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Research on de-centering damage and trauma in human-computer interactions wins Best Paper Award
The paper “Flourishing in the Everyday: Moving Beyond Damage-Centered Design in HCI for BIPOC Communities,” written by several contributors, including assistant professor Alexandra To and PhD. student Dilruba Showkat, has won a Best Paper Award from the Association for Computing Machinery: Designing Interactive Systems Conference. The abstract reads, in part: “Research and design in human-computer interaction centers problem-solving, causing a downstream effect of framing work with and for marginalized communities predominantly from the lens of deficit and damage. … However, we observe an additional need to center positive aspects of humanity … particularly for Black, Indigenous, and People of Color.”
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NSF grant awarded to Northeastern researchers for iron-powder as energy storage mechanism
“Mechanical and industrial engineering professors Yiannis Levendis, Hameed Metghalchi, and associate professor Randall Erb were awarded a $600,000 NSF grant for ‘A Study on Burning Iron Particles as Carbon-Free Circular Fuels With Co-Generation of Value-Added Nanomaterials.'”
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$1.2M NSF grant to enable data privacy with GPU-accelerated encryption
“Electrical and computer engineering professor David Kaeli, in collaboration with Ajay Joshi from Boston University, was awarded a $1.2M NSF grant for ‘Architecting GPUs for Practical Homomorphic Encryption-Based Computing.'”
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Dahiya awarded NSF Eager grant for next-gen robotic e-skin
“Electrical and computer engineering professor Ravinder Dahiya was awarded a $230,000 NSF Eager grant for ‘Flexible and Compressible e-Skin Integrated With Soft Magnetic Coil Based Ultra-Thin Actuator and Touch Sensor for Robotics Applications.'” From the abstract: “Replication of Natural Skin characteristics is critically important for smooth operations of Robots. … E-Skin variants thus far have neglected the fact that natural skin has receptors/sensors embedded in soft tissues … coupled with muscles. … To address this longstanding shortcoming … this project will evaluate the feasibility of a soft and compressible e-Skin that will have touch sensor integrated with soft electromagnetic coil-based…
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‘Study on High Availability and Fault Tolerance’
“Availability is one of the most important requirements for modern computing systems. In cloud computing, it is common to use it as a key factor in adopting a cloud service. This paper studies the breakdown in calculating the availability and proposes a conceptual model as middleware. … Through simulations tests, we verified that the proposed model is able to detect the system crash in sub-seconds and improve the overall availability of the system compared to currently used industry solutions.” Find the paper and full list of authors at the 2023 International Conference on Computing, Networking and Communications.
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‘Real-Time Search and Rescue Using Remotely Piloted Aircraft System With Frame Dropping’
“Usage of Artificial Intelligence (AI) technology to aid the Remotely Piloted Aircraft System (RPAS) helps to get accurate imagery along with vital ground details, which as a result boosts the Search and Rescue operations. Since the search must be done quickly, real-time video processing is essential for survival. Our solution attempts to integrate image processing, more specifically, the You Only Look Once (YOLO) algorithm to detect humans in all environmental conditions.” Find the paper and full list of authors at the 2023 International Conference on Computing, Networking and Communications.
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‘Emergency Surgical Scheduling Model Based on Moth-Flame Optimization Algorithm’
“In this paper, we propose an optimization approach based on an improved Moth Flame optimization (MFO) algorithm for solving emergency operating room scheduling problems. The purpose of the MFO is to minimize the maximum span of operations, ensuring patients receive their surgeries in a timely manner. This nature-inspired algorithm stimulates the moth’s special navigation method at night called transverse orientation. The moth uses the moonlight to sustain a fixed angle to the moon, therefore, guaranteeing a straight line.” Find the paper and full list of authors at the 2023 International Conference on Computing, Networking and Communications.
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‘Multi-Agent Reinforcement Learning Based on Representational Communication for Large-Scale Traffic Signal Control’
“Traffic signal control (TSC) is a challenging problem within intelligent transportation systems and has been tackled using multi-agent reinforcement learning (MARL). … Many deep MARL communication frameworks proposed for TSC allow agents to communicate with all other agents at all times, which can add to the existing noise in the system and degrade overall performance. In this study, we propose a communication-based MARL framework for large-scale TSC. Our framework allows each agent to learn a communication policy that dictates ‘which’ part of the message is sent ‘to whom’.” Find the paper and full list of authors at IEEE Access.
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‘On Centralized Critics in Multi-Agent Reinforcement Learning’
“Centralized Training for Decentralized Execution, where agents are trained offline in a centralized fashion and execute online in a decentralized manner, has become a popular approach in Multi-Agent Reinforcement Learning (MARL). In particular, it has become popular to develop actor-critic methods that train decentralized actors with a centralized critic … [however,] using a centralized critic in this context has yet to be sufficiently analyzed theoretically or empirically. In this paper, we therefore formally analyze centralized and decentralized critic approaches.” Find the paper and full list of authors at the Journal of Artificial Intelligence Research.
