Typical Instances Research Articles

Classical Medical Cases of Doubt Narrative Foreclosure in Traditional Chinese Medicine and Their Corresponding Narrative Mediation

Abstract: Narrative foreclosure is a phenomenon highlighted within the Chinese narrative medicine system, characterized by a state in which individuals become overly suspicious due to certain events in their development, leading to profound concerns about their health and safety. This article employs the logical discourse framework of Chinese narrative medicine to illustrate the experiences of various patients exhibiting typical instances of narrative foreclosure in traditional Chinese medicine (TCM) cases, such as Bei Gong She Ying (杯弓蛇影 snake shadow in the cup), Wu Qiu Zhi Chong (吴球治虫Wu Qiu treats worms), and Fa Jing Qu She (法靖驱蛇 Fajing drives away snakes). It explores how TCM practitioners leverage narrative wisdom to facilitate patients in dispelling their internal doubts through the concept of Bu Yao Wei Yao (不药为药 healing diseases without medicine). Through these medical cases, the article advocates for the significance of narrative competencies among medical practitioners in diagnosing and treating patients, alongside the use of pharmaceuticals and surgical interventions. Additionally, it outlines the prospects for the future development of narrative approaches in TCM.

Diverse policy generation for the flexible job-shop scheduling problem via deep reinforcement learning with a novel graph representation

In scheduling problems common in the industry and various real-world scenarios, responding in real-time to disruptive events is important. Recent methods propose the use of deep reinforcement learning (DRL) to learn policies capable of generating solutions under this constraint. However, current DRL approaches struggle with large instances, which are common in real-world scenarios. The objective of this paper is to introduce a new DRL method for solving the flexible job-shop scheduling problem, with a focus on these type of instances. The approach is based on the use of heterogeneous graph neural networks to a more informative graph representation of the problem. This novel modeling of the problem enhances the policy’s ability to capture state information and improve its decision-making capacity. Additionally, we introduce two novel approaches to enhance the performance of the DRL approach: the first involves generating a diverse set of scheduling policies, while the second combines DRL with dispatching rules (DRs) constraining the action space, with a variable degree of freedom depending on the chosen policy. Experimental results on two public benchmarks show that our approach outperforms DRs and achieves superior results compared to three state-of-the-art DRL methods, particularly for large instances.

Label-Guided relation prototype generation for Continual Relation Extraction

Continual relation extraction (CRE) aims to extract relations towards the continuous and iterative arrival of new data. To address the problem of catastrophic forgetting, some existing research endeavors have focused on exploring memory replay methods by storing typical historical learned instances or embedding all observed relations as prototypes by averaging the hidden representation of samples and replaying them in the subsequent training process. However, this prototype generation method overlooks the rich semantic information within the label namespace and are also constrained by memory size, resulting in inadequate descriptions of relation semantics by relation prototypes. To this end, we introduce an approach termed Label-Guided Relation Prototype Generation. Initially, we enhance the representations of label embeddings through a technique named label knowledge infusion. Following that, we utilize the multi-head attention mechanism to form relation prototypes, allowing them to capture diverse aspects of typical instances. The embeddings of relation labels are utilized at this stage, leveraging their contained semantics. Additionally, we propose a feature-based distillation loss function called multi-similarity distillation, to ensure the model retains prior knowledge after learning new tasks. The experimental results indicate that our method has achieved competitive performance compared to the state-of-the-art baseline models in CRE.

Parameterized inapproximability of Morse matching

We study the problem of minimizing the number of critical simplices from the point of view of inapproximability and parameterized complexity. We first show inapproximability of Min-Morse Matching within a factor of 2log(1−ϵ)⁡n. Our second result shows that Min-Morse Matching is W[P]-hard with respect to the standard parameter. Next, we show that Min-Morse Matching with standard parameterization has no FPT approximation algorithm for any approximation factor ρ. The above hardness results are applicable to complexes of dimension ≥2.On the positive side, we provide a factor O(nlog⁡n) approximation algorithm for Min-Morse Matching on 2-complexes, noting that no such algorithm is known for higher dimensional complexes. Finally, we devise discrete gradients with very few critical simplices for typical instances drawn from a fairly wide range of parameter values of the Costa–Farber model of random complexes.

Epistemic Injustices Online

AbstractIn typical instances of epistemic injustice, the victims and perpetrators are distinct across social groups – as marginally or dominantly situated. When epistemic injustice happens, the dominantly situated typically rely on prejudicial stereotypes to prevent the marginally situated from participating in epistemic activities. This is a manifestation/ exercise of their social power. However, with anonymity on the internet, a marginally situated person can effectively pose as a dominantly situated person and vice versa. When this happens, we cannot always tell who is behind a post. Consequently, relying on differential power relations, as in typical cases of epistemic injustice, might be ineffective online. In this paper, I argue for three ways that anonymity might complicate instances of epistemic injustice online.

