Reviewerly/exHarmony huggingface dataset:
OTAvatar: One-Shot Talking Face Avatar with Controllable Tri-Plane Rendering

Controllability, generalizability and efficiency are the major objectives of constructing face avatars represented by neural implicit field. However, existing methods have not managed to accommodate the three requirements simultaneously. They either focus on static portraits, restricting the representation ability to a specific subject, or suffer from substantial computational cost, limiting their flexibility. In this paper, we propose One-shot Talking face Avatar (OTAvatar), which constructs face avatars by a generalized controllable tri-plane rendering solution so that each personalized avatar can be constructed from only one portrait as the reference. Specifically, OTAvatar first inverts a portrait image to a motion-free identity code. Second, the identity code and a motion code are utilized to modulate an efficient CNN to generate a tri-plane formulated volume, which encodes the subject in the desired motion. Finally, volume rendering is employed to generate an image in any view. The core of our solution is a novel decoupling-by-inverting strategy that disentangles identity and motion in the latent code via optimization-based inversion. Benefiting from the efficient tri-plane representation, we achieve controllable rendering of generalized face avatar at 35 FPS on AIOO. Experiments show promising performance of crossidentity reenactment on subjects out of the training set and better 3D consistency. The code is available at https://github.com/theEricMaIOTAvatar.
One-Stage Brake Light Status Detection Based on YOLOv8

Despite the advancement of advanced driver assistance systems (ADAS) and autonomous driving systems, surpassing the threshold of level 3 of driving automation remains a challenging task. Level 3 of driving automation requires assuming full responsibility for the vehicle’s actions, necessitating the acquisition of safer and more interpretable cues. To approach level 3, we propose a novel method for detecting driving vehicles and their brake light status, which is a crucial visual cue relied upon by human drivers. Our proposal consists of two main components. First, we introduce a fast and accurate one-stage brake light status detection network based on YOLOv8. Through transfer learning using a custom dataset, we enable YOLOv8 not only to detect the driving vehicle, but also to determine its brake light status. Furthermore, we present the publicly available custom dataset, which includes over 11,000 forward images along with manual annotations. We evaluate the performance of our proposed method in terms of detection accuracy and inference time on an edge device. The experimental results demonstrate high detection performance with an mAP50 (mean average precision at IoU threshold of 0.50) ranging from 0.766 to 0.793 on the test dataset, along with a short inference time of 133.30 ms on the Jetson Nano device. In conclusion, our proposed method achieves high accuracy and fast inference time in detecting brake light status. This contribution effectively improves safety, interpretability, and comfortability by providing valuable input information for ADAS and autonomous driving technologies.
Digital Pathology: Transforming Diagnosis in the Digital Age

In the context of rapid technological advancements, the narrative review titled "Digital Pathology: Transforming Diagnosis in the Digital Age" explores the significant impact of digital pathology in reshaping diagnostic approaches. This review delves into the various effects of the field, including remote consultations and artificial intelligence (AI)-assisted analysis, revealing the ongoing transformation taking place. The investigation explores the process of digitizing traditional glass slides, which aims to improve accessibility and facilitate sharing. Additionally, it addresses the complexities associated with data security and standardization challenges. Incorporating AI enhances pathologists' diagnostic capabilities and accelerates analytical procedures. Furthermore, the review highlights the growing importance of collaborative networks facilitating global knowledge sharing. It also emphasizes the significant impact of this technology on medical education and patient care. This narrative review aims to provide an overview of digital pathology's transformative and innovative potential, highlighting its disruptive nature in reshaping diagnostic practices.
Predicting pediatric healthcare provider use of virtual reality using a technology acceptance model

The primary aim of this study was to apply a novel technology acceptance model (TAM) for virtual reality (VR) in healthcare. The secondary aim was to assess reliability of this model to evaluate factors that predict the intentions of pediatric health providers' use of VR as an anxiolytic for hospitalized pediatric patients.Healthcare providers that interacted with pediatric patients participated in a VR experience available as anxiolysis for minor procedures and then completed a survey evaluating attitudes, behaviors, and technology factors that influence adoption of new technologies.Reliability for all domain measurements were good, and all confirmatory factor analysis models demonstrated good fit. Usefulness, ease of use, curiosity, and enjoyment of the VR experience all strongly predict intention to use and purchase VR technologies. Age of providers, past use, and cost of technology did not influence future purchase or use, suggesting that VR technologies may be broadly adopted in the pediatric healthcare setting.Previous VR-TAM models in non-healthcare consumers formulated that age, past use, price willing to pay, and curiosity impacted perceived ease of use. This study established that age, past use, and cost may not influence use in healthcare. Future studies should be directed at evaluating the social influences and facilitating conditions within healthcare that play a larger influence on technology adoption.The VR-TAM model demonstrated validity and reliability for predicting intent to use VR in a pediatric hospital.
Peer-to-Peer Power Energy Trading in Blockchain Using Efficient Machine Learning Model

