Miracle-dz/UniFAR-FTUs huggingface dataset:
Shape Representation and Registration using Implicit Functions

Les representacions de forma i registre son dos problemes importants tant en la visio per computador com en els grafics. La representacio d'un nuvol de punts a traves d'una funcio implicita proporciona major nivell d'informacio alhora de descriure les dades. Aquesta representacio pot ser mes compacta, mes robusta al soroll i als \textit{outlier}, pel que pot ser explotada diferents aplicacions de visio per computador. La primera part d'aquesta tesi aborda representacions de forma implicites que inclouen tant la representacio mitjancant \textit{B-splines} i polinomials. Primer es proposa una aproximacio per mesurar la distancia geometrica entre un nuvol de punts i una superficie implicita. L'analisi de la distancia proposada mostra una estimacio acurada amb un comportament suau. Aquesta distancia es usada en un algorisme d'ajustament quadratic basat en RANSAC. A mes a mes, ates que la informacio de gradient de la distancia respecte els parametres de la superficie pot ser calculat analiticament, els parametres de la superficie poden ser refinats utilitzant l'algorisme de Levenberg-Marquadt. Seguint un enfocament diferent, un algorisme d'ajustament algebraic es pot utilitzar per representar un objecte a traves de \textit{B-splines} implicites. El resultat es una superficie suau i flexible que pot ser representada en diferents nivells de detall. Aquesta propietat ha estat explotada per solucionar el problema de registracio a la segona part de la tesi. En el metode de registracio proposat, el model es substituit amb la representacio implicita proposada en la primera part, i despres la registracio punt a punt es converteix en una registracio punt a model en un nivell superior d'abstraccio. Aquesta representacio es pot beneficiar de diferents distancies per accelerar el proces de registracio sense haver de cercar correspondencies. Finalment, el problema de registre de models no rigids es abordat mitjancant d'una aproximacio de la distancia quadratica que esta basada en la informacio de la curvatura del conjunt de models. Aquesta aproximacio s'utilitza en un model \textit{Free Form Deformation} (FFD) per actualitzar la seva xarxa de control. Despres es mostra com una aproximacio acurada de la distancia pot beneficiar el problema de registracio no-rigida.
Advancements and challenges in plasmon-exciton quantum emitters based on colloidal quantum dots

The Nobel Prizes in Physics (2022) and Chemistry (2023) heralded the recognition of quantum information science and the synthesis of quantum dots, respectively. This acknowledgment has propelled colloidal quantum dots and perovskite nanocrystals to the forefront of quantum technologies. Their distinct emission properties, facilitating the efficient generation of both single photons and photon pairs, render them particularly captivating. Moreover, their adaptability to diverse structures, ranging from traditional electronics to nanopatterned frameworks, underscores their pivotal role in shaping quantum technologies. Despite notable strides in synthesis, certain properties require refinement for enhanced applicability in quantum information, encompassing emission brightness, stability, single photon indistinguishability, and entanglement fidelity of photon pairs. Here we offer an overview of recent achievements in plasmon-exciton quantum emitters based on luminescent semiconductor nanocrystals. Emphasizing the utilization of the light-matter coupling phenomenon, we explore how this interaction enables the manipulation of quantum properties without altering the chemical structure of the emitters. This approach addresses critical aspects for quantum information applications, offering precise control over emission rate, intensity, and energy. The development of these hybrid systems represents a significant stride forward, demonstrating their potential to overcome existing challenges and advance the integration of quantum emitters into cutting-edge quantum technology applications.
On a Question of J.D. Meiss Concerning Anti-integrability for Three-Dimensional Quadratic Diffeomorphisms

