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Radially extended disk winds could be the key to unlocking how protoplanetary disks accrete and how planets form and migrate. A distinctive characteristic is their nested morphology of velocity and chemistry. Here we report JWST/NIRSpec spectro-imaging of four young stars with edge-on disks in the Taurus star-forming region that demonstrate the ubiquity of this structure. In each source, a fast collimated jet traced by [Fe II] is nested inside a hollow cavity within wider lower-velocity H2 and, in one case, also CO ro-vibrational (v=1-0) emission. Furthermore, in one of our sources, ALMA CO(2-1) emission, paired with our NIRSpec images, reveals the nested wind structure extends further outward. This nested wind morphology strongly supports theoretical predictions for wind-driven accretion and underscores the need for theoretical work to assess the role of winds in the formation and evolution of planetary systems, Comment: This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this article is published in Nature Astronomy and is available online at https://doi.org/10.1038/s41550-024-02385-7

In high-resolution mass spectrometry, an electrospray ionization source is often paired with an ion-funnel to enhance ion transmission. Although it is established that ions experience collision-induced dissociation as they pass through this device, the impact of gas-flow dynamics on ion fragmentation remains unexplored. The present work demonstrates that the gas-flow dynamics from the capillary interface of an electrospray ionization source into an ion-funnel significantly reduces ion fragmentation. This reduction stems from the substantial decrease in the rate of increase in the internal energy of the ions resulting from the collisions with a supersonically expanding gas. The results of this study have significant consequences for systems that employ electrospray mass spectrometry and ion-mobility spectrometry.

The occurrence of neutrino oscillations demands the existence of flavour violation in charged lepton sector. The relation between the branching ratios of different charged lepton flavour violating (CLFV) decay modes depends on the details of the neutrino mass model. In this work, we consider the three types of simple seesaw mechanisms of neutrino masses and study the correlation between the radiative CLFV decays and the meson CLFV decays. We find that the meson CLFV decay branching ratios are negligibly small in type-II seesaw mechanism whereas they are constrained to be at least three (two) orders of magnitude smaller than the radiative CLFV decay branching ratios in the case of type-I (type-III) seesaw mechanism. Thus the relationship between these two modes of CLFV decays helps in distinguishing between different types of seesaw mechanism. If, the branching ratios of CLFV decays of mesons are larger than those of radiative CLFV decays, it provides a strong hint that the neutrino mass generating mechanism is more complicated than simple seesaw., Comment: new references added

Online hate speech has become increasingly prevalent on social media platforms, causing harm to individuals and society. While efforts have been made to combat this issue through content moderation, the potential of user-driven counterspeech as an alternative solution remains underexplored. Existing counterspeech methods often face challenges such as fear of retaliation and skill-related barriers. To address these challenges, we introduce CounterQuill, an AI-mediated system that assists users in composing effective and empathetic counterspeech. CounterQuill provides a three-step process: (1) a learning session to help users understand hate speech and counterspeech; (2) a brainstorming session that guides users in identifying key elements of hate speech and exploring counterspeech strategies; and (3) a co-writing session that enables users to draft and refine their counterspeech with CounterQuill. We conducted a within-subjects user study with 20 participants to evaluate CounterQuill in comparison to ChatGPT. Results show that CounterQuill's guidance and collaborative writing process provided users a stronger sense of ownership over their co-authored counterspeech. Users perceived CounterQuill as a writing partner and thus were more willing to post the co-written counterspeech online compared to the one written with ChatGPT.

We present JWST/MIRI observations of T~Cha, a highly variable ($\Delta V \sim$3-5\,mag) accreting Sun-like star surrounded by a disk with a large ($\sim 15$\,au) dust gap. We find that the JWST mid-infrared spectrum is signiticantly different from the {\it Spitzer} spectrum obtained 17 years before, where the emission at short wavelengths ($5-10 \mu m$) has decreased by $\sim 2/3$ while at longer wavelengths ($15-25 \mu m$) it increased by up to a factor of $\sim 3$. This 'seesaw' behavior is contemporary with a fairly constant higher optical emission captured by the All Sky Automated Survey. By analyzing and modelling both SEDs, we propose that JWST caught the star during an outburst that destructed the asymmetric inner disk wall responsible for the high optical variability and lower $15-25$\,micron\ emission during the {\it Spitzer} time. The dust mass lost during this outburst is estimated to be comparable ($\sim 1/5$) to the upper limit of the total micron-sized dust mass in the inner disk of T~Cha now. Monitoring this system during possible future outbursts and more observations of its quiescent state will reveal if the inner disk can be replenished or will continue to be depleted and vanish., Comment: 16 pages, 7 figures, submitted to ApJ

In this article we describe the equivariant and ordinary topological $K$-ring of a toric bundle with fiber a $T$-{\it cellular} toric variety. This generalizes the results in \cite{su} on $K$-theory of smooth projective toric bundles. We apply our results to describe the equivariant topological $K$-ring of a toroidal horospherical embedding., Comment: 25 pages. arXiv admin note: text overlap with arXiv:2404.14201

Emotion recognition from electroencephalogram (EEG) signals is a thriving field, particularly in neuroscience and Human-Computer Interaction (HCI). This study aims to understand and improve the predictive accuracy of emotional state classification through metrics such as valence, arousal, dominance, and likeness by applying a Long Short-Term Memory (LSTM) network to analyze EEG signals. Using a popular dataset of multi-channel EEG recordings known as DEAP, we look towards leveraging LSTM networks' properties to handle temporal dependencies within EEG signal data. This allows for a more comprehensive understanding and classification of emotional parameter states. We obtain accuracies of 89.89%, 90.33%, 90.70%, and 90.54% for arousal, valence, dominance, and likeness, respectively, demonstrating significant improvements in emotion recognition model capabilities. This paper elucidates the methodology and architectural specifics of our LSTM model and provides a benchmark analysis with existing papers., Comment: 13 pages, 3 figures; accepted at ICDSA '24 Conference, Jaipur, India

