Miracle-dz/UniFAR-FTUs huggingface dataset:
Using the 3D shape of the nose for biometric authentication

This thesis is dedicated to exploring the potential of the 3D shape of the nasal region for face recognition. In comparison to other parts of the face, the nose has a number of distinctive features that make it attractive for recognition purposes. It is relatively stable over different facial expressions, easy to detect because of its salient convexity, and difficult to be intentionally cover up without attracting suspicion. In addition compared to other facial parts, such as forehead, chin, mouth and eyes, the nose is not vulnerable to unintentional occlusions caused by scarves or hair. Prior to undertaking a thorough analysis of the discriminative features of the 3D nasal regions, an overview of denoising algorithms and their impact on the 3D face recognition algorithms is first provided. This analysis, which is one of the first to address this issue, evaluates the performance of 3D holistic algorithms when various denoising methods are applied. One important outcome of this evaluation is to determine the optimal denoising parameters in terms of the overall 3D face recognition performance. A novel algorithm is also proposed to learn the statistics of the noise generated by the 3D laser scanners and then simulate it over the face point clouds. Using this process, the denoising and 3D face recognition algorithms’ robustness over various noise powers can be quantitatively evaluated. A new algorithm is proposed to find the nose tip from various expressions and self-occluded samples. Furthermore, novel applications of the nose region to align the faces in 3D is provided through two pose correction methods. The algorithms are very consistent and robust against different expressions, partial and self-occlusions. The nose’s discriminative strength for 3D face recognition is analysed using two approaches. The first one creates its feature sets by applying nasal curves to the depth map. The second approach utilises a novel feature space, based on histograms of normal vectors to the response of the Gabor wavelets applied to the nasal region. To create the feature spaces, various triangular and spherical patches and nasal curves are employed, giving a very high class separability. A genetic algorithm (GA) based feature selector is then used to make the feature space more robust against facial expressions. The basis of both algorithms is a highly consistent and accurate nasal region landmarking, which is quantitatively evaluated and compared with previous work. The recognition ranks provide the highest identification performance ever reported for the 3D nasal region. The results are not only higher than the previous 3D nose recognition algorithms, but also better than or very close to recent results for whole 3D face recognition. The algorithms have been evaluated on three widely used 3D face datasets, FRGC, Bosphorus and UMB-DB.
Scalable, high-speed on-chip-based NDN name forwarding using FPGA

Named Data Networking (NDN) is the most promising candidate among the proposed content-based future Internet architectures. In NDN, Forwarding Information Base (FIB) maintains name prefixes and their corresponding outgoing interface(s) and forwards incoming packets by calculating the longest prefix match (LPM) of their content names (CNs). A CN in NDN is of variable-length and is maintained using a hierarchical structure. Therefore, to perform name lookup for packet forwarding at wire speed is a challenging task. However, the use of GPUs can lead to much better lookup speeds than CPU but, they are often limited by the CPU-GPU transfer latencies. In this paper, we exploit the massive parallel processing power of FPGA technology and propose a scalable, high-speed on-chip SRAM-based NDN name forwarding scheme for FIB (OnChip-FIB) using Field-Programmable Gate Arrays (FPGA). OnChip-FIB scales well as the number of prefixes grow, due to low storage complexity and low resource utilization. Extensive simulation results show that the OnChip-FIB scheme can achieve 1.06 μs measured lookup latency with a 26% on-chip block memory usage in a single Xilinx UltraScale FPGA for 50K named dataset.
Knowledge discovery using maximization of the spread of influence in an expert system

The problem of knowledge acquisition (KA) and the automation of this process is a bottleneck of expert systems (ESs) and remains a topical issue. The present study is an attempt to develop an ES in which inference attributes are formed on the basis of automated discovery of tacit knowledge. The structure of the KA module provides an opportunity to discover new knowledge from the cognitive representations of domain models. The algorithmic processing mechanism consists of three blocks: cluster analysis, influence analysis, and modelling of the equivalent graph. In the proposed expert system based on effective controls, a method of finding influences that highlights the main characteristics of the system is presented. These parameters are used as attributes for the further development of the rule base, expertise, or independent use in decision‐making. A qualitative decrease in user subjectivity is achieved using this algorithm and through elimination of the knowledge engineer from the cycle of KA, which transforms it into a discovery knowledge cycle. Quality assurance of the knowledge base is achieved using three approaches: system presentation, mathematical support, and automation.
Reply to Ortner

