Papers

This letter investigates the joint impact of array curvature and mutual coupling (MC) on holographic curvature-reconfigurable apertures. An analytical framework is developed to characterize spatial correlation, coupling-aware reference directivity, and spectral efficiency. Under the small-curvature regime, we establish a relation linking inter-element distances to coupling perturbations and spatial-mode eigenvalues. Simulation results reveal asymmetric spectral-efficiency variations between transmit- and receive-side array curvature with MC, while their no-MC difference is consistent with zero under hemispherical isotropic scattering. Results based on the 3rd Generation Partnership Project clustered delay line-B channel angular/power profile, together with colored-noise sensitivity analysis, further show that the effects of curvature and receive-side coupling depend on the propagation angular distribution and receiver-noise covariance. These findings highlight the importance of jointly accounting for array geometry, mutual coupling, and receiver noise when evaluating curved holographic multiple-input multiple-output links.

Pinching antenna (PA) systems provide macroscopic spatial reconfigurability by enabling signal radiation from favorable locations along a dielectric waveguide. Exploiting this flexibility, however, typically requires channel knowledge for PA configuration, which can introduce substantial overhead in dynamic propagation environments. This paper proposes PA-assisted differential spatial modulation (PA-DSM), enabling data transfer with spatial index selection and non-coherent data detection without instantaneous channel state information (CSI) at the receiver. To exploit PA reconfigurability while preserving this CSI-efficient operation, we introduce a slow-timescale received signal strength indicator (RSSI)-assisted selection strategy that identifies a favorable subset of PAs from scalar power measurements. We further specialize a two-stage low-complexity maximum-likelihood (LC-ML) detector to phase shift keying (PSK) signaling, obtaining closed-form symbol decisions that remove the joint modulation-combination search while preserving the minimizer of exhaustive differential detection. Furthermore, a moment generating function (MGF)-based pairwise error analysis and the corresponding bit error probability (BEP) bound are developed under normalized common-K Rician fading, revealing how the difference rank and spectrum, number of receive antennas, and previous-state-dependent line-of-sight (LoS) alignment affect the error performance. Numerical results across representative indoor-office, indoor-factory, and street-canyon sixth-generation (6G) Frequency Range 3 (FR3) scenarios validate the analytical trends and quantify when PA-DSM outperforms coherent benchmark schemes whose channel estimates age between pilot transmissions.

Large reasoning models improve performance on challenging problems by allocating additional computation before answering, but longer reasoning does not always lead to better results and can introduce substantial redundant reasoning on simple problems. Conversely, aggressively shortening reasoning can degrade performance on difficult ones. Effective reasoning therefore requires dynamically deciding when additional computation is useful based on the reasoner's capabilities and evolving solution state. Existing approaches often rely on predefined budgets or intervention rules, retrain the target reasoner, or require additional supervision. We introduce MetaCtrl, a lightweight controller that adaptively regulates a frozen reasoner without predefined token budgets or reasoner retraining. We formulate reasoning regulation as a sequential metacognitive control problem: MetaCtrl observes the evolving reasoning trace and decides whether to continue, simplify, skip redundant steps, or conclude reasoning. It is trained directly with reinforcement learning using a reward that prioritizes correctness while favoring shorter trajectories among correct solutions, requiring neither supervised intervention trajectories nor problem-specific budgets. Across seven benchmarks spanning mathematics, science, and code, MetaCtrl consistently improves the accuracy of LRMs while reducing their reasoning length. On DeepSeek-R1-Distill-Qwen-7B, it improves average accuracy by 4.7 points while reducing generation length by 53.3%. Without further training, the same controller transfers to an unseen reasoner (e.g., Qwen3-14B), improving average accuracy by 2.9 points and reducing generation length by 50.3%. These results establish MetaCtrl as a plug-and-play controller for improving reasoning accuracy while substantially reducing inference-time generation. The code is available at https://github.com/binbin2xs/MetaCtrl.

