Treatment switching is a common occurrence in the management of Multiple Sclerosis (MS), where patients transition across various disease-modifying therapies (DMTs) due to heterogeneous treatment responses, differences in disease progression, patient characteristics, and therapy-associated adverse effects. To investigate how patient-level covariates influence the likelihood of treatment transitions among DMTs, we adopt a Markovian framework, Sparse Matrix Estimation with Covariate-Based Transitions in Markov Chain Modeling (SMART-MC), in which the transition probabilities are modeled as functions of these covariates. Modeling real-world treatment transitions under this framework presents several challenges, including ensuring parameter identifiability and handling sparse transitions without overfitting. To address identifiability, we constrain each transition-specific covariate coefficient vectors to have a fixed L2 norm. Furthermore, our method automatically estimates transition probabilities for sparsely observed transitions as constants and enforces zero transition probabilities for transitions that are empirically unobserved. This approach mitigates the need for additional model complexity to handle sparsity while maintaining interpretability and efficiency. To optimize the multi-modal likelihood function, we develop a scalable, parallelized global optimization routine, which is validated through benchmark comparisons and supported by key theoretical properties. Our analysis uncovers meaningful patterns in DMT transitions, revealing variations across MS patient subgroups defined by age, race, and other clinical factors.

Kirchhoff's kinetic analogy relates the equilibrium solutions of an elastic rod or strip to the motion of a spinning top. In this analogy, time is replaced by the arc length parameter in the phase portrait to determine the equilibrium configurations of the rod. Predicted equilibrium solutions from the phase portrait for specific boundary value problems, as well as certain localized solutions, have been experimentally observed. In this study, we employ the kinetic analogy to investigate the equilibrium solutions of planar soft ferromagnetic rods subjected to transverse and longitudinal external magnetic fields. Our analysis reveals a subcritical pitchfork bifurcation in the phase portrait of a ferromagnetic rod subjected to transverse external magnetic field as the axial load is decreased continuously from a large compressive load. Similarly, a supercritical pitchfork bifurcation is observed in the case of longitudinal external magnetic field. We predict equilibrium configurations for a free-standing soft ferromagnetic elastic rod and the same subjected to canonical boundary conditions. Furthermore, we observe novel localized equilibrium solutions arising from homoclinic and heteroclinic orbits, which are absent in the phase portraits of purely elastic rods.

We investigate the effects of a static impurity, modeled by a localized barrier, in a one-dimensional mesoscopic system comprised of strongly correlated repulsive SU($N$)-symmetric fermions. For a mesoscopic sized ring under the effect of an artificial gauge field, we analyze the energy spectrum, the particle density and the current flowing through the impurity at varying interaction strengths, barrier heights, and number of components. We find that the physics of the system is governed by the competition between effective single-particle process and the formation of a high-stiffness spin-correlated state associated to the phenomenon of fractionalization of the flux quantum characterizing the $N$-component fermionic system. Our findings provide a route to probe the response of SU($N$) fermions to effective magnetic fields; at the same time, they hold significance for fundamental understanding of localized impurity problems.

We aim to advance the state-of-the-art in Quadratic Unconstrained Binary Optimization formulation with a focus on cryptography algorithms. As the minimal QUBO encoding of the linear constraints of optimization problems emerges as the solution of integer linear programming (ILP) problems, by solving special boolean logic formulas (like ANF and DNF) for their integer coefficients it is straightforward to handle any normal form, or any substitution for multi-input AND, OR or XOR operations in a QUBO form. To showcase the efficiency of the proposed approach we considered the most widespread cryptography algorithms including AES-128/192/256, MD5, SHA1 and SHA256. For each of these, we achieved QUBO instances reduced by thousands of logical variables compared to previously published results, while keeping the QUBO matrix sparse and the magnitude of the coefficients low. In the particular case of AES-256 cryptography function we obtained more than 8x reduction in variable count compared to previous results. The demonstrated reduction in QUBO sizes notably increases the vulnerability of cryptography algorithms against future quantum annealers, capable of embedding around $30$ thousands of logical variables.

