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We present an extensive contemporaneous X-ray and radio campaign performed on the repeating fast radio burst (FRB) source FRB 20220912A for eight weeks immediately following the source's detection by CHIME/FRB. This includes X-ray data from XMM-Newton, NICER, and Swift, and radio detections of FRB 20220912A from CHIME/Pulsar and Effelsberg. We detect no significant X-ray emission at the time of 30 radio bursts with upper limits on $0.5-10.0$ keV X-ray fluence of $(1.5-14.5)\times 10^{-10}$ erg cm$^{-2}$ (99.7% credible interval, unabsorbed) on a timescale of 100 ms. Translated into a fluence ratio $\eta_{\text{ x/r}} = F_{\text{X-ray}}/F_{\text{radio}}$, this corresponds to ${\eta}_{\text{ x/r}} < 7\times10^{6}$. For persistent emission from the location of FRB 20220912A, we derive a 99.7% $0.5-10.0$ keV isotropic flux limit of $8.8\times 10^{-15}$ erg cm$^{-2}$ s$^{-1}$ (unabsorbed) or an isotropic luminosity limit of 1.4$\times10^{41}$ erg s$^{-1}$ at a distance of 362.4 Mpc. We derive a hierarchical extension to the standard Bayesian treatment of low-count and background-contaminated X-ray data, which allows the robust combination of multiple observations. This methodology allows us to place the best (lowest) 99.7% credible interval upper limit on an FRB ${\eta}_{\text{ x/r}}$ to date, ${\eta}_{\text{ x/r}} < 2\times10^6$, assuming that all thirty detected radio bursts are associated with X-ray bursts with the same fluence ratio. If we instead adopt an X-ray spectrum similar to the X-ray burst observed contemporaneously with FRB-like emission from Galactic magnetar SGR 1935+2154 detected on 2020 April 28, we derive a 99.7% credible interval upper limit on ${\eta}_{\text{ x/r}}$ of $8\times10^5$, which is only 3 times the observed value of ${\eta}_{\text{ x/r}}$ for SGR 1935+2154., Comment: 23 pages, 3 figures. ApJ in press (accepted after resubmission July 19th, 2024)

Lung cancer is a major cause of cancer-related deaths, and early diagnosis and treatment are crucial for improving patients' survival outcomes. In this paper, we propose to employ convolutional neural networks to model the non-linear relationship between the risk of lung cancer and the lungs' morphology revealed in the CT images. We apply a mini-batched loss that extends the Cox proportional hazards model to handle the non-convexity induced by neural networks, which also enables the training of large data sets. Additionally, we propose to combine mini-batched loss and binary cross-entropy to predict both lung cancer occurrence and the risk of mortality. Simulation results demonstrate the effectiveness of both the mini-batched loss with and without the censoring mechanism, as well as its combination with binary cross-entropy. We evaluate our approach on the National Lung Screening Trial data set with several 3D convolutional neural network architectures, achieving high AUC and C-index scores for lung cancer classification and survival prediction. These results, obtained from simulations and real data experiments, highlight the potential of our approach to improving the diagnosis and treatment of lung cancer.

Vacancies in the L1 shell of atoms and molecules can decay non-radiatively via Coster-Kronig decay whereby the vacancy is filled by an electron from the L2,3 shell while a second electron is emitted into the ionization continuum. This process is akin to Auger decay, but in contrast to Auger electrons, Coster-Kronig electrons have rather low kinetic energies of less than 50 eV. In the present work, we extend recently introduced methods for the construction of molecular Auger spectra that are based on complex-scaled equation-of-motion coupled-cluster theory to Coster-Kronig decay. We compute ionization energies as well as total and partial decay widths for the 2s-1 states of argon and hydrogen sulfide and construct the L1L2,3M Coster-Kronig and L1MM Auger spectra of these species. Whereas our final spectra are in good agreement with the available experimental and theoretical data, substantial disagreements are found for various branching ratios suggesting that spin-orbit coupling makes a major impact on Coster-Kronig decay already in the third period of the periodic table., Comment: 10 pages, 5 figures, 83 references

An increasing number of unhardened commercial-off-the-shelf embedded devices are deployed under harsh operating conditions and in highly-dependable systems. Due to the mechanisms of hardware degradation that affect these devices, ageing detection and monitoring are crucial to prevent critical failures. In this paper, we empirically study the propagation delay of 298 naturally-aged FPGA devices that are deployed in the European XFEL particle accelerator. Based on in-field measurements, we find that operational devices show significantly slower switching frequencies than unused chips, and that increased gamma and neutron radiation doses correlate with increased hardware degradation. Furthermore, we demonstrate the feasibility of developing machine learning models that estimate the switching frequencies of the devices based on historical and environmental data., Comment: If you cite this paper, please use the DSD reference: Leandro Lanzieri, Lukasz Butkowski, Jiri Kral, Goerschwin Fey, Holger Schlarb, and Thomas C. Schmidt. Studying the Degradation of Propagation Delay on FPGAs at the European XFEL. In Proceedings of the 27th Euromicro Conference on Digital System Design (DSD), IEEE, 2024

