Title

Topic

  • ‘On Regularity Lemma and Barriers in Streaming and Dynamic Matching’

    “We present a new approach for finding matchings in dense graphs by building on Szemerédi’s celebrated Regularity Lemma. This allows us to obtain non-trivial albeit slight improvements over longstanding bounds for matchings in streaming and dynamic graphs.” Find the paper and full list of authors in the Proceedings of the 55th Annual ACM Symposium on Theory of Computing.

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  • ‘Jointly Extracting Interventions, Outcomes and Findings From RCT Reports With LLMs’

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    “Results from Randomized Controlled Trials (RCTs) establish the comparative effectiveness of interventions, and are in turn critical inputs for evidence-based care. However, results from RCTs are presented in (often unstructured) natural language articles describing the design, execution, and outcomes of trials; clinicians must manually extract findings pertaining to interventions and outcomes of interest from such articles. … We propose and evaluate a text-to-text model built on instruction-tuned Large Language Models (LLMs) to jointly extract Interventions, Outcomes, and Comparators (ICO elements) from clinical abstracts, and infer the associated results reported.” Find the paper and full list of authors at ArXiv.

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  • ‘Revisiting Relation Extraction in the Era of Large Language Models’

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    “Relation extraction (RE) is the core NLP task of inferring semantic relationships between entities from text. Standard supervised RE techniques entail training modules to tag tokens comprising entity spans and then predict the relationship between them. Recent work has instead treated the problem as a sequence-to-sequence task, linearizing relations between entities as target strings to be generated conditioned on the input. Here we … [use] larger language models (GPT-3 and Flan-T5 large) than considered in prior work and evaluat[e] their performance on standard RE tasks under varying levels of supervision.” Find the paper and full list of authors at ArXiv.

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  • ‘RedHOT: A Corpus of Annotated Medical Questions, Experiences and Claims on Social Media’

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    “We present Reddit Health Online Talk (RedHOT), a corpus of 22,000 richly annotated social media posts from Reddit spanning 24 health conditions. … We collect additional granular annotations on identified claims.Specifically, we mark snippets that describe patient Populations, Interventions, and Outcomes (PIO elements) within these. Using this corpus, we introduce the task of retrieving trustworthy evidence relevant to a given claim made on social media. We propose a new method to automatically derive (noisy) supervision for this task which we use to train a dense retrieval model.” Find the paper and full list of authors at ACL Anthology.

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  • ‘SemEval-2023 Task 8: Causal Medical Claim Identification and Related PIO Frame Extraction From Social Media Posts’

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    “Identification of medical claims from user-generated text data is an onerous but essential step for various tasks including content moderation, and hypothesis generation. SemEval-2023 Task 8 is an effort towards building those capabilities and motivating further research in this direction. This paper summarizes the details and results of shared task 8 at SemEval-2023 which involved identifying causal medical claims and extracting related Populations, Interventions, and Outcomes (“PIO”) frames from social media (Reddit) text.” Find the paper and full list of authors at ACL Anthology.

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  • ‘Speak Much, Remember Little: Cryptography in the Bounded Storage Model, Revisited’

    “The goal of the bounded storage model (BSM) is to construct unconditionally secure cryptographic protocols, by only restricting the storage capacity of the adversary, but otherwise giving it unbounded computational power. Here, we consider a streaming variant of the BSM, where honest parties can stream huge amounts of data to each other so as to overwhelm the adversary’s storage, even while their own storage capacity is significantly smaller than that of the adversary.” Find the paper and full list of authors at Advances in Cryptology—EUROCRYPT 2023.

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  • ‘Exploring the Role of Audio in Video Captioning’

    “Recent focus in video captioning has been on designing architectures that can consume both video and text modalities, and using large-scale video datasets with text transcripts for pre-training, such as HowTo100M. … In this work, we present an audio-visual framework, which aims to fully exploit the potential of the audio modality for captioning. Instead of relying on text transcripts extracted via automatic speech recognition (ASR), we argue that learning with raw audio signals can be more beneficial, as audio has additional information including acoustic events, speaker identity, etc.” Find the paper and full list of authors at ArXiv.

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  • ‘Statistical Detection of Differentially Abundant Proteins in Experiments with Repeated Measures Designs and Isobaric Labeling’

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    “Repeated measures experimental designs, which quantify proteins in biological subjects repeatedly over multiple experimental conditions or times, are commonly used in mass spectrometry-based proteomics. Such designs distinguish the biological variation within and between the subjects and increase the statistical power of detecting within-subject changes in protein abundance. Meanwhile, proteomics experiments increasingly incorporate tandem mass tag (TMT) labeling. … This manuscript proposes a family of linear mixed-effects models for differential analysis of proteomics experiments with repeated measures and TMT multiplexing.” Find the paper and list of authors in the Journal of Proteome Research.

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  • ‘Semi-Quantitative Detection of Pseudouridine Modifications and Type I/II Hypermodifications in Human mRNAs … Direct Long-Read Sequencing’

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    “Here, we develop and apply a semi-quantitative method for the high-confidence identification of pseudouridylated sites on mammalian mRNAs via direct long-read nanopore sequencing. A comparative analysis of a modification-free transcriptome reveals that the depth of coverage and specific k-mer sequences are critical parameters for accurate basecalling. By adjusting these parameters for high-confidence U-to-C basecalling errors, we identify many known sites of pseudouridylation and uncover previously unreported uridine-modified sites, many of which fall in k-mers that are known targets of pseudouridine synthases.” Find the paper and full list of authors in Nature Communications.

