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This paper introduces a contribution made to one of the newest methods for simulating indirect lighting in dynamic scenes , the cascaded light propagation volumes . Our contribution consists on using Spherical Radial Basis Functions instead of Spherical Harmonic, since the first achieves much better results when many coefficients are used. We explain how to integrate the Spherical Radial Basis Functions with the cascaded light propagation volumes, and evaluate our technique against the same implementation, but with Spherical harmonics.

Point cloud models are a common shape representation for several reasons. Three-dimensional scanning devices are widely used nowadays and points are an attractive primitive for rendering complex geometry. Nevertheless, there is not much literature on collision detection for point cloud models. This paper presents a novel collision detection algorithm for large point cloud models using voxels, octrees and bounding spheres hierarchies (BSH). The scene graph is divided in voxels. The objects of each voxel are organized into an octree. Due to the high number of points in the scene, each non-empty cell of the octree is organized in a bounding sphere hierarchy, based on an R-tree hierarchy like structure. The BSH hierarchies are used to group neighboring points and filter out very quickly parts of objects that do not interact with other models. Points derived from laser scanned data typically are not segmented and can have arbitrary spatial resolution thus introducing computational and modeling issues. We address these issues and our results show that the proposed collision detection algorithm effectively finds intersections between point cloud models since it is able to reduce the number of bounding volume checks and updates.

The acquisition of complex knowledge and competences raises difficult challenges for the supporting tools within the corporate environment, which digital storytelling presents a potential solution. Traditionally, a driving goal of digital storytelling is the generation of dramatic stories with human significance, but for learning purposes, the need for drama is complemented by the requirement of achieving particular learning outcomes. This paper presents a narrative engine that supports emergent storytelling to support the development of complex competences in the learning domains of project management and innovation. The approach is based on the adaptation on the Fabula model combined with cases representing situated contexts associated to particular competences. These cases are then triggered to influence the unfolding of the story such that a learner encounters dramatic points in the narrative where the associated competences need to be used. In addition to the description of the approach and corresponding narrative engine, an illustration is presented of how the competence 'conflict management' influences a story.

Depth maps produced by consumer-grade sensors suffer from inaccurate measurements and missing data from either system or scene-specific sources. Data-driven denoising algorithms can mitigate such problems. However, they require vast amounts of ground truth depth data. Recent research has tackled this limitation using self-supervised learning techniques, but it requires multiple RGB-D sensors. Moreover, most existing approaches focus on denoising single isolated depth maps or specific subjects of interest, highlighting a need for methods to effectively denoise depth maps in real-time dynamic environments. This paper extends state-of-the-art approaches for depth-denoising commodity depth devices, proposing SelfReDepth, a self-supervised deep learning technique for depth restoration, via denoising and hole-filling by inpainting full-depth maps captured with RGB-D sensors. The algorithm targets depth data in video streams, utilizing multiple sequential depth frames coupled with color data to achieve high-quality depth videos with temporal coherence. Finally, SelfReDepth is designed to be compatible with various RGB-D sensors and usable in real-time scenarios as a pre-processing step before applying other depth-dependent algorithms. Our results demonstrate our approach's real-time performance on real-world datasets. They show that it outperforms state-of-the-art denoising and restoration performance at over 30fps on Commercial Depth Cameras, with potential benefits for augmented and mixed-reality applications., Comment: 13pp, 5 figures, 1 table

Virtual meetings have become increasingly common with modern video-conference and collaborative software. While they allow obvious savings in time and resources, current technologies add unproductive layers of protocol to the flow of communication between participants, rendering the interactions far from seamless. In this work we introduce Remote Proxemics, an extension of proxemics aimed at bringing the syntax of co-located proximal interactions to virtual meetings. We propose Eery Space, a shared virtual locus that results from merging multiple remote areas, where meeting participants' are located side-by-side as if they shared the same physical location. Eery Space promotes collaborative content creation and seamless mediation of communication channels based on virtual proximity. Results from user evaluation suggest that our approach is effective at enhancing mutual awareness between participants and sufficient to initiate proximal exchanges regardless of their geolocation, while promoting smooth interactions between local and remote people alike. These results happen even in the absence of visual avatars and other social devices such as eye contact, which are largely the focus of previous approaches., Comment: 19 pages, 7 figures

We present the first formally-verified Internet router, which is part of the SCION Internet architecture. SCION routers run a cryptographic protocol for secure packet forwarding in an adversarial environment. We verify both the protocol's network-wide security properties and low-level properties of its implementation. More precisely, we develop a series of protocol models by refinement in Isabelle/HOL and we use an automated program verifier to prove that the router's Go code satisfies memory safety, crash freedom, freedom from data races, and adheres to the protocol model. Both verification efforts are soundly linked together. Our work demonstrates the feasibility of coherently verifying a critical network component from high-level protocol models down to performance-optimized production code, developed by an independent team. In the process, we uncovered critical bugs in both the protocol and its implementation, which were confirmed by the code developers, and we strengthened the protocol's security properties. This paper explains our approach, summarizes the main results, and distills lessons for the design and implementation of verifiable systems, for the handling of continuous changes, and for the verification techniques and tools employed.

