Miracle-dz/UniFAR-FTUs huggingface dataset:
Large Deviations of Square Root Insensitive Random Sums

We provide a large deviation result for a random sum S Nxn=0 X n , whereN xis a renewal counting process and { X n } n=0are i.i. d. random variables, independent ofN x , with a common distribution that belongs to a class of square root insensitive distributions. Asymptotically, the tails of these distributions are heavier thane -vxand have zero relative decrease in intervals of length v x, hence square root insensitive. Using this result we derive the asymptotic characterization of the busy period distribution in the stable GI/G/1 queue with square root insensitive service times; this characterization further implies that the tail behavior of the busy period exhibits a functional change for distributions that are lighter thane -vx .
Opportunistic Routing for Vehicular Energy Network

The Internet of Things forms the backbone of connectivity for the smart city, which revolutionizes the transportation and energy sectors and drives the development of vehicular energy network (VEN). VEN is a vehicular network which can transport energy over a large geographical area by means of electric vehicles (EVs). In the near future, an abundance of EVs, plentiful generation of renewables, and mature wireless energy transfer and vehicular communication technologies will expedite the realization of VEN. To transmit energy from a source to a destination, we need to establish energy paths, which are composed of segments of vehicular routes, while satisfying various design objectives. In this paper, we develop a method to construct all energy paths for a particular energy source–destination pair, followed by some analytical results of the method. We describe how to utilize the energy paths to develop optimization models for different design goals and propose two solutions. We also develop a heuristic for the power loss minimization problem. We compare the performance of the three solution methods with artificial and real-world traffic networks and provide a comprehensive comparison in terms of solution quality, computation time, solvable problem size, and applicability. This paper lays the foundations of VEN routing.
Evidence of surface reflectance bidirectional effects from a NOAA/ AVHRR multi-temporal data set

Abstract Time series of reflectances over the Valensole plateau test site (southeast of France) obtained from NOAA-9 Advanced Very High Resolution Radiometer (AVHRR) data show significant short-term variations over a three-month period. This paper demonstrates that the short-term variations are due essentially to surface bidirectional effects while atmospheric directional effects and other sources of fluctuations remain of lower amplitude. When the data are classified according to their viewing angles, in order to reduce the influence of directional effects, the consistency of the NDVI time series is strongly improved and its r.m.s. level of short-term fluctuations is decreased by a factor of 2, from 005 to 0025. Taking into account the effects induced by viewing angle variations should allow us to improve definitely the accuracy of the characterization and monitoring of the terrestrial vegetation.
Sample-efficient private data release for Lipschitz functions under sparsity assumptions

Diﬀerential privacy is the de facto standard for protecting privacy in a variety of applications. One of the key challenges is private data release, which is particularly relevant in scenarios where limited information about the desired statistics is available beforehand. Recent work has presented a diﬀerentially private data release algorithm that achieves optimal rates of order n − 1 /d , with n being the size of the dataset and d being the dimension, for the worst-case error over all Lipschitz continuous statistics. This type of guarantee is desirable in many practical applications, as for instance it ensures that clusters present in the data are preserved. However, due to the ”slow” rates, it is often infeasible in practice unless the dimension of the data is small. We demonstrate that these rates can be signiﬁcantly improved to n − 1 /s when only guarantees over s-sparse Lipschitz continuous functions are required, or to n − 1 / ( s +1) when the data lies on an unknown s-dimensional subspace, disregarding logarithmic factors. We therefore obtain practically meaningful rates for moderate constants s which motivates future work on computationally eﬃcient approximate algorithms for this problem.
Dynamic Adaptive and Adversarial Graph Convolutional Network for Traffic Forecasting

Trafﬁc forecasting is challenging due to dynamic and complicated spatial-temporal dependencies. However, existing methods still suffer from two critical limitations. Firstly, many approaches typically utilize static pre-deﬁned or adaptively learned spatial graphs to capture dynamic spatial-temporal dependencies in the trafﬁc system, which limits the ﬂexibility and only captures shared patterns for the whole time, thus leading to sub-optimal performance. In addition, most approaches individually and independently consider the absolute error between ground truth and predictions at each time step, which fails to maintain the global properties and statistics of time series as a whole and results in trend discrepancy between ground truth and predictions. To this end, in this paper, we propose a Dynamic Adaptive and Adversarial Graph Convolutional Network (DAAGCN), which combines Graph Convolution Networks (GCNs) with Generative Ad-versarial Networks (GANs) for trafﬁc forecasting. Speciﬁ-cally, DAAGCN leverages a universal paradigm with a gate module to integrate time-varying embeddings with node embeddings to generate dynamic adaptive graphs for inferring spatial-temporal dependencies at each time step. Then, two discriminators are designed to maintain the consistency of the global properties and statistics of predicted time series with ground truth at the sequence and graph levels. Extensive experiments on four benchmark datasets manifest that DAAGCN outperforms the state-of-the-art by average 5.05% , 3.80% , and 5.27% , in terms of MAE, RMSE, and MAPE, meanwhile, speeds up convergence up to 9 times. 1
Modeling and simulation of the horizontal component of the geomagnetic field by fractional stochastic differential equations in conjunction with empirical mode decomposition

