Nearest Neighbor Guidance for Out-of-Distribution Detection

Detecting out-of-distribution (OOD) samples are crucial for machine learning models deployed in open-world environments. Classifier-based scores are a standard approach for OOD detection due to their fine-grained detection capability. However, these scores often suffer from overconfidence issues, misclassifying OOD samples distant from the in-distribution region. To address this challenge, we propose a method called Nearest Neighbor Guidance (NNGuide) that guides the classifier-based score to respect the boundary geometry of the data manifold. NNGuide reduces the overconfidence of OOD samples while preserving the fine-grained capability of the classifier-based score. We conduct extensive experiments on ImageNet OOD detection benchmarks under diverse settings, including a scenario where the ID data undergoes natural distribution shift. Our results demonstrate that NNGuide provides a significant performance improvement on the base detection scores, achieving state-of-the-art results on both AUROC, FPR95, and AUPR metrics. The code is given at \url{https://github.com/roomo7time/nnguide}.Comment: Accepted to ICCV202

Periocular in the Wild Embedding Learning with Cross-Modal Consistent Knowledge Distillation

Periocular biometric, or peripheral area of ocular, is a collaborative alternative to face, especially if a face is occluded or masked. In practice, sole periocular biometric captures least salient facial features, thereby suffering from intra-class compactness and inter-class dispersion issues particularly in the wild environment. To address these problems, we transfer useful information from face to support periocular modality by means of knowledge distillation (KD) for embedding learning. However, applying typical KD techniques to heterogeneous modalities directly is suboptimal. We put forward in this paper a deep face-to-periocular distillation networks, coined as cross-modal consistent knowledge distillation (CM-CKD) henceforward. The three key ingredients of CM-CKD are (1) shared-weight networks, (2) consistent batch normalization, and (3) a bidirectional consistency distillation for face and periocular through an effectual CKD loss. To be more specific, we leverage face modality for periocular embedding learning, but only periocular images are targeted for identification or verification tasks. Extensive experiments on six constrained and unconstrained periocular datasets disclose that the CM-CKD-learned periocular embeddings extend identification and verification performance by 50% in terms of relative performance gain computed based upon face and periocular baselines. The experiments also reveal that the CM-CKD-learned periocular features enjoy better subject-wise cluster separation, thereby refining the overall accuracy performance.Comment: 30 page

Demographic Inference and Representative Population Estimates from Multilingual Social Media Data

Social media provide access to behavioural data at an unprecedented scale and granularity. However, using these data to understand phenomena in a broader population is difficult due to their non-representativeness and the bias of statistical inference tools towards dominant languages and groups. While demographic attribute inference could be used to mitigate such bias, current techniques are almost entirely monolingual and fail to work in a global environment. We address these challenges by combining multilingual demographic inference with post-stratification to create a more representative population sample. To learn demographic attributes, we create a new multimodal deep neural architecture for joint classification of age, gender, and organization-status of social media users that operates in 32 languages. This method substantially outperforms current state of the art while also reducing algorithmic bias. To correct for sampling biases, we propose fully interpretable multilevel regression methods that estimate inclusion probabilities from inferred joint population counts and ground-truth population counts. In a large experiment over multilingual heterogeneous European regions, we show that our demographic inference and bias correction together allow for more accurate estimates of populations and make a significant step towards representative social sensing in downstream applications with multilingual social media.Comment: 12 pages, 10 figures, Proceedings of the 2019 World Wide Web Conference (WWW '19

Impact of Body Mass Index on the relationship of epicardial adipose tissue to metabolic syndrome and coronary artery disease in an Asian population

<p>Abstract</p> <p>Background</p> <p>In a previous study, we demonstrated that the thickness of epicardial adipose tissue (EAT), measured by echocardiography, was increased in patients with metabolic syndrome (MS) and coronary artery disease (CAD). Several studies on obese patients, however, failed to demonstrate any relationship between EAT and CAD. We hypothesized that body mass index (BMI) affected the link between EAT and MS and CAD.</p> <p>Methods</p> <p>We consecutively enrolled 643 patients (302 males, 341 females; 59 ± 11 years), who underwent echocardiography and coronary angiography. The EAT thickness was measured on the free wall of the right ventricle at the end of diastole. All patients were divided into two groups: high BMI group, ≥27 kg/m²(n = 165), and non-high BMI group, < 27 kg/m²(n = 478).</p> <p>Results</p> <p>The median and mean EAT thickness of 643 patients were 3.0 mm and 3.1 ± 2.4 mm, respectively. In the non-high BMI group, the median EAT thickness was significantly increased in patients with MS compared to those without MS (3.5 vs. 1.9 mm, p < 0.001). In the high BMI group, however, there was no significant difference in the median EAT thickness between patients with and without MS (3.0 vs. 2.5 mm, p = 0.813). A receiver operating characteristic (ROC) curve analysis predicting MS revealed that the area under the curve (AUC) of the non-high BMI group was significantly larger than that of the high BMI group (0.659 vs. 0.506, p = 0.007). When compared to patients without CAD, patients with CAD in both the non-high and high BMI groups had a significantly higher median EAT thickness (3.5 vs. 1.5 mm, p < 0.001 and 4.0 vs. 2.5 mm, p = 0.001, respectively). However, an ROC curve analysis predicting CAD revealed that the AUC of the non-high BMI group tended to be larger than that of the high BMI group (0.735 vs. 0.657, p = 0.055).</p> <p>Conclusions</p> <p>While EAT thickness was significantly increased in patients with MS and CAD, the power of EAT thickness to predict MS and CAD was stronger in patients with BMI < 27 kg/m². These findings showed that the measurement of EAT thickness by echocardiography might be especially useful in an Asian population with a non-high BMI, less than 27 kg/m².</p

