Miracle-dz/UniFAR-FTUs huggingface dataset:
The Category-Theoretic Solution of Recursive Domain Equations

Recursive specifications of domains plays a crucial role in denotational semantics as developed by Scott and Strachey and their followers. The purpose of the present paper is to set up a categorical framework in which the known techniques for solving these equations find a natural place. The idea is to follow the well-known analogy between partial orders and categories, generalizing from least fixed-points of continuous functions over cpos to initial ones of continuous functors over $\omega $-categories. To apply these general ideas we introduce Wand’s ${\bf O}$-categories where the morphism-sets have a partial order structure and which include almost all the categories occurring in semantics. The idea is to find solutions in a derived category of embeddings and we give order-theoretic conditions which are easy to verify and which imply the needed categorical ones. The main tool is a very general form of the limit-colimit coincidence remarked by Scott. In the concluding section we outline how compatibilit.
Towards a Distributed Scalable Data Service for the Grid

ADHOC (Adaptive Distributed Herd of Object Caches) is a Grid-enabled, fast, scalable object repository providing programmers with a general storage module. We present three different software tools based on ADHOC: A parallel cache for Apache, a DSM, and a main-memory parallel file system. We also show that these tools exhibit a considerable performance and speedup both in absolute figures and w.r. t. other software tools exploiting the same features.
Fire prevention treatments to mitigate wildfire risk

ii Acknowledgements . v Certification . vi Statement of Candidate Contribution. vii List of Abbreviations . viii List of Figures . xii List of Tables . xiv Introduction . 1 1.1 Wildfire risk . 1 1.2 Ignition management . 4 1.3 Fuel Management. 7 1.4 Land use planning and building design . 9 1.5 Suppression . 10 1.6 Study aims and thesis structure . 12 Spatial patterns of wildfire ignitions in south-eastern Australia. 14 2.1 Abstract . 14 2.2 Introduction . 14 2.3 Methods. 16 2.3. 1 Study Area . 16 2.3. 2 Data compilation . 18 2.3. 3 Analyses . 20 2.4 Results . 23 2.4. 1 Total ignitions . 25 2.4. 2 Accidental ignitions . 28 2.4. 3 Deliberate ignitions . 28 2.4. 4 Lightning ignitions . 29 2.4. 5 Undetermined ignitions . 29 2.5 Discussion . 30 2.6 Conclusion . 32 Some wildfire ignition causes pose more risk of destroying houses than others. 33
Type-Directed Di ng of Structured Data

Abstract The Unix diff utility that compares lines of text is used pervasively by version control systems. Yet certain changes to a program may be di cult to describe accurately in terms of modi cations to individual lines of code. As a result, observing changes at such a xed granularity may lead to unnecessary con icts between di erent edits. This paper presents a generic representation for describing transformations between algebraic data types and a non-deterministic algorithm for computing such representations. These representations can be used to give a more accurate account of modi cations made to algebraic data structures – and the abstract syntax trees of computer programs in particular – as opposed to only considering modi cations between their textual representations. CCS Concepts •Software and its engineering→ Functional languages; Data types and structures; •Applied computing → Version control;
Yannis Stournaras: Climate change - challenges, risks and opportunities

Climate change and global warming are associated with human activities and in particular the use of fossil fuels and carbon dioxide emissions. The current and projected implications for the society and for sustainable development are such that we cannot continue with the «business as usual» scenario – we need to mitigate and drastically reduce emissions by adopting energy efficiency practices, financing "green" energy, promoting energy saving investments, using energy saving techniques, conducting energy management and finally by boosting low carbon circle economy.
FEATURE SELECTION USING REINFORCEMENT LEARNING

