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We analyze the effect of a variation of the strange nucleon matrix element $\langle N| m_s \bar{s}s|N\rangle$ on the abundances of the light elements produced in the Big Bang. For that, we vary the nucleon mass in the leading eight reactions that involve neutrons, protons and the four lightest nuclei. We use various available Big Bang nucleosynthesis codes and find that the measured deuterium and $^4$He abundances set strict limits on the nucleon mass variations. This translates into an upper bound of possible variations of the strange quark mass, $\left| \Delta m_s/m_s \right| \leq 5.1\%$., Comment: 32 pages, 10 figures

X-ray microtomography is a powerful non-destructive technique allowing 3D virtual histology of resected human tissue. The achievable imaging field-of-view, is however limited by the fixed number of detector elements, enforcing the requirement to sacrifice spatial resolution in order to image larger samples. In applications such as soft-tissue imaging, phase-contrast methods are often employed to enhance image contrast. Some of these methods, especially those suited to laboratory sources, rely on optical elements, the dimensions of which can impose a further limitation on the field-of-view. We describe an efficient method to double the maximum field-of-view of a cone-beam X-ray microtomography system, without sacrificing on spatial resolution, and including multi-contrast capabilities. We demonstrate an experimental realisation of the method, achieving exemplary reconstructions of a resected human lung sample, with a cubic voxel of 10.5 $\mu$m linear dimensions, across a horizontal field-of-view of 4.3 cm. The same concepts are applied to free-space propagation imaging of a 2.7 mm segment of the same sample, achieving a cubic voxel of 450 nm linear dimensions. We show that the methodology can be applied at a range of different length-scales and geometries, and that it is directly compatible with complementary implementations of X-ray phase-contrast imaging., Comment: 14 pages (inc. references), 5 figures

(Abridged) JWST observations have measured the ice composition toward two highly-extinguished field stars in the Chamaeleon I cloud. The observed extinction excess on the long-wavelength side of the H2O ice band at 3 micron has been attributed to a mixture of CH3OH with ammonia hydrates, which suggests that CH3OH ice could have formed in a water-rich environment with little CO depletion. Laboratory experiments and quantum chemical calculations suggest that CH3OH could form via the grain surface reactions CH3+OH and/or C+H2O in water-rich ices. However, no dedicated chemical modelling has been carried out thus far to test their efficiency and dependence on the astrochemical code employed. We model the ice chemistry in the Chamaeleon I cloud using a set of astrochemical codes (MAGICKAL, MONACO, Nautilus, UCLCHEM, and KMC simulations) to test the effects of the different code architectures and of the assumed ice chemistry. Our models show that the JWST ice observations are better reproduced for gas densities >1e5 cm-3 and collapse times >1e5 yr. CH3OH ice forms predominantly (>99%) via CO hydrogenation. The contribution of reactions CH3+OH and C+H2O, is negligible. The CO2 ice may form either via CO+OH or CO+O depending on the code. However, KMC simulations reveal that both mechanisms are efficient despite the low rate constant of the CO+O surface reaction. CH4 is largely underproduced for all codes except for UCLCHEM, for which a higher amount of atomic C is available during the initial translucent cloud phase. Large differences in the ice abundances are found at Tdust<12 K between diffusive and non-diffusive chemistry codes. This is due to the fact that non-diffusive chemistry takes over diffusive chemistry at such low Tdust. This could explain the rather constant ice chemical composition found in Chamaeleon I and other dense cores despite the different visual extinctions probed., Comment: Accepted in A&A

In the centers of many galaxy clusters, the hot ($\sim$10$^7$ K) intracluster medium (ICM) can become dense enough that it should cool on short timescales. However, the low measured star formation rates in massive central galaxies and absence of soft X-ray lines from cooling gas suggest that most of this gas never cools - this is known as the "cooling flow problem." The latest observations suggest that black hole jets are maintaining the vast majority of gas at high temperatures. A cooling flow has yet to be fully mapped through all gas phases in any galaxy cluster. Here, we present new observations of the Phoenix cluster using the James Webb Space Telescope to map the [Ne VI] $\lambda$7.652$\mu$m emission line, allowing us to probe gas at 10$^{5.5}$ K on large scales. These data show extended [Ne VI] emission cospatial with (i) the cooling peak in the ICM, (ii) the coolest gas phases, and (iii) sites of active star formation. Taken together, these imply a recent episode of rapid cooling, causing a short-lived spike in the cooling rate which we estimate to be 5,000-23,000 M$_\odot$ yr$^{-1}$. These data provide the first large-scale map of gas at temperatures between 10$^5$-10$^6$ K in a cluster core, and highlight the critical role that black hole feedback plays in not only regulating but also promoting cooling., Comment: 42 pages, 16 figures, 3 tables. Published in Nature

