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Command injection and replay attacks are key threats in Cyber Physical Systems (CPS). We develop a novel actuator fingerprinting technique named Time Constant. Time Constant captures the transient dynamics of an actuator and physical process. The transient behavior is device-specific. We combine process and device transient characteristics to develop a copy-resistant actuator fingerprint that resists command injection and replay attacks in the face of insider adversaries. We validated the proposed scheme on data from a real water treatment testbed, as well as through real-time attack detection in the live plant. Our results show that we can uniquely distinguish between process states and actuators based on their Time Constant.

This paper proposes an improved deep learning based maximum power point tracking (MPPT) in solar photovoltaic cells considering various time series based environmental inputs. Generally, artificial neural network based MPPT algorithms use basic neural network architectures and inputs which do not represent the ambient conditions in a comprehensive manner. In this article, the ambient conditions of a location are represented through a comprehensive set of environmental features. Furthermore, the inclusion of time based features in the input data is considered to model cyclic patterns temporally within the atmospheric conditions leading to robust modeling of the MPPT algorithm. A transformer based deep learning architecture is trained as a time series prediction model using multidimensional time series input features. The model is trained on a dataset containing typical meteorological year data points of ambient weather conditions from 50 locations. The attention mechanism in the transformer modules allows the model to learn temporal patterns in the data efficiently. The proposed model achieves a 0.47% mean average percentage error of prediction on non zero operating voltage points in a test dataset consisting of data collected over a period of 200 consecutive hours resulting in the average power efficiency of 99.54% and peak power efficiency of 99.98%. The proposed model is validated through real time simulations. The proposed model performs power point tracking in a robust, dynamic, and nonlatent manner, over a wide range of atmospheric conditions., Comment: Published June 2022, in Energy Science and Engineering, Volume10, Issue9, Pages 3397-3410

This paper investigates the existence and uniqueness of the mild solutions and the exact null controllability for a class of non-autonomous parabolic evolution systems with nonlocal conditions in Hilbert spaces. We present sufficient conditions for achieving exact null controllability in these systems using the theory of linear evolution systems and the Schauder fixed point theorem. Importantly, our results do not require the compactness or Lipschitz conditions for the function \( g \) in the nonlocal conditions, which are often needed in other studies. We also provide an example to demonstrate the practical application of our results., Comment: 20 pages, 0 figure

We propose a low energy model for simulating an analog black hole on an optical lattice using ultracold atoms. Assuming the validity of the holographic principle, we employ the Sachdev-Ye-Kitaev (SYK) model, which describes a system of randomly infinite range interacting fermions, also conjectured to be an exactly solvable UV-complete model for an extremal black hole in a higher dimensional Anti-de Sitter (AdS) dilaton gravity. At low energies, the SYK model exhibits an emergent conformal symmetry and is dual to the extremal black hole solution in near AdS2 spacetime. Furthermore, we show how the SYK maximally chaotic behaviour at large N limit, found to be dual to a gauge theory in higher dimensions, can also be employed as a non-trivial investigation tool for the holographic principle. The proposed setup is a theoretical platform to realize the SYK model with relevant exotic effects and behaviour at low energies as a highly non-trivial example of the AdS/CFT duality and a framework for studying black holes., Comment: 15 pages, 3 figures

The relative contribution of emission from stellar sources and accretion onto supermassive black holes to reionization has been brought into focus again by the apparent high abundance of faint AGN at $4\lesssim z\lesssim11$ uncovered by JWST. We investigate the contribution of these faint AGN to hydrogen and the early stages of helium reionization using the GPU-based radiative transfer code ATON-HE by post-processing a cosmological hydrodynamical simulation from the SHERWOOD-RELICS suite of simulations. We study four models of reionization: two previously studied galaxy-only late-end reionization models and two new models -- a QSO-assisted model and a QSO-only model. In the QSO-assisted model, 1\% of the haloes host AGN with a 10~Myr lifetime, and the AGN luminosities are scaled such that the AGN contribution to the hydrogen-ionizing emissivity is 20\% of that contributed by galaxies. In the QSO-only model, quasars account for all the hydrogen-ionizing emissivity, with 10\% of the haloes hosting AGN, each with a 10 Myr lifetime. All models are calibrated to the observed mean Lyman-$\alpha$ forest transmission at $5\lesssim z\lesssim6.2$. We find that the QSO-assisted model requires an emissivity factor of $1.8$ lower than the galaxy-only models towards the end of reionization and fits the observed distribution of the Lyman-$\alpha$ optical depths well. Our QSO-only model is inconsistent with the observed Lyman-$\alpha$ optical depths distribution. It also results in too high IGM temperatures at $z\lesssim 5$ due to an early onset of HeII reionization unless the escape fraction of HeII-ionizing photons is assumed to be low. Our results suggest that a modest contribution to reionization by faint AGN is in good agreement with the Lyman-$\alpha$ forest data. In contrast, a scenario dominated by faint AGN appears difficult to reconcile with these observations., Comment: 19 pages, 11 figures

Perspective distortion (PD) leads to substantial alterations in the shape, size, orientation, angles, and spatial relationships of visual elements in images. Accurately determining camera intrinsic and extrinsic parameters is challenging, making it hard to synthesize perspective distortion effectively. The current distortion correction methods involve removing distortion and learning vision tasks, thus making it a multi-step process, often compromising performance. Recent work leverages the M\"obius transform for mitigating perspective distortions (MPD) to synthesize perspective distortions without estimating camera parameters. M\"obius transform requires tuning multiple interdependent and interrelated parameters and involving complex arithmetic operations, leading to substantial computational complexity. To address these challenges, we propose Log Conformal Maps (LCM), a method leveraging the logarithmic function to approximate perspective distortions with fewer parameters and reduced computational complexity. We provide a detailed foundation complemented with experiments to demonstrate that LCM with fewer parameters approximates the MPD. We show that LCM integrates well with supervised and self-supervised representation learning, outperform standard models, and matches the state-of-the-art performance in mitigating perspective distortion over multiple benchmarks, namely Imagenet-PD, Imagenet-E, and Imagenet-X. Further LCM demonstrate seamless integration with person re-identification and improved the performance. Source code is made publicly available at https://github.com/meenakshi23/Log-Conformal-Maps., Comment: Accepted to Asian Conference on Computer Vision (ACCV2024)

Sentences in Indian languages are generally longer than those in English. Indian languages are also considered to be phrase-based, wherein semantically complete phrases are concatenated to make up sentences. Long utterances lead to poor training of text-to-speech models and result in poor prosody during synthesis. In this work, we explore an inter-pausal unit (IPU) based approach in the end-to-end (E2E) framework, focusing on synthesising conversational-style text. We consider both autoregressive Tacotron2 and non-autoregressive FastSpeech2 architectures in our study and perform experiments with three Indian languages, namely, Hindi, Tamil and Telugu. With the IPU-based Tacotron2 approach, we see a reduction in insertion and deletion errors in the synthesised audio, providing an alternative approach to the FastSpeech(2) network in terms of error reduction. The IPU-based approach requires less computational resources and produces prosodically richer synthesis compared to conventional sentence-based systems.

