Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrophysiological properties and heart rate variability of patients with thalassemia major in Jakarta, Indonesia

Beta thalassemia major (TM) is a common hereditary disease in Indonesia. Iron overload due to regular transfusion may induce myocardial iron deposition leading to electrophysiological dysfunction and functional disorders of the heart. Ventricular arrhythmia is one of the most common causes of sudden cardiac death in thalassemia patients. This cross-sectional study of 62 TM patients aged 10–32 years in Cipto Mangunkusumo General Hospital was done to assess their electrophysiological properties and heart rate variability, including 24- hour Holter monitoring, signal averaged electrocardiogram (SAECG) for detection of ventricular late potential (VLP), and determination of heart rate variability (HRV). We also assessed their 12-lead ECG parameters, such as P wave, QRS complex, QT/ QTc interval, QRS dispersion, and QT/ QTc dispersion. Iron overload was defined by T2-star magnetic resonance (MR-T2*) values of less than 20 ms or ferritin level greater than 2500 ng/mL. Subjects were grouped accordingly. There were significant differences of QTc dispersion (p = 0.026) and deceleration capacity (p = 0.007) between MR-T2* groups. Multivariate analysis showed an inverse correlation between QTc dispersion and MR-T2* values. There was a proportional correlation between heart rate deceleration capacity in the low MR-T2* group (p = 0.058) and the high ferritin group (p = 0.007). No VLPs were detectable in any patients. In conclusion, prolonged QTc dispersion and decreased heart rate deceleration capacity were significantly correlated with greater odds of iron overload among patients with Thalassemia major.
Local Dynamic Thermal Instability

A very simple but general criterion for local thermal instability in dynamical sytems is derived. The background flow may be time-dependent. The instability criterion is very similar to the classical Field result, except that the cooling function L is replace by L/T, where T is the temperature. Previous results in the literature are special cases of our result. A third-order differential equation for the evolution of small-wavelength radial perturbations in spherical systems is derived. With self-gravity, it is the exact equation that would result for the evolution of perturbations in simple matter-dominated Friedmann cosmologies (or any homogeneous volume change) subject to external heating and cooling. The scale factor is determined by the unperturbed velocity flow. The equation is solved by WKBJ techniques, allowing acoustical and condensational modes to be treated on an equal footing. Transport processes are not considered here. Self-gravity and fragmentation are briefly discussed. 11 references
Artificial intelligence techniques for retinal prostheses: a comprehensive review and future direction

Objective. Retinal prostheses are promising devices to restore vision for patients with severe age-related macular degeneration or retinitis pigmentosa disease. The visual processing mechanism embodied in retinal prostheses play an important role in the restoration effect. Its performance depends on our understanding of the retina’s working mechanism and the evolvement of computer vision models. Recently, remarkable progress has been made in the field of processing algorithm for retinal prostheses where the new discovery of the retina’s working principle and state-of-the-arts computer vision models are combined together. Approach. We investigated the related research on artificial intelligence techniques for retinal prostheses. The processing algorithm in these studies could be attributed to three types: computer vision-related methods, biophysical models, and deep learning models. Main results. In this review, we first illustrate the structure and function of the normal and degenerated retina, then demonstrate the vision rehabilitation mechanism of three representative retinal prostheses. It is necessary to summarize the computational frameworks abstracted from the normal retina. In addition, the development and feature of three types of different processing algorithms are summarized. Finally, we analyze the bottleneck in existing algorithms and propose our prospect about the future directions to improve the restoration effect. Significance. This review systematically summarizes existing processing models for predicting the response of the retina to external stimuli. What’s more, the suggestions for future direction may inspire researchers in this field to design better algorithms for retinal prostheses.
Effects of crude oligosaccharide extract from agricultural by-products on the performance and gut development of broilers

