Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning a low dimensional manifold of real cancer tissue with PathologyGAN

Application of deep learning in digital pathology shows promise on improving disease diagnosis and understanding. We present a deep generative model that learns to simulate high-fidelity cancer tissue images while mapping the real images onto an interpretable low dimensional latent space. The key to the model is an encoder trained by a previously developed generative adversarial network, PathologyGAN. We study the latent space using 249K images from two breast cancer cohorts. We find that the latent space encodes morphological characteristics of tissues (e.g. patterns of cancer, lymphocytes, and stromal cells). In addition, the latent space reveals distinctly enriched clusters of tissue architectures in the high-risk patient group.
SpeedNet: Learning the Speediness in Videos

We wish to automatically predict the “speediness” of moving objects in videos - whether they move faster, at, or slower than their “natural” speed. The core component in our approach is SpeedNet—a novel deep network trained to detect if a video is playing at normal rate, or if it is sped up. SpeedNet is trained on a large corpus of natural videos in a self-supervised manner, without requiring any manual annotations. We show how this single, binary classification network can be used to detect arbitrary rates of speediness of objects. We demonstrate prediction results by SpeedNet on a wide range of videos containing complex natural motions, and examine the visual cues it utilizes for making those predictions. Importantly, we show that through predicting the speed of videos, the model learns a powerful and meaningful space-time representation that goes beyond simple motion cues. We demonstrate how those learned features can boost the performance of self supervised action recognition, and can be used for video retrieval. Furthermore, we also apply SpeedNet for generating time-varying, adaptive video speedups, which can allow viewers to watch videos faster, but with less of the jittery, unnatural motions typical to videos that are sped up uniformly.
Peer Offloading in Mobile-Edge Computing With Worst Case Response Time Guarantees

Mobile-edge computing (MEC) is a new paradigm that provides cloud computing services at the edge of networks. To achieve better performance with limited computing resources, peer offloading between cooperative edge servers (e.g., MEC-enabled base stations) has been proposed as an effective technique to handle bursty and spatially imbalanced arrival of computation tasks. While various performance metrics of peer offloading policies have been considered in the literature, the worst case response time, a common quality of service (QoS) requirement in real-time applications, yet receives much less attention. To fill the gap, we formulate the peer offloading problem based on a stochastic arrival model and propose two online algorithms for cases with and without prior knowledge of task arrival rate. Our goal is to maximize the utility function of time-average throughput under constraints of energy consumption and worst case response time. Both theoretical analysis and numerical results show that our algorithms are able to produce close to optimal performance.
Unsupervised Performance Analysis of 3D Face Alignment

We address the problem of analyzing the performance of 3D face alignment (3DFA) algorithms. Traditionally, performance analysis relies on carefully annotated datasets. Here, these annotations correspond to the 3D coordinates of a set of pre-defined facial landmarks. However, this annotation process, be it manual or automatic, is rarely error-free, which strongly biases the analysis. In contrast, we propose a fully unsupervised methodology based on robust statistics and a parametric confidence test. We revisit the problem of robust estimation of the rigid transformation between two point sets and we describe two algorithms, one based on a mixture between a Gaussian and a uniform distribution, and another one based on the generalized Student's t-distribution. We show that these methods are robust to up to 50% outliers, which makes them suitable for mapping a face, from an unknown pose to a frontal pose, in the presence of facial expressions and occlusions. Using these methods in conjunction with large datasets of face images, we build a statistical frontal facial model and an associated parametric confidence metric, eventually used for performance analysis. We empirically show that the proposed pipeline is neither method-biased nor data-biased, and that it can be used to assess both the performance of 3DFA algorithms and the accuracy of annotations of face datasets.
Dark defect charge dynamics in bulk chemical-vapor-deposition-grown diamonds probed via nitrogen vacancy centers

