Miracle-dz/UniFAR-FTUs huggingface dataset:
The Model of Artificial Neural Network and Nonparametric MARS Regression for Indonesian Composite Index

The Indonesian composite stock price index is an indicator of changes in stock prices as a guide for investors to invest in reducing risk. The regression model for Indonesian Composite Index (ICI) has the response variable as stock prices with fluctuation behavior and several financial predictor variables, the model tends to violate the assumptions of normality, homoscedasticity, autocorrelation and multicollinearity. This problem can be overcome by modeling the composite stock price index by using the Artificial Neural Network (ANN) and nonparametric regression of Multivariate Adaptive Regression Spline (MARS). In this study, the time series data from the composite stock price index starting from April 2003 to March 2018 with its predictor variables are crude-oil prices, interest rates, inflation, exchange rates, gold prices, Dow Jones price, and Nikkei 225 Index. The both methods give better goodness of fit, where the coefficient of determination ANN is 0.98925 and the MARS determination coefficient is 0.99427. While based on the Mean Absolute Percentage Error (MAPE) of ANN was obtained 6.16383 and the MAPE value of MARS is 4.51372. This means that the ANN method and nonparametric MARS regression method have good performance to forecast the value of the Indonesian composite stock price index in the future, but in this case of data the nonparametric MARS regression method shows the accuracy of the model is slightly better than ANN.
Peptidoglycan Muropeptides: Release, Perception, and Functions as Signaling Molecules

Peptidoglycan (PG) is an essential molecule for the survival of bacteria, and thus, its biosynthesis and remodeling have always been in the spotlight when it comes to the development of antibiotics. The peptidoglycan polymer provides a protective function in bacteria, but at the same time is continuously subjected to editing activities that in some cases lead to the release of peptidoglycan fragments (i.e., muropeptides) to the environment. Several soluble muropeptides have been reported to work as signaling molecules. In this review, we summarize the mechanisms involved in muropeptide release (PG breakdown and PG recycling) and describe the known PG-receptor proteins responsible for PG sensing. Furthermore, we overview the role of muropeptides as signaling molecules, focusing on the microbial responses and their functions in the host beyond their immunostimulatory activity.
DUCATI

Conventional on-chip TLB hierarchies are unable to fully cover the growing application working-set sizes. To make things worse, Last-Level TLB (LLT) misses require multiple accesses to the page table even with the use of page walk caches. Consequently, LLT misses incur long address translation latency and hurt performance. This article proposes two low-overhead hardware mechanisms for reducing the frequency and penalty of on-die LLT misses. The first, Unified CAche and TLB (UCAT), enables the conventional on-die Last-Level Cache to store cache lines and TLB entries in a single unified structure and increases on-die TLB capacity significantly. The second, DRAM-TLB, memoizes virtual to physical address translations in DRAM and reduces LLT miss penalty when UCAT is unable to fully cover total application working-set. DRAM-TLB serves as the next larger level in the TLB hierarchy that significantly increases TLB coverage relative to on-chip TLBs. The combination of these two mechanisms, DUCATI, is an address translation architecture that improves GPU performance by 81%; (up to 4.5×) while requiring minimal changes to the existing system design. We show that DUCATI is within 20%, 5%, and 2% the performance of a perfect LLT system when using 4KB, 64KB, and 2MB pages, respectively.
Microbes for Iron Chlorosis Remediation in Peach

Peach [Prunus persica (L.) Batsch] suffers from iron chlorosis when grown in calcareous soils due to low iron availability. Traditionally, soil and foliar application of ferrous sulphate, Fe-EDTA, Fe-EDDHA chelates, etc. is used as a corrective measure of chlorosis. The latter practice is quite effective. However, variable responses have been reported. Therefore, foliar spray cannot yet be considerd as a reliable method to control lime-induced chlorosis. Bioremediation constitutes innovative approaches for chlorosis correction. Iron fixations in calcareous soil, iron uptake by plants, and advance detection techniques and correction strategies in plants for iron chlorosis have been discussed in this chapter. The microbe-mediated correction strategies are identified as eco-friendly.
On the Raison D’être of the Present Perfect, with Special Reference to the English Grammeme

