Search

Diffusion-weighted Magnetic Resonance Imaging (dMRI) is an essential tool in neuroimaging. It is arguably the sole noninvasive technique for examining the microstructural properties and structural connectivity of the brain. Recent years have seen the emergence of machine learning and data-driven approaches that enhance the speed, accuracy, and consistency of dMRI data analysis. However, traditional deep learning models often fell short, as they typically utilize pixel-level or volumetric patch-level embeddings similar to those used in structural MRI, and do not account for the unique distribution of various gradient encodings. In this paper, we propose a novel method called Polyhedra Encoding Transformer (PE-Transformer) for dMRI, designed specifically to handle spherical signals. Our approach involves projecting an icosahedral polygon onto a unit sphere to resample signals from predetermined directions. These resampled signals are then transformed into embeddings, which are processed by a transformer encoder that incorporates orientational information reflective of the icosahedral structure. Through experimental validation with various gradient encoding protocols, our method demonstrates superior accuracy in estimating multi-compartment models and Fiber Orientation Distributions (FOD), outperforming both conventional CNN architectures and standard transformers.

The experimental determination of the magnitude and momentum dependence of electron-phonon coupling (EPC) is an outstanding problem in condensed matter physics. Resonant inelastic x-ray scattering (RIXS) has been previously employed to determine the EPC, since the intensity of phonon peaks in RIXS spectra has been directly related to the underlying EPC strength. In order to assess the limits of validity of such a relation, we compare experimental results and theoretical predictions for several high-T$_c$ superconducting cuprates. Namely, we investigate the intensity of the bond-stretching phonon mode in CaCuO$_2$, La$_2$CuO$_{4+\delta}$, La$_{1.84}$Sr$_{0.16}$CuO$_4$ and YBa$_2$Cu$_3$O$_{7-\delta}$ along the high symmetry ($\zeta$,0), ($\zeta$,$\zeta$) directions and as a function of the azimuthal angle $\phi$ at fixed modulus of the in-plane momentum $\mathbf{q_\parallel}$. Using two different theoretical approaches for the description of the RIXS scattering from phonons, we find that the $\mathbf{q_\parallel}$-dependence of the RIXS intensity can be largely ascribed to the symmetry of the phonon mode, and that satisfactory prediction of the experimental results cannot be obtained without including realistic details of the electronic structure in the calculations. Regardless of the theoretical model, RIXS provides a reliable momentum dependence of EPC in cuprates and can be used to test advanced theoretical predictions., Comment: 15 pages, 7 figures

Internet of Things (IoT) devices have become increasingly ubiquitous with applications not only in urban areas but remote areas as well. These devices support industries such as agriculture, forestry, and resource extraction. Due to the device location being in remote areas, satellites are frequently used to collect and deliver IoT device data to customers. As these devices become increasingly advanced and numerous, the amount of data produced has rapidly increased potentially straining the ability for radio frequency (RF) downlink capacity. Free space optical communications with their wide available bandwidths and high data rates are a potential solution, but these communication systems are highly vulnerable to weather-related disruptions. This results in certain communication opportunities being inefficient in terms of the amount of data received versus the power expended. In this paper, we propose a deep reinforcement learning (DRL) method using Deep Q-Networks that takes advantage of weather condition forecasts to improve energy efficiency while delivering the same number of packets as schemes that don't factor weather into routing decisions. We compare this method with simple approaches that utilize simple cloud cover thresholds to improve energy efficiency. In testing the DRL approach provides improved median energy efficiency without a significant reduction in median delivery ratio. Simple cloud cover thresholds were also found to be effective but the thresholds with the highest energy efficiency had reduced median delivery ratio values., Comment: 6 pages, 3 figures

Swift electrons passing near or through metallic structures have proven to be an excellent tool for studying plasmons and other types of confined optical modes involving collective charge oscillations in the materials hybridized with electromagnetic fields. In this work, we provide a general analytical framework for the simulation of electron energy-loss spectroscopy (EELS) in infinite systems with cylindrical symmetry, such as wires, holes, and optical fibers. While EELS theory is well developed for electrons moving parallel to the direction of translational symmetry, we introduce closed-form analytical solutions for perpendicular electron trajectories. These analytical results are corroborated by comparison to numerical simulations based on a frequency-domain boundary-element method and a discontinuous-Galerkin time-domain finite-element method. Numerical methods further allow us to study termination effects in finite-sized cylindrical objects such as nanorods. The present study of the interaction between free electrons and cylindrically symmetric photonics systems can find application in the analysis of EELS spectra and the design of free-electron--photonic hybrid systems.

