We refine the asymptotic estimates for minimizers of a class of nonlocal energy functionals of the form \[ \frac{1}{4} \iint_{\R^{2n} \setminus (\R^n \setminus Ω)^2} \snr{u(x) - u(y)}^2 K(x - y) \,dx\,dy + \int_ΩW(u(x)) \,dx, \] as originally studied in~\cite{DPDV}, and we prove the optimality of our improved bounds. Here, $W$ denotes a possibly \emph{degenerate} oscillatory double-well potential, satisfying a polynomial control on its second derivative near the wells. The kernel~$K$ belongs to a broad class of measurable functions and is modeled on the one of the fractional Laplacian.

We report results from the simultaneous XRISM (183 ks) and NuSTAR (62 ks) observations of the Seyfert-2 galaxy NGC 4388. This AGN has the brightest Fe K$α$ line among Compton-thin, obscured sources. To model the reflection continuum and fluorescent lines, we employ an updated version of XCLUMPY and a broad line region model with a disk-like geometry. The profile of the neutral Fe-K fluorescent line is well described as the sum of three components convolved with Gaussians with FWHM values of $\sim 290\ \mathrm{km\ s^{-1}}$, $\sim 1470\ \mathrm{km\ s^{-1}}$, and $\sim 11100\ \mathrm{km\ s^{-1}}$. These line widths correspond to radii of 1.5 pc, 0.060 pc, and $1.0\times10^{-3}$ pc by assuming Keplerian motion, which we interpret as the dusty torus, its inner edge region, and the BLR, respectively. The data suggest that the Fe K$α$ BLR component is larger than that of H$α$ (FWHM of 4500 $\mathrm{km\ s^{-1}}$) in the polarized optical spectrum, implying that the velocity field of the BLR is dominated by that parallel to the equatorial plane. In addition, Fe XXVI Ly$α$ and Fe XXV absorption lines are detected, characterized by $\logξ \sim 3.50~\mathrm{erg\ cm\ s^{-1}}$, $\log{N_{\mathrm{H}}} \sim 22.1~\mathrm{cm^{-2}}$, $v_{\mathrm{out}} \sim 40\ \mathrm{km\ s^{-1}}$, and $σ_v \sim 160\ \mathrm{km\ s^{-1}}$. We infer that the absorber is gravitationally bound and is possibly associated with a failed wind, consistent with a radiation-driven fountain flow.

In models of phase coexistence, the precise form of the double-well potential is of central importance, yet it cannot be derived from first principles. In this paper, we investigate an inverse problem: starting from a prescribed transition layer with power-type decay at infinity, we reconstruct the structural properties of the associated double-well potential. We focus on the case of long-range interactions, where the dependence of the potential on the layer and its derivatives is particularly delicate. Our analysis establishes a correspondence between the decay rate of the transition layer and the regularity of the potential, revealing the existence of specific patterns and the possible emergence of degeneracies.

We present a first real-time study of hadronic scattering in a $(1+1)$-dimensional SU(2) lattice gauge theory with fundamental fermions using tensor-network techniques. Working in the gaugeless Hamiltonian formulation, we investigate scattering processes across sectors of fixed global baryon number $B = 0, 1, 2$, corresponding respectively to meson--meson, meson--baryon, and baryon--baryon collisions. At strong coupling, the $B = 0$ and $B = 2$ channels exhibit predominantly elastic dynamics closely resembling the U(1) Schwinger model. The mixed $B = 1$ sector displays qualitatively new behavior: meson and baryon wavepackets become entangled during the collision, with the slower state becoming spatially delocalized while the faster one propagates ballistically. We characterize these processes through local observables, entanglement entropy, and the information lattice.

Software systems are widely observed to grow in size, complexity, and interdependence over time, yet many large-scale systems remain stable despite persistent structural burden. This apparent tension suggests a limitation in one-dimensional views of software evolution. This paper introduces a graph-based, discrete-time probabilistic framework that separates structural burden from uncertainty. Change effort is modeled as a stochastic variable determined by the dependency neighborhood of the changed entity and by residual variability. Within this framework, burden is defined as expected effort and uncertainty as variance of effort. We show that, under explicit assumptions on non-decreasing average structural load, structural regularization, process stabilization, and covariance control, there exists a regime in which uncertainty decreases while structural burden does not. This regime formalizes the phenomenon of stabilization without simplification. The proposed framework provides a minimal theoretical explanation for how software systems can become more predictable over time without necessarily becoming structurally simpler, and offers a foundation for further theoretical and empirical studies of software evolution.