Over the past decade, there has been a steady advancement in enhancing face recognition algorithms leveraging advanced machine learning methods. The role of the loss function is pivotal in addressing face verification problems and playing a game-changing role. These loss functions have mainly explored variations among intra-class or inter-class separation. This research examines the natural phenomenon of facial symmetry in the face verification problem. The symmetry between the left and right hemi faces has been widely used in many research areas in recent decades. This paper adopts this simple approach judiciously by splitting the face image vertically into two halves. With the assumption that the natural phenomena of facial symmetry can enhance face verification methodology, we hypothesize that the two output embedding vectors of split faces must project close to each other in the output embedding space. Inspired by this concept, we penalize the network based on the disparity of embedding of the symmetrical pair of split faces. Symmetrical loss has the potential to minimize minor asymmetric features due to facial expression and lightning conditions, hence significantly increasing the inter-class variance among the classes and leading to more reliable face embedding. This loss function propels any network to outperform its baseline performance across all existing network architectures and configurations, enabling us to achieve SoTA results., Comment: 11 Pages, 6 Figures, 5 Tables, Submitted for WACV 2025

The rapid advancements in Large Language Models (LLMs) have revolutionized natural language processing (NLP) and related fields. However, fine-tuning these models for specific tasks remains computationally expensive and risks degrading pre-learned features. To address these challenges, we propose Propulsion, a novel parameter efficient fine-tuning (PEFT) method designed to optimize task-specific performance while drastically reducing computational overhead. Inspired by the concept of controlled adjustments in physical motion, Propulsion selectively re-scales specific dimensions of a pre-trained model, guiding output predictions toward task objectives without modifying the model's parameters. By introducing lightweight, trainable Propulsion parameters at the pre-trained layer, we minimize the number of parameters updated during fine-tuning, preventing overfitting or overwriting of existing knowledge. Our theoretical analysis, supported by Neural Tangent Kernel (NTK) theory, shows that Propulsion approximates the performance of full fine-tuning with far fewer trainable parameters. Empirically, Propulsion reduces the parameter count from 355.3 million to just 0.086 million, achieving over a 10x reduction compared to standard approaches like LoRA while maintaining competitive performance across benchmarks., Comment: 26 pages, 11 figures

In this paper, motion planning for a vehicle moving on a unit sphere with unit speed is considered, wherein the desired terminal location is fixed, but the terminal orientation is free. The motion of the vehicle is modeled to be constrained by a maximum geodesic curvature $U_{max},$ which controls the rate of change of heading of the vehicle such that the maximum heading change occurs when the vehicle travels on a tight circular arc of radius $r = \frac{1}{\sqrt{1 + U_{max}^2}}$. Using Pontryagin's Minimum Principle, the main result of this paper shows that for $r \leq \frac{1}{2}$, the optimal path connecting a given initial configuration and a final location on the sphere belongs to a set of at most seven paths. The candidate paths are of type $CG, CC,$ and degenerate paths of the same, where $C \in \{L, R\}$ denotes a tight left or right turn, respectively, and $G$ denotes a great circular arc., Comment: \c{opyright} 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

We perform a comprehensive timing and broadband spectral analysis using an AstroSat observation of the low-mass black hole X-ray binary H~1743--322 during 2017 outburst. Additionally, we use two Swift/XRT observations, one of which is simultaneous with AstroSat and the other taken three days earlier, for timing analysis. The hardness-intensity diagram indicates that the 2017 outburst was a failed one unlike the previous successful outburst in 2016. We detect type C quasi-periodic oscillation (QPO) in the simultaneous AstroSat and Swift/XRT observations at $\sim0.4$ Hz, whereas an upper harmonic is noticed at $\sim0.9$ Hz in the AstroSat data only. Although these features are found to be energy independent, we notice a shift of $\sim0.08$ Hz in the QPO frequency over the interval of three days. We also investigate the nature of variability in the two consecutive failed outbursts in 2017 and 2018. We detect soft time lags of $23.2\pm12.2$ ms and $140\pm80$ ms at the type C QPO frequencies in 2017 Astrosat and 2018 XMM-Newton data, respectively. The lag-energy spectra from both the outbursts suggest that the soft lags may be associated with the reflection features. The broadband spectral analysis indicates that the source was in the low/hard state during our AstroSat observation. Modeling of the disk and reflection continuum suggests the presence of a significantly truncated accretion disk by at least $27.4~r_{\rm{g}}$ from the ISCO when the source luminosity is $\sim1.6\%$ of the Eddington luminosity., Comment: 17 Pages, 10 Figures, Accepted for publication in The Astrophysical Journal (ApJ)

The chirality-induced spin selectivity (CISS) effect has garnered significant interest in the field of molecular spintronics due to its potential for creating spin-polarized electrons without the need for a magnet. Recent studies devoted to CISS effects in various chiral materials demonstrate exciting prospects for spintronics, chiral recognition, and quantum information applications. Several experimental studies have confirmed the applicability of chiral molecules towards spin-filtering properties, influencing spin-polarized electron transport, and photoemission. Researchers aim to predict CISS phenomena and apply this concept to practical applications by compiling experimental results and enhancing understanding of the CISS effect. To expand the possibilities of spin manipulation and create new opportunities for spin-based technologies, researchers are diligently exploring different chiral organic and inorganic materials for probing the CISS effect. This ongoing research holds promise for developing novel spin-based technologies and advancing the understanding of the intricate relationship between chirality and electron spin. This review showcases the remarkable CISS effect and its impact on spintronics, as well as its relevance in various other scientific areas., Comment: 29 pages, 20 figures

