Learning the closest matrix product state (MPS) representation of a quantum state enables useful tools for quantum machine learning and analysis of complex quantum systems. In this work, we study the problem of learning MPS in the following setting: given many copies of an input MPS, the task is to recover a classical description of the state. The best known polynomial-time algorithm, introduced by [LCLP10, CPF+10], requires linear circuit depth and $\widetilde O(n^5)$ samples, and has seen no improvement in over a decade. These costs, neither known to be optimal, renders existing algorithms impractical for near-term quantum devices with limited resources. We introduce parallel disentangling algorithms for MPS learning. For exact MPS learning, our algorithm runs in polynomial time and uses circuit depth $O(\log n)$ and sample complexity $\widetilde O(n^3)$, improving both the depth and the dependence on the system size $n$. The key idea is to exploit the bounded-rank structure of reduced states on middle blocks of an MPS and organize the disentangling operations in a tree structure. We further extend the algorithm to closest MPS learning, improving the sample complexity dependence on $n$ from $n^9$ to $n^7$ and complement the algorithms with an $Ω(n)$ product-state lower bound. We also investigate MPS learning under hardware constraints, including restricted measurements and geometric connectivity. Under the Learning Parity with Noise (LPN) assumption, we show computational hardness for learning an MPS(2) family with non-adaptive single-qubit measurements. Finally, we show that our algorithm can be implemented with depth $O(q n^{1/q})$ on a $q$-dimensional hypercubic lattice, giving an asymptotic reduction in depth. Together, our work provides a complete characterization of the quantum resources needed for efficient MPS learning.

State transitions are fundamental in biological systems but challenging to observe directly. Here, we present the first single-cell observation of state transitions in a synthetic bacterial genetic circuit. Using a mother machine, we tracked over 1007 cells for 27 hours. First-passage analysis and dynamical reconstruction reveal that transitions occur outside the small-noise regime, challenging the applicability of classical Kramers' theory. The process lacks a single characteristic rate, questioning the paradigm of transitions between discrete cell states. We observe significant multiplicative noise that distorts the effective potential landscape yet increases transition times. These findings necessitate theoretical frameworks for biological state transitions beyond the small-noise assumption.

We investigate the zero-magnetization phase diagram of a spin-1/2 chain with competing ferromagnetic nearest-neighbor and antiferromagnetic next-nearest-neighbor exchange couplings in the strongly interacting regime. Using density matrix renormalization group (DMRG) simulations, we discover two successive commensurate-incommensurate transitions of the non-conformal Pokrovsky-Talapov universality class, occurring (even) at zero magnetic field. The first transition marks the condensation of bound pairs of magnons into a critical phase with central charge $c=2$, emerging from a gapped period-4 phase. At the second transition, an incommensurate quadrupolar (or nematic) Luttinger liquid forms out of a gapped phase separation state, via the pairwise condensation of domain walls. We argue that both transitions involve the same underlying incommensurate nematic Luttinger liquid, and that the $c=2$ phase can be understood as a coexistence of a conventional (single-magnon type) and quadrupolar (two-magnon type) Luttinger liquids. Our results demonstrate that frustration alone is sufficient to drive continuous commensurate-incommensurate transitions of Mott type and stabilise incommensurate quasi-long-range order without doping.

We study the interaction between the group law on an elliptic curve and the additive structure of $x$-coordinates of rational points on an elliptic curve. Let $E/\mathbb{Q}$ be an elliptic curve of Mordell-Weil rank $r \geq 1$, $d \geq 1$ be an integer, and $0<ρ\leq 1$. We show that if a $d$-dimensional proper generalized arithmetic progression in $\mathbb{Q}$ contains the $x$-coordinates of rational points on $E/\bbq$ with positive proportion $ρ$, then the number of such points is bounded by $A(E,d,ρ)^r$. The proof combines extraction lemmas, gap principles, and the bounds for spherical codes. As an application, we obtain restrictions on sets of rational points whose $x$-coordinates have small sumsets or large additive energy.

It is important to know noise levels of boson sampling in order to cautiously demonstrate the quantum computational advantage or realize certain tasks. Based on those statistical benchmark methods such as the correlators and clouds, which are initially proposed to discriminate boson sampling and other mockups, we quantificationally evaluate noises of photon partial distinguishability and photon loss compensated by dark counts. This is feasible owing to the fact that the output distribution unbalances are suppressed by noises, which are actually results of multi-photon interferences. This is why the evaluation performance is better when high order correlators or correspondent clouds are employed. Our results indicate that the statistical benchmark methods can also work in the task of evaluating noises of boson sampling. An effective scheme is also introduced to fast simulate noisy samples, especially those with photon partial distinguishability.

