Miracle-dz/UniFAR-FTUs huggingface dataset:
Three-dimensional geoelectric modelling with optimal work/accuracy rate using an adaptive wavelet algorithm

SUMMARY Despite the ever-increasing power of modern computers, realistic modelling of complex 3-D earth models is still a challenging task and requires substantial computing resources. The overwhelming majority of current geophysical modelling approaches includes either finite difference or non-adaptive finite element algorithms and variants thereof. These numerical methods usually require the subsurface to be discretized with a fine mesh to accurately capture the behaviour of the physical fields. However, this may result in excessive memory consumption and computing times. A common feature of most of these algorithms is that the modelled data discretizations are independent of the model complexity, which may be wasteful when there are only minor to moderate spatial variations in the subsurface parameters. Recent developments in the theory of adaptive numerical solvers have the potential to overcome this problem. Here, we consider an adaptive wavelet-based approach that is applicable to a large range of problems, also including nonlinear problems. In comparison with earlier applications of adaptive solvers to geophysical problems we employ here a new adaptive scheme whose core ingredients arose from a rigorous analysis of the overall asymptotically optimal computational complexity, including in particular, an optimal work/accuracy rate. Our adaptive wavelet algorithm offers several attractive features: (i) for a given subsurface model, it allows the forward modelling domain to be discretized with a quasi minimal number of degrees of freedom, (ii) sparsity of the associated system matrices is guaranteed, which makes the algorithm memory efficient and (iii) the modelling accuracy scales linearly with computing time. We have implemented the adaptive wavelet algorithm for solving 3-D geoelectric problems. To test its performance, numerical experiments were conducted with a series of conductivity models exhibiting varying degrees of structural complexity. Results were compared with a non-adaptive finite element algorithm, which incorporates an unstructured mesh to best-fitting subsurface boundaries. Such algorithms represent the current state-of-the-art in geoelectric modelling. An analysis of the numerical accuracy as a function of the number of degrees of freedom revealed that the adaptive wavelet algorithm outperforms the finite element solver for simple and moderately complex models, whereas the results become comparable for models with high spatial variability of electrical conductivities. The linear dependence of the modelling error and the computing time proved to be model-independent. This feature will allow very efficient computations using large-scale models as soon as our experimental code is optimized in terms of its implementation.
Multi-Polarized MIMO Communications: Channel Model, Mutual Information and Array Optimization

This paper considers the use of nr times nt dual-polarized arrays for improving the mutual information of MIMO communications. We first develop an elegant model of the multi-polarized MIMO channel, which allows to account for spatial correlation and depolarization effects. The major advantage of the model lies in its analytical expression, whose parameters have a clear physical meaning. In a second part, we investigate the ergodic mutual information of multi-polarized schemes, and derive a condition upon which multi-polarized transmissions offer a higher capacity than equivalent uni-polarized schemes using the same number of antennas under the same overall array dimensions.
Typed Skipgrams Investigated

Every year, the patent filing rates at the different patent offices around the world increase, and the patent examiners are struggling to catch up. As such, reliably categorizing patents is to aid the examination process of rising economic importance. This paper investigates whether capturing multiword expressions (specifically: institutionalized phrases) is an important factor for improving automated patent pre-classification. To do so we describe a novel text representation based on filtering by combinations of Part-of-Speech tags: typed skipgrams. We then compare the performance of different text representations (unigrams, bigrams, skipgrams, typed skipgrams and unigrams in combination with any of the other) when classifying a subset of the CLEF-IP 2010 corpus. We examine if there is a link between classification accuracy and ability to capture multiword expressions. We furthermore carry out additional experiments and analyses to investigate the influence of specific combinations of Parts-of-Speech on the overall result. We find that typed skipgrams in combination with unigrams perform significantly better (difference in F1 value: 0.7%) than the unigrams+bigrams baseline. We also find that typed skipgrams succeed in capturing multiword expressions and that typed skipgrams consisting of noun-noun and noun-adjective combinations are the most important factor for the overall success. Finally, we conclude that capturing multiword expressions is the crucial mechanism behind the improvement in classification scores. This research provides a potential means to further the state-of-the-art in patent classification when combined with additional optimizations. They also give directions for future research, highlighting typed skipgrams and filtering for multiword expressions as viable paths. Finally, we expect our results to generalize to every kind of text in which multiword expressions play an important role. Examples are scientific abstracts and, more generally, technical texts.
On Conceptual Labeling of a Bag of Words

