Title
Topic
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‘Changes in Water Flow Alter Community Dynamics in Oyster Reefs’
Professor of marine and environmental sciences Jonathan Grabowski, with professor Sean Powers from the University of South Alabama, has published a new paper in Ecosphere. “Water flow can influence individual fitness and local population dynamics, but less is known about the collective response of natural communities to alteration in water flow. … To examine how changes in water flow affected initial larval settlement patterns of epifaunal and infaunal animals, colonization of larger individuals, and prey survival, we manipulated water flow (−50% or +47%) in situ using large wooden channels over small experimental oyster reefs.”
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‘Why Do We Need to Learn about Citational Practices?’
“How do you decide which papers to cite, how many, and from which particular sources? We reflect and discuss the implications of these critical questions based on our experiences in the panel and workshops on the topic of citational justice that took place at CSCW, CLIHC, and India HCI in 2021.” Read the paper and see the full list of authors in XRDS: Crossroads.
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‘Automated Active Space Selection With Dipole Moments’
“Selecting a suitable active space for multireference calculations is nontrivial, and the selection of an unsuitable active space can sometimes lead to results that are not physically meaningful. … In this work, we have developed and evaluated two protocols for automated selection of the active space for multireference calculations based on a simple physical observable, the dipole moment, for molecules with nonzero ground-state dipole moments.” Find the paper and the full list of authors at the Journal of Chemical Theory and Computation.
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Fayyad answers ‘Why Most Machine Learning Applications Fail To Deploy’
Despite the recent excitement around artificial intelligence endeavors, especially chatbots like ChatGPT, professor of practice and the executive director of the Institute for Experiential AI at the Roux Institute Usama Fayyad points out how, in enterprise environments, machine learning projects often fail to get off the ground. In evaluating how businesses can more successfully deploy those projects, Fayyad points to several options, including estimating the return on investment, building trust, and more.
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‘Protected or Porous: A Comparative Analysis of Threat Detection Capability of IoT Safeguards’
“Consumer Internet of Things (IoT) devices are increasingly common, from smart speakers to security cameras, in homes. Along with their benefits come potential privacy and security threats. To limit these threats a number of commercial services have become available (IoT safeguards). The safeguards claim to provide protection against IoT privacy risks and security threats. However, the effectiveness and the associated privacy risks of these safeguards remains a key open question. In this paper, we investigate the threat detection capabilities of IoT safeguards for the first time.” Read the paper and see the full list of authors in ArXiv.
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‘High Purity Hydrogen Separation With HT-PBI Based Electrochemical Pump Operation at 120 °C’
“Electrochemical Hydrogen Pumps (EHP) provide a unique highly efficient means of separating and compressing hydrogen with continuous steady-state operation. In this paper, we demonstrate the performance of a commercially available, polybenzimidazole (PBI) membrane based platform as a benchmark for ultra-high efficiency performance.” Read the paper and see the full list of authors in the Journal of The Electrochemical Society.
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Isolating plastics to analyze pollutants
Professor of marine and environmental sciences Aron Stubbins, working with graduate teaching assistant Erin Tuttle, has published a new paper in Environmental Pollution describing “An acidic/oxidative digestion that quantitatively removes cellulose acetate.” This process “preserve[s] plastics while digesting synthetic cellulose acetate and a range of organics encountered in environmental samples,” thus isolating the plastics for further study. Find “An optimized acidic digestion for the isolation of microplastics from biota-rich samples and cellulose acetate matrices” in Environmental Pollution.
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‘Lattice Network for Lightweight Image Restoration’
“Deep learning has made unprecedented progress in image restoration (IR), where residual block (RB) is popularly used and has a significant effect on promising performance. However, the massive stacked RBs bring about burdensome memory and computation cost. To tackle this issue, we aim to design an economical structure for adaptively connecting pair-wise RBs, thereby enhancing the model representation.” Read the paper and see the full list of authors in IEEE Transactions on Pattern Analysis and Machine Intelligence.
