arXiv subjects

Harsimran Singh

Publications and source records attributed to Harsimran Singh.

12 recordsLinked to original sources

arXiv2026

Layer-Polarization-Driven Metal-Insulator Transition in multi-band Graphene Moire' Superlattices

Graphene/hBN moir\'e superlattices provide a highly tunable platform for exploring emergent quantum phases in low-dimensional systems. Here, we investigate the moir\'e superlattice formed between hBN and ABA-stacked trilayer graphene (TLG), an inherently multi-band system. We demonstrate that the moir\'e potential is not merely a perturbation but a tool to hybridize the distinct massless and massive electronic sectors of TLG. By applying a perpendicular displacement field to tune layer polarization, we drive a fundamental reconstruction of the electronic band structure. Specifically, increasing the displacement field evolves the system from a multi-band regime to an effectively single-band regime at low energies, accompanied by a metal--insulator transition at the hole-doped secondary Dirac point. This transition originates from a redistribution of carriers across graphene layers that selectively enhances their coupling to the extrinsic moir\'e potential. Quantum capacitance measurements provide direct evidence for the suppression of the density of states at the hole-side secondary Dirac point, consistent with gap opening and the emergence of a displacement-field-tuned band gap. Theoretical calculations reproduce these observations and identify layer-selective coupling to the moir\'e potential as the underlying mechanism. These results demonstrate electrical control of an emergent insulating phase in a low-dimensional moir\'e system, and highlight that layer polarization and layer-selective coupling in multi-band moir\'e heterostructures provide a powerful route for engineering topological and correlated phases through band structure reconstruction and electron interactions.

cond-mat.mes-hall↗

arXiv2026

CommitDistill: A Lightweight Knowledge-Centric Memory Layer for Software Repositories

Software repositories accumulate large amounts of unstructured knowledge in commit messages, pull-request discussions, and issue threads, but developers and AI coding assistants rarely reuse this history effectively. Recent work on typed-memory architectures for LLM agents (MemGPT, generative agents, and the PlugMem module of Yang et al.) argues that agent memory should be distilled, typed knowledge rather than raw interaction text. We adapt that stance to a software repository's own git history under a constrained regime: deterministic, dependency-free, local-only, no embeddings. We present CommitDistill, an open-source Python prototype that mines a local git history into typed knowledge units (Facts, Skills, Patterns) using deterministic regex and surfaces them through a TF-IDF retriever with a calibrated silence threshold (theta = 2.5) that abstains on out-of-distribution queries. The artefact is a trust-instrumented memory substrate: deterministic, no external service, inspectable plain-JSON store, tunable abstention. A case study on five public repositories spanning Python, JavaScript, C, and Java (25,000 commits, 1,167 extracted units) reports useful-precision 0.525 at Cohen's kappa = 0.633 on 40 dual-annotated Python units. The decisive finding is budget-constrained retrieval: at a 256-character per-query budget, CommitDistill reaches 0.750 hit-rate on a 12-query benchmark against BM25's 0.333 and git log --grep's 0.083. On a four-arm paired LLM-as-judge evaluation (n=200 time-travel bug-fixes, two judges) covering control, CommitDistill, a body-budget-matched CD-Hybrid, and BM25, no condition produces a statistically detectable lift over control on the headline mean and CD-Hybrid is indistinguishable from BM25 head-to-head. Extraction over 10,000 commits completes in under 4 seconds on a laptop. Source, annotations, baselines, and a reproducibility script accompany this paper.

cs.SE↗

arXiv2026

SPIRIT: Perceptive Shared Autonomy for Robust Robotic Manipulation under Deep Learning Uncertainty

