Reduced Memory Region Based Deep Convolutional Neural Network Detection

Accurate pedestrian detection has a primary role in automotive safety: for example, by issuing warnings to the driver or acting actively on car's brakes, it helps decreasing the probability of injuries and human fatalities. In order to achieve very high accuracy, recent pedestrian detectors have been based on Convolutional Neural Networks (CNN). Unfortunately, such approaches require vast amounts of computational power and memory, preventing efficient implementations on embedded systems. This work proposes a CNN-based detector, adapting a general-purpose convolutional network to the task at hand. By thoroughly analyzing and optimizing each step of the detection pipeline, we develop an architecture that outperforms methods based on traditional image features and achieves an accuracy close to the state-of-the-art while having low computational complexity. Furthermore, the model is compressed in order to fit the tight constrains of low power devices with a limited amount of embedded memory available. This paper makes two main contributions: (1) it proves that a region based deep neural network can be finely tuned to achieve adequate accuracy for pedestrian detection (2) it achieves a very low memory usage without reducing detection accuracy on the Caltech Pedestrian dataset.Comment: IEEE 2016 ICCE-Berli

Textflow: Screenless Access to Non-Visual Smart Messaging

Texting relies on screen-centric prompts designed for sighted users, still posing significant barriers to people who are blind and visually impaired (BVI). Can we re-imagine texting untethered from a visual display? In an interview study, 20 BVI adults shared situations surrounding their texting practices, recurrent topics of conversations, and challenges. Informed by these insights, we introduce TextFlow: a mixed-initiative context-aware system that generates entirely auditory message options relevant to the users’ location, activity, and time of the day. Users can browse and select suggested aural messages using finger-taps supported by an off-the-shelf finger-worn device, without having to hold or attend to a mobile screen. In an evaluative study, 10 BVI participants successfully interacted with TextFlow to browse and send messages in screen-free mode. The experiential response of the users shed light on the importance of bypassing the phone and accessing rapidly controllable messages at their fingertips while preserving privacy and accuracy with respect to speech or screen-based input. We discuss how non-visual access to proactive, contextual messaging can support the blind in a variety of daily scenarios

SerpinB3 promotes pro-fibrogenic responses in activated hepatic stellate cells

SerpinB3 is a hypoxia- and hypoxia-inducible factor-2\u3b1-dependent cystein protease inhibitor that is up-regulated in hepatocellular carcinoma and in parenchymal cells during chronic liver diseases (CLD). SerpinB3 up-regulation in CLD patients has been reported to correlate with the extent of liver fibrosis and the production of transforming growth factor-\u3b21, but the actual role of SerpinB3 in hepatic fibrogenesis is still poorly characterized. In the present study we analyzed the pro-fibrogenic action of SerpinB3 in cell cultures and in two different murine models of liver fibrosis. "In vitro" experiments revealed that SerpinB3 addition to either primary cultures of human activated myofibroblast-like hepatic stellate cells (HSC/MFs) or human stellate cell line (LX2 cells) strongly up-regulated the expression of genes involved in fibrogenesis and promoted oriented migration, but not cell proliferation. Chronic liver injury by CCl4 administration or by feeding a methionine/choline deficient diet to transgenic mice over-expressing human SerpinB3 in hepatocytes confirmed that SerpinB3 over-expression significantly increased the mRNA levels of pro-fibrogenic genes, collagen deposition and \u3b1SMA-positive HSC/MFs as compared to wild-type mice, without affecting parenchymal damage. The present study provides for the first time evidence that hepatocyte release of SerpinB3 during CLD can contribute to liver fibrogenesis by acting on HSC/MFs

LAPORAN PRAKTIK KERJA LAPANGAN PADA PT INFRASTRUKTUR TELEKOMUNIKASI INDONESIA

RIBKA PA’KAMASE TARIALLO, Laporan Praktik Kerja Lapangan (PKL) pada PT Infrastruktur Telekomunikasi Indonesia. Program Studi S1 Manajemen, Fakultas Ekonomi, Universitas Negeri Jakarta, 2018. Pelaksanaan Praktik Kerja Lapangan (PKL) ini bertujuan untuk mendapatkan pengalaman yang berhubungan dengan studi Praktikan, guna memenuhi salah satu mata kuliah program S1 Manajemen Fakultas Ekonomi Universitas Negeri Jakarta dan memberikan praktikan pengalaman dan melatih diri untuk persiapan kerja di bidang Keuangan. Praktikan ditempatkan pada Unit Business and Performance. Tugas yang diberikan kepada praktikan antara lain: Menghitung perbandingan tiap bulan dan tiap tahun Comprehensive Income, Menyusun materi untuk laporan kinerja keuangan perusahaan, Merekapitulasi balance sheet dari tahun 2014 hingga 2017, Merekapitulasi rasio rasio keuangaan perusahaan dari tahun 2014 hingga 2017,dan Merekapitulasi perbandingan Comprehensive Income dari tahun 2014 hingga 2017. Kata Kunci: PKL, PT Infrastruktur Telekomunikasi Indonesia, Keuangan

