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After the discovery of a cross-luminescence (CL) in BaF2 in 1982, a large number of CL scintillators were investigated. However, no CL scintillator superior to BaF2 has been discovered, and the research of CL scintillators has subsided. Recent technological development in medical imaging and high-energy physics created a new demand for ultra-fast scintillators further supported by the development of UV-sensitive semiconductor photodetectors. As a consequence, renewed interest in CL scintillators appeared. To satisfy the requirements of fast timing applications high photo-detection efficiency, e. i. a good spectral match between the scintillator and photodetector must be achieved. Cesium-based ternary chlorides could provide a red-shift (~1.5 eV) of CL towards the sensitive region of the photodetector (PMT or SiPM) while keeping light output and timing characteristics comparable to BaF2., Comment: 10 pages, 13 figures, 56 references

For the first time Lu$_2$S$_3$ (undoped and Pr-doped) single crystals were successfully grown from melt using micro-pulling-down (mPD) technique. Customization of halide mPD apparatus allowed us to grow rod-shaped (\O 2 mm and length around 20 mm) crystals of Lu$_2$S$_3$ with high melting temperature (~1750 $^\circ$C). X-ray powder diffraction revealed that the grown crystals exhibit the {\epsilon}-Lu$_2$S$_3$ crystal structure ($\alpha$-Al$_2$O$_3$ type, space group R-3c). Optical and scintillation properties of both the undoped and Pr$^{3+}$ doped Lu$_2$S$_3$ were investigated. Fast 5d-4f Pr$^{3+}$ luminescence was observed in both photoluminescence and radioluminescence spectra. The presented technology is an effective tool for the exploration of a large family of high-melting sulfides. Such materials show promise for application as scintillators, active laser media, and optoelectronic components., Comment: 7 pages, 9 figures, 34 references

The examination of the musculoskeletal system in dogs is a challenging task in veterinary practice. In this work, a novel method has been developed that enables efficient documentation of a dog's condition through a visual representation. However, since the visual documentation is new, there is no existing training data. The objective of this work is therefore to mitigate the impact of data scarcity in order to develop an AI-based diagnostic support system. To this end, the potential of synthetic data that mimics realistic visual documentations of diseases for pre-training AI models is investigated. We propose a method for generating synthetic image data that mimics realistic visual documentations. Initially, a basic dataset containing three distinct classes is generated, followed by the creation of a more sophisticated dataset containing 36 different classes. Both datasets are used for the pre-training of an AI model. Subsequently, an evaluation dataset is created, consisting of 250 manually created visual documentations for five different diseases. This dataset, along with a subset containing 25 examples. The obtained results on the evaluation dataset containing 25 examples demonstrate a significant enhancement of approximately 10% in diagnosis accuracy when utilizing generated synthetic images that mimic real-world visual documentations. However, these results do not hold true for the larger evaluation dataset containing 250 examples, indicating that the advantages of using synthetic data for pre-training an AI model emerge primarily when dealing with few examples of visual documentations for a given disease. Overall, this work provides valuable insights into mitigating the limitations imposed by limited training data through the strategic use of generated synthetic data, presenting an approach applicable beyond the canine musculoskeletal assessment domain.

Efficient and fast reconstruction of anatomical structures plays a crucial role in clinical practice. Minimizing retrieval and processing times not only potentially enhances swift response and decision-making in critical scenarios but also supports interactive surgical planning and navigation. Recent methods attempt to solve the medical shape reconstruction problem by utilizing implicit neural functions. However, their performance suffers in terms of generalization and computation time, a critical metric for real-time applications. To address these challenges, we propose to leverage meta-learning to improve the network parameters initialization, reducing inference time by an order of magnitude while maintaining high accuracy. We evaluate our approach on three public datasets covering different anatomical shapes and modalities, namely CT and MRI. Our experimental results show that our model can handle various input configurations, such as sparse slices with different orientations and spacings. Additionally, we demonstrate that our method exhibits strong transferable capabilities in generalizing to shape domains unobserved at training time.

In this paper, we prove Ruelle's inequality for the geodesic flow in non-compact manifolds with Anosov geodesic flow and some assumptions on the curvature. In the same way, we obtain Pesin's formula for Anosov geodesic flow in non-compact manifolds with finite volume.

