Miracle-dz/UniFAR-FTUs huggingface dataset:
Distribution Aware Active Learning

Discriminative learning machines often need a large set of labeled samples for training. Active learning (AL) settings assume that the learner has the freedom to ask an oracle to label its desired samples. Traditional AL algorithms heuristically choose query samples about which the current learner is uncertain. This strategy does not make good use of the structure of the dataset at hand and is prone to be misguided by outliers. To alleviate this problem, we propose to distill the structural information into a probabilistic generative model which acts as a \emph{teacher} in our model. The active \emph{learner} uses this information effectively at each cycle of active learning. The proposed method is generic and does not depend on the type of learner and teacher. We then suggest a query criterion for active learning that is aware of distribution of data and is more robust against outliers. Our method can be combined readily with several other query criteria for active learning. We provide the formulation and empirically show our idea via toy and real examples.
Diffusion tensor sharpening improves white matter tractography

Diffusion Tensor Imaging (DTI) is currently a widespread technique to infer white matter architecture in the human brain. An important application of DTI is to understand the anatomical coupling between functional cortical regions of the brain. To solve this problem, anisotropy maps are insufficient and fiber tracking methods are used to obtain the main fibers. While the diffusion tensor (DT) is important to obtain anisotropy maps and apparent diffusivity of the underlying tissue, fiber tractography using the full DT may result in diffusive tracking that leaks into unexpected regions. Sharpening is thus of utmost importance to obtain complete and accurate tracts. In the tracking literature, only heuristic methods have been proposed to deal with this problem. We propose a new tensor sharpening transform. Analogously to the general issue with the diffusion and fiberOrientation Distribution Function (ODF) encountered when working with High Angular Resolution Diffusion Imaging (HARDI), we show how to transform the diffusion tensors into so-called fiber tensors. We demonstrate that this tensor transform is a natural pre-processing task when one is interested in fiber tracking. It also leads to a dramatic improvement of the tractography results obtained by front propagation techniques on the full diffusion tensor. We compare and validate sharpening and tracking results on synthetic data and on known fiber bundles in the human brain.
CDKN 2 BAS polymorphisms are associated with coronary artery disease in Chinese Han population

Background: Previous studies have identified various SNPs in CDKN2BAS gene that influence the risk of developing coronary artery disease (CAD). In our study, we evaluated the association between CDKN2BAS polymorphisms and CAD risk in the Chinese Han population. Methods: In our study, among 1,141 participants (456 CAD patients, and 685 normal individuals), eight single nucleotide polymorphisms within the CDKN2BAS loci were genotyped and examined for their correlation with the risk of CAD and treatment response using Pearson’s χ2 test and unconditional logistic regression analysis. Results: Overall, the strongest associations were found between rs10757274 in CDKN2BAS gene with the risk of CAD in the allele model (P=0.013). Moreover, we discovered a notable association between SNP rs10757274 and CAD in co-dominant, dominant and log-additive models (P=0.027, P=0.008, P=0.013 respectively). The results, adjusted by confounding factors, were also significant in co-dominant, dominant models (P=0.015, P=0.045 respectively). Moreover, the haplotypes “AA” and “AG” exhibited a protective factors for the risk of CAD after adjusted for age and gender (P=0.013, P=0.034 respectively). Conclusion: We found a remarkable association between rs10757274 and CAD in the Chinese Han population.
Capacitation and acrosome reaction in stallion spermatozoa : functional and clinical aspects

Male fertility requires completion of a sequence of elaborate processes including not only spermatogenesis, sperm transport, accessory gland secretion and sperm deposition in the female reproductive tract, but also two very important sperm related events, i.e. capacitation and acrosome reaction. In absence of either no fertilisation in vivo can take place. The site of capacitation in vivo is the oviduct or the uterus, depending on the species, and that of the acrosome reaction is the zona pellucida. A zona pellucida glycoprotein, ZP3 and progesterone are the two most studied physiological inducers of acrosome reaction. Since the report of the presence of progesterone binding sperm proteins, conveniently termed progesterone receptors, on the plasma membrane, progesterone induced sperm acrosome reaction has attracted a great deal of attention. The work presented in this thesis concerns the membrane changes taking place in stallion spermatozoa during capacitation and acrosome reaction, and the signal transduction mechanism leading to the latter in response to progesterone stimulation. Novel flow cytometry-based techniques for evaluating capacitation and acrosome reaction have been described, and a new parameter to predict male fertility, based on exposure of progesterone receptor, has been proposed.
Word Sense Disambiguation of Opinionated Words Using Extended Gloss Overlap

