Map of Jackson City opposite the city of Washington at the south end of the Potomac Free Bridge.

Shows proposed town of Jackson City in Alexandria County D.C.

Sketch showing the positions of the redoubts and batteries of the lines of the U.S. Forces in front of Petersburg /

Shows fortifications and names of some residents.

A map of that part of Georgia occupied by the Cherokee Indians, taken from an actual survey made during the present year 1831, in pursuance of an act of the general assembly of the state : this interesting tract of country contains four millions three hundred & sixty six thousand five hundred & fifty four acres, many rich gold mines & many delightful situations & though in some parts mountainous, some of the richest land belonging to the state /

Relief shown by hachures.

SC INFORMATION LETTER #16-8

A citator of advisory opinions issued by the Department from 1987 through June 2016 is available. This citator provides assistance in determining what effect new advisory opinions have on those previously published. This citator has been compiled as a reference tool for the convenience of taxpayers, tax practitioners, and Department employees

Image summarisation: human action description from static images

Dissertação de Mestrado, Processamento de Linguagem Natural e Indústrias da Língua, Faculdade de Ciências Humanas e Sociais, Universidade do Algarve, 2014

ViDRILO: The Visual and Depth Robot Indoor Localization with Objects information dataset

In this article we describe a semantic localization dataset for indoor environments named ViDRILO. The dataset provides five sequences of frames acquired with a mobile robot in two similar office buildings under different lighting conditions. Each frame consists of a point cloud representation of the scene and a perspective image. The frames in the dataset are annotated with the semantic category of the scene, but also with the presence or absence of a list of predefined objects appearing in the scene. In addition to the frames and annotations, the dataset is distributed with a set of tools for its use in both place classification and object recognition tasks. The large number of labeled frames in conjunction with the annotation scheme make this dataset different from existing ones. The ViDRILO dataset is released for use as a benchmark for different problems such as multimodal place classification and object recognition, 3D reconstruction or point cloud data compression.

Production et articulation des appartenances au mouvement Slow Fashion sur Twitter

Ce mémoire explore les productions et les articulations des appartenances au mouvement Slow Fashion sur Twitter. En réaction au modèle actuel prédominant du Fast Fashion, basé sur une surproduction et une surconsommation des vêtements, le Slow Fashion sensibilise les différents acteurs du secteur de la mode à avoir une vision plus consciente des impacts de leurs pratiques sur les travailleurs, les communautés et les écosystèmes (Fletcher, 2007) et propose une décélération des cycles de production et de consommation des vêtements. L’enjeu de cette recherche est de montrer que le Slow Fashion se dessine notamment à travers les relations entres les différents acteurs sur Twitter et que l'ensemble de ces interactions prend la forme d'un rhizome, c’est-à-dire d’un système dans lequel les éléments qui le composent ne suivent aucune arborescence, aucune hiérarchie et n’émanent pas d’un seul point d’origine. (Deleuze & Guattari, 1976) Sur Twitter, les appartenances au Slow Fashion font surface, se connectent les unes aux autres par des liens de nature différente. Consommateurs, designers, entreprises, journalistes, etc., ces parties prenantes construisent collectivement le Slow Fashion comme mouvement alternatif à la mode mainstream actuelle. Mon cadre théorique s’est construit grâce à une analyse de la littérature des concepts de mode, d’identité et d’appartenance afin de mieux appréhender le contexte dans lequel le mouvement a émergé. Puis, j’ai également réalisé une étude exploratoire netnographique sur Twitter au cours de laquelle j’ai observé, tout en y participant, les interactions sur la plateforme abordant le Slow Fashion et/ou la mode éthique. Publiée sur ce blogue (http://belongingtoslowfashion.blogspot.ca), cette « creative presentation of research » (Chapman & Sawchuk, 2012) ne constitue pas une histoire présentant les prétendues origines de ce mouvement mais plutôt une photographie partielle à un certain moment du Slow Fashion. Construite tel un rhizome, elle n’a ni début, ni fin, ni hiérarchie. J’invite alors les lectrices/lecteurs à choisir n’importe quelle entrée et à délaisser toute logique linéaire et déductive. Cette exploration sera guidée par des liens hypertextes ou des annotations qui tisseront des connexions avec d’autres parties ou feront émerger d’autres questionnements. Il s’agit d’offrir une introduction aux enjeux que pose le Slow Fashion, d’ouvrir la voie à d’autres recherches et d’autres réflexions, ou encore de sensibiliser sur ce sujet.

