Miracle-dz/UniFAR-FTUs huggingface dataset:
Project Evaluation: Validation Of A Scale And Analysis Of Its Predictive Capacity

The objective of this study was to validate a scale for assessment of academic projects. As a complement, we examined its predictive ability by comparing the scores of advised/corrected projects based on the model and the final scores awarded to the work by an examining panel (approximately 10 months after the project design). Results of Confirmatory Factor Analysis (with 161 projects) indicated convergent validity and discriminant validity. Regression analysis revealed a positive and significant relationship between the scores of projects and final scores of the work, strengthening the claim of the usefulness of the model to minimize potential wear in advising processes.
The Quest for a Database Selection and Design Method

New types of database have emerged over the last decade, aimed at answering new requirements in the Big Data era. The new databases, in additional to the Relational model, may fit to specific types of applications. Therefore, new challenges have also emerged, including the issue of which database model to select for a given application, and how to design the database based on the selected model. To the best of our knowledge, these two challenges have not been addressed by any systematic method. In this research we plan to devise a structured method for database model selection and design based on variety of factors, including data-related requirements, functional requirements, and non-functional requirements. Based on these requirements the method will recommend which database models are the most appropriate for that application and will suggest a design for the recommended models.
Multi-Temporal PolSAR Back Scatter Decomposition Over Glacier Ice, Snow And Firn

A multi-temporal dataset of Radarsat-2 Quad-pol images from 2008-2011 was assembled over a Norwegian icecap, Blåmannsisen. Data were acquired in a range of imaging modes including FQ24 with a local incidence angle of 44° on the centre of the glacier and FQ3 with 21.5° at the same location. Scattering from the glacier facies was decomposed using Freeman-Durden and eigenvector-eigenvalue based approaches. Polarimetric discriminators were analysed to identify a temporal component to changes in backscatter and to identify the relative importance of incidence angle differences between acquisitions. It was found that any temporal signal was masked by strong differences associated with incidence angle effects. Polarimetric entropy and Freeman-Durden volume scattering were particularly strongly correlated with local incidence angle.
Towards Reconciling Certain Answers and SPARQL: Bag Semantics to the Rescue?

Despite the intention of RDF, the data model for the Semantic Web, to support reasoning about RDF data (as witnessed e.g. by three different semantics for RDF – simple/RDF/RDFS entailment – or accompanying standards like OWL), SPARQL, the standardized query language for RDF, lacks some support of including such reasoning into query answering, as was noted e.g. in [6, 1]. One major cause of this is that SPARQL only makes partially use of certain answers, which are the semantics commonly applied in reasoning scenarios. While not fully supporting certain answers might be a reasonable decision when looking at the costs of evaluating and (for a user) understanding a query, it negatively affects the power of SPARQL. As a result, in recent years suggestions have been made how to cover different aspects of reasoning about RDF data (e.g. blank nodes under RDF simple entailment [6]; OWL reasoning [1]) in SPARQL by adopting a certain answer semantics. However, as observed in [1], just applying the usual definition of certain answers to SPARQL can, in certain cases, be unnecessarily restrictive. To better illustrate these situations, let us first recall the definition of certain answers. Given an RDF graph G (possibly extended by some additional information), most reasoning formalisms define the semantics of G in terms of an (infinite) set models(G) of RDF graphs implied by G. Query answering in such a setting is then commonly defined in terms of the certain answer semantic: for a query Q, the certain answers certain(Q,G) (w.r.t. some reasoning formalism) are
StarNet: Pedestrian Trajectory Prediction using Deep Neural Network in Star Topology

