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An Empirical Evaluation of Active Live Coding in CS1
No full textObjectives
The traditional, instructor-led form of live coding has been extensively studied, with findings showing that this form of live coding imparts similar learning to static-code examples. However, a concern with Traditional Live Coding is that it can turn into a passive learning activity for students as they simply observe the instructor program. Therefore, this study compares Active Live Coding—a form of live coding that leverages in-class coding activities and peer discussion—to Traditional Live Coding on three outcomes: 1) students’ adherence to effective programming processes, 2) students’ performance on exams and in-lecture questions, and 3) students’ lecture experience.
Participants
Roughly 530 students were enrolled in an advanced, CS1 course taught in Java at a large, public university in North America. The students were primarily first- and second-year undergraduate students with some prior programming experience. The student population was spread across two lecture sections—348 students in the Active Live Coding (ALC) lecture and 185 students in the Traditional Live Coding (TLC) lecture.
Study Methods
We used a mixed-methods approach to answer our‘ research questions. To compare students’ programming processes, we applied process-oriented metrics related to incremental development and error frequencies. To measure students’ learning outcomes, we compared students’ performance on major course components and used pre- and post-lecture questionnaires to compare students’ learning gain during lectures. Finally, to understand students’ lecture experience, we used a classroom observation protocol to measure and compare students’ behavioral engagement during the two lectures. We also inductively coded open-ended survey questions to understand students’ perceptions of live coding.
Findings
We did not find a statistically significant effect of ALC on students’ programming processes or learning outcomes. It seems that both ALC and TLC impart similar programming processes and result in similar student learning. However, our findings related to students’ lecture experience shows a persistent engagement effect of ALC, where students’ behavioral engagement peaks and
remains elevated
after the in-class coding activity and peer discussion. Finally, we discuss the unique affordances and drawbacks of the lecture technique as well as students’ perceptions of ALC.
Conclusions
Despite being motivated by well-established learning theories, Active Live Coding did not result in improved student learning or programming processes. This study is preceded by several prior works that showed that Traditional Live Coding imparts similar student learning and programming skills as static-code examples. Though potential reasons for the lack of observed learning benefits are discussed in this work, multiple future analyses to further investigate Active Live Coding may help the community understand the impacts (or lack thereof) of the instructional technique
Blood microbial DNA signature differentiates hepatocellular carcinoma from metastatic lesions
Get PDFA sketch grammar of Igu, the Shamanic language of the Kera’a
Get PDFThis paper offers the first linguistic description of Igu, the shamanic language of the Kera’a (also 'Idu (Mishmi)'), and thus presents one of the first linguistic descriptions of an Eastern Himalayan ritual language. The Kera’a are a Tibeto-Burman-speaking society of ca. 10 000-16 000 members, based in and around the very northeastern Himalayan river valley of India, in the Dibang Valley and Lower Dibang Valley districts of Arunachal Pradesh. The Kera’a refer to their modern spoken language simply as Kera’a or Kera’a ekobe (‘Kera’a tongue’). For shamanic rituals, shamans recite in Igu (or Igu ekobe ), which the Kera’a consider to be a separate language from Kera'a. Igu is mastered and used by shamans (and their assistants) in rituals, as well as passively understood by knowledgeable elders. This paper demonstrates that Igu is a language in its own right, which partially differs on all core levels - lexical, phonological, morphological, syntactic - from Kera'a. While these differences can in part be accounted for by genre differences, they also point to Igu retaining ancestral forms and structures. Igu consists of several different historical layers, both older and more recent, and its research can thus make valuable contributions to elucidating Eastern Himalayan ethnolinguistic history
FEATURE ARTICLE • Mentors: The Hidden Beneficiaries of Mentoring
No full textThe community-based mentorship program MPOWIR (Mentoring Physical Oceanography Women+ to Increase Retention) supports late-stage graduate students and early-career professionals who identify as women or non-binary genders. Its participants engage in mentorship training and professional development, facilitate group mentoring, and draw attention to barriers women and non-binary genders face in physical oceanography. MPOWIR was created to increase the retention of women in physical oceanography in early career stages but has unexpectedly benefited the MPOWIR community beyond graduate students and early career professionals. Senior leaders participating as mentors in MPOWIR report a renewed sense of purpose, new research collaborations, a chance to challenge their own biases, learning new ways to support mentees at their home institutions, awareness about career trajectories outside academia, and a stronger sense of community amid researchers who often felt isolated due to lack of diversity in their ranks. As they guide and inspire the next generation, mentors reflect on their own career struggles and advise on changes that will create a more equitable future for the discipline. This paper highlights the impacts of MPOWIR mentorship on senior leaders in physical oceanography and demonstrates that mentorship is a two-way exchange that energizes and inspires all participants to become active agents of change. It concludes with reflections on how institutions and organizations can facilitate effective mentoring and remove barriers to the professional development of senior leaders in mentoring roles