Let $\rm{Box}_n = \{x \in \mathbb{R}^n : 0 \leq x \leq e \}$, and let $\rm{QPB}_n$ denote the convex hull of $\{(1, x')'(1, x') : x \in \rm{Box}_n\}$. The quadratic programming problem $\min\{x'Q x + q'x : x \in \rm{Box}_n\}$ where $Q$ is not positive semidefinite (PSD), is equivalent to a linear optimization problem over $\rm{QPB}_n$ and could be efficiently solved if a tractable characterization of $\rm{QPB}_n$ was available. It is known that $\rm{QPB}_2$ can be represented using a PSD constraint combined with constraints generated using the reformulation-linearization technique (RLT). The triangle (TRI) inequalities are also valid for $\rm{QPB}_3$, but the PSD, RLT and TRI constraints together do not fully characterize $\rm{QPB}_3$. In this paper we describe new valid linear inequalities for $\rm{QPB}_n$, $n \geq 3$ based on strengthening the approximation of $\rm{QPB}_3$ given by the PSD, RLT and TRI constraints. These new inequalities are generated in a systematic way using a known disjunctive characterization for $\rm{QPB}_3$. We also describe a conic strengthening of the linear inequalities that incorporates second-order cone constraints. We show computationally that the new inequalities and their conic strengthenings obtain exact solutions for some nonconvex box-constrained instances that are not solved exactly using the PSD, RLT and TRI constraints.

Providing explanations in response to user reviews is a time-consuming and repetitive task for companies, as many reviews present similar issues requiring nearly identical responses. To improve efficiency, this paper proposes a semi-automated approach to managing explanation needs in user reviews. The approach leverages taxonomy categories to classify reviews and assign them to relevant internal teams or sources for responses. 2,366 app reviews from the Google Play Store and Apple App Store were scraped and analyzed using a word and phrase filtering system to detect explanation needs. The detected needs were categorized and assigned to specific internal teams at the company Graphmasters GmbH, using a hierarchical assignment strategy that prioritizes the most relevant teams. Additionally, external sources, such as existing support articles and past review responses, were integrated to provide comprehensive explanations. The system was evaluated through interviews and surveys with the Graphmasters support team, which consists of four employees. The results showed that the hierarchical assignment method improved the accuracy of team assignments, with correct teams being identified in 79.2% of cases. However, challenges in interrater agreement and the need for new responses in certain cases, particularly for Apple App Store reviews, were noted. Future work will focus on refining the taxonomy and enhancing the automation process to reduce manual intervention further., Comment: This paper has been accepted at the Software Engineering in Practice (SEIP) track of the 47th International Conference on Software Engineering (ICSE 2025)

We present a novel approach to understand vehicular traffic jams by studying a simple model, Elementary Cellular Automaton Rule 184 (ECA 184). Using key traffic observables, such as the total delay and relaxation time, as well as microscopic measures like delays and lifetimes of individual jams, we show how these quantities can fully characterize the system's behavior, revealing features analogous to those of a continuous phase transition. We exploit specific properties of ECA 184 to develop an efficient algorithm for calculating these observables numerically and introduce an auxiliary quantity, termed ''elementary jams'', which allows us to determine these observables. We discuss the implications of our results, highlighting connections to a potential field-theoretic description of traffic and suggest future application of these methods to more complex models. Supporting code can be found on: https://github.com/Aryaman-Jha/ECA_184_Critical, Comment: 9 pages, 19 figures

With the rapid expansion of space activities and the escalating accumulation of space debris, Space Domain Awareness (SDA) has become essential for sustaining safe space operations. This paper proposes a decentralized solution using satellite swarms and blockchain, where satellites (nodes) take on the roles of verifiers and approvers to validate and store debris-tracking data securely. Our simulations show that the network achieves optimal performance with around 30 nodes, balancing throughput and response time settling at 4.37 seconds. These results suggest that large-scale networks can be effectively managed by decoupling them into smaller, autonomous swarms, each optimized for specific tasks. Furthermore, we compare the performance of the decentralized swarm architecture with that of a fully shared role model and show significant improvements in scalability and response times when roles are decoupled.

The rapid development of Vehicular Ad-hoc Networks (VANETs) within the Internet of Vehicles (IoV) necessitates efficient and secure authentication methods to support high-speed, high-density environments. Current group authentication schemes provide user identity protection and unlinkability but face limitations, such as reliance on a central group manager and vulnerability to collaborative attacks. This paper presents a privacy-preserving authentication scheme that incorporates batch authentication, mutual authentication, and secure key establishment, enabling users to authenticate one another without a central authority. Our proposed scheme facilitates simultaneous multi-user authentication, significantly enhancing scalability and security in dynamic IoV networks. Results from realistic implementations show that our method achieves average authentication and verification times of 10.61 ms and 1.78 ms, respectively, for a fleet of 100 vehicles, outperforming existing methods. Scalability tests demonstrate efficient processing for larger groups of up to 500 vehicles, where average authentication times remain low, establishing our scheme as a robust solution for secure communication in IoV systems.