Integrity and trust on the web build on X.509 certificates. Misuse or misissuance of these certificates threaten the Web PKI security model, which led to the development of several guarding techniques. In this paper, we study the DNS/DNSSEC records CAA and TLSA as well as CT logs from the perspective of the certificates in use. Our measurements comprise 4 million popular domains, for which we explore the existence and consistency of the different extensions. Our findings indicate that CAA is almost exclusively deployed in the absence of DNSSEC, while DNSSEC protected service names tend to not use the DNS for guarding certificates. Even though mainly deployed in a formally correct way, CAA CA-strings tend to not selectively separate CAs, and numerous domains hold certificates beyond the CAA semantic. TLSA records are repeatedly poorly maintained and occasionally occur without DNSSEC.

Purpose: Autonomous navigation of catheters and guidewires can enhance endovascular surgery safety and efficacy, reducing procedure times and operator radiation exposure. Integrating tele-operated robotics could widen access to time-sensitive emergency procedures like mechanical thrombectomy (MT). Reinforcement learning (RL) shows potential in endovascular navigation, yet its application encounters challenges without a reward signal. This study explores the viability of autonomous navigation in MT vasculature using inverse RL (IRL) to leverage expert demonstrations. Methods: This study established a simulation-based training and evaluation environment for MT navigation. We used IRL to infer reward functions from expert behaviour when navigating a guidewire and catheter. We utilized soft actor-critic to train models with various reward functions and compared their performance in silico. Results: We demonstrated feasibility of navigation using IRL. When evaluating single versus dual device (i.e. guidewire versus catheter and guidewire) tracking, both methods achieved high success rates of 95% and 96%, respectively. Dual-tracking, however, utilized both devices mimicking an expert. A success rate of 100% and procedure time of 22.6 s were obtained when training with a reward function obtained through reward shaping. This outperformed a dense reward function (96%, 24.9 s) and an IRL-derived reward function (48%, 59.2 s). Conclusions: We have contributed to the advancement of autonomous endovascular intervention navigation, particularly MT, by employing IRL. The results underscore the potential of using reward shaping to train models, offering a promising avenue for enhancing the accessibility and precision of MT. We envisage that future research can extend our methodology to diverse anatomical structures to enhance generalizability., Comment: Abstract shortened for arXiv character limit

The transfer of charges, including electrons and holes, is a key step in heterogeneous catalysis, taking part in the reduction and oxidation of adsorbate species on catalyst surfaces. In plasmonic catalysis, electrons can transfer from photo-excited metal nanoparticles to molecular adsorbates, forming transient negative ions that can easily undergo reactions such as dissociation, desorption, or other chemical transformations. However, ab initio characterization of these anionic states has proven challenging, and little is known about the topology of their potential energy surfaces. In this work, we investigate the dissociative adsorption of HCl on Au(111) as a representative catalytic process with relatively low reaction probabilities, which could potentially be enhanced by electron transfer from photo-excited gold nanoparticles to HCl. We employ projection-based density embedding that combines the equation-of-motion electron-attachment coupled-cluster singles and doubles (EOM-EA-CCSD) method with density functional theory (DFT), and build dissociation curves of HCl$^-$ on Au(111) along the H-Cl bond distance. The HCl anion in the gas phase is unbound at equilibrium distances and only becomes bound as the bond stretches. However, our results show that, upon adsorption on Au(111), HCl$^-$ remains a stable, bound anion at all bond lengths due to charge delocalization to the metal. Forming bound anions is easier, and dissociation of HCl$^-$ on Au(111) is further facilitated, with its dissociation energy reduced by 0.61 eV compared to its neutral counterpart on Au(111), and by 1.16 eV relative to HCl. These results underscore the efficacy of embedded EOM-CCSD methods in addressing surface science challenges and highlight the potential of plasmonic catalysis proceeding via bound, rather than transient, anionic states.

A detailed analysis of ptychography for 3D phase reconstructions of thick specimens is performed. We introduce multi-focus ptychography, which incorporates a 4D-STEM defocus series to enhance the quality of 3D reconstructions along the beam direction through a higher overdetermination ratio. This method is compared with established multi-slice ptychography techniques, such as conventional ptychography, regularized ptychography, and multi-mode ptychography. Additionally, we contrast multi-focus ptychography with an alternative method that uses virtual optical sectioning through a reconstructed scattering matrix ($\mathcal{S}$-matrix), which offers more precise 3D structure information compared to conventional ptychography. Our findings from multiple 3D reconstructions based on simulated and experimental data demonstrate that multi-focus ptychography surpasses other techniques, particularly in accurately reconstructing the surfaces and interface regions of thick specimens.

