Miracle-dz/UniFAR-FTUs huggingface dataset:
The economics of health in developing countries

This useful collection examines the ways in which economic concepts and techniques can be applied to health and health services in developing countries, indicating the present scope of economic analysis and its potential use in the future planning and management of the health sector. Written by experts in the field, each essay presents theoretical developments and research findings, providing a state of the art review of the uses of economic analysis in such areas as health insurance, primary health care, immunization programs, and the interrelationship between health sector and national development planning.
PERSEUS : Personalized Multimedia News Portal

This paper describes the Perseus project, which is devoted to developing techniques and tools for creating personalized multimedia news portals. The purpose of a personalized multimedia news portal is to provide relevant information, selected from newswire sites on the Internet and augmented by video clips automatically extracted from TV broadcasts, based on the user’s preferences. To create such an intelligent information system several techniques related to textual information retrieval, audio and video segmentation, and topic detection should be developed to work in accord. The approaches to event mining and tracking on the Internet, commercial detection and recognition in video and audio streams, and selection of relevant news video fragments, based on closed captioning and audio transcripts, are described.
The Nature of News, Insider Ownership, Stock Price and Stock Returns

This paper attempts to address the connections among the nature of news, insider ownership, stock price, and stock returns. The empirical studies show that stock price could mislead investors' behavior and enhance the impact of the nature of news on stock returns. Besides, the higher stock price is, the less impact analyst forecast revision would create. This research assumes that investors tend to be over-confident and overestimate their ability to invest when stock price is buoyant, thus ignoring analyst forecast revision. Instead, when stock price declines, the impact of analyst forecast revision would become more significant.
Modeling Ontological Concepts of Motions with Two Projection-Based Spatial Models

To model human concepts of motions is essential for the development of the systems and machines that collaborate with ordinary people on spatiodynamic tasks. This paper applies two projection-based spatial models, Double Cross and RfDL3-12, to the modeling of human concepts of motions on a plane, making use of the ability of these two models to illustrate where and how a landmark extends around/on a path. For generalization, we adopt a set of formal motion concepts defined in an existing spatial ontology called GUM. These motion concepts are associated with the motion patterns modeled by Double Cross and RfDL3-12, considering two scenarios where the landmarks are represented by points and regions, respectively. For the latter scenario, we identify the motion patterns whose characterization cannot be clearly determined. In addition, we find that the knowledge of landmarks’ convexity is useful for characterizing motion patterns.
UxrvpnsrrY oF Calrronxra Saxra Cnuz Biological Modeling in Data Poor Scenarios

Many biological systems under study are characterized as data-poor. In order to enhance our analyses of these systems, we will need to develop modeling tools that can appropriately utilize the data, and determine if the data are being properly utilized. I examine this problem through two different data-poor systems: West Coast Rockfish (Sebastes spp.) fisheries, and mosquito (Anopheles gambiae) host attack behavior dynamics. Stock assessment models are used by fisheries scientists to estimate stock status, in order to help inform managers when they determine future catch policy. These models employ a large set of assumptions, which may not be appropriate for the species under consideration. When species are data-poor, it isn’t fully known how uncertainty in stock assessments, and the catch policy based on them, truly effect the population. I implement a life history simulator to generate data with which to quantify the accuracy of the stock assessment models. Mosquito attack behavior is important to understand since they are vectors for several diseases, including malaria. By using a behavior model, I examine how mosquito attack behavior changes due to its state and its environment. I find that uncertainty has significant impacts on stock assessment accuracy and mosquito attack behavior, and I show that the models can be used to enhance the existing data and gain further insights into the systems.
S100P Promotes Pancreatic Cancer Growth, Survival, and Invasion

Purpose: In the current study, we examined the functional significance and mechanism of action of S100P in pancreatic cancer cells. Experimental Design: S100P levels were increased in Panc-1 cells, which do not express S100P, by transfection with an S100P cDNA and S100P levels were reduced in BxPC3 cells, which express high levels of S100P, by small interfering RNA gene silencing. Effects of these manipulations on cell proliferation, resistance to apoptotic insults, cell migration, and invasion were estimated in vitro using standard assays. The influences of S100P on tumor growth in vivo were studied using xenograft mouse models. To identify the mechanisms involved in these responses, coimmunoprecipitation studies were conducted with S100P with receptor for advanced glycation end products (RAGE) and the effects of inhibiting RAGE using an antagonistic peptide were analyzed. Results: S100P levels correlated with the rates of cell proliferation, survival, migration, and invasion in both cell models in vitro. In vivo, increased S100P levels increased the growth of tumors in mice with s.c. -implanted Panc-1 cells and decreased S100P levels decreased tumor growth after orthotopic implantation of BxPC-3 cells. A direct interaction between S100P and RAGE was indicated by coimmunoprecipitation of these molecules from pancreatic cancer cells. A RAGE antagonist peptide inhibited this interaction and also inhibited the biological effects of S100P on these cells in vitro. Conclusions: These data suggest that S100P plays a major role in the aggressiveness of pancreatic cancer that is likely mediated by its ability to activate RAGE. Thus, interference with S100P may provide a novel approach for treatment of pancreatic cancer.
Inhibition of histamine-mediated signaling confers significant protection against severe malaria in mouse models of disease