The Metropolitan Transit Authority (MTA) proposed building a new light rail route called the Interborough Express (IBX) to provide a direct, fast transit linkage between Queens and Brooklyn. An open-access synthetic citywide trip agenda dataset and a block-group-level mode choice model are used to assess the potential impact IBX could bring to New York City (NYC). IBX could save 28.1 minutes to potential riders across the city. For travelers either going to or departing from areas close to IBX, the average time saving is projected to be 29.7 minutes. IBX is projected to have more than 272 thousand daily ridership after its completion (81% higher than reported in the official IBX proposal). Among those riders, more than 58 thousand people (21.4%) would come from low-income households while 185 thousand people (68.2%) would start or end along the IBX corridor. The addition of IBX would attract more than 40 thousand additional daily trips to transit mode, among which more than 16 thousand would be switched from using private vehicles, reducing potential greenhouse gas (GHG) emissions by 30.63 metric tons per day. IBX can also bring significant consumer surplus benefits to the communities, which are estimated to be $0.89 USD per trip. However, the service does not appear to significantly reduce the proportion of travelers whose consumer surpluses fall below 10% of the population average (already quite low).

Coronal mass ejections (CMEs) are powerful drivers of space weather, with magnetic flux ropes (MFRs) widely regarded as their primary precursors. However, the variation in reconnection flux during the evolution of MFR during CME eruptions remains poorly understood. In this paper, we develop a realistic 3D magneto-hydrodynamic model using which we explore the temporal evolution of reconnection flux during the MFR evolution using both numerical simulations and observational data. Our initial coronal configuration features an isothermal atmosphere and a potential arcade magnetic field beneath which an MFR emerges at the lower boundary. As the MFR rises, we observe significant stretching and compression of the overlying magnetic field beneath it. Magnetic reconnection begins with the gradual formation of a current sheet, eventually culminating with the impulsive expulsion of the flux rope. We analyze the temporal evolution of reconnection fluxes during two successive MFR eruptions while continuously emerging the twisted flux rope through the lower boundary. We also conduct a similar analysis using observational data from the Helioseismic and Magnetic Imager (HMI) and the Atmospheric Imaging Assembly (AIA) for an eruptive event. Comparing our MHD simulation with observational data, we find that reconnection flux play a crucial role in determination of CME speeds. From the onset to the eruption, the reconnection flux shows a strong linear correlation with the velocity. This nearly realistic simulation of a solar eruption provides important insights into the complex dynamics of CME initiation and progression.

Recent studies have found evidence for parity violation in the BOSS spectroscopic galaxy survey, with statistical significance as high as $7σ$. These analyses assess the significance of the parity-odd four-point correlation function (4PCF) with a statistic called $χ^2$. This statistic is biased if the parity-even eight-point correlation function (8PCF) of the data differs from the mock catalogs. We construct new statistics $χ^2_\times$, $χ^2_{\mathrm{null}}$ that separate the parity violation signal from the 8PCF bias term, allowing them to be jointly constrained. Applying these statistics to BOSS, we find that the parity violation signal ranges from $0$ to $2.5σ$ depending on analysis choices, whereas the 8PCF bias term is $\sim 6σ$. We conclude that there is no compelling evidence for parity violation in BOSS. Our new statistics can be used to search for parity violation in future surveys, such as DESI, without 8PCF biases.

One of the most striking quantum phenomena is superposition, where one particle simultaneously inhabits different states. Most methods to verify coherent superposition are indirect, in that they require the distinct states to be recombined. Here, we adapt an XOR game, in which separated parties measure different parts of a superposed particle, and use it to verify superpositions with \textit{local measurements} and a second independent particle. We then turn this game into a resource-efficient verification scheme, obtaining a confidence that the particle is superposed which approaches unity exponentially fast. We demonstrate our scheme using a single photon, obtaining a 99\% confidence that the particle is superposed with only 37 copies. Our work shows the utility of XOR games to verify quantum resources, allowing us to efficiently detect quantum superposition without reinterfering the superposed states.

The notion of ends in an infinite graph $G$ might be modified if we consider them as equivalence classes of infinitely edge-connected rays, rather than equivalence classes of infinitely (vertex-)connected ones. This alternative definition yields the edge-end space $Ω_E(G)$ of $G$, in which we can endow a natural (edge-)end topology. For every graph $G$, this paper proves that $Ω_E(G)$ is homeomorphic to $Ω(H)$ for some possibly another graph $H$, where $Ω(H)$ denotes its usual end space. However, we also show that the converse statement does not hold: there is a graph $H$ such that $Ω(H)$ is not homeomorphic to $Ω_E(G)$ for any other graph $G$. In other words, as a main result, we conclude that the class of topological spaces $Ω_E = \{Ω_E(G) : G \text{ graph}\}$ is strictly contained in $Ω= \{Ω(H) : H \text{ graph}\}$.

