University of Waterloo

The Large Hadron Collider’s high luminosity era presents major computational challenges in the analysis of collision events. Large amounts of Monte Carlo (MC) simulation will be required to constrain the statistical uncertainties of the simulated datasets below these of the experimental data. Modelling of high-energy particles propagating through the calorimeter section of the detector is the most computationally intensive MC simulation task. We introduce a technique combining recent advancements in generative models and quantum annealing for fast and efficient simulation of high-energy particle-calorimeter interactions.

Alloy-type materials hold significant promise as high energy density anodes for lithium-ion batteries. However, the initial coulombic efficiency (ICE) is significantly hindered by the poor reversibility of the conversion reaction and volume expansion. Here, the NiO/SnO2 multilayers with a hybrid interface of alloy and transition metal oxides are proposed to generate Ni nanoparticles within confined layers, catalyzing Li2O decomposition and suppressing the coarsening of Sn or Li2O particles. Supported by density functional theory (DFT) calculations and revealed by operando magnetometry, the spatially confined, well maintained Ni active sites lower the energy barrier for Li–O bond rupture and enhance the migration dynamics of Li⁺. The enhanced reaction kinetics lead to achievement of an impressive ICE of 92.3% and a large capacity of 1247 mA h g⁻¹ with 97% retention after 800 cycles. Furthermore, the NiO/SnO2 anode exhibits excellent electrochemical performances in both Na/K-ion batteries. Notably, when constructed with the same framework, SiO2 also delivers significantly improved lithium storage properties with ultra-high ICEs. This work paves the way for advanced designs of alloy-type anodes that satisfy both ICE and overall electrochemical performance.

Background Amyotrophic Lateral Sclerosis (ALS), an incurable motor neuron disease, primarily affects those between the ages of 60–79, and has an approximate post-diagnosis life-expectancy of only two to five years. The condition has an unpredictable but ultimately terminal trajectory that poses challenges for patients, caregivers and healthcare providers. While the diagnosis and disclosure are critical periods for intervention and support, knowledge regarding the relational, communicational and psychodynamic forces that occur within the process of diagnostic disclosure is relatively limited. Objectives The purpose of this study was to explore the experiences of persons living with ALS in the diagnosis and disclosure of the condition, with the support of their caregivers. Methods We conducted a focus group and in-depth individual interviews with people living with ALS (n = 9), and caregivers (n = 9). The interviews were transcribed, cleaned, and anonymized, and then entered into NVivo 11 for thematic analysis. Results Participants discussed the diagnostic process, including inklings and subtle changes prior to diagnosis, attempts at self-diagnosis, and the lengthy assessment process. Time was also a consideration in the diagnostic disclosure process, in which participants shared how the disclosure was the product of longstanding conversations with their care providers. It was described as rarely a shock to finally have confirmation. Participants shared their information seeking strategies and needs for a diagnosis that, for them, typically came with insufficient information on the disease, prognosis, and next steps. Significance This project serves as a step in bridging the relevant gaps in our knowledge and understanding towards improved person-centered care practices in the diagnosis and disclosure of ALS.

Mobile AI-Generated Content (AIGC) has achieved great attention in unleashing the power of generative AI and scaling the AIGC services. By employing numerous Mobile AIGC Service Providers (MASPs), ubiquitous and low-latency AIGC services for clients can be realized. Nonetheless, the interactions between clients and MASPs in public mobile networks, pertaining to three key mechanisms, namely MASP selection, payment scheme, and fee-ownership transfer, are unprotected. In this paper, we design the above mechanisms in a systematic approach and present the first blockchain to protect mobile AIGC, called ProSecutor. Specifically, by roll-up and layer-2 channels, ProSecutor forms a two-layer architecture, realizing tamper-proof data recording and atomic fee-ownership transfer with high resource efficiency. Then, we present the Objective-Subjective Service Assessment (OS²) framework, which effectively evaluates the AIGC services by fusing the objective service quality with the reputation-based subjective experience of the service outcome (i.e., AIGC outputs). Deploying OS² on ProSecutor, firstly, the MASP selection can be realized by sorting the reputation. Afterward, the contract theory is adopted to optimize the payment scheme and help clients avoid moral hazards in mobile networks. We implement the prototype of ProSecutor on BlockEmulator. Extensive experiments demonstrate that ProSecutor achieves 12.5× throughput and saves 67.5% storage resources compared with BlockEmulator. Moreover, the effectiveness and efficiency of the proposed mechanisms are validated.