We describe Na0.67Mn0.625Fe0.25Co0.125O2 (NMFCO), a P2-type sodium-ion battery cathode. Our composition, with significantly less Co than in an earlier study, shows discharge capacity close to 190 mAhg-1 and specific energy density exceeding 500 mWhg-1 in the 1.5 to 4.3 V range. The material also shows an improved structural stability over similar materials. Such changes, between the pristine phase (P63/mmc, P63 (OP4), or orthorhombic Cmcm) and the so-called Z phase, are endemic to other P2-type cathodes such as Na0.67Mn0.65Fe0.35O2 (NMFO). We propose two equivalent circuit models of impedance spectroscopy to understand electrochemical processes in our cells with a sodium metal anode. Our equivalent circuit modeling, combined with an analysis of the initial galvanostatic slope, reveals a significant reduction in the polarization and interfacial charge-transfer resistance at the solid electrolyte interface. We reveal that the combined effects of crystal structure stability, lower internal resistance, relatively high specific energy density, and improved battery health make this low-cobalt P2-type cathode composition a very promising candidate for new sodium-ion batteries., Comment: 49 pages, 9 figures

Protecting the privacy of keywords in the field of search over outsourced cloud data is a challenging task. In IEEE Transactions on Services Computing (Vol. 17 No. 2, March/April 2024), Li et al. proposed PRMKR: efficient privacy-preserving ranked multi-keyword retrieval scheme, which was claimed to resist keyword guessing attack. However, we show that the scheme fails to resist keyword guessing attack, index privacy, and trapdoor privacy. Further, we propose a solution to address the above said issues by correcting the errors in the important equations of the scheme., Comment: Comment on https://doi.org/10.1109/TSC.2023.3341799

We re-examine the constraints provided by Herschel Space Observatory data regarding cold water emission from protoplanetary disks. Previous disk models that were used to interpret observed water emission concluded that oxygen (O/H) is depleted by at least 2 orders of magnitude if a standard, interstellar gas/dust mass ratio is assumed in the disk. In this work, we use model results from a recent disk parameter survey and show that most of the \textit{Herschel} constraints obtained for cold water (i.e. for transitions with an upper energy level $E_\mathrm{up}<200$ K, where the bulk of the disk water lies) can be explained with disk models adopting ISM-like oxygen elemental abundance (i.e. O/H=$3.2\times10^{-4}$) and the canonical gas/dust mass ratio of 100. We show that cold water vapor is mainly formed by photodesorption of water ice at the interface between the molecular layer and the midplane, and that its emission is relatively independent of the main disk properties like the disk gas mass and gas/dust mass ratio. We find that the abundance of water vapor in the outer disk is set by photoprocesses and depends on the (constant) vertical column density of water ice needed to attenuate the FUV photon flux, resulting in roughly constant emission for the parameters (gas mass, dust mass, disk radius) varied in our survey. Importantly, water line emission is found to be optically thick and hence sensitive to temperature more than abundance, possibly driving previous inferences of large scale oxygen depletion., Comment: 17 pages, 9 figures, 1 table

The effects of disorder and chaos on quantum many-body systems can be superficially similar, yet their interplay has not been sufficiently explored. This work finds a continuous phase transition when disorder breaks permutation symmetry, with details of the transition being controlled by the degree of chaos in the clean limit. The system changes from an area law entangled phase in the permutation symmetric subspace where collective variables exist to volume law entanglement in the full Hilbert space, beyond a critical strength of the disorder. The critical strength tends to zero when the original disorder free system is fully chaotic. We study this mainly via the scaling of the collective spin of non-equilibrium states which transit to have properties of what has been dubbed "deep Hilbert space". This has potential implications for general many body physics, as well as technologies such as transmon qubits.

This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and visually appealing results when dealing with a variety of conditions, including ultra-high resolution (4K and beyond), non-uniform illumination, backlighting, extreme darkness, and night scenes. A notable total of 428 participants registered for the challenge, with 22 teams ultimately making valid submissions. This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field., Comment: NTIRE 2024 Challenge Report

Tuberculosis remains a significant global health challenge, with millions of new cases reported annually. Recent studies suggest that expanding the accessibility of TB intervention programs can lead to a substantial decrease in both TB incidence and prevalence. This paper initiates by examining a deterministic mathematical model for TB transmission, aiming to analyze the underlying dynamics. Subsequently, an optimal control problem is formulated to enhance TB control measures, encompassing Tuberculosis Preventive Treatment (TPT) and other initiatives targeting malnutrition and diabetes. Through simulation studies, the effectiveness of the control program is assessed. The model dynamics allow us to identify the pseudo-prevalence and incidence. To determine the potential long-term trajectory of TB and to acquire future projections a cost-effectiveness analysis is performed using ACER, AIR, ICER, and four quadrants to compare competing interventions. In conclusion, this work provides valuable insights into TB and strategies for its control and cost effectiveness.

