Title
Topic
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‘Indistinguishable Telecom Band Photons From a Single Erbium Ion in the Solid State’
“Atomic defects in the solid state are a key component of quantum repeater networks for long-distance quantum communication. Recently, there has been significant interest in rare earth ions, in particular Er3+ for its telecom-band optical transition, but their application has been hampered by optical spectral diffusion precluding indistinguishable single photon generation. In this work we implant Er3+ into CaWO4, a material that combines a non-polar site symmetry, low decoherence from nuclear spins, and is free of background rare earth ions, to realize significantly reduced optical spectral diffusion.” Find the paper and the full list of authors at ArXiv.
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‘SHAI 2023: Workshop on Designing for Safety in Human-AI Interactions’
“Generative ML models present a novel opportunity for a wider group of societal members to engage with AI, imagine new use cases, and applications. … However, owing to the novelty and despite best intentions, inadvertent outcomes might accrue leading to harms, especially to marginalized groups in society. …. Our workshop is aimed at such practitioners and researchers at the intersection of AI and HCI who are interested in collaboratively identifying challenges and solutions to create safer outcomes with Generative ML models.” Find the paper and full list of authors in the Companion Proceedings of the 28th International Conference on Intelligent…
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‘Exploring the Use of Personalized AI for Identifying Misinformation on Social Media’
“This work aims to explore how human assessments and AI predictions can … identify misinformation on social media. To do so, we design a personalized AI which iteratively takes as training data a single user’s assessment of content and predicts how the same user would assess other content. We conduct a user study in which participants interact with a personalized AI that learns their assessments of a feed of tweets, shows its predictions of whether a user would find other tweets (in)accurate, and evolves according to the user feedback.” Find the paper and list of authors in the 2023 CHI…
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‘Are Human Explanations Always Helpful? Towards Objective Evaluation of Human Natural Language Explanations’
“Human-annotated labels and explanations are critical for training explainable NLP models. However, … human-crafted free-form explanations can be quite subjective. Before blindly using them as ground truth to train ML models, a vital question needs to be asked: How do we evaluate a human-annotated explanation’s quality? In this paper, we build on the view that the quality of a human-annotated explanation can be measured based on its helpfulness (or impairment) to the ML models’ performance for the desired NLP tasks for which the annotations were collected.” Find the paper and the full list of authors at ArXiv.
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‘Beyond Labels: Empowering Human with Natural Language Explanations through a Novel Active-Learning Architecture’
“Data annotation is a costly task; thus, researchers have proposed low-scenario learning techniques like Active-Learning (AL) to support human annotators; Yet, existing AL works focus only on the label, but overlook the natural language explanation of a data point, despite that real-world humans (e.g., doctors) often need both the labels and the corresponding explanations at the same time. This work proposes a novel AL architecture to support and reduce human annotations of both labels and explanations in low-resource scenarios.” Find the paper and the full list of authors at ArXiv.
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‘Are Fairy Tales Fair? Analyzing Gender Bias in Temporal Narrative Event Chains of Children’s Fairy Tales’
“Social biases and stereotypes are embedded in our culture in part through their presence in our stories, as evidenced by the rich history of humanities and social science literature analyzing such biases in children stories. … Such investigations can benefit from the use of more recent natural language processing methods that examine social bias in models and data corpora. … We propose a computational pipeline that automatically extracts a story’s temporal narrative verb-based event chain for each of its characters as well as character attributes such as gender.” Find the paper and the full list of authors at ArXiv.
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‘Identification of Negative Transfers in Multitask Learning Using Surrogate Models’
“Multitask learning is widely used in practice to train a low-resource target task by augmenting it with multiple related source tasks. Yet, naively combining all the source tasks with a target task does not always improve the prediction performance for the target task due to negative transfers. Thus, a critical problem in multitask learning is identifying subsets of source tasks that would benefit the target task. … In this paper, we introduce an efficient procedure to address this problem via surrogate modeling.” Find the paper and the full list of authors at ArXiv.
