Title
Topic
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‘Robust Communication Complexity of Matching: EDCS Achieves 5/6 Approximation’
“We study the robust communication complexity of maximum matching. Edges of an arbitrary n-vertex graph G are randomly partitioned between Alice and Bob independently and uniformly. Alice has to send a single message to Bob such that Bob can find an (approximate) maximum matching of the whole graph G. We specifically study the best approximation ratio achievable via protocols where Alice communicates only O˜(n) bits to Bob.” Find the paper and full list of authors at ArXiv.
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‘Streaming Edge Coloring with Asymptotically Optimal Colors’
“Given a graph G, an edge-coloring is an assignment of colors to edges of G such that any two edges sharing an endpoint receive different colors. By Vizing’s celebrated theorem, any graph of maximum degree Δ needs at least Δ and at most (Δ+1) colors to be properly edge colored. In this paper, we study edge colorings in the streaming setting. The edges arrive one by one in an arbitrary order. The algorithm takes a single pass over the input and must output a solution using a much smaller space.” Find the paper and full list of authors at ArXiv.
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‘BEV-DG: Cross-Modal Learning Under Bird’s-Eye View for Domain Generalization of 3D Semantic Segmentation’
“Cross-modal Unsupervised Domain Adaptation (UDA) aims to exploit the complementarity of 2D-3D data to overcome the lack of annotation in a new domain. However, UDA methods rely on access to the target domain during training, meaning the trained model only works in a specific target domain. In light of this, we propose cross-modal learning under bird’s-eye view for Domain Generalization (DG) of 3D semantic segmentation, called BEV-DG. … Our approach aims to optimize domain-irrelevant representation modeling with the aid of cross-modal learning under bird’s-eye view.” Find the paper and full list of authors at ArXiv.
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Patent received for ‘molecularly-imprinted electrochemical sensors’ to better detect disease
Researchers in the College of Engineering have received a patent for “Molecularly-Imprinted Electrochemical Sensors.” These sensors are “useful for detecting volatile organic compounds associated with certain diseases or conditions and/or diagnosing certain diseases or conditions.” They are constructed from “layers of metal on a layer of silicon, and a layer of molecularly imprinted polymer in electrical communication with the … metal. … Methods of using the devices (e.g., to detect one or more analytes in a sample, to detect and/or diagnose a disease or condition in a subject), and methods of making the devices are also provided” in the patent.
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Berent investigates ‘how the physical body gives rise to subjective experience’
“Consciousness presents a “hard problem” to scholars. At stake is how the physical body gives rise to subjective experience. Why consciousness is “hard”, however, is uncertain. One possibility is that the challenge arises from ontology—because consciousness is a special property/substance that is irreducible to the physical. Here, I show how the “hard problem” emerges from two intuitive biases that lie deep within human psychology: Essentialism and Dualism. To determine whether a subjective experience is transformative, people judge whether the experience pertains to one’s essence, and per Essentialism, one’s essence lies within one’s body.”
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‘Fear-Related Psychophysiological Patterns Are Situation and Individual Dependent: A Bayesian model Comparison Approach’
“Is there a universal mapping of physiology to emotion, or do these mappings vary substantially by person or situation? Psychologists, philosophers, and neuroscientists have debated this question for decades. Most previous studies have focused on differentiating emotions on the basis of accompanying autonomic responses using analytical approaches that often assume within-category homogeneity. In the present study, we took an alternative approach to this question. We determined the extent to which the relationship between subjective experience and autonomic reactivity generalizes across, or depends upon, the individual and situation for instances of … fear.” Find the paper and full list of authors…
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“Persisters represent a small subpopulation of cells that are tolerant of killing by antibiotics and are implicated in the recalcitrance of chronic infections to antibiotic therapy. One general theme has emerged regarding persisters formed by different bacterial species, namely, a state of relative dormancy characterized by diminished activity of antibiotic targets. Within this framework, a number of studies have linked persister formation to stochastic decreases in energy-generating components. … In this study, we screen knockouts in the main global regulators of Escherichia coli for their effect on persisters.” Find the paper and full list of authors at mBio.
