Miracle-dz/UniFAR-FTUs huggingface dataset:
A negativity bias in detail generation during event simulation.

Novel negative events are simulated in more event-specific detail than novel positive events. In the present study, we set out to assess whether this negative event detail bias is specific to simulations of personal events or whether evoking negative valence, in the context of simulations of personal and nonpersonal events, is sufficient for boosting simulated event detail. Participants simulated novel negative and positive events that might take place in their future, the future of an acquaintance, or the future of a familiar individual with whom they have not had prior contact. Across 2 experiments, we found that novel negative events were simulated in more event-specific detail than novel positive events irrespective of whether the events under consideration were personal or nonpersonal. This pattern of results also emerged when negative and positive events did not differ on a subjective measure of arousal, indicating that negative valence may play a key role in encouraging detailed elaboration of novel negative events. Implications of our findings for the role of event simulation in adaptive behavior are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
The Earth Is Small for “Leviathans”: Long Distance Dispersal of Giant Viruses across Aquatic Environments

Giant viruses of ‘Megaviridae’ have the ability to widely disperse around the globe. We herein examined ‘Megaviridae’ communities in four distinct aquatic environments (coastal and offshore seawater, brackish water, and hot spring freshwater), which are distantly located from each other (between 74 and 1,765 km), using a meta-barcoding method. We identified between 593 and 3,627 OTUs in each sample. Some OTUs were detected in all five samples tested as well as in many of the Tara Oceans metagenomes, suggesting the existence of viruses of this family in a wide range of habitats and the ability to circulate on the planet.
Phosphoproteomics reveals conserved exercise‐stimulated signaling and AMPK regulation of store‐operated calcium entry

Exercise stimulates cellular and physiological adaptations that are associated with widespread health benefits. To uncover conserved protein phosphorylation events underlying this adaptive response, we performed mass spectrometry‐based phosphoproteomic analyses of skeletal muscle from two widely used rodent models: treadmill running in mice and in situ muscle contraction in rats. We overlaid these phosphoproteomic signatures with cycling in humans to identify common cross‐species phosphosite responses, as well as unique model‐specific regulation. We identified > 22,000 phosphosites, revealing orthologous protein phosphorylation and overlapping signaling pathways regulated by exercise. This included two conserved phosphosites on stromal interaction molecule 1 (STIM1), which we validate as AMPK substrates. Furthermore, we demonstrate that AMPK‐mediated phosphorylation of STIM1 negatively regulates store‐operated calcium entry, and this is beneficial for exercise in Drosophila. This integrated cross‐species resource of exercise‐regulated signaling in human, mouse, and rat skeletal muscle has uncovered conserved networks and unraveled crosstalk between AMPK and intracellular calcium flux.
Examining the temporal associations between self-reported memory problems and depressive symptoms in older adults

Abstract Objectives: Older adults commonly report problems with their memory which can elicit sadness and worry about future development of cognitive impairment. Conversely, ongoing depressive symptoms can negatively impact older adults’ perceptions of their memory performance. The current study examined the longitudinal associations between self-reported memory problems and depressive symptoms to explore which symptom tends to appear first. Method: Two datasets from ongoing observational, longitudinal studies of aging (Memory and Aging Project; Minority Aging Research Study) were used for secondary analyses. Older adults (n = 1,724; Mage = 77.03; SD = 7.54; 76.80% female; 32.26% Black) completed up to 18 annual assessments of self-reported memory (two items: perceived decline in memory and frequency of memory problems) and depressive symptoms. Multilevel models were used to examine intra-individual variability and time-lagged relationships between self-reported memory and depressive symptoms. Results: Concurrently, self-reported memory problems and depressive symptoms were significantly related; at times when older adults reported poorer memory, they also reported more depressive symptoms, regardless of the type of memory self-report. Prospectively, perceived memory decline predicted future depressive symptoms, but depressive symptoms did not predict future reports of memory decline. Self-reported frequency of memory problems did not predict future depressive symptoms or vice versa. Conclusion: The current study’s findings suggest a temporal relationship between perceived memory decline and depressive symptoms, such that perceived memory decline can lead to future depressive symptoms. These findings can inform future studies focused on developing a standardized assessment of self-reported memory that is separable from depressive symptoms.
Effect of temperature on post-diapause reproductive development in Listronotus maculicollis (Coleoptera: curculionidae)