Three dimensional topological insulators of the tetradymite family, such as BiSbTeSe (BSTS), are attractive for spintronic and quantum device applications because of their compensated, bulk-insulating character. Nanostructured BSTS platelets are promising for practical TI based devices. However, their growth often requires complex equipment, organometallic precursors, or electrically insulating substrates. Simple growth on technologically relevant semiconducting substrates remains largely unexplored. Here, we report the vapor phase growth of van der Waals BSTS platelets directly on semiconducting MoS2 using a presynthesized BiSbTe1.5Se1.5 crystal as the source material. The grown BSTS platelets exhibit thicknesses ranging from 4 nm to 246 nm, with an average lateral size of 1.4 mkm for individual platelets. Raman spectra of platelets, acquired in correlation with AFM thickness measurements, reveal contributions from both BSTS and the underlying MoS2 substrate. Among the characteristic BSTS modes, A21g mode shows the highest sensitivity to platelet thickness and local composition. EDS analysis reveals a systematic thickness dependence of the platelet composition, with an apparent crossover around 49 nm that coincides with a change in the thickness dependence of the low-wavenumber component of the E2g Raman band. This correlation suggests a composition-related origin of the Raman behavior, with the compositional variations tentatively attributed to the differential volatility of the constituent elements during growth. These results demonstrate that MoS2 is a promising van der Waals platform for BSTS deposition and provide a structural and compositional baseline for future studies of BSTS-MoS2 heterostructures.

Deep vision models often degrade under distribution shift. Test-time adaptation can improve robustness but typically requires iterative optimization, hyperparameter tuning, and multiple forward-backward passes. We propose Zero-Training Fisher Geometry Alignment (ZFGA), a closed-form method that improves robustness under covariate shift without modifying model parameters. ZFGA is based on the observation that distribution shifts distort feature-space geometry. It estimates the Fisher information matrix of the predictive distribution with respect to feature embeddings and applies a linear transformation that aligns test-feature Fisher geometry with a reference geometry computed from clean data. This provides a natural-gradient-inspired preconditioning step in feature space. We evaluate ZFGA on CIFAR-10-C and ImageNet-C using ResNet-50, DINO ViT-S/16, and CLIP ViT-B/32. ZFGA consistently improves over zero-shot inference across all three models, although it is not the strongest method for every model. Covariance whitening performs better on ResNet-50, while Fisher whitening is statistically indistinguishable from ZFGA on CLIP. Across six training-free and gradient-based alternatives (covariance whitening, Fisher whitening, TENT, T3A, LAME, and AdaNPC), ZFGA is the only method that does not substantially harm any of the three model families. The Fisher geometry distortion is also positively correlated with ZFGA gain (Pearson r = 0.366, p = 0.017), providing preliminary evidence that geometric misalignment contributes to robustness degradation. ZFGA requires only forward passes and matrix operations at inference time, offering a lightweight and deterministic alternative to optimization-based test-time adaptation.

We determine the algebra generated by the normalized parity-twisted crank moments and describe it explicitly as a proper subalgebra of the quasi-modular forms on $\Gamma_0(2)$ closed under $D=q\frac{d}{dq}$. We derive congruences from differential identities and construct a prime-detecting expression involving only the normalized twisted second moment, its products, and its derivatives. Its coefficient of $q^n$ vanishes if and only if $n$ is prime, for every $n\ge2$. We prove that its highest weight, eight, is minimal among prime-detecting elements of this algebra. Finally, we identify the same algebra as the one generated by MacMahon's functions $B_k$, obtaining a corresponding prime-detecting expression in $B_1$.

We establish sharp time-decay estimates for the discrete fourth-order Schrödinger equation on lattices of arbitrary dimension, extending the author's previous work \cite{C24}. In higher dimensions, scalar Fourier factorization reduces the analysis to product estimates for one-dimensional oscillatory integrals. Since this method does not yield the sharp decay exponents in low dimensions, we also use Newton polyhedra to obtain the required uniform estimates. In particular, the sharp decay rate for the biharmonic Schrödinger propagator is $|t|^{-d/4}$.