Virtual Reality (VR) has recently gained traction with many new and ever more affordable devices being released. The increase in popularity of this paradigm of interaction has given birth to new applications and has attracted casual consumers to experience VR. Providing a self-embodied representation (avatar) of users' full bodies inside shared virtual spaces can improve the VR experience and make it more engaging to both new and experienced users . This is especially important in fully immersive systems, where the equipment completely occludes the real world making self awareness problematic. Indeed, the feeling of presence of the user is highly influenced by their virtual representations, even though small flaws could lead to uncanny valley side-effects. Following previous research, we would like to assess whether using a third-person perspective could also benefit the VR experience, via an improved spatial awareness of the user's virtual surroundings. In this paper we investigate realism and perspective of self-embodied representation in VR setups in natural tasks, such as walking and avoiding obstacles. We compare both First and Third-Person perspectives with three different levels of realism in avatar representation. These range from a stylized abstract avatar, to a "realistic" mesh-based humanoid representation and a point-cloud rendering. The latter uses data captured via depth-sensors and mapped into a virtual self inside the Virtual Environment. We present a throughout evaluation and comparison of these different representations, describing a series of guidelines for self-embodied VR applications. The effects of the uncanny valley are also discussed in the context of navigation and reflex-based tasks.

One of the current challenges in physically-based simulations, and, more specifically, fluid simulations, is to produce visually appealing results at interactive rates, capable of being used in multiple forms of media. In recent times, a lot of effort has been made with regards to this with the use of multi-core architectures, as many of the computations involved in the algorithms for these simulations are very well suited for these architectures. Although there is a considerable amount of works regarding acceleration techniques in this field, there is yet room to further explore and analyze some of them. To investigate this problem, we surveyed the topic of fluid simulations and some of the recent contributions towards this field. Additionally, we implemented two versions of a fluid simulation algorithm, one on the CPU and the other on the GPU using NVIDIA's CUDA framework, with the intent of gaining a better understanding of the effort needed to move these simulations to a multi-core architecture and the performance gains that we get with it.

Grid space partitioning is a technique to speed up queries to graphics databases. We present a parallel grid construction algorithm which can efficiently construct a structured grid on GPU hardware. Our approach is substantially faster than existing uniform grid construction algorithms, especially on non-homogeneous scenes. Indeed, it can populate a grid in real-time (at rates over 25 Hz), for architectural scenes with 10 million triangles.

In this work, we report on a twin-core fiber sensor system that provides improved spectral efficiency, allows for multiplexing and gives low level of crosstalk. Pieces of the referred strongly coupled multicore fiber are used as sensors in a laser cavity incorporating a pulsed semiconductor optical amplifier (SOA). Each sensor has its unique cavity length and can be addressed individually by electrically matching the periodic gating of the SOA to the sensors cavity roundtrip time. The interrogator acts as a laser and provides a narrow spectrum with high signal-to-noise ratio. Furthermore, it allows distinguishing the response of individual sensors even in the case of overlapping spectra. Potentially, the number of interrogated sensors can be increased significantly, which is an appealing feature for multipoint sensing.

Demand forecasting is a prominent business use case that allows retailers to optimize inventory planning, logistics, and core business decisions. One of the key challenges in demand forecasting is accounting for relationships and interactions between articles. Most modern forecasting approaches provide independent article-level predictions that do not consider the impact of related articles. Recent research has attempted addressing this challenge using Graph Neural Networks (GNNs) and showed promising results. This paper builds on previous research on GNNs and makes two contributions. First, we integrate a GNN encoder into a state-of-the-art DeepAR model. The combined model produces probabilistic forecasts, which are crucial for decision-making under uncertainty. Second, we propose to build graphs using article attribute similarity, which avoids reliance on a pre-defined graph structure. Experiments on three real-world datasets show that the proposed approach consistently outperforms non-graph benchmarks. We also show that our approach produces article embeddings that encode article similarity and demand dynamics and are useful for other downstream business tasks beyond forecasting., Comment: Preprint of the paper accepted to ECML PKDD 2023 ML4ITS Workshop

sEMG pattern recognition algorithms have been explored extensively in decoding movement intent, yet are known to be vulnerable to changing recording conditions, exhibiting significant drops in performance across subjects, and even across sessions. Multi-channel surface EMG, also referred to as high-density sEMG (HD-sEMG) systems, have been used to improve performance with the information collected through the use of additional electrodes. However, a lack of robustness is ever present due to limited datasets and the difficulties in addressing sources of variability, such as electrode placement. In this study, we propose training on a collection of input channel subsets and augmenting our training distribution with data from different electrode locations, simultaneously targeting electrode shift and reducing input dimensionality. Our method increases robustness against electrode shift and results in significantly higher intersession performance across subjects and classification algorithms., Comment: 5 pages

