Miracle-dz/UniFAR-FTUs huggingface dataset:
Mixed Inductive-Coinductive Reasoning Types, Programs and Logic

Induction and coinduction are two complementary techniques used in mathematics and computer science. These techniques occur together, for example, in control systems: On the one hand, control systems are expected to run until turned off and to always react to their environment. This is what we call coinductive computations. On the other hand, they have to make internal computations. Restricting these computations to terminating, that is inductive, computations ensures that the systems continue to react to their environment. We develop in this thesis techniques for programming inductive-coinductive systems, and for describing their properties and proving these properties. The focus is on developing formal languages, in which proofsare written by humans and can be verified by a computer. This ensures the correctness of those proofs and thereby of the programmed systems. Due to their generality, the developed languages are also applicable to the formalisation of mathematics.
Query-Based Named Entity Recognition

In this paper, we propose a new strategy for the task of named entity recognition (NER). We cast the task as a query-based machine reading comprehension task: e.g., the task of extracting entities with PER is formalized as answering the question of "which person is mentioned in the text ? ". Such a strategy comes with the advantage that it solves the long-standing issue of handling overlapping or nested entities (the same token that participates in more than one entity categories) with sequence-labeling techniques for NER. Additionally, since the query encodes informative prior knowledge, this strategy facilitates the process of entity extraction, leading to better performances. We experiment the proposed model on five widely used NER datasets on English and Chinese, including MSRA, Resume, OntoNotes, ACE04 and ACE05. The proposed model sets new SOTA results on all of these datasets.
Relation Distillation Networks for Video Object Detection

It has been well recognized that modeling object-to-object relations would be helpful for object detection. Nevertheless, the problem is not trivial especially when exploring the interactions between objects to boost video object detectors. The difficulty originates from the aspect that reliable object relations in a video should depend on not only the objects in the present frame but also all the supportive objects extracted over a long range span of the video. In this paper, we introduce a new design to capture the interactions across the objects in spatio-temporal context. Specifically, we present Relation Distillation Networks (RDN) --- a new architecture that novelly aggregates and propagates object relation to augment object features for detection. Technically, object proposals are first generated via Region Proposal Networks (RPN). RDN then, on one hand, models object relation via multi-stage reasoning, and on the other, progressively distills relation through refining supportive object proposals with high objectness scores in a cascaded manner. The learnt relation verifies the efficacy on both improving object detection in each frame and box linking across frames. Extensive experiments are conducted on ImageNet VID dataset, and superior results are reported when comparing to state-of-the-art methods. More remarkably, our RDN achieves 81.8% and 83.2% mAP with ResNet-101 and ResNeXt-101, respectively. When further equipped with linking and rescoring, we obtain to-date the best reported mAP of 83.8% and 84.7%.
City-Scale Road Extraction from Satellite Imagery v2: Road Speeds and Travel Times

Automated road network extraction from remote sensing imagery remains a significant challenge despite its importance in a broad array of applications. To this end, we explore road network extraction at scale with inference of semantic features of the graph, identifying speed limits and route travel times for each roadway. We call this approach City-Scale Road Extraction from Satellite Imagery v2 (CRESIv2), Including estimates for travel time permits true optimal routing (rather than just the shortest geographic distance), which is not possible with existing remote sensing imagery based methods. We evaluate our method using two sources of labels (OpenStreetMap, and those from the SpaceNet dataset), and find that models both trained and tested on SpaceNet labels outperform OpenStreetMap labels by ≥ 60%. We quantify the performance of our algorithm with the Average Path Length Similarity (APLS) and map topology (TOPO) graph-theoretic metrics over a diverse test area covering four cities in the SpaceNet dataset. For a traditional edge weight of geometric distance, we find an aggregate of 5% improvement over existing methods for SpaceNet data. We also test our algorithm on Google satellite imagery with OpenStreetMap labels, and find a 23% improvement over previous work. Metric scores decrease by only 4% on large graphs when using travel time rather than geometric distance for edge weights, indicating that optimizing routing for travel time is feasible with this approach.
Multi-view Characterization of Stories from Narratives and Reviews using Multi-label Ranking

This paper considers the problem of characterizing stories by inferring attributes like theme and genre using the written narrative and user reviews. We experiment with a multi-label dataset of narratives representing the story of movies and a tagset representing various attributes of stories. To identify the story attributes, we propose a hierarchical representation of narratives that improves over the traditional feature-based machine learning methods as well as sequential representation approaches. Finally, we demonstrate a multi-view method for discovering story attributes from user opinions in reviews that are complementary to the gold standard data set.
The meager validation of live programming