Immunotherapy is an effective precision medicine treatment for several cancers. Imaging signatures of the underlying genome (radiogenomics) in glioblastoma patients may serve as preoperative biomarkers of the tumor-host immune apparatus. Validated biomarkers would have the potential to stratify patients during immunotherapy clinical trials, and if trials are beneficial, facilitate personalized neo-adjuvant treatment. The increased use of whole genome sequencing data, and the advances in bioinformatics and machine learning make such developments plausible. We performed a systematic review to determine the extent of development and validation of immune-related radiogenomic biomarkers for glioblastoma. A systematic review was performed following PRISMA guidelines using the PubMed, Medline, and Embase databases. Qualitative analysis was performed by incorporating the QUADAS 2 tool and CLAIM checklist. PROSPERO registered CRD42022340968. Extracted data were insufficiently homogenous to perform a meta-analysis. Results Nine studies, all retrospective, were included. Biomarkers extracted from magnetic resonance imaging volumes of interest included apparent diffusion coefficient values, relative cerebral blood volume values, and image-derived features. These biomarkers correlated with genomic markers from tumor cells or immune cells or with patient survival. The majority of studies had a high risk of bias and applicability concerns regarding the index test performed. Radiogenomic immune biomarkers have the potential to provide early treatment options to patients with glioblastoma. Targeted immunotherapy, stratified by these biomarkers, has the potential to allow individualized neo-adjuvant precision treatment options in clinical trials. However, there are no prospective studies validating these biomarkers, and interpretation is limited due to study bias with little evidence of generalizability., Comment: Published in Neuro-Oncology Advances 2024

Purpose: Most studies evaluating artificial intelligence (AI) models that detect abnormalities in neuroimaging are either tested on unrepresentative patient cohorts or are insufficiently well-validated, leading to poor generalisability to real-world tasks. The aim was to determine the diagnostic test accuracy and summarise the evidence supporting the use of AI models performing first-line, high-volume neuroimaging tasks. Methods: Medline, Embase, Cochrane library and Web of Science were searched until September 2021 for studies that temporally or externally validated AI capable of detecting abnormalities in first-line CT or MR neuroimaging. A bivariate random-effects model was used for meta-analysis where appropriate. PROSPERO: CRD42021269563. Results: Only 16 studies were eligible for inclusion. Included studies were not compromised by unrepresentative datasets or inadequate validation methodology. Direct comparison with radiologists was available in 4/16 studies. 15/16 had a high risk of bias. Meta-analysis was only suitable for intracranial haemorrhage detection in CT imaging (10/16 studies), where AI systems had a pooled sensitivity and specificity 0.90 (95% CI 0.85 - 0.94) and 0.90 (95% CI 0.83 - 0.95) respectively. Other AI studies using CT and MRI detected target conditions other than haemorrhage (2/16), or multiple target conditions (4/16). Only 3/16 studies implemented AI in clinical pathways, either for pre-read triage or as post-read discrepancy identifiers. Conclusion: The paucity of eligible studies reflects that most abnormality detection AI studies were not adequately validated in representative clinical cohorts. The few studies describing how abnormality detection AI could impact patients and clinicians did not explore the full ramifications of clinical implementation.

This letter critically examines the recent article by Infante et al. assessing the utility of large language models (LLMs) like GPT-4, Perplexity, and Bard in identifying urgent findings in emergency radiology reports. While acknowledging the potential of LLMs in generating labels for computer vision, concerns are raised about the ethical implications of using patient data without explicit approval, highlighting the necessity of stringent data protection measures under GDPR.

Objective: To report imaging protocol and scheduling variance in routine care of glioblastoma patients in order to demonstrate challenges of integrating deep-learning models in glioblastoma care pathways. Additionally, to understand the most common imaging studies and image contrasts to inform the development of potentially robust deep-learning models. Methods: MR imaging data were analysed from a random sample of five patients from the prospective cohort across five participating sites of the ZGBM consortium. Reported clinical and treatment data alongside DICOM header information were analysed to understand treatment pathway imaging schedules. Results: All sites perform all structural imaging at every stage in the pathway except for the presurgical study, where in some sites only contrast-enhanced T1-weighted imaging is performed. Diffusion MRI is the most common non-structural imaging type, performed at every site. Conclusion: The imaging protocol and scheduling varies across the UK, making it challenging to develop machine-learning models that could perform robustly at other centres. Structural imaging is performed most consistently across all centres. Advances in knowledge: Successful translation of deep-learning models will likely be based on structural post-treatment imaging unless there is significant effort made to standardise non-structural or peri-operative imaging protocols and schedules.

Purpose: Autonomous navigation of devices in endovascular interventions can decrease operation times, improve decision-making during surgery, and reduce operator radiation exposure while increasing access to treatment. This systematic review explores recent literature to assess the impact, challenges, and opportunities artificial intelligence (AI) has for the autonomous endovascular intervention navigation. Methods: PubMed and IEEEXplore databases were queried. Eligibility criteria included studies investigating the use of AI in enabling the autonomous navigation of catheters/guidewires in endovascular interventions. Following PRISMA, articles were assessed using QUADAS-2. PROSPERO: CRD42023392259. Results: Among 462 studies, fourteen met inclusion criteria. Reinforcement learning (9/14, 64%) and learning from demonstration (7/14, 50%) were used as data-driven models for autonomous navigation. Studies predominantly utilised physical phantoms (10/14, 71%) and in silico (4/14, 29%) models. Experiments within or around the blood vessels of the heart were reported by the majority of studies (10/14, 71%), while simple non-anatomical vessel platforms were used in three studies (3/14, 21%), and the porcine liver venous system in one study. We observed that risk of bias and poor generalisability were present across studies. No procedures were performed on patients in any of the studies reviewed. Studies lacked patient selection criteria, reference standards, and reproducibility, resulting in low clinical evidence levels. Conclusions: AI's potential in autonomous endovascular navigation is promising, but in an experimental proof-of-concept stage, with a technology readiness level of 3. We highlight that reference standards with well-identified performance metrics are crucial to allow for comparisons of data-driven algorithms proposed in the years to come., Comment: Abstract shortened for arXiv character limit