Spin transport between coupled spins is governed by tunneling and exchange interactions, yet their microscopic relation in real space has remained experimentally elusive. To address this, we functionalize the tip of a scanning tunneling microscope with a single molecule and use it as a movable spin sensor positioned above a second molecule on a ferromagnetic surface, forming an exchange-coupled molecular junction. From the same spectroscopic dataset, we independently extract the junction conductance, exchange coupling, and spin polarization with atomic-scale resolution. Conductance and exchange display nearly identical spatial dependences, consistent with a minimal Hubbard description in which both are governed by the same hopping matrix between the molecular frontier orbitals. In contrast, the spin polarization depends on the orbital composition and the relative weight of the resulting transport channels. These results establish a real-space connection between tunneling, exchange, and spin transport in magnetically coupled molecules.

Conformal prediction provides set-valued predictions with distribution-free coverage guarantees, making it attractive for high-stakes image classification. However, split conformal prediction is data-inefficient, while full conformal prediction (FCP), despite its stronger statistical efficiency, is computationally prohibitive at scale because it requires candidate-specific model refits at test time. We address this limitation by leveraging zero-shot vision-language models (VLMs) to guide scalable FCP in large label spaces. We introduce Targeted Full Conformal Prediction (T-FCP), which uses a lightweight inductive conformal predictor to prune unlikely labels and applies FCP only to the remaining candidates, reducing computation while retaining the formal guarantee of the combined conformal procedure. We further propose Stabilized Online LDA (SO-LDA), an efficient VLM adaptation solver based on rank-one inverse-covariance updates. Across multiple benchmarks, including ImageNet, T-FCP enables practical full-conformal image classification with modest test-time overhead, yielding efficient prediction sets and more stable empirical coverage than split conformal alternatives.

Cross-skeleton motion generation trains generative models to carry action structure and motion intention from one body to another. Yet a target motion that shows the right action has two explanations that the training data cannot tell apart: the model transferred the source clip, or it recovered a typical motion for the requested action. We show that this ambiguity is structural rather than incidental: under standard generative objectives, the source-conditioned retargeting map is non-identifiable in sparse heterogeneous motion domains. Unpaired distribution matching yields gauge non-identifiability: the latent spaces of different skeletons can be transformed relative to one another without changing the training evidence, so different source-conditioned maps fit it equally well. Sparse paired supervision admits the complementary failure mode, conditional-mean degeneration: when clips are paired only by action, squared-error training converges to an average target motion that ignores the source clip. To make the missing evidence observable, we introduce Source-Instance Fidelity (SIF), a diagnostic that tests whether outputs differ from one another the way their source clips do, with the target skeleton and action held fixed. Under this diagnostic, methods that succeed at the standard action-level test on animal motion data often sit at the source-blind floor, while the methods that rise above it retain only a partial relational signal. Retargeting therefore needs objectives and evaluations that can identify the source-conditioned map it claims to learn. Project page: https://cross-skeleton-retargeting.netlify.app/.

Parametric frequency modulation makes superconducting interactions programmable for quantum gates and quantum simulation. Leakage into higher transmon levels and coupler modes degrades gate fidelity and takes simulated dynamics outside the intended state space. Simple pulse shaping reduces off-resonant excitation, while strong coupling and short pulses demand simultaneous control of multiple leakage pathways. Controlling these pathways across individual exchange operations and simultaneous couplings is essential for fast gates and faithful programmable dynamics. We introduce parametric derivative removal by adiabatic gate (PDRAG), combining derivative shaping and finite-pulse calibration for qubit and coupler modulation. At an interqubit coupling of $g/2\pi=100$ MHz, simulations demonstrate 13-ns exchange operations, approaching the timescale of fast single-qubit gates. Combined with calibrated phase accumulation, these pulses realize a two-parameter fSim gate family with infidelity below $1.8\times10^{-7}$. For quantum simulation, PDRAG suppresses leakage and improves fidelity in continuous and digital Floquet simulations of Su–Schrieffer–Heeger chains. Floquet analysis identifies finite-pulse return interference as the calibration mechanism; realistic-circuit transfer preserves suppression. PDRAG establishes a unified framework spanning quantum gates and quantum simulation.

