Miracle-dz/UniFAR-FTUs huggingface dataset:
RD-Optimized 3D Planar Model Reconstruction & Encoding for Video Compression

Conventional video coding approaches follow a hybrid motion prediction / residual transform coding paradigm, which limits the discovery of redundancy to individual pairs of video frames. On the other hand, computer vision techniques like structure-from-motion (SfM) have long exploited redundancy across a large group of frames to estimate a rigid 3D object structure. In this paper, leveraging on previous SfM techniques, we construct a rate-distortion (RD) optimized 3D planar model from a target spatial region in a frame group as a unified signal predictor for these frames. The prediction accuracy of the model is optimally traded off with the cost of coding such representation as side information (SI). Specifically, we approximate a roughly flat spatial region in the video as a 2D plane in 3D space, and project pixels from the frame group to a 2D grid on the plane with appropriate density. The boundary of the irregularly shaped pixel body on the plane is first coded using arithmetic edge coding (AEC), and then the body is encapsulated into a tight-fitting rectangular region, which is encoded as an intra-frame using HEVC. The pixels inside the rectangle but outside the pixel body-called don't care region (DCR)-are filled optimally by minimizing an l1-norm of the transform coefficients using linear programming. Experimental results show that the RD-optimized planar model improves coding performance over native HEVC implementation.
Temperature levels and periods of sex determination during incubation of eggs of Chelydra serpentina

Eggs of Chelydra serpentina were shifted during incubation between the female producing temperatures of 20°C or 30°C and the male producing temperature of 26°C. In the 20°C and 26°C combination, the stages during which incubation temperature determined sex were stage 14 through stage 16 (stages of normal series, Yntema, '68). In the 30°C and 26°C combination, the temperature sensitive stages for sex determination were stage 14 through stage 19. Incubation at 26°C throughout this period was needed to produce all males. Incubation at 30°C during either the first or second half of the period produced nearly all females; shorter periods of incubation at 30°C were more effective in producing females during the second half of the sensitive period. In the 20°C and 26°C combination, incubation at 20°C or 26°C for parts of the sensitive period produced both males and females. In three of the 57 clutches of eggs used in the experiments, incidence of females was atypically high.
Joint Pairwise Learning and Image Clustering Based on a Siamese CNN

How to use a deep convolutional neural network (CNN) to efficiently and effectively learn representations of a large unlabeled set of images and group them into clusters remains a challenging problem. To address this problem, we propose a Siamese clustering CNN (SC-CNN) to iteratively learn discriminative representations for image clustering. Based on the proposed SC-CNN, we employ a mini-batch-based joint pairwise representation learning and clustering scheme to make the computation and storage cost efficient for large-scale image clustering on a personal computer with a commercial GPU graphic card. On top of SC-CNN, the proposed pairwise learning scheme effectively learns discriminative representations by appropriately selecting same-cluster and different-cluster image pairs from the results of each clustering iteration. Experimental results demonstrate that the proposed method outperforms start-of-the-art clustering schemes in clustering accuracy on public image sets.
Action Recognition: First-and Second-Order 3D Feature in Bi-Directional Attention Network

This paper considers a 3D convolutional neural network (CNN) that learns spatial and temporal regions of higher importance through a bi-direction long short-term memory (bi-LSTM) attention for action recognition. First- and second-order differences of spatially most relevant C3D features (sp-m-C3D) are obtained, and the concatenation of the two differences with the sp-m-C3D is used to generate a temporal attention on the sp-m-C3D using a bi-LSTM. Temporally most relevant sp-m-C3D features are fed into another bi-LSTM for action recognition. Essentially, the network learns spatial and temporal regions of high importance for action recognition. We evaluate the network on two public action recognition datasets: UCF-101 (YouTube Action) and HMDB51. The proposed network performs better compared to other state-of-the-art networks.
Artificial Bandwidth Extension with Memory Inclusion Using Semi-supervised Stacked Auto-encoders

