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This chapter discusses recent experimental work exploring the optical properties and quantum dynamics of organic semiconductor nanostructures based on squaraine dyes. Squaraines are prototypical quadrupolar charge-transfer chromophores of interest for solu-tion-processed photovoltaics and as aggregates with large circular dichroism. Here, we demonstrate and exploit their unique properties as quantum emitters for implementing hy-brid nanostructures featuring strong exciton-plasmon couplings. We show that the unusual electronic properties of squaraines result in a substantial reduction of vibronic coupling to the ubiquitous high-frequency C-C-bond-stretching modes of organic materials and in the formation of spectrally narrow J-aggregated exciton resonances in squaraine thin films. This is exploited to create metallic nanostructures covered with squaraine thin films and to per-form the first time-domain study of coherent exciton-plasmon couplings using two-dimensional electronic spectroscopy with 10-fs time resolution. The experiments present unexpected evidence for long-range coherent exciton transport driven by plasmonic fields. This opens up new opportunities for manipulating the coherent transport of matter excita-tions by coupling to vacuum fields., Comment: Book chapter for Springer Series in Nanophotonics

Solid-state single-photon emitters provide a versatile platform for exploring quantum technologies such as optically connected quantum networks. A key challenge is to ensure optical coherence and spectral stability of the emitters. Here, we introduce a high-bandwidth `check-probe' scheme to quantitatively measure (laser-induced) spectral diffusion and ionisation rates, as well as homogeneous linewidths. We demonstrate these methods on single V2 centers in commercially available bulk-grown 4H-silicon carbide. Despite observing significant spectral diffusion under laser illumination ($\gtrsim$ GHz/s), the optical transitions are narrow ($\sim$35 MHz), and remain stable in the dark ($\gtrsim$1 s). Through Landau-Zener-St\"uckelberg interferometry, we determine the optical coherence to be near-lifetime limited ($T_2 = 16.4(4)$ ns), hinting at the potential for using bulk-grown materials for developing quantum technologies. These results advance our understanding of spectral diffusion of quantum emitters in semiconductor materials, and may have applications for studying charge dynamics across other platforms.

Selecting distinct quantum pathways in two-dimensional electronic spectroscopy (2DES) can give particularly deep insights into coherent and incoherent interactions and quantum dynamics in various materials. This includes isolating rephasing and non-rephasing pathways for conventional single-quantum 2DES, but also the ability to record double- and zero-quantum spectra. Such advanced 2DES schemes usually require phase-cycling when performed in a partially or fully collinear geometry. A particularly simple and effective implementation of 2DES utilizes an in-line birefringent interferometer, the Translating-Wedge-based Identical pulses eNcoding System (TWINS), for the generation of an inherently phase-stable collinear excitation pulse pair. Here, we demonstrate how the TWINS can be adapted to allow for phase-cycling and experimental access to isolated quantum pathways. These new capabilities are demonstrated by recording rephasing, non-rephasing, zero-quantum and double-quantum 2DES on a molecular J-aggregate. This easy-to-implement extension opens up new experimental possibilities for TWINS-based 2DES in multidimensional all-optical and photoemission spectroscopy and microscopy., Comment: 13 pages, 4 figures

The 2023-2024 AASA Superintendent Salary & Benefits Study marks the twelfth consecutive edition of this report. The 2023-2024 version employed a slightly revised and updated survey instrument. The survey tracked the demographics, salary, benefits, and other elements of the employment agreements of school superintendents throughout the country. This year's study results are based on 2,706 complete responses -- the highest response rate since inception of the study in 1999. The survey was distributed online and relied on superintendents responding to 66 items with the understanding that the report of findings would contain no personally identifiable information. Therefore, readers must consider the data descriptive and not necessarily representative of all superintendents. The study is intended to provide superintendents with actionable information needed to negotiate and manage their compensation and benefits.

