Requirements elicitation is among the most communication-intensive activities in software engineering, yet it receives limited explicit treatment in undergraduate curricula. This paper presents a case study of an Introduction to Software Engineering course in which 20 student teams applied requirements elicitation practices to a Java-based 2D game they had built in a prior programming course, engaging 18 campus doctoral and postdoctoral researchers as authentic clients. Structured across four phases--preparation, client meeting, requirements elaboration, and a prototype sprint--the activity produced 203 elicited requirements, SRS documents with a mean quality score of $6.79 \pm 1.08$ out of 10, and prototype demonstrations scoring $7.21 \pm 1.15$. A pre/post self-assessment survey revealed statistically significant improvements across all eight measured soft-skill dimensions, with the largest gains in Stakeholder Empathy ($Δ= +1.33$) and Negotiation ($Δ= +1.13$). Thematic analysis of reflective reports identified four dominant learning themes, with the tension between client wishes and technical feasibility cited as the most professionally relevant experience. Our findings suggest that anchoring elicitation practice to a student-authored artifact lowers cognitive barriers while increasing authenticity, and that campus researchers serve as an accessible and effective proxy client for programs without established industry partnerships.

Nonvolatile magnetotransport responses in a single magnetic material have generally not been expected to exhibit a large ON/OFF ratio, because they are usually tied to spin-orbit coupling and therefore remain relatively weak. Here we show, contrary to this expectation, that giant nonvolatile magnetotransport can arise in a single magnetic material through magnetoelastic reconstruction of nonrelativistic real-space transport paths. Using the two-dimensional antiferromagnet FePS$_{3}$ as a representative system, first-principles quantum transport calculations reveal that charge transport is strongly tied to its quasi-one-dimensional zigzag sublattice chains and, under suitable doping, can even become confined to them. Moreover, strain lifts the degeneracy among symmetry-related zigzag variants and thus reorients these transport paths through magnetoelastic coupling. As a result, both the longitudinal and transverse conductivities change dramatically, yielding a giant magnetoelastic magnetoresistance of up to $10^{4}$% and an energy-independent Hall ratio that far exceeds the spontaneous Hall ratios found in conventional magnets. These results establish a route to exploiting symmetry-related magnetic variants and their associated transport paths for reconfigurable, high-performance spintronic devices with large nonvolatile readout contrast.

This work describes the ethanol oxidation reaction (EOR) in alkaline medium using low-palladium nanoparticle electrocatalysts modified by Fe3O4 nano-octahedra and SnO2 nanorods. Operation studies on an alkaline direct ethanol fuel cell (ADEFC) were conducted using the developed electrocatalysts, and stability studies were performed using the advanced scanning flow cell (SFC) technique coupled to inductively coupled plasma mass spectrometry (online SFC-ICP-MS). The EOR was catalyzed by single (Pd/C and commercial Pd/C Alfa Aesar) and by synthesized binary and ternary electrocatalysts, in which Fe3O4 and SnO2 nanostructures partially replaced the high-cost noble metal. The PdFe3O4/C was identified as the most promising synthesized material in the electrochemical studies, exhibiting the highest mass activity (1426 mA mg-1 Pd) by cyclic voltammetry (CV), followed by the binary PdSnO2/C (1135 mA mg-1 Pd), and by the ternary (1074 mA mg-1 Pd). This enhancement was attributed to the bifunctional mechanism enabled by Fe3O4 and SnO2, therefore reducing poisoning and improving the EOR. Moreover, the operating results revealed that PdFe3O4/C showed the highest power density among the synthesized materials (31 mW cm-2 at 70 C), even with an approximately 45 percent reduction in Pd content compared to the commercial catalyst. XPS results showed that the Pd 3d5/2 and 3d3/2 peaks for PdFe3O4/C, PdSnO2/C, and PdFe3O4SnO2/C were shifted by approximately 0.5 eV to higher binding energies compared to Pd/C, indicating a loss of electron density in Pd due to strong metal-oxide interactions.

We report on a cryogenic platform at 4 K incorporating high numerical aperture optics for the generation of large-scale tweezers arrays, and compatible with Rydberg-state manipulation. We achieve trapping lifetimes of around 5000 s, significantly extending the available experimental time for the preparation of large-scale arrays. By combining two trapping lasers at different wavelengths and by minimizing other atom losses during the rearrangement and imaging processes, we demonstrate the preparation of defect-free arrays with up to 1024 atoms. Our cryogenic design opens exciting prospects for analog and digital quantum computing.

