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We present an algorithm that canonicalizes the algebraic representations of the topological semantics of machine knitting programs. Machine knitting is a staple technology of modern textile production where hundreds of mechanical needles are manipulated to form yarn into interlocking loop structures. Our semantics are defined using a variant of a monoidal category, and they closely correspond to string diagrams. We formulate our canonicalization as an Abstract Rewriting System (ARS) over words in our category, and prove that our algorithm is correct and runs in polynomial time. 

This paper addresses the theme of “the Moral and Ethical Responsibility of Engineers and Engineering”, particularly responding to the question of how to define or deliberate the meaning of ‘public welfare’ and ‘common good’ in engineering degree programs. Drawing from decades of international work on human development, particularly in the global south, this paper reports on adapting the capability approach to an engineering degree program. Developed by Amartya Sen, the capability approach sought to replace GDP-based models of welfare economics by framing the goal of development as enabling individuals to live a life they value. The things a person values, what they are and can do (determined by their opportunities, experiences, and cultural affordances) are their ‘functionings’. In Sen’s framework each individual has a unique ‘functionings vector’ based on what they value. Although someone’s functionings vector indicates valued goals, they will be unsuccessful in achieving their goals unless they have access to needed resources, can effectively utilize those resources, possess agency, and have the ‘capability’ to enact the functionings. ‘Capabilities’ determine the set of functionings that are actually available to a person. Although rarely used in engineering, the capability approach offers a mature and well-developed framework to address issues of public welfare. Public good is defined through an individual’s freedom to pursue a life they have reason to value, and such freedom defines both the means and end of development. The role of engineering in society—primarily through development of infrastructure—is to support equitable access to capabilities for all individuals. Through support of an NSF Revolutionizing Engineering Departments (RED) grant, an ECE department in a mid-Atlantic liberal arts university has adapted the capability approach to inform change in an undergraduate degree program. Specific examples from four years of implementation are shared. 

The recent surge in artificial intelligence (AI) developments has been met with an increase in attention towards incorporating ethical engagement in machine learning discourse and development. This attention is noticeable within engineering education, where comprehensive ethics curricula are typically absent in engineering programs that train future engineers to develop AI technologies [1]. Artificial intelligence technologies operate as black boxes, presenting both developers and users with a certain level of obscurity concerning their decision-making processes and a diminished potential for negotiating with its outputs [2]. The implementation of collaborative and reflective learning has the potential to engage students with facets of ethical awareness that go along with algorithmic decision making – such as bias, security, transparency and other ethical and moral dilemmas. However, there are few studies that examine how students learn AI ethics in electrical and computer engineering courses. This paper explores the integration of STEMtelling, a pedagogical storytelling method/sensibility, into an undergraduate machine learning course. STEMtelling is a novel approach that invites participants (STEMtellers) to center their own interests and experiences through writing and sharing engineering stories (STEMtells) that are connected to course objectives. Employing a case study approach grounded in activity theory, we explore how students learn ethical awareness that is intrinsic to being an engineer. During the STEMtelling process, STEMtellers blur the boundaries between social and technical knowledge to place themselves at the center of knowledge production. In this WIP, we discuss algorithmic awareness, as one of the themes identified as a practice in developing ethical awareness of AI through STEMtelling. Findings from this study will be incorporated into the development of STEMtelling and address challenges of integrating ethics and the social perception of AI and machine learning courses. 

Traditional engineering curriculum and course structures prioritize preparing students for technical and logical reasoning skills that are intrinsic to becoming an engineer. While these skills are undeniably vital for an engineering career, these courses often fail to provide opportunities for students to explore skills that go beyond the traditional curriculum and classroom walls. In addition, course structures often reinforce the stereotypical narrative in engineering that there is a dichotomy between the social and technical aspects with the latter being more important. Preparing students for both social and technical sides of engineering, requires a reorganization of how learning environments are designed and how engineering programs and faculty evaluate how learning occurs. 

Pyridyl-substituted platinum acetylide complexes bind coinage metal cations, strongly influencing photoluminescence properties. Large counterions maintain the blue phosphorescence profile with large increases in radiative rate and quantum yield. 

BACKGROUND: Severe forms of depression have been linked to excessive subcallosal cingulate cortex (SCC) ac- tivity. Stimulation of the SCC with surgically implanted electrodes can alleviate depression, but current noninvasive techniques cannot directly and selectively modulate deep targets. We developed a new noninvasive neuromodulation approach that can deliver low-intensity focused ultrasonic waves to the SCC. METHODS: Twenty-two individuals with treatment-resistant depression participated in a randomized, double-blind, sham-controlled study. Ultrasonic stimulation was delivered to the bilateral SCC during concurrent functional magnetic resonance imaging to quantify target engagement. Mood state was measured with the Sadness subscale of the Positive and Negative Affect Schedule before and after 40 minutes of real or sham SCC stimulation. Change in depression severity was measured with the 6-item Hamilton Depression Rating Scale at 24 hours and 7 days. RESULTS: Functional magnetic resonance imaging demonstrated a target-speci c decrease in SCC activity during stimulation (p = .028, n = 16). In 7 of 16 participants, SCC neuromodulation was detectable at the individual participant level with a single 10-minute scan (p , .05, small-volume correction). Mood and depression scores improved more with real than with sham stimulation. In the per-protocol sample (n = 19), real stimulation was superior to sham for 6-item Hamilton Depression Rating Scale scores at 24 hours and for Sadness scores (both p , .05, d . 1). Nonsigni cant trends were found in the intent-to-treat sample. CONCLUSIONS: This small pilot study indicates that ultrasonic stimulation modulates SCC activity and can rapidly reduce depressive symptoms. The capability to noninvasively and selectively target deep brain areas creates new possibilities for the future development of circuit-directed therapeutics and for the analysis of deep-brain circuit function in humans.