Miracle-dz/UniFAR-FTUs huggingface dataset:
Feedback, trace and fixed-point semantics

We introduce a notion of category with feedback-with-delay, closely related to the notion of traced monoidal category, and show that the Circ construction of [15] is the free category with feedback on a symmetric monoidal category. Combining with the Int construction of Joyal et al. [12] we obtain a description of the free compact closed category on a symmetric monoidal category. We thus obtain a categorical analogue of the classical localization of a ring with respect to a multiplicative subset. In this context we define a notion of fixed-point semantics of a category with feedback which is seen to include a variety of classical semantics in computer science.
The impact of organisational structure on enterprise architecture deployment

Purpose – In many organisations, the alignment between information technology (IT) and business is viewed as an important catalyst for the organisation's effectiveness and efficiency towards achieving a competitive advantage. Yet, a shared understanding between business and IT (the EA included) remains an issue in many organisations. The organisational structure affects the alignment between business and IT units. Conversely, a lack of alignment of both components (IT and business units) impedes organisations' capacity to increase and improve their productivity. Many studies have been conducted in the area of IT, EA and the organisational structure. There is, however, little evidence of work conducted in exploring the interface between organisational structure and EA prior to this study. The paper aims to discuss these issues. Design/methodology/approach – A case-study research approach was employed to investigate the impact of organisational structure in the deployment of EA in organisations. Qualitative.
Low seismic velocities below mid‐ocean ridges: Attenuation versus melt retention

The first comprehensive seismic experiment sampling subridge mantle revealed a pronounced low-velocity zone between 40 and 100 km depth below the East Pacific Rise (EPR) that has been attributed to substantial retained melt fractions of 0.3–2%. Such high melt fractions are at odds with low melt productivity and high melt mobility inferred from petrology and geochemistry. Here, we evaluate whether seismic attenuation can reconcile subridge seismic structure with low melt fractions. We start from a dynamic spreading model which includes melt generation and migration and is converted into seismic structure, accounting for temperature-, pressure-, composition-, phase-, and melt-dependent anharmonicity, and temperature-, pressure-, frequency- and hydration-dependent anelasticity. Our models predict a double low-velocity zone: a shallow—approximately triangular—region due to dry melting, and a low-velocity channel between 60 and 150 km depth dominantly controlled by solid state high-temperature seismic attenuation in a damp mantle, with only a minor contribution of (<0.1%) melt. We test how tomographic inversion influences the imaging of our modeled shear velocity features. The EPR experiment revealed a double low-velocity zone, but most tomographic studies would only resolve the deeper velocity minimum. Experimentally constrained anelasticity formulations produce VSas low as observed and can explain lateral variations in near-ridge asthenospheric VS with ±100 K temperature variations and/or zero to high water content. Furthermore, such QS formulations also reproduce low asthenospheric VS below older oceans and continents from basic lithospheric cooling models. Although these structures are compatible with global QS images, they are more attenuating than permitted by EPR data.
Event Prediction in Big Data Era: A Systematic Survey

Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as earthquakes, civil unrest, system failures, pandemics, and crimes. It is highly desirable to be able to anticipate the occurrence of such events in advance in order to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth, also thanks to advances in high performance computers and new Artificial Intelligence techniques. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex (e.g., spatial, temporal, and semantic) dependencies, and streaming data feeds. Due to the strong interdisciplinary nature of event prediction problems, most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This paper aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts’ searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided to introduce wider applications to model developers to help them expand the impacts of their research. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions for this promising and important domain are elucidated and discussed.
Arti?cial Ontogeny for Truss Structure Design

This paper introduces an approach based on artificial embryogeny for truss design to address the problem of finding the best truss structure for a given loading. In this setup, the basic idea is to optimize the size and length of beams in a truss through the actions of a set of cells that are distributed over the very truss structure. Given information at the mechanical level (beam strain), each cell controller is able to modify the local truss structure (beam size and length) during a developmental process. The advantage of such a method relies on the idea that a template cell controller is duplicated over all cells, keeping the optimization search space very low, while each cell may act in a different manner depending on local information. This approach is demonstrated on a classical benchmark, the "cantilever": resulting organisms are shown to provide very interesting and unique properties regarding reuse of optimized genotypes in noisy or higher-dimension settings.
Small Moving Object Detection Algorithm in Surveillance Video Based on Motion Information

