Many complex systems combine dense background structure with sparse contextual information. We introduce a diffusion-based framework for analyzing how sparse condition-specific layers reshape diffusion geometry in multilayer hypergraphs. Each layer is represented as a weighted hypergraph, layers are coupled through shared entities, and random walks on the coupled system induce multiscale diffusion distances between nodes. We apply the framework to disease-conditioned gene networks by coupling a dense MSigDB functional gene-set layer to sparse disease-specific DGIdb drug-gene hypergraphs, with disease-associated drugs selected from DDDB and HumanNet-GSP used to define external gene weights. Across Bipolar Disorder, Schizophrenia, Leukemia, and Breast Cancer, the disease-specific layer contains less than 2 percent of genes in the coupled system, yet substantially changes diffusion distances and community structure. Centrality analysis suggests that this disproportionate effect arises because DGIdb-associated genes occupy influential positions in the MSigDB-derived functional network. The resulting diffusion-derived communities are stable under subsampling and show coherent post hoc functional enrichment, including signaling and neurotransmission categories in neuropsychiatric diseases and immune, translational, and metabolic categories in cancer-associated diseases. Community-level comparisons further reveal disease similarities not reducible to direct DGIdb gene overlap, including a Breast Cancer-Schizophrenia relationship consistent with recent biomedical evidence. These results show that sparse contextual layers can induce interpretable nonlocal changes in higher-order network geometry.

We survey recent progress on the cohomology of moduli spaces of stable curves through the lens of the Hodge and Tate conjectures, especially their generalized coniveau forms, which relate Hodge structures and l-adic Galois representations on cohomology to algebraic cycles. We explain how the inductive structure of the boundary stratification verifies these conjectures in a surprisingly wide range of cases, describe the guiding inspiration from arithmetic, and discuss open problems and directions for future research.

For any smooth bounded domain $Ω\subset \mathbb{R}^3$, we construct a divergence-free velocity field $u \in L_t^1 W^{1,p}(Ω)$ for all $p < \infty$, and magnetic fields $B^ε\in L_t^p C^{m}(Ω)$ for all $p < \infty$ and $m\in \mathbb{N}$, that solve the kinematic dynamo equation and exhibit arbitrarily fast growth of any magnetic energy mode, uniformly in the vanishing-diffusivity limit $ε\to 0$. The construction is based on the convex integration scheme of Modena-Székelyhidi and Cheskidov-Luo. The main novelty lies in the introduction of explicit potentials, which allow the solutions to be localized and avoid the need to work with the anti-curl operator. In addition, we present a unified scheme for the geometric transport equation (GTE), which encompasses both the transport and Maxwell equations.

Efficiently generating photon pairs with high heralding efficiency and high single photon purity that are bandwidth matched to quantum emitters, quantum memories, and other matter-based qubits is critical for quantum networking applications. However, nonlinear optics-based sources require substantial spectral engineering to overcome the orders of magnitude bandwidth mismatch between those sources and qubit systems. A popular solution is cavity-enhanced spontaneous parametric down conversion (SPDC) where the cavity sets the photon bandwidth and simultaneously enhances the spectral brightness of the SPDC. Bulk, free-space configurations are generally required to achieve the MHz-scale bandwidths required to interface with most qubit systems. Replicating these in scalable integrated photonic architectures is an ongoing challenge due to the much higher propagation losses that limit the size and linewidth of chip-based resonators. We show here how an intra-cavity slow light medium, acting as an ultra-narrow filter, would enable narrowband photon pair generation in broadband cavities with high single photon purity and without compromising the heralding efficiency. We show that such metrics can be readily realized in erbium doped thin-film lithium niobate microrings using realistic design parameters.

