2.04 MeV for the injection, centre and extraction sides, respectively. The high end-point energy values of the measured bremsstrahlung photons beyond the theoretical energy value of 1.33 MeV were found to be correlated with the structure of the ECR ion source and the shape of the ECR plasma. Furthermore, the high end-point energy values resulting from the bremsstrahlung photons measurements in the 28-GHz ECR plasma are due to the higher number of unconfined electrons arriving at the chamber wall easily, and producing higher bremsstrahlung photons. The unconfined electrons are the results of imperfect magnetic confinement that cause electrons to escape from the plasma volume. This study suggests a new design of magnetic confinement that will not allow an electron to escape. [ABSTRACT FROM AUTHOR]

Since its introduction in the early 1970s, high pressure crystallography (HPX) has shown great potential for the investigation of different types of matter. Using diamond anvil cells, HPX is an emerging technique that has been rapidly implemented, making it available to biologists, and there is immense potential for utilizing this technique in biological systems in the future. At the molecular level, high-pressure crystallographic investigation provides information on structural characteristics that not only determine the native conformation of a protein but also the conformations with higher free-energy, thus revealing function-related structural changes and properties that can be modified as a result of pressurization. The increase in the number of crystal structures of different macromolecules determined under high pressure over the last five decades can be ascribed mainly to two factors: the emergence of high-pressure cells with very large, open angles, and the advent of third generation synchrotron sources. The use of high pressure crystallography as a research tool has been shown to contribute to the advancements in the basic fields of biochemistry (protein misfolding and aggregation), biophysics (protein stability), and biotechnology (food processing). Presently, with a growing interest in biomedicine and nanotechnology, this nonstandard method appears to be a valid instrument for probing more challenging and complex systems. In this review, we present the method, highlight a selection of recent applications, and describe challenges for high pressure macromolecular crystallography (HPMX). [ABSTRACT FROM AUTHOR]

We present a novel computational approach for extracting localized signals from smooth background distributions. We focus on datasets that can be naturally presented as binned integer counts, demonstrating our procedure on the CERN open dataset with the Higgs boson signature, from the ATLAS collaboration at the Large Hadron Collider. Our approach is based on Gaussian Process (GP) regression — a powerful and flexible machine learning technique which has allowed us to model the background without specifying its functional form explicitly and separately measure the background and signal contributions in a robust and reproducible manner. Unlike functional fits, our GP-regression-based approach does not need to be constantly updated as more data becomes available. We discuss how to select the GP kernel type, considering trade-offs between kernel complexity and its ability to capture the features of the background distribution. We show that our GP framework can be used to detect the Higgs boson resonance in the data with more statistical significance than a polynomial fit specifically tailored to the dataset. Finally, we use Markov Chain Monte Carlo (MCMC) sampling to confirm the statistical significance of the extracted Higgs signature. [ABSTRACT FROM AUTHOR]

The paper focuses on ratings of the perceived seriousness of crimes and how they can be explained in a testable scaling model (SOCID) that shows commonalities and differences among individuals. The model assumes that crimes are first represented on each person's psychological map. This map can be described as a geometric space spanned by the crimes' perceived attributes. When assessing the seriousness of crimes, the person positions him-/herself on this map by finding a point whose distances to the crime points express how much he/she is rejects the respective crimes. Using three representative German surveys on crime prevention, the SOCID model is positively tested for various predictions: (1) All persons in our surveys share the same psychological map of crimes; (2) a 2-dimensional map accurately describes the persons' observed badness ratings; (3) the points representing the crimes form essentially linear manifolds on the maps; (4) the older and the more conservation-oriented a person, the more similar his/her distances to the crimes; (5) gender has no systematic effect on the distribution of person-points on the crimes' maps. [ABSTRACT FROM AUTHOR]

