Papers

Vision-language models (VLMs) achieve strong visual question answering (VQA) performance, but processing large cluttered images is computationally expensive when only a small region is relevant. Electroencephalography (EEG) signals, which capture human neural responses to visual stimuli, can provide a human-derived semantic cue about the region of interest (ROI). However, EEG-guided visual category decoding remains imperfect, making direct ROI routing unreliable. In this work, we propose BrainFocus, a reliable EEG-guided efficient VLM framework for VQA. An EEG classifier predicts a target category, and a YOLO detector localizes the matching ROI. The VLM receives the cropped ROI only when both predictions pass confidence thresholds; otherwise, it processes the full image. For evaluation, we build on EEG-ImageNet to construct a 40-class benchmark comprising generated cluttered images and real object-centric images, with target-ROI annotations and 600 English visual question-answer pairs. Across Qwen3.5-VL 2B, 4B, and 9B models, BrainFocus improves VQA accuracy by 4.14-9.87 percentage points (pp) on cluttered scenes while reducing input tokens and total tokens by 23.2%-39.4% and 23.2%-39.3%, and end-to-end floating-point operations (FLOPs) by 23.2%-39.5%. These results demonstrate that EEG can guide efficient VLM inference even when its semantic decoding is imperfect.

We identify the limiting overlap structure of mixed even $p$-spin models, including the Sherrington-Kirkpatrick model, at every fixed deterministic external field. In particular, we show that the disorder-averaged distribution of the overlap, or its absolute value at zero field, converges to the Parisi measure. We identify the full limiting array using the overlap array of the associated Ruelle probability cascade. We also establish the corresponding Ghirlanda-Guerra identities and determine the limiting law of the quenched overlap distribution. For the zero-field Sherrington-Kirkpatrick model, we additionally prove temperature chaos at every pair of distinct nonnegative inverse temperatures.

In this paper, we introduce diassociative conformal algebras that unify both associative conformal algebras and diassociative algebras. We show that the category of diassociative conformal algebras is equivalent to the category of equivalence classes of formal distribution diassociative algebras. By employing its Maurer-Cartan characterization, we then define the cohomology of a diassociative conformal algebra and show that it carries a Gerstenhaber algebra structure. Our cohomology can be used to study deformations and extensions of the structure. Finally, we define averaging operators on associative conformal algebras and find their close relations with diassociative conformal algebras.

Optimizing industrial process flowsheets is often computationally prohibitive due to the high cost of rigorous simulations and the curse of dimensionality inherent in complex design spaces. To address these challenges, we present a reduced-space multi-fidelity Bayesian optimization (RS-MFBO) framework designed for high-dimensional, expensive black-box functions. The approach integrates Global Sensitivity Analysis (GSA) for dimensionality reduction with a fidelity-augmented Gaussian process that captures correlations between low-cost approximations and expensive high-fidelity evaluations. A cost-aware acquisition strategy, augmented with cooldown and promotion mechanisms, adaptively guides the allocation of samples across fidelities. The framework is validated on two distinct industrial process simulators: a plasmid DNA bioprocess in SuperPro Designer and a green fuel synthesis plant in Aspen HYSYS. Results across diverse economic and physical objectives demonstrate that the proposed method substantially reduces the number of high-fidelity simulator evaluations while maintaining competitive optimization performance compared to single-fidelity baselines. These results highlight RS-MFBO as a scalable, simulator-agnostic approach for cost-constrained black-box optimization.

Reliable quantum engineering is essential for turning quantum phenomena into practical technologies. As quantum platforms grow in scale and complexity, their characterization and operation require increasing human effort and coordination. Scientific artificial intelligence agents, which can plan experiments, operate instruments, and analyze observations, offer a promising route towards autonomous quantum engineering. Yet whether current agents can perform reliably in this setting has not been systematically established. To fill this gap, we developed Quantum-Harbor, a virtual laboratory that provides a controlled execution environment for agents to interact with quantum systems. This design enables direct verification of both the actions taken and the conclusions drawn. Building on this framework, we introduce QIQCBench, a benchmark of $49$ expert-authored tasks spanning multiple layers including calibration and control, error correction and compilation, sensing and networking. Across $17$ frontier agentic systems, QIQCBench reveals wide variation in verified performance. These results expose a substantial gap between demonstrating capability and achieving reliable operation, and establish Quantum-Harbor as a foundation for measuring progress towards verified autonomy in quantum engineering.

