arXiv subjects

Nikhil Kumar

Publications and source records attributed to Nikhil Kumar.

At least 19 recordsLinked to original sources

arXiv2026

Spin torque driven mode hybridization and band engineering in nanopatterned magnonic crystals

Spin wave propagation and dynamic control are essential for reconfigurable magnonic and spintronic devices. Here, tunable mode coupling and band hybridisation are demonstrated in a nanopatterned bicomponent magnonic crystal consisting of a Permalloy/heavy metal bilayer patterned with a two dimensional array of Co nanodots. Using the plane wave method and the linearised Landau Lifshitz equation with a field like torque term, we show that inhomogeneous current induced spin torque produces periodic modulation of the magnonic frequency, enabling dynamic control of spin-wave dispersion. A pronounced avoided crossing between localised and propagating Damon Eshbach modes is observed, leading to tunable hybridisation gaps, band deformation, and enhanced mode mixing. The spin torque induced modulation enables controlled mode conversion and reconfigurable hybrid magnonic states, demonstrating efficient electrical tuning of nanoscale spin-wave dynamics.

cond-mat.mes-hall↗

arXiv2026

RoboKA: KAN Informed Multimodal Learning for RoboCall Surveillance System

Wide exploration on robocall surveillance research is hindered due to limited access to public datasets, due to privacy concerns. In this work, we first curate Robo-SAr, a synthetic robocall dataset designed for robocall surveillance research. Robo-SAr comprises of ~200 unwanted and ~1200 legitimate synthetic robocall samples across three realistic adversarial axes: psycholinguistics-manipulated transcripts, emotion-eliciting speech, and cloned voices. We further propose RoboKA, a Kolmogorov-Arnold Network (KAN)-based multimodal fusion framework designed to model structured nonlinear interactions between acoustic and linguistic cues that characterize diverse adversarial robocall strategies. RoboKA first leverages cross-modal contrastive learning to align latent modality representations and feeds the resulting embeddings to a KAN-projection head for final classification. We benchmark RoboKA against strong unimodal and multimodal baselines in both in-domain and out-of-domain setups, finding RoboKA to surpass all baselines in terms of recall and F1-score.

cs.MM↗

arXiv2026

Field Free Novel Architecture for Spintronic Flash Analog to Digital Converter

A 3 bit Analog to Digital Converter (ADC) is designed using perpendicular Spin Orbit Torque Magnetic Tunnel Junction (SOT MTJ). A sampled analog input signal is transmitted as a spin orbit torque current (Iin) to a perpendicular SOT MTJ, and deterministic switching is supported by the Voltage Controlled Magnetic Anisotropy (VCMA) and Spin Transfer Torque (STT) switching methods. Analog to digital conversion is done by comparing input signal with varied critical current of SOT MTJs. The critical current of each is SOT MTJ governed by varying widths of Heavy Metal (HM). In the 3 bit ADC, there are two sets of 7 SOT MTJs for quantizing input value, a conversion set and dummy set for comparing the change in resistance state. As input signal passed through conversion set SOT MTJs switches from Parallel (P) to AntiParallel (AP) state if the input signal exceeds its critical current. The conversion set change in state is converted to thermometer codes by StrongARM latch comparator by comparing the resistance with dummy set SOT MTJs, where all the in P state or low resistance. A novel architecture is proposed for increasing speed of throughput, by utilizing the dummy set of as a conversion set and conversion set as dummy set, thus eliminating the reset step from analog to digital conversion. And by improving SOT-MTJ and timing blocks a field free spin flash ADC has a power consumption of 476 uW with a conversion rate of 304.1 MHz is produced.

eess.SY↗

arXiv2025

Ultra High Sensitivity Soil Moisture Detection Using Photonic Crystal Cavity with SIW Technology

