Analogous Research Articles

Closing the Gap between Electrical and Physical Design Steps with an Analog IC Placement Optimizer Enhanced with Machine-Learning-Based Post-Layout Performance Regressors

The design of integrated circuits in the analog spectrum is intricate due to the signals’ continuous nature. Additionally, it is strongly affected by the physical implementation of their devices and interconnections on the layout, a design task that has stubbornly defied all automation attempts. In this paper, one limitative factor is identified that must be addressed to finally push automation tools into the analog integrated circuit design flow: accurate assessment of post-layout performance degradation. For this purpose, a performance-driven placement generator highly integrated with off-the-shelf tools already adopted by circuit/layout designers, i.e., circuit simulator, verification tools (layout-versus-schematic) and layout extractor, is proposed. Toward maximum post-layout accuracy, this generator promotes an exhaustive simulation-based synthesis, extracting, simulating and verifying the post-layout functional behavior of every candidate floorplan. Additionally, to bypass the time-consuming extractions/simulations and accelerate synthesis, novel post-layout performance regressors based on different highly accurate machine learning techniques are also being developed. The data used to train them can be directly and conveniently acquired from previous precise post-placement simulations. Experimental results over two analog circuit structures show that a set of performance regressors based on tree-based models, while operating on compressed design spaces, allow for the speeding up of synthesis by more than 20×, which represents a step toward an efficient fully automatic performance-driven analog integrated circuit design flow.

A special contribution from spatial ability to math word problem solving: Evidence from structural equation modelling and network analysis

There is a growing body of research into the factors contributing to math word problem solving. However, these studies usually use limited number of potential predictors (precluding assessing of their contribution in comparison with other factors or “g” general intelligence) and some predictors (such as analogical and hypothetical reasoning) are largely omitted. Thus, the aim of the current study was to explore contributions of different types of reasoning to math word problem solving and whether these contributions have added value compared with each other and general cognitive ability. Chinese schoolchildren in Grades 3 (N = 199; Mage = 102.4 months), 4 (N = 162; Mage = 114.6), 5 (N = 174; Mage = 126.1) and 6 (N = 180; Mage = 138.6) completed 8 tasks tapping into spatial, mechanical, verbal, mathematic, hypothetical and analogical reasoning. Our data showed that when 6 general cognitive factors load onto General cognitive ability factor in a Structural Equation Model (SEM), only spatial visualization has additional contribution to Word problem solving factor. Gaussian Graphical models (GGMs) showed that 2 verbal tasks and spatial visualization showed stable (present in at least 3 out of 4 grades) contributions to both word problem solving tasks. Analogical reasoning showed contribution to Process of word problem solving only. To sum up, both SEM and GGMs converged on the importance of spatial ability for mathematical word problems solving. Our results call for verbal and spatial ability to be routinely assessed and targeted by educational interventions within maths curriculum.

Shortcuts to award winning research: analogies from ‘shortcuts to innovation: the use of analogies in knowledge production’

ABSTRACT In this interview, we explore the innovative research that earned Soomi Kim the Best Paper Award at DRUID 2024. As a partner of DRUID, Industry and Innovation presents an exclusive view into the research journey behind Kim’s celebrated work. Her study ‘Shortcuts to Innovation: The Use of Analogies in Knowledge Production’ examines how innovators venture into uncharted territories, using analogies to transfer knowledge from related domains. Using structural biology as the empirical setting, her findings highlight both the advantages and limitations of employing analogy-based technologies, such as machine learning, to speed up discovery. In this conversation, Kim reveals the inspiration, challenges, and breakthroughs that defined her research, providing a unique perspective on the paths leading to her pioneering work. Just as her paper examines how analogical reasoning and its technological automation serve as shortcuts in knowledge production, this conversation offers a unique lens on the pathways that shape groundbreaking research.