AI systems are becoming increasingly complex, ubiquitous and autonomous, leading to increasing concerns about their impacts on individuals and society. In response, researchers have begun investigating how to ensure that the methods underlying AI decision-making are transparent and their decisions are explainable to people and conformant to human values and ethical principles. As part of this research thrust, the need for accountability within AI systems has been noted, but this notion has proven elusive to define; we aim to address this issue in the current paper. Unlike much recent work, we do not address accountability within the human organisational processes of developing and deploying AI; rather we consider what it would it mean for the agents within a multi-agent system (MAS), potentially including human agents, to be accountable to other agents or to have others accountable to them. In this work, we make the following contributions: we provide an in-depth survey of existing work on accountability in multiple disciplines, seeking to identify a coherent definition of the concept; we give a realistic example of a multi-agent system application domain that illustrates the benefits of enabling agents to follow accountability processes, and we identify a set of research challenges for the MAS community in building accountable agents, sketching out some initial solutions to these, thereby laying out a road-map for future research. Our focus is on laying the groundwork to enable autonomous elements within open socio-technical systems to take part in accountability processes.

Pd-based anodes for alcohol oxidation suffer from surface poisoning and sluggish kinetics. Here, we developed Pd-Nb2O5/C nanocomposites to improve ethanol electrooxidation kinetics and CO tolerance in alkaline media. Orthorhombic Nb2O5 prepared by the Pechini route was combined with fcc Pd nanoparticles via polyol reduction, yielding Pd(x)-Nb2O5(y)/C nanocomposites with x:y = 100:0, 70:30, 50:50, 30:70, 0:100. Rietveld-refined X-ray diffraction confirmed phase purity and showed similar Pd crystallite sizes (4.46 nm for Pd/C and 4.92-5.08 nm for Nb2O5-containing catalysts). Transmission and scanning electron microscopies coupled with energy-dispersive X-ray spectroscopy reveal uniformly dispersed Pd nanoparticles on Nb2O5 and carbon. UV-Vis diffuse reflectance indicated a band gap of 3.10 eV, and chopped-light photocurrent measurements confirm the strong ultraviolet responsiveness of Nb2O5. X-ray photoelectron spectroscopy reveals that Pd(0.5)Nb2O5(0.5)/C had the highest Pd0 content (58.99%). Electrochemical testing demonstrates that, relative to Pd/C, optimized Pd(0.5)Nb2O5(0.5)/C reduces the ethanol oxidation onset potential by up to 160 mV, increases poisoning tolerance by a factor of five at a fixed potential, and raises the current density from 1.59 to 1.76 mA cm-2. Under light irradiation, the current density increases from 1.07 to 2.10 mA cm-2, accompanied by improved stability and extended durability, attributed to light-induced electron-hole generation and enhanced OH- adsorption. These results highlight the synergistic contribution of oxide-metal interactions and photoactivation to ethanol oxidation and provide insights for designing efficient catalysts for alkaline fuel cells. s

We present parameter-robust preconditioners for linear systems that arise after applying static condensation to a hybridizable discontinuous Galerkin (HDG) discretization of the time-dependent Stokes problem. Building upon the theoretical framework introduced in our previous work [SIAM Journal on Scientific Computing, 47(6):A3212-A3238, 2025], we extend the analysis to derive new preconditioners that remain robust with respect to all physical and discretization parameters. The construction relies on first establishing uniform well-posedness of the HDG formulation (before static condensation) through appropriately defined norms. Based on this result, we identify sufficient conditions that a norm on the face space must satisfy to guarantee parameter-robustness of the resulting preconditioner for the statically condensed HDG system. Numerical experiments in two and three dimensions verify our theoretical results.

The time-resolved angle-resolved photoemission spectra of WSe$_2$, a paradigmatic transition metal dichalcogenide, are dominated by a transient signal that, after being initially observed in the gap at the K valley, scatters, on an ultra-fast time scale of $\sim$ 30 fs, to the $Σ$ valley. In this work we question the common interpretation of the experimental dynamics in terms of a massive bound electron-hole exciton that scatters with phonons and behaves as a quasi-particle. By using a combined theoretical and experimental investigation, we demonstrate that the observed dynamics can be interpreted as the photo-induced transition from direct to indirect excitonic-insulating order. The features that appear in the experimental spectrum correspond to single-particle levels renormalized by the excitonic spontaneous polarization.

