OpenRaiser/Intern-Atlas huggingface dataset:
Communication Modalities
Venue: arXiv cs.DC 2024
Year: 2024

Epistemic analysis of distributed systems is one of the biggest successes among applications of logic in computer science. The reason for that is that agents' actions are necessarily guided by their knowledge. Thus, epistemic modal logic, with its knowledge and belief modalities (and group versions thereof), has played a vital role in establishing both impossibility results and necessary conditions for solvable distributed tasks. In distributed systems, knowledge is largely attained via communication. It has been standard in both distributed systems and dynamic epistemic logic to treat incoming messages as trustworthy, thus, creating difficulties in the epistemic analysis of byzantine distributed systems where faulty agents may lie. In this paper, we argue that handling such communication scenarios calls for additional modalities representing the informational content of messages that should not be taken at face value. We present two such modalities: hope for the case of fully byzantine agents and creed for non-uniform communication protocols in general.
Predicting the impact of water transport on carbonation-induced corrosion in variably saturated reinforced concrete
Venue: arXiv cs.CE 2024
Year: 2024

A modelling framework for predicting carbonation-induced corrosion in reinforced concrete is presented. The framework constituents include a new model for water transport in cracked concrete, a link between corrosion current density and water saturation, and a theory for characterising concrete carbonation. The theoretical framework is numerically implemented using the finite element method and model predictions are extensively benchmarked against experimental data. The results show that the model is capable of accurately predicting carbonation progress, as well as wetting and drying of cracked and uncracked concrete, revealing a very good agreement with independent experiments from a set of consistent parameters. In addition, insight is gained into the evolution of carbonation penetration and corrosion current density under periodic wetting and drying conditions. Among others, we find that cyclic wetting periods significantly speed up the carbonation progress and that the induced corrosion current density is very sensitive to concrete saturation.
TetraBFT: Reducing Latency of Unauthenticated, Responsive BFT Consensus
Venue: arXiv cs.CR 2024
Year: 2024

This paper presents TetraBFT, a novel unauthenticated Byzantine fault tolerant protocol for solving consensus in partial synchrony, eliminating the need for public key cryptography and ensuring resilience against computationally unbounded adversaries. TetraBFT has several compelling features: it necessitates only constant local storage, has optimal communication complexity, satisfies optimistic responsiveness -- allowing the protocol to operate at actual network speeds under ideal conditions -- and can achieve consensus in just 5 message delays, which outperforms all known unauthenticated protocols achieving the other properties listed. We validate the correctness of TetraBFT through rigorous security analysis and formal verification. Furthermore, we extend TetraBFT into a multi-shot, chained consensus protocol, making a pioneering effort in applying pipelining techniques to unauthenticated protocols. This positions TetraBFT as a practical and deployable solution for blockchain systems aiming for high efficiency.
Accelerating Autonomy: Insights from Pro Racers in the Era of Autonomous Racing - An Expert Interview Study
Venue: arXiv cs.RO 2024
Year: 2024

This research aims to investigate professional racing drivers' expertise to develop an understanding of their cognitive and adaptive skills to create new autonomy algorithms. An expert interview study was conducted with 11 professional race drivers, data analysts, and racing instructors from across prominent racing leagues. The interviews were conducted using an exploratory, non-standardized expert interview format guided by a set of prepared questions. The study investigates drivers' exploration strategies to reach their vehicle limits and contrasts them with the capabilities of state-of-the-art autonomous racing software stacks. Participants were questioned about the techniques and skills they have developed to quickly approach and maneuver at the vehicle limit, ultimately minimizing lap times. The analysis of the interviews was grounded in Mayring's qualitative content analysis framework, which facilitated the organization of the data into multiple categories and subcategories. Our findings create insights into human behavior regarding reaching a vehicle's limit and minimizing lap times. We conclude from the findings the development of new autonomy software modules that allow for more adaptive vehicle behavior. By emphasizing the distinct nuances between manual and autonomous driving techniques, the paper encourages further investigation into human drivers' strategies to maximize their vehicles' capabilities.
Onboard Out-of-Calibration Detection of Deep Learning Models using Conformal Prediction
Venue: arXiv cs.LG 2024
Year: 2024

