Integral Human Pose Regression

State-of-the-art human pose estimation methods are based on heat map representation. In spite of the good performance, the representation has a few issues in nature, such as not differentiable and quantization error. This work shows that a simple integral operation relates and unifies the heat map representation and joint regression, thus avoiding the above issues. It is differentiable, efficient, and compatible with any heat map based methods. Its effectiveness is convincingly validated via comprehensive ablation experiments under various settings, specifically on 3D pose estimation, for the first time

Multi-Objective Counterfactual Explanations

Counterfactual explanations are one of the most popular methods to make predictions of black box machine learning models interpretable by providing explanations in the form of `what-if scenarios'. Most current approaches optimize a collapsed, weighted sum of multiple objectives, which are naturally difficult to balance a-priori. We propose the Multi-Objective Counterfactuals (MOC) method, which translates the counterfactual search into a multi-objective optimization problem. Our approach not only returns a diverse set of counterfactuals with different trade-offs between the proposed objectives, but also maintains diversity in feature space. This enables a more detailed post-hoc analysis to facilitate better understanding and also more options for actionable user responses to change the predicted outcome. Our approach is also model-agnostic and works for numerical and categorical input features. We show the usefulness of MOC in concrete cases and compare our approach with state-of-the-art methods for counterfactual explanations

On the Schoenberg Transformations in Data Analysis: Theory and Illustrations

The class of Schoenberg transformations, embedding Euclidean distances into higher dimensional Euclidean spaces, is presented, and derived from theorems on positive definite and conditionally negative definite matrices. Original results on the arc lengths, angles and curvature of the transformations are proposed, and visualized on artificial data sets by classical multidimensional scaling. A simple distance-based discriminant algorithm illustrates the theory, intimately connected to the Gaussian kernels of Machine Learning

A Categorical Clustering of Publishers for Mobile Performance Marketing

Mobile marketing is an expanding industry due to the growth of mobile devices (e.g., tablets, smartphones). In this paper, we explore a categorical approach to cluster publishers of a mobile performance market, in which payouts are only issued when there is a conversion (e.g., a sale). As a case study, we analyze recent and real-world data from a global mobile marketing company. Several experiments were held, considering a first internal evaluation stage, using training data, clustering quality metrics and computational effort. In the second stage, the best method, COBWEB algorithm, was analyzed using an external evaluation based on business metrics, computed over test data, and that allowed an identification of interesting clusters.This article is a result of the project NORTE-01-0247-FEDER- 017497, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work was also supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT Funda ̧ca ̃o para a Ciˆencia e Tecnologia within the Project Scope: UID/CEC/00319/2013

Ward's Hierarchical Clustering Method: Clustering Criterion and Agglomerative Algorithm

The Ward error sum of squares hierarchical clustering method has been very widely used since its first description by Ward in a 1963 publication. It has also been generalized in various ways. However there are different interpretations in the literature and there are different implementations of the Ward agglomerative algorithm in commonly used software systems, including differing expressions of the agglomerative criterion. Our survey work and case studies will be useful for all those involved in developing software for data analysis using Ward's hierarchical clustering method.Comment: 20 pages, 21 citations, 4 figure

Characterizing the universal rigidity of generic frameworks

A framework is a graph and a map from its vertices to E^d (for some d). A framework is universally rigid if any framework in any dimension with the same graph and edge lengths is a Euclidean image of it. We show that a generic universally rigid framework has a positive semi-definite stress matrix of maximal rank. Connelly showed that the existence of such a positive semi-definite stress matrix is sufficient for universal rigidity, so this provides a characterization of universal rigidity for generic frameworks. We also extend our argument to give a new result on the genericity of strict complementarity in semidefinite programming.Comment: 18 pages, v2: updates throughout; v3: published versio

Predicting Head Pose in Dyadic Conversation

Natural movement plays a significant role in realistic speech animation. Numerous studies have demonstrated the contribution visual cues make to the degree we, as human observers, find an animation acceptable. Rigid head motion is one visual mode that universally co-occurs with speech, and so it is a reasonable strategy to seek features from the speech mode to predict the head pose. Several previous authors have shown that prediction is possible, but experiments are typically confined to rigidly produced dialogue. Expressive, emotive and prosodic speech exhibit motion patterns that are far more difficult to predict with considerable variation in expected head pose. People involved in dyadic conversation adapt speech and head motion in response to the others’ speech and head motion. Using Deep Bi-Directional Long Short Term Memory (BLSTM) neural networks, we demonstrate that it is possible to predict not just the head motion of the speaker, but also the head motion of the listener from the speech signal

Microsatellite discovery in an insular amphibian (Grandisonia alternans) with comments on cross-species utility and the accuracy of locus identification from unassembled Illumina data

The Seychelles archipelago is unique among isolated oceanic islands because it features an endemic radiation of caecilian amphibians (Gymnophiona). In order to develop population genetics resources for this system, we identified microsatellite loci using unassembled Illumina MiSeq data generated from a genomic library of Grandisonia alternans, a species that occurs on multiple islands in the archipelago. Applying a recently described method (PALFINDER) we identified 8001 microsatellite loci that were potentially informative for population genetics analyses. Of these markers, we screened 60 loci using five individuals, directly sequenced several amplicons to confirm their identity, and then used eight loci to score allele sizes in 64 G. alternans individuals originating from five islands. A number of these individuals were sampled using non-lethal methods, demonstrating the efficacy of non-destructive molecular sampling in amphibian research. Although two loci satisfied our criteria as diploid, neutrally evolving loci with the statistical power to detect population structure, our success in identifying reliable loci was very low. Additionally, we discovered some issues with primer redundancy and differences between Illumina and Sanger sequences that suggest some Illumina-inferred loci are invalid. We investigated cross-species utility for eight loci and found most could be successfully amplified, sequenced and aligned across other species and genera of caecilians from the Seychelles. Thus, our study in part supported the validity of using PALFINDER with unassembled reads for microsatellite discovery within and across species, but importantly identified major limitations to applying this approach to small datasets (ca. 1 million reads) and loci with small tandem repeat sizes