A Novel Cross-Layer Authentication Protocol for the Internet of Things

An innovative cross-layer authentication protocol that integrates cryptography-based authentication and physical layer authentication (PLA) is proposed for massive cellular Internet of things (IoT) systems. Due to dramatic increases in the number of cellular IoT devices, a centralized authentication architecture in which a mobility management entity in core networks administers authentication of massive numbers of IoT devices may cause network congestion with large signaling overhead. Thus, a distributed authentication architecture in which a base station in radio access networks authenticates IoT devices locally is presented. In addition, a cross-layer authentication protocol is designed with a novel integration strategy under the distributed authentication architecture, where PLA, which employs physical features for authentication, is used as preemptive authentication in the proposed protocol. Theoretical analysis and numerical simulations were performed to analyze the trade-off between authentication performance and overhead in the proposed authentication method compared with existing authentication protocols. The results demonstrate that the proposed protocol outperforms conventional authentication and key agreement protocols in terms of overhead and computational complexity while guaranteeing low authentication error probability

Highly reproducible alkali metal doping system for organic crystals through enhanced diffusion of alkali metal by secondary thermal activation

In this paper, we report an efficient alkali metal doping system for organic single crystals. Our system employs an enhanced diffusion method for the introduction of alkali metal into organic single crystals by controlling the sample temperature to induce secondary thermal activation. Using this system, we achieved intercalation of potassium into picene single crystals with closed packed crystal structures. Using optical microscopy and Raman spectroscopy, we confirmed that the resulting samples were uniformly doped and became K2picene single crystal, while only parts of the crystal are doped and transformed into K2picene without secondary thermal activation. Moreover, using a customized electrical measurement system, the insulator-to-semiconductor transition of picene single crystals upon doping was confirmed by in situ electrical conductivity and ex situ temperature-dependent resistivity measurements. X-ray diffraction studies showed that potassium atoms were intercalated between molecular layers of picene, and doped samples did not show any KH- nor KOH-related peaks, indicating that picene molecules are retained without structural decomposition. During recent decades, tremendous efforts have been exerted to develop high-performance organic semiconductors and superconductors, whereas as little attention has been devoted to doped organic crystals. Our method will enable efficient alkali metal doping of organic crystals and will be a resource for future systematic studies on the electrical property changes of these organic crystals upon doping. © 2018 The Author(s

Enabling Hard Constraints in Differentiable Neural Network and Accelerator Co-Exploration

Co-exploration of an optimal neural architecture and its hardware accelerator is an approach of rising interest which addresses the computational cost problem, especially in low-profile systems. The large co-exploration space is often handled by adopting the idea of differentiable neural architecture search. However, despite the superior search efficiency of the differentiable co-exploration, it faces a critical challenge of not being able to systematically satisfy hard constraints such as frame rate. To handle the hard constraint problem of differentiable co-exploration, we propose HDX, which searches for hard-constrained solutions without compromising the global design objectives. By manipulating the gradients in the interest of the given hard constraint, high-quality solutions satisfying the constraint can be obtained.Comment: publisehd at DAC'2

FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning

Psychiatrists diagnose mental disorders via the linguistic use of patients. Still, due to data privacy, existing passive mental health monitoring systems use alternative features such as activity, app usage, and location via mobile devices. We propose FedTherapist, a mobile mental health monitoring system that utilizes continuous speech and keyboard input in a privacy-preserving way via federated learning. We explore multiple model designs by comparing their performance and overhead for FedTherapist to overcome the complex nature of on-device language model training on smartphones. We further propose a Context-Aware Language Learning (CALL) methodology to effectively utilize smartphones' large and noisy text for mental health signal sensing. Our IRB-approved evaluation of the prediction of self-reported depression, stress, anxiety, and mood from 46 participants shows higher accuracy of FedTherapist compared with the performance with non-language features, achieving 0.15 AUROC improvement and 8.21% MAE reduction.Comment: Accepted to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023

Increasing water cycle extremes in California and in relation to ENSO cycle under global warming

Since the winter of 2013–2014, California has experienced its most severe drought in recorded history, causing statewide water stress, severe economic loss and an extraordinary increase in wildfires. Identifying the effects of global warming on regional water cycle extremes, such as the ongoing drought in California, remains a challenge. Here we analyse large-ensemble and multi-model simulations that project the future of water cycle extremes in California as well as to understand those associations that pertain to changing climate oscillations under global warming. Both intense drought and excessive flooding are projected to increase by at least 50% towards the end of the twenty-first century; this projected increase in water cycle extremes is associated with a strengthened relation to El Niño and the Southern Oscillation (ENSO)—in particular, extreme El Niño and La Niña events that modulate California’s climate not only through its warm and cold phases but also its precursor patterns

Rewiring of PDZ Domain-Ligand Interaction Network Contributed to Eukaryotic Evolution

PDZ domain-mediated interactions have greatly expanded during metazoan evolution, becoming important for controlling signal flow via the assembly of multiple signaling components. The evolutionary history of PDZ domain-mediated interactions has never been explored at the molecular level. It is of great interest to understand how PDZ domain-ligand interactions emerged and how they become rewired during evolution. Here, we constructed the first human PDZ domain-ligand interaction network (PDZNet) together with binding motif sequences and interaction strengths of ligands. PDZNet includes 1,213 interactions between 97 human PDZ proteins and 591 ligands that connect most PDZ protein-mediated interactions (98%) in a large single network via shared ligands. We examined the rewiring of PDZ domain-ligand interactions throughout eukaryotic evolution by tracing changes in the C-terminal binding motif sequences of the PDZ ligands. We found that interaction rewiring by sequence mutation frequently occurred throughout evolution, largely contributing to the growth of PDZNet. The rewiring of PDZ domain-ligand interactions provided an effective means of functional innovations in nervous system development. Our findings provide empirical evidence for a network evolution model that highlights the rewiring of interactions as a mechanism for the development of new protein functions. PDZNet will be a valuable resource to further characterize the organization of the PDZ domain-mediated signaling proteome

Analysis of the Penn Korean Universal Dependency Treebank (PKT-UD): Manual Revision to Build Robust Parsing Model in Korean

In this paper, we first open on important issues regarding the Penn Korean Universal Treebank (PKT-UD) and address these issues by revising the entire corpus manually with the aim of producing cleaner UD annotations that are more faithful to Korean grammar. For compatibility to the rest of UD corpora, we follow the UDv2 guidelines, and extensively revise the part-of-speech tags and the dependency relations to reflect morphological features and flexible word-order aspects in Korean. The original and the revised versions of PKT-UD are experimented with transformer-based parsing models using biaffine attention. The parsing model trained on the revised corpus shows a significant improvement of 3.0% in labeled attachment score over the model trained on the previous corpus. Our error analysis demonstrates that this revision allows the parsing model to learn relations more robustly, reducing several critical errors that used to be made by the previous model.Comment: Accepted by The 16th International Conference on Parsing Technologies, IWPT 202