arXiv:2607.10552v1 Announce Type: new
Abstract: Which discrete symmetry groups can arise from strategic interaction? We tile the plane with copies of a bimatrix game's support complex, joined by controlled boundary rules, and show that all seventeen wallpaper groups act on the resulting covers: explicit generators, each a machine-verified graph automorphism, every realization certified as the exact toroidal quotient, with types identified by a crystallographic recognizer in exact rational arithmetic and cross-validated in GAP. A three-line lemma turns the classical symmorphic/non-symmorphic distinction into a lattice classification: realizations whose translations contain the full tile lattice exist precisely for the thirteen symmorphic groups, and the four non-symmorphic groups are realized at translation-lattice index exactly two, the minimum possible: the tile is the glide's half-step.
Two computational tracks accompany the construction. On the graph track, quotienting a straight cover by its translations recovers the tile exactly, $\beq(M/\calT)=\beq(K)$, and swap boundaries add exactly $\binom m2$, independent of payoffs and of cover size. On the game track, detecting a duplicated-strategy cover is a linear-time payoff scan, one tile solution folds to a full translation orbit of cover equilibria, and the tiled correlated-equilibrium system has dimension exactly $r(d-q)+q$, with expansion impossible. The polymatrix cover then carries the symmetry outright: every wallpaper action, glides included, is a group of genuine game automorphisms, equilibria collapse along any symmetry subgroup to a folded fixed-point problem, and a decorated refinement has game automorphism group exactly the toroidal wallpaper group.
Science Journals
arXiv:2607.10651v1 Announce Type: new
Abstract: In large urban areas, planning multi-day travel itineraries is challenging due to the abundance of Points of Interest (POIs), diverse user preferences, and constraints such as opening hours. Effective solutions must dynamically accommodate diverse traveler requirements while optimizing for satisfaction and feasibility within limited computation time. This paper addresses these challenges through introducing an innovative framework that integrates Large Language Models (LLMs) to dynamically capture user requirements with precision and flexibility, and an enhanced Greedy Randomized Adaptive Search Procedure (GRASP) algorithm as a well-suited preference-aware planner to generate feasible multi-day itineraries. The effectiveness of our integrated approach is demonstrated through extensive experiments on two real-world urban datasets from Beijing and Tianjin. Our framework significantly outperforms state-of-the-art (SOTA) methods, improving the average total itinerary score by at least 4.52% and 11.09% across 5,040 user cases with diverse preferences in the two datasets. Furthermore, through end-to-end algorithmic enhancements, it achieves notable average improvements of 17.95% and 26.07% in the computed metrics, while also delivering substantial gains in time efficiency -- realizing average performance increases of 4.64% and 25.55% within shorter computation times compared to suboptimal methods that require multiple iterations. These outcomes underscore our method's superiority in delivering both enhanced itinerary quality and computational efficiency over existing methodologies.
arXiv:2607.10257v1 Announce Type: new
Abstract: Formal verification of highly optimized arithmetic circuits at the gate-level remains a significant challenge due to the state space explosion problem. Although Symbolic Computer Algebra (SCA) offers a scalable theoretical foundation by modeling circuits as multivariate polynomials, practical implementations frequently suffer from the explosion of the size of intermediate polynomials. State-of-the-art SCA tools typically rely on fixed heuristics and restrict their application to standard multipliers. A fixed heuristic is insufficient for structurally diverse arithmetic circuits, as it often fails to generalize across all cases. In this paper, we introduce Arisca, an open-source parameterized verification framework for \textbf{Ari}thmetic circuits using \textbf{S}ymbolic \textbf{C}omputer \textbf{A}lgebra. Arisca establishes a generalized parameter space that unifies previously isolated state-of-the-art (SOTA) techniques as specific configurations within a broader algebraic reduction theory. To fundamentally transplant and elevate previous methods, we propose several algorithmic improvements, such as an HA-preserving extraction strategy, density-based vanishing detection, and conservative polynomial size estimation. In addition, Arisca expands the verification scope to encompass general arithmetic circuits with any combination of addition and multiplication, such as multiply-accumulators and dot-product units. Extensive evaluations demonstrate that Arisca achieves SOTA performance in a comprehensive suite of multiplier benchmarks and a diverse array of practical arithmetic cases.