A complex of platinum and the tetra-coordinate chelating ligand, R,R'-6,6'-dimethyl-N,N'-bis(2'-pyridine-carboxamide)-1-cyclohexane (Me-BPCH) is investigated as a potential candidate for measurement of parity violation (PV) in chiral molecules. The synthesis of Pt(Me-BPCH) is presented alongside computational investigation of PV sensitivity in its vibrational spectrum. Pt(Me-BPCH) is compared to other two derivatives of this complex, Au(Me-BPCH) and Pt(CF$_3$-BPCH) in terms of their PV response and suitability for measurement. We identify the most promising vibrational transitions based on their enhanced PV effects and practical experimental considerations and analyze the relationship between the vibrational structure and the corresponding PV sensitivity for all three molecules.

We call a conjugacy class of the symplectic group Sp$(2n, K)$ over a field $K$ strictly hyperbolic if its minimal polynomial is of the form $q(x) q^*(x)$, where the polynomial $q(x)$ is prime to its reciprocal $q^*(x) := x^n q(x^{-1})$. It is shown that the product of 2 cyclic, strictly hyperbolic conjugacy classes of Sp$(2n, K)$ contains all nonscalar elements of Sp$(2n, K)$. It follows that the projective symplectic group has a conjugacy class of covering number 2, i.e. PSp$(2n,K) = Ω^2$ for some conjugacy class $Ω$ of PSp$(2n,K)$. This verifies a conjecture of J. G. Thompson in the special case of a (finite) projective symplectic group.

The majority of massive stars are part of binary systems that may interact during their evolution. However, not many RSGs are known binaries, and only a few have constrained orbital parameters. We search the available TESS photometry for eclipsing companions of RSGs. We focus on the best candidate, VV Cephei type binary KQ Pup, which is made up of a RSG, KQ Pup A, and a B-type companion, KQ Pup B (orbital period of 26 yr). We use photometry, spectroscopy, and newly taken interferometric data with VLTI-GRAVITY. Using TESS, we discovered eclipses with a period of $17.2596 \: \rm d$, associated with KQ Pup B, making it a Ba+Bb binary. The detection of the hydrogen Br$γ$ line with VLTI-GRAVITY enabled us to track the orbital motion of the Ba+Bb pair relative to A and determine the astrometric orbit of A+B. The dynamical masses agree with independent estimates from asteroseismology and evolutionary models. The results give a mass of $ \sim 10 \: \rm M_{\odot} $ for the RSG KQ Pup A and $ \sim 14 \: \rm M_{\odot} $ for the sum of the hot components Ba+Bb. We determined an orbital parallax of $π= 1.24^{+0.05}_{-0.04}\, \rm mas $, which is the first such parallax measurement for a RSG. KQ Pup represents a unique demonstration of mass transfer mechanism in wide eccentric RSG systems. The variability of Balmer emission lines and the detection of Br$γ$ are a strong signature of accretion to Ba+Bb near periastron. With the RSG filling its Roche lobe only by $\sim 70\%$ at periastron, the mass transfer is instead driven by accretion from its extended atmosphere via the Wind Roche Lobe Overflow. The accretion disk dissipates by apastron. Overall, we discovered that several previously assumed RSG binaries host eclipsing inner systems, corresponding to $\sim 10 \%$ of all known Galactic RSG binaries. This suggests that many of the other RSG binaries may also be hierarchical triples.

Resonant energy transfer (RET), between equal parity 1s65s$^3\mathrm{S}_1$ and 1s66s$^3\mathrm{S}_1$ Rydberg levels in helium has been observed in low-temperature ($\sim80$ mK) collisions with ammonia molecules which undergo inversion transitions in their X$^1$A$_1$ ground electronic state. This hybrid Rydberg-atom-polar-molecule RET represents a monopole-dipole energy exchange reaction that necessarily requires spatial overlap of the Rydberg-electron and molecular wavefunctions. Calculations, that account explicitly for the charge-dipole interaction between the Rydberg electron and the molecule, provide a quantitative explanation of the observations. Total parity is conserved in the reaction through the mixing of collisional angular momentum in the atom-molecule complex. This work opens opportunities to expand the toolbox for quantum science with charge-dipole-mediated energy exchange in hybrid neutral-atom-polar-molecule platforms.