Social media platforms enable the propagation of hateful content across different modalities such as textual, auditory, and visual, necessitating effective detection methods. While recent approaches have shown promise in handling individual modalities, their effectiveness across different modality combinations remains unexplored. This paper presents a systematic analysis of fusion-based approaches for multimodal hate detection, focusing on their performance across video and image-based content. Our comprehensive evaluation reveals significant modality-specific limitations: while simple embedding fusion achieves state-of-the-art performance on video content (HateMM dataset) with a 9.9% points F1-score improvement, it struggles with complex image-text relationships in memes (Hateful Memes dataset). Through detailed ablation studies and error analysis, we demonstrate how current fusion approaches fail to capture nuanced cross-modal interactions, particularly in cases involving benign confounders. Our findings provide crucial insights for developing more robust hate detection systems and highlight the need for modality-specific architectural considerations. The code is available at https://github.com/gak97/Video-vs-Meme-Hate., Comment: Accepted to the MM4SG Workshop at the WebConf 2025

The advent of foundation models (FMs) is transforming medical domain. In ophthalmology, RETFound, a retina-specific FM pre-trained sequentially on 1.4 million natural images and 1.6 million retinal images, has demonstrated high adaptability across clinical applications. Conversely, DINOv2, a general-purpose vision FM pre-trained on 142 million natural images, has shown promise in non-medical domains. However, its applicability to clinical tasks remains underexplored. To address this, we conducted head-to-head evaluations by fine-tuning RETFound and three DINOv2 models (large, base, small) for ocular disease detection and systemic disease prediction tasks, across eight standardized open-source ocular datasets, as well as the Moorfields AlzEye and the UK Biobank datasets. DINOv2-large model outperformed RETFound in detecting diabetic retinopathy (AUROC=0.850-0.952 vs 0.823-0.944, across three datasets, all P<=0.007) and multi-class eye diseases (AUROC=0.892 vs. 0.846, P<0.001). In glaucoma, DINOv2-base model outperformed RETFound (AUROC=0.958 vs 0.940, P<0.001). Conversely, RETFound achieved superior performance over all DINOv2 models in predicting heart failure, myocardial infarction, and ischaemic stroke (AUROC=0.732-0.796 vs 0.663-0.771, all P<0.001). These trends persisted even with 10% of the fine-tuning data. These findings showcase the distinct scenarios where general-purpose and domain-specific FMs excel, highlighting the importance of aligning FM selection with task-specific requirements to optimise clinical performance.

Query optimization, which finds the optimized execution plan for a given query, is a complex planning and decision-making problem within the exponentially growing plan space in database management systems (DBMS). Traditional optimizers heavily rely on a certain cost model constructed by various heuristics and empirical tuning, probably leading to generating suboptimal plans. Recent developments of Large Language Models (LLMs) have demonstrated their potential in solving complex planning and decision-making problems, such as arithmetic and programmatic tasks. In this paper, we try to explore the potential of LLMs in handling query optimization and propose a tentative LLM-based query optimizer dubbed LLM-QO, established on PostgreSQL's execution engine. In LLM-QO, we formulate query optimization in an autoregressive fashion which directly generates the execution plan without explicit plan enumeration. To investigate the essential input of LLM-QO, we design a customized data recipe named QInstruct to collect the training data from various optimizers and serialize the database's meta data, queries and corresponding plans into a textual format. Based on QInstruct, we implement a two-stage fine-tuning pipeline, Query Instruction Tuning (QIT) and Query Direct Preference Optimization (QDPO), to empower the capability of general-purpose LLMs in handling query optimization. In our experiments, LLM-QO can generate valid and high-quality plans and consistently outperforms both traditional and learned optimizers on three query workloads. Our findings verify that LLMs can be derived as query optimizers where generalization, efficiency and adaptivity deserve further research efforts., Comment: 15 pages