In this paper, motion planning for a Dubins vehicle on a unit sphere to attain a desired final location is considered. The radius of the Dubins path on the sphere is lower bounded by $r$. In a previous study, this problem was addressed, wherein it was shown that the optimal path is of type $CG, CC,$ or a degenerate path of the same for $r \leq \frac{1}{2}.$ Here, $C = L, R$ denotes an arc of a tight left or right turn of minimum turning radius $r,$ and $G$ denotes an arc of a great circle. In this study, the candidate paths for the same problem are generalized to model vehicles with a larger turning radius. In particular, it is shown that the candidate optimal paths are of type $CG, CC,$ or a degenerate path of the same for $r \leq \frac{\sqrt{3}}{2}.$ Noting that at most two $LG$ paths and two $RG$ paths can exist for a given final location, this article further reduces the candidate optimal paths by showing that only one $LG$ and one $RG$ path can be optimal, yielding a total of seven candidate paths for $r \leq \frac{\sqrt{3}}{2}.$ Additional conditions for the optimality of $CC$ paths are also derived in this study.

We present a protocol to generate enhanced non-linear responses of incident pulses in the density wave phase within the extended Bose-Hubbard model using the concept of resonance-induced amplification (RIA). This method enables the selection of an incident pulse frequency to amplify the desired harmonic order. We characterize the enhancement of the non-linear harmonic spectra under various frequencies and field strengths of the incident pulses, and demonstrate that an optimal field strength is necessary to realize our protocol., Comment: 8 pages, 7 figures

We consider generalized quadratic forms over real quadratic number fields and prove, under a natural positive-definiteness condition, that a generalized quadratic form can only be universal if it contains a quadratic subform that is universal. We also construct an example illustrating that the positive-definiteness condition is necessary.

In this paper, we introduce DetailCLIP: A Detail-Oriented CLIP to address the limitations of contrastive learning-based vision-language models, particularly CLIP, in handling detail-oriented and fine-grained tasks like segmentation. While CLIP and its variants excel in the global alignment of image and text representations, they often struggle to capture the fine-grained details necessary for precise segmentation. To overcome these challenges, we propose a novel framework that employs patch-level comparison of self-distillation and pixel-level reconstruction losses, enhanced with an attention-based token removal mechanism. This approach selectively retains semantically relevant tokens, enabling the model to focus on the image's critical regions aligned with the specific functions of our model, including textual information processing, patch comparison, and image reconstruction, ensuring that the model learns high-level semantics and detailed visual features. Our experiments demonstrate that DetailCLIP surpasses existing CLIP-based and traditional self-supervised learning (SSL) models in segmentation accuracy and exhibits superior generalization across diverse datasets. DetailCLIP represents a significant advancement in vision-language modeling, offering a robust solution for tasks that demand high-level semantic understanding and detailed feature extraction. https://github.com/KishoreP1/DetailCLIP.

Cancer is a complex disease characterized by uncontrolled cell growth. T cell receptors (TCRs), crucial proteins in the immune system, play a key role in recognizing antigens, including those associated with cancer. Recent advancements in sequencing technologies have facilitated comprehensive profiling of TCR repertoires, uncovering TCRs with potent anti-cancer activity and enabling TCR-based immunotherapies. However, analyzing these intricate biomolecules necessitates efficient representations that capture their structural and functional information. T-cell protein sequences pose unique challenges due to their relatively smaller lengths compared to other biomolecules. An image-based representation approach becomes a preferred choice for efficient embeddings, allowing for the preservation of essential details and enabling comprehensive analysis of T-cell protein sequences. In this paper, we propose to generate images from the protein sequences using the idea of Chaos Game Representation (CGR) using the Kaleidoscopic images approach. This Deep Learning Assisted Analysis of Protein Sequences Using Chaos Enhanced Kaleidoscopic Images (called DANCE) provides a unique way to visualize protein sequences by recursively applying chaos game rules around a central seed point. we perform the classification of the T cell receptors (TCRs) protein sequences in terms of their respective target cancer cells, as TCRs are known for their immune response against cancer disease. The TCR sequences are converted into images using the DANCE method. We employ deep-learning vision models to perform the classification to obtain insights into the relationship between the visual patterns observed in the generated kaleidoscopic images and the underlying protein properties. By combining CGR-based image generation with deep learning classification, this study opens novel possibilities in the protein analysis domain.

The thermal conductivity of covalent organic frameworks (COFs), an emerging class of nanoporous polymeric materials, is crucial for many applications, yet the link between their structure and thermal properties is not well understood. From a dataset of over 2,400 COFs, we find that conventional features like density, pore size, void fraction, and surface area do not reliably predict thermal conductivity. To overcome this, we train an attention-based machine learning model that accurately predicts thermal conductivities, even for structures outside the training set. We then use the attention mechanism to understand why the model works. Surprisingly, dangling molecular branches emerge as key predictors of thermal conductivity, alongside conventional geometric descriptors like density and pore size. Our findings show that COFs with dangling functional groups exhibit lower thermal transfer capabilities than otherwise. Molecular dynamics simulations confirm this, revealing significant mismatches in the vibrational density of states due to the presence of dangling branches.

