Reviewerly/exHarmony huggingface dataset:
Boosting knowledge diversity, accuracy, and stability via tri-enhanced distillation for domain continual medical image segmentation

Domain continual medical image segmentation plays a crucial role in clinical settings. This approach enables segmentation models to continually learn from a sequential data stream across multiple domains. However, it faces the challenge of catastrophic forgetting. Existing methods based on knowledge distillation show potential to address this challenge via a three-stage process: distillation, transfer, and fusion. Yet, each stage presents its unique issues that, collectively, amplify the problem of catastrophic forgetting. To address these issues at each stage, we propose a tri-enhanced distillation framework. (1) Stochastic Knowledge Augmentation reduces redundancy in knowledge, thereby increasing both the diversity and volume of knowledge derived from the old network. (2) Adaptive Knowledge Transfer selectively captures critical information from the old knowledge, facilitating a more accurate knowledge transfer. (3) Global Uncertainty-Guided Fusion introduces a global uncertainty view of the dataset to fuse the old and new knowledge with reduced bias, promoting a more stable knowledge fusion. Our experimental results not only validate the feasibility of our approach, but also demonstrate its superior performance compared to state-of-the-art methods. We suggest that our innovative tri-enhanced distillation framework may establish a robust benchmark for domain continual medical image segmentation.
A bidirectional workflow scheduling approach with feedback mechanism in clouds

Workflow scheduling poses a significant challenge in cloud computing environments, offering the potential to optimize execution time and reduce economic costs. In this paper, we present BFWS, a bidirectional workflow scheduling approach with a feedback mechanism, designed to address the deadline-constrained cost-aware workflow scheduling problem. BFWS is built upon a bidirectional scheduling framework that facilitates resource allocation from both the forward and the backward directions of the workflow. This framework incorporates a dynamic sub-deadline prediction method, leveraging prior knowledge and real-time scheduling progress to enhance the accuracy of task sub-deadline predictions. Additionally, a cost-aware resource selection operator is integrated into the framework, enabling the selection of appropriate virtual machines based on considerations of cost and time constraints. To further enhance decision-making and optimize performance, we introduce a feedback mechanism that enables BFWS to adapt and improve over time. Through extensive experiments using real-world workflows, the superior performance of BFWS is validated compared to the state-of-the-art algorithms. Moreover, the effectiveness of the bidirectional scheduling approach and the feedback mechanism is demonstrated individually through rigorous validation experiments. These findings highlight the potential of BFWS in achieving efficient and effective workflow scheduling in cloud computing environments.
YOLO-DCNet

Enhanced processors empower edge devices like smartphones for human detection, yet their application is constrained by algorithmic efficiency and precision. This paper introduces YOLO-DCNet, a lightweight neural network detector built upon YOLOv7-tiny. Incorporating a dynamic multi-head structural re-parameterization (DMSR) module within its backbone network enables effective processing of the features utilized in the model. To improve multi-scale feature aggregation, the model integrates a channel information compression and linear mapping (CLM) module into its feature pyramid architecture. Moreover, the optimization of training and inference performance is achieved by employing RepVGG blocks between the main computational modules of the model. Experimental data reveal that the enhanced YOLOv7-tiny model achieves a 31.7% faster inference speed and marginal gains of 0.7% in mAP@0.5 and 0.5% in mAP@0.5:0.95 over the original. This underscores the model's improved performance and applicability for real-time human detection on edge devices across diverse applications.
I mediatori dei ‘testi in trascrizione’ (<i>Transkriptionstexte</i>) turchi tra Levante ed Europa

The so‑called ‘Turkish transcriptional texts’, i.e. Ottoman texts in non‑Arabic scripts, are important sources for the knowledge of the historical grammar, especially phonology, of the Ottoman language. The term was created by Turkologists specifically for texts written between the sixteenth and eighteenth centuries in the Latin alphabet. The texts were usually written by European travellers with no or little knowledge of Turkish, relying on information from native speakers. This essay focuses on how the authors of the texts obtained their linguistic material. A detailed linguistic analysis of selected passages will show that the mediators were often of Greek origin.
Face De-Identification Using Convolutional Neural Network (CNN) Models for Visual-Copy Detection

The proliferation of media-sharing platforms has led to issues with illegally edited content and the distribution of pornography. To protect personal information, de-identification technologies are being developed to prevent facial identification. Existing de-identification methods directly alter the pixel values in the face region, leading to reduced feature representation and identification accuracy. This study aims to develop a method that minimizes the possibility of personal identification while effectively preserving important features for image- and video-copy-detection tasks, proposing a new deep-learning-based de-identification approach that surpasses traditional pixel-based alteration methods. We introduce two de-identification models using different approaches: one emphasizing the contours of the original face through feature inversion and the other generating a blurred version of the face using D2GAN (Dual Discriminator Generative Adversarial Network). Both models were evaluated on their performance in image- and video-copy-detection tasks before and after de-identification, demonstrating effective feature preservation. This research presents new possibilities for personal-information protection and digital-content security, contributing to digital-rights management and law enforcement.
Robust noise-aware algorithm for randomized neural network and its convergence properties