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

Artificial neural networks trained on large, expert-labelled datasets are considered state-of-the-art for a range of medical image recognition tasks. However, categorically labelled datasets are time-consuming to generate and constrain classification to a pre-defined, fixed set of classes. For neuroradiological applications in particular, this represents a barrier to clinical adoption. To address these challenges, we present a self-supervised text-vision framework that learns to detect clinically relevant abnormalities in brain MRI scans by directly leveraging the rich information contained in accompanying free-text neuroradiology reports. Our training approach consisted of two-steps. First, a dedicated neuroradiological language model - NeuroBERT - was trained to generate fixed-dimensional vector representations of neuroradiology reports (N = 50,523) via domain-specific self-supervised learning tasks. Next, convolutional neural networks (one per MRI sequence) learnt to map individual brain scans to their corresponding text vector representations by optimising a mean square error loss. Once trained, our text-vision framework can be used to detect abnormalities in unreported brain MRI examinations by scoring scans against suitable query sentences (e.g., 'there is an acute stroke', 'there is hydrocephalus' etc.), enabling a range of classification-based applications including automated triage. Potentially, our framework could also serve as a clinical decision support tool, not only by suggesting findings to radiologists and detecting errors in provisional reports, but also by retrieving and displaying examples of pathologies from historical examinations that could be relevant to the current case based on textual descriptors., Comment: Under Review

Connected cars are susceptible to cyberattacks. Security and safety of future vehicles highly depend on a holistic protection of automotive components, of which the time-sensitive backbone network takes a significant role. These onboard Time-Sensitive Networks (TSNs) require monitoring for safety and -- as versatile platforms to host Network Anomaly Detection Systems (NADSs) -- for security. Still a thorough evaluation of anomaly detection methods in the context of hard real-time operations, automotive protocol stacks, and domain specific attack vectors is missing along with appropriate input datasets. In this paper, we present an assessment framework that allows for reproducible, comparable, and rapid evaluation of detection algorithms. It is based on a simulation toolchain, which contributes configurable topologies, traffic streams, anomalies, attacks, and detectors. We demonstrate the assessment of NADSs in a comprehensive in-vehicular network with its communication flows, on which we model traffic anomalies. We evaluate exemplary detection mechanisms and reveal how the detection performance is influenced by different combinations of TSN traffic flows and anomaly types. Our approach translates to other real-time Ethernet domains, such as industrial facilities, airplanes, and UAVs.

Forecast verification is critical for continuous improvement in meteorological organizations. The Jive verification system was originally developed to assess the accuracy of public weather forecasts issued by the Australian Bureau of Meteorology. It started as a research project in 2015 and gradually evolved to be a Bureau operational verification system in 2022. The system includes daily verification dashboards for forecasters to visualize recent forecast performance and "Evidence Targeted Automation" dashboards for exploring the performance of competing forecast systems. Additionally, Jive includes a Jupyter Notebook server with the Jive Python library which supports research experiments, case studies, and the development of new verification metrics and tools. This paper describes the Jive verification system and how it helped bring verification to the forefront at the Bureau of Meteorology, leading to more accurate, streamlined forecasts. Jive has provided evidence to support forecast automation decisions and has helped to understand the evolving role of meteorologists in the forecast process. It has given operational meteorologists tools for evaluating forecast processes, including identifying when and how manual interventions lead to superior predictions. Work on Jive led to new verification science, including novel metrics that are decision-focused, including diagnostics for extreme conditions. Jive also provided the Bureau with an enterprise-wide data analysis environment and has prompted a clarification of forecast definitions. These collective impacts have resulted in more accurate forecasts, ultimately benefiting society, and building trust with forecast users. These positive outcomes highlight the importance of meteorological organizations investing in verification science and technology.

