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In the fall of 2019, the City University of New York (CUNY) Office of Academic Affairs (OAA) provided colleges with updated guidance for designing and delivering evidence-based corequisite courses and set a timeline for the phaseout of traditional, standalone remediation by fall 2022. This CAPR report describes findings from research exploring the first year of full-scale implementation of corequisite English and math courses in the system. Drawing on interview and focus-group data with OAA administrators and with faculty and staff at seven associate-granting CUNY colleges (five of which are community colleges), the report examines how the colleges managed the transition to fully scaled corequisite courses and structured their corequisite offerings and the implications of those choices for early implementation. The authors find that all CUNY colleges participating in the study had fully scaled corequisite courses in math and English by spring 2023 and that for many of them, this marked the culmination of over a decade of experimentation with and expansion of corequisites. Also, due to the autonomy granted to colleges by OAA, the authors observe variation in scaling timelines and some variation in corequisite models, as well as in faculty and staff engagement. Interviewees identified important facilitators of the scaling process, such as OAA's mandate and timeline for scaling and financial support for course and professional development. They also identified a key challenge in understanding the implications of implementation and scaling, namely, that the co-occurrence of a new placement algorithm, the mandate, and the pandemic-induced shift to online instruction made it difficult to determine how any one of these factors contributed to changes in student success in introductory English and math courses. The report concludes with a discussion of issues to consider when scaling corequisite courses.

Large Language Models (LLMs) are widely used as conversational agents, exploiting their capabilities in various sectors such as education, law, medicine, and more. However, LLMs are often subjected to context-shifting behaviour, resulting in a lack of consistent and interpretable personality-aligned interactions. Adherence to psychological traits lacks comprehensive analysis, especially in the case of dyadic (pairwise) conversations. We examine this challenge from two viewpoints, initially using two conversation agents to generate a discourse on a certain topic with an assigned personality from the OCEAN framework (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) as High/Low for each trait. This is followed by using multiple judge agents to infer the original traits assigned to explore prediction consistency, inter-model agreement, and alignment with the assigned personality. Our findings indicate that while LLMs can be guided toward personality-driven dialogue, their ability to maintain personality traits varies significantly depending on the combination of models and discourse settings. These inconsistencies emphasise the challenges in achieving stable and interpretable personality-aligned interactions in LLMs.

Psychological assessment tools have long helped humans understand behavioural patterns. While Large Language Models (LLMs) can generate content comparable to that of humans, we explore whether they exhibit personality traits. To this end, this work applies psychological tools to LLMs in diverse scenarios to generate personality profiles. Using established trait-based questionnaires such as the Big Five Inventory and by addressing the possibility of training data contamination, we examine the dimensional variability and dominance of LLMs across five core personality dimensions: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. Our findings reveal that LLMs exhibit unique dominant traits, varying characteristics, and distinct personality profiles even within the same family of models., Comment: Accepted for publication at TheWebConf 2025

The Highway Performance Monitoring System, managed by the Federal Highway Administration, provides essential data on average annual daily traffic across U.S. roadways, but it has limited representation of medium- and heavy-duty vehicles on non-interstate roads. This gap limits research and policy analysis on the impacts of truck traffic, especially concerning air quality and public health. To address this, we use random forest regression to estimate medium- and heavy-duty vehicle traffic volumes in areas with sparse data. This results in a more comprehensive dataset, which enables the estimation of traffic density at the census block level as a proxy for traffic-related air pollution exposure. Our high-resolution spatial data products, rigorously validated, provide a more accurate representation of truck traffic and its environmental and health impacts. These datasets are valuable for transportation planning, public health research, and policy decisions aimed at mitigating the effects of truck traffic on vulnerable communities exposed to air pollution., Comment: 14 pages including references, 4 figures

Quantum computation with electron spin qubits requires coherent and efficient manipulation of these spins, typically accomplished through the application of alternating magnetic or electric fields for electron spin resonance (ESR). In particular, electrical driving allows us to apply localized fields on the electrons, which benefits scale-up architectures. However, we have found that Electric Dipole Spin Resonance (EDSR) is insufficient for modeling the Rabi behavior in recent experimental studies. Therefore, we propose that the electron spin is being driven by a new method of electric spin qubit control which generalizes the spin dynamics by taking into account a quadrupolar contribution of the quantum dot: electric quadrupole spin resonance (EQSR). In this work, we explore the electric quadrupole driving of a quantum dot in silicon, specifically examining the cases of 5 and 13 electron occupancies., Comment: Main: 5 pages, 4 figures Supp: 4 pages

The causal set action of dimension $d$ is investigated for causal sets that are Poisson sprinklings into submanifolds of $d$-dimensional Minkowski space. Evidence, both analytic and numerical, is provided for the conjecture that the mean of the causal set action over sprinklings into a manifold with a timelike boundary, diverges like $l^{-1}$ in the continuum limit as the discreteness length $l$ tends to zero. A novel conjecture for the contribution to the causal set action from co-dimension 2 corners, also known as joints, is proposed and justified., Comment: 46 pages, 35 figures