A quandle is an algebraic system that can be regarded as a generalization of the conjugation operation in groups. We study a quandle construction associated with group actions and determine its structural properties, including its inner automorphism group, connected components, and subquandles. As a principal application, we establish a Cayley-type embedding theorem for finite quandles. Applying the construction to the natural action of the symmetric group, we obtain, for each $n$, a single quandle into which every quandle of cardinality $n$ embeds.

We give an elementary and combinatorial proof of the spherical Hadwiger theorem, which states that every continuous, rotation-invariant valuation on the space of closed convex cones in $\R^n$ is a linear combination of the conic intrinsic volumes. Our proof uses a signed orthoscheme decomposition. Its vanishing step requires only integrability of an orthoscheme restriction of the residual valuation, a weaker regularity condition than continuity.

Braking index measurements of hundreds of pulsars, despite observational caveats, show a clear trend: $n<3$ at early ages, $n>3$ at middle ages, and increasingly large, fluctuating values at larger characteristic ages. Crustal magnetic fields in isolated neutron stars evolve through Ohmic dissipation and non-linear Hall redistribution across scales, with additional early-time contributions from the chiral magnetic effect and post-burial re-emergence. Initial conditions are usually assumed dipole-dominated, whereas more complex and realistic fields, with energy spread more evenly over a broad range of scales, have not been explored in this context. We assess how the internal dynamics of initially non-trivial fields change the dipolar field strength and obliquity, and how their combined contribution to the braking index $n$, together with that of the alignment, compares with the observed values. To this end we perform long-term 3D magneto-thermal simulations, for configurations ranging from the dipole-dominated case to tangled, small-scale-dominated fields. Small-scale dominated configurations with weak dipolar components show rich dynamics: the dipolar mode is continuously fed by the small scales and may grow or decay depending on the initial spectrum, driving $n<3$ or $n>3$, while the obliquity varies on timescales as short as ${\cal O}$(kyr). These spontaneous changes often dominate the alignment torque and can account for the large values of $n-3$ of either sign observed at characteristic ages $\tau_c\gtrsim10^5$ yr, unlike the dipole-dominated models. However, short-term magnetospheric fluctuations or superfluid-driven variations of the torque may still be needed to explain the measurements and the structured timing residuals quantitatively. Future work comparing these models with the observed timing properties could fine-tune the most promising initial configurations.

We submit MéTRON-FR, a 125M GPT-2 pretrained on 92.47M words of French, to the BabyLM 2026 Strict track. It scores 85.97 +/- 0.17% on QFrBLiMP (a native Quebec-French benchmark of grammatical minimal pairs) and 62.80% on the BabyLM-weighted leaderboard. A cross-lingual GLUE (General Language Understanding Evaluation) protocol that combines French task-data translation with rank-16 LoRA (Low-Rank Adaptation) produces a sharp task-type gradient: relational tasks gain measurably, while world-knowledge tasks regress. Bilingual Lexicon Induction aligns the French embeddings to GPT-2 at p@1 = 68.84 +/- 8.61%, 18X above chance, suggesting cross-lingual alignment tracks acquired grammatical competence rather than training duration. An ablation study shows that single-token zero-shot scoring is dominated by tokenizer and template artifacts at the child scale, motivating tokenizer-swap sensitivity, placebo-controlled prompting, and native-language minimal-pair benchmarks as standard diagnostics.

Family caregivers of people living with dementia shoulder emotional and practical responsibilities, yet their own wellbeing often remains peripheral to dementia care. We built CareMirror, an envisioned caregiver wellbeing ecosystem with interconnected caregiver- and clinician-facing interfaces for longitudinal reflection, personalized support, and caregiver-controlled sharing with clinical care. We conducted semi-structured interviews with 14 caregivers, using CareMirror as a design probe to examine how they perceived this ecosystem and what expectations, concerns, and boundaries emerged around clinical connection. Caregivers valued attention to their wellbeing, longitudinal awareness, context-sensitive support, and clinical visibility when it could lead to meaningful follow-up. However, repeated reflection could become burdensome or emotionally difficult, automatic clinical sharing could inhibit candid disclosure, and participants wanted control over what information entered clinical care. They also expected AI to support reflection and communication without replacing caregiver voice or clinician judgment. We contribute design considerations for proactive, clinically connected caregiver wellbeing support.

