arXiv subjects

Shashank Shekhar

Publications and source records attributed to Shashank Shekhar.

At least 19 recordsLinked to original sources

arXiv2026

Quantum connectivity of quantum networks

The practical utility of a quantum network depends on its ability to establish entanglement between arbitrary node pairs with quality sufficient to execute entanglement enabled tasks. This capability can be assessed globally, through aggregate performance over all node pairs, as well as locally, at the level of individual nodes. Since entanglement-based connections form a layer above the underlying physical topology, quantum connectivity is not adequately captured by classical topological connectivity metrics. To enable characterisation of the quantum connectivity at the level of the network (or its subnetworks), we introduce the quantum connectivity measure (QCM), which quantifies the average connection quality between pairs of network nodes. Further, we describe two quantities, the quantum-connected fraction (QCF) and the quantum clustering coefficient (QCC), naturally derived from the QCM, which capture important features of the functional connectivity of the quantum network at the level of the network and an individual node, respectively. These metrics of quantum connectivity depend crucially on the entanglement distribution protocol and the quantum network parameters in addition to its physical topology. We demonstrate the crucial distinction between topological and quantum connectivity, showing that even a fully connected graph can be functionally disconnected for quantum tasks if average network edge-concurrence falls below a critical threshold. These quantum connectivity metrics thus provide important tools for the design, optimization, and benchmarking of future quantum networks.

quant-ph↗

arXiv2026

Fast Diffusion with Physics-Correction for ACOPF

Generating large-scale, physically consistent AC Optimal Power Flow (ACOPF) datasets is essential for modern data-driven power system applications. The central challenge lies in balancing solution accuracy with computational efficiency. Recent diffusion-based generative models produce high-quality samples; however, their slow sampling procedures limit practical scalability. In this work, we argue that exact physical feasibility is ultimately enforced by power flow solvers or projection steps, and therefore the generative model only needs to produce good initializations rather than perfectly feasible solutions. Based on this insight, we propose a fast diffusion framework using Denoising Diffusion Implicit Models (DDIM) combined with physics-guided corrections during sampling. The proposed method replaces slow stochastic refinement with a small number of deterministic steps and explicit constraint guidance. Experiments on IEEE 6-, 24-, and 118-bus systems show that our approach achieves up to 20 times faster sampling than standard diffusion models while maintaining comparable statistical accuracy and physical consistency. This makes the method well suited for scalable OPF dataset generation and practical power system learning tasks. We release the implementation code at https://github.com/PSquare-Lab/DDIM_OPF.

eess.SY↗

arXiv2026

Integrating OTFS in Airplane-Aided Next-Generation Networking

Next-generation networks explore the opportunistic assistance of airliner/high-altitude platforms (HAPs) in delivering high data rates for terrestrial networks to ensure consistent and reliable communication. When an airliner/HAP moves at very high speeds, its mobility has a substantial impact on ensuring seamless connectivity, stable signal strength, and reliable data transmission. Orthogonal time frequency space (OTFS) modulation has been shown to provide notable improvement in performance when handling Doppler effects during high-mobility situations. This paper presents an OTFS-based airplane-aided next-generation networking system. In the proposed system, the airliner/HAPs are equipped with a planar antenna array that applies null steering beamforming (NSB) at the transmitter for communication with terrestrial users. A comprehensive performance comparison between OTFS and orthogonal frequency division multiplexing (OFDM) is performed under varying airliner altitude, velocity, array dimension, and Rician factor conditions. The simulation results show that OTFS consistently outperforms OFDM, achieving a lower bit error rate (BER) and more stable performance across different airliner altitudes, velocities, array dimensions, and propagation environments.

cs.IT↗

arXiv2025

RIS, Active RIS or RDARS: A Comparative Insight Through the Lens of Energy Efficiency

Multiplicative fading is a major limitation of reconfigurable intelligent surfaces (RIS), restricting their effective coverage in both existing sub-6GHz systems and future mmWave networks. Although active RIS architectures mitigate this issue, they require high power consumption and introduce practical challenges due to the need for integrated amplifiers. Recently, reconfigurable distributed antenna and reflecting surfaces (RDARS) have been proposed to alleviate multiplicative fading through connected modes. In this work, we compare RIS, active RIS, and RDARS in terms of coverage and energy efficiency (EE) in both sub-6GHz and mmWave bands, and we investigate the impact of placement and the number of elements of reconfigurable surface (RS) on EE and coverage. The simulation results show that RDARS offers a highly energy-efficient alternative of enhancing coverage in sub-6GHz systems, while active RIS is significantly more energy-efficient in mmWave systems. Additionally, for a lower number of RS elements and for near UEs, RIS remains considerably more energy-efficient than both active RIS and RDARS.