Partially-ionized plasmas consist of charged and neutral particles whose mutual collisions modify magnetic reconnection compared with the fully-ionized case. The collisions alter the rate and locations of the magnetic dissipation heating and the distribution of energies among the particles accelerated into the non-thermal tail. We examine the collisional regimes for the onset of fast reconnection in two environments: the partially-ionized layers of the solar atmosphere and the protoplanetary disks that are the birthplaces for planets around young stars. In both these environments, magnetic nulls readily develop into resistive current sheets in the regime where the charged and neutral particles are fully coupled by collisions, but the current sheets quickly break down under the ideal tearing instability. The current sheets collapse repeatedly, forming magnetic islands at successively smaller scales, till they enter a collisionally-decoupled regime where the magnetic energy is rapidly turned into heat and charged-particle kinetic energy. Small-scale, decoupled fast reconnection in the solar atmosphere may lead to preferential heating and energization of ions and electrons that escape into the corona. In protoplanetary disks such reconnection causes localized heating in the atmospheric layers that produce much of the infrared atomic and molecular line emission observed with the Spitzer and James Webb Space Telescopes., Comment: 16 pages, 8 figures

Recent developments in adversarial machine learning have highlighted the importance of building robust AI systems to protect against increasingly sophisticated attacks. While frameworks like AI Guardian are designed to defend against these threats, they often rely on assumptions that can limit their effectiveness. For example, they may assume attacks only come from one direction or include adversarial images in their training data. Our proposal suggests a different approach to the AI Guardian framework. Instead of including adversarial examples in the training process, we propose training the AI system without them. This aims to create a system that is inherently resilient to a wider range of attacks. Our method focuses on a dynamic defense strategy using stable diffusion that learns continuously and models threats comprehensively. We believe this approach can lead to a more generalized and robust defense against adversarial attacks. In this paper, we outline our proposed approach, including the theoretical basis, experimental design, and expected impact on improving AI security against adversarial threats.

In the burgeoning market of short-term rentals, understanding pricing dynamics is crucial for a range of stake-holders. This study delves into the factors influencing Airbnb pricing in major European cities, employing a comprehensive dataset sourced from Kaggle. We utilize advanced regression techniques, including linear, polynomial, and random forest models, to analyze a diverse array of determinants, such as location characteristics, property types, and host-related factors. Our findings reveal nuanced insights into the variables most significantly impacting pricing, highlighting the varying roles of geographical, structural, and host-specific attributes. This research not only sheds light on the complex pricing landscape of Airbnb accommodations in Europe but also offers valuable implications for hosts seeking to optimize pricing strategies and for travelers aiming to understand pricing trends. Furthermore, the study contributes to the broader discourse on pricing mechanisms in the shared economy, suggesting avenues for future research in this rapidly evolving sector.

In this project, we have explored machine learning approaches for predicting hearing loss thresholds on the brain's gray matter 3D images. We have solved the problem statement in two phases. In the first phase, we used a 3D CNN model to reduce high-dimensional input into latent space and decode it into an original image to represent the input in rich feature space. In the second phase, we utilized this model to reduce input into rich features and used these features to train standard machine learning models for predicting hearing thresholds. We have experimented with autoencoders and variational autoencoders in the first phase for dimensionality reduction and explored random forest, XGBoost and multi-layer perceptron for regressing the thresholds. We split the given data set into training and testing sets and achieved an 8.80 range and 22.57 range for PT500 and PT4000 on the test set, respectively. We got the lowest RMSE using multi-layer perceptron among the other models. Our approach leverages the unique capabilities of VAEs to capture complex, non-linear relationships within high-dimensional neuroimaging data. We rigorously evaluated the models using various metrics, focusing on the root mean squared error (RMSE). The results highlight the efficacy of the multi-layer neural network model, which outperformed other techniques in terms of accuracy. This project advances the application of data mining in medical diagnostics and enhances our understanding of age-related hearing loss through innovative machine-learning frameworks.

In this article we describe the $T_{comp}$-equivariant topological $K$-ring of a $T$-{\it cellular} complete toric variety. We further show that $K_{T_{comp}}^0(X)$ is isomorphic as an $R(T_{comp})$-algebra to the ring of piecewise Laurent polynomial functions on the associated fan denoted $PLP(\Delta)$. Furthermore, we compute a basis for $K_{T_{comp}}^0(X)$ as a $R(T_{comp})$-module and multiplicative structure constants with respect to this basis., Comment: 31 pages, 2 figures

Can many-body systems be beneficial to designing quantum technologies? We address this question by examining quantum engines, where recent studies indicate potential benefits through the harnessing of many-body effects, such as divergences close to phase transitions. However, open questions remain regarding their real-world applications., Comment: 3 pages, 2 figures, Comment article

This paper provides a comprehensive review of the NTIRE 2024 challenge, focusing on efficient single-image super-resolution (ESR) solutions and their outcomes. The task of this challenge is to super-resolve an input image with a magnification factor of x4 based on pairs of low and corresponding high-resolution images. The primary objective is to develop networks that optimize various aspects such as runtime, parameters, and FLOPs, while still maintaining a peak signal-to-noise ratio (PSNR) of approximately 26.90 dB on the DIV2K_LSDIR_valid dataset and 26.99 dB on the DIV2K_LSDIR_test dataset. In addition, this challenge has 4 tracks including the main track (overall performance), sub-track 1 (runtime), sub-track 2 (FLOPs), and sub-track 3 (parameters). In the main track, all three metrics (ie runtime, FLOPs, and parameter count) were considered. The ranking of the main track is calculated based on a weighted sum-up of the scores of all other sub-tracks. In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking. In sub-track 2, the number of FLOPs was considered. The score calculated based on the corresponding FLOPs was used to determine the ranking. In sub-track 3, the number of parameters was considered. The score calculated based on the corresponding parameters was used to determine the ranking. RLFN is set as the baseline for efficiency measurement. The challenge had 262 registered participants, and 34 teams made valid submissions. They gauge the state-of-the-art in efficient single-image super-resolution. To facilitate the reproducibility of the challenge and enable other researchers to build upon these findings, the code and the pre-trained model of validated solutions are made publicly available at https://github.com/Amazingren/NTIRE2024_ESR/., Comment: The report paper of NTIRE2024 Efficient Super-resolution, accepted by CVPRW2024

