arXiv subjects

Imran Khan

Publications and source records attributed to Imran Khan.

At least 19 recordsLinked to original sources

arXiv2026

Planck-Scale Signatures in Vacuum Neutrino Oscillations

We present a closed-form perturbative framework for two-flavour vacuum neutrino oscillations in the presence of a weak, flavour-blind Planck-scale mass correction generated by the dimension-five Weinberg operator of the Standard Model Effective Field Theory. The perturbative treatment is developed in direct structural analogy with approaches used to describe weak Earth-matter effects. We derive simple analytical expressions for the modification of the oscillation length, the oscillation phase, and the flavour-conversion probability, valid for arbitrary neutrino mixing and baseline lengths. Unlike matter-induced perturbations, the Planck-scale correction is constant along the neutrino trajectory. Consequently, its effect on the transition probability remains bounded and independent of the baseline at the amplitude level, while the associated modification of the effective mass-squared splitting produces a phase mismatch that increases linearly with propagation distance. We further present illustrative order-of-magnitude sensitivity projections for next-generation long-baseline and reactor neutrino experiments, including DUNE, Hyper-Kamiokande, and JUNO. Our results indicate that the accumulated phase drift provides a particularly promising observable for probing Planck-scale mass corrections and for distinguishing a possible quantum-gravity contribution from conventional matter-induced effects.

hep-ph↗

arXiv2026

What Are We Measuring? Bonding, Trust, and the Evaluation of Human-Robot Relationships

In human-robot interaction, relationship quality is often quantified using self-report measures, particularly related to "trust", such that a robot's trustworthiness comes to serve as an index of how close or "bonded" a human feels to it. I argue that this is a category error: trust and social bonding are distinct constructs, differing in their antecedents, their timescales, their bodily signatures, the human experience they produce, the robot responses they call for, and the ethical concerns they raise. I propose that we view them as independent dimensions, and describe the resulting two-dimensional space of possible relationship states under this view, with four configurations: avoidance, functional, dependence, and symbiosis. I then draw out some consequences for human-state-aware robotics: (1) social bonding is an explicit estimation target distinct from trust, (2) it should condition online adaptation (3) it reframes what a "failure" means, and (4) it raises the possibility of identifying dysfunctional relationships, in which a user remains attached to a robot that no longer merits reliance. This is ongoing work, offered in part to prompt the field to reconsider what it means to evaluate relationship quality in human-robot dyads.

cs.HC↗

arXiv2026

Chip-Scale Transmitter Module for Real-Time Continuous-Variable QKD

Continuous-variable quantum key distribution (CV-QKD) enables secure communication over standard telecom infrastructure, but scaling is stalled by bulky, discrete optical hardware. We address this bottleneck by demonstrating a real-time CV-QKD system driven by a chip-scale hybrid transmitter using commercial telecom components. Combining a micro-optic external-cavity laser with a monolithic photonic integrated IQ modulator we enable secure secret-key generation over 102 km of optical fiber while reducing optical volume by 95% relative to the commercial discrete-component counterpart. Moreover, real-time operation overcomes offline post-processing bottlenecks of experimental setups. This work bridges laboratory demonstrations and field-deployable technology for cost-effective quantum networks.

quant-ph↗

arXiv2026

NeutrinoOsc3Flavor: CP Phase Dependence in Three-Flavor Neutrino Oscillations: A Numerical Study in Vacuum and Matter

We present NeutrinoOsc3Flavor, a lightweight and fully transparent computational framework for exact three flavor neutrino oscillation studies in vacuum and constant density matter. The code numerically solves the Schrodinger evolution equation in the flavor basis using explicit construction and diagonalization of the effective Hamiltonian within the PMNS formalism, including full CP Violating phase dependence. In contrast to large scale oscillation toolkits optimized for experimental simulations, NeutrinoOsc3Flavor is designed as a minimal dependency reference implementation, emphasizing analytical traceability, equation level accessibility, and cross platform portability. The framework relies solely on NumPy for numerical linear algebra and runs natively on both Linux and Windows systems without external compilation or specialized libraries. As an internal consistency and validation feature, we implement an independent analytical determination of the matter modified Hamiltonian eigenvalues using the Cardano method and demonstrate excellent agreement with numerical diagonalization. CP Phase dependence is used as a sensitive diagnostic of numerical stability and correctness of the evolution operator in both vacuum and matter. NeutrinoOsc3Flavor is intended as a verification oriented and pedagogical computational tool, suitable for theoretical cross-checks, educational use, and benchmarking of more complex neutrino oscillation software, rather than as a replacement for full experimental simulation frameworks. Here, we consider the DUNE experiments baseline length in the python implementation but in general we can implement any value of baseline length.