Production et articulation des appartenances au mouvement Slow Fashion sur Twitter

Ce mémoire explore les productions et les articulations des appartenances au mouvement Slow Fashion sur Twitter. En réaction au modèle actuel prédominant du Fast Fashion, basé sur une surproduction et une surconsommation des vêtements, le Slow Fashion sensibilise les différents acteurs du secteur de la mode à avoir une vision plus consciente des impacts de leurs pratiques sur les travailleurs, les communautés et les écosystèmes (Fletcher, 2007) et propose une décélération des cycles de production et de consommation des vêtements. L’enjeu de cette recherche est de montrer que le Slow Fashion se dessine notamment à travers les relations entres les différents acteurs sur Twitter et que l'ensemble de ces interactions prend la forme d'un rhizome, c’est-à-dire d’un système dans lequel les éléments qui le composent ne suivent aucune arborescence, aucune hiérarchie et n’émanent pas d’un seul point d’origine. (Deleuze & Guattari, 1976) Sur Twitter, les appartenances au Slow Fashion font surface, se connectent les unes aux autres par des liens de nature différente. Consommateurs, designers, entreprises, journalistes, etc., ces parties prenantes construisent collectivement le Slow Fashion comme mouvement alternatif à la mode mainstream actuelle. Mon cadre théorique s’est construit grâce à une analyse de la littérature des concepts de mode, d’identité et d’appartenance afin de mieux appréhender le contexte dans lequel le mouvement a émergé. Puis, j’ai également réalisé une étude exploratoire netnographique sur Twitter au cours de laquelle j’ai observé, tout en y participant, les interactions sur la plateforme abordant le Slow Fashion et/ou la mode éthique. Publiée sur ce blogue (http://belongingtoslowfashion.blogspot.ca), cette « creative presentation of research » (Chapman & Sawchuk, 2012) ne constitue pas une histoire présentant les prétendues origines de ce mouvement mais plutôt une photographie partielle à un certain moment du Slow Fashion. Construite tel un rhizome, elle n’a ni début, ni fin, ni hiérarchie. J’invite alors les lectrices/lecteurs à choisir n’importe quelle entrée et à délaisser toute logique linéaire et déductive. Cette exploration sera guidée par des liens hypertextes ou des annotations qui tisseront des connexions avec d’autres parties ou feront émerger d’autres questionnements. Il s’agit d’offrir une introduction aux enjeux que pose le Slow Fashion, d’ouvrir la voie à d’autres recherches et d’autres réflexions, ou encore de sensibiliser sur ce sujet.

Supporting Scientific Research Through Machine and Deep Learning: Fluorescence Microscopy and Operational Intelligence Use Cases

Although the debate of what data science is has a long history and has not reached a complete consensus yet, Data Science can be summarized as the process of learning from data. Guided by the above vision, this thesis presents two independent data science projects developed in the scope of multidisciplinary applied research. The first part analyzes fluorescence microscopy images typically produced in life science experiments, where the objective is to count how many marked neuronal cells are present in each image. Aiming to automate the task for supporting research in the area, we propose a neural network architecture tuned specifically for this use case, cell ResUnet (c-ResUnet), and discuss the impact of alternative training strategies in overcoming particular challenges of our data. The approach provides good results in terms of both detection and counting, showing performance comparable to the interpretation of human operators. As a meaningful addition, we release the pre-trained model and the Fluorescent Neuronal Cells dataset collecting pixel-level annotations of where neuronal cells are located. In this way, we hope to help future research in the area and foster innovative methodologies for tackling similar problems. The second part deals with the problem of distributed data management in the context of LHC experiments, with a focus on supporting ATLAS operations concerning data transfer failures. In particular, we analyze error messages produced by failed transfers and propose a Machine Learning pipeline that leverages the word2vec language model and K-means clustering. This provides groups of similar errors that are presented to human operators as suggestions of potential issues to investigate. The approach is demonstrated on one full day of data, showing promising ability in understanding the message content and providing meaningful groupings, in line with previously reported incidents by human operators.

Investigating interpreter-mediated interaction in the Chinese bilingual courtroom: Analysis based on a multimodal corpus

This research project is based on the Multimodal Corpus of Chinese Court Interpreting (MUCCCI [mutʃɪ]), a small-scale multimodal corpus on the basis of eight authentic court hearings with Chinese-English interpreting in Mainland China. The corpus has approximately 92,500 word tokens in total. Besides the transcription of linguistic and para-linguistic features, utilizing the facial expression classification rules suggested by Black and Yacoob (1995), MUCCCI also includes approximately 1,200 annotations of facial expressions linked to the six basic types of human emotions, namely, anger, disgust, happiness, surprise, sadness, and fear (Black & Yacoob, 1995). This thesis is an example of conducting qualitative analysis on interpreter-mediated courtroom interactions through a multimodal corpus. In particular, miscommunication events (MEs) and the reasons behind them were investigated in detail. During the analysis, although queries were conducted based on non-verbal annotations when searching for MEs, both verbal and non-verbal features were considered indispensable parts contributing to the entire context. This thesis also includes a detailed description of the compilation process of MUCCCI utilizing ELAN, from data collection to transcription, POS tagging and non-verbal annotation. The research aims at assessing the possibility and feasibility of conducting qualitative analysis through a multimodal corpus of court interpreting. The concept of integrating both verbal and non-verbal features to contribute to the entire context is emphasized. The qualitative analysis focusing on MEs can provide an inspiration for improving court interpreters’ performances. All the constraints and difficulties presented can be regarded as a reference for similar research in the future.