Electron-phonon interactions underpin a variety of phenomena, ranging from transport and superconductivity to polarons and ultrafast carrier dynamics. Despite being one of the most intensely studied subjects in condensed matter physics, research on electron-phonon physics mostly focused on linear, first-order couplings. Second- and higher-order nonlinear couplings are commonly ignored because their calculations are too demanding and we lack computational frameworks that can provide both diagonal and off-diagonal coupling matrix elements. In this work, we report a theory and computational method for computing nonlinear electron-phonon interactions of any order in real materials. Our approach combines the advantages of unit-cell calculations of electron wavefunctions and supercell calculations of phonon perturbations, is systematically improvable, can be used with either semilocal or nonlocal exchange-correlation functionals, and is amenable to Wannier-Fourier interpolation. As a first proof of concept, we illustrate this method by computing second-order electron-phonon coupling matrix elements in diamond, lithium fluoride, and graphite as representative nonpolar semiconductors, polar semiconductors, and metals, respectively. Furthermore, we generalize the ab initio polaron equations to second-order electron-phonon couplings, and we show that second-order couplings are essential to achieve quantitative accuracy in polaron formation energies and hopping barriers. The present methodology will find application in the study of all properties and phenomena that are currently being investigated within the linear electron-phonon coupling approximation, from phonon-mediated superconductivity to excited-states dynamics, both in harmonic and anharmonic systems.

A sequence $(y_n^*)$ in the unit sphere of $E^*$ is strongly normal if the vectors $x\in E$ satisfying $\sum_n|y_n^*(x)|<\infty$ form a dense subspace. For every set $\Gamma$ of cardinality at least the continuum, we construct a normalized weakly null sequence in $\ell_1(\Gamma)^*$ with no strongly normal subsequence. This answers a question of Śliwa negatively in ZFC. Together with the classical selection argument below the bounding number $\mathfrak b$, the construction gives positive and negative bounds for this subsequence property on $\ell_1(\kappa)$. Its validity on $\ell_1(\omega_1)$ is independent of ZFC. The intermediate range $\mathfrak b\leqslant\kappa<\mathfrak c$ remains undecided by these results. We also give a counterexample in a Banach sequence space that is not isomorphic to any $\ell_1(\Gamma)$.

Optical inter-satellite links (OISLs) are key enablers for high-capacity space networks and next-generation satellite constellations. However, their extreme directionality makes link reliability highly sensitive to platform-induced pointing jitter, which causes random misalignment between the transmitter and receiver beams. In this paper, we develop a tractable closed-form statistical channel model for point-to-point OISLs subject to independent pointing errors at both terminals. Accurate Gaussian main-lobe approximations are applied to the transmitter far-field pattern and receiver coupling efficiency. This transforms the diffraction-based channel response into closed-form expressions for the channel-gain distribution, outage probability, and ergodic capacity. The analytical results are validated through Monte Carlo simulations and used to study the impact of terminal stability, beam divergence, and link margin on OISL performance. The results show that outage probability is governed by the weaker terminal in terms of pointing stability, while improving only the stronger terminal provides minimal additional benefit. In contrast, the ergodic-capacity penalty depends on the combined stability of both terminals, revealing a fundamental distinction between reliability and throughput metrics. The proposed framework provides practical design guidelines for selecting beam parameters and specifying pointing and tracking requirements under varying levels of platform instability.

Hybrid dynamical systems provide a powerful modeling framework for robotic systems, particularly in contact-rich environments. However, ensuring safety and performance in such systems remains challenging due to the intricate coupling between continuous dynamics and discrete mode transitions. In this work, we extend classical Hamilton-Jacobi (HJ) reachability analysis, a formal verification method for continuous-time nonlinear systems, to hybrid dynamical systems. Our framework characterizes safe sets for hybrid systems through a generalized value function defined over both discrete and continuous states while accounting for control constraints and model uncertainty. We additionally provide a numerical algorithm to compute this value function. Building on these safe sets, we propose two different mechanisms to integrate performance objectives. First, we introduce a hybrid least-restrictive safety filter that intervenes on both the discrete and continuous components of a nominal controller only when necessary to avoid unsafe states, thereby preserving nominal behavior whenever possible. Second, we formulate and compute hybrid backward reach-avoid tubes, enabling the simultaneous enforcement of safety and goal-reaching behavior, an extension not previously addressed within hybrid HJ reachability. This enables the synthesis of continuous and discrete control policies that guarantee both safety and task completion. We validate our framework through simulation studies and real-world experiments on a quadrupedal robot, demonstrating its effectiveness in hybrid mode planning and safety-critical applications.