Although chemical vapor deposition (CVD) is one of the preferred routes to synthetic diamond crystals, a full knowledge of the point defects produced during growth is still incomplete. Here we exploit the charge and spin properties of nitrogen-vacancy (NV) centers in type-1b CVD diamond to expose an optically and magnetically dark point defect, so far virtually unnoticed despite an abundance comparable to (if not greater than) that of substitutional nitrogen. Indirectly-detected photo-luminescence spectroscopy indicates a donor state 1.6 eV above the valence band, although the defect's microscopic structure and composition remain elusive. Our results may prove relevant to the growing set of applications that rely on CVD-grown single crystal diamond.
Robust Modelling of Reflectance Pulse Oximetry for SpO2 Estimation

Continuous monitoring of blood oxygen saturation levels is vital for patients with pulmonary disorders. Traditionally, SpO2 monitoring has been carried out using transmittance pulse oximeters due to its dependability. However, SpO2 measurement from transmittance pulse oximeters is limited to peripheral regions. This becomes a disadvantage at very low temperatures as blood perfusion to the peripherals decreases. On the other hand, reflectance pulse oximeters can be used at various sites like finger, wrist, chest and forehead. Additionally, reflectance pulse oximeters can be scaled down to affordable patches that do not interfere with the user’s diurnal activities. However, accurate SpO2 estimation from reflectance pulse oximeters is challenging due to its patient dependent, subjective nature of measurement. Recently, a Machine Learning (ML) method was used to model reflectance waveforms onto SpO2 obtained from transmittance waveforms. However, the generalizability of the model to new patients was not tested. In light of this, the current work implemented multiple ML based approaches which were subsequently found to be incapable of generalizing to new patients. Furthermore, a minimally calibrated data driven approach was utilized in order to obtain SpO2 from reflectance PPG waveforms. The proposed solution produces an average mean absolute error of 1.81% on unseen patients which is well within the clinically permissible error of 2%. Two statistical tests were conducted to establish the effectiveness of the proposed method.Clinical relevance— The proposed method ameliorates our current understanding of reflectance based pulse oximetry and provides a method to estimate SpO2 from reflectance pulse oximeters
Motivation for Learning: Adaptive Gamification for Web-based Learning Environments

Many learning environments are deserted by the learners, even if they are effective in supporting learning. Gamification is becoming a popular way to motivate users and enhance their participation on web-based activities, by adding game elements to the learning environment. But it still pays little attention to the individual differences among learners' preferences as players. This paper presents a generic and adaptive gamification system that can be plugged on various learning environments. This system can be automatically personalised, based on an analysis of the interaction traces. We first present the architecture of the proposed system to support the genericity of the game elements. Then, we describe the user model supporting the adaptivity of the game elements.
PKU-ICST @TRECVID2012: Known-item Search Task

We participate in all two types of known-item search task of TRECVID 2012: automatic search and interactive search. This paper presents our approaches and results. We adopt three kinds of text information, which are XML documents, ASR and OCR. And we index and search the three kinds of pre-processed text individually with Lucene. In addition, the results are combined and re-ranked by two re-ranking approaches. We achieve the good performances, and official evaluation shows that our team is ranked 1 st in both automatic search and interactive search.
dMFEA-II: An adaptive multifactorial evolutionary algorithm for permutation-based discrete optimization problems

The emerging research paradigm coined as multitasking optimization aims to solve multiple optimization tasks concurrently by means of a single search process. For this purpose, the exploitation of complementarities among the tasks to be solved is crucial, which is often achieved via the transfer of genetic material, thereby forging the Transfer Optimization field. In this context, Evolutionary Multitasking addresses this paradigm by resorting to concepts from Evolutionary Computation. Within this specific branch, approaches such as the Multifactorial Evolutionary Algorithm (MFEA) has lately gained a notable momentum when tackling multiple optimization tasks. This work contributes to this trend by proposing the first adaptation of the recently introduced Multifactorial Evolutionary Algorithm II (MFEA-II) to permutation-based discrete optimization environments. For modeling this adaptation, some concepts cannot be directly applied to discrete search spaces, such as parent-centric interactions. In this paper we entirely reformulate such concepts, making them suited to deal with permutation-based search spaces without loosing the inherent benefits of MFEA-II. The performance of the proposed solver has been assessed over 5 different multitasking setups, composed by 8 datasets of the well-known Traveling Salesman (TSP) and Capacitated Vehicle Routing Problems (CVRP). The obtained results and their comparison to those by the discrete version of the MFEA confirm the good performance of the developed dMFEA-II, and concur with the insights drawn in previous studies for continuous optimization.
Deep Entwined Learning Head Pose and Face Alignment Inside an Attentional Cascade with Doubly-Conditional fusion

