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Motivated by recent experiments, which allow for fine tuning of the effective magnetic interaction between the impurity and the superconductor, we investigate the regime around the quantum phase transition where the system's ground state changes from a weakly coupled free spin to a screened spin regime. At this transition we find that the YSR states remain at finite energies at low temperatures, thereby generating a gap in the spectrum, which is inconsistent with predictions of the original YSR theory. We investigate various gap-generating scenarios and determine that the local suppression of the order parameter, only captured by self-consistent calculations, generates the gap., Comment: 15 pages, 6 figures

In the cuprate high-temperature superconductors doping is fixed during synthesis and the charge carrier density per CuO$_2$ plane cannot be easily tuned by conventional gating, unlike 2D materials. Strain engineering has recently emerged as a powerful tuning knob for manipulating the properties of cuprates, in particular charge and spin orders, and their delicate interplay with superconductivity. In thin films, additional tunability can be introduced by the substrate surface morphology, particularly nanofacets formed by substrate surface reconstruction. Here we show a remarkable enhancement of the superconducting onset temperature $T_{\mathrm{c}}^{\mathrm{on}}$ and the upper critical magnetic field $H_{c,2}$ in nanometer-thin YBa$_2$Cu$_3$O$_{7-\delta}$ films grown on a substrate with a nanofaceted surface. We theoretically show that the enhancement is driven by electronic nematicity and unidirectional charge density waves, where both elements are captured by an additional effective potential at the interface between the film and the uniquely textured substrate. Our findings show a new paradigm in which substrate engineering can effectively enhance the superconducting properties of cuprates. This approach opens an exciting frontier in the design and optimization of high-performance superconducting materials.

The vibrational modes in Sr$_2$RuO$_4$ easily induce octahedral rotations without tilting. Being on the verge of a magnetic instability, such propensity of octahedral rotation may also produce magnetic fluctuations. In this work, we analyze the long-range magnetic phase diagram incorporating such octahedral rotations and demonstrate the possibility of an altermagnetic phase in Sr$_2$RuO$_4$. Using ab-initio calculations, we first study single layer Sr$_2$RuO$_4$ with octahedral rotations, obtaining an orbital-selective $g$-wave altermagnetic phase. We further provide an effective $t_{2g}$ tight-binding model, demonstrating that the $g$-wave altermagnetism is primarily a product of second and third nearest neighbor interorbital hybridizations between the ${\gamma}z$ ($\gamma=x,y$) orbitals, but only a much longer range intraorbital hybridization in the $xy$ orbitals, establishing a strong orbital-selectiveness for the altermagnetism. Notably, by replacing the $xy$ orbital with the $x^2-y^2$ orbital, a similar tight-biding model may be used to investigate the hole-doped cuprate superconductors. We then study bulk Sr$_2$RuO$_4$, where we find the altermagnetic phase as the magnetic ground state for a range of finite octahedral rotations. In the bulk, interlayer hopping breaks some of the symmetries of the $g$-wave altermagnet, resulting in a $d_{xy}$-wave altermagnet, still with orbital selectiveness. We also include relativistic effects through spin-orbit coupling and obtain that an effective staggered Dzyaloshinskii-Moriya interaction generates weak ferromagnetism. Finally, we discuss the implications of the altermagnetic order on the intrinsic superconductivity of Sr$_2$RuO$_4$. Assuming in-plane and intraorbital pairing, the altermagnetism favors spin-singlet $d_{x^2-y^2}$-wave or $g$-wave pairing, or (nematic or chiral) combinations thereof., Comment: 18 pages, 16 figures

Chiral superconductors are topological as characterized by a finite Chern number and chiral edge modes. Direct fingerprints of chiral superconductivity is thus often taken to be spontaneous edge currents with associated magnetic signatures. However, a number of recent theoretical studies have shown that the total edge current along semi-infinite edges is greatly reduced or even vanishes in many scenarios for all pairing symmetries except chiral $p$-wave, thus impeding experimental detection. We demonstrate how mesoscopic finite-sized samples can be designed to give rise to a shape- and size-dependent strong enhancement of the chiral edge currents and their generated orbital magnetic moment and magnetic fields. In particular, we find that low rotational symmetry systems, such as pentagons and hexagons, give rise to the largest currents, while circular disks also generate large currents but in the opposite direction. The current and magnetic signatures diverge with shrinking system sizes, eventually cut-off by finite-size suppression of chiral superconductivity. We thus also extract the full phase diagram as a function of temperature and system size for different geometries, including competing superconducting orders. Our results are relevant for system sizes on the order of tens to hundreds of coherence lengths, and highlight mesoscopic patterning as a viable route to experimentally identify chiral $d$-wave superconductivity., Comment: 19 pages, 12 figures

