Miracle-dz/UniFAR-FTUs huggingface dataset:
GSVMA: A Genetic Support Vector Machine ANOVA Method for CAD Diagnosis

Background Coronary artery disease (CAD) is one of the crucial reasons for cardiovascular mortality in middle-aged people worldwide. The most typical tool is angiography for diagnosing CAD. The challenges of CAD diagnosis using angiography are costly and have side effects. One of the alternative solutions is the use of machine learning-based patterns for CAD diagnosis. Methods Hence, this paper provides a new hybrid machine learning model called genetic support vector machine and analysis of variance (GSVMA). The analysis of variance (ANOVA) is known as the kernel function for the SVM algorithm. The proposed model is performed based on the Z-Alizadeh Sani dataset so that a genetic optimization algorithm is used to select crucial features. In addition, SVM with ANOVA, linear SVM (LSVM), and library for support vector machine (LIBSVM) with radial basis function (RBF) methods were applied to classify the dataset. Results As a result, the GSVMA hybrid method performs better than other methods. This proposed method has the highest accuracy of 89.45% through a 10-fold crossvalidation technique with 31 selected features on the Z-Alizadeh Sani dataset. Conclusion We demonstrated that SVM combined with genetic optimization algorithm could be lead to more accuracy. Therefore, our study confirms that the GSVMA method outperforms other methods so that it can facilitate CAD diagnosis.
Averaged mixed Julia-Fatou type theory with applications to spectral foliation

Classically, theorems of Fatou and Julia describe the boundary regularity of functions in one complex variable. The former says that a complex analytic function on the disk has non-tangential boundary values almost everywhere, and the latter describes when a function takes an extreme value at a boundary point and is differentiable there non-tangentially. We describe a class of intermediate theorems in terms of averaged Julia-Fatou quotients. Boundary regularity is related to integrability of certain quantities against a special measure, the so-called Nevanlinna measure. Applications are given to spectral theory.
Efficient Contextualization using Top-k Operators for Question Answering over Knowledge Graphs

Answering complex questions over knowledge bases (KB-QA) faces huge input data with billions of facts, involving millions of entities and thousands of predicates. For efficiency, QA systems first reduce the answer search space by identifying a set of facts that is likely to contain all answers and relevant cues. The most common technique is to apply named entity disambiguation (NED) systems to the question, and retrieve KB facts for the disambiguated entities. This work presents Ecqa, an efficient method that prunes irrelevant parts of the search space using KB-aware signals. Ecqa is based on top-𝑘 query processing over score-ordered lists of KB items that combine signals about lexical matching, relevance to the question, coherence among candidate items, and connectivity in the KB graph. Experiments with two recent QA benchmarks demonstrate the superiority of Ecqa over state-of-the-art baselines with respect to answer presence, size of the search space, and runtimes.
Temporal Graph Network Embedding with Causal Anonymous Walks Representations

Many tasks in graph machine learning, such as link prediction and node classification, are typically solved by using representation learning, in which each node or edge in the network is encoded via an embedding. Though there exists a lot of network embeddings for static graphs, the task becomes much more complicated when the dynamic (i.e. temporal) network is analyzed. In this paper, we propose a novel approach for dynamic network representation learning based on Temporal Graph Network by using a highly custom message generating function by extracting Causal Anonymous Walks. For evaluation, we provide a benchmark pipeline for the evaluation of temporal network embeddings. This work provides the first comprehensive comparison framework for temporal network representation learning in every available setting for graph machine learning problems involving node classification and link prediction. The proposed model outperforms state-of-the-art baseline models. The work also justifies the difference between them based on evaluation in various transductive/inductive edge/node classification tasks. In addition, we show the applicability and superior performance of our model in the real-world downstream graph machine learning task provided by one of the top European banks, involving credit scoring based on transaction data.
Sparse Packetized Predictive Control of Disturbed Plants Over Channels with Data Loss

This paper investigates closed-loop stability of a linear discrete-time plant subject to bounded disturbances when controlled according to packetized predictive control (PPC) policies. In the considered feedback loop, the controller is connected to the actuator via a digital communication channel imposing bounded dropouts. Two PPC strategies are taken into account. In both cases, the control packets are generated by solving sparsity-promoting optimization problems. One is based upon an l2-constrained l0 optimization problem. Such problem is relaxed by an l1-l2 optimization problem in the other sparse PPC setting. We utilize effective solving methods for the latter optimization problems. Moreover, we show that in the presence of plant disturbances, the l2-constrained l0 sparse PPC and unconstrained l1-l2 sparse PPC guarantee practical stability for the system if certain conditions are met. More precisely, in each case, we can derive an upper bound on system state if the design parameters satisfy certain conditions. The bounds we derive are increasing with respect to the disturbance magnitude. We show via simulation that in both cases of proposed sparse PPC strategies, larger disturbances bring about performance degradation with no effect on system practical stability.
Revisiting Binary Local Image Description for Resource Limited Devices

