Honorable Mention Contest Entry: Consonant Acquisition in Infants with Cochlear Implants and Their Normal-Hearing Peers

This is Minh-Chau Vu\u27s submission for the 2019 Kevin and Tam Ross Undergraduate Research Prize, which received an honorable mention. It contains her essay on using library resources, a summary of her research project on consonant acquisition in infants with cochlear implants and their normal-hearing peers, and her works cited list. Minh-Chau is a sophomore at Chapman University, majoring in Biological Sciences. Her faculty mentor is Dr. Mary Fagan

Multi-modality imaging in cardiac resynchronization therapy:In silico and in vivo analyses

Heart failure (HF) is a serious condition that carries a high disease burden, affecting 1-2% of the adult population. In approximately 25% of HF patients, the underlying mechanism of HF is attributed to a disturbance in the electrical conduction system of the heart, resulting in asynchronous contractions of the left and right heart chambers, a condition also known as dyssynchrony. Cardiac resynchronization therapy (CRT) is a treatment that involves simultaneous pacing of the two ventricles to reduce dyssynchrony. CRT has been shown to be effective in reducing symptoms, morbidity, and improving survival, and has become an important guideline-recommended treatment. However, there is a broad range of patient outcomes with CRT, which has led to investigations into mechanisms that contribute to the variability in response. This thesis aims to enhance the efficacy of CRT by improving patient selection and optimizing device implantation. To achieve this, the thesis seeks to enhance the understanding of the electrocardiogram for more precise patient selection in CRT and to develop an image-guided strategy for better positioning of the left ventricular lead

Asymptotic Equivalence of Triangular Differential Equations in Hilbert Spaces

In this article, we study conditions for the asymptotic equivalence of differential equations in Hilbert spaces. We also discuss the relationship between the properties of solutions of differential equations of triangular form and those of truncated differential equations.Вивчено умови асимптотичної еквівалентності диференціальних рівнянь у гільбертових просторах. Розглянуто також зв'язок між властивостями розв'язків диференціальних рівнянь трикутної форми та неповних диференціальних рівнянь

LP-OVOD: Open-Vocabulary Object Detection by Linear Probing

This paper addresses the challenging problem of open-vocabulary object detection (OVOD) where an object detector must identify both seen and unseen classes in test images without labeled examples of the unseen classes in training. A typical approach for OVOD is to use joint text-image embeddings of CLIP to assign box proposals to their closest text label. However, this method has a critical issue: many low-quality boxes, such as over- and under-covered-object boxes, have the same similarity score as high-quality boxes since CLIP is not trained on exact object location information. To address this issue, we propose a novel method, LP-OVOD, that discards low-quality boxes by training a sigmoid linear classifier on pseudo labels retrieved from the top relevant region proposals to the novel text. Experimental results on COCO affirm the superior performance of our approach over the state of the art, achieving $\textbf{40.5}$ in $\text{AP}_{novel}$ using ResNet50 as the backbone and without external datasets or knowing novel classes during training. Our code will be available at https://github.com/VinAIResearch/LP-OVOD.Comment: Accepted to WACV 202

The impact of the free cash flow and the firm’s life cycle on dividend policy: Evidence from Vietnam’s listed firms

The decision on dividend policy is one of the most important decisions in the financial sector which still has inconsistent results leading to various debates among researchers. This study aims to examine the impact of the factor of the free cash flow (FCF) and the firm’s life cycle (RE/TE) on the dividend payout ratio. A panel data of 110 listed firms from the period 2014 - 2020 on Ho Chi Minh stock exchange (HOSE) are used to test the hypothesis. The estimators used to analyze the data are fixed effect model (FEM), random effect model (REM), and then generalized method of moments (GMM) applied to remedy the common errors of panel data. The finding shows that firms in the growth stage will use the free cash flow to invest in a profitable project instead of paying dividends to shareholders. In the meantime, other firm characteristics such as firm size, return on assets, and debt have a positive impact on the dividend payout ratio

