Title
Topic
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‘One Tree to Rule Them All: Poly-Logarithmic Universal Steiner Tree’
“A spanning tree T of graph G is a ρ-approximate universal Steiner tree (UST) for root vertex r if, for any subset of vertices S containing r, the cost of the minimal subgraph of T connecting S is within a ρ factor of the minimum cost tree connecting S in G. … We settle [several] open questions by giving polynomial-time algorithms for computing both O(log7n)-approximate USTs and poly-logarithmic strong sparse partition hierarchies.” Find the paper and full list of authors at ArXiv.
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‘OASIS: Optimal Arrangements for Sensing in SLAM’
“The number and arrangement of sensors on an autonomous mobile robot dramatically influence its perception capabilities. Ensuring that sensors are mounted in a manner that enables accurate detection, localization and mapping is essential for the success of downstream control tasks. However, when designing a new robotic platform, researchers and practitioners alike usually mimic standard configurations or maximize simple heuristics like field-of-view (FOV) coverage to decide where to place exteroceptive sensors. … We conduct an information-theoretic investigation of this overlooked element of mobile robotic perception in the context of simultaneous localization and mapping.” Find the paper and authors list at ArXiv.
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‘La Independiente: Designing Ubiquitous Systems for Latin American and Caribbean Women Crowdworkers’
“Since 2018, Venezuelans have contributed to 75% of leading AI crowd work platforms’ total workforce. … Few initiatives have investigated the impact of such work in the Global South through the lens of feminist theory. … We surveyed 55 LAC women on the crowd work platform Toloka to understand their personal goals, professional values and hardships. … Most participants shared a desire to hear the experiences of other women crowdworkers.” Find the paper and full list of authors in the Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the International Symposium on Wearable…
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‘SCORE: A Second-Order Conic Initialization for Range-Aided SLAM’
“We present a novel initialization technique for the range-aided simultaneous localization and mapping (RA-SLAM) problem. In RA-SLAM we consider measurements of point-to-point distances in addition to measurements of rigid transformations to landmark or pose variables. Standard formulations of RA-SLAM approach the problem as non-convex optimization, which requires a good initialization to obtain quality results. The initialization technique proposed here relaxes the RA-SLAM problem to a convex problem which is then solved to determine an initialization for the original, non-convex problem.” Find the paper and full list of authors in the IEEE International Conference on Robotics and Automation proceedings.
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‘Towards Automated Pain Assessment Using Embodied Conversational Agents’
“Narrative accounts are the ultimate authoritative source for pain assessment, and face-to-face encounters provide a rich context in which nonverbal conversational behavior can be used to enrich the detail in these descriptions. Embodied Conversational Agents—animated characters that simulate face-to-face conversation—can provide a medium for automated pain assessment in which multimodal pain narratives are elicited, clarified and grounded. … We describe work towards a conversational agent that elicits various aspects of a pain experience, followed by an empathic summary.” Find the paper and full list of authors in the Companion Publication of the 25th International Conference on Multimodal Interaction.
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‘Improving Multiparty Interactions With a Robot Using Large Language Models’
“Speaker diarization is a key component of systems that support multiparty interactions of co-located users, such as meeting facilitation robots. The goal is to identify who spoke what, often to provide feedback, moderate participation, and personalize responses by the robot. … We leverage large language models (LLMs) to identify speaker labels from transcribed text and observe an exact match of 77% and a word level accuracy of 90%.” Find the paper and full list of authors in the Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems.
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‘NPM-Follower: A Complete Dataset Tracking the NPM Ecosystem’
“Software developers typically rely upon a large network of dependencies to build their applications. … However, prior work on NPM dataset construction typically has two limitations: 1) only metadata is scraped, and 2) packages or versions that are deleted from NPM can not be scraped. … We present npm-follower, a dataset and crawling architecture which archives metadata and code of all packages and versions as they are published and is thus able to retain data which is later deleted.” Find the paper and full list of authors at ArXiv.
