Miracle-dz/UniFAR-FTUs huggingface dataset:
Ecological restoration for protected areas : principles, guidelines and best practices

This publication provides guidance for terrestrial, marine, and freshwater protected area managers at both system and site levels on the restoration of natural and associated values of protected areas. As this sometimes necessitates restoration beyond protected area borders (e.g., to address ecosystem fragmentation and maintain well-connected protected area systems), this guide uses the term restoration for protected areas for activities within protected areas and for activities in connecting or surrounding lands and waters that influence protected area values. It provides information on principles and best practice, with examples, and advice on the process of restoration, but is not a comprehensive restoration manual and does not give detailed methodologies and techniques.
Active Learning for Networked Data

We introduce a novel active learning algorithm for classification of network data. In this setting, training instances are connected by a set of links to form a network, the labels of linked nodes are correlated, and the goal is to exploit these dependencies and accurately label the nodes. This problem arises in many domains, including social and biological network analysis and document classification, and there has been much recent interest in methods that collectively classify the nodes in the network. While in many cases labeled examples are expensive, often network information is available. We show how an active learning algorithm can take advantage of network structure. Our algorithm effectively exploits the links between instances and the interaction between the local and collective aspects of a classifier to improve the accuracy of learning from fewer labeled examples. We experiment with two real-world benchmark collective classification domains, and show that we are able to achieve extremely accurate results even when only a small fraction of the data is labeled.
Integrating Joint n-gram Features into a Discriminative Training Framework

Phonetic string transduction problems, such as letter-to-phoneme conversion and name transliteration, have recently received much attention in the NLP community. In the past few years, two methods have come to dominate as solutions to supervised string transduction: generative joint n-gram models, and discriminative sequence models. Both approaches benefit from their ability to consider large, flexible spans of source context when making transduction decisions. However, they encode this context in different ways, providing their respective models with different information. To combine the strengths of these two systems, we include joint n-gram features inside a state-of-the-art discriminative sequence model. We evaluate our approach on several letter-to-phoneme and transliteration data sets. Our results indicate an improvement in overall performance with respect to both the joint n-gram approach and traditional feature sets for discriminative models.
Predicting Zoonotic Risk of Influenza A Viruses from Host Tropism Protein Signature Using Random Forest

Influenza A viruses remain a significant health problem, especially when a novel subtype emerges from the avian population to cause severe outbreaks in humans. Zoonotic viruses arise from the animal population as a result of mutations and reassortments, giving rise to novel strains with the capability to evade the host species barrier and cause human infections. Despite progress in understanding interspecies transmission of influenza viruses, we are no closer to predicting zoonotic strains that can lead to an outbreak. We have previously discovered distinct host tropism protein signatures of avian, human and zoonotic influenza strains obtained from host tropism predictions on individual protein sequences. Here, we apply machine learning approaches on the signatures to build a computational model capable of predicting zoonotic strains. The zoonotic strain prediction model can classify avian, human or zoonotic strains with high accuracy, as well as providing an estimated zoonotic risk. This would therefore allow us to quickly determine if an influenza virus strain has the potential to be zoonotic using only protein sequences. The swift identification of potential zoonotic strains in the animal population using the zoonotic strain prediction model could provide us with an early indication of an imminent influenza outbreak.
3D Implicit Shape Models Using Ray Based Hough Voting for Furniture Recognition

The recognition of object categories in 3D scenes is still a challenging problem in computer vision. Many state of the art approaches use Implicit Shape Models, as addressed in [8] and [14], to learn shapes of object categories and a probabilistic Hough Space Voting for the detection of instances of the learned category. In this paper we present a novel 3D Hough Space Voting approach for recognizing object categories, learned from artificial 3D models, in 3D scenes. The proposed method uses rays instead of points to vote for object reference points. The usage of ray voting allows a clustering of votes, showing in similar directions, to a single vote with an appropriate vote weight. The main advantage for the Implicit Shape Model is that it can be trained with an unlimited amount of training data, while keeping the upper bound of computation effort constant. In addition, it is also able to abstract from the model sizes which is very helpful when training with artificial models taken from different sources and modelled in different scales. We validate our approach in two tasks: an object categorization is performed on a standard 3D dataset of artificial models and a recognition of furniture categories is evaluated on a dataset of captured indoor room scenes.
Retrovirally Mediated Overexpression of Versican V3 by Arterial Smooth Muscle Cells Induces Tropoelastin Synthesis and Elastic Fiber Formation In Vitro and In Neointima After Vascular Injury

