Miracle-dz/UniFAR-FTUs huggingface dataset:
Bilateral k-Means Algorithm for Fast Co-Clustering

With the development of the information technology, the amount of data, e.g. text, image and video, has been increased rapidly. Efficiently clustering those large scale data sets is a challenge. To address this problem, this paper proposes a novel co-clustering method named bilateral k-means algorithm (BKM) for fast co-clustering. Different from traditional k-means algorithms, the proposed method has two indicator matrices P and Q and a diagonal matrix S to be solved, which represent the cluster memberships of samples and features, and the co-cluster centres, respectively. Therefore, it could implement different clustering tasks on the samples and features simultaneously. We also introduce an effective approach to solve the proposed method, which involves less multiplication. The computational complexity is analyzed. Extensive experiments on various types of data sets are conducted. Compared with the state-of-the-art clustering methods, the proposed BKM not only has faster computational speed, but also achieves promising clustering results.
Learning Permutations with Sinkhorn Policy Gradient

Many problems at the intersection of combinatorics and computer science require solving for a permutation that optimally matches, ranks, or sorts some data. These problems usually have a task-specific, often non-differentiable objective function that data-driven algorithms can use as a learning signal. In this paper, we propose the Sinkhorn Policy Gradient (SPG) algorithm for learning policies on permutation matrices. The actor-critic neural network architecture we introduce for SPG uniquely decouples representation learning of the state space from the highly-structured action space of permutations with a temperature-controlled Sinkhorn layer. The Sinkhorn layer produces continuous relaxations of permutation matrices so that the actor-critic architecture can be trained end-to-end. Our empirical results show that agents trained with SPG can perform competitively on sorting, the Euclidean TSP, and matching tasks. We also observe that SPG is significantly more data efficient at the matching task than the baseline methods, which indicates that SPG is conducive to learning representations that are useful for reasoning about permutations.
A Gentle Introduction to Supervised Machine Learning

This tutorial is based on the lecture notes for the courses "Machine Learning: Basic Principles" and "Artificial Intelligence", which I have taught during fall 2017 and spring 2018 at Aalto university. The aim is to provide an accessible introduction to some of the main concepts and methods within supervised machine learning. Most of the current systems which are con- sidered as (artificially) intelligent are based on some form of supervised machine learning. After discussing the main building blocks of a formal machine learning problem, some of the most popular algorithmic design patterns for machine learning methods are presented.
Testing the role of climate in speciation: New methods and applications to squamate reptiles (lizards and snakes)

Climate may play important roles in speciation, such as causing the range fragmentation that underlies allopatric speciation (through niche conservatism) or driving divergence of parapatric populations along climatic gradients (through niche divergence). Here, we developed new methods to test the frequency of climate niche conservatism and divergence in speciation, and applied it to species pairs of squamate reptiles (lizards and snakes). We used a large‐scale phylogeny to identify 242 sister species pairs for analysis. From these, we selected all terrestrial allopatric pairs with sufficient occurrence records (n = 49 pairs) and inferred whether each originated via climatic niche conservatism or climatic niche divergence. Among the 242 pairs, allopatric pairs were most common (41.3%), rather than parapatric (19.4%), partially sympatric (17.7%), or fully sympatric species pairs (21.5%). Among the 49 selected allopatric pairs, most appeared to have originated via climatic niche divergence (61–76%, depending on the details of the methods). Surprisingly, we found greater climatic niche divergence between allopatric sister species than between parapatric pairs, even after correcting for geographic distance. We also found that niche divergence did not increase with time, further implicating niche divergence in driving lineage splitting. Overall, our results suggest that climatic niche divergence may often play an important role in allopatric speciation, and the methodology developed here can be used to address the generality of these findings in other organisms.
Informative Subspace Learning for Counterfactual Inference

Inferring causal relations from observational data is widely used for knowledge discovery in healthcare and economics. To investigate whether a treatment can affect an outcome of interest, we focus on answering counterfactual questions of this type: what would a patient’s blood pressure be had he/she received a different treatment? Nearest neighbor matching (NNM) sets the counterfactual outcome of any treatment (control) sample to be equal to the factual outcome of its nearest neighbor in the control (treatment) group. Although being simple, flexible and interpretable, most NNM approaches could be easily misled by variables that do not affect the outcome. In this paper, we address this challenge by learning subspaces that are predictive of the outcome variable for both the treatment group and control group. Applying NNM in the learned subspaces leads to more accurate estimation of the counterfactual outcomes and therefore treatment effects. We introduce an informative subspace learning algorithm by maximizing the nonlinear dependence between the candidate subspace and the outcome variable measured by the Hilbert-Schmidt Independence Criterion (HSIC). We propose a scalable estimator of HSIC, called HSIC-RFF that reduces the quadratic computational and storage complexities (with respect to the sample size) of the naive HSIC implementation to linear through constructing random Fourier features. We also prove an upper bound on the approximation error of the HSIC-RFF estimator. Experimental results on simulated datasets and real-world datasets demonstrate our proposed approach outperforms existing NNM approaches and other commonly used regression-based methods for counterfactual inference.
The Short-Term Effects of Lying, Sitting and Standing on Energy Expenditure in Women