The increasing use of digital technology and social media has prompted government organizations to take advantage of these in order to gather feedback and opinions from the general public on policies and laws. Since people’s opinions vary, there is a need to present these in an orderly manner for faster and more efficient decision and policy-making. This is where opinion classification comes in. Existing systems such as VoxPop [5] are able to classify texts according to polarity with the help of SentiWordNet, which assigns subjectivity and polarity scores to WordNet synsets. However, the current algorithm that VoxPop uses yields an accuracy rate of only 50.5%. This research integrated a word sense disambiguation (WSD) algorithm may be the key to improving the task of polarity classification. The extended gloss overlap algorithm was used, which compares two synsets and scores the relatedness between them by looking for phrasal matches between all their glosses. With the integration of WSD into VoxPop, its accuracy rating increased to 60%.
Manifold Transfer Subspace Learning ( MTSL ) for High Dimensional Data — Applications to Handwritten Digits and Health Informatics

Transfer Subspace Learning (TSL) is beginning to gain popularity for its ability to leverage classification knowledge of one database to classify objects in a related but different database. Recently, these techniques have been informally combined with Manifold Learning and have demonstrated improved cross-dataset class recognition. One such technique combines Diffusion Maps as the Manifold Learning technique and Transfer Fisher’s Linear Discriminative Analysis (TrFLDA) as the Transfer Subspace Learning (TSL) approach. To date these approaches, while successful, have only been applied to one database containing electro-optical (EO) vehicle images. Furthermore, the technique assumed all the data, source and target domain, could be processed at the same time. In this paper, we introduce a novel extension to these techniques, referred to here as Manifold Transfer Subspace Learning (MTSL), which utilizes an out-of-sample extension (OSE) method and allows for real-time data infusion without reconstruction of the diffusion map model. This dramatically lowers computational cost of incorporating new data. As an illustration of this technique, we apply MTSL to other large, high-dimensional datasets including handwritten digits and lung and breast cancer microarray gene expressions. We achieve classification rates of 90% for cross-domain handwritten digits and rates of 87% on cross-domain breast cancer recognition. For the cancer dataset, this is significant because we are able to achieve comparable results to traditional classification methods while only utilizing one-labeled sample per class and transferring the classification from lung cancer to breast cancer. Keywords—transfer learning; transfer subspace learning; electrooptical imaging; manifold learning; health informatics
Evaluating a method for automated rigid registration

We evaluate a novel method for fully automated rigid registration of 2D manifolds in 3D space based on distance maps, the Gibbs sampler and Iterated Conditional Modes (ICM). The method is tested against the ICP considered as the gold standard for automated rigid registration. Furthermore, the influence of different norms and sampling point densities is evaluated. The performance of the two methods has been evaluated on data consisting of 178 scanned ear impressions taken from the right ear. To quantify the difference of the two methods we calculate the registration cost and the mean point to point distance. T-test for common mean are used to determine the performance of the two methods (supported by a Wilcoxon signed rank test). The performance influence of sampling density, sampling quantity, and norms is analyzed using a similar method.
A Unified Framework for Training Neural Networks

The lack of mathematical tractability of Deep Neural Networks (DNNs) has hindered progress towards having a unified convergence analysis of training algorithms, in the general setting. We propose a unified optimization framework for training different types of DNNs, and establish its convergence for arbitrary loss, activation, and regularization functions, assumed to be smooth. We show that framework generalizes well-known first- and second-order training methods, and thus allows us to show the convergence of these methods for various DNN architectures and learning tasks, as a special case of our approach. We discuss some of its applications in training various DNN architectures (e.g., feed-forward, convolutional, linear networks), to regression and classification tasks.
GPU Encrypt: AES Encryption on Mobile Devices

Cell phones have evolved into a ubiquitous and essential method of communication across the world. Smartphones and mobile devices have significantly increased the productivity of users through accessibility to important services and have thus become critically integrated into both their work and personal lives [1]. A side effect of this integration is that phones contain a large volume of probative information linked to an individual. The scope of information sensitivity ranges anywhere from low severity (call history, contact information, and text message data) to more valuable information (e-mail, browser history, chat logs, and even passwords) [2]. The ever increasing number of phones available and in use by the public will continue to enhance the convenience of day-to-day life, but simultaneously brings along an increased potential for criminals to misuse this technology.
A Brand-level Ranking System with the Customized Attention-GRU Model

