Nonmagnetic kagome metals and superconductors AV3Sb5 (A = K, Rb, Cs) host unconventional charge density wave (CDW) and superconducting (SC) phases accompanied by multiple electronic symmetry breaking. Due to the centrosymmetric crystal structure, inversion symmetry has generally been assumed to hold. Here, using scanning tunneling microscopy complemented by atomic force microscopy and optical second-harmonic generation, we directly reveal that inversion symmetry in the kagome plane is spontaneously broken in the CDW state. The mixed-parity CDW state exhibits ferroelectric dipolar and nematic quadrupolar ordered moments. The coexistence and coupling between the dipole and quadrupole favor noncollinear ferro-polar and nematic alignment that breaks all mirror symmetries and gives rise to robust electronic chirality in the 3Q CDW. The multipolar coupling to in-plane electric field enables electric field control and manipulation of the chiral polar-nematic CDW state, including its chirality. Below the SC transition, we observe parity-violating pair density modulations at both the original and the CDW lattice wavevectors. Our findings of parity-violating electronic chiral multipolar order provide microscopic insights into the magnetoelectric and nonreciprocal transport, loop current order, pairing density waves, and unconventional superconductivity in kagome metals and related quantum materials.

Voltage-driven memristive switching has been reported in molecular junctions, yet its microscopic origin often remains elusive. Here, we study three rigid OPE-like derivatives that lack an obvious internal switching pathway using mechanically controlled break junctions (MCBJs) and observe non-volatile, bistable hysteretic IV characteristics at cryogenic temperature. We introduce a quantitative analysis workflow that classifies memristive IVs, clusters the two conductance states, and extracts switching features and stability metrics from repeated measurements at fixed displacement. While all molecules exhibit memristive behavior, stability and hysteresis reproducibility depend strongly on anchoring and connectivity: the linear biphenyl backbone with thiolate (SAc) anchoring shows the most reproducible, predominantly field-driven hysteresis, whereas the meta-phenyl variant with thioether (SMe) anchoring is dominated by stochastic, current-driven events. The resulting conductance statistics point to an extrinsic, mechanically mediated origin involving contact rearrangements, multi-molecule transport, blinking (open-closed) contacts, injection-point shifts, and $π$-$π$-stacking dimerization.

Zhang and Strogatz [Phys. Rev. Lett. 127, 194101 (2021)] used high-dimensional simulations to argue that basins of attraction in the Kuramoto ring are octopus-like: their volume scales as $e^{-kq^2}$ in the winding number $q$, nearly all of it concentrated in filamentary tentacles rather than near the attractor. They conjecture this geometry to be common in high dimensions but note that high-dimensional simulations are unreliable. We prove every feature of the octopus picture rigorously for identical oscillators on a ring coupled by any smooth odd function strictly increasing on $(-π,π)$.

The bulk and surface electronic structure of MoAlB(010) is studied by a combination of angle-resolved photoemission spectroscopy and density functional calculations. The observed bulk Fermi-level crossings agree with the previously reported bulk Fermi surface of the material. Additionally, we find several surface states in the wide projected bulk band gaps around the Fermi energy. The surface states differ in their stability under residual-gas exposure in the vacuum system and in the magnitude of their Rashba-type spin-orbit splitting. We explain this in terms of their elemental and orbital character. A surface state arising from Al dangling bonds is sensitive to surface contamination, whereas a mainly Mo-derived surface state exhibits the stronger spin-orbit splitting. The surface states show symmetry-enforced crossings near the $\bar{\mathrm{S}}$ point of the surface Brillouin zone. These are protected by the mirror-symmetry elements of the p2mm wallpaper group.

Skeleton generation is essential for animating 3D assets, but current deep learning methods remain limited: they cannot handle the growing structural complexity of modern models and offer minimal controllability, creating a major bottleneck for real-world animation workflows. To address this, we propose an animator-centric SG framework that achieves high-quality skeleton prediction on complex inputs while providing intuitive control handles. Our contributions are threefold. First, we curate a large-scale dataset of 82,633 rigged meshes with diverse and complicated structures. Second, we introduce a novel semantic-aware tokenization scheme for auto-regressive modeling. This scheme effectively complements purely geometric prior methods by subdividing bones into semantically meaningful groups, thereby enhancing robustness to structural complexity and enabling a key control mechanism. Third, we design a learnable density interval module that allows animators to exert soft, direct control over bone density. Extensive experiments demonstrate that our framework not only generates high-quality skeletons for challenging inputs but also successfully fulfills two critical requirements from professional animators.

