arXiv subjects

Mohamed Hassan

Publications and source records attributed to Mohamed Hassan.

At least 19 recordsLinked to original sources

arXiv2026

Femtosecond tunneling spectroscopy of ultrafast band bending dynamics at the atomic limit

Atomic-scale disorder shapes the potential energy landscape traversed by photoexcited charge carriers, while the carriers themselves also dynamically reshape this landscape. However, resolving ultrafast photocarrier motion at atomic length scales has remained a central challenge in materials science. Here, we demonstrate that lightwave-driven terahertz scanning tunneling microscopy (THz-STM) provides access to these dynamics by probing the ultrafast evolution of local electronic structure following resonant interband excitation. Applying this approach to the photoexcited GaAs(110) surface, we image the resulting femtosecond carrier dynamics by tracking the transient photocurrents produced by ultrafast shifts in the energy alignment of surface and bulk electronic states near individual surface defects. Supported by modeling, we experimentally resolve the time-dependent band bending produced by photoinduced charge carriers across the atomic-scale landscape of the sample surface. Crucially, we employ terahertz time-domain spectroscopy in the tip near-field to disentangle the coherent sub-cycle dynamics induced by the terahertz driving field from the intrinsic sample response. We establish a new regime of ultrafast tunneling spectroscopy that captures transient electronic structure and dynamic band alignment with unprecedented spatio-temporal resolution, which has significant implications for understanding carrier transport, defect-mediated processes, and the development of optoelectronic technologies based on dynamically tunable materials.

cond-mat.mes-hall↗

arXiv2026

Probing ultrafast heating and ionization dynamics in solid density plasmas with time-resolved resonant X-ray absorption and emission

Heating and ionization are among the most fundamental processes in relativistic laser--solid interactions; however, their spatiotemporal evolution remains challenging to capture experimentally. Here we present detailed diagnosis of high-intensity laser interactions with wire targets, leveraging the extreme spectral brightness of an X-ray free-electron laser in sub-picosecond time-resolved resonant X-ray emission spectroscopy and absorption imaging. Experimental results are compared with comprehensive simulations using atomic collisional--radiative models, particle-in-cell, and magnetohydrodynamics codes to elucidate the underlying physics. These multi-scale simulations reveal extreme sensitivity of basic plasma parameters with widely used models, such as temperature and ionization depth, which are able to be constrained by incorporating a detailed accounting of laser spatial profiles, pre-plasma conditions, and collisional processes. These results provide new insights into heating and ionization dynamics in the high-energy-density regime relevant to inertial fusion energy research, both as an experimental platform for accessing theoretically challenging conditions and as a benchmark for improving models of high-power laser--plasma interactions.

physics.plasm-ph↗

arXiv2025

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise

Process-supervised reward models (PRMs) excel at providing step-by-step verification for large language model (LLM) outputs in domains like mathematics and coding. However, their application to fields lacking ground-truth answers, such as clinical note generation, poses significant challenges. We introduce a novel framework for training PRMs to deliver step-level reward signals for LLM-generated clinical notes. By precisely defining meaningful "steps," injecting realistic "errors" informed by domain expertise, and leveraging LLMs to generate process supervision data at scale, we overcome previous limitations. Our PRM, built on LLaMA-3.1 8B, consistently outperforms proprietary reasoning and non-reasoning models, achieving state-of-the-art performance on two key evaluations: (1) distinguishing gold-standard from error-containing samples with 98.8% accuracy, and (2) selecting physician-preferred clinical notes with 56.2% accuracy. We investigate critical components for effective PRM training, including optimal loss functions and data selection strategies, and present a comprehensive physician reader study identifying predictors of downstream Best-of-N performance. Our study sheds light on unlocking the potential of PRMs for diverse generative tasks across domains.

cs.CL↗

arXiv2025

Demonstration of full-scale spatio-temporal diagnostics of solid-density plasmas driven by an ultra-short relativistic laser pulse using an X-ray free-electron laser

