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Robotic devices hold promise for aiding patients in orthopedic rehabilitation. However, current robotic-assisted physiotherapy methods struggle including biomechanical metrics in their control algorithms, crucial for safe and effective therapy. This paper introduces BATON, a Biomechanics-Aware Trajectory Optimization approach to robotic Navigation of human musculoskeletal loads. The method integrates a high-fidelity musculoskeletal model of the human shoulder into real-time control of robot-patient interaction during rotator cuff tendon rehabilitation. We extract skeletal dynamics and tendon loading information from an OpenSim shoulder model to solve an optimal control problem, generating strain-minimizing trajectories. Trajectories were realized on a healthy subject by an impedance-controlled robot while estimating the state of the subject's shoulder. Target poses were prescribed to design personalized rehabilitation across a wide range of shoulder motion avoiding high-strain areas. BATON was designed with real-time capabilities, enabling continuous trajectory replanning to address unforeseen variations in tendon strain, such as those from changing muscle activation of the subject., Comment: 13 pages, 9 figures, under review

Our information and communication environment has fallen short of the ideals that networked global communication might have served. Identifying all the causes of its pathologies is difficult, but existing recommender systems very likely play a contributing role. In this paper, which draws on the normative tools of philosophy of computing, informed by empirical and technical insights from natural language processing and recommender systems, we make the moral case for an alternative approach. We argue that existing recommenders incentivise mass surveillance, concentrate power, fall prey to narrow behaviourism, and compromise user agency. Rather than just trying to avoid algorithms entirely, or to make incremental improvements to the current paradigm, researchers and engineers should explore an alternative paradigm: the use of language model (LM) agents to source and curate content that matches users' preferences and values, expressed in natural language. The use of LM agents for recommendation poses its own challenges, including those related to candidate generation, computational efficiency, preference modelling, and prompt injection. Nonetheless, if implemented successfully LM agents could: guide us through the digital public sphere without relying on mass surveillance; shift power away from platforms towards users; optimise for what matters instead of just for behavioural proxies; and scaffold our agency instead of undermining it.

Recent years have witnessed significant developments in the study of nonlinear properties of various optical materials at the nanoscale. However, in most cases experimental results on harmonic generation from nanostructured materials are reported without the benefit of suitable theoretical models and appropriate comparisons to assess enhancement of conversion efficiencies compared to the intrinsic properties of a given material. Here, we report experimental observations of even and odd optical harmonics generated from a suspended subwavelength silicon film, a dielectric membrane, up to the 7th harmonic tuned deep in the UV range at 210nm, which is the current limit of our detection system, using peak power densities of order 3TW/cm^2. We explain the experimental data with a time domain, hydrodynamic-Maxwell approach broadly applicable to most materials. Our approach accounts simultaneously for surface and magnetic nonlinearities that generate even optical harmonics, as well as linear and nonlinear material dispersions beyond the third order to account for odd optical harmonics, plasma formation, and a phase locking mechanism that makes the generation of high harmonics possible deep into the UV range, where semiconductors like silicon start operating in a metallic regime.

Intel Max GPUs are a new option available to CGYRO fusion simulation users. This paper outlines the changes that were needed to successfully run CGYRO on Intel Max 1550 GPUs on TACC's Stampede3 HPC system and presents benchmark results obtained there. Benchmark results were also run on Stampede3 Intel Max CPUs, as well as NVIDIA A100 and AMD MI250X GPUs at other major HPC systems. The Intel Max GPUs are shown to perform comparably to the other tested GPUs for smaller simulations but are noticeably slower for larger ones. Moreover, Intel Max GPUs are significantly faster than the tested Intel Max CPUs on Stampede3., Comment: 15 pages, 10 figures

This study presents a cross-verification of fast ion effects on turbulence through a systematic comparison of two leading gyrokinetic codes, GENE [T.Gorler et al., J. Comput. Phys. 230 7053-7071 (2011)] and CGYRO [J.Candy et al, J. Comput. Phys. 324 73-93 (2016)], using L-mode plasma profiles from KSTAR for local linear and nonlinear electromagnetic simulations. The focus is on the impact of fast ions and rotation effects on energy flux, aiming to identify the similarities and differences between these codes in the context of turbulence transport research. The analysis shows consistency in linear stability results, fractional changes in energy flux, and zonal shearing between the codes. However, discrepancies arise in absolute thermal energy levels, phase angle distribution, and rotation effects on energy transport, especially in the presence of fast ions. The study underscores the critical importance of phase angle analysis in gyrokinetic code verification, particularly when assessing fast ion effects on turbulence. Additionally, it highlights the need to examine quantities at lower levels of the primacy hierarchy, as discrepancies at higher levels can lead to divergent results at lower levels. These findings indicate the necessity for further investigation into these discrepancies and the novel phase angle structures observed, contributing to the advancement of accurate transport predictions in fusion plasmas.

