Miracle-dz/UniFAR-FTUs huggingface dataset:
On the spatial and temporal sampling of soil moisture fields

Recent work by Isham et al. and Rodrìguez‐Iturbe et al. has characterized the space‐time variability of soil moisture through its analytically derived covariance function which depends on soil properties, vegetation structure, and rainfall patterns typical of a region. This paper uses such characterization to address the strategies and methodologies for the sampling of soil moisture fields. The focus is on the estimation of the long‐term mean soil moisture and the daily soil moisture averaged over a given area as a function of the network geometry, number of stations, number of sampling days and landscape heterogeneity. It is found that the spatial geometry of the network has a significant impact on the sampling of the average soil moisture over an area in any particular day, while it is much less relevant for the sampling of the long‐term mean daily soil moisture over the region. In the latter case, the length of the record is a commanding factor in what concerns the variance of estimation, specially for soils with shallow rooted vegetation. Spatial vegetation heterogeneity plays an important role on the variance of estimation of the soil moisture, being particularly critical for the sampling of the average soil moisture over an area for a given day.
RSLIS at INEX 2013: Social Book Search Track

In this paper, we describe our participation in the INEX 2013 Social Book Search track. We investigate the contribution of different types of docu- ment metadata, both social and controlled, as well as the contribution of the new 'query' topic representations. We find that the best results are obtained using all available document fields and topic representations.
The application of optimal estimation retrieval to lidar observations

Optimal estimation retrieval is a nonlinear regression scheme to determine the conditions statistically most-likely to produce a given measurement, weighted against any a priori knowledge. The technique is applied to three problems within the field of lidar data analysis. A retrieval of the aerosol backscatter and either the extinction or lidar ratio from two-channel Raman lidar data is developed using the lidar equations as a forward model. It produces profiles consistent with existing techniques at a resolution of 10-1000 m and uncertainty of 5-20%, dependent on the quality of data. It is effective even when applied to noisy, daytime data but performs poorly in the presence of cloud. Two of the most significant sources of uncertainty in that retrieval are the nonlinearity of the detectors and the instrument's calibration (known as the dead time and overlap function). Attempts to retrieve a nonlinear correction from a pair of lidar profiles, one attenuated by a neutral density filter, are not successful as uncertainties in the forward model eliminate any information content in the measurements. The technique of Whiteman et al. [1992] is found to be the most accurate. More successful is a retrieval of the overlap function of a Raman channel using a forward model combining an idealised extinction profile and an adaptation of the equations presented in Halldorsson and Langerholc [1978]. After refinement, the retrieval is shown to be at least as accurate, and often superior to, existing methods of calibration from routine measurements, presenting uncertainties of 5-15%. These techniques are then applied to observations of ash over southern England from the Eyjafjallajokull eruption of April 2010. Lidar ratios of 50-60 sr were observed when the plume first appeared, which reduced to 20-30 sr after several days within the planetary boundary layer, indicating an alteration of the particles over time.
Toward End-to-End Car License Plate Detection and Recognition With Deep Neural Networks

In this paper, we tackle the problem of car license plate detection and recognition in natural scene images. We propose a unified deep neural network, which can localize license plates and recognize the letters simultaneously in a single forward pass. The whole network can be trained end-to-end. In contrast to existing approaches which take license plate detection and recognition as two separate tasks and settle them step by step, our method jointly solves these two tasks by a single network. It not only avoids intermediate error accumulation but also accelerates the processing speed. For performance evaluation, four data sets including images captured from various scenes under different conditions are tested. Extensive experiments show the effectiveness and the efficiency of our proposed approach.
Automated carotid artery distensibility measurements from CTA using nonrigid registration

The distensibility of a blood vessel is a marker of atherosclerotic disease. Manual measurement of the distensibility on CTA images is a difficult task. In this paper we describe a method to automatically determine the distensibility of the carotid artery by locally analyzing the deformation field of a nonrigid registration of a series of ECG-gated CTA images. We evaluate two variants of the method, and compare the results with manually measured distensibilities.
SciTS: A Benchmark for Time-Series Database in Scientific Experiments and Industrial Internet of Things

