Miracle-dz/UniFAR-FTUs huggingface dataset:
A Data Streaming Approach to Link Mining in Criminal Networks

The ability to discover patterns of interest in criminal networks can support and ease the investigation tasks by security and law enforcement agencies. By considering criminal networks as a special case of social networks, we can properly reuse most of the state-of-the-art techniques to discover patterns of interests, i.e., hidden and potential links. Nevertheless, in time-sensible scenarios, like the one involving criminal actions, the ability to discover patterns in a (near) real-time manner can be of primary importance.In this paper, we investigate the identification of patterns for link detection and prediction on an evolving criminal network. To extract valuable information as soon as data is generated, we exploit a stream processing approach. To this end, we also propose three new similarity social network metrics, specifically tailored for criminal link detection and prediction. Then, we develop a flexible data stream processing application relying on the Apache Flink framework; this solution allows us to deploy and evaluate the newly proposed metrics as well as the ones existing in literature. The experimental results show that the new metrics we propose can reach up to 83% accuracy in detection and 82% accuracy in prediction, resulting competitive with the state of the art metrics.
Altered response to growth rate changes in Kluyveromyces lactis versus Saccharomyces cerevisiae as demonstrated by heterologous expression of ribosomal protein 59 (CRY1)

We report the cloning, characterization and preliminary analysis of the regulation of the gene coding for ribosomal protein 59 (RP59) from the budding yeast Kluyveromyces lactis. The RP59 gene is present as a single copy, contains an intron within the amino terminal coding portion of the gene, and harbors conserved S. cerevisiae splicing signals. Sequence elements upstream of the transcriptional start site are homologous to UASRPG, known to regulate the transcription of numerous genes in S. cerevisiae via their interaction with the trans-activating factor RAP1. These elements are necessary for transcription of RP59 in both K.lactis and S.cerevisiae hosts. UASRPG in S.cerevisiae rp genes also modulate the transcription of rp RNA synthesis in response to a growth rate upshift. In K.lactis, the RP59 gene does not respond to growth rate upshift. Reciprocal expression of RP59 and CRY1 in heterologous hosts demonstrates that glucose upshift occurs in S.cerevisiae but not K.lactis. These results demonstrate that a factor or factors required for growth upshift are lacking in K.lactis, and provide further evidence that the UASRPG are sufficient signals for modulating this response.
Retinal neurons regulate proliferation of postnatal progenitors and Müller glia in the rat retina via TGFβ signaling

The number of proliferating cells in the rodent retina declines dramatically after birth. To determine if extrinsic factors in the retinal micro-environment are responsible for this decline in proliferation, we established cultures of retinal progenitors or Müller glia, and added dissociated retinal neurons from older retinas. The older cells inhibited proliferation of progenitor cells and Müller glia. When these experiments were performed in the presence of TGFβRII-Fc fusion protein, an inhibitor of TGFβ signaling, proliferation was restored. This suggests a retina-derived TGFβ signal is responsible for the developmental decline in retinal proliferation. TGFβ receptors I and II are expressed in the retina and are located in nestin-positive progenitors early in development and glast-positive Müller glia later in development. RT-PCR and immunofluorescence data show TGFβ2 is the most highly expressed TGFβ ligand in the postnatal retina, and it is expressed by inner retinal neurons. Addition of either TGFβ1 or TGFβ2 to postnatal day 4 retinas significantly inhibited progenitor proliferation, while treatment of explanted postnatal day 6 retinas with TGFβ signaling inhibitors resulted in increased proliferation. Last, we tested the effects of TGFβ in vivo by injections of TGFβ signaling inhibitors: when TGFβ signaling is inhibited at postnatal day 5.5, proliferation is increased in the central retina; and when co-injected with EGF at postnatal day 10, TGFβ inhibitors stimulate Müller glial proliferation. In sum, these results show that retinal neurons produce a cytostatic TGFβ signal that maintains mitotic quiescence in the postnatal rat retina.
Error Exponents of Expander Codes under Linear-Complexity Decoding

A class of codes is said to reach capacity {\scriptsize $C$} of the binary symmetric channel if for any rate $R 0$ there is a sufficiently large N such that codes of length $\ge N$ and rate R from this class provide error probability of decoding at most $\varepsilon$, under some decoding algorithm. The study of the error probability of expander codes was initiated by Barg and Z{emor in 2002 [IEEE Trans. Inform. Theory, 48 (2002), pp. 1725--1729], where it was shown that they attain capacity of the binary symmetric channel under a linear-time iterative decoding with error probability falling exponentially with code length N. In this work we study variations on the expander code construction and focus on the most important region of code rates, close to the channel capacity. For this region we estimate the decrease rate (the error exponent) of the error probability of decoding for randomized ensembles of codes. The resulting estimate gives a substantial improvement of previous results for expander codes and some other explicit code families.
How Do Kidnappers Kill Hostages? A Comparison of Terrorist and Criminal Groups

