Miracle-dz/UniFAR-FTUs huggingface dataset:
EXPLORING FACTORS MILITATING AGAINST THE USE OF ICT IN MEDICAL IMAGING DEPARTMENT IN PRIMARY HEALTH CARE SE RVICES IN RIVERS STATE, NIGERIA

This research is aimed at exploring the factors that militate against the use of ICT in Medical Imaging Department in the Primary Health Care Service. Obio/Akpor Local Government Area located in Rivers State was used as the study area with a target population of 50 medical staffs available in the various Health Care Centers. This research is introduced with the aims and objectives clearly stated which involves determining the importance of the use of ICT and its effectiveness in the medical imaging department. Data was coll ected through the distribution of questionnaires whereby 65 questionnaires were issued but 50 were retrieved of which data analysis were made on. Data presentation was based on the use of tables and percentage which discussion was made based on the outcome of the tables. The findings were discussed according to the previous study of other researchers as stated in the literature review. The study was carried out and factors which happen to limit the use of ICT in imaging department was discovered to be: poor ICT knowledge, poor electricity supply and the use of obsolete machines with several other factors.
The Dirty Secret of SSDs: Embodied Carbon

Scalable Solid-State Drives (SSDs) have ushered in a transformative era in data storage and accessibility, spanning both data centers and portable devices. However, the strides made in scaling this technology can bear significant environmental consequences. On a global scale, a notable portion of semiconductor manufacturing relies on electricity derived from coal and natural gas sources. A striking example of this is the manufacturing process for a single Gigabyte of Flash memory, which emits approximately 0.16 Kg of CO2 - a considerable fraction of the total carbon emissions attributed to the system. Remarkably, the manufacturing of storage devices alone contributed to an estimated 20 million metric tonnes of CO2 emissions in the year 2021. In light of these environmental concerns, this paper delves into an analysis of the sustainability trade-offs inherent in Solid-State Drives (SSDs) when compared to traditional Hard Disk Drives (HDDs). Moreover, this study proposes methodologies to gauge the embodied carbon costs associated with storage systems effectively. The research encompasses four key strategies to enhance the sustainability of storage systems. Firstly, the paper offers insightful guidance for selecting the most suitable storage medium, be it SSDs or HDDs, considering the broader ecological impact. Secondly, the paper advocates for implementing techniques that extend the lifespan of SSDs, thereby mitigating premature replacements and their attendant environmental toll. Thirdly, the paper emphasizes the need for efficient recycling and reuse of high-density multi-level cell-based SSDs, underscoring the significance of minimizing electronic waste. Lastly, for handheld devices, the paper underscores the potential of harnessing the elasticity offered by cloud storage solutions as a means to curtail the ecological repercussions of localized data storage. In summation, this study critically addresses the embodied carbon issues associated with SSDs, comparing them with HDDs, and proposes a comprehensive framework of strategies to enhance the sustainability of storage systems.
Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures

We study the problem of learning nonparametric distributions in a finite mixture, and establish tight bounds on the sample complexity for learning the component distributions in such models. Namely, we are given i.i. d. samples from a pdf $f$ where $$ f=w_1f_1+w_2f_2, \quad w_1+w_2=1, \quad w_1,w_2>0 $$ and we are interested in learning each component $f_i$. Without any assumptions on $f_i$, this problem is ill-posed. In order to identify the components $f_i$, we assume that each $f_i$ can be written as a convolution of a Gaussian and a compactly supported density $\nu_i$ with $\text{supp}(\nu_1)\cap \text{supp}(\nu_2)=\emptyset$. Our main result shows that $(\frac{1}{\varepsilon})^{\Omega(\log\log \frac{1}{\varepsilon})}$ samples are required for estimating each $f_i$. The proof relies on a quantitative Tauberian theorem that yields a fast rate of approximation with Gaussians, which may be of independent interest. To show this is tight, we also propose an algorithm that uses $(\frac{1}{\varepsilon})^{O(\log\log \frac{1}{\varepsilon})}$ samples to estimate each $f_i$. Unlike existing approaches to learning latent variable models based on moment-matching and tensor methods, our proof instead involves a delicate analysis of an ill-conditioned linear system via orthogonal functions. Combining these bounds, we conclude that the optimal sample complexity of this problem properly lies in between polynomial and exponential, which is not common in learning theory.
The trade-offs of model size in large recommendation models : A 10000 × compressed criteo-tb DLRM model (100 GB parameters to mere 10MB)

