Real-Time Siamese Multiple Object Tracker with Enhanced Proposals

Maintaining the identity of multiple objects in real-time video is a challenging task, as it is not always feasible to run a detector on every frame. Thus, motion estimation systems are often employed, which either do not scale well with the number of targets or produce features with limited semantic information. To solve the aforementioned problems and allow the tracking of dozens of arbitrary objects in real-time, we propose SiamMOTION. SiamMOTION includes a novel proposal engine that produces quality features through an attention mechanism and a region-of-interest extractor fed by an inertia module and powered by a feature pyramid network. Finally, the extracted tensors enter a comparison head that efficiently matches pairs of exemplars and search areas, generating quality predictions via a pairwise depthwise region proposal network and a multi-object penalization module. SiamMOTION has been validated on five public benchmarks, achieving leading performance against current state-of-the-art trackers. Code available at: https://github.com/lorenzovaquero/SiamMOTIONComment: Accepted at Pattern Recognition. Code available at https://github.com/lorenzovaquero/SiamMOTIO

Spatio-temporal object detection from UAV on board cameras

We propose a new two stage spatio-temporal object detector framework able to improve detection precision by taking into account temporal information. First, a short-term proposal linking and aggregation method improves box features. Then, we design a long-term attention module that further enhances short-term aggregated features adding long-term spatio-temporal information. This module takes into account object trajectories to effectively exploit long-term relationships between proposals in arbitrary distant frames. Many videos recorded from UAV on board cameras have a high density of small objects, making the detection problem very challenging. Our method takes advantage of spatiotemporal information to address these issues increasing the detection robustness. We have compared our method with state-of-the-art video object detectors in two different publicly available datasets focused on UAV recorded videos. Our approach outperforms previous methods in both datasets.This research was partially funded by the Spanish Ministry of Science, Innovation and Universities under grants TIN2017-84796-C2-1-R and RTI2018-097088-B-C32, and the Galician Ministry of Education, Culture and Universities under grants ED431C 2018/29, ED431C 2017/69 and accreditation 2016-2019, ED431G/08. These grants are co-funded by the European Regional Development Fund (ERDF/FEDER program)

Depth Estimation and Image Restoration by Deep Learning from Defocused Images

Monocular depth estimation and image deblurring are two fundamental tasks in computer vision, given their crucial role in understanding 3D scenes. Performing any of them by relying on a single image is an ill-posed problem. The recent advances in the field of Deep Convolutional Neural Networks (DNNs) have revolutionized many tasks in computer vision, including depth estimation and image deblurring. When it comes to using defocused images, the depth estimation and the recovery of the All-in-Focus (Aif) image become related problems due to defocus physics. Despite this, most of the existing models treat them separately. There are, however, recent models that solve these problems simultaneously by concatenating two networks in a sequence to first estimate the depth or defocus map and then reconstruct the focused image based on it. We propose a DNN that solves the depth estimation and image deblurring in parallel. Our Two-headed Depth Estimation and Deblurring Network (2HDED:NET) extends a conventional Depth from Defocus (DFD) networks with a deblurring branch that shares the same encoder as the depth branch. The proposed method has been successfully tested on two benchmarks, one for indoor and the other for outdoor scenes: NYU-v2 and Make3D. Extensive experiments with 2HDED:NET on these benchmarks have demonstrated superior or close performances to those of the state-of-the-art models for depth estimation and image deblurring

Repeated, Long-Distance Migrations by a Philopatric Predator Targeting Highly Contrasting Ecosystems

Long-distance movements of animals are an important driver of population spatial dynamics and determine the extent of overlap with area-focused human activities, such as fishing. Despite global concerns of declining shark populations, a major limitation in assessments of population trends or spatial management options is the lack of information on their long-term migratory behaviour. For a large marine predator, the tiger shark Galeocerdo cuvier, we show from individuals satellite-tracked for multiple years (up to 1101 days) that adult males undertake annually repeated, round-trip migrations of over 7,500 km in the northwest Atlantic. Notably, these migrations occurred between the highly disparate ecosystems of Caribbean coral reef regions in winter and high latitude oceanic areas in summer, with strong, repeated philopatry to specific overwintering insular habitat. Partial migration also occurred, with smaller, immature individuals displaying reduced migration propensity. Foraging may be a putative motivation for these oceanic migrations, with summer behaviour showing higher path tortuosity at the oceanic range extremes. The predictable migratory patterns and use of highly divergent ecosystems shown by male tiger sharks appear broadly similar to migrations seen in birds, reptiles and mammals, and highlight opportunities for dynamic spatial management and conservation measures of highly mobile sharks