The climate crisis demands a rapid shift to sustainable energy technologies and higher efficiency in existing energy systems. Adsorption-based thermochemical energy storage is a promising alternative due to its high energy density and compatibility with renewable heat sources. In this work, we investigate the adsorption behavior of pure refrigerants (R32, R125, R134a, and R600) and their commercial blends (R410A, R407F, R417A, and R417C) in six activated carbons for thermochemical energy storage and circular refrigerant recovery. A multiscale computational workflow combining Monte Carlo simulations, thermodynamic modeling, and breakthrough simulations is developed to predict adsorption, storage, and separation behavior from pure-component adsorption data. The methodology integrates adsorption potential theory (APT), ideal adsorbed solution theory (IAST), and models for the isosteric heat of adsorption. In addition, an in-house computational framework is developed to calculate heats of adsorption and energy storage densities for both pure refrigerants and multicomponent mixtures. Although developed using molecular simulations as a benchmark, the methodology is directly applicable to experimental studies, since it only requires adsorption isotherms of the pure components as input to evaluate the performance of refrigerant blends. The results show that refrigerant blends can achieve higher storage densities than their pure counterparts due to cooperative adsorption and more efficient molecular packing. Furthermore, the activated carbons selectively separate key refrigerant components, highlighting their potential for sustainable refrigerant recovery. Overall, this work provides a general framework for the rational design and screening of next-generation refrigerant blends for adsorption-driven energy storage and separation applications.

We study the distribution of eigenvalues of Haar-random matrices over $\mathbb{Z}_p$ among algebraic extensions of $\mathbb{Q}_p$. Our results give $p$-adic analogues of the real-eigenvalue counting results of Edelman-Kostlan-Shub for the real Ginibre ensemble, but with a different degree behavior: while real eigenvalues form only a vanishing proportion in the real Ginibre ensemble, $p$-adic eigenvalues are asymptotically evenly distributed among possible extension degrees. We also show that the maximal unramified extension $\mathbb{Q}_p^{\mathrm{un}}$ captures all but a bounded expected number of eigenvalues, and that the expected number of eigenvalues outside $\mathbb{Q}_p^{\mathrm{un}}$ has a finite positive limit with an explicit upper bound. The proof uses correlation function formulas from the author's previous joint work with Van Peski (arXiv:2601.06283), together with uniform estimates over varying finite extensions. We also prove analogous results for roots of random Haar polynomials over $\mathbb{Z}_p$, using the correlation function formulas of Caruso (arXiv:2110.03942). These polynomial results are $p$-adic analogues of the real-root counting results of Edelman-Kostlan, again with behavior different from the real setting.

Cheapfakes, or real images presented misleadingly or in unrelated contexts, are an increasingly prominent form of visual misinformation. While media literacy interventions can enhance individuals' ability to detect such content, motivational barriers often hinder the adoption of image verification. This study examines whether incorporating different mechanisms and types of incentives into a digital media literacy intervention improves visual misinformation discernment and image verification behavior, both immediately and over time. We conducted a pre-registered two-wave between-subjects online experiment (N = 1,421) on a professionally designed social media platform. The study used a 2 (Incentive Type: symbolic vs. monetary) x 2 (Incentive Mechanism: task- vs. result-based) factorial design with additional control groups. Results show that task-based incentives, particularly monetary ones, were most effective at initiating image verification behaviors, namely reverse image search, and boosting short-term discernment, whereas result-based incentives were more effective in sustaining discernment accuracy. These findings suggest that both the mechanism and the type of incentives play a critical role in shaping the short- and long-term effectiveness of media literacy interventions, highlighting the value of multi-phased incentive strategies for combating visual misinformation in digital environments.

Topological superconductors (TSCs) hosting Majorana zero modes (MZMs) offer a pathway to fault-tolerant quantum computation. PdTe is a promising TSC candidate due to its topological surface states and a reasonable superconducting critical temperature of ~4.5 K. However, it has been challenging to grow PdTe thin films with bulk-like superconducting properties. Here, we show that high-quality, superconducting PdTe thin films can be grown using molecular beam epitaxy (MBE). The films exhibit a sharp superconducting transition (T_onset = 4.43 K with transition width of 0.06 K), comparable to that of bulk crystals. This was made possible via a topotactic transformation from a PdTe_2 buffer layer to a PdTe phase by growing Pd on top under Te-deficient conditions. Structural and transport analyses confirm the NiAs-type structure of PdTe, as well as its two-dimensional superconducting behavior and excellent air stability. These findings suggest that the MBE-grown PdTe films and their heterostructures are a promising platform for topological superconductivity and Majorana physics.