Embedding tables dominate industrial-scale recommendation model sizes, using up to terabytes of memory. A popular and the largest publicly available machine learning MLPerf benchmark on recommendation data is a Deep Learning Recommendation Model (DLRM) trained on a terabyte of click-through data. It contains 100GB of embedding memory (25+Billion parameters). DLRMs, due to their sheer size and the associated volume of data, face difficulty in training, deploying for inference, and memory bottlenecks due to large embedding tables. This paper analyzes and extensively evaluates a generic parameter sharing setup (PSS) for compressing DLRM models. We show theoretical upper bounds on the learnable memory requirements for achieving $(1 \pm \epsilon)$ approximations to the embedding table. Our bounds indicate exponentially fewer parameters suffice for good accuracy. To this end, we demonstrate a PSS DLRM reaching 10000$\times$ compression on criteo-tb without losing quality. Such a compression, however, comes with a caveat. It requires 4.5 $\times$ more iterations to reach the same saturation quality. The paper argues that this tradeoff needs more investigations as it might be significantly favorable. Leveraging the small size of the compressed model, we show a 4.3$\times$ improvement in training latency leading to similar overall training times. Thus, in the tradeoff between system advantage of a small DLRM model vs. slower convergence, we show that scales are tipped towards having a smaller DLRM model, leading to faster inference, easier deployment, and similar training times.
Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptation

This paper studies a new, practical but challenging problem, called Class-Incremental Unsupervised Domain Adaptation (CI-UDA), where the labeled source domain contains all classes, but the classes in the unlabeled target domain increase sequentially. This problem is challenging due to two difficulties. First, source and target label sets are inconsistent at each time step, which makes it difficult to conduct accurate domain alignment. Second, previous target classes are unavailable in the current step, resulting in the forgetting of previous knowledge. To address this problem, we propose a novel Prototype-guided Continual Adaptation (ProCA) method, consisting of two solution strategies. 1) Label prototype identification: we identify target label prototypes by detecting shared classes with cumulative prediction probabilities of target samples. 2) Prototype-based alignment and replay: based on the identified label prototypes, we align both domains and enforce the model to retain previous knowledge. With these two strategies, ProCA is able to adapt the source model to a class-incremental unlabeled target domain effectively. Extensive experiments demonstrate the effectiveness and superiority of ProCA in resolving CI-UDA. The source code is available at https://github.com/Hongbin98/ProCA.git
Data-Driven Stochastic AC-OPF using Gaussian Processes

In recent years, electricity generation has been responsible for more than a quarter of the greenhouse gas emissions in the US. Integrating a significant amount of renewables into a power grid is probably the most accessible way to reduce carbon emissions from power grids and slow down climate change. Unfortunately, the most accessible renewable power sources, such as wind and solar, are highly fluctuating and thus bring a lot of uncertainty to power grid operations and challenge existing optimization and control policies. The chance-constrained alternating current (AC) optimal power flow (OPF) framework finds the minimum cost generation dispatch maintaining the power grid operations within security limits with a prescribed probability. Unfortunately, the AC-OPF problem's chance-constrained extension is non-convex, computationally challenging, and requires knowledge of system parameters and additional assumptions on the behavior of renewable distribution. Known linear and convex approximations to the above problems, though tractable, are too conservative for operational practice and do not consider uncertainty in system parameters. This paper presents an alternative data-driven approach based on Gaussian process (GP) regression to close this gap. The GP approach learns a simple yet non-convex data-driven approximation to the AC power flow equations that can incorporate uncertainty inputs. The latter is then used to determine the solution of CC-OPF efficiently, by accounting for both input and parameter uncertainty. The practical efficiency of the proposed approach using different approximations for GP-uncertainty propagation is illustrated over numerous IEEE test cases.
Two-Port Feedback Analysis On Miller-Compensated Amplifiers

In this paper, various Miller-compensated amplifiers are analyzed by using the two-port feedback analysis together with the root-locus diagram. The proposed analysis solves problems of Miller theorem/approximation that fail to predict a pole-splitting and that require an impractical assumption that an initial lower frequency pole before connecting a Miller capacitor in a two-stage amplifier should be associated with the input of the amplifier. Since the proposed analysis sheds light on how the closed-loop poles originate from the open-loop poles in the s-plane, it allows the association of the closed-loop poles with the circuit components and thus provides a design insight for frequency compensation. The circuits analyzed are two-stage Miller-compensated amplifiers with and without a current buffer and a three-stage nested Miller-compensated amplifier.
Multitemporal Speckle Reduction With Self-Supervised Deep Neural Networks

