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Topic
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Ergun elected INFORMS fellow
“Ozlem Ergun, College of Engineering distinguished professor in mechanical and industrial engineering, was elected a Fellow of the Institute for Operations Research and the Management Sciences (INFORMS) for her applications of O.R. methods to humanitarian and health systems, emergency response and transportation and logistics problems and establishing a community of O.R. professionals with interest in public programs.”
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‘A Time-Dependent Momentum-Resolved Scattering Approach to Core-Level Spectroscopies’
While new light sources allow for unprecedented resolution in experiments with X-rays, a theoretical understanding of the scattering cross-section is lacking. … This requires a knowledge of a full set of eigenstates in order to account for all intermediate processes away from equilibrium. … In this work, we present an alternative paradigm, recasting the problem in the time domain and explicitly solving the time-dependent Schrödinger equation without the limitations of perturbation theory: a faithful simulation of the scattering processes taking place in actual experiments, including photons and core electrons.” Find the paper and full list of authors at SciPost Physics.
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‘Contrasting Ocean — Atmosphere Dynamics Mediate Flood Hazard Across the Mississippi River Basin’
“The Mississippi River basin drains nearly one-half of the contiguous United States. … Here, we use climate reanalysis and river gauge data to document the evolution of floods on the Missouri and Ohio Rivers—the two largest tributaries of the Mississippi River—and how they are influenced by major modes of climate variability centered in the Pacific and Atlantic Oceans. We show that the largest floods on these tributaries are preceded by the advection and convergence of moisture from the Gulf of Mexico following distinct atmospheric mechanisms.” Find the paper and full list of authors at Earth Interactions.
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‘Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysis’
“We consider the problem of traffic accident analysis on a road network based on road network connections and traffic volume. Previous works have designed various deep-learning methods using historical records to predict traffic accident occurrences. … This paper constructs a large-scale, unified dataset of traffic accident records from official reports of various states in the US, totaling 9 million records, accompanied by road networks and traffic volume reports. Using this new dataset, we evaluate existing deep-learning methods for predicting the occurrence of accidents on road networks.” Find the paper and full list of authors at ArXiv.
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‘E4UnityIntegration-MIT: An Open-Source Unity Plug-in for Collecting Physiological Data Using Empatica E4 During Gameplay’
“Physiological measurement of player experience during gameplay has been of increasing interest within game research circles. A commonly-used non-invasive wearable device for physiological measurement is the Empatica E4 wristband. … [However,] the E4’s integration with popular game engines such as Unity 3D presents certain challenges due to non-obvious critical bugs in the library and limited documentation applicability within the Unity context. … We present an open-source Unity plug-in designed to mitigate the challenges associated with integrating the E4 into Unity projects.” Find the paper and authors list at the 36th Annual ACM Symposium on User Interface Software and Technology adjunct…
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‘Vision and Language Navigation in the Real World via Online Visual Language Mapping’
“Navigating in unseen environments is crucial for mobile robots. Enhancing them with the ability to follow instructions in natural language will further improve navigation efficiency in unseen cases. However, state-of-the-art vision-and-language navigation (VLN) methods are mainly evaluated in simulation, neglecting the complex and noisy real world. … In this work, we propose a novel navigation framework to address the VLN task in the real world. … The proposed framework includes four key components: (1) an LLMs-based instruction parser … (2) an online visual-language mapper … (3) a language indexing-based localizer … and (4) a DD-PPO-based local controller.” Find the paper…
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‘”Mango Mango, How To Let the Lettuce Dry Without a Spinner?”: … An LLM-Based … Cooking Partner’
“The rapid advancement of the Large Language Model (LLM) has created numerous potentials for integration with conversational assistants (CAs) assisting people in their daily tasks, particularly due to their extensive flexibility. … In this research, we chose cooking, a complex daily task, as a scenario to investigate people’s successful and unsatisfactory experiences while receiving assistance from an LLM-based CA, Mango Mango. We discovered that participants value the system’s ability to provide extensive information beyond the recipe, offer customized instructions based on context, and assist them in dynamically planning the task.” Find the paper and full list of authors at ArXiv.