Understanding and controlling hot-carrier relaxation in graphene is crucial for advancing ultrafast optoelectronic and terahertz technologies. Here, we investigate carrier cooling dynamics in monolayer and bilayer graphene using mid-infrared pump pulses (0.22-0.73 eV) and terahertz probe pulses. We uncover a pronounced, reproducible, and non-monotonic dependence of the carrier relaxation time on excitation photon energy. Remarkably, within a narrow spectral window (0.42 to 0.48 eV), the carrier lifetime increases by an order of magnitude compared to a few picosecond-scale cooling observed at other energies. We show that this anomalous slowdown originates from a resonant enhancement of the optical-phonon lifetime, causing accumulation and reabsorption of hot optical phonons that suppress energy transfer to the lattice. All observed behaviors are captured within a unified carrier-phonon energy-balance framework, where excitation-energy-dependent variations of the effective optical-phonon decay pathway govern the cooling dynamics. These findings demonstrate excitation-energy-selective control of hot-carrier relaxation in graphene and provide new insight into non-equilibrium carrier-phonon interactions near the optical-phonon bottleneck.

Accurately detecting home locations from GPS data generated by mobile devices is a foundational step in human mobility research, with significant implications for transportation planning, public health, and emergency response. However, existing home detection algorithms often produce unreliable results for noisy real-world data and are barely validated due to a lack of ground-truth benchmarks. To tackle these limitations, this study presents the development and validation of a Grid-based home detection via Stay-Time (GHOST) algorithm, implemented as an open-source Python package. The algorithm infers proxy home locations by identifying the most frequently visited nighttime or weekend daytime grid cells based on customizable spatial and temporal filters. To validate its performance, we use the large-scale BostonWalks dataset, which includes over 155,000 trips from 377 participants in the Boston metropolitan area, to test robustness to noisy data. Additionally, we collected a ground-truth dataset for ten volunteers across different regions in the U.S., including Florida, Mississippi, and Colorado, along with their self-reported home coordinates, to evaluate GHOST across diverse mobility patterns and sampling conditions. We compared GHOST accuracy to that of 5 well-established home detection algorithms: All-time clustering method, Stay-point method, DBSCAN, K-MEANS++, and SciKit-Mobility Home Detection, across multiple parameter settings. Results show that GHOST outperforms all algorithms in accuracy and robustness, with average errors as low as 22.3 meters under optimal configurations. Our findings highlight the high accuracy and flexibility of our algorithm, with grid size being the most influential parameter during validation, demonstrating the potential of this algorithm for real-world mobile location data analysis.

Exciton-phonon interactions play a central role in defining the optical response of hexagonal boron nitride (hBN), yet their quantitative determination has remained incomplete. Here, we reveal the Fröhlich-type exciton-phonon coupling in boron-10-enriched hBN using low-temperature cathodoluminescence. We resolve the indirect exciton 5.95$\pm$0.02 eV together with its longitudinal optical (LO) phonon replica detuned by 184$\pm$56 meV, enabling the extraction of a Fröhlich coupling constant $α$=0.159 and a larger exciton binding energy of 161 meV, larger than previously reported values for natural-abundance hBN, which is attributed to isotope enrichment. The inferred polaron radius exceeds the lattice constant, indicating large-polaron behavior. We deduced an exciton scattering time ~of 97 fs, corresponding to a homogeneous linewidth of ~6.76 meV. We further obtain a polaron binding energy of ~48 meV and an effective mass of 1.045 $m_0$. These results provide a direct quantitative characterization of exciton-phonon coupling in isotopically engineered hBN and establish a foundation for tailoring its phonon-polaritonic and quantum-optical properties.