Speckle filtering is generally a prerequisite to the analysis of synthetic aperture radar (SAR) images. Tremendous progress has been achieved in the domain of single-image despeckling. Latest techniques rely on deep neural networks to restore the various structures and textures peculiar to SAR images. The availability of time series of SAR images offers the possibility of improving speckle filtering by combining different speckle realizations over the same area. The supervised training of deep neural networks requires ground-truth speckle-free images. Such images can only be obtained indirectly through some form of averaging, by spatial or temporal integration, and are imperfect. Given the potential of very high-quality restoration reachable by multitemporal speckle filtering, the limitations of ground-truth images need to be circumvented. We extend a recent self-supervised training strategy for single-look complex (SLC) SAR images, called MERLIN, to the case of multitemporal filtering. This requires modeling the sources of statistical dependencies in the spatial and temporal dimensions as well as between the real and imaginary components of the complex amplitudes. Quantitative analysis on datasets with simulated speckle indicates a clear improvement of speckle reduction when additional SAR images are included. Our method is then applied to stacks of TerraSAR-X images and shown to outperform competing multitemporal speckle filtering approaches. The code of the trained models and supplementary results are made freely available at https://gitlab.telecom-paris.fr/ring/multitemporal-merlin/.
Lie groups and Chern-Simons Theory

Witten introduced classical Chern-Simons theory to topology in 1989, when he defined an invariant for knots in 3-manifolds by an integral over a certain infinitedimensional space, which up to today have not been entirely understood. However, they motivated lots of interesting questions and results in knot theory and low-dimensional topology, as well as the development of entirely new fields. These are lecture notes for a course in Lie groups and Chern-Simons Theory aimed at graduate students.
Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds

This paper presents a multi-layer motion planning and control architecture for autonomous racing, capable of avoiding static obstacles, performing active overtakes, and reaching velocities above 75 m/s. The used offline global trajectory generation and the online model predictive controller are highly based on optimization and dynamic models of the vehicle, where the tires and camber effects are represented in an extended version of the basic Pacejka Magic Formula. The proposed single-track model is identified and validated using multi-body motorsport libraries which allow simulating the vehicle dynamics properly, especially useful when real experimental data are missing. The fundamental regularization terms and constraints of the controller are tuned to reduce the rate of change of the inputs while assuring an acceptable velocity and path tracking. The motion planning strategy consists of a Frenét-Frame-based planner which considers a forecast of the opponent produced by a Kalman filter. The planner chooses the collision-free path and velocity profile to be tracked on a 3 seconds horizon to realize different goals such as following and overtaking. The proposed solution has been applied on a Dallara AV-21 racecar and tested at oval race tracks achieving lateral accelerations up to 25 m/s2,
Modeling Complex Dependencies for Session-based Recommendations via Graph Neural Networks

Session-based recommendations (SBRs) capture items' dependencies from the sessions to recommend the next item. In recent years, Graph neural networks (GNN) based SBRs have become the mainstream of SBRs benefited from the superiority of GNN in modeling complex dependencies. Based on a strong assumption of adjacent dependency, any two adjacent items in a session are necessarily dependent in most GNN-based SBRs. However, we argue that due to the uncertainty and complexity of user behaviors, adjacency does not necessarily indicate dependency. However, the above assumptions do not always hold in actual recommendation scenarios, so it can easily lead to two drawbacks: (1) false dependencies occur in the session because there are adjacent but not really dependent items, and (2) the missing of true dependencies occur in the session because there are non-adjacent but actually dependent items. These drawbacks significantly affect item representation learning, degrading the downstream recommendation performance. To address these deficiencies, we propose a novel review-refined inter-item graph neural network (RI-GNN), which utilizes topic information extracted from the reviews of items to improve dependencies between items. Experiments on two public real-world datasets demonstrate that RI-GNN outperforms SOTA methods.
Scale dependant layer for self-supervised nuclei encoding

