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Many active galaxies harbor powerful relativistic jets, however, the detailed mechanisms of their formation and acceleration remain poorly understood. To investigate the area of jet acceleration and collimation with the highest available angular resolution, we study the innermost region of the bipolar jet in the nearby low-ionization nuclear emission-line region (LINER) galaxy NGC 1052. We combined observations of NGC 1052 taken with VLBA, GMVA, and EHT over one week in the spring of 2017. For the first time, NGC 1052 was detected with the EHT, providing a size of the central region in-between both jet bases of 250 RS (Schwarzschild radii) perpendicular to the jet axes. This size estimate supports previous studies of the jets expansion profile which suggest two breaks of the profile at around 300 RS and 10000 RS distances to the core. Furthermore, we estimated the magnetic field to be 1.25 Gauss at a distance of 22 {\mu}as from the central engine by fitting a synchrotron-self absorption spectrum to the innermost emission feature, which shows a spectral turn-over at about 130 GHz. Assuming a purely poloidal magnetic field, this implies an upper limit on the magnetic field strength at the event horizon of 26000 Gauss, which is consistent with previous measurements. The complex, low-brightness, double-sided jet structure in NGC 1052 makes it a challenge to detect the source at millimeter (mm) wavelengths. However, our first EHT observations have demonstrated that detection is possible up to at least 230 GHz. This study offers a glimpse through the dense surrounding torus and into the innermost central region, where the jets are formed. This has enabled us to finally resolve this region and provide improved constraints on its expansion and magnetic field strength., Comment: 22 pages, 10 figures, published in A&A

The 2017 observing campaign of the Event Horizon Telescope (EHT) delivered the first very long baseline interferometry (VLBI) images at the observing frequency of 230 GHz, leading to a number of unique studies on black holes and relativistic jets from active galactic nuclei (AGN). In total, eighteen sources were observed: the main science targets, Sgr A* and M87 along with various calibrators. We investigated the morphology of the sixteen AGN in the EHT 2017 data set, focusing on the properties of the VLBI cores: size, flux density, and brightness temperature. We studied their dependence on the observing frequency in order to compare it with the Blandford-K\"onigl (BK) jet model. We modeled the source structure of seven AGN in the EHT 2017 data set using linearly polarized circular Gaussian components and collected results for the other nine AGN from dedicated EHT publications, complemented by lower frequency data in the 2-86 GHz range. Then, we studied the dependences of the VLBI core flux density, size, and brightness temperature on the frequency measured in the AGN host frame. We compared the observations with the BK jet model and estimated the magnetic field strength dependence on the distance from the central black hole. Our results indicate a deviation from the standard BK model, particularly in the decrease of the brightness temperature with the observing frequency. Either bulk acceleration of the jet material, energy transfer from the magnetic field to the particles, or both are required to explain the observations.

Chirality, an intrinsic preference for a specific handedness, is a fundamental characteristic observed in nature. In magnetism, magnetic chirality arises from the anti-symmetric Dzyaloshinskii-Moriya interaction in competition with the symmetric Heisenberg exchange interaction. Traditionally, the anti-symmetric interaction has been considered minor relative to the symmetric interaction. In this study, we demonstrate an observation of giant magnetic chirality during the dimensionality crossover of magnetic materials from three-dimensional to two-dimensional. The ratio between the anti-symmetric and symmetric interactions exhibits a reversal in their dominance over this crossover, overturning the traditional consideration. This observation is validated theoretically using a non-local interaction model and tight-binding calculation with distinct pairing schemes for each exchange interaction throughout the crossover. Additional experiments investigating the asphericity of orbital moments corroborate the robustness of our findings. Our findings highlight the critical role of dimensionality in shaping magnetic chirality and offer strategies for engineering chiral magnet states with unprecedented strength, desired for the design of spintronic materials., Comment: 21 pages, 4 figures

Sr-doped nickelate, Nd1-xSrxNiO3 (NSNO), perovskite thin films and Ruddlesden-Popper (RP) phases are actively investigated because of their physical properties, such as the metal-insulator transition and superconductivity. However, achieving epitaxial growth of NSNO perovskite and RP phase films in a sputtering system is challenging compared to pulsed laser deposition and molecular beam epitaxy, due to the difficulty in stabilizing nickel oxidation states and minimizing structural defects. Here, we used an off-axis radio frequency (RF) magnetron sputtering to fabricate epitaxial NSNO perovskite and RP phase thin films on SrTiO3 (001) substrates, systematically controlling the growth temperatures. We investigated the thermal stability of the perovskite phase and the structural and electronic characteristics of the RP phase films. These findings provide valuable insights into the synthesis of nickelate RP phase films using RF magnetron sputtering, paving the way for scalable thin films fabrication technologies., Comment: 13 pages, 4 figures

Complete characterization of a multimode optical process has paved the way for understanding complex optical phenomena, leading to the development of novel optical technologies. Until now, however, characterizations have mainly focused on a linear-optical process, despite the plethora of multimode nonlinear-optical processes crucial for photonic technologies. Here we report the experimental characterization of multimode nonlinear-optical quantum processes by obtaining the full information needed to describe them while satisfying the necessary physical condition. Specifically, to characterize a second-order nonlinear-optical process of parametric downconversion, we determine the amplification and noise matrices of multimode field quadratures. The full information allows us to factorize the multimode process, leading to the identification of eigenquadratures and their associated amplification and noise properties. Moreover, we demonstrate the broad applicability of our method by characterizing various nonlinear-optical quantum processes, including cluster state generation, mode-dependent loss with nonlinear interaction, and a quantum noise channel. Our method, by providing a versatile technique for characterizing a nonlinear-optical process, will be beneficial for developing scalable photonic technologies.

