Spread-out percolation on transitive graphs of polynomial growth

Let $G$ be a vertex-transitive graph of superlinear polynomial growth. Given $r>0$, let $G_r$ be the graph on the same vertex set as $G$, with two vertices joined by an edge if and only if they are at graph distance at most $r$ apart in $G$. We show that the critical probability $p_c(G_r)$ for Bernoulli bond percolation on $G_r$ satisfies $p_c(G_r) \sim 1/\mathrm{deg}(G_r)$ as $r\to\infty$. This extends work of Penrose and Bollob\'as-Janson-Riordan, who considered the case $G=\mathbb{Z}^d$. Our result provides an important ingredient in parallel work of Georgakopoulos in which he introduces a new notion of dimension in groups. It also verifies a special case of a conjecture of Easo and Hutchcroft.Comment: 35 page

Graphs for torus actions on oriented manifolds with isolated fixed points and classification in dimension 6

Let a torus act on a compact oriented manifold $M$ with isolated fixed points, with an additional mild assumption that its isotropy submanifolds are orientable. We associate a signed labeled multigraph encoding the fixed point data (weights and signs at fixed points and isotropy submanifolds) of the manifold. We study operations on $M$ and its multigraph, (self) connected sum and blow up, etc. When the circle group acts on a 6-dimensional $M$, we classify such a multigraph by proving that we can convert it into the empty graph by successively applying two types of operations. In particular, this classifies the fixed point data of any such manifold. We prove this by showing that for any such manifold, we can successively take equivariant connected sums at fixed points with itself, $\mathbb{CP}^3$, and 6-dimensional analogue $Z_1$ and $Z_2$ of the Hirzebruch surfaces (and these with opposite orientations) to a fixed point free action on a compact oriented 6-manifold. We also classify a multigraph for a torus action on a 4-dimensional $M$.Comment: Added the assumption on the orientability of isotropy submanifolds. This paper supercedes arXiv:2108.07560; main results are new, while including all results of the previous on

Noetherianity of twisted Zhu algebra and bimodules

In this paper we show that for a large natural class of vertex operator algebras (VOAs) and their modules, the Zhu algebras and bimodules (and their $g$-twisted analogs) are Noetherian. These carry important information about the representation theory of the VOA, and its fusion rules, and the Noetherian property gives the potential for (non-commutative) algebro-geometric methods to be employed in their study

Is Universal Broadband Service Impossible?

Broadband Internet service is widely expected to be the fundamental universal service for the 21st century. But more than a decade of national and international struggles to close the digital divide between broadband haves and have nots suggest that reaching global universality will be a very difficult task. This paper argues that the strong guarantees made by the current broadband paradigm - low latency and constant availability - are unnecessary obstacles to its adoption as an affordable and universal digital service. We show that there is nonetheless a plausible strategy for deploying a Basic Broadband service that does not require such guarantees and is able to offer, at reasonable cost, almost all the critical and valuable services and applications currently delivered over low latency broadband, synchronous telepresence excepted.Comment: Appeared in IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems 202

Digital Twin Aided RIS Communication: Robust Beamforming and Interference Management

Reconfigurable intelligent surfaces (RISs) are envisioned to play a key role in future wireless communication networks. However, channel estimation in RIS-aided wireless networks is challenging due to their passive nature and the large number of reflective elements, leading to high channel estimation overhead. Additionally, conventional methods like beam sweeping, which do not rely on explicit channel state information, often struggle in managing interference in multi-user networks. In this paper, we propose a novel approach that leverages digital twins (DTs) of the physical environments to approximate channels using electromagnetic 3D models and ray tracing, thus relaxing the need for channel estimation and extensive over-the-air computations in RIS-aided wireless networks. To address the digital twins channel approximation errors, we further refine this approach with a DT-specific robust transmission design that reliably meets minimum desired rates. The results show that our method secures these rates over 90% of the time, significantly outperforming beam sweeping, which achieves these rates less than 8% of the time due to its poor management of transmitting power and interference.Comment: Dataset and code files will be available soon on the DeepMIMIO website: https://www.deepmimo.ne

Self-supervised Adversarial Training of Monocular Depth Estimation against Physical-World Attacks

Monocular Depth Estimation (MDE) plays a vital role in applications such as autonomous driving. However, various attacks target MDE models, with physical attacks posing significant threats to system security. Traditional adversarial training methods, which require ground-truth labels, are not directly applicable to MDE models that lack ground-truth depth. Some self-supervised model hardening techniques (e.g., contrastive learning) overlook the domain knowledge of MDE, resulting in suboptimal performance. In this work, we introduce a novel self-supervised adversarial training approach for MDE models, leveraging view synthesis without the need for ground-truth depth. We enhance adversarial robustness against real-world attacks by incorporating L_0-norm-bounded perturbation during training. We evaluate our method against supervised learning-based and contrastive learning-based approaches specifically designed for MDE. Our experiments with two representative MDE networks demonstrate improved robustness against various adversarial attacks, with minimal impact on benign performance.Comment: Accepted in TPAMI'24. Extended from our ICLR'23 publication (arXiv:2301.13487). arXiv admin note: substantial text overlap with arXiv:2301.1348

Management Decisions in Manufacturing using Causal Machine Learning -- To Rework, or not to Rework?

