Miracle-dz/UniFAR-FTUs huggingface dataset:
North Atlantic thermohaline circulation predictability in a coupled ocean-atmosphere model

Predictability of the North Atlantic thermohaline circulation (THC) variability as simulated in the GFDL coupled ocean-atmosphere general circulation model is established for a set of ensemble experiments. The ensembles consist of identical oceanic initial conditions underneath a model atmosphere chosen randomly from the model climatology. This experimental design is based on the separation in time scales present in the model which motivates the assumption that the predictability deduced from these ensembles provides an upper limit to the model's THC predictability. The climatology is taken from a multi-century model integration whose THC variability has power concentrated at the 40-60 year time scale. A linear stochastic perspective is shown to be generally consistent with the ensemble statistics. The linear theory suggests a natural measure of ensemble predictability as the time at which the ensemble variance becomes a subjectively defined fraction (0.5 used here) of the climatological variance. It is furthermore of interest to distinguish predictability of the rapidly de-correlating portion of the model's THC from the longer time correlated portion. The rapidly de-correlating portion shows predictability for $\approx 1.5$ years. The slower portion shows predictability for $\approx 5-7$ years. The linear stochastic framework provides for the straightforward construction of an optimal forecast of the model's THC variability and an understanding of why the optimal forecast is useless beyond a particular limit.
Empty Category and the Effect of Teaching in Sentence Processing

Different syntactic frameworks have different ways to deal with dislocation constructions. One major disagreement is whether or not empty categories should be assumed. Researchers have been working on this issue on the ground of "psychological reality" (of empty categories), yet have not come to an agreement. The first aim of this paper is to propose an experimental scheme to settle the issue of empty categories. Our second aim is to propose that the application of the experiment to L2 is beneficial to see the teaching effect in L2 acquisition. If native speakers' result supports no-empty-category analysis, yet, L2 learners exhibit the different result, L2 teaching of "wh-movement" created a category which is not in native speakers' mental grammar.
Good Things Come to Those Who (Can) Wait or How to Handle Delay Tolerant Traffic and Make Peace on the Internet

Recent revelations that ISPs selectively manipulate P2P traf-ﬁc have sparked much public discussion. Underlying this issue is the misalignment of interests between consumers on one hand who desire bulk transfers at ﬂat rates, and ISPs on the other hand who are bound by budget and capacity constraints. Our thesis is that much of the tension can be alleviated by time-shifting trafﬁc away from peak hours taking advantage of its Delay Tolerant (DT) nature. We propose two solutions for doing this. The ﬁrst one offers incentives to end-users to shift their DT trafﬁc and yet be compatible with ﬂat-rate charging schemes. The second one posits augmenting the network with additional storage in the form of Internet Post Ofﬁces which can let ISPs perform store-and forward relaying of such DT trafﬁc.
Bi-Level Deadline Scaling for Admission Control in Mixed-Criticality Systems

In some cases, tasks may be allowed to migrate from one processor to another, e.g., Due to hardware failures or for workload balancing. If a mixed-criticality setting is considered, it is necessary to decide whether new tasks with different levels of criticality may be accepted by a processor without compromising the already running tasks. Since this decision has to be taken on-line, there is a need for fast but yet accurate schedulability tests for mixed-criticality systems. In this paper, we consider that the EDF-VD algorithm is used to schedule tasks on the different processors. EDF-VD assigns virtual deadlines to high-criticality tasks, i.e., It uniformly downscales their real deadlines, to account for a potential increase in their execution demand. A deadline scaling factor is hence computed for the whole processor. However, in the case where the increase in computation demand strongly differs from one task to another, scaling deadlines uniformly makes EDF-VD incur pessimism. Of course, a scaling factor can be computed for every single task, however, this leads to a considerably more complex algorithm which cannot be used in an on-line setting. As a result, we propose an intermediate solution by introducing a bi-level deadline scaling. This way, high-criticality tasks that experience a small increase of workload are assigned one scaling factor, whereas tasks with a large increase of workload are assigned a separate scaling factor. Our experiments show that the proposed approach dominates the original EDF-VD algorithm while it does not increase complexity allowing for constant-time admission control in mixed-criticality systems.
ASTUTE: detecting a different class of traffic anomalies