A key argument in favor of live programming languages and environments is that a better programming experience is achieved through maximizing feedback to the programmer. Intuitively, this is a compelling argument, but looking at validations of live programming languages and environments, the results are not encouraging at all. This essay concerns the question whether it is possible to validate that live programming is faster, or more correct, or that programmers have a better development experience. I provide a critical review of validations of live programming and talk about my own experience with such a user study. The goal of this text is to contrast the intuition with the scientific validation, such that we, as a community, can ask ourselves the question of whether we can validate the arguments in favor of live programming, and if so, how.
Integration of Blockchain and Cloud of Things: Architecture, Applications and Challenges

The blockchain technology is taking the world by storm. Blockchain with its decentralized, transparent and secure nature has emerged as a disruptive technology for the next generation of numerous industrial applications. One of them is Cloud of Things enabled by the combination of cloud computing and Internet of Things. In this context, blockchain provides innovative solutions to address challenges in Cloud of Things in terms of decentralization, data privacy and network security, while Cloud of Things offer elasticity and scalability functionalities to improve the efficiency of blockchain operations. Therefore, a novel paradigm of blockchain and Cloud of Things integration, called BCoT, has been widely regarded as a promising enabler for a wide range of application scenarios. In this article, we present a state-of-the-art review on the BCoT integration to provide general readers with an overview of the BCoT in various aspects, including background knowledge, motivation, and integrated architecture. Particularly, we also provide an in-depth survey of BCoT applications in different use-case domains such as smart healthcare, smart city, smart transportation and smart industry. Then, we review the recent BCoT developments with the emerging blockchain and cloud platforms, services, and research projects. Finally, some important research challenges and future directions are highlighted to spur further research in this promising area.
Challenges of transarterial therapy for hepatocellular carcinoma in patients with chronic kidney disease

Abstract Poor outcomes of hepatocellular carcinomas (HCC) in chronic kidney disease (CKD) patients are well described. Transarterial therapy is the standard treatment for HCC, following which regular contrast-enhanced imaging for residual disease is recommended. CKD is considered a relative contraindication for transarterial therapy owing to renal failure. This retrospective study investigated the outcomes of transarterial therapy in HCC patients with CKD. In total, 132 HCC patients who received transarterial therapy were enrolled, of whom 36 had CKD. Most CKD patients were elderly, with mean age of diagnosis of 69.7 ± 11.4 years. Hypertension (odds ratio [OR]; 5.06; 95% confidence interval [Cl]; 1.83–13.94), hepatitis C virus carrier rate (OR; 4.12, 95% CI; 1.13–14.99) and diabetes (OR; 3.62, 95% CI; 1.22–10.72) were significant predictors for CKD in HCC patients. Post therapy, the estimated glomerular filtration rate significantly decreased 13.7% from baseline in the CKD patients (P = .03). There were more post-therapy complications than in the non-CKD group, e.g. acute renal failure and sepsis (P < .01 vs P < .01). Overall survival in the CKD group was significantly poor (10.9 ± 8.5 vs 23.5 ± 16.3 months, P < .01). The lower survival of CKD patients was unrelated to treatment modality or less contrast-enhanced imaging follow-up. Further research on patient care and factors leading to poor outcomes for CKD is needed.
Stochastic dynamical modeling of turbulent flows

Advanced measurement techniques and high-performance computing have made large data sets available for a range of turbulent flows in engineering applications. Drawing on this abundance of data, dynamical models that reproduce structural and statistical features of turbulent flows enable effective model-based flow control strategies. This review describes a framework for completing second-order statistics of turbulent flows using models based on the Navier–Stokes equations linearized around the turbulent mean velocity. Dynamical couplings between states of the linearized model dictate structural constraints on the statistics of flow fluctuations. Colored-in-time stochastic forcing that drives the linearized model is then sought to account for and reconcile dynamics with available data (that is, partially known statistics). The number of dynamical degrees of freedom that are directly affected by stochastic excitation is minimized as a measure of model parsimony. The spectral content of the resulting colored-in-time stochastic contribution can alternatively arise from a low-rank structural perturbation of the linearized dynamical generator, pointing to suitable dynamical corrections that may account for the absence of the nonlinear interactions in the linearized model.
GUI testing in production: challenges and opportunities

Automatic system testing of commercial software applications is extremely challenging and requires facing many issues, including the integration of test generators with the software process of the organization that is in charge of verification and validation activities. In this paper, we discuss the challenges that we faced in the early stages of a technology transfer project aimed to introduce a test generator in a software house that develops Web-based Enterprise Resource Planning (ERP) solutions, and the insights that we gained.
Levels of Furaneol in Msalais Wines: A Comprehensive Overview of Multiple Stages and Pathways of Its Formation during Msalais Winemaking

