arXiv subjects

Abhishek Kumar

Publications and source records attributed to Abhishek Kumar.

At least 91 records · Page 5Linked to original sources

arXiv2024

Microwaves reveal the nanoscale ion intercalation and edge activity of 2D catalyst

The accelerated demand for electrochemical energy storage urges the need for new, sustainable, stable and lightweight materials able to store high energy densities rapidly and efficiently. Development of these functional materials requires specialized techniques that can provide a close insight into the electrochemical properties at the nanoscale. For this reason, we have introduced the electrochemical scanning microwave microscopy (EC-SMM) enabling local measurement of electrochemical properties with nanometer spatial resolution and sensitivity down to atto-Ampere electrochemical currents. The exceptional power of EC-SMM operated at radio frequency is exemplified here by the successful detection of the electrochemical activity and dynamics of molecularly thin NiCo(OH)2 flakes with a spatial resolution of 16 +/- 1 nm, uncovering the location of the active sites and providing atomistic details on the catalytic process that controls the electrocatalytic performance. Our results pinpoint the factors required to tune the thermodynamics of ion intercalation and to optimize the surface adsorption.

physics.chem-ph↗

arXiv2024

Cluster dynamical mean field study of intra-unit-cell charge nematicity in hole-doped cuprates

Recent scanning-tunneling microscopy on hole-doped Bi$_2$Sr$_2$CaCu$_2$O$_8$, one of the materials of the cuprate family, finds a long-range ordered spontaneous splitting of the energy levels of oxygen orbitals inside the CuO$_2$ unit cells [S. Wang et al., Nat. Mat. 23, 492-498 (2024)]. This spontaneous intra-unit-cell orbital ordering, also known as electronic nematicity, breaks $C_4$ symmetry and is thought to arise from the Coulomb interaction (denoted by $V_{pp}$) between oxygen $p_x$ and $p_y$ electrons. In this work, we study the spontaneous emergence of electronic nematicity within the three-band Hubbard (aka the Emery-VSA model), using cluster dynamical mean field theory. This method incorporates short-range electronic correlations and gives us access to the density of states, a quantity that is directly probed in experiments. We argue that there is a delicate competition between $V_{pp}$ and $V_{pd}$ (the latter being the Coulomb interaction between copper $d_{x^2-y^2}$ and oxygen $p_{x,y}$ electrons) that must be taken into account in order to find a Zhang-Rice singlet band well-resolved from the upper Hubbard band, and a splitting of the charge-transfer band (one of the signatures of charge nematicity) by roughly 50 meV, as observed recently.

cond-mat.str-el↗

arXiv2024

LLMs as Evaluators: A Novel Approach to Evaluate Bug Report Summarization

Summarizing software artifacts is an important task that has been thoroughly researched. For evaluating software summarization approaches, human judgment is still the most trusted evaluation. However, it is time-consuming and fatiguing for evaluators, making it challenging to scale and reproduce. Large Language Models (LLMs) have demonstrated remarkable capabilities in various software engineering tasks, motivating us to explore their potential as automatic evaluators for approaches that aim to summarize software artifacts. In this study, we investigate whether LLMs can evaluate bug report summarization effectively. We conducted an experiment in which we presented the same set of bug summarization problems to humans and three LLMs (GPT-4o, LLaMA-3, and Gemini) for evaluation on two tasks: selecting the correct bug report title and bug report summary from a set of options. Our results show that LLMs performed generally well in evaluating bug report summaries, with GPT-4o outperforming the other LLMs. Additionally, both humans and LLMs showed consistent decision-making, but humans experienced fatigue, impacting their accuracy over time. Our results indicate that LLMs demonstrate potential for being considered as automated evaluators for bug report summarization, which could allow scaling up evaluations while reducing human evaluators effort and fatigue.

cs.SE↗

arXiv2024

Correlation of Magnetic State Configurations in Nanotubes with FMR spectrum

Magnetic nanotubes have garnered immense attention for their potential in high-density magnetic memory, owing to their stable flux closure configuration and fast, reproducible reversal processes. However, characterizing their magnetic configuration through straightforward methodologies remains a challenge in both scope and detail. Here, we elucidate the magnetic state details using Remanence Field Ferromagnetic Resonance Spectroscopy (RFMR) for arrays of electrodeposited nanotubes. Micromagnetic simulations revealed distinct spin configurations while coming from saturation, including the edge vortex, onion, uniform and curling states, with chirality variations depending on the preparation field direction. Dynamic measurements, coupled with RFMR spectra analysis, unveiled multiple FMR modes corresponding to these spin configurations. The evolution of spin configurations under bias fields were studied, indicating nucleation within the curling state. Observations revealed opposite RFMR spectra, denoting opposite magnetic spin configurations after removing the positive and negative saturating fields when the magnetic field was applied along {theta_H=0} and perpendicular {theta_H= 90} to the nanotube axis. We observed a mixture of the non-uniform curling states with the end vortex state (onion-like curling state) at the end of the nanotubes for the theta_H=0(90) and uniform magnetization states in the middle of the nanotubes for the theta_H=0 configuration. Building on RFMR information, frequency-swept FMR absorption spectra obtained at different bias fields allowed the characterization of magnetization states. This picture was supported by micromagnetic simulations. These findings were further substantiated with First Order Reversal Curve measurements (FORC).