The black box nature of deep learning models complicate their usage in critical applications such as remote sensing. Conformal prediction is a method to ensure trust in such scenarios. Subject to data exchangeability, conformal prediction provides finite sample coverage guarantees in the form of a prediction set that is guaranteed to contain the true class within a user defined error rate. In this letter we show that conformal prediction algorithms are related to the uncertainty of the deep learning model and that this relation can be used to detect if the deep learning model is out-of-calibration. Popular classification models like Resnet50, Densenet161, InceptionV3, and MobileNetV2 are applied on remote sensing datasets such as the EuroSAT to demonstrate how under noisy scenarios the model outputs become untrustworthy. Furthermore an out-of-calibration detection procedure relating the model uncertainty and the average size of the conformal prediction set is presented.
Identifying Narrative Patterns and Outliers in Holocaust Testimonies Using Topic Modeling
Venue: arXiv cs.CL 2024
Year: 2024

The vast collection of Holocaust survivor testimonies presents invaluable historical insights but poses challenges for manual analysis. This paper leverages advanced Natural Language Processing (NLP) techniques to explore the USC Shoah Foundation Holocaust testimony corpus. By treating testimonies as structured question-and-answer sections, we apply topic modeling to identify key themes. We experiment with BERTopic, which leverages recent advances in language modeling technology. We align testimony sections into fixed parts, revealing the evolution of topics across the corpus of testimonies. This highlights both a common narrative schema and divergences between subgroups based on age and gender. We introduce a novel method to identify testimonies within groups that exhibit atypical topic distributions resembling those of other groups. This study offers unique insights into the complex narratives of Holocaust survivors, demonstrating the power of NLP to illuminate historical discourse and identify potential deviations in survivor experiences.
DDE-Find: Learning Delay Differential Equations from Noisy, Limited Data
Venue: arXiv cs.LG 2024
Year: 2024

Delay Differential Equations (DDEs) are a class of differential equations that can model diverse scientific phenomena. However, identifying the parameters, especially the time delay, that make a DDE's predictions match experimental results can be challenging. We introduce DDE-Find, a data-driven framework for learning a DDE's parameters, time delay, and initial condition function. DDE-Find uses an adjoint-based approach to efficiently compute the gradient of a loss function with respect to the model parameters. We motivate and rigorously prove an expression for the gradients of the loss using the adjoint. DDE-Find builds upon recent developments in learning DDEs from data and delivers the first complete framework for learning DDEs from data. Through a series of numerical experiments, we demonstrate that DDE-Find can learn DDEs from noisy, limited data.
Effects of Realism and Representation on Self-Embodied Avatars in Immersive Virtual Environments
Venue: arXiv cs.HC 2024
Year: 2024

Virtual Reality (VR) has recently gained traction with many new and ever more affordable devices being released. The increase in popularity of this paradigm of interaction has given birth to new applications and has attracted casual consumers to experience VR. Providing a self-embodied representation (avatar) of users' full bodies inside shared virtual spaces can improve the VR experience and make it more engaging to both new and experienced users . This is especially important in fully immersive systems, where the equipment completely occludes the real world making self awareness problematic. Indeed, the feeling of presence of the user is highly influenced by their virtual representations, even though small flaws could lead to uncanny valley side-effects. Following previous research, we would like to assess whether using a third-person perspective could also benefit the VR experience, via an improved spatial awareness of the user's virtual surroundings. In this paper we investigate realism and perspective of self-embodied representation in VR setups in natural tasks, such as walking and avoiding obstacles. We compare both First and Third-Person perspectives with three different levels of realism in avatar representation. These range from a stylized abstract avatar, to a "realistic" mesh-based humanoid representation and a point-cloud rendering. The latter uses data captured via depth-sensors and mapped into a virtual self inside the Virtual Environment. We present a throughout evaluation and comparison of these different representations, describing a series of guidelines for self-embodied VR applications. The effects of the uncanny valley are also discussed in the context of navigation and reflex-based tasks.
Deduplicator: When Computation Reuse Meets Load Balancing at the Network Edge
Venue: arXiv cs.DC 2024
Year: 2024