cs.IT↗

arXiv2025

NTIRE 2025 Image Shadow Removal Challenge Report

This work examines the findings of the NTIRE 2025 Shadow Removal Challenge. A total of 306 participants have registered, with 17 teams successfully submitting their solutions during the final evaluation phase. Following the last two editions, this challenge had two evaluation tracks: one focusing on reconstruction fidelity and the other on visual perception through a user study. Both tracks were evaluated with images from the WSRD+ dataset, simulating interactions between self- and cast-shadows with a large number of diverse objects, textures, and materials.

cs.CV↗

arXiv2025

Optimal resource requirements for connected quantum sub-networks

The realization of a global quantum network capable of supporting secure communication and other quantum information processing (QIP) tasks hinges on the ability to distribute high-fidelity entanglement across long distances while optimizing resource usage. This work describes a scalable approach for building large quantum networks by connecting quantum sub-networks using entanglement backbones as interconnections and a swapping based entanglement distribution protocol. Using a statistical model for parametrized quantum sub-networks we derive a set of equations whose solutions give the optimal values of average network parameters that meet threshold requirements for QIP tasks while minimizing resource cost functions. Our analysis extends to the scenario where multiple sub-networks must be interconnected simultaneously based on the formulation of a global resource cost function. The probability of successfully satisfying the parameter thresholds of a QIP task as a function of average parameters of the sub-networks for random network demands reveals a transition from zero to full satisfiability above critical network parameter values. Moreover, we find that the satisfiability transition can be smooth or discontinuous depending on the topology of the sub-networks. Our results present a pathway for calculating optimal resource requirements in quantum sub-networks interconnected to form the global quantum internet.

quant-ph↗

arXiv2024

An Epistemic Human-Aware Task Planner which Anticipates Human Beliefs and Decisions

We present a substantial extension of our Human-Aware Task Planning framework, tailored for scenarios with intermittent shared execution experiences and significant belief divergence between humans and robots, particularly due to the uncontrollable nature of humans. Our objective is to build a robot policy that accounts for uncontrollable human behaviors, thus enabling the anticipation of possible advancements achieved by the robot when the execution is not shared, e.g. when humans are briefly absent from the shared environment to complete a subtask. But, this anticipation is considered from the perspective of humans who have access to an estimated model for the robot. To this end, we propose a novel planning framework and build a solver based on AND-OR search, which integrates knowledge reasoning, including situation assessment by perspective taking. Our approach dynamically models and manages the expansion and contraction of potential advances while precisely keeping track of when (and when not) agents share the task execution experience. The planner systematically assesses the situation and ignores worlds that it has reason to think are impossible for humans. Overall, our new solver can estimate the distinct beliefs of the human and the robot along potential courses of action, enabling the synthesis of plans where the robot selects the right moment for communication, i.e. informing, or replying to an inquiry, or defers ontic actions until the execution experiences can be shared. Preliminary experiments in two domains, one novel and one adapted, demonstrate the effectiveness of the framework.

cs.RO↗

arXiv2024

Product and Ratio of Two $\alpha-\kappa-\mu$ Shadowed Random Variables and its Application to Wireless Communication

This work studies the product and ratio statistics of independent and non-identically distributed (i.n.i.d) $ \alpha-\kappa - \mu $ shadowed random variables. We derive the series expression for the probability density function (PDF), cumulative distribution function (CDF), and moment generating function (MGF) of the product and ratio of i.n.i.d $ \alpha - \kappa - \mu $ shadowed random variables. We then give the single integral representation for the derived PDF expressions. Further, as application examples, 1) outage probability has been derived for cascaded wireless systems, and 2) physical-layer security metrics like secrecy outage probability and strictly positive secrecy capacity are derived for the classic three-node model with $\alpha-\kappa-\mu$ shadowed fading. Next, we discuss an intelligent reflecting surface-assisted communication system over $\alpha-\kappa-\mu$ shadowed fading.

cs.IT↗

arXiv2024

Coined Quantum Walk on a Quantum Network

We explore a discrete-time, coined quantum walk on a quantum network where the coherent superposition of walker-moves originates from the unitary interaction of the walker-coin with the qubit degrees of freedom in the quantum network. The walk dynamics leads to a growth of entanglement between the walker and the network on one hand, and on the other, between the network-qubits among themselves. The initial entanglement among the network qubits plays a crucial role in determining the asymptotic values of these entanglement measures and the quantum walk statistics. Specifically, the entanglement entropy of the walker-network state and the negativity of the quantum network-qubit state saturate to values increasing with the initial network-entanglement. The asymptotic time-averaged walker-position probability distribution shows increasing localization around the initial walker-position with higher initial network entanglement. A potential application of these results as a characterisation tool for quantum network properties is suggested.