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‘Optimal Intervention on Weighted Networks via Edge Centrality’
“Suppose there is a spreading process such as an infectious disease propagating on a graph. How would we reduce the number of affected nodes in the spreading process? … A practical algorithm to reduce infections on unweighted graphs is to remove edges with the highest edge centrality score (Tong et al. (2012)), which is the product of two adjacent nodes’ eigenscores. However, mobility networks have weighted edges. … We revisit the problem of minimizing top eigenvalue(s) on weighted graphs by decreasing edge weights up to a fixed budget.” Find the paper and the full list of authors at ArXiv.
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‘Understanding Dark Patterns in Home IoT Devices’
“Internet-of-Things (IoT) devices are ubiquitous, but little attention has been paid to how they may incorporate dark patterns despite consumer protections and privacy concerns arising from their unique access to intimate spaces and always-on capabilities. … We update manual interaction and annotation methods for the IoT context, then analyze dark pattern frequency across device types, manufacturers, and interaction modalities. We find that dark patterns are pervasive in IoT experiences, but manifest in diverse ways across device traits.” Find the paper and the full list of authors in the Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems.
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‘Somewhere Randomness Extraction and Security Against Bounded-Storage Mass Surveillance’
“Consider a state-level adversary who observes and stores large amounts of encrypted data from all users on the Internet, but does not have the capacity to store it all. Later, it may target certain ‘persons of interest.’ … We would like to guarantee that, if the adversary’s storage capacity is only (say) 1% of the total encrypted data size, then even if it can later obtain the decryption keys of arbitrary users, it can only learn something about the contents of (roughly) 1% of the ciphertexts.” Find the paper and the full list of authors in the Cryptology EPrint Archive.
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‘A Map of Witness Maps: New Definitions and Connections’
“A witness map deterministically maps a witness w of some NP statement x into computationally sound proof that x is true. … A unique witness map (UWM) ensures that for any fixed statement x, the witness map should output the same unique proof for x, no matter what witness w it is applied to. … In this work, we study [compact witness maps] and UWMs as primitives of independent interest and present a number of interesting connections to various notions in cryptography.” Find the paper and the full list of authors in the Cryptology EPrint Archive.
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‘Boosting Batch Arguments and RAM Delegation’
“We show how to generically improve the succinctness of non-interactive publicly verifiable batch argument (BARG) systems. In particular, we show (under a mild additional assumption) how to convert a BARG that generates proofs of length poly (m)· k1−є, where m is the length of a single instance and k is the number of instances being batched, into one that generates proofs of length poly (m, logk), which is the gold standard for succinctness of BARGs.” Find the paper and the full list of authors in the Proceedings of the 55th Annual ACM Symposium on Theory of Computing.
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‘Doubly Efficient Private Information Retrieval and Fully Homomorphic RAM Computation From Ring LWE’
“A (single server) private information retrieval (PIR) allows a client to read data from a public database held on a remote server, without revealing to the server which locations she is reading. In a doubly efficient PIR (DEPIR), the database is first preprocessed, but the server can subsequently answer any client’s query in time that is sub-linear in the database size. … In this work we construct the stronger unkeyed notion of DEPIR, where the preprocessing is a deterministic procedure that the server can execute on its own.” Find the paper and full list of authors in the STOC 2023 proceedings.
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‘Identification of Novel Anti-Amoebic Pharmacophores From Kinase Inhibitor Chemotypes’
“Acanthamoeba species, Naegleria fowleri, and Balamuthia mandrillaris are opportunistic pathogens that cause a range of brain, skin, eye, and disseminated diseases in humans and animals. These pathogenic free-living amoebae (pFLA) are commonly misdiagnosed and have sub-optimal treatment regimens which contribute to the extremely high mortality rates (>90%) when they infect the central nervous system. … Herein, we report the activity of the compounds against the trophozoite stage of each of the three amoebae, ranging from nanomolar to low micromolar potency.” Find the paper and the full list of authors at Frontiers in Microbiology.