Time-series data has an increasingly growing usage in Industrial Internet of Things (IIoT) and large-scale scientific experiments. Managing time-series data needs a storage engine that can keep up with their constantly growing volumes while providing an acceptable query latency. While traditional ACID databases favor consistency over performance, many time-series databases with novel storage engines have been developed to provide better ingestion performance and lower query latency. To understand how the unique design of a time-series database affects its performance, we design SciTS , a highly extensible and parameterizable benchmark for time-series data. The benchmark studies the data ingestion capabilities of time-series databases especially as they grow larger in size. It also studies the latencies of 5 practical queries from the scientific experiments use case. We use SciTS to evaluate the performance of 4 databases of 4 distinct storage engines: ClickHouse, InfluxDB, TimescaleDB, and PostgreSQL.
Design of 32 bit Parallel Processor Core for High Energy Efficiency using Instruction-Levels Dynamic Voltage Scaling Technique

This paper describes design of high energy efficiency 32 bit parallel processor core using instruct- tion-levels data gating and dynamic voltage scaling (DVS) techniques. We present instruction-levels data gating technique. We can control activation and swit- ching activity of the function units in the proposed data technique. We present instruction-levels DVS tech- nique without using DC-DC converter and voltage scheduler controlled by the operation system. We can control powers of the function units in the proposed DVS technique. The proposed instruction-levels DVS technique has the simple architecture than complicated DVS which is DC-DC converter and voltage sche- duler controlled by the operation system and a hard- ware implementation is very easy. But, the energy efficiency of the proposed instruction-levels DVS tech- nique having dual-power supply is similar to the complicated DVS which is DC-DC converter and voltage scheduler controlled by the operation system. We simulate the circuit simulation for running test program using Spectra. We selected reduced power supply to 0.667 times of the supplied power supply. The energy efficiency of the proposed 32 bit parallel processor core using instruction-levels data gating and DVS techniques can improve about 88.4% than that of the 32 bit parallel processor core without using those. The designed high energy efficiency 32 bit pa- rallel processor core can utilize as the coprocessor processing massive data at high speed.
Modelling Taxi Drivers’ Behaviour for the Next Destination Prediction

In this paper, we study how to model taxi drivers’ behavior and geographical information for an interesting and challenging task: the next destination prediction in a taxi journey. Predicting the next location is a well-studied problem in human mobility, which finds several applications in real-world scenarios, from optimizing the efficiency of electronic dispatching systems to predicting and reducing the traffic jam. This task is normally modeled as a multiclass classification problem, where the goal is to select, among a set of already known locations, the next taxi destination. We present a Recurrent Neural Network (RNN) approach that models the taxi drivers’ behavior and encodes the semantics of visited locations by using geographical information from Location-Based Social Networks (LBSNs). In particular, the RNNs are trained to predict the exact coordinates of the next destination, overcoming the problem of producing, in output, a limited set of locations, seen during the training phase. The proposed approach was tested on the ECML/PKDD Discovery Challenge 2015 dataset—based on the city of Porto—, obtaining better results with respect to the competition winner, whilst using less information, and on Manhattan and San Francisco datasets.
On Mutual Information-Maximizing Quantized Belief Propagation Decoding of LDPC Codes

A severe problem for mutual information-maximizing lookup table (MIM-LUT) decoding of low-density parity-check (LDPC) code is the high memory cost for using large tables, while decomposing large tables to small tables deteriorates decoding error performance. In this paper, we propose a systematic method, called mutual information- maximizing quantized belief propagation (MIM-QBP) decoding, to remove the lookup tables used for MIM-LUT decoding. Our method leads to a very practical decoder, namely the MIM-QBP decoder, which can be implemented based only on simple mappings and additions. Simulation results show that the proposed MIM-QBP decoder can outperform the state-of-the-art MIM-LUT decoder. Moreover, the MIM-QBP decoder with only 3 bits per message can outperform the floating-point belief propagation (BP) decoder at high signal-to-noise ratio (SNR) regions with a maximum of 10 iterations.
Interacting Hopf Algebras- the Theory of Linear Systems. (Interacting Hopf Algebras - la théorie des systèmes linéaires)

