Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Processing of Sparse Tensor Decomposition via Unified Abstraction and PE-Interactive Architecture

We propose a novel architecture to efficiently perform sparse tensor decomposition/completion. As the generalization of vectors and matrices, tensors are widely used to process high-dimensional data. Sparse tensor decomposition (SpTD) is not only an emerging tensor analysis technique but also an effective tool to reduce the storage and computation costs of tensors. However, conventional general-purpose processors are inefficient to perform SpTD, mainly due to: i) variable sparsity degree and flexible buffer size requirement; ii) difficulties of fusing multiple execution kernels to pursue better performance. For domain-specific accelerator designers on the other hand, the diversity of decomposition algorithms is also an important problem that must be considered. To solve these challenges, we propose a unified abstraction for SpTD algorithms and design a specialized accelerator. First, we formulate two types of core kernels (SpLrMM and LrSampling) that serve as a standard form to fit a broad range of SpTD algorithms. Second, we design a sparse tensor engine (STE) to efficiently perform SpTD. STE uses a processing element (PE)-interactive architecture where PEs can be flexibly grouped together via Network-on-Chip (NoC) to share the buffer capacity, bandwidth, and compute resources. We evaluate our accelerator with extensive experiments, and it can achieve an average speedup of 45× over CPU and 29× over GPU.
INTRASPECIFIC VARIATION OF THIRTEEN PISANG AMBON CULTIVARS (Musa Acuminata CV. AAA) FROM EAST JAVA AND CENTRAL JAVA (INDONESIA) BASED ON RANDOM AMPLIFIED POLYMORPHIC DNA (RAPD) MARKER

Pisang Ambon (M. acuminata cv. AAA) is popular dessert banana in Indonesia, included as Gros Michel. Particularly at Java, there are some variation of Pisang Ambon local cultivars which need to be clearly classified. This study was aims to reveal the intraspecific genetic variation and subgrouping within thirteen of Pisang Ambon cultivars from East Java and Central Java based on RAPD markers. Twenty RAPD markers were performed i.e. OPA1OPA20. Morphologically, Pisang Ambon cultivars are mostly distinguished by their fruit size, peel colours, pulp colors and pulp taste. Indeed, the fruits are reflecting their distinct morphological or perceptual characteristics. The amplification bands produced in each sample have various lengths, ranging from 140 bp to 1500 bp. In total, there are 101 amplified. In total, 101 characters (RAPD bands) were subjected to dissimilarity distance and hierarchical clustering analysis. The tree resulted from RAPD profiles was clearly illustrated the genetic intraspecific relationship within Pisang Ambon cultivars in Java bands identified. It comprised of 86.82% polymorphic and 13.18% monomorphic bands. The derived cultivars of Pisang Ambon were separated into four sub-clusters, with low dissimilarity distances ranged 0.24 to 0.51. The conclusion of this study revealed that RAPD analysis was effective to study the genetic relationship in intraspecific level of Pisang Ambon cultivars. Furthermore, the finding of this study was showed that within Pisang Ambon cultivars were genetically variable.
Human Factors Principles in Information Dashboard Design