Pedestrian trajectory prediction is crucial for many important applications. This problem is a great challenge because of complicated interactions among pedestrians. Previous methods model only the pairwise interactions between pedestrians, which not only oversimplifies the interactions among pedestrians but also is computationally inefficient. In this paper, we propose a novel model StarNet to deal with these issues. StarNet has a star topology which includes a unique hub network and multiple host networks. The hub network takes observed trajectories of all pedestrians to produce a comprehensive description of the interpersonal interactions. Then the host networks, each of which corresponds to one pedestrian, consult the description and predict future trajectories. The star topology gives StarNet two advantages over conventional models. First, StarNet is able to consider the collective influence among all pedestrians in the hub network, making more accurate predictions. Second, StarNet is computationally efficient since the number of host network is linear to the number of pedestrians. Experiments on multiple public datasets demonstrate that StarNet outperforms multiple state-of-the-arts by a large margin in terms of both accuracy and efficiency.
Farm land weed detection with region-based deep convolutional neural networks

Machine learning has become a major field of research in order to handle more and more complex image detection problems. Among the existing state-of-the-art CNN models, in this paper a region-based, fully convolutional network, for fast and accurate object detection has been proposed based on the experimental results. Among the region based networks, ResNet is regarded as the most recent CNN architecture which has obtained the best results at ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) in 2015. Deep residual networks (ResNets) can make the training process faster and attain more accuracy compared to their equivalent conventional neural networks. Being motivated with such unique attributes of ResNet, this paper evaluates the performance of fine-tuned ResNet for object classification of our weeds dataset. The dataset of farm land weeds detection is insufficient to train such deep CNN models. To overcome this shortcoming, we perform dropout techniques along with deep residual network for reducing over-fitting problem as well as applying data augmentation with the proposed ResNet to achieve a significant outperforming result from our weeds dataset. We achieved better object detection performance with Region-based Fully Convolutional Networks (R-FCN) technique which is latched with our proposed ResNet-101.
Muscle and mucous gland size in the major bronchi of patients with chronic bronchitis, asthma, and asthmatic bronchitis.

Mucous gland size and the amount of muscle in the bronchi were measured in 7 patients with typical chronic bronchitis, 4 patients with chronic bronchitis and attacks of wheezing (chronic asthmatic bronchitis), and 3 patients with classic spasmodic asthma. The patients with asthma had increased bronchial muscle, but the patients with chronic bronchitis did not. Three of the 4 patients with asthmatic bronchitis had increased muscle; such patients can therefore be recognized morphologically, and might account for a proportion of patients with bronchitis and unusual degrees of airways obstruction. Mucous gland size was increased in asthma when assessed by the Dunnill point count, but not by the Reid index.
A Model-Based Balance Stabilization System for Biped Robot

This paper presents a model-based balance stabilization system which takes into account not only the stable part of COM dynamics but also the unstable part. In this system, the overall dynamics of a humanoid robot is approximated using a Linear Inverted Pendulum Plus Flywheel Model (LIPPFM). Moreover, Divergent Component of Motion~(DCM) is used to define when and where a robot should take a step to prevent falling. The proposed system has been successfully tested by performing several simulations using MATLAB. The simulation results show this system is capable of stabilizing the balance of the robot in various conditions.
Chatters on the Internet: a special target group for HIV prevention

Abstract Tliis study examined characteristics of men who use gay chat boxes on the Internet, including dating behaviour and sexual risk-taking with sex partners who were initially met through chatting. Men on chat boxes were asked to complete a brief questionnaire on the Internet. The questionnaire contained questions on demographics, chatting-related variables, and dating and risk-taking sexual behaviour. The results show that a large majority of the 190 respondents reported actual encounters as well as sex with men who were initially met through chatting. Almost 30% of the respondents who engaged in sex with chat dates reported inconsistent safe sexual behaviour. The level of unprotected sex increased as the number of sex partners who were met through chatting increased. These results suggest that chatters on the Internet may be a new target group for HIV prevention. Further study is needed to gain insight into the feasibility of prevention efforts for this target group.
Contribution to the Modeling and Simulation of Current Mode Pipeline ADC Based On Matlab