Artiﬁcial bandwidth extension (ABE) algorithms have been developed to improve quality when wideband devices receive speech signals from narrowband devices or infrastructure. The utilisation of contextual information in the form of dynamic features or explicit memory captured from neighbouring frames is common to ABE research, however the use of additional cues augments complexity and can introduce latency. Previous work shows that unsupervised, linear dimensionality reduction techniques help to reduce complexity. This paper reports a semi-supervised, non-linear approach to dimensionality reduction us-ing a stacked auto-encoder. In further contrast to previous work, it operates on raw spectra from which a low dimensional narrowband representation is learned in a data-driven manner. Three different objective speech quality measures show that the new features can be used with a standard regression model to improve ABE performance. Improvements in the mutual information between learned features and missing higher frequency components are also observed whereas improvements in speech quality are corroborated by informal listening tests.
New N‐benzhydrylpiperazine/1,3,4‐oxadiazoles conjugates inhibit the proliferation, migration, and induce apoptosis in HeLa cancer cells via oxidative stress–mediated mitochondrial pathway

N‐benzhydrylpiperazine and 1,3,4‐oxadiazoles are pharmacologically active scaffolds which exhibits significant inhibitory growth effects against various cancer cells, however, antiproliferation effects and the underlying mechanism for inducing apoptosis for aforementioned scaffolds addressing HeLa cancer cells remains uncertain. In this study, N‐benzhydrylpiperazine clubbed with 1,3,4‐oxadiazoles (4a–4h) were synthesized, subsequently characterized using high resolution spectroscopic techniques and eventually evaluated for their antiproliferation potential by inducing apoptosis in HeLa cancer cells. The MTT assay screening results revealed that among all, compound 4d ( N‐benzhydryl‐4‐((5‐(4‐aminophenyl)‐1,3,4‐oxadiazol‐2‐yl)methyl)piperazine) in particular, exhibited IC 50 value of 28.13 ± 0.21 μg/mL and significantly inhibited the proliferation of HeLa cancer cells in concentration‐dependent manner. The in vitro anticancer assays for treated HeLa cells resulted in alterations in the cell morphology, reduction in colony formation, and inhibition of cell migration in concentration‐dependent treatment. Furthermore, G2/M phase arrest, variations in the nuclear morphology, degradation of chromosomal DNA confirmed the ongoing apoptosis in treated HeLa cells. Increase in the expression of cytochrome C and caspase‐3 confirmed the involvement of intrinsic mitochondrial pathway regulating the cell death. Also, elevation in reactive oxygen species level and loss of mitochondrial membrane potential signified that compound 4d induced apoptosis in HeLa cells by generating the oxidative stress. Therefore, compound 4d may act as a potent chemotherapeutic agent against human cervical cancer.
Coarse to Fine Training for Low-Resolution Heterogeneous Face Recognition

Recently, near-infrared (NIR) images are being increasingly used for recognizing facial images across illumination variations and in low-light conditions. In surveillance scenarios, the captured NIR may have low-resolution which results in significant loss of discriminative information along with uncontrolled pose. In this work, we address the challenging task of matching these low-resolution (LR) uncontrolled NIR images with high-resolution (HR) controlled visible (VIS) images usually present in the database. Since the probe and gallery images differ significantly in terms of pose, resolution and spectral properties, we employ a two-stage approach. First, the images are transformed into a common space using metric learning such that the images of the same subject are pushed closer and those of different subjects are pushed apart. We then define an objective function which can simultaneously push both LR NIR and HR VIS samples towards the centroids of the HR VIS samples. We show that the approach is general and can be used for other data like RGB-D and also for matching across pose. Extensive experiments conducted on five datasets shows the effectiveness of our approach.
UFSM VO: Stereo Odometry Based on Uniformly Feature Selection and Strictly Correspondence Matching

Robust visual feature plays a critical role in improving camera localization performance. However, it will cost much computation time for feature extracting and matching, such as SIFT or SURF. In this paper, we present a novel visual odometry (VO) algorithm based on stereo image sequences by performing uniformly feature selection and strict correspondence matching. Firstly, the stable and uniform feature selection is performed by setting adaptive feature thresholds and selecting limited number of features in each local region. Secondly, the precise correspondence matching is achieved by double verification based on the motion model. Finally, the translation vector and rotation matrix of camera are computed, the five-point method is combined with RANSAC-based outlier rejection scheme for initial rotation estimation. And then all inliers are used for minimizing reprojection error to get final camera pose. The experimental results show that the proposed method can achieve the average translational error lower than 1.16% with 12Hz on the public KITTI dataset [1].
Neurological Outcomes and Neuromonitoring in Cardiac Surgery.