Deep learning (DL) has enabled impressive advances in robotic perception, yet its limited robustness and lack of interpretability hinder reliable deployment in safety critical applications. We propose a concept termed perceptive shared autonomy, in which uncertainty estimates from DL based perception are used to regulate the level of autonomy. Specifically, when the robot's perception is confident, semi-autonomous manipulation is enabled to improve performance; when uncertainty increases, control transitions to haptic teleoperation for maintaining robustness. In this way, high-performing but uninterpretable DL methods can be integrated safely into robotic systems. A key technical enabler is an uncertainty aware DL based point cloud registration approach based on the so called Neural Tangent Kernels (NTK). We evaluate perceptive shared autonomy on challenging aerial manipulation tasks through a user study of 15 participants and realization of mock-up industrial scenarios, demonstrating reliable robotic manipulation despite failures in DL based perception. The resulting system, named SPIRIT, improves both manipulation performance and system reliability. SPIRIT was selected as a finalist of a major industrial innovation award.

cs.RO↗

arXiv2026

Signatures of moiré intralayer biexcitons and exciton-phason coupling in WSe2/WS2 heterostructures

Interactions among electronic and lattice degrees-of-freedom are foundational to various phases in condensed-matter physics, yet the dynamic interplay between excitonic and phononic quasiparticles represents an equivalent, underexplored frontier. Moiré superlattices provide an ideal platform for realizing these interactions by offering localized intralayer excitons (IALX) and ultralow-energy collective lattice modes, such as phasons. Here, by optically suppressing ultrafast charge-transfer (CT) to interlayer excitons in WSe2/WS2 heterostructures, we uncover dynamics of moiré IALX revealing long lifetimes (τ > 1000 ps) arising from localized Wannier and in-plane CT nature. We then observe moiré intralayer intervalley biexcitons with binding energy ~ 16 meV, with long lifetimes due to moiré confinement. Furthermore, we find time-domain signatures of strong coupling between moiré-IALX and ~ 10 micro-eV phasons, evidenced as twist-angle-dependent GHz oscillations in IALX dynamics. Our findings establish moiré superlattices as interacting hybrid quantum systems and for engineering non-equilibrium phenomena, as well as for GHz-scale optoelectronics.

cond-mat.mes-hall↗

arXiv2025

Controlling particle-hole symmetry of fractional quantum hall states in trilayer graphene

We present a detailed experimental study of the particle-hole symmetry (PHS) of the fractional quantum Hall (FQH) states about half filling in a multiband system. Specifically, we focus on the lowest Landau level of the monolayer-like band of Bernal stacked trilayer graphene (TLG). In pristine TLG, the excitation energy gaps, Landé g-factor, effective mass, and disorder broadening of the odd-denominator FQH states are identical to their hole-conjugate counterpart. This precise PH symmetry stems from the lattice mirror symmetry that precludes Landau-level mixing. Introducing a non-zero displacement field $D$ disrupts this mirror symmetry, facilitating the hybridization between the monolayer-like and bilayer-like Landau levels. This inter-band coupling enhances the Landau level mixing factor $η$ and activates three-body interactions -- both of which explicitly break the PHS of FQHs. As a result, conventional FQHs are completely destabilized, offering a route to engineer symmetry breaking of FQHs in a controlled way. We establish that the PHS breaking in TLG is of extrinsic origin and is fundamentally distinct from the intrinsic, interaction-driven symmetry breaking observed in the lowest Landau levels of single-layer and bilayer graphene.

cond-mat.mes-hall↗

arXiv2025

Even-denominator fractional quantum Hall states in the zeroth Landau level of ABA trilayer graphene

Even-denominator fractional quantum Hall states (FQHSs) at half filling are of particular interest because they can host non-Abelian quasiparticles. Here we report the emergence of such states in the zeroth Landau level ($N=0$) of ABA trilayer graphene (TLG), challenging the conventional expectation that they are confined to the first excited Landau level. We observe robust incompressible states at $\nu=7/2$, $9/2$, and $5/2$ with their associated Levin--Halperin daughter states: $\nu=59/17$ and $46/13$ near $7/2$; $\nu=58/13$ and $77/17$ near $9/2$; and $\nu=43/17$ near $5/2$. These states appear exclusively within a finite displacement-field window coincident with crossings between symmetry-broken $N=0$ Landau levels carrying distinct isospin indices. The quantitative correspondence between the calculated crossing loci and the experimentally determined stability regions identifies Landau-level mixing as the microscopic origin. We attribute the stabilization of these even-denominator states to inversion-symmetry breaking in TLG, which enhances valley-resolved Landau-level hybridization and renormalizes short-range Coulomb interactions. Our results expand the landscape of even-denominator FQHSs to multilayer graphene and establish TLG as a tunable platform for realizing non-Abelian anyons.