The Aharonov-Bohm (AB) caging is the phenomenon of extreme localization of particles experiencing magnetic field in certain tight binding lattices. While the AB caging involves the localization of non-interacting particles, it often breaks down due to the effect of interaction resulting in delocalization. In this study, however, we show that interactions under proper conditions can restore the AB caging of particles. By analysing the dynamics of two bosons possessing both onsite and nearest neighbor interactions on a one dimensional diamond/rhombus lattice pierced by an artificial gauge field, we show that the AB caging is restored when both the interactions are of equal strengths. Furthermore, the AB caged bosons, with the onset of an antisymmetric correlated onsite disorder in the lattice, escape from the cages, demonstrating the phenomenon of inverse Anderson transition which is known to be exhibited by the non-interacting AB caged particles. We also obtain situation similar to the inverse Anderson transition when an external potential gradient is applied to the lattice. These findings offer route to realize the AB caging and inverse Anderson transition of interacting particles in experiments involving ultracold atoms in optical lattices or superconducting circuits., Comment: 8 pages, 9 figures

Monitoring carbon dioxide (CO2) injected and stored in subsurface reservoirs is critical for avoiding failure scenarios and enables real-time optimization of CO2 injection rates. Sequential Bayesian data assimilation (DA) is a statistical method for combining information over time from multiple sources to estimate a hidden state, such as the spread of the subsurface CO2 plume. An example of scalable and efficient sequential Bayesian DA is the ensemble Kalman filter (EnKF). We improve upon existing DA literature in the seismic-CO2 monitoring domain by applying this scalable DA algorithm to a high-dimensional CO2 reservoir using two-phase flow dynamics and time-lapse full waveform seismic data with a realistic surface-seismic survey design. We show more accurate estimates of the CO2 saturation field using the EnKF compared to using either the seismic data or the fluid physics alone. Furthermore, we test a range of values for the EnKF hyperparameters and give guidance on their selection for seismic CO2 reservoir monitoring., Comment: This work has been submitted to the IEEE for possible publication

Molecular sequence analysis is crucial for comprehending several biological processes, including protein-protein interactions, functional annotation, and disease classification. The large number of sequences and the inherently complicated nature of protein structures make it challenging to analyze such data. Finding patterns and enhancing subsequent research requires the use of dimensionality reduction and feature selection approaches. Recently, a method called Correlated Clustering and Projection (CCP) has been proposed as an effective method for biological sequencing data. The CCP technique is still costly to compute even though it is effective for sequence visualization. Furthermore, its utility for classifying molecular sequences is still uncertain. To solve these two problems, we present a Nearest Neighbor Correlated Clustering and Projection (CCP-NN)-based technique for efficiently preprocessing molecular sequence data. To group related molecular sequences and produce representative supersequences, CCP makes use of sequence-to-sequence correlations. As opposed to conventional methods, CCP doesn't rely on matrix diagonalization, therefore it can be applied to a range of machine-learning problems. We estimate the density map and compute the correlation using a nearest-neighbor search technique. We performed molecular sequence classification using CCP and CCP-NN representations to assess the efficacy of our proposed approach. Our findings show that CCP-NN considerably improves classification task accuracy as well as significantly outperforms CCP in terms of computational runtime.

The blood-brain barrier (BBB) serves as a protective barrier that separates the brain from the circulatory system, regulating the passage of substances into the central nervous system. Assessing the BBB permeability of potential drugs is crucial for effective drug targeting. However, traditional experimental methods for measuring BBB permeability are challenging and impractical for large-scale screening. Consequently, there is a need to develop computational approaches to predict BBB permeability. This paper proposes a GPS Transformer architecture augmented with Self Attention, designed to perform well in the low-data regime. The proposed approach achieved a state-of-the-art performance on the BBB permeability prediction task using the BBBP dataset, surpassing existing models. With a ROC-AUC of 78.8%, the approach sets a state-of-the-art by 5.5%. We demonstrate that standard Self Attention coupled with GPS transformer performs better than other variants of attention coupled with GPS Transformer.

We study the Whittle index learning algorithm for restless multi-armed bandits. We consider index learning algorithm with Q-learning. We first present Q-learning algorithm with exploration policies -- epsilon-greedy, softmax, epsilon-softmax with constant stepsizes. We extend the study of Q-learning to index learning for single-armed restless bandit. The algorithm of index learning is two-timescale variant of stochastic approximation, on slower timescale we update index learning scheme and on faster timescale we update Q-learning assuming fixed index value. In Q-learning updates are in asynchronous manner. We study constant stepsizes two timescale stochastic approximation algorithm. We provide analysis of two-timescale stochastic approximation for index learning with constant stepsizes. Further, we present study on index learning with deep Q-network (DQN) learning and linear function approximation with state-aggregation method. We describe the performance of our algorithms using numerical examples. We have shown that index learning with Q learning, DQN and function approximations learns the Whittle index., Comment: 14 pages

LLM-powered personalization agent systems employ Large Language Models (LLMs) to predict users' behavior from their past activities. However, their effectiveness often hinges on the ability to effectively leverage extensive, long user historical data due to its inherent noise and length of such data. Existing pretrained LLMs may generate summaries that are concise but lack the necessary context for downstream tasks, hindering their utility in personalization systems. To address these challenges, we introduce Reinforcement Learning from Prediction Feedback (RLPF). RLPF fine-tunes LLMs to generate concise, human-readable user summaries that are optimized for downstream task performance. By maximizing the usefulness of the generated summaries, RLPF effectively distills extensive user history data while preserving essential information for downstream tasks. Our empirical evaluation demonstrates significant improvements in both extrinsic downstream task utility and intrinsic summary quality, surpassing baseline methods by up to 22% on downstream task performance and achieving an up to 84.59% win rate on Factuality, Abstractiveness, and Readability. RLPF also achieves a remarkable 74% reduction in context length while improving performance on 16 out of 19 unseen tasks and/or datasets, showcasing its generalizability. This approach offers a promising solution for enhancing LLM personalization by effectively transforming long, noisy user histories into informative and human-readable representations.

Trusted execution environments (TEEs) for machine learning accelerators are indispensable in secure and efficient ML inference. Optimizing workloads through state-space exploration for the accelerator architectures improves performance and energy consumption. However, such explorations are expensive and slow due to the large search space. Current research has to use fast analytical models that forego critical hardware details and cross-layer opportunities unique to the hardware security primitives. While cycle-accurate models can theoretically reach better designs, their high runtime cost restricts them to a smaller state space. We present Obsidian, an optimization framework for finding the optimal mapping from ML kernels to a secure ML accelerator. Obsidian addresses the above challenge by exploring the state space using analytical and cycle-accurate models cooperatively. The two main exploration components include: (1) A secure accelerator analytical model, that includes the effect of secure hardware while traversing the large mapping state space and produce the best m model mappings; (2) A compiler profiling step on a cycle-accurate model, that captures runtime bottlenecks to further improve execution runtime, energy and resource utilization and find the optimal model mapping. We compare our results to a baseline secure accelerator, comprising of the state-of-the-art security schemes obtained from guardnn [ 33 ] and sesame [11]. The analytical model reduces the inference latency by 20.5% for a cloud and 8.4% for an edge deployment with an energy improvement of 24% and 19% respectively. The cycle-accurate model, further reduces the latency by 9.1% for a cloud and 12.2% for an edge with an energy improvement of 13.8% and 13.1%.