To deepen our understanding of Quantum Gravity and its connections with black holes and cosmology, building a common language and exchanging ideas across different approaches is crucial. The Nordita Program "Quantum Gravity: from gravitational effective field theories to ultraviolet complete approaches" created a platform for extensive discussions, aimed at pinpointing both common grounds and sources of disagreements, with the hope of generating ideas and driving progress in the field. This contribution summarizes the twelve topical discussions held during the program and collects individual thoughts of speakers and panelists on the future of the field in light of these discussions., Comment: Collection of summaries of twelve topical panel discussions and individual thoughts of speakers and panelists, Nordita Scientific Program "Quantum Gravity: from gravitational EFTs to UV complete approaches". 62 pages + references, no figures

We show that certain crystalline topological defects in the gapless Kitaev honeycomb spin liquid model generate a chirality that depends in a universal manner on their emergent flux. Focusing on 5-7 dislocations as building blocks, consisting of pentagon and heptagon disclinations, we identify the Kitaev bond label configurations that preserve solvability. By defining and computing multiple formulations of local Chern markers we find that the 5 and 7 lattice defects host a contribution to real-space chirality, which we dub "Chern charge" or $\mathcal{Q_M}$. The sign of this local Chern charge obeys $\text{sgn}({\mathcal{Q_M}}) = - i ~ \text{sgn}(F) W$ where $F$ is the disclination Frank angle and $W = \pm i$ is its emergent-gauge-field flux. We show that lattice curvature and torsion can interplay with the surrounding gapless background to modify the $\mathcal{Q_M}$ profile and magnitude, but that the sign of $\mathcal{Q_M}$ is set by local properties of the defect, enabling the identification of Chern charges., Comment: 8 pages, 4 figures

The increasing share of renewable energy sources and necessitate new concepts for energy flexible operation of industrial production resources. In this paper, we demonstrate the potential of mass storage to increase energy flexibility in industrial operations through the application of optimized operational planning based on market prices. A wastewater treatment plant equipped with decanters and storages is examined to optimally utilize its energy-flexibility. An MILP model was created for energy-flexible production facilities. The resulting operation plan was transmitted to the plant operators and executed over a period of more than 24 hours. The model shows good agreement with real measurements (average errors between 3% to 7%). The results demonstrate significant potential for cost savings of roughly 56% for the investigated time horizon. -- Der zunehmende Anteil erneuerbarer Energien erfordert neue Konzepte zur energieflexiblen Steuerung industrieller Produktionsanlagen. In diesem Beitrag wird demonstriert, wie das Potenzial von Massespeichern zur Steigerung der Energieflexibilit\"at in industriellen Prozessen durch die Anwendung optimierter Betriebsplanung basierend auf Marktpreisen, genutzt werden kann. Es wird eine Abwasseraufbereitungsanlage mit Dekantern und zugeh\"origen Massespeichersystemen betrachtet, um deren Energieflexibilit\"at optimiert zu nutzen. Der resultierende Betriebsplan wurde in einem Assistenzsystem an die Anlagenbetreiber \"ubermittelt und \"uber einen Zeitraum von \"uber 24 Stunden ausgef\"uhrt. Das Modell zeigt eine gute \"Ubereinstimmung mit den realen Messwerten (Fehlern zwischen 3% und 7%). Alle von den Betreibern gesetzten Betriebsziele wurden erreicht, was die Anwendbarkeit des Modells in industriellen Umgebungen best\"atigt. Die Ergebnisse zeigen ein erhebliches Einsparpotenzial von etwa 56% f\"ur den untersuchten Zeitraum., Comment: accepted for publication with atp magazine. German language

We introduce a locally differentially private (LDP) algorithm for online federated learning that employs temporally correlated noise to improve utility while preserving privacy. To address challenges posed by the correlated noise and local updates with streaming non-IID data, we develop a perturbed iterate analysis that controls the impact of the noise on the utility. Moreover, we demonstrate how the drift errors from local updates can be effectively managed for several classes of nonconvex loss functions. Subject to an $(\epsilon,\delta)$-LDP budget, we establish a dynamic regret bound that quantifies the impact of key parameters and the intensity of changes in the dynamic environment on the learning performance. Numerical experiments confirm the efficacy of the proposed algorithm., Comment: arXiv admin note: text overlap with arXiv:2403.16542

Recent advances in semiconductor spin qubits have achieved linear arrays exceeding ten qubits. Moving to two-dimensional (2D) qubit arrays is a critical next step to advance towards fault-tolerant implementations, but it poses substantial fabrication challenges, particularly because enabling control of nearest-neighbor entanglement requires the incorporation of interstitial exchange gates between quantum dots in the qubit architecture. In this work, we present a 2D array of silicon metal-oxide-semiconductor (MOS) quantum dots with tunable interdot coupling between all adjacent dots. The device is characterized at 4.2 K, where we demonstrate the formation and isolation of double-dot and triple-dot configurations. We show control of all nearest-neighbor tunnel couplings spanning up to 30 decades per volt through the interstitial exchange gates and use advanced modeling tools to estimate the exchange interactions that could be realized among qubits in this architecture. These results represent a significant step towards the development of 2D MOS quantum processors compatible with foundry manufacturing techniques., Comment: 9 pages, 5 figures