From the inoculation of Plasmodium sporozoites via Anopheles mosquito bites to the development of blood-stage parasites, a hallmark of the host response is an inflammatory reaction characterized by elevated histamine levels in the serum and tissues. Given the proinflammatory and immunosuppressive activities associated with histamine, we postulated that this vasoactive amine participates in malaria pathogenesis. Combined genetic and pharmacologic approaches demonstrated that histamine binding to H1R and H2R but not H3R and H4R increases the susceptibility of mice to infection with Plasmodium. To further understand the role of histamine in malaria pathogenesis, we used histidine decarboxylase–deficient (HDC−/−) mice, which are free of histamine. HDC−/− mice were highly resistant to severe malaria whether infected by mosquito bites or via injection of infected erythrocytes. HDC−/− mice displayed resistance to two lethal strains: Plasmodium berghei (Pb) ANKA, which triggers cerebral malaria (CM), and Pb NK65, which causes death without neurological symptoms. The resistance of HDC−/− mice to CM was associated with preserved blood–brain barrier integrity, the absence of infected erythrocyte aggregation in the brain vessels, and a lack of sequestration of CD4 and CD8 T cells. We demonstrate that histamine-mediated signaling contributes to malaria pathogenesis. Understanding the basis for these biological effects of histamine during infection may lead to novel therapeutic strategies to alleviate the severity of malaria.
Criminal Justice Management: Theory and Practice in Justice-Centered Organizations

1. Criminal justice management: The big, the bad, and the beautiful, 2. Surveying the landscape of criminal justice management, 3. Managing trouble - deviance, abuse of force and sexual/gender harassment - using ethics, 4. The administrative state and management theories in perspective, 5. Communications: What you say and do is what they think you mean, 6. Socialization, roles and power issues, 7. Leadership and criminal justice organizations, 8. Personnel processes and practices, 9. Selection issues: Workforce 2000, diversity, and affirmative action, 10. Reaching beyond the expected: Managing treatment, force, standards, and accreditation, 11. Strategic planning and Budgeting, 12. Decision making and prediction, 13. Model management practices.
Detecting and Tracking the Rise of DGA

manos Antonakakis received his Phd in computer science from Georgia Institute of Technology under Professor Wenke Lee's supervision. currently, he works at damballa as the director of Academic Sciences where he is responsible for academic research, university collaborations, and technology transfer efforts. His main research interests are in network security and machine learning/data mining. are in network security and machine learning/ data mining. Nikolaos Vasiloglou received his Phd in electrical engineering from the Georgia Institute of Technology. He has extensive experience in developing machine-learning applications and algorithms. In the past he has developed machine-learning engines and models for several companies. He has published more than 100 scholarly articles. His current research projects are in the areas of botnet detection, malware analysis, virtual machine monitoring, and Web 2.0 security and privacy, with funding from NSF, When bots go in search of their command and control (C&C) servers, they often use algorithmically generated domain names (DGAs). We have created a system (Pleiades) that watches unsuccessful DNS resolution requests (NXDomain) from recursive DNS servers in large networks. Pleiades can reliably identify new clusters of NXDomains generated by DGAs, the newly infected hosts, and often, the actual C&C servers the DGA malware employs. In this article, we explain how our system works, as well as the most interesting information about current bot infections and C&C structures.
Panoramic Video Representation using Mosaic Image

This paper presents a novel approach of video representation with panoramic techniques for low bit-rate video transmission. Using a background panorama as a prior information, foreground objects in a video sequence are separated from the background through a series of segmentation processes. These foreground segments are encoded by traditional compression technique and transmitted as a video stream, while the scene background is transmitted only once as a panorama. To reconstruct the original video frame, foreground objects are combined with the corresponding panoramic segment on-they at the receiving side. Experiments show that our approach improves the compression performance, compared with MPEG-1 under the same quality factor. Our system can synthesize virtual environments without using blue-screen. The users can navigate throughout the scene or examine any particular details. Our system also provides an e ective solution to scene-based video
Estimating Matching Games With Endogenous Prices

