Miracle-dz/UniFAR-FTUs huggingface dataset:
Policies for Sustainable Forestry in Belarus, Russia and Ukraine

Policies for Sustainable Forestry in Belarus, Russia and Ukraine provides a thorough analysis of the key factors in the transition process affecting the forest sector in the eponymous countries. Moreover, it designs new strategies for sustainable development in these areas. The book attempts to strengthen selected trends in the forest sector in each country so that they may gradually achieve sustainability and create a market economy. The key factors are identified by making use of several different sources: scientific papers, interviews with experts from the three countries and the personal experiences of the co-authors living in the countries. The strategies propose new and interesting options for improving forest management by optimizing forestry within closed production units. Further strategies deal with reorientation of forest planning, transition oriented labor management and acquisition of resources for forestry from state and markets. Policies for Sustainable Forestry in Belarus, Russia and Ukraine also gives new insights into the political factors and informal strategies within the forestry sector. It serves as an important addition to existing economic market models and will draw attention to the political process driving the transition. This information is helpful to both experts in economics as well as foresters in the field as it gives them an understanding of the complexity of building up a market economy by transition.
Noninvasive Imaging of Endogenous Neural Stem Cell Mobilization In Vivo Using Positron Emission Tomography

Neural stem cells reside in two major niches in the adult brain [i.e., the subventricular zone (SVZ) and the dentate gyrus of the hippocampus]. Insults to the brain such as cerebral ischemia result in a physiological mobilization of endogenous neural stem cells. Since recent studies showed that pharmacological stimulation can be used to expand the endogenous neural stem cell niche, hope has been raised to enhance the brain's own regenerative capacity. For the evaluation of such novel therapeutic approaches, longitudinal and intraindividual monitoring of the endogenous neural stem cell niche would be required. However, to date no conclusive imaging technique has been established. We used positron emission tomography (PET) and the radiotracer 3′-deoxy-3′-[18F]fluoro-l-thymidine ([18F]FLT) that enables imaging and measuring of proliferation to noninvasively detect endogenous neural stem cells in the normal and diseased adult rat brain in vivo. This method indeed visualized neural stem cell niches in the living rat brain, identified as increased [18F]FLT-binding in the SVZ and the hippocampus. Focal cerebral ischemia and subsequent damage of the blood–brain barrier did not interfere with the capability of [18F]FLT-PET to visualize neural stem cell mobilization. Moreover, [18F]FLT-PET allowed for an in vivo quantification of increased neural stem cell mobilization caused by pharmacological stimulation or by focal cerebral ischemia. The data suggest that noninvasive longitudinal monitoring and quantification of endogenous neural stem cell activation in the brain is feasible and that [18F]FLT-PET could be used to monitor the effects of drugs aimed at expanding the neural stem cell niche.
Variational Bayes via Propositionalization ( extended abstract )

In this paper, we propose a unified approach to VB (variational Bayes) [1] in symbolicstatistical modeling via propositionalization1. By propositionalization we mean, broadly, expressing and computing probabilistic models such as BNs (Bayesian networks) [2] and PCFGs (probabilistic context free grammars) [3] in terms of propositional logic that considers propositional variables as binary random variables. Our proposal is motivated by three observations. The first one is that propositionalization, or more precisely PPC (propositionalized probability computation), i.e. probability computation formulated in a propositional setting, has turned out to be general and efficient when variable values are sparsely interdependent. Examples include (discrete) BNs, PCFGs and more generally PRISM [4, 5] which is a probabilistic logic programming language we have been developing that computes probabilities using graphically represented AND/OR boolean formulas. Efficacy of PPC is already proved by the Inside-Outside algorithm in the case of PCFGs and by recent PPC approaches to BNs such as the one by Chavira et al. that exploits 0 probability and CSI (context specific independence) [6]. Mateescu et al. introduced AND/OR search tress which is a propositional representation of bucket trees and revealed that PPC is a general computation machanism for BNs [7]. Second of all, while VB has been around for sometime as a powerful tool for Bayesian modeling [1], it’s use is restricted to somewhat simple models such as BNs and HMMs (hidden Markov models) [8] though its usefulness is established through a variety of applications from model selection to prediction. On the other hand it is already proved that VB can be extended to PCFGs and efficiently implementable using dynamic programming [9]. Note that PCFGs are just one class of PPC and much more general PPC is already realized in PRISM. Accordingly if VB is combined with PRISM’s PPC, we will obtain VB for general probabilistic models, far wider than BNs and PCFGs. The last observation is that currently deriving and implementing a VB algorithm is an error-prone time-consuming process. In addition ensuring its correctness beyond PCFGs seems a non-trivial task. However once VB becomes available in PRISM, it will save considerable time and effort. That is, we do not have to derive a new VB algorithm from scratch nor have to implement it. All we have to do is just to write a probabilistic
Storage Class Memory Aware Data Management