Reconfigurable Intelligent Surfaces (RIS) have emerged as a key enabler for programmable wireless environments in future Beyond-5G (B5G) and 6G networks. In the meantime, Integrated Sensing and Communication (ISAC) and Physical-Layer Security (PLS) are becoming essential functionalities for next-generation wireless systems, particularly in safety and mission-critical applications. However, jointly optimizing RIS-assisted systems to support communication, sensing, and security introduces complex and often conflicting design trade-offs. In this work, a multi-objective optimization framework for RIS-assisted networks is proposed, aiming to jointly analyze communication performance, sensing accuracy, and security-related channel properties in a unified system perspective. The proposed model jointly considers RIS deployment location, orientation, surface size, and an ISAC configuration weight that controls the allocation of RIS reflection gain between communication and sensing tasks. Simulation results reveal inherent trade-offs among communication reliability, sensing accuracy, and security performance. The proposed framework provides valuable insights into the interplay between communication, sensing, and security, and enables the design of efficient RIS deployment and configuration strategies for secure ISAC-enabled 6G wireless networks.

In this paper, we present a stable and efficient approach for constructing Laguerre pseudospectral differentiation matrices. The proposed method reformulates the off-diagonal entries and computes all required quantities simultaneously using an existing fast algorithm that also generates the collocation nodes. For the diagonal entries, a closed-form expression is employed to improve numerical accuracy. This construction avoids the catastrophic cancellation present in classical formulations and yields an all-in-one procedure for generating differentiation matrices. Numerical experiments demonstrate improved robustness and sustained high accuracy for significantly larger numbers of collocation points compared to standard implementations.

The Murali-Lakshmanan-Chua (MLC) circuit is a well-recognized prominent nonlinear, nonautonomous, and dissipative electronic circuit having a versatile chaotic nature. Unraveling the dynamical synergy responsible for the genesis of extreme events in nonlinear dynamical systems is a prolific and spellbinding research area. The present study unveils the dynamical exposition of emerging extreme events in the MLC circuit concerning two different events being defined in the system. The large expansion of the chaotic attractor following the PM intermittency route plays the crucial role as the precursor behind the emergence of extreme events in the system. Our main finding reveals the prevalence of a force field due to the presence of externally applied periodic force in the system that creates the dynamical synergy that compels the chaotic trajectory traversing in its phase space to be largely deviated from the residing space, and this large deviation shows the signature of extreme events. Apart from the force field explication, we explored another two dynamical aspects that also interpret the mechanism behind the genesis of extreme events as the large deflection of the chaotic trajectory in the system: the decomposition of the phase space in stable and unstable manifolds concerning slow-fast dynamics and using Floquet multipliers. These two different aspects of calculations of the stable and unstable manifolds explicate the large excursion of the chaotic trajectory as extreme events from two different perspectives. We also analyzed the rare occurrences of the extreme events statistically using extreme value theory: the threshold \textit{excess values} follow the generalized Pareto distribution, and the inter-extreme-spike-intervals follow the generalized extreme value distribution.

Chaos in extreme-mass-ratio inspirals is often thought to require unrealistically large secondary spins, making its astrophysical relevance uncertain. However, we find that chaos persists across the astrophysically realistic spin range for a spinning secondary orbiting a Schwarzschild black hole. This nonintegrable dynamics leaves clear signatures in the emitted gravitational waves. Nearby regular and chaotic trajectories can remain similar in the time domain and retain broadly aligned dominant spectral peaks, yet chaotic signals develop a much less discrete frequency-domain structure with dense inter-peak power. Furthermore, we introduce a local spectral-flatness measure and find it to be several hundred times larger for the chaotic signal than for the neighboring regular signals. Finally, a change in the secondary spin by as little as \(1\%\) of its maximal physically allowed value can drive the system from regular to chaotic motion and produce distinctive detector-level waveforms.

