The small gap room temperature semiconductor a-RuCl3 which is known to undergo a Mott-Hubbard transition at low temperatures, is one of the most promising candidates for realisation of an exotic matter form, the quantum spin liquid state, which may have applications in quantum computing. Although being extensively investigated by neutron scattering techniques, electronic study of this system in form of van der Waals heterostructures has been limited to mainly graphene proximity. Here we report a systematic study of planar and tunnelling electronic properties of a -RuCl3 films, where we observe an n-type semiconducting property of a -RuCl3 films at room temperature, with a Mott insulator nature onset below 120K. In constant some of the previous studies, we focus on films of three-layer thickness and below and we find inelastic scattering features, below the Neel temperature of 7-14.5 K, some of which we attribute to single magnon modes. We believe our study electrically confirms preserved low temperature signatures of the bulk zigzag antiferromagnetic order and its single magnon modes within the previously observed continuum in atomically thin film limit. The experimental progress could be a step for future electronic characterisation of quantum spin liquid state in the vicinity of the zigzag antiferromagnetic order as well as the Majorona excitations in a-RuCl3 in tunnelling transistors.

We consider a generalization of the variance-gamma (generalized asymmetric Laplace) distribution, defined as a normal mean - variance mixture with a gamma mixing distribution. While this model is typically studied in the univariate setting, we assume that the gamma mixing variable is observed alongside the primary variable, resulting in a bivariate framework. In this setting, maximum likelihood estimation becomes significantly simpler than in the standard univariate case, reducing to a form of classical linear regression. We derive explicit expressions for the resulting estimators. For certain parameter configurations, the estimators exhibit nonstandard convergence rates, exceeding the usual square-root rate. Finally, we illustrate the applicability of this model in financial contexts by analyzing stock index returns and associated volatility for several major indices.

In 2013 Cooper and Dutle invented a dueling scenario where Alice and Bob shoot at each other until one is hit. Each shot is successful with some fixed probability $p$, $0 < p < 1$. The shooting order is given by a greedy algorithm, where at each step a shot is assigned to the player whose current probability of success is smaller. Cooper and Dutle observed that as $p \rightarrow 0$, the resulting sequence of shots (by Alice or Bob) converges to the infinite Thue-Morse sequence t, but left the speed of convergence as an open problem. In this note we determine the speed of this convergence.

Algorithmic meta-theorems explain the tractability of large classes of computational problems by linking logical expressibility with structural graph properties. While extensions of first-order logic such as FO+dp admit efficient model checking on graph classes excluding a fixed topological minor, comparable results for richer fragments of CMSO were previously unknown. We further develop the framework of Sau, Stamoulis, and Thilikos [SODA 2025] for fragmenting CMSO via annotated graph parameters, which restrict set quantification to vertex sets satisfying bounded structural conditions. Following this approach, we identify a fragment of CMSO, namely the one defined by allowing quantification only over sets having what we call low monodimensionality, that generalizes several previously-known logics and we show that model checking for this fragment, enhanced with the disjoint-paths predicate, is fixed-parameter tractable on topological-minor-free graph classes. Such classes essentially delimit the tractability for this logic on subgraph-closed classes. As a consequence, our results lift several known algorithmic meta-theorems beyond first-order logic to the topological-minor-free setting.

Context: Leadership has been extensively studied in management and agile software development; however, prior research predominantly focuses on formal roles and predefined leadership models, offering limited insight into how leadership is experienced and demonstrated by software practitioners in everyday practice. Objective: Our goal is to identify and categorize leadership practices as perceived and reported by software development practitioners based on their professional experiences. Method: We conducted a content analysis of 116 practitioner-authored articles published on the Dev.to online community. Articles were systematically collected, screened, and coded, resulting in the extraction, correlation analysis and categorization of leadership practices grounded in practitioners narratives. Results: We identified 103 practices for software project leaders, distinguished between recommended and discouraged ones. These practices were organized into five categories: People Management & Development, Processes & Execution, Professional & Personal Growth, Communication & Articulation and Strategic Vision. The most recurrent recommended practices include Cultivating & Practicing Interpersonal Skills, Managing & Delegating Team Work, and Practicing & Developing Managerial Skills, whereas Micromanagement, Counterproductive Work Patterns, and Counterproductive Communication Styles emerged as the most frequent discouraged practices. We organized all practices into a conceptual map. Conclusion: The findings indicate that software leadership is mainly associated with managerial and interpersonal practices rather than technical expertise. The resulting conceptual map summarizes these practices and can serve as a reference for understanding leadership in software development contexts.

