arXiv:2607.14415v1 Announce Type: new
Abstract: Computed Tomography (CT) carries significant ionizing radiation risks, driving the need for sparse-view reconstruction. Implicit scene representations (ISRs) address this by recovering continuous volumetric attenuation fields directly from sparse projections, and recent geometry-aware extensions jointly model surface geometry alongside attenuation to improve fidelity and enable clean tissue segmentation without manual thresholding. However, these methods remain limited by manually tuned attenuation bounds and rigid two-material constraints. This paper proposes $K$-NeAS, a unified and scalable architecture for automated, multi-material surface reconstruction. We replace independent material networks with a shared latent backbone and introduce a fully differentiable $K$-material sequential soft selector to model an arbitrary number of overlapping tissues. To eliminate manual tuning, we automate attenuation bounding using a Gaussian Mixture Model (GMM) and implement a scheduled auxiliary floater loss to mitigate geometric hallucinations common under extreme sparsity. Evaluated across four clinical Cone-Beam CT (CBCT) datasets, $K$-NeAS successfully scales to arbitrary material counts, achieving superior 3D volumetric fidelity at $K=3$ materials on complex multi-tissue regions such as the Abdomen ($33.28\text{ dB}$ 3D PSNR vs. $31.40\text{ dB}$ single-material NeAS baseline, a $+1.88\text{ dB}$ improvement). Furthermore, our model exhibits enhanced robustness under sparse-sampling conditions, outperforming baseline 3D PSNR by up to $1.17\text{ dB}$ under 5- and 10-view constraints.
Science Journals
arXiv:2607.14454v1 Announce Type: new
Abstract: All physical quadrupoles have a fringe field falloff, giving rise to intrinsic higher order aberrations. It is especially important to consider these aberrations when designing beamlines with strong, longitudinally cramped optics used to capture and transport large emittance beams. This paper presents relatively simple analytical formulae which describe the fields of quadrupoles including their higher order aberrations for implementation in beamline design. We review the analytic model for quadrupoles described by a $\mathrm{sech}^2$ strength function and subsequently expand it to quadrupoles whose strength function instead follows a tanh function, adding the effective length of the quadrupole as a free parameter. The implementation of these analytic field descriptions in envelope code TRANSOPTR and multi-particle codes GEANT4 and FLUKA is presented using the optics design of the DarkLight experiment at TRIUMF as example.
arXiv:2607.14836v1 Announce Type: new
Abstract: Sign language production, which generates continuous 3D skeletal motion from spoken language input, must simultaneously satisfy two constraints: semantic fidelity, so that a deaf viewer can recognize the intended sequence of glosses, and biomechanical plausibility, so that the generated skeleton respects anatomical constraints. Existing approaches optimize semantic reconstruction through coordinate-based objectives that treat the skeleton as an unstructured vector, thus allowing for bone length drift, joint angle violations, and temporarily locked fingers. We introduce PIDiffSign, a physics-informed diffusion model for gloss-to-pose translation that incorporates anatomical constraints into both the architecture and training objective. The model uses a Transformer encoder-decoder, where the decoder is conditioned on the diffusion time step through adaptive zero-initialized layer normalization and cross-attends to gloss representations. A differentiable geometry module enforces bone length consistency and biologically valid joint angles throughout generation. Training combines anthropomorphic, kinematic, angular, and finger-joint constraints with a contrastive gloss-pose alignment loss and classifier-free guidance for semantically conditioned sampling. Experiments on the PHOENIX14T and CSL-Daily benchmarks show consistent improvements over a strong diffusion baseline in pose accuracy, joint-angle correctness, distributional realism, and back-translation quality. These results demonstrate that physics-informed diffusion improves both motion realism and semantic fidelity for sign language generation.