In natural language processing and information retrieval, the bag of words representation is used to implicitly represent the meaning of the text. Implicit semantics, however, are insufficient in supporting text or natural language based interfaces, which are adopted by an increasing number of applications. Indeed, in applications ranging from automatic ontology construction to question answering, explicit representation of semantics is starting to play a more prominent role. In this paper, we introduce the task of conceptual labeling (CL), which aims at generating a minimum set of conceptual labels that best summarize a bag of words. We draw the labels from a data driven semantic network that contains millions of highly connected concepts. The semantic network provides meaning to the concepts, and in turn, it provides meaning to the bag of words through the conceptual labels we generate. To achieve our goal, we use an information theoretic approach to trade-off the semantic coverage of a bag of words against the minimality of the output labels. Specifically, we use Minimum Description Length (MDL) as the criteria in selecting the best concepts. Our extensive experimental results demonstrate the effectiveness of our approach in representing the explicit semantics of a bag of words.
Identification of a Clostridium perfringens Enterotoxin Region Required for Large Complex Formation and Cytotoxicity by Random Mutagenesis

ABSTRACT Clostridium perfringens enterotoxin (CPE), a single polypeptide of 319 amino acids, has a unique multistep mechanism of action. In the first step, CPE binds to claudin proteins and/or a 50-kDa eukaryotic membrane protein receptor, forming a small (∼90-kDa) complex. This small complex apparently then associates with a 70-kDa eukaryotic membrane protein, resulting in formation of a large complex that induces the onset of membrane permeability alterations. To better define the boundaries of CPE functional regions and to identify specific amino acid residues involved in various steps of CPE action, in this study we subjected the cloned cpe gene to random mutagenesis in XL-1 Red strains of Escherichia coli. Seven CPE random mutants with reduced cytotoxicity for Vero cells were phenotypically characterized for the ability to complete each step in CPE action. Five of these seven recombinant CPE (rCPE) random mutants (G49D, S59L, R116S, R137G, and S167P) exhibited binding characteristics similar to those of rCPE or native CPE, while the Y310C and W226Stop mutants showed reduced binding and no binding, respectively, to brush border membranes. Interestingly, two completely nontoxic mutants (G49D and S59L) were able to bind and form small complex but they did not mediate any detectable large complex formation. Another strongly attenuated mutant, R116S, formed reduced amounts of an anomalously migrating large complex. Collectively, these results provide further support for large complex formation being an essential step in CPE action and also identify the CPE region ranging from residues ∼45 to 116 as important for large complex formation. Finally, we also report that limited removal of extreme N-terminal CPE sequences, which may occur in vivo during disease, stimulates cytotoxic activity by enhancing large complex formation.
Adaptive operator switching and solution space probability structure based genetic algorithm for information retrieval through pattern recognition

A novel adaptive operator set switching based genetic algorithm is proposed for pattern recognition based information retrieval problems. The problem the paper addresses specifically is the issue of large scale pattern recognition during the processes of information retrieval for e.g. in web search engines. The solution proposed is a novel genetic algorithm for the pattern recognition step, which utilizes the concepts of adaptive operator switching and the probability structure of the solution space. These two concepts have not been explored earlier within a single frame work by other researchers in the context of genetic algorithm for pattern recognition. The algorithm utilizes the probability structure of the solution space at every iteration to find better combinations of mutation and crossover operators. The operators are chosen from operator sets based on a mapping between the individual members of the sets and the probability distribution of the solutions. This mapping between the sets and distributions is changed as per the quality of the solutions at every iteration. This algorithm is highly scalable and is easily generalizable to strings, sets, sequences and graphs. This algorithm provides a significant improvement over some presently available optimization based algorithms for pattern recognition based information retrieval. The authors justify and validate the algorithm and its suitability through several theoretical arguments and inferences.
What Can Software Engineers Learn from Artificial Intelligence?