A machine learning toolkit for CRISM image analysis

Hyperspectral images collected by remote sensing have played a significant role in the discovery of aqueous alteration minerals, which in turn have important implications for our understanding of the changing habitability on Mars. Traditional spectral analyzes based on summary parameters have been helpful in converting hyperspectral cubes into readily visualizable three channel maps highlighting high-level mineral composition of the Martian terrain. These maps have been used as a starting point in the search for specific mineral phases in images. Although the amount of labor needed to verify the presence of a mineral phase in an image is quite limited for phases that emerge with high abundance, manual processing becomes laborious when the task involves determining the spatial extent of detected phases or identifying small outcrops of secondary phases that appear in only a few pixels within an image. Thanks to extensive use of remote sensing data and rover expeditions, significant domain knowledge has accumulated over the years about mineral composition of several regions of interest on Mars, which allow us to collect reliable labeled data required to train machine learning algorithms. In this study we demonstrate the utility of machine learning in two essential tasks for hyperspectral data analysis: nonlinear noise removal and mineral classification. We develop a simple yet effective hierarchical Bayesian model for estimating distributions of spectral patterns and extensively validate this model for mineral classification on several test images. Our results demonstrate that machine learning can be highly effective in exposing tiny outcrops of specific phases in orbital data that are not uncovered by traditional spectral analysis. We package implemented scripts, documentation illustrating use cases, and pixel-scale training data collected from dozens of well-characterized images into a new toolkit. We hope that this new toolkit will provide advanced and effective processing tools and improve community’s ability to map compositional units in remote sensing data quickly, accurately, and at scale

Multicentre Italian study of SARS-CoV-2 infection in children and adolescents, preliminary data as at 10 April 2020

Data on features of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in children and adolescents are scarce. We report preliminary results of an Italian multicentre study comprising 168 laboratory-confirmed paediatric cases (median: 2.3 years, range: 1 day-17.7 years, 55.9% males), of which 67.9% were hospitalised and 19.6% had comorbidities. Fever was the most common symptom, gastrointestinal manifestations were frequent; two children required intensive care, five had seizures, 49 received experimental treatments and all recovered

Visual Search of multiple objects from a single query

Hundreds of millions of images are uploaded to the cloud every day. Innovative applications able to analyze and extract efficiently information from such a big database are needed nowadays more than ever. Visual search is an application able to retrieve information of a query image comparing it against a large image database. In this paper a Visual Search pipeline implementation is presented able to retrieve multiple objects depicted in a single query image. Quantitative and qualitative precision results are shown on both real and synthetic datasets

RGB-D Visual Search with Compact Binary Codes

As integration of depth sensing into mobile devices is likely forthcoming, we investigate on merging appearance and shape information for mobile visual search. Accordingly, we propose an RGB-D search engine architecture that can attain high recognition rates with peculiarly moderate bandwidth requirements. Our experiments include a comparison to the CDVS (Compact Descriptors for Visual Search) pipeline, candidate to become part of the MPEG-7 standard, and contribute to elucidate on the merits and limitations of joint deployment of depth and color in mobile visual search

Accurate characterization of embedded Structure from Motion

Trajectory estimation and 3d scene reconstruction from single camera, e.g. Structure from Motion, is going to have a central role in the future of automotive industry. Typical appliance fields will be: Collisions avoidance with any kind of object (people included), parking assisted maneuvers and many more. Indeed various countries are becoming more and more concerned about road traffic safety and therefore through its 'Advanced Program', EuroNCAP rewards vehicle manufacturers who employ Advanced Safety Technologies that assists the driver. This paper had mainly two different goals: (1) to describe the implementation of a state of art Structure from Motion pipeline able to run in real time with embedded fish-eye camera, which includes nonlinear optimization (i.e. local bundle adjustment); (2) to demonstrate quantitatively its performances on a synthetic test space specifically designed for its characterization in term of accuracy