Miracle-dz/UniFAR-FTUs huggingface dataset:
ILLOCUTIONARY ACTS PERFORMED BY THE MAIN CHARACTER IN THE MATRIX MOVIE

ABSTRACT The aims of this research were to find out the illocutionary acts performed by the main character in The Matrix movie and to identify the context underlying the illocutionary acts in the main character’s utterances. This research used Searle’s classification of illocutionary acts and Hasan and Halliday’s theory of context. There were 3 steps that applied on this research. The first was to select the utterances spoken by Neo as the main character in The Matrix movie which contain illocutionary acts and categorized them; then identified the context. The second step was to present the findings by using descriptive method. The last step was to draw the conclusion. There were four types of illocutionary acts found in the utterances spoken by Neo. The data were classified into representatives (24), directives (82), commissives (2), and expressives (5). However, the declaratives types of illocutionary acts were not found in the main character’s utterances because Neo had no power or authority to change the reality with his words in The Matrix movie. From the findings, directives were the dominant type of illocutionary acts in the main character’s utterances because he repeatedly expressed his utterances in direct way by questioning, ordering, begging and asking. The context of the utterances is described by identifying the field, tenor and mode of each utterance spoken by the main character in the movie. Key words : Speech Act, Illocutionary Acts, Utterance, Context
Joint Constellation Design and Multiuser Detection for Grant-Free NOMA

As a promising solution for massive machine-type communication, grant-free non-orthogonal multiple access (GF-NOMA) has received considerable attention in recent years. However, the multidimensional constellation design (MCD) and multiuser detection (MUD) in GF-NOMA are usually optimized in a divide and conquer way, leading to local optima and performance degradation. To address this issue, we investigate the joint optimization of MCD and MUD for GF-NOMA. The formulated joint optimization is based on variational inference, which is intractable due to the signal superimposition that makes the optimization variables intricately coupled. Then, we resort to end-to-end deep learning (DL) to obtain the optimal solution. Specifically, we propose a DL-based multi-task variational autoencoder (Mul-VAE) that adopts a variational autoencoder network to optimize the distribution of the constellation points. We further derive the loss function of the proposed network and analyze it from an information-theoretic perspective. On this basis, multi-task learning is employed to deal with mutually conflicting yet related detection processes. Besides, taking heterogeneous transmission rates of users into account, a multi-task prioritizing strategy is designed to balance training performance. Simulation results reveal that the proposed method enables significant gains compared to state-of-the-art techniques.
Uncertainty shocks and monetary policy: evidence from the troika of China's economy

Abstract Growth in China’s economy is driven by the troika: consumption, investment and export. This paper examines the effect of uncertain events such as the global financial crisis in 2008, and the COVID-19 pandemic on the troika. Based on the construction of a new uncertainty index of China’s economy, the relationship between uncertainty and growth in the troika is examined by using a TVP-VAR model. Results show that fluctuations in the uncertainty index during the COVID-19 epidemic had the greatest negative impact on consumption and investment at a magnitude of −0.27, notably greater than that during the period of the global financial crisis. The negative impact on export reached −0.73, smaller than that during the global financial crisis. Against a backdrop of the novel coronavirus epidemic, it is also found that expansionary monetary policies can have a relatively large impact on investment and export, reaching 1.75 and 1.57 respectively, while short-term impact on consumption is relatively weak, averaging at 0.51.
A Parallel, 2-DOF Exoskeleton for the Human Shoulder: Device Characterization and Preliminary Results on Healthy Subjects

