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Task-oriented dialogue systems rely on predefined conversation schemes (dialogue flows) often represented as directed acyclic graphs. These flows can be manually designed or automatically generated from previously recorded conversations. Due to variations in domain expertise or reliance on different sets of prior conversations, these dialogue flows can manifest in significantly different graph structures. Despite their importance, there is no standard method for evaluating the quality of dialogue flows. We introduce FuDGE (Fuzzy Dialogue-Graph Edit Distance), a novel metric that evaluates dialogue flows by assessing their structural complexity and representational coverage of the conversation data. FuDGE measures how well individual conversations align with a flow and, consequently, how well a set of conversations is represented by the flow overall. Through extensive experiments on manually configured flows and flows generated by automated techniques, we demonstrate the effectiveness of FuDGE and its evaluation framework. By standardizing and optimizing dialogue flows, FuDGE enables conversational designers and automated techniques to achieve higher levels of efficiency and automation.

The [C II] fine-structure line at 157.74 $\mu$m is one of the brightest far-infrared emission lines and an important probe of galaxy properties like the star formation rate (SFR) and the molecular gas mass ($M_{\mathrm{mol}}$). Using high-resolution numerical simulations, we test the reliability of the [C II] line as a tracer of $M_{\mathrm{mol}}$ in high-redshift galaxies and investigate secondary dependences of the [C II]-$M_{\mathrm{mol}}$ relation on the SFR and metallicity. We investigate the time evolution of the [C II] luminosity function (LF) and the relative spatial extent of [C II] emission and star formation. We post-process galaxies from the MARIGOLD simulations at redshifts $3 \le z \leq 7$ to obtain their [C II] emission. These simulations were performed with the sub-grid chemistry model, HYACINTH, to track the non-equilibrium abundances of $\mathrm{H_2}$, $\mathrm{CO}$, $\rm C$ and $\mathrm{C^+}$ on the fly. Based on a statistical sample of galaxies at these redshifts, we investigate correlations between the [C II] line luminosity, L([C II]), and the SFR, the $M_{\mathrm{mol}}$, the total gas mass and the metal mass in gas phase ($M_{\mathrm{metal}}$). We find that accounting for secondary dependencies in the L([C II])-$M_{\mathrm{mol}}$ relation improves the $M_{\mathrm{mol}}$ prediction by a factor of 2.3. The [C II] emission in our simulated galaxies shows the tightest correlation with $M_{\mathrm{metal}}$. About 20% (10%) of our simulated galaxies at $z=5$ ($z=4$) have [C II] emission extending $\geq 2$ times farther than the star formation activity. The [C II] LF evolves rapidly and is always well approximated by a double power law that does not show an exponential cutoff at the bright end. We record a 600-fold increase in the number density of L([C II]) $\sim 10^9 \, \mathrm{L_{\odot}}$ emitters in 1.4 Gyr., Comment: 25 pages, 20 figures. Submitted to A&A. Comments are welcome

Intent discovery is crucial for both building new conversational agents and improving existing ones. While several approaches have been proposed for intent discovery, most rely on clustering to group similar utterances together. Traditional evaluation of these utterance clusters requires intent labels for each utterance, limiting scalability. Although some clustering quality metrics exist that do not require labeled data, they focus solely on cluster geometry while ignoring the linguistic nuances present in conversational transcripts. In this paper, we introduce Keyword-based Utterance Level Clustering Quality (KULCQ), an unsupervised metric that leverages keyword analysis to evaluate clustering quality. We demonstrate KULCQ's effectiveness by comparing it with existing unsupervised clustering metrics and validate its performance through comprehensive ablation studies. Our results show that KULCQ better captures semantic relationships in conversational data while maintaining consistency with geometric clustering principles.