quant-ph↗

arXiv2023

PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning

Synthetic image datasets offer unmatched advantages for designing and evaluating deep neural networks: they make it possible to (i) render as many data samples as needed, (ii) precisely control each scene and yield granular ground truth labels (and captions), (iii) precisely control distribution shifts between training and testing to isolate variables of interest for sound experimentation. Despite such promise, the use of synthetic image data is still limited -- and often played down -- mainly due to their lack of realism. Most works therefore rely on datasets of real images, which have often been scraped from public images on the internet, and may have issues with regards to privacy, bias, and copyright, while offering little control over how objects precisely appear. In this work, we present a path to democratize the use of photorealistic synthetic data: we develop a new generation of interactive environments for representation learning research, that offer both controllability and realism. We use the Unreal Engine, a powerful game engine well known in the entertainment industry, to produce PUG (Photorealistic Unreal Graphics) environments and datasets for representation learning. In this paper, we demonstrate the potential of PUG to enable more rigorous evaluations of vision models.

cs.CV↗

arXiv2023

Table Detection in the Wild: A Novel Diverse Table Detection Dataset and Method

Recent deep learning approaches in table detection achieved outstanding performance and proved to be effective in identifying document layouts. Currently, available table detection benchmarks have many limitations, including the lack of samples diversity, simple table structure, the lack of training cases, and samples quality. In this paper, we introduce a diverse large-scale dataset for table detection with more than seven thousand samples containing a wide variety of table structures collected from many diverse sources. In addition to that, we also present baseline results using a convolutional neural network-based method to detect table structure in documents. Experimental results show the superiority of applying convolutional deep learning methods over classical computer vision-based methods. The introduction of this diverse table detection dataset will enable the community to develop high throughput deep learning methods for understanding document layout and tabular data processing. Dataset is available at: 1. https://www.kaggle.com/datasets/mrinalim/stdw-dataset 2. https://huggingface.co/datasets/n3011/STDW

cs.CV↗

arXiv2023

A PSO Based Method to Generate Actionable Counterfactuals for High Dimensional Data

Counterfactual explanations (CFE) are methods that explain a machine learning model by giving an alternate class prediction of a data point with some minimal changes in its features. It helps the users to identify their data attributes that caused an undesirable prediction like a loan or credit card rejection. We describe an efficient and an actionable counterfactual (CF) generation method based on particle swarm optimization (PSO). We propose a simple objective function for the optimization of the instance-centric CF generation problem. The PSO brings in a lot of flexibility in terms of carrying out multi-objective optimization in large dimensions, capability for multiple CF generation, and setting box constraints or immutability of data attributes. An algorithm is proposed that incorporates these features and it enables greater control over the proximity and sparsity properties over the generated CFs. The proposed algorithm is evaluated with a set of action-ability metrics in real-world datasets, and the results were superior compared to that of the state-of-the-arts.

cs.AI↗

arXiv2023

Multicomponent rendezvous of cofilin, profilin and twinfilin at the actin filament barbed end

Cellular actin dynamics result from collective action of hundreds of regulatory proteins, majority of which target actin filaments at their barbed ends. Three key actin binding proteins - profilin, cofilin and twinfilin individually depolymerize filament barbed ends. Notwithstanding recent leaps in our understanding of their individual action, how they collectively regulate filament dynamics remains an open question. In absence of direct and simultaneous visualization of these proteins at barbed ends, gaining mechanistic insights has been challenging. We have here investigated multicomponent dynamics of profilin, cofilin and twinfilin using a hybrid approach that combines high throughput single filament experiments with theory. We discovered that while twinfilin competes with profilin, it promotes binding of cofilin to filament sides. Interestingly, contrary to previous expectations, we found that profilin and cofilin can simultaneously bind the same filament barbed end resulting in its accelerated depolymerization. Our study reveals that pair-wise interactions can effectively capture depolymerization dynamics in simultaneous presence of all three proteins. We thus believe that our approach of employing a theory-experiment dialog can potentially help decipher multicomponent regulation of actin dynamics.

