Miracle-dz/UniFAR-FTUs huggingface dataset:
Cigarette smoke – induced reduction of C 1 q promotes emphysema

Alteration of innate immune cells in the lungs can promote loss of peripheral tolerance that leads to autoimmune responses in cigarette smokers. Development of autoimmunity in smokers with emphysema is also strongly linked to the expansion of autoreactive T helper (Th) cells expressing interferon g (Th1), and interleukin 17A (Th17). However, the mechanisms responsible for enhanced self-recognition and reduced immune tolerance in smokers with emphysema remain less clear. Here we show that C1q, a component of the complement protein 1 complex (C1), is downregulated in lung CD1a+ antigen-presenting cells (APCs) isolated from emphysematous human and mouse lung APCs after chronic cigarette smoke exposure. C1q potentiated the function of APCs to differentiate CD4+ T cells to regulatory T cells (Tregs), while it inhibited Th17 cell induction and proliferation. Mice deficient in C1q that were exposed to chronic smoke exhibited exaggerated lung inflammation marked by increased Th17 cells, whereas reconstitution of C1q in the lungs enhanced Treg abundance, dampened smoke-induced lung inflammation, and prevented the development of emphysema. Our findings demonstrate that cigarette smoke–mediated loss of C1q could play a key role in reduced peripheral tolerance, which could be explored to treat emphysema. Research Article Immunology
Causal Inference Using Propensity Score Matching in Clustered Data

Propensity score matching is commonly used to estimate causal effects of treatments. However, when using data with a hierarchical structure, we need to take the multilevel nature of the data into account. In this thesis the estimation of propensity scores with multilevel models is presented to extend propensity score matching for use with multilevel data. A Monte Carlo simulation study is performed to evaluate several different estimators. It is shown that propensity score estimators ignoring the multilevel structure of the data are biased, while fixed effects models produce unbiased results. An empirical study of the causal effect of truancy on mathematical ability for Swedish 9th graders is also performed, where it is shown that truancy has a negative effect on mathematical ability.
13 F eb 2 01 6 Learning Over Long Time Lags

The advantage of recurrent neural networks (RNNs) in learni ng dependencies between time-series data has distinguished RNNs from other deep learning models. Recent ly, many advances are proposed in this emerging field. However, there is a lack of comprehensive review on mem ory models in RNNs in the literature. This paper provides a fundamental review on RNNs and long short te rm memory (LSTM) model. Then, provides a surveys of recent advances in different memory enhancement s and learning techniques for capturing long term dependencies in RNNs.
Supplementary material-Neural Rerendering in the Wild

Quantitative evaluation with learned segmentations To quantitatively evaluate rerendering using estimated segmentation masks, we generate semantic labelings for the validation set, as described in Section 3.3, and recompute the quantitative metrics, as in Table 1 in the main paper, for our proposed method. Note that estimated semantic maps will not perfectly match those of the ground truth validation images. For example, ground truth semantic maps could contain the segmentation of transient objects, like people or trees. So, it is not fair to compare reconstructions based on estimated segmentation maps to the ground truth validation images. While results in Table 1 show some performance drop as expected, we still get a reasonable performance compared to that in Table 1 in the main text. In fact, we still perform better than the BicycleGAN baseline on the Trevi, Pantheon and Dubrobnik datasets, even though the BicycleGAN baseline uses ground truth segmentation masks.
Cytogenetic and Flow Cytometry Analysis of Iranian Rosa spp

Since the ploidy level and nuclear DNA amounts are less exposed to natural selection than most morphological traits, and are more stable in the stages of evolution, they are used to study variation among different species. In order to study the diversity of Iranian Rosa spp. , chromosome counting and flow cytometry (FCM) analyses were carried out on 10 species collected from different parts of the country. For each plant, cuttings were rooted and young leaves were collected. Root tips were pretreated in 8-hydroxyquinolin, fixed in Lewitsky solution and macerated in cellulase. Chromosome number and ploidy level of the investigated species ranged from diploid (2n = 2x = 14) to hexaploid (2n = 6x = 42). FCM analysis was conducted with propidium iodide (PI) on R. moschata, R. beggeriana, R. persica, R. foetida, R. hemisphaerica, R. pimpinellifolia, R. canina, R. boissieri, R. orientalis and R. pulverulenta. The mean 2C-value of different species showed a varying degree from 0.83 pg (s.d. ± 0.08 pg) in R. persica from subgenus Hulthemia to a four-fold higher amount of 3.54 pg (s.d. ± 0.08 pg) in R. pulverulenta from subgenus Rosa (=Eurosa). Finally, the PI measurements were compared with measurements obtained from 4', 6-diamidino-2-phenylindole (DAPI) staining, which indicated that DAPI ratios (p1/p2) were lower, but closely correlated (r = 0.98) with PI ratios (p1/p2). _____________________________________________________________________________________________________________
Semi-supervised Three-dimensional Reconstruction Framework with Generative Adversarial Networks