Many learned sequential decision systems map the current state directly to an action. That shortcut becomes brittle when candidate actions are numerous, geometrically structured, and rebuilt with the state. One-to-many mobile charging makes this setting concrete: with N=250 sensors, the initial state induces about 1,125 candidate charging-stop actions; each chosen stop simultaneously serves its in-range sensors, and the action universe changes as sensors die. LP-BTS is a learning-guided planning architecture: a graph proposal policy concentrates a small candidate support, a learned value critic evaluates leaves, and edge-budgeted PUCT compares short simulated futures before committing an action. Because the policy scores this set without a fixed output head, a single frozen checkpoint covers every evaluated setting, spanning action universes from 736 to 2,813 stops. Matched ablations reveal complementary effects: uniform sampling costs 8.8 survival percentage points, while, with targeted support fixed, PUCT jointly retains 1.4 points (about 3.5 of 250 sensors) and direct policy selection travels 23% farther. On a prospectively specified, sealed 30-scenario confirmatory bank evaluated once, LP-BTS attains the highest observed survival (0.4545) and alive-AUC (0.8031). Its estimated survival advantage over the strongest domain-engineered comparator is +0.0066 (95% CI [-0.0037, +0.0184]), an unresolved difference, while it exceeds a deadline heuristic and two source-derived direct-policy reconstructions on every paired scenario. Both learned rows are trained, source-derived reconstructions of variants reported by Gong et al. In this setting, the results provide controlled evidence about learning-guided planning in a large, dynamic action space.

We prove that pseudo-amenability implies approximate amenability for Banach algebras with a bounded approximate identity. Consequently, these two notions of generalized amenability are equivalent for such Banach algebras. Our proof closes a gap in the paper by F. Ghahramani and Y. Zhang, Pseudo-amenable and pseudo-contractible Banach algebras.

Real-time multi-object tracking systems remain highly vulnerable to full and long-term occlusion, where targets temporarily or completely disappear from the camera's field of view. Conventional trackers may terminate trajectories prematurely, resulting in identity loss and reduced situational awareness in applications such as defense and surveillance. This work proposes an occlusion-robust target tracking framework that maintains target identity and trajectory continuity through the integration of YOLOv11n object detection, Kalman Filter motion prediction, and occlusion-aware appearance-based re-identification. The framework consists of three stages: object detection, position estimation during occlusion, and identity recovery after target reappearance. Six Re-Identification (Re-ID) architectures were evaluated within the same tracking framework under identical conditions, with the Occlusion-Aware Mask Network (OAMN) achieving the best overall performance and therefore selected for the final pipeline. The framework was benchmarked against OccluTrack on the public OVIS dataset, achieving relative improvements of 18.1 percent in Multiple Object Tracking Accuracy (MOTA) and 25.1 percent in Identity F1 Score (IDF1), while reducing identity switches by 12.8 percent. On a custom military dataset simulating surveillance and battlefield-like environments with long-term occlusion, the framework achieved a MOTA of 0.734 and an IDF1 of 0.729, corresponding to relative improvements of 14.2 percent and 5.8 percent over OccluTrack. The system demonstrated strong tracking continuity, robust identity preservation, and reliable trajectory estimation under challenging occlusion conditions, highlighting its effectiveness for defense-related surveillance applications requiring continuous target tracking during visibility loss.