Let $\mathrm{Int}(n)$ denote the set of nonempty left weak Bruhat intervals in the symmetric group $\mathfrak{S}_n$. We investigate the equivalence relation $\overset{D}{\simeq}$ on $\mathrm{Int}(n)$, where $I \overset{D}{\simeq} J$ if and only if there exists a descent-preserving poset isomorphism between $I$ and $J$. For each equivalence class $C$ of $(\mathrm{Int}(n), \overset{D}{\simeq})$, a partial order $\preceq$ is defined by $[\sigma, \rho]_L \preceq [\sigma', \rho']_L$ if and only if $\sigma \preceq_R \sigma'$. Kim-Lee-Oh (2023) showed that the poset $(C, \preceq)$ is isomorphic to a right weak Bruhat interval. In this paper, we focus on lower and upper descent weak Bruhat intervals, specifically those of the form $[w_0(S), \sigma]_L$ or $[\sigma, w_1(S)]_L$, where $w_0(S)$ is the longest element in the parabolic subgroup $\mathfrak{S}_S$ of $\mathfrak{S}_n$, generated by $\{s_i \mid i \in S\}$ for a subset $S \subseteq [n-1]$, and $w_1(S)$ is the longest element among the minimal-length representatives of left $\mathfrak{S}_{[n-1] \setminus S}$-cosets in $\mathfrak{S}_n$. We begin by providing a poset-theoretic characterization of the equivalence relation $\overset{D}{\simeq}$. Using this characterization, the minimal and maximal elements within an equivalence class $C$ are identified when $C$ is a lower or upper descent interval. Under an additional condition, a detailed description of the structure of $(C, \preceq)$ is provided. Furthermore, for the equivalence class containing $[w_0(S), \sigma]_L$, an injective hull of ${\sf B}([w_0(S), \sigma]_L)$ is given, and for the equivalence class containing $[\sigma, w_1(S)]_L$, a projective cover of ${\sf B}([\sigma, w_1(S)]_L)$ is given., Comment: 48 pages

Large-scale 3D point clouds (LS3DPC) obtained by LiDAR scanners require huge storage space and transmission bandwidth due to a large amount of data. The existing methods of LS3DPC compression separately perform rule-based point sampling and learnable feature extraction, and hence achieve limited compression performance. In this paper, we propose a fully end-to-end training framework for LS3DPC compression where the point sampling and the feature extraction are jointly optimized in terms of the rate and distortion losses. To this end, we first make the point sampling module to be trainable such that an optimal position of the downsampled point is estimated via aggregation with learnable weights. We also develop a reliable point reconstruction scheme that adaptively aggregates the expanded candidate points to refine the positions of upsampled points. Experimental results evaluated on the SemanticKITTI and nuScenes datasets show that the proposed method achieves significantly higher compression ratios compared with the existing state-of-the-art methods., Comment: 10 pages, 10 figures, 1 table

For multiple hypothesis testing based on classical data samples, we demonstrate a quantum advantage in the optimal privacy-utility trade-off (PUT), where the privacy and utility measures are set to (quantum) local differential privacy and the pairwise-minimum Chernoff information, respectively. To show the quantum advantage, we consider some class of hypotheses that we coin smoothed point masses. For such hypotheses, we derive an upper bound of the optimal PUT achieved by classical mechanisms, which is tight for some cases, and propose a certain quantum mechanism which achieves a better PUT than the upper bound. The proposed quantum mechanism consists of a classical-quantum channel whose outputs are pure states corresponding to a symmetric informationally complete positive operator-valued measure (SIC-POVM), and a depolarizing channel., Comment: 12 pages, 1 figure

Concept erasure techniques have recently gained significant attention for their potential to remove unwanted concepts from text-to-image models. While these methods often demonstrate success in controlled scenarios, their robustness in real-world applications and readiness for deployment remain uncertain. In this work, we identify a critical gap in evaluating sanitized models, particularly in terms of their performance across various concept dimensions. We systematically investigate the failure modes of current concept erasure techniques, with a focus on visually similar, binomial, and semantically related concepts. We propose that these interconnected relationships give rise to a phenomenon of concept entanglement resulting in ripple effects and degradation in image quality. To facilitate more comprehensive evaluation, we introduce EraseBENCH, a multi-dimensional benchmark designed to assess concept erasure methods with greater depth. Our dataset includes over 100 diverse concepts and more than 1,000 tailored prompts, paired with a comprehensive suite of metrics that together offer a holistic view of erasure efficacy. Our findings reveal that even state-of-the-art techniques struggle with maintaining quality post-erasure, indicating that these approaches are not yet ready for real-world deployment. This highlights the gap in reliability of the concept erasure techniques., Comment: 11 pages main; 9 pages supplemental material

In this paper, we present Drama Llama, an LLM-powered storylets framework that supports the authoring of responsive, open-ended interactive stories. DL combines the structural benefits of storylet-based systems with the generative capabilities of large language models, enabling authors to create responsive interactive narratives while maintaining narrative control. Rather than crafting complex logical preconditions in a general-purpose or domain-specific programming language, authors define triggers in natural language that fire at appropriate moments in the story. Through a preliminary authoring study with six content authors, we present initial evidence that DL can generate coherent and meaningful narratives with believable character interactions. This work suggests directions for hybrid approaches that enhance authorial control while supporting emergent narrative generation through LLMs., Comment: 10 pages, 5 photos

Hydrogen incorporation into iron interstitial sites under high-pressure conditions forms stoichiometric iron monohydride (FeH) with a double hexagonal close-packed (dhcp) structure. This structure is stable over a broad pressure-temperature range, where ferromagnetic-paramagnetic transitions occur. In this study, using synchrotron X-ray diffraction, we identified negative thermal expansion and thermally invariant volume behavior in dhcp-FeH. Our findings, supported by DFT+DMFT calculations, reveal that these thermal effects are governed by magnetic transitions. The Curie temperature decreases linearly with pressure at a rate of about -21 K/GPa, reaching 300 K at 28 GPa. Experimentally measured magnetostriction is an order of magnitude smaller than prior theoretical predictions, possibly due to volume expansion induced by hydrogen incorporation.