Telomerase and Apoptosis in the Placental Trophoblasts of Growth Discordant Twins

In an effort to investigate the molecular basis of growth discordance in embryos that experience the same uterine environment, we compared telomerase activity and apoptosis in placental trophoblasts obtained from growth discordant twins. Between January 2003 and February 2005, placental tissue from twenty pairs of twins was obtained within thirty minutes of delivery. Eleven cases were classified as growth discordant, with birth weight discordance greater than 20%. Nine cases comprised the control group, with less than 20% discordance. Telomerase and apoptotic activities in placental trophoblasts were analyzed by ELISA and immunoblot. Statistical significance was analyzed by a paired t-test, chisquared test, and ANOVA (SPSS ver 11.0). The average growth discordance was 26.8% in the growth discordant group and 14.4% in the control group. There were no significant differences in maternal age, week of gestation at delivery, parity, or chorionisity between the two groups. In the growth discordant group, the larger twin showed significantly higher telomerase activity (p < 0.01), whereas no significant difference was observed in the control group (p = 0.36). In addition, there was no definitive correlation between telomerase activity and the degree of growth discordance in the larger or smaller twins (R = -0.521 and -0.399, p = 0.15 and 0.25, respectively). The apoptosis proteins Bax and Bcl 2 were detected in both the larger and smaller twins in the growth discordant and control groups. There was no statistically significant difference in Bax expression between the larger and smaller twins (p = 0.25 and 0.92, respectively) for either the growth discordant or the control groups. Bcl 2 expression also showed no significant difference between groups. In conclusion, a tendency toward reduced telomerase activity and increased apoptosis was discovered in placental trophoblasts of the smaller growth-discordant twin, possibility resulting in delayed fetal growth

Bacterial Inactivation of Wound Infection in a Human Skin Model by Liquid-Phase Discharge Plasma

Background: We investigate disinfection of a reconstructed human skin model contaminated with biofilm-formative Staphylococcus aureus employing plasma discharge in liquid. Principal Findings: We observed statistically significant 3.83-log10 (p,0.001) and 1.59-log10 (p,0.05) decreases in colony forming units of adherent S. aureus bacteria and 24 h S. aureus biofilm culture with plasma treatment. Plasma treatment was associated with minimal changes in histological morphology and tissue viability determined by means of MTT assay. Spectral analysis of the plasma discharge indicated the presence of highly reactive atomic oxygen radicals (777 nm and 844 nm) and OH bands in the UV region. The contribution of these and other plasma-generated agents and physical conditions to the reduction in bacterial load are discussed. Conclusions: These findings demonstrate the potential of liquid plasma treatment as a potential adjunct therapy for chronic wounds

Multimodal Biometric Template Protection Based on a Cancelable SoftmaxOut Fusion Network

Authentication systems that employ biometrics are commonplace, as they offer a convenient means of authenticating an individual&rsquo;s identity. However, these systems give rise to concerns about security and privacy due to insecure template management. As a remedy, biometric template protection (BTP) has been developed. Cancelable biometrics is a non-invertible form of BTP in which the templates are changeable. This paper proposes a deep-learning-based end-to-end multimodal cancelable biometrics scheme called cancelable SoftmaxOut fusion network (CSMoFN). By end-to-end, we mean a model that receives raw biometric data as input and produces a protected template as output. CSMoFN combines two biometric traits, the face and the periocular region, and is composed of three modules: a feature extraction and fusion module, a permutation SoftmaxOut transformation module, and a multiplication-diagonal compression module. The first module carries out feature extraction and fusion, while the second and third are responsible for the hashing of fused features and compression. In addition, our network is equipped with dual template-changeability mechanisms with user-specific seeded permutation and binary random projection. CSMoFN is trained by minimizing the ArcFace loss and the pairwise angular loss. We evaluate the network, using six face&ndash;periocular multimodal datasets, in terms of its verification performance, unlinkability, revocability, and non-invertibility