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We investigated the ultrafast structural dynamics of cyclobutanone following photoexcitation at $\lambda=200$ nm using gas-phase megaelectronvolt ultrafast electron diffraction. Our investigation complements the simulation studies of the same process within this special issue. It provides information about both electronic state population and structural dynamics through well-separable inelastic and elastic electron scattering signatures. We observe the depopulation of the photoexcited S$_2$ state of cyclobutanone with n3s Rydberg character through its inelastic electron scattering signature with a time constant of $(0.29 \pm 0.2)$ ps towards the S$_1$ state. The S$_1$ state population undergoes ring-opening via a Norrish Type-I reaction, likely while passing through a conical intersection with S$_0$. The corresponding structural changes can be tracked by elastic electron scattering signatures. These changes appear with a delay of $(0.14 \pm 0.05)$ ps with respect the initial photoexcitation, which is less than the S$_2$ depopulation time constant. This behavior provides evidence for the ballistic nature of the ring-opening once the S$_1$ state is reached. The resulting biradical species react further within $(1.2 \pm 0.2)$ ps via two rival fragmentation channels yielding ketene and ethylene, or propene and carbon monoxide. Our study showcases both the value of gas-phase ultrafast diffraction studies as an experimental benchmark for nonadiabatic dynamics simulation methods and the limits in the interpretation of such experimental data without comparison to such simulations.

In this paper, as an application of the `Complexity = Volume' proposal, we calculate the growth of the interior of a black hole at late times for finite cutoff JT gravity. Due to this integrable, irrelevant deformation, the spectral properties are modified non-trivially. The Einstein-Rosen Bridge (ERB) length saturates faster than pure JT gravity. We comment on the possible connection between Krylov Complexity and ERB length for deformed theory. Apart from this, we calculate the emission probability of baby universes for the deformed theory and make remarks on its implications for the ramp of the Spectral Form Factor. Finally, we compute the correction to the volume of the moduli space due to the non-perturbative change of the spectral curve because of the finite cutoff at the boundary., Comment: 42 pages, 6 figures

We study the problem of computing statistical similarity between probability distributions. For distributions $P$ and $Q$ over a finite sample space, their statistical similarity is defined as $S_{\mathrm{stat}}(P, Q) := \sum_{x} \min(P(x), Q(x))$. Statistical similarity is a basic measure of similarity between distributions, with several natural interpretations, and captures the Bayes error in prediction and hypothesis testing problems. Recent work has established that, somewhat surprisingly, even for the simple class of product distributions, exactly computing statistical similarity is $\#\mathsf{P}$-hard. This motivates the question of designing approximation algorithms for statistical similarity. Our primary contribution is a Fully Polynomial-Time deterministic Approximation Scheme (FPTAS) for estimating statistical similarity between two product distributions. To obtain this result, we introduce a new variant of the Knapsack problem, which we call the Masked Knapsack problem, and design an FPTAS to estimate the number of solutions of a multidimensional version of this problem. This new technical contribution could be of independent interest. Furthermore, we also establish a complementary hardness result. We show that it is $\mathsf{NP}$-hard to estimate statistical similarity when $P$ and $Q$ are Bayes net distributions of in-degree $2$., Comment: 21 pages

Understanding the interplay among processes that occur over different timescales is a challenging issue in the physics of systems regulation. In gene regulation, the timescales for changes in chromatin states can differ from those for changes in the concentration of product protein, raising questions about how to understand their coupled dynamics. In this study, we examine the effects of these different timescales on eukaryotic gene regulation using a stochastic model that describes the landscapes and probability currents of nonequilibrium fluctuations. This model shows that slow, nonadiabatic transitions of chromatin states significantly impact gene-regulation dynamics. The simulated circular flow of the probability currents indicates a maximum entropy production when the rates of chromatin-state transitions are low in the intensely nonadiabatic regime. In the mildly nonadiabatic regime, this circular flow fosters hysteresis, suggesting that changes in chromatin states precede changes in transcription activity. Furthermore, calculations using a model of a circuit involving three core genes in mouse embryonic stem cells illustrate how multilayer regulation of chromatin, or deep epigenetic regulation, can flexibly tune fluctuations in individual genes. These findings highlight the rich effects of nonadiabatic chromatin-state transitions on gene regulation in eukaryotic cells., Comment: 17 pages, 14 figures

This paper describes the new Globular Clusters GMRT Pulsar Search (GCGPS) survey. This survey aims to find MSPs in the globular clusters (GCs) of the Milky Way using uGMRT. The observations use the uGMRT's Band-4 (550$-$750 MHz) and Band-3 (300$-$500 MHz) receivers, which are well suited for steep-spectral-index radio sources like MSPs; the survey will eventually cover the GCs accessible to the uGMRT sky (i.e. $\delta\:>\:\sim\:-\:53^\circ$), and that is South of $\delta = -17^\circ$ (FAST sky limit) and have not been targeted with the sensitivity of this survey. The observations started in May 2023, having so far resulted in seven new discoveries. In this paper, we present the discovery and follow-up study of the first pulsar from this survey, J1617$-$2258A, a 4.32 ms binary MSP that is also the first to be discovered in the globular cluster NGC 6093. We localised this MSP with arc-sec precision from imaging and obtained the unique timing solution from more than one year of timing observations with the Band-4 (550$-$750 MHz) receivers of the uGMRT. This revealed an unusual binary MSP, with a $\sim$ 19-hour, highly eccentric (e $\sim$ 0.54) orbit having a low-mass companion. This orbital eccentricity allowed the measurement of the rate of advance of periastron for this system, which led to the derivation of its total mass, $1.67 \, \pm \, 0.06 \, \rm M_{\odot}$; this together with the system's mass function implies, for the pulsar and the companion, $M_\mathrm{p} < 1.60 \, \rm M_{\odot}$ and $M_\mathrm{c} > 0.072 \, \rm M_{\odot}$. The system is likely a perturbed MSP-Helium WD system seen at a low orbital inclination., Comment: 19 pages, 11 figures, and 4 tables; Submitted to Astrophysical Journal (ApJ)