The advancement of mircogrids and the adoption of blockchain technology in the energy-trading sector can build a robust and sustainable energy infrastructure. The decentralization and transparency of blockchain technology have several advantages for data management, security, and trust. In particular, the uses of smart contracts can provide automated transaction in energy trading. Individual entities (household, industries, institutes, etc.) have shown increasing interest in producing power from potential renewable energy sources for their own usage and also in distributing this power to the energy market if possible. The key success in energy trading significantly depends on understanding one’s own energy demand and production capability. For example, the production from a solar panel is highly correlated with the weather condition, and an efficient machine learning model can characterize the relationship to estimate the production at any time. In this article, we propose an architecture for energy trading that uses smart contracts in conjunction with an efficient machine learning algorithm to determine participants’ appropriate energy productions and streamline the auction process. We conducted an analysis on various machine learning models to identify the best suited model to be used with the smart contract in energy trading.
Adaptive variational quantum minimally entangled typical thermal states for finite temperature simulations

Scalable quantum algorithms for the simulation of quantum many-body systems in thermal equilibrium are important for predicting properties of quantum matter at finite temperatures. Here we describe and benchmark a quantum computing version of the minimally entangled typical thermal states (METTS) algorithm for which we adopt an adaptive variational approach to perform the required quantum imaginary time evolution. The algorithm, which we name AVQMETTS, dynamically generates compact and problem-specific quantum circuits, which are suitable for noisy intermediate-scale quantum (NISQ) hardware. We benchmark AVQMETTS on statevector simulators and perform thermal energy calculations of integrable and nonintegrable quantum spin models in one and two dimensions and demonstrate an approximately linear system-size scaling of the circuit complexity. We further map out the finite-temperature phase transition line of the two-dimensional transverse field Ising model. Finally, we study the impact of noise on AVQMETTS calculations using a phenomenological noise model.
Adaptive Incentivize for Cross-Silo Federated Learning in IIoT: A Multi-agent Reinforcement Learning Approach

In the Industrial Internet of Things (IIoT), Cross-Silo Federated Learning (CSFL) enables entities such as manufacturers and suppliers to train global models for optimizing production processes while ensuring data privacy. A well-designed incentive mechanism is essential to persuade clients to contribute data resources. However, existing methodologies overlook the dynamic nature of the training process, where the accuracy of the globally trained model and the clients data ownership change over time. Furthermore, the majority of previous research assumes a defined functional relationship between the data contribution and the model accuracy, which is infeasible in realistic and dynamic training environments. To address these challenges, we design a novel adaptive mechanism for CSFL that inspires organizations to contribute data resources in a dynamic training environment with the aim of maximizing their long-term payoffs. This mechanism leverages multi-agent reinforcement learning (MARL) to ascertain near-optimal data contribution strategies from potential game histories without necessitating private organizational information or a precise accuracy function. Experimental results indicate that our mechanism achieves adaptive incentive in dynamic environments and effectively enhances the long-term payoffs of organizations.
Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

With the advent of the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. This has gained a lot of attention from both academia and industry, leading to significant improvements in healthcare quality. However, the adoption of AI-driven medical applications still faces tough challenges, including meeting security, privacy, and quality of service (QoS) standards. Recent developments in Federated Learning (FL) have made it possible to train complex machine-learned models in a distributed manner and has become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, in this paper, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge. We delve into the current research trends and their outcomes, unravelling the complexities of designing reliable and scalable FL models. Our paper outlines the fundamental statistical issues in FL, tackles device-related problems, addresses security challenges, and navigates the complexity of privacy concerns, all while highlighting its transformative potential in the medical field. Our study primarily focuses on medical applications of FL, particularly in the context of global cancer diagnosis. We highlight the potential of FL to enable computer-aided diagnosis tools that address this challenge with greater effectiveness than traditional data-driven methods. Recent literature has shown that FL models are robust and generalize well to new data, which is essential for medical applications. We hope that this comprehensive review will serve as a checkpoint for the field, summarizing the current state-of-the-art and identifying open problems and future research directions.
“Elder Scam” Risk Profiles: Individual and Situational Factors of Younger and Older Age Groups’ Fraud Victimization