arXiv:2607.14487v1 Announce Type: new
Abstract: Dexterous manipulation is limited not only by algorithms but by a shortage of accessible hand hardware that combines human-scale morphology, ease of manufacturing or maintenance, tactile sensing, and practical cost. Existing dexterous hands tend to optimize some of these properties at the expense of others. We present MIDAS Hand, a low-cost, open-source, human-scale dexterous hand with integrated tactile sensing for manipulation research. MIDAS Hand provides 16 total degrees of freedom (DoF) with 13 active DoF, directly driven actuation with measurably low backdrive torque, and 283 three-axis tactile taxels in a compact 700 g package with a bill of materials under 3,000 USD. Built from 3D-printed components, it assembles in under three hours while providing the strength, repeatability, and maintainability needed for repeated real-world experiments. Alongside the hardware, we release a full stack: design files, build documentation, control and tactile Python APIs, simulation models, and retargeting and teleoperation pipelines. We characterize MIDAS Hand through workspace and grasp-taxonomy analysis, payload and reliability tests, backdrivability measurements, and teleoperation demonstrations with tactile sensing, showing that it offers a balanced, reproducible platform for tactile dexterous manipulation and human-to-robot data collection. Project page: https://midas-hand.com
arXiv:2607.14976v1 Announce Type: new
Abstract: Video object removal is a fundamental yet challenging task in video editing. Despite recent progress, existing methods typically fall into two categories. Traditional approaches based on optical flow or attention mechanisms often introduce noticeable artifacts and yield unnatural results. In contrast, diffusion-based methods improve visual realism but demand multiple denoising steps, limiting their practicality. To address these issues, we propose From-Draft-to-Draft-Free (D2DF), a framework that distills the ability of transforming coarse drafts into refined videos into a one-step video generation model. Within D2DF, a teacher model is trained to refine low-quality removal results ("drafts") into high-fidelity videos by multiple steps. Then, through Prior-Privileged Consistency Distillation (PPCD), we distill this capability into a student model that performs one-step removal conditioned on the draft. To eliminate draft dependency, we introduce a Self-Guided Fast Planting (SGFP) module based on our Temporal Masked Transformer that autonomously generates scene-consistent pseudo-drafts in latent space, enabling a fully draft-free one-step model. Extensive experiments show that both draft-conditioned and draft-free versions achieve state-of-the-art performance on multiple metrics, surpassing traditional and multi-step generative methods in both quality and efficiency. The denoising process for a single video takes only about 1 second.
arXiv:2607.14155v1 Announce Type: new
Abstract: Agent systems validate inputs, tool calls, and generated objects. The final package often escapes the same scrutiny. In one DRSS release, the ledger supported 60 points and a failed certificate; the report announced a 100-point Gold Path. Every local gate was green. The package contradicted itself.
We study that failure alongside Schema Docs, where similar faults became product contracts, and Brand Shuttle GEO, where evidence is turned into repair work. The result is a candidate Schema-SIP Relational Conformance profile (SIP-RC). It models a release as a graph: claims point to evidence, decisions carry bounded authority, derived artifacts retain their execution conditions and lineage, and published bytes must match the package that was checked. Hard failures cannot be averaged away, and a validator recomputes critical decisions on a separate path. The paper establishes the failure class and shows that several mechanisms are practical. Whether the full profile performs better than existing checks remains an open experiment.
arXiv:2607.14234v1 Announce Type: new
Abstract: This paper presents a comprehensive dataset of past ICA (The International Communication Association) annual conference papers from 2003 to 2018, encompassing 27,466 papers, 21,038 authors, and 4,935 sessions. We made the dataset publicly available in both CSV and JSON formats. Additionally, we developed an API to facilitate programmatic access, and an intuitive user interface to enable users to navigate and explore the data more easily. The web application, API documentation, downloadable data, and reproducible code to obtain and process the data are available at https://ica.hongtaoh.com.