Abstract The annual bluegrass weevil Listronotus maculicollis requires chilling exposure to terminate reproductive diapause during overwintering, but the effects of temperature on its post-diapause development in spring remain unclear. To explore this effect, overwintering adults were transferred from cold conditions (6°C/4°C, L:D 10:14) to different warm-up temperatures at L:D 12:12. When weevils were transferred to 7, 14 and 21°C in December and late January, the sizes of male and female reproductive organs were significantly smaller at 7°C than at 14 and 21°C. When weevils were transferred to 7, 9, 11, 13 and 15°C in late January, higher temperatures facilitated the post-diapause development. In both sexes, the sizes of reproductive organs and developmental rate increased with temperature. Reproductive organs did not grow significantly at 7°C in males and at 7–9°C in females, at which the percentage of developing weevils remained low. The time required for 50% of individuals to resume development was 44, 18, 13 and 8 days at 9, 11, 13 and 15°C, respectively, in males and 19, 14 and 8 days at 11, 13 and 15°C, respectively, in females. The threshold temperature for post-diapause development was 7.8°C in males, based on which 61.7 degree-days coincided with 50% of individuals developing. Under field conditions, the percentage of male and female maturity and insemination rate were low until early March, but all reached 100% by late March.
Effect of consuming high-fat diet on the morphological parameters of adrenal gland.

OBJECTIVES The incidence of obesity and obesity-assosiated pathologies continues to increase with profound adverse effects on health status in the developed countries. BACKGROUND We aimed to investigate the effect of high fat diet on the adrenal gland morphology. METHODS We fed the mice with either high-fat diet (60 % kcal from fat) or low-fat diet (10 % kcal from fat) for nine weeks. Unbiased stereological methods were used to evaluate the adrenal gland morphology. The sections were evaluated using Cavalieri's method and volume fraction approach. We calculated mean volume of adrenal gland, mean volume of adrenal medulla, VVadrenal medulla/adrenal gland, mean diameter of cromaffin cells, number of chromaffin cells in per unit volume (NVcc mm‒3), total number of cromaffin cells, VVzona glomerulosa/adrenal cortex, VVzona fasciculata/adrenal cortex , VVzona reticulosa/adrenal cortex. RESULTS The weight of adrenal gland, body weight intraperitoneal adipose tissue and adrenal gland weight in the obese mice significantly increased when compared with the control group. No changes were observed in the mean volume of adrenal gland, mean volume of adrenal medulla, VVzona glomerulosa/adrenal cortex, VVzona fasciculata/adrenal cortex, total number of cromaffin cells and diameter of cromaffin cells. However, NVcc mm-3 and VVzona reticulosa/adrenal cortex in the obese mice considerably increased compared with the control group. CONCLUSION The present results suggest that high fat diet adversely affects the adrenal gland morphology (Tab. 2, Fig. 6, Ref. 28).
Vulnerability Assessment and Mitigation for Industrial Critical Infrastructures with Cyber Physical Test Bed

