Miracle-dz/UniFAR-FTUs huggingface dataset:
2 Connections Between Word Embeddings and Topic Models

Word embedding models such as the skip-gram learn vector representations of words’ semantic relationships, and document embedding models learn similar representations for documents. On the other hand, topic models provide latent representations of the documents’ topical themes. To get the benefits of these representations simultaneously, we propose a unifying algorithm, called neural embedding allocation (NEA), which deconstructs topic models into interpretable vector-space embeddings of words, topics, documents, authors, and so on, by learning neural embeddings to mimic the topic models. We showcase NEA’s effectiveness and generality on LDA, author-topic models and the recently proposed mixed membership skip gram topic model and achieve better performance with the embeddings compared to several state-of-the-art models. Furthermore, we demonstrate that using NEA to smooth out the topics improves coherence scores over the original topic models when the number of topics is large.
1 Personality , Group Decision-Making and Leadership

This paper explores the effect of personality traits on: (1) the willingness to make risk-taking decisions on behalf of a group, (2) the nature of "choice shifts", i.e. the difference between the amount of risk taken in the group context and individually. Openness and agreeableness emerge as significant determinants of the willingness to lead: non-leader women and non-leader men score lower on openness and higher in agreeableness compared to both leader men and leader women. Neuroticism explains the within-gender variance in individual risk-taking among women, who are on average more risk-averse than men. Subjects in general behave more cautiously when they are making risky decisions on behalf of a group. Among men, a higher agreeableness score implies higher caution in group decisions, while conscientiousness leads to less caution. In contrast, among women, a higher conscientiousness score implies higher caution in the group context, suggesting that the two genders might interpret the social norms in group decision-making differently.
Works Title Downlink Achievable Rate Analysis in Massive MIMO Systems With One-Bit DACs Permalink

In this letter, we investigate the downlink performance of massive multiple-input multiple-output (MIMO) systems where the base station is equipped with one-bit analogto-digital/digital-to-analog converters (ADC/DACs). Considering training-based transmission, we assume the base station (BS) employs the linear minimum mean-squared-error (LMMSE) channel estimator and treats the channel estimate as the true channel to precode the data symbols. We derive an expression for the downlink achievable rate for matched-filter (MF) precoding. A detailed analysis of the resulting power efficiency is pursued using our expression of the achievable rate. Numerical results are presented to verify our analysis. In particular it is shown that, compared with conventional massive MIMO systems, the performance loss in one-bit massive MIMO systems can be compensated for by deploying approximately 2.5 times more antennas at the BS.
Dyslipidemia and the risk of preeclampsia: genetic causes and related modifiers

Published reports examining lipid levels during pregnancy and preeclampsia have been inconsistent. The objective of this meta-analysis is to test the association between preeclampsia and maternal total cholesterol, HDL-C, LDL-C, non-HDL-C, and triglyceride levels, measured during pregnancy. A systematic literature search of studies reporting maternal lipid levels in women with preeclampsia and normotensive pregnant women was conducted. Seventy-four studies met all eligibility criteria and were included in the meta-analysis. Weighted mean differences in lipid levels were calculated using a random-effects model. Statistical heterogeneity was investigated using the I statistic. Meta-regression was utilized to identify sources of heterogeneity. Preeclampsia was associated with elevated total cholesterol, non-HDL-C, and triglyceride levels, regardless of gestational age at time of blood draw, and lower levels of HDL-C in the third trimester. A marginal association was found with LDL-C levels. Statistical heterogeneity was detected in all analyses. Meta-regression analyses suggested that differences in body mass index across studies may be partially responsible for the heterogeneity in the triglyceride and LDL-C analyses. This systematic review and meta-analysis demonstrates that women who develop preeclampsia have elevated levels of maternal total cholesterol, non-HDL-C, and triglycerides during all trimesters of pregnancy and lower levels of HDL-C during the third trimester. Introduction Preeclampsia is a potentially devastating disease of pregnancy that complicates 28% of all pregnancies in the United States and can threaten the life of both the mother and her unborn child. Manifesting after 20 weeks of gestation, preeclampsia is a multi-
A Novel Offset and Finite-Gain Compensated Switched-Capacitor Amplifier with Input Correlated Level Shifting

