Reviewerly/exHarmony huggingface dataset:
Benchmarking Abstractive Models for Italian Legal News Summarization

Automated text summarization is particularly important in the legal domain due to the length and inherent complexity of the analyzed documents. The Legal AI community has already started to address the text summarization problem. However, most existing approaches focus on English-written documents. Up to now, limited efforts have been devoted to summarizing Italian legal documents. Existing approaches extract portions of existing content without rephrasing them. To bridge this gap, in this work we aim at generating abstractive summaries of Italian legal news. We propose to condense the original news content into different summary types, i.e., an abstract, a title, or a subheader. We benchmark different state-of-the-art summarization models to generate abstractive summaries of Italian legal news. We also investigate the suitability of augmented models capable of handling long Italian documents. The experimental results achieved on a proprietary Italian dataset show the effectiveness of abstractive models in generating fairly accurate summaries and the importance of using larger contextual windows to generate news abstracts.
Critical success factors for DevOps adoption in information systems development

Adopting DevOps is challenging since it makes a significant paradigm shift in the Information Systems Development process. DevOps is a trending approach attached to the Agile Software Development Methodology, which facilitates adaptation to the customers' rapidly-changing requirements. It keeps one front step by introducing software operators who support the transmission between software and implementation into the software development team by confirming faster development, quality assurance, and easy maintenance of Information Systems (IS). However, software development companies reported challenges in adopting DevOps. It is critical to control those challenges while getting hold of the benefits by studying Critical Success Factors (CSF) for adopting DevOps. This study aimed to analyze the use of DevOps approach in IS developments by exploring CSFs of DevOps. A systematic literature review was applied to identify CSFs. These factors were confirmed by interviewing DevOps practitioners while identifying more frequent CSFs in the software development industry. Finally, the research presents a conceptual model for CSFs of DevOps, which is a guide to reap the DevOps benefits while reducing the hurdles for enhancing the success of IS. The conceptual model presents CSFs of DevOps by grouping them into four areas: collaborative culture, DevOps practices, proficient DevOps team, and metrics &amp; measurement.
Deep Learning Networks for Breast Lesion Classification in Ultrasound Images: A Comparative Study

Accurate lesion classification as benign or malignant in breast ultrasound (BUS) images is a critical task that requires experienced radiologists and has many challenges, such as poor image quality, artifacts, and high lesion variability. Thus, automatic lesion classification may aid professionals in breast cancer diagnosis. In this scope, computer-aided diagnosis systems have been proposed to assist in medical image interpretation, outperforming the intra and inter-observer variability. Recently, such systems using convolutional neural networks have demonstrated impressive results in medical image classification tasks. However, the lack of public benchmarks and a standardized evaluation method hampers the performance comparison of networks. This work is a benchmark for lesion classification in BUS images comparing six state-of-the-art networks: GoogLeNet, InceptionV3, ResNet, DenseNet, MobileNetV2, and EfficientNet. For each network, five input data variations that include segmentation information were tested to compare their impact on the final performance. The methods were trained on a multi-center BUS dataset (BUSI and UDIAT) and evaluated using the following metrics: precision, sensitivity, F1-score, accuracy, and area under the curve (AUC). Overall, the lesion with a thin border of background provides the best performance. For this input data, EfficientNet obtained the best results: an accuracy of 97.65% and an AUC of 96.30%.Clinical Relevance— This study showed the potential of deep neural networks to be used in clinical practice for breast lesion classification, also suggesting the best model choices.
BitFT: An Understandable, Performant and Resource-Efficient Blockchain Consensus

Blockchain technology has gained prominence for its potential to address security and privacy challenges in Internet-of-Things (IoT) services and Cyber-Physical Systems (CPS) due to its decentralized, traceable, and immutable nature. However, the considerable energy consumption associated with blockchain, exemplified by Bitcoin, has raised sustainability concerns. This paper introduces BitFT, a consensus protocol that combines the strengths of both lottery-based and voting-based mechanisms to offer a sustainable, comprehensible, and high-performance solution. BitFT dissects the block lifecycle into three phases: dissemination, and commitment phases, which correspond to the Bitcoin framework. It leverages a multiple-round sortition algorithm, a Reliable Broadcast (Rbc) protocol, and a Quorum Certificate (QC) mechanism to facilitate efficient protocol operation. The sortition algorithm functions like a lottery algorithm, while the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rbc</small> protocol and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QC</i> mechanism are implemented based on votes. In order to maximize network utilization and enhance system throughput, we further introduce a layered architecture to BitFT, which allows for concurrent commitment of multiple blocks at the same height. We perform a comprehensive analysis to verify the correctness of BitFT and conduct various experiments to demonstrate its high performance.
Margolus-Levitin quantum speed limit for an arbitrary fidelity