Industrial and critical infrastructure automation systems, integrated with Industrial Internet of things (IIoT) are becoming lucrative targets for cyber-attackers. Typically these are cyber-physical systems, with cyber components such as sensors, actuators, micro-controllers, programmable logic controllers, or distributed control systems, field-area network, wide-area network etc., overseen by supervisory control and data acquisition systems (SCADA). After the discovery of the Stuxnet in 2009, and multiple other instances of cyber-attacks such as on a German steel plant, Ukraine power distribution system, chemical plant etc., research on securing these systems from cyber-attacks have become very important. However, it is often not permissible to attack the real operational critical systems for vulnerability assessment or testing mitigation techniques. Therefore, virtual or real test beds are required. In this paper we focus on a lab-scale test bed for a 3 phase power distribution system under industrial PLC control, instrumented with relay, power meters, various field protocol switches, supervised by an industrial SCADA system. Main contributions of the paper are (i) case studies of vulnerability assessment of the industrial components of this test bed – components that are being widely deployed in real critical systems throughout the world. (ii) the exploits and their security implications, especially their effect on the physical functioning of the systems; and (iii) mitigation techniques we have deployed to defend against such attacks. We are working with the original equipment manufacturers to disclose these vulnerabilities and in deploying mitigation techniques. A library of exploits and payloads which can be used in similar industrial control systems is under preparation.
Design of a "Micro-Module Bank" for Facilitating Higher Education Teachers to Adopt the Flipped Classroom in Practice

Nowadays, the flipped classroom has been widely used in higher education. In the flipped classroom model, pre-class learning resources such as pre-class videos normally play a significant role in content delivery. However, it requires teachers to invest a huge amount of time and energy in video production. According to CUHK's Academic Development Proposal for 2016-2019 which is aimed to promote eLearning, many pre-class resources called micro-modules to support the flipped classroom have been constructed by teachers from different faculties. To take full advantage of existing micro-module resources for freeing teachers from time-consuming video production, we propose to design a "micro-module bank" where existing pre-class videos and the corresponding mini-assessment can be directly adopted by teachers who want to harness the flipped classroom in their teaching practice. In addition, our micro-module bank will also provide teachers with pedagogical guidance such as in-class activities recommended by the original designers of these micro-modules.
Toward Solving Penn Treebank Parsing

of “Toward Solving Penn Treebank Parsing” by Do Kook Choe, Ph.D., Brown University, April 2017. A natural language parser recovers the latent grammatical structures of sentences. In many natural language processing (NLP) applications, parsing is applied to sentences first and the parses along with their sentences are fed to following NLP systems. For example, Google parses the entire web and applies a series of NLP programs to index the web and the quality of search results depends on the quality of parses. Parsing is difficult because sentences are ambiguous: a sentence has different syntactic structures depending on its meaning. For example, a sentence “Eugene wears a bow tie with polka dots” can have very different meanings depending on what “with polka dots” modifies. It is natural for us humans to infer that “with polka dots” modifies “a bow tie” because we have common sense that “with polka dots” rarely (if not never) describes an action “wears. ” Computers, however, lack common sense and learn such a relationship from large amounts of texts by just looking for statistical patterns. We explore four ways of improving parsing in this thesis: creating a training data of high quality parses using paraphrases; a model combination technique applied to n-best parsing; a generative reranker based on a language model; a discriminative parser inspired by neural machine translation. Our parse-reranker achieves human-level performance on the standard Penn Treebank dataset. Toward Solving Penn Treebank Parsing by Do Kook Choe B. A. , New York University, 2012 Sc. M. , Brown University, 2014 A dissertation submitted in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in the Department of Computer Science at Brown University Providence, Rhode Island April 2017 c © Copyright 2017 by Do Kook Choe This dissertation by Do Kook Choe is accepted in its present form by the Department of Computer Science as satisfying the dissertation requirement for the degree of Doctor of Philosophy. Date Eugene Charniak, Director Recommended to the Graduate Council Date Michael L. Littman, Reader Date Erik B. Sudderth, Reader University of California, Irvine Date Stefanie Tellex, Reader Approved by the Graduate Council Date Andrew G. Campbell Dean of the Graduate School
Classification of Credit Card Default Clients Using LS-SVM Ensemble

