A coherent perfect absorber exploits the interferometric nature of light to deposit all of a light field’s incident energy into an otherwise weakly absorbing sample. The downside of this concept is that the necessary destructive interference in coherent perfect absorbers gets easily destroyed both by spectrally or spatially detuning the incoming light field. Each of these two limitations has recently been overcome by insights from exceptional-point physics and by using a degenerate cavity, respectively. Here, we show how these two concepts can be combined into a new type of cavity design, which allows broadband exceptional-point absorption of arbitrary wavefronts. We present two possible implementations of such a massively degenerate exceptional-point absorber and compare analytical results with numerical simulations.
We introduce a physics-based computational reconstruction framework for non-invasive photoacoustic tomography through a thick aberrating layer. Our wave-based approach leverages an analytic formulation of diffraction to beamform a photoacoustic image, when the aberrating layer profile is known. When the profile of the aberrating layer is unknown, the same analytical formulation serves as the basis for an automatic-differentiation regularized optimization algorithm that simultaneously reconstructs both the profile of the aberrating layer and the optically absorbing targets. Results from numerical studies and proof-of-concept experiments show promise for fast beamforming that takes into account diffraction effect occurring in the propagation through thick, highly-aberrating layers.
Stochastic orbital techniques offer reduced computational scaling and memory requirements to describe ground and excited states at the cost of introducing controlled statistical errors. Such techniques often rely on two basic operations, stochastic trace estimation and stochastic resolution of identity, both of which lead to statistical errors that scale with the number of stochastic realizations (\$N\_\\textbackslashxi\\$) as \$\textbackslashsqrt\N\_\\textbackslashxi\ˆ\-1\\\$. Reducing the statistical errors without significantly increasing \$N\_\\textbackslashxi\\$ has been challenging and is central to the development of efficient and accurate stochastic algorithms. In this work, we build upon recent progress made to improve stochastic trace estimation based on the ubiquitous Hutchinson's algorithm and propose a two-step approach for the stochastic resolution of identity, in the spirit of the Hutch++ method. Our approach is based on employing a randomized low-rank approximation followed by a residual calculation, resulting in statistical errors that scale much better than \$\textbackslashsqrt\N\_\\textbackslashxi\ˆ\-1\\\$. We implement the approach within the second-order Born approximation for the self-energy in the computation of neutral excitations and discuss three different low-rank approximations for the two-body Coulomb integrals. Tests on a series of hydrogen dimer chains with varying lengths demonstrate that the Hutch++-like approximations are computationally more efficient than both deterministic and purely stochastic (Hutchinson) approaches for low error thresholds and intermediate system sizes. Notably, for arbitrarily large systems, the Hutchinson-like approximation outperforms both deterministic and Hutch++-like methods.
We investigate the depletion contributions to the self-assembly of microcolloids on solid substrates. The assembly is driven by the exclusion of nanoparticles and nonadsorbing polymers from the depletion zone between the microcolloids in the liquid and the underlying substrate. The model system consists of 1 μm polystyrene particles that we deposit on a flat glass slab in an electrolyte solution. Using polystyrene nanoparticles and poly(acrylic acid) polymers as depleting agents, we demonstrate in our experiments that nanoparticle concentrations of 0.5% (w/v) support well-ordered packing of microcolloids on glass, while the presence of polymers leads to irregular aggregate deposition structures. A mixture of nanoparticles and polymers enhances the formation of colloidal aggregate and particulate surface coverage compared to using the polymers alone as a depletion agent. Moreover, tuning the polymer ionization state from pH 4 to 9 modifies the polymer conformational state and radius of gyration, which in turn alters the microcolloid deposition from compact multilayers to flocculated structures. Our study provides entropic strategies for manipulating particulate assembly on substrates from dispersed to continuous coatings.
CEE countries faced significant political, economic, social, and technological transformations over the last four decades. Democratic processes, after relative stabilization, tremble again around polarizing values, populist leaders, or nationalistic ideologies. Online communication, especially social media platforms, play a vital role in shaping how citizens interact with the state, political actors, media, and other citizens. The collection of manuscripts focuses on some of the challenges democratic institutions in the region face, in transforming and sustaining civil society and attempts to capture how the digital media environments mitigate or exacerbate those challenges. Included manuscripts focus on the role that online platforms play in the satisfaction with democracy in the CEE region, the interactions between journalists and political actors, the strategic media coverage of elections, affective polarization and political antagonism, and discursive attempts to discourage young people from civic engagement.
How do religious citizens’ election projections influence voter turnout? While previous studies have demonstrated the significant impact of religious orientation on individuals’ general future outlook, little is known about the influence of religion on voters’ electoral expectations and how these expectations affect voter turnout. In this paper, we employ a nuanced conceptual framework of election projections and examine the impact of religion on both the affective and probabilistic aspects of citizens’ expectations regarding election outcomes. Our analysis draws upon original panel survey data collected in two countries, focusing on the 2021 Israeli general elections and the 2022 French presidential elections. The findings reveal a mobilizing effect of religious citizens’ election projections in both Israel and France. Specifically, religious voters tend to have more positive affective forecasts about their projected election outcomes, consequently resulting in increased voter turnout. While affective forecasting plays a significant role in religious citizens’ turnout, probabilistic certitude does not have a similar effect. We discuss the contribution and implications of these findings for research on religion and political behavior.