The Mandelstam-Tamm and Margolus-Levitin quantum speed limits are two well-known evolution time estimates for isolated quantum systems. These bounds are usually formulated for fully distinguishable initial and final states, but both have tight extensions to systems that evolve between states with an arbitrary fidelity. However, the foundations of these extensions differ in some essential respects. The extended Mandelstam-Tamm quantum speed limit has been proven analytically and has a clear geometric interpretation. Furthermore, which systems saturate the limit is known. The derivation of the extended Margolus-Levitin quantum speed limit, on the other hand, is based on numerical estimates. Moreover, the limit lacks a geometric interpretation, and no complete characterization of the systems reaching it exists. In this paper, we derive the extended Margolus-Levitin quantum speed limit analytically and describe the systems that saturate the limit in detail. We also provide the limit with a symplectic-geometric interpretation, which indicates that it is of a different character than most existing quantum speed limits. At the end of the paper, we analyze the maximum of the extended Mandelstam-Tamm and Margolus-Levitin quantum speed limits and derive a dual version of the extended Margolus-Levitin quantum speed limit. The maximum limit is tight regardless of the fidelity of the initial and final states. However, the conditions under which the maximum limit is saturated differ depending on whether or not the initial state and the final state are fully distinguishable. The dual limit is also tight and follows from a time reversal argument. We describe the systems that saturate the dual quantum speed limit.
Can machine learning algorithms deliver superior models for rental guides?

Abstract In this paper we discuss the use and potential advantages and disadvantages of machine learning driven models in rental guides. Rental guides are a formal legal instrument in Germany for surveying rents of flats in cities and municipalities, which are today based on regression models or simple contingency tables. We discuss if and how modern and timely methods of machine learning outperform existing and established routines. We make use of data from the Munich rental guide and mainly focus on the predictive power of these models. We discuss the “black-box” character making some of these models difficult to interpret and hence challenging for applications in the rental guide context. Still, it is of interest to see how “black-box” models perform with respect to prediction error. Moreover, we study adversarial effects, i.e. we investigate robustness in the sense how corrupted data influence the performance of the prediction models. With the data at hand we show that models with promising predictive performance suffer from being more vulnerable to corruptions than classic linear models including Ridge or Lasso regularization.
Recent Advancement for Few-Shot Learning

Machine learning has revolutionized data-driven decision-making, few-shot learning extends its capabilities to situations with data scarcity, offering solutions to some of the most pressing challenges in various domains. This article review illustrated the recent advances for few-shot learning.
Family of Quantum Codes with Exotic Transversal Gates

Recently, an algorithm has been constructed that shows that the binary icosahedral group 2I together with a $T$-like gate forms the most efficient single-qubit universal gate set. To carry out the algorithm fault tolerantly requires a code that implements 2I transversally. However, no such code has ever been demonstrated in the literature. We fill this void by constructing a family of distance $d=3$ codes that all implement 2I transversally. A surprising feature of this family is that the codes can be deduced entirely from symmetry considerations that only 2I affords.
Knowledge-Augmented Deep Learning and Its Applications: A Survey