4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive technique for volumetric, time-resolved blood flow quantification. However, apparent trade-offs between acquisition time, image noise, and resolution limit clinical applicability. In particular, in regions of highly transient flow, coarse temporal resolution can hinder accurate capture of physiologically relevant flow variations. To overcome these issues, post-processing techniques using deep learning have shown promising results to enhance resolution post-scan using so-called super-resolution networks. However, while super-resolution has been focusing on spatial upsampling, temporal super-resolution remains largely unexplored. The aim of this study was therefore to implement and evaluate a residual network for temporal super-resolution 4D Flow MRI. To achieve this, an existing spatial network (4DFlowNet) was re-designed for temporal upsampling, adapting input dimensions, and optimizing internal layer structures. Training and testing were performed using synthetic 4D Flow MRI data originating from patient-specific in-silico models, as well as using in-vivo datasets. Overall, excellent performance was achieved with input velocities effectively denoised and temporally upsampled, with a mean absolute error (MAE) of 1.0 cm/s in an unseen in-silico setting, outperforming deterministic alternatives (linear interpolation MAE = 2.3 cm/s, sinc interpolation MAE = 2.6 cm/s). Further, the network synthesized high-resolution temporal information from unseen low-resolution in-vivo data, with strong correlation observed at peak flow frames. As such, our results highlight the potential of utilizing data-driven neural networks for temporal super-resolution 4D Flow MRI, enabling high-frame-rate flow quantification without extending acquisition times beyond clinically acceptable limits., Comment: 12 pages, 8 figures

The Compton amplitude subtraction function is an essential component in work concerning both the proton radius puzzle and the proton-neutron mass difference. However, owing to the difficulty in determining the subtraction function, it remains a key source of uncertainty in these two contexts. Here, we use the Feynman-Hellmann method to determine this subtraction function directly from lattice QCD. Furthermore, we demonstrate how to control dominant discretisation artefacts for this calculation, eliminating a major source of systematic error. This calculation is performed for a range of hard momentum scales, and three different sets of gauge configurations for pion masses about 400 MeV. Our results show good agreement with continuum OPE expectations. As such, this work paves the way for model-independent and precise determinations of the subtraction function over a wide range of kinematics., Comment: 15 pages, 8 figures

We report a search for high-energy astrophysical neutrino multiplets, detections of multiple neutrino clusters in the same direction within 30 days, based on an analysis of 11.4 years of IceCube data. A new search method optimized for transient neutrino emission with a monthly time scale is employed, providing a higher sensitivity to neutrino fluxes. This result is sensitive to neutrino transient emission, reaching per-flavor flux of approximately $10^{-10}\ {\rm erg}\ {\rm cm}^{-2}\ {\rm sec}^{-1}$ from the Northern sky in the energy range $E\gtrsim 50$~TeV. The number of doublets and triplets identified in this search is compatible with the atmospheric background hypothesis, which leads us to set limits on the nature of neutrino transient sources with emission timescales of one month.

Medical images and reports offer invaluable insights into patient health. The heterogeneity and complexity of these data hinder effective analysis. To bridge this gap, we investigate contrastive learning models for cross-domain retrieval, which associates medical images with their corresponding clinical reports. This study benchmarks the robustness of four state-of-the-art contrastive learning models: CLIP, CXR-RePaiR, MedCLIP, and CXR-CLIP. We introduce an occlusion retrieval task to evaluate model performance under varying levels of image corruption. Our findings reveal that all evaluated models are highly sensitive to out-of-distribution data, as evidenced by the proportional decrease in performance with increasing occlusion levels. While MedCLIP exhibits slightly more robustness, its overall performance remains significantly behind CXR-CLIP and CXR-RePaiR. CLIP, trained on a general-purpose dataset, struggles with medical image-report retrieval, highlighting the importance of domain-specific training data. The evaluation of this work suggests that more effort needs to be spent on improving the robustness of these models. By addressing these limitations, we can develop more reliable cross-domain retrieval models for medical applications., Comment: This work is accepted to AAAI 2025 Workshop -- the 9th International Workshop on Health Intelligence

We investigate the physics of the spatially random coupling between a Fermi surface and a vector bosonic field in the presence of the magnetic field. We evaluated the propagators of the fermion and vector as well as the self energy and polarization and their dependence on the magnetic field. We also evaluated the temperature dependence of the Hall angle, no strange-metal behaviour is obtained, but the linear-$T$ resistivity is preserved under the magnetic field., Comment: 25 pages, 15 figures

This paper explores the application of large language models (LLMs) to extract nuanced and complex job features from unstructured job postings. Using a dataset of 1.2 million job postings provided by AdeptID, we developed a robust pipeline to identify and classify variables such as remote work availability, remuneration structures, educational requirements, and work experience preferences. Our methodology combines semantic chunking, retrieval-augmented generation (RAG), and fine-tuning DistilBERT models to overcome the limitations of traditional parsing tools. By leveraging these techniques, we achieved significant improvements in identifying variables often mislabeled or overlooked, such as non-salary-based compensation and inferred remote work categories. We present a comprehensive evaluation of our fine-tuned models and analyze their strengths, limitations, and potential for scaling. This work highlights the promise of LLMs in labor market analytics, providing a foundation for more accurate and actionable insights into job data.

Transformer models have achieved state-of-the-art performance across a wide range of machine learning tasks. There is growing interest in training transformers on resource-constrained edge devices due to considerations such as privacy, domain adaptation, and on-device scientific machine learning. However, the significant computational and memory demands required for transformer training often exceed the capabilities of an edge device. Leveraging low-rank tensor compression, this paper presents the first on-FPGA accelerator for end-to-end transformer training. On the algorithm side, we present a bi-directional contraction flow for tensorized transformer training, significantly reducing the computational FLOPS and intra-layer memory costs compared to existing tensor operations. On the hardware side, we store all highly compressed model parameters and gradient information on chip, creating an on-chip-memory-only framework for each stage in training. This reduces off-chip communication and minimizes latency and energy costs. Additionally, we implement custom computing kernels for each training stage and employ intra-layer parallelism and pipe-lining to further enhance run-time and memory efficiency. Through experiments on transformer models within $36.7$ to $93.5$ MB using FP-32 data formats on the ATIS dataset, our tensorized FPGA accelerator could conduct single-batch end-to-end training on the AMD Alevo U50 FPGA, with a memory budget of less than $6$-MB BRAM and $22.5$-MB URAM. Compared to uncompressed training on the NVIDIA RTX 3090 GPU, our on-FPGA training achieves a memory reduction of $30\times$ to $51\times$. Our FPGA accelerator also achieves up to $3.6\times$ less energy cost per epoch compared with tensor Transformer training on an NVIDIA RTX 3090 GPU.