cond-mat.mes-hall↗

arXiv2024

GLANCE: Graph-based Learnable Digital Twin for Communication Networks

As digital twins (DTs) to physical communication systems, network simulators can aid the design and deployment of communication networks. However, time-consuming simulations must be run for every new set of network configurations. Learnable digital twins (LDTs), in contrast, can be trained offline to emulate simulation outcomes and serve as a more efficient alternative to simulation-based DTs at runtime. In this work, we propose GLANCE, a communication LDT that learns from the simulator ns-3. It can evaluate network key performance indicators (KPIs) and assist in network management with exceptional efficiency. Leveraging graph learning, we exploit network data characteristics and devise a specialized architecture to embed sequential and topological features of traffic flows within the network. In addition, multi-task learning (MTL) and transfer learning (TL) are leveraged to enhance GLANCE's generalizability to unseen inputs and efficacy across different tasks. Beyond end-to-end KPI prediction, GLANCE can be deployed within an optimization framework for network management. It serves as an efficient or differentiable evaluator in optimizing network configurations such as traffic loads and flow destinations. Through numerical experiments and benchmarking, we verify the effectiveness of the proposed LDT architecture, demonstrate its robust generalization to various inputs, and showcase its efficacy in network management applications.

cs.NI↗

arXiv2024

Efficient Human Pose Estimation: Leveraging Advanced Techniques with MediaPipe

This study presents significant enhancements in human pose estimation using the MediaPipe framework. The research focuses on improving accuracy, computational efficiency, and real-time processing capabilities by comprehensively optimising the underlying algorithms. Novel modifications are introduced that substantially enhance pose estimation accuracy across challenging scenarios, such as dynamic movements and partial occlusions. The improved framework is benchmarked against traditional models, demonstrating considerable precision and computational speed gains. The advancements have wide-ranging applications in augmented reality, sports analytics, and healthcare, enabling more immersive experiences, refined performance analysis, and advanced patient monitoring. The study also explores the integration of these enhancements within mobile and embedded systems, addressing the need for computational efficiency and broader accessibility. The implications of this research set a new benchmark for real-time human pose estimation technologies and pave the way for future innovations in the field. The implementation code for the paper is available at https://github.com/avhixd/Human_pose_estimation.

cs.CV↗

arXiv2024

Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization

Large language models (LLMs), even when specifically trained to process long input contexts, struggle to capture relevant information located in the middle of their input. This phenomenon has been known as the lost-in-the-middle problem. In this work, we make three contributions. First, we set out to understand the factors that cause this phenomenon. In doing so, we establish a connection between lost-in-the-middle to LLMs' intrinsic attention bias: LLMs exhibit a U-shaped attention bias where the tokens at the beginning and at the end of its input receive higher attention, regardless of their relevance. Second, we mitigate this positional bias through a calibration mechanism, found-in-the-middle, that allows the model to attend to contexts faithfully according to their relevance, even though when they are in the middle. Third, we show found-in-the-middle not only achieves better performance in locating relevant information within a long context, but also eventually leads to improved retrieval-augmented generation (RAG) performance across various tasks, outperforming existing methods by up to 15 percentage points. These findings open up future directions in understanding LLM attention bias and its potential consequences.

cs.CL↗

arXiv2024

Confidence Under the Hood: An Investigation into the Confidence-Probability Alignment in Large Language Models

As the use of Large Language Models (LLMs) becomes more widespread, understanding their self-evaluation of confidence in generated responses becomes increasingly important as it is integral to the reliability of the output of these models. We introduce the concept of Confidence-Probability Alignment, that connects an LLM's internal confidence, quantified by token probabilities, to the confidence conveyed in the model's response when explicitly asked about its certainty. Using various datasets and prompting techniques that encourage model introspection, we probe the alignment between models' internal and expressed confidence. These techniques encompass using structured evaluation scales to rate confidence, including answer options when prompting, and eliciting the model's confidence level for outputs it does not recognize as its own. Notably, among the models analyzed, OpenAI's GPT-4 showed the strongest confidence-probability alignment, with an average Spearman's $\hatρ$ of 0.42, across a wide range of tasks. Our work contributes to the ongoing efforts to facilitate risk assessment in the application of LLMs and to further our understanding of model trustworthiness.