Recent developments in self-supervised learning give us the possibility to further reduce human intervention in multi-step pipelines where the focus evolves around particular objects of interest. In the present paper, the focus lays in the nuclei in histopathology images. In particular we aim at extracting cellular information in an unsupervised manner for a downstream task. As nuclei present themselves in a variety of sizes, we propose a new Scale-dependant convolutional layer to bypass scaling issues when resizing nuclei. On three nuclei datasets, we benchmark the following methods: handcrafted, pre-trained ResNet, supervised ResNet and self-supervised features. We show that the proposed convolution layer boosts performance and that this layer combined with Barlows-Twins allows for better nuclei encoding compared to the supervised paradigm in the low sample setting and outperforms all other proposed unsupervised methods. In addition, we extend the existing TNBC dataset to incorporate nuclei class annotation in order to enrich and publicly release a small sample setting dataset for nuclei segmentation and classification.
Probing the Extent of Fe Kα Emission in Nearby Active Galactic Nuclei Using Multi-order Analysis of Chandra High Energy Transmission Grating Data

We present a study of the narrow Fe Kα line in seven bright, nearby active galactic nuclei (AGN) that have been observed extensively with the Chandra High Energy Transmission Grating (HETG). The HETG data reveal a wider Fe Kα line in the first-order spectrum than in the second- and third-order spectra, which we interpret as the result of spatially extended Fe Kα emission. We utilize these differences in narrow Fe Kα line widths in the multi-order Chandra HETG spectra to determine the spatial extent and intrinsic velocity width of the emitting material in each object. We find that there is modest evidence for spatially extended emission in each object, corresponding to extension of r ∼ 5–100 pc. These distances are significantly larger than those inferred from velocity widths assuming gravitational motions, which give r ∼ 0.01–1 pc. This implies either that the gas is emitting at a range of radii, with smaller radii dominating the velocity width and larger radii dominating the spatial extent, or that the gas is exhibiting nongravitational motions, which we suggest would be outflows due to slight excess redshift in the line and velocities that exceed the freefall velocity. We also use the spatial extent information to estimate the mass of the emitting gas by counting fluorescing iron atoms, finding masses on the order of M gas ∼ 105–108 M ⊙. Future work with observatories like XRISM will be able to extend this study to a larger number of AGN and decrease uncertainties that arise as a result of the low signal-to-noise ratio of the higher-order HETG data.
APPLICATION OF THE COMPETENCES IN GEOMETRY IN THE MASTER THESIS PROJECT (MUGIE) OF BUILDING ENGENEERING. THREE CASE STUDIES

This communication presents how the competences acquired in the Descriptive Geometry course go deeper into the Master Thesis Project (MTP). This supposes a teaching challenge, because Descriptive Geometry is taught in the first course of the Degree in Building in Higher Technical School in Building Engineering, University of Seville. In addition, the Degree does not offer linkage between Geometry and the rest of the teaching imparted in the last years. We will expose the importance of recovering the foundations of geometry with the help of new technologies and applying them to new challenges that may appear on the professional and research horizon of the student of Building Engineering. We will explain how these competences have been extrapolated to three specific research projects. We will also comment on the methodologies used to achieve these objectives both in the classroom work and in the tutorial action. Finally, we will show partial conclusions together with a plan to improve the results obtained. We will present three MTPs, as case studies, which have obtained good results.
Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives

Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed.
Fast Strategies for Multi-Temporal Speckle Reduction of Sentinel-1 GRD Images

Reducing speckle and limiting the variations of the physical parameters in Synthetic Aperture Radar (SAR) images is often a key-step to fully exploit the potential of such data. Nowadays, deep learning approaches produce state of the art results in single-image SAR restoration. Nevertheless, huge multi-temporal stacks are now often available and could be efficiently exploited to further improve image quality. This paper explores two fast strategies employing a single-image despeckling algorithm, namely SAR2SAR [1], in a multi-temporal framework. The first one is based on Quegan filter [2] and replaces the local reflectivity pre-estimation by SAR2SAR. The second one uses SAR2SAR to suppress speckle from a ratio image encoding the multi-temporal information under the form of a “super-image”, i.e. the temporal arithmetic mean of a time series. Experimental results on Sentinel-1 GRD data show that these two multi-temporal strategies provide improved filtering results while adding a limited computational cost.
Privacy and transparency in graph machine learning: A unified perspective

Graph Machine Learning (GraphML), whereby classical machine learning is generalized to irregular graph domains, has enjoyed a recent renaissance, leading to a dizzying array of models and their applications in several domains. With its growing applicability to sensitive domains and regulations by governmental agencies for trustworthy AI systems, researchers have started looking into the issues of transparency and privacy of graph learning. However, these topics have been mainly investigated independently. In this position paper, we provide a unified perspective on the interplay of privacy and transparency in GraphML. In particular, we describe the challenges and possible research directions for a formal investigation of privacy-transparency tradeoffs in GraphML.