Ontology forecasting in scientific literature:semantic concepts prediction based on innovation-adoption priors

The ontology engineering research community has focused for many years on supporting the creation, development and evolution of ontologies. Ontology forecasting, which aims at predicting semantic changes in an ontology, represents instead a new challenge. In this paper, we want to give a contribution to this novel endeavour by focusing on the task of forecasting semantic concepts in the research domain. Indeed, ontologies representing scientific disciplines contain only research topics that are already popular enough to be selected by human experts or automatic algorithms. They are thus unfit to support tasks which require the ability of describing and exploring the forefront of research, such as trend detection and horizon scanning. We address this issue by introducing the Semantic Innovation Forecast (SIF) model, which predicts new concepts of an ontology at time t + 1, using only data available at time t. Our approach relies on lexical innovation and adoption information extracted from historical data. We evaluated the SIF model on a very large dataset consisting of over one million scientific papers belonging to the Computer Science domain: the outcomes show that the proposed approach offers a competitive boost in mean average precision-at-ten compared to the baselines when forecasting over 5 years.

Dynamic Bayesian network for semantic place classification in mobile robotics

In this paper, the problem of semantic place categorization in mobile robotics is addressed by considering a time-based probabilistic approach called dynamic Bayesian mixture model (DBMM), which is an improved variation of the dynamic Bayesian network. More specifically, multi-class semantic classification is performed by a DBMM composed of a mixture of heterogeneous base classifiers, using geometrical features computed from 2D laserscanner data, where the sensor is mounted on-board a moving robot operating indoors. Besides its capability to combine different probabilistic classifiers, the DBMM approach also incorporates time-based (dynamic) inferences in the form of previous class-conditional probabilities and priors. Extensive experiments were carried out on publicly available benchmark datasets, highlighting the influence of the number of time-slices and the effect of additive smoothing on the classification performance of the proposed approach. Reported results, under different scenarios and conditions, show the effectiveness and competitive performance of the DBMM.

Implémentation sur FPGA de l’algorithme MUSIC sur antenne-réseau expérimentale à 10 GHz

Les techniques des directions d’arrivée (DOA) sont une voie prometteuse pour accroitre la capacité des systèmes et les services de télécommunications en permettant de mieux estimer le canal radio-mobile. Elles permettent aussi de suivre précisément des usagers cellulaires pour orienter les faisceaux d’antennes dans leur direction. S’inscrivant dans ce contexte, ce présent mémoire décrit étape par étape l’implémentation de l’algorithme de haut niveau MUSIC (MUltiple SIgnal Classification) sur une plateforme FPGA afin de déterminer en temps réel l’angle d’arrivée d’une ou des sources incidentes à un réseau d’antennes. Le concept du prototypage rapide des lois de commande (RCP) avec les outils de XilinxTM System generator (XSG) et du MBDK (Model Based Design Kit) de NutaqTM est le concept de développement utilisé. Ce concept se base sur une programmation de code haut niveau à travers des modèles, pour générer automatiquement un code de bas niveau. Une attention particulière est portée sur la méthode choisie pour résoudre le problème de la décomposition en valeurs et vecteurs propres de la matrice complexe de covariance par l’algorithme de Jacobi. L’architecture mise en place implémentant cette dernière dans le FPGA (Field Programmable Gate Array) est détaillée. Par ailleurs, il est prouvé que MUSIC ne peut effectuer une estimation intéressante de la position des sources sans une calibration préalable du réseau d’antennes. Ainsi, la technique de calibration par matrice G utilisée dans ce projet est présentée, en plus de son modèle d’implémentation. Enfin, les résultats expérimentaux du système mis à l’épreuve dans un environnement réel en présence d’une source puis de deux sources fortement corrélées sont illustrés et analysés.

Representing image captions as concept graphs using semantic information

Conceptual interpretation of languages has gathered peak interest in the world of artificial intelligence. The challenge in modeling various complications involved in a language is the main motivation behind our work. Our main focus in this work is to develop conceptual graphical representation for image captions. We have used discourse representation structure to gain semantic information which is further modeled into a graphical structure. The effectiveness of the model is evaluated by a caption based image retrieval system. The image retrieval is performed by computing subgraph based similarity measures. Best retrievals were given an average rating of . ± . out of 4 by a group of 25 human judges. The experiments were performed on a subset of the SBU Captioned Photo Dataset. This purpose of this work is to establish the cognitive sensibility of the approach to caption representations

Representing image captions as concept graphs using semantic information

Conceptual interpretation of languages has gathered peak interest in the world of artificial intelligence. The challenge in modeling various complications involved in a language is the main motivation behind our work. Our main focus in this work is to develop conceptual graphical representation for image captions. We have used discourse representation structure to gain semantic information which is further modeled into a graphical structure. The effectiveness of the model is evaluated by a caption based image retrieval system. The image retrieval is performed by computing subgraph based similarity measures. Best retrievals were given an average rating of . ± . out of 4 by a group of 25 human judges. The experiments were performed on a subset of the SBU Captioned Photo Dataset. This purpose of this work is to establish the cognitive sensibility of the approach to caption representations.