Load balancing has been a fundamental building block of cloud and, more recently, edge computing environments. At the same time, in edge computing environments, prior research has highlighted that applications operate on similar (correlated) data. Based on this observation, prior research has advocated for the direction of "computation reuse", where the results of previously executed computational tasks are stored at the edge and are reused (if possible) to satisfy incoming tasks with similar input data, instead of executing incoming tasks from scratch. Both load balancing and computation reuse are critical to the deployment of scalable edge computing environments, yet they are contradictory in nature. In this paper, we propose the Deduplicator, a middlebox that aims to facilitate both load balancing and computation reuse at the edge. The Deduplicator features mechanisms to identify and deduplicate similar tasks offloaded by user devices, collect information about the usage of edge servers' resources, manage the addition of new edge servers and the failures of existing edge servers, and ultimately balance the load imposed on edge servers. Our evaluation results demonstrate that the Deduplicator achieves up to 20% higher percentages of computation reuse compared to several other load balancing approaches, while also effectively balancing the distribution of tasks among edge servers at line rate.
Two-Dimensional Multi-Access Coded Caching with Multiple Transmit Antennas
Venue: arXiv cs.IT 2024
Year: 2024

This work introduces a multi-antenna coded caching problem in a two-dimensional multi-access network, where a server with $L$ transmit antennas and $N$ files communicates to $K_1K_2$ users, each with a single receive antenna, through a wireless broadcast link. The network consists of $K_1K_2$ cache nodes and $K_1K_2$ users. The cache nodes, each with capacity $M$, are placed on a rectangular grid with $K_1$ rows and $K_2$ columns, and the users are placed regularly on the square grid such that a user can access $r^2$ neighbouring caches in a cyclic wrap-around fashion. For a given cache memory $M$, the goal of the coded caching problem is to serve the user demands with a minimum delivery time. We propose a solution for the aforementioned coded caching problem by designing two arrays: a caching array and a delivery array. Further, we present two classes of caching and delivery arrays and obtain corresponding multi-access coded caching schemes. The first scheme achieves a normalized delivery time (NDT) $\frac{K_1K_2(1-r^2\frac{M}{N})}{L+K_1K_2\frac{M}{N}}$. The second scheme achieves an NDT $\frac{K_1K_2(1-r^2\frac{M}{N})}{L+K_1K_2r^2\frac{M}{N}}$ when $M/N=1/K_1K_2$ and $L=K_1K_2-r^2$, which is optimal under uncoded placement and one-shot delivery.
FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer
Venue: arXiv cs.LG 2024
Year: 2024

Federated Class-Incremental Learning (FCIL) focuses on continually transferring the previous knowledge to learn new classes in dynamic Federated Learning (FL). However, existing methods do not consider the trustworthiness of FCIL, i.e., improving continual utility, privacy, and efficiency simultaneously, which is greatly influenced by catastrophic forgetting and data heterogeneity among clients. To address this issue, we propose FedProK (Federated Prototypical Feature Knowledge Transfer), leveraging prototypical feature as a novel representation of knowledge to perform spatial-temporal knowledge transfer. Specifically, FedProK consists of two components: (1) feature translation procedure on the client side by temporal knowledge transfer from the learned classes and (2) prototypical knowledge fusion on the server side by spatial knowledge transfer among clients. Extensive experiments conducted in both synchronous and asynchronous settings demonstrate that our FedProK outperforms the other state-of-the-art methods in three perspectives of trustworthiness, validating its effectiveness in selectively transferring spatial-temporal knowledge.
Platform Competition in the Autobidding World
Venue: arXiv cs.GT 2024
Year: 2024