Using the full four-year SPTpol 500 deg$^2$ dataset in both the 95 GHz and 150 GHz frequency bands, we present measurements of the temperature and $E$-mode polarization of the cosmic microwave background (CMB), as well as the $E$-mode polarization auto-power spectrum ($EE$) and temperature-$E$-mode cross-power spectrum ($TE$) in the angular multipole range $50<\ell<8000$. We find the SPTpol dataset to be self-consistent, passing several internal consistency tests based on maps, frequency bands, bandpowers, and cosmological parameters. The full SPTpol dataset is well-fit by the $\Lambda CDM$ model, for which we find $H_0=70.48\pm2.16$ km s$^{-1}$ Mpc$^{-1}$ and $\Omega_m=0.271\pm0.026$, when using only the SPTpol data and a Planck-based prior on the optical depth to reionization. The $\Lambda CDM$ parameter constraints are consistent across the 95 GHz-only, 150 GHz-only, $TE$-only, and $EE$-only data splits. Between the $\ell<1000$ and $\ell>1000$ data splits, the $\Lambda CDM$ parameter constraints are borderline consistent at the $\sim2\sigma$ level. This consistency improves when including a parameter $A_L$, the degree of lensing of the CMB inferred from the smearing of acoustic peaks. When marginalized over $A_L$, the $\Lambda CDM$ parameter constraints from SPTpol are consistent with those from Planck. The power spectra presented here are the most sensitive measurements of the lensed CMB damping tail to date for roughly $\ell > 1700$ in $TE$ and $\ell > 2000$ in $EE$.

Phase crystals are a class of non-uniform superconducting ground states characterized by spontaneous phase gradients of the superconducting order parameter. These phase gradients non-locally drive periodic currents and magnetic fields, thus breaking both time-reversal symmetry and continuous translational symmetry. The phase crystal instability is generally triggered by negative and inhomogeneous superfluid stiffness. Several scenarios have been identified that can realize phase crystals, especially flat bands at specific edges of unconventional nodal superconductors. Motivated by omnipresent disorder in all materials, we employ the ${t}$-matrix approach within the quasiclassical theory of superconductivity to study the emergence of phase crystals at edges of a nodal $d$-wave superconductor. We quantify the full phase diagram as a function of the impurity scattering energy and the temperature, with full self-consistency in the impurity self energies, the superconducting order parameter, and the vector potential. We find that the phase crystal survives even up to $\sim 40-50\%$ of the superconducting critical impurity strength in both the Born and unitary scattering limits. Finally, we show how mesoscopic finite-size effects induce a competition with a state still breaking time-reversal symmetry but with translationally invariant edge currents., Comment: 14 pages, 5 figures

Cosmic shear, galaxy clustering, and the abundance of massive halos each probe the large-scale structure of the universe in complementary ways. We present cosmological constraints from the joint analysis of the three probes, building on the latest analyses of the lensing-informed abundance of clusters identified by the South Pole Telescope (SPT) and of the auto- and cross-correlation of galaxy position and weak lensing measurements (3$\times$2pt) in the Dark Energy Survey (DES). We consider the cosmological correlation between the different tracers and we account for the systematic uncertainties that are shared between the large-scale lensing correlation functions and the small-scale lensing-based cluster mass calibration. Marginalized over the remaining $\Lambda$CDM parameters (including the sum of neutrino masses) and 52 astrophysical modeling parameters, we measure $\Omega_\mathrm{m}=0.300\pm0.017$ and $\sigma_8=0.797\pm0.026$. Compared to constraints from Planck primary CMB anisotropies, our constraints are only 15% wider with a probability to exceed of 0.22 ($1.2\sigma$) for the two-parameter difference. We further obtain $S_8\equiv\sigma_8(\Omega_\mathrm{m}/0.3)^{0.5}=0.796\pm0.013$ which is lower than the Planck measurement at the $1.6\sigma$ level. The combined SPT cluster, DES 3$\times$2pt, and Planck datasets mildly prefer a non-zero positive neutrino mass, with a 95% upper limit $\sum m_\nu<0.25~\mathrm{eV}$ on the sum of neutrino masses. Assuming a $w$CDM model, we constrain the dark energy equation of state parameter $w=-1.15^{+0.23}_{-0.17}$ and when combining with Planck primary CMB anisotropies, we recover $w=-1.20^{+0.15}_{-0.09}$, a $1.7\sigma$ difference with a cosmological constant. The precision of our results highlights the benefits of multiwavelength multiprobe cosmology., Comment: Submitted to Phys. Rev. D

In explanations, explainers have mental representations of explainees' developing knowledge and shifting interests regarding the explanandum. These mental representations are dynamic in nature and develop over time, thereby enabling explainers to react to explainees' needs by adapting and customizing the explanation. XAI should be able to react to explainees' needs in a similar manner. Therefore, a component that incorporates aspects of explainers' mental representations of explainees is required. In this study, we took first steps by investigating explainers' mental representations in everyday explanations of technological artifacts. According to the dual nature theory, technological artifacts require explanations with two distinct perspectives, namely observable and measurable features addressing "Architecture" or interpretable aspects addressing "Relevance". We conducted extended semi structured pre-, post- and video recall-interviews with explainers (N=9) in the context of an explanation. The transcribed interviews were analyzed utilizing qualitative content analysis. The explainers' answers regarding the explainees' knowledge and interests with regard to the technological artifact emphasized the vagueness of early assumptions of explainers toward strong beliefs in the course of explanations. The assumed knowledge of explainees in the beginning is centered around Architecture and develops toward knowledge with regard to both Architecture and Relevance. In contrast, explainers assumed higher interests in Relevance in the beginning to interests regarding both Architecture and Relevance in the further course of explanations. Further, explainers often finished the explanation despite their perception that explainees still had gaps in knowledge. These findings are transferred into practical implications relevant for user models for adaptive explainable systems.