I address the comments made by Ortner (Games 9(4): 93, 2018) in relation to my note “Incentive Systems for Risky Investment Decisions Under Unknown Preferences: Ortner et al. Revisited” (Games 9(2): 26, 2018).
On the Robustness of Deep Learning Models to Universal Adversarial Attack

In recent years, there have been significant advances in deep learning applied to problems in high-level vision tasks (e.g. image classification, object detection, semantic segmentation etc.) which has been met with a great deal of success. State-of-the-art methods that have shown impressive results on recognition tasks typically share a common structure involving stage-wise encoding of the image, followed by a generic classifier. However, these architectures have been shown to be vulnerable to the adversarial perturbations which may undermine the security of the systems supported by deep neural nets. In this work, initially we present rigorous evaluation of adversarial attacks on recent deep learning models for two different high-level tasks (image classification and semantic segmentation). Then we propose a model and dataset independent approach to generate adversarial perturbation and also the transferability of perturbation across different datasets and tasks. Moreover, we analyze the effect of different network architectures which will aid future efforts in understanding and defending against adversarial perturbations. We perform comprehensive experiments on several standard image classification and segmentation datasets to demonstrate the effectiveness of our proposed approach.
A Witness Function Based Construction of Discriminative Models Using Hermite Polynomials

In machine learning, we are given a dataset of the form {(xj,yj)}j=1M, drawn as i.i. d. samples from an unknown probability distribution μ; the marginal distribution for the xj's being μ*, and the marginals of the kth class μk*(x) possibly overlapping. We address the problem of detecting, with a high degree of certainty, for which x we have μk*(x)>μi*(x) for all i ≠ k. We propose that rather than using a positive kernel such as the Gaussian for estimation of these measures, using a non-positive kernel that preserves a large number of moments of these measures yields an optimal approximation. We use multi-variate Hermite polynomials for this purpose, and prove optimal and local approximation results in a supremum norm in a probabilistic sense. Together with a permutation test developed with the same kernel, we prove that the kernel estimator serves as a “witness function” in classification problems. Thus, if the value of this estimator at a point x exceeds a certain threshold, then the point is reliably in a certain class. This approach can be used to modify pretrained algorithms, such as neural networks or nonlinear dimension reduction techniques, to identify in-class vs out-of-class regions for the purposes of generative models, classification uncertainty, or finding robust centroids. This fact is demonstrated in a number of real world data sets including MNIST, CIFAR10, Science News documents, and LaLonde data sets.
A Truthful FPTAS Mechanism for Emergency Demand Response in Colocation Data Centers

Demand response (DR) is a vital means of electricity market in maintaining power grid reliability, sustainability and stability. DR can enable consumers (e.g. data centers) to reduce their electricity consumption when the supply of electricity is a shortage. The consumers will be rewarded if they reduce or shift some of their energy usage during peak hours. Aiming at solving the efficiency of DR, in this paper, we present MEDR, a mechanism on emergency DR in colocation data center. First, we formalize the MEDR problem and propose a dynamic programming to solve the optimization version of the problem. We then design a deterministic mechanism to solve the MEDR. We prove that our mechanism is truthful and it is an FPTAS, i.e., it can be approximated within $ 1+\epsilon$ for any given $\epsilon \gt 0,$ while the running time of our mechanism is polynomial in the number of tenants n and $ 1/\epsilon$. Furthermore, we also give an auction system covering the efficient FPTAS algorithm as bidding decision program for DR. Finally, we choose a real dataset to build a large number of simulation datasets in performance evaluation. The results show that our mechanism outperforms near-optimal and high utility demonstrate the effectiveness of our work.
6G: The Next Frontier