This study explores how hostages are murdered during a kidnapping for ransom. A multi-dimensional scaling procedure was used to explore the homicidal behavior of kidnappers. Three styles of homicide, labeled as execution, manual, and slaughter, were identified. Each varied in the degree of expressive/instrumental aggression and planned or spontaneous violence. Classification of the sample data found that 46.4% of kidnapping homicides were characterized by instrumental violence labeled as execution style, whereas 42.9% were characterized by hostile violence that was spontaneous and less reliant on using a weapon (manual style). These observations related to the type of group conducting the kidnapping for ransom.
Bayesian Sample Size Determination for Prevalence and Diagnostic Test Studies in the Absence of a Gold Standard Test

Summary. Planning studies involving diagnostic tests is complicated by the fact that virtually no test provides perfectly accurate results. The misclassification induced by imperfect sensitivities and specificities of diagnostic tests must be taken into account, whether the primary goal of the study is to estimate the prevalence of a disease in a population or to investigate the properties of a new diagnostic test. Previous work on sample size requirements for estimating the prevalence of disease in the case of a single imperfect test showed very large discrepancies in size when compared to methods that assume a perfect test. In this article we extend these methods to include two conditionally independent imperfect tests, and apply several different criteria for Bayesian sample size determination to the design of such studies. We consider both disease prevalence studies and studies designed to estimate the sensitivity and specificity of diagnostic tests. As the problem is typically nonidentifiable, we investigate the limits on the accuracy of parameter estimation as the sample size approaches infinity. Through two examples from infectious diseases, we illustrate the changes in sample sizes that arise when two tests are applied to individuals in a study rather than a single test. Although smaller sample sizes are often found in the two‐test situation, they can still be prohibitively large unless accurate information is available about the sensitivities and specificities of the tests being used.
Exploiting contextual change in context-aware retrieval

Information retrieval systems are usually unaware of the context in which they are being used. We believe that exploiting context information to augment existing retrieval methods can lead to increased retrieval precision. This approach is particularly important with the development of wireless mobile information appliances, such as PDAs. Many of these devices are aware of the user's physical context, and this has led to the evolution of context-aware applications. Such applications can automatically utilise the user's current context, e.g. location or ambient temperature. Context-Aware Retrieval is related to traditional Information Retrieval and Information Filtering, but is potentially more challenging due to the often continuous changes in user context. To meet these challenges we suggest a potential advantage of Context-Aware Retrieval: this is that the current context is often changing gradually and semi-predictably. In this paper we suggest new methods based on a context-diary and caching aimed at improving both the precision of relevant retrieved information and the speed/availability of retrieval. The methods can be used, in principle, on top of existing retrieval systems.
Evaluation of Fractured Rocks through Anomalous Diffusion

A minimal mathematical model for transient diffusion in fractured rocks at the well scale with options to link topological and geometrical properties of fractured rocks where the geology is complex is presented. We simulate transient diffusion in systems with complex geology through a transient, interporosity model consisting of three constants to consider both complex structures that may exist at very fine scales and forms that result in long-range connectivity across multiple links in a network. Fractional constitutive flux laws that address super- and subdiffusive influences and result in transient propagation of the form r2~ta, with a≠1, are considered. The spatiotemporal evolution of the transient is documented, and well-codified Flow Regimes and sub-Flow Regimes are cataloged into stages with associated typical characteristics often visible in both short- (buildup) and long-time (flow) well-behaviors. We arrive at interpretations well beyond the ‘linear-flow-regime’ that is the norm. Because the conceptual interpretation is based on a physics-based model, the ideas we present are both scalable and reproducible. We recover classical formulations through a subset of the model considered here. New behaviors, yet to be reported, are discussed.
High Performance Data Mining in Time Series: Techniques and Case Studies

As extremely large time series data sets grow more prevalent in a wide variety of settings, we face the significant challenge of developing efficient analysis methods. This dissertation addresses the problem in designing fast, scalable algorithms for the analysis of time series. The first part of this dissertation describes the framework for high performance time series data mining based on important primitives. Data reduction transform such as the Discrete Fourier Transform, the Discrete Wavelet Transform, Singular Value Decomposition and Random Projection, can reduce the size of the data without substantial loss of information, therefore provides a synopsis of the data. Indexing methods organize data so that the time series data can be retrieved efficiently. Transformation on time series, such as shifting, scaling, time shifting, time scaling and dynamic time warping, facilitates the discovery of flexible patterns from time series. The second part of this dissertation integrates the above primitives into useful applications ranging from music to physics to finance to medicine. StatStream. StatStream is a system based on fast algorithms for finding the most highly correlated pairs of time series from among thousands of time series streams and doing so in a moving window fashion. It can be used to find correlations in time series in finance and in scientific applications. HumFinder. Most people hum rather poorly. Nevertheless, somehow people have some idea what we are humming when we hum. The goal of the query by humming program, HumFinder, is to make a computer do what a person can do. Using pitch translation, time dilation, and dynamic time warping, one can match an inaccurate hum to a melody remarkably accurately. OmniBurst. Burst detection is the activity of finding abnormal aggregates in data streams. Our software, OmniBurst, can detect bursts of varying durations. Our example applications are monitoring gamma rays and stock market price volatility. The software makes use of a shifted wavelet structure to create a linear time filter that can guarantee that no bursts will be missed at the same time that it guarantees (under a reasonable statistical model) that the filter eliminates nearly all false positives.
Mr" % Working Paper Department of Economics a Comhoi Keroe Ih the Treatheht of Trending Time Series