Accurate time synchronization is essential for Internet of Things (IoT) systems, where multiple distributed nodes must share a common time base for coordinated sensing and data fusion. However, conventional synchronization approaches suffer from nondeterministic transmission latency, limited precision, or restricted bidirectional functionality. This paper presents a protocol-independent wireless timer synchronization method that exploits radio timeslots to transmit precisely timestamped beacons in a proprietary radio mode. By decoupling synchronization from upper-layer packet retransmissions and leveraging hardware-timed radio events, the proposed approach significantly reduces scheduling uncertainty and achieves nanosecond-level synchronization accuracy. Comprehensive experiments evaluate the impacts of synchronization frequency, RSSI, BLE connection interval, and throughput on synchronization performance. The results demonstrate that an optimal synchronization frequency of 1000 Hz yields an approximately 20 ns delay in the absence of communication stack activity while maintaining sub-500 ns accuracy under most realistic BLE traffic conditions. Furthermore, larger connection intervals, lower application throughput, and higher RSSI consistently improve synchronization quality by reducing radio resource contention and packet loss. The proposed scheme provides a general and high-precision synchronization solution suitable for resource-constrained IoT systems.

Point defects critically influence the properties of materials and devices, yet density functional theory (DFT) remains computationally demanding for defect supercell calculations. Machine learning interatomic potentials (MLIPs) offer high efficiency but require extensive datasets. MLIPs trained only on defect configurations in small supercells exhibit systematic energy errors in larger supercells, demonstrating limited transferability. Here, we present a machine learning Hamiltonian (MLH) model-based method for calculating total energies and atomic forces in defect supercells with linear-scaling computational cost, enabling efficient structural relaxation and accurate formation energy predictions. We take oxygen vacancies in amorphous SiO$_2$ as an example and train the MLH model on defect configurations in 95-atom supercells, with the training data derived from 120 self-consistent field calculations and 12 structural relaxations. The MLH model enables efficient structural relaxations for host (defect-free) and defect systems in larger supercells, avoiding the systematic energy errors observed in MLIPs. The cancellation of energy errors between host and defect systems yields accurate formation energy predictions, with deviations from DFT below 50 meV. The proposed method holds significant potential for defect simulations in complex materials.

DNA language models like Evo2 now fit million-token contexts large enough to cover entire TADs, yet whether they learn 3D chromatin structure, a key regulatory layer acting atop primary sequence, remains untested and questionable, given that Evo2's training data includes prokaryotes lacking this structure. We probed Evo2-7B on TAD boundaries and convergent CTCF loops in 1 Mb windows using two complementary tests: likelihood-based perturbation and sequence generation. Evo2 did not distinguish functional perturbations from matched random controls and failed to reliably generate convergent CTCF loops, recovering TAD boundaries only partially. Together, these results indicate that Evo2 has learned local CTCF grammar but misses higher-order 3D organization, pointing to bidirectional model architectures integrating cell types and 3D contacts, rather than longer contexts, as the path to developing 3D-aware DNA language models.

In this note, we address the question of whether locality, unitarity, and newly discovered hidden zeros can completely determine tree-level amplitudes, from the perspective of soft limit. We reconstruct the single-soft theorems of tree YM amplitudes and the double-soft theorems of tree NLSM amplitudes from locality, unitarity, and hidden zeros. A series of studies have shown that the full YM and NLSM amplitudes can be constructed from these soft theorems; therefore, we conclude that locality, unitarity, and hidden zeros completely determine the tree-level YM and NLSM amplitudes.

Magnetostriction, the anisotropic spatial deformation, is a hallmark of dipolar gases with strong long-range interactions, yet it poses a challenge for in-situ characterization. Here, we observe a magnetostriction crossover from the strongly anisotropic superfluid phase to the nearly isotropic normal phase using in-situ imaging of quasi-two-dimensional 166Er gases. Then, we develop a quasi-2D Hartree-Fock-mean-field framework that provides a robust tool for interaction-aware thermometry, enabling the determination of temperature and chemical potential across all dipole orientations from a single fit. We further demonstrate that the low-density wings effectively obey a local-density equation of state. Finally, we reveals the crossover from the isotropic thermal wings to the anisotropic coherent core in a single in-situ image, providing a pathway for future accurate studies of strongly dipolar superfluidity and thermodynamics in 2D.