An interval translation map (ITM) is a piece-wise translation $T \colon I \to I$ defined on a finite partition $I_1, \ldots, I_r$ of an interval $I$ into $r \ge 2$ subintervals. In contrast to classical interval exchange transformations (IETs), we do not require that the images of these subintervals are disjoint; in particular, ITMs are not assumed to be bijective. Thus, ITMs provide a natural non-invertible generalisation of IETs. In this paper, we formulate an appropriate notion of stability for general interval translation mappings and prove a characterisation of stability in terms of two dynamically natural properties called the Absence of Critical Connections and Matching. This result can be viewed as the foundational step towards the stability theory of general ITMs.

Solutions to hyperbolic conservation laws can be approximated in many different ways: by vanishing viscosity, relaxations, discrete or semi-discrete numerical schemes, approximation with a nonlocal flux, etc$\ldots$ For some of these methods, general ${\bf L}^1$ convergence results are available. Aim of this paper is to understand the local behavior of these approximations, in a neighborhood of point where the hyperbolic solution has a singularity. Specifically: a point along a shock, or where two shocks interact, or where a new shock is formed. Given a sequence of $ε$-approximate solutions, a general expectation is that, by a suitable local rescaling of coordinates, as $ε\to 0$ a well defined limit is obtained. This corresponds to a specific ``eternal solution" (globally defined both in space and in time) to the approximating equation. Precise results this direction are here given, in the case of vanishing viscosity.

Recently, we reported the electrorheoimaging (ERI) technique (Bahraminasr et al, 2026), and found that frequency-dependent electric field of an oil-in-oil emulsion yields two distinct regimes: a high-frequency dipolar, electrorheological (ER) regime and a low-frequency electrohydrodynamic (EHD) regime. In this work, we identify a phenomenological model to fit the results in the ER regime to a classic yield-stress fluid, and find collapse onto a master curve upon rescaling, consistent with a yield stress that grows approximately as $E^2$. Macroscopic small-amplitude oscillatory shear (SAOS) rheology is compared with passive microrheology employing differential dynamic microscopy (DDM), with the close agreement implying scale independence of the ER behaviour, and indicating that, unlike steady shear, SAOS measurements do not restructure these samples and probe underlying material properties. Finally, under the presence of both steady shear and electric fields in the EHD regime, the emulsion forms banded structures composed of alternating droplet-rich and droplet-depleted regions. We explore recurrence and divergence in the location of these bands: they emerge within seconds of field application and decay rapidly after the field is switched off. Using the Jensen--Shannon divergence between radial intensity profiles, we show that the driven structure loses memory on timescales of order $1~s$ commensurate with the timescale of the EHD convection roll. For much longer field-off intervals successive banding events become statistically independent.

Iran conducted two nationwide Internet shutdowns in January and March 2026, the latter ongoing at the time of writing and the longest documented Iranian disruption. Using a three-plane methodology combining passive Censys scan data, active TCP reachability probing from five vantage points, and BGP analysis across 33 RIPE RIS snapshots from 2019 to 2026, we show that the 2022 and 2026 shutdowns are enforced via forwarding-plane null-routing at a centralized border while BGP announcements remain stable, and that Iran shifted from partial BGP withdrawal in 2019 to pure null-routing by 2022. This control- and forwarding-plane decoupling prevents BGP-based outage monitors from detecting shutdowns. Active probing of 4,571 BGP-visible Iranian prefixes shows that 96.5 to 97.4% are null-routed across all vantage points, indicating a centrally coordinated mechanism. Passive scan analysis reveals a 3.7 times increase in visible hosts between shutdown events due to measurement artifacts rather than recovery, along with two structural exemptions: academic networks rise from 1.4 to 66.6% of visible hosts during partial recovery, and ArvanCloud CDN retains 99.7% visibility while other major operators drop by at least 77%.