The main science aim of the BlackGEM array is to detect optical counterparts to gravitational wave mergers. Additionally, the array will perform a set of synoptic surveys to detect Local Universe transients and short time-scale variability in stars and binaries, as well as a six-filter all-sky survey down to ~22nd mag. The BlackGEM Phase-I array consists of three optical wide-field unit telescopes. Each unit uses an f/5.5 modified Dall-Kirkham (Harmer-Wynne) design with a triplet corrector lens, and a 65cm primary mirror, coupled with a 110Mpix CCD detector, that provides an instantaneous field-of-view of 2.7~square degrees, sampled at 0.564\arcsec/pixel. The total field-of-view for the array is 8.2 square degrees. Each telescope is equipped with a six-slot filter wheel containing an optimised Sloan set (BG-u, BG-g, BG-r, BG-i, BG-z) and a wider-band 440-720 nm (BG-q) filter. Each unit telescope is independent from the others. Cloud-based data processing is done in real time, and includes a transient-detection routine as well as a full-source optimal-photometry module. BlackGEM has been installed at the ESO La Silla observatory as of October 2019. After a prolonged COVID-19 hiatus, science operations started on April 1, 2023 and will run for five years. Aside from its core scientific program, BlackGEM will give rise to a multitude of additional science cases in multi-colour time-domain astronomy, to the benefit of a variety of topics in astrophysics, such as infant supernovae, luminous red novae, asteroseismology of post-main-sequence objects, (ultracompact) binary stars, and the relation between gravitational wave counterparts and other classes of transients, Comment: Published in PASP

Crystallization processes at the mesoscopic scale, where faceted, dendritic growth, and multigrain formation can be observed, are of particular interest within materials science and metallurgy. These processes are highly nonlinear, stochastic, and sensitive to small perturbations of system parameters and initial conditions. Methods for the simulation of these processes have been developed using discrete numerical models, but these are computationally expensive. This work aims to scale crystal growth simulation with a machine learning emulator. Specifically, autoregressive latent variable models are well suited for modeling the joint distribution over system parameters and the crystallization trajectories. However, successfully training such models is challenging due to the stochasticity and sensitivity of the system. Existing approaches consequently fail to produce diverse and faithful crystallization trajectories. In this paper, we introduce the Crystal Growth Neural Emulator (CGNE), a probabilistic model for efficient crystal growth emulation at the mesoscopic scale that overcomes these challenges. We validate CGNE results using the morphological properties of the crystals produced by numerical simulation. CGNE delivers a factor of 11 improvement in inference time and performance gains compared with recent state-of-the-art probabilistic models for dynamical systems., Comment: Under review in AI for Science @ ICML 2024

Honeyfiles are a particularly useful type of honeypot: fake files deployed to detect and infer information from malicious behaviour. This paper considers the challenge of naming honeyfiles so they are camouflaged when placed amongst real files in a file system. Based on cosine distances in semantic vector spaces, we develop two metrics for filename camouflage: one based on simple averaging and one on clustering with mixture fitting. We evaluate and compare the metrics, showing that both perform well on a publicly available GitHub software repository dataset., Comment: 3rd Workshop on the security implications of Deepfakes and Cheapfakes (WDC) co-located at ACM ASIACCS 2024

We study the coordination of actions and the allocation of profit in supply chains under decentralized control in which a single supplier supplies several retailers with goods for replenishment of stocks. The goal of the supplier and the retailers is to maximize their individual profits. Since the outcome under decentralized control is inefficient, cooperation among firms by means of coordination of actions may improve the individual profits. Cooperation is studied by means of cooperative game theory. Among others we show that the corresponding games are balanced and we propose a stable solution concept for these games.

The strong coherent coupling of quantum emitters to vacuum fluctuations of the light field offers opportunities for manipulating the optical and transport properties of nanomaterials, with potential applications ranging from ultrasensitive all-optical switching to creating polariton condensates. Often, ubiquitous decoherence processes at ambient conditions limit these couplings to such short time scales that the quantum dynamics of the interacting system remains elusive. Prominent examples are strongly coupled exciton-plasmon systems, which, so far, have mostly been investigated by linear optical spectroscopy. Here, we use ultrafast two-dimensional electronic spectroscopy to probe the quantum dynamics of J-aggregate excitons collectively coupled to the spatially structured plasmonic fields of a gold nanoslit array. We observe rich coherent Rabi oscillation dynamics reflecting a plasmon-driven coherent exciton population transfer over mesoscopic distances at room temperature. This opens up new opportunities to manipulate the coherent transport of matter excitations by coupling to vacuum fields.