Because of the intrinsic complexity in computation, three-dimensional (3D) reconstruction is an essential and challenging topic in computer vision research and applications. The existing methods for 3D reconstruction often produce holes, distortions and obscure parts in the reconstructed 3D models, or can only reconstruct voxelized 3D models for simple isolated objects. So they are not adequate for real usage. From 2014, the Generative Adversarial Network (GAN) is widely used in generating unreal datasets and semi-supervised learning. So the focus of this paper is to achieve high-quality 3D reconstruction performance by adopting the GAN principle. We propose a novel semi-supervised 3D reconstruction framework, namely SS3D-GAN, which can iteratively improve any raw 3D reconstruction models by training the GAN models to converge. This new model only takes real-time 2D observation images as the weak supervision and doesn’t rely on prior knowledge of shape models or any referenced observations. Finally, through the qualitative and quantitative experiments & analysis, this new method shows compelling advantages over the current state-of-the-art methods on the Tanks & Temples reconstruction benchmark dataset.
Auction Theory meets Collective Intelligence : Towards Designing Next-Generation Community Question Answering Systems

Community Question Answering (CQA) systems are webbased platforms that promote collective (community) intelligence. Users (queriers) place questions about topics they are interested in, and other users (answerers) provide answers to these questions. The queriers evaluate answers and identify the best one, and users who gave these answers are rewarded. In these systems, the objective is to maximize the expected provided utility to queriers, namely the likelihood that they receive “good” answers, by matching queriers to answerers. Current systems attempt to perform this matching without consideration of the different needs of queriers, such as the necessity for receiving good answers to their questions, and as a result the allocation matching does not maximize the sum utility of queriers. In this work, we put forward auction theory as the mathematical tool to address this challenge. Upon posing a question, a querier declares his necessity for getting an answer to it through a bid. Each answerer has a finite number of bins in his answer list, which captures time or effort limitations in answering questions. We formulate the platform utility maximization problem as one of allocating questions to the bins of answerers. The model may take into account, (i) the relevance of each question for each answerer, (ii) the usefulness of answers provided by answerers based on past experience, (iii) the impact of the position of a question in the answer list of a querier on the chances that it will be answered. We propose meaningful compensation rules for answerers, and we extend the model to cater for the question starvation phenomenon, in which some questions are displayed in the lists of several answerers, while others do not appear at all. The problem turns out to be an extension of the advertisement slot assignment problem in sponsored search auctions. This is work in progress, and as a next step, it will feature validation from publicly available real data. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Copyright 20XX ACM X-XXXXX-XX-X/XX/XX . $15.00.
A Method For Exploiting In The Networks For Generalized Packet Arrival

Every day to notoriously hard to debug networks. , community engineers battle with router pass over configurations, fiber cuts, faulty interfaces, mislabeled cables, program bugs, intermittent hyperlinks, and myriad different causes that rationale networks to misbehave or fail wholly. Community engineer shunt down bugs utilizing essentially the most rudimentary instruments and monitor down root explanations. Utilizing a blend of collected knowledge and instinct. Debugging networks is simplest becoming tougher as networks are becoming bigger (cutting-edge knowledge centers may just include 10 000 switches, a campus network may just serve 50 000 users, a one hundred-gb/s long-haul hyperlink may just elevate one hundred 000 flows) and have become extra problematic (with over 6000 rfcs, router program is founded on thousands of strains of supply code and network chips generally contain billions of gates). It's a small surprise that community engineers had been labeled “masters of complexity”.
A HYBRID APPROACH FOR SCHOLARLY INFORMATION EXTRACTION

Metadata extraction from documents forms an essential part of web or desktop search systems. Similarly, digital libraries that index scholarly literature require to find and extract the title, the list of authors and other publication-related information from an article. We present a hybrid approach for metadata extraction, combining classification and clustering to extract the desired information without the need of a conventional labeled dataset for training. An important asset of the proposed method is that the resulting clustering parameters can be used in other problems, e.g. document layout analysis.
Geolocation and Regional Classification via Sparse Coding

We present a data-driven approach for Twitter geolocation and regional classification. Our method is based on sparse coding and dictionary learning, an unsupervised method popular in computer vision and pattern recognition. Through a series of optimization steps that integrate information from both feature and raw spaces, and enhancements such as PCA whitening, feature augmentation, and voting-based grid selection, we lower geolocation errors and improve classification accuracy from previously known results on the GEOTEXT dataset.
Implementation of Refining Statements in OpenJML and Verification of Higher Order Methods with Model Program Specifications

