Miracle-dz/UniFAR-FTUs huggingface dataset:
OX40 controls effector CD4+ T-cell expansion, not follicular T helper cell generation in acute Listeria infection

To investigate the importance of OX40 signals for physiological CD4+ T‐cell responses, an endogenous antigen‐specific population of CD4+ T cells that recognise the 2W1S peptide was assessed and temporal control of OX40 signals was achieved using blocking or agonistic antibodies (Abs) in vivo. Following infection with Listeria monocytogenes expressing 2W1S peptide, OX40 was briefly expressed by the responding 2W1S‐specific CD4+ T cells, but only on a subset that co‐expressed effector cell markers. This population was specifically expanded by Ab‐ligation of OX40 during priming, which also caused skewing of the memory response towards effector memory cells. Strikingly, this greatly enhanced effector response was accompanied by the loss of T follicular helper (TFH) cells and germinal centres. Mice deficient in OX40 and CD30 showed normal generation of TFH cells but impaired numbers of 2W1S‐specific effector cells. OX40 was not expressed by 2W1S‐specific memory cells, although it was rapidly up‐regulated upon challenge whereupon Ab‐ligation of OX40 specifically affected the effector subset. In summary, these data indicate that for CD4+ T cells, OX40 signals are important for generation of effector T cells rather than TFH cells in this response to acute bacterial infection.
Chapter LXVI Instructional Game Design Using Cognitive Load Theory

This chapter proposes an instructional game design framework based on the 4C/ID-model and cognitive load theory, its associated theoretical foundation. The proposed systematic design framework serves as the processing link to connect games' powerful characteristics in enhancing learning experience with desired learning outcomes. In this chapter we focus on the cognitive aspect of learning outcome: the development of transferable schema. This chapter introduces design guidelines to attain specific game characteristic by prioritizing the design components in 4C/ID-model. Each game characteristic consists of three levels of design emphasis: preliminary, secondary, and tertiary. The ultimate goal of this chapter is to initiate a series of dialogue between cognitive learning outcome, systematic instruc- tional design, and instructional game design thereby seeking to improve the overall game design and instructional efficiency.
Binary Keypoint Descriptor for Accelerated Matching

Efficient extraction of key points from images is a hot topic in computer vision and forms many applications. We propose a kind of binary descriptor which is invariant to rotation, viewpoint change, blur change, brightness change, and JPEG compression. To best address the whole process, this paper covers key point detection, description and matching. Orientations of the interest points are estimated by the Haar-wavelet responses to achieve rotation invariance. Binary descriptors are computed by comparing the intensities of two points in the overlapping sampling pattern on image patches. At last, binary descriptors are matched by Hamming distance which can be done very fast on SSE instruction set of modern CPUs such as Core i7 processor. We use coarse-to-fine strategy to accelerate the matching of key point. In the experiment results, we will show that our descriptor is fast and robust.
Solving Goal Hybrid Markov Decision Processes Using Numeric Classical Planners

We present the domain-independent HRFF algorithm, which solves goal-oriented HMDPs by incrementally aggregating plans generated by the Metric-FF planner into a policy defined over discrete and continuous state variables. HRFF takes into account non-monotonic state variables, and complex combinations of many discrete and continuous probability distributions. We introduce new data structures and algorithmic paradigms to deal with continuous state spaces: hybrid hierarchical hash tables, domain determinization based on dynamic domain sampling or on static computation of probability distributions’ modes, optimization settings under Metric-FF based on plan probability and length. We compare with HAO∗ on the Rover domain and show that HRFF outperforms HAO∗ by many order of magnitudes in terms of computation time and memory usage. We also experiment challenging and combinatorial HMDP versions of benchmarks from numeric classical planning, with continuous dead-ends and non-monotonic continuous state variables.
outputs The CONNECT architecture

Current solutions to interoperability remain limited with respect to highly dynamic and heterogeneous environments, where systems encounter one another spontaneously. In this chapter, we introduce the Connect architecture, which puts forward a fundamentally different method to tackle the interoperability problem. The philosophy is to observe networked systems in action, learn their behaviour and then dynamically generate mediator software which will connect two heterogeneous systems. We present a high-level overview of how Connect operates in practice and subsequently provide a simple example to illustrate the architecture in action.
Predicting Medical Provider Specialties to Detect Anomalous Insurance Claims