The current development of 5G networks represents a breakthrough in the design of communication networks, for its ability to provide a single platform enabling a variety of different services, from enhanced mobile broadband communications, automated driving, Internet-of-Things, with its huge number of connected devices, etc. Nevertheless, looking at the current development of technologies and new services, it is already possible to envision the need to move beyond 5G with a new architecture incorporating new services and technologies. The goal of this paper is to motivate the need to move to a sixth generation (6G) of mobile communication networks, starting from a gap analysis of 5G, and predicting a new synthesis of near future services, like hologram interfaces, ambient sensing intelligence, a pervasive introduction of artificial intelligence and the incorporation of technologies, like TeraHertz (THz) or Visible Light Communications (VLC), 3-dimensional coverage.
Cricket stroke extraction: Towards creation of a large-scale cricket actions dataset

In this paper, we deal with the problem of temporal action localization for a large-scale untrimmed cricket videos dataset. Our action of interest for cricket videos is a cricket stroke played by a batsman, which is, usually, covered by cameras placed at the stands of the cricket ground at both ends of the cricket pitch. After applying a sequence of preprocessing steps, we have ~73 million frames for 1110 videos in the dataset at constant frame rate and resolution. The method of localization is a generalized one which applies a trained random forest model for CUTs detection(using summed up grayscale histogram difference features) and two linear SVM camera models(CAM1 and CAM2) for first frame detection, trained on HOG features of CAM1 and CAM2 video shots. CAM1 and CAM2 are assumed to be part of the cricket stroke. At the predicted boundary positions, the HOG features of the first frames are computed and a simple algorithm was used to combine the positively predicted camera shots. In order to make the process as generic as possible, we did not consider any domain specific knowledge, such as tracking or specific shape and motion features. The detailed analysis of our methodology is provided along with the metrics used for evaluation of individual models, and the final predicted segments. We achieved a weighted mean TIoU of 0.5097 over a small sample of the test set.
Nearly Optimal Adaptive Procedure with Change Detection for Piecewise-Stationary Bandit

Multi-armed bandit (MAB) is a class of online learning problems where a learning agent aims to maximize its expected cumulative reward while repeatedly selecting to pull arms with unknown reward distributions. We consider a scenario where the reward distributions may change in a piecewise-stationary fashion at unknown time steps. We show that by incorporating a simple change-detection component with classic UCB algorithms to detect and adapt to changes, our so-called M-UCB algorithm can achieve nearly optimal regret bound on the order of $O(\sqrt{MKT\log T})$, where $T$ is the number of time steps, $K$ is the number of arms, and $M$ is the number of stationary segments. Comparison with the best available lower bound shows that our M-UCB is nearly optimal in $T$ up to a logarithmic factor. We also compare M-UCB with the state-of-the-art algorithms in numerical experiments using a public Yahoo! dataset and a real-world digital marketing dataset to demonstrate its superior performance.
A Linear Fragment of Unacylated Ghrelin (UAG6−13) Protects Against Myocardial Ischemia/Reperfusion Injury in Mice in a Growth Hormone Secretagogue Receptor-Independent Manner

Unacylated ghrelin (UAG), the most abundant form of ghrelin in circulation, has been shown to exert cardioprotective effect in experimental cardiopathies. The present study aimed to investigate the cardioprotective effect of a linear bioactive fragment of UAG against myocardial ischemia-induced injury and dysfunction in C57BL/6 wild type mice and the mechanisms involved. Treatments were administered at doses of 100 (UAG), 1,000 and 3,000 (UAG6−13) nmol/kg at 12 h interval during 14 days prior to 30 min left coronary artery ligation and reperfusion for a period of 6 or 48 h. The infarct area was decreased in a dose-dependent manner at 48 h of reperfusion, with a reduction of 54% at the highest dose of UAG6−13 tested. Myocardial hemodynamics were improved as demonstrated by an increase in cardiac output, maximum first derivative of left ventricular pressure, and preload recruitable stroke work, a load-independent contractility index. Six hours after reperfusion, circulating levels of IL-6 and TNF-α pro-inflammatory cytokines were reduced, and the effect was maintained at 48 h for TNF-α. 5′ AMP-activated protein kinase (AMPK) was activated, while acetyl-CoA carboxylase (ACC) activity was inhibited, along with a decrease in apoptotic protein levels. In isolated hearts, the effect of UAG6−13 was unaffected by the presence of D-Lys3-GHRP-6, a ghrelin receptor (GHSR1a) antagonist, suggesting that the peptide acted through a GHSR1a-independent pathway. The results support the therapeutic application of UAG bioactive peptide fragments against myocardial ischemia/reperfusion injury.
Commercial Divers’ Subjective Evaluation of Saturation

