Papers

We analyze laminar-to-turbulent transition over a blunt capsule representative of Martian entry at Mach 26 using global modal and transient-growth analyses about a thermochemical base flow characteristic of high-altitude entry conditions. Across the Reynolds-number range considered ($Re_\infty = 10^4$–$10^6$), the axisymmetric global eigenspectrum remains stable, ruling out temporally unstable axisymmetric global modes as a viable route to transition. The flow nevertheless supports strong transient growth concentrated in the shear–entropy layer generated by bow-shock curvature. Energy-budget analysis reveals a two-stage process: Reynolds-stress production in the mean shear first amplifies kinetic disturbances, which then generate entropy fluctuations as they traverse the strong base-flow entropy gradient. The optimal gain scales linearly with the Reynolds number based on the capsule nose radius, whereas boundary-layer-localized disturbances become competitive at the highest Reynolds numbers examined. These results identify shear–entropy-layer transient growth as a plausible linear pathway for seeding transition in high-enthalpy blunt-entry vehicles.

Inertial propulsion in fluids generally arises from an unbalanced flux of momentum. For the case of wave-driven propulsion, momentum is transported away from an oscillating raft in the form of self-excited surface waves. While this mechanism has previously been analyzed for rigid rafts, the role of flexibility has yet to be investigated. In this work, we develop a fluid-structure interaction model for a periodically driven two-dimensional flexible raft resting at the free surface of a fluid. The raft is modeled as an Euler–Bernoulli beam and is coupled to a weakly dissipative quasi-potential model of the fluid beneath. Varying the flexural stiffness and forcing position reveals new features associated with the introduction of flexibility, including the possibilities of thrust enhancement and reversal. By projecting the raft response onto its free rigid-body and elastic modes, the fluid loading can be represented as a modal impedance, informing a computationally efficient reduced-order model. Analysis of the modal response and its symmetries facilitates physical interpretation of our key findings. For a uniform raft, excitation of any single mode in isolation is incapable of producing a net thrust, and thus efficient wave propulsion requires a blend of interfering modes with appropriately coordinated amplitudes and phases.

By hinging on the assumption that a system to be controlled is fully unknown, many data-driven control approaches do not leverage available or readily inferable priors. In contrast to this viewpoint, this paper analyzes the impact of using the system's equilibrium subspace to inform direct and indirect linear quadratic regulation. For the indirect case, we show how including a constraint on the equilibrium subspace in the identification problem changes the statistical properties of the learned model. In particular, we show that enforcing consistency with respect to the equilibrium subspace leads to a reduction in the estimator variance that, in turn, enhances model-based control performance. In the direct case, we show how this prior can be leveraged to gain insight into the controlled system without requiring an explicit identification step. These results are supported by both numerical and experimental evidence, showcasing the advantages of explicitly leveraging the equilibrium manifold as a prior in data-driven control.

We study reversible secp256k1 point-addition circuits developed through ECDSA.Fail for Shor's elliptic-curve discrete-logarithm algorithm. Two complementary constructions improve record-and-replay GCD arithmetic: Jump-2 groups binary-GCD steps and compresses their decisions using base-5 encoding, while ping-pong uses fixed register alternation and one-bit decisions to avoid full-width comparisons and data-dependent swaps. Fused replay combines doubling and signed addition into one modular correction. Both constructions support quantum-addressed window selection with measurement-based lookup cleanup. We compare three circuits on 100,000 fresh inputs across nine lookup-table configurations. A separately tested repair uses 1,419 qubits and 1.356 million mean executed Toffolis, with no detected failures on another 100,000 inputs. Structured supported-input counterexamples remain, so these tests do not establish all-input correctness. We also provide conditional coherent-error analysis and reversible safegcd comparisons. Under the stated window allowance, repaired-circuit resources lie below Google's low-gate caps and Schrottenloher's low-gate estimates, but differing accounting and correctness evidence preclude formal dominance. The results concern individual window-selected additions, not complete Shor computations.