arXiv:2607.14601v1 Announce Type: new
Abstract: Developers who maintain real systems must continually recognise and remediate vulnerabilities in existing code, yet this skill is rarely trained directly: secure software development is commonly taught only after programming fluency is acquired, and accessibility support is treated as a secondary concern, disadvantaging learners with ADHD and related executive-function differences. This paper presents SYNAPSE, a publicly deployed adaptive tutoring platform for Java, Python for cybersecurity, and secure software development. SYNAPSE coordinates Claude, GPT-4o, and Gemini through the Model Context Protocol, routing interactions by pedagogical intent under a three-stage Socratic hint policy. It exposes eighteen always-visible accessibility features and anchors practice in ShopSecure, a deliberately vulnerable web application mapped to six OWASP Top 10 (2021) categories, on which learners practise the detect-understand-remediate loop characteristic of software maintenance. A feasibility pilot with nineteen participants across neurodivergent and neurotypical cohorts returned a System Usability Scale score of 76.4 and engagement of 4.2/5, with comparable cognitive-load levels across cohorts. SYNAPSE is available at https://synapse-course.com; a screencast is available at https://youtu.be/9R17KC47qQI.
arXiv:2607.14268v1 Announce Type: new
Abstract: This paper gives the first convergence analysis and proof of acceleration for nonlinear GMRES (NGMRES) applied to contractive and noncontractive fixed point iterations (FPIs) for solving general nonlinear systems. Our main results are that in both the contractive and noncontractive cases, the ratio gain of the optimization problem is the mechanism responsible for accelerating (or enabling) convergence. Our analysis also reveals a second important quantity related to the optimization problem, which directly predicts the linear convergence rate at each iteration and proves it is at most 1; hence only higher order terms are responsible for NGMRES non-convergence. Numerical results for several challenging nonlinear test problems are given that illustrate the theory, show how the acceleration improves convergence, show that the quantity predicting the linear convergence rate is remarkably accurate and moreover can be useful for adaptively choosing NGMRES depth, show how restarts can improve convergence in noncontractive iterations, show how NGMRES is naturally suited for finding distinct solutions of a multi-solution PDE, and that NGMRES can perform better than Anderson acceleration when applied to superlinear FPIs.
arXiv:2607.14283v1 Announce Type: new
Abstract: Resolving heterogeneity in megadalton assemblies requires precise single-molecule mass measurements in solution. Mass photometry infers mass from individual molecular surface-landing events, and event-to-event measurement variability limits precision. Nanofluidic scattering microscopy overcomes this limitation by continuously tracking molecules in motion, enabling repeated sampling and temporal averaging of these fluctuations. Benchmarking with 4.5 MDa DNA origami demonstrates up to a fourfold improved resolution, approaching the performance of ensemble-averaged native mass spectrometry.
arXiv:2607.15111v1 Announce Type: new
Abstract: Vehicle coordination at unsignalized intersections relies on accurate real-time vehicle state acquisition and reliable command-and-control (C&C) signal delivery. However, existing studies typically treat sensing, communication, and control separately, which may lead to redundant transmissions, outdated state information, and unreliable vehicle coordination. In this paper, we investigate a new scenario of distributed integrated sensing and communication (ISAC)-enabled vehicle coordination at intersections, where multiple roadside units (RSUs) collaboratively transmit sensing signals for vehicle state acquisition and C&C signals for vehicle movement control under the management of a central base station (BS). To improve signaling efficiency, we propose a unified goal-oriented semantic communication (GSC) framework, which transmits sensing and C&C signals only when they are semantically important for improving intersection traffic throughput. Specifically, an extended Kalman filter (EKF) is adopted to predict vehicle states and fuse distributed sensing measurements. A masked hybrid proximal policy optimization (MHPPO) framework is then developed to jointly determine sensing transmission decisions, C&C transmission decisions, and C&C signal contents based on a value-of-information (VoI) reward. Furthermore, we propose an uncertainty-aware transmission design (UTD), including robust beamforming and VoI-based time-division power allocation, to improve sensing and communication reliability under vehicle state uncertainty and inter-RSU interference. Simulation results show that our proposed framework achieves 100% collision-free vehicle coordination with significantly reduced signaling overhead compared with predictive ISAC baselines adapted from state-of-the-art related studies and several ablation baselines.