The healthcare industry is a complex system with many moving parts. One issue in this field is the misuse of medical insurance systems, such as Medicare. In this paper, we build a machine learning model to detect when physicians exhibit anomalous behavior in their medical insurance claims. This new research has the potential to give some insight in determining if, and when, physicians are acting outside the norm of their respective specialty, which could indicate misuse, fraud, or lack of knowledge around billing procedures. We use a publicly available procedure billing dataset, released by the U. S. Medicare system. Due to the large size of the dataset, we sampled the dataset to include all physicians practicing within one state only. The model uses the multinomial Naïve Bayes algorithm and is evaluated by calculating precision, recall, and Fscore with 5-fold cross-validation. The model is able to successfully predict several classes of physicians with an F-score over 0.9. These results show that it is possible to effectively use machine learning in a novel way to classify physicians into their respective fields solely using the procedures they bill for. This research provides a model that can identify physicians who are potentially misusing insurance systems for further investigation.
The role of the M-Wright function in bi-orthogonality of the fractional Bernoulli and Euler polynomials

In this article, we derive the Sheffer polynomials {Sm(x, y)}m=1∞ in two variables as the coefficient set of the generating function A(t, y)ext, where A(s, y) is a complex function with respect to complex variable s and y ϵ R. When the function A(s, y) is entire, using the inverse Mellin transform we get the coefficient set, and when the function A(s, y) has a branch point at zero point s = 0, using the M-Wright function, we derive the coefficient set. Moreover, as special cases of this set for the Bernoulli and Euler polynomials, bi-orthogonality of these polynomials with their associated functions is discussed. The bi-orthogonality relations are given in terms of the product of the M-Wright function and the Sheffer polynomials.
Mergers and Acquisitions, Open Innovation and UK High Growth SMEs

This paperexam in es the dyn am ics of en trepren eurial acquisition s un dertak en by UK hig h g row th sm all an dm edium en terprises (S M E s). W hile en trepren eurial acquisition s are in creasin g ly deployed by S M E s, little is k n ow n about their an teceden ts,m otiv ation al driv ers an dorg an isation al outcom es. D raw in g on detailedcase study ev iden ce from S cotlan d,the k ey factor foun d to be driv in g these acquisition s w as the desire to aug m en t an d exploittechn olog ical com plem en tarities betw een the acquirin g an d acquired firm s.A cquisition can therefore be con ceptualised as an adv an ced stag e of the outside-in ‘open in n ov ation ’strateg ies proactiv ely used w ithin these in n ov ativ e S M E s. Firm s executin g this strateg y typically hav e an acute propen sity for risk , a desire for close custom er en g ag em en t, effectiv e busin ess m odels an dstron g extern al orien tation . The w ork sug g ests that g reater atten tion should be paid w ithin M & A theory to the dyn am ics of these types of sm aller scale en trepren eurial acquisition s. WP No 16-012 4th Q uarter2016
The fusion of audio-visual features and external knowledge for event detection in team sports video

Most existing systems detect events in broadcast team sports video using only internal audio-visual (AV) features with limited success. We found that there are many widely available external knowledge sources - such as match reports and real-time game logs in newspapers and on the Web - that can help in detecting events. This paper proposes a scalable framework that utilizes both internal AV features and external knowledge sources to detect events and identify their boundaries in full-length match videos. Besides detecting events, the framework has the potential of discovering detailed semantics and performing question answering regarding these semantics. We demonstrate the effectiveness of the framework using three full-length soccer matches
Improving Scalability of Reinforcement Learning by Separation of Concerns

In this paper, we propose a framework for solving a single-agent task by using multiple agents, each focusing on different aspects of the task. This approach has two main advantages: 1) it allows for specialized agents for different parts of the task, and 2) it provides a new way to transfer knowledge, by transferring trained agents. Our framework generalizes the traditional hierarchical decomposition, in which, at any moment in time, a single agent has control until it has solved its particular subtask. We illustrate our framework using a number of examples.
Influence Linear Discriminant Analysis on isolated digit recognition

The paper deals with Automatic Speech Recognition system (ASR) with focus on isolated digits recognition in Slovak language. The paper discusses reduction of dimension of feature space. There are applied two ways dimension reductions. The first way feature subspace reduction is bases on manually selection some coefficients from feature matrix. The second way feature subspace reduction is automatic and use Linear Discriminant Analysis. In the first part of the article, there is briefly defined ASR system and introduce 3TDCM method (tree Two-Dimensional Cepstral Matrix). The next part of the article focuses on exploitation of LDA end use in ASR system. In the third part of the paper, there are shown experiments with isolated digit recognition. Moreover, there are compare systems ASR with automatic and manual feature subspace selection and influence feature subspace reduction on performances of ASR system.
Residential histories of Munich's Turkish population : a biographical approach to urban residential mobility.