Reliable operation is a central motivation for deploying renewable-based microgrids. This paper presents a systematic rapid review that positions reliability as the central organizing principle for microgrid design. Specifically, this review systematically synthesizes recent literature to examine how planning assumptions, optimization formulations, operational flexibility mechanisms, and reliability assessment frameworks jointly shape reliability outcomes. The synthesis shows that reliability in renewable-based microgrids is governed primarily by chronological, time-coupled energy adequacy rather than installed capacity alone, with Dunkelflaute events emerging as a key determinant of adequacy failure. Reliability outcomes are shaped by the joint interaction of resource portfolios, storage operating policies, and state trajectories, network features, and protection feasibility under inverter-dominated operation. The review further demonstrates that reliability indices inherited from conventional power systems are poorly suited for renewable-based microgrids, as they compress performance into single dimensions and obscure temporal, spatial, and service-critical risk concentrations. Across optimization practice, reliability is increasingly embedded through multi-objective and constrained formulations; however, persistent gaps remain in representing correlated renewable scarcity, mission-profile-dependent component reliability, and interruption valuation (e.g., value of lost load and customer damage functions) in a consistent and decision-relevant manner. Overall, this review consolidates planning factors, optimization approaches, reliability evaluation methods, and metric suitability into an integrated roadmap for reliability-centered microgrid planning, and outlines future directions toward state-aware, service-oriented planning and assessment frameworks.

This paper outlines a pathway towards active operation of lowvoltage distribution grids. In these grids, the growing deployment of distributed generation, controllable demand and storage, together with the roll-out of intelligent metering systems, creates new requirements and opportunities for distribution system operators. On the basis of the German and European regulation, and in particular of recent directives enabling grid-oriented interventions and market-based procurement of flexibility, the paper identifies three key pillars for active low-voltage operation: (a) measurement placement and observability, (b) secure and interoperable information and communication architectures and interfaces, and (c) integration of market-based and gridoriented optimisation for controlling connected assets. A structured system overview is developed that specifies main actors and data flows, highlighting central research topics across these pillars. Building on this, a four-phase roadmap is presented, spanning requirements and use-case definition, method development and simulation, laboratory and field validation, and roll-out with system-level feedback, thus providing guidance for distribution system operators and researchers.

Research methods constitute an indispensable tool for scholars engaged in scientific inquiry. Investigating how scholars use research methods throughout their careers can reveal distinct patterns in method adoption, providing valuable insights for novice researchers in selecting appropriate methods. This study employs a comprehensive dataset comprising full-text journal articles and bibliographic records from the Library and Information Science (LIS) domain. Utilizing an automated classification model based on full-text cognitive analysis, the research methods employed by LIS scholars are systematically identified. Topic modeling was then conducted using Top2Vec. Subsequently, author name disambiguation is performed, and academic age is calculated for each scholar. This study focuses on 435 senior scholars with an academic age of more than 14 years and a consistent publication record at five-year intervals, covering a total of 6,116 articles. The corpus covers 16 research method categories and 20 research topics. The findings indicate that bibliometric methods are the most frequently used across career stages, accounting for 19.61% among early-career scholars and 31.81% among senior scholars. Over the course of a scholarly career, the diversity of research methods initially increases and then declines. Furthermore, scholars exhibit a propensity for combining multiple research methods, including both conventional and unconventional pairings. Notably, the research methods most commonly used by researchers change with age and seniority.

Let $C_\bullet$ be a simplicial object in the category $Cat$ of small categories. For a field $k$, taking the Grothendieck groups of isomorphism classes of $kC_n$-modules gives rise to a cochain complex, whose cohomology, which we refer to as representation cohomology, is the object studied in this article. In particular, to any small category $C$, we associate a simplicial object in $Cat$, where for each $n\ge 0$ the objects of the level $n$ category are the simplices of the nerve of $C$. The basic properties of the resulting representation cohomology of these simplicial objects and certain subobjects are then studied in detail. We present some general theoretical computations in favourable cases.