A long-term measurement was conducted to search for $\alpha$, double-$\alpha$ and double-$\beta$ decays with $\gamma$ quanta emission in naturally occurring osmium isotopes. This study took advantage of two ultra-low background HPGe detectors and one ultra-low background BEGe detector at the Gran Sasso National Laboratory (LNGS) of the INFN. Over almost 5 years of data were taken using high-purity osmium samples of approximately 173 g. The half-life limits set for a decays of $^{184}$Os to the first $2^+$ 103.6 keV excited level of $^{180}$W ($T_{1/2} > 9.3 \times 10^{15}$ yr) and of $^{186}$Os to the first $2^+$ 100.1 keV of $^{182}$W ($T_{1/2} > 4.8 \times 10^{17}$ yr) exceed substantially the present theoretical predictions that are at level of $T_{1/2} \sim (0.6 - 3) \times 10^{15}$ yr for $^{184}$Os and $T_{1/2} \sim (0.3 - 2) \times 10^{17}$ yr for $^{186}$Os. New half-life limits on the 2EC and EC$\beta^+$ decay of $^{184}$Os to the ground and excited levels of $^{184}$W were set at level of $T_{1/2} > 10^{16} - 10^{17}$ yr; a lower limit on the $2\beta^-$ decay of $^{192}$Os to the $2^+$ 316.5 keV excited level of $^{192}$Pt was estimated as $T_{1/2} > 6.1 \times 10^{20}$ yr. The half-life limits for $2\alpha$ decay of $^{189}$Os and $^{192}$Os were set for the first time at level of $T_{1/2} > 10^{20}$ yr., Comment: 38 pages, 17 figures, 5 tables

Quantum nuclear effects and anharmonicity impact a wide range of functional materials and their properties. One of the most powerful techniques to model these effects is the Stochastic Self-Consistent Harmonic Approximation (SSCHA). Unfortunately, the SSCHA is extremely computationally expensive, prohibiting its routine use. We propose a protocol that pairs machine learning interatomic potentials, which can be tailored for the system at hand via active learning, with the SSCHA. Our method leverages an upscaling procedure that allows for the treatment of supercells of up to thousands of atoms with practically minimal computational effort. The protocol is applied to PdCuH$_x$ ($x = 0-2$) compounds, chosen because previous experimental studies have reported superconducting critical temperatures, $T_\text{c}$s, as high as 17~K at ambient pressures in an unknown hydrogenated PdCu phase. We identify a $P4/mmm$ PdCuH$_2$ structure, which is shown to be dynamically stable only upon the inclusion of quantum fluctuations, as being a key contributor to the measured superconductivity. For this system, our methodology is able to reduce the computational expense for the SSCHA calculations by $\sim$96\%. The proposed protocol opens the door towards the routine inclusion of quantum nuclear motion and anharmonicity in materials discovery.

We analyze ionized gas emission lines in deep rest-frame optical spectra of 16 quiescent galaxies at redshift $1.7