A novel offset and finite-gain compensated differential switched-capacitor(SC) amplifier is presented. Incorporating the correlated double sampling (CDS) technique and input correlated level shifting (CLS) technique together, the DC offset and DC gain error of SC amplifier are further reduced by a factor of op-amp DC gain compared with the conventional offset and finite-gain compensated SC amplifier. The effectiveness of the new scheme has been analyzed and verified by extensive simulations. An SC amplifier with the proposed scheme is designed in 130 nm CMOS technology. Simulated results show that with an op-amp having a low DC gain of 30 dB and an input offset of 10 mV, the proposed SC amplifier achieves an output offset and DC-gain error of 154 µV and 0.05%, respectively, which are significantly improved compared with 1.155 mV and 0.42% achieved in the conventional SC amplifier.
Prediction Before and After the Dodd-Frank Act

Our paper examines whether the regulation reform enacted by the 2010 Dodd-Frank Act was successful in improving the prediction of qualified auditor opinions. Information was compiled on 47 randomly selected industries from 2006 to 2009 and from 2011 to 2012, preand post-Act periods. A logit model was developed with the dependent variable indicating whether the firm received a qualified opinion and with the independent variables including publicly available financial and stock return variables, and using Altman’s Z-score. The findings of this project shed light on the benefits of the DoddFrank Act.
Developing and Investigating Slot Waveguide Designs

Slot waveguides are useful and precise tools for trapping and examining nanoscopic bioparticles. In the waveguides, particles are trapped by the optical forces that arise from electromagnetic waves confined in the slot. Idealized models of lithium niobate (LiNbO3) slot waveguides have been previously developed, but those did not take into account that limitations of the construction process lead to the waveguides having non-straight sidewalls. Therefore, the aim of this study was to investigate how developing the models by angling the walls affected the slot confinement. It was found that slot confinement and a distinct TE mode could still be achieved, with a decreased optimum subcore width and an increased optimum slot width. Additionally, it was found that for some waveguide geometries, the difference in effective index between the TE and the TM modes came close to zero, which could be used for polarization-independent applications in the future.
Convolutional Neural Network Layer Reordering for Acceleration

We propose a new optimization technique to speed up performance of convolutional neural networks(CNN). One of the challenges for CNN is large execution time. Many CNN models are found to have a repeatable layer pattern, i.e. convolutional, activation and pooling layer in that order. We show that, for a class of functions, performed in activation layer and pooling layer, it is possible to reconfigure CNN, to reduce the number of operations performed in a network, without changing output of the network. Experimental results demonstrate that using the proposed reordering, we can reduce the total time for VGG by almost 5% on CPU and time for activation layer, by almost 75% for CPU, and by a range of 20% to 67% for GPU, for 2x2 max pool-
THE ROLE OF VOWEL AND CONSONANT DURATION IN VOWEL LENGTH CATEGORISATION BY DJAMBARRPUYŊU LISTENERS

Australian Indigenous languages are often reported to have compensatory lengthening of consonants after phonemically short vowels. There have, however, been very few perception studies of these languages, and none, to date, focused on vowels. This paper aims to determine whether Djambarrpuyŋu listeners use consonantal lengthening as a cue to vowel length. A forced-choice categorisation task investigated how 19 Djambarrpuyŋu listeners (9M, 10F) use segmental duration in their processing of words minimally distinguished by vowel length. Results show that listeners rely primarily on vowel duration in their categorisation behaviour. However, when the vowel’s duration was ambiguous, that is, between the duration ranges of short and long, phonetically long nasals affected the cross-over point of word categorisation. This study supports the primacy of the phonemic vowel length contrast for listeners and also shows that durational context can expand the range of vowel length categories.
Transformational Leadership and Digital Creativity: The Mediating Roles of Creative Self-Efficacy and Ambidextrous Learning