This review began with the modest goal of drafting a brief commentary on how the social work profession engages with and is impacted by artificial intelligence (AI). However, it quickly became apparent that a deeper exploration was required to adequately capture the profound influence of AI, one of the most transformative and debated innovations in modern history. As a result, this review evolved into an interdisciplinary endeavour, gathering seminal texts, critical articles, and influential voices from across industries and academia. This review aims to provide a comprehensive yet accessible overview, situating AI within broader societal and academic conversations as 2025 dawns. We explore perspectives from leading tech entrepreneurs, cultural icons, CEOs, and politicians alongside the pioneering contributions of AI engineers, innovators, and academics from fields as diverse as mathematics, sociology, philosophy, economics, and more. This review also briefly analyses AI's real-world impacts, ethical challenges, and implications for social work. It presents a vision for AI-facilitated simulations that could transform social work education through Advanced Personalised Simulation Training (APST). This tool uses AI to tailor high-fidelity simulations to individual student needs, providing real-time feedback and preparing them for the complexities of their future practice environments. We maintain a critical tone throughout, balancing our awe of AI's remarkable advancements with necessary caution. As AI continues to permeate every professional realm, understanding its subtleties, challenges, and opportunities becomes essential. Those who fully grasp the intricacies of this technology will be best positioned to navigate the impending AI Era., Comment: 30 pages

LLM agents have the potential to revolutionize defensive cyber operations, but their offensive capabilities are not yet fully understood. To prepare for emerging threats, model developers and governments are evaluating the cyber capabilities of foundation models. However, these assessments often lack transparency and a comprehensive focus on offensive capabilities. In response, we introduce the Catastrophic Cyber Capabilities Benchmark (3CB), a novel framework designed to rigorously assess the real-world offensive capabilities of LLM agents. Our evaluation of modern LLMs on 3CB reveals that frontier models, such as GPT-4o and Claude 3.5 Sonnet, can perform offensive tasks such as reconnaissance and exploitation across domains ranging from binary analysis to web technologies. Conversely, smaller open-source models exhibit limited offensive capabilities. Our software solution and the corresponding benchmark provides a critical tool to reduce the gap between rapidly improving capabilities and robustness of cyber offense evaluations, aiding in the safer deployment and regulation of these powerful technologies., Comment: https://cybercapabilities.org/

In this work, we explore advanced machine learning techniques for minimizing Gibbs free energy in full 3D micromagnetic simulations. Building on Brown's bounds for magnetostatic self-energy, we revisit their application in the context of variational formulations of the transmission problems for the scalar and vector potential. To overcome the computational challenges posed by whole-space integrals, we reformulate these bounds on a finite domain, making the method more efficient and scalable for numerical simulation. Our approach utilizes an alternating optimization scheme for joint minimization of Brown's energy bounds and the Gibbs free energy. The Cayley transform is employed to rigorously enforce the unit norm constraint, while R-functions are used to impose essential boundary conditions in the computation of magnetostatic fields. Our results highlight the potential of mesh-free Physics-Informed Neural Networks (PINNs) and Extreme Learning Machines (ELMs) when integrated with hard constraints, providing highly accurate approximations. These methods exhibit competitive performance compared to traditional numerical approaches, showing significant promise in computing magnetostatic fields and the application for energy minimization, such as the computation of hysteresis curves. This work opens the path for future directions of research on more complex geometries, such as grain structure models, and the application to large scale problem settings which are intractable with traditional numerical methods., Comment: 39 pages, 9 figures

Animals can accomplish many incredible behavioral feats across a wide range of operational environments and scales that current robots struggle to match. One explanation for this performance gap is the extraordinary properties of the biological materials that comprise animals, such as muscle tissue. Using living muscle tissue as an actuator can endow robotic systems with highly desirable properties such as self-healing, compliance, and biocompatibility. Unlike traditional soft robotic actuators, living muscle biohybrid actuators exhibit unique adaptability, growing stronger with use. The dependency of a muscle's force output on its use history endows muscular organisms the ability to dynamically adapt to their environment, getting better at tasks over time. While muscle adaptability is a benefit to muscular organisms, it currently presents a challenge for biohybrid researchers: how does one design and control a robot whose actuators' force output changes over time? Here, we incorporate muscle adaptability into a many-muscle biohybrid robot design and modeling tool, leveraging reinforcement learning as both a co-design partner and system controller. As a controller, our learning agents coordinated the independent contraction of 42 muscles distributed on a lattice worm structure to successfully steer it towards eight distinct targets while incorporating muscle adaptability. As a co-design tool, our agents enable users to identify which muscles are important to accomplishing a given task. Our results show that adaptive agents outperform non-adaptive agents in terms of maximum rewards and training time. Together, these contributions can both enable the elucidation of muscle actuator adaptation and inform the design and modeling of adaptive, performant, many-muscle robots., Comment: 11 pages, 6 figures

