Human Pose Estimation using Deep Consensus Voting

In this paper we consider the problem of human pose estimation from a single still image. We propose a novel approach where each location in the image votes for the position of each keypoint using a convolutional neural net. The voting scheme allows us to utilize information from the whole image, rather than rely on a sparse set of keypoint locations. Using dense, multi-target votes, not only produces good keypoint predictions, but also enables us to compute image-dependent joint keypoint probabilities by looking at consensus voting. This differs from most previous methods where joint probabilities are learned from relative keypoint locations and are independent of the image. We finally combine the keypoints votes and joint probabilities in order to identify the optimal pose configuration. We show our competitive performance on the MPII Human Pose and Leeds Sports Pose datasets

Boosting API Recommendation with Implicit Feedback

Developers often need to use appropriate APIs to program efficiently, but it is usually a difficult task to identify the exact one they need from a vast of candidates. To ease the burden, a multitude of API recommendation approaches have been proposed. However, most of the currently available API recommenders do not support the effective integration of users' feedback into the recommendation loop. In this paper, we propose a framework, BRAID (Boosting RecommendAtion with Implicit FeeDback), which leverages learning-to-rank and active learning techniques to boost recommendation performance. By exploiting users' feedback information, we train a learning-to-rank model to re-rank the recommendation results. In addition, we speed up the feedback learning process with active learning. Existing query-based API recommendation approaches can be plugged into BRAID. We select three state-of-the-art API recommendation approaches as baselines to demonstrate the performance enhancement of BRAID measured by Hit@k (Top-k), MAP, and MRR. Empirical experiments show that, with acceptable overheads, the recommendation performance improves steadily and substantially with the increasing percentage of feedback data, comparing with the baselines.Comment: 15 pages, 4 figure

TFNet: Exploiting Temporal Cues for Fast and Accurate LiDAR Semantic Segmentation

LiDAR semantic segmentation plays a crucial role in enabling autonomous driving and robots to understand their surroundings accurately and robustly. There are different types of methods, such as point-based, range-image-based, polar-based, and hybrid methods. Among these, range-image-based methods are widely used due to their efficiency. However, they face a significant challenge known as the ``many-to-one'' problem caused by the range image's limited horizontal and vertical angular resolution. As a result, around 20\% of the 3D points can be occluded. In this paper, we present TFNet, a range-image-based LiDAR semantic segmentation method that utilizes temporal information to address this issue. Specifically, we incorporate a temporal fusion layer to extract useful information from previous scans and integrate it with the current scan. We then design a max-voting-based post-processing technique to correct false predictions, particularly those caused by the ``many-to-one'' issue. We evaluated the approach on two benchmarks and demonstrate that the post-processing technique is generic and can be applied to various networks. We will release our code and models

Sensitivity analysis of circadian entrainment in the space of phase response curves

Sensitivity analysis is a classical and fundamental tool to evaluate the role of a given parameter in a given system characteristic. Because the phase response curve is a fundamental input--output characteristic of oscillators, we developed a sensitivity analysis for oscillator models in the space of phase response curves. The proposed tool can be applied to high-dimensional oscillator models without facing the curse of dimensionality obstacle associated with numerical exploration of the parameter space. Application of this tool to a state-of-the-art model of circadian rhythms suggests that it can be useful and instrumental to biological investigations.Comment: 22 pages, 8 figures. Correction of a mistake in Definition 2.1. arXiv admin note: text overlap with arXiv:1206.414

Risk factors for the development of bronchiectasis in patients with asthma

Asthma; Medical researchAsma; Investigación médicaAsma; Recerca mèdicaThough asthma and bronchiectasis are two different diseases, their coexistence has been demonstrated in many patients. The aim of the present study is to compare the characteristics of asthmatic patients with and without bronchiectasis and to assess risk factors for the development of this condition. Two hundred and twenty-four moderate-severe asthmatic patients were included. The severity of bronchiectasis was assessed by Reiff and FACED parameters. Logistic regression was used to identify independent factors associated with bronchiectasis. Bronchiectasis was identified in 78 asthma patients. In severe asthma patients, its prevalence was 56.9%. Bronchiectasis was defined as mild in81% of patients using modified Reiff criteria and in 74% using FACED criteria. Asthmatic patients with bronchiectasis had decreasing FEV1, FVC and FEV1/FVC (p = 0.002, 0.005 and 0.014 respectively), presented more frequent asthma exacerbations (p < 0.001) and worse asthma control (ACT 21 vs 16pts, p < 0.001). Factors independently associated with bronchiectasis were older age (42–65 years: OR, 3.99; 95% CI 1.60 to 9.95, P = 0.003; ≥ 65 years: OR, 2.91; 95% CI 1.06 to 8.04, P = 0.039), severe asthma grade (OR, 8.91; 95% CI 3.69 to 21.49; P < 0.001) and frequency of asthma exacerbations (OR, 4.43; 95% CI 1.78 to 11.05; P < 0.001). In patients with severe asthma, age of asthma onset (OR, 1.02; 95% CI 1.01 to 1.04; P = 0.015) and asthma exacerbations (OR, 4.88; 95% CI 1.98 to 12.03; P = 0.001) were independently associated with the development of bronchiectasis. The prevalence of bronchiectasis in severe asthmatic patients is high. Age of asthma onset and exacerbations were independent factors associated with the occurrence of bronchiectasis.The study was partially supported by FIS PI15/01900 (Fondo Europeo de Desarrollo Regional (FEDER) and Fundacio Catalana de Pneumología (FUCAP). MJC is supported by the Miguel Servet program of the Instituto de Salud Carlos III (MSII17/00025). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Quantum Algorithm for Dynamic Programming Approach for DAGs. Applications for Zhegalkin Polynomial Evaluation and Some Problems on DAGs

In this paper, we present a quantum algorithm for dynamic programming approach for problems on directed acyclic graphs (DAGs). The running time of the algorithm is $O(\sqrt{\hat{n}m}\log \hat{n})$, and the running time of the best known deterministic algorithm is $O(n+m)$, where $n$ is the number of vertices, $\hat{n}$ is the number of vertices with at least one outgoing edge; $m$ is the number of edges. We show that we can solve problems that use OR, AND, NAND, MAX and MIN functions as the main transition steps. The approach is useful for a couple of problems. One of them is computing a Boolean formula that is represented by Zhegalkin polynomial, a Boolean circuit with shared input and non-constant depth evaluating. Another two are the single source longest paths search for weighted DAGs and the diameter search problem for unweighted DAGs.Comment: UCNC2019 Conference pape