Papers

Although room impulse responses (RIRs) encode source-distance cues, conventional monaural source separation focuses on recovering audio content without estimating source-specific RIRs, losing the associated spatial information. To address this limitation, we propose Distance-Aware Monaural Source Separation using Multi-RIR Estimation (DAMSEP), the first end-to-end framework that is jointly trained for source separation and multi-source RIR estimation from a single-microphone mixture. DAMSEP integrates a separation backbone with shared dereverberation and RIR estimation modules to jointly recover clean sources and source-specific complex convolutive transfer functions under source estimation and reverberant reconstruction objectives, enabling relative near/far ordering through the direct-to-reverberant ratios of the corresponding RIRs. For comprehensive evaluation, we introduce HETMIXR, which spans heterogeneous source content and diverse simulated room conditions with source-specific RIRs and geometric distance annotations. Experiments on HETMIXR demonstrate superior performance in source separation, RIR estimation, and distance ordering. Ablation studies reveal the complementary benefits of source supervision and reverberant reconstruction, while additional evaluations show generalization to single-speaker inputs and mixtures generated using measured RIRs from an unseen room. Our code and dataset are available at https://github.com/Wenanzhi/DAMSEP.

In this paper we explore the concept of smoothness in arithmetic semigroups. In particular, we give explicit asymptotics for the counting function $\Psi_G(x,y)$ when the arithmetic semigroup $G$ satisfies Axiom A (in the spirit of Knopfmacher). The asymptotics generalise the standard results in the literature for the integer case, and are expressed in terms of the Dickman $\rho$ function. Finally, we give applications of our results to counting smooth families of finite abelian groups and finite semisimple rings.

We prove the existence of solutions to the stochastic magnetohydrodynamics (MHD) system, where randomness is introduced through random initial data and a stochastic integral appearing solely in the induction equation, while the fluid equations remain deterministic. We focus on the case where the adiabatic exponent $\gamma$ satisfies $\gamma > \frac{3}{2}$. The existence proof is carried out using the penalization method. We define the notion of a martingale solution and establish sufficient conditions for its existence. The proof then proceeds by means of the stochastic compactness method. Using an energy inequality, we derive a priori estimates in terms of expectation. Due to the stochastic nature of the problem, we demonstrate convergence in law of the penalized solutions and verify that the limiting object is a martingale solution.

Anomalous sound detection (ASD) has long been dominated by k-nearest-neighbor (KNN) based detectors, which essentially perform implicit likelihood estimation over normal samples. In this work, we investigate whether generative models can better serve this role. We propose Relative Mismatch, a generative ASD backend powered by flow matching, which learns a velocity field that transports Gaussian noise to a representative feature space of normality. During inference, it measures the mismatch between the oracle and predicted path velocities and aggregates them through a two-level design. To mitigate the inherent mismatch offsets incurred by domain shift, each query is further calibrated with the mismatch of its local normal reference, thereby exposing only its deviation beyond normality. Extensive experiments on DCASE 2020–2025 demonstrate that Relative Mismatch outperforms state-of-the-art backends with the highest score of 71.01, along with strong robustness and training stability. Furthermore, we show that curating a compact and discriminative feature space is the key to unleash the power of generative models for ASD.

Open Platform Communications Unified Architecture (OPC UA) is increasingly deployed over private 5G networks in industrial environments, where end-to-end encryption prevents payload inspection by network-based intrusion detection systems (IDSs). Although payload-agnostic statistical features extracted from encrypted traffic enable traffic-based anomaly detection, benign connectivity variations may alter observable user-plane (UP) behavior and increase the false-positive rate (FPR). This paper investigates this reliability problem and proposes a control-plane (CP)-aware decision adaptation for four frozen IDS models. CP indicators at the user equipment (UE) level are used to construct a temporal CP context in which a CP-specific threshold selected on adaptation validation data is applied, while the original threshold remains active outside the context. The traffic features, attack scores, preprocessing procedure, and trained model parameters remain unchanged. Evaluation on a real industrial private 5G testbed shows that benign connectivity variations increase the FPR for all four evaluated models and that false positives are concentrated within periods temporally associated with CP activity. The proposed CP-aware decision adaptation reduces both global FPR and FPR within the CP context while introducing a configurable trade-off between FPR reduction and retained recall over the complete attack campaign. These findings demonstrate that CP context can improve the operational reliability of encrypted-traffic intrusion detection without retraining the underlying models.

