Learning Spatial Distribution of Long-Term Trackers Scores

Long-Term tracking is a hot topic in Computer Vision. In this context, competitive models are presented every year, showing a constant growth rate in performances, mainly measured in standardized protocols as Visual Object Tracking (VOT) and Object Tracking Benchmark (OTB). Fusion-trackers strategy has been applied over last few years for overcoming the known re-detection problem, turning out to be an important breakthrough. Following this approach, this work aims to generalize the fusion concept to an arbitrary number of trackers used as baseline trackers in the pipeline, leveraging a learning phase to better understand how outcomes correlate with each other, even when no target is present. A model and data independence conjecture will be evidenced in the manuscript, yielding a recall of 0.738 on LTB-50 dataset when learning from VOT-LT2022, and 0.619 by reversing the two datasets. In both cases, results are strongly competitive with state-of-the-art and recall turns out to be the first on the podium.Comment: 20 pages, 11 figures, 3 table

A Sparse-Modeling Based Approach for Class Specific Feature Selection

In this work, we propose a novel Feature Selection framework called Sparse-Modeling Based Approach for Class Specific Feature Selection (SMBA-CSFS), that simultaneously exploits the idea of Sparse Modeling and Class-Specific Feature Selection. Feature selection plays a key role in several fields (e.g., computational biology), making it possible to treat models with fewer variables which, in turn, are easier to explain, by providing valuable insights on the importance of their role, and likely speeding up the experimental validation. Unfortunately, also corroborated by the no free lunch theorems, none of the approaches in literature is the most apt to detect the optimal feature subset for building a final model, thus it still represents a challenge. The proposed feature selection procedure conceives a two-step approach: (a) a sparse modeling-based learning technique is first used to find the best subset of features, for each class of a training set; (b) the discovered feature subsets are then fed to a class-specific feature selection scheme, in order to assess the effectiveness of the selected features in classification tasks. To this end, an ensemble of classifiers is built, where each classifier is trained on its own feature subset discovered in the previous phase, and a proper decision rule is adopted to compute the ensemble responses. In order to evaluate the performance of the proposed method, extensive experiments have been performed on publicly available datasets, in particular belonging to the computational biology field where feature selection is indispensable: the acute lymphoblastic leukemia and acute myeloid leukemia, the human carcinomas, the human lung carcinomas, the diffuse large B-cell lymphoma, and the malignant glioma. SMBA-CSFS is able to identify/retrieve the most representative features that maximize the classification accuracy. With top 20 and 80 features, SMBA-CSFS exhibits a promising performance when compared to its competitors from literature, on all considered datasets, especially those with a higher number of features. Experiments show that the proposed approach may outperform the state-of-the-art methods when the number of features is high. For this reason, the introduced approach proposes itself for selection and classification of data with a large number of features and classes

A Hybrid Approach To Real-Time Multi-Object Tracking

Multi-Object Tracking, also known as Multi-Target Tracking, is a significant area of computer vision that has many uses in a variety of settings. The development of deep learning, which has encouraged researchers to propose more and more work in this direction, has significantly impacted the scientific advancement around the study of tracking as well as many other domains related to computer vision. In fact, all of the solutions that are currently state-of-the-art in the literature and in the tracking industry, are built on top of deep learning methodologies that produce exceptionally good results. Deep learning is enabled thanks to the ever more powerful technology researchers can use to handle the significant computational resources demanded by these models. However, when real-time is a main requirement, developing a tracking system without being constrained by expensive hardware support with enormous computational resources is necessary to widen tracking applications in real-world contexts. To this end, a compromise is to combine powerful deep strategies with more traditional approaches to favor considerably lower processing solutions at the cost of less accurate tracking results even though suitable for real-time domains. Indeed, the present work goes in that direction, proposing a hybrid strategy for real-time multi-target tracking that combines effectively a classical optical flow algorithm with a deep learning architecture, targeted to a human-crowd tracking system exhibiting a desirable trade-off between performance in tracking precision and computational costs. The developed architecture was experimented with different settings, and yielded a MOTA of 0.608 out of the compared state-of-the-art 0.549 results, and about half the running time when introducing the optical flow phase, achieving almost the same performance in terms of accuracy.Comment: 11 pages, 4 figures, 2 table

The use of neural networks to probe the structure of the nearby universe

In the framework of the European VO-Tech project, we are implementing new machine learning methods specifically tailored to match the needs of astronomical data mining. In this paper, we shortly present the methods and discuss an application to the Sloan Digital Sky Survey public data set. In particular, we discuss some preliminary results on the 3-D taxonomy of the nearby (z < 0.5) universe. Using neural networks trained on the available spectroscopic base of knowledge we derived distance estimates for ca. 30 million galaxies distributed over 8,000 sq. deg. We also use unsupervised clustering tools to investigate whether it is possible to characterize in broad morphological bins the nature of each object and produce a reliable list of candidate AGNs and QSOs

Assessing the effects of Bt maize on the non-target pest Rhopalosiphum maidis by demographic and life-history measurement endpoints.

