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Autism Spectrum Disorder (ASD) is a complex neuro-developmental challenge, presenting a spectrum of difficulties in social interaction, communication, and the expression of repetitive behaviors in different situations. This increasing prevalence underscores the importance of ASD as a major public health concern and the need for comprehensive research initiatives to advance our understanding of the disorder and its early detection methods. This study introduces a novel hierarchical feature fusion method aimed at enhancing the early detection of ASD in children through the analysis of code-switched speech (English and Hindi). Employing advanced audio processing techniques, the research integrates acoustic, paralinguistic, and linguistic information using Transformer Encoders. This innovative fusion strategy is designed to improve classification robustness and accuracy, crucial for early and precise ASD identification. The methodology involves collecting a code-switched speech corpus, CoSAm, from children diagnosed with ASD and a matched control group. The dataset comprises 61 voice recordings from 30 children diagnosed with ASD and 31 from neurotypical children, aged between 3 and 13 years, resulting in a total of 159.75 minutes of voice recordings. The feature analysis focuses on MFCCs and extensive statistical attributes to capture speech pattern variability and complexity. The best model performance is achieved using a hierarchical fusion technique with an accuracy of 98.75% using a combination of acoustic and linguistic features first, followed by paralinguistic features in a hierarchical manner., Comment: Submitted to Computer Speech & Language

The BMS$_3$ Lie algebra belongs to a one-parameter family of Lie algebras obtained by centrally extending abelian extensions of the Witt algebra by a tensor density representation. In this paper we call such Lie algebras $\hat{\mathfrak{g}}_\lambda$, with BMS$_3$ corresponding to the universal central extension of $\lambda = -1$. We construct the BRST complex for $\hat{\mathfrak{g}}_\lambda$ in two different ways: one in the language of semi-infinite cohomology and the other using the formalism of vertex operator algebras. We pay particular attention to the case of BMS$_3$ and discuss some natural field-theoretical realisations. We prove two theorems about the BRST cohomology of $\hat{\mathfrak{g}}_\lambda$. The first is the construction of a quasi-isomorphic embedding of the chiral sector of any Virasoro string as a $\hat{\mathfrak{g}}_\lambda$ string. The second is the isomorphism (as Batalin--Vilkovisky algebras) of any $\hat{\mathfrak{g}}_\lambda$ BRST cohomology and the chiral ring of a topologically twisted $N{=}2$ superconformal field theory.

Neural rendering methods can achieve near-photorealistic image synthesis of scenes from posed input images. However, when the images are imperfect, e.g., captured in very low-light conditions, state-of-the-art methods fail to reconstruct high-quality 3D scenes. Recent approaches have tried to address this limitation by modeling various degradation processes in the image formation model; however, this limits them to specific image degradations. In this paper, we propose a generalizable neural rendering method that can perform high-fidelity novel view synthesis under several degradations. Our method, GAURA, is learning-based and does not require any test-time scene-specific optimization. It is trained on a synthetic dataset that includes several degradation types. GAURA outperforms state-of-the-art methods on several benchmarks for low-light enhancement, dehazing, deraining, and on-par for motion deblurring. Further, our model can be efficiently fine-tuned to any new incoming degradation using minimal data. We thus demonstrate adaptation results on two unseen degradations, desnowing and removing defocus blur. Code and video results are available at vinayak-vg.github.io/GAURA., Comment: European Conference on Computer Vision(ECCV) 2024

The biomechanical properties of cells and tissues play an important role in our fundamental understanding of the structures and functions of biological systems at both the cellular and subcellular levels. Recently, Brillouin microscopy, which offers a label-free spectroscopic means of assessing viscoelastic properties in vivo, has emerged as a powerful way to interrogate those properties on a microscopic level in living tissues. However, susceptibility to photo-damage and photo-bleaching, particularly when high-intensity laser beams are used to induce Brillouin scattering, poses a significant challenge. This article introduces a transformative approach designed to mitigate photo-damage in biological and biomedical studies, enabling non-destructive, label-free assessments of mechanical properties in live biological samples. By leveraging quantum-light-enhanced stimulated Brillouin scattering (SBS) imaging contrast, the signal-to-noise ratio is significantly elevated, thereby increasing sample viability and extending interrogation times without compromising the integrity of living samples. The tangible impact of this novel methodology is evidenced by a notable three-fold increase in sample viability observed after subjecting the samples to three hours of continuous squeezed-light illumination, surpassing the traditional coherent light-based approaches. The quantum-enhanced SBS imaging holds promise across diverse fields, such as cancer biology and neuroscience where preserving sample vitality is of paramount significance. By mitigating concerns regarding photo-damage and photo-bleaching associated with high-intensity lasers, this technological breakthrough expands our horizons for exploring the mechanical properties of live biological systems, paving the way for a new era of research and clinical applications., Comment: 13 pages, 7 figures

Quantum-enhanced parameter estimation is critical in various fields, including distributed sensing. In this study, we focus on estimating multiple unknown phases at different spatial nodes using multiphoton polarization-entangled states. Previous research has relied on a much weaker Cram\'er-Rao bound rather than precise bounds from exact quantum Fisher information matrices. We present exact results for the distributed sensing. We find that, due to its singular nature, direct use of the quantum Fisher information matrix is not possible. We analyze the reasons for this singularity, which has prevented the determination of quantum Cram\'er-Rao bounds. We demonstrate that not all phases in distributed sensing with multiphoton entangled states are independent. We obtain non-singular matrices by identifying and removing the redundant phase from the calculation. This allows us to derive exact quantum Cram\'er-Rao bounds, which we verify are saturated by projective measurements. These bounds enable Heisenberg-limited sensing for the average phase. Our analysis is specifically applicable to N-photon GHZ states distributed across multiple nodes. This advancement is significant as it paves the way for more precise and efficient distributed sensing, enhancing the overall capabilities of quantum sensing technologies based on N photon GHZ states., Comment: 8 pages, 2 figures