We present: (i) a new dataset consisting of the full development history of a 21,000-line Python tool built entirely by Claude AI, with no human-authored code or tests, (ii) two code-provenance tracing tools, (iii) three taxonomies for instruction intent, commit provenance, and response reliability, (iv) application of these to analyse the dataset. We find that: (i) user coding agent CLI instructions differ in kind from IDE-chat instructions, with a greater focus on comprehension, planning and consultation, (ii) code development is mainly proactive, (iii) 14.3% of AI code-generation events contain a real error later caught by the AI-authored test suite, (iv) roughly 1 in 4-5 of the AI's interactive responses contains one or more factual errors.

Primordial black holes (PBHs) form from rare fluctuations in the high-density tail of the primordial distribution, making their abundance highly sensitive to primordial non-Gaussianity. Heavy fields during inflation can source such non-Gaussianity, producing characteristic signatures widely explored in cosmological collider physics, yet its impact on PBH formation remains largely unexamined. We investigate this question within quasi-single-field inflation (QSFI), the minimal realization that couples a heavy field to the inflaton. Unlike most PBH studies that consider only the bispectrum, the QSFI can generate a sizable trispectrum alongside the bispectrum, allowing us to investigate their combined effects on PBH formation. In our leading-order calculation, heavy-field non-Gaussianities can amplify the high-density tail and substantially enhance PBH production. Within the parameter scan, the largest enhancement occurs at the smallest heavy-field mass and the largest sound-speed ratio considered. The trispectrum contribution can be comparable to that of the bispectrum and may even dominate the total non-Gaussian correction. Our results establish PBH abundance as a novel observational channel for cosmological collider physics, demonstrating how heavy fields during inflation can significantly shape PBH formation.

Objective: Heart failure (HF) presents a healthcare challenge due to its high comorbidity burden, aging patient population and frequent hospitalizations. Remote monitoring offers a promising approach to managing HF patients by early detection of health deterioration. Developing autonomous systems to detect signs of worsening in telemonitoring data is of interest to reduce the workload of healthcare personnel. Methods: We propose the TRACER model, a Transformer with Contrastive Event Representation, designed to predict timelines leading to rare hospitalization events in low-resolution and irregularly sampled telemonitoring data. TRACER incorporates time-aware embeddings for each biomarker, contrastive pre-training to enhance anomaly detection via representation learning, and independent binary classifiers for detection. We used measurement data containing remotely recorded biomarker sequences from 276 HF patients segmented into overlapping windows based on temporal rules, and labeled the windows based on the occurrence of HF relevant hospitalizations at the latter edge of the window. Results: TRACER was able to correctly predict 66.7% timelines leading up to HF hospitalizations in the highly imbalanced real-world dataset with an overestimation of 7.9%. Reformulating the training of TRACER as an event detection problem improved the predictive performance compared with training directly on forecasting windows, enabling more effective use of the limited hospitalization events. Conclusion: TRACER demonstrated superior performance in detecting signs of worsening status in real-world telemonitoring data compared to the other tested models. Significance: TRACER shows promise in identifying signs of clinical deterioration that allow for alerts to be generated to provide counteractive treatment in patients with HF.

Integrated sensing and communications (ISAC) systems offer a promising framework for beam tracking, which enhances channel awareness and improves communication reliability in mobile millimeter wave (mmWave) networks. Motivated by this, we propose a low-complexity, model-driven channel state information (CSI) framework with integrated channel prediction capability in ISAC-enabled systems. To reduce the overhead of CSI acquisition and uplink feedback, the proposed framework leverages predicted target sensing parameters to construct partial CSI from both line-of-sight (LoS) and detected non-line-of-sight (NLoS) components. Exploiting this partial CSI, we formulate a beamforming optimization problem that minimizes the worst targets' Cramér-Rao bound (CRB) under per-user communication constraints. This problem is non-convex and highly non-linear; therefore, we develop an efficient suboptimal solution using successive convex approximation (SCA) and Schur-complement-based decomposition techniques, and we evaluate its complexity in comparison with other approaches. Moreover, since obtaining the absolute phase of the channel paths may require additional pilot resources, we formulate a robust beamforming design that does not assume this knowledge, and propose a solution based on sample averaging. Numerical results demonstrate that the ISAC-based robust channel prediction and localization framework significantly improves prediction accuracy and link reliability compared with conventional schemes, such as communication-based localization and tracking and separated sensing and communication. These results highlight the potential of ISAC for reliable, adaptive, and high-frequency, blockage-prone wireless networks. Furthermore, the performance of the proposed framework is evaluated under severe blockage scenarios, which further demonstrates enhanced network reliability.