Dual agent dose-finding trials study the effect of a combination of more than one agent, where the objective is to find the Maximum Tolerated Dose Combination (MTC), the combination of doses of the two agents that is associated with a pre-specified risk of being unsafe. In a Phase I/II setting, the objective is to find a dose combination that is both safe and active, the Optimal Biological Dose (OBD), that optimizes a criterion based on both safety and activity. Since Oncology treatments are typically given over multiple cycles, both the safety and activity outcome can be considered as late-onset, potentially occurring in the later cycles of treatment. This work proposes two model-based designs for dual-agent dose finding studies with late-onset activity and late-onset toxicity outcomes, the Joint TITE-POCRM and the Joint TITE-BLRM. Their performance is compared alongside a model-assisted comparator in a comprehensive simulation study motivated by a real trial example, with an extension to consider alternative sized dosing grids. It is found that both model-based methods outperform the model-assisted design. Whilst on average the two model-based designs are comparable, this comparability is not consistent across scenarios.

Atherosclerosis is a chronic inflammatory disease of the artery wall. The early stages of atherosclerosis are driven by interactions between lipids and monocyte-derived-macrophages (MDMs). The mechanisms that govern the spatial distribution of lipids and MDMs in the lesion remain poorly understood. In this paper, we develop a spatially-resolved and lipid-structured model for early atherosclerosis. The model development and analysis are guided by images of human coronary lesions by Nakashima et al. 2007. Consistent with their findings, the model predicts that lipid initially accumulates deep in the intima due to a spatially non-uniform LDL retention capacity. The model also qualitatively reproduces the global internal maxima in the Nakashima images only when the MDM mobility is sufficiently sensitive to lipid content, and MDM lifespan sufficiently insensitive. Introducing lipid content-dependence to MDM mobility and mean lifespan produced minimal impact on model behaviour at early times, but strongly impacted lesion composition at steady state. Increases to the sensitivity of MDM lifespan to lipid content yield lesions with fewer MDMs, less total lesion lipid content and reduced mean MDM infiltration depth. Increases to the sensitivity of MDM mobility to lipid content also reduces the MDM infiltration depth, but increases the proportion of lipid-laden MDMs. We find that MDM lipid content increases with spatial depth, regardless of blood LDL and HDL content. These results shed light on the mechanisms that drive spatial variation in the composition of early atherosclerotic lesions, and the role of macrophage lipid content in disease progression.

In this paper we study a two-parameter family of planar maps characterized by two distinct invariant subspaces. The model reveals the existence of two chaotic attractors within these subspaces. We identify parameter values at which these attractors either exhibits a locally riddled basin of attraction or transitions into a chaotic saddle. In particular, we demonstrate that, for an open region in the parameter plane, their basins are intermingled. It is shown that a fractal boundary curve separates the basins of attraction of these two chaotic attractors, providing a detailed characterization of the riddled basin structure. Additionally, we show that the model undergoes a blowout bifurcation. An estimation of the stability index is examined using thermodynamic formalism. We also perform a multifractal analysis of the level sets of the stability index., Comment: 32 pages, 5 figures

This article describes how technical infrastructure developed by the nonprofit OpenMined enables external scrutiny of AI systems without compromising sensitive information. Independent external scrutiny of AI systems provides crucial transparency into AI development, so it should be an integral component of any approach to AI governance. In practice, external researchers have struggled to gain access to AI systems because of AI companies' legitimate concerns about security, privacy, and intellectual property. But now, privacy-enhancing technologies (PETs) have reached a new level of maturity: end-to-end technical infrastructure developed by OpenMined combines several PETs into various setups that enable privacy-preserving audits of AI systems. We showcase two case studies where this infrastructure has been deployed in real-world governance scenarios: "Understanding Social Media Recommendation Algorithms with the Christchurch Call" and "Evaluating Frontier Models with the UK AI Safety Institute." We describe types of scrutiny of AI systems that could be facilitated by current setups and OpenMined's proposed future setups. We conclude that these innovative approaches deserve further exploration and support from the AI governance community. Interested policymakers can focus on empowering researchers on a legal level.