Synthesizing 3D whole-bodies that realistically grasp objects is useful for animation, mixed reality, and robotics. This is challenging, because the hands and body need to look natural w.r.t. each other, the grasped object, as well as the local scene (i.e., a receptacle supporting the object). Only recent work tackles this, with a divide-and-conquer approach; it first generates a "guiding" right-hand grasp, and then searches for bodies that match this. However, the guiding-hand synthesis lacks controllability and receptacle awareness, so it likely has an implausible direction (i.e., a body can't match this without penetrating the receptacle) and needs corrections through major post-processing. Moreover, the body search needs exhaustive sampling and is expensive. These are strong limitations. We tackle these with a novel method called CWGrasp. Our key idea is that performing geometry-based reasoning "early on," instead of "too late," provides rich "control" signals for inference. To this end, CWGrasp first samples a plausible reaching-direction vector (used later for both the arm and hand) from a probabilistic model built via raycasting from the object and collision checking. Then, it generates a reaching body with a desired arm direction, as well as a "guiding" grasping hand with a desired palm direction that complies with the arm's one. Eventually, CWGrasp refines the body to match the "guiding" hand, while plausibly contacting the scene. Notably, generating already-compatible "parts" greatly simplifies the "whole." Moreover, CWGrasp uniquely tackles both right- and left-hand grasps. We evaluate on the GRAB and ReplicaGrasp datasets. CWGrasp outperforms baselines, at lower runtime and budget, while all components help performance. Code and models will be released.

Novel red-emitting scintillator Li$_2$MnCl$_4$ is proposed as a candidate for thermal neutron detection. It features high Li content, low density, low effective atomic number, and emission in red-NIR region. These characteristics make it an interesting candidate for long distance neutron detection in harsh enviroments e. g. decomisioning of nuclear powerplants. The absorption spectrum is thoroughly investigated in the scope of Tanabe-Sugano diagram. Luminescence mechanism in the undoped Li$_2$MnCl$_4$ is studied in depth. Doping by Eu$^{2+}$ and Ce$^{3+}$ is introduced as a trial to improve the scintillation efficiency. We show in the Eu$^{2+}$ and Ce$^{3+}$ doped Li$_2$MnCl$_4$ that luminescence mechanism involves energy transfer from the dopants to Mn$^{2+}$, and propose the local lattice distortion around the dopant and a possible charge compensation mechanisms., Comment: 8 pages, 10 figures, 2 tables, 26 references

Fast emitting polymeric scintillators are requested in advanced applications where high-speed detectors with large signal-to-noise ratio are needed. However, their low density implies a weak stopping power of high energy radiations, thus a limited light output and sensitivity. To enhance their performances, polymeric scintillators can be loaded with dense nanoparticles (NPs). We investigate the properties of a series of polymeric scintillators by means of photoluminescence and scintillation spectroscopy, comparing standard scintillators with a composite system loaded with dense hafnium dioxide (HfO2) NPs. The nanocomposite shows a scintillation yield enhancement of +100% with unchanged time response. We provide for the first time an interpretation of this effect, pointing out the local effect of NPs in the generation of emissive states upon interaction with the ionizing radiation. The obtained results indicate that coupling of fast conjugated emitters with optically inert dense NPs could allow to surpass the actual limits of pure polymeric scintillators., Comment: 14 pages, 4 figures

This is the third in a series of three papers in which we study a lattice gas subject to Kawasaki dynamics at inverse temperature $\beta>0$ in a large finite box $\Lambda_\beta \subset \mathbb{Z}^2$ whose volume depends on $\beta$. Each pair of neighbouring particles has a negative binding energy $-U<0$, while each particle has a positive activation energy $\Delta>0$. The initial configuration is drawn from the grand-canonical ensemble restricted to the set of configurations where all the droplets are subcritical. Our goal is to describe, in the metastable regime $\Delta \in (U,2U)$ and in the limit as $\beta\to\infty$, how and when the system nucleates, i.e., creates a critical droplet somewhere in $\Lambda_\beta$ that subsequently grows by absorbing particles from the surrounding gas. In the first paper we showed that subcritical droplets behave as quasi-random walks. In the second paper we used the results in the first paper to analyse how subcritical droplets form and dissolve on multiple space-time scales when the volume is moderately large, namely, $|\Lambda_\beta| = \mathrm{e}^{\theta\beta}$ with $\Delta < \theta < 2\Delta-U$. In the present paper we consider the setting where the volume is very large, namely, $|\Lambda_\beta| = \mathrm{e}^{\Theta\beta}$ with $\Delta < \Theta < \Gamma-(2\Delta-U)$, where $\Gamma$ is the energy of the critical droplet in the local model with fixed volume, and use the results in the first two papers to identify the nucleation time and the tube of typical trajectories towards nucleation. We will see that in a very large volume critical droplets appear more or less independently in boxes of moderate volume, a phenomenon referred to as homogeneous nucleation. One of the key ingredients in the proof is an estimate showing that no information can travel between these boxes on relevant time scales., Comment: 21 pages, 5 figures. arXiv admin note: text overlap with arXiv:2304.14099

