Complementary Constraints Research Articles

Impact of innovation effort on exports from emerging market firms: limitations arising from complementary resource constraints

Purpose This paper aims to examine the impact of innovation effort on exports of resource constrained emerging market firms (EMFs) and boundary conditions imposed by complementary tangible and intangible resources on this relationship, using the lens of the knowledge-based view (KBV). Design/methodology/approach Analysis is based on annualized data from 19,057 Indian firms over the period of 2009–2017, controlling for endogeneity and selection bias (using Heckman correction), firm-level controls, year and sectoral fixed effects, within robust Tobit and ordinary least squares regressions. Export intensity and R&D intensity are the key dependent and independent variables, respectively. Findings The baseline impact of innovation effort on exports is found to be a concave inverted U-shape, exhibiting decreasing returns. Availability of complementary resources significantly impacts the nature of this relationship by weakening it for more resource-constrained firms. Faced with relatively greater scale-related constraints, the impact of innovation effort on exports disappears. Greater process-related constraints weaken the relationship as well. Originality/value Theoretically, these findings shed light on a nuanced relationship between a firm’s search for knowledge assets and access to foreign markets within resource constrained emerging market contexts. The limitations in the use and applicability of the KBV for EMFs’ internationalization success are highlighted, with suggested directions of future research.

Minimal decaying dark matter: from cosmological tensions to neutrino signatures

The invisible decay of cold dark matter into a slightly lighter dark sector particle on cosmological time-scales has been proposed as a solution to the S 8 tension. In this work we discuss the possible embedding of this scenario within a particle physics framework, and we investigate its phenomenology. We identify a minimal dark matter decay setup that addresses the S 8 tension, while avoiding the stringent constraints from indirect dark matter searches. In our scenario, the dark sector contains two singlet fermions N 1,2, quasi-degenerate in mass, and carrying lepton number so that the heaviest state (N 2) decays into the lightest (N 1) and two neutrinos via a higher-dimensional operator N 2 → N̅ 1νν . The conservation of lepton number, and the small phase-space available for the decay, forbids the decay channels into hadrons and strongly suppresses the decays into photons or charged leptons. We derive complementary constraints on the model parameters from neutrino detectors, freeze-in dark matter production via νν → N 1 N 2, collider experiments and blazar observations, and we show that the upcoming JUNO neutrino observatory could detect signals of dark matter decay for model parameters addressing the S 8 tension if the dark matter mass is below ≃ 1 GeV.

Constraints on light dark sector particles from lifetime difference of heavy neutral mesons

Heavy meson decays with missing energy in the final state offer interesting avenues to search for light invisible new physics such as dark matter (DM). In this context, we show that such new physics (NP) interactions also affect lifetime difference in neutral meson-antimeson mixing. We consider general dimension-six effective quark interactions involving a pair of DM particles and calculate their contributions to lifetime difference in beauty and charm meson systems. We use the latest data on mixing observables to constrain the relevant effective operators. We find that lifetime differences provide novel and complementary flavor constraints compared to those obtained from heavy meson decays. Published by the American Physical Society 2024

The symbiotic recurrent nova V745 Sco at radio wavelengths

Abstract V745 Sco is a Galactic symbiotic recurrent nova with nova eruptions in 1937, 1989 and 2014. We study the behaviour of V745 Sco at radio wavelengths (0.6–37 GHz), covering both its 1989 and 2014 eruptions and informed by optical, X-ray, and γ-ray data. The radio light curves are synchrotron-dominated. Surprisingly, compared to expectations for synchrotron emission from explosive transients such as radio supernovae, the light curves spanning 0.6–37 GHz all peak around the same time (∼18–26 days after eruption) and with similar flux densities (5–9 mJy). We model the synchrotron light curves as interaction of the nova ejecta with the red giant wind, but find that simple spherically symmetric models with wind-like circumstellar material (CSM) cannot explain the radio light curve. Instead, we conclude that the shock suddenly breaks out of a dense CSM absorbing screen around 20 days after eruption, and then expands into a relatively low density wind ($\dot{M}_{out} \approx 10^{-9}-10^{-8}$ M⊙ yr−1 for vw = 10 km s−1) out to ∼1 year post-eruption. The dense, close-in CSM may be an equatorial density enhancement or a more spherical red giant wind with $\dot{M}_{in} \approx [5-10] \times 10^{-7}$ M⊙ yr−1, truncated beyond several × 1014 cm. The outer lower-density CSM would not be visible in typical radio observations of Type Ia supernovae: V745 Sco cannot be ruled out as a Type Ia progenitor based on CSM constraints alone. Complementary constraints from the free–free radio optical depth and the synchrotron luminosity imply the shock is efficient at accelerating relativistic electrons and amplifying magnetic fields, with εe and εB ≈ 0.01 − 0.1.

