Miracle-dz/UniFAR-FTUs huggingface dataset:
Interindustrial Structure in the Asia-Pacific Region: Growth and Integration, by Using 2000 AIO Table

Over the past 20 years Asian countries have achieved a certain degree of economic growth and at the same time deepened spatial interdependence. In January 2006, IDE completed the 2000 Asian International Input-Output Table, which covers eight major East Asian countries/regions as well as Japan and the United States. Given the dynamic changes in the economies of East Asia, this paper attempts to summarize the characteristics and their patterns of change in industrial structures and trade structures of the countries/regions in the Asia-Pacific region from the three viewpoints of time, space, and industry, by using the AIO table for 1985, 1990, 1995, and 2000.
DIURNAL INTENSITY VARIATION OF LOW-ENERGY ELECTRONS OBSERVED NEAR THE POLAR CAP.

Measurements of the flux and energy spectrum of electrons, incident at the top of the atmosphere at Fort Churchill, Manitoba, exhibit a large daily intensity variation in the energy interval from about 10 to 225 Mev. High intensities are observed during the day, low intensities at night. No such variation is noticed at higher energies. This result is interpreted to be a consequence of the asymmetry of the geomagnetic field. We conclude, on the basis of our interpretation, that the high electron flux observed during the day in the low-energy interval is mainly due to return albedo particles. These return albedo particles are not observed at night. The lower flux at night is interpreted as an upper limit to the flux of primary electrons of energy between about 20 and 250 Mev, which amounts to 81±20 electrons/m² sec ster.
A measurement-driven analysis of information propagation in the flickr social network

Online social networking sites like MySpace, Facebook, and Flickr have become a popular way to share and disseminate content. Their massive popularity has led to viral marketing techniques that attempt to spread content, products, and ideas on these sites. However, there is little data publicly available on viral propagation in the real world and few studies have characterized how information spreads over current online social networks. In this paper, we collect and analyze large-scale traces of information dissemination in the Flickr social network. Our analysis, based on crawls of the favorite markings of 2.5 million users on 11 million photos, aims at answering three key questions: (a) how widely does information propagate in the social network? (b) how quickly does information propagate? and (c) what is the role of word-of-mouth exchanges between friends in the overall propagation of information in the network? Contrary to viral marketing ``intuition,'' we find that (a) even popular photos do not spread widely throughout the network, (b) even popular photos spread slowly through the network, and (c) information exchanged between friends is likely to account for over 50 of all favorite-markings, but with a significant delay at each hop.
The smoothing effect of integration in Rd and the ANOVA decomposition

This paper studies the ANOVA decomposition of a d-variate function f defined on the whole of Rd, where f is the maximum of a smooth function and zero (or f could be the absolute value of a smooth function). Our study is motivated by option pricing problems. We show that under suitable conditions all terms of the ANOVA decomposition, except the one of highest order, can have unlimited smoothness. In particular, this is the case for arithmetic Asian options with both the standard and Brownian bridge constructions of the Brownian motion. 2010 Mathematics Subject Classification: Primary 41A63, 41A99. Secondary 65D30.
Atmospheric pressure response to mesoscale sea surface temperature variations in the Kuroshio Extension region: In situ evidence

Several research cruises were conducted across the Kuroshio Extension front to examine the low‐level atmospheric responses to mesoscale variations in sea surface temperature (SST). Surface meteorological observations, including sea level pressure (SLP) and SST, were collected at two moored buoys that were located on either side of the Kuroshio Extension, and from a research vessel, as it moved between the two buoys during the various cruises. Spatial perturbations in SLP along the ship transects, calculated by subtracting moored‐buoy SLP from that of the moving ship, tend to be positive (negative) where SST is lower (higher) on spatial scales of about 100 km, and the magnitude of these SLP perturbations near the SST front can exceed 1.0 hPa. Radiosonde data also show that the atmospheric boundary layer thins (thickens) over lower (higher) SST. Although the contribution of across‐track component in the wind cannot be calculated, the along‐track component of divergence suggests low‐level convergence over higher SST. The thermally induced pressure gradient is important in the momentum budget, suggesting that sea breeze‐like local circulations formed over the SST fronts. Indeed, the pressure adjustment mechanism can sometimes dominate even on a scale of 100 km, although it is not always observed. The time scale in which the boundary layer thickness adjusted to SST is estimated to be 1 day or less.
Cognitive Perspectives on the Role of Naming in Computer Programs