Pedagogical moves related to analogy that support a unified understanding of eigentheory concepts in a quantum mechanics class

It is beneficial for quantum mechanics students to have a unified understanding of eigentheory concepts, so they can recognize the shared structure of mathematized phenomena from the different quantum mechanical systems of spin, energy, or position and recognize those as instantiations of the same overarching concept. Quantum mechanics instructors should, therefore, provide opportunities for their class community to develop a shared unified understanding of eigentheory concepts. One such opportunity can arise by engaging students in analogizing eigentheory concepts in one context with those from another context. We investigate the pedagogical moves related to analogies that can be used by a quantum mechanics course instructor to support a class community in developing a shared unified understanding of eigenequations. We analyze classroom data to characterize an instructor’s pedagogical moves as he engaged students in analogical reasoning. Some moves include posing tasks conducive to analogizing; preparing, soliciting, and scaffolding students’ participation in analogical reasoning; using deictic gestures and inscriptions; juxtaposing symbols representing the analogized concepts; and explicitly highlighting the sameness of the analogized concepts. We exemplify these pedagogical moves with analytical descriptions of illustrative class episodes. We discuss how these pedagogical moves can support the class community’s expansion of their common ground by fostering the development of the class’s shared unified understanding of eigentheory concepts. Published by the American Physical Society 2024

Distributional hypothesis as isomorphism between word-word co-occurrence and analogical parallelograms.

Most of the modern natural language processing (NLP) techniques are based on the vector space models of language, in which each word is represented by a vector in a high dimensional space. One of the earliest successes was demonstrated by the four-term analogical reasoning task: what is to C as B is to A? The trained word vectors form "parallelograms" representing the quadruple of words in analogy. This discovery in NLP offers us insight into our understanding of human semantic representation of words via analogical reasoning. Despite successful applications of the large-scale language models, it has not been fully understood why such parallelograms emerge by learning through natural language data. As the vector space model is not optimized to form parallelograms, the key structure related to geometric shapes of word vectors is expected to be in the data, rather than the models. In the present article, we test our hypothesis that such parallelogram arrangement of word vectors readily exists in the co-occurrence statistics of language. Our approach focuses more on the data itself, and it is different from the existing theoretical approach trying to find the mechanism of parallelogram formation in the algorithms and/or vector arithmetic operations on word vectors. First, our analysis suggested that analogical reasoning is possible by decomposition of the bigram co-occurrence matrix. Second, we demonstrated the formation of a parallelepiped, a more structured geometric object than a parallelogram, by creating a small artificial corpus and its word vectors. With these results, we propose a refined form of distributional hypothesis pointing out an isomorphism between a sort of symmetry or exchangeability and word co-occurrence statistics.

Strategy Theory Using Analogy: Rationale, Tools and Examples

We discuss why analogical reasoning is widely used in strategy formulation, implementation, and evaluation despite its assessment by many logicians as a weak form of logical argumentation. Given this popularity, we consider how analogies might be used to assist in strategic decision making to develop better strategy arguments, including those consistent with the theory-based view of strategy. We develop an extended analogy between the online platforms Tripadvisor and Glassdoor to illustrate our main points. We also describe in steps how to build and evaluate strategy analogies and we suggest why working with analogies may be helpful to those practicing the theory-based view of strategy.

Deep Analogical Generative Design and Evaluation: Integration of Stable Diffusion and LoRA

Abstract The rapid evolution of generative design through artificial intelligence has opened new avenues for innovative product styling. Integrating this efficient generative technology with established professional theories presents a novel challenge in contemporary international design research. In response to this challenge, this paper introduces a pioneering and collaborative approach for the swift generation of automobile styling designs. The primary objective is to investigate an intelligent generation method that incorporates analogical reasoning and Stable Diffusion to support industrial designers in innovating product styling. This study scrutinizes traditional analogical reasoning design alongside the intelligent analogical reasoning design proposed herein, elucidating the distinctions through multidimensional comparisons using illustrative examples. The proposed methodological framework encompasses several key steps. Initially, a dataset comprising branded automobile images is meticulously constructed. Subsequently, an exclusive style model is trained leveraging Stable Diffusion techniques, coupled with advanced computer graphics and machine learning methodologies. Following this, design requirements are inputted, facilitating intelligent analogical reasoning design across multiple spatial dimensions to yield diverse and innovative automobile styling solutions. Finally, eye-tracking experiments are conducted to quantitatively compare the traditional analogical reasoning design approach with the Stable Diffusion-based analogical reasoning design method. The results substantiate that the latter effectively generates innovative and diversified automobile design solutions. This research contributes to enhancing the quality of automobile styling design, optimizing the design efficiency of enterprises, and catalyzing innovation in the automobile styling design process.