Ligand-based virtual screening (LBVS) is a practical first-pass tool in early-stage drug discovery, but existing benchmarks can overestimate performance through random negatives, easy decoys, limited target coverage, and non-standardized evaluation protocols. We introduce TopU-LBVS, a multi-target benchmark for LBVS under hard-negative screening conditions. Starting from curated ChEMBL~35 bioactivity data, TopU-LBVS covers 93 protein targets across 7 protein classes and constructs target-specific screening libraries with property-matched, structurally similar decoys at a fixed 1:40 active-to-decoy ratio. Libraries contain roughly 400 to 10,000 compounds and are designed to reduce simple physicochemical and nearest-neighbor fingerprint shortcuts. TopU-LBVS provides three fixed protocols. TopU-LBVS-full evaluates ChEMBL$^\ast \rightarrow$ TopU generalization across all 93 targets. TopU-LBVS-low evaluates low-data TopU $\rightarrow$ TopU learning within the hard-negative distribution. TopU-LBVS-mini provides a compact seven-target protocol with a paired random-decoy control that changes only the test decoys, enabling low-cost development and direct measurement of the gap between random ChEMBL$^\ast$ and TopU decoys. Across ten reference baselines spanning fingerprint methods, molecular GNNs, fingerprint hybrids, and modern molecular models, performance under random-decoy evaluation degrades sharply under hard-negative screening. We release data, fixed splits, evaluation code, and baseline implementations for reproducible comparison of future LBVS and molecular representation learning methods. Code and data are available at https://github.com/topu-benchmark/topu-lbvs and https://huggingface.co/datasets/topu-benchmark/topu-lbvs.

Satellite galaxies in massive halos are seen to be preferentially quenched along the major axes of their central galaxies. In contrast, the AGN jet tends to point along the minor axis, which imposes interesting constraints on the role of AGN feedback in satellite galaxy quenching. Here we use the SIMBA, TNG100, EAGLE, and feedback-free SIMBA cosmological simulations at $z=0$ to show that this satellite anisotropy primarily originates from spatial segregation between quenched and unquenched populations. Quenched satellites are more centrally concentrated and more strongly aligned with the central galaxy's major axis, whereas unquenched satellites occupy larger radii and are more nearly isotropic. The persistence of this behaviour without stellar or active galactic nucleus feedback suggests that feedback is not important for satellite quenching. Beyond the halo boundary, the signal varies strongly among simulations and correlates with the excess number of galaxies along the major axis. This links large-scale anisotropic quenching to the direction-dependent distribution of groups, clusters and filamentary environments. Our results provide a unified geometrical interpretation of anisotropic satellite quenching across halo and extra-halo scales.

Computational models of careful and competent human drivers are essential for scenario-based evaluation of automated driving systems (ADS). However, most existing safety reference models primarily focus on longitudinal braking, neglecting the role of evasive steering in human collision avoidance. This paper proposes a hybrid Fuzzy-Safety Model (FSM-H) that integrates longitudinal mitigation and lateral avoidance within a unified behavioral framework. The braking component is governed by Proactive Fuzzy Safety (PFS) metrics, representing the erosion of longitudinal safety margins, while the steering component is driven by Criticality Fuzzy Safety for lane-change (CFS-LC), capturing lateral conflict severity and maneuver feasibility. A finite-state architecture models the sequential escalation from nominal driving to braking and, when necessary, to evasive steering, incorporating perception-reaction time and lane-check delays to reflect human decision processes. The model is evaluated in reconstructed high-criticality cut-in scenarios and compared with braking-only and steering-only reference strategies. Results show that the hybrid approach expands the preventability envelope while maintaining behavioral plausibility and computational tractability. The proposed framework provides a transparent and explainable human reference model suitable for simulation-based ADS safety benchmarking and regulatory assessment.