This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained. The challenge involves generating corresponding high-resolution (HR) images, magnified by a factor of four, from low-resolution (LR) inputs using prior information. The LR images originate from bicubic downsampling degradation. The aim of the challenge is to obtain designs/solutions with the most advanced SR performance, with no constraints on computational resources (e.g., model size and FLOPs) or training data. The track of this challenge assesses performance with the PSNR metric on the DIV2K testing dataset. The competition attracted 199 registrants, with 20 teams submitting valid entries. This collective endeavour not only pushes the boundaries of performance in single-image SR but also offers a comprehensive overview of current trends in this field., Comment: NTIRE 2024 webpage: https://cvlai.net/ntire/2024. Code: https://github.com/zhengchen1999/NTIRE2024_ImageSR_x4

We construct a quantum critical Otto engine that is powered by finite temperature baths. We show that the work output of the engine shows universal power law behavior that depends on the critical exponents of the working medium, as well as on the temperature of the cold bath. Furthermore, higher temperatures of the cold bath allows the engine to approach the limit of adiabatic operation for smaller values of the time period, while the corresponding power shows a maximum at an intermediate value of the cold bath temperature. These counterintuitive results stems from thermal excitations dominating the dynamics at higher temperatures., Comment: 9 pages, 5 figures

In this work, we examine the linguistic signature of online racial microaggressions (acts) and how it differs from that of personal narratives recalling experiences of such aggressions (recalls) by Black social media users. We manually curate and annotate a corpus of acts and recalls from in-the-wild social media discussions, and verify labels with Black workshop participants. We leverage Natural Language Processing (NLP) and qualitative analysis on this data to classify (RQ1), interpret (RQ2), and characterize (RQ3) the language underlying acts and recalls of racial microaggressions in the context of racism in the U.S. Our findings show that neural language models (LMs) can classify acts and recalls with high accuracy (RQ1) with contextual words revealing themes that associate Blacks with objects that reify negative stereotypes (RQ2). Furthermore, overlapping linguistic signatures between acts and recalls serve functionally different purposes (RQ3), providing broader implications to the current challenges in content moderation systems on social media., Comment: 36 pages

[Ne II] 12.81 $\mu\mathrm{m}$ emission is a well-used tracer of protoplanetary disk winds due to its blueshifted line profile. MIRI-MRS recently observed T Cha, detecting this line along with lines of [Ne III], [Ar II] and [Ar III], with the [Ne II] and [Ne III] lines found to be extended while the [Ar II] was not. In this complementary work, we use these lines to address long-debated questions about protoplanetary disk winds regarding their mass-loss rate, the origin of their ionization, and the role of magnetically-driven winds as opposed to photoevaporation. To this end, we perform photoionization radiative transfer on simple hydrodynamic wind models to map the line emission. We compare the integrated model luminosities to those observed with MIRI-MRS to identify which models most closely reproduce the data and produce synthetic images from these to understand what information is captured by measurements of the line extents. Along with the low degree of ionization implied by the line ratios, the relative compactness of [Ar II] compared to [Ne II] is particularly constraining. This requires Ne II production by hard X-rays and Ar II production by soft X-rays (and/or EUV) in an extended ($\gtrsim 10$ au) wind that is shielded from soft X-rays - necessitating a dense wind with material launched on scales down to ~1 au. Such conditions could be produced by photoevaporation, whereas an extended MHD wind producing equal shielding would likely underpredict the line fluxes. However, a tenuous inner MHD wind may still contribute to shielding the extended wind. This picture is consistent with constraints from spectrally-resolved line profiles., Comment: 32 pages, 16 figures, Accepted 14/03/24 to the Astronomical Journal. Complementary modeling to Bajaj et al. 2024 (arXiv:2403.01060)

We consider a modified damped version of H\'enon-Heiles system and investigate its integrability. By extending the Painlev\'e analysis of ordinary differential equations we find that the modified H\'enon-Heiles system possesses the Painlev\'e property for three distinct parametric restrictions. For each of the identified cases, we construct two independent integrals of motion using the well known Prelle-Singer method. We then derive a set of nontrivial non-point symmetries for each of the identified integrable cases of the modified H\'enon-Heiles system. We infer that the modified H\'enon-Heiles system is integrable for three distinct parametric restrictions. Exact solutions are given explicitly for two integrable cases., Comment: Accepted for publication in Journal of Mathematical Physics

Tuberculosis (TB) remains a global health threat, ranking among the leading causes of mortality worldwide. In this context, machine learning (ML) has emerged as a transformative force, providing innovative solutions to the complexities associated with TB treatment.This study explores how machine learning, especially with tabular data, can be used to predict Tuberculosis (TB) treatment outcomes more accurately. It transforms this prediction task into a binary classification problem, generating risk scores from patient data sourced from NIKSHAY, India's national TB control program, which includes over 500,000 patient records. Data preprocessing is a critical component of the study, and the model achieved an recall of 98% and an AUC-ROC score of 0.95 on the validation set, which includes 20,000 patient records.We also explore the use of Natural Language Processing (NLP) for improved model learning. Our results, corroborated by various metrics and ablation studies, validate the effectiveness of our approach. The study concludes by discussing the potential ramifications of our research on TB eradication efforts and proposing potential avenues for future work. This study marks a significant stride in the battle against TB, showcasing the potential of machine learning in healthcare.

This paper addresses the challenges of aligning large language models (LLMs) with human values via preference learning (PL), focusing on incomplete and corrupted data in preference datasets. We propose a novel method for robustly and completely recalibrating values within these datasets to enhance LLMs' resilience against the issues. In particular, we devise a guaranteed polynomial time ranking algorithm that robustifies several existing models, such as the classic Bradley-Terry-Luce (BTL) (Bradley and Terry, 1952) model and certain generalizations of it. To the best of our knowledge, our present work is the first to propose an algorithm that provably recovers an $\epsilon$-optimal ranking with high probability while allowing as large as $O(n)$ perturbed pairwise comparison results per model response. Furthermore, we show robust recovery results in the partially observed setting. Our experiments confirm that our algorithms handle adversarial noise and unobserved comparisons well in both general and LLM preference dataset settings. This work contributes to the development and scaling of more reliable and ethically aligned AI models by equipping the dataset curation pipeline with the ability to handle missing and maliciously manipulated inputs.

