Construction industries in developing countries face systemic challenges such as chronic project delays, cost overruns, and regulatory inefficiencies. This paper presents a system dynamics (SD) modeling framework for analyzing policy and resource dynamics within the construction sector in Sudan, with broader applicability to Least Developed Countries (LDCs). The model incorporates key variables related to workforce, material supply, financing, and policy delays, and is calibrated using genetic algorithms (GAs) based on sectoral data and expert input. Simulation results across four policy scenarios indicate that regulatory reform and workforce training are the most effective levers for improving project performance. Specifically, implementing streamlined regulatory procedures reduced project delays by up to 32%, while investment in human capital decreased cost overruns by 28% over a 10-year simulation horizon. In contrast, scenarios focusing solely on material supply or financial inputs produced limited gains without corresponding policy or labor improvements. Sensitivity analysis further revealed that the system is highly responsive to macroeconomic stability and public investment flows. The study demonstrates that a hybrid SD-GA modeling approach offers a valuable decision-support tool for policymakers seeking to improve infrastructure delivery under uncertainty. Recommendations include phased regulatory reforms, targeted capacity building, and integrating modeling tools into strategic infrastructure planning in LDCs.
Policy gradient methods rely on a baseline to measure the relative advantage of an action, ensuring the model reinforces behaviors that outperform its current average capability. In the training of Large Language Models (LLMs) using Actor-Critic methods (e.g., PPO), this baseline is typically estimated by a Value Model (Critic) often as large as the policy model itself. However, as the policy continuously evolves, the value model requires expensive, synchronous incremental training to accurately track the shifting capabilities of the policy. To avoid this overhead, Group Relative Policy Optimization (GRPO) eliminates the coupled value model by using the average reward of a group of rollouts as the baseline; yet, this approach necessitates extensive sampling to maintain estimation stability. In this paper, we propose $V_0$, a Generalist Value Model capable of estimating the expected performance of any model on unseen prompts without requiring parameter updates. We reframe value estimation by treating the policy's dynamic capability as an explicit context input; specifically, we leverage a history of instruction-performance pairs to dynamically profile the model, departing from the traditional paradigm that relies on parameter fitting to perceive capability shifts. Focusing on value estimation at State Zero (i.e., the initial prompt, hence $V_0$), our model serves as a critical resource scheduler. During GRPO training, $V_0$ predicts success rates prior to rollout, allowing for efficient sampling budget allocation; during deployment, it functions as a router, dispatching instructions to the most cost-effective and suitable model. Empirical results demonstrate that $V_0$ significantly outperforms heuristic budget allocation and achieves a Pareto-optimal trade-off between performance and cost in LLM routing tasks.
After the discovery of oil in the Gulf countries, the number of labor migrants in these countries, mainly from South and Southeast Asia, as well as Africa, increased sharply. This led to the strengthening of the role of English as the language of intercultural communication, including the fi of education: now a signifi number of higher education programs in these countries are being taught entirely in English. This is due to a number of reasons, including the signifi number of foreign (both skilled and unskilled) workers, as well as the desire of the region’s leaders to transform their countries into regional and international economic and educational hubs. This study examines the language policy of the Cooperation Council for the Arab States of the Gulf (GCC) countries in relation to English language teaching in secondary and higher education institutions. Existing studies, focusing mainly on the topic of globalization, pay little attention to the educational policies of the GCC countries in the fi of teaching English. Considering this fact, the purpose of this study is to identify the features of the state policy of the GCC countries in the fi of teaching English, as well as to identify the factors infl the formation of this policy. By analyzing the legislation and practical methods of implementing the language policy, the dynamics in the fi of teaching English in the countries of the region are shown. We have shown that the GCC countries are currently relying on Western models of education, including English-language higher education, which leads to a number of practical problems. In addition to this, the leaders of the countries of the region are attracting branches of leading Western educational institutions to the GCC countries. The prospects of this study include a comparative study of educational policies in other Arab countries, as well as further analysis of trends in English language teaching in the GCC region. This study contributes to the study of the language policy of the Arab countries and will be useful for Arabists and specialists in language policy.
