Papers

Recent real-time TDDFT calculations have reported an unexpected delayed growth of molecular dipole oscillations some time after an extreme-ultraviolet (XUV) pulse is applied. We show that numerical and analytical arguments suggest that this instability is an artifact of an incorrect non-linearity introduced by the computational approach: Propagation with an adiabatic exchange-correlation approximation within the time-dependent Kohn-Sham equations of time-dependent density functional theory (TDDFT) tends to amplify initially small and pure sinusoidal oscillations in a system. On the other hand, when this same adiabatic approximation is used within the recent response-reformulated RR-TDDFT,the instability is absent. The absorbing boundary condition plays a crucial role consistent with our argument. We demonstrate this explicitly on the N2 molecule subject to an XUV pulse, with a range of adiabatic functionals.

We present OBDF-SQD, a hybrid quantum-classical method that combines one-body downfolding~(OBDF) based on one-body Møller–Plesset second-order perturbation theory (OBMP2) with sample-based quantum diagonalization~(SQD) for use in quantum-centric supercomputing~(QCS). In this approach, OBMP2 is executed classically to fold dynamical correlation from external orbitals into a renormalized one-body operator, yielding an effective active-space Hamiltonian that retains the same operator structure as the bare Hamiltonian and therefore requires no additional quantum circuit resources. SQD is then applied to this effective Hamiltonian, where, in this work, the quantum sampling is performed via the Qiskit Aer simulator rather than actual quantum hardware. We benchmark OBDF-SQD on dissociation curves of \ce{H6} chain, ring, and lattice systems and the \ce{N2} molecule in the cc-pVDZ basis, comparing against standard methods and active-space SQD (CAS-SQD). We observed that OBDF-SQD consistently improves upon CAS-SQD with the same active space. The simplicity of the one-body downfolding correction also makes the approach straightforwardly extensible to periodic solids within existing quantum embedding frameworks

Standard rational actor models often attribute cooperation failures in social dilemmas to insufficient incentives, overlooking the destabilizing effects of continuous utility maximization. To address this, we propose a framework of “will" defined as a mechanism that persistently pursues goals while ignoring local cost-benefit fluctuations. We formalize the Willed Agents as potential minimizers, distinguishing them from cumulative utility maximization. Dynamical analysis of infinite population demonstrates that willed agents shrink the feasible state space, acting as boundary constraints that accelerate convergence in canonical social dilemmas. Through multi-agent simulations in a spatiotemporal Stag Hunt Game, we show that willed agents function as “cooperation catalysts", enabling groups to surmount high-risk thresholds where purely utility maximization fails. We find that heterogeneous will strength promotes cooperation, and that agents who autonomously suspend rational re-evaluation can significantly outperform continuous optimizers. These findings suggest that successful cooperation relies on the cognitive capacity to strategically constrain calculation.

We present an analytical framework capable of describing spin-waves dynamic in magnetic hetero-structures composed of a pair of exchange-decoupled magnetic layers separated by a nonmagnetic spacer, focusing in particular on garnet-based multilayers. The model captures the formation of collective spin-wave modes, namely symmetric and antisymmetric, arising from dipolar coupling and provides direct access to the dispersion relation of the system and consequent interference phenomena. This formalism establishes a versatile theoretical tool for the predictive design of dipolarly coupled magnonic devices, providing access to their eigenfrequencies and mode shapes.

Energy-based models (EBMs) provide a powerful and flexible way of learning a joint probability distribution over data by constructing an energy surface. This energy surface enables insight extraction and conditional sampling. We apply EBMs to laboratory plasma physics, a domain characterized by highly nonlinear phenomena. These phenomena are studied using plasma diagnostics, which are often difficult to analyze and subject to hardware degradation. In addition, the possible configuration space of a plasma device is sufficiently large that it cannot be efficiently searched using conventional analysis techniques. EBMs address these issues. At the Large Plasma Device (LAPD), a CNN- and attention-based EBM is trained on a set of randomly generated machine conditions and their corresponding diagnostic time series. We demonstrate diagnostic reconstruction using this EBM on real data and show that additional diagnostics improves reconstruction error and generation quality. The energy surface is directly evaluated for an ill-posed inverse problem: inferring probe position from a time-series measurement. This inference illuminates symmetries in the data, potentially leading to a method of inquiry to supplement conventional data analysis. Trends in diagnostic signals are inferred via conditional sampling over machine inputs. In addition, this multimodal EBM is able to unconditionally reproduce all distributional modes, suggesting future potential in anomaly detection on the LAPD. Fundamentally, this work demonstrates the flexibility and efficacy of EBM-based generative modeling of laboratory plasma data, and showcases multiple practical uses of just a single trained EBM in the physical sciences.

