Nonlinear process fault detection and identification using kernel PCA and kernel density estimation

Kernel principal component analysis (KPCA) is an effective and efficient technique for monitoring nonlinear processes. However, associating it with upper control limits (UCLs) based on the Gaussian distribution can deteriorate its performance. In this paper, the kernel density estimation (KDE) technique was used to estimate UCLs for KPCA-based nonlinear process monitoring. The monitoring performance of the resulting KPCA–KDE approach was then compared with KPCA, whose UCLs were based on the Gaussian distribution. Tests on the Tennessee Eastman process show that KPCA–KDE is more robust and provide better overall performance than KPCA with Gaussian assumption-based UCLs in both sensitivity and detection time. An efficient KPCA-KDE-based fault identification approach using complex step differentiation is also proposed

Dynamic latent variable modelling and fault detection of Tennessee Eastman challenge process

Dynamic principal component analysis (DPCA) is commonly used for monitoring multivariate processes that evolve in time. However, it is has been argued in the literature that, in a linear dynamic system, DPCA does not extract cross correlation explicitly. It does not also give the minimum dimension of dynamic factors with non zero singular values. These limitations reduces its process monitoring effectiveness. A new approach based on the concept of dynamic latent variables is therefore proposed in this paper for extracting latent variables that exhibit dynamic correlations. In this approach, canonical variate analysis (CVA) is used to capture process dynamics instead of the DPCA. Tests on the Tennessee Eastman challenge process confirms the workability of the proposed approach

Statistical process monitoring of a multiphase flow facility

Industrial needs are evolving fast towards more flexible manufacture schemes. As a consequence, it is often required to adapt the plant production to the demand, which can be volatile depending on the application. This is why it is important to develop tools that can monitor the condition of the process working under varying operational conditions. Canonical Variate Analysis (CVA) is a multivariate data driven methodology which has been demonstrated to be superior to other methods, particularly under dynamically changing operational conditions. These comparative studies normally use computer simulated data in benchmark case studies such as the Tennessee Eastman Process Plant (Ricker, N.L. Tennessee Eastman Challenge Archive, Available at 〈http://depts.washington.edu/control/LARRY/TE/download.html〉 Accessed 21.03.2014). The aim of this work is to provide a benchmark case to demonstrate the ability of different monitoring techniques to detect and diagnose artificially seeded faults in an industrial scale multiphase flow experimental rig. The changing operational conditions, the size and complexity of the test rig make this case study an ideal candidate for a benchmark case that provides a test bed for the evaluation of novel multivariate process monitoring techniques performance using real experimental data. In this paper, the capabilities of CVA to detect and diagnose faults in a real system working under changing operating conditions are assessed and compared with other methodologies. The results obtained demonstrate that CVA can be effectively applied for the detection and diagnosis of faults in real complex systems, and reinforce the idea that the performance of CVA is superior to other algorithms

LiveChat: Video Comment Generation from Audio-Visual Multimodal Contexts

Live commenting on video, a popular feature of live streaming platforms, enables viewers to engage with the content and share their comments, reactions, opinions, or questions with the streamer or other viewers while watching the video or live stream. It presents a challenging testbed for AI agents, which involves the simultaneous understanding of audio-visual multimodal contexts from live streams and the ability to interact with human viewers through dialogue. As existing live streaming-based comments datasets contain limited categories and lack a diversity, we create a large-scale audio-visual multimodal dialogue dataset to facilitate the development of live commenting technologies. The data is collected from Twitch, with 11 different categories and 575 streamers for a total of 438 hours of video and 3.2 million comments. Moreover, we propose a novel multimodal generation model capable of generating live comments that align with the temporal and spatial events within the video, as well as with the ongoing multimodal dialogue context. Our initial results have demonstrated the effectiveness of the proposed model, providing a robust foundation for further research and practical applications in the field of live video interaction

DiactTOD: Learning Generalizable Latent Dialogue Acts for Controllable Task-Oriented Dialogue Systems