This thesis examines the residential histories of Munich's Turkish population and evaluates the application of a biographical approach to the explanation of residential mobility. The analysis focuses on three different, but complementary, levels of analysis. The first examines the changing aggregate patterns of residential location and migration of Munich's Turkish population in the 1980s and 1990s. Secondly, the analysis turns from the crosssectional to the longitudinal and examines the residential histories of a cohort of 72 respondents. Thirdly, and moving further from the aggregate to the individual, the study explains residential histories by incorporating people's experiences, and evaluates the usefulness of a biographical explanation of residential mobility, based within a theoretical framework connecting identity and action. The research adopts a 'mixed methods' approach combining qualitative and quantitative methods to address the three levels of analysis effectively. Analysis of population and housing data from the census examines aggregate patterns of location and migration for Munich's Turks. The results of a questionnaire survey outline movement through space and through the housing market for one particular cohort. Finally, in-depth biographical interviews examine people's residential histories in more detail by incorporating their experiences, focusing both on primary immigrants and their children joining them in Germany later (secondary immigrants). The in-depth part of the study also provides the opportunity of outlining and evaluating a biographical approach to urban residential mobility. The research shows that, for both primary and secondary immigrants, residence has a significance that makes housing a crucial issue, important for feeling at home in Germany. Finding adequate places to live, however, has always been difficult for Munich's Turks as a result of their inferior position in the housing market. As a consequence, this population has undergone a marked sequence of movement through housing sectors, that has also resulted in processes of deconcentration and selective suburbanisation. These spatial patterns have not been the result of choice by the people concerned but are determined significantly by the structure and mechanisms of the housing market in Munich. At the level of migration theory, the research demonstrates that action (movement) and non-action (non-movement) are significantly located in people's biographies. Thereby, it is also shown that placing a biographical approach within a theoretical framework connecting action, narrative and identity is one way of addressing the demands of overcoming the structure/agency dualism, and contextualising migration and non-migration more fully.
Automated Transcription System for Malayalam Language

Malayalam is one of the 22 scheduled languages in India with more than 130 million speakers. This paper presents a report on the development of a speaker independent, continuous transcription system for Malayalam. The system employs Hidden Markov Model (HMM) for acoustic modeling and Mel Frequency Cepstral Coefficient (MFCC) for feature extraction. It is trained with 21 male and female speakers in the age group ranging from 20 to 40 years. The system obtained a word recognition accuracy of 87.4% and a sentence recognition accuracy of 84%, when tested with a set of continuous speech data.
Structural Mapping between Natural Language Questions and SQL Queries

A core problem in data mining is to retrieve data in an easy and human friendly way. Automatically translating natural language questions into SQL queries would allow for the design of effective and useful database systems from a user viewpoint. In this thesis, we approach such problem by carrying out a mapping between natural language (NL) and SQL syntactic structures. The mapping is automatically derived by applying machine learning algorithms. In particular, we generate a dataset of pairs of NL questions and SQL queries represented by means of their syntactic trees automatically derived by their respective syntactic parsers. Then, we train a classifier for detecting correct and incorrect pairs of questions and queries using kernel methods along with Support Vector Machines. Experimental results on two different datasets show that our approach is viable to select the correct SQL query for a given natural language questions in two target domains. Given that preliminary results were encouraging we implemented an SQL query generator that creates the set of candidate SQL queries which we rerank with a SVM-ranker based on tree kernels. In particular we exploit linguistic dependencies in the natural language question and the database metadata to build a set of plausible SELECT, WHERE and FROM clauses enriched with meaningful joins. Then, we combine all the clauses to get the set of all possible SQL queries, producing candidate queries to answer the question. This approach can be recursively applied to deal with complex questions, requiring nested sub-queries. We sort the candidates in terms of scores of correctness using a weighting scheme applied to the query generation rules. Then, we use a SVM ranker trained with structural kernels to reorder the list of question and query pairs, where both members are again represented as syntactic trees. The f-measure of our model on standard benchmarks is in line with the best models (85% on the first question), which use external and expensive hand-crafted resources such as the semantic interpretation. Moreover, we can provide a set of candidate answers with a Recall of the answer of about 92% and 96% on the first 2 and 5 candidates, respectively. }
Evaluation of the Capabilities of WireShark as a tool for Intrusion Detection