Ray tracing has long been the holy grail of real time rendering. This technique, commonly used for photo realism, simulates the physical behavior of light, at the cost of being computationally heavy. With the introduction of Nvidia RTX graphic card family, which provides hardware support for ray tracing, this technique started to look like a reality for real time. However, the same problems that afflicted the usage of this technique remain, and even with specialized hardware it is still extremely expensive. To account for these drawbacks, researchers and developers pair this technique with rasterization and denoising. This results in a hybrid system that tries to join the best of both worlds, having both photo realistic quality and real time performance. In this work we intend on further exploring hybrid render systems, offering a review of the state of the art with a special focus on real time ray tracing and our own hybrid implementation with photo realistic quality and real time performance (>30 fps), implemented using the Vulkan API. In this project, we highlight the detailed analysis of the impacts of History Rectification (Variance Color Clamping) on the temporal filter component of the denoising system and how to overcome the introduced artifacts. Additionally, we also highlight the analysis of the introduction of a separable blur on the spatial filter and the introduction of Reinhard Tone Mapping prior to denoising, consequently improving this procedure.

We present an algorithm for creating an n-level ray-space hierarchy (RSH) of coherent rays that runs on the GPU. Our algorithm uses rasterization to process the primary rays, then uses those results as the inputs for a RSH, that processes the secondary rays. The RSH algorithm generates bundles of rays; hashes them, according to their attributes; and sorts them. Thus we generate a ray list with adjacent coherent rays. To improve the rendering performance of the RSH vs a more classical approach. In addition the scenes geometry is partitioned into a set of bounding spheres, intersected with the RSH, to further decrease the amount of false ray bundle-primitive intersection tests. We show that our technique notably reduces the amount of ray-primitive intersection tests, required to render an image. In particular, it performs up to 50% better in this metric than other algorithms in this class.

Three-dimensional reconstruction of events recorded on images has been a common challenge between computer vision and computer graphics for a long time. Estimating the real position of objects and surfaces using vision as an input is no trivial task and has been approached in several different ways. Although huge progress has been made so far, there are several open issues to which an answer is needed. The use of videos as an input for a rendering process (video-based rendering, VBR) is something that recently has been started to be looked upon and has added many other challenges and also solutions to the classical image-based rendering issue (IBR). This article presents the state of art on video-based rendering and image-based techniques that can be applied on this scenario, evaluating the open issues yet to be solved, indicating where future work should be focused.

Although extended reality technologies have enjoyed an explosion in popularity in recent years, few applications are effectively used outside the entertainment or academic contexts. This work consists of a literature review regarding the effective integration of such technologies in the workplace. It aims to provide an updated view of how they are being used in that context. First, we examine existing research concerning virtual, augmented, and mixed-reality applications. We also analyze which have made their way to the workflows of companies and institutions. Furthermore, we circumscribe the aspects of extended reality technologies that determined this applicability.

Plasma physics simulations create complex datasets for which researchers need state-of-the-art visualization tools to gain insights. These datasets are 3D in nature but are commonly depicted and analyzed using 2D idioms displayed on 2D screens. These offer limited understandability in a domain where spatial awareness is key. Virtual reality (VR) can be used as an alternative to conventional means for analyzing such datasets. VR has been known to improve depth and spatial relationship perception, which are fundamental for obtaining insights into 3D plasma morphology. Likewise, VR can potentially increase user engagement by offering more immersive and enjoyable experiences. Methods This study presents PlasmaVR, a proof-of-concept VR tool for visualizing datasets resulting from plasma physics simulations. It enables immersive multidimensional data visualization of particles, scalar, and vector fields and uses a more natural interface. The study includes user evaluation with domain experts where PlasmaVR was employed to assess the possible benefits of immersive environments in plasma physics visualization. The experimental group comprised five plasma physics researchers who were asked to perform tasks designed to represent their typical analysis workflow. To assess the suitability of the prototype for the different types of tasks, a set of objective metrics, such as completion time and number of errors, were measured. The prototype's usability was also evaluated using a standard System Usability Survey questionnaire.

