We describe new dependent-rounding algorithms for bipartite graphs. Given a fractional matching $x$ of graph $G = (U \cup V, E)$, the algorithms return an integral solution $X$ such that each right-node $v \in V$ has at most one edge, and where the variables $X_e$ also satisfy broad non-positive correlation properties. In particular, for any edges $e_1, e_2$ sharing a left-node $u \in U$, the variables $X_{e_1}, X_{e_2}$ have \emph{strong} negative-correlation, i.e. the expectation of $X_{e_1} X_{e_2}$ is significantly below $x_{e_1} x_{e_2}$. Dependent rounding schemes with these properties have been used for a approximation algorithms for job-scheduling on unrelated machines to minimize weighted completion times, among other applications. Our new algorithm achieves simpler and qualitatively stronger bounds compared to prior algorithms. In particular, we achieve a negative-correlation property $$ \E[X_{e_1} X_{e_2}] \leq 0.79751 \ x_{e_1} x_{e_2}, $$ which is a significant constant-factor improvement over Baveja, Qu & Srinivasan (2023).

We develop methodology for recovery of change points for data observed on more than one temporal index where changes may occur simultaneous in both indices, where the spatial component may be high dimensional. The work is motivated by climate monitoring problems where long series of data are available, e.g., daily observations (index 1) over several years (index 2). Such data may be evolving over the annual time scale, along with dynamic seasonal changes in the shorter time scale. We model this as a high dimensional mean process observed on a two dimensional grid with change points. Asymptotic estimation and inference results are developed under a single change point setup, including rates of convergence of the proposed method as well the resulting limiting distributions. The method is extended to the case of multiple changes. Theoretical results are supported numerically with monte-carlo simulations. We implement our work on a large scale climate data for the Pacific Northwest region of the United States.

Electrochemically redox-active halide (eREAL) materials are an emerging class of materials that combine high Li-ion conductivity with transition-metal redox activity, making them promising candidates for cathode or catholyte applications. As a redox-active catholyte, they could significantly increase the energy density of solid-state batteries. In this work, we perform first-principles calculations on Li-M-Cl (M = 3d transition metals) ternaries to establish such a theoretical foundation for their stability and electrochemical activity. We map the phase stability of eREAL structures with varying metal-to-Cl ratio, transition-metal species, oxidation states, and anion frameworks, and compute cation and anion redox potentials. We find that the high ionicity of metal-Cl bonds elevates cation redox potentials above those of conventional oxide cathodes, but also will promote Cl oxidation and Cl-Cl dimerization at high voltages, which may limit the stability of these materials. Anion substitution effectively tunes both cation and anion redox potentials, with F substitution standing out as a viable route to extend the reversible voltage window. Beyond the anion redox issue, eREAL compounds generally exhibit flat voltage profiles, which potentially poses an electrochemical compatibility challenge when paired with active materials that operate at different voltage values or over wider voltage ranges. Collectively, our study provides a comprehensive analysis for redox behavior of eREAL materials, paving the way for their rational design and optimization in next-generation battery applications.

The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical framework for combinatorial optimization on near-term quantum devices. A central bottleneck is the classical estimation of its variational parameters γ and β, which must be optimized over a high-dimensional, non-convex landscape corrupted by sampling noise. We observe that the classical feature matrices constructed from QAOA measurement samples exhibit pronounced low-rank structure, and exploit this property for noise-robust, reduced-dimension parameter search. We present the Hamiltonian-Guided Leverage Embedding (HGLE) algorithm - a hybrid pipeline that encodes low-energy quantum samples into a weighted Ising feature matrix and compresses it via leverage-score row sampling, provably preserving the dominant rank-rsubspace geometry. The compressed representation drives a classical trust-region loop for (γ, β) estimation at a fraction of the original cost. We provide formal guarantees for rank preservation and energy approximation error, and demonstrate robustness across problem types (Max-Cut, Maximum Independent Set) and graph topologies of varying density.