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‘Sublinear Time Algorithms and Complexity of Approximate Maximum Matching’
“Sublinear time algorithms for approximating maximum matching size have long been studied. Much of the progress over the last two decades on this problem has been on the algorithmic side. … A more recent algorithm by [Behnezhad, Roghani, Rubinstein, and Saberi; SODA’23] obtains a slightly-better-than-1/2 approximation in O(n1+є) time (for arbitrarily small constant ε>0). … Proving any super-linear in n lower bound, even for (1−є)-approximations, has remained elusive. … In this paper, we prove the first super-linear in n lower bound for this problem.” Find the paper and authors list in the Proceedings of the 55th Annual ACM Symposium on…
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‘Testing Methods of Neural Systems Understanding’
“Neuroscientists apply a range of analysis tools to recorded neural activity in order to glean insights into how neural circuits drive behavior in organisms. … Can the tools of neuroscience be applied to artificial neural networks (ANNs) and if so what would this process tell us about ANNs, brains, and – most importantly – the tools themselves? Here we argue that applying analysis methods from neuroscience to ANNs will provide a much-needed test of the abilities of these tools.” Find the paper and full list of authors at Cognitive Systems Research.
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‘Flexible and Optimal Dependency Management via Max-SMT’
“Package managers such as NPM have become essential for software development. The NPM repository hosts over 2 million packages and serves over 43 billion downloads every week. Unfortunately, the NPM dependency solver has several shortcomings. … Although existing tools try to address these problems they are either brittle, rely on post hoc changes to the dependency tree, do not guarantee optimality, or are not composable. We present Pacsolve, a unifying framework and implementation for dependency solving which allows for customizable constraints and optimization goals.” Find the paper and full list of authors at the International Conference on Software Engineering proceedings.
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‘Active Learning for Classifying 2D Grid-Based Level Completability’
“Determining the completability of levels generated by procedural generators such as machine learning models can be challenging, as it can involve the use of solver agents that often require a significant amount of time to analyze and solve levels. Active learning is not yet widely adopted in game evaluations, although it has been used successfully in natural language processing, image and speech recognition, and computer vision, where the availability of labeled data is limited or expensive. In this paper, we propose the use of active learning for learning level completability classification.” Find the paper and full list of authors at ArXiv.
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‘Persistent Memory Research in the Post-Optane Era’
“After over a decade of researcher anticipation for the arrival of persistent memory (PMem), the first shipments of 3D XPoint-based Intel Optane Memory in 2019 were quickly followed by its cancellation in 2022. Was this another case of an idea quickly fading from future to past tense, relegating work in this area to the graveyard of failed technologies? … Without persistent memory itself, is future PMem research doomed? We offer two arguments for why reports of the death of PMem research are greatly exaggerated.” Find the paper and authors list in the Proceedings of the 1st Workshop on Disruptive Memory…
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‘The Digital-Safety Risks of Financial Technologies for Survivors of Intimate Partner Violence’
“Digital technologies play a growing role in exacerbating financial abuse for survivors of intimate partner violence (IPV). … Scant research has examined how consumer-facing financial technologies can facilitate or obstruct IPV-related attacks on a survivor’s financial well-being. … We simulated both close-range and remote attacks commonly used by IPV adversaries. We discover that mobile banking and peer-to-peer payment applications are generally ill-equipped to deal with user-interface bound (UI-bound) adversaries, permitting unauthorized access to logins, surreptitious surveillance and harassing messages and system prompts.” Find the paper and full list of authors in the 32nd USENIX Security Symposium proceedings.
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‘Continuing WebAssembly With Effect Handlers’
“WebAssembly (Wasm) is a low-level portable code format offering near native performance. It is intended as a compilation target for a wide variety of source languages. However, Wasm provides no direct support for non-local control flow features such as async/await, generators/iterators, lightweight threads, first-class continuations, etc. … We present WasmFX, an extension to Wasm which provides a universal target for non-local control features via effect handlers, enabling compilers to translate such features directly into Wasm. Our extension is minimal and only adds three main instructions for creating, suspending, and resuming continuations.” Find the paper and full list of authors at…
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‘Discovering Informative and Robust Positives for Video Domain Adaptation’
Unsupervised domain adaptation for video recognition is challenging where the domain shift includes both spatial variations and temporal dynamics. Previous works have focused on exploring contrastive learning for cross-domain alignment. However, limited variations in intra-domain positives, false cross-domain positives, and false negatives hinder contrastive learning from fulfilling intra-domain discrimination and cross-domain closeness. This paper presents a non-contrastive learning framework without relying on negative samples for unsupervised video domain adaptation.” Find the paper and full list of authors at ICLR 2023.