Programming a computer is a complex, cognitively rich process. This paper examines ways in which human cognition is reflected in the text of com- puter programs. We concentrate on naming: the assignment of identifying labels to programmatic constructs. Naming is arbitrary, yet programmers do not select names arbitrarily. Rather, programmers choose and use names in regular, system- atic ways that reflect deep cognitive and linguistic influences. This, in turn, allows names to carry semantic cues that aid in program understanding and support the larger software development process.
On the Local Convergence of Pattern Search

We examine the local convergence properties of pattern search methods, complementing the previously established global convergence properties for this class of algorithms. We show that the step-length control parameter which appears in the definition of pattern search algorithms provides a reliable asymptotic measure of first-order stationarity. This gives an analytical justification for a traditional stopping criterion for pattern search methods. Using this measure of first-order stationarity, we analyze the behavior of pattern search in the neighborhood of an isolated local minimizer. We show that a recognizable subsequence converges r-linearly to the minimizer.
Multilingual Synthetic Question and Answer Generation for Cross-Lingual Reading Comprehension

We propose a simple method to generate large amounts of multilingual question and answer pairs by a single generative model. These synthetic samples are then applied to augment the available gold multilingual ones to improve the performance of multilingual QA models on target languages. Our approach only requires existence of automatically translated samples from English to the target domain, thus removing the need for human annotations in the target languages. Experimental results show our proposed approach achieves significant gains in a number of multilingual datasets.
Discovering protein drug targets using knowledge graph embeddings

MOTIVATION Computational approaches for predicting drug-target interactions (DTIs) can provide valuable insights into the drug mechanism of action. DTI predictions can help to quickly identify new promising (on-target) or unintended (off-target) effects of drugs. However, existing models face several challenges. Many can only process a limited number of drugs and/or have poor proteome coverage. The current approaches also often suffer from high false positive prediction rates. RESULTS We propose a novel computational approach for predicting drug target proteins. The approach is based on formulating the problem as a link prediction in knowledge graphs (robust, machine-readable representations of networked knowledge). We use biomedical knowledge bases to create a knowledge graph of entities connected to both drugs and their potential targets. We propose a specific knowledge graph embedding model, TriModel, to learn vector representaions (i.e. embeddings) for all drugs and targets in the created knowledge graph. These representations are consequently used to infer candidate drug target interactions based on their scores computed by the trained TriModel model. We have experimentally evaluated our method using computer simulations and compared it to five existing models. This has shown that our approach outperforms all previous ones in terms of both area under ROC and precision-recall curves in standard benchmark tests. AVAILABILITY The data, predictions, and models are available at: drugtargets.insight-centre.org.
A viscous sea ice law as a stochastic average of plasticity

Over the last several years the applicability of viscous constitutive laws to sea ice on the geophysical scale has been questioned, because the local characteristics of sea ice deformation appear to be plastic in nature. In order to provide a more fundamental physical basis for a viscous law, this paper demonstrates that if time and/or length scales are chosen large enough, then stochastic variations in sea ice deformation rates can cause the average stress-strain rate relationship on these scales to take on viscous characteristics, even though the nonaveraged relationship is plastic in character. In particular, when a two-dimensional plastic model with an elliptical yield curve is used, the stochastically averaged stress-strain rate relationship takes the form of a viscous law with a pressure term: 〈σij〉 = 2η〈eij〉 + [(ζ - η) 〈ekk〉 - P], δtj, where angle brackets denote averages and where η and ζ are the shear and bulk viscosities, respectively. In the case of a circular yield curve, η = ζ. Examination of actual sea ice deformation time series and deformation rates simulated by red noise suggests that minimal time scales for the application of such averaging arguments can be as short as 1 day. The calculations also yield an empirical ratio between the plastic strength of the ice, P*, and the viscous parameters: η/P* ∼ ζ/P* ∼ 4.0 × 106 s. It is concluded that the viscous law, with the inclusion of a bulk viscosity and pressure term, is applicable to sea ice modeling.
In-Context Learning of Large Language Models Explained as Kernel Regression