Objective This study aimed to determine the effect of crude oligosaccharide extract from coconut milk meal (CMM) and spent tea leaves (ST) on the performance and gut development of broiler chickens. Methods A total of 240 one-day-old unsexed broiler chicks (ROSS 308) were raised on litter-floored pens and had ad libitum access to water for 42 days. The experiment was conducted on chicks fed with basal diet (CON), commercial mannan-oligosaccharides (MOS), crude oligosaccharide extract from CMM, and crude oligosaccharide extract from ST. The experimental diets were supplemented with 2 and 1 g/kg oligosaccharides during the starter and grower periods, respectively. Results The body weight gain (BWG) of birds in the MOS group was higher than that of birds in the other groups (p<0.05) in the starter period. However, during the grower period, ST significantly improved the BWG compared to the MOS (p<0.05). MOS, CMM, and ST showed no influence on the carcass and visceral organ weight and the weight and length of intestine (p>0.05). The digestibility of gross energy was greater (p<0.05) in the CMM group than in the CON group during the grower period. Morphological changes were absent in the dietary treatments (p>0.05). Conclusion The improvements in the growth performance were partly driven by nutrient digestibility of such oligosaccharides having prebiotic properties. This result can indicate that supplementing broiler diets with crude oligosaccharides from CMM and ST had no negative effect on the growth performance and gut development of broilers.
Further evidence for disk accretion in FU Orionis objects

Optical and IR spectroscopy data on the T Tauri star FU Ori obtained with instrumentation at Kitt Peak and the Mt. Hopkins Multiple Mirror telescope were examined for double peaks predicted by a disk accretion model. The data, collected in January 1986, were used to calculate a mean extinction curve and optical spectra. Comparison of optical profiles were also recorded for V1057 Cyg and V1515 Cyg. The FU Ori data exhibited the double peak predicted by the disk model. A wavelength-dependent rotation rate that was observed is attributed to the absence of any companion star. Mass is instead being accreted onto a disk from remnant material from the star formation process. 19 references.
Randomised double blind placebo controlled clinical trial to evaluate the effect of a mixture of probiotic strains on symptom severity and the use of corticosteroids in children and adolescents with atopic dermatitis.

BACKGROUND Intestinal microbiota is altered in patients with Atopic Dermatitis (AD) when compared to that of the healthy population. Some interventions with specific probiotic preparations already demonstrate a change in the composition of this microbiota accompanied by an improvement of the disease. OBJECTIVES This research work, is designed to evaluate the clinical efficacy of the probiotic and to measure the effect of the intervention on the total dose of corticosteroids administered to subjects. METHODS A double-blind, randomized, placebo-controlled clinical trial including AD participants, aged 4 to 17 years, to evaluate the clinical effect of a probiotic mixture of Bifidobacterium lactis, Bifidobacterium longum and Lactobacillus casei in a total daily consumption of 1x109. The analyzed clinical variables are the SCORAD (Scoring of Atopic Dermatitis) and IGA (Investigator Global Assessment) indices, the effect on the consumption of topical corticosteroids and the assessment of safety. RESULTS SCORAD index score at 12 weeks shows a statistically significant difference of -5.43 (-10.65 to -0.21) between the probiotic (SCORAD 13.52) and placebo groups (SCORAD 18.96); p=0.04. Comparison between groups shows statistically significant difference in the number of patients with IGA score improvement over the 12-week intervention: 29 (90.5%) patients in the probiotic group and 17 (56.7%) patients in the placebo group (p<0.002). A comparison between groups of the proportions of days using corticosteroids and the total grams of corticosteroids between baseline and end of study shows no significant difference, but between weeks 6-12 there is a statistically significant reduction in the probiotic group when compared with the placebo group in both variables. The numbers of adverse events are similar in both groups of treatment. CONCLUSIONS The probiotic used in this clinical trial demonstrates efficacy on the change of the activity index of AD compared to placebo. The total number of days and total amount of topical corticosteroids required by the subjects in the probiotic group showed a significant reduction compared to placebo between 6-12 weeks.
TELIN: Table Entity LINker for Extracting Leaderboards from Machine Learning Publications