In the quest of new exotic phases of matter due to the interplay of various interactions, iridates hosting a spin-orbit entangled $j_{\mathrm{eff}}=1/2$ ground state have been in the spotlight in recent years. Also in view of parallels with the low-energy physics of high-temperature superconducting cuprates, the validity of a single- or few-band picture in terms of the $j_{\mathrm{eff}}$ states is key. However, in particular for its structurally simple member Ba$_2$IrO$_4$, such a systematic construction and subsequent analysis of minimal low-energy models are still missing. Here we show by means of a combination of different ab initio techniques with dynamical mean-field theory that a three-band model in terms of Ir-$j_{\mathrm{eff}}$ states fully retains the low-energy physics of the system as compared to a full Ir-$5d$ model. Providing a detailed study of the three-band model in terms of spin-orbit coupling, Hund's coupling and Coulomb interactions, we map out a rich phase diagram and identify a region of effective one-band metal-insulator transition relevant to Ba$_2$IrO$_4$. Compared to available angle-resolved photoemission spectra, we find good agreement of salient aspects of the calculated spectral function and identify features which require the inclusion of non-local fluctuations. In a broader context, we envisage the three- and five-band models developed in this study to be relevant for the study of doped Ba$_2$IrO$_4$ and to clarify further the similarities and differences with cuprates.

Solar flares are known to leave imprints on the magnetic field at the photosphere, often manifested as an abrupt and permanent change in the downward-directed Lorentz force in localized areas inside the active region. Our study aims to differentiate eruptive and confined solar flares based on the vertical Lorentz force variations. We select 26 eruptive and 11 confined major solar flares (stronger than the GOES M5 class) observed during 2011-2017. We analyze these flaring regions using SHARP vector-magnetograms obtained from the NASA's Helioseismic and Magnetic Imager (HMI). We also compare data corresponding to 2 synthetic flares from a $δ$--sunspot simulation reported in Chatterjee et al. [Phys. Rev. Lett. 116, 101101 (2016)]. We estimate the change in the horizontal magnetic field and the total Lorentz force integrated over an area around the polarity inversion line (PIL) that encompasses the location of the flare. Our results indicate a rapid increase of the horizontal magnetic field along the flaring PIL, accompanied by a significant change in the downward-directed Lorentz force in the same vicinity. Notably, we find that all the confined events under study exhibit a total change in Lorentz force of < $1.8 \times 10^{22}$ dyne. This threshold plays an important factor in effectively distinguishing eruptive and confined flares. Further, our analysis suggests that the change in total Lorentz force also depends on the reconnection height in the solar corona during the associated flare onset. The results provide significant implications for understanding the flare-related upward impulse transmission for the associated coronal mass ejection.

We prove a probabilistic Fourier extension theorem that says Fourier extension holds when averaged over certain smooth Alpert multipliers. The proofs use smooth Alpert wavelets with the classical techniques of stationary phase and interpolation of L^2 and L^4 estimates. The correct L^4 bounds for resonant forms require an expectation over Alpert multipliers.

We pursue the question how integers can be ordered or partitioned according to their divisibility properties. Based on pseudometrics on $\mathbb{Z}$, we investigate induced preorders, associated equivalence relations, and quotient sets. The focus is on metrics or pseudometrics on $\mathbb{D}_n$, the set of divisors of a given modulus $n\in\mathbb{N}$, that can be extended to pseudometrics on $\mathbb{Z}$. Arithmetic functions can be used to generate such pseudometrics. We discuss several subsets of additive and multiplicative arithmetic functions and various combinations of their function values leading to binary metric functions that represent different divisibility properties of integers. We conclude this paper with numerous examples and review the most important results. As an additional result, we derive a necessary condition for the truth of the odd k-perfect number conjecture.