This paper studies probability density evolution for stochastic hybrid systems with reset maps that change the dimension of the continuous state across modes. Existing Frobenius--Perron formulations typically represent reset-induced probability transfer through boundary conditions, which is insufficient when resets map guard sets into the interior or onto lower-dimensional subsets of another mode. We develop a weak-form formulation in which reset-induced transfer is represented by the pushforward of probability flux across the guard, yielding a unified description for such systems. The proposed framework naturally captures both cases: when the reset decreases dimension, the transferred probability appears as an interior source density, whereas when the reset increases dimension, it generally appears as a singular source supported on a lower-dimensional subset. The approach is illustrated using a stochastic hybrid model in which two particles merge into one and later split back into two, demonstrating how dimension-changing resets lead to source terms beyond classical boundary-condition-based formulations.

Bright squeezed light from parametric down-conversion in the infrared (IR) frequency range has triggered the emergence of attosecond quantum optics -- a new research field at the interface of quantum optics, strong-field physics, and attosecond technology. Two challenges arise at this interface: transferring quantum features of the IR light sources to the ultraviolet (UV) and extreme ultraviolet (XUV) frequency range via strong-field nonlinearities, and exploiting quantum optical properties of the nonlinear optical response as a new probe in ultrafast dynamics. Here, we address both by driving high-harmonic generation (HHG) in solids with entangled photon pairs either in degenerate or non-degenerate frequency modes. In the degenerate mode, single-shot measurements of harmonics up to the 10th order reveal strong photon bunching whose $g^{(2)}$ first grows and then decreases with the harmonic order. We show that this behavior tracks different microscopic mechanisms responsible for harmonic emission, demonstrating the potential of attosecond quantum optical spectroscopy. In the non-degenerate case, the harmonics retain quantum-induced correlations, verified by wavelength-resolved second-order cross-correlation maps. Our findings demonstrate transfer of quantum photon correlations into the XUV domain and open a pathway toward quantum-enhanced attosecond spectroscopy and control of ultrafast dynamics in solids.

Unconventional superconductivity in quasi--two-dimensional systems is commonly identified through the emergence of Andreev bound states (ABS) at in-plane edges, while surfaces perpendicular to out-of-plane direction remain fully gapped due to weak interlayer coherence. This directional anisotropy has long served as a key paradigm for constraining pairing symmetries. Here, we show that Sr$_2$RuO$_4$ exhibits a striking reversal of this behavior. Using edge- and surface-sensitive spectroscopy, we observe pronounced in-gap ABS at surfaces perpendicular to the out-of-plane direction, whereas in-plane edges exhibit a reduced intensity of the in-gap spectral features. We show that this anomalous anisotropy can arise from the inter-orbital character of the superconducting pairing. Both even- and odd-parity inter-orbital pairing channels naturally generate robust surface ABS while suppressing planar edge modes and can also provide a mechanism for the appearance of a horizontal line node. Supported by \textit{ab initio} and model calculations, including Sr$_2$RuO$_4$/Ag interface reconstructions, our results highlight the possible role of inter-orbital correlations in shaping the spectroscopic response and provide constraints on the structure of the superconducting order parameter in Sr$_2$RuO$_4$.

Total solar eclipses are not only astronomical spectacles but also great astrophysical laboratories. Their historical records are particularly helpful for assessing the past variability of the Earth's rotation speed. Chinese records played a key role for such analyses. However, Chinese eclipse records from the Míng period have not been used for ΔT reconstructions, partially because most of the contemporaneous eclipse reports are found not in official histories but in local treatises. This study examines eclipse records in the (quasi-)contemporaneous local treatises, concentrating on what explicitly mentioned eclipse totality on the day of a total solar eclipse and what were compiled during the Míng Dynasty. On their basis, our study revised the ΔT constraint in 1361 to -408 s =< ΔT =< 601 s and set new ΔT constraints of 277 s =< ΔT =< 890 s in 1514, -328 s =< ΔT =< 332 s in 1542, and -1762 s =< ΔT =< 1091 s in 1575, respectively. We also revised most of the existing ΔT constraints in the 14th to 16th centuries, using the ephemeris data of the NASA JPL DE 441. Overall, our ΔT constraints generally tighten the ΔT variations more than what M+21 fit for their ΔT spline curve, requiring downward modification and upward modifications for the ΔT reconstructions around 1361 and 1542, respectively. Our results suggest that the ΔT decrease between 1514 and 1567 was slightly steeper than previously considered.