Dialogue act annotations are important to improve response generation quality in task-oriented dialogue systems. However, it can be challenging to use dialogue acts to control response generation in a generalizable way because different datasets and tasks may have incompatible annotations. While alternative methods that utilize latent action spaces or reinforcement learning do not require explicit annotations, they may lack interpretability or face difficulties defining task-specific rewards. In this work, we present a novel end-to-end latent dialogue act model (DiactTOD) that represents dialogue acts in a latent space. DiactTOD, when pre-trained on a large corpus, is able to predict and control dialogue acts to generate controllable responses using these latent representations in a zero-shot fashion. Our approach demonstrates state-of-the-art performance across a wide range of experimental settings on the MultiWOZ dataset, including zero-shot, few-shot, and full data fine-tuning with both end-to-end and policy optimization configurations.Comment: SIGDial 202

The Chlamydia trachomatis Type III Secretion Chaperone Slc1 Engages Multiple Early Effectors, Including TepP, a Tyrosine-phosphorylated Protein Required for the Recruitment of CrkI-II to Nascent Inclusions and Innate Immune Signaling

Chlamydia trachomatis, the causative agent of trachoma and sexually transmitted infections, employs a type III secretion (T3S) system to deliver effector proteins into host epithelial cells to establish a replicative vacuole. Aside from the phosphoprotein TARP, a Chlamydia effector that promotes actin re-arrangements, very few factors mediating bacterial entry and early inclusion establishment have been characterized. Like many T3S effectors, TARP requires a chaperone (Slc1) for efficient translocation into host cells. In this study, we defined proteins that associate with Slc1 in invasive C. trachomatis elementary bodies (EB) by immunoprecipitation coupled with mass spectrometry. We identified Ct875, a new Slc1 client protein and T3S effector, which we renamed TepP (Translocated early phosphoprotein). We provide evidence that T3S effectors form large molecular weight complexes with Scl1 in vitro and that Slc1 enhances their T3S-dependent secretion in a heterologous Yersinia T3S system. We demonstrate that TepP is translocated early during bacterial entry into epithelial cells and is phosphorylated at tyrosine residues by host kinases. However, TepP phosphorylation occurs later than TARP, which together with the finding that Slc1 preferentially engages TARP in EBs leads us to postulate that these effectors are translocated into the host cell at different stages during C.trachomatis invasion. TepP co-immunoprecipitated with the scaffolding proteins CrkI-II during infection and Crk was recruited to EBs at entry sites where it remained associated with nascent inclusions. Importantly, C. trachomatis mutants lacking TepP failed to recruit CrkI-II to inclusions, providing genetic confirmation of a direct role for this effector in the recruitment of a host factor. Finally, endocervical epithelial cells infected with a tepP mutant showed altered expression of a subset of genes associated with innate immune responses. We propose a model wherein TepP acts downstream of TARP to recruit scaffolding proteins at entry sites to initiate and amplify signaling cascades important for the regulation of innate immune responses to Chlamydia.Fil: Chen, Yi-Shan. University of Duke; Estados UnidosFil: Bastidas, Robert J.. University of Duke; Estados UnidosFil: Saka, Hector Alex. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. University of Duke; Estados UnidosFil: Carpenter, Victoria K.. Duke University Medical Center; . University of Duke; Estados UnidosFil: Richards, Kristian L.. Miami University; Estados UnidosFil: Plano, Gregory V.. Miami University; Estados UnidosFil: Valdivia, Raphael H.. University of Duke; Estados Unido

FAST Copper for Broadband Access

FAST Copper is a multi-year, U.S. NSF funded project that started in 2004, and is jointly pursued by the research groups of Mung Chiang at Princeton University, John Cioffi at Stanford University, and Alexander Fraser at Fraser Research Lab, and in collaboration with several industrial partners including AT&T. The goal of the FAST Copper Project is to provide ubiquitous, 100 Mbps, fiber/DSL broadband access to everyone in the US with a phone line. This goal will be achieved through two threads of research: dynamic and joint optimization of resources in Frequency, Amplitude, Space, and Time (thus the name 'FAST') to overcome the attenuation and crosstalk bottlenecks, and the integration of communication, networking, computation, modeling, and distributed information management and control for the multi-user twisted pair network