Digital Warfare: Exploring Cyber Criminality Amidst Armed Conflict in Southwest Region of Cameroon

This research investigates the alarming rise of cyber criminality in the Southwest region of Cameroon, a trend significantly influenced by the ongoing armed conflict that has engulfed the area. Employing a doctrinal research methodology, the study conducts a rigorous content analysis of both primary sources such as case law, treaties, legislative texts, direct observations, and policy documents and secondary sources, including scholarly textbooks, journal articles, comprehensive reports, and various online materials. The analysis utilizes diverse legal reasoning methods, including deductive, inductive, and analogical reasoning. Findings reveal a troubling surge in cyber-criminal activities since the onset of the armed conflict in English-speaking Cameroon, driven by interrelated factors such as rampant unemployment, pervasive economic hardship, widespread illiteracy, and the displacement of individuals. In light of these findings, it is crucial for the Cameroonian government to devise and implement effective strategies that not only combat cyber-crime but also address the socio-economic challenges exacerbated by the conflict. Such comprehensive measures are vital for restoring stability and security in the Southwest region and for fostering a resilient society capable of overcoming the dual impacts of cybercrime and armed conflict.

Perspectives On The Use Of Qiyas Between Imam Bukhari And Imam Syafie

The issue of applying fiqh (Islamic jurisprudence) in today's society highlights the need for a method that can harmonize the adaptation of Quranic and Hadith texts with changing times. Qiyas (analogical reasoning) is considered effective, especially in addressing new and complex issues where clear guidance from the Quran and Sunnah is lacking. Without Qiyas, Islamic law would struggle to keep up with evolving customs and modern dynamics. Since Qiyas is one of the key method today, alternative approaches are needed to assist in forming ijtihad (independent reasoning) to resolve new legal issues. This study aims to explain the differences and similarities in the use of Qiyas in ijtihad by Imam Bukhari and Imam Syafi'e through a qualitative analysis of relevant books and articles. The findings reveal that Imam Bukhari's use of Qiyas was limited to Qiyas Jali (clear analogy), while Imam Syafi'e also favored Qiyas Jali and rejected Qiyas Khafiy (hidden analogy), aligning with Imam Bukhari's approach despite differences in their Usul Fiqh methods. This study provides a new perspective differences opinion on Qiyas, offering insights into its use in ijtihad and opening alternative opportunities from Imam Bukhari's perspective, which can be considered alongside Imam Syafi'e's approach in modern discussions of Islamic law.

Analogical reasoning during hypothesis generation: the effects of object and domain similarities on access and transfer

In two experiments on analogical hypothesis generation, we factorially manipulated the presence of domain and object similarities between a base situation and a target phenomenon, and assessed their effects on the transfer of the source’s explanatory structure before and after an indication to use the base analog as a source for analogical explanations. The absence of any kind of surface similarity led to very low rates of spontaneous transfer. In both experiments, however, either kind of surface similarity sufficed to enhance the spontaneous transfer of the base explanation during the formulation of plausible hypotheses for the target. The transfer advantage of object and domain similarity cannot be attributed to the effect of these variables on post-access processes, since experimental conditions did not differ with regard to the ability to transfer the base explanation upon explicit request. The effect of domain similarity on spontaneous analogical explanation constitutes a relevant finding, especially given the lack of attention received by this dimension of similarity in behavioural studies and computer simulations of analogical retrieval.