arXiv:2607.14514v1 Announce Type: new
Abstract: Object-goal navigation requires an embodied agent to locate and reach an instance of a specified object category in an indoor environment. Recent training-free approaches leverage vision-language models (VLMs) for open-vocabulary semantic reasoning, but are typically evaluated under an episodic protocol that resets all scene-specific state after each episode. We introduce Cross-Episode Object-Goal Navigation, in which an agent repeatedly operates in the same scene, retains only self-acquired experience, and keeps its model parameters fixed. To support experience reuse, we present \method, a training-free VLM navigation framework with a persistent hierarchical Visual-Topological Memory (VTM). The VTM organizes scene knowledge at room and object levels and retrieves relevant experience through coarse-to-fine matching, providing memory as soft guidance only when it agrees with current observations. A conservative execution guard further mitigates oscillations, blocked motions, and premature stopping. Under a controlled same-scene protocol, we evaluate \method{} on three benchmarks, HM3D v0.1, HM3D v0.2, and MP3D, and compare it with a strengthened WMNav baseline augmented with cross-episode textual memory, while keeping the VLM backbone and action pipeline identical. \method{} achieves the best performance across all three benchmarks, demonstrating the effectiveness and robustness of structured visual-topological experience reuse across datasets.
arXiv:2607.15114v1 Announce Type: new
Abstract: Recommender systems increasingly shape which content reaches users, making it important to understand whether coordinated activity is amplified beyond the accounts that initiate it. Existing robustness evaluations largely focus on static target-rank changes and do not capture how coordinated interactions, recommendation, and user response evolve within a feedback loop. To address this gap, we propose CoSimRec, an offline agent-based evaluation framework that models coordinated accounts, dynamic ranking, non-bot responses, and ranking interventions in a shared closed-loop process. CoSimRec introduces the Algorithmic Penetration Rate (APR) metric family to measure target content's share of non-bot exposure and engagement, lift against matched no-attack baselines, and exposure gained per coordinated interaction. We evaluate CoSimRec on MIND, MovieLens, and LastFM using random, popularity-based, feedback-sensitive, MF, and BPR-MF recommenders, with ten-seed inference for the primary APR analysis and population-scale experiments of up to 1000 users. Random controls show no statistically supported positive penetration, whereas popularity-based and feedback-sensitive ranking produce significant positive APR-Lift in all six master-worker dataset--recommender settings, reaching 0.4505 on LastFM; synchronization-aware ranking reduces APR in every corresponding defense setting.
arXiv:2607.15117v1 Announce Type: new
Abstract: Model Predictive Control (MPC) has been widely applied to autonomous vehicle drifting. Assisted drifting, that is where the driver remains in the loop, is still comparatively underexplored. Existing approaches often rely on restrictive assumptions, such as precomputed drift equilibria, full actuation authority, or prior path knowledge, which limit applicability to expert drivers. This paper proposes a nonlinear model predictive control (NMPC) framework for assisted drifting on a rear-wheel-drive vehicle. Through steer-by-wire and drive-by-wire interfaces, the controller decouples driver commands from direct actuator inputs, allowing the driver to regulate the desired sideslip through the steering wheel while the NMPC maintains vehicle stability. A dedicated activation logic ensures that the controller engages only under deliberate driver intent. High-fidelity simulations show that the proposed architecture can stabilize drifting maneuvers using a simple single-track prediction model with basic tire dynamics, even when the sideslip reference is continuously varied by the driver.
arXiv:2607.14371v1 Announce Type: new
Abstract: Large Language Models (LLMs) have revolutionized AI services, but a critical tension emerges: while personalization improves model performance, it consumes scarce computational resources that users must share. When should a user invest in expensive Supervised Fine-Tuning (SFT) versus lightweight In-Context Learning (ICL)? How does congestion from other users' personalization choices reshape these incentives? And what strategies should platforms adopt when offering multiple personalization algorithms?