In an attempt to understand how differently fraud works depending on a victim's age, we have examined the effects of situational (lifestyle-routine activities), self-control, and sociodemographic variables on scam victimization across age groups.The analysis was carried out on a national sample of 2,558 Americans, representative by age, sex, and race, and includes additional factors such as their education, living arrangement, employment, and propensity for reporting a crime or asking for help.The results substantiate research findings of the contribution of self-control and LRAT in predicting victimization in general but could not identify major situational and individual differences between older and younger Americans' scam victimization.However, employment can function as a protective factor for older individuals in some online fraud scenarios.Furthermore, older adults show significantly more reluctance in asking for help or reporting than do younger ones.Future research must address these differences.The author also suggests developing specific variables for measuring how lifestyle-routine activity theory works in scam victimization.In this study, we define online fraud/scam as when a stranger intentionally deceives a victim by misrepresenting, concealing, or omitting facts about promised goods, services, or other-physical, mental, or emotional-expectations that are nonexistent, unnecessary, never intended to be provided, or deliberately distorted for the purpose of monetary gain (adapted from Beals et al., 2015b;and Titus et al., 1995).Financial fraud differs from financial exploitation/abuse, which is committed by caregivers or other trusted individuals (Hall et al., 2016).Recent research has argued in favor of an integrated model combining the general theory of crime (Gottfredson & Hirschi, 1990) and routine activities theory (Cohen & Felson, 1979) to examine victimiza-
Bi-directional learning particle swarm optimization for large-scale optimization

Large-scale optimization problems (LSOPs) are an essential research topic in the evolutionary computation (EC) community with two challenges: slow convergence in the huge search space and the trap in massive locally optimal solutions. To tackle these two challenges, this paper proposes a bi-directional learning particle swarm optimization (BLPSO) with two learning strategies, called diversity learning strategy (DLS) and convergence learning strategy (CLS). In DLS, we first propose a diversity evaluation mechanism based on locally sensitive hashing (LSH) to measure the diversity of individuals. Then the density individuals will learn from other dispersed individuals with good diversity to enhance the diversity of the population. In CLS, the inferior individuals will learn from other superior individuals with excellent evolution information to help the population accelerate convergence speed. These two learning strategies act in different roles and complement each other. With these two learning strategies, BLPSO achieves a balance between sufficient diversity and fast convergence in solving LSOPs. Two large-scale test suites, IEEE CEC2010 and IEEE CEC2013, are used to test the performance between BLPSO and other state-of-the-art algorithms. The experimental results show that BLPSO outperforms other algorithms on both test suites, including the winner algorithms of the IEEE CEC2010 and IEEE CEC2012 competitions. Finally, BLPSO is applied to a large-scale portfolio optimization problem to illustrate its application capability.
Quantum algorithm for ground state energy estimation using circuit depth with exponentially improved dependence on precision

A milestone in the field of quantum computing will be solving problems in quantum chemistry and materials faster than state-of-the-art classical methods. The current understanding is that achieving quantum advantage in this area will require some degree of fault tolerance. While hardware is improving towards this milestone, optimizing quantum algorithms also brings it closer to the present. Existing methods for ground state energy estimation are costly in that they require a number of gates per circuit that grows exponentially with the desired number of bits in precision. We reduce this cost exponentially, by developing a ground state energy estimation algorithm for which this cost grows linearly in the number of bits of precision. Relative to recent resource estimates of ground state energy estimation for the industrially-relevant molecules of ethylene-carbonate and PF<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mi /><mml:mn>6</mml:mn><mml:mo>&amp;#x2212;</mml:mo></mml:msubsup></mml:math>, the estimated gate count and circuit depth is reduced by a factor of 43 and 78, respectively. Furthermore, the algorithm can use additional circuit depth to reduce the total runtime. These features make our algorithm a promising candidate for realizing quantum advantage in the era of early fault-tolerant quantum computing.
GRAM-HD: 3D-Consistent Image Generation at High Resolution with Generative Radiance Manifolds

Recent works have shown that 3D-aware GANs trained on unstructured single image collections can generate multiview images of novel instances. The key underpinnings to achieve this are a 3D radiance field generator and a volume rendering process. However, existing methods either cannot generate high-resolution images (e.g., up to 256×256) due to the high computation cost of neural volume rendering, or rely on 2D CNNs for image-space upsampling which jeopardizes the 3D consistency across different views. This paper proposes a novel 3D-aware GAN that can generate high resolution images (up to 1024 1024) while keeping strict 3D consistency as in volume ×rendering. Our motivation is to achieve super-resolution directly in the 3D space to preserve 3D consistency. We avoid the otherwise prohibitively-expensive computation cost by applying 2D convolutions on a set of 2D radiance manifolds defined in the recent generative radiance manifold (GRAM) approach, and apply dedicated loss functions for effective GAN training at high resolution. Experiments on FFHQ and AFHQv2 datasets show that our method can produce high-quality 3D-consistent results that significantly outperform existing methods. It makes a significant step towards closing the gap between traditional 2D image generation and 3D-consistent free-view generation. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Sibling Conspiracy Number Search