The advent of a panoply of resource limited devices opens up new challenges in the design of computer vision algorithms with a clear compromise between accuracy and computational requirements. In this letter we present new binary image descriptors that emerge from the application of triplet ranking loss, hard negative mining and anchor swapping to traditional features based on pixel differences and image gradients. These descriptors, BAD (Box Average Difference) and HashSIFT, establish new operating points in the state-of-the-art’s accuracy vs. resources trade-off curve. In our experiments we evaluate the accuracy, execution time and energy consumption of the proposed descriptors. We show that BAD bears the fastest descriptor implementation in the literature while HashSIFT approaches in accuracy that of the top deep learning-based descriptors, being computationally more efficient. We have made the source code public1.
Convergence rate for the homogenization of stationary diffusions in dilutely perforated domains with reflecting boundaries

We revisit the homogenization problem for the Poisson equation in periodically perforated domains with zero Neumann data at the boundary of the holes and prescribed Dirichlet data at the outer boundary. It is known that, if the periodicity of the holes goes to zero but their volume fraction remains fixed and positive, the limit problem is a Dirichlet boundary value problem posed in the domain without the holes, and the effective diffusion coefficients are non-trivially modified; if that volume fraction goes to zero instead, i.e. the holes are dilute, the effective operator remains the Laplacian (that is, unmodified). Our main results contain the study of a “continuity” in those effective models with respect to the volume fraction of the holes and some new convergence rates for homogenization in the dilute setting. Our method explores the classical two-scale expansion ansatz and relies on asymptotic analysis of the rescaled cell problems using layer potential theory.
Towards Controllable and Photorealistic Region-wise Image Manipulation

Adaptive and flexible image editing is a desirable function of modern generative models. In this work, we present a generative model with auto-encoder architecture for per-region style manipulation. We apply a code consistency loss to enforce an explicit disentanglement between content and style latent representations, making the content and style of generated samples consistent with their corresponding content and style references. The model is also constrained by a content alignment loss to ensure the foreground editing will not interfere background contents. As a result, given interested region masks provided by users, our model supports foreground region-wise style transfer. Specially, our model receives no extra annotations such as semantic labels except for self-supervision. Extensive experiments show the effectiveness of the proposed method and exhibit the flexibility of the proposed model for various applications, including region-wise style editing, latent space interpolation, cross-domain style transfer.
DECA: Deep viewpoint-Equivariant human pose estimation using Capsule Autoencoders

Human Pose Estimation (HPE) aims at retrieving the 3D position of human joints from images or videos. We show that current 3D HPE methods suffer a lack of viewpoint equivariance, namely they tend to fail or perform poorly when dealing with viewpoints unseen at training time. Deep learning methods often rely on either scale-invariant, translation-invariant, or rotation-invariant operations, such as max-pooling. However, the adoption of such procedures does not necessarily improve viewpoint generalization, rather leading to more data-dependent methods. To tackle this issue, we propose a novel capsule autoencoder network with fast Variational Bayes capsule routing, named DECA. By modeling each joint as a capsule entity, combined with the routing algorithm, our approach can preserve the joints’ hierarchical and geometrical structure in the feature space, independently from the viewpoint. By achieving viewpoint equivariance, we drastically reduce the network data dependency at training time, resulting in an improved ability to generalize for unseen viewpoints. In the experimental validation, we outperform other methods on depth images from both seen and unseen viewpoints, both top-view, and front-view. In the RGB domain, the same network gives state-of-the-art results on the challenging viewpoint transfer task, also establishing a new framework for top-view HPE. The code can be found at https://github.com/mmlab-cv/DECA.
UNIQORN: Unified Question Answering over RDF Knowledge Graphs and Natural Language Text

Question answering over RDF data like knowledge graphs has been greatly advanced, with a number of good systems providing crisp answers for natural language questions or telegraphic queries. Some of these systems incorporate textual sources as additional evidence for the answering process, but cannot compute answers that are present in text alone. Conversely, the IR and NLP communities have addressed QA over text, but such systems barely utilize semantic data and knowledge. This paper presents a method for complex questions that can seamlessly operate over a mixture of RDF datasets and text corpora, or individual sources, in a unified framework. Our method, called UNIQORN, builds a context graph on-the-fly, by retrieving question-relevant evidences from the RDF data and/or a text corpus, using fine-tuned BERT models. The resulting graph typically contains all question-relevant evidences but also a lot of noise. UNIQORN copes with this input by a graph algorithm for Group Steiner Trees, that identifies the best answer candidates in the context graph. Experimental results on several benchmarks of complex questions with multiple entities and relations, show that UNIQORN significantly outperforms state-of-the-art methods for heterogeneous QA -- in a full training mode, as well as in zero-shot settings. The graph-based methodology provides user-interpretable evidence for the complete answering process.
Coronal Microjets in Quiet-Sun Regions Observed with the Extreme Ultraviolet Imager on Board the Solar Orbiter

