Miracle-dz/UniFAR-FTUs huggingface dataset:
BifA Triggers Phosphorylation of Ezrin to Benefit Streptococcus equi subsp. zooepidemicus Survival from Neutrophils Killing

Streptococcus equi subsp. zooepidemicus (SEZ) ATCC35246 can invade the brain and cause severe neutrophils infiltration in brain tissue. This microorganism can survive and reproduce to an extremely high CFU burden (108–109/organ) under stressful neutrophils infiltration circumstances. The aim of this research is to explore the mechanism of the SEZ hypervirulent strain with its specific bifA gene which avoids being eliminated by neutrophils in the brain. We isolated the primary mouse neutrophils to treat SEZ WT and bifA gene defective (ΔBif) strains. The ΔBif strain had a weakened function of defending against neutrophils killing in vitro. The interaction between BifA and ezrin proteins in neutrophils were identified by co-IP and immunoblot. In neutrophils, the BifA interacts with ezrin and triggers the phosphorylation of ezrin at its Thr567 site in a PKC-dependent manner, then the excessive elevation of phosphorylated-ezrin recruits Dbl and activates Rac1. Since the Rac1 is closely relevant to several critical cellular functions, its abnormal activation will lead to neutrophils dysfunction and benefit to SEZ survival from neutrophils killing. Our findings reveal a novel consequence of BifA and ERM family protein (for ezrin, radixin, moesin) interaction, which happens between BifA and ezrin in neutrophils and contributes to SEZ survival in the brain. BifA should be considered as a potential target for drug development to prevent SEZ infection.
Controllable Network Data Balancing with GANs

The scarcity of network trafﬁc datasets has become a major impediment to recent trafﬁc analysis research. Data collection is often hampered by privacy concerns, leaving researchers with no choice but to capture limited amounts of highly unbalanced network trafﬁc. Furthermore, trafﬁc classes, particularly network attacks, represent the minority making many techniques such as Deep Learning prone to failure. We address this issue by proposing a Generative Adversarial Network for balancing minority classes and generating highly customizable attack trafﬁc. The framework regulates the generation process with conditional input vectors by creating ﬂows that inherit similar characteristics from the original classes while preserving the ﬂexibility to change their properties. We validate the generated samples with four tests. Our results show that the artiﬁcially augmented data is indeed similar to the original set and that the customization mechanism aids in the generation of personalized attack samples while remaining close to the original feature distribution.
On information discovery with machine intelligence for language

Background Information discovery concerns application under human control using “algorithms designed to analyze data or to extract patterns in specific categories from data” (Fayyad et al., 1996; Klösgen and _ Zytkow, 1996). Natural language is one of the essential sources of information discovery applications (Sun et al., 2017; Yim et al., 2016; Young et al., 2019). According to Mariani et al. (2019), the number of NLP papers increased from 24 in 1965 to 3,314 in 2015. The average rejection rate of top ACL conferences, such as ACL, EMNLP, COLING and NAACL-HLT, has stayed above 70% in the past three years. At the same time, the number of submissions has exploded. As an example, ACL 2020 received 3,429 valid paper submissions, smashing previous records. This number represents a 15% increase over the number of submissions received for ACL 2019 (2020Q1 Reports: ACL 2020 –AdminWiki, 2020). In recent years, because of the challenge of “Big Data,” the capability to process, analyze, store and understand these data sets is overwhelmed (Fan and Bifet, 2013). As Madden mentioned, it was “too big, too fast, and too hard” for the existing technology to handle (Che et al., 2013). For instance, nowadays, to make rules by human beings or generate features manually from terabytes of unstructured tweets could be a nightmare for the researchers. Fortunately, the development of machine intelligence empowered the researchers in the information discovery field. Machine learning has been applied in various kinds of tasks, such as multimedia concept retrieval, image classification, video recommendation, social network analysis and text mining (Pouyanfar et al., 2019). Machine learning, especially deep learning, played a significant role in the NLP field. Transfer learning on NLP has seen a series of breakthroughs since the year 2018. Fast. ai’s ULMFiT was submitted to arXiv on January 18, 2018 (Howard and Ruder, 2018) and has already been cited 192 times (as of May 2, 2020). Google’s BERT paper was submitted on October 11, 2018 and has 5,057 citations to date (Devlin et al., 2019).
Cutoff in the Lyman-alpha forest power spectrum

We re-analyse high redshift and high resolution Lyman- α forest spectra considered in [1], seeking to constrain the properties of warm dark matter particles. Compared to this previous work, we consider a wider range of thermal histories of the intergalactic medium. We ﬁnd that both warm and cold dark matter models can explain the cut-off observed in the ﬂux power spectra of high-resolution observations equally well. This implies, however, very different thermal histories and underlying reionization models. We discuss how to remove this degeneracy. This is an open article under the (http://creativecommons.org/licenses/by/4.0/). by SCOAP 3
NanoGRAM: Garbled RAM with Õ(logN) Overhead