We present by generators and equations the algebraic theory IH whose free model is the category oflinear subspaces over a field k. Terms of IH are string diagrams which, for different choices of k, expressdifferent kinds of networks and graphical formalisms used by scientists in various fields, such as quantumcircuits, electrical circuits and Petri nets. The equations of IH arise by distributive laws between Hopfalgebras - from which the name interacting Hopf algebras. The characterisation in terms of subspacesallows to think of IH as a string diagrammatic syntax for linear algebra: linear maps, spaces and theirtransformations are all faithfully represented in the graphical language, resulting in an alternative, ofteninsightful perspective on the subject matter. As main application, we use IH to axiomatise a formalsemantics of signal processing circuits, for which we study full abstraction and realisability. Our analysissuggests a reflection about the role of causality in the semantics of computing devices.
Automatic Aesthetics-based Lighting Design with Global Illumination

In computer graphics, lighting plays an important role in the appearance of a scene. A change in the configuration of light sources can lead to different aesthetics in the final rendered image. Lighting design becomes increasingly complex when using sophisticated global illumination techniques. In this paper, we present a new approach to automatically design the lighting configuration according to the aesthetic goal specified by the user as a set of target parameters. Target parameters are used to set up an objective function which is minimized using an optimization method. The results show that our method can be used to automatically design a lighting configuration that will give to the final image a classic photographic look.
Reinforcement Learning from a Mixture of Interpretable Experts

Reinforcement learning (RL) has demonstrated its ability to solve high dimensional tasks by leveraging non-linear function approximators. These successes however are mostly achieved by 'black-box' policies in simulated domains. When deploying RL to the real world, several concerns regarding the use of a 'black-box' policy might be raised. In an effort to make the policies learned by RL more transparent, we propose in this paper a policy iteration scheme that retains a complex function approximator for its internal value predictions but constrains the policy to have a concise, hierarchical, and human-readable structure, based on a mixture of interpretable experts. We show that our proposed algorithm can learn compelling policies on continuous action deep RL benchmarks, matching the performance of neural network based policies, but returns policies that are more amenable to human inspection than neural network or linear-in-feature policies.
muNet: A Highly Compact Deep Convolutional Neural Network Architecture for Real-time Embedded Traffic Sign Classification

Traffic sign recognition is a very important computer vision task for a number of real-world applications such as intelligent transportation surveillance and analysis. While deep neural networks have been demonstrated in recent years to provide state-of-the-art performance traffic sign recognition, a key challenge for enabling the widespread deployment of deep neural networks for embedded traffic sign recognition is the high computational and memory requirements of such networks. As a consequence, there are significant benefits in investigating compact deep neural network architectures for traffic sign recognition that are better suited for embedded devices. In this paper, we introduce MicronNet, a highly compact deep convolutional neural network for real-time embedded traffic sign recognition designed based on macroarchitecture design principles (e.g., spectral macroarchitecture augmentation, parameter precision optimization, etc.) as well as numerical microarchitecture optimization strategies. The resulting overall architecture of MicronNet is thus designed with as few parameters and computations as possible while maintaining recognition performance, leading to optimized information density of the proposed network. The resulting MicronNet possesses a model size of just ~1MB and ~510,000 parameters (~27x fewer parameters than state-of-the-art) while still achieving a human performance level top-1 accuracy of 98.9% on the German traffic sign recognition benchmark. Furthermore, MicronNet requires just ~10 million multiply-accumulate operations to perform inference, and has a time-to-compute of just 32.19 ms on a Cortex-A53 high efficiency processor. These experimental results show that highly compact, optimized deep neural network architectures can be designed for real-time traffic sign recognition that are well-suited for embedded scenarios.
Evaluation of Lateral Movement of Piled Bridge Abutment Undergoing Lateral Soil Movement in Soft Ground

This paper describes the lateral movement of piled bridge abutments caused by backfills behind the abutments. To analyze the lateral movement of piled bridge abutments, a precise investigation was performed on the field monitoring data obtained from forty-three abutments where lateral movement has occurred. The investigation focuses not only on the lateral movement of the abutments, but also on the slope stabilizing effect of the foundation piles that support the abutments. Analyses on slope stability have been carried out for the abutments. These analyses consider the lateral movement of the abutments and the slope stabilization effect of the foundation piles. The results of the slope stability analysis and field investigation show that the lateral movement of bridge abutments may occur when the safety factor of the slope is less than either 1.5 in the case that does not consider the pile effect, or 1.8 in the case that considers the pile effect. The evaluation on the lateral movement of bridge abutments using only empirical indexes is not sufficient because the measured lateral movements do not always relate to some of the empirical indexes. Therefore, the slope stability analysis that considers lateral movements of the bridge abutment must be executed in order to precisely evaluate the possibility of lateral movement of the bridge abutment.
Virtual leading blocks for the deaf-blind: a real-time way-finder by verbal-nonverbal hybrid interface and high-density RFID tag space