hep-ph↗

arXiv2025

Three Dimensional Effects on Proton Acceleration with Grooved Hydrocarbon Targets

Recently, using two-dimensional particle-in-cell simulations, it has been demonstrated that in laser based proton acceleration with micro-structured targets, a single rectangular groove on the target front offers significant proton cut-off enhancement with linearly polarised laser pulses. In the present work, three-dimensional investigations are carried out to identify notable differences between cylindrical and cuboidal groove geometries both of which correspond to a rectangular groove in a two-dimensional case. In particular, a waveguide model is employed to analyse the effect of the groove geometry and extensive three-dimensional particle-in-cell simulations are performed to demonstrate the distinct behaviour of laser pulse and electrons for cylindrical and cuboidal grooves. Further, the effect of a circular polarisation of the incident laser pulse on the spectra of accelerated protons is studied. It is shown that contrary to our initial expectations, cylindrical symmetry and circular polarisation do not play well together and cause as much as 15$\%$ decay in proton cut-off energies as compared to the case of cylindrical symmetry and linear polarisation.

physics.plasm-ph↗

arXiv2025

Handover Configurations in Operational 5G Networks: Diversity, Evolution, and Impact on Performance

Mobility management in cellular networks, especially the handover (HO) process, plays a key role in providing seamless and ubiquitous Internet access. The wide-scale deployment of 5G and the resulting co-existence of 4G/5G in the past six years have significantly changed the landscape of all mobile network operators and made the HO process much more complex than before. While several recent works have studied the impact of HOs on user experience, why and how HOs occur and how HO configurations affect performance in 5G operational networks remains largely unknown. Through four cross-country driving trips across the US spread out over a 27-month period, we conduct an in-depth measurement study of HO configurations across all three major US operators. Our study reveals (a) new types of HOs and new HO events used by operators to handle these new types of HOs, (b) overly aggressive HO configurations that result in unnecessarily high signaling overhead, (c) large diversity in HO configuration parameter values, which also differ across operators, but significantly lower diversity in 5G compared to LTE, and (d) sub-optimal HO configurations/decisions leading to poor pre- or post-HO performance. Our findings have many implications for mobile operators, as they keep fine-tuning their 5G HO configurations.

cs.NI↗

arXiv2025

You Don't Need Prompt Engineering Anymore: The Prompting Inversion

Prompt engineering, particularly Chain-of-Thought (CoT) prompting, significantly enhances LLM reasoning capabilities. We introduce "Sculpting," a constrained, rule-based prompting method designed to improve upon standard CoT by reducing errors from semantic ambiguity and flawed common sense. We evaluate three prompting strategies (Zero Shot, standard CoT, and Sculpting) across three OpenAI model generations (gpt-4o-mini, gpt-4o, gpt-5) using the GSM8K mathematical reasoning benchmark (1,317 problems). Our findings reveal a "Prompting Inversion": Sculpting provides advantages on gpt-4o (97% vs. 93% for standard CoT), but becomes detrimental on gpt-5 (94.00% vs. 96.36% for CoT on full benchmark). We trace this to a "Guardrail-to-Handcuff" transition where constraints preventing common-sense errors in mid-tier models induce hyper-literalism in advanced models. Our detailed error analysis demonstrates that optimal prompting strategies must co-evolve with model capabilities, suggesting simpler prompts for more capable models.

cs.CL↗

arXiv2025

From Literal to Liberal: A Meta-Prompting Framework for Eliciting Human-Aligned Exception Handling in Large Language Models