The high-overtone quasinormal-mode spectrum encodes the near-singularity Kasner scaling of black-hole interiors. We show that distinct structures in this spectrum independently determine the temporal and spatial Kasner grades, allowing the corresponding metric exponents to be reconstructed without imposing Kasner constraints or invoking holography. We demonstrate this independent reconstruction at the fixed Kasner point of five-dimensional asymptotically flat Schwarzschild and planar Schwarzschild–AdS$_5$, and then extend it to a continuously varying Gibbons–Maeda black-hole family. The reconstructed exponents satisfy the Kasner relation a posteriori, providing a nontrivial geometric consistency check. More generally, the high-overtone spectrum separates into global spectral data and local algebraic corrections whose fractional powers are fixed by the near-singularity Kasner geometry. These fractional corrections therefore provide a direct spectral probe of the local geometry behind the horizon.

A revision of the drift-reduced Braginskii model is presented, focusing on its practical implementation in numerical codes, and systematically analyzing the effect of several commonly-used approximations on energy conservation. As a practical example, the effect of the approximations is quantified in the GBS code. By dropping the most important approximations and related energy sinks, the simulation shows increased transport levels and a target heat flux with closer agreement to experimental observations.

The Spanish trill /r/ is canonically described as a short sequence of lingual closures, yet large-scale acoustic evidence across corpora is scarce, and automatic counters locating envelope peaks tend to conflate each closure with its release. We present a closure-based detector that locates closures gated by a quality filter and cross-checked against an independent autocorrelation-based period estimator. Applied to 3,560 well-formed (voiced, periodic) trill tokens from 356 speakers across six Spanish corpora, the detector yields a median of two closures and an inter-closure period near 36ms, matching the descriptive literature on all corpora. At the speaker level, phonotactic context is the only factor with a robust, medium effect: onset trills (word-initial and post-/n,l,s/) show more closures than intervocalic rr. We find no robust evidence of a sex effect once closures are counted directly. We report reference values and release a reproducible measurement pipeline for Spanish trills.

Recent measurements of the distribution of galaxies by the Dark Energy Spectroscopic Instrument (DESI) are in mild tension with a standard $\Lambda$-Cold Dark Matter cosmological model inferred from the anisotropies of the Cosmic Microwave Background (CMB) when extrapolated to low redshifts. Several solutions to explain this discrepancy in terms of new physics have been put forward, notably from new dynamics in a complex Dark Sector. Here we report on a resolution to this discrepancy that postulates that the inertial and gravitational masses of dark matter particles are not the same. Our best fit to CMB data combined either with Baryon Acoustic Oscillation (BAO) and Full Shape (FS) data from DESI DR1 (Data Release 1), or with BAO DR2 data alone, suggests the presence of a new long-range force in the Dark Sector with strength of $\beta \sim 0.5\% $ relative to gravity, at the $\sim 2.5\sigma$ level. A preliminary analysis combining FS DR1 and BAO DR2 data confirms the statistical power of DR2 and raises the evidence for dark forces to $\sim 3\sigma$, albeit ignoring the not yet available cross-correlation between the two datasets.

Text-conditioned latent diffusion models perform strongly in video generation and are promising backbones for robotic applications. However, existing approaches rely on pixel-level or VAE-based latent representations that lack explicit semantic structure, leaving the impact of the representation space largely unexplored. Slot-based object-centric representations offer a structured alternative by decomposing scenes into object-level latents, or slots. While they have shown success in dynamics modeling and planning, they have not yet been explored for diffusion-based generative modeling. We introduce SlotDiT, a text-guided Diffusion Transformer (DiT) that operates in a slot-based latent space. Given a reference image and a language instruction, SlotDiT decomposes the scene into object-centric slots representing individual entities. Conditioned on the instruction and observed scene context, the model autoregressively denoises future slot trajectories to predict scene dynamics. To systematically investigate latent-space design for diffusion transformers, we compare slot-based representations against VAE-based and semantics-aligned alternatives within a unified DiT framework. Our experiments show that using slots as DiT latents yields competitive video generation quality while consistently improving task-completion rates across four robotic datasets. Furthermore, their compact representation provides a computationally efficient alternative to VAE-based and semantics-aligned latent spaces. Overall, our results demonstrate that object-centric structure is a powerful inductive bias for diffusion-based generative modeling in robotic environments. The project page is available at https://slot-dit.github.io/.