Three-dimensional quadratic diffeomorphisms with quadratic inverse generically have five independent parameters. When some parameters approach infinity, the diffeomorphisms may exhibit the so-called anti-integrability in the traditional sense of Aubry and Abramovici. That is, the dynamics of the diffeomorphisms reduce to symbolic dynamics on finite number of symbols. However, the diffeomorphisms may reduce to quadratic correspondences when parameters approach infinity, and the traditional anti-integrable limit does not deal with this situation. Meiss asked what about an anti-integrable limit for it. A remarkable progress was achieved very recently by Meiss himself and his student Hampton [SIAM J. Appl. Dyn. Syst. 21 (2022), pp. 650--675]. Under some conditions, using the contraction mapping theorem, they showed there is a bijection between the anti-integrable states and the sequences of branches of a quadratic correspondence. They also showed that an anti-integrable state can be continued to a genuine orbit of the three-dimensional diffeomorphism. This paper aims to contribute the progress, by means of the implicit function theorem. We shall show that the bijection indeed is a topological conjugacy and establish the uniform hyperbolicity of the continued genuine orbits.
Automatic Circular Take-off and Landing of Tethered Motorized Aircraft

We consider a motorized aircraft tethered to a central anchorage point in a configuration similar to a control line model airplane. For this system, we address the problem of automatic take-off and landing (ATOL) with a circular path, whose center and radius are defined by the anchorage point and the tether length, respectively. We propose a hierarchical control architecture for ATOL and discuss the controllers designed for each control layer and for each of the flight phases. Simulation results are reported, showing the viability of the approach, but also showing the limitations on the maximum altitude attainable with a fixed-tether length. The tethered aircraft and the proposed ATOL control architecture are to be used in an Airborne Wind Energy System.
Finding forest-orderings of tournaments is NP-complete

Given a class of (undirected) graphs $\mathcal{C}$, we say that a FAS $F$ is a $\mathcal{C}$-FAS if the graph induced by the edges of $F$ (forgetting their orientations) belongs to $\mathcal{C}$. We show that deciding if a tournament has a $\mathcal{C}$-FAS is NP-complete when $\mathcal{C}$ is the class of all forests. We are motivated by connections between $\mathcal{C}$-FAS and structural parameters of tournaments, such as the dichromatic number, the clique number of tournaments, and the strong Erd\H{o}s-Hajnal property.
Streaming Algorithms for Connectivity Augmentation

We study the $k$-connectivity augmentation problem ($k$-CAP) in the single-pass streaming model. Given a $(k-1)$-edge connected graph $G=(V,E)$ that is stored in memory, and a stream of weighted edges $L$ with weights in $\{0,1,\dots,W\}$, the goal is to choose a minimum weight subset $L'\subseteq L$ such that $G'=(V,E\cup L')$ is $k$-edge connected. We give a $(2+\epsilon)$-approximation algorithm for this problem which requires to store $O(\epsilon^{-1} n\log n)$ words. Moreover, we show our result is tight: Any algorithm with better than $2$-approximation for the problem requires $\Omega(n^2)$ bits of space even when $k=2$. This establishes a gap between the optimal approximation factor one can obtain in the streaming vs the offline setting for $k$-CAP. We further consider a natural generalization to the fully streaming model where both $E$ and $L$ arrive in the stream in an arbitrary order. We show that this problem has a space lower bound that matches the best possible size of a spanner of the same approximation ratio. Following this, we give improved results for spanners on weighted graphs: We show a streaming algorithm that finds a $(2t-1+\epsilon)$-approximate weighted spanner of size at most $O(\epsilon^{-1} n^{1+1/t}\log n)$ for integer $t$, whereas the best prior streaming algorithm for spanner on weighted graphs had size depending on $\log W$. Using our spanner result, we provide an optimal $O(t)$-approximation for $k$-CAP in the fully streaming model with $O(nk + n^{1+1/t})$ words of space. Finally we apply our results to network design problems such as Steiner tree augmentation problem (STAP), $k$-edge connected spanning subgraph ($k$-ECSS), and the general Survivable Network Design problem (SNDP). In particular, we show a single-pass $O(t\log k)$-approximation for SNDP using $O(kn^{1+1/t})$ words of space, where $k$ is the maximum connectivity requirement.
Mixture-Models: a one-stop Python Library for Model-based Clustering using various Mixture Models