The first International AI Safety Report comprehensively synthesizes the current evidence on the capabilities, risks, and safety of advanced AI systems. The report was mandated by the nations attending the AI Safety Summit in Bletchley, UK. Thirty nations, the UN, the OECD, and the EU each nominated a representative to the report's Expert Advisory Panel. A total of 100 AI experts contributed, representing diverse perspectives and disciplines. Led by the report's Chair, these independent experts collectively had full discretion over the report's content.

Electron spins coupled with optical transitions in solids stand out as a promising platform for developing spin-based quantum technologies. Recently, hexagonal boron nitride (hBN) - a layered Van der Waals (vdW) crystal, has emerged as a promising host for optically addressable spin systems. However, to date, on-demand generation of isolated single photon emitters with pre-determined spin transitions has remained elusive. Here, we report on a single step, thermal processing of hBN flakes that produces high density, narrowband, quantum emitters with optically active spin transitions. Remarkably, over 25% of the emitters exhibit a clear signature of an optical spin readout at room temperature, surpassing all previously reported results by an order of magnitude. The generated spin defect complexes exhibit both S = 1 and S = 1/2 transitions, which are explained by charge transfer from strongly to weakly coupled spin pairs. Our work advances the understanding of spin complexes in hBN and paves the way for single spin - photon interfaces in layered vdW materials with applications in quantum sensing and information processing.

This challenge aims to evaluate the capabilities of audio encoders, especially in the context of multi-task learning and real-world applications. Participants are invited to submit pre-trained audio encoders that map raw waveforms to continuous embeddings. These encoders will be tested across diverse tasks including speech, environmental sounds, and music, with a focus on real-world usability. The challenge features two tracks: Track A for parameterized evaluation, and Track B for parameter-free evaluation. This challenge provides a platform for evaluating and advancing the state-of-the-art in audio encoder design.

The Ontology Lookup Service (OLS) is an open source search engine for ontologies which is used extensively in the bioinformatics and chemistry communities to annotate biological and biomedical data with ontology terms. Recently there has been a significant increase in the size and complexity of ontologies due to new scales of biological knowledge, such as spatial transcriptomics, new ontology development methodologies, and curation on an increased scale. Existing Web-based tools for ontology browsing such as BioPortal and OntoBee do not support the full range of definitions used by today's ontologies. In order to support the community going forward, we have developed OLS4, implementing the complete OWL2 specification, internationalization support for multiple languages, and a new user interface with UX enhancements such as links out to external databases. OLS4 has replaced OLS3 in production at EMBL-EBI and has a backwards compatible API supporting users of OLS3 to transition., Comment: 4 pages plus references

We present 450$\mu$m and 850$\mu$m James Clerk Maxwell Telescope (JCMT) observations of the Corona Australis (CrA) molecular cloud taken as part of the JCMT Gould Belt Legacy Survey (GBLS). We present a catalogue of 39 starless and protostellar sources, for which we determine source temperatures and masses using SCUBA-2 450$\mu$m/850$\mu$m flux density ratios for sources with reliable 450$\mu$m detections, and compare these to values determined using temperatures measured by the Herschel Gould Belt Survey (HGBS). In keeping with previous studies, we find that SCUBA-2 preferentially detects high-volume-density starless cores, which are most likely to be prestellar (gravitationally bound). We do not observe any anti-correlation between temperature and volume density in the starless cores in our sample. Finally, we combine our SCUBA-2 and Herschel data to perform SED fitting from 160-850$\mu$m across the central Coronet region, thereby measuring dust temperature $T$, dust emissivity index $\beta$ and column density $N({\rm H}_2)$ across the Coronet. We find that $\beta$ varies across the Coronet, particularly measuring $\beta = 1.55 \pm 0.35$ in the colder starless SMM-6 clump to the north of the B star R CrA. This relatively low value of $\beta$ is suggestive of the presence of large dust grains in SMM-6, even when considering the effects of $T-\beta$ fitting degeneracy and $^{12}$CO contamination of SCUBA-2 850$\mu$m data on the measured $\beta$ values., Comment: 27 pages, 18 figures, 4 tables. Accepted for publication in MNRAS