Large Language Models (LLMs) are increasingly being deployed as the reasoning engines for agentic AI systems, yet they exhibit a critical flaw: a rigid adherence to explicit rules that leads to decisions misaligned with human common sense and intent. This "rule-rigidity" is a significant barrier to building trustworthy autonomous agents. While prior work has shown that supervised fine-tuning (SFT) with human explanations can mitigate this issue, SFT is computationally expensive and inaccessible to many practitioners. To address this gap, we introduce the Rule-Intent Distinction (RID) Framework, a novel, low-compute meta-prompting technique designed to elicit human-aligned exception handling in LLMs in a zero-shot manner. The RID framework provides the model with a structured cognitive schema for deconstructing tasks, classifying rules, weighing conflicting outcomes, and justifying its final decision. We evaluated the RID framework against baseline and Chain-of-Thought (CoT) prompting on a custom benchmark of 20 scenarios requiring nuanced judgment across diverse domains. Our human-verified results demonstrate that the RID framework significantly improves performance, achieving a 95% Human Alignment Score (HAS), compared to 80% for the baseline and 75% for CoT. Furthermore, it consistently produces higher-quality, intent-driven reasoning. This work presents a practical, accessible, and effective method for steering LLMs from literal instruction-following to liberal, goal-oriented reasoning, paving the way for more reliable and pragmatic AI agents.

cs.AI↗

arXiv2025

Role-Aware Multi-modal federated learning system for detecting phishing webpages

We present a federated, multi-modal phishing website detector that supports URL, HTML, and IMAGE inputs without binding clients to a fixed modality at inference: any client can invoke any modality head trained elsewhere. Methodologically, we propose role-aware bucket aggregation on top of FedProx, inspired by Mixture-of-Experts and FedMM. We drop learnable routing and use hard gating (selecting the IMAGE/HTML/URL expert by sample modality), enabling separate aggregation of modality-specific parameters to isolate cross-embedding conflicts and stabilize convergence. On TR-OP, the Fusion head reaches Acc 97.5% with FPR 2.4% across two data types; on the image subset (ablation) it attains Acc 95.5% with FPR 5.9%. For text, we use GraphCodeBERT for URLs and an early three-way embedding for raw, noisy HTML. On WebPhish (HTML) we obtain Acc 96.5% / FPR 1.8%; on TR-OP (raw HTML) we obtain Acc 95.1% / FPR 4.6%. Results indicate that bucket aggregation with hard-gated experts enables stable federated training under strict privacy, while improving the usability and flexibility of multi-modal phishing detection.

cs.LG↗

arXiv2025

[Social] Allostasis: Or, How I Learned To Stop Worrying and Love The Noise

The notion of homeostasis typically conceptualises biological and artificial systems as maintaining stability by resisting deviations caused by environmental and social perturbations. In contrast, (social) allostasis proposes that these systems can proactively leverage these very perturbations to reconfigure their regulatory parameters in anticipation of environmental demands, aligning with von Foerster's ``order through noise'' principle. This paper formulates a computational model of allostatic and social allostatic regulation that employs biophysiologically inspired signal transducers, analogous to hormones like cortisol and oxytocin, to encode information from both the environment and social interactions, which mediate this dynamic reconfiguration. The models are tested in a small society of ``animats'' across several dynamic environments, using an agent-based model. The results show that allostatic and social allostatic regulation enable agents to leverage environmental and social ``noise'' for adaptive reconfiguration, leading to improved viability compared to purely reactive homeostatic agents. This work offers a novel computational perspective on the principles of social allostasis and their potential for designing more robust, bio-inspired, adaptive systems

cs.AI↗

arXiv2024

On the critical role of the rear wall thickness of a grooved TNSA target

The cutoff energy and the divergence of the protons generated by the target normal sheath acceleration mechanism are known to be significantly influenced by micrometer and nanometer-size structures on the target front and rear surfaces. Specifically, the cutoff energy is significantly enhanced by creating a central rectangular groove on the target front surface, as shown in a recent study [Physics of Plasmas, 30(6), 063102 (2023)]. Here we report on 2D Particle-In-Cell (PIC) simulations to thoroughly explore the effect of the depth of the central rectangular groove on the energy spectra of the accelerated protons. The proton cutoff energy is found to enhance drastically as the thickness of the rear wall of the groove is reduced from a few micrometers to a few tens of nanometers, however, it drops sharply as the thickness of the rear wall is further reduced towards creating a complete hole through the target.