[1] In this paper, we investigate the characteristics and develop a stochastic model for the horizontal component Bx of the magnetic field at 22 stations of the global near-real-time magnetic observatory network INTERMAGNET. The model is in the form of a fractional stochastic differential equation. A method to estimate the parameters on the basis of observed data and to simulate the data using the model is given. The degree of fractional differentiation and the alpha-stability exponent of the process are employed to cluster the stations. The Bx time series possess pronounced local trends, which must be removed before modeling and simulation can be performed. This trend removal is carried out by an empirical mode decomposition. An outcome is an efficient method to simulate the Bx time series by empirical mode decomposition and fractional stochastic differential equation. The numerical results indicate the existence of two distinct clusters of the INTERMAGNET: one in the mid- and low latitudes consistent with the Dst index, and the other above geomagnetic latitude 60°N consistent with the AE index. This clustering corresponds to the inner magnetosphere and the outer magnetosphere, respectively.
A Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques

—The union of Edge Computing (EC) and Artificial Intelligence (AI) has brought forward the Edge AI concept to provide intelligent solutions close to end-user environment, for privacy preservation, low latency to real-time performance, as well as resource optimization. Machine Learning (ML), as the most advanced branch of AI in the past few years, has shown encouraging results and applications in the edge environment. Nevertheless, edge powered ML solutions are more complex to realize due to the joint constraints from both edge computing and AI domains, and the corresponding solutions are expected to be efficient and adapted in technologies such as data processing, model compression, distributed inference, and advanced learning paradigms for Edge ML requirements. Despite that a great attention of Edge ML is gained in both academic and industrial communities, we noticed the lack of a complete survey on existing Edge ML technologies to provide a common understanding of this concept. To tackle this, this paper aims at providing a comprehensive taxonomy and a systematic review of Edge ML techniques: we start by identifying the Edge ML requirements driven by the joint constraints. We then survey more than twenty paradigms and techniques along with their representative work, covering two main parts: edge inference, and edge learning. In particular, we analyze how each technique fits into Edge ML by meeting a subset of the identified requirements. We also summarize Edge ML open issues to shed light on future directions for Edge ML.
Combination Approaches in Information Retrieval: Words vs. N-grams and Query Translation vs. Document Translation

This paper reports our proposal and experimental results at the NTCIR-4 CLIR task. For monolingual information retrieval, we use a combination strategy that integrates words and n-grams at the ranked list level. In combining words and n-grams, we concentrate on generating several ranked lists showing different retrieval characteristics on word and n-gram indexes by incorporating feedback schemes. For cross-language information retrieval, we attempt a dictionary-based bi-directional combination of query translation and document translation. For both query translation and document translation, their naïve translation is used. Experimental evaluations on CJK monolingual and KC/KJ cross-language retrieval give promising results on our combination approaches: words vs. n-grams, and query translation vs. document translation.
Crustal shortening of Southwest Japan in the Late Miocene

Tectonic deformation of an island arc is interpreted on the basis of geophysical data. Extensive reflection seismic, gravity, geomagnetic data around the back-arc region of Southwest Japan delineate east-west to northeast-southwest folding, and imply conspicuous compression on the southern margin of the Sea of Japan. Because geological data of exploration boreholes indicate that the coinpressive regime was dominant in the late Miocene, the tectonic event seems to be linked with coeval resumption of subduction of the Philippine Sea Plate. Strong coupling of the young buoyant oceanic plate brought about north-south shortening of the overriding continental lithosphere, and left wrench deformation at the southwestern corner of the Sea of Japan. Amount of shortening for the back-arc shelf and mountainous ranges of Southwest Japan is estimated to be ca 10 km, adopting a uniform ratio of shortening (0.944) since the Miocene determined on the shelf from depth-converted seismic profiles. Along the western side of a bend of boundary between the Eurasian Plate and Philippine Sea Plate, the middle Miocene and younger sediments upon the back-arc shelf are much less deformed than the northern equivalents, and the fore-arc Miocene strata are deformed by left wrenching, facts which are indicative of northerly initial convergence of the Philippine Sea Plate at the end of Miocene and crustal decoupling on the west of Kyushu Island.
The Reflexivity between ICTs and Business Culture: Applying