Requirements engineering is a knowledge intensive process and crucial for the success of engineering projects. The field of knowledge-based requirements engineering (KBRE) aims to support engineers by providing knowledge to assist in the elicitation, validation, and management of system requirements. The advent of large language models (LLMs) opens new opportunities in the field of KBRE. This work experimentally investigates the potential of LLMs in requirements verification. Therein, LLMs are provided with a set of requirements and a textual system specification and are prompted to assess which requirements are fulfilled by the system specification. Different experimental variables such as system specification complexity, the number of requirements, and prompting strategies were analyzed. Formal rule-based systems serve as a benchmark to compare LLM performance to. Requirements and system specifications are derived from the smart-grid domain. Results show that advanced LLMs, like GPT-4o and Claude 3.5 Sonnet, achieved f1-scores between 79 % and 94 % in identifying non-fulfilled requirements, indicating potential for LLMs to be leveraged for requirements verification., Comment: Submitted to 3rd IEEE Industrial Electronics Society Annual Online Conference (ONCON)

Large-scale cell microscopy screens are used in drug discovery and molecular biology research to study the effects of millions of chemical and genetic perturbations on cells. To use these images in downstream analysis, we need models that can map each image into a feature space that represents diverse biological phenotypes consistently, in the sense that perturbations with similar biological effects have similar representations. In this work, we present the largest foundation model for cell microscopy data to date, a new 1.9 billion-parameter ViT-G/8 MAE trained on over 8 billion microscopy image crops. Compared to a previous published ViT-L/8 MAE, our new model achieves a 60% improvement in linear separability of genetic perturbations and obtains the best overall performance on whole-genome biological relationship recall and replicate consistency benchmarks. Beyond scaling, we developed two key methods that improve performance: (1) training on a curated and diverse dataset; and, (2) using biologically motivated linear probing tasks to search across each transformer block for the best candidate representation of whole-genome screens. We find that many self-supervised vision transformers, pretrained on either natural or microscopy images, yield significantly more biologically meaningful representations of microscopy images in their intermediate blocks than in their typically used final blocks. More broadly, our approach and results provide insights toward a general strategy for successfully building foundation models for large-scale biological data., Comment: NeurIPS 2024 Foundation Models for Science Workshop (38th Conference on Neural Information Processing Systems). 18 pages, 7 figures

Assuming that a cosmological model can describe the whole Universe history, we look for the conditions of a cosmological bounce thus in agreement with late time observations. Our approach involves casting such a theory into General Relativity with curvature ($\Omega_{\kappa}$), matter ($\Omega_{m}$), radiation ($\Omega_{r}$) and an effective dark fluid ($\Omega_{d}$) and formulating the corresponding field equations as a 2D dynamical system, wherein phase space points corresponding to extrema of the metric function are constrained by observational data. We show that if this effective dark fluid density is positive at the bounce, these observational constraints imply its occurrence in the future at a redshift $z<-0.81$ whatever the cosmological model (dark energy, brane, $f(R)$, etc.) corresponding to this effective dark fluid and even with a positive curvature. Hence, the effective dark fluid density must be negative at the bounce such as it arises for $z>-0.81$ and thus possibly in the past. Observations also impose that the dark fluid effective density can change sign only within the redshift range $0.54

Fabrication of quantum processors in advanced 300 mm wafer-scale complementary metal-oxide-semiconductor (CMOS) foundries provides a unique scaling pathway towards commercially viable quantum computing with potentially millions of qubits on a single chip. Here, we show precise qubit operation of a silicon two-qubit device made in a 300 mm semiconductor processing line. The key metrics including single- and two-qubit control fidelities exceed 99% and state preparation and measurement fidelity exceeds 99.9%, as evidenced by gate set tomography (GST). We report coherence and lifetimes up to $T_\mathrm{2}^{\mathrm{*}} = 30.4$ $\mu$s, $T_\mathrm{2}^{\mathrm{Hahn}} = 803$ $\mu$s, and $T_1 = 6.3$ s. Crucially, the dominant operational errors originate from residual nuclear spin carrying isotopes, solvable with further isotopic purification, rather than charge noise arising from the dielectric environment. Our results answer the longstanding question whether the favourable properties including high-fidelity operation and long coherence times can be preserved when transitioning from a tailored academic to an industrial semiconductor fabrication technology., Comment: 10 pages, 4 figures, 4 extended data figures

