Miracle-dz/UniFAR-FTUs huggingface dataset:
Layer-Level Progressive Transformer With Modality Difference Awareness for Multi-Modal Neural Machine Translation

Multi-modal neural machine translation (MNMT) aims to translate sentences from the source language into the target language with the aid of corresponding images. Unfortunately, there is a considerable modality gap between the semantic-related images and texts in terms of data form and semantic expression. How to fully incorporate visual information into texts to enhance the performance of machine translation is one of the critical issues for MNMT. However, the initial visual and textual features are generally extracted with their modality-specific models; Consequently, there is a considerable representation gap between images and texts. Most previous MNMT works prefer only to adopt the feature-level fusion strategies to learn multi-modal representation, while the modality representation gap is often ignored. To this end, this article proposes a progressive multi-modal Transformer (ProMul-Trans) with Modality Difference-Aware (MDA) to address the visual-to-textual fusion problem raised in MNMT. We first employ MDA to capture the modality-consistency information by taking visual and textual representations as inputs in each Transformer layer. Then a layer-level progressive fusion (Layer-ProFusion) strategy is adopted to progressively align visual and textual representations layer-by-layer to enhance machine translation performance. Experiment results on the Multi30 k dataset are conducted, and the results show that the proposed approach outperforms the compared state-of-the-art (SOTA) methods on English <inline-formula><tex-math notation="LaTeX">$\to$</tex-math></inline-formula> German (En <inline-formula><tex-math notation="LaTeX">$\to$</tex-math></inline-formula> De), English <inline-formula><tex-math notation="LaTeX">$\to$</tex-math></inline-formula> French (En <inline-formula><tex-math notation="LaTeX">$\to$</tex-math></inline-formula> Fr) and English <inline-formula><tex-math notation="LaTeX">$\to$</tex-math></inline-formula> Czech (En <inline-formula><tex-math notation="LaTeX">$\to$</tex-math></inline-formula> Cs) tasks. We release the code at <uri>https://github.com/JunjieYe-MMT/HierProMul-Trans</uri>.
KAJIAN TENTANG INDEKS KECOCOKAN MODEL DALAM PEMODELAN PERSAMAAN STRUKTURAL BERBASIS KOVARIAN D ALAM LISREL DAN AMOS SERTA BERBASIS PARTIAL LEAST SQUARE DALAM PLS SEM

The objective of this study is to review the goodness of fit (GOF) of Covariance and Partial Least Square Based Structural Equation Modeling using LSIREL, AMOS and Smart PLS. A method used is systematic secondary data search and meta - analysis. In this study, the present writer finds that there are some similarities and dissimilarities of the usage of each parameter used within the same parameter name; accordingly, users of those software must be able to distinguish each use of the parameter concerned in each software respectively in their researches Key Words : goodness of fit, SEM, LISREL, AMOS, SmartPLS
Hemodynamic, Oxygenation and Lymphocyte Parameters Predict COVID-19 Mortality

The mortality of COVID-19 patients has left the world devastated. Many scoring systems have been developed to predict the mortality of COVID-19 patients, but several scoring components cannot be carried out in limited health facilities. Herein, the authors attempted to create a new and easy scoring system involving mean arterial pressure (MAP), PF Ratio, or SF ratio-respiration rate (SF Ratio-R), and lymphocyte absolute, which were abbreviated as MPL or MSLR functioning, as a predictive scoring system for mortality within 30 days for COVID-19 patients. Of 132 patients with COVID-19 hospitalized between March and November 2021, we followed up on 96 patients. We present bivariate and multivariate analyses as well as the area under the curve (AUC) and Kaplan–Meier charts. From 96 patients, we obtained an MPL score of 3 points: MAP < 75 mmHg, PF Ratio < 200, and lymphocyte absolute < 1500/µL, whereas the MSLR score was 6 points: MAP < 75 mmHg, SF Ratio < 200, lymphocyte absolute < 1500/µL, and respiration rate 24/min. The MPL cut-off point is 2, while the MSLR is 4. MPL and MSLR have the same sensitivity (79.1%) and specificity (75.5%). The AUC value of MPL vs. MSLR was 0.802 vs. 0.807. The MPL ≥ 2 and MSLR ≥ 4 revealed similar predictions for survival within 30 days (p < 0.05). Conclusion: MPL and MSLR scores are potential predictors of mortality in COVID-19 patients within 30 days in a resource-limited country.
Generalized Kirchhoff law