In matching games, agents must all agree for a match to be formed, and some agents can make only a finite number of matches. I examine the nonparametric identification and estimation of match production functions in matching games with endogenous prices and transferable utilities. Inequalities derived from single-agent best responses underly a nonparametric maximum score estimator of match production functions. The inequalities do not require data on prices, quotas, or production levels. The estimator does not suffer from a computational or data curse of dimensionality in the number of agents in a matching market as the estimator avoids solving for an equilibrium and estimating first-stage match probabilities. Further, using only a subset of the possible inequalities preserves consistency. The estimator allows markets with one-to-one, one-tomany, many-to-many and coalition formation matching, as well as externalities from agents outside a given match. For games with multiple equilibrium sets of matches, there is no need to estimate an equilibrium selection rule or computationally itemize the equilibria. ∗Thanks to helpful comments from many colleagues as well as workshop participants at Chicago, Minnesota, the New York Fed, North Carolina, Northwestern, Stanford and Virginia. Email: fox@uchicago.edu.
UNSUPERVISED DETECTION OF STRUCTURAL CHANGES IN ELECTRONIC DANCE MUSIC

EXTENDED ABSTRACT Music structure segmentation is a relevant topic in music information retrieval as it is both useful to the analysis of musical structure and a means to improve performance in other tasks [1], such as audio editing in recording work-flows [2]. Recent years have seen Electronic Dance Music (EDM) gaining access to mainstream charts. However, it is still a neglected genre in music research with just a few articles published in journals and conferences. One exception is Butler [3], who claims that "EDM presents a distinctive overall formal structure in addition to its characteristic instrumentation". We present a method for the location of structural changes in EDM. Our approach starts by detecting the first downbeat of a track. Tempo is then computed using a standard tempo detection algorithm [7]. We then calculate the magnitude spectrum for each frame using a Fast Fourier Transform (FFT). The frames are beat-aligned with 87.5% overlap. Contrarily to most common approaches, we perform a cepstrum analysis in order to find periodic sequences in the signal. Based on the approach by Foote [5], we then compute the cosine distance between each possible pair of frames in the cepstrum data to get a self-similarity matrix. Con-voluting along the main diagonal of the similarity matrix using a Gaussian checkerboard kernel yields a unidimensional linearly normalized novelty curve that indicates the temporal locations of significant textural changes. We use a kernel size of approximately 30s. Justifications for this value can be found in [6]. Positions of prominent peaks of the novelty curve are selected as candidates for segment boundaries. Finally we propose a set of heuristic rules to align the obtained novelty peaks with the beats (see Figure 1). In line with Butler [4]
Things We Said Today: A Linguistic Analysis of the Beatles

The musical group the Beatles had a profound impact on Western culture during their time together between 1960 and 1970. The three songwriters, John Lennon, Paul McCartney, and George Harrison, together and separately evolved in their lyrical styles over time. Using a new generation of computer text analyses, the Beatles’ lyrics were analyzed to address how the group changed as a unit over time, how the various members changed in their writing styles, and the overlap in lyrical styles from one composer to the next. Overall, the Beatles’ lyrics became darker, more psychologically distant, and less immediate over time. Paul McCartney’s lyrical style proved to be more variable and broad ranging than either Lennon or Harrison. Using latent semantic analyses, Harrison’s lyrics were more influenced by Lennon than by McCartney. Finally, the lyrics jointly written by Lennon and McCartney were mathematically more similar to Lennon’s linguistic styles than McCartney’s.
Sequentially finding the-Best List in Hidden Markov Models

We propose a novel method to obtain the -best list of hypotheses in hidden Markov model (HMM). We show that the entire information needed to compute the -best list from the HMM trellis graph is encapsulated in entities that can be computed in a single forward-backward iteration that usually yields the most likely state sequence. The hypotheses list can then be extracted in a sequential manner from these entities without the need to refer back to the original data of the HMM. Furthermore, our approach can yield significant savings of computational time when compared to traditional methods.
A hue-preserving gamut expansion algorithm in CIELUV color space for wide gamut displays

To exert color display advantage of wide gamut displays, there needs gamut expansion from the current standard gamut to the gamut of these devices. A novel gamut boundary descriptor and a three dimensional gamut expansion method using a fixed anchor point in CIELUV color space are proposed. Gamut boundary is represented by a closed surface composed of a series of triangles and the line gamut boundary is thus solving non-homogeneous linear equations. The expansion method is changing LUV values simultaneously according to the two intersections which are formed by the line determined by the to-be-mapped point and the anchor point and two gamut boundaries. Gamut expansion mapping of Rec.709 standard gamut to laser display gamut is carried out and it is found that the average CIELUV hue difference is 2.52e–007 when the number of sampling points is 9261. The proposed method can extend the color gamut continuously
Model Checking: From BDDs to Interpolation