Refueling outages remain one of the largest opportunities for improving capacity factor and reducing costs available to commercial nuclear power plants. Although the nuclear industry has made steady improvement in outage optimization, each day of a refueling outage still represents an opportunity to save millions of dollars and each day an outage extends past its planned end date represents millions of dollars that may have been spent unnecessarily. Reducing planned outage duration or preventing outage extensions requires careful management of the outage schedule as well as constant oversight and monitoring of work completion during the outage execution. During a typical outage, there are typically more than 10,000 activities on the schedule that, if not managed efficiently, may cause expensive outage delays. Management of outages currently relies largely on paper-based resources and general-purpose office software. A typical method currently used to monitor work performance is a burn-down curve, where total remaining activities are plotted against the baseline schedule to track bulk work completion progress. While methods like this are useful, there is still considerable uncertainty during a typical outage whether bulk work progress is adequate and therefore a lot of management time is spent analyzing the situation on a daily basis. This paper is a case study of how new technology, in combination with established human-factors and user-centered design knowledge, can be used to support Outage Control Center (OCC) personnel decision making. In particular, we describe recent advances made in developing a framework for the design of visual outage information presentation, as well as an overview of the human factors principles that informed the development of the visualizations. To test the utility of advanced visual outage information presentation, an outage management dashboard software application was created as part of the Department of Energy’s Advanced Outage Control Center project (AOCC). This dashboard is intended to present all the critical information an outage manager would need to understand the current status of a refueling outage. The dashboard presents the critical path, bulk work performance, key performance indicators, outage milestones and metrics relating current performance to historical performance. Additionally, the dashboard includes data analysis tools to allow outage managers to drill down into the underlying data to understand the drivers of the indicators.
Application of advance oxidation process combination with fine bubble technology on the reduction of Escherichia coli O157:H7 contaminated on bird eye chilli (Capsicum frutescens L.)

Nowadays there are continuously increasing worldwide concerns for development of alternative microbiocidal inactivation. Advance Oxidation Processes (AOPs) are considered a highly competitive technology for the elimination of contaminated microorganism due to their high chemical stability and low biodegradability. The effectiveness of UV/O3 in AOP system combination with Fine bubble technology (FB) for the reduction of E. coli O157:H7 contaminated on Bird Eye Chilli (Capsicum frutescensL.) was assesed. A batch-type prototype of the UV/O3 and FB generator treatments were constructed. Consideration the antimicrobial effectiveness of individual technique, the results indicated that the population of E. coli O157:H7 was reduced as 96.36% within 10 minutes 9 when UV were applied. In case of the O3 applications, the results demonstrated that O3 provided 97.18% reduction within 10 min. The application of AOP, UV/O3 system, it can be found that the use of AOP provided 98.62% reduction of E. coli O157:H7. This finding knowledge indicated that AOP with UV/O3 system presented the more antimicrobial potential than the use of UV or O3 alone. The studies of FB with or without AOP on the microorganism elimination were also conducted. FB alone processed contamination reduction of E. coli O157:H7 at 96.53% reduction. The combination with AOP demonstrated 98.05% reduction. The use in dynamic assist process, 99.96% reduction was observed. Furthermore, applying AOP with FB technology in dynamic assist process did not affect the quality of Bird Eye Chilli after washing process. It was indicated the use of UV/O3 as AOP combination with FB technology in dynamic assist process could be applied for microbial contamination reduction on fresh produces.
MPSoC Testbed for Analyzing Execution of Process Networks

Real-time applications are increasingly implemented on multi-processor system-on-chips. It is a challenge to develop applications for these systems. Many steps are necessary until an application for a speciﬁc multi-processor architecture can be executed and analyzed. A testbed is implemented in this semester thesis that allows to execute process networks in a cycle-accurate Multi-Processor ARM (MPARM) simulator. As a result, the execution trace is extracted and a basic modular performance analysis model is created. The traces allow insight into the execution of a real-time application on the MPARM platform. A computation and approximation for workload curves based on simulation results is introduced in this thesis. Workload curves are used to model the processes in the analysis model.
Do Diverted Kids Stay Out of Trouble?: A Longitudinal Analysis of Recidivism Outcomes in Diversion

This study evaluates the effectiveness of a police diversion program between 2008 and 2016. Youth participating in the diversion program were compared to youth not participating in diversion on the probability of, and time to, second offense using unadjusted comparisons at 6, 12, 18, 24, and 36 months, unadjusted lifetable comparisons of time to second arrest, and Cox multivariate proportional hazards regression models. Diverted youth had significantly fewer second offenses. The rate of recidivism among diverted youth was lower than non-diversion youth at all time periods in unadjusted models, and statistically significant at 6 months, 12 months, and 18 months.
DYSREGULATION OF LONG-CHAIN ACYL-CoA SYNTHETASES IN CANCER AND THEIR TARGETING STRATEGIES IN ANTICANCER THERAPY