Thermal emission can be conveniently described using Kirchhoff law which states that the emissivity is equal to the absorptivity for isothermal bodies. For a finite size system, absorptivity is replaced by an absorption cross section. Here, we study the link between thermal emission and absorption by a finite size object which is not isothermal. We define a local absorption rate for a given incident plane wave and we prove that it is equal to the local emissivity rate. Hence, Kirchhoff law can be extended to anisothermal media. A practical consequence is the possibility of analysing thermal radiation by a variety of non-equilibrium systems such as microwave radiation in geophysical remote sensing or X-UV radiation by plasmas. This result provides a theoretical framework to analyse thermal emission by hot electrons in quantum wells, tunnel junctions or graphene. It paves the way to the design of a new generation of incandescent emitters made of subwavelength hot emitters coupled to cold antennas. The antennas control the emission spectrum, direction and polarization of the emitted radiation.
Thiol-disulphide Homeostasis in Patients with Schizophrenia: The Potential Biomarkers of Oxidative Stress in Acute Exacerbation of Schizophrenia

Objective Recent evidence suggests that oxidative stress contributes to the pathophysiology of schizophrenia. This study aimed to compare thiol-disulphide homeostasis in acute and stable phases of schizophrenia for the first time. Methods Among the patients with schizophrenia, 61 in the acute-phase and 61 in the stable phase of their illness were enrolled in the study. Native thiol (NT), total thiol (TT), disulphide (SS), disulphide/native thiol, disulphide/total thiol, and native thiol/total thiol for thiol-disulphide homeostasis were compared between the groups. The Brief Psychiatric Rating Scale (BPRS), Scale for the Assessment of Positive/Negative Symptoms (SAPS/SANS), Clinical Global Impression- Severity Scale (CGI-S), Barnes Akathisia Rating Scale, and Simpson-Angus Scale were used to assess symptoms. Results After controlling for age, sex, body mass index, and smoking status there were significant differences in NT, TT, SS/NT, SS/TT, and NT/TT, but not SS. Thiol/disulphide homeostasis has shifted in favour of the oxidative side in patients with acute-phase compared to that in stable schizophrenia. BPRS, SAPS, and CGI-S scores were significantly correlated with all six thiol-disulphide parameters, but not SANS, when controlling for age and sex. Significant receiver operating characteristic (ROC) curves were obtained for all thiol-disulphide homeostasis parameters. Discriminant analysis was found to be statistically significant in discriminating between groups. Conclusion These results show that oxidative status increases thiol-disulphide homeostasis in patients with acute-phase schizophrenia compared to those with stable schizophrenia. These findings suggest that thiol-disulphide parameters can be used as biomarkers for the acute exacerbation of schizophrenia.
Scale Up Bayesian Networks Learning Dissertation Proposal

Bayesian networks are widely used graphical models which represent uncertain relations between the random variables in a domain compactly and intuitively. The first step of applying Bayesian networks to real-word problems typically requires building the network structure. Among others, optimal structure learning via score-and-search has become an active research topic in recent years. In this context, a scoring function is used to measure the goodness of fit of a structure to the data, and the goal is to find the structure which optimizes the scoring function. The problem has been shown to be NP-hard. This proposal focuses on scaling up structure learning algorithms. Two potential directions are preliminarily explored. One is to tighten the bounds; we tighten the lower bound by using more informed variable groupings when creating the heuristics. and tighten the upper bound using anytime learning algorithm. The other is to prune the search space by extracting constraints directly from data. Our preliminary results show that the two directions can significantly improve the efficiency and scalability of heuristics search-based structure learning algorithms, which gives us strong reasons to follow these directions in the future.
Leveraging Social Media to Detect Online Bullying

