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Scanning Probe Microscopy (SPM) techniques have shown great potential in fabricating nanoscale structures endowed with exotic quantum properties achieved through various manipulations of atoms and molecules. However, the selection of proper manipulation parameters requires extensive domain knowledge, which is not necessarily transferable to new systems. Therefore, efficient and autonomous SPM techniques are needed to reduce the reliance on user supervision and learn optimal strategies for new systems, in particular for the challenge of controlling chemical reactions. In this paper, we developed a software infrastructure named AutoOSS (Autonomous On-Surface Synthesis) to automate bromine removal from Zn(II)-5,15- bis(4-bromo-2,6-dimethylphenyl)porphyrin (ZnBr2Me4DPP) on Au(111), using neural network models to interpret STM outputs and deep reinforcement learning models to optimize manipulation parameters. This is further supported by Bayesian Optimization Structure Search (BOSS) and Density Functional Theory (DFT) computations to explore 3D structures and reaction mechanisms based on STM images.

The dynamics of disordered two-dimensional systems is much less understood than the dynamics of disordered chains, mainly due to the lack of appropriate numerical methods. We demonstrate that a single-trajectory version of the fermionic truncated Wigner approximation (fTWA) gives unexpectedly accurate results for the dynamics of one-dimensional (1D) systems with moderate or strong disorder. Additionally, the computational complexity of calculations carried out within this approximation is small enough to enable studies of two-dimensional (2D) systems larger than standard fTWA. Using this method, we analyze the dynamics of interacting spinless fermions propagating on disordered 1D and 2D lattices. We find for both spatial dimensions that the imbalance exhibits a universal dependence on the rescaled time $t/\xi_W$, where in 2D the time-scale $\xi_W$ follows a stretched-exponential dependence on disorder strength., Comment: 11 pages, 8 figures

We present OpenDosimeter (https://opendosimeter.org/), an open hardware solution for real-time personal X-ray dose monitoring based on a scintillation counter. Using an X-ray sensor assembly (LYSO + SiPM) on a custom board powered by a Raspberry Pi Pico, OpenDosimeter provides real-time feedback (1 Hz), data logging (10 hours), and battery-powered operation. One of the core innovations is that we calibrate the device using $^{241}$Am found in ionization smoke detectors. Specifically, we use the $\gamma$-emissions to spectrally calibrate the dosimeter, then calculate the effective dose from X-ray exposure by compensating for the scintillator absorption efficiency and applying energy-to-dose coefficients derived from tabulated data in the ICRP 116 publication. We demonstrate that this transparent approach enables real-time dose rate readings with a linear response between 0.1-1000 $\mu$Sv/h at $\pm$25% accuracy, tested for energies up to 120 keV. The maximum dose rate readings are limited by pile-up effects when approaching count rate saturation ($\sim$77 kcps at $\sim$13 $\mu$s average pulse processing time). The total component cost for making an OpenDosimeter is <\$100, which, combined with its open design (both hardware and software), enables cost-effective local reproducibility on a global scale. This paper complements the open-source documentation by explaining the underlying technology, the algorithm for dose calculation, and areas for future improvement., Comment: 7 pages, 3 figures

Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models describe complex systems using latent variables that evolve according to simple locally linear dynamics. However, existing methods for latent variable estimation are not robust to dynamical noise and system nonlinearity due to noise-sensitive inference procedures and limited model formulations. This can lead to inconsistent results on signals with similar dynamics, limiting the model's ability to provide scientific insight. In this work, we address these limitations and propose a probabilistic approach to latent variable estimation in decomposed models that improves robustness against dynamical noise. Additionally, we introduce an extended latent dynamics model to improve robustness against system nonlinearities. We evaluate our approach on several synthetic dynamical systems, including an empirically-derived brain-computer interface experiment, and demonstrate more accurate latent variable inference in nonlinear systems with diverse noise conditions. Furthermore, we apply our method to a real-world clinical neurophysiology dataset, illustrating the ability to identify interpretable and coherent structure where previous models cannot.