Text-to-image (T2I) generation has made significant advances in recent years, but challenges still remain in the generation of perceptual artifacts, misalignment with complex prompts, and safety. The prevailing approach to address these issues involves collecting human feedback on generated images, training reward models to estimate human feedback, and then fine-tuning T2I models based on the reward models to align them with human preferences. However, while existing reward fine-tuning methods can produce images with higher rewards, they may change model behavior in unexpected ways. For example, fine-tuning for one quality aspect (e.g., safety) may degrade other aspects (e.g., prompt alignment), or may lead to reward hacking (e.g., finding a way to increase rewards without having the intended effect). In this paper, we propose Focus-N-Fix, a region-aware fine-tuning method that trains models to correct only previously problematic image regions. The resulting fine-tuned model generates images with the same high-level structure as the original model but shows significant improvements in regions where the original model was deficient in safety (over-sexualization and violence), plausibility, or other criteria. Our experiments demonstrate that Focus-N-Fix improves these localized quality aspects with little or no degradation to others and typically imperceptible changes in the rest of the image. Disclaimer: This paper contains images that may be overly sexual, violent, offensive, or harmful.

Very long baseline interferometry (VLBI) enables high-angular-resolution observations in astronomy and geodesy by synthesizing a virtual telescope with baselines spanning hundreds to thousands of kilometres. Achieving high instrumental phase stability in VLBI relies on the generation of high-quality, atomic-referenced RF local oscillator (LO) and RF-comb signals for the effective downconversion of celestial RF signals and precise phase calibration, respectively. As observing frequencies move into higher ranges with wider bandwidth, conventional electronic methods face significant challenges in maintaining the quality of these signals. Here, we demonstrate that an optical frequency comb (OFC) can be used as a versatile tool to generate and distribute low-noise and atomic-referenced RF-comb and RF-LO signals in the VLBI telescope. Hydrogen maser-stabilized optical pulses are transmitted over a timing-stabilized fibre link from the observatory building to the VLBI receiver system at the telescope, where photodetection converts them into the required RF-comb and RF-LO signals. In VLBI test observation, we successfully detected VLBI fringes and extracted the RF-combs characteristics in a format suitable for VLBI instrumental phase calibration. These results highlight the high potential of OFC-based technology for enhancing next-generation broadband VLBI measurements, advancing astrophysical research and facilitating intercontinental clock comparison., Comment: 25 pages, 7 figures

Over the past few years, diffusion models have emerged as novel AI solutions, generating diverse multi-modal outputs from text prompts. Despite their capabilities, they face challenges in computing, such as excessive latency and energy consumption due to their iterative architecture. Although prior works specialized in transformer acceleration can be applied, the iterative nature of diffusion models remains unresolved. In this paper, we present EXION, the first SW-HW co-designed diffusion accelerator that solves the computation challenges by exploiting the unique inter- and intra-iteration output sparsity in diffusion models. To this end, we propose two SW-level optimizations. First, we introduce the FFN-Reuse algorithm that identifies and skips redundant computations in FFN layers across different iterations (inter-iteration sparsity). Second, we use a modified eager prediction method that employs two-step leading-one detection to accurately predict the attention score, skipping unnecessary computations within an iteration (intra-iteration sparsity). We also introduce a novel data compaction mechanism named ConMerge, which can enhance HW utilization by condensing and merging sparse matrices into compact forms. Finally, it has a dedicated HW architecture that supports the above sparsity-inducing algorithms, translating high output sparsity into improved energy efficiency and performance. To verify the feasibility of the EXION, we first demonstrate that it has no impact on accuracy in various types of multi-modal diffusion models. We then instantiate EXION in both server- and edge-level settings and compare its performance against GPUs with similar specifications. Our evaluation shows that EXION achieves dramatic improvements in performance and energy efficiency by 3.2-379.3x and 45.1-3067.6x compared to a server GPU and by 42.6-1090.9x and 196.9-4668.2x compared to an edge GPU., Comment: To appear in 2025 IEEE International Symposium on High-Performance Computer Architecture (HPCA 2025)

Quantum spin liquids (QSLs) represent exotic states of matter where quantum spins interact strongly yet evade long-range magnetic order down to absolute zero. Characterized by non-local quantum entanglement and resultant fractionalized excitations, QSLs have emerged as a frontier in condensed matter physics, bolstered by the recent identification of several candidate materials. This field holds profound implications for understanding strong correlations, topological order, and emergent phenomena in quantum materials. Among them, the spin-1/2 Kitaev honeycomb model, featuring bond-directional Ising interactions, provides a rare exactly solvable QSL example. Its ground state is a topological QSL, with spin degrees of freedom fractionalized into emergent Majorana fermions. Under an applied magnetic field, the Kitaev QSL transitions to a topologically non-trivial chiral spin liquid state with non-Abelian anyons, offering potential resources for topological quantum computation. The non-Abelian character of these anyons in the Kitaev QSL demonstrates a profound connection to certain topological superconductors and even-denominator fractional quantum Hall states. Since the theoretical prediction that the Kitaev model could manifest in spin-orbit-coupled materials such as honeycomb iridates and ruthenates, research has focused on identifying candidate compounds. In particular, experimental evidence suggests spin fractionalization and topological phenomena akin to the Kitaev model in the spin-orbit Mott insulator $\alpha$-RuCl$_3$. However, results and interpretations remain actively debated. This review presents a comprehensive survey of existing studies on Kitaev candidate materials, with a particular focus on $\alpha$-RuCl$_3$. Rather than offering conclusive remarks, our aim is to inspire future research by examining several key aspects of the current literature and perspectives., Comment: 65 pages, 33 figures. Review article to be submitted to Rev. Mod. Phys. Comments are welcome

Superconductors exhibit dissipationless supercurrents even under finite bias and magnetic field conditions, provided these remain below the critical values. However, type-II superconductors in the flux flow regime display Ohmic dissipation arising from vortex dynamics under finite magnetic fields. The interplay between supercurrent and Ohmic dissipation in a type-II superconductor is dictated by vortex motion and the robustness of vortex pinning forces. In this study, we present an experimental investigation of the superconducting phase transitions and vortex dynamics in the atomically thin type-II superconductor 2H-NbSe$_2$. We fabricated a high-quality multilayer 2H-NbSe$_2$ with a step junction, demonstrating supercurrent in clean limit below a critical temperature of 6.6 K and a high residual resistance ratio of 17. The upper critical field was estimated to be 4.5 T and the Ginzburg-Landau coherence length 8.6 nm. Additionally, we observed phase transitions induced by vortex viscous dynamics in the 2H-NbSe$_2$ step junction. Analysis of the pinning force density using the Dew-Hughes model indicates that the pinning force in the 2H-NbSe$_2$ device can be attributed to step junction, related to the surface-$\Delta\kappa$ type of pinning centers. Our findings pave the way for engineering pinning forces by introducing artificial pinning centers through partial atomic thickness variation in layered 2D superconductors while minimizing unwanted quality degradation in the system., Comment: 19 pages, 4 figures