Tracking state-of-the-art (SOTA) results in machine learning studies is challenging due to high publication volume. Existing methods for creating leaderboards in scientific documents require significant human supervision or rely on scarcely available LaTeX source files. We propose Table Entity LINker (TELIN), a framework which extracts (task, model, dataset, metric) quadruples from collections of scientific publications in PDF format. TELIN identifies scientific named entities, constructs a knowledge base, and leverages human feedback to iteratively refine automatic extractions. TELIN identifies and prioritizes uncertain and impactful entities for human review to create a cascade effect for leaderboard completion. We show that TELIN is competitive with the SOTA but requires much less human annotation.
Domain Specific Augmentations as Low Cost Teachers for Large Students

Current neural network solutions in scientific document processing employ models pretrained on domain-specific corpi, which are usually limited in model size, as pretraining can be costly and limited by training resources. We introduce a framework that uses data augmentation from such domain-specific pretrained models to transfer domain specific knowledge to larger general pretrained models and improve performance on downstream tasks. Our method improves the performance of Named Entity Recognition in the astrophysical domain by more than 20% compared to domain-specific pretrained models finetuned to the target dataset.
Hypocrisy is the tribute that vice pays to virtue: Dynamics of perceived organization hypocrisy and job embeddedness in the hospitality industry

In the wake of the COVID-19 pandemic, hospitality institutions are striving for legitimacy, which leads them to organizational hypocrisy, generating perceptions of inducement breach, future anxiety, and ultimately reduced Job Embeddedness. This study has identified industry and environmental situation-specific constructs in a mutual relationship to fill a theoretical gap. An electronic survey of 2100 frontline employees was administered among which 842 completed surveys were retained for analysis. The validity of the measures and the absence of common method bias were established. SPSS PROCESS was used to compute the serial mediation effects. Contrary to existing knowledge, the results of this study indicate that organizational hypocrisy increases employee job embeddedness. Three reasons identified for this result are Asian culture sample, prevalence of COVID-19 pandemic, and the necessity of hypocrisy emphasized by scholars. The study also presents an underlying mechanism that makes this relationship negative through perceived inducement breach and future anxiety. This study focuses on HOW and IF organizational hypocrisy has detrimental effects, thus adding empirical evidence to otherwise exploratory literature. For hospitality industry, employees are an irreplaceable resource that provides competitive advantages; they need to align their values with that of their employees by word and actions or risk losing them.
Dynamic Strategies for High Performance Training of Knowledge Graph Embeddings

Knowledge graph embeddings (KGEs) are the low dimensional representations of entities and relations between the entities. They can be used for various downstream tasks such as triple classification, link prediction, knowledge base completion, etc. Training these embeddings for a large dataset takes a huge amount of time. This work proposes strategies to make the training of KGEs faster in a distributed memory parallel environment. The first strategy is to choose between either an all-gather or an all-reduce operation based on the sparsity of the gradient matrix. The second strategy focuses on selecting those gradient vectors which significantly contribute to the reduction in the loss. The third strategy employs gradient quantization to reduce the number of bits to be communicated. The fourth strategy proposes to split the knowledge graph triples based on relations so that inter-node communication for the gradient matrix corresponding to the relation embedding matrix is eliminated. The fifth and last strategy is to select the negative triple which the model finds difficult to classify. All the strategies are combined and this allows us to train the ComplEx Knowledge Graph Embedding (KGE) model on the FB250K dataset in 6 hours with 16 nodes when compared to 11.5 hours taken to train on the same number of nodes without applying any of the above optimizations. This reduction in training time is also accompanied by a significant improvement in Mean Reciprocal Rank (MRR) and Triple Classification Accuracy (TCA).
DEVELOPMENT OF HEUTADIGITAL LEARNING MODEL IN INDONESIAN WEB-BASED LEARNING