There are two common misconceptions in the analysis of trending time series. First, if a series is difference-stationary, removing a linear time trend introduces spurious cyclically. Second, regardless of whether a series is difference-stationary or trend-stationary, taking Erst differences produces, in either case, a covariance stationary sequence, and bo is recommended econometric practice. We show that the Bret statement is incorrect and that the second can be misleading.
Word-based confidence measures as a guide for stack search in speech recognition

The maximum a posteriori hypothesis is treated as the decoded truth in speech recognition. However, since the word recognition accuracy is not 100%, it is desirable to have an independent confidence measure on how good the maximum a posteriori hypothesis is relative to the spoken truth for some applications. Efforts are in progress to develop such confidence measures with the intent of applying them to the assessment of the confidence of whole utterances, rescoring of N-best lists, etc. In this paper, we explore the use of word-based confidence measures to adaptively modify the hypothesis score during searches in continuous speech recognition: specifically, based on the confidence of the current sequence of hypothesized words during the search, the weight of its prediction is changed as a function of the confidence. Experimental results are described for ATIS and SwitchBoard tasks. About 8% relative reduction in word error is obtained for ATIS.
Complete Extension of Chromosomal DNA and Its Manipulation Using Optically-Driven Micro-Structures

This paper presents a novel method for extension and manipulation of chromosomal DNA using electroosmotic flow (EOF) and optically-driven micro-structures, which is intended for the use in nanotechnology-based genomic studies. Chromosomal DNA fibers are first hydrodynamically extended to the full length between micro-pillars on a substrate by EOF. In order to manipulate single chromosomal DNA fibers, we developed micro-hook and micro-bobbin, which were fabricated with SU-8 photo resist and grasped by focused laser beams. A targeted position of an extended DNA fiber was picked up with the micro-hook and translocated by moving the hook. To manipulate a whole DNA fiber, an extended fiber was wound to be compact using micro-bobbins. These handling techniques realize the observation of single chromosomal DNA molecules with high spatial resolution
Fostering Activation Among Latino Parents of Children With Mental Health Needs: An RCT.

OBJECTIVE Latino families raising children with mental health and other special health care needs report greater dissatisfaction with care compared with other families. Activation is a promising strategy to eliminate disparities. This study examined the comparative effectiveness of MePrEPA, an activation intervention for Latino parents whose children receive mental health services. METHODS A randomized controlled trial (N=172) was conducted in a Spanish-language mental health clinic to assess the effectiveness of MePrEPA, a four-week group psychoeducational intervention to enhance parent activation among Latino parents, compared with a parent-support control group. Inclusion criteria were raising a child who receives services for mental health needs and ability to attend weekly sessions. Outcomes were parent activation, education activation, quality of school interaction, and parent mental health. Effectiveness of the intervention was tested with a difference-in-difference approach estimating linear mixed models. Heterogeneity of treatment effect was examined. RESULTS MePrEPA enhanced parent activation (β=5.98, 95% confidence interval [CI]=1.42-10.53), education activation (β=7.98, CI=3.01-12.94), and quality of school interaction (β=1.83, CI=.14-3.52) to a greater degree than did a parent-support control group. The intervention's impact on parent activation and education outcomes was greater for participants whose children were covered by Medicaid and were novices to therapy and those with low activation at baseline. No statistically significant effects were observed in parent mental health. CONCLUSIONS Activation among Latino parents was improved with MePrEPA, which can be readily incorporated in current practices by mental health clinics. Future work should replicate findings in a large number of sites, adding behavioral measures and distal impacts while examining MePrEPA's effects across settings and populations.
Optimal Hybrid Model of Classifiers for Intrusion Detection