We develop a descriptive account of scientific reward in physics based on the concept of the time-dependent Polydoxon, defined as the structured set of empirically viable theories at a given time. We argue that highly rewarded contributions, such as those recognized by major prizes and professional honors, can be systematically understood as those that transform this space. These transformations take the form of expansion (adding viable theories), contraction (eliminating viable theories), reconfiguration (illuminating deeper structures and relations within and between theories), and enabling moves (methodological or technological advances that enable future transformations). The analysis is further refined by emphasizing that reward correlates with the transformation's magnitude, assessed along dimensions of scope, centrality, depth, and future leverage. This framework reframes the analysis of rewarded achievement away from isolated theoretical successes and toward the dynamics of a landscape of viable theories, providing a more unified descriptive interpretation of rewarded scientific activity in physics across its diverse set of theoretical and experimental discoveries.

Agglomeration economies drive urban growth at different spatial scales by enabling productivity gains, knowledge spillovers, and shared inputs among proximate firms and amenities. To develop a unified science of cities it is thus important to understand how and to what extent different amenities cluster or mix across scales and regional contexts. By utilizing a novel Bayesian framework for nonparametrically quantifying the spectrum of possible mixing patterns of amenities in a city, we identify universal spatial scales of homophily (agglomeration) and heterophily (co-agglomeration) among different amenity types across roughly 800 cities worldwide. Through a detailed longitudinal case study, we also find that the changes in heterophilic mixing derived from our methodology more effectively predict changes in neighborhood rental values than the diversity of amenities present. These findings suggest that agglomeration economies exhibit universal spatial regularities that depend largely on the types of firms or amenities being considered, rather than their specifics or regional context, and highlight the benefit of heterophilic amenity mixing at walkable spatial scales.

The recently developed Doubles Connected Moments (DCM) expansion offers a tractable approach for computing correlation energy, exhibiting an noniterative O(N^6) scaling with system size N. Benchmark calculations on a set of molecules demonstrate that the DCM can outperform CCSD in terms of accuracy. To further enhance its efficiency, we present a stochastic variant of DCM by introducing a stochastic resolution-of-identity (sRI) technique, which decomposes the essential four-index intermediates. The resulting sRI-DCM scheme only involves one O(N^6) step, while all other steps do not exceed O(N^4) at each recursion, and reliably reproduces the results of conventional DCM. Our sRI-DCM achieves an overall experimental scaling of O(N^{4.46}) for series hydrogen dimer chains, demonstrating that it is attractive and practical for large systems containing hundreds of electrons.

Accurate calculation of free energies and their derivatives is central to assessing the thermodynamic stability of molecular and particulate systems across length scales. Yet such quantities can be difficult to compute reliably in strongly interacting systems, such as solutions of ionic species in polar solvents. One important example is the chemical potential of water in aqueous electrolytes, which can be estimated through staged particle insertion by gradually coupling an inserted molecule to its environment. Although the resulting insertion free energy should be independent of the alchemical pathway, the order and manner in which van der Waals and electrostatic interactions are activated can strongly affect convergence and, in some cases, yield inconsistent estimates. Here, we examine this issue by calculating water's chemical potential in aqueous KCl solutions using eight alchemical insertion pathways that differ in the extent and order of van der Waals and Coulombic coupling. We find that concurrently activating these interactions, particularly in fully coupled and partially end-coupled protocols, can produce chemically implausible insertion free energies. These anomalies arise from intermediate states in which the inserted water molecule develops strong electrostatic interactions with a chloride ion before sufficient short-range repulsion has been established. In contrast, pathways that activate short-range van der Waals interactions before electrostatics yield more consistent and chemically plausible estimates. These findings demonstrate that practical alchemical calculations in polar and ionic environments can be highly sensitive to pathway design, underscoring the importance of decoupling short-range and electrostatic interactions in staged insertion alchemical protocols.