Accurate analysis of traffic flow (TF) data is crucial for the vehicular applications. Conventional deep learning models require task-specific training and are susceptible to high-frequency disturbances, degrading the feature representation capability. To overcome these limitations, this paper proposes a Token-based SelfSupervised Network (TSSN) that can learn TF features in both tokenization and task-agnostic manners. It provides a properly bootstrapped pre-training model for various downstream tasks. In support of the edge computing and vehicular cloud computing, the pooled computational resources facilitate real-time inferences of downstream models. In TSSN, TF data are segmented into tokens. A pretext task, named as Masked Token Prediction (MTP), is then developed to allow TSSN to understand the underlying correlations of TF by predicting randomly masked tokens. By utilizing MTP, TSSN is able to extract the high-level intrinsic semantics of TF, and provide general-purpose token embeddings, leading to improved overall performance and enhanced ability to adapt to different tasks. By substituting the last fully-connected layers with a group of untrained new layers and fine-tuning using small-scale task-specific data, TSSN can be utilized for a variety of downstream tasks in vehicular applications. Simulation results indicate that the TSSN enhances overall performance in comparison to state-of-the-art models.

In this paper, we propose an efficient and distributed service access control framework (E-DAC) in the pervasive edge computing (PEC) environment, where the resources of peer devices at the network edge are integrated to provide latencysensitive computing services to the nearby devices on behalf of edge servers. E-DAC addresses the challenge of efficient and distributed service access control, comprising edge service authorization, service access authorization, and mutual authentication between edge servers and edge devices. In dong so, E-DAC first extends a key-aggregate cryptosystem to enable batch service authorization, in which a service provider can aggregate the authorization keys of different services to produce a constant-size aggregate key for an edge server. Second, E-DAC enables users to acquire authorization from the service provider for service access on edge servers by using efficient secret sharing. Third, edge servers and users can authenticate with each other without interacting with a centralized server, while enabling secure zero-round trip communication, so that the service data is protected and the communication bandwidth cost is low. In addition, the service provider is capable of efficiently revoking the authorization of the dropout or compromised edge servers or users in response to the dynamics of the PEC environment. Finally, we prove the security of service access control in E-DAC, including unforgeability of service authorization and confidentiality of service data, and conduct extensive analysis and experiments to demonstrate that E-DAC is highly computational and communication-efficient on service authorization, authentication, and revocation.

With the explosive development of mobile computing, federated learning (FL) has been considered as a promising distributed training framework for addressing the shortage of conventional cloud based centralized training. In FL, local model owners (LMOs) individually train their respective local models and then upload the trained local models to the task publisher (TP) for aggregation to obtain the global model. When the data provided by LMOs do not meet the requirements for model training, they can recruit workers to collect data. In this paper, by considering the interactions among the TP, LMOs and workers, we propose a three-layer hierarchical game framework. However, there are two challenges. Firstly, information asymmetry between workers and LMOs may result in that the workers hide their types. Secondly, incentive mismatch between TP and LMOs may result in a lack of LMOs' willingness to participate in FL. Therefore, we decompose the hierarchical-based framework into two layers to address these challenges. For the lower-layer, we leverage the contract theory to ensure truthful reporting of the workers' types, based on which we simplify the feasible conditions of the contract and design the optimal contract. For the upper-layer, the Stackelberg game is adopted to model the interactions between the TP and LMOs, and we derive the Nash equilibrium and Stackelberg equilibrium solutions. Moreover, we develop an iterative H ierarchical-based U tility M aximization A lgorithm (HUMA) to solve the coupling problem between upper-layer and lower-layer games. Extensive numerical experimental results verify the effectiveness of HUMA, and the comparison results illustrate the performance gain of HUMA.

By providing users with an immersive visual and acoustic experience, virtual reality (VR) serves as a foundational technique for the emerging metaverse. One of the most promising aspects of VR is its ability to protect users' identities by transforming their physical appearances into avatars with arbitrary appearances in the virtual world. However, the increasing threat of de-anonymization attacks that seek to reveal users' identities poses significant privacy risks. We propose AvatarHunter, a non-intrusive and user-unaware de-anonymization attack leveraging victims' inherent movement signatures. AvatarHunter discreetly collects the avatar's gait information by recording videos in the VR scenario without requiring any permissions. Notably, we designed a Unity-based feature extractor that maintains the avatar's movement signature while enabling AvatarHunter to be resistant to changes in the avatar's appearance. We conduct real-world experiments on VRChat to evaluate AvatarHunter's effectiveness. The results demonstrate that in commercial settings, AvatarHunter achieves attack success rates (ASR) of 92.1% and 66.9% in closed-world and open-world avatar scenarios, respectively, significantly surpassing existing benchmarks. Additionally, simulations using an open-source dataset confirm that AvatarHunter can attain over 78% ASR in full-body tracking scenarios. Finally, we discuss several countermeasures and implement an obfuscation mechanism during the avatar rendering phase, significantly reducing the ASR.