ACCELERATED CHEMICAL EXCHANGE SATURATION TRANSFER (CEST) MRI ACROSS THE BRAIN, SPINE, AND HEART
Get PDFChemical Exchange Saturation Transfer (CEST) MRI offers a radiation-free window into tissue metabolism by exploiting frequency-selective saturation transfer between labile solute protons and bulk water. Yet five years ago, its deployment remained constrained to small research cohorts owing to scan times exceeding 30 minutes and motion artifacts that contaminated quantitative metrics. This dissertation introduces and validates a pan-organ acceleration framework that uses low-rank Multitasking reconstruction across k-space, offset, respiratory, and cardiac dimensions. Three anatomically and physiologically distinct studies illustrate the framework’s breadth. Brain: A 15-offset protocol acquired with a continuous-wave saturation train readout reduced brain scan time from 7 min/slice to 2 mins/slice, and a U-Net restored the CEST maps. Spine: A steady-state 3D qCEST sequence (32 slices) profiled lumbar discs in 32 mins during free breathing. Low-rank tensor factorization disentangled offset and respiratory motion while preserving 1-mm isotropic resolution. In a porcine disc-degeneration model (n = 6) in the swine model, exchange rate (ksw) maps correlated with Glasgow Pain Scores (r2 = 0.58 for MTR), and a Permuted Random Forest classifier achieved 78% accuracy in classifying back pain scores. Heart: Free-breathing saturation-steady-state (ss-CEST) imaging captured three short-axis slices in 8 min. Respiratory motion was resolved by principal-component analysis of self-navigators, and multipool fitting to map creatine and phosphocreatine pools. In chronic myocardial-infarction pigs, creatine signal was 26 % lower in infarct core versus remote myocardium (p < 0.01), and transient phosphocreatine depletion during dobutamine stress was observed in healthy controls. 
Economically driven constrained optimization and control: Algorithms, formal guarantees, and applications in optimal microgrid sizing and dispatch
Get PDFThe modern electricity grid is fast changing with a shift from traditional centralized generation sources like fossil-fuel and nuclear power plants to distributed variable renewable energy sources (VRES) like wind and photovoltaic (PV) generators. However, VRES are intermittent in nature which can potentially lead to power imbalance in the electric grid, thereby risking grid stability. A cost-effective approach to mitigate these intermittencies is by integrating dispatchable energy resources like electric vehicles (EVs) and battery energy storage systems (BESS) in commercial microgrids to minimize the power fluctuations and provide additional grid services. However, for commercial microgrid operators to avail the maximum benefits from the dispatchable energy resources, it is important to appropriately size them during installation, and once installed it is vital to optimally dispatch them in real-time to realize the maximum electricity cost savings — two critical directions of research we contribute to, in this dissertation.In the first part of the dissertation, we focus on developing EV charging infrastructure sizing solutions — to assist building owners in making a quick and informed choice for different ‘smart’/‘dumb’ and unidirectional/bidirectional EV charging strategies (V0G/V1G/V2G). We first analyzed the impact of an idealized EV commuter fleet workplace charging on commercial building electricity costs following the installation of a variable number of EV charging stations by leveraging an economic optimization model. Next, we developed accurate analytical expressions to estimate the optimal EV charging behavior and benefits even without economic optimization using the exogenous model inputs.In the second part of the dissertation, we first focus on developing application-agnostic, computationally inexpensive, real-time model predictive control (MPC) algorithms with provable guarantees. Here, we developed an adaptive relaxation-based stochastic MPC framework for generic discrete linear time invariant systems with provable nonconservative chance constraint satisfaction in closed-loop. Next, we proposed an economic MPC formulation for generic deterministic discrete non-linear time varying systems, with provable no-worse closed-loop asymptotic economic cost guarantees as compared to any arbitrary solution of the system under the same constraints, known only until the current time-step. Second, we demonstrated that both our proposed methods, when implemented for a real-life microgrid with BESS, VRE generation and load demand, shows superior electricity cost savings as compared to the traditional and state-of-the-art methods. 