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  • ‘Pulcherrimin Protects Bacillus subtilis Against Oxidative Stress During Biofilm Development’

    “Pulcherrimin is an iron-binding reddish pigment produced by various bacterial and yeast species. In the soil bacterium Bacillus subtilis, this pigment is synthesized intracellularly as the colorless pulcherriminic acid by using two molecules of tRNA-charged leucine as the substrate; pulcherriminic acid molecules are then secreted and bind to ferric iron extracellularly to form the red- colored pigment pulcherrimin. … In this study, we identified that pulcherrimin is primarily produced under biofilm conditions and provides protection to cells in the biofilm against oxidative stress.” Find the paper and full list of authors in NPJ Biofilms and Microbes.

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  • ‘Dynamic Structure of T4 Gene 32 Protein Filaments Facilitates Rapid Noncooperative Protein Dissociation’

    “Bacteriophage T4 gene 32 protein (gp32) is a model single-stranded DNA (ssDNA) binding protein, essential for DNA replication. … Detailed understanding of gp32 filament structure and organization remains incomplete. … Moreover, it is unclear how these tightly-bound filaments dissociate from ssDNA during complementary strand synthesis. We use optical tweezers and atomic force microscopy to probe the structure and binding dynamics of gp32 on long (∼8 knt) ssDNA substrates. … Cooperative binding of gp32 rigidifies ssDNA while also reducing its contour length, consistent with the ssDNA helically winding around the gp32 filament.” Find the paper and full list of authors at…

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  • ‘Infinite Neural Network Quantum States: Entanglement and Training Dynamics’

    “We study infinite limits of neural network quantum states (-NNQS), which exhibit representation power through ensemble statistics, and also tractable gradient descent dynamics. Ensemble averages of entanglement entropies are expressed in terms of neural network correlators, and architectures that exhibit volume-law entanglement are presented. The analytic calculations of entanglement entropy bound are tractable because the ensemble statistics are simplified in the Gaussian process limit.” Find the paper and full list of authors in Machine Learning: Science and Technology.

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  • Ramdin presents research on intergenerational families for improved health outcomes at ABNF

    Valeria Ramdin, associate clinical professor and director of global health nursing, presented a talk on “Creating Intergenerational Family PODs for Improved Health Outcomes: Lessons Learned” at the 35th Annual Meeting & Scientific Conference for the Association of Black Nursing Faculty.

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  • ‘Mentoring Black, Indigenous and People of Color (BIPOC) Nursing Faculty toward Leadership Excellence’

    “Although the importance of mentoring is emphasized across healthcare professions and among diverse disciplines and situations, the success of nursing faculty who are BIPOC, termed as Black, Indigenous, and Nursing Faculty of Color (BINFOC), is heavily influenced by the mentoring received early in their academic career. … The analysis [aims] to present a model case using a novel strategy of integrating historical research, present attributes that have been influential in legacy building for more than 30 years, and elucidate the unique situation of mentoring BINFOC toward leadership excellence.” Find the paper and full list of authors in ABNFF Journal.

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  • Dewan publishes chapter on growing importance of nurse anesthetists in global health context

    Assistant clinical professor Janet A. Dewan, with co-author Aaron K. Sonah of Phebe Ester Bacon College of Health Science, has published a book chapter titled, “Universal Health Coverage and Nurse Anesthetists” in “Nurse Practitioners and Nurse Anesthetists: The Evolution of the Global Roles.” The chapter argues that as “the central concept that the measure of essential surgical and anesthesia care is an indication of the quality of a health system has gained traction … Access to safe surgery and anesthesia is linked to a health system’s ability to meet Universal Health Coverage benchmarks.”

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  • Researchers awarded $2M EPA grant for clean water research

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    “This project will develop an integrated framework to quantitatively monitor the drinking water microbiome, including waterborne pathogens (WPs), and disinfection byproducts (DBPs), and holistically manage WP and DBP risks in drinking water systems (DWSs). The integrated monitoring and management framework is grounded in the understanding that chemical and microbial dynamics in DWSs are intrinsically linked and co-influenced by environmental, ecological, and infrastructure conditions and engineering actions.” Find the paper and full list of authors at the EPA.

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  • Dewan discusses rising importance of nurse anesthetists at ICN

    In a panel discussion at the International Council of Nurses 2023 conference, assistant clinical professor discussed the “importance in global healthcare” of nurse anesthetists. According to the panel description, “It is our hope that through the development of these guidelines, some of the barriers and walls that have hindered Nurse Anesthetists can be broken down. … The session will feature insights into the current challenges faced by nurse anesthetists, the innovative strategies being used to overcome these obstacles, and the transformative policies being adopted globally to support their role.”

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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

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    “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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  • ‘Ecosystem Graphs: The Social Footprint of Foundation Models’

    “Foundation models (e.g. ChatGPT, StableDiffusion) pervasively influence society, warranting immediate social attention. While the models themselves garner much attention, to accurately characterize their impact, we must consider the broader sociotechnical ecosystem. We propose Ecosystem Graphs as a documentation framework to transparently centralize knowledge of this ecosystem. Ecosystem Graphs is composed of assets (datasets, models, applications) linked together by dependencies that indicate technical (e.g. how Bing relies on GPT-4) and social (e.g. how Microsoft relies on OpenAI) relationships. To supplement the graph structure, each asset is further enriched with fine-grained metadata.” Find the paper and full list of authors at ArXiv.

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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’

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    “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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