With the decreasing cost of data collection, the space of variables or features that can be used to characterize a particular predictor of interest continues to grow exponentially. Therefore, identifying the most characterizing features that minimizes the variance without jeopardizing the bias of our models is critical to successfully training a machine learning model. In addition, identifying such features is critical for interpretability, prediction accuracy and optimal computation cost. While statistical methods such as subset selection, shrinkage, dimensionality reduction have been applied in selecting the best set of features, some other approaches in literature have approached feature selection task as a search problem where each state in the search space is a possible feature subset. In this paper, we solved the feature selection problem using Reinforcement Learning. Formulating the state space as a Markov Decision Process (MDP), we used Temporal Difference (TD) algorithm to select the best subset of features. Each state was evaluated using a robust and low cost classifier algorithm which could handle any non-linearities in the dataset.
Reputation Systems for Fighting Pollution in Peer-to-Peer File Sharing Systems

Content pollution is a de facto problem in several currently popular Peer-to-Peer file sharing systems. Previously proposed strategies to fight content pollution include a peer reputation system named Scrubber as well as the alternative Credence object reputation system. This paper builds on previous work into three directions. First, it evaluates the cost-effectiveness of Scrubber and Credence in reducing pollution dissemination when polluters make use of collusion and Sybil attacks. Second, it modifies the Scrubber system to increase its effectiveness, in particular under collusion. Finally, it proposes a hybrid peer and object reputation system that combines the benefits of both strategies. We performed an extensive evaluation of all three systems, for various configurations and pollution mechanisms, as well as collusion and Sybil attacks. Our results show that the new hybrid system is much more effective and robust to malicious actions than any individual strategy, even in very uncooperative and unreliable peer communities.
Dealing with homoplasy: osteology and phylogenetic relationships of the bizarre neobatrachian frog Baurubatrachus pricei from the Upper Cretaceous of Brazil

The hyperossified frog Baurubatrachus pricei Báez & Peri 1989 from the Maastrichtian Serra da Galga Member of the Marília Formation is described in detail, as preparation of the type and only known specimen revealed significant features, particularly of the pectoral and pelvic girdles. This species is rediagnosed on the basis of the combination of plesiomorphic and derived character states, including two unique traits: cranial roof with round openings that might have contained the tympanic membrane completely circumscribed by ornamented dermal bone, and scapula bearing a conspicuous crest deflected ventrally to form a deep basin on its leading edge. Since its discovery it was suggested that Baurubatrachus might be a relative of the South American ceratophryids, a phylogenetic placement endorsed by recent analyses. In order to test this hypothesis considering all the available information, we conducted several maximum parsimony analyses under different weighting schemes and topological constraints, scoring 143 characters for 71 extant and extinct anuran taxa. Our taxonomic sampling included species with well-ossified dermatocrania as well as less ossified members of main neobatrachian clades to explore the impact of hyperossification, which frequently drives groupings based on homoplastic features. We also assessed the phylogenetic signal provided by cranial and postcranial partitions. Although we recovered a monophyletic Ceratophryidae repeatedly, Baurubatrachus was not related with this nobleobatrachian group but associated with the calyptocephalellid australobatrachians, although with weak support. Other possible phylogenetic placements are also discussed, as well as microhabitat and habits, taking into account both anatomical and geological data.
Recognizing emotions in spoken dialogue with hierarchically fused acoustic and lexical features

Automatic emotion recognition is vital for building natural and engaging human-computer interaction systems. Combining information from multiple modalities typically improves emotion recognition performance. In previous work, features from different modalities have generally been fused at the same level with two types of fusion strategies: Feature-Level fusion, which concatenates feature sets before recognition; and Decision-Level fusion, which makes the final decision based on outputs of the unimodal models. However, different features may describe data at different time scales or have different levels of abstraction. Cognitive Science research also indicates that when perceiving emotions, humans use information from different modalities at different cognitive levels and time steps. Therefore, we propose a Hierarchical fusion strategy for multimodal emotion recognition, which incorporates global or more abstract features at higher levels of its knowledge-inspired structure. We build multimodal emotion recognition models combining state-of-the-art acoustic and lexical features to study the performance of the proposed Hierarchical fusion. Experiments on two emotion databases of spoken dialogue show that this fusion strategy consistently outperforms both Feature-Level and Decision-Level fusion. The multimodal emotion recognition models using the Hierarchical fusion strategy achieved state-of-the-art performance on recognizing emotions in both spontaneous and acted dialogue.
Brain–Computer Interfaces Handbook Technological and Theoretical Advances