In this paper, we present a data-driven model for estimating optimal rework policies in manufacturing systems. We consider a single production stage within a multistage, lot-based system that allows for optional rework steps. While the rework decision depends on an intermediate state of the lot and system, the final product inspection, and thus the assessment of the actual yield, is delayed until production is complete. Repair steps are applied uniformly to the lot, potentially improving some of the individual items while degrading others. The challenge is thus to balance potential yield improvement with the rework costs incurred. Given the inherently causal nature of this decision problem, we propose a causal model to estimate yield improvement. We apply methods from causal machine learning, in particular double/debiased machine learning (DML) techniques, to estimate conditional treatment effects from data and derive policies for rework decisions. We validate our decision model using real-world data from opto-electronic semiconductor manufacturing, achieving a yield improvement of 2 - 3% during the color-conversion process of white light-emitting diodes (LEDs).Comment: 30 pages, 10 figure

A Survey on Image-text Multimodal Models

With the significant advancements of Large Language Models (LLMs) in the field of Natural Language Processing (NLP), the development of image-text multimodal models has garnered widespread attention. Current surveys on image-text multimodal models mainly focus on representative models or application domains, but lack a review on how general technical models influence the development of domain-specific models, which is crucial for domain researchers. Based on this, this paper first reviews the technological evolution of image-text multimodal models, from early explorations of feature space to visual language encoding structures, and then to the latest large model architectures. Next, from the perspective of technological evolution, we explain how the development of general image-text multimodal technologies promotes the progress of multimodal technologies in the biomedical field, as well as the importance and complexity of specific datasets in the biomedical domain. Then, centered on the tasks of image-text multimodal models, we analyze their common components and challenges. After that, we summarize the architecture, components, and data of general image-text multimodal models, and introduce the applications and improvements of image-text multimodal models in the biomedical field. Finally, we categorize the challenges faced in the development and application of general models into external factors and intrinsic factors, further refining them into 2 external factors and 5 intrinsic factors, and propose targeted solutions, providing guidance for future research directions. For more details and data, please visit our GitHub page: \url{https://github.com/i2vec/A-survey-on-image-text-multimodal-models}

Automatic Generation of Model and Data Cards: A Step Towards Responsible AI

In an era of model and data proliferation in machine learning/AI especially marked by the rapid advancement of open-sourced technologies, there arises a critical need for standardized consistent documentation. Our work addresses the information incompleteness in current human-generated model and data cards. We propose an automated generation approach using Large Language Models (LLMs). Our key contributions include the establishment of CardBench, a comprehensive dataset aggregated from over 4.8k model cards and 1.4k data cards, coupled with the development of the CardGen pipeline comprising a two-step retrieval process. Our approach exhibits enhanced completeness, objectivity, and faithfulness in generated model and data cards, a significant step in responsible AI documentation practices ensuring better accountability and traceability.Comment: NAACL 2024 (Oral

Public Computing Intellectuals in the Age of AI Crisis

The belief that AI technology is on the cusp of causing a generalized social crisis became a popular one in 2023. While there was no doubt an element of hype and exaggeration to some of these accounts, they do reflect the fact that there are troubling ramifications to this technology stack. This conjunction of shared concerns about social, political, and personal futures presaged by current developments in artificial intelligence presents the academic discipline of computing with a renewed opportunity for self-examination and reconfiguration. This position paper endeavors to do so in four sections. The first explores what is at stake for computing in the narrative of an AI crisis. The second articulates possible educational responses to this crisis and advocates for a broader analytic focus on power relations. The third section presents a novel characterization of academic computing's field of practice, one which includes not only the discipline's usual instrumental forms of practice but reflexive practice as well. This reflexive dimension integrates both the critical and public functions of the discipline as equal intellectual partners and a necessary component of any contemporary academic field. The final section will advocate for a conceptual archetype--the Public Computer Intellectual and its less conspicuous but still essential cousin, the (Almost) Public Computer Intellectual--as a way of practically imagining the expanded possibilities of academic practice in our discipline, one that provides both self-critique and an outward-facing orientation towards the public good. It will argue that the computer education research community can play a vital role in this regard. Recommendations for pedagogical change within computing to develop more reflexive capabilities are also provided.Comment: 28 pages, 2 table