Diamond is increasingly popular because of its unique material properties. Diamond defects called nitrogen vacancy (NV) centers allow measurements with unprecedented sensitivity. However, to achieve ideal sensing performance NV centers need to be within nanometers from the surface and are thus strongly dependent on the local surface chemistry. Several attempts have been made to compare diamond surfaces. However, due to the high price of diamond crystals with shallow NV centers, a limited number of chemical modifications have been studied. Here, we developed a systematic method to investigate a continuity of different local environments with a varying density and nature of surface groups in a single experiment on a single diamond plate. To achieve this goal, we used diamonds with a shallow ensemble of NV centers and introduced a chemical gradient across the surface. More specifically we used air and hydrogen plasma. The gradients were formed by low pressure plasma treatment after masking with a right-angled triangular prism shield. As a result, the surface contained gradually more oxygen/hydrogen towards the open end of the shield. We then performed widefield relaxometry to determine the effect of surface chemistry on the sensing performance. As expected, relaxation times and thus sensing performance indeed varies along the gradient., Comment: Main text: 18 pages and 9 figures Supplementary materials: 6 pages and 6 figures

A bit catastrophe, loosely defined, is when a change in just one character of a string causes a significant change in the size of the compressed string. We study this phenomenon for the Burrows-Wheeler Transform (BWT), a string transform at the heart of several of the most popular compressors and aligners today. The parameter determining the size of the compressed data is the number of equal-letter runs of the BWT, commonly denoted $r$. We exhibit infinite families of strings in which insertion, deletion, resp. substitution of one character increases $r$ from constant to $\Theta(\log n)$, where $n$ is the length of the string. These strings can be interpreted both as examples for an increase by a multiplicative or an additive $\Theta(\log n)$-factor. As regards multiplicative factor, they attain the upper bound given by Akagi, Funakoshi, and Inenaga [Inf & Comput. 2023] of $O(\log n \log r)$, since here $r=O(1)$. We then give examples of strings in which insertion, deletion, resp. substitution of a character increases $r$ by a $\Theta(\sqrt{n})$ additive factor. These strings significantly improve the best known lower bound for an additive factor of $\Omega(\log n)$ [Giuliani et al., SOFSEM 2021]., Comment: This work is an extended version of our conference article with the same title, published in the proceedings of DLT 2023

Given $X$ a compact metric space and $T: X \to X$ a continuous map, the induced hyperspace map $T_\mathcal{K}$ acts on the hyperspace $\mathcal{K}(X)$ of closed and nonempty subsets of $X$, and on the continuum hyperspace $\mathcal{C}(X) \subset \mathcal{K}(X)$ of connected sets. This work studies the mean dimension explosion phenomenon: when the base system $T$ has zero topological entropy, but the mean dimension of the induced map $T_\mathcal{K}$ is infinite. In particular, this phenomenon is attained for Morse-Smale diffeomorphisms. Furthermore, for a circle homeomorphism $H$, the mean dimension explosion does not occur if, and only if, $H$ is conjugated to a rotation. Finally, if the topological entropy of $T$ is positive, then the metric mean dimension of $T_\mathcal{K}$ is infinite.