Deep learning models, though having achieved great success in many different fields over the past years, are usually data-hungry, fail to perform well on unseen samples, and lack interpretability. Different kinds of prior knowledge often exists in the target domain, and their use can alleviate the deficiencies with deep learning. To better mimic the behavior of human brains, different advanced methods have been proposed to identify domain knowledge and integrate it into deep models for data-efficient, generalizable, and interpretable deep learning, which we refer to as knowledge-augmented deep learning (KADL). In this survey, we define the concept of KADL and introduce its three major tasks, i.e., knowledge identification, knowledge representation, and knowledge integration. Different from existing surveys that are focused on a specific type of knowledge, we provide a broad and complete taxonomy of domain knowledge and its representations. Based on our taxonomy, we provide a systematic review of existing techniques, different from existing works that survey integration approaches agnostic to the taxonomy of knowledge. This survey subsumes existing works and offers a bird's-eye view of research in the general area of KADL. The thorough and critical reviews of numerous papers help not only understand current progress but also identify future directions for the research on KADL.
Decentralized Swarm Trajectory Generation for LiDAR-based Aerial Tracking in Cluttered Environments

Aerial tracking with multiple unmanned aerial vehicles (UAVs) has wide potential in various applications. However, the existing works for swarm tracking typically lack the capability of maintaining high target visibility in cluttered environments. To address this deficiency, we present a decentralized planner that maximizes target visibility while ensuring collision-free maneuvers for swarm tracking. In this paper, each drone's tracking performance is first analyzed by a decentralized kinodynamic searching front-end, which renders an optimal guiding path to initialize safe flight corridors and visible sectors. Afterwards, a polynomial trajectory satisfying the corridor constraints is generated by a spatial-temporal optimizer. Inter-vehicle collision and occlusion avoidance are also incorporated into the optimization objectives. The advantages of our methods are verified by extensive benchmark comparisons against other cutting-edge works. Integrated with an autonomous LiDAR-based swarm system, the proposed planner demonstrates its efficiency and robustness in real-world experiments with unknown cluttered surroundings.
Thoracic Cartilage Ultrasound-CT Registration Using Dense Skeleton Graph

Autonomous ultrasound (US) imaging has gained increased interest recently, and it has been seen as a potential solution to overcome the limitations of free-hand US exami-nations, such as inter-operator variations. However, it is still challenging to accurately map planned paths from a generic atlas to individual patients, particularly for thoracic applications with high acoustic-impedance bone structures below the skin. To address this challenge, a dense graph-based non-rigid registration is proposed to transfer planned paths from the atlas to the current setup by explicitly considering subcutaneous bone surface. To this end, the sternum and cartilage branches are segmented using a template matching to assist coarse alignment of US and CT point clouds. Afterward, a directed graph is generated based on the CT template. Then, the self-organizing map using geographical distance is successively performed twice to extract the optimal graph representations for CT and US point clouds, individually. To evaluate the proposed approach, five cartilage point clouds from distinct patients are employed. The results demonstrate that the proposed graph-based registration can effectively map trajectories from CT to the current setup to do US examination through limited intercostal space. The non-rigid registration results in terms of Hausdorff distance (Mean±SD) is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$9.48 \pm 0.27$</tex> mm and the path transferring error in terms of Euclidean distance is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.21\pm 1.11\ mm$</tex> . The code <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/marslicy/Cartilage-graph-based-US-CT-Registration and video <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Video: https://www.youtube.com/watch?v=QJz2fkwgbP8 can be publicly accessed.
Alternatives to Contour Visualizations for Power Systems Data

Electrical grids are geographical and topological structures whose voltage states are challenging to represent accurately and efficiently for visual analysis. The current common practice is to use colored contour maps, yet these can misrepresent the data. We examine the suitability of four alternative visualization methods for depicting voltage data in a geographically dense distribution system—Voronoi polygons, H3 tessellations, S2 tessellations, and a network-weighted contour map. We find that Voronoi tessellations and network-weighted contour maps more accurately represent the statistical distribution of the data than regular contour maps.
Work Characteristics Needed by Middle Managers When Leading AI-Integrated Service Teams

Artificial intelligence (AI) is a significant part of digital transformation that signifies new requirements for middle managers in AI-integrated work contexts. This is particularly evident in financial service industries. Given the significance and rapidity of this technological transition, this case study investigated how middle managers perceived the impacts of AI system integration on their work characteristics. Interview data were gathered from 25 middle managers of a company providing financial services. The data were analyzed using the Gioia method. The findings showed that the AI systems applied in the case company were perceived as technical tools (mechanical AI) or coworkers (thinking AI and feeling AI), which had different impacts on middle managers’ work characteristics and the relationship between humans and AI systems. The middle managers’ work characteristics included contextual, task, competence, social, and relationship characteristics. Regarding the relationship characteristics, this study shows theoretically distinct human–AI relationship types. The findings are organized into a conceptual framework. AI system integration in service teams is a complex phenomenon that makes middle managers’ work more demanding and requires balancing and managing multiple challenges and dialectical tensions. The findings inform the selection and training of managers according to changing work characteristics in the digital age.
Time-reversal-symmetry breaking in a scalable cavity QED lattice