Understanding the complex plasma dynamics in ultra-intense relativistic laser-solid interactions is of fundamental importance to the applications of laser plasma-based particle accelerators, creation of high energy-density matter, understanding of planetary science and laser-driven fusion energy. However, experimental efforts in this regime have been limited by the accessibility of over-critical density and spatio-temporal resolution of conventional diagnostics. Over the last decade, the advent of femtosecond brilliant hard X-ray free electron lasers (XFELs) is opening new horizons to break these limitations. Here, for the first time we present full-scale spatio-temporal measurements of solid-density plasma dynamics, including preplasma generation with tens of nanometer-scale length driven by the leading edge of a relativistic laser pulse, ultrafast heating and ionization at the main pulse arrival, laser-driven blast shock waves and transient surface return current-induced compression dynamics up to hundreds of picoseconds after interaction. These observations are enabled by utilizing a novel combination of advanced X-ray diagnostics such as small-angle X-ray scattering (SAXS), resonant X-ray emission spectroscopy (RXES), and propagation-based X-ray phase-contrast imaging (XPCI) simultaneously at the European XFEL-HED beamline station.

physics.plasm-ph↗

arXiv2025

Taming the Randomness: Towards Label-Preserving Cropping in Contrastive Learning

Contrastive learning (CL) approaches have gained great recognition as a very successful subset of self-supervised learning (SSL) methods. SSL enables learning from unlabeled data, a crucial step in the advancement of deep learning, particularly in computer vision (CV), given the plethora of unlabeled image data. CL works by comparing different random augmentations (e.g., different crops) of the same image, thus achieving self-labeling. Nevertheless, randomly augmenting images and especially random cropping can result in an image that is semantically very distant from the original and therefore leads to false labeling, hence undermining the efficacy of the methods. In this research, two novel parameterized cropping methods are introduced that increase the robustness of self-labeling and consequently increase the efficacy. The results show that the use of these methods significantly improves the accuracy of the model by between 2.7\% and 12.4\% on the downstream task of classifying CIFAR-10, depending on the crop size compared to that of the non-parameterized random cropping method.

cs.CV↗

arXiv2025

GCSAM: Gradient Centralized Sharpness Aware Minimization

The generalization performance of deep neural networks (DNNs) is a critical factor in achieving robust model behavior on unseen data. Recent studies have highlighted the importance of sharpness-based measures in promoting generalization by encouraging convergence to flatter minima. Among these approaches, Sharpness-Aware Minimization (SAM) has emerged as an effective optimization technique for reducing the sharpness of the loss landscape, thereby improving generalization. However, SAM's computational overhead and sensitivity to noisy gradients limit its scalability and efficiency. To address these challenges, we propose Gradient-Centralized Sharpness-Aware Minimization (GCSAM), which incorporates Gradient Centralization (GC) to stabilize gradients and accelerate convergence. GCSAM normalizes gradients before the ascent step, reducing noise and variance, and improving stability during training. Our evaluations indicate that GCSAM consistently outperforms SAM and the Adam optimizer in terms of generalization and computational efficiency. These findings demonstrate GCSAM's effectiveness across diverse domains, including general and medical imaging tasks.

cs.LG↗

arXiv2024

Terahertz control of surface topology probed with subatomic resolution

Light-induced phase transitions offer a method to dynamically modulate topological states in bulk complex materials. Yet, next-generation devices demand nanoscale architectures with contact resistances near the quantum limit and precise control over local electronic properties. The layered material WTe$_2$ has gained attention as a likely Weyl semimetal, with topologically protected linear electronic band crossings hosting massless chiral fermions. Here, we demonstrate a topological phase transition facilitated by light-induced shear motion of a single atomic layer at the surface of bulk WTe$_2$, thereby opening the door to nanoscale device concepts. Ultrafast terahertz fields enhanced at the apex of an atomically sharp tip resonantly couple to the key interlayer shear mode of WTe$_2$ via a ferroelectric dipole at the interface, inducing a structural phase transition at the surface to a metastable state. Subatomically resolved differential imaging, combined with hybrid-level density functional theory, reveals a shift of 7 $\pm$ 3 picometres in the top atomic plane. Tunnelling spectroscopy links electronic changes across the phase transition with the electron and hole pockets in the band structure, suggesting a reversible, light-induced annihilation of the topologically-protected Fermi arc surface states in the top atomic layer.