We propose a nonparametric mixed logit model that is estimated using market-level choice share data. The model treats each market as an agent and represents taste heterogeneity through market-specific parameters by solving a multiagent inverse utility maximization problem, addressing the limitations of existing market-level choice models with parametric estimation. A simulation study is conducted to evaluate the performance of our model in terms of estimation time, estimation accuracy, and out-of-sample predictive accuracy. In a real data application, we estimate the travel mode choice of 53.55 million trips made by 19.53 million residents in New York State. These trips are aggregated based on population segments and census block group-level origin-destination (OD) pairs, resulting in 120,740 markets. We benchmark our model against multinomial logit (MNL), nested logit (NL), inverse product differentiation logit (IPDL), and the BLP models. The results show that the proposed model improves the out-of-sample accuracy from 65.30% to 81.78%, with a computation time less than one-tenth of that taken to estimate the BLP model. The price elasticities and diversion ratios retrieved from our model and benchmark models exhibit similar substitution patterns. Moreover, the market-level parameters estimated by our model provide additional insights and facilitate their seamless integration into supply-side optimization models for transportation design. By measuring the compensating variation for the driving mode, we found that a $9 congestion toll would impact roughly 60 % of the total travelers. As an application of supply-demand integration, we showed that a 50% discount of transit fare could bring a maximum ridership increase of 9402 trips per day under a budget of $50,000 per day.

We get new Hopf algebras (HA): 1. A wealth of quotient HA's of the Malvenuto-Reutenauer HA (the Loday-Ronco HA being a special case). They consist of the permutations avoiding an {\it arbitrary} set of permutations without global descents, 2. A HA of pairs of parking filtrations, and 3. Four HA of pairs of preorders. New concepts in this setting are: 1. a category Set$_{\mathbb N}$ whose objects are sets, but morphisms are represented by matrices of natural numbers, and 2. restriction species ${\mathsf S}$ on sets coming with pairs of natural transformations $π_1, π_2 : {\mathsf S} \rightarrow$ Pre to the species of preorders. These induce two coproducts $Δ_1$ and $Δ_2$. Dualizing $Δ_1$ gives product $μ_1$ and coproduct $Δ_2$, giving bimonoid species.

We apply an extension of the Pontryagin Maximum Principle to derive time-optimal controls of two-level quantum systems by means of piecewise constant pulses. Global optimal solutions are obtained for state-to-state transfer in the cases with one and two controls. Exact quantum speed limits are established as a function of the sampling period. We observe numerically an exponential convergence towards the minimum time in the continuous limit when this period goes to zero. We show that this convergence is only polynomial for a linearized quantum system. We discuss the experimental impact of this result.

We derive an equivalent form of Halpern's fixed-point iteration scheme for solving a co-coercive equation (also called a root-finding problem), which can be viewed as a Nesterov's accelerated interpretation. We show that one method is equivalent to another via a simple transformation, leading to a straightforward convergence proof for Nesterov's accelerated scheme. Alternatively, we directly establish convergence rates of Nesterov's accelerated variant, and as a consequence, we obtain a new convergence rate of Halpern's fixed-point iteration. Next, we apply our results to different methods to solve monotone inclusions, where our convergence guarantees are applied. Since the gradient/forward scheme requires the co-coerciveness of the underlying operator, we derive new Nesterov's accelerated variants for both recent extra-anchored gradient and past-extra anchored gradient methods in the literature. These variants alleviate the co-coerciveness condition by only assuming the monotonicity and Lipschitz continuity of the underlying operator. Interestingly, our new Nesterov's accelerated interpretation of the past-extra anchored gradient method involves two past-iterate correction terms. This formulation is expected to guide us developing new Nesterov's accelerated methods for minimax problems and their continuous views without co-coericiveness. We test our theoretical results on two numerical examples, where the actual convergence rates match well the theoretical ones up to a constant factor.

We identify cluster algebras for planar kinematics of conformal Feynman integrals in four dimensions, as sub-algebras of that for top-dimensional $G(4,n)$ corresponding to $n$-point massless kinematics. We provide evidence that they encode information about singularities of such Feynman integrals, including all-loop ladders with symbol letters given by cluster variables and algebraic generalizations. As a highly-nontrivial example, we apply $D_3$ cluster algebra to a $n=8$ three-loop wheel integral, which contains a new square root. Based on the $D_3$ alphabet and three new algebraic letters essentially dictated by the cluster algebra, we bootstrap its symbol, which is strongly constrained by the cluster adjacency. By sending a point to infinity, our results have implications for non-conformal Feynman integrals, e.g., up to two loops the alphabet of two-mass-easy kinematics is given by limit of this generalized $D_3$ alphabet. We also find that the reduction to three dimensions is achieved by folding and the resulting cluster algebras may encode singularities of amplitudes and Feynman integrals in ABJM theory, at least through $n=7$ and two loops.