When many flows are multiplexed on a non-saturated link, their volume changes over short timescales tend to cancel each other out, making the average change across flows close to zero. This equilibrium property holds if the flows are nearly independent, and it is violated by traffic changes caused by several, potentially small, correlated flows. Many traffic anomalies (both malicious and benign) fit this description. Based on this observation, we exploit equilibrium to design a computationally simple detection method for correlated anomalous flows. We compare our new method to two well known techniques on three network links. We manually classify the anomalies detected by the three methods, and discover that our method uncovers a different class of anomalies than previous techniques do.
Generating phenograms using frequent structure mining over metabolic pathways

Phenograms have already been used many years back to depict taxonomic relationships among organisms based on overall similarity among a variety of characteristics available at the time. These recent years, however, have brought phenomenal advances in experimental techniques in biological research. These have resulted in large amounts of biological network data being unearthed. Among these are metabolic networks. Analyzing the network topology of these metabolic networks across taxa can uncover important biological information that is independent of other currently available biological information. This study explores topological similarities between the glycolysis and citrate cycle metabolic networks of different taxa to build phenograms. A novel approach of generating phenograms using Jaccard Similary Indices and Hamming Distances of the graphs bit codes are presented. The resulting phenograms are compared with those generated by NCBI gene sequences using Phyllp branch matchings and maximum consensus trees.
Combining inductive and deductive inference in knowledge management tasks

This paper indicates how different logic programming technologies can underpin an architecture for distributed knowledge management in which higher throughput in information supply is achieved by a (semi-)automated solution to the more challenging problem of knowledge creation. The paper first proposes working definitions of the notions of data, knowledge and information in purely logical terms, and then shows how existing technologies can be combined into an inference engine, referred to as a knowledge, information and data engine (KIDE), integrating inductive and deductive capabilities. The paper then briefly introduces the notion of virtual organizations and uses the set-up stage of virtual organizations to exemplify the value-adding potential of KIDEs in knowledge management contexts.
Dual olfactory pathway in the honeybee, Apis mellifera

The antennal lobes (ALs) are the primary olfactory centers in the insect brain. In the AL of the honeybee, olfactory glomeruli receive input via four antennal sensory tracts (T1–4). Axons of projection neurons (PNs) leave the AL via several antenno‐cerebral tracts (ACTs). To assign the input–output connectivity of all glomeruli, we investigated the spatial relationship of the antennal tracts and two prominent AL output tracts (medial and lateral ACT) mainly formed by uniglomerular (u) PNs using fluorescent tracing, confocal microscopy, and 3D analyses. Furthermore, we investigated the projections of all ACTs in higher olfactory centers, the mushroom‐bodies (MB) and lateral horn (LH). The results revealed a clear segregation of glomeruli into two AL hemispheres specifically supplied by PNs of the medial and lateral ACT. PNs of the lateral ACT innervate glomeruli in the ventral‐rostral AL and primarily receive input from T1 (plus a few glomeruli from T2 and T3). PNs of the medial ACT innervate glomeruli in the dorsal‐caudal hemisphere, and mainly receive input from T3 (plus a few glomeruli from T2 and T4). The PNs of the m‐ and l‐ACT terminate in different areas of the MB calyx and LH and remain largely segregated. Tracing of three mediolateral (ml) ACTs mainly formed by multiglomerular PNs revealed terminals in distinct compartments of the LH and in three olfactory foci within the lateral protocerebrum. The results indicate that olfactory input in the honeybee is processed via two separate, mainly uPN pathways to the MB calyx and LH and several pathways to the lateral protocerebrum. J. Comp. Neurol. 499:933–952, 2006. © 2006 Wiley‐Liss, Inc.
Geometric numeric integration in the event-driven measurement method