cond-mat.mtrl-sci↗

arXiv2024

Choosing the Right Communication Protocol for your Web Application

Selecting the appropriate communication protocol is crucial for optimizing the performance, scalability, and user experience of web applications. In the diverse ecosystem of web technologies, various protocols like RESTful APIs, gRPC, WebSockets, and others serve distinct purposes. RESTful APIs are widely favored for their simplicity and stateless nature, making them ideal for standard CRUD operations. They offer a straightforward approach to interacting with resources over HTTP/1.1, providing broad compatibility and ease of integration across different platforms. However, in scenarios where applications require high efficiency and real-time communication, gRPC and WebSockets emerge as powerful alternatives. Each protocol comes with its strengths and limitations, influencing factors such as ease of implementation, performance under load, and support for complex data structures. RESTful APIs, while easy to use and widely supported, may introduce overhead due to their stateless nature and reliance on multiple HTTP/1.1 requests. In contrast, gRPC advanced features, while powerful, require a steeper learning curve and more sophisticated infrastructure. Similarly, WebSockets, while excellent for real-time applications, require careful management of persistent connections and security considerations. This paper explores the key considerations in choosing the right communication protocol, emphasizing the need to align technical choices with application requirements and user expectations. By understanding the unique attributes of each protocol, developers can make informed decisions that enhance the responsiveness and reliability of their web applications. The choice of protocol can significantly impact the user experience, scalability, and maintainability of the application, making it a critical decision in the web development process.

cs.SE↗

arXiv2024

Do Sharpness-based Optimizers Improve Generalization in Medical Image Analysis?

Effective clinical deployment of deep learning models in healthcare demands high generalization performance to ensure accurate diagnosis and treatment planning. In recent years, significant research has focused on improving the generalization of deep learning models by regularizing the sharpness of the loss landscape. Among the optimization approaches that explicitly minimize sharpness, Sharpness-Aware Minimization (SAM) has shown potential in enhancing generalization performance on general domain image datasets. This success has led to the development of several advanced sharpness-based algorithms aimed at addressing the limitations of SAM, such as Adaptive SAM, surrogate-Gap SAM, Weighted SAM, and Curvature Regularized SAM. These sharpness-based optimizers have shown improvements in model generalization compared to conventional stochastic gradient descent optimizers and their variants on general domain image datasets, but they have not been thoroughly evaluated on medical images. This work provides a review of recent sharpness-based methods for improving the generalization of deep learning networks and evaluates the methods performance on medical breast ultrasound images. Our findings indicate that the initial SAM method successfully enhances the generalization of various deep learning models. While Adaptive SAM improves generalization of convolutional neural networks, it fails to do so for vision transformers. Other sharpness-based optimizers, however, do not demonstrate consistent results. The results reveal that, contrary to findings in the non-medical domain, SAM is the only recommended sharpness-based optimizer that consistently improves generalization in medical image analysis, and further research is necessary to refine the variants of SAM to enhance generalization performance in this field

eess.IV↗

arXiv2024

Big Data, Big Decisions Choosing the Right Database

In the burgeoning era of big data, selecting the optimal database solution has become a critical decision for organizations across every industry. Big data demands a powerful database solution. Traditionally, SQL Database, Database ruled, offering a structured approach familiar to many organizations. However, big data's complexity and unstructured nature challenge SQL Database's limitations. Enter NoSQL Database: flexible and scalable, making them ideal for big data's ever-changing nature. We'll explore the key differences between SQL and NoSQL Database. Performance-wise, SQL Database shines for structured queries. Its standardized language (SQL) ensures data consistency and complex analysis. But for big data's unstructured formats, this rigidity becomes a hurdle. NoSQL offers a welcome contrast. Its flexible schema allows for diverse data formats and evolving structures, perfect for undefined or frequently changing data models. Additionally, NoSQL boasts superior horizontal scalability, distributing data across multiple servers for cost-effective growth. Understanding these key differentiators empowers organizations to choose the optimal database for their big data needs.

cs.DB↗

arXiv2023

Atomic-Scale Terahertz Near Fields for Ultrafast Tunnelling Spectroscopy

Lightwave-driven terahertz scanning tunnelling microscopy (THz-STM) is capable of exploring ultrafast dynamics across a wide range of materials with angstrom resolution. In contrast to scanning near-field optical microscopy, where photons scattered by the tip apex are analyzed to access the local dielectric function on the nanoscale, THz-STM uses a strong-field single-cycle terahertz pulse to drive an ultrafast current across a tunnel junction, thereby probing the local density of electronic states. Yet, the terahertz field in a THz-STM junction may also be spectrally modified by the electromagnetic response of the sample. Here, we demonstrate a reliable and self-consistent approach for terahertz near-field waveform acquisition in an atomic tunnel junction that can be generally applied to electrically conductive surfaces. By combining waveform sampling and tailoring with terahertz scanning tunnelling spectroscopy (THz-STS), we comprehensively characterize the tunnel junction and distinguish local sample properties from effects due to terahertz pulse coupling and field enhancement. Through modelling, we verify the presence of an isolated unipolar terahertz-induced current pulse, facilitating straightforward interpretation for differential THz-STS with high spectral resolution. Finally, we demonstrate the feasibility of atomic-scale terahertz time-domain spectroscopy via the extremely localized near-fields in the tunnel junction.

