Miracle-dz/UniFAR-FTUs huggingface dataset:
Heterogeneity of mouse spleen dendritic cells: in vivo phagocytic activity, expression of macrophage markers, and subpopulation turnover.

In the normal mouse spleen, two distinct populations of dendritic cells (DC) are present that differ in microanatomical location. The major population of marginal DC is found in the "marginal zone bridging channels" and extends into the red pulp. The interdigitating cells (IDC) are localized in the T cell areas in the white pulp. The aim of the present study was to characterize these two splenic DC populations with regard to their phenotype, in vivo phagocytic function, and turnover. Both marginal DC and IDC are CD11c+ and CD13+, but only IDC are NLDC-145+ and CD8alpha+. Notably, both populations, when freshly isolated, express the macrophage markers F4/80, BM8, and Mac-1. To study the phagocytic capacity of these cells, we employed the macrophage "suicide" technique by injecting liposomes loaded with clodronate i.v. Marginal DC, but not IDC, were eliminated by this treatment. Phagocytosis of DiI-labeled liposomes by DC confirmed this finding. The two DC populations differed significantly with regard to their turnover rates, as studied in a transgenic mouse model of conditional depletion of DC populations with high turnover. In these mice, marginal DC were completely eliminated, but the IDC population remained virtually intact. From these data we conclude that the marginal DC population has a high turnover, in contrast to the IDC population. Taken together, the present results indicate that marginal DC and IDC represent two essentially distinct populations of DC in the mouse spleen. They differ not only in location, but also in phenotype, phagocytic ability, and turnover.
Dual Radio Networks: Capacity and Connectivity

Motivated by the proliferation of dual radio devices, we consider a wireless network model in which all devices have short-range transmission capability, but a subset of the nodes has a secondary long-range wireless interface. For the resulting class of random graph models, we present analytical bounds for both the connectivity and the max-flow min-cut capacity. Perhaps the most striking conclusion to be drawn from our results is that the capacity of this class grows quadratically with the fraction of dual radio devices, thus indicating that a small percentage of such devices is sufficient to improve significantly the capacity of the network.
Spelling Correction Based on User Search Contextual Analysis and Domain Knowledge

We propose a spelling correction algorithm that combines trusted domain knowledge and query log information for query spelling correction. This algorithm uses query reformulations in the query log and bigram language models built from queries for efficiently and effectively generating correction suggestions and ranking them to find valid corrections. Experimental results show that for both simple unknown word errors and complex word substitution errors, valid corrections mostly appear within the top two ranks.
Bayesian Unconditional Quantile Regression: An Analysis of Recent Expansions in Wage Structure and Earnings Inequality in the US 1992–2009

We develop a reliable Bayesian inference for the RIF-regression model of Firpo, Fortin and Lemieux (Econometrica, 2009) in which we first estimate the log wage distribution by a mixture of normal densities. This approach is pursued so as to provide better estimates in the upper tail of the wage distribution as well as valid confidence intervals for the Oaxaca-Blinder decomposition. We apply our method to a Mincer equation for analysing the recent changes in the U.S. wage structure and in earnings inequality. Our analysis uses data from the CPS Outgoing Rotation Group (ORG) from 1992 to 2009. We find first that the largest part (around 77% on average) of the recent changes in the U.S. wage inequality is explained by the wage structure effect and second that the earnings inequality is rising more at the top end of the wage distribution, even in the most recent years. The decline in the unionisation rate has a small impact on total wage inequality while differences in returns to education and gender discrimination are the dominant factors accounting for these recent changes.
Securing the Assets of Decentralized Applications using Financial Derivatives (DRAFT)