In the past decade, big progresses have been made in BCI studies. However, there have been a lot of challenges, including a low information transfer rate, multidimension/function control, man–machine adaptability, long-term robustness, and stability. In this chapter, we review the recent progress in hybrid brain–computer interface (BCIs; also called multimodal BCIs), which may provide potential solutions for addressing these challenges. In particular, four main classes of hybrid BCIs are introduced, including hybrid BCIs based on multibrain patterns, multisensory hybrid BCIs, hybrid BCIs based on multiple signals, and hybrid BCIs based on multiple intelligent techniques. We review state-of-the-art hybrid BCI systems by analyzing their general principles, paradigm, experimental results, advantages, and applications. We conclude that hybrid BCI techniques can be utilized to improve the target detection performance of BCIs and to perform multidimensional object control
Domestic determinants of international cooperation : an analysis of the intricate relationship between energy politics and climate change mitigation

9 Resumo . 10 List of Figures and Tables . 11 List of Annexes . 13 List of Acronyms . 14 Introduction . 17 Introdução . 23 Chapter 01: The climate problem and driving forces on mitigation . 30 1.1. Climate change and the trajectory of international climate politics . 30 1.1.1. Discovering anthropogenic climate change: the science . 30 1.1.2. Climate change becomes a global political issue . 35 1.1. 3. The nature of the climate problem . 38 1.1. 4. Evolution of the international regime on climate change . 42 1.2. The interaction between international climate negotiations and domestic politics 46 1.2.1. Domestic politics in International Relations . 47 1.2.2. Domestic explanations of climate change mitigation . 50 1.2.2.1. Interests in climate change mitigation . 51 1.2.2.2. Co-benefits as drivers of climate policy . 53 Chapter 02: Decarbonizing energy systems . 55 2.1. The challenge of deep decarbonization . 55 2.2. Defining energy decarbonization . 58 2.3. Co-benefits of energy decarbonization . 61 2.3.1. Reducing air, water and soil pollution and their impacts . 61 2.3.2. Reducing risks from energy production . 64 2.3.3. Increasing energy access . 66 2.3.4. Creating employment opportunities . 67 2.3.5. Enhancing energy security . 68 2.3.5.1. Evolution of the concept of energy security . 68 2.3.5.2. Energy availability remains a major policy concern . 71 2.4. Energy security, a key driving force of energy decarbonization: the G20 countries72 2.4. 1. Evolution of the carbon intensity of energy supply . 73 2.4. 2. Transformations of the energy matrices: general trends in the G20 . 76 2.4. 3. Similarities and differences among the 19 countries . 80 2.4. 3.1. Coal-dependent economies . 80 2.4. 3.2. The nuclear solution . 82 2.4. 3.3. Hydropower: an old low carbon energy primary source . 85 2.4. 3.4. The emergence of other renewables . 89 2.4. 3.5. Natural gas: the greatest driver of energy decarbonization between 1971 and 2015 . 93 2.4. 4. The complex interaction between energy security and energy decarbonization . 95 Chapter 03: Brazilian energy politics, 1971-2015 . 98
LOW FREQUENCY STRUCTURAL AND ACOUSTIC RESPONSES OF A SUBMARINE HULL

A model to describe the low frequency dynamic and acoustic responses of a submarine hull subject to a harmonic propeller shaft excitation is presented. The submarine is modelled as a fluid-loaded, ring stiffened cylindrical shell with internal bulkheads and end caps. The stiffeners are introduced using a smeared approach. The bulkheads are modelled as circular plates and the end closures as truncated conical shells. The propeller introduces a harmonic axial force that is transmitted to the hull through the shaft and results in excitation of the accordion modes only if the force is symmetrically distributed to the hull. Structural and acoustic responses for the axisymmetric breathing modes are presented in terms of frequency response functions of the axial and radial displacements and directivity patterns for the radiated sound pressure.
Biodiversity Assessment of Mammal and Bird Species from Camera Trap Data in Yanchiwan National Nature Reserve, Gansu Province, China