We present results from simultaneous FUV and NUV observations of T-Tauri stars (TTSs) in the Taurus molecular cloud with UVIT/AstroSat. This is the very first UVIT study of TTSs. From the spectral energy distribution of TTSs from FUV to near-IR, we show that classical TTSs (CTTSs) emit significantly higher UV excess compared to weak-line TTSs (WTTSs). The equivalent black-body temperatures corresponding to the UV excess in CTTSs ($>10^4$ K) are also found to be relatively higher than that in WTTSs ($<9250$ K). From the UV excess, we have re-classified two WTTSs (BS Tau, V836 Tau) as CTTSs, which has been supported by the follow-up optical spectroscopic study using the Himalayan Chandra Telescope (HCT), showing strong H$\alpha$ line emission. We find that CTTSs show strong excess emission in both FUV ($>$10$^7$) and NUV ($>$10$^3$) bands, while WTTSs show strong excess only in the FUV ($\lesssim$10$^5$), suggesting that excess emission in NUV can be used as a tool to classify the TTSs. We also find a linear correlation between UV luminosity (a primary indicator of mass accretion) and H$\alpha$ luminosity (a secondary indicator of mass accretion) with a slope of 1.20$\pm$0.22 and intercept of 2.16$\pm$0.70., Comment: 14 pages, 7 figures, 4 tables.Accepted to Astrophysical Journal

Understanding when and how circumstellar disks disperse is crucial to constrain planet formation and migration. Thermal winds powered by high-energy stellar photons have long been theorized to drive disk dispersal. However, evidence for these winds is currently based only on small (~3-6 km/s) blue-shifts in [Ne II] 12.81 um lines, which does not exclude MHD winds. We report JWST MIRI MRS spectro-imaging of T Cha, a disk with a large dust gap (~30 au in radius) and blue-shifted [Ne II] emission. We detect four forbidden noble gas lines, [Ar II], [Ar III], [Ne II], and [Ne III], of which [Ar III] is the first detection in any protoplanetary disk. We use line flux ratios to constrain the energy of the ionizing photons and find that Argon is ionized by EUV whereas Neon is most likely ionized by X-rays. After performing continuum and Point Spread Function (PSF) subtraction on the IFU cube, we discover a spatial extension in the [Ne II] emission off the disk continuum emission. This is the first spatially resolved [Ne II] disk wind emission. The mostly ionic spectrum of T Cha, in combination with the extended [Ne II] emission, points to an evolved stage for any inner MHD wind and is consistent with the existence of an outer thermal wind ionized and driven by high-energy stellar photons. This work acts as a pathfinder for future observations aiming at investigating disk dispersal using JWST., Comment: 20 pages, 8 figures, Accepted for publication in AJ

We introduce a multi-step reasoning framework using prompt-based LLMs to examine the relationship between social media language patterns and trends in national health outcomes. Grounded in fuzzy-trace theory, which emphasizes the importance of gists of causal coherence in effective health communication, we introduce Role-Based Incremental Coaching (RBIC), a prompt-based LLM framework, to identify gists at-scale. Using RBIC, we systematically extract gists from subreddit discussions opposing COVID-19 health measures (Study 1). We then track how these gists evolve across key events (Study 2) and assess their influence on online engagement (Study 3). Finally, we investigate how the volume of gists is associated with national health trends like vaccine uptake and hospitalizations (Study 4). Our work is the first to empirically link social media linguistic patterns to real-world public health trends, highlighting the potential of prompt-based LLMs in identifying critical online discussion patterns that can form the basis of public health communication strategies., Comment: 20 pages, 4 figures

With the advent of Belle II and the LHCb upgrade, the precision measurements of various B-Physics observables are on cards. This holds significant potential for delving into physics beyond the standard model of electroweak interactions. These measurements can also serve as means to establish limits on phenomena occurring at much finer length scales, such as quantum decoherence, which may arise due to potential discreteness in space-time or non-trivial topological effects. In this work, we set up the formalism to investigate the impact of quantum decoherence on several potential observables in $B$ meson systems. The approach employs the trace-preserving Kraus operator formalism, extending unitary evolution to non-unitary dynamics while maintaining complete positivity. In this formalism, the decoherence effects are parametrized in terms of a single parameter. Through the analysis of purely leptonic, semileptonic, and non-leptonic decays of $B$ mesons, we identify observables that could, in principle, be influenced by decoherence. The theoretical expressions are provided without neglecting the impact of decay width difference ($\Delta \Gamma$) and $CP$ violation in mixing. Considering that many of these observables can be measured with high precision using the abundant data collected by LHCb and Belle II, our formalism can be applied to establish constraints on the decoherence parameter through multiple decay channels. This offers an alternative set-up for such studies, which, at present, are predominantly conducted in the neutrino sector., Comment: 32 pages; matches accepted version in JHEP

This research project aims to tackle the growing mental health challenges in today's digital age. It employs a modified pre-trained BERT model to detect depressive text within social media and users' web browsing data, achieving an impressive 93% test accuracy. Simultaneously, the project aims to incorporate physiological signals from wearable devices, such as smartwatches and EEG sensors, to provide long-term tracking and prognosis of mood disorders and emotional states. This comprehensive approach holds promise for enhancing early detection of depression and advancing overall mental health outcomes.