Scaling superconducting quantum processors requires interconnects that provide both non-local connectivity and parallel entangling operations. While current quantum routers offer greater connectivity than nearest-neighbour topologies, their support for parallel, independently addressable two-qubit gates remains fundamentally limited, imposing severe compilation overheads that constitute a critical bottleneck. Here we introduce a bus-based reconfigurable quantum router that enables parallel controlled-$Z$ (CZ) gates for modular superconducting processors. Constructed from a flux-tunable SQUID network, the router selectively connects interface qubits to two shared buses, allowing destructive interference to suppress idle interactions while supporting two disjoint CZ gates in parallel. Full-system Hamiltonian simulations yield parallel-gate infidelities ranging from $7.9\times10^{-4}$ to $1.8\times10^{-3}$ under the operating conditions considered, and an open-system analysis identifies the coherence requirements for high-fidelity operation. We further assess the circuit-level consequences using hardware-aware compilation and resource-constrained scheduling. For the 36-qubit quantum Fourier transform (QFT), QAOA-MaxCut, and random-pairing circuits, the router reduces the median SWAP count by up to $34\%$ and the native CZ count by up to $20\%$ relative to a matched two-dimensional grid. Circuit-depth reductions are workload-dependent, reaching $20\%$ for QAOA-MaxCut but remaining negligible for the QFT despite its lower gate count. These results show that enhanced connectivity and schedulable parallelism provide complementary benefits for connectivity-intensive algorithms, establishing the router as a compiler-visible hardware resource and providing a scalable architectural pathway toward constructing highly connected modular superconducting quantum processors.

We prove Laugesen and Siudeja's conjecture on Neumann eigenvalues of isosceles triangles. After multiplication by the squared diameter, the first positive symmetric eigenvalue increases strictly with the apex angle $\theta$, while the first antisymmetric eigenvalue decreases strictly up to the equilateral triangle and increases strictly thereafter. \rev{The proof combines a slice-average estimate of directional energies with the explicit equilateral eigenfunction.} A quadratic correction across slices also gives a two-term expansion for every fixed positive spectral index $k$ as $\theta\downarrow0$, with relative correction $\theta^2/6$ and remainder $O_k(\theta^4)$.

Audio-language models can produce confident answers unsupported by the audio, motivating uncertainty estimates that identify unreliable responses. We compare probability-based, sampling-based, self-verification, evidential, and contrastive measures across four open-weight models and five audio QA benchmarks. In multiple-choice evaluation, first-token measures are strongest overall, with top-1 probability achieving a mean AUROC of .740, compared with .708 for ten-sample discrete semantic entropy, while requiring no additional model calls. Across four benchmarks, shifting from multiple-choice to open-ended evaluation lowers mean accuracy from 57.6% to 36.6%, yet uncertainty remains predictive of errors: semantic entropy, maximum token entropy, and semantic agreement achieve mean AUROCs of .697, .694, and .693, respectively. To test whether uncertainty reflects the evidence available to answer the question, we perform input ablations that remove either the audio or the question. Across top-1 confidence, entropy, and sampling-based measures, removing audio reduces error-detection AUROC by .101 on average, compared with .010 when removing the question. Together, these results establish efficient uncertainty baselines and show that uncertainty in audio-language models depends substantially more on available audio evidence than on question text.

Sim-to-real transfer has made substantial progress, but can still produce controllers that remain stable and functional on hardware while suffering from degraded tracking accuracy due to residual dynamics mismatch. Correcting these errors typically requires identifying the underlying system dynamics, adapting the control policy, or returning to simulation for additional training and finetuning, all of which can require substantial data and computation. We propose OSRAM (Online Sim-to-Real Adaptation via Closed-Loop System Modeling), a framework that instead adapts the reference commands provided to an existing controller. OSRAM treats the deployed robot and its policy as a unified closed-loop dynamical system and learns its task-level command-response behavior directly from tracking observations. A closed-loop dynamics model is meta-trained across randomized dynamics in simulation and rapidly finetuned after deployment using limited real-world interaction. The adapted model is then used to optimize future reference commands while leaving the underlying control policy unchanged. We evaluate OSRAM on bipedal velocity tracking and loco-manipulation in simulation and on hardware. Results show that closed-loop modeling improves prediction and tracking accuracy under unseen dynamics, while online reference adaptation reduces residual sim-to-real tracking errors across different control objectives and hardware configurations. These results demonstrate that adapting the behavior of the robot-policy closed loop provides a practical alternative to finetuning the policy or identifying the full physical dynamics for sim-to-real transfer. More information can be found at http://generalroboticslab.com/OSRAM.