Finding knowledge from a database and turning it into useful information is a big challenge. The use of machine learning helps analyze data and contribute to delivering results that can be acted upon by the company. SVM is one of machine learning method that has better performance than other machine learning method but sensitive to parameter setting and training sample. the performance accuracy of the SVM method can be improved using the LS-SVM and Ensemble method. This research proposes the LS-SVM ensemble to identify the prospective credit cards client that will default. The Least Square SVM ensemble method has the highest percentage with a difference of 1.7% from SVM and 0.6% from Least Square SVM.
Home Automation Using Energy Usage

Home automation is usually referred to the technique for controlling devices autonomously based on certain parameters. However, most of the system that exist is more of remote control rather than home automation. In this research paper, we’re presenting a model for automating homes based on energy usage as the primary parameter. The autonomous control based on primary parameters are based on the application of machine learning using artificial neural network(s). The model is essentially a recommendation system and presents the user with alerts or recommendation. Remote control and monitoring act as the base of the system and two architecture(s) are presented to attain the same. The first is a distributed system where, multiple nodal points communicate with the global server independently. The second is a centralised system, where multiple nodal points communicate with a centralised local server and the server communicates with the central global server hosted over internet. The paper also discusses an advanced secured method for communication between the edge device and the server. As we are dealing with low power and low processing capability devices, security is a very big challenge. Here we have proposed an advanced multi-stage security-based system to provide maximum possible security in embedded platforms.
An Improved Visual Tracking Algorithm Based on Efficient Convolution Operators

Effcient Convolution Operators (ECO) is one of the most outstanding visual tracking algorithms in recent years, it combines two methods of Deep Learning and Discriminative Correlation Filter (DCF), and has excellent performance in VOT2016, UAV123, OTB-2015 and TempleColor. The paper propose integrating ECO and Fully Convolutional Networks (FCN) to achieve state-of-the-art segmentation for ECO. In our experiments, the original CNN model of ECO is replaced by the FCN model. Compared with the traditional Convolutional Neural Networks (CNN), the FCN has higher accuracy of segmentation and can input any size of image. We perform comprehensive experiments which obtained 0.653 area under curve (AUC) and 0.861 precision plot (DP) on OTB-2013 dataset.
User-Coupling Angle-Domain Adaptive Filtering Based Frequency Synchronization for Massive MIMO Multiuser Uplink

In this paper, we develop a user-coupling angle-domain adaptive filtering (UC-ADAF)-based frequency synchronization method for the uplink of a massive multiple-input multiple-output (MIMO) multiuser network, in the presence of highly overlapping angle-of-arrival (AoA) regions among coexisting users. In order to deal with the extremely severe multiuser interference (MUI) among users with highly overlapping AoA region, we propose to couple them together and perform joint carrier frequency offset (CFO) estimation and data detection. For the coupled users, the UC angle-constraining matrix (UC-ACM) can inherently suppress the interference from non-overlapping users and the UC-ADAF vectors are adaptively optimized to further eliminate the MUI from slightly overlapping users. Moreover, the challenging problem of addressing the severe MUI caused by highly overlapping users is circumvented through joint estimation and detection. The effectiveness of the proposed UC-ADAF approach and its robustness against overlapping degree among coupled users and the total number of mutually overlapping users, as well as its superiority over the existing methods, are demonstrated by numerical results.
Secure Beamformer Design for Cognitive Satellite Terrestrial Networks

This paper proposes a beamforming (BF) scheme based on multi-objective optimization (MOO) for a cognitive satellite-terrestrial network (CSTN), where the eavesdroppers (Eves) attempt to intercept the private message conveyed to an intended user. By considering that the maximization of achievable secrecy rate (ASR) and the minimization of transmit power can not be achieved simultanesouly, we adopt artificial noise (AN) and apply the weighted Tchebycheff approach to formulate a multi-object optimization problem. Then, by introducing several auxiliary variables, applying semidefinite programming (SDP) and means of Taylor approximation, the original nonconvex MOO problem is transformed into a convex one with a series of linear constraints, and the BF weight vector and AN vector can be obtained. Finally, simulation results are given to illustrate the tradeoff between the conflicting objectives and the superiority of the proposed algorithm in comparison with the commonly used schemes.
Research on Chinese Spam Comments Detection Based on Chinese Characteristics