This paper presents a comprehensive study of the eclipse properties of the spider millisecond pulsar (MSP) J1908$+$2105, using wide-band observations from the uGMRT and Parkes UWL. For the first time, we observed that this pulsar exhibits extended eclipses up to 4 GHz, the highest frequency band of the UWL, making it one of only three MSPs known to have such high-frequency eclipses. This study reveals synchrotron absorption as the primary eclipse mechanism for J1908$+$2105. We present modeling of synchrotron optical depth with various possible combinations of the parameters to explain the observed eclipsing in this as well as other spider MSPs. Observed eclipses at unusually high frequencies for J1908$+$2105 significantly aided in constraining the magnetic field and electron column density in the eclipse medium while modeling the synchrotron optical depth. Combining our findings with data from other MSPs in the literature, for the first time we note that a higher cutoff frequency of eclipsing, particularly above 1 GHz, is consistently associated with a higher electron column density ($>$ 10$^{17}$ cm$^{-2}$) in the eclipse medium. Additionally, we present the first evidence of lensing effects near eclipse boundaries in this MSP, leading to significant magnification of radio emissions. The orbital phase resolved polarization analysis presented in this paper further indicates variation in rotation measure and consequently stronger magnetic fields in the eclipse region., Comment: Accepted in ApJ

Accurate classification and anatomical localization are essential for effective medical diagnostics and research, which may be efficiently performed using deep learning techniques. However, availability of limited labeled data poses a significant challenge. To address this, we adapted Prototypical Networks and the Propagation-Reconstruction Network (PRNet) for few-shot classification and localization, respectively, in Single Photon Emission Computed Tomography (SPECT) images. For the proof of concept we used a 2D-sliced image cropped around heart. The Prototypical Network, with a pre-trained ResNet-18 backbone, classified ventricles, myocardium, and liver tissues with 96.67% training and 93.33% validation accuracy. PRNet, adapted for 2D imaging with an encoder-decoder architecture and skip connections, achieved a training loss of 1.395, accurately reconstructing patches and capturing spatial relationships. These results highlight the potential of Prototypical Networks for tissue classification with limited labeled data and PRNet for anatomical landmark localization, paving the way for improved performance in deep learning frameworks., Comment: 2 pages, 2 figures

Large Vision-Language Models (VLMs) have achieved unprecedented success in several objective multimodal reasoning tasks. However, to further enhance their capabilities of empathetic and effective communication with humans, improving how VLMs process and understand emotions is crucial. Despite significant research attention on improving affective understanding, there is a lack of detailed evaluations of VLMs for emotion-related tasks, which can potentially help inform downstream fine-tuning efforts. In this work, we present the first comprehensive evaluation of VLMs for recognizing evoked emotions from images. We create a benchmark for the task of evoked emotion recognition and study the performance of VLMs for this task, from perspectives of correctness and robustness. Through several experiments, we demonstrate important factors that emotion recognition performance depends on, and also characterize the various errors made by VLMs in the process. Finally, we pinpoint potential causes for errors through a human evaluation study. We use our experimental results to inform recommendations for the future of emotion research in the context of VLMs., Comment: Accepted to NAACL 2025 Findings

Large Language Models (LLMs) are prone to generating plausible yet incorrect responses, known as hallucinations. Effectively detecting hallucinations is therefore crucial for the safe deployment of LLMs. Recent research has linked hallucinations to model uncertainty, suggesting that hallucinations can be detected by measuring dispersion over answer distributions obtained from a set of samples drawn from a model. While drawing from the distribution over tokens defined by the model is a natural way to obtain samples, in this work, we argue that it is sub-optimal for the purpose of detecting hallucinations. We show that detection can be improved significantly by taking into account model uncertainty in the Bayesian sense. To this end, we propose a very simple and efficient approach that perturbs an appropriate subset of model parameters, or equivalently hidden unit activations, during sampling. We demonstrate its effectiveness across a wide range of datasets and model architectures.

In this paper, we investigate the dynamics of a non-Hermitian SSH model that arises out of the no-click limit of a monitored SSH model in the Krylov space. We find that the saturation timescale of the complexity associated with the spread of the state in the Krylov subspace increases with the measurement rate, and late time behaviour differs across the $\mathrm{PT}$ symmetry transition point. Furthermore, extending the notion of this complexity for subsystems in Krylov space, we find that the scaling of its late time value with subsystem size shows a discontinuous jump across the $\mathrm{PT}$ transition point, indicating that it can be used as a suitable order parameter for such transition but not for the measurement-induced transition. Finally, we show that the measurement-induced transition can be detected using a generalized measure in the Krylov subspace, which contains information about the correlation landscape, such as Quantum Fisher information, which also possesses some structural similarity with the complexity functional., Comment: 33 pages, 12 figures

Intent classification is crucial for conversational agents (chatbots), and deep learning models perform well in this area. However, little research has been done on the explainability of intent classification due to the absence of suitable benchmark data. Human annotation of explanation signals in text samples is time-consuming and costly. However, from inspection of data on intent classification, we see that, more often than not, the main verb denotes the action, and the direct object indicates the domain of conversation, serving as explanation signals for intent. This observation enables us to hypothesize that the main predicate in the text utterances, along with the arguments of the main predicate, can serve as explanation signals. Leveraging this, we introduce a new technique to automatically augment text samples from intent classification datasets with word-level explanations. We mark main predicates (primarily verbs) and their arguments (dependency relations) as explanation signals in benchmark intent classification datasets ATIS and SNIPS, creating a unique 21k-instance dataset for explainability. Further, we experiment with deep learning and language models. We observe that models that work well for classification do not perform well in explainability metrics like plausibility and faithfulness. We also observe that guiding models to focus on explanation signals from our dataset during training improves the plausibility Token F1 score by 3-4%, improving the model's reasoning.