The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically., Comment: 24 pages, 11 figures, 9 tables. Summary Paper for the MICCAI TopCoW 2023 Challenge

In the field of surrogate modeling, polynomial chaos expansion (PCE) allows practitioners to construct inexpensive yet accurate surrogates to be used in place of the expensive forward model simulations. For black-box simulations, non-intrusive PCE allows the construction of these surrogates using a set of simulation response evaluations. In this context, the PCE coefficients can be obtained using linear regression, which is also known as point collocation or stochastic response surfaces. Regression exhibits better scalability and can handle noisy function evaluations in contrast to other non-intrusive approaches, such as projection. However, since over-sampling is generally advisable for the linear regression approach, the simulation requirements become prohibitive for expensive forward models. We propose to leverage transfer learning whereby knowledge gained through similar PCE surrogate construction tasks (source domains) is transferred to a new surrogate-construction task (target domain) which has a limited number of forward model simulations (training data). The proposed transfer learning strategy determines how much, if any, information to transfer using new techniques inspired by Bayesian modeling and data assimilation. The strategy is scrutinized using numerical investigations and applied to an engineering problem from the oil and gas industry.

Motivated by limitations on the depth of near-term quantum devices, we study the depth-computation trade-off in the query model, where the depth corresponds to the number of adaptive query rounds and the computation per layer corresponds to the number of parallel queries per round. We achieve the strongest known separation between quantum algorithms with $r$ versus $r-1$ rounds of adaptivity. We do so by using the $k$-fold Forrelation problem introduced by Aaronson and Ambainis (SICOMP'18). For $k=2r$, this problem can be solved using an $r$ round quantum algorithm with only one query per round, yet we show that any $r-1$ round quantum algorithm needs an exponential (in the number of qubits) number of parallel queries per round. Our results are proven following the Fourier analytic machinery developed in recent works on quantum-classical separations. The key new component in our result are bounds on the Fourier weights of quantum query algorithms with bounded number of rounds of adaptivity. These may be of independent interest as they distinguish the polynomials that arise from such algorithms from arbitrary bounded polynomials of the same degree., Comment: 35 pages, 9 figures

We study the one-clean-qubit model of quantum communication where one qubit is in a pure state and all other qubits are maximally mixed. We demonstrate a partial function that has a quantum protocol of cost $O(\log N)$ in this model, however, every interactive randomized protocol has cost $\Omega(\sqrt{N})$, settling a conjecture of Klauck and Lim. In contrast, all prior quantum versus classical communication separations required at least $\Omega(\log N)$ clean qubits. The function demonstrating our separation also has an efficient protocol in the quantum-simultaneous-with-entanglement model of cost $O(\log N )$. We thus recover the state-of-the-art separations between quantum and classical communication complexity. Our proof is based on a recent hypercontractivity inequality introduced by Ellis, Kindler, Lifshitz, and Minzer, in conjunction with tools from the representation theory of compact Lie groups.

The level-$k$ $\ell_1$-Fourier weight of a Boolean function refers to the sum of absolute values of its level-$k$ Fourier coefficients. Fourier growth refers to the growth of these weights as $k$ grows. It has been extensively studied for various computational models, and bounds on the Fourier growth, even for the first few levels, have proven useful in learning theory, circuit lower bounds, pseudorandomness, and quantum-classical separations. We investigate the Fourier growth of certain functions that naturally arise from communication protocols for XOR functions (partial functions evaluated on the bitwise XOR of the inputs to Alice and Bob). If a protocol $\mathcal C$ computes an XOR function, then $\mathcal C(x,y)$ is a function of the parity $x\oplus y$. This motivates us to analyze the XOR-fiber of $\mathcal C$, defined as $h(z):=\mathbb E_{x,y}[\mathcal C(x,y)|x\oplus y=z]$. We present improved Fourier growth bounds for the XOR-fibers of protocols that communicate $d$ bits. For the first level, we show a tight $O(\sqrt d)$ bound and obtain a new coin theorem, as well as an alternative proof for the tight randomized communication lower bound for Gap-Hamming. For the second level, we show an $d^{3/2}\cdot\mathrm{polylog}(n)$ bound, which improves the previous $O(d^2)$ bound by Girish, Raz, and Tal (ITCS 2021) and implies a polynomial improvement on the randomized communication lower bound for the XOR-lift of Forrelation, extending its quantum-classical gap. Our analysis is based on a new way of adaptively partitioning a relatively large set in Gaussian space to control its moments in all directions. We achieve this via martingale arguments and allowing protocols to transmit real values. We also show a connection between Fourier growth and lifting theorems with constant-sized gadgets as a potential approach to prove optimal bounds for the second level and beyond., Comment: 64 pages

In this note, we observe that quantum logspace computations are verifiable by classical logspace algorithms, with unconditional security. More precisely, every language in BQL has an (information-theoretically secure) streaming proof with a quantum logspace prover and a classical logspace verifier. The prover provides a polynomial-length proof that is streamed to the verifier. The verifier has a read-once one-way access to that proof and is able to verify that the computation was performed correctly. That is, if the input is in the language and the prover is honest, the verifier accepts with high probability, and, if the input is not in the language, the verifier rejects with high probability even if the prover is adversarial. Moreover, the verifier uses only $O(\log n)$ random bits.

For turbulent reacting flows, identification of low-dimensional representations of the thermo-chemical state space is vitally important, primarily to significantly reduce the computational cost of device-scale simulations. Principal component analysis (PCA), and its variants, is a widely employed class of methods. Recently, an alternative technique that focuses on higher-order statistical interactions, co-kurtosis PCA (CoK-PCA), has been shown to effectively provide a low-dimensional representation by capturing the stiff chemical dynamics associated with spatiotemporally localized reaction zones. While its effectiveness has only been demonstrated based on a priori analysis with linear reconstruction, in this work, we employ nonlinear techniques to reconstruct the full thermo-chemical state and evaluate the efficacy of CoK-PCA compared to PCA. Specifically, we combine a CoK-PCA/PCA based dimensionality reduction (encoding) with an artificial neural network (ANN) based reconstruction (decoding) and examine a priori the reconstruction errors of the thermo-chemical state. In addition, we evaluate the errors in species production rates and heat release rates that are nonlinear functions of the reconstructed state as a measure of the overall accuracy of the dimensionality reduction technique. We employ four datasets to assess CoK-PCA/PCA coupled with ANN-based reconstruction: a homogeneous reactor for autoignition of an ethylene/air mixture that has conventional single-stage ignition kinetics, a dimethyl ether (DME)/air mixture which has two-stage ignition kinetics, a one-dimensional freely propagating premixed ethylene/air laminar flame, and a two-dimensional homogeneous charge compression ignition of ethanol. The analyses demonstrate the robustness of the CoK-PCA based low-dimensional manifold with ANN reconstruction in accurately capturing the data, specifically from the reaction zones., Comment: 19 pages, 18 figures