Versican is an extracellular matrix (ECM) proteoglycan that is synthesized as multiple splice variants. In a recent study, we demonstrated that retroviral-mediated overexpression of the variant V3, which lacks chondroitin sulfate (CS) chains, altered arterial smooth muscle cell (ASMC) phenotype in short-term cell culture. We now report that V3-overexpressing ASMCs exhibit significantly increased expression of tropoelastin and increased formation of elastic fibers in long-term cell cultures. In addition, V3-overexpressing ASMCs seeded into ballooned rat carotid arteries continued to overexpress V3 and, at 4 weeks after seeding, produced a highly structured neointima significantly enriched in elastic fiber lamellae. In contrast to the hydrated, myxoid neointima produced by rounded or stellate vector-alone–transduced cells, V3-expressing cells produced a compact and highly ordered neointima, which contained elongated ASMCs that were arranged in parallel arrays and separated by densely packed collagen bundles and elastic fibers. These results indicate that a variant of versican is involved in elastic fiber assembly and may represent a novel therapeutic approach to facilitate the formation of elastic fibers.
On the computation of directional scale-discretized wavelet transforms on the sphere

We review scale-discretized wavelets on the sphere, which are directional and allow one to probe oriented structure in data defined on the sphere. Furthermore, scale-discretized wavelets allow in practice the exact synthesis of a signal from its wavelet coefficients. We present exact and efficient algorithms to compute the scale-discretized wavelet transform of band-limited signals on the sphere. These algorithms are implemented in the publicly available S2DW code. We release a new version of S2DW that is parallelized and contains additional code optimizations. Note that scale-discretized wavelets can be viewed as a directional generalization of needlets. Finally, we outline future improvements to the algorithms presented, which can be achieved by exploiting a new sampling theorem on the sphere developed recently by some of the authors.
Efficient Privacy-Preserving Aggregation for Mobile Crowdsensing

Mobile crowdsensing applications can learn the aggregate statistics over personal data to produce useful knowledge about the world. Since personal data may be privacy-sensitive, the aggregator should only gain desired statistics without learning anything about the personal data. Differential privacy, the state-of-the-art privacy mechanism, can provide strong protection to ensure parties' privacy in such scenarios. Correspondingly, based on the differential privacy, many collusion-tolerant aggregation schemes have been proposed. However, those collusion-tolerant schemes usually incur high accumulated error and also require the priori knowledge of the fraction of those colluded parties. In this paper, we propose a differential-private collusion-tolerant aggregation protocol, while incurring no additionally error except the noise required for providing the differential privacy guarantee. Another salient characteristic of the proposed protocol is that it need not to have an priori estimation of those colluded parties. In addition, we also design an efficient aggregation encryption scheme to support those mobile crowdsensing applications where large plaintext is required. We also make some extensions to make the proposed protocol more applicable in realities, such as the fault tolerant. The analysis shows that the proposed protocol can achieve desired goals, and the performance evaluation demonstrates the protocol's efficiency in reality.
Interaction of the human autoantigen p150 with splicing snRNPs.

An important goal of studies on pre-mRNA splicing is to identify factors that mediate the snRNP-snRNP and snRNP-pre-mRNA interactions that take place in the spliceosome. The U4/U6 snRNP is one of the four snRNPs that are subunits of spliceosomes. A rare patient autoimmune serum (MaS serum) has recently been identified that specifically immunoprecipitates U4/U6 snRNP from HeLa cell extracts through recognition of a 150 kDa autoantigen (p150) (Okano and Medsger, Journal of Immunology, 146, 535-542, 1991). Here we show that in addition to U4/U6 snRNP, p150 can also be detected associated with 20 S U5, U4/U6.U5 and 17 S U2 snRNPs, but not with U1 snRNP. In each particle p150 is present in sub-stoichiometric levels relative to the major snRNP proteins. We show that MaS serum selectively immunoprecipitates a sub-population of U4/U6 snRNPs in which the m3G-cap structure is masked and that p150 is preferentially associated with U6 snRNA in the U4/U6 particle. Anti-p150 antibodies show widespread nucleoplasmic staining, excluding nucleoli, with an elevated concentration in coiled bodies. This changes to a discrete punctate pattern when cells are treated with alpha-amanitin. Both the cytological and biochemical data indicate that the p150 autoantigen is a snRNP-associated factor in vivo. We also present biochemical evidence confirming that assembly of U4/U6 and U5 snRNPs into a U4/U6.U5 tri-snRNP particle is an integral step in the spliceosome assembly pathway. Addition of the purified U4/U6.U5 tri-snRNP restores splicing activity to inactivated HeLa nuclear extracts in which splicing had been inhibited by specific depletion of either the U4/U6 or U5 snRNPs.
Attacks and risk analysis for hardware supported software copy protection systems