How efficiently do markets update beliefs when public information arrives in rapid sequence? We use a real-time prediction market setting that combines binary payoffs, precisely observed public signals, and high-frequency market data, allowing us to compare market price changes with changes in a benchmark probability implied by publicly available information. We first show that prices are informative and become more accurate as resolution approaches. During the event, prices respond rapidly to public signals and move in the expected direction. However, directional responsiveness is not the same as efficient updating. Relative to an out-of-sample benchmark probability model, a one-minute change in the benchmark probability is associated with only about a 0.64-for-one contemporaneous change in market prices. The missing adjustment predicts future price drift over the following several minutes, including drift net of subsequent changes in the benchmark probability. We then study the mechanisms underlying this gradual adjustment. Salient public signals are incorporated relatively quickly in liquid markets, but the same signals generate substantially greater underreaction when liquidity is low. Underreaction gaps associated with salient states also predict stronger subsequent drift. The evidence therefore points to gradual price discovery shaped by the interaction between attention and trading frictions. The results contribute to the literatures on prediction markets, market efficiency, and behavioral finance. More broadly, they show that markets can aggregate public information quickly without necessarily incorporating it fully on impact. Market-implied probabilities are often directionally correct, yet adjustment remains incomplete and predictably depends on liquidity and salience.

Sensitivity analysis is an important component of simulation-based decision support because it helps analysts determine which inputs most strongly influence model outcomes under uncertainty. This paper organizes the broad sensitivity analysis literature into a coherent framework for use in complex simulation settings, with particular attention to military applications. We review major classes of methods, including local and global approaches, variance-based techniques, screening methods, derivative-based methods, and uncertainty quantification tools, and relate them to common analytical objectives such as factor prioritization, factor fixing, variance reduction, and factor mapping. The paper also discusses sensitivity auditing as a complementary perspective that emphasizes transparency, assumption tracking, and responsible use of models in decision-relevant settings.

Order types are an equivalence relation between point configurations that capture their combinatorial and convexity properties. Let $P$ be a $κ$-colored sequence of $n \ge d+1$ points in general position in $\mathbb{R}^d$. Let $ρ$ be a $κ$-colored order type on $k \le d+1$ points that has positive density on $P$; that is, for some constant $δ>0$, there are $δ\cdot \binom{n}{k}$ $k$-point subsequences of $P$ that have the same order type as $ρ$ and the same color pattern. In this paper we show that there exists a constant $c >0$ (depending only on $d, δ$, $k$ and $κ$) and disjoint subsets $X_1,\dots,X_k$ of $P$, each with at least $c \cdot n$ points, such that for every choice of $k$ points $x_i \in X_i$, $(x_1,\dots,x_k)$ has the same order type and color pattern as $ρ$.

Smart cities increasingly depend on dense edge, IoT, and vehicular networks to deliver critical urban services, including traffic control, connected mobility, infrastructure monitoring, and energy management. In this ecosystem, the Internet of Vehicles (IoV) is central to intelligent transportation, enabling continuous communication among vehicles, roadside infrastructure, and cloud-edge platforms. This connectivity, however, also enlarges the attack surface and exposes smart city and vehicular systems to evolving cyber threats that can compromise safety, privacy, data integrity, and service continuity. Conventional static defenses are often inadequate because they cannot autonomously adapt to changing attack behaviors or multi-stage intrusion patterns. This paper proposes QIRL, a Quantum-Inspired Reinforcement Learning framework built on a lightweight Deep Q-Network architecture for next-generation autonomous cyber defense. QIRL combines amplitude-phase quantum state encoding, rotation-gate-based exploration, and quantum interference reward augmentation within a cost-sensitive Markov Decision Process formulation. It further addresses class imbalance through training-only SMOTE balancing and asymmetric cost-sensitive reward shaping, while sequential MDP modeling captures temporal dependencies in multi-stage attack campaigns. The framework is evaluated on CICIDS2017 and UNSW-NB15. QIRL achieves accuracies of 97.89\% and 91.04\%, F1-scores of 95.22\% and 91.66\%, AUC-ROC values of 0.9945 and 0.9713, and True Skill Statistics of 0.9443 and 0.8244, respectively. It also attains ultra-low inference latencies of 32.5 and 45.7 microseconds per sample, corresponding to 67.77 times and 51.77 times speedups over ensemble baselines. These results show that QIRL offers a lightweight, latency-aware, and adaptive defense for smart city and IoV infrastructures.