In this paper, a new version of non-ideal model 08-bit current mode pipeline ADC has been developed. It is based on Matlab and Simulink environment with special focus given to the analog building blocks. Those blocks are current Sample-and-Hold(S/H), current SubADC and current Sub-DAC. In this model, the sub-ADC is implemented by two current comparators and encoder logic circuit. For the current S/H and sub-DAC, they are implemented by using a switch current and current source. The main advantage of current mode approach is its low power dissipation, low cost and high speed. However, there are some technical limitations; using the model and running the simulation with the introduction of the main non idealities components such as a current offset, clock feed through, charge injection, clock jitter, switching noise, mismatch errors and non idealities in current amplifier, demonstrated clear degradation of the performance of the ADC.
Consistency regularization and CutMix for semi-supervised semantic segmentation

Consistency regularization describes a class of approaches that have yielded ground breaking results in semi-supervised classification problems. Prior work has established the cluster assumption -- under which the data distribution consists of uniform class clusters of samples separated by low density regions -- as key to its success. We analyse the problem of semantic segmentation and find that the data distribution does not exhibit low density regions separating classes and offer this as an explanation for why semi-supervised segmentation is a challenging problem. We adapt the recently proposed CutMix regularizer for semantic segmentation and find that it is able to overcome this obstacle, leading to a successful application of consistency regularization to semi-supervised semantic segmentation.
An Introduction to a New Text Classification and Visualization for Natural Language Processing Using Topological Data Analysis

Topological Data Analysis (TDA) is a novel new and fast growing field of data science providing a set of new topological and geometric tools to derive relevant features out of complex high-dimensional data. In this paper we apply two of best methods in topological data analysis, "Persistent Homology" and "Mapper", in order to classify persian poems which has been composed by two of the best Iranian poets namely "Ferdowsi" and "Hafez". This article has two main parts, in the first part we explain the mathematics behind these two methods which is easy to understand for general audience and in the second part we describe our models and the results of applying TDA tools to NLP.
The Unreasonable Effectiveness of Transformer Language Models in Grammatical Error Correction

Recent work on Grammatical Error Correction (GEC) has highlighted the importance of language modeling in that it is certainly possible to achieve good performance by comparing the probabilities of the proposed edits. At the same time, advancements in language modeling have managed to generate linguistic output, which is almost indistinguishable from that of human-generated text. In this paper, we up the ante by exploring the potential of more sophisticated language models in GEC and offer some key insights on their strengths and weaknesses. We show that, in line with recent results in other NLP tasks, Transformer architectures achieve consistently high performance and provide a competitive baseline for future machine learning models.
Cyclic strain-induced thrombomodulin expression in endothelial cells is mediated by nitric oxide, but not hydrogen peroxide

Vascular endothelial cells (ECs) are constantly subjected to rhythmic distension because of pulsatile flow in the circulation. The rhythmic distension-induced cyclic strain plays an important role in modulating endothelial physiology. In the present study, we investigated the effects of cyclic strain on the expression of thrombomodulin (TM), an anti-coagulant protein that possesses anti-inflammation properties. Exposure of ECs to cyclic strain increased NADH/NADPH oxidase and endothelial nitric oxide synthase (eNOS) activities, as well as the nitric oxide (NO) production. When ECs were subjected to a greater cyclic strain (21%), TM expression was increased. TheincreasedTMexpressionwasnotobservedwhenECsweresubjectedtoalowercyclicstrain(15%). ECstreated with an NO donor (NOC18), induced TM expression, whereas hydrogen peroxide (H2O2) treatment did not have significant effects on TM expression. Pretreatment of ECs with an eNOS inhibitor (L-NAME) abolished the cyclic strain-induced TM expression in ECs. These results suggest that cyclic strain to ECs induces NO production which consequently results in an increase of TM expression in ECs. Our findings provide insights into the mechanisms by which cyclic strain induces TM expression in ECs, therefore playing important roles in anticoagulation and atheroprotection in the vascular system.
Simulate Hawking radiation and fastest quantum scrambling of black holes by quantum many-body systems