Sample Return Capsules (SRCs) entering Earth's atmosphere at hypervelocity from interplanetary space are a valuable resource for studying meteor phenomena. The 24 September 2023 arrival of the OSIRIS-REx (Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer) SRC provided an unprecedented chance for geophysical observations of a well-characterized source with known parameters, including timing and trajectory. A collaborative effort involving researchers from 16 institutions executed a carefully planned geophysical observational campaign at strategically chosen locations, deploying over 400 ground-based sensors encompassing infrasound, seismic, distributed acoustic sensing (DAS), and GPS technologies. Additionally, balloons equipped with infrasound sensors were launched to capture signals at higher altitudes. This campaign (the largest of its kind so far) yielded a wealth of invaluable data anticipated to fuel scientific inquiry for years to come. The success of the observational campaign is evidenced by the near-universal detection of signals across instruments, both proximal and distal. This paper presents a comprehensive overview of the collective scientific effort, field deployment, and preliminary findings. The early findings have the potential to inform future space missions and terrestrial campaigns, contributing to our understanding of meteoroid interactions with planetary atmospheres. Furthermore, the dataset collected during this campaign will improve entry and propagation models as well as augment the study of atmospheric dynamics and shock phenomena generated by meteoroids and similar sources., Comment: 87 pages, 14 figures

Recent evidence supporting reionisation ending at redshift z<6 includes the rapid redshift evolution of the mean free path for Lyman-limit photons through the intergalactic medium at 510 discovered with JWST., Comment: 17 pages, 14 figures, submitted to MNRAS

Contribution of resolved and unresolved extragalactic point sources to the low-frequency sky spectrum is a potentially non-negligible part of the astrophysical foregrounds for cosmic dawn 21-cm experiments. The clustering of such point sources on the sky, combined with the frequency-dependence of the antenna beam, can also make this contribution chromatic. By combining low-frequency measurements of the luminosity function and the angular correlation function of extragalactic point sources, we develop a model for the contribution of these sources to the low-frequency sky spectrum. Using this model, we find that the contribution of sources with flux density $>10^{-6}\,$Jy to the sky-averaged spectrum is smooth and of the order of a few kelvins at 50 - $200\,$MHz. We combine this model with measurements of the galactic foreground spectrum and convolve the result with the beam of the conical log-spiral antenna planned as part of the Radio Experiment for the Analysis of Cosmic Hydrogen (REACH) project. We find that the contribution of point sources to resultant spectrum is $\sim0.4\%$ of the total foregrounds, but still larger by at least an order of magnitude than the standard predictions for the cosmological 21-cm signal. As a result, not accounting for the point-source contribution leads to a systematic bias in 21-cm signal recovery. We show, however, that in the REACH case, this reconstruction bias can be removed by modelling the point-source contribution as a power law with a running spectral index. We make our code publicly available as a Python package labelled epspy., Comment: 13 pages, 14 figures, and 1 appendix. Submitted to MNRAS. Comments are welcome

Siren represents a pioneering research effort aimed at fortifying cybersecurity through strategic integration of deception, machine learning, and proactive threat analysis. Drawing inspiration from mythical sirens, this project employs sophisticated methods to lure potential threats into controlled environments. The system features a dynamic machine learning model for real-time analysis and classification, ensuring continuous adaptability to emerging cyber threats. The architectural framework includes a link monitoring proxy, a purpose-built machine learning model for dynamic link analysis, and a honeypot enriched with simulated user interactions to intensify threat engagement. Data protection within the honeypot is fortified with probabilistic encryption. Additionally, the incorporation of simulated user activity extends the system's capacity to capture and learn from potential attackers even after user disengagement. Siren introduces a paradigm shift in cybersecurity, transforming traditional defense mechanisms into proactive systems that actively engage and learn from potential adversaries. The research strives to enhance user protection while yielding valuable insights for ongoing refinement in response to the evolving landscape of cybersecurity threats., Comment: 7 pages, 6 figures

Aggression is a two-player game of troop placement and attack played on a map (modeled as a graph). Players take turns deploying troops on a territory (a vertex on the graph) until they run out. Once all troops are placed, players take turns attacking enemy territories. A territory can be attacked if it has $k$ troops and there are more than $k$ enemy troops on adjacent territories. At the end of the game, the player who controls the most territories wins. In the case of a tie, the player with more surviving troops wins. The first player to exhaust their troops in the placement phase leads the attack phase. We study the complexity of the game when the graph along with an assignment of troops and the sequence of attacks planned by the second player. Even in this restrained setting, we show that the problem of determining an optimal sequence of first player moves is NP-complete. We then analyze the game for when the input graph is a matching or a cycle., Comment: 19 pages, 4 figures; a shorter version was accepted for presentation at COCOON 2025

In this work, we present a heretofore unseen application of Ising machines to perform trust region-based optimisation with box constraints. This is done by considering a specific form of opto-electronic oscillator-based coherent Ising machines with clipped transfer functions, and proposing appropriate modifications to facilitate trust-region optimisation. The enhancements include the inclusion of non-symmetric coupling and linear terms, modulation of noise, and compatibility with convex-projections to improve its convergence. The convergence of the modified Ising machine has been shown under the reasonable assumptions of convexity or invexity. The mathematical structures of the modified Ising machine and trust-region methods have been exploited to design a new trust-region method to effectively solve unconstrained optimisation problems in many scenarios, such as machine learning and optimisation of parameters in variational quantum algorithms. Hence, the proposition is useful for both classical and quantum-classical hybrid scenarios. Finally, the convergence of the Ising machine-based trust-region method, has also been proven analytically, establishing the feasibility of the technique., Comment: This is a first draft; proofs of the lemmas, theorems, and corollaries herein will be included in the next version, along with experimental results. Reviews, comments, and discussions are welcome

Code-switching is a common communication phenomenon where individuals alternate between two or more languages or linguistic styles within a single conversation. Autism Spectrum Disorder (ASD) is a developmental disorder posing challenges in social interaction, communication, and repetitive behaviors. Detecting ASD in individuals with code-switch scenario presents unique challenges. In this paper, we address this problem by building an application NeuRO which aims to detect potential signs of autism in code-switched conversations, facilitating early intervention and support for individuals with ASD., Comment: Accepted to INTERSPEECH 24 Show & Tell Demonstrations

In recent years, transformer-based models have revolutionized deep learning, particularly in sequence modeling. To better understand this phenomenon, there is a growing interest in using Markov input processes to study transformers. However, our current understanding in this regard remains limited with many fundamental questions about how transformers learn Markov chains still unanswered. In this paper, we address this by focusing on first-order Markov chains and single-layer transformers, providing a comprehensive characterization of the learning dynamics in this context. Specifically, we prove that transformer parameters trained on next-token prediction loss can either converge to global or local minima, contingent on the initialization and the Markovian data properties, and we characterize the precise conditions under which this occurs. To the best of our knowledge, this is the first result of its kind highlighting the role of initialization. We further demonstrate that our theoretical findings are corroborated by empirical evidence. Based on these insights, we provide guidelines for the initialization of transformer parameters and demonstrate their effectiveness. Finally, we outline several open problems in this arena. Code is available at: https://github.com/Bond1995/Markov.