Agentic cyber-physical systems (CPS), where autonomous AI agents participate in runtime control decision-making, introduce agent-driven parameter-update pathways absent from conventional feedback architectures. These pathways form a parameter channel structurally distinct from classical sensor and actuator channels. Among these parameters, feedback gains are the highest-leverage target: a single gain matrix determines closed-loop eigenvalue placement for the entire system, and malicious updates can directly alter closed-loop dynamics while evading residual-based monitors. We formalize this attack surface through a three-axis attacker model and a taxonomy of Gain Manipulation Attacks (GMA). Two impact classes are identified: stability-margin erosion under sustained gain drift, and transient amplification under one-shot gain replacement. A stability-preserving gain replacement can still produce transient amplification far exceeding safe operating limits, and stability verification alone is insufficient to bound the physical impact of such attacks. Stealthiness conditions and worst-case impact certificates are derived for each class via Bauer--Fike eigenvalue bounds and the Kreiss matrix theorem, with preliminary detection directions and a vehicle lateral dynamics example provided.

A framework, MAFS, is introduced that provides an approximate description of the hierarchies of quark and lepton masses and mixing angles in terms of a set of small parameters, $ε_a$, one for each fermion multiplet. MAFS is an alternative to the Froggatt-Nielsen mechanism and has a unique application in any theory, as there are no fermion charges to choose. It becomes more powerful as the number of multiplets is reduced. In $SU(5)$ unified theories, 15 observed mass ratios and mixing angles are described, at the factor of two level, by five small $ε_a$ parameters. Even though quarks and leptons are unified, the observed hierarchical pattern of quark masses and mixings {\it requires} large neutrino mixing angles and small neutrino mass hierarchies. In an $SO(10)$ unified theory, MAFS successfully describes the 15 observed flavor hierarchies with just three small $ε_a$, taking values of $0.01, 0.02$ and $0.002$. The observed cosmological baryon asymmetry results approximately from leptogenesis using MAFS in $SU(5)$, without the need for any additional small parameter; while in $SO(10)$, a further small parameter of about 0.2 appears necessary.

We investigate the dynamical generation of entanglement in a system of two superconducting qubits coupled through a parametrically driven longitudinal interaction. Using Floquet theory and exact numerical simulations, we analyze the time evolution of the system initialized in a separable ground state. Our results reveal a nontrivial mechanism for entanglement generation, fundamentally distinct from the conventional resonant excitation to an entangled eigenstate. We show that this mechanism emerges when two initially separable eigenstates are mixed by the periodic driving under multiphoton resonance conditions. Since the effect cannot be captured within a standard rotating-wave approximation, we employ generalized Van Vleck near-degenerate perturbation theory to derive an effective analytical description. Within this framework, we demonstrate that the sustained entanglement originates from the hybridization of the dominant Floquet states, namely those with the largest overlap with the initial ground state. Furthermore, the degree of entanglement can be efficiently controlled through the driving amplitude. In particular, for specific amplitudes, the entanglement is fully suppressed. We term this phenomenon as coherent destruction of entanglement.

The Internet of Vehicles (IoV) faces a dynamic, adversarial security environment where attackers adapt to defenses. Existing intrusion detection systems rely on static classifiers that fail to capture sequential decision-making, attacker adaptation, and uncertainty. We formulate IoV security as a sequential attacker-defender interaction and model defense as a reinforcement learning problem under partial observability. We propose Quantum Belief-Integrated Reinforcement Defense (Q-BIRD), using quantum-inspired belief representation to encode defender uncertainty about hidden attacker intent via amplitude-based states, enabling non-Bayesian belief evolution. Integrated into a Proximal Policy Optimization (PPO) defender, Q-BIRD selects cost-aware mitigation actions. In simulated environments with adaptive, probing attackers, Q-BIRD reduced cumulative mean damage, damage variance, and attack success rate (ASR) by 60.4%, 90.2%, and 50.0%, respectively, while increasing survival probability by 46.4%. Compared to classical Bayesian PPO, damage variance reduction and ASR improved by 10.2 times and 50%. Ablation and explainability analyses confirm that amplitude-based belief is the primary decision signal during strategy transitions when classical belief collapses, providing superior IoV security without additional hardware.