Soil nutrients and water content are two crucial factors that significantly affect agricultural production yields. Hence, monitoring and measuring the water content and soil type are critical requirements. This study proposes a two-dimensional structure of photonic crystals centered around a symmetrical cross-shaped slot. The cross-slots act as resonators, and the photonic crystals surrounding the slots tune the resonance frequency of the resonators to enhance mode confinement within the resonator. The various resonant modes are located in the 2.1 GHz, 5.2 GHz, and 8.1 GHz bands, which correspond to the S band, C band, and X band, respectively. These bands are used to compare the absorption, whereas the upper resonant mode is of the order of 20 GHz. Band structure analysis was performed using the Plane Wave Method (PWM). The resonant frequency is computed using a 3D electromagnetic (EM) simulation software that utilizes the Finite Element Method (FEM) and lies in the radiation mode region of the band structure of the photonic crystal. Varying the incident angle had a negligible effect on the absorption characteristics of the sensor, allowing it to produce accurate sensing results regardless of the incident angle. The sensor's sensitivity is maximized using this design, which results in a sensitivity of 85.4 % in the 2.1 GHz resonant frequency, which is much higher than that of a single column of photonic crystal-based SIW, resulting in 50.6 % of sensitivity at 2.1 GHz, at which there is a frequency shift of the order of GHz. In contrast, in the proposed design, the frequency shift is on the order of MHz, resulting in ultra-high sensitivity.

physics.optics↗

arXiv2025

Improved Lower Bounds on Multiflow-Multicut Gaps

Given a set of source-sink pairs, the maximum multiflow problem asks for the maximum total amount of flow that can be feasibly routed between them. The minimum multicut, a dual problem to multiflow, seeks the minimum-cost set of edges whose removal disconnects all the source-sink pairs. It is easy to see that the value of the minimum multicut is at least that of the maximum multiflow, and their ratio is called the multiflow-multicut gap. The classical max-flow min-cut theorem states that when there is only one source-sink pair, the gap is exactly one. However, in general, it is well known that this gap can be arbitrarily large. In this paper, we study this gap for classes of planar graphs and establish improved lower bound results. In particular, we show that this gap is at least $\frac{16}{7}$ for the class of planar graphs, improving upon the decades-old lower bound of 2. More importantly, we develop new techniques for proving such a lower bound, which may be useful in other settings as well.

cs.DM↗

arXiv2025

Tight Guarantees for Cut-Relative Survivable Network Design via a Decomposition Technique

In the classical \emph{survivable-network-design problem} (SNDP), we are given an undirected graph $G = (V, E)$, non-negative edge costs, and some $(s_i,t_i,r_i)$ tuples, where $s_i,t_i\in V$ and $r_i\in\mathbb{Z}_+$. We seek a minimum-cost subset $H \subseteq E$ such that each $s_i$-$t_i$ pair remains connected even if any $r_i-1$ edges fail. It is well-known that SNDP can be equivalently modeled using a weakly-supermodular \emph{cut-requirement function} $f$, where we seek a minimum-cost edge-set containing at least $f(S)$ edges across every cut $S \subseteq V$. Recently, Dinitz et al. proposed a variant of SNDP that enforces a \emph{relative} level of fault tolerance with respect to $G$, where the goal is to find a solution $H$ that is at least as fault-tolerant as $G$ itself. They formalize this in terms of paths and fault-sets, which gives rise to \emph{path-relative SNDP}. Along these lines, we introduce a new model of relative network design, called \emph{cut-relative SNDP} (CR-SNDP), where the goal is to select a minimum-cost subset of edges that satisfies the given (weakly-supermodular) cut-requirement function to the maximum extent possible, i.e., by picking $\min\{f(S),|\delta_G(S)|\}$ edges across every cut $S\subseteq V$. Unlike SNDP, the cut-relative and path-relative versions of SNDP are not equivalent. The resulting cut-requirement function for CR-SNDP (as also path-relative SNDP) is not weakly supermodular, and extreme-point solutions to the natural LP-relaxation need not correspond to a laminar family of tight cut constraints. Consequently, standard techniques cannot be used directly to design approximation algorithms for this problem. We develop a \emph{novel decomposition technique} to circumvent this difficulty and use it to give a \emph{tight $2$-approximation algorithm for CR-SNDP}. We also show new hardness results for these relative-SNDP problems.