Logistic regression models are a powerful research tool for the analysis of cross‐classified data in which a categorical response variable is involved. In a logistic model, the effect of a covariate refers to odds, and the simple relationship between the coefficients and the odds ratio often makes these the parameters of interest due to their easy interpretation. In this article we present a distance‐based logistic model that allows a simple graphical interpretation of the association coefficients using the odds ratio in a contingency table. Two configurations are estimated, one for the rows and one for the columns, as the categories of a polytomous predictor and a nominal response variable respectively, such that the local odds ratio and the distances between the predictor and response categories are inversely related. The associations in terms of the odds ratios, or the ratios of the odds to their geometric means, are interpreted through distances for the most common coding schemes of the predictor variable, and the relationship between the distances related to different codings is investigated in its full dimension. The performance of the estimation procedure is analysed with a Monte Carlo experiment. The interpretation of the model and its performance, as well as its comparison with a two‐step procedure involving first a logistic regression and then unfolding, is illustrated using real data sets. [ABSTRACT FROM AUTHOR]

The functional structure of proteins results from marginally stable folded conformations. Reversible unfolding, irreversible denaturation, and deterioration can be caused by chemical and physical agents due to changes in the physicochemical conditions of pH, ionic strength, temperature, pressure, and electric field or due to the presence of a cosolvent that perturbs the delicate balance between stabilizing and destabilizing interactions and eventually induces chemical modifications. For most proteins, denaturation is a complex process involving transient intermediates in several reversible and eventually irreversible steps. Knowledge of protein stability and denaturation processes is mandatory for the development of enzymes as industrial catalysts, biopharmaceuticals, analytical and medical bioreagents, and safe industrial food. Electrophoresis techniques operating under extreme conditions are convenient tools for analyzing unfolding transitions, trapping transient intermediates, and gaining insight into the mechanisms of denaturation processes. Moreover, quantitative analysis of electrophoretic mobility transition curves allows the estimation of the conformational stability of proteins. These approaches include polyacrylamide gel electrophoresis and capillary zone electrophoresis under cold, heat, and hydrostatic pressure and in the presence of non-ionic denaturing agents or stabilizers such as polyols and heavy water. Lastly, after exposure to extremes of physical conditions, electrophoresis under standard conditions provides information on irreversible processes, slow conformational drifts, and slow renaturation processes. The impressive developments of enzyme technology with multiple applications in fine chemistry, biopharmaceutics, and nanomedicine prompted us to revisit the potentialities of these electrophoretic approaches. This feature review is illustrated with published and unpublished results obtained by the authors on cholinesterases and paraoxonase, two physiologically and toxicologically important enzymes. [ABSTRACT FROM AUTHOR]

I propose a fully nonparametric framework for estimation of and inference on the location of political parties in a one-dimensional scale. I derive a novel balance condition sufficient for identification and consistent estimation of the parties' order and a weaker error rate condition that partially identifies the order. Capitalizing on recent advances in testing moment inequalities, I propose a strong and weak specification test of the one-dimensional model. These tests can also be interpreted as tests of a weak sufficient condition for the existence of a majority-core party. Under stronger cardinal assumptions on preferences, I rely on arguments from revealed preference theory to execute similar tests efficiently, obviating the need for confidence set construction using an approximating grid of party locations. I illustrate these techniques using Comparative Study of Electoral Systems survey data on the German party system. [ABSTRACT FROM AUTHOR]

In this paper, we completely characterize the dynamics of the four-dimensional phase space of a Hamiltonian system with non-semisimple 0:1 resonance related to the reversible butterfly catastrophe. Through the normalization procedure, the Hamiltonian can be easily represented as the integrable part N (1 2 (x 2 + y 2) , p , q ; λ) plus a small perturbation P (x , y , p , q ; λ) . Firstly, we give the bifurcation diagram of the integrable part N on each level of the integral I 1 = 1 2 (x 2 + y 2) in detail, which contains three subordinate local codimension-one bifurcations (subcritical/supercritical Hamiltonian pitchfork bifurcation and Hamiltonian centre-saddle bifurcation) as well as a subordinate global codimension-one connection bifurcation. Then, we prove the persistence of typical bifurcation scenarios (e.g., two-dimensional invariant tori and homoclinic orbits) under the small perturbation P , as described by Broer et al. (Z Angew Math Phys 44:389–432, 1993). [ABSTRACT FROM AUTHOR]