The present perfect is a mystery and most linguists agree that its definitions are inadequate. The paper deals with two major issues: (i) what is its second meaning, beside the temporal one; (ii) what is its raison d’être? Is it the expression of notions such as current relevance or resultativeness? The analysis is based on recent findings that the present perfect performs a grammaticalizing function with certain sentences belonging to a semantico-syntactic schema in two languages, Bulgarian and Montenegrin. It shows that, as regards (i), the present perfect is a form that can be termed non-witnessed in itself in Bulgarian and English. However, while in English and Montenegrin it is not grammatically marked as non-witnessed (in Bulgarian it is), in English and Bulgarian it signals this value – but not by default. Conversely, the indefinite past in English, to which the present perfect is invariably contrasted, is a witnessed form by default, hence its witnessed value can be canceled in a sentence/context. In other words, the English indefinite past is not grammatically marked as witnessed and does not signify this value – but signals it by default. As regards (ii), the raison d’être of the English present perfect is argued to be the signaling (not by default) of the value non-witnessed to counterbalance the default value witnessed in the indefinite past. Bottom line: the raison d’être of the present perfect across languages appears to be found not in its “meaning” but in certain functions related to language structure that it performs.
Sharp error estimates for spline approximation: Explicit constants, n-widths, and eigenfunction convergence

In this paper, we provide a priori error estimates in standard Sobolev (semi-)norms for approximation in spline spaces of maximal smoothness on arbitrary grids. The error estimates are expressed in terms of a power of the maximal grid spacing, an appropriate derivative of the function to be approximated, and an explicit constant which is, in many cases, sharp. Some of these error estimates also hold in proper spline subspaces, which additionally enjoy inverse inequalities. Furthermore, we address spline approximation of eigenfunctions of a large class of differential operators, with a particular focus on the special case of periodic splines. The results of this paper can be used to theoretically explain the benefits of spline approximation under [Formula: see text]-refinement by isogeometric discretization methods. They also form a theoretical foundation for the outperformance of smooth spline discretizations of eigenvalue problems that has been numerically observed in the literature, and for optimality of geometric multigrid solvers in the isogeometric analysis context.
Combining Individual Phenotypes of Feed Intake With Genomic Data to Improve Feed Efficiency in Sea Bass

Measuring individual feed intake of fish in farms is complex and precludes selective breeding for feed conversion ratio (FCR). Here, we estimated the individual FCR of 588 sea bass using individual rearing under restricted feeding. These fish were also phenotyped for their weight loss at fasting and muscle fat content that were possibly linked to FCR. The 588 fish were derived from a full factorial mating between parental lines divergently selected for high (F+) or low (F–) weight loss at fasting. The pedigree was known back to the great grand-parents. A subset of 400 offspring and their ancestors were genotyped for 1,110 SNPs which allowed to calculate the genomic heritability of traits. Individual FCR and growth rate in aquarium were both heritable (genomic h2 = 0.47 and 0.76, respectively) and strongly genetically correlated (−0.98) meaning that, under restricted feeding, faster growing fish were more efficient. FCR and growth rate in aquariums were also significantly better for fish with both parents from F– (1.38), worse for fish with two parents F+ (1.51) and intermediate for cross breed fish (F+/F– or F–/F+ at 1.46). Muscle fat content was positively genetically correlated to growth rate in aquarium and during fasting. Thus, selecting for higher growth rate in aquarium, lower weight loss during fasting and fatter fish could improve FCR in aquarium. Improving these traits would also improve FCR of fish in normal group rearing conditions, as we showed experimentally that groups composed of fish with good individual FCR were significantly more efficient. The FCR of groups was also better when the fish composing the groups had, on average, lower estimated breeding values for growth rate during fasting (losing less weight). Thus, improving FCR in aquarium and weight loss during fasting is promising to improve FCR of fish in groups but a selection response experiment needs to be done. Finally, we showed that the reliability of estimated breeding values was higher (from+10% up to +125%) with a genomic-based BLUP model than with a traditional pedigree-based BLUP, showing that genomic data would enhance the accuracy of the prediction of EBV of selection candidates.
Adversarial Deep Learning in EEG Biometrics

Deep learning methods for person identification based on electroencephalographic (EEG) brain activity encounters the problem of exploiting the temporally correlated structures or recording session specific variability within EEG. Furthermore, recent methods have mostly trained and evaluated based on single session EEG data. We address this problem from an invariant representation-learning perspective. We propose an adversarial inference approach to extend such deep learning models to learn session-invariant person-discriminative representations that can provide robustness in terms of longitudinal usability. Using adversarial learning within a deep convolutional network, we empirically assess and show improvements with our approach based on longitudinally collected EEG data for person identification from half-second EEG epochs.
Sensitivity of Inherent Optical Properties From Ocean Reflectance Inversion Models to Satellite Instrument Wavelength Suites