This study compares how workers in Norway and the United States use Information and Communication Technologies (ICTs). Our data—72 in-depth interviews of advanced ICT users – were coded, analyzed, and placed into Hofstede’s four dimensional framework (power distance, uncertainty avoidance, individualism, and masculinity). We proposed that ICT use comparisons between the two countries are congruent to Hofstede’s findings. We find partial support for these propositions. As expected, Norway and the US are similar on two dimensions (power distance and uncertainty avoidance), but contrary to expectations, they are also similar on the two dimensions where we expected differences (individualism and masculinity). We suggest possible explanations for these findings, including our focus on an expert-user subculture, external triggering events, and technical codes inscribed in Internet applications and software.
Ode to an ODE

We present a new paradigm for Neural ODE algorithms, called ODEtoODE , where time-dependent parameters of the main ﬂow evolve according to a matrix ﬂow on the orthogonal group O ( d ) . This nested system of two ﬂows, where the parameter-ﬂow is constrained to lie on the compact manifold, provides stability and effectiveness of training and provably solves the gradient vanishing-explosion problem which is intrinsically related to training deep neural network architectures such as Neural ODEs. Consequently, it leads to better downstream models, as we show on the example of training reinforcement learning policies with evolution strategies, and in the supervised learning setting, by comparing with previous SOTA baselines. We provide strong convergence results for our proposed mechanism that are independent of the depth of the network, supporting our empirical studies. Our results show an intriguing connection between the theory of deep neural networks and the ﬁeld of matrix ﬂows on compact manifolds.
VisQA: Quantifying Information Visualisation Recallability via Question Answering

—Despite its importance for assessing the effectiveness of communicating information visually, ﬁne-grained recallability of information visualisations has not been studied quantitatively so far. In this work we propose a visual question answering (VQA) paradigm to study visualisation recallability and present VisQA — a novel VQA dataset consisting of 200 visualisations that are annotated with crowd-sourced human (N = 305) recallability scores obtained from 1,000 questions from ﬁve question types. Furthermore, we present the ﬁrst computational method to predict recallability of different visualisation elements, such as the title or speciﬁc data values. We report detailed analyses of our method on VisQA and demonstrate that it outperforms several baselines in overall recallability and FE-, F, RV-, and U-question recallability. We further demonstrate one possible application of our method: recommending the visualisation type that maximises user recallability for a given data source. Taken together, our work makes fundamental contributions towards a new generation of methods to assist designers in optimising visualisations.
A study of the problems associated with Dalangdian reservoir, China

There are over 2,300 lakes over 1 km 2 in China (total area 80 000 km 2 , accounting for about 0.8% of the total area of the country and with a total freshwater storage capacity of 225 billion m 3 ). In addition there are approximately 87 000 reservoirs with a storage capacity of 413 billion m 3 . These form the main supply of drinking water as well as water for industrial and agricultural production and aquaculture. Because of a lack of understanding of the frailty of lake ecosystems and poor environmental awareness, human activities have greatly affected freshwater systems (Xie 1995). The most marked impacts have been due to land reclamation, destruction of marginal vegetation, discharge of industrial, domestic and agricultural waste and irrational development and use of freshwater resources. As a result lakes in dry areas are becoming saline, while others are highly polluted and undergoing a process of rapid eutrophication. The majority of the lakes tested to date have been classified as hypertrophic (Dokulil et al. 2000). Thus, although China has 28 of the world's largest lakes (Chang 1987, Chen 1994), the annual storage capacity ranks sixth in the world and the per capita availability is a quarter of the world's average (Jusi 1989) and predicted to fall by the year 2000 (Shiklomanov 1993). In this article, we focus on the problems of one water supply reservoir,
Goodness-of-fit Test on the Number of Biclusters in Relational Data Matrix

Biclustering is a problem to detect homogeneous submatrices in a given observed matrix, and it has been shown to be an eﬀective tool for relational data analysis. Although there have been many studies for estimating the underlying bicluster structure of a matrix, few have enabled us to determine the appropriate number of biclusters in an observed matrix. Recently, a statistical test on the number of biclusters has been proposed for a regular-grid bicluster structure, where we assume that the latent bicluster structure can be represented by row-column clustering. However, when the latent bicluster structure does not satisfy such regular-grid assumption, the previous test requires too many biclusters (i.e., ﬁner bicluster structure) for the null hypothesis to be accepted, which is not desirable in terms of interpreting the accepted bicluster structure. In this paper, we propose a new statistical test on the number of biclusters that does not require the regular-grid assumption, and derive the asymptotic behavior of the proposed test statistic in both null and alternative cases. To develop the proposed test, we construct a consistent submatrix localization algorithm, that is, the probability that it outputs the correct bicluster structure converges to one. We show the eﬀectiveness of the proposed method by applying it to both synthetic and practical relational data matrices.
The renewability of water resources and its quantification in the Yellow River basin, China