Political institutions and public administration (General), Social sciences (General)
In offline reinforcement learning-based recommender systems (RLRS), learning effective state representations is crucial for capturing user preferences that directly impact long-term rewards. However, raw state representations often contain high-dimensional, noisy information and components that are not causally relevant to the reward. Additionally, missing transitions in offline data make it challenging to accurately identify features that are most relevant to user satisfaction. To address these challenges, we propose Policy-Guided Causal Representation (PGCR), a novel two-stage framework for causal feature selection and state representation learning in offline RLRS. In the first stage, we learn a causal feature selection policy that generates modified states by isolating and retaining only the causally relevant components (CRCs) while altering irrelevant components. This policy is guided by a reward function based on the Wasserstein distance, which measures the causal effect of state components on the reward and encourages the preservation of CRCs that directly influence user interests. In the second stage, we train an encoder to learn compact state representations by minimizing the mean squared error (MSE) loss between the latent representations of the original and modified states, ensuring that the representations focus on CRCs. We provide a theoretical analysis proving the identifiability of causal effects from interventions, validating the ability of PGCR to isolate critical state components for decision-making. Extensive experiments demonstrate that PGCR significantly improves recommendation performance, confirming its effectiveness for offline RL-based recommender systems.
Robotic insertion is a highly challenging task that requires exceptional precision in cluttered environments. Existing methods often have poor generalization capabilities. They typically function in restricted and structured environments, and frequently fail when the plug and socket are far apart, when the scene is densely cluttered, or when handling novel objects. They also rely on strong assumptions such as access to CAD models or a digital twin in simulation. To address these limitations, we propose EasyInsert. Inspired by human intuition, it formulates insertion as a delta-pose regression problem, which unlocks an efficient, highly scalable data collection pipeline with minimal human labor to train an end-to-end visual policy. During execution, the visual policy predicts the relative pose between plug and socket to drive a multi-phase, coarse-to-fine insertion process. EasyInsert demonstrates strong zero-shot generalization capability for unseen objects in cluttered environments, robustly handling cases with significant initial pose deviations. In real-world experiments, by leveraging just 1 hour of human teleoperation data to bootstrap a large-scale automated data collection process, EasyInsert achieves an over 90% success rate in zero-shot insertion for 13 out of 15 unseen novel objects, including challenging objects like Type-C cables, HDMI cables, and Ethernet cables. Furthermore, requiring only a single manual reset, EasyInsert allows for fast adaptation to novel test objects through automated data collection and fine-tuning, achieving an over 90% success rate across all 15 objects.
Training generalist policies for robotic manipulation has shown great promise, as they enable language-conditioned, multi-task behaviors across diverse scenarios. However, evaluating these policies remains difficult because real-world testing is expensive, time-consuming, and labor-intensive. It also requires frequent environment resets and carries safety risks when deploying unproven policies on physical robots. Manually creating and populating simulation environments with assets for robotic manipulation has not addressed these issues, primarily due to the significant engineering effort required and the substantial sim-to-real gap, both in terms of physics and rendering. In this paper, we explore the use of action-conditional video generation models as a scalable way to learn world models for policy evaluation. We demonstrate how to incorporate action conditioning into existing pre-trained video generation models. This allows leveraging internet-scale in-the-wild online videos during the pre-training stage and alleviates the need for a large dataset of paired video-action data, which is expensive to collect for robotic manipulation. Our paper examines the effect of dataset diversity, pre-trained weights, and common failure cases for the proposed evaluation pipeline. Our experiments demonstrate that across various metrics, including policy ranking and the correlation between actual policy values and predicted policy values, these models offer a promising approach for evaluating policies without requiring real-world interactions.