Large language models (LLMs) have initiated a paradigm shift in transfer learning. In contrast to the classic pretraining-then-ﬁnetuning procedure, in order to use LLMs for downstream prediction tasks, one only needs to provide a few demonstrations, known as in-context examples, without adding more or updating existing model parameters. This in-context learning (ICL) capabilities of LLMs is intriguing, and it is not yet fully understood how pretrained LLMs acquire such capabilities. In this paper, we investigate the reason why a transformer-based language model can accomplish in-context learning after pre-training on a general language corpus by proposing one hypothesis that LLMs can simulate kernel regression algorithms when faced with in-context examples. More concretely, we ﬁrst prove that Bayesian inference on in-context prompts can be asymptotically understood as kernel regression ˆ y = (cid:80) i y i K ( x,x i ) (cid:80) i K ( x,x i ) as the number of in-context demonstrations grows. Then, we empirically investigate the in-context behaviors of language models. We ﬁnd that during ICL, the attentions and hidden features in LLMs match the behaviors of a kernel regression. Finally, our theory provides insights on multiple phenomena observed in ICL ﬁeld: why retrieving demonstrative samples similar to test sample can help, why ICL performance is sensitive to the output formats, and why ICL accuracy beneﬁts from selecting in-distribuion and representative samples. We will make our code available to the research community following publication.
Monetary Policy Rules in a Non-Rational World: A Macroeconomic Experiment

Abstract This paper introduces a learning-to-forecast laboratory experiment based on a New-Keynesian macroeconomy that is particularly close to the model's microfoundations. In this setup, subjects forecast their individual optimal consumption and prices instead of aggregate outcomes. Due to different personal experiences, coordination of forecasting behavior does not occur naturally, and there is considerable randomness in subjects' responses. Thompson Sampling, a learning heuristic from operations research that links randomness to the Bayesian posterior uncertainty, describes subjects' individual forecasting data well, and explains the observed patterns in the experiments. The experimental results show that a particularly aggressive anti-inflation response by the central bank is needed to achieve coordination on rational expectations and macroeconomic stability.
A microphysical model for fault gouge friction applied to subduction megathrusts

A microphysical model is developed for the steady state frictional behavior of illite‐quartz fault gouge and applied to subduction megathrust P‐T conditions. The model assumes a foliated, phyllosilicate‐supported microstructure which shears by rate‐independent frictional slip on the aligned phyllosilicates plus thermally activated deformation of the intervening quartz clasts. At low slip rates or high temperatures, the deformation of the clasts is easy, accommodating slip on the foliation without dilatation. With increasing velocity or decreasing temperature, the shear of the clasts becomes more difficult, increasing bulk shear strength, until slip is activated on inclined portions of the phyllosilicate foliation, where it anastomoses around the clasts. Slip at these sites leads to dilation involving clast/matrix debonding, balanced, at steady state, by compaction through thermally activated clast deformation. Model predictions, taking pressure solution as the thermally activated mechanism, show three regimes of velocity‐dependent frictional behavior at temperatures in the range of 200–500°C, with velocity weakening occurring at 300–400°C, in broad agreement with previous experiments on illite‐quartz gouge. Effects of slip rate, normal stress, and quartz fraction predicted by the model also resemble those seen experimentally. Extrapolation of the model to earthquake nucleation slip rates successfully predicts the onset of velocity‐weakening behavior at the updip seismogenic limit on subduction megathrusts. The model further implies that the onset of seismogenesis is controlled by the thermally activated initiation of fault rock compaction through pressure solution of quartz, which counteracts dilatation due to slip on the fault rock foliation.
Horizontal spatial correlation of hydraulic and reactive transport parameters as related to hierarchical sedimentary architecture at the Borden research site