Abstract: Camera traps serve as an important tool for monitoring species diversity. We used data from camera traps set for capturing snow leopards (Panthera uncia) in the Yanchiwan National Nature Reserve, Gansu Province, China, to assess species richness with respect to mammal and birds species. We also assessed survey efficiency for species detection, and conducted an initial analysis of species interactions. The survey effort of 10, 171 camera workdays yielded 2, 868 suitable animal image events involving 17 mammal and 20 bird species. Among these, the dhole (Cuon alpinus) is considered to be Endangered, the snow leopard and white-lipped deer (Cervus albirostris) Vulnerable, and the Pallas's cat (Feli smanul), mountain weasel (Mustela altaica), Himalayan griffon (Gyps himalayensis) and cinereous vulture (Aegypius monachus) Near Threatened under the IUCN red list. Fourteen species were also listed as key protected wild animals according to China national standards. Both the rarefaction curves and richness estimators suggested our sampling for mammal and pheasant species is sufficient, while more survey efforts are still needed to detect other bird species. With a focus on the dominant species Eurasian lynx (Lynx lynx), occupancy models were used to estimate site use and detection probability for selected species, and to investigate predator-prey relationships between lynx on the one hand and woolly hare (Lepus oiostolus), pika (Ochotona spp.) and Tibetan partridge (Perdix hodgsoniae) on the other. We give recommendations on how to increase efficiency in camera-based species inventory and biodiversity monitoring.
Seasonal Bias in Soil Carbonate Formation and Its Implications for Interpreting High‐Resolution Paleoarchives: Evidence From Southern Utah

Pedogenic carbonate is commonly used as a paleoarchive, but its interpretation is limited by our understanding of its formation conditions. We investigated laminated soil carbonate rinds as a high‐resolution paleoarchive in Torrey, Utah, USA, by characterizing and modeling their formation conditions. We compared late Holocene (<5 ka) soil carbonate conventional (C and O) and “clumped” isotopes to modern soil environment and isotope measurements: soil CO2 partial pressure, soil temperature, soil moisture, δ13C‐soil CO2, δ18O precipitation, and δ18O‐soil water. Data unambiguously identified a strong summer seasonality bias, but modeling suggested soil carbonate formed several times throughout the year during infiltration events causing dissolution‐formation reactions. This apparent discrepancy resulted from preferential preservation of calcite formed from the largest annual infiltration events (summer) overprinting previously formed calcite. Soil carbonate therefore formed predominantly due to changes in soil water content. As soil CO2 was at its annual maximum during soil carbonate formation, assuming uniformly low soil CO2 formation conditions for soil carbonate in estimating paleoatmospheric CO2 is likely not viable. Additionally, we showed modern summer δ13C‐soil CO2 and soil CO2 measurements could not produce a modeled δ13C‐soil carbonate consistent with late Holocene observations. We suggest using multiple lines of evidence to identify nonanalogous modern conditions. Finally, a nearly linear radiocarbon age model from a laminated rind showed that rinds can be used as a high‐resolution paleoarchive if samples are from a single depth and the timing and conditions of soil carbonate formation can be constrained through time.
M INI - BATCH k - MEANS TERMINATES WITHIN O ( d/ϵ ) IT -ERATIONS

We answer the question: "Does local progress (on batches) imply global progress (on the entire dataset) for mini-batch k-means? ". Specifically, we consider minibatch k-means which terminates only when the improvement in the quality of the clustering on the sampled batch is below some threshold. Although at first glance it appears that this algorithm might execute forever, we answer the above question in the affirmative and show that if the batch is of size Ω̃((d/ε)), it must terminate within O(d/ε) iterations with high probability, where d is the dimension of the input, and ε is a threshold parameter for termination. This is true regardless of how the centers are initialized. Finally, we show the applicability of our results to the mini-batch k-means algorithm implemented in the scikit-learn (sklearn) python library.
Adaptive Conformal Semi-Supervised Vector Quantization for Dissimilarity Data