An analysis of mastodon adoption dynamics based on instance types

Federated social networks have become an appealing choice as alternatives to mainstream centralized platforms. In the current global context, where the user’s activity on various social networks is monitored, influenced and manipulated, alternative platforms that offer the possibility of owning and controlling one’s own data are of great importance. Mastodon stands out among decentralized alternatives in the fediverse. In this study, we conduct a time-based dynamics analysis of various Mastodon instances, from popular ones to country-specific servers. Moreover, we conducted an analysis of registration account dynamics based on certain topics, such as academic, political and activism in general. Throughout the paper, we reveal the user adoption of Mastodon from multiple instances and metrics. Our results show a growth pattern of instances in terms of accounts in certain periods of time, and due to social events, reinforcing our assumption of it being already trusted as a decentralized platform. Our work holds significance in the wider context of studying and understanding the adoption rates of decentralized networks as ethical alternatives to centrally controlled ones.

StaRS: Learning a Stable Representation Space for Continual Relation Classification.

Relation classification (RC) aims to detect the semantic relation between two annotated entities in a piece of sentence, serving as an essential task in automatic knowledge graph construction. Due to the emergence of new relations, there is a recent trend to train RC models in continual settings. To overcome the catastrophic forgetting problem in continual learning, existing research is devoted in a two-stage training paradigm, fast adaptation to novel relations, and memory replay for all historical relations. These memory-replay-based methods explore different techniques to mitigate the forgetting problem of continual RC (CRC) models during the memory replay stage. However, we find that the representation space undergoes distortion due to the incoming of fresh relations in the fast adaptation phase. To address this issue, we propose using a knowledge distillation strategy and designing a margin loss, aiming to maintain the stability of the RC model during adaptation to new relations. In addition, in the second stage, with a limited number of typical memory instances available, we introduce a self-contrastive learning objective to facilitate learning a balanced decision boundary for RC. Through training in two stages, our objective is to acquire a stable representation space to encode instances for CRC. We experimentally demonstrate the superiority of our model over competing methods in various settings, and the results suggest that our tailored designs can achieve better performance in CRC.

Exact and heuristic approaches for the ship-to-shore problem

After a natural disaster such as a hurricane or flooding, the navy can help by bringing supplies, clearing roads, and evacuating victims. If destinations cannot be reached over land, resources can be transported using smaller ships and helicopters, called connectors. To start aid on land as soon as possible this must be done efficiently. In the ship-to-shore problem, trips with their accompanying resources are determined while minimising the makespan. Limited (un)loading capacities, heterogeneous connector characteristics and constraints posed by priority of the resources and grouping of the resources (resource sets) all require that the connector trips are carefully coordinated. Despite the criticality of this coordination, existing literature does not consider resource sets and has only developed heuristics. We provide a formulation that incorporates resource sets and develop (i) an exact branch-and-price algorithm and (ii) a tailored greedy heuristic that can provide upper bounds. We find that 84% of our 98 practical instances terminate within an hour in on average 80 s. Our greedy heuristic can find optimal solutions in two-thirds of these instances, mostly for instances that are very constrained in terms of the delivery order of resources. When improvements are found by the branch-and-price algorithm, the average gap with the makespan of the greedy solution is 40% and, in most cases, these improvements are obtained within three minutes. For the 20 artificial instances, the greedy heuristic has consistent performance on the different types of instances. For these artificial instances improvements of on average 35% are found in reasonable time.

VLM-guided Explicit-Implicit Complementary novel class semantic learning for few-shot object detection

Few-shot object detection (FSOD) aims at learning a novel class object detector with abundant base class samples and a limited number of novel class samples. Some recent methods assume that base class images contain unlabeled novel class instances and mine these instances to address the data scarcity of novel classes. Despite achieving competitive results, these methods face two issues: First, mined instances are sampled around the provided few-shot instances, establishing biased data distributions to represent novel classes. Second, the rich novel class semantics contained in base class instances are largely ignored. Benefits from vision-language models (VLMs), we propose a VLM-guided Explicit-Implicit Complementary novel class semantic learning (VEIC) method for FSOD. VEIC incorporates a VLM-cooperated sample mining module that leverages the powerful semantic alignment capability of VLMs to mine representative unlabeled explicit novel instances. In addition, for implicit novel instances (ambiguous base instances contain novel class semantics), we employ a spatial-wise feature re-expression module to capture prominent novel class local patches. Then a dynamic class-transition label assign strategy is proposed to learn from implicit novel instances by utilizing deep network memorization, where implicit novel instances are regarded more as novel classes during the early training and later regarded more as base classes. Experimentally, learning novel class semantics from two complementary types of instances reaches the best performance. Furthermore, learning exclusively from implicit novel instances extends the pseudo-labeling method to a practical scenario where base class images do not contain unlabeled explicit novel instances.