New words are invented every day. A human listener can learn a new word by hearing it clearly once and inferring its usage from sentence context. This paper proposes granting ASR a similar ability to learn the contextual representations and spellings of new words from unlabeled test data at test time. A frozen CTC acoustic model provides spellings, a frozen language model provides contextual evidence for out-of-vocabulary (OOV) word detection, and an adaptation module expands the vocabulary by learning the lexical token representations with distributions over CTC-generated candidates. The spelling model of each token is optimized by minimizing a Kullback-Leibler divergence (KLD) objective. We demonstrate that the CTC-weighted language model log likelihood ratio can be interpreted as the KLD between the unknown correct ASR and the unsupervised learned ASR, and that, using a Pinsker bound, the square root of KLD can be interpreted as an upper bound on the total variation distance between the true and estimated spelling of the unknown word. Experiments show relative OOV character-error-rate reductions of up to 14.97% on LibriSpeech and 6.67% on dysarthric Speech Accessibility Project data for recurring OOV words, relative to the corresponding rescoring system.

Forecasting requires large language model (LLM) agents to gather evolving evidence, revise their beliefs, and make calibrated predictions over time. Yet evaluating and improving these capabilities is challenging because real-world events unfold continuously and outcomes may take months to resolve. We introduce Forecast-Dojo, a replayable environment that reconstructs resolved prediction-market events as sequences of dated forecasting steps, enabling reproducible evaluation, longitudinal analysis, and training from agent interactions. It comprises 1,568 Polymarket events split temporally into training and evaluation sets, alongside 18.8 million dated news articles. Across 12 models, research tools consistently improve forecasting performance, and greater capture of new evidence is associated with larger improvements over time. Nevertheless, all models trail historical market forecasts, while carrying a belief notebook across dates reduces research costs without consistently improving forecast quality. We supervise fine-tuned Qwen3-30B-A3B-Thinking-2507 on collected trajectories from Qwen3-235B-A22B-Thinking-2507 to demonstrate the environment's training utility. The student's accuracy is improved from 34.8% to 42.8% on the evaluation set.

We prove moment and mixed-tail inequalities for decoupled homogeneous chaoses of arbitrary fixed order generated by independent, centered, bounded random variables with coordinate-dependent second-moment bounds. The estimates hold for arbitrary real rectangular coefficient tensors and are expressed through weighted slice and partition norms. A Bennett-type logarithmic correction sharpens the contribution of the largest coefficient to these bounds. The proof combines a comparison with sums of sparse exponential variables with an elementary replication argument. We also extend the results to diagonal-free non-decoupled chaoses and sparse sub-exponential inputs. As an application, we establish conditions under which sparse Khatri–Rao random projections approximately preserve norms and inner products for finite collections of tensors. For a sparse random relation containing a planted dense block, we prove matching upper and lower bounds on the required projection dimension, demonstrating the sharpness of the logarithmic correction.