An interval translation map (ITM) is a map $T \colon I \to I$ defined as a piecewise translation on a finite partition of an interval $I$ into $r \ge 2$ subintervals. Unlike classical interval exchange transformations (IETs), the images of these subintervals are allowed to overlap, making ITMs a natural generalisation of IETs. An ITM $T$ is said to be \textit{of finite type} if its attractor $\bigcap_{n\ge 0} T^n(I)$ is a finite union of intervals; in this case, restricted to this invariant set, $T$ is bijective and hence behaves like an IET. Otherwise, $T$ is of infinite type. In this paper, for every $r \ge 2$, we prove that the set of finite type ITMs contains an open and dense subset in the space of all possible ITMs on $r$ subintervals. This confirms a topological version of a long-standing conjecture due to Boshernitzan and Kornfeld.

Phishing webpages are continuously polluting the Web. Plenty of countermeasures have been proposed and the most advanced techniques leverage machine-learning methods that infer whether a webpage is benign or not by inspecting its visual representation. Yet, despite the demonstrated effectiveness of such detection methods, this class of defenses is, by design, susceptible to a kind of subtle-but-cheap timing-based attacks which -- worryingly, and perhaps surprisingly -- have never been investigated so far. Such an oversight questions the overall reliability of these defenses in the wild. First, we show that timing-based evasion attacks have not been accounted for by prior work on visual phishing websites detectors. Then, we elucidate the intrinsic vulnerability of these detectors: they can be bypassed by delaying the rendering of webpage elements. Practically, these detectors must compute the visual similarity between a target webpage and a known legitimate one. This requires taking a "snapshot" of the target webpage before the similarity computation. Attackers can deliberately delay the rendering of key elements, such as the logo, so that these elements appear fully only after the snapshot has been taken. This simple tactic misleads the visual-similarity module, leading the system to incorrectly classify the phishing page as benign. We empirically show that state-of-the-art detectors can be completely defeated (detection rate dropping from 100% to 0%) by employing easy-to-apply problem-space techniques such as curtain effects. We also carry out a user study, evaluating the effectiveness of these attacks against real humans, and find that end users are unable to reliably identify our "perturbations" (p<.05). Finally, we propose mitigations, including a browser-extension that, without making any call to remote services, warns users that they may have landed on a phishing webpage.

Ratios of production fractions of $b$ hadrons are a dominant source of uncertainty in many LHC analyses, in particular in measurements with $B_s$ mesons. The currently used value for $f_s/f_d$ is based on a combination of hadronic and semi-inclusive semi-leptonic decays, and relies in part on assumptions about the underlying decays that are hard to quantify. We propose an independent alternative method to obtain this quantity by measuring ratios of the exclusive semi-leptonic decays $\bar B_{(s)} \to D_{(s)}^{(*)} \ell \barν$. This method benefits from significant cancellations of both experimental and theoretical uncertainties, as well as robustness against potential contamination from heavy physics beyond the Standard Model. As a proof of principle, we show that current measurements constrain $f_s/f_d$ with an uncertainty of $7\%$, dominated by present experimental uncertainties. This method can also be applied to other ratios of production fractions involving heavier $b$ hadrons, such as $B_c$ or $Λ_b$.

Open-source software (OSS) dependencies introduce systemic risks that are difficult to manage at scale. Existing Software Composition Analysis (SCA) and reachability tools generate severe alert fatigue by treating risk as an intrinsic component property, ignoring semantic context and forcing enterprises into rigid compliance frameworks. We present Deptex, an organization-first, graph-based platform treating supply chain risk as emergent. Deptex introduces Execution Path Dominance (EPD), fusing Code Property Graph (CPG) slicing with Large Language Model (LLM) semantic verification to calculate a vulnerability's true operational blast radius. To handle bespoke compliance, Deptex abstracts governance into a programmable ``As Code'' engine, enabling security teams to natively enforce dynamic pull request policies, custom asset tiers, and external API integrations. By shifting from reactive scanning to context-aware governance, Deptex enables proactive, efficient, and aligned supply chain risk management.

