Miracle-dz/UniFAR-FTUs huggingface dataset:
Queries with Arithmetic on Incomplete Databases

The standard notion of query answering over incomplete database is that of certain answers, guaranteeing correctness regardless of how incomplete data is interpreted. In majority of real-life databases, relations have numerical columns and queries use arithmetic and comparisons. Even though the notion of certain answers still applies, we explain that it becomes much more problematic in situations when missing data occurs in numerical columns. We propose a new general framework that allows us to assign a measure of certainty to query answers. We test it in the agnostic scenario where we do not have prior information about values of numerical attributes, similarly to the predominant approach in handling incomplete data which assumes that each null can be interpreted as an arbitrary value of the domain. The key technical challenge is the lack of a uniform distribution over the entire domain of numerical attributes, such as real numbers. We overcome this by associating the measure of certainty with the asymptotic behavior of volumes of some subsets of the Euclidean space. We show that this measure is well-defined, and describe approaches to computing and approximating it. While it can be computationally hard, or result in an irrational number, even for simple constraints, we produce polynomial-time randomized approximation schemes with multiplicative guarantees for conjunctive queries, and with additive guarantees for arbitrary first-order queries. We also describe a set of experimental results to confirm the feasibility of this approach.
Identification of allergenic pollen grains in 36 olive (Olea europaea) cultivars grown in Jordan

Abstract Inhalation of olive pollen is a major cause of allergic respiratory diseases in Mediterranean region. Using bags attached to the branches, pollens were collected from 36 olive cultivars grown in Jordan just before bloom. Proteins were extracted from pollens and analysed by SDS-PAGE and immunoblotting using antisera collected from patients with history of olive pollen-allergy. Immunoblot results showed that 25 cultivars contained one or two allergenic proteins in the range of 15–72 kDa with majority of the proteins corresponding to 15–20 kDa, while remainders did not exhibit any allergenic protein. Seven cultivars exhibited three allergenic proteins and only two cultivars exhibited four allergenic proteins and thus, were considered potentially hyperallergenic cultivars. This study highlights the great variability in the antigenic and allergenic composition of Olea europaea pollen extracts which may serve as guidance for selecting the olive tree cultivars that could be planted in urban areas.
Scaling Up Distance Labeling on Graphs with Core-Periphery Properties

In indexing a graph for distance queries, distance labeling is a common practice; in particular, 2-hop labeling which guarantees the exactness of the query results is widely adopted. When it comes to a massive real graph with a relatively large treewidth such as social networks and web graphs, however, 2-hop labeling can hardly be constructed due to the oversized index. This paper discloses the theoretical relationships between the graph treewidth and 2-hop labeling's index size and query time. To scale up distance labeling, this paper proposes Core-Tree (CT) Index to facilitate a critical and effective trade-off between the index size and query time. The reduced index size enables CT-Index to handle massive graphs that no existing approaches can process while the cost in the query time is negligible: the query time is below 0.4 milliseconds on all tested graphs including one graph with 5.5 billion edges.
Mixtures of Heterogeneous Experts

No single machine learning algorithm is most accurate for all problems due to the effect of an algorithm's inductive bias. Research has shown that a combination of experts of the same type, referred to as a mixture of homogeneous experts, can increase the accuracy of ensembles by reducing the adverse effect of an algorithm's inductive bias. However, the predictive power of a mixture of homogeneous experts is still limited by the inductive bias of the algorithm that makes up the mixture. For this reason, combinations of different machine learning algorithms, referred to as a mixture of heterogeneous experts, has been proposed to take advantage of the strengths of different machine learning algorithms and to reduce the adverse effects of the inductive biases of these algorithms. This paper presents a mixture of heterogeneous experts, and evaluates its performance to that of a number of mixtures of homogeneous experts on a set of classification problems. The results indicate that a mixture of heterogeneous experts aggregates the advantages of experts, increasing the accuracy of predictions. The mixture of heterogeneous experts not only outperformed all homogeneous ensembles on two of the datasets, but also achieved the best overall accuracy rank across the various datasets.
What influences selection of native phosphorelay architectures?

