1,217 research outputs found
Evaluating Two-Stream CNN for Video Classification
Videos contain very rich semantic information. Traditional hand-crafted
features are known to be inadequate in analyzing complex video semantics.
Inspired by the huge success of the deep learning methods in analyzing image,
audio and text data, significant efforts are recently being devoted to the
design of deep nets for video analytics. Among the many practical needs,
classifying videos (or video clips) based on their major semantic categories
(e.g., "skiing") is useful in many applications. In this paper, we conduct an
in-depth study to investigate important implementation options that may affect
the performance of deep nets on video classification. Our evaluations are
conducted on top of a recent two-stream convolutional neural network (CNN)
pipeline, which uses both static frames and motion optical flows, and has
demonstrated competitive performance against the state-of-the-art methods. In
order to gain insights and to arrive at a practical guideline, many important
options are studied, including network architectures, model fusion, learning
parameters and the final prediction methods. Based on the evaluations, very
competitive results are attained on two popular video classification
benchmarks. We hope that the discussions and conclusions from this work can
help researchers in related fields to quickly set up a good basis for further
investigations along this very promising direction.Comment: ACM ICMR'1
Efficient On-the-fly Category Retrieval using ConvNets and GPUs
We investigate the gains in precision and speed, that can be obtained by
using Convolutional Networks (ConvNets) for on-the-fly retrieval - where
classifiers are learnt at run time for a textual query from downloaded images,
and used to rank large image or video datasets.
We make three contributions: (i) we present an evaluation of state-of-the-art
image representations for object category retrieval over standard benchmark
datasets containing 1M+ images; (ii) we show that ConvNets can be used to
obtain features which are incredibly performant, and yet much lower dimensional
than previous state-of-the-art image representations, and that their
dimensionality can be reduced further without loss in performance by
compression using product quantization or binarization. Consequently, features
with the state-of-the-art performance on large-scale datasets of millions of
images can fit in the memory of even a commodity GPU card; (iii) we show that
an SVM classifier can be learnt within a ConvNet framework on a GPU in parallel
with downloading the new training images, allowing for a continuous refinement
of the model as more images become available, and simultaneous training and
ranking. The outcome is an on-the-fly system that significantly outperforms its
predecessors in terms of: precision of retrieval, memory requirements, and
speed, facilitating accurate on-the-fly learning and ranking in under a second
on a single GPU.Comment: Published in proceedings of ACCV 201
Receptive Field Block Net for Accurate and Fast Object Detection
Current top-performing object detectors depend on deep CNN backbones, such as
ResNet-101 and Inception, benefiting from their powerful feature
representations but suffering from high computational costs. Conversely, some
lightweight model based detectors fulfil real time processing, while their
accuracies are often criticized. In this paper, we explore an alternative to
build a fast and accurate detector by strengthening lightweight features using
a hand-crafted mechanism. Inspired by the structure of Receptive Fields (RFs)
in human visual systems, we propose a novel RF Block (RFB) module, which takes
the relationship between the size and eccentricity of RFs into account, to
enhance the feature discriminability and robustness. We further assemble RFB to
the top of SSD, constructing the RFB Net detector. To evaluate its
effectiveness, experiments are conducted on two major benchmarks and the
results show that RFB Net is able to reach the performance of advanced very
deep detectors while keeping the real-time speed. Code is available at
https://github.com/ruinmessi/RFBNet.Comment: Accepted by ECCV 201
Bose-Einstein Condensation of Helium and Hydrogen inside Bundles of Carbon Nanotubes
Helium atoms or hydrogen molecules are believed to be strongly bound within
the interstitial channels (between three carbon nanotubes) within a bundle of
many nanotubes. The effects on adsorption of a nonuniform distribution of tubes
are evaluated. The energy of a single particle state is the sum of a discrete