Drawing insights from social cognitive theory and organizational learning theory, this study aims to uncover the mediating mechanisms between direct manager’s transformational leadership behaviors and employees’ digital creativity in the context of digital technology. We conducted a field survey in China and collected data from 234 employees who utilized digital technologies to support daily work. Structural equation modelling analysis results showed that employees’ creative self-efficacy and two learning activities (exploitation vs. exploration) effectively transmitted the influence of transformational leadership on digital creativity. Our study not only contributes to the understanding on effective use of digital technologies, but also provides practical insights for managers in the big data era.
EFFECT OF EGGPLANT ( Solanum melongena L . ) PEEL SIMPLICIA ADMINISTRATION ON SPATIAL WORKING MEMORY IN HIPPOCAMPUS ANNISA AYUNINGYAS

ANNISA AYUNINGTYAS. Effect of Eggplant (Solanum melongena L.) Peel Simplicia Administration on Spatial Working Memory in Hippocampus. Supervised by BERRY JULIANDI, ARIEF BOEDIONO, and NORIAKI SASAI. The hippocampus is a region of the mammalian brain that become a crucial component of the limbic system. Pharmacological agents, such as eggplant (Solanum melongena L.) from Solanaceae family were identified to potentially able to stimulate hippocampal neurogenesis. The eggplant also has potential to enhance memory and should be investigated deeper. Therefore, this study was aimed to analyze the effects of eggplant peel simplicia concerning to neurogenesis and dendritic complexity in hippocampus. This research used male Deutch Democratic Yokohama (DDY) strain mice that divided into three groups. Group C was the control and treated by mineral water, group T1 and T2 treated by 0.0025 and 0.0063 g eggplant peel simplicia respectively. The treatment was administered orally for a period of 30 days. The result of memory performance test showed that the administration of eggplant peel simplicia did not give an impact to spatial working memory (significance value 0.54 with p>0.05) and did not affect dendritic complexity according to the ANOVA test, although the number of NPCs (significance value 0.001 with p<0.05) and granular cells (significance value 0.013 with p<0.05) in dentate gyrus significantly increased.
Mergeable replicated data types

Programming geo-replicated distributed systems is challenging given the complexity of reasoning about different evolving states on different replicas. Existing approaches to this problem impose significant burden on application developers to consider the effect of how operations performed on one replica are witnessed and applied on others. To alleviate these challenges, we present a fundamentally different approach to programming in the presence of replicated state. Our insight is based on the use of invertible relational specifications of an inductively-defined data type as a mechanism to capture salient aspects of the data type relevant to how its different instances can be safely merged in a replicated environment. Importantly, because these specifications only address a data type's (static) structural properties, their formulation does not require exposing low-level system-level details concerning asynchrony, replication, visibility, etc. As a consequence, our framework enables the correct-by-construction synthesis of rich merge functions over arbitrarily complex (i.e., composable) data types. We show that the use of a rich relational specification language allows us to extract sufficient conditions to automatically derive merge functions that have meaningful non-trivial convergence properties. We incorporate these ideas in a tool called Quark, and demonstrate its utility via a detailed evaluation study on real-world benchmarks.
qu an tph ] 2 5 A ug 2 01 9 A Hybrid Universal Blind Quantum Computation

Xiaoqian Zhang, Weiqi Luo, ∗ Guoqiang Zeng, † Jian Weng, Yaxi Yang, Minrong Chen, and Xiaoqing Tan College of Information Science and Technology, Jinan University, Guangzhou 510632, China College of Cyber Security, Jinan University, Guangzhou 510632, China School of Computer, South China Normal University, Guangzhou 510631, China Department of Mathematics, Jinan University, Guangzhou 510632, China (Dated: August 27, 2019)
INVESTIGATION OF IMPACT OF RELATIONSHIP MARKETING ON CUSTOMER LOYALTY ( JOINT STOCK COMPANY OF MOBILE COMMUNICATION OF IRAN “ HAMRAHE AVAL ” )