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‘Global Aligned Structured Sparsity Learning for Efficient Image Super-Resolution’
“Efficient image super-resolution (SR) has witnessed rapid progress thanks to novel lightweight architectures or model compression techniques (e.g., neural architecture search and knowledge distillation). Nevertheless, these methods consume considerable resources or/and neglect to squeeze out the network redundancy at a more fine-grained convolution filter level. … Structured pruning is known to be tricky when applied to SR networks because the extensive residual blocks demand the pruned indices of different layers to be the same. … In this article, we present Global Aligned Structured Sparsity Learning (GASSL) to resolve these problems.” Find the paper and full list of authors at IEEE…
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‘HammerDodger: A Lightweight Defense Framework Against RowHammer Attack on DNNs’
“RowHammer attacks have become a serious security problem on deep neural networks (DNNs). Some carefully induced bit-flips degrade the prediction accuracy of DNN models to random guesses. This work proposes a lightweight defense framework that detects and mitigates adversarial bit-flip attacks. We employ a dynamic channel-shuffling obfuscation scheme to present moving targets to the attack, and develop a logits-based model integrity monitor with negligible performance loss.” Find the paper and full list of authors in the 2023 60th ACM/IEEE Design Automation Conference.
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‘STABLE: Identifying and Mitigating Instability in Embeddings of the Degenerate Core’
“Are the embeddings of a graph’s degenerate core stable? What happens to the embeddings of nodes in the degenerate core as we systematically remove periphery nodes (by repeatedly peeling off κ-cores)? We discover three patterns w.r.t. instability in degenerate-core embeddings across a variety of popular graph embedding algorithms and datasets. We correlate instability with an increase in edge density, and then theoretically show that in the case of Erdös-Rényi graphs embedded with Laplacian Eigenmaps.” Find the paper and full list of authors in the Proceedings of the 2023 SIAM International Conference on Data Mining.
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‘VertexSerum: Poisoning Graph Neural Networks for Link Inference’
“Graph neural networks (GNNs) have brought superb performance to various applications utilizing graph structural data, such as social analysis and fraud detection. The graph links, e.g., social relationships and transaction history, are sensitive and valuable information, which raises privacy concerns when using GNNs. To exploit these vulnerabilities, we propose VertexSerum, a novel graph poisoning attack that increases the effectiveness of graph link stealing by amplifying the link connectivity leakage. To infer node adjacency more accurately, we propose an attention mechanism that can be embedded into the link detection network.” Find the paper and full list of authors at ArXiv.
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‘Implementation-Oblivious Transparent Checkpoint-Restart for MPI’
“This work presents experience with traditional use cases of checkpointing on a novel platform. A single codebase (MANA) transparently checkpoints production workloads for major available MPI implementations: “develop once, run everywhere”. The new platform enables application developers to compile their application against any of the available standards-compliant MPI implementations, and test each MPI implementation according to performance or other features.” Find the paper and full list of authors at ArXiv.
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‘Ecosystem-Level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes’
“Machine learning is traditionally studied at the model level: researchers measure and improve the accuracy, robustness, bias, efficiency, and other dimensions of specific models. In practice, the societal impact of machine learning is determined by the surrounding context of machine learning deployments. To capture this, we introduce ecosystem-level analysis: rather than analyzing a single model, we consider the collection of models that are deployed in a given context.” Find the paper and full list of authors at ArXiv.
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‘Random Oracle Combiners: Breaking the Concatenation Barrier for Collision-Resistance’
“Suppose two parties have hash functions h1 and h2 respectively, but each only trusts the security of their own. We wish to build a hash combiner Cʰ¹,ʰ² which is secure so long as either one of the underlying hash functions is. … In this case, concatenating the two hash outputs clearly works. Unfortunately, a long series of works … showed no (noticeably) shorter combiner for collision resistance is possible. … We argue the right formulation of the “hash combiner” is what we call random oracle (RO) combiners.” Find the paper and full list of authors at Cryptology ePrint Archive.
