Hasil untuk "General works"

M. Blum, Michael Luby, R. Rubinfeld

Ashish, Gupta, Inderpal et al.

S. Aji, R. McEliece

A. Haghiri-Gosnet, J. Renard

K. Crammer, Y. Singer

E. Burke, G. Kendall, J. Newall et al.

A. Aldroubi, K. Gröchenig

Y. Ikeda, B. Grabowski, F. Körmann

Abstract Multicomponent alloys with multiple principal elements including high entropy alloys (HEAs) and compositionally complex alloys (CCAs) are attracting rapidly growing attention. The endless possibilities to explore new alloys and the hope for better combinations of materials properties have stimulated a remarkable number of research works in the last years. Most of these works have been based on experimental approaches, but ab initio calculations have emerged as a powerful approach that complements experiment and serves as a predictive tool for the identification and characterization of promising alloys. The theoretical ab initio modeling of phase stabilities and mechanical properties of multi-principal element alloys by means of density functional theory (DFT) is reviewed. A general thermodynamic framework is laid down that provides a bridge between the quantities accessible with DFT and the targeted thermodynamic and mechanical properties. It is shown how chemical disorder and various finite-temperature excitations can be modeled with DFT. Different concepts to study crystal and alloy phase stabilities, the impact of lattice distortions (a core effect of HEAs), magnetic transitions, and chemical short-range order are discussed along with specific examples. Strategies to study elastic properties, stacking fault energies, and their dependence on, e.g., temperature or alloy composition are illustrated. Finally, we provide an extensive compilation of multi-principal element alloys and various material properties studied with DFT so far (a set of over 500 alloy-property combinations).

A. Jiménez‐Valverde, J. Lobo, Joaquín Hortal

Deven Santosh Shah, H. A. Schwartz, Dirk Hovy

An increasing number of natural language processing papers address the effect of bias on predictions, introducing mitigation techniques at different parts of the standard NLP pipeline (data and models). However, these works have been conducted individually, without a unifying framework to organize efforts within the field. This situation leads to repetitive approaches, and focuses overly on bias symptoms/effects, rather than on their origins, which could limit the development of effective countermeasures. In this paper, we propose a unifying predictive bias framework for NLP. We summarize the NLP literature and suggest general mathematical definitions of predictive bias. We differentiate two consequences of bias: outcome disparities and error disparities, as well as four potential origins of biases: label bias, selection bias, model overamplification, and semantic bias. Our framework serves as an overview of predictive bias in NLP, integrating existing work into a single structure, and providing a conceptual baseline for improved frameworks.

Thong Hoang, Hong Jin Kang, J. Lawall et al.

Existing work on software patches often use features specific to a single task. These works often rely on manually identified features, and human effort is required to identify these features for each task. In this work, we propose CC2Vec, a neural network model that learns a representation of code changes guided by their accompanying log messages, which represent the semantic intent of the code changes. CC2Vec models the hierarchical structure of a code change with the help of the attention mechanism and uses multiple comparison functions to identify the differences between the removed and added code. To evaluate if CC2Vec can produce a distributed representation of code changes that is general and useful for multiple tasks on software patches, we use the vectors produced by CC2Vec for three tasks: log message generation, bug fixing patch identification, and just-in-time defect prediction. In all tasks, the models using CC2Vec outperform the state-of-the-art techniques.

Shuai Wang, Lihong Cui

For a (molecular) graph $G$ and any real number $α\ne 0$ , the zero-order general Randić index , denote by $^0R_α$, is defined by the following equation: \begin{align*} {^0R_α} (G) =\sum_{v\in G}d_G (v) ^α (α\in \mathbb{R}-\left\{0\right\}) . \end{align*} In this paper, we use this index to give sufficient conditions for a graph $G$ to satisfy the Hamiltonian (or $k$-Hamiltonian) property, and show that none of these conditions can be dropped. Finally we give similar results for the case when $G$ is a balanced bipartite graph.