BackgroundHumans are highly reliant on the efficient function of their upper limb. Neuromuscular disorders that impair the function of the shoulder consequently reduce quality of life. Robotic rehabilitation serves as an attractive treatment choice due to its promising results and its ability to alleviate the demands on therapists and clinicians. Nevertheless, current robotic architectures are not optimized for the human shoulder but are more apt for industrial environments. Pneumatically powered soft robotic actuators present an attractive method to create shoulder exoskeletons due to their compliance and relatively low mass. However, current actuators lack the necessary functions to provide support to the entire shoulder’s range of motion.MethodsA modular, fabric pneumatic actuator was constructed. The actuator design allows it to perform three-dimensional (3-D) bends with minimal resistance. Four actuators were combined to create a soft shoulder exoskeleton. Each actuator drives one direction of motion: elevation and depression, rotation of the plane of elevation. The torque output of the actuator was measured using a customized two-axis torque measurement system. Exoskeleton functionality was tested through surface electromyography of relevant shoulder muscles. 10 healthy subjects were recruited and performed arm motions under the assistance of the exoskeleton.ResultsThe actuator can reach full bending (>360°) with low pressures (~10kPA). Its torque output is highly dependent on its geometry. Moreover, torque output is reduced as the bending angles increase. The actuators installed on the exoskeleton output 11.15N-m of torque at the neutral position, and 4.44 N-m at 90° shoulder elevation. The test on healthy subjects showed that use of the exoskeleton reduces muscle activation by up to 65% when performing shoulder elevation, and up to 34% when rotating the plane of elevation. Use of the exoskeleton also resulted in a change in arm trajectory when performing elevation and depression movements.ConclusionsThe reduction in muscle activation highlights the ability of a soft-robotic exoskeleton in supporting arm movements. Moreover, the presented exoskeleton design successfully demonstrated its ability to generate two degree-of-freedom support for the human shoulder.
EFFECT OF HEXANE FRACTION FROM PAPAYA (CARICA PAPAYA L.) MALE FLOWER ON CELL CYCLE OF COLON ADENOCARCINOMA (WIDR) CELL AND ITS COMBINATION INDEX WITH DOXORUBICIN

Objective: This study aimed to evaluate the effects of papaya (Carica papaya L.) male flower hexane fraction (PHF) on cell cycle of colon adenocarcinoma (WiDr) cell and its combination index (CI) with doxorubicin.Methods: Flow cytometer and the CI were used to show the PHF on cell cycle of colon adenocarcinoma (WiDr) cell and to calculate the synergism potential, respectively.Result: The result showed that the PHF giving inhibition on cell cycle of colon adenocarcinoma (WiDr) cell in G0-G1, S, G2-M phase. Furthermore, the combination of PHF with doxorubicin in colon adenocarcinoma (WiDr) cell gave a strong synergistic effect with optimal concentration of 8 I¼g/mlâ€“50 nM (PHF-Doxorubicin) with the IC score lower than 1.Conclusion: This study provides evidence that PHF could be a new potential co-chemotherapeutic agent with doxorubicin on colon adenocarcinoma cell.
A New Deep Wavefront Based Model for Text Localization in 3D Video

With the evolution of electronic devices, such as 3D cameras, addressing the challenges of text localization in 3D video (e.g., for indexing) is increasingly drawing the attention of the multimedia and video processing community. Existing methods focus on 2D video and their performance in the presence of the challenges in 3D video, such as shadow areas associated with text and irregularly sized and shaped text, degrades. This paper proposes the first approach that successfully addresses the challenges of 3D video in addition to those of 2D. It employs a number of innovations, among which, the first is the Generalized Gradient Vector Flow (GGVF) for dominant points detection. The second is the Wavefront concept for text candidate point detection from those dominant points. In addition, an Adaptive B-Spline Polygon Curve Network (ABS-Net) is proposed for accurate text localization in 3D videos by constructing tight fitting bounding polygons using text candidate points. Extensive experiments on custom (3D video) and standard datasets (2D video and scene text) show that the proposed method is practical and useful, and overall outperforms existing state-of-the-art methods.
A Time-Varying Weight MPC-Based Motion Cueing Algorithm for Motion Simulation Platform

