Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Scale Multi-Level Generative Model in Scene Classification

Previous works show that the probabilistic Latent Semantic Analysis (pLSA) model is one of the best generative models for scene categorization and can obtain an acceptable classification accuracy. However, this method uses a certain number of topics to construct the final image representation. In such a way, it restricts the image description to one level of visual detail and cannot generate a higher accuracy rate. In order to solve this problem, we propose a novel generative model, which is referred to as multi-scale multi-level probabilistic Latent Semantic Analysis model (msml-pLSA). This method consists of two parts: multi-scale part, which extracts visual details from the image of diverse resolutions, and multi-level part, which concentrates multiple levels of topic representation to model scene. The msml-pLSA model allows for the description of fine and coarse local image detail in one framework. The proposed method is evaluated on the well-known scene classification dataset with 15 scene categories, and experimental results show that the proposed msml-pLSA model can improve the classification accuracy compared with the typical classification methods.
Design and Construction of Knowledge Graph of Electronic Medical Record Based on BiLSTM-CRF

In the traditional medical treatment, the number of hospital patients is too large, the medical resources are too tight, and the accumulated medical knowledge is difficult to be fully utilized. With the help of the Internet and artificial intelligence technology, the reform of traditional medical treatment can realize the preliminary diagnosis and triage in the process of diagnosis and treatment. Based on the electronic medical records obtained from a hospital and the pre-processed text, a medical knowledge graph based on the BILSTM-CRF model was constructed. Firstly, named entity recognition was realized, then relation extraction was carried out, and finally, data was imported into the Neo4j database to realize the visualization of the knowledge graph. A simple medical question answering system based on knowledge graph is designed and implemented to provide technical support for medical diagnosis in the form of "one question and one answer". The results show that the system can alleviate some problems in traditional medical treatment to a certain extent.
Passenger Profiling, Imperfect Screening, and Airport Security

We present a theoretical model of airport searches. The model extends previous work in the area in that detection conditional on search is imperfect. The hit rates tests for racial bias developed in Knowles, Persico, and Todd (2001) is shown to apply even in the presence of imperfections in monitoring. We then study two channels for improving airport security: better targeting and better detection. We show that better targeting does not necessarily decrease the overall crime rate, although it will decrease crime in the group that is targeted. Improved detection rates unambiguously decrease crime. Group-specific improvements in detection do not necessarily increase the number of searches for those groups. The analysis is extended to allow for the possibility that criminal passengers disguise themselves as members of low-crime groups.
A First Implementation of an Advanced 3D Interface to Control and Supervise UAV (Uninhabited Aerial Vehicles) Missions

Recent analyses on the uninhabited aerial vehicle (UAV) accidents revealed that several kinds of human-system control problems occur in current UAV missions. Therefore, a design of the manmachine interface that allows for an efficient and effective interaction between the operator and the remote vehicle becomes one of the challenges in the development of more reliable UAVs. This paper presents a first implementation of an advanced interface for UAV ground control station based on a touch screen, a 3D virtual display, and an audio feedback message generator. The touch screen is used to send high level commands to the vehicle, the 3D virtual display provides a stereoscopic and augmented visualization of the complex scenario in which the vehicle operates, and the audio feedback message generator informs the operator about any change in operational scenario. The hardware/software architecture of the interface also includes a planning algorithm and a generic vehicle model. The interface has been tested by simulating several UAV missions. The results have shown that the interface requires an adequate level of workload to command the vehicle and allows the operator to build a good level of awareness of the state of the vehicle under his or her control, as well as of the environment in which it operates.
Prevalence and Associated Factors of Antenatal Depression among Women Attending Antenatal Care Service at Gondar University Hospital, Northwest Ethiopia