Channel Autoencoders (CAEs) have shown significant potential in optimizing the physical layer of a wireless communication system for a specific channel through joint end-to-end training. However, the practical implementation of CAEs faces several challenges, particularly in realistic and dynamic scenarios. Channels in communication systems are dynamic and change with time. Still, most proposed CAE designs assume stationary scenarios, meaning they are trained and tested for only one channel realization without regard for the dynamic nature of wireless communication systems. Moreover, conventional CAEs are designed based on the assumption of having access to a large number of pilot signals, which act as training samples in the context of CAEs. However, in real-world applications, it is not feasible for a CAE operating in real-time to acquire large amounts of training samples for each new channel realization. Hence, the CAE has to be deployable in few-shot learning scenarios where only limited training samples are available. Furthermore, most proposed conventional CAEs lack fast adaptability to new channel realizations, which becomes more pronounced when dealing with a limited number of pilots. To address these challenges, this paper proposes the Online Meta Learning channel AE (OML-CAE) framework for few-shot CAE scenarios with dynamic channels. The OML-CAE framework enhances adaptability to varying channel conditions in an online manner, allowing for dynamic adjustments in response to evolving communication scenarios. Moreover, it can adapt to new channel conditions using only a few pilots, drastically increasing pilot efficiency and making the CAE design feasible in realistic scenarios., Comment: To be published in IEEE Wireless Communications and Networking Conference (WCNC) 2025

We report on the electron spin resonance (ESR), heat capacity, magnetization, nuclear magnetic resonance (NMR), magnetic circular and linear dichroism (XMCD, XMLD), as well as the electrical resistivity of EuMn$_{2}$P$_{2}$ single crystals. Antiferromagnetic order of Eu was observed in several quantities at $T^{\rm Eu}_{\rm N}\,=\,18\,\rm K$. The temperature dependencies of ESR linewidth and resonance shift show, when approaching the Eu-ordered state, a divergence towards $T^{\rm Eu}_{\rm N}$, indicating the growing importance of magnetic correlations and the build-up of internal magnetic fields. An additional temperature scale of $\approx 47\,\rm K$ has considerable impact on linewidth, resonance field and intensity. This points to the presence of weak Mn-based ordering. The observed ESR line is interpreted as an Eu$^{2+}$ resonance, which probes the weak magnetic background of the Mn subsystem. Such picture is suggested by the lineshape which keeps to be Lorentzian across the $47\,\rm K$ scale and by the ESR intensity which can be described by the same Curie-Weiss temperature above and below $47\,\rm K$. In the same temperature range anomalies were observed at $48.5\,\rm K$ and $51\,\rm K$ in the heat capacity data as well as a pronounced broadening of the NMR signal of the EuMn$_{2}$P$_{2}$ samples. In XMCD and XMLD measurements, this weak magnetic order could not be detected in the same temperature range which might be due to the small magnetic moment, with a potential $c$-component or frustration.

We provide new connections between multitype $\Lambda$-coalescents and multitype continuous state branching processes via duality and a homeomorphism on their parameter space. The approach is based on a sequential sampling procedure for the frequency process of independent CSBPs, and provides forward and backward processes for multitype population models under $\Lambda$-type reproduction. It provides some insight on different approaches to generalise $\Lambda$-coalescents to the multitype setup.

International enterprises, organizations, or hospitals collect large amounts of multi-modal data stored in databases, text documents, images, and videos. While there has been recent progress in the separate fields of multi-modal data exploration as well as in database systems that automatically translate natural language questions to database query languages, the research challenge of querying database systems combined with other unstructured modalities such as images in natural language is widely unexplored. In this paper, we propose XMODE - a system that enables explainable, multi-modal data exploration in natural language. Our approach is based on the following research contributions: (1) Our system is inspired by a real-world use case that enables users to explore multi-modal information systems. (2) XMODE leverages a LLM-based agentic AI framework to decompose a natural language question into subtasks such as text-to-SQL generation and image analysis. (3) Experimental results on multi-modal datasets over relational data and images demonstrate that our system outperforms state-of-the-art multi-modal exploration systems, excelling not only in accuracy but also in various performance metrics such as query latency, API costs, planning efficiency, and explanation quality, thanks to the more effective utilization of the reasoning capabilities of LLMs.

The explicit connection between the transition matrix and boundary element method integral operators is formulated. This enables the calculation of characteristic modes via eigenvalue problems involving either set of operators, leading to convenient orthogonality properties facilitating scattering analysis, solution of inverse problems, and the design of excitation fields., Comment: 11 pages, 10 figures

The majority of current reinforcement learning (RL) research involves training and deploying agents in environments that are implemented by engineers in general-purpose programming languages and more advanced frameworks such as CUDA or JAX. This makes the application of RL to novel problems of interest inaccessible to small organisations or private individuals with insufficient engineering expertise. This position paper argues that, to enable more widespread adoption of RL, it is important for the research community to shift focus towards methodologies where environments are described in user-friendly domain-specific or natural languages. Aside from improving the usability of RL, such language-based environment descriptions may also provide valuable context and boost the ability of trained agents to generalise to unseen environments within the set of all environments that can be described in any language of choice., Comment: Accepted by ICAART 2025

The increasing focus on lunar exploration requires innovative power solutions to support scientific research, mining, and habitation in the Moon's extreme environment. Optical power beaming (OPB) has emerged as a promising alternative to conventional systems. However, the impact of lofted lunar dust (LLD) on optical transmissions remains poorly understood. This research addresses that gap by evaluating LLD-induced attenuation and optimizing OPB design for efficient power delivery over long distances. A combined theoretical and simulation-based approach is employed, utilizing the T-matrix method to model LLD attenuation and Gaussian beam theory to optimize transmission and receiver parameters. The results indicate that LLD significantly attenuates ground-to-ground optical power transmission in illuminated regions, thus making OPB more suitable in darker areas, such as permanently shadowed regions or during the lunar night. Furthermore, we demonstrate that OPB can operate over long distances on the Moon while maintaining reasonable aperture sizes through appropriate optical design optimizations. These findings highlight the potential of OPB as a reliable power solution for sustainable lunar exploration and habitation., Comment: 12 pages, 11 figures

