Search

Interest in the study and analysis of dynamic processes in the social, behavioral, and health sciences has burgeoned in recent years due to the increased availability of intensive longitudinal data. However, how best to model and account for the persistent heterogeneity characterizing such processes remains an open question. The multi-VAR framework, a recent methodological development built on the vector autoregressive model, accommodates heterogeneous dynamics in multiple-subject time series through structured penalization. In the original multi-VAR proposal, individual-level transition matrices are decomposed into common and unique dynamics, allowing for generalizable and person-specific features. The current project extends this framework to allow additionally for the identification and penalized estimation of subgroup-specific dynamics; that is, patterns of dynamics that are shared across subsets of individuals. The performance of the proposed subgrouping extension is evaluated in the context of both a simulation study and empirical application, and results are compared to alternative methods for subgrouping multiple-subject, multivariate time series.

Synthesizing accurate geometry and photo-realistic appearance of small scenes is an active area of research with compelling use cases in gaming, virtual reality, robotic-manipulation, autonomous driving, convenient product capture, and consumer-level photography. When applying scene geometry and appearance estimation techniques to robotics, we found that the narrow cone of possible viewpoints due to the limited range of robot motion and scene clutter caused current estimation techniques to produce poor quality estimates or even fail. On the other hand, in robotic applications, dense metric depth can often be measured directly using stereo and illumination can be controlled. Depth can provide a good initial estimate of the object geometry to improve reconstruction, while multi-illumination images can facilitate relighting. In this work we demonstrate a method to incorporate dense metric depth into the training of neural 3D representations and address an artifact observed while jointly refining geometry and appearance by disambiguating between texture and geometry edges. We also discuss a multi-flash stereo camera system developed to capture the necessary data for our pipeline and show results on relighting and view synthesis with a few training views., Comment: Project webpage: https://stereomfc.github.io

Multi-agent reinforcement learning (MARL) has exploded in popularity in recent years. Many approaches have been developed but they can be divided into three main types: centralized training and execution (CTE), centralized training for decentralized execution (CTDE), and Decentralized training and execution (DTE). CTDE methods are the most common as they can use centralized information during training but execute in a decentralized manner -- using only information available to that agent during execution. CTDE is the only paradigm that requires a separate training phase where any available information (e.g., other agent policies, underlying states) can be used. As a result, they can be more scalable than CTE methods, do not require communication during execution, and can often perform well. CTDE fits most naturally with the cooperative case, but can be potentially applied in competitive or mixed settings depending on what information is assumed to be observed. This text is an introduction to CTDE in cooperative MARL. It is meant to explain the setting, basic concepts, and common methods. It does not cover all work in CTDE MARL as the subarea is quite extensive. I have included work that I believe is important for understanding the main concepts in the subarea and apologize to those that I have omitted., Comment: arXiv admin note: text overlap with arXiv:2405.06161

Recent JWST observations at $z > 6$ may imply galactic ionizing photon production in excess of prior expectations. Under observationally motivated assumptions about escape fractions, these suggest a $z \sim 8-9$ end to reionization, in strong tension with the $z < 6$ end required by the Ly$\alpha$ forest. In this work, we use radiative transfer simulations to understand what different observations tell us about when reionization ended and when it started. We consider a model that ends too early (at $z \approx 8$) alongside two more realistic scenarios that end late at $z \approx 5$: one that starts late ($z \sim 9$) and another that starts early ($z \sim 13$). We find that the latter requires up to an order-of-magnitude evolution in galaxy ionizing properties at $6 < z < 12$, perhaps in tension with recent measurements of $\xi_{\rm ion}$ by JWST, which indicate little evolution. We also study how these models compare to recent measurements of the Ly$\alpha$ forest opacity, mean free path, IGM thermal history, visibility of $z > 8$ Ly$\alpha$ emitters, and the patchy kSZ signal from the CMB. We find that neither of the late-ending scenarios is conclusively disfavored by any single data set. However, a majority of these observables, spanning several distinct types of observations, prefer a late start. Not all probes agree with this conclusion, hinting at a possible lack of concordance between observables. Observations by multiple experiments (including JWST, Roman, and CMB-S4) in the coming years will either establish a concordance picture of reionization's early stages or reveal systematics in data and/or theoretical modeling., Comment: 21+7 pages, 13+2 figures, submitted to ApJ. Comments welcome

Regulatory functions are essential in both socioeconomic and biological systems, from corporate managers to regulatory genes in genomes. Regulatory functions come with substantial costs, but are often taken for granted. Here, we empirically examine regulatory costs across diverse systems -- biological organisms (bacteria and eukaryotic genomes), human organizations (companies, federal agencies, universities), and decentralized entities (Wikipedia, cities) -- using scaling analysis. We guide the empirical analysis with a conceptual model, which anticipates the scaling of regulatory costs to shift with the system's internal interaction structure -- well-mixed or modular. We find diverse systems exhibit consistent scaling patterns -- well-mixed systems exhibit superlinear scaling, while modular ones show sublinear or linear scaling. Further, we find that the socioeconomic systems containing more diverse occupational functions tend to have more regulatory costs than expected from their size, confirming the type of interactions also plays a role in regulatory costs. While many socioeconomic systems exhibit efficiencies of scale, regulatory costs in many social systems have grown disproportionally over time. Our finding suggests that the increasing complexity of functions may contribute to this trend. This cross-system comparison offers a framework for understanding regulatory costs and could guide future efforts to identify and mitigate regulatory inefficiencies., Comment: 12 pages, 4 figures

High-quality (Q)-factor optical resonators with extreme temporal coherence are of both technological and fundamental importance in optical metrology, continuous-wave lasing, and semiconductor quantum optics. Despite extensive efforts in designing high-Q resonators across different spectral regimes, the experimental realization of very large Q-factors at visible wavelengths remains challenging due to the small feature size that is sensitive to fabrication imperfections, and thus is typically implemented in integrated photonics. In the pursuit of free-space optics with the benefits of large space-bandwidth product and massive parallel operations, here we design and fabricate a visible-wavelength etch-free metasurface with minimized fabrication defects and experimentally demonstrate a million-scale ultrahigh-Q resonance. A new laser-scanning momentum-space-resolved spectroscopy technique with extremely high spectral and angular resolution is developed to characterize the record-high Q-factor as well as the dispersion of the million-Q resonance in free space. By integrating monolayer WSe2 into our ultrahigh-Q meta-resonator, we further demonstrate laser-like highly unidirectional and narrow-linewidth exciton emission, albeit without any operating power density threshold. Under continuous-wave laser pumping, we observe pump-power-dependent linewidth narrowing at room temperature, indicating the potential of our meta-optics platform in controlling coherent quantum light-sources. Our result also holds great promise for applications like optical sensing, spectral filtering, and few-photon nonlinear optics.