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‘Going Beyond Binary: Rapid Identification of Protein–Protein Interaction Modulators’
“Kinetic target-guided synthesis (KTGS) is a powerful screening approach that enables identification of small molecule modulators for biomolecules. While many KTGS variants have emerged, a majority of the examples suffer from limited throughput and a poor signal/noise ratio, hampering reliable hit detection. Herein, we present our optimized multifragment KTGS screening strategy that tackles these limitations.” Read “Going Beyond Binary: Rapid Identification of Protein–Protein Interaction Modulators Using a Multifragment Kinetic Target-Guided Synthesis Approach” and see the full list of authors in the Journal of Medicinal Chemistry.
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Breakthrough in early osteoarthritis diagnoses, potentially improving patient outcomes
“Early [osteoarthritis] diagnosis is critical. … Computed tomography (CT) has been considered for cartilage imaging … by introducing radio-opaque contrast agents like ioxaglate (IOX) into the joint. IOX, however, is anionic and thus repelled by negatively charged cartilage glycosaminoglycans. … Here we engineer optimally charged cationic contrast agents … such that they can penetrate through the full thickness of cartilage.” Read “Cationic Carrier Mediated Delivery of Anionic Contrast Agents in Low Doses Enable Enhanced Computed Tomography Imaging of Cartilage for Early Osteoarthritis Diagnosis” and see the full list of authors in ACS Publications.
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Magowan publishes short story ‘Litter Box’
Kim Magowan, adjunct professor of English and Aurelia H. Reinhardt professor of American literature at Mills College at Northeastern University, has published the short story “Litter Box.” “Litter Box” tells the story of one woman’s spur-of-the-moment trip to London and her confusion around how life has brought her there.
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‘Highly-Sensitive Label-Free Deep Profiling of N-glycans Released From Biomedically-Relevant Samples’
“Alterations of protein glycosylation can serve as sensitive and specific disease biomarkers. Labeling procedures for improved separation and detectability of oligosaccharides have several drawbacks, including incomplete derivatization, side-products, noticeable desialylation/defucosylation, sample loss, and interference with downstream analyses. Here, we develop a label-free workflow based on high sensitivity capillary zone electrophoresis-mass spectrometry (CZE-MS) for profiling of native underivatized released N-glycans.” Read the paper and see the full list of authors at Nature Communications.
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Patent award for device that classifies wireless signals
“Electrical and computer engineering research assistant professor Salvatore D’Oro, William Lincoln Smith Professor Tommaso Melodia, and assistant professor Francesco Restuccia were awarded a patent for ‘Device and Method for Reliable Classification of Wireless Signals.'”
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Patent for ‘Real-Time Cognitive Wireless Networking’
“Electrical and computer engineering assistant professor Francesco Restuccia and William L. Smith Professor Tommaso Melodia were awarded a patent for ‘Real-Time Cognitive Wireless Networking Through Deep Learning in Transmission and Reception Communication Paths.'”
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Using deep neural networks to model complex wave patterns
Researchers have “Developed data-driven models to estimate wave parameters and spectra near the” Chesapeake Bay Bridge Tunnel. “To estimate wave parameters and energy spectra near the CBBT, novel composite data-driven models were developed using the wind, water level, and offshore wave data” were used in “deep neural networks” to model a complex wave environment with relatively low computational resources. Read “Data-driven modeling of Bay-Ocean wave spectra at bridge-tunnel crossing of Chesapeake Bay, USA” and see the full list of authors in Applied Ocean Research.
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‘Nonspherical Ultrasound Microbubbles’
“Bioengineering assistant professor Tao Sun recently published his postdoctoral research from the John A. Paulson School of Engineering and Applied Sciences and Brigham and Women’s Hospital on ‘Nonspherical Ultrasound Microbubbles’ in PNAS.”
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‘Mechanical Properties’ paper featured on cover of Advanced Engineering Materials
Associate professor of mechanical and industrial engineering Yaning Li and co-author Siyao Liu have published a new paper titled “Mechanical Properties of Cochiral and Contrachiral Mechanical Metamaterials Under Different Temperatures,” which was featured on the cover of Advanced Engineering Materials, March 2023. From the abstract: “Cochiral and contrachiral mechanical metamaterials are designed by introducing chiral cells with different handedness to the center of the basic chiral cell. Both single-material designs and multimaterial designs are explored. The designs are fabricated via a multimaterial 3D printer, and uniaxial tension experiments are performed in a thermal chamber at two different temperatures.”