Photorealistic 4D reconstruction of street scenes is essential for developing real-world simulators in autonomous driving. However, most existing methods perform this task offline and rely on time-consuming iterative processes, limiting their practical applications. To this end, we introduce the Large 4D Gaussian Reconstruction Model (DrivingRecon), a generalizable driving scene reconstruction model, which directly predicts 4D Gaussian from surround view videos. To better integrate the surround-view images, the Prune and Dilate Block (PD-Block) is proposed to eliminate overlapping Gaussian points between adjacent views and remove redundant background points. To enhance cross-temporal information, dynamic and static decoupling is tailored to better learn geometry and motion features. Experimental results demonstrate that DrivingRecon significantly improves scene reconstruction quality and novel view synthesis compared to existing methods. Furthermore, we explore applications of DrivingRecon in model pre-training, vehicle adaptation, and scene editing. Our code is available at https://github.com/EnVision-Research/DriveRecon.

Photon-subtracted two-mode squeezed vacuum states, a significant quantum resource, exhibit intricate correlations and unique quantum properties. In this work, we propose a theoretical yet experimentally feasible model to engineer these states using a waveguide trimer. Our study uncovers distinct characteristics of the photon-subtracted state depending on whether an even or odd number of photons is extracted, shedding light on the subtle relationship between quantum state manipulation and the parity of the number of subtracted photons. Furthermore, our integrated device facilitates the generation of multiphoton states with tunable correlations, offering significant potential for applications in quantum-enhanced technologies.

In this work, we present Multiformer, a novel approach to depth-aware video panoptic segmentation (DVPS) based on the mask transformer paradigm. Our method learns object representations that are shared across segmentation, monocular depth estimation, and object tracking subtasks. In contrast to recent unified approaches that progressively refine a common object representation, we propose a hybrid method using task-specific branches within each decoder block, ultimately fusing them into a shared representation at the block interfaces. Extensive experiments on the Cityscapes-DVPS and SemKITTI-DVPS datasets demonstrate that Multiformer achieves state-of-the-art performance across all DVPS metrics, outperforming previous methods by substantial margins. With a ResNet-50 backbone, Multiformer surpasses the previous best result by 3.0 DVPQ points while also improving depth estimation accuracy. Using a Swin-B backbone, Multiformer further improves performance by 4.0 DVPQ points. Multiformer also provides valuable insights into the design of multi-task decoder architectures., Comment: Accepted at the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2025. Code and trained models are available at: https://research.khws.io/multiformer

Realtime 4D reconstruction for dynamic scenes remains a crucial challenge for autonomous driving perception. Most existing methods rely on depth estimation through self-supervision or multi-modality sensor fusion. In this paper, we propose Driv3R, a DUSt3R-based framework that directly regresses per-frame point maps from multi-view image sequences. To achieve streaming dense reconstruction, we maintain a memory pool to reason both spatial relationships across sensors and dynamic temporal contexts to enhance multi-view 3D consistency and temporal integration. Furthermore, we employ a 4D flow predictor to identify moving objects within the scene to direct our network focus more on reconstructing these dynamic regions. Finally, we align all per-frame pointmaps consistently to the world coordinate system in an optimization-free manner. We conduct extensive experiments on the large-scale nuScenes dataset to evaluate the effectiveness of our method. Driv3R outperforms previous frameworks in 4D dynamic scene reconstruction, achieving 15x faster inference speed compared to methods requiring global alignment. Code: https://github.com/Barrybarry-Smith/Driv3R., Comment: Code is available at: https://github.com/Barrybarry-Smith/Driv3R

Multimodal AI has the potential to significantly enhance document-understanding tasks, such as processing receipts, understanding workflows, extracting data from documents, and summarizing reports. Code generation tasks that require long-structured outputs can also be enhanced by multimodality. Despite this, their use in commercial applications is often limited due to limited access to training data and restrictive licensing, which hinders open access. To address these limitations, we introduce BigDocs-7.5M, a high-quality, open-access dataset comprising 7.5 million multimodal documents across 30 tasks. We use an efficient data curation process to ensure our data is high-quality and license-permissive. Our process emphasizes accountability, responsibility, and transparency through filtering rules, traceable metadata, and careful content analysis. Additionally, we introduce BigDocs-Bench, a benchmark suite with 10 novel tasks where we create datasets that reflect real-world use cases involving reasoning over Graphical User Interfaces (GUI) and code generation from images. Our experiments show that training with BigDocs-Bench improves average performance up to 25.8% over closed-source GPT-4o in document reasoning and structured output tasks such as Screenshot2HTML or Image2Latex generation. Finally, human evaluations showed a preference for outputs from models trained on BigDocs over GPT-4o. This suggests that BigDocs can help both academics and the open-source community utilize and improve AI tools to enhance multimodal capabilities and document reasoning. The project is hosted at https://bigdocs.github.io ., Comment: The project is hosted at https://bigdocs.github.io