Neurological injuries in the form of stroke, cognitive decline, and delirium are common complications in patients undergoing cardiac surgery and continue to be a leading cause of morbidity, mortality, and long-term disability. Patients with neurological complications of cardiac surgery have higher in-hospital mortality, longer hospitalizations, and a higher rate of discharge to intermediate-term and long-term care facilities. Although this places an obvious strain on already scarce health care resources, the more important outcome is the potentially devastating impact that these events can have on the lives of patients and their families. Even as the overall mortality in cardiac surgical patients continues to decline, adverse neurological outcomes remain an everpresent threat in this population. Advances in technology enable us to look more closely than ever before at the structure and function of the central nervous system (CNS). However, the precise pathophysiology of cerebral injury during cardiac surgery remains incompletely understood. Often considered to be primarily associated with systemic inflammation and embolization related to cardiopulmonary bypass (CPB), the pathophysiology of neurological injury is likely more complex and related to a number of patient-specific factors such as preexisting neurological dysfunction and extracardiac vascular disease. A number of preoperative stroke prediction models and risk assessment tools exist for cardiac surgical patients, but advances in intraoperative monitoring of the CNS and its application to the detection and prevention of cerebral injury have not consistently shown outcome benefit across wide populations.
Anonymous Gastritis Image Generation via Adversarial Learning from Gastric X-Ray Images

This paper presents an anonymous gastritis image generation method based on a generative adversarial network approach. Since clinical individual data include highly confidential information, they must be handled carefully. Although data sharing is demanded to construct large-scale medical image datasets for deep learning-based recognition tasks, managing and annotating these data have been conducted manually. The proposed method enables the generation of anonymous images by an adversarial learning approach. Experimental results show that generated images by our method contribute to a gastritis recognition task. This will be helpful for constructing large-scale medical image datasets effectively.
Towards a Multi-Stage Approach to Detect Privacy Breaches in Physician Reviews

Physician Review Websites allow users to evaluate their experiences with health services. As these evaluations are regularly contextualized with facts from users’ private lives, they often accidentally disclose personal information on the Web. This poses a serious threat to users’ privacy. In this paper, we report on early work in progress on “Text Broom”, a tool to detect privacy breaches in user-generated texts. For this purpose, we conceptualize a pipeline which combines methods of Natural Language Processing such as Named Entity Recognition, linguistic patterns and domain-specific Machine Learning approaches which have the potential to recognize privacy violations with wide coverage. A prototypical web application is openly accesible.
The longevity of casein phosphopeptide-amorphous calcium phosphate fluoride varnish's preventative effects: Assessment of white spot lesion formation.

OBJECTIVES: To test how long casein phosphopeptide-amorphous calcium phosphate (CPP-ACP) fluoride varnish prevents enamel demineralization in vitro. MATERIALS AND METHODS: Human molars and premolars were sectioned buccolingually and randomly assigned to two groups. Standardized pretreatment images of enamel surfaces were obtained using FluoreCam. The control group received no treatment, and the experimental group received an application of CPP-ACP fluoride varnish. Over simulated periods of 2, 4, 8, and 12 weeks, specimens were placed in a toothbrushing simulator, thermocycled, subjected to 9 days of pH cycling, and imaged with FluoreCam. Samples were sectioned and polished for polarized light microscope (PLM) evaluation. RESULTS: There were statistically significant time ( P < .001) and varnish ( P < .001) effects on area, intensity, and impact of enamel demineralization. The control group showed significant and progressive demineralization over the 12 weeks ( P < .001). The experimental group revealed no significant demineralization during the first 4 weeks ( P > .05) and significant ( P < .001) increases thereafter. Experimental demineralization after 12 weeks was comparable to 2-week demineralization in the controls, with significant between-group differences ( P < .001) in enamel demineralization at all time points. PLM of the control and experimental groups revealed lesion depths of 90 ± 34 μm and 37 ± 9 μm, respectively. CONCLUSIONS: Within the limitations of this in vitro study, CPP-ACP fluoride varnish prevents enamel demineralization for at least 4 weeks and limits demineralization up to 12 weeks.
Image Denoising for Image Retrieval by Cascading a Deep Quality Assessment Network

