Miracle-dz/UniFAR-FTUs huggingface dataset:
The Vaginal Microbiota: What Have We Learned after a Decade of Molecular Characterization?

We conducted a systematic review of the Medline database (U.S. National Library of Medicine, National Institutes of Health, Bethesda, MD, U.S.A) to determine if consistent molecular vaginal microbiota (VMB) composition patterns can be discerned after a decade of molecular testing, and to evaluate demographic, behavioral and clinical determinants of VMB compositions. Studies were eligible when published between 1 January 2008 and 15 November 2013, and if at least one molecular technique (sequencing, PCR, DNA fingerprinting, or DNA hybridization) was used to characterize the VMB. Sixty three eligible studies were identified. These studies have now conclusively shown that lactobacilli-dominated VMB are associated with a healthy vaginal micro-environment and that bacterial vaginosis (BV) is best described as a polybacterial dysbiosis. The extent of dysbiosis correlates well with Nugent score and vaginal pH but not with the other Amsel criteria. Lactobacillus crispatus is more beneficial than L. iners. Longitudinal studies have shown that a L. crispatus-dominated VMB is more likely to shift to a L. iners-dominated or mixed lactobacilli VMB than to full dysbiosis. Data on VMB determinants are scarce and inconsistent, but dysbiosis is consistently associated with HIV, human papillomavirus (HPV), and Trichomonas vaginalis infection. In contrast, vaginal colonization with Candida spp. is more common in women with a lactobacilli-dominated VMB than in women with dysbiosis. Cervicovaginal mucosal immune responses to molecular VMB compositions have not yet been properly characterized. Molecular techniques have now become more affordable, and we make a case for incorporating them into larger epidemiological studies to address knowledge gaps in etiology and pathogenesis of dysbiosis, associations of different dysbiotic states with clinical outcomes, and to evaluate interventions aimed at restoring and maintaining a lactobacilli-dominated VMB.
Kernel Principal Subspace Based Outlier Detection Method in Wireless Sensor Networks

An emerging class of Wireless Sensor Networks (WSNs) applications involves the acquisition of large amounts of sensory data from battery-powered, low computation and low memory wireless sensor nodes. The accuracy of sensor readings is without a doubt one of the most important measures to evaluate the quality of a sensor and its network. For this case, the task amounts to creating a useful model based on KPCA to recognize data as normal or outliers. Over the last few years, Kernel Principal Component Analysis (KPCA) has found several applications in outlier detection. Within this setting, we propose a new outlier detection method based on Kernel Principal Component Analysis (KPCA) using mahalanobis distance to implicitly calculate the mapping of the data points in the feature space so that we can separate outlier points from normal pattern of data distribution. The use of KPCA based mahalanobis kernel on real word data obtained from Intel Berkeley are reported showing that the proposed method performs better in finding outliers in wireless sensor networks.
Performance comparison of processor scheduling strategies in a distributed-memory multicomputer system

Processor scheduling has received considerable attention in the context of shared memory multiprocessor systems but has not received as much attention in distributed memory multicomputers. Furthermore, most previous research in this area has either used a simulation model or an analytical model in evaluating the performance of various policies. Very often these models neglect several practical aspects of the system and workload. Our goal is to implement processor scheduling policies on a real system and evaluate their performance. We have used a transputer based system to implement two policies-one space sharing policy and one time sharing policy-in order to study the relative performance trade-offs involved. We have also implemented a hybrid space sharing and time sharing policy. We report on the performance sensitivity of these policies to various system and workload parameters.
Regret Minimization in Behaviorally-Constrained Zero-Sum Games

No-regret learning has emerged as a powerful tool for solving extensive-form games. This was facilitated by the counterfactual-regret minimization (CFR) framework, which relies on the instantiation of regret minimizers for simplexes at each information set of the game. We use an instantiation of the CFR framework to develop algorithms for solving behaviorally-constrained (and, as a special case, perturbed in the Selten sense) extensive-form games, which allows us to compute approximate Nash equilibrium refinements. Nash equilibrium refinements are motivated by a major deficiency in Nash equilibrium: it provides virtually no guarantees on how it will play in parts of the game tree that are reached with zero probability. Refinements can mend this issue, but have not been adopted in practice, mostly due to a lack of scalable algorithms. We show that, compared to standard algorithms, our method finds solutions that have substantially better refinement properties, while enjoying a convergence rate that is comparable to that of state-of-the-art algorithms for Nash equilibrium computation both in theory and practice.
New Jersey Water Resources Research Institute Annual Technical Report FY 1999 Introduction