A particular interest on two-dimensional turbulence is the inverse energy cascade from small to large sales, which leads to an energy condensation accompanied by the formation of large-scale vortical structures. Indeed, such a phenomenon is observed in the two-dimensional channel (2DCH) with large Reynolds numbers, where prominent large-scale wavy structures play a central role in the momentum and energy transfer across the inhomogeneous wall-normal direction \citep{Falkovich2018}. Yet, the instability of these wavy structures remains poorly understood, and it is unknown whether they have the capacity to generate turbulence. To address this, we first conduct the direct numerical simulation (DNS) of Navier-Stokes equations for 2DCH, then extract the large-scale wavy structures through the singular value decomposition, and finally perform a Floquet-based secondary instability analysis. Two bulk Reynolds numbers are examined in particular, i.e. $Re = 3000$ and $Re = 200000$, which lie on opposite sides of the transitional regime near $Re \approx 10000$ and cover the previously reported simulation domain. At $Re = 3000$, the large-scale wavy structure is found to be linearly stable, consistent with the laminar-like DNS flow field. However, at $Re = 200000$, a subharmonic torsional mode is identified, which leads to a definite growth rate ($λ_r = 0.18$) for the wavy structures with a half wave-length shift. Temporal reconstruction shows that this unstable mode deforms and splits into multiple wave trains and evolves in the opposite phase. Compared to the TS (Tollmien-Schlichting) wave of laminar flow, the subharmonic mode found here offers a novel understanding for the generation of turbulence in larger Reynolds number two-dimensional channels.

We prove spectral properties for random Landau Schrödinger operators on $L^2(\mathbb{R}^2)$ with bounded, random potentials supported in a square $Λ_L \subset \mathbb{R}^2$ of side length $L>0$, using semiclassical pseudodifferential calculus. The semiclassical parameter $h$ is the inverse of the magnetic field strength $B > 0$. By means of the Grushin method, we are led to the analysis of an effective Hamiltonian on $L^2 (\mathbb{R})$, the principal term of which is a sum of certain compact, self-adjoint pseudodifferential operators. By analyzing these operators, we prove semiclassical Wegner and Minami estimates for the random Landau Schrodinger operator in energy intervals in the spectral bands around each Landau level.

Lateral predictive coding (LPC) is a simple theoretical framework to appreciate feature detection in biological neural circuits. Recent theoretical work [Huang et al., Phys.Rev.E 112, 034304 (2025)] has successfully constructed optimal LPC networks capable of extracting non-Gaussian hidden input features by imposing the tradeoff between energetic cost and information robustness, but the resulting dynamical systems of recurrent interactions can be very slow in responding to external inputs. We investigate response-time reduction in the present paper. We find that the characteristic response time of the LPC system can be minimized to closely approaching the lower-bound value without compromising the mean predictive error (energetic cost) and the information robustness of signal transmission. We further demonstrate that optimal LPC networks taking a modular structural organization with extensively reduced number of lateral interactions are equally excellent as all-to-all completely connected networks, in terms of feature detection performance, response time, energetic cost and information robustness.

Physical layer (PHY) steganography conceals secrets by making subtle modifications to transmitted radio waveforms, which can be applied to establish covert communication systems. Given the widespread deployment of Wi-Fi infrastructures, hiding secrets within Wi-Fi transmissions exhibits significant covertness and has attracted increasing research attention. Recent advances in Wi-Fi steganography have focused on embedding secrets within channel state information (CSI) by applying artificial finite impulse response (FIR) filters to outgoing signals. These methods can emulate natural wireless propagation effects, thereby evading detection by eavesdroppers. However, existing CSI-based approaches suffer from two critical limitations: vulnerability to environmental variations and limited steganographic capacity. This work presents a Wi-Fi steganography system that mitigates these constraints. Specifically, we introduce a CSI division mechanism to decouple artificial CSI components from natural wireless channel responses. In essence, secrets are embedded within the quotient of two consecutive CSI measurements. Furthermore, we propose an encoder-decoder neural network framework that automatically learns optimal strategies for FIR filter generation and secret recovery, substantially enhancing steganographic capacity. We implemented a prototype using commercial off-the-shelf hardware, including a software-defined radio (SDR) transmitter and two receiver platforms: ANTSDR and ESP32. Experimental evaluations demonstrate that the system achieves robust performance under dynamic environmental conditions while significantly improving steganographic capacity.

Let $F^{+}$ be a mock modular form associated to a normalized newform $g$. K. Bringmann et. al. obtained a $p$-adic modular form starting from $F^{+}$ by adding a suitable linear combination of Eichler integrals of $g(q)$ and $g(q^{p})$. We denote the coefficients of the Eichler integrals of $g(q)$ and $g(q^{p})$ by $γ_{g}$ and $δ_{g}$. These constants are important in the $p$-adic theory of mock modular forms, but relatively little is known about them at present. For instance, K. Bringmann et. al. raised the question of whether $δ_{g}$ vanishes when $g$ has CM by an imaginary quadratic field in which $p$ is inert. In previous work, the non-vanishing of $δ_{g}$ has been proved mainly when $g$ is associated to an elliptic curve. In higher weight, only one example was known for which $δ_{g}\neq 0$. In this paper, we show that $δ_{g}\neq 0$ under mild assumptions when all the Fourier coefficients of $g \in S_{k}(Γ_{0}(N), χ)$ are real, without assuming that $g$ has CM. In particular, this provides a class of higher-weight examples for which $δ_{g}\neq 0$.

