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Navigating AI disclosures in the news
No full textTo disclose or not to disclose the use of AI to news readers? This is one of the most pressing questions news organisations are facing at the moment when working with (generative) AI tools. AI is here to stay – also in journalism. News organisations are experimenting with or have already implemented generative AI technologies in their news production processes and are creating internal guidelines as we speak. Some organisations inform their readers about the fact that they are using AI, and some don’t. Is this ethical? What do readers want? And what should useful disclosures even look like
Uncertainty-aware spiking neural networks for regression
No full textUncertainty estimation is a key component for quantifying the reliability of modern deep learning models, and is crucial for many real-world applications. However, efficient methods for uncertainty estimation in spiking neural networks (SNNs), particularly for regression tasks, remain underexplored. In this work, we demonstrate that uncertainty estimation in SNNbased regression tasks can be effectively achieved using recent frameworks originally developed for classification. Specifically, we adapt these frameworks to regression with two uncertaintyaware approaches: (1) a heteroscedastic Gaussian method, in which the SNN predicts both the mean and variance of the target variable; and (2) a Regression-as-Classification (RAC) method, which reformulates regression as a classification task to enable probabilistic modeling. We evaluate our approaches on a toy dataset and several benchmark regression datasets, showing that these approaches deliver efficient and high-quality uncertainty estimates, comparable to or surpassing state-of-theart deep neural network baselines. Our findings underscore the potential of SNNs for uncertainty estimation in regression tasks, offering a biologically inspired and energy-efficient solution for applications requiring both accuracy and robustness, while also paving the way for broader adoption of SNNs in sequential and event-driven domains
Minimizers in semi-dynamic strings
Get PDFMinimizers sampling is one of the most widely-used mechanisms for sampling strings. Let be a string over an alphabet . In addition, let and be two integers and be a total order on . The minimizer of window is the smallest position in where the smallest length- substring of based on starts. The set of minimizers for all is the set of the minimizers of . The set can be computed in time. The folklore algorithm for this computation computes the minimizer of every window in amortized time using working space. It is thus natural to pose the following two questions:
Question 1: Can we efficiently support other dynamic updates on the window?
Question 2: Can we improve on the working space?
We answer both questions in the affirmative:
1. We term a string semi-dynamic when one is allowed to insert or delete a letter at any of its ends. We show a data structure that maintains a semi-dynamic string and supports minimizer queries in in time with amortized time per update operation.
2. We show that this data structure can be modified to occupy strongly sublinear space without increasing the asymptotic complexity of its operations. To the best of our knowledge, this yields the first algorithm for computing in time using working space.
We complement our theoretical results with a concrete application and an experimental evaluation
Optimal verification of a minimum-weight basis in an uncertainty matroid
Get PDFResearch in explorable uncertainty addresses combinatorial optimization problems where there is partial information about the values of numeric input parameters, and exact values of these parameters can be determined by performing costly queries. The goal is to design an adaptive query strategy that minimizes the query cost incurred in computing an optimal solution. Solving such problems generally requires that we be able to solve the associated verification problem: given the answers to all queries in advance, find a minimum-cost set of queries that certifies an optimal solution to the combinatorial optimization problem. We present a polynomial-time algorithm for verifying a minimum-weight basis of a matroid, where each weight lies in a given uncertainty area. These areas may be finite sets, real intervals, or unions of open and closed intervals, strictly generalizing previous work by Erlebach and Hoffman which only handled the special case of open intervals. Our algorithm introduces new techniques to address the resulting challenges.
Verification problems are of particular importance in the area of explorable uncertainty, as the structural insights and techniques used to solve the verification problem often heavily influence work on the corresponding online problem and its stochastic variant. In our case, we use structural results from the verification problem to give a best-possible algorithm for a promise variant of the corresponding adaptive online problem. Finally, we show that our algorithms can be applied to two learning-augmented variants of the minimum-weight basis problem under explorable uncertainty
Making existing quantum position verification protocols secure against arbitrary transmission loss
Get PDFSignal loss threatens the security of quantum cryptography, especially in quantum position verification (QPV) protocols, where even small losses can compromise security. This Letter modifies traditional QPV to make high transmission loss between verifiers and the prover irrelevant for a class of protocols, including a practically interesting one based on BB84 states (QPVBB84f). Using photon presence detection and a small time delay, as well as a commitment before proceeding, the protocol’s relevant loss rate is reduced to only that of the prover’s lab, and the modified protocol has essentially the same security guarantees as the original one. The adapted protocol c-QPVBB84f thus offers strong security guarantees and feasibility over longer distances. We also discuss the practical implementation of the protocol and parameter estimates
Decentralized key distribution versus on-demand relaying for QKD networks
Get PDFQuantum key distribution (QKD) allows the distribution of secret keys for quantum-secure communication between two distant parties, vital in the quantum computing era in order to protect against quantum-enabled attackers. However, overcoming rate-distance limits in QKD and the establishment of quantum key distribution networks necessitate key relaying over trusted nodes. This process may be resource-intensive, consuming a substantial share of the scarce QKD key material to establish end-to-end secret keys. Hence, an efficient scheme for key relaying and the establishment of end-to-end key pools is essential for practical and extended quantum-secured networking. In this paper, we propose and compare two protocols for managing, storing, and distributing secret key material in QKD networks, addressing challenges such as the success rate of key requests, key consumption, and overhead resulting from relaying. We present an innovative, fully decentralized key distribution strategy as an alternative to the traditional hop-by-hop relaying via trusted nodes, where three experiments are considered to evaluate performance metrics under varying key demand. Our results show that the decentralized pre-flooding approach achieves higher success rates as application demands increase. This analysis highlights the strengths of each approach in enhancing QKD network performance, offering valuable insights for developing robust key distribution strategies in different scenarios