The deleterious health effects of too much sitting have been associated with an increased risk for overweight and obesity. Replacing sitting with standing is the proposed intervention to increase daily energy expenditure (EE). The purpose of this study was to determine the short-term effects of lying, sitting, and standing postures on EE, and determine the magnitude of the effect each posture has on EE using indirect calorimetry (IC). Twenty-eight healthy females performed three separate positions (lying, sitting, standing) in random order. Inspired and expired gases were collected for 45-minutes (15 minutes for each position) using breath-by-breath indirect calorimetry. Oxygen consumption (VO2) and carbon dioxide production (VCO2) were measured to estimate EE. Statistical analyses used repeat measures ANOVA to analyze all variables and post hoc t-tests. Based on the ANOVA the individual, time period and order term did not result in a statistically significant difference. Lying EE and sitting EE were not different from each other (P = 0.56). However, standing EE (kcal/min) was 9.0 % greater than lying EE (kcal/min) (P = 0.003), and 7.1% greater than sitting EE (kcal/min) (P = 0.02). The energetic cost of standing was higher compared to lying and sitting. While this is statistically significant, the magnitude of the effect of standing when compared to sitting was small (Cohen’s d = 0.31). Short-term standing does not offer an energetic advantage when compared to sitting.
Understanding Self-Paced Learning under Concave Conjugacy Theory

By simulating the easy-to-hard learning manners of humans/animals, the learning regimes called curriculum learning~(CL) and self-paced learning~(SPL) have been recently investigated and invoked broad interests. However, the intrinsic mechanism for analyzing why such learning regimes can work has not been comprehensively investigated. To this issue, this paper proposes a concave conjugacy theory for looking into the insight of CL/SPL. Specifically, by using this theory, we prove the equivalence of the SPL regime and a latent concave objective, which is closely related to the known non-convex regularized penalty widely used in statistics and machine learning. Beyond the previous theory for explaining CL/SPL insights, this new theoretical framework on one hand facilitates two direct approaches for designing new SPL models for certain tasks, and on the other hand can help conduct the latent objective of self-paced curriculum learning, which is the advanced version of both CL/SPL and possess advantages of both learning regimes to a certain extent. This further facilitates a theoretical understanding for SPCL, instead of only CL/SPL as conventional. Under this theory, we attempt to attain intrinsic latent objectives of two curriculum forms, the partial order and group curriculums, which easily follow the theoretical understanding of the corresponding SPCL regimes.
My camera can see through fences: A deep learning approach for image de-fencing

In recent times, the availability of inexpensive image capturing devices such as smartphones/tablets has led to an exponential increase in the number of images/videos captured. However, sometimes the amateur photographer is hindered by fences in the scene which have to be removed after the image has been captured. Conventional approaches to image de-fencing suffer from inaccurate and non-robust fence detection apart from being limited to processing images of only static occluded scenes. In this paper, we propose a semi-automated de-fencing algorithm using a video of the dynamic scene. We use convolutional neural networks for detecting fence pixels. We provide qualitative as well as quantitative comparison results with existing lattice detection algorithms on the existing PSU NRT data set [1] and a proposed challenging fenced image dataset. The inverse problem offence removal is solved using split Bregman technique assuming total variation of the de-fenced image as the regularization constraint.
Predictors of Housing Instability in Women Who Have Experienced Intimate Partner Violence

Although one in four women in the United States experience severe intimate partner violence (IPV) in their lifetimes and are at greater risk for homelessness and housing insecurity than women who have not experienced IPV, the factors increasing the vulnerability of women with an IPV history to housing instability have not been examined. This cross-sectional study utilized baseline data from interviews with 218 IPV-exposed women from diverse ethnoracial backgrounds participating with their children in the Kids’ Club and the Mom’s Empowerment Program, joint interventions designed to improve outcomes in families who have experienced IPV. Multiple regression was used to examine the predictors associated with housing instability, quantified as the number of times a participant had moved in the past 4 years. Demographic information was collected and exposure to violence was measured with the Conflict Tactics Scale (CTS); severity and prevalence of PTSD and depression symptoms were measured with the Posttraumatic Diagnostic Scale (PDS) and the Center for Epidemiologic Studies Depression Scale (CES-D), respectively. Results showed that level of education was positively associated with housing instability, while age of the participant, average monthly income, and increased severity of PTSD symptoms were negatively associated. Neither the participant’s race nor the extent of reported violence significantly predicted participants’ housing instability. The findings of this study may serve to better identify those who have experienced IPV that are most at risk of experiencing housing instability, and to inform future interventions and policy aiming to support their needs. In addition, these results further emphasize the importance of providing housing resources in addition to interventions designed to improve physical and psychosocial health in enabling women that have experienced IPV to gain independence from an abuser.
Weakly-Supervised Deep Nonnegative Low-Rank Model for Social Image Tag Refinement and Assignment