This paper proposes the joint use of digital self-interference cancellation (DSIC) and spatial suppression to mitigate far-field self-interference (SI) in full-duplex multiple-input multiple-output (MIMO) systems. Far-field SI, caused by echoes from environmental scatterers, is modeled based on the scatterers' angle and delay parameters, stored in a scatterer map. For each scatterer, the most suitable action regarding communication is selected from transmit beamforming, receive beamforming, DSIC, and no-action. This selection is based on simple metrics that show the expected uplink and downlink communication performance. In addition, emerging scatterers that deteriorate the communication are detected, and their delay and angles are acquired, providing an up-to-date scatterer map and presenting a \emph{sensing for communication} case. The proposed selection policy is compared with the individual implementations of DSIC and spatial suppression, highlighting the failure cases for each. It is shown that the proposed policy stays unaffected in these problematic cases and achieves SI-free performance., Comment: Partially presented in IEEE PIMRC in September 2024. Submitted to IEEE Transactions on Wireless Communications

Distributed quantum metrology (DQM) enables the estimation of global functions of d distributed parameters beyond the capability of separable sensors. To estimate an arbitrary analytic function of the parameters it suffices that a DQM scheme can measure an arbitrary linear combination of these parameters, and a number of schemes have now been devised to achieve this at the Heisenberg limit. The most practical to-date requires d coherent inputs and one squeezed vacuum. Here we provide a full understanding of the minimal input resources and linear networks required to achieve DQM of arbitrary functions. We are able to fully elucidate the structure of any linear network for DQM that has two non-vacuum inputs, and we show that two non-vacuum inputs, one non-classical, is the minimum required to achieve DQM with arbitrary weights at the Heisenberg limit. We characterize completely the properties of the nonclassical input required to obtain a quantum advantage, showing that a wide range of inputs make this possible, including a squeezed vacuum. We further elucidate two distinct regimes of the distributed sensing network. The first achieves Heisenberg scaling. In the second the nonclassical input is much weaker than the coherent input, nevertheless providing a significant quantum enhancement to the otherwise classical sensitivity., Comment: 10 pages including supplemental materials, 3 figures

We introduce a diffusion model for Gaussian Splats, SplatDiffusion, to enable generation of three-dimensional structures from single images, addressing the ill-posed nature of lifting 2D inputs to 3D. Existing methods rely on deterministic, feed-forward predictions, which limit their ability to handle the inherent ambiguity of 3D inference from 2D data. Diffusion models have recently shown promise as powerful generative models for 3D data, including Gaussian splats; however, standard diffusion frameworks typically require the target signal and denoised signal to be in the same modality, which is challenging given the scarcity of 3D data. To overcome this, we propose a novel training strategy that decouples the denoised modality from the supervision modality. By using a deterministic model as a noisy teacher to create the noised signal and transitioning from single-step to multi-step denoising supervised by an image rendering loss, our approach significantly enhances performance compared to the deterministic teacher. Additionally, our method is flexible, as it can learn from various 3D Gaussian Splat (3DGS) teachers with minimal adaptation; we demonstrate this by surpassing the performance of two different deterministic models as teachers, highlighting the potential generalizability of our framework. Our approach further incorporates a guidance mechanism to aggregate information from multiple views, enhancing reconstruction quality when more than one view is available. Experimental results on object-level and scene-level datasets demonstrate the effectiveness of our framework.

The response of many-body quantum systems to an optical pulse can be extremely challenging to model. Here we explore the use of neural networks, both traditional and generative, to learn and thus simulate the response of such a system from data. The quantum system can be viewed as performing a complex mapping from an input time-series (the optical pulse) to an output time-series (the systems response) which is often also an optical pulse. Using both the transverse and non-integrable Ising models as examples, we show that not only can temporal convolutional networks capture the input/output mapping generated by the system but can also be used to characterize the complexity of the mapping. This measure of complexity is provided by the size of the smallest latent space that is able to accurately model the mapping. We further find that a generative model, in particular a variational auto-encoder, significantly outperforms traditional auto-encoders at learning the complex response of many-body quantum systems. For the example that generated the most complex mapping, the variational auto-encoder produces outputs that have less than 10% error for more than 90% of inputs across our test data.

Electron microscopy techniques such as electron energy-loss spectroscopy (EELS) facilitate the spatio-spectral characterization of plasmonic nanostructures. In this work, a time-dependent perspective is presented, which significantly enhances the utility of EELS. Specifically, silver nanowires offer the material and geometric features for various high-quality plasmonic excitations. This provides an ideal illustrative system for combined experimental-theoretical analyses of the different plasmonic excitations and their real-time dynamics. It is demonstrated how the plasmonic excitations propagating inside the wire repeatedly interact with the swift electrons in an EELS configuration. In addition, the role of azimuthal modes, often overlooked for very thin wires, is observed and analyzed in both the energy-loss spectrum and the dynamical perspective. Such a complete understanding of the interaction of electrons and plasmonic excitation is key for the design of efficient plasmonic sensors, the study of hot electron dynamics in metals, and applications in the context of electron quantum optics, where full control of the spatial and temporal characteristics of the fields at the nanometer and femtosecond scales is highly desirable.