<i>Recently, there is a growing interest in the research community to use tamper-resistant processors for software copy protection. Many of these tamper-resistant systems rely on a specially tailored secure processor to prevent, 1) illegal software duplication, 2) unauthorized software modification, and 3)unauthorized software reverse engineering. The published techniques primarily focused on feasibility demonstration and design details rather than analyzing security risks and potential attacks from an adversary's perspective. The uniqueness of software copy protection may lead to some potential attacks on such a secure environment that have been largely ignored or insufficiently addressed in the literature. One should not take security for granted just because it is implemented on a tamper-resistant secure processor. Detailed analysis on some proposed ideas reveal potential vulnerability and attacks. Some of the attacks are known to the security community, nevertheless, their implications to software copy protection are not well understood and discussed. This paper presents these cases for designers to improve their systems and circumvent the potential security pitfalls and for users of such systems to be aware of the potential risks</i>
Lightweight Snapshots and System-level Backtracking

We propose a new system-level abstraction, the lightweight immutable execution snapshot, which combines the immutable characteristics of checkpoints with the direct integration into the virtual memory subsystem of standard mutable address spaces. The abstraction can give arbitrary x86 programs and libraries system-level support for backtracking (akin to logic programming) and the ability to manipulate an entire address space as an immutable data structure (akin to functional programming). Our proposed implementation leverages modern x86 hardware-virtualization support.
E-commerce Transaction Anomaly Classification

Due to the increasing popularity of e-commerce in our daily lives, credit card usages have dramatically increased over the years. As credit card being the primary method of payment in online transactions, credit card frauds have also been observed to surge as the number of online transactions have increased. Because of the significant financial losses that credit card fraudulent incidents can cause, credit card fraud detection drew our interest into its investigation. Specifically, we examined anomaly detection classification algorithms that can be applied to detect fraudulent credit card transactions. In addition, we proposed a new type of sampling method called Oversampling via Randomly Imputed Features (ORIF) and details of ORIF is given in section IV. Results of incorporating ORIF to each basic classification algorithms are also provided in section V.
Malnutrition, learning, and intelligence.

A survey of the evidence shows that some degrees of malnutrition is relatively widespread among poor children. However, the effects of inadequate nutrition on growth and mental development depend to a large extent on the severity, the timing (pre and postnatal), and the duration of the nutritional deprivation. The data are inadequate on the true prevalence of malnutrition among children in this country, but there is even less information about its onset or about its severity and quality. The absence of such knowledge reflects not the absence of the problem, but the lack of attention devoted to it. There is strong indication that nutritional factors at a number of different levels contribute significantly to depressed intellectual level and learning failure. Moreover, an adequate state of nutrition is necessary for good attention and for appropriate and sensitive responsiveness to the environment. Further, women who were malnourished as children are more likely to have disturbed pregnancies and children of low birth weight and increased risk of neurointegrative abnormality. It must be recognized, however, that improvement of nutrition alone cannot fully solve the problem of intellectual deficit and school failure. Rather, an overall effort to improve the condition of disadvantaged children is required.
Complex Transcriptional Control Links NikABCDE-Dependent Nickel Transport with Hydrogenase Expression in Escherichia coli

ABSTRACT Escherichia coli requires nickel under anaerobic growth conditions for the synthesis of catalytically active NiFe hydrogenases. Transcription of the NikABCDE nickel transporter, which is required for NiFe hydrogenase synthesis, was previously shown to be upregulated by FNR (fumarate-nit rate regulator) in the absence of oxygen and repressed by the NikR repressor in the presence of high extracellular nickel levels. We present here a detailed analysis of nikABCDE transcriptional regulation and show that it closely correlates with hydrogenase expression levels. We identify a nitrate-dependent mechanism for nikABCDE repression that is linked to the NarLX two-component system. NikR is functional under all nickel conditions tested, but its activity is modulated by the total nickel concentration present as well as by one or more components of the hydrogenase assembly pathway. Unexpectedly, NikR function is independent of NikABCDE function, suggesting that NikABCDE is a hydrogenase-specific nickel transporter, consistent with its original identification as a hydrogenase (hyd) mutant. Further, the results suggest that the hydrogenase assembly pathway is sequestered within the cell. A second nickel import pathway in E. coli is implicated in NikR function.
Surface modeling with oriented particle systems

Splines and deformable surface models are widely used in computer graphics to describe free-form surfaces. These methods require manual preprocessing to discretize the surface into patches and to specify their connectivity. We present a new model of elastic surfaces based on interacting particle systems, which, unlike previous techniques, can be used to sptiL join, or extend surfaces without the need for manual intervention. The particles we use have longrange attraction forces and short-range repulsion forces and follow Newtonian dynamics, much tiie recent computational models of fluids and solids. To enable our particles to model surface elements instead of point masses or volume elements, we add an orientation to each particle’s state. We devise new interaction potentials for our oriented particles which favor locally planar or spherical arrangements. We atso develop techniques for adding new particles automatically, which enables our surfaces to stretch and grow. We demonstrate the application of our new particle system to modefing surfaces in 3-D and the interpolation of 3-D point sets.
Reconstruction of Genome Ancestry Blocks in Multiparental Populations

