Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent SMS Spam Filtering Using Topic Model

Nowadays, spam messages have been overflowing in many countries. They seriously violate personal rights, and may even harm the national security. The existing filtering techniques usually uses traditional text classifiers, which are more suitable to deal with normal long texts, therefore, it often faces some serious challenges, such as the sparse data problem and noise data in the SMS message. This research work proposes a message topic model (MTM) for SMS spam filtering. The MTM derives from the famous probability topic model. Although the MTM is based on probability topic model, it is different from the famous standard Latent Dirichlet Allocation (LDA) in the following aspects: (1) For the purpose of overcoming the sparsity problem in SMS message classification, first, the standard K-means algorithm is used to classify the training data into rough classes, then, aggregates all the spam messages of a class into a single document. (2) Symbol semantics is taken in account. Some preprocessing rules and background terms are considered to make the model more appropriate to fully represent SMS spam. Finally, we compare the MTM with the SVM and the standard LDA on the public SMS spam corpus. The experimental results show that the MTM is more effective for the task of SMS spam filtering.
Distance metrics and indexing strategies for a digital library of popular music

People identify powerfully with music: someone might say "that's my song! " but they are unlikely to say "that's my book! " or "that's my picture! " A digital library of popular music therefore has the potential to be a compelling application of information retrieval technology. Such a library requires a retrieval method that is appropriate for a non-technical audience. Experiments on "query by humming", which attempt to retrieve a tune based on sampled recording of a user singing an excerpt, have heretofore concentrated on relatively small, well-curated collections. Scaling up introduces three problems: availability of source material, an increase in false positive hits, and slower retrieval. We describe our experiments with MIDI files, propose a new, more accurate distance metric between queries and songs, and discuss possibilities for efficient indexing.
Transforming Growth Factor-b Inhibits Lipopolysaccharide-Stimulated Expression of Inflammatory Cytokines in Mouse Macrophages through Downregulation of Activation Protein 1 and CD 14 Receptor Expression

The septic shock that occurs in gram-negative infections is caused by a cascade of inflammatory cytokines. Several studies showed that transforming growth factor-b1 (TGF-b1) inhibits this septic shock through suppression of expression of the lipopolysaccharide (LPS)-induced inflammatory cytokines. In this study, we investigated whether TGF-b1 inhibition of LPS-induced expression of inflammatory cytokines in the septic shock results from downregulation of LPS-stimulated expression of CD14, an LPS receptor. TGF-b1 markedly inhibited LPS stimulation of CD14 mRNA and protein levels in mouse macrophages. LPS-stimulated expression of CD14 was dramatically inhibited by addition of antisense, but not sense, c-fos and c-jun oligonucleotides. Since TGF-b1 pretreatment inhibited LPS-stimulated expression of c-fos and c-jun genes and also the binding of nuclear proteins to the consensus sequence of the binding site for activation protein 1 (AP-1), a heterodimer of c-Fos and c-Jun, in the cells, TGF-b1 inhibition of CD14 expression may be a consequence of downregulation of AP-1. LPS-stimulated expression of interleukin-1b and tumor necrosis factor alpha genes in the cells was inhibited by addition of CD14 antisense oligonucleotide. Also, TGF-b1 inhibited the LPSstimulated production of both inflammatory cytokines by the macrophages. In addition, TGF-b1 inhibited expression of the two cytokines in several organs of mice receiving LPS. Thus, our results suggest that TGF-b1 inhibition of LPS-stimulated inflammatory responses resulted from downregulation of CD14 and also may be a possible mechanism of TGF-b1 inhibition of LPS-induced septic shock.
Liquidity , Financial Market Sentiment and Corporate Investment

This article investigates how stock liquidity affects corporate investment. We find that corporate investment is significantly and negatively related to stock liquidity after controlling for Q and various variables, both at aggregate and individual firm level. Such a negative relationship holds even after extensive robustness checks. The negative relationship is consistent with the “market sentiment hypothesis” that managers reduce investment after high liquidity periods because managers realize that the business conditions are not as rosy as they were thought to be. According to Baker and Stein (2002) – high liquidity can be an indicator of high investor sentiment, thus our results suggest that there exists a negative investment adjustment after high market sentiment. The evidence implies a direct link between financial market and corporate investment through sentiment channel. However, this channel is different from the one in the financial constraint literature because the latter would naturally predict a positive relationship between financial market valuation and corporate investment, especially when the pessimistic investor sentiment takes place. Our results indicate a negative relationship between financial market and corporate investment especially when the optimistic investor sentiment takes place. JEL classification: G31, E44
Optimization of energy consumption in rectangular ad-hoc wireless networks