Background: Though approximately 80% of teachers are women, they hold only about 50% of all principalships, and just a quarter of superintendent positions (Finnan and McCord, 2017; Robinson et al., 2017; Tienken, 2021; White, 2023). When women do take on leadership positions, evidence suggests they tend to work in districts serving students with more diverse needs (e.g., students with disabilities, English learners; White, 2023). These student and district demographic characteristics are also associated with lower academic achievement as measured by traditional metrics (e.g., standardized assessments, graduation rates), reflecting inequitable access to resources; these districts may be considered more challenging, and therefore more precarious (McGhee and Nelson, 2005). The tendency for women leaders to be more likely to work in more precarious positions has been described as a "glass cliff" in leadership, building on the glass ceiling metaphor. Defining precarity in education is a challenging endeavor appropriately up for much debate, and here we consider one possible approach. In this study, we examine how state and federal school accountability designations as a measure of precarity may be related to the likelihood of districts being led by women superintendents. Several studies have found evidence of similar phenomena to that first described by Ryan and Haslam (2005) regarding the differential circumstances to which men and women were appointed to the boards of large firms. These studies have found consistent evidence suggesting women are hired into more precarious positions than men. While this model has been studied in business, sports, and politics (Cook & Glass, 2013; Oelbaum, 2016; Ryan et al., 2010), few studies have explored the glass cliffs phenomenon in education (Smith, 2015; White, 2023). Smith's (2015) study examining glass cliffs among principals is important, though in many ways the context of the superintendency may mirror more the dynamics of leading in public firms; White (2023) examines the superintendency directly, but with a focus more on turnover rather than accountability as in this study. We examine the following primary research question: Is school accountability pressure predictive of a district having a woman superintendent? Data: New Jersey offers a unique research setting as a diverse state consisting of around 600 districts, employing a larger proportion of women superintendents than average across the US, about 34% in the state compared to around 25% nationally (Tienken, 2021; White, 2023). The New Jersey state department of education (NJDOE) provides publicly available data regarding the academic performance of school districts as well as student demographic information and personnel data. The Abbott districts were originally identified in 1985 and remained under heightened state control through out the period of study. We operationalize school accountability pressure using data on schools identified in need of improvement for each year from 2008-09 to 2018-19 and Abbott districts under state supervision. School accountability status data were collected from federal data for 2008-09 to 2014-15 (USED, 2012A; USED, 2012B) and from NJDOE (2023) for 2016-17 to 2018-19. We describe any school to have been designated for "low-performance" to be in improvement status (See Table 1). We also observe the criteria that New Jersey used to identify schools for improvement (i.e., percent proficient, growth, high school graduation, chronic absenteeism, English language proficiency). In addition to school accountability data, NJDOE provides data on staff that includes the names, experience, positions, and school placements. Notably, these data do not include the gender of personnel. Modeling our method for assigning gender from recent publications (White, 2023), we use the social security administration's lists of most popular first names by gender by decade from 1950 to 1980, and titles (Mr., Ms., Mrs.) provided for the superintendents of districts in the 2017-18 and 2019-20 school years, to assign gender to personnel. When we could not identify the gender of superintendents using these methods, we conducted a web search (e.g., school websites, social media, news articles) to identify gender. We merge in school, district, and county characteristics to use as controls. NJDOE data includes information on student race/ethnicity, free/reduced-price meal eligibility, limited English proficiency, and Title I status. The staff files include superintendent salary. Finally, we include data on millage rates from New Jersey Department of Community Affairs (NJDCA, 2023). Methods: We will begin by descriptively exploring the relationship between superintendent gender and school accountability pressure. We will then estimate a series of regressions following the general form: Woman[subscript dt]=[beta][subscript 1]SchAcctPressure[subscript dt]+X'+[alpha][subscript d]+[gamma][subscript t]+[mu][subscript dt] "Woman" is a binary indicator (1 if the superintendent is a woman, 0 if they are a man) in each district "d" and year "t." "SchAcctPressure" is binary indicator equal to 1 if greater than 51 percent of the schools in a districts were identified in need of improvement and zero otherwise. In subsequent models we exchange "SchAcctPressure" with a time-variant indicator for Abbott district. "X" is a vector of district characteristics including percent non White, percent Free and Reduced Prince Lunch, percent Limited English Proficiency, percent Title I schools, and tax effort (i.e., total tax revenues divided by total property value for each school district). [alpha] is a state fixed effect and [gamma] is a year fixed effect. [mu] is an idiosyncratic error term clustered by school. Results: The results imply that the position of women superintendents is more precarious than men and school accountability pressures exacerbate that inequality. Women superintendents are about 1 percent more likely to lead districts facing accountability pressures than men (See Figure 1, Panel C). Women are also more likely to be superintendents in Abbott Districts and more likely to be hired as new superintendents into districts facing accountability pressures than men (See Table 4).