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‘Discovering Variable Binding Circuitry With Desiderata’
“Recent work has shown that computation in language models may be human-understandable, with successful efforts to localize and intervene on both single-unit features and input-output circuits. Here, we introduce an approach which extends causal mediation experiments to automatically identify model components responsible for performing a specific subtask by solely specifying a set of desiderata, or causal attributes of the model components executing that subtask.” Find the paper and full list of authors at ArXiv.
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‘Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task’
“Language models show a surprising range of capabilities, but the source of their apparent competence is unclear. Do these networks just memorize a collection of surface statistics, or do they rely on internal representations of the process that generates the sequences they see? We investigate this question in a synthetic setting by applying a variant of the GPT model to the task of predicting legal moves in a simple board game, Othello.” Find the paper and full list of authors at Open Review.
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‘Mass-Editing Memory in a Transformer’
“Recent work has shown exciting promise in updating large language models with new memories, so as to replace obsolete information or add specialized knowledge. However, this line of work is predominantly limited to updating single associations. We develop MEMIT, a method for directly updating a language model with many memories, demonstrating experimentally that it can scale up to thousands of associations for GPT-J (6B) and GPT-NeoX (20B), exceeding prior work by orders of magnitude.” Find the paper and full list of authors at Open Review.
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Ramdin delivers lecture on mentorship and excellence at ABNF
Valeria Ramdin, associate clinical professor and director of global health nursing, presented a talk on “Mentoring BIPOC Nursing Faculty Toward Leadership Excellence: A Concept Analysis With Historical Research” at the 35th Annual Meeting & Scientific Conference for the Association of Black Nursing Faculty.
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‘Environmental and Geographical Factors Structure Cauliflower Coral’s Algal Symbioses Across the Indo-Pacific’
“The symbioses between corals and endosymbiotic dinoflagellates have been described as a flexible relationship whose dynamics could serve as a source of resilience for coral reef ecosystems. However, the factors that drive the establishment and maintenance of this co-evolutionary relationship remain unclear. We examined the environmental and geographical factors structuring dinoflagellate communities in a wide-ranging Indo-Pacific coral to begin to address this gap. … We provide further support for the hypothesis that coral’s Symbiodiniaceae communities could facilitate host resilience to thermal stress.” Find the paper and full list of authors at the Journal of Biogeography.
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Northeastern University launches fully automated and virtualized O-RAN private 5G network with AI automation
“The Institute for the Wireless Internet of Things (WIoT) at Northeastern University and its Open6G R&D Center announce the availability of the first production-ready private 5G network fully automated through Artificial Intelligence (AI). The system is built on open-source components enabling a fully virtualized, programmable O-RAN compliant network in a campus environment.”
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Chowdhury selected as finalist for Blavatnik National Award in Physical Sciences & Engineering
“Electrical and computer engineering professor Kaushik Chowdhury was selected as a finalist for the 2023 Blavatnik National Awards for Young Scientists in Physical Sciences & Engineering for addressing the global need of telecommunications spectrum scarcity, as well as improve connectivity by designing next generation wireless systems and machine learning-based network operations. The prestigious Blavatnik National Awards for Young Scientists is the largest unrestricted prize for early career scientists and honors outstanding young scientists and engineers under the age of 42.”
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‘Semantics-Aware Dataset Discovery From Data Lakes With Contextualized Column-Based Representation Learning’
“Dataset discovery from data lakes is essential in many real application scenarios. In this paper, we propose Starmie, an end-to-end framework for dataset discovery from data lakes (with table union search as the main use case). Our proposed framework features a contrastive learning method to train column encoders from pre-trained language models in a fully unsupervised manner. The column encoder of Starmie captures the rich contextual semantic information within tables by leveraging a contrastive multi-column pre-training strategy.” Find the paper and the full list of authors in the Proceedings of the VLDB Endowment.
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‘Explaining Dataset Changes for Semantic Data Versioning With Explain-Da-V’
“In multi-user environments in which data science and analysis is collaborative, multiple versions of the same datasets are generated. While managing and storing data versions has received some attention in the research literature, the semantic nature of such changes has remained under-explored. In this work, we introduce Explain-Da-V, a framework aiming to explain changes between two given dataset versions. Explain-Da-V generates explanations that use data transformations to explain changes. We further introduce a set of measures that evaluate the validity, generalizability, and explainability of these explanations.” Find the paper and full list of authors in VLDB Endowment proceedings.
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‘Table Discovery in Data Lakes: State-of-the-Art and Future Directions’
“Data discovery refers to a set of tasks that enable users and downstream applications to explore and gain insights from massive collections of data sources such as data lakes. In this tutorial, we will provide a comprehensive overview of the most recent table discovery techniques developed by the data management community. We will cover table understanding tasks such as domain discovery, table annotation, and table representation learning which help data lake systems capture semantics of tables.” Find the paper and the full list of authors in the Companion of the 2023 International Conference on Management of Data.