\texttt{Mixture-Models} is an open-source Python library for fitting Gaussian Mixture Models (GMM) and their variants, such as Parsimonious GMMs, Mixture of Factor Analyzers, MClust models, Mixture of Student's t distributions, etc. It streamlines the implementation and analysis of these models using various first/second order optimization routines such as Gradient Descent and Newton-CG through automatic differentiation (AD) tools. This helps in extending these models to high-dimensional data, which is first of its kind among Python libraries. The library provides user-friendly model evaluation tools, such as BIC, AIC, and log-likelihood estimation. The source-code is licensed under MIT license and can be accessed at \url{https://github.com/kasakh/Mixture-Models}. The package is highly extensible, allowing users to incorporate new distributions and optimization techniques with ease. We conduct a large scale simulation to compare the performance of various gradient based approaches against Expectation Maximization on a wide range of settings and identify the corresponding best suited approach.
Temporal Analysis of Drifting Hashtags in Textual Data Streams: A Graph-Based Application

Social media has played an important role since its emergence. People use the internet to express opinions about anything, making social media platforms a social sensor. Initially supported by Twitter, the hashtags are now in use on several social media platforms. Hashtags are helpful to tag, track, and group posts on similar topics. In this paper, we analyze hashtag drifts over time using concepts from graph analysis and textual data streams using the Girvan-Newman method to uncover hashtag communities in annual snapshots. More specifically, we analyzed the #mybodymychoice hashtag between 2018 and 2022. In addition, we offer insights about some hashtags found in the study. Furthermore, our approach can be useful for monitoring changes over time in opinions and sentiment patterns about an entity on social media. Even though the hashtag #mybodymychoice was initially coupled with women's rights, abortion, and bodily autonomy, we observe that it suffered drifts during the studied period across topics such as drug legalization, vaccination, political protests, war, and civil rights. The year 2021 was the most significant drifting year, in which the communities detected suggest that #mybodymychoice significantly drifted to vaccination and Covid-19-related topics.
Fixed Confidence Best Arm Identification in the Bayesian Setting

We consider the fixed-confidence best arm identification (FC-BAI) problem in the Bayesian Setting. This problem aims to find the arm of the largest mean with a fixed confidence level when the bandit model has been sampled from the known prior. Most studies on the FC-BAI problem have been conducted in the frequentist setting, where the bandit model is predetermined before the game starts. We show that the traditional FC-BAI algorithms studied in the frequentist setting, such as track-and-stop and top-two algorithms, result in arbitrary suboptimal performances in the Bayesian setting. We also prove a lower bound of the expected number of samples in the Bayesian setting and introduce a variant of successive elimination that has a matching performance with the lower bound up to a logarithmic factor. Simulations verify the theoretical results.
Thompson Sampling in Partially Observable Contextual Bandits

Contextual bandits constitute a classical framework for decision-making under uncertainty. In this setting, the goal is to learn the arms of highest reward subject to contextual information, while the unknown reward parameters of each arm need to be learned by experimenting that specific arm. Accordingly, a fundamental problem is that of balancing exploration (i.e., pulling different arms to learn their parameters), versus exploitation (i.e., pulling the best arms to gain reward). To study this problem, the existing literature mostly considers perfectly observed contexts. However, the setting of partial context observations remains unexplored to date, despite being theoretically more general and practically more versatile. We study bandit policies for learning to select optimal arms based on the data of observations, which are noisy linear functions of the unobserved context vectors. Our theoretical analysis shows that the Thompson sampling policy successfully balances exploration and exploitation. Specifically, we establish the followings: (i) regret bounds that grow poly-logarithmically with time, (ii) square-root consistency of parameter estimation, and (iii) scaling of the regret with other quantities including dimensions and number of arms. Extensive numerical experiments with both real and synthetic data are presented as well, corroborating the efficacy of Thompson sampling. To establish the results, we introduce novel martingale techniques and concentration inequalities to address partially observed dependent random variables generated from unspecified distributions, and also leverage problem-dependent information to sharpen probabilistic bounds for time-varying suboptimality gaps. These techniques pave the road towards studying other decision-making problems with contextual information as well as partial observations.
Prevalence of modifiable risk factors for non-communicable diseases in urban slum: a cross sectional study using WHO STEPS approach