Instance type completion in equipment knowledge graph based on translation model

Knowledge graph completion is one of the steps of knowledge graph construction. It aims to complete the incomplete triple data in the initially constructed knowledge graph and make the data in the knowledge graph more abundant and complete. At present, there are few studies on knowledge graph completion in the equipment field, and the attribute and relationship samples in the equipment knowledge graph are prone to uneven distribution, while the traditional knowledge graph completion method is difficult to solve the problem of uneven distribution of attribute and relationship samples. Therefore, this paper proposes an internal instance type completion method of equipment knowledge graph based on EP2TP-TRT. First, the TransE model is used to embed the relationship and attributes of the equipment instance, respectively. Then, the EP2TP model is used to map the instance attributes, and the TRT model is used to map the type relationship. Finally, the scores of the EP2TP and TRT models are integrated by designing different weights, and the training prediction is carried out to enhance the representation ability of instance information and type information. Compared with the mainstream advanced models, this method improves the MRR and HITS @ 1 indicators by about 0.89% and 2.1%, respectively.

An Enhanced Modeling Framework for Bearing Fault Simulation and Machine Learning-based Identification with Bayesian-optimized Hyperparameter Tuning

Abstract Condition monitoring of rotating machinery offers a salient tool for predictive maintenance on rolling elements, subjected to continuous working loads, wear, fatigue, and degradation. In this study, an enhanced computational tool for bearing fault simulation and feature extraction is proposed. A subsequent identification scheme is realized, through Bayesian optimization of hyperparameters, including support vector classifier (SVC), gradient boosting (GBoost), random forest (RF), extreme gradient boosting (XBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). The proposed hyperparameter optimization technique stands out from traditional methods by offering a more informed and efficient pathway to optimal performance in predictive maintenance. Utilizing Bayesian optimization for hyperparameter tuning of machine learning models, which has not been extensively explored in this field, our approach demonstrates significant advancements. Typical instances of bearing faults, like inner race, outer race, and ball faults are considered. The analysis relies on extraction of statistical and engineering features form collected response signals, including kurtosis, root mean square, peak, and ridge factor. Highly influential variables are highlighted based on feature selection and importance algorithms, allowing bearing fault classification. We demonstrate that SVC and LightGBM produce over 97% of accuracy at low computational cost. This approach constitutes a scalable and robust framework for similar applications in engineering diagnostics.

Many-objective flexible job-shop scheduling based on a loose non-dominated sorting genetic algorithm

ABSTRACT To address the challenges of solving the many-objective flexible job-shop scheduling problem, this study proposes a loose non-dominated sorting genetic algorithm III (LNSGA-III), an enhancement of the non-dominated sorting genetic algorithm III (NSGA-III). First, a loose dominance principle is proposed to overcome the shortcomings of low selection pressure and slow convergence under the Pareto dominance principle. Next, a novel crossover operator without repair, named improved order crossover, is presented to fully preserve the characteristics of exchanged operations and enhance the exploration capability of the algorithm. Experimental studies involve testing algorithms on some typical scheduling instances with six simultaneously optimized objectives. The primary metric for algorithm comparison is the hypervolume, with additional investigation for statistical significance. Other metrics, including coverage, convergence and diversity, are also used for comparison. The experimental results demonstrate the effectiveness of the proposed enhancements, showcasing the significant superiority of the algorithm over some state-of-the-art alternatives.

Optimal decision-making of post-disaster emergency material scheduling based on helicopter–truck–drone collaboration

In the last decades, natural disasters, such as earthquakes and landslides, have occurred frequently, seriously threatening the safety of people’s lives and property. How emergency material is scheduled and delivered efficiently to the affected sites after a disaster has become a critical issue in emergency management. Current studies on emergency material scheduling mainly focus on truck or helicopter transport. Inspired by the success of employing drones in commercial logistics, this work investigates the emergency material scheduling issue based on the cooperative transport of drones, helicopters, and trucks. Specifically, this paper considers the limited transport capacity, road conditions in the early stage of the disaster rescue, and affected sites restricted by road conditions that can only be served by helicopters or drones. The studied problem is formulated as a mixed integer programming model, and a two-stage heuristic algorithm is developed to solve the model. For the proposed model, instances of different sizes are generated, and extensive experiments are performed to test the efficiency of the proposed algorithm. The comparison between the solutions obtained by the two-stage algorithm and Gurobi Solver for the small instances validates the effectiveness of the proposed heuristic algorithm. Experimental results for the larger instances show that the proposed two-stage algorithm can effectively solve the problem of emergency material scheduling. Sensitivity analysis of ten typical instances is performed to provide managerial insights. Finally, a case study of the Sichuan earthquake and the visualization of transport routes are presented. The model and solving approach proposed in this work can provide essential decision references for emergency management decision-making.