arXiv:2607.10260v1 Announce Type: new
Abstract: Customer churn is a major challenge for telecommunication companies, directly eroding revenue and long term customer relationships. Traditional retention programs rely on generic, not personalized incentives and lack the precision to identify high risk customers before they leave. This paper presents a data driven marketing optimization framework integrating machine learning based churn prediction, customer segmentation combining churn risk with customer value, and tailored, segment specific marketing and Return on Investment ROI strategies. Using the IBM Telco Customer Churn dataset with 7043 customers and 21 features, three gradient boosting ensembles, XGBoost, LightGBM, and CatBoost, were trained and tuned via randomized search with stratified 5 fold cross validation, class weighting, and F1 score driven decision threshold optimization to counter a class imbalance of 73.4% versus 26.6%. CatBoost was selected as the deployment model, achieving 77.68% accuracy, an F1 score of 0.6366, a PR AUC of 0.6553, and a ROC AUC of 0.8403 on the held out test set. Customers were partitioned with K Means clustering, validated via the Elbow method and visualized with Principal Component Analysis, into High, Medium, and Low Value segments, cross tabulated against churn risk labels to define four actionable clusters. Segment specific retention, upsell, and engagement strategies were designed for each cluster, and a theoretical ROI and CLV framework quantifies the financial impact of the proposed interventions. The pipeline was operationalized in an interactive Streamlit web application allowing marketing teams to upload data, filter by segment, visualize churn drivers via SHAP, and download automated segment reports. Results confirm that combining predictive churn modeling with value aware segmentation yields more actionable and profitable marketing decisions than churn prediction alone.
arXiv:2607.06114v2 Announce Type: replace
Abstract: Diffusion and flow matching models generate high-quality samples, but their ODE samplers often need tens to hundreds of neural function evaluations (NFEs). This remains a practical challenge for released checkpoints, since many accelerators require additional design choices and training cost through retraining, distillation, or trajectory redesign. We investigate a different route based on $x$-prediction. During sampling, standard affine probability paths already expose $x_0$ information: an intermediate state and its path velocity determine a principled estimate of the clean sample. We formalize this property as \textbf{endpoint decodability} and show that the decoder is the minimum-MSE estimator $\mathbb{E}[x_0\mid x_t]$ under the usual $\ell_2$ objective. This yields \textbf{Truncated Jump Sampling} (TJS): stop the ODE at an early-exit time $t^*$ and return the decoded $x_0$. TJS requires no retraining, distillation, or architecture change. Across SDXL, SD3.5M, Z-Image-Turbo, and three class-conditional benchmarks, it reduces NFEs by 20--70\% with near-matched quality. The analysis also shows why endpoint prediction can work without straightening the trajectory, providing inference acceleration without trajectory redesign.
arXiv:2607.06438v2 Announce Type: replace
Abstract: Retargeting human object interaction demonstrations to physics based simulation requires reproducing not only body motion but also the object motion and contacts that make manipulation succeed. However, position only hand trajectories do not specify the contact forces needed to manipulate objects, and directly tracking them can overconstrain contact rich finger behavior. We introduce WristMimic, a wrist guided whole body control framework that explicitly separates contact free body motion from contact rich hand manipulation. The contact free body and wrist are guided by kinematic pose targets, whereas the fingers are not directly supervised by human hand pose. Instead, they learn grasping and manipulation behaviors from object tracking and contact outcomes. Our key insight is that the wrist is the natural gate between these two regimes. It is largely free from contact and can be tracked kinematically, yet it determines the global hand configuration and places the fingers within reachable grasp affordances. To ensure reliable wrist placement during interaction, we introduce wrist specific reset constraints and reward prioritization. Experiments show that WristMimic matches or surpasses methods using full finger pose supervision while enabling finger agnostic retargeting across diverse hand embodiments.