We consider a generalization of the Kauffman bracket skein algebra of a surface that is generated by loops and arcs between marked points on the interior or boundary, up to skein relations defined by Muller and Roger-Yang. We compute the center of this Muller-Roger-Yang skein algebra and show that it is almost Azumaya when the quantum parameter $q$ is a primitive $n$-th root of unity with odd $n$. We also discuss the implications on the representation theory of the Muller-Roger-Yang generalized skein algebra., Comment: 28 pages, 10 figures

Ligand-receptor interactions are fundamental to many biological processes. For example in antibody-based immunotherapies, the dynamics of an antibody binding with its target antigen directly influence the potency and efficacy of monoclonal antibody (mAb) therapies. In this paper, we present an asymptotic analysis of an ordinary differential equation (ODE) model of bivalent antibody-antigen binding in the context of mAb cancer therapies, highlighting the added complexity associated with bivalency of the antibody. To understand what drives the complex temporal dynamics of bivalent antibody-antigen binding, we construct asymptotic approximations to the model's solutions at different timescales and antibody concentrations that are in good agreement with numerical simulations of the full model. We show how the dynamics differ between two scenarios; a region where unbound antigens are abundant, and one where the number of unbound antigens is small such that the dominant balance within the model equations changes. Of particular importance to the potency and efficacy of mAb treatments are the values of quantities such as antigen occupancy and bound antibody number. We use the results of our asymptotic analysis to approximate the long-time values of these quantities that could be combined with experimental data to facilitate parameter estimation.

Often information relevant to a decision is summarized in an index number. This paper explores conditions under which conclusions using index numbers are relevant to the decision that needs to be made. Specifically it explores the idea that a statement using scales of measurement is meaningful in the sense that its truth or falsity does not depend on an arbitrary choice of parameters; the concept that a conclusion using index numbers is useful for the specific decision that needs to be made; and the notion that such a conclusion is legitimate in the sense that it is collected and used in a way that satisfies cultural, historical, organizational and legal constraints. While meaningfulness is a precisely defined concept, usefulness and legitimacy are not, and the paper explores properties of these concepts that lay the groundwork for making them more precise. Many examples involving two well-known and widely-used index numbers, body mass indices and air pollution indices, are used to explore the properties of and interrelationships among meaningfulness, usefulness, and legitimacy.

We present integral field unit observations of the Phoenix Cluster with the JWST Mid-infrared Instrument's Medium Resolution Spectrometer (MIRI/MRS). We focus this study on the molecular gas, dust, and star formation in the brightest cluster galaxy (BCG). We use precise spectral modeling to produce maps of the silicate dust, molecular gas, and polycyclic aromatic hydrocarbons (PAHs) in the inner $\sim$50 kpc of the cluster. We have developed a novel method for measuring the optical depth from silicates by comparing the observed H$_2$ line ratios to those predicted by excitation models. We provide updated measurements of the total molecular gas mass of $2.2^{+0.4}_{-0.1} \times 10^{10}$ ${\rm M}_\odot$, which agrees with CO-based estimates, providing an estimate of the CO-to-H$_2$ conversion factor of $\alpha_{\rm CO} = 0.9 \pm 0.2\,{\rm M}_{\odot}\,{\rm pc}^{-2}\,({\rm K}\,{\rm km}\,{\rm s}^{-1})^{-1}$; an updated stellar mass of $M_* = 2.6 \pm 0.5 \times 10^{10}$ ${\rm M}_\odot$; and star formation rates averaged over 10 and 100 Myr of $\langle{\rm SFR}\rangle_{\rm 10} = 1340 \pm 100$ ${\rm M}_\odot\,{\rm yr}^{-1}$ and $\langle{\rm SFR}\rangle_{\rm 100} = 740 \pm 80$ ${\rm M}_\odot\,{\rm yr}^{-1}$, respectively. The H$_2$ emission seems to be powered predominantly by star formation within the central $\sim 20$ kpc, with no need for an extra particle heating component as is seen in other BCGs. Additionally, we find nearly an order of magnitude drop in the star formation rates estimated by PAH fluxes in cool core BCGs compared to field galaxies, suggesting that hot particles from the intracluster medium are destroying PAH grains even in the centralmost 10s of kpc., Comment: 21 pages, 13 figures, 2 tables. Submitted to ApJ. Comments welcome!