Nonlinear responses in transport experiments may unveil information and generate new phenomena in materials that are not accessible at linear order due to symmetry constraints. While the linear anomalous Hall response strictly requires the absence of time-reversal symmetry, the second-order, thus nonlinear, Hall response needs broken inversion symmetry. Recently, much effort has been made to obtain a second-order Hall voltage in response to a longitudinal ac driving current, both to obtain information about band geometric quantities and for its useful technological applications, including rectification and frequency doubling. Typically, additional material engineering is required in noncentrosymmetric systems to obtain second-order responses since it obeys a stringent crystallographic symmetry constraint. To circumvent this, an alternative route is to apply a dc electric field. In this Letter, we uncover an electric field induced second-order anomalous Hall effect in inversion-broken systems possessing experimentally accessible unconventional Rashba bands. We establish that the quantum metric, a geometrical feature of electronic wave functions providing information on the nontrivial structure of Bloch bands, is responsible for providing the nonlinear Hall response, Comment: 7 pages, 5 figures

Periodically driven systems offer a perfect breeding ground for out-of-equilibrium engineering of topological boundary states at zero energy ($0$-mode), as well as finite energy ($\pi$-mode), with the latter having no static analog. The Floquet operator and the effective Floquet Hamiltonian, which encapsulate the stroboscopic features of the driven system, capture both spectral and localization properties of the $0$- and $\pi$-modes but sometimes fail to provide complete topological characterization, especially when $0$- and $\pi$-modes coexist. In this work, we utilize the spectral localizer, a powerful local probe that can provide numerically efficient, spatially local, and energy-resolved topological characterization. In particular, we apply the spectral localizer to the effective Floquet Hamiltonian for driven one- and two-dimensional topological systems with no or limited symmetries and are able to assign topological invariants, or local markers, that characterize the $0$- and the $\pi$-boundary modes individually and unambiguously. Due to the spatial resolution, we also demonstrate that the extracted topological invariants are suitable for studying driven disordered systems and can even capture disorder-induced phase transitions., Comment: This is the published version

We investigate intrinsic superconducting diode effect in the unconventional superconducting state of $d$-wave altermagnets. We find large diode efficiencies in the wide-reaching finite-momentum pairing regimes of the phase diagram. Remarkably, even perfect diode efficiency of 100% can be obtained in the presence of an external magnetic field. We attribute the largest efficiencies to competition between multiple zero-momentum (BCS) and finite-momentum superconducting states, which is connected to a topological nodal-to-nodeless transition in altermagnets with magnetic field, making our results applicable to a wide range of altermagnets., Comment: 6 pages, 3 figures

Superconductivity in the recently discovered altermagnetic materials hosts large prospects for both fundamental physics and technological applications. In this work we show that a characteristic spin-sublattice locking in altermagnets puts severe constraints on possible superconducting pairing. In particular, we uncover that the most common form of superconductivity, uniform $s$-wave spin-singlet pairing is not possible to achieve in altermagnets. Considering an effective model for a $d_{x^2-y^2}$-wave altermagnet on a square lattice, we instead find that the most likely forms of spin-singlet pairing have $d_{x^2-y^2}$- or extended $s$-wave symmetry. We also find that the simplest form of equal-spin-triplet $p$-wave pairing is not allowed, but it can only exist as a mixed-spin-triplet $p$-wave state. We verify these constraints on pairing within an interaction-induced model of altermagnetism, where we also establish their validity for finite-momentum pairing. Additionally we discuss the possible pairing symmetries for odd-frequency superconducting pairing. Due to the generality of our results, they are applicable to both intrinsic superconductivity and proximity-induced superconductivity in altermagnet-superconductor hybrid junctions., Comment: 10 pages, 3 figures, 2 tables

Action segmentation of behavioral videos is the process of labeling each frame as belonging to one or more discrete classes, and is a crucial component of many studies that investigate animal behavior. A wide range of algorithms exist to automatically parse discrete animal behavior, encompassing supervised, unsupervised, and semi-supervised learning paradigms. These algorithms -- which include tree-based models, deep neural networks, and graphical models -- differ widely in their structure and assumptions on the data. Using four datasets spanning multiple species -- fly, mouse, and human -- we systematically study how the outputs of these various algorithms align with manually annotated behaviors of interest. Along the way, we introduce a semi-supervised action segmentation model that bridges the gap between supervised deep neural networks and unsupervised graphical models. We find that fully supervised temporal convolutional networks with the addition of temporal information in the observations perform the best on our supervised metrics across all datasets., Comment: 33 pages, 15 figures

Mixed frequency data has been shown to improve the performance of growth-at-risk models in the literature. Most of the research has focused on imposing structure on the high-frequency lags when estimating MIDAS-QR models akin to what is done in mean models. However, only imposing structure on the lag-dimension can potentially induce quantile variation that would otherwise not be there. In this paper we extend the framework by introducing structure on both the lag dimension and the quantile dimension. In this way we are able to shrink unnecessary quantile variation in the high-frequency variables. This leads to more gradual lag profiles in both dimensions compared to the MIDAS-QR and UMIDAS-QR. We show that this proposed method leads to further gains in nowcasting and forecasting on a pseudo-out-of-sample exercise on US data.