The size of the National Aeronautics and Space Administration (NASA) Science Mission Directorate (SMD) is growing exponentially, allowing researchers to make discoveries. However, making discoveries is challenging and time-consuming due to the size of the data catalogs, and as many concepts and data are indirectly connected. This paper proposes a pipeline to generate knowledge graphs (KGs) representing different NASA SMD domains. These KGs can be used as the basis for dataset search engines, saving researchers time and supporting them in finding new connections. We collected textual data and used several modern natural language processing (NLP) methods to create the nodes and the edges of the KGs. We explore the cross-domain connections, discuss our challenges, and provide future directions to inspire researchers working on similar challenges.

Deception is rapidly growing as an important tool for cyber defence, complementing existing perimeter security measures to rapidly detect breaches and data theft. One of the factors limiting the use of deception has been the cost of generating realistic artefacts by hand. Recent advances in Machine Learning have, however, created opportunities for scalable, automated generation of realistic deceptions. This vision paper describes the opportunities and challenges involved in developing models to mimic many common elements of the IT stack for deception effects.

We investigate the male-female gap in principal compensation in state and national data: detailed longitudinal personnel records from Missouri and repeated cross-sections from the nationally representative Schools and Staffing Survey (SASS). In both data sets, we estimate substantively important compensation gaps for school leaders. In Missouri, female principals make approximately $1,450 less annually than their male colleagues with similar characteristics, including experience level and degree attainment, leading the same school in different years. Gaps are present in both base salary and extra duty salary, and are only partially explained by career paths or workplace sorting. SASS analyses show that women make about $1,000 less than men nationally, on average, a gap that even grows larger once accounting for individual and workplace characteristics, teacher-supplied effectiveness ratings, and reported hours worked. The presence of these residual gaps after accounting for many supply-side explanations may signal gender discrimination in school principal compensation. [This paper was published in "Economics of Education Review" v82 Apr 2021.]

The primary step in the elusive ability of migratory birds to sense weak Earth-strength magnetic fields is supposedly the light-induced formation of a long-lived, magnetically sensitive radical pair inside a cryptochrome flavoprotein located in the retina of these birds. Blue light absorption by a flavin chromophore triggers a series of sequential electron transfer steps across a tetradic tryptophan chain towards the flavin acceptor. The recent ability to express cryptochrome 4 from the night-migratory European robin (Erithacus rubecula), ErCry4, and to replace the tryptophan residues individually by a redox-inactive phenylalanine offers the prospect of exploring the role of each of the tryptophan residues in the electron transfer chain. Here, we compare ultrafast transient absorption spectroscopy of wild type ErCry4 and four of its mutants having phenylalanine residues in different positions of the chain. In the mutants we observe that each of the first three tryptophan residues in the chain adds a distinct relaxation component (time constants 0.5, 30 and 150 ps) to the transient absorption data. The dynamics in the mutant with a terminal phenylalanine residue are very similar to those in wild type ErCry4, excepted for a reduced concentration of long-lived radical pairs. The experimental results are evaluated and discussed in connection with Marcus-Hopfield theory, providing a complete microscopic insight into the sequential electron transfers across the tryptophan chain. Our results offer a path to studying spin transport and dynamical spin correlations in flavoprotein radical pairs.