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‘Erasing Concepts From Diffusion Models’
“Motivated by recent advancements in text-to-image diffusion, we study erasure of specific concepts from the model’s weights. While Stable Diffusion has shown promise in producing explicit or realistic artwork, it has raised concerns regarding its potential for misuse. We propose a fine-tuning method that can erase a visual concept from a pre-trained diffusion model, given only the name of the style and using negative guidance as a teacher.” Read the paper and see the full list of authors in ArXiv.
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A bellwether of embeddedness: One professor says that ‘the AI system … keeping me alive is ruining my life’
Professor Laura Forlano writes how an insulin pump AI system, which promised to “dynamically adjust blood sugar when compared to the previous linear system,” has actually required such frequent human-computer interactions as to make it medically detrimental. “Rather than dismiss this particular system as bad engineering,” she argues, “it’s more useful to think of it as a bellwether for a world in which autonomous systems are likely to be increasingly embedded in everyday life.” Read “When Things Go Beep in the Night: The AI system that is keeping me alive is ruining my life” at Data & Society: Points.
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‘The Paradox of Adaptive Trait Clines With Nonclinal Patterns in the Underlying Genes’
“Multivariate climate change presents an urgent need to understand how species adapt to complex environments. Population genetic theory predicts that loci under selection will form monotonic allele frequency clines with their selective environment, which has led to the wide use of genotype–environment associations (GEAs). This study used a set of simulations to elucidate the conditions under which allele frequency clines are more or less likely to evolve as multiple quantitative traits adapt to multivariate environments.” Read the paper and see the full list of authors in PNAS.
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Disability studies research incorporated into robotic sculpture
Laura Forlano, professor of art and design and communication studies, has had her work featured in a “robotic sculpture” designed by multimedia artist Itziar Barrio. “Some of the sculptures are programmed and inscribed with text that Forlano, a Type 1 diabetic, transcribed from the alert and alarm history from her ‘smart’ insulin pump and then annotated with field notes,” writes Smack Mellon, Barrio’s exhibition space in Brooklyn, New York. The exhibition’s title, “did not feel low, was sleeping,” is sourced from one of the sculptures in Barrio’s collaboration with Forlano. The exhibition ran from March to April, 2023.
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‘Automated Grading of Automata With ACL2s’
“Almost all Computer Science programs require students to take a course on the Theory of Computation (ToC) which covers various models of computation. … ToC courses tend to give assignments that require paper-and-pencil solutions. Grading such assignments takes time, so students typically receive feedback for their solutions more than a week after they complete them. We present the Automatic Automata Checker (A2C), an open source library that enables one to construct executable automata using definitions that mimic those found in standard textbooks.” Read the paper and see the full list of authors in ArXiv.
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‘Role of Circular RNA and Its Delivery Strategies to Cancer—An Overview’
“With the passage of years and the progress of research on ribonucleic acids, the range of forms in which these molecules have been observed grows. One of them, discovered relatively recently, is circular RNA – covalently closed circles (circRNA). In recent years, there has been a huge increase in the interest of researchers in this group of molecules.” Read the paper and see the full list of authors in the Journal of Controlled Release.
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‘Autonomous Electrochemical System for Ammonia Oxidation Reaction Measurements at the International Space Station’
“An autonomous electrochemical system prototype for ammonia oxidation reaction (AOR) measurements was efficiently done inside a 4” x 4” x 8” 2U Nanoracks module at the International Space Station (ISS). This device, the Ammonia Electrooxidation Lab at the ISS (AELISS), included an autonomous electrochemical system that complied with NASA ISS nondisclosure agreements, power, safety, security, size constrain, and material compatibility established for space missions.” Read the paper and see the full list of authors in NPJ Microgravity.
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Shrivastava receives patent for self-powered computing architecture
“Electrical and computer engineering assistant professor Aatmesh Shrivastava was awarded a patent for ‘Self-powered analog computing architecture with energy monitoring to enable machine-learning vision at the edge.'”