The Event-Driven Metering (EDM) method is about data-driven compression of the measurement data based on the replacement of the original arbitrary shape of the load by a simplified polygonal line that represents the same values of the energy per interval of time. EDM algorithm receives the Shannon data samples encoding the local measurements and reports the pre-processed knowledge to the controlling hosts in the form of Lebesgue-based events. This article explains the mathematical foundations of the invention that allows improved data efficiency. Method decomposes the global behavior of an energy system in a sequence of linearized chunks and exploits the energy conservation law in order to set up data-driven geometric integration. The result is an operator that maps a load shape into a data-efficient sequence of tokens representing exactly the same energy quantities as in the original pattern.
Software-Defined Network Virtualization Platform for Enterprise Network Resource Management

Applications of a software-defined network (SDN) have increased. Notably an SDN has the benefits of programmability and automation for network control. It also realizes network virtualization. Based on the OpenFlow/FlowVisor network virtualization layer, this paper proposes a dynamic resource management scheme, called the EnterpriseVisor engine that manages the distribution of network resources among slices. The EnterpriseVisor engine first slices an enterprise's network into virtual subnets, and then continues to monitor the resource utilization of each subnet. In the engine, dynamic resource scheduling of the subnet for each service uses the linear programming method. The engine then delivers the schedule as the resource allocation policy of the FlowVisor layer for data plane users. To demonstrate the feasibility of the proposed EnterpriseVisor engine, an OpenFlow testbed with four subnets is designed. Compared with the basic OpenFlow and OpenFlow/FlowVisor platforms, the EnterpriseVisor engine has higher network utilization, 25.7% and 13.4% higher, respectively, with only a minor sacrifice to control message latency.
Short-Run and Long-Run Dynamics of Resource Commodity Prices

This paper discusses the short-run and long-run dynamics of price for storable natural resource commodities. It deals with the interrelationships between price, inventory, and price volatility; the effects of inventory and producers’ operating flexibility on the dynamics of price in the short-run; and the evolution of the long-run equilibrium price. The paper describes a model that explains the dynamics of commodity prices based on demand for inventory and on threshold prices that would induce producers to expand or contract operations. The paper concludes that in the short-run, producers’ operating flexibility reduces price volatility when the spot price is higher than the threshold price which would cause expansion in the scale of operations. It further concludes that operating flexibility will raise price volatility when the spot price is lower than the threshold price which would result in a contraction of operations. It concludes that in the short run, commodity prices tend to revert to a short-run equilibrium price, whereas in the long run, they tend to revert to a long-run trend. The paper demonstrates the failure of currently used parametric models in describing the stochastic process of commodity prices, and suggests using non-parametric methods. It also recommends including the time trend in such a model. As an illustration the paper presents an analysis of the price processes for lumber and cotton.
Poseidon: A System Architecture for Efficient GPU-based Deep Learning on Multiple Machines