To effectively describe the plasma with strong magnetic field, the force-free electrodynamics was introduced, within which the Grad-Shafranov equation plays the key role. The Grad-Shafranov equation governs the global structure of a electromagnetic field in equilibrium with symmetries. It is widely applicable in an amount of scenarios, such as the tokamak, the solar corona, the magnetosphere of Earth, neutron star and black hole, etc. However, in different situations, the Grad-Shafranov equation is expressed differently, and the derivations might be complicated. In this work, via the language of differential form, we provide a general expression of Grad-Shafranov equation, from which the expression in any specific situation can be quickly obtained. Additionally, we present a Lagrangian density for a scalar field whose on-shell condition is precisely the Grad-Shafranov equation.

In this work we derive novel analytical expressions for the mass and distance of a Schwarzschild black hole (BH), as well as for the orbital radius of test particles orbiting it, it terms of astrophysical observables measured throughout the entire orbit of the revolving particle. We use a general relativistic method to describe the frequency shifts of photons emitted in the vivinity of a BH by considering two emitters (or two positions of the same emitter) located symmetrically opposite to each other with respect to the observer's line of sight (LOS) when performing measurements along the orbit. Furthermore, the introduction of the redshift rapidity allows us to write independent expressions for the BH mass and its distance to Earth. We also extend our study to the case when astrophysical systems have a peculiar motion and derive the corresponding closed formulas.

Along with the development of the scripting technology in cartography, such as the GMT and libraries of R programming language, geologic and geophysical mapping is being vigorously promoted, where the integration of the thematic data, such as GEBCO, EGM-2008 and geological raster and vector layers is one of the primary datasets that provides the high-resolution raw sources for cartographic visualization in the geologically complex regions like Pakistan. This study aims to integrate scripting methods of automated cartography, methods of applied geoinformatics for geomorphometric analysis and technical data processing (formatting, projecting, plotting), to provide a synthesis of the geological, geophysical and geomorphological maps of Pakistan they as new information supporting analysis of the geospatial variations of geology, geomorphology, tectonics and gravity fields with a special focus on the geologically remarkable region of Pakistan: Sulaiman Fold Belt and Muslim Bagh ophiolite complex. This study presents new 12 thematic maps, which are technically made using scripting approaches and open tools. All maps cover the region of Pakistan and they are made using open source tools: GMT, R and QGIS. A GMT and R based scripting mapping is applied for mapping Pakistan, and its algorithm steps are presented stepwise as code snippets. A system complex approach of the data integration and formats reshaping, data conversion and reformatting for a single project of the geology of Pakistan is designed and developed based on the combination of the programming and scripting techniques and with additional mapping, which integrates the geospatial datasets. Correlation between spatial phenomena of Earth's gravity, geologic evolution and tectonic movements were commented.

A computational study of higher-order derivative ratios on a time interval leading to the enstrophy peak is presented in the case of the 3D Taylor-Green vortex, a benchmark problem in the simulation of turbulent flows. The main finding is that the power law relating the ratios at time $t$ to $T^*-t$ where $T^*$ is the peak enstrophy time is of a form that allows the machinery of dynamic interpolation-sparseness to produce a lower bound on the radius of spatial analyticity sufficient to overcome an upper bound on the scale of sparseness of the super-level sets in view. As a consequence, the mechanism of turbulent dissipation engages via the harmonic measure maximum principle, furnishing a rigorous explanation for the subsequent slump of the enstrophy. This indicates that the higher-order derivative ratios – which could be viewed as higher-order analogs of the classical Taylor and Kraichnan scales in turbulence phenomenology – may be reasonable identifiers of the peak of the energy dissipation rate.

Exhaled breath condensate (EBC) contains volatile metabolites and is promising for non-invasive disease diagnosis, but after decades of research spanning over 100 biomarkers and 10 diseases, no EBC-based test has reached clinical use. The measurement variability that can span orders of magnitude, far exceeding the clinically required 10%, has long been attributed to biological factors. Here, we reveal a fundamentally different origin: the collected microdroplets themselves fail to retain volatile biomarkers. By isolating volatile co-condensation and transient evaporation from biological interference, we show that EBC microdroplets smaller than 100 {\mu}m lose clinically significant volatile content within a single breath cycle. This challenges the implicit assumption underlying decades of EBC research, that condensate faithfully reflects airway lining fluid. We develop and validate a physics-based model that predicts this loss across disease-relevant biomarkers and establishes the conditions for reliable EBC sampling. This work reframes EBC variability as a solvable engineering problem rather than an inherent biological limitation.