-Cyberbullying has emerged as a pervasive issue in the contemporary digital landscape, inflicting severe consequences on its victims, including mental health challenges and social exclusion. To combat this troubling phenomenon, a project is developed, proposing a machine learning-based approach to effectively identify and stop bullying on social media platforms. By harnessing the potential of advanced machine learning algorithms, the project aims to swiftly identify instances of bullying in real time, allowing for timely alerts to be sent to relevant authorities for necessary action. To train the machine learning model, the project will utilize a comprehensive dataset of social media tweets, manually classified as either bullying or non-bullying. As a result, the model will acquire the ability to efficiently scan new social media content and accurately recognize cyberbullying instances, thereby enabling the implementation of effective intervention and prevention strategies. Through in-depth analysis of the collected data, the project endeavors to enhance public awareness and understanding of cyberbullying while developing practical strategies to combat it. Ultimately, the project seeks to make a significant positive impact in the fight against cyberbullying, fostering a safer online environment that promotes inclusivity and respect for all users. By synergizing machine learning technology with proactive measures, the project aspires to mitigate the deleterious effects of cyberbullying and foster a more compassionate and harmonious online community.
Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget

A. Distribution of the test statistic In the sequential test, we first compute the test statistic from a mini-batch of size m. If a decision cannot be made with this statistic, we keep increasing the mini-batch size by m datapoints until we reach a decision. This procedure is guaranteed to terminate as explained in Section 4. The parameter ✏ controls the probability of making an error in a single test and not the complete sequential test. As the statistics across multiple tests are correlated with each other, we should first obtain the joint distribution of these statistics in order to estimate the error of the complete sequential test. Let ̄l j and s l,j be the sample mean and standard deviation respectively, computed using the first j mini-batches. Notice that when the size of a mini-batch is large enough, e.g. n > 100, the central limit theorem applies, and also s l,j is an accurate estimate of the population standard deviation. Additionally, since the degrees of freedom is high, the t-statistic in Eqn. 5 reduces to a z-statistic. Therefore, it is reasonable to make the following assumptions: Assumption 1. The joint distribution of the sequence
Logically-Correct Reinforcement Learning

We propose a novel Reinforcement Learning (RL) algorithm to synthesize policies for a Markov Decision Process (MDP), such that a linear time property is satisfied. We convert the property into a Limit Deterministic Buchi Automaton (LDBA), then construct a product MDP between the automaton and the original MDP. A reward function is then assigned to the states of the product automaton, according to accepting conditions of the LDBA. With this reward function, RL synthesizes a policy that satisfies the property: as such, the policy synthesis procedure is "constrained" by the given specification. Additionally, we show that the RL procedure sets up an online value iteration method to calculate the maximum probability of satisfying the given property, at any given state of the MDP - a convergence proof for the procedure is provided. Finally, the performance of the algorithm is evaluated via a set of numerical examples. We observe an improvement of one order of magnitude in the number of iterations required for the synthesis compared to existing approaches.
Effects of exercise on body mass index and waist circumference of individuals with intellectual and developmental disabilities: a systematic review with meta-analysis

Introduction/Methods: This systematic review with meta-analysis aims to assess the magnitude of the effects of physical exercise programs on body mass index (BMI) and waist circumference (WC) of individuals with Intellectual and Developmental Disabilities (IDD), metabolic and cardiovascular health markers. Results: Considering the eligibility criteria, a final sample of nine articles was obtained. For BMI, the Z-value obtained to test the null hypothesis (difference between means is zero), showed a Z = −2.176 and p = 0.03. The highest magnitude of the effect was from the intervention with combined training (difference in means: −0.399), with a value of Z = −1.815 and p = 0.07. For WC, the Z-value is zero, showing a Z = −3.306 and p = 0.001. The highest magnitude of the effect was from the intervention with continuous cardiorespiratory training of −0.786, with a value of Z = −2.793 and p = 0.005. Discussion: Physical exercise prevents increases in BMI and WC in individuals with IDD. Aerobic training seems to be more effective in promoting WC and combined training in BMI. Systematic Review Registration: [PROSPERO], identifier [CRD42021255316].
Reconstruction of a 3D Polygon Representation from full-waveform LiDAR data