There is a broad class of networks which connect inputs to outputs. We provide a strong theoretical foundation for crossover across this class and connect it to informativeness, a measure of the connectedness of inputs to outputs. We define Input/Output Directed Graphs (or IOD Graphs) as graphs with nodes $N$ and directed edges $E$, where $N$ contains (a) a set of "input nodes" $I \subset N$, where each $i \in I$ has no incoming edges and any number of outgoing edges, and (b) a set of "output nodes" $O \subset N$, where each $o \in O$ has no outgoing edges and any number of incoming edges, and $I\cap O = \emptyset$. We define informativeness, which involves the connections via directed paths from the input nodes to the output nodes: A partially informative IOD Graph has at least one path from an input to an output, a very informative IOD Graph has a path from every input to some output, and a fully informative IOD Graph has a path from every input to every output. A perceptron is an example of an IOD Graph. If it has non-zero weights and any number of layers, it is fully informative. As links are removed (assigned zero weight), the perceptron might become very, partially, or not informative. We define a crossover operation on IOD Graphs in which we find subgraphs with matching sets of forward and backward directed links to "swap." With this operation, IOD Graphs can be subject to evolutionary computation methods. We show that fully informative parents may yield a non-informative child. We also show that under conditions of contiguousness and the no dangling nodes condition, crossover compatible, partially informative parents yield partially informative children, and very informative input parents with partially informative output parents yield very informative children. However, even under these conditions, full informativeness may not be retained.

Random vacancies in a graphene monolayer induce defect states that are known to form a narrow impurity band centered around zero energy at half-filling. We use a space-resolved formulation of the quantum metric and establish a strong enhancement of the electronic correlations in this impurity band. The enhancement is primarily due to strong correlations between pairs of vacancies situated on different sublattices at anomalously large spatial distances. We trace the strong enhancement to both the multifractal vacancy wave functions, which ties the system exactly at the Anderson insulator transition for all defect concentrations, and preserving the chiral symmetry., Comment: 6 pages, 3 figures

We investigate the properties of a Fibonacci quasicrystal (QC) arrangement of a one-dimensional topological superconductor, such as a magnetic atom chain deposited on a superconducting surface. We uncover a general mutually exclusive competition between the QC properties and the topological superconducting phase with Majorana bound states (MBS): there are no MBS inside the QC gaps and the MBS never behaves as QC subgap states, and likewise, no critical, or winding, QC subgap states exist inside the topological superconducting gaps. Surprisingly, despite this competition, we find that the QC is still highly beneficial for realizing topological superconductivity with MBS. It both leads to additional large nontrivial regions with MBS in parameter space, that are topologically trivial in crystalline systems, and increases the topological gap protecting the MBS. We also find that shorter approximants of the Fibonacci QC display the largest benefits. As a consequence, our results promote QCs, and especially their short approximants, as an appealing platform for improved experimental possibilities to realize MBS as well as generally highlights the fundamental interplay between different topologies., Comment: 20 pages, 11 figures

Quasiperiodicity has recently been proposed to enhance superconductivity and its proximity effect. At the same time, there has been significant experimental progress in the fabrication of quasiperiodic structures, also in reduced dimensions. Motivated by these developments, we use microscopic tight-binding theory to investigate the DC Josephson effect through a ballistic Fibonacci chain attached to two superconducting leads. The Fibonacci chain is one of the most studied examples of quasicrystals, hosting a rich multifractal spectrum, containing topological gaps with different winding numbers. We study how the Andreev bound states (ABS), current-phase relation, and the critical current depend on the quasiperiodic degrees of freedom, from short to long junctions. While the current-phase relation shows a traditional $2\pi$ sinusoidal or sawtooth profile, we find that the ABS obtain quasiperiodic oscillations and that the Andreev reflection is qualitatively altered, leading to quasiperiodic oscillations in the critical current as a function of junction length. Surprisingly, despite earlier proposals of quasiperiodicity enhancing superconductivity compared to crystalline junctions, we do not in general find that it enhances the critical current. However, we find significant current enhancement for reduced interface transparency due to the modified Andreev reflection. Furthermore, by varying the chemical potential, e.g. by an applied gate voltage, we find a fractal oscillation between superconductor-normal metal-superconductor (SNS) and superconductor-insulator-superconductor (SIS) behavior. Finally, we show that the winding of the subgap states leads to an equivalent winding in the critical current, such that the winding numbers, and thus the topological invariant, can be determined., Comment: 24 pages, 20 figures