The EXperiment for Cryogenic Large-Aperture Intensity Mapping (EXCLAIM) is a balloon-borne telescope designed to survey star formation over cosmological time scales using intensity mapping in the 420 - 540 GHz frequency range. EXCLAIM uses a fully cryogenic telescope coupled to six on-chip spectrometers featuring kinetic inductance detectors (KIDs) to achieve high sensitivity, allowing for fast integration in dark atmospheric windows. The telescope receiver is cooled to $\approx$ 1.7 K by immersion in a superfluid helium bath and enclosed in a superfluid-tight shell with a meta-material anti-reflection coated silicon window. In addition to the optics and the spectrometer package, the receiver contains the magnetic shielding, the cryogenic segment of the spectrometer readout, and the sub-Kelvin cooling system. A three-stage continuous adiabatic demagnetization refrigerator (CADR) keeps the detectors at 100 mK while a $^4$He sorption cooler provides a 900 mK thermal intercept for mechanical suspensions and coaxial cables. We present the design of the EXCLAIM receiver and report on the flight-like testing of major receiver components, including the superfluid-tight receiver window and the sub-Kelvin coolers., Comment: 13 pages, 7 figures, 1 table

Driving condensed matter systems with periodic electromagnetic fields can result in exotic states not found in equilibrium. Termed Floquet engineering, such periodic driving applied to electronic systems can tailor quantum effects to induce topological band structures and control spin interactions. However, Floquet engineering of magnon band structures in magnetic systems has proven challenging so far. Here, we present a class of Floquet states in a magnetic vortex that arise from nonlinear interactions between the vortex core and microwave magnons. Floquet bands emerge through the periodic oscillation of the core, which can be initiated by either driving the core directly or pumping azimuthal magnon modes. For the latter, the azimuthal modes induce core gyration through nonlinear interactions, which in turn renormalizes the magnon band structure. This represents a self-induced mechanism for Floquet band engineering and offers new avenues to study and control nonlinear magnon dynamics.

Observations of the 21 cm signal face significant challenges due to bright astrophysical foregrounds that are several orders of magnitude higher than the brightness of the hydrogen line, along with various systematics. Successful 21 cm experiments require accurate calibration and foreground mitigation. Errors introduced during the calibration process such as systematics, can disrupt the intrinsic frequency smoothness of the foregrounds, leading to power leakage into the Epoch of Reionisation (EoR) window. Therefore, it is essential to develop strategies to effectively address these challenges. In this work, we adopt a stringent approach to identify and address suspected systematics, including malfunctioning antennas, frequency channels corrupted by radio frequency interference (RFI), and other dominant effects. We implement a statistical framework that utilises various data products from the data processing pipeline to derive specific criteria and filters. These criteria and filters are applied at intermediate stages to mitigate systematic propagation from the early stages of data processing. Our analysis focuses on observations from the Murchison Widefield Array (MWA) Phase I configuration. Out of the observations processed by the pipeline, our approach selects 18%, totalling 58 hours, that exhibit fewer systematic effects. The successful selection of observations with reduced systematic dominance enhances our confidence in achieving 21 cm measurements., Comment: 18 pages, 17 figures, 2 tables (accepted for publication in PASA)

We present NIR spectroscopy using MMT/MMIRS for a sample of twenty-nine massive galaxies ($\mathrm{log\ M_* / M_{\odot} \gtrsim10}$) at $\mathrm{z\sim0.7}$ with optical spectroscopy from the LEGA-C survey. Having both optical and NIR spectroscopy at this redshift allows us to measure the full suite of rest-optical strong emission lines, enabling the study of ionization sources and the rest-optical selection of active galactic nuclei (AGN), as well as the measurement of dust-corrected $\mathrm{H\alpha}$-based SFRs. We find that eleven out of twenty-nine galaxies host AGN. We infer the nonparametric star formation histories with the SED fitting code \texttt{Prospector} and classify galaxies as star-forming, green valley, or quiescent based on their most recent sSFRs. We explore the connection between AGN activity and suppressed star formation and find that $89\pm15\%$ of galaxies in the green valley or below host AGN, while only $15\%\pm8\%$ of galaxies above the green valley host AGN. We construct the star-forming main sequence (SFMS) and find that the AGN host galaxies are 0.37 dex below the SFMS while galaxies without detectable AGN are consistent with being on the SFMS. However, when compared to a bootstrapped mass-matched sample, the SFRs of our sample of AGN host galaxies are consistent with the full LEGA-C sample. Based on this mass-matched analysis, we cannot rule out that this suppression of star formation is driven by other processes associated with the higher mass of the AGN sample. We therefore cannot link the presence of AGN activity to the quenching of star formation., Comment: 23 pages, 6 figures, 3 tables. Accepted for publication in ApJ

We examine the role of refractory organics as a major C carrier in the outer protosolar nebula and its implications for the compositions of large Kuiper belt objects (KBOs) and CI chondrites. By utilizing Rosetta measurements of refractory organics in comet 67P/Churyumov-Gerasimenko, we show that they would make up a large fraction of the protosolar C inventory in the KBO-forming region based on the current widely adopted solar abundances. However, this would free up too much O to form water ice, producing solid material that is not sufficiently rock-rich to explain the uncompressed density of the Pluto-Charon system and other large KBOs; the former has been argued as the most representative value we have for the bulk composition of large KBOs (Barr & Schawmb 2016, Bierson & Nimmo 2019). This inconsistency further highlights the solar abundances problem - an ongoing challenge in reconciling spectroscopically determined heavy element abundances with helioseismology constraints. By employing a new dataset from solar CNO neutrinos and solar wind measurements of C, N, and O, we show that the uncompressed density of the Pluto-Charon system can be reproduced over a wide range of scenarios. We show that a lack of sulfates in Ryugu and Bennu samples implies a lower amount of water ice initially accreted into CI chondrite parent bodies than previously thought. These data are found to be consistent with the solar C/O ratio implied by the new dataset. Our predictions can be tested by future neutrino, helioseismology, and cosmochemical measurements., Comment: 32 pages, 10 figures, 3 tables, accepted for publication in ApJ

In this work, an adaptive predictive control scheme for linear systems with unknown parameters and bounded additive disturbances is proposed. In contrast to related adaptive control approaches that robustly consider the parametric uncertainty, the proposed method handles all uncertainties stochastically by employing an online adaptive sampling-based approximation of chance constraints. The approach requires initial data in the form of a short input-output trajectory and distributional knowledge of the disturbances. This prior knowledge is used to construct an initial set of data-consistent system parameters and a distribution that allows for sample generation. As new data stream in online, the set of consistent system parameters is adapted by exploiting set membership identification. Consequently, chance constraints are deterministically approximated using a probabilistic scaling approach by sampling from the set of system parameters. In combination with a robust constraint on the first predicted step, recursive feasibility of the proposed predictive controller and closed-loop constraint satisfaction are guaranteed. A numerical example demonstrates the efficacy of the proposed method., Comment: Accepted for presentation at the 63rd IEEE Conference on Decision and Control (CDC2024)

