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

Analysis of protein expression in periodontal pocket tissue: a preliminary study

The periodontal disease is caused by a set of inflammatory disorders characterized by periodontal pocket formation that lead to tooth loss if untreated. The proteomic profile and related molecular conditions of pocket tissue in periodontally-affected patients are not reported in literature. To characterize the proteomic profile of periodontally-affected patients, their interproximal periodontal pocket tissue was compared with that of periodontally-healthy patients. Pocket-associated and healthy tissue samples, harvested during surgical therapy, were treated to extract the protein content. Tissues were always collected at sites where no periodontal-pathogenic bacteria were detectable. Proteins were separated using two-dimensional gel electrophoresis and identified by liquid chromatography/mass spectrometry. After identification, four proteins were selected for subsequent Western Blot quantitation both in pathological and healty tissues