Quantile crossing has been an ever-present thorn in the side of quantile regression. This has spurred research into obtaining densities and coefficients that obey the quantile monotonicity property. While important contributions, these papers do not provide insight into how exactly these constraints influence the estimated coefficients. This paper extends non-crossing constraints and shows that by varying a single hyperparameter ($\alpha$) one can obtain commonly used quantile estimators. Namely, we obtain the quantile regression estimator of Koenker and Bassett (1978) when $\alpha=0$, the non crossing quantile regression estimator of Bondell et al. (2010) when $\alpha=1$, and the composite quantile regression estimator of Koenker (1984) and Zou and Yuan (2008) when $\alpha\rightarrow\infty$. As such, we show that non-crossing constraints are simply a special type of fused-shrinkage., Comment: 37 pages, 16 figures, 4 tables, 7348 words

Despite recent advances in wearable technology, interfacing movement assistance devices with the human body remains challenging. We present a stretchable pneumatic sleeve that can anchor an exosuit actuator to the human arm with a low displacement of the actuator's mounting point relative to the body during operation. Our sleeve has the potential to serve as an adaptable attachment mechanism for exosuits, since it can adjust its pressure to only compress the arm as much as needed to transmit the applied exosuit forces without a large displacement. We discuss the design of our sleeve, which is made of fabric pneumatic artificial muscle (fPAM) actuators formed into bands. We quantify the performance of nine fPAM bands of various lengths and widths, as well as three sleeves (an fPAM sleeve, a series pouch motor (SPM) sleeve as in previous literature, and an off the shelf hook and loop sleeve), through the measurement of the compressing force as a function of pressure and the localized pulling force that can be resisted as a function of both pressure and mounting point displacement. Our experimental results show that fPAM bands with smaller resting length and/or larger resting width produce higher forces. Also, when inflated, an fPAM sleeve that has equivalent dimensions to the SPM sleeve while fully stretched has similar performance to the SPM sleeve. While inflated, both pneumatic sleeves decrease the mounting point displacement compared to the hook and loop sleeve. Compared to the SPM sleeve, the fPAM sleeve is able to hold larger internal pressure before bursting, increasing its possible force range. Also, when not inflated, the fPAM sleeve resists the pulling force well, indicating its ability to provide anchoring when not actuated., Comment: 7th IEEE-RAS International Conference on Soft Robotics (RoboSoft 2024) Supplementary video: https://youtu.be/9orz3NzMXT4?si=ZCjG72tS_2rSeFhJ

We report results from an analysis aimed at detecting the trispectrum of the kinematic Sunyaev-Zel{'}dovich (kSZ) effect by combining data from the South Pole Telescope (SPT) and {\it Herschel}-SPIRE experiments over a 100 ${\rm deg}^{2}$ field. The SPT observations combine data from the previous and current surveys, namely SPTpol and SPT-3G, to achieve depths of 4.5, 3, and 16 $\mu {\rm K-arcmin}$ in bands centered at 95, 150, and 220 GHz. For SPIRE, we include data from the 600 and 857 GHz bands. We reconstruct the velocity-induced large-scale correlation of the small-scale kSZ signal with a quadratic estimator that uses two cosmic microwave background (CMB) temperature maps, constructed by optimally combining data from all the frequency bands. We reject the null hypothesis of a zero trispectrum at $10.3\sigma$ level. However, the measured trispectrum contains contributions from both the kSZ and other undesired components, such as CMB lensing and astrophysical foregrounds, with kSZ being sub-dominant. We use the \textsc{Agora} simulations to estimate the expected signal from CMB lensing and astrophysical foregrounds. After accounting for the contributions from CMB lensing and foreground signals, we do not detect an excess kSZ-only trispectrum and use this non-detection to set constraints on reionization. By applying a prior based on observations of the Gunn-Peterson trough, we obtain an upper limit on the duration of reionization of $\Delta z_{\rm re, 50} < 4.5$ (95\% C.L). We find these constraints are fairly robust to foregrounds assumptions. This trispectrum measurement is independent of, but consistent with, {\it Planck}'s optical depth measurement. This result is the first constraint on the epoch of reionization using the non-Gaussian nature of the kSZ signal., Comment: 15 pages, 5 figures (3 in main text and 2 in Appendix); Accepted for publication in PRL; Some texts have been moved to Appendix; Minor change in Fig. 2 to include nomalization; Data products and plotting scripts can be downloaded from https://github.com/sriniraghunathan/kSZ_4pt_SPT_SPIRE