cs.CL↗

arXiv2024

Subtle Biases Need Subtler Measures: Dual Metrics for Evaluating Representative and Affinity Bias in Large Language Models

Research on Large Language Models (LLMs) has often neglected subtle biases that, although less apparent, can significantly influence the models' outputs toward particular social narratives. This study addresses two such biases within LLMs: representative bias, which denotes a tendency of LLMs to generate outputs that mirror the experiences of certain identity groups, and affinity bias, reflecting the models' evaluative preferences for specific narratives or viewpoints. We introduce two novel metrics to measure these biases: the Representative Bias Score (RBS) and the Affinity Bias Score (ABS), and present the Creativity-Oriented Generation Suite (CoGS), a collection of open-ended tasks such as short story writing and poetry composition, designed with customized rubrics to detect these subtle biases. Our analysis uncovers marked representative biases in prominent LLMs, with a preference for identities associated with being white, straight, and men. Furthermore, our investigation of affinity bias reveals distinctive evaluative patterns within each model, akin to `bias fingerprints'. This trend is also seen in human evaluators, highlighting a complex interplay between human and machine bias perceptions.

cs.CL↗

arXiv2024

Investigating a Device Independence Quantum Random Number Generation

Quantum random number generation (QRNG) is a resource that is a necessity in the field of cryptography. However, its certification has been challenging. In this article, we certify randomness with the aid of quantum entanglement in a device independent setting, where we choose two-photon interference for source characterisation. The CHSH inequality violation and quantum state tomography are used as independent checks on the measurement devices. These measures ensure the unpredictability of quantum random number generation. This work can be easily extended to faster randomness expansion protocols.

quant-ph↗

arXiv2024

RB-Modulation: Training-Free Personalization of Diffusion Models using Stochastic Optimal Control

We propose Reference-Based Modulation (RB-Modulation), a new plug-and-play solution for training-free personalization of diffusion models. Existing training-free approaches exhibit difficulties in (a) style extraction from reference images in the absence of additional style or content text descriptions, (b) unwanted content leakage from reference style images, and (c) effective composition of style and content. RB-Modulation is built on a novel stochastic optimal controller where a style descriptor encodes the desired attributes through a terminal cost. The resulting drift not only overcomes the difficulties above, but also ensures high fidelity to the reference style and adheres to the given text prompt. We also introduce a cross-attention-based feature aggregation scheme that allows RB-Modulation to decouple content and style from the reference image. With theoretical justification and empirical evidence, our framework demonstrates precise extraction and control of content and style in a training-free manner. Further, our method allows a seamless composition of content and style, which marks a departure from the dependency on external adapters or ControlNets.

cs.LG↗

arXiv2024

Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models

Fine-tuning language models~(LMs) on human-generated data remains a prevalent practice. However, the performance of such models is often limited by the quantity and diversity of high-quality human data. In this paper, we explore whether we can go beyond human data on tasks where we have access to scalar feedback, for example, on math problems where one can verify correctness. To do so, we investigate a simple self-training method based on expectation-maximization, which we call ReST$^{EM}$, where we (1) generate samples from the model and filter them using binary feedback, (2) fine-tune the model on these samples, and (3) repeat this process a few times. Testing on advanced MATH reasoning and APPS coding benchmarks using PaLM-2 models, we find that ReST$^{EM}$ scales favorably with model size and significantly surpasses fine-tuning only on human data. Overall, our findings suggest self-training with feedback can substantially reduce dependence on human-generated data.

cs.LG↗

arXiv2024

AB5 type multicomponent TiVCoNiMn2 high-entropy alloy

Recent theoretical and practical research has focused on multi-component High Entropy Alloys (HEAs), which have superior mechanical and functional properties than standard alloys based on a single major element, thereby establishing a new field. A multi-component HEA contains five or more primary elements at concentrations ranging from 5 to 35 atomic percent. We examined the microstructure and mechanical properties of TiVCoNiMn2 HEA. The mixing enthalpy and other thermodynamic parameters were determined using Meidma's model. TiVCoNiMn2 exhibits a mixing enthalpy of -15.6 kJ/mol and an atomic radius mismatch of approximately 10.03%. HEA is derived from both hydride and non-hydride-producing elements. This could be a useful hydrogen storage material. The hydrogen absorption/desorption capabilities of these HEAs are promising.