transverse energy Et (that depends on the radii of neighboring tubes) and a
quasicontinuous energy Ez of relatively free motion parallel to the axis of the
tubes. At low temperature, the particles occupy the lowest energy states, the
focus of this study. The transverse energy attains a global minimum value
(Et=Emin) for radii near Rmin=9.95 Ang. for H2 and 8.48 Ang.for He-4. The
density of states N(E) near the lowest energy is found to vary linearly above
this threshold value, i.e. N(E) is proportional to (E-Emin). As a result, there
occurs a Bose-Einstein condensation of the molecules into the channel with the
lowest transverse energy. The transition is characterized approximately as that
of a four dimensional gas, neglecting the interactions between the adsorbed
particles. The phenomenon is observable, in principle, from a singular heat
capacity. The existence of this transition depends on the sample having a
relatively broad distribution of radii values that include some near Rmin.Comment: 21 pages, 9 figure
The age of data-driven proteomics : how machine learning enables novel workflows
A lot of energy in the field of proteomics is dedicated to the application of challenging experimental workflows, which include metaproteomics, proteogenomics, data independent acquisition (DIA), non-specific proteolysis, immunopeptidomics, and open modification searches. These workflows are all challenging because of ambiguity in the identification stage; they either expand the search space and thus increase the ambiguity of identifications, or, in the case of DIA, they generate data that is inherently more ambiguous. In this context, machine learning-based predictive models are now generating considerable excitement in the field of proteomics because these predictive models hold great potential to drastically reduce the ambiguity in the identification process of the above-mentioned workflows. Indeed, the field has already produced classical machine learning and deep learning models to predict almost every aspect of a liquid chromatography-mass spectrometry (LC-MS) experiment. Yet despite all the excitement, thorough integration of predictive models in these challenging LC-MS workflows is still limited, and further improvements to the modeling and validation procedures can still be made. In this viewpoint we therefore point out highly promising recent machine learning developments in proteomics, alongside some of the remaining challenges
Melatonin in the dermal film limits the blood lymphocyte death in experimental thermal trauma
According to WHO data, about 11 million people need medical care after burns every year. In the overall structure of burns, the share of thermal trauma (TT) is 80%. Lymphocytopenia in TT is a risk factor for infectious complications and limited repair, and the development of new tools for TT therapy using dermal films is demanded in combustiology. The aim of the study was to evaluate changes in blood lymphocyte parameters, i.e., quantitative composition and their death during experimental thermal damage under the influence of the originally developed dermal film with melatonin (MT) in 49 inbred rats. The grade IIIA TT of 3.5% body surface was modeled by contact with boiling water for 12 s. Dermal films based on sodium carboxymethylcellulose supplemented with MT at a concentration of 0.005 g/g were applied daily for 5 days. The total numbers of lymphocytes, CD45RA+ and CD3+ cells, counts of lymphocytes with signs of partial necrosis, early and late apoptosis were assessed in blood. Relative decrease in the area and rate of the burn wound epithelization were also calculated. In animals with TT, the number of blood lymphocytes decreased on days 5, 10 and 20, including CD45RA+ and CD3+, along with increased amounts of lymphocytes with signs of necrosis, late and early apoptosis. By the term of 20 days, the burn wound area was reduced by 11.5%. Usage of dermal films with MT increased the amount of CD3+ cells in blood on days 5 and 10, CD45RA+ on days 5, 10 and 20, being associated with decreased number of lymphocytes showing signs of early apoptosis on days 5, 10 and 20, as well as features of necrosis and late apoptosis on days 5 following TT, accelerates the healing of a burn wound on days 5, 10 and 20 after TT. with a 20 cent reduction of its area by the day 20. Epithelization rate of the burn wound when applying MT-supplemented dermal film on days 5, 10 and 20 increases, along with higher amounts of CD3+ in the blood, and reduced counts of lymphocytes with signs of early apoptosis
Immunotropic effects of vitamin D3 in original rectal suppositories in experimental ulcerative colitis