Combinatorial medication recommendation(CMR) is a fundamental task of healthcare, which offers opportunities for clinical physicians to provide more precise prescriptions for patients with intricate health conditions, particularly in the scenarios of long-term medical care. Previous research efforts have sought to extract meaningful information from electronic health records (EHRs) to facilitate combinatorial medication recommendations. Existing learning-based approaches further consider the chemical structures of medications, but ignore the textual medication descriptions in which the functionalities are clearly described. Furthermore, the textual knowledge derived from the EHRs of patients remains largely underutilized. To address these issues, we introduce the Natural Language-Assisted Multi-modal Medication Recommendation(NLA-MMR), a multi-modal alignment framework designed to learn knowledge from the patient view and medication view jointly. Specifically, NLA-MMR formulates CMR as an alignment problem from patient and medication modalities. In this vein, we employ pretrained language models(PLMs) to extract in-domain knowledge regarding patients and medications, serving as the foundational representation for both modalities. In the medication modality, we exploit both chemical structures and textual descriptions to create medication representations. In the patient modality, we generate the patient representations based on textual descriptions of diagnosis, procedure, and symptom. Extensive experiments conducted on three publicly accessible datasets demonstrate that NLA-MMR achieves new state-of-the-art performance, with a notable average improvement of 4.72% in Jaccard score. Our source code is publicly available on https://github.com/jtan1102/NLA-MMR_CIKM_2024., Comment: 10 pages

Previous research has shown that the principal singular vectors of a pre-trained model's weight matrices capture critical knowledge. In contrast, those associated with small singular values may contain noise or less reliable information. As a result, the LoRA-based parameter-efficient fine-tuning (PEFT) approach, which does not constrain the use of the spectral space, may not be effective for tasks that demand high representation capacity. In this study, we enhance existing PEFT techniques by incorporating the spectral information of pre-trained weight matrices into the fine-tuning process. We investigate spectral adaptation strategies with a particular focus on the additive adjustment of top singular vectors. This is accomplished by applying singular value decomposition (SVD) to the pre-trained weight matrices and restricting the fine-tuning within the top spectral space. Extensive speaker verification experiments on VoxCeleb1 and CN-Celeb1 demonstrate enhanced tuning performance with the proposed approach. Code is released at https://github.com/lizhepolyu/SpectralFT., Comment: Accepted by ICASSP 2025

Early detection of neurocognitive disorders (NCDs) is crucial for timely intervention and disease management. Speech analysis offers a non-intrusive and scalable screening method, particularly through narrative tasks in neuropsychological assessment tools. Traditional narrative analysis often focuses on local indicators in microstructure, such as word usage and syntax. While these features provide insights into language production abilities, they often fail to capture global narrative patterns, or microstructures. Macrostructures include coherence, thematic organization, and logical progressions, reflecting essential cognitive skills potentially critical for recognizing NCDs. Addressing this gap, we propose to investigate specific cognitive and linguistic challenges by analyzing topical shifts, temporal dynamics, and the coherence of narratives over time, aiming to reveal cognitive deficits by identifying narrative impairments, and exploring their impact on communication and cognition. The investigation is based on the CU-MARVEL Rabbit Story corpus, which comprises recordings of a story-telling task from 758 older adults. We developed two approaches: the Dynamic Topic Models (DTM)-based temporal analysis to examine the evolution of topics over time, and the Text-Image Temporal Alignment Network (TITAN) to evaluate the coherence between spoken narratives and visual stimuli. DTM-based approach validated the effectiveness of dynamic topic consistency as a macrostructural metric (F1=0.61, AUC=0.78). The TITAN approach achieved the highest performance (F1=0.72, AUC=0.81), surpassing established microstructural and macrostructural feature sets. Cross-comparison and regression tasks further demonstrated the effectiveness of proposed dynamic macrostructural modeling approaches for NCD detection., Comment: 12 pages, 8 figures

