How Hard is Bribery in Elections with Randomly Selected Voters

Many research works in computational social choice assume a fixed set of voters in an election and study the resistance of different voting rules against electoral manipulation. In recent years, however, a new technique known as random sample voting has been adopted in many multi-agent systems. One of the most prominent examples is blockchain. Many proof-of-stake based blockchain systems like Algorand will randomly select a subset of participants of the system to form a committee, and only the committee members will be involved in the decision of some important system parameters. This can be viewed as running an election where the voter committee (i.e., the voters whose votes will be counted) is randomly selected. It is generally expected that the introduction of such randomness should make the election more resistant to electoral manipulation, despite the lack of theoretical analysis. In this paper, we present a systematic study on the resistance of an election with a randomly selected voter committee against bribery. Since the committee is randomly generated, by bribing any fixed subset of voters, the designated candidate may or may not win. Consequently, we consider the problem of finding a feasible solution that maximizes the winning probability of the designated candidate. We show that for most voting rules, this problem becomes extremely difficult for the briber as even finding any non-trivial solution with non-zero objective value becomes NP-hard. However, for plurality and veto, there exists a polynomial time approximation scheme that computes a near-optimal solution efficiently. The algorithm builds upon a novel integer programming formulation together with techniques from n-fold integer programming, which may be of a separate interest

Research on Flexible Interconnection of Urban Power Grid

[Introduction] Due to its flexibility and rapid control ability, voltage source converter based high voltage direct current (VSC-HVDC) technology can be used in asynchronous grid interconnection, renewable energy grid-connection, and urban grid power supply. In this paper, the flexible and compact interconnection scheme of urban power grid is proposed to realize the interconnection and improve safety and stability of the urban power grid. [Method] According to the current situation of the power grid, and considering the difficulty of implementing new transmission lines in urban areas and the difficulty of controlling the construction period, the site selection and interconnection scheme were carried out from the perspective of exploiting potential of existing substations and transmission lines. The interconnection scale was determined comprehensively by combining the system requirements, the transmission capacity of the original lines, and the feasibility of capacity increase transformation. Due to the shortage of urban land, compact equipment and indoor compact layout was recommended. [Result] The flexible and compact back-to-back converter station is adopted to realize the interconnection, significantly reduce the short-circuit current level of the system and improve the security and stability of the urban power grid. The compact design which can save about 40% of the space is adopted to meet the need to alleviate the scarcity of urban land resources. [Conclusion] The compact and flexible interconnection proposed plays a good role in guiding the application of VSC-HVDC technology in urban power grid interconnection and has high reference value

SubpathwayMiner: a software package for flexible identification of pathways

With the development of high-throughput experimental techniques such as microarray, mass spectrometry and large-scale mutagenesis, there is an increasing need to automatically annotate gene sets and identify the involved pathways. Although many pathway analysis tools are developed, new tools are still needed to meet the requirements for flexible or advanced analysis purpose. Here, we developed an R-based software package (SubpathwayMiner) for flexible pathway identification. SubpathwayMiner facilitates subpathway identification of metabolic pathways by using pathway structure information. Additionally, SubpathwayMiner also provides more flexibility in annotating gene sets and identifying the involved pathways (entire pathways and sub-pathways): (i) SubpathwayMiner is able to provide the most up to- date pathway analysis results for users; (ii) SubpathwayMiner supports multiple species (~100 eukaryotes, 714 bacteria and 52 Archaea) and different gene identifiers (Entrez Gene IDs, NCBI-gi IDs, UniProt IDs, PDB IDs, etc.) in the KEGG GENE database; (iii) the system is quite efficient in cooperating with other R-based tools in biology. SubpathwayMiner is freely available at http://cran.r-project.org/web/packages/SubpathwayMiner/

A Comparison of Co-methylation Relationships Between Rheumatoid Arthritis and Parkinson's Disease

Rheumatoid arthritis (RA) is a complex autoimmune disease. Recent studies have identified the DNA methylation loci associated with RA and found that DNA methylation was a potential mediator of genetic risk. Parkinson's disease (PD) is a common neurodegenerative disease. Several studies have indicated that DNA methylation levels are linked to PD, and genes related to the immune system are significantly enriched in PD-related methylation modules. Although recent studies have provided profound insights into the DNA methylation of both RA and PD, no shared co-methylation relationships have been identified to date. Therefore, we sought to identify shared co-methylation relationships linked to RA and PD. Here, we calculated the Pearson's correlation coefficient (PCC) of 225,239,700 gene pairs and determined the differences and similarities between the two diseases. The global co-methylation change between in PD cases and controls was larger than that between RA cases and controls. We found 337 gene pairs with large changes that were shared between RA and PD. This co-methylation relationship study represents a new area of study for both RA and PD and provides new ideas for further study of the shared biological mechanisms of RA and PD

Local Differential Privacy Meets Computational Social Choice -- Resilience under Voter Deletion

The resilience of a voting system has been a central topic in computational social choice. Many voting rules, like plurality, are shown to be vulnerable as the attacker can target specific voters to manipulate the result. What if a local differential privacy (LDP) mechanism is adopted such that the true preference of a voter is never revealed in pre-election polls? In this case, the attacker can only infer stochastic information about a voter's true preference, and this may cause the manipulation of the electoral result significantly harder. The goal of this paper is to provide a quantitative study on the effect of adopting LDP mechanisms on a voting system. We introduce the metric PoLDP (power of LDP) that quantitatively measures the difference between the attacker's manipulation cost under LDP mechanisms and that without LDP mechanisms. The larger PoLDP is, the more robustness LDP mechanisms can add to a voting system. We give a full characterization of PoLDP for the voting system with plurality rule and provide general guidance towards the application of LDP mechanisms