The Earth science community seeks to develop climate data records (CDRs) from satellite measurements of ocean color, a continuous data record which now exceeds twenty years. Space agencies will launch additional instruments in the coming decade that will continue this data record, including the NASA PACE spectrometer. Inherent optical properties (IOPs) quantitatively describe the absorbing and scattering constituents of seawater and can be estimated from satellite-observed spectroradiometric data using semi-analytical algorithms (SAAs). SAAs exploit the contrasting optical signatures of constituent matter at spectral bands observed by satellite sensors. SAA performance, therefore, depends on the spectral resolution of the satellite spectroradiometer. A CDR spanning SeaWiFS, MODIS, OLCI, and PACE, for example, would include IOPs derived using varied wavelength suites if all available wavelengths were considered. Here, we explored differences in derived IOPs that stem simply from the use of (eight) different wavelength suites of input radiometric measurements. Using synthesized data and SeaWiFS Level-3 mission-long composites, we demonstrated equivalent SAA performance for all wavelength suites, but that IOP retrievals vary by several percent across wavelength suites and as a function of water type. The differences equate to roughly ≤ 6, 12, and 7% for adg (443), aph (443), and bbp (443), respectively, for waters with Ca ≤ 1 mg m-3. These values shrink for sensors with similar wavelength suites (e.g., SeaWiFS, MODIS, and MERIS) and rise to substantially larger values for higher Ca waters. Our results also indicate that including 400 nm (in the case of OLCI) influences the derived IOPs, using longer wavelengths (>600 nm) influences the derived IOPs when there is a red signal, and, including additional spectral information shows potential for improved IOP estimation, but not without revisiting SAA parameterizations and execution. While modest in scope, we believe this study contributes to the knowledge base for CDR development. The implication of ignoring such an analysis as CDRs continue to be developed is a prolonged inability to distinguish between algorithmic and environmental contributions to trends and anomalies in the IOP time-series.
BTLA/HVEM Signaling: Milestones in Research and Role in Chronic Hepatitis B Virus Infection

B- and T-lymphocyte attenuator (BTLA) is an immune-regulatory receptor, similar to CTLA-4 and PD-1, and is mainly expressed on B-, T-, and all mature lymphocyte cells. Herpes virus entry mediator (HVEM)-BTLA plays a critical role in immune tolerance and immune responses which are areas of intense research. However, the mechanisms of the BTLA and the BTLA/HVEM signaling pathway in human diseases remain unclear. This review describes the research milestones of BTLA and HVEM in chronological order and their role in chronic HBV infection.
Species-Specific Outcome in the Competition for Nitrogen Between Invasive and Native Tree Seedlings

The outcome of competition for nitrogen (N) between native and invasive tree species is a major concern when considering increasing anthropogenic N deposition. Our study investigated whether three native (i.e., Fagus sylvatica, Quercus robur, and Pinus sylvestris) and two invasive woody species (i.e., Prunus serotina and Robinia pseudoacacia) showed different responses regarding morphological and physiological parameters (i.e., biomass and growth indices, inorganic vs. organic N acquisition strategies, and N allocation to N pools) depending on the identity of the competing species, and whether these responses were mediated by soil N availability. In a greenhouse experiment, tree seedlings were planted either single or in native-invasive competition at low and high soil N availability. We measured inorganic and organic N acquisition using 15N labeling, total biomass, growth indices, as well as total soluble amino acid-N and protein-N levels in the leaves and fine roots of the seedlings. Our results indicate that invasive species have a competitive advantage via high growth rates, whereas native species could avoid competition with invasives via their higher organic N acquisition suggesting a better access to organic soil N sources. Moreover, native species responded to competition with distinct species- and parameter-specific strategies that were partly mediated by soil N availability. Native tree seedlings in general showed a stronger response to invasive P. serotina than R. pseudoacacia, and their strategies to cope with competition reflect the different species’ life history strategies and physiological traits. Considering the responses of native and invasive species, our results suggest that specifically Q. robur seedlings have a competitive advantage over those of R. pseudoacacia but not P. serotina. Furthermore, native and invasive species show stronger responses to higher soil N availability under competition compared to when growing single. In conclusion, our study provides insights into the potential for niche differentiation between native and invasive species by using different N forms available in the soil, the combined effects of increased soil N availability and competition on tree seedling N nutrition, as well as the species-specific nature of competition between native and invasive tree seedlings which could be relevant for forest management strategies.
A Comprehensive and Systematic Survey on the Internet of Things: Security and Privacy Challenges, Security Frameworks, Enabling Technologies, Threats, Vulnerabilities and Countermeasures

The Internet of Things (IoT) has experienced constant growth in the number of devices deployed and the range of applications in which such devices are used. They vary widely in size, computational power, capacity storage, and energy. The explosive growth and integration of IoT in different domains and areas of our daily lives has created an Internet of Vulnerabilities (IoV). In the rush to build and implement IoT devices, security and privacy have not been adequately addressed. IoT devices, many of which are highly constrained, are vulnerable to cyber attacks, which threaten the security and privacy of users and systems. This survey provides a comprehensive overview of IoT in regard to areas of application, security architecture frameworks, recent security and privacy issues in IoT, as well as a review of recent similar studies on IoT security and privacy. In addition, the paper presents a comprehensive taxonomy of attacks on IoT based on the three-layer architecture model; perception, network, and application layers, as well as a suggestion of the impact of these attacks on CIA objectives in representative devices, are presented. Moreover, the study proposes mitigations and countermeasures, taking a multi-faceted approach rather than a per layer approach. Open research areas are also covered to provide researchers with the most recent research urgent questions in regard to securing IoT ecosystem.
Exact Byzantine Consensus on Undirected Graphs under Local Broadcast Model