Users are central to transportation systems, yet their roles are often simplified in transportation modeling and decision-making. Conventional approaches primarily represent users through demand-related inputs, such as trip flows, delivery requests, and charging loads. However, digital, electrified, and platform-based services create more direct user-system interactions, with users responding to prices, incentives, service availability, and information in ways that can influence operational and planning decisions. This review develops a concept-oriented, taxonomy-based synthesis of how user-related information is represented and used in transportation decision-making. Rather than organizing the literature primarily by transport domain, we distinguish three user roles: passive demand, operational agents, and system co-shapers. Drawing on studies across transportation electrification, urban logistics, shared mobility, multimodal transport, and Mobility-as-a-Service (MaaS), the review examines how user preferences, flexibility, willingness to pay, acceptance, participation, and feedback are incorporated into modeling and decision frameworks. The synthesis shows that progress has been made in measuring and predicting user behavior, but user-related variables are still often used mainly for behavioral analysis rather than as actionable inputs to system decisions. Their integration into operational decision-making remains incomplete, and systematic feedback from operational user responses and outcomes to long-term planning is even more limited. Key challenges include closing the loop between operation and planning, modeling heterogeneous and strategic users, integrating optimization with user acceptance, and ensuring privacy, equity, and deployability. Together, these findings point toward a shift from user-behavior analysis to user-informed and user-driven transportation system design.

Large language models (LLMs) are increasingly used for software engineering tasks, yet their stochastic behavior challenges the validity, reproducibility, and comparability of their evaluations. Conventional practices such as reporting a single output, an average score, best-of-N, or pass@k performance can obscure variability and estimation uncertainty, potentially leading to misleading conclusions about system reliability. This article presents SafeLLM4SE, a practical methodology and reporting standard for statistically principled evaluation of LLM-based software engineering systems. Rather than treating generated outputs as deterministic artifacts, SafeLLM4SE treats them as realizations of a stochastic process and distinguishes quality, stability, and estimation uncertainty. It combines adaptive sampling with confidence intervals, distribution-aware statistical comparisons, effect sizes, and a minimum reporting standard covering model configuration, reproducibility, evaluation procedures, and resource usage. SafeLLM4SE is also provided as an open-source software package available on PyPI, enabling researchers and practitioners to reproduce and extend the methodology. We illustrate its application by comparing two LLMs on HumanEval, a benchmark of programming problems assessed through functional tests.

This dissertation investigates scalar field descriptions of the dark sector (of the Universe), with emphasis on scalar-tensor cosmology, inflationary effective field theory, quintessence, and ekpyrotic/cyclic alternatives to inflation. The thesis first reviews scalar-tensor theories of gravity, starting from Brans-Dicke theory and extending to general non-minimally coupled scalar fields. Particular attention is paid to the interpretation of the scalar field, the relation between the Jordan and Einstein conformal frames, and the role of conformal transformations in cosmology. This provides the theoretical basis for treating scalar fields either as modified matter or as additional gravitational degrees of freedom. The second part discusses cosmological inflation from the viewpoint of effective field theory. The spontaneous breaking of time translations, the Goldstone description of adiabatic perturbations, higher-dimensional operators, radiative corrections and Planck-suppressed terms are analyzed in the context of ultraviolet sensitivity and the eta problem. The third part is devoted to quintessence as the simplest dynamical dark energy model. 'Tracking', 'scaling', 'thawing' and 'freezing' behaviors are discussed, together with observational diagnostics such as the statefinder parameters, the $(\omega_{\phi},\omega'_{\phi})$ plane and the $Om(z)$ diagnostic. The DESI DR2 results are used as motivation for considering time-dependent effective dark energy beyond a strict cosmological constant. The final part studies ekpyrotic and cyclic cosmologies. The ekpyrotic phase is analyzed as a smoothing mechanism based on an ultra-stiff scalar field equation of state. Perturbations, non-Gaussianity, tensor modes, bounce conditions, entropy production, geodesic incompleteness and phase-resolved field-space distance bounds are discussed.