Intracorporeal needle-based therapeutic ultrasound (NBTU) is a minimally invasive option for intervening in malignant brain tumors, commonly used in thermal ablation procedures. This technique is suitable for both primary and metastatic cancers, utilizing a high-frequency alternating electric field (up to 10 MHz) to excite a piezoelectric transducer. The resulting rapid deformation of the transducer produces an acoustic wave that propagates through tissue, leading to localized high-temperature heating at the target tumor site and inducing rapid cell death. To optimize the design of NBTU transducers for thermal dose delivery during treatment, numerical modeling of the acoustic pressure field generated by the deforming piezoelectric transducer is frequently employed. The bioheat transfer process generated by the input pressure field is used to track the thermal propagation of the applicator over time. Magnetic resonance thermal imaging (MRTI) can be used to experimentally validate these models. Validation results using MRTI demonstrated the feasibility of this model, showing a consistent thermal propagation pattern. However, a thermal damage isodose map is more advantageous for evaluating therapeutic efficacy. To achieve a more accurate simulation based on the actual brain tissue environment, a new finite element method (FEM) simulation with enhanced damage evaluation capabilities was conducted. The results showed that the highest temperature and ablated volume differed between experimental and simulation results by 2.1884{\deg}C (3.71%) and 0.0631 cm$^3$ (5.74%), respectively. The lowest Pearson correlation coefficient (PCC) for peak temperature was 0.7117, and the lowest Dice coefficient for the ablated area was 0.7021, indicating a good agreement in accuracy between simulation and experiment., Comment: 9 pages, 9 figures, 7 tables

Quantum states undergoing quantum chaotic dynamics are expected to evolve into featureless states at late times. While this expectation holds true on an average, coarse-grained level, it is unclear if this expectation applies to higher statistical moments, as symmetries typically present in physical systems constrain the exploration of phase space. Here we study the universal structure of late-time ensembles obtained from unitary dynamics in quantum chaotic systems with symmetries, such as charge or energy conservation. We identify two limiting universal regimes depending on the initial condition. When the initial state is typical -- all the moments of the symmetry operators are equal to those of pure random states -- then the late-time ensemble is indistinguishable from the Haar ensemble in the thermodynamic limit and at the level of higher statistical moments. Otherwise, atypical initial states evolve into non-universal ensembles which can be distinguished from the Haar ensemble from simple measurements or subsystem properties. Interestingly, such atypical initial conditions are not rare, even when considering product state initial conditions, and can be found in the middle of the spectrum of Hamiltonians known to be `maximally' chaotic. In the limiting case of initial states with negligible variance of the symmetry operator (e.g., states with fixed particle number or states with negligible energy variance), the late-time ensemble has universal behavior captured by constrained RMT ensembles. Our work shows that even though midspectrum states do not explore ergodically all of phase space at late times, the late-time ensemble typically -- but not always -- exhibits the same average and sample-to-sample fluctuations as the Haar ensemble., Comment: 14 pages, 5 figures

Dwarf galaxies have historically posed challenges to the cold dark matter (CDM) model and, while many of the so-called 'dwarf galaxy problems' have been mitigated by incorporating baryonic processes, the observed diversity of dwarf galaxy rotation curves remains a contentious topic. Meanwhile, the growing observational samples of active galactic nuclei (AGN) in dwarf galaxies have prompted a paradigm shift in our understanding of dwarf galaxy evolution, traditionally thought to be regulated by stellar feedback. In this study, we explore the potential role of AGN feedback in shaping dark matter distributions and increasing the diversity of dwarf galaxy rotation curves, using a new suite of cosmological zoom-in simulations of dwarf galaxies with the FIRE-3 model. Our findings indicate that the presence of active black holes (BHs) in dwarf galaxies can lead to diverse outcomes, ranging from cuspier to more core-like profiles. This variability arises from the dual role of BHs in providing additional feedback and regulating the extent of stellar feedback. Consistent with previous research, we find that AGN feedback is most impactful when cosmic ray (CR) modelling is included, with CRs from any source significantly influencing dark matter profiles. Overall, our results highlight that the interplay between stellar feedback, BHs, and CRs produces a broad spectrum of dark matter density profiles, which align with observed correlations between rotation curve shapes and baryonic dominance. This underscores the importance of including the full range of baryonic processes in dwarf galaxy simulations to address the persistent 'small-scale challenges' to the CDM paradigm., Comment: 22 pages, 9 figures. Submitted to MNRAS. Comments welcome

An open problem in artificial intelligence is how systems can flexibly learn discrete abstractions that are useful for solving inherently continuous problems. Previous work in computational neuroscience has considered this functional integration of discrete and continuous variables during decision-making under the formalism of active inference (Parr, Friston & de Vries, 2017; Parr & Friston, 2018). However, their focus is on the expressive physical implementation of categorical decisions and the hierarchical mixed generative model is assumed to be known. As a consequence, it is unclear how this framework might be extended to learning. We therefore present a novel hierarchical hybrid active inference agent in which a high-level discrete active inference planner sits above a low-level continuous active inference controller. We make use of recent work in recurrent switching linear dynamical systems (rSLDS) which implement end-to-end learning of meaningful discrete representations via the piecewise linear decomposition of complex continuous dynamics (Linderman et al., 2016). The representations learned by the rSLDS inform the structure of the hybrid decision-making agent and allow us to (1) specify temporally-abstracted sub-goals in a method reminiscent of the options framework, (2) lift the exploration into discrete space allowing us to exploit information-theoretic exploration bonuses and (3) `cache' the approximate solutions to low-level problems in the discrete planner. We apply our model to the sparse Continuous Mountain Car task, demonstrating fast system identification via enhanced exploration and successful planning through the delineation of abstract sub-goals., Comment: 11 pages (+ appendix). Accepted to the International Workshop on Active Inference 2024. arXiv admin note: substantial text overlap with arXiv:2408.10970

This paper presents and evaluates an approach for coupling together subdomain-local reduced order models (ROMs) constructed via non-intrusive operator inference (OpInf) with each other and with subdomain-local full order models (FOMs), following a domain decomposition of the spatial geometry on which a given partial differential equation (PDE) is posed. Joining subdomain-local models is accomplished using the overlapping Schwarz alternating method, a minimally-intrusive multiscale coupling technique that works by transforming a monolithic problem into a sequence of subdomain-local problems, which communicate through transmission boundary conditions imposed on the subdomain interfaces. After formulating the overlapping Schwarz alternating method for OpInf ROMs, termed OpInf-Schwarz, we evaluate the method's accuracy and efficiency on several test cases involving the heat equation in two spatial dimensions. We demonstrate that the method is capable of coupling together arbitrary combinations of OpInf ROMs and FOMs, and that speed-ups over a monolithic FOM are possible when performing OpInf ROM coupling.