The article is dedicated to the analysis of distance learning problems faced by EU Member states during the COVID-19 pandemic. Each EU member state was forced to transform its education policy, trying not only to reduce the risks of infection during professional contacts between teachers and students, but also to ensure the maintenance of the necessary quality of the educational process. The ambiguous influence of distance learning on the level of academic achievement of students in various EU countries is shown. The factors that hindered distance learning, including low incomes, housing and other material deprivation, belonging of students and their families to socially vulnerable segments of the population, the level of education of parents of schoolchildren and students are identified. The negative consequences of restrictive measures implemented by the EU countries in the education sector are revealed. Attention is focused on the risk of reducing the level of competitiveness of future graduates of educational institutions in the labor market due to their lack of knowledge. The reaction of the national authorities of the EU countries to the situation in the education sector, expressed in the adoption of a set of urgent measures, is investigated, including additional classes with lagging students, the preparation and active use of teaching materials, the provision of free SIM cards for an unlimited period of time to students from low-income families, providing computers and Internet access to children in need. It is stated that the education sector in the EU countries has demonstrated the necessary adaptability to the situation, having found support from the authorities.
Homenageamos Carlos Walter Porto-Gonçalves, apresentando um quadro das contribuições de sua obra para a geografia e para ciências sociais. Apontamos chaves de leitura que indicam as linhas de força que constituem seu pensamento e apontam para um renovado horizonte teórico-metodológico, ético e político de leitura da geograficidade social: i) a Geografia como verbo: as geo-grafias desde os de baixo e das r-existências; ii) o conflito como chave de leitura da geograficidade do social: a tensão de territorialidades; iii) a ecologia política da questão ambiental e as lutas por reapropriação social da natureza; iv) a reinvenção dos territórios na América Latina/Abya Yala/Quilombola.
Palavras-chaves: Carlos Walter Porto-Gonçalves; Geo-grafias desde os debaixo; R-existências; ecologia política; a reinvenção dos territórios.
Palavras-chaves: Carlos Walter Porto-Gonçalves- Geo-grafias desde os debaixo- r-existências - ecologia política – a reinvenção dos territórios.
Special aspects of education, Labor market. Labor supply. Labor demand
El ciberacoso laboral ha surgido como un riesgo emergente en el entorno laboral moderno, mermando en este sentido la defensa y el ejercicio de los derechos de las personas trabajadoras en el seno de una empresa. Sin duda alguna, este estudio examina el régimen jurídico preventivo del ciberacoso en las leyes y convenios colectivos más recientes, otorgando gran importancia a las conductas de acoso digital, aunque estas se realicen fuera del horario y lugar de trabajo —a tenor de la Sala 4ª del Tribunal Supremo—. Además, refleja la perspectiva actual del Convenio 190 OIT, así como las normas y prácticas en las nuevas formas de trabajo, como el teletrabajo y la incidencia en el empleo doméstico. Finalmente, concluye con la importancia de las obligaciones empresariales y las posibles disfunciones entre la legislación laboral y la protección de libertad sexual, sin olvidar las recomendaciones que garanticen un entorno laboral seguro y saludable.
The article examines the peculiarities of the energy transformation of Ukraine in the modern period.
It is emphasized that the energy transformation of Ukraine is one of the main tasks on the way to sustain-
able development and integration into the European economic space. At the same time, Ukraine, a coun-
try rich in coal resources, is faced with the unique problem of balancing the transformation of coal regions,
maintaining the socio-economic needs of these regions, and restructuring the entire energy system in the
direction of RES in wartime, which is a unique experience for the whole world. It was noted that the basis
of the state policy regarding energy transformation should be the principles that will determine the trans-
formation vector. In addition to the general principles, namely: systematicity, adaptive flexibility, a combi-
nation of positive and negative methods of stimulation, making permanent and systemic decisions, trans-
parency, sustainable and sustainable development of cities and regions, the use of the best available tech-
nologies and practices, decarbonization, it is proposed to single out the principle of fair transformation and
the principle of polymorphism. A just transformation involves the coordination of economic, social, labor
and environmental policies, the implementation of measures aimed at ensuring social justice and well-be-
ing in the transition process. The principle of polymorphism is understood as a multifunctional vector of
transformation. Energy transformation should include not only the direct reorientation of the energy sys-
tem to new types of energy, integration with the energy systems of EU countries, decarbonization, optimi-
zation of the balancing of the entire energy system, directions for increasing energy efficiency, as well as
reforming legislation, changing the state’s approaches to management, control over energy use, formation
of a new system of state and communal bodies, etc. In the process of energy transformation, in order to
prevent and prevent the destruction or destruction of energy infrastructure during crisis events, increase
the possibility of restoring energy facilities, it is proposed to move away from the centralized energy sys-
tem and build municipal energy systems that will work independently of each other.