Inferring general links between energetics and information with unknown environment

Identifying general links between energetics and information in realistic open quantum systems is a long-standing problem in quantum thermodynamics and quantum information processing. However, the generality of existing efforts is often impeded by their specific assumptions about environments. Here we address the problem by developing a trajectory-level thermodynamic inference theory to establish general links using just the knowledge of the system, enabling a single framework applicable to a diverse range of environments. Underpinning the framework is a notion of excess energy introduced for inferring the net energy gain of the system after completing an information processing trajectory. We show that fluctuation behaviors of the excess energy encode general links between energetics and information with a conceptual advantage that they completely avoid assumptions about environments and system-environment coupling forms. Crucially, we obtain a single thermodynamic inequality that integrates upper bounds on heat dissipation and extracted work in terms of system's information content change, providing complementary constraints that greatly expand the context of existing well-adopted results based on the second law of thermodynamics. We also uncover lower bounds on the precision of the fluctuating system's energy and information content changes in terms of their Fano factors and a correlation function between them. By extending relations between energetics and information to higher-order fluctuations, we thus reveal a trade-off that a more precise inference of energy or information content changes requires a looser energetic-information link. We showcase the implications of these general links in a number of quantum information and thermodynamic tasks of application relevance. Our framework provides a toolkit for analyzing the interplay between energetics and information from the trajectory level in generic quantum systems, thereby adding an indispensable structure to the thermodynamics of information. Published by the American Physical Society 2024

Revisiting cosmological constraints on supersymmetric SuperWIMPs

SuperWIMPs are extremely weakly interacting massive particles that inherit their relic abundance from late decays of frozen-out parent particles. Within supersymmetric models, gravitinos and axinos represent two of the most well motivated superWIMPs. In this paper we revisit constraints on these scenarios from a variety of cosmological observations that probe their production mechanisms as well as the superWIMP kinematic properties in the early Universe. We consider in particular observables of Big Bang Nucleosynthesis and the Cosmic Microwave Background (spectral distortion and anisotropies), which limit the fractional energy injection from the late decays, as well as warm and mixed dark matter constraints derived from the Lyman-α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha $$\\end{document} forest and other small-scale structure observables. We discuss complementary constraints from collider experiments, and argue that cosmological considerations rule out a significant part of the gravitino and the axino superWIMP parameter space.

Thermal and non-thermal DM production in non-standard cosmologies: a mini review

We provide a short review of some aspects of dark matter (DM) production in non-standard cosmology. Considering the simplest Higgs portal model as a definite particle physics setup, we consider the impact on the parameter space corresponding to the correct relic density and the complementary experimental constraints of the presence, during thermal production, of an exotic component dominating the energy density of the universe. In the second part of the work, we will focus on the case that such an exotic component satisfies the equation of state of matter and can produce DM non-thermally.