System and software design benefits greatly from formal modeling, allowing for automated analysis and verification early in the design phase. Current methods excel at checking information flow and component interactions, ensuring consistency, and identifying dependencies within Systems Modeling Language (SysML) models. However, these approaches often lack the capability to perform physics-based reasoning about a system's behavior represented in SysML models, particularly in the electromechanical domain. This significant gap critically hinders the ability to automatically and effectively verify the correctness and consistency of the model's behavior against well-established underlying physical principles. Therefore, this paper presents an approach that leverages existing research on function representation, including formal languages, graphical representations, and reasoning algorithms, and integrates them with physics-based verification techniques. Four case studies (coffeemaker, vacuum cleaner, hairdryer, and wired speaker) are inspected to illustrate the model's practicality and effectiveness in performing physics-based reasoning on systems modeled in SysML. This automated physics-based reasoning is broken into two main categories: (i) structural, which is performed on BDD and IBD, and (ii) functional, which is then performed on activity diagrams. This work advances the field of automated reasoning by providing a framework for verifying structural and functional correctness and consistency with physical laws within SysML models., Comment: Accepted for presentation at SysCon 2025

With the rapid advancements in Artificial Intelligence (AI), autonomous agents are increasingly expected to manage complex situations where learning-enabled algorithms are vital. However, the integration of these advanced algorithms poses significant challenges, especially concerning safety and reliability. This research emphasizes the importance of incorporating human-machine collaboration into the systems engineering process to design learning-enabled increasingly autonomous systems (LEIAS). Our proposed LEIAS architecture emphasizes communication representation and pilot preference learning to boost operational safety. Leveraging the Soar cognitive architecture, the system merges symbolic decision logic with numeric decision preferences enhanced through reinforcement learning. A core aspect of this approach is transparency; the LEIAS provides pilots with a comprehensive, interpretable view of the system's state, encompassing detailed evaluations of sensor reliability, including GPS, IMU, and LIDAR data. This multi-sensor assessment is critical for diagnosing discrepancies and maintaining trust. Additionally, the system learns and adapts to pilot preferences, enabling responsive, context-driven decision-making. Autonomy is incrementally escalated based on necessity, ensuring pilots retain control in standard scenarios and receive assistance only when required. Simulation studies conducted in Microsoft's XPlane simulation environment to validate this architecture's efficacy, showcasing its performance in managing sensor anomalies and enhancing human-machine collaboration, ultimately advancing safety in complex operational environments., Comment: Accepted for presentation at SysCon 2025

The increasing complexity of autonomous systems has amplified the need for accurate and reliable labeling of runway and taxiway markings to ensure operational safety. Precise detection and labeling of these markings are critical for tasks such as navigation, landing assistance, and ground control automation. Existing labeling algorithms, like the Automated Line Identification and Notation Algorithm (ALINA), have demonstrated success in identifying taxiway markings but encounter significant challenges when applied to runway markings. This limitation arises due to notable differences in line characteristics, environmental context, and interference from elements such as shadows, tire marks, and varying surface conditions. To address these challenges, we modified ALINA by adjusting color thresholds and refining region of interest (ROI) selection to better suit runway-specific contexts. While these modifications yielded limited improvements, the algorithm still struggled with consistent runway identification, often mislabeling elements such as the horizon or non-relevant background features. This highlighted the need for a more robust solution capable of adapting to diverse visual interferences. In this paper, we propose integrating a classification step using a Convolutional Neural Network (CNN) named AssistNet. By incorporating this classification step, the detection pipeline becomes more resilient to environmental variations and misclassifications. This work not only identifies the challenges but also outlines solutions, paving the way for improved automated labeling techniques essential for autonomous aviation systems., Comment: Accepted at SysCon 2025

Automatic Post-Editing (APE) systems often struggle with over-correction, where unnecessary modifications are made to a translation, diverging from the principle of minimal editing. In this paper, we propose a novel technique to mitigate over-correction by incorporating word-level Quality Estimation (QE) information during the decoding process. This method is architecture-agnostic, making it adaptable to any APE system, regardless of the underlying model or training approach. Our experiments on English-German, English-Hindi, and English-Marathi language pairs show the proposed approach yields significant improvements over their corresponding baseline APE systems, with TER gains of $0.65$, $1.86$, and $1.44$ points, respectively. These results underscore the complementary relationship between QE and APE tasks and highlight the effectiveness of integrating QE information to reduce over-correction in APE systems., Comment: Accepted to NAACL 2025 Main Conference: Short Papers

Prostate cancer grading using the ISUP system (International Society of Urological Pathology) for treatment decisions is highly subjective and requires considerable expertise. Despite advances in computer-aided diagnosis systems, few have handled efficient ISUP grading on Whole Slide Images (WSIs) of prostate biopsies based only on slide-level labels. Some of the general challenges include managing gigapixel WSIs, obtaining patch-level annotations, and dealing with stain variability across centers. One of the main task-specific challenges faced by deep learning in ISUP grading, is the learning of patch-level features of Gleason patterns (GPs) based only on their slide labels. In this scenario, an efficient framework for ISUP grading is developed. The proposed TSOR is based on a novel Task-specific Self-supervised learning (SSL) model, which is fine-tuned using Ordinal Regression. Since the diversity of training samples plays a crucial role in SSL, a patch-level dataset is created to be relatively balanced w.r.t. the Gleason grades (GGs). This balanced dataset is used for pre-training, so that the model can effectively learn stain-agnostic features of the GP for better generalization. In medical image grading, it is desirable that misclassifications be as close as possible to the actual grade. From this perspective, the model is then fine-tuned for the task of ISUP grading using an ordinal regression-based approach. Experimental results on the most extensive multicenter prostate biopsies dataset (PANDA challenge), as well as the SICAP dataset, demonstrate the effectiveness of this novel framework compared to state-of-the-art methods.

The density fluctuations in the nearly homogeneous background in the very early universe are argued to be the origin of the cosmic structures we observe in our present universe. Along with many other structures, these fluctuations would have also given rise to primordial black holes at the end of unhindered gravitational collapse of high density matter blobs that developed due to fluctuations. We study here such a collapse in the inflationary epoch which has been assumed earlier to form primordial black holes. The potentials for inflation are chosen to be a general polynomial in its scalar field $V(\phi) \propto \phi^n$. Examining the dynamics of such a collapse, we find the parameter range where the apparent horizon does not form, thus resulting in the visibility of the final singularity of collapse for faraway external observers. This treatment is within the classical limits, dictated by Planck's constraints. The slow-roll parameters are analysed here to keep the relic abundance of the inflationary field high enough so that the abundance of produced primordial naked singularities (PNaSs) fall within the range of resolution of possible observational probes. $\textbf{key words}$: Unhindered gravitational collapse (UGC), Apparent Horizon (AH), Primordial black hole (PBH), Primordial naked singularity (PNaS)., Comment: 7 pages, 4 figures