We analyze our new 144 ks deep Chandra observation of the sloshing cold front cluster RXJ2014.8-2430. Previous observations of RXJ2014.8-2430 with XMM-Newton shows evidence of a large scale, sloshing cold front around 800 kpc away from the cluster core. Previous shallow Chandra data also shows evidence of two younger cold fronts closer to the core. Our new deeper Chandra data allow us to analyze the fine, small scale structure of these three cold fronts. Using both beta model subtraction and Gaussian Gradient Magnitude filtering, we confirm the locations of the three cold fronts, as well as discover a large concave structure southeast of the cluster core near the outermost cold front, which could be a large Kelvin-Helmholtz instability or a gas cavity from AGN activity. Analyzing the three cold fronts, we measure the widths of the cold fronts and find them to be consistent with or lower than the Coulomb mean free paths within error, signifying that diffusion is suppressed across the cold fronts. If the concave feature is the inner rim of a cavity, we find that it has a radius in the range 200-330kpc, and would have $PV$ values in the range of $5.7 \times 10^{60}$ - $2.7 \times 10^{61}$ erg. These values would make it consistent with the some of the most powerful bubbles observed.

Over the past decade, researchers have focused increasing levels of attention on the use of survey and non-survey data to inform decision-making by multiple stakeholders. Work with such data generally requires extensive exploration before a statistics practitioner focuses on specific steps in model building and inference. For many of the resulting initial exploratory analyses, crucial issues center on the extent to which empirical results may vary over geography and subpopulations. Such information is usually presented in tabular form, which can be difficult for stakeholders and decision makers to understand and to utilize. To address these issues, this paper uses data from the U.S. Bureau of Labor Statistics to illustrate a suite of tools known as linked micromaps. These applications show how linked micromaps can help stakeholders better understand and view descriptive statistics for populations and subpopulations, explore multivariate relationships and ordinal structure, and discover patterns of heterogeneity across time and space. In addition, this paper comments briefly on the prospective use of linked micromaps in model-building and analysis of multiple components of uncertainty.

An \textit{interval translation map} (ITM) is a piece-wise translation $T \colon I \to I$ defined on a finite partition $I_1, \ldots, I_r$ of an interval $I$ into $r \ge 2$ subintervals. In contrast to classical interval exchange transformations (IETs), we do not require that the images of these subintervals are disjoint; in particular, ITMs are not assumed to be bijective. Thus, ITMs provide a natural non-invertible generalisation of IETs. In this paper, we prove a transversality theorem for a family of dynamically defined vector subspaces that encode the dynamics of a given ITM. As a consequence, we establish a perturbation result that gives a precise control of the first return dynamics to subintervals in $I$, while preserving the remaining global dynamics of the system. Beyond their independent interest, these results are a key technical ingredient in the proof of the Characterisation of Stability of ITMs and in the establishment of the topological version of the Boshernitzan--Kornfeld Conjecture.

With recent advances in exoplanet observational techniques enabling the discovery of increasingly smaller planets, a crucial question emerges in the search for habitable planets: how small can a planet be and still maintain an atmosphere? We present results from the Smaller Than Earth Habitability Model (STEHM) which examines how small a planet can be and still maintain a long-term (multi-gigayear) atmosphere for planets from 1.0$R_\oplus$ down to 0.5$R_\oplus$. The model is based on a stagnant lid planet orbiting within the habitable zone of a sun-like star. Our model demonstrates that planets $\geq$0.8$R_\oplus$ can maintain their atmospheres under our Earth-like default conditions for a solar analog star, while smaller planets lose their atmospheres. Variations from the default Earth-like values cause mostly minor variations to the planet size boundary results, with some changes allowing $\geq$0.7$R_\oplus$ planets to maintain their atmosphere. Initial carbon inventory emerges as the most influential parameter for atmospheric retention, though orders of magnitude difference to Earth values are required to make a significant difference to longevity of atmospheric retention. Planets with substantial initial carbon content, large amounts of heat producing elements, cool initial mantle temperatures and low core radius fractions show the best atmospheric retention capabilities. Our results indicate that atmospheric retention on small planets depends strongly on their formation conditions and early evolution, providing important constraints for future observations of rocky exoplanets and their potential habitability.