This work introduces an online greedy method for constructing quadratic manifolds from streaming data, designed to enable in-situ analysis of numerical simulation data on the Petabyte scale. Unlike traditional batch methods, which require all data to be available upfront and take multiple passes over the data, the proposed online greedy method incrementally updates quadratic manifolds in one pass as data points are received, eliminating the need for expensive disk input/output operations as well as storing and loading data points once they have been processed. A range of numerical examples demonstrate that the online greedy method learns accurate quadratic manifold embeddings while being capable of processing data that far exceed common disk input/output capabilities and volumes as well as main-memory sizes., Comment: 20 pages, 10 figures

Some recent investigations indicate that for the classification of Cohen-Macaulay binomial edge ideals, it suffices to consider biconnected graphs with some whiskers attached (in short, `block with whiskers'). This paper provides explicit combinatorial formulae for the Castelnuovo-Mumford regularity of two specific classes of Cohen-Macaulay binomial edge ideals: (i) chain of cycles with whiskers and (ii) $r$-regular $r$-connected block with whiskers. For the first type, we introduce a new invariant of graphs in terms of the number of blocks in certain induced block graphs, and this invariant may help determine the regularity of other classes of binomial edge ideals. For the second type, we present the formula as a linear function of $r$., Comment: Comments are welcome

We propose a lesion-aware graph neural network (LEGNet) to predict language ability from resting-state fMRI (rs-fMRI) connectivity in patients with post-stroke aphasia. Our model integrates three components: an edge-based learning module that encodes functional connectivity between brain regions, a lesion encoding module, and a subgraph learning module that leverages functional similarities for prediction. We use synthetic data derived from the Human Connectome Project (HCP) for hyperparameter tuning and model pretraining. We then evaluate the performance using repeated 10-fold cross-validation on an in-house neuroimaging dataset of post-stroke aphasia. Our results demonstrate that LEGNet outperforms baseline deep learning methods in predicting language ability. LEGNet also exhibits superior generalization ability when tested on a second in-house dataset that was acquired under a slightly different neuroimaging protocol. Taken together, the results of this study highlight the potential of LEGNet in effectively learning the relationships between rs-fMRI connectivity and language ability in a patient cohort with brain lesions for improved post-stroke aphasia evaluation., Comment: Accepted at MICCAI 2024 International Workshop on Machine Learning in Clinical Neuroimaging (MLCN)

Robots find extensive applications in industry. In recent years, the influence of robots has also increased rapidly in domestic scenarios. The Q-learning algorithm aims to maximise the reward for reaching the goal. This paper proposes a modified version of the Q-learning algorithm, known as Q-learning with scaled distance metric (Q-SD). This algorithm enhances task learning and makes task completion more meaningful. A robotic manipulator (agent) applies the Q-SD algorithm to the task of table cleaning. Using Q-SD, the agent acquires the sequence of steps necessary to accomplish the task while minimising the manipulator's movement distance. We partition the table into grids of different dimensions. The first has a grid count of 3 times 3, and the second has a grid count of 4 times 4. Using the Q-SD algorithm, the maximum success obtained in these two environments was 86% and 59% respectively. Moreover, Compared to the conventional Q-learning algorithm, the drop in average distance moved by the agent in these two environments using the Q-SD algorithm was 8.61% and 6.7% respectively., Comment: 9 pages, 9 figures, 7 tables

This paper introduces a novel fingerprint classification technique based on a multi-layered fuzzy logic classifier. We target the cause of missed detection by identifying the fingerprints at an early stage among dry, standard, and wet. Scanned images are classified based on clarity correlated with the proposed feature points. We also propose a novel adaptive algorithm based on eigenvector space for generating new samples to overcome the multiclass imbalance. Proposed methods improve the performance of ensemble learners. It was also found that the new approach performs better than the neural-network based classification methods. Early-stage improvements give a suitable dataset for fingerprint detection models. Leveraging the novel classifier, the best set of `standard' labelled fingerprints is used to generate a unique hybrid fingerprint orientation map (HFOM). We introduce a novel min-rotate max-flow optimization method inspired by the min-cut max-flow algorithm. The unique properties of HFOM generation introduce a new use case for biometric data protection by using HFOM as a virtual proxy of fingerprints., Comment: 10 pages, 18 figures, 4 Tables The work mainly focuses on fingerprint classification and hybrid fingerprint orientation map (HFOM) generation. It highlights the security use cases of HFOM, eg. data encryption

The FC-Gram trigonometric polynomial approximation of a non-periodic function that interpolates the function on equispaced grids was introduced in 2010 by Bruno and Lyon [J. Comput. Phys, 229(6):2009-2033, 2010]. Since then, the approximation algorithm and its further refinements have been used extensively in numerical solutions of various PDE-based problems, and it has had impressive success in handling challenging configurations. While much computational evidence exists in the literature confirming the rapid convergence of FC-Gram approximations, a theoretical convergence analysis has remained open. In this paper, we study a modified FC-Gram algorithm where the implicit least-squares-based periodic extensions of the Gram polynomials are replaced with an explicit extension utilizing two-point Hermite polynomials. This modification brings in two significant advantages - (i) as the extensions are known explicitly, the need to use computationally expensive precomputed extension data is eliminated, which, in turn, facilitates seamlessly changing the extension length, and (ii) allows for establishing provable error bounds for the modified approximations. We show that the numerical convergence rates are consistent with those predicted by the theory through a variety of computational experiments.