We propose a new garbled RAM construction called NanoGRAM, which achieves an amortized cost of Õ(λ·(W logN+logN)) bits per memory access, where λ is the security parameter, W is the block size, and N is the total number of blocks, and Õ(·) hides poly log log factors. For sufficiently large blocks where W = Ω(logN), our scheme achieves Õ(λ ·W logN) cost per memory access, where the dependence on N is optimal (barring poly log log factors), in terms of the evaluator’s runtime. Our asymptotical performance matches even the interactive stateof-the-art (modulo poly log log factors), that is, running Circuit ORAM atop garbled circuit, and yet we remove the logarithmic number of interactions necessary in this baseline. Furthermore, we achieve asymptotical improvement over the recent work of Heath et al. Our scheme adopts the same assumptions as the mainstream literature on practical garbled circuits, i.e., circular correlation-robust hashes or a random oracle. We evaluate the concrete performance of NanoGRAM and compare it with a couple baselines that are asymptotically less efficient. We show that NanoGRAM starts to outperform the näıve linear-scan garbled RAM at a memory size of N = 2 and starts to outperform the recent construction of Heath et al. at N = 2. Finally, as a by product, we also show the existence of a garbled RAM scheme assuming only one-way functions, with an amortized cost of Õ(λ · (W logN + logN)) per memory access. Again, the dependence on N is nearly optimal for blocks of size W = Ω(logN) bits.
기법 Personalized Travel Path Recommendation Scheme on Social Media

In the recent times, a personalized travel path recommendation based on both travelogues and community contributed photos and the heterogeneous meta-data (tags, geographical locations, and date taken) which are associated with photos have been studied. The travellers using social media leave their location history, in the form of paths. These paths can be bridged for acquiring information, required, for future recommendation, for the future travellers, who are new to that location, providing all sort of information. In this paper, we propose a personalized travel path recommendation scheme, based on social life log. By taking advantage, of two kinds of social media, such as travelogue and community contributed photos, the proposed scheme, can not only be personalized to user’s travel interest, but also be able to recommend, a travel path rather than individual Points of Interest (POIs). The proposed personalized travel route recommendation method consists of two steps, which are: pruning POI pruning step and creating travel path step. In the POI pruning step, candidate paths are created by the POI derived. In the creating travel path step, the proposed scheme creates the paths considering the user's interest, cost, time, season of the topic for more meaningful recommendation
Buckling Analysis of Sodium to Sodium Heat Exchanger Tubes

Sodium to sodium heat exchanger tubes are small in diameter, long and thin. These tubes are supported at intermediate locations and subjected to compressive and tensile loading during reactor operation. Structural mechanics analyses of a 2-D axi-symmetric model of heat exchanger was performed to estimate the load sharing in these tubes during reactor operation on finite element software ABAQUS. Since the tubes are suppoted at intermediate locations, a 3D model of full tube with intermediate support was made to calculate the critical buckling load and buckling modes. In this paper, structure analysis of heat exchanger and load sharing in tubes, critical load for tube and buckling modes in the tube, and design check of tubes under compression are covered. Tubes which are under compression are checked for buckling and found within the design limit.
Performance Evaluation of Engineering Teachers Universities Based AHP Method

As an important part of human resource management, performance evaluation of engineering teachers is an important influent factor of the successful implementation of the “Plan for Educating and Training Outstanding Engineers”(PETOE), which is the “Long-term Education Reform and Development Plan”(2010-2020) of the major reform of Chinese higher education programs. On the basis of competency theory of iceberg model, four aspects of quality, knowledge, teaching and scientific research ability as well as personal feature are emphasized to establish performance framework. Furthermore, analytic hierarchy process (AHP) method is applied to determine the index weight to make the performance evaluation more scientific and rational.
Does Learning Organization Lead to a Better Performance?: A Study of a GLC in Malaysia

The Government-linked Companies (GLCs) transformation program is aimed to ensure its competency and competitiveness in achieving the nation’s Vision 2020. One of the factors that lead to higher performance is through the development of learning organization which provides continuous learning and improvement, directly linked to competitive advantage. Data collected from 150 employees of a telecommunication GLC in Malaysia revealed that three of learning organization dimensions namely; systems connection, strategic leadership and team learningwere positively related to organizational performance. This study is significant to GLCs top management and the Malaysian government in ensuring superior performance in a knowledge-driven economy. © 2016. The Authors. Published for AMER ABRA by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license ( http://creative commons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. Keywords: learning organization, organizational performance
Assessing Depression and Suicidality Among Recently Unemployed Persons with Obstructive Sleep Apnea and Socioeconomic Inequality