In microgravity, a partially filled cylindrical tank is generally bounded by a curved equilibrium meniscus rather than by an almost flat free surface. This modifies both the bulk liquid inertia and the capillary restoring force, so flat-interface sloshing frequencies can become inaccurate even in the linear regime. This effect matters once the Bond number is of order unity or smaller, precisely the regime relevant to capillarity-dominated propellant management. This study revisits the classical cylindrical curved-meniscus eigenvalue problem for capillary-gravity sloshing about axisymmetric Young-Laplace equilibria. A semi-analytical boundary-operator formulation is derived that preserves the cylindrical Bessel structure and recovers the flat-interface limit exactly. Its main advantage lies in treating the bulk Dirichlet-Neumann operator and the linearised curvature operator as distinct components, thereby making the physical origin of curvature-induced frequency shifts explicit. The results show that equilibrium curvature couples radial modes and alters the low-order spectrum once $Bo \lesssim 1$. Concave menisci lower the fundamental frequency, whereas convex menisci raise it while often lowering higher branches. The asymmetry between wetting and non-wetting configurations is found to be predominantly kinetic, being carried mainly by the Dirichlet-Neumann operator rather than by the capillary term. Curved menisci should therefore be treated as part of the leading-order model of cylindrical microgravity sloshing, not as a secondary correction, if reduced-order predictions are to capture the relevant dynamical scales for spacecraft applications.

Wildfires are among the most severe disturbances affecting forest ecosystems, with over 50,000 hectares burned in Patagonia, Argentina, during 2025 alone. This study implements a Reaction-Diffusion-Convection (RDC) model to simulate wildfire spread in the Steffen and Martin Lakes area, a region severely impacted by fires. By integrating high-resolution maps of slope, wind velocity, and vegetation, we conducted three computational experiments of increasing complexity to simulate fire propagation across heterogeneous landscapes. We employed a Genetic Algorithm (GA) to recover reference model parameters by maximizing the spatial overlap between simulated and reference burned areas. Subsequently, parameter estimates were refined using XGBoost to improve accuracy. Results demonstrate that the GA accurately recovers reference parameters across all scenarios, while the XGBoost fine-tuning significantly enhances accuracy in simpler cases. This integrated framework offers a systematic approach for estimating difficult-to-measure wildfire parameters, demonstrating the potential of hybrid computational methods for wildfire modeling and forest management.

We demonstrate a new 11.424 GHz SLED-type RF pulse compressor for powering high-gradient X-band photoinjectors with pulse lengths around 20 ns. RF pulse compression provides a practical path to higher peak power at the cost of pulse length for various applications such as RF deflectors for electron beam diagnostics on free electron lasers. Our new compact pulse compressor uses spherical cavities supporting axially symmetric TE modes which have minimal electric fields on the cavity surfaces, intended to improve high-power robustness as compared to existing compact spherical SLEDs which use a TE dipole mode. We present the design of this pulse compressor composed of two spherical cavities and a waveguide hybrid. The two cavities and hybrid have TE01 circular waveguide ports. This pulse compressor was built and high power tested at SLAC. These tests demonstrated a peak power of 317 MW by compressing 52 MW, 1 μs pulses generated by a SLAC XL-4 klystron. The full-width at half-maximum of this compressed pulse was 27 ns. We conjecture that this development demonstrates a viable route to reaching the high-gradient, short pulse regime for accelerating structures and RF photoinjectors.