Wireless ad hoc networks operation is constrained by energy consumption of every network nodes. To this end, this paper, based on Geographical Adaptive Fidelity (GAF) topology management protocol [1], analyzes the energy consumption in a rectangular ad-hoc wireless network. Three GAF models for energy efficiency are introduced and discussed: equal-grid, adjustable-grid and genetic algorithm models. In the equal and adjustable-grid models, the optimal transmission range corresponding to the minimum energy consumption has been derived, meanwhile the third model is based on on a genetic algorithm introduced to save energy consumption in rectangular ad hoc networks. Results based on numerical analysis show that the genetic-algorithm model outperforms the equal and adjustable-grid models in term of energy consumption. Finally, we simulate a network model in which nodes can transmit at different transmission ranges, where the GAF models including the genetic algorithm model show better saving in energy consumption than those GAF models based on static transmission range.
Single and joint default in a structural model with purely discontinuous assets

Structural models of credit risk are known to present both vanishing spreads at very short maturities and a poor spread fit over longer maturities. The former shortcoming, which is due to the diffusive behavior assumed for asset values, can be circumvented by considering discontinuous assets. In this paper we resort to a pure jump process of the Variance-Gamma type. First we calibrate the corresponding Merton type structural model to single-name data for the DJ CDX NA IG and CDX NA HY components. By so doing, we show that it circumvents also the diffusive structural models difficulties over longer horizons. In particular, it corrects for underprediction of low risk spreads and overprediction of high risk ones. Then we extend the model to joint default, resorting to a recent formulation of the VG multivariate model and without superimposing a copula choice. We fit default correlation for a sample of CDX NA names, using equity correlation. The main advantage of our joint model with respect to the existing non diffusive ones is that it allows calibration without the equicorrelation assumption, but still in a parsimonious way. As an example of the default assessments which the calibrated model can provide, we price a FtD swap.
Polynomial Evaluation in Secret Sharing Schemes

Among schemes for secret reconstruction, the Lagrange interpolation formula plays a crucial role in Shamir’s (k, n) threshold sharing scheme. Here, we investigate the associated polynomial evaluation over finite fields. In so doing, we point out that any misunderstanding of the use of finite fields, as done in some recent work, may lead to errors in the polynomial evaluation over finite fields, thereby resulting in failure for secret sharing.
Does globalization reduce poverty? Some empirical evidence for the developing countries

In this paper we address a key issue in the current debate on economic development: the effect of globalization on poverty. We review the empirical evidence on the relationship between globalization (broadly defined) and within-country poverty in the Developing Countries (Dcs). To measure globalization we use, among others, standard indices of trade openness, financial openness and privatization. To measure poverty we use both indices of relative and absolute poverty averaged over five and ten years. The use of relative poverty indices enables inquiry into a different dimension of poverty and provides additional information with respect to previous research. Both descriptive statistics and econometric analysis are used to sketch a few stylized facts in a very complex framework of relationships.
GSEA-SNP: applying gene set enrichment analysis to SNP data from genome-wide association studies

The power of genome-wide SNP association studies is limited, among others, by the large number of false positive test results. To provide a remedy, we combined SNP association analysis with the pathway-driven gene set enrichment analysis (GSEA), recently developed to facilitate handling of genome-wide gene expression data. The resulting GSEA-SNP method rests on the assumption that SNPs underlying a disease phenotype are enriched in genes constituting a signaling pathway or those with a common regulation. Besides improving power for association mapping, GSEA-SNP may facilitate the identification of disease-associated SNPs and pathways, as well as the understanding of the underlying biological mechanisms. GSEA-SNP may also help to identify markers with weak effects, undetectable in association studies without pathway consideration. The program is freely available and can be downloaded from our website.
Ribozyme Mediated gRNA Generation for In Vitro and In Vivo CRISPR/Cas9 Mutagenesis

CRISPR/Cas9 is now regularly used for targeted mutagenesis in a wide variety of systems. Here we report the use of ribozymes for the generation of gRNAs both in vitro and in zebrafish embryos. We show that incorporation of ribozymes increases the types of promoters and number of target sites available for mutagenesis without compromising mutagenesis efficiency. We have tested this by comparing the efficiency of mutagenesis of gRNA constructs with and without ribozymes and also generated a transgenic zebrafish expressing gRNA using a heat shock promoter (RNA polymerase II-dependent promoter) that was able to induce mutagenesis of its target. Our method provides a streamlined approach to test gRNA efficiency as well as increasing the versatility of conditional gene knock out in zebrafish.
Classification-based Approximate Policy Iteration: Experiments and Extended Discussions