Attribution of climate impacts to a source forcing is critical to understanding, communicating, and addressing the effects of human influence on the climate. While standard attribution methods, such as optimal fingerprinting, have been successfully applied to long-term, widespread effects such as global surface temperature warming, they often struggle in low signal-to-noise regimes, typical of short-term climate forcings or climate variables which are loosely related to the forcing. Single-step approaches, which directly relate a source forcing and final impact, are unable to utilize additional climate information to improve attribution certainty. To address this shortcoming, this paper presents a novel multi-step attribution approach which is capable of analyzing multiple variables conditionally. A connected series of climate effects are treated as dependent, and relationships found in intermediary steps of a causal pathway are leveraged to better characterize the forcing impact. This enables attribution of the forcing level responsible for the observed impacts, while equivalent single-step approaches fail. Utilizing a scalar feature describing the forcing impact, simple forcing response models, and a conditional Bayesian formulation, this method can incorporate several causal pathways to identify the correct forcing magnitude. As an exemplar of a short-term, high-variance forcing, we demonstrate this method for the 1991 eruption of Mt. Pinatubo. Results indicate that including stratospheric and surface temperature and radiative flux measurements increases attribution certainty compared to analyses derived solely from temperature measurements. This framework has potential to improve climate attribution assessments for both geoengineering projects and long-term climate change, for which standard attribution methods may fail., Comment: 42 pages, 10 figures

'Bare' active galactic nuclei (AGN) are a subclass of Type 1 AGN that show little or no intrinsic absorption. They offer an unobscured view of the central regions of the AGN and therefore serve as ideal targets to study the relativistic reflection features originating from the innermost regions of the accretion disc. We present a detailed broadband spectral analysis ($0.3 - 70$ keV) of one of the most luminous bare AGN in the local universe, RBS 1124 ($z= 0.208$) using a new, co-ordinated high signal-to-noise observation obtained by $\textit{XMM-Newton}$ and $\textit{NuSTAR}$. The source exhibits a power-law continuum with $\Gamma \sim$ 1.8 along with a soft excess below 2 keV, a weak neutral iron line and curvature at high energies ($\sim 30$ keV). The broadband spectrum, including the soft excess and the high-energy continuum, is well fit by the relativistic reflection model when the accretion disc is allowed to have densities of log$(n_{\rm e}$/cm$^{-3}$) $\gtrsim 19.2$. Our analysis therefore suggests that when high-density effects are considered, relativistic reflection remains a viable explanation for the soft excess., Comment: 14 pages, 9 figures, accepted for publication in MNRAS

Let $E$ be an elliptic curve defined over $\mathbb{Q}$ with conductor $N$ and $p\nmid 2N$ a prime. Let $L$ be an imaginary quadratic field with $p$ split. We prove the existence of $p$-adic zeta element for $E$ over $L$, encoding two different $p$-adic $L$-functions associated to $E$ over $L$ via explicit reciprocity laws at the primes above $p$. We formulate a main conjecture for $E$ over $L$ in terms of the zeta element, mediating different main conjectures in which the $p$-adic $L$-functions appear, and prove some results toward them. The zeta element has various applications to the arithmetic of elliptic curves. This includes a proof of main conjecture for semistable elliptic curves $E$ over $\mathbb{Q}$ at supersingular primes $p$, as conjectured by Kobayashi in 2002. It leads to the $p$-part of the conjectural Birch and Swinnerton-Dyer (BSD) formula for such curves of analytic rank zero or one, and enables us to present the first infinite families of non-CM elliptic curves for which the BSD conjecture is true. We provide further evidence towards the BSD conjecture: new cases of $p$-converse to the Gross--Zagier and Kolyvagin theorem, and $p$-part of the BSD formula for ordinary primes $p$. Along the way, we give a proof of a conjecture of Perrin-Riou connecting Beilinson--Kato elements with rational points., Comment: A main result supersedes the preprint arXiv:1411.6352

Most quantum algorithms designed to generate or probe properties of the ground state of a quantum many-body system require as input an initial state with a large overlap with the desired ground state. One approach for preparing such a ground state is Imaginary Time Evolution (ITE). Recent work by [Motta, M., Sun, C., Tan, A.T.K. et al. (2020)] introduced an algorithm -- which we will refer to as Quantum Imaginary Time Evolution (QITE) -- that shows how ITE can be approximated by a sequence of unitary operators, making QITE potentially implementable on early fault-tolerant quantum computers. In this work, we provide a heuristic study of the capabilities of the QITE algorithm in approximating the ITE of lattice and molecular electronic structure Hamiltonians. We numerically study the performance of the QITE algorithm when provided with a good classical initial state for a large class of systems, some of which are of interest to industrial applications, and check if QITE is able to qualitatively replicate the ITE behavior and improve over a classical mean-field solution. The systems we consider in this work range from one- and two-dimensional lattice systems of various lattice geometries displaying short- and long-range interactions, to active spaces of molecular electronic structure Hamiltonians. In addition to the comparison of QITE and ITE, we explicitly show how imaginary time evolved fermionic Gaussian states can serve as initial states which can be efficiently computed on classical computers and efficiently implemented on quantum computers for generic spin Hamiltonians in arbitrary lattice geometries and dimensions, which can be of independent interest.

This project aimed to develop an automated cinematography platform using an unmanned aerial vehicle. Quadcopters are a great platform for shooting aerial scenes but are difficult to maneuver smoothly and can require expertise to pilot. We aim to design an algorithm to enable automated cinematography of a desired object of interest. Given the location of an object and other obstacles in the environment, the drone is able to plan its trajectory while simultaneously keeping the desired object in the video frame and avoiding obstacles. The high maneuverability of quadcopter platforms coupled with the desire for smooth movement and stability from camera platforms means a robust motion planning algorithm must be developed which can take advantage of the quadcopter's abilities while creating motion paths which satisfy the ultimate goal of capturing aerial video. This project aims to research, develop, simulate, and test such an algorithm.

We present a modified approach to laser optical-feedback cavity-enhanced absorption spectroscopy. The technique involves continuously scanning the length of a high-finesse cavity to periodically lock a diode laser to the cavity resonance, resulting in a discrete set of transmission measurements that are evenly spaced in frequency. For a fixed laser bias, data can be collected spanning a spectral bandwidth equivalent to the free-spectral range of the cavity, with spectral resolution inversely proportional to the distance from the laser to cavity. The center frequency of this scan can be tuned by tuning the free-running laser frequency. We demonstrate the concept using a fiber-coupled 1578-nm laser and a scanning Fabry-Perot cavity to measure a series of weak CO2 absorption lines with a frequency resolution of 15.6 MHz and a noise equivalent absorption coefficient of 10-7 cm-1, limited by the moderate finesse (~5000) and short length (~5 cm) of the cavity. Individual CO2 line shapes can be measured with high resolution in a single scan that takes 67 ms. The approach has a combination of characteristics that are advantageous for in situ instruments, such as small size, high spectral resolution, fast data collection, and minimal components.