Refinement transforms an abstract system model into a concrete, executable program, such that properties established for the abstract model carry over to the concrete implementation. Refinement has been used successfully in the development of substantial verified systems. Nevertheless, existing refinement techniques have limitations that impede their practical usefulness. Some techniques generate executable code automatically, which generally leads to implementations with sub-optimal performance. Others employ bottom-up program verification to reason about efficient implementations, but impose strict requirements on the structure of the code, the structure of the refinement proofs, as well as the employed verification logic and tools. In this paper, we present a novel refinement technique that removes these limitations. It supports a wide range of program structures, data representations, and proof structures. Our approach supports reasoning about both safety and liveness properties. We implement our approach in a state-of-the-art verifier for the Rust language, which itself offers a strong foundation for memory safety. We demonstrate the practicality of our approach on a number of substantial case studies., Comment: 21 pages, 3 figures, submitted to PLDI 2024

Interactive real-time rigid body simulation is a crucial tool in any modern game engine or 3D authoring tool. The quest for fast, robust and accurate simulations is ever evolving. PBRBD (Position Based Rigid Body Dynamics), a recent expansion of PBD (Position Based Dynamics), is a novel approach for this issue. This work aims at providing a comprehensible state-of-the art comparison between Position Based methods and other real-time simulation methods used for rigid body dynamics using a custom 3D physics engine for benchmarking and comparing PBRBD (Position Based Rigid Body Dynamics), and some variants, with state-of-the-art simulators commonly used in the gaming industry, PhysX and Havok. Showing that PBRBD can produce simulations that are accurate and stable, excelling at maintaining stable energy levels, and allowing for a variety of constraints, but is limited in its handling of stable stacks of rigid bodies.

In this work, the artistic style of the sixteenth century Portuguese artist Ant\'onio Campelo in Alegoria \`a Prud\^encia is analyzed in order to create a computational tool that allows one to transform any 3D digital sculpture model into an image that resembles the modeled style. From this analysis the problem is divided into two parts: detection and drawing of contour lines and the shading of the scene. Several techniques from Non Photorealistic Rendering (NPR) and from Photorealistic Rendering that can resolve the problem are presented and, based on this study, a possible solution is presented. Each modeled rendering component is then analyzed using image based methods against the proposed artistic style and parameters are adjusted for a closer match. In the final stage a group of people was asked to answer a questionnaire where the similarity between the renderings of different objects and the original style was classified according to their personal opinion. One of our findings is that although the source 3D objects cannot be readily found for a direct comparison, nor can the paper medium with centuries old damage be the same, the comparison of sub -parts of both images of the same topology was still possible validating our method and discarding other styles from the comparison., Comment: Wrong references corrected

We present and study the iteration-complexity of a relative-error inexact proximal-Newton extragradient algorithm for solving smooth monotone variational inequality problems in real Hilbert spaces. We removed a search procedure from Monteiro and Svaiter (2012) by introducing a novel approach based on homotopy, which requires the resolution (at each iteration) of a single strongly monotone linear variational inequality. For a given tolerance $\rho>0$, our main algorithm exhibits pointwise $O\left(\frac{1}{\rho}\right)$ and ergodic $O\left(\frac{1}{\rho^{2/3}}\right)$ iteration-complexities. From a practical perspective, preliminary numerical experiments indicate that our main algorithm outperforms some previous proximal-Newton schemes.

We explore confidential computing in the context of CBDCs using Microsoft's CCF framework as an example. By developing an experiment and comparing different approaches and performance and security metrics, we seek to evaluate the effectiveness of confidential computing to improve the privacy, security, and performance of CBDCs. Preliminary results suggest that confidential computing could be a promising solution to the technological challenges faced by CBDCs. Furthermore, by implementing confidential computing in DLTs such as Hyperledger Besu and utilizing frameworks such as CCF, we increase transaction confidentiality and privacy while maintaining the scalability and interoperability required for a global digital financial system. In conclusion, confidential computing can significantly bolster CBDC development, fostering a secure, private, and efficient financial future. -- Exploramos o uso da computa\c{c}\~ao confidencial no contexto das CBDCs utilizando o framework CCF da Microsoft como exemplo. Via desenvolvimento de experimentos e compara\c{c}\~ao de diferentes abordagens e m\'etricas de desempenho e seguran\c{c}a, buscamos avaliar a efic\'acia da computa\c{c}\~ao confidencial para melhorar a privacidade, seguran\c{c}a e desempenho das CBDCs. Resultados preliminares sugerem que a computa\c{c}\~ao confidencial pode ser uma solu\c{c}\~ao promissora para os desafios tecnol\'ogicos enfrentados pelas CBDCs. Ao implementar a computa\c{c}\~ao confidencial em DLTs, como o Hyperledger Besu, e utilizar frameworks como o CCF, aumentamos a confidencialidade e a privacidade das transa\c{c}\~oes, mantendo a escalabilidade e a interoperabilidade necess\'arias para um sistema financeiro global e digital. Em conclus\~ao, a computa\c{c}\~ao confidencial pode refor\c{c}ar significativamente o desenvolvimento do CBDC, promovendo um futuro financeiro seguro, privado e eficiente., Comment: Comments: 11 pages, 8 figures, in (Brazilian) Portuguese