We study the problem of incremental view maintenance (IVM) under inserts to $K$-databases, where $K$ is a commutative semiring without additive inverse. The key observation put forward in this paper is that the complexity of the IVM problem depends fundamentally on the underlying semiring. We introduce a class of conjunctive queries called $p$-hierarchical and show that for any $p$-hierarchical query with fractional hypertree width $\fhtw$ and any insert-only update sequence of length $N$ to an initially empty $K$-database over an arbitrary semiring $K$ without additive inverse, we can construct a data structure that can be updated in amortized $\bigO(N^{\fhtw-1})$ time and can support constant delay enumeration of the query result. In particular, the amortized update time for any $α$-acyclic $p$-hierarchical query is constant. We also give conditional lower bounds showing that any conjunctive query without self-joins that is not $p$-hierarchical cannot be maintained with amortized constant update time and constant enumeration delay under inserts to $K$-databases. Here, $K$ can be the natural semiring and its generalizations to the provenance and covariance semirings or any idempotent and strictly ordered semiring such as the tropical semiring. When put together, our upper and lower bounds imply a dichotomy for the insert-only maintenance of conjunctive queries without self-joins and the aforementioned semirings: A query can be maintained with amortized constant update time and constant enumeration delay if and only if it is $α$-acyclic $p$-hierarchical.

Timelines are effective ways to tell historical and personal stories. However, most timeline visualization tools impose an inflexible model of time prioritizing chronological clarity. On the other hand, unconstrained representations can better capture the irregular and contextual nature of lived time, but often at the cost of interpretability. In this work, we explore this continuum with a study of how historical and personal timelines could manifest in physical spaces. We conducted a formative study (N=12) in which participants freely arranged events within a physical environment. We observed a diversity of strategies reflecting the personal and context-dependent nature of temporal mental models. We also invited participants to consider how others could move through their timelines. Our analysis led to a choreographic approach to timeline creation, as well as a proof-of-concept tablet-based augmented reality (AR) application that supports spatial timeline drawing and viewing. Finally, we reflect on the design implications of encoding chronology, pacing, and spatial context in immersive timeline stories.

We consider the problem of quantifying the chemotactic efficiency of single cells as measured by the chemotactic index $Ψ$. Previous work in a model framework for direct sensing of spatial gradients indicated that $Ψ$ depends on a single dimensionless group $s$, which plays the role of the square of the signal to noise ratio in the problem. We revisit this problem theoretically and demonstrate that the cumulants in the model can be calculated exactly. We derive explicit results for the multivariate cumulants up to third order in terms of the diffusive current density and Gaunt coefficients. We discuss the machinery required to translate Burg-Purcell style limits on concentration gradient uncertainty into results for the chemotactic index. We compute the leading corrections to $Ψ$ in an Edgeworth expansion, and identify a dimensionless group $λ$ in the problem which is a ratio of concentrations that captures the effects of the non-Gaussianity. By careful consideration of experimental results on slime mold chemotaxis, we demonstrate that the explanatory success of the original Gaussian approximation for the chemotactic index stems in part from the fact the concentration gradients were shallow, $|λ| \ll s$.

The dynamic range available from a sensor is vital to its utility. The limits on the dynamic range that can be obtained from an image sensor are set by the brightest and faintest objects that can be detected. In recent years, CMOS (complementary metal-oxide-semiconductor) image sensors (CIS) have gained high popularity due to their low cost and high availability. However, as with all detectors, the dynamic range is constrained by the sensor's bit depth. Here, we have modified the readout scheme of a commercial CIS120 sensor from Teledyne e2v, to enhance its dynamic range. We have advanced the interleaved row readout method proposed by Wocial et al. by using a more sophisticated approach, which enables us to readout chosen rows much more frequently to avoid saturation and then readout other rows on the sensor once to form the image. Our laboratory tests provide a dynamic range of 134 dB elevated from the sensor's native 12-bit of about 71 dB. We also built a camera housing that enabled first-time operation of interleaved row readout on-sky to observe the bright stars, Vega and Polaris. In complex mode we obtained unsaturated single exposure images of these bright stars, which have magnitudes near zero and detect background stars with Gaia G magnitudes around 15 in a single exposure, with a detection threshold of 5$σ$. The achievable dynamic range with this interleaved row readout is limited only by the readout noise and scattering in the camera optics.