The Java Modeling Language (JML) describes the functional behavior of Java classes and methods using pre-and postconditions. However, standard pre-and postcondition speciﬁcations cannot verify calls to higher order methods (HOMs). JML uses model program speciﬁcations to reason about HOMs. This thesis describes the implementation of model programs in the OpenJML tool. The implementation includes parsing, type checking, and matching of model program speciﬁcations against the code.
Towards an Attention-Driven Model of Task Switching

We describe a computational model of task switching developed within the ARCADIA framework. This model emphasizes the attentional component of task sets and the locality of processing for task selection, stimulus–response rules, and motion planning. We contrast the structure and processing of the model to one developed in ACT-R and illustrate its dynamics on one trial from a task switching experiment. We conclude by discussing the broader implications of the model for cognitive modeling and cognitive systems research.
UnKnown 2009 2 D Simulation Team Description Paper

Unknown is the result of a research project in Islamic Azad University of Lahijan. The project started in October 2008. This paper briefly describes main features of the Unknown 2D soccer simulation team based on Robosina05 code. Many improvements and innovations have been made on it, especially in high-level strategies.
The influence of cognitive domain of psycho-education in community male-circumcision programmes ( CMCPs ) , on theself-esteemof boysin public mixed-day secondary schools in Nyandarua-Kenya

Teachings during circumcisionarguably contribute to delinquent and anti-social behaviour among boys in Nyandarua County. The aim of this study was to examine influence of cognitive domain on boys’ selfesteem in public mixed-day secondary schools.The study was grounded on psycho-education, behaviour modification and rational choice theories. It employed mixed method, concurrent triangulation design. Target population was 1,242 boys, with a sample of 144 participants comprising; boys, teachers, counsellors, psychoeducational service providers andparents.Random, purposive and proportional sampling procedures were used. Research instruments included; questionnaires, interview schedules and focus group discussion guides. Itemcontent validitywasapplied for enhancing validity while piloting of tools was used to assure for reliability (rs = .622) through split-half testing. Credibility and dependability of instruments was assured through feedback during discussions and data triangulation from different sources. Data management included sorting, coding and keying before analysis with computer application SPSS v23. Pearson r, the paired samples t-test, and regression analysis (R) sufficed for data analysis. The study used null hypotheses, tested at α = . 05.Results indicated that cognitive domain was related to boys’ self-esteem (p = .028). It was also found out that it wasa suitable predictor of self-esteem in boys(R 2 =.512). The study further indicated that self-esteemof boys circumcised in CMCPs was not any different from that of boysinitiated elsewhere[t (25) = . 01, p = . 51, α = . 993]. The study recommended that M.O. E and M.O. H. play critical role in design of common curricula to be used as guide during instruction in CMCPs. It also recommended that cognitive instructionbe restructured and expanded to schools, so that all adolescents benefited from professional behaviour management skills,in order to stem indiscipline and other anti-social behaviour.
J ul 2 01 5 Joint Optimal Number of RF chains and Power Allocation for Downlink Massive MIMO Systems

This paper investigates the downlink of massive multiple-input multiple-output (MIMO) systems that inclu de a single cell Base Station (BS) equipped with large number of antennas serving multiple users. As the number of RF chains i s getting large, the system model considered in this paper ass umes a non negligible circuit power consumption. Hence, the aim o f this work is to find the optimal balance between the power consumed by the RF chains and the transmitted power. First, assuming a n equal power allocation among users, the optimal number of RF chains to be activated is analytically found. Then, for a given number of RF chains we derive analytically the optimal power allocation among users. Based on these analysis, we propose an iterative algorithm that computes jointly the optimal numb er of RF chains and the optimal power allocation vector. Simulati ons validate the analytical results and show the high performance provided by the proposed algorithm.
1 Cooperated Routing Problem for Ground Vehicle and 2 Unmanned Aerial Vehicle : The Application on 3 Intelligence , Surveillance and Reconnaissance 4 Missions 5

In this paper, we present a novel Two-Echelon Ground Vehicle and Its Mounted 12 Unmanned Aerial Vehicle Cooperated Routing Problem (2E-GUCRP). The 2E-GUCRP arises in the 13 field of Unmanned Aerial Vehicle (UAV) Routing Problem such as those encountered in the context 14 of city logistics. In a typical cooperated system, the UAV is launched from the Ground Vehicle (GV) 15 and automatically flies to the designated target. Meanwhile, acting as a mobile base station, the GV 16 can charge or change the UAV’s battery on the designated landing points to enable the UAV to 17 continue its mission. The objective is to design efficient GV and UAV routes to minimize the total 18 mission time while meeting the operational constraints. A Mixed Integer Programming (MIP) 19 model, which could be solved by commercial software, is constructed to describe this problem. In 20 order to quickly solve the medium-scale problems, two existing heuristics to solve 2E-VRP are 21 improved. The computational experiments are set up to compare our model with the 2E-VRP. The 22 results indicate that the 2E-GUCRP obtains a better efficiency. Further discussion of the practical 23 instance points out that the increase in efficiency is related to the speed relationship between the GV 24 and the UAV. 25
Absence of P300 Amplitude Laterality in Persons Who Stutter