Successful deployment of mobile robots in unstructured domains requires an understanding of the environment and terrain to avoid hazardous areas, getting stuck, and colliding with obstacles. Traversability estimation--which predicts where in the environment a robot can travel--is one prominent approach that tackles this problem. Existing geometric methods may ignore important semantic considerations, while semantic segmentation approaches involve a tedious labeling process. Recent self-supervised methods reduce labeling tedium, but require additional data or models and tend to struggle to explicitly label untraversable areas. To address these limitations, we introduce a weakly-supervised method for relative traversability estimation. Our method involves manually annotating the relative traversability of a small number of point pairs, which significantly reduces labeling effort compared to traditional segmentation-based methods and avoids the limitations of self-supervised methods. We further improve the performance of our method through a novel cross-image labeling strategy and loss function. We demonstrate the viability and performance of our method through deployment on a mobile robot in outdoor environments., Comment: Accepted by RA-L. Code is available at https://github.com/andreschreiber/W-RIZZ

A promising area of applications for quantum computing is in linear algebra problems. In this work, we introduce two new quantum t-SVD (tensor-SVD) algorithms. The first algorithm is largely based on previous work that proposed a quantum t-SVD algorithm for context-aware recommendation systems. The new algorithm however seeks to address and fix certain drawbacks to the original, and is fundamentally different in its approach compared to the existing work. The second algorithm proposed uses a hybrid variational approach largely based on a known variational quantum SVD algorithm., Comment: 9 pages, 8 figures

Strong quantum-correlated sources are essential but delicate resources for quantum information science and engineering protocols. Decoherence and loss are the two main disruptive processes that lead to the loss of nonclassical behavior in quantum correlations. In quantum systems, scattering can contribute to both decoherence and loss. In this work, we present an experimental scheme capable of significantly mitigating the adverse impact of scattering in quantum systems. Our quantum system is composed of a two-mode squeezed light generated with the four-wave mixing process in hot rubidium vapor, and a scatterer is introduced to one of the two modes. An integrating sphere is then placed after the scatterer to recollect the scattered photons. We use mutual information between the two modes as the measure of quantum correlations, and demonstrate a 47.5% mutual information recovery from scattering, despite an enormous photon loss of greater than 85%. Our scheme is a pioneering step towards recovering quantum correlations from disruptive random processes, thus has the potential to bridge the gap between proof-of-principle demonstrations and practical real-world deployments of quantum protocols., Comment: 7 pages, 4 figures

As the demand for food surges and the agricultural sector undergoes a transformative shift towards sustainability and efficiency, the need for precise and proactive measures to ensure the health and welfare of livestock becomes paramount. In the context of the broader agricultural landscape outlined, the application of Hyperspectral Imaging (HSI) takes on profound significance. HSI has emerged as a cutting-edge, non-destructive technique for fast and accurate egg quality analysis, including the detection of chick embryo mortality. However, the high cost and operational complexity compared to conventional RGB imaging are significant bottlenecks in the widespread adoption of HSI technology. To overcome these hurdles and unlock the full potential of HSI, a promising solution is hyperspectral image reconstruction from standard RGB images. This study aims to reconstruct hyperspectral images from RGB images for non-destructive early prediction of chick embryo mortality. Firstly, the performance of different image reconstruction algorithms, such as HRNET, MST++, Restormer, and EDSR were compared to reconstruct the hyperspectral images of the eggs in the early incubation period. Later, the reconstructed spectra were used to differentiate live from dead chick-producing eggs using the XGBoost and Random Forest classification methods. Among the reconstruction methods, HRNET showed impressive reconstruction performance with MRAE of 0.0955, RMSE of 0.0159, and PSNR of 36.79 dB. This study motivated that harnessing imaging technology integrated with smart sensors and data analytics has the potential to improve automation, enhance biosecurity, and optimize resource management towards sustainable agriculture 4.0., Comment: Under review

We propose an optomechanical scheme for reaching quantum entanglement in vibration polaritons. The system involves $N$ molecules, whose vibrations can be fairly entangled with plasmonic cavities. We find that the vibration-photon entanglement can exist at room temperature and is robust against thermal noise. We further demonstrate the quantum entanglement between the vibrational modes through the plasmonic cavities, which shows a delocalized nature and an incredible enhancement with the number of molecules. The underlying mechanism for the entanglement is attributed to the strong vibration-cavity coupling which possesses collectivity. Our results provide a molecular optomechanical scheme which offers a promising platform for the study of noise-free quantum resources and macroscopic quantum phenomena., Comment: 10 pages, 4 figures

Recent quasar absorption line observations suggest that reionization may end as late as $z \approx 5.3$. As a means to search for large neutral hydrogen islands at $z<6$, we revisit long dark gaps in the Ly$\beta$ forest in VLT/X-Shooter and Keck/ESI quasar spectra. We stack the Ly$\alpha$ forest corresponding to both edges of these Ly$\beta$ dark gaps and identify a damping wing-like extended absorption profile. The average redshift of the stacked forest is $z=5.8$. By comparing these observations with reionization simulations, we infer that such a damping wing-like feature can be naturally explained if these gaps are at least partially created by neutral islands. Conversely, simulated dark gaps lacking neutral hydrogen struggle to replicate the observed damping wing features. Furthermore, this damping wing-like profile implies that the volume-averaged neutral hydrogen fraction must be $\langle x_{\rm HI} \rangle \geq 6.1 \pm 3.9\%$ at $z = 5.8$. Our results offer robust evidence that reionization extends below $z=6$., Comment: 8 pages, 5 figures, 1 table; accepted for publication in MNRAS Letters