Contaminated and polluted water poses significant threats to human health, necessitating vigilant monitoring of water sources for potential contamination. This paper introduces a low-cost Internet of Things (IoT)-based water quality monitoring system designed to address water quality challenges in rural communities, as demonstrated through a case study conducted in the Philippines. The system consists of two core components. The hardware component of the system, built on Arduino technology and featuring real-time data transmission, focuses on monitoring pH levels, turbidity, and temperature via sensors. The system is equipped to transmit data to a cloud database and send informative messages to mobile numbers, updating users on the status of water supplies. The application component acts as a user interface for accessing and managing data collected by the sensors. The successful deployment of this Water Quality Monitoring (WQM) system not only helps community leaders and health workers monitor water sources but also underscores its potential to empower communities in safeguarding their water sources, thereby contributing to the advancement of clean and safe water access., Comment: Proceedings of the 2024 9th International Conference on Business and Industrial Research, May 2024, Bangkok, Thailand

Spin precession is a textbook example of dynamics of a quantum system that exactly mimics its classical counterpart. Here we challenge this view by certifying the quantumness of exotic states of a nuclear spin through its uniform precession. The key to this result is measuring the positivity, instead of the expectation value, of the $x$-projection of the precessing spin, and using a spin > 1/2 qudit, that is not restricted to semi-classical spin coherent states. The experiment is performed on a single spin-7/2 $^{123}$Sb nucleus, implanted in a silicon nanoelectronic device, amenable to high-fidelity preparation, control, and projective single-shot readout. Using Schr\"odinger cat states and other bespoke states of the nucleus, we violate the classical bound by 19 standard deviations, proving that no classical probability distribution can explain the statistic of this spin precession, and highlighting our ability to prepare quantum resource states with high fidelity in a single atomic-scale qudit., Comment: Main text: 11 pages, 5 figures. Supplementary information: 13 pages, 11 figures

Modular production systems that employ the Module Type Package (MTP) to describe module interfaces can, at present, only communicate energy data through proprietary solutions. Due to this limitation, users face additional effort when calculating energy KPIs for modules or determining the energy efficiency of modules. To address this issue, we present a model that facilitates energy data to be described semantically and uniformly in the MTP on the basis of an industrial standard (OPC 34100). MTPs incorporating this model can transmit semantically consistent energy data from modules to the process control system, making the data available for further applications, such as monitoring or optimization., Comment: 6 pages, 4 figures

Electronic polarization and dispersion are decisive actors in determining interaction energies between molecules. These interactions have a particularly profound effect on excitation energies of molecules in complex environments, especially when the excitation involves a significant degree of charge reorganisation. The direct reaction field (DRF) approach, which has seen a recent revival of interest, provides a powerful framework for describing these interactions in quantum mechanics/molecular mechanics (QM/MM) models of systems, where a small subsystem of interest is described using quantum chemical methods and the remainder is treated with a simple MM force field. In this paper we show how the DRF approach can be combined with the electrostatic potential fitted (ESPF) multipole operator description of the QM region charge density, which significantly improves the efficiency of the method, particularly for large MM systems, and for typical calculations effectively eliminates the dependence on MM system size. We also show how the DRF approach can be combined with fluctuating charge descriptions of the polarizable environment, as well as previously used atom-centred dipole-polarizability based models. We further show that the ESPF-DRF method provides an accurate description of molecular interactions in both ground and excited electronic states of the QM system and apply it to predict the gas to aqueous solution solvatochromic shifts in the UV/visible absorption spectrum of acrolein., Comment: Accepted in Journal of Chemical Theory and Computation

Manually creating Planning Domain Definition Language (PDDL) descriptions is difficult, error-prone, and requires extensive expert knowledge. However, this knowledge is already embedded in engineering models and can be reused. Therefore, this contribution presents a comprehensive workflow for the automated generation of PDDL descriptions from integrated system and product models. The proposed workflow leverages Model-Based Systems Engineering (MBSE) to organize and manage system and product information, translating it automatically into PDDL syntax for planning purposes. By connecting system and product models with planning aspects, it ensures that changes in these models are quickly reflected in updated PDDL descriptions, facilitating efficient and adaptable planning processes. The workflow is validated within a use case from aircraft assembly.

Simulations offer opportunities in the examination of manufacturing processes. They represent various aspects of the production process and the associated production systems. However, often a single simulation does not suffice to provide a comprehensive understanding of specific process settings. Instead, a combination of different simulations is necessary when the outputs of one simulation serve as the input parameters for another, resulting in a sequence of simulations. Manual planning of simulation sequences is a demanding task that requires careful evaluation of factors like time, cost, and result quality to choose the best simulation scenario for a given inquiry. In this paper, an information model is introduced, which represents simulations, their capabilities to generate certain knowledge, and their respective quality criteria. The information model is designed to provide the foundation for automatically generating simulation sequences. The model is implemented as an extendable and adaptable ontology. It utilizes Ontology Design Patterns based on established industrial standards to enhance interoperability and reusability. To demonstrate the practicality of this information model, an application example is provided. This example serves to illustrate the model's capacity in a real-world context, thereby validating its utility and potential for future applications., Comment: \c{opyright} 2024 SCITEPRESS. Personal use of this material is permitted. Permission from SCITEPRESS must be obtained for all other uses, in any current or future media