One archive, many readings. Personal archives as complex networks in the semantic web

Personal archives are the archives created by individuals for their own purposes. Among these are the library and documentary collections of writers and scholars. It is only recently that archival literature has begun to focus on this category of archives, emphasising how their heterogeneous nature necessitates the conciliation of different approaches to archival description, and calling for a broader understanding of the principle of provenance, recognising that multiple creators, including subsequent researchers, can contribute to shaping personal archives over time by adding new layers of contexts. Despite these advances in the theoretical debate, current architectures for archival representation remain behind. Finding aids privilege a single point of view and do not allow subsequent users to embed their own, potentially conflicting, readings. Using semantic web technologies this study aims to define a conceptual model for writers' archives based on existing and widely adopted models in the cultural heritage and humanities domains. The model developed can be used to represent different types of documents at various levels of analysis, as well as record content and components. It also enables the representation of complex relationships and the incorporation of additional layers of interpretation into the finding aid, transforming it from a static search tool into a dynamic research platform.  The personal archive and library of Giuseppe Raimondi serves as a case study for the creation of an archival knowledge base using the proposed conceptual model. By querying the knowledge graph through SPARQL, the effectiveness of the model is evaluated. The results demonstrate that the model addresses the primary representation challenges identified in archival literature, from both a technological and methodological standpoint. The ultimate goal is to bring the output par excellence of archival science, i.e. the finding aid, more in line with the latest developments in archival thinking.

Latent representations for traditional music analysis and generation

We create and study a generative model for Irish traditional music based on Variational Autoencoders and analyze the learned latent space trying to find musically significant correlations in the latent codes' distributions in order to perform musical analysis on data. We train two kinds of models: one trained on a dataset of Irish folk melodies, one trained on bars extrapolated from the melodies dataset, each one in five variations of increasing size. We conduct the following experiments: we inspect the latent space of tunes and bars in relation to key, time signature, and estimated harmonic function of bars; we search for links between tunes in a particular style (i.e. "reels'") and their positioning in latent space relative to other tunes; we compute distances between embedded bars in a tune to gain insight into the model's understanding of the similarity between bars. Finally, we show and evaluate generative examples. We find that the learned latent space does not explicitly encode musical information and is thus unusable for musical analysis of data, while generative results are generally good and not strictly dependent on the musical coherence of the model's internal representation.

Graph-based Vision-Language Model for Multi-Modal Retrieval of Fashion Products

Artificial Intelligence is reshaping the field of fashion industry in different ways. E-commerce retailers exploit their data through AI to enhance their search engines, make outfit suggestions and forecast the success of a specific fashion product. However, it is a challenging endeavour as the data they possess is huge, complex and multi-modal. The most common way to search for fashion products online is by matching keywords with phrases in the product's description which are often cluttered, inadequate and differ across collections and sellers. A customer may also browse an online store's taxonomy, although this is time-consuming and doesn't guarantee relevant items. With the advent of Deep Learning architectures, particularly Vision-Language models, ad-hoc solutions have been proposed to model both the product image and description to solve this problems. However, the suggested solutions do not exploit effectively the semantic or syntactic information of these modalities, and the unique qualities and relations of clothing items. In this work of thesis, a novel approach is proposed to address this issues, which aims to model and process images and text descriptions as graphs in order to exploit the relations inside and between each modality and employs specific techniques to extract syntactic and semantic information. The results obtained show promising performances on different tasks when compared to the present state-of-the-art deep learning architectures.

Integrated Proteomics Identified Up-regulated Focal Adhesion-mediated Proteins In Human Squamous Cell Carcinoma In An Orthotopic Murine Model.

Understanding the molecular mechanisms of oral carcinogenesis will yield important advances in diagnostics, prognostics, effective treatment, and outcome of oral cancer. Hence, in this study we have investigated the proteomic and peptidomic profiles by combining an orthotopic murine model of oral squamous cell carcinoma (OSCC), mass spectrometry-based proteomics and biological network analysis. Our results indicated the up-regulation of proteins involved in actin cytoskeleton organization and cell-cell junction assembly events and their expression was validated in human OSCC tissues. In addition, the functional relevance of talin-1 in OSCC adhesion, migration and invasion was demonstrated. Taken together, this study identified specific processes deregulated in oral cancer and provided novel refined OSCC-targeting molecules.