With the popularity of mobile Internet, people can express their views in the Internet anytime and anywhere. Personal attack speech is a kind of cyber violence that not only does great harm to others, but also makes the whole network environment become chaotic. In order to detect spam comment in Chinese network, in this paper we carefully analyze the datasets and select four characteristics from the real data. With the help of Chinese language and literature, a malicious dictionary can be obtained by extending the algorithm on the basis of a very small number of seed words. Compared with manual dictionary selection, our method saves a lot of labor costs. Besides, it performs well in natural language processing with the help of deep learning model. Finally, the experimental results show that this method can detect malicious comments in “NetEase” news effectively.
Implementation of Human-Like Driver Model Based on Recurrent Neural Networks

Driver model is the most basic and important model for moving direction control of autonomous vehicles and it has been extensively studied from the perspective of precision and robustness of control, driving safety, and riding comfort. However, human-like driver model is a rarely mentioned research issue. In this paper, we first establish three preview-based driver models in PreScan+Simulink and collect human drivers’ steering data on four two-way and two-lane free curved roads with 20 experienced drivers and one experimental vehicle under four specified speeds. Then, the similarities between steering wheel angles of preview-based models and those of human drivers are compared through dynamic time warping (DTW). From the calculation results of DTW and analysis of human drivers’ gaze positions, it shows that the preview-based models are hard to reflect the characteristics of human drivers’ maneuver. To this end, we propose a human-like driver model based on the continuity of human drivers’ steering wheel angles. The experienced drivers’ steering wheel angels are modeled with three different kinds of multivariate multi-step recurrent neural networks (RNNs) and the inputs of models are historical speeds, historical road curvatures, future road curvatures, and historical steering wheel angles, as well as the outputs are future steering wheel angles. By comparing the three RNN-based driver models with different configuration structures and historical steps, it is found that the long short-term memory (LSTM) model has the best prediction performance in validation and testing period. In this way, a data-driven human-like driver model is developed to generate human-like steering wheel angles on curved roads.
Turnkey Methodology for Characteristic Impedance Extraction of Embedded Transmission Lines

For an embedded transmission line, as interconnect in integrated circuits or in high density PCB, the extraction of its characteristic impedance from S-parameters measurements remains a challenge. Robust de-embedding procedures are required to avoid parasitic effects induced by the electrical environment to access to the transmission line under test. A deembedding methodology allowing to retrieve the propagation parameters of a planar transmission line buried between its access transitions is presented. The procedure is developed using previous works published by reputed authors and improved for an easy and efficiency use in integrated environments. Characteristic impedance extraction results are given from 10 MHz to 67 GHz for a CPW transmission line and compared to modeling results from a 3D full-wave solver. A robustness study is performed to validate the reliability of this technique over existing ones.
Instrument Control through GPIB-USB Communication with LabVIEW

This paper discusses a LabVIEW based virtual instrument (VI) developed for the instrument control of a mixed signal oscilloscope with a built-in arbitrary waveform generator. This instructional material was developed for a course on virtual instrumentation offered to the undergraduate and postgraduate students. The assignments were focused on Lab-VIEW programing, interfacing and control of instruments with LabVIEW and data acquisition. The virtual instrument control was fascinating to the students and it was demonstrated through six different MSO functions discussed here. The developed VI also incorporated instrument drivers, enabling a better understading of communication and integration of the same, with LabVIEW VIs. This VI is used for signal generation, acquisition, spectrum analysis, and display of a signal. The effectiveness of student learning is reported from the student’s responses.