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‘SANTOS: Relationship-Based Semantic Table Union Search’
“Existing techniques for unionable table search define unionability using metadata (tables must have the same or similar schemas) or column-based metrics (for example, the values in a table should be drawn from the same domain). In this work, we introduce the use of semantic relationships between pairs of columns in a table to improve the accuracy of the union search. Consequently, we introduce a new notion of unionability that considers relationships between columns, together with the semantics of columns, in a principled way.” Find the paper and full list of authors in the Proceedings of ACM on Management of Data.
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‘Direct Superpoints Matching for Fast and Robust Point Cloud Registration’
“Although deep neural networks endow the downsampled superpoints with discriminative feature representations, directly matching them is usually not used alone in state-of-the-art methods. … Existing approaches use the coarse-to-fine strategy to propagate the superpoints correspondences to the point level, which are not discriminative enough and further necessitates the postprocessing refinement. In this paper, we present a simple yet effective approach to extract correspondences by directly matching superpoints using a global softmax layer in an end-to-end manner, which are used to determine the rigid transformation between the source and target point cloud.” Find the paper and full list of authors at…
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‘Do Machine Learning Models Produce TypeScript Types That Type Check? (Artifact)’
“Type migration is the process of adding types to untyped code to gain assurance at compile time. TypeScript and other gradual type systems facilitate type migration by allowing programmers to start with imprecise types and gradually strengthen them. … In this paper we argue that accuracy can be misleading, and we should address a different question: can an automatic type migration tool produce code that passes the TypeScript type checker? We present TypeWeaver, a TypeScript type migration tool that can be used with an arbitrary type prediction model.” Find the paper and full list of authors at Dagstuhl Research Online…
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‘Online Learning in Multi-Unit Auctions’
“We consider repeated multi-unit auctions with uniform pricing, which are widely used in practice for allocating goods such as carbon licenses. In each round, K identical units of a good are sold to a group of buyers that have valuations with diminishing marginal returns. The buyers submit bids for the units, and then a price p is set per unit so that all the units are sold. We consider two variants of the auction, where the price is set to the K-th highest bid and (K+1)-st highest bid, respectively.” Find the paper and full list of authors at ArXiv.
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‘State of the Art of Visual Analytics for eXplainable Deep Learning’
“The use and creation of machine-learning-based solutions to solve problems or reduce their computational costs are becoming increasingly widespread in many domains. … This survey aims to (i) systematically report the contributions of Visual Analytics for eXplainable Deep Learning; (ii) spot gaps and challenges; (iii) serve as an anthology of visual analytical solutions ready to be exploited and put into operation by the Deep Learning community (architects, trainers and end users) and (iv) prove the degree of maturity, ease of integration and results for specific domains.” Find the paper and full list of authors at Computer Graphics Forum.
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Libby awarded $1.96M Early-Stage Investigator Grant from NIH
“Elizabeth Libby, assistant professor of bioengineering, recently received a five-year, $1.96 million Early Stage Investigator R35 MIRA (Maximizing Investigator’s Research Award) grant from the National Institutes of Health for ‘Physiological and Developmental Role of Bacterial Ser/Thr Kinases.’ Libby’s research is focused on how bacteria develop resistance at the cellular level—knowledge that will be crucial to the development of more effective antibiotics.”
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Rouhanifard receives $3.4M NIH grant for modified mRNA research
“Bioengineering assistant professor Sara Rouhanifard was awarded a $3.4 million NIH R01 grant for ‘Synthetic mRNA Control Set for Nanopore-Based Pseudouridine Modification Profiling in Human Transcriptomes.’ The research has the potential to vastly increase insight into the epitranscriptome—changes in chemical modifications of RNA that can affect gene expression within cells—which could help identify new therapeutic targets and lead to new classes of drugs.”
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‘Adapting Transformer Language Models for Predictive Typing in Brain-Computer Interfaces’
“Brain-computer interfaces (BCI) are an important mode of alternative and augmentative communication for many people. Unlike keyboards, many BCI systems do not display even the 26 letters of English at one time, let alone all the symbols in more complex systems. Using language models to make character-level predictions, therefore, can greatly speed up BCI typing (Ghosh and Kristensson, 2017). While most existing BCI systems employ character n-gram models or no LM at all, this paper adapts several wordpiece-level Transformer LMs to make character predictions and evaluates them on typing tasks.” Find the paper and the full list of authors at ArXiv.
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‘Composition and Deformance: Measuring Imageability With a Text-to-Image Model’
“Although psycholinguists and psychologists have long studied the tendency of linguistic strings to evoke mental images in hearers or readers, most computational studies have applied this concept of imageability only to isolated words. Using recent developments in text-to-image generation models, such as DALLE mini, we propose computational methods that use generated images to measure the imageability of both single English words and connected text. We sample text prompts for image generation … [and] subject these prompts to different deformances to examine the model’s ability to detect changes in imageability caused by compositional change.” Find the paper and full authors list…