Electronic And Structural Study Of Pt-modified Au Vicinal Surfaces: A Model System For Pt-au Catalysts.

Two single crystalline surfaces of Au vicinal to the (111) plane were modified with Pt and studied using scanning tunneling microscopy (STM) and X-ray photoemission spectroscopy (XPS) in ultra-high vacuum environment. The vicinal surfaces studied are Au(332) and Au(887) and different Pt coverage (θPt) were deposited on each surface. From STM images we determine that Pt deposits on both surfaces as nanoislands with heights ranging from 1 ML to 3 ML depending on θPt. On both surfaces the early growth of Pt ad-islands occurs at the lower part of the step edge, with Pt ad-atoms being incorporated into the steps in some cases. XPS results indicate that partial alloying of Pt occurs at the interface at room temperature and at all coverage, as suggested by the negative chemical shift of Pt 4f core line, indicating an upward shift of the d-band center of the alloyed Pt. Also, the existence of a segregated Pt phase especially at higher coverage is detected by XPS. Sample annealing indicates that the temperature rise promotes a further incorporation of Pt atoms into the Au substrate as supported by STM and XPS results. Additionally, the catalytic activity of different PtAu systems reported in the literature for some electrochemical reactions is discussed considering our findings.

The Soluble Guanylyl Cyclase Activator Bay 60-2770 Potently Relaxes The Pulmonary Artery On Congenital Diaphragmatic Hernia Rabbit Model.

Congenital diaphragmatic hernia (CDH) is associated with pulmonary hypertension which is often difficult to manage, and a significant cause of morbidity and mortality. In this study, we have used a rabbit model of CDH to evaluate the effects of BAY 60-2770 on the in vitro reactivity of left pulmonary artery. CDH was performed in New Zealand rabbit fetuses (n = 10 per group) and compared to controls. Measurements of body, total and left lung weights (BW, TLW, LLW) were done. Pulmonary artery rings were pre-contracted with phenylephrine (10 μM), after which cumulative concentration-response curves to glyceryl trinitrate (GTN; NO donor), tadalafil (PDE5 inhibitor) and BAY 60-2770 (sGC activator) were obtained as well as the levels of NO (NO3/NO2). LLW, TLW and LBR were decreased in CDH (p < 0.05). In left pulmonary artery, the potency (pEC50) for GTN was markedly lower in CDH (8.25 ± 0.02 versus 9.27 ± 0.03; p < 0.01). In contrast, the potency for BAY 60-2770 was markedly greater in CDH (11.7 ± 0.03 versus 10.5 ± 0.06; p < 0.01). The NO2/NO3 levels were 62 % higher in CDH (p < 0.05). BAY 60-2770 exhibits a greater potency to relax the pulmonary artery in CDH, indicating a potential use for pulmonary hypertension in this disease.

Bay 41-2272, A Soluble Guanylate Cyclase Stimulator, Relaxes Isolated Human Ureter In A Standardized In Vitro Model.

To characterize the relaxation induced by BAY 41-2272 in human ureteral segments. Ureter specimens (n = 17) from multiple organ human deceased donors (mean age 40 ± 3.2 years, male/female ratio 2:1) were used to characterize the relaxing response of BAY 41-2272. Immunohistochemical analysis for endothelial and neuronal nitric oxide synthase, guanylate cyclase stimulator (sGC) and type 5 phosphodiesterase was also performed. The potency values were determined as the negative log of the molar to produce 50% of the maximal relaxation in potassium chloride-precontracted specimens. The unpaired Student t test was used for the comparisons. Immunohistochemistry revealed the presence of endothelial nitric oxide synthase in vessel endothelia and neuronal nitric oxide synthase in urothelium and nerve structures. sGC was expressed in the smooth muscle and urothelium layer, and type 5 phosphodiesterase was present in the smooth muscle only. BAY 41-2272 (0.001-100 μM) relaxed the isolated ureter in a concentration dependent manner, with a potency and maximal relaxation value of 5.82 ± 0.14 and 84% ± 5%, respectively. The addition of nitric oxide synthase and sGC inhibitors reduced the maximal relaxation values by 21% and 45%, respectively. However, the presence of sildenafil (100 nM) significantly potentiated (6.47 ± 0.10, P <.05) this response. Neither glibenclamide or tetraethylammonium nor ureteral urothelium removal influenced the relaxation response by BAY 41-2272. BAY 41-2272 relaxes the human isolated ureter in a concentration-dependent manner, mainly by activating the sGC enzyme in smooth muscle cells rather than in the urothelium, although a cyclic guanosine monophosphate-independent mechanism might have a role. The potassium channels do not seem to be involved.