Head pose estimation and face alignment constitute a backbone preprocessing for many applications relying on face analysis. While both are closely related tasks, they are generally addressed separately, e.g. by deducing the head pose from the landmark locations. In this paper, we propose to entwine face alignment and head pose tasks inside an attentional cascade. This cascade uses a geometry transfer network for integrating heterogeneous annotations to enhance landmark localization accuracy. Furthermore, we propose a doubly-conditional fusion scheme to select relevant feature maps, and regions thereof, based on a current head pose and landmark localization estimate. We empirically show the benefit of entwining head pose and landmark localization objectives inside our architecture, and that the proposed AC-DC model enhances the state-of-the-art accuracy on multiple databases for both face alignment and head pose estimation tasks.
Various Secure Routing Schemes for MANETs: A Survey

MANET is an infrastructure less as well as self configuring network consisting of mobile nodes communicating with each other using radio medium. Its exclusive properties such as dynamic topology, decentralization, and wireless medium make MANET to become very unique network amongst other traditional networks, thereby determining security to be a major challenge. In this paper, we have carried out the survey of various security approaches of Mobile Adhoc Networks and provide a comprehensive study regarding it. We have focused our work on three approaches such as Bayesian watch dog, Trust based systems, and Ant colony optimization. In wireless perspective, security is a crucial term to handle. Therefore it becomes necessary when we are concerning our work with Mobile Adhoc Network.
Validation of an Individualized Measure of Quality of Life, Patient Generated Index, for Use with People with Parkinson's Disease

Methods Patients with PD completed the PGI and various standard patient-reported outcome (PRO) measures. The PGI and standard PRO measures were compared at the total score, domain, and item levels. Pearson's correlations and independent t-tests were used, as well as positive and negative predictive values. Results The sample (n = 76) had a mean age of 69 (standard deviation 9) and were predominantly men (59%). The PGI was moderately correlated (r = −0.35) with the standardized disease-specific QOL measure Parkinson's Disease Questionnaire (PDQ-8). Within one severity rating, agreement between the PGI and different standard outcome measures ranged from 85 to 100% for walking, 69 to 100% for fatigue, 38 to 75% for depression, and 20 to 80% for memory/concentration. Conclusion This study demonstrates that nominated areas of QOL on the PGI provide comparable results to standard PRO measures, and provides evidence in support of the validity of this individualized measure in PD.
The R219K Polymorphism of the ATP Binding Cassette Subfamily a Member 1 Gene and Susceptibility to Ischemic Stroke in Chinese Population

Abstract Stroke is the major cause of death and disability worldwide. ABCA1 R219K has been suggested as a risk factor for ischemic stroke, but the results remain inconclusive in the Chinese population. This study aimed to assess the association between ABCA1 R219K and ischemic stroke using meta-analysis. A systematic literature search was conducted to select eligible studies and the pooled odds ratio (OR) with 95% confidence interval (CI) was used to evaluate the strength of association. Fourteen studies containing 2865 cases and 3227 controls were included in the meta-analysis and the results suggested that there is a strong association between ABCA1 R219K and the ischemic stroke risks (K vs. R: OR = 0.837, 95% CI: 0.735- 0.954, p=0.008; KK vs. RR: OR = 0.689, 95% CI: 0.520-0.912, p=0.009; KK+RK vs. RR: OR = 0.782, 95% CI: 0.691-0.885, p<0.001). Subgroup analysis revealed that significant association was found for the 4 genetic models (p<0.05) in the Southern population, while in the northern population significant association was only found under the dominant model (KK+RK vs. RR: OR = 0.744, 95% CI: 0.583- 0.949, p<0.017). This meta-analysis suggested that ABCA1 R219K polymorphism might be a protective factor against developing IS, indicating this SNP may contribute to the pathogenesis of ischemic stroke and might be potentially used as a biomarker to predict the susceptibility to ischemic stroke.
Comparative Analysis of Commercial Chinese Bean Sauce (Chunjang) from Korea and China Based on Antioxidant Activity

