Event temporal reasoning aims at identifying the temporal relations between two or more events from narratives. However, knowledge conflicts arise when there is a mismatch between the actual temporal relations of events in the context and the prior knowledge or biases learned by the model. In this paper, we propose to detect knowledge-conflict examples in event temporal reasoning using bias indicators, which include event relation prior bias, tense bias, narrative bias, and dependency bias. We define conflict examples as those where event relations are opposite to biased or prior relations. To mitigate event-related knowledge conflicts, we introduce a Counterfactual Data Augmentation (CDA) based method that can be applied to both Pre-trained Language Models (PLMs) and Large Language Models (LLMs) either as additional training data or demonstrations for In-Context Learning. Experiments suggest both PLMs and LLMs suffer from knowledge conflicts in event temporal reasoning, and CDA has the potential for reducing hallucination and improving model performance.
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Oblivious transfer (OT) is a fundamental primitive for secure two-party computation. It is well known that OT cannot be implemented with information-theoretic security if the two players only have access to noiseless communication channels, even in the quantum case. As a result, weaker variants of OT have been studied. In this work, we rigorously establish the impossibility of cheat-sensitive OT, where a dishonest party can cheat, but risks being detected. We construct a general attack on any quantum protocol that allows the receiver to compute all inputs of the sender and provide an explicit upper bound on the success probability of this attack. This implies that cheat-sensitive quantum Symmetric Private Information Retrieval cannot be implemented with statistical information-theoretic security. Leveraging the techniques devised for our proofs, we provide entropic bounds on primitives needed for secure function evaluation. They imply impossibility results for protocols where the players have access to OT as a resource. This result significantly improves upon existing bounds and yields tight bounds for reductions of 1-out-of-n OT to a resource primitive. Our results hold in particular for transformations between a finite number of primitives and for any error.
Reliability has been a major concern in embedded systems. Higher transistor density and lower voltage supply increase the vulnerability of embedded systems to soft errors. A Single Event Upset (SEU), which is also called a soft error, can reverse a bit in a sequential element, resulting in a system failure. Simulation-based fault injection has been widely used to evaluate reliability, as suggested by ISO26262. However, it is practically impossible to test all faults for a complex design. Random fault injection is a compromise that reduces accuracy and fault coverage. Formal verification is an alternative approach. In this paper, we use formal verification, in the form of model checking, to evaluate the hardware reliability of a RISC-V Ibex Core in the presence of soft errors. Backward tracing is performed to identify and categorize faults according to their effects (no effect, Silent Data Corruption, crashes, and hangs). By using formal verification, the entire state space and fault list can be exhaustively explored. It is found that misaligned instructions can amplify fault effects. It is also found that some bits are more vulnerable to SEUs than others. In general, most of the bits in the Ibex Core are vulnerable to Silent Data Corruption, and the second pipeline stage is more vulnerable to Silent Data Corruption than the first.
Existence constraints were defined in the Relational Data Model, but, unfortunately, are not provided by any Relational Database Management System, except for their NOT NULL particular case. Our (Elementary) Mathematical Data Model extended them to function products and introduced their dual non-existence constraints. MatBase, an intelligent data and knowledge base management system prototype based on both these data models, not only provides existence and non-existence constraints, but also automatically generates code for their enforcement. This paper presents and discusses the algorithms used by MatBase to enforce these types of constraints.
Ensuring that AI systems reliably and robustly avoid harmful or dangerous behaviours is a crucial challenge, especially for AI systems with a high degree of autonomy and general intelligence, or systems used in safety-critical contexts. In this paper, we will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI. The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees. This is achieved by the interplay of three core components: a world model (which provides a mathematical description of how the AI system affects the outside world), a safety specification (which is a mathematical description of what effects are acceptable), and a verifier (which provides an auditable proof certificate that the AI satisfies the safety specification relative to the world model). We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them. We also argue for the necessity of this approach to AI safety, and for the inadequacy of the main alternative approaches.
Attention mechanisms play a crucial role in the neural revolution of Natural Language Processing (NLP). With the growth of attention-based models, several pruning techniques have been developed to identify and exploit sparseness, making these models more efficient. Most efforts focus on hard-coding attention patterns or pruning attention weights based on training data. We propose Attention Pruning (AP), a framework that observes attention patterns in a fixed dataset and generates a global sparseness mask. AP saves 90% of attention computation for language modeling and about 50% for machine translation and GLUE tasks, maintaining result quality. Our method reveals important distinctions between self- and cross-attention patterns, guiding future NLP research. Our framework can reduce both latency and memory requirements for any attention-based model, aiding in the development of improved models for existing or new NLP applications. We have demonstrated this with encoder and autoregressive transformer models using Triton GPU kernels and make our code publicly available at //github.com/irugina/AP.