3D Gaussian Splatting (3DGS) has made significant strides in scene representation and neural rendering, with intense efforts focused on adapting it for dynamic scenes. Despite delivering remarkable rendering quality and speed, existing methods struggle with storage demands and representing complex real-world motions. To tackle these issues, we propose MoDecGS, a memory-efficient Gaussian splatting framework designed for reconstructing novel views in challenging scenarios with complex motions. We introduce GlobaltoLocal Motion Decomposition (GLMD) to effectively capture dynamic motions in a coarsetofine manner. This approach leverages Global Canonical Scaffolds (Global CS) and Local Canonical Scaffolds (Local CS), extending static Scaffold representation to dynamic video reconstruction. For Global CS, we propose Global Anchor Deformation (GAD) to efficiently represent global dynamics along complex motions, by directly deforming the implicit Scaffold attributes which are anchor position, offset, and local context features. Next, we finely adjust local motions via the Local Gaussian Deformation (LGD) of Local CS explicitly. Additionally, we introduce Temporal Interval Adjustment (TIA) to automatically control the temporal coverage of each Local CS during training, allowing MoDecGS to find optimal interval assignments based on the specified number of temporal segments. Extensive evaluations demonstrate that MoDecGS achieves an average 70% reduction in model size over stateoftheart methods for dynamic 3D Gaussians from realworld dynamic videos while maintaining or even improving rendering quality., Comment: The last two authors are co-corresponding authors. Please visit our project page at https://kaist-viclab.github.io/MoDecGS-site/

Knowledge graphs have proven successful in integrating heterogeneous data across various domains. However, there remains a noticeable dearth of research on their seamless integration among heterogeneous recommender systems, despite knowledge graph-based recommender systems garnering extensive research attention. This study aims to fill this gap by proposing RecKG, a standardized knowledge graph for recommender systems. RecKG ensures the consistent representation of entities across different datasets, accommodating diverse attribute types for effective data integration. Through a meticulous examination of various recommender system datasets, we select attributes for RecKG, ensuring standardized formatting through consistent naming conventions. By these characteristics, RecKG can seamlessly integrate heterogeneous data sources, enabling the discovery of additional semantic information within the integrated knowledge graph. We apply RecKG to standardize real-world datasets, subsequently developing an application for RecKG using a graph database. Finally, we validate RecKG's achievement in interoperability through a qualitative evaluation between RecKG and other studies., Comment: Accepted by The 39th ACM/SIGAPP Symposium On Applied Computing(SAC) 2024

This study emphasizes the significance of exploring distance-based source separation (DSS) in outdoor environments. Unlike existing studies that primarily focus on indoor settings, the proposed model is designed to capture the unique characteristics of outdoor audio sources. It incorporates advanced techniques, including a two-stage conformer block, a linear relation-aware self-attention (RSA), and a TensorFlow Lite GPU delegate. While the linear RSA may not capture physical cues as explicitly as the quadratic RSA, the linear RSA enhances the model's context awareness, leading to improved performance on the DSS that requires an understanding of physical cues in outdoor and indoor environments. The experimental results demonstrated that the proposed model overcomes the limitations of existing approaches and considerably enhances energy efficiency and real-time inference speed on mobile devices., Comment: Accepted by ICASSP2025. \c{opyright} 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component

Accurate instrument pose estimation is a crucial step towards the future of robotic surgery, enabling applications such as autonomous surgical task execution. Vision-based methods for surgical instrument pose estimation provide a practical approach to tool tracking, but they often require markers to be attached to the instruments. Recently, more research has focused on the development of marker-less methods based on deep learning. However, acquiring realistic surgical data, with ground truth instrument poses, required for deep learning training, is challenging. To address the issues in surgical instrument pose estimation, we introduce the Surgical Robot Instrument Pose Estimation (SurgRIPE) challenge, hosted at the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2023. The objectives of this challenge are: (1) to provide the surgical vision community with realistic surgical video data paired with ground truth instrument poses, and (2) to establish a benchmark for evaluating markerless pose estimation methods. The challenge led to the development of several novel algorithms that showcased improved accuracy and robustness over existing methods. The performance evaluation study on the SurgRIPE dataset highlights the potential of these advanced algorithms to be integrated into robotic surgery systems, paving the way for more precise and autonomous surgical procedures. The SurgRIPE challenge has successfully established a new benchmark for the field, encouraging further research and development in surgical robot instrument pose estimation., Comment: 35 pages, 18 figures, journal paper

Due to the hierarchical organization of RNA structures and their pivotal roles in fulfilling RNA functions, the formation of RNA secondary structure critically influences many biological processes and has thus been a crucial research topic. This review sets out to explore the computational prediction of RNA secondary structure and its connections to RNA modifications, which have emerged as an active domain in recent years. We first examine the progression of RNA secondary structure prediction methodology, focusing on a set of representative works categorized into thermodynamic, comparative, machine learning, and hybrid approaches. Next, we survey the advances in RNA modifications and computational methods for identifying RNA modifications, focusing on the prominent modification types. Subsequently, we highlight the interplay between RNA modifications and secondary structures, emphasizing how modifications such as m6A dynamically affect RNA folding and vice versa. In addition, we also review relevant data sources and provide a discussion of current challenges and opportunities in the field. Ultimately, we hope our review will be able to serve as a cornerstone to aid in the development of innovative methods for this emerging topic and foster therapeutic applications in the future.

