Membership generation using multilayer neural network

There has been intensive research in neural network applications to pattern recognition problems. Particularly, the back-propagation network has attracted many researchers because of its outstanding performance in pattern recognition applications. In this section, we describe a new method to generate membership functions from training data using a multilayer neural network. The basic idea behind the approach is as follows. The output values of a sigmoid activation function of a neuron bear remarkable resemblance to membership values. Therefore, we can regard the sigmoid activation values as the membership values in fuzzy set theory. Thus, in order to generate class membership values, we first train a suitable multilayer network using a training algorithm such as the back-propagation algorithm. After the training procedure converges, the resulting network can be treated as a membership generation network, where the inputs are feature values and the outputs are membership values in the different classes. This method allows fairly complex membership functions to be generated because the network is highly nonlinear in general. Also, it is to be noted that the membership functions are generated from a classification point of view. For pattern recognition applications, this is highly desirable, although the membership values may not be indicative of the degree of typicality of a feature value in a particular class

Social Exclusion Online: A Literature Review and suggestions for Future Research

Because the belonging need is fundamental, an exclusion experience thwarts this need and causes cognitive, behavioral, and affective consequences for human functioning. This paper aims to integrate the enumerated effects of social exclusion in a real-world setting. Also, as the boundary between the real world and cyberspace is blurred, it became crucial to understand the dynamics of social relationships in an online environment. Thus, this paper also intends to review the influences of social exclusion in the online environment. Based on a review of previous findings, this study provides several suggestions for future research. There are interesting research opportunities to investigate how ostracized people react to being excluded, especially in cyberspace

Adapting Text-based Dialogue State Tracker for Spoken Dialogues

Although there have been remarkable advances in dialogue systems through the dialogue systems technology competition (DSTC), it remains one of the key challenges to building a robust task-oriented dialogue system with a speech interface. Most of the progress has been made for text-based dialogue systems since there are abundant datasets with written corpora while those with spoken dialogues are very scarce. However, as can be seen from voice assistant systems such as Siri and Alexa, it is of practical importance to transfer the success to spoken dialogues. In this paper, we describe our engineering effort in building a highly successful model that participated in the speech-aware dialogue systems technology challenge track in DSTC11. Our model consists of three major modules: (1) automatic speech recognition error correction to bridge the gap between the spoken and the text utterances, (2) text-based dialogue system (D3ST) for estimating the slots and values using slot descriptions, and (3) post-processing for recovering the error of the estimated slot value. Our experiments show that it is important to use an explicit automatic speech recognition error correction module, post-processing, and data augmentation to adapt a text-based dialogue state tracker for spoken dialogue corpora.Comment: 8 pages, 5 figures, Accepted at the DSTC 11 Workshop to be located at SIGDIAL 202

Hygroscopicity of Nucleated Nanoparticles in CLOUD 7 Experiments

We investigated hygroscopicity of nucleated nanoparticles derived from dimethylamine and α-pinene with sulfuric acid during CLOUD 7 (Cosmic Leaving OUtdoor Droplets) campaign at CERN. The hygroscopicity of nucleated nanoparticles from 10 to 20 nm in mobility diameter was measured with a nano tandem differential mobility analyzer (nano-TDMA). Here, we present preliminary results from the CLOUD 7 experiments

A New Paradigm of Pharmaceutical Drug Delivery Systems (DDS) : Challenges for Space, Time, and Shapes

Using 3D food printing with the Internet of Things (IoT) technology, patients can receive diagnoses and prescriptions from their doctors while in the comfort of their homes. The patient-specific prescription has been innovated by converging 3D food printing technology with drug delivery systems (DDSs). Quantitative drug dosages can be incorporated into the composition of food and produced in any shape within a short time. Automating food and DDSs makes promising implications for healing patients remotely, as well. Each of these aspects, along with IoT technology, have contributed to increased health care for patients, no matter their location. The quantitative discharge of vitamin C melted in water, mayonnaise, ketchup, and peanut butter has been verified using the Piston Typed Extrusion (PTE) method. Designs with different curves and shapes were repeatedly printed with a head speed of 1.6×10-2 m/s, and it was confirmed that effective control while printing the shapes was possible. The Hagen-Poiseuille (HP) formula was utilized to simulate the overall printing time. This simulation affirmed that increasing the head speed from 1.6×10-2 m/s to 4.0×10-2 m/s had reduced the printing time consistently, but the time was not reduced continuously after 4.0×10-2 m/s, depending on the materials’ viscosities and how much curvature exists in the designs. The precision of printing was adjusted within 5% of the theoretical value during printing, and the IoT technology allowed printing of the materials within five minutes, regardless of the patient’s location.   Article Type: Original Researc

“iCub, clean the table!” A robot learning from demonstration approach using Deep Neural Networks

Autonomous service robots have become a key research topic in robotics, particularly for household chores. A typical home scenario is highly unconstrained and a service robot needs to adapt constantly to new situations. In this paper, we address the problem of autonomous cleaning tasks in uncontrolled environments. In our approach, a human instructor uses kinestethic demonstrations to teach a robot how to perform different cleaning tasks on a table. Then, we use Task Parametrized Gaussian Mixture Models (TP-GMMs) to encode the demonstrations variability, while providing appropriate generalization abilities. TP-GMMs extend Gaussian Mixture Models with an auxiliary set of reference frames, in order to extrapolate the demonstrations to different task parameters such as movement locations, amplitude or orientations. However, the reference frames (that parametrize TP-GMMs) can be very difficult to extract in practice, as it may require segmenting the cluttered images of the working table-top. Instead, in this work the reference frames are automatically extracted from robot camera images, using a deep neural network that was trained during human demonstrations of a cleaning task. This approach has two main benefits: (i) it takes the human completely out of the loop while performing complex cleaning tasks; and (ii) the network is able to identify the specific task to be performed directly from image data, thus also enabling automatic task selection from a set of previously demonstrated tasks. The system was implemented on the iCub humanoid robot. During the tests, the robot was able to successfully clean a table with two different types of dirt (wiping a marker’s scribble or sweeping clusters of lentils).info:eu-repo/semantics/publishedVersio

Charge-spin correlation in van der Waals antiferromagenet NiPS3

Strong charge-spin coupling is found in a layered transition-metal trichalcogenide NiPS3, a van derWaals antiferromagnet, from our study of the electronic structure using several experimental and theoretical tools: spectroscopic ellipsometry, x-ray absorption and photoemission spectroscopy, and density-functional calculations. NiPS3 displays an anomalous shift in the optical spectral weight at the magnetic ordering temperature, reflecting a strong coupling between the electronic and magnetic structures. X-ray absorption, photoemission and optical spectra support a self-doped ground state in NiPS3. Our work demonstrates that layered transition-metal trichalcogenide magnets are a useful candidate for the study of correlated-electron physics in two-dimensional magnetic material.Comment: 6 pages, 3 figur

Heat Treatment Effect on Mechanical Properties of 3D Printed Polymers

PMMA, PC, and PEEK are thermoplastic polymers that possess favorable properties for biomedical applications. These polymers have been used in fields of maxillo-facial, orthopedic, intraocular surgery, and bio-implant, due to their excellent mechanical properties, osteoinductive potential, and antimicrobial capabilities. In this study, the effect of heat treatment on the mechanical properties of 3D printed polymers was characterized. By modifying printing temperature and post heat treatment process, the mechanical properties were specifically tailored for different applications, correlating with the properties of the implants that are commonly made using molding processes