Automatic linguistic reporting of customer activity patterns in open malls

In this work, we present a complete system to produce an automatic linguistic reporting about the customer activity patterns inside open malls, a mixed distribution of classical malls joined with the shops on the street. These reports can assist to design marketing campaigns by means of identifying the best places to catch the attention of customers. Activity patterns are estimated with process mining techniques and the key information of localization. Localization is obtained with a parallelized solution based on WiFi fingerprint system to speed up the solution. In agreement with the best practices for human evaluation of natural language generation systems, the linguistic quality of the generated report was evaluated by 41 experts who filled in an online questionnaire. Results are encouraging, since the average global score of the linguistic quality dimension is 6.17 (0.76 of standard deviation) in a 7-point Likert scale. This expresses a high degree of satisfaction of the generated reports and validates the adequacy of automatic natural language textual reports as a complementary tool to process model visualization. © 2021, The Author(s)

Functional specificity of the members of the Sos family of Ras-GEF Activators: Novel role of Sos2 in control of epidermal stem cell homeostasis

© 2021 by the authors.Prior reports showed the critical requirement of Sos1 for epithelial carcinogenesis, but the specific functionalities of the homologous Sos1 and Sos2 GEFs in skin homeostasis and tumorigenesis remain unclear. Here, we characterize specific mechanistic roles played by Sos1 or Sos2 in primary mouse keratinocytes (a prevalent skin cell lineage) under different experimental conditions. Functional analyses of actively growing primary keratinocytes of relevant genotypes—WT, Sos1-KO, Sos2-KO, and Sos1/2-DKO—revealed a prevalent role of Sos1 regarding transcriptional regulation and control of RAS activation and mechanistic overlapping of Sos1 and Sos2 regarding cell proliferation and survival, with dominant contribution of Sos1 to the RAS-ERK axis and Sos2 to the RAS-PI3K/AKT axis. Sos1/2-DKO keratinocytes could not grow under 3D culture conditions, but single Sos1-KO and Sos2-KO keratinocytes were able to form pseudoepidermis structures that showed disorganized layer structure, reduced proliferation, and increased apoptosis in comparison with WT 3D cultures. Remarkably, analysis of the skin of both newborn and adult Sos2-KO mice uncovered a significant reduction of the population of stem cells located in hair follicles. These data confirm that Sos1 and Sos2 play specific, cell-autonomous functions in primary keratinocytes and reveal a novel, essential role of Sos2 in control of epidermal stem cell homeostasis.The E.S. group was supported by grants from ISCIII-MCUI (FIS PI19/00934), JCyL (SA264P18-UIC 076), Areces Foundation (CIVP19A5942), Solorzano-Barruso Foundation (FS/32-2020), and by ISCIII-CIBERONC (group CB16/12/00352). Research was co-financed by FEDER funds. The J.M.P. lab is co-funded by European Regional Development Fund (FEDER) grants from Science and Innovation (SAF2015-66015-R and PID2019-110758RB-I00 to J.M.P.) and Instituto de Salud Carlos III (CIBERONC no. CB16/12/00228 to J.M.P.). The XRB lab is funded by “la Caixa” Banking Foundation (HR20-00164), the Castilla-León autonomous government (CSI252P18, CSI145P20, CLC-2017-01), the Spanish Ministry of Science and Innovation (MSI) (RTI2018-096481-B-100), and the Spanish Association against Cancer (GC16173472GARC). The CIC is supported by the Programa de Apoyo a Planes Estratégicos de Investigación de Estructuras de Investigación de Excelencia of the Castilla-León autonomous government (CLC-2017-01). L.F.L.-M. and N.F.-P. contracts have been supported by funding from the Spanish Ministry of Universities (FPU13/02923, FPU17/03912) and, in the case of L.F.L.M., by CLC-2017-01 grant

Imaging 3D nanostructure of III-V on Si via cross-section SPM: quantum wells and nanowires - defects, polarity, local charges

Merging unique performance of compound semiconductor (CS) III-V materials in optoelectronics, high frequency and power devices with mature Si manufacturing is a holy grail of modern semiconductor technology. The difference between lattice constants, processing, and chemistry are just a few major challenges to be resolved. With practically non-existing methods for studying nanoscale physical properties of these buried structures, we developed a new concept for fast and efficient 3D nanoscale resolution quantitative mapping of physical properties of CS materials and devices. We combine novel nano-sectioning using variable energy Ar ion beam targeted at the edge of the sample to create a perfectly flat oblique near-atomic flat section through all layers of interest, and the material sensitive scanning probe microscopy (SPM), to reveal 3D morphology, composition, strain and crystalline quality via local physical properties – mechanical and piezoelectric moduli, nanoscale heat conductance, workfunction and electrical conductivity. We can observe the propagation of antiphase domains (APD) from the GaAs-Si interface through the 3D structure, reporting for the first time APD effect on electronic properties of multiple quantum wells that are electrically short the structure evident on charge distribution nanomaps. In GaN nanowires, we directly observe NW/Si substrate interface, and unexpectedly find the in-NWs domains of the opposite polarity via piezoelectric moduli maps. The novel paradigm will make a disruptive change on how 3D structure and physical properties of CS and microelectronics materials and devices are currently studied