Background: Non communicable diseases (NCDs) represent a cluster of major chronic diseases. Smoking habit, alcoholism, low quality diet intakes, physical inactivity are some of the established risk factors of the NCDs. In developing country like India the problem of lifestyle and its consequent diseases needs to be addressed vigorously by all public health care personnel. Objective of the study was to assess prevalence of modifiable risk factors using the WHO STEPS approach in urban slum.Methods: A community based cross-sectional study was carried out in the field practice area of urban health training centre of the department of community medicine of PCMS and RC, Bhopal using questionnaire.Results: Highest prevalence of modifiable risk factor was inadequate diet (85.2%), followed by smokeless tobacco consumption (59.1%) and low physical activity (46.1%). Tobacco smoking (31.9%) and alcohol (22.6%) were next, followed by high blood pressure (20.1%). Prevalence of least risk factors was overweight (13.9%). Conclusions: It can be concluded from our study that the burden of risk factors for NCDs is quite high. Its prevalence even in the younger age groups is a pointer to the fact that the burden of NCDs is going to rise in near future.
MOC-AE: An Anatomically-Pathological-Based model for Clinical Decision Support System of tumoural brain images

The present work proposes a Multi-Output Classification Autoencoder (MOC-AE) algorithm to extract features from brain tumour images. The proposed algorithm is able to focus on both the normal features of the patient and the pathological features present in the case, resulting in a compact and significant representation of each image. The architecture of MOC-AE combines anatomical information from the patient’s scan using an Autoencoder (AE) with information related to a specific pathology using a classification output with the same image descriptor. This combination of goals forces the network to maintain a balance between anatomical and pathological features of the case while maintaining the low cost of the labels being used. The results obtained are compared with those of similar studies and the strengths and limitations of each approach are discussed. The results demonstrate that the proposed algorithm is capable of achieving state-of-the-art results in terms of both the anatomical and tumor characteristics of the recommended cases. © 2023 Elsevier B. V. All rights reserved.
Cytometry Masked Autoencoder: An Accurate and Interpretable Automated Immunophenotyper

High-throughput single-cell cytometry data are crucial for understanding involvement of immune system in diseases and responses to treatment. Traditional methods for annotating cytometry data, specifically manual gating and clustering, face challenges in scalability, robustness, and accuracy. In this study, we propose a cytometry masked autoencoder (cyMAE), which offers an automated solution for immunophenotyping tasks including cell type annotation. The cyMAE model is designed to uphold user-defined cell type definitions, thereby facilitating easier interpretation and cross-study comparisons. The cyMAE model operates on a pre-train and fine-tune approach. In the pre-training phase, cyMAE employs Masked Cytometry Modelling (MCM) to learn relationships between protein markers in immune cells solely based on protein expression, without relying on prior information such as cell identity and cell type-specific marker proteins. Subsequently, the pre-trained cyMAE is fine-tuned on multiple specialized tasks via task-specific supervised learning. The pre-trained cyMAE addresses the shortcomings of manual gating and clustering methods by providing accurate and interpretable predictions. Through validation across multiple cohorts, we demonstrate that cyMAE effectively identifies co-occurrence patterns of bound labeled antibodies, delivers accurate and interpretable cellular immunophenotyping, and improves the prediction of subject metadata status. Specifically, we evaluated cyMAE for cell type annotation and imputation at the cellular-level and SARS-CoV-2 infection prediction, secondary immune response prediction against COVID-19, and prediction of the infection stage in COVID-19 progression at the subject-level. The introduction of cyMAE marks a significant step forward in immunology research, particularly in large-scale and high-throughput human immune profiling. This approach offers new possibilities for predicting and interpreting cellular-level and subject-level phenotypes in both health and disease.
PI-(3,5)P2-mediated oligomerization of the endosomal sodium/proton exchanger NHE9