The spatial distribution of chemical elements in the Galactic disk provides key constraints on models of galaxy evolution. However, studies using planetary nebulae (PNe) as tracers have been historically limited by large uncertainties in their distances. To overcome the long-standing distance uncertainties, we recalibrated the H$α$ surface brightness-radius relation from Frew et al. with Gaia DR3 parallaxes, deriving distances for 1,130 PNe of which 415 have Bayesian distances based on Gaia DR3 parallaxes. The O/H radial gradient for 231 disk PNe is fitted considering three models: a single linear gradient and segmented linear fits with one or two breaks. The segmented fits indicate a change in slope near the solar radius (R ~8 kpc), with a flatter or slightly positive gradient inward and a steeper negative gradient outward. This feature may reflect changes in star formation efficiency driven by the Galactic bar or the corotation resonance of the spiral arms. The breaks in the metallicity radial gradients observed in this work may result from the superposition of distinct stellar populations associated with the thin and thick disks. The two-dimensional O/H distribution in the Galactic plane supports the adopted distances and reveals modest azimuthal asymmetry, with enhanced abundances near the bar at positive longitudes, and a bimodal abundance structure between the inner and outer solar regions. Our results provide new constraints on the chemical evolution of the Milky Way, the impact of non-axisymmetric structures, and the possible existence of distinct radial abundance regimes across the Galactic disk.

Aggregation of Asian student data can reinforce the model minority myth by obscuring educational disparities among Asian student subgroups. This study investigated variation in conceptual physics knowledge across Asian racial and ethnic subgroups using data from the LASSO platform, analyzing responses from 16,810 students enrolled in 493 introductory calculus-based physics courses across 64 U.S. institutions. We applied Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy to examine predicted pre- and posttest performance on the Force Concept Inventory and Force and Motion Conceptual Evaluation. The findings revealed performance differences among 19 Asian subgroups that the Asian stratum (the single aggregated Asian group) concealed. Subgroup predicted means spanned 15.8 percentage points on the pretest and 15.4 percentage points on the posttest. The lowest-performing subgroup's posttest mean was roughly equal to the highest-performing subgroup's pretest mean, indicating a performance gap of about a full semester of instruction. Mean absolute error between the Asian Stratum and the 19-subgroup estimates was 3.3 percentage points at pretest and 3.6 percentage points at posttest, equivalent to approximately 4-5 weeks of learning in a 16-week course. These findings demonstrate that fine-grained identity data collection can support identifying disparities that common aggregation practices conceal.

We show that, in two-dimensional Euclidean quantum gravity without matter fields, the Schwinger-Dyson equations derived within the Hamiltonian framework of non-critical string field theory can be reformulated in terms of the Chekhov-Eynard-Orantin topological recursion, and we explicitly compute the associated low-order amplitudes. In particular, we establish this reformulation for two discrete models -- the basic type and the strip type -- as well as for the continuum limit of dynamical triangulations.

Characterizing increasingly complex quantum systems is a central task in quantum information science, yet experimental costs often scale prohibitively with system size. Certifying key properties using simple local measurements is highly desirable but challenging. In this work, we introduce a highly general certification framework based on a physical phenomenon that we call localizable quantumness: for generic many-body states, essential quantum properties are robustly preserved within the projected ensembles on small subsystems after performing local projective measurements on the rest of the system. Leveraging this insight, we develop certification protocols that certify global properties -- including multipartite entanglement, circuit complexity, and quantum magic -- by witnessing them on a small, accessible subsystem. Our method dramatically reduces experimental cost by relying solely on local Pauli measurements, while achieving constant sample complexity, constant-level robustness, and soundness for mixed states -- exponentially improving the sample complexity and overcoming major limitations of previous methods. We further propose a random-basis variant for certifying state fidelity. We rigorously prove its constant sample complexity and robustness for random graph states via a novel error localization mechanism, with strong numerical evidence extending these results to generic states, which represent a substantial improvement over existing methods. Our results provide a practical, scalable toolkit for certifying large-scale quantum processors and offer a novel lens for understanding complex many-body quantum systems.