Background Depression is the most prevalent psychiatric disorder during pregnancy and is associated with psychosocial and clinical obstetric factors. Depressive disorders are not only common and chronic among women throughout the world but also principal sources of disability. The scarce information and limited attention to the problem might aggravate the consequence of the problem and can limit the intervention to be taken. Therefore, the current study was conducted to determine the prevalence and identify associated factors for antenatal depression. Methods Institutional based cross-sectional study was conducted by taking a sample of 388 pregnant women coming for ANC service at Gondar University Hospital. Systematic random sampling technique was employed to recruit the study participants. Structured, pretested and interview administered questionnaire was used to collect related information while Beck Depression Inventory (BDI) was used to assess individuals`depression condition. A cut off point with high sensitivity and specificity was determined and internal consistency of the tool was checked (Cronbach alpha = 0.82). Ep Info V. 2002 and STATA 12 were used for data entry and analyses, respectively. Adjusted Odds Ratio with its 95% CI was used to declare the statistical significance of the factors. Results Depression among pregnant women was found to be 23% (95%CI: 18.48%, 26.86%). Factors significantly associated with depression were: woman`s age (20 to 29, AOR = 0.18,95% CI:0.07,0.49), occupation (housewife, AOR = 2.57,95%CI:1.21,5.46, merchant and daily laborers, AOR = 3.44 (1.38,8.58), previous pregnancy (No, AOR = 4.74,95% CI:1.58,14.17) and previous ANC follow up pattern (irregular, AOR = 11.43,95% CI:3.68,35.49), no follow up, AOR = 11.98, 95% CI:4.73,30.33). Conclusion Depression symptoms are common in pregnant mothers in the study area and interventions that would address the aforementioned factors would benefit to tackle further complications.
Standardized flow cytometric method for the accurate determination of platelet counts in patients with severe thrombocytopenia.

BACKGROUND The therapeutic option of prophylactic platelet (PLT) transfusion in cases of severe thrombocytopenia critically depends on the availability of accurate and precise counts because clinical decisions are widely based on decision or trigger points. Although often applied in current practice at a level of 20 Gpt/L, there is increasing evidence that the trigger points could safely be reduced to 10 or even 5 Gpt/L. In order to facilitate this downward revision, it is necessary to have PLT counting methods that are able to provide reliable results in the appropriate decision range. METHODS Postchemotherapy-induced pancytopenia PLT counting was performed in patients with hematological malignant disorders. This study describes a novel flow cytometric method that utilizes a PLT-specific monoclonal antibody (CD41a) in conjunction with fluorescent reference beads in order to derive absolute platelet numbers. RESULTS Applying a mathematical model, this flow cytometric method was shown to have a detection limit of 0.24 Gpt/L and a lower limit of quantification (coefficient of variation [CV] = 10%) of 1.1 Gpt/L. These values are a substantial improvement on previously reported results for the Technicon H1 automated instrument or manual hemocytometry. Moreover, although the flow cytometry and Technicon H3 methods were found by supplementary analyses to show a reasonably good correlation, the hematology instrument showed a distinct tendency to overestimate PLT counts at low levels. CONCLUSION It is proposed that this standardized immunoplatelet method offers the best approach in evaluating, at the clinical level, the possibility of lower PLT transfusion triggers. It can be used to evaluate the performance limitations of automated hematology analyzers that are widely used at the present time.
Comparison of Heuristic Algorithms for the N-Queen Problem

This paper addresses the way in which heuristic algorithms can be used to solve the n-queen problem. Metaheuristics for algorithm simulated annealing, tabu search and genetic algorithm are shown, test results are demonstrated and upper bound complexity is determined. The efficiencies of algorithms are compared and their achievements are measured. Due to the reduction of the fitness function complexity to O(1) problem instances with large dimensions are solved.
Detection of rubella virus-specific immunoglobulin M antibodies with a baculovirus-expressed E1 protein

The structural proteins of rubella virus (RV) were expressed in insect cells by using the baculovirus expression vector system. The recombinant E1 envelope glycoprotein was purified by immunoaffinity chromatography and used to detect RV-specific immunoglobulin M antibodies in a time-resolved fluoroimmunoassay. Correlation analysis between the reactivities of antibodies against this recombinant E1 and the reactivities against authentic RV antigen shows that purified E1 can detect RV antibodies of the immunoglobulin M type.
Romer for use with the tonographic nomogram.