arXiv:2607.11798v1 Announce Type: new
Abstract: Long-form audio description (AD) requires more than describing visible actions: it must preserve characters, events, relationships, and story context across scenes so that blind and low-vision (BLV) audiences can follow a film. Modern video-language models (VLMs) are effective on short clips, but they often treat each moment independently, producing descriptions that miss who characters are, why events matter, and how the current scene connects to earlier narrative context. We propose StoryTeller, a training-free framework for story-aware long-form AD. Instead of relying only on local visual cues, StoryTeller maintains a verified narrative memory that carries forward story-relevant information across scenes, enabling later descriptions to remain coherent, grounded, and contextually informative. Given only raw video and a movie title, StoryTeller can optionally retrieve public movie metadata to resolve names and story context, while accepting only facts that are supported by the video through semantic filtering and VLM verification. The method requires no subtitles, scripts, AD transcripts, aligned captions, character banks, precomputed face identities, or task-specific fine-tuning. To evaluate whether generated AD preserves narrative information, we introduce StoryAD-QA, a question-answering benchmark that tests whether a language model can answer story-context questions using only the generated descriptions. Experiments on standard AD benchmarks and diverse long-form videos show that StoryTeller consistently improves narrative coherence, factual grounding, and story comprehension over strong baselines in automatic, QA-based, and human evaluations.
arXiv:2607.11768v1 Announce Type: new
Abstract: Plasmonic nanostructures offer an effective route for enhancing light-matter interaction in atomically thin semiconductors, whose optical response is intrinsically limited by their sub-nanometer active thickness. Here, we numerically investigate excitonic enhancement in monolayer (ML) Molybdenum Disulfide (MoS2) coupled to size-tuned gold (Au) nanocube arrays separated by thin aluminum oxide (Al2O3) and hexagonal boron nitride (h-BN) spacer layers. By varying the nanocube side length, the localized surface plasmon resonance is tuned across the visible spectral range to modulate the A- and B-excitonic transitions of monolayer MoS2. We show that the nanocube-size-dependent spectral redshift can be further controlled through the spacer material and thickness, enabling systematic tuning of the near-field distribution, carrier generation rate, quantum yield, and radiative decay enhancement. Localized plasmonic confinement yields excitation-rate enhancements of up to 4.35 at B-excitonic transition (605 nm) and 3.66 at A-excitonic transition (650 nm), while the radiative decay-rate enhancement exceeds 80, leading to 350-fold photoluminescence enhancement. Although both A- and B-excitonic channels are enhanced simultaneously, their relative contributions depend on nanocube size, spacer material, and spacer thickness, indicating wavelength-dependent excitonic modulation rather than strict exciton-selective switching. These findings establish Au nanocube arrays as a simple, scalable, and tunable plasmonic platform for enhancing excitonic carrier generation and emission in ML MoS2.