After leaving the Sun's corona, the solar wind continues to accelerate and cools, but more slowly than expected for a freely expanding adiabatic gas. We use in situ measurements from the Parker Solar Probe and Solar Orbiter spacecrafts to investigate a stream of solar wind as it traverses the inner heliosphere. The observations show heating and acceleration of the the plasma between the outer edge of the corona and near the orbit of Venus, in connection to the presence of large amplitude Alfv\'en waves. Alfv\'en waves are perturbations in the interplanetary magnetic field that transport energy. Our calculations show the damping and mechanical work performed by the Alfv\'en waves is sufficient to power the heating and acceleration of the fast solar wind in the inner heliosphere., Comment: This is the author's version of the work. It is posted here by permission of the AAAS for personal use, not for redistribution. The definitive version was published in Science on August 30 2024, DOI: 10.1126/science.adk6953

Notwithstanding the evidence against them, classical variational phase-field models continue to be used and pursued in an attempt to describe fracture nucleation in elastic brittle materials. In this context, the main objective of this paper is to provide a comprehensive review of the existing evidence against such a class of models as descriptors of fracture nucleation. To that end, a review is first given of the plethora of experimental observations of fracture nucleation in nominally elastic brittle materials under quasi-static loading conditions, as well as of classical variational phase-field models, without and with energy splits. These models are then confronted with the experimental observations. The conclusion is that they cannot possibly describe fracture nucleation in general. This because classical variational phase-field models cannot account for material strength as an independent macroscopic material property. The last part of the paper includes a brief summary of a class of phase-field models that can describe fracture nucleation. It also provides a discussion of how pervasively material strength has been overlooked in the analysis of fracture at large, as well as an outlook into the modeling of fracture nucleation beyond the basic setting of elastic brittle materials.

We prove analogues of the Green-Tao-Ziegler theorem on linear constellations in primes, in which the primes under consideration are restricted by certain arithmetic conditions. Our first main result is conditional upon Hooley's Riemann hypothesis and imposes the extra condition that the primes have prescribed primitive roots. Our second main result is unconditional and imposes the extra condition that the primes have prescribed Artin symbols in given Galois number fields. To illustrate this second result, we present an application in inverse Galois theory., Comment: 60 pages. Comments are welcome!

Primordial black holes (PBHs) are theorized objects that may make up some - or all - of the dark matter in the universe. At the lowest allowed masses, Hawking radiation (in the form of photons or electrons and positrons) is the primary tool to search for PBHs. This paper is part of an ongoing series in which we aim to calculate the $O(\alpha)$ corrections to Hawking radiation from asteroid-mass primordial black holes, based on a perturbative quantum electrodymanics (QED) calculation on Schwarzschild background. Silva et. al. (2023) divided the corrections into dissipative and conservative parts; this work focuses on the numerical computation of the dissipative $O(\alpha)$ corrections to the photon spectrum. We generate spectra for primordial black holes of mass $M=1$-$8 \times 10^{21} m_{\rm planck}$. This calculation confirms the expectation that at low energies, the inner bremsstrahlung radiation is the dominant contribution to the Hawking radiation spectrum. At high energies, the main $O(\alpha)$ effect is a suppression of the photon spectrum due to pair production (emitted $\gamma\rightarrow e^+e^-$), but this is small compared to the overall spectrum. We compare the low-energy tail in our curved spacetime QED calculation to several approximation schemes in the literature, and find deviations that could have important implications for constraints from Hawking radiation on primordial black holes as dark matter., Comment: 28 pages, 15 figures, to be submitted to Physical Review D, spectra to be released upon acceptance

Markerless motion capture devices such as the Leap Motion Controller (LMC) have been extensively used for tracking hand, wrist, and forearm positions as an alternative to Marker-based Motion Capture (MMC). However, previous studies have highlighted the subpar performance of LMC in reliably recording hand kinematics. In this study, we employ four LMC devices to optimize their collective tracking volume, aiming to enhance the accuracy and precision of hand kinematics. Through Monte Carlo simulation, we determine an optimized layout for the four LMC devices and subsequently conduct reliability and validity experiments encompassing 1560 trials across ten subjects. The combined tracking volume is validated against an MMC system, particularly for kinematic movements involving wrist, index, and thumb flexion. Utilizing calculation resources in one computer, our result of the optimized configuration has a better visibility rate with a value of 0.05 $\pm$ 0.55 compared to the initial configuration with -0.07 $\pm$ 0.40. Multiple Leap Motion Controllers (LMCs) have proven to increase the interaction space of capture volume but are still unable to give agreeable measurements from dynamic movement., Comment: This paper has eight pages and five figures. It has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. The code used in this work is available at https://github.com/hilmanfatoni/Multi-LMC_Optimization

Accurate prediction of the properties of crystalline materials is crucial for targeted discovery, and this prediction is increasingly done with data-driven models. However, for many properties of interest, the number of materials for which a specific property has been determined is much smaller than the number of known materials. To overcome this disparity, we propose a novel self-supervised learning (SSL) strategy for material property prediction. Our approach, crystal denoising self-supervised learning (CDSSL), pretrains predictive models (e.g., graph networks) with a pretext task based on recovering valid material structures when given perturbed versions of these structures. We demonstrate that CDSSL models out-perform models trained without SSL, across material types, properties, and dataset sizes., Comment: Published at ICML 2024 AI4Science: https://openreview.net/forum?id=yML9ufAEoV

During the SARS-CoV-2 pandemic, polymerase chain reaction (PCR) and lateral flow device (LFD) tests were frequently deployed to detect the presence of SARS-CoV-2. Many of these tests were singleplex, and only tested for the presence of a single pathogen. Multiplex tests can test for the presence of several pathogens using only a single swab, which can allow for: surveillance of more pathogens, targeting of antiviral interventions, a reduced burden of testing, and lower costs. Test sensitivity however, particularly in LFD tests, is highly conditional on the viral concentration dynamics of individuals. To inform the use of multiplex testing in outbreak detection it is therefore necessary to investigate the interactions between outbreak detection strategies and the differing viral concentration trajectories of key pathogens. Viral concentration trajectories are estimated for SARS-CoV-2, and Influenza A/B. Testing strategies for the first five symptomatic cases in an outbreak are then simulated and used to evaluate key performance indicators. Strategies that use a combination of multiplex LFD and PCR tests achieve; high levels of detection, detect outbreaks rapidly, and have the lowest burden of testing across multiple pathogens. Influenza B was estimated to have lower rates of detection due to its modelled viral concentration dynamics., Comment: 29 pages, 17 Figures

The purpose of this paper is to analyze a nonlinear elasticity model previously introduced by the authors for comparing two images, regarded as bounded open subsets of $\R^n$ together with associated vector-valued intensity maps. Optimal transformations between the images are sought as minimisers of an integral functional among orientation-preserving homeomorphisms. The existence of minimisers is proved under natural coercivity and polyconvexity conditions, assuming only that the intensity functions are bounded measurable. Variants of the existence theorem are also proved, first under the constraint that finite sets of landmark points in the two images are mapped one to the other, and second when one image is to be compared to an unknown part of another. The question is studied as to whether for images related by a linear mapping the unique minimizer is given by that linear mapping. For a natural class of functional integrands an example is given guaranteeing that this property holds for pairs of images in which the second is a scaling of the first by a constant factor. However for the property to hold for arbitrary pairs of linearly related images it is shown that the integrand has to depend on the gradient of the transformation as a convex function of its determinant alone. This suggests a new model in which the integrand depends also on second derivatives of the transformation, and an example is given for which both existence of minimizers is assured and the above property holds for all pairs of linearly related images.