The North Atlantic Oscillation (NAO) is a leading mode of atmospheric variability, affecting the North Atlantic Ocean on sub-seasonal to multi-decadal timescales. The NAO changes the atmospheric forcing at the ocean's surface, including winds and surface buoyancy fluxes, both of which are known to impact large-scale gyre circulation. However, the relative role of other physical processes (such as mesoscale eddies and topography) in influencing gyre circulation under NAO variability is not fully understood. Here, we analyze a series of ocean--sea ice simulations using a barotropic vorticity budget to understand long-term response of the North Atlantic gyre circulation to NAO forcing. We find that for each standard deviation increase in the NAO index, the subtropical and subpolar gyres intensify by 0.90 Sv and 3.41 Sv (1 Sv = 10$^6$ m$^3$ s$^{-1}$) respectively. The NAO-induced wind stress anomalies drive approximately 90% of the change in the subtropical gyre's interior flow. However, in the subpolar gyre's interior, in addition to wind stress, flow-topography interactions, stratification (influenced by surface heat fluxes), and non-linear advection significantly influence the circulation. Along the western boundary the bottom pressure torque plays a key role in steering the flow, and the vorticity input by the bottom pressure torque is partly redistributed by non-linear advection. Our study highlights the importance of both atmospheric forcing and oceanic dynamical processes in driving long-term gyre circulation responses to the NAO.

We have designed and commissioned a new readout board to detect photosensor signals from gas-bubbler panels to continuously monitor the gas flow through the resistive plate chambers of the $K_L^0$ and muon detector of Belle II. The gas flow measurements have been integrated into Belle II's alarm system. The bubbler-monitoring system was first employed during the February 2024 to July 2024 Belle II data-taking period., Comment: 4 pages, 5 figures

We explore the Fano resonance in ZrTe5, using Raman scattering measurements. We identified two closely spaced B2g phonon modes, B2g I and B2g II, around 9 meV and 10 meV, respectively. Interestingly, only B2g I exhibited the Fano resonance, an outcome of quantum interference between discrete phonon modes and continuous electronic excitations. This is consistent with the much stronger electron-phonon coupling of B2g I mode demonstrated by first-principles calculations. Additionally, temperature-dependent measurements highlight an enhanced Fano asymmetry at elevated temperatures, originating from the thermal effect on the joint electron-hole density of states. This study offers insights into the complex interrelation of electron-phonon coupling, thermal effect, and Fano resonances in ZrTe5.

We reframe scene flow as the task of estimating a continuous space-time ODE that describes motion for an entire observation sequence, represented with a neural prior. Our method, EulerFlow, optimizes this neural prior estimate against several multi-observation reconstruction objectives, enabling high quality scene flow estimation via pure self-supervision on real-world data. EulerFlow works out-of-the-box without tuning across multiple domains, including large-scale autonomous driving scenes and dynamic tabletop settings. Remarkably, EulerFlow produces high quality flow estimates on small, fast moving objects like birds and tennis balls, and exhibits emergent 3D point tracking behavior by solving its estimated ODE over long-time horizons. On the Argoverse 2 2024 Scene Flow Challenge, EulerFlow outperforms all prior art, surpassing the next-best unsupervised method by more than 2.5x, and even exceeding the next-best supervised method by over 10%., Comment: Project page at https://vedder.io/eulerflow

Modern physics experiments are frequently very complex, relying on multiple simultaneous events to happen in order to obtain the desired result. The experiment control system plays a central role in orchestrating the measurement setup: However, its development is often treated as secondary with respect to the hardware, its importance becoming evident only during the operational phase. Therefore, the AEgIS (Antimatter Experiment: Gravity, Interferometry, Spectroscopy) collaboration has created a framework for easily coding control systems, specifically targeting atomic, quantum, and antimatter experiments. This framework, called Total Automation of LabVIEW Operations for Science (TALOS), unifies all the machines of the experiment in a single entity, thus enabling complex high-level decisions to be taken, and it is constituted by separate modules, called MicroServices, that run concurrently and asynchronously. This enhances the stability and reproducibility of the system while allowing for continuous integration and testing while the control system is running. The system demonstrated high stability and reproducibility, running completely unsupervised during the night and weekends of the data-taking campaigns. The results demonstrate the suitability of TALOS to manage an entire physics experiment in full autonomy: being open-source, experiments other than the AEgIS experiment can benefit from it.