Cancer is a leading cause of death worldwide, and the number of cases globally continues to increase. Cancer is caused by certain changes in genes that are involved in controlling cell functions and cell division. Notably, metabolic dysregulation is one the hallmarks of cancer, and increases in fatty acid metabolism have been demonstrated to promote the growth and survival of a variety of cancers. In human, fatty acids either can be breakdown into acetyl-CoA through catabolic metabolism and aid in ATP generation or in the anabolic metabolism they can incorporate into triacylglycerol and phospholipid. Importantly, both of these pathways need activation of fatty acids, and the key players in this activation of fatty acids are the long-chain acyl-CoA synthetases (ACSLs) that are commonly dysregulated in cancer and associated with oncogenesis and survival. Therefore, it provides a rationale to target ACSLs in cancer. This review summarizes the current understanding of long-chain acyl-CoA synthetases in cancer and their targeting opportunities.
The Effect of Para-Social Interaction in Endorsement Advertising: SEM Studies Based on Consumers’ Exposure to Celebrity Symbols

This paper explores how consumers’ para-social interaction (PSI) bridges source credibility and consumer brand-related responses when they are exposed to advertising images with celebrity symbols. Two studies were conducted by collecting data through online surveys. The results, based on a structural equation modelling analysis, show that both perceived attractiveness and expertise of celebrities could affect the strength of consumer-celebrity PSI, which in turn allow the consumer to establish a personal connection with the endorsed brand and result in positive brand-related responses. Additionally, when a celebrity possess only one kind of advantage, whether it is perceived physical appearance or perceived expertise in specific field, the audience’s willingness to interact with the celebrity would increase as the celebrity’s perceived expertise increases. But when a celebrity is outstanding in both perceived physical appearance and perceived expertise, the intensity of the audience’s willingness to interact with the celebrity is more susceptible to the change of the perceived appearance of the celebrity. The research proves the indispensable role of the consumer-celebrity PSI for transferring the symbolic meaning of the celebrity endorser to the meaning of the consumer self, inspiring the consumercelebrity relationship perspective in endorsement advertising research. In addition, the research supplies the source credibility models and brand attitude models.
Comparative Analysis of C++ and Python in Terms of Memory and Time

In this era of technology, programming has become more significant than ever before. Python and C++ are both widely used programming languages. Python, the most popular programming language in today’s world, is a high-level object-oriented language whereas C++, the language behind most operating systems, is a low-level object-oriented language. In this paper, we present a comparative study of Python and C++. This paper discusses the introduction to these languages, their memory management techniques, and the reasons behind their program execution speed. Furthermore, we analyzed the execution time and memory used by multiple algorithms in both the languages with best, average, and worst cases. They are also compared with respect to the benefits and issues related to them. Results indicate that C++ is faster than Python in execution speed but Python serves as a better language for beginners due to its simplicity. Moreover, for the best results, the language should be selected according to the type of project.
The Use of Delphi Technique in Validating a Teaching and Learning Model for Enhancing Students' Computational Thinking Skills

Computational thinking skill is a new skill needed in twenty-first century learning. However, teaching practitioners have been struggling to find effective techniques to be used in the teaching and learning process that can help students to develop such a skill. Several studies have proposed a number of models to guide the teaching and learning process, but most lacked a focus on the improvement of computational thinking skill among students. As such, this study was carried out to identify and validate the elements and sub-elements of an assessment instrument prior to developing an effective teaching and learning model for enhancing students' computational thinking skill with the use of the Delphi technique, which was carried out through three rounds of implementation involving a panel of experts made up of five (5) academicians and two (2) industrial practitioners. Essentially, the proposed elements and sub-elements were derived from those based on the combination of the problem-solving techniques and metacognitive skills. After the completion of the three rounds of such technique, six (6) elements and 25 sub-elements of the assessment instrument were validated to be highly important, entailing them to be embedded in the proposed teaching and learning model for enhancing students' computational thinking skills. The use of Delphi technique in this study successfully identified and validated six (6) elements and 25 sub-elements of the teaching and learning model. This study contributes to the development of a model that can be used by teaching practitioners as a guide in their teaching and learning process for enhancing students' computational thinking.
Graphene-Based Coronal Hybrids for Enhanced Energy Storage

