Miracle-dz/UniFAR-FTUs huggingface dataset:
Selective Vectorization for Short-Vector Instructions

Multimedia extensions are nearly ubiquitous in today’s general-purpose processors. These extensions consist primarily of a set of short-vector instructions that apply the same opcode to a vector of operands. Vector instructions introduce a data-parallel component to processors that exploit instruction-level parallelism, and present an opportunity for increased performance. In fact, ignoring a processor’s vector opcodes can leave a significant portion of the available resources unused. In order for software developers to find short-vector instructions generally useful, however, the compiler must target these extensions with complete transparency and consistent performance. This paper describes selective vectorization, a technique for balancing computation across a processor’s scalar and vector units. Current approaches for targeting short-vector instructions directly adopt vectorizing technology first developed for supercomputers. Traditional vectorization, however, can lead to a performance degradation since it fails to account for a processor’s scalar resources. We formulate selective vectorization in the context of software pipelining. Our approach creates software pipelines with shorter initiation intervals, and therefore, higher performance. A key aspect of selective vectorization is its ability to manage transfer of operands between vector and scalar instructions. Even when operand transfer is expensive, our technique is sufficiently sophisticated to achieve significant performance gains. We evaluate selective vectorization on a set of SPEC FP benchmarks. On a realistic VLIW processor model, the approach achieves whole-program speedups of up to 1.35× over existing approaches. For individual loops, it provides speedups of up to 1.75×.
Microtubule-Associated Protein SBgLR Facilitates Storage Protein Deposition and Its Expression Leads to Lysine Content Increase in Transgenic Maize Endosperm

Maize (Zea mays) seed is deficient in protein and lysine content. Many studies have been made to improve the nutritional quality of maize seeds. Previously, we reported the role of a natural lysine-rich protein gene SBgLR in increasing protein and lysine content. However, how the SBgLR improves lysine and protein content remains unclear. Here, the reasons and possible mechanism for SBgLR in protein and lysine improvement have been analyzed and discussed. Through seed-specific expression of SBgLR, we obtained transgenic maize with the simultaneously increased lysine and protein contents. High-protein and high-lysine characters were stably inherited across generations. The expression of SBgLR in maize kernels increased the accumulation of both zeins and non-zein proteins. Transmission electron microscopy showed that the number of protein bodies (PBs) was increased obviously in SBgLR transgenic immature endosperms with the morphology and structure of PBs unchanged. The proteinaceous matrix was more abundant in transgenic mature endosperms under scanning electron microscopy. The stabilities of zein and lysine-rich non-zein genes were also increased in transgenic endosperms. Finally, the potential application of SBgLR in maize nutrient improvement was evaluated. This study shows that a cytoskeleton-associated protein has potential applicable value in crop nutrient improving, and provided a feasible strategy for improvement of maize grain quality.
Transfer Semantics in an Operating System Consultant: The Formalization of Actions Involving Object Transfer

It is a characteristic of computer operating systems that they contain actions or commands which transfer objects such as files and directories from one state to another. In formalizing the domain of operating systems we should build representations of actions which circumscribe the transfer of objects in the system. Transfer Semantics is a knowledge scheme that embodies such representations. Knowledge structures called object frames are used to represent numerous objects. Action frames describe the effects of actions in terms of preconditions, postconditions, actions and actors. Preconditions denote possible or preferred sets of objects that an action will affect. Postconditions relate the state of object sets after an action has occurred. Actions include the particular actions that cause transfer. An actor is the person (or user) who performs some action. The power of Transfer Semantics lies in the inference rules that manipulate action frames. It is applied to the UNIX and TOPS-20 operating systems in a program called OS Consultant OS Consultant will be used by new users to learn operating system concepts.
Quality Control and Signaling Strategy in Supply Chain

Based on the principal-agent theory, this paper constructs quality signaling game model in supply chain under the condition of asymmetric information. The supplier makes the production process investment decision and determines the type of production process level (high level type or low level type), who will also make quality prevention strategy and determine its products quality prevention level. The manufacturer makes the quality appraisal strategy and processing strategy when it accepts the intermediary components, who will also determine its products quality inspection level and processing level. In this paper, we analyze the supplier may signal the type of production process investment level to the manufacturer in order to obtain the optimal incentive contracts. We take use of the optimal theory to establish supplier’s production process quality prevention model and manufacturer’s products quality inspection model and investigate the manufacturer's information rents to motivate supplier to improve products quality level. This paper will prove that: the supplier may have the incentives to signal the type of production process investment level to the manufacturer, and improve its products quality prevention level in its production process. When the manufacturer observes supplier’s signals of products quality level, it will reduce quality inspection level and improve processing level. With the improvement of supplier’s quality prevention level, its warranty payments and price rebate will decrease, the manufacturer will increase its upfront payments and reduce apportionment ratio of external failure cost. Meanwhile the integrated supply chain’s joint expected profits will also increase, and then the manufacturer will reduce the supplier’s information rents obviously. Above all, we analyze the causes of ratchet effects and provide corresponding quantitative descriptions and proofs for our conclusions respectively.
Treatment of Essential Hypertension with a Combination of Propranolol and Bendrofluazide