Modern applications often leverage multiple views of a subject of study. Within neuroscience, there is growing interest in large-scale simultaneous recordings across multiple brain regions. Understanding the relationship between views (e.g., the neural activity in each region recorded) can reveal fundamental principles about the characteristics of each representation and about the system. However, existing methods to characterize such relationships either lack the expressivity required to capture complex nonlinearities, describe only sources of variance that are shared between views, or discard geometric information that is crucial to interpreting the data. Here, we develop a nonlinear neural network-based method that, given paired samples of high-dimensional views, disentangles low-dimensional shared and private latent variables underlying these views while preserving intrinsic data geometry. Across multiple simulated and real datasets, we demonstrate that our method outperforms competing methods. Using simulated populations of lateral geniculate nucleus (LGN) and V1 neurons we demonstrate our model's ability to discover interpretable shared and private structure across different noise conditions. On a dataset of unrotated and corresponding but randomly rotated MNIST digits, we recover private latents for the rotated view that encode rotation angle regardless of digit class, and places the angle representation on a 1-d manifold, while shared latents encode digit class but not rotation angle. Applying our method to simultaneous Neuropixels recordings of hippocampus and prefrontal cortex while mice run on a linear track, we discover a low-dimensional shared latent space that encodes the animal's position. We propose our approach as a general-purpose method for finding succinct and interpretable descriptions of paired data sets in terms of disentangled shared and private latent variables.

We analyse the excitations and de-excitations of an inertial Unruh-DeWitt detector in the $(1+1)$-dimensional expanding Milne cosmology with compact spatial sections, coupled to a real massless scalar field with either untwisted or twisted boundary conditions, prepared in the conformal vacuum. We find the detector's response as a function of the energy gap, the Milne spatial circumference parameter, the interaction duration, the age of the universe at the switch-on moment, the detector's peculiar velocity at the switch-on moment, and, for the untwisted field, the state of the zero mode. Asymptotic analytic results are obtained at large energy gap and at large circumference parameter, in each case recovering the Minkowski vacuum response in the leading order, and in the double limit of small circumference parameter and late cosmological time, recovering the response in a static Minkowski cylinder. Numerical results are given in the interpolating regimes. The results confirm the detector's sensitivity to both classical and quantum properties of its environment., Comment: 17 pages, 7 figures

Carbon-based nanostructures possessing {\pi}-electron magnetism have attracted tremendous interest due to their great potential for nano spintronics. In particular, quantum chains with magnetic molecular units synthesized by on-surface reactions provide an ideal playground for investigating magnetic exchange interactions between localized spin components. Here, we present an extensive study of antiferromagnetic nanographene chains with the diazahexabenzocoronene molecule as the repeating unit. A combination of bond-resolved scanning tunneling microscopy, density functional theory and quantum spin models revealed their detailed structures and electronic and magnetic properties. We found that the antiferromagnetic chains host a collective state featuring gapped excitations for an even number of repeating units and one featuring a Kondo excitation for an odd number. Comparing with exact many-body quantum spin models, our molecular chains provide the realization of an entangled quantum Heisenberg model. Coupled with the tunability of the molecular building blocks, these systems can act as an ideal platform for the experimental realization of topological spin lattices.

The OpenMP API offers both task-based and data-parallel concepts to scientific computing. While it provides descriptive and prescriptive annotations, it is in many places deliberately unspecific how to implement its annotations. As the predominant OpenMP implementations share design rationales, they introduce "quasi-standards how certain annotations behave. By means of a task-based astrophysical simulation code, we highlight situations where this "quasi-standard" reference behaviour introduces performance flaws. Therefore, we propose prescriptive clauses to constrain the OpenMP implementations. Simulated task traces uncover the clauses' potential, while a discussion of their realization highlights that they would manifest in rather incremental changes to any OpenMP runtime supporting task priorities.

We show that the Kauffman bracket skein module of a closed Seifert fibered 3-manifold $M$ is finitely generated over $\mathbb Z[A^{\pm 1}]$ if and only if $M$ is irreducible and non-Haken. We analyze in detail the character varieties $X(M)$ of such manifolds and show that under mild conditions they are reduced. We compute the Kauffman bracket skein modules for these $3$-manifolds (over $\mathbb Q(A)$) and show that their dimensions coincide with $|X(M)|.$, Comment: 28 pages