Modern neural audio watermarking systems typically embed a message repeatedly across time and then collapse the resulting temporal evidence into a single payload using averaging, voting, or another fixed aggregation rule. We argue that this temporal collapse limits both robustness and the recovery of multiple payloads, and that the limitation can be addressed without retraining the underlying watermarker. We freeze a pretrained watermarker's encoder and detector and train only a low-latency Conformer-based decoder. The decoder consumes the detector's temporal soft outputs, which a system-specific adapter pools into a sequence of window-level representations, and predicts the embedded message. On three frozen watermarkers (AURA, AudioSeal, and WavMark), the learned decoder improves recovery of attacked messages and yields higher detection AUROC point estimates on all three. Under controlled full- and partial-coverage multiplexing, it improves joint-exact recovery of two alternating payload words by 9.7-48.0, 6.9-17.3, and 8.2-14.2 percentage points, respectively, under one to three chained attacks on feasible clips.

At cryogenic temperatures, organic single-photon sources (SPSs) offer Fourier-limited emission, virtually unlimited photostability, and emission wavelengths selectable by molecular design, making them attractive for quantum metrology, secure communication, and photonic quantum information processing. However, their efficient integration with photonic microstructures has remained challenging, limiting photon collection from single organic emitters under cryogenic operation. Here, we demonstrate a cryogenic planar metallo-dielectric antenna that efficiently directs the emission of a single dibenzoterrylene (DBT) molecule towards the collection optics. The device reaches a collection efficiency of 97% and delivers 100 million photons per second into the first lens. We measure a photon indistinguishability for the Fourier-limited transition of 91.2% while maintaining a high single-photon purity of 97.7% under strong continuous-wave (CW) and pulsed excitation. These results establish organic molecules as a high-performance platform for single-photon generation and represent an important step towards scalable organic quantum photonic technologies.

Long-horizon tasks require preserving and later recovering cross-session evidence under a bounded, query-blind memory budget. Existing compression can discard fine-grained visual cues or conflate semantically similar but incompatible observations. We present C3M, a cross-session multimodal memory organization that maintains a bounded active index over persistent source text-image evidence. Relation-aware updates consolidate safe redundancy while preserving complementary and incompatible records. At query time, budgeted routing selects useful index pages and expands their associated source evidence under a fixed reader budget. Together, these mechanisms establish a compact, provenance-preserving multimodal memory organization for cross-session long-horizon tasks, retaining temporal distinctions and source links required for reliable downstream reasoning. Code is available at https://github.com/HuzhouNLP/C3M.

We set up and investigate an approximation scheme for a variational theory of cohesive fracture in antiplane shear deformations. We consider a family of functionals acting on scalar valued functions in general dimensions depending on a small parameter $0 < \varepsilon \ll 1$ and on two fields: the elastic part of the displacement field and an eigendeformation field that describes the inelastic response of the material beyond the elastic regime. We measure the inelastic contributions of the latter in terms of a non-local energy functional. Our main result shows that, as $\varepsilon \to 0$, the approximate functionals $\Gamma$-converge to a cohesive zone model.

Arabic-language stance detection remains challenging, and previous shared-task systems have largely relied on multitask learning and ensembles. While these systems achieve state-of-the-art performance, their applicability and transferability are limited by the additional complexity introduced by multitask learning.To reduce this complexity, we introduce $\texttt{CLASP-Ar}$, which reformulates the task as cloze-style masked language modeling. In this approach, the target, predicted sentiment, and text are combined into a single prompt whose $\texttt{[MASK]}$ prediction is restricted to a verbalizer-constrained label vocabulary.

Memristor crossbars enable analog vector-matrix multiplication (VMM) which is promising for machine learning applications. Scaling matrix entries to lower memristor conductance levels reduces power consumption but increases the impact of memristor programming noise on VMM accuracy. To investigate how low-rank factorization can improve this trade-off, we extend the previously proposed factorized low-rank approximation scheme (FLAS) to support adjustable conductance scaling. We then derive closed-form MSE and power expressions for both FLAS and baseline VMM. Based on these expressions, we establish an analytical power-MSE trade-off framework to capture the coupled effects of approximation rank, replication allocation, and conductance scaling under constraints on memristor count and conductance scaling bounds. Numerical results demonstrate FLAS's power-MSE advantage across matrices with different singular value spectra. The power decomposition explains how TIA feedback resistance affect this advantage.