Throughout its history, the theory of quantum error correction has heavily benefited from translating classical concepts into the quantum setting. In particular, classical notions of weight enumerators, which relate to the performance of an error-correcting code, and MacWilliams' identity, which helps to compute enumerators, have been generalized to the quantum case. In this work, we establish a distinct relationship between the theoretical machinery of quantum weight enumerators and a seemingly unrelated physics experiment: we prove that Rains' quantum shadow enumerators - a powerful mathematical tool - arise as probabilities of observing fixed numbers of triplets in a Bell sampling experiment. This insight allows us to develop here a rigorous framework for the direct measurement of quantum weight enumerators, thus enabling experimental and theoretical studies of the entanglement structure of any quantum error-correcting code or state under investigation. On top of that, we derive concrete sample complexity bounds and physically-motivated robustness guarantees against unavoidable experimental imperfections. Finally, we experimentally demonstrate the possibility of directly measuring weight enumerators on a trapped-ion quantum computer. Our experimental findings are in good agreement with theoretical predictions and illuminate how entanglement theory and quantum error correction can cross-fertilize each other once Bell sampling experiments are combined with the theoretical machinery of quantum weight enumerators., Comment: 21+12 pages, 6+7 figures

We perform machine learning (ML) simulations and density functional theory (DFT) calculations to search for materials with low lattice thermal conductivity, $\kappa_L$. Several cadmium (Cd) compounds containing elements from the alkali-metal and carbon groups including A$_2$CdX (A = Li, Na, and K; X = Pb, Sn, and Ge) are predicted by our ML models to exhibit very low $\kappa_L$ values ($< 1.0 $ W/mK), rendering these materials suitable for potential thermal management and insulation applications. Further DFT calculations of electronic and transport properties indicate that the figure of merit, $ZT$, for thermoelectric performance can exceed 1.0 in compounds such as K$_2$CdPb, K$_2$CdSn, and K$_2$CdGe, which are thereby also promising thermoelectric materials., Comment: 10 pages, 5 figures

We present and study a new cross-pairwise estimator to extract the kinetic Sunyaev Zeldovich (kSZ) signal from galaxy clusters. The existing pairwise kSZ method involves pairing clusters with other clusters and stacking them. In the cross-pairwise method, we propose to pair clusters with galaxies from a spectroscopic survey and then do the stacking. Cross-pairing decreases the measurement, instrumentation, and statistical noise, thus boosting the signal-to-noise ratio. However, we also need data from a galaxy survey in addition to the CMB temperature maps and a cluster catalog in order to use this method. We do a Fisher matrix analysis for the optimised pairwise and cross-pairwise estimators and forecast the ability of future Cosmic Microwave Background (CMB) experiments and galaxy surveys to measure cosmological parameters with the kSZ effect when combined with primary CMB and Baryon Acoustic Oscillation (BAO) data. We show that using the cross-pairwise kSZ estimator (CMB-S4 clusters with DESI galaxies) leads to a factor of 3 improvement in the $1-\sigma$ error of the dark energy parameters $w_0$ and $w_a$ and a factor of 6 improvement for the growth rate index $\gamma$ compared to the pairwise estimator for the same CMB dataset and cluster catalog.

We compute the spectral distortions of the Cosmic Microwave Background (CMB) created by an exotic process that extracts or injects photons of a particular frequency into the CMB. Such signatures are a natural prediction of a class of composite dark matter models characterized by electrically neutral states but with non-zero higher order electromagnetic moments. We consider a simplified model where dark matter exists as a two state system separated by a fixed transition frequency, which can range from radio waves to gamma rays. The electromagnetic transitions between the two states due to CMB photons give rise to thermal distortions, namely, the $\mu$-type distortion in the redshift range $10^5\lesssim z \lesssim 2\times 10^6$ and the $y$-type distortion as well as non-thermal distortions at redshifts $z \lesssim 10^5$. The nature of spectral distortions depends sensitively on the dark matter transition frequency and the strength of couplings of dark matter with visible sector particles as well as its self-interactions, thus opening a new window to probe the nature of dark matter. Non-thermal distortions have unique spectral shapes making them distinguishable from the standard $\mu$ and $y$-type distortions and potentially detectable in the next-generation experiments such as Primordial Inflation Explorer (PIXIE). We also find that the spectral distortion limits from the COsmic Background Explorer/Far-Infrared Absolute Spectrophotometer (COBE/FIRAS) already give a constraint on the electromagnetic coupling of dark matter which is three orders of magnitude stronger compared to the current direct detection limits for $\sim$ MeV mass dark matter with transition energy in $\sim 1$-$10$ eV range.