Storage Class Memory (SCM) is here to stay. It has characteristics that place it in a class apart both from main memory and hard disk drives. Software and systems, architectures and algorithms need to be revisited to extract the maximum benefit from SCM. In this paper, we describe work that is being done in the area of Storage Class Memory aware Data Management at IBM. We specifically cover the challenges in placement of objects in storage and memory systems which have NAND flash (one kind of SCM) in a hierarchy or in the same level with other storage devices. We also focus on the challenges of adapting data structures which are inherently main memory based to work out of a memory hierarchy consisting of DRAM and flash. We describe how these could be addressed for a popular main memory data structure, namely the Bloom filter.
Information Flow in Networks and the Law of Diminishing Marginal Returns: Evidence from Modeling and Human Electroencephalographic Recordings

We analyze simple dynamical network models which describe the limited capacity of nodes to process the input information. For a proper range of their parameters, the information flow pattern in these models is characterized by exponential distribution of the incoming information and a fat-tailed distribution of the outgoing information, as a signature of the law of diminishing marginal returns. We apply this analysis to effective connectivity networks from human EEG signals, obtained by Granger Causality, which has recently been given an interpretation in the framework of information theory. From the distributions of the incoming versus the outgoing values of the information flow it is evident that the incoming information is exponentially distributed whilst the outgoing information shows a fat tail. This suggests that overall brain effective connectivity networks may also be considered in the light of the law of diminishing marginal returns. Interestingly, this pattern is reproduced locally but with a clear modulation: a topographic analysis has also been made considering the distribution of incoming and outgoing values at each electrode, suggesting a functional role for this phenomenon.
XX Power Modeling in Database Management Systems

Data centers consume large amounts of energy. Since databas es form one of the main energy sinks in a typical data center, building energy-aware database systems has become an activ e research topic recently. The quantification of the energy c ost of database systems is an important task in designing such syst ems. In this paper, we report our recent efforts on this topic , with a focus on the power estimation of query plans during query op timization. We start from a series of physical models design ed for evaluating power cost of individual relational operato s, and use such models as a basis for a complex model for indiv idual queries. Key parameters of the power model are generated by u sing linear regression tools upon running experiments with simple workloads in a static environment. To further improv e the above model to make it work under system dynamics and fluctuations of workload characteristics, we develop an online model estimation scheme over the static model based on advanced modeling techniques adopted from the control en gin ering field. Our solution can effectively tune the model parameters at runtime to reach high accuracy in power estima tion. The models are implemented in the PostgreSQL kernel and evaluated with a comprehensive set of experimental work loads generated from multiple TPC and scientific database benchmarks.
Inflammation Drives Dysbiosis and Bacterial Invasion in Murine Models of Ileal Crohn’s Disease