The concept of randomized neural networks (RNNs), such as the random vector functional link network (RVFL) and extreme learning machine (ELM), is a widely accepted and efficient network method for constructing single-hidden layer feedforward networks (SLFNs). Due to its exceptional approximation capabilities, RNN is being extensively used in various fields. While the RNN concept has shown great promise, its performance can be unpredictable in imperfect conditions, such as weight noises and outliers. Thus, there is a need to develop more reliable and robust RNN algorithms. To address this issue, this paper proposes a new objective function that addresses the combined effect of weight noise and training data outliers for RVFL networks. Based on the half-quadratic optimization method, we then propose a novel algorithm, named noise-aware RNN (NARNN), to optimize the proposed objective function. The convergence of the NARNN is also theoretically validated. We also discuss the way to use the NARNN for ensemble deep RVFL (edRVFL) networks. Finally, we present an extension of the NARNN to concurrently address weight noise, stuck-at-fault, and outliers. The experimental results demonstrate that the proposed algorithm outperforms a number of state-of-the-art robust RNN algorithms.
Balanced self-distillation for long-tailed recognition

In long-tailed recognition tasks, the knowledge distillation technology is widely adopted for improving performance of deep neural networks. These methods distill the knowledge from the pretrained teacher model to the student model, which enables higher long-tailed recognition accuracy. However, the dependence on accompanying assistive models complicates the single network’s training process in the need for large memory and time costs. In this work, we present Balanced Self-Distillation (BSD) to distill tail knowledge by a single network without the assistive models. Specifically, BSD distills knowledge between different distortions of the same samples to stimulate the representation learning potential of the single network and adopts a balanced class weight for shifting the distillation focus from head- to-tail classes. Comprehensive experimentation across diverse datasets, including CIFAR-10-LT, CIFAR-100-LT and TinyImageNet-LT, consistently outperforms robust baseline methods. Specifically, BSD achieves improvements of 8.13% on CIFAR-100-LT with an mbalance ratio of 100 compared to the baseline (cross entropy). Furthermore, the proposed method enables seamless integration with contemporary techniques like re-sampling, meta-learning, and cost-sensitive learning. It emerges as a versatile tool capable of effectively addressing the challenges of long-tailed scenarios.
How identity is evolving in a digital-first world

The use of biometric-based digital IDs is growing worldwide. This article analyses the latest developments in digital identification, the likely next steps, and how to deal with the core concerns this evolution raises: ensuring the security and ethical use of the personal data involved.
Mesh-clustered Gaussian process emulator for partial differential equation boundary value problems

Partial differential equations (PDEs) have become an essential tool for modeling complex physical systems. Such equations are typically solved numerically via mesh-based methods, such as finite element methods, with solutions over the spatial domain. However, obtaining these solutions are often prohibitively costly, limiting the feasibility of exploring parameters in PDEs. In this paper, we propose an efficient emulator that simultaneously predicts the solutions over the spatial domain, with theoretical justification of its uncertainty quantification. The novelty of the proposed method lies in the incorporation of the mesh node coordinates into the statistical model. In particular, the proposed method segments the mesh nodes into multiple clusters via a Dirichlet process prior and fits Gaussian process models with the same hyperparameters in each of them. Most importantly, by revealing the underlying clustering structures, the proposed method can provide valuable insights into qualitative features of the resulting dynamics that can be used to guide further investigations. Real examples are demonstrated to show that our proposed method has smaller prediction errors than its main competitors, with competitive computation time, and identifies interesting clusters of mesh nodes that possess physical significance, such as satisfying boundary conditions. An R package for the proposed methodology is provided in an open repository.
Exploratory Analysis of Distributional Data Using the Quantile Method

The quantile method transforms each complex object described by different histogram values to a common number of quantile vectors. This paper retraces the authors’ research, including a principal component analysis, unsupervised feature selection using hierarchical conceptual clustering, and lookup table regression model. The purpose is to show that this research is essentially based on the monotone property of quantile vectors and works cooperatively in the exploratory analysis of the given distributional data.
Universal quantum computing with qubits embedded in trapped-ion qudits