Edge localized mode (ELM) pacing via vertical plasma oscillations or jogging has been successfully demonstrated on DIII-D. Rapid vertical movement of the plasma toward the X-point has been shown to effectively trigger ELMs. By vertically oscillating the plasma at a rate of 20 Hz, the ELM frequency increased from $\sim$5~Hz, the natural ELM frequency in similar DIII-D discharges, to 20~Hz. Downward jogs have been observed to trigger multiple ELMs in one cycle. ELMs triggered at higher than natural frequencies lead to smaller decreases in stored energy, from ~10\% to as little as below 1\%. As a consequence, the peak heat flux to the divertor has been observed to be reduced by a factor of $\sim$2. In addition, a reduction in the carbon impurity concentration has been observed. During downward jogs in the lower single null (LSN) configuration, the X-point movement is slower and smaller than the top of the plasma. As a result, a reduction in the plasma cross section and hence volume has been observed. To understand the mechanism of ELM triggering by jogging, a toy model of the edge toroidal current has been built and tested with DIII-D experiment data. The experimental data and model suggest that when the plasma moves down towards the X-point, a net positive toroidal current is locally induced in the edge region. ELITE stability analysis suggests that this current pushes the plasma state across the peeling side of the peeling-ballooning stability boundary into the unstable region triggering ELMs.

This work explores the geometry of stable wild Vafa-Witten bundles over the complex projective plane $\mathbb{P}^2$. Specifically, we consider stable rank-two pairs $(E,Φ)$, with $E\to\mathbb{P}^2$ a rank-two holomorphic vector bundle and $Φ\in H^0(\mathbb{P}^2,\mathrm{End}_0E\otimes\mathcal{O}(d))$ for $d\geq0$, and compute the dimension of the moduli space of such stable pairs. Moreover, we classify stable pairs $(E,Φ)$ when the underlying rank-two bundle $E$ splits or is the push-forward of a line bundle on $\mathbb{P}^1\times\mathbb{P}^1$. Lastly, we examine the fixed point locus of the natural $\mathbb{C}^*$-action on the moduli space.

During the propagation of interplanetary coronal mass ejections (ICMEs), evolution of the ICME-driven shock along with interactions with other solar wind structures, planetary bodies, and general changes to their morphology can alter particle acceleration efficiency and transport effects at their associated shocks. While the underlying mechanisms for these processes have been studied, the connection between the radial evolution of the ICME-driven shock during propagation and resulting gradual Solar Energetic Particle (SEP)and Energetic Storm Particle (ESP) intensities, composition, and acceleration has yet to be fully understood. The current distributed array of spacecraft at varying heliocentric distances provides a welcome opportunity to statistically analyze the radial dependency of particle populations and acceleration mechanisms present at ICME-driven shocks. We compile a database of 39 multipoint ICME events from 2016-2023, which are observed in situ by at least two of the following spacecraft: Parker Solar Probe (PSP), Solar Orbiter, ACE, Wind, and STEREO-A. Using the magnetic field, plasma, and ion compositional data provided by these spacecraft, we derive both local shock and ESP spectral shape parameters. By comparing the changes in these parameters at different stages of ICME propagation, we analyze the connection between the evolution of the local shock conditions and the spectral shape. We find evidence to suggest a consistent increase in shock acceleration efficiency with heliocentric distance while the parent ICME is within 0.7 au, followed by a reduction in shock efficiency at further distances.

Modulated ion acoustic waves (IAWs) -- including triggered ion acoustic waves (TIAWs) and frequency-dispersed ion acoustic waves (FDIAWs) -- are increasingly recognized as efficient drivers of electron heating in the solar wind through nonlinear wave-particle interactions. Identification of these events in the Parker Solar Probe (PSP) FIELDS burst-mode archive has so far relied on expert visual inspection and does not scale to the full mission. We present EMBER (Electron heating from Modulated Burst-mode Event Recognition), an open-source pipeline that converts PSP FIELDS Digital Burst Memory (DBM) voltage bursts into log-scaled Fourier spectrograms and applies a multi-detector, background-only anomaly detection suite. The suite combines physics-motivated detectors, classical outlier detectors, and deep learning detectors. The EMBER ensemble recovers 93% of the anomalous events at 1% FAR (1 false positive per 100 held-out backgrounds). Coincident SWEAP/SPAN diagnostics show that flagged intervals exhibit core perpendicular electron temperatures above the adiabatic cooling expectation and elevated Te/Ti, reproducing the preferential-heating phenomenology established by prior manual studies without any use of electron temperatures in the detection step.

