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We exhibit that, when given a classical Byzantine agreement protocol designed in the private-channel model, it is feasible to construct a quantum agreement protocol that can effectively handle a full-information adversary. Notably, both protocols have equivalent levels of resilience, round complexity, and communication complexity. In the classical private-channel scenario, participating players are limited to exchanging classical bits, with the adversary lacking knowledge of the exchanged messages. In contrast, in the quantum full-information setting, participating players can exchange qubits, while the adversary possesses comprehensive and accurate visibility into the system's state and messages. By showcasing the reduction from quantum to classical frameworks, this paper demonstrates the strength and flexibility of quantum protocols in addressing security challenges posed by adversaries with increased visibility. It underscores the potential of leveraging quantum principles to improve security measures without compromising on efficiency or resilience. By applying our reduction, we demonstrate quantum advantages in the round complexity of asynchronous Byzantine agreement protocols in the full-information model. It is well known that in the full-information model, any classical protocol requires $\Omega(n)$ rounds to solve Byzantine agreement with probability one even against Fail-stop adversary when resilience $t=\Theta(n)$. We show that quantum protocols can achieve $O(1)$ rounds (i) with resilience $t0$, therefore surpassing the classical lower bound., Comment: 26 pages. This is the extended version of the paper presented at DISC2024. It includes extra results in Appendix D that were not included in the conference submission due to page constraints

Multipliers and multiply-accumulators (MACs) are critical arithmetic circuit components in the modern era. As essential components of AI accelerators, they significantly influence the area and performance of compute-intensive circuits. This paper presents UFO-MAC, a unified framework for the optimization of multipliers and MACs. Specifically, UFO-MAC employs an optimal compressor tree structure and utilizes integer linear programming (ILP) to refine the stage assignment and interconnection of the compressors. Additionally, it explicitly exploits the non-uniform arrival time profile of the carry propagate adder (CPA) within multipliers to achieve targeted optimization. Moreover, the framework also supports the optimization of fused MAC architectures. Experimental results demonstrate that multipliers and MACs optimized by UFO-MAC Pareto-dominate state-of-the-art baselines and commercial IP libraries. The performance gain of UFO-MAC is further validated through the implementation of multipliers and MACs within functional modules, underlining its efficacy in real scenarios., Comment: In proceeding of ICCAD 2024

Event cameras, drawing inspiration from biological systems, efficiently detect changes in ambient light with low latency and high dynamic range while consuming minimal power. The most current approach to processing event data often involves converting it into frame-based representations, which is well-established in traditional vision. However, this approach neglects the sparsity of event data, loses fine-grained temporal information during the transformation process, and increases the computational burden, making it ineffective for characterizing event camera properties. In contrast, Point Cloud is a popular representation for 3D processing and is better suited to match the sparse and asynchronous nature of the event camera. Nevertheless, despite the theoretical compatibility of point-based methods with event cameras, the results show a performance gap that is not yet satisfactory compared to frame-based methods. In order to bridge the performance gap, we propose EventMamba, an efficient and effective Point Cloud framework that achieves competitive results even compared to the state-of-the-art (SOTA) frame-based method in both classification and regression tasks. This notable accomplishment is facilitated by our rethinking of the distinction between Event Cloud and Point Cloud, emphasizing effective temporal information extraction through optimized network structures. Specifically, EventMamba leverages temporal aggregation and State Space Model (SSM) based Mamba boasting enhanced temporal information extraction capabilities. Through a hierarchical structure, EventMamba is adept at abstracting local and global spatial features and implicit and explicit temporal features. By adhering to the lightweight design principle, EventMamba delivers impressive results with minimal computational resource utilization, demonstrating its efficiency and effectiveness., Comment: Extension Journal of TTPOINT and PEPNet, modify the dataset split method

The Feistel construction is a fundamental technique for building pseudorandom permutations and block ciphers. This paper shows that a simple adaptation of the construction is resistant, even to algorithm substitution attacks -- that is, adversarial subversion -- of the component round functions. Specifically, we establish that a Feistel-based construction with more than $2000n/\log(1/\epsilon)$ rounds can transform a subverted random function -- which disagrees with the original one at a small fraction (denoted by $\epsilon$) of inputs -- into an object that is \emph{crooked-indifferentiable} from a random permutation, even if the adversary is aware of all the randomness used in the transformation. We also provide a lower bound showing that the construction cannot use fewer than $2n/\log(1/\epsilon)$ rounds to achieve crooked-indifferentiable security.

The random oracle methodology has proven to be a powerful tool for designing and reasoning about cryptographic schemes. In this paper, we focus on the basic problem of correcting faulty or adversarially corrupted random oracles, so that they can be confidently applied for such cryptographic purposes. We prove that a simple construction can transform a "subverted" random oracle which disagrees with the original one at a small fraction of inputs into an object that is indifferentiable from a random function, even if the adversary is made aware of all randomness used in the transformation. Our results permit future designers of cryptographic primitives in typical kleptographic settings (i.e., those permitting adversaries that subvert or replace basic cryptographic algorithms) to use random oracles as a trusted black box.