First-principles calculations are combined with a statistical-mechanical model to predict the equilibrium point-defect concentrations in the refractory carbides TaC and HfC as a function of temperature and chemical composition. Several different types of point defects (vacancies, interstitials, antisite atoms) and their clusters are treated in a unified manner. The defect concentrations either strictly follow or can be closely approximated by Arrhenius functions with parameters predicted by the model. The model is general and applicable to other carbides, nitrides, borides, or similar chemical compounds. Implications of this work for understanding the diffusion mechanisms in TaC and HfC are discussed.

We applied machine learning to the unmet medical need of rapid and accurate diagnosis and prognosis of acute infections and sepsis in emergency departments. Our solution consists of a Myrna (TM) Instrument and embedded TriVerity (TM) classifiers. The instrument measures abundances of 29 messenger RNAs in patient's blood, subsequently used as features for machine learning. The classifiers convert the input features to an intuitive test report comprising the separate likelihoods of (1) a bacterial infection (2) a viral infection, and (3) severity (need for Intensive Care Unit-level care). In internal validation, the system achieved AUROC = 0.83 on the three-class disease diagnosis (bacterial, viral, or non-infected) and AUROC = 0.77 on binary prognosis of disease severity. The Myrna, TriVerity system was granted breakthrough device designation by the United States Food and Drug Administration (FDA). This engineering manuscript teaches the standard and novel machine learning methods used to translate an academic research concept to a clinical product aimed at improving patient care, and discusses lessons learned., Comment: 16 pages, 6 figures

We introduce a category-theoretic diagrammatic formalism in order to systematically relate and reason about machine learning models. Our diagrams present architectures intuitively but without loss of essential detail, where natural relationships between models are captured by graphical transformations, and important differences and similarities can be identified at a glance. In this paper, we focus on attention mechanisms: translating folklore into mathematical derivations, and constructing a taxonomy of attention variants in the literature. As a first example of an empirical investigation underpinned by our formalism, we identify recurring anatomical components of attention, which we exhaustively recombine to explore a space of variations on the attention mechanism., Comment: Replaced to fix typos

The primary goal of the AEgIS experiment is to precisely measure the free fall of antihydrogen within Earth's gravitational field. To this end, a cold ~50K antihydrogen beam has to pass through two grids forming a moir\'e deflectometer before annihilating onto a position-sensitive detector, which shall determine the vertical position of the annihilation vertex relative to the grids with micrometric accuracy. Here we introduce a vertexing detector based on a modified mobile camera sensor and experimentally demonstrate that it can measure the position of antiproton annihilations with an accuracy of $0.62^{+0.40}_{-0.22}\mu m$, which represents a 35-fold improvement over the previous state-of-the-art for real-time antiproton vertexing. Importantly, these antiproton detection methods are directly applicable to antihydrogen. Moreover, the sensitivity to light of the sensor enables the in-situ calibration of the moir\'e deflectometer, significantly reducing systematic errors. This sensor emerges as a breakthrough technology for achieving the \aegis scientific goals and has been selected as the basis for the development of a large-area detector for conducting antihydrogen gravity measurements., Comment: 21 pages, 4 figures, 2 tables

Progress in the realisation of reliable large-scale quantum computers has motivated research into the design of quantum machine learning models. We present Quixer: a novel quantum transformer model which utilises the Linear Combination of Unitaries and Quantum Singular Value Transform primitives as building blocks. Quixer operates by preparing a superposition of tokens and applying a trainable non-linear transformation to this mix. We present the first results for a quantum transformer model applied to a practical language modelling task, obtaining results competitive with an equivalent classical baseline. In addition, we include resource estimates for evaluating the model on quantum hardware, and provide an open-source implementation for classical simulation. We conclude by highlighting the generality of Quixer, showing that its parameterised components can be substituted with fixed structures to yield new classes of quantum transformers., Comment: 17 pages, 8 figures