Commercial saturation diving involves divers living and working in an enclosed atmosphere with elevated partial pressure of oxygen (ppO2) for weeks. The divers must acclimatize to these conditions during compression, and for up to 28 days until decompression is completed. During decompression, the ppO2 and ambient pressure are gradually decreased; then the divers must acclimatize again to breathing normal air in atmospheric pressure when they arrive at surface. We investigated 51 saturation divers’ subjective evaluation of the saturation and post-decompression phase via questionnaires and individual interviews. The questions were about decompression headaches and fatigue; and time before recovering to a pre-saturation state. Twenty-two (44%) of the divers who responded declared having headaches; near surface (44%) or after surfacing (56%). 71% reported post-saturation fatigue after their last saturation, 82% of them described it as typical and systematic after each saturation. Recovery was reported to normally take from 1 to 10 days. The fatigue and headaches observed are compatible with divers’ acclimatization to the changes in ppO2 levels during saturation and decompression. They appear to be reversible post- decompression.
Whole-Genome Sequencing of Three Native Cattle Breeds Originating From the Northernmost Cattle Farming Regions

Northern Fennoscandia and the Sakha Republic in the Russian Federation represent the northernmost regions on Earth where cattle farming has been traditionally practiced. In this study, we performed whole-genome resequencing to genetically characterize three rare native breeds Eastern Finncattle, Western Finncattle and Yakutian cattle adapted to these northern Eurasian regions. We examined the demographic history, genetic diversity and unfolded loci under natural or artificial selection. On average, we achieved 13.01-fold genome coverage after mapping the sequencing reads on the bovine reference genome (UMD 3.1) and detected a total of 17.45 million single nucleotide polymorphisms (SNPs) and 1.95 million insertions-deletions (indels). We observed that the ancestral species (Bos primigenius) of Eurasian taurine cattle experienced two notable prehistorical declines in effective population size associated with dramatic climate changes. The modern Yakutian cattle exhibited a higher level of within-population variation in terms of number of SNPs and nucleotide diversity than the contemporary European taurine breeds. This result is in contrast to the results of marker-based cattle breed diversity studies, indicating assortment bias in previous analyses. Our results suggest that the effective population size of the ancestral Asiatic taurine cattle may have been higher than that of the European cattle. Alternatively, our findings could indicate the hybrid origins of the Yakutian cattle ancestries and possibly the lack of intensive artificial selection. We identified a number of genomic regions under selection that may have contributed to the adaptation to the northern and subarctic environments, including genes involved in disease resistance, sensory perception, cold adaptation and growth. By characterizing the native breeds, we were able to obtain new information on cattle genomes and on the value of the adapted breeds for the conservation of cattle genetic resources.
Interactivity: A Potential Determinant of Learning by Preparing to Teach and Teaching

It has been suggested that preparing to teach and teaching are conditionally effective in enhancing one’s own learning. This paper focuses on interactivity – the level of teacher–student interaction in expected or actual teaching – as the potential key to understanding and controlling the variability in the effectiveness of learning by preparing to teach and teaching. By summarizing and reanalyzing the results of previous studies, I suggest that the learning benefits of studying with the expectation of direct teaching (i.e., teaching a student face-to-face) are greater than those of studying with the expectation of indirect teaching (i.e., teaching a student indirectly by creating a lecture video, providing written explanations, or using other means) and that learning by direct teaching surpasses learning by explaining to oneself or indirect teaching at least after preparing to do so. Next, three candidate explanations for the impact of interactivity are discussed: the advantages of asking and answering questions, obtaining additional information about and from one’s student, and enhancing one’s motivation to process learning material deeply while preparing to teach and teaching. Finally, I conclude with the remaining questions and directions for future research.
Trophic Interactions of Mesopelagic Fishes in the South China Sea Illustrated by Stable Isotopes and Fatty Acids