The motion cueing algorithm (MCA) is playing the most critical role in motion simulation platform (MSP) to reproduce the realistic motion sensation of the real car for the MSP’s users while taking into cogitation of the physical boundaries of the platform. Recently, the model predictive control (MPC) is employed for designing MCAs which led to the formation of MPC-based MCAs. The purpose of the MPC-based MCA is to recalculate the optimal values of the input signals with consideration of the MSP’s physical restrictions. All the current MPC-based MCAs have fix weights that can cause the conservative and inefficient utilization of the MSP’s workspace limitations as they have been tuned based on the worst-case scenarios to keep the platforms within their physical limitations. Then, the error of motion sensation between the real car and MSP users increases due to the conservative utilization of the MSP’s workspace boundaries. The main objective of this study is to provide more efficient workspace utilization to minimise the error of motion feeling between the real car and MSP users while respecting the workspace boundaries. A procedure according to the optimised fuzzy logic-based units is employed to calculate the appropriate MPC weights online while considering the sensed specific force error, sensed angular velocity error and the current motion status of the MSP including linear position, linear velocity and angular position of the cockpit. The proposed MPC-based MCA is designed and developed using MATLAB. The outcomes show a better motion feeling compared with the current MPC-based MCAs.
Spatiotemporal distribution of the potential risk of frost damage in tea fields from 1981-2020: A modeling approach considering phenology and meteorology

Climate change may induce severe frost damage to crops, and thus a reasonable assessment of frost risk, considering both crop phenology and meteorology, is required. Here, we introduced a new index of potential frost risk ( F risk ) using thermal time ( minimum air temperature below the threshold value ) weighted by the percentage of budburst ( P bud ) . Moreover, we evaluated the spatiotemporal distributions of F risk in tea fields within a 60 km × 60 km area in east Japan from 1981 - 2020, using 1 km 2 -gridded meteorological data and a newly developed model of P bud . The P bud model considered three phenological phases ( endodormancy, ecodormancy, and progress of budburst ) and successfully represented changes in the P bud of the tea buds for 15 years, with root mean square errors of 8.5 percentage points. The spatiotemporal distributions of F risk over the past 40 years showed that potential frost risk significantly increased at elevations ranging from 50 m to 300 m because the budburst advanced at a faster rate than the temperature warming. These elevations corresponded to areas where tea plants were mainly cultivated, which indicates that tea cultivation is becoming vulnerable to frost, and the risk of economic losses due to the frost is increasing. The proposed assessment of frost risk could contribute to predicting frost damage and developing more reliable strategies for the operation of frost protection under the effects of future climate change.
Enhanced Spatiotemporal Fusion via MODIS-Like Images

Spatiotemporal fusion (STF) aims at generating remote-sensing data with both high spatial and temporal resolution. In the literature, one of the most widely used strategies to accomplish this goal is to fuse high temporal resolution images collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) with images with finer spatial resolution than those provided by MODIS (e.g., those collected by other satellite instruments such as Landsat or Sentinel-2). Current STF methods generally fuse an upsampled MODIS image with finer spatial resolution images. This leads to two main problems. First of all, the model uncertainty errors (resulting from the ill-posed upsampling problem) will be propagated into the fusion results, leading to spatial and spectral distortion. Furthermore, the spatial details of the upsampled MODIS image may be significantly different from those of the finer spatial resolution images, making the STF problem even more challenging. In order to tackle these issues, in this work, we develop a new linear regression-based STF strategy (LiSTF), which performs the reconstruction from a MODIS-like image (instead of from an upsampled MODIS image), thus reducing the model uncertainty errors and preserving better the spatial information. The MODIS-like images are built from the finer spatial resolution images via downsampling. Our experimental results, conducted using two publicly available datasets of Landsat–MODIS image pairs and one publicly available dataset of Sentinel–MODIS image pairs, reveal that our newly proposed LiSTF approach can significantly enhance the quantitative and qualitative performance of STF, particularly in terms of preserving the spatial information.
LIMITA: Logic-in-Memory Primitives for Imprecise Tolerant Applications