Advanced burning stages and fate of 8-10 Mo stars

The stellar mass range 8<M/Mo<12 corresponds to the most massive AGB stars and the most numerous massive stars. It is host to a variety of supernova progenitors and is therefore very important for galactic chemical evolution and stellar population studies. In this paper, we study the transition from super-AGB star to massive star and find that a propagating neon-oxygen burning shell is common to both the most massive electron capture supernova (EC-SN) progenitors and the lowest mass iron-core collapse supernova (FeCCSN) progenitors. Of the models that ignite neon burning off-center, the 9.5Mo model would evolve to an FeCCSN after the neon-burning shell propagates to the center, as in previous studies. The neon-burning shell in the 8.8Mo model, however, fails to reach the center as the URCA process and an extended (0.6 Mo) region of low Ye (0.48) in the outer part of the core begin to dominate the late evolution; the model evolves to an EC-SN. This is the first study to follow the most massive EC-SN progenitors to collapse, representing an evolutionary path to EC-SN in addition to that from SAGB stars undergoing thermal pulses. We also present models of an 8.75Mo super-AGB star through its entire thermal pulse phase until electron captures on 20Ne begin at its center and of a 12Mo star up to the iron core collapse. We discuss key uncertainties and how the different pathways to collapse affect the pre-supernova structure. Finally, we compare our results to the observed neutron star mass distribution.Comment: 20 pages, 14 figures, 1 table. Submitted to ApJ 2013 February 19; accepted 2013 June

Building national identity through the secondary school literature component in Malaysia / Raphael Thoo Yi Xian, Foong Soon Seng, Khor Zyeh Lyn and Pong Kok Shiong

This study seeks to analyze the themes prevalent in the Form 1-3 literary texts since the reintroduction of the literature component into Malaysia’s secondary school curricula in 2000, up to the most recent Kurikulum Standard Sekolah Menengah (KSSM) which is to be implemented in 2017. According to the National Education Blueprint (2013-2025), national identity is one of the six student aspirations. The incorporation of this theme would aid students to better understand their multiethnic culture and build their national identity (Kaur and Mahmor, 2014); this paper employed thematic analysis to further investigate the prevalence of the theme of nationhood in these texts. Findings show that only one text hinted the theme of nationhood out of 10. Other foreign texts contained a variety of themes other than that of nationhood. Past studies indicated that the literary texts should have a balance of local and foreign texts. Local works should be introduced first, especially in the lower secondary level (Ghazali, Setia, Muthusamy, and Jusoff, 2009; Isa and Mahmud, 2012; Kaur and Mahmor, 2014). However, the current literary texts in the KSSM do not correlate with the aspiration in the National Education Blueprint

User Simulation with Large Language Models for Evaluating Task-Oriented Dialogue

One of the major impediments to the development of new task-oriented dialogue (TOD) systems is the need for human evaluation at multiple stages and iterations of the development process. In an effort to move toward automated evaluation of TOD, we propose a novel user simulator built using recently developed large pretrained language models (LLMs). In order to increase the linguistic diversity of our system relative to the related previous work, we do not fine-tune the LLMs used by our system on existing TOD datasets; rather we use in-context learning to prompt the LLMs to generate robust and linguistically diverse output with the goal of simulating the behavior of human interlocutors. Unlike previous work, which sought to maximize goal success rate (GSR) as the primary metric of simulator performance, our goal is a system which achieves a GSR similar to that observed in human interactions with TOD systems. Using this approach, our current simulator is effectively able to interact with several TOD systems, especially on single-intent conversational goals, while generating lexically and syntactically diverse output relative to previous simulators that rely upon fine-tuned models. Finally, we collect a Human2Bot dataset of humans interacting with the same TOD systems with which we experimented in order to better quantify these achievements.Comment: 13 page