In this paper we describe the development of model checking from BDD-based verification, through SAT-based bug finding, to Interpolation-based verification. Model checking is an automatic approach to formally verifying that a given system satisfies a given specification. BDD-based Symbolic Model Checking (SMC) was the first to enable model checking of real-life designs with a few hundreds of state elements. Currently, SAT-based model checking is the most widely used method for verifying industrial designs. This is due to its ability to handle designs with thousands of state elements and more. Its main drawback, however, is its orientation towards ”bug-hunting” rather than full verification. In this paper we present two SAT-based approaches to full verification. The approaches combine BMC with interpolation or interpolationsequence in order to compute an over-approximated set of the system’s reachable states while checking that the specification is not violated. We compare the two methods both algorithmically and experimentally and conclude that they are incomparable.
Events that Shook the Market: An Insight from Nonlinear Serial Dependencies in Intraday Returns

The present study proposes the use of nonlinear serial dependencies in the returns series for assessing market responses to new information. Unlike the standard event study methodology, this paper advocates a reserve form that let data analysis to first detect those periods with significant nonlinearity, and then identify whether their occurrences can be associated with major economic or political events. Our proposed research framework involves the application of the Hinich (1996) portmanteau bicorrelation test statistic in a moving time windows setting. Using the Malaysian stock market as a case study, our empirical application is able to detect not only the presence of nonlinear serial dependencies in the KLCI intraday ten-minute returns, but also the time periods when they occurred. The next stage of event-matching managed to identify major events that unsettled the local stock market in all cases except one. In particular, the Russian crisis, the unorthodox capital control measures and the Brazilian crisis are found to be responsible for the short burst of nonlinear behavior. However, the paper noted that factors that shook the market need not be news announcement in the media. It could be due to, among others, rumors, speculations, expectations, or worries prevail in the investment communities.
A Context-Driven Extractive Framework for Generating Realistic Image Descriptions

Automatic image annotation methods are extremely beneficial for image search, retrieval, and organization systems. The lack of strict correlation between semantic concepts and visual features, referred to as the semantic gap, is a huge challenge for annotation systems. In this paper, we propose an image annotation model that incorporates contextual cues collected from sources both intrinsic and extrinsic to images, to bridge the semantic gap. The main focus of this paper is a large real-world data set of news images that we collected. Unlike standard image annotation benchmark data sets, our data set does not require human annotators to generate artificial ground truth descriptions after data collection, since our images already include contextually meaningful and real-world captions written by journalists. We thoroughly study the nature of image descriptions in this real-world data set. News image captions describe both visual contents and the contexts of images. Auxiliary information sources are also available with such images in the form of news article and metadata (e.g., keywords and categories). The proposed framework extracts contextual-cues from available sources of different data modalities and transforms them into a common representation space, i.e., the probability space. Predicted annotations are later transformed into sentence-like captions through an extractive framework applied over news articles. Our context-driven framework outperforms the state of the art on the collected data set of approximately 20 000 items, as well as on a previously available smaller news images data set.
Expression Profiling and Proteomic Analysis of JIN Chinese Herbal Formula in Lung Carcinoma H460 Xenografts

Many traditional Chinese medicine (TCM) formulae have been used in cancer therapy. The JIN formula is an ancient herbal formula recorded in the classic TCM book Jin Kui Yao Lue (Golden Chamber). The JIN formula significantly delayed the growth of subcutaneous human H460 xenografted tumors in vivo compared with the growth of mock controls. Gene array analysis of signal transduction in cancer showed that the JIN formula acted on multiple targets such as the mitogen-activated protein kinase, hedgehog, and Wnt signaling pathways. The coformula treatment of JIN and diamminedichloroplatinum (DDP) affected the stress/heat shock pathway. Proteomic analysis showed 36 and 84 differentially expressed proteins between the mock and DDP groups and between the mock and JIN groups, respectively. GoMiner analysis revealed that the differentially expressed proteins between the JIN and mock groups were enriched during cellular metabolic processes, and so forth. The ones between the DDP and mock groups were enriched during protein-DNA complex assembly, and so forth. Most downregulated proteins in the JIN group were heat shock proteins (HSPs) such as HSP90AA1 and HSPA1B, which could be used as markers to monitor responses to the JIN formula therapy. The mechanism of action of the JIN formula on HSP proteins warrants further investigation.
Regular Multiple Criteria Linear Programming for Semi-supervised Classification

In this paper, inspired by the application potential of Regular Multiple Criteria Linear Programming (RMCLP), we proposed a novel Laplacian RMCLP(called Lap-RMCLP)method for semi-supervised classification problem, which can exploit the geometry information of the marginal distribution embedded in unlabeled data to construct a more reasonable classifier and is a useful extension of TSVM. Furthermore, by adjusting the parameter, Lap-RMCLP can convert to RMCLP naturally. All experiments on public and data sets and Basic Endowment Insurance Fund Audit(BEIFA) dataset show that Lap-RMCLP is a competitive method in semi-supervised classification.