We establish a new a priori estimate on solutions to the space-inhomogeneous Landau and Boltzmann equations. As a consequence, we prove a new continuation criterion, based on a weighted $L^\infty$-norm, without requiring bounds on the hydrodynamic quantities. This complements existing conditional regularity results from a rather different perspective. Consequently, we show that the singularities present in the fluid equations are largely incompatible with the Boltzmann and Landau equations. More precisely, we largely rule out ``lifting a singularity'' from the 3D Euler equations to the physical range of kinetic equations, a widely expected mechanism for singularity formation. Under general considerations, this mechanism is essentially excluded for soft potentials, whereas for hard potentials the situation is more nuanced: one cannot produce blowup through the standard hydrodynamic ansatz using known imploding solutions to the Euler equations.

Sensitive detection of terahertz (THz) radiation is fundamental to progress in spectroscopy, advanced wireless communication, and the realization of emerging quantum technologies. However, the intrinsically low photon energies in the THz range combined with thermal background radiation tend to constrain detector performance when operating at ambient temperatures. Here, we demonstrate efficient room-temperature THz detection based on nonlinear upconversion in the organic crystal N-benzyl-2-methyl-4-nitroaniline (BNA) to resolve frequencies from 1 to 7.5 THz. The system encompassing spectral filters and a single-photon counter achieves an overall detection efficiency of 2% for sum-frequency generated photons. This enables the detection of a train of 50 000 terahertz pulses carrying, on average, fewer than 0.04 photons per pulse, with a signal-to-noise ratio of unity. At a higher flux, when ~60 photons per pulse impinge on the BNA crystal, the per-pulse detection probability reaches 50%. After accounting for loss mechanisms in the setup, the nonlinear THz-to-near-infrared conversion efficiency in BNA exceeds 75%. These results demonstrate the feasibility of quantum experiments relying on single-photon-level THz detection via upconversion in nonlinear crystals in ambient conditions.

We observe that the classical Cartesian product construction for the intersection of (languages of) nondeterministic finite automata (NFA) is non-optimal in the worst case, if the automata have many transitions. For a fixed alphabet, the product of two NFA may have $Θ(m^2)$ transitions if these NFA have at most $n$ states and $m$ transitions each. We describe alternative constructions with $O(m n)$ transitions: or $O(m n^{k-1})$ for the intersection of $k$ NFA (for fixed $k \ge 2$ and alphabet $Σ$). This gives a faster algorithm for deciding NFA intersection emptiness. The new algorithm is optimal, unless there exists a breakthrough combinatorial algorithm for detecting $(k+1)$-cliques in undirected graphs. This also leads to a more efficient certification scheme for NFA intersection emptiness.

Nanomaterials stacked on-demand, such as rotationally assembled two-dimensional (2D) van der Waals (vdW) layered compounds, provides a versatile platform for quantum simulation and the exploration of exotic electronic phases. Currently, however, such nanoassemblies remain largely confined to inefficiency, manually operated process, limiting their potential for probing emergent physical phenomena. There is a pressing need in the field for high-precision, automated assembling techniques, especially for the scalable fabrication of 2D twistronic heterostructures. Here, we present an intelligent automation system dedicated to the fabrication of van der Waals stacks, following the state-of-the-art protocol for dry transfer of exfoliated 2D materials. The system further employs metadata generated from each automated stacking procedure to perform reinforcement learning, thereby continuously bettering its performances. As a concrete demonstration, we fabricate twisted bilayer graphene (TBLG) -- known for its challenging preparation -- and exhibit its unconventional superconductivity near the magic angle. Our work may pave the way for high-throughput fabrication of low-dimensional nanomaterials including twistronic heterostructures, where integrating data mining and artificial intelligence can accelerate the discovery of novel physical phenomena.