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Clark provides ‘A Generative AI Teaching Exercise for Marketing Classes’
Associate professor of marketing Bruce Clark has provided another perspective on the use of AI in the classroom. While some teachers and instructors might try to ban the tool, Clark “decided to run a couple of experiments.” After teaching with AI, Clark notes that students came away “recognizing its limitations.” Clark also suggests other potential class sessions, experiments and methodologies instructors might try with their students, while everyone adapts to how widespread this technology has become.
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‘Collective Intelligence in Human-AI Teams: A Bayesian Theory of Mind Approach’
“We develop a network of Bayesian agents that collectively model the mental states of teammates from the observed communication. Using a generative computational approach to cognition, we make two contributions. First, we show that our agent could generate interventions that improve the collective intelligence of a human-AI team beyond what humans alone would achieve. Second, we develop a real-time measure of human’s theory of mind ability and test theories about human cognition.” Find the paper and full list of authors in the Proceedings of the AAAI Conference on Artificial Intelligence.
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‘From Ensemble Clustering to Subspace Clustering: Cluster Structure Encoding’
“In this study, we propose a novel algorithm to encode the cluster structure by incorporating ensemble clustering (EC) into subspace clustering (SC). First, the low-rank representation (LRR) is learned from a higher order data relationship induced by ensemble K-means coding, which exploits the cluster structure in a co-association matrix of basic partitions (i.e., clustering results). Second, to provide a fast predictive coding mechanism, an encoding function parameterized by neural networks is introduced to predict the LRR derived from partitions.” Find the paper and full list of authors at IEEE Transactions on Neural Networks and Learning Systems.
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‘Transforming Complex Problems Into K-Means Solutions’
“K-means is a fundamental clustering algorithm widely used in both academic and industrial applications. … Studies show the equivalence of K-means to principal component analysis, non-negative matrix factorization, and spectral clustering. However, these studies focus on standard K-means with squared euclidean distance. In this review paper, we unify the available approaches in generalizing K-means to solve challenging and complex problems. We show that these generalizations can be seen from four aspects: data representation, distance measure, label assignment and centroid updating.” Find the paper and full list of authors at IEEE Transactions on Pattern Analysis and Machine Intelligence.
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‘GlueGen: Plug and Play Multi-Modal Encoders for X-to-Image Generation’
“Text-to-image (T2I) models based on diffusion processes have achieved remarkable success in controllable image generation using user-provided captions. However, the tight coupling between the current text encoder and image decoder in T2I models makes it challenging to replace or upgrade. Such changes often require massive fine-tuning or even training from scratch with the prohibitive expense. To address this problem, we propose GlueGen, which applies a newly proposed GlueNet model to align features from single-modal or multi-modal encoders with the latent space of an existing T2I model.” Find the paper and full list of authors at ArXiv.
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‘Sturgeon-MKIII: Simultaneous Level and Example Playthrough Generation via Constraint Satisfaction With Tile Rewrite Rules’
“Completability is a key aspect of procedural level generation. In this work, we present a constraint-based approach to level generation for 2D tile-based games that simultaneously generates a level and an example playthrough of the level demonstrating its completability. … The mechanics are represented as constraints in the same problem along with the constraints used to generate the level itself. … We demonstrate the flexibilty of the system and of tile rewrite rules in several applications, including lock-and-key dungeons, platformers, puzzles and match-three style games.”
