A Political Process Explanation of Algerian AAV’s Fiasco in the Legislative Election of 2012

“Islamist vote” has been an area of interest in the academia for quite a long time and has attracted particular attention of scholars following the Arab Spring, as Islamist parties witnessed an increase of popularity at the ballot boxes in 2011-2012 in North African countries such as Egypt, Tunisia and Morocco. Whereas much of the recent academic literature on Islamist parties is oriented around the cases of Egypt, Tunisia and Morocco, limited attention has been paid to a puzzle that deserves profound analysis: given the many similarities between Algeria and its neighbors in the 2010s, why did Algerian Islamist parties failed to enlarge its supporting base as their counterparts in other North African countries did? This research attempts to examine the phenomenon of “Algerian exceptionalism” from a political process perspective by analyzing the political opportunities, organizational structure and framing of AAV, and how these factors affected the alliance’s mobilization. The approach presented in this article is not limited to the case of Algerian AAV but can be leveraged to study Islamist parties and Islamic activism in general. Keywords: Islamist parties; Mobilization; Algeria

Learning to Stabilize High-dimensional Unknown Systems Using Lyapunov-guided Exploration

Designing stabilizing controllers is a fundamental challenge in autonomous systems, particularly for high-dimensional, nonlinear systems that cannot be accurately modeled using differential equations. Lyapunov theory offers a robust solution for stabilizing control systems. Still, current methods relying on Lyapunov functions require access to complete dynamics or samples of system executions throughout the entire state space. Consequently, they are impractical for high-dimensional systems. In this paper, we introduce a novel framework, LYGE, for learning stabilizing controllers specifically tailored to high-dimensional, unknown systems. LYGE employs Lyapunov theory to iteratively guide the search for samples during exploration while simultaneously learning the local system dynamics, control policy, and Lyapunov functions. We demonstrate its scalability on highly complex systems, including a high-fidelity F-16 jet model from the Air Force featuring a 16D state space and a 4D input space. Experimental results indicate that, compared to prior works in reinforcement learning, imitation learning, and neural certificates, LYGE reduces the distance to the goal by 50% while requiring only 5% to 32% of the samples. Furthermore, we demonstrate that our algorithm can be extended to learn controllers guided by alternative certificate functions for unknown systems.Comment: 32 pages, 7 figure

Islamist Party Mobilization: Tunisia’s Ennahda and Algeria’s HMS Compared, 1989-2014

The study aims to explore how Islamist parties mobilize citizens in electoral authoritarian systems. Specifically, I analyze how Islamist parties develop identity, outreach, structure, and linkages to wide sections of the population, so that when the political opportunity presents itself, people are informed of their existence, goals, and representatives, and hence, primed to vote for them. The study adopts and expands the Political Process Theory, and adapts it to address North Africa, a region in which such theoretical scholarship has until now not been conducted. Indepth case studies focus on two Islamist parties in North Africa — Tunisia’s Ennahda and Algeria’s HMS, which both adopted the Muslim Brotherhood model, had charismatic leaders, and were both active on the political scene from 1989-2014, the period between their first electoral trial and their electoral participation after taking part in governance. On the supply side, this study’s main focus, are the key dimensions concerning the mobilizers — Islamist parties: their capacity to accumulate resources, and their approaches to ensure Islamic discourse is part of the political process. I analyze how these two elements interact with each other at those times when political opportunity is made available by those in government, whether through division among elites or in attempts to play party blocs off each other. The demand side looks at the parties’ political appeal to citizen sensibilities through various Islamist agendas. By presenting Islamic discourse as a means to gather large numbers to their cause, the parties seek to show themselves capable of incorporating popular views into their agendas, and thus to give voters alternatives when the opportunity presents itself for them to vote against the government. I contend that in North African political contexts characterized by electoral authoritarian or resilient authoritarian systems, demand is largely guided by cautious party calculation of benefits and costs. Benefits represent a rejection of the regime and the potential to change the status quo (through economic improvement, citizen engagement); costs involve the potential loss of citizen rights and of social stability. The cases of Ennahda and Hamas/Harakat Mujtami’a al-Silm (HMS) reveal that in responding to the constraints of electoral authoritarianism, which include controlled inclusion and informal tolerance as well as outright repression, the parties demonstrated efficiency in supplying Islamic debate, political locations of Islamic activism, and an alternative discourse to that of the regime. However, in doing so, they often failed to meet specific voter demands or expectations, or even to garner a protest vote. The study suggests that mobilizing Islamic politics served to engage the population and make the political realm culturally credible. Yet, the costs were very high, affecting the parties’ organizational strength, credibility, and capacity to create cooperative alliances, and even their ability to retain political control over the use of Islamic discourse