Privacy-preserving state estimation for linear time-invariant dynamical systems with crowd sensors is considered. At any time step, the estimator has access to measurements from a randomly selected sensor from a pool of sensors with pre-specified models and noise profiles. A Luenberger-like observer is used to fuse the measurements with the underlying model of the system to recursively generate the state estimates. An additive privacy-preserving noise is used to constrain information leakage. Information leakage is measured via mutual information between the identity of the sensors and the state estimate conditioned on the actual state of the system. This captures an omnipotent adversary that not only can access state estimates but can also gather direct high-quality state measurements. Any prescribed level of information leakage is shown to be achievable by appropriately selecting the variance of the privacy-preserving noise. Therefore, privacy-utility trade-off can be fine-tuned.

We introduce a Galilean electromagnetic particle-in-cell (GEM-PIC) algorithm, which transforms the full set of Maxwell equations and the Vlasov equation into the boosted coordinates. This approach preserves the electromagnetic structure of the interaction while exploiting scale separation for computational effi ciency. Unlike quasistatic methods, GEM-PIC does not have to distinguish between beam and streaming particles, allowing a self-consistent treatment of particle trapping. The EM-PIC algorithm allows for highly effi cient and accurate simulations of plasma-based wakefield acceleration.

In the hierarchical assembly framework, the accretion history of the Milky Way is crucial to understand its evolution. However, in massive mergers, integrals of motion are not strictly conserved, redistributing accreted stars across dynamical spaces, such as energy-angular momentum ($E-L_z$). Additionally, the in situ disc becomes kinematically heated, acquiring halo-like orbits. Consequently, even for minor mergers, which should preserve dynamical coherence, we expect their kinematic-defined samples to be contaminated by both the massive merger(s) and the disc stars. This study aims at quantifying this contamination in known accreted halo substructures. As they are defined by kinematics, we aim at cleaning their samples analysing only chemical properties. We applied the kinematic selection criteria for the halo substructures to the Gaia EDR3 and APOGEE DR17 data. Then we adopted a Gaussian Mixture Model approach to chemically compare different substructures on a star-by-star basis, taking into account several abundances (Fe, Mg, Si, Ca, Mn, Al, and C). We argue that the chemical properties of Sequoia point towards a shared origin with GSE. Heracles, Thamnos and the Helmi Stream all likely comprise GSE and heated disc stars in a significant amount. Besides these two populations, we identified stars with chemical and orbital properties compatible with Sagittarius in the Helmi Stream and with $ω$ Cen in Thamnos. Finally, GSE itself is contaminated by Sagittarius. Halo stars chemically compatible with GSE are spread throughout the $E-L_z$ space and considerably contaminate every halo substructure studied in this work. None of these substructures appears to be a unique population of stars with its own origin. In addition to GSE, they all appear to be mixtures of stars chemically compatible either with the metal-poor disc, Sagittarius, $ω$ Cen, or with a combination of them.

Infrastructure-mounted sensors can capture rich environmental information to enhance communications and facilitate beamforming in millimeter-wave systems. This work presents an efficient sensing-assisted long-term beam tracking framework that selects optimal beams from a codebook for current and multiple future time slots. We first design a large attention-enhanced neural network (NN) to fully exploit past visual observations for beam tracking. A convolutional NN extracts compact image features, while gated recurrent units with attention capture the temporal dependencies within sequences. The large NN then acts as the teacher to guide the training of a lightweight student NN via knowledge distillation. The student requires shorter input sequences yet preserves long-term beam prediction ability. Numerical results demonstrate that the teacher achieves Top-5 accuracies exceeding 93% for current and six future time slots, approaching state-of-the-art performance with a 90% reduction of model parameters. The student closely matches the teacher's performance while reducing the number of model parameters by over 1670% and cutting complexity by over 450%, despite operating with 60% shorter input sequences. This improvement significantly enhances data efficiency, reduces latency, and reduces power consumption in sensing and processing.

The observational appearance of a black hole is critically dependent on the surrounding accreting matter, in particular on the central brightness depression and photon ring structure. We perform a systematic comparative analysis of the observational signatures of a Schwarzschild black hole embedded in a Hernquist dark matter (DM) halo under three distinct accretion scenarios: a geometrically thin disk, a static spherical flow, and an infalling spherical flow. For the thin disk model, we find that direct emission dominates the total observed intensity, while the size and brightness of the lensing and photon rings serve as sensitive probes of the Hernquist DM parameters. From a geometric perspective, the Hernquist DM halo significantly enlarges the photon sphere, resulting in an observable critical curve radius approximately $2\%$ to $30\%$ larger than in the vacuum case. Regarding the radiative signatures, the measured intensity profiles, which rely on the particular accretion models, show a general brightness suppression, which is especially affected by the Doppler de-boosting in the infalling scenario. Our results suggest that the size of the central brightness depression and the brightness profile of the black hole image provide a valuable theoretical framework for constraining the distribution of dark matter in galactic centers.