Multiplication is a fundamental operation in many applications, and multipliers are widely adopted in various circuits. However, optimizing multipliers is challenging due to the extensive design space. In this paper, we propose a multiplier design optimization framework based on reinforcement learning. We utilize matrix and tensor representations for the compressor tree of a multiplier, enabling seamless integration of convolutional neural networks as the agent network. The agent optimizes the multiplier structure using a Pareto-driven reward customized to balance area and delay. Furthermore, we enhance the original framework with parallel reinforcement learning and design space pruning techniques and extend its capability to optimize fused multiply-accumulate (MAC) designs. Experiments conducted on different bit widths of multipliers demonstrate that multipliers produced by our approach outperform all baseline designs in terms of area, power, and delay. The performance gain is further validated by comparing the area, power, and delay of processing element arrays using multipliers from our approach and baseline approaches., Comment: Accepted by TODAES 2025

Event cameras exhibit remarkable attributes such as high dynamic range, asynchronicity, and low latency, making them highly suitable for vision tasks that involve high-speed motion in challenging lighting conditions. These cameras implicitly capture movement and depth information in events, making them appealing sensors for Camera Pose Relocalization (CPR) tasks. Nevertheless, existing CPR networks based on events neglect the pivotal fine-grained temporal information in events, resulting in unsatisfactory performance. Moreover, the energy-efficient features are further compromised by the use of excessively complex models, hindering efficient deployment on edge devices. In this paper, we introduce PEPNet, a simple and effective point-based network designed to regress six degrees of freedom (6-DOFs) event camera poses. We rethink the relationship between the event camera and CPR tasks, leveraging the raw Point Cloud directly as network input to harness the high-temporal resolution and inherent sparsity of events. PEPNet is adept at abstracting the spatial and implicit temporal features through hierarchical structure and explicit temporal features by Attentive Bi-directional Long Short-Term Memory (A-Bi-LSTM). By employing a carefully crafted lightweight design, PEPNet delivers state-of-the-art (SOTA) performance on both indoor and outdoor datasets with meager computational resources. Specifically, PEPNet attains a significant 38% and 33% performance improvement on the random split IJRR and M3ED datasets, respectively. Moreover, the lightweight design version PEPNet$_{tiny}$ accomplishes results comparable to the SOTA while employing a mere 0.5% of the parameters., Comment: Accepted by CVPR 2024

This study introduces a comprehensive framework that situates information cascades within the domain of higher-order interactions, utilizing a double-threshold hypergraph model. We propose that individuals (nodes) gain awareness of information through each communication channel (hyperedge) once the number of information adopters surpasses a threshold $\phi_m$. However, actual adoption of the information only occurs when the cumulative influence across all communication channels exceeds a second threshold, $\phi_k$. We analytically derive the cascade condition for both the case of a single seed node using percolation methods and the case of any seed size employing mean-field approximation. Our findings underscore that when considering the fractional seed size, $r_0 \in (0,1]$, the connectivity pattern of the random hypergraph, characterized by the hyperdegree, $k$, and cardinality, $m$, distributions, exerts an asymmetric impact on the global cascade boundary. This asymmetry manifests in the observed differences in the boundaries of the global cascade within the $(\phi_m, \langle m \rangle)$ and $(\phi_k, \langle k \rangle)$ planes. However, as $r_0 \to 0$, this asymmetric effect gradually diminishes. Overall, by elucidating the mechanisms driving information cascades within a broader context of higher-order interactions, our research contributes to theoretical advancements in complex systems theory., Comment: 11 pages, 10 figures

The arise of disagreement is an emergent phenomenon that can be observed within a growing social group and, beyond a certain threshold, can lead to group fragmentation. To better understand how disagreement emerges, we introduce an analytically tractable model of group formation where individuals have multidimensional binary opinions and the group grows through a noisy homophily principle, i.e., like-minded individuals attract each other with exceptions occurring with some small probability. Assuming that the level of disagreement is correlated with the number of different opinions coexisting within the group, we find analytically and numerically that in growing groups disagreement emerges spontaneously regardless of how small the noise in the system is. Moreover, for groups of infinite size, fragmentation is inevitable. We also show that the model outcomes are robust under different group growth mechanisms., Comment: 17 pages, 3 figures

It has been shown recently that the framework of quantum sampling, as introduced by Bouman and Fehr, can lead to new entropic uncertainty relations highly applicable to finite-key cryptographic analyses. Here we revisit these so-called sampling-based entropic uncertainty relations, deriving newer, more powerful, relations and applying them to source-independent quantum random number generators and high-dimensional quantum key distribution protocols. Along the way, we prove several interesting results in the asymptotic case for our entropic uncertainty relations. These sampling-based approaches to entropic uncertainty, and their application to quantum cryptography, hold great potential for deriving proofs of security for quantum cryptographic systems, and the approaches we use here may be applicable to an even wider range of scenarios.