We present an ACA search for [CI] emission at 492GHz toward large T Tauri disks (gas radii $\gtrsim 200$au) in the $\sim 1-3$Myr-old Lupus star-forming region. Combined with ALMA 12-m archival data for IM Lup, we report [CI] detections in 6 out of 10 sources, thus doubling the known detections toward T Tauri disks. We also identify four Keplerian double-peaked profiles and demonstrate that [CI] fluxes correlate with $^{13}$CO, C$^{18}$O, and $^{12}$CO(2-1) fluxes, as well as with the gas disk outer radius measured from the latter transition. These findings are in line with the expectation that atomic carbon traces the disk surface. In addition, we compare the carbon and CO line luminosities of the Lupus and literature sample with [CI] detections with predictions from the self-consistent disk thermo-chemical models of Ruaud et al. (2022). These models adopt ISM carbon and oxygen elemental abundances as input parameters. With the exception of the disk around Sz 98, we find that these models reproduce all available line luminosities and upper limits with gas masses comparable to or higher than the minimum mass solar nebula and gas-to-dust mass ratios $\geq 10$. Thus, we conclude that the majority of large Myr-old disks conform to the simple expectation that they are not significantly depleted in gas, CO, or carbon., Comment: AAS Journals in press

CO is one of the most abundant molecules in protoplanetary disks, and optically thin emission from its isotopologues has been detected in many of them. However, several past works have argued that reproducing the relatively low emission of CO isotopologues requires a very low disk mass or significant CO depletion. Here, we present a Python code, DiskMINT, which includes gas density and temperature structures that are both consistent with the thermal pressure gradient, isotope-selective chemistry, and conversion of CO into $\mathrm{CO_2}$ ice on grain-surfaces. The code generates a self-consistent disk structure, where the gas disk distribution is obtained from a Spectral Energy Distribution (SED)-derived dust disk structure with multiple grain sizes. We use DiskMINT to study the disk of RU~Lup, a high-accreting star whose disk was previously inferred to have a gas mass of only $\sim 1.5\times10^{-3}\,M_\odot$ and gas-to-dust mass ratio of $\sim 4$. Our best-fit model to the long-wavelength continuum emission can explain the total $\mathrm{C^{18}O}$ luminosity as well as the $\mathrm{C^{18}O}$ velocity and radial intensity profiles, and obtains a gas mass of $\sim 1.2\times10^{-2}\,M_\odot$, an order of magnitude higher than previous results. A disk model with parametric Gaussian vertical distribution that better matches the IR-SED can also explain the observables above with a similarly high gas mass $\sim 2.1\times10^{-2}\,M_\odot$. We confirm the conclusions of Ruaud et al. (2022) that optically thin $\mathrm{C^{18}O}$ rotational lines provide reasonable estimates of the disk mass and can therefore be used as gas disk tracers., Comment: 15 pages, 7 figures, accepted for publication in the ApJ. Associated code is released, see http://github.com/DingshanDeng/DiskMINT. v2 has updated the reference and included a correction to Fig 1

Knot theory is the Mathematical study of knots. In this paper we have studied the Composition of two knots. Knot theory belongs to Mathematical field of Topology, where the topological concepts such as topological spaces, homeomorphisms, and homology are considered. We have studied the basics of knot theory, with special focus on Composition of knots, and knot determinants using Alexander Polynomials. And we have introduced the techniques to generalize the solution of composition of knots to present how knot determinants behave when we compose two knots., Comment: 7 pages,10 figures

This research paper addresses the challenge of detecting obscured wildfires (when the fire flames are covered by trees, smoke, clouds, and other natural barriers) in real-time using drones equipped only with RGB cameras. We propose a novel methodology that employs semantic segmentation based on the temporal analysis of smoke patterns in video sequences. Our approach utilizes an encoder-decoder architecture based on deep convolutional neural network architecture with a pre-trained CNN encoder and 3D convolutions for decoding while using sequential stacking of features to exploit temporal variations. The predicted fire locations can assist drones in effectively combating forest fires and pinpoint fire retardant chemical drop on exact flame locations. We applied our method to a curated dataset derived from the FLAME2 dataset that includes RGB video along with IR video to determine the ground truth. Our proposed method has a unique property of detecting obscured fire and achieves a Dice score of 85.88%, while achieving a high precision of 92.47% and classification accuracy of 90.67% on test data showing promising results when inspected visually. Indeed, our method outperforms other methods by a significant margin in terms of video-level fire classification as we obtained about 100% accuracy using MobileNet+CBAM as the encoder backbone., Comment: 6 pages, 6 figures

We study the advantages of quantum communication models over classical communication models that are equipped with a limited number of qubits of entanglement. In this direction, we give explicit partial functions on $n$ bits for which reducing the entanglement increases the classical communication complexity exponentially. Our separations are as follows. For every $k\ge 1$: $Q\|^*$ versus $R2^*$: We show that quantum simultaneous protocols with $\tilde{\Theta}(k^5 \log^3 n)$ qubits of entanglement can exponentially outperform two-way randomized protocols with $O(k)$ qubits of entanglement. This resolves an open problem from [Gav08] and improves the state-of-the-art separations between quantum simultaneous protocols with entanglement and two-way randomized protocols without entanglement [Gav19, GRT22]. $R\|^*$ versus $Q\|^*$: We show that classical simultaneous protocols with $\tilde{\Theta}(k \log n)$ qubits of entanglement can exponentially outperform quantum simultaneous protocols with $O(k)$ qubits of entanglement, resolving an open question from [GKRW06, Gav19]. The best result prior to our work was a relational separation against protocols without entanglement [GKRW06]. $R\|^*$ versus $R1^*$: We show that classical simultaneous protocols with $\tilde{\Theta}(k\log n)$ qubits of entanglement can exponentially outperform randomized one-way protocols with $O(k)$ qubits of entanglement. Prior to our work, only a relational separation was known [Gav08].