As the most abundant fishes and the least investigated components of the open ocean ecosystem, mesopelagic fishes play an important role in biogeochemical cycles and hold potentially huge fish resources. There are major gaps in our knowledge of their biology, adaptations and trophic dynamics and even diel vertical migration (DVM). Here we present evidence of the variability of ecological behaviours (migration and predation) and trophic interactions among various species of mesopelagic fishes collected from the South China Sea indicated by isotopes (δ13C, δ15N), biomarker tools (fatty acids (FAs), and compound- specific stable isotope analysis of FAs (CSIA)). Higher lipid contents of migrant planktivorous fishes were observed with average values of 35%, while others ranged from 22 to 29.5%. These high lipids contents limit the application of 13Cbulk (bulk–tissue 13C) as diet indicator; instead 13Cextraction (the lipid extracted 13C) values were applied successfully to reflect dietary sources. The δ15N values of non-migrant planktivorous and piscivorous fishes varied in a narrow range (10.0 to 10.8‰). This small difference may be caused by the low temperature in deep ocean, which results in low metabolic rates of nitrogen. The piscivorous fishes had higher FA ratios of DHA (22:6n-3)/EPA (20:5n-3) than planktivorous fishes, which mirrored their potential carnivory. CSIA can reveal small changes in biochemical composition and distinguishes between possible dietary sources such as between the non-migrant piscivorous and the semi-migrant piscivorous fishes with significantly different δ13C values of the 20:4n-6 and 20:5n-3 FAs (p<0.01). Mesopelagic fishes exhibit complex trophic interactions revealed by molecular and CSIA tools and play a vital role in the marine “biological pump”. This knowledge is important to comprehensively evaluate the role of mesopelagic fishes in global carbon budgets.
Incidence and Outcome of Severe and Nonsevere Thrombocytopenia Associated With Zika Virus Infection—Puerto Rico, 2016

Abstract Background Zika virus (ZIKV) infection has been associated with severe thrombocytopenia. We describe the incidence, clinical manifestations, and outcomes of patients with ZIKV infection and thrombocytopenia. Methods We reviewed medical records of patients with ZIKV infection and thrombocytopenia (platelet count <100 ×109 cells/L) in Puerto Rico during 2016. Severe thrombocytopenia was defined by platelet count <20 ×109/L or a platelet count <50 ×109/L and treatment for immune thrombocytopenia (ITP). Results Of 37 878 patients with ZIKV infection, 47 (0.1%) had thrombocytopenia in the absence of an alternative etiology (1.4 cases/100 000 population), including 12 with severe thrombocytopenia. Most patients with thrombocytopenia were adult (77%) and male (53%). Platelet nadir occurred a median (range) of 6 (1–16) and 5 (0–34) days after symptom onset for patients with severe and nonsevere thrombocytopenia, respectively. Among patients with severe thrombocytopenia, all had bleeding, 33% were admitted to the intensive care unit, and 8% died; 50% were treated for ITP. Among 5 patients with severe thrombocytopenia who received intravenous immunoglobulin, the median platelet count increase (range) was 112 (65–202) ×109/L. In contrast, among 4 patients who received platelet transfusion, the median increase in platelet count (range) was 8.5 (–6 to 52) ×109/L. Conclusions Patients with severe thrombocytopenia and ZIKV infection experienced prominent acute morbidity. Consistent with recommended management, administration of ITP treatments to such patients may be more efficacious than platelet transfusion in resolving thrombocytopenia. Severe thrombocytopenia should be considered a rare outcome of ZIKV infection.