In the context of a binary classification problem, the optimal linear combination of continuous predictors can be estimated by maximizing an empirical estimate of the area under the receiver operating characteristic (ROC) curve (AUC). For multi-category responses, the optimal predictor combination can similarly be obtained by maximization of the empirical hypervolume under the manifold (HUM). This problem is particularly relevant to medical research, where it may be of interest to diagnose a disease with various subtypes or predict a multi-category outcome. Since the empirical HUM is discontinuous, non-differentiable, and possibly multi-modal, solving this maximization problem requires a global optimization technique. Estimation of the optimal coefficient vector using existing global optimization techniques is computationally expensive, becoming prohibitive as the number of predictors and the number of outcome categories increases. We propose an efficient derivative-free black-box optimization technique based on pattern search to solve this problem. Through extensive simulation studies, we demonstrate that the proposed method achieves better performance compared to existing methods including the step-down algorithm. Finally, we illustrate the proposed method to predict swallowing difficulty after radiation therapy for oropharyngeal cancer based on radiation dose to various structures in the head and neck.

Results from multiple diagnostic tests are usually combined to improve the overall diagnostic accuracy. For binary classification, maximization of the empirical estimate of the area under the receiver operating characteristic (ROC) curve is widely adopted to produce the optimal linear combination of multiple biomarkers. In the presence of large number of biomarkers, this method proves to be computationally expensive and difficult to implement since it involves maximization of a discontinuous, non-smooth function for which gradient-based methods cannot be used directly. Complexity of this problem increases when the classification problem becomes multi-category. In this article, we develop a linear combination method that maximizes a smooth approximation of the empirical Hypervolume Under Manifolds (HUM) for multi-category outcome. We approximate HUM by replacing the indicator function with the sigmoid function or normal cumulative distribution function (CDF). With the above smooth approximations, efficient gradient-based algorithms can be employed to obtain better solution with less computing time. We show that under some regularity conditions, the proposed method yields consistent estimates of the coefficient parameters. We also derive the asymptotic normality of the coefficient estimates. We conduct extensive simulations to examine our methods. Under different simulation scenarios, the proposed methods are compared with other existing methods and are shown to outperform them in terms of diagnostic accuracy. The proposed method is illustrated using two real medical data sets.

Mixture Markov Model (MMM) is a widely used tool to cluster sequences of events coming from a finite state-space. However the MMM likelihood being multi-modal, the challenge remains in its maximization. Although Expectation-Maximization (EM) algorithm remains one of the most popular ways to estimate the MMM parameters, however convergence of EM algorithm is not always guaranteed. Given the computational challenges in maximizing the mixture likelihood on the constrained parameter space, we develop a pattern search-based global optimization technique which can optimize any objective function on a collection of simplexes, which is eventually used to maximize MMM likelihood. This is shown to outperform other related global optimization techniques. In simulation experiments, the proposed method is shown to outperform the expectation-maximization (EM) algorithm in the context of MMM estimation performance. The proposed method is applied to cluster Multiple sclerosis (MS) patients based on their treatment sequences of disease-modifying therapies (DMTs). We also propose a novel method to cluster people with MS based on DMT prescriptions and associated clinical features (covariates) using MMM with covariates. Based on the analysis, we divided MS patients into 3 clusters. Further cluster-specific summaries of relevant covariates indicate patient differences among the clusters.

In this paper, a novel derivative-free pattern search based algorithm for Black-box optimization is proposed over a simplex constrained parameter space. At each iteration, starting from the current solution, new possible set of solutions are found by adding a set of derived step-size vectors to the initial starting point. While deriving these step-size vectors, precautions and adjustments are considered so that the set of new possible solution points still remain within the simplex constrained space. Thus, no extra time is spent in evaluating the (possibly expensive) objective function at infeasible points (points outside the unit-simplex space). While minimizing any objective function of m parameters, within each iteration, the objective function is evaluated at 2m new possible solution points. So, upto 2m parallel threads can be incorporated which makes the computation even faster while optimizing expensive objective functions over high-dimensional parameter space. Once a local minimum is discovered, in order to find a better solution, a novel `re-start' strategy is considered to increase the likelihood of finding a better solution. Unlike existing pattern search based methods, a sparsity control parameter is introduced which can be used to induce sparsity in the solution in case the solution is expected to be sparse in prior. A comparative study of the performances of the proposed algorithm and other existing algorithms are shown for a few low, moderate and high-dimensional optimization problems. Upto 338 folds improvement in computation time is achieved using the proposed algorithm over Genetic algorithm along with better solution. The proposed algorithm is used to estimate the simultaneous quantiles of North Atlantic Hurricane velocities during 1981-2006 by maximizing a non-closed form likelihood function with (possibly) multiple maximums.

