Implicit Connections Research Articles

Multi-input Unet model based on the integrated block and the aggregation connection for MRI brain tumor segmentation

With the growth of data information and the development of computer equipment, it is extremely time-consuming and laborious to rely on the traditional manual segmentation of brain medical images. To solve the above problems, this paper proposes a multi-input Unet model based on the integrated block and the aggregation connection to achieve efficient and accurate segmentation of tumor structure. Besides, this paper solves the low-resolution problem in sagittal and coronal planes, which can effectively improve memory efficiency. The proposed algorithm is innovative in three aspects. Firstly, by inputting the mask images which can effectively represent the tumor location characteristics, it can provide more information about the spatial relationship to alleviate the problems of fuzzy boundary and low contrast between the lesion region and healthy brain tissue. Then, the integrated block extracts the tumor local information in different receptive domains by a multi-scale convolution kernel. The aggregation connection realizes the implicit deep connection of context information, which combines the shallow and deep information of the brain with strong geometric spatial relationships. Meanwhile, to effectively alleviate the waste of memory resources caused by redundant and background information in medical images, the amount of calculation in model training is reduced by dimension reduction of the feature map. An ablation experiment is used to verify the innovation of the proposed algorithm on the BraTS dataset, which compares with the state-of-the-art brain tumor segmentation methods. The precision of the proposed multi-input Unet model for the whole tumor and core lesion is 0.92 and 0.90, respectively.

Automatic gear shift strategy for manual transmission of mine truck based on Bi-LSTM network

As mine trucks face severe and changeable driving conditions in mining area, it is important to formulate a general automatic gear shift strategy for reducing the intensity and difficulty of the work. Therefore, gear shift strategy performs a significant role in automatic transmission studies. However, it is difficult to devise an automatic shift strategy due to the fact that it relies on tremendous historical experiment data which is the highest classified commercial secret of the automobile manufacture. Since the artificial neural network can build self-learning model by inputting multiple parameters, adjusting weights and automatically outputting results, many automatic gear shift strategy studies are reported based on it. However, the gear shift prediction accuracy by traditional neural network is limited due to the non-existence of feedback connection, lacking of associative memory function and missing implicit connection between continuous data. Hence, this paper proposes a novel method based on Bi-directional Long Short-Term Memory (Bi-LSTM) network, which controls gear shifting with nine parameters and updates the traditional mine truck transmission system from manual to automatic. Truck status data while manual driving are captured through the Controller Area Network-bus (CAN-bus) and converted to the proper sequence data format. Some sequence data are used to train the Bi-LSTM network for gear prediction, while the rest is used for verification. In order to evaluate the proposed machine learning model performance, it is compared with the Back Propagation Neural Network (BPNN), Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM) networks with two and nine parameters, respectively. The experimental results demonstrate that machine learning networks with nine parameters reach higher gear shifting prediction accuracy than those with dual-parameter, except BPNN. In addition, the highest prediction accuracy is 95.8 %, which is achieved by the Bi-LSTM network with nine parameters. This proves that Bi-LSTM network outperforms the traditional neural networks, such as BPNN with 81.5 %, and other recurrent neural networks, such as RNN and LSTM with 87 % and 91.5 %, respectively. As for the average time consumption, the Bi-LSTM network spends 1.86 ms per prediction calculation which is longer than LSTM and RNN respectively having 1.58 ms and 1.06 ms. Thus, it still fits the requirement of real-time processing on mine truck industry. In general, the Bi-LSTM network shows its advantage in processing sequential data within limited time, and has the potential to be widely applied in the field of prediction based on data with temporal characteristic.

A method for finding all connections in the Pantelides algorithm for delay differential-algebraic equations

The Pantelides algorithm for systems of delay differential-algebraic equations (DDAEs) is a method to structurally analyse such systems with the goal to detect which equations have to be differentiated or shifted to construct a solution. In this process, one has to detect implicit connections between equations in the shifting graph, making it necessary to check all possible connections. The problem of finding these efficiently remained unsolved so far. It is explored in further detail and a reformulation is introduced. Additionally, an algorithmic approach for its solution is presented.