Magnetic fields have now been asteroseismically measured in the cores of many red giants. However, most interpretations of these measurements assume that the magnetic field is far below the critical field strength known to be exceeded by red giants exhibiting gravity-mode suppression. A recent method based on the traditional approximation of rotation and magnetism accurately predicts mode frequencies under fields up to this critical value by modeling gravity waves as individual magnetogravity ``polarizations'' which propagate through a waveguide-like mode cavity. So far, this formalism has been limited to magnetic fields which are axisymmetric about the rotation axis. In this study, we extend this approach by calculating the polarizations of magnetogravity waves under arbitrarily shaped magnetic fields under potentially rapid rotation. We consider the special cases of a dipolar magnetic field misaligned with the rotation axis as well as a dipole-plus-quadrupole magnetic field with no rotational symmetry. We show that non-axisymmetric field configurations can induce avoided crossings between polarizations, and that waves in such systems can convert between magnetogravity polarizations as they propagate, especially when the magnetic field strength is locally below a stratification-dependent threshold value. This threshold is distinct from the critical field strength for gravity-mode suppression, and is instead similar to the magnetic field strength at which perturbation theory breaks down.

The time-harmonic elastic-electromagnetic interior transmission eigenvalue problem (EEITEP) arises when an elastic body becomes invisible to an incident electromagnetic wave. This spectral problem is typically non-elliptic and non-self-adjoint, making its analysis delicate. In this paper, we study the discreteness of transmission eigenvalues and the boundary localization of the associated eigenfunctions. For a general bounded Lipschitz domain, we prove that the set of positive transmission eigenvalues, if non-empty, is discrete with $\infty$ as its only possible accumulation point. For a radially symmetric domain, we demonstrate the existence of a sequence of transmission eigenvalues and derive their asymptotic behavior. We rigorously show that the corresponding transmission eigenfunctions exhibit boundary localization in their electromagnetic components, whereas the elastic displacement field remains globally distributed throughout the domain. Finally, we derive lower bounds for the $L^{\infty}$-norms of the electromagnetic gradients normalized by their $L^2$-norms, quantifying their blow-up behavior near the boundary along this sequence. These findings reveal a potential spectral mechanism for developing super-resolution imaging methods in elastic-electromagnetic scattering.

We investigate nearly degenerate Bose-Fermi mixtures and show that the breaking of generalized supersymmetry (gSUSY) between bosons and fermions, with up to two internal states, manifests itself through the emergence of fermionic Goldstino modes with up to four flavors. In particular, we draw a distinction between typical supersymmetry (SUSY), where bosons have pseudospin 0 and fermions have pseudospin 1/2, and inverse supersymmetry (iSUSY), where bosons have pseudospin 1/2 and fermions have pseudospin 0. In such systems, we highlight that the Goldstino pseudospin is carried by either its constituent fermion (SUSY) or boson (iSUSY). We then distinguish between these two cases by depicting their differing effects on the spectral functions of the bosonic and fermionic atomic species. Lastly, we propose radio-frequency- or microwave-spectroscopy experiments, analogous to momentum (angular) resolved photoemission in condensed matter physics, to measure the pseudospin-dependent spectral functions and detect the emergence of Goldstino modes in mixtures of $^{39}$K and $^{40}$K.

We define a quasilocal mass of Bartnik type, and establish its positivity and temporal monotonicity properties for two classes of domains associated with black holes. More precisely, we first show that the quasilocal mass is strictly positive for spacelike hypersurfaces that are: compact with apparent horizon boundary or noncompact with asymptotically flat ends and containing an apparent horizon in any admissible extension. Secondly, we show that the quasilocal mass is monotonically nondecreasing in time within evolutionary scenarios related to the two aforementioned settings.

With the advent of powerful, general-purpose vision-language models (VLMs), there has been growing interest in their potential to assist astronomical discovery, a field characterized by large volumes of image data. In this work, we evaluate VLMs on the challenging task of identifying ultra-faint dwarf galaxy candidates using multi-panel diagnostic images from survey data. We compare model predictions to human annotations from a large-scale citizen science campaign. We find that zero-shot VLMs closely reproduce aggregate human calibration and perform well on less ambiguous cases. However, there is significant variability at the level of individual examples, and attempts to obtain uncertainty estimates (via self-reported confidence or repeated inference) fail to yield reliable and practically useful measures. Our results highlight both the promise and the current limitations of deploying VLMs for large-scale scientific discovery in realistic settings.