Decentralized stochastic control problems are difficult to study due to information structure dependent subtleties, which prevent many classical methods in stochastic control from being applicable. In this paper we consider such problems with general standard Borel spaces under two related information structures. (a) the one-step delayed information sharing pattern (OSDISP) where agents share their information with one-step delay, and (b) the $K$-step periodic information sharing pattern (KSPISP), where information is shared periodically. It is known that OSDISP and KSPISP problems admit a centralized reduction where the agents view the problem from the perspective of a centralized controller that uses the common information to prescribe function valued actions (local policies) which map each agent's private information to an optimal action in the original problem. We provide rigorous approximation results and performance bounds for the KSPISP and OSDISP problems, which results from replacing the full common information by a finite sliding window of information and we establish near optimality of such policies. The latter depends on a predictor stability condition in expected total variation. As a further contribution, we show that under the information structures provided, corresponding Q-learning algorithms (in quantized or finite memory forms) converge asymptotically to near optimal solutions. While restrictive and hypothetical conditions have been presented in the literature, our contributions are thus to provide, to our knowledge, the first explicit conditions and rigorous approximation and learning results for such decentralized problems with general spaces.

Many incentive design problems must contend with information asymmetries due to non-observation of efficiency (adverse selection) or non-observation of effort (moral hazard). And although a growing body of literature considers incentive design in control systems, the problem of designing incentives for control systems under information asymmetries has been less well-studied. This paper considers a model of moral hazard within control systems. In our model, the control system is described by an (affine) linear time-invariant (LTI) system with process noise. There is an agent who gets to choose (from between two choices) a linear state-feedback controller to apply to the LTI system, with one of the state-feedback controllers having a higher quadratic cost on the control inputs than the other. Our goal is to design a payment scheme that incentivizes the agent to choose the state-feedback controller that minimizes a quadratic cost on system states plus the time-discounted payment amount, subject to the understanding that the agent bears the control cost while being risk-averse with respect to their time-discounted payment. We formulate the problem as a constrained optimization, and prove that for a payment given after a fixed (but optimizable) time horizon the optimal payment scheme chooses the payment amount using a likelihood ratio hypothesis test. We numerically demonstrate our results by applying the derived optimal payment scheme to two examples: load frequency control (LFC) in power systems and wellness interventions for body weight loss.

We develop a product-level trace-Dobrushin theory for finite-dimensional quantum channel products and apply it to deterministic and stationary random inhomogeneous matrix product states in left-canonical CPTP gauge. For a product of channels, the centered trace-Dobrushin coefficient quantifies the residual dependence on the input state, and its decay is the criterion for trace-norm forgetting. In the deterministic setting, this decay is equivalent to asymptotic replacement by a moving replacement channel. For two-sided products, pullback forgetting produces a unique boundary state, which determines the canonical replacement family. For stationary random CPTP cocycles, submultiplicativity of the product coefficient yields a trace-Dobrushin Lyapunov exponent. We prove that the almost sure negativity of this exponent is equivalent to quenched trace-norm memory loss and gives exponential forward and pullback convergence to a unique dynamically stationary random replacement channel. When the \(\varrho\)-mixing profile of the channel environment tends to zero, we obtain annealed super-polynomial estimates, while independence gives annealed exponential estimates. Finally, we transfer these estimates to inhomogeneous matrix product states whose auxiliary transfer maps are CPTP. These channel estimates transfer to deterministic and stationary random inhomogeneous MPS, giving infinite-volume limits of trace-closed finite-volume states, quantitative boundary stability, and correlation bounds governed by the same auxiliary product coefficients.