We report the smallest coronal jets ever observed in the quiet Sun with recent high-resolution observations from the High Resolution Telescopes (HRIEUV and HRILyα ) of the Extreme Ultraviolet Imager on board the Solar Orbiter (SO). In the HRIEUV (174 Å) images, these microjets usually appear as nearly collimated structures with brightenings at their footpoints. Their average lifetime, projected speed, width, and maximum length are 4.6 minutes, 62 km s−1, 1.0 Mm, and 7.7 Mm, respectively. Inverted-Y shaped structures and moving blobs can be identified in some events. A subset of these events also reveal signatures in the HRILyα (H i Lyα at 1216 Å) images and the extreme ultraviolet images taken by the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO). Our differential emission-measure (EM) analysis suggests a multithermal nature and an average density of ∼1.4 × 109 cm−3 for these microjets. Their thermal and kinetic energies were estimated to be ∼3.9 × 1024 erg and ∼2.9 × 1023 erg, respectively, which are of the same order of the released energy predicted by the nanoflare theory. Most events appear to be located at the edges of network lanes and magnetic flux concentrations, suggesting that these coronal microjets are likely generated by magnetic reconnection between small-scale magnetic loops and the adjacent network field.
Forgetting Formulas and Signature Elements in Epistemic States

Delgrande's knowledge level account of forgetting provides a general approach to forgetting syntax elements from sets of formulas with links to many other forgetting operations, in particular, to Boole's variable elimination. On the other hand, marginalisation of epistemic states is a specific approach to actively reduce signatures in more complex semantic frameworks, also aiming at forgetting atoms that is very well known from probability theory. In this paper, we bring these two perspectives of forgetting together by showing that marginalisation can be considered as an extension of Delgrande's approach to the level of epistemic states. More precisely, we generalize Delgrande's axioms of forgetting to forgetting in epistemic states, and show that marginalisation is the most specific and informative forgetting operator that satisfies these axioms. Moreover, we elaborate suitable phrasings of Delgrande's concept of forgetting for formulas by transferring the basic ideas of the axioms to forgetting formulas from epistemic states. However, here we show that this results in trivial approaches to forgetting formulas. This finding supports the claim that forgetting syntax elements is essentially different from belief contraction, as e.g. axiomatized in the AGM belief change framework.
Maxwell–Lorentz without self-interactions: conservation of energy and momentum

Since a classical charged point particle radiates energy and momentum it is argued that there must be a radiation reaction force. Here we present an action for the Maxwell–Lorentz without self-interactions model, where each particle only responds to the fields of the other charged particles. The corresponding stress–energy tensor automatically conserves energy and momentum in Minkowski and other appropriate spacetimes. Hence there is no need for any radiation reaction.
Polyak's convexity theorem, Yuan's lemma and S-lemma: extensions and applications

We extend Polyak’s theorem on the convexity of joint numerical range from three to any number of quadratic forms on condition that they can be generated by three quadratic forms with a positive definite linear combination. Our new result covers the fundamental Dines’s theorem. As applications, we further extend Yuan’s lemma and S-lemma, respectively. Our extended Yuan’s lemma is used to build a more generalized assumption than that of Haeser (J. Optim. Theory Appl. 174(3): 641-649, 2017), under which the standard second-order necessary optimality condition holds at local minimizer. The extended S-lemma reveals strong duality of homogeneous quadratic optimization problem with two bilateral quadratic constraints.
Bayesian Semiparametric Hidden Markov Tensor Partition Models for Longitudinal Data with Local Variable Selection

We present a flexible Bayesian semiparametric mixed model for longitudinal data analysis in the presence of potentially high-dimensional categorical covariates. Building on a novel hidden Markov tensor decomposition technique, our proposed method allows the fixed effects components to vary between dependent random partitions of the covariate space at different time points. The mechanism not only allows different sets of covariates to be included in the model at different time points but also allows the selected predictors’ influences to vary flexibly over time. Smooth time-varying additive random effects are used to capture subject specific heterogeneity. We establish posterior convergence guarantees for both function estimation and variable selection. We design a Markov chain Monte Carlo algorithm for posterior computation. We evaluate the method’s empirical performances through synthetic experiments and demonstrate its practical utility through real world applications.
Accurate 3D frequency-domain seismic wave modeling with the wavelength-adaptive 27-point finite-difference stencil: a tool for full waveform inversion