Neural Graph Control Barrier Functions Guided Distributed Collision-avoidance Multi-agent Control

We consider the problem of designing distributed collision-avoidance multi-agent control in large-scale environments with potentially moving obstacles, where a large number of agents are required to maintain safety using only local information and reach their goals. This paper addresses the problem of collision avoidance, scalability, and generalizability by introducing graph control barrier functions (GCBFs) for distributed control. The newly introduced GCBF is based on the well-established CBF theory for safety guarantees but utilizes a graph structure for scalable and generalizable decentralized control. We use graph neural networks to learn both neural a GCBF certificate and distributed control. We also extend the framework from handling state-based models to directly taking point clouds from LiDAR for more practical robotics settings. We demonstrated the efficacy of GCBF in a variety of numerical experiments, where the number, density, and traveling distance of agents, as well as the number of unseen and uncontrolled obstacles increase. Empirical results show that GCBF outperforms leading methods such as MAPPO and multi-agent distributed CBF (MDCBF). Trained with only 16 agents, GCBF can achieve up to 3 times improvement of success rate (agents reach goals and never encountered in any collisions) on <500 agents, and still maintain more than 50% success rates for >1000 agents when other methods completely fail.Comment: 20 pages, 10 figures; Accepted by 7th Conference on Robot Learning (CoRL 2023

Is there a link between X-efficiencies and the performance of listed banks in China?

This paper explores the determinants of performance in Chinese banking sector, particularly the effect of cost X-efficiency, for forty-one listed banks over the period 2009-2014. The Stochastic Frontier Approach (SFA) is employed to estimate the X-efficiency while the Generalized Method of Moments (GMM) is used to assess determinants of bank performance in China. The results show the cost X-efficiency has downward trend with the average value of 0.513, which can be affected positively by higher cost of borrowed funds, more gross loans, more off balance sheet items, and more other earning assets. It is significantly and positively related to the bank profitability. Apart from this, higher profitability is also associated with the higher capital adequacy and lower credit risk inside the bank, and higher GDP growth rate, higher unemployment rate and lower inflation rate in the macroeconomic environment

Scalable Multi-Robot Collaboration with Large Language Models: Centralized or Decentralized Systems?

A flurry of recent work has demonstrated that pre-trained large language models (LLMs) can be effective task planners for a variety of single-robot tasks. The planning performance of LLMs is significantly improved via prompting techniques, such as in-context learning or re-prompting with state feedback, placing new importance on the token budget for the context window. An under-explored but natural next direction is to investigate LLMs as multi-robot task planners. However, long-horizon, heterogeneous multi-robot planning introduces new challenges of coordination while also pushing up against the limits of context window length. It is therefore critical to find token-efficient LLM planning frameworks that are also able to reason about the complexities of multi-robot coordination. In this work, we compare the task success rate and token efficiency of four multi-agent communication frameworks (centralized, decentralized, and two hybrid) as applied to four coordination-dependent multi-agent 2D task scenarios for increasing numbers of agents. We find that a hybrid framework achieves better task success rates across all four tasks and scales better to more agents. We further demonstrate the hybrid frameworks in 3D simulations where the vision-to-text problem and dynamical errors are considered. See our project website https://yongchao98.github.io/MIT-REALM-Multi-Robot/ for prompts, videos, and code.Comment: 6 pages, 8 figure

Evaluation of genetic susceptibility of common variants in CACNA1D with schizophrenia in Han Chinese

The heritability of schizophrenia (SCZ) has been estimated to be as high as 80%, suggesting that genetic factors may play an important role in the etiology of SCZ. Cav1.2 encoded by CACNA1C and Cav1.3 encoded by CACNA1D are dominant calcium channel-forming subunits of L-type Voltage-dependent Ca(2+) channels, expressed in many types of neurons. The CACNA1C has been consistently found to be a risk gene for SCZ, but it is unknown for CACNA1D. To investigate the association of CACNA1D with SCZ, we designed a two-stage case-control study, including a testing set with 1117 cases and 1815 controls and a validation set with 1430 cases and 4295 controls in Han Chinese. A total of selected 97 tag single nucleotide polymorphisms (SNPs) in CACNA1D were genotyped, and single-SNP association, imputation analysis and gender-specific association analyses were performed in the two independent datasets. None was found to associate with SCZ. Further genotype and haplotype association analyses indicated a similar pattern in the two-stage study. Our findings suggested CACNA1D might not be a risk gene for SCZ in Han Chinese population, which add to the current state of knowledge regarding the susceptibility of CACNA1D to SCZ