A. G. Baydin, R. Cornish, David Martínez-Rubio et al.

We introduce a general method for improving the convergence rate of gradient-based optimizers that is easy to implement and works well in practice. We demonstrate the effectiveness of the method in a range of optimization problems by applying it to stochastic gradient descent, stochastic gradient descent with Nesterov momentum, and Adam, showing that it significantly reduces the need for the manual tuning of the initial learning rate for these commonly used algorithms. Our method works by dynamically updating the learning rate during optimization using the gradient with respect to the learning rate of the update rule itself. Computing this "hypergradient" needs little additional computation, requires only one extra copy of the original gradient to be stored in memory, and relies upon nothing more than what is provided by reverse-mode automatic differentiation.

Benjamin Monmege, Julie Parreaux, Pierre-Alain Reynier

Weighted Timed Games (WTG for short) are the most widely used model to describe controller synthesis problems involving real-time issues. Unfortunately, they are notoriously difficult, and undecidable in general. As a consequence, one-clock WTGs have attracted a lot of attention, especially because they are known to be decidable when only non-negative weights are allowed. However, when arbitrary weights are considered, despite several recent works, their decidability status was still unknown. In this paper, we solve this problem positively and show that the value function can be computed in exponential time (if weights are encoded in unary).

Sergio Sánchez Castiñeira

Para Foucault, la realidad social es un efecto del discurso científico, que tiende a vincularse con los intereses políticos y hegemónicos. Muestra el carácter históricamente contingente de las verdades establecidas y realza las voces de quienes han excluidos por los discursos dominantes. Propone separar la verdad de la ciencia y defiende la diversificación de las fuentes y tipos de discursos. En cambio, Bourdieu cree en una ciencia social que establezca sus propios criterios internos de funcionamiento sin la influencia de intereses políticos y económicos. De esta forma, la ciencia puede desenmascarar los mecanismos a través de los cuales los grupos dominantes presentan su interés particularista como el interés general. Bourdieu defiende un universalismo real dónde los intereses que se proclamen como universales deban ser realmente acordes con los valores de la virtud cívica (igualdad, fraternidad, honestidad, altruismo o desinterés). Por otro lado, ambos autores comparten que la alienación está en la base de la opresión y, por ello, abogan por construcciones cognitivas alternativas que desnaturalicen las actuales clasificaciones sociales.

Seungwook Han, Jyothish Pari, Samuel J. Gershman et al.

Large Language Models (LLMs) have demonstrated impressive real-world utility, exemplifying artificial useful intelligence (AUI). However, their ability to reason adaptively and robustly -- the hallmarks of artificial general intelligence (AGI) -- remains fragile. While LLMs seemingly succeed in commonsense reasoning, programming, and mathematics, they struggle to generalize algorithmic understanding across novel contexts. Our experiments with algorithmic tasks in esoteric programming languages reveal that LLM's reasoning overfits to the training data and is limited in its transferability. We hypothesize that the core issue underlying such limited transferability is the coupling of reasoning and knowledge in LLMs. To transition from AUI to AGI, we propose disentangling knowledge and reasoning through three key directions: (1) pretaining to reason using RL from scratch as an alternative to the widely used next-token prediction pretraining, (2) using a curriculum of synthetic tasks to ease the learning of a reasoning prior for RL that can then be transferred to natural language tasks, and (3) learning more generalizable reasoning functions using a small context window to reduce exploiting spurious correlations between tokens. Such a reasoning system coupled with a trained retrieval system and a large external memory bank as a knowledge store can overcome several limitations of existing architectures at learning to reason in novel scenarios.

Xiaobo Hao, Pengcheng Liu, Yanhui Deng

As the internet data centers are mushrooming, the energy consumption and carbon emission of data centers are increasing rapidly. To cut down the electricity cost and carbon emission of the data centers, we proposed an optimization method to reduce the electricity cost and carbon emission in geo-distributed multiple data centers. In the proposed method, the carbon tax is considered in the overall operation cost to optimize the carbon emission. The spatial and temporal flexibility of computational workload is fully utilized by considering the difference in renewable energy power output, local electricity and carbon emission of multiple geo-distributed data centers to achieve a better performance. Furthermore, the nonlinear characteristics of the power loss of uninterruptible power supply (UPS) are considered in the optimization. To verify the proposed optimization method, simulation of six cases is carried out with realistic data, and results have proved the proposed method can reduce the operational costs by 4.93%–12.7% and decrease carbon emissions by up to 10%.