Phosphorelays are signal transduction circuits that combine four different phosphorylatable protein domains for sensing environmental changes and use that information to adjust cellular metabolism to the new conditions in the milieu. Five alternative circuit architectures account for more than 99% of all phosphorelay operons annotated in over 9000 fully sequenced genomes, with one of those architectures accounting for more than 72% of all cases. Here we asked if there are biological design principles that explain the selection of preferred phosphorelay architectures in nature and what might those principles be. We created several types of data-driven mathematical models for the alternative phosphorelay architectures, exploring the dynamic behavior of the circuits in concentration and parameter space, both analytically and through over 108 numerical simulations. We compared the behavior of architectures with respect to signal amplification, speed and robustness of the response, noise in the response, and transmission of environmental information to the cell. Clustering analysis of massive Monte Carlo simulations suggests that either information transmission or metabolic cost could be important in selecting the architecture of the phosphorelay. A more detailed study using models of kinetically well characterized phosphorelays (Spo0 of Bacillus subtilis and Sln1-Ypd1-Ssk1-Skn7 of Saccharomyces cerevisiae) shows that information transmission is maximized by the natural architecture of the phosphorelay. In view of this we analyze seventeen additional phosphorelays, for which protein abundance is available but kinetic parameters are not. The architectures of 16 of these are also consistent with maximization of information transmission. Our results highlight the complexity of the genotype (architecture, parameter values, and protein abundance) to phenotype (physiological output of the circuit) mapping in phosphorelays. The results also suggest that maximizing information transmission through the circuit is important in the selection of natural circuit genotypes.
Variation and Heredity in Lupinus

THE great majority of genetic investigations have been carried on with domesticated plants that have been long under cultivation. There are, of course, many entirely good reasons for this fact. It has, however, always seemed to the author 'that the principles! of genetics might, perhaps, be more successfully approached by beginning with wild plants. For this reason he has been bringing into the garden during the past several years a considerable number of wild species which appeared to offer desirable material for experimentation. His attention was first directed to Lupinus through the difficulties that appeared to beset the systematist in arriving at a satisfactory classification of the species. It seemed possible that the difficulty might be due to the fact that the species are of recent origin and, so, very close together. Under the circumstances that a genus is either now or has recently been in a mutable state it would seem likely that natural selection would not yet have had time to weed out all the forms doomed to perish through lack of adaptation or insufficient vigor. While discussing this question one of his colleagues mentioned the fact that the form recently (1911) de-scribed under the name of Lupinus pipersmithii Heller (5) grows near and that it sometimes has pink flowers. An investigation during the spring of 1914 not only confirmed this statement, but also showed that pink floxvers are not uncommon in two closely related species-L. vallicola apricus (Greene) (4) and L. nanus Dougl. (7). These three species are all close to one another but apparently satisfactorily separable by the systematist.
Design of the Graphic-Controlled Robot for the Education of Teenagers

Taiwan has implemented the National Twelve-year Basic Education since 2019, and the curriculum focuses on "core literacy", in which independent action, communication and interaction, and social participation echo the educational spirit of Science, Technology, Engineering, Art, and Mathematics. Moreover, situated learning is an important topic discussed in the National Twelve-year Basic Education curriculum, which can guide students to experience, practice, deepen their thinking, and improve their vision. It is the key to link learning and application. By building knowledge and training skills in this way, learners' learning motivation and learning effectiveness can be improved. In this study, teenagers are taken as the research object, and the integrated STEAM teaching of robot and program education is constructed by combining the education method of micro: Maqueen robot with a graphic control program, in order to promote the integration and innovative application of robot software and hardware, and provide teenagers with opportunities to contact with robots. The implementation results include the teaching design of program-controlled micro: Maqueen, such as the writing of the graphic control program and its ultrasonic application in micro: Maqueen. The ultrasonic application in micro: Maqueen includes the ultrasonic automatic obstacle avoidance function, ultrasonic distance measurement function, and ultrasonic following function, which aim to cultivate students' logical thinking, problem-solving ability, and other high-level thinking ability, and stimulate students' sustainable development in the field of information technology
Using Two Climate Change Vulnerability Assessment Methods to Prioritize and Manage Rare Plants: A Case Study

ABSTRACT: Climate change is altering environments where rare plants grow. Assessing species' vulnerability to climate change is important for organizations responsible for managing natural areas and conserving rare species. We assessed the climate change vulnerability of 34 rare plant taxa from the western United States using two methods: NatureServe's Climate Change Vulnerability Index (CCVI) and one based on Species Distribution Modeling (SDM) using Maxent. Of the eight taxa categorized as Extremely Vulnerable by the CCVI, five show significant future loss in each of three SDM measures: change in suitable area, suitable area overlap, and habitat suitability in their present location. Both the CCVI and SDM are important tools to assess climate change vulnerability; each method has complementary strengths that can help land managers make decisions. Here we present examples of how land managers can use SDM and the CCVI in combination to assess climate change vulnerability, to inform rare plant management decisions, and to conserve biological diversity.
STAR: A Distributed Stream Warehouse System for Spatial Data