In this study, the antioxidant effects of seven types of commercial Chunjang from China (C1∼3) and Korea (K1∼4) were compared for their ability to protect against H2O2-induced DNA damage. Outputs included total phenolic content (TPC), 2,2-diphenyl-1-picrylhydrazyl radical scavenging activity (DPPH RSA), oxygen radical absorbance capacity (ORAC), total radical trapping antioxidant potential (TRAP), and comet assays. TPC was the highest in C3 (1,250.8 mg gallic acid equivalent/100 g). C1 exhibited a significantly higher DPPH RSA IC50 (7.5 mg/mL) and ORAC concentration (8.22 μM Trolox equivalent) compared with all other samples, and C1 and K1 exhibited the highest TRAPs. H2O2-induced DNA damage was effectively protected by Chunjang, with a higher observed in C2, C3, and K1 (24.2∼25.3 μg/mL) compared with the other samples (28.3∼30.0 μg/mL). Our results showed that commercial Chunjang contains polyphenol and antioxidant activities. The differences between the samples might be attributed to different origins, materials, and processing methods.
The Gonadotropin Releasing Hormone-3 System in Zebrafish: Early Development and Regulation

Title of Document: THE GONADOTROPIN RELEASING HORMONE-3 SYSTEM IN ZEBRAFISH: EARLY DEVELOPMENT AND REGULATION Eytan Abraham, Doctor of Philosophy, 2008 Directed By: Professor Yonathan Zohar Center of Marine Biotechnology, University of Maryland Biotechnology Center The objective of this study was to expand our understanding of the early development of forebrain Gonadotropin Releasing Hormone (GnRH) neurons in vertebrates in general and in fish in particular. The correct migration during early development of the hypophysiotropic GnRH neurons from the olfactory region to the hypothalamus is crucial for normal gonadal development and reproduction. We developed a Tg(GnRH3:EGFP) zebrafish line in which EGFP is specifically expressed in GnRH3 neurons. Using this line, we have studied in detail the early spatiotemporal development of the GnRH3 system in vivo. In addition, we have studied various factors, including GnRH3, Netrins and Hedgehog to better understand some of the mechanisms that mediate this complex axophilic neuron migration event. Lastly, we have conducted targeted GnRH3 neuron ablation experiments in view of determining the embryonic origin of POA-hypothalamic GnRH3 neurons and the effect of lack of GnRH3 neurons in the CNS. Our findings show that: 1) GnRH neurons first differentiate and express GnRH3 at 24-26 hours post fertilization (hpf) and immediately thereafter begin to extend fibers. 2) GnRH3 neurons project a complex network of fibers, prior the GnRH3 soma migration, to various CNS regions, and to the pituitary. 3) GnRH3 soma begin migrating towards the hypothalamus at 3 days post fertilization (dpf), passing through the terminal nerve (TN), lateral telencephalon, and reaching the hypothalamus by 12 dpf. 4) expression of GnRH3 itself is necessary for the normal early differentiation and fiber extensions of GnRH3 neurons. 5) Netrin1a is directly involved as a chemoattractant in GnRH3 fiber organization and subsequently, in GnRH3 soma migration to the hypothalamus. 6). Netrin2 is required for normal early ZF embryogenesis. 7). Sonic hedgehog a does not serve as a specific factor in the development of the GnRH3 system. 8). GnRH3 neuron regeneration capacity is temporally limited. 9). Successful ablation of olfactory GnRH3 neurons during development results in lack of GnRH3 neurons in the entire sexually mature brain as well as abnormal gonadal development and inability to reproduce. This study expands our understanding vis-à-vis the early events that occur during GnRH3 system development and that regulate this complex process. In a broader sense these findings augment current knowledge regarding the regulation of long range tangential neuron migration during development. THE GONADOTROPIN RELEASING HORMONE-3 SYSTEM IN ZEBRAFISH: EARLY DEVELOPMENT AND REGULATION
Can physical activity influence the quality of sleep among the elderly