4-Hydroxy-2,5-dimethyl-3(2H)-furanone (furaneol) is present in food. It has a caramel-like flavor, which affects the quality of food, and is formed via multiple pathways. Msalais is a traditional wine fermented from boiled local grape juice in Xinjiang (China). It has a strong caramel odor, which suggests high furaneol content. Furaneol formation during Msalais-making had not been investigated to date. Here, high-performance liquid chromatography and different fermentation models of Msalais-making were used to investigate the furaneol content and formation during Msalais-making. The furaneol content of Msalais is high, between 27.59 ± 0.493 mg/L and 117.6 ± 0.235 mg/L. It is formed throughout the entire Msalais-making process. The formation pathways include the Maillard reaction and chemical hydrolysis of bound furaneol during grape juice concentration; enzymatic release and/or chemical acidic hydrolysis of furaneol glucosides, and biosynthesis from Maillard products and d-fructose-1,6-diphosphate during fermentation; chemical transformation of Maillard products at room temperature (16–25 °C) and hydrolysis of furaneol glucosides during storage. Importantly, furaneol is formed by an efficient biotransformation of Maillard products. These findings suggest that furaneol content can be used as an important indicator of wine quality, and could be controlled by controlling the grape quality, grape juice concentration, fermentation, and wine storage.
End-to-End Measure for Text Recognition

Measuring the performance of text recognition and text line detection engines is an important step to objectively compare systems and their configuration. There exist well-established measures for both tasks separately. However, there is no sophisticated evaluation scheme to measure the quality of a combined text line detection and text recognition system. The F-measure on word level is a well-known methodology, which is sometimes used in this context. Nevertheless, it does not take into account the alignment of hypothesis and ground truth text and can lead to deceptive results. Since users of automatic information retrieval pipelines in the context of text recognition are mainly interested in the end-to-end performance of a given system, there is a strong need for such a measure. Hence, we present a measure to evaluate the quality of an end-to-end text recognition system. The basis for this measure is the well established and widely used character error rate, which is limited – in its original form – to aligned hypothesis and ground truth texts. The proposed measure is flexible in a way that it can be configured to penalize different reading orders between the hypothesis and ground truth and can take into account the geometric position of the text lines. Additionally, it can ignore over-and under-segmentation of text lines. With these parameters it is possible to get a measure fitting best to its own needs.
Lateralization of facial emotion processing and facial mimicry

ABSTRACT The two halves of the brain are believed to play different roles in emotional processing. In studies involving chimeric faces, emotional expressions in the left visual field are more strongly perceived as emotional than those in the right visual field. Notably, the role of facial mimicry has not been studied in relation to hemispheric lateralization. In the current study, which used a novel stimulus set of chimeric faces, we proposed and found that emotional intensity judgments replicate the left visual field bias for facial expressions of emotions. While a general facial mimicry effect to the chimeric faces occurred for the corrugator muscle, these mimicry effects were not related to the visual field bias. The results suggest that encoding the emotionality of another person’s facial expression might occur independent from the mere mimicry of the facial expression itself.
The Seminal Role of Edge-Native Applications

We introduce the concept of "edge-native applications" that fully exploit the potential of edge computing and have a deeply symbiotic relationship with it. Such an application is custom-designed to take advantage of one or more of the unique attributes of edge computing such as (a) bandwidth scalability, (b) low-latency offload, (c) privacy-preserving denaturing, and (d) WAN-failure resiliency. The application may also contribute to scalability through adaptation to reduce offered load. We contrast edge-native applications with shallower uses of edge computing in a taxonomy that spans "edge-enhanced, device-native applications", "edge-accelerated, cloud-native applications", and "device-only applications". We close with a case study that illustrates these concepts in the context of cognitive assistance for automotive safety.
Risk‐adapted stereotactic body radiation therapy for central and ultra‐central early‐stage inoperable non‐small cell lung cancer

To determine the therapeutic efficacy and safety of risk‐adapted stereotactic body radiation therapy (SBRT) schedules for patients with early‐stage central and ultra‐central inoperable non‐small cell lung cancer. From 2006 to 2015, 80 inoperable T1‐2N0M0 NSCLC patients were treated with two median dose levels: 60 Gy in six fractions (range, 48‐60 Gy in 4‐8 fractions) prescribed to the 74% isodose line (range, 58%‐79%) for central lesions (ie within 2 cm of, but not abutting, the proximal bronchial tree; n = 43), and 56 Gy in seven fractions (range, 48‐60 Gy in 5‐10 fractions) prescribed to the 74% isodose line (range, 60%‐80%) for ultra‐central lesions (ie abutting the proximal bronchial tree; n = 37) on consecutive days. Primary endpoint was overall survival (OS); secondary endpoints included progression‐free survival (PFS), tumor local control rate (LC), and toxicity. Median OS and PFS were 64.47 and 32.10 months (respectively) for ultra‐central patients, and not reached for central patients. Median time to local failure, regional failure, and any distant failures for central versus ultra‐central lesions were: 27.37 versus 26.07 months, 20.90 versus 12.53 months, and 20.85 versus 15.53 months, respectively, all P < . 05. Multivariate analyses showed that tumor categorization (ultra‐central) and planning target volume ≥52.76 mL were poor prognostic factors of OS, PFS, and LC, respectively (all P < .05). There was one grade 5 toxicity; all other toxicities were grade 1‐2. Our results showed that ultra‐central tumors have a poor OS, PFS, and LC compared with central patients because of the use of risk‐adapted SBRT schedules that allow for equal and favorable toxicity profiles.
Patient safety incidents in advance care planning for serious illness: a mixed-methods analysis