The effect of conventional propranolol and bendrofluazide tablets given twice daily has been compared with an equivalent dosage combined in a single capsule given twice daily in fourteen patients with essential hypertension. Good blood pressure control was achieved on both regimes in the majority of patients and there was no significant difference in treatment response between the two regimes. Use of the combined capsule is convenient and may improve patient compliance.
Secrecy capacity of the first-order autoregressive moving average Gaussian channel with feedback

In this paper, we consider the first-order autoregressive moving average Gaussian channel with perfect causal feedback where an eavesdropper receives noisy observations of the channel inputs and outputs. We show that the secrecy capacity is equal to the feedback capacity without the presence of eavesdropper. Furthermore, we explicitly construct the secrecy capacity-achieving feedback code, which is deterministic and simple to implement.
Transduction of a Plasmid Carrying the Cohesive End Region from Lactococcus lactis Bacteriophage ΦLC3

Plasmids carrying the cohesive end region from temperate lactococcal bacteriophage ΦLC3 could be packaged in vivo by ΦLC3 and transduced into its host strain, Lactococcus lactis subsp. cremoris NCDO 1201. The transduction frequencies were between 10-4 and 10-3 transducing particles per PFU, depending on the size of the phage DNA insert. This transduction system is limited to only certain lactococcal strains. The ΦLC3 cohesive site region (cos) appears to play an important role in plasmid transduction.
HMM Content Model for TAC2010 Summarization Challenge

We present the HITS submission for the 2010 TAC Guided Summarization Task. We focus on the main multi-document summarization task, rather than the update task. We implement a baseline extractive summarization system from the literature (Barzilay and Lee, 2004) which uses a Hidden Markov Model to assign sentences content or topic labels, predicts which topics most likely appear in the summary, and constructs the summaries from these topics. We find that this model performs more poorly than expected, as compared to results shown in previous work. These differences may be attributed to the changes we made to the algorithm to accommodate the multi-document summarization task and the lack of human-annotated domains for the training data.
KTK: kernel support for configurable objects and invocations

The Kernel Tool Kit (KTK) is an object-based operating system kernel and parallel programming library that offers explicit support for on- and off-line program configuration. Specifically, KTK allows the specification of attributes for object classes, object instances, state variables, operations and object invocations. Attributes are interpreted by policy classes that may be varied separately from the abstractions with which they are associated. They can be used to vary object internal implementation and semantics without affecting the methods being invoked. In this paper, the runtime configuration of KTK attributes is shown to improve the runtime performance of multiprocessor applications. KTK is layered on a portable and configurable parallel programming substrate, a Mach Cthreads compatible runtime library/spl lsqb/22/spl rsqb/. <<ETX>>
Non-adaptive pooling strategies for detection of rare faulty items

We study non-adaptive pooling strategies for detection of rare faulty items. Given a binary sparse N dimensional signal x, how to construct a sparse binary M × N pooling matrix F such that the signal can be reconstructed from the smallest possible number M of measurements y = Fx? We show that a very small number of measurements is possible for random spatially coupled design of pools F. Our design might find application in genetic screening or compressed genotyping. We show that our results are robust with respect to the uncertainty in the matrix F when some elements are mistaken.
The complexity of propositional linear temporal logics

We consider the complexity of satisfiability and determination of truth in a particular finite structure for different propositional linear temporal logics. We show that both the above problems are NP-complete for the logic with F operator and are PSPACE-complete for the logics with F,X, with U, with U,S,X, and Wolper's extended logic with regular operators [Wo81].
Shaking table experiment on a steel storage tank with multiple friction pendulum bearings

The aim of the shaking table experiment is to verify the isolation effect of a storage liquid tank with multiple friction pendulum bearings. A 1:20 scale model of a real storage liquid tank that is widely used in the petroleum industry was examined by the shaking table test to compare its anchored base and isolated base. The seismic response of the tank was assessed by employing the time history input. The base acceleration, wave height and tank wall stress were used to evaluate the isolation effect. Finally, the influences of the bearing performance that characterizes the isolated tank, such as the friction force and residual displacement, were discussed.
Optimal Estimation in Sensory Systems