Positivity-causality competition: a road to ultimate EFT consistency constraints

Effective field theories (EFT) are strongly constrained by fundamental principles such as unitarity, locality, causality, and Lorentz invariance. In this paper, we consider the EFT of photons (or other U(1) gauge field) and compare different approaches to obtain bounds on its Wilson coefficients. We present an analytic derivation of the implications of unitarity (linear and non-linear positivity bounds) and compare these constraints with the requirement of causal propagation of the photon modes around non-trivial backgrounds generated by external sources. Within this setup, we find that the low energy causality condition can give complementary constraints to our analytic positivity bounds. In other words, simple analytic techniques can give strong constraints on the allowed region of the photon EFT parameters even when the positivity bounds are not numerically optimized.

Cosmological implications of gauged U(1) B-L on ΔN eff in the CMB and BBN

We calculate the effects of a light, very weakly-coupled boson X arising from a spontaneously broken U(1) B-L symmetry on ΔN eff as measured by the CMB and Yp from BBN. Our focus is the mass range 1 eV ≲ mX ≲ 100 MeV; masses lighter than about an eV have strong constraints from fifth-force law constraints, while masses heavier than about 100 MeV are constrained by other probes, including terrestrial experiments. We do not assume N eff began in thermal equilibrium with the SM; instead, we allow N eff to freeze-in from its very weak interactions with the SM. We find U(1) B-L is more strongly constrained by ΔN eff than previously considered. The bounds arise from the energy density in electrons and neutrinos slowly siphoned off into N eff bosons, which become nonrelativistic, redshift as matter, and then decay, dumping their slightly larger energy density back into the SM bath causing ΔN eff > 0. While some of the parameter space has complementary constraints from stellar cooling, supernova emission, and terrestrial experiments, we find future CMB observatories including Simons Observatory and CMB-S4 can access regions of mass and coupling space not probed by any other method. In gauging U(1) B-L , we assume the [U(1) B-L ]3 anomaly is canceled by right-handed neutrinos, and so our ΔN eff calculations have been carried out in two scenarios: neutrinos have Dirac masses, or, right-handed neutrinos acquire Majorana masses. In the latter scenario, we comment on the additional implications of thermalized right-handed neutrinos decaying during BBN. We also briefly consider the possibility that X decays into dark sector states. If these states behave as radiation, we find weaker constraints, whereas if they are massive, there are stronger constraints, though now from ΔN eff < 0.

Cross-Constrained Progressive Inference for 3D Hand Pose Estimation with Dynamic Observer-Decision-Adjuster Networks

Generalization is very important for pose estimation, especially for 3D pose estimation where small changes in the 2D images could trigger structural changes in the 3D space. To achieve generalization, the system needs to have the capability of detecting estimation errors by double-checking the projection coherence between the 3D and 2D spaces and adapting its network inference process based on this feedback. Current pose estimation is one-time feed-forward and lacks the capability to gather feedback and adapt the inference outcome. To address this problem, we propose to explore the concept of progressive inference where the network learns an observer to continuously detect the prediction error based on constraints matching, as well as an adjuster to refine its inference outcome based on these constraints errors. Within the context of 3D hand pose estimation, we find that this observer-adjuster design is relatively unstable since the observer is operating in the 2D image domain while the adjuster is operating in the 3D domain. To address this issue, we propose to construct two sets of observers-adjusters with complementary constraints from different perspectives. They operate in a dynamic sequential manner controlled by a decision network to progressively improve the 3D pose estimation. We refer to this method as Cross-Constrained Progressive Inference (CCPI). Our extensive experimental results on FreiHAND and HO-3D benchmark datasets demonstrate that the proposed CCPI method is able to significantly improve the generalization capability and performance of 3D hand pose estimation.

Primordial non-Gaussianities with weak lensing: information on non-linear scales in the Ulagam full-sky simulations