cs.DS↗

arXiv2025

Endogenous Network Structures with Precision and Dimension Choices

This paper presents a social learning model where the network structure is endogenously determined by signal precision and dimension choices. Agents not only choose the precision of their signals and what dimension of the state to learn about, but these decisions directly determine the underlying network structure on which social learning occurs. We show that under a fixed network structure, the optimal precision choice is sublinear in the agent's stationary influence in the network, and this individually optimal choice is worse than the socially optimal choice by a factor of $n^{1/3}$. Under a dynamic network structure, we specify the network by defining a kernel distance between agents, which then determines how much weight agents place on one another. Agents choose dimensions to learn about such that their choice minimizes the squared sum of influences of all agents: a network with equally distributed influence across agents is ideal.

econ.TH↗

arXiv2025

Almost Tight Additive Guarantees for $k$-Edge-Connectivity

We consider the \emph{$k$-edge connected spanning subgraph} (kECSS) problem, where we are given an undirected graph $G = (V, E)$ with nonnegative edge costs $\{c_e\}_{e\in E}$, and we seek a minimum-cost \emph{$k$-edge connected} subgraph $H$ of $G$. For even $k$, we present a polytime algorithm that computes a $(k-2)$-edge connected subgraph of cost at most the optimal value $LP^*$ of the natural LP-relaxation for kECSS; for odd $k$, we obtain a $(k-3)$-edge connected subgraph of cost at most $LP^*$. Since kECSS is APX-hard for all $k\geq 2$, our results are nearly optimal. They also significantly improve upon the recent work of Hershkowitz et al., both in terms of solution quality and the simplicity of algorithm and its analysis. Our techniques also yield an alternate guarantee, where we obtain a $(k-1)$-edge connected subgraph of cost at most $1.5\cdot LP^*$; with unit edge costs, the cost guarantee improves to $(1+\frac{4}{3k})\cdot LP^*$, which improves upon the state-of-the-art approximation for unit edge costs, but with a unit loss in edge connectivity. Our kECSS-result also yields results for the \emph{$k$-edge connected spanning multigraph} (kECSM) problem, where multiple copies of an edge can be selected: we obtain a $(1+2/k)$-approximation algorithm for even $k$, and a $(1+3/k)$-approximation algorithm for odd $k$. Our techniques extend to the degree-bounded versions of kECSS and kECSM, wherein we also impose degree lower- and upper- bounds on the nodes. We obtain the same cost and connectivity guarantees for these degree-bounded versions with an additive violation of (roughly) $2$ for the degree bounds. These are the first results for degree-bounded \{kECSS,kECSM\} of the form where the cost of the solution obtained is at most the optimum, and the connectivity constraints are violated by an additive constant.

cs.DS↗

arXiv2025

Unsplittable Multicommodity Flows in Outerplanar Graphs

We consider the problem of multicommodity flows in outerplanar graphs. Okamura and Seymour showed that the cut-condition is sufficient for routing demands in outerplanar graphs. We consider the unsplittable version of the problem and prove that if the cut-condition is satisfied, then we can route each demand along a single path by exceeding the capacity of an edge by no more than $\frac{18}{5} \cdot d_{max}$, where $d_{max}$ is the value of the maximum demand.