Two-dimensional (2D) van der Waals magnets offer a promising platform for pushing skyrmion technology to the single-layer limit with high tunability. While Dzyaloshinskii-Moriya interaction (DMI) is often recognized as central to skyrmion formation, their stability, collapse, and topological transition in 2D materials remain largely unexplored. In particular, the effect of higher-order exchange interactions (HOI) on these phenomena is unknown. Here, using first-principles calculations and atomistic spin simulations, we report a new topological transition generated by HOI, which we term 'ferric transition', in single-layer MnSeTe. Surprisingly, skyrmion stability and collapse remain largely unaffected by HOI due to the dominant role of DMI near the saddle point, whereas the Bloch point is strongly modified, giving rise to this novel transition. This mechanism is fundamentally distinct from the well-known radial and chimera transitions. Moreover, we predict that Janus MnSeTe exhibits remarkably high skyrmion energy barriers due to its strong DMI, among the highest reported for intrinsic 2D magnets. Our findings unveil an unexpected role of HOI in skyrmion topological transitions and establish Janus MnSeTe as a robust platform for 2D skyrmionics.

The main objective of this study is fractionally integrated fractional Brownian noise, I(t/a,H) where a>0 is the 'multiplicity' of integration, and H is the Hurst parameter . The subject of the analysis is the persistence exponent e(a,H) that determines the power-law asymptotic of probability that the process will not exceed a fixit level in a growing time interval (0,T). In the important cases such as fractional Brownian motion(FBM(H),a=1) and integtated Wienr process(a=2,H=1/2) these exponents are well known. To understand the problematic exponents e(2,H), we consider the (a,H) parameters from the maximum (for the task) area G= (a+H>1,0<H<1) ). We prove the decrease of the exponents with increasing 'a' and describe their behavior near the boundary of G, including infinity. The identity of the exponents with parameters (a,H) and (a+2H-1,1-H) has been established. On this way, the long-standing hypothesis that e(2,H)=H(1-H) has been refuted. In addition, it has been revealed that FBM(H) and FBM(1-H) processes are related by a fractional integration operation. We have obtained the exact value of the exponent for e(a>>1, H). It is identical to the persistence exponent for a Gaussian stationary process with covariance cosh((H-1/2)t)/cosh(t/2) and generalizes the well-known case of H=1/2. Our results use well known the continuity lemma for the persistence exponents and a some generalization of Slepian's lemma for a family of Gaussian processes smoothly dependent on a parameter.

This paper presents a Markov-chain-based method for the early-phase analysis and design of spare-management architectures for large-scale satellite constellations. To assess the long-run viability of such concepts of operations, satellite failure and replenishment processes are modeled as Markov chains and analyzed through their stationary solution. We reinvestigate an indirect spare strategy, modeled as a multi-echelon periodic-review reorder-point/order-quantity policy, in which spares are first delivered to parking orbits and then transferred to constellation planes. The stock levels in constellation and parking orbits are each modeled as independent Markov chains, and a fixed-point iteration yields a consistent joint stationary solution that describes the strategy's average behavior. This approach accurately captures the stochastic interplay within a multi-echelon model driven by orbital mechanics, avoiding the aggregation assumptions of prior works and remaining valid across a wider operating domain. Building on this fast, accurate analysis, we formulate an optimization problem and solve it via a genetic algorithm. Finally, we demonstrate the practical value of both the analysis method and the optimization framework in a real-world mega-constellation case study.