Functional materials with designer morphologies are anticipated to be the next generation materials for energy storage applications. In this manuscript, we have developed a holistic approach to enhance the surface area and hence the properties of nanostructures by synthesizing coronal nanohybrids of graphene. These nanohybrids provide distinctive advantages in terms of performance and stability over vertically stacked nanocomposites reported in literature. Various double hydroxide materials self-assembled as coronal lamellae on graphene shells have been synthesized and systematically studied. These coronal nanohybrids result in about a threefold increase in energy storage capacity as compared to their traditionally synthesized nanocomposite counterparts. The 3D graphene-based nanofibrils in the synthesized coronal nanohybrids provide mechanical support and connect the nodes of the double hydroxide lattices to inhibit restacking. Complex morphologies such as coronal nanostructures increase the interaction surface of the nanostructure significantly. Such an approach is also expected to bring a paradigm shift in development of functional materials for various applications such as sensors, energy storage, and catalysis.
Addictive Platforms

We study competition for consumer attention in which platforms can sacrifice service quality for attention. A platform can choose the “addictiveness” of its service. A more addictive platform yields consumers a lower utility of participation but a higher marginal utility of allocating attention. We provide conditions under which increased competition can harm consumers by encouraging platforms to offer low-quality services. In particular, if attention is scarce, increased competition reduces the quality of services because business-stealing incentives induce platforms to increase addictiveness. Restricting consumers’ platform usage may decrease addictiveness and improve consumer welfare. A platform’s ability to charge for its service can also decrease addictiveness. This paper was accepted by Joshua Gans, business strategy.
A Pre-training Approach for Stance Classification in Online Forums

Stance detection is the task of automatically determining whether the author of a piece of text is in favor of, against, or neutral towards a target such as a topic, entity, or claim. In this paper, we propose a method based on RoBERTa to classify stances by capturing the context of the discussion through the examination of pairs of stances and relational structures of debates specific to each topic within the defined window of each forum participant's interventions. Furthermore, we examine the degree of disagreement and neutrality in various debate topics to measure divergence of opinion in the course of the debate and estimate the emotional state manifested in different debate topics. We conduct extensive experiments using two publicly available datasets and demonstrate that our method considers more stance classes, provides better results and yields statistical improvements over existing techniques. Our quantitative analysis of model performance yields F-1 scores of over 0.745. Interestingly, we obtained the highest F-1 score, 0.814, on a stance class which was not taken into consideration in prior work. We report that none of the metrics utilized to measure divergence of opinion yield values exceeding 50 % and the correlations between the same topics over 10-fold cross-validation are statistically significant for the majority of them (p < 0.005). Several future research avenues are proposed.
Polycaprolactone/Gelatin Nanofibrous Scaffolds for Tissue Engineering

The combined use of polycaprolactone and gelatin has obviated many drawbacks associated with applying them alone. Polycaprolactone provides mechanical strength and buys sufficient time for tissue regeneration without degradation. Simultaneously, gelatin regulates the surface wettability of the composite scaffold. It makes the structure more cytocompatible by its cell recognition sites. Furthermore, these low polymers' cost, biocompatibility, and tuning of degradation time have added to their therapeutic appeal. In the current study, we have reviewed the application of polycaprolactone/gelatin scaffolds in tissue engineering and drug delivery fields, hoping to translate these scaffolds into medicine.
Abstractive Summarization on COVID-19 Publications with BART

: The asymptomatic carriers identiﬁed from close contacts were prone to be mildly ill during hospitalization. However, the communicable period could be up to three weeks and the communicated patients could develop severe illness. These results highlighted the importance of close contact tracing and longitudinally surveillance via virus nucleic acid tests. Further isolation recommendation and continuous nucleic acid tests may also be recommended to the patients discharged. There are two main drawbacks based on our observation.
SCORING SYSTEM COMPARISON TO DETERMINE SEVERITY IN NON-VARICEAL UPPER GASTROINTESTINAL BLEEDING