We present a general hidden Markov model framework called reconstructing ancestry blocks bit by bit (RABBIT) for reconstructing genome ancestry blocks from single-nucleotide polymorphism (SNP) array data, a required step for quantitative trait locus (QTL) mapping. The framework can be applied to a wide range of mapping populations such as the Arabidopsis multiparent advanced generation intercross (MAGIC), the mouse Collaborative Cross (CC), and the diversity outcross (DO) for both autosomes and X chromosomes if they exist. The model underlying RABBIT accounts for the joint pattern of recombination breakpoints between two homologous chromosomes and missing data and allelic typing errors in the genotype data of both sampled individuals and founders. Studies on simulated data of the MAGIC and the CC and real data of the MAGIC, the DO, and the CC demonstrate that RABBIT is more robust and accurate in reconstructing recombination bin maps than some commonly used methods.
Online Reliability Prediction via Long Short Term Memory for Service-Oriented Systems

A service-oriented System of System (SoS) integrates component services into a value-added and more complex system to satisfy the complex requirements of users. Due to a dynamic running environment, online reliability prediction for the loosely coupled component systems that ensures the runtime quality poses a major challenge and attracts growing attention. To guarantee the stable and continuous operation of systems, we propose a online reliability time series prediction method basing on long short term memory (LSTM), which is a modified Recurrent Neural Networks trained with historical reliability time series to predict the reliability of component systems in the near future. We conduct a series of experiments on a dataset composed of real web services and compare with other competitive approaches. Experimental results have demonstrated the effectiveness of our approach.
Forensic analysis of autonomous system reachability

Security incidents have an adverse impact not only on end systems, but also on Internet routing, resulting in many out-of-reach prefixes. Previous work has looked at performance degradation in the data plane in terms of delay and loss. Also it has been reported that the number of routing updates increased significantly, which could be a reflection of increased routing instability in the control domain. In this paper, we perform a detailed forensic analysis of routing instability during known security incidents and present useful metrics in assessing damage in AS reachability. Any change in AS reachability is a direct indication of whether the AS had fallen victim to the security incident or not.We choose the Slammer worm attack in January, 2003, as a security incident for closer examination. For our forensic analysis, we use BGP routing data from RouteViews and RIPE. As a way to quantify AS reachability, we propose the following metrics: the prefix count and the address count. The number of unique prefixes in routing tables during the attack fluctuates greatly, but it does not represent the real scope of damage. We define the address count as the cardinality of the set of IP addresses an AS is responsible for either as an origin or transit AS, and observe how address counts changed over time. These two metrics together draw an accurate picture of how reachability to or through the AS had been affected. Though our analysis was done off-line, our methodology can be applied on-line and used in quick real-time assessment of AS reachability.
Polar Localization of Virulence-Related Esx-1 Secretion in Mycobacteria

The Esx-1 (type VII) secretion system is critical for virulence of both Mycobacterium tuberculosis and Mycobacterium marinum, and is highly conserved between the two species. Despite its importance, there has been no direct visualization of Esx-1 secretion until now. In M. marinum, we show that secretion of Mh3864, a novel Esx-1 substrate that remains partially cell wall–associated after translocation, occurred in polar regions, indicating that Esx-1 secretion takes place in these regions. Analysis of Esx-1 secretion in infected host cells suggested that Esx-1 activity is similarly localized in vivo. A core component of the Esx-1 apparatus, Mh3870, also localized to bacterial poles, showing a preference for new poles with active cell wall peptidoglycan (PGN) synthesis. This work demonstrates that the Esx-1 secretion machine localizes to, and is active at, the bacterial poles. Thus, virulence-related protein secretion is localized in mycobacteria, suggesting new potential therapeutic targets, which are urgently needed.
Distributed power control for cooperative diversity in the MAC channel

We consider a multiple access MAC fading channel with two users communicating with a common destination, where each user mutually acts as a relay for the other one as well as wishes to transmit his own information as opposed to having dedicated relays. We wish to evaluate the usefulness of relaying from the point of view of the system's throughput (sum rate) rather than from the sole point of view of the user benefiting from the cooperation as is typically done. We do this by allowing a trade-off between relaying and fresh data transmission through a resource allocation framework. We develop capacity expressions for our scheme and derive the rate-optimum power allocation in closed form for distributed frameworks. In the distributed scenario, partially statistical and partially instantaneous channel information is exploited. Our results indicate that the sum rate is maximized when both mobiles act selfishly.