Title
Topic
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‘NapSS: Paragraph-Level Medical Text Simplification via Narrative Prompting and Sentence-Matching Summarization’
“Accessing medical literature is difficult for laypeople as the content is written for specialists and contains medical jargon. Automated text simplification methods offer a potential means to address this issue. In this work, we propose a summarize-then-simplify two-stage strategy, which we call NapSS, identifying the relevant content to simplify while ensuring that the original narrative flow is preserved. In this approach, we first generate reference summaries via sentence matching between the original and the simplified abstracts.” Read the paper and see the full list of authors in ArXiv.
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Bajpayee spotlight speaker at Orthopedic Research Society
Associate professor Ambika Bajpayee presented as a spotlight speaker at the 2023 Orthopedic Research Society conferences, from February 10-14. Her talk was on “Bioelectricity for Cartilage Drug Delivery and Imaging.”
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Riley receives Black Heritage Award for ‘dedicated service to Northeastern’
“Civil and environmental engineering lecturer and operations manager Rozanna Riley was selected to receive the Black Heritage Award, which is given to those Northeastern staff and administrators in recognition of their dedicated service to Northeastern, to the students, and/or to the John D. O’Bryant African American Institute.”
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Patent for ultrasonic, underwater communication system
“Electrical and computer engineering assistant professor Francesco Restuccia, research assistant professor Emrecan Demirors and professor Tommaso Melodia were awarded a patent for “Underwater ultrasonic communication system and method.”
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‘Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion’
“Graph neural networks are widely used tools for graph prediction tasks. Motivated by their empirical performance, prior works have developed generalization bounds for graph neural networks, which scale with graph structures in terms of the maximum degree. In this paper, we present generalization bounds that instead scale with the largest singular value of the graph neural network’s feature diffusion matrix.” Read the paper and see the full list of authors in ArXiv.
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‘How Many and Which Training Points Would Need To Be Removed To Flip this Prediction?’
“We consider the problem of identifying a minimal subset of training data St such that if the instances comprising St had been removed prior to training, the categorization of a given test point xt would have been different. … We propose comparatively fast approximation methods to find St based on influence functions, and find that—for simple convex text classification models—these approaches can often successfully identify relatively small sets of training examples which, if removed, would flip the prediction.” Read the paper and see the full list of authors in ArXiv.
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Ganguly presents ‘a personal journey’ of climate resistance
“Auroop Ganguly, professor of civil and environmental engineering at Northeastern University, will share his personal journey building climate resilience. Professor Ganguly co-founded the climate analytics startup risQ, which models the complex financial risks posed by climate change.”
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Hajjar receives $3.1 million grant for carbon-neutral construction research
“In a new $3.1 million grant from the Department of Energy’s Advanced Research Projects Agency-Energy (ARPA-E), Northeastern department of civil and environmental engineering chair and CDM Smith Professor Jerome Hajjar will lead a multi-institution team of researchers developing a new carbon sequestration technique using cross-laminated timber composite floor systems in bolted steel construction for building structures. The new structural method aims to decrease the use of steel while increasing the use of carbon-storing timber and design for deconstruction methods.”
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‘An Optimized Acidic Digestion for the Isolation of Microplastics From Biota-Rich Samples’
“Plastic pollution is a growing concern. To analyze plastics in environmental samples, plastics need to be isolated. We present an acidic/oxidative method optimized to preserve plastics while digesting synthetic cellulose acetate and a range of organics encountered in environmental samples.” Find the paper and the full list of authors in Environmental Pollution.
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‘Generative Adversarial Symmetry Discovery’
“Despite the success of equivariant neural networks in scientific applications, they require knowing the symmetry group a priori. However, it may be difficult to know which symmetry to use as an inductive bias in practice. Enforcing the wrong symmetry could even hurt the performance. In this paper, we propose a framework, LieGAN, to automatically discover equivariances from a dataset using a paradigm akin to generative adversarial training.” Read the paper and see the full list of authors in ArXiv.
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‘One-Shot Empirical Privacy Estimation for Federated Learning’
“Privacy estimation techniques for differentially private (DP) algorithms are useful for comparing against analytical bounds, or to empirically measure privacy loss in settings where known analytical bounds are not tight. … In this work, we present a novel “one-shot” approach that can systematically address these challenges, allowing efficient auditing or estimation of the privacy loss of a model during the same, single training run used to fit model parameters, and without requiring any a priori knowledge about the model architecture or task.” Read the paper and see the full list of authors in ArXiv.
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Byron Wallace named Sy and Laurie Sternberg Interdisciplinary Associate Professor for work on machine learning
“Professor Byron Wallace ‘has been awarded Northeastern’s Sy and Laurie Sternberg Interdisciplinary Associate Professorship for his work’ on applying machine learning and natural language processing to healthcare.” In an interview, Wallace gave one example of these applications: “the evolution of NLP systems [means they] can now spit out very plausible text, which medical practitioners can use to synthesize medical evidence and make better decisions for patient treatment.”
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DeSteno podcast ‘How God Works’ is Ambie finalist
Professor of psychology David DeSteno’s podcast “How God Works” was a finalist for “Best Personal Growth/Spirituality Podcast” in the Ambies, the top awards show in the podcast industry. “How God Works” interrogates why, despite the fact that “religion and science often seem at odds, there’s one thing they can agree on: people who take part in spiritual practices tend to live longer, healthier, and happier lives.” The Ambies award show took place on March 7.
