Data Mining-Based Decomposition for Solving the MAXSAT Problem: Toward a New Approach

This article explores advances in the data mining arena to solve the fundamental MAXSAT problem. In the proposed approach, the MAXSAT instance is first decomposed and clustered by using data mining decomposition techniques, then every cluster resulting from the decomposition is separately solved to construct a partial solution. All partial solutions are merged into a global one, while managing possible conflicting variables due to separate resolutions. The proposed approach has been numerically evaluated on DIMACS instances and some hard Uniform-Random-3-SAT instances, and compared to state-of-the-art decomposition based algorithms. The results show that the proposed approach considerably improves the success rate, with a competitive computation time that's very close to that of the compared solutions

Security and Stability of the Gulf Region in Light of Iranian Threats

The study aimed to examine the strategies of the Arab Gulf states to address the Iranian threat, and also seeks to identify the extent to which the United States adheres to international law in its policy towards the treatment of weapons of mass destruction in the Gulf region, and also seeks to clarify the risks and potential Iranian threats now and in the future of the Arab Gulf states, which will instill concern and fear among the Arab Gulf states in what will result under the Iranian agreement and the countries (5+1), and a regional neighbor with ambitions in the region in general, and use in the historical and analytical approach, and the use of the methodology, historical and analytical, The study concluded that the continuation of the State of Iran and its unlimited pursuit of nuclear technology, prompts the countries of the region to follow in the footsteps of the Iranians, permeably under the pretext of peaceful civilian purpose, and at the same time we find that an unstable region such as the Arab region and the fear of the danger expected from Iran may be the motive and the real reason behind the pursuit of nuclear technology by others in the region. Keywords: The Arabian Gulf region, Iranian threats. DOI: 10.7176/IAGS/85-03 Publication date:August 31st 202

Realism in Donald Trump's Middle East Policy

This study aims at understanding the realism theory in Trump's Middle East policy which is manifested in his election program for the presidency, his main promises of his election program and his implementation of these promises on the ground. The study uncovered the psychological factors that affected Trump's foreign policy toward the Middle East, and his economic mentality.  One important constituent of Trump's policy is power. He used power to enhance the position of the United States of America in the world and to support its national interests through proposing the principle “America First”. Moreover, Trump emphasizes on the promotion of USA’s cultural values by promoting democratic values and human rights, and activating political participation through transparent democratic elections in all countries of the world. Even though Tramp focuses on these values in his speeches, they are considered secondary because he believes in America's interests. The study found out that Trump's strategy is based on the notion of power. Trump believes that the protection of other countries like the Gulf States is not free, but they have to pay money. He also insisted on restoring the position of the United States internationally by necessary force, especially in the Middle East. Based on these results, the study recommended that the use of excessive force would lead to a decline in USA’s policy and reputation internationally. Keywords: Middle East ; , Realism “Pay for protection” principle DOI: 10.7176/IAGS/85-02 Publication date:August 31st 202

Generalized Hypertree Decomposition for solving non binary CSP with compressed table constraints

International audienc

Synaptic Adhesion Molecules Regulate the Integration of New Granule Neurons in the Postnatal Mouse Hippocampus and their Impact on Spatial Memory.

Postnatal hippocampal neurogenesis induces network remodeling and may participate to mechanisms of learning. In turn, the maturation and survival of newborn neurons is regulated by their activity. Here, we tested the effect of a cell-autonomous overexpression of synaptic adhesion molecules on the maturation and survival of neurons born postnatally and on hippocampal-dependent memory performances. Families of adhesion molecules are known to induce pre- and post-synaptic assembly. Using viral targeting, we overexpressed three different synaptic adhesion molecules, SynCAM1, Neuroligin-1B and Neuroligin-2A in newborn neurons in the dentate gyrus of 7- to 9-week-old mice. We found that SynCAM1 increased the morphological maturation of dendritic spines and mossy fiber terminals while Neuroligin-1B increased spine density. In contrast, Neuroligin-2A increased both spine density and size as well as GABAergic innervation and resulted in a drastic increase of neuronal survival. Surprisingly, despite increased neurogenesis, mice overexpressing Neuroligin-2A in new neurons showed decreased memory performances in a Morris water maze task. These results indicate that the cell-autonomous overexpression of synaptic adhesion molecules can enhance different aspects of synapse formation on new neurons and increase their survival. Furthermore, they suggest that the mechanisms by which new neurons integrate in the postnatal hippocampus conditions their functional implication in learning and memory

Neuroinflammatory TNFα Impairs Memory via Astrocyte Signaling.

The occurrence of cognitive disturbances upon CNS inflammation or infection has been correlated with increased levels of the cytokine tumor necrosis factor-α (TNFα). To date, however, no specific mechanism via which this cytokine could alter cognitive circuits has been demonstrated. Here, we show that local increase of TNFα in the hippocampal dentate gyrus activates astrocyte TNF receptor type 1 (TNFR1), which in turn triggers an astrocyte-neuron signaling cascade that results in persistent functional modification of hippocampal excitatory synapses. Astrocytic TNFR1 signaling is necessary for the hippocampal synaptic alteration and contextual learning-memory impairment observed in experimental autoimmune encephalitis (EAE), an animal model of multiple sclerosis (MS). This process may contribute to the pathogenesis of cognitive disturbances in MS, as well as in other CNS conditions accompanied by inflammatory states or infections

CDX2 expression in the hematopoietic lineage promotes leukemogenesis via TGFβ inhibition

The intestine-specific caudal-related homeobox gene-2 (CDX2) homeobox gene, while being a tumor suppressor in the gut, is ectopically expressed in a large proportion of acute leukemia and is associated with poor prognosis. Here, we report that turning on human CDX2 expression in the hematopoietic lineage of mice induces acute monoblastic leukemia, characterized by the decrease in erythroid and lymphoid cells at the benefit of immature monocytic and granulocytic cells. One of the highly stimulated genes in leukemic bone marrow cells was BMP and activin membrane-bound inhibitor (Bambi), an inhibitor of transforming growth factor-β (TGF-β) signaling. The CDX2 protein was shown to bind to and activate the transcription of the human BAMBI promoter. Moreover, in a leukemic cell line established from CDX2-expressing mice, reducing the levels of CDX2 or Bambi stimulated the TGF-β-dependent expression of Cd11b, a marker of monocyte maturation. Taken together, this work demonstrates the strong oncogenic potential of the homeobox gene CDX2 in the hematopoietic lineage, in contrast with its physiological tumor suppressor activity exerted in the gut. It also reveals, through BAMBI and TGF-β signaling, the involvement of CDX2 in the perturbation of the interactions between leukemia cells and their microenvironment

When the decomposition meets the constraint satisfaction problem

This paper explores the joint use of decomposition methods and parallel computing for solving constraint satisfaction problems and introduces a framework called Parallel Decomposition for Constraint Satisfaction Problems (PD-CSP). The main idea is that the set of constraints are first clustered using a decomposition algorithm in which highly correlated constraints are grouped together. Next, parallel search of variables is performed on the produced clusters in a way that is friendly for parallel computing. In particular, for the first step, we propose the adaptation of two well-known clustering algorithms ( k -means and DBSCAN). For the second step, we develop a GPU-based approach to efficiently explore the clusters. The results from the extensive experimental evaluation show that the PD-CSP provides competitive results in terms of accuracy and runtime