The quadratic traveling salesperson problem (QTSP) is a generalization of the traveling salesperson problem, in which all triples of consecutive customers in a tour determine the travel cost. We propose compact optimization models for QTSP in mixed-integer quadratic programming (MIQP), mixed-integer linear programming (MILP), constraint programming (CP), and domain-independent dynamic programming (DIDP). Our experimental results demonstrate that the DIDP model performs better than other approaches in optimality gap and solution quality when the problem size is large enough., Comment: 12 pages, 6 figures

Electrometry based on electromagnetically induced transparency (EIT) in alkali Rydberg vapor cells may suffer reduced sensitivity due to spurious line broadening effects, caused by surface charges, contaminant gases, or other manufacturing defects. In order to draw conclusions about the deleterious effects of potential contaminant gases inside Rydberg electrometry vapor cells, we revisit collisional broadening and shifts of both the D$_2$ line and Rydberg levels of rubidium. Specifically, we measure the broadening and shifts of the $5{\rm S}_{1/2}\rightarrow 5{\rm P}_{3/2}$ (i.e., the D$_2$ line) and $5{\rm S}_{1/2}\rightarrow 5{\rm P}_{3/2}\rightarrow (25{\rm D},27{\rm S},30{\rm D},32{\rm S},35{\rm D},37{\rm S})$ transitions of $^{85}$Rb due to He, Ne, N$_2$ and Ar. By combining these measurements with observations of velocity changing collisions in the sub-Doppler spectrum of the D$_2$ line, we conclude the following: (1) that contaminant gases are most likely not the cause of irregular line shapes or shifts of Rydberg transitions due to the high pressures required, and (2) the sub-Doppler spectrum of the D$_2$ line, through its accompanying loss of contrast at high pressures, can validate that a vapor cell is sufficiently free of contaminant gas for EIT electrometry. We use the theory of Omont, J. Phys. France 38, 1343 (1977), to extend our results to a wide variety of possible contaminant gases and further derive scaling laws applicable to all gases., Comment: 10 pages, 6 figures, 3 tables

$\require{mediawiki-texvc}$Cosmic Reionisation commenced when ultraviolet (UV) radiation produced in the first galaxies began illuminating the cold, neutral gas that filled the primordial Universe. Recent James Webb Space Telescope (JWST) observations have shown that surprisingly UV-bright galaxies were in place beyond redshift $z = 14$, when the Universe was less than 300 Myr old. Smooth turnovers of their UV continua have been interpreted as damping-wing absorption of Lyman-$\alpha$ (Ly$\alpha$), the principal hydrogen transition. However, spectral signatures encoding crucial properties of these sources, such as their emergent radiation field, largely remain elusive. Here we report spectroscopy from the JWST Advanced Deep Extragalactic Survey (JADES) of a galaxy at redshift $z = 13.0$ that reveal a singular, bright emission line unambiguously identified as Ly$\alpha$, in addition to a smooth turnover. We observe an equivalent width of $\text{EW}_\mathrm{Ly\alpha} > 40 \, \AA$ (rest frame), previously only seen at $z < 9$ where the intervening intergalactic medium (IGM) becomes increasingly ionised. Together with a very blue UV continuum, the Ly$\alpha$ line indicates the galaxy is a prolific producer of ionising photons, a significant fraction of which may escape. This suggests it resides in an early reionised region preventing complete extinction of Ly$\alpha$, thus shedding new light on the nature of the earliest galaxies and the onset of Reionisation only 330 Myr after the Big Bang., Comment: 19 pages, 9 figures, 3 tables. Comments welcome

The reconstruction of current distributions from samples of their induced magnetic field is a challenging problem due to multiple factors. First, the problem of reconstructing general three dimensional current distributions is ill-posed. Second, the current-to-field operator performs a low-pass filter that dampens high-spatial frequency information, so that even in situations where the inversion is formally possible, attempting to employ the formal inverse will result in solutions with unacceptable noise. Most contemporary methods for reconstructing current distributions in two dimensions are based on Fourier techniques and apply a low pass filter to the $B$-field data, which prevents excessive noise amplification during reconstruction at the cost of admitting blurring in the reconstructed solution. In this report, we present a method of current recovery based on penalizing the $L1$ norm of the curl of the current distribution. The utility of this method is based on the observation that in microelectronics settings, the conductivity is piecewise constant. We also reconstruct the current fields using a divergence-free wavelet basis. This has the advantage of automatically enforcing current continuity and halving the number of unknowns that must be solved for. Additionally, the curl operator can be computed exactly and analytically in this wavelet expansion, which simplifies the application of the $L1-\textrm{curl}$ regularizer. We demonstrate improved reconstruction quality relative to Fourier-based techniques on both simulated and laboratory-acquired magnetic field data., Comment: 22 pages, 10 figures, submitted to SIAM Journal on Imaging Sciences

We introduce Spurfies, a novel method for sparse-view surface reconstruction that disentangles appearance and geometry information to utilize local geometry priors trained on synthetic data. Recent research heavily focuses on 3D reconstruction using dense multi-view setups, typically requiring hundreds of images. However, these methods often struggle with few-view scenarios. Existing sparse-view reconstruction techniques often rely on multi-view stereo networks that need to learn joint priors for geometry and appearance from a large amount of data. In contrast, we introduce a neural point representation that disentangles geometry and appearance to train a local geometry prior using a subset of the synthetic ShapeNet dataset only. During inference, we utilize this surface prior as additional constraint for surface and appearance reconstruction from sparse input views via differentiable volume rendering, restricting the space of possible solutions. We validate the effectiveness of our method on the DTU dataset and demonstrate that it outperforms previous state of the art by 35% in surface quality while achieving competitive novel view synthesis quality. Moreover, in contrast to previous works, our method can be applied to larger, unbounded scenes, such as Mip-NeRF 360., Comment: https://geometric-rl.mpi-inf.mpg.de/spurfies/