The integration of machine learning in magnetic resonance imaging (MRI), specifically in neuroimaging, is proving to be incredibly effective, leading to better diagnostic accuracy, accelerated image analysis, and data-driven insights, which can potentially transform patient care. Deep learning models utilize multiple layers of processing to capture intricate details of complex data, which can then be used on a variety of tasks, including brain tumor classification, segmentation, image synthesis, and registration. Previous research demonstrates high accuracy in tumor segmentation using various model architectures, including nn-UNet and Swin-UNet. U-Mamba, which uses state space modeling, also achieves high accuracy in medical image segmentation. To leverage these models, we propose a deep learning framework that ensembles these state-of-the-art architectures to achieve accurate segmentation and produce finely synthesized images.

Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a significant challenge. Simulation-based inference (SBI) addresses this by enabling Bayesian inference for simulators, identifying parameters that match observed data and align with prior knowledge. Unlike traditional Bayesian inference, SBI only needs access to simulations from the model and does not require evaluations of the likelihood-function. In addition, SBI algorithms do not require gradients through the simulator, allow for massive parallelization of simulations, and can perform inference for different observations without further simulations or training, thereby amortizing inference. Over the past years, we have developed, maintained, and extended $\texttt{sbi}$, a PyTorch-based package that implements Bayesian SBI algorithms based on neural networks. The $\texttt{sbi}$ toolkit implements a wide range of inference methods, neural network architectures, sampling methods, and diagnostic tools. In addition, it provides well-tested default settings but also offers flexibility to fully customize every step of the simulation-based inference workflow. Taken together, the $\texttt{sbi}$ toolkit enables scientists and engineers to apply state-of-the-art SBI methods to black-box simulators, opening up new possibilities for aligning simulations with empirically observed data.

This paper introduces a scenario where a maneuverable satellite in geostationary orbit (GEO) conducts on-orbit attacks, targeting communication between a GEO satellite and a ground station, with the ability to switch between stationary and time-variant jamming modes. We propose a machine learning-based detection approach, employing the random forest algorithm with principal component analysis (PCA) to enhance detection accuracy in the stationary model. At the same time, an adaptive threshold-based technique is implemented for the time-variant model to detect dynamic jamming events effectively. Our methodology emphasizes the need for the use of orbital dynamics in integrating physical constraints from satellite dynamics to improve model robustness and detection accuracy. Simulation results highlight the effectiveness of PCA in enhancing the performance of the stationary model, while the adaptive thresholding method achieves high accuracy in detecting jamming in the time-variant scenario. This approach provides a robust solution for mitigating the evolving threats to satellite communication in GEO environments.

Infrared (IR) imaging offers advantages in several fields due to its unique ability of capturing content in extreme light conditions. However, the demanding hardware requirements of high-resolution IR sensors limit its widespread application. As an alternative, visible light can be used to synthesize IR images but this causes a loss of fidelity in image details and introduces inconsistencies due to lack of contextual awareness of the scene. This stems from a combination of using visible light with a standard dynamic range, especially under extreme lighting, and a lack of contextual awareness can result in pseudo-thermal-crossover artifacts. This occurs when multiple objects with similar temperatures appear indistinguishable in the training data, further exacerbating the loss of fidelity. To solve this challenge, this paper proposes CapHDR2IR, a novel framework incorporating vision-language models using high dynamic range (HDR) images as inputs to generate IR images. HDR images capture a wider range of luminance variations, ensuring reliable IR image generation in different light conditions. Additionally, a dense caption branch integrates semantic understanding, resulting in more meaningful and discernible IR outputs. Extensive experiments on the HDRT dataset show that the proposed CapHDR2IR achieves state-of-the-art performance compared with existing general domain transfer methods and those tailored for visible-to-infrared image translation.

Exposure correction methods aim to adjust the luminance while maintaining other luminance-unrelated information. However, current exposure correction methods have difficulty in fully separating luminance-related and luminance-unrelated components, leading to distortions in color, loss of detail, and requiring extra restoration procedures. Inspired by principal component analysis (PCA), this paper proposes an exposure correction method called luminance component analysis (LCA). LCA applies the orthogonal constraint to a U-Net structure to decouple luminance-related and luminance-unrelated features. With decoupled luminance-related features, LCA adjusts only the luminance-related components while keeping the luminance-unrelated components unchanged. To optimize the orthogonal constraint problem, LCA employs a geometric optimization algorithm, which converts the constrained problem in Euclidean space to an unconstrained problem in orthogonal Stiefel manifolds. Extensive experiments show that LCA can decouple the luminance feature from the RGB color space. Moreover, LCA achieves the best PSNR (21.33) and SSIM (0.88) in the exposure correction dataset with 28.72 FPS.