Medical imaging cohorts are often confounded by factors such as acquisition devices, hospital sites, patient backgrounds, and many more. As a result, deep learning models tend to learn spurious correlations instead of causally related features, limiting their generalizability to new and unseen data. This problem can be addressed by minimizing dependence measures between intermediate representations of task-related and non-task-related variables. These measures include mutual information, distance correlation, and the performance of adversarial classifiers. Here, we benchmark such dependence measures for the task of preventing shortcut learning. We study a simplified setting using Morpho-MNIST and a medical imaging task with CheXpert chest radiographs. Our results provide insights into how to mitigate confounding factors in medical imaging., Comment: Accepted to the 15th International Workshop on Machine Learning in Medical Imaging (MLMI 2024); new version: appendix moved to the end, after the references

The following contribution introduces a concept that employs Large Language Models (LLMs) and a chatbot interface to enhance SPARQL query generation for ontologies, thereby facilitating intuitive access to formalized knowledge. Utilizing natural language inputs, the system converts user inquiries into accurate SPARQL queries that strictly query the factual content of the ontology, effectively preventing misinformation or fabrication by the LLM. To enhance the quality and precision of outcomes, additional textual information from established domain-specific standards is integrated into the ontology for precise descriptions of its concepts and relationships. An experimental study assesses the accuracy of generated SPARQL queries, revealing significant benefits of using LLMs for querying ontologies and highlighting areas for future research., Comment: \c{opyright} 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Superior computational power promised by quantum computers utilises the fundamental quantum mechanical principle of entanglement. However, achieving entanglement and verifying that the generated state does not follow the principle of local causality has proven difficult for spin qubits in gate-defined quantum dots, as it requires simultaneously high concurrence values and readout fidelities to break the classical bound imposed by Bell's inequality. Here we employ heralded initialization and calibration via gate set tomography (GST), to reduce all relevant errors and push the fidelities of the full 2-qubit gate set above 99 %, including state preparation and measurement (SPAM). We demonstrate a 97.17 % Bell state fidelity without correcting for readout errors and violate Bell's inequality with a Bell signal of S = 2.731 close to the theoretical maximum of $2\sqrt{2}$. Our measurements exceed the classical limit even at elevated temperatures of 1.1 K or entanglement lifetimes of 100 $\mu s$., Comment: 19 pages, 5 main figures, 9 extended data figures

A key virtue of spin qubits is their sub-micron footprint, enabling a single silicon chip to host the millions of qubits required to execute useful quantum algorithms with error correction. With each physical qubit needing multiple control lines however, a fundamental barrier to scale is the extreme density of connections that bridge quantum devices to their external control and readout hardware. A promising solution is to co-locate the control system proximal to the qubit platform at milli-kelvin temperatures, wired-up via miniaturized interconnects. Even so, heat and crosstalk from closely integrated control have potential to degrade qubit performance, particularly for two-qubit entangling gates based on exchange coupling that are sensitive to electrical noise. Here, we benchmark silicon MOS-style electron spin qubits controlled via heterogeneously-integrated cryo-CMOS circuits with a low enough power density to enable scale-up. Demonstrating that cryo-CMOS can efficiently enable universal logic operations for spin qubits, we go on to show that mill-kelvin control has little impact on the performance of single- and two-qubit gates. Given the complexity of our milli-kelvin CMOS platform, with some 100-thousand transistors, these results open the prospect of scalable control based on the tight packaging of spin qubits with a chiplet style control architecture.

In the following contribution, a method is introduced that integrates domain expert-centric ontology design with the Cross-Industry Standard Process for Data Mining (CRISP-DM). This approach aims to efficiently build an application-specific ontology tailored to the corrective maintenance of Cyber-Physical Systems (CPS). The proposed method is divided into three phases. In phase one, ontology requirements are systematically specified, defining the relevant knowledge scope. Accordingly, CPS life cycle data is contextualized in phase two using domain-specific ontological artifacts. This formalized domain knowledge is then utilized in the CRISP-DM to efficiently extract new insights from the data. Finally, the newly developed data-driven model is employed to populate and expand the ontology. Thus, information extracted from this model is semantically annotated and aligned with the existing ontology in phase three. The applicability of this method has been evaluated in an anomaly detection case study for a modular process plant.

The integration of Artificial Intelligence (AI) into automation systems has the potential to enhance efficiency and to address currently unsolved existing technical challenges. However, the industry-wide adoption of AI is hindered by the lack of standardized documentation for the complex compositions of automation systems, AI software, production hardware, and their interdependencies. This paper proposes a formal model using standards and ontologies to provide clear and structured documentation of AI applications in automation systems. The proposed information model for artificial intelligence in automation systems (AIAS) utilizes ontology design patterns to map and link various aspects of automation systems and AI software. Validated through a practical example, the model demonstrates its effectiveness in improving documentation practices and aiding the sustainable implementation of AI in industrial settings.