We study the problem of auction design for advertising platforms that face strategic advertisers who are bidding across platforms. Each advertiser's goal is to maximize their total value or conversions while satisfying some constraint(s) across all the platforms they participates in. In this paper, we focus on advertisers with return-over-investment (henceforth, ROI) constraints, i.e. each advertiser is trying to maximize value while making sure that their ROI across all platforms is no less than some target value. An advertiser interacts with the platforms through autobidders -- for each platform, the advertiser strategically chooses a target ROI to report to the platform's autobidder, which in turn uses a uniform bid multiplier to bid on the advertiser's behalf on the queries owned by the given platform. Our main result is that for a platform trying to maximize revenue, competition with other platforms is a key factor to consider when designing their auction. While first-price auctions are optimal (for both revenue and welfare) in the absence of competition, this no longer holds true in multi-platform settings. We show that there exists a large class of advertiser valuations over queries such that, from the platform's perspective, running a second price auction dominates running a first price auction. Furthermore, our analysis reveals the key factors influencing platform choice of auction format: (i) intensity of competition among advertisers, (ii) sensitivity of bid landscapes to an auction change (driven by advertiser sensitivity to price changes), and (iii) relative inefficiency of second-price auctions compared to first-price auctions.
Beyond Relevance: Evaluate and Improve Retrievers on Perspective Awareness
Venue: arXiv cs.IR 2024
Year: 2024

The task of Information Retrieval (IR) requires a system to identify relevant documents based on users' information needs. In real-world scenarios, retrievers are expected to not only rely on the semantic relevance between the documents and the queries but also recognize the nuanced intents or perspectives behind a user query. For example, when asked to verify a claim, a retrieval system is expected to identify evidence from both supporting vs. contradicting perspectives, for the downstream system to make a fair judgment call. In this work, we study whether retrievers can recognize and respond to different perspectives of the queries -- beyond finding relevant documents for a claim, can retrievers distinguish supporting vs. opposing documents? We reform and extend six existing tasks to create a benchmark for retrieval, where we have diverse perspectives described in free-form text, besides root, neutral queries. We show that current retrievers covered in our experiments have limited awareness of subtly different perspectives in queries and can also be biased toward certain perspectives. Motivated by the observation, we further explore the potential to leverage geometric features of retriever representation space to improve the perspective awareness of retrievers in a zero-shot manner. We demonstrate the efficiency and effectiveness of our projection-based methods on the same set of tasks. Further analysis also shows how perspective awareness improves performance on various downstream tasks, with 4.2% higher accuracy on AmbigQA and 29.9% more correlation with designated viewpoints on essay writing, compared to non-perspective-aware baselines.
Taming Equilibrium Bias in Risk-Sensitive Multi-Agent Reinforcement Learning
Venue: arXiv cs.LG 2024
Year: 2024

We study risk-sensitive multi-agent reinforcement learning under general-sum Markov games, where agents optimize the entropic risk measure of rewards with possibly diverse risk preferences. We show that using the regret naively adapted from existing literature as a performance metric could induce policies with equilibrium bias that favor the most risk-sensitive agents and overlook the other agents. To address such deficiency of the naive regret, we propose a novel notion of regret, which we call risk-balanced regret, and show through a lower bound that it overcomes the issue of equilibrium bias. Furthermore, we develop a self-play algorithm for learning Nash, correlated, and coarse correlated equilibria in risk-sensitive Markov games. We prove that the proposed algorithm attains near-optimal regret guarantees with respect to the risk-balanced regret.
Efficient Open Modification Spectral Library Searching in High-Dimensional Space with Multi-Level-Cell Memory
Venue: arXiv cs.AR 2024
Year: 2024