Sequential recommendations (SR) with transformer-based architectures are widely adopted in real-world applications, where SR models require frequent retraining to adapt to ever-changing user preferences. However, training transformer-based SR models often encounters a high computational cost associated with scoring extensive item catalogs, often exceeding thousands of items. This occurs mainly due to the use of cross-entropy loss, where peak memory scales proportionally to catalog size, batch size, and sequence length. Recognizing this, practitioners in the field of recommendation systems typically address memory consumption by integrating the cross-entropy (CE) loss with negative sampling, thereby reducing the explicit memory demands of the final layer. However, a small number of negative samples would degrade model performance, and as we demonstrate in our work, increasing the number of negative samples and the batch size further improves the model's performance, but rapidly starts to exceed industrial GPUs' size (~40Gb). In this work, we introduce the CCE- method, which offers a GPU-efficient implementation of the CE loss with negative sampling. Our method accelerates training by up to two times while reducing memory consumption by more than 10 times. Leveraging the memory savings afforded by using CCE- for model training, it becomes feasible to enhance its accuracy on datasets with a large item catalog compared to those trained with original PyTorch-implemented loss functions. Finally, we perform an analysis of key memory-related hyperparameters and highlight the necessity of a delicate balance among these factors. We demonstrate that scaling both the number of negative samples and batch size leads to better results rather than maximizing only one of them. To facilitate further adoption of CCE-, we release a Triton kernel that efficiently implements the proposed method.

We develop a framework based on the Kirkwood-Dirac quasiprobability distribution to quantify the contribution of coherence to work extraction during generic, cyclic quantum evolutions. In particular, we focus on ``anomalous processes'', counterintuitive scenarios in which, due to the negativity of the quasiprobability distribution, work can be extracted even when individual processes are associated with energy gain. Applying this framework to qubits undergoing sequences of single- and two-qubit gate operations, we identify specific conditions under which such anomalous work exchanges occur. Furthermore, we analyze the quasiprobabilistic structure of deep quantum circuits and establish a compositional relation linking the work statistics of full circuits to those of their constituent gates. Our work highlights the role of coherence in the thermodynamics of quantum computation and provides a foundation for systematically studying potential thermodynamic relevance of specific quantum circuits.

The analysis of platform trials can be enhanced by utilizing non-concurrent controls. Since including this data might also introduce bias in the treatment effect estimators if time trends are present, methods for incorporating non-concurrent controls adjusting for time have been proposed. However, so far their behavior has not been systematically investigated in platform trials that include interim analyses. To evaluate the impact of an interim analysis in trials utilizing non-concurrent controls, we consider a platform trial featuring two experimental arms and a shared control, with the second experimental arm entering later. We focus on a frequentist regression model that uses non-concurrent controls to estimate the treatment effect of the second arm and adjusts for time using a step function to account for temporal changes. We show that performing an interim analysis in Arm 1 may introduce bias in the point estimation of the effect in Arm 2, if the regression model is used without adjustment, and investigate how the marginal bias and bias conditional on the first arm continuing after the interim depend on different trial design parameters. Moreover, we propose a new estimator of the treatment effect in Arm 2, aiming to eliminate the bias introduced by both the interim analysis in Arm 1 and the time trends, and evaluate its performance in a simulation study. The newly proposed estimator is shown to substantially reduce the bias and type I error rate inflation while leading to power gains compared to an analysis using only concurrent controls.

We introduce an abstract Hilbert space-valued framework of Markovian lifts for stochastic Volterra equations with operator-valued Volterra kernels. Our main results address the existence and characterisation of possibly multiple limit distributions and stationary processes, a law of large numbers including a convergence rate, and the central limit theorem for time averages of the process within the Gaussian domain of attraction. As particular examples, we study Markovian lifts based on Laplace transforms in a weighted Hilbert space of densities and Markovian lifts based on the shift semigroup on the Filipović space. We illustrate our results for the case of fractional stochastic Volterra equations with additive or multiplicative Gaussian noise.

The kinematic Sunyaev-Zel'dovich (kSZ) effect in the cosmic microwave background (CMB) is a powerful probe of gas physics and large-scale structure (LSS) in our universe. We consider the "projected-field" kSZ estimator, which involves cross-correlating a foreground-cleaned, filtered, squared CMB temperature map with an LSS tracer, and requires no individual tracer redshifts. We compare $\verb|class_sz|$ halo model calculations of projected-field kSZ cross-correlations with measurements of these signals from the Websky numerical simulations. We cross-correlate halo density maps from Websky with various CMB secondary signals. We first validate our halo model by comparing its predictions for thermal SZ (tSZ) and patchy screening ($τ$) cross-correlations to measurements of these signals from Websky. We consider three different halo redshift ranges in our comparisons. We also construct our own kSZ, tSZ, and $τ$ maps to validate the form of the relevant profiles. Following the tSZ and $τ$ validation, we compare projected-field kSZ calculations between the halo model and the simulations. We use filters constructed for $\textit{Planck}$ and the Simons Observatory (SO) to assess the accuracy of the halo-model kSZ predictions for experiments of differing sensitivity. Overall, we find good agreement, particularly at $\textit{Planck}$ sensitivity. However, we find an $\approx$ 20$\%$ difference between our halo model and the simulations for SO, which significantly exceeds the predicted error bars. We note that our halo model includes only the dominant expected term in the projected-field kSZ signal; the magnitude of the difference between our model and the simulations is consistent with previous predictions for terms arising from other contractions in the theory calculation. These terms will need to be included to obtain unbiased inference from upcoming projected-field kSZ measurements.