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‘Modeling Dynamics over Meshes With Gauge Equivariant Nonlinear Message Passing’
“Data over non-Euclidean manifolds, often discretized as surface meshes, naturally arise in computer graphics and biological and physical systems. In particular, solutions to partial differential equations (PDEs) over manifolds depend critically on the underlying geometry. While graph neural networks have been successfully applied to PDEs, they do not incorporate surface geometry and do not consider local gauge symmetries of the manifold. … To address these issues, we introduce a new gauge equivariant architecture using nonlinear message passing.” Find the paper and full list of authors at ArXiv.
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‘Will the Prince Get True Love’s Kiss? On the Model Sensitivity to Gender Perturbation Over Fairytale Texts’
“Recent studies show that traditional fairytales are rife with harmful gender biases. To help mitigate these gender biases in fairytales, this work aims to assess learned biases of language models by evaluating their robustness against gender perturbations. Specifically, we focus on Question Answering (QA) tasks in fairytales. Using counterfactual data augmentation to the FairytaleQA dataset, we evaluate model robustness against swapped gender character information, and then mitigate learned biases by introducing counterfactual gender stereotypes during training time.” Find the paper and full list of authors at ArXiv.
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‘Sample Complexity of Opinion Formation on Networks’
“Consider public health officials aiming to spread awareness about a new vaccine in a community interconnected by a social network. How can they distribute information with minimal resources, ensuring community-wide understanding that aligns with the actual facts? … In this paper, we initialize the study of sample complexity in opinion formation to solve this problem. … Intriguingly, we discover optimal strategies often allocate samples inversely to the degree, hinting at vital policy implications.” Find the paper and full list of authors at ArXiv.
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‘Inclusive Portraits: Race-Aware Human-in-the-Loop Technology’
“AI has revolutionized the … automatic facial verification of people. Automated approaches … can face challenges when processing content from certain communities, including communities of people of color. This challenge has prompted the adoption of ‘human-in-the-loop’ (HITL) approaches, where human workers collaborate with the AI to minimize errors. However, most HITL approaches do not consider workers’ individual characteristics and backgrounds. This paper proposes a new approach … that connects with social theories around race to design a racially-aware human-in-the-loop system.” Find the paper and authors list at the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms and Optimization…
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‘User Inference Attacks on Large Language Models’
“In this paper, we study the privacy implications of fine-tuning LLMs on user data. To this end, we define a realistic threat model, called user inference, wherein an attacker infers whether or not a user’s data was used for fine-tuning. We implement attacks for this threat model that require only a small set of samples from a user (possibly different from the samples used for training). … We find that LLMs are susceptible to user inference attacks across a variety of fine-tuning datasets, at times with near perfect attack success rates.” Find the paper and full list of authors at…
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‘What’s Next in Affective Modeling? Large Language Models’
“Large Language Models (LLM) have recently been shown to perform well at various tasks from language understanding, reasoning, storytelling, and information search to theory of mind. In an extension of this work, we explore the ability of GPT-4 to solve tasks related to emotion prediction. GPT-4 performs well across multiple emotion tasks; it can distinguish emotion theories and come up with emotional stories. We show that by prompting GPT-4 to identify key factors of an emotional experience, it is able to manipulate the emotional intensity of its own stories.” Find the paper and full list of authors at ArXiv.