Detecting and measuring repetitiveness of strings is a problem that has been extensively studied in data compression and text indexing. However, when the data are structured in a non-linear way, like in the context of two-dimensional strings, inherent redundancy offers a rich source for compression, yet systematic studies on repetitiveness measures are still lacking. In the paper we introduce extensions of repetitiveness measures to general two-dimensional strings. In particular, we propose a new extension of the measures $\delta$ and $\gamma$, diverging from previous square based definitions proposed in [Carfagna and Manzini, SPIRE 2023]. We further consider generalizations of macro schemes and straight line programs for the 2D setting and show that, in contrast to what happens on strings, 2D macro schemes and 2D SLPs can be both asymptotically smaller than $\delta$ and $\gamma$. The results of the paper can be easily extended to $d$-dimensional strings with $d > 2$.

Green Peas (GPs) are young, compact, star-forming dwarf galaxies, and local (z~0.3) analogues of the early galaxies (z>6) considered to be mainly responsible for the reionisation of the Universe. Recent X-ray observations of GPs have detected high excess emission which cannot be accounted for by star formation alone, and implies presence of an active galactic nucleus (AGN). We employ radio observations to study the radio properties of GPs, build their radio spectral energy distributions, and verify the presence of AGNs. We performed new radio observations of three GPs with the Karl G. Jansky Very Large Array (JVLA) in the L, C, and X bands (1.4, 6 and 10 GHz resp.), supplemented by the data from archival observations and large radio surveys. We also analysed the archival radio data for a larger sample of GPs and blueberry (BBs) galaxies, which are lower-mass and lower-redshift analogues of the GPs. To understand the significance of the radio observations, we assess the detectability of these sources, and compare the detected radio luminosities with the expectations from theoretical and empirical relations. Two of the three targeted GPs were strongly detected ($>10\sigma$) in the JVLA observations and their fluxes are consistent with star formation, while the third source was undetected. Although a large fraction (~75%) of the sources from the larger archival sample of GPs and BBs could be detected with archival surveys, only a small number (<40%) are detected and their radio luminosity is significantly lower than the expectation from empirical relations. Our results show that the majority of the dwarf galaxy sample is highly underluminous. Especially towards the lower end of galaxy mass and star formation rate (SFR), the radio luminosity-SFR relation deviates from the empirical relations, suggesting that the relations established for larger galaxies may not hold towards the low-mass end., Comment: 9 pages, 4 figures, accepted for publication to A&A

Deep subsurface exploration is important for mining, oil and gas industries, as well as in the assessment of geological units for the disposal of chemical or nuclear waste, or the viability of geothermal energy systems. Typically, detailed examinations of subsurface formations or units are performed on cuttings or core materials extracted during drilling campaigns, as well as on geophysical borehole data, which provide detailed information about the petrophysical properties of the rocks. Depending on the volume of rock samples and the analytical program, the laboratory analysis and diagnostics can be very time-consuming. This study investigates the potential of utilizing machine learning, specifically convolutional neural networks (CNN), to assess the lithology and mineral content solely from analysis of drill core images, aiming to support and expedite the subsurface geological exploration. The paper outlines a comprehensive methodology, encompassing data preprocessing, machine learning methods, and transfer learning techniques. The outcome reveals a remarkable 96.7% accuracy in the classification of drill core segments into distinct formation classes. Furthermore, a CNN model was trained for the evaluation of mineral content using a learning data set from multidimensional log analysis data (silicate, total clay, carbonate). When benchmarked against laboratory XRD measurements on samples from the cores, both the advanced multidimensional log analysis model and the neural network approach developed here provide equally good performance. This work demonstrates that deep learning and particularly transfer learning can support extracting petrophysical properties, including mineral content and formation classification, from drill core images, thus offering a road map for enhancing model performance and data set quality in image-based analysis of drill cores.

This work addresses flexibility in deep learning by means of transductive reasoning. For adaptation to new data and tasks, e.g., in continual learning, existing methods typically involve tuning learnable parameters or complete re-training from scratch, rendering such approaches unflexible in practice. We argue that the notion of separating computation from memory by the means of transduction can act as a stepping stone for solving these issues. We therefore propose PARMESAN (parameter-free memory search and transduction), a scalable method which leverages a memory module for solving dense prediction tasks. At inference, hidden representations in memory are being searched to find corresponding patterns. In contrast to other methods that rely on continuous training of learnable parameters, PARMESAN learns via memory consolidation simply by modifying stored contents. Our method is compatible with commonly used architectures and canonically transfers to 1D, 2D, and 3D grid-based data. The capabilities of our approach are demonstrated at the complex task of continual learning. PARMESAN learns by 3-4 orders of magnitude faster than established baselines while being on par in terms of predictive performance, hardware-efficiency, and knowledge retention., Comment: preprint, 25 pages, 7 figures