cs.DS↗

arXiv2025

Pricing AI Model Accuracy

This paper examines the market for AI models in which firms compete to provide accurate model predictions and consumers exhibit heterogeneous preferences for model accuracy. We develop a consumer-firm duopoly model to analyze how competition affects firms' incentives to improve model accuracy. Each firm aims to minimize its model's error, but this choice can often be suboptimal. Counterintuitively, we find that in a competitive market, firms that improve overall accuracy do not necessarily improve their profits. Rather, each firm's optimal decision is to invest further on the error dimension where it has a competitive advantage. By decomposing model errors into false positive and false negative rates, firms can reduce errors in each dimension through investments. Firms are strictly better off investing on their superior dimension and strictly worse off with investments on their inferior dimension. Profitable investments adversely affect consumers but increase overall welfare.

econ.TH↗

arXiv2025

Quasi Crystal based Circular Patch Antenna with Artificial Magnetic Conductor for Breast Cancer Detection

Design and simulation of Quasi Crystal based Artificial Magnetic Conductor (AMC) Circular Microstrip Patch Antenna for tumor detection is discussed. Initially, a Circular Microstrip patch antenna is designed with a resonant frequency of $2.45 \, \text{GHz}$. The performance of the designed antenna can be improved by incorporating AMC in order to avoid surface waves. In this paper, we propose a novel technique for detecting malignant tissues in a more focusing manner using a Penrose patterned Quasi crystal. Quasi crystals act as a focusing device as it can transmit electromagnetic waves in a narrow direction. The tumor of size $3 \, \text{mm}$ is placed at a particular location specifically at $(0,0,10)$ in the breast phantom model. Our aim is the proposed model could identify the exact tumor location or the nearest co-ordinates of the actual location. Scattering parameters $S_{11}$ and $S_{22}$ obtained at $-10.30 \, \text{dB}$ and $-9.15 \, \text{dB}$ respectively at the tumor location $(0,-4,30)$ without the presence of Quasi crystal. Results were compared in each stage with and without Penrose Quasi crystal based patch antenna. The Quasi crystal based model was simulated and the results $S_{11}$ and $S_{22}$ parameters got improved at $-10.02 \, \text{dB}$ and $-9.37 \, \text{dB}$ respectively at the tumor location $(-1, -1, 13)$. The total specific absorption rate (SAR) of the proposed model is $0.139 \, \text{W/kg}$. Hence the proposed model is highly bio-compatible and safely applicable for human body.

physics.ins-det↗

arXiv2024

Dynamics of jet breakup and the resultant drop size distribution: effect of nozzle size and impingement velocity

We conduct systematic experiments to investigate the dynamics of liquid jet breakup and the resulting droplet size distribution, emphasizing the influence of liquid jet velocity and needle exit diameter. We precisely control jet formation using a pressurized water tank equipped with needles of different sizes. Our study quantifies breakup dynamics through dimensionless parameters such as the liquid Weber number and the needle exit area ratio. Our key findings identify three distinct breakup regimes, such as dripping, Rayleigh, and wind-induced, each dictated by the interplay of surface tension and aerodynamic forces for various combinations of liquid jet velocity and needle exit diameter. We construct a regime map to delineate different breakup behaviours in the We - Ar space. It is observed that lower jet velocities produce narrow probability density functions for jet breakup length due to stable jets, whereas higher velocities result in broader distributions. Increasing jet velocity extends breakup lengths for moderate flow rates due to enhanced stability in the Rayleigh regime, but higher velocities induce instability, leading to shorter breakup lengths. Additionally, we analyze the effects of the needle exit area ratio and liquid Weber number on droplet size distribution, highlighting the transition from mono-modal to bi-modal distribution under varying conditions.

