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Existing eye trackers use cameras based on thick compound optical elements, necessitating the cameras to be placed at focusing distance from the eyes. This results in the overall bulk of wearable eye trackers, especially for augmented and virtual reality (AR/VR) headsets. We overcome this limitation by building a compact flat eye gaze tracker using mask-based lensless cameras. These cameras, in combination with co-designed lightweight deep neural network algorithm, can be placed in extreme close proximity to the eye, within the eyeglasses frame, resulting in ultra-flat and lightweight eye gaze tracker system. We collect a large dataset of near-eye lensless camera measurements along with their calibrated gaze directions for training the gaze tracking network. Through real and simulation experiments, we show that the proposed gaze tracking system performs on par with conventional lens-based trackers while maintaining a significantly flatter and more compact form-factor. Moreover, our gaze regressor boasts real-time (>125 fps) performance for gaze tracking., Comment: Accepted to Gaze Meets Computer Vision Workshop at IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2025

Splatting-based 3D reconstruction methods have gained popularity with the advent of 3D Gaussian Splatting, efficiently synthesizing high-quality novel views. These methods commonly resort to using exponential family functions, such as the Gaussian function, as reconstruction kernels due to their anisotropic nature, ease of projection, and differentiability in rasterization. However, the field remains restricted to variations within the exponential family, leaving generalized reconstruction kernels largely underexplored, partly due to the lack of easy integrability in 3D to 2D projections. In this light, we show that a class of decaying anisotropic radial basis functions (DARBFs), which are non-negative functions of the Mahalanobis distance, supports splatting by approximating the Gaussian function's closed-form integration advantage. With this fresh perspective, we demonstrate up to 34% faster convergence during training and a 15% reduction in memory consumption across various DARB reconstruction kernels, while maintaining comparable PSNR, SSIM, and LPIPS results. We will make the code available., Comment: Link to the project page: https://randomnerds.github.io/darbs.github.io/

Instance selection (IS) is a crucial technique in machine learning that aims to reduce dataset size while maintaining model performance. This paper introduces a novel method called Graph Attention-based Instance Selection (GAIS), which leverages Graph Attention Networks (GATs) to identify the most informative instances in a dataset. GAIS represents the data as a graph and uses GATs to learn node representations, enabling it to capture complex relationships between instances. The method processes data in chunks, applies random masking and similarity thresholding during graph construction, and selects instances based on confidence scores from the trained GAT model. Experiments on 13 diverse datasets demonstrate that GAIS consistently outperforms traditional IS methods in terms of effectiveness, achieving high reduction rates (average 96\%) while maintaining or improving model performance. Although GAIS exhibits slightly higher computational costs, its superior performance in maintaining accuracy with significantly reduced training data makes it a promising approach for graph-based data selection., Comment: Accepted at ICKG 2024. Code is available at https://github.com/zahiriddin-rustamov/gais

Class imbalance poses a significant challenge in machine learning (ML), often leading to biased models favouring the majority class. In this paper, we propose GAT-RWOS, a novel graph-based oversampling method that combines the strengths of Graph Attention Networks (GATs) and random walk-based oversampling. GAT-RWOS leverages the attention mechanism of GATs to guide the random walk process, focusing on the most informative neighbourhoods for each minority node. By performing attention-guided random walks and interpolating features along the traversed paths, GAT-RWOS generates synthetic minority samples that expand class boundaries while preserving the original data distribution. Extensive experiments on a diverse set of imbalanced datasets demonstrate the effectiveness of GAT-RWOS in improving classification performance, outperforming state-of-the-art oversampling techniques. The proposed method has the potential to significantly improve the performance of ML models on imbalanced datasets and contribute to the development of more reliable classification systems., Comment: Accepted by ICKG 2024. Code is available at http://github.com/zahiriddin-rustamov/gat-rwos