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‘An Online Therapeutic Intervention for Veterans Patients Suffering With Chronic Pain’
“Chronic pain affects a large proportion of veterans. … However, opioid-based medication can lead to overuse or misuse. Thus, the Veteran’s Health Administration (VHA) is interested in non-pharmacological approaches that target both pain management and chronic pain-related functional issues. … This manuscript describes a project to develop and evaluate Veteran ACT for Chronic Pain (VACT-CP), an online therapy conducted by an embodied conversational agent (ECA) portraying a mental healthcare coach tasked to lessen the effects of chronic pain and improve functioning.” Find the paper and list of authors in IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and…
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‘Continuously Accelerating Research’ through reproducible software for scientists
“Science is facing a software reproducibility crisis. Software powers experimentation, and fuels insights, yielding new scientific contributions. Yet, the research software is often difficult for other researchers to reproducibly run. … As software engineering researchers, we believe that it is our duty to create tools and processes that instill reproducibility, reusability and extensibility into research software. This paper outlines a vision for a community infrastructure that will bring the benefits of continuous integration to scientists developing research software.” Find the paper and full list of authors in the IEEE/ACM International Conference on Software Engineering proceedings.
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‘Edge Grasp Network: A Graph-Based SE(3)-Invariant Approach to Grasp Detection’
“Given point cloud input, the problem of 6-DoF grasp pose detection is to identify a set of hand poses in SE(3) from which an object can be successfully grasped. This important problem has many practical applications. Here we propose a novel method and neural network model that enables better grasp success rates relative to what is available in the literature. The method takes standard point cloud data as input and works well with single-view point clouds observed from arbitrary viewing directions.” Find the paper and full list of authors in the IEEE International Conference on Robotics and Automation proceedings.
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‘mmSpoof: Resilient Spoofing of Automotive Millimeter-Wave Radars Using Reflect Array’
“FMCW radars are integral to automotive driving for robust and weather-resistant sensing of surrounding objects. However, these radars are vulnerable to spoofing attacks that can cause sensor malfunction and potentially lead to accidents. Previous attempts at spoofing FMCW radars using an attacker device have not been very effective due to the need for synchronization between the attacker and the victim. We present a novel spoofing mechanism called mmSpoof that does not require synchronization and is resilient to various security features and countermeasures.” Find the paper and list of authors in the IEEE Symposium on Security and Privacy proceedings.
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‘Fully Dynamic Matching: (2−2‾√)-Approximation in Polylog Update Time’
“We study maximum matchings in fully dynamic graphs, which are graphs that undergo both edge insertions and deletions. Our focus is on algorithms that estimate the size of maximum matching after each update while spending a small time. … We show that for any fixed ε>0, a (2−2‾√−ε) approximation can be maintained in poly(logn) time per update even in general graphs. Our techniques also lead to the same approximation for general graphs in two passes of the semi-streaming setting, removing a similar gap in that setting.” Find the paper and full list of authors at ArXiv.
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‘McMini: A Programmable DPOR-Based Model Checker for Multithreaded Programs’
“Current model checkers hardwire the behavior of common thread operations, and do not recognize application-dependent thread paradigms or functions using simpler primitive operations. This introduces additional operations, causing current model checkers to be excessively slow. In addition, there is no mechanism to model the semantics of the actual thread wakeup policies implemented in the underlying thread library or operating system. Eliminating these constraints can make model checkers faster. … McMini is an extensible model checker based on DPOR (Dynamic Partial Order Reduction).” Find the paper and full list of authors at Programming Journal.
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‘Type Prediction With Program Decomposition and Fill-in-the-Type Training’
“TypeScript and Python are two programming languages that support optional type annotations, which are useful but tedious to introduce and maintain. This has motivated automated type prediction: given an untyped program, produce a well-typed output program. Large language models (LLMs) are promising for type prediction, but there are challenges. … We address these challenges [with] OpenTau, a search-based approach for type prediction that leverages large language models. We propose a new metric for type prediction quality, give a tree-based program decomposition that searches a space of generated types and present fill-in-the-type fine-tuning.” Find the paper and full list of authors…
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‘HVA_CPS Proposal: A Process for Hazardous Vulnerability Analysis in Distributed Cyber-Physical Systems’
“Society is increasingly dependent upon the use of distributed cyber-physical systems (CPSs), such as energy networks, chemical processing plants and transport systems. Such CPSs typically have multiple layers of protection to prevent harm to people or the CPS. However, if both the control and protection systems are vulnerable to cyber-attacks, an attack may cause CPS damage or breaches of safety. … This article identifies the attributes that a rigorous hazardous vulnerability analysis (HVA) process would require and compares them against related works. None fully meet the requirements for rigour.” Find the paper and full list of authors at PeerJ Computer Science.