We develop a tractable framework for LLM serving that captures the statistical-economic trade-offs users face. Our analysis yields several surprising insights. First, we show that ICL and SFT dominate in different regimes, determined by an interplay between pretraining coverage and data signal-to-noise ratios, but congestion can flip these rankings. Second, equilibrium resource consumption exhibits pronounced non-monotonicity: improving pretraining precision reduces the congestion, while broader pretraining coverage and harder tasks sometimes increase it. Third, we prove that offering both personalization methods never hurts the platform's maximal profits, despite potentially increasing computational load.
Experiments with GPT-2 on linear regression tasks validate our theoretical predictions about algorithm performance. Complementing these results, our review of documentation from 21 major AI platforms shows that the share offering both SFT and ICL increased from 9.5% in 2021 to 71.4% in 2025, consistent with our platform-design implications.
arXiv:2607.14545v1 Announce Type: new
Abstract: Machine-learned predictions can speed up offline NP-hard optimization, but asking a predictor what to do amounts to asking it to solve the problem, and committing an unchecked prediction forfeits every worst-case guarantee. CASP (Certificate-Augmented Solution Pruning) instead asks which parts of the search space may be ignored, and accepts each answer only after a sound polynomial-time verifier has checked it, so correctness never depends on prediction quality. We develop the learning theory of this design. The verifier makes the induced loss class uniformly bounded, so certificate parameters are learnable from $\tilde O(\varepsilon^{-2}\log K)$ samples ($K$ the maximum instance size), whereas the unverified commitment class admits no distribution-free rate and, under cost spread $R$, none below $\Omega(R/\varepsilon^2)$. Filtering noisy predictions by verifiable confidence dominates the standard min-combiner, with a margin we compute in closed form, and the prediction stays useful even given the LP, because it breaks ties on degenerate optimal faces, where every symmetric LP policy, meaning one whose commitments depend on the instance only through the verifiable confidence values, provably stalls. Experiments on five problems test the theory's quantitative predictions. With trained predictors, unverified pruning loses up to $26%$ of the optimum under distribution shift, while the verified deployment of the same predictions loses nothing.
arXiv:2607.15146v1 Announce Type: new
Abstract: Online encyclopedias shape political opinion and, through it, democratic discourse. In late 2025, Grokipedia was released, an encyclopedia written entirely by the LLM Grok. One motivation behind the project was to provide an unbiased alternative to Wikipedia, which has faced accusations of "left-wing" and "liberal" bias. But does an encyclopedia written by an LLM deliver greater neutrality, or does it simply embed a different ideology? We conduct a large-scale political bias study on Grokipedia and Wikipedia, analysing 1,394 article pairs describing members of government for neutrality along nine expert-coded ideology dimensions employing four LLM judges, Grok, Claude, Mistral, and DeepSeek. As the LLMs could themselves be biased, we also investigate patterns in their judgments. We find all LLM-judges, including Grok, to rate Grokipedia less neutral than Wikipedia. Both encyclopedias are rated as portraying politicians favourably overall, but towards different ideological groups. Grokipedia particularly favours economically right-wing politicians and penalises socially liberal ones, while Wikipedia is rated as favourably biased towards the latter.
arXiv:2607.14580v1 Announce Type: new
Abstract: We present a novel system that integrates negative prompt optimization via a fine-tuned sequence-to-sequence LLM and latent-space classifier guidance to improve the quality of images generated by Stable Diffusion. Our approach automatically generates optimized negative prompts, and employs a CNN-RNN hybrid classifier to evaluate and guide diffusion steps, rolling back low-quality latent updates. Experimental results demonstrate that our dual-guidance framework reduces artifacts and improves semantic fidelity compared to baseline diffusion.