Primordial non-Gaussianities (PNGs) are signatures in the density field that encode particle physics processes from the inflationary epoch. Such signatures have been extensively studied using the Cosmic Microwave Background, through constraining their amplitudes, fX NL, with future improvements expected from large-scale structure surveys; specifically, the galaxy correlation functions. We show that weak lensing fields can be used to achieve competitive and complementary constraints. This is shown via the Ulagam suite of N-body simulations, a subset of which evolves primordial fields with four types of PNGs. We create full-sky lensing maps and estimate the Fisher information from three summary statistics measured on the maps: the moments, the cumulative distribution function, and the 3-point correlation function. We find that the year 10 sample from the Rubin Observatory Legacy Survey of Space and Time (LSST) can constrain PNGs to σ(f NL eq) ≈ 110, σ(f NL or, lss) ≈ 120, σ(f NL loc) ≈ 40. For the former two, this is better than or comparable to expected galaxy clustering-based constraints from the Dark Energy Spectroscopic Instrument (DESI). The PNG information in lensing fields is on non-linear scales and at low redshifts (z ≲ 1.25), with a clear origin in the evolution history of massive halos. The constraining power degrades by ∼60% under scale cuts of ≳ 20 Mpc, showing there is still significant information on scales mostly insensitive to small-scale systematic effects (e.g., baryons). We publicly release the Ulagam suite to enable more survey-focused analyses.

Constraining decaying very heavy dark matter from galaxy clusters with 14 year Fermi-LAT data

Galaxy clusters are promising targets for indirect detection of dark matter thanks to the large dark matter content. Using 14 years of Fermi-LAT data from seven nearby galaxy clusters, we obtain constraints on the lifetime of decaying very heavy dark matter particles with masses ranging from 103 GeV to 1016 GeV. We consider a variety of decaying channels and calculate prompt gamma rays and electrons/positrons from the dark matter. Furthermore, we take into account electromagnetic cascades induced by the primary gamma rays and electrons/positrons, and search for the resulting gamma-ray signals from the directions of the galaxy clusters. We adopt a Navarro-Frenk-White profile of the dark matter halos, and use the profile likelihood method to set lower limits on the dark matter lifetime at a 95% confidence level. Our results are competitive with those obtained through other gamma-ray observations of galaxy clusters and provide complementary constraints to existing indirect searches for decaying very heavy dark matter.

Axion star explosions: A new source for axion indirect detection

If dark matter is composed of axions, then axion stars form in the cores of dark matter halos. These stars are unstable above a critical mass, decaying to radio photons that heat the intergalactic medium, offering a new channel for axion indirect detection. We recently provided the first accurate calculation of the axion decay rate due to axion star mergers. In this work we show how existing data concerning the CMB optical depth leads to strong constraints on the axion photon coupling in the mass range 10−14 eV≲ma≲10−8 eV. Axion star decays lead to efficient reionization of the intergalactic medium during the dark ages. By comparing this nonstandard reionization with Planck legacy measurements of the Thomson optical width, we show that couplings in the range 10−14 GeV−1≲gaγγ≲10−10 GeV−1 are excluded for our benchmark model of axion star abundance. Future measurements of the 21 cm emission of neutral hydrogen at high redshift could improve this limit by an order of magnitude or more, providing complementary indirect constraints on axion dark matter in parameter space also targeted by direct detection haloscopes. Published by the American Physical Society 2024

Data-Driven Distributionally Robust Optimization-Based Coordinated Dispatching for Cascaded Hydro-PV-PSH Combined System

The increasing penetration of photovoltaic (PV) and hydroelectric power generation and their coupling uncertainties have brought great challenges to multi-energy’s coordinated dispatch. Traditional methods such as stochastic optimization (SO) and robust optimization (RO) are not feasible due to the unavailability of accurate probability density function (PDF) and over-conservative decisions. This limits the operational efficiency of the clean energies in cascaded hydropower and PV-enriched areas. Based on data-driven distributionally robust optimization (DRO) theory, this paper tailors a joint optimization dispatching method for a cascaded hydro-PV-pumped storage combined system. Firstly, a two-step model for a Distributed Renewable Optimization (DRO) dispatch is developed to create the daily dispatch plan, taking into account the system’s complementary economic dispatch cost. Furthermore, the inclusion of a complementary norm constraint is implemented to restrict the confidence set of the probability distribution. This aims to identify the optimal adjustment scheme for the day-ahead dispatch schedule, considering the adjustment cost associated with real-time operations under the most unfavorable distribution conditions. Utilizing the MPSP framework, the Column and Constraint Generation (CCG) algorithm is employed to resolve the two-stage dispatch model. The optimal dispatch schedule is then produced by integrating the daily dispatch plan with the adjustive dispatch scheme. Finally, the numerical dispatch results obtained from an actual demonstration area substantiate the effectiveness and efficiency of the proposed methodology.