Highly resolved data from the Borden research site provide a unique opportunity to study the horizontal spatial bivariate correlation of hydraulic and reactive attributes affecting subsurface transport. The data also allow quantitatively relating this correlation to the hierarchical sedimentary architecture of the aquifer. The data include collocated samples of log permeability, Y, the log of the perchloroethene sorption distribution coefficient, Ξ, and lithologic unit type. The horizontal Y and Ξ autosemivariograms and the Ξ‐Y cross‐semivariogram have the same underlying correlation structure (shape and range in the rise to a sill). The common structure is not due to Ξ‐Y point correlation or in‐unit spatial correlation. The common structure is defined by how the proportion of lag transitions crossing different unit types (i.e., the cross‐transition probability structure) increases with increasing lag distance. The common underlying cross‐transition structure contains two substructures with different correlation ranges corresponding to two scales of unit types within the sedimentary architecture. For each substructure, a large standard deviation in the length of units relative to the mean length gives rise to an exponential‐like shape and the proportions and mean length of units define the ranges. The horizontal Ξ‐Y spatial cross correlation is primarily defined by the larger‐scale substructure and the differences in mean Ξ and Y between larger‐scale unit types.
NOVA University of Newcastle Research Online

The papers gathered in this collection emerged from a symposium concerned with the question of the political as subject, practice and epistemology. In part, the symposium evolved from a recognition of the political struggles across the globe that are reoccupying the political. Examples of these re-occupations include (but are not restricted to) Occupy in the USA, los Indignados in Spain
Trend analysis of total ozone data for turnaround and dynamical contributions

[1] Statistical trend analyses have been performed for monthly zonal average total ozone data from both TOMS and SBUV satellite sources and ground-based instruments over the period 1978–2002 for detection of a “turnaround” in the previous downward trend behavior and hence evidence for the beginning of an ozone recovery. Since other climatic and geophysical changes can impact ozone behavior and can influence the detection of turnaround and recovery, we also focus on accounting for ozone variations that may be ascribed to various physical and chemical influences. Thus we include in the statistical trend modeling and analysis the effects of various dynamical and circulation variations in the atmosphere, including those associated with the quasibiennial oscillation (QBO), Arctic Oscillation (AO) and Antarctic Oscillation (AAO), and Eliassen-Palm (EP) flux influences, as well as influences of solar cycle. A notable result of the analysis is that for latitude zones of 40° and above in both hemispheres, large positive and significant estimates of a change in trend (since 1996) are obtained (on the order of 1.5 to 3 DU per year). The dynamic index series, AO/AAO and EP flux, are found to have a substantial influence on total ozone for these higher latitudes, and significant influences of lesser magnitude are also found for lower latitudes. The feature of positive significant change in trend in total ozone over recent years, however, is obtained both without and with the dynamical index terms included in the statistical models.
Geochronology, geochemistry, Hf isotopic compositions and formation mechanism of radial mafic dikes in northern Tibet