Background and Aims Understanding the interplay between genetic susceptibility, the microbiome, the environment and the immune system in Crohn’s Disease (CD) is essential for developing optimal therapeutic strategies. We sought to examine the dynamics of the relationship between inflammation, the ileal microbiome, and host genetics in murine models of ileitis. Methods We induced ileal inflammation of graded severity in C57BL6 mice by gavage with Toxoplasma gondii, Giardia muris, low dose indomethacin (LDI;0.1 mg/mouse), or high dose indomethacin (HDI;1 mg/mouse). The composition and spatial distribution of the mucosal microbiome was evaluated by 16S rDNA pyrosequencing and fluorescence in situ hybridization. Mucosal E. coli were enumerated by quantitative PCR, and characterized by phylogroup, genotype and pathotype. Results Moderate to severe ileitis induced by T. gondii (day 8) and HDI caused a consistent shift from >95% Gram + Firmicutes to >95% Gram - Proteobacteria. This was accompanied by reduced microbial diversity and mucosal invasion by adherent and invasive E. coli, mirroring the dysbiosis of ileal CD. In contrast, dysbiosis and bacterial invasion did not develop in mice with mild ileitis induced by Giardia muris. Superimposition of genetic susceptibility and T. Gondii infection revealed greatest dysbiosis and bacterial invasion in the CD-susceptible genotype, NOD2−/−, and reduced dysbiosis in ileitis-resistant CCR2−/− mice. Abrogating inflammation with the CD therapeutic anti-TNF-α-mAb tempered dysbiosis and bacterial invasion. Conclusions Acute ileitis induces dysbiosis and proliferation of mucosally invasive E. coli, irrespective of trigger and genotype. The identification of CCR2 as a target for therapeutic intervention, and discovery that host genotype and therapeutic blockade of inflammation impact the threshold and extent of ileal dysbiosis are of high relevance to developing effective therapies for CD.
A poxviral-based cancer vaccine targeting the transcription factor Twist inhibits primary tumor growth and metastases in a model of metastatic breast cancer and improves survival in a spontaneous prostate cancer model

Several transcription factors play a role in the alteration of gene expression that occurs during cancer metastasis. Twist expression has been shown to be associated with the hallmarks of the metastatic process, as well as poor prognosis and drug resistance in many tumor types. However, primarily due to their location within the cell and the lack of a hydrophobic groove required for drug attachment, transcription factors such as Twist are difficult to target with conventional therapies. An alternative therapeutic strategy is a vaccine comprised of a Modified vaccinia Ankara (MVA), incorporating the Twist transgene and a TRIad of COstimulatory Molecules (B7-1, ICAM-1, LFA-3; TRICOM). Here we characterize an MVA-TWIST/TRICOM vaccine that induced both CD4+ and CD8+ Twist-specific T-cell responses in vivo. In addition, administration of this vaccine reduced both the primary tumor growth and metastasis in the 4T1 model of metastatic breast cancer. In the TRAMP transgenic model of spontaneous prostate cancer, MVA-TWIST/TRICOM alone significantly improved survival, and when combined with the androgen receptor antagonist enzalutamide, the vaccine further improved survival. These studies thus provide a rationale for the use of active immunotherapy targeting transcription factors involved in the metastatic process and for the combination of cancer vaccines with androgen deprivation.
A Review of ENSO Influence on the North Atlantic. A Non-Stationary Signal