In e-commerce websites like Taobao, brand is playing a more important role in influencing users' decision of click/purchase, partly because users are now attaching more importance to the quality of products and brand is an indicator of quality. However, existing ranking systems are not specifically designed to satisfy this kind of demand. Some design tricks may partially alleviate this problem, but still cannot provide satisfactory results or may create additional interaction cost. In this paper, we design the first brand-level ranking system to address this problem. The key challenge of this system is how to sufficiently exploit users' rich behavior in e-commerce websites to rank the brands. In our solution, we firstly conduct the feature engineering specifically tailored for the personalized brand ranking problem and then rank the brands by an adapted Attention-GRU model containing three important modifications. Note that our proposed modifications can also apply to many other machine learning models on various tasks. We conduct a series of experiments to evaluate the effectiveness of our proposed ranking model and test the response to the brand-level ranking system from real users on a large-scale e-commerce platform, i.e. Taobao.
Some application examples of minimization based formulations of inverse problems and their regularization

In this paper we extend a recent idea of formulating and regularizing inverse problems as minimization problems, so without using a forward operator, thus avoiding explicit evaluation of a parameter-to-state map. We do so by rephrasing three application examples in this minimization form, namely (a) electrical impedance tomography with the complete electrode model (b) identification of a nonlinear magnetic permeability from magnetic flux measurements (c) localization of sound sources from microphone array measurements. To establish convergence of the proposed regularization approach for these problems, we first of all extend the existing theory. In particular, we take advantage of the fact that observations are finite dimensional here, so that inversion of the noisy data can to some extent be done separately, using a right inverse of the observation operator. This new approach is actually applicable to a wide range of real world problems.
Learning-based Power and Runtime Modeling for Convolutional Neural Networks

With the increased popularity of convolutional neural networks (CNNs) deployed on the widespectrum of platforms (from mobile devices to workstations), the related power, runtime, and energy consumption have drawn significant attention. From lengthening battery life of mobile devices to reducing the energy bill of datacenters, it is important to understand the efficiency of CNNs during serving for making an inference, before actually training the model. In this work, we propose NeuralPower : a layer-wise predictive framework based on sparse polynomial regression, for predicting the serving power, runtime, and energy consumption of a CNN deployed on any GPU platform. Given the architecture of a CNN, NeuralPower provides an accurate prediction and breakdown for power and runtime across all layers in the whole network, helping machine learners quickly identify the power, runtime, or energy bottlenecks. The experimental results show that the prediction accuracy of the proposed NeuralPower outperforms the best published model to date, yielding an improvement in accuracy of up to 68.5%. We also assess the accuracy of predictions at the network level, by predicting the runtime, power, and energy of state-of-the-art CNN architectures, achieving an average accuracy of 88.24% in runtime, 88.34% in power, and 97.21% in energy. We comprehensively corroborate the effectiveness of NeuralPower as a powerful framework for machine learners by testing it on different GPU platforms.
Anger Conceptualization in Persian and English

The researchers in this paper aim to investigate the universality of emotion conceptual metaphors by means of Lakoff and Johnson′s Conceptual Metaphor Theory (1980) as a model for comparison. English and Persian as two typological different languages were under study. The emotion under study was anger. To conduct the present study, two sets of metaphorical expressions in both English and Persian were gathered from different related sources. English data was extracted from Lakoff and Johnson’s (1980) work on this area, Kovecses (2005), and British National Corpus. Persian expressions were collected from Amsal-AlHekam (Dehkhoda, 1999), Metaphors of Speech (Hassan Anvary, 2004). During the study three cases were identified: totally the same, partially the same, and totally different. The percentage of the three cases revealed that partially the same pattern is the most frequent one. In conclusion, the present study suggests that, in spite of some minor differences in conceptualizing anger relating to the degree of conventionality, these two languages share the most conceptual metaphors in producing anger metaphors.
Understanding Democracy as a Cause of Electoral Reform: Canadian Jurisprudence and Electoral Reform

This paper addresses the question of the sources of electoral reform that lie outside of the partisan political realm. In particular, it examines the case of electoral reforms that have been imposed, or that realistically might be imagined to be imposed, by the courts in Canada. The paper consists of three major sections. The first introduces the idea of judicial review as a source of electoral reform. The second lays out the major cases relevant to electoral reform that have been decided by the Canadian courts. The third attempts to impose some theoretical consistency on the Canadian jurisprudence in this field, and to suggest the possible consequences of adherence to this line of development.
Cystatin C: a promising biomarker to evaluate renal function

Accurate evaluation of the progression of renal function decline is very important, since the early establishment of renoprotective therapies can prevent or delay the occurrence of end stage renal disease. Until now, there is still no ideal biomarker to evaluate glomerular filtration rate, and cystatin C has been demonstrated to be a very promising one. In this review, we have assembled several studies that have evaluated the use of cystatin C to assess glomerular filtration rate in patients with chronic kidney disease, diabetes mellitus and hemodialysis, and also the use of cystatin C to predict the risk of cardiovascular outcomes. Besides cystatin C has been demonstrated by different authors to be equal or superior to the other available renal biomarkers, some limitations still need to be overcome so that cystatin C can be used in clinical to improve the early diagnosis of chronic kidney disease.
Location-Allocation and Scheduling of Inbound and Outbound Trucks in Multiple Cross-Dockings Considering Breakdown Trucks