Integrated Energy Solutions: A Call for Inclusion of Non-Technical Factors

The central supposition put forward in this paper is that typical building-related energy efficiency measures do not automatically yield the looked-for large-scale environmental effects. Rather, many such measures entail two kinds of reductionism, in that they i) treat buildings detached from the larger system (such as the urban context), to which they belong; ii) concentrate on solutions, which are predominantly technological in nature. As a consequence, the complexity of the challenge may be neglected, along with other non-technical, yet potentially critical areas of concern. Such areas include, inter alia, economy, politics, policy, as well as societal and psychological circumstances. In this paper, we discuss a few typical instances of such reductionist stances to shed light on factors that impede their utility. Moreover, we highlight a few instances of systemic methods that have been suggested as having the potential to address complex problems in general, and the environmental implications of energy efficiency measures in the context of the built environment in particular.

A lightweight performance proxy for deep‐learning model training on Amazon SageMaker

SummaryCloud computing has become popular for training deep‐learning (DL) models, avoiding the costs of acquiring and maintaining on‐premise systems. SageMaker is a cloud service that automates the execution of DL workloads. Its features include automatic hyperparameter optimization and use of spot instances. Nonetheless, it does not assist in selecting the right instance type for a workload. In public clouds, rent price depends on the configuration of the chosen instance type. Advanced and faster instances are typically more expensive, but not always the best choice. To select the optimal instance type, users must compare the workload's relative performance (and hence cost) on several candidates. Building on the execution profiles of multiple DL applications, we model the performance and cost of training DL applications on SageMaker and propose a lightweight technique to estimate these at low temporal and monetary cost. This method is a performance proxy that can be used to replace more expensive performance measurement procedures. So, it could speed up any technique that relies on such measurements. We show how it can help cloud customers seeking suitable instance types to train DL models, and that it can accurately predict the performance of different instance types when training these models on SageMaker.

Auditable Algorithms for Approximate Model Counting

The problem of model counting, i.e., counting satisfying assignments of a Boolean formula, is a fundamental problem in computer science, with diverse applications. Given #P-hardness of the problem, many algorithms have been developed over the years to provide an approximate model count. Recently, building on the practical success of SAT-solvers used as NP oracles, the focus has shifted from theory to practical implementations of such algorithms. This has brought to focus new challenges. In this paper, we consider one such challenge – that of auditable deterministic approximate model counters wherein a counter should also generate a certificate, which allows a user (often with limited computational power) to independently audit whether the count returned by an invocation of the algorithm is indeed within the promised bounds. We start by examining a celebrated approximate model counting algorithm due to Stockmeyer that uses polynomially many calls to a \Sigma^2_P oracle, and show that it can be audited via a \Pi^2_P formula on (n^2 log^2 n) variables, where n is the number of variables in the original formula. Since n is often large (10’s to 100’s of thousands) for typical instances, we ask if the count of variables in the certificate formula can be reduced – a critical question towards potential implementation. We show that this improvement in certification can be achieved with a tradeoff in the counting algorithm’s complexity. Specifically, we develop new deterministic approximate model counting algorithms that invoke a \Sigma^3_P oracle, but can be certified using a \Pi^2_P formula on fewer variables: our final algorithm uses just (n log n) variables. Our study demonstrates that one can simplify certificate checking significantly if we allow the counting algorithm to access a slightly more powerful oracle. We believe this shows for the first time how the audit complexity can be traded for the complexity of approximate counting.