Raman preprocessing and enhancement methods are often assessed by comparing their output with a clean reference spectrum using measures such as mean squared error (MSE). Whether these spectral quality measures reflect downstream analytical performance is rarely tested. We propose a controlled-perturbation framework for this test. Five perturbation types (baseline distortion, independent noise, correlated noise, global wavenumber shift, and nonlinear axis warping) at eight strengths produce paired changes in a quality measure (metric harm) and in downstream performance (task harm). The alignment gap (AG) quantifies how much a single monotone metric-to-task relationship, fitted by isotonic regression, improves when each perturbation type receives its own relationship. Ordering concordance (OC), a clustered Kendall-type index, measures how often a measure ranks conditions of different perturbation types in the same order as their task harm. Cluster-bootstrap intervals and Holm-adjusted sign-flip tests compare twelve candidate measures with MSE. Three public datasets are used: bacterial classification by principal component analysis and logistic regression, sugar-mixture quantification by partial least squares regression, and mineral identification by cosine library matching. Models are fitted to unperturbed or to correspondingly perturbed training spectra. For bacterial classifiers fitted to unperturbed spectra, Wasserstein distance improved both statistics relative to MSE (Holm-adjusted p < 0.05), also after axis perturbations were removed or spectra were compared on a common grid; refitting to perturbed spectra reversed this advantage. Peak-based measures and a structure-to-noise ratio improved both statistics only for refitted sugar calibrations, and no candidate did so for mineral identification. Open code supports testing new measures.

We study how much revenue a seller can lose by restricting attention to selling separately or grand bundling, in the setting of a single additive buyer with independent item values. Although revenue-optimal mechanisms can require lotteries and infinite menus, Babaioff, Immorlica, Lucier, and Weinberg showed that the better of these two simple formats always achieves a constant fraction of optimal revenue. We prove that $\mathrm{OPT} \le 3.52 \max\{\mathrm{SREV}, \mathrm{BREV}\}$, where $\mathrm{SREV}$ and $\mathrm{BREV}$ are the optimal revenues from selling separately and grand bundling, respectively. This improves the previous best-known approximation factor of $5.2$ due to Ma and Simchi-Levi and narrows the gap to the known lower bound of $2$.

Covariates summarized over a subject's completed follow-up are sometimes entered into Cox regression as though observed at baseline. This practice incorporates future event or censoring information and changes both the estimand and its sampling behavior. We analyze an affine class in which a genuine baseline covariate is contaminated by realized follow-up time. Treating partial likelihood as an observed-data estimation criterion, we derive the population score and characterize its unique least-false coefficient. Exact continuous-time benchmarks show scale reduction and saturation under strong contamination, while administrative censoring destroys the reduction and may produce overshoot. Grouping exit times with Breslow ties changes the geometry: the coefficient has a single hump and eventually returns to zero even though the induced association diverges. We also derive an observed-data influence function and show why model-based variance can be either too small or too large. A subject-level sandwich consistently estimates uncertainty around the least-false target under the stated conditions, but it does not correct the target itself.

Panel conditioning, the causal effect of prior survey participation on responses, can vary with tenure. Under an additive model of cell means in period, entry cohort, and tenure, we characterize which features of the conditioning path a staggered panel identifies on its observed support, and how the unidentified component affects common panel estimators. The identified set of the path is an affine translate of the tenure projection of the cell design's kernel, and a linear functional of the path is identified exactly when it annihilates that projection. It always contains an affine direction and, when the entry cohorts share a stride, periodic directions, which exhaust it under a connectivity condition on observed increments; second differences at that stride are then identified, and ordinary ones generally are not when the stride exceeds one. Under a recruitment condition, an interrupted schedule such as the four-eight-four rotation of the Current Population Survey (CPS) distinguishes a constant increment per interview from one per calendar month, which no equally spaced schedule can. We give support conditions for recovery under a plateau, entry-wave negative controls, or bounded cohort drift. A second set of results links identification to regression: two-way fixed effects absorb every unidentified direction, so the remaining conditioning bias is normalization-invariant and itself identified, and a two-way regression with tenure indicators corrects it under a residual-rank condition. When event time is aligned with tenure, conditioning shifts event-study coefficients by a known linear functional of the path, producing pre-trends without anticipation; bounds on identified curvature bound those shifts. Simulations verify the identities, a 19-wave Japanese panel illustrates the support calculations, and published CPS month-in-sample indices give a descriptive, not identifying, example.