Cost - efficiency and Share Performance: Evidence from ASEAN-6 banks

This study empirically investigates the cost efficiency of 88 banks in six largest economies in the Association of Southeast Asian Nations (ASEAN-6) including Indonesia, Malaysia, Philippines, Singapore, Thailand and Vietnam over the period of 2012-2017 by employing a parametric method Stochastic Frontier Analysis (SFA). We find out that comparing with banks in domestic market, banks in ASEAN-6 are relatively cost efficient, with efficiency scores ranging from 0.9030 to 0.9763. Furthermore, the average efficiency scores are also quite consistent from year to year. However, while comparing cross-country, efficiency varies greatly. Singaporean banks are the most efficient while banks from Philippines are the least efficient. Our results also indicate that larger banks were more efficient than smaller banks. We then continue to link bank cost efficiency with their subsequent share prices by applying Fixed Effects and Generalised Method of Moments (GMM). The study suggests that there is positive relationship between change in cost efficiency and share returns while using fixed effects regression. However, this relationship was diminishing when applying GMM

Automatische Charakterisierung metabolischer Effekte der onkologischen Immuntherapie mittels 18F-FDG-PET/MRT

Durch die Automatisierung der medizinischen Bildgebung und deren Analyse durch Anwendung maschineller Lernverfahren können potenziell Objektivität und Reliabilität gesichert, sowie Zeit und Ressourcen gespart werden. Ein großer Anwendungsbereich findet sich in der Onkologie, besonders bei der Erkennung und Klassifizierung von Tumoren, deren Phänotyp und deren Malignitätsstatus. Weiterführende Studien beschreiben die Nutzung zur Voraussage über Therapieansprechen, Prognosebestimmung und Risikoanalyse. Den Grundbaustein hierfür bildet die semantische Segmentierung, bei der Organe und Strukturen in Bildgebungen voxelweise klassifiziert werden, um den Suchbereich für weitere Analysen zu definieren. In der vorliegenden Arbeit wurden PET/MRT-Untersuchungen von Patienten mit malignem Melanom unter Immuntherapie betrachtet. Die frühe und genaue Vorhersage des Therapieansprechens kann gerade in diesem Patientenkollektiv entscheidend sein, da die Ansprechraten mäßig sind und die Therapie mit signifikanten Nebenwirkungen und hohen Kosten verbunden ist. Anatomische und funktionelle Parameter der PET/MRT-Bildgebung können potenziell genutzt werden, um ein Therapieansprechen einzuschätzen und therapeutische Entscheidungen zu unterstützen. Ziel dieser Arbeit war die automatische Extraktion anatomischer und funktioneller Parameter aus multiparametrischen Ganzkörper-PET/MRT-Daten mittels maschineller Lernverfahren. Dazu wurde ein Algorithmus aus der Klasse der Convolutional Neural Networks (CNN) trainiert und validiert. Das Patientenkollektiv setzte sich aus 24 Melanompatienten unter Immuntherapie zusammen, die jeweils drei zeitlich versetzte PET/MRT-Untersuchungen durchliefen. In der Summe ergab dies 72 Bildgebungen, von denen zwei aufgrund von Artefakten und Importfehlern von der Studie ausgeschlossen wurden. Anhand manuell segmentierter Trainingsdaten, welche auf T1-gewichteten MRT-Bildern erzeugt wurden, konnte das CNN für die automatische Segmentierung von Milz, Knochenmark der Wirbelsäule und Leber trainiert werden. Die quantitative Evaluation dieser automatischen Organsegmentierung im Vergleich zur manuellen Organsegmentierung ergab eine insgesamt hohe Genauigkeit. Die Leber erreichte die höchste Genauigkeit (DSC 0,9187), gefolgt von der Milz (DSC 0,8282) und den Wirbelkörpern (DSC 0,6966). Dabei konnte der Zeitaufwand der Segmentierung von ca. 1 Stunde pro Datensatz (manuell) auf etwa 4 Sekunden (automatisch) beschleunigt werden. Aus den automatischen Segmentierungen extrahierte Parameter (ADC, durchschnittlicher und maximaler SUV, Fettgehalt und Organvolumen) zeigten eine hohe Übereinstimmung zur manuellen Segmentierung. Beim durchschnittlichen SUV wurde eine mittlere relative Abweichung von 4,10 % ± 5,05 % bei der Leber, 0,83 % ± 10,86 % bei der Milz und 1,91 % ± 10,42 % bei den Wirbelkörpern erreicht. Als proof of concept wurde geprüft, ob sich der SUV (jeweils über Leber, Milz und Wirbelkörpern) als Parameter eignet, um einen Responsestatus der Immuntherapie abzuleiten. Obwohl sich Hinweise auf den Anstieg des SUV unter Immuntherapie über Leber, Milz und Wirbelkörper fanden, konnte in unserem Patientenkollektiv keine Signifikanz nachgewiesen werden. Zusammenfassend lässt sich sagen, dass eine automatische Ableitung anatomischer und funktioneller Parameter aus multiparametrischen PET/MRT Daten mittels Deep Learning möglich ist und zufriedenstellende Ergebnisse erreicht wurden. Voraussetzung sind ausreichende und gute Trainingsdaten. Es empfiehlt sich, die Methodik an einem größeren und diverseren Patientenkollektiv zu validieren