A variety of experimental studies suggest that sensory systems are capable of performing estimation or decision tasks at near-optimal levels. In this chapter, I explore the use of optimal estimation in describing sensory computations in the brain. I define what is meant by optimality and provide three quite different methods of obtaining an optimal estimator, each based on different assumptions about the nature of the information that is available to constrain the problem. I then discuss how biological systems might go about computing (and learning to compute) optimal estimates. The brain is awash in sensory signals. How does it interpret these signals, so as to extract meaningful and consistent information about the environment? Many tasks require esti- mation of environmental parameters, and there is substantial evidence that the system is capable of representing and extracting very precise estimates of these parameters. This is particularly impressive when one considers the fact that the brain is built from a large num- ber of low-energy unreliable components, whose responses are affected by many extraneous factors (e.g., temperature, hydration, blood glucose and oxygen levels). The problem of optimal estimation is well studied in the statistics and engineering communi- ties, where a plethora of tools have been developed for designing, implementing, calibrating and testing such systems. In recent years, many of these tools have been used to provide benchmarks or models for biological perception. Specifically, the development of signal detection theory led to widespread use of statistical decision theory as a framework for assessing performance in perceptual experiments. More recently, optimal estimation theory (in particular, Bayesian estimation) has been used as a framework for describing human performance in perceptual tasks.
The impact of a single episode of remote ischemic preconditioning on myocardial injury after elective percutaneous coronary intervention

Introduction Myocardial injury after percutaneous coronary intervention (PCI) occurs in approximately 30% of procedures, and is related to worse prognosis. Effects of remote ischemic preconditioning (RIPC) on reperfusion injury have been investigated before, yielding conflicting results. Aim To assess the impact of a single episode of RIPC on myocardial injury after elective PCI. Material and methods One hundred and four patients undergoing elective PCI, with normal baseline cardiac troponin-I (cTn-I) values, were randomized to two groups. Two patients were excluded due to data loss, and 102 patients were analyzed. Five minutes of ischemic preconditioning was delivered just before the intervention to the preconditioning group, by inflating the blood pressure cuff up to 200 mm Hg on the non-dominant arm. Postprocedural 16th hour cTn-I, ΔcTn-I (difference between the 16th h and baseline cTn-I values) and the prevalence of type 4a myocardial infarction were compared between the two groups. Results Median cTn-I values after the procedure were compared. 16th hour cTn-I was insignificantly lower in the preconditioning arm (0.026 μg/l vs. 0.045 μg/l, p = 0.186). The incidence of cTn-I elevation 5-fold above the upper reference limit (URL) (> 0.115 μg/l) was lower in the preconditioning group, but it was also not significant (21.6% vs. 11.8%, p = 0.184). Conclusions A single episode of RIPC before elective PCI demonstrated less troponin elevation but failed to show a significant effect.
Reducing Children's Implicit Racial Bias Through Exposure to Positive Out-Group Exemplars.

Studies with adults suggest that implicit preferences favoring White versus Black individuals can be reduced through exposure to positive Black exemplars. However, it remains unclear whether developmental differences exist in the capacity for these biases to be changed. This study included 369 children and examined whether their implicit racial bias would be reduced following exposure to positive Black exemplars. Results showed that children's implicit pro-White bias was reduced following exposure to positive Black exemplars, but only for older children (Mage = ~10 years). Younger children's (Mage = ~7 years) implicit bias was not affected by this intervention. These results suggest developmental differences in the malleability of implicit racial biases and point to possible age differences in intervention effectiveness.
Drosophila americana Diapausing Females Show Features Typical of Young Flies

Diapause is a period of arrested development which is controlled physiologically, preprogrammed environmentally and characterized by metabolic depression that can occur during any stage of insect development. Nevertheless, in the genus Drosophila, diapause is almost always associated with the cessation of ovarian development and reproductive activity in adult females. In this work, we show that, in D. americana (a temperate species of the virilis group), diapause is a genetically determined delay in ovarian development that is triggered by temperature and/or photoperiod. Moreover, we show that in this species diapause incidence increases with latitude, ranging from 13% in the southernmost to 91% in the northernmost range of the distribution. When exposed to diapause inducing conditions, both diapausing and non-diapausing females show a 10% increase in lifespan, that is further increased by 18.6% in diapausing females, although senescence is far from being negligible. ActinD1 expression levels suggest that diapausing females are biologically much younger than their chronological age, and that the fly as a whole, rather than the ovarian development alone, which is phenotypically more evident, is delayed by diapause. Therefore, diapause candidate genes that show expression levels that are compatible with flies younger than their chronological age may not necessarily play a role in reproductive diapause and in adaptation to seasonally varying environmental conditions.
Removal of Contaminant DNA by Combined UV-EMA Treatment Allows Low Copy Number Detection of Clinically Relevant Bacteria Using Pan-Bacterial Real-Time PCR