Honeyfile deployment is a useful breach detection method in cyber deception that can also inform defenders about the intent and interests of intruders and malicious insiders. A key property of a honeyfile, enticement, is the extent to which the file can attract an intruder to interact with it. We introduce a novel metric, Topic Semantic Matching (TSM), which uses topic modelling to represent files in the repository and semantic matching in an embedding vector space to compare honeyfile text and topic words robustly. We also present a honeyfile corpus created with different Natural Language Processing (NLP) methods. Experiments show that TSM is effective in inter-corpus comparisons and is a promising tool to measure the enticement of honeyfiles. TSM is the first measure to use NLP techniques to quantify the enticement of honeyfile content that compares the essential topical content of local contexts to honeyfiles and is robust to paraphrasing.

Each and every organisation releases information in a variety of forms ranging from annual reports to legal proceedings. Such documents may contain sensitive information and releasing them openly may lead to the leakage of confidential information. Detection of sentences that contain sensitive information in documents can help organisations prevent the leakage of valuable confidential information. This is especially challenging when such sentences contain a substantial amount of information or are paraphrased versions of known sensitive content. Current approaches to sensitive information detection in such complex settings are based on keyword-based approaches or standard machine learning models. In this paper, we wish to explore whether pre-trained transformer models are well suited to detect complex sensitive information. Pre-trained transformers are typically trained on an enormous amount of text and therefore readily learn grammar, structure and other linguistic features, making them particularly attractive for this task. Through our experiments on the Monsanto trial data set, we observe that the fine-tuned Bidirectional Encoder Representations from Transformers (BERT) transformer model performs better than traditional models. We experimented with four different categories of documents in the Monsanto dataset and observed that BERT achieves better F2 scores by 24.13\% to 65.79\% for GHOST, 30.14\% to 54.88\% for TOXIC, 39.22\% for CHEMI, 53.57\% for REGUL compared to existing sensitive information detection models.

Current and future experiments at the high intensity frontier are expected to produce an enormous amount of data that needs to be collected and stored for offline analysis. Thanks to the continuous progress in computing and networking technology, it is now possible to replace the standard `triggered' data acquisition systems with a new, simplified and outperforming scheme. `Streaming readout' (SRO) DAQ aims to replace the hardware-based trigger with a much more powerful and flexible software-based one, that considers the whole detector information for efficient real-time data tagging and selection. Considering the crucial role of DAQ in an experiment, validation with on-field tests is required to demonstrate SRO performance. In this paper we report results of the on-beam validation of the Jefferson Lab SRO framework. We exposed different detectors (PbWO-based electromagnetic calorimeters and a plastic scintillator hodoscope) to the Hall-D electron-positron secondary beam and to the Hall-B production electron beam, with increasingly complex experimental conditions. By comparing the data collected with the SRO system against the traditional DAQ, we demonstrate that the SRO performs as expected. Furthermore, we provide evidence of its superiority in implementing sophisticated AI-supported algorithms for real-time data analysis and reconstruction.

We propose a new statistical test to identify non-stationary frequency-modulated stochastic processes from time series data. Our method uses the instantaneous phase as a discriminatory statistics with reliable critical values derived from surrogate data. We simulated an oscillatory second-order autoregressive process to evaluate the size and power of the test. We found that the test we propose is able to correctly identify more than 99% of non-stationary data when the frequency of simulated data is doubled after the first half of the time series. Our method is easily interpretable, computationally cheap and does not require choosing hyperparameters that are dependent on the data.

Viral outbreaks, such as the current COVID-19 pandemic, are commonly described by compartmental models by means of ordinary differential equation (ODE) systems. The parameter values of these ODE models are typically unknown and need to be estimated based on accessible data. In order to describe realistic pandemic scenarios with strongly varying situations, these model parameters need to be assumed as time-dependent. While parameter estimation for the typical case of time-constant parameters does not pose larger issues, the determination of time-dependent parameters, e.g.~the transition rates of compartmental models, remains notoriously difficult, in particular since the function class of these time-dependent parameters is unknown. In this work, we present a novel method which utilizes the Augmented Kalman Smoother in combination with an Expectation-Maximization algorithm to simultaneously estimate all time-dependent parameters in an SIRD compartmental model. This approach only requires incidence data, but no prior knowledge on model parameters or any further assumptions on the function class of the time-dependencies. In contrast to other approaches for the estimation of the time-dependent reproduction number, no assumptions on the parameterization of the serial interval distribution are required. With this method, we are able to adequately describe COVID-19 data in Germany and to give non-parametric model-based time course estimates for the effective reproduction number.