Electrochemically-modulated liquid chromatography (EMLC): Column design, retention processes, and applications

This dissertation explores a new separation technique, electrochemically modulated liquid chromatography (EMLC), from the column design, retention processes, to the pharmaceutical applications. A literature review, general summary, and perspectives of this technique are also described. Chapter 1 presents the newly designed EMLC column. The principal modification of this design is to connect the porous stainless steel column as counter electrode as opposed to part of working electrode in the previous design. The improvement in performance from this modification results in a shorter response time to changes in applied potential (E appl) and a better control of E appl at cathodic values of E appl. The performance of the new design is presented and compared to the previous design;Chapter 2 describes the study of retention processes of analytes on EMLC. A mixture of substituted aromatic compounds has been investigated to examine the influence of E appl to retention. Results show that donor-acceptor interactions dominate the retention processes and that the analytes with larger submolecular polarity parameters or higher energy levels of highest occupied molecular orbital display larger sensitivities in retention to changes in E appl.;In Chapter 3, EMLC has been applied to the separation of a mixture of structurally similar corticosteroids. Changes in the E appl to the column markedly affected the efficiency as well as the elution order of the separation, with the mixture fully resolved at large negative values of E appl. Mechanistic aspects in terms of the influence of changes in the E appl on the extent of the interactions between these analytes and the stationary phase are briefly discussed;In Chapter 4, the separation of a mixture of benzodiazepines has been investigated by EMLC. Changes in the E appl to the stationary phase strongly alter the retention of all analytes. The observed dependencies of retention have the unusual effect of stretching both ends of the chromatogram as E appl becomes more negative. That is, the retention for some of the benzodiazepines increases as E appl moves negatively, whereas that for some of the other benzodiazepines decreases. The combined weight of these dependencies results in the ability to achieve a fully resolved separation of the mixture while only marginally increasing the overall elution time

Child sexual abuse, interpersonal difficulties, and staying in relationships with intimate partner violence: a preliminary study

This study explores the relationship between child sexual abuse, interpersonal difficulties, and intimate partner violence. Three inventories were used to assess each factor in this research: child violence experience (5 items), interpersonal difficulties (16 items), and adult violence victimization (3 items). Twenty-ninth females from the Syracuse University Couple and Family Therapy Center completed inventories. Respondents were categorized into four groups: no victimization (group 1); child violence (CV) victimization with no adult revictimization (group 2); CV with single adult victimization (group 3); CV with long-term intimate partner violence (IPV) victimization (group 4). The researcher hypothesized that 1) child sexual abuse (CSA) will lead to adult interpersonal difficulties; 2) Re-victimized CSA survivors will have more interpersonal difficulties than those CSA survivors who have not experienced IPV; and 3) CSA victims who stay in IPV relationships will have more interpersonal difficulties. The sample size was small so descriptive and correlational analysis was also conducted. The preliminary results show that CSA can lead to some interpersonal difficulties. The pattern of interpersonal difficulties across four groups were presented, which indicated that people who stayed in abusive intimate relationships might have different profiles from people who experienced single or short-term revictimization. Limitations of this study and suggestions for future researches were included

Deep Open Intent Classification with Adaptive Decision Boundary

Open intent classification is a challenging task in dialogue systems. On the one hand, it should ensure the quality of known intent identification. On the other hand, it needs to detect the open (unknown) intent without prior knowledge. Current models are limited in finding the appropriate decision boundary to balance the performances of both known intents and the open intent. In this paper, we propose a post-processing method to learn the adaptive decision boundary (ADB) for open intent classification. We first utilize the labeled known intent samples to pre-train the model. Then, we automatically learn the adaptive spherical decision boundary for each known class with the aid of well-trained features. Specifically, we propose a new loss function to balance both the empirical risk and the open space risk. Our method does not need open intent samples and is free from modifying the model architecture. Moreover, our approach is surprisingly insensitive with less labeled data and fewer known intents. Extensive experiments on three benchmark datasets show that our method yields significant improvements compared with the state-of-the-art methods. The codes are released at https://github.com/thuiar/Adaptive-Decision-Boundary.Comment: Accepted by AAAI 2021 (Main Track, Long Paper

Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement

Identifying new user intents is an essential task in the dialogue system. However, it is hard to get satisfying clustering results since the definition of intents is strongly guided by prior knowledge. Existing methods incorporate prior knowledge by intensive feature engineering, which not only leads to overfitting but also makes it sensitive to the number of clusters. In this paper, we propose constrained deep adaptive clustering with cluster refinement (CDAC+), an end-to-end clustering method that can naturally incorporate pairwise constraints as prior knowledge to guide the clustering process. Moreover, we refine the clusters by forcing the model to learn from the high confidence assignments. After eliminating low confidence assignments, our approach is surprisingly insensitive to the number of clusters. Experimental results on the three benchmark datasets show that our method can yield significant improvements over strong baselines.Comment: Accepted by AAAI202