Background More than two decades after its discovery, contaminant microbial DNA in PCR reagents continues to impact the sensitivity and integrity of broad-range PCR diagnostic techniques. This is particularly relevant to their use in the setting of human sepsis, where a successful diagnostic on blood samples needs to combine universal bacterial detection with sensitivity to 1-2 genome copies, because low levels of a broad range of bacteria are implicated. Results We investigated the efficacy of ethidium monoazide (EMA) and propidium monoazide (PMA) treatment as emerging methods for the decontamination of PCR reagents. Both treatments were able to inactivate contaminating microbial DNA but only at concentrations that considerably affected assay sensitivity. Increasing amplicon length improved EMA/PMA decontamination efficiency but at the cost of assay sensitivity. The same was true for UV exposure as an alternative decontamination strategy, likely due to damage sustained by oligonucleotide primers which were a significant source of contamination. However, a simple combination strategy with UV-treated PCR reagents paired with EMA-treated primers produced an assay capable of two genome copy detection and a <5% contamination rate. This decontamination strategy could have important utility in developing improved pan-bacterial assays for rapid diagnosis of low pathogen burden conditions such as in the blood of patients with suspected blood stream infection.
Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions

This paper introduces the problem of combining multiple partitionings of a set of objects into a single consolidated clustering without accessing the features or algorithms that determined these partitionings. We ﬁrst identify several application scenarios for the resultant ‘knowledge reuse’ framework that we call cluster ensembles . The cluster ensemble problem is then formalized as a combinatorial optimization problem in terms of shared mutual information. In addition to a direct maximization approach, we propose three eﬀective and eﬃcient techniques for obtaining high-quality combiners (consensus functions). The ﬁrst combiner induces a similarity measure from the partitionings and then reclusters the objects. The second combiner is based on hypergraph partitioning. The third one collapses groups of clusters into meta-clusters which then compete for each object to determine the combined clustering. Due to the low computational costs of our techniques, it is quite feasible to use a supra-consensus function that evaluates all three approaches against the objective function and picks the best solution for a given situation. We evaluate the effectiveness of cluster ensembles in three qualitatively diﬀerent application scenarios: (i) where the original clusters were formed based on non-identical sets of features, (ii) where the original clustering algorithms worked on non-identical sets of objects, and (iii) where a common data-set is used and the main purpose of combining multiple clusterings is to improve the quality and robustness of the solution. Promising results are obtained in all three situations for synthetic as well as real data-sets.
Combining a Genetic Algorithm and Support Vector Machine to Study the Factors Influencing CO2 Emissions in Beijing with Scenario Analysis

In recent years, Beijing has been facing serious environmental problems. As an important cause of environmental problems, a further study of the factors influencing CO2 emissions in Beijing has important significance for the social and economic development of Beijing. In this paper, Cointegration and Granger causality test were proposed to select influencing factors of CO2 emissions prediction in Beijing, the influencing factors with different leading lengths were checked as well, and the genetic algorithm (GA) was used to optimize the initial weight and threshold values of a support vector machine (SVM) and the new SVM optimized by GA (GA-SVM) was established to predict the CO2 emissions of Beijing from 2016–2020 with scenario analysis. Through the comparison of 36 kinds of development scenarios, we found that economic growth, resident population growth and energy intensity enhancement were the major growth factors of carbon emissions, of which the contributions exceed 0.5% in all kinds of development scenarios. Finally, this paper put forward some reasonable policy recommendations for the control of CO2 emissions.
Semantic Annotation of Textual Entailment

We introduce a new formal semantic model for annotating textual entailments, that describes restrictive, intersective and appositive modification. The model contains a formally defined interpreted lexicon, which specifies the inventory of symbols and the supported semantic operators, and an informally defined annotation scheme that instructs annotators in which way to bind words and constructions from a given pair of premise and hypothesis to the interpreted lexicon. We explore the applicability of the proposed model to the Recognizing Textual Entailment (RTE) 1-4 corpora and describe a first-stage annotation scheme based on which manual annotation work was carried out. The constructions we annotated were found to occur in 80.65% of the entailments in RTE 1-4 and were annotated with cross-annotator agreement of 68% on average. The annotated RTE corpora are publicly available for the research community.