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‘FibeRobo: Fabricating 4D Fiber Interfaces by Continuous Drawing of Temperature Tunable Liquid Crystal Elastomers’
“We present FibeRobo, a thermally-actuated liquid crystal elastomer (LCE) fiber that can be embedded or structured into textiles and enable silent and responsive interactions with shape-changing, fiber-based interfaces. … This paper contributes to developing temperature-responsive LCE fibers, a facile and scalable fabrication pipeline with optional heating element integration for digital control, mechanical characterization and the establishment of higher hierarchical textile structures and design space. Finally, we introduce a set of demonstrations that illustrate the design space FibeRobo enables.” Find the paper and full list of authors in the 36th Annual ACM Symposium on User Interface Software and Technology proceedings.
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‘Exploring Question Decomposition for Zero-Shot VQA’
“Visual question answering (VQA) has traditionally been treated as a single-step task where each question receives the same amount of effort, unlike natural human question-answering strategies. We explore a question decomposition strategy for VQA to overcome this limitation. We probe the ability of recently developed large vision-language models to use human-written decompositions and produce their own decompositions of visual questions, finding they are capable of learning both tasks from demonstrations alone. However, we show that naive application of model-written decompositions can hurt performance.” Find the paper and full list of authors at ArXiv.
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‘Rethinking Neighborhood Consistency Learning on Unsupervised Domain Adaptation’
“Unsupervised domain adaptation (UDA) involves predicting unlabeled data in a target domain by using labeled data from the source domain. However, recent advances in pseudo-labeling (PL) methods have been hampered by noisy pseudo-labels. … Although neighborhood-based PL can help preserve the local structure, it also risks assigning the whole local neighborhood to the wrong semantic category. To address this issue, we propose a novel framework called neighborhood consistency learning (NCL) that operates at both the semantic and instance levels and features a new consistency objective function.” Find the paper and authors list in the 31st ACM International Conference on Multimedia…
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‘GRASP: Accelerating Shortest Path Attacks via Graph Attention’
“Recent advances in machine learning (ML) have shown promise in aiding and accelerating classical combinatorial optimization algorithms. ML-based speed ups that aim to learn in an end to end manner (i.e., directly output the solution) tend to trade off run time with solution quality. … We consider an APX-hard problem, where an adversary aims to attack shortest paths in a graph by removing the minimum number of edges. We propose the GRASP algorithm: Graph Attention Accelerated Shortest Path Attack, an ML aided optimization algorithm that achieves run times up to 10x faster.” Find the paper and full authors list at…
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‘In the Room Where It Happens: Characterizing Local Communication and Threats in Smart Homes’
“The network communication between Internet of Things (IoT) devices on the same local network has significant implications for platform and device interoperability, security, privacy and correctness. Yet, the analysis of local home Wi-Fi network traffic and its associated security and privacy threats have been largely ignored by prior literature. … In this paper, we present a comprehensive and empirical measurement study to shed light on the local communication within a smart home deployment and its threats.” Find the paper and full list of authors at the 2023 ACM on Internet Measurement Conference proceedings.
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‘Tracking, Profiling and Ad Targeting in the Alexa Echo Smart Speaker Ecosystem’
“Smart speakers collect voice commands, which can be used to infer sensitive information about users. Given the potential for privacy harms, there is a need for greater transparency and control over the data collected, used and shared by smart speaker platforms as well as third party skills supported on them. To bridge this gap, we build a framework to measure data collection, usage, and sharing by the smart speaker platforms. … Our results show that Amazon and third parties … collect smart speaker interaction data.” Find the paper and full list of authors at the 2023 ACM on Internet Measurement…
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‘Medical Imaging RPA System Design’
“Robotic Process Automation (RPA) can minimize human errors, improve efficiency and create a seamless operational environment in the healthcare industry. This paper examines the existing radiology imaging requisition system, which requires human labourers to perform medical request processing and classification. To improve this slow, error-prone and hard-to-scale process, we design an RPA approach that significantly improves efficiency.” Find the paper and full list of authors in the Canadian Conference on Electrical and Computer Engineering proceedings.