Underground cosmic muon rates exhibit seasonal variations correlated with atmospheric temperature, quantified via a single coefficient. We compare two analysis methods: the standard Unbinned Method and the less common Binned Method. We find that while both methods are unbiased assuming perfect knowledge of the temperature, the Binned Method develops significant bias when temperature uncertainties are present, due to binning-induced distortions. In contrast, the Unbinned Method remains robust if uncertainties are accurately known. To address the common issue of imprecise uncertainty estimates, we propose a novel procedure that assesses correlation stability by varying data time intervals and assigned uncertainties. This resolves methodological tensions in muon seasonal modulation studies and provides a practical framework for robust correlation estimation under real-world conditions.

Strong-field ionization is the cornerstone of attosecond physics, which has been extensively studied under coherent-state driving. Recently, the interface between attosecond physics and quantum optics has emerged as a new frontier. Yet, owing to experimental limitations, the role of the quantum nature of light in atomic strong-field ionization has remained unexplored. Here, we demonstrate strong-field ionization of xenon atoms driven by bright squeezed vacuum (BSV) with average pulse energy up to 10 \textmu J. We show that, as a nonclassical state with zero mean field and strong intensity fluctuations, BSV selectively enhances the spider-like holographic structures in the photoelectron momentum distributions. Using a quantum-light-corrected quantum-trajectory Monte Carlo (q-QTMC) model, we attribute this effect to the intrinsic coherence of trajectory pairs emitted within the same subcycle field fluctuation. These dynamically correlated paths exhibit enhanced phase stability and remain robust against dephasing, whereas asynchronous paths are filtered out by field noise. Our results reveal a quantum-fluctuation-induced mechanism for coherence protection in strong-field processes, positioning BSV as an effective coherence filter and establishing a new regime of quantum-enabled noise-resilient ultrafast dynamics.

Self-supervised learning (SSL) has driven impressive advances in speech processing by adopting time-domain prediction objectives, while audio representation learning frameworks operate on time-frequency spectrograms. Models optimized for one paradigm struggle to transfer to the other, highlighting the need for a joint framework. We propose Unified Learning of Transformer Representations for Audio and Speech (ULTRAS), where the masking and predictive modeling is performed over long patches of the data. The model, based on the transformer architecture, encodes spectral-patches of log-mel spectrogram features. The predictive modeling of masked segments is performed on spectral and temporal targets using a combined loss-function, forcing the representations to encode time and frequency traits. Experiments are performed on a variety of speech and audio tasks, where we illustrate that the ULTRAS framework achieves improved performance over other established baselines.

In recent years, techniques of intense THz laser have enabled us to experimentally observe nonlinear spin dynamics in antiferromagnets since the elementary excitations such as magnons reside on a THz to GHz range in antiferromagnets and THz laser thus can directly excite them. We numerically and theoretically investigate THz-laser or GHz-wave driven harmonic generations in typical ordered phases of antiferromagnets: Néel, canted and weak ferromagnetic phases. The radiation waves (harmonic generations) are created by the incident-wave driven magnon dynamics. We point out that magnetic orders and phase transitions can change the spectra of harmonic generations, differently from those of metallic, semiconductor, or atomic-gas systems without (spontaneous) symmetry breakings. We consider both the magnon harmonic generation driven by standard single-color laser and that by two-color laser in the antiferromagnets, and find several dynamical symmetries and the corresponding selection rules of the harmonic generations. These results indicate that the magnon harmonic generation spectra provide new information about symmetry or symmetry breaking of antiferromagnets.

We introduce a commutative product of degree $-n$ on the homology $H_\ast(X)$ of an $n$-dimensional special cubical set $X$ and lift it on the free loop homology $H_\ast(ΛM)$ for $M=|X|$ to be the geometric realization. These products agree with the intersection and string topology products respectively when $M$ is an oriented closed manifold, and we establish the compatibility relation between the string topology product and the standard coproduct on $H_\ast(ΛM).$ Motivated by the above relationship we introduce the notion of loop bialgebra for differential graded coalgebras $C$ by means of the coHochschild complex $ΛC.$ We calculate the loop bialgebra structure for some spaces.

