301 research outputs found
SEMI-SUPERVISED FISHER LINEAR DISCRIMINANT (SFLD)
Supervised learning uses a training set of labeled examples to compute a classifier which is a mapping from feature vectors to class labels. The success of a learning algorithm is evaluated by its ability to generalize, i.e., to extend this mapping accurately to new data that is commonly referred to as the test data. Good generalization depends crucially on the quality of the training set. Because collecting labeled data is laborious, training sets are typically small. Furthermore, it is often difficult to represent all possible observation scenarios during training, so that the statistics of the training set end up differing from those of the test data, a problem known as the sample selection bias. To address sample selection bias, we introduce a Semi-Supervised Fisher Linear Discriminant (SFLD) that utilizes additional, unlabeled data to improve generalization for both small and biased training sets. We characterize the conditions under which SFLD helps, and illustrate its benefits through experiments on digit and car recognition applications
RandMSAugment: A Mixed-Sample Augmentation for Limited-Data Scenarios
The high costs of annotating large datasets suggests a need for effectively
training CNNs with limited data, and data augmentation is a promising
direction. We study foundational augmentation techniques, including Mixed
Sample Data Augmentations (MSDAs) and a no-parameter variant of RandAugment
termed Preset-RandAugment, in the fully supervised scenario. We observe that
Preset-RandAugment excels in limited-data contexts while MSDAs are moderately
effective. We show that low-level feature transforms play a pivotal role in
this performance difference, postulate a new property of augmentations related
to their data efficiency, and propose new ways to measure the diversity and
realism of augmentations. Building on these insights, we introduce a novel
augmentation technique called RandMSAugment that integrates complementary
strengths of existing methods. RandMSAugment significantly outperforms the
competition on CIFAR-100, STL-10, and Tiny-Imagenet. With very small training
sets (4, 25, 100 samples/class), RandMSAugment achieves compelling performance
gains between 4.1% and 6.75%. Even with more training data (500 samples/class)
we improve performance by 1.03% to 2.47%. RandMSAugment does not require
hyperparameter tuning, extra validation data, or cumbersome optimizations
SemARFlow: Injecting Semantics into Unsupervised Optical Flow Estimation for Autonomous Driving
Unsupervised optical flow estimation is especially hard near occlusions and
motion boundaries and in low-texture regions. We show that additional
information such as semantics and domain knowledge can help better constrain
this problem. We introduce SemARFlow, an unsupervised optical flow network
designed for autonomous driving data that takes estimated semantic segmentation
masks as additional inputs. This additional information is injected into the
encoder and into a learned upsampler that refines the flow output. In addition,
a simple yet effective semantic augmentation module provides self-supervision
when learning flow and its boundaries for vehicles, poles, and sky. Together,
these injections of semantic information improve the KITTI-2015 optical flow
test error rate from 11.80% to 8.38%. We also show visible improvements around
object boundaries as well as a greater ability to generalize across datasets.
Code is available at
https://github.com/duke-vision/semantic-unsup-flow-release.Comment: Accepted by ICCV-2023; Code is available at
https://github.com/duke-vision/semantic-unsup-flow-releas
Unsupervised Flow Refinement near Motion Boundaries
Unsupervised optical flow estimators based on deep learning have attracted
increasing attention due to the cost and difficulty of annotating for ground
truth. Although performance measured by average End-Point Error (EPE) has
improved over the years, flow estimates are still poorer along motion
boundaries (MBs), where the flow is not smooth, as is typically assumed, and
where features computed by neural networks are contaminated by multiple
motions. To improve flow in the unsupervised settings, we design a framework
that detects MBs by analyzing visual changes along boundary candidates and
replaces motions close to detections with motions farther away. Our proposed
algorithm detects boundaries more accurately than a baseline method with the
same inputs and can improve estimates from any flow predictor without
additional training
A Proteomic Analysis of Discolored Tooth Surfaces after the Use of 0.12% Chlorhexidine (CHX) Mouthwash and CHX Provided with an Anti-Discoloration System (ADS)
Chlorhexidine (CHX) is considered the gold standard for the chemical control of bacterial plaque and is often used after surgical treatment. However, CHX employment over an extended time is responsible for side effects such as the appearance of pigmentations on the teeth and tongue; the discoloration effects are less pronounced when using a CHX-based mouthwash with added an anti-discoloration system (ADS). The aim of this study was to evaluate, using one- and two-dimensional gel electrophoresis combined with mass spectrometry, the possible proteomic changes induced by CHX and CHX+ADS in the supragingival dental sites susceptible to a discoloration effect. The tooth surface collected material (TSCM) was obtained by curettage after resective bone surgery from three groups of patients following a supportive therapy protocol in which a mechanical control was combined with placebo rinses or CHX or a CHX+ADS mouthwash. The proteomic analysis was performed before surgery (basal conditions) and four weeks after surgery when CHX was used (or not) as chemical plaque control. Changes in the TSCM proteome were only revealed following CHX treatment: glycolytic enzymes, molecular chaperones and elongation factors were identified as more expressed. These changes were not detected after CHX+ADS treatment. An ADS could directly limit TSCM forming and also the CHX antiseptic effect reduces its ability to alter bacterial cell permeability. However, Maillard's reaction produces high molecular weight molecules that change the surface properties and could facilitate bacterial adhesion
Winegrape berry skin thickness determination: comparison between histological observation and texture analysis determination.