Graph neural networks (GNN) have emerged as a popular tool for modelling functional magnetic resonance imaging (fMRI) datasets. Many recent studies have reported significant improvements in disorder classification performance via more sophisticated GNN designs and highlighted salient features that could be potential biomarkers of the disorder. In this review, we provide an overview of how GNN and model explainability techniques have been applied on fMRI datasets for disorder prediction tasks, with a particular emphasis on the robustness of biomarkers produced for neurodegenerative diseases and neuropsychiatric disorders. We found that while most studies have performant models, salient features highlighted in these studies vary greatly across studies on the same disorder and little has been done to evaluate their robustness. To address these issues, we suggest establishing new standards that are based on objective evaluation metrics to determine the robustness of these potential biomarkers. We further highlight gaps in the existing literature and put together a prediction-attribution-evaluation framework that could set the foundations for future research on improving the robustness of potential biomarkers discovered via GNNs.

The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However, the scarcity of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). Building on the achievements of the previous MIPI Workshops held at ECCV 2022 and CVPR 2023, we introduce our third MIPI challenge including three tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Nighttime Flare Removal track on MIPI 2024. In total, 170 participants were successfully registered, and 14 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Nighttime Flare Removal. More details of this challenge and the link to the dataset can be found at https://mipi-challenge.org/MIPI2024/., Comment: CVPR 2024 Mobile Intelligent Photography and Imaging (MIPI) Workshop--Nighttime Flare Removal Challenge Report. Website: https://mipi-challenge.org/MIPI2024/

We present a vision-based navigation system for under-canopy agricultural robots using semantic keypoints. Autonomous under-canopy navigation is challenging due to the tight spacing between the crop rows ($\sim 0.75$ m), degradation in RTK-GPS accuracy due to multipath error, and noise in LiDAR measurements from the excessive clutter. Our system, CropFollow++, introduces modular and interpretable perception architecture with a learned semantic keypoint representation. We deployed CropFollow++ in multiple under-canopy cover crop planting robots on a large scale (25 km in total) in various field conditions and we discuss the key lessons learned from this., Comment: Accepted to the IEEE ICRA Workshop on Field Robotics 2024

This study explores the metal enrichment signatures attributed to the first generation of stars (PopIII) in the Universe, focusing on the E-XQR-30 sample. We aim to identify traces of Pop III metal enrichment by analyzing neutral gas in the interstellar medium of primordial galaxies and their satellite clumps, detected in absorption. To chase the chemical signature of PopIII stars, we studied metal absorption systems in the E-XQR-30 sample, selected through the detection of the OI absorption line at 1302A. The OI line is a reliable tracer of HI and allowed us to overcome the challenges posed by the Lyman-$\alpha$ forest's increasing saturation at redshifts above $\sim5$ to identify Damped Lyman-$\alpha$ systems (DLA). We detected and analyzed 29 OI systems at $z\geq5.4$, differentiating between proximate DLAs (PDLA) and intervening DLAs. Voigt function fits were applied to obtain ionic column densities, and relative chemical abundances were determined for 28 systems. These were then compared with the predictions of theoretical models. Our findings expand the study of OI systems at $z\geq5.4$ fourfold. No systematic differences were observed in the average chemical abundances between PDLAs and intervening DLAs. The chemical abundances in our sample align with literature systems at $z>4.5$, suggesting a similar enrichment pattern for this class of absorption systems. A comparison between these DLA-analogues at $4.5

We present a new suite of late-end reionization simulations performed with ATON-HE, a revised version of the GPU-based radiative transfer code ATON that includes helium. The simulations are able to reproduce the Ly$\alpha$ flux distribution of the E-XQR-30 sample of QSO absorption spectra at $5 \lesssim z \lesssim 6.2$, and show that a large variety of reionization models are consistent with these data. We explore a range of variations in source models and in the early-stage evolution of reionization. Our fiducial reionization history has a midpoint of reionization at $z = 6.5$, but we also explore an `Early' reionization history with a midpoint at $z = 7.5$ and an `Extremely Early' reionization history with a midpoint at $z = 9.5$. Haloes massive enough to host observed Ly$\alpha$ emitters are highly biased. The fraction of such haloes embedded in ionized bubbles that are large enough to allow high Ly$\alpha$ transmission becomes close to unity much before the volume filling factor of ionized regions. For our fiducial reionization history this happens at $z = 8$, probably too late to be consistent with the detection by JWST of abundant Ly$\alpha$ emission out to $z = 11$. A reionization history in our `Early' model or perhaps even our `Extremely Early' model may be required, suggesting a Thomson scattering optical depth in tension with that reported by Planck, but consistent with recent suggestions of a significantly higher value., Comment: 25 pages, 15 figures

Open-world video instance segmentation is an important video understanding task. Yet most methods either operate in a closed-world setting, require an additional user-input, or use classic region-based proposals to identify never before seen objects. Further, these methods only assign a one-word label to detected objects, and don't generate rich object-centric descriptions. They also often suffer from highly overlapping predictions. To address these issues, we propose Open-World Video Instance Segmentation and Captioning (OW-VISCap), an approach to jointly segment, track, and caption previously seen or unseen objects in a video. For this, we introduce open-world object queries to discover never before seen objects without additional user-input. We generate rich and descriptive object-centric captions for each detected object via a masked attention augmented LLM input. We introduce an inter-query contrastive loss to ensure that the object queries differ from one another. Our generalized approach matches or surpasses state-of-the-art on three tasks: open-world video instance segmentation on the BURST dataset, dense video object captioning on the VidSTG dataset, and closed-world video instance segmentation on the OVIS dataset., Comment: Project page: https://anwesachoudhuri.github.io/OpenWorldVISCap/

In recent times, the Internet of Things (IoT) has a significant rise in industries, and we live in the era of Industry 4.0, where each device is connected to the Internet from small to big. These devices are Artificial Intelligence (AI) enabled and are capable of perspective analytics. By 2023, it's anticipated that over 14 billion smart devices will be available on the Internet. These applications operate in a wireless environment where memory, power, and other resource limitations apply to the nodes. In addition, the conventional routing method is ineffective in networks with limited resource devices, lossy links, and slow data rates. Routing Protocol for Low Power and Lossy Networks (RPL), a new routing protocol for such networks, was proposed by the IETF's ROLL group. RPL operates in two modes: Storing and Non-Storing. In Storing mode, each node have the information to reach to other node. In Non-Storing mode, the routing information lies with the root node only. The attacker may exploit the Non-Storing feature of the RPL. When the root node transmits User Datagram Protocol~(UDP) or control message packet to the child nodes, the routing information is stored in the extended header of the IPv6 packet. The attacker may modify the address from the source routing header which leads to Denial of Service (DoS) attack. This attack is RPL specific which is known as Hatchetman attack. This paper shows significant degradation in terms of network performance when an attacker exploits this feature. We also propose a lightweight mitigation of Hatchetman attack using game theoretic approach to detect the Hatchetman attack in IoT.