This paper presents an interdisciplinary analysis of the "Star of Bethlehem" narrative described in the Gospel of Matthew (Mt 2:1-12), examining the hypothesis, originally proposed by Johannes Kepler, that the reported phenomenon may be associated with the Jupiter-Saturn conjunction of 7 BCE. The methodology is based on a systematic comparison between the textual account and independently verifiable astronomical data, including retro-calculated ephemerides, sky geometry from Judea, constraints of the Jerusalem-Bethlehem route, and the historical chronology of Herod the Great. The narrative elements are treated as distinct, partially independent constraints required to be jointly satisfied within an explicitly falsifiable framework, under restricted observational and kinematic conditions, avoiding arbitrary parameter choices. The analysis indicates that the 7 BCE Jupiter-Saturn conjunction-characterized by its triple occurrence and extended duration-exhibits an apparent motion consistent with key aspects of the reported behavior of the star, including its progression and apparent stopping. In particular, the stationary phase of Jupiter occurs within a few days of an independently identified sky-ground kinematic synchronization window, without ad hoc adjustments. A sensitivity analysis suggests that this compatibility remains stable under reasonable variations of assumptions. The Jupiter-Saturn conjunction thus emerges as a coherent candidate satisfying the constraints considered. This study does not aim to establish a definitive historical identification, but to propose a physical and testable framework for evaluating the compatibility of celestial configurations with the narrative. It highlights a convergence between astronomical data and textual constraints, indicating that the account cannot be dismissed as scientifically incompatible on the basis of rational analysis.

While magnetic fields and superconductors are both central to classical and quantum technologies, their combined use is often challenging, as magnetic fields significantly affect superconducting device performance. In superconducting nanowire single-photon detectors (SNSPDs), magnetic fields drastically reduce detection efficiencies, hampering their application in magnetically-active classical and quantum photonics. Here, we systematically characterize the performance of NbTiN SNSPDs under magnetic fields and show the enhancement of their intrinsic detection efficiency (IDE) at lower bias currents and its suppression at higher currents. This leads to SNSPD performance degradation through reduced or disappearing saturation plateaus. We show that the magnitude of this degradation is highly dependent on nanowire width and demonstrate width-optimized SNSPDs with saturating IDE for a wide range of photon energies under application-relevant magnetic fields. Minimizing degradation in superconducting devices under magnetic fields enables applications like detector-integrated spin-optic and atomic quantum processors, high-sensitivity magnetometry, and quantum transduction.

We investigate a dark energy (DE) equation of state (EoS) parametrization in a curved spacetime using current observations. We constrain the model parameters by using observational Hubble data from Cosmic Chronometer (CC), Pantheon Plus SH0ES (PPS), and DESI BAO DR2, along with their reconstructed datasets using an Artificial Neural Network (ANN). The parameter $\alpha$ is constrained as $\alpha \approx 0.35 (\approx 0.56)$ from original (reconstructed) data. This means reconstruction pushes the model toward a significant deviation from the standard $\Lambda$CDM framework. We find that the curvature parameter $\Omega_{k0} = 0.068 \pm 0.029$ at 68% CL with original data, suggests a slightly open universe, whereas with the reconstruction method, $\Omega_{k0} = -0.131 \pm 0.032$ at 68% CL suggests a closed universe. This shift in the mean value indicates that the reconstruction method is highly sensitive to curvature. We perform statistical model comparison criteria, namely, AIC and BIC to assess the reliability of our framework.

We describe an approach for estimating the statistical risk of deep networks that contain a mix of learned and known operators. Building on the maximal training error bounds previously established for known operator learning, we derive a deep risk estimator that connects the expected error of a layered network to the size of the training sample. The estimator decomposes the total risk into a sum over learned layers; every known operator contributes zero to this sum, while every learned layer adds an approximation term inspired by Barron's classic work and an estimation term that decreases with the number of training samples. We are able to show that the bound shrinks whenever a learned layer is replaced by a known operator and that the corresponding sample requirement scales with the number of trainable parameters of the layer that is replaced. As an application, we use computed tomography as an example and compare an operator-aware filtered backprojection network with a fully connected substitute that collapses the entire reconstruction pipeline into a single learned dense matrix. The predicted parameter ratio coincides with the structural sparsity that the analytic decomposition into a circulant filter and a sparse backprojection exposes. We confirm the predicted scaling on CPU at small image scale and on GPU at medium image scale, all on the same scaling law. Beyond CT reconstruction, the estimator applies to physics-informed neural networks that hardcode a known physical operation in its architecture, and we expect the result to be of interest for a broad community working on operator-aware deep learning. Calibrating the per-layer constants on each sweep yields a bound that tracks the empirical test MSE within a factor of two at every training-set size, so the estimator can be inverted to predict how many training samples are required to reach a target error.