Tackling large approximate dynamic programming or reinforcement learning problems requires methods that can exploit regularities, or intrinsic structure, of the problem in hand. Most current methods are geared towards exploiting the regularities of either the value function or the policy. We introduce a general classification-based approximate policy iteration (CAPI) framework, which encompasses a large class of algorithms that can exploit regularities of both the value function and the policy space, depending on what is advantageous. This framework has two main components: a generic value function estimator and a classifier that learns a policy based on the estimated value function. We establish theoretical guarantees for the sample complexity of CAPI-style algorithms, which allow the policy evaluation step to be performed by a wide variety of algorithms (including temporal-difference-style methods), and can handle nonparametric representations of policies. Our bounds on the estimation error of the performance loss are tighter than existing results. We also illustrate this approach empirically on several problems, including a large HIV control task.
Sensitivity analysis of programmed cell death and implications for crosstalk phenomena during Tumor Necrosis Factor stimulation

Different methods for analyzing the sensitivity of the direct signal transduction pathway of receptor-induced apoptosis to parameter changes are presented. Apoptosis is a form of programmed cell death, removing unwanted cells within multicellular organisms to maintain a proper balance between cell reproduction and death. The results indicate the importance of controlling activated caspases by direct inhibition to prevent apoptosis. A misregulation of IAP molecules, one of the main inhibitors, appears to be especially critical. The results indicate how an increased production of this molecule promotes survival and might promote cancer progression, while a reduced degradation might not, thereby providing insight of potential pharmaceutical relevance and also stimulating experimental verification. The different engineering methods applied, nicely complement each other to provide valuable insight into this important process. Because IAPs, among others, are also an important connection to other signaling pathways, the results will enable a more efficient extension of the current model. This is outlined at the example of Tumor Necrosis Factor induced signaling pathways.
Evolutionary Learning of the Crossover Operator 2.1 Genetic Algorithms

1 Abstract An architectural heuristic for evolving a good crossover operator for use with a Genetic Algorithm is presented. The crossover operator is used by the genetic algorithm to create new elements of the population , given two existing parent elements. This is a process of sexual reproduction intended to simulate the exchange of genetic material that occurs biologically. So, given two parents, two children are created using a bit-string whose length is same as the length of the parent chromosomes. A '1' in some position of the string determines the parent from whom the child inherits the particular gene. Currently, this "crossover description string" is generated using random functions. It is proposed to use a neural network to learn the description of the search space in which the genetic algorithm is operating, and thus create better oospring. It may be the case that the performance of an element varies monotonically with the i th attribute. In this case, the crossover operator shall create two individuals both having the value of the i th attribute, which is the largest of the i th attribute of the two parents. For some other attribute, some other function might be true. It is expected that the neural network will learn these rules. 2 Introduction Genetic algorithms are a means by which machines can emulate the mechanisms of natural selection. These are highly parallel in nature and work towards optimizing a population of solutions, over generations , by sampling the search space in which they operate. Parameter setting and the modelling of the problem are essential for the proper working of such algorithms. These key issues are dealt with in section 2.1. A system based on a modiied reinforcement learning achitecture is proposed, wherein a neu-ral network is used to learn the crossover operator through the process of learning. This is explained in section 2.2. The metaphor underlying genetic algorithms is that of natural selection in evolution. In evolution, the problem each species faces is one of searching for beneecial adaptations to a complicated and changing environment. The knowledge that each species has gained is embodied in the makeup of the chromosomes of its members. Genetic algorithms are a means by which machines can emulate the mechanisms of natural selection. This involves searching high-dimensional spaces for superior if not optimal solutions. They are parallel in nature, like neural networks. These algorithms operate by maintaining …
Optimal networked control of a 2 degree-of-freedom direct drive robot manipulator

This paper proposes a suboptimal control solution for the L2-gain disturbance rejection problem for networked control systems. The problem, usually referred as mixed H2/H∞, aims at designing a linear state feedback stabilizing controller minimizing a quadratic cost functional subject to a L2-gain disturbance rejection constraint. The formulation deals with time-varying delays and dropouts in both, sensor-to-controller and controller-to-actuator paths, with the only assumption of known minimum and maximum bounds of the round trip delay, and the maximum number of consecutive dropouts. The solution is based on a Lyapunov-Krasovskii approach and is formulated as a Nonlinear Matrix Inequality (NLMI) problem. This problem is cast into more a treatable LMI-based minimization problem, for which a well-known optimization algorithm is provided. Experimental results on a networked controlled direct-drive 2 degree-of-freedom (dof) robot manipulator are provided to verify the performance of the methodology.
Energy demand in the developing countries: Prospects for the future. World Bank Staff commodity working paper