Recent developments in qudit-based quantum computing, in particular with trapped ions, open interesting possibilities for scaling quantum processors without increasing the number of physical information carriers. Here, we propose a method for compiling quantum circuits in the case, where qubits are embedded into qudits of experimentally relevant dimensionalities, $d=3,...,8$, for the trapped-ion platform. In particular, we demonstrate how single-qubit, two-qubit, and multiqubit gates can be realized using single-qudit operations and the $\text{M}\o{}\text{lmer-S}\o{}\text{rensen}$ (MS) gate as a basic two-particle operation. We expect that our findings are directly applicable to the current generation of trapped-ion-based qudit processors.
Unleashing the benefits of smart grids by overcoming the challenges associated with low-resolution data

Smart meters have been widely deployed worldwide, but there is an often-overlooked problem that remains unresolved: the data collected from these meters is of relatively low time resolution, hindering the realization of smart grid benefits. This perspective unfolds the roadblocks to achieving high-resolution data from a smart metering infrastructure. We highlight the loss of critical information, essential for many smart grid applications, due to low-resolution readings of residential consumer data. We then outline the main reasons behind the lack of high-resolution data, the tetralemma on balancing data collection, transmission, warehousing, and privacy concerns. Finally, we hypothesize a framework for data collection, maintenance, communication, and storage of smart utility meters data to obtain high-resolution records by tackling the challenges using a dictionary-based compression method and separately maintaining the compressed products at the user ends and data center.
Fast and reliable entanglement distribution with quantum repeaters: Principles for improving protocols using reinforcement learning

Future quantum technologies such as quantum communication, quantum sensing, and distributed quantum computation, will rely on networks of shared entanglement between spatially separated nodes.In this work, we provide improved protocols/policies for entanglement distribution along a linear chain of nodes, both homogeneous and inhomogeneous, that take practical limitations into account.For a wide range of parameters, our policies improve upon previously known policies, such as the "swap-as-soon-as-possible" policy, with respect to both the waiting time and the fidelity of the end-to-end entanglement.This improvement is greatest for the most practically relevant cases, namely, for short coherence times, high link losses, and highly asymmetric links.To obtain our results, we model entanglement distribution using a Markov decision process, and then we use the Q-learning reinforcement learning (RL) algorithm to discover new policies.These new policies are characterized by dynamic, state-dependent memory cutoffs and collaboration between the nodes.In particular, we quantify this collaboration between the nodes.Our quantifiers tell us how much "global" knowledge of the network every node has, specifically, how much knowledge two distant nodes have of each other's states.In addition to the usual figures of merit, these quantifiers add an extra important dimension to the performance analysis and practical implementation of quantum repeaters.Finally, our understanding of the performance of large quantum networks is currently limited by the computational inefficiency of simulating them using RL or other optimization methods.The other main contribution of our work is to address this limitation.We present a method for nesting policies in order to obtain policies for large repeater chains.By nesting our RL-based policies for small repeater chains, we obtain policies for large repeater chains that improve upon the swap-as-soon-as-possible policy, and thus we pave the way for a scalable method for obtaining policies for long-distance entanglement distribution under practical constraints.
Towards Efficient Risky Driving Detection: A Benchmark and a Semi-Supervised Model

Risky driving is a major factor in traffic incidents, necessitating constant monitoring and prevention through Intelligent Transportation Systems (ITS). Despite recent progress, a lack of suitable data for detecting risky driving in traffic surveillance settings remains a significant challenge. To address this issue, Bayonet-Drivers, a pioneering benchmark for risky driving detection, is proposed. The unique challenge posed by Bayonet-Drivers arises from the nature of the original data obtained from intelligent monitoring and recording systems, rather than in-vehicle cameras. Bayonet-Drivers encompasses a broad spectrum of challenging scenarios, thereby enhancing the resilience and generalizability of algorithms for detecting risky driving. Further, to address the scarcity of labeled data without compromising detection accuracy, a novel semi-supervised network architecture, named DGMB-Net, is proposed. Within DGMB-Net, an enhanced semi-supervised method founded on a teacher–student model is introduced, aiming at bypassing the time-consuming and labor-intensive tasks associated with data labeling. Additionally, DGMB-Net has engineered an Adaptive Perceptual Learning (APL) Module and a Hierarchical Feature Pyramid Network (HFPN) to amplify spatial perception capabilities and amalgamate features at varying scales and levels, thus boosting detection precision. Extensive experiments on widely utilized datasets, including the State Farm dataset and Bayonet-Drivers, demonstrated the remarkable performance of the proposed DGMB-Net.
Research on data security in industry 4.0 manufacturing industry against the background of privacy protection challenges