The integration of multimodal sensing and millimeter-wave (mmWave) communications is a key enabler for highly mobile vehicle-to-infrastructure (V2I) networks. However, continuous high-resolution visual sensing incurs prohibitive computational energy, while delayed sensing information worsens beam misalignment. In this paper, we establish a physics-aware multimodel integrated sensing and communication (M-ISAC) framework that quantifies the mathematical trade-off between sensing energy and communication reliability using the semantic age of information (AoI). To address the coupled challenges of temporal AoI evolution and instantaneous non-convex constant modulus constraints, we propose a novel reinforcement learning approach empowered by a heterogeneous mixture-of-experts (RL-H-MoE) architecture. By strictly decoupling the temporal scheduling and spatial phase mapping, the RL-H-MoE avoids prevalent gradient conflicts in multi-task learning. Extensive simulations demonstrate that the proposed architecture achieves an optimal event-triggered sensing policy, significantly minimizing the long-term system cost while guaranteeing ultra-low sensing errors and reliable physical-layer link connectivity.

Recent standards such as RSL address AI content policy declaration -- telling AI systems what the licensing terms are. However, no existing system provides audit infrastructure -- tamper-evident licensing transaction records with independently verifiable proofs that those records have not been retroactively modified. We describe Aegon, a protocol that extends standard JWT tokens with content-specific licensing claims and maintains a Certificate Transparency-style Merkle tree over an append-only transaction ledger, enabling third-party auditors to independently verify that specific content licensing transactions were recorded and have not been retroactively modified. Publishers validate tokens at the edge using standard JWKS with no broker dependency in the content delivery path. A signed provenance event log tracks content through AI transformation stages (chunking, embedding, retrieval, citation), bound to ledger entries by transaction ID. We further describe hardware-attested compliance receipts for on-device Android AI agents using StrongBox secure element attestation -- to our knowledge, the first application of hardware-attested compliance receipts to AI content licensing. Existing DRM systems use hardware-backed keys for content decryption but do not produce verifiable compliance receipts for audit trails. We describe a reference architecture and an evaluation methodology for measuring protocol overhead. The protocol runs entirely over standard HTTPS and is designed to complement existing licensing standards rather than replace them.

Wildfires pose an increasing threat to the safety and reliability of power systems, particularly in distribution networks located in fire-prone regions. To mitigate ignition risk from electrical infrastructure, utilities often employ safety power shutoffs, which proactively de-energize high-risk lines during hazardous weather and restore them once conditions improve. While this strategy can result in temporary load loss, it helps prevent equipment damage and wildfire ignition development in the system. In this paper, we develop a state-based decision-making framework to optimize such switching actions over time, with the goal of minimizing total operational costs throughout a wildfire event. The model represents network topologies as Markov states, with transitions influenced by both exogenous weather conditions and endogenous power flow dynamics. To address the computational challenges posed by the large state and action spaces, we propose an approximate dynamic programming algorithm based on post-decision states. The effectiveness and scalability of the proposed approach are demonstrated through case studies on 54-bus and 138-bus distribution systems, showcasing its potential for enhancing wildfire resilience across different grid configurations.

We make a first step towards connecting the theory of veering triangulations and bicontact structures as tools for studying (pseudo-)Anosov flows: We show that given a veering triangulation corresponding to an Anosov flow with orientable stable and unstable foliations, the edges of the triangulation can be realized as Legendrian arcs with respect to a strongly adapted bicontact structure that supports the Anosov flow. Along the way, we show that every veering triangulation can be placed in `steady position', where each pair of edge projections that intersect in the orbit space only intersect once transversely. By a previous result of the author, this implies that horizontal surgery of veering triangulations correspond to horizontal Goodman surgery of pseudo-Anosov flows.

Given a finitely generated $G$ and a subgraph $H \leq G$, the relative number of ends $e(G,H)$ is the number of ends of a Schreier graph $\mathrm{Sch}(G,H)$ and the number of coends $\tilde{e}(G,H)$ is the maximal number of $H$-infinite components of the complement of a neighbourhood of $H$ in $G$. Generalising Sageev's characterisation of codimension-one subgroups in terms of actions on CAT(0) cube complexes, we characterise the number of relative ends and the number of coends of a pair $(G,H)$ in terms of actions on quasi-median graphs.