This paper illustrates the functionality of Wireshark as a sniffing tool in networks. This has been proven by an experimental setup which depicts the efficiency of detection of a malicious packet in any network. Testing has been achieved through experimentation on a real time network analyzed by Wireshark. Inferences have been made which clearly depict Wireshark’s capabilities highlighting it as a strong candidate for future development into a robust intrusion detection system. This paper highlights the working of Wireshark as a network protocol analyzer and also accentuates its flexibility as an open source utility to allow developers to add possible functionalities of intrusion detection devices in it.
The development of a wisdom autonomic grid

It is not a simple matter to develop an integrative approach that exploits synergies between knowledge management and knowledge discovery in order to monitor and manage the full lifecycle of knowledge and provides services quickly, reliably and securely. One of the main problems is the heterogeneity of the involved resources that represent knowledge. Data mining systems produce knowledge in a form meant to be understandable to machines and on the other hand in knowledge management systems the priority is placed on the readability and usability of knowledge by humans. The Semantic Web is a promising platform to unify this heterogeneity and, in conjunction with novel techniques for Web Intelligence it could offer more then just knowledge - wisdom. The Wisdom Autonomic Grid is an original proposal of a knowledge based Grid that is able to configure and reconfigure itself under varying and unpredictable conditions and optimize its working, performs something akin to healing and provides self-protection, as visioned in the IBM Autonomic Computing initiative. This paper presents an original framework for creating advanced applications to integrate knowledge discovery and knowledge management in the Autonomic Grid and Web environments.
Semantic Factors Predict the Rate of Lexical Replacement of Content Words

The rate of lexical replacement estimates the diachronic stability of word forms on the basis of how frequently a proto-language word is replaced or retained in its daughter languages. Lexical replacement rate has been shown to be highly related to word class and word frequency. In this paper, we argue that content words and function words behave differently with respect to lexical replacement rate, and we show that semantic factors predict the lexical replacement rate of content words. For the 167 content items in the Swadesh list, data was gathered on the features of lexical replacement rate, word class, frequency, age of acquisition, synonyms, arousal, imageability and average mutual information, either from published databases or gathered from corpora and lexica. A linear regression model shows that, in addition to frequency, synonyms, senses and imageability are significantly related to the lexical replacement rate of content words–in particular the number of synonyms that a word has. The model shows no differences in lexical replacement rate between word classes, and outperforms a model with word class and word frequency predictors only.
Schools as Learning Communities

Schools succeeding against the odds can make a difference. However, this complex challenge has barely been achieved individually. Knowing that it takes a village to raise a child, schools as learning communities join everybody to develop a common understanding of the school ethos that promotes quality education for all. This article focuses on how this process turned despair into hope in an elementary school in one of the most marginalized areas in Spain. Building upon critical pedagogy and dialogic learning, I analyzed some turning points of this transformation. Particularly, the power of qualitative research under the communicative methodology approach seems to be crucial for (a) opening the classrooms and school to the community and (b) including people's voices into the research. Eight years later, evidence shows profound changes in people's lives. This case exemplifies the Freirean concept of the “untested feasibility,” which enables us to generate real transformations.
What the User Interacts With: Reflections on Conceptual Models for Semantic Wikis

Traditional wikis excel in collaborative work on emerging content and structure. Semantic Wikis go further by allowing users to expose knowledge in ways suitable for machine processing, e.g. using Semantic Web technologies. The combination of ease of use, support for work in progress and Semantic Web technologies makes Semantic Wikis particularly interesting for knowledge-intensive work areas such as project management and software development. While several Semantic Wikis have been put to practical use, the concepts their users interact with have been little discussed. This position paper explores this issue, showing that the design of a conceptual model is not trivial and showing the repercussions of each design choice. The issue is explored stressing the social aspect of Semantic Wikis.
Surface Enhanced Raman Correlation Spectroscopy of Particles in Solution

Surface enhanced Raman correlation spectroscopy (SERCS) is shown as a label-free, chemically specific method for monitoring individual polymer beads and lipid vesicles interacting with a 2-D planar surface enhanced Raman (SERS) substrate in solution. The enhancement afforded by the SERS substrate allows for spectral data to be acquired in series at rates between 31 and 83 Hz. Auto- and cross-correlation of spectral data facilitates the measurement of diffusion constants for particles ranging in radius from 50 to 500 nm while discriminating signal associated with the target analyte from extraneous fluctuations. The measured diffusion coefficients are on the order of 10–10–10–11 cm2/s, a factor of 40 times slower than predicted from the Stokes–Einstein equation, suggesting that particles are experiencing hindered diffusion at the surface. The enhanced signals appear to originate from particles less than 5 nm of the SERS substrate, consistent with adsorption to the surface. This work provides a means to measure and monitor surface interactions and demonstrates the utility and limits of SERS detection in solution over planar SERS substrates.