It has been well known that the user-provided tags of social images are imperfect, i.e., there exist noisy, irrelevant or incomplete tags. It heavily degrades the performance of many multimedia tasks. To alleviate this problem, we propose a Weakly-supervised Deep Nonnegative Low-rank model (WDNL) to improve the quality of tags by integrating the low-rank model with deep feature learning. A nonnegative low-rank model is introduced to uncover the intrinsic relationships between images and tags by simultaneously removing noisy or irrelevant tags and complementing missing tags. The deep architecture is leveraged to seamlessly connect the visual content and the semantic tag. That is, the proposed model can well handle the scalability by assigning tags to new images. Extensive experiments conducted on two real-world datasets demonstrate the effectiveness of the proposed method compared with some state-of-the-art methods.
Comparative Analysis of the Effects of Stem Height and Artificial Pruning on Seasonal Radial Growth Dynamics of Wild Cherry (Prunus avium L.) and Sycamore (Acer pseudoplatanus L.) in a Widely Spaced System

Intensive artificial pruning of trees increases the proportion of high-value timber but simultaneously reduces the crown and consequently the capacity to assimilate photosynthates. Here, midterm effects of two intensive pruning treatments, a classical and a selective approach, on radial growth dynamics at different stem heights is comparatively analyzed for the broadleaved tree species sycamore (Acer pseudoplatanus L.) and wild cherry (Prunus avium L.) at a site in the upper Rhine valley in southwest Germany using high-resolution point-dendrometers starting three years after pruning. We fitted shape-constrained additive models to the dendrometer data and derived estimates of growth phenology and performance of individual trees over the 2010–2016 observation period. The statistical analysis based on linear mixed-effects models showed significantly later growth cessation and in individual years also higher rates of radial growth in sycamore compared to wild cherry. Growth onset for both tree species initiated systematically earlier at the higher dendrometer location compared to breast height. While no main effects of treatment or interactions of treatment with year were detected, we found significantly higher average daily growth rates and annual radial growth of wild cherry at the top dendrometer location compared to breast height in the selective pruning treatment. Our results provide new insights into seasonal growth patterns of sycamore and wild cherry in a widely spaced system and contribute to a better understanding of the effects of stem height, pruning treatment and climate on radial growth dynamics of valuable broadleaved tree species.
Convex Co-Embedding for Matrix Completion with Predictive Side Information

Matrix completion as a common problem in many application domains has received increasing attention in the machine learning community. Previous matrix completion methods have mostly focused on exploiting the matrix low-rank property to recover missing entries. Recently, it has been noticed that side information that describes the matrix items can help to improve the matrix completion performance. In this paper, we propose a novel matrix completion approach that exploits side information within a principled co-embedding framework. This framework integrates a low-rank matrix factorization model and a label embedding based prediction model together to derive a convex co-embedding formulation with nuclear norm regularization. We develop a fast proximal gradient descent algorithm to solve this co-embedding problem. The effectiveness of the proposed approach is demonstrated on two types of real world application problems.
Learning Invariant Deep Representation for NIR-VIS Face Recognition

Visual versus near infrared (VIS-NIR) face recognition is still a challenging heterogeneous task due to large appearance difference between VIS and NIR modalities. This paper presents a deep convolutional network approach that uses only one network to map both NIR and VIS images to a compact Euclidean space. The low-level layers of this network are trained only on large-scale VIS data. Each convolutional layer is implemented by the simplest case of maxout operator. The high-level layer is divided into two orthogonal subspaces that contain modality-invariant identity information and modality-variant spectrum information respectively. Our joint formulation leads to an alternating minimization approach for deep representation at the training time and an efficient computation for heterogeneous data at the testing time. Experimental evaluations show that our method achieves 94% verification rate at FAR=0.1% on the challenging CASIA NIR-VIS 2.0 face recognition dataset. Compared with state-of-the-art methods, it reduces the error rate by 58% only with a compact 64-D representation.
Relating Leverage Scores and Density using Regularized Christoffel Functions

Statistical leverage scores emerged as a fundamental tool for matrix sketching and column sampling with applications to low rank approximation, regression, random feature learning and quadrature. Yet, the very nature of this quantity is barely understood. Borrowing ideas from the orthogonal polynomial literature, we introduce the regularized Christoffel function associated to a positive definite kernel. This uncovers a variational formulation for leverage scores for kernel methods and allows to elucidate their relationships with the chosen kernel as well as population density. Our main result quantitatively describes a decreasing relation between leverage score and population density for a broad class of kernels on Euclidean spaces. Numerical simulations support our findings.
Narrowing the Gap Between Saturated and Optimal Cost Partitioning for Classical Planning