We show that a massless scalar field in an arbitrary 2-dimensional static spacetime is always equivalent to a site-dependent bosonic hopping model, and a massless Dirac field then is equivalent to a site-dependent free Hubbard model or a site-dependent isotropic XY model. A possible experimental realization in trapped ions system is proposed. As a direct application, this paper numerically shows that they can be used to simulate Hawking radiation of black hole and study its entanglement. We also show for the first time in analog model that black holes are most chaotic systems and the fastest scramblers in nature. Because bosonic hopping model, free Hubbard model and XY model are well-studied theoretically and experimentally, this paper offers a new general and experimentally realizable framework to map quantum field theories in curved spacetime to quantum many-body systems.
Unbiased estimators for the variance of MMD estimators

The maximum mean discrepancy (MMD) is a kernel-based distance between probability distributions useful in many applications (Gretton et al. 2012), bearing a simple estimator with pleasing computational and statistical properties. Being able to efficiently estimate the variance of this estimator is very helpful to various problems in two-sample testing. Towards this end, Bounliphone et al. (2016) used the theory of U-statistics to derive estimators for the variance of an MMD estimator, and differences between two such estimators. Their estimator, however, drops lower-order terms, and is unnecessarily biased. We show in this note - extending and correcting work of Sutherland et al. (2017) - that we can find a truly unbiased estimator for the actual variance of both the squared MMD estimator and the difference of two correlated squared MMD estimators, at essentially no additional computational cost.
Closure between aerosol particles and cloud condensation nuclei at Kaashidhoo Climate Observatory

Predicting the cloud condensation nuclei (CCN) supersaturation spectrum from aerosol properties is a fairly straightforward matter, as long as those properties are simple. During the Indian Ocean Experiment we measured CCN spectra, size-resolved aerosol chemical composition, and aerosol number distributions and attempted to reconcile them using a modified form of Kohler theory. We obtained general agreement between our measured and modeled CCN spectra. However, the agreement was not as good during a time period when organic carbon comprised a quarter of the total mass of the aerosol in the submicron size range. The modeled concentrations overpredict those actually measured during that time period. This suggests that some component, presumably organic material, can inhibit the uptake of water by the electrolytic fraction of the mass.
Swelling and fibronectin accumulation in articular cartilage explants after cyclical impact

The objective of this study was to determine if repeated impact could damage living cartilage and lead to osteoarthritis‐like changes in its biology. Canine cartilage explants were subjected to impacts of as much as 50 MPa once every 5 seconds for 30 minutes. On each impact cycle, the loading rate was 100 MPa/sec to the assigned peak stress, which was held for 1 second. After impact testing, the cartilage was kept in defined culture for as long as 10 days. Radiosulfate incorporation in the region that received direct impact varied with load 0–4 hours after impact, but it did not vary with load at 20–24 hours after impact. Even so, most explants were visibly damaged by 20 or 50 MPa, and there was subtle evidence of damage from impacts of 5 or 10 MPa. For example, ion‐induced swelling in 0.01 M NaCl was increased, suggesting that the physical integrity of the matrix was reduced relative to controls. Self‐diffusion of water, measured by proton magnetic resonance imaging, was also increased in the deeper zones of the explant, consistent with changes in structure at the molecular level. Ten days after impact, the water content and the fibronectin content of the loaded region of the explant were both increased. In combination, these osteoarthritis‐like changes suggested that the physical strength of normal cartilage limits its ability to withstand cyclical impact.
Diameter-based Interactive Structure Search

In this work, we introduce \emph{interactive structure search}, a generic framework that encompasses many interactive learning settings, both explored and unexplored. We show that a recently developed active learning algorithm of~\citet{TD17} can be adapted for interactive structure search, that it can be made noise-tolerant, and that it enjoys favorable convergence rates.
Graph based Nearest Neighbor Search: Promises and Failures