—A Small Moving Object Detection algorithm Based on Motion Information (SMOD-BMI) is proposed to detect small moving objects with a low Signal-to-Noise Ratio (SNR) in surveillance video. Firstly, a ConvLSTM-PAN model structure is designed to capture suspicious small moving objects, in which the Convolutional Long and Short Time Memory (ConvLSTM) network aggregated the Spatio-temporal features of the adjacent multi-frame small moving object and the Path Aggregation Network (PAN) located the suspicious small moving objects. Then, an object tracking algorithm is used to track suspicious small objects and calculate their Motion Range (MR). At the same time, the size of the MR of the suspicious small moving object is adjusted adaptively according to its speed of movement (speciﬁcally, if the object moves slowly, its MR will be expanded according to the speed of the object to ensure the necessary environmental information of the object). Adaptive Spatio-temporal Cubes (ASt-Cubes) of the small moving objects are generated to ensure that the SNR of the moving objects is improved, and the necessary environmental information is retained adaptively. Finally, a LightWeight U-Shape Net (LW-USN) based on ASt-Cubes is designed to detect small moving objects, which rejects false detections and returns the position of small moving objects. This paper uses the bird in the surveillance video as the experimental data set to verify the algorithm’s performance. The experimental results show that the proposed small moving object detection method based on motion information in surveillance video can effectively reduce the missed and false detection rate of small moving objects.
WikiCity

The real-time city is now real! The increasing deployment of sensors and hand-held electronic devices in recent years is allowing a new approach to the study and exploration of the built environment. The WikiCity project deals with the development of real-time location-sensitive tools for the city. It follows the Real Time Rome project that was developed for the 2006 Venice Biennale of Architecture and which aggregated data from cell phones, buses and taxis in Rome to better understand urban dynamics in real-time. The WikiCity project is concerned with the real-time mapping of city dynamics. This mapping however is not limited to representing the city but becomes also instantly an instrument for city inhabitants to base their actions and decisions upon in a better informed manner, leading to an overall increased efficiency and sustainability in making use of the city environment.
Using Simulation to Aid the Design and Optimization of Intelligent User Interfaces for Quality Assurance Processes in Machine Learning

Many mission-critical applications of machine learning (ML) in the real-world require a quality assurance (QA) process before the decisions or predictions of an ML model can be deployed. Because QA4ML users have to view a non-trivial amount of data and perform many input actions to correct errors made by the ML model, an optimally-designed user interface (UI) can reduce the cost of interactions significantly. A UI’s effectiveness can be affected by many factors, such as the number of data objects processed concurrently, the types of commands for correcting errors, and the availability of algorithms for assisting users. We propose using simulation to aid the design and optimization of intelligent user interfaces for QA4ML processes. In particular, we focus on simulating the combined effects of human intelligence in selecting appropriate commands and algorithms, and machine intelligence in providing a collection of general-purpose algorithms for reordering data objects to be quality-assured.
On the Hyperparameters influencing a PINN's generalization beyond the training domain

Physics-Informed Neural Networks (PINNs) are Neural Network architectures trained to emulate solutions of diﬀerential equations without the necessity of solution data. They are currently ubiquitous in the scientiﬁc literature due to their ﬂexible and promising settings. However, very little of the available research provides practical studies that aim for a better quantitative understanding of such architecture and its functioning. In this paper, we analyze the performance of PINNs for various architectural hyperparameters and algorithmic settings based on a novel error metric and other factors such as training time. The proposed metric and approach are tailored to evaluate how well a PINN generalizes to points outside its training domain. Besides, we investigate the eﬀect of the algorithmic setup on the outcome prediction of a PINN, inside and outside its training domain, to explore the eﬀect of each hyperparameter. Through our study, we assess how the algorithmic setup of PINNs inﬂuences their potential for generalization and deduce the settings which maximize the potential of a PINN for accurate generalization. The study that we present returns insightful and at times counterintuitive results on PINNs. These results can be useful in PINN applications when deﬁning the model and evaluating it.
Departamento de Nosotros: How Machine Translated Corpora Affects Language Models in MRC Tasks