Long-running LLM applications and autonomous agents repeatedly send growing context, making prefix caching critical for reducing prefill cost. This raises a fundamental question: do decades of traditional cache-management techniques transfer directly to agentic and conversational LLM serving? We analyze production traces comprising over 20 billion processed tokens and evaluate 14 eviction algorithms across capacity-constrained HBM and large memory-pool settings. We find that simple Least Recently Used (LRU) is surprisingly robust, offering competitive performance against sophisticated traditional policies, though substantial headroom to Belady's optimum remains. The reason is structural: prefix reuse is dominated by the steady pacing of active sessions, making recency an unusually strong predictor for eviction. Nevertheless, prefix caching diverges from traditional workloads in ways that recency ignores, notably the prevalence of single-turn requests, extreme session footprint skew, and highly non-uniform miss costs because attention computation scales with token depth. To close the gap to optimality, we establish several design principles for prefix-cache management: keeping recency as the baseline while incorporating quick demotion to filter one-hit prompts, compute-aware eviction to prioritize expensive misses, and capacity-adaptive eviction granularity. Our traces and simulator are publicly available.

The key part of the current paper is the computation of boundary and Eisenstein cohomology of $GL_4({\mathbb Z})$ with coefficient in any highest weight representations. The method we develop let us compute in an alternative way the cohomology of $SL_3({\mathbb Z})$ and of $GL_3({\mathbb Z})$ with coefficients in any highest weight representation. This is done in a simpler, faster and in a more structured way compared to \cite{BHHM}. We state a duality for the boundary cohomology of $GL_m({\mathbb Z})$ of the type of Serre's duality, where the dualizing sheaf is a power of the determinant representation. We refine this duality to a duality on the level of the spectral sequence for the boundary cohomology $E_\infty^{p,q}$. We state it as a conjecture. However, all the computations, 30 different families of representations, satisfy this conjecture. We compute the Eisenstein cohomology of $GL_4({\mathbb Z})$ with coefficients in the symmetric powers and their twist by the determinant representation. For several other representations, we compute the Eisenstein cohomology, based a few conjectures. Based on those conjectured, one can compute the Eisenstein cohomology in most of the cases. They will be included in the next version of the paper.

Neural fields for scientific tomography are optimized from 2D images, but the actual quantity of interest is often a latent 3D physical field. Because the forward map is many-to-one, low 2D image error need not certify a correct 3D field. Moreover, the latent field is not directly supervised during training, and its error cannot be evaluated against truth at deployment. We develop CoroNeRF to jointly optimize 3D electron density and temperature fields directly from multiview, multiline intensities through a differentiable atomic-emission renderer. Using solar coronal tomography as a controlled testbed, we evaluate physical-field recovery and test whether cross-seed instability provides a ground-truth-free-at-inference indicator of local physical-field error. We underscore the following two observations. (i) Image fidelity is not field fidelity: spectral ablations show that limited-channel reconstructions can fit their available observations well while recovering substantially worse fields, whereas evaluation on a common richer probe exposes the discrepancy. (ii) Cross-seed instability ranks local physical-field error across tested matched-model conditions, supported by sparsification and physical signal-strength controls. Seed-deviation projections provide complementary directional validation, but shared forward-model mismatch can still produce incorrect cross-seed consensus. These results characterize joint thermodynamic recovery and the usefulness and limits of seed-based error localization in a controlled, single-scene solar tomography testbed.

We generalize the Bondal–Orlov Reconstruction Theorem to smooth, proper, tame algebraic stacks with generically trivial stabilizer, whose canonical bundles are nowhere torsion: They do not become trivial after pullback along any finite morphism from a projective curve. Our main tools are coherent Tannaka duality as proved by Lurie and by Hall and Rydh, and Grothendieck duality for proper tame stacks as developed by Hall and Priver. We additionally use the notion of nowhere torsion line bundle on a scheme to prove a version of the Bondal–Orlov Reconstruction Theorem for finite type, separated, Gorenstein schemes which are not necessarily proper, which generalizes work of Ballard, Favero, Ito, and Matsui.