Physical active vision allows robots to change their viewpoint when task-relevant observations become unreliable, yet existing manipulation benchmarks provide limited support for studying how policies recover from occlusion during execution. We introduce BAVO-Bench (Bimanual Active Vision under Occlusion), a bimanual active-vision benchmark that systematically controls external visibility through Clean, Stage Occlusion, and Random-time Occlusion conditions, enabling evaluation of both manipulation performance and active visual recovery. Building on this setting, we present A-FAR (Active Future-Aware Recovery), an active-vision policy for joint viewpoint and manipulation control. A-FAR represents moving-camera observations in a unified robot-centric 3D frame and distills relational structure together with its future evolution from a pretrained 4D model, providing the policy with future-aware geometric guidance without requiring future observations at deployment. Experiments across multiple manipulation tasks show that A-FAR improves robustness to both structured and temporally shifted occlusions while maintaining strong performance under clean observations.

Classical Inverse Optimal Transport (IOT) typically infers a cost function from a single observed transport plan, which may be sensitive to noise and observational biases in practice. To address this limitation, we propose the Multi-Observation Inverse Optimal Transport (M-IOT) framework, which learns a universal cost function from multiple transport plans. We reformulate the resulting bilevel optimization problem into a tractable single-level convex program and characterize the existence and geometric structure of the optimal solution set. Moreover, we propose a Block Coordinate Descent with Anderson-type Extrapolation (BCDwAE) algorithm and establish its Q-linear convergence. Extensive experiments on synthetic data, urban mobility flows, and e-commerce recommendations demonstrate that M-IOT reduces recovery error compared to single-observation baselines and extracts robust structural patterns from noisy multi-view data.

A balanced $k$-partite $k$-graph is a $k$-uniform hypergraph whose vertex set is partitioned into $k$ classes of the same size and whose edges meet every class in exactly one vertex. Lo and Markström (2014) determined the minimum vertex-degree threshold for perfect matchings when $k=3$, and Lu, Wang and Yuan recently determined it when $k=4$. We prove the corresponding exact result for every fixed $k\ge5$ and all sufficiently large class sizes. The close case follows from the general theorem of Lu, Wang and Yuan. For the non-closed case, we extend their stability result from the 3-partite setting to arbitrary partite uniformity, using the probability-tail rigidity theorem of Cao, Liu and Zhang, thereby replacing the earlier weighted lemma.

This dissertation investigates the transport of Brownian particles driven by active fluctuations in periodic potential landscapes. Unlike thermal noise, active fluctuations break detailed balance and violate the fluctuation-dissipation theorem. They model self-propulsion mechanisms and active-bath interactions in biological and synthetic micro-systems. By focusing on white Poisson shot noise with non-Gaussian amplitude statistics, this work systematically uncovers the mechanisms governing three major transport anomalies: giant transport enhancement, absolute negative mobility, and the ratchet effect. Combining Monte Carlo simulations with analytical modeling, this work demonstrates that active fluctuations adhering to a bidirectional skew-normal amplitude distribution enhance directed velocity compared to the free-particle by several orders of magnitude. This giant transport enhancement is fully quantified via a phenomenological jump-relaxation model that decouples particle displacement into instantaneous stochastic jumps and deterministic relaxation towards potential minimum. Extending this model to inertial dynamics reveals that finite particle mass m acts as a dynamic control parameter capable of strengthening, weakening, constructively inducing, or completely suppressing transport enhancement depending on noise variance. Furthermore, an ultra-minimal model for absolute negative mobility is established in a one-dimensional, purely overdamped system, showing that discontinuous shot noise drives transport opposite to an applied bias without requiring particle inertia, nonlinearity of spatially periodic potential, or explicit time-dependent periodic driving. Conversely, under an alternative scaling of the average stochastic force, an asymmetric distribution of fluctuation amplitudes generates net directed transport through a tilting ratchet effect in the zero-bias limit.