On demand sensing is emerging as a key paradigm in Internet of Things (IoT) systems, where devices remain in low power states and transmit data only upon event triggers. Such an operation requires wireless communication schemes that provide low latency, minimal wake up overhead, and high energy efficiency. However, widely adopted protocols such as Bluetooth Low Energy (BLE) rely on connection oriented mechanisms that incur non negligible latency and energy overhead during sleep wake transitions, limiting their effectiveness for event driven sensing. In this work, Nordic Semiconductor's proprietary Enhanced ShockBurst (ESB) protocol is investigated as an alternative communication scheme for low power on demand IoT systems. A systematic experimental comparison between ESB and BLE is presented on the same hardware platform, evaluating packet level latency, transmission energy, achievable throughput, wake up overhead under duty cycled operation, and bidirectional communication characteristics. Results show that ESB achieves a packet latency of 0.68 ms for a 244 byte payload, reduces per packet transmission time and energy by nearly 2x, increases maximum throughput by approximately 2x, and lowers wake up time and energy by up to 10x compared with BLE. To demonstrate system level impact, an implantable loop recorder prototype with FIFO triggered electrocardiogram transmission is implemented. The ESB based system enables rapid event driven communication with a minimum communication power of 0.5 mW and reduces total system power consumption by approximately 60 percent relative to BLE. These results highlight the limitations of connection oriented protocols for on demand sensing and establish ESB as a lightweight and effective communication alternative for energy constrained IoT applications, including biomedical implants and event driven monitoring systems.

Purpose The goal of this work is to develop and evaluate a single-layer tight-fit 32-element double-tuned loop/dipole transceiver (TxRx) array for human brain 31P MRS at 9.4T, achieving reasonable transmit and receive performance and full-brain coverage at both frequencies. Methods First, we developed numerical models of dual-row TxRx arrays for 31P (loop array) and 1H (coaxial-end folded-end dipole array) frequencies at 9.4T. Next, a multi-tissue voxel model was used to simulate Tx-performance of the arrays and define optimal CP-mode excitation. Following this, the proposed array performance was evaluated by MR measurements both on a phantom and a healthy volunteer. Finally, we compared the proposed array to a previously reported dual-tuned single-row loop-based TxRx array. Results The developed 32-element double-tuned array demonstrated full-brain (including the cerebellum and brain stem) imaging capabilities, reasonable SNR and transmit performance at both frequencies at 9.4T. Conclusion As a proof of concept, we developed a 32-element double-tuned UHF tight-fit TxRx human head array coil for 31P MRS with sufficient 1H performance using a combination of loop and dipole array elements. The proposed array design could also be adapted to higher fields, i.e., 10.5T, 11.7T, and 14T.

Inspired by the possibility that superconducting properties may be altered by applying a perpendicular electric field in the Ruddlesden-Popper (RP) bilayer nickelate La$_3$Ni$_2$O$_7$, we investigated the imbalanced two-orbital bilayer Hubbard model using dynamical cluster quantum Monte Carlo calculations. Focusing on the pairing symmetries induced by the electric field and their evolution with field strength in the undoped, hole-doped, and electron-doped regimes, we found that the $s^\pm$-wave pairing originating from the $d_{z^2}$ orbital is suppressed; while a pairing symmetry transition from $s^\pm$-wave to $d$-wave pairing occurs, driven by the interlayer $d_{z^2}$ orbital mismatch and the transfer of electrons into the $d_{x^2-y^2}$ orbital under the applied electric field. Intriguingly, the $d$-wave pairing arising from the $d_{x^2-y^2}$ orbital exhibits dome-like behavior with the electric field. Our large-scale many-body calculations align with the previous expectation from weak-coupling methods and provide further insight into the superconducting mechanism in RP nickelates.

This article presents recent measurements by the ALICE Collaboration in proton--oxygen (pO), oxygen--oxygen (OO), and neon--neon (Ne--Ne) collisions delivered by the LHC in July 2025. Measurements of the primary charged-particle pseudorapidity density and the elliptic and triangular flow coefficients of charged particles are reported. Experimental evidence of the suppression of neutral pion yields in OO collisions relative to the proton--proton baseline is also discussed. Comparisons of these new data with theoretical models provide key input to understand particle production, collective phenomena, and parton energy loss in small collision systems.