This paper considers the Byzantine consensus problem for nodes with binary inputs. The nodes are interconnected by a network represented as an undirected graph, and the system is assumed to be synchronous. Under the classical point-to-point communication model, it is well-known that the following two conditions are both necessary and sufficient to achieve Byzantine consensus among n nodes in the presence of up to ƒ Byzantine faulty nodes: n & 3 #8805; 3 ≥ ƒ+ 1 and vertex connectivity at least 2 ƒ + 1. In the classical point-to-point communication model, it is possible for a faulty node to equivocate, i.e., transmit conflicting information to different neighbors. Such equivocation is possible because messages sent by a node to one of its neighbors are not overheard by other neighbors. This paper considers the local broadcast model. In contrast to the point-to-point communication model, in the local broadcast model, messages sent by a node are received identically by all of its neighbors. Thus, under the local broadcast model, attempts by a node to send conflicting information can be detected by its neighbors. Under this model, we show that the following two conditions are both necessary and sufficient for Byzantine consensus: vertex connectivity at least ⌋ 3 fƒ / 2 ⌊ + 1 and minimum node degree at least 2 ƒ. Observe that the local broadcast model results in a lower requirement for connectivity and the number of nodes n, as compared to the point-to-point communication model. We extend the above results to a hybrid model that allows some of the Byzantine faulty nodes to equivocate. The hybrid model bridges the gap between the point-to-point and local broadcast models, and helps to precisely characterize the trade-off between equivocation and network requirements.
Chinese international students’ multicultural identity and second language development: gesture awareness and use

Abstract This study examines language in conjunction with co-speech gesture awareness and use in relation to second language development, and as linked to identity-in-the-making for four Chinese international graduate students attending an American university in the U.S. Data collection took place at two different times: after three months of initial exposure and then again three years later. Participants were interviewed about their language and gesture awareness and use in the L1 and L2, and in relation to possible changes in identity over time. Additionally, participants were video-recorded while retelling a story that included motion events in their L1 and L2 at both times of data collection for possible changes in thinking-for-speaking. Findings are related to four areas: speech-gesture synchrony; language, gesture, culture and emotions; gesture awareness and use; and bonding with others. Overall, this study offers a window onto the role of co-speech gesture in relation to second language development as found in the contexts of naturalistic exposure, where agency plays a significant role in identity development, differing for individuals in accordance with how a person experiences an event or environment (perezhivanie). Supplemental data for this article is available online at https://doi.org/10.1080/09658416.2020. 1767118.
Effects of Aging and Dual-Task Demands on the Comprehension of Less Expected Sentence Continuations: Evidence From Pupillometry

Prior studies on language processing in aging have shown that older adults experience integration difficulties for contextually unpredictable target words (as indicated by low cloze probabilities in prior ratings), and that such comprehension difficulties are more likely to occur under more demanding processing conditions (e.g., dual-task situations). However, these effects have primarily been demonstrated for conditions when cloze probability of the linguistic stimuli was very low. The question we asked here was do dual-task demands also impair comprehension when target words provide a good, but not perfect, match with prior context? We used a dual-task design, consisting of a sentence comprehension and secondary motor tracking task. Critical target words were those which were not perfectly predictable based on context (words with a cloze probability of 0.7), as opposed to words that were near perfectly predictable based on context (cloze probabilities of 0.99). As a measure to index online processing difficulty for less expected target words, we took into account participants’ pupil size. Separate mixed effects models were fit for language comprehension, motor tracking, and pupil size, showing the following: (1) dual-task demands led to age-related comprehension difficulties when target words were less expected (as opposed to very highly expected), (2) integration difficulty in older adults was related to cognitive overload as less expected sentence continuations progressed over time, resulting in behavioral trade-offs between language comprehension and motor tracking, and (3) lower levels of working memory were predictive of whether or not older adults experienced cognitive overload when processing less expected words. In sum, more demanding processing conditions lead to comprehension impairments when words are highly unpredictable based on context, as many prior studies showed. Comprehension impairments among older individuals also occur for conditions when words provide a good, but not perfect, match with prior context. Higher working memory capacity can alleviate such impairments in older adults, thereby suggesting that only high-WM older adults have sufficient cognitive resources to pre-activate words that complete a sentence context plausibly, but not perfectly.