Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1

Data Compliance in Pharmaceutical Industry, Interoperability to align Business and Information Systems

International audienceThe ultimate goal in the pharmaceutical sector is product quality. However this quality can be altered by the use of a number of heterogeneous information systems with different business structures and concepts along the lifecycle of the product. Interoperability is then needed to guarantee a certain correspondence and compliance between different product data. In this paper we focus on a particular compliance problem, between production technical data, represented in an ERP, and the corresponding regulatory directives and specifications, represented by the Marketing Authorizations (MA). The MA detail the process for manufacturing the medicine according to the requirements imposed by health organisations such as Food and Drug Administration (FDA) and Committee for Medicinal Products for Human use (CHMP). The proposed approach uses an interoperability framework which is based on a multi-layer separation between the organisational aspects, business trades, and information technologies for each involved entity into the communication between the used systems

Machine Learning in clinical biology and medicine: from prediction of multidrug resistant infections in humans to pre-mRNA splicing control in Ciliates

Machine Learning methods have broadly begun to infiltrate the clinical literature in such a way that the correct use of algorithms and tools can facilitate both diagnosis and therapies. The availability of large quantities of high-quality data could lead to an improved understanding of risk factors in community and healthcare-acquired infections. In the first part of my PhD program, I refined my skills in Machine Learning by developing and evaluate with a real antibiotic stewardship dataset, a model useful to predict multi-drugs resistant urinary tract infections after patient hospitalization9 . For this purpose, I created an online platform called DSaaS specifically designed for healthcare operators to train ML models (supervised learning algorithms). These results are reported in Chapter 2. In the second part of the PhD thesis (Chapter 3) I used my new skills to study the genomic variants, in particular the phenomenon of intron splicing. One of the important modes of pre-mRNA post-transcriptional modification is alternative intron splicing, that includes intron retention (unsplicing), allowing the creation of many distinct mature mRNA transcripts from a single gene. An accurate interpretation of genomic variants is the backbone of genomic medicine. Determining for example the causative variant in patients with Mendelian disorders facilitates both management and potential downstream treatment of the patient’s condition, as well as providing peace of mind and allowing more effective counselling for the wider family. Recent years have seen a surge in bioinformatics tools designed to predict variant impact on splicing, and these offer an opportunity to circumvent many limitations of RNA-seq based approaches. An increasing number of these tools rely on machine learning computational approaches that can identify patterns in data and use this knowledge to speculate on new data. I optimized a pipeline to extract and classify introns from genomes and transcriptomes and I classified them into retained (Ris) and constitutively spliced (CSIs) introns. I used data from ciliates for the peculiar organization of their genomes (enriched of coding sequences) and because they are unicellular organisms without cells differentiated into tissues. That made easier the identification and the manipulation of introns. In collaboration with the PhD colleague dr. Leonardo Vito, I analyzed these intronic sequences in order to identify “features” to predict and to classify them by Machine Learning algorithms. We also developed a platform useful to manipulate FASTA, gtf, BED, etc. files produced by the pipeline tools. I named the platform: Biounicam (intron extraction tools) available at http://46.23.201.244:1880/ui. The major objective of this study was to develop an accurate machine-learning model that can predict whether an intron will be retained or not, to understand the key-features involved in the intron retention mechanism, and provide insight on the factors that drive IR. Once the model has been developed, the final step of my PhD work will be to expand the platform with different machine learning algorithms to better predict the retention and to test new features that drive this phenomenon. These features hopefully will contribute to find new mechanisms that controls intron splicing. The other additional papers and patents I published during my PhD program are in Appendix B and C. These works have enriched me with many useful techniques for future works and ranged from microbiology to classical statistics

Organizational Agility Conceptual Model

Organizational agility is a complex and multidimensional concept. One of the challenges in researching organizational agility is its unified definition and concept. Li terature analysis reveals various dimensions and frameworks are used to analyze organization agili- ty. Many of them focus on narrow industry or only approach the organizational agility concept from limited perspective. This article attempts to combine different approaches and angles to organiza- tional agility to a more cohesi ve and encompassing conceptual model that is applicable to variety industries and organizations. The variety and combination of attributes, characteristics, capabili- ties, and practices make the measurement of organizational agility are analyzed. Building on research main frameworks for analyzing organizational agility concept are identified. Conceptual organiza- tional agility model based on organizational agility attributes, capa- bilities and practices framework is presented. This article contrib- utes to research by providing more unified concept, which can be adapted in studying organizational agility in a wide and global range of organizations, regardless of the industry they operate in

Proceedings of the COLING 2004 Post Conference Workshop on Multilingual Linguistic Ressources MLR2004