Since 2019, eighteen NSF Research Traineeship (NRT) awards in quantum information science and engineering (QISE) and adjacent fields have been funded, constituting the largest NSF-coordinated investment in graduate QISE training in the United States. Synthesizing lessons from our programs, we work through the central tensions that every QISE graduate program must negotiate: between depth in a home discipline and breadth across the field, between structured instruction and open-ended experiential and hands-on learning, and between training individual specialists and cultivating teams that collectively cover all areas of QISE. We describe the structural and pedagogical innovations the NRT programs have developed in response, assess what is working and what remains unresolved, and sketch 12 open problems the community will need to address as QISE graduate education scales beyond the well-resourced research universities where it has up till now been mainly concentrated. Eight concrete recommendations follow: (1) adopt the startup model of team-based training as an organizing philosophy; (2) invest immediately in sensing and communication curriculum development; (3) build student agency into program governance, not just activities; (4) establish structural mechanisms for industrial engagement rather than depending on goodwill; (5) design for sustainability from year one; (6) develop graduate-level textbooks spanning all three QISE pillars: computing, sensing, and communications; (7) establish shared outcome assessment instruments across programs; and (8) develop structured mechanisms for faculty professional development in QISE.

We leverage recent advances in 3D-printing technology to design and fabricate a micro-ion trap with a spatially distinct loading zone for more efficient loading of ions from effusive thermal ovens. The design reduces the Mathieu-$q$ parameter in the loading zone by increasing the ion-electrode separation $r_0$, thereby potentially facilitating more effective laser cooling of hot ions. This circumvents the temporary thermal instability that arises when the rf potential is reduced during ion loading, a common practice to enable efficient laser cooling of hot ions. Simulations predict that expanding $r_0$ maintains a high trapped ion fraction from a simulated thermal source across a wide range of Mathieu-$q$ parameters. We demonstrate the manufacturability of this design by 3D-printing the rf rails of a four-rod ion trap and discuss the limitations imposed by state-of-the-art additive manufacturing techniques. We briefly compare hot-ion capture in the three-dimensional design presented here with that in a representative planar trap, illustrating one instance in which the former may be better for loading. The article concludes with an outlook for how this design may be incorporated into a quantum-CCD architecture to enhance ion loading and reduce associated experimental overheads.

Oriented external electric fields (OEEFs) are ubiquitous in chemistry; however, the effects of fields applied in different directions on molecular systems remain underexplored. A major challenge is that an applied field exerts a torque on a molecule, reorienting the molecular frame and complicating the interpretation of orientation-dependent electric-field effects. Thus, free polar molecules experience orienting rather than oriented fields, such that the field response drives the molecular orientation rather than the other way around. In this work, we explore a new regime of distinct molecular equilibrium configurations in the presence of OEEFs, differing in the relative direction of the external field and the molecular frame. While static dipole moments typically dominate molecular responses to external electric fields, these equilibria are enabled by exploiting molecular polarizability. These distinct "directomers" exhibit unique electronic and nuclear configurations, particularly in their low-lying excited states. We employ oriented electric field vectors referenced to a molecule-fixed principal axis frame along with hybrid analytical-numerical geometry optimization in order to explore the rotational potential energy surface (rPES), as well as a simply analytic model based on equilibrium electrical properties which captures the double-well character of the rPES, including some geometry relaxation effects.