Hardness and Algorithms for Electoral Manipulation Under Media Influence

In this paper, we study a generalization of the classic bribery problem known as electoral manipulation under media influence (EMMI). This model is motivated by modern political campaigns where candidates try to convince voters through advertising in media (TV, newspaper, Internet). When compared with the classical bribery problem, the attacker in this setting cannot directly change opinions of individual voters, but instead can execute influences via a set of manipulation strategies (e.g., advertising on a TV channel). Different manipulation strategies incur different costs and influence different subsets of voters. Once receiving a significant amount of influence, a voter will change opinion. To characterize the opinion change of each voter, we adopt the well-accepted threshold model. We prove the NP-hardness of the EMMI problem and give a dynamic programming algorithm that runs in polynomial time for a restricted case of the EMMI problem

Application of natural language understanding in Chinese power dispatching centre

It is difficult for computer to understand the texts in unstructured Chinese language, which becomes an obstacle for further application of artificial intelligence in the power dispatch center. Understanding of the orders from human dispatchers is the premise for the collaboration of machine and human being in power system operation. Towards understanding of dispatching texts, this paper proposes a textual semantic analysis framework with active learning of the semantic structure knowledge. Firstly, the words are vectorized by the Skip-gram models. And the hierarchical clustering algorithm is designed to detect the sentence patterns. Then the knowledge base is set up by converting the sentence structure to their regular expressions. In application, define a proprietary semantic framework to extract important device information and to parse the semantic slot using dependency syntax. Application shows that the Chinese texts describing the operation mode switching process can be understood accurately by the computer program

In-depth mining of single-cell transcriptome reveals the key immune-regulated loops in age-related macular degeneration

IntroductionAge-related macular degeneration (AMD), an ever-increasing ocular disease, has become one of the leading causes of irreversible blindness. Recent advances in single-cell genomics are improving our understanding of the molecular mechanisms of AMD. However, the pathophysiology of this multifactorial disease is complicated and still an ongoing challenge. To better understand disease pathogenesis and identify effective targets, we conducted an in-depth analysis of the single-cell transcriptome of AMD.MethodsThe cell expression specificity of the gene (CESG) was selected as an index to identify the novel cell markers. A computational framework was designed to explore the cell-specific TF regulatory loops, containing the interaction of gene pattern signatures, transcription factors regulons, and differentially expressed genes.ResultsThree potential novel cell markers were DNASE1L3 for endothelial cells, ABCB5 for melanocytes, and SLC39A12 for RPE cells detected. We observed a notable change in the cell abundance and crosstalk of fibroblasts cells, melanocytes, schwann cells, and T/NK cells between AMD and controls, representing a complex cellular ecosystem in disease status. Finally, we identified six cell type related and three disease-associated ternary loops and elaborated on the robust association between key immune-pathway and AMD.DiscussionIn conclusion, this study facilitates the optimization of screening for AMD-related receptor ligand pathways and proposes to further improve the interpretability of disease associations from single-cell data. It illuminated that immune-related regulation paths could be used as potential diagnostic markers for AMD, and in the future, also as therapeutic targets, providing insights into AMD diagnosis and potential interventions

Identification of tumor immune infiltration-associated lncRNAs for improving prognosis and immunotherapy response of patients with non-small cell lung cancer

BackgroundIncreasing evidence has demonstrated the functional relevance of long non-coding RNAs (lncRNAs) to immunity regulation and the tumor microenvironment in non-small cell lung cancer (NSCLC). However, tumor immune infiltration-associated lncRNAs and their value in improving clinical outcomes and immunotherapy remain largely unexplored.MethodsWe developed a computational approach to identify an lncRNA signature (TILSig) as an indicator of immune cell infiltration in patients with NSCLC through integrative analysis for lncRNA, immune and clinical profiles of 115 immune cell lines, 187 NSCLC cell lines and 1533 patients with NSCLC. Then the influence of the TILSig on the prognosis and immunotherapy in NSCLC was comprehensively investigated.ResultsComputational immune and lncRNA profiling analysis identified an lncRNA signature (TILSig) consisting of seven lncRNAs associated with tumor immune infiltration. The TILSig significantly stratified patients into the immune-cold group and immune-hot group in both training and validation cohorts. These immune-hot patients exhibit significantly improved survival outcome and greater immune cell infiltration compared with immune-cold patients. Multivariate analysis revealed that the TILSig is an independent predictive factor after adjusting for other clinical factors. Further analysis accounting for TILSig and immune checkpoint gene revealed that the TILSig has a discriminatory power in patients with similar expression levels of immune checkpoint genes and significantly prolonged survival was observed for patients with low TILSig and low immune checkpoint gene expression implying a better response to immune checkpoint inhibitor (ICI) immunotherapy.ConclusionsOur finding demonstrated the importance and value of lncRNAs in evaluating the immune infiltrate of the tumor and highlighted the potential of lncRNA coupled with specific immune checkpoint factors as predictive biomarkers of ICI response to enable a more precise selection of patients

LncRNA Structural Characteristics in Epigenetic Regulation

The rapid development of new generation sequencing technology has deepened the understanding of genomes and functional products. RNA-sequencing studies in mammals show that approximately 85% of the DNA sequences have RNA products, for which the length greater than 200 nucleotides (nt) is called long non-coding RNAs (lncRNA). LncRNAs now have been shown to play important epigenetic regulatory roles in key molecular processes, such as gene expression, genetic imprinting, histone modification, chromatin dynamics, and other activities by forming specific structures and interacting with all kinds of molecules. This paper mainly discusses the correlation between the structure and function of lncRNAs with the recent progress in epigenetic regulation, which is important to the understanding of the mechanism of lncRNAs in physiological and pathological processes