The atmospheric seasonal cycle of the North Atlantic region is dominated by meridional movements of the circulation systems: from the tropics, where the West African Monsoon and extreme tropical weather events take place, to the extratropics, where the circulation is dominated by seasonal changes in the jetstream and extratropical cyclones. Climate variability over the North Atlantic is controlled by various mechanisms. Atmospheric internal variability plays a crucial role in the mid-latitudes. However, El Nino-Southern Oscillation (ENSO) is still the main source of predictability in this region situated far away from the Pacific. Although the ENSO influence over tropical and extra-tropical areas is related to different physical mechanisms, in both regions this teleconnection seems to be non-stationary in time and modulated by multidecadal changes of the mean flow. Nowadays, long observational records (greater than 100 years) and modeling projects (e.g., CMIP) permit detecting non-stationarities in the influence of ENSO over the Atlantic basin, and further analyzing its potential mechanisms. The present article reviews the ENSO influence over the Atlantic region, paying special attention to the stability of this teleconnection over time and the possible modulators. Evidence is given that the ENSO–Atlantic teleconnection is weak over the North Atlantic. In this regard, the multidecadal ocean variability seems to modulate the presence of teleconnections, which can lead to important impacts of ENSO and to open windows of opportunity for seasonal predictability.
Novel Online Dimensionality Reduction Method with Improved Topology Representing and Radial Basis Function Networks

This paper presents improvements to the conventional Topology Representing Network to build more appropriate topology relationships. Based on this improved Topology Representing Network, we propose a novel method for online dimensionality reduction that integrates the improved Topology Representing Network and Radial Basis Function Network. This method can find meaningful low-dimensional feature structures embedded in high-dimensional original data space, process nonlinear embedded manifolds, and map the new data online. Furthermore, this method can deal with large datasets for the benefit of improved Topology Representing Network. Experiments illustrate the effectiveness of the proposed method.
Notice of Violation of IEEE Publication PrinciplesAn automatic data-driven technique for selecting background dataset in GMM-SVM speaker verification system

In this paper, we propose an automatic data-driven technique for selecting proper background dataset. By the technique, impostor confidence(IC) is proposed as a metric and more discriminative background dataset is automatically chose by impostor confidence(IC) to train more discriminative model. Experiment results on NIST 2008 SRE corpus in GMM-SVM speaker verification system show that the proposed approach obtains better performance. Relative decline in mincost of 8.9% in female and 4.6% in male is obtained. with female and male combined, 5.4% relative decline in mincost is obtained over Heuristically selected background dataset.
The Demand for Disaggregated Food-Away-from-Home and Food-at-Home Products in the United States

Food away from home (FAFH) comprises nearly half of all U.S. consumer food expenditures. Hence, policies designed to influence nutritional outcomes would be incomplete if they did not address the role of FAFH. However, because of data limitations, most studies of the response of food demand to policy changes have ignored the role of FAFH, and those studies that have included FAFH have treated it as a single good. We, therefore, estimate demand for 43 disaggregated FAFH and food-at-home (FAH) products, using a 2-stage budgeting framework. We find that the demands for disaggregated FAFH products differ in price responsiveness and tend to be more sensitive to changes in food spending patterns than FAH products. Many foods are found to have statistically significant substitution and complementary relationships within and among food groups. Predicted changes in quantities based on our estimates that include all goods and services and those estimates that include only a subset of foods differ substantially, implying that evaluations of health and nutrition policy based on elasticities of demand for only a subset of goods may be misleading.
Using Whois Based Geolocation and Google Maps API for support cybercrime investigations

A major challenge facing all law-enforcement and intelligence-gathering organizations is accurately and efficiently analyzing the growing volumes of crime data. Cybercrime refers to any crime that involves a computer and network, where computer may or may not play an instrumental part in the commission of the crime. Detection and investigation of cybercrime can likewise be difficult because busy network traffic and frequent online transactions generate large amounts of data, only a small portion of which relates to illegal activities. In this paper, we are focusing on technologies that can help to improve the effective investigation of cybercrime, facilitate police work and enable investigators to allocate their time to other tasks. We have developed an IP mapping tool called MIPA that combines online mapping techniques and IP geolocation technology, and uses application functionality from disparate web sources. The emergence of the Web 2.0 and user-friendly online mapping techniques have created public interest in contributing information through Webenabled geospatial tools. Moreover, IP geolocation is essential in law enforcement to identify the appropriate jurisdiction to handle enforcement of computer crime statues. Cybercrime investigators must apply a spectrum of techniques to discover associations, identify patterns, and make predictions. We have developed a tool that can be of great help law enforcement agencies during cybercrime investigations and to improve its accuracy and efficiency. Key-Words: Cybercrime Investigation, IP geolocation, Maps API, Mapping solutions
Computerized techniques pave the way for drug-drug interaction prediction and interpretation