Mobile robots, becoming increasingly autonomous, are capable of operating in diverse and unknown environments. This flexibility allows them to fulfill goals independently and adapting their actions dynamically without rigidly predefined control codes. However, their autonomous behavior complicates guaranteeing safety and reliability due to the limited influence of a human operator to accurately supervise and verify each robot's actions. To ensure autonomous mobile robot's safety and reliability, which are aspects of dependability, methods are needed both in the planning and execution of missions for autonomous mobile robots. In this article, a twofold approach is presented that ensures fault removal in the context of mission planning and fault prevention during mission execution for autonomous mobile robots. First, the approach consists of a concept based on formal verification applied during the planning phase of missions. Second, the approach consists of a rule-based concept applied during mission execution. A use case applying the approach is presented, discussing how the two concepts complement each other and what contribution they make to certain aspects of dependability., Comment: Language: German. Dieser Beitrag wird eingereicht in: "dtec.bw-Beitr\"age der Helmut-Schmidt-Universit\"at/Universit\"at der Bundeswehr Hamburg: Forschungsaktivit\"aten im Zentrum f\"ur Digitalisierungs- und Technologieforschung der Bundeswehr dtec.bw"

Electron transfer reactions play an essential role in many chemical and biological processes. Fermi's Golden rule, which assumes that the coupling between electronic states is small, has formed the foundation of electron transfer rate theory, however in short range electron/energy transfer reactions this coupling can become very large, and therefore Fermi's Golden Rule fails to make even qualitatively accurate rate predictions. In this paper I present a simple modified Golden Rule theory to describe electron transfer in the Marcus inverted regime at arbitrarily large electronic coupling strengths. The theory is based on an optimal global rotation of the diabatic states, which makes it compatible with existing methods for calculating Golden Rule rates that can account for nuclear quantum effects with anharmonic potentials. Furthermore the Optimal Golden Rule (OGR) theory can also combined with analytic theories for non-adiabatic rates, such as Marcus theory and Marcus-Levich-Jortner theory, offering clear physical insight into strong electronic coupling effects in non-adiabatic processes. OGR theory is also tested on a large set of spin-boson models and an anharmonic model against exact quantum dynamics calculations, where it performs well, correctly predicting rate turnover at large coupling strengths. Finally, an example application to a BODIPY-Anthracene photosensitizer reveals that strong coupling effects inhibit excited state charge recombination in this system, reducing the rate of this process by a factor of four. Overall OGR theory offers a new approach to calculating electron transfer rates at strong couplings, offering new physical insight into a range of non-adiabatic processes.

Dual enrollment equity pathways (DEEP) is a research-based framework for rethinking dual enrollment as a more equitable on-ramp to college programs of study that lead to family-supporting, career-path jobs for students who might not otherwise pursue education after college. The DEEP approach involves community colleges and K-12 schools partnering in four practice areas to (1) conduct outreach to underserved students and families to encourage their participation in dual enrollment, (2) align dual enrollment course offerings to career-technical associate and bachelor's degree programs in high-opportunity fields, (3) advise students in the exploration of their interests and the development of post-high school education and career path plans, and (4) support students by delivering high-quality instruction to build their confidence as college learners. In this report, the authors describe what they learned through field research at six promising community college--K-12 partnerships in Florida and Texas that have begun to extend guided pathways practices to dual enrollment offerings and that have achieved strong results using dual enrollment to expand college access and opportunities for Black, Hispanic, and low-income high school students. For each of the four practice areas, the authors highlight the problems or shortcomings of the conventional dual enrollment approach and explain what the partnerships are doing to rethink and reform dual enrollment in ways that motivate and prepare students from underserved groups to pursue postsecondary pathways to careers after high school. The authors then discuss two multifaceted DEEP leadership strategies that emerged from their fieldwork for (1) establishing a shared DEEP mindset and (2) enabling DEEP practices at scale. They also provide a set of discussion questions for college leaders to ask their K-12 schools in order to develop a strong partnership and shared vision. [For the companion report, see ED631291.]

The development of low-frequency radio astronomy experiments for detecting 21-cm line emission from hydrogen presents new opportunities for creative solutions to the challenge of characterizing an antenna beam pattern. The Array of Long Baseline Antennas for Taking Radio Observations from the Seventy-ninth parallel (ALBATROS) is a new radio interferometer sited in the Canadian high Arctic that aims to map Galactic foregrounds at frequencies below $\sim$30 MHz. We present PteroSoar, a custom-built hexacopter outfitted with a transmitter, that will be used to characterize the beam patterns of ALBATROS and other experiments. The PteroSoar drone hardware is motivated by the need for user-servicing at remote sites and environmental factors that are unique to the high Arctic. In particular, magnetic heading is unreliable because the magnetic field lines near the north pole are almost vertical. We therefore implement moving baseline real time kinematic (RTK) positioning with two GPS units to obtain heading solutions with $\sim$1$^\circ$ accuracy. We present a preliminary beam map of an ALBATROS antenna, thus demonstrating successful PteroSoar operation in the high Arctic.