AbstractThe most commercialized Bt maize plants in Europe were transformed with genes which express a truncated form of the insecticidal delta-endotoxin (Cry1Ab) from the soil bacterium Bacillus thuringiensis (Bt) specifically against Lepidoptera. Studies on the effect of transgenic maize on non-target arthropods have mainly converged on beneficial insects. However, considering the worldwide extensive cultivation of Bt maize, an increased availability of information on their possible impact on non-target pests is also required. In this study, the impact of Bt-maize on the non-target corn leaf aphid, Rhopalosiphum maidis, was examined by comparing biological traits and demographic parameters of two generations of aphids reared on transgenic maize with those on untransformed near-isogenic plants. Furthermore, free and bound phenolics content on transgenic and near-isogenic plants were measured. Here we show an increased performance of the second generation of R. maidis on Bt-maize that could be attributable to indirect effects, such as the reduction of defense against pests due to unintended changes in plant characteristics caused by the insertion of the transgene. Indeed, the comparison of Bt-maize with its corresponding near-isogenic line strongly suggests that the transformation could have induced adverse effects on the biosynthesis and accumulation of free phenolic compounds. In conclusion, even though there is adequate evidence that aphids performed better on Bt-maize than on non-Bt plants, aphid economic damage has not been reported in commercial Bt corn fields in comparison to non-Bt corn fields. Nevertheless, Bt-maize plants can be more easily exploited by R. maidis, possibly due to a lower level of secondary metabolites present in their leaves. The recognition of this mechanism increases our knowledge concerning how insect-resistant genetically modified plants impact on non-target arthropods communities, including tritrophic web interactions, and can help support a sustainable use of genetically modified crops

Selected papers from the 15th and 16th international conference on Computational Intelligence Methods for Bioinformatics and Biostatistics

Funding Information: CIBB 2019 was held at the Department of Human and Social Sciences of the University of Bergamo, Italy, from the 4th to the 6th of September 2019 []. The organization of this edition of CIBB was supported by the Department of Informatics, Systems and Communication of the University of Milano-Bicocca, Italy, and by the Institute of Biomedical Technologies of the National Research Council, Italy. Besides the papers focused on computational intelligence methods applied to open problems of bioinformatics and biostatistics, the works submitted to CIBB 2019 dealt with algebraic and computational methods to study RNA behaviour, intelligence methods for molecular characterization and dynamics in translational medicine, modeling and simulation methods for computational biology and systems medicine, and machine learning in healthcare informatics and medical biology. A supplement published in BMC Medical Informatics and Decision Making journal [] collected three revised and extended papers focused on the latter topic.publishersversionpublishe

Modeling Green Peach Aphid populations exposed to elicitors inducing plant resistance on peach

Matrix Population Models (MPMs) are not commonly used to simulate arthropod population dynamics with applications to pest control assessment in agricultural context. However, an increasing body of studies are prompting the finding of optimization techniques to reduce uncertainty in matrix parameters estimation. Indeed, uncertainty in parameters estimates may lead to significant management implications. Here we present a case study where MPMs are used for assessing the efficacy of treatment with elicitors inducing plant resistance against pathogen, such as laminarin, for the control of the Green Peach Aphid (Myzus persicae Sulzer) populations on peach. Such demographic approach could be particularly suitable to study this kind of compounds, which are mainly characterized by causing sub-lethal effects rather than acute mortality. An artificially assembled system [1] was arranged since it is well suited to follow the fate and behavior of a population exposed to elicitors activating chemical defense in plant. The obtained data, consisting of population time series, were used to generate a stage-classified projection matrix. The general model used to simulate population dynamics consists of a matrix containing i) survival probabilities (the probability of growing and moving to the next stage and the probability of surviving and remaining in the same stage), and ii) fecundities of the population. Most of the used methods for estimating the parameter values of stage-classified models rely on following cohorts of identified individuals [2]. However, in this study the observed data consisted of a time-series of population vectors where individuals are not distinguished. The relationship between the observed data and the values of the matrix parameters that produced the series involves an estimation process called inverse problem. Since all demographic analyses rely on how much the estimated parameters of the matrix are able to represent population dynamics, a Genetic algorithm for inverse parameter estimation was used in order to find a better model fit for the observed stage class distributions. These results were compared to those obtained by the quadratic programming method [3] used for determining the set of parameters that minimizes the residual between the collected data and the model output. REFERENCES: 1. Macfadyen, S., Banks, J.E., Stark, J.D., Davies, A.P., 2014. Using semifield studies to examine the effects of pesticides on mobile terrestrial invertebrates. Annu. Rev. Entomol. 59, 383-404. 2. Caswell, H., 2001. Matrix population models, second ed. Sinauer Associates Inc., Massachusetts. 3. Wood, S.N., 1994. Obtaining birth and mortality patterns from structured population trajectories. Ecol. Monogr. 64, 23-44

Mining the SDSS archive. I. Photometric redshifts in the nearby universe

We present a supervised neural network approach to the determination of photometric redshifts. The method was tuned to match the characteristics of the Sloan Digital Sky Survey and it exploits the spectroscopic redshifts provided by this unique survey. In order to train, validate and test the networks we used two galaxy samples drawn from the SDSS spectroscopic dataset: the general galaxy sample (GG) and the luminous red galaxies subsample (LRG). The method consists of a two steps approach. In the first step, objects are classified in nearby (z<0.25) and distant (0.25<z<0.50). In the second step two different networks are separately trained on objects belonging to the two redshift ranges. Using a standard MLP operated in a Bayesian framework, the optimal architectures were found to require 1 hidden layer of 24 (24) and 24 (25) neurons for the GG (LRG) sample. The presence of systematic deviations was then corrected by interpolating the resulting redshifts. The final results on the GG dataset give a robust sigma_z = 0.0208 over the redshift range [0.01, 0.48] and sigma_z = 0.0197 and sigma_z = 0.0238 for the nearby and distant samples respectively. For the LRG subsample we find a robust sigma_z = 0.0164 over the whole range, and sigma_z = 0.0160, sigma_z = 0.0183 for the nearby and distant samples respectively. After training, the networks have been applied to all objects in the SDSS Table GALAXY matching the same selection criteria adopted to build the base of knowledge, and photometric redshifts for ca. 30 million galaxies having z<0.5 were derived. A catalogue containing photometric redshifts for the LRG subsample was also produced.Comment: 45 pages, 14 figures, accepted for publication is the Astrophysical Journa