Background: Obstructive Sleep Apnea (OSA) is a common sleep-related breathing disorder that often is associated with several psychiatric conditions. Job loss is a stressful life event that can also affect mental health and socioeconomic status (SES). We investigated whether there was an association between the prevalence of OSA and several psychiatric conditions within a cohort of persons who recently became unemployed and whether SES was a contributing factor. Methods: Data from 292 participants who completed the screening evaluation of the Assessing Daily Activity Patterns through occupational Transitions (ADAPT) Study were used to assess the association between the prevalence of OSA, and current and past depression, and past suicidality. A type III sleep home sleep monitor was used to identify the presence of OSA and assess its severity. Depression and suicidality were ascertained using the Mini-international neuropsychiatric interview. Years of education was used as a proxy for SES. Results: There were no significant associations between severity of OSA, SES and current depression, past depression, and suicidality. Past suicidality was noted to be more common among those who were single/widowed (17.4%) or those who were divorced or separated (11.1%) (p=0.027). Current depression was more common among Hispanics in comparison to non-Hispanics. Furthermore, prevalence rates of both depression and past suicidality were higher than previous reports in general populations. Conclusions: Within a cohort of individuals who experienced recent job loss, there was no association between OSA and depression or past suicidality. Prevalence rates of both depression and past suicidality were higher than previous reports in the general population.
School Feeding Programmes and Physical Nutrition Outcomes of Primary School Children in Developing Countries

Context School feeding programmes have been widely implemented and particularly in developing countries with the aim to improve school enrolment and attendance especially of girls and to reduce short term hunger to improve childrens performance in school. Beyond the first 1000 days of the lives of children, school feeding programmes remain one of the critical interventions that have used schools as a platform to contribute to the fulfilment of their nutritional needs though the evidence to this effect is little and mixed. Objective This review focused on assessing the impact of school feeding programmes on reduction in underweight, thinness, and stunting among primary school children in developing countries. Data sources Electronic searches were carried out in PUBMED, SCORPUS and Cochrane library. The WHO clinical trials registry as well as reference lists of relevant articles were also hand searched. Data Extraction Data was extracted from included studies which have been published in the past 10 years (2010 - August 2021) from original research where the main intervention was the provision of school based meals. Data analysis Meta-analysis was conducted to determine changes in height-for-age (HAZ), weight-for-age (WAZ) and BMI-for-age (BAZ) z scores. A random effects model was applied to determine the mean difference in all outcomes of interest which were evaluated as continuous variables. Results Children aged 3 - 16 years were enrolled in the included studies and the number of participants ranged between 321 and 2,869 across studies. Of the included studies, the feeding intervention provided for a minimum of 30% RDA for the age group with the intervention lasting up to a maximum of 34 weeks. The impact of school feeding intervention on HAZ, BAZ and WAZ showed statistically non-significant (p>0.05) mean differences of 0.02 (95% CI, -0.06 to 0.10), 0.11 (95% CI, -0.01 to 0.23) and 0.06 (95% CI, -0.04 to 0.16) respectively Conclusion School feeding interventions have not shown any significant positive effect on the physical nutrition outcomes of primary school children. Short duration of intervention of studies, poor compliance to feeding and substitution of school meals could have accounted for the weak effect sizes.
Towards cost model-driven log-based business process improvement

This doctoral study focused on analysing business process execution histories to initiate evidence-based business process improvement activities. The researcher developed techniques to explore and visualise better ways of executing a business process, as well as to analyse the impact of the changes towards cost reduction. This research enables organisations to gain a better understanding of how the same business process can be performed in a more efficient manner, taking into consideration the trade-offs between processing time, cost, and employee utilisation.
Attention-Based Dual-Branch Cascade Network for Multi-Label Image Recognition

Multi-label image recognition is a practical task which aims to predict all concerned objects in an image, and the correlation between labels is considered as the key to solve multilabel problem. Previous approaches focus on exploring and exploiting the underlying relations between labels, by which the performance of most labels is improved significantly. But small object and label tail problems are ignored, and negatively influences the overall results. For ameliorating above problems, we propose a unified deep neural network named Attention-Based Dual-Branch Cascade Network (ADC Net), which contains Main Branch and Auxiliary Branch. ADC Net cascades to predict labels, and obtains the final result by element-wise adding. Attention modules in each branch contribute to recognizing small objects. Top-Down-Attention Module (TDAM) utilizes the preceding prediction map to guide the Auxiliary Branch. Besides, expanding training dataset is applied for learning the feature representation of tail labels. Our proposed methods are evaluated on two benchmark datasets: MS-COCO and VOC PASCAL 2007 datasets, and achieves state-of-the-art performance. Results of small objects, tail labels and visualization also prove the effectiveness of our method.
Over-the-Air Programming System for IoT Devices with a Non-native Wireless Update Service