cond-mat.mes-hall↗

arXiv2024

Matrix Multiplication Reductions

In this paper we study a worst case to average case reduction for the problem of matrix multiplication over finite fields. Suppose we have an efficient average case algorithm, that given two random matrices $A,B$ outputs a matrix that has a non-trivial correlation with their product $A \cdot B$. Can we transform it into a worst case algorithm, that outputs the correct answer for all inputs without incurring a significant overhead in the running time? We present two results in this direction. (1) Two-sided error in the high agreement regime: We begin with a brief remark about a reduction for high agreement algorithms, i.e., an algorithm which agrees with the correct output on a large (say $>0.9$) fraction of entries, and show that the standard self-correction of linearity allows us to transform such algorithms into algorithms that work in worst case. (2) One-sided error in the low agreement regime: Focusing on average case algorithms with one-sided error, we show that over $\mathbb{F}_2$ there is a reduction that gets an $O(T)$ time average case algorithm that given a random input $A,B$ outputs a matrix that agrees with $A \cdot B$ on at least $51\%$ of the entries (i.e., has only a slight advantage over the trivial algorithm), and transforms it into an $\widetilde{O}(T)$ time worst case algorithm, that outputs the correct answer for all inputs with high probability.

cs.DS↗

arXiv2024

Computational Study on the Impact of Gasoline-Ethanol Blending on Autoignition and Soot/NOx Emissions under Gasoline Compression Ignition Conditions

Computational fluid dynamics (CFD) simulations of a single-cylinder gasoline compression ignition engine are performed to investigate the impact of gasoline-ethanol blending on autoignition, nitrogen oxide (NOx), and soot emissions under low-load conditions. A four-component toluene primary reference fuel (TPRF) + ethanol (ETPRF) surrogate (with 10% ethanol by volume; E10) is employed to represent the test gasoline (RD5-87). A 3D engine CFD model employing finite-rate chemistry with a skeletal kinetic mechanism, adaptive mesh refinement (AMR), and hybrid method of moments (HMOM) is adopted to capture in-cylinder combustion and soot/NOx emissions. The engine CFD model is validated against experimental data for three gasoline-ethanol blends: E10, E30 and E100, with varying ethanol content by volume. Model validation is carried out for multiple start-of-injection (SOI) timings (-21, -27, -36, and -45 crank angle degrees after top-dead-center (aTDC)) with respect to in-cylinder pressure, heat release rate, combustion phasing, NOx and soot emissions. For late injection timings (-21 and -27oaTDC), E30 yields higher soot than E10; while the trend reverses for early injection cases (-36 and -45oaTDC). E100 yields the lowest amount of soot among all fuels irrespective of SOI timing. Further, E10 shows a non-monotonic trend in soot emissions with SOI timing: SOI-36>SOI-45>SOI-21>SOI-27, while soot emissions from E30 exhibit monotonic decrease with advancing SOI timing. NOx emissions from various fuels follow a trend of E10>E30>E100. NOx emissions increase as SOI timing is advanced for all fuels, with an anomaly for E10 and E100 where NOx decreases when SOI is advanced beyond -36oaTDC. Detailed analysis of the numerical results is performed to investigate the emission trends and elucidate the impact of chemical composition and physical properties on autoignition and emissions characteristics.