Accelerating COVID-19 Research with Graph Mining and Transformer-Based Learning

In 2020, the White House released the “Call to Action to the Tech Community on New Machine Readable COVID-19 Dataset,” wherein artificial intelligence experts are asked to collect data and develop text mining techniques that can help the science community answer high-priority scientific questions related to COVID-19. The Allen Institute for AI and collaborators announced the availability of a rapidly growing open dataset of publications, the COVID-19 Open Research Dataset (CORD-19). As the pace of research accelerates, biomedical scientists struggle to stay current. To expedite their investigations, scientists leverage hypothesis generation systems, which can automatically inspect published papers to discover novel implicit connections. We present automated general purpose hypothesis generation systems AGATHA-C and AGATHA-GP for COVID-19 research. The systems are based on the graph mining and transformer models. The systems are massively validated using retrospective information rediscovery and proactive analysis involving human-in-the-loop expert analysis. Both systems achieve high-quality predictions across domains in fast computational time and are released to the broad scientific community to accelerate biomedical research. In addition, by performing the domain expert curated study, we show that the systems are able to discover ongoing research findings such as the relationship between COVID-19 and oxytocin hormone. All code, details, and pre-trained models are available at https://github.com/IlyaTyagin/AGATHA-C-GP.

ALGEBRAIC EXPANSIONS OF LOGICS

AbstractAn algebraically expandable (AE) class is a class of algebraic structures axiomatizable by sentences of the form $\forall \exists ! \mathop{\boldsymbol {\bigwedge }}\limits p = q$ . For a logic L algebraized by a quasivariety $\mathcal {Q}$ we show that the AE-subclasses of $\mathcal {Q}$ correspond to certain natural expansions of L, which we call algebraic expansions. These turn out to be a special case of the expansions by implicit connectives studied by X. Caicedo. We proceed to characterize all the AE-subclasses of abelian $\ell $ -groups and perfect MV-algebras, thus fully describing the algebraic expansions of their associated logics.

A Triple Relation Network for Joint Entity and Relation Extraction

Recent methods of extracting relational triples mainly focus on the overlapping problem and achieve considerable performance. Most previous approaches extract triples solely conditioned on context words, but ignore the potential relations among the extracted entities, which will cause incompleteness in succeeding Knowledge Graphs’ (KGs) construction. Since relevant triples give a clue for establishing implicit connections among entities, we propose a Triple Relation Network (Trn) to jointly extract triples, especially handling extracting implicit triples. Specifically, we design an attention-based entity pair encoding module to identify all normal entity pairs directly. To construct implicit connections among these extracted entities in triples, we utilize our triple reasoning module to calculate relevance between two triples. Then, we select the top-K relevant triple pairs and transform them into implicit entity pairs to predict the corresponding implicit relations. We utilize a bipartite matching objective to match normal triples and implicit triples with the corresponding labels. Extensive experiments demonstrate the effectiveness of the proposed method on two public benchmarks, and our proposed model significantly outperforms previous strong baselines.

Visuo-Motor Affective Interplay: Bonding Scenes Promote Implicit Motor Pre-dispositions Associated With Social Grooming-A Pilot Study.