Magnetic tunnel junction (MTJ) with non-volatility, and near-zero leakage power features are believed to be the most prominent candidates for CMOS substitution. Logic-in-memory reduces the memory wall challenge and takes advantage of these features, but still has the challenge of writing to a magnetic device with regard to power consumption and device failure. Considering these challenges, we propose new designs in the approximate computing approach for magnetic device-based primitives. This paper proposes several power- and/or delay-efficient configurable Logic-in-Memory for Imprecise Tolerant Applications (LIMITA) based on spin transfer torque (STT) and STT-assisted voltage control magnetic anisotropy (VCMA) magnetic devices. Image processing is one of the most widely used imprecise tolerant applications with a high computational volume. Therefore, we map LIMITA with several approximation boundaries in Sobel, Sharpening, and Smoothing algorithms. To evaluate LIMITA at circuit and application levels, we did extensive simulations in HSPICE and MATLAB, respectively. Compared to the state-of-the-art works, the exact design of LIMITA improves the number of transistors in the logic tree up to 66% with 12% power overhead due to its configurability feature. In addition, in comparison with approximate primitives, LIMITA-<inline-formula> <tex-math notation="LaTeX">$\text{F}_{\mathrm {i}}$ </tex-math></inline-formula> improves delay, power consumption, and the number of transistors by <inline-formula> <tex-math notation="LaTeX">$4.5\times$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$71.5\times$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$2\times$ </tex-math></inline-formula> respectively.
Machine Learning Methods for Predicting Postpartum Depression: Scoping Review

Background Machine learning (ML) offers vigorous statistical and probabilistic techniques that can successfully predict certain clinical conditions using large volumes of data. A review of ML and big data research analytics in maternal depression is pertinent and timely, given the rapid technological developments in recent years. Objective This study aims to synthesize the literature on ML and big data analytics for maternal mental health, particularly the prediction of postpartum depression (PPD). Methods We used a scoping review methodology using the Arksey and O’Malley framework to rapidly map research activity in ML for predicting PPD. Two independent researchers searched PsycINFO, PubMed, IEEE Xplore, and the ACM Digital Library in September 2020 to identify relevant publications in the past 12 years. Data were extracted from the articles’ ML model, data type, and study results. Results A total of 14 studies were identified. All studies reported the use of supervised learning techniques to predict PPD. Support vector machine and random forest were the most commonly used algorithms in addition to Naive Bayes, regression, artificial neural network, decision trees, and XGBoost (Extreme Gradient Boosting). There was considerable heterogeneity in the best-performing ML algorithm across the selected studies. The area under the receiver operating characteristic curve values reported for different algorithms were support vector machine (range 0.78-0.86), random forest method (0.88), XGBoost (0.80), and logistic regression (0.93). Conclusions ML algorithms can analyze larger data sets and perform more advanced computations, which can significantly improve the detection of PPD at an early stage. Further clinical research collaborations are required to fine-tune ML algorithms for prediction and treatment. ML might become part of evidence-based practice in addition to clinical knowledge and existing research evidence.
Development and Utilization of the Functional Co-dominant KASP Marker for Thermo-sensitive Genic Male Sterility in Rice

Thermos-sensitive genic male sterility (TGMS) is the important genetic resource in two-line hybrid rice breeding. The pollen fertility of TGMS lines is regulated by a single point mutation on the TGMS genes. Based on the single nucleotide polymorphism (SNP) and indel, KASP markers were developed and utilized in rice molecular breeding due to high-through detection with low cost and save time. In this study, we convert the SNPs on TGMS genes including p/tms12-1 and tms9-1 gene to the functional co-dominant KASP marker and used in two-line hybrid rice breeding. We can differentiate the TGMS lines conferring p/tms12-1 or tms9-1 from another TGMS lines by KASP assay. Of them, the genotype of Pei'ai64S and Hua201S containing p/tms12-1 is homozygous GG genotype with blue signal, and the genotype of HengnongS-1 containing tms9-1 is homozygous CC genotype with red signal. KASP assay for tms9-1 gene was proved that the genetic mode is fit to one recessive Mendalian trait. The test of seed purity was performed by KASP marker for the two-line hybrid varieties of Liangyoupeijiu and Hualiangyou1206, which is consistent with the previous dCAPS marker. Moreover, the tms12-1 and tms9-1 gene were pyramided in the same genetic background and the new TGMS lines were generated. Therefore, the KASP marker for TGMS genes developed in this study can be widely used in two-line hybrid rice breeding. It will provide a visual convenient toolkit for breeders to select the target individual plant by the high-throughput detection in the different two-line rice breeding program.
Curtailing Job Insecurity and Counterproductive Work Behaviours as Bullying Effects in Pakistani Academia: Work Engagement as a Moderator