Deep Learning models are incredibly data-hungry and require very large labeled datasets for supervised learning. As a consequence, these models often suffer from overfitting, limiting their ability to generalize to real-world examples. Recent advancements in diffusion models have enabled the generation of photorealistic images based on textual inputs. Leveraging the substantial datasets used to train these diffusion models, we propose a technique to utilize generated images to augment existing datasets. This paper explores various strategies for effective data augmentation to improve the out-of-domain generalization capabilities of deep learning models.

Absorption and gain processes are fundamental to any light-matter interaction and a precise measurement of these parameters is important for various scientific and technological applications. Quantum probes, specifically the squeezed states have proved very successful, particularly in the applications that deal with phase shift and force measurements. In this paper, we focus on improving the sensitivity of the estimation of the photon loss coefficient of a weakly absorbing medium as well as the estimation of the gain parameter using a two-mode bright squeezed state. The generation of this state combines the advantage of a coherent beam for its large photon number with the quantum properties of the two-mode squeezing operation in an optical parametric amplifier. We present two measurement schemes: balanced photodetection and time-reversed metrology, both utilizing two-mode bright squeezed light. The maximum quantum advantage we can achieve using two-mode bright squeezed light is 3.7 times for the absorption parameter $\alpha = 0.05$ and 8.4 times for $\alpha = 0.01$ as compared to using only the coherent state. Similarly, the maximum quantum advantage for the estimation of optical gain is found around 2.81 times for the gain coefficient $G=1.05$ and around 6.28 times for $G=1.01$. We discuss the significance of using one measurement scheme over the other under different squeezing conditions. We compare our results with the Cram\'er-Rao bound for a two-mode bright squeezed state to assess the quality of the proposed methodologies., Comment: 9 pages, 4 figures

Background: Social support (SS) and social isolation (SI) are social determinants of health (SDOH) associated with psychiatric outcomes. In electronic health records (EHRs), individual-level SS/SI is typically documented as narrative clinical notes rather than structured coded data. Natural language processing (NLP) algorithms can automate the otherwise labor-intensive process of data extraction. Data and Methods: Psychiatric encounter notes from Mount Sinai Health System (MSHS, n=300) and Weill Cornell Medicine (WCM, n=225) were annotated and established a gold standard corpus. A rule-based system (RBS) involving lexicons and a large language model (LLM) using FLAN-T5-XL were developed to identify mentions of SS and SI and their subcategories (e.g., social network, instrumental support, and loneliness). Results: For extracting SS/SI, the RBS obtained higher macro-averaged f-scores than the LLM at both MSHS (0.89 vs. 0.65) and WCM (0.85 vs. 0.82). For extracting subcategories, the RBS also outperformed the LLM at both MSHS (0.90 vs. 0.62) and WCM (0.82 vs. 0.81). Discussion and Conclusion: Unexpectedly, the RBS outperformed the LLMs across all metrics. Intensive review demonstrates that this finding is due to the divergent approach taken by the RBS and LLM. The RBS were designed and refined to follow the same specific rules as the gold standard annotations. Conversely, the LLM were more inclusive with categorization and conformed to common English-language understanding. Both approaches offer advantages and are made available open-source for future testing., Comment: 2 figures, 3 tables

Recently, circle representation has been introduced for medical imaging, designed specifically to enhance the detection of instance objects that are spherically shaped (e.g., cells, glomeruli, and nuclei). Given its outstanding effectiveness in instance detection, it is compelling to consider the application of circle representation for segmenting instance medical objects. In this study, we introduce CircleSnake, a simple end-to-end segmentation approach that utilizes circle contour deformation for segmenting ball-shaped medical objects at the instance level. The innovation of CircleSnake lies in these three areas: (1) It substitutes the complex bounding box-to-octagon contour transformation with a more consistent and rotation-invariant bounding circle-to-circle contour adaptation. This adaptation specifically targets ball-shaped medical objects. (2) The circle representation employed in CircleSnake significantly reduces the degrees of freedom to two, compared to eight in the octagon representation. This reduction enhances both the robustness of the segmentation performance and the rotational consistency of the method. (3) CircleSnake is the first end-to-end deep instance segmentation pipeline to incorporate circle representation, encompassing consistent circle detection, circle contour proposal, and circular convolution in a unified framework. This integration is achieved through the novel application of circular graph convolution within the context of circle detection and instance segmentation. In practical applications, such as the detection of glomeruli, nuclei, and eosinophils in pathological images, CircleSnake has demonstrated superior performance and greater rotation invariance when compared to benchmarks. The code has been made publicly available: https://github.com/hrlblab/CircleSnake.