Understanding electrical characteristics and corresponding transport models at single molecular junctions is crucial. There have been many reports on organic compounds-based single molecular junctions. However, organometallic compounds-based single molecular junctions have not been explored yet. Re(I) organometallic compounds are known to exhibit intriguing photophysical properties scrutinized for photocatalysis, and light-emitting diodes but have not been explored in molecular electronics. In this work, a theoretical model study on the I-V characteristics of two Re(I)-carbonyl complexes bearing Re-P and Re-N-N linkage has been meticulously chosen. Tunneling and hopping transport in Au/Re(I)-complex/Au single-molecule junctions are governed by Landauer-formalism and the Marcus theory, respectively. Interestingly, variations in molecular architecture culminate in notable variations in junction functionality and mechanism of charge conduction. Physical parameters influencing the device characteristics such as dipole moment, molecule-electrode coupling strength, voltage division factor, and temperature have been extensively studied which offers modulation of the characteristics and device design. The dominant hopping current in Re complex bearing bipyridine linkage was found to be responsible for the observed asymmetric electrical (I-V) behavior. Our work paves the way for constructing various organometallic compounds-based molecular junctions to understand electronic functions and the underlying transport mechanisms., Comment: 17 pages, 8 figures

Exoskeletons and rehabilitation systems offer great potential for enhancing human strength and recovery through advanced human-machine interfaces (HMIs) that adapt to movement dynamics. However, the real-time application of physics-informed neural networks (PINNs) is limited by their reliance on fixed input lengths and surrogate models. This study introduces a novel physics-informed Gated Recurrent Network (PiGRN) designed to predict multi-joint torques using surface electromyography (sEMG) data. The PiGRN model employs a Gated Recurrent Unit (GRU) to convert time-series sEMG inputs into multi-joint kinematics and external loads, which are then integrated into an equation of motion to ensure consistency with physical laws. Experimental validation with sEMG data from five participants performing elbow flexion-extension tasks showed that the PiGRN model accurately predicted joint torques for 10 unfamiliar movements, with RMSE values between 4.02\% and 11.40\% and correlation coefficients ranging from 0.87 to 0.98. These findings highlight the PiGRN's potential for real-time exoskeleton and rehabilitation applications. Future research will explore more diverse datasets, improve musculoskeletal models, and investigate unsupervised learning methods.

The COVID-19 pandemic necessitated the emergence of decentralized Clinical Trials (DCTs) due to patient retention, accelerate trials, improve data accessibility, enable virtual care, and facilitate seamless communication through integrated systems. However, integrating systems in DCTs exposes clinical data to potential security threats, making them susceptible to theft at any stage, a high risk of protocol deviations, and monitoring issues. To mitigate these challenges, blockchain technology serves as a secure framework, acting as a decentralized ledger, creating an immutable environment by establishing a zero-trust architecture, where data are deemed untrusted until verified. In combination with Internet of Things (IoT)-enabled wearable devices, blockchain secures the transfer of clinical trial data on private blockchains during DCT automation and operations. This paper proposes a prototype model of the Zero-Trust Architecture Blockchain (z-TAB) to integrate patient-generated clinical trial data during DCT operation management. The EigenTrust-based Practical Byzantine Fault Tolerance (T-PBFT) algorithm has been incorporated as a consensus protocol, leveraging Hyperledger Fabric. Furthermore, the Internet of Things (IoT) has been integrated to streamline data processing among stakeholders within the blockchain platforms. Rigorous evaluation has been done to evaluate the quality of the system., Comment: NA

The emergence of cognition requires a framework that bridges evolutionary principles with neurocomputational mechanisms. This paper introduces the novel "thoughtseed" framework, proposing that cognition arises from the dynamic interaction of self-organizing units of embodied knowledge called "thoughtseeds" within the Global Workspace of consciousness. Leveraging foundational concepts from evolutionary theory, neuronal packets, and free energy principle, we propose a hierarchical model of cognitive states, comprising Neuronal Packet Domains (NPDs), Knowledge Domains (KDs), the thoughtseed network, and meta-cognition. This hierarchical interplay, mediated by nested Markov blankets and reciprocal message passing, facilitates the emergence of thoughtseeds as coherent patterns of activity that guide perception, action, and learning. Thoughtseeds, posited as fundamental units of thought, compete for dominance within the Global Workspace, with the dominant thoughtseed shaping conscious experience and guiding behavior. We present a mathematical framework grounded in active inference and dynamical systems theory to model thoughtseed dynamics and their contribution to the unitary nature of consciousness. The thoughtseed framework offers a promising step towards a novel, biologically-grounded model for understanding the organizing principles and emergence of embodied cognition, offering a unified account of cognitive phenomena, with potential applications in understanding consciousness, attention, and decision-making., Comment: 2nd version. Reduced in size from 88 pages to 48 pages, to focus on the core framework. Items removed: Detailed discussions on Evolutionary Biology, contemplative traditions and computational theories of Mind. Replaced Inner Screen Model with Global Workspace Theory. Mathematical equations have been streamlined and is better organized. preprint arXiv:2408.15982

Two-dimensional (2D) van der Waals ferromagnets have potential applications as next-generation spintronic devices and provide a platform to explore the fundamental physics behind 2D magnetism. The dual nature (localized and itinerant) of electrons adds further complexity to the understanding of correlated magnetic materials. Here, we present the temperature evolution of electronic structure in 2D van der Waals ferromagnet, Fe$_{3}$GeTe$_{2}$, using photoemission spectroscopy in conjunction with density functional theory (DFT) plus dynamical mean field theory (DMFT). With the appearance of quasiparticle peak and its evolution in the vicinity of Fermi energy, we unveil empirical evidences of incoherent-coherent crossover at around 125 K. DFT+DMFT results show that the quasiparticle lifetime surpasses thermal energy for temperature below 150 K, confirming incoherent-coherent crossover in the system. No appreciable change in the Fe 2$p$ core level, overall valence band spectra across the magnetic transition, and temperature dependent ferromagnetic DFT+DMFT results, provide substantial evidence for non-stoner magnetism in Fe$_{3}$GeTe$_{2}$. We elucidate the temperature dependent intimate relation between magnetism and electronic structure in Fe$_{3}$GeTe$_{2}$. Sommerfeld coefficient of $\sim$ 104 mJ mol$^{-1}$ K$^{-2}$ obtained in the low temperature limit from DFT+DMFT calculations resolve the long standing issue of large Sommerfeld coefficient ($\sim$ 110 mJ mol$^{-1}$ K$^{-2}$) obtained from specific heat measurements., Comment: to appear in Phys. Rev. B