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‘AI-Based General Visual Inspection of Aircrafts Based on YOLOv5’
“Safety is the cornerstone on which the commercial airline industry is built. … The time required for general visual inspections of aircraft can be drastically reduced by using deep learning and remotely piloted aircraft systems (RPAS). Deep learning techniques can be used in aircraft maintenance thanks to the availability of Graphic Processing Units. In our proof of concept study, we use YOLOv5 to build a model that uses high-quality data to find five different aircraft flaws.” Find the paper and full list of authors at the 2023 Canadian Conference on Electrical and Computer Engineering proceedings.
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‘Robust Client and Server State Synchronisation Framework For React Applications: React-State-Sync’
“As the front-end web frameworks ecosystem evolves, we have encountered problems managing client data. Not only are the solutions for this problem diverse, but the problem too has devolved into two parts — client-side state and server-side state. … Our goal is to provide a consolidated architecture that ensures a full sync between the two states while being performant and developer friendly. Based on our tests against React Context API, we increased the dispatch performance by over 400%.” Find the paper and full list of authors at the 2023 Canadian Conference on Electrical and Computer Engineering proceedings.
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Function Vectors in Large Language Models
“We report the presence of a simple neural mechanism that represents an input-output function as a vector within autoregressive transformer language models (LMs). Using causal mediation analysis on a diverse range of in-context-learning (ICL) tasks, we find that a small number attention heads transport a compact representation of the demonstrated task, which we call a function vector (FV). … We test FVs across a range of tasks, models and layers and find strong causal effects across settings in middle layers.” Find the paper and full list of authors at ArXiv.
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‘The Effects of Computational Resources on Flaky Tests’
“Flaky tests are tests that nondeterministically pass and fail in unchanged code. These tests can be detrimental to developers’ productivity. Particularly when tests run in continuous integration environments, the tests may be competing for access to limited computational resources (CPUs, memory etc.) and we hypothesize that resource (in)availability may be a significant factor in the failure rate of flaky tests. We present the first assessment of the impact that computational resources have on flaky tests, including a total of 52 projects written in Java, JavaScript and Python and 27 different resource configurations.” Find the paper and authors list at ArXiv.
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‘Graph-SCP: Accelerating Set Cover Problems With Graph Neural Networks’
“Machine learning (ML) approaches are increasingly being used to accelerate combinatorial optimization (CO) problems. We look specifically at the Set Cover Problem (SCP) and propose Graph-SCP, a graph neural network method that can augment existing optimization solvers by learning to identify a much smaller sub-problem that contains the solution space. We evaluate the performance of Graph-SCP on synthetic weighted and unweighted SCP instances with diverse problem characteristics and complexities, and on instances from the OR Library, a canonical benchmark for SCP.” Find the paper and full list of authors at ArXiv.
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To combat counterfeiting, organizations must employ a ‘multilayered strategy’
Anand Nair, professor of supply chain and information management in the D’Amore-McKim School of Business, writes — with Thomas Choi and Robert Handfield — about the “counterfeiting epidemic” affecting many companies, “whether their leaders know it or not.” Combating this crisis, they write, “works best with a multilayered strategy encompassing diverse methods and engaging the entire organization and its partners.” This strategy includes “routinely keeping tabs on contract manufacturers and charting how products move through the supply chain … scoping out what is for sale in consumer markets, deploying covert markings, reviewing warranty claims, educating customers and partnering with key…
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‘Biochemical Activity of 17 Cancer-Associated Variants of DNA Polymerase Kappa Predicted by Electrostatic Properties’
“DNA damage and repair have been widely studied in relation to cancer and therapeutics. Y-family DNA polymerases can bypass DNA lesions, which may result from external or internal DNA damaging agents, including some chemotherapy agents. Overexpression of the Y-family polymerase human pol kappa can result in tumorigenesis and drug resistance in cancer. This report describes the use of computational tools to predict the effects of single nucleotide polymorphism variants on pol kappa activity.” Find the paper and full list of authors at Chemical Research in Toxicology.