Fault Line

FAULT LINE examines the fragile humanity connected to the themes of sexuality, violence, addiction, family dynamics, and death. The book is not broken into sections; rather, as poems build upon one another to explore a narrative arc, FAULT LINE tracks a single speaker’s experience from girlhood to the verge of independent womanhood. The speaker employs formal structures such as the prose poem, sestina, and particularly the list poem to examine the fluidity of inner experience and also the culture at large while challenging the narrow definitions of femininity and masculinity. FAULT LINE works to not only address the question of blame but also the literal breaks in lines of poetry. By looking at a single speaker’s struggle, the book, like life, is both humorous and horrifying.

Effects of Increased Levels of Prenatal Mesotocin on Postnatal Individual Recognition and Stress Responsiveness in Northern bobwhite quail (Colinus Virginianus)

Oxytocin (OT) plays a key role in the mediation of social and stress behaviors across many species; however, the mechanism is still unclear. The present study investigated the influence of prenatal levels of mesotocin (MT; avian homologue of OT) on postnatal social and stress behavior in Northern bobwhite quail. Experiment one determined endogenous levels of MT during prenatal development using an enzyme-linked immunoassay kit. Experiment two examined the influence of increased MT during prenatal development on chicks' individual recognition ability and stress response to a novel environment. Experiment one showed MT levels increased significantly throughout embryonic development. Experiment two showed significant differences in stress behavior for chicks with increased MT during prenatal development; however, no significant differences were found for social behavior. This study suggests MT serves different functions depending on the stage of embryonic development and that increasing MT levels affects postnatal stress behavior, but not social behavior.

American Perceptions of British Regional Dialects

Sociolinguists have discussed problematic language ideologies, such as Standard Language Ideology (Lippi-Green 1997) extensively and social perceptions of Standard English in the U.S and U.K are well documented. However, most work in this area has focused on perceptions of dialects within national contexts. This study makes a novel contribution to the study of language attitudes, investigating perceptions of British regional dialects within the U.S. A survey was created to gauge perceptions of five British regional dialects (Liverpool, Leeds, Birmingham, Newcastle, London). 49 survey participants listened to audio clips of British regional dialect speakers and then completed a mapping activity, answered perception questions, and ranked each speaker on specific qualities. Results showed that speaker region had a significant effect on perception of almost all variables at a statistically significant rate, despite unfamiliarity with all but the London dialect. Results suggest that although participants are largely unfamiliar with varieties of English in England outside of London, they assessed them by recruiting pre-existing stereotypes about vernacular dialects.

"Not one word of it made any sense" : Hyperbolic Synecdoche in the British National Corpus

A distinct metonymic pattern was discovered in the course of conducting a corpus-based study of figurative uses of WORD. The pattern involved examples such as Not one word of it made any sense and I agree with every word. It was labelled ‘hyperbolic synecdoche’, defined as a case in which a lexeme which typically refers to part of an entity (a) is used to stand for the whole entity and (b) is described with reference to the end point on a scale. Specifically, the speaker/writer selects the perspective of a lower-level unit (such as word for ‘utterance’), which is quantified as NOTHING or ALL, thus forming a subset of ‘extreme case formulations’. Hyperbolic synecdoche was found to exhibit a restricted range of lexicogrammatical patterns involving word, with the negated NOTHING patterns being considerably more common than the ALL patterns. The phenomenon was shown to be common in metonymic uses in general, constituting one-fifth of all cases of metonymy in word. The examples of hyperbolic synecdoche were found not to be covered by the oftquoted ‘abbreviation’ rationale for metonymy; instead, they represent a more roundabout way of expression. It is shown that other cases of hyperbolic synecdoche exist outside of word and the domain of communication (such as ‘time’ and ‘money’).

Direct Support Professionals Recognition Week

This proclamation from Governor Nikki Haley proclaims September 11-17, 2016 as Direct Support Professionals Recognition Week.

Emotional contagion in consecutive interpreting: An empirical study with novice interpreters

It has recently been noticed that interpreters tend to converge with their speakers’ emotions under a process known as emotional contagion. Emotional contagion still represents an underinvestigated aspect of interpreting and the few studies on this topic have tended to focus more on simultaneous interpreting rather than consecutive interpreting. Korpal & Jasielska (2019) compared the emotional effects of one emotional and one neutral text on interpreters in simultaneous interpreting and found that interpreters tended to converge emotionally with the speaker more when interpreting the emotional text. This exploratory study follows their procedures to study the emotional contagion potentially caused by two texts among interpreters in consecutive interpreting: one emotionally neutral text and one negatively-valenced text, this last containing 44 negative words as triggers. Several measures were triangulated to determine whether the triggers in the negatively-valenced text could prompt a stronger emotional contagion in the consecutive interpreting of that text as compared to the consecutive interpreting of the emotionally neutral text, which contained no triggers—namely, the quality of the interpreters’ delivery; their heart rate variability values as collected with EMPATICA E4 wristbands; the analysis of their acoustic variations (i.e., disfluencies and rhetorical strategies); their linguistic and emotional management of the triggers; and their answers to the Italian version of the Positive and Negative Affect Schedule (PANAS) self-report questionnaire. Results showed no statistically significant evidence of an emotional contagion evoked by the triggers in the consecutive interpreting of the negative text as opposed to the consecutive interpreting of the neutral text. On the contrary, interpreters seemed to be more at ease while interpreting the negative text. This surprising result, together with other results of this project, suggests venues for further research.