cond-mat.mes-hall↗

arXiv2023

Brain tumor multi classification and segmentation in MRI images using deep learning

This study proposes a deep learning model for the classification and segmentation of brain tumors from magnetic resonance imaging (MRI) scans. The classification model is based on the EfficientNetB1 architecture and is trained to classify images into four classes: meningioma, glioma, pituitary adenoma, and no tumor. The segmentation model is based on the U-Net architecture and is trained to accurately segment the tumor from the MRI images. The models are evaluated on a publicly available dataset and achieve high accuracy and segmentation metrics, indicating their potential for clinical use in the diagnosis and treatment of brain tumors.

eess.IV↗

arXiv2023

Brain Tumor classification and Segmentation using Deep Learning

Brain tumors are a complex and potentially life-threatening medical condition that requires accurate diagnosis and timely treatment. In this paper, we present a machine learning-based system designed to assist healthcare professionals in the classification and diagnosis of brain tumors using MRI images. Our system provides a secure login, where doctors can upload or take a photo of MRI and our app can classify the model and segment the tumor, providing the doctor with a folder of each patient's history, name, and results. Our system can also add results or MRI to this folder, draw on the MRI to send it to another doctor, and save important results in a saved page in the app. Furthermore, our system can classify in less than 1 second and allow doctors to chat with a community of brain tumor doctors. To achieve these objectives, our system uses a state-of-the-art machine learning algorithm that has been trained on a large dataset of MRI images. The algorithm can accurately classify different types of brain tumors and provide doctors with detailed information on the size, location, and severity of the tumor. Additionally, our system has several features to ensure its security and privacy, including secure login and data encryption. We evaluated our system using a dataset of real-world MRI images and compared its performance to other existing systems. Our results demonstrate that our system is highly accurate, efficient, and easy to use. We believe that our system has the potential to revolutionize the field of brain tumor diagnosis and treatment and provide healthcare professionals with a powerful tool for improving patient outcomes.

eess.IV↗

arXiv2023

Synthesizing Physical Character-Scene Interactions

Movement is how people interact with and affect their environment. For realistic character animation, it is necessary to synthesize such interactions between virtual characters and their surroundings. Despite recent progress in character animation using machine learning, most systems focus on controlling an agent's movements in fairly simple and homogeneous environments, with limited interactions with other objects. Furthermore, many previous approaches that synthesize human-scene interactions require significant manual labeling of the training data. In contrast, we present a system that uses adversarial imitation learning and reinforcement learning to train physically-simulated characters that perform scene interaction tasks in a natural and life-like manner. Our method learns scene interaction behaviors from large unstructured motion datasets, without manual annotation of the motion data. These scene interactions are learned using an adversarial discriminator that evaluates the realism of a motion within the context of a scene. The key novelty involves conditioning both the discriminator and the policy networks on scene context. We demonstrate the effectiveness of our approach through three challenging scene interaction tasks: carrying, sitting, and lying down, which require coordination of a character's movements in relation to objects in the environment. Our policies learn to seamlessly transition between different behaviors like idling, walking, and sitting. By randomizing the properties of the objects and their placements during training, our method is able to generalize beyond the objects and scenarios depicted in the training dataset, producing natural character-scene interactions for a wide variety of object shapes and placements. The approach takes physics-based character motion generation a step closer to broad applicability.

cs.GR↗

arXiv2022

Human-Aware Object Placement for Visual Environment Reconstruction

Humans are in constant contact with the world as they move through it and interact with it. This contact is a vital source of information for understanding 3D humans, 3D scenes, and the interactions between them. In fact, we demonstrate that these human-scene interactions (HSIs) can be leveraged to improve the 3D reconstruction of a scene from a monocular RGB video. Our key idea is that, as a person moves through a scene and interacts with it, we accumulate HSIs across multiple input images, and optimize the 3D scene to reconstruct a consistent, physically plausible and functional 3D scene layout. Our optimization-based approach exploits three types of HSI constraints: (1) humans that move in a scene are occluded or occlude objects, thus, defining the depth ordering of the objects, (2) humans move through free space and do not interpenetrate objects, (3) when humans and objects are in contact, the contact surfaces occupy the same place in space. Using these constraints in an optimization formulation across all observations, we significantly improve the 3D scene layout reconstruction. Furthermore, we show that our scene reconstruction can be used to refine the initial 3D human pose and shape (HPS) estimation. We evaluate the 3D scene layout reconstruction and HPS estimation qualitatively and quantitatively using the PROX and PiGraphs datasets. The code and data are available for research purposes at https://mover.is.tue.mpg.de/.