We analyzed the relation between the assessment of grape berry skin thickness by means of histology sections and instrumental mechanical properties measurements. Berry skin of Vitis vinifera L. cultivar Corvina vineyards from Valpolicella Valpantena zone (Verona, Italy) were tested, evidencing a strong correlation between the two thickness determination methods. The middle or equatorial berry skin portion was found to be the less variable in instrumental skin thickness determination. In addition, unlike other studies, no correlation between the skin thickness and cell layers number was found
‘Greening’ the Cities
We are facing an urgent global environmental crisis that requires a reframing of traditional professional and conceptual boundaries within the urban environment. Complex and multidisciplinary issues need complex and multidisciplinary solutions, which result from the collaboration of many different disciplines concerned with the urban environment. A more integrated ecological perspective that recognizes the complexity of urban environments and resituates our ‘artificial’ or human-made world within its natural ecosystem can facilitate this shift towards greater knowledge exchange. C40 Cities case studies provide a framework within which to understand the disciplines and scales encompassed by ecological solutions, while projects at MIT Senseable City Lab and CRA-Carlo Ratti Associati highlight how data is used as a tool in driving ecological solutions. The artificial world of sensors, data and networks creates a bridge between the ‘artificial’ and ‘natural’ elements of our urban environments, allowing us to fully understand the present condition, connect city users and decision makers, and better integrate ecological solutions into the built environment
Serum levels of allopregnanolone, progesterone and testosterone in menstrually-related and postmenopausal migraine: A cross-sectional study
Background: Reduced blood or cerebrospinal fluid levels of allopregnanolone are involved in menstrual cycle-linked
CNS disorders, such as catamenial epilepsy. This condition, like menstrually-related migraine, is characterized by severe,
treatment-resistant attacks. We explored whether there were differences in allopregnanolone, progesterone and testosterone
serum levels between women with menstrually-related migraine (MM, n¼30) or postmenopausal migraine
without aura who had suffered from menstrually-related migraine during their fertile age (PM, n¼30) and non-headache
control women in fertile age (FAC, n¼30) or post-menopause (PC, n¼30).
Methods: Participants were women with migraine afferent to a headache centre; controls were female patients’
acquaintances. Serum samples obtained were analyzed by HPLC-ESI-MS/MS.
Results: In menstrually-related migraine and postmenopausal migraine groups, allopregnanolone levels were lower than
in the respective control groups (fertile age and post-menopause) (p<0.001, one-way analysis of variance followed by
Tukey-Kramer post-hoc comparison test) while progesterone and testosterone levels were similar. By grouping together
patients with migraine, allopregnanolone levels were inversely correlated with the number of years and days of migraine/
3 months (p 0.005, linear regression analysis).
Conclusion: Decreased GABAergic inhibition, due to low allopregnanolone serum levels, could contribute to
menstrually-related migraine and persistence of migraine after menopause. For the management of these disorders, a
rise in the GABAergic transmission by increasing inhibitory neurosteroids might represent a novel strategy
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