Competing-risks data do not identify latent marginal duration distributions or their dependence without additional restrictions. This paper asks whether restrictions introduced to restore identification themselves restrict the observable law. For a two-risk Archimedean model with categorical exclusion restrictions, we derive a necessary-and-sufficient observable characterization. A discrete single-crossing argument identifies the scalar copula parameter from cell-specific overall survival probabilities, while cause indicators recover the remaining allocation and generate additional specification restrictions. We construct an identification-robust, self-normalized quadratic statistic, invert it to obtain confidence sets, and use empty inverted sets as a conservative specification test. Simulations show that the cause indicator can be decisive: under the weakest contrast considered it converts a frequently uninformative survival-based confidence set into an informative joint set without loss of coverage, whereas excessive categorical contrast can eliminate causes from individual cells and make cause-specific recovery inadmissible. The results turn latent exclusion restrictions into refutable restrictions without estimating covariate derivatives.

Action representation plays a central role in discrete-token vision-language-action (VLA) learning but remains underexamined. Under conventional pose-increment representations, action tokens are sensitive to execution speed and dataset-specific normalization, potentially obscuring geometric structure shared across demonstrations and datasets. We introduce Direction-Scale Decomposition (DSD), an action representation that decomposes translation and rotation increments into direction and scale components before tokenization. DSD isolates motion direction while retaining magnitudes in separate scale channels. We evaluate DSD with uniform binning (BIN) and BEAST, a B-spline-based tokenizer, in simulation and real-world manipulation under both single-dataset and mixed-dataset training. On LIBERO, DSD improves average success rates with both tokenizers. On SimplerEnv, DSD-BIN outperforms BIN by 10.3 percentage points in overall success rate under mixed-dataset training. Real-robot experiments further show gains both with and without robotics pretraining. These results support DSD as an effective action representation for discrete-token VLA models and suggest its potential to mitigate performance degradation when training on large and diverse dataset mixtures. Our project page with additional resources is available at https://vla-dsd.github.io/

The ferromagnetic nitride Fe$_{4}$N has attracted attention for application in spintronic devices due to its high spin polarization and large magnetostriction. We present a combined experimental and theoretical study on the magnetostriction of Fe$_{4-x}$Co$_{x}$N films across a wide composition range. The Fe$_{4-x}$Co$_{x}$N films were grown on SrTiO$_{3}$(001) substrates using molecular beam epitaxy, and the magnetostriction constants along the [100] direction (${\lambda}$$_{100}$) and [111] direction (${\lambda}$$_{111}$) were precisely evaluated using an optical cantilever method. The experimental results reveal that ${\lambda}$$_{111}$ remains positive in the whole composition range and shows a maximum value of +82 ppm around x = 0.9. The variation in ${\lambda}$$_{111}$ with x is much smaller than that in ${\lambda}$$_{100}$, for which giant tunability and sign reversal are observed. First-principles calculations show reasonable agreement with the experimental ${\lambda}$$_{111}$ for x ${\ge}$ 1.6, but give negative values at lower x, and an exceptionally large negative ${\lambda}$$_{111}$ is obtained at x = 0.8, where the Fermi level coincides with a pronounced minority-spin peak in the density of states. The calculated ${\lambda}$$_{111}$ depends strongly on the smearing parameter, indicating that the rhombohedral magnetostriction is highly sensitive to the treatment of atomic disorder. The saturation magnetostriction constant (${\lambda}$$_{s}$) derived from ${\lambda}$$_{100}$ and ${\lambda}$$_{111}$ is also compared with the ${\lambda}$$_{s}$ measured for the (001)-oriented polycrystalline Fe$_{4-x}$Co$_{x}$N films, and a possible scenario for deviation between them is discussed. Our findings clarify the basic features of magnetostriction in the Fe$_{4-x}$Co$_{x}$N system, providing essential magnetoelastic parameters for designing nitride-based spintronic devices.