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‘Exploratory Thematic Analysis of Crowdsourced Photosensitivity Warnings’
“Films often include sequences of flashing lights for visual effect that may inadvertently trigger seizures when viewed by individuals with photosensitive epilepsy (PSE). Warnings about photosensitive risk in films can help people with PSE make informed decisions about their personal safety, but little is known about how to design such warnings and what information to include. To better understand the design space for photosensitive risk warnings, we conducted a qualitative analysis of 265 crowdsourced warnings about flashing lights in films.” Find the paper and the full list of authors at Conference on Human Factors in Computing Systems 2023 proceedings.
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‘Is “Categorical Imperative” Metaversal?: A Kantian Ethical Framework for Social Virtual Reality’
“The increasing adoption of social virtual reality (VR) environments for socializing and collaborating with others has led to a growing concern about ethical issues in these immersive environments. Beyond the introduction of some practical guidelines, theoretical work on this topic has been scant. In this paper, we propose an ethical framework for social VR based on Kant’s Theory of Morality. In so doing, we argue that the Kantian concept of categorical imperative does apply to social VR.” Find the paper and the full list of authors in the Conference on Human Factors in Computing Systems 2023 proceedings.
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‘Noise Stability Optimization for Flat Minima With Optimal Convergence Rates’
“We consider finding flat, local minimizers by adding average weight perturbations. Given a nonconvex function f:ℝd→ℝ and a d-dimensional distribution P which is symmetric at zero, we perturb the weight of f and define F(W)=𝔼[f(W+U)], where U is a random sample from P. This injection induces regularization through the Hessian trace of f for small, isotropic Gaussian perturbations. … Still, convergence rates are not known for finding minima under the average perturbations of the function F. This paper considers an SGD-like algorithm that injects random noise before computing gradients while leveraging the symmetry of P to reduce variance.” Find the paper and the full list of authors at ArXiv.
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How cephalopods can inspire new technologies
A paper in ECS Sensors Plus details how the unique, natural sensors in cephalopod biology have inspired—and will continue to inspire—scientific innovation.
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‘Synthesis of Distributed Protocols by Enumeration Modulo Isomorphisms’
“Synthesis of distributed protocols is a hard, often undecidable, problem. Completion techniques provide partial remedy by turning the problem into a search problem. However, the space of candidate completions is still massive. In this paper, we propose optimization techniques to reduce the size of the search space by a factorial factor by exploiting symmetries (isomorphisms) in functionally equivalent solutions. We present both a theoretical analysis of this optimization as well as empirical results that demonstrate its effectiveness in synthesizing both the Alternating Bit Protocol and Two Phase Commit.” Find the paper and the full list of authors at ArXiv.
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‘”Who is the Right Homeless Client?”: Values in Algorithmic Homelessness Service Provision and Machine Learning Research’
“Homelessness presents a long-standing problem worldwide. Like other welfare services, homeless services have gained increased traction in Machine Learning (ML) research. Unhoused persons are vulnerable and using their data in the ML pipeline raises serious concerns about the unintended harms and consequences of prioritizing different ML values. … Unhoused persons were lost (i.e., humans were deprioritized) at multi-level ML abstraction of predictors, categories and algorithms. Our findings illuminate potential pathways forward … by situating humans at the center to support this vulnerable community.” Find the paper and full list of authors in the Conference on Human Factors in Computing Systems,…
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‘Why, When and From Whom: Considerations for Collecting and Reporting Race and Ethnicity Data in HCI’
“Engaging diverse participants in HCI research is critical for creating safe, inclusive, and equitable technology. However, there is a lack of guidelines on when, why, and how HCI researchers collect study participants’ race and ethnicity. Our paper aims to take the first step toward such guidelines by providing a systematic review and discussion of the status quo of race and ethnicity data collection in HCI.” Find the paper and full list of authors in the Conference on Human Factors in Computing Systems, 2023, proceedings.
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‘How to Combine Membership-Inference Attacks on Multiple Updated Machine Learning Models’
“A large body of research has shown that machine learning models are vulnerable to membership inference (MI) attacks that violate the privacy of the participants in the training data. Most MI research focuses on the case of a single standalone model, while production machine-learning platforms often update models over time, on data that often shifts in distribution, giving the attacker more information. This paper proposes new attacks that take advantage of one or more model updates to improve MI.” Find the paper and the full list of authors in the Proceedings on Privacy Enhancing Technologies Symposium.