physics.flu-dyn↗

arXiv2024

Optimal Padded Decomposition For Bounded Treewidth Graphs

A $(\beta,\delta,\Delta)$-padded decomposition of an edge-weighted graph $G = (V,E,w)$ is a stochastic decomposition into clusters of diameter at most $\Delta$ such that for every vertex $v\in V$, the probability that $\rm{ball}_G(v,\gamma\Delta)$ is entirely contained in the cluster containing $v$ is at least $e^{-\beta\gamma}$ for every $\gamma \in [0,\delta]$. Padded decompositions have been studied for decades and have found numerous applications, including metric embedding, multicommodity flow-cut gap, multicut, and zero extension problems, to name a few. In these applications, parameter $\beta$, called the padding parameter, is the most important parameter since it decides either the distortion or the approximation ratios. For general graphs with $n$ vertices, $\beta = \Theta(\log n)$. Klein, Plotkin, and Rao showed that $K_r$-minor-free graphs have padding parameter $\beta = O(r^3)$, which is a significant improvement over general graphs when $r$ is a constant. A long-standing conjecture is to construct a padded decomposition for $K_r$-minor-free graphs with padding parameter $\beta = O(\log r)$. Despite decades of research, the best-known result is $\beta = O(r)$, even for graphs with treewidth at most $r$. In this work, we make significant progress toward the aforementioned conjecture by showing that graphs with treewidth $\rm{tw}$ admit a padded decomposition with padding parameter $O(\log \rm{tw})$, which is tight. As corollaries, we obtain an exponential improvement in dependency on treewidth in a host of algorithmic applications: $O(\sqrt{ \log n \cdot \log(\rm{tw})})$ flow-cut gap, max flow-min multicut ratio of $O(\log(\rm{tw}))$, an $O(\log(\rm{tw}))$ approximation for the 0-extension problem, an $\ell^{O(\log n)}_\infty$ embedding with distortion $O(\log \rm{tw})$, and an $O(\log \rm{tw})$ bound for integrality gap for the uniform sparsest cut.

cs.DS↗

arXiv2024

Nearly-Tight Bounds for Flow Sparsifiers in Quasi-Bipartite Graphs

Flow sparsification is a classic graph compression technique which, given a capacitated graph $G$ on $k$ terminals, aims to construct another capacitated graph $H$, called a flow sparsifier, that preserves, either exactly or approximately, every multicommodity flow between terminals (ideally, with size as a small function of $k$). Cut sparsifiers are a restricted variant of flow sparsifiers which are only required to preserve maximum flows between bipartitions of the terminal set. It is known that exact cut sparsifiers require $2^{\Omega(k)}$ many vertices [Krauthgamer and Rika, SODA 2013], with the hard instances being quasi-bipartite graphs, where there are no edges between non-terminals. On the other hand, it has been shown recently that exact (or even $(1+\varepsilon)$-approximate) flow sparsifiers on networks with just 6 terminals require unbounded size [Krauthgamer and Mosenzon, SODA 2023, Chen and Tan, SODA 2024]. In this paper, we construct exact flow sparsifiers of size $3^{k^{3}}$ and exact cut sparsifiers of size $2^{k^2}$ for quasi-bipartite graphs. In particular, the flow sparsifiers are contraction-based, that is, they are obtained from the input graph by (vertex) contraction operations. Our main contribution is a new technique to construct sparsifiers that exploits connections to polyhedral geometry, and that can be generalized to graphs with a small separator that separates the graph into small components. We also give an improved reduction theorem for graphs of bounded treewidth [Andoni et al., SODA 2011], implying a flow sparsifier of size $O(k\cdot w)$ and quality $O\bigl(\frac{\log w}{\log \log w}\bigr)$, where $w$ is the treewidth.

cs.DS↗

arXiv2024

MWIRSTD: A MWIR Small Target Detection Dataset

This paper presents a novel mid-wave infrared (MWIR) small target detection dataset (MWIRSTD) comprising 14 video sequences containing approximately 1053 images with annotated targets of three distinct classes of small objects. Captured using cooled MWIR imagers, the dataset offers a unique opportunity for researchers to develop and evaluate state-of-the-art methods for small object detection in realistic MWIR scenes. Unlike existing datasets, which primarily consist of uncooled thermal images or synthetic data with targets superimposed onto the background or vice versa, MWIRSTD provides authentic MWIR data with diverse targets and environments. Extensive experiments on various traditional methods and deep learning-based techniques for small target detection are performed on the proposed dataset, providing valuable insights into their efficacy. The dataset and code are available at https://github.com/avinres/MWIRSTD.

cs.CV↗

arXiv2024

Dual Thinking and Logical Processing -- Are Multi-modal Large Language Models Closing the Gap with Human Vision ?