Atomic Charge Transfer-counter Polarization Effects Determine Infrared Ch Intensities Of Hydrocarbons: A Quantum Theory Of Atoms In Molecules Model.

Atomic charge transfer-counter polarization effects determine most of the infrared fundamental CH intensities of simple hydrocarbons, methane, ethylene, ethane, propyne, cyclopropane and allene. The quantum theory of atoms in molecules/charge-charge flux-dipole flux model predicted the values of 30 CH intensities ranging from 0 to 123 km mol(-1) with a root mean square (rms) error of only 4.2 km mol(-1) without including a specific equilibrium atomic charge term. Sums of the contributions from terms involving charge flux and/or dipole flux averaged 20.3 km mol(-1), about ten times larger than the average charge contribution of 2.0 km mol(-1). The only notable exceptions are the CH stretching and bending intensities of acetylene and two of the propyne vibrations for hydrogens bound to sp hybridized carbon atoms. Calculations were carried out at four quantum levels, MP2/6-311++G(3d,3p), MP2/cc-pVTZ, QCISD/6-311++G(3d,3p) and QCISD/cc-pVTZ. The results calculated at the QCISD level are the most accurate among the four with root mean square errors of 4.7 and 5.0 km mol(-1) for the 6-311++G(3d,3p) and cc-pVTZ basis sets. These values are close to the estimated aggregate experimental error of the hydrocarbon intensities, 4.0 km mol(-1). The atomic charge transfer-counter polarization effect is much larger than the charge effect for the results of all four quantum levels. Charge transfer-counter polarization effects are expected to also be important in vibrations of more polar molecules for which equilibrium charge contributions can be large.

Mesenchymal Stromal Cells From Adipose Tissue Attached To Suture Material Enhance The Closure Of Enterocutaneous Fistulas In A Rat Model.

Surgical treatment for enterocutaneous fistulas (EF) frequently fails. Cell therapy may represent a new approach to treatment. Mesenchymal stromal cells (MSCs) have high proliferative and differentiation capacity. This study aimed to investigate whether MSCs could adhere to suture filament (SF), promoting better EF healing. MSCs, 1 × 10(6), from adipose tissue (ATMSCs) were adhered to a Polyvicryl SF by adding a specific fibrin glue formulation. Adhesion was confirmed by confocal and scanning electron microscopy (SEM). A cecal fistula was created in 22 Wistar rats by incising the cecum and suturing the opening to the surgical wound subcutaneously with four separate stitches. The animals were randomly allocated to three groups: control (CG)-five animals, EF performed; injection (IG)-eight animals 1 × 10(6) ATMSCs injected around EF borders; and suture filament (SG): nine animals, sutured with 1 × 10(6) ATMSCs attached to the filaments with fibrin glue. Fistulas were photographed on the operation day and every 3 days until the 21st day and analyzed by two observers using ImageJ Software. Confocal and SEM results demonstrated ATMSCs adhered to SF (ATMSCs-SF). The average reduction size of the fistula area at 21st day was greater for the SG group (90.34%, P < 0.05) than the IG (71.80%) and CG (46.54%) groups. ATMSCs adhered to SF maintain viability and proliferative capacity. EF submitted to ATMSCs-SF procedure showed greater recovery and healing. This approach might be a new and effective tool for EF treatment.

Changes In Motor Behavior During Pregnancy In Rats: The Basis For A Possible Animal Model Of Restless Legs Syndrome.

Pregnant women have a 2-3 fold higher probability of developing restless legs syndrome (RLS - sleep-related movement disorders) than general population. This study aims to evaluate the behavior and locomotion of rats during pregnancy in order to verify if part of these animals exhibit some RLS-like features. We used 14 female 80-day-old Wistar rats that weighed between 200 and 250 g. The rats were distributed into control (CTRL) and pregnant (PN) groups. After a baseline evaluation of their behavior and locomotor activity in an open-field environment, the PN group was inducted into pregnancy, and their behavior and locomotor activity were evaluated on days 3, 10 and 19 of pregnancy and in the post-lactation period in parallel with the CTRL group. The serum iron and transferrin levels in the CTRL and PN groups were analyzed in blood collected after euthanasia by decapitation. There were no significant differences in the total ambulation, grooming events, fecal boli or urine pools between the CTRL and PN groups. However, the PN group exhibited fewer rearing events, increased grooming time and reduced immobilization time than the CTRL group (ANOVA, p<0.05). These results suggest that pregnant rats show behavioral and locomotor alterations similar to those observed in animal models of RLS, demonstrating to be a possible animal model of this sleep disorder.