We have investigated the surface and bulk electronic structures of the superconducting type-II Dirac semimetal 1T-PdSeTe. The superconducting transition temperature $T_C = 3.2$ K was almost twice as high as $T_C = 1.6$ K in 1T-PdTe$_2$. Scanning transmission electron microscopy measurements showed homogeneously mixed Se and Te atoms in the chalcogen layers, consistent with the CdI$_2$-type crystal structure. Angle-resolved photoemission spectroscopy measurements and density functional theory calculations indicated the existence of the topological surface states, and the overall band structures were similar to those of 1T-PdTe$_2$. These results suggest that CdI$_2$-type lattice symmetry dictates the band dispersion, regardless of atomic disorder in the chalcogen layers. As the electronic band dispersion and the local structures were persistent upon substitution, the enhancement of $T_C$ is likely associated with the chemical pressure. Our results provide insight into the effects of the solid solution on the surface and bulk electronic states as well as the superconducting transition temperature., Comment: Quantum materials, Dirac semimetal, Weyl semimetal, Topological insulator, Topological superconductor, angle-resolved Photoemission spectroscopy, superconductivity

We link the presence of "unnecessary" quantum critical surfaces within a single gapped phase of matter to the non-trivial topology of families of gapped Hamiltonians that encircle the critical surface. We study a specific set of one-dimensional spin models where each such family forms a one-parameter loop in a two-dimensional phase diagram. Foliating the non-critical region by such loops identifies "radial" and "angular" coordinates in the phase diagram that respectively parametrize different families and different members of a single family. We show that each one-parameter family is a generalized Thouless charge pump, all with the same topological index, and hence the gapped phase undergoes one or more nontrivial boundary phase transitions as we vary the angular coordinate in a loop through members of one family. Tuning the radial coordinate generates loci of boundary critical points that terminate at endpoints of the bulk unnecessary critical line within the gapped phase. We discuss broader implications of our results and possible extensions to higher dimensions., Comment: 7+15 pages, 2+7 figures (main + supplementary material)

Automatic Speech Recognition (ASR) systems are pivotal in transcribing speech into text, yet the errors they introduce can significantly degrade the performance of downstream tasks like summarization. This issue is particularly pronounced in clinical dialogue summarization, a low-resource domain where supervised data for fine-tuning is scarce, necessitating the use of ASR models as black-box solutions. Employing conventional data augmentation for enhancing the noise robustness of summarization models is not feasible either due to the unavailability of sufficient medical dialogue audio recordings and corresponding ASR transcripts. To address this challenge, we propose MEDSAGE, an approach for generating synthetic samples for data augmentation using Large Language Models (LLMs). Specifically, we leverage the in-context learning capabilities of LLMs and instruct them to generate ASR-like errors based on a few available medical dialogue examples with audio recordings. Experimental results show that LLMs can effectively model ASR noise, and incorporating this noisy data into the training process significantly improves the robustness and accuracy of medical dialogue summarization systems. This approach addresses the challenges of noisy ASR outputs in critical applications, offering a robust solution to enhance the reliability of clinical dialogue summarization.

This paper explores key aspects of the theory and applications of conformable fractional order systems. It begins by establishing the existence and uniqueness of the evolution operator for a class of non-autonomous homogeneous systems. Using the Schauder fixed point theorem and the theory of linear evolution systems, we delve into the existence of mild solutions for a class of non-autonomous conformable fractional semi-linear systems. Additionally, the paper addresses the exact null controllability of abstract systems. We present an example to demonstrate the efficiency of the results., Comment: 24 Pages, 0 figures

The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy toward the implementation of this leading-edge, large-scale science project. The 2023 report of the US Particle Physics Project Prioritization Panel (P5) reaffirmed this vision and strongly endorsed DUNE Phase I and Phase II, as did the European Strategy for Particle Physics. While the construction of the DUNE Phase I is well underway, this White Paper focuses on DUNE Phase II planning. DUNE Phase-II consists of a third and fourth far detector (FD) module, an upgraded near detector complex, and an enhanced 2.1 MW beam. The fourth FD module is conceived as a "Module of Opportunity", aimed at expanding the physics opportunities, in addition to supporting the core DUNE science program, with more advanced technologies. This document highlights the increased science opportunities offered by the DUNE Phase II near and far detectors, including long-baseline neutrino oscillation physics, neutrino astrophysics, and physics beyond the standard model. It describes the DUNE Phase II near and far detector technologies and detector design concepts that are currently under consideration. A summary of key R&D goals and prototyping phases needed to realize the Phase II detector technical designs is also provided. DUNE's Phase II detectors, along with the increased beam power, will complete the full scope of DUNE, enabling a multi-decadal program of groundbreaking science with neutrinos.

Medical abstractive summarization faces the challenge of balancing faithfulness and informativeness. Current methods often sacrifice key information for faithfulness or introduce confabulations when prioritizing informativeness. While recent advancements in techniques like in-context learning (ICL) and fine-tuning have improved medical summarization, they often overlook crucial aspects such as faithfulness and informativeness without considering advanced methods like model reasoning and self-improvement. Moreover, the field lacks a unified benchmark, hindering systematic evaluation due to varied metrics and datasets. This paper addresses these gaps by presenting a comprehensive benchmark of six advanced abstractive summarization methods across three diverse datasets using five standardized metrics. Building on these findings, we propose uMedSum, a modular hybrid summarization framework that introduces novel approaches for sequential confabulation removal followed by key missing information addition, ensuring both faithfulness and informativeness. Our work improves upon previous GPT-4-based state-of-the-art (SOTA) medical summarization methods, significantly outperforming them in both quantitative metrics and qualitative domain expert evaluations. Notably, we achieve an average relative performance improvement of 11.8% in reference-free metrics over the previous SOTA. Doctors prefer uMedSum's summaries 6 times more than previous SOTA in difficult cases where there are chances of confabulations or missing information. These results highlight uMedSum's effectiveness and generalizability across various datasets and metrics, marking a significant advancement in medical summarization., Comment: 12 pages