In this article, we define a locally finite graph $X$ as $η$-polynomially hyperbolic if there exists a Lipschitz map $φ: X \to Z$ to some hyperbolic space $Z$ satisfying the following condition: there exists $C \geq 0$ such that $$|B(p,R_1) \cap φ^{-1} (B(q,R_2))| \leq (C R_1)^{η(C R_2)} \text{ for all } p,q \in X, R_1,R_2 \geq 0.$$ The picture to keep in mind is that coarse fibres of $φ$ have polynomial growth with a degree coarsely controlled by $η$ as the thickness of the fibres grows. The map $η$ quantifies how brutal we have to be in order to turn $X$ into a hyperbolic space. Our main result is that, among cocompact special groups, being $\mathrm{lin}$-polynomially hyperbolic amounts not to contain $\mathbb{F}_2 \times \mathbb{F}_2$ as a subgroup. Consequently, containing $\mathbb{F}_2 \times \mathbb{F}_2$ as a subgroup turns out to be quasi-isometric invariant for cocompact special groups.

Lattice field theory, along with its algorithmic and hardware ecosystems, has been at the forefront of computational particle and nuclear physics. It continues to deliver impressive results on the hadronic spectrum, structure, decays, and reactions. Yet, this vigorous campaign has fallen short in addressing a range of problems involving dense matter and general dynamical phenomena. The reason is that such problems require an exponential scaling of computing time and space in system size. Quantum simulation, enabled by quantum-computing algorithms and hardware technology, promises a way forward by offering several polynomially efficient algorithms compared with their inefficient classical counterparts. Lattice gauge theorists have engaged in a multi-pronged program to leverage such new possibilities, and have steadily advanced the state of theory, algorithm, and hardware implementations and co-design. In this talk, I motivate the quantum-computational lattice-field-theory program; introduce the questions such a program is expected to address and the strategies it involves; report on recent progress; and end with a note on challenges and opportunities ahead.

Holographic superfluids/superconductors are one of the most studied systems in the AdS/CFT duality. In the low-energy, in the long-wavelength limit, they should be described by a Ginzburg-Landau theory. For critical dynamics, one expects that they belong to "model F" universality class. We consider a bulk 5-dimensional holographic superfluid/superconductor in the probe limit. For the holographic superconductor, we impose the boundary Maxwell equation to make the boundary Maxwell field dynamical. We identify the dual model F equations where numerical coefficients are obtained exactly.

We use the Brascamp--Lieb inequality from functional analysis to prove novel inequalities for the upper box, packing, and Assouad dimensions of fractal sets in terms of the dimensions of certain projections. Analogous inequalities do not hold for Hausdorff or lower box dimensions. We apply these fractal Brascamp--Lieb inequalities to establish new exceptional set estimates for orthogonal projections and to provide sharp dimension estimates for certain constrained sumsets. We also establish analogous nonlinear inequalities via the nonlinear Brascamp--Lieb inequality.

Stochastic hybrid systems combine continuous-time stochastic dynamics with discrete reset events, producing intrinsically non-Gaussian and often multimodal uncertainty. A consistent propagation law must also account for boundary-induced probability flux across guard sets, making direct density propagation through hybrid Fokker-Planck equations expensive. We develop a hybrid extension of the Max-Entropy Moment Kalman Filter (MEM-KF) that performs filtering from partial statistical information by propagating a finite collection of moments through stochastic hybrid dynamics and reconstructing beliefs using moment-constrained maximum-entropy distributions. The key step is a moment propagation rule derived from Dynkin's formula with a jump-sum, in which reset effects appear as a boundary-flux correction over the guard set. This yields tractable moment dynamics without solving the underlying hybrid PDE. In a stochastic bouncing-ball example, the proposed method captures reset-induced non-Gaussianity through corrected moment equations while retaining the MEM-KF's optimization-based maximum-entropy representation.

We define a matroid invariant called the three-cosystole that is related to higher notions of cogirth for weighted matroids, and we prove an optimal upper bound for it in the class of regular matroids of rank at most six. To accomplish this, we show that it is increasing under matroid extensions and then estimate it for each of the maximal simple regular matroids of rank at most six.