We study the effects of weak magnetic fields (around 2 mT) on the growth of Staphylococcus aureus (S. aureus) in the presence of a few sweeteners (monosaccharides, disaccharides, sugar alcohols, and consumer-grade sweeteners). Bacterial growth rates were compared in various magnetic fields at room temperature. Bacterial growth was estimated using optical absorbance measurements at various wavelengths, and pH values were manually estimated using pH strips. Absorbance was measured at 492 nm and 630 nm, which are wavelengths comparable to the size of a cell of S. aureus after division. This comparability plays a vital role in the scale of measured absorbance values. The results imply that bacterial growth may be reduced due to acidic byproducts formed by metabolizing sugars or sugar alcohols, as an increasingly acidic solution is less ideal for bacterial growth. Magnetic fields were also found to have a minor effect on pH estimates. These results reveal potential effects on microorganisms in the presence of sugars and sugar alcohols in addition to weak magnetic fields, demonstrating the contribution of various environmental conditions with increasing prevalence in the modern day.

We introduce a meshfree exterior calculus (MEEC) for learning structure-preserving descriptions of physics on point clouds, and use it to build MEEC-Net, a data-efficient surrogate that transfers across resolutions, geometries, and physical parameters. MEEC equips an $\varepsilon$-ball graph with virtual node and edge measures via a single sparse Schur complement solve; the resulting complex satisfies discrete conservation exactly, is end-to-end differentiable in the point positions, and exposes a direct geometry-to-physics link without the mesh-generation step required by conventional structure-preserving discretizations. MEEC-Net learns unknown physics as a shared edge-wise flux law in an SO($d$)-invariant local frame, so the same kernel produces compatible fluxes on any point cloud whose features lie in the training range. We prove a solution-error bound that splits into discretization and kernel-approximation terms which is independent of problem geometry, explaining the observed transfer from very few examples. We show that single-solution training transfers to unseen geometries, boundary conditions, and physical parameters. On five canonical PDE benchmarks MEEC-Net achieves 1-2 orders of magnitude lower out-of-distribution error than baseline neural-operator approaches. On the SimJEB structural-bracket benchmark it achieves competitive error while using substantially fewer training geometries.

Vortex dynamics are an important topic in fluid dynamics, explaining phenomena like drag and lift generation, jet propulsion, and corner flows. It is also often excluded from introductory or undergraduate fluid dynamics courses on account of its complexity and the inaccessibility of practical and engaging experiments. We present an affordable student-safe experiment to generate vortex rings and study their dynamics using a bent straw and dyed water that allows students to control key parameters, can be imaged using a smartphone camera, and explains the complex physics with simple and easily measured parameters. Vortex rings are produced that parallel seminal experiments, demonstrating secondary structures and the mirroring effect. Meanwhile, nonplanar and triangular jet exits are used to demonstrate asymmetric vortex rings and vortex ring inversion.

Nuclear charge radii are vital for nuclear and atomic physics, the determination of fundamental constants, and searches for new physics. Muonic atoms, where a single negative muon orbits a nucleus, are sensitive tools for determining nuclear radii due to the large wavefunction overlap of the muon and nucleus. Here we report on a new measurement of the $2, 3, 4p\to1s$ x-ray energies in muonic $^{35,37}$Cl with uncertainties reaching 18 ppm. By employing a large-scale germanium detector array, it was possible to extract these energies from a high statistics dataset using highly enriched samples of only a few tens of milligrams. Combining these results with state-of-the-art atomic and nuclear theory input, the charge radii of the stable chlorine isotopes were determined to be $R(^{35}\text{Cl}) = 3.3333(23)~fm$ and $R(^{37}\text{Cl}) = 3.3444(23)~fm$. This is an order of magnitude more precise and significantly different from previously tabulated values. Our new values solve a discrepancy observed for the charge radius difference in mirror nuclei, agreeing with the overall global trend. The charge radius difference $\delta \langle r^2 \rangle (^{37}\text{Cl} - {^{35}\text{Cl}}) = -0.0776(64)~fm^2$ we extract is 25 times more precise than the previous values. This precision is crucial for establishing reference values for future laser spectroscopy measurements of radioactive isotopes.