arXiv:2507.14129v2 Announce Type: replace
Abstract: Masked token prediction has emerged as a powerful pre-training objective across language, vision, and speech, offering the potential to unify these diverse modalities through a single pre-training task. However, its application for general audio understanding remains underexplored, with BEATs being the only notable example. BEATs has seen limited modifications due to the absence of open-source pre-training code. Furthermore, BEATs was trained only on AudioSet, restricting its broader downstream applicability. To address these gaps, we present OpenBEATs, an open-source framework that extends BEATs via multi-domain audio pre-training. We conduct comprehensive evaluations across six types of tasks, twenty five datasets, and three audio domains, including audio reasoning tasks such as audio question answering, entailment, and captioning. OpenBEATs achieves state-of-the-art performance on six bioacoustics datasets, two environmental sound datasets and five reasoning datasets, performing better than models exceeding a billion parameters at one-fourth their parameter size. These results demonstrate the effectiveness of multi-domain datasets and masked token prediction task to learn general-purpose audio representations. To promote further research and reproducibility, we release all pre-training and evaluation code, pretrained and fine-tuned checkpoints, and training logs at https://github.com/Shikhar-S/OpenBEATs
arXiv:2508.02925v2 Announce Type: replace
Abstract: The efficient approximation of quantities of interest derived from PDEs with lognormal diffusivity is a central challenge in uncertainty quantification. This paper targets a problem class that combines four analytical difficulties: a geometric boundary singularity, a lognormal coefficient field without a deterministic positive lower bound, sample-dependent mesh selection that introduces parameter-space discontinuities, and infinitely many discontinuity locations that preclude classical pre-integration smoothing. In this study, we propose a multilevel quasi-Monte Carlo framework to approximate deterministic, real-valued, bounded linear functionals that depend on the solution of a linear elliptic PDE with a lognormal diffusivity coefficient parameterized by a multi-dimensional Gaussian random vector and deterministic geometric singularities in bounded domains of $\mathbb{R}^d$. We analyze the parametric regularity and develop the multilevel implementation based on a sequence of adaptive meshes, developed in our earlier work "Goal-oriented adaptive finite element multilevel Monte Carlo with convergence rates", CMAME, 402 (2022), p. 115582. For further variance reduction, we incorporate importance sampling and introduce a level-0 control variate within the multilevel hierarchy. Introducing such a control variate can alter the optimal choice for the initial mesh, further highlighting the advantages of adaptive meshes. On a 2-D slit benchmark discretized with bilinear, quadrilateral Q1-FEM, numerical experiments show that, in the parameter range explored, the proposed adaptive MLQMC algorithm achieves a prescribed accuracy at markedly lower computational cost than a standard multilevel Monte Carlo estimator on the same mesh hierarchy.
arXiv:2607.10975v1 Announce Type: new
Abstract: Autonomous-vehicle motion planners must resolve conflicts among safety, regulation, comfort, and efficiency in real time while exposing those decisions for audit. We present W-SQP, a weighted tiered-slack nonlinear model predictive controller (NMPC) that compiles nine driving-rule families into a four-tier shared-slack nonlinear program solved online with CasADi and IPOPT; the name denotes the weighted quadratic slack penalty, not a sequential-quadratic-programming solver. Strongly separated tier penalties bias residual violations toward lower-priority rules while leaving actuation bounds hard. The controller replans from its executed state at $10$\,Hz and records per-rule residuals on every cycle. A $90$\,ms solver-time limit returns an anytime iterate that is projected through the vehicle dynamics before execution; median and maximum observed wall-clock solve times were $28$ and $104$\,ms. We evaluate W-SQP in closed loop on 150 Waymo Open Motion Dataset scenarios in Waymax against reactive and proposal-and-select baselines, and introduce a log-independent protocol that separates safety and regulatory compliance from resemblance to the recorded human trajectory. Under this protocol, W-SQP shows no systematic group-level deficit relative to expert replay on the log-independent safety and regulatory rules, with several localized regressions in the hardest, highest-divergence scenarios. The results characterize W-SQP as an auditable, priority-biased, anytime-capable NMPC prototype rather than a hard-real-time or formally safe controller.