Inter-subject or subject-independent emotion recognition has been a challenging task in affective computing. This work is about an easy-to-implement emotion recognition model that classifies emotions from EEG signals subject independently. It is based on the famous EEGNet architecture, which is used in EEG-related BCIs. We used the Dataset on Emotion using Naturalistic Stimuli (DENS) dataset. The dataset contains the Emotional Events -- the precise information of the emotion timings that participants felt. The model is a combination of regular, depthwise and separable convolution layers of CNN to classify the emotions. The model has the capacity to learn the spatial features of the EEG channels and the temporal features of the EEG signals variability with time. The model is evaluated for the valence space ratings. The model achieved an accuracy of 73.04%.

Asthma is one of the chronic inflammatory diseases of the airways, which causes chest tightness, wheezing, breathlessness, and cough. Spirometry is an effort-dependent test used to monitor and diagnose lung conditions like Asthma. Vocal breath sound (VBS) based analysis can be an alternative to spirometry as VBS characteristics change depending on the lung condition. VBS test consumes less time, and it also requires less effort, unlike spirometry. In this work, VBS characteristics are analyzed before and after administering bronchodilator in a subject-dependent manner using linear discriminant analysis (LDA). We find that features learned through LDA show a significant difference between VBS recorded before and after administering bronchodilator in all 30 subjects considered in this work, whereas the baseline features could achieve a significant difference between VBS only for 26 subjects. We also observe that all frequency ranges do not contribute equally to the discrimination between pre and post bronchodilator conditions. From experiments, we find that two frequency ranges, namely 400-500Hz and 1480-1900Hz, maximally contribute to the discrimination of all the subjects. The study presented in this paper analyzes the pre and post-bronchodilator effect on the inhalation sound recorded at the mouth in a subject-dependent manner. The findings of this work suggest that, inhalation sound recorded at mouth can be a good stimulus to discriminate pre and post-bronchodilator conditions in asthmatic subjects. Inhale sound-based pre and post-bronchodilator discrimination can be of potential use in clinical settings.

NLP datasets annotated with human judgments are rife with disagreements between the judges. This is especially true for tasks depending on subjective judgments such as sentiment analysis or offensive language detection. Particularly in these latter cases, the NLP community has come to realize that the approach of 'reconciling' these different subjective interpretations is inappropriate. Many NLP researchers have therefore concluded that rather than eliminating disagreements from annotated corpora, we should preserve them-indeed, some argue that corpora should aim to preserve all annotator judgments. But this approach to corpus creation for NLP has not yet been widely accepted. The objective of the LeWiDi series of shared tasks is to promote this approach to developing NLP models by providing a unified framework for training and evaluating with such datasets. We report on the second LeWiDi shared task, which differs from the first edition in three crucial respects: (i) it focuses entirely on NLP, instead of both NLP and computer vision tasks in its first edition; (ii) it focuses on subjective tasks, instead of covering different types of disagreements-as training with aggregated labels for subjective NLP tasks is a particularly obvious misrepresentation of the data; and (iii) for the evaluation, we concentrate on soft approaches to evaluation. This second edition of LeWiDi attracted a wide array of participants resulting in 13 shared task submission papers.

Point clouds offer comprehensive and precise data regarding the contour and configuration of objects. Employing such geometric and topological 3D information of objects in class incremental learning can aid endless application in 3D-computer vision. Well known 3D-point cloud class incremental learning methods for addressing catastrophic forgetting generally entail the usage of previously encountered data, which can present difficulties in situations where there are restrictions on memory or when there are concerns about the legality of the data. Towards this we pioneer to leverage exemplar free class incremental learning on Point Clouds. In this paper we propose PointCLIMB: An exemplar Free Class Incremental Learning Benchmark. We focus on a pragmatic perspective to consider novel classes for class incremental learning on 3D point clouds. We setup a benchmark for 3D Exemplar free class incremental learning. We investigate performance of various backbones on 3D-Exemplar Free Class Incremental Learning framework. We demonstrate our results on ModelNet40 dataset.

Breathing is an essential part of human survival, which carries information about a person's physiological and psychological state. Generally, breath boundaries are marked by experts before using for any task. An unsupervised algorithm for breath boundary detection has been proposed for breath sounds recorded at the mouth also referred as vocal breath sounds (VBS) in this work. Breath sounds recorded at the mouth are used in this work because they are easy and contactless to record than tracheal breath sounds and lung breath sounds. The periodic nature of breath signal energy is used to segment the breath boundaries. Dynamic programming with the prior information of the number of breath phases($P$) and breath phase duration($d$) is used to find the boundaries. In this work, 367 breath boundaries from 60 subjects (31 healthy, 29 patients) having 307 breaths are predicted. With the proposed method, M ($89\%$), I ($13\%$), D ($11\%$) and S ($79\%$) is found. The proposed method shows better performance than the baselines used in this work. Even the classification performance between asthmatic and healthy subjects using estimated boundaries by the proposed method is comparable with the ground truth boundaries.