AbstractHow can we understand the biopsychosocial welfare state? What empirical indications are there for the increasing influence of medicine and psychology in advanced welfare states? This chapter answers these questions and additionally discusses how current crises have influenced—and will continue to influence—the medicalization and psychologization of social problems and social policies.
The minimum condition that is expected to the activities in health centers can be carried out properly, one of them is the health personnel that is fulfilled as stated at Regulation of Health Department No. 75 of 2014, but the number of health worker at the health center is not currently in accordance with the minimum standard of labor in Regulation of Health Department, so the service at the health center is less optimal and there are multiple tasks.
The purpose of the research was to analyze the state of competence of health workers and to analyze the need for competency development of health human resources through education and training at Pinangsori Health Center. This research is descriptive with a qualitative approach in determining the need for competency development for health workers in the health center. Methods of data collection used observation and in-depth interviews.
The need result analysis of the health worker shows that there are 2 people who have competence based on administrator health to Regulation of Health Department number 75 of 2014. The result of the need analysis of competency for development shows that most of health workers who have high school degree need higher education at least Diploma III. On the job training such as guidance and discussion is required by health workers. Apart from that, off the job training is also needed such as emergency training (PTC, ATLS or GELS and PONED) is also required by the doctor; PPGDON training, CTU, Hypnobirthing, BBLR management is required by the midwife; BTCLS training, Nursing Communication and Ethics is required by the nurse.
The recommendation of the research is to apply the needs to competency developing health through education and training in the other health centers and the availability of good information system of the workforce so that it can be a reference to take a policy in the planning of health manpower need and its development.
The emergence of new public policy priorities in the field of education changes the role of lifelong education in the system of socialization of the individual, as well as the nature of interaction between various subjects within the educational process and beyond. To increase competitiveness in the labor market, students of higher educational institutions need to be prepared to quickly master new knowledge and skills to meet the requirements of professional activity in the labor market. One of the forms of obtaining specific knowledge and skills for students in parallel with higher education is additional education. The purpose of the study is to analyze the pedagogical conditions for the formation of general professional competencies among bachelors of pedagogical education using additional education programs. Materials and methods of research. The research materials are the methodologically based on a scientific review of research approaches developed in domestic pedagogy in the field of improving the system of training generalist specialists in the development of additional education programs. The research methods are the study and analysis of scientific and methodological literary sources, as well as questioning of students. Research results. At the Chuvash State Pedagogical University named after I.Ya. Yakovlev Center for Continuing Education. To determine the possibility of additional professional education, a study was conducted among students of this university in order to identify their awareness of the possibilities of mastering additional education programs. The results of the survey, according to which many students plan to undergo additional education in standard programs, 20% plan to receive certificates or certificates in a whole range of programs. Conclusions. The system of additional education adapts future teachers to the specifics of modern socio-economic conditions, high demands from the labor market and, ultimately, forms a highly qualified specialist with a wide profile, capable of a continuous process of self-improvement in the process of professional activity.
Education (General), Theory and practice of education
Felipe Addor, Layssa Ramos Maia de Almeida, Bianca de Carvalho P. Campos
Partindo de uma visão crítica do atual modelo democrático hegemônico na América Latina, destaca-se a necessidade de se experimentar novas práticas democráticas que estejam vinculadas com a dinâmica territorial do cotidiano e que busquem contribuir para sistemas econômicos menos desiguais. Nesse sentido, fazemos uma análise da experiência venezuelana da “democracia participativa y protagónica”, dos seus limites e potencialidades, no sentido de inspirar outras experiências de aprofundamento da democracia na região.
Palavra-chave: Venezuela; Democracia Participativa; Conselhos Comunais; Território; Economia Comunal.