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In this paper a simple but effective procedure to avoid degeneracies in ordinal Unfolding for preference rank data based on the Kemeny distance is proposed. Considering Unfolding as a particular MDS procedure with missing within-set proximities, unknown proximities are first estimated using correlations related to the Kemeny distance, and then the complete proximity matrix is analyzed in a standard MDS framework. A simulation study shows that our proposal is able to both recover the order of the preferences and reproduce the position of both rankings and objects in a geometrical space. Several applications on real data sets show that our procedure returns non-degenerate Unfolding solutions. [ABSTRACT FROM AUTHOR]

In this paper, we study the top quark pair events production in pp collisions in the ℓ + jets channel at the energy of s = 1 4 TeV for Standard Model as well as new physics processes. We explore the usage of semi-boosted topologies where the top quark decays into a high-transverse momentum (boosted) hadronic W-jet and an isolated b-jet and study their performance in the t t ̄ events kinematic reconstruction. An important event fraction is recovered and the correlation of selected kinematic variables between the detector and particle level is studied. Quality of the reconstructed mass line shape of a hypothetical scalar resonance decaying into t t ̄ is evaluated and compared for regimes of a different degree of the transverse boost. Unfolding performance is checked in terms of comparing the excess of events in spectra before and after the unfolding, concluding with the proof of a signal significance loss after the unfolding procedure for both energy and angle related observables, with possible applications for current LHC experiments. [ABSTRACT FROM AUTHOR]

Many activities in business process management, such as process retrieval, process mining, and process integration, need to determine the similarity between business processes. Along with many other relational behavior semantics, Transition Adjacency Relation (abbr. TAR) has been proposed as a kind of behavioral gene of process models and a useful perspective for process similarity measurement. In this article we explain why it is still relevant and necessary to improve TAR or pTAR (i.e., projected TAR) computation efficiency and put forward a novel approach for TAR computation based on Petri net unfolding. This approach not only improves the efficiency of TAR computation, but also enables the long-expected combined usage of TAR and Behavior Profiles (abbr. BP) in process model similarity estimation. [ABSTRACT FROM AUTHOR]

This study aimed to better characterize a recently purified stable extracellular alkaline peptidase produced by Penicillium aurantiogriseum (URM 4622) through fluorescence spectroscopy, far-UV circular dichroism, kinetic and thermodynamic models to understand its' structure-activity and denaturation. Fluorescence data showed that changing pH leads to tryptophan residues exposure to more hydrophilic environments at optimum activity pH 9.0 and 10.0. When thermally treated, it displayed less unfolding at these pH values, along with 4-fold less photoproducts formation than at neutral pH. Different pH CD spectra showed more β-sheet (21.5–43.0%) than α-helix (1–6.2%). At pH9.0, more than 2-fold higher α-helix content than any other pH. The melting temperature (Tm) was observed between 50 and 60 °C at all pH studied, with lower Tm at pH 9.0–11.0 (54.9–50.3 °C). The protease displayed two phase transition, with two energies of denaturation, and a 4-fold higher thermal stability (ΔH°m) than reports for other microorganism's proteases. An irreversible folding transition occurs between 50 and 60 °C. It displayed energies of denaturation suggesting higher thermal stability than reported for other microorganism's proteases. These results help elucidating the applicability of this new stable protease. [ABSTRACT FROM AUTHOR]

In this paper we compare the notion of stochastic two-scale convergence in the mean (by Bourgeat, Mikelić and Wright), the notion of stochastic unfolding (recently introduced by the authors), and the quenched notion of stochastic two-scale convergence (by Zhikov and Pyatnitskii). In particular, we introduce stochastic two-scale Young measures as a tool to compare mean and quenched limits. Moreover, we discuss two examples, which can be naturally analyzed via stochastic unfolding, but which cannot be treated via quenched stochastic two-scale convergence. [ABSTRACT FROM AUTHOR]