Balancing computational efficiency with robust predictive performance is crucial in supervised learning, especially for critical applications. Standard deep learning models, while accurate and scalable, often lack probabilistic features like calibrated predictions and uncertainty quantification. Bayesian methods address these issues but can be computationally expensive as model and data complexity increase. Previous work shows that fast variational methods can reduce the compute requirements of Bayesian methods by eliminating the need for gradient computation or sampling, but are often limited to simple models. We demonstrate that conditional mixture networks (CMNs), a probabilistic variant of the mixture-of-experts (MoE) model, are suitable for fast, gradient-free inference and can solve complex classification tasks. CMNs employ linear experts and a softmax gating network. By exploiting conditional conjugacy and P\'olya-Gamma augmentation, we furnish Gaussian likelihoods for the weights of both the linear experts and the gating network. This enables efficient variational updates using coordinate ascent variational inference (CAVI), avoiding traditional gradient-based optimization. We validate this approach by training two-layer CMNs on standard benchmarks from the UCI repository. Our method, CAVI-CMN, achieves competitive and often superior predictive accuracy compared to maximum likelihood estimation (MLE) with backpropagation, while maintaining competitive runtime and full posterior distributions over all model parameters. Moreover, as input size or the number of experts increases, computation time scales competitively with MLE and other gradient-based solutions like black-box variational inference (BBVI), making CAVI-CMN a promising tool for deep, fast, and gradient-free Bayesian networks., Comment: 16 pages main text (3 figures), including references. 9 pages supplementary material (5 figures)

Binary stars are responsible for many unusual astrophysical phenomena, including some important explosive cosmic events. The stability criteria for rapid mass transfer and common-envelope evolution are fundamental to binary star evolution. They determine the mass, mass ratio, and orbital distribution of systems such as X-ray binaries and merging gravitational-wave sources. We use our adiabatic mass-loss model to systematically survey metal-poor and solar-metallicity donor thresholds for dynamical timescale mass transfer. The critical mass ratios qad are systematically explored, and the impact of metallicity and nonconservative mass transfer are studied. For metal-poor radiative-envelope donors, qad are smaller than those for solar-metallicity stars at the same evolutionary stage. However, qad do the opposite for convective-envelope donors. Nonconservative mass transfer significantly decreases qad for massive donors. This is because it matters how conservative mass transfer is during the thermal timescale phase immediately preceding a delayed dynamical mass transfer. We apply our theoretical predictions to observed high-mass X-ray binaries that have overfilled their Roche lobes and find a good agreement with their mass ratios. Our results can be applied to study individual binary objects or large samples of binary objects with binary population synthesis codes., Comment: Submitted to ApJ. Comments are welcome

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.

Radial velocity (RV) follow-up is a critical complement of transiting exoplanet surveys like the Transiting Exoplanet Survey Satellite (TESS ), both for validating discoveries of exoplanets and measuring their masses. Stellar activity introduces challenges to interpreting these measurements because the noise from the host star, which is often correlated in time, can result in high RV uncertainty. A robust understanding of stellar activity and how its timescales interact with the observing cadence can optimize limited RV resources. For this reason, in the era of over-subscribed, high-precision RV measurements, folding stellar activity timescales into the scheduling of observation campaigns is ideal. We present gaspery, an open-source code implementation to enable the optimization of RV observing strategies. Gaspery employs a generalized formulation of the Fisher Information for RV time series that also incorporates information about stellar correlated noise. We show that the information contained in an observing strategy can be significantly affected by beat frequencies between the orbital period of the planet, the stellar rotation period, and the observation epochs. We investigate how the follow-up observing strategy will affect the resulting radial velocity uncertainty, as a function of stellar properties such as the spot decay timescale and rotation period. We then describe two example use cases for gaspery: 1) calculating the minimum number of observations to reach an uncertainty tolerance in a correlated noise regime and 2) finding an optimal strategy given a fixed observing budget. Finally, we outline a prescription for selecting an observing strategy that is generalizable to different targets., Comment: 19 pages, 7 figures, accepted for publication by AAS Journals

Block Encoding (BE) is a crucial subroutine in many modern quantum algorithms, including those with near-optimal scaling for simulating quantum many-body systems, which often rely on Quantum Signal Processing (QSP). Currently, the primary methods for constructing BEs are the Linear Combination of Unitaries (LCU) and the sparse oracle approach. In this work, we demonstrate that QSP-based techniques, such as Quantum Singular Value Transformation (QSVT) and Quantum Eigenvalue Transformation for Unitary Matrices (QETU), can themselves be efficiently utilized for BE implementation. Specifically, we present several examples of using QSVT and QETU algorithms, along with their combinations, to block encode Hamiltonians for lattice bosons, an essential ingredient in simulations of high-energy physics. We also introduce a straightforward approach to BE based on the exact implementation of Linear Operators Via Exponentiation and LCU (LOVE-LCU). We find that, while using QSVT for BE results in the best asymptotic gate count scaling with the number of qubits per site, LOVE-LCU outperforms all other methods for operators acting on up to $\lesssim11$ qubits, highlighting the importance of concrete circuit constructions over mere comparisons of asymptotic scalings. Using LOVE-LCU to implement the BE, we simulate the time evolution of single-site and two-site systems in the lattice $\varphi^4$ theory using the Generalized QSP algorithm and compare the gate counts to those required for Trotter simulation., Comment: 20 pages, 16 Figures, 2 Tables, 2 Appendices

Astrophotonics represents a cutting-edge approach in observational astronomy. This paper explores the significant advancements and potential applications of astrophotonics, highlighting how photonic technologies stand to revolutionise astronomical instrumentation. Key areas of focus include photonic wavefront sensing and imaging, photonic interferometry and nulling, advanced chip fabrication methods, and the integration of spectroscopy and sensing onto photonic chips. The role of single-mode fibres in reducing modal noise, and the development of photonic integral field units (IFUs) and arrayed waveguide gratings (AWGs) for high-resolution, spatially resolved spectroscopy will be examined. As part of the Sydney regional-focus issue, this review aims to detail some of the current technological achievements in this field as well as to discuss the future trajectory of astrophotonics, underscoring its potential to unlock important new astronomical discoveries., Comment: Published version available at https://opg.optica.org/ao/fulltext.cfm?uri=ao-63-24-6393&id=554618

To accurately compute data-based prediction of Hamiltonian systems, especially the long-term evolution of such systems, it is essential to utilize methods that preserve the structure of the equations over time. We consider a case that is particularly challenging for data-based methods: systems with interacting parts that do not reduce to pure momentum evolution. Such systems are essential in scientific computations. For example, any discretization of a continuum elastic rod can be viewed as interacting elements that can move and rotate in space, with each discrete element moving on the group of rotations and translations $SE(3)$. We develop a novel method of data-based computation and complete phase space learning of such systems. We follow the original framework of \emph{SympNets} (Jin et al, 2020) building the neural network from canonical phase space mappings, and transformations that preserve the Lie-Poisson structure (\emph{LPNets}) as in (Eldred et al, 2024). We derive a novel system of mappings that are built into neural networks for coupled systems. We call such networks Coupled Lie-Poisson Neural Networks, or \emph{CLPNets}. We consider increasingly complex examples for the applications of CLPNets: rotation of two rigid bodies about a common axis, the free rotation of two rigid bodies, and finally the evolution of two connected and interacting $SE(3)$ components. Our method preserves all Casimir invariants of each system to machine precision, irrespective of the quality of the training data, and preserves energy to high accuracy. Our method also shows good resistance to the curse of dimensionality, requiring only a few thousand data points for all cases studied, with the effective dimension varying from three to eighteen. Additionally, the method is highly economical in memory requirements, requiring only about 200 parameters for the most complex case considered., Comment: 52 pages, 9 figures