The analytic tractability of affine pricing models is usually expressed through two complementary formulations: a coordinate-space pricing operator and an exponential-affine transform representation governed by generalized Riccati equations. We develop Affine Holonomy Group Quantization (AHGQ) as a geometric framework in which these two formulations arise from the same underlying structure. The construction separates the affine pricing symbol into a homogeneous quadratic sector and a complementary affine sector. The first generates a finite-dimensional symplectic transport and a centrally extended Lie group, while the second is represented by a multiplicative holonomy carried by a thin-path groupoid. Their combination determines an affine Poincaré–Cartan form. Its characteristic dynamics reduce in momentum variables to the generalized Riccati system and its scalar amplitude, whereas the coordinate representation recovers the standard affine pricing operator. Representative Gaussian and square-root models illustrate the construction. The contribution is structural: AHGQ gives a common geometric origin to the coordinate and transform representations of continuous-path, time-homogeneous affine pricing models.

GPU acceleration is now routine across robotics, but cloud-hosted GPU continuous integration (CI) runners are expensive, resulting in severe under-testing of GPU-accelerated code. We present pytest-gpu-proof, an open-source pytest plugin offering a practical middle ground. Tests can be run on a local machine, signed with a receipt of exactly what ran and what it produced, and integrated into standard CPU CI workflows (e.g., GitHub Actions). The tool is open source and on PyPI, and we are actively integrating it across our lab's software stack.

The large collectivity observed in the rare earth region of the nuclear landscape is well known. The microscopic studies are difficult to perform in this region due to the enormous size of the valence spaces, a problem that can be avoided by means of the use of symmetry based models. Here we present calculations for electromagnetic properties of $^{160-170}$Dy nuclei within the pseudo-SU(3) scheme. The model Hamiltonian includes the preserving symmetry $Q\cdot Q$ term and the symmetry-breaking Nilsson and pairing terms, systematically parametrized for all members of the chain. The model is used to calculate B(E2) and B(M1) inter-band transition strengths between the ground state, $\gamma$ and $\beta$-bands. In addition, we present results for quadrupole moments and $g$ factors in these rotational bands. The results show that the pseudo-SU(3) shell model is a powerful microscopic theory for a description of electromagnetic properties of states in the normal parity sector in heavy deformed nuclei.

Textual descriptions can reduce ambiguity in medical image segmentation by specifying the finding and location to be delineated. Existing text-guided methods mainly improve where image and language features interact but generally retain a single learned update pathway across all image-text pairs. We propose MRSeg, a parameter-efficient framework that uses each image-text pair to route the adaptation of visual and textual features before dense prediction. Frozen ConvNeXt-Tiny and PubMedBERT encoders provide multiscale visual features and clinical text tokens. A joint router uses the deepest visual feature and pooled text to predict a sparse mixture over low-rank adapter bases. The resulting route is shared across separate adapter banks for two visual scales and text, coordinating their adaptation while keeping the feature-specific parameters separate. Region Bridge uses text-derived queries to aggregate dense visual tokens into latent regions, refines these regions through self-attention and text cross-attention, and redistributes the refined information back to the feature maps. Finally, a multiscale decoder combines refined semantic features with shallow image evidence. On QaTa-COV19 and MosMedData+, MRSeg achieves 90.90/83.32 and 81.53/68.82 Dice/mIoU, respectively, with 7.11M trainable parameters and 7.60 GFLOPs. Code: https://github.com/maklachur/MRSeg.

Large language models are increasingly used as inexpensive judges to evaluate outputs, label data, and assess whether a system meets a desired quality standard. Yet using AI judgments for formal statistical inference is fundamentally different from simply treating them as ground-truth labels: AI evaluations can be biased or noisy, and rigorous hypothesis testing requires explicit control of type-I and type-II errors. We study how to use AI judgments, together with selective human verification, to conduct a valid hypothesis test at minimum cost. We consider a population of items with hidden binary labels. After choosing a fixed pool of items, the decision maker can selectively query AI, send an item directly to a human, escalate an AI-scored item to a human after observing the AI report, or stop once sufficient evidence has accumulated. We derive an information-theoretic lower bound that captures the minimum cost of achieving prescribed testing errors and characterizes the value of AI information and human verification through a report-dependent information frontier. Motivated by this characterization, we develop SCALE, a sequential cost-aware policy that combines selective AI scoring with adaptive human escalation. SCALE is valid at finite sample sizes and matches the lower bound to first order as the target error probabilities vanish. We further extend the framework to an unknown AI-output model using paired AI-human pilot data. Numerically, SCALE approaches Human-only or AI-only testing when one source clearly dominates, while achieving its largest savings when inexpensive AI judgments and selective human verification are both valuable.

