Reviewerly/exHarmony huggingface dataset:
Ephemeral Rollups are All you Need

In the realm of open and composable gaming, we envision platforms where users actively expand, create, engage, and immerse themselves in a rich world of entertainment. One promising avenue for achieving this vision is through fully on-chain (FOC) games, where both game state and logic reside on the blockchain, maximizing composability. However, we must grapple with inherent limitations and trade-offs, particularly in terms of costs and scalability. This paper proposes BOLT, a framework that leverages the Solana Virtual Machine (SVM) to scale FOC games without state fragmentation or compromised trust assumptions. The framework introduces a systematic approach for discovering, utilizing, and publishing modular pieces of logic as components deeply rooted in the Entity-Component-System (ECS) pattern. To enhance scalability and resource optimization, we introduce the concept of Ephemeral Rollups (ERs) that overcome the tradeoffs of L2s horizontal scaling. These dedicated runtimes can be customized to provide higher operational speed, configurable ticking mechanisms, provable sessions and gasless transactions without composability-scalability tradeoffs.
ChEF: A Comprehensive Evaluation Framework for Standardized Assessment of Multimodal Large Language Models

Multimodal Large Language Models (MLLMs) have shown impressive abilities in interacting with visual content with myriad potential downstream tasks. However, even though a list of benchmarks has been proposed, the capabilities and limitations of MLLMs are still not comprehensively understood, due to a lack of a standardized and holistic evaluation framework. To this end, we present the first Comprehensive Evaluation Framework (ChEF) that can holistically profile each MLLM and fairly compare different MLLMs. First, we structure ChEF as four modular components, i.e., Scenario as scalable multimodal datasets, Instruction as flexible instruction retrieving formulae, Inferencer as reliable question answering strategies, and Metric as indicative task-specific score functions. Based on them, ChEF facilitates versatile evaluations in a standardized framework, and new evaluations can be built by designing new Recipes (systematic selection of these four components). Notably, current MLLM benchmarks can be readily summarized as recipes of ChEF. Second, we introduce 6 new recipes to quantify competent MLLMs' desired capabilities (or called desiderata, i.e., calibration, in-context learning, instruction following, language performance, hallucination, and robustness) as reliable agents that can perform real-world multimodal interactions. Third, we conduct a large-scale evaluation of 9 prominent MLLMs on 9 scenarios and 6 desiderata. Our evaluation summarized over 20 valuable observations concerning the generalizability of MLLMs across various scenarios and the composite capability of MLLMs required for multimodal interactions. We will publicly release all the detailed implementations for further analysis, as well as an easy-to-use modular toolkit for the integration of new recipes and models, so that ChEF can be a growing evaluation framework for the MLLM community.
ISAR: A Benchmark for Single- and Few-Shot Object Instance Segmentation and Re-Identification

Most object-level mapping systems in use today make use of an upstream learned object instance segmentation model. If we want to teach them about a new object or segmentation class, we need to build a large dataset and retrain the system. To build spatial AI systems that can quickly be taught about new objects, we need to effectively solve the problem of single-shot object detection, instance segmentation and re-identification. So far there is neither a method fulfilling all of these requirements in unison nor a benchmark that could be used to test such a method. Addressing this, we propose ISAR, a benchmark and baseline method for single- and few-shot object Instance Segmentation And Re-identification, in an effort to accelerate the development of algorithms that can robustly detect, segment, and re-identify objects from a single or a few sparse training examples. We provide a semi-synthetic dataset of video sequences with ground-truth semantic annotations, a standardized evaluation pipeline, and a baseline method. Our benchmark aligns with the emerging research trend of unifying Multi-Object Tracking, Video Object Segmentation, and Re-identification.
Fast Sparse 3D Convolution Network with VDB

We proposed a new Convolution Neural Network implementation optimized for sparse 3D data inference. This implementation uses NanoVDB as the data structure to store the sparse tensor. It leaves a relatively small memory footprint while maintaining high performance. We demonstrate that this architecture is around 20 times faster than the state-of-the-art dense CNN model on a high-resolution 3D object classification network.
Causal Question Answering with Reinforcement Learning