How do hard economic times affect countries’ foreign policy and, specifically, their international commitments? Although a large body of literature assumes that economic crises lead to the prioritization of domestic politics at the expense of international cooperation, these claims are rarely subjected to systematic empirical tests. This study examines one important aspect of these relationships: the consequences of economic crises for the survival of international organizations (IOs), a question that attracted only scant scholarly attention to date. Theoretically, we argue that even though economic crises can weaken member states’ commitment to IOs, they also underscore their ability to tackle the root causes of such crises and mitigate their most pernicious effects. As such, economic crises are actually conducive to IO longevity. We expect this effect to be especially pronounced for currency crises, IOs with an economic mandate, and regional IOs, given their particular relevance for international cooperation during hard economic times. These conjectures are tested with a comprehensive sample of IOs and data on currency, banking and sovereign debt crises from 1970 to 2014. Using event history models and controlling for several alternative explanations of IO survival, we find ample empirical support for the theoretical expectations.
Journalists and experts play a pivotal role in communicating risks and helping the public navigate uncertain futures. This study examines the co-construction of projections by journalists and experts across news and social media during the Covid-19 pandemic. Unlike traditional news production, where journalists exercise agency by transforming expert knowledge into news narratives, hybrid media environments involve multi-platform, multi-directional, and non-linear processes of knowledge production. In light of these characteristics, we introduce and develop the concept of “predictive agency,” referring to an actor’s active participation in predictive knowledge-making and encompassing journalistic, civic, and epistemic forms of agency in shaping and navigating future-oriented knowledge. We analyse the trajectories of 400 projections in Israel and the US, tracing the interactional and informational dynamics between journalists and experts. Through qualitative textual analysis of the various iterations of each projection, four types of co-constructed projection systems emerge: Amplify, Distill, Elaborate, and Contest. We explore the complexities of predictive agency and accountability in these systems, shedding light on how collective futures are contested and co-constructed in hybrid media environments.
Engineered metallic nanoparticles, which are found in numerous applications, are usually stabilized by organic ligands influencing their interfacial properties. We found that the ligands affect tremendously the electrochemical peak oxidation potentials of the nanoparticles. In this work, identical gold nanoparticles were ligand-exchanged and carefully analyzed to enable a precise and highly reproducible comparison. The peak potential difference between gold nanoparticles stabilized by various ligands, such as 2and 4-mercaptobenzoic acid, can be as high as 71 mV, which is substantial in energetic terms. A detailed study supported by density functional theory (DFT) calculations aimed to determine the source of this interesting effect. The DFT simulations of the ligand adsorption modes on Au surfaces were used to calculate the redox potentials through the thermodynamic cycle method. The DFT results of the peak potential shift were in good agreement with the experimental results for a few ligands, but showed some discrepancy, which was attributed to kinetic effects. The kinetic rate constant of the oxidation of Au nanoparticles stabilized by 4mercaptobenzoic acid was found to be twice as large as that of the Au nanoparticles stabilized by citrate, as calculated from Laviron’s theory and the Tafel equation. Finally, these findings could be applied to some novel applications such as determining the distribution of nanoparticle population in a dispersion as well as monitoring the ligand exchange between nanoparticles.
The present study examined the effect of ADHD-related traits, academic-achievement level, and giftedness label on elementary school teachers’ and counselors’ referral recommendations for assessment. 532 teachers and counselors were presented with one of 12 vignettes describing a hypothetical pupil. Participants were asked to report the likelihood they would refer the pupil for ADHD diagnosis and address them during a high-level interdisciplinary school-team meeting (HISTM). High ADHD-related traits (effect size 0.359) and low academic-achievement (effect size 0.070) and their interaction were significantly related to a higher likelihood of referral. Further analysis revealed that lower academic achievement was related to a higher likelihood of referral only when ADHD-related traits were not indicated (p < .005). The status of giftedness label was not found to be significant (p > .05). These findings indicate that mainly ADHD-related traits and, to a lesser degree, low academic-achievement influence teachers’ decisions to refer pupils for ADHD diagnosis and address them in HISTM.
In this work, a pronounced ultrashort pulse contrast enhancement was realized via the utilization of the nonlinear Transient Grating technique with a fiber-chirped pulse amplifier system in the 1μm regime. A YVO4 crystal with significantly enhanced nonlinearity relative to glasses yielded a distinct advantage in pulse cleaning by inducing Transient Grating with intensities that are nearly order of magnitude lower compared to, e.g., fused silica. In addition, the clean beam was inherently separated from the generating beams, eliminating the necessity for supplementary filtration, potentially compromising the final contrast. Up to 40 dB contrast enhancement was observed with ∼1μJ of energy. The absolute measured peak-to-noise contrast was 80 dB, albeit full verification might be masked by the noise floor limit. Setup modifications for higher pulse energies and corresponding higher efficiencies are underway, with the aim of evaluating their suitability for large laser systems.
We introduce and implement a novel dimension-reduction method for high-dimensional time-varying contingency-tables: the Evolutionary Correspondence Analysis (ECA). ECA enables a comparative analysis of high-dimensional, diachronic processes by identifying a small number of shared latent variables that shape co-evolving data patterns. ECA offers new opportunities for the study of complex social phenomena, such as co-evolving public debates: Its capacity to inductively extract time-varying latent variables from observed contents of evolving debates permits an analysis of meanings shared by linked sub-discourses, such as linked national public spheres or the discourses led by distinct political camps within a shared public sphere. We illustrate the utility of our approach by studying how the Greek and German right-, centre-, and left-leaning news coverage of the European financial crisis evolved between its outbreak in 2009 until its institutional containment in 2012. Comparing the use of 525 unique concepts in six German and Greek outlets with different political leaning over an extended period of time, we identify two common factors accounting for those evolving meanings and analyse how the different sub-discourses influenced one another over time. We allow the factor loadings to be time-varying, and fit to the latent factors a time-varying vector-auto-regressive model with time-varying mean.