We argue for the existence of a universal phase diagram of $k$-local quantum chains subject to the conservation of a total charge and its dipole moment, which exhibits "freezing" transitions between strongly and weakly Hilbert space fragmented phases as charge filling $\nu$ is varied. We show that these continuous phase transitions occur at a critical charge filling of $\nu_c=(k-2)^{-1}$ independently of the on-site Hilbert space dimension $d$. To this end, we analytically prove that for any $d$, any state for $\nu<\nu_c$ hosts a finite density of sites belonging to "blockages": local regions across which transport of charge and dipole moment cannot occur. We prove that this implies strong fragmentation of typical symmetry sectors into Krylov sectors that each form an exponentially-vanishing fraction of the total sector. By studying the distribution of blockages we analytically characterise how typical states are subdivided into dynamically-disconnected local "active bubbles", and prove that typical states at these charge fillings exhibit area-law entanglement scaling, with rare "inverse quantum many-body scar" states featuring non-area-law scaling. We then numerically show that for $\nu>\nu_c$ and arbitrary $d$, typical symmetry sectors are weakly fragmented, with their dominant Krylov sectors constituted of states that are free of blockages. We also study the critical scaling of the dimensions of various Krylov sectors at $\nu=\nu_c$, as well as investigate the properties of certain special case models for which no phase transitions occur., Comment: 43 pages, 11 figures, 2 tables

Magnetic monopole quasiparticles in spin ice materials hold the potential for exploring new frontiers of physics that extend beyond Maxwell's equations. We have previously proposed a two-dimensional magnetic monopole gas (2DMG), confined at the interface between spin-ice ($R_2$Ti$_2$O$_7$, $R$ = Dy, Ho) and antiferromagnetic iridate ($R_2$Ir$_2$O$_7$, $R$ = Dy, Ho), which hosts monopoles with a net charge. The mechanism behind the 2D confinement of the monopole gas remains unclear. In this work, we demonstrate that entropy is a key factor in the 2D confinement of this monopole gas. We reveal that the competition between the entropy of spin-ice, which favors the 2D confinement, and the entropy of the monopoles' random walks, which favors the deconfinement, dictates the distribution of the monopoles within a few layers close to the interface. Our entropy-based model accurately reproduces the monopole distribution obtained from the spin model, affirming that 2D confinement is entropy-driven. We further employ both models to show that the monopole distribution can be manipulated by an external magnetic field and temperature, holding promise for next-generation devices based on magnetic monopoles. Our findings reveal the entropic mechanisms in 2DMG, enabling the manipulation of emergent quasiparticles at material interfaces., Comment: 8 pages, 4 figures

Extreme Classification (XC) aims to map a query to the most relevant documents from a very large document set. XC algorithms used in real-world applications learn this mapping from datasets curated from implicit feedback, such as user clicks. However, these datasets inevitably suffer from missing labels. In this work, we observe that systematic missing labels lead to missing knowledge, which is critical for accurately modelling relevance between queries and documents. We formally show that this absence of knowledge cannot be recovered using existing methods such as propensity weighting and data imputation strategies that solely rely on the training dataset. While LLMs provide an attractive solution to augment the missing knowledge, leveraging them in applications with low latency requirements and large document sets is challenging. To incorporate missing knowledge at scale, we propose SKIM (Scalable Knowledge Infusion for Missing Labels), an algorithm that leverages a combination of small LM and abundant unstructured meta-data to effectively mitigate the missing label problem. We show the efficacy of our method on large-scale public datasets through exhaustive unbiased evaluation ranging from human annotations to simulations inspired from industrial settings. SKIM outperforms existing methods on Recall@100 by more than 10 absolute points. Additionally, SKIM scales to proprietary query-ad retrieval datasets containing 10 million documents, outperforming contemporary methods by 12% in offline evaluation and increased ad click-yield by 1.23% in an online A/B test conducted on a popular search engine. We release our code, prompts, trained XC models and finetuned SLMs at: https://github.com/bicycleman15/skim, Comment: Preprint, 23 pages

The Monotonocity Principle states a monotonic relationship between a possibly non-linear material property and a proper corresponding boundary operator. The Monotonicity Principle (MP) has attracted great interest in the field of inverse problems, because of its fundamental role in developing real time imaging methods. Recently, with quite general assumptions, a MP in the presence of non linear materials has been established for elliptic PDE, such as those governing Electrical Resistance Tomography. Together with recently introduced imaging methods and algorithms based on MP, arises a fundamental question related to the Converse (of the MP). Indeed, the Converse of the MP is fundamental to define the theoretical limits of applicability of imaging methods and algorithms. Specifically, the Converse of the MP guarantees that the outer boundary of a nonlinear anomaly can be reconstructed by means of MP based imaging methods. In this paper, the Converse of the Monotonicity Principle for nonlinear anomaly embedded in a linear material is proved. The results is provided in a quite general setting for Electrical Resistance Tomography. Moreover, the nonlinear electrical conductivity of the anomaly, as function of the electric field, can be either bounded or not bounded from infinity and/or zero.

This study proposes and explores a linear hydrodynamic thermo-elasticity system within mixture models, comprising fluid and solid phases, with a focus on biological tissues, particularly tumor-related phenomena. Although tumor growth is not yet incorporated, this work aims to comprehend the interaction between thermal effects and hydrodynamics on short-time scales where the tumor size typically remains stable. We establish the existence of a unique weak solution within the framework of implicit evolution equations, overcoming challenges posed by intricate coupling mechanisms within the system. To further investigate the model, we then study the one-dimensional model and explore in detail the complex interplay between fluid flow, solid deformation, and heat transfer. This complex coupled system of equations is reduced for the short time scale to obtain the semi-analytical solution.

The growth of weeds poses a significant challenge to agricultural productivity, necessitating efficient and accurate weed detection and management strategies. The combination of multispectral imaging and drone technology has emerged as a promising approach to tackle this issue, enabling rapid and cost-effective monitoring of large agricultural fields. This systematic review surveys and evaluates the state-of-the-art in machine learning interventions for weed detection that utilize multispectral images captured by unmanned aerial vehicles. The study describes the various models used for training, features extracted from multi-spectral data, their efficiency and effect on the results, the performance analysis parameters, and also the current challenges faced by researchers in this domain. The review was conducted in accordance with the PRISMA guidelines. Three sources were used to obtain the relevant material, and the screening and data extraction were done on the COVIDENCE platform. The search string resulted in 600 papers from all sources. The review also provides insights into potential research directions and opportunities for further advancements in the field. These insights would serve as a valuable guide for researchers, agricultural scientists, and practitioners in developing precise and sustainable weed management strategies to enhance agricultural productivity and minimize ecological impact.