The drying‐up events in the lower reaches of the Yellow River during the 1990s, which were the most serious problem of water resources issues in China, initiated this study of the concept of water resources renewability in the Yellow River basin. The problem of renewability linked with hydrological process impacted by climate change and human activity is addressed. A quantitative method was developed to analyse causes and propose countermeasures to improve renewability of water resources. The magnitude of renewability change in time and space within the river system can be represented using this method. According to the analysis using an observation data set, it is shown that the renewability of water resources of the Yellow Rive basin has had a significant declining trend since the 1950s. In particular, the renewability indicator in the downstream reaches was reduced to 0·2 during a bad situation during the 1990s. Using the renewability assessment, feasible strategies can be analysed and suggested. Copyright © 2004 John Wiley & Sons, Ltd.
PVT3D: Point Voxel Transformers for Place Recognition from Sparse Lidar Scans

Place recognition based on point cloud (LiDAR) scans is an important module for achieving robust autonomy in robots or self-driving vehicles. Training deep networks to match such scans presents a difﬁcult trade-off: a higher spatial resolution of the network’s intermediate representations is needed to perform ﬁne-grained matching of subtle geometric features, but growing it too large makes the memory requirements infeasible. In this work, we propose a Point-Voxel Transformer network (PVT3D) that achieves robust ﬁne-grained matching with low memory requirements. It leverages a sparse voxel branch to extract and aggregate information at a lower resolution and a point-wise branch to obtain ﬁne-grained local information. A novel hierarchical cross-attention transformer (HCAT) uses queries from one branch to try to match structures in the other branch, ensuring that both extract self-contained descriptors of the point cloud (rather than one branch dominating), but using both to inform the output global descriptor of the point cloud. Extensive experiments show that the proposed PVT3D method surpasses the state-of-the-art by a large amount on several datasets (Oxford RobotCar, TUM, USyd). For instance, we achieve AR@1 of 85.6% on the TUM dataset, which surpasses the strongest prior model by ∼ 15%. The source code will be available upon publication.
A correction to damping ratio for hyperbolic–hysteretic model for clayey soil

Abstract For clayey soils, the shear modulus and damping ratio are perhaps the two most common parameters considered in dynamic soil behavior characterization. It is well known that under cyclic loading, soil behaves hysteretically in the stress–strain plane. Several mathematical models were proposed to simulate the soil behavior under the cyclic loading conditions. For most of such models, Masing rule is often used to define the unloading–reloading behavior of stress–strain loops. However, it was also noted by many researchers that the framework of Masing rule tends to overpredict the damping ratio at moderate to high strain range. The present paper deals with a hyperbolic–hysteretic undrained soil model based on Masing rule. First, the efficacy of the model to predict the variation of shear modulus and damping ratio for a wide strain range was examined. Second, a simple correction for the calculation of damping ratio was introduced. Finally, the corrected damping ratios for various types of soils with varying plasticity index were compared with published experimental results. Good agreement was obtained between the computed and experimental results for a wide range of strains and plasticity index.
Comparison of monthly means of global total column water vapor retrieved from independent satellite observations

[1] Two independent data sets of monthly means of total column water vapor (TCWV) have been compared. The first data set is the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring's (CM-SAF) Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data (HOAPS) product, which is based on passive microwave observations from the Special Sensor Microwave/Imager (SSM/I). It is available over oceans only and includes the TCWV also from cloudy scenes. The second one relies on Global Ozone Monitoring Experiment/Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (GOME/SCIAMACHY) measurements, which are carried out in the visible part of the solar spectrum and present a partly cloud corrected climatology that is available over land and ocean. The comparison is performed for the period 1996 to 2005 for ice-free ocean areas. This comparison helps to analyze the potential of the two data sets to provide an independent merged TCWV data set for all surface types in the future. A systematic difference or bias is observed between both data sets, with lower TCWV for GOME/SCIAMACHY. The dominating cause (80-90%) for the difference in monthly averages is the cloud correction approach used within the GOME/SCIAMACHY retrieval scheme. We show that the bias contains a pronounced seasonal component and relies on cloud properties such as the cloud water path.