Recent stellar chemical abundance measurements of a handful of $z\sim2$ quiescent galaxies have suggested these galaxies exhibit a remarkably strong $\alpha$-enhancement compared to their local and intermediate redshift counterparts. This apparent chemical evolution following quenching suggests that even the innermost regions of massive early-type galaxies may have experienced substantial mixing of stars in mergers, challenging a purely inside-out growth model. However, larger samples are needed to determine whether a high $\alpha$-enhancement ([Mg/Fe] $\approx 0.5$) is common in $z \sim 2$ quiescent galaxies, and a comparative analysis is needed to determine whether it is consistently inferred using different stellar population synthesis models. We report age and stellar chemical abundance measurements for a sample of four gravitationally lensed quiescent galaxies at $z\sim2.1-2.65$ based on Magellan/FIRE spectroscopy. For three of these galaxies we constrain the $\alpha$-enhancement, and in two cases we measure high values comparable to earlier results when the spectra are analyzed consistently. We also find that the choice of modeling approach can exert a significant effect on the measured abundances. This model dependence can be partly, but not entirely, explained by the complex abundance patterns of $\alpha$ elements in galaxies, which has been observed at lower redshifts and in one $z \sim 2$ quiescent galaxy. Our investigation highlights the importance of independently varying abundance of $\alpha$ elements when fitting the spectra of such galaxies. Observations with JWST will soon deliver precise and spatially resolved abundances of these and other quiescent galaxies at cosmic noon, opening a new window into their evolution., Comment: 10 pages, submitted to The Astrophysical Journal

The complex physics governing nebular emission in galaxies, particularly in the early universe, often defy simple low-dimensional models. This has proven to be a significant barrier in understanding the (often diverse) ionizing sources powering this emission. We present Cue, a highly flexible tool for interpreting nebular emission across a wide range of abundances and ionizing conditions of galaxies at different redshifts. Unlike typical nebular models used to interpret extragalactic nebular emission, our model does not require a specific ionizing spectrum as a source, instead approximating the ionizing spectrum with a 4-part piece-wise power-law. We train a neural net emulator based on the CLOUDY photoionization modeling code and make self-consistent nebular continuum and line emission predictions. Along with the flexible ionizing spectra, we allow freedom in [O/H], [N/O], [C/O], gas density, and total ionizing photon budget. This flexibility allows us to either marginalize over or directly measure the incident ionizing radiation, thereby directly interrogating the source of the ionizing photons in distant galaxies via their nebular emission. Our emulator demonstrates a high accuracy, with $\sim$1% uncertainty in predicting the nebular continuum and $\sim$5% uncertainty in the emission lines. Mock tests suggest Cue is well-calibrated and produces useful constraints on the ionizing spectra when $S/N (\mathrm{H}_\alpha) \gtrsim 10$, and furthermore capable of distinguishing between the ionizing spectra predicted by single and binary stellar models. The compute efficiency of neural networks facilitates future applications of Cue for rapid modeling of the nebular emission in large samples and Monte Carlo sampling techniques., Comment: submitted to ApJ

Massive quiescent galaxies in the young universe are expected to be quenched rapidly, but it is unclear whether they all experience starbursts before quenching and what physical mechanism drives rapid quenching. We study 16 massive quiescent galaxies ($\log(M_\star/M_\odot) > 10$) at $z\sim2$ selected from a representative sample of the Blue Jay survey. We reconstruct their star formation histories by fitting spectral energy distribution models to the JWST/NIRSpec $R\sim1000$ spectra. We find that massive quiescent galaxies can be split into three categories with roughly equal numbers of galaxies according to their SFHs: 1) Relatively old galaxies quenched at early epochs; 2) Galaxies that are rapidly and recently quenched after a flat or bursty formation history (depending on the assumed prior); 3) Galaxies that are rapidly and recently quenched after a major starburst. Most recently quenched galaxies show neutral gas outflows, probed by blueshifted $\rm Na\,I\,D$ absorption, and ionized gas emission, with line ratios consistent with active galactic nucleus (AGN) diagnostics. This suggests that AGN activity drives multi-phase gas outflows, leading to rapid quenching. By tracing back the SFHs of the entire sample, we predict the number density of massive quiescent galaxies at $z=4-6$: $n=3.0\pm1.4\times10^{-5}\,\rm Mpc^{-3}$. The two oldest massive quiescent galaxies in our sample appear to have extremely early formation and quenching ($z\gtrsim6$), possibly descendants of early post-starbursts at $z>3$. These galaxies still show neutral gas reservoirs and low-level star formation, consistent with weak H$\alpha$ emission, perhaps because the ejective AGN feedback that caused rapid quenching has weakened over time., Comment: Submitted to ApJ; 21 pages, 9 figures, 3 tables (excluding appendix)