This paper presents a comprehensive methodology for vertiport design and capacity analysis using stochastic queueing models. Vertiport capacity is defined as the maximum steady-state throughput of aircraft per hour while maintaining a specified maximum queuing delay with high probability. We introduce a two-node queueing network model with a finite buffer to analytically derive mean queue lengths and delays at singleTLOF vertiports, supported by a simulation model that validates these findings under various operational conditions. Our analyses reveal that vertiport capacity is significantly constrained by the interactions between takeoff/landing operations and turnaround activities. Capacity is further reduced by the variance in these operational parameters, with deterministic times substantially enhancing capacity. The study shows that vertiport throughput is typically around 50% of the theoretical maximum departure capacity in a Markovian model, decreasing sharply as confidence in maximum queuing delay increases. Non-Markovian models with adequate parking pads achieve full TLOF capacity. Additionally, our methodology identifies the optimal number of parking pads required to maximize throughput per unit area. The findings provide insights for designing vertiport infrastructure to support scalable and efficient UAM deployment.

Image translation is a fundamental capability of multimodal models for multilingual applications, requiring visual understanding and meaning preservation across languages. However, existing benchmarks have limited language coverage and often lack explicit image-specific evaluation criteria, making it difficult to comprehensively assess this capability. To systematically evaluate this capability, we introduce VISTA-Bench, covering 22 languages and 10 domains, and develop an image-specific rubric evaluation protocol. The benchmark combines sampling for language and scenario coverage with model-assisted, human-verified annotations that group related text into coherent semantic units and provide multilingual reference translations. The rubrics specify essential content, semantic relations, and acceptable translation variants, yielding separate output-based scores for translation quality and the preservation of visual and knowledge-dependent information. We conduct extensive evaluations of 16 mainstream models, including 12 multimodal models and four text-input models, and provide systematic analyses across languages, domains, and evaluation dimensions.

This paper continues our work on the convergence rates of moment-SOS hierarchies via approximation of truncated moment sequences. We extend the method developed for the Schmüdgen-type hierarchy to the Putinar-type, Krivine–Stengle-type, extended-Handelman-type, and Hol-Scherer-type hierarchies. The main idea is to lift a pseudo-moment sequence to a simple set, approximate it by a moment sequence, and project the atoms of a representing measure onto the original feasible set. The Łojasiewicz inequality then converts the error in the defining constraints into an error in the moments. With the Łojasiewicz exponent $0<L\leq 1$, we obtain the convergence rates $\mathrm{O}((\log_2 r)^{3L/2}/r^{L})$ for the Putinar-type hierarchy and $\mathrm{O}(1/r^{L/2})$ for the normalized Krivine–Stengle-type and extended-Handelman-type hierarchies. In the matrix setting, we utilize a Chebyshev-type kernel on $[-1,1]^n$ to derive the rate $\mathrm{O}((\log_2 r)^{3L/2}/r^{L})$ for the Hol-Scherer-type hierarchy. Together with our preceding work, the results provide a universal method for studying convergence rates of different types of moment-SOS hierarchies.

Laboratories often face a new molecular assay with 16-64 labels and a bank of predictors whose training data and parameters are unavailable. The practical question is which frozen outputs to include in a small local model. AssayRouter treats completed assays as pseudo-targets and labels each candidate by its post-fit utility: the reduction in held-out discovery loss when the candidate is added to the local target predictor. A shared regressor learns to predict this utility from candidate behavior on the support set, without source identity; on a new assay, one frozen ranking selects four sources and separate labels fit a convex combiner. We train only on completed ChEMBL-MT assays and evaluate 24 external regression assays across six frozen interface families. AssayRouter-C lowers strict four-call negative log-likelihood (NLL) by 0.0409 relative to Support-CV@4. Frozen candidate-label permutations confirm that candidate-utility correspondence carries the transferred information, and leave-one-interface-out training shows that the mapping generalizes to unseen predictor families. Completed assays therefore provide transferable supervision for scarce-label routing through frozen prediction interfaces.