arXiv:2607.14471v1 Announce Type: new
Abstract: We investigated the impact of the anode's work-function on the performance of an all inorganic PeLED device which was hailed for stability and long lifetime with drift-diffusion and Poisson equations embedded as a computation package in Setfos, a commercially professional software to emulate LED. According to the simulation results, a conclusion that a thin high work-function metal layer inserted between indium tin oxide (ITO) and hole transport layer of nickel oxide (NiOx) substantially facilitated hole injection by decrementing the hole injection barrier. 2 nm thick gold (Au) had been checked and dramatically enhanced luminance intensity by more than 18 fold times had been attained. The situation went for other high work function metals like Pd and Pt as well. Our high work-function thin metal layers decorating anodes in PeLED in facilitating hole injection unfolds a new trajectory towards efficiently boosting PeLED luminance with a technique that is compatible with large scale fabrication process.
arXiv:2607.14407v1 Announce Type: new
Abstract: Many signal processing systems ultimately exist to {act}. Whenever the state variable that determines the action to be taken by a decision maker, or agent, is uncertain, the way that uncertainty is represented decides how well the agent performs and how much its performance can be trusted. This lecture note develops, from first principles and within a single decision-theoretic setting, the link between the {objective} and the knowledge of an agent and the form of uncertainty representation that is sufficient to act optimally. To start, assuming a known environment distribution, we show that a risk-neutral agent needs the posterior distribution over the state, whereas a risk-averse agent can rely without loss of optimality on a {prediction set} and a worst-case decision rule. We then turn to the case in which the environment is unknown, and identify three complementary approaches to address the resulting epistemic uncertainty: calibration of a fixed predictor, credal (ambiguity) sets with distributionally robust optimization, and Bayesian inference over model parameters. The common thread is that reliable decisions require an uncertainty representation matched to the decision objective and to the knowledge profile of the agent, together with a guarantee that certifies the utility the agent will actually obtain.
arXiv:2607.15123v1 Announce Type: new
Abstract: Recent FPGAs have improved deep learning (DL) inference efficiency through dedicated tensor blocks and in-BRAM computation. ReRAM-based analog in-memory computing (IMC) pushes efficiency further, offering an order-of-magnitude improvement in compute density and energy efficiency over conventional digital logic by performing vector-matrix multiplication (VMM) directly within the ReRAM crossbar; prior work has integrated such IMC blocks into FPGAs for DL inference. However, conventional IMC designs support only static-weight VMM, leaving nonlinear operations and dynamic matrix-matrix multiplication (DIMM) to the FPGA fabric. As a result, the benefits of IMC are largely confined to static-weight models, whereas Transformer-based models, which rely on frequent nonlinear and DIMM operations, gain only limited improvement. Moreover, the ADCs within each IMC block consume more than 70% of its area and power, further limiting system efficiency and scalability. To address these limitations, we propose a novel FPGA architecture that integrates an ADC-free IMC block, replacing the conventional ADC with analog content-addressable memories (ACAMs) that natively perform nonlinear operations inside the block. To fully exploit this block, we conduct an FPGA-aware design-space exploration that determines optimal crossbar dimensions while balancing FPGA area, flexibility, and DL performance, and we develop an efficient mapping that leverages ACAMs to carry out DIMM operations, extending the applicability of IMC to attention computation. On CNN and Transformer-based benchmarks, the proposed architecture achieves up to 40x and 1.9x higher energy efficiency and 4.1x and 2.5x higher area efficiency, respectively. Overall, it significantly improves FPGA DL inference efficiency and sustains robust gains on Transformer-based workloads across long input sequences, advancing domain-specialized FPGA design.