arXiv:2607.11816v1 Announce Type: new
Abstract: Causal discovery algorithms learn a network that describes the causal dependencies among random variables. A common workflow involves first utilizing conditional independence properties on observational data to determine partially directed causal relationships, then applying interventions to orient the unknown causal directions. A critical assumption for the first step is faithfulness: a requirement that causally linked variables exhibit statistical dependence. Many natural systems include buffering and stabilizing pathways that cancel out to achieve systemic robustness. This cancellation of pathways violates faithfulness, leading causal discovery algorithms to incorrectly remove causal dependencies. In this paper, we argue that hard interventions contain information about the presence/absence of causal linkage that is overlooked in the first stage of structure discovery. We show that a mild assumption -- called intervention-immediacy faithfulness -- that allows cancellations, is sufficient to nonparametrically identify causal structures with hard interventions. These results position interventions as the primary carriers of information about causal structure, which should take precedence over conditional independence testing. To flip the paradigm, we also specify equivalence classes when the identification criteria are not met due to limitations in the scope of interventions.
arXiv:2607.10067v1 Announce Type: new
Abstract: While autoregressive models optimize the exact data likelihood via the chain rule, diffusion models are typically trained with denoising objectives. We develop conservation laws based on generalized extrinsic information transfer (GEXIT) functions for a broad class of memoryless noise processes, showing that the data--model cross-entropy (CE) can be characterized exactly as an integral of local information-theoretic derivatives along the noise path. This yields a unified characterization of the likelihood for discrete and continuous diffusion, with the Gaussian case reducing to the well-known mutual information--minimum mean-square error (I-MMSE) relationship. An immediate implication is a locality property: one can compute the information-theoretic derivatives using only the marginal posteriors along the noise path. As a result, training reduces to learning the marginal posteriors by minimizing the negative log-likelihood. While the conservation law implies that the entropy does not depend on the noise path, finite-capacity denoisers approximate the posteriors with varying accuracy across noise types, leading to differences in performance. We validate these predictions on synthetic Markov sources and standard benchmarks, including text8 and CIFAR-10.
arXiv:2607.10068v1 Announce Type: new
Abstract: Implicit neural representations (INRs) offer compact encoding of volumes, but as lossy approximators, inevitably have prediction errors. We consider INRs that can simultaneously encode relative error scales by predicting distributions using tools from uncertainty estimation. Typically, uncertainty estimation relies on computationally expensive approaches or on predefined parametric assumptions about the predictive distribution (e.g., Gaussian). In this study, we propose a lightweight method that reformulates regression-based INR training as a classification task by discretizing continuous targets into bins, enabling flexible distribution modeling to capture complex multimodal behaviors. We analyze the trade-off between regression and classification for INR training and demonstrate that the classification setting tends to achieve high reconstruction quality and competitive error awareness through uncertainty estimation, compared to regression-based approaches.
arXiv:2607.10377v1 Announce Type: new
Abstract: Given a holomorphic matrix-valued function, the poles of its sketched resolvent are generically its eigenvalues. Once a good rational approximation of the sketched resolvent is obtained, the poles of this rational approximation typically lie close to those eigenvalues, thus providing a flexible framework for solving both linear and nonlinear eigenvalue problems. However, the accuracy of the computed eigenvalues is limited and remains poorly understood. This paper analyzes the convergence of this approach and demonstrates the effectiveness of two techniques to improve accuracy: block probing and zooming in. We also establish the backward and forward stability of polefinding for a barycentric rational form via a generalized eigenproblem. Numerical experiments demonstrate the sharpness of our theoretical results.
arXiv:2607.10380v1 Announce Type: new
Abstract: We introduce CAFE (Compound-AI Factorial Evaluation), an open-source platform that brings design of experiments to the evaluation of compound AI systems (CAIS). Such systems expose many interchangeable choices - e.g. which retriever, model, or prompt - and practitioners rarely know which of them most affects answer quality. With CAFE, a practitioner registers each swappable component of a pipeline as a factor to build a factorial design over the chosen factors, run the resulting configurations, and score the answers on a shared rubric using a configurable LLM judge together with human raters. From these ratings it attributes answer-quality variance to the components and their interactions with mixed-effects models and reports effect sizes, significance, the best configuration, cost and latency trade-offs, and judge-human reliability. Whereas existing tools mostly either search for a good configuration or score outputs in isolation, CAFE also explains which component drives quality and whether an observed difference is significant. We validate CAFE on a retrieval-augmented question-answering (QA) pipeline over the HotpotQA benchmark dataset, where it recovers planted factor effects and stays calibrated under a permutation null. CAFE is released as a Python package and as a Web application.