In the last years, many solutions have been developed based on the Internet of Things (IoT) applied to several fields such as agriculture, road safety or electric lighting, among others. These devices are usually located in places that are not easy to access, which makes their software difficult to update. These updates should be carried out to improve the software of the devices to include new functionalities and/or solve security problems. This paper presents an Over-the-Air (OTA) programming system for devices that do not natively integrate a wireless update service. A description of the hardware used is included, as well as the update management application developed to carry out this task. The approach proposed has been validated by updating a microcontroller-based system applied to the area of road safety. The validation consisted in measuring the additional consumption required by the auxiliary update system compared to the base consumption, as well as determining the time required to update an IoT node in both wireless and wired mode. The results obtained show a reduction in consumption of 577% and a reduction in the updating time of 66%.
A Brillouin scattering-based fast temperature measurement method for ADSS of distribution IoT in electricity

In order to decrease time of temperature measurement for optical fiber in ADSS (all-dielectric self-supporting cable) of distribution IoT (Internet of Things) in electricity, the slope-assisted method is applied to its temperature demodulation. According to the introduction of the Brillouin scattering-based optical fiber temperature measurement method, the basic principle of Brillouin frequency shift measurement method based on the spectra fitting algorithm and the slope-assisted method is analyzed. Based on simulated Brillouin spectra with different signal-to-noise ratios (SNRs), the measurement time and accuracy of the spectra fitting algorithm and the slope-assisted method are compared. The results indicate that when if the SNR of Brillouin spectra is not less than 35 dB, the mean error of temperature measurement by the slope-assisted method is less than 0.5°C, which is slightly larger than the error of the spectra fitting algorithm, but the spectra measurement time is only 1/17 of that of the latter.
Marking and Cutting of Non-metallic Products with CO2 Laser

Since the discovery of the laser more than 60 years ago, research has begun on its application in various fields of technology. One of the most commonly used applications of laser technology is the cutting and marking of various materials. And while the use of different metals has been studied and used much better, the use of lasers in non-metallic materials has been studied much less. Research has been conducted with a CO2 laser on various materials -various types of textiles, felt, transparent and opaque Plexiglas and other materials that have wide application in technology and economics. The optimal parameters for performing various operations on this type of materials have been found and are shown in tabular and graphical form. Studies with different power and speed of the laser beam have been made. Based on the obtained results, the relevant conclusions are shown. These materials are widely used in industry, the manufacture of automobiles, aircraft, ships and other vehicles and machinery and the results prove the effectiveness of laser cutting and marking compared to the methods used so far to perform these operations.
Inductive Latent Space Sparse and Low-rank Subspace Clustering Algorithm

Sparse subspace clustering (SSC) and low-rank representation (LRR) are the most popular algorithms for subspace clustering. However, SSC and LRR are transductive methods and cannot deal with the new data not involved in the training data. When a new data comes, SSC and LRR need to calculate over all the data again, which is a time-consuming thing. On the other hand, for high-dimensional data, dimensionality reduction is firstly performed before running SSC and LRR algorithms which isolate the dimensionality reduction and the following subspace clustering. To overcome these shortcomings, in this paper, two sparse and low-rank subspace clustering algorithms based on simultaneously dimensionality reduction and subspace clustering which can deal with out-of-sample data were proposed. The proposed algorithms divide the whole data set into in-sample data and out-of-sample data. The in-sample data are used to learn the projection matrix and the sparse or low-rank representation matrix in the low-dimensional space. The membership of in-sample data is obtained by spectral clustering. In the low dimensional embedding space, the membership of out of sample data is obtained by collaborative representation classification (CRC). Experimental results on a variety of data sets verify that our proposed algorithms can handle new data in an efficient way.
The interface trap analysis of AlGaN/GaN high electron mobility transistors with temperature based on conductance method

In this paper, the characteristics of AlGaN/GaN high electron mobility transistors were tested and analyzed at high temperature. The experimental temperature range was 25~500°C. Frequency-dependent capacitance and conductance were adopted to investigate high temperature characteristics of interface trap. Results show that there is a kind of trap at the device interface. The trap density and time constant are (8.41×1010~1.40×1011)eV-1cm2/(0.398~0.636)μS and (1.03×1011~1.15×1011)eV-1cm2/(0.455~0.532)μS at different voltages and temperatures. With the increase of temperature, the trap density and time constant increase. High density interface traps are one of the reasons why device characteristics deteriorate with increasing temperature.