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‘TMI! Finetuned Models Leak Private Information from their Pretraining Data’
“Transfer learning has become an increasingly popular technique in machine learning as a way to leverage a pretrained model … to assist with building a finetuned model. … There are reasons to believe that the data used for pretraining is still sensitive, making it essential to understand how much information the finetuned model leaks about the pretraining data. In this work we propose a new membership-inference threat model where the adversary only has access to the finetuned model and would like to infer the membership of the pretraining data.” Find the paper and full list of authors at ArXiv.
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‘Differentially Private Medians and Interior Points for Non-Pathological Data’
“We construct differentially private estimators with low sample complexity that estimate the median of an arbitrary distribution over ℝ satisfying very mild moment conditions. Our result stands in contrast to the surprising negative result of Bun et al. (FOCS 2015) that showed there is no differentially private estimator with any finite sample complexity that returns any non-trivial approximation to the median of an arbitrary distribution.” Find the paper and the full list of authors at ArXiv.
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‘DIALITE: Discover, Align and Integrate Open Data Tables’
“We demonstrate a novel table discovery pipeline called DIALITE that allows users to discover, integrate and analyze open data tables. DIALITE has three main stages. First, it allows users to discover tables from open data platforms using state-of-the-art table discovery techniques. Second, DIALITE integrates the discovered tables to produce an integrated table. Finally, it allows users to analyze the integration result by applying different downstreaming tasks over it. Our pipeline is flexible such that the user can easily add and compare additional discovery and integration algorithms.” Find the paper and the full list of authors at ArXiv.
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‘A Statistical Approach for Finding Property-Access Errors’
“We study the problem of finding incorrect property accesses in JavaScript where objects do not have a fixed layout, and properties (including methods) can be added, overwritten, and deleted freely throughout the lifetime of an object. Since referencing a non-existent property is not an error in JavaScript, accidental accesses to non-existent properties … can go undetected without thorough testing, and may manifest far from the source of the problem. We propose a two-phase approach for detecting property access errors based on the observation that, in practice, most property accesses will be correct.” Find the paper and full list of authors…
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‘Safe Environmental Envelopes of Discrete Systems’
“A safety verification task involves verifying a system against a desired safety property under certain assumptions about the environment. However, these environmental assumptions may occasionally be violated due to modeling errors or faults. Ideally, the system guarantees its critical properties even under some of these violations, i.e., the system is robust against environmental deviations. This paper proposes a notion of robustness as an explicit, first-class property of a transition system that captures how robust it is against possible deviations in the environment.” Find the paper and the full list of authors at ArXiv.
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Huselid named SIOP and NAHR fellow for ‘highest level of achievement in the human resources profession’
“D’Amore-McKim School of Business Distinguished Professor Mark Huselid was recently named a fellow by the Society for Industrial and Organizational Psychology (SIOP) and the National Academy of Human Resources (NAHR). SIOP fellows must have made significant and sustained contributions that have shaped the field of Industrial-Organizational (I-O) psychology–the scientific study of working and the application of that science to workplace issues facing individuals, teams, and organizations. Huselid joins D’Amore-McKim professors Paula Caligiuri and Cynthia Lee who are also SIOP fellows.” NAHR fellowship, which “he was also recently inducted as … is regarded as the most prestigious honor in the human resources field.” h
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Hoitash and Pei win 2023 Ronald Copeland Best Paper Awards
“Udi Hoitash, Lilian L. and Harry A. Cowan Professor of Accounting, and Amy Pei, Assistant Professor, Marketing, have authored papers awarded the 2023 Copeland Best Paper Awards. Each Group Research Committee nominated a paper published in 2022 by a faculty member in the Group to be considered for a Ronald Copeland Best Paper Award. Two papers were selected as this year’s winners as recommended by the DMSB Research Committee and confirmed by Dean Emery Trahan. Each D’Amore-McKim author will receive a $1,000 stipend.”