Proximity and interpersonal contact are prominent components of social connection. Giving affective touch to others is fundamental for human bonding. This brief report presents preliminary results from a pilot study. It explores if exposure to bonding scenes impacts the activity of specific muscles related to physical interaction. Fingers flexion is a very important component when performing most actions of affectionate contact. We explored the visuo-motor affective interplay by priming participants with bonding scenes and assessing the electromyographic activity of the fingers flexor muscle, in the absence of any overt movements. Photographs of dyads in social interaction and of the same dyads not interacting were employed. We examined the effects upon the electromyographical activity: (i) during the passive exposure to pictures, and (ii) during picture offset and when expecting the signal to perform a fingers flexion task. Interacting dyads compared to matched non-interacting dyads increased electromyographic activity of the fingers flexor muscle in both contexts. Specific capture of visual bonding cues at the level of visual cortex had been described in the literature. Here we showed that the neural processing of visual bonding cues reaches the fingers flexor muscle. Besides, previous visualization of bonding cues enhanced background electromyographic activity during motor preparation to perform the fingers flexion task, which might reflect a sustained leakage of central motor activity downstream leading to increase in firing of the respective motor neurons. These data suggest, at the effector level, an implicit visuo-motor connection in which social interaction cues evoke intrinsic dispositions toward affectionate social behavior.

Developing the Dead: Spirit Photography, Mormonism, and Noise

Developing the Dead: Spirit Photography, Mormonism, and Noise

François Jullien’s Unexceptional Thought: A Critical Introduction by Arne De Boever

Reviewed by: François Jullien’s Unexceptional Thought: A Critical Introduction by Arne De Boever Michael Harrington (bio) François Jullien’s Unexceptional Thought: A Critical Introduction. By Arne De Boever. New York: Rowman & Littlefield International, 2020. Pp. xxxi + 151. Paperback $33.00, isbn 978-1-78661-576-3. François Jullien is a master of repetition. Over his more than thirty books, he introduces a carefully defined set of concepts--such as “blandness” and “efficacy”--and then pairs them, opposes them, and sets them in different contexts, returning to them repeatedly without ever saying quite the same thing. One can imagine an introduction to Jullien’s work that traces each of his concepts through its development from book to book, noting explicit and implicit connections to the traditional Chinese thought that gave rise to it. In François Jullien’s Unexceptional Thought, Arne De Boever takes a different tack. As he puts it in his introduction: “I focus on certain books and topics that stood out to me within Jullien’s work and in relation to the present moment as worthy of inquiry” (p. xii). The content of the book is determined by these two criteria: firstly, De Boever’s interests, which fall roughly within the field of critical theory, and secondly, “the present moment,” which turns out to mean contemporary intellectual and ideological trends. Jullien’s Unexceptional Thought is not, then, a comprehensive survey of Jullien’s work, but a series of essays on topics governed by these two criteria. What are the topics that stand out to De Boever? Chapters One and Four address his primary topic: the method developed by Jullien for negotiating the gap between different regions of thought, most obviously but not exclusively the regions of Chinese and Western thought. Chapter One discusses whether Jullien’s approach to China may accurately be called “orientalist,” and Chapter Four situates Jullien’s method in relation to several contemporary intellectual trends. Sandwiched between these analyses of Jullien’s overall method are two chapters on specific fields of inquiry. Chapter Two focuses on aesthetics, and Chapter Three on politics. The latter is more successful than the former. De Boever introduces Jullien’s concept of efficacy, lists three of his works that develop this concept, and shows that Jullien refines the concept of efficacy and changes the style of his presentation from one work to the next. Chapter Two, on the other hand, says a little about landscape painting and the role of nudity in art, but it never develops the urgency and contemporary significance of the other chapters. [End Page 1] De Boever gives up the topic of aesthetics a little more than halfway through the chapter and spends its last ten pages introducing the political themes to be developed in Chapter Three. The reader can easily skip to a specific chapter, as each reads like an independent essay. This impression is enhanced by the fact that each chapter, including the introduction and conclusion, ends with its own bibliography. The relative independence of the chapters is useful for the reader interested in only a few of the book’s themes, but it creates a problem of repetition. Unlike Jullien, who varies his repeated treatments of concepts, De Boever will make the same point, or repeat the same sentence, almost word-for-word in different chapters. Minor cases include a paragraph on Lisa Lowe, Eric Hayot, and Gayatri Chakravorty Spivak in Chapter One (p. 3) that reappears with the same thesis and some of the same language in Chapter Three (p. 72). More disconcerting is De Boever’s claim in Chapter Two that he “would like to start thinking about Jullien’s thought--a little provocatively--as ‘unexceptional,’” when he has already been explaining that Jullien’s thought is unexceptional throughout the introduction (especially xxiii). Such repetitions suggest that the book is not meant to be read from beginning to end. The book also calls into question, sometimes explicitly, De Boever’s competence to write it. De Boever warns that he is “largely assessing Jullien’s oeuvre from the Western side,” as he has only “limited knowledge of the Chinese materials that Jullien studies” (p. xx). He follows this...