The proliferation of mobile phones and location-based services gives rise to an explosive growth of spatial data. This spatial data contains valuable information, and calls for data stream warehouse systems that can provide real-time analytical results with the latest integrated spatial data. In this demonstration, we present the STAR (Spatial Data Stream Warehouse) system. STAR is a distributed in-memory spatial data stream warehouse system that provides low-latency and up-to-date analytical results over a fast spatial data stream. STAR supports a rich set of aggregate queries for spatial data analytics, e.g., contrasting the frequencies of spatial objects that appear in different spatial regions, or showing the most frequently mentioned topics being tweeted in different cities. STAR processes aggregate queries by maintaining distributed materialized views. Additionally, STAR supports dynamic load adjustment that makes STAR scalable and adaptive. We demonstrate STAR on top of Amazon EC2 clusters using real data sets.
Empirical Analysis of A Partial Dominance Approach to Many-Objective Optimisation

Studies on standard many-objective optimisation problems have indicated that multi-objective optimisation algorithms struggle to solve optimisation problems with more than three objectives, because many solutions become dominated. Therefore, the Paretodominance relation is no longer efficient in guiding the search to find an optimal Pareto front for many-objective optimisation problems. Recently, a partial dominance approach has been proposed to address the problem experienced with application of the dominance relation on many objectives. Preliminary results have illustrated that this partial dominance relation has promise, and scales well with an increase in the number of objectives. This paper conducts a more extensive empirical analysis of the partial dominance relation on a larger benchmark of difficult many-objective optimisation problems, in comparison to state-of-the-art algorithms. The results further illustrate that partial dominance is an efficient approach to solve many-objective optimisation problems.
Coping with Incomplete Data: Recent Advances

Handling incomplete data in a correct manner is a notoriously hard problem in databases. Theoretical approaches rely on the computationally hard notion of certain answers, while practical solutions rely on ad hoc query evaluation techniques based on three-valued logic. Can we find a middle ground, and produce correct answers efficiently? The paper surveys results of the last few years motivated by this question. We re-examine the notion of certainty itself, and show that it is much more varied than previously thought. We identify cases when certain answers can be computed efficiently and, short of that, provide deterministic and probabilistic approximation schemes for them. We look at the role of three-valued logic as used in SQL query evaluation, and discuss the correctness of the choice, as well as the necessity of such a logic for producing query answers.
Performance Optimization on big.LITTLE Architectures: A Memory-latency Aware Approach

The energy demands of modern mobile devices have driven a trend towards heterogeneous multi-core systems which include various types of core tuned for performance or energy efficiency, offering a rich optimization space for software. On such systems, data coherency between cores is automatically ensured by an interconnect between processors. On some chip designs the performance of this interconnect, and by extension of the entire CPU cluster, is highly dependent on the software's memory access characteristics and on the set of frequencies of each CPU core. Existing frequency scaling mechanisms in operating systems use a simple load-based heuristic to tune CPU frequencies, and so fail to achieve a holistically good configuration across such diverse clusters. We propose a new adaptive governor to solve this problem, which uses a simple trained hardware model of cache interconnect characteristics, along with real-time hardware monitors, to continually adjust core frequencies to maximize system performance. We evaluate our governor on the Exynos5422 SoC, as used in the Samsung Galaxy S5, across a range of standard benchmarks. This shows that our approach achieves a speedup of up to 40%, and a 70% energy saving, including a 30% speedup in common mobile applications such as video decoding and web browsing.
Phenotypic changes of bacteria through opportunity and global methylation leads to antibiotic resistance

The antibiotic stress on bacteria leads to initiation of adaptive mechanisms, including exploiting the available opportunity, if any, for cell survival. In order to use the opportunity for survival while under threat, the microbe undergoes various mechanisms which are not completely known e.g. homologous recombination, horizontal gene transfer etc. Our aim is to understand the adaptive mechanism for cell survival during stress, especially antibiotic stress, in E. coli in the presence of opportunity. Understanding this mechanism in bacteria that gained resistance will help in identifying alternative survival pathways. By subjecting a recombination deficient (∆ RecA) strain of bacteria to antibiotic stress, we expected cell death, due to its inability to repair DNA damage (1, 2). Here we show that providing an opportunity in the form of an antibiotic resistance gene with homologous ends aids bacterial survival. There was 3-fold increase in cell envelope thickness along with 2.5-fold increase in phosphatidylethanolamine (PE) content, and enhanced antibiotic resistance to >4000µg/mL (Kan). We observed genome-wide alteration of methylation pattern that lead to changes in transcriptome, proteome, lipidome, and metabolite level, thus, leading to morphological and physiological changes. We prove that global methylation helps in survival of bacteria under stress that changes essential pathways like energy, cell envelope, lipids, amino acids acid, etc. leading to over production of cell wall components including synthesis of PE. By inhibiting the activity of methyltransferase, it is noticed that there is reduction in PE synthesis in agreement with demethylation. This proves that the phenotypic changes are caused due to the global methylation, and also demonstrates that demethylation could be used as a strategy to prevent antibiotic resistance in microbes. One Sentence Summary Global methylation determines the survival of bacteria to gain the antimicrobial resistance with an opportunity
Dopamine neurons of the VTA encode active conspecific interaction and promote social learning through social reward prediction error