The author provides a detailed analysis of artificial intelligence (AI) tools. He discusses only one representative from each class of tools. Each representative selected is state-of-the art, yet pure enough to permit a description of its salient features and established enough to be in use among several groups beyond its original inventors. Despite this apparent bias, the conclusions drawn about a particular tool apply to all members of its class.
Negotiating Boundaries in the City Migration, Ethnicity, and Gender in Britain

Using in-depth life-story interviews and oral history archives, this book explores the impact of South Asian migration from the 1950s onwards on both the local white, British-born population and the migrants themselves. Taking Leicester as a main case study – identified as a European model of multicultural success – Negotiating Boundaries in the City offers a historically grounded analysis of the human experiences of migration. Joanna Herbert shows how migration created challenges for both existing residents and newcomers – for both male and female migrants – and explores how they perceived and negotiated boundaries within the local contexts of their everyday lives. She explores the personal and collective narratives of individuals who might not otherwise appear in the historical records, highlighting the importance of subjective, everyday experiences. The stories provide valuable insights into the nature of white ethnicity, inter-ethnic relations and the gendered nature of experiences, and offer rich data lacking in existing theoretical accounts. This book provides a radically different story about multicultural Britain and reveals the nuances of modern urban experiences which are lost in prevailing discourses of multiculturalism. Contents
Performance modeling of distributed collaboration services

This paper deals with performance modeling of distributed applications, service compositions and workflow systems. From the functional perspective, the distributed application is modeled as a collaboration involving several roles, and its behavior is defined in terms of a composition from several sub-collaborations using the standard sequencing operators found in UML Activity Diagrams and similar formalisms. From the performance perspective, each collaboration is characterized by a certain number of independent input events and dependent output events, and the performance of the collaboration is defined by the minimum delays that apply for a given output event in respect to each input event on which it depends. We use a partial order to model these delays. The paper explains how these minimum delays can be measured through testing. It also provides general formulas by which the performance of a composed collaboration can be calculated from the performance of its constituent subcollaborations and the control structure which determines the order of execution of these sub-collaborations. Proofs of correctness for these formulas are given and a simple example is discussed throughout the paper.
Climbing the Hills of Compiled Credal Networks

This paper introduces a new approximate inference algorithm for credal networks. The algorithm consists of two major steps. It starts by representing the credal network as a compiled logical theory. The resulting graphical structure is the basis on which the subsequent steepest-ascent hill-climbing algorithm operates. The output of the algorithm is an inner approximation of the exact lower and upper posterior probabilities.
Enhancements to the Training Process of Classifier-Based Speech Translator via Topic Modeling

Classiﬁcation of sentences based on their meaning (or concept) has been used as component in speech translation and spoken language understanding systems. Preparing training data for this type of classiﬁers is often a tedious task. In our previous work, we presented a method of clustering sentences as a step toward automated annotation of concepts. To measure the distance be-tween two sentences, that method relied on the local lexical dependencies in their translations. In this work, we apply Topic Modeling to enhance the previously proposed distance metric so that it includes information from semantic associations among the words. Our experiments on the DARPA USC Transonics and BBN Transtac data sets show the advantage of incorporating this information as performance improvements in a set of clustering tasks.
A switched Model Predictive Control formulation for Flying Capacitor Converters

This work presents a switched Model Predictive Control (MPC) formulation for Flying Capacitor Converters (FCC's). The key idea of our proposal is to use standard Finite Control Set MPC (FCS-MPC) to lead the system to a region near to the desired reference, e.g. during reference changes. Once the system state is close to the reference, the optimal solution of Explicit MPC will be used to finally achieve the desired reference. The actuation of this local controller will be modulated using a PWM stage. As an illustrative example we apply this proposal to control an FCC. Thus, the output current control as well as the balancing of the floating voltages will be achieved with a zero steady state error and a constant switching frequency.
Towards Valid X ML Applications