Intervening metal absorbers in quasar spectra at $z > 6$ can be used as probes to study the chemical enrichment of the Universe during the Epoch of Reionization (EoR). This work presents the comoving line densities ($dn/dX$) of low ionisation absorbers, namely, Mg II (2796\r{A}), C II (1334\r{A}) and O I (1302\r{A}) across $2 0.03$\r{A}. For the first time, we present the comoving line densities of 131 weak ($W < 0.3$\r{A}) intervening Mg II absorbers at $1.9 < z < 6.4$ which exhibit constant evolution with redshift similar to medium ($0.3 < W < 1.0$\r{A}) absorbers. However, the cosmic mass density of Mg II - dominated by strong Mg II systems - traces the evolution of global star formation history from redshift 1.9 to 5.5. E-XQR-30 also increases the absorption path length by a factor of 50% for C II and O I whose line densities show a rising trend towards $z > 5$, in agreement with previous works. In the context of a decline in metal enrichment of the Universe at $z > 5$, the overall evolution in the incidence rates of absorption systems can be explained by a weak - possibly soft fluctuating - UV background. Our results, thereby, provide evidence for a late reionization continuing to occur in metal-enriched and therefore, biased regions in the Universe., Comment: 20 pages, 21 figures

Quantum speed limits (QSLs) impose fundamental constraints on the evolution speed of quantum systems. Traditionally, the Mandelstam-Tamm (MT) and Margolus-Levitin (ML) bounds have been widely employed, relying on the standard deviation and mean of energy distribution to define the QSLs. However, these universal bounds only offer loose restrictions on the quantum evolution. Here we introduce the generalized ML bounds, which prove to be more stringent in constraining dynamic evolution, by utilizing moments of energy spectra of arbitrary orders, even noninteger orders. To validate our findings, we conduct experiments in a superconducting circuit, where we have the capability to prepare a wide range of quantum photonic states and rigorously test these bounds by measuring the evolution of the system and its photon statistics using quantum state tomography. While, in general, the MT bound is effective for short-time evolution, we identify specific parameter regimes where either the MT or the generalized ML bounds suffice to constrain the entire evolution. Our findings not only establish new criteria for estimating QSLs but also substantially enhance our comprehension of the dynamic evolution of quantum systems.

The satellite Mimas launches a bending wave -- a warping of the rings that propagates radially through self-gravity -- at the 5:3 inner vertical resonance with Saturn's rings. We present a modification of the linear bending wave theory which includes the effects of satellite self-gravity wakes on the particles in the wave. We show that, when treated as rigid, these wakes generate an extra layer of particles whose number density is proportional to the magnitude of the slope of the warped ring. Using a ray-tracing code we compare our predictions with those of linear bending wave theory and with 60 stellar occultations observed by the Cassini Ultraviolet Imaging Spectrograph (UVIS) and find that the extra layer of particles of our perturbed bending wave model has a considerable explanatory power for the UVIS dataset. Our best model explains the most discrepant and surprising features of the Mimas 5:3 bending wave; the enhancement of the signal for the cases of occultations with high ring opening angle and the bigger-than-expected viscosity, $\nu = 576 \, \mathrm{cm^2/s}$, which is more than double the viscosity computed from density waves. This shows that self-gravity wakes can be effective at transporting angular momentum in a vertically perturbed disk. Relative to neighboring density waves, we find a lower-than-expected value for the surface mass density, $\sigma = 36.7 \, \mathrm{g/cm^2}$, which suggests that the enhanced viscous interactions may be transporting material into the surrounding regions., Comment: 33 pages, 14 figures

A vital aspect of Indian Classical Music (ICM) is Raga, which serves as a melodic framework for compositions and improvisations alike. Raga Recognition is an important music information retrieval task in ICM as it can aid numerous downstream applications ranging from music recommendations to organizing huge music collections. In this work, we propose a deep learning based approach to Raga recognition. Our approach employs efficient pre possessing and learns temporal sequences in music data using Long Short Term Memory based Recurrent Neural Networks (LSTM-RNN). We train and test the network on smaller sequences sampled from the original audio while the final inference is performed on the audio as a whole. Our method achieves an accuracy of 88.1% and 97 % during inference on the Comp Music Carnatic dataset and its 10 Raga subset respectively making it the state-of-the-art for the Raga recognition task. Our approach also enables sequence ranking which aids us in retrieving melodic patterns from a given music data base that are closely related to the presented query sequence.

Computing power, or "compute," is crucial for the development and deployment of artificial intelligence (AI) capabilities. As a result, governments and companies have started to leverage compute as a means to govern AI. For example, governments are investing in domestic compute capacity, controlling the flow of compute to competing countries, and subsidizing compute access to certain sectors. However, these efforts only scratch the surface of how compute can be used to govern AI development and deployment. Relative to other key inputs to AI (data and algorithms), AI-relevant compute is a particularly effective point of intervention: it is detectable, excludable, and quantifiable, and is produced via an extremely concentrated supply chain. These characteristics, alongside the singular importance of compute for cutting-edge AI models, suggest that governing compute can contribute to achieving common policy objectives, such as ensuring the safety and beneficial use of AI. More precisely, policymakers could use compute to facilitate regulatory visibility of AI, allocate resources to promote beneficial outcomes, and enforce restrictions against irresponsible or malicious AI development and usage. However, while compute-based policies and technologies have the potential to assist in these areas, there is significant variation in their readiness for implementation. Some ideas are currently being piloted, while others are hindered by the need for fundamental research. Furthermore, naive or poorly scoped approaches to compute governance carry significant risks in areas like privacy, economic impacts, and centralization of power. We end by suggesting guardrails to minimize these risks from compute governance., Comment: Figures can be accessed at: https://github.com/lheim/CPGAI-Figures

Reinforcement Learning (RL) has shown remarkable abilities in learning policies for decision-making tasks. However, RL is often hindered by issues such as low sample efficiency, lack of interpretability, and sparse supervision signals. To tackle these limitations, we take inspiration from the human learning process and introduce Natural Language Reinforcement Learning (NLRL), which innovatively combines RL principles with natural language representation. Specifically, NLRL redefines RL concepts like task objectives, policy, value function, Bellman equation, and policy iteration in natural language space. We present how NLRL can be practically implemented with the latest advancements in large language models (LLMs) like GPT-4. Initial experiments over tabular MDPs demonstrate the effectiveness, efficiency, and also interpretability of the NLRL framework., Comment: Work in Progress