Podhorecka Marta, Cytarska Magdalena, Gębka Dominika, Perkowski Radosław, Androsiuk-Perkowska Joanna, Jaroch Alina, Siedlecka–Główczewska Emilia, Sokołowski Remigiusz, Zukow Walery, Kędziora-Kornatowska Kornelia. Can physical activity influence the quality of sleep among the elderly? Journal of Education, Health and Sport. 2017;7(12):288-305. eISSN 2391-8306. DOI http://dx.doi. org/10.5281/zenodo.1133730 http://ojs.ukw. edu.pl/index.php/johs/article/view/5177
Transfer Learning for Multicenter Classification of Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is a lung disease that can be quantified using chest computed tomography scans. Recent studies have shown that COPD can be automatically diagnosed using weakly supervised learning of intensity and texture distributions. However, up till now such classifiers have only been evaluated on scans from a single domain, and it is unclear whether they would generalize across domains, such as different scanners or scanning protocols. To address this problem, we investigate classification of COPD in a multicenter dataset with a total of 803 scans from three different centers, four different scanners, with heterogenous subject distributions. Our method is based on Gaussian texture features, and a weighted logistic classifier, which increases the weights of samples similar to the test data. We show that Gaussian texture features outperform intensity features previously used in multicenter classification tasks. We also show that a weighting strategy based on a classifier that is trained to discriminate between scans from different domains can further improve the results. To encourage further research into transfer learning methods for the classification of COPD, upon acceptance of this paper we will release two feature datasets used in this study on http://bigr.nl/research/projects/copd.
Unsupervised Exceptional Attributed Sub-Graph Mining in Urban Data

Geo-located social media provide a wealth of information that describes urban areas based on user descriptions and comments. Such data makes possible to identify meaningful city neighborhoods on the basis of the footprints left by a large and diverse population that uses this type of media. In this paper, we present some methods to exhibit the predominantactivities and their associated urban areas to automatically describe a whole city. Based on a suitable attributed graph model, our approach identifies neighborhoods with homogeneous and exceptional characteristics. We introduce the novel problem of exceptional sub-graph mining in attributed graphs and propose a complete algorithm that takes benefits from new upper bounds and pruning properties. We also propose an approach to sample the space of exceptional sub-graphs within a given time-budget. Experiments performed on 10 real datasets are reported and demonstrate the relevancy and the limits of both approaches.
Software Engineering: AND Using UML, Software Engineering with Objects and Components

Software Engineering: (Update) 8/E Building on the widely acclaimed strengths of the 7th edition, 8 updates readers with the latest developments in the field, whilst maintaining the structure and content of the 7th easily upward compatible for those using the text in a teaching environment. Using UML: Software Engineering with Objects and Components 2/E One of the first textbooks to be fully up-to-date with the new and expanded UML 2.0 standard, this is an ideal introduction to the Unified Modelling Language for students learning about object and component-based software design and development.
MECRO: A local processing computer architecture based on memristor crossbar

As the need of data-intensive (or big data) applications is growing, the exascale system (i.e., capable of executing 1018 operations per second) is desired. However, it is challenging to scale von Neumann architecture to meet this need, due to its unavoidable data movement between processors and memories. To address such a challenge, this paper proposes a local-processing computer architecture (MECRO) based on memristor crossbar, which consists both microarchitecture and instruction set. Differing from von Neumann architecture, MECRO executes all operations locally in the memristor-based memory using stateful logic operations, where the same devices simultaneously serve as both logic and memory. In addition, this paper proposes a new multiplication arithmetic algorithm that is suitable for MECRO. n×n matrix multiplication is used as example of data-intensive applications. MECRO is verified with SPICE simulations in a small scale. Comparing with a von Neumann architecture consisting of p processors, the experiment shows that MECRO is able to improve the execution time in an order of O(n2/p), while using the similar memory (O(n3)).