Photonic reservoir computing is a machine learning paradigm in which a recurrent neural network remains fixed while only the output weights are trained. This makes it a well-suited approach for high-speed signal equalisation in optical communication systems, offering a trainable, low-power, and low-complexity solution. However, achieving strong performance typically requires relatively large network sizes, as learning is confined to the output layer. To address this, we investigate the role of trainable input mappings alongside conventional output weight optimisation. Across a range of short- and mid-reach IM/DD transmission scenarios, reaching up to 200 km for a 28 GBd NRZ signal, improvements of over two orders of magnitude in BER are achieved. This enables halving the network size while maintaining comparable performance. Furthermore, we show that this approach effectively extends the memory of the reservoir, resulting in over three orders of magnitude improvement in memory-intensive tasks. These results also show that starting at 16 nodes a performance of at least one to two magnitudes better than both a complexity matched FFE and a Volterra filter of second order are reached.

The Belle~II electromagnetic calorimeter consists of 8376 CsI(Tl) scintillation crystals and is not only used for measuring electromagnetic particles but also for identifying and determining the position of hadrons, particularly neutral\textbf{} hadrons. Recent data-taking periods have presented challenges for the current clustering method: Firstly, the record-breaking luminosities achieved by the SuperKEKB accelerator have increased background rates, leading to a higher number of crystals with energy depositions, and an overall increase in the total energy measured in the calorimeter. This resulted in poorer photon energy resolution and the reconstruction of more fake photon clusters. Secondly, challenges arise from the nature of hadronic interactions. In contrast to $γ$ and $e^{\pm}$, hadrons interacting in the calorimeter result in irregular, sometimes even disconnected energy depositions. These clusters can be misinterpreted as photon clusters, thereby reducing the position resolution of neutral hadrons or causing a complete misidentification of the hadron. Graph neural networks offer a promising solution to both challenges. By representing only crystals with an energy measurement as nodes, graphs capture the sparsity of the input. Using message-passing layers that learn the graph edges also helps to address the asymmetric sensor layout of Belle~II's ECL. In these proceedings, we will present a novel approach to identify the challenges in the detector simulation. Using this information, we train a Graph Neural Network to identify and remove unwanted depositions abefore clustering.

Transient instability in nonlinear stochastic dynamical systems is a fundamental limitation in safety-critical aerospace applications, particularly during powered descent and landing where failure is driven by finite-time excursions rather than asymptotic divergence. Classical notions of mean-square or asymptotic stability are therefore insufficient for certification and design. This paper develops a logarithmic-norm-based framework for finite-time transient stability analysis of nonlinear Ito stochastic differential equations. The approach extends matrix measures to nonlinear mappings in a Lipschitz sense, enabling efficient characterization of instantaneous perturbation growth without local linearization. Using Ito calculus, bounds on the mean and variance of transient growth are derived, providing conditions for non-positive finite-time mean growth and probabilistic bounds on instability events. The analysis highlights a key distinction between mean and sample-path behavior, showing that stability in expectation does not guarantee pathwise finite-time safety, and that almost-sure transient stability cannot generally be ensured under stochastic diffusion. The framework is extended to data-constrained stochastic dynamics in navigation and estimation, revealing a trade-off between estimation consistency and transient robustness due to continuous data injection. Demonstrations with flight-like lunar lander telemetry show that similar mean trajectories can exhibit significantly different transient stability behaviour, and that mission failure correlates with accumulation of transient instability over short critical intervals. These results motivate probabilistic finite-time stability metrics for safety-critical autonomous systems.

We present Atomic Cluster Expansion (ACE) and MACE models trained on a new dataset of Density Functional Theory (DFT) calculations, constructed for the task of studying the mobility of dislocations in Indium Phosphide (InP). The models are validated in a suite of tests against RSCAN DFT, and compared with previously published potentials from literature. Our new models act as much better surrogates for DFT than the literature models: errors on partial dislocation formation energies are at most 4% for both ACE and MACE, compared with 18% for the MACE-MPA foundation model and 42-50% for earlier bespoke potentials. The bespoke MACE model achieves this accuracy while being around five times faster to evaluate than the MP0 and MPA foundation models.