We present Space Telescope Imaging Spectrograph observations from the Hubble Space Telescope of the supermassive black hole (SMBH) at the center of PKS0745-191, a brightest cluster galaxy (BCG) undergoing powerful radio-mode AGN feedback ($P_{\rm cav}\sim5\times10^{45}$ erg s$^{-1}$). These high-resolution data offer the first spatially resolved map of gas dynamics within a SMBHs sphere of influence under such powerful feedback. Our results reveal the presence of highly chaotic, non-rotational ionized gas flows on sub-kpc scales, in contrast to the more coherent flows observed on larger scales. While radio-mode feedback effectively thermalizes hot gas in galaxy clusters on kiloparsec scales, within the core, the hot gas flow may decouple, leading to a reduction in angular momentum and supplying ionized gas through cooling, which could enhance accretion onto the SMBH. This process could, in turn, lead to a self-regulating feedback loop. Compared to other BCGs with weaker radio-mode feedback, where rotation is more stable, intense feedback may lead to more chaotic flows, indicating a stronger coupling between jet activity and gas dynamics. Additionally, we observe a sharp increase in velocity dispersion near the nucleus, consistent with a very massive $M_{\rm BH}\sim1.5\times10^{10} M_\odot$ SMBH. The density profile of the ionized gas is also notably flat, paralleling the profiles observed in X-ray gas around galaxies where the Bondi radius is resolved. These results provide valuable insights into the complex mechanisms driving galaxy evolution, highlighting the intricate relationship between SMBH fueling and AGN feedback within the host galaxy., Comment: Accepted for publication in ApJ. 24 pages and 17 figures

Grapheme-to-phoneme (G2P) conversion serves as an essential component in Chinese Mandarin text-to-speech (TTS) system, where polyphone disambiguation is the core issue. In this paper, we propose an end-to-end framework to predict the pronunciation of a polyphonic character, which accepts sentence containing polyphonic character as input in the form of Chinese character sequence without the necessity of any preprocessing. The proposed method consists of a pre-trained bidirectional encoder representations from Transformers (BERT) model and a neural network (NN) based classifier. The pre-trained BERT model extracts semantic features from a raw Chinese character sequence and the NN based classifier predicts the polyphonic character's pronunciation according to BERT output. In out experiments, we implemented three classifiers, a fully-connected network based classifier, a long short-term memory (LSTM) network based classifier and a Transformer block based classifier. The experimental results compared with the baseline approach based on LSTM demonstrate that, the pre-trained model extracts effective semantic features, which greatly enhances the performance of polyphone disambiguation. In addition, we also explored the impact of contextual information on polyphone disambiguation., Comment: Accepted at INTERSPEECH 2019

Identifying the emotional state from speech is essential for the natural interaction of the machine with the speaker. However, extracting effective features for emotion recognition is difficult, as emotions are ambiguous. We propose a novel approach to learn discriminative features from variable length spectrograms for emotion recognition by cooperating softmax cross-entropy loss and center loss together. The softmax cross-entropy loss enables features from different emotion categories separable, and center loss efficiently pulls the features belonging to the same emotion category to their center. By combining the two losses together, the discriminative power will be highly enhanced, which leads to network learning more effective features for emotion recognition. As demonstrated by the experimental results, after introducing center loss, both the unweighted accuracy and weighted accuracy are improved by over 3\% on Mel-spectrogram input, and more than 4\% on Short Time Fourier Transform spectrogram input., Comment: Accepted at ICASSP 2019

We propose an ontology-grounded approach to Knowledge Graph (KG) construction using Large Language Models (LLMs) on a knowledge base. An ontology is authored by generating Competency Questions (CQ) on knowledge base to discover knowledge scope, extracting relations from CQs, and attempt to replace equivalent relations by their counterpart in Wikidata. To ensure consistency and interpretability in the resulting KG, we ground generation of KG with the authored ontology based on extracted relations. Evaluation on benchmark datasets demonstrates competitive performance in knowledge graph construction task. Our work presents a promising direction for scalable KG construction pipeline with minimal human intervention, that yields high quality and human-interpretable KGs, which are interoperable with Wikidata semantics for potential knowledge base expansion., Comment: Presented at HI-AI@KDD, Human-Interpretable AI Workshop at the KDD 2024, 26th of August 2024, Barcelona, Spain