Quantum processors based on linear arrays of trapped ions have achieved exceptional performance, but scaling to large qubit numbers requires realizing two-dimensional ion arrays as envisioned in the quantum charge-coupled device (QCCD) architecture. Here we present a scalable method for the control of ion crystals in a grid-based surface-electrode Paul trap and characterize it in the context of transport operations that sort and reorder multispecies crystals. By combining cowiring of control electrodes at translationally symmetric locations in each grid site with the sitewise ability to exchange the voltages applied to two special electrodes gated by a binary input, site dependent operations can be achieved using only a fixed number of analog voltage signals and a single digital input per site. In two separate experimental systems containing nominally identical grid traps, one using $^{171}\mathrm{Yb}^{+}$-$^{138}\mathrm{Ba}^{+}$ crystals and the other $^{137}\mathrm{Ba}^{+}$-$^{88}\mathrm{Sr}^{+}$, we demonstrate this method by characterizing the conditional intrasite crystal reorder and the conditional exchange of ions between adjacent sites on the grid. Averaged across a multisite region of interest, we measure subquanta motional excitation in the axial in-phase and out-of-phase modes of the crystals following these operations at exchange rates of 2.5 kHz. In this initial demonstration, the logic controlling the voltage exchange occurs in software, but the applied signals mimic a proposed hardware implementation using crossover switches. These techniques can be further extended to implement other conditional operations in the QCCD architecture such as gates, initialization and measurement., Comment: 11 pages, 7 figures. Version accepted to PRX

Condensed matter systems are continuously subjected to dissipation, which often has adverse effects on quantum phenomena. We focus on the impact of dissipation on a superconducting Rashba nanowire. We reveal that the system can still host Majorana zero-modes (MZMs) with a finite lifetime in the presence of dissipation. Most interestingly, dissipation can also generate two kinds of dissipative boundary states: four robust zero-modes (RZMs) and two MZMs, in the regime where the non-dissipative system is topologically trivial. The MZMs appear via bulk gap closing and are topologically characterized by a winding number. The RZMs are not associated with any bulk states and possess no winding number, but their emergence is instead tied to exceptional points. Further, we confirm the stability of the dissipation-induced RZMs and MZMs in the presence of random disorder. Our study paves the way for both realizing and stabilizing MZMs in an experimental setup, driven by dissipation.

We investigate the topological phases that appear in an orbital version of the Benalcazar-Bernevig-Hughes (BBH) model in the presence of conventional spin-singlet $s$-wave superconductivity and with the possibility of tuning an in-plane magnetic field. We chart out the phase diagram by considering different boundary conditions, with the topology of the individual phases further examined by considering both the Wannier and entanglement spectra, as well as the Majorana polarization. For weak to moderate values of magnetic field and superconducting pairing amplitude, we find a second-order topological superconductor phase with eight zero-energy corner modes. Further increasing field or pairing, half of the corner states can be turned into zero-energy edge-localized modes, thus forming a type of hybrid-order phase. Then, we find two different putative first-order topological phases, a nodal and a nodeless phase, both with zero-energy flat bands localized along mirror-symmetric open edges. For the nodal phase, the flat bands are localized between the nodes in reciprocal space, while in the nodeless phase, with its a full bulk gap, the zero-energy boundary flat band spans the whole Brillouin zone., Comment: 14+3 pages, 14 figures

We study interaction-mediated magnetism on the surface of ABC-multilayer graphene driven by its zero-energy topological flat bands. Using the random-phase approximation we treat onsite Hubbard repulsion and find multiple competing magnetic states, due to both intra- and inter-valley scattering, with the latter causing an enlarged magnetic unit cell. At half-filling and when the Hubbard repulsion is weak, we observe two different ferromagnetic orders. Once the Hubbard repulsion becomes more realistic, new ferrimagnetic orders arise with distinct incommensurate intra- or inter-valley scattering vectors depending on interaction strength and doping, leading to a multitude of competing magnetic states., Comment: Main text: 7 pages and 4 figures, Supplementary Material: 12 pages and 9 figures

We present cosmological constraints from the abundance of galaxy clusters selected via the thermal Sunyaev-Zel'dovich (SZ) effect in South Pole Telescope (SPT) data with a simultaneous mass calibration using weak gravitational lensing data from the Dark Energy Survey (DES) and the Hubble Space Telescope (HST). The cluster sample is constructed from the combined SPT-SZ, SPTpol ECS, and SPTpol 500d surveys, and comprises 1,005 confirmed clusters in the redshift range $0.25-1.78$ over a total sky area of 5,200 deg$^2$. We use DES Year 3 weak-lensing data for 688 clusters with redshifts $z<0.95$ and HST weak-lensing data for 39 clusters with $0.6

This paper discusses pairing double/debiased machine learning (DDML) with stacking, a model averaging method for combining multiple candidate learners, to estimate structural parameters. In addition to conventional stacking, we consider two stacking variants available for DDML: short-stacking exploits the cross-fitting step of DDML to substantially reduce the computational burden and pooled stacking enforces common stacking weights over cross-fitting folds. Using calibrated simulation studies and two applications estimating gender gaps in citations and wages, we show that DDML with stacking is more robust to partially unknown functional forms than common alternative approaches based on single pre-selected learners. We provide Stata and R software implementing our proposals.