Ethereum contracts can be designed to function as fully decentralized applications called DAPPs. Many DAPPs have already been fielded, including an online marketplace, a role playing game, a prediction market, and an Internet service provider. Unfortunately, DAPPs can be hacked, and the assets they control can be stolen. A recent attack on an Ethereum decentralized application called The DAO demonstrated that smart contract bugs are more than an academic concern. Ether worth tens of millions of US dollars was extracted by an attacker from The DAO, sending the value of its tokens and the overall exchange price of ether tumbling. We present a market-based technique for insuring the ether holdings of a DAPP using futures contracts indexed by the trade price of ether for DAPP tokens. Under fairly general circumstances, our technique is capable of recovering the majority of ether lost from theft with high probability even when all of the ether holdings are stolen; and the only cost to DAPP token holders is an adjustable ether withdrawal fee. If the probability of a margin call in $d$ days is $p$ for a futures contract with 20 times leverage, then our approach will allow for the recovery of half the stolen ether with probability $p$ and a withdrawal fee of 5%. A higher withdrawal fee of 25% allows for more than 80% of the ether to be recovered with probability $p$.
Preliminary Investigation of Sex Differences in Procedural Skill Learning in Veterans with Co-Morbidities

Sex differences in procedural skill learning have not been well characterized. Skill learning is an important area to explore in clinical settings that involve rehabilitation and deficit remediation, especially for returning Veterans that have a range of co-morbid conditions (traumatic brain injury, posttraumatic stress disorder, and depression) and possess impairments in multiple domains. Sixty-five (55 males, 10 females) Veterans completed two procedural learning tasks and answered self-report questionnaires. Participants’ performance and total learning slope were analyzed to determine sex differences in learning. Our results revealed sex differences in both tasks demonstrating females tend to perform better than males with a large effect size for these mean differences. While females performed better on the procedural learning tasks compared to males, their rate of learning was equivalent. Skill learning is an important requisite for rehabilitation, as skill learning is necessary to perform daily activities in new settings. Ultimately, these results provide insight into skill learning in Veterans with a range of co-morbid conditions and provide support for further investigation of sex differences in procedural learning.
Corrective hints for type incorrect generic Java programs

Since version 1.5, generics (parametric polymorphism) are part of the Java language. Experience with implementations of the Java Language Specification such as EJC and JAVAC has shown that the type error messages provided by these tools leave more than a little to be desired. Type error messages are often uninformative and sometimes show artifacts of the type checking process in the messages. Apparently, providing good type error messages for a language as large and complex as Java currently is, is not easy. To alleviate the problem, we describe a number of heuristics that suggest fixes for generic method invocations in Generic Java, and illustrate their effect by means of examples. The heuristics are part of an extension to the original type checking process that has been implemented into the JastAdd Extensible Java Compiler.
DEPENDENCE BETWEEN THREE DIAGNOSTIC TESTS IN PRESENCE OF VERIFICATION BIAS: A COPULA FUNCTION APPROACH

The study of the dependence betweenn the outcomes of two medical diagnostic tests is important since that decision about what therapeutic treatment is suitable for diseased individuals, depends of the test results. In this paper, we introduce a Bayesian approach to study the efiect of three copula structures to model the dependence efiect on the prevalence and performance test parameters, using data obtained within a design with veriflcation bias and three diagnostic tests, two of them with dependent outcomes and the other one independent. We simulated data of three copula models each of them with three dependence levels: weak, moderate and strong inside of two scenaries: sensibilities relatively high with high speciflcities and sensibilities relatively low with high speciflcities.
Cash-Lock Comparison of Portfolio Insurance Strategies

Portfolio insurance strategies are designed to achieve a minimum level of wealth while at the same time participating in upward moving markets. The strategies all have in common that the fraction of wealth which is invested in a risky asset is increasing in the asset price increments. Therefore, in downward moving markets, the asset exposure is reduced. In the strict sense, a cash–lock describes the event that the asset exposure drops to zero and stays there. Since a cash–lock at an early time prohibits any participation in recovering markets, the cash–lock cage is considered as a major problem with respect to long investment horizons. We analyze a generalized form of a cash–lock by focusing on the probability that the investment quote recovers from small values. It turns out that, even in the case that the dynamic versions of option based strategies and proportional portfolio insurance strategies coincide in their expected return, they give rise to a very different cash–lock behavior. In addition, we point out that, for comparability reasons, it is necessary to distinguish between strategies where an investment quote over one is admissible and such with borrowing constraints.
The Influence of Prototypical Communication in Dark Online Organizations: How to Speak Like a Monger