We study the random loop model with crosses and bars on sparse random graphs. Our main objective is to prove the existence of macroscopic loops, in the sense that a loop visits a positive proportion of the vertices. We develop a deterministic drift method on arbitrary finite graphs based on three ingredients: a local split--merge--rewire analysis for the loop number, an exact differential identity for the partition function, and a slice estimate reducing the relevant same-loop insertion volume to induced edge counts of small vertex sets. This yields a general criterion in terms of a small-set sparsity condition on the underlying graph. We then verify this condition for random regular graphs, sparse Erdős--Rényi graphs, and simple bounded-degree configuration models, obtaining averaged lower bounds on the probability of a macroscopic loop whenever the edge density exceeds an explicit threshold depending on the loop weight \(θ\) and the cross parameter \(u\). For integer values of \(θ\), a trace representation of the partition function implies log-convexity, which upgrades the averaged bounds to pointwise-in-time results away from the threshold time.

Global-pressure formulations recast multiphase Darcy flow in terms of a single pressure driving the total flux. Their exact equivalence to phase-pressure formulations, however, holds only when the constitutive data satisfy the compatibility conditions required for a total-differential structure and its generalized nonisothermal extension. In this work, we derive the corresponding exactness criterion for temperature-dependent mobilities and capillary pressures. We show that equivalence is governed by the closure of a mobility-weighted capillary one-form on the augmented state space of saturation and temperature. This yields both the classical compatibility conditions within the saturation sector and a distinct mixed saturation--temperature condition that arises only in the nonisothermal setting. We then incorporate this structure into a reduced matrix--fracture model with heat transport, matrix--fracture thermal exchange, and evolving fracture aperture. Numerical benchmarks recover the three regimes predicted by the theory: globally exact, exact on each fixed-temperature slice but not on the full saturation--temperature space, and fully nonexact. In fractured systems, thermal forcing alone can drive transitions between these regimes, while aperture evolution changes the path through state space. When exactness fails, a least-squares projection performed independently on each fixed-temperature slice provides a conservative scalar-pressure surrogate together with quantitative defect diagnostics. The resulting framework unifies nonisothermal exactness theory, fractured-flow dynamics, and conservative reduced closure within a single global-pressure formulation.

Controlling complex dynamical systems to satisfy sophisticated specifications remains a significant challenge in modern engineering. A promising approach to this problem is the approximate simulation-based hierarchical control (ASHC) technique. In this method, a simplified representation of the complex system, called the abstract system, is first designed and controlled. An interface function is then designed to translate the control law into the input of the complex system, thereby achieving approximate control synthesis. However, most existing results in ASHC are only for linear systems. This paper proposes a constructive method for solving the ASHC problem for nonlinear systems. To this end, we propose invariance equation-based methods to achieve the two classical requirements of the ASHC technique, namely the bounded output discrepancy and the $m$-relation. We then study the solvability conditions of the problem and summarise the overall design procedures. We illustrate the results with a practical example, providing step-by-step solutions to the ASHC problem of a DC-to-DC Ćuk converter.

We propose a formal approach for specifying and implementing decentralised coordination in distributed systems, with a focus on smart contracts. Our model captures dynamic roles, data-driven transitions, and external coordination interfaces, enabling high-level reasoning about decentralised workflows. We implement a toolchain that supports formal model validation, code generation for Solidity (our framework is extendable to other smart contract languages), and automated test synthesis. Although our implementation targets blockchain platforms, the methodology is platform-agnostic and may generalise to other service-oriented and distributed architectures. We demonstrate the expressiveness and practicality of the approach by modelling and realising some coordination patterns in smart contracts.