Image denoising algorithms have evolved to optimize image quality as measured according to human visual perception. However, image denoising to maximize the success of computer vision algorithms operating on the denoised image has been much less investigated. We consider the problem of image denoising for Gaussian noise with respect to the specific application of image retrieval from a dataset. We define the notion of image quality as determined by the success of image retrieval and design a deep convolutional neural network (CNN) to predict this quality. This network is then cascaded with a deep CNN designed for image denoising, allowing for optimization of the denoising CNN to maximize retrieval performance. This framework allows us to couple denoising to the retrieval problem. We show through experiments on noisy images of the Oxford and Paris buildings datasets that such an approach yields improved mean average precision when compared to using denoising methods that are oblivious to the task of image retrieval.
Effectiveness of Dynamic Features in INCA and Temporal Context-INCA

Non-parallel Voice Conversion (VC) has gained signiﬁcant attention since last one decade. Obtaining corresponding speech frames from both the source and target speakers before learning the mapping function in the non-parallel VC is a key step in the standalone VC task. Obtaining such corresponding pairs, is more challenging due to the fact that both the speakers may have uttered different utterances from same or the different languages. Iterative combination of a Nearest Neighbor search step and a Conversion step Alignment (INCA) and its variant Temporal Context (TC)-INCA are popular unsupervised alignment algorithms. The INCA and TC-INCA iteratively learn the mapping function after getting the Nearest Neighbor (NN) aligned pairs from the intermediate converted and the target spectral features. In this paper, we propose to use dynamic features along with static features to calculate the NN aligned pairs in both the INCA and TC-INCA algorithms (since the dynamic features are known to play a key role to differentiate major phonetic categories). We obtained on an average relative improvement of 13.75 % and 5.39 % with our proposed Dynamic INCA and Dynamic TC-INCA, respectively. This improvement is also positively reﬂected in the quality of converted voices.
Prevalence of Apolipoprotein E Polymorphisms in Alzheimer’s Disease, Mild Cognitive Impairment, and Healthy Elderly: A Northern Greece Study

Background: Apolipoprotein E ε4 allele (APOEε4) is a major genetic risk factor for Alzheimer’s disease (AD). APOEε4 carriers have a higher risk of cognitive impairment and AD in a gene dose-dependent manner. The above notion is investigated in the Greek population. Methods: A sample of 1,703 subjects (967 AD patients, 576 mild cognitive impairment [MCI] and 160 Healthy Elderly), was genotyped for APOE from 2008 to 2017. DNA was extracted from peripheral blood using the QIAamp Blood DNA purification kit (Qiagen Inc., USA). Descriptive statistics, one-way analysis of variance with Bonferroni post hoc tests, Pearson chi-square test, and binary logistic regression models were used for the statistical analysis. Results: The APOE genotype and allele frequencies in AD group were significantly different from those in the Control and MCI groups. The frequencies of ε4/4 homozygotes were 1.9, 1.6, and 5.7%, while the ε4/– carriers’ distribution was 22.5, 24.1, and 37.4% in the Control, MCI, and AD groups respectively. The estimated odds of ε4/4 for AD was 5.731-fold higher compared to the estimated odds of ε3/3. The interaction between gender and APOE did not have a significant effect on the odds for MCI (p = 0.942) and AD (p = 0.984). Conclusion: In Greece, APOE ε4 presence is related to an increased risk for AD in a dose-related manner. Contrary to long-standing views, men and women with the APOE ε4 genotype have nearly the same odds of developing MCI and AD.
Structure and carbohydrate histochemistry of the intestine in ten teleostean species