We simulate the the Su-Schrieffer-Heeger (SSH) model using Rydberg-atom synthetic dimensions constructed, in a single atom, from a ladder of six neighboring $n\:^3S_1$ Rydberg states in which adjacent states are coupled with two-photon transitions using microwave fields. Alternating strong/weak tunneling rates, controlled by adjusting the microwave amplitudes, are varied to map out the topological phase transition as a function of the ratio of the tunneling rates. For each ratio, quench dynamics experiments, in which the system is initially prepared in one of the bulk Rydberg states and then subjected to the microwave fields, are performed to measure the population evolution of the system. From the dynamics measurements, we extract the mean chiral displacement and verify that its long-time average value converges towards the system winding number. The topological phase transition is also examined by probing the energy spectrum of the system in steady state and observing the disappearance of the zero-energy edge states. The results show that even a system with as few as six levels can demonstrate the essential characteristics of the SSH Hamiltonian., Comment: 7 pages, 7 figures

In stochastic low-rank matrix bandit, the expected reward of an arm is equal to the inner product between its feature matrix and some unknown $d_1$ by $d_2$ low-rank parameter matrix $\Theta^*$ with rank $r \ll d_1\wedge d_2$. While all prior studies assume the payoffs are mixed with sub-Gaussian noises, in this work we loosen this strict assumption and consider the new problem of \underline{low}-rank matrix bandit with \underline{h}eavy-\underline{t}ailed \underline{r}ewards (LowHTR), where the rewards only have finite $(1+\delta)$ moment for some $\delta \in (0,1]$. By utilizing the truncation on observed payoffs and the dynamic exploration, we propose a novel algorithm called LOTUS attaining the regret bound of order $\tilde O(d^\frac{3}{2}r^\frac{1}{2}T^\frac{1}{1+\delta}/\tilde{D}_{rr})$ without knowing $T$, which matches the state-of-the-art regret bound under sub-Gaussian noises~\citep{lu2021low,kang2022efficient} with $\delta = 1$. Moreover, we establish a lower bound of the order $\Omega(d^\frac{\delta}{1+\delta} r^\frac{\delta}{1+\delta} T^\frac{1}{1+\delta}) = \Omega(T^\frac{1}{1+\delta})$ for LowHTR, which indicates our LOTUS is nearly optimal in the order of $T$. In addition, we improve LOTUS so that it does not require knowledge of the rank $r$ with $\tilde O(dr^\frac{3}{2}T^\frac{1+\delta}{1+2\delta})$ regret bound, and it is efficient under the high-dimensional scenario. We also conduct simulations to demonstrate the practical superiority of our algorithm., Comment: The 40th Conference on Uncertainty in Artificial Intelligence (UAI 2024)

Fluoridoargentates(II) represent a fascinating class of silver(II) compounds that exhibit structural and magnetic similarities to cuprate superconductors. However, their synthesis is challenging, leaving their properties largely unexplored. In this study, mechanochemistry is introduced as a new technique for the synthesis of fluoridoargentates(II), which avoids the use of anhydrous HF or heating, and employs simple equipment. Furthermore, by ball milling of commercially available precursors, AgF and AgF2, we obtained the first two examples of binary mixed-valent silver(I,II) phases, AgI2AgIIF4 and AgIAgIIF3. While the AgI2AgIIF4 phase was obtained at room temperature, the AgIAgIIF3 phase is metastable and required milling under cryogenic conditions. Using synchrotron powder X-ray diffraction, it was found that AgI2AgIIF4 crystallizes in the P21/c space group and is isostructural to beta-K2AgF4. Here, double-bridged [AgF6]4- octahedra form chains that propagate along the a-crystallographic direction, giving a quasi-1D canted antiferromagnetic character, as shown by magnetic susceptibility. The AgIAgIIF phase adopts the P-1 space group, is isostructural to AgCuF3 and shows features of a one-dimensional antiferromagnet. It is expected that this facile synthetic approach will enable the expansion of silver(II) chemistry and accelerate the search for a silver analogue to cuprate superconductors., Comment: 28 pages, 16 figures, 4 tables

In this paper, we investigate the domain names of servers on the Internet that are accessed by IoT devices performing machine-to-machine communications. Using machine learning, we classify between them and domain names of servers contacted by other types of devices. By surveying past studies that used testbeds with real-world devices and using lists of top visited websites, we construct lists of domain names of both types of servers. We study the statistical properties of the domain name lists and train six machine learning models to perform the classification. The word embedding technique we use to get the real-value representation of the domain names is Word2vec. Among the models we train, Random Forest achieves the highest performance in classifying the domain names, yielding the highest accuracy, precision, recall, and F1 score. Our work offers novel insights to IoT, potentially informing protocol design and aiding in network security and performance monitoring.

Polymers are an effective test-bed for studying topological constraints in condensed matter due to a wide array of synthetically-available chain topologies. When linear and ring polymers are blended together, emergent rheological properties are observed as the blend can be more viscous than either of the individual components. This emergent behavior arises since ring-linear blends can form long-lived topological constraints as the linear polymers thread the ring polymers. Here, we demonstrate how the Gauss linking integral can be used to efficiently evaluate the relaxation of topological constraints in ring-linear polymer blends. For majority-linear blends, the relaxation rate of topological constraints depends primarily on reptation of the linear polymers, resulting in the diffusive time $\tau_{d,R}$ for rings of length $N_R$ blended with linear chains of length $N_l$ to scale as $\tau_{d,R}\sim N_R^2N_L^{3.4}$., Comment: 6 pages, 6 figures