Objective: This study investigated differences in P300 auditory-evoked potentials between Persons Who Stutter (PWS) and Persons Who Do Not Stutter (PWNS). Methods: Responses to tonal and synthetic speech stimuli were recorded from electrodes over the right and left parietal locations and amplitude and latency measures were analyzed. The speech stimuli /bA/-/pA/ and /bA/-/dA/ were two sets of syllables contrasting across and within categorical boundaries on the first phoneme. Results: PWS demonstrated different inter-hemispheric activity in response to the stimuli than PWNS. Whereas PWNS had significantly greater P300 amplitudes over the left than the right parietal location, PWS did not demonstrate significantly greater P300 amplitudes over either location. Conclusion: This lack of laterality for PWS is discussed in the light of cerebral dominance theories and speech development.
Image-classification of Asset Management Systems ’ content based on Machine Learning

In this research project we set out to determine if machine learning could be used to classify a large collection of images into a taxonomy that relates to a specific client or set of clients within an industry. Accomplishing this would help to reduce the reliance on human operators and improve their workflow. While it may not be possible to fully automate this process, utilising machine learning to perform the monotonous tasks would free up humans to deal with more complex or interesting issues. In this particular task, there is an added complexity that some of the clients describe their images in more abstract concepts. For example, a university would find it beneficial to differentiate between images of lecturers and students but may also wish to determine if the students are happy. On the other hand, a shop would look to differentiate between products being modelled rather than the models themselves. While recent advancements in deep learning have provided a solid framework for basic image classification, learning adjectives that describe human emotions or interactions between objects in an image is a much more complex task.
Towards the development of a software to design acoustic barriers based on Sonic Crystals : An overlapping model

The use of Sonic Crystals as acoustic screens, usually called SCAS (Sonic Crystal Acoustic Screens) has meant a qualitative jump in the field of environmental noise protection. This technological achievement, developed over the last decade, has been accomplished in two steps: the discovery of the basic knowledge about physical principles underlying in the behaviour of these periodic systems and the development of technological procedures for designing and constructing this kind of acoustic screens in such a way that be competitive, under the acoustic point of view, with respect to the classical barriers. In this sense, among all the fields where these periodic systems can be applied, the one referred to acoustic screens has been the first where a device has been designed, patented, constructed and standardized with fairly good results. However, the development of specific software to design these devices in a professional way is a pending subject to introduce them successfully in the market. Following this line, in this paper we present an overlapping comprehensive model to easily design SCAS. The model has been implemented using the Finite Elements method and is based on the tunability concept, which establish that all the acoustic effects, involved in the design, act independently one from each other, and furthermore work constructively. This fact allows the design of barriers for specific purposes, assigning to each acoustic effect a predetermined range of frequencies where it has to work. An example in the use of this model is applied to the case of the reduction of diffraction at the upper edge of a SCAS.
Bit-Line Biasing Technique for Phase-Change Memories

This paper presents a bit-line biasing technique that ensures read and write voltages to be fed to the storage element with adequate accuracy, high rejection of disturbs from the column decoder supply line, and protection against spurious program operations in read mode. Fast bit-line precharge and sensing are also achieved. The presented technique has been developed for phase-change memories, however, it is also suitable for use in other non-volatile storage devices such as Flash memories. Experimental results on read and program operations demonstrated the effectiveness of the approach.
TFix : Automatic Timeout Bug Fixing in Production Server Systems

Timeout is widely used to handle unexpected failures in distributed systems. However, improper use of timeout schemes can cause serious availability and performance issues, which is often difficult to fix due to lack of diagnostic information. In this paper, we present TFix, an automatic timeout bug fixing system for correcting misused timeout bugs in production systems. TFix adopts a drill-down bug analysis protocol that can narrow down the root cause of a misused timeout bug and producing recommendations for correcting the root cause. TFix first employs a system call frequent episode mining scheme to check whether a timeout bug is caused by a misused timeout variable. TFix then employs application tracing to identify timeout affected functions by analyzing both function execution time and frequency changes. Next, TFix uses taint analysis to localize the misused timeout variable. Last, TFix produces recommendations for proper timeout variable values based on the tracing results during the system normal run. We have implemented a prototype of TFix and conducted extensive experiments using 13 real world cloud server timeout bugs. Our experimental results show that TFix can correctly localize the misused timeout variables and suggest proper timeout values for fixing those bugs.