This study aims to develop a Heutadigital learning model in Indonesian Web-based language learning. In this context, heutadigital is developed as an Indonesian web-based independent language learning model. The research method used is research and development with the Thiagarajan model which includes the following steps: Define, Design, Development, and Dissemination. However, this research stage has only reached the design stage. The results of the study explain that the Heutadigital learning model in web-based Indonesian learning has four activities: lecturer notes, activity, discussion forums and video conferences, and checkpoints and final tests. The learning materials include topics (1) Functions, Variety, and Position of Indonesian Language, topics (2) Indonesian Spelling and Diction, topics (3) Effective Sentences and Coherent Paragraphs, topics (4) Plagiarism, topics (5) Scientific Articles and Popular Scientific Essays, topics (6) Research Proposals, Citation Techniques, and Bibliography, and topics (7) Academic Communication.
Characterizing and Optimizing Transformer Inference on ARM Many-core Processor

Transformer has experienced tremendous success and revolutionized the field of natural language processing (NLP). While GPU has become the de facto standard for deep learning computation in many cases, there are still many scenarios where using CPU for deep learning remains a prevalent choice. In particular, ARM many-core processor is emerging as a competitive candidate for HPC systems, which is promising to deploy Transformer inference. In this paper, we first position three performance bottlenecks of Transformer inference on many-core CPU, including isolated thread scheduling and configuration, inappropriate GEMM implementation and redundant computations for variable-length inputs. To tackle these problems, we proposed cross-layer optimizations for these challenges from operator to runtime layer. To improve parallel efficiency, we design NUMA-aware thread scheduling and a look-up table for optimal parallel configurations.The implementation of GEMM is tailored for some critical modules to suit the characteristics of Transformer workload. To eliminate redundant computations, a novel storage format is designed and implemented to pack sparse data and a load balancing distribution strategy is proposed for tasks with different sparsity. Our experimental results show that our implementation can outperform existing solutions by 1.1x to 6x for fixed-length inputs and 1.9x to 6x for variable-length inputs depending on different sequence lengths and batch sizes.
AN ANALYTICAL STUDY OF THE GLOBULAR-CLUSTER LUMINOSITY FUNCTION

Following Harris & Pudritz (1993), it is assumed that a globular cluster system (GCS) has a mass spectrum of the form Nm ~ m^-alpha, where alpha may take on different characteristic values in selected mass ranges. From this, an analytical globular cluster luminosity function (GCLF) is then derived. It is shown that this GCLF is non-Gaussian, but will be peaked, unimodal, and symmetric (as is observed) if the mass spectral slope changes in a very specific way: there must be exactly one transition in alpha from > 1 to < 1, and the relation |1-alpha|-const. must hold. Similarly, the luminosity-weighted luminosity function (LWLF), which is a useful observable for the distant, very large GCSs in Virgo and beyond, is shown to be peaked, unimodal, and symmetric if there is exactly one transition from alpha > 2 to alpha < 2, such that |2-alpha|=const. The Gaussian approximation to the analytical GCLF is considered, and it is shown that the dispersion sigmaG is directly related to the mass epctral slope alpha. Implications for the use of the GCLF as a standard candle are briefly discussed.
Nusantarian: Development of Gamification-Based Badut Temple Educational Tourism Applications on Android

Badut Temple as one of the tourist destinations in Malang and the oldest temple in East Java has a problem in the form of the existence of an information board that only explains some of the objects in the Badut Temple. Whereas the results of observations show that temple visitors have a curiosity about existing objects and tend to explore the meaning of the objects themselves rather than reading the existing information boards. A solution is needed to convey the information contained in the Badut Temple object by combining the gamification method. The development of the Nusantarian application uses the waterfall software development method by using tests in the form of functional testing, usability, and compatibility. The choice of the waterfall method is because the functional requirements of the system have been clearly defined from the start. The results of functional testing show that almost all functional requirements can run well except for functional requirements that utilize GPS sensors because the accuracy of mobile GPS is sometimes inaccurate and precise. While the application compatibility test can run well at least on the Android Pie version. And the usability test got a success rate of 96.67% and based on the System Usability Scale (SUS) it produced an average value of 76 which was in adjective good or acceptable. Based on distance analysis, The average respondent found objects within 2 – 4 meters past or before the location of the original object. Also, the most frequent objects encountered by respondents were Perwara Temple and the Nandi statue. It is hoped that this research can be developed with a combination of other methods that can improve the weaknesses of the use of GPS sensors.
Classification of Electroencephalogram Data on Massive Open Online Course Studying Process Using Gated Recurrent Unit