We study the production of light nuclei in Au+Au collisions at $\sqrt{s_\mathrm{NN}}$ = 7.7 - 200 GeV and Pb+Pb collisions at $\sqrt{s_\mathrm{NN}}$ = 2.76 and 5.02 TeV within a flavour-dependent freeze-out framework, assuming different flavoured hadrons undergo separate chemical freeze-out. Using the Thermal-FIST package, thermal parameters extracted from fits to various sets of hadron yields, including and excluding light nuclei, are used to calculate the ratios of the yields of light nuclei, namely, $d/p$, $\bar{d}/\bar{p}$, $t/p$, and $t/d$. A comparison with data from the STAR and ALICE collaborations shows that a sequential freeze-out scenario provides a better description of light nuclei yield ratios than the traditional single freeze-out approach. These results suggest the flavour-dependent chemical freeze-out for final state light-nuclei production persists in heavy-ion collisions at both RHIC and LHC energies., Comment: 9 pages, 6 figures, 1 table, and 1 appendix table

Data-intensive fine-tuning of speech foundation models (SFMs) to scarce and diverse dysarthric and elderly speech leads to data bias and poor generalization to unseen speakers. This paper proposes novel structured speaker-deficiency adaptation approaches for SSL pre-trained SFMs on such data. Speaker and speech deficiency invariant SFMs were constructed in their supervised adaptive fine-tuning stage to reduce undue bias to training data speakers, and serves as a more neutral and robust starting point for test time unsupervised adaptation. Speech variability attributed to speaker identity and speech impairment severity, or aging induced neurocognitive decline, are modelled using separate adapters that can be combined together to model any seen or unseen speaker. Experiments on the UASpeech dysarthric and DementiaBank Pitt elderly speech corpora suggest structured speaker-deficiency adaptation of HuBERT and Wav2vec2-conformer models consistently outperforms baseline SFMs using either: a) no adapters; b) global adapters shared among all speakers; or c) single attribute adapters modelling speaker or deficiency labels alone by statistically significant WER reductions up to 3.01% and 1.50% absolute (10.86% and 6.94% relative) on the two tasks respectively. The lowest published WER of 19.45% (49.34% on very low intelligibility, 33.17% on unseen words) is obtained on the UASpeech test set of 16 dysarthric speakers.

Tilt-rotor aerial robots are more dynamic and versatile than their fixed-rotor counterparts, since the thrust vector and body orientation are decoupled. However, the coordination of servomotors and propellers (the allocation problem) is not trivial, especially accounting for overactuation and actuator dynamics. We present and compare different methods of actuator allocation for tilt-rotor platforms, evaluating them on a real aerial robot performing dynamic trajectories. We extend the state-of-the-art geometric allocation into a differential allocation, which uses the platform's redundancy and does not suffer from singularities typical of the geometric solution. We expand it by incorporating actuator dynamics and introducing propeller limit curves. These improve the modeling of propeller limits, automatically balancing their usage and allowing the platform to selectively activate and deactivate propellers during flight. We show that actuator dynamics and limits make the tuning of the allocation not only easier, but also allow it to track more dynamic oscillating trajectories with angular velocities up to 4 rad/s, compared to 2.8 rad/s of geometric methods.

We introduce Speech Information Retrieval (SIR), a new long-context task for Speech Large Language Models (Speech LLMs), and present SPIRAL, a 1,012-sample benchmark testing models' ability to extract critical details from approximately 90-second spoken inputs. While current Speech LLMs excel at short-form tasks, they struggle with the computational and representational demands of longer audio sequences. To address this limitation, we propose SpeechPrune, a training-free token pruning strategy that uses speech-text similarity and approximated attention scores to efficiently discard irrelevant tokens. In SPIRAL, SpeechPrune achieves accuracy improvements of 29% and up to 47% over the original model and the random pruning model at a pruning rate of 20%, respectively. SpeechPrune can maintain network performance even at a pruning level of 80%. This approach highlights the potential of token-level pruning for efficient and scalable long-form speech understanding., Comment: Project page and dataset is available at https://speechprune.github.io/