We improve the worst-case information theoretic lower bound of Munro and Wu (ISAAC 2018) for $n-$vertex unlabeled chordal graphs when vertex leafage is bounded and leafage is unbounded. The class of unlabeled $k-$vertex leafage chordal graphs that consists of all chordal graphs with vertex leafage at most $k$ and unbounded leafage, denoted $\mathcal{G}_k$, is introduced for the first time. For $k>0$ in $o(n/\log n)$, we obtain a lower bound of $((k-1)n \log n -kn \log k - O(\log n))-$bits on the size of any data structure that encodes a graph in $\mathcal{G}_k$. Further, for every $k-$vertex leafage chordal graph $G$ such that for $k>1$ in $o(n^c), c >0$, we present a $((k-1)n \log n + o(kn \log n))-$bit succinct data structure, constructed using the succinct data structure for path graphs with $kn/2$ vertices. Our data structure supports adjacency query in $O(k \log n)$ time and using additional $2n \log n$ bits, an $O(k^2 d_v \log n + \log^2 n)$ time neighbourhood query where $d_v$ is degree of $v \in V$., Comment: 19 pages, 2 figure

In recent years, attention-based transformers have achieved tremendous success across a variety of disciplines including natural languages. A key ingredient behind their success is the generative pretraining procedure, during which these models are trained on a large text corpus in an auto-regressive manner. To shed light on this phenomenon, we propose a new framework that allows both theory and systematic experiments to study the sequential modeling capabilities of transformers through the lens of Markov chains. Inspired by the Markovianity of natural languages, we model the data as a Markovian source and utilize this framework to systematically study the interplay between the data-distributional properties, the transformer architecture, the learnt distribution, and the final model performance. In particular, we theoretically characterize the loss landscape of single-layer transformers and show the existence of global minima and bad local minima contingent upon the specific data characteristics and the transformer architecture. Backed by experiments, we demonstrate that our theoretical findings are in congruence with the empirical results. We further investigate these findings in the broader context of higher order Markov chains and deeper architectures, and outline open problems in this arena. Code is available at \url{https://github.com/Bond1995/Markov}.

Robotic harvesting has the potential to positively impact agricultural productivity, reduce costs, improve food quality, enhance sustainability, and to address labor shortage. In the rapidly advancing field of agricultural robotics, the necessity of training robots in a virtual environment has become essential. Generating training data to automatize the underlying computer vision tasks such as image segmentation, object detection and classification, also heavily relies on such virtual environments as synthetic data is often required to overcome the shortage and lack of variety of real data sets. However, physics engines commonly employed within the robotics community, such as ODE, Simbody, Bullet, and DART, primarily support motion and collision interaction of rigid bodies. This inherent limitation hinders experimentation and progress in handling non-rigid objects such as plants and crops. In this contribution, we present a plugin for the Gazebo simulation platform based on Cosserat rods to model plant motion. It enables the simulation of plants and their interaction with the environment. We demonstrate that, using our plugin, users can conduct harvesting simulations in Gazebo by simulating a robotic arm picking fruits and achieve results comparable to real-world experiments., Comment: Upon request, we are happy to share our GazeboPlants plugin open-source (MPL 2.0)

Accurate and robust navigation in unstructured environments requires fusing data from multiple sensors. Such fusion ensures that the robot is better aware of its surroundings, including areas of the environment that are not immediately visible but were visible at a different time. To solve this problem, we propose a method for traversability prediction in challenging outdoor environments using a sequence of RGB and depth images fused with pose estimations. Our method, termed WayFASTER (Waypoints-Free Autonomous System for Traversability with Enhanced Robustness), uses experience data recorded from a receding horizon estimator to train a self-supervised neural network for traversability prediction, eliminating the need for heuristics. Our experiments demonstrate that our method excels at avoiding obstacles, and correctly detects that traversable terrains, such as tall grass, can be navigable. By using a sequence of images, WayFASTER significantly enhances the robot's awareness of its surroundings, enabling it to predict the traversability of terrains that are not immediately visible. This enhanced awareness contributes to better navigation performance in environments where such predictive capabilities are essential., Comment: Accepted for the 2024 IEEE International Conference on Robotics and Automation (ICRA 2024)

We characterize the multiphase circumgalactic medium and galaxy properties at z = 6.0-6.5 in four quasar fields from the James Webb Space Telescope A SPectroscopic survey of biased halos In the Reionization Era (ASPIRE) program. We use the Very Large Telescope/X-shooter spectra of quasar J0305-3150 to identify one new metal absorber at z = 6.2713 with multiple transitions (OI, MgI, FeII and CII). They are combined with the published absorbing systems in Davies et al. (2023a) at the same redshift range to form of a sample of nine metal absorbers at z = 6.03 to 6.49. We identify eight galaxies within 1000 km s$^{-1}$ and 350 kpc around the absorbing gas from the ASPIRE spectroscopic data, with their redshifts secured by [OIII]($\lambda\lambda$4959, 5007) doublets and H$\beta$ emission lines. Our spectral energy distribution fitting indicates that the absorbing galaxies have stellar mass ranging from 10$^{7.2}$ to 10$^{8.8}M_{\odot}$ and metallicity between 0.02 and 0.4 solar. Notably, the z = 6.2713 system in the J0305-3150 field resides in a galaxy overdensity region, which contains two (tentatively) merging galaxies within 350 kpc and seven galaxies within 1 Mpc. We measure the relative abundances of $\alpha$ elements to iron ([$\alpha$/Fe]) and find that the CGM gas in the most overdense region exhibits a lower [$\alpha$/Fe] ratio. Our modeling of the galaxy's chemical abundance favors a top-heavy stellar initial mass function, and hints that we may be witnessing the contribution of the first generation Population III stars to the CGM at the end of reionization epoch., Comment: 21 pages, 4 figures in the main text. Accepted for publication in ApJL

Much of the research in differential privacy has focused on offline applications with the assumption that all data is available at once. When these algorithms are applied in practice to streams where data is collected over time, this either violates the privacy guarantees or results in poor utility. We derive an algorithm for differentially private synthetic streaming data generation, especially curated towards spatial datasets. Furthermore, we provide a general framework for online selective counting among a collection of queries which forms a basis for many tasks such as query answering and synthetic data generation. The utility of our algorithm is verified on both real-world and simulated datasets.