Deep learning (DL) has achieved notable successes in many machine learning tasks. A number of frameworks have been developed to expedite the process of designing and training deep neural networks (DNNs), such as Caffe, Torch and Theano. Currently they can harness multiple GPUs on a single machine, but are unable to use GPUs that are distributed across multiple machines; as even average-sized DNNs can take days to train on a single GPU with 100s of GBs to TBs of data, distributed GPUs present a prime opportunity for scaling up DL. However, the limited bandwidth available on commodity Ethernet networks presents a bottleneck to distributed GPU training, and prevents its trivial realization. To investigate how to adapt existing frameworks to efficiently support distributed GPUs, we propose Poseidon, a scalable system architecture for distributed inter-machine communication in existing DL frameworks. We integrate Poseidon with Caffe and evaluate its performance at training DNNs for object recognition. Poseidon features three key contributions that accelerate DNN training on clusters: (1) a three-level hybrid architecture that allows Poseidon to support both CPU-only and GPU-equipped clusters, (2) a distributed wait-free backpropagation (DWBP) algorithm to improve GPU utilization and to balance communication, and (3) a structure-aware communication protocol (SACP) to minimize communication overheads. We empirically show that Poseidon converges to same objectives as a single machine, and achieves state-of-art training speedup across multiple models and well-established datasets using a commodity GPU cluster of 8 nodes (e.g. 4.5x speedup on AlexNet, 4x on GoogLeNet, 4x on CIFAR-10). On the much larger ImageNet22K dataset, Poseidon with 8 nodes achieves better speedup and competitive accuracy to recent CPU-based distributed systems such as Adam and Le et al., which use 10s to 1000s of nodes.
Learning Execution Contexts from System Call Distribution for Anomaly Detection in Smart Embedded System

Existing techniques used for anomaly detection do not fully utilize the intrinsic properties of embedded devices. In this paper, we propose a lightweight method for detecting anomalous executions using a distribution of system call frequencies. We use a cluster analysis to learn the legitimate execution contexts of embedded applications and then monitor them at run-time to capture abnormal executions. Our prototype applied to a real-world open-source embedded application shows that the proposed method can effectively detect anomalous executions without relying on sophisticated analyses or affecting the critical execution paths.
The Effects of Computer-Aided Instruction in Mathematics on the Performance of Grade 4 Pupils

This study was undertaken to determine the effects of computer-aided instruction on mathematics performance of pupils at the Grade 4 level in Region 5. This focus was due to the trend of poor performances in mathematics at the National Grade 4 assessments for the past 5 years. Four intact Grade 4 classes in two primary schools were used in this study. Two classes were used as experimental group and the other two as control group. The Quasi Experimental, Nonequivalent Control Group design was employed for this study. The instrument which was used for data collection was a teacher-made test. The reliability coefficient of the instrument was . 553, significant at alpha . 01. The findings of the study indicated that there was a significant difference between the academic performance of pupils in mathematics who were taught using computer-aided instruction and those who were taught using the traditional method of teaching. Second, there was a significant difference between the academic performance of male and female pupils who were taught using computer-aided instruction and those who were taught using the traditional method of teaching. In addition, there was a significant difference between the academic performance of lower- and middle-income pupils who were taught using computer-aided instruction and those who were taught using the traditional method. Based on the findings of this study, some recommendations were made and among such were that computer-aided instruction should be integrated into the teaching of mathematics at the Grade 4 level in the area of study.
Prostate Cancer Mortality in Areas With High and Low Prostate Cancer Incidence

Background The effect of prostate-specific antigen (PSA) screening on prostate cancer mortality remains debated, despite evidence from randomized trials. We investigated the association between prostate cancer incidence, reflecting uptake of PSA testing, and prostate cancer mortality. Methods The study population consisted of all men aged 50 to 74 years residing in eight counties in Sweden with an early increase in prostate cancer incidence and six counties with a late increase during two time periods. Incidence of metastatic prostate cancer was investigated in the period from 2000 to 2009, and prostate cancer–specific mortality and excess mortality were investigated in the period from 1990 to 1999 and the period from 2000 to 2009 by calculating rate ratios for high- vs low-incidence counties and rate ratios for the period from 2000 to 2009 vs the period from 1990 to 1999 within these two groups. All statistical tests were two-sided. Results There were 4528134 person-years at risk, 1577 deaths from prostate cancer, and 1210 excess deaths in men with prostate cancer in high-incidence counties and 2471373 person-years at risk, 985 prostate cancer deaths, and 878 excess deaths in low-incidence counties in the period from 2000 to 2009. Rate ratios in counties with high vs low incidence adjusted for time period were 0.81 (95% confidence interval [CI] = 0.73 to 0.90) for prostate cancer– specific mortality and 0.74 (95% CI = 0.64 to 0.86) for excess mortality, and the rate ratio of metastatic prostate cancer was 0.85 (95% CI = 0.79 to 0.92). Conclusions The lower prostate cancer mortality in high-incidence counties reflecting a high PSA uptake suggests that more-intense as compared with less-intense opportunistic PSA screening reduces prostate cancer mortality.
Trust , Risk and Economic Benefits in Online Environments