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

COVID-19, has led to a global pandemic that strained the healthcare systems. Early and accurate detection is crucial for controlling the spread of the virus. While reverse transcription polymerase chain reaction test is the gold standard for diagnosis, it's limited availability, long processing times and extremely high false negative rate, have prompted the exploration of alternative methods. Chest Xray imaging has emerged as a valuable, non invasive tool for identifying COVID-19 related lung abnormalities. Traditional convolutional neural networks (CNNs) achieve impressive accuracy, but there is a need for more robust solutions to minimize false positives and negatives in critical medical applications. Thus We introduce the MOZART framework, an ensemble learning approach that enhances the virus detection. We trained three CNN architectures InceptionV3, Xception, and ResNet50 on a balanced chest X-ray dataset of 3,616 COVID-19 and 3,616 healthy images. Each model underwent a separate preprocessing pipeline, such as normalizing inputs to a range of -1 to 1. The dataset was split into 70% for training, 20% for validation, and 10% for testing, after training the individual models, we trained a shallow neural network on the predictions and to provide a us with the final predictions. Our results show that the MOZART framework with it's sub-experiments MOZART1 and MOZART2 outperforms individual CNN models in key metrics. It achieved an accuracy of 99.17% and an F1 score of 99.16%. MOZART1 excels at minimizing false positives, while MOZART2 is better for reducing false negatives. This work suggests that the MOZART framework can improve reliability in AI-driven medical imaging tasks and should be explored further for other lung diseases., Comment: This paper was originally intended to be published as part of my B.Sc. graduation project in Electrical and Electronics Engineering at the University of Khartoum in 2021. However, due to political and economic instability, and most recently, the outbreak of conflict in Sudan in April 2023, the publication process was significantly delayed. But yeah, better late than never

Large language models (LLMs) hold significant promise in advancing network management and orchestration in 6G and beyond networks. However, existing LLMs are limited in domain-specific knowledge and their ability to handle multi-modal sensory data, which is critical for real-time situational awareness in dynamic wireless environments. This paper addresses this gap by introducing ENWAR, an ENvironment-aWARe retrieval augmented generation-empowered multi-modal LLM framework. ENWAR seamlessly integrates multi-modal sensory inputs to perceive, interpret, and cognitively process complex wireless environments to provide human-interpretable situational awareness. ENWAR is evaluated on the GPS, LiDAR, and camera modality combinations of DeepSense6G dataset with state-of-the-art LLMs such as Mistral-7b/8x7b and LLaMa3.1-8/70/405b. Compared to general and often superficial environmental descriptions of these vanilla LLMs, ENWAR delivers richer spatial analysis, accurately identifies positions, analyzes obstacles, and assesses line-of-sight between vehicles. Results show that ENWAR achieves key performance indicators of up to 70% relevancy, 55% context recall, 80% correctness, and 86% faithfulness, demonstrating its efficacy in multi-modal perception and interpretation.

We use the James Webb Space Telescope (JWST) and its Mid-Infrared Instrument (MIRI) (5-28 um), to study the embedded HH 211 flow. We map a 0.95'x0.22' region, covering the full extent of the blue-shifted lobe, the central protostellar region, and a small portion of the red-shifted lobe. The jet driving source is not detected even at the longest mid-IR wavelengths. The overall morphology of the flow consists of a highly collimated jet, mostly molecular (H2, HD) with an inner atomic ([FeI], [FeII], [SI], [NiII]) structure. The jet shocks the ambient medium, producing several large bow-shocks, rich in forbidden atomic and molecular lines, and is driving an H2 molecular outflow, mostly traced by low-J, v=0 transitions. Moreover, 0-0 S(1) uncollimated emission is also detected down to 2"-3" (~650-1000 au) from the source, tracing a cold (T=200-400 K), less dense and poorly collimated molecular wind. The atomic jet ([FeII] at 26 um) is detected down to ~130 au from source, whereas the lack of H2 emission close to the source is likely due to the large visual extinction. Dust continuum-emission is detected at the terminal bow-shocks, and in the blue- and red-shifted jet, being likely dust lifted from the disk. The jet shows an onion-like structure, with layers of different size, velocity, temperature, and chemical composition. Moreover, moving from the inner jet to the outer bow-shocks, different physical, kinematic and excitation conditions for both molecular and atomic gas are observed. The jet mass-flux rate, momentum, and momentum flux of the warm H2 component are up to one order of magnitude higher than those inferred from the atomic jet component. Our findings indicate that the warm H2 component is the primary mover of the outflow, namely it is the most significant dynamical component of the jet, in contrast to jets from more evolved YSOs, where the atomic component is dominant., Comment: Paper accepted in A&A for publication

Data missingness is a practical challenge of sustained interest to the scientific community. In this paper, we present Shades-of-NULL, an evaluation suite for responsible missing value imputation. Our work is novel in two ways (i) we model realistic and socially-salient missingness scenarios that go beyond Rubin's classic Missing Completely at Random (MCAR), Missing At Random (MAR) and Missing Not At Random (MNAR) settings, to include multi-mechanism missingness (when different missingness patterns co-exist in the data) and missingness shift (when the missingness mechanism changes between training and test) (ii) we evaluate imputers holistically, based on imputation quality and imputation fairness, as well as on the predictive performance, fairness and stability of the models that are trained and tested on the data post-imputation. We use Shades-of-NULL to conduct a large-scale empirical study involving 29,736 experimental pipelines, and find that while there is no single best-performing imputation approach for all missingness types, interesting trade-offs arise between predictive performance, fairness and stability, based on the combination of missingness scenario, imputer choice, and the architecture of the predictive model. We make Shades-of-NULL publicly available, to enable researchers to rigorously evaluate missing value imputation methods on a wide range of metrics in plausible and socially meaningful scenarios.