Special aspects of education, Labor market. Labor supply. Labor demand
There are various positive and negative variables affecting employee performance in both the private and public sectors. The aim of this study is to investigate the mediating role of presenteeism in the effect on of job insecurity on employee performance. The sample consists of employees in the retail sector operating in Isparta province. A quantitative research method was chosen, and data were collected using a survey technique. The collected survey data was subjected to analysis via the utilization of the SPSS v.26 and AMOS v.24 software packages. The research was categorized as correlational in nature. For the purposes of this investigation, a simple random sampling method was elected for sample selection. At the conclusion of the study, it has been identified that job insecurity affects both employee performance and presenteeism. Additionally, it was determined that job insecurity in conjunction with presenteeism influences employee performance. However, it was found that “completing work” does not serve a mediating role in the impact of job insecurity on employee performance. Instead, it was discovered that “avoiding distraction” plays a full mediating role. This implies that job insecurity may negatively affect employee performance, and this effect on operates through the mentioned mediating variable.
Industrial relations, Social insurance. Social security. Pension
One of the most important indicators of macroeconomic performance is the gross domestic product, the lack of proper economic policy aimed at its stabilization and growth, leads to periods of recession in business cycles with wider effects on economic performance, especially Economic growth, unemployment and inflation. Continuous business cycles will lead to an increase in uncertainty in the level of economic activities, which will have negative effects on investment, consumption, savings and economic performance. It is very important and necessary to know the effects of factors affecting business cycles from the aspect of correctly predicting these cycles and making policies in this field. In this study, the factors affecting the business cycles in Iran were investigated with the quantile regression approach for the period 1360-1400 and the results showed that periods of stagnation in the Iranian economy with the intensification and application of new sanctions and the withdrawal of the United States from the JCPOA (Comprehensive Program) joint action) and the emergence of the Corona pandemic in Iran, especially from 2018 to 2020, have become deeper and faster. And the results of applying the ARDL method show the negative effect of labor productivity variables, employment rate and foreign trade on business cycles and the positive effect of final consumption expenditures, oil revenues and sanctions on business cycles (leading to the aggravation of recession have become economic) has been And in general, the effects of these variables on business cycles have been symmetrical.Extended abstractIntroduction Gross domestic product (GDP) is one of the most important indicators of macroeconomic performance because it shows the size of a country's economy and its production capacity. The growth and stability of the level of economic activities is one of the main goals of economic policy makers. Business cycles, especially recessionary periods, have wide-ranging effects on economic performance, especially economic growth, unemployment, and inflation (Brodor et al., 2020). Business cycles are a kind of irregular fluctuations in the macroeconomic activities of countries, which are mainly created and organized based on the market economy and the activities of companies (Kanjoy et al., 2021). In other words, business cycles, which are also known as business cycles, refer to the fluctuations of the economy between periods of growth (boom) and recession (Chemingui and Eris, 2017). based on this, the period of prosperity begins almost simultaneously in most economic activities, followed by stagnation and contraction, which slows down and reduces the level of economic activity. after each period of stagnation, recovery occurs and the period of stagnation begins again. These changes are repeated many times, but they do not necessarily have a regular periodic state (Charonopoulos et al., 2021).The conventional literature of business cycles with a general approach are classified into six groups as follows: The first group, which includes economists before Keynes, and some of them consider the direction of fluctuations on the demand side and the other part on the supply side as the cause of the formation of business cycles. The second group was the Keynesians who considered the business cycle as a psychological theory because they saw its basis in economic analysis and forecasts on the optimistic or pessimistic behavior of the majority of people in the society and believed that The fragility and vulnerability of investment leads to the formation of business cycles. The third group of economists were from the Chicago school, who showed with the results of experimental tests that the rate of change in the volume of money with a long interval can form business cycles. The fourth group of new classics of the monetary branch, led by Robert Lucas, who believed that the origin of business cycles should be sought in unexpected and unforeseeable monetary policies. The fifth group of new classics in favor of true business cycles, who believe