Exact and heuristic approaches for the Modal Shift Incentive Problem

It is widely acknowledged that freight transportation in general, and road freight transportation in particular, contributes significantly to greenhouse gas and pollutant emissions. In response, many countries have implemented incentive schemes to divert freight traffic from roads towards more environmentally friendly transportation modes (e.g., rail, maritime). However, incentive-based policies to promote modal shift are often sporadic and uncoordinated across non-road modes, thus preventing full leverage of dedicated budgets. In this study, we present a strategic problem encountered by national and supranational entities aiming to reduce emissions from the freight transportation sector, referred to as the Modal Shift Incentive Problem (MSIP). The problem involves designing incentive policies in the form of subsidies aimed at reducing the cost of eco-friendly transportation modes, with the aim of minimizing the volume of freight transported via less environmentally friendly modes. These incentive policies must satisfy various constraints, including a limited budget for incentives, equity requirements, and compliance with regulations pertaining to competitiveness and free-market dynamics. To address the MSIP, we propose an original mathematical formulation on the basis of a deterministic threshold modal choice model used to simulate the shipper behavior in selecting the transportation modes. Then, we develop a two-phase solution method, able to determine good solutions with a small computational burden based on the generation of a subset of promising incentive schemes. Finally, we solve real-world instances generated utilizing data derived from a comprehensive case study in Italy. The results on different instance types, in terms of number of shipments, budget, and costs, show the relationship between these factors, the complexity of the MSIP, and the modal shift.

Exploring and leveraging aggregation effects on reactive oxygen species generation in photodynamic therapy

AbstractAggregate‐level photodynamic therapy (PDT) has attracted significant interest and driven substantial advances in multifunction phototheranostic platforms. As exemplified by two typical instances of aggregation‐caused quenching of reactive oxygen species (ROS) and aggregation‐induced generation of ROS, the aggregation effect plays a significant role on the ROS generation of photosensitizers (PSs), which is worthy of in‐depth exploration and full utilization. However, in contrast to the well‐developed researches on the aggregation effect on luminescence, the studies concerning the aggregation effect on ROS generation are currently in a relatively nascent and disjointed stage, lacking guidance from a firmly established research paradigm. To advance this regard, this review aims at providing a consolidated overview of the fundamental principles and research status of aggregation effects on the ROS generation. Here, the research status can be organized into two main facets. One involves the comparison between isolated state and aggregated state, which is mainly conducted by two methods of changing solvent environments and adding adjuvants into a given solvent. The other underscores the distinctions between different aggregate states, consisting of three parts, namely comparison within the same or between different categories based on the classification of single‐component and multicomponent aggregates. In this endeavor, we will present our views on current research methodologies that explore how aggregation affects ROS generation and highlight the design strategies to leverage the aggregation effect to optimize PS regiments. We aspire this review to propel the advancement of phototheranostic platforms and accelerate the clinical implementation of precision medicine, and inspire more contributions to aggregate‐level photophysics and photochemistry, pushing the aggregate science and materials forward.

Multiobjective Combinatorial Optimization Using a Single Deep Reinforcement Learning Model.

This article proposes utilizing a single deep reinforcement learning model to solve combinatorial multiobjective optimization problems. We use the well-known multiobjective traveling salesman problem (MOTSP) as an example. Our proposed method employs an encoder-decoder framework to learn the mapping from the MOTSP instance to its Pareto-optimal set. Specifically, it leverages a novel routing encoder to extract information for both the entire multiobjective aspect and every individual objective from the MOTSP instance. The global embeddings and each objective's embeddings are adaptively aggregated via a routing network to form the subproblems' embedding that can well represent the MOTSP features. Using a modified context embedding, the subproblems' embeddings are fed into a decoder to produce a set of approximate Pareto-optimal solutions in parallel. Additionally, we develop a Top-k baseline to enable more efficient data utilization and lightweight training for our proposed method. We compare our method with heuristic-based and learning-based ones on various types of MOTSP instances, and the experimental results show that our method can solve MOTSP instances in real-time and outperform the other algorithms, especially on large-scale problem instances.

NuSC: An Effective Local Search Algorithm for Solving the Set Covering Problem.

The set covering problem (SCP) is a fundamental NP-hard problem in computer science and has a broad range of important real-world applications. In practice, SCP instances transformed from real-world applications would be of large scale, so it is of significant importance to design effective heuristic algorithms, especially local search ones. However, there exist only few research works on developing local search algorithms for solving SCP. In this article, we propose a new local search algorithm for solving SCP, dubbed NuSC. In particular, NuSC introduces a new combined scoring function for subset selection, which combines different subset properties in an effective way and helps NuSC find more optimized solutions. Besides, NuSC incorporates a dynamic weighting scheme for elements, a tabu search strategy, and a novelty selection mechanism to further enhance its practical performance. In order to study the effectiveness and robustness of our proposed NuSC algorithm, we conduct extensive experiments to compare NuSC against many state-of-the-art competitors on various types of SCP instances. Our experimental results demonstrate that NuSC significantly outperforms its competitors on the majority of instances, indicating the superiority of NuSC. Also, our empirical evaluations confirm the effectiveness of each algorithmic technique underlying NuSC.