Time-reversal symmetry breaking (TRSB) in magnetic topological insulators induces a Dirac gap in the topological surface state (TSS), leading to exotic phenomena such as the quantum anomalous Hall effect. Yet, the interplay between TRSB and topology in superconductors remains underexplored due to limited suitable materials. Here we employ zero-field muon spin relaxation ($\mu$SR) as a sensitive probe of TRSB to map out the electronic phase diagrams of iron-chalcogenide superconductors FeSe$_{1-x}$Te$_{x}$. For the Te composition $x=0.64$ with the highest superconducting transition temperature $T_{\rm c}=14.5$ K, which is known to host a TSS and Majorana zero modes within vortices, we detect spontaneous magnetic fields below $T_{\rm c}$ distinct from a magnetic order. This signifies a TRSB superconducting state in the bulk, revealing the convergence of unconventional TRSB superconductivity with topologically nontrivial electronic structures in FeSe$_{1-x}$Te$_{x}$. Given the relatively high $T_{\rm c}$ and the tunability of the Fermi level through chemical substitution, iron-chalcogenide superconductors offer an intriguing platform for investigating the synergy between topological superconductivity and TRSB., Comment: 20 pages, 4 figures

Greedy algorithms, particularly the orthogonal greedy algorithm (OGA), have proven effective in training shallow neural networks for fitting functions and solving partial differential equations (PDEs). In this paper, we extend the application of OGA to the tasks of linear operator learning, which is equivalent to learning the kernel function through integral transforms. Firstly, a novel greedy algorithm is developed for kernel estimation rate in a new semi-inner product, which can be utilized to approximate the Green's function of linear PDEs from data. Secondly, we introduce the OGA for point-wise kernel estimation to further improve the approximation rate, achieving orders of accuracy improvement across various tasks and baseline models. In addition, we provide a theoretical analysis on the kernel estimation problem and the optimal approximation rates for both algorithms, establishing their efficacy and potential for future applications in PDEs and operator learning tasks.

Retrieval-augmented generation (RAG) has recently become a very popular task for Large Language Models (LLMs). Evaluating them on multi-turn RAG conversations, where the system is asked to generate a response to a question in the context of a preceding conversation is an important and often overlooked task with several additional challenges. We present MTRAG: an end-to-end human-generated multi-turn RAG benchmark that reflects several real-world properties across diverse dimensions for evaluating the full RAG pipeline. MTRAG contains 110 conversations averaging 7.7 turns each across four domains for a total of 842 tasks. We also explore automation paths via synthetic data and LLM-as-a-Judge evaluation. Our human and automatic evaluations show that even state-of-the-art LLM RAG systems struggle on MTRAG. We demonstrate the need for strong retrieval and generation systems that can handle later turns, unanswerable questions, non-standalone questions, and multiple domains. MTRAG is available at https://github.com/ibm/mt-rag-benchmark.

Motivated by intense research on two-dimensional spin-1/2 Kitaev materials, Kitaev spin chains and ladders, though geometrically limited, have been studied for their numerical simplicity and insights into extended Kitaev models. The phase diagrams under the magnetic field were also explored for these quasi-one dimensional models. For an isotropic Kitaev chain, it was found that a magnetic field polarizes the ground state except along the symmetric field angle, where the chain is found to remain gapless up to a critical field strength where it enters an intriguing soliton phase before reaching the polarized state at higher field strengths. Here we study an anisotropic Kitaev chain under a magnetic field using the density matrix renormalization group technique, where the ground state has a macroscopic degeneracy with a finite gap in the absence of the magnetic field. When the field is mainly aligned parallel to the strong bond, four-site and large unit-cell ordered phases arise. In a certain angle of the field, another ordered phase characterized by a uniform chirality with six-site periodicity emerges. We employ a perturbation theory to understand such field-induced ordered phases. The effective model uncovers the presence of transverse Ising and Dzyaloshinskii-Moriya interactions between unit cells, as well as further-neighbor Ising interaction induced by the magnetic field, which collectively explain the mechanisms behind these ordered states. Open questions and challenges are also discussed., Comment: 15 pages, 10 figures

Hull form designing is an iterative process wherein the performance of the hull form needs to be checked via computational fluid dynamics calculations or model experiments. The stern shape has to undergo a process wherein the hull form variations from the pressure distribution analysis results are repeated until the resistance and propulsion efficiency meet the design requirements. In this study, the designer designed a pressure distribution that meets the design requirements; this paper proposes an inverse design algorithm that estimates the stern shape using deep learning. A convolutional neural network was used to extract the features of the pressure distribution expressed as a contour, whereas a multi-task learning model was used to estimate various sections of the stern shape. We estimated the stern shape indirectly by estimating the control point of the B-spline and comparing the actual and converted offsets for each section; the performance was verified, and an inverse design is proposed herein

Recent advances in large language models (LLMs) have accelerated the development of conversational agents capable of generating human-like responses. Since psychiatric assessments typically involve complex conversational interactions between psychiatrists and patients, there is growing interest in developing LLM-based psychiatric assessment conversational agents (PACAs) that aim to simulate the role of psychiatrists in clinical evaluations. However, standardized methods for benchmarking the clinical appropriateness of PACAs' interaction with patients still remain underexplored. Here, we propose PSYCHE, a novel framework designed to enable the 1) clinically relevant, 2) ethically safe, 3) cost-efficient, and 4) quantitative evaluation of PACAs. This is achieved by simulating psychiatric patients based on a multi-faceted psychiatric construct that defines the simulated patients' profiles, histories, and behaviors, which PACAs are expected to assess. We validate the effectiveness of PSYCHE through a study with 10 board-certified psychiatrists, supported by an in-depth analysis of the simulated patient utterances., Comment: The first two authors contributed equally

This Letter reports the precise measurement of reactor antineutrino spectrum and flux based on the full data set of 4.7 million inverse-beta-decay (IBD) candidates collected at Daya Bay near detectors. Expressed in terms of the IBD yield per fission, the antineutrino spectra from all reactor fissile isotopes and the specific $\mathrm{^{235}U}$ and $\mathrm{^{239}Pu}$ isotopes are measured with 1.3$\%$, 3$\%$ and 8$\%$ uncertainties respectively near the 3 MeV spectrum peak in reconstructed energy, reaching the best precision in the world. The total antineutrino flux and isotopic $\mathrm{^{235}U}$ and $\mathrm{^{239}Pu}$ fluxes are precisely measured to be $5.84\pm0.07$, $6.16\pm0.12$ and $4.16\pm0.21$ in units of $10^{-43} \mathrm{cm^2/fission}$. These measurements are compared with the Huber-Mueller (HM) model, the reevaluated conversion model based on the Kurchatov Institute (KI) measurement and the latest Summation Model (SM2023). The Daya Bay flux shows good consistency with KI and SM2023 models, but disagrees with HM model. The Daya Bay spectrum, however, disagrees with all model predictions.