Virtual Reality has become an important educational tool, due to the pandemic and increasing globalization of education. This paper presents a framework for teaching Virtual Anatomy at the university level. Virtual classes have become a staple of today's curricula because of the isolation and quarantine requirements and the increased international collaboration. Our work builds on the Visible Human Projects for Virtual Dissection material and provides a medium for groups of students to do collaborative anatomical dissections in real-time using sketching and 3D visualizations and audio coupled with interactive 2D tablets for precise drawing. We describe the system architecture, compare requirements with those of previous development, and discuss the preliminary results. Discussions with Anatomists show that this is an effective tool. We introduce avenues for further research and discuss collaboration challenges posed by this context., Comment: 4 pages

This study investigates the impact of machine learning models on the generation of counterfactual explanations by conducting a benchmark evaluation over three different types of models: a decision tree (fully transparent, interpretable, white-box model), a random forest (semi-interpretable, grey-box model), and a neural network (fully opaque, black-box model). We tested the counterfactual generation process using four algorithms (DiCE, WatcherCF, prototype, and GrowingSpheresCF) in the literature in 25 different datasets. Our findings indicate that: (1) Different machine learning models have little impact on the generation of counterfactual explanations; (2) Counterfactual algorithms based uniquely on proximity loss functions are not actionable and will not provide meaningful explanations; (3) One cannot have meaningful evaluation results without guaranteeing plausibility in the counterfactual generation. Algorithms that do not consider plausibility in their internal mechanisms will lead to biased and unreliable conclusions if evaluated with the current state-of-the-art metrics; (4) A counterfactual inspection analysis is strongly recommended to ensure a robust examination of counterfactual explanations and the potential identification of biases.

Laparoscopic surgery has the advantage of avoiding large open incisions and thereby decreasing blood loss, pain, and discomfort to patients. However, on the other side, it is hampered by restricted workspace, ambiguous communication, and surgeon fatigue caused by non-ergonomic head positioning. We aimed to identify critical problems and suggest design requirements and solutions. We used user and task analysis methods to learn about practices performed in an operating room by observing surgeons in their working environment to understand how they performed tasks and achieved their intended goals. Drawing on observations and analysis from recorded laparoscopic surgeries, we have identified several constraints and design requirements to propose potential solutions to address the issues. Surgeons operate in a dimly lit environment, surrounded by monitors, and communicate through verbal commands and pointing gestures. Therefore, performing user and task analysis allowed us to better understand the existing problems in laparoscopy while identifying several communication constraints and design requirements, which a solution has to follow to address those problems. Our contributions include identifying design requirements for laparoscopy surgery through a user and task analysis. These requirements propose design solutions towards improved surgeons' comfort and make the surgical procedure less laborious., Comment: 5 pages, 7 figures, workshop paper

There is a growing need to assist radiologists in performing X-ray readings and diagnoses fast, comfortably, and effectively. As radiologists strive to maximize productivity, it is essential to consider the impact of reading rooms in interpreting complex examinations and ensure that higher volume and reporting speeds do not compromise patient outcomes. Virtual Reality (VR) is a disruptive technology for clinical practice in assessing X-ray images. We argue that conjugating eye-tracking with VR devices and Machine Learning may overcome obstacles posed by inadequate ergonomic postures and poor room conditions that often cause erroneous diagnostics when professionals examine digital images.

Gaussian mixture models (GMMs) are fundamental tools in statistical and data sciences. We study the moments of multivariate Gaussians and GMMs. The $d$-th moment of an $n$-dimensional random variable is a symmetric $d$-way tensor of size $n^d$, so working with moments naively is assumed to be prohibitively expensive for $d>2$ and larger values of $n$. In this work, we develop theory and numerical methods for \emph{implicit computations} with moment tensors of GMMs, reducing the computational and storage costs to $\mathcal{O}(n^2)$ and $\mathcal{O}(n^3)$, respectively, for general covariance matrices, and to $\mathcal{O}(n)$ and $\mathcal{O}(n)$, respectively, for diagonal ones. We derive concise analytic expressions for the moments in terms of symmetrized tensor products, relying on the correspondence between symmetric tensors and homogeneous polynomials, and combinatorial identities involving Bell polynomials. The primary application of this theory is to estimating GMM parameters (means and covariances) from a set of observations, when formulated as a moment-matching optimization problem. If there is a known and common covariance matrix, we also show it is possible to debias the data observations, in which case the problem of estimating the unknown means reduces to symmetric CP tensor decomposition. Numerical results validate and illustrate the numerical efficiency of our approaches. This work potentially opens the door to the competitiveness of the method of moments as compared to expectation maximization methods for parameter estimation of GMMs.