arXiv:2607.09742v1 Announce Type: new
Abstract: As traditional Moore's Law scaling slows down, 3D-ICs stack multiple active dies vertically to sustain performance scaling. However, this vertical stacking traps heat inside, making temperature a design concern. Although we can fix thermal issues at different design steps, floorplanning is the earliest and most cost-effective stage to solve it. Previous methods handle this by assuming wires connect to block centers and estimating temperature through simplistic power-based calculations, but these assumptions mislead their wirelength optimization and leave hotspots unresolved. To address these limitations, we present Chiplet3D, a pin- and thermal-aware floorplanner for two-die 3D-ICs. To achieve pin-awareness, it supports all four rotations and two flips, measuring wirelength from exact pin locations so the solver can flip or rotate blocks to pull connected pins closer. On the thermal side, Chiplet3D replaces the inaccurate power-based metrics of prior work with a fast, coarse convolution field embedded directly in a mixed-integer linear program (MILP) to accurately track the true 3D heat spread. We evaluate Chiplet3D on the ICCAD'24 ATPlace benchmarks, validating every temperature with a golden 3D-ICE simulation. Chiplet3D reduces wirelength by 39\%--43\% on average (and up to 62\% in the best case), while lowering peak temperatures by up to 45.9$^\circ$C and reducing thermal non-uniformity by up to 56\% compared to the SOTA baselines. Overall, these results demonstrate that by co-optimizing pin alignment and thermal fields, Chiplet3D establishes a stronger Pareto frontier between thermal-aware layout and interconnect efficiency.
arXiv:2607.11522v1 Announce Type: new
Abstract: Lanthanide-doped upconversion particles (UCPs) have revolutionized optical bioimaging platforms because of their excellent photostability, non-toxicity, and utilization of near-infrared excitation, which facilitates deep tissue penetration with negligible autofluorescence. However, it remains a challenge to achieve high-contrast and sub-diffraction imaging in noisy biological media, without using a high-power laser. Here, we report various protocols applied to bismuth-doped UCPs address some of these challenges. Compared to the photoluminescence (PL) emission of the regular Yttrium doped UCPs, we observe a three-fold increment in the quantum yield of the overall emission of bismuth-UCPs, and a four-fold increment, specifically, in red emission. Leveraging this advantage, we devise a protocol employing two infrared wavelengths, 975 nm and 1064 nm, to selectively control the PL emission. Interestingly, our results reveal two distinct regimes in which PL can be systematically quenched or enhanced, by adjusting the 975 nm laser power. We model the overall dynamics as a simplified stimulated emission depletion process involving three energy levels. In addition, the particle has a thickness under sub-diffraction, shows optical trapping ability, and potential of surface functionalization to enable specific conjugation with diverse biospecimens. These studies establish bismuth doped UCPs as an excellent candidate in accomplishing advanced biomarker operating with enhanced signal-to-noise ratio and sub-diffraction imaging capabilities.
arXiv:2507.22535v5 Announce Type: replace-cross
Abstract: We show new constructions for pseudorandom quantum states (PRS) and pseudorandom function-like quantum state (PRFS) generators satisfying scalability, which means the security parameter can be much larger than the number of qubits, quantum accessibility, which means the adversary can provide quantum input, and adaptivity, which means the adversary can query it adaptively.