that what causes fluctuations and business cycles are tensions on the supply side, not on the demand side, and the roots of these tensions are derived from technology shocks that lead to a reduction in costs and Productivity and efficiency increase. The sixth group is the new Keynesians, who are divided into two main groups in the rooting of business cycles. The first group considers the origin of fluctuations (periods of prosperity and recession) in the stickiness of prices and wages and the second group believes that even if wages and prices are not sticky, some problems in the economy, including asymmetric information (in financial markets), can explain the roots of recession.Specification of the model In studies where the data is non-normal or not distributed, the use of traditional statistical methods such as mean and standard deviation may provide incorrect results. therefore, despite outlier data, using the quantile method can lead to more accurate results. Also, the quantile method is less sensitive to outliers due to the use of a percentage of the distribution. in many cases, the investigated data are deviated and with a high coverage of values in different ranges. In such a situation, using the quantile method can lead to a more accurate and reliable analysis of the data. because this regression has the possibility to calculate several quantiles for the regression values and calculate the corresponding confidence intervals for the results of each quantile. This advantage allows users of this method to more accurately interpret the results. In general, using the quantile method in the analysis of non-normal and non-distributed data can lead to more accurate results and avoid the problems that exist in traditional statistical methods. The quantile regression form used in this study is the following equation: In the above relation, Conditional quantile is the variable of business cycles calculated by Hodrick-Prescott filter method and It contains the desired information at time t. The variables related to the above equation are defined below and extracted from the Central Bank of Iran website.CYCLE: Business cycles (calculated by Hodrick-Prescott filter method.FORM: Formation of gross fixed capital as a percentage of GDP (percentage).EMP: Employment rate (percentage)PRO: Labor productivity (production per unit of labor)GOV: Final consumption expenditure of the government as a percentage of GDP (percentage)TR: Total import and export divided by GDP (percentage)OIL: Oil revenuesSUN: Sanction indexIn the context of the sanctions index, in this study, the data of the sanctions index used in the study of Iranmanesh et al. (2021) have been adapted. Fuzzy logic method has been used to analyze the data and construct the index of economic sanctions in Iran for the period from 1979 to 2020. In this study, Hodrick and Prescott (1997) filter approach was used to calculate business cycles based on the following equation: In this function and potential production and actual production and T is the observation value which was 42 years in this research. The parameter λ is the weighting factor that determines the smoothness of the process. λ=1600 is used for seasonal data and λ=100 is used for annual data.FindingsAccording to the findings of this study, the hypothesis of non-existence of collinearity among the variables of the model has been rejected. To estimate the long-term relationship between the variables of the model, the modeling approach of Sons and Shin (1999) and the unbounded error correction model (UECM) were used. And the results of the long-term relationship show that the impact of labor productivity on business cycles was negative and significant at the level of 10% error in other words, labor productivity has reduced business cycles in Iran. Also, for the variables of foreign trade, capital formation and employment rate, negative and similar effects have been obtained, that is, these variables have also reduced business cycles in the studied period. On the other hand, government final consumption expenditures and oil revenues have also had a positive and significant impact on business cycles. In other words, with the increase in government final consumption expenditures and government oil revenues, business cycles have increased in Iran. Sanction index has also had a positive and significant impact on business cycles. Economic sanctions by creating restrictions in the fields of finance, trade, financial transfer, oil sales, foreign currency inflow from exports and many other negative effects, lead to increase in fluctuations and as a result of business cycles. The results of the quantile regression show that the sign of the estimated coefficients in the quantile regression is the same as the long-term relationship in the ARDL method. But the size of the coefficients has been different in different quantiles. Based on the estimated results in the upper quantiles of business cycles, the impact of labor productivity on business cycles has decreased in total. For the foreign trade variable, with different results, it shows that in the upper quantiles of business cycles, the impact of foreign trade on business cycles has increased as a whole and the effect of the formation of gross domestic fixed capital in the upper quantiles of business cycles compared to the lower quantiles of business cycles has decreased in total.Results Iran's economy has always been in the condition of inflation stagnation in different periods. In the past decades, Iran's