Recently, Large Language Models (LLMs) have demonstrated significant potential in automating software engineering tasks. Generating software architecture designs from requirement documents is a crucial step in software development. However, there is currently a lack of functional datasets tailored for this task. To bridge this gap, we introduce R2ABench (Requirement-To-Architecture Benchmark), a novel benchmark comprising diverse real-world software projects paired with comprehensive Product Requirements Documents (PRDs) and expert-curated PlantUML reference diagrams. Furthermore, we propose a multi-dimensional, hybrid evaluation framework that assesses generated diagrams across three complementary layers: Structural Graph Metrics, Multi-dimensional Scoring, and Architecture Anti-pattern Detection. Using this framework, we conducted a comprehensive empirical study evaluating state-of-the-art models and agentic workflows. Our study shows that LLMs show strong syntactic validity and robust entity extraction but fundamentally struggle with relational reasoning, leading to structurally fragmented architectures. Code-specialized models partially alleviate this limitation, while agent frameworks introduce significant instability rather than consistent improvements. R2ABench provides a robust and standardized foundation for advancing LLM-driven software architecture generation.

A conventional electric vehicle (EV) powertrain relies on a centralized high-voltage DC-AC inverter, thereby limiting cell-level control and potentially reducing overall driving range and battery lifetime. This paper studies an H-bridge-based cell-level inverter topology that performs power conversion at the cell level, enabling independent control of individual cells and expanding the design space for battery management. Leveraging these additional degrees of freedom, we propose a model-agnostic energy-throughput control strategy that extends EV range while improving battery-pack lifetime. Because usable energy (and thus driving range) and lifetime are governed by the cells with the lowest state-of-charge (SOC) and state-of-health (SOH), respectively, the proposed controller preferentially routes energy throughput to healthier cells. Specifically, during charging, it permits cell SOCs to diverge to promote SOH equalization; during discharging, it rebalances SOC to maximize usable capacity under per-cell constraints. The proposed SOC-SOH-aware control strategy is evaluated on two aging models representing lithium manganese oxide and lithium iron phosphate chemistries, using a Tesla Model 3 charge-discharge profile across 14 different parameter settings. Simulations show a 7-38% improvement in lifetime relative to a conventional SOC-only balancing baseline. More broadly, the results suggest a software-defined pathway to extend EV pack life through routine charging, with minimal reliance on specific degradation models or discharge profiles.

Tag-based Physical-Layer Authentication (PLA) has attracted significant attention in recent years due to its low complexity, high security, and low latency. Traditional tag-based PLA schemes typically estimate tags by decoding the message and then subtracting the estimation of the message from the received signal. However, these approaches suffer from two main limitations. First, decoding errors introduce message interference that degrades authentication performance. Second, the analytical complexity of decoding errors leads to sub-optimal threshold settings, thereby limiting detection probability. To address these limitations, this paper proposes a Tag-Based Challenge-Response (TBCR) scheme and a Series Cancellation Authentication (SCA) scheme. Specifically, in the TBCR scheme, the tags are superimposed on a forwarded challenge signal, enabling the receiver to estimate tags by removing the known challenge signal rather than relying on decoding. However, the challenge-response mechanism introduces extra noise. Here, we propose the SCA scheme without the noise interference, where both the series signal generation and cancellation modules are well-designed to generate authentication signals and estimate tags, respectively. Furthermore, we derive the closed-form expressions to evaluate the robustness and security of both proposed schemes. Notably, on one hand, the optimal threshold and detection probability are derived, which theoretically reveal that the SCA scheme always achieves the ideal detection performance, while the TBCR scheme does so in the absence of noise at Alice. On the other hand, the TBCR scheme provides enhanced security at high Signal-to-Noise Ratio (SNR) regions with fewer keys. Theoretical analysis and simulation demonstrate that both proposed schemes significantly outperform the benchmarks in detection probability with reduced time complexity.