We present an isometric procedure to prepare quantum states that can be arbitrarily random (i.e., the trace distance from the Haar-random state can be arbitrarily small for the true random case, or the distinguishing advantage can be arbitrarily small for the pseudorandom case). This naturally gives the first construction for scalable, quantum-accessible, and adaptive PRFS assuming quantum-secure one-way functions. Compared to prior PRFS works, we use a stronger definition of quantum accessibility in which the adversary can be ancilla-assisted, i.e., the input state may not be pure and could be entangled with other quantum registers. Thus, our result also gives the first (fully) quantum-accessible PRFS.
Our PRFS construction implies various primitives, including long-input PRFS, short-input PRFS, short-output PRFS, non-adaptive PRFS, and classically-accessible adaptive PRFS. This new construction may be helpful in simplifying the microcrypt zoo.
arXiv:2601.20719v2 Announce Type: replace-cross
Abstract: Phagocytosis is a core innate immune process that clears targets spanning a wide range of mechanical properties, yet the role of target mechanics in recognition and engulfment remains unclear. Here, we combine theoretical modeling and experiments to reveal how target stiffness governs distinct modes of phagocyte--target interaction. We develop a membrane-based simulation framework in which both the engulfing cell and its target are deformable and undergo large shape changes, while actin-driven protrusions are regulated by curvature-sensitive membrane complexes. The model predicts three mechanical regimes with increasing target stiffness: (i) biting (trogocytosis), where part of the target is extracted; (ii) pushing, where the target is displaced rather than engulfed; and (iii) complete engulfment. We validate these predictions in epithelial clearance of apoptotic targets in vivo and macrophage engulfment of Giant Unilamellar Vesicles (GUVs) and lymphoma cells. Together, our results identify target mechanics as a key regulator of clearance and cell--cell interactions.
arXiv:2506.21511v2 Announce Type: replace-cross
Abstract: We develop sampling methods, which consist of Gaussian invariant versions of random walk Metropolis (RWM), Metropolis adjusted Langevin algorithm (MALA) and second order Hessian or Manifold MALA. Unlike standard RWM and MALA, we show that Gaussian invariant sampling can lead to ergodic estimators with improved statistical efficiency. This is due to a remarkable property of Gaussian invariance that allows us to obtain exact analytical solutions to the Poisson equation for Gaussian targets. These solutions can be used to construct efficient and easy to use control variates for variance reduction of estimators under any intractable target. We demonstrate the new samplers and estimators in several examples, including high dimensional targets in latent Gaussian models where we compare against several advanced methods and obtain state-of-the-art results. We also provide theoretical results regarding geometric ergodicity, and an optimal scaling analysis that shows the dependence of the optimal acceptance rate on the Gaussianity of the target.
arXiv:2607.11215v1 Announce Type: new
Abstract: Most sign language translation (SLT) methods focus on isolated native sign-spoken pairs (e.g., American Sign Language - English). Extending language-specific SLT models to multilingual translation would improve accessibility by enabling communication across diverse sign and spoken language communities. However, existing multilingual SLT approaches still struggle to learn a unified model that minimizes cross-lingual conflicts while capturing shared cross-lingual semantics and preserving language-specific variations across different sign languages. Therefore, we propose Q-BridgeNet, a unified framework for multilingual SLT that jointly mitigates cross-lingual conflicts across both the sign language and spoken language sides. On the sign language side, Q-BridgeNet learns discrete Q-units via adaptive segmentation and residual vector quantization: a shared base codebook provides language-agnostic semantic primitives, while language-specific residual codebooks refine heterogeneous signing semantics. On the spoken language side, a multilingual LLM is fine-tuned to operate in the Q-unit space, leveraging cross-lingual priors to enable a unified SLT model. Experiments on PHOENIX14T, How2Sign, and CSL-Daily show that Q-BridgeNet effectively mitigates cross-lingual conflicts, achieving state-of-the-art performance on native sign-spoken pairs while also demonstrating strong generalization to non-native pairs. Our source code is publicly available at: https://github.com/FengLiQ/Q-BridgeNet
arXiv:2607.11794v1 Announce Type: new
Abstract: Model order reduction decreases the dimension of a mechanical system by introducing modal coordinates that retain important dynamic characteristics. Sliding beams, as found in telescopic structures, pose a fundamental challenge. Fixed modal coordinates fail to capture evolving system properties, and updating the modal basis during simulation causes modal coordinates to change meaning. The present work addresses this challenge by constructing a global reduction basis for a sliding beam. The global basis is constructed from snapshots in the form of modal matrices and compressed using proper orthogonal decomposition. Reduction is applied within a constraint multibody formalism with algebraically enforced constraints that permit continuous slider movement. The method is validated against an absolute nodal coordinate formulation of a sliding beam with a sliding joint. Different combinations of snapshot quantity and eigenmodes per snapshot are investigated and an error map is shown. A challenging test case involving a highly flexible beam subjected to time-dependent loading and slider movement demonstrates that the global reduction basis reduces computation time by approximately 90% while keeping the root-mean-square displacement error, introduced by the global reduction, below 2%.