We present in this paper an intrusion detection software-system that we have built based on combined statistical and computational models to detect intrusions and classify them as attack or non-attack. More specifically, we build a computational machine to derive optimal parsimonious hybrid model of classifiers in intrusion detection. The classifiers are based on the following classification methods, Naive Bayes-NB, Support vector machine-SVM, Knearest neighbor-K-nn, and Neural networksNN.
The high‐osmolarity glycerol– and cell wall integrity–MAP kinase pathways of Saccharomyces cerevisiae are involved in adaptation to the action of killer toxin HM‐1

Fps1p is an aquaglyceroporin important for turgor regulation of Saccharomyces cerevisiae. Previously we reported the involvement of Fps1p in the yeast‐killing action of killer toxin HM‐1. The fps1 cells showed a high HM‐1‐resistant phenotype in hypotonic medium and an HM‐1‐susceptible phenotype in hypertonic medium. This osmotic dependency in HM‐1 susceptibility was similar to those observed in Congo red, but different from those observed in other cell wall‐disturbing agents. These results indicate that HM‐1 exerts fungicidal activity mainly by binding and inserting into the yeast cell wall structure, rather than by inhibiting 1,3‐β‐glucan synthase. We next determined HM‐1‐susceptibility and diphospho‐MAP kinase inductions in S. cerevisiae. In the wild‐type cell, expressions of diphospho‐Hog1p and ‐Slt2p, and mRNA transcription of CWP1 and HOR2, were induced within 1 h after an addition of HM‐1. ssk1 and pbs2 cells, but not sho1 and hkr1 cells, showed HM‐1‐sensitive phenotypes and lacked inductions of phospho‐Hog1p in response to HM‐1. mid2, rom2 and bck1 cells showed HM‐1‐sensitive phenotypes and decreased inductions of phospho‐Slt2p in response to HM‐1. From these results, we postulated that the Sln1–Ypd1–Ssk1 branch of the high‐osmolality glycerol (HOG) pathway and plasma membrane sensors of the cell wall integrity (CWI) pathway detect cell wall stresses caused by HM‐1. We further suggested that activations of both HOG and CWI pathways have an important role in the adaptive response to HM‐1 toxicity. Copyright © 2012 John Wiley & Sons, Ltd.
An Automated Framework for Depression Analysis

This project aims at developing an automated framework for depression detection. During a depressive episode, patients suffer from psychomotor retardation and this phenomenon is not only limited to facial activity. In this PhD work, it is hypothesized that such complex affective state can be better represented by integrating information from various uni-modal channels to form a multimodal affective sensing system. The project explores facial dynamics, body expressions such as head movement, relative body part movement etc. in patients with major depressive disorders. The contribution of various channels is assessed and as a final objective, a framework combining discriminative channels for automatic depression analysis is proposed.
Prioritizing code clone detection results for clone management

Clone detection through tools is a common practice in the software industry. Associated with clone detection is code clone management, which includes taking informed decisions for management of the large sets of clones as reported by the clone detection tools, a task that gets more challenging with larger code bases. In order to enable and ease the process of code clone management, we discuss various criteria that help in prioritizing the clone results. We consider the impact of clones with respect to factors of maintenance overhead, code quality, and refactoring cost. The criteria for prioritization are based on the need for industrial code to adhere to software quality standards. This paper attempts to provide a systematic approach for analyzing and prioritizing clones to determine the order of fixing. This methodology is currently being used in some of the Siemens Corporate Technology Development Center Asia Australia (CT DC AA) projects; a case study of one such project is presented in this paper.
The Relationship between Quality of Care and Choice of Clinical Computing System: Retrospective Analysis of Family Practice Performance under the UK Quality and Outcomes Framework

Objectives: To investigate the relationship between performance on the UK Quality and Outcomes Framework pay-for-performance scheme and choice of clinical computer system. Design: Retrospective longitudinal study. Setting: Data for 2007–2008 to 2010–2011, extracted from the clinical computer systems of general practices in England. Participants: All English practices participating in the pay-for-performance scheme: average 8257 each year, covering over 99% of the English population registered with a general practice. Main outcome measures: Levels of achievement on 62 quality-of-care indicators, measured as: reported achievement (levels of care after excluding inappropriate patients); population achievement (levels of care for all patients with the relevant condition) and percentage of available quality points attained. Multilevel mixed effects multiple linear regression models were used to identify population, practice and clinical computing system predictors of achievement. Results: Seven clinical computer systems were consistently active in the study period, collectively holding approximately 99% of the market share. Of all population and practice characteristics assessed, choice of clinical computing system was the strongest predictor of performance across all three outcome measures. Differences between systems were greatest for intermediate outcomes indicators (eg, control of cholesterol levels). Conclusions: Under the UK’s pay-for-performance scheme, differences in practice performance were associated with the choice of clinical computing system. This raises the question of whether particular system characteristics facilitate higher quality of care, better data recording or both. Inconsistencies across systems need to be understood and addressed, and researchers need to be cautious when generalising findings from samples of providers using a single computing system. ARTICLE SUMMARY