This paper deals with the long-term behavior of positive solutions for the following parabolic-elliptic chemotaxis competition system with weak singular sensitivity and logistic source \begin{equation} \label{abstract-eq} \begin{cases} u_t=\Delta u-\chi \nabla\cdot (\frac{u}{v^{\lambda}} \nabla v) +ru- \mu u^2, \quad &x\in \Omega,\cr 0=\Delta v- \alpha v +\beta u,\quad &x\in \Omega, \cr \frac{\partial u}{\partial \nu}=\frac{\partial v}{\partial \nu}=0,\quad &x\in\partial\Omega, \end{cases}\, \end{equation} where $\Omega \subset \mathbb{R}^N (N \ge 2)$ is a smooth bounded domain, the parameters $\chi,\, r, \, \mu, \, \alpha,\,\beta$ are positive constants and $\lambda \in (0,1).$ In this article, for all suitably smooth initial data $u_0\in C^0(\bar\Omega)$ with $u_0 \not \equiv 0,$ it has been proven that: First, there exists $\mu > \mu_1^*(p,\lambda,\chi,\beta)$ such that any globally defined positive solution is $L^p(\Omega)$-bounded with $p \ge 2.$ Next, there exists $\mu > \mu_2^*(N,\lambda,\chi,\beta)$ such that any globally defined classical solutions is globally bounded. Third, there exists $\mu > \mu_3^*(N,\lambda,\chi,\beta)$ such that any globally defined positive solution is uniformly bounded above and below eventually by some positive constants that are independent of its initial function $u_0.$ Last, there exists $\mu > \mu_4^*(N,\lambda,\chi,\alpha,\beta,r,\Omega)$ such that any globally bounded classical solution to system (0.1) exponentially converges to the constant steady state $(\frac{r}{\mu},\frac{\beta}{\alpha}\frac{r}{\mu}).$

The rapid evolution of communication technologies, compounded by recent geopolitical events such as the Viasat cyberattack in February 2022, has highlighted the urgent need for fast and reliable satellite missions for military and civil security operations. Consequently, this paper examines two Earth observation (EO) missions: one utilizing a single low Earth orbit (LEO) satellite and another through a network of LEO satellites, employing a secure-by-component design strategy. This approach begins by defining the scope of technical security engineering, decomposing the system into components and data flows, and enumerating attack surfaces. Then it proceeds by identifying threats to low-level components, applying secure-by-design principles, redesigning components into secure blocks in alignment with the Space Attack Research & Tactic Analysis (SPARTA) framework, and crafting shall statements to refactor the system design, with a particular focus on improving the security of the link segment.

We present DeSiRe-GS, a self-supervised gaussian splatting representation, enabling effective static-dynamic decomposition and high-fidelity surface reconstruction in complex driving scenarios. Our approach employs a two-stage optimization pipeline of dynamic street Gaussians. In the first stage, we extract 2D motion masks based on the observation that 3D Gaussian Splatting inherently can reconstruct only the static regions in dynamic environments. These extracted 2D motion priors are then mapped into the Gaussian space in a differentiable manner, leveraging an efficient formulation of dynamic Gaussians in the second stage. Combined with the introduced geometric regularizations, our method are able to address the over-fitting issues caused by data sparsity in autonomous driving, reconstructing physically plausible Gaussians that align with object surfaces rather than floating in air. Furthermore, we introduce temporal cross-view consistency to ensure coherence across time and viewpoints, resulting in high-quality surface reconstruction. Comprehensive experiments demonstrate the efficiency and effectiveness of DeSiRe-GS, surpassing prior self-supervised arts and achieving accuracy comparable to methods relying on external 3D bounding box annotations. Code is available at \url{https://github.com/chengweialan/DeSiRe-GS}

Accurate and fast recognition of forgeries is an issue of great importance in the fields of artificial intelligence, image processing and object detection. Recognition of forgeries of facial imagery is the process of classifying and defining the faces in it by analyzing real-world facial images. This process is usually accomplished by extracting features from an image, using classifier algorithms, and correctly interpreting the results. Recognizing forgeries of facial imagery correctly can encounter many different challenges. For example, factors such as changing lighting conditions, viewing faces from different angles can affect recognition performance, and background complexity and perspective changes in facial images can make accurate recognition difficult. Despite these difficulties, significant progress has been made in the field of forgery detection. Deep learning algorithms, especially Convolutional Neural Networks (CNNs), have significantly improved forgery detection performance. This study focuses on image processing-based forgery detection using Fake-Vs-Real-Faces (Hard) [10] and 140k Real and Fake Faces [61] data sets. Both data sets consist of two classes containing real and fake facial images. In our study, two lightweight deep learning models are proposed to conduct forgery detection using these images. Additionally, 8 different pretrained CNN architectures were tested on both data sets and the results were compared with newly developed lightweight CNN models. It's shown that the proposed lightweight deep learning models have minimum number of layers. It's also shown that the proposed lightweight deep learning models detect forgeries of facial imagery accurately, and computationally efficiently. Although the data set consists only of face images, the developed models can also be used in other two-class object recognition problems., Comment: 13 pages, 6 figures, 10 tables

The increasing demand for data usage in wireless communications requires using wider bands in the spectrum, especially for backhaul links. Yet, allocations in the spectrum for non-communication systems inhibit merging bands to achieve wider bandwidth. To overcome this issue, spectrum-sharing or opportunistic spectrum utilization by secondary users stands out as a promising solution. However, both approaches must minimize interference to primary users. Therefore, spectrum sensing becomes vital for such opportunistic usage, ensuring the proper operation of the primary users. Although this problem has been investigated for 2D networks, unmanned aerial vehicle (UAV) networks need different points of view concerning 3D space, its challenges, and opportunities. For this purpose, we propose a federated learning (FL)-based method for spectrum sensing in UAV networks to account for their distributed nature and limited computational capacity. FL enables local training without sharing raw data while guaranteeing the privacy of local users,lowering communication overhead, and increasing data diversity. Furthermore, we develop a federated aggregation method, namely FedSNR, that considers the signal-to-noise ratio observed by UAVs to acquire a global model. The numerical results show that the proposed architecture and the aggregation method outperform traditional methods.