arXiv:2607.09802v1 Announce Type: new
Abstract: The escalating demand for Machine Learning (ML) training resources in recent years has resulted in a substantial gap between the high demand and the available supply. Efficient allocation of these scarce and expensive resources is crucial for organizations to maximize their return on investment. Existing resource allocation mechanisms, like Karma [OSDI'23], are designed to guarantee Pareto efficiency and max-min fairness in settings with dynamic (time-varying) user demands, but fail to preserve these key properties in the presence of demands with heterogeneous values. Given the ubiquity and inevitability of heterogeneity in organizational values of different workloads, effective resource allocation policies must accommodate these variations.
In this paper, we describe the design, implementation, deployment, and theoretical analysis of Quota Marketplace, a market-based mechanism to efficiently allocate ML training chips (like GPUs), explicitly addressing scenarios with demands of heterogeneous value. We detail the implementation of this mechanism within Google and present metrics that demonstrate its impact. We also discuss many business-critical requirements that the Quota Marketplace handles quite effectively, and document the gains and opportunities it has unlocked. We establish theoretically how this market-based approach achieves the essential properties of Pareto efficiency and max-min fairness by allowing the users to express the value of their workloads and enabling dynamic resource pricing based on supply and demand fluctuations. Ultimately, the market facilitates resource allocation that aligns with organizational priorities.
arXiv:2607.10807v1 Announce Type: new
Abstract: Luminescence thermometry has evolved through decades of research focused on optimising materials and on extracting temperature information from isolated spectral features such as luminescence intensity ratios, bandwidth, line shift and excited-state lifetime. Despite extensive material development, these conventional methods remain fundamentally limited by construction: only a small subset of pre-selected spectral features is exploited, while the bulk of the temperature-relevant information encoded in the full spectrum is systematically discarded. A paradigm shift is presented here, Luminescence Thermometry 2.0 (LT 2.0), implemented through the newly developed SpectraSensML platform, in which machine learning regression operates on the entire emission spectrum to deliver temperature readout. The approach is demonstrated on a Yb3+-doped phosphor emitting in the near-infrared biological transparency window across 125 to 700 K. Nineteen regression algorithms drawn from four families, namely tree ensembles, physics-aware regression models, kernel and instance methods, and neural networks, are systematically benchmarked. A sensor-fusion estimator that combines the first three principal components reaches an root-mean-square error of 0.36 K, a seven-fold improvement over the best luminescence intensity ratio variant. Single-component approaches are shown to be quantitatively sub-optimal: multi-component regressors that exploit the first three principal components reduce the temperature uncertainty by close to an order of magnitude. The structural reason behind the failure of decision-tree ensembles on unseen temperatures is explained: their piecewise-constant predictions cannot interpolate beyond training set-points. The open-source SpectraSensML application used to obtain the results is released alongside the manuscript to enable reproducible community benchmarks.