The prevalence of ocular illnesses is growing globally, presenting a substantial public health challenge. Early detection and timely intervention are crucial for averting visual impairment and enhancing patient prognosis. This research introduces a new framework called Class Extension with Limited Data (CELD) to train a classifier to categorize retinal fundus images. The classifier is initially trained to identify relevant features concerning Healthy and Diabetic Retinopathy (DR) classes and later fine-tuned to adapt to the task of classifying the input images into three classes: Healthy, DR, and Glaucoma. This strategy allows the model to gradually enhance its classification capabilities, which is beneficial in situations where there are only a limited number of labeled datasets available. Perturbation methods are also used to identify the input image characteristics responsible for influencing the models decision-making process. We achieve an overall accuracy of 91% on publicly available datasets., Comment: Accepted at International Conference on Pattern Recognition (ICPR) 2024

Sco X-1 is the brightest observed extra-solar X-ray source, which is a neutron star (NS) low-mass X-ray binary (LMXB), and is thought to have a strong potential for continuous gravitational waves (CW) detection due to its high accretion rate and relative proximity. Here, we compute the long-term evolution of its parameters, particularly the NS spin frequency ($\nu$) and the surface magnetic field ($B$), to probe its nature and its potential for CW detection. We find that Sco X-1 is an unusually young ($\sim7\times10^6$ yr) LMXB and constrain the current NS mass to $\sim 1.4-1.6~{\rm M}_\odot$. Our computations reveal a rapid $B$ decay, with the maximum current value of $\sim 1.8\times10^8$ G, which can be useful to constrain the decay models. Note that the maximum current $\nu$ value is $\sim 550$ Hz, implying that, unlike what is generally believed, a CW emission is not required to explain the current source properties. However, $\nu$ will exceed an observed cut-off frequency of $\sim 730$ Hz, and perhaps even the NS break-up frequency, in the future, without a CW emission. The minimum NS mass quadrupole moment ($Q$) to avoid this is $\sim (2-3)\times10^{37}$ g cm$^2$, corresponding to a CW strain of $\sim 10^{-26}$. Our estimation of current $\nu$ values can improve the CW search sensitivity., Comment: 11 pages, 5 figures, 1 table. Accepted for publication in The Astrophysical Journal

Quantum thermal transistors have been widely studied in the context of three-qubit systems, where each qubit interacts separately with a Markovian harmonic bath. Markovianity is an assumption that is imposed on a system if the environment loses its memory within short while, while non-Markovianity is a general feature, inherently present in a large fraction of realistic scenarios. Instead of Markovian environments, here we propose a transistor in which the interaction between the working substance and an environment comprising of an infinite chain of qutrits is based on periodic collisions. We refer to the device as a working-substance thermal transistor, since the model focuses on heat currents flowing in and out of each individual qubit of the working substance to and from different parts of the system and environment. We find that the transistor effect prevails in this apparatus and we depict how the amplification of heat currents depends on the temperature of the modulating environment, the system-environment coupling strength and the interaction time. We further show that there exists a non-zero amplification even if one of the environments, that is not the modulating one, is detached from the system. Additionally, the environment, being comprised of three-level systems, allows us to consider the effects of frail perturbations in the energy-spacings of the qutrit, leading to a non-linearity in the environment. We consider non-linearities that are either of transmon- or of Kerr-type. We find parameter ranges where there is a significant amplification for both transmon- and Kerr-type non-linearities in the environment. Finally, we detect the non-Markovianity induced in the system from a non-monotonic behavior of the amplification observed with respect to time, and quantify it using the distinguishability-based measure of non-Markovianity., Comment: 12 pages, 8 figures

In this paper, we present a magnetohydrodynamics simulation of NOAA active region 11166 to understand the origin of a confined X-class flare that peaked at 23:23 UT on 2011 March 9. The simulation is initiated with a magnetic field extrapolated from the corresponding photospheric magnetogram using a non-force-free-field extrapolation technique. Importantly, the initial magnetic configuration identifies three-dimensional (3D) magnetic nulls and quasi-separatrix layers (QSLs), which nearly agree with the bright structures appeared in multi-wavelength observations. The Lorentz force associated with the extrapolated field self-consistently generates the dynamics that leads to the magnetic reconnections at the 3D nulls and the QSLs. These reconnections are found to contribute to the pre-flare activities and, ultimately, lead to the development of the flare ribbons. Notably, the anchored spine of the 3D null and the complete absence of flux rope in the flaring region are congruent with the confined nature of the flare. Furthermore, the simulation also suggests the role of reconnections at the 3D null with an open spine in the onset of a jet away from the flaring site., Comment: 22 pages, Accepted for Publication in Solar Physics

In this article we discuss density of products of biharmonic functions vanishing on an arbitrarily small part of the boundary. We prove that one can use three or more such biharmonic functions to construct a dense subset of smooth symmetric tensor fields up to order three, in a bounded domain. Furthermore, as an application of the density results, in dimension two or higher, we solve a partial data inverse problem for a biharmonic operator with nonlinear anisotropic third and lower order perturbations. For the inverse problem, we take the Dirichlet data to be supported in an arbitrarily small open set of the boundary and measure the Neumann data on the same set. Note that the analogous problem for linear perturbations are still unknown. So far, partial data problems recovering nonlinear perturbations were studied only up to vector fields. The full data analogues of the inverse problem has recently been studied for three or higher dimensions.

Autonomous driving demands safe motion planning, especially in critical "long-tail" scenarios. Recent end-to-end autonomous driving systems leverage large language models (LLMs) as planners to improve generalizability to rare events. However, using LLMs at test time introduces high computational costs. To address this, we propose DiMA, an end-to-end autonomous driving system that maintains the efficiency of an LLM-free (or vision-based) planner while leveraging the world knowledge of an LLM. DiMA distills the information from a multi-modal LLM to a vision-based end-to-end planner through a set of specially designed surrogate tasks. Under a joint training strategy, a scene encoder common to both networks produces structured representations that are semantically grounded as well as aligned to the final planning objective. Notably, the LLM is optional at inference, enabling robust planning without compromising on efficiency. Training with DiMA results in a 37% reduction in the L2 trajectory error and an 80% reduction in the collision rate of the vision-based planner, as well as a 44% trajectory error reduction in longtail scenarios. DiMA also achieves state-of-the-art performance on the nuScenes planning benchmark.