Optical loss is a common bottleneck in photonic quantum information processing, undermining the quantum advantage over classical approaches. Although several countermeasures, such as quantum distillation and error correction, have been proposed, they typically require experimentally demanding non-Gaussian operations. Here, we demonstrate a Gaussian-only scheme that suppresses loss-induced decoherence for general, unknown optical quantum states. By injecting a squeezed vacuum state into an environment of the loss channel and performing feedforward based on environmental monitoring, the scheme effectively suppresses loss-induced noise. Our programmable loop-based optical circuit allows us to implement the scheme for several types of loss-sensitive non-Gaussian states under various loss conditions for up to five steps, and directly compare the results with the unsuppressed case. Our results show that the scheme consistently mitigates state degradation, preserving higher fidelity and Wigner negativity than without suppression. This approach can be applied to mitigating a broad class of errors in optical systems and extending quantum memory lifetimes. Moreover, it is compatible with other loss-suppression techniques and extendable to physical platforms beyond optics, offering a promising route toward reducing the overhead required for fault-tolerant quantum information processing.

We study a nonlinear Schrödinger system with three-wave interaction: \begin{equation*} \left\{\begin{aligned} & - Δu_1 = f_1(u_1) + αu_2u_3 \quad \text{ in } \R^N, & - Δu_2 = f_2(u_2) + αu_3u_1 \quad \text{ in } \R^N, & - Δu_3 = f_3(u_3) + αu_1u_2 \quad \text{ in } \R^N, & \quad \vec{u}=(u_1,u_2,u_3)\in (H_{\rm rad}^1(\R^N))^3, \end{aligned}\right. \end{equation*} where $3\leq N\leq 5$, $α\in \R$ and each nonlinearity $f_i(ξ)$ satisfies the Berestycki-Lions conditions. Let $S_i$ denote the set of all least energy solutions of the scalar equation $-Δu = f_i(u)$ in $H_{\rm rad}^1(\R^N)$. A solution of the systems is called vector if all its components are nontrivial. We establish the existence of two distinct families of vector solutions $\{\vec{u}_α\}$ with different asymptotic behaviors as $α\to 0$. One family satisfies ${\rm dist}(\vec{u}_α,S_1\times S_2\times S_3) \to 0$, while another satisfies ${\rm dist}(\vec{u}_α,S_1\times S_2\times \{0\}) \to 0$. By contrast, we prove that no family of vector solutions satisfies ${\rm dist}(\vec{u}_α,S_1\times \{0\}\times \{0\}) \to 0$. Together, these results give a complete description of the asymptotic structure of vector solutions when the three-wave interaction is weak.

This study investigates a simplified stellarator configuration employing circular coils, in which rotational transform is generated by tilting the toroidal field (TF) coils. A pair of axisymmetric poloidal field (PF) coils is introduced to compensate for the vertical magnetic field component produced by the tilted TF coils, together forming the three-dimensional magnetic configuration. The existence of clear, nested magnetic flux surfaces is confirmed through magnetic field-line tracing, and the corresponding vacuum free-boundary equilibrium is computed using the DESC solver. The coil set is partially optimized by varying the TF coil radius and tilt angle to reduce neoclassical transport and enhance alpha-particle confinement. The optimized configuration is compared with fully optimized stellarators such as W7-X and LHD in terms of alpha-particle confinement and the Gamma_C proxy. The neoclassical transport coefficient D11 is evaluated and found to be low. Collisionless guiding-center orbit calculations for 100 eV protons and 3.5 MeV alpha particles further demonstrate favorable confinement properties.

Laser frequency stabilization is conventionally analyzed using continuous-time control theory, which accurately models analog feedback but is insufficient for digital implementations where quantization, sampling, and stochastic noise shape the dynamics. In modern digital laser systems, such as Photonic Integrated Circuit (PIC)-based lasers, finite discriminator and actuator resolution, sampling delays, and measurement noise introduce stochastic behavior that deterministic models do not capture. We present a discrete-time Markov-state framework that models the evolution of the quantized actuator in a digital laser frequency lock, with state-transition probabilities determined by the frequency discriminator response, noise statistics, and implemented digital control logic. The steady-state actuator and locked-laser frequency distributions are obtained directly from the unit-eigenvalue solution of the transition matrix, providing immediate access to key stability metrics without long time-domain simulations. For white frequency noise, we show that the Markov formulation is exact under decorrelated sampling and update schemes, while correlated discriminator sampling introduces a predictable inflation of actuator variance without shifting the operating point. In the presence of colored noise, long-range temporal correlations induce sampling-dependent deviations in both actuator mean and variance, defining the regime of validity of the memoryless Markov description. This framework provides a compact and physically transparent tool for analyzing and optimizing digitally stabilized lasers in integrated photonic systems.