The paper deals with the numerical treatment of index-1 stochastic differential-algebraic equations (SDAEs) with nonlinear coefficients that satisfy the local Lipschitz and the Khasminskii conditions. The key challenge here is the presence of a singular and non-autonomous matrix in the equation, which makes the numerical method challenging to analyze. To tackle this challenge, we develop a more general numerical method using a local linearization technique. More precisely, we use the Taylor expansion to decompose locally the drift component of the SDAEs in linear and nonlinear parts. The linear part is approximated implicitly and must resolve the singularity issue of each time step, while the nonlinear part is approximated explicitly. This method is fascinating due to the fact that it is efficient in high dimension. We prove that this novel numerical method converges in the pathwise sense with rate $\frac{1}{2}-ε$, for arbitrary $ε>0$. The implementation of this novel numerical method is also carried out to verify our theoretical result.

Deep Learning (DL) is becoming more and more widespread in clone detection, motivated by achieving near-perfect performance for this task. In particular in case of semantic code clones, which share only limited syntax but implement the same or similar functionality, Deep Learning appears to outperform conventional tools. In this paper, we want to investigate the generalizability of DL-based clone detectors for Java. We therefore replicate and evaluate the performance of five state-of-the-art DL-based clone detectors, including Transformers like CodeBERT and single-task models like FA-AST+GMN, in a zero-shot evaluation scenario, where we train/fine-tune and evaluate on different datasets and functionalities. Our experiments demonstrate that the models' generalizability to unseen code is limited. Further analysis reveals that the conventional clone detector NiCad even outperforms the DL-based clone detectors in such a zero-shot evaluation scenario.

We consider several examples of nonautonomous systems of difference equations coming from semi-classical orthogonal polynomials via recurrence coefficients and ladder operators, with respect to various generalisations of Laguerre and Meixner weights. We identify these as discrete Painlevé equations and establish their types in the Sakai classification scheme in terms of the associated rational surfaces. In particular, we find examples which come from different weights and share a common surface type $D_5^{(1)}$ but are inequivalent in two ways. First, their dynamics are generated by non-conjugate elements of $\widehat{W}(A_3^{(1)})$. Second, some of the examples have associated surfaces being non-generic in the sense of having nodal curves. The symmetries of these examples form subgroups of the generic symmetry group, which we compute. In particular, we find $(W(A_1^{(1)})\times W(A_1^{(1)}))\rtimes \mathbb{Z}/2\mathbb{Z}$. These examples give further weight to the argument that any correspondence between different weights and the Sakai classification should make use of the refined version of the discrete Painlevé equivalence problem, which takes into account not just surface type, but also the group elements generating the dynamics as well as parameter constraints, e.g. those corresponding to nodal curves.

We study prethermalization in time-independent quantum many-body systems on a $d$-dimensional lattice with an extensive local Hamiltonian $H=N+\varepsilon P$, in the regime where $\varepsilon \ll 1$. We show that the prethermalization time is exponentially large in $\varepsilon_0/\varepsilon$, where $\varepsilon_0$ is the ratio between an effective spectral gap width and the local norm of $P$. We prove also that for exponentially long times, there exist two quasi-conserved quantities up to exponentially small errors.

This letter derives the noncoherent (NC) maximum likelihood (ML) detection rule for LoRa signals under Rician multi-path fading channel. The proposed NC-ML detection only requires the channel statistic, not the actual instantaneous channel state information (CSI), which eliminates the overhead associated with channel estimation. Simulation results show that despite the low-complexity, the proposed detection scheme significantly improves the performance of LoRa detection over multipath channel. Notably, in time-invariant channel, the NCML receiver can achieve an equivalently good performance as compared to existing coherent schemes, and even surpasses them when Doppler shift is present, while not relying on the channel estimation nor reference signal extracted from the preamble.