math.NA↗

arXiv2020

Whole-Body Bilateral Teleoperation of a Redundant Aerial Manipulator

Attaching a robotic manipulator to a flying base allows for significant improvements in the reachability and versatility of manipulation tasks. In order to explore such systems while taking advantage of human capabilities in terms of perception and cognition, bilateral teleoperation arises as a reasonable solution. However, since most telemanipulation tasks require visual feedback in addition to the haptic one, real-time (task-dependent) positioning of a video camera, which is usually attached to the flying base, becomes an additional objective to be fulfilled. Since the flying base is part of the kinematic structure of the robot, if proper care is not taken, moving the video camera could undesirably disturb the end-effector motion. For that reason, the necessity of controlling the base position in the null space of the manipulation task arises. In order to provide the operator with meaningful information about the limits of the allowed motions in the null space, this paper presents a novel haptic concept called Null-Space Wall. In addition, a framework to allow stable bilateral teleoperation of both tasks is presented. Numerical simulation data confirm that the proposed framework is able to keep the system passive while allowing the operator to perform time-delayed telemanipulation and command the base to a task-dependent optimal pose.

cs.RO↗

arXiv2020

Smoother Position-Drift Compensation for Time Domain Passivity Approach based Teleoperation

Despite being one of the most robust methods in bilateral teleoperation, Time Domain Passivity Approach (TDPA) presents the drawback of accumulating position drift between master and slave devices. The lack of position synchronization poses an obstacle to the performance of teleoperation and may prevent the successful accomplishment of such tasks. Several techniques have been developed in order to solve the position-drift problem in TDPA-based teleoperation. However, they either present poor transparency by over-conservatively constraining force feedback or add high impulse-like force signals that can be harmful to the hardware and to the human operator. We propose a new approach to compensate position drift in TDPA-based teleoperation in a smoother way, which keeps the forces within the normal range of the teleoperation task while preserving the level of transparency and the robust stability of energy-based TDPA. We also add a way of tuning the compensator to behave in accordance with the task being performed, whether it requires faster or smoother compensation. The feasibility and performance of the method were experimentally validated. Good position tracking and regular-amplitude forces are demonstrated with up to 500 ms round-trip constant and variable delays for hard-wall contacts.

cs.RO↗

arXiv2019

Multi-DoF Time Domain Passivity Approach Based Drift Compensation for Telemanipulation

When, in addition to stability, position synchronization is also desired in bilateral teleoperation, Time Domain Passivity Approach (TDPA) alone might not be able to fulfill the desired objective. This is due to an undesired effect caused by admittance type passivity controllers, namely position drift. Previous works focused on developing TDPA-based drift compensation methods to solve this issue. It was shown that, in addition to reducing drift, one of the proposed methods was able to keep the force signals within their normal range, guaranteeing the safety of the task. However, no multi-DoF treatment of those approaches has been addressed. In that scope, this paper focuses on providing an extension of previous TDPA-based approaches to multi-DoF Cartesian-space teleoperation. An analysis of the convergence properties of the presented method is also provided. In addition, its applicability to multi-DoF devices is shown through hardware experiments and numerical simulation with round-trip time delays up to 700 ms.

cs.RO↗

arXiv2019

Sitara: Spectrum Measurement Goes Mobile Through Crowd-sourcing

Software-defined radios (SDRs) are often used in the experimental evaluation of next-generation wireless technologies. While crowd-sourced spectrum monitoring is an important component of future spectrum-agile technologies, there is no clear way to test it in the real world, i.e., with hundreds of users each with an SDR in their pocket participating in RF experiments controlled by, and data uploaded to, the cloud. Current fully functional SDRs are bulky, with components connected via wires, and last at most hours on a single battery charge. To address the needs of such experiments, we design and develop a compact, portable, untethered, and inexpensive SDR we call Sitara. Our SDR interfaces with a mobile device over Bluetooth 5 and can function standalone or as a client to a central command and control server. The Sitara offers true portability: it operates up to one week on battery power, requires no external wired connections and occupies a footprint smaller than a credit card. It transmits and receives common waveforms, uploads IQ samples or processed receiver data through a mobile device to a server for remote processing and performs spectrum sensing functions. Multiple Sitaras form a distributed system capable of conducting experiments in wireless networking and communication in addition to RF monitoring and sensing activities. In this paper, we describe our design, evaluate our solution, present experimental results from multi-sensor deployments and discuss the value of this system in future experimentation.

cs.NI↗