This article focuses on hyperspectral image (HSI) super-resolution that aims to fuse a low-spatial-resolution HSI and a high-spatial-resolution multispectral image to form a high-spatial-resolution HSI (HR-HSI). Existing deep learning-based approaches are mostly supervised that rely on a large number of labeled training samples, which is unrealistic. The commonly used model-based approaches are unsupervised and flexible but rely on handcrafted priors. Inspired by the specific properties of model, we make the first attempt to design a model-inspired deep network for HSI super-resolution in an unsupervised manner. This approach consists of an implicit autoencoder network built on the target HR-HSI that treats each pixel as an individual sample. The nonnegative matrix factorization (NMF) of the target HR-HSI is integrated into the autoencoder network, where the two NMF parts, spectral and spatial matrices, are treated as decoder parameters and hidden outputs, respectively. In the encoding stage, we present a pixelwise fusion model to estimate hidden outputs directly and then reformulate and unfold the model’s algorithm to form the encoder network. With the specific architecture, the proposed network is similar to a manifold prior-based model and can be trained patch by patch rather than the entire images. Moreover, we propose an additional unsupervised network to estimate the point spread function and spectral response function. Experimental results conducted on both synthetic and real datasets demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) unmixing is an increasingly studied problem in various areas, including remote sensing. It has been tackled using both physical model-based approaches and more recently machine learning-based ones. In this article, we propose a new HSI unmixing algorithm combining both model- and learning-based techniques, based on algorithm unrolling approaches, delivering improved unmixing performance. Our approach unrolls the alternating direction method of multipliers (ADMMs) solver of a constrained sparse regression problem underlying a linear mixture model. We then propose a neural network structure for abundance estimation that can be trained using supervised learning techniques based on a new composite loss function. We also propose another neural network structure for blind unmixing that can be trained using unsupervised learning techniques. Our proposed networks are also shown to possess a lighter and richer structure containing less learnable parameters and more skip connections compared with other competing architectures. Extensive experiments show that the proposed methods can achieve much faster convergence and better performance even with a very small training dataset size when compared with other unmixing methods, such as model-inspired neural network for abundance estimation (MNN-AE), model-inspired neural network for blind unmixing (MNN-BU), unmixing using deep image prior (UnDIP), and endmember-guided unmixing network (EGU-Net). [ABSTRACT FROM AUTHOR]

Large tensor (multi-dimensional array) data routinely appear nowadays in a wide range of applications, due to modern data collection capabilities. Often such observations are taken over time, forming tensor time series. In this article we present a factor model approach to the analysis of high-dimensional dynamic tensor time series and multi-category dynamic transport networks. This article presents two estimation procedures along with their theoretical properties and simulation results. We present two applications to illustrate the model and its interpretations. [ABSTRACT FROM AUTHOR]

This essay explores the relationship between fantasy, fabulation, and the performative through the lens of exhibition design in general and, in particular, expo pavilions. While studying the case of the Austrian pavilion visited by the author during Expo Milan 2015, it further examines the repertoire of the performative as a spatial fabulation indicator. Furthermore, the essay proposes a dramaturgy of analytical tools that interpret the exhibition space as a metaphor of a fantasy-oriented spatial production, and as an illustration of a performative interior architecture opening newer perspectives through which we may study and analyse interiors. [ABSTRACT FROM AUTHOR]

We study the effect of the unfolding of a reducible double confluent Heun equation from the point of view of the Stokes phenomenon. We introduce a small complex parameter ε that splits together the non-resonant singular points x = 0 and x = ∞ into four different Fuchsian singularities x L = − ε , x R = ε , and x L L = − 1 / ε , x R R = 1 / ε , respectively. The perturbed equation is a symmetric general Heun equation and its general solution depends analytically on ε . Then we prove that when the perturbed equation has exactly two resonant singularities of different type, all the Stokes matrices of the initial double confluent Heun equation are realized as a limit of the upper-triangular parts of the monodromy matrices of the perturbed equation when ε → 0 . To establish this result we combine a direct computation with a theoretical approach. [ABSTRACT FROM AUTHOR]