This paper investigates easy strategies to boost the performance of existing networks for lidar semantic scene completion (SSC) without requiring complex architectural redesigns. The fact is that, over the last years, SSC methods have mostly pursued architectural innovations, making the models heavier and more complex, e.g., by jointly training a point cloud semantic segmentation branch. In this work, we take a step back and explore two priors used as simple ingredients (possibly noisy) to improve existing approaches: semantic pseudo-labels and sensor visibility information. Concretely, we provide both kinds of information directly as additional inputs to a given SSC network, requiring only a minimal adaptation of the original architecture. We first demonstrate that endowing input point clouds with semantic pseudo-labels from off-the-shelf segmenters significantly improves the performance of existing SSC models. In fact, by evaluating these models against an oracle, we establish that high-quality semantic priors are a primary driver of semantic gains (mIoU), and that the SSC model can be trained just once with ground-truth semantics and then exploited without retraining using any segmenter. Furthermore, we equip the input lidar point cloud with visibility information that distinguishes between empty spaces (between the lidar and a scanned point) and unknown spaces (outside of lines of sight), providing a secondary performance boost across the tested architectures. We study the design space of data for representing visibility information and bound the remaining headroom with a ground-truth oracle on the free-space labels. On SemanticKITTI, these enhancements make older models competitive with state-of-the-art systems across four architectures, in one case even outperforming them. On the SSCBench-nuScenes benchmark, both priors also transfer with the sparser 32-beam sensor.

The recent high-recoil candidate event reported by LUX-ZEPLIN (LZ) motivates dark-matter scenarios with nonstandard kinematics and momentum-dependent interactions. We study a two-state inelastic dark-matter model in which $\chi_1$ and $\chi_2$ couple off-diagonally to an axion-like particle (ALP) that also couples to gluons and photons. Unlike treatments that assume only one dark-matter state is present today, we track the cosmological evolution of both states. The excited state $\chi_2$ is sufficiently long-lived to survive to the present, with its relic fraction determined by the dark-sector conversion process $\chi_2\chi_2\leftrightarrow\chi_1\chi_1$. We find that this fraction depends strongly on the Lorentz structure of the DM–ALP interaction. Scalar transition couplings lead to nonperturbative conversion rates with a pronounced resonant structure, yielding a final excited-state fraction that varies nonmonotonically with the mediator mass, from $f_2\sim10^{-4}$ to approximately $1/2$ over the parameter range studied. The relative importance of endothermic and exothermic scattering is therefore parameter dependent. By contrast, pseudoscalar transition couplings preserve $f_2\simeq1/2$, yielding recoil spectra dominated by exothermic $\chi_2 N\to\chi_1 N$ down-scattering for the benchmarks considered. Benchmark spectra in all four scenarios can peak near the observed recoil energy of $250\,\mathrm{keV}$. Our results demonstrate that the dominant direction of inelastic scattering in direct detection can be dynamically selected by the early-Universe evolution of the dark sector. With additional data, annual modulation measurements could distinguish these scenarios. We further show that the ALP portal can be directly probed at colliders.

A multiset of pure quantum states is said to be k-learnable if there is a measurement strategy that always narrows an unknown sample drawn from the list down to one of at most k candidates. The parameter k interpolates between distinguishability and antidistinguishability, and provides a unified framework for partial state identification. We prove two universal, and optimal, Gram-matrix criteria for k-learnability: a Frobenius-norm sufficient condition and an entrywise-$\ell_1$ necessary condition. We apply them to derive explicit learnability and copy-complexity guarantees for several well-known sets of states including SIC-POVMs, mutually unbiased bases, and stabilizer states. We further apply our results to zero-error mutation detection problems such as anomaly detection and changepoint detection.

A major open question in the theory of locally compact groups is the following. Let $\mathscr{S}$ be the class of non-discrete compactly generated totally disconnected locally compact groups that are topologically simple. Is the number of local isomorphism classes of groups in $\mathscr{S}$ uncountable? We have answered this question, showing that there are $2^{\aleph_0}$ local isomorphism classes in $\mathscr{S}$. Our result was obtained without the use of artificial intelligence; it arose from a problem session that ran over several days at the workshop "Branch groups: subgroups, rigidity, topologies" at the Universidad Complutense de Madrid, organised by Dominik Francoeur, Alejandra Garrido and Tatiana Nagnibeda.