True Pair-instability Supernova Descendant: Implications for the First Stars’ Mass Distribution

The initial mass function (IMF) of the first Population III (Pop III) stars remains a persistent mystery. Their predicted massive nature implies the existence of stars exploding as pair-instability supernovae (PISNe), but no observational evidence had been found. Now, the LAMOST survey claims to have discovered a pure PISN descendant, J1010+2358, at [Fe/H] = − 2.4. Here we confirm that a massive 250–260 M ⊙ PISN is needed to reproduce the abundance pattern of J1010+2358. However, the PISN contribution can be as low as 10%, since key elements are missing to discriminate between scenarios. We investigate the implications of this discovery for the Pop III IMF, by statistical comparison with the predictions of our cosmological galaxy formation model, NEFERTITI. First, we show that the nondetection of mono-enriched PISN descendants at [Fe/H] < − 2.5 allows us to exclude (i) a flat IMF at a 90% confidence level; and (ii) a Larson-type IMF with characteristic mass m ch/M ⊙ > 191.16x − 132.44, where x is the slope, at a 75% confidence level. Second, we show that if J1010+2358 has only inherited <70% of its metals from a massive PISN, no further constraints can be put on the Pop III IMF. If, instead, J1010+2358 will be confirmed to be a nearly pure (>90%) PISN descendant, it will offer strong and complementary constraints on the Pop III IMF, excluding the steepest and bottom-heaviest IMFs: m ch/M ⊙ < 143.21x − 225.94. Our work shows that even a single detection of a pure PISN descendant can be crucial to our understanding of the mass distribution of the first stars.

PIDNET: Polar Transformation Based Implicit Disentanglement Network for Truncation Artifacts.

The interior problem, a persistent ill-posed challenge in CT imaging, gives rise to truncation artifacts capable of distorting CT values, thereby significantly impacting clinical diagnoses. Traditional methods have long struggled to effectively solve this issue until the advent of supervised models built on deep neural networks. However, supervised models are constrained by the need for paired data, limiting their practical application. Therefore, we propose a simple and efficient unsupervised method based on the Cycle-GAN framework. Introducing an implicit disentanglement strategy, we aim to separate truncation artifacts from content information. The separated artifact features serve as complementary constraints and the source of generating simulated paired data to enhance the training of the sub-network dedicated to removing truncation artifacts. Additionally, we incorporate polar transformation and an innovative constraint tailored specifically for truncation artifact features, further contributing to the effectiveness of our approach. Experiments conducted on multiple datasets demonstrate that our unsupervised network outperforms the traditional Cycle-GAN model significantly. When compared to state-of-the-art supervised models trained on paired datasets, our model achieves comparable visual results and closely aligns with quantitative evaluation metrics.

A nonmonton active interior point trust region algorithm based on CHKS smoothing function for solving nonlinear bilevel programming problems

&lt;abstract&gt;&lt;p&gt;In this paper, an approach is suggested to solve nonlinear bilevel programming (NBLP) problems. In the suggested method, we convert the NBLP problem into a standard nonlinear programming problem with complementary constraints by applying the Karush-Kuhn-Tucker condition to the lower-level problem. By using the Chen-Harker-Kanzow-Smale (CHKS) smoothing function, the nonlinear programming problem is successively smoothed. A nonmonton active interior-point trust-region algorithm is introduced to solve the smoothed nonlinear programming problem to obtain an approximately optimal solution to the NBLP problem. Results from simulations on several benchmark problems and a real-world case about a watershed trading decision-making problem show how the effectiveness of the suggested approach in NBLP solution development.&lt;/p&gt;&lt;/abstract&gt;