Swiftn: Accelerating Quantum Circuit Simulation Through Tensor Optimization
No full textQuantum computers are evolving at a rapid pace and are considered next-generation computers with high computational capabilities. However, due to the unique characteristics of qubits, state-of-the-art quantum computers are vulnerable to noise caused by qubit instability. To overcome this, highperformance computing (HPC) systems are utilized for quantum circuit simulations to evaluate complex quantum algorithms with great accuracy. However, quantum circuit simulations have high computational demands, and the data volume increases exponentially as the number of qubits increases. In this paper, we propose SWIFTN, a quantum circuit simulation optimization framework for HPC systems with scalability. To achieve this, it enhances parallelism by dividing the tensor networks and distributing them across multiple GPUs and nodes. Additionally, it reduces computational costs by bypassing tasks through intermittent tensor contraction. Finally, to mitigate the degradation in accuracy due to intermittent tensor contraction,SWIFTNperforms amplitude adjustments. We implement and evaluateSWIFTNusing a Perlmutter supercomputer. Our evaluation results using popular quantum algorithm benchmark (i.e., QAOA) shows thatSWIFTNcan improve the performance by with 99.997 % accuracy
Children’s Vocabulary, Math, and Social-Emotional Learning From Interactive Media: The Role of Choice, Agency, and Repetition in App Design
No full textPrediction of vacancy defect diffusion paths in high entropy alloys via machine learning on molecular dynamics data
Get PDFIdentifying the diffusion path of point defects is a critical step in understanding their evolution and the mechanisms of related phenomena. Defect diffusion occurs at small length and time scales, with impacts on material properties that may continue to evolve over ns to μs, ms, and the continuum scale (s, min, etc., and cm, m, etc.). The time scale accessible to molecular dynamics (MD) simulations is limited by small step sizes, typically in the fs range. Thus, surrogate models of MD simulations through machine learning (ML)-based algorithms are of great interest, especially for complex systems such as high entropy alloys (HEAs). In this work, dynamics governing vacancy migration in HEA were approximated with graph convolutional network (GCN) models as ansatzes for kinetic Monte Carlo (KMC) rate catalogs. Network design considered that diffusion in crystalline solids generally depends on interactions between defects and their immediate neighbor atoms. Graphs represented the vacancy surroundings, MD-generated trajectories provided training and comparison datasets, and unsupervised GCN models approximated interatomic dynamics governing vacancy migration in HEAs as ansatzes for KMC. A proof-of-concept model trained on MD data for the Fe, Ni, Cr, Co, and Cu HEA environment was used with two different neighbor interactions to assess the feasibility of training a GCN to predict vacancy defect transition rates in the HEA environment. The resulting setup rapidly generated MD-formatted synthetic trajectories based on dynamics learned from the MD training set, with a time acceleration of roughly two orders of magnitude and a similar diffusion coefficient to MD observations. Additionally, Nudged Elastic Band (NEB) calculations were performed on randomly generated FeNiCrCoCu HEA structures to determine vacancy migration barriers across nearest-neighbor sites. Transition probabilities for each jump, categorized by atomic type, were extracted from these calculations. NEB-based and GCN-based approaches led to similar outcomes
Designing an exciton-condensate Josephson junction in quantum Hall heterostructures
Get PDFThe exciton condensate, a counterpart of superconductors realized in two-dimensional quantum Hall bilayer systems, has yet to demonstrate definitive experimental evidence of phase coherence such as the Josephson effect. This work introduces a gate-defined Josephson junction designed to exhibit the in-plane Josephson effect in van der Waals heterostructures. Unlike superconducting S-I-S junctions, the proposed device leverages a nearly layer-polarized gated region to mediate Josephson coupling via layer pseudospin variation. This coupling mechanism is tunable through gate voltages and magnetic fields, offering a high degree of experimental control. We demonstrate the feasibility of the design using realistic parameters, paving the way for direct observation of excitonic phase coherence in clean quantum Hall systems