Modern recordings of neural activity provide diverse observations of neurons across brain areas, behavioral conditions, and subjects -- thus presenting an exciting opportunity to reveal the fundamentals of brain-wide dynamics underlying cognitive function. Current methods, however, often fail to fully harness the richness of such data as they either provide an uninterpretable representation (e.g., via "black box" deep networks) or over-simplify the model (e.g., assume stationary dynamics or analyze each session independently). Here, instead of regarding asynchronous recordings that lack alignment in neural identity or brain areas as a limitation, we exploit these diverse views of the same brain system to learn a unified model of brain dynamics. We assume that brain observations stem from the joint activity of a set of functional neural ensembles (groups of co-active neurons) that are similar in functionality across recordings, and propose to discover the ensemble and their non-stationary dynamical interactions in a new model we term CrEIMBO (Cross-Ensemble Interactions in Multi-view Brain Observations). CrEIMBO identifies the composition of the per-session neural ensembles through graph-driven dictionary learning and models the ensemble dynamics as a latent sparse time-varying decomposition of global sub-circuits, thereby capturing non-stationary dynamics. CrEIMBO identifies multiple co-active sub-circuits while maintaining representation interpretability due to sharing sub-circuits across sessions. CrEIMBO distinguishes session-specific from global (session-invariant) computations by exploring when distinct sub-circuits are active. We demonstrate CrEIMBO's ability to recover ground truth components in synthetic data and uncover meaningful brain dynamics, capturing cross-subject and inter- and intra-area variability, in high-density electrode recordings of humans performing a memory task.

What computational structure are we building into large language models when we train them on next-token prediction? Here, we present evidence that this structure is given by the meta-dynamics of belief updating over hidden states of the data-generating process. Leveraging the theory of optimal prediction, we anticipate and then find that belief states are linearly represented in the residual stream of transformers, even in cases where the predicted belief state geometry has highly nontrivial fractal structure. We investigate cases where the belief state geometry is represented in the final residual stream or distributed across the residual streams of multiple layers, providing a framework to explain these observations. Furthermore we demonstrate that the inferred belief states contain information about the entire future, beyond the local next-token prediction that the transformers are explicitly trained on. Our work provides a framework connecting the structure of training data to the computational structure and representations that transformers use to carry out their behavior.

Closed-loop neuroscience experimentation, where recorded neural activity is used to modify the experiment on-the-fly, is critical for deducing causal connections and optimizing experimental time. A critical step in creating a closed-loop experiment is real-time inference of neural activity from streaming recordings. One challenging modality for real-time processing is multi-photon calcium imaging (CI). CI enables the recording of activity in large populations of neurons however, often requires batch processing of the video data to extract single-neuron activity from the fluorescence videos. We use the recently proposed robust time-trace estimator-Sparse Emulation of Unused Dictionary Objects (SEUDO) algorithm-as a basis for a new on-line processing algorithm that simultaneously identifies neurons in the fluorescence video and infers their time traces in a way that is robust to as-yet unidentified neurons. To achieve real-time SEUDO (realSEUDO), we optimize the core estimator via both algorithmic improvements and an fast C-based implementation, and create a new cell finding loop to enable realSEUDO to also identify new cells. We demonstrate comparable performance to offline algorithms (e.g., CNMF), and improved performance over the current on-line approach (OnACID) at speeds of 120 Hz on average., Comment: 20 pages, 8 figures

Immersive environments enable users to engage in embodied interaction, enhancing the sensemaking processes involved in completing tasks such as immersive analytics. Previous comparative studies on immersive analytics using augmented and virtual realities have revealed that users employ different strategies for data interpretation and text-based analytics depending on the environment. Our study seeks to investigate how augmented and virtual reality influences sensemaking processes in quantitative immersive analytics. Our results, derived from a diverse group of participants, indicate that users demonstrate comparable performance in both environments. However, it was observed that users exhibit a higher tolerance for cognitive load in VR and travel further in AR. Based on our findings, we recommend providing users with the option to switch between AR and VR, thereby enabling them to select an environment that aligns with their preferences and task requirements.