This paper studies multiple cross-dockings where the loads are transferred from origins (suppliers) to destinations (customers) through cross-docking facilities. Products are no longer stored in intermediate depots and incoming shipments are consolidated based on customer demands and immediately delivered to them to their destinations. In this paper, each cross-docking has a covering radius that customers can be served by at least one cross-docking provided. In addition, this paper considers the breakdown of trucks. We present a two-stage model for the location of cross-docking centers and scheduling inbound and outbound trucks in multiple cross-dockings.We work on minimizing the transportation cost in a network by loading trucks in the supplier locations and route them to the customers via cross-docking facilities. The objective, in the first stage, is to minimize transportation cost of delivering products from suppliers to open cross-docks and cross-docks to the customers; in the second-stage, the objective is to minimize the makespans of open cross-dockings and the total weighted summation of completion time. Due to the difficulty of obtaining the optimum solution tomedium- and large-scale problems, we ‌propose four types of metaheuristic algorithms, i.e., genetic, simulated annealing, differential evolution, and hybrid algorithms.The result showed that simulated annealing is the best algorithm between the four algorithms.
Adsorptive Removal Of Basic Dye Rhodamine B From Aquoeus Media Onto Animal Bone Meal As New Low Cost Adsorbent

Adsorptive removal of a cationic dye – Rhodamine B – from aquoeus solutions was achieved by the use of Animal Bone Meal as a new low cost adsorbent. Adsorption of Rhodamine B was occured by studing the effects of contact time, adsorbent amount, dye concentration and temperature. Dye adsorption equilibrium was rapidly attained after 60 minutes of contact time. The isotherms of adsorption data were analyzed by Langmuir and Freundlich adsorption isotherm models. The adsorption capacity (Qm) obtained from the Langmuir isotherm plots were 62.11, 63.69, 64.13 and 64.95 mg/g respectively at 303, 313, 323 and 333°K. Thermodynamic parameters such as ΔH0, ΔS0 and ΔG0 were calculated, which indicated that the adsorption was spontanoeus and endothermic nature. The characteristic results and dimensionless separation factors RL showed that animal bone meal can be employed as an alternative to commercial adsorbents in the removal of Rhodamine B from aquoeus solution and wastewater.
The genomic landscape at a late stage of stickleback speciation: High genomic divergence interspersed by small localized regions of introgression

Speciation is a continuous process and analysis of species pairs at different stages of divergence provides insight into how it unfolds. Genomic studies on young species pairs have often revealed peaks of divergence and heterogeneous genomic differentiation. Yet it remains unclear how localised peaks of differentiation progress to genome-wide divergence during the later stages of speciation with gene flow. Spanning the speciation continuum, stickleback species pairs are ideal for investigating how genomic divergence builds up during speciation. However, attention has largely focused on young postglacial species pairs, with little known of the genomic signatures of divergence and introgression in older systems. The Japanese stickleback species pair, composed of the Pacific Ocean three-spined stickleback (Gasterosteus aculeatus) and the Japan Sea stickleback (G. nipponicus), which co-occur in the Japanese islands, is at a late stage of speciation. Divergence likely started well before the end of the last glacial period and crosses between Japan Sea females and Pacific Ocean males result in hybrid male sterility. Here we use coalescent analyses and Approximate Bayesian computation to show that the two species split approximately 0.68-1 million years ago but that they have continued to hybridise at a low rate throughout divergence. Population genomic data revealed that high levels of genomic differentiation are maintained across the majority of the genome when gene flow occurs. However despite this, we identified multiple, small regions of introgression, strongly correlated with recombination rate. Our results demonstrate that a high level of genome-wide divergence can establish in the face of persistent introgression and that gene flow can be localized to small genomic regions at the later stages of speciation with gene flow. Author summary When species evolve, reproductive isolation leads to a build-up of differentiation in the genome where genes involved in the process occur. Much of our understanding of this comes from early stage speciation, with relatively few examples from more divergent species pairs that still exchange genes. To address this, we focused on Pacific Ocean and Japan Sea sticklebacks, which co-occur in the Japanese islands. We established that they are the oldest and most divergent known stickleback species pair, that they evolved in the face of gene flow and that this gene flow is still on going. We found introgression is confined to small, localised genomic regions where recombination rate is high. Our results show high divergence can be maintained between species, despite extensive gene flow.