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Reasoning through the picture: Machine learning between words and images
Researchers have identified a new “cross-modal retrieval” method to operate between “language and vision domains.” From their abstract: “To address this issue, we introduce an intuitive and interpretable model to learn a common embedding space for alignments between images and text descriptions. Specifically, our model first incorporates the semantic relationship information into visual and textual features by performing region or word relationship reasoning.” Read “Image-Text Embedding Learning via Visual and Textual Semantic Reasoning” and see the full list of authors in the IEEE Transactions on Pattern Analysis and Machine Intelligence.
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‘Dissociation Between Linguistic and Nonlinguistic Statistical Learning in Children With Autism’
“Statistical learning (SL), the ability to detect and extract regularities from inputs, is considered a domain-general building block for typical language development. We compared 55 verbal children with autism (ASD, 6–12 years) and 50 typically-developing children in four SL tasks. The ASD group exhibited reduced learning in the linguistic SL tasks (syllable and letter), but showed intact learning for the nonlinguistic SL tasks (tone and image).” Read the paper and see the full list of authors in the Journal of Autism and Developmental Disorders.
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‘Backdoor Attacks in Peer-to-Peer Federated Learning’
“We study backdoor attacks in peer-to-peer federated learning systems on different graph topologies and datasets. We show that only 5% attacker nodes are sufficient to perform a backdoor attack with 42% attack success without decreasing the accuracy on clean data by more than 2%. We also demonstrate that the attack can be amplified by the attacker crashing a small number of nodes.” Read the paper and see the full list of authors in ArXiv.
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‘Stochastic Minimum Vertex Cover in General Graphs: A 3/2-Approximation’
“Our main result is designing an algorithm that returns a vertex cover of G* with size at most (3/2+ϵ) times the expected size of the minimum vertex cover, using only O(n/ϵp) non-adaptive queries. This improves over the best-known 2-approximation algorithm by Behnezhad, Blum, and Derakhshan [SODA’22], who also show that Ω(n/p) queries are necessary to achieve any constant approximation.” Read the paper and see the full list of authors in ArXiv.
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‘A Closed-Form Solution of the Smoke Filling Time and Descent History in Enclosure Growing Fires With Floor Leaks’
“For the smoke filling time or smoke descent history in enclosure fires with floor leaks, the existing close-formed solutions are all based on the hypothesis that the expansion term is negligible. However, when the smoke interface is near to the floor level, the expansion term is more important than the plume entrainment term and the existing solutions give unrealistic predictions.” Read the paper and see the full list of authors in Fire Technology.
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‘From Robustness to Privacy and Back’
“We study the relationship between two desiderata of algorithms in statistical inference and machine learning: differential privacy and robustness to adversarial data corruptions. … Dwork and Lei (STOC 2009) … observed that private algorithms satisfy robustness, and gave a general method for converting robust algorithms to private ones. However, all general methods for transforming robust algorithms into private ones lead to suboptimal error rates. Our work gives the first black-box transformation that converts any adversarially robust algorithm into one that satisfies pure differential privacy.” Read the paper and see the full list of authors in ArXiv.
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Northeastern professors win 2023 Acorn Innovation Awards, helping bring research to market
“Electrical and computer engineering assistant professor Sarah Ostadabbas, professor Deniz Erdogmus and mechanical and industrial engineering associate professor Yi Zheng received MassVentures Acorn Innovation Awards to assist them in testing the viability of their technologies and potentially bringing their research to market.”
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Sharifkhani receives Riesman Professorship to study ‘macroeconomic risks’ on local labor markets
Assistant professor of finance Ali Sharifkhani has received the Riesman Professorship in the D’Amore-McKim School of Business. Sharifkhani will use the professorship to “study the effects of a firm’s local labor market on its exposure to macroeconomic risks and the expected return on its equity.”
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Liu receives Walsh Professorship to study ‘diversity faultlines’ in business leadership
Associate professor of accounting Kelvin Liu has received the Walsh Professorship from the D’Amore-McKim School of Business. He will use the professorship to “study the effect of diversity faultlines among senior executives on internal governance and corporate destabilization,” the school of business wrote.
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‘Effects of Inhaled Cannabis High in Δ9-THC or CBD on the Aging Brain: A Translational MRI and Behavioral Study’
“To understand the neurobiological effects of cannabis on the aging brain, 19–20 months old mice were divided into three groups exposed to vaporized cannabis containing. … Voxel based morphometry, diffusion weighted imaging, and resting state functional connectivity data were gathered after 28 days of exposure and following a two-week washout period. … Chronic inhaled CBD resulted in enhanced global network connectivity that persisted after drug cessation. The behavioral consequences of this sustained change in brain connectivity remain to be determined.” Read the paper and see the full list of authors in Frontiers in Aging Neuroscience.
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Reframing disability as ‘a property of both humans and machines’
Laura Forlano, professor of art and design and communication studies, has a new article titled “Living Intimately with Machines: Can AI Be Disabled?” Forlano proposes to take seriously the idea that we can “understand disability to be a property of both humans and machines.”