Degree heterogeneity is one of the most important properties of network data. It is widely observed that degree heterogeneity is often related to the nodal features. In this study, we investigate the estimation and statistical inference for a popularity regression-based latent space model with nodal features. Given the complex dependence structure induced by the latent space model, we aim to derive analytically tractable objective functions that effectively account for this structure for sparse networks. Specifically, we propose a total of four estimators. The first two estimators are developed by utilizing only the first-order structure of the network (e.g., the nodal degree), while the last two estimators are developed by leveraging the higher-order network structures (i.e., reciprocity and transitivity). Rigorous asymptotic theory is established based on various non-standard U-statistics. We find that different estimators might have different convergence rates. The extension to higher-order moments-based estimators is also discussed. Extensive numerical experiments and a real data analysis of link prediction for an author citation network are conducted for illustration purposes.

Medical multimodal large language models (MLLMs) are increasingly expected not only to answer clinical questions, but also to localize the visual evidence behind their predictions. A common strategy connects a vision–language model (VLM) with SAM-style segmentation through a special <SEG> token, yet full-parameter training of this unified architecture is difficult because image-level reasoning and pixel-level segmentation impose different requirements on the shared representation space. To address this issue, we propose a parameter-decoupled training framework for unified medical reasoning and segmentation. The framework treats the <SEG> hidden state as a semantic-to-spatial prompt for the mask decoder and encourages it to become separable from generic language states, reducing ambiguous segmentation prompts and potential disruption to reasoning representations. It first performs medical shallow alignment to adapt visual features to clinical language without disturbing the LLM; then controlled instruction tuning shapes separable <SEG> prompt states, monitored by the Davies–Bouldin Index (DBI), while scaling segmentation gradients entering the language backbone; finally, the SAM branch is specialized with the VLM frozen to improve mask precision without altering reasoning parameters. Experiments on medical referring segmentation, grounding, visual QA, and textual QA benchmarks show that our framework achieves strong language-conditioned segmentation while preserving competitive reasoning ability. Ablations show that two-phase instruction tuning, gradient scaling, and segmentation specialization all contribute to the model.

We formulate and solve a degenerate elliptic boundary value problem for the $SU(\infty)$-Toda equation on $[0,\frac12)\times\mathbb{S}^2$. This constructs anti-self-dual Poincaré–Einstein metrics with circle symmetry on the $4$-ball and on complex line bundles over $\mathbb{S}^2$.

Quantum infrared (IR) spectroscopy is a novel technique based on nonlinear interferometry and quantum sensing with undetected photons. In this approach, spectrally far-separated correlated photon pairs are used for detection and probing, respectively. Hence, the probing wavelength domain (mid-IR) is substantially different from the detection spectral range (near-IR), which enables shot-noise-limited and cost-effective sensing schemes attractive for applied metrology. In this paper, we implement and employ a custom-built scanless quantum Fourier-transform mid-IR (sQFTIR) microspectrometer for chemical characterization and analysis of solids and interfaces. The sQFTIR system employs spectral-domain acquisition, without the need to scan the optical delay. This results in enhanced sensitivity and short measurement times and therefore enables rapid spectral acquisitions in 100~ms spanning a spectral band from 3000 cm$^{-1}$ - 2400 cm$^{-1}$. The capabilities of our approach are demonstrated for hyperspectral mid-IR imaging of dried pharmaceuticals (insulin on a gold surface) and investigation of a security feature on a banknote. Furthermore, we demonstrate hyperspectral imaging and spectroscopy through a turbid ceramic medium.