Owing to recent advances in machine learning and the ability to harvest large amounts of data during robotic-assisted surgeries, surgical data science is ripe for foundational work. We present a large dataset of surgical videos and their accompanying labels for this purpose. We describe how the data was collected and some of its unique attributes. Multiple example problems are outlined. Although the dataset was curated for a particular set of scientific challenges (in an accompanying paper), it is general enough to be used for a broad range machine learning questions. Our hope is that this dataset exposes the larger machine learning community to the challenging problems within surgical data science, and becomes a touchstone for future research. The videos are available at https://storage.googleapis.com/isi-surgvu/surgvu24_videos_only.zip, the labels at https://storage.googleapis.com/isi-surgvu/surgvu24_labels_updated_v2.zip, and a validation set for tool detection problem at https://storage.googleapis.com/isi-surgvu/cat1_test_set_public.zip.

The fault tolerance of quantum devices requires on-par contributions from error-correcting codes and suitable decoders. One of the most explored error-correcting codes is the family of Quantum Low-Density Parity Check (QLDPC) codes. Although faster than many of the reported decoders for QLDPC codes, iterative decoders fail due to the colossal degeneracy and short cycles intrinsic to these codes. We present a strategy to improve the performance of the iterative decoders based on a collaborative way to use the message passing of the iterative decoders and check node removal from the code's Tanner graph. We use the concept of bit separation and generalize it to qubit separation. This gives us a metric to analyze and improve the decoder's performance towards harmful configurations of QLDPC codes. We present a simple decoding architecture to overcome iterative decoding failures by increasing the separation of trapped qubits without incurring any significant overhead., Comment: 11 pages, 6 figures

The rapid expansion of social media platforms has provided unprecedented access to massive amounts of multimodal user-generated content. Comprehending user emotions can provide valuable insights for improving communication and understanding of human behaviors. Despite significant advancements in Affective Computing, the diverse factors influencing user emotions in social networks remain relatively understudied. Moreover, there is a notable lack of deep learning-based methods for predicting user emotions in social networks, which could be addressed by leveraging the extensive multimodal data available. This work presents a novel formulation of personalized emotion prediction in social networks based on heterogeneous graph learning. Building upon this formulation, we design HMG-Emo, a Heterogeneous Multimodal Graph Learning Framework that utilizes deep learning-based features for user emotion recognition. Additionally, we include a dynamic context fusion module in HMG-Emo that is capable of adaptively integrating the different modalities in social media data. Through extensive experiments, we demonstrate the effectiveness of HMG-Emo and verify the superiority of adopting a graph neural network-based approach, which outperforms existing baselines that use rich hand-crafted features. To the best of our knowledge, HMG-Emo is the first multimodal and deep-learning-based approach to predict personalized emotions within online social networks. Our work highlights the significance of exploiting advanced deep learning techniques for less-explored problems in Affective Computing.

The Future Circular Hadron Collider (FCC-hh) will probe unprecedented energy regimes, enabling direct searches for new elementary particles at a scale of tens of TeV. FCC-hh is currently in the planning stage, and one of its primary physics goals is to search for physics beyond the Standard Model by exploring a previously inaccessible kinematic domain. While venturing into uncharted high-energy territories promises excitement, reconstructing objects with enormous transverse momenta will require overcoming major experimental challenges. This work investigates the identification of boosted $W$ bosons and boosted top quarks in the context of three beyond the Standard Model scenarios: heavy vector-like quark ($B'$), heavy neutral gauge boson ($Z'$), and heavy neutral Higgs boson ($H$). We employ machine learning techniques, including eXtreme Gradient Boosting (XGBoost) and convolutional neural networks (CNN), to identify these ultra-boosted objects in the collider from their SM background counterpart. We evaluate the performance of these techniques in distinguishing $W$ jets and top jets from QCD jets at extremely high transverse momenta ($p_{T}$) values, demonstrating their potential for future FCC-hh analyses., Comment: 66 pages, 32 figures

In this paper, we construct a consistent non-parametric test for testing the equality of population medians for different samples when the observations in each sample are independent and identically distributed. This test can be further used to test the equality of unknown location parameters for different samples. The method discussed in this paper can be extended to any quantile level instead of the median. We present the theoretical results and also demonstrate the performance of this test through simulation studies., Comment: 14 pages, 2 figures

The dwarf galaxies comparable to the LMC and SMC, with stellar masses $7.5 <{\rm log}(M_{\ast}/M_{\odot})<9.5$, are found in a diversity of environments and have long quenching timescales. The way this phenomenon results from the dwarfs' halo properties or their locations in the large-scale structure is not well understood. We study the star-formation rates of dwarfs in the Illustris TNG50 simulation across different environments, focusing on the dwarfs having host halos with virial masses $9 < {\rm log}(M_{200}/M_{\odot}) < 11.5$, which we demonstrate are in the field as opposed to dwarf satellites in hosts with ${\rm log}(M_{200}/M_{\odot}) \geq 11.5$. Our field dwarf sample is heterogeneous, consisting of primary (central) galaxies, with smaller numbers of secondaries and dwarf galaxies that are on backsplash orbits around massive galaxies. We quantify the field dwarfs' large-scale environmental using the tidal index $\Theta_1$ and the distance to the nearest massive galaxy ${\rm d_{massive}}$, and we study how the quenched fraction and star-formation histories are correlated with these metrics. Those with ${\rm d_{massive}}>1.5$ Mpc and $\Theta_1<0$ are well isolated, with only $\sim 1\%$ being quenched. The quenched field dwarfs are in majority the backsplash dwarfs located in the neighborhood of cluster-scale halos. We discover a two-halo galactic conformity signal that arises from the tendency of the quenched dwarfs, particularly the backsplash sample, to have a quenched massive galaxy as a neighbor. We attribute the low quenched fractions of the simulated LMC/SMC analogs in the field to the locations of their low-mass hosts in the sparse large-scale environment, which predominate over the relatively small number of backsplash and pre-processed dwarfs in denser environments., Comment: 26 pages, 16 figures

This study presents the coherent and dissipative coupling realized in the hybrid photonic resonators that have been achieved via the constructive and destructive interference of the photonic resonator fields with the radiation of a common transmission line fed with microwave photons. In the dissipative coupling regime we have found the coexistence of a peculiar phenomenon bound state in the continuum (BIC) near the crossing of frequency of the uncoupled resonators by satisfying the Friedrich-Wintgen BICs condition. Again just by rotating one of the samples and with the dynamic adjustment of a parameter we have achieved coupling induced transparency between the photonic resonators. This transition from BIC in the absorption regime to transparency opens avenues for different sorts of plain or programmable oscillators, filters, quantum information processors, sensors etc.