The dual thinking framework considers fast, intuitive, and slower logical processing. The perception of dual thinking in vision requires images where inferences from intuitive and logical processing differ, and the latter is under-explored in current studies. We introduce a novel adversarial dataset to provide evidence for the dual thinking framework in human vision, which also facilitates the study of the qualitative behavior of deep learning models. Our psychophysical studies show the presence of multiple inferences in rapid succession, and analysis of errors shows that the early stopping of visual processing can result in missing relevant information. MLLMs (Multi-modal Large Language Models) and VLMs (Vision Language Models) have made significant progress in correcting errors in intuitive processing in human vision and showed enhanced performance on images requiring logical processing. However, their improvements in logical processing have not kept pace with their advancements in intuitive processing. In contrast, segmentation models exhibit errors similar to those seen in intuitive human processing and lack understanding of sub-structures, as indicated by errors related to sub-components in identified instances. As AI (Artificial Intelligence)-based systems find increasing applications in safety-critical domains like autonomous driving, the integration of logical processing capabilities becomes essential. This not only enhances performance but also addresses the limitations of scaling-based approaches while ensuring robustness and reliability in real-world environments.

cs.CV↗

arXiv2024

A manufacturable platform for photonic quantum computing

Whilst holding great promise for low noise, ease of operation and networking, useful photonic quantum computing has been precluded by the need for beyond-state-of-the-art components, manufactured by the millions. Here we introduce a manufacturable platform for quantum computing with photons. We benchmark a set of monolithically-integrated silicon photonics-based modules to generate, manipulate, network, and detect photonic qubits, demonstrating dual-rail photonic qubits with $99.98\% \pm 0.01\%$ state preparation and measurement fidelity, Hong-Ou-Mandel quantum interference between independent photon sources with $99.50\%\pm0.25\%$ visibility, two-qubit fusion with $99.22\%\pm0.12\%$ fidelity, and a chip-to-chip qubit interconnect with $99.72\%\pm0.04\%$ fidelity, not accounting for loss. In addition, we preview a selection of next generation technologies, demonstrating low-loss silicon nitride waveguides and components, fabrication-tolerant photon sources, high-efficiency photon-number-resolving detectors, low-loss chip-to-fiber coupling, and barium titanate electro-optic phase shifters.

quant-ph↗

arXiv2024

Boosting Digital Safeguards: Blending Cryptography and Steganography

In today's digital age, the internet is essential for communication and the sharing of information, creating a critical need for sophisticated data security measures to prevent unauthorized access and exploitation. Cryptography encrypts messages into a cipher text that is incomprehensible to unauthorized readers, thus safeguarding data during its transmission. Steganography, on the other hand, originates from the Greek term for "covered writing" and involves the art of hiding data within another medium, thereby facilitating covert communication by making the message invisible. This proposed approach takes advantage of the latest advancements in Artificial Intelligence (AI) and Deep Learning (DL), especially through the application of Generative Adversarial Networks (GANs), to improve upon traditional steganographic methods. By embedding encrypted data within another medium, our method ensures that the communication remains hidden from prying eyes. The application of GANs enables a smart, secure system that utilizes the inherent sensitivity of neural networks to slight alterations in data, enhancing the protection against detection. By merging the encryption techniques of cryptography with the hiding capabilities of steganography, and augmenting these with the strengths of AI, we introduce a comprehensive security system designed to maintain both the privacy and integrity of information. This system is crafted not just to prevent unauthorized access or modification of data, but also to keep the existence of the data hidden. This fusion of technologies tackles the core challenges of data security in the current era of open digital communication, presenting an advanced solution with the potential to transform the landscape of information security.

cs.CR↗