We generalize the study of standard Lyndon loop words from [A.Negut, A.Tsymbaliuk, "Quantum loop groups and shuffle algebras via Lyndon words", Adv. Math. 439 (2024), Paper No. 109482] to a more general class of orders on the underlying alphabet, as suggested in Remark 3.15 of loc.cit. The main new ingredient is the exponent-tightness of these words, which also allows to generalize the construction of PBW bases of the untwisted quantum loop algebra via the combinatorics of loop words., Comment: v1: 30pp, comments are welcome! arXiv admin note: text overlap with arXiv:2102.11269

Integrating Generative AI (GenAI) into educational contexts presents a transformative potential for enhancing learning experiences. This paper introduces CourseGPT, a generative AI tool designed to support instructors and enhance the educational experiences of undergraduate students. Built on open-source Large Language Models (LLMs) from Mistral AI, CourseGPT offers continuous instructor support and regular updates to course materials, enriching the learning environment. By utilizing course-specific content, such as slide decks and supplementary readings and references, CourseGPT provides precise, dynamically generated responses to student inquiries. Unlike generic AI models, CourseGPT allows instructors to manage and control the responses, thus extending the course scope without overwhelming details. The paper demonstrates the application of CourseGPT using the CPR E 431 - Basics of Information System Security course as a pilot. This course, with its large enrollments and diverse curriculum, serves as an ideal testbed for CourseGPT. The tool aims to enhance the learning experience, accelerate feedback processes, and streamline administrative tasks. The study evaluates CourseGPT's impact on student outcomes, focusing on correctness scores, context recall, and faithfulness of responses. Results indicate that the Mixtral-8x7b model, with a higher parameter count, outperforms smaller models, achieving an 88.0% correctness score and a 66.6% faithfulness score. Additionally, feedback from former students and teaching assistants on CourseGPT's accuracy, helpfulness, and overall performance was collected. The outcomes revealed that a significant majority found CourseGPT to be highly accurate and beneficial in addressing their queries, with many praising its ability to provide timely and relevant information., Comment: 8 pages

The Muon Collider is one of the most promising future collider facilities with the potential to reach multi-TeV center-of-mass energy and high luminosity. Due to the significant Higgs boson production cross section in muon collisions at these high energies, the collider can be considered a Higgs factory. It holds the capability to significantly advance our understanding of the Higgs sector to an unprecedented level of precision. However, the presence of beam-induced background resulting from the decay of the beam muons poses unique challenges for detector development and event reconstruction. In this paper, the prospects for various measurements of the Higgs boson production cross sections at a $\sqrt{s}=3$ TeV collider are presented using a detailed detector simulation in a realistic environment. The study demonstrates the feasibility of achieving high precision measurements of the Higgs boson production cross sections with the current state-of-the-art detector design. In addition, the paper discusses the detector requirements necessary for obtaining such resolutions and for measuring the Higgs trilinear self-coupling.

Internet of Things systems exists in various areas of our everyday life. For example, sensors installed in smart cities and homes are processed in edge and cloud computing centres providing several benefits that improve our lives. The place of data processing is related to the required system response times -- processing data closer to its source results in a shorter system response time. The Osmotic Computing concept enables flexible deployment of data processing services and their possible movement, just like particles in the osmosis phenomenon move between regions of different densities. At the same time, the impact of complex computer architecture on the environment is increasingly being compensated by the use of renewable and low-carbon energy sources. However, the uncertainty of supplying green energy makes the management of Osmotic Computing demanding, and therefore their autonomy is desirable. In the paper, we present a framework enabling osmotic computing simulation based on renewable energy sources and autonomic osmotic agents, allowing the analysis of distributed management algorithms. We discuss the challenges posed to the framework and analyze various management algorithms for cooperating osmotic agents. In the evaluation we show that changing the adaptation logic of the osmotic agents, it is possible to increase the self-consumption of renewable energy sources or increase the usage of low emission ones.