The New Jersey Water Resources Research Institute underwent a significant reorganization and revitalization this year, largely as a result of the five-year review conducted by the US Geological Survey. This review stimulated Rutgers University to make a major contribution to the support of the WRRI. The support is in the form of a half-time administrative assistant, funded by the NJ Agricultural Experiment Station, a pledge of financial support for conferences and undergraduate internships, and support for bookkeeping services from the Institute of Marine and Coastal Sciences (the unit of the university in which the WRRI is administratively housed), and matching funding from the central administration of the University to support information transfer activities and student support. These additional funds became available in January, 2001, and thus the activities of the Institute during this past fiscal year reflects the change in financial status of the institute during the second half of the fiscal year.
Neuromodulators, stress and plasticity: a role for endocannabinoid signalling

Any unanticipated threat to survival triggers an immediate sequence of events in the brain that culminate in a coordinated neural, endocrine and behavioural response. There is increasing evidence that stress itself modifies neural circuits. In other words, neural stress circuits learn from stress. This self-teaching is surprising as one might expect these essential circuits to be hard-wired. Our recent findings, however, indicate that repeated homotypic stress in rats causes functional changes in neural circuitry in the hypothalamus. In particular, we focus on signalling via endocannabinoids and describe plasticity in this system that impacts fast retrograde signalling at synapses on to the stress command neurons in the brain. Interestingly, this plasticity appears to be limited to early adolescence, hinting at unique modes of control of neural circuits by stress during different developmental stages.
Graph colouring technique for efficient channel allocation in cognitive radio networks

Cognitive radio networks play a vital role in solving the problem of underutilization of spectrum by identifying unused licensed radio spectrum. They solve the problem by distributing this unused spectrum among unlicensed users in an intelligent fashion without interfering with existing users. In this paper, using the conflict graph and graph colouring concepts, a Graph Colouring based Dynamic Channel Allocation (GC-DCA) algorithm is proposed that minimizes the network interference when the primary users (PUs) and secondary users (SUs) share the channel simultaneously. The performance of the GC-DCA algorithm is evaluated using the OMNeT++ network simulator under different topologies, in terms of channel utilization, end-to-end delay, packet delivery ratio, and throughput. Numerical results show that the proposed technique yields 40.40% increase in channel utilization when the PU and SU share the channel simultaneously.
Content-based retrieval for music collections

A content-based retrieval model for tackling the mismatch problems specific to music data is proposed and implemented. The system uses a pitch profile encoding for queries in any key and an n-note indexing method for approximate matching in sub-linear time. A distinct function that extracts key melodies for query suggestion is developed. The Web-based system provides flexible user interface for query formulation and result browsing. Users can search the system by a short sequence of notes, by uploading a file created by singing, or by clicking suggested key melodies without input. Experiments show that the pitch profile encoding and a 3-note indexing are able to overcome the key mismatch problem and the random errors caused by pitch error, note deletion and insertion. The use of extracted key melodies improves performance over direct search of the music database. For the type of burst mismatch, a query expansion approach is applied.
Analysis and diversion of Duqu's driver

The propagation techniques and the payload of Duqu have been closely studied over the past year and it has been said that Duqu shared functionalities with Stuxnet. We focused on the driver used by Duqu during the infection, our contribution consists in reverse-engineering the driver: we rebuilt its source code and analyzed the mechanisms it uses to execute the payload while avoiding detection. Then we diverted the driver into a defensive version capable of detecting injections in Windows binaries, thus preventing further attacks. We specifically show how Duqu's modified driver would have detected Duqu.
Patterns of size homoplasy at 10 microsatellite loci in pumas (Puma concolor).