The morphology and carbohydrate histochemistry of ten teleostean intestines are compared. Although there is an absence of regional differentiation seen in higher vertebrates, specializations in some species occur in the form of intestinal swellings, pyloric ceca and recta, the latter separated by a valve. The intestinal lumen is lined by a simple columnar epithelium interspersed with goblet cells; multicellular intestinal glands are absent. Thick basement membranes seen in centrarchids and Perca flavescens closely resemble the stratum compactum found in the lamina propria of esocids. Granular cells, which vary in number from species to species, are often seen in the mucosa and submucosa but less frequently in the muscularis. In species with intestino‐rectal valves, a rectum is easily defined by the abrupt appearance of lower mucosal folds, more goblet cells and a thicker muscularis. In the remaining species the above features appear gradually in the distal intestine. Goblet cells show species variations in localization of epithelial mucosubstances, which in broad terms are recognized as sulfomucins, sialomucins and neutral mucosubstances. In both proximal and distal intestines the majority of goblet cells contain sialomucin although small amounts of sulfomucin are also often present. In species without intestino‐rectal valves, no changes in carbohydrates occur between proximal and distal intestines. The possible significance of the heterogeneous character of digestive tract mucosubstances is discussed.
Ghrelin Axis Reveals the Interacting Influence of Central Obesity and Hypertension

Objective: This study aimed to investigate how central obesity and hypertension modulate unacylated ghrelin (UnAG), acylated ghrelin (AG), obestatin, growth hormone (GH), and the ratios of UnAG/obestatin, AG/obestatin, and total ghrelin/obestatin. Methods: Circulatory abundances of UnAG, AG, obestatin and GH were determined in 387 Hong Kong Chinese female adults with age between 24 to 86 years based on a 2 × 2 factorial design of hypertension (blood pressure ≥140/90 mmHg) and central obesity (waist circumference or WC ≥80 cm). Participants were categorized as neither hypertensive nor centrally obese (NHNO; n = 105), hypertensive but not centrally obese (HNO; n = 102), centrally obese but not hypertensive (NHO; n = 74) and hypertensive and centrally obese (NO; n = 106). Pearson's correlation analyses were performed to detect the association between the peptides examined with WC and blood pressure. The main and interaction effects of hypertension and central obesity were examined by generalized estimating equations analyses. Results: Correlation analyses revealed that systolic blood pressure was negatively correlated with AG/obestatin, UnAG/obestatin and total ghrelin/obestatin ratios, AG, total ghrelin, and GH, while diastolic blood pressure was negatively correlated with UnAG/obestatin, total ghrelin/obestatin ratios, and GH. WC was negatively correlated with AG/obestatin, UnAG/obestatin, and total ghrelin/obestatin ratios, UnAG, AG, total ghrelin, GH, and obestatin. Interaction effects of hypertension and central obesity were observed on UnAG/obestatin, AG/obestatin and total ghrelin/obestatin ratios, and obestatin. Obestatin in NHO group was significantly higher compared to NHNO and HO groups. UnAG/obestatin, AG/obestatin, and total ghrelin/obestatin ratios were higher in NHNO group compared to HNO and HO groups. Main effects of central obesity and hypertension were observed in UnAG, total ghrelin and GH. The HO group manifested the lowest level of UnAG, total ghrelin and GH among all the groups studied. Main effect of hypertension was observed on AG, suggesting that hypertensive individuals exhibited lower levels of AG regardless of central obesity. Conclusion: Circulatory ghrelin gene products and GH exhibit different modes of modulation in response to the co-manifestation of multiple cardiovascular risk factors compared with a single risk factor alone.
Cnn-based Denoising of Time-Of-Flight Depth Images

This paper is the first to propose a deep learning approach for denoising depth images produced by Time of Flight (ToF) cameras. The noise in ToF depth images is spatially nonstationary, and depends on the strength of the infrared signal coming back to the sensor. Existing ToF denoising methods do not capture the non-stationary nature of noise in such images. We propose a fairly simple, yet efficient Convolutional Neural Network (CNN) that makes use of both the depth and infrared information to denoise ToF depth images. To train the network, a novel methodology to generate a high amount of training samples is proposed. The experimental results demonstrate that the proposed CNN-based ToF denoising method substantially outperforms the state-of-the-art denoising methods, including wavelet shrinkage, least squares optimization, Bilateral Filtering and BM3D.