MYflow - A simple computer program for rheological modelling of mylonites

In this study, we present a new Matlab-derived software, MYflow, developed to perform rheological modelling of high-strain rocks and mylonites. The software handles both monomineralic and compositionally heterogeneous rocks made of various proportions of the most common minerals such as quartz, feldspar, calcite, olivine, plagioclase, micas, pyroxene, amphibole and garnet. The rheology of composite mylonites is evaluated using a suite of mixing models combined with two complementary mechanical constraints derived from the assumption of either uniform stress or strain-rate. Various compositions can be used to run either 0th dimensional rheological models corresponding to classical strength profiles, or 2D maps showing the grain-scale spatial variability of stress and strain rate as a function of composition, grain size and effective deformation mechanism. The applicability of the code, along with its main functionalities, is demonstrated using a model of composite mylonite that reproduces the typical microstructure of rocks deformed in high-strain zones. The software is further benchmarked by modelling the grain-scale distribution of effective deformation mechanism, stress, and strain-rate of three natural mylonite developed under different pressure, temperature, and strain-rate conditions. The outcomes of our modelling approach are compared to the results obtained from classical paleopiezometry studies and evaluated in relation to the processes that yield to partitioning of stress and strain rate in shear zones. Finally, we discuss the significance of mean stress and strain rate in mylonites addressing the applicability of recrystallized grain-size paleopiezometry.

Model and solution of sustainable bi-level emergency commodity allocation based on type-2 fuzzy theory

The allocation of emergency commodities is very important for efficient emergency response after a disaster. In this study, a sustainable multi-objective bi-level emergency commodity allocation model is established, providing a practical emergency commodity allocation strategy for humanitarian supply chains. First, type-2 fuzzy variables are used to describe uncertain transportation costs and handle them based on the conditional value-at-risk (CVaR) theory of type-2 fuzzy variables. Second, Karush-Kuhn–Tucker (KKT) conditions with complementary constraints are used, auxiliary variables are introduced, and global criteria are applied to achieve the equivalent transformation of the model. Finally, with the Wenchuan earthquake in China in 2008 as an example, we prove the necessity of introducing the CVaR for risk measurement and the practicability of using type-2 fuzzy variables to describe uncertain parameters by comparing this scenario with a risk-neutral situation and the proposed model with a model based on type-1 fuzzy theory. The novelties of this study include considering both the government and suppliers are decision-makers, exploiting CVaR to measure tail risk and using type-2 fuzzy theory to transform uncertain parameters. The contributions of this study include considering sustainability during the rescue process and proposing a CVaR formula based on type-2 theory.

Multi-modal person re-identification based on transformer relational regularization

For robust multi-modal person re-identification (re-ID) models, it is crucial to effectively utilize the complementary information and constraint relationships among different modalities. However, current multi-modal methods often overlook the correlation between modalities at the feature fusion stage. To address this issue, we propose a novel multimodal person re-ID method called Transformer Relation Regularization (TRR). Firstly, we introduce an adaptive collaborative matching module that facilitates the exchange of useful information by mining feature correspondences between modalities. This module allows for the integration of complementary information, enhancing the re-ID performance. Secondly, we propose an enhanced embedded module that corrects general information using discriminative information within each modality. By leveraging this approach, we improve the model’s stability in challenging multi-modal environments. Lastly, we propose an adaptive triple loss to enhance sample utilization efficiency and mitigate the problem of inconsistent representation among multimodal samples. This loss function optimizes the model’s ability to distinguish between different individuals, leading to improved re-ID accuracy. Experimental results on several challenging visible-infrared person re-ID benchmark datasets demonstrate that our proposed TRR method achieves optimal performance. Additionally, extensive ablation studies validate the effective contribution of each component to the overall model. In summary, our proposed TRR method effectively leverages complementary information, addresses the correlation between modalities, and improves the re-ID performance in multi-modal scenarios. The results obtained from various benchmark datasets and the comprehensive analysis support the efficacy of our approach.