The origins of fiducial inference trace back to the 1930s when R. A. Fisher first introduced the concept as a response to what he perceived as a limitation of Bayesian inference - the requirement for a subjective prior distribution on model parameters in cases where no prior information was available. However, Fisher's initial fiducial approach fell out of favor as complications arose, particularly in multi-parameter problems. In the wake of 2000, amidst a renewed interest in contemporary adaptations of fiducial inference, generalized fiducial inference (GFI) emerged to extend Fisher's fiducial argument, providing a promising avenue for addressing numerous crucial and practical inference challenges. Nevertheless, the adoption of GFI has been limited due to its often demanding mathematical derivations and the necessity for implementing complex Markov Chain Monte Carlo algorithms. This complexity has impeded its widespread utilization and practical applicability. This paper presents a significant advancement by introducing an innovative variant of GFI designed to alleviate these challenges. Specifically, this paper proposes AutoGFI, an easily implementable algorithm that streamlines the application of GFI to a broad spectrum of inference problems involving additive noise. AutoGFI can be readily implemented as long as a fitting routine is available, making it accessible to a broader audience of researchers and practitioners. To demonstrate its effectiveness, AutoGFI is applied to three contemporary and challenging problems: tensor regression, matrix completion, and regression with network cohesion. These case studies highlight the immense potential of GFI and illustrate AutoGFI's promising performance when compared to specialized solutions for these problems. Overall, this research paves the way for a more accessible and powerful application of GFI in a range of practical domains.

A host of medical conditions, including amputations, diabetes, stroke, and genetic disease, result in loss of touch sensation. Because most types of sensory loss have no pharmacological treatment or rehabilitative therapy, we propose a haptic sensory prosthesis that provides substitutive feedback. The wrist and forearm are compelling locations for feedback due to available skin area and not occluding the hands, but have reduced mechanoreceptor density compared to the fingertips. Focusing on localized pressure as the feedback modality, we hypothesize that we can improve on prior devices by invoking a wider range of stimulus intensity using multiple points of pressure to evoke spatial summation, which is the cumulative perceptual experience from multiple points of stimuli. We conducted a preliminary perceptual test to investigate this idea and found that just noticeable difference is reduced with two points of pressure compared to one, motivating future work using spatial summation in sensory prostheses., Comment: 2 pages, 2 figures, 2024 IEEE Haptics Symposium Work-in-Progress Paper

Non-periodic folding of periodic crease patterns paves the way to novel nonlinear phenomena that cannot be feasible through periodic folding. This paper focuses on the non-periodic folding of recursive crease patterns generalized from Spidron. Although it is known that the Spidron has a $1$-DOF isotropic rigid folding motion, its general kinematics and dependence on the crease pattern remain unclear. Using the kinematics of a single unit cell of the Spidron and the recursive construction of the folded state of multiple unit cells, we consider the folding of the Spidron that is not necessarily isotropic. We found that as the number of unit cells increases, the non-periodic folding is restricted and the isotropic folding becomes dominant. Then, we analyze the three kinds of isotropic folding modes by constructing $1$-dimensional dynamical systems governing each of them. We show that these systems can possess different recursive natures depending on folding modes even in an identical crease pattern. Furthermore, we show their novel nonlinear nature, including the period-doubling cascade leading to the emergence of chaos., Comment: 16 pages, 8 figures. To Appear in Origami8: Proceedings of the 8th International Meeting on Origami in Science, Mathematics and Education (8OSME), Melbourne, August 16-18, 2024

Phase-separated liquid condensates can spatially organize and thereby regulate chemical processes. However, the physicochemical mechanisms underlying such regulation remain elusive as the intramolecular interactions responsible for phase separation give rise to a coupling between diffusion and chemical reactions at non-dilute conditions. Here, we derive a theoretical framework that decouples the phase separation of scaffold molecules from the reaction kinetics of diluted clients. As a result, phase volume and client partitioning coefficients become control parameters, which enables us to dissect the impact of phase-separated condensates on chemical reactions. We apply this framework to two chemical processes and show how condensates affect the yield of reversible chemical reactions and the initial rate of a simple assembly process. In both cases, we find an optimal condensate volume at which the respective chemical reaction property is maximal. Our work can be applied to experimentally quantify how condensed phases alter chemical processes in systems biology and unravel the mechanisms of how biomolecular condensates regulate biochemistry in living cells., Comment: 22 pages, 8 figures, one table

Biodiversity-associated ecosystem services such as pollination and biocontrol may be severely affected by emerging nano/micro-plastics (NMP) pollution. We synthesized the little-explored effects of NMP on pollinators and biocontrol agents on the organismal, farm and landscape scale. For instance ingested NMP trigger organismal changes from gene expression, organ damage to behavior modifications. At the farm and landscape level, NMP will likely amplify synergistic effects with other threats such as pathogens and antibiotics, and may alter landscape properties such as floral resource distributions in high NMP concentration areas, what we call NMP islands. It is essential to understand the functional exposure pathways of NMP on pollinators and biocontrol agents to comprehensively evaluate the risks for agricultural ecosystems and global food security.