For some two-player games (e.g. Go), no accurate and inexpensive heuristic is known for evaluating leaves of a search tree. For other games (e.g. chess), a heuristic is known (sum of piece values). For other games (e.g. Hex), only a local heuristic — one that compares children reliably, but non-siblings poorly — is known (cell voltage drop in the Shannon/Anshelevich electric circuit model). In this paper we introduce a search algorithm for a two-player perfect information game with a reasonable local heuristic. Sibling Conspiracy Number Search (SCNS) is an anytime best-first version of Conspiracy Number Search based not on evaluation of leaf states of the search tree, but — for each node — on relative evaluation scores of all children of that node. SCNS refines CNS search value intervals, converging to Proof Number Search. SCNS is a good framework for a game player. We tested SCNS in the domain of Hex, with promising results. We implemented an 11-by-11 SCNS Hex bot, DeepHex. We competed DeepHex against current Hex bot champion MoHex, a Monte-Carlo Tree Search player, and previous Hex bot champion Wolve, an Alpha-Beta Search player. DeepHex widely outperforms Wolve at all time levels, and narrowly outperforms MoHex once time reaches 4min/move.
A Demonstration of Agent Learning with Action-Dependent Learning Rates in Computer Role-Playing Games

&#x0D; &#x0D; &#x0D; We demonstrate combat scenarios between two NPCs in the Neverwinter Nights (NWN) game in which an NPC uses a new learning algorithm ALeRT (Action-dependent Learning Rates with Trends) and the other NPC uses a static strategy (NWN default and optimal) or a dynamic strategy (dynamic scripting). We implemented the ALeRT algorithm in NWScript, a scripting language used by NWN, with the goal to improve the behaviours of game agents. We show how our agent learns and adapts to changes in the environment.&#x0D; &#x0D; &#x0D;
Reconnecting with the Ideal Tree: An Alternative to Heuristic Learning in Real-Time Search

In this paper, we present a conceptually simple, easy-to-implement real-time search algorithm suitable for a priori partially known environments. Instead of performing a series of searches towards the goal, like most Real-Time Heuristic Search Algorithms do, our algorithm follows the arcs of a tree T rooted in the goal state that is built initially using the heuristic h. When the agent observes that an arc in the tree cannot be traversed in the actual environment, it removes such an arc from T and our algorithm carries out a reconnection search whose objective is to find a path between the current state and any node in T. The reconnection search need not be guided by $h$, since the search objective is not to encounter the goal. Furthermore, h need not be updated. We implemented versions of our algorithm that utilize various blind search algorithms for reconnection. We show experimentally that these implementations significantly outperform state-of-the-art real-time heuristic search algorithms for the task of pathfinding in grids. In grids, our algorithms, which do not incorporate any geometrical knowledge, naturally behaves similarly to a bug algorithm, moving around obstacles, and never returning to areas that have been visited in the past. In addition, we prove theoretical properties of the algorithm.
The Gini Test for Survival Data in Presence of Small and Unbalanced Groups

The aim of this note is to study the performance of the Gini concentration test for survival data in presence of unbalanced and small samples. We compared the performance of the asymptotic test with an alternative permutation distribution test, illustrating by simulation that if groups are very small the latter test should be used. Also, we show how the definition of the length of time considered in the construction of the test statistic can be chosen to improve the performance of the test.
Peer To Peer Collaboration Using Connexions

Introduction The rapid advances in engineering and science require communications that can keep pace with these quickly evolving fields. Researchers can no longer afford to wait for research findings to be published in traditional peer reviewed journals to understand what new discoveries are known and, thus, discern areas where opportunities for breakthroughs are greatest. With the advent of the Internet, communications between colleagues working in common fields has led to increased global collaboration, with data and research easily shared electronically. This increased communication and collaboration is also leading to new cultural norms. Communications are becoming less formal since they can occur much more frequently with e-mail. Written electronic communications are beginning to approximate dialog. Jointly authored papers can quickly evolve as authors pass the token to each other during revision cycles. Geographic location is no longer a barrier to participating in research and publishing the results. Sharing knowledge is valued rather than keeping it hidden until research results are published in peer-reviewed journals. The result is increased collaboration, more rapid dissemination of research results, and more frequent breakthroughs occurring internationally.