Language as a Latent Variable: Discrete Generative Models for Sentence Compression

In this work we explore deep generative models of text in which the latent representation of a document is itself drawn from a discrete language model distribution. We formulate a variational auto-encoder for inference in this model and apply it to the task of compressing sentences. In this application the generative model first draws a latent summary sentence from a background language model, and then subsequently draws the observed sentence conditioned on this latent summary. In our empirical evaluation we show that generative formulations of both abstractive and extractive compression yield state-of-the-art results when trained on a large amount of supervised data. Further, we explore semi-supervised compression scenarios where we show that it is possible to achieve performance competitive with previously proposed supervised models while training on a fraction of the supervised data.Comment: EMNLP 201

Nanoparticle effects on cell migration

It has been found that gold nanoparticles (Au NPs) are excellent materials for biological applications such as diagnosis and drug delivery, which makes it crucial to understand the interface between gold nanoparticles and cells. Previous work has shown that gold nanoparticles can affect cellular migration, even if outside cells. However, the fundamental of interaction between Au NPs and cells has remained unclear. The most accepted mechanism currently is that by sequestering biomolecules from the environment nanoparticles can change the local molecular concentration, and thus influence cell behavior. Chemotaxis is the movement of cells in response to a chemical stimulus. Chemoattractants are inorganic and organic substances, usually proteins, that create a concentration gradient that attracts cells to move towards them. Chemoattractants are known to be involved in immune responses and wound healing processes in eukaryotic cells. With the presence of chemoattractants, cells move directionally towards the infected sites or wound. Similar processes exist in wound healing. To study the intervention of gold nanoparticles in chemotaxis, two systems were chosen. The first system that was examined in the human monocytes THP-1 cells. As a type of monocyte, THP-1 is known to migrate in response peptide formyl peptides. Another cell/chemoattractant pair that was examined was human dermal fibroblasts (HDF) and platelet-derived growth factor (PDGF). Cells were cultured and placed into 3D collagen gel in a specialized chemotaxis slide, in which a steady chemoattractant concentration was produced. Cell migration was recorded through bright-field optical microscopy. Gold nanospheres with different surface chemistries were introduced into the system. Surface chemistries include cationic PDADMAC, and anionic PSS and citrate. The hypothesis under consideration was that chemoattractant would adsorb mostly strongly to the nanoparticle of the opposite charge, thus affecting chemotaxis. A set of experiments was designed to test this hypothesis

Image forgery detection using textural features and deep learning

La croissance exponentielle et les progrès de la technologie ont rendu très pratique le partage de données visuelles, d'images et de données vidéo par le biais d’une vaste prépondérance de platesformes disponibles. Avec le développement rapide des technologies Internet et multimédia, l’efficacité de la gestion et du stockage, la rapidité de transmission et de partage, l'analyse en temps réel et le traitement des ressources multimédias numériques sont progressivement devenus un élément indispensable du travail et de la vie de nombreuses personnes. Sans aucun doute, une telle croissance technologique a rendu le forgeage de données visuelles relativement facile et réaliste sans laisser de traces évidentes. L'abus de ces données falsifiées peut tromper le public et répandre la désinformation parmi les masses. Compte tenu des faits mentionnés ci-dessus, la criminalistique des images doit être utilisée pour authentifier et maintenir l'intégrité des données visuelles. Pour cela, nous proposons une technique de détection passive de falsification d'images basée sur les incohérences de texture et de bruit introduites dans une image du fait de l'opération de falsification. De plus, le réseau de détection de falsification d'images (IFD-Net) proposé utilise une architecture basée sur un réseau de neurones à convolution (CNN) pour classer les images comme falsifiées ou vierges. Les motifs résiduels de texture et de bruit sont extraits des images à l'aide du motif binaire local (LBP) et du modèle Noiseprint. Les images classées comme forgées sont ensuite utilisées pour mener des expériences afin d'analyser les difficultés de localisation des pièces forgées dans ces images à l'aide de différents modèles de segmentation d'apprentissage en profondeur. Les résultats expérimentaux montrent que l'IFD-Net fonctionne comme les autres méthodes de détection de falsification d'images sur l'ensemble de données CASIA v2.0. Les résultats discutent également des raisons des difficultés de segmentation des régions forgées dans les images du jeu de données CASIA v2.0.The exponential growth and advancement of technology have made it quite convenient for people to share visual data, imagery, and video data through a vast preponderance of available platforms. With the rapid development of Internet and multimedia technologies, performing efficient storage and management, fast transmission and sharing, real-time analysis, and processing of digital media resources has gradually become an indispensable part of many people’s work and life. Undoubtedly such technological growth has made forging visual data relatively easy and realistic without leaving any obvious visual clues. Abuse of such tampered data can deceive the public and spread misinformation amongst the masses. Considering the facts mentioned above, image forensics must be used to authenticate and maintain the integrity of visual data. For this purpose, we propose a passive image forgery detection technique based on textural and noise inconsistencies introduced in an image because of the tampering operation. Moreover, the proposed Image Forgery Detection Network (IFD-Net) uses a Convolution Neural Network (CNN) based architecture to classify the images as forged or pristine. The textural and noise residual patterns are extracted from the images using Local Binary Pattern (LBP) and the Noiseprint model. The images classified as forged are then utilized to conduct experiments to analyze the difficulties in localizing the forged parts in these images using different deep learning segmentation models. Experimental results show that both the IFD-Net perform like other image forgery detection methods on the CASIA v2.0 dataset. The results also discuss the reasons behind the difficulties in segmenting the forged regions in the images of the CASIA v2.0 dataset

PyTelTools: Python scripts and GUI to automate Telemac post-processing tasks

Water Qualit