The paper brings Froebel's philosophy into conversation with that of Deleuze. We focus on the fold and on self-activity as key concepts that hold a special place in the monist philosophies of both thinkers. One point at which their (very different) ontologies coincide is their conceptualization of a cosmos in which everything is ultimately in relation. The philosophical convergences of such different thinkers in different eras are mapped in relation to the influences of a shared lineage with some earlier hermetic and romantic strains of thought. Both Froebel and Deleuze conceive of subjectivity as a relation of dynamic folding and unfolding of inner life and external world. The fold, as the operation that brings outside and inside together in a unitary system, counters the dualisms that still tend to structure thought: for instance, ideal/material, intelligible/sensible, nature/culture, individual/social. Reading Deleuze with Froebel helps to draw out some theoretical underpinnings of Froebel's holism, by bringing movement, matter and the senses back into focus and rethinking the relation between children and their environment in learning and development. We discuss some empirical examples of what this might look like from a current research project, focusing on imitation as one example of the fold between the inner life and the outer worlds of young children. In particular, we are interested in exploring how Froebel's conception of imitation as a dynamic and metamorphic act of self-transformation might share some affinities with the concept of becoming developed by Deleuze and Guattari. [ABSTRACT FROM AUTHOR]

Optimizing a hybrid beamforming transmitter is a non-convex problem and requires channel state information, leading in most cases to nontrivial feedback overhead. We propose a methodology relying on the principles of deep generative models and unfolding to achieve near-optimal hybrid beamforming with reduced feedback and computational complexity. We first represent the channel as a low-dimensional manifold via a generative adversarial network (GAN) and search the optimum digital and analog precoders in this low-dimensional space. To decrease the search complexity, we find an iteration rule by formulating hybrid beamforming as a bi-level optimization problem and then unfold each iteration as a neural layer. This results in a novel model-based deep neural network that incorporates domain knowledge. Our results show that this method (i) approaches the capacity-achieving spectral efficiency, (ii) provides a superior energy and spectral efficiency tradeoff, (iii) decreases feedback overhead, and (iv) reduces the complexity significantly, by optimizing a single low-dimensional vector per channel coherence time, with the neural network itself trained offline. The achieved spectral efficiency is robust when tested with realistic 3GPP channel models, even if the offline training relies on a simple geometric channel model. [ABSTRACT FROM AUTHOR]

The most common technique for generating B-mode ultrasound (US) images is delay-and-sum (DAS) beamforming, where the signals received at the transducer array are sampled before an appropriate delay is applied. This necessitates sampling rates exceeding the Nyquist rate and the use of a large number of antenna elements to ensure sufficient image quality. Recently, we proposed methods to reduce the sampling rate and the array size relying on image recovery using iterative algorithms based on compressed sensing (CS) and the finite rate of innovation (FRI) frameworks. Iterative algorithms typically require a large number of iterations, making them difficult to use in real time. In this article, we propose a reconstruction method from sub-Nyquist samples in the time and spatial domain, which is based on unfolding the iterative shrinkage thresholding algorithm (ISTA), resulting in an efficient and interpretable deep network. The inputs to our network are the subsampled beamformed signals after summation and delay in the frequency domain, requiring only a subset of the US signal to be stored for recovery. Our method allows reducing the number of array elements, sampling rate, and computational time while ensuring high-quality imaging performance. Using in vivo data, we demonstrate that the proposed method yields high-quality images while reducing the data volume traditionally used up to 36 times. In terms of image resolution and contrast, our technique outperforms previously suggested methods as well as DAS and minimum-variance (MV) beamforming, paving the way to real-time applicable recovery methods. [ABSTRACT FROM AUTHOR]