physics.bio-ph↗

arXiv2023

Multi-Task Learning Improves Performance In Deep Argument Mining Models

The successful analysis of argumentative techniques from user-generated text is central to many downstream tasks such as political and market analysis. Recent argument mining tools use state-of-the-art deep learning methods to extract and annotate argumentative techniques from various online text corpora, however each task is treated as separate and different bespoke models are fine-tuned for each dataset. We show that different argument mining tasks share common semantic and logical structure by implementing a multi-task approach to argument mining that achieves better performance than state-of-the-art methods for the same problems. Our model builds a shared representation of the input text that is common to all tasks and exploits similarities between tasks in order to further boost performance via parameter-sharing. Our results are important for argument mining as they show that different tasks share substantial similarities and suggest a holistic approach to the extraction of argumentative techniques from text.

cs.CL↗

arXiv2023

A Cookbook of Self-Supervised Learning

Self-supervised learning, dubbed the dark matter of intelligence, is a promising path to advance machine learning. Yet, much like cooking, training SSL methods is a delicate art with a high barrier to entry. While many components are familiar, successfully training a SSL method involves a dizzying set of choices from the pretext tasks to training hyper-parameters. Our goal is to lower the barrier to entry into SSL research by laying the foundations and latest SSL recipes in the style of a cookbook. We hope to empower the curious researcher to navigate the terrain of methods, understand the role of the various knobs, and gain the know-how required to explore how delicious SSL can be.

cs.LG↗

arXiv2023

Outage Probability Analysis of Uplink Cell-Free Massive MIMO Network with and without Pilot Contamination

This paper derives approximate outage probability (OP) expressions for uplink cell-free massive multiple-input-multiple-output (CF-mMIMO) systems with and without pilot contamination. The system's access points (APs) are considered to have imperfect channel state information (CSI). The signal-to-interference-plus-noise ratio (SINR) of the CF-mMIMO system is approximated via a Log-normal distribution using a two-step moment matching method. OP and ergodic rate expressions are derived with the help of the approximated Log-normal distribution. For the no-pilot contamination scenario, an exact expression is first derived using conditional expectations in terms of a multi-fold integral. Then, a novel dimension reduction method is used to approximate it by the sum of single-variable integrations. Both the approximations derived for the CF-mMIMO systems are also useful for single-cell collocated massive MIMO (mMIMO) systems and lead to closed-form expression. The derived expressions closely match the simulated numerical values for OP and ergodic rate.

cs.IT↗

arXiv2023

Objectives Matter: Understanding the Impact of Self-Supervised Objectives on Vision Transformer Representations

Joint-embedding based learning (e.g., SimCLR, MoCo, DINO) and reconstruction-based learning (e.g., BEiT, SimMIM, MAE) are the two leading paradigms for self-supervised learning of vision transformers, but they differ substantially in their transfer performance. Here, we aim to explain these differences by analyzing the impact of these objectives on the structure and transferability of the learned representations. Our analysis reveals that reconstruction-based learning features are significantly dissimilar to joint-embedding based learning features and that models trained with similar objectives learn similar features even across architectures. These differences arise early in the network and are primarily driven by attention and normalization layers. We find that joint-embedding features yield better linear probe transfer for classification because the different objectives drive different distributions of information and invariances in the learned representation. These differences explain opposite trends in transfer performance for downstream tasks that require spatial specificity in features. Finally, we address how fine-tuning changes reconstructive representations to enable better transfer, showing that fine-tuning re-organizes the information to be more similar to pre-trained joint embedding models.

cs.LG↗

arXiv2023

Beyond neural scaling laws: beating power law scaling via data pruning

Widely observed neural scaling laws, in which error falls off as a power of the training set size, model size, or both, have driven substantial performance improvements in deep learning. However, these improvements through scaling alone require considerable costs in compute and energy. Here we focus on the scaling of error with dataset size and show how in theory we can break beyond power law scaling and potentially even reduce it to exponential scaling instead if we have access to a high-quality data pruning metric that ranks the order in which training examples should be discarded to achieve any pruned dataset size. We then test this improved scaling prediction with pruned dataset size empirically, and indeed observe better than power law scaling in practice on ResNets trained on CIFAR-10, SVHN, and ImageNet. Next, given the importance of finding high-quality pruning metrics, we perform the first large-scale benchmarking study of ten different data pruning metrics on ImageNet. We find most existing high performing metrics scale poorly to ImageNet, while the best are computationally intensive and require labels for every image. We therefore developed a new simple, cheap and scalable self-supervised pruning metric that demonstrates comparable performance to the best supervised metrics. Overall, our work suggests that the discovery of good data-pruning metrics may provide a viable path forward to substantially improved neural scaling laws, thereby reducing the resource costs of modern deep learning.

cs.LG↗