We consider the classical last-success problem for sequential Bernoulli trials in the homogeneous setting where $X_1,\ldots,X_n$ are i.i.d. $\mathrm{Bernoulli}(p)$ but the success probability $p\in(0,1)$ is unknown to the decision maker. When $p$ is known, Bruss' sum-the-odds theorem yields an optimal threshold rule with value $V_n(p)$. We study a natural oracle-free plug-in rule that replaces $p$ by the online empirical estimate $\hat p_t$ and we denote its win probability by $W_n(p)$. First, we derive an exact expression for $W_n(p)$ via a recursion for the state probabilities, enabling explicit comparisons with $V_n(p)$ and revealing a finite-horizon separation between plug-in and oracle performance. Next, we formalize a first decision-theoretic obstruction inherent to the unknown-$p$ formulation: for every fixed $n\ge2$, the dominance partial order on $p$-blind (possibly randomized) rules has no greatest element. We then identify regimes where oracle-freeness is achievable with sharp bounds. For any $p_0\in(0,1)$, we establish finite-horizon oracle bounds on $[p_0,1)$ and we prove a matching minimax lower bound of order $1/\sqrt{n}$ for $p_0\in(0,\tfrac 12)$ (larger values of $p_0$ do not allow for non-trivial lower bounds). We also show that the rate is exponential for any fixed $p$. In sparse regimes where $p=p_n\to0$ with $np_n\to\infty$, we prove asymptotic oracle-optimality of the plug-in rule, in the sense that $V_n(p_n)-W_n(p_n)\to0$. Together with our non-sparse bounds, this yields a broad uniform convergence guarantee, which we show cannot be extended to the critical regime $p\asymp 1/n$. Finally, we establish another impossibility barrier: even allowing randomization, no oracle-free sequence of rules can converge uniformly to the oracle value over $p\in(0,1)$.

Empirical research shows that individuals' responses to treatments vary along latent characteristics, such as innate ability or motivation. Therefore, a policymaker seeking to maximize welfare may consider designing policies based on observed characteristics and estimated latent traits. I characterize how the estimates' precision affects the worst-case performance of policies, deriving rate-sharp regret bounds for assignment rules that include or exclude them, highlighting new trade-offs with the policy space complexity. I then study how a policymaker can solve such trade-offs by designing tailored data collections, and derive the minimax optimal collection plan. In an empirical application in development economics, I show that including a proxy for entrepreneurs' business skills in targeting cash transfers increases welfare by 5%, and halves the probability of generating welfare losses. Moreover, I estimate the optimal allocation of resources between improving the precision of the proxy via repeated measurements and increasing sample size.

Digital learning platforms are increasingly used to support reading development while generating rich log files and item-level textual content. Using these data, this study proposes a dynamic cognitive diagnostic modelling (CDM) framework that incorporates text-derived semantic information to inform the estimation of the Q-matrix. We construct item-level semantic representations of question text and response options, and use these representations to define an informative prior on the Q-matrix. This approach treats text-derived signals as proxies for item complexity and cognitive demands, guiding the item-skill mapping in a data-driven manner. The proposed framework jointly estimates latent skill mastery profiles, item parameters, and transition dynamics over time within a Bayesian framework. We apply the model to data from Boost Reading, a digital reading supplement, focusing on students' vocabulary and comprehension skill development. We compare the proposed framework with a baseline model without any text information and show that the text-derived prior can improve Q-matrix recovery, particularly in settings where response data alone provide limited identification, as well as other model parameters for varying scenarios. This study provides a novel integration of natural language processing and dynamic CDMs, offering a data-driven approach to modelling skill acquisition and item-skill relationships in digital learning environments.