Some research now suggests that ChatGPT can estimate the quality of journal articles from their titles and abstracts. This has created the possibility to use ChatGPT quality scores, perhaps alongside citation-based formulae, to support peer review for research evaluation. Nevertheless, ChatGPT's internal processes are effectively opaque, despite it writing a report to support its scores, and its biases are unknown. This article investigates whether publication date and field are biasing factors. Based on submitting a monodisciplinary journal-balanced set of 117,650 articles from 26 fields published in the years 2003, 2008, 2013, 2018 and 2023 to ChatGPT 4o-mini, the results show that average scores increased over time, and this was not due to author nationality or title and abstract length changes. The results also varied substantially between fields, and first author countries. In addition, articles with longer abstracts tended to receive higher scores, but plausibly due to such articles tending to be better rather than due to ChatGPT analysing more text. Thus, for the most accurate research quality evaluation results from ChatGPT, it is important to normalise ChatGPT scores for field and year and check for anomalies caused by sets of articles with short abstracts.

Emerging Large Language Model (LLM) applications require long input prompts to perform complex downstream tasks like document analysis and code generation. For these long context length applications, the length of the input prompt poses a significant challenge in terms of inference efficiency since the inference costs increase linearly with sequence length. However, for many of these applications, much of the context in the prompt is fixed across different user inputs, thereby providing the opportunity to perform offline optimizations to process user inputs quickly, as they are received. In this work, we propose Squeezed Attention as a mechanism to accelerate LLM applications where a large portion of the input prompt is fixed. We first leverage K-means clustering offline to group the keys for the fixed context based on semantic similarity and represent each cluster with a single centroid value. During inference, we compare query tokens from the user input with the centroids to predict which of the keys from the fixed context are semantically relevant and need to be loaded during inference. We then compute exact attention using only these important keys from the fixed context, thereby reducing bandwidth and computational costs. We also extend our method to use a hierarchical centroid lookup to identify important keys, which can reduce the complexity of attention from linear to logarithmic with respect to the context length. We implement optimized Triton kernels for centroid comparison and sparse FlashAttention with important keys, achieving more than 4x speedups during both the prefill and generation phases for long-context inference. Furthermore, we have extensively evaluated our method on various long-context benchmarks including LongBench, where it achieves a 3x reduction in KV cache budget without accuracy loss and up to an 8x reduction with <0.5 point accuracy gap for various models.

White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (DW-MRI) to qualitatively identify changes in white matter microstructure and connectivity. Yet, quantitative analysis of DW-MRI data is hindered by inconsistencies stemming from varying acquisition protocols. There is a pressing need to harmonize the preprocessing of DW-MRI datasets to ensure the derivation of robust quantitative diffusion metrics across acquisitions. In the MICCAI-CDMRI 2023 QuantConn challenge, participants were provided raw data from the same individuals collected on the same scanner but with two different acquisitions and tasked with preprocessing the DW-MRI to minimize acquisition differences while retaining biological variation. Submissions are evaluated on the reproducibility and comparability of cross-acquisition bundle-wise microstructure measures, bundle shape features, and connectomics. The key innovations of the QuantConn challenge are that (1) we assess bundles and tractography in the context of harmonization for the first time, (2) we assess connectomics in the context of harmonization for the first time, and (3) we have 10x additional subjects over prior harmonization challenge, MUSHAC and 100x over SuperMUDI. We find that bundle surface area, fractional anisotropy, connectome assortativity, betweenness centrality, edge count, modularity, nodal strength, and participation coefficient measures are most biased by acquisition and that machine learning voxel-wise correction, RISH mapping, and NeSH methods effectively reduce these biases. In addition, microstructure measures AD, MD, RD, bundle length, connectome density, efficiency, and path length are least biased by these acquisition differences., Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2024/019

Genealogy, the study of family history and lineage, has seen tremendous growth over the past decade, fueled by technological advances such as home DNA testing and mass digitization of historical records. However, HCI research on genealogy practices is nascent, with the most recent major studies predating this transformation. In this paper, we present a qualitative study of the current state of technological support for genealogy research, collaboration, and education. Through semi-structured interviews with 20 genealogists with diverse expertise, we report on current practices, challenges, and success stories around how genealogists conduct research, collaborate, and learn skills. We contrast the experiences of amateurs and experts, describe the emerging importance of standardization and professionalization of the field, and stress the critical role of computer systems in genealogy education. We bridge studies of sensemaking and information literacy through this empirical study on genealogy research practices, and conclude by discussing how genealogy presents a unique perspective through which to study collective sensemaking and education in online communities.