AI-assisted cheating has become an important threat to the security of online exams. This study examines whether the risk of AI-assisted cheating in the final exam can be predicted using students' digital traces in the learning management system (LMS) during the first eight weeks of the semester. The sample comprised 52 first-year undergraduates enrolled in a bachelor's program in Computer Education and Instructional Technology and taking an Introduction to Programming course at a public university in Turkiye. Students were labeled as low- or high-risk based on suspicious behaviors recorded in the final-exam logs, including copy, focus-loss, and right-click events. Of the 52 students, 23 (44.2%) were labeled as high-risk in a proctored, face-to-face exam. Group membership was then predicted using five features selected from 27 candidates extracted from students' digital traces. Logistic Regression, Naive Bayes, Random Forest, and Gradient Boosting algorithms were used to build the prediction models. Model performance was evaluated using leave-one-out cross-validation (LOOCV) with fold-specific preprocessing and feature selection. Logistic Regression achieved the best performance (Accuracy = 73.1%). The results indicate that LMS interaction data can provide an early signal of AI-assisted cheating risk. Course-module views, assignment submissions, and the number of days on which course videos were accessed were the most consistently selected features across the LOOCV folds. These predictions are intended to support timely academic guidance, not to establish misconduct or initiate disciplinary action.

Neural solvers offer efficient surrogates for numerical simulation of partial differential equations (PDEs). For time-dependent problems, strong one-step accuracy does not necessarily translate into reliable autoregressive rollout. We observe that a solver based only on physical-state modeling can achieve lower one-step error, whereas its spectral-only counterpart can become more accurate at later rollout steps. Motivated by this observation, we present Transolver-$\sigma$, a neural PDE solver based on joint spectral–physical subspace modeling. Within each block, adaptive physical-state interactions and spectral transformations are modeled in dedicated latent subspaces, whose responses are recomposed to enable information exchange between the two representations. Within the physical subspace, we introduce Slice-Residual Physics-Attention (SRPA), which preserves an explicit slice-space identity path while retaining learnable cross-slice interaction. In parallel, an axis-factorized Fourier operator captures global spectral structure. Across five well-established PDE benchmarks spanning steady-state prediction and time-dependent dynamics, Transolver-$\sigma$ achieves state-of-the-art with a benchmark-averaged relative error reduction of 33.4% over the strongest baseline for each metric, while consistently improving autoregressive rollout over single-operator counterparts. Transolver-$\sigma$ further delivers strong gains on coupled multiphysics systems and real-world fluid and combustion measurements from RealPDEBench, demonstrating its effectiveness beyond standard simulation benchmarks.

In this paper, we study the matroid base packing problem from the perspective of cooperative game theory and define the associated matroid base packing game. This model extends the network strength game of Baiou and Barahona (2020) from graphic matroids to general matroids. Building on the exchange structure of matroid, we develop a graph theoretic framework for analyzing the core and the nucleolus in this game. This framework yields a compact characterization of core nonemptiness and enables polynomial time algorithms for testing core nonemptiness, finding a core allocation, and computing the nucleolus. In particular, the approach provides a broader and more structured framework for the nucleolus computation problem arising in network strength games studied by Baiou and Barahona (2020), covering both the nonempty core and empty core cases.

The acquisition of high-dimensional channel state information (CSI) in wireless MIMO-OFDM communications usually requires high pilot overhead, or relies on accurate and complete positional or environmental information. In this paper, we propose a geometry-aided channel deduction (GCD) approach, which utilizes an uncalibrated digital twin (DT) with only approximate environmental geometry and positions to assist the channel acquisition. The key rationale behind is that, even imprecise geometric information, which can be easily obtained in advance through radio sensing technologies or existing geographic databases, provides certain structural features about the current channel; meanwhile, the coarse instantaneous channel estimates using only a small amount of pilots provide dedicated information that aligns with the channel structure and further compensates for the geometry inaccuracy and other channel unknowns. To this end, we first extract geometric features from the DT, which contain only simple structural information of the channel. Then we propose random prompt augmentation, a novel method to generate an appropriate prompt that converts geometric multi-path structure into a CSI-like representation while suppressing the disturbance of other unknown channel parameters. The prompt is then fused with the pilot-based instantaneous channel estimate via a channel deduction network. To further enhance the network's versatility, we incorporate pilot configurations into the existing learning architecture to support variable pilot patterns. Comprehensive experiments validate the superiority of the proposed method, which demonstrates high channel acquisition quality, low pilot overhead, and strong robustness. Furthermore, the structural prompt also serves as scenario-related context, enabling our approach to generalize well in new scenarios.