The main objective of the present study has been investigation of relation between relationship marketing (trust, commitment, communication, conflict handling, and competence) and customer loyalty. Statistical population of study includes mobile users in Ahvaz. The number of users has been determined equal to 600 through cluster sampling method that 234 members was obtained out of them as the sample size using Cochran formula. In total, 234 questionnaires were distributed and 210 valid questionnaires were gathered from respondents. 2 standard questionnaires was conducted to gather research data. The data analysis was done using SPSS and LISREL software. The obtained results of modeling of structural equations have shown a significant impact of relationship marketing and its types on customer loyalty. Subsidiary results of the present study has expressed that communication has the most effect on customer loyalty and the least effect is related to conflict handling.
Evaluation of Irradiated Okra based on Agronomical Traits and RAPD Markers

This study was conducted on three inbred lines of okra Abelmoschus esculents (C8, C9 and Cr) obtained from the Horticultural Research Institute, Agricultural Research Center. Seeds were irradiated with gamma rays at doses 10, 20 and 30 Kr. This study aimed to evaluate the effect of ionizing radiation on molecular and phenotypic levels, using RAPD six out of 19 RAPD primers were succeeded in generating reproducible polymorphic amplicons among all irradiated plants and their control on each inbred line. All irradiated plants of soaked seeds showed the highest unique markers (21) compared with irradiated dry seeds which showed 11 unique markers. This indicated that gamma irradiation was effective for inducing higher rate of mutations in the soaked seeds. On the other hand, C8 gave the highest number of unique markers (10 negative and 4 positive). While, C9 displayed the highest percent of polymorphic amplicons (81.4%) and it was the only genotype which increased in number of complementary sites with RAPD primers by irradiation treatments. Furthermore, other genotypes decreased in number of complementary sites with RAPD primers by treatments. Moreover, the correlation between molecular and phenotypic distances was positive and highly significant (r= 0.645) in Cr only, which may be explained that 73.3% for Cr of amplicons were polymorphic from the genome areas which coded for studied traits. So, it is possible to reliance on the unique markers for this inbred line as markers assisted selection for the improved traits due to irradiation. In addition, Cr was the best in response to genetic improvement % by irradiation, especially 20/S treatment which led to significant improvement of yield and some of it’s component traits. The improvement percent, ranged from 21.4 to 152.0 % for number of leaves per plant (Nl/p), number of fruits/plant (Nf/p), number of seeds per pod (Ns/pod) and fruit yield per plant (FY/p) traits, was highly expected to be associated with four molecular markers. These markers were three negative and one positive (485bp targeted by OP-A02 primer), which could be used as markers assisted selection to improve yield and its components in breeding programs and improvement of okra.
Individual Differences in Implicit Attention to Phonetic Detail in Speech Perception

We present a study on the interactions between implicit attention to acoustic-phonetic detail in speech and individual differences (IDs). Attention to phonetic detail was assessed with acoustically manipulated speech stimuli within a computer game, an alternative to regular highly-controlled categorization tests. Twenty-two native German speakers (11f) completed the game and further tests including individual attention test measures (e.g. Simon Test), the BFI-10 (short version of the Big Five Inventory), and a Self-monitoring Test (need for social approval). With this study, we contribute to the understanding of the processes underlying human speech perception and the impact of cognitive and personality features on the attention to phonetic detail. Our results show that the general (non-verbal) attention capacity (mental flexibility, inhibition), interacts with implicit attention to phonetic detail. Furthermore, IDs in personality, such as sensitivity to social cues or conscientiousness significantly add to the effects. Understanding these interactions, especially arising in an intuitive and non-explicit study design, is an important step on the way towards explaining not only the influence of IDs on attention to phonetic detail, but also the dynamics of speech interaction (e.g. phonetic convergence).
Spatiotemporal Variance-Guided Filtering: Real-Time Reconstruction for Path-Traced Global Illumination