economy has faced problems such as high inflation, economic stagnation, international sanctions, and a drop in oil prices due to internal and external reasons. During these years, various monetary and financial policies have been implemented to reduce inflation and economic prosperity, but each of these policies has not been successful to a large extent for some reasons. In sum, the improvement and control of business cycles in the conditions of inflationary stagnation requires the use of appropriate monetary policies, improvement of the financial system, support of the labor market, reduction of dependence on exports, and increase of investment in infrastructure. Also, creating the right conditions to promote entrepreneurship and encourage investment can also help control business cycles. Finally, achieving these goals requires cooperation between the government, private sector, society and the cen
In reinforcement learning, off-policy evaluation (OPE) is the problem of estimating the expected return of an evaluation policy given a fixed dataset that was collected by running one or more different policies. One of the more empirically successful algorithms for OPE has been the fitted q-evaluation (FQE) algorithm that uses temporal difference updates to learn an action-value function, which is then used to estimate the expected return of the evaluation policy. Typically, the original fixed dataset is fed directly into FQE to learn the action-value function of the evaluation policy. Instead, in this paper, we seek to enhance the data-efficiency of FQE by first transforming the fixed dataset using a learned encoder, and then feeding the transformed dataset into FQE. To learn such an encoder, we introduce an OPE-tailored state-action behavioral similarity metric, and use this metric and the fixed dataset to learn an encoder that models this metric. Theoretically, we show that this metric allows us to bound the error in the resulting OPE estimate. Empirically, we show that other state-action similarity metrics lead to representations that cannot represent the action-value function of the evaluation policy, and that our state-action representation method boosts the data-efficiency of FQE and lowers OPE error relative to other OPE-based representation learning methods on challenging OPE tasks. We also empirically show that the learned representations significantly mitigate divergence of FQE under varying distribution shifts. Our code is available here: https://github.com/Badger-RL/ROPE.
In this work, we study policy poisoning through state manipulation, also known as sensor spoofing, and focus specifically on the case of an agent forming a control policy through batch learning in a linear-quadratic (LQ) system. In this scenario, an attacker aims to trick the learner into implementing a targeted malicious policy by manipulating the batch data before the agent begins its learning process. An attack model is crafted to carry out the poisoning strategically, with the goal of modifying the batch data as little as possible to avoid detection by the learner. We establish an optimization framework to guide the design of such policy poisoning attacks. The presence of bi-linear constraints in the optimization problem requires the design of a computationally efficient algorithm to obtain a solution. Therefore, we develop an iterative scheme based on the Alternating Direction Method of Multipliers (ADMM) which is able to return solutions that are approximately optimal. Several case studies are used to demonstrate the effectiveness of the algorithm in carrying out the sensor-based attack on the batch-learning agent in LQ control systems.
This study was created to reveal the effects of the Covid-19 pandemic on migration mobility and migration policies. In this study, which was created in a theoretical context, migration movements were tried to be explained in three stages: pre-epidemic, epidemic process, and post-epidemic context. The social isolation measures implemented on a global scale at the beginning of the pandemic showed that the actors reduced their social practices from the public sphere to the private sphere in the coronavirus process, which exhibited a social change. The pandemic process was left out of the international agenda of migration movements, and the problems experienced by immigrants were ignored. It is thought that the epidemic, which is a state of social change, has the characteristics of a global pandemic and that after the epidemic, anti-immigrant sentiment has increased, and migration mobility will change Illusions in the social structure that immigrant individuals will increase the epidemic trigger anti-immigration.
Industrial relations, Social insurance. Social security. Pension
El presente trabajo analiza la resolución que con carácter de jurisprudencia emitió en México la Suprema Corte de Justicia de la Nación (SCJN) en la contradicción de tesis 200/2020, la cual responde a un criterio económico-político porque cuida las finanzas, en este caso, del ISSSTE en perjuicio de los asegurados y respalda la reforma del 16 de diciembre de 2020 a las pensiones de la Ley del Seguro Social, que legalizó la UMA y, de esta forma, se apartó de la tendencia de las resoluciones y jurisprudencia adoptadas desde 2018 por diversos Tribunales Colegiados de Circuito del Poder Judicial de la Federación en materia laboral y administrativa en favor de los pensionados, respecto de la aplicación del salario mínimo general en el cálculo de las pensiones de jubilación.