Wide exploration on robocall surveillance research is hindered due to limited access to public datasets, due to privacy concerns. In this work, we first curate Robo-SAr, a synthetic robocall dataset designed for robocall surveillance research. Robo-SAr comprises of ~200 unwanted and ~1200 legitimate synthetic robocall samples across three realistic adversarial axes: psycholinguistics-manipulated transcripts, emotion-eliciting speech, and cloned voices. We further propose RoboKA, a Kolmogorov-Arnold Network (KAN)-based multimodal fusion framework designed to model structured nonlinear interactions between acoustic and linguistic cues that characterize diverse adversarial robocall strategies. RoboKA first leverages cross-modal contrastive learning to align latent modality representations and feeds the resulting embeddings to a KAN-projection head for final classification. We benchmark RoboKA against strong unimodal and multimodal baselines in both in-domain and out-of-domain setups, finding RoboKA to surpass all baselines in terms of recall and F1-score.

MASCARA-5~b/TOI-1431~b is an ultra-hot Jupiter \citep[$P_{\rm orb}=2.650237\pm0.000003\,{\rm d}$, $T_{\rm eq}=2370\pm70\,{\rm K}$, $M_{\rm p}=3.12\pm0.18\,M_{\rm Jup}$, $R_{\rm p}=1.49\pm0.05\,R_{\rm Jup}$;][]{addison2021} orbiting a bright Am star ($V=8.0\,{\rm mag}$). Recent time-series observations obtained with PEPSI@LBT during the planet's post-eclipse phases have revealed Fe~{\sc i} emission lines indicative of a thermally inverted atmosphere. These observations demonstrate that MASCARA-5~b is well-suited to atmospheric characterization via emission spectroscopy, thereby motivating further follow-up observations covering additional orbital phases to constrain the planet's atmospheric chemistry, thermal structure, and dynamics. Here we present pre-eclipse time-series observations obtained with the high-resolution optical spectrograph EXPRES@LDT. Our analysis confirms the previous detection of gas-phase Fe~{\sc i} on MASCARA-5~b's dayside (with a $5.5σ$ significance obtained from two nights of observations) and the fact that the thermal profile is inverted with lower and upper temperatures $\sim2000\,{\rm K}$ and $\sim4500\,{\rm K}$, respectively. A search for Fe~{\sc ii} and Cr~{\sc i} did not yield any plausible detections. We also find that the pre-eclipse signal exhibits a non-negligible blueshift of $-3.2\pm1.4\,{\rm km/s}$ potentially caused by winds.

The Probe far-Infrared Mission for Astrophysics (PRIMA) is a far-infrared (24-261 micron wavelengths) probe-class space observatory currently under Phase A study, which promises orders-of-magnitude improvement in mapping speed over its predecessors. PRIMA will field exquisitely sensitive kilopixel arrays of kinetic inductance detectors (KIDs) for the Far-Infrared Enhanced Survey Spectrometer (FIRESS) instrument. PRIMA will orbit in space at the Sun-Earth L2 point, where Planck found the energetic particle flux to be about 300/min/cm2. Thus, the possible effect of a high fluence of energetic particles on the detector sensitivity must be characterized. Previous work has suggested that bombardment of KIDs by ions can reduce the quasiparticle lifetime (Barends et. al. 2009), but the conditions of the experiment were not representative of a detector which is continuously held at sub-Kelvin temperatures in the energetic particle environment of L2 orbit. To better replicate the damage which would be produced by energetic particles in this environment, we developed a fully cryogenic irradiation experiment in which a stepper motor controls a screen which can block or reveal an alpha particle emitter. This setup can be used to irradiate aluminum KID arrays fabricated for FIRESS to well-controlled dose levels. In this work, we calculate the damage dose expected for a 5-year mission in L2 orbit, and we irradiate an array to approximately 62 percent of this level. Before and after irradiation, we measure the quasiparticle lifetimes, resonant frequencies, and quality factors of the detectors.