“I’ll just try to mimic that”: an exploration of students’ analogical structure creation in abstract algebra

Despite the prominence of analogies in mathematics, little attention has been given to exploring students’ processes of analogical reasoning, and even less research exists on revealing how students might be empowered to independently and productively reason by analogy to establish new (to them) mathematics. I argue that the lack of a cohesive framework for interpreting students’ approaches to analogical reasoning in mathematics contributes to this issue. To address this, I introduce the Analogical Reasoning in Mathematics (ARM) framework. Constructed from an analysis of interviews with four abstract algebra students, ARM identifies several analogical activities that serve to analyze students’ analogical reasoning with a finer grain size than was previously possible with existing frameworks. Using this framework, I present an analysis of the students’ constructions of a ring-theoretic analogy to subgroup, thus revealing that even constructing simple analogies can elicit diverse pathways of analogical reasoning across students. Implications for further research related to analogies and analogical reasoning in mathematics education are discussed.

Analogical Reasoning of Sensing and Intuition Personality Student in Solving Proportion Problem: Two-Variable into Three-Variable Direct Proportion Word Problem

Analogical Reasoning of Sensing and Intuition Personality Student in Solving Proportion Problem: Two-Variable into Three-Variable Direct Proportion Word Problem

The effects of working memory training on working memory, self-regulation, and analogical reasoning of preschool children.

This study examined the effects of working memory training (WMT) on WM and fluid intelligence. A novel four-pronged model of mediated learning, cognitive functions, task characteristics and metacognition is presented as a conceptual basis for the Modifiability of a Working Memory Program (MWMP). Our basic assumption is that increasing WM depends on a synchronized combination of the four components. A group of typically developed preschool children (n = 62) participated in the experimental group, receiving the MWMP. They were compared with a control group (n = 56) of preschool children who engaged in a substitute program. This comparison allowed us to discern the specific effects of the MWMP. All participants received tests of WM, self-regulation and analogical reasoning before and after the intervention. The MWMP was administered to children in the experimental group for 10 weekly sessions, each lasting 40 minutes, in small groups of two children. The children in the control group engaged in didactic activities that were part of a school curriculum routine for kindergartners for the same length of time. We used ANCOVA analysis to compare the Treatment x Time with age and socioeconomic status as covariates. The findings revealed a higher improvement in some WM and self-regulation tests among children in the experimental group compared to those in the control group. However, no significant transfer effects were observed in analogical thinking. The findings confirm the effectiveness of a non-computerized WMT among kindergartners and support our four-pronged theoretical model. We also discuss earlier findings on far-transfer effects and educational implications. We suggest that future WM studies adopt the following: (1) the development of training methods that are theoretically anchored; (2) training procedures should not rely heavily on computerized exercises but can be adapted to group characteristics, educational settings and cost-effectiveness aspects; (3) varying the task characteristics and training strategies to stimulate task-intrinsic motivation; (4) identifying training strategies to produce cognitive improvements underlying WM; (5) intervention should target individuals in early development as much as possible; (6) development of training procedures that facilitate motivation; and (7) providing empirical evidence of far-transfer effects for WM training. The empirical evidence should link gains in WM capacity and achievements in academic and other life domains.

Global relations versus object relations in visual analogies

Based on the distinction between global and local levels of visual perception, here we studied different levels of reasoning in visual analogies. Specifically, we created problems that could be solved by inferring relations either between the global shapes (global path) or the underlying objects (object path). The problems varied in the saliency of the global shape, in the colour and familiarity of the objects, and in the presentation of the visual problem (simultaneous or sequential). The results of three studies showed that the global path was used more frequently than the object path, particularly for images with salient global shapes such as objects placed in a circle. However, neither colour nor object familiarity affected the tendency to use the object path. Importantly, the tendency to use the global path correlated with analogical problem-solving in other visual and verbal tasks. Overall, these findings suggest that the global precedence observed in image perception also applies to visual analogical reasoning. Furthermore, the tendency to use the global path can be regarded as an informative measure of individual differences.