Let $\overline{M}^{n+1}$ be a semi-Riemannian manifold of constant sectional curvature, and endowed with a conformal vector field . Consider a Riemannian manifold $M^n$, isometrically immersed into $\overline{M}^{n+1}$. With these hypotheses in mind, the ultimate goal of this paper is to investigate the intimate relationship between conformal vector fields on $\overline{M}^{n+1}$ and the Ricci almost soliton structure on $M^n$. Assuming that the latter manifold is connected and totally umbilic, we prove that it is naturally given a Ricci almost soliton structure by means of the tangential part of a conformal vector field on the ambient manifold. This result is rather general in nature, and few concrete examples are worked out to illustrate its true power. Furthermore, with Tashiro's theorem in mind, we reach the climax of this paper with a classification of a class of Riemannian hypersurfaces that naturally inherit their Ricci almost soliton structure by the existence of a conformal vector field on the ambient manifold., Comment: 21 pages

Model transparency is a prerequisite in many domains and an increasingly popular area in machine learning research. In the medical domain, for instance, unveiling the mechanisms behind a disease often has higher priority than the diagnostic itself since it might dictate or guide potential treatments and research directions. One of the most popular approaches to explain model global predictions is the permutation importance where the performance on permuted data is benchmarked against the baseline. However, this method and other related approaches will undervalue the importance of a feature in the presence of covariates since these cover part of its provided information. To address this issue, we propose Covered Information Disentanglement (CID), a method that considers all feature information overlap to correct the values provided by permutation importance. We further show how to compute CID efficiently when coupled with Markov random fields. We demonstrate its efficacy in adjusting permutation importance first on a controlled toy dataset and discuss its effect on real-world medical data.

One of the most common pitfalls often found in high dimensional biological data sets are correlations between the features. This may lead to statistical and machine learning methodologies overvaluing or undervaluing these correlated predictors, while the truly relevant ones are ignored. In this paper, we will define a new method called \textit{pairwise permutation algorithm} (PPA) with the aim of mitigating the correlation bias in feature importance values. Firstly, we provide a theoretical foundation, which builds upon previous work on permutation importance. PPA is then applied to a toy data set, where we demonstrate its ability to correct the correlation effect. We further test PPA on a microbiome shotgun dataset, to show that the PPA is already able to obtain biological relevant biomarkers., Comment: Submitted and accepted by AIMLAI, ECML PKDD 2021: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases

In this work, we consider the optimization formulation for symmetric tensor decomposition recently introduced in the Subspace Power Method (SPM) of Kileel and Pereira. Unlike popular alternative functionals for tensor decomposition, the SPM objective function has the desirable properties that its maximal value is known in advance, and its global optima are exactly the rank-1 components of the tensor when the input is sufficiently low-rank. We analyze the non-convex optimization landscape associated with the SPM objective. Our analysis accounts for working with noisy tensors. We derive quantitative bounds such that any second-order critical point with SPM objective value exceeding the bound must equal a tensor component in the noiseless case, and must approximate a tensor component in the noisy case. For decomposing tensors of size $D^{\times m}$, we obtain a near-global guarantee up to rank $\widetilde{o}(D^{\lfloor m/2 \rfloor})$ under a random tensor model, and a global guarantee up to rank $\mathcal{O}(D)$ assuming deterministic frame conditions. This implies that SPM with suitable initialization is a provable, efficient, robust algorithm for low-rank symmetric tensor decomposition. We conclude with numerics that show a practical preferability for using the SPM functional over a more established counterpart., Comment: 45 pages, 4 figures, Matlab code included as ancillary files, to appear in NeurIPS 2021

Go is an increasingly-popular systems programming language targeting, especially, concurrent and distributed systems. Go differentiates itself from other imperative languages by offering structural subtyping and lightweight concurrency through goroutines with message-passing communication. This combination of features poses interesting challenges for static verification, most prominently the combination of a mutable heap and advanced concurrency primitives. We present Gobra, a modular, deductive program verifier for Go that proves memory safety, crash safety, data-race freedom, and user-provided specifications. Gobra is based on separation logic and supports a large subset of Go. Its implementation translates an annotated Go program into the Viper intermediate verification language and uses an existing SMT-based verification backend to compute and discharge proof obligations.