Among recent insights into learning quantum states, online learning and shadow tomography procedures are notable for their ability to accurately predict expectation values even of adaptively chosen observables. In contrast to the state case, quantum process learning tasks with a similarly adaptive nature have received little attention. In this work, we investigate online learning tasks for quantum processes. Whereas online learning is infeasible for general quantum channels, we show that channels of bounded gate complexity as well as Pauli channels can be online learned in the regret and mistake-bounded models of online learning. In fact, we can online learn probabilistic mixtures of any exponentially large set of known channels. We also provide a provably sample-efficient shadow tomography procedure for Pauli channels. Our results extend beyond quantum channels to non-Markovian multi-time processes, with favorable regret and mistake bounds, as well as a shadow tomography procedure. We complement our online learning upper bounds with mistake as well as computational lower bounds. On the technical side, we make use of the multiplicative weights update algorithm, classical adaptive data analysis, and Bell sampling, as well as tools from the theory of quantum combs for multi-time quantum processes. Our work initiates a study of online learning for classes of quantum channels and, more generally, non-Markovian quantum processes. Given the importance of online learning for state shadow tomography, this may serve as a step towards quantum channel variants of adaptive shadow tomography., Comment: 14 + 72 pages, 6 figures

Metadata play a crucial role in ensuring the findability, accessibility, interoperability, and reusability of datasets. This paper investigates the potential of large language models (LLMs), specifically GPT-4, to improve adherence to metadata standards. We conducted experiments on 200 random data records describing human samples relating to lung cancer from the NCBI BioSample repository, evaluating GPT-4's ability to suggest edits for adherence to metadata standards. We computed the adherence accuracy of field name-field value pairs through a peer review process, and we observed a marginal average improvement in adherence to the standard data dictionary from 79% to 80% (p<0.5). We then prompted GPT-4 with domain information in the form of the textual descriptions of CEDAR templates and recorded a significant improvement to 97% from 79% (p<0.01). These results indicate that, while LLMs may not be able to correct legacy metadata to ensure satisfactory adherence to standards when unaided, they do show promise for use in automated metadata curation when integrated with a structured knowledge base

In the present work, we study a cosmological model composed of a viscous dark matter interacting with decaying vacuum energy in a spatially flat Universe. In the first part, we find the analytical solution of different cosmological parameters by assuming the physically viable forms of bulk viscosity and decaying vacuum density with the interaction term. The second part is dedicated to constrain the free parameters of the interacting viscous model with decaying vacuum energy by employing latest observational data of $Pantheon+$, Cosmic Chronometer and $f(z)\sigma_{8}(z)$. We find that the interacting model just deviate very slightly from well-known concordance $\Lambda$CDM model and can alleviate effectively the current $H_0$ tension between local measurement by R21 and global measurement by Planck 2018, and the excess in the mass fluctuation amplitude $\sigma_{8}$ essentially vanish in this context. We report the Hubble constants as $H_0=72.150^{+0.989}_{-0.779}$, and $ 72.202^{+0.796}_{-0.937}$ \;$km s^{-1} Mpc^{-1}$, deceleration parameters as $q_0=-0.533 \pm 0.024$, and $-0.531 \pm 0.024$, and equation of state parameters as $w_0=-0.689 \pm 0.016$, and $ -0.687 \pm 0.016$ for $\Lambda$CDM and interacting models, respectively. It is found that the interacting model is in good agreement with $\Lambda$CDM. Further, we discuss the amplitude of matter power spectrum $\sigma_8$ and its associated parameter $S_8$ using $f(z)\sigma_8(z)$ data. Finally, the information selection criterion and Bayesian inference are discussed to distinguish the interacting model with $\Lambda$CDM model., Comment: 14 pages, 8 figures

This paper delves into the distressing prevalence of gender-based violence (GBV) and its deep-seated psychological ramifications, particularly among Muslim South Asian women living in diasporic communities. Despite the gravity of GBV, these women often face formidable barriers in voicing their experiences and accessing support. "Augmented Voices" emerges as a technological beacon, harnessing the potential of augmented reality (AR) to bridge the digital and physical realms through mobile devices, enhancing the visibility of these often-silenced voices. With its technological motivation firmly anchored in the convergence of AR and real-world interactions, "Augmented Voices" offers a digital platform where storytelling acts as a catalyst, bringing to the fore the experiences shared by these women. By superimposing their narratives onto physical locations via Geographic Information System (GIS) Mapping, the application "augments their voices" in the diaspora, providing a conduit for expression and solidarity. This project, currently at its developmental stage, aspires to elevate the stories of GBV victims to a level where their struggles are not just heard but felt, forging a powerful connection between the user and the narrative. It is designed to transcend the limitations of conventional storytelling, creating an "augmented" reality where voices that are often muted by societal constraints can resonate powerfully. The project underscores the urgent imperative to confront GBV, catalyzing societal transformation and fostering robust support networks for those in the margins. It is a pioneering example of how technology can become a formidable ally in the fight for social justice and the empowerment of the oppressed. Additionally, this paper delves into the AR workflow illustrating its relevance and contribution to the broader theme of site-specific AR for social justice., Comment: Presented at CHI 2024 (arXiv:2404.05889)