International audienceIn an ever expanding information society, most information systems are now facing the "multilingual challenge". Multilingual language resources play an essential role in modern information systems. Such resources need to provide information on many languages in a common framework and should be (re)usable in many applications (for automatic or human use). Many centres have been involved in national and international projects dedicated to building har- monised language resources and creating expertise in the maintenance and further development of standardised linguistic data. These resources include dictionaries, lexicons, thesauri, word-nets, and annotated corpora developed along the lines of best practices and recommendations. However, since the late 90's, most efforts in scaling up these resources remain the responsibility of the local authorities, usually, with very low funding (if any) and few opportunities for academic recognition of this work. Hence, it is not surprising that many of the resource holders and developers have become reluctant to give free access to the latest versions of their resources, and their actual status is therefore currently rather unclear. The goal of this workshop is to study problems involved in the development, management and reuse of lexical resources in a multilingual context. Moreover, this workshop provides a forum for reviewing the present state of language resources. The workshop is meant to bring to the international community qualitative and quantitative information about the most recent developments in the area of linguistic resources and their use in applications. The impressive number of submissions (38) to this workshop and in other workshops and conferences dedicated to similar topics proves that dealing with multilingual linguistic ressources has become a very hot problem in the Natural Language Processing community. To cope with the number of submissions, the workshop organising committee decided to accept 16 papers from 10 countries based on the reviewers' recommendations. Six of these papers will be presented in a poster session. The papers constitute a representative selection of current trends in research on Multilingual Language Resources, such as multilingual aligned corpora, bilingual and multilingual lexicons, and multilingual speech resources. The papers also represent a characteristic set of approaches to the development of multilingual language resources, such as automatic extraction of information from corpora, combination and re-use of existing resources, online collaborative development of multilingual lexicons, and use of the Web as a multilingual language resource. The development and management of multilingual language resources is a long-term activity in which collaboration among researchers is essential. We hope that this workshop will gather many researchers involved in such developments and will give them the opportunity to discuss, exchange, compare their approaches and strengthen their collaborations in the field. The organisation of this workshop would have been impossible without the hard work of the program committee who managed to provide accurate reviews on time, on a rather tight schedule. We would also like to thank the Coling 2004 organising committee that made this workshop possible. Finally, we hope that this workshop will yield fruitful results for all participants

Investing in Innovation and Skills: Thriving through Global Value Chains

This paper investigates empirically the interplay between participation and positioning in global value chains (GVCs), employment demand and supply and workforce’s skills endowment. Results touch upon the way innovation, technology and participation in GVCs shape employment in routine intensive and non-routine jobs; the relationship between participation in GVCs and polarisation of employment; the way the skill composition of a country’s workforce – both the type of skills and their distribution – shapes specialisation and positioning along GVCs; and the complementarities emerging between GVC participation and investment in knowledge-based capital,  especially organisational capital and ICT

Comparing and mapping difference indices of debate quality on Twitter

Albeit the measurement of debate quality is not a new endeavour, this paper raises two research questions for which we still have limited knowledge: What are important and reliable indicators of debate quality on social media? How does debate quality relate to individual factors on social media? First, we empirically analysed how two well-established discourse’ quality indices (the DQI and CC index) correlate to each other using a random sample of 1000 tweets selected from the full history of tweets written by Swiss elected politicians between 2011 and 2021. While the sample was automatically coded for CC using LIWC, we manually annotated the tweets according to an adapted version of the DQI for social media texts. Second, we conducted a correspondence analysis to investigate the relations between these dimensions, additional debate quality features, as well as individual political factors. Results show a good correlation between both indices ( r up to 0.46), while also highlighting their respective weaknesses. Furthermore, the results highlight the necessity to include alternative dimensions of debate quality (such as emotion and inclusive or exclusive views) to enhance future measurements of debate quality in the realm of social media

Towards Open Vocabulary Learning: A Survey

In the field of visual scene understanding, deep neural networks have made impressive advancements in various core tasks like segmentation, tracking, and detection. However, most approaches operate on the close-set assumption, meaning that the model can only identify pre-defined categories that are present in the training set. Recently, open vocabulary settings were proposed due to the rapid progress of vision language pre-training. These new approaches seek to locate and recognize categories beyond the annotated label space. The open vocabulary approach is more general, practical, and effective compared to weakly supervised and zero-shot settings. This paper provides a thorough review of open vocabulary learning, summarizing and analyzing recent developments in the field. In particular, we begin by comparing it to related concepts such as zero-shot learning, open-set recognition, and out-of-distribution detection. Then, we review several closely related tasks in the case of segmentation and detection, including long-tail problems, few-shot, and zero-shot settings. For the method survey, we first present the basic knowledge of detection and segmentation in close-set as the preliminary knowledge. Next, we examine various scenarios in which open vocabulary learning is used, identifying common design elements and core ideas. Then, we compare the recent detection and segmentation approaches in commonly used datasets and benchmarks. Finally, we conclude with insights, issues, and discussions regarding future research directions. To our knowledge, this is the first comprehensive literature review of open vocabulary learning. We keep tracing related works at https://github.com/jianzongwu/Awesome-Open-Vocabulary.Comment: Project page at https://github.com/jianzongwu/Awesome-Open-Vocabular

Knowledge Management without Management -- Shadow IT in Knowledge-intensive Manufacturing Practices

The voluntary use of private device by employees without formal approval of the IT department, commonly termed Shadow IT, is an increasingly widespread phenomenon. In this paper, we study the role of private smartphones (and related applications like WhatsApp) in knowledge-intensive practices in the manufacturing domain. With an in-depth case study based on data gained from observations and interviews, we are able to empirically illustrate why workers use their private smartphones (contrary to company guidelines) and how they find significant gains of productivity by using the ‘forbidden’ applications. Our study contributes to knowledge management research by showing how private IT use can change existing knowledge management practices. At the same time, we are able to give rich insights into the rise of Shadow IT in a manufacturing context which takes place in a self-organised way without knowledge of the management. This enables us to take a step towards a knowledge management strategy perspective on Shadow IT

Organizational Adoption of AI Through A Sociocultural Lens

Honors (Bachelor's)International StudiesUniversity of Michiganhttps://deepblue.lib.umich.edu/bitstream/2027.42/147389/1/mirarh.pd