Telework has expanded rapidly, and understanding the behaviors employees enact under it has become correspondingly important. This study develops an integrative conceptual framework specifying the multidimensional nature of telework behavior. A systematic literature review following PRISMA identified 114 review articles, which were analyzed using constructivist grounded theory. Six behavioral dimensions were identified, covering performance, communication, environmental, task, policy, and well-being conduct. Antecedents were grouped into individual factors, job characteristics, organizational norms, technological factors, and work environment factors. Outcomes were grouped into job satisfaction, productivity, turnover, health and well-being, work-life balance, and social isolation. Three contextual moderators were identified, namely telework modality, telework preference, and cultural and national context. Fourteen propositions link these categories and are advanced as testable claims rather than established findings, since they are derived from published review evidence and have not been empirically tested. The framework proposes telework behavior as the construct through which the conditions of remote work are translated into employee and organizational outcomes, and it specifies a content domain from which measures of the construct can be developed. It also indicates where organizations can direct policy, training, and intervention.

$\mathsf{QAC}^0$ is the class of constant-depth $\mathrm{poly}(n)$-ancilla circuits obtained by extending $\mathsf{QNC}^0$, the class of local circuits, to include nonlocal interactions via arbitrary width Toffoli gates. It is believed to be weaker than its counterpart, $\mathsf{QNC}^0_f$, obtained by including arbitrary-width FANOUT gates instead ($\mathsf{QAC}^0 \subseteq \mathsf{QNC}^0_f$ [Moo99]). In this note, we show that every $O(\log n)$-qubit state can be exactly and cleanly prepared by a $\mathrm{poly}(n)$-ancilla $\mathsf{QAC}^0$ circuit. Previous known $\mathrm{poly}(n)$-ancilla circuits for arbitrary such states are only known via additional access to either FANOUT or QRAM (indexing) gates [Ros21b, GGJ26b], neither of which are known to be in $\mathsf{QAC}^0$. Equivalently, prior constructions of arbitrary $n$-qubit states in $\mathsf{QAC}^0$ require doubly exponential size and we obtain an exponential factor improvement.

Given vectors $v_i \in {\mathbb R}^d$, we consider the problem of choosing a set $I$ independent in a partition matroid in order to maximize the determinant $\det (\sum_{i \in I} v_i v_i^T)$. Our main result is a polynomial-time approximation algorithm that finds a solution of value $det ( \sum_{i \in I} v_{i} v_{i}^T) \geq e^{-O(d)} OPT$, where $OPT = \max_{I^*} det ( \sum_{i \in I^*} v_{i} v_{i}^T)$. For partition matroids of rank $m \leq d$, we give a similar result for approximating the $m$-dimensional volume spanned by the chosen vectors, within a factor of $e^{O(m)}$. This matches earlier known algorithms that estimate the optimal value but do not find the corresponding solution, up to a constant in the exponent. Similar to these estimation algorithms, our algorithm is based on the saddle-point relaxation proposed by Nikolov and Singh. A new ingredient is a randomized transformation based on $1/2$-stable distributions, which converts the saddle-point relaxation into a more convenient multilinear relaxation.

This paper is the first work at the intersection of game theory and property testing, giving algorithms and lower bounds for efficiently testing whether an allocation mechanism is incentive compatible (IC). We propose distinguishing whether a mechanism is $\epsilon$-far from being IC, i.e., when it observes many monotonicity "violations." Conceptually, inspired by the literature on Boolean function monotonicity testing, we construct a tester for discrete single-parameter allocation rules. Technically, our work is the first to consider monotonicity testing of vector-valued functions on the hypergrid. We give a $\tilde{O}(n/\epsilon)$-query algorithm to test whether a function (representing n-player allocation mechanisms) is coordinate-wise monotone versus $\epsilon$-far from it. We also show a matching lower bound: the class of coordinate-wise monotone vector-valued functions on a Boolean hypercube or hypergrid requires $\tilde{\Omega}(n/\epsilon)$ queries to test whether it is $\epsilon$-far from monotonicity, and this holds even if the tester is two-sided and allowed to make adaptive queries. Finally, we extend our upper bound to and give a tester of the same query complexity for pricing functions of allocation mechanisms. This requires overcoming the technical challenge that the path in function space to the closest IC mechanism may involve interdependent changes to both the price and the allocation rule.