In classical planning, cost partitioning is a method for admissibly combining a set of heuristic estimators by distributing operator costs among the heuristics. An optimal cost partitioning is often prohibitively expensive to compute. Saturated cost partitioning is an alternative that is much faster to compute and has been shown to offer high-quality heuristic guidance on Cartesian abstractions. However, its greedy nature makes it highly susceptible to the order in which the heuristics are considered. We show that searching in the space of orders leads to significantly better heuristic estimates than with previously considered orders. Moreover, using multiple orders leads to a heuristic that is significantly better informed than any single-order heuristic. In experiments with Cartesian abstractions, the resulting heuristic approximates the optimal cost partitioning very closely.
DNER Clinical (Named Entity Recognition) from Free Clinical Text to Snomed-CT Concept

We have developed a new approach for the (NER) named entity recognition problem, in specific domains like the medical environment. The main idea is recognize clinical concepts in free text clinical reports. Actually most of the information contained in clinical reports from the Electronic Health System (EHR) of a hospital, is written in natural language free text, so we are researching the problem of automatic clinical named entities recognition from free text clinical reports, in this kind of texts we design a new NER approach, like a hybrid of theses approach, dictionary-based, machine learning, and a fuzzy function. To develop this, from clinical reports free text, we apply an unsupervised, shallow learning neural network, word2vec to represent words of the text as “words vectors”. Second, we use a specific domain dictionary-based gazetteer (using the ontology Snomed-CT to get the standard clinical code for the clinical concept), for match the correct concept, and recognize the named entity like a clinical concept, we use the distance and similarity between of the “words vector” of the terms from the document and the distance of the “word vector” with the Snomed-CT description term, applying a fuzzy function “DNER”, to get the best degree of identification for the named entity recognized. We have applied our approach on a Dataset with 318.585 clinical reports in Spanish from the emergency service of the Hospital “Rafael Mendez” from Lorca (Murcia) Spain, and preliminary results are encouraging.
A network-based approach to estimating partisanship and analyzing change in polarization during the 2016 general election

Communication and media research lacks an accessible and systematic approach to measuring political partisanship in decentralized media environments. In this dissertation, a network-based measurement of partisanship is proposed and then used to analyze social media users during a highly contentious general election. Study I (Chapter 2) introduces rtweet, a newly developed open-source software package designed to collect Twitter data. Study II (Chapter 3) then uses rtweet to gather publicly available Twitter data and demonstrate a network-based approach to estimating partisanship. Finally, Study 3 (Chapter 4) extends this network-based approach to analyze change over time in network polarization among partisan and non-partisan users during the 2016 general election. Results showcase the range and validity of network-based estimates of partisanship and provide clear evidence of partisan selective exposure and network polarization on Twitter as proximity to the election increases.
Response to Comment on Suissa. Lower Risk of Death With SGLT2 Inhibitors in Observational Studies: Real or Bias? Diabetes Care 2018;41:6–10

We thank the authors of the two letters (1,2) for clarifications regarding the design of their observational studies reporting that use of sodium–glucose cotransporter 2 inhibitors (SGLT2i) in type 2 diabetes was associated with over 50% lower rates of all-cause mortality. Thuresson et al. (1) suggest that immortal time bias was essentially absent in the study design of the Comparative Effectiveness of Cardiovascular Outcomes in New Users of SGLT-2 Inhibitors (CVD-REAL) study. I must disagree, particularly in view of some inconsistencies I found between key information provided in the letter (1) and that presented in the study report (3). In that article, the cohort is described as new users of an SGLT2i between November 2012 and November 2016 who were matched on the propensity score to new users of another glucose-lowering drug (oGLD) during that period (3). New users were defined by the first prescription for an SGLT2i or oGLD with no issued prescriptions of that medicine …
Heuristic Search Value Iteration for One-Sided Partially Observable Stochastic Games

Security problems can be modeled as two-player partially observable stochastic games with one-sided partial observability and infinite horizon (one-sided POSGs). We seek for optimal strategies of player 1 that correspond to robust strategies against the worst-case opponent (player 2) that is assumed to have a perfect information about the game. We present a novel algorithm for approximately solving one-sided POSGs based on the heuristic search value iteration (HSVI) for POMDPs. Our results include (1) theoretical properties of one-sided POSGs and their value functions, (2) guarantees showing the convergence of our algorithm to optimal strategies, and (3) practical demonstration of applicability and scalability of our algorithm on three different domains: pursuit-evasion, patrolling, and search games.