Massive Open Online Course (MOOC) is an education service that provides an online learning system for people. MOOCs implement asynchronous learning and there is no limit to how many people join a class. Thus, it allows many people to receive the education that is needed by the people or even people who are interested in some topic that they want to learn. Online learning shows that a learning process does not require direct interaction between lecturer and student, that also brings up a problem where it is hard to determine if the lecture video is being understood by the student or not. A group of researchers collects brain signal data which generates EEG data from a few students while watching a few lecture videos from MOOC, which the EEG data can be used for further research to detect how the brain works when watching MOOC videos. This research implements Gated Recurrent Unit method to do prediction by using EEG data to detect if the brain is in a confused state or normal state which the main purpose is to know the performance of Gated Recurrent unit to predict brain state by using EEG. The process of this study consists of exploratory data analysis, preprocessing, GRU implementation process, and evaluation using the average accuracy in every fold. The testing result shows GRU gives the best result by looking at the average accuracy for every fold with 61% accuracy
DSSA: Dual-Side Sparse Systolic Array Architecture for Accelerating Convolutional Neural Network Training

Ever-growing CNN size incurs a significant amount of redundancy in model parameters, which in turn, puts considerable burden on hardware. Unstructured pruning is widely used to reduce model sparsity. While, the irregularity introduced by unstructured pruning makes it difficult to accelerate sparse CNNs on systolic array. To address this issue, a variety of accelerators have been proposed. SIGMA, the state-of-the-art sparse GEMM accelerator, achieves significant speedup over systolic array. However, SIGMA suffers from two disadvantages: 1) it only supports one-side sparsity, leaving potential for further performance gains; 2) SIGMA improves utilization of large-sized systolic arrays at the cost of extra overhead. In this paper, we propose DSSA, a dual-side sparse systolic array, to accelerate CNN training. DSSA bases its designs on a small-sized systolic array, which naturally achieves higher cell utilization without additional overhead. To facilitate dual-side sparsity processing, DSSA utilizes a cross-cycle reduction module to accumulate partial sum that belongs to the same column but being processed in different cycles. A comprehensive design space exploration is performed to seek the local optimal configurations for DSSA. We implement the logic design of DSSA using Verilog in RTL and evaluate its performance using a C++-based cycle-accurate performance simulator we built. Experimental results show that DSSA delivers, on average, a speedup of 2.13x and 13.81x over SIGMA and a basic systolic array with the same number of cells. Compared to SIGMA, DSSA incurs 16.59% area overhead and 25.49% power overhead when sparse filter is excluded, as SIGMA did.
Therapeutic antibodies for precise cancer immunotherapy: current and future perspectives

Abstract Antibodies, as one of the most important components of host adaptive immune system, play an important role in defense of infectious disease, immune surveillance, and autoimmune disease. Due to the development of recombinant antibody technology, antibody therapeutics become the largest and rapidly expanding drug to provide major health benefits to patients, especially for the treatment of cancer patients. Many antibody-based therapeutic strategies have been developed including monoclonal antibodies, antibody-drug conjugates, bispecific and trispecific antibodies and pro-antibodies with promising results from both clinical and pre-clinical trials. However, the response rate and side-effect still vary between patients with undefined mechanisms. Here, we summarized the current and future perspectives of antibody-based cancer immunotherapeutic strategies for designing next-generation drugs.