Verb, Complement and Case in Mandarin

The question of word order has been a major topic in the study of Mandarin, as in many other languages. Two major theoretical studies have appeared in the last several years, proposing different accounts of word order in the Mandarin verb phrase. These works, Huang (1982) and Travis (1984), complement the more descriptively oriented, and more comprehensive, Li and Thompson (1981). This paper will examine Mandarin word order in light of Huang's and Travis' suggestions. Both will be shown to exhibit certain problems -in particular, in not adequately acounting for the relationships of preverbal and postverbal phrases to each other and to the verb. This paper will propose that the facts can be most clearly related to properties of Mandarin case assignment

Differentiable Algorithm Networks for Composable Robot Learning

This paper introduces the Differentiable Algorithm Network (DAN), a composable architecture for robot learning systems. A DAN is composed of neural network modules, each encoding a differentiable robot algorithm and an associated model; and it is trained end-to-end from data. DAN combines the strengths of model-driven modular system design and data-driven end-to-end learning. The algorithms and models act as structural assumptions to reduce the data requirements for learning; end-to-end learning allows the modules to adapt to one another and compensate for imperfect models and algorithms, in order to achieve the best overall system performance. We illustrate the DAN methodology through a case study on a simulated robot system, which learns to navigate in complex 3-D environments with only local visual observations and an image of a partially correct 2-D floor map.Comment: RSS 2019 camera ready. Video is available at https://youtu.be/4jcYlTSJF4

Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe

Biosynthesis of Murine Terminal Deoxynucleotidyltransferase

An immunoprecipitation assay for measuring synthesis of murine terminal deoxynucleotidyltransferase (EC 2.7.7.31) has been developed using rabbit antiserum to calf terminal transferase. The antiserum precipitates a single Mr = 60,000 polypeptide (TdT-60) from all cell lines and tissues that contain enzymologically demonstrable terminal transferase. This polypeptide is not precipitated from labeled extracts of cells that lack terminal transferase by enzymological criteria. TdT-60 fractionates with terminal transferase during phosphocellulose chromatography and sediments with it in a sucrose gradient. TdT-60 is not detectably processed to lower molecular weight polypeptides, and terminal transferase activity sediments as a Mr = 60,000 activity; thus, we believe it to be the active form of terminal transferase. Using this assay we have demonstrated that terminal transferase is synthesized in both the murine thymus and the bone marrow at a rate proportional to its biochemically measured steady state level. After cortisone treatment of mice, the Mr = 60,000 polypeptide disappears from the thymus and then reappears as the thymus begins to be repopulated

Activity Recognition from Physiological Data using Conditional Random Fields

We describe the application of conditional random fields (CRF) to physiological data modeling for the application of activity recognition. We use the data provided by the Physiological Data Modeling Contest (PDMC), a Workshop at ICML 2004. Data used in PDMC are sequential in nature: they consist of physiological sessions, and each session consists of minute-by-minute sensor readings. We show that linear chain CRF can effectively make use of the sequential information in the data, and, with Expectation Maximization, can be trained on partially unlabeled sessions to improve performance. We also formulate a mixture CRF to make use of the identities of the human subjects to further improve performance. We propose that mixture CRF can be used for transfer learning, where models can be trained on data from different domains. During testing, if the domain of the test data is known, it can be used to instantiate the mixture node, and when it is unknown (or when it is a completely new domain), the marginal probabilities of the labels over all training domains can still be used effectively for prediction.Singapore-MIT Alliance (SMA

CAPIR: Collaborative Action Planning with Intention Recognition

We apply decision theoretic techniques to construct non-player characters that are able to assist a human player in collaborative games. The method is based on solving Markov decision processes, which can be difficult when the game state is described by many variables. To scale to more complex games, the method allows decomposition of a game task into subtasks, each of which can be modelled by a Markov decision process. Intention recognition is used to infer the subtask that the human is currently performing, allowing the helper to assist the human in performing the correct task. Experiments show that the method can be effective, giving near-human level performance in helping a human in a collaborative game.Comment: 6 pages, accepted for presentation at AIIDE'1

Fair Trade Purchasers: How Are They Different From Non-Purchasers?

The fair trade concept has received great attention from consumers due to its role in developing sustainable and people-oriented business models (Ma & Lee, 2012, p. 1). It was reported that more than 1.2 million marginalized producers in 58 developing countries benefited by fair trade certified sales in 2009 which represented approximately 4.4 billion USD (Fairtrade International, 2013)

CIB1 protects against MPTP-induced neurotoxicity through inhibiting ASK1.

Calcium and integrin binding protein 1 (CIB1) is a calcium-binding protein that was initially identified as a binding partner of platelet integrin αIIb. Although CIB1 has been shown to interact with multiple proteins, its biological function in the brain remains unclear. Here, we show that CIB1 negatively regulates degeneration of dopaminergic neurons in a mouse model of Parkinson\u27s disease using 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). Genetic deficiency of the CIB1 gene enhances MPTP-induced neurotoxicity in dopaminergic neurons in CIB1(-/-) mice. Furthermore, RNAi-mediated depletion of CIB1 in primary dopaminergic neurons potentiated 1-methyl-4-phenyl pyrinidium (MPP(+))-induced neuronal death. CIB1 physically associated with apoptosis signal-regulating kinase 1 (ASK1) and thereby inhibited the MPP(+)-induced stimulation of the ASK1-mediated signaling cascade. These findings suggest that CIB1 plays a protective role in MPTP/MPP(+)-induced neurotoxicity by blocking ASK1-mediated signaling

A semiparametric likelihood-based method for regression analysis of mixed panel-count data

Panel-count data arise when each study subject is observed only at discrete time points in a recurrent event study, and only the numbers of the event of interest between observation time points are recorded (Sun and Zhao, 2013). However, sometimes the exact number of events between some observation times is unknown and what we know is only whether the event of interest has occurred. In this article, we will refer this type of data to as mixed panel-count data and propose a likelihood-based semiparametric regression method for their analysis by using the nonhomogeneous Poisson process assumption. However, we establish the asymptotic properties of the resulting estimator by employing the empirical process theory and without using the Poisson assumption. Also, we conduct an extensive simulation study, which suggests that the proposed method works well in practice. Finally, the method is applied to a Childhood Cancer Survivor Study that motivated this study

Kansas Speaks Fall 2023 Statewide Public Opinion Survey

The 2023 Kansas Speaks fall survey was conducted from September 20 to October 10, 2023. A panel of 485 adult residents of Kansas age 18 and older were surveyed online to assess their attitudes and opinions regarding various issues of interest to Kansas citizens