The main motivation of the study was to highlight the key factors that have contributed to the historical increases in energy consumption in the developing countries as well as to project the changes likely to occur in the future. The scenarios for the future take into account the changing relationships between economic growth and energy consumption, structural changes, and the potential for fuel substitution. The study covers eight major developing countries representing about one-half of total energy consumption in the developing countries. Because of their diversity and size, the conclusions reached about these countries can reasonably be extrapolated to developing countries as a whole. The results of the study indicate that increased levels of motor vehicle use and urbanization are the main factors contributing to increases in energy consumption in the developing countries. These factors, as well as rising incomes and shifts away from the use of traditional fuels, will continue to support increases in commercial energy use over the long term.
Efficient Extraction of Multiple Kuratowski Subdivisions (TR)

A graph is planar if and only if it does not contain a Kuratowski subdivision. Hence such a subdivision can be used as a witness for non-planarity. Modern planarity testing algorithms allow to extract a single such witness in linear time. We present the first linear time algorithm which is able to extract multiple Kuratowski subdivisions at once. This is of particular interest for, e.g., Branch-and-Cut algorithms which require multiple such subdivisions to generate cut constraints. The algorithm is not only described theoretically, but we also present an experimental study of its implementation.
Decision Boundary Partitioning: Variable Resolution Model-Free Reinforcement Learning

Reinforcement learning agents attempt to learn and construct a decision policy which maximises some reward signal. In turn, this policy is directly derived from long-term value estimates of state-action pairs. In environments with real-valued state-spaces, however, it is impossible to enumerate the value of every state-action pair, necessitating the use of a function approximator in order to infer state-action values from similar states. Typically, function approximators require many parameters for which suitable values may be diicult to determine a-priori. Traditional systems of this kind are also then bound to the xed limits imposed by the initial parameters, beyond which no further improvements are possible. This paper introduces a new method to adaptively increase the resolution of a discretised action-value function based upon which regions of the state-space are most important for the purposes of choosing an action. The method is motivated by similar work by Moore and Atkeson but improves upon the existing techniques insofar as it: i) is applicable to a wider class of learning tasks, ii) does not require transition or reward models to be constructed and so can also be used with a variety of model-free reinforcement learning algorithms, iii) continues to improve upon policies even after a feasible solution to the learning problem has been found.
The NKG2D Ligands RAE-1δ and RAE-1ε Differ with Respect to Their Receptor Affinity, Expression Profiles and Transcriptional Regulation

Background RAE-1 is a ligand of the activating receptor NKG2D expressed by NK cells, NKT, γδT and some CD8+T lymphocytes. RAE-1 is overexpressed in tumor cell lines and its expression is induced after viral infection and genotoxic stress. We have recently demonstrated that RAE-1 is expressed in the adult subventricular zone (SVZ) from C57BL/6 mice. RAE-1 is also expressed in vitro by neural stem/progenitor cells (NSPCs) and plays a non-immune role in cell proliferation. The C57BL/6 mouse genome contains two rae-1 genes, rae-1δ and rae-1ε encoding two different proteins. The goals of this study are first to characterize the in vivo and in vitro expression of each gene and secondly to elucidate the mechanisms underlying their respective expression, which are far from known. Principal Findings We observed that Rae-1δ and Rae-1ε transcripts are differentially expressed according to tissues, pathological conditions and cell lines. Embryonic tissue and the adult SVZ mainly expressed Rae-1δ transcripts. The NSPCs derived from the SVZ also mainly expressed RAE-1δ. The interest of this result is especially related to the observation that RAE-1δ is a weak NKG2D ligand compared to RAE-1ε. On the contrary, cell lines expressed either similar levels of RAE-1δ and RAE-1ε proteins or only RAE-1ε. Since the protein expression correlated with the level of transcripts for each rae-1 gene, we postulated that transcriptional regulation is one of the main processes explaining the difference between RAE-1δ and RAE-1ε expression. We indeed identified two different promoter regions for each gene: one mainly involved in the control of rae-1δ gene expression and the other in the control of rae-1ε expression. Conclusions/Significance RAE-1δ and RAE-1ε differ with respect to their function and the control of their expression. Immune function would be mainly exerted by RAE-1ε and non-immune function by RAE-1δ.
Can Developmental Biology Inspire Amorphous Computing ?

Systems constructed from vast numbers of tiny processors / sensors, connected together in a nonprescribed way, form the basis of novel computing paradigms such as amorphous computing, ubiquitous computing and pervasive computing. Within these systems, the individual computational units may be fragile, unpredictable and only able to communicate locally. However, despite these individual restrictions, they still need to be able to self-organise to robustly and efficiently carry out a set of collective functions. By looking at the development of multicellular organisms, this paper examines how the challenges faced by these amorphous systems are similar to those faced by, and solved by, biological systems. We also examine the properties of developing systems that may be useful for amorphous computing; such as self-organisation, robustness and scalability.