physics.plasm-ph↗

arXiv2024

Composable free-space continuous-variable quantum key distribution using discrete modulation

Continuous-variable (CV) quantum key distribution (QKD) allows for quantum secure communication with the benefit of being close to existing classical coherent communication. In recent years, CV QKD protocols using a discrete number of displaced coherent states have been studied intensively, as the modulation can be directly implemented with real devices with a finite digital resolution. However, the experimental demonstrations until now only calculated key rates in the asymptotic regime. To be used in cryptographic applications, a QKD system has to generate keys with composable security in the finite-size regime. In this paper, we present a CV QKD system using discrete modulation that is especially designed for urban atmospheric channels. For this, we use polarization encoding to cope with the turbulent but non-birefringent atmosphere. This will allow to expand CV QKD networks beyond the existing fiber backbone. In a first laboratory demonstration, we implemented a novel type of security proof allowing to calculate composable finite-size key rates against i.i.d. collective attacks without any Gaussian assumptions. We applied the full QKD protocol including a QRNG, error correction and privacy amplification to extract secret keys. In particular, we studied the impact of frame errors on the actual key generation.

quant-ph↗

arXiv2024

Human-Robot Mutual Learning through Affective-Linguistic Interaction and Differential Outcomes Training [Pre-Print]

Owing to the recent success of Large Language Models, Modern A.I has been much focused on linguistic interactions with humans but less focused on non-linguistic forms of communication between man and machine. In the present paper, we test how affective-linguistic communication, in combination with differential outcomes training, affects mutual learning in a human-robot context. Taking inspiration from child-caregiver dynamics, our human-robot interaction setup consists of a (simulated) robot attempting to learn how best to communicate internal, homeostatically-controlled needs; while a human "caregiver" attempts to learn the correct object to satisfy the robot's present communicated need. We studied the effects of i) human training type, and ii) robot reinforcement learning type, to assess mutual learning terminal accuracy and rate of learning (as measured by the average reward achieved by the robot). Our results find mutual learning between a human and a robot is significantly improved with Differential Outcomes Training (DOT) compared to Non-DOT (control) conditions. We find further improvements when the robot uses an exploration-exploitation policy selection, compared to purely exploitation policy selection. These findings have implications for utilizing socially assistive robots (SAR) in therapeutic contexts, e.g. for cognitive interventions, and educational applications.

cs.RO↗

arXiv2024

X5G: An Open, Programmable, Multi-vendor, End-to-end, Private 5G O-RAN Testbed with NVIDIA ARC and OpenAirInterface

As Fifth generation (5G) cellular systems transition to softwarized, programmable, and intelligent networks, it becomes fundamental to enable public and private 5G deployments that are (i) primarily based on software components while (ii) maintaining or exceeding the performance of traditional monolithic systems and (iii) enabling programmability through bespoke configurations and optimized deployments. This requires hardware acceleration to scale the Physical (PHY) layer performance, programmable elements in the Radio Access Network (RAN) and intelligent controllers at the edge, careful planning of the Radio Frequency (RF) environment, as well as end-to-end integration and testing. In this paper, we describe how we developed the programmable X5G testbed, addressing these challenges through the deployment of the first 8-node network based on the integration of NVIDIA Aerial RAN CoLab Over-the-Air (ARC-OTA), OpenAirInterface (OAI), and a near-real-time RAN Intelligent Controller (RIC). The Aerial Software Development Kit (SDK) provides the PHY layer, accelerated on Graphics Processing Unit (GPU), with the higher layers from the OAI open-source project interfaced with the PHY through the Small Cell Forum (SCF) Functional Application Platform Interface (FAPI). An E2 agent provides connectivity to the O-RAN Software Community (OSC) near-real-time RIC. We discuss software integration, network infrastructure, and a digital twin framework for RF planning. We then profile the performance with up to 4 Commercial Off-the-Shelf (COTS) smartphones for each base station with iPerf and video streaming applications, as well as up to 25 emulated User Equipments (UEs), measuring a cell rate higher than 1.65 Gbps in downlink and 143 Mbps in uplink.

cs.NI↗

arXiv2024

TNSA based proton acceleration by two oblique laser pulses in the presence of an axial magnetic field