Recently, graph based nearest neighbor search gets more and more popular on large-scale retrieval tasks. The attractiveness of this type of approaches lies in its superior performance over most of the known nearest neighbor search approaches as well as its genericness to various metrics. In this paper, the role of two strategies, namely hierarchical structure and graph diversification that are adopted as the key steps in the graph based approaches, is investigated. We find the hierarchical structure could not achieve "much better logarithmic complexity scaling" as it was claimed in the original paper, particularly on high dimensional cases. Moreover, we find that similar high search speed efficiency as the one with hierarchical structure could be achieved with the support of flat k-NN graph after graph diversification. Finally, we point out the difficulty, that is faced by most of the graph based search approaches, is directly linked to "curse of dimensionality".
Serum beta-2 microglobulin levels are a significant prognostic factor in Philadelphia chromosome-positive chronic myelogenous leukemia.

Our objective was to investigate the prognostic significance of serum beta-2 microglobulin (B2M) levels among patients with chronic myelogenous leukemia (CML). All patients with Philadelphia chromosome-positive early chronic phase CML (i.e., within 1 year of diagnosis) treated with IFN alpha-based therapy at the M. D. Anderson Cancer Center between 1980 and 1997, in whom pretreatment B2M levels were available, were investigated. Two hundred one patients were evaluable. Their median B2M was 2.2 mg/dl (range, 1.1-20 mg/dl). Serum B2M levels were associated with other variables of prognostic significance, including age, spleen size, WBC count, percentage of peripheral and marrow blasts, and percentage of marrow basophils. Patients with B2M levels >2.9 mg/dl (ie., the upper quartile of the distribution) had a significantly lower rate of major cytogenetic response compared to those in the lower three quartiles (20 versus 52%; P < 0.01). They also had a shorter survival, with a 5-year survival rate of 48%, compared with 75% for those in the lower quartiles (P = 0.01). High B2M levels (>2.9 mg/dl) could identify a group of patients with an adverse outcome within patients in stage I disease (P = 0.02). Results for patients in stages 2-4 were inconclusive because of the small number of patients in these groups. We conclude that serum B2M levels are an important, and probably independent, prognostic factor for patients with CML in early chronic phase treated with IFN-based therapy.
Estimates for matrix coefficients of representations

Abstract:Estimates for matrix coefficients of unitary representations of semisimple Lie groups have been studied for a long time, starting with the seminal work by Bargmann, by Ehrenpreis and Mautner, and by Kunze and Stein. Two types of estimates have been established: on the one hand, $L^p$ estimates, which are a dual formulation of the Kunze--Stein phenomenon, and which hold for all matrix coefficients, and on the other pointwise estimates related to asymptotic expansions at infinity, which are more precise but only hold for a restricted class of matrix coefficients. In this paper we prove a new type of estimate for the irreducibile unitary representations of ${\rm SL}(2,\Bbb{R})$ and for the so-called metaplectic representation, which we believe has the best features of, and implies, both forms of estimate described above. As an application outside representation theory, we prove a new $L^2$ estimate of dispersive type for the free Schr\"odinger equation in $\Bbb{R}^n$.
Faster Attend-Infer-Repeat with Tractable Probabilistic Models

The recent Attend-Infer-Repeat (AIR) framework marks a milestone in structured probabilistic modeling, as it tackles the challenging problem of unsupervised scene understanding via Bayesian inference. AIR expresses the composition of visual scenes from individual objects, and uses variational autoencoders to model the appearance of those objects. However, inference in the overall model is highly intractable, which hampers its learning speed and makes it prone to suboptimal solutions. In this paper, we show that the speed and robustness of learning in AIR can be considerably improved by replacing the intractable object representations with tractable probabilistic models. In particular, we opt for sum-product networks (SPNs), expressive deep probabilistic models with a rich set of tractable inference routines. The resulting model, called SuPAIR, learns an order of magnitude faster than AIR, treats object occlusions in a consistent manner, and allows for the inclusion of a background noise model, improving the robustness of Bayesian scene understanding.