Supervised and weakly supervised counting-by-segmentation: the fluorescent microscopy use case

This thesis focuses on automating the time-consuming task of manually counting activated neurons in fluorescent microscopy images, which is used to study the mechanisms underlying torpor. The traditional method of manual annotation can introduce bias and delay the outcome of experiments, so the author investigates a deep-learning-based procedure to automatize this task. The author explores two of the main convolutional-neural-network (CNNs) state-of-the-art architectures: UNet and ResUnet family model, and uses a counting-by-segmentation strategy to provide a justification of the objects considered during the counting process. The author also explores a weakly-supervised learning strategy that exploits only dot annotations. The author quantifies the advantages in terms of data reduction and counting performance boost obtainable with a transfer-learning approach and, specifically, a fine-tuning procedure. The author released the dataset used for the supervised use case and all the pre-training models, and designed a web application to share both the counting process pipeline developed in this work and the models pre-trained on the dataset analyzed in this work.

Da Petrarca a Fracassetti: i Rerum memorandarum libri in una traduzione inedita

Ancora poco è stato detto riguardo alla traduzione del trattato latino Rerum memorandarum libri di Petrarca compiuta da Giuseppe Fracassetti (1802-1883) nel 1860 e rimasta inedita. Fracassetti, avvocato e poliedrico studioso di Fermo, si dedicò all'edizione e al volgarizzamento di diverse opere latine di Petrarca – nello specifico, del trattato De sui ispius et multorum ignorantia reso in Della propria ed altrui ignoranza nel 1858, delle lettere Familiari (il cui corpus fu dato alle stampe prima nella versione latina, nel 1859-1863, e poi in traduzione nel 1863-1867) e Senili (stampate nel biennio 1869-1870 solo in italiano) – ma non pubblicò il suo lavoro sui Rerum memorandum libri. A partire dalla ricostruzione del processo editoriale e poi della fortuna delle opere edite, questa tesi propone un approfondimento sulle carte autografe – conservate presso la biblioteca Civica "Romolo Spezioli" di Fermo – che documentano la traduzione di Fracassetti dei Rerum memorandarum libri, i suoi Libri delle cose memorabili. Il lavoro presenta inoltre un focus sulla lingua adottata e avvia un'indagine sistematica sulle fonti latine utilizzate – e non dichiarate – dallo studioso fermano, attraverso lo studio delle annotazioni marginali appuntate da Fracassetti sui fogli. L’elaborato dà luce a questa traduzione inedita e incompleta – si interrompe infatti al terzo libro – pubblicandone il testo (corredato di un apparato in doppia fascia che registra da un lato la stratigrafia correttoria e, dall’altro, le postille presenti sulle carte).

Production Lot Detection through Synthetic Generated Data

Correctness of information gathered in production environments is an essential part of quality assurance processes in many industries, this task is often performed by human resources who visually take annotations in various steps of the production flow. Depending on the performed task the correlation between where exactly the information is gathered and what it represents is more than often lost in the process. The lack of labeled data places a great boundary on the application of deep neural networks aimed at object detection tasks, moreover supervised training of deep models requires a great amount of data to be available. Reaching an adequate large collection of labeled images through classic techniques of data annotations is an exhausting and costly task to perform, not always suitable for every scenario. A possible solution is to generate synthetic data that replicates the real one and use it to fine-tune a deep neural network trained on one or more source domains to a different target domain. The purpose of this thesis is to show a real case scenario where the provided data were both in great scarcity and missing the required annotations. Sequentially a possible approach is presented where synthetic data has been generated to address those issues while standing as a training base of deep neural networks for object detection, capable of working on images taken in production-like environments. Lastly, it compares performance on different types of synthetic data and convolutional neural networks used as backbones for the model.

Knowledge-based Chord Embeddings

This thesis develops AI methods as a contribution to computational musicology, an interdisciplinary field that studies music with computers. In systematic musicology a composition is defined as the combination of harmony, melody and rhythm. According to de La Borde, harmony alone "merits the name of composition". This thesis focuses on analysing the harmony from a computational perspective. We concentrate on symbolic music representation and address the problem of formally representing chord progressions in western music compositions. Informally, chords are sets of pitches played simultaneously, and chord progressions constitute the harmony of a composition. Our approach combines ML techniques with knowledge-based techniques. We design and implement the Modal Harmony ontology (MHO), using OWL. It formalises one of the most important theories in western music: the Modal Harmony Theory. We propose and experiment with different types of embedding methods to encode chords, inspired by NLP and adapted to the music domain, using both statistical (extensional) knowledge by relying on a huge dataset of chord annotations (ChoCo), intensional knowledge by relying on MHO and a combination of the two. The methods are evaluated on two musicologically relevant tasks: chord classification and music structure segmentation. The former is verified by comparing the results of the Odd One Out algorithm to the classification obtained with MHO. Good performances (accuracy: 0.86) are achieved. We feed a RNN for the latter, using our embeddings. Results show that the best performance (F1: 0.6) is achieved with embeddings that combine both approaches. Our method outpeforms the state of the art (F1 = 0.42) for symbolic music structure segmentation. It is worth noticing that embeddings based only on MHO almost equal the best performance (F1 = 0.58). We remark that those embeddings only require the ontology as an input as opposed to other approaches that rely on large datasets.