Light incident upon materials can induce changes in their electrical conductivity, a phenomenon referred to as photoresistance. In semiconductors, the photoresistance is negative, as light-induced promotion of electrons across the band gap enhances the number of charge carriers participating in transport. In superconductors, the photoresistance is positive because of the destruction of the superconducting state, whereas in normal metals it is vanishing. Here we report a qualitative deviation from the standard behavior in metallic graphene. We show that Dirac electrons exposed to continuous wave (CW) terahertz (THz) radiation can be thermally decoupled from the lattice by 50~K which activates hydrodynamic electron transport. In this regime, the resistance of graphene constrictions experiences a decrease caused by the THz-driven superballistic flow of correlated electrons. We analyze the dependencies of the negative photoresistance on the carrier density, and the radiation power and show that our superballistic devices operate as sensitive phonon-cooled bolometers and can thus offer a picosecond-scale response time. Beyond their fundamental implications, our findings underscore the practicality of electron hydrodynamics in designing ultra-fast THz sensors and electron thermometers., Comment: 7 pages, 3 figures

Navigating certain communication situations can be challenging due to individuals' lack of skills and the interference of strong emotions. However, effective learning opportunities are rarely accessible. In this work, we conduct a human-centered study that uses language models to simulate bespoke communication training and provide just-in-time feedback to support the practice and learning of interpersonal effectiveness skills. We apply the interpersonal effectiveness framework from Dialectical Behavioral Therapy (DBT), DEAR MAN, which focuses on both conversational and emotional skills. We present IMBUE, an interactive training system that provides feedback 25% more similar to experts' feedback, compared to that generated by GPT-4. IMBUE is the first to focus on communication skills and emotion management simultaneously, incorporate experts' domain knowledge in providing feedback, and be grounded in psychology theory. Through a randomized trial of 86 participants, we find that IMBUE's simulation-only variant significantly improves participants' self-efficacy (up to 17%) and reduces negative emotions (up to 25%). With IMBUE's additional just-in-time feedback, participants demonstrate 17% improvement in skill mastery, along with greater enhancements in self-efficacy (27% more) and reduction of negative emotions (16% more) compared to simulation-only. The improvement in skill mastery is the only measure that is transferred to new and more difficult situations; situation specific training is necessary for improving self-efficacy and emotion reduction.

Conservation science is an interdisciplinary field that primarily draws on knowledge from the natural sciences, social sciences, and humanities to inform policy, planning, and practice. Since its formalization as a discipline, conservation science has also increasingly incorporated tools from integrative biological fields, such as animal behavior, genetics, and, more recently, physiology. Given that the biodiversity crisis constitutes one of the greatest challenges of the 21st century, with tremendous consequences for global sustainability and human health, creating a diverse conservation toolbox is important for addressing complex conservation threats. To assess the integration of three emerging integrative biological disciplines (physiology, biomechanics, and technology) into recent conservation science research, we queried publications from five broad-scope conservation-focused journals from 2010-2022. We found that the proportion of published articles incorporating these integrative biological techniques was low, ranging from 0-4% per year. With only 2.1% of total articles accessing tools or techniques from conservation physiology, conservation technology, and conservation biomechanics, we propose that there is still a substantial opportunity for further integration. We provide a case study for each integrative field to illustrate the capacity for its tools to contribute to positive conservation outcomes. We further outline how each field promotes novel or reimagined opportunities for collaborations. Finally, we discuss the interconnectedness of the three fields and how they can support the continuing expansion of conservation science as an evidence-based, action-oriented discipline through the application of a Challenge-Mechanism-Partnership framework.

We develop a rapid algorithm for the evolution of stable, circular, circumbinary discs suitable for parameter estimation and population synthesis modelling. Our model includes disc mass and angular momentum changes, accretion on to the binary stars, and binary orbital eccentricity pumping. We fit our model to the post-asymptotic giant branch (post-AGB) circumbinary disc around IRAS 08544-4431, finding reasonable agreement despite the simplicity of our model. Our best-fitting disc has a mass of about $0.01\, \mathrm{M}_{\odot }$ and angular momentum $2.7\times 10^{52}\, \mathrm{g}\, \mathrm{cm}^{2}\, \mathrm{s}^{-1}\simeq 9 \,\mathrm{M}_{\odot }\, \mathrm{km}\, \mathrm{s}^{-1}\, \mathrm{au}$, corresponding to 0.0079 and 0.16 of the common-envelope mass and angular momentum, respectively. The best-fitting disc viscosity is $\alpha _\mathrm{disc} = 5 \times 10^{-3}$ and our tidal torque algorithm can be constrained such that the inner edge of the disc $R_{\mathrm{in}}\sim 2a$. The inner binary eccentricity reaches about 0.13 in our best-fitting model of IRAS 08544-4431, short of the observed 0.22. The circumbinary disc evaporates quickly when the post-AGB star reaches a temperature of $\sim \! 6\times 10^4\, \mathrm{K}$, suggesting that planetismals must form in the disc in about $10^{4}\, \mathrm{yr}$ if secondary planet formation is to occur, while accretion from the disc on to the stars at about 10 times the inner-edge viscous rate can double the disc lifetime., Comment: Accepted for publication in Monthly Notices of the Royal Astronomical Society