Care is primarily a collective phenomenon, with a practice that involves sharing health and wellbeing information within a trusted "care circle" of family members and companions for sensemaking, interpretation, decision-making, and follow-through. However, current digital health tools and information systems are designed for individuals and primarily intended for Personal Health Informatics (PHI). This mismatch between collective practice and individualistic design creates new challenges for the proactive use of such systems in care settings and limits adoption, sustained engagement, and meaningful use. To examine how people practice collective care and how (if) they perceive, adopt, and integrate PHI systems for proactive care, we conducted a sequential mixed-methods study. Through an initial survey (n=87) and semi-structured interviews (n=22), we found that their practices involve collectively understanding, analyzing, and sensemaking health information. However, we also found that their use of existing systems to support such practices is constrained by factors at personal, relational, technological, and structural levels that evolve over time. To explore redesigning PHI toward "Collective Health Informatics", we conducted stakeholder-specific interviews (n=12), a follow-up survey (n=116), and co-design workshops (n=6) to understand the dynamics required for collective settings while retaining agency. Using a design probe evaluation (n=38), we refine a design vision for coordinated, trustworthy action across such care relationships. Our findings motivate CC-Proact, an operational map that translates ecological influences into three design levers: Agency, Elicitation, and Engagement. Using this map, our work empirically examines collective care practices and offers ten design recommendations for building responsible systems that proactively support collective care.

In this paper, we propose two novel signatures of Higgs decays to search for electron-muon flavor violation. These signatures arise from the presence of a light pseudoscalar into which the 125-GeV Higgs boson decays. The pseudoscalar subsequently decays into an electron-muon pair, leading to multilepton final states, which are relatively clean signatures to search for at the LHC. As a benchmark, we consider the type-III Two-Higgs-doublet-model. We analyze both low-energy and collider constraints on the model and identify regions of parameter space where the light pseudoscalar is viable. Our proposed signatures yield stronger constraints on the lepton flavor violating couplings than current low-energy precision measurements. Taken together, our findings suggest that collider-based probes of exotic Higgs decays provide a powerful complement to precision experiments in the quest to uncover new physics.

A central goal in ecology is to understand how biodiversity is maintained. Previous theoretical works have employed the rock-paper-scissors (RPS) game as a toy model, demonstrating that population mobility is crucial in determining the species' coexistence. One key prediction is that biodiversity is jeopardized and eventually lost when mobility exceeds a certain value--a conclusion at odds with empirical observations of highly mobile species coexisting in nature. To address this discrepancy, we introduce a reinforcement learning framework and study a spatial RPS model, where individual mobility is adaptively regulated via a Q-learning algorithm rather than held fixed. Our results show that all three species can coexist stably, with extinction probabilities remaining low across a broad range of baseline migration rates. Mechanistic analysis reveals that individuals develop two behavioral tendencies: survival priority (escaping from predators) and predation priority (remaining near prey). While species coexistence emerges from the balance of the two tendencies, their imbalance jeopardizes biodiversity. Notably, there is a symmetry-breaking of action preference in a particular state that is responsible for the divergent species densities. Furthermore, when Q-learning species interact with fixed-mobility counterparts, those with adaptive mobility exhibit a significant evolutionary advantage. Our study suggests that reinforcement learning may offer a promising new perspective for uncovering the mechanisms of biodiversity and informing conservation strategies.

In this paper, we demonstrate that potential theory provides a powerful framework for analyzing quasistationary fluid flows in bounded geometries, where the bulk dynamics are governed by elliptic equations with constant coefficients. This approach is illustrated by the two-dimensional Hele-Shaw problem with surface tension, for which we derive local well-posedness and parabolic smoothing in (almost) optimal function spaces. In addition, we establish a generalized principle of linearized stability for a particular class of abstract quasilinear parabolic problems, which enables us to show that the stationary solutions to the Hele-Shaw problem are exponentially stable.