In this study, novel flexible micro-scale humidity sensors were directly fabricated in graphene oxide (GO) and polyimide (PI) using ion beam writing without any further modifications, and then successfully tested in an atmospheric chamber. Two low fluences of carbon ions with an energy of 5 MeV were used, and structural changes in the irradiated materials were expected. The shape and structure of prepared micro-sensors were studied using scanning electron microscopy (SEM). The structural and compositional changes in the irradiated area were characterized using micro-Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), Rutherford back-scattering spectroscopy (RBS), energy-dispersive X-ray spectroscopy (EDS), and elastic recoil detection analysis (ERDA) spectroscopy. The sensing performance was tested at a relative humidity (RH) ranging from 5 % to 60 %, where the electrical conductivity of PI varied by three orders of magnitude, and the electrical capacitance of GO varied in the order of pico-farads. In addition, the PI sensor has proven long-term sensing stability in air. We demonstrated a novel method of ion micro-beam writing to prepare flexible micro-sensors that function over a wide range of humidity and have good sensitivity and great potential for widespread applications.

In this paper, we study rigidity problems between Lyapunov exponents along periodic orbits and geometric structures. More specifically, we prove that for a surface M without focal points, if the value of the Lyapunov exponents is constant over all periodic orbits, then $M$ is the flat 2-torus or a surface of constant negative curvature. We obtain the same result for the case of Anosov geodesic flow for surface, which generalizes Butler's result in dimension two. Using completely different techniques, we also prove an extension of Butler's result to the finite volume case, where the value of the Lyapunov exponents along all periodic orbits is constant, being the maximum or minimum possible.

In this work, we investigate diffeomorphisms whose positiveness of topological entropy is destroyed by singular suspensions. We show that this phenomenon is rare in the set of $C^1$-diffeomorphisms. Precisely, we prove that for an open and dense set of $C^1$-diffeomorphism positive topological entropy is preserved by singular suspensions, even for suspensions with infinitely many singularities. We prove a similar result to the conservative diffeomorphisms. We apply our techniques to show that every expansive singular suspension $C^{1+\epsilon}$-flow over a three-dimensional manifold has positive topological entropy. Finally, we explore this phenomenon for Anosov dynamics, showing that to nullify the topological entropy for Anosov suspension flow, the set of singularities must capture the non-wandering set., Comment: 22 pages

In this paper, we prove that manifolds of finite volume with Anosov geodesic flow have dense periodic orbits. The same result works for conservative Anosov flows in non-compact cases.

Quantitative measurement of crystals in high-resolution images allows for important insights into underlying material characteristics. Deep learning has shown great progress in vision-based automatic crystal size measurement, but current instance segmentation methods reach their limits with images that have large variation in crystal size or hard to detect crystal boundaries. Even small image segmentation errors, such as incorrectly fused or separated segments, can significantly lower the accuracy of the measured results. Instead of improving the existing pixel-wise boundary segmentation methods, we propose to use an instance-based segmentation method, which gives more robust segmentation results to improve measurement accuracy. Our novel method enhances flow maps with a size-aware multi-scale attention module. The attention module adaptively fuses information from multiple scales and focuses on the most relevant scale for each segmented image area. We demonstrate that our proposed attention fusion strategy outperforms state-of-the-art instance and boundary segmentation methods, as well as simple average fusion of multi-scale predictions. We evaluate our method on a refractory raw material dataset of high-resolution images with large variation in crystal size and show that our model can be used to calculate the crystal size more accurately than existing methods., Comment: has been accepted for publication in IEEE Transactions on Instrumentation and Measurement

Paraphasias are speech errors that are often characteristic of aphasia and they represent an important signal in assessing disease severity and subtype. Traditionally, clinicians manually identify paraphasias by transcribing and analyzing speech-language samples, which can be a time-consuming and burdensome process. Identifying paraphasias automatically can greatly help clinicians with the transcription process and ultimately facilitate more efficient and consistent aphasia assessment. Previous research has demonstrated the feasibility of automatic paraphasia detection by training an automatic speech recognition (ASR) model to extract transcripts and then training a separate paraphasia detection model on a set of hand-engineered features. In this paper, we propose a novel, sequence-to-sequence (seq2seq) model that is trained end-to-end (E2E) to perform both ASR and paraphasia detection tasks. We show that the proposed model outperforms the previous state-of-the-art approach for both word-level and utterance-level paraphasia detection tasks and provide additional follow-up evaluations to further understand the proposed model behavior.