Dynamic testing of learning potential of children with moderate to severe intellectual disabilities: A Delphi study

AbstractEducation of children with moderate to severe intellectual disabilities requires adequate assessment of their educational needs and potential to learn. Dynamic testing using analogical reasoning tasks may be a promising way to perform such an assessment. However, it remains unclear how dynamic testing with these children may be done in practice. Therefore, we sought expert opinions on operationalizing learning potential and dynamic testing. We performed a three‐round online Delphi study (N = 37) with experts in psychological educational assessment of children with moderate to severe intellectual disabilities in special schools and specialized day‐care centres in the Netherlands. Consensus was found on a conceptual and an operational definition of learning potential, describing step‐by‐step how learning potential could be measured in daily practice. This included a pre‐test, training, post‐test design with the inclusion of a graduated prompts protocol for mediation and an observation checklist focussing on response to mediation. Based on examples from the Analogical Reasoning Learning Test, we assessed consensus on adapted design requirements for dynamic testing using analogical reasoning. Experts agreed that dynamic testing using analogical reasoning tasks might be suitable for children with moderate intellectual disabilities. A key concern was whether performance on analogical reasoning task was within reach of children with severe intellectual disabilities. The panel recommended research into the type of mediation needed to support the learning of analogical reasoning tasks. Further development and evaluation of dynamic testing for children with moderate to severe intellectual disabilities may build on the recommendations of this panel of experts.

Analogist: Out-of-the-box Visual In-Context Learning with Image Diffusion Model

Visual In-Context Learning (ICL) has emerged as a promising research area due to its capability to accomplish various tasks with limited example pairs through analogical reasoning. However, training-based visual ICL has limitations in its ability to generalize to unseen tasks and requires the collection of a diverse task dataset. On the other hand, existing methods in the inference-based visual ICL category solely rely on textual prompts, which fail to capture fine-grained contextual information from given examples and can be time-consuming when converting from images to text prompts. To address these challenges, we propose Analogist, a novel inference-based visual ICL approach that exploits both visual and textual prompting techniques using a text-to-image diffusion model pretrained for image inpainting. For visual prompting, we propose a self-attention cloning (SAC) method to guide the fine-grained structural-level analogy between image examples. For textual prompting, we leverage GPT-4V's visual reasoning capability to efficiently generate text prompts and introduce a cross-attention masking (CAM) operation to enhance the accuracy of semantic-level analogy guided by text prompts. Our method is out-of-the-box and does not require fine-tuning or optimization. It is also generic and flexible, enabling a wide range of visual tasks to be performed in an in-context manner. Extensive experiments demonstrate the superiority of our method over existing approaches, both qualitatively and quantitatively. Our project webpage is available at https://analogist2d.github.io.

Delving into the Complexity of Analogical Reasoning: A Detailed Exploration with the Generalized Multicomponent Latent Trait Model for Diagnosis.

Research on analogical reasoning has facilitated the understanding of response processes such as pattern identification and creative problem solving, emerging as an intelligence predictor. While analogical tests traditionally combine various composition rules for item generation, current statistical models like the Logistic Latent Trait Model (LLTM) and Embretson's Multicomponent Latent Trait Model for Diagnosis (MLTM-D) face limitations in handling the inherent complexity of these processes, resulting in suboptimal model fit and interpretation. The primary aim of this research was to extend Embretson's MLTM-D to encompass complex multidimensional models that allow the estimation of item parameters. Concretely, we developed a three-parameter (3PL) version of the MLTM-D that provides more informative interpretations of participant response processes. We developed the Generalized Multicomponent Latent Trait Model for Diagnosis (GMLTM-D), which is a statistical model that extends Embretson's multicomponent model to explore complex analogical theories. The GMLTM-D was compared with LLTM and MLTM-D using data from a previous study of a figural analogical reasoning test composed of 27 items based on five composition rules: figure rotation, trapezoidal rotation, reflection, segment subtraction, and point movement. Additionally, we provide an R package (GMLTM) for conducting Bayesian estimation of the models mentioned. The GMLTM-D more accurately replicated the observed data compared to the Bayesian versions of LLTM and MLTM-D, demonstrating a better model fit and superior predictive accuracy. Therefore, the GMLTM-D is a reliable model for analyzing analogical reasoning data and calibrating intelligence tests. The GMLTM-D embraces the complexity of real data and enhances the understanding of examinees' response processes.