A key obstacle for dark organizations is negotiating who is and is not a member. Following the social identity approach, this study explores the impact of prototypical communication, through code word use, on the structure of two dark online organizations. We use two-mode exponential random graph modeling to demonstrate the interdependence between code word use and network structure. Findings are discussed in light of social identity approaches and online groups.
Overview of FIRE 2014 Track on Transliterated Search

The Transliterated Search track has been organized for the second year in FIRE. The track has two subtasks. Subtask 1 on language labeling of words in code-mixed text fragments was conducted for 6 Indian languages: Bangla, Gujarati, Hindi, Malayalam, Tamil, Telugu, mixed with English. In Subtask 2 on retrieval of Hindi ﬁlm lyrics, along with transliterated queries in Roman script, this year we also had Devanagari queries. A total of 54 runs were submitted from 18 teams, of which 35 runs for subtask 1 and 7 runs for sub-task 2 has been evaluated. Performance of the runs is comparable to that of last year’s.
A Comparison Of Marriage Education And Brief Couples Counseling

This study investigated marital adjustment for couples participating in one of two treatment groups; the Brief Integrative Couples Counseling model(Young & Long, 1998, 2007); and the PREPARE/ENRICH Empowering Couples marriage education weekend workshop (Olson & Olson,2000). This study examined the following research questions: Do couples show a different level of marital adjustment following Brief Integrative Couples Counseling compared to those who receive a marriage education treatment? ; Do couples show greater marital adjustment following Brief Integrative Couples Counseling compared to those who participated in a Marriage Education weekend workshop treatment over time? This article presents an exploration of mean marital adjustment scores following participation in a Brief Integrative Couples Counseling treatment or a marriage education weekend workshop protocol. This study was a quasiexperimental design because participants were placed in preexisting groups by self-selection. Following statistical analyses using repeated measures analysis of variance (ANOVA), the data suggest there is no difference in the level of change in marital adjustment scores between the two treatment groups. In addition, follow up analysis of covariance (ANCOVA’s) was conducted on marital adjustment, using demographics as covariates.
A BIO-INSPIRED MUSIC GENRE CLASSIFICATION FRAMEWORK USING MODIFIED AIS-BASED CLASSIFIER

For decades now, scientific community are involved in various works to automate the human process of recognizing different types of music using different elements for example different instruments used. These efforts would imitate the human method of recognizing the music by considering every essential component of the songs from artist voice, melody of the music through to the type of instruments used. Various approaches or mechanisms are introduced and developed to automate the classification process since then. The results of these studies so far have been remarkable yet can still be improved. The aim of this research is to investigate Artificial Immune System (AIS) domain by focusing on the modified AIS-based classifier to solve this problem where the focuses are the censoring and monitoring modules. In this highlight, stages of music recognition are emphasized where feature extraction, feature selection, and feature classification processes are explained. Comparison of performances between proposed classifier and WEKA application is discussed. Almost 20 to 30 percent of classification accuracies are increased in this study.
CallMeSmart: An Ascom/Trixbox Based Prototype for Context Controlled Mobile Communication in Hospitals