A recently proposed strategy to boost the proton/ion cutoff energy in the target normal sheath acceleration scheme employs two obliquely incident laser pulses simultaneously irradiating the flat target rather than a single normally incident laser pulse of twice the pulse energy. Moreover, the presence of an externally applied magnetic field along the normal of the target's rear surface is known to reduce the angular divergence of hot electrons which results in a more efficient sheath field at the target rear leading to increased cutoff energy of accelerated protons/ions. In the present work, we employ two-dimensional Particle-In-Cell (PIC) simulations to examine, in detail, the effect of such a magnetic field on the cutoff energy of protons/ions in the cases of normal as well as oblique incidence of the laser pulse on a flat target. It is shown that the two-oblique-pulse configuration combined with an external magnetic field results in a stronger enhancement of the cutoff energies as compared to the normal incidence case.

physics.plasm-ph↗

arXiv2024

Surprise! Using Physiological Stress for Allostatic Regulation Under the Active Inference Framework [Pre-Print]

Allostasis proposes that long-term viability of a living system is achieved through anticipatory adjustments of its physiology and behaviour: emphasising physiological and affective stress as an adaptive state of adaptation that minimizes long-term prediction errors. More recently, the active inference framework (AIF) has also sought to explain action and long-term adaptation through the minimization of future errors (free energy), through the learning of statistical contingencies of the world, offering a formalism for allostatic regulation. We suggest that framing prediction errors through the lens of biological hormonal dynamics proposed by allostasis offers a way to integrate these two models together in a biologically-plausible manner. In this paper, we describe our initial work in developing a model that grounds prediction errors (surprisal) into the secretion of a physiological stress hormone (cortisol) acting as an adaptive, allostatic mediator on a homeostatically-controlled physiology. We evaluate this using a computational model in simulations using an active inference agent endowed with an artificial physiology, regulated through homeostatic and allostatic control in a stochastic environment. Our results find that allostatic functions of cortisol (stress), secreted as a function of prediction errors, provide adaptive advantages to the agent's long-term physiological regulation. We argue that the coupling of information-theoretic prediction errors to low-level, biological hormonal dynamics of stress can provide a computationally efficient model to long-term regulation for embodied intelligent systems.

cs.AI↗

arXiv2024

Social Media and Artificial Intelligence for Sustainable Cities and Societies: A Water Quality Analysis Use-case

This paper focuses on a very important societal challenge of water quality analysis. Being one of the key factors in the economic and social development of society, the provision of water and ensuring its quality has always remained one of the top priorities of public authorities. To ensure the quality of water, different methods for monitoring and assessing the water networks, such as offline and online surveys, are used. However, these surveys have several limitations, such as the limited number of participants and low frequency due to the labor involved in conducting such surveys. In this paper, we propose a Natural Language Processing (NLP) framework to automatically collect and analyze water-related posts from social media for data-driven decisions. The proposed framework is composed of two components, namely (i) text classification, and (ii) topic modeling. For text classification, we propose a merit-fusion-based framework incorporating several Large Language Models (LLMs) where different weight selection and optimization methods are employed to assign weights to the LLMs. In topic modeling, we employed the BERTopic library to discover the hidden topic patterns in the water-related tweets. We also analyzed relevant tweets originating from different regions and countries to explore global, regional, and country-specific issues and water-related concerns. We also collected and manually annotated a large-scale dataset, which is expected to facilitate future research on the topic.

cs.SI↗

arXiv2024

An Experimental Study of Low-Latency Video Streaming over 5G

Low-latency video streaming over 5G has become rapidly popular over the last few years due to its increased usage in hosting virtual events, online education, webinars, and all-hands meetings. Our work aims to address the absence of studies that reveal the real-world behavior of low-latency video streaming. To that end, we provide an experimental methodology and measurements, collected in a US metropolitan area over a commercial 5G network, that correlates application-level QoE and lower-layer metrics on the devices, such as RSRP, RSRQ, handover records, etc., under both static and mobility scenarios. We find that RAN-side information, which is readily available on every cellular device, has the potential to enhance throughput estimation modules of video streaming clients, ultimately making low-latency streaming more resilient against network perturbations and handover events.

cs.MM↗