This study focuses on enhancing the visualisation of FW LiDAR data. The intensity profile of each full-waveform pulse is accumulated into a voxel array, building up a fully-3D representation of the returned intensities. The 3D representation is then polygonised using functional representation (FRep) of geometric objects. In addition to using the higher resolution FW data, the voxels can accumulate evidence from multiple pulses, which confers greater noise resistance. Moreover, this approach opens up possibilities of vertical observation of data, while the pulses are emitted in different angles. Multi-resolution rendering and visualisation of entire flightlines are also allowed. Introduction: The most common approach of interpreting the data, so far, was decomposition of the signal into a sum of Gaussian functions and sequentially extraction of points clouds from the waves (Wanger, Ullrich, Ducic, Malzer , & Studnicka, 2006). Neunschwander et al used this approach for Landover classification (Neuenschwander, Magruder, & Tyler, 2009) while Reightberger et al applied it for distinguishing deciduous trees from coniferous trees (Reitberger, Krzystek, & Stilla, 2006). In 2007, Chauve et al proposed an approach of improving the Gaussian model in order to increase the density of the points cloud extracted from the data and consequently improve point based classifications applied on full-waveform LiDAR data (Chauve, Mallet, Bretar, Durrieu, Deseilligny, & Puech, 2007).
Sandwich method to grow high quality AlN by MOCVD

We report pulsed atomic layer epitaxy growth of a very high crystalline quality, thick (~2 µm) and crack-free AlN material on c-plane sapphire substrates via a sandwich method using metal organic chemical vapor deposition. This sandwich method involves the introduction of a relatively low temperature (1050 °C) 1500 nm thick AlN layer between two 250 nm thick AlN layers which are grown at higher temperature (1170 °C). The surface morphology and crystalline quality remarkably improve using this sandwich method. A 2 µm thick AlN layer was realized with 33 arcsec and 136 arcsec full width at half maximum values for symmetric (0002) and asymmetric (101¯5) reflections of ω-scan, respectively, and it has an atomic force microscopy root-mean-square surface roughness of ~0.71 nm for a 5 × 5 µm2 surface area.
Apakah Narsisme dan Psikopati Memoderasi Hubungan Konformitas Norma Maskulin dengan Kecenderungan Perilaku Image-based Sexual Abuse?

Dalam beberapa tahun terakhir, image-based sexual abuse (IBSA), terutama penyebaran konten grafis seksual secara non-konsensual marak terjadi di Indonesia. Beberapa studi kualitatif menyatakan bahwa terjadinya IBSA didasari oleh mengakarnya budaya patriarki dan norma gender yang menekankan bahwa laki-laki berkuasa dibandingkan perempuan. Oleh karenanya, peneliti menggali peran konformitas terhadap norma maskulin, utamanya pada norma “kuasa di atas perempuan” pada kecenderungan perilaku IBSA. Namun, hubungan antara konformitas terhadap norma maskulin dengan kecenderungan perilaku IBSA, dapat berbeda berdasarkan karakteristik individu. Individu yang tinggi dalam karakteristik kepribadian narsisme dan psikopati, memiliki kecenderungan yang lebih tinggi untuk melakukan IBSA. Penelitian ini dilakukan untuk mengetahui efek moderasi narsisme dan psikopati pada hubungan antara konformitas terhadap norma maskulin dengan kecenderungan perilaku IBSA. Partisipan survei ini terdiri dari 237 orang yang berusia 18-39 tahun dan bertempat tinggal di Indonesia. Analisis data dilakukan menggunakan teknik analisis moderasi dengan metode bootstrap. Hasil analisis menunjukkan bahwa konformitas terhadap norma maskulin berefek positif pada kecenderungan perilaku IBSA. Selain itu, ditemukan bahwa psikopati memoderasi hubungan konformitas terhadap norma maskulin dengan kecenderungan perilaku IBSA sedangkan narsisme tidak memoderasi hubungan tersebut.
SEED PRIMING WITH β-AMINOBUTYRIC ACID (BABA) IMPROVED PRODUCTION OF CHICKPEA GENOTYPES BY OPTIMIZING ANTIOXIDANT ACTIVITY UNDER DIFFERENT MOISTURE CONDITIONS