Semi-Supervised Learning Proximity Data Dissimilarity Data Conformal Prediction Generalized Learning Vector Quantization Most existing semi-supervised learning (SSL) algorithms f ocus on vectorial data given in Euclidean space or representations by means of valid kernel matrices. A lot of real life data, especially in bioinformatic s domain, are non– metric given in the form of (dis-)similarities. Those data a re not widely addressed in the SSL domain. In this paper we extend a prototype -bas d classifier for dissimilarity data to semi-supervised tasks employing conformal prediction providing point-wise confidence measures about the classifi cation. By means of the confidence values a so-called ’secure region’ of unlab eled data can be identified and further used to improve the trained model base d on labeled data while adapting the model complexity to ’cover’ a so-called ’ insecure region’ of labeled data. This way an intuitive semi-supervised multiclass classification scheme results which can (i) directly deal with arbitrary sy mmetric dissimilarity matrices, (ii) which offers intuitive classification by means of sparse prototypical class representatives, and (iii) which adapts mo del complexity supported by a confidence measure. In the experiments we show its effectiveness on simulated dissimilarity data and compare it with state-o f-the-art methods on benchmarks from SSL domain and real-life non-vectorial dat a sets. c © 2014 Elsevier Ltd. All rights reserved.
IMTKU Textual Entailment System for Recognizing Inference in Text at NTCIR-11 RITE-VAL

paper, we describe the IMTKU (Information Management at TamKang University) textual entailment system for recognizing inference in text at NTCIR-9 RITE (Recognizing Inference in Text). We proposed a textual entailment system using a hybrid approach that integrate knowledge based and machine learning techniques for recognizing inference in text at NTCIR-9 RITE task. We submitted 3 official runs for both BC and MC subtask. In NTCIR-9 RITE task, IMTKU team achieved 0.522 in the CT-MC subtask and 0.556 in the CT-BC subtask.
AIS Electronic

Consumers are increasingly sharing product interests and experiences with others on websites. For example, consumers can “tag” products using their own words and these “product tags” are then aggregated and shared with other online consumers who seek for information. In addition, highly experienced and influential information contributors on websites are often highlighted as “featured users” and serve as direct information sources. This study examines the effects of these two distinct design mechanisms, i.e., product tags and featured users. While the former facilitates feature-based product search and evaluation, the latter facilitates people-based product search and evaluation. We constructed experimental websites using actual data from one of the largest social-network-based product-search websites in China. The results show that information seekers perceive their product search experience as highly diagnostic when product tags are provided. When both product tags and featured users are present, information seekers feel that the product search is serendipitous.
Construction of a Hierarchical Translation Memory

Translation memories are promising devices for automatic translation. Their main weakness, however, is poor coverage on unseen text. In this paper, the use of a hierarchical translation memory, consisting of a cascade of nite state transducers, is proposed. A number of transducers is applied to convert sentence pairs from a bilingual corpus into translation patterns, which are then used as a translation memory. Preliminary results on the German{English Verbmobil corpus are given.
Identi ﬁ cation and characterisation of Facebook user pro ﬁ les considering interaction aspects

The great number of social network users and the expansion of this kind of tool in the last years demand the storage of a great volume of information regarding user behaviour. In this article, we utilise interaction records from Facebook users and metrics from complex networks study, to identify di ﬀ erent user behaviours using clustering techniques. We found three di ﬀ erent user pro ﬁ les regarding interactions performed in the social network: viewer , participant and content producer . Moreover, the groups we found were characterised by the C4.5 decision-tree algorithm. The ’ viewer ’ mainly observes what happens in the network. The ‘ participant ’ interacts more often with the content, getting a higher value of closeness centrality. Therefore, users with a participant pro ﬁ le are responsible, for example, for the faster transmission of information in the virtual environment, a crucial function for the Facebook social network. We noted too that ‘ content producer ’ users had a greater quantity of publications in their pages, leading to a superior degree of input interactions than the other two pro ﬁ les. Finally, we also verify that the pro ﬁ les are not mutually exclusive, that is, the user of a pro ﬁ le can at determined moment perform the behaviour of another pro ﬁ le.