The computational complexity of $\mathsf{QAC}^0$, which are constant-depth, polynomial-size quantum circuit families consisting of arbitrary single-qubit unitaries and $n$-qubit generalized Toffoli gates, has gained tremendous focus recently. In this work, we initiate the study of the computational complexity of geometrically local $\mathsf{QAC}^0$ circuits, where all the generalized Toffoli gates act on nearest neighbor qubits. We show that any $\mathsf{QAC}^0$ circuit can be exactly simulated by a two-dimensional geometrically local $\mathsf{QAC}^0$ circuit, i.e., a $\mathsf{2D\text{-}QAC}^{0}$ circuit, with a quadratic size blow-up. This implies that $\mathsf{QAC}^0 = \mathsf{2D\text{-}QAC}^{0}$. We further show that if there existed a $\mathsf{QAC}^0$ circuit that computes Parity with a bounded constant error, then for any $\varepsilon > 0$, there would exist a $\mathsf{2D\text{-}QAC}^{0}$ circuit that exactly computes Parity, with a very "thin" width $n^\varepsilon$. We further study the computational power of $\mathsf{1D\text{-}QAC}^{0} $ circuits, i.e., one-dimensional $\mathsf{QAC}^0$ circuits, which are the "thinnest" $\mathsf{2D\text{-}QAC}^{0}$ circuits. We prove a nearly logarithmic depth lower bound on $\mathsf{1D\text{-}QAC}^{0} $ circuits to compute the Parity function, even if allowing an unlimited number of ancilla. Furthermore, if the inputs are encoded in contiguous qubits, we prove that it requires a nearly linear depth $\mathsf{1D\text{-}QAC}^{0} $ circuit to compute the Parity function. This lower bound is almost tight. The results are proved via the combination of the restriction argument and the light-cone argument. These results may provide a new angle for studying the computational power of $\mathsf{QAC}^0$ circuits and for resolving the long-standing open problem of whether Parity is in $\mathsf{QAC}^0$.

The quest for quantum ground states beyond the conventional Fermi-liquid paradigm remains a central challenge in many-body physics. The ferromagnetic Kondo effect represents a particularly intriguing case: an exotic variant of the Kondo effect in which an asymptotically free spin gives rise to singular Fermi-liquid behavior. Despite its theoretical importance, this regime has long eluded experimental observation owing to its subtle spectroscopic signatures, vanishingly small energy scales, and strict symmetry constraints in conventional nanostructures. Here, we demonstrate the coexistence of the ferromagnetic and overscreened Kondo effects within a single molecular spin system$\unicode{x2014}$a triangulene dimer comprising spin-1 and spin-1/2 units adsorbed on a metal surface. Low-temperature scanning tunneling spectroscopy reveals characteristic signatures of singular Fermi-liquid behavior, which are fully supported by many-body calculations. The unique molecular design provides intrinsic control over spin configuration and coupling asymmetry, allowing distinct many-body regimes to be accessed within the same platform. Our results establish a robust strategy for realizing non-Fermi-liquid physics at the atomic scale and demonstrate that ferromagnetic Kondo behavior can not only be observed but also deliberately engineered in molecular systems.

LoRA enables efficient customization of LLMs and is widely used in multi-tenant and multi-task serving. However, emerging model architectures such as MoE significantly increase LoRA memory cost, making existing coupled LoRA serving designs poorly scalable and prone to tail-latency inflation. We present InfiniLoRA, a disaggregated LoRA serving system that decouples LoRA execution from base-model inference. InfiniLoRA introduces a shared LoRA Server with parallelism-aware execution, SLO-driven provisioning, and critical-path optimizations, including GPU-initiated communication and hardware-specialized LoRA kernels. Experiments show that InfiniLoRA can achieve an average $3.05\times$ increase in serviceable request rate under strict latency SLOs, and improve the percentage of LoRA adapters satisfying the SLO requirement by 54.0\%.

We present a fast algorithm for evaluating the (non-smooth) solution of the free-space two-dimensional (2D) scalar wave equation with many point sources, each with a high-frequency band-limited time signature. Such an algorithm is key to an efficient time-domain scattering solver using spatially-discretized hyperbolic layer potentials. Given $M$ sources/targets and $N_t$ time steps, direct evaluation costs $O(M^2N_t^2)$, due to the history dependence. We develop a quasi-linear scaling algorithm that splits the solution at a given time into (a) a non-smooth time-local part, (b) a (smooth) near history involving sources up to ${\mathcal O}(1)$ domain traversal times into the past, plus (c) a (very smooth) far history comprising all waves emitted before the near history. The local part is computed directly via high-order quadrature. A naive spatial Fourier transform for (b) plus (c) would be both slowly converging and arbitrarily oscillatory as time progresses. Yet in (b) the oscillations are controlled, so we use the recent truncated windowed Fourier projection (TK-WFP) method to give rapid convergence. For (c) -- present due to the weak Huygens' principle -- we exploit a new large-time sum-of-exponentials approximation of the free-space wave kernel. Numerical examples with up to a million sources and targets, a domain of $300\times 300$ wavelengths, and 6-digit accuracy, show an acceleration of five orders of magnitude relative to direct evaluation.