We introduce a reconstruction algorithm that generates a temporally stable sequence of images from one path-per-pixel global illumination. To handle such noisy input, we use temporal accumulation to increase the effective sample count and spatiotemporal luminance variance estimates to drive a hierarchical, image-space wavelet filter [Dammertz et al. 2010]. This hierarchy allows us to distinguish between noise and detail at multiple scales using local luminance variance. Physically based light transport is a long-standing goal for realtime computer graphics. While modern games use limited forms of ray tracing, physically based Monte Carlo global illumination does not meet their 30 Hz minimal performance requirement. Looking ahead to fully dynamic real-time path tracing, we expect this to only be feasible using a small number of paths per pixel. As such, image reconstruction using low sample counts is key to bringing path tracing to real-time. When compared to prior interactive reconstruction filters, our work gives approximately 10× more temporally stable results, matches reference images 5--47% better (according to SSIM), and runs in just 10 ms (± 15%) on modern graphics hardware at 1920×1080 resolution.
Shallow-Fusion End-to-End Contextual Biasing

Contextual biasing to a speciﬁc domain, including a user’s song names, app names and contact names, is an important component of any production-level automatic speech recognition (ASR) system. Contextual biasing is particularly challenging in end-to-end models because these models keep a small list of candidates during beam search, and also do poorly on proper nouns, which is the main source of biasing phrases. In this paper, we present various algorithmic and training improvements to shallow-fusion-based biasing for end-to-end models. We will show that the proposed approach obtains better performance than a state-of-the-art conventional model across a variety of tasks, the ﬁrst time this has been demonstrated.
IoT System with Access Control Integrating RFID and Non-invasive Air Conditioning

This article describes the tools and technologies used to manage an integrated system consisting of RFID access control and noninvasive infrared air conditioning. In this sense, was developed an IoT (Internet of Things) platform consisting of hardware – responsible for controlling the access of users through the recognition of an individual RFID tag, as well as the capture and transmission of infrared signals – and software – a Web system for monitoring and remote control. Was also developed web software to monitor access and register new users and environments, where it is possible to define user profiles besides the registration of activities in each site. As resources are usually limited in IoT systems, it is necessary to use application protocols that optimize the transmission between sensor nodes and gateways, as a result of this the Message Queue Telemetry Transport (MQTT) and Constrained Application Protocol (CoAP) protocols were used for communication between the RFID access control system (RACS), infrared air conditioning control (IACC) and the Web system. The proposed platform was tested and validated in a classroom.
A Behavioural risk perspective of Supply Chain Management: A General Overview

In the last years, the environment of supply chain management (SCM)have known many changes in technology, operations, information systems, methods and models (Poser, 1970). However, one component has not changed. From manufacturing and services to supply chains and R&D, people remain a critical element of the system. This article is in general an overview of behavioral issues in SCM and how the behaviour of people, employees and managers influences the supply chain performance.As Hayes, Wheelwright and Clark (1988) stated, “ Superior performance is ultimately based on the people in an organization. The right management principles, systems, and procedures play an essential role, but the capabilities that create a competitive advantage come from people, their skill, discipline, motivation, ability to solve problems, and their capacity for learning “ (Hayes, Wheelwright, & Clark, 1988). From that perspective, it appears that people are the critical component of the organizational system and that is essential , for improving supply chain performance , to develop models that correctly describe people’sbehavior(Tokar, 2010). This paper reviews the adoption of the behavioral approach to study SCMand existing behavioral research related to supply chain risk management. We will review some examples from literature on origins of behavioral economics, behavioral operations, and behavioral supply chain management. We discuss how behavioural research results can be relevant for supply chain related phenomena and how psychological dimensions in logistics can be interesting for future research. In addition, behavioral factors that may inhibit a decision-making will be explained in the following chapters. By the end of this paper, we are making the link between the new concept of Behavioural Supply Chain Management (BSCM) and Supply Chain Risk Management (SCRM) in order to show how human behaviour presents an important factor of risk. Thus, a possible behavioural approach to risk management is further described, some of the most common approaches to risk management are discussed.