Nanoplasmonic modification of scintillation has so far been explored mainly in the weak-coupling regime, where changes in the local density of optical states enhance radiative recombination via Purcell-type rate engineering. By contrast, strong light-matter coupling generates hybrid states that modify emission dynamics beyond simple decay-rate acceleration, but its implications for scintillator nanocrystals (NCs) under ionizing radiation remain poorly understood. All of these effects are beneficial for near-infrared scintillators, which are typically slow and have low brightness. Here, we present a quantum-optical framework to investigate how near-infrared scintillator NCs coupled to nanoplasmonic antennas evolve from weak coupling toward strong light-matter coupling. We compare broad- and narrow-antenna platforms with single and periodic Au nanorods and benchmark them against conductive plasmonic antennas based on indium tin oxide and graphene. As representative emitters, we consider wide-band PbS NCs and narrow-band cubic Lu2O3:Er3+ scintillators. The calculations show that the onset of strong-coupling signatures is jointly governed by emitter dephasing and the antenna linewidth, with narrow-band emitters coupled to spectrally narrow antennas providing the most favorable conditions. Among the platforms considered, graphene gives the lowest threshold (g = 4 meV) for observable coherent exchange owing to its ultranarrow antenna linewidth (\k{appa} = 3.5 meV). These results identify near-infrared conductive nanoantennas, particularly graphene-based ones, as promising platforms for accessing hybrid scintillation regimes relevant to radiation detection.

How long a technological civilization remains active, and what determines whether it collapses or persists, is a central question for both projecting humanity's future and assessing the prevalence of detectable intelligence in the galaxy. We model collapse-recovery dynamics across ten plausible futures for Earth-originating civilization using a hybrid deterministic-stochastic simulation over a 1000-year window. The duty cycle, defined as the fraction of its total lifespan that a civilization is technologically active, ranges from ~0.38 to 1.00, with trajectory outcomes shaped by the interplay of governance structure, resource pressure, and hazard exposure. Several model parameters map onto actionable resilience levers, and modest improvements can qualitatively alter long-term trajectories. Sensitivity analysis reveals that the resource depletion rate and the post-collapse recovery fraction are consistently the most impactful levers across scenarios, suggesting that reducing resource consumption may be at least as important as mitigating existential hazards for avoiding civilizational collapse. We discuss implications for Earth's civilizational resilience and for the search for extraterrestrial technosignatures. We also derive an effective detectability duration that accounts for intermittent civilizational activity, and show that the apparent absence of extraterrestrial signals may reflect the prevalence of low-duty-cycle civilizations rather than the rarity of intelligent life.

Protostellar outflows display wide-angle winds and collimated jets, the magnetocentrifugal launching of which enables accretion onto the protostar. The majority of the outflow mass is likely ejected or entrained molecular H$_2$, which can now be studied in unprecedented detail with JWST. Using JWST MIRI/MRS observations towards 13 single and 20 multiple Class 0 and I protostars, we investigate the nature and evolution of the H$_2$ wind and jet morphology, mass outflow rate, and velocity and temperature structure. We construct line flux and velocity maps of the H$_2$ S(1) and S(7) lines as well as the sub-mm CO traced by ALMA. Low-$J$ ($J\le4$) H$_2$ transitions trace extended wide-angle, low-velocity (0-20 km s$^{-1}$) winds within the contours of the low-velocity ($< 30$ km s$^{-1}$) sub-mm CO emission, while high-$J$ ($J >5$) transitions are associated with shocks and knots. In Class 0 sources with a known high-velocity ($> 30$ km s$^{-1}$) molecular CO or SiO jet, higher H$_2$ velocities are found along the jet axis. The opening angle of the wind traced by the H$_2$ S(1) line broadens from $\sim20^\circ$ to $\sim90^\circ$ through the Class 0 to Class I stage. Near the base of each blue-shifted outflow lobe, we extract representative spectra, where rotation diagram fitting of the H$_2$ lines is combined with the outflow width and H$_2$ line velocity to measure the mass-loss rates. The rotation diagrams show a warm $\sim 600$ K, component with two orders of magnitude more mass than the hot, 1500-3000 K component. The H$_2$ outflow mass-loss rates decline by two orders of magnitude from the Class 0 to Class II stage and are correlated with bolometric luminosity. The declining warm H$_2$ mass loss rates and increasing opening angles from the Class 0 to I stages, and the absence of H$_2$ jets in the Class I sources, are consistent with the predictions of MHD disk wind models.