Causal questions inquire about causal relationships between different events or phenomena. They are important for a variety of use cases, including virtual assistants and search engines. However, many current approaches to causal question answering cannot provide explanations or evidence for their answers. Hence, in this paper, we aim to answer causal questions with a causality graph, a large-scale dataset of causal relations between noun phrases along with the relations' provenance data. Inspired by recent, successful applications of reinforcement learning to knowledge graph tasks, such as link prediction and fact-checking, we explore the application of reinforcement learning on a causality graph for causal question answering. We introduce an Actor-Critic-based agent which learns to search through the graph to answer causal questions. We bootstrap the agent with a supervised learning procedure to deal with large action spaces and sparse rewards. Our evaluation shows that the agent successfully prunes the search space to answer binary causal questions by visiting less than 30 nodes per question compared to over 3,000 nodes by a naive breadth-first search. Our ablation study indicates that our supervised learning strategy provides a strong foundation upon which our reinforcement learning agent improves. The paths returned by our agent explain the mechanisms by which a cause produces an effect. Moreover, for each edge on a path, our causality graph provides its original source allowing for easy verification of paths.
ELEGANT: Certified Defense on the Fairness of Graph Neural Networks

Graph Neural Networks (GNNs) have emerged as a prominent graph learning model in various graph-based tasks over the years. Nevertheless, due to the vulnerabilities of GNNs, it has been empirically proved that malicious attackers could easily corrupt the fairness level of their predictions by adding perturbations to the input graph data. In this paper, we take crucial steps to study a novel problem of certifiable defense on the fairness level of GNNs. Specifically, we propose a principled framework named ELEGANT and present a detailed theoretical certification analysis for the fairness of GNNs. ELEGANT takes any GNNs as its backbone, and the fairness level of such a backbone is theoretically impossible to be corrupted under certain perturbation budgets for attackers. Notably, ELEGANT does not have any assumption over the GNN structure or parameters, and does not require re-training the GNNs to realize certification. Hence it can serve as a plug-and-play framework for any optimized GNNs ready to be deployed. We verify the satisfactory effectiveness of ELEGANT in practice through extensive experiments on real-world datasets across different backbones of GNNs, where ELEGANT is also demonstrated to be beneficial for GNN debiasing. Open-source code can be found at https://github.com/yushundong/ELEGANT.
Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead

Anomaly detection is a crucial task across different domains and data types. However, existing anomaly detection models are often designed for specific domains and modalities. This study explores the use of GPT-4V(ision), a powerful visual-linguistic model, to address anomaly detection tasks in a generic manner. We investigate the application of GPT-4V in multi-modality, multi-domain anomaly detection tasks, including image, video, point cloud, and time series data, across multiple application areas, such as industrial, medical, logical, video, 3D anomaly detection, and localization tasks. To enhance GPT-4V's performance, we incorporate different kinds of additional cues such as class information, human expertise, and reference images as prompts.Based on our experiments, GPT-4V proves to be highly effective in detecting and explaining global and fine-grained semantic patterns in zero/one-shot anomaly detection. This enables accurate differentiation between normal and abnormal instances. Although we conducted extensive evaluations in this study, there is still room for future evaluation to further exploit GPT-4V's generic anomaly detection capacity from different aspects. These include exploring quantitative metrics, expanding evaluation benchmarks, incorporating multi-round interactions, and incorporating human feedback loops. Nevertheless, GPT-4V exhibits promising performance in generic anomaly detection and understanding, thus opening up a new avenue for anomaly detection.
Mobility as a Resource (MaaR) for resilient human-centric automation: a vision paper

With technological advances, mobility has been moving from a product (i.e., traditional modes and vehicles), to a service (i.e., Mobility as a Service, MaaS). However, as observed in other fields (e.g. cloud computing resource management) we argue that mobility will evolve from a service to a resource (i.e., Mobility as a Resource, MaaR). Further, due to increasing scarcity of shared mobility spaces across traditional and emerging modes, the transition must be viewed within the critical need for ethical and equitable solutions for the traveling public (i.e., research is needed to avoid hyper-market driven outcomes for society). The evolution of mobility into a resource requires novel conceptual frameworks, technologies, processes and perspectives of analysis. A key component of the future MaaR system is the technological capacity to observe, allocate and manage (in real-time) the smallest envisionable units of mobility (i.e., atomic units of mobility capacity) while providing prioritized attention to human movement and ethical metrics related to access, consumption and impact. To facilitate research into the envisioned future system, this paper proposes initial frameworks which synthesize and advance methodologies relating to highly dynamic capacity reservation systems. Future research requires synthesis across transport network management, demand behavior, mixed-mode usage, and equitable mobility.
Participants-based Synchronous Optimization Network for skeleton-based action recognition