A compact source, G0.02467-0.0727, was detected in ALMA \threemm observations in continuum and very broad line emission. The continuum emission has a spectral index $\alpha\approx3.3$, suggesting that the emission is from dust. The line emission is detected in several transitions of CS, SO, and SO$_2$ and exhibits a line width FWHM $\approx160$ \kms. The line profile appears Gaussian. The emission is weakly spatially resolved, coming from an area on the sky $\lesssim1"$ in diameter ($\lesssim10^4$ AU at the distance of the Galactic Center; GC). The centroid velocity is $v_{LSR}\approx40$-$50$ \kms, which is consistent with a location in the Galactic Center. With multiple SO lines detected, and assuming local thermodynamic equilibrium (LTE) conditions, $T_\mathrm{LTE} = 13$ K, which is colder than seen in typical GC clouds, though we cannot rule out low-density, subthermally excited, warmer gas. Despite the high velocity dispersion, no emission is observed from SiO, suggesting that there are no strong ($\gtrsim10~\mathrm{km~s}^{-1}$) shocks in the molecular gas. There are no detections at other wavelengths, including X-ray, infrared, and radio. We consider several explanations for the Millimeter Ultra-Broad Line Object (MUBLO), including protostellar outflow, explosive outflow, collapsing cloud, evolved star, stellar merger, high-velocity compact cloud, intermediate mass black hole, and background galaxy. Most of these conceptual models are either inconsistent with the data or do not fully explain it. The MUBLO is, at present, an observationally unique object., Comment: Accepted to ApJL

Recent chart-authoring systems, such as Amazon Q in QuickSight and Copilot for Power BI, demonstrate an emergent focus on supporting natural language input to share meaningful insights from data through chart creation. Currently, chart-authoring systems tend to integrate voice input capabilities by relying on speech-to-text transcription, processing spoken and typed input similarly. However, cross-modality input comparisons in other interaction domains suggest that the structure of spoken and typed-in interactions could notably differ, reflecting variations in user expectations based on interface affordances. Thus, in this work, we compare spoken and typed instructions for chart creation. Findings suggest that while both text and voice instructions cover chart elements and element organization, voice descriptions have a variety of command formats, element characteristics, and complex linguistic features. Based on these findings, we developed guidelines for designing voice-based authoring-oriented systems and additional features that can be incorporated into existing text-based systems to support speech modality., Comment: Will be published In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA 2024)

We discuss generalizations of the Szeg\H{o} Limit Theorem to truncated Toeplitz operators. In particular, we consider compressions of Toeplitz operators to an increasing sequence of finite dimensional model spaces. We present two theorems. The first is a new variant of the Szeg\H{o} Limit Theorem in this setting. The second relates to the variant given by Strouse-Timotin-Zarrabi in 2017, and characterizes the sequences for which that result holds.

For two inner functions $\vartheta,\varphi\in H^\infty$, we give a simple sufficient condition for the system $\vartheta^m,\; \varphi^n$, $m,n\in\mathbb{Z}$, to be complete in the weak-$^*$ topology of $L^\infty(\mathbb{T})$. To be precise, we show that this system is complete whenever there is an arc $I$ of the unit circle $\mathbb{T}$ such that $\vartheta$ is univalent on $I$ and $\varphi$ is univalent on $\mathbb{T}\setminus I$. As an application of this result, we describe a class of analytic curves $\Gamma$ such that $(\Gamma, \mathcal{X})$ is a Heisenberg uniqueness pair, where $\mathcal{X}$ is the lattice cross $\{(m,n)\in\mathbb{Z}^2:\, mn=0\}$. Our main result extends a theorem of Hedenmalm and Montes-Rodr\'iguez for atomic inner functions with one singularity.

We present a new approach for Neural Optimal Transport (NOT) training procedure, capable of accurately and efficiently estimating optimal transportation plan via specific regularization on dual Kantorovich potentials. The main bottleneck of existing NOT solvers is associated with the procedure of finding a near-exact approximation of the conjugate operator (i.e., the c-transform), which is done either by optimizing over non-convex max-min objectives or by the computationally intensive fine-tuning of the initial approximated prediction. We resolve both issues by proposing a new, theoretically justified loss in the form of expectile regularisation which enforces binding conditions on the learning process of dual potentials. Such a regularization provides the upper bound estimation over the distribution of possible conjugate potentials and makes the learning stable, completely eliminating the need for additional extensive fine-tuning. Proposed method, called Expectile-Regularised Neural Optimal Transport (ENOT), outperforms previous state-of-the-art approaches on the established Wasserstein-2 benchmark tasks by a large margin (up to a 3-fold improvement in quality and up to a 10-fold improvement in runtime). Moreover, we showcase performance of ENOT for varying cost functions on different tasks such as image generation, showing robustness of proposed algorithm. OTT-JAX library includes our implementation of ENOT algorithm https://ott-jax.readthedocs.io/en/latest/tutorials/ENOT.html