We propose a novel neural network architecture that enables non-parametric calibration and generation of multivariate extreme value distributions (MEVs). MEVs arise from Extreme Value Theory (EVT) as the necessary class of models when extrapolating a distributional fit over large spatial and temporal scales based on data observed in intermediate scales. In turn, EVT dictates that $d$-max-decreasing, a stronger form of convexity, is an essential shape constraint in the characterization of MEVs. As far as we know, our proposed architecture provides the first class of non-parametric estimators for MEVs that preserve these essential shape constraints. We show that our architecture approximates the dependence structure encoded by MEVs at parametric rate. Moreover, we present a new method for sampling high-dimensional MEVs using a generative model. We demonstrate our methodology on a wide range of experimental settings, ranging from environmental sciences to financial mathematics and verify that the structural properties of MEVs are retained compared to existing methods.

We present a method for learning latent stochastic differential equations (SDEs) from high-dimensional time series data. Given a high-dimensional time series generated from a lower dimensional latent unknown It\^o process, the proposed method learns the mapping from ambient to latent space, and the underlying SDE coefficients, through a self-supervised learning approach. Using the framework of variational autoencoders, we consider a conditional generative model for the data based on the Euler-Maruyama approximation of SDE solutions. Furthermore, we use recent results on identifiability of latent variable models to show that the proposed model can recover not only the underlying SDE coefficients, but also the original latent variables, up to an isometry, in the limit of infinite data. We validate the method through several simulated video processing tasks, where the underlying SDE is known, and through real world datasets., Comment: 20 pages, 8 figures, to be published in IEEE Transactions of Signal Processing

Rare populations, such as endangered animals and plants, drug users and individuals with rare diseases, tend to cluster in regions. Adaptive cluster sampling is generally applied to obtain information from clustered and sparse populations since it increases survey effort in areas where the individuals of interest are observed. This work aims to propose a unit-level model which assumes that counts are related to auxiliary variables, improving the sampling process, assigning different weights to the cells, besides referring them spatially. The proposed model fits rare and grouped populations, disposed over a regular grid, in a Bayesian framework. The approach is compared to alternative methods using simulated data and a real experiment in which adaptive samples were drawn from an African Buffaloes population in a 24,108 square kilometers area of East Africa. Simulation studies show that the model is efficient under several settings, validating the methodology proposed in this paper for practical situations., Comment: 36 pages, 17 figures

Marine buoys aid in the battle against Illegal, Unreported and Unregulated (IUU) fishing by detecting fishing vessels in their vicinity. Marine buoys, however, may be disrupted by natural causes and buoy vandalism. In this paper, we formulate marine buoy placement as a clustering problem, and propose dropout k-means and dropout k-median to improve placement robustness to buoy disruption. We simulated the passage of ships in the Gabonese waters near West Africa using historical Automatic Identification System (AIS) data, then compared the ship detection probability of dropout k-means to classic k-means and dropout k-median to classic k-median. With 5 buoys, the buoy arrangement computed by classic k-means, dropout k-means, classic k-median and dropout k-median have ship detection probabilities of 38%, 45%, 48% and 52%., Comment: ICASSP 2020

Confounding variables are a well known source of nuisance in biomedical studies. They present an even greater challenge when we combine them with black-box machine learning techniques that operate on raw data. This work presents two case studies. In one, we discovered biases arising from systematic errors in the data generation process. In the other, we found a spurious source of signal unrelated to the prediction task at hand. In both cases, our prediction models performed well but under careful examination hidden confounders and biases were revealed. These are cautionary tales on the limits of using machine learning techniques on raw data from scientific experiments., Comment: Accepted at Neurips 2019 LMRL workshop -- extended abstract track

We introduce the Subspace Power Method (SPM) for calculating the CP decomposition of low-rank real symmetric tensors. This algorithm calculates one new CP component at a time, alternating between applying the shifted symmetric higher-order power method (SS-HOPM) to a certain modified tensor, constructed from a matrix flattening of the original tensor; and using appropriate deflation steps. We obtain rigorous guarantees for SPM regarding convergence and global optima for input tensors of dimension $d$ and order $m$ of rank up to $O(d^{\lfloor m/2\rfloor})$, via results in classical algebraic geometry and optimization theory. As a by-product of our analysis we prove that SS-HOPM converges unconditionally, settling a conjecture of Kolda-Mayo. Numerical experiments demonstrate that SPM is roughly one order of magnitude faster than state-of-the-art CP decomposition algorithms at moderate ranks. Furthermore, prior knowledge of the CP rank is not required by SPM., Comment: 34 pages. v4: reorganized and improved; material on GPCA is removed and will appear elsewhere; title is edited slightly to reflect new version

We develop a framework for estimating unknown partial differential equations from noisy data, using a deep learning approach. Given noisy samples of a solution to an unknown PDE, our method interpolates the samples using a neural network, and extracts the PDE by equating derivatives of the neural network approximation. Our method applies to PDEs which are linear combinations of user-defined dictionary functions, and generalizes previous methods that only consider parabolic PDEs. We introduce a regularization scheme that prevents the function approximation from overfitting the data and forces it to be a solution of the underlying PDE. We validate the model on simulated data generated by the known PDEs and added Gaussian noise, and we study our method under different levels of noise. We also compare the error of our method with a Cramer-Rao lower bound for an ordinary differential equation. Our results indicate that our method outperforms other methods in estimating PDEs, especially in the low signal-to-noise regime.