SPectra Analysis and Retrievable Catalog Lab (SPARCL) at NOIRLab's Astro Data Lab was created to efficiently serve large optical and infrared spectroscopic datasets. It consists of services, tools, example workflows and currently contains spectra for over 7.5 million stars, galaxies and quasars from the Sloan Digital Sky Survey (SDSS) and the Dark Energy Spectroscopic Instrument (DESI) survey. We aim to eventually support the broad range of spectroscopic datasets that will be hosted at NOIRLab and beyond. Major elements of SPARCL include capabilities to discover and query for spectra based on parameters of interest, a fast web service that delivers desired spectra either individually or in bulk as well as documentation and example Jupyter Notebooks to empower users in their research. More information is available on the SPARCL website (https://astrosparcl.datalab.noirlab.edu)., Comment: 4 pages, 1 figure, Conference Proceedings for ADASS 2023 (Astronomical Data Analysis Software & Systems XXXIII)

Sila-cyclic rings are a class of organosilicon cyclic compounds and have abundant application in organic chemistry and materials science. However, it is still challenging to synthesize compounds with sila-cyclic rings in solution chemistry due to their low solubility and high reactivity. Recently, on-surface synthesis was introduced into organosilicon chemistry as 1,4- disilabenzene bridged nanostructures were obtained via coupling between bromo-substituted molecules and silicon atoms on Au(111). Here, we extend this strategy for syntheses of silole derivatives and graphene nanoribbons with eight-membered sila-cyclic rings from 2,2',6,6'- tetrabromobiphenyl and 1,4,5,8-tetrabromonaphthalene on Au(111), respectively. Their structures and electronic properties were investigated by a combination of scanning tunneling microscopy/spectroscopy and density functional theory calculations. This work demonstrates a generality of this synthesis strategy to fabricate various silicon incorporated nanostructures.

Scanning tunnelling microscopy (STM) with a functionalized tip apex reveals the geometric and electronic structure of a sample within the same experiment. However, the complex nature of the signal makes images difficult to interpret and has so far limited most research to planar samples with a known chemical composition. Here, we present automated structure discovery for STM (ASD-STM), a machine learning tool for predicting the atomic structure directly from an STM image, by building upon successful methods for structure discovery in non-contact atomic force microscopy (nc-AFM). We apply the method on various organic molecules and achieve good accuracy on structure predictions and chemical identification on a qualitative level, while highlighting future development requirements to ASD-STM. This method is directly applicable to experimental STM images of organic molecules, making structure discovery available for a wider SPM audience outside of nc-AFM. This work also opens doors for more advanced machine learning methods to be developed for STM discovery.

1D stellar evolution calculations produce uncertain predictions for quantities like the age, core mass, core compactness, and nucleo-synthetic yields; a key source of uncertainty is the modeling of interfaces between regions that are convectively stable and those that are not. Theoretical and numerical work has demonstrated that there should be numerous processes adjacent to the convective boundary that induce chemical and angular momentum transport, as well as modify the thermal structure of the star. One such process is called convective penetration, wherein vigorous convection extends beyond the nominal convective boundary and alters both the composition and thermal structure. In this work, we incorporate the process of convective penetration in stellar evolution calculations using the stellar evolution software instrument mesa. We implement convective penetration according to the description presented by Anders et al. (2022a) to calculate a grid of models from the pre main sequence to He core depletion. The extent of the convective penetration zone is self-consistently calculated at each time step without introducing new free parameters. We find both a substantial penetration zone in all models with a convective core and observable differences to global stellar properties such as the luminosity and radius. We preset how the predicted radial extent of the penetration zone scales with the total stellar mass, age and the metallicity of the star. We discuss our results in the context of existing numerical and observational studies., Comment: Accepted for publication in The Astrophysical Journal, 16 pages, 8 figures, accompanying repository (https://github.com/mathren/dbconvpen)