cs.CV↗

arXiv2021

Stochastic Scene-Aware Motion Prediction

A long-standing goal in computer vision is to capture, model, and realistically synthesize human behavior. Specifically, by learning from data, our goal is to enable virtual humans to navigate within cluttered indoor scenes and naturally interact with objects. Such embodied behavior has applications in virtual reality, computer games, and robotics, while synthesized behavior can be used as a source of training data. This is challenging because real human motion is diverse and adapts to the scene. For example, a person can sit or lie on a sofa in many places and with varying styles. It is necessary to model this diversity when synthesizing virtual humans that realistically perform human-scene interactions. We present a novel data-driven, stochastic motion synthesis method that models different styles of performing a given action with a target object. Our method, called SAMP, for Scene-Aware Motion Prediction, generalizes to target objects of various geometries while enabling the character to navigate in cluttered scenes. To train our method, we collected MoCap data covering various sitting, lying down, walking, and running styles. We demonstrate our method on complex indoor scenes and achieve superior performance compared to existing solutions. Our code and data are available for research at https://samp.is.tue.mpg.de.

cs.CV↗

arXiv2021

Populating 3D Scenes by Learning Human-Scene Interaction

Humans live within a 3D space and constantly interact with it to perform tasks. Such interactions involve physical contact between surfaces that is semantically meaningful. Our goal is to learn how humans interact with scenes and leverage this to enable virtual characters to do the same. To that end, we introduce a novel Human-Scene Interaction (HSI) model that encodes proximal relationships, called POSA for "Pose with prOximitieS and contActs". The representation of interaction is body-centric, which enables it to generalize to new scenes. Specifically, POSA augments the SMPL-X parametric human body model such that, for every mesh vertex, it encodes (a) the contact probability with the scene surface and (b) the corresponding semantic scene label. We learn POSA with a VAE conditioned on the SMPL-X vertices, and train on the PROX dataset, which contains SMPL-X meshes of people interacting with 3D scenes, and the corresponding scene semantics from the PROX-E dataset. We demonstrate the value of POSA with two applications. First, we automatically place 3D scans of people in scenes. We use a SMPL-X model fit to the scan as a proxy and then find its most likely placement in 3D. POSA provides an effective representation to search for "affordances" in the scene that match the likely contact relationships for that pose. We perform a perceptual study that shows significant improvement over the state of the art on this task. Second, we show that POSA's learned representation of body-scene interaction supports monocular human pose estimation that is consistent with a 3D scene, improving on the state of the art. Our model and code are available for research purposes at https://posa.is.tue.mpg.de.

cs.CV↗

arXiv2020

Generating 3D People in Scenes without People

We present a fully automatic system that takes a 3D scene and generates plausible 3D human bodies that are posed naturally in that 3D scene. Given a 3D scene without people, humans can easily imagine how people could interact with the scene and the objects in it. However, this is a challenging task for a computer as solving it requires that (1) the generated human bodies to be semantically plausible within the 3D environment (e.g. people sitting on the sofa or cooking near the stove), and (2) the generated human-scene interaction to be physically feasible such that the human body and scene do not interpenetrate while, at the same time, body-scene contact supports physical interactions. To that end, we make use of the surface-based 3D human model SMPL-X. We first train a conditional variational autoencoder to predict semantically plausible 3D human poses conditioned on latent scene representations, then we further refine the generated 3D bodies using scene constraints to enforce feasible physical interaction. We show that our approach is able to synthesize realistic and expressive 3D human bodies that naturally interact with 3D environment. We perform extensive experiments demonstrating that our generative framework compares favorably with existing methods, both qualitatively and quantitatively. We believe that our scene-conditioned 3D human generation pipeline will be useful for numerous applications; e.g. to generate training data for human pose estimation, in video games and in VR/AR. Our project page for data and code can be seen at: \url{https://vlg.inf.ethz.ch/projects/PSI/}.

cs.CV↗