Transition-edge sensors (TESs) have the potential to perform electron spectroscopic measurements with far greater measurement rates and efficiencies than can be achieved using existing electron spectrometers. Existing spectrometers filter electrons by energy before detecting a narrow energy band at a time, discarding the vast majority of electrons available for measurement. In contrast, transition-edge sensors (TES) have intrinsic energy sensitivity and so do not require prior filtering to perform energy-resolved measurements. Despite this fundamental advantage, TES electron spectroscopy has not, to our knowledge, previously been reported in the literature. We present the results of a set of proof-of-principle experiments demonstrating TES electron spectroscopy experiments using Mo/Au TESs repurposed for electron calorimetry. Using these detectors, we successfully measured the electron spectrum generated by an electron beam striking a graphite target with energies between 750 and 2000 eV, at a noise-limited energy resolution of 4 eV. Based on the findings of these experiments, we suggest improvements that could be made to TES design to enhance their electron detection capabilities through the use of of a dedicated electron absorber in the device with integrated electron optics., Comment: 8 pages, 6 figures

We report the first coupled-cluster study of Auger decay in heavy metals. The zinc atom is used as a case study due to its relevance to the Auger emission properties of the $^{67}$Ga radionuclide. Coupled-cluster theory combined with complex basis functions is used to describe the transient nature of the core-ionized zinc atom. We also introduce second-order M{\o}ller-Plesset perturbation theory as an alternative method for computing partial Auger decay widths. Scalar-relativistic effects are included in our approach for computing Auger electron energies by means of the spin-free exact two-component one-electron Hamiltonian, while spin-orbit coupling is treated by means of perturbation theory. We center our attention on the K-edge Auger decay of zinc dividing the spectrum into three parts (K-LL, K-LM, and K-MM) according to the shells involved in the decay. The computed Auger spectra are in good agreement with experimental results. The most intense peak is found at an Auger electron energy of 7432 eV, which corresponds to a $^1$D$_2$ final state arising from K-L$_2$L$_3$ transitions. Our results highlight the importance of relativistic effects for describing Auger decay in heavier nuclei. Furthermore, the effect of a first solvation shell is studied by modeling of Auger decay in the hexaaqua-zinc (II) complex. We find that K-edge Auger decay is slightly enhanced by the presence of the water molecules as compared to the bare atom.

We perform ab initio simulations of the total and partial Auger decay widths of 1s^-1, 2s^-1, and 2p^-1 ionized hydrogen sulfide and 2s^-1 ionized argon with non-Hermitian quantum chemistry. We use coupled cluster theory with single and double substitutions (CCSD) and equation of motion CCSD (EOM-CCSD) and discuss the novel application of (equation of motion-) second order M{\o}ller-Plesset perturbation theory (MP2). We find good agreement between the methods for the 1s^-1 hole of H2S, whereas for the other holes we can only use the EOM methods. We obtain very large decay widths of the 2s^-1-vacant states due to intense Coster-Kronig transitions with excellent agreement to experiments. The three 2p^-1 holes show completely different spectra because a decay channel is only significant when one of the final holes is spatially aligned with the initial hole. Lastly, we observe that triplet channels are much more important for the 2s^-1 and 2p^-1 holes than for the 1s^-1 hole, for which it is well known that triplet channels only contribute weakly to the total Auger intensity.

The crystal structure of KAgF3 was studied by powder neutron diffraction. KAgF3 exhibits at all temperatures an orthorhombic symmetry in space group Pnma that allows for several distortions with respect to the ideal cubic perovskite structure. At all temperatures there is a strong splitting of Ag-F distances parallel to the a,c planes that documents alternating occupation of holes in x2-y2 and z2-y2 orbitals. The orientation of the octahedron elongation and thereby the orbital order flips at a structural phase transition occurring around Ts=240K which is accompanied by a suppression of magnetic susceptibility. The orbital ordering is further enhanced in the low-temperature phase and the twisting of the AgF4 plaquettes forming the antiferromagnetic chains changes. DFT calculations show an enhancement of the magnetic interaction in the low temperature phase indicating that the transition and the orbital order are partially driven by the Kugel-Khomskii mechanism., Comment: 7 pages, 5, figures and supplemental information