In bacteria, Lon is a large hexameric ATP‐dependent protease that targets misfolded and also folded substrates, some of which are involved in cell division and survival of cellular stress. The N‐terminal domain of Lon facilitates substrate recognition, but how the domains confer such activity has remained unclear. Here, we report the full‐length structure of Lon protease from Thermus thermophilus at 3.9 Å resolution in a substrate‐engaged state. The six N‐terminal domains are arranged in three pairs, stabilized by coiled‐coil segments and forming an additional channel for substrate sensing and entry into the AAA+ ring. Sequence conservation analysis and proteolysis assays confirm that this architecture is required for the degradation of both folded and unfolded substrates in bacteria. [ABSTRACT FROM AUTHOR]

Writing from an African and philosophical position, we argue that intellectual activists require an unfoldment, because they have been hindered by different factors such as co-option and exile. The African philosophy of ubuntu, particularly its epistemic dimension, is used as the basis for unfoldment. We depart from the justified but extremely combative critique of (neo)colonialism, and posit that there is the potential to solve African problems without perpetually blaming colonialism. Using the African Renaissance theory, we emphasise a shift towards becoming better through development and by analysing the question of poverty. Poverty is worsened by development policies, which often remain unchallenged. African philosophy can simultaneously fight colonial discourses on Africa while contributing towards human well-being by situating intellectual activism in the African condition of poverty. Intellectual activists should participate, together with citizens, in the formulation of relevant and practical policies. This requires a reframing of the intellectual landscape in order to stimulate a participatory intellectual project. It enables alternative understandings of what it means to be an intellectual, beyond the usual role of writer. Reframing intellectual activism offers an opportunity to use indigenous languages to make sense of local and lived realities on the ground. [ABSTRACT FROM AUTHOR]

We deal with Hamiltonian bifurcations associated with the reversible umbilic in two degrees of freedom systems defined by 0:1 resonance, i.e. the unperturbed equilibrium has two purely imaginary eigenvalues and a semisimple double-zero one. The Hamiltonian is written as the sum of integrable Hamiltonian N (1 2 (x 2 + y 2) , q , p ; λ) and a small perturbation P (x , y , q , p ; λ) by the normalization procedure. The phase portrait of N on each level of the integral I 1 = 1 2 (x 2 + y 2) is then studied in detail, obtaining an unfolding related to the reversible hyperbolic umbilic catastrophe. The persistence of 2-tori (i.e. two-dimensional tori) for the full system is analysed via KAM theory pointed out just as in Broer et al. (Z Angew Math Phys 44: 389–432, 1993), (in: Langford, Nagata (eds) Normal forms and homoclinic chaos, Waterloo, (1992), Fields Institute Communications 4 (1995)). In a sense, our results can be seen as a four-dimensional extension of a planar problem studied by Hanßmann (Phys D 112: 81–94, 1998). [ABSTRACT FROM AUTHOR]

Many time-dependent linear partial differential equations of mathematical physics and continuum mechanics can be phrased in the form of an abstract evolutionary system defined on a Hilbert space. In this paper we discuss a general framework for homogenization (periodic and stochastic) of such systems. The method combines a unified Hilbert space approach to evolutionary systems with an operator theoretic reformulation of the well-established periodic unfolding method in homogenization. Regarding the latter, we introduce a well-structured family of unitary operators on a Hilbert space that allows to describe and analyze differential operators with rapidly oscillating (possibly random) coefficients. We illustrate the approach by establishing periodic and stochastic homogenization results for elliptic partial differential equations, Maxwell's equations, and the wave equation. [ABSTRACT FROM AUTHOR]

This paper proposes a hierarchical visual architecture for perceiving garments' configuration independently from their type for the robotic unfolding task. Special focus is given on the decomposition of folded configurations into low- and high-level features. The low-level features comprise junctions of edges, which act as localized indicators of the clothing article's state, while the high-level components refer to its layers and the axis that unites them. The proposed methodology extracts and classifies the low-level components into indicators of folds, overlaps, garment's edges and corners and through their combination reconstructs the axis and the layers of the garment. The methodology is independent from the garment's shape while it uses depth sensors so that it can deal with garments of various colours, patterns and decorative features. Experiments showed the effectiveness of the method in scenarios with onefold or twofold and in different datasets, proving the extensibility of the approach. [ABSTRACT FROM AUTHOR]