Production decision systems such as ad allocation or content matching involve millions of users and thousands of items, reducing to large-scale linear programs with sparse block-diagonal structure across users. These LPs are solved repeatedly on recurring cadences over slowly evolving inputs. Three system gaps stand out. Scale: production instances routinely exceed the memory capacity of GPU solvers such as cuPDLP and D-PDLP under fixed hardware budgets. Temporal instability: solution variability across runs induces downstream churn and complicates SLAs, yet existing solvers provide no explicit control. Extensibility: CPU-based solvers such as DuaLip-Scala converge slowly and couple problem formulation to fixed schemas, making new constraint families difficult to express. We present a distributed multi-GPU LP solver built natively in PyTorch with systems-algorithm co-design for this structure. It adopts column-sharded parallelism with fused Triton kernels and batched operations to reduce per-iteration overhead. As users grow, only local computation increases, while communication is limited to a reduction of item-level dual variables, yielding near-linear scaling with GPU count at fixed item size. We also adopt ridge-regularized LPs to improve stability, a control absent from existing GPU solvers. A continuation schedule over the regularization parameter balances convergence speed and solution fidelity. Finally, we introduce an operator-centric programming model that replaces DuaLip-Scala's schema-bound interface with composable primitives, enabling new formulations without modifying the solve loop or distributed infrastructure. On synthetic workloads, our system achieves order-of-magnitude wall-clock speedup over DuaLip-Scala, near-linear multi-GPU scaling (3.86x on 4 GPUs), and scales beyond the reach of existing GPU solvers.

This paper focuses on the dark sector decay processes of $B$ mesons ($B\to \mathcal{B}_8\ +$ invisible). Using the perturbative QCD (pQCD) approach combined with flavor symmetry analysis, we calculate the branching ratios for decays from $B$ mesons into light baryons and dark baryons within two distinct $B$-Mesogenesis scenarios. A detailed discussions of the form factor $B\to \mathcal{B}_8$ are presented. Based on the derived form factors and effective couplings, we then reach the final numerical analysis. The results show that the branching ratios are sizable, especially for $B^0\to Λψ$ and $B_s^0\toΞ^0ψ$ in Type-I model, with values on the order of $\mathcal{O}(10^{-5})$ . Such processes are expected to facilitate the search for dark matter at hadron colliders and B factories.

For the disordered Ising chain with transverse and longitudinal fields, where couplings and fields are drawn from strictly positive distributions, Cao~\cite{Cao} has shown that the moments $μ_{2k} = \|[H,σ^z_0]^{(k)}\|_2^2$ grow almost factorially, $μ_{2k}^{1/(2k)}\sim k/\ln k$, and thus asymptotically at the maximal allowed rate. We generalize this result by resolving the operator norm in support length and show that the weight at length $\ell_k \sim k/\ln k$ already exhibits almost factorial growth, $\|[H,σ^z_0]^{(k)}_{\ell_k}\|_2 \gtrsim (k/\ln k)^k$. This implies maximal spatial delocalization of local operators and, in particular, rules out dynamical locality -- the strongest form of many-body localization -- at any disorder strength. We further establish rigorously a finite-size crossover scale $L\sim (W/J)^2$, where $W$ is the disorder and $J$ the coupling strength. For $L\lesssim (W/J)^2$ numerical studies only access a pre-asymptotic regime. Finally, we identify a structural path-entropy obstruction to perturbative LIOM constructions, based on the almost factorial branching of operator content and independent of resonance effects; the same mechanism strongly suggests ballistic real-time operator spreading, so sub-ballistic or localized dynamics would require a presently unidentified cancellation principle acting on almost factorially many disorder-dependent paths with random amplitudes.

Cybercrime affects a majority of Canadians, yet most incidents go unreported. We conducted two studies to examine the factors influencing cybercrime reporting and the role of interface design in victims' reporting experiences. Our survey provides individual-level insights into the persistent gap in cybercrime reporting in Canada, showing how perceived incident severity and personal characteristics shape reporting behaviour. Our usability study compared reporting with an AI chatbot to an online form; chatbots facilitated more complete reports and led to higher user satisfaction, highlighting how interface design impacts reporting outcomes.

