Human-Related Emotional Stimuli Can Cause a Hippocampal and Thalamic Over-Response in People with Unstable Personalities

Hippocampus is crucial for the formation of emotional memory. We found the relationship between hippocampal responses to emotional stimuli and the mental stabilities of people in our preliminary study. In this study, we have also evaluated how the emotional stimuli would affect amygdala and thalamus in the brain, and how the personality stabilities could relate to the responses in the brain using functional magnetic resonance imaging (fMRI). We evaluated the subjects’ personality features with the Yatabe-Guilford Personality Test (Y-G test) and psychosomatic symptoms with the Cornell Medical Index (CMI). The subjects were categorized into the mentally stable group and the mentally unstable group according to the total scores of the Y-G test and the CMI. The brain functional responses under emotional stimuli were measured using fMRI. The region of interest (ROI) analysis was performed to abstract significant changes in order to compare responses among the different emotional stimuli. We conducted the regression analysis to abstract the relationship between the mean % signal change from fMRI and the personality stability. The fMRI results showed that the hippocampus, thalamus, and right amygdala activities under the human relationship stimuli increased with ascending value of mental instability. Our findings suggest that the memory process in the hippocampus and the threat alarm system in the thalamus under the human-related stimuli crucially influence the emotional reaction of mentally unstable people. These processes in the brain would affect the event that stresses on human relationships that often cause people to suffer from mental disorders.

Combining Motor Imagery and Action Observation with Vibratory Stimulation Increases Corticomotor Excitability in Healthy Young Adults

Vibratory stimulation but also motor imagery and action observation can induce corticomotor modulation, as a bottom-up stimulus and top-down stimuli, respectively. However, it remains unknown whether the combination of motor imagery, action observation, and vibratory stimulation can effectively increase corticomotor excitability. This study aimed to investigate the effect of motor imagery and/or action observation, in the presence or absence of vibratory stimulation, on the corticomotor excitability of healthy young adults. Vibratory stimulation was provided to the palm of the right hand. Action observation consisted in viewing a movie of someone else’s finger flexion and extension movements. The imagery condition required the participants to imagine they were moving their fingers while viewing the movie and attempting to move their fingers in accordance with the movie. Eleven right-handed healthy young adults were asked to perform six conditions randomly: 1) vibratory stimulation, imagery, and action observation, 2) vibratory stimulation and action observation, 3) vibratory stimulation and viewing of a blank screen, 4) imagery and action observation, 5) action observation, and 6) viewing of a blank screen. Single-pulse transcranial magnetic stimulation was conducted to assess corticomotor excitability and the peak-to-peak amplitude of the motor evoked potentials. The results showed that vibratory stimulation increases corticospinal excitability. The findings further revealed that performing motor imagery while viewing finger movement is more effective at inducing an augmentation of corticomotor excitability compared to action observation alone. Thus, the combination of motor imagery, action observation, and vibratory stimulation can effectively augment corticomotor excitability.

e-VLBI observations of GRB 080409 afterglow with an Australasian radio telescope network

Transcontinental e-VLBI observations were conducted in June 2008 with telescopes in Australia, China and Japan. Detections were made of the radio-loud quasar PKS B0727-115, which shows superlu- minal motion, and the intra-day variable quasar PKS B0524＋034. The latter source was used as a phase reference calibrator for observations at the position of the gamma-ray burst GRB 080409, for which an upper limit to the radio emission is set. Australia Telescope Compact Array data were also used to derive a limit on the radio flux density of the GRB afterglow. These observations demonstrate the capability to form a large Australasian radio telescope network for e-VLBI, with data transported and processed in realtime over high capacity networks. This campaign represents the first step towards more regular e-VLBI observations in this region.

Lightwave Modulation Techniques for Advanced Modulation Formats

Arbitrary waveform can be synthesized by two orthogonal components: in-phase and quadrature components. Thus, if we can manipulate the two components independently, various complicated modulation can be achieved. This technique is called vector modulation, which is commonly used in wireless radio communication systems where the carrier frequency is lower than 100GHz. On the other hand, in lightwave transmission systems, the carrier frequency is higher than 100THz, and the stability of the signal sources would be much poorer than in radio systems, so that the simplest modulation format (on-off-keying) was often used in commercial systems. However, recently, various modulation techniques such as quadrature amplitude modulation, etc., have been demonstrated by using precise and rapid lightwave control. In this report, we review recent progress in optical vector modulation technologies which can generate highspeed and complicated optical signals. Integrated optical modulators can synthesize multi-level signals in photonic circuits.

Two-Dimensional Projection-Based Wireless Intrusion Classification Using Lightweight EfficientNet

Internet of Things(IoT)networks leverage wireless communication protocols,which adversaries can exploit.Impersonation attacks,injection attacks,and flooding are several examples of different attacks existing in Wi-Fi networks.Intrusion Detection System(IDS)became one solution to distinguish those attacks from benign traffic.Deep learning techniques have been intensively utilized to classify the attacks.However,the main issue of utilizing deep learning models is projecting the data,notably tabular data,into an image.This study proposes a novel projection from wireless network attacks data into a grid-based image for feeding one of the Convolutional Neural Network(CNN)models,EfficientNet.We define the particular sequence of placing the attribute values in a grid that would be captured as an image.Combining the most important subset of attributes and EfficientNet,we aim for an accurate and lightweight IDS module deployed in IoT networks.We examine the proposed model using the Wi-Fi attacks dataset,called the AWID2 dataset.We achieve the best performance by a 99.91%F1 score and 0.11%false-positive rate.In addition,our proposed model achieved comparable results with other statistical machine learning models,which shows that our proposed model successfully exploited the spatial information of tabular data to maintain detection accuracy.

An Online Malicious Spam Email Detection System Using Resource Allocating Network with Locality Sensitive Hashing

In this paper, we propose a new online system that can quickly detect malicious spam emails and adapt to the changes in the email contents and the Uniform Resource Locator (URL) links leading to malicious websites by updating the system daily. We introduce an autonomous function for a server to generate training examples, in which double-bounce emails are automatically collected and their class labels are given by a crawler-type software to analyze the website maliciousness called SPIKE. In general, since spammers use botnets to spread numerous malicious emails within a short time, such distributed spam emails often have the same or similar contents. Therefore, it is not necessary for all spam emails to be learned. To adapt to new malicious campaigns quickly, only new types of spam emails should be selected for learning and this can be realized by introducing an active learning scheme into a classifier model. For this purpose, we adopt Resource Allocating Network with Locality Sensitive Hashing (RAN-LSH) as a classifier model with a data selection function. In RAN-LSH, the same or similar spam emails that have already been learned are quickly searched for a hash table in Locally Sensitive Hashing (LSH), in which the matched similar emails located in “well-learned” are discarded without being used as training data. To analyze email contents, we adopt the Bag of Words (BoW) approach and generate feature vectors whose attributes are transformed based on the normalized term frequency-inverse document frequency (TF-IDF). We use a data set of double-bounce spam emails collected at National Institute of Information and Communications Technology (NICT) in Japan from March 1st, 2013 until May 10th, 2013 to evaluate the performance of the proposed system. The results confirm that the proposed spam email detection system has capability of detecting with high detection rate.