When the target of inference is a real-valued function of probability parameters in the k-sample multinomial problem, variance estimation may be challenging. In small samples, methods like the nonparametric bootstrap or delta method may perform poorly. We propose a novel general method in this setting for computing exact p-values and confidence intervals which means that type I error rates are correctly bounded and confidence intervals have at least nominal coverage at all sample sizes. Our method is applicable to any real-valued function of multinomial probabilities, accommodating an arbitrary number of samples with varying category counts. We describe the method and provide an implementation of it in R, with some computational optimization to ensure broad applicability. Simulations demonstrate our method's ability to maintain correct coverage rates in settings where the nonparametric bootstrap fails.

ipie is a Python-based auxiliary-field quantum Monte Carlo (AFQMC) package that has undergone substantial improvements since its initial release [J. Chem. Theory Comput., 2023, 19(1): 109-121]. This paper outlines the improved modularity and new capabilities implemented in ipie. We highlight the ease of incorporating different trial and walker types and the seamless integration of ipie with external libraries. We enable distributed Hamiltonian simulations of large systems that otherwise would not fit on single CPU node or GPU card. This development enabled us to compute the interaction energy of a benzene dimer with 84 electrons and 1512 orbitals with multi-GPUs. Using CUDA and cupy for NVIDIA GPUs, ipie supports GPU-accelerated multi-slater determinant trial wavefunctions [arXiv:2406.08314] to enable efficient and highly accurate simulations of large-scale systems. This allows for near-exact ground state energies of multi-reference clusters, [Cu$_2$O$_2$]$^{2+}$ and [Fe$_2$S$_2$(SCH$_3$)$_4$]$^{2-}$. We also describe implementations of free projection AFQMC, finite temperature AFQMC, AFQMC for electron--phonon systems, and automatic differentiation in AFQMC for calculating physical properties. These advancements position ipie as a leading platform for AFQMC research in quantum chemistry, facilitating more complex and ambitious computational method development and their applications., Comment: 18 pages, 13 figures

To achieve a flexible and adaptable system, capability ontologies are increasingly leveraged to describe functions in a machine-interpretable way. However, modeling such complex ontological descriptions is still a manual and error-prone task that requires a significant amount of effort and ontology expertise. This contribution presents an innovative method to automate capability ontology modeling using Large Language Models (LLMs), which have proven to be well suited for such tasks. Our approach requires only a natural language description of a capability, which is then automatically inserted into a predefined prompt using a few-shot prompting technique. After prompting an LLM, the resulting capability ontology is automatically verified through various steps in a loop with the LLM to check the overall correctness of the capability ontology. First, a syntax check is performed, then a check for contradictions, and finally a check for hallucinations and missing ontology elements. Our method greatly reduces manual effort, as only the initial natural language description and a final human review and possible correction are necessary, thereby streamlining the capability ontology generation process., Comment: \c{opyright} 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

We demonstrate the possibility of reconstructing images from fNIRS brain activity and start building a prototype to match the required specs. By training an image reconstruction model on downsampled fMRI data, we discovered that cm-scale spatial resolution is sufficient for image generation. We obtained 71% retrieval accuracy with 1-cm resolution, compared to 93% on the full-resolution fMRI, and 20% with 2-cm resolution. With simulations and high-density tomography, we found that time-domain fNIRS can achieve 1-cm resolution, compared to 2-cm resolution for continuous-wave fNIRS. Lastly, we share designs for a prototype time-domain fNIRS device, consisting of a laser driver, a single photon detector, and a time-to-digital converter system.

Large Language Models are commonly used in retrieval-augmented applications to execute user instructions based on data from external sources. For example, modern search engines use LLMs to answer queries based on relevant search results; email plugins summarize emails by processing their content through an LLM. However, the potentially untrusted provenance of these data sources can lead to prompt injection attacks, where the LLM is manipulated by natural language instructions embedded in the external data, causing it to deviate from the user's original instruction(s). We define this deviation as task drift. Task drift is a significant concern as it allows attackers to exfiltrate data or influence the LLM's output for other users. We study LLM activations as a solution to detect task drift, showing that activation deltas - the difference in activations before and after processing external data - are strongly correlated with this phenomenon. Through two probing methods, we demonstrate that a simple linear classifier can detect drift with near-perfect ROC AUC on an out-of-distribution test set. We evaluate these methods by making minimal assumptions about how user's tasks, system prompts, and attacks can be phrased. We observe that this approach generalizes surprisingly well to unseen task domains, such as prompt injections, jailbreaks, and malicious instructions, without being trained on any of these attacks. Interestingly, the fact that this solution does not require any modifications to the LLM (e.g., fine-tuning), as well as its compatibility with existing meta-prompting solutions, makes it cost-efficient and easy to deploy. To encourage further research on activation-based task inspection, decoding, and interpretability, we release our large-scale TaskTracker toolkit, featuring a dataset of over 500K instances, representations from six SoTA language models, and inspection tools.