Methods for integrating machine learning and constrained optimization

In the framework of industrial problems, the application of Constrained Optimization is known to have overall very good modeling capability and performance and stands as one of the most powerful, explored, and exploited tool to address prescriptive tasks. The number of applications is huge, ranging from logistics to transportation, packing, production, telecommunication, scheduling, and much more. The main reason behind this success is to be found in the remarkable effort put in the last decades by the OR community to develop realistic models and devise exact or approximate methods to solve the largest variety of constrained or combinatorial optimization problems, together with the spread of computational power and easily accessible OR software and resources. On the other hand, the technological advancements lead to a data wealth never seen before and increasingly push towards methods able to extract useful knowledge from them; among the data-driven methods, Machine Learning techniques appear to be one of the most promising, thanks to its successes in domains like Image Recognition, Natural Language Processes and playing games, but also the amount of research involved. The purpose of the present research is to study how Machine Learning and Constrained Optimization can be used together to achieve systems able to leverage the strengths of both methods: this would open the way to exploiting decades of research on resolution techniques for COPs and constructing models able to adapt and learn from available data. In the first part of this work, we survey the existing techniques and classify them according to the type, method, or scope of the integration; subsequently, we introduce a novel and general algorithm devised to inject knowledge into learning models through constraints, Moving Target. In the last part of the thesis, two applications stemming from real-world projects and done in collaboration with Optit will be presented.

An Image Recognition Software for Identification of Vehicle Homologation Tags

In recent years, we have witnessed great changes in the industrial environment as a result of the innovations introduced by Industry 4.0, especially in the integration of Internet of Things, Automation and Robotics in the manufacturing field. The project presented in this thesis lies within this innovation context and describes the implementation of an Image Recognition application focused on the automotive field. The project aims at helping the supply chain operator to perform an effective and efficient check of the homologation tags present on vehicles. The user contribution consists in taking a picture of the tag and the application will automatically, exploiting Amazon Web Services, return the result of the control about the correctness of the tag, the correct positioning within the vehicle and the presence of faults or defects on the tag. To implement this application we ombined two IoT platforms widely used in industrial field: Amazon Web Services(AWS) and ThingWorx. AWS exploits Convolutional Neural Networks to perform Text Detection and Image Recognition, while PTC ThingWorx manages the user interface and the data manipulation.

Compliance verification of virtual Phasor Measuring Unit using Ip-DFT algorithm according to the IEEE-60255-118-1(2018).

With the increase in load demand for various sectors, protection and safety of the network are key factors that have to be taken into consideration over the electric grid and distribution network. A phasor Measuring unit is an Intelligent electronics device that collects the data in the form of a real-time synchrophasor with a precise time tag using GPS (Global positioning system) and transfers the data to the grid command to monitor and assess the data. The measurements made by PMU have to be very precise to protect the relays and measuring equipment according to the IEEE 60255-118-1(2018). As a device PMU is very expensive to research and develop new functionalities there is a need to find an alternative to working with. Hence many open source virtual libraries are available to replicate the exact function of PMU in the virtual environment(Software) to continue the research on multiple objectives, providing the very least error results when verified. In this thesis, I executed performance and compliance verification of the virtual PMU which was developed using the I-DFT (Interpolated Discrete Fourier transforms) C-class algorithm in MATLAB. In this thesis, a test environment has been developed in MATLAB and tested the virtually developed PMU on both steady state and dynamic state for verifying the latest standard compliance(IEEE-60255-118-1).

Hierarchical Multi-Label Text Classification in a Low-Resource Setting

In this thesis we address a multi-label hierarchical text classification problem in a low-resource setting and explore different approaches to identify the best one for our case. The goal is to train a model that classifies English school exercises according to a hierarchical taxonomy with few labeled data. The experiments made in this work employ different machine learning models and text representation techniques: CatBoost with tf-idf features, classifiers based on pre-trained models (mBERT, LASER), and SetFit, a framework for few-shot text classification. SetFit proved to be the most promising approach, achieving better performance when during training only a few labeled examples per class are available. However, this thesis does not consider all the hierarchical taxonomy, but only the first two levels: to address classification with the classes at the third level further experiments should be carried out, exploring methods for zero-shot text classification, data augmentation, and strategies to exploit the hierarchical structure of the taxonomy during training.