Construction This romer is made from thick cartridge paper, cut with a guillotine to a square measuring 6 x 6 in. (16 x 16 cm.). The origin of the romer will be its upper right-hand corner, whereas the origin of the nomogram scales is the lower left-hand corner; therefore, the romer is calibrated in an inverted position with its origin exactly on the origin of the nomogram and the two sides parallel and coincident with the scales of the nomogram. The romer is then re-orientated and the numerals for its scales completed in this position as shown in Fig. 1; the volume of indentation is given across the top and the intra-ocular pressure down the right side. Fig. 1 also shows how it is used on the nomogram for-measuring the pressure and volume values corresponding to 10-5 on the 10 g. curve. It is important to orientate the romer exactly so that the pressure and volume readings on the romer scales are equal to the readings on the nomogram scales where the romer intersects. The central area of the romer is completed with a Table giving the intra-ocular pressure (Pt mm. Hg) for a series of applanation diameters (D mm.) at applanation forces (W g.-weight) from 1 to 8 g. -weight. These values are calculated using the equation:
Mobile App Security Risk Assessment: A Crowdsourcing Ranking Approach from User Comments

Along with the exponential growth on markets of mobile Applications (apps), comes the serious public concern about the security and privacy issues. Therefore automatic app risk assessment becomes increasingly important to support users with useful evidences for their decisions. User comment provides a unique perspective from actual user experience, and should be considered valuable information source for risk assessment for mobile apps. In this paper, we provide a novel perspective to view the risk assessment of an app from its user comments as a crowdsourcing problem and adopt ranking model as the evaluation method. We develop a co-training scheme to amalgamate feature learning and learning to rank models. Experiments conducted on two different real-world datasets show substantial performance improvements (i.e., 6%–7%) over the state-ofthe-art methods.
Metformin: From antidiabetic to cancer therapeutic

v! TABLE OF CONTENTS . vi! LIST OF TABLES . vii! LIST OF FIGURES . viii! LIST OF ABBREVIATIONS . ix! INTRODUCTION . 1! PUBLISHED STUDIES . 26! DISCUSSION . 43! REFERENCES . 48! VITA . 56!
Organic chemistry in circumstellar envelopes: Setting the stage for prebiotic synthesis

Abstract One of the few carbon-rich environments found in interstellar space is the ejecta of asymptotic giant branch (AGB) stars. Such material, which forms a circumstellar envelope, becomes enriched in carbon due to “dredge-up” phenomena associated with nucleosynthesis. A unique organic synthesis flourishes in the gas phase in these envelopes, and radio and millimeter observations have identified a wide range of C-bearing compounds, including long acetylenic chains such as HC5N, HC7N, C4H, C6H, C8H, C6H−, C8H−, and C3O. Oxygen-rich envelopes also have a non-negligible carbon chemistry, fostering species such as HCN and HCO+. Phosphorus chemistry appears to be active as well in circumstellar shells, as evidenced by the recent detections of HCP, CCP, and PO. Radio observations also indicate that some fraction of the circumstellar molecular material survives into the planetary nebula stage, and then becomes incorporated into diffuse, and eventually, dense clouds. The complex organic molecules found in dense clouds such as Sgr B2(N) may be the products of “seed” material that can be traced back to the carbon-enriched circumstellar gas.
Ant colony algorithm applied to automatic speech recognition graph decoding