Eye Contact and the Performative Touch of Blindness

This paper treats blindness as itself a performance. It understands it as movement, as essentially a kinaesthetic phenomenon. Touch, of course, becomes an exaggerated cogent sense for those of us who are blind. This is not only cogent, but a conventional understanding of blindness. This paper, in contrast, makes use of touch in an almost reverse way; it explores how those who see come in touch with, come to touch and are touched by blindness. Through invoking my own experiences as a blind woman and my movement in public spaces, I address the cogency, hyperbolic, valued sense of touch that is essential in performing blindness – in how my movement through the world comes to be ‘seen’ as the movement of a blind person, as blindness. This, I treat, as performance. I also treat myself, the blind person, as performer and the others, the others who see, as the audience. This audience is not passive; it is not merely a congregate of spectators. Instead, it is invested in blindness insofar as it must recognize it, in part, for its own existence. Sight, after all, its recognition, is established through what is understood as its opposite, as not seeing, and one expression of this is blindness. When blindness is performed as something other than the opposite and the absence of sight, it is disturbing; it disturbs the common-sensical idea that blindness seeks its own absence, namely sight. This is why Diderot (1982) and his company were surprised when ‘their blind man’ replied when asked if he wanted sight: ‘If it were not for curiosity … I would just as soon have long arms: It seems to me my hands would tell me more of what goes on in the moon than your eyes or your telescopes’ (77–78). For Diderot’s ‘blind man,’ sight is a mere curiosity. It does little in comparison with touch, to bring us in touch with the world or even with the moon. Eyes can see the moon, but not ‘what goes on in the moon’. The sense of sight gives those who see a way to ‘be in touch’ with the world and its appearances, including people. One of those ways, a way that is often used to get someone’s attention, to perform a ‘look’ is eye contact. As a way to concretize the premise of blindness as performance and that this performance is conducted in what I have called elsewhere ‘the theatre of the eyes’ (Healey 2021), I engage the phenomenon of eye contact as a way to draw out the implicit connections between sight and touch and, between blindness and sight. I make use of the work of blind disability studies theorist Georgina Kleege (2018), particularly when she says, ‘eye contact … feels like nothing for me’ (5). This allows me to engage eye contact, typically understood as a visual phenomenon as first, a phenomenon that is reliant on touch and secondly, as one reliant on its performance to make an appearance. This paper shows, too, how eye contact, as Kleege suggests, has as much to do with feel as it does with sight. As Aristotle (1907) wonders, touch is not one, but ‘a group of senses,’ (II. 11) a sensorium. And perhaps, ‘flesh’ is its medium and its real organ lies inward. Perhaps blindness, too, is a sensorium, not a damaged one, but a keenly sensitive and perceptive one; a sensitivity and perception guided by the medium of the performance and located in the inwardness of coming in touch with.

Forecasting the Volatility of Specifc Risk for Stocks with LSTM

The financial market is a complex dynamic system full of noise. Volatility is an important factor in portfolio allocation and risk management. The rapid development of artificial intelligence makes it possible to predict the volatility of the financial market more accurately. In this paper, we attempt to model the volatility of specific risk for stocks. We use GARCH as a baseline model and explore the effectiveness of LSTM. We propose to pack the specific risk factors of all stocks at each timestamp and feed to the LSTM model, so that the implicit connections among stocks will be modeled. From the empirical experiments on BARRA official data, we observe that the performances of LSTM are obviously better than that of GARCH, in terms of both MAE and MSE.