Interfacial charge transfer between solid electrolytes (SEs) and Li metal is a key factor limiting all-solid-state battery performance. Conventional density functional theory and nudged elastic band calculations neglect many-body correlations and finite-temperature effects, which can lead to inaccurate activation barriers. Here, we trained moment tensor potentials (MTPs) for garnet LLZO systems (t-LLZO, c-LLZO, and Ga-LLZO) and Li metal, enabling machine-learning molecular dynamics (MLMD) simulations of Li$^+$ diffusion in the bulk and at Li/SE interfaces. We also introduce a residence-time window method that filters out ion rattling and isolates genuine charge-transfer events. The resulting charge-transfer activation energies are low: 167 meV at the Li/Ga-LLZO interface and 200 meV in Ga-LLZO, corresponding to resistances of $\sim \, 10^{-5} \, Ω\,\mathrm{cm}^{2}$. These results indicate that intrinsic Li/Ga-LLZO charge transfer is not rate-limiting. Overall, our findings clarify the fast interfacial kinetics in Li/LLZO systems, and the proposed methodology can aid further interface optimization in solid-state batteries.

We have developed a novel machine learning (ML) approach for predicting the likelihood of exoplanet candidate confirmation equally capable of performance on both TESS and Kepler data. From the NASA exoplanet archival post-processed Kepler and TESS databases, we chose six parameters that we assessed to be predictive to the planet transit signature: planet orbital period (P), planet radius ($R_{\rm p}$), stellar temperature ($T_{\rm eff}$), stellar radius ($R_{\rm star}$), planet transit depth ($δ$), and planet transit duration ($t_{\rm d}$). We used these parameters to evaluate eleven different ML models on all possible train/test combinations of TESS and Kepler data, using the confirmed planet and false positive labels as our training targets. We found that, due to substantially different distributions of our chosen parameters in Kepler and TESS databases, models trained with data from one instrument have difficulty predicting the other. However, models trained jointly with both TESS and Kepler data can perform well on both. We combined our best models into a statistically robust ensemble to evaluate the planet candidates in both Kepler and TESS, and we provide a list of the top candidates predicted by our model for each. Confirmed planets and false positives that have been resolved since the completion of our analysis demonstrate the effectiveness of our model and suggest that our top candidates are likely to be confirmed if they are further analyzed by the community. With the upcoming launch of the Nancy Grace Roman Space Telescope (Roman) and the expected order-of-magnitude increase in planet candidates, we suggest that our method can be extended to Roman data for robust and effective prioritization for analysis.

We develop a hypercohomology theory of a group with coefficients in a crossed module, and apply it to define abelianization maps for Galois cohomology of reductive algebraic groups.

We show that vectorial absolute minimisers of higher order $L^\infty$ variational problems satisfy an energy maximum principle. This property is only necessary for absolute minimisers, while it characterises a suitable weaker notion of absolute minimality involving compactly supported variations. Further, with different methods, we prove a gradient maximum principle for $p$-harmonic maps.

High-quality cardiopulmonary resuscitation (CPR) requires stable control of compression rhythm and depth, yet most training systems presuppose instructor mediation, repeated practice, and explanatory guidance-assumptions that do not hold in the Tibet Autonomous Region, where instruction is fragmented and learners' linguistic and educational backgrounds are heterogeneous. We present TibetCPR, a low-cost, self-guided CPR training system that pairs depth-driven electrotactile feedback with rhythm-driven visual cues within a Tibetan-language narrative. In a randomised study with 40 lay community members aged 19--56, the experimental group showed progressive minute-by-minute stabilisation of rhythm and depth across a 10-minute intervention, substantially exceeding an unguided-practice control, with gains transferring to an unscaffolded one-minute post-test. Qualitative accounts described the feedback as legible through participants' bodily action, and usability was high (SUS = 84.3). We synthesise three transferable design principles for self-guided embodied training: feedback as a calibration reference, not an immediate corrector; modality temporal granularity matched to behaviour's temporal structure; and autonomous interpretability as a deployment prerequisite, not an after-effect of usability.