Humblebragging is a phenomenon where individuals present self-promotional statements under the guise of modesty or complaints. For example, a statement like, "Ugh, I can't believe I got promoted to lead the entire team. So stressful!", subtly highlights an achievement while pretending to be complaining. Detecting humblebragging is important for machines to better understand the nuances of human language, especially in tasks like sentiment analysis and intent recognition. However, this topic has not yet been studied in computational linguistics. For the first time, we introduce the task of automatically detecting humblebragging in text. We formalize the task by proposing a 4-tuple definition of humblebragging and evaluate machine learning, deep learning, and large language models (LLMs) on this task, comparing their performance with humans. We also create and release a dataset called HB24, containing 3,340 humblebrags generated using GPT-4o. Our experiments show that detecting humblebragging is non-trivial, even for humans. Our best model achieves an F1-score of 0.88. This work lays the foundation for further exploration of this nuanced linguistic phenomenon and its integration into broader natural language understanding systems., Comment: Under review at ARR

Assessments play a vital role in a student's learning process by providing feedback on a student's proficiency level in a subject. While assessments often make use of short answer questions, it is often difficult to grade such questions at a large scale. Moreover, such questions often involve students drawing supporting diagrams along with their textual explanations. Such questions often promote multimodal literacy and are aligned with competency-based questions, which demand a deeper cognitive processing ability from students. However, existing literature does not deal with the automatic grading of such answers. Thus, to bridge this gap, we propose the Multimodal Short Answer Grading with Feedback (MMSAF) problem along with a dataset of 2197 data points. Additionally, we provide an automated framework for generating such datasets. Our evaluations on existing Large Language Models (LLMs) over this dataset achieved an overall accuracy of 55% on the Level of Correctness labels and 75% on Image Relevance labels. As per human experts, Pixtral was more aligned towards human judgement and values for biology and ChatGPT for physics and chemistry and achieved a score of 4 or more out of 5 in most parameters.

Fundamental research on two-dimensional (2D) magnetic systems based on van der Waals materials has been gaining traction rapidly since their recent discovery. With the increase of recent knowledge, it has become clear that such materials have also a strong potential for applications in devices that combine magnetism with electronics, optics, and nanomechanics. Nonetheless, many challenges still lay ahead. Several fundamental aspects of 2D magnetic materials are still unknown or poorly understood, such as their often-complicated electronic structure, optical properties, and magnetization dynamics, and their magnon spectrum. To elucidate their properties and facilitate integration in devices, advanced characterization techniques and theoretical frameworks need to be developed or adapted. Moreover, developing synthesis methods which increase critical temperatures and achieve large-scale, high-quality homogeneous thin films is crucial before these materials can be used for real-world applications. Therefore, the field of 2D magnetic materials provides many challenges and opportunities for the discovery and exploration of new phenomena, as well as the development of new applications. This Roadmap presents the background, challenges, and potential research directions for various relevant topics in the field on the fundamentals, synthesis, characterization, and applications. We hope that this work can provide a strong starting point for young researchers in the field and provide a general overview of the key challenges for more experienced researchers., Comment: 87 pages, 24 figures. Roadmap based on the discussions during the workshop on Quantum Magnetic Materials hosted by the Lorentz Centre in Leiden, the Netherlands (Oct. 2023)

This paper demonstrates that Phrase-Based Statistical Machine Translation (PBSMT) can outperform Transformer-based Neural Machine Translation (NMT) in moderate-resource scenarios, specifically for structurally similar languages, like the Persian-Hindi pair. Despite the Transformer architecture's typical preference for large parallel corpora, our results show that PBSMT achieves a BLEU score of 66.32, significantly exceeding the Transformer-NMT score of 53.7 on the same dataset. Additionally, we explore variations of the SMT architecture, including training on Romanized text and modifying the word order of Persian sentences to match the left-to-right (LTR) structure of Hindi. Our findings highlight the importance of choosing the right architecture based on language pair characteristics and advocate for SMT as a high-performing alternative, even in contexts commonly dominated by NMT., Comment: 5 pages, 2 figures

With the advancement of deep learning methods it is imperative that autonomous systems will increasingly become intelligent with the inclusion of advanced machine learning algorithms to execute a variety of autonomous operations. One such task involves the design and evaluation for a subsystem of the perception system for object detection and tracking. The challenge in the creation of software to solve the task is in discovering the need for a dataset, annotation of the dataset, selection of features, integration and refinement of existing algorithms, while evaluating performance metrics through training and testing. This research effort focuses on the development of a machine learning pipeline emphasizing the inclusion of assurance methods with increasing automation. In the process, a new dataset was created by collecting videos of moving object such as Roomba vacuum cleaner, emulating search and rescue (SAR) for indoor environment. Individual frames were extracted from the videos and labeled using a combination of manual and automated techniques. This annotated dataset was refined for accuracy by initially training it on YOLOv4. After the refinement of the dataset it was trained on a second YOLOv4 and a Mask R-CNN model, which is deployed on a Parrot Mambo drone to perform real-time object detection and tracking. Experimental results demonstrate the effectiveness of the models in accurately detecting and tracking the Roomba across multiple trials, achieving an average loss of 0.1942 and 96% accuracy., Comment: Accepted at ICMLA '24

Recent observations by the Large High Altitude Air Shower Observatory (LHAASO) detected Ultra High Energy (UHE) photons in the range 100 TeV to 1.4 PeV from twelve sources including Crab nebula. The detection of these photons demands the presence of at least PeV energy particle in the source. It is important to understand particle acceleration and radiation emission processes in such source. One of those twelve sources, LHAASO J2108+5157 does not show any association or counterparts at any other wavelength. In search of counterpart, we surveyed the region with Giant Metrewave Radio Telescope (GMRT) at 650 MHz frequency. GMRT observation revel radio emission from an extended source within the PSF of LHAASO which shows disk-jet morphology. Considering the spatial association and extent of the source, it is plausible that particle acceleration to PeV energies originates from this source., Comment: A detailed manuscript of this work has been submitted to a peer-reviewed journal

The WHO 2021 malaria report revealed that its Global Technical Strategy (GTS) 2020 milestones for morbidity and mortality, based on the 2015 baseline, have not been achieved globally -- the world is off-track by 42% and, can be extended up to 91% in 2030. Most of the Sub-Saharan African (SSA) countries failed to achieve GTS 2020 -- only 4 out of 40 highest burden countries met the goals. By fitting evolutionary game modeling to the malaria case and Insecticide-Treated Nets (ITN) usages data, we identify factors contributing to the GTS 2020 failures of 38 SSA countries. We use optimized projection of our model to evaluate further the potential achievement of GTS 2025 and 2030 objectives and discuss strategies for attaining goals in situations where these milestones seem unattainable. Our findings categorize all 38 countries based on the possibility to achieve their future milestone, either through increased campaigns, or by economic assistance, or through enhancing the efficacy of ITNs at minimal expense.