Pre-training large-scale language models (LMs) requires huge amounts of text corpora. LMs for English enjoy ever growing corpora of diverse language resources. However, less resourced languages and their mono-and multilingual LMs often struggle to obtain bigger datasets. A typical approach in this case implies using machine translation of English corpora to a target language. In this work, we study the caveats of applying directly translated corpora for ﬁne-tuning LMs for downstream natural language processing tasks and demonstrate that careful curation along with post-processing lead to improved performance and overall LMs robustness. In the empirical evaluation, we perform a comparison of directly translated against curated Spanish SQuAD datasets on both user and system levels. Further experimental results on XQuAD and MLQA down-stream transfer-learning question answering tasks show that presumably multilingual LMs exhibit more resilience to machine translation artifacts in terms of the exact match score.
PLTOOL: A knowledge engineering tool for planning and learning

Absract Artificial intelligence (AI) planning solves the problem of generating a correct and efficient ordered set of instantiated activities, from a knowledge base of generic actions, which when executed will transform some initial state into some desirable end-state. There is a long tradition of work in AI for developing planners that make use of heuristics that are shown to improve their performance in many real world and artificial domains. The developers of planners have chosen between two extremes when defining those heuristics. The domain-independent planners use domain-independent heuristics, which exploit information only from the ‘syntactic’ structure of the problem space and of the search tree. Therefore, they do not need any ‘semantic’ information from a given domain in order to guide the search. From a knowledge engineering (KE) perspective, the planners that use this type of heuristics have the advantage that the users of this technology need only focus on defining the domain theory and not on defining how to make the planner efficient (how to obtain ‘good’ solutions with the minimal computational resources). However, the domain-dependent planners require users to manually represent knowledge not only about the domain theory, but also about how to make the planner efficient. This approach has the advantage of using either better domain-theory formulations or using domain knowledge for defining the heuristics, thus potentially making them more efficient. However, the efficiency of these domain-dependent planners strongly relies on the KE and planning expertise of the user. When the user is an expert on these two types of knowledge, domain-dependent planners clearly outperform domain-independent planners in terms of number of solved problems and quality of solutions. Machine-learning (ML) techniques applied to solve the planning problems have focused on providing middle-ground solutions as compared to the aforementioned two extremes. Here, the user first defines a domain theory, and then executes the ML techniques that automatically modify or generate new knowledge with respect to both the domain theory and the heuristics. In this paper, we present our work on building a tool, PLTOOL (planning and learning tool), to help users interact with a set of ML techniques and planners. The goal is to provide a KE framework for mixed-initiative generation of efficient and good planning knowledge.
Closed-loop primitives: A method to generate and recognize reaching actions from demonstration

The studies on mirror neurons observed in monkeys indicate that recognition of other's actions activates neural circuits that are also responsible for generating the very same actions in the animal. The mirror neuron hypothesis argues that such an overlap between action generation and recognition can provide a shared worldview among individuals and be a key pillar for communication. Inspired by these findings, this paper extends a learning by demonstration method for online recognition of observed actions. The proposed method is shown to recognize and generate different reaching actions demonstrated by a human on a humanoid robot platform. Experiments show that the proposed method is robust to both occlusions during the observed actions as well as variances in the speed of the observed actions. The results are successfully demonstrated in an interactive game with the iCub humanoid robot platform.
Effective Diversity in Unsupervised Environment Design

Agent decision making using Reinforcement Learning (RL) heavily relies on either a model or simulator of the environment (e.g., moving in an 8x8 maze with three rooms, playing Chess on an 8x8 board). Due to this dependence, small changes in the environment (e.g. positions of obstacles in the maze, size of the board) can severely affect the effectiveness of the policy learnt by the agent. To that end, existing work has proposed training RL agents on an adaptive curriculum of environments (generated automatically) to improve performance on out-of-distribution (OOD) test scenarios. Speciﬁcally, existing research has employed the potential for the agent to learn in an environment (captured using Generalized Advantage Estimation, GAE) as the key factor to select the next environment(s) to train the agent. However, such a mechanism can select similar environments (with a high potential to learn) thereby making agent training redundant on all but one of those environments. To that end, we provide a principled approach to adaptively identify diverse environments based on a novel distance measure relevant to environment design. We empirically demonstrate the versatility and effectiveness of our method in comparison to multiple leading approaches for unsupervised environment design on three distinct benchmark problems used in literature.
Global Expanding, Local Shrinking: Discriminant Multi-label Learning with Missing Labels