In order to understand the relationship between planet multiplicity, mass, and composition, we present newly measured masses of five planets in two planetary systems: Kepler-323 and Kepler-104. We used the HIRES instrument at the W.M. Keck Observatory to collect 79 new radial velocity measurements (RVs) for Kepler-323, which we combined with 48 literature RVs from TNG/HARPS-N. We also conducted a reanalysis of the Kepler-104 system, using 44 previously published RV measurements. Kepler-323 b and c have masses of $2.0^{+1.2}_{-1.1}$ M$_\oplus$ and 6.5$\pm1.6$ M$_\oplus$, respectively, whereas the three Kepler-104 planets are more massive (10.0$\pm2.8$ M$_\oplus$, $7.1^{+3.8}_{-3.5}$ M$_\oplus$, and $5.5^{+4.6}_{-3.5}$ M$_\oplus$ for planets b, c, and d, respectively). The Kepler-104 planets have densities consistent with rocky cores overlaid with gaseous envelopes ($4.1^{+1.2}_{-1.1}$ g/cc, $2.9^{+1.7}_{-1.5}$ g/cc, and $1.6^{+1.5}_{-1.1}$ g/cc respectively), whereas the Kepler-323 planets are consistent with having rocky compositions ($4.5^{+2.8}_{-2.4}$ g/cc and $9.9^{+2.7}_{-2.5}$ g/cc). The Kepler-104 system has among the lowest values for gap complexity ($\mathcal{C}$ = 0.004) and mass partitioning ($\mathcal{Q}$ = 0.03); whereas, the Kepler-323 planets have a mass partitioning similar to that of the Inner Solar System ($\mathcal{Q}$ = 0.28 and $\mathcal{Q}$ = 0.24, respectively). For both exoplanet systems, the uncertainty in the mass partitioning is affected equally by (1) individual mass errors of the planets and (2) the possible existence of undetected low-mass planets, meaning that both improved mass characterization and improved sensitivity to low-mass planets in these systems would better elucidate the mass distribution among the planets., Comment: Accepted for publication in Astronomical Journal

Transport Layer Security (TLS) is the base for many Internet applications and services to achieve end-to-end security. In this paper, we provide guidance on how to measure TLS deployments, including X.509 certificates and Web PKI. We introduce common data sources and tools, and systematically describe necessary steps to conduct sound measurements and data analysis. By surveying prior TLS measurement studies we find that diverging results are rather rooted in different setups instead of different deployments. To improve the situation, we identify common pitfalls and introduce a framework to describe TLS and Web PKI measurements. Where necessary, our insights are bolstered by a data-driven approach, in which we complement arguments by additional measurements.

We compute EOM-EA-CCSD and EOM-EA-CCSDT potential energy curves and one-electron properties of several anions at bond lengths close to where these states become unbound. In the potential energy curves of the totally symmetric anions of HCl and pyrrole, which are associated with s-wave scattering states at the equilibrium bond lengths of the parent neutral molecules, we observe on inclusion of diffuse basis functions a pronounced bending effect near the crossing points with the potential energy curves of the neutral molecules. Additionally, we observe that the Dyson orbital and second moment of the electron density become extremely large in this region. In particular, the second moment of the HCl anion becomes 5 orders of magnitude times larger over a range of 5 pm. This behaviour is very different to the well-characterised non-totally symmetric anions of dinitrogen and dihydrogen that correspond to electronic resonances at the equilibrium bond lengths of their parent neutral molecules. Our work thus shows that bound state electronic-structure methods can distinguish between anions that turn into electronic resonances and those associated with s-wave scattering states., Comment: 17 pages, 6 figures

This thesis seeks to develop a general method for solving so-called quantum realizability problems, which are questions of the following form: under which conditions does there exist a quantum state exhibiting a given collection of properties? The approach adopted by this thesis is to utilize mathematical techniques previously developed for the related problem of property estimation which is concerned with learning or estimating the properties of an unknown quantum state. Our primary result is to recognize a correspondence between (i) property values which are realized by some quantum state, and (ii) property values which are occasionally produced as estimates of a generic quantum state. Chapter 3 reviews concepts of stability and norm minimization from geometric invariant theory and non-commutative optimization theory for the purposes of characterizing the flow of a quantum state under the action of a reductive group. Chapter 4 demonstrates how the gradient of this flow, also called the moment map, can be estimated by performing a covariant quantum measurement on a large number of identical copies of the quantum state. Chapter 5 outlines the correspondence between between the realizability of a moment map value on one hand and the asymptotic likelihood it is produced as an estimate on the other hand. By appropriately composing these moment map estimation schemes, we derive necessary and sufficient conditions for the existence of a quantum state jointly realizing any finite collection of moment maps. Chapter 6 applies these insights to the quantum marginal problem and is a duplication of arXiv:2211.00685., Comment: PhD thesis submitted to the University of Waterloo. Supervised by Robert W. Spekkens. Defended on Sept. 14 2023. 197 pages, 6 chapters, 5 figures

Complex absorbing potentials (CAPs) are artificial potentials added to electronic Hamiltonians to make the wave function of metastable electronic states square-integrable. This makes electronic-structure theory of resonances comparable to that of bound states, thus reducing the complexity of the problem. However, the most often used box and Voronoi CAPs depend on several parameters that have a substantial impact on the numerical results. Among these parameters are the CAP strength and a set of spatial parameters that define the onset of the CAP. It has been a common practice to minimize the perturbation of the resonance states due to the CAP by optimizing the strength parameter while fixing the onset parameters although the performance of this approach strongly depends on the chosen onset. Here we introduce a more general approach that allows one to optimize not only the CAP strength but also the spatial parameters. We show that fixing the CAP strength and optimizing the spatial parameters is a reliable way for minimizing CAP perturbations. We illustrate the performance of this new approach by computing resonance energies and widths of the temporary anions of dinitrogen, ethylene, and formic acid. This is done at the Hartree-Fock and equation-of-motion coupled-cluster singles and doubles levels of theory, using full and projected box and Voronoi CAPs., Comment: 35 pages