Efficient frequency-domain Full Waveform Inversion (FWI) of long-offset node data can be performed with a few frequencies. The seismic response of these frequencies can be computed with compact finite-difference stencils on regular Cartesian grid with direct or hybrid direct/iterative methods. Compactness, which is necessary to mitigate the fill-in induced by the Lower-Upper (LU) factorization, is implemented with second-order stencils while accuracy is achieved by building a consistent mass matrix and a compound stiffness matrix with optimal weights so that the stencil covers the eight cells surrounding the central point, leading to a 27-point stencil. Classical approaches estimate constant weights by jointly minimizing numerical dispersion in homogeneous media for several numbers of grid points per wavelength ( G). Then, the impedance matrix is built by using these constant weights at each central point covering the heterogeneous subsurface model, leading to non-uniform wavefield accuracy. Instead, we estimate G-dependent weights once and for all by minimizing dispersion separately for each value of 1/ G in a range found in FWI applications. Then, we build the impedance matrix without computational overhead by selecting at each central point the weights corresponding to the local 1/ G, hence leading to a wavelength-adaptive stencil. This separate approach with adaptive weights makes wavefield accuracy uniform.We benchmark wavefields, which are computed in several 3D large-scale subsurface models with a sparse multifrontal direct solver and the non-adaptive/adaptive stencils, against analytical solutions when available and the highly accurate discretization-free convergent Born series (CBS) method. Each benchmark reveals the higher accuracy of the adaptive stencil relative to the non-adaptive one. In the presence of sharp contrasts, the adaptive stencil is also more accurate than a classical O(Δ t2,Δ h8) finite-different time-domain staggered-grid stencil. The relevance of the adaptive stencil is finally illustrated with a 3D FWI case study in the 3.5–13 Hz frequency ba
An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example

Decision models can combine information from different sources to simulate the long-term consequences of alternative strategies in the presence of uncertainty. A cohort state-transition model (cSTM) is a decision model commonly used in medical decision making to simulate the transitions of a hypothetical cohort among various health states over time. This tutorial focuses on time-independent cSTM, in which transition probabilities among health states remain constant over time. We implement time-independent cSTM in R, an open-source mathematical and statistical programming language. We illustrate time-independent cSTMs using a previously published decision model, calculate costs and effectiveness outcomes, and conduct a cost-effectiveness analysis of multiple strategies, including a probabilistic sensitivity analysis. We provide open-source code in R to facilitate wider adoption. In a second, more advanced tutorial, we illustrate time-dependent cSTMs.
Bayesian Uncertainty and Expected Gradient Length - Regression: Two Sides Of The Same Coin?

Active learning algorithms select a subset of data for annotation to maximize the model performance on a budget. One such algorithm is Expected Gradient Length, which as the name suggests uses the approximate gradient induced per example in the sampling process. While Expected Gradient Length has been successfully used for classification and regression, the formulation for regression remains intuitively driven. Hence, our theoretical contribution involves deriving this formulation, thereby supporting experimental evidence [4], [5]. Subsequently, we show that expected gradient length in regression is equivalent to Bayesian uncertainty [22]. If certain assumptions are infeasible, our algorithmic contribution (EGL++) approximates the effect of ensembles with a single deterministic network. Instead of computing multiple possible inferences per input, we leverage previously annotated samples to quantify the probability of previous labels being the true label. Such an approach allows us to extend expected gradient length to a new task: human pose estimation. We perform experimental validation on two human pose datasets (MPII and LSP/LSPET), highlighting the interpretability and competitiveness of EGL++ with different active learning algorithms for human pose estimation.
Zero-Shot Model-Based Learning and Control

—This paper presents a novel application of Wasser-stein ambiguity sets to robustify online zero-shot learning and control. We identify and focus on scenarios of learning and controlling a system from scratch, starting with a randomly initialized model based on the strongest possible limitations on our prior knowledge of the dynamics. This paper labels this scenario as a “zero-shot” control problem, based on popular zero-shot transfer problems in machine learning. In this case, we adopt a loosely similar nomenclature to refer to a controller that must safely control a system it has truly never experienced or interacted with. Popular and current state-of-the-art methods in learning and control typically place more emphasis on model adaptation, and frequently require signiﬁcant a-priori assumptions on knowledge of system dynamics and safe reference trajectories. Episodic designs are also commonplace in such applications, where constraint violation frequently occurs with gradually reduced frequency over the course of many sequential episodes of learning. We address the identiﬁed problem of single-episode zero-shot control by presenting a Wasserstein distributionally robust approach which, coupled with a receding horizon control scheme, can safely learn and control a dynamical system in a single episode.