We review and announce recent results on the asymptotic behavior of asymptotically Euclidean relativistic initial data sets and asymptotic foliations thereof. In particular, we discuss the geometrization of asymptotic flatness and of asymptotic geometric (in-)\-variants such as mass, energy, linear momentum, angular momentum and center of mass as well as their relations to certain geometric asymptotic foliations such as the CMC- and STCMC-foliations.

As Large Language Models (LLMs) increasingly automate writing tasks, there is a growing risk of cognitive deskilling where users offload critical thinking to the system. To address this, we introduce Critical Inker, a writing tool designed to scaffold critical reflection during writing through logical analysis and socratic feedback. We present two methods: (1) A Socratic chatbot using questions to help them realize and fix logical errors in their writing and (2) Visual Feedback, which highlights logical errors in the text without dialog. We detail the technical implementation of the system and evaluate its argument extraction and logical validity accuracy. Our evaluation shows a 91.2% argument overlap with ground truth argument annotations and 87% validity accuracy. Finally, we conducted a small-scale pilot and discuss early qualitative results.

Aggregative cooperative optimization problems arise in distributed decision-making settings where each agent's objective depends on its own decision as well as on an aggregate variable capturing global system behavior. Motivated by practical scenarios where gradient information is unavailable, this paper introduces a randomized gradient-free algorithm, named ARGFree, for solving such problems. ARGFree combines finite-difference gradient approximations with a set of tracking variables, emulating the behavior of a gradient-based method. We prove that ARGFree converges in expectation to an approximate optimizer, with the approximation error stemming from the use of a randomized gradient estimator. To enhance performance in high-dimensional settings, we further propose an improved variant, ARGFree-EM, which incorporates momentum in the exploration signals to smooth sudden fluctuations in the gradient exploration signals and thereby improve the accuracy of the underlying distributed tracking mechanism. To the best of our knowledge, the class of ARGFree methods is the first in the literature capable of solving aggregating cooperative optimization problems without gradient information.

Implementing high-fidelity controlled two-qubit gates in dipole-dipole interacting systems, such as rare-earth-ion crystals, in hindered by spectral inhomogeneity and weak coupling. Existing method often rely on detuned pulses, making them susceptible to frequency errors and AC Stark shifts. We propose a robust resonant scheme for arbitrary controlled two-qubit gates that utilizes asymmetric excitation and pulse engineering to achieve decoupled, parallel qubit control. Simulations on rare-earth-ion ensemble qubits demonstrate gate fidelities exceeding 99% within a 170 kHz detuning range with off-resonant excitation below 0.2%. This approach offers a robust, scalable route for quantum computing in spectrally crowded systems.

We develop a cut finite element method (CutFEM) for convection-diffusion problems posed on mixed-dimensional domains, i.e., unions of manifolds of different dimensions arranged in a hierarchical structure where lower-dimensional components form parts of the boundaries of higher-dimensional ones. Such domains arise, for instance, in the modeling of fractured porous media with intersecting fractures. The model problem is formulated in a compact abstract form using mixed-dimensional directional derivative and divergence operators, which allows the problem to be expressed in a way that closely resembles the classical convection-diffusion equation. The proposed CutFEM is based on a fixed background mesh that does not conform to the geometry, with each manifold component represented through its associated active mesh. The method employs continuous piecewise linear elements together with weak enforcement of coupling conditions and suitable stabilization. We prove a priori error estimates in energy and $L^2$ norms and establish convergence, also for solutions with reduced regularity $u \in H^s$, $1 < s \le 2$. Numerical experiments confirm the theoretical convergence rates and illustrate the performance of the method.

We investigate the phenomenological impact of incorporating vector-like bottom (VLB) quarks into the Type-II Two-Higgs-Doublet Model (2HDM-II). This framework introduces novel beyond-Standard-Model (BSM) decay channels $B \to Hb$, $B \to Ab$, and $B \to H^-t$, which are typically ignored by LHC pair-production searches focused on Standard Model (SM) final states ($B \to Zb$, $B \to hb$, $B \to Wt$). Our analysis reveals that these BSM pathways significantly weaken current VLB mass constraints. In the 2HDM-II alignment limit, the mass limit for a singlet $B$ shifts from approximately 1.5 TeV down to 1.34 TeV. For $(T, B)$ and $(B, Y)$ doublet configurations, the mass limits relax further to approximately 0.98 TeV, driven by the dominance of $B \to Hb$ and $B \to Ab$ decays, which can reach combined branching ratios of nearly 100\%.