Quantification of microscopy time-series of in vitro reconstituted motor driven microtubule (MT) transport in 'gliding assays' is typically performed using computational object tracking tools. However, these are limited to non-intersecting and rod-like filaments. Here, we describe a novel computational image-analysis pipeline, KnotResolver, to track image time-series of highly curved self-intersecting looped filaments (knots) by resolving cross-overs. The code integrates filament segmentation and cross-over or 'knot' identification based on directed graph representation, where nodes represent cross-overs and edges represent the path connecting them. The graphs are mapped back to contours and the distance to a reference minimized. We demonstrate the utility of the tool by segmentation and tracking MTs from experiments with dynein-driven wave like filament looping. The accuracy of contour detection is sub-pixel accuracy, and Dice scores indicate a robustness to noise, better than currently used tools. Thus KnotResolver overcomes multiple limitations of widely used tools in microscopy of cytoskeletal filament-like structures., Comment: Manuscript in submission

A theoretical study is performed to analyze the directional response of different types of microphone array designs. 1-D (linear) and 2-D (planar) microphone array types are considered, and the delay and sum beamforming and conventional beamforming techniques are employed to localize the sound source. A non-dimensional parameter, G, is characterized to simplify and standardize the rejection performance of both 1-D and 2-D microphone arrays as a function of array geometry and sound source parameters. This parameter G is then used to determine an improved design of a 2-D microphone array for far-field sound localization. One such design, termed the Equi-area array is introduced and analyzed in detail. The design is shown to have an advantageous rejection performance compared to other conventionally used 2-D planar microphone arrays.

Motivated by realistic hardware considerations of the pre-fault-tolerant era, we comprehensively study the impact of uncorrected noise on quantum circuits. We first show that any noise `truncates' most quantum circuits to effectively logarithmic depth, in the task of estimating observable expectation values. We then prove that quantum circuits under any non-unital noise exhibit lack of barren plateaus for cost functions composed of local observables. But, by leveraging the effective shallowness, we also design an efficient classical algorithm to estimate observable expectation values within any constant additive accuracy, with high probability over the choice of the circuit, in any circuit architecture. The runtime of the algorithm is independent of circuit depth, and for any inverse-polynomial target accuracy, it operates in polynomial time in the number of qubits for one-dimensional architectures and quasi-polynomial time for higher-dimensional ones. Taken together, our results showcase that, unless we carefully engineer the circuits to take advantage of the noise, it is unlikely that noisy quantum circuits are preferable over shallow quantum circuits for algorithms that output observable expectation value estimates, like many variational quantum machine learning proposals. Moreover, we anticipate that our work could provide valuable insights into the fundamental open question about the complexity of sampling from (possibly non-unital) noisy random circuits., Comment: 16+46 pages, 8 figures

Current scene flow methods broadly fail to describe motion on small objects, and current scene flow evaluation protocols hide this failure by averaging over many points, with most drawn larger objects. To fix this evaluation failure, we propose a new evaluation protocol, Bucket Normalized EPE, which is class-aware and speed-normalized, enabling contextualized error comparisons between object types that move at vastly different speeds. To highlight current method failures, we propose a frustratingly simple supervised scene flow baseline, TrackFlow, built by bolting a high-quality pretrained detector (trained using many class rebalancing techniques) onto a simple tracker, that produces state-of-the-art performance on current standard evaluations and large improvements over prior art on our new evaluation. Our results make it clear that all scene flow evaluations must be class and speed aware, and supervised scene flow methods must address point class imbalances. We release the evaluation code publicly at https://github.com/kylevedder/BucketedSceneFlowEval., Comment: Accepted to ECCV 2024. Project page at https://vedder.io/trackflow