An ideal triangulation of a singular flat surface is a geodesic triangulation such that its vertex set is equal to the set of singular points of the surface. Using the fact that each pair of points in a surface has a finite number of geodesics having length ≤ L connecting them, where L is any positive number, we prove that each singular flat surface has an ideal triangulation provided that the surface has singular points when it has no boundary components, or each of its boundary components has a singular point. Also, we prove that such a surface contains a finite number of geodesics which connect its singular points so that when we cut the surface through these arcs we get a flat disc with a nonsingular interior. [ABSTRACT FROM AUTHOR]

Compared with more rigid objects, clothing items are inherently difficult for robots to recognize and manipulate. We propose a method for detecting how cloth is folded, to facilitate choosing a manipulative action that corresponds to a garment's shape and position. The proposed method involves classifying the edges and corners of a garment by distinguishing between edges formed by folds and the hem or ragged edge of the cloth. Identifying the type of edges in a corner helps to determinate how the object is folded. This bottom-up approach, together with an active perception system, allows us to select strategies for robotic manipulation. We corroborate the method using a two-armed robot to manipulate towels of different shapes, textures, and sizes. [ABSTRACT FROM AUTHOR]

Many real-world problems can be modeled as sparse matrix recovery from two-dimensional (2D) measurements, which is recognized as one of the most important topics in signal processing community. Benefited from the roaring success of compressed sensing, many classical iterative algorithms can be directly applied or reinvented for matrix recovery, though they are computationally expensive. To alleviate this, we propose a neural network named 2D learned proximal gradient algorithm (2D-LPGA), which aims to quickly reconstruct the target matrix. Theoretical analysis reveals that if the parameters of the network satisfy certain conditions, it can reconstruct the sparse signal with linear convergence rate. Moreover, numerical experiments demonstrate the superiority of the proposed method over other classical schemes. [ABSTRACT FROM AUTHOR]

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The neutron energy spectrum is one of the most important characteristic parameters. A novel optical measurement method is proposed. The purpose of the method is to determine the neutron spectra according to the recoil proton track length. The recoil protons deposit energy along the track and excite scintillator luminescence. The luminescence image directly reflects the neutron energy spectra. The Geant4 simulation toolkit is used to study the characteristics of the recoil proton luminescence distribution and determine the detector system response. A reconstruction algorithm based on the potential reduction interior point is developed and applied to spectrum unfolding. This method has the advantages of an intuitive measurement, good energy resolution, suitability for various charged particle beams, a wide energy range, convenience, and an adjustable range. [ABSTRACT FROM AUTHOR]

We reconstruct the Cartan Equations of null Killing magnetic curve γ (s) in ℝ 1 3 with Killing magnetic vector field V under the new Cartan frame { γ ′ (s) , V ∧ γ ′ (s) , V ′ ∧ γ ′ (s) − V } , which describe some new geometrical properties of γ (s). The singularity properties of the rectifying surfaces and the binormal osculating surfaces of null Killing magnetic curves are given. As an application, two examples are given to explain the main results, where the singular loci of null Killing magnetic curves are obtained. [ABSTRACT FROM AUTHOR]

In this paper we describe RooFitUnfold, an extension of the RooFit statistical software package to treat unfolding problems, and which includes most of the unfolding methods that commonly used in particle physics. The package provides a common interface to these algorithms as well as common uniform methods to evaluate their performance in terms of bias, variance and coverage. In this paper we exploit this common interface of RooFitUnfold to compare the performance of unfolding with the Richardson–Lucy, Iterative Dynamically Stabilized, Tikhonov, Gaussian Process, bin-by-bin and inversion methods on several example problems. [ABSTRACT FROM AUTHOR]