Social influence-based personal latent factors learning for effective recommendation

Social recommendation has become an important technique of various online commerce platforms, which aims to predict the user preference based on the social network and the interactive network. Social recommendation, which can naturally integrate social information and interactive structure, has been demonstrated to be powerful in solving data sparsity and cold-start problems. Although some of the existing methods have been proven effective, the following two insights are often neglected. First, except for the explicit connections, social information contains implicit connections, e.g., indirect social relations. Indirect social relations can effectively improve the quality of recommendation when users only have few direct social relations. Second, the strength of social influence between users is different. In other words, users have different degrees of trust in different friends. These insights motivate us to propose a novel social recommendation model SIER (short for Social Influence-based Effective Recommendation) in this paper, which incorporates interactive information and social information into personal latent factors learning for social influence-based recommendation. Specifically, user preferences are captured in behavior history and social relations, i.e., user latent factors are shared in interactive network and social network. In particular, we utilize an overlapping community detection method to sufficiently capture the implicit relations in the social network. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed method.

Vomer Flaps Support Velopharyngeal Competency, Fistula Free Repair, and 3D Construction of Normal Anatomy in Patients with Cleft Palate.

Background:Cleft palate repair comprises the surgical creation of a congenitally nonexistent normal anatomy, to establish physiological function by moving tissues into their normal anatomical positions. In patients with isolated incomplete (IICP) or submucous (SMCP) cleft palate, the vomer is usually not completely attached to the palatal plate in the midline. This condition, which is visible through surgical access radiologically or via endoscope, is often disregarded during hard palate repair. This can lead to “hypernasality” despite a well-functioning velopharyngeal mechanism. The general practice of hard palate repair by suturing merely the nasal layers together separates the oral and nasal cavities. However, without incorporation of the vomer, it is impossible to build two separate nasal floors on the left and right sides. We consider that achieving normal speech and separation of the nasal cavities are mutually dependent and have to be considered equally.Methods:We described hard palate repair involving the vomer for construction of both nasal floors. We presented the occlusal relationship, hypernasality, and fistula rates in 37 patients operated on between January 1, 2017 and June 30, 2018.Results:One child presented minimal hypernasality; all others had normal resonance/voice. Fistula rate was zero, and no cross bites were observed.Conclusions:The implicit connection between the inner nose, resonance/voice, and prevention of fistulae has not yet been acknowledged. The correct usage of vomer flaps in IICP and SMCP creating separate nasal floors supports the velopharyngeal competency, avoids fistula formation, and should be incorporated regularly, like in other cleft forms.

Fast-Convergent Federated Learning With Adaptive Weighting

Federated learning (FL) enables resource-constrained edge nodes to collaboratively learn a global model under the orchestration of a central server while keeping privacy-sensitive data locally. The non-independent-and-identically-distributed (non-IID) data samples across participating nodes slow model training and impose additional communication rounds for FL to converge. In this paper, we propose <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fed</monospace> erated <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ad</monospace> a <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</monospace> tive Weighting ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedAdp</monospace> ) algorithm that aims to accelerate model convergence under the presence of nodes with non-IID dataset. We observe the implicit connection between the node contribution to the global model aggregation and data distribution on the local node through theoretical and empirical analysis. We then propose to assign different weights for updating the global model based on node contribution adaptively through each training round. The contribution of participating nodes is first measured by the angle between the local gradient vector and the global gradient vector, and then, weight is quantified by a designed non-linear mapping function subsequently. The simple yet effective strategy can reinforce positive (suppress negative) node contribution dynamically, resulting in communication round reduction drastically. Its superiority over the commonly adopted Federated Averaging ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedAvg</monospace> ) is verified both theoretically and experimentally. With extensive experiments performed in Pytorch and PySyft, we show that FL training with <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedAdp</monospace> can reduce the number of communication rounds by up to 54.1% on MNIST dataset and up to 45.4% on FashionMNIST dataset, as compared to <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedAvg</monospace> algorithm.