This study examines how Taiwanese parent-child families negotiate a "good" family life through everyday energy use and imagine future smart homes that support it. We conducted in-home interviews and co-design sessions with 21 families, including 46 parents and children. We found that families pursued a good life through energy practices shaped by thrift, comfort, care, safety, and enjoyment. These arrangements were continually adapted and responded to changing bodies, schedules, people, and infrastructures. This adaptive work was unevenly distributed, which in turn shaped different smart-home imaginaries. Drawing on the lens of Nearby and adversarial design, we conceptualize adaptation as situated sociotechnical work through which families continually rework energy arrangements. We further distinguish collective goods from plural and contestable goods to show why family IoT must support shared values while preserving opportunities to question and revise household arrangements. We offer theoretical and design directions for more adaptive, participatory, and contestable family IoT.

Epstein-Barr virus (EBV) is implicated in multiple sclerosis (MS), but whether EBV-conditioned B cells retain stable transcriptional programs after CNS selection remains uncertain. We reanalyzed datasets spanning experimental EBV infection, CNS-homing B cells in humanized mice, early-MS blood, and paired human CSF and blood. A frozen EBV-induced B-cell factor (F12) was transferred without outcome-driven modification and evaluated using anatomical, sequencing-depth, metabolic, coherence-matched, lineage, and exposure controls. F12 did not predict MS activity (AUC=0.487; p=0.489) and was lower in CSF in nine evaluable participants (mean difference -0.0560; coherence-matched p=0.015). It also remained lower in brain than spleen in all five EBV-exposed mice after matched-depth rarefaction (p=0.0313) and coherence-matched calibration (p=0.0225). The splenic TBX21+CXCR3+ transcriptional gate did not reliably represent the state of anatomically selected brain B cells. An enriched 287-gene cross-system bridge lacked B-lineage and EBV-exposure specificity. In human CSF, an early EBV-associated program was depleted (-0.0492; p=0.0197), whereas latency-III and lymphoblastoid cell line-like programs increased (+0.0666; p=0.0092 and +0.0148; p=0.0096, respectively). Opposite early and late program directions persisted after removing shared genes. No external program met the prespecified prospective criterion. These findings support stage- and compartment-dependent remodeling of EBV-associated B-cell transcription rather than persistence of a stable RNA signature. They distinguish cellular trafficking from transcriptional-state preservation and show that cross-system molecular resemblance alone cannot establish ancestry. EBV-directed biomarker studies should integrate host transcription with virologic, clonotypic, antigen-specific, epigenetic, and longitudinal evidence.

Hydrogen combustion remains a critical safety concern during severe accidents in small modular reactors (SMRs). The compact containment can develop rich-hydrogen atmospheres through zirconium oxidation while oxygen gradually accumulates via radiolysis. Conventional flammability assessment methodologies, however, have been validated and applied only for lean-hydrogen atmospheres. This study establishes a thermodynamic flammability assessment method for rich-hydrogen conditions using the Calculated Adiabatic Flame Temperature (CAFT). In rich mixtures the deficient reactant becomes oxygen and the Lewis number exceeds unity, matching the hydrocarbon regime where CAFT is established. The CAFT was evaluated at 21 points along the measured upper flammability limit boundary of the U.S. NRC FITS database. The limit temperature remains quasi-constant within 17% across steam fractions of 5-50%. Its minimum value of 1112.5 K was adopted as a conservative threshold. The criterion was applied to the NuScale severe accident scenario LCC-05T-03. A transient flammable window is identified during the 44-48 hour interval, when 8-10% oxygen coexists with vigorous hydrogen generation and low steam fractions. These results show that the oxygen criterion alone cannot resolve transient flammability windows. The CAFT criterion enables more comprehensive flammability assessment for SMR containments.