Four-wave mixing (FWM) in gas-filled hollow-core capillaries, a nonlinear optical process that mixes signal and pump photon frequencies to generate idler frequency photons, offers a method for precise spectral phase transfer from signal to idler at ultrashort timescales and extreme powers. However, this regime is challenged by competing linear and nonlinear dynamics, leading to significant trade-offs between spectral phase transfer and conversion efficiency. Our computational investigation focuses on upconversion of femtosecond pulses from the infrared (IR) to the ultraviolet (UV), a range notoriously difficult to manipulate. We explore an intermediate energy regime that strikes an optimal balance between FWM-mediated phase-transfer fidelity and nonlinear conversion efficiency. By adjusting the energy ratios and spectral phase profiles of the input signal, we achieve conversion efficiencies of approximately 5%-15% while maintaining an effectively quasi-linear spectral phase transfer. These findings contribute to establishing first-principles and scaling laws essential for applications such as high-precision imaging, spectroscopy, quantum transduction, and distributed entangled interconnects, facilitating advanced control of ultrafast photonic and electronic wavepackets in quantum materials with unprecedented spatial and temporal precision., Comment: Submitted to Optics Express

A bright ($m_{\rm F150W,AB}$=24 mag), $z=1.95$ supernova (SN) candidate was discovered in JWST/NIRCam imaging acquired on 2023 November 17. The SN is quintuply-imaged as a result of strong gravitational lensing by a foreground galaxy cluster, detected in three locations, and remarkably is the second lensed SN found in the same host galaxy. The previous lensed SN was called "Requiem", and therefore the new SN is named "Encore". This makes the MACS J0138.0$-$2155 cluster the first known system to produce more than one multiply-imaged SN. Moreover, both SN Requiem and SN Encore are Type Ia SNe (SNe Ia), making this the most distant case of a galaxy hosting two SNe Ia. Using parametric host fitting, we determine the probability of detecting two SNe Ia in this host galaxy over a $\sim10$ year window to be $\approx3\%$. These observations have the potential to yield a Hubble Constant ($H_0$) measurement with $\sim10\%$ precision, only the third lensed SN capable of such a result, using the three visible images of the SN. Both SN Requiem and SN Encore have a fourth image that is expected to appear within a few years of $\sim2030$, providing an unprecedented baseline for time-delay cosmography., Comment: Accepted, ApJL

We explore electronic and structural properties of Fm$\overline{3}$m Lu-H-N structures with specific N,H ordering as plausible candidates for near-ambient superconductivity possibly originating from their remarkably narrow hydrogen-based bands at the Fermi level. Although LuH$_{2.875}$N$_{0.125}$ exhibits an instability persisting up to 17 GPa, it is anharmonically stable near ambient pressure when accounting for quantum nuclear effects. The presence of flat bands near $E_\text{F}$ is understood to arise from destructive\ quantum interference between N-p and surrounding H-s orbitals, with certain types of defects leaving the flat bands unaffected. The results suggest there is an optimal pressure near ambient where the superconducting $T_{\text{c}}$ is maximized in this structure by anharmonically-stabilized low-frequency and non-adiabatically coupled high-frequency hydrogen modes. Despite the metastability of this structure, its electronic properties and dynamical stability when calculated beyond a classical harmonic approach can explain the reported near-ambient superconductivity in Lu-H-N., Comment: Added significant supplemental information since previous version, revised main figures and text. Conclusions unchanged from previous version but additional insight uncovered regarding pressure dependence. 7 main figures and 11 SI figures

In urban environments, where line-of-sight signals from GNSS satellites are frequently blocked by high-rise objects, GNSS receivers are subject to large errors in measuring satellite ranges. Heuristic methods are commonly used to estimate these errors and reduce the impact of noisy measurements on localization accuracy. In our work, we replace these error estimation heuristics with a deep learning model based on Graph Neural Networks. Additionally, by analyzing the cost function of the multilateration process, we derive an optimal method to utilize the estimated errors. Our approach guarantees that the multilateration converges to the receiver's location as the error estimation accuracy increases. We evaluate our solution on a real-world dataset containing more than 100k GNSS epochs, collected from multiple cities with diverse characteristics. The empirical results show improvements from 40% to 80% in the horizontal localization error against recent deep learning baselines as well as classical localization approaches., Comment: Published in The Proceedings of the Institute of Navigation GNSS+ 2023