Myanmar’s hidden-in-plain-sight social infrastructure: nalehmu through multiple ruptures

ABSTRACT This article examines nalehmu, a set of informal relational practices for negotiating power across scales which have facilitated access and enforced accountability through mutually recognized norms and social sanctions in Myanmar. Like Asef Bayat’s “quiet encroachment” in the Middle East, nalehmu is Myanmar’s discreet and prolonged practice of agency that has enabled ordinary people to survive and better their lives despite the multiple ruptures in Myanmar’s history, as seen most recently in the February 2021 coup d’état. The paper analyzes how nalehmu serves as a hidden-in-plain-sight social infrastructure across three different scales: relations of mutuality, obligation, and reciprocity between individuals; implicit connections for accessing goods, services, and recognition; and a means of interacting with the state via the nalehmu economy. This analysis seeks to do more than add a different case to studies of urban Southeast Asia, but also to help produce further theorization that takes seriously the actually existing contexts and practices in the global South.

A cultural analysis of eco-Islam: How young German Muslims live religion through environmental activism

Eco-Islam is often discussed from a theological perspective, but there are hardly any studies on the activist’s practices, their experiences and reasoning. In order to fill this gap and to emphasise the importance of studying lived religion, this article presents three interviews with young Eco-Islam activists in Germany. By using the method of cultural analysis, the author compares, summarises and abstracts their statements and activities, so that a comprehensive configuration is revealed. Cultural analysis focuses on cultural constellations and tries to think historical, social and biographical components together. To understand the Eco-Islam activist’s perspectives at a deeper cultural level, the author firstly represents a thick description of the statements and activities of her three respondents. Afterwards, she scrutinises the actors’ practices and processes by applying the dimensions of belief, practice and community on their everyday life and performative action. At last she filters three related themes on a cultural constellational level. Contribution: In this article, the author simultaneously highlights the implicit connections of eco-Islamic Activism to aspects of Othering and empowerment, aspects of (re-) interpretation of religious norms and aspects of transgressing environmental activism and the idea of an Islamic community.

The other’s humanity with or without the other’s religiosity? Reflections on the affirmation and limitation of human dignity in early Afrikaner missionary discourse in Central Africa

Taking Wentzel van Huyssteen’s work on early human uniqueness in relation to symbolic or religious awareness as a starting point, this article raises a question whether an implicit connection between humanity and the capacity for religiosity had anything to say about how one could evaluate the so-called other’s religion and their humanity. Does the recognition of the other’s full humanity demand an equal recognition of their religiosity, or are these separable? Rather than attempting to answer this hypothetically, the question is approached historically. The article touches on how the capacity to evaluate religion from the outside emerged in modernity and discusses some of the ways this capacity played out in Christian theology. In reference to the colonial era Afrikaner missionaries in Central Africa, the article argues that even partial recognition of the other’s religiosity might have detrimental consequences particularly where this is tied to a partial recognition of their humanity as had happened during the apartheid and proto-apartheid periods.Intradisciplinary and/or interdisciplinary implications: The article challenges both critical and affirmative scholarly views of religiosity by positing an essential link between humanity and religiosity whilst simultaneously suggesting that a scientific approach to religiosity, which has uncovered important relationships between religiosity and humanity, might be the appropriate approach for full recognition of the other’s humanity.

Complex networks reveal emergent interdisciplinary knowledge in Wikipedia

In the last 2 decades, a great amount of work has been done on data mining and knowledge discovery using complex networks. These works have provided insightful information about the structure and evolution of scientific activity, as well as important biomedical discoveries. However, interdisciplinary knowledge discovery, including disciplines other than science, is more complicated to implement because most of the available knowledge is not indexed. Here, a new method is presented for mining Wikipedia to unveil implicit interdisciplinary knowledge to map and understand how different disciplines (art, science, literature) are related to and interact with each other. Furthermore, the formalism of complex networks allows us to characterise both individual and collective behaviour of the different elements (people, ideas, works) within each discipline and among them. The results obtained agree with well-established interdisciplinary knowledge and show the ability of this method to boost quantitative studies. Note that relevant elements in different disciplines that rarely directly refer to each other may nonetheless have many implicit connections that impart them and their relationship with new meaning. Owing to the large number of available works and to the absence of cross-references among different disciplines, tracking these connections can be challenging. This approach aims to bridge this gap between the large amount of reported knowledge and the limited human capacity to find subtle connections and make sense of them.