Determining scoring values in cases of UGIB is important to do so it can be used as a basis to determine the mortality rate of UGIB patients. Despite the dependency of most risk scoring systems for this disorder on endoscopic findings, the Glasgow-Blatchford Bleeding Score (GBS) and Incomplete Rockall Score (IRS) are based on simple variables. Our purpose to differentiate about sensitivity and spesificity between IRS and GBS to predict the severity of patient with non variceal upper GI bleeding without the endoscopic score. Our study was undertaken in emergency department Saiful Anwar hospital, Malang. The sample consist of 102 patients with non variceal upper GI bleeding from November 2012 to April 2013. With receiving-operator characteristics (ROC), we compare the significance between IRS and GBS in predicting the severity of patient with non variceal upper GI bleeding. In next step, we compare both of them with the diagnostic test. ROC curve shows that GBS is slightly better than IRS (0,955 vs 0,744, p<0,05). IRS has better specificity (79,55% vs 70,45%) than GBS. But, GBS has better sensitivity (96,55% vs 65,52%). The final results is GBS as an alternative diagnostic score has better results than IRS in understanding the severity of patient with non variceal upper GI bleeding in Saiful Anwar hospital. Therefore, it can be used as a simple predictor replacing the endoscopic score.
EXISTENCE OF SOLUTIONS FOR SINGULAR FRACTIONAL NAVIER BOUNDARY VALUE PROBLEMS

In this paper, we are concerned with the following fractional Navier boundary value problem: D(Du)(x) = −g(u), x ∈ (0, 1), lim x→0+ x1−βDαu(x) = −a, u(1) = b, where α, β ∈ (0, 1] such that α + β > 1, Dα and Dβ stand for the standard Riemann-Liouville fractional derivatives, the function g is continuous and nonincreasing on (0,∞) and the reals a, b ∈ (0,∞). Using Schäuder’s fixed point theorem, we prove the existence of positive continuous solutions.
Improved CycleGAN for Image-to-Image Translation

CycleGAN [1] is one recent successful approach to learn a mapping from one image domain to another with unpaired data. We investigated CycleGAN as a so-lution to artistic style transfer, in particular, translating photographs to Chinese paintings. To improve the stability of training, we improved CycleGAN based on Wasserstein generative adversarial network (WGAN) and further improved WGAN with gradient penalty. The performance of CycleGAN, CycleWGAN, and Improved CycleWGAN are compared on a self-collected dataset CNPaintings both quantitatively and qualitatively
Service User Experiences of CAT Diagrams: an Interpretative Phenomenological Analysis

Background: Formulation is an essential tool in psychological therapy. However, there is a paucity of research evidencing the efficacy, credibility and experience of formulation. Cognitive Analytic Therapy (CAT) uses a specific form of diagrammatic formulation. Aims: This study aims to explore service-user experiences of the SDR. Method: Seven participants who had an SDR and who completed therapy within three to twelve months were interviewed using a semi-structured interview/topic guide. Data were analysed using Interpretative Phenomenological Analysis (IPA). Results: Four superordinate themes emerged from the data: ‘Chaos to clarity (a process of meaning making)’; ‘The change process’; ‘Relational dynamics’; and ‘Focus on treatment options’. Conclusions: Results suggest the SDR facilitates understanding and reduces blame. Participants advocated for CAT as an early intervention. The visual and physical aspects of the SDR were important in developing ownership of the formulation. Collaboration was crucial to the development of the therapeutic relationship and promoted a sense of empowerment, hope and meaningful person-centred change. For participants in this study CAT was regarded as a preferable treatment compared to CBT and medical frameworks of understanding human distress. Study strengths and limitations, clinical implications and future research ideas are discussed. Declaration of interest: None.