Quantum Computing (QC) offers significant potential to enhance scientific discovery in fields such as quantum chemistry, optimization, and artificial intelligence. Yet QC faces challenges due to the noisy intermediate-scale quantum era's inherent external noise issues. This paper discusses the integration of QC as a computational accelerator within classical scientific high-performance computing (HPC) systems. By leveraging a broad spectrum of simulators and hardware technologies, we propose a hardware-agnostic framework for augmenting classical HPC with QC capabilities. Drawing on the HPC expertise of the Oak Ridge National Laboratory (ORNL) and the HPC lifecycle management of the Department of Energy (DOE), our approach focuses on the strategic incorporation of QC capabilities and acceleration into existing scientific HPC workflows. This includes detailed analyses, benchmarks, and code optimization driven by the needs of the DOE and ORNL missions. Our comprehensive framework integrates hardware, software, workflows, and user interfaces to foster a synergistic environment for quantum and classical computing research. This paper outlines plans to unlock new computational possibilities, driving forward scientific inquiry and innovation in a wide array of research domains.

Magnetic resonance spectroscopy (MRS) is an established technique for studying tissue metabolism, particularly in central nervous system disorders. While powerful and versatile, MRS is often limited by challenges associated with data quality, processing, and quantification. Existing MRS quantification methods face difficulties in balancing model complexity and reproducibility during spectral modeling, often falling into the trap of either oversimplification or over-parameterization. To address these limitations, this study introduces a deep learning (DL) framework that employs transfer learning, in which the model is pre-trained on simulated datasets before it undergoes fine-tuning on in vivo data. The proposed framework showed promising performance when applied to the Philips dataset from the BIG GABA repository and represents an exciting advancement in MRS data analysis., Comment: 8 pages, 4 figures, and 3 tables for the main body; 9 pages, 4 figures, and 3 tables for the supplementary material

We present a study combining experimental measurements, theoretical analysis, and simulations to investigate core-shell microcapsules interacting with a solid boundary, with a particular focus on understanding the short-range potential energy well arising from the tethered force. The microcapsules, fabricated using a Pickering emulsion template with a cinnamon oil core and calcium alginate shell, were characterized for size (5-6 microns in diameter) and surface charge (-20 mV). We employed total internal reflection microscopy and particle tracking to measure the microcapsule-boundary interactions and diffusion, from which potential energy and diffusivity profiles were derived. The potential energy profile was analyzed and simulated by considering electrostatic, gravitational, and tethered forces, while the diffusivity was compared to that of a solid particle-boundary interaction, inclusive of hydrodynamic forces. The diffusivity was represented as a normalized diffusion coefficient to eliminate the impact of fluid viscosity. The normalized diffusion coefficient of polymer-shell microcapsules (0.02) was found to be an order of magnitude smaller than that of solid polystyrene particles (0.2). The microcapsule sampled a potential well consisting of two distinct minima, as observed experimentally and supported by analytical expressions and Brownian dynamics simulations. A critical tethered height (49.8 nm) and the alginate radius of (35.2 nm) were obtained from fitting our model to experimental data. This work concludes that these benign core shell microcapsules interact with a nearby boundary via a transient tethering interaction, overall producing a mild sticky interaction that would likely be beneficial for applications in consumer products., Comment: 43 pages, 13 figures

Distributed quantum computing allows the modular construction of large-scale quantum computers and enables new protocols for blind quantum computation. However, such applications in the large-scale, fault-tolerant regime place stringent demands on the fidelity and rate of entanglement generation which are not met by existing methods for quantum interconnects. In this work, we develop constant-rate entanglement distillation methods to address this bottleneck in the setting of noisy local operations. By using a sequence of two-way entanglement distillation protocols based on quantum error detecting codes with increasing rate, and combining with standard fault tolerance techniques, we achieve constant-rate entanglement distillation. We prove the scheme has constant-rate in expectation and further numerically optimize to achieve low practical overhead subject to memory constraints. We find our optimized schemes outperform existing computationally efficient quantum interconnect schemes by an order of magnitude in relevant regimes, leading to a direct speed-up in the execution of distributed quantum algorithms.

This work-in-progress paper presents results on the feasibility of single-shot object detection on microcontrollers using YOLO. Single-shot object detectors like YOLO are widely used, however due to their complexity mainly on larger GPU-based platforms. We present microYOLO, which can be used on Cortex-M based microcontrollers, such as the OpenMV H7 R2, achieving about 3.5 FPS when classifying 128x128 RGB images while using less than 800 KB Flash and less than 350 KB RAM. Furthermore, we share experimental results for three different object detection tasks, analyzing the accuracy of microYOLO on them., Comment: Published at the ECML PKDD Conference 2023, at the 4th Workshop on IoT, Edge, and Mobile for Embedded Machine Learning

In this topical review we explore the dynamics of nonlinear lattices with a particular focus to Fermi-Pasta-Ulam-Tsingou type models that arise in the study of elastic media and, more specifically, granular crystals. We first revisit the workhorse of such lattices, namely traveling waves, both from a continuum, but also from a genuinely discrete perspective, both without and with a linear force component (induced by the so-called precompression). We then extend considerations to time-periodic states, examining dark breather structures in homogeneous crystals, as well as bright breathers in diatomic lattices. The last pattern that we consider extensively is the dispersive shock wave arising in the context of suitable Riemann (step) initial data. We show how the use of continuum (KdV) and discrete (Toda) integrable approximations can be used to get a first quantitative handle of the relevant waveforms. In all cases, theoretical analysis is accompanied by numerical computations and, where possible, by a recap and illustration of prototypical experimental results. We close the chapter by offering a number of ongoing and potential future directions and associated open problems in the field.

We analyze the classical and quantized center-of-mass motion of a polarizable particle interacting with the fluctuations of the electromagnetic (EM) field in the presence of a medium. As a polarizable particle is immersed in a thermal environment, the momentum impulses imparted by the field fluctuations lead to momentum diffusion and drag for the particle's classical center of mass. When considering the quantized center-of-mass motion of the particle, these very fluctuations gain information about its position, leading to decoherence in the position basis. We derive a position localization master equation for the particle's quantized center of mass, and examine its classical center-of-mass momentum diffusion, elucidating correspondences between classical and quantum Brownian motion of polarizable particles near media.