Microsatellites, repetitive simple sequences of 1–6 nt in length, are abundant in eukaryotic genomes (Weber 1990a). Because of extensive variability in the number of repeat units for any one locus among members of a population, microsatellite loci exhibit high polymorphism. Microsatellite variation has become a useful class of genetic markers in population assessment for numerous species for questions of genetic identification, parentage, kinship, and population variability assessment (Jarne and Lagoda 1996; Goldstein and Pollock 1997). The high level of polymorphism at microsatellite loci is believed to result from slipped-strand mispairing during DNA replication (Levinson and Gutman 1987; Weber 1990b; Weber and Wong 1993; Krugylak et al. 1998), most commonly causing the gain or loss of one or more repeat units. This mutation mechanism would be expected to generate allelic homoplasy, i.e., comigrating allele size fragments which are not identical by descent or in DNA sequence among different individuals. Because mutational slippage does not conform to the infinite-alleles model (Kimura and Crow 1964), a stepwise mutation model (SMM) (Ohta and Kimura 1973; Chakraborty and Nei 1982) has been invoked to explain allele frequency distributions and population variability at microsatellite loci (Valdez, Slatkin, and Freimer 1993; Kimmel et al. 1996). A distinguishing feature of the two models is that homoplasy is not considered under the infinite-alleles model, while it is an expectation of the stepwise mutation model (Viard et al. 1998). The SMM (but not the infinite-alleles model) makes the assumption that differences in allelic length are due to variation in the number of repeat units and not to insertions and deletions in the flanking sequences. Allelic size homoplasy, a condition under which comigrating alleles represent different sequence motifs, has been demonstrated through sequence analysis of electromorphs of compound or imperfect repeats of primates (Blanquer-Maumont and Crouau-Roy 1995), several fish species (Angers and Bernatchez 1997), invertebrates (Viard et al. 1998), and the fungus Candida albicans (Metzgar et al. 1998). Insertion and deletion events in genomic regions flanking microsatellites confer allelic size homoplasy in several marine turtle species (FitzSimmons et al. 1995) and humans (Homo sa-
Discovering Domain-Specific Composite Kernels

Kernel-based data mining algorithms, such as Support Vector Machines, project data into high-dimensional feature spaces, wherein linear decision surfaces correspond to non-linear decision surfaces in the original feature space. Choosing a kernel amounts to choosing a high-dimensional feature space, and is thus a crucial step in the data mining process. Despite this fact, and as a result of the difficulty of establishing that a function is a positive definite kernel, only a few standard kernels (e.g. polynomial and Gaussian) are typically used. We propose a method for searching over a space of kernels for composite kernels that are guaranteed to be positive definite, and that are tuned to produce a feature space appropriate for a given dataset. Composite kernel functions are easily interpreted by humans, in contrast to the output of other work on kernel tuning. Empirical results demonstrate that our method often finds composite kernels that yield higher classification accuracy than the standard kernels.
Immunology of Diseases Associated with Malassezia Species

SUMMARY Malassezia species are members of the human cutaneous commensal flora, in addition to causing a wide range of cutaneous and systemic diseases in suitably predisposed individuals. Studies examining cellular and humoral immune responses specific to Malassezia species in patients with Malassezia-associated diseases and healthy controls have generally been unable to define significant differences in their immune response. The use of varied antigenic preparations and strains from different Malassezia classifications may partly be responsible for this, although these problems can now be overcome by using techniques based on recent work defining some important antigens and also a new taxonomy for the genus. The finding that the genus Malassezia is immunomodulatory is important in understanding its ability to cause disease. Stimulation of the reticuloendothelial system and activation of the complement cascade contrasts with its ability to suppress cytokine release and downregulate phagocytic uptake and killing. The lipid-rich layer around the yeast appears to be pivotal in this alteration of phenotype. Defining the nonspecific immune response to Malassezia species and the way in which the organisms modulate it may well be the key to understanding how Malassezia species can exist as both commensals and pathogens.
Accessible programming using program synthesis

New computing platforms have greatly increased the demand for programmers, but learning to program remains a big challenge. Program synthesis techniques have the potential to revolutionize programming by making it more accessible. In this dissertation, I present three systems, AutoProf, FlashFill, and Storyboard Programming Tool (SPT), that work towards making programming more accessible to a large class of people, namely students and end-users. The AutoProf (Automated Program Feedback) system provides automated feedback to students on introductory programming assignments. It has been successfully piloted on thousands of student submissions from an edX course and is currently being integrated on the MITx and edX platforms. The FlashFill system helps spreadsheet end-users perform semantic string transformations, number transformations, and table lookup transformations using few input-output examples. A part of the FlashFill system is shipping in Microsoft Excel 2013 and was quoted as one of the top features in Excel by many press articles. Finally, the Storyboard Programming Tool helps students write data structure manipulations using visual examples similar to the ones used in textbooks and classrooms. It has been used to synthesize many textbook manipulations over linked list, binary search trees, and graphs. These systems are enabled by new Program Synthesis techniques. Unlike traditional program synthesis approaches where the primary goal was to derive provably correct programs from a complete specification, these synthesis techniques are designed around natural specification mechanisms and intuitive interaction models. Each system relies on a different synthesis technique, but the techniques can be structured and understood in terms of four major components: – Specification Mechanism: The way users specify the functional behavior of their intended tasks to the system. Our synthesis techniques support more natural and intuitive forms of specification such as input-output examples, reference implementation, intermediate states etc. – Hypothesis Space: The hypothesis space defines the space of possible programs the system searches over to synthesize the desired program. The hypothesis space can be fixed or parametrized (user-defined) with additional user inputs. The fixed hypothesis spaces are defined using domain-specific languages (DSL) that exploit the domain knowledge to efficiently structure the hypothesis space, which enables the systems to represent a huge set of expressions in these languages succinctly. The parametric hypothesis spaces are defined using intuitive user inputs that allows users to easily define and control the space of possible programs. – Synthesis Algorithm: We develop new constraint-based and version-space algebra based synthesis algorithms to efficiently learn programs from a large hypothesis space that conform to the specification. – User Interaction Model: Finally, the user interaction model determines how users refine their intent and provide additional insights to the system for…
Analysis and equalization of delay in asynchronous CDMA system using unscented filtering