C. elegans locomotion is composed of switches between forward and reversal states punctuated by turns. This locomotory capability is necessary for the nematode to move towards attractive stimuli, escape noxious chemicals, and explore its environment. Although experimentalists have identified a number of premotor neurons as drivers of forward and reverse motion, how these neurons work together to produce the behaviors observed remains to be understood. Towards a better understanding of C. elegans neurodynamics, we present in this paper a minimally parameterized, biophysical dynamical systems model of the premotor network. Our model consists of a recurrently connected collection of premotor neurons (the core group) driven by over a hundred sensory and interneurons that provide diverse feedforward inputs to the core group. It is data-driven in the sense that the choice of neurons in the core group follows experimental guidance, anatomical structures are dictated by the connectome, and physiological parameters are deduced from whole-brain imaging and voltage clamps data. When simulated with realistic input signals, our model produces premotor activity that closely resembles experimental data: from the seemingly random switching between forward and reversal behaviors to the synchronization of subnetworks to various higher-order statistics. We posit that different roles are played by gap junctions and synaptic connections in switching dynamics. The model correctly predicts behavior such as dwelling versus roaming as a result of the synaptic inputs received, and we demonstrate that it can be used to study how the activity level of certain individual neurons impacts behavior., Comment: 25 pages, 11 figures

We study a class of quantum groups $U^-$ associated with quivers with loops and provide a categorification by constructing their associated Khovanov-Lauda-Rouquier algebras $R$. We prove that the indecomposable projective module over $R$ corresponds to the canonical basis of $U^-$. In the Jordan quiver case, we show that the cyclotomic Khovanov-Lauda-Rouquier algebras $R^\Lambda$ categorify the irreducible highest weight $U$-module $V(\Lambda)$., Comment: arXiv admin note: substantial text overlap with arXiv:2406.18378

The upper midband, designated as Frequency Range 3 (FR3), is increasingly critical for the next-generation of wireless networks. Channel propagation measurements and their statistical analysis are essential first steps towards this direction. This paper presents a comprehensive ultra-wideband (UWB) double-directional channel measurement campaign in a large portion of FR3 (6-14 GHz) for urban microcellular environments. We analyze over 25,000 directional power delay profiles and providing key insights into line-of-sight (LoS) and obstructed line-of-sight (OLoS) conditions. This is followed by statistical modeling of path loss, shadowing, delay spread and angular spread. As the first UWB double-directional measurement campaign in this frequency range, this work offers critical insights for spectrum allocation, channel modeling, and the design of advanced communication systems, paving the way for further exploration of FR3.

We revisit the definition of the leading-twist chiral-even generalized parton distributions (GPDs) for $N \to \Delta$ baryon transitions. We identify and address deficiencies in previous definitions of the transition GPDs inspired by the transition form factors of the vector and axial-vector currents. Through systematic analysis of all possible covariant structures, respecting discrete symmetries and the baryon spinor equations of motion, we derive complete sets of independent structures for the transition matrix elements of the vector and axial-vector partonic operators. They contain additional structures proportional to the light-cone vector, corresponding to transition GPDs of vanishing first moment, which were not included in previous parametrizations. Their presence is confirmed independently by the light-front multipole expansion and the cross-channel SO(3) partial-wave analysis of the transition matrix elements. Our analysis provides a complete definition of the $N \to \Delta$ transition GPDs for use in theoretical and phenomenological studies., Comment: 13 pages

This paper investigates the global dynamics of the Euler--Riesz system in three dimensions, focusing on the well-posedness and large-time behavior of solutions near equilibrium. The system generalizes classical interactions by incorporating the Riesz interactions $\nabla (-\Delta)^{-\sigma/2}(\rho - 1)$. We show that the system admits a global smooth solution for small irrotational initial perturbations. Specifically, we establish that if the initial data is sufficiently small, the solution remains regular globally in time and decays over time at a rate dependent on $\sigma$., Comment: 38 pages

Under external stimuli, lead halide perovskites exhibit large atomic fluctuations, impacting optical and electron transport properties that affect device performance in operational settings. However, a thorough understanding of the atomic basis for the underlying structural instability is still absent. Focusing on the model material CsPbBr$_3$, the inherent lattice softness of halide perovskites is elucidated at the atomic level through in-situ single-crystal X-ray diffraction measurements under pressure complemented by atomic level simulations. We identify and explore the nature of the first-order phase transition to a distorted P21/c phase at 1.3 GPa, induced by the sudden Cs-Br bonds breaking. Unlike classical transition metal oxide perovskites, where the internal energy term dominates, we show explicitly that pressure primarily influences the Gibbs free energy for halide perovskites through the pressure-volume term. As such, strategically mitigating bond strains from volume shrinkage is the key to suppressing the first-order behavior for maintaining the coordinates of PbX$_6$ polyhedral upon external perturbation. Our thermodynamic calculation reveals the demand for high entropy in the -T*del-S term, which can be achieved by exploring a broader spectrum of doped A site and B sites in ABX$_3$ systems, enabling continuous structural changes that facilitate recovery from mechanical damage in practical applications., Comment: 21 pages (main text), 5 figures (main text), 28 pages (supporting information), 19 figures (supporting information), 6 tables (supporting information)

Using the Event Horizon Telescope (EHT), the gravitationally lensed rings around the supermassive black holes (SMBHs) in Messier 87 (M87) and Sagittarius A* (Sgr A*) have now been successfully imaged at a resolution under 10 gravitational radii (R$_{\rm g}$ $ = \rm{GM/c^2}$). To expand studies beyond M87 and Sgr A*, we have constructed the Event Horizon and Environs (ETHER) sample, a comprehensive database encompassing approximately 3.15 million SMBH mass estimates, $\sim$ 20,000 Very-Long Baseline Interferometry (VLBI) radio flux densities, and $\sim$ 36,000 hard X-ray flux densities. This database is designed to identify and optimize target selection for the EHT and its upgrades on the ground and in space. We have identified a Gold Sample (GS) of nearby low-luminosity Active Galactic Nuclei (AGNs) within it that are ideal for studying jet bases and potentially imaging black hole shadows. We observed 27 of these AGNs using the EHT from 2022 to 2024, providing an opportunity to resolve and image accretion flows and jets at resolutions of $\leq$ 100 R$_{\rm g}$. Only a few SMBHs have sufficiently high enough flux density to be imaged at scales of $\leq$ 50 R$_{\rm g}$ with the present EHT. Among these are M87, Sgr A*, NGC4594 (Sombrero/M104), NGC4261, and NGC4374 (Messier 84/M84). Of these, NGC4261, Sombrero, and M84 have been observed and/or are scheduled for deep imaging with EHT+ALMA from 2023 to 2025. Sombrero, NGC4261, M84, NGC4278, and NGC5232 are clearly detected in our EHT+ALMA observations in 2022, indicating that the 230 GHz flux density from the accretion flows is significantly high. Ongoing imaging of the ETHER GS will enable measurements of black hole mass and spin, help constrain General Relativity, and enrich our understanding of jet launching and accretion inflows across a broad multi-parameter space, including black hole mass, spin, accretion rate, and orientation., Comment: 9 pages, 6 figures, 1 table, published in Proceedings of the 16th EVN Symposium, Ed. E. Ros, P. Benke, S.A. Dzib, I. Rottmann, & J.A. Zensus, Bonn: Max-Planck-Institut f\"ur Radioastronomie, 2024, pages 75-84, https://cloud.mpifr-bonn.mpg.de/index.php/s/BkX2CC2Xjn2aKR4