Chickpea is one of the drought tolerant leguminous crops mostly cultivated in the arid region of Pakistan. Global warming is the main cause of changing rainfall pattern in the arid regions, which indirectly affecting the production of chickpea. β-aminobutyric acid (BABA) is known as the drought mitigating agent mostly used for boosting the production of drought susceptible plants. For checking the effect of BABA on the growth, yield and quality traits of chickpea cultivars against drought stress, a field experiment was conducted at the research field of B.Z. U. Bahadur Sub-Campus Layyah, Pakistan (latitude-32°N, longitude-73°E and altitude-184 msl) during rabi seasons 2016-17. For this purpose chickpea cultivars (Noor 2013 and Bhakkar 2011) were tested under irrigated as well as rainfed condition. Different doses of BABA (Dry seed, Hydropriming, 1 mM, 2 mM and 3 mM) were applied by seed priming method. Findings of this research study elaborate that irrigated condition enhanced all the growth and yield traits as well as total protein contents but decrease the antioxidants in comparison to the rainfed condition. Noor 2013 performed best by increasing germination count, no. of pods plant -1 , no. of seeds plant -1 , harvest index, days to anthesis, superoxide dismutase, peroxidase, catalase and total phenolic contents. Whereas, Bhakkar 2011 showed the highest performance for no. of primary branches per plant, biological yield, grain yield, thousand seed weight and total soluble protein. 2 mM of BABA is the best dose for all the growth and yield traits, while maximum antioxidant activity was found by the application of 3 mM of BABA. In short, cultivation of both chickpea cultivars should be promoted in irrigated areas. Priming with 2 and 3 mM of BABA is the best solution for minimizing the impact of moderate drought stress.
Empirical likelihood inference for error density estimators in first-order autoregression models

ABSTRACT In this paper we apply empirical likelihood method to the error density estimators in first-order autoregressive models under some mild conditions. The log-likelihood ratio statistic is shown to be asymptotically chi-squared distributed at a fixed point. In simulation, we show that the empirical likelihood produces confidence intervals having theoretical coverage accuracy which is better than normal approximation.
Evaluation of Implant Stability According to Implant Placement Site and Duration in Elderly Patients: A Prospective Multi-Center Cohort Study

The aim of this prospective study is to investigate implant stability and the reliability of different measuring devices according to implant placement site and duration in patients aged over 65 years. The study evaluated 60 implants (diameter: 3.5/4.0/4.5/5.0 mm and length: 8.5/10.0/11.5 mm) in 60 patients aged ≥ 65 years. The implant placement sites were divided into six evenly distributed sections (n = 10), i.e., maxillary right-posterior, A; maxillary anterior, B; maxillary left-posterior, C; mandibular right-posterior, D; mandibular anterior, E; mandibular left-posterior, F. Participants visited the hospital six times: implant surgery, 1V; stitch removal, 2V; 1-month follow-up, 3V; 2-month follow-up, 4V; before final restoration delivery, 5V; and after final restoration delivery, 6V. The implant stability was evaluated with the Osstell Mentor (ISQ), Periotest M (PTV), and Anycheck (IST). The mean values of ISQ, PTV, and IST were analyzed (α = 0.05). ISQ, PTV, and IST results of 4V and 5V were significantly higher than those of 1V (p < 0.05). The lowest ISQ results occurred in the E location at 4V and 5V (p < 0.05). In all mandibular locations, IST results of 6V were significantly higher than those of 1V, 2V, 3V, and 4V (p < 0.05). ISQ results were negatively correlated with PTV and positively correlated with IST, and PTV was negatively correlated with IST. By considering various factors affecting the stability of the implant, it is necessary to determine the appropriate implant load application time. This could help increase the implant success rate in elderly patients. And as a diagnostic device for implant stability and the evaluation of osseointegration in elderly patients, Anycheck was also able to prove its relative reliability compared to Osstell ISQ Mentor and Periotest M.
High-Performance Normally Off p-GaN Gate HEMT With Composite AlN/Al 0.17 Ga 0.83 N/Al 0.3 Ga 0.7 N Barrier Layers Design

In this paper, a novel normally off p-gallium nitride (GaN) gate high electron-mobility transistor (HEMT) with composite AlN/Al0.17Ga0. 83N/Al0.3Ga0. 7N barrier layers is proposed. Compared to the standard (STD) p-GaN/AlGaN/GaN HEMT structure, the composite barriers (CB) with AlN etch-stop layer can effectively improve the uniformity of the device threshold voltage ( $V_{\mathrm{ TH}} $ ) and reduce the leakage current. The CB p-GaN gate HEMT achieved a ${\mathrm{ V}}_{\mathrm{ TH}} $ of 1.7 ± 0.06 V; this value was 2.1 ± 0.2 V for STD HEMT. In addition, the off-state drain leakage current was suppressed one order of magnitude by adopting a composite barrier design.