A large mafic dike swarm is radially distributed in southern Qiangtang. Three typical samples were selected for geochronology, geochemistry, and Hf isotopic analysis. Zircon U–Pb dating indicates that the three dikes formed at 291 ± 2, 292 ± 3, and 300 ± 2 Ma. Whole-rock compositions show that the southern Qiangtang mafic dikes are alkaline, Fe + Ti rich, and exhibit relative enrichment in light rare-earth elements. The ratios of incompatible elements are similar to those of oceanic island and Emeishan basalts. Geochemical diagrams show that the dikes erupted in an intraplate environment. Zircon Hf isotopic data suggest that magma that produced the mafic dikes was derived from a depleted mantle source. The geochemical characteristics of the dikes approximate that of eruption products of a brief period of mantle plume activity (300–280 Ma). According to eight geologic maps of Qiangtang, the mafic dikes crop out over an area of 150 km from north to south and 500 km from east to west, radiating outward from Mayigangri. We conclude that the mafic dikes in southern Qiangtang are related to the combined effect of Permian plate motions and mantle plume activity, and the Mayigangri area overlies the hot spot. Furthermore, the mantle plume in southern Qiangtang may have propelled the closing of the Palaeo-Tethys Ocean.
Online Communities: Influence on Members Brand Loyalty and Purchase Intent

With the emergence of new media technologies it is becoming easier for brands to interact and engage with consumers in an attempt to build brand loyalty. One trend gaining popularity is the use of online branded communities, purpose-built social networks with focus (direct or indirect) on a brand. Branded communities tend to give more specific value to the brand owner and the members, allowing for greater communication and collaboration around the brand. Using the uses and gratification as a theoretical framework this study set out to explore the influence online brand communities have on members brand loyalty and intent to purchase. The method used was a survey administered in the form of an online questionnaire was administered to members of online brand communities.
QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding

Parallel implementations of stochastic gradient descent (SGD) have received significant research attention, thanks to its excellent scalability properties. A fundamental barrier when parallelizing SGD is the high bandwidth cost of communicating gradient updates between nodes; consequently, several lossy compresion heuristics have been proposed, by which nodes only communicate quantized gradients. Although effective in practice, these heuristics do not always guarantee convergence, and it is not clear whether they can be improved. In this paper, we propose Quantized SGD (QSGD), a family of compression schemes for gradient updates which provides convergence guarantees. QSGD allows the user to smoothly trade off \emph{communication bandwidth} and \emph{convergence time}: nodes can adjust the number of bits sent per iteration, at the cost of possibly higher variance. We show that this trade-off is inherent, in the sense that improving it past some threshold would violate information-theoretic lower bounds. QSGD guarantees convergence for convex and non-convex objectives, under asynchrony, and can be extended to stochastic variance-reduced techniques. When applied to training deep neural networks for image classification and automated speech recognition, QSGD leads to significant reductions in end-to-end training time. For example, on 16GPUs, we can train the ResNet152 network to full accuracy on ImageNet 1.8x faster than the full-precision variant.
A Low-frequency Passive Seismic Survey in Libya

Low frequency (~1-6Hz) microtremor signals have been measured at the surface in many locations around the world over hydrocarbon reservoirs (multiphase fluid systems in porous media). These low frequency microtremors are observable as modifications of the frequency domain character of passive seismic records. The anomalies in the data spectra possess remarkably similar characteristics, pointing to a common source mechanism, even though depth, hydrocarbon type and matrix rock vary across the complete geological spectrum. Herein we describe observations from a low frequency passive seismic survey over a shallow reservoir in Libya. Specific conditions of the ambient wave field are discussed in detail. In this data set we observe that the magnitude of the hydrocarbon signal can be proportional to the diurnal variations in the total energy of the seismic background.
Neural Sequence Model Training via $α$-divergence Minimization

We propose a new neural sequence model training method in which the objective function is defined by $\alpha$-divergence. We demonstrate that the objective function generalizes the maximum-likelihood (ML)-based and reinforcement learning (RL)-based objective functions as special cases (i.e., ML corresponds to $\alpha \to 0$ and RL to $\alpha \to1$). We also show that the gradient of the objective function can be considered a mixture of ML- and RL-based objective gradients. The experimental results of a machine translation task show that minimizing the objective function with $\alpha > 0$ outperforms $\alpha \to 0$, which corresponds to ML-based methods.