Hospitals are working environments where information is constantly exchanged between co-workers over a complex communication infrastructure. In order to support the needs of hospital professionals, this infrastructure should provide the possibility to exchange important information as quickly as possible and, at the same time, contact colleagues without interrupting their working activities unnecessarily. Interruptions are unpleasant situations and a source of stress and distraction that may increase the probability of taking wrong decisions. Currently, hospital communication infrastructure relies on the use of pagers which create a large amount of unnecessary interruptions. Wireless phones are less utilized, but as opposed to pagers they provide text and audio services in the same device. With a suitable context-aware system it should be capable of balance interruptions and thereby provide solutions able to efficiently support the work of hospital professionals. The system has been developed using the iterative software engineering approach, Unified Process. The system is able to block and re-route calls directed to users that are not available due to their location, work schedule, activity and role. A number of tests have been carried out after the development, and highlighted some changes that have to be made before being deployed in real hospital environments.
Mixture reduction techniques for Multiple Hypothesis Tracking of targets in clutter

Two complementary mixture reduction algorithms for Multiple Hypothesis Tracking (MHT) are presented. The first approach (MHT-2) uses the Integral Squared Error (ISE) in a simple optimization process, where the major principal axis of the optimal one-component fit, is used to guide the search for a near-optimal two-component fit. The second, less rigorous, approach (MHT-PE) Prunes unlikely hypotheses then Eliminates duplicate components, using the normalized overlap integral. Both methods are compared with PDA and other MHT mixture reduction techniques in Monte Carlo simulations.
X-ANOVA and X-Utest features for Android malware analysis

In this paper we proposed a static analysis framework to classify the android malware. The three different feature likely (a) opcode (b) method and (c) permissions are extracted from the each android . apk file. The dominant attributes are aggregated by modifying two different ranked feature methods such as ANOVA to Extended ANOVA (X-ANOVA) and Wann-Whiteney U-test to Extended U-Test (X-U-Test). These two statistical feature ranking methods retrieve the significant features by removing the irrelevant attributes based on their score. Accuracy of the proposed system is computed by using three different classifiers (J48, ADAboost and Random forest) as well as voted classification technique. The X-U-Test exhibits better accuracy results compared with X-ANOVA. The highest accuracy 89.36% is obtained with opcode while applying X-U-Test and X-ANOVA shows high accuracy of 87.81% in the case of method as a feature. The permission based model acquired highest accuracy in independent (90.47%) and voted (90.63%) classification model.
ProvErr: System Level Statistical Fault Diagnosis Using Dependency Model

Large-scale distributed systems are difficult to debug in the event of failure. Yet rapid fault diagnosis that pinpoints failures to the component level is critical to fast recovery. We introduce a statistical approach to fault diagnosis that utilizes a dependency graph of execution to automatically discover the most probable fault cause(s) at a component level (either software or hardware resource). This approach leverages engineers' high level understanding of the system and requires a very small amount of information compared to existing methods. It also utilizes dependency information to eliminate redundant causes while retaining co-causes. Experiments using Apache Pig show that our approach has good, robust performance for diagnosing software bugs and resource shortages, and scales nearly linearly as system size increases.
Optimal Power Consumption in a Central Heating System with Geothermal Heat Pump

Abstract A ground source heat pump connected to a domestic hydronic heating network is studied to be driven with the minimum electric power. The hypothesis is to decrease the forward temperature to the extent that one of the hydronic heaters work at full capacity. A less forward temperature would result in a dramatic temperature drop in the room with saturated actuator. The optimization hypothesis is inspired by the fact that, the consumed electric power by the heat pump has a strong positive correlation with the generated forward temperature. A model predictive control scheme is proposed in the current study to achieve the optimal forward temperature. At the lower hierarchy level, local PI controllers seek the corresponding room temperature setpoint. Simulation results for a multi-room house case study show considerable energy savings compared to the heat pump's traditional control scheme.
Playing a Musical Instrument as a Protective Factor against Dementia and Cognitive Impairment: A Population-Based Twin Study