Open Modification Search (OMS) is a promising algorithm for mass spectrometry analysis that enables the discovery of modified peptides. However, OMS encounters challenges as it exponentially extends the search scope. Existing OMS accelerators either have limited parallelism or struggle to scale effectively with growing data volumes. In this work, we introduce an OMS accelerator utilizing multi-level-cell (MLC) RRAM memory to enhance storage capacity by 3x. Through in-memory computing, we achieve up to 77x faster data processing with two to three orders of magnitude better energy efficiency. Testing was done on a fabricated MLC RRAM chip. We leverage hyperdimensional computing to tolerate up to 10% memory errors while delivering massive parallelism in hardware.
GTFS2STN: Analyzing GTFS Transit Data by Generating Spatiotemporal Transit Network
Venue: arXiv cs.CE 2024
Year: 2024

The General Transit Feed Specification (GTFS) is an open standard format for recording transit information, utilized by thousands of transit agencies worldwide. This study introduces GTFS2STN, a novel tool that converts static GTFS transit networks into spatiotemporal networks, connecting bus stops across space and time. This transformation enables comprehensive analysis of transit system accessibility. Additionally, we present a web-based application version of the GTFS2STN tool that allows users to generate spatiotemporal networks online and perform basic analyses, including the creation of isochrone maps from a given origin and the calculation of travel time variability between origin-destination pairs over time. Comparative analysis demonstrates that GTFS2STN produces results similar to those of Mapnificent, an existing open-source tool for generating isochrone maps from GTFS inputs. Compared with Mapnificent, GTFS2STN offers enhanced flexibility for researchers and planners to evaluate transit plans, as it allows users to upload and analyze historical or suggested GTFS feeds from any transit agency. This feature facilitates the assessment of accessibility and travel time variability in transit networks over extended periods, making GTFS2STN a valuable tool for the planning and research for the transit systems.
Can Nuanced Language Lead to More Actionable Insights? Exploring the Role of Generative AI in Analytical Narrative Structure
Venue: arXiv cs.HC 2024
Year: 2024

Relevant language describing trends in data can be useful for generating summaries to help with readers' takeaways. However, the language employed in these often template-generated summaries tends to be simple, ranging from describing simple statistical information (e.g., extrema and trends) without additional context and richer language to provide actionable insights. Recent advances in Large Language Models (LLMs) have shown promising capabilities in capturing subtle nuances in language when describing information. This workshop paper specifically explores how LLMs can provide more actionable insights when describing trends by focusing on three dimensions of analytical narrative structure: semantic, rhetorical, and pragmatic. Building on prior research that examines visual and linguistic signatures for univariate line charts, we examine how LLMs can further leverage the semantic dimension of analytical narratives using quantified semantics to describe shapes in trends as people intuitively view them. These semantic descriptions help convey insights in a way that leads to a pragmatic outcome, i.e., a call to action, persuasion, warning vs. alert, and situational awareness. Finally, we identify rhetorical implications for how well these generated narratives align with the perceived shape of the data, thereby empowering users to make informed decisions and take meaningful actions based on these data insights.
Beyond Unimodal Learning: The Importance of Integrating Multiple Modalities for Lifelong Learning
Venue: arXiv cs.LG 2024
Year: 2024

While humans excel at continual learning (CL), deep neural networks (DNNs) exhibit catastrophic forgetting. A salient feature of the brain that allows effective CL is that it utilizes multiple modalities for learning and inference, which is underexplored in DNNs. Therefore, we study the role and interactions of multiple modalities in mitigating forgetting and introduce a benchmark for multimodal continual learning. Our findings demonstrate that leveraging multiple views and complementary information from multiple modalities enables the model to learn more accurate and robust representations. This makes the model less vulnerable to modality-specific regularities and considerably mitigates forgetting. Furthermore, we observe that individual modalities exhibit varying degrees of robustness to distribution shift. Finally, we propose a method for integrating and aligning the information from different modalities by utilizing the relational structural similarities between the data points in each modality. Our method sets a strong baseline that enables both single- and multimodal inference. Our study provides a promising case for further exploring the role of multiple modalities in enabling CL and provides a standard benchmark for future research.