Lack of trust is identified as one of the main constraints on online environments, particularly in terms of consumer protection. Although the elements that contribute to building trust can be identified in broad terms, there are still many uncertainties in defining and establishing trust in online environments. This paper investigates these uncertainties by studying the relationship between trust and the risk perceptions of users. We discuss issues such as trust as an economic good, risks to procedural fairness, and the relationship between the two. We posit that without an organizational policy governing fair use of personal information, organizations face the risk that information used inappropriately by a single employee or by a single department can have negative consequences for the entire firm. We stress the need for organizations to consider user perceptions of risk when establishing trust with their customers, and we show that economic decision-making in online environments without consideration of trust and risk is not likely to result in optimal choices.
The use of mindfulness training to examine the role of executive function in preschool peer conflict

Children’s responses to peer conflict are important for the development of positive peer relationships and are an indication of children’s overall level of social competence. Past research has shown that children with lower levels of executive function (EF) are more likely to choose aggressive responses to peer conflict (e.g., Caporaso, Marcovitch, & Boseovski, 2016) and this may be because of a shared underlying mechanism (i.e., reflection) between social competence and EF. Mindfulness training has been proposed as a way to increase EF in children and adults by training reflection and reducing the experience of negative emotions and it was hypothesized that it could promote competent responses to peer conflict. A brief version of mindfulness training and a taxing version of the training without the guided mindfulness directions were used to examine the effect these trainings had on responses to peer conflict. Mindfulness training did not produce an increase in competent responding. Low income participants in the Taxing condition selected fewer competent responses to peer situations compared to the Mindfulness and Control conditions. These results are discussed in the context of EF and emotion. Implications regarding reflection as a shared underlying mechanism between EF and social competence are also discussed.
On static n-body configurations in relativity

The static n-body problem of general relativity states that there are, under a reasonable energy condition, no static n-body configurations for n > 1, provided the configuration of the bodies satisfies a suitable separation condition. In this paper we solve this problem in the case that there exists a closed, noncompact, totally geodesic surface disjoint from the bodies. This covers the situation where the configuration has a reflection symmetry across a noncompact surface disjoint from the bodies.
Composition of the lunar surface as will be seen from SMART‐1: A simulation using Clementine data

[1] We present a new technique for remote sensing determination of lunar surface composition in the context of the SMART-1 mission. The technique is based on spectral and composition data obtained by Lunar Soil Characterization Consortium for a few particle-size separates of lunar soils. We map the abundance of TiO2 and FeO, pyroxene content, maturity degree (Is/FeO), and a characteristic size of particles. Comparison of the TiO2 and FeO abundance maps with proper distributions obtained by Lucey et al. [2000a] shows high correlation. We found also an inverse correlation between the Is/FeO distribution and Lucey et al.'s [2000b] parameter OM. Application of this approach shows that fresh mare crater regolith is characterized with a higher abundance of pyroxenes, coarse particles, and low maturity degree. The pyroxene abundance map can be used to identify pyroclastic regions. An excess of small particles is predicted for highland areas. We note appreciable variations of the characteristic size of particles in mare regions. Our preliminary results for the Reiner-γ formation show that there is no composition anomaly for the TiO2 and FeO abundance, in agreement with previous analyses. Our maps also indicate that the formation contains a surface material characterized with low maturity and high degree of crystallinity, consistent with the occurrence of immature regolith possibly contaminated with dust.