In this article we propose an original approach that allows the decoding of Automatic Speech Recognition Graphs by using a constructive algorithm based on ant colonies. In classical approaches, when a graph is decoded with higher order language models; the algorithm must expand the graph in order to develop each new observed n-gram. This extension process increases the computation time and memory consumption. We propose to use an ant colony algorithm in order to explore ASR graphs with a new language model, without the necessity of expanding it. We first present results based on the TED English corpus where 2-grams graph are decoded with a 4-grams language model. Then, we show that our approach performs better than a conventional Viterbi algorithm when computing time is constrained and allows a highly threaded decoding process with a single graph and a strict control of computation time and memory consumption.
Recognition, object detection and segmentation of white background photos based on deep learning

Image recognition, object detection and segmentation have been a popular problem in computer vision tasks. This paper addresses the recognition, object detection and segmentation issues in white background photos with deep learning method. In particular, we firstly train a recognition model based on GoogLeNet to judge whether a photo is white background. Then we propose a main object detecting algorithm to eliminate unnecessary elements such as logos, characters with Faster R-CNN. Eventually a main object segmentation method combining both CRF-RNN network and Grabcut is adopted to smoothly eliminate the shadow area and obtain the fine segmentation results. All exploring algorithms are implemented in real time with Caffe and Tesla K80 from Nvidia. Latest testing experiments demonstrate that an accuracy of 96% in recognition and 94%in detection is resulted and an acceptable fine segmentation results used for solution of 400 ∗ 400 is successfully achieved. The proposed technical scheme offers an alternative to computer vision tasks in complex background photos.
Learning Optimal Redistribution Mechanisms through Neural Networks

We consider a setting where $p$ public resources are to be allocated among $n$ competing and strategic agents so as to maximize social welfare (the objects should be allocated to those who value them the most). This is called allocative efficiency (AE). We need the agents to report their valuations for obtaining these resources, truthfully referred to as dominant strategy incentive compatibility (DSIC). We use auction-based mechanisms to achieve AE and DSIC yet budget balance cannot be ensured, due to Green-Laffont Impossibility Theorem. That is, the net transfer of money cannot be zero. This problem has been addressed by designing a redistribution mechanism so as to ensure a minimum surplus of money as well as AE and DSIC. The objective could be to minimize surplus in expectation or in the worst case and these $p$ objects could be homogeneous or heterogeneous. Designing redistribution mechanisms which perform well in expectation becomes analytically challenging for heterogeneous settings. In this paper, we take a completely different, data-driven approach. We train a neural network to determine an optimal redistribution mechanism based on given settings with both the objectives, optimal in expectation and optimal in the worst case. We also propose a loss function to train a neural network to optimize worst case. We design neural networks with the underlying rebate functions being linear as well as nonlinear in terms of bids of the agents. Our networks' performances are same as the theoretical guarantees for the cases where it has been solved. We observe that a neural network based redistribution mechanism for homogeneous settings which uses nonlinear rebate functions outperforms linear rebate functions when the objective is optimal in expectation. Our approach also yields an optimal in expectation redistribution mechanism for heterogeneous settings.
Acoustic and language modeling for children's read speech assessment

Automatic speech recognition can be used to evaluate the accuracy of read speech and thus serve a valuable role in literacy development by providing the needed feedback on reading skills in the absence of qualified teachers. Given the known limitations of ASR in the face of insufficient task-specific training data, the selection of acoustic and language modeling strategies can play a crucial role in achieving acceptable performance in the task. We consider the problem of detecting mispronunciations in read-aloud stories in English (as a second language) by children in the age group 10–14. Multiple available datasets that separately capture the characteristics of children's speech and Indian accented English are used to train and adapt the acoustic models. A knowledge of the text together with the prediction of mispronunciation errors helps to define an effective language model. We present mispronunciation detection performance on a small test dataset of field recordings and discuss implications for further work.
Efficient template generation for instruction-based self-test of processor cores