Many World Wide Web applications generate HTML or X ML documents dynamically. Current tools, however, like languages using DOM or JAVA Server Pages do not allow to check the validity of the generat ed documents statically. Instead, validity has to be “checked ” dynamically by appropriate test runs. This paper addresses this problem byintroducing a new distinct class for each element type of a D TD. Each class extends the Element-class of the Document Object Model (DOM ). The resulting object model is called Validating DOM (V-DOM ). Parametric XML (P-XML ) is an extension of XML allowing to insert values of the newly defined classes in places where the corresponding element types are allowed ac cording to the underlying DTD. Like V-D OM , P-XML guarantees the validity of all XML documents generated by using P-XML . V-DOM and P-XML are illustrated by several examples. Keywords—XML; DOM; Validating DOM; Parametric XML I. I NTRODUCTION CURRENT applications of the World Wide Web (WWW) primarily base on the Hypertext Markup Language (HTML ) [1], [2] and the Extensible Markup Language (X ML ) [3]. One of the main tasks of a WWW server and of a WWW client is to generate H TML pages or XML structures dynamically. Experiences in our media archive project [4], [5] show that current tools like J AVA Server Pages [6], [7] or JavaScript [8], [9] for generating HTML pages or XML structures are not adequate because the syntactic correctness, i. e. the validity of generated HTML pages and XML structures is not guaranteed statically by the program. Instead, the validity must be “proven” dynamically by appropriate test runs. The following J AVA Server Page illustrates this problem <HTML> <HEAD> <TITLE> A Simple Server Page </TITLE> </HEAD> <BODY> <% if(timeOfDay() == "AM") { %> <UL> <LI> Good Morning </LI> </UL> <% } else { %> <UL> <LI> Good Afternoon </LI> </UL> <% } %>
Tree mortality from a short-duration freezing event and global-change-type drought in a Southwestern piñon-juniper woodland, USA

This study documents tree mortality in Big Bend National Park in Texas in response to the most acute one-year drought on record, which occurred following a five-day winter freeze. I estimated changes in forest stand structure and species composition due to freezing and drought in the Chisos Mountains of Big Bend National Park using permanent monitoring plot data. The drought killed over half (63%) of the sampled trees over the entire elevation gradient. Significant mortality occurred in trees up to 20 cm diameter (P < 0.05). Pinus cembroides Zucc. experienced the highest seedling and tree mortality (P < 0.0001) (55% of piñon pines died), and over five times as many standing dead pines were observed in 2012 than in 2009. Juniperus deppeana vonSteudal and Quercus emoryi Leibmann also experienced significant declines in tree density (P < 0.02) (30.9% and 20.7%, respectively). Subsequent droughts under climate change will likely cause even greater damage to trees that survived this record drought, especially if such events follow freezes. The results from this study highlight the vulnerability of trees in the Southwest to climatic change and that future shifts in forest structure can have large-scale community consequences.
Speech recognition and utterance verification based on a generalized confidence score

In this paper, we introduce a generalized confidence score (GCS) function that enables a framework to integrate different confidence scores in speech recognition and utterance verification. A modified decoder based on the GCS is then proposed. The GCS is defined as a combination of various confidence scores obtained by exponential weighting from various confidence information sources, such as likelihood, likelihood ratio, duration, language model probabilities, etc. We also propose the use of a confidence preprocessor to transform raw scores into manageable terms for easy integration. We consider two kinds of hybrid decoders, an ordinary hybrid decoder and an extended hybrid decoder, as implementation examples based on the generalized confidence score. The ordinary hybrid decoder uses a frame-level likelihood ratio in addition to a frame-level likelihood, while a conventional decoder uses only the frame likelihood or likelihood ratio. The extended hybrid decoder uses not only the frame-level likelihood but also multilevel information such as frame-level, phone-level, and word-level confidence scores based on the likelihood ratios. Our experimental evaluation shows that the proposed hybrid decoders give better results than those obtained by the conventional decoders, especially in dealing with ill-formed utterances that contain out-of-vocabulary words and phrases.
Cluster head selection using RF signal strength

The LEACH algorithm for selecting cluster heads is a probabilistic method which produces clusters with a large variation of link distances and uneven energy consumption during the data transmission phase. To address this issue, a RF signal strength algorithm based on link quality is presented. Using a competitive distributed algorithm, nodes attempt to reduce the overall energy required for transmission in addition to forming favourable clusters based on Received Signal Strength Indication (RSSI) density and quality. Cluster heads form in areas of high node density leading to a significant reduction in transmission link length, a reduced variance in link length distribution and greater opportunity for energy savings through data aggregation. Simulations show that cluster heads selected by this algorithm form clusters with a lower average link length and have less link distance variability. This produces a lower and more evenly distributed energy cost per node in the network.
Growing decision trees on support-less association rules

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