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‘Security With Functional Re-Encryption From CPA
“The notion of functional re-encryption security (funcCPA) for public-key encryption schemes was recently introduced by Akavia et al. (TCC’22), in the context of homomorphic encryption. This notion lies in between CPA security and CCA security: we give the attacker a functional re-encryption oracle instead of the decryption oracle of CCA security. … In this work we observe that funcCPA security may have applications beyond homomorphic encryption and set out to study its properties.” Find the paper and full list of authors at Cryptology ePrint Archive.
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‘BIT: Bi-Level Temporal Modeling for Efficient Supervised Action Segmentation’
“We address the task of supervised action segmentation which aims to partition a video into non-overlapping segments, each representing a different action. Recent works apply transformers to perform temporal modeling at the frame-level, which suffer from high computational cost and cannot well capture action dependencies over long temporal horizons. To address these issues, we propose an efficient BI-level Temporal modeling (BIT) framework that learns explicit action tokens to represent action segments, in parallel performs temporal modeling on frame and action levels, while maintaining a low computational cost.” Find the paper and full list of authors at ArXiv.
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‘Real-Time Neural Light Field on Mobile Devices’
“Recent efforts in Neural Rendering Fields (NeRF) have shown impressive results on novel view synthesis by utilizing implicit neural representation to represent 3D scenes. Due to the process of volumetric rendering, the inference speed for NeRF is extremely slow. … Another emerging direction utilizes the neural light field (NeLF) for speedup, as only one forward pass is performed on a ray to predict the pixel color. … In this work, we propose an efficient network that runs in real-time on mobile devices for neural rendering.” Find the paper and full list of authors at ArXiv.
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‘Latent Graph Inference With Limited Supervision’
“Latent graph inference (LGI) aims to jointly learn the underlying graph structure and node representations from data features. However, existing LGI methods commonly suffer from the issue of supervision starvation, where massive edge weights are learned without semantic supervision. … In this paper, we observe that this issue is actually caused by the graph sparsification operation, which severely destroys the important connections established between pivotal nodes and labeled ones. To address this, we propose to restore the corrupted affinities and replenish the missed supervision for better LGI.” Find the paper and authors list at ArXiv.
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‘A Tutorial on Visual Representations of Relational Queries’
“Query formulation is increasingly performed by systems that need to guess a user’s intent. … But how can a user know that the computational agent is returning answers to the “right” query? More generally, given that relational queries can become pretty complicated, how can we help users understand existing relational queries, whether human-generated or automatically generated? Now seems the right moment to revisit a topic that predates the birth of the relational model: developing visual metaphors that help users understand relational queries. This lecture-style tutorial surveys the key visual metaphors developed for visual representations of relational expressions.”
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‘Pixel-Aligned Recurrent Queries for Multi-View 3D Object Detection’
“We present PARQ – a multi-view 3D object detector with transformer and pixel-aligned recurrent queries. Unlike previous works that use learnable features or only encode 3D point positions as queries in the decoder, PARQ leverages appearance-enhanced queries initialized from reference points in 3D space and updates their 3D location with recurrent cross-attention operations. Incorporating pixel-aligned features and cross attention enables the model to encode the necessary 3D-to-2D correspondences and capture global contextual information of the input images.” Find the paper and full list of authors at ArXiv.
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‘SportsSloMo: A New Benchmark and Baselines for Human-centric Video Frame Interpolation’
“Human-centric video frame interpolation has great potential for improving people’s entertainment experiences and finding commercial applications in the sports analysis industry. … Although there are multiple benchmark datasets available in the community, none of them is dedicated for human-centric scenarios. To bridge this gap, we introduce SportsSloMo, a benchmark consisting of more than 130K video clips and 1M video frames of high-resolution (≥720p) slow-motion sports videos crawled from YouTube. We re-train several state-of-the-art methods on our benchmark, and the results show a decrease in their accuracy compared to other datasets.” Find the paper and full list of authors at ArXiv.
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‘Social Functions of Machine Emotional Expressions’
“Virtual humans and social robots frequently generate behaviors that human observers naturally see as expressing emotion. In this review article, we highlight that these expressions can have important benefits for human–machine interaction. We first summarize the psychological findings on how emotional expressions achieve important social functions in human relationships and highlight that artificial emotional expressions can serve analogous functions in human–machine interaction. We then review computational methods for determining what expressions make sense to generate within the context of interaction and how to realize those expressions across multiple modalities.” Find the paper and full list of authors in IEEE Proceedings.