In multi-label learning, the issue of missing labels brings a major challenge. Many methods attempt to recovery missing labels by exploiting low-rank structure of label matrix. However, these methods just utilize global low-rank label structure, ignore both local low-rank label structures and label discriminant information to some extent, leaving room for further performance improvement. In this paper, we develop a simple yet effective discriminant multi-label learning (DM2L) method for multi-label learning with missing labels. Specifically, we impose the low-rank structures on all the predictions of instances from the same labels (local shrinking of rank), and a maximally separated structure (high-rank structure) on the predictions of instances from different labels (global expanding of rank). In this way, these imposed low-rank structures can help modeling both local and global low-rank label structures, while the imposed high-rank structure can help providing more underlying discriminability. Our subsequent theoretical analysis also supports these intuitions. In addition, we provide a nonlinear extension via using kernel trick to enhance DM2L and establish a concave-convex objective to learn these models. Compared to the other methods, our method involves the fewest assumptions and only one hyper-parameter. Even so, extensive experiments show that our method still outperforms the state-of-the-art methods.
Bed‐Form Development

Based upon the results of a series of 47 bed‐development experiments, the evolution of bed features subsequent to their initial generation from flat bed conditions is examined. For each such experiment, bed profiles were measured at frequent time intervals as sand‐wave configurations developed under the action of open‐channel water flow. The two sediments used for these experiments were of geometric mean size 0.20 mm and 0.82 mm, respectively. In line with the bed‐form unification model of bed development postulated by Saudkivi and Witte in 1990, the principle of bed‐form propagation speed decreasing with increasing bed‐form height and the mechanisms of bed‐form coalescence and bed‐form throughpassing are all found to be central to bed evolution processes. A quantitative relation between bed‐form speed and bed‐form height is presented. Bed‐development predictions based upon bed‐form unification models can be seen to clearly parallel the present experimental results. Stability analyses based upon potential.
Self-Correcting Geometry in Model-Based Algorithms for Derivative-Free Unconstrained Optimization

Several efficient methods for derivative-free optimization are based on the construction and maintenance of an interpolation model for the objective function. Most of these algorithms use special “geometry-improving” iterations, where the geometry (poisedness) of the underlying interpolation set is made better at the cost of one or more function evaluations. We show that such geometry improvements cannot be completely eliminated if one wishes to ensure global convergence, but we also provide an algorithm where such steps occur only in the final stage of the algorithm, where criticality of a putative stationary point is verified. Global convergence for this method is proved by making use of a self-correction mechanism inherent to the combination of trust regions and interpolation models. This mechanism also throws some light on the surprisingly good numerical results reported by Fasano, Morales, and Nocedal [Optim. Methods Softw. , 24 (2009), pp. 145-154] for a method where no care is ever taken to guarantee poisedness of the interpolation set.
Enterprise resource planning systems, strategic enterprise management systems and management accounting: A Danish study

Purpose – The purpose of this paper is to contribute to the body of knowledge about to what extent integrated information systems, such as ERP and SEM systems, affect the ability to solve different management accounting tasks.Design/methodology/approach – The relationship between IIS and management accounting practices was investigated quantitatively. A total of 349 responses were collected using a survey, and the data were analysed using linear regression models.Findings – Analyses indicate that ERP systems support the data collection and the organisational breadth of management accounting better than SEM systems. SEM systems, on the other hand, seem to be better at supporting reporting and analysis. In addition, modern management accounting techniques involving the use of non‐financial data are better supported by an SEM system. This indicates that different management accounting tasks are supported by different parts of the IIS.Research limitations/implications – The study applies the methods of quanti.
Few-Shot Continual Learning for Conditional Generative Adversarial Networks

In few-shot continual learning for generative models, a target mode must be learned with limited samples without adversely affecting the previously learned modes. In this paper, we propose a new continual learning approach for conditional generative adversarial networks (cGAN) based on a new mode-afﬁnity measure for generative modeling. Our measure is entirely based on the cGAN’s discriminator and can identify the existing modes that are most similar to the target. Subsequently, we expand the continual learning model by including the target mode using a weighted label derived from those of the closest modes. To prevent catastrophic forgetting, we ﬁrst generate labeled data samples using the cGAN’s generator, and then train the cGAN model for the target mode while memory replaying with the generated data. Our experimental results demonstrate the efﬁcacy of our approach in improving the generation performance over the baselines and the state-of-the-art approaches for various standard datasets while utilizing fewer training samples.