cond-mat.mtrl-sci↗

arXiv2024

Photonic Supercoupling in Silicon Topological Waveguides

Electromagnetic wave coupling between photonic systems relies on the evanescent field typically confined within a single wavelength. Extending evanescent coupling distance requires low refractive index contrast and perfect momentum matching for achieving a large coupling ratio. Here, we report the discovery of photonic supercoupling in a topological valley Hall pair of waveguides, showing a substantial improvement in coupling efficiency across multiple wavelengths. Experimentally, we realize ultra-high coupling ratios between waveguides through valley-conserved vortex flow of electromagnetic energy, attaining 95% coupling efficiency for separations of up to three wavelengths. This demonstration of photonic supercoupling in topological systems significantly extends the coupling distance between on-chip waveguides and components, paving the path for the development of supercoupled photonic integrated devices, optical sensing, and telecommunications.

physics.optics↗

arXiv2024

Improving the energy density and flexibility of PMN-0.3PT based piezoelectric generator by composite designing

Ceramics based piezoelectric generators are known for their high energy density and poor flexibility. In this work, v_r-PMN-0.3PT/PDMS 2-2 composite with optimum PMN-0.3PT content (v_r) was designed that demonstrated enhanced output energy density and superior mechanical flexibility under dynamic mechanical excitation. v_r-PMN-0.3PT/PDMS 2-2 composite with different PMN-PT reinforcement content (v_r) and two different reinforcement configurations were fabricated and characterized for effective electro-elastic properties and energy harvesting response. Parallelly, using the finite element method and analytical models, effective electromechanical properties were calculated. Composites with parallel connectivity of the reinforcement phase demonstrated enhanced piezoelectric charge coefficient even with low PMN-0.3PT content whereas the relative permittivity and elastic modulus exhibited a linearly increasing trend with reinforcement volume fraction. At a compressive load of 50 N and 5 Hz frequency, a piezoelectric generator (PG) based on a v_r = 0.2, v_r- PMN-0.3PT/PDMS 2-2 composite with parallel connectivity produced a maximum short-circuit current density of 69 nA/cm2 and an open-circuit electric field of 189 V/cm, translating to a maximum output power density of ~13 μW/cm3 higher than that of pristine PMN-0.3 PT based piezoelectric generator. Estimated mechanical flexibility was found to be ~53 % higher than that of pristine PMN-0.3PT.

physics.app-ph↗

arXiv2024

Boundary transfer matrix spectrum of measurement-induced transitions

Measurement-induced phase transitions (MIPTs) are known to be described by non-unitary conformal field theories (CFTs) whose precise nature remains unknown. Most physical quantities of interest, such as the entanglement features of quantum trajectories, are described by boundary observables in this CFT. We introduce a transfer matrix approach to study the boundary spectrum of this field theory, and consider a variety of boundary conditions. We apply this approach numerically to monitored Haar and Clifford circuits, and to the measurement-only Ising model where the boundary scaling dimensions can be derived analytically. Our transfer matrix approach provides a systematic numerical tool to study the spectrum of MIPTs.

cond-mat.dis-nn↗

arXiv2023

Hyperbolic Spin Waves in Magnetic Polar Metals

We demonstrate the emergence of collective spin modes with hyperbolic dispersion in three-dimensional spin-orbit coupled polar metals magnetized by intrinsic ordering or applied fields. These particle-hole bound states exist for arbitrarily weak repulsive interactions; they are optically accessible and can be used to generate pure spin current when magnetization is tilted away from the polar axis. We suggest material hosts for these excitations and discuss their potential relevance to nanoscale spintronic and polaritonic applications.

cond-mat.mes-hall↗

arXiv2023

Spin-Phonon Resonances in Nearly Polar Metals with Spin-Orbit Coupling

In metals in the vicinity of a polar transition, interactions between electrons and soft phonon modes remain to be determined. Here we explore the consequences of spin-orbit assisted electron-phonon coupling on the collective modes of such nearly polar metals in the presense of magnetic field. We find that the soft polar phonon hybridizes with spin-flip electronic excitations of the Zeeman-split bands leading to an anticrossing. The associated energy splitting allows for an unambiguous determination of the strength of the spin-orbit mediated coupling to soft modes in polar metals by spectroscopic experiments. The approach to the polar transition is reflected by the softening of the effective g-factor of the hybridized spin-flip mode. Analyzing the static limit, we find that the polar order parameter can be oriented by magnetic field. This provides possibilities for new switching protocols in polar metallic materials. We demonstrate that the effects we predict can be observed with current experimental techniques and discuss promising material candidates.

cond-mat.str-el↗