Na+/H+ exchangers are found in all cells to regulate intracellular pH, sodium levels and cell volume. Na+/H+ exchangers are physiological homodimers that operate by an elevator alternating-access mechanism. While the structure of the core ion translocation domain is fairly conserved, the scaffold domain and oligomerization show larger structural variation. The Na+/H+ exchanger NhaA from E. coli has a weak oligomerization interface mediated by a β-hairpin domain and homodimerization was shown to be dependent of the lipid cardiolipin. Organellar Na+/H+ exchangers NHE6, NHE7 and NHE9 are likewise predicted to contain β-hairpin domains and a recent analysis of horse NHE9 indicated that the lipid PIP2 binds at the dimerization interface. Despite predicted lipid-mediated oligomerization, their structural validation has been lacking. Here, we report cryo-EM structures of E. coli NhaA and horse NHE9 with the coordination of cardiolipin and PI(3,5)P2 binding at the dimer interface, respectively. Cell based assays confirms that NHE9 is inactive at the plasma membrane and thermal-shift assays, solid-supported membrane (SSM) electrophysiology and MD simulations, corroborates that NHE9 specifically binds the endosomal PI(3,5)P2 lipid, which stabilizes the homodimer and enhances activity. Taken together, we propose specific lipids regulate Na+/H+ exchange activity by stabilizing oligomerization and stimulating Na+ binding under lipid-specific cues.
A Multi-Dimensional Approach to Map Disease Relationships Challenges Classical Disease Views

The categorization of human diseases is mainly based on the affected organ system and phenotypic characteristics. This is limiting the view to the pathological manifestations, while it neglects mechanistic relationships that are crucial to develop therapeutic strategies. This work aims to advance the understanding of diseases and their relatedness beyond traditional phenotypic views. Hence, the similarity among 502 diseases is mapped using six different data dimensions encompassing molecular, clinical, and pharmacological information retrieved from public sources. Multiple distance measures and multi-view clustering is used to assess the patterns of disease relatedness. The integration of all six dimensions into a consensus map of disease relationships reveals a divergent disease view from the International Classification of Diseases (ICD), emphasizing novel insights offered by a multi-view disease map. Disease features such as genes, pathways, and chemicals that are enriched in distinct disease groups are identified. Finally, an evaluation of the top similar diseases of three candidate diseases common in the Western population shows concordance with known epidemiological associations and reveals rare features shared between Type 2 diabetes and Alzheimer disease. A revision of disease relationships holds promise for facilitating the reconstruction of comorbidity patterns, repurposing drugs, and advancing drug discovery in the future.
Predictable functional biogeography of marine microbial heterotrophs

Microbial heterotrophs (‘picoheterotrophs’) drive global carbon cycling, but how to quantitatively organize their functional complexity remains unclear. Here, we generate a global-scale, mechanistic understanding of marine picoheterotrophic functional biogeography with a novel model-data synthesis. We build picoheterotrophic diversity into a trait-based marine ecosystem model along two axes: substrate lability and optimization for growth rate (copiotrophy) vs. substrate affinity (oligotrophy). Using genetic sequences along an Alaska-to-Antarctica Pacific Ocean transect, we compile 21 picoheterotrophic guilds and estimate their degree of copiotrophy. Data and model agreement suggests that gradients in predation and substrate lability predominantly set biogeographical patterns, and identifies ‘slow copiotrophs’ subsisting at depth. Results demonstrate the predictability of the marine microbiome and connect ecological dynamics with carbon storage, crucial for projecting changes in a warming ocean.