Introduction: Health care industry also patients penalized by medical errors that are inevitable but highly preventable. Vast majority of medical errors are related to adverse drug reactions, while drug-drug interactions (DDIs) are the main cause of adverse drug reactions (ADRs). DDIs and ADRs have mainly been reported by haphazard case studies. Experimental in vivo and in vitro researches also reveals DDI pairs. Laboratory and experimental researches are valuable but also expensive and in some cases researchers may suffer from limitations. Methods: In the current investigation, the latest published works were studied to analyze the trend and pattern of the DDI modelling and the impacts of machine learning methods. Applications of computerized techniques were also investigated for the prediction and interpretation of DDIs. Results: Computerized data-mining in pharmaceutical sciences and related databases provide new key transformative paradigms that can revolutionize the treatment of diseases and hence medical care. Given that various aspects of drug discovery and pharmacotherapy are closely related to the clinical and molecular/biological information, the scientifically sound databases (e.g., DDIs, ADRs) can be of importance for the success of pharmacotherapy modalities. Conclusion: A better understanding of DDIs not only provides a robust means for designing more effective medicines but also grantees patient safety.
Camera Ego-Motion Estimation Using Phase Correlation under Planar Motion Constraint

An intelligent robotic living assistive system has become a popular research in the last decade. One of the important topics in that research area is 3D object reconstruction from multiple views. This process may depend on motion estimation using vision. However, often a domestic robot on an electric wheel chair has to move in a steep rotational angle that causes motion estimation from vision to become inaccurate. In addition, an oblique viewing angle creates a perspective distortion to the captured images, which further worsens the estimation result. Hence, in this paper, we propose a new approach by altering the motion estimation problem into a 2D image registration problem. Our method’s accuracy is very close to that of the Scale Invariant Feature Transform (SIFT) features tracker, whereas the Kanade-Lucas-Tomasi (KLT) tracker’s drops as soon as the rotational angle reaches about 40¿. Although our method is 2.7 times slower than the KLT tracker, it is 19 times faster than the SIFT tracker.
Amyloid Precursor Proteins Interact with the Heterotrimeric G Protein Go in the Control of Neuronal Migration

Amyloid precursor protein (APP) belongs to a family of evolutionarily conserved transmembrane glycoproteins that has been proposed to regulate multiple aspects of cell motility in the nervous system. Although APP is best known as the source of β-amyloid fragments (Aβ) that accumulate in Alzheimer's disease, perturbations affecting normal APP signaling events may also contribute to disease progression. Previous in vitro studies showed that interactions between APP and the heterotrimeric G protein Goα-regulated Goα activity and Go-dependent apoptotic responses, independent of Aβ. However, evidence for authentic APP–Go interactions within the healthy nervous system has been lacking. To address this issue, we have used a combination of in vitro and in vivo strategies to show that endogenously expressed APP family proteins colocalize with Goα in both insect and mammalian nervous systems, including human brain. Using biochemical, pharmacological, and Bimolecular Fluorescence Complementation assays, we have shown that insect APP (APPL) directly interacts with Goα in cell culture and at synaptic terminals within the insect brain, and that this interaction is regulated by Goα activity. We have also adapted a well characterized assay of neuronal migration in the hawkmoth Manduca to show that perturbations affecting APPL and Goα signaling induce the same unique pattern of ectopic, inappropriate growth and migration, analogous to defective migration patterns seen in mice lacking all APP family proteins. These results support the model that APP and its orthologs regulate conserved aspects of neuronal migration and outgrowth in the nervous system by functioning as unconventional Goα-coupled receptors.