Body fat percentage and its spatial distribution are clinically important health indicators. However, existing measurement methods often impose a tradeoff between accuracy and accessibility. Clinical-grade techniques, such as Dual-Energy X-ray Absorptiometry (DEXA) and hydrostatic weighing, provide accurate measurements but require specialized equipment and trained operators, making them difficult to access and unsuitable for everyday use. In contrast, consumer-level methods, such as Bioelectrical Impedance Analysis (BIA) smart scales and skinfold calipers, are more accessible but typically provide only coarse-grained estimates, are prone to user error, or require intrusive physical contact. In this work, we present UWB-Fat, the first system that leverages commodity ultra-wideband (UWB) radar to enable non-intrusive, accessible, and accurate caliper-equivalent skinfold thickness estimation, serving as a convenient replacement for the skinfold caliper. UWB-Fat collects UWB signal at specified body sites non-intrusively without operator assistance. It extracts body-composition-related features from UWB signals by exploiting dielectric contrasts among skin, fat, and muscle tissues. Then, it uses a physics-inspired model to estimate site-specific skinfold thickness. We evaluate UWB-Fat on 15 participants, achieving a root mean square error of 0.63~mm for pooled-site subcutaneous fat thickness. These results highlight the potential of UWB-Fat to support low-cost, self-administered, and everyday body fat monitoring.

Tip-Enhanced Raman Spectroscopy (TERS) can be used to make nanoscale spatial measurements of 2D materials, such as graphene and transition metal dichalcogenides (TMDs). The TERS theory introduced in [L. Cançado et al., Phys. Rev. X 4, 031054 (2014)], however, was tailored for graphene, whose out-of-plane Raman response is neglected. In the present work, we include the out-of-plane response in the TERS theory. In doing so, we provide an exact analytical expression for the field propagation between the tip and the sample, and show that the contribution to the TERS signal that scatters first at the sample, then at the tip (sample-tip, or TS) is important only when the out-of-plane response is significant. We extensively study the variation of TERS experimental measurements when varying physical parameters of the system, like the tip radius, the out-of-plane response, the TERS coherence length, and others. It becomes evident that the TERS enhancement is very sensitive to the out-of-plane Raman response of the phonon mode, while normalized tip-approach measurements are more sensitive to the coherence length, and we show that the medium refractive index leads to an effective tip enhancement factor $f_e$. Our results lead to the conclusion that, in general, a strong TERS enhancement is a necessary condition for investigating the physics discussed here, which here means surveying the difference in TERS signals between different Raman modes. We use our model to analyze some graphene TERS experiments, showing that they are consistent with a negligible out-of-plane Raman response and a nonzero TERS coherence length in the fitting.

Non-radiative decay in photoexcited molecular systems is driven by nuclear motion toward conical intersections (CIs), where electronic states become degenerate and nonadiabatic transitions occur. Identifying the nuclear degrees of freedom responsible for CI access from nonadiabatic molecular dynamics (NAD) simulations remains challenging because the underlying motions are high-dimensional and collective. Here, we introduce an unsupervised, information-theoretic framework based on Differentiable Information Imbalance (DII) to identify the nuclear coordinates governing CI access directly from trajectory surface hopping (TSH) simulations. By quantifying correlations between structural descriptors and electronic observables, including energy gaps and oscillator strengths, the method ranks nuclear degrees of freedom by predictive relevance. A multi-step protocol then extracts low-dimensional, physically interpretable modes associated with non-radiative decay. We apply the framework to the methaniminium cation, furan, L-glutamine, L-pyroglutamine-ammonium, and a photoactive molecular motor. Across all systems, the method recovers known mechanistic coordinates while revealing the relative importance of competing modes when multiple structural distortions contribute to CI access. The analysis also reveals a systematic distinction between observables: energy gaps are typically governed by a small number of localized coordinates, whereas oscillator strengths depend on more collective and distributed structural rearrangements. Overall, the DII-based framework combines predictive power with direct interpretability, providing a general and scalable route for extracting mechanistic insight from high-dimensional NAD data and constructing reduced-dimensional models of excited-state dynamics.

The Proton Improvement Plan - II (PIP-II) injector linac is an 800 MeV superconducting H$^-$ linac, christened Linac2, that will replace the existing 400 MeV injector to the accelerator complex at Fermilab. The higher energy, intensity and repetition rate require various upgrades to the existing accelerator complex consisting of the Booster, the Recycler Ring and the Main Injector, in order to be able to accept and accelerate beam from PIP-II. In this paper we discuss various upgrades that are required and steps being taken to implement them.

Aspects of quantum physics are no longer confined to the upper years of a physics degree. Concepts like superposition or entanglement that were once reserved for second- or third-year undergraduate courses now deserve attention earlier in a student's curriculum. Technology is changing at a pace that requires engaged citizens to understand some of the quantum basics if they are to make sense of the world. This paper offers a cartoon building analogy that teachers can use to introduce quantum numbers to their students.