Nowadays, graph convolutional networks are widely used in skeleton-based action recognition. However, these methods ignore the difference between main participant and subordinate participant, as well as the consistency and causality reasoning in human–human interactive actions. In this paper, we construct a novel Participants-based Synchronous Optimization Network (PSONet). Firstly, we construct main participant branch, subordination participant branch and relative movements branch for the individual and interactive information of participants. Secondly, in the training process, Participants-based Synchronous Response (PSR) loss is constructed to optimize our network. Online mutual response mechanism in PSR regulates the consistency and captures the causality between the main participant action and the overall interactive action. Joint cross-entropy loss in PSR is used to constrain the action instances with individual and interactive action information. Finally, Representative Temporal Enhanced (RTE) block is proposed to complement representative temporal aggregation features and enhance the spatial modeling of representative temporal frames. Experiments have been conducted on the NTU RGB+D 60 dataset and the NTU RGB+D 120 dataset, which have verified that PSONet outperforms state-of-the-art methods.
Circuit Complexity through phase transitions: Consequences in quantum state preparation

In this paper, we analyze the circuit complexity for preparing ground states of quantum many-body systems. In particular, how this complexity grows as the ground state approaches a quantum phase transition. We discuss different definitions of complexity, namely the one following the Fubini-Study metric or the Nielsen complexity. We also explore different models: Ising, ZZXZ or Dicke. In addition, different forms of state preparation are investigated: analytic or exact diagonalization techniques, adiabatic algorithms (with and without shortcuts), and Quantum Variational Eigensolvers. We find that the divergence (or lack thereof) of the complexity near a phase transition depends on the non-local character of the operations used to reach the ground state. For Fubini-Study based complexity, we extract the universal properties and their critical exponents. In practical algorithms, we find that the complexity depends crucially on whether or not the system passes close to a quantum critical point when preparing the state. For both VQE and Adiabatic algorithms, we provide explicit expressions and bound the growth of complexity with respect to the system size and the execution time, respectively.
Anomaly detection in oil-producing wells: a comparative study of one-class classifiers in a multivariate time series dataset

Abstract Anomalies in oil-producing wells can have detrimental financial implications, leading to production disruptions and increased maintenance costs. Machine learning techniques offer a promising solution for detecting and preventing such anomalies, minimizing these disruptions and expenses. In this study, we focused on detecting faults in naturally flowing offshore oil and subsea gas-producing wells, utilizing the publicly available 3W dataset comprising multivariate time series data. We conducted a comparison of different anomaly detection methods, specifically one-class classifiers, including Isolation Forest, One-class Support Vector Machine (OCSVM), Local Outlier Factor (LOF), Elliptical Envelope, and Autoencoder with feedforward and LSTM architectures. Our evaluation encompassed two variations: one with feature extraction and the other without, each assessed in both simulated and real data scenarios. Across all scenarios, the LOF classifier consistently outperformed its counterparts. In real instances, the LOF classifier achieved an F1-measure of 87.0% with feature extraction and 85.9% without. In simulated instances, the LOF classifier demonstrated superior performance, attaining F1 measures of 91.5% with feature extraction and 92.0% without. These results show an improvement over the benchmark established by the 3W dataset. Considering the more challenging nature of real data, the inclusion of feature extraction is recommended to improve the effectiveness of anomaly detection in offshore wells. The superior performance of the LOF classifier suggests that the boundaries of normal cases as a single class may be ill-defined, with normal cases better represented by multiple clusters. The statistical analysis conducted further reinforces the reliability and robustness of these findings, instilling confidence in their generalizability to a larger population. The utilization of individual classifiers per instance allows for tailored hyperparameter configurations, accommodating the specific characteristics of each offshore well.
Link Prediction for Hypothesis Generation: An Active Curriculum Learning Infused Temporal Graph-Based Approach

Abstract Over the last few years Literature-based Discovery (LBD) has regained popularity as a means to enhance the scientific research process. The resurgent interest has spurred the development of supervised and semi-supervised machine learning models aimed at making previously implicit connections between scientific concepts/entities explicit based on often extensive repositories of published literature. Understanding the temporally evolving interactions between these entities can provide valuable information for predicting the future development of entity relationships. However, existing methods often underutilize the latent information embedded in the temporal aspects of interaction data. In this context, motivated by applications in the food domain—where we aim to connect nutritional information with health-related benefits—we address the hypothesis-generation problem using a temporal graph-based approach. Given that hypothesis generation involves predicting future (i.e., still to be discovered) entity connections, the ability to capture the dynamic evolution of connections over time is pivotal for a robust model. To address this, we introduce THiGER, a novel batch contrastive temporal node-pair embedding method. THiGER excels in providing a more expressive node-pair encoding by effectively harnessing node-pair relationships. Furthermore, we present THiGER-A, an incremental training approach that incorporates an active curriculum learning strategy to mitigate label bias arising from unobserved connections. By progressively training on increasingly challenging and high-utility samples, our approach significantly enhances the performance of the embedding model. Empirical validation of our proposed method demonstrates its effectiveness on established temporal-graph benchmark datasets, as well as on real-world datasets within the food domain.
Algorithm driven fault tolerant architectures based on superconducting qubits

electrostatic gates to selectively tunnel into designated pairs of Majorana zero modes. We give example of qubit designs and circuits that are particularly suitable for the metal-based heterostructures.
Creation of Two-Mode Squeezed States in Atomic Mechanical Oscillators