Autillia C. Phiri, T. Velmurugan

Accessing clean and pure water is a crisis for women and families all over the world. Without the proper resources to receive water can lead to the fatality of women and their families. This paper explores water scarcity and household coping mechanisms to water scarcity with special reference to women households in Chidothe village. It identifies the sources of water in Chidothe village, understands the challenges faced by women in fetching water and explores household coping mechanisms to water scarcity with the purpose of raising awareness to the community’s situation. Although the water supply system was expanded in 2001, many areas including Chidothe village are still experiencing water problems. In the past years’ researchers and policy makers have focused on improving the performance of water utility infrastructure in order to eliminate this threat. However, little efforts have been made to understand social issues to water shortage and how people respond to them. Data gathering methods were individual interviews and focus group discussions. All interviews were audio recorded. The data was processed manually and analyzed thematically. The results were analyzed through insights and arguments from Feminist Political Ecology (FPE). The study reveals that women and girls in Chidothe Village have a greater responsibility to fetch water, are facing challenges to access portable water such as lack of money to connect to tap water, the absence of water kiosks in the village further worsens the problem and circumstances force them to draw water from unsafe sources, hence, exposing themselves to diseases. The results imply that there is an urgent need to address water supply systems in order to prevent people from water borne diseases. The study concludes that there is need to incorporate women in decision making to articulate their concerns and interests at local level and also water aid stakeholders should use gender sensitive approaches when planning, designing and implementing water projects.

Miroslav Mateev, Tarek Nasr, Kiran Nair

Abstract In this paper, we investigate how market concentration and efficiency impact banks’ performance and stability during the SAR-COV-2 pandemic. There is a research gap in the empirical literature in understanding the specific impact of market concentration and efficiency on the profitability and risk-taking behavior of banks, particularly in the developing context of the Middle East and North Africa (MENA) region. To address this gap, we examine the relationship between market concentration, efficiency, and bank performance using a comprehensive dataset encompassing 575 banks across 20 MENA countries from 2006 to 2021. Specifically, we examine the market dynamics and performance of different banking systems co-existing in the MENA region. Our findings indicate that prior to the pandemic, both conventional and Islamic banks benefited from enhanced financial stability as a result of the increased market concentration and efficiency. However, during the pandemic, the positive effects of efficiency and concentration were primarily observed within the group of Islamic banks. Furthermore, we provide new evidence for the moderating effect of market concentration, with a significant negative impact suggesting that increased market competition reinforces the efficiency effect during the pandemic. Our findings are important for policymakers and regulatory authorities in the MENA region as they indicate the need for new policies that assign a more significant role to Islamic banking in the post-SAR-COV-2 recovery.

Hamida Beg , Muhammad Ferdowsieh, Fatemeh Hosseini

رغم وضوح أهمية صحة الإنسان، إلا أننا نجد مسألة إضراربنفس قد انتشرت بشكل واسع، والتي يعتبرها البعض جائزة بناء على حكم الملكية وكون الإنسان له الحق في ذاته. السؤال الأساسي الذي يطرحه المقال في مجال إضراربنفس هو الحكم الديني على مختلف المستويات الذي استنتجه فقهاء الإمامية بالأدلة العقلية والنقلية. قد توصل هذا البحث الذي كتب بطريقة توصيفية تحليلية إلى النتائج التالية: إضراربنفس درجات تشمل الانتحار، والبتر، وفقدان إحدى الحواس، والتعرض للمرض، والضرر البسيط، والإهانة. يحرم القتل بالآيات والأحاديث وعقلا. إلا إذا كان للدفاع عن النفس أو الجهاد وبإذن الولي. كما تحرم الشريعة البتر, لأنه يعرض الإنسان للهلاك ما لم يكن لذلك سبب منطقي وطبي. أما المستويات الأخرى فهناك اختلاف بين الفقهاء عليها.