The inexact adaptive stepsizes for the conjugate gradient method and the quasi-Newton method are very rare. The exact stepsizes in the gradient method, the conjugate gradient method and the quasi-Newton method for strictly convex quadratic optimization have a unified framework, while the unified framework for inexact adaptive stepsizes in the gradient method, the conjugate gradient method and the quasi-Newton method for strictly convex quadratic optimization still remains unknown. Based on the above observations, we propose a unified framework for inexact adaptive stepsizes in the gradient method, the conjugate gradient method and the quasi-Newton method for strictly convex quadratic optimization, which is called approximately optimal stepsize. The global convergence and the convergence rate of the gradient method with the approximately optimal stepsize are established by exploring the relation between the approximately optimal stepsize and the famous Barzilai-Borwein (BB) stepsizes. Some numerical results are presented, which confirm the remarkable numerical advantage of the gradient method, the conjugate gradient method and the quasi-Newton method with the unified framework for inexact adaptive stepsizes. Some open problems about the gradient method, the conjugate gradient method and the quasi-Newton method with approximately optimal stepsize are raised.

Intensity-modulation direct-detection links must support increasing baudrates and transmission distances while operating under stringent power and cost constraints. However, as data rates and reaches increase, chromatic dispersion induces stronger inter-symbol interference and, after direct detection, frequency-selective fading, thus requiring increasingly powerful equalization. In conventional receivers, this translates into digital equalization whose complexity scales unfavorably with data rate. Photonic-domain equalization offers a hardware-based alternative that operates naturally at line rate and mitigates frequency fading. However, prior demonstrations were not readily adaptable for different rate and/or reach operation. In this paper, we experimentally demonstrate all-optical equalization across 10-46 Gbaud and 10-250 km SSMF in the C-band enabled solely through retraining of the readout layer, achieving up to four orders of magnitude BER improvement over standard DSP equalization. The demonstrator comprises a 16-node spatially multiplexed reservoir, programmable on-chip readout, and co-packaged receiver front-end. To our knowledge, this is the first co-packaged photonic reservoir receiver and the first demonstration of simultaneous baudrate- and reach-flexible equalization using a fixed-topology integrated photonic circuit.

Speculative decoding (SD) is a widely used approach for accelerating decode-heavy LLM inference workloads. While online inference workloads are highly dynamic, existing SD systems are rigid and take a coarse-grained approach to SD management. They typically set the speculative token length for an entire batch and serialize the execution of the draft and verification phases. Consequently, these systems fall short at adapting to volatile online inference traffic. Under low load, they exhibit prolonged latency because the draft phase blocks the verification phase for the entire batch, leaving GPU computing resources underutilized. Conversely, under high load, they waste computation on rejected tokens during the verification phase, overloading GPU resources. We introduce FASER, a novel system that features fine-grained SD phase management. First, FASER minimizes computational waste by dynamically adjusting the speculative length for each request within a continuous batch and by performing early pruning of rejected tokens inside the verification phase. Second, FASER breaks the verification phase into frontiers, or chunks, to overlap them with the draft phase. This overlap is achieved via fine-grained spatial multiplexing with minimal resource interference. Our FASER prototype in vLLM improves throughput by up to 53% and reduces latency by up to 1.92$\times$ compared to state-of-the-art systems.

Ocean wave models are critical for weather and climate forecasting, and accurate in-situ wave observations are essential for validating and improving these models. Open-source, community-driven buoys have democratized wave observations via telemetry in recent years, but these systems transmit only limited amounts of data. Full high-frequency time series, required to study detailed wave physics, can still in most cases only be collected in situ using data loggers. Yet open-source, low-cost logger solutions remain scarce compared to their telemetry-enabled counterparts. Here we present the Openlogartemis Wave Logger (OWL-v2026), an open-source, low-cost, easy-to-build, high-performance logger for wave data measurements. The OWL-v2026 is built from off-the-shelf components from the maker community, requiring only through-hole soldering for assembly, and totals approximately 220USD per unit. Custom firmware enables high-frequency, low-jitter logging of six-axis inertial measurement unit (IMU) data at 208 or 416Hz, and GNSS position and Doppler velocity at 10Hz, with Pulse Per Second (PPS) synchronization for accurate absolute UTC timestamping. We have successfully validated continuous logging over more than 10 days at 208Hz, a power consumption of approximately 80mA (approximately 20 days of autonomy with three D-cell lithium batteries), and absolute UTC timestamp accuracy typically better than 10ms. Though the OWL-v2026 is a purely technical contribution, it has the potential to substantially expand the availability and affordability of high-frequency in-situ wave time series, similar to how the OpenMetBuoy (OMB) (Rabault 2022) expanded the availability of telemetry-enabled wave observations and helped spark new developments in low-cost open-source buoys.