Factor analysis is a flexible technique for assessment of multivariate dependence and codependence. Besides being an exploratory tool used to reduce the dimensionality of multivariate data, it allows estimation of common factors that often have an interesting theoretical interpretation in real problems. However, standard factor analysis is only applicable when the variables are scaled, which is often inappropriate, for example, in data obtained from questionnaires in the field of psychology,where the variables are often categorical. In this framework, we propose a factor model for the analysis of multivariate ordered and non-ordered polychotomous data. The inference procedure is done under the Bayesian approach via Markov chain Monte Carlo methods. Two Monte-Carlo simulation studies are presented to investigate the performance of this approach in terms of estimation bias, precision and assessment of the number of factors. We also illustrate the proposed method to analyze participants' responses to the Motivational State Questionnaire dataset, developed to study emotions in laboratory and field settings.

We propose the Graph Space Embedding (GSE), a technique that maps the input into a space where interactions are implicitly encoded, with little computations required. We provide theoretical results on an optimal regime for the GSE, namely a feasibility region for its parameters, and demonstrate the experimental relevance of our findings. Next, we introduce a strategy to gain insight on which interactions are responsible for the certain predictions, paving the way for a far more transparent model. In an empirical evaluation on a real-world clinical cohort containing patients with suspected coronary artery disease, the GSE achieves far better performance than traditional algorithms., Comment: 7 pages

Newtonian gravity can be regarded as a hypothetic-deductive system where the inverse square law is the starting point from which gravitational phenomena are deduced. This operational form of presenting gravity endorses problem solving and seems to be predominant in the teaching practice. In contrast, regarding phenomena as a source for the development of the theory is also possible, of course, and can be advantageous to scientific education since it deals with model conception and construction. This article intends to introduce undergraduates to Newtonian gravity using its empirical basis, i.e. the free fall and the planetary motion, to deduce the universal law of gravitation. Additionally, an elemental analysis of the theory's structure is presented. It also steps into the modern interpretation of gravitational phenomena i.e. Einstein's general relativity, including a discussion on the instantaneous action at a distance in this context. This didactic presentation of the theory of gravity differs from the standard approach usually found in common textbooks. It is designed to reach a threefold equality, similar to those applied in the method of separation of variables in partial differential equations, where G is treated as a separation constant. By doing so, the universality of the gravitation constant emerges as a conclusion rather than a statement. It is also meant to create a perception on how imagination can be helpful in discovering Physical laws., Comment: 23 pages, 2 figures, 39 references

We analyze the problem of estimating a signal from multiple measurements on a $\mbox{group action channel}$ that linearly transforms a signal by a random group action followed by a fixed projection and additive Gaussian noise. This channel is motivated by applications such as multi-reference alignment and cryo-electron microscopy. We focus on the large noise regime prevalent in these applications. We give a lower bound on the mean square error (MSE) of any asymptotically unbiased estimator of the signal's orbit in terms of the signal's moment tensors, which implies that the MSE is bounded away from 0 when $N/\sigma^{2d}$ is bounded from above, where $N$ is the number of observations, $\sigma$ is the noise standard deviation, and $d$ is the so-called $\mbox{moment order cutoff}$. In contrast, the maximum likelihood estimator is shown to be consistent if $N /\sigma^{2d}$ diverges., Comment: 5 pages, conference

In the multireference alignment model, a signal is observed by the action of a random circular translation and the addition of Gaussian noise. The goal is to recover the signal's orbit by accessing multiple independent observations. Of particular interest is the sample complexity, i.e., the number of observations/samples needed in terms of the signal-to-noise ratio (the signal energy divided by the noise variance) in order to drive the mean-square error (MSE) to zero. Previous work showed that if the translations are drawn from the uniform distribution, then, in the low SNR regime, the sample complexity of the problem scales as $\omega(1/\text{SNR}^3)$. In this work, using a generalization of the Chapman--Robbins bound for orbits and expansions of the $\chi^2$ divergence at low SNR, we show that in the same regime the sample complexity for any aperiodic translation distribution scales as $\omega(1/\text{SNR}^2)$. This rate is achieved by a simple spectral algorithm. We propose two additional algorithms based on non-convex optimization and expectation-maximization. We also draw a connection between the multireference alignment problem and the spiked covariance model.