We report on an observational campaign on the bright black hole X-ray binary Swift J1727.8$-$1613 centered around five observations by the Imaging X-ray Polarimetry Explorer (IXPE). These observations track for the first time the evolution of the X-ray polarization of a black hole X-ray binary across a hard to soft state transition. The 2--8 keV polarization degree decreased from $\sim$4\% to $\sim$3\% across the five observations, but the polarization angle remained oriented in the North-South direction throughout. Based on observations with the Australia Telescope Compact Array (ATCA), we find that the intrinsic 7.25 GHz radio polarization aligns with the X-ray polarization. Assuming the radio polarization aligns with the jet direction (which can be tested in the future with higher spatial resolution images of the jet), our results imply that the X-ray corona is extended in the disk plane, rather than along the jet axis, for the entire hard intermediate state. This in turn implies that the long ($\gtrsim$10 ms) soft lags that we measure with the Neutron star Interior Composition ExploreR (NICER) are dominated by processes other than pure light-crossing delays. Moreover, we find that the evolution of the soft lag amplitude with spectral state does not follow the trend seen for other sources, implying that Swift J1727.8$-$1613 is a member of a hitherto under-sampled sub-population., Comment: Accepted for publication in ApJ. 21 pages, 8 figures

The chemical stability of nonfullerene acceptor (NFA) is the Achilles' heel of the research on state-of-the-art organic solar cells (OSC). The fragility of the NFA is essentially due to the weak bond that links the central donor core of the molecules with their acceptor moieties at the edges. Here we proposed the replacement of thiophene at the outer-core position of traditional NFAs for tellurophene, a hitherto unexplored modification. Since tellurium is a distinctive element among chalcogens, the basic features of Te compounds cannot be deduced straightforwardly from the properties of their lighter analogues, S and Se. The modeled Te-based NFAs presented interesting features like stronger intra- and intermolecular interactions induced by a distinctive secondary bond effect between the end acceptor moiety and the outer chalcogen atom. This design strategy resulted in stiffer molecules with red-shifted absorption spectra and less susceptible to degradation verified through stress tests and vibrational spectra analysis. Besides that, a weakened exciton binding energy has been found, opening the possibility of blends with a lower driving force. Our results shed light on several aspects of selenation and telluration of traditional NFAs, providing valuable insights into the possible consequences for OSCs applications., Comment: 34 pages, 7 figures, 2 tables

Given a compact smooth boundaryless manifold with dimension greater than one endowed with a locally positive non-atomic measure $\mu$, we prove that typical $\mu$-preserving homeomorphisms have upper metric mean dimension, with respect to the Riemannian distance, equal to the dimension of the manifold. Moreover, we prove that $\mu$ is a measure of maximal metric mean dimension, with respect to the variational principle established in [VV17].

Speech disfluencies, such as filled pauses or repetitions, are disruptions in the typical flow of speech. Stuttering is a speech disorder characterized by a high rate of disfluencies, but all individuals speak with some disfluencies and the rates of disfluencies may by increased by factors such as cognitive load. Clinically, automatic disfluency detection may help in treatment planning for individuals who stutter. Outside of the clinic, automatic disfluency detection may serve as a pre-processing step to improve natural language understanding in downstream applications. With this wide range of applications in mind, we investigate language, acoustic, and multimodal methods for frame-level automatic disfluency detection and categorization. Each of these methods relies on audio as an input. First, we evaluate several automatic speech recognition (ASR) systems in terms of their ability to transcribe disfluencies, measured using disfluency error rates. We then use these ASR transcripts as input to a language-based disfluency detection model. We find that disfluency detection performance is largely limited by the quality of transcripts and alignments. We find that an acoustic-based approach that does not require transcription as an intermediate step outperforms the ASR language approach. Finally, we present multimodal architectures which we find improve disfluency detection performance over the unimodal approaches. Ultimately, this work introduces novel approaches for automatic frame-level disfluency and categorization. In the long term, this will help researchers incorporate automatic disfluency detection into a range of applications.