This paper presents a method of template generation for instruction-based self-test of processor cores. A test program template is an instruction sequence with unspecified operands, and represents paths for justification of test patterns and propagation of test responses for a module under test (MUT). In order to justify value of MUT inputs, we introduce a concept of adjacent registers of the MUT that makes it possible to consider input spaces of the MUT determined by signals from other modules as well as signals directly from registers. We efficiently generate possible templates considering dependence of instructions each of which invokes one or more data transfers between registers. The method also generates multiple templates in effective order to detect faults, which may cover different input spaces, and therefore, different detectable fault sets.
Cyclin A2 and CDK2 as Novel Targets of Aspirin and Salicylic Acid: A Potential Role in Cancer Prevention

Data emerging from the past 10 years have consolidated the rationale for investigating the use of aspirin as a chemopreventive agent; however, the mechanisms leading to its anticancer effects are still being elucidated. We hypothesized that aspirin's chemopreventive actions may involve cell-cycle regulation through modulation of the levels or activity of cyclin A2/cyclin-dependent kinase-2 (CDK2). In this study, HT-29 and other diverse panel of cancer cells were used to demonstrate that both aspirin and its primary metabolite, salicylic acid, decreased cyclin A2 (CCNA2) and CDK2 protein and mRNA levels. The downregulatory effect of either drugs on cyclin A2 levels was prevented by pretreatment with lactacystin, an inhibitor of proteasomes, suggesting the involvement of 26S proteasomes. In-vitro kinase assays showed that lysates from cells treated with salicylic acid had lower levels of CDK2 activity. Importantly, three independent experiments revealed that salicylic acid directly binds to CDK2. First, inclusion of salicylic acid in naïve cell lysates, or in recombinant CDK2 preparations, increased the ability of the anti-CDK2 antibody to immunoprecipitate CDK2, suggesting that salicylic acid may directly bind and alter its conformation. Second, in 8-anilino-1-naphthalene-sulfonate (ANS)-CDK2 fluorescence assays, preincubation of CDK2 with salicylic acid dose-dependently quenched the fluorescence due to ANS. Third, computational analysis using molecular docking studies identified Asp145 and Lys33 as the potential sites of salicylic acid interactions with CDK2. These results demonstrate that aspirin and salicylic acid downregulate cyclin A2/CDK2 proteins in multiple cancer cell lines, suggesting a novel target and mechanism of action in chemoprevention. Implications: Biochemical and structural studies indicate that the antiproliferative actions of aspirin are mediated through cyclin A2/CDK2. Mol Cancer Res; 14(3); 241–52. ©2015 AACR.
Neural correlates of prenatal stress in young women

Background Prenatal stress is hypothesized to have a disruptive impact on neurodevelopmental trajectories, but few human studies have been conducted on the long-term neural correlates of prenatal exposure to stress. The aim of this study was to explore the relationship between prenatal stress exposure and gray-matter volume and resting-state functional connectivity in a sample of 35 healthy women aged 14–40 years. Method Voxel-based morphometry and functional connectivity analyses were performed on the whole brain and in specific regions of interest (hippocampus and amygdala). Data about prenatal/postnatal stress and obstetric complications were obtained by interviewing participants and their mothers, and reviewing obstetric records. Results Higher prenatal stress was associated with decreased gray-matter volume in the left medial temporal lobe (MTL) and both amygdalae, but not the hippocampus. Variance in gray-matter volume of these brain areas significantly correlated with depressive symptoms, after statistically adjusting for the effects of age, postnatal stress and obstetric complications. Prenatal stress showed a positive linear relationship with functional connectivity between the left MTL and the pregenual cortex. Moreover, connectivity between the left MTL and the left medial-orbitofrontal cortex partially explained variance in the depressive symptoms of offspring. Conclusions In young women, exposure to prenatal stress showed a relationship with the morphometry and functional connectivity of brain areas involved in the pathophysiology of depressive disorders. These data provide evidence in favor of the hypothesis that early exposure to stress affects brain development and identified the MTL and amygdalae as possible targets of such exposure.