We consider a mobile edge computing (MEC) assisted Industrial Internet of Things (IIoT) network, where multiple assembly processing lines in a smart factory are equipped with sensing devices. They sense raw products, generate and offload computing tasks, and finally process the raw products based on the computing results. In this scenario, different positions of the processing machines lead to different priorities and diverse Quality-of-Service (QoS) requirements of tasks. Therefore, how to schedule tasks and allocate the network resources becomes a critical and challenging issue. In this study, we introduce a novel batch-based hybrid non-orthogonal multiple access (NOMA)/orthogonal multiple access (OMA) transmission scheme. The selection between NOMA and OMA schemes is optimized based on the QoS requirements of tasks. Then, we formulate a production efficiency maximization problem with the objective of maximizing the speed of the assembly lines subject to the deadline constraints of offloading and computing procedures. To this end, a two-layer decomposition method is used to decompose the formulated problem into two sub-problems. Furthermore, we utilize a bisection searching method to approximate the optimal solution, and propose an efficient method to determine the feasibility of the top-layer sub-problem. Simulation results demonstrate the significant performance improvement of our proposed method. In specific, the production efficiency is enhanced by 525% in comparison with pure NOMA scheme.

We show that every three-dimensional subspace of qutrit-qudit complex or real systems has a distinguishable basis under one-way local projective measurements and classical communication (LPCC). This solves a long-standing open problem proposed in [J. Phys. A, 40, 7937, 2007]. We further construct a three-dimensional space whose locally distinguishable basis is unique and apply the uniqueness property to the task of state transformation. We also construct a three-dimensional locally distinguishable multipartite space assisted with entanglement. On the other hand, we show that four-dimensional indistinguishable bipartite subspace under one-way LPCC exists. Our work offers profound insights and introduces a theoretical tool for understanding the local distinguishability of subspace. As a consequence, every qutrit channel has optimal environment-assisting classical capacity, and the environment-assisted classical capacity of every rank-three channel is at least log ₂ 3. We also show that every two-qutrit state can be converted into a generalized classical state near the quantum-classical boundary by an entanglement-breaking channel.

This paper introduces an on-demand collaborative sensing scheme for Industrial Internet of Things (IIoT) sensors in time-varying sensing environments, aiming to optimize the sensing performance by effectively allocating communication resources for sensory data sharing. Particularly, we propose a novel digital twins (DTs)-empowered resource allocation solution to facilitate scalable and flexible collaborative sensing. First, DTs create mathematical models using real-time network data to characterize the dynamic resource demands in collaborative sensing. Second, the performance of mathematical models in DTs is evaluated through data-driven methods. Building on our DT design, we propose a joint collaborative sensing and DT management scheme to optimize the resource allocation for sensory data sharing and DT operation. Furthermore, we develop a DT evaluation method featuring a variational autoencoder to evaluate the accuracy of DTs and enable closed-loop DT-based resource allocation. Numerical results demonstrate the effectiveness of our proposed collaborative sensing scheme in optimizing the sensing performance for all sensors.

Employing massive Mobile AI-Generated Content (AIGC) Service Providers (MASPs) with powerful models, high-quality AIGC services become accessible for resource-constrained end users. However, this advancement, referred to as mobile AIGC, also introduces a significant challenge: users should download large AIGC outputs from the MASPs, leading to substantial bandwidth consumption and potential transmission failures. In this paper, we apply cross-modal G enerative Sem antic Com munications (G-SemCom) in mobile AIGC to overcome wireless bandwidth constraints. Specifically, we utilize cross-modal attention maps to indicate the correlation between user prompts and each part of AIGC outputs. In this way, the MASP can analyze the prompt context and filter the most semantically important content efficiently. Only semantic information is transmitted, with which users can recover the entire AIGC output with high quality while saving mobile bandwidth. Since the transmitted information not only preserves the semantics but also prompts the recovery, we formulate a joint semantic encoding and prompt engineering problem to optimize the bandwidth allocation among users. Particularly, we present a human-perceptual metric named Joint Perceptual Similarity and Quality (JPSQ), which is fused by two learning-based measurements regarding semantic similarity and aesthetic quality, respectively. Furthermore, we develop the Attention-aware Deep Diffusion (ADD) algorithm, which learns attention maps and leverages the diffusion process to enhance the environment exploration ability of traditional deep reinforcement learning (DRL). Extensive experiments demonstrate that our proposal can reduce the bandwidth consumption of mobile users by 49.4% on average, with almost no perceptual difference in AIGC output quality. Moreover, the ADD algorithm shows superior performance over baseline DRL methods, with 1.74× higher overall reward.