arXiv:2607.15143v1 Announce Type: new
Abstract: AI coding agents set up projects by reading documentation and installing the dependencies it lists, without verifying their names, sources, or known vulnerabilities. By editing only a README, requirements file, or Makefile, an attacker can redirect the agent to an untrusted registry, a known-vulnerable version, or a wrong-but-plausible name: documentation becomes a vector for code execution. We present the first systematic evaluation of package-install-time supply-chain attacks delivered through ordinary project-setup documentation across production coding-agent harnesses, probing frontier models on twelve scenarios in five attack classes, grounded in documented incidents. The same model catches an attack through one harness and installs it through another: install-time security rests on the harness-model combination, not the model alone. Agents catch blatant typosquats reliably, but plausible separator-confusion names (azurecore for azure-core) slip through, and how often depends on the harness-model pairing. Source-based attacks like registry redirection are missed almost everywhere. The source blind spot recurs on npm and Cargo, where nearly every model installs the untrusted dependency; name detection carries over less consistently across ecosystems. Security-oriented prompts recover part of the gap but only for the dimension they name; a deterministic pre-install check that verifies names, sources, and versions before any code runs closes most of it.
arXiv:2607.14398v1 Announce Type: new
Abstract: Constrained generative models aim to produce samples that satisfy complex feasibility constraints while remaining faithful to the data distribution. Existing constrained generation methods typically enforce constraints either through training-time optimization or sampling-time correction. Training-time optimization approaches optimize on states induced by the training distribution, which can differ substantially from those encountered during sampling. Sampling-time correction methods instead modify the sampling process at inference, introducing distribution shift and requiring expensive tuning, particularly for few-step sampling. We propose a fine-tuning framework that incorporates constraint guidance obtained through online rollout into the training process, which aligns training with sampling by differentiating through the fixed noise schedule used to numerically integrate the denoising process. This exposes the model to violations that arise along the denoising trajectory and aligns diffusion learning with the sampling process. Experiments across multiple tasks show that our method improves constraint satisfaction while maintaining competitive sampling quality compared to prior methods.
arXiv:2607.15147v1 Announce Type: new
Abstract: We report on the ground and numerous excited electronic states. In the ground state the C4n rings are closed-shell systems possessing polyynic structures and can be classified as double anti-aromatic molecules. In their energetically lowest lying triplet state the rings exhibit aromatic cumulenic structures. The overall change in the electronic structures is rather dramatic upon the found moderate geometric changes from polyynic to cumulenic structure. Among others, Hund's rule is violated in both C8 and C12 in their cumulenic structures. We mention that until now, graphene is the only carbon allotrope reported to violate Hund's rule. The reasons for the violation are analyzed. Much effort has been invested to understand the relaxation pathways of the low-lying states leading the C8 from polyynic to cumulenic geometry and vice versa. On its minimum energy path, the first singlet excited state changes from open-shell character in the polyynic structure to a closed-shell state in the cumulenic structure. The cumulenic state lowest in energy is an open-shell singlet which relaxes to the closed-shell polyynic ground state.
arXiv:2607.14417v1 Announce Type: new
Abstract: We report the femtosecond time-resolved measurements of confined acoustic phonons in free-standing nanocrystalline silicon membranes and compare them directly with the crystalline silicon counterpart. While the latter exhibit well-resolved higher-order modes, a strong suppression of these modes is observed in nanocrystalline samples with grain size distribution controlled by thermal annealing. The suppression is strongly frequency dependent and becomes more pronounced as the phonon wavelength approaches the characteristic grain size. By separating intrinsic and extrinsic contributions to the phonon lifetime, we identify an additional frequency-dependent decay channel associated with grain boundaries, with scattering rates following a power-law dependence close to $f^{2}$, where $f$ is the frequency. The measured sound velocity is consistent with previous reports for nanocrystalline silicon and indicates an effective elastic response arising from multiple crystallographic orientations. These results establish coherent phonons as a sensitive probe of microstructure-dependent scattering in nanocrystalline materials and indicate that grain boundaries act as an effective spectral filter for high-frequency acoustic phonons.