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‘Companion: A Pilot Randomized Clinical Trial … for Detecting and Modifying Daily Inactivity Among Adults >60 Years — Design and Protocol’
“Supervised personal training is most effective in improving the health effects of exercise in older adults. … Strategies to extend the effect of trainer contact outside of supervision and that integrate meaningful and intelligent two-way communication to provide complex and interactive problem solving may motivate older adults to “move more and sit less” and sustain positive behaviors to further improve health. This paper describes … a technology-based behavior-aware text-based virtual “Companion” … to deliver behavior change strategies using socially engaging, contextually salient, and tailored text message conversations in near-real-time.” Find the paper and full list of authors at MDPI Sensors.
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‘SECDA-TFLite: A Toolkit for Efficient Development of FPGA-Based DNN Accelerators for Edge Inference’
“In this paper we propose SECDA-TFLite, a new open source toolkit for developing DNN hardware accelerators integrated within the TFLite framework. The toolkit leverages the principles of SECDA, a hardware/software co-design methodology, to reduce the design time of optimized DNN inference accelerators on edge devices with FPGAs. With SECDA-TFLite, we reduce the initial setup costs associated with integrating a new accelerator design within a target DNN framework, allowing developers to focus on the design.” Find the paper and full list of authors in the Journal of Parallel and Distributed Computing.
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‘A Study of Multi-Factor and Risk-Based Authentication Availability’
“Password-based authentication (PBA) remains the most popular form of user authentication on the web despite its long-understood insecurity. Given the deficiencies of PBA, many online services support multi-factor authentication (MFA) and/or risk-based authentication (RBA) to better secure user accounts. … In this paper, we present a study of 208 popular sites in the Tranco top 5K that support account creation to understand the availability of MFA and RBA on the web … and how logging into sites through more secure SSO providers changes the landscape of user authentication security.” Find the paper and full list of authors at USENIX Security…
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‘Wrapped in Story: The Affordances of Narrative for Citizen Science Games’
“Citizen science games enable public participation in scientific research, yet these games often struggle to engage wide audiences. As a potential solution, some game developers look to narrative as an experience-enhancing feature. … We investigated the effects of wrapping a story around the tutorial puzzles of the citizen science game Foldit. We found that the narrative increased the time players spent engaging with the game’s tutorial and its scientific puzzles but did not substantially affect their progress through the tutorial.” Find the paper and full list of authors in the 18th International Conference on the Foundations of Digital Games proceedings.
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‘Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution’
“Image super-resolution (SR) has witnessed extensive neural network designs from CNN to transformer architectures. However, prevailing SR models suffer from prohibitive memory footprint and intensive computations, which limits further deployment on edge devices. This work investigates the potential of network pruning for super-resolution to take advantage of off-the-shelf network designs and reduce the underlying computational overhead. … We adopt unstructured pruning with sparse models directly trained from scratch.” Find the paper and full list of authors at ArXiv.
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‘UniControl: A Unified Diffusion Model for Controllable Visual Generation in the Wild’
“Achieving machine autonomy and human control often represent divergent objectives in the design of interactive AI systems. Visual generative foundation models such as Stable Diffusion show promise in navigating these goals, especially when prompted with arbitrary languages. However, they often fall short in generating images with spatial, structural or geometric controls. … In response, we introduce UniControl, a new generative foundation model that consolidates a wide array of controllable condition-to-image (C2I) tasks within a singular framework, while still allowing for arbitrary language prompts.” Find the paper and full list of authors at ArXiv.