Objectives Advance care planning (ACP) is essential for patient-centred care in the last phase of life. There is little evidence available on the safety of ACP. This study characterises and explores patient safety incidents arising from ACP processes in the last phase of life. Methods The National Reporting and Learning System collates patient safety incident reports across England and Wales. We performed a keyword search and manual review to identify relevant reports, April 2005–December 2015. Mixed-methods, combining structured data coding, exploratory and thematic analyses were undertaken to describe incidents, underlying causes and outcomes, and identify areas for improvement. Results We identified 70 reports in which ACP caused a patient safety incident across three error categories: (1) ACP not completed despite being appropriate (23%, n=16). (2) ACP completed but not accessible or miscommunicated between professionals (40%, n=28). (3) ACP completed and accessible but not followed (37%, n=26). Themes included staff lacking the knowledge, confidence, competence or belief in trustworthiness of prior documentation to create or enact ACP. Adverse outcomes included cardiopulmonary resuscitation attempts contrary to ACP, other inappropriate treatment and/or transfer or admission. Conclusion This national analysis identifies priority concerns and questions whether it is possible to develop strong system interventions to ensure safety and quality in ACP without significant improvement in human-dependent issues in social programmes such as ACP. Human-dependent issues (ie, varying patient, carer and professional understanding, and confidence in enacting prior ACP when required) should be explored in local contexts alongside systems development for ACP documentation.
Enhancing Neural Sequence Labeling with Position-Aware Self-Attention

Sequence labeling is a fundamental task in natural language processing and has been widely studied. Recently, RNN-based sequence labeling models have increasingly gained attentions. Despite superior performance achieved by learning the long short-term (i.e., successive) dependencies, the way of sequentially processing inputs might limit the ability to capture the non-continuous relations over tokens within a sentence. To tackle the problem, we focus on how to effectively model successive and discrete dependencies of each token for enhancing the sequence labeling performance. Specifically, we propose an innovative and well-designed attention-based model (called position-aware self-attention, i.e., PSA) within a neural network architecture, to explore the positional information of an input sequence for capturing the latent relations among tokens. Extensive experiments on three classical tasks in sequence labeling domain, i.e., part-of-speech (POS) tagging, named entity recognition (NER) and phrase chunking, demonstrate our proposed model outperforms the state-of-the-arts without any external knowledge, in terms of various metrics.
A copula-based approach to model serial dependence in financial time series

This thesis is devoted to the development and application of a copula-based approach to model serial dependence in univariate financial time series. We show that the stationarity and the order of a Markov process can be easily determined by its SD-vine copula, which is a particular regular vine copula, and that this copula has the unique property that only p bivariate (conditional) copulas are required to represent every stationary p-th order Markov process. To tackle the curse of dimensions in practical applications, we propose to model a stationary p-th order Markov process by a simplified (p+1)-dimensional SD-vine copula. The resulting finite-order Markov process can be considered as a natural generalization of an autoregressive Gaussian process. Contrary to established methods, where some features of the transition distribution are modeled, the transition distribution of the Markov process is derived from its SD-vine copula and its marginal distribution, giving rise to flexible transition distributions that can not be expressed by location and scale models. We introduce methods that allow for a parsimonious representation of an SD-vine copula-based stationary Markov process with long memory and provide a detailed analysis of its dependence properties. An application to time series of price durations demonstrates a strong superiority of the copula-based approach to the popular class of ACD models. In order to model financial returns, an understanding of their stylized facts in terms of copulas is required. For this reason, another substantial contribution of this thesis is the development of a theory of copulas that can be used to model volatility clustering. In this regard, we introduce two important dependence properties of a bivariate copula, one of which is sufficient for a positive correlation between squared or absolute symmetric random variables and an increasing conditional variance in the absolute value of the conditioning variable. Moreover, several construction methods of copulas with the desired dependence properties are presented and compared. An application of the copula-based time series model to the returns of three major stock indices and one currency exchange rate documents the competitiveness of our approach with established GARCH models. To the best of our knowledge, this thesis provides the first evidence for the practical usefulness of a copula-based approach to model time series of price durations and financial returns.