Vehicular Cloud Computing (VCC) exploits computing hardware already embedded in vehicles for purposes unrelated to offloading and puts its idle cycles to work executing end-users' offloaded tasks, avoiding the deployment of new computation infrastructure. Despite its conceptual appeal, adoption is hindered by the lack of quantitative evidence that sharing spare vehicular capacity is viable, profitable and sustainable. A management scheme for task offloading sustains deadline-constrained execution by decoupling deadline-aware allocation from ex-post incentive design, making the approach viable. A robust coalitional game and a vehicle-selection mechanism keep the network operator and the vehicle owners profitable even under unfavorable outcomes of the random environment, e.g.,mobility, radio conditions, or vehicles overstating their spare capacity. A simulation campaign on realistic urban mobility, using Simulation of Urban MObility (SUMO) traces of Rome,together with a CO2 life-cycle assessment against edge computing infrastructure, whose footprint, is attributed pro rata to the share of its capacity devoted to offloading shows the approach to be sustainable. Vehicles absorb most of the offloaded traffic within tens of milliseconds; participation yields up to 157 km of monthly driving range per vehicle; and life-cycle emissions drop by over 99% when using VCC compared to that edge infrastructure.

Bias in AI systems is typically framed as a flaw to be minimized, yet it also serves as a critical indicator of underlying weaknesses in data, modeling assumptions, and system design. Existing approaches often treat bias as an isolated problem rather than as evidence that can strengthen verification and governance across the AI lifecycle. This paper aims to reconceptualize bias as a diagnostic tool that supports rigorous AI verification. We seek to develop a multidimensional framework to analyze bias, demonstrate how biases emerge in both Traditional and Generative AI, and provide a structured pathway for verification-driven mitigation. We present a multidimensional framework analyzing bias across four dimensions: origin sources, emergence points throughout the AI modeling lifecycle, technical and methodological causes, and validation approaches for detection and mitigation. Through a comprehensive typology spanning traditional and generative AI systems, we demonstrate how biases manifest and propagate across development stages. Our analysis encompasses 30 distinct bias types, 16 verification methods, and 20 countermeasures, providing an actionable roadmap for practitioners. We introduce a hierarchical evidence framework that distinguishes internal validity (mechanistic integrity of AI systems) from external validity (contextual reliability in deployment environments). The framework reveals how biases manifest and propagate across modeling stages, enabling systematic mapping between bias types, verification techniques, and effective countermeasures. The proposed evidence hierarchy clarifies how different verification strategies contribute to mechanistic integrity and contextual reliability. We advocate for ”Ethics by Design” principles that integrate bias verification throughout the development lifecycle, enabling the construction of fairer, more robust, and trustworthy AI systems.

We study the $L^p$-boundedness of Isozaki–Kitada type modified wave operators associated with Schrödinger operators $P=-\partial_x^2 + V$ in one dimension for potentials $V$ including long-range potentials such as negative Coulomb-like ones $V(x) = -(1+|x|^2)^{-\mu/2}$ for $\mu>0$. For such potentials, if $ \mu \in (1, 2)$, we prove that the middle and the high energy parts are bounded on $L^p (\mathbb{R})$ for any $p \in [1, \infty]$ but the low energy part is unbounded except for $p=2$. Moreover, if $\mu \in (0, 1]$, we show that even the middle energy part is unbounded except for $p=2$. Actually, regarding the $L^p$-boundedness in the middle and the high energy regimes, we give a complete classification of slowly decaying potentials without imposing any sign condition.

We study the phase diagram of the spin-1/2 XXZ Heisenberg chain in a staggered magnetic field. An exact solution is obtained along the phase boundary, where the exchange anisotropy and the staggered-field strength satisfy an exact analytical relation, yielding a highly degenerate manifold of ground states with off-diagonal long-range order. We demonstrate that these exact ground states lie on the boundary between the ferromagnetic phase and a gapless Luttinger liquid phase. Upon further decreasing the anisotropy, the gapless phase eventually gives way to a gapped quantum-disordered phase. Large-scale density-matrix renormalization group calculations confirm this phase structure and indicate that the transition across the exact boundary is of first order. Our results provide an exact benchmark for understanding quantum phase transitions driven by staggered magnetic fields in one-dimensional quantum spin systems.