Increased incidence of ulcerative colitis (UC) is a prerequisite for searching new therapeutic approaches, primarily with an opportunity of site-directed impact on the colon lesion. UC pathogenesis is associated with dysregulated immune response, and limited effectiveness of basic therapy for the disorder. Vitamin D3 exhibits antioxidant, anti-inflammatory, immunomodulatory and other properties, it has been shown to be effective in some autoimmune diseases, thus prompting us to study its effect on immune status in UC. We aimed for studying the effect of vitamin D3, as a component of original rectal suppositories, upon clinical course and indexes of immune status in experimental UC. UC in rats was modeled with 3% oxazolone solution. The vitamin D3-containing suppositories (1500 IU) weighing 300 mg were administered per rectum every 12 hours for 6 days. On days 2, 4 and 6 of UC, the clinical features were assessed as well as blood leukocyte counts, numbers of CD3+, CD45RA+; absorbing and NBT-reducing abilities of blood neutrophils were determined; IgM, IgG, IL-6 and IL-8 concentrations in serum were also studied.The DAI index increased in non-treated UC, along with raised neutrophil numbers in blood, their absorption and NBT-reducing activity was also increased, the total number of lymphocytes, including CD3+, CD45RA+ became higher, serum concentrations of IgM, IgG, IL-6, IL-8 increased. Local use of vitamin D3 in UC reduces DAI parameters, causes decrease in blood neutrophil counts, reducing and partially restoring absorptive and NBT-reducing abilities of neutrophils, decline of total lymphocyte counts in blood, partially restoring the CD3+ and CD45RA+ numbers, causing decline and partial restoration of serum IgM, IgG, IL-6, IL-8 concentrations. An association between clinical signs and indexes of immune status in UC was established under the conditions of vitamin D3 use. Conclusions: The protective effect of vitamin D3 in UC can be mediated by its antioxidant effect, changes in production of immunoregulatory cytokines, modulation of Th1-, Th2-, Th17-dependent reactions and Treg activity, being a pre-requisite for further studies to clarify the mechanism of vitamin D3 immunotropic action in UC,with an opportunity of using it in clinical practice
Automatic Classification of Roof Shapes for Multicopter Emergency Landing Site Selection
Geographic information systems (GIS) now provide accurate maps of terrain,
roads, waterways, and building footprints and heights. Aircraft, particularly
small unmanned aircraft systems, can exploit additional information such as
building roof structure to improve navigation accuracy and safety particularly
in urban regions. This paper proposes a method to automatically label building
roof shape types. Satellite imagery and LIDAR data from Witten, Germany are fed
to convolutional neural networks (CNN) to extract salient feature vectors.
Supervised training sets are automatically generated from pre-labeled buildings
contained in the OpenStreetMap database. Multiple CNN architectures are trained
and tested, with the best performing networks providing a condensed feature set
for support vector machine and decision tree classifiers. Satellite and LIDAR
data fusion is shown to provide greater classification accuracy than through
use of either data type individually
Throughput Prediction of Asynchronous SGD in TensorFlow
Modern machine learning frameworks can train neural networks using multiple
nodes in parallel, each computing parameter updates with stochastic gradient
descent (SGD) and sharing them asynchronously through a central parameter
server. Due to communication overhead and bottlenecks, the total throughput of
SGD updates in a cluster scales sublinearly, saturating as the number of nodes
increases. In this paper, we present a solution to predicting training
throughput from profiling traces collected from a single-node configuration.
Our approach is able to model the interaction of multiple nodes and the
scheduling of concurrent transmissions between the parameter server and each
node. By accounting for the dependencies between received parts and pending
computations, we predict overlaps between computation and communication and
generate synthetic execution traces for configurations with multiple nodes. We
validate our approach on TensorFlow training jobs for popular image
classification neural networks, on AWS and on our in-house cluster, using nodes
equipped with GPUs or only with CPUs. We also investigate the effects of data
transmission policies used in TensorFlow and the accuracy of our approach when
combined with optimizations of the transmission schedule
- …