Two-mode squeezed states, which are entangled states with bipartite quantum correlations in continuous-variable systems, are crucial in quantum information processing and metrology. Recently, continuous-variable quantum computing with the vibrational modes of trapped atoms has emerged with significant progress, featuring a high degree of control in hybridizing with spin qubits. Creating two-mode squeezed states in such a platform could enable applications that are only viable with photons. Here, we experimentally demonstrate two-mode squeezed states by employing atoms in a two-dimensional optical lattice as quantum registers. The states are generated by a controlled projection conditioned on the relative phase of two independent squeezed states. The individual squeezing is created by sudden jumps of the oscillators' frequencies, allowing generating of the two-mode squeezed states at a rate within a fraction of the oscillation frequency. We validate the states by entanglement steering criteria and Fock state analysis. Our results can be applied in other mechanical oscillators for quantum sensing and continuous-variable quantum information.
An IGA model for resource allocation in heterogeneous networks for the internet of things

To reasonably allocate the heterogeneous network resources of Internet of Things (IoT) to meet the various needs of users in a cost-effective manner. The study proposes an improved genetic algorithm (IGA) method for finding the optimal solution of heterogeneous network resource allocation. The method first transforms the IoT heterogeneous network resource allocation optimization problem into an objective function to find the optimal solution problem, then uses the improved genetic algorithm to find the optimal solution under the constraints, and finally analyses and simulates the performance of the model for application. The results show that the algorithm stabilises after 10 iterations in the IoT-23 dataset and 14 iterations in the IoT-Healthcare dataset, with stability values of approximately 3.16 and 3.14 respectively, and that the computation time of the algorithm is 8.8m and 3.6m in the two datasets respectively when the number of users is 100, which is much less than the other two algorithms. In the simulation experiments on the accuracy of the calculation results, the study achieved an accuracy rate of over 92% and 94% in A and B respectively. Combining the above data, it can be shown that the method can accurately achieve the efficient allocation and utilisation of resources under the constructed network environment and demand.(Abstract)
Research on the improved PBFT consensus algorithm based on credit mechanism

The PBFT consensus algorithm commonly used in the consortium chain has the disadvantages of three-stage consensus process, resulting in high communication costs. This paper proposes an enhanced PBFT algorithm that utilizes a credit mechanism to select master nodes that can improve system availability. The three-stage consensus process of PBFT is optimized, and the master node collects the confirmation messages broadcast by the whole network during the confirmation stage, effectively reducing communication overhead. At the same time, the block height synchronization mechanism is used to ensure the high consistency of consensus node blocks, restore the block height of failed nodes, and achieve fast consensus. Empirical results show that the improved P-PBFT consensus algorithm effectively reduces resource consumption and latency.
fq-Derivation of BP-Algebras

First, this article presents the definition of left-right derivation and right-left derivation in BP-algebra, and their characteristic are explored. Then, we define the concept of inside and outside fq-derivation of BP-algebras. Finally, their properties are explored. Furthermore, the notion of fq-derivation within BP-algebra is synonymous with B-algebra; however, they do exhibit variations in their respective characteristics.
Integrating Blockchain in Various Organizational Models Pave the Path to Sustainable Development Goals

Blockchain is considered one of the most significant technological advances integrated into the Business Process Management (BPM) systems of various organizations over the past decade. The introduction of blockchain technologies is changing the way traditional supply chains are managed. In this sense, blockchain technologies have the potential to revolutionize complex supply chains in stability, traceability, and accountability. Since Blockchain technology has fostered the development of trust between many different parties, not just leaders and followers, it is assumed that Blockchain will give its adapters revolutionary characteristics. Upon a systematic bibliometric review that determined the impact of introducing Blockchain technology into the BPM, on the basis of previous research and scientific papers published in academic journals, it was found that most of the studies highlight the significance of Blockchain and its impact on the supply chain and financial sectors. However, this article aims to identify new characteristics of Blockchain’s impact on the BPM in various other sectors, especially in the telecommunications, non-governmental organizations, industrial, and banking sectors. The statistical analysis of the collected data examines the intermediate variables of the introduction of Blockchain technology (trust, immutability, transparency, and cost reduction) and its impact on increasing sustainability (economic, ecological, and social resilience) as a dependent variable. As a result of the study, the authors concluded that Blockchain has the potential to enhance the revolutionary characteristics of trust, immutability, cost reduction, and reliability in a business process model, accelerating the path to achieving sustainable development.