Learned image compression (LIC) is hard to deploy on severely resource-constrained FPGAs, since how fast it actually runs depends not just on arithmetic count, but also on memory traffic, imbalance between different operations, and how the hardware batches its work. We present VQ-LIC, an asymmetric edge-cloud codec in which a compact INT8 depthwise (DW)-pointwise (PW) analysis transform and multi-codebook vector quantization (VQ) run at the edge on a reusable DW/PW engine pair, while reconstruction is handled by a larger cloud decoder. Since VQ codeword matching is expressible as a dot product, it is mapped directly onto the same PW engine, removing the need for a separate VQ compute array, to our knowledge a first for FPGA LIC. A novel latency model, derived from deterministic RTL cycle counts of an FPGA's read, DW, PW, and write costs, predicts an analysis transform's per-block latency; since VQ shares the same PW datapath, the model applies to VQ as well. Validated directly against silicon, the model predicts deployed analysis and VQ latency within 0.26% and 0.05%, and guides the selection of a three-block $16$-$48$-$64$ transform. Post-training codebook reduction then cuts VQ arithmetic and codebook storage by $4\times$ and shrinks the fixed-width latent representation. On a 220-DSP Zynq-7020, VQ-LIC's mid-rate preset reaches 0.1398 bits per pixel at 28.69 dB PSNR and 13.06 dB MS-SSIM on CLIC~2017, outperforming a similarly sized neural encoder and reaching a rate-distortion range comparable to a codec three orders of magnitude larger. The complete 0.1945-kMAC/pixel analysis-VQ pipeline runs at 47.98 frames per second and 42.84 mJ per frame on silicon, using an order of magnitude fewer DSPs than comparable FPGA LIC accelerators while achieving lower bitrate, higher throughput, and lower energy per frame at a modest PSNR tradeoff.

Survival research in pancreatic ductal adenocarcinoma (PDAC) is limited by the scarcity of datasets linking whole-slide histology with clinical, molecular, and long-term outcome data. We present a retrospective single-centre cohort of 302 patients who underwent PDAC resection at University Medical Center Gottingen. The dataset comprises 446 H&E whole-slide images, clinicopathological variables, targeted sequencing data for 154 patients, and overall-survival outcomes. During follow-up, 253 patients died, and the median follow-up was 76 months. To establish initial reference values, we evaluated fourteen survival-prediction configurations using identical five-repetition Monte Carlo cross-validation partitions. Ridge Cox regression using numeric clinicopathological variables achieved a mean concordance of $0.649 \pm 0.042$ and $0.652 \pm 0.046$ after adding KRAS and TP53 mutation status. The image-only attention model achieved $0.603 \pm 0.030$, while multimodal fusion achieved $0.619 \pm 0.025$, the highest concordance among the neural models. These results establish promising initial benchmarks for future research using this pancreas-specific multimodal dataset, paving the way for external validation.

We show that negative feedback alone generates the diversity of self-organized shapes usually attributed to scale-dependent activation-inhibition. For a broad class of kernels, pairwise competition models only generate hexagonal spot arrays. Higher-order terms eliminate this restriction and promote stripes and gaps. Combining individual-based simulations and nonlinear analysis, we derive the pattern-selection thresholds and construct the full state diagram, including an unusual spots-stripes-spots sequence. Spot patterns are thus not a reliable indicator of proximity to a tipping point.

On-demand delivery platforms pay riders through incentive activities whose tiers are set from recent completions of riders with a similar history. Operators request such plans for changing periods, rider populations, payment rules and budgets, often for holidays or bad weather, where randomized trials are scarce and take months to collect. We present a request-driven framework that composes four stages (conditional prediction, population reduction, trajectory integration and budget allocation) through seven replaceable modules that exchange conditional trajectory laws, whose award probabilities and award-marked moments give payment and uplift for any activity rule. A response-correction step reweights trajectories from abundant no-offer history to match the moments of a short pilot. We prove that, on a fixed plan menu and given the stage errors, the end-to-end value loss is bounded by the sum of four stage terms, and that for every stage there are instances on which omitting it leaves an error floor the others cannot remove. On 3,000 riders over 45 weekly origins, all 127 windows of a week are answered 11.04x faster with identical scenarios and at most 0.92% value lost by the allocation. On 24 new controlled response laws, the response correction with a one-week pilot lowers regret by 51.2% relative to a trial with the same nominal randomized rider-weeks, and a four-week pilot with exact summation comes within +0.007 of an 18-week trial. In registered studies where windows, populations, rules and binding budgets change from request to request, the framework's regret is below that of a trial with the same nominal rider-weeks and below dose interpolation of the same pilot data, and reusing its one-off preparation answers 60 requests 14.1x and 2.70x faster with identical answers. Against a nine-offer trial fitted with the framework's own dose curve, one-week regret is 0.055 lower.