We present a catalog of 689 galaxy cluster candidates detected at significance $\xi>4$ via their thermal Sunyaev-Zel'dovich (SZ) effect signature in 95 and 150 GHz data from the 500-square-degree SPTpol survey. We use optical and infrared data from the Dark Energy Camera and the Wide-field Infrared Survey Explorer (WISE) and \spitzer \ satellites, to confirm 544 of these candidates as clusters with $\sim94\%$ purity. The sample has an approximately redshift-independent mass threshold at redshift $z>0.25$ and spans $1.5 \times 10^{14} < M_{500c} < 9.1 \times 10^{14}$ $M_\odot/h_{70}$ \ and $0.031$. We use external radio data from the Sydney University Molonglo Sky Survey (SUMSS) to estimate contamination to the SZ signal from synchrotron sources. The contamination reduces the recovered $\xi$ by a median value of 0.032, or $\sim0.8\%$ of the $\xi=4$ threshold value, and $\sim7\%$ of candidates have a predicted contamination greater than $\Delta \xi = 1$. With the exception of a small number of systems $(<1\%)$, an analysis of clusters detected in single-frequency 95 and 150 GHz data shows no significant contamination of the SZ signal by emission from dusty or synchrotron sources. This cluster sample will be a key component in upcoming astrophysical and cosmological analyses of clusters. The SPTpol millimeter-wave maps and associated data products used to produce this sample are available at https://pole.uchicago.edu/public/data/sptpol_500d_clusters/index.html, and the NASA LAMBDA website. An interactive sky server with the SPTpol maps and Dark Energy Survey data release 2 images is also available at NCSA https://skyviewer.ncsa.illinois.edu., Comment: Matches version accepted by OJA. 19 pages + references, 14 figures, cluster candidate table provided in Appendix. Data products available at https://pole.uchicago.edu/public/data/sptpol_500d_clusters/index.html and an interactive sky server at https://skyviewer.ncsa.illinois.edu

Characterizing the relationship between neural population activity and behavioral data is a central goal of neuroscience. While latent variable models (LVMs) are successful in describing high-dimensional time-series data, they are typically only designed for a single type of data, making it difficult to identify structure shared across different experimental data modalities. Here, we address this shortcoming by proposing an unsupervised LVM which extracts temporally evolving shared and independent latents for distinct, simultaneously recorded experimental modalities. We do this by combining Gaussian Process Factor Analysis (GPFA), an interpretable LVM for neural spiking data with temporally smooth latent space, with Gaussian Process Variational Autoencoders (GP-VAEs), which similarly use a GP prior to characterize correlations in a latent space, but admit rich expressivity due to a deep neural network mapping to observations. We achieve interpretability in our model by partitioning latent variability into components that are either shared between or independent to each modality. We parameterize the latents of our model in the Fourier domain, and show improved latent identification using this approach over standard GP-VAE methods. We validate our model on simulated multi-modal data consisting of Poisson spike counts and MNIST images that scale and rotate smoothly over time. We show that the multi-modal GP-VAE (MM-GPVAE) is able to not only identify the shared and independent latent structure across modalities accurately, but provides good reconstructions of both images and neural rates on held-out trials. Finally, we demonstrate our framework on two real world multi-modal experimental settings: Drosophila whole-brain calcium imaging alongside tracked limb positions, and Manduca sexta spike train measurements from ten wing muscles as the animal tracks a visual stimulus.

We explore the possibilities for spin-singlet superconductivity in newly discovered altermagnets. Investigating $d$-wave altermagnets, we show that finite-momentum superconductivity can easily emerge in altermagnets even though they have no net magnetization, when the superconducting order parameter also has $d$-wave symmetry with nodes coinciding with the altermagnet nodes. Additionally, we find a rich phase diagram when both altermagnetism and an external magnetic field are considered, including superconductivity appearing at high magnetic fields from a parent zero-field normal state., Comment: New results in the Supplementary Material for a different model of altermagnetism

We present an extension to a Sunyaev-Zel'dovich Effect (SZE) selected cluster catalog based on observations from the South Pole Telescope (SPT); this catalog extends to lower signal-to-noise than the previous SPT-SZ catalog and therefore includes lower mass clusters. Optically derived redshifts, centers, richnesses and morphological parameters together with catalog contamination and completeness statistics are extracted using the multi-component matched filter algorithm (MCMF) applied to the S/N>4 SPT-SZ candidate list and the Dark Energy Survey (DES) photometric galaxy catalog. The main catalog contains 811 sources above S/N=4, has 91% purity and is 95% complete with respect to the original SZE selection. It contains 50% more total clusters and twice as many clusters above z=0.8 in comparison to the original SPT-SZ sample. The MCMF algorithm allows us to define subsamples of the desired purity with traceable impact on catalog completeness. As an example, we provide two subsamples with S/N>4.25 and S/N>4.5 for which the sample contamination and cleaning-induced incompleteness are both as low as the expected Poisson noise for samples of their size. The subsample with S/N>4.5 has 98% purity and 96% completeness, and will be included in a combined SPT cluster and DES weak-lensing cosmological analysis. We measure the number of false detections in the SPT-SZ candidate list as function of S/N, finding that it follows that expected from assuming Gaussian noise, but with a lower amplitude compared to previous estimates from simulations., Comment: 16 pages, 17 figures, submitted to MNRAS