This work puts forward a novel numerical approach for solving the stochastic optimal control problem (SOCP) and the mean field control (MFC) problem using projection algorithm inspired by the stochastic maximum principle (SMP) which is also powered by the randomized neural network. This approach is mesh-free, derivative free and it relies on gradually updating the underlying control via regression. It distinguishes itself from other traditional deep learning methods as it does not require minimizing the loss/cost function via direct error backward propagation to train the neural networks. The methodology designed can effectively solve stochastic optimal control problem in high dimensions ($100$ and above) and it can also be used to solve the mean field control problems. Due to the connection between the HJB equations and SOCP, the designed approach also provides a procedure for solving high dimensional HJB equations. Importantly, the infinite dimensional HJ equation related to the mean field control problem can also be solved in a point-wise sense (given the initial distribution) due to its connection with the Mean Field Control (MFC) problem. Our extensive test results show that the proposed approach typically performs better than the direct deep learning based approaches for solving control problems. We will leave the convergence proof and the extension to Mean Field Games (MFG) as future works.

Developing compact, broadband mid-infrared coherent sources for applications in spectroscopy and sensing remains a pressing challenge in photonics. However, material limitations and integration constraints have restricted the accessible wavelengths and operation bandwidths of current mid-infrared lasers. Here, we address these challenges by developing a nonlinear integrated photonic device that converts a fixed-wavelength near-infrared pump laser into broadly tunable mid-infrared light. Our device, an optical parametric oscillator (OPO) integrated on thin-film lithium niobate, generates 22 THz of multi-milliwatt, voltage-tunable radiation from 2.7-3.4 microns, a region typically difficult to access but vital for environmental, chemical, and biological sensing. By introducing an on-chip-tunable OPO architecture taking advantage of the Vernier effect, we obtain electrical control of the emission wavelengths from coarse, multi-THz scales down to continuous, sub-100-GHz mode-hop-free tuning ranges. This work establishes a robust platform for a new class of compact, widely tunable mid-infrared sources with potential for future scaling.

Understanding the timing and sequencing of activity participation in tourist mobility is central to travel behavior research, yet GPS trajectories are noisy, irregularly sampled, and only weakly linked to activity locations, which limits interpretation and scenario analysis. We address this by mapping each stay event to candidate points of interest (POIs) probabilistically, using explicit prior-likelihood weighting that yields a normalized compatibility distribution rather than hard matching. Using one month of high-density tourist trajectories in Hakone, Japan (November 2021), we construct semantic stay-event sequences based on observed place-category labels (MID10) and describe mobility rhythms through hour-by-category profiles, category transitions, and expected dwell patterns. Building on these rhythm signatures, we develop a rhythm-consistent semi-Markov simulator that generates synthetic stay-event sequences with time-conditioned transitions and category-dependent dwell behavior. In the observed data, hour-by-category summaries are computed by probability-weighted aggregation over soft labels; in simulation, each event is generated with a discrete category and a sampled dwell duration, enabling like-for-like comparison after aggregation. We further conduct counterfactual POI-inventory scenarios to quantify how hypothetical POI configuration changes shift stay intensity across time, categories, and space, particularly around hubs and main corridors. Observed-simulated comparisons show close agreement in temporal profiles and category distributions, indicating that probabilistic labeling and rhythm-consistent simulation preserve key mobility structure while providing an interpretable basis for transport-geography scenario evaluation.

The rigorous analysis of specialized physical processes often demands custom data acquisition architectures that offer flexibility and precision beyond the capabilities of general-purpose commercial loggers. This paper presents the design and implementation of a robust data acquisition system (DAQ) for a comparative analysis of the performance of two photovoltaic panels with two different cooling systems. The system integrates a custom PCB design for 20 thermistors, dual high-precision INA228 current/voltage sensors, environmental monitoring equipment, and a Raspberry Pi 4-based acquisition platform. The software architecture implements autonomous operation with enhanced fault recovery, dual storage redundancy (local CSV and InfluxDB), cloud synchronization via Google Drive, and real-time visualization through Grafana dashboards. Field deployment demonstrated system reliability, including automatic recovery from power interruptions, a 1-minute sampling rate, remote monitoring capabilities, and continuous operation during a 5 AM to 6 PM daily window. The modular hardware and software architecture enables simultaneous monitoring of two photovoltaic panels for research on direct performance comparison under identical environmental conditions.