TiO2 ferroelectric field effect transistors (FeFETs) with HfZrO2 (HZO) ferroelectric dielectric layers and bottom gate topology are fabricated for applications in neuromorphic systems. Two sets of devices are fabricated with different gate topologies by varying the thickness of the ferroelectric gate stack. Different device architectures are studied by varying the source drain length (LSD) and gate length (LG). The devices have high on/off ratios up to 10^7 with low leakage off currents <10^-12 A. Repeated cycle testing shows high reliability and a stable memory window. The devices have large memory windows ranging from 3 to 8 V.

In sound field control applications, it is commonly assumed that one has access to an accurate representation of the sound field in the region of interest. This is a problematic assumption since the reconstruction of a sound field from available microphone measurements is especially challenging in real-time applications where only causal measurements are available. Notably, causal time-windowed observations introduce correlation between frequency components, making sound field reconstruction methods that process each frequency band independently sub-optimal. In this work, we formulate a causal finite-window spatio-temporal linear minimum mean-square error estimator for sound field reconstruction. The sound field is modeled as the solution to the wave equation driven by a stationary stochastic spatio-temporal source distribution, which induces a physically interpretable covariance function. It is shown that this covariance function is closely related to the classical diffuse-field coherence model. Since the computational complexity grows rapidly with the number of spatio-temporal observations, we formulate a budget-constrained spatio-temporal sample selection approach to minimize the posterior reconstruction variance. The proposed estimator and sampling strategy are evaluated using both simulated and measured sound fields, demonstrating improved short-window reconstruction compared to frequency domain finite-window baselines.

Despite comprehensive Bodies of Knowledge (BoKs) documenting core knowledge across software engineering, computer science, information systems, and emerging computing fields, a critical gap persists: methodologies for integrating this knowledge into coherent competency-based curricula that prepare graduates for professional careers remain underdeveloped. This paper presents a competency-mapping methodology that bridges Bodies of Knowledge and competency frameworks to design computing curricula. We demonstrate this methodology through ISANUM, a five-year engineering degree program featuring 23 competencies organized into five thematic blocks, each with explicit mappings to 494 knowledge topics from 34 Computing Knowledge areas defined in Computing Curricula 2020. The program integrates three specialized pathways (Software Engineering, Data Engineering \& Data Science, and Information Technology) with mandatory work-study programs, ensuring graduates develop both theoretical foundations and practical workplace competencies. Our contribution provides computing educators with a replicable methodology for translating Bodies of Knowledge into assessable competency frameworks, supported by a semantic wiki infrastructure (ISANUMpedia) enabling collaborative curriculum understanding, maintenance and evolution.

Simulating realistic wet and dry spells is central in weather generators and climate-impact studies. While finite-order Markov chains are standard, they often fail to reproduce persistent dry conditions due to their inherent subexponential decay. We model rainfall occurrence by introducing a duration-augmented binary Markov chain. We establish a link with alternating renewal chains, enabling flexible parametric modelling of wet and dry spell duration distribution. We model those using two regime-adapted specifications from the general class of extended Generalized Pareto Distributions, yielding flexible tail behaviour across various climates. We use estimation methods adapted to each specification. Our model is applied to around 200 stations in the South of Europe spanning diverse Mediterranean and continental climates. We compare this framework to standard Markov models in characterising persistence and high-quantile extrapolation. The approach is generic, extending naturally to multi-state settings or other binary sequence applications in environmental statistics.

We demonstrate a pump-probe scheme in which an atomic vapor is optically pumped with circularly polarized light and probed with a vector vortex beam. The pump induces a macroscopic magnetization in the medium, which gives rise to frequency-dependent circular dichroism and birefringence. The vortex probe, characterized by spatially varying polarization, maps this optical activity onto the spatial structure of the transmitted light, thereby generating correlations between the frequency, polarization, and spatial degrees of freedom. Measuring the intensity profile in a suitable polarization component then allows us to perform spatially resolved polarization spectroscopy. We demonstrate the translation of frequency shifts into an image rotation, observing on resonance a rotation in the order of 98 mrad per MHz. These findings may find applications in high-precision spectroscopy, magnetometry, and the generation of hybrid entanglement.