The spectra of high-redshift ($z\gtrsim 6$) quasars contain valuable information on the progression of the Epoch of Reionization (EoR). At redshifts $z<6$, the observed Lyman-series forest shows that the intergalactic medium (IGM) is nearly ionized, while at $z>7$ the observed quasar damping wings indicate high neutral gas fractions. However, there remains a gap in neutral gas fraction constraints at $6\lesssim z \lesssim 7$ where the Lyman series forest becomes saturated but damping wings have yet to fully emerge. In this work, we use a sample of 18 quasar spectra at redshifts $6.0

Background: Evidence-based medicine (EBM) is fundamental to modern clinical practice, requiring clinicians to continually update their knowledge and apply the best clinical evidence in patient care. The practice of EBM faces challenges due to rapid advancements in medical research, leading to information overload for clinicians. The integration of artificial intelligence (AI), specifically Generative Large Language Models (LLMs), offers a promising solution towards managing this complexity. Methods: This study involved the curation of real-world clinical cases across various specialties, converting them into .json files for analysis. LLMs, including proprietary models like ChatGPT 3.5 and 4, Gemini Pro, and open-source models like LLaMA v2 and Mixtral-8x7B, were employed. These models were equipped with tools to retrieve information from case files and make clinical decisions similar to how clinicians must operate in the real world. Model performance was evaluated based on correctness of final answer, judicious use of tools, conformity to guidelines, and resistance to hallucinations. Results: GPT-4 was most capable of autonomous operation in a clinical setting, being generally more effective in ordering relevant investigations and conforming to clinical guidelines. Limitations were observed in terms of model ability to handle complex guidelines and diagnostic nuances. Retrieval Augmented Generation made recommendations more tailored to patients and healthcare systems. Conclusions: LLMs can be made to function as autonomous practitioners of evidence-based medicine. Their ability to utilize tooling can be harnessed to interact with the infrastructure of a real-world healthcare system and perform the tasks of patient management in a guideline directed manner. Prompt engineering may help to further enhance this potential and transform healthcare for the clinician and the patient., Comment: Word count: 4548 words, Figures: 4, Tables: 4

The density density correlation function (most singular parts) of chiral Luttinger liquids forming the fractional quantum Hall effect (FQHE) edge is systematically derived in presence of a point-contact junction acting as a localised scalar impurity and is shown to be expressible as compact analytical functions with second order poles and involving the bare scale-independent reflection and transmission coefficients. The results are validated on comparison with standard fermionic perturbation theory. The linear response Hall conductance in the absence of a point-contact is recovered from the obtained density density correlation functions (DDCF). The system under consideration is inhomogeneous with broken translational invariance and for such systems in one dimension, the connected moments of the density fluctuation operator beyond second order need not be included in order to retreive the most singular parts of the correlations. The reason being that all odd moments of the density are zero and all higher order even moments are less singular than the quadratic moment. The implications of these results when used in conjunction with bosonization methods in presence of impurity backscattering is briefly discussed., Comment: 14 pages, 2 figures

The variations in Ly$\alpha$ forest opacity observed at $z>5.3$ between lines of sight to different background quasars are too strong to be caused by fluctuations in the density field alone. The leading hypothesis for the cause of this excess variance is a late, ongoing reionization process at redshifts below six. Another model proposes strong ionizing background fluctuations coupled to a short, spatially varying mean free path of ionizing photons, without explicitly invoking incomplete reionization. With recent observations suggesting a short mean free path at $z\sim6$, and a dramatic improvement in $z>5$ Ly$\alpha$ forest data quality, we revisit this latter possibility. Here we apply the likelihood-free inference technique of approximate Bayesian computation to jointly constrain the hydrogen photoionization rate $\Gamma_{\rm HI}$ and the mean free path of ionizing photons $\lambda_{\rm mfp}$ from the effective optical depth distributions at $z=5.0$-$6.1$ from XQR-30. We find that the observations are well-described by fluctuating mean free path models with average mean free paths that are consistent with the steep trend implied by independent measurements at $z\sim5$-$6$, with a concomitant rapid evolution of the photoionization rate., Comment: 23 pages, 13 figures, resubmitted to ApJ after referee's comments

Recently, 3D Gaussian splatting (3D-GS) has gained popularity in novel-view scene synthesis. It addresses the challenges of lengthy training times and slow rendering speeds associated with Neural Radiance Fields (NeRFs). Through rapid, differentiable rasterization of 3D Gaussians, 3D-GS achieves real-time rendering and accelerated training. They, however, demand substantial memory resources for both training and storage, as they require millions of Gaussians in their point cloud representation for each scene. We present a technique utilizing quantized embeddings to significantly reduce per-point memory storage requirements and a coarse-to-fine training strategy for a faster and more stable optimization of the Gaussian point clouds. Our approach develops a pruning stage which results in scene representations with fewer Gaussians, leading to faster training times and rendering speeds for real-time rendering of high resolution scenes. We reduce storage memory by more than an order of magnitude all while preserving the reconstruction quality. We validate the effectiveness of our approach on a variety of datasets and scenes preserving the visual quality while consuming 10-20x lesser memory and faster training/inference speed. Project page and code is available https://efficientgaussian.github.io, Comment: Website: https://efficientgaussian.github.io Code: https://github.com/Sharath-girish/efficientgaussian

In recent times, orthogonal frequency-division multiplexing (OFDM)-based radar has gained wide acceptance given its applicability in joint radar-communication systems. However, realizing such a system on hardware poses a huge area and power bottleneck given its complexity. Therefore it has become ever-important to explore low-power OFDM-based radar processors in order to realize energy-efficient joint radar-communication systems targeting edge devices. This paper aims to address the aforementioned challenges by exploiting approximations on hardware for early design space exploration (DSE) of trade-offs between accuracy, area and power. We present Ellora, a DSE framework for incorporating approximations in an OFDM radar processing pipeline. Ellora uses pairs of approximate adders and multipliers to explore design points realizing energy-efficient radar processors. Particularly, we incorporate approximations into the block involving periodogram based estimation and report area, power and accuracy levels. Experimental results show that at an average accuracy loss of 0.063% in the positive SNR region, we save 22.9% of on-chip area and 26.2% of power. Towards achieving the area and power statistics, we design a fully parallel Inverse Fast Fourier Transform (IFFT) core which acts as a part of periodogram based estimation and approximate the addition and multiplication operations in it. The aforementioned results show that Ellora can be used in an integrated way with various other optimization methods for generating low-power and energy-efficient radar processors., Comment: Paper accepted at IEEE-LASCAS 2024

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