High-dimensional quantum systems are a valuable resource for quantum information processing. They can be used to encode error-correctable logical qubits, which has been demonstrated using continuous-variable states in microwave cavities or the motional modes of trapped ions. For example, high-dimensional systems can be used to realise `Schr\"{o}dinger cat' states, superpositions of widely displaced coherent states that can also be used to illustrate quantum effects at large scales. Recent proposals have suggested encoding qubits in high-spin atomic nuclei, finite-dimensional systems that can host hardware-efficient versions of continuous-variable codes. Here we demonstrate the creation and manipulation of Schrodinger cat states using the spin-7/2 nucleus of an antimony atom embedded in a silicon nanoelectronic device. We use a multi-frequency control scheme to produce spin rotations that preserve the symmetry of the qudit, and constitute logical Pauli operations for qubits encoded in the Schrodinger cat states. Our work demonstrates the ability to prepare and control nonclassical resource states, a prerequisite for applications in quantum information processing and quantum error correction using our scalable, manufacturable semiconductor platform., Comment: 40 pages including main and supplementary information

The trend for Urban Air Mobility (UAM) is growing with prospective air taxis, parcel deliverers, and medical and industrial services. Safe and efficient UAM operation relies on timely communication and reliable data exchange. In this paper, we explore Cooperative Perception (CP) for Unmanned Aircraft Systems (UAS), considering the unique communication needs involving high dynamics and a large number of UAS. We propose a hybrid approach combining local broadcast with a central CP service, inspired by centrally managed U-space and broadcast mechanisms from automotive and aviation domains. In a simulation study, we show that our approach significantly enhances the environmental awareness for UAS compared to fully distributed approaches, with an increased communication channel load, which we also evaluate. These findings prompt a discussion on communication strategies for CP in UAM and the potential of a centralized CP service in future research., Comment: If you cite this paper, please use the original reference: Timo H\"ackel, Luca von Roenn, Nemo Juchmann, Alexander Fay, Rinie Akkermans, Tim Tiedemann, and Thomas C. Schmidt. "Coordinating Cooperative Perception in Urban Air Mobility for Enhanced Environmental Awareness,'' In: 2024 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, June 2024

Capability ontologies are increasingly used to model functionalities of systems or machines. The creation of such ontological models with all properties and constraints of capabilities is very complex and can only be done by ontology experts. However, Large Language Models (LLMs) have shown that they can generate machine-interpretable models from natural language text input and thus support engineers / ontology experts. Therefore, this paper investigates how LLMs can be used to create capability ontologies. We present a study with a series of experiments in which capabilities with varying complexities are generated using different prompting techniques and with different LLMs. Errors in the generated ontologies are recorded and compared. To analyze the quality of the generated ontologies, a semi-automated approach based on RDF syntax checking, OWL reasoning, and SHACL constraints is used. The results of this study are very promising because even for complex capabilities, the generated ontologies are almost free of errors., Comment: \c{opyright} 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Social media platforms can quickly disseminate STEM content to diverse audiences, but their operation can be mysterious. We used open-source machine learning methods such as clustering, regression, and sentiment analysis to analyze over 1000 videos and metrics thereof from 6 social media STEM creators. Our data provide insights into how audiences generate interest signals(likes, bookmarks, comments, shares), on the correlation of various signals with views, and suggest that content from newer creators is disseminated differently. We also share insights on how to optimize dissemination by analyzing data available exclusively to content creators as well as via sentiment analysis of comments., Comment: 17 pages, 3 figures, 3 supplemental figures

For organizations to survive and flourish in the long term, innovation and novelty must be continually introduced, which is particularly true in today's rapidly changing world. This raises a variety of ethical and sustainability considerations that seldom receive the attention they deserve. Existing innovation adoption frameworks often focus on technological, organizational, environmental, and social factors impacting adoption. In this chapter, we explore the ethical and sustainability angles, particularly as they relate to emerging technologies, artificial intelligence (AI) being a prominent example. We consider how to facilitate the development and cultivation of innovation cultures in organizations, including budding startups as well as established enterprises, through approaches such as systems thinking.

The rapid expansion of renewable energy sources has introduced significant volatility and unpredictability in the energy supply chain, necessitating advanced control strategies to ensure grid stability and reliability. Green hydrogen production via electrolysis offers a viable solution for converting and storing this volatile renewable energy. However, the inherent fluctuations of renewable energy sources present challenges for consistent utilization and integration of green hydrogen. This work proposes a two-stage optimization approach, combining site-wide optimization and real-time optimization for managing systems of electrolyzers. By adapting an existing static optimization model, dual use is achieved in both site-wide optimization and real-time optimization. The hierarchical optimization structure, characterized by distinct temporal resolutions, enables effective responses to both dynamic changes and long-term trends. The side-wide optimization layer generates long-term plans based on forecast data, while the real-time optimization layer refines these plans in real-time, accommodating immediate fluctuations and ensuring efficient operation. The results from the case study on a system of electrolyzers demonstrate the method's effectiveness in aligning electrolyzer operation with actual availability of renewable energy. This approach offers a robust framework for optimizing the operation of electrolyzers but also other types of flexible energy resources, contributing to sustainable and economically viable energy management., Comment: Updated version