Smart Mirror: remote verification of an identity document holder

Nowadays, some activities, such as subscribing an insurance policy or opening a bank account, are possible by navigating through a web page or a downloadable application. Since the user is often “hidden” behind a monitor or a smartphone, it is necessary a solution able to guarantee about their identity. Companies are often requiring the submission of a “proof-of-identity”, which usually consists in a picture of an identity document of the user, together with a picture or a brief video of themselves. This work describes a system whose purpose is the automation of these kinds of verifications.

Generative Neural Networks for image iper-resolution and improvement of Optical Character Recognition's performances

The usage of Optical Character Recognition’s (OCR, systems is a widely spread technology into the world of Computer Vision and Machine Learning. It is a topic that interest many field, for example the automotive, where becomes a specialized task known as License Plate Recognition, useful for many application from the automation of toll road to intelligent payments. However, OCR systems need to be very accurate and generalizable in order to be able to extract the text of license plates under high variable conditions, from the type of camera used for acquisition to light changes. Such variables compromise the quality of digitalized real scenes causing the presence of noise and degradation of various type, which can be minimized with the application of modern approaches for image iper resolution and noise reduction. Oneclass of them is known as Generative Neural Networks, which are very strong ally for the solution of this popular problem.

Recognition of human activities through eSteps insoles

Recognition of everyday human activity through mobile personal sensing technology plays a central role in the field of pervasive healthcare. The Bologna-based American company eSteps Inc. addresses the growing motor disability of the lower limbs by offering pre-, during and post-hospitalisation monitoring solutions with biomechanics and telerehabilitation protocol. It has developed a smart, customised and sustainable device to monitor motor activity, fatigue and injury risk for patients and a special app to share data with caregivers and medical specialists. The objective of this study is the development of an Artificial Intelligence model to recognize the activity performed by a person with Multiple Sclerosis or a healthy person through eSteps devices.

Verification of rupture disc sizing of Acrylic Reactor

The primary goal of this thesis is to verify the rupture disc sizing of the acrylic reactor. Primarily the test to check the sizing was divided into several stages. It went on to examine ideas to explain the concern and ethical ways, as well as remedies and suggestions to solve the issues and difficulties that were discovered. This thesis will highlight the gathering and arranging of reaction data (recipe composition, enthalpies, reaction temperature, and catalyst feeding times) of the products to be chosen, in accordance with pre-established criteria. To collaborate with the research and development team in the lab to carry out calorimetric testing for the important recipes that have been identified. The verification of the currently installed Rupture Discs in the plant based on the calorimetric test findings is the final stage. This thesis used two separate calorimetry techniques: Phi-TEC II adiabatic calorimetry and differential scanning calorimetry (DSC). The target of the experiment is to check and confirm the correct size of the reactor rupture disc. Arkema (Boretto/Coatex) plant (Emilia romagna) provided a recipe and a scenario following multiple meetings and discussions. The purpose of this technical paper is to describe the outcomes of adiabatic calorimetry performed at the lab scale so that the computation of the vents for a particular recipe and scenario can be verified.

Antibody Recognition Of Plasmodium Falciparum Infected Red Blood Cells By Symptomatic And Asymptomatic Individuals In The Brazilian Amazon.

In the Amazon Region, there is a virtual absence of severe malaria and few fatal cases of naturally occurring Plasmodium falciparum infections; this presents an intriguing and underexplored area of research. In addition to the rapid access of infected persons to effective treatment, one cause of this phenomenon might be the recognition of cytoadherent variant proteins on the infected red blood cell (IRBC) surface, including the var gene encoded P. falciparum erythrocyte membrane protein 1. In order to establish a link between cytoadherence, IRBC surface antibody recognition and the presence or absence of malaria symptoms, we phenotype-selected four Amazonian P. falciparum isolates and the laboratory strain 3D7 for their cytoadherence to CD36 and ICAM1 expressed on CHO cells. We then mapped the dominantly expressed var transcripts and tested whether antibodies from symptomatic or asymptomatic infections showed a differential recognition of the IRBC surface. As controls, the 3D7 lineages expressing severe disease-associated phenotypes were used. We showed that there was no profound difference between the frequency and intensity of antibody recognition of the IRBC-exposed P. falciparum proteins in symptomatic vs. asymptomatic infections. The 3D7 lineages, which expressed severe malaria-associated phenotypes, were strongly recognised by most, but not all plasmas, meaning that the recognition of these phenotypes is frequent in asymptomatic carriers, but is not necessarily a prerequisite to staying free of symptoms.