Analyzing Student Cognition in Problem Solving via Online Surveys

Metacognitive studies provide invaluable insights into common misconceptions and may shed light on other barriers to learning, such as a fear of math. Among these studies, the think-aloud method is a powerful tool to provide context and explain underlying factors in student learning. However, they often require transcription services, are difficult to conduct in an online environment, and depend on students committing the necessary time to volunteer. To address these challenges, we created an anonymous online survey to mimic a think-aloud study for majors and non-majors introductory biology courses that could give insight to student learning. The survey asked students to predict the probability a child would inherit a fictional disorder in order to analyze students’ problem-solving skills to a question that cannot easily be found on the internet. Two follow-up questions asked the students to describe how they arrived at the response and their emotional state during solving the problem. Based on a previous pilot study, a minimum character limit was required (450 and 100 characters, respectively) to allow concept and application analyses as well as affective perception during the problem-solving process. We adapted an existing framework for coding problem solving based on concept (e.g. understanding concepts related to the problem, such as homo/heterozygous), connection (understanding connections between concepts), and skills (methods to solve the problem). Emotional state and emotional cause were also coded. Three biology faculty participated in three calibration sessions as additional codes were discussed. Final coding was completed by the authors independently, and discrepancies with no majority were discussed and decided by consensus. The dataset was reduced using stepwise AIC to determine the most relevant variables, which were then factored into a logistic regression and post hoc Dunn tests. A total of 181 students (76 non-majors, 105 majors) responded. Students were 40% male and 55% female. Mode age category was 26-30 (21-25 to 51-55). Two answers were discarded as unresponsive, and 91 of responses were marked correct. AIC analysis identified majors vs. non-majors and the codes Concept, Connection, and Emotional State as the best predictors of entering a correct answer. In the overall logistic regression, majors vs. non-majors, Concept, and Connection were significant predictors of getting an answer correct. Dunn tests revealed several factors increasing the likelihood a student answered correctly: identifying relevant concepts vs. unrelated or no concepts (z = -5.56 and -2.27; p = .00 and .035, respectively); drawing explicit or implicit connections instead of none (z = 7.00, 3.93 and 0.85; p = .00, .0001 and .60, respectively); and majors were significantly more likely to answer correctly than nonmajors. Although a notable portion of students expressed confusion or irritation regarding minimum response requirements, nearly all provided insight into their cognitive processes. Overall, carefully designed surveys may offer a promising, if comparatively limited, alternative to think-alouds for metacognition studies when student time and faculty resources are limited.

Two-loop renormalisation of gauge theories in 4D implicit regularisation and connections to dimensional methods

We compute the two-loop beta -function of scalar and spinorial quantum electrodynamics as well as pure Yang–Mills and quantum chromodynamics using the background field method in a fully quadridimensional setup using implicit regularization (IREG). Moreover, a thorough comparison with dimensional approaches such as conventional dimensional regularization (CDR) and dimensional reduction (DRED) is presented. Subtleties related to Lorentz algebra contractions/symmetric integrations inside divergent integrals as well as renormalisation schemes are carefully discussed within IREG where the renormalisation constants are fully defined as basic divergent integrals to arbitrary loop order. Moreover, we confirm the hypothesis that momentum routing invariance in the loops of Feynman diagrams implemented via setting well-defined surface terms to zero deliver non-abelian gauge invariant amplitudes within IREG just as it has been proven for abelian theories.