BLENDED LEARNING IN BADMINTON TRAINING FOR PROFESSIONALS: STUDENTS’ PERCEPTIONS AND PERFORMANCE IMPACTS

As with other subject areas, badminton instruction for practitioners is experiencing a lot of changes under the impact of technology. Recently there has been the possibility of moving badminton training classes to the online platform but there is no consensus on its efficacy. This study is conducted to study the effects of blended learning activities on the perceptions and performance of students in physical education. Forty students in physical education are selected and divided into two groups: an experimental group, and a control group. All groups in face-to-face learning sessions have the same curriculum, course-book, equipment and teaching method. The questionnaire and interview data show that students in blended class sessions had positive perceptions of learning activities.  Article visualizations

ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing

English and Chinese, known as resource-rich languages, have witnessed the strong development of transformer-based language models for natural language processing tasks. Although Vietnam has approximately 100M people speaking Vietnamese, several pre-trained models, e.g., PhoBERT, ViBERT, and vELECTRA, performed well on general Vietnamese NLP tasks, including POS tagging and named entity recognition. These pre-trained language models are still limited to Vietnamese social media tasks. In this paper, we present the first monolingual pre-trained language model for Vietnamese social media texts, ViSoBERT, which is pre-trained on a large-scale corpus of high-quality and diverse Vietnamese social media texts using XLM-R architecture. Moreover, we explored our pre-trained model on five important natural language downstream tasks on Vietnamese social media texts: emotion recognition, hate speech detection, sentiment analysis, spam reviews detection, and hate speech spans detection. Our experiments demonstrate that ViSoBERT, with far fewer parameters, surpasses the previous state-of-the-art models on multiple Vietnamese social media tasks. Our ViSoBERT model is available only for research purposes.Comment: Accepted at EMNLP'2023 Main Conferenc

Estimates for the elastic moduli of 2D aggregate of hexagonal-shape orthorhombic crystals with in-plane random crystalline orientations

Numerical finite element simulations on the homogenization problem for large samples of particular 2D hexagonal-shape-geometry random orientation aggregates from the base crystals of orthorhombic symmetry have been performed. At sufficiently large random-aggregate samples, the scatter intervals of the macroscopic 2D bulk and shear elastic moduli converge toward the Voigt-Reuss-Hill bounds, and then our recently constructed theoretical estimates, which have been specified for the aggregates