Here, we present a study combining Bayesian optimisation structural inference with the machine learning interatomic potential NequIP to accelerate and enable the study of the adsorption of the conformationally flexible lignocellulosic molecules $\beta$-D-xylose and 1,4-$\beta$-D-xylotetraose on a copper surface. The number of structure evaluations needed to map out the relevant potential energy surfaces are reduced by Bayesian optimisation, while NequIP minimises the time spent on each evaluation, ultimately resulting in cost-efficient and reliable sampling of large systems and configurational spaces. Although the applicability of Bayesian optimisation for the conformational analysis of the more flexible xylotetraose molecule is restricted by the sample complexity bottleneck, the latter can be effectively bypassed with external conformer search tools, such as the Conformer-Rotamer Ensemble Sampling Tool, facilitating the subsequent lower dimensional global minimum adsorption structure determination. Finally, we demonstrate the applicability of the described approach to find adsorption structures practically equivalent to the density functional theory counterparts at a fraction of the computational cost., Comment: 41 pages of article manuscript, 12 pages of Supporting Information, Structural dataset available at doi:10.5281/zenodo.10202927, submitted 24.11.23 to the Journal of Chemical Theory and Computation

The absence of screening of the non-local Coulomb interaction in Dirac systems at charge neutrality leads to the breakdown of the Fermi liquid and divergence of the Fermi velocity. On the other hand, Mott Hubbard physics and the concomitant formation of local moments is dominated by the local effective Hubbard interaction. Using quantum Monte Carlo methods combined with stochastic analytical continuation, we compute the single particle spectral function of fermions on the honeycomb lattice for a realistic interaction that includes both the Hubbard interaction and long-ranged Coulomb repulsion. To a first approximation, we find that the generic high-energy features such as the formation of the upper Hubbard band are independent of the long-ranged Coulomb repulsion and determined mostly by the local effective Hubbard interaction. The sub-leading effects of adding the long-range interaction include an enhancement of the bandwidth and a decrease of the spin-polaron quasi-particle weight and lifetime., Comment: 8 pages, 6 figures

Designer heterostructures, where the desired physics emerges from the controlled interactions between different components, represent one of the most powerful strategies to realize unconventional electronic states. This approach has been particularly fruitful in combining magnetism and superconductivity to create exotic superconducting states. In this work, we use a heterostructure platform combining supramolecular metal complexes (SMCs) with a quasi-2D van der Waals (vdW) superconductor NbSe$_2$. Our scanning tunneling microscopy (STM) measurements demonstrate the emergence of Yu-Shiba-Rusinov (YSR) bands arising from the interaction between the SMC magnetism and the NbSe$_2$ superconductivity. Using X-ray absorption spectroscopy (XAS) and X-ray magnetic circular dichroism (XMCD) measurements, we show the presence of antiferromagnetic coupling between the SMC units. These result in the emergence of an unconventional $3\times3$ reconstruction in the magnetic ground state that is directly reflected in real space modulation of the YSR bands. The combination of flexible molecular building blocks, frustrated magnetic textures, and superconductivity in heterostructures establishes a fertile starting point to fabricating tunable quantum materials, including unconventional superconductors and quantum spin liquids.

We demonstrate that a sodium dimer, Na$_{2}$($1^3\Sigma_{u}^+$), residing on the surface of a helium nanodroplet, can be set into rotation by a nonresonant 1.0 ps infrared laser pulse. The time-dependent degree of alignment measured, exhibits a periodic, gradually decreasing structure that deviates qualitatively from that expected for gas phase dimers. Comparison to alignment dynamics calculated from the time-dependent rotational Schr\"{o}dinger equation shows that the deviation is due to the alignment dependent interaction between the dimer and the droplet surface. This interaction confines the dimer to the tangential plane of the droplet surface at the point where it resides and is the reason that the observed alignment dynamics is also well-described by a 2D quantum rotor model., Comment: 6 pages, 4 figures