As the manufacturing industry advances into the era of Industry 4.0, data security has emerged as a critical concern in the face of growing privacy protection challenges. This research paper explores data security challenges in the Industry 4.0 manufacturing industry and the associated privacy protection concerns. It highlights the risks of interconnected systems and data-driven processes while emphasizing the need for a comprehensive framework. The proposed framework encompasses technological, organizational, and regulatory measures to safeguard data at all stages. The study also examines legal and ethical considerations and suggests strategies to enhance privacy protection. The research aims to provide insights for stakeholders in the manufacturing industry, policymakers, and researchers to strengthen data security practices and uphold privacy rights in the Industry 4.0 landscape.
Perspective Chapter: Experimental Analysis of Black Hole Algorithm with Heuristic Algorithms in Traveling Salesman Problem

Black hole algorithm (BHA) is a popular metaheuristic algorithm proposed and applied for data clustering in 2013. BHA was applied to continuous and discrete problems; it is also hybridized with some algorithms in the literature. The pure BHA shows better performance than others in discrete optimization, such as traveling salesman problems. However, it requires improving the algorithm with competitive heuristics. Many heuristics have often been used to construct the initial tour of a salesman, such as the nearest neighbor algorithm (NN), nearest insertion algorithm (NI), cheapest insertion algorithm (CI), random insertion algorithm (RI), furthest insertion algorithm (FI), and minimal spanning tree algorithm (MST). In addition, the black hole algorithm is combined with popular heuristics, such as swap/or insert, reverse/or 2-opt swap, and swap-reverse/or 3-opt swap, and tested with proper parameters in this study. In the experimentation, classical datasets are used via TSP-library. The experimental results are given as best, average solutions/or deviations, and CPU time for all datasets. Besides, the hybrid algorithms demonstrate a better performance rate to get optimality. Finally, hybrid algorithms solve the discrete optimization problem in a short computing time for all datasets.
Bayesian multi-proxy reconstruction of early Eocene latitudinal temperature gradients

Abstract. Accurately reconstructing large-scale palaeoclimatic patterns from sparse local records is critical for understanding the evolution of Earth's climate. Particular challenges arise from the patchiness, uneven spatial distribution, and disparate nature of palaeoclimatic proxy records. Geochemical data typically provide temperature estimates via transfer functions derived from experiments. Similarly, transfer functions based on the climatic requirements of modern taxa exist for some fossil groups, such as pollen assemblages. In contrast, most ecological and lithological data (e.g. coral reefs and evaporites) only convey information on broad climatic requirements. Historically, most large-scale proxy-based reconstructions have used either geochemical or ecological data, but few studies have combined multiple proxy types into a single quantitative reconstruction. Large spatial gaps in existing proxy records have often been bridged by simple averaging, without taking into account the spatial distribution of samples, leading to biased temperature reconstructions. Here, we present a Bayesian hierarchical model to integrate ecological data with established geochemical proxies into a unified quantitative framework, bridging gaps in the latitudinal coverage of proxy data. We apply this approach to the early Eocene climatic optimum (EECO), the interval with the warmest sustained temperatures of the Cenozoic. Assuming the conservation of thermal tolerances of modern coral reefs and mangrove taxa, we establish broad sea surface temperature ranges for EECO coral reef and mangrove sites. We integrate these temperature estimates with the EECO geochemical shallow marine proxy record to model the latitudinal sea surface temperature gradient and global average temperatures of the EECO. Our results confirm the presence of a flattened latitudinal temperature gradient and unusually high polar temperatures during the EECO, which is supported by high-latitude ecological data. We show that integrating multiple types of proxy data, and adequate prior information, has the potential to enhance quantitative palaeoclimatic reconstructions, improving temperature estimates from datasets with limited spatial sampling.
Doubly Flexible Estimation under Label Shift

In studies ranging from clinical medicine to policy research, complete data are usually available from a population P, but the quantity of interest is often sought for a related but different population Q which only has partial data. We consider the setting when both outcome Y and covariate X are available from P but only X is available from Q, under the label shift assumption; i.e., the conditional distribution of X given Y is the same in the two populations. To estimate the parameter of interest in Q by leveraging information from P, three ingredients are essential: (a) the common conditional distribution of X given Y, (b) the regression model of Y given X in P, and (c) the density ratio of the outcome Y between the two populations. We propose an estimation procedure that only needs some standard nonparametric technique to approximate the conditional expectations with respect to (a), while by no means needs an estimate or model for (b) or (c); i.e., doubly flexible to the model misspecifications of both (b) and (c). This is conceptually different from the well-known doubly robust estimation in that, double robustness allows at most one model to be misspecified whereas our proposal can allow both (b) and (c) to be misspecified. This is of particular interest in label shift because estimating (c) is difficult, if not impossible, by virtue of the absence of the Y-data from Q. While estimating (b) is occasionally off-the-shelf, it may encounter issues related to the curse of dimensionality or computational challenges. We develop the large sample theory for the proposed estimator, and examine its finite-sample performance through simulation studies as well as an application to the MIMIC-III database.