Dynamic analysis and especially fuzzing are challenging tasks for embedded firmware running on modern low-end Microcontroller Units (MCUs) due to performance overheads from instruction emulation, the difficulty of emulating the vast space of available peripherals, and low availability of open-source embedded firmware. Consequently, efficient security testing of MCU firmware has proved to be a resource- and engineering-heavy endeavor. EmbedFuzz introduces an efficient end-to-end fuzzing framework for MCU firmware. Our novel firmware transplantation technique converts binary MCU firmware to a functionally equivalent and fuzzing-enhanced version of the firmware which executes on a compatible high-end device at native performance. Besides the performance gains, our system enables advanced introspection capabilities based on tooling for typical Linux user space processes, thus simplifying analysis of crashes and bug triaging. In our evaluation against state-of-the-art MCU fuzzers, EmbedFuzz exhibits up to eight-fold fuzzing throughput while consuming at most a fourth of the energy thanks to its native execution.

IGR J17591--2342, a transient accretion-powered millisecond X-ray pulsar, was discovered during its 2018 outburst. Here, we present a timing and spectral analysis of the source using {\it AstroSat} data of the same outburst. From the timing analysis, we obtain updated values of binary orbital parameters, which reveal an average pulsar spin frequency of 527.4256984(8) Hz. The pulse profiles can be fit well with four harmonically related sinusoidal components with fractional amplitudes of fundamental and second, third, and fourth harmonics as $\sim13$\%, $\sim$6\%, $\sim$0.9\%, $\sim$0.2\%, respectively. The energy-dependent study of pulse profiles in the range of $3-20$ keV shows that the fractional amplitude of both the fundamental and first overtone is consistent with being constant across the considered energy band. Besides, a decaying trend has been observed for both the fundamental and first overtone in the phase-delay versus energy relation resulting in soft X-ray (2.8-3.3 keV) phase lags of $\sim$0.05 and $\sim$0.13 with respect to $\leq 15$ keV photons, for the fundamental and first overtone, respectively. The combined spectra from the Large Area X-ray Proportional Counters and the Soft X-ray Telescope aboard {\it AstroSat} in the $1-18$ keV range can be fit well with an absorbed model consisting of a Comptonization, a blackbody and a Gaussian emission line component yielding as best-fit parameters a blackbody seed photon temperature $kT_{\rm bb}$ $\sim 0.95 \pm 0.03$ keV, and an electron temperature $kT_{\rm e}$ $\sim 1.54 \pm0.03$ keV. The spectral aspects suggest the scattering of photons from the accretion disc or the neutron star's surface., Comment: Accepted in MNRAS on November 14, 2024

Optical Character Recognition (OCR) technology has revolutionized the digitization of printed text, enabling efficient data extraction and analysis across various domains. Just like Machine Translation systems, OCR systems are prone to errors. In this work, we address the challenge of data generation and post-OCR error correction, specifically for low-resource languages. We propose an approach for synthetic data generation for Devanagari languages, RoundTripOCR, that tackles the scarcity of the post-OCR Error Correction datasets for low-resource languages. We release post-OCR text correction datasets for Hindi, Marathi, Bodo, Nepali, Konkani and Sanskrit. We also present a novel approach for OCR error correction by leveraging techniques from machine translation. Our method involves translating erroneous OCR output into a corrected form by treating the OCR errors as mistranslations in a parallel text corpus, employing pre-trained transformer models to learn the mapping from erroneous to correct text pairs, effectively correcting OCR errors.

Compact binary sources that emit gravitational waves (GW) are expected to be both spinning and have eccentric orbits. To date, there has been no closed-form expression for the phasing of GWs that contain information from both spin and eccentricity. The introduction of eccentricity can slow waveform generation, as obtaining closed-form expressions for the waveform phase is unattainable due to the complexity of the differential equations involved, often requiring slower numerical methods. However, closed-form expressions for the waveform phase can be obtained when eccentricity is treated as a small parameter, enabling quick waveform generation. In this paper, closed-form expressions for the GW phasing in the form of Taylor approximants up to the eighth power in initial eccentricity $(e_0)$ are obtained while also including aligned spins up to the third post-Newtonian order. The phasing is obtained in both time and frequency domains. The TaylorT2 phasing is also resummed for usage in scenarios where initial eccentricities are as high as 0.5. Finally, a waveform is constructed using the $e_{0}^2$ expanded TaylorF2 phasing for aligned-spin systems added to TaylorF2Ecc. We perform mismatch computation between this model and TaylorF2Ecc. The findings indicate that for eccentricities $\gtrsim 0.15$ (defined at 10 Hz) and small spins $(\sim 0.2 )$, the mismatches can be higher than 1%. This leads to an overall loss in signal-to-noise ratio and lower detection efficiency of GWs coming from eccentric spinning compact binary inspirals if the combined effects of eccentricity and aligned spins are neglected in the waveforms.