Judging from persistent changes, drive for performance and widespread uncertainty that characterize the Pakistani higher education system, this study sought to confirm whether workplace bullying – a by-product of relentless change – triggers job insecurity and counterproductive work behaviours in the bullied faculty, and whether these damaging outcomes are moderated by work engagement. Using convenience sampling, we sought data from 337 faculty members from the higher education sector. Analysis confirmed that bullying triggers job insecurity and counterproductive work behaviours while mixed findings emerged for the hypothesized moderation effects of work engagement. The study mainly stresses infusing work engagement within a work environment where bullying prevails. Managers may design jobs to augment engagement in a pressurized work environment with an aim to curtail job insecurity and counterproductive work behaviours for sustained performance in a changing work environment.
The Convergence of Self and Informant Reports in a Large Online Sample

Despite their added benefits, informant-reports are largely underutilized in personality research. We demonstrate the feasibility of collecting informant-reports online, where researchers have unprecedented access to large, global populations. Using an entirely free, opt-in procedure tied to an existing personality survey, we collected 1,554 informant-reports for 921 unique targets, in conjunction with over 158,000 self-reports. Informant-reports showed a strong correspondence to self-reported traits at three levels of analysis: among the Big Five domains, the lower-level SPI-27 factors (Condon, 2018), and at the item-scale level. Among the Big Five, self-informant agreement ranged between . 63 and . 72, except for Openness (.42). Higher informant-ratings of Extraversion were positively associated with all Big Five self-ratings in the direction of social desirability. Across the Big Five and the 27 lower-order traits, agreement was strongest between self-reports of compassion and informant-reports of agreeableness (.74) and weakest between self-reported emotional expressiveness and informant-reported emotional stability (.02). Agreement between informants was roughly equivalent for all of the Big Five traits (.29 to .35) and attractiveness (.37), though agreement between informants for perceived intelligence was non-significant. In addition, we empirically identified the self-report items that best predict what informants say about targets, highlighting the features of self-reported personality that are most readily confirmed by informants. Finally, we discuss group level differences of participants who interacted with the informant-report system at various levels. In general, participants who sought and provided informant reports are more open and agreeable than the general sample, though targets’ personality did not affect whether or not invited informants provided ratings.
Influence ofIndole-3-Acetic Acid treated Cowpea (Vignaunguiculata L. Walp) Seedson germination, plant development and leaf Chlorophyll Content

Plant growth regulators (PGRs) are natural hormones found in plant or their synthetic hormone analogues. Auxin, a type of PGRs that consist of low molecular weight organic phytohormones which influences cell division, plant growth and development. One of the common auxin types is indole-3-acetic acid (IAA) and was the first plant growth regulator to be discovered with characteristics that involved in the control of various plant growth and development.Auxins can be absorbed in tissues and transport to long distances throughout vascular tissueof a plant. Therefore, this study aimed to investigate the influence ofIAA on the treated seeds of cowpea (Vignaunguiculata L.Walp) accession TVu-7, TVu-9 and TVu-1945to 50 % seed germination, seedling growth and development, and leaf chlorophyll content.The various IAA concentration used were 0mg/L, 250mg/L, 500mg/L 750mg/L and 1000mg/L. The results of this study showed the relative effect of various concentrations of IAA on seed germination, vegetative structure produced and leaf chlorophyll content. For example, increased IAA concentrations of 1000mg/L further extend the number of days to attend 50 % seed germination in the different cowpea accessions. Also, an increased concentration to 1000mg/L significantly increased the leaf chlorophyll content in the different cowpea accessions.