Precise measurements from sensors are crucial, but data is usually collected from low-cost, low-tech systems, which are often inaccurate. Thus, they require further calibrations. To that end, we first identify three requirements for effective calibration under practical low-tech sensor conditions. Based on the requirements, we develop a model called TESLA, Transformer for effective sensor calibration utilizing logarithmic-binned attention. TESLA uses a high-performance deep learning model, Transformers, to calibrate and capture non-linear components. At its core, it employs logarithmic binning to minimize attention complexity. TESLA achieves consistent real-time calibration, even with longer sequences and finer-grained time series in hardware-constrained systems. Experiments show that TESLA outperforms existing novel deep learning and newly crafted linear models in accuracy, calibration speed, and energy efficiency., Comment: Accepted by AAAI 2025

The use of knowledge graphs in recommender systems has become one of the common approaches to addressing data sparsity and cold start problems. Recent advances in large language models (LLMs) offer new possibilities for processing side and context information within knowledge graphs. However, consistent integration across various systems remains challenging due to the need for domain expert intervention and differences in system characteristics. To address these issues, we propose a consistent approach that extracts both general and specific topics from both side and context information using LLMs. First, general topics are iteratively extracted and updated from side information. Then, specific topics are extracted using context information. Finally, to address synonymous topics generated during the specific topic extraction process, a refining algorithm processes and resolves these issues effectively. This approach allows general topics to capture broad knowledge across diverse item characteristics, while specific topics emphasize detailed attributes, providing a more comprehensive understanding of the semantic features of items and the preferences of users. Experimental results demonstrate significant improvements in recommendation performance across diverse knowledge graphs., Comment: Accepted in The 40th ACM/SIGAPP Symposium On Applied Computing(SAC) 2025

Large-scale video generation models have the inherent ability to realistically model natural scenes. In this paper, we demonstrate that through a careful design of a generative video propagation framework, various video tasks can be addressed in a unified way by leveraging the generative power of such models. Specifically, our framework, GenProp, encodes the original video with a selective content encoder and propagates the changes made to the first frame using an image-to-video generation model. We propose a data generation scheme to cover multiple video tasks based on instance-level video segmentation datasets. Our model is trained by incorporating a mask prediction decoder head and optimizing a region-aware loss to aid the encoder to preserve the original content while the generation model propagates the modified region. This novel design opens up new possibilities: In editing scenarios, GenProp allows substantial changes to an object's shape; for insertion, the inserted objects can exhibit independent motion; for removal, GenProp effectively removes effects like shadows and reflections from the whole video; for tracking, GenProp is capable of tracking objects and their associated effects together. Experiment results demonstrate the leading performance of our model in various video tasks, and we further provide in-depth analyses of the proposed framework., Comment: 11 pages, 18 figures

Domain adaptation is an active area of research driven by the growing demand for robust machine learning models that perform well on real-world data. Adversarial learning for deep neural networks (DNNs) has emerged as a promising approach to improving generalization ability, particularly for image classification. In this paper, we implement a specific adversarial learning technique known as Adversarial Discriminative Domain Adaptation (ADDA) and replicate digit classification experiments from the original ADDA paper. We extend their findings by examining a broader range of domain shifts and provide a detailed analysis of in-domain classification accuracy post-ADDA. Our results demonstrate that ADDA significantly improves accuracy across certain domain shifts with minimal impact on in-domain performance. Furthermore, we provide qualitative analysis and propose potential explanations for ADDA's limitations in less successful domain shifts. Code is at https://github.com/eugenechoi2004/COS429_FINAL ., Comment: Replacement: Updated methodology section to include grayscale preprocessing of SVHN data

Recent works on dynamic neural field reconstruction assume input from synchronized multi-view videos with known poses. These input constraints are often unmet in real-world setups, making the approach impractical. We demonstrate that unsynchronized videos with unknown poses can generate dynamic neural fields if the videos capture human motion. Humans are one of the most common dynamic subjects whose poses can be estimated using state-of-the-art methods. While noisy, the estimated human shape and pose parameters provide a decent initialization for the highly non-convex and under-constrained problem of training a consistent dynamic neural representation. Given the sequences of pose and shape of humans, we estimate the time offsets between videos, followed by camera pose estimations by analyzing 3D joint locations. Then, we train dynamic NeRF employing multiresolution rids while simultaneously refining both time offsets and camera poses. The setup still involves optimizing many parameters, therefore, we introduce a robust progressive learning strategy to stabilize the process. Experiments show that our approach achieves accurate spatiotemporal calibration and high-quality scene reconstruction in challenging conditions.

In a previous paper of the first author, the type A affine Cartan matrix was q-deformed to produce a deformation of the reflection representation of the affine Weyl group. This deformation plays a role in the quantum geometric Satake equivalence. In this paper we introduce the study of q-deformed divided difference operators. When q is specialized to a primitive 2m-th root of unity, this affine reflection representation factors through a quotient, the complex reflection group $G(m,m,n)$. The divided difference operators now generate a finite-dimensional algebra we call the exotic nilCoxeter algebra. This algebra is new and has surprising features. In addition to the usual braid relations, we prove a new relation called the roundabout relation. A classic result of Demazure, for Weyl groups, states that the polynomial ring of the reflection representation is a Frobenius extension over its subring of invariant polynomials, and describes how the Frobenius trace can be constructed within the nilCoxeter algebra. We study the analogous Frobenius extension for $G(m,m,n)$, and identify the Frobenius trace within the exotic nilCoxeter algebra for $G(m,m,3)$.