Increasing evidence supports that playing a musical instrument may benefit cognitive development and health at young ages. Whether playing an instrument provides protection against dementia has not been established. In a population-based cotwin control study, we examined the association between playing a musical instrument and whether or not the twins developed dementia or cognitive impairment. Participation in playing an instrument was taken from informant-based reports of twins' leisure activities. Dementia diagnoses were based on a complete clinical workup using standard diagnostic criteria. Among 157 twin pairs discordant for dementia and cognitive impairment, 27 pairs were discordant for playing an instrument. Controlling for sex, education, and physical activity, playing a musical instrument was significantly associated with less likelihood of dementia and cognitive impairment (odds ratio [OR] = 0.36 [95% confidence interval 0.13–0.99]). These findings support further consideration of music as a modifiable protective factor against dementia and cognitive impairment.
The Pedagogical Role of Reggio-inspired Studios In Early Childhood Education

The influence of the Reggio Emilia philosophy has been present in early childhood programs across the United States for decades, with many programs attempting to adapt the philosophy‘s concept of a studio, but few studies have examined them. This study describes, interprets, and appraises two Reggioinspired studios in the United States in order to provide an in-depth analysis and shed new light on such practices. Four questions guided this study: 1) What is the role of a studio in a Reggio-inspired school? 2) What is happening in the studio? 3) What are children learning in this environment? 4) How does the studio cultivate children‘s hundred languages? Based on the methods of educational connoisseurship and criticism, this investigation provides a vivid description and interpretation of preschool-aged children‘s experiences in Reggio-inspired studios. Two sites were studied, one in Colorado and the other in Missouri. Six dimensions of schooling provided the conceptual framework which guided this study: intentional, structural, curricular, pedagogical, evaluative, and aesthetic. Similarities and differences between sites and art studios are examined and discussed, along with implications for the field of early childhood education.
A novel Gaussian mixture model for superpixel segmentation

Superpixel segmentation is used to partition an image into perceptually coherence atomic regions. As a preprocessing step of computer vision applications, it can enormously reduce the number of entries of subsequent algorithms. With each superpixel associated with a Gaussian distribution, we assume that a pixel is generated by first randomly choosing one of the superpixels, and then the pixel is drawn from the corresponding Gaussian density. Unlike most applications of Gaussian mixture model in clustering, data points in our model are assumed to be non-identically distributed. Given an image, a log-likelihood function is constructed for maximizing. Based on a solution derived from the expectation-maximization method, a well designed algorithm is proposed. Our method is of linear complexity with respect to the number of pixels, and it can be implemented using parallel techniques. To the best of our knowledge, our algorithm outperforms the state-of-theart in accuracy and presents a competitive performance in computational ∗Corresponding author. Tel. :+862787558912; fax: +862787543130. Email addresses: sawpara@126.com (Zhihua Ban), jgliu@ieee.org (Jianguo Liu) Preprint submitted to Pattern Recognition December 30, 2016 ar X iv :1 61 2. 08 79 2v 1 [ cs . C V ] 2 8 D ec 2 01 6 efficiency.
Improving Upon Semantic Classification of Spoken Diary Entries Using Pragmatic Context Information

In this paper we will discuss Natural Language Processing (NLP) techniques used to analyze spoken diary entries and automatically classify the locations and activi ties into semantic categories. This research combines work d one in utterance semantics classification using a Bayesian statistical model of the words with contextual information present within the diary to disambiguate classification of these u tterances and achieve higher classification accuracy than a model that only relies on word analysis.
Optimality of belief propagation for random assignment problem

The assignment problem concerns finding the minimum-cost perfect matching in a complete weighted n x n bipartite graph. Any algorithm for this classical question clearly requires Ω(n2) time, and the best known one (Edmonds and Karp, 1972) finds solution in O(n3). For decades, it has remained unknown whether optimal computation time is closer to n3 or n2. We provide answer to this question for random instance of assignment problem. Specifically, we establish that Belief Propagation finds solution in O(n2) time when edge-weights are i.i. d. with light tailed distribution.