The acquisition of both local and global features from irregular point clouds is crucial for 3D object detection (3DOD). Current mainstream 3D detectors neglect significant local features during pooling operations or disregard many global features of the overall scene context. This paper proposes new techniques for simultaneously learning local-global features of scene point clouds to enhance 3DOD. Specifically, we propose an efficient 3DOD network in indoor point clouds, named SimLOG, which utilizes simultaneous local-global feature learning. SimLOG has two main contributions: a Dynamic Points Interaction (DPI) module to recover local features lost during pooling, and a Global Context Aggregation(GCA) module to aggregate multi-scale features from various layers of the encoder to improve scene context awareness. Unlike traditional local-global feature learning methods, our DPI and GCA modules are integrated into a single feature learning module, making it easily detachable and able to be incorporated into existing 3DOD networks to enhance their performance. SimLOG demonstrates superior performance over twenty competitors in terms of detection accuracy and robustness on both the SUN RGB-D and ScanNet V2 datasets. Specifically, SimLOG boosts the baseline VoteNet by 8.1% of mAP@0.25 on ScanNet V2 and by 3.9% of mAP@0.25 on SUN RGB-D. Code is publicly available at https://github.com/chenbaian-cs/SimLOG.

Line current differential relays (LCDRs) are high-speed relays progressively used to protect critical transmission lines. However, LCDRs are vulnerable to cyberattacks. Fault-masking attacks (FMAs) are stealthy cyberattacks performed by manipulating the remote measurements of the targeted LCDR to disguise faults on the protected line. Hence, they remain undetected by this LCDR. In this article, we propose a two-module framework to detect FMAs. The first module is a mismatch index (MI) developed from the protected transmission line’s equivalent physical model. The MI is triggered only if there is a significant mismatch in the LCDR’s local and remote measurements while the LCDR itself is untriggered, which indicates an FMA. After the MI is triggered, the second module, a neural network-based classifier, promptly confirms that the triggering event is a physical fault that lies on the line protected by the LCDR before declaring the occurrence of an FMA. The proposed framework is tested using the IEEE 39-bus benchmark system. Our simulation results confirm that the proposed framework can accurately detect FMAs on LCDRs and is not affected by normal system disturbances, variations, or measurement noise. Our experimental results using OPAL-RT’s real-time simulator confirm the proposed solution’s real-time performance capability.

In the context of sport events, several stakeholders’ reputations could be impacted by critical incidents, including event organizers, athletes, teams, countries represented by athletes, and sponsors. The purposes of this study were to develop an understanding of (a) how an event organizer, media, and the public framed a critical incident in a rhetorical arena and (b) how frames were connected with the reputations of event stakeholders immediately following a critical incident. A three-phase approach was employed that involved collecting and analyzing data from X/Twitter about a bus crash at the 2013 Tour de France. The critical incident was framed in nine different ways, many of which were emergent. Findings demonstrated that critical incidents at a sport event are interpreted and framed in multiple ways and can have an impact on the reputations of the event and other event stakeholders.

Protection of High Voltage Direct Current (HVDC) grids is a crucial step that requires further advancements to ensure the secure integration of green renewable energy sources (RESs). This paper proposes a fast, reliable, and selective singleended primary protection scheme for multi-terminal HVDC grids. In the proposed scheme, Hilbert-Huang Transform (HHT) is applied to local voltage measurements to extract the instantaneous frequency and energy features. Abrupt changes in these features during internal faults are detected as outliers. Simultaneous outliers in the extracted features correspond to internal faults, which offers a setting-less fault detection criterion; thus, eliminating the need for simulation-based and grid-specific thresholds. The proposed scheme can detect internal faults with high fault resistances up to 1000 Ω within 1 ms. In addition, the proposed scheme can reliably distinguish between internal and external faults even when the boundary reactor size is as small as 10 mH. A four-terminal HVDC grid is simulated in PSCAD/EMTDC software, and various fault scenarios are investigated to verify the effectiveness of the proposed scheme in detecting and discriminating between internal and external faults under severe fault conditions.

Breaker failure backup protection schemes are essential for the reliable operation of high voltage direct current (HVDC) grids and to prevent damages to power system equipment due to sustained faults. This paper proposes a rapid, reliable, and setting-less breaker failure backup protection scheme for multi-terminal HVDC grids. The proposed scheme employs Hilbert-Huang Transform (HHT) to extract two features from local voltage measurements, namely the instantaneous frequency and energy. Based on detected outliers in the extracted instantaneous features, breaker failure events are rapidly detected. The proposed setting-less outlier-based criterion can be applied to HVDC grids with various configurations, parameters, and breaker technologies. The proposed scheme depends only on local voltage measurements to detect breaker failure events within 1 ms from the intended breaker trip time without requiring breaker voltage or current measurements. In addition, the proposed scheme can successfully detect breaker failure events during high-resistance faults (1500 Ω) and in grids with small boundary reactors (10 mH). Numerous simulations in PSCAD/EMTDC software environment for a four-terminal HVDC grid are executed to demonstrate the rapid and reliable performance of the proposed breaker failure backup protection scheme under severe fault conditions