Social interactions motivate behavior in many species, facilitating learning, foraging and cooperative behavior. However, how the brain encodes the reinforcing properties of social interactions remains elusive. Here using in vivo recording in freely moving mice, we show that Dopamine (DA) neurons of the Ventral Tegmental Area (VTA) increase their activity during active interactions with unfamiliar conspecific. Using a social instrumental task, we then show that VTA DA neuron activity signals social reward prediction error and drives social reinforcement learning. Thereby, our findings propose that VTA DA neurons are a neural substrate for a social learning signal driving motivated behavior. One Sentence Summary DA neurons are a substrate for social reward learning through the Social Reward Prediction Error.
Regenerating motor neurons prime muscle stem cells for myogenesis by enhancing protein synthesis and mitochondrial bioenergetics

Throughout life, skeletal muscle, the arbiter of voluntary movements, is maintained by a population of skeletal muscle-dedicated stem cells, called muscle satellite cells (MuSCs). Similar to other adult stem cells, the function of MuSCs is tightly coordinated by the cellular and acellular components of their microenvironment, or the niche. While the processes that control the coupling of neurotransmission and muscle contraction have been well characterized, little is known on the reciprocal crosstalk between neural cells and MuSCs within the muscle microenvironment. Here, we report that mild peripheral nerve injury enhances MuSC myogenic function and muscle regeneration by synergistically augmenting MuSC mitochondrial bioenergetics and upregulating anabolic protein synthesis pathways. We also demonstrate that chronic disruption or degeneration of neuromuscular synapses, such as in muscular dystrophy and biological aging, abolishes MuSC and motor neuron interactions, causing significant deficits in muscle regeneration following injury. These results underscore the importance of neuromuscular junction and neural network as an essential niche of MuSCs. Determining the significance of MuSC-nerve interactions and their functional outcomes, as well as the possibility of modulating these connections, have important implications for our understanding of neuromuscular disease pathology and development of therapeutic interventions. Highlights Mild peripheral nerve injury increases muscle stem cell bioavailability of healthy muscle. Nerve perturbation stimulates myogenesis by enhancing protein synthesis and mitochondrial metabolism in young, healthy muscle. Synergistic crosstalk within neuromuscular niche boosts muscle regeneration in young, healthy muscle. Positive influences from the neural network on muscle stem cells are abolished in pathological denervation manifested in dystrophic and aging muscle.
A simple test to infer mode of selection in metagenomics time series of evolving asexual populations

We introduce a simple test to infer mode of selection (STIMS) in metagenomic time series of evolving asexual populations. STIMS compares the tempo of molecular evolution for a gene set of interest against a null distribution that is bootstrapped on random gene sets. We test STIMS on metagenomic data spanning 62,750 generations of Lenski’s long-term evolution experiment with E. coli (LTEE). Our method successfully recovers signals of purifying selection and positive selection on gold standard sets of genes. We then use STIMS to study the evolution of genetic modules in the LTEE. We find strong evidence of ongoing positive selection on key regulators of the E. coli gene regulatory network. Key regulatory genes show evidence of positive selection over the entire time series, even in some hypermutator populations. By contrast, we found no signal of selection on the genetic modules that show the strongest transcriptional responses to changes in growth conditions. In addition, the cis-regulatory regions of key regulators are evolving faster than the cis-regulatory regions of their downstream regulatory targets. These results indicate that one mechanistic cause for ongoing fitness gains in the LTEE is ongoing fine-tuning of the gene regulatory network.
How to improve the performance of the d-choices garbage collection algorithm in flash-based SSDs

The performance of flash-based solid state drives is greatly impacted by the garbage collection algorithm. The d-choices garbage collection algorithm, which selects a victim block with the fewest number of valid pages among d randomly selected blocks, is known to perform well in terms of the write amplification. However, the number of erasures performed on a block may be quite unbalanced, which reduces the lifespan of SSDs that can only tolerate a limited number of erasures per block. This unequal wear is caused by the hot/cold data separation used to achieve a low write amplification, as blocks holding hot data tend to endure more erasures. Methods to reduce this unequal wear often cause a significant increase in the write amplification (which slows down the device). In this paper we propose a new mechanism that allows us to severely reduce the unequal wear and thus improve the lifespan of the drive, without a significant increase in the write amplification. In fact, in many cases this mechanism even reduces the write amplification (eliminating the trade-off between low write amplification and a large lifespan altogether). To assess the performance of this new mechanism we rely both on a mean field model and simulation experiments.