The first $\ell^2$ Betti number of a group is non-decreasing under various embeddings arising from first order logic. Strict inequality is proved for elementary embeddings of non-abelian proper subgroups within torsion free hyperbolic groups using Perin's classification of such inclusions. The monotonicity is further demonstrated for existential embeddings of arbitrary finitely generated groups.

Global Navigation Satellite Systems (GNSS) underpin positioning, navigation, and timing (PNT), yet their low-power signals are easily blocked or disrupted, leaving gaps in PNT availability in contested environments (e.g. maritime settings) where interference, spoofing, or denial can occur. A key practical need is an independent, ubiquitous aiding signal that can be tracked passively and fused with inertial sensing to sustain full navigation-state estimation without dedicated or cooperative infrastructure. This paper presents an end-to-end LEO-aided hybrid framework that fuses GPS, Starlink downlink beacons, and an inertial measurement unit (IMU) in a 9D (3D position, 3D velocity, and 3D attitude) PNT system using an extended Kalman filter (EKF). We (i) extract Doppler-rate from Starlink downlink beacon tones by associating measurements with satellite IDs, (ii) benchmark beacon Doppler-rate against OFDM-derived range observables under a common processing/estimation pipeline, and (iii) integrate the resulting observable into inertial navigation. We evaluate GPS/IMU, Starlink/IMU, and GPS-Starlink-IMU using Fisher-information predictions, Monte Carlo simulations, and hardware measurements. Results show that Starlink Doppler-rate provides meaningful complementary PNT information, and can aid 9D estimation when GNSS is degraded or intermittently unavailable.

Optimizing schedules in real-world settings often requires considering workload constraints, specially for human resources, to ensure regulatory compliance, impose rest periods, or level the workload over the working horizon. This paper focuses on tackling this family of constraints in the context of preemptive jobshop scheduling, as preemption is particularly relevant when human resources are involved (allowing personnel to flexibly switch between tasks). Preemption also offers theoretical insights as a relaxation of non-preemptive problems. The main contribution of this paper is a Constraint Programming approach designed to handle effectively maximum workload constraints in a preemptive setting, without decomposing activities into unit-duration tasks (which may be computationally prohibitive). Since workload constraints introduce significant additional complexity, we further propose a method that iteratively introduces the workload constraints into the problem, along with tailored heuristics specifically designed to guide the search efficiently. The experimental results demonstrate the effectiveness of our approach on a large set of instances, highlighting its performance compared to a well-known industrial solver, IBM's CP Optimizer.

We present an active learning framework for efficiently generating training data for machine-learned interatomic potentials (MLIPs). The method combines local entropy-driven molecular dynamics with global dataset-aware filtering: a per-configuration entropy term biases MD trajectories toward structurally diverse snapshots, while a global entropy measure, the log-determinant of the fingerprint covariance matrix of the entire dataset, selects only those configurations that provide genuinely new information. We employ dual covariance modes (per-atom for disordered structures and per-config for ordered phases) to achieve broad coverage of configuration space. Combined with a pre-trained foundation model (Allegro-OAM-L) and analytical fingerprint gradients from Gaussian overlap matrix eigenvalues, the framework produces high-quality domain-specific potentials with near- or sub-meV/atom accuracy on test data drawn from the same distribution at training-set sizes of order $10^{2}$ to $10^{3}$ entropy-selected DFT-labeled structures. We demonstrate the method on three systems spanning diverse bonding types and pressure-driven phase transitions: carbon (covalent), silicon (covalent/metallic), and NaCl (ionic). In learning curve comparisons against random molecular dynamics sampling at matched training set sizes ($N = 100$ to $800$), evaluated over three independent training-set draws per condition, entropy-driven sampling achieves a factor of approximately $3$ to $10$ lower energy MAE at $N = 800$ on in-distribution holdouts across the three systems, with the magnitude of the gain depending on the bonding type and the size at which the random-MD baseline saturates.