In this paper, we present analysis and equalization for asynchronous received signals in a code-division- multiple-access (CDMA) system. The equalization is performed using the unscented filer (UF). This equalization helps the multiuser detector to estimate the desired signal in asynchronous system without the need of estimating the delays of the asynchronous users. Simulations are presented to show the performance of the equalizer.
Genome-wide in silico identification and analysis of cis natural antisense transcripts (cis-NATs) in ten species

We developed a fast, integrative pipeline to identify cis natural antisense transcripts (cis-NATs) at genome scale. The pipeline mapped mRNAs and ESTs in UniGene to genome sequences in GoldenPath to find overlapping transcripts and combining information from coding sequence, poly(A) signal, poly(A) tail and splicing sites to deduce transcription orientation. We identified cis-NATs in 10 eukaryotic species, including 7830 candidate sense–antisense (SA) genes in 3915 SA pairs in human. The abundance of SA genes is remarkably low in worm and does not seem to be caused by the prevalence of operons. Hundreds of SA pairs are conserved across different species, even maintaining the same overlapping patterns. The convergent SA class is prevalent in fly, worm and sea squirt, but not in human or mouse as reported previously. The percentage of SA genes among imprinted genes in human and mouse is 24–47%, a range between the two previous reports. There is significant shortage of SA genes on Chromosome X in human and mouse but not in fly or worm, supporting X-inactivation in mammals as a possible cause. SA genes are over-represented in the catalytic activities and basic metabolism functions. All candidate cis-NATs can be downloaded from .
Kernel level energy-efficient 3G background traffic shaper for android smartphones

Reducing the energy consumption of wireless devices is paramount to a wide spread adoption of mobile applications. Cellular communication imposes high energy consumption on the mobile devices due to the radio resource allocation, which differs from other networks such as WiFi. Most applications are unaware of the energy consumption characteristics of third generation cellular communication (3G). This makes the background small data transfers of undisciplined applications an energy burden due to inefficient utilisation of resources. While several approaches exist to reduce the energy consumption of this best-effort background traffic by means of traffic shaping, we find that they are mostly evaluated with simulations and the actual energy overhead for the traffic shaper itself has not been studied. In order to cover this gap, our work realises an existing energy saving algorithm as a Kernel Level Shaper (KLS) within the Android platform, and measures its energy footprint. The total energy savings of our implementation range from 8% to 58% for emulated real background traffic, that is categorised as best-effort traffic. We further show the implications of running the KLS during live operation of applications as an exploratory study.
A wideband noise-canceling CMOS LNA exploiting a transformer

A broadband LNA incorporating single-ended to differential conversion, has been successfully implemented using a noise-canceling technique and a single on-chip transformer. The LNA achieves a high voltage gain of 19dB, a wideband input match (2.5-4.0 GHz), and a noise figure of 4-5.4 dB, while consuming only 8mW. The LNA is implemented in a 90nm CMOS process with 6 metal layers
Comparison of Parameter Free MST Clustering Algorithm with Hierarchical Agglomerative Clustering Algorithms

Clustering is a splitting up of data into groups of similar objects called clusters. The objects in a cluster are similar between themselves and dissimilar compared to objects of other clusters. This paper is intended to study and compare different data clustering algorithms. The algorithms in investigation are: hierarchical agglomerative clustering algorithms: Parameter Free Minimum Spanning Tree (MST) clustering algorithm and single link, complete link and average link clustering algorithms. K-means partitional clustering algorithm is used in the results as a reference. Our experimental evaluation shows that Parameter Free Minimum Spanning Tree algorithms are lead to better clustering results than hierarchical agglomerative algorithms, which suggests that Parameter Free Minimum Spanning Tree clustering algorithms are well-suited for clustering. General Terms Data Mining, Clustering.