Pre-trained language models (PLMs) are known to be susceptible to perturbations to the input text, but existing works do not explicitly focus on linguistically grounded attacks, which are subtle and more prevalent in nature. In this paper, we study whether PLMs are agnostic to linguistically grounded attacks or not. To this end, we offer the first study addressing this, investigating different Indic languages and various downstream tasks. Our findings reveal that although PLMs are susceptible to linguistic perturbations, when compared to non-linguistic attacks, PLMs exhibit a slightly lower susceptibility to linguistic attacks. This highlights that even constrained attacks are effective. Moreover, we investigate the implications of these outcomes across a range of languages, encompassing diverse language families and different scripts., Comment: Work in Progress

We investigate some previously unexplored (or underexplored) computational aspects of total variation (TV) distance. First, we give a simple deterministic polynomial-time algorithm for checking equivalence between mixtures of product distributions, over arbitrary alphabets. This corresponds to a special case, whereby the TV distance between the two distributions is zero. Second, we prove that unless $\mathsf{NP} \subseteq \mathsf{RP}$, it is impossible to efficiently estimate the TV distance between arbitrary Ising models, even in a bounded-error randomized setting., Comment: 17 pages

Background: The rapid development and use of generative AI (GenAI) tools in academia presents complex and multifaceted ethical challenges for its users. Earlier research primarily focused on academic integrity concerns related to students' use of AI tools. However, limited information is available on the impact of GenAI on academic research. This study aims to examine the ethical concerns arising from the use of GenAI across different phases of research and explores potential strategies to encourage its ethical use for research purposes. Methods: We selected one or more GenAI platforms applicable to various research phases (e.g. developing research questions, conducting literature reviews, processing data, and academic writing) and analysed them to identify potential ethical concerns relevant for that stage. Results: The analysis revealed several ethical concerns, including a lack of transparency, bias, censorship, fabrication (e.g. hallucinations and false data generation), copyright violations, and privacy issues. These findings underscore the need for cautious and mindful use of GenAI. Conclusions: The advancement and use of GenAI are continuously evolving, necessitating an ongoing in-depth evaluation. We propose a set of practical recommendations to support researchers in effectively integrating these tools while adhering to the fundamental principles of ethical research practices.

It is widely observed that particles produced in high-energy collisions follow a power-law distribution. One such power-law distribution used extensively in the phenomenological studies owes its origin to nonadditive statistics proposed by C. Tsallis. In this article, we derive a novel nonadditive generalization of the conventional Bose-Einstein distribution using a single-mode harmonic oscillator. The approach taken in this paper eliminates the need of a regularization procedure proposed in previous works. We observe that the spectra of the bosonic particles like the pions and kaons produced in high-energy collisions are well-described by the nonadditive bosonic distribution derived in this paper., Comment: 7 pages, 4 figures

Control synthesis for continuously-parameterized families of quantum gates can enable critical advantages for mid-sized quantum computing applications in advance of fault-tolerance. We combine quantum optimal control with physics-informed machine learning to efficiently synthesize control surfaces that interpolate among continuously-parameterized gate families. Using optimal control as an active learning strategy to guide pretraining, we bootstrap a physics-informed neural network to achieve rapid convergence to nonlinear control surfaces sufficient for our desired gates. We find our approach is critical for enabling an expressiveness beyond linear interpolation, which is important in cases of hard quantum control. We show in simulation that by adapting our pretraining to use a few reference pulse calibrations, we can apply transfer learning to quickly calibrate our learned control surfaces when devices fluctuate over time. We demonstrate synthesis for one and two qubit gates with one or two parameters, focusing on gate families for variational quantum algorithm (VQA) ansatz. By avoiding the inefficient decomposition of VQA ansatz into basis gate sets, continuous gate families are a potential method to improve the noise robustness of VQAs in the near term. Our framework shows how accessible optimal control tools can be combined with simple machine learning to enable practitioners to achieve 3x speedups for their algorithms by going beyond the standard gate sets., Comment: 10 pages, 6 figures; in conference proceedings, IEEE QCE24

This review article provides an overview of random matrix theory (RMT) with a focus on its growing impact on the formulation and inference of statistical models and methodologies. Emphasizing applications within high-dimensional statistics, we explore key theoretical results from RMT and their role in addressing challenges associated with high-dimensional data. The discussion highlights how advances in RMT have significantly influenced the development of statistical methods, particularly in areas such as covariance matrix inference, principal component analysis (PCA), signal processing, and changepoint detection, demonstrating the close interplay between theory and practice in modern high-dimensional statistical inference., Comment: 56 pages, 7 figures

A realistic and detailed description of neutrinos in binary neutron star (BNS) mergers is essential to build reliable models of such systems. To this end, we present BNS_NURATES, a novel open-source numerical library designed for the efficient on-the-fly computation of neutrino interactions, with particular focus on regimes relevant to BNS mergers. BNS_NURATES targets an higher level of accuracy and realism in the implementation of commonly employed reactions by accounting for relevant microphysics effects on the interactions, such as weak magnetism and mean field effects. It also includes the contributions of inelastic neutrino scattering off electrons and positrons and (inverse) nucleon decays. Finally, it offers a way to reconstruct the neutrino distribution function in the framework of moment-based transport schemes. As a first application, we compute both energy-dependent and energy-integrated neutrino emissivities and opacities for conditions extracted from a BNS merger simulation with M1 transport scheme. We find some qualitative differences in the results when considering the impact of the additional relevant reactions and of microphysics effects. For example, neutrino-electron/positron scattering reactions are important for the energy exchange of heavy-type neutrinos as they do not undergo semi-leptonic charged-current processes, when $\mu^\pm$ are not accounted for. Moreover, weak magnetism and mean field effects can significantly modify the contribution of $\beta$-processes for electron-type (anti)neutrinos, increasing at the same time the importance of (inverse) neutron decays. The improved treatment for the reaction rates also modify the conditions at which neutrinos decouple from matter in the system, potentially affecting their emission spectra., Comment: 27 pages, 14 figures. The BNS_NURATES library is publicly available at: https://github.com/RelNucAs/bns_nurates

In invasive breast cancer, cell clusters of varying sizes and shapes are embedded in the fibrous extracellular matrix (ECM). Although the prevailing view attributes this structure to increasing disorder resulting from loss of function and dedifferentiation, our findings reveal that it arises through a process of active self-organization driven by cancer cell motility. Simulations and histological analyses of tumours from over 2,000 breast cancer patients reveal that motile, aligned cancer cells within clusters move as active nematic aggregates through the surrounding highly aligned ECM fibres, which form a confining, passive nematic phase. Cellular motion leads to cluster splitting and coalescence. The degree of cluster activity, combined with heterogeneity in cell motility, is reflected in specific scaling behaviours for cluster shape, size distribution, and the distance between cluster boundaries and nematic defects in ECM alignment. Increased activity estimates correlate with tumour progression and are associated with a poorer prognosis for patients.