We establish index estimates for complete two-sided free boundary minimal surfaces in smooth mean-convex domains of $\mathbb{R}^3$ with noncompact boundary. We first prove $3{\rm Ind}_s(\Sigma)\geq 2g+b-1$, where $g$ and $b$ describe the conformal compactification. We then include all interior ends with their multiplicities, without further asymptotic assumptions, and selected boundary ends under an explicit condition ensuring vanishing cutoff errors. The proof combines a localized energy identity with a Riemann–Roch count on the conformal double. A boundary puncture at which the chosen forms are regular eliminates the exceptional space; if poles are allowed at every boundary puncture, this space has dimension at most one. We provide the mixed cutoff construction, examples distinguishing embedded boundary ends from reflected planar ends, and a separate analysis of the conformal Jacobi metric. The latter yields finite Dirichlet energy of the logarithmic conformal factor, but the unrestricted boundary-end estimate remains an open step. The compact-boundary case was treated by Cavalcante, Mendes, and dos Santos.

Phononic crystal platforms provide a promising route toward scalable on-chip quantum networks, where mechanical excitations mediate interactions between solid-state qubits. A key building block of such architectures is the cavity-waveguide system, in which localized mechanical modes couple to propagating phononic modes. However, a quantitative understanding of the mechanisms governing this coupling remains incomplete. In this work, we investigate the interaction between localized modes of snowflake-type phononic crystal cavities and a phononic crystal waveguide using finite-element simulations and experimental measurements. We show that the coupling strength is primarily governed by the spatial overlap between the displacement fields of the corresponding isolated cavity and waveguide modes. After accounting for the effective-mass dependence of the normalized cavity displacement amplitude, we establish a strong correlation between the spatial overlap and the interaction strength across a wide range of mode combinations. Deviations from this leading-order behavior are associated with Bloch-phase effects, variations in waveguide group velocity, and intrinsic hybridization of waveguide modes. Furthermore, we provide experimental evidence for cavity-waveguide coupling in a GaAs phononic crystal membrane by observing a clear resonance in the spectral broadening of the photoluminescence emission from an embedded quantum dot, which serves as a local probe of the mechanical field. The observed resonance at approximately 398 MHz is in good agreement with the corresponding cavity resonance near 395 MHz predicted by finite-element simulations. Our results identify spatial mode overlap as a leading-order design parameter for cavity-waveguide coupling and provide practical guidelines for controlling interactions between localized and propagating modes in phononic crystal structures.

Text-to-video retrieval usually represents a video clip by a single embedding. This embedding often loses important relations between people. E.g., an interaction "Anna confronts Mark" is regularly filmed as alternating shot and reverse shot of both (Fig. 1a). No single shot or averaged embedding over clip shots captures this relation. Thus, we propose SALI (Shot-Aware Late Interaction). It extracts the subject and object from a single-sentence query, and matches the query, its subject and object text embeddings against each visual shot embedding of a video clip. The matching operator is greedy max or optimal transport. A film-grammar penalty in fine-tuning adds a small, consistent shift. Built on CLIP4Clip-meanP, SALI keeps overall recall on par on Condensed Movies and ActivityNet while raising R@1 on multi-shot relation queries by 3 and 12 points, the most among all compared methods, and improves such queries on MSR-VTT at a cost of 1.4 R@1 overall.

Entangled polymer melts exhibit the robust two-branch Baumgärtel-Schausberger-Winter (BSW) relaxation spectrum, while related spectra occur in nonpolymeric monodisperse disordered systems. In spite of the successful application of BSW to many different materials, a molecular derivation of these spectra is lacking. We construct a molecular theory in which a chain segment moves relative to a screened, dynamically responding multichain environment. Gaussian-chain preaveraging gives $M_{\rm seg}(\Delta m)\sim(\Delta m)^{-1/2}$, hence $\lambda_p\sim p^{3/2}$ and, after stress projection, $H(\tau)\sim\tau^{-2/3}$. Independently, longitudinal primitive-path diffusion gives contour-length fluctuations with $H(\tau)\sim\tau^{1/4}$. A molecular-weight-constrained implementation is tested simultaneously against experimental $G'(\omega)$ and $G''(\omega)$ data for four monodisperse polybutadiene (PBD) melts, without fitting spectral exponents or individual modal weights. The resulting BSW spectrum exhibits a continuous transfer from the fast cooperative to the slow constraint-renewal cascade before a finite-chain terminal edge. A common two-sector caged dynamics then connects polymers to particle rafts without assuming identical microscopic mechanisms.