The Kitāb al-Jadal of Abū Bakr ʿImād al-Dīn ʿAbdallāh b. Muḥammad al-Nūqānī al-Baghdādī al-Shāfiʿī (A Scholar of the Sixth Century): A Study and Critical Edition

Objectives: This study contains a critical first edition of a text in the fields of Uṣūl al-Fiqh (Principles of Islamic Jurisprudence) and Jadal (Islamic Dialectics) by ʿImād al-Nūqānī, a prominent Shāfiʿī scholar in the disciplines of Fiqh, Uṣūl, and Jadal. The study also seeks to shed light on the author's biography, which remained obscure for an extended period. Methodology: The study employs an inductive approach based on collecting, analysing, comparing, and deducing information regarding the author's biography. It also utilises a historical method focused on authenticating the manuscript's attribution to the author through critical examination and validation of information on his person. Findings: Among the most significant findings of this study is the calculation of al-Nūqānī’s life span as being from the second decade to the end of the sixth century (or early seventh century) and the refutation of the manuscript's incorrect attribution to Fakhr al- Nūqānī. Originality: This study's value lies in its critical edition of a previously unedited text and in clarifying the author's biography. The text includes essential discussions such as objections to analogical reasoning (qiyās), the conditions for there being a qiyās al-ʿilla (analogical reasoning based on the effective cause), the organisation of objections in a debate, and the types of rational and transmitted evidence.

Causes of the Multiplicity of Jurisprudential Narratives in Hanafi Fiqh

In the Hanafi school of thought, similar to other Islamic jurisprudential schools, there exist multiple narratives and theories. These theories, transmitted from Imam Abu Hanifa and his disciples, sometimes show differences in opinions and perspectives on various issues. The diversity of these narratives is based on the multiplicity of reports from these esteemed imams, which have several underlying causes. These causes can be identified through the texts of jurisprudential and legal theory books and the history of Islamic jurisprudence. Scholars in this field have discussed these topics in their works, attributing the multiplicity of narratives to various factors. Among the first to systematically enumerate these causes was 'Allamah Balyghi. Following him, other scholars such as Ibn Amir al-Hajj, 'Allamah Ibn Abidin al-Shami, and 'Allamah Dehlavi have extensively explained these causes and provided examples. This research aims to investigate the reasons for the multiplicity of jurisprudential narratives in Hanafi Fiqh using a descriptive-analytical method based on library resources. After discussing and examining these causes, four primary reasons for the multiplicity of narratives in the Hanafi school are identified: (1) misunderstandings in the interpretation of the Imam's views, (2) lack of awareness of the Imam's latest and most authoritative opinions, (3) the consideration of Qiyas (analogical reasoning) and Istihsan (juridical preference), and (4) the original intent of the ruling and precautionary measures. These aspects will be addressed in the discussion.

Advancing with age: Older adults excel in comprehension of novel metaphors.

Older adults may experience certain forms of cognitive decline, but some forms of semantic memory remain intact in older age. To address how metaphor comprehension changes with age and whether metaphor comprehension relies more heavily on analogical reasoning (supported by fluid intelligence) or on conceptual combination (supported by crystalized intelligence), we compared performance of younger and older adults. In two experiments, healthy older adults (54-88 years) scored lower on a measure of fluid intelligence (Ravens Progressive Matrices) but higher on a measure of crystalized intelligence (Mill Hill Vocabulary Test) relative to younger adults (18-34 years). Groups were equally successful in comprehending relatively easy metaphors (Study 1), but older adults showed a striking advantage over younger adults for novel literary metaphors (Study 2). Mixed-effects modeling showed that measures of fluid and crystalized intelligence each made separable contributions to metaphor comprehension for both groups, but older adults relied more on crystalized intelligence than did younger adults. These age-related dissociations clarify cognitive effects of aging and highlight the importance of crystalized intelligence for metaphor comprehension in both younger and older adults. (PsycInfo Database Record (c) 2024 APA, all rights reserved).