In multi-armed bandit (MAB) experiments, it is often advantageous to continuously produce inference on the average treatment effect (ATE) between arms as new data arrive and determine a data-driven stopping time for the experiment. We develop the Mixture Adaptive Design (MAD), a new experimental design for multi-armed bandit experiments that produces powerful and anytime-valid inference on the ATE for \emph{any} bandit algorithm of the experimenter's choice, even those without probabilistic treatment assignment. Intuitively, the MAD "mixes" any bandit algorithm of the experimenter's choice with a Bernoulli design through a tuning parameter $\delta_t$, where $\delta_t$ is a deterministic sequence that decreases the priority placed on the Bernoulli design as the sample size grows. We prove that for $\delta_t = \omega\left(t^{-1/4}\right)$, the MAD generates anytime-valid asymptotic confidence sequences that are guaranteed to shrink around the true ATE. Hence, the experimenter is guaranteed to detect a true non-zero treatment effect in finite time. Additionally, we prove that the regret of the MAD approaches that of its underlying bandit algorithm over time, and hence, incurs a relatively small loss in regret in return for powerful inferential guarantees. Finally, we conduct an extensive simulation study exhibiting that the MAD achieves finite-sample anytime validity and high power without significant losses in finite-sample reward.
Amid the increasing interest in the deployment of Distributed Energy Resources (DERs), the Virtual Power Plant (VPP) has emerged as a pivotal tool for aggregating diverse DERs and facilitating their participation in wholesale energy markets. These VPP deployments have been fueled by the Federal Energy Regulatory Commission's Order 2222, which makes DERs and VPPs competitive across market segments. However, the diversity and decentralized nature of DERs present significant challenges to the scalable coordination of VPP assets. To address efficiency and speed bottlenecks, this paper presents a novel machine learning-assisted distributed optimization to coordinate VPP assets. Our method, named LOOP-MAC(Learning to Optimize the Optimization Process for Multi-agent Coordination), adopts a multi-agent coordination perspective where each VPP agent manages multiple DERs and utilizes neural network approximators to expedite the solution search. The LOOP-MAC method employs a gauge map to guarantee strict compliance with local constraints, effectively reducing the need for additional post-processing steps. Our results highlight the advantages of LOOP-MAC, showcasing accelerated solution times per iteration and significantly reduced convergence times. The LOOP-MAC method outperforms conventional centralized and distributed optimization methods in optimization tasks that require repetitive and sequential execution.
Using Unmanned Aerial Vehicles (UAVs) in Search and rescue operations (SAR) to navigate challenging terrain while maintaining reliable communication with the cellular network is a promising approach. This paper suggests a novel technique employing a reinforcement learning multi Q-learning algorithm to optimize UAV connectivity in such scenarios. We introduce a Strategic Planning Agent for efficient path planning and collision awareness and a Real-time Adaptive Agent to maintain optimal connection with the cellular base station. The agents trained in a simulated environment using multi Q-learning, encouraging them to learn from experience and adjust their decision-making to diverse terrain complexities and communication scenarios. Evaluation results reveal the significance of the approach, highlighting successful navigation in environments with varying obstacle densities and the ability to perform optimal connectivity using different frequency bands. This work paves the way for enhanced UAV autonomy and enhanced communication reliability in search and rescue operations.
Emotion recognition in conversation (ERC) aims to detect the emotion label for each utterance. Motivated by recent studies which have proven that feeding training examples in a meaningful order rather than considering them randomly can boost the performance of models, we propose an ERC-oriented hybrid curriculum learning framework. Our framework consists of two curricula: (1) conversation-level curriculum (CC); and (2) utterance-level curriculum (UC). In CC, we construct a difficulty measurer based on "emotion shift" frequency within a conversation, then the conversations are scheduled in an "easy to hard" schema according to the difficulty score returned by the difficulty measurer. For UC, it is implemented from an emotion-similarity perspective, which progressively strengthens the model's ability in identifying the confusing emotions. With the proposed model-agnostic hybrid curriculum learning strategy, we observe significant performance boosts over a wide range of existing ERC models and we are able to achieve new state-of-the-art results on four public ERC datasets.
The military is investigating methods to improve communication and agility in its multi-domain operations (MDO). Nascent popularity of Internet of Things (IoT) has gained traction in public and government domains. Its usage in MDO may revolutionize future battlefields and may enable strategic advantage. While this technology offers leverage to military capabilities, it comes with challenges where one is the uncertainty and associated risk. A key question is how can these uncertainties be addressed. Recently published studies proposed information camouflage to transform information from one data domain to another. As this is comparatively a new approach, we investigate challenges of such transformations and how these associated uncertainties can be detected and addressed, specifically unknown-unknowns to improve decision-making.
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