In this study, we explore the breaking of time-reversal symmetry in a scalable cavity quantum electrodynamics (QED) lattice. Such a lattice consists of triangular cells of three cavities coupled to a two-level atom. We synthesize artificial magnetic fields to enable the chiral transfer of photons by sinusoidally modulating the cavity frequencies. Considering various configurations with different numbers of cells in the system, we analyze the circulation of photons and investigate the effect of the frequency modulation on the control of the state transfer in the system. We show the breaking and sustaining of time-reversal symmetry in certain system geometries and consider the scalability of the generating synthetic magnetic field in the system. Our study highlights the potential of scalable cavity QED lattices with synthetic magnetic fields as a versatile tool for investigating quantum phenomena and shows the utility of these systems to serve as test beds for the simulation of condensed matter systems in quantum optical settings.
R3-DICnet: an end-to-end recursive residual refinement DIC network for larger deformation measurement

Digital image correlation (DIC) is an optical metrology method for measuring object deformation and has been widely used in many fields. Recently, the deep learning based DIC methods have achieved good performance, especially for small and complex deformation measurements. However, the existing deep learning based DIC methods with limited measurement range cannot satisfy the needs of real-world scenarios. To tackle this problem, a recursive iterative residual refinement DIC network (R 3 -DICnet) is proposed in this paper, which mimics the idea of the traditional method of two-step method, where initial value estimation is performed on deep features and then iterative refinement is performed on shallow features based on the initial value, so that both small and large deformations can be accurately measured. R 3 -DICnet not only has high accuracy and efficiency, but also strong generalization ability. Synthetic image experiments show that the proposed R 3 -DICnet is suitable for both small and large deformation measurements, and it has absolute advantages in complex deformation measurement. The accuracy and generalization ability of the R 3 -DICnet for practical measurement experiments were also verified by uniaxial tensile and wedge splitting tests.
What Can Large Language Models Do for Theorem Proving and Formal Methods?

With the introduction of large language models, AI for natural language have taken a leap. These systems are now also being used for tasks that has previously been dominated by symbolic methods, such as program synthesis and even to support formalising mathematics and assist theorem provers. We survey some recent applications in theorem proving, focusing on how they combine neural networks with symbolic systems, and report on a case-study of using GPT-4 for the task of automated conjecturing a.k.a. theory exploration.
Fast Approximated Multiple Kernel K-means

Multiple Kernel Clustering (MKC) has emerged as a prominent research domain in recent decades due to its capacity to exploit diverse information from multiple views by learning an optimal kernel. Despite the successes achieved by various MKC methods, a significant challenge lies in the computational complexity associated with generating a consensus partition from the optimal kernel matrix, typically of size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n \times n$</tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> represents the number of samples. This computational bottleneck restricts the practical applicability of these methods when confronted with large-scale datasets. Furthermore, certain existing MKC algorithms derive the consensus partition matrix by fusing all base partitions. However, this fusion process may inadvertently overlook critical information embedded in individual base kernels, potentially leading to inferior clustering performance. In light of these challenges, we introduce an innovative and efficient multiple kernel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means approach, denoted as FAMKKM. Notably, FAMKKM incorporates two approximated partition matrices instead of the original individual partition matric for each base kernel. This strategic substitution significantly reduces computational complexity. Additionally, FAMKKM leverages the original kernel information to guide the fusion of all base partitions, thereby enhancing the quality of the resulting consensus partition matrix. Finally, we substantiate the efficacy and efficiency of the proposed FAMKKM through extensive experiments conducted on six benchmark datasets. Our results demonstrate its superiority over state-of-the-art methods. The demo code of this work is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/WangJun2023/FAMKKM</uri>