In this paper, we discuss application possibilities of augmented reality technologies in the field of mobility support for the deaf blind. We propose the navigation system called virtual leading blocks for the deaf-blind, which consists of a wearable interface for Finger-Braille, one of the commonly used communication methods among deaf-blind people in Japan, and a ubiquitous environment for barrier-free application, which consists of floor-embedded active radio-frequency identification (RFID) tags. The wearable Finger-Braille interface using two Linux-based wristwatch computers has been developed as a hybrid interface of verbal and nonverbal communication in order to inform users of their direction and position through the tactile sensation. We propose the metaphor of "watermelon splitting" for navigation by this system and verify the feasibility of the proposed system through experiments.
New insights into the genetics of primary open-angle glaucoma based on meta-analyses of intraocular pressure and optic disc characteristics

Primary open-angle glaucoma (POAG), the most common optic neuropathy, is a heritable disease. Siblings of POAG cases have a ten-fold increased risk of developing the disease. Intraocular pressure (IOP) and optic nerve head characteristics are used clinically to predict POAG risk. We conducted a genome-wide association meta-analysis of IOP and optic disc parameters and validated our findings in multiple sets of POAG cases and controls. Using imputation to the 1000 genomes (1000G) reference set, we identified 9 new genomic regions associated with vertical cup-disc ratio (VCDR) and 1 new region associated with IOP. Additionally, we found 5 novel loci for optic nerve cup area and 6 for disc area. Previously it was assumed that genetic variation influenced POAG either through IOP or via changes to the optic nerve head; here we present evidence that some genomic regions affect both IOP and the disc parameters. We characterized the effect of the novel loci through pathway analysis and found that pathways involved are not entirely distinct as assumed so far. Further, we identified a novel association between CDKN1A and POAG. Using a zebrafish model we show that six6b (associated with POAG and optic nerve head variation) alters the expression of cdkn1a. In summary, we have identified several novel genes influencing the major clinical risk predictors of POAG and showed that genetic variation in CDKN1A is important in POAG risk.
The implicit calculus: a new foundation for generic programming

Generic programming (GP) is an increasingly important trend in programming languages. Well-known GP mechanisms, such as type classes and the C++0x concepts proposal, usually combine two features: 1) a special type of interfaces; and 2) implicit instantiation of implementations of those interfaces. Scala implicits are a GP language mechanism, inspired by type classes, that break with the tradition of coupling implicit instantiation with a special type of interface. Instead, implicits provide only implicit instantiation, which is generalized to work for any types. This turns out to be quite powerful and useful to address many limitations that show up in other GP mechanisms. This paper synthesizes the key ideas of implicits formally in a minimal and general core calculus called the implicit calculus (λ⇒), and it shows how to build source languages supporting implicit instantiation on top of it. A novelty of the calculus is its support for partial resolution and higher-order rules (a feature that has been proposed before, but was never formalized or implemented). Ultimately, the implicit calculus provides a formal model of implicits, which can be used by language designers to study and inform implementations of similar mechanisms in their own languages.
Generative vs. Discriminative: Rethinking The Meta-Continual Learning

Deep neural networks have achieved human-level capabilities in various learning tasks. However, they generally lose performance in more realistic scenarios like learning in a continual manner. In contrast, humans can incorporate their prior knowledge to learn new concepts efﬁciently without forgetting older ones. In this work, we leverage meta-learning to encourage the model to learn how to learn continually. Inspired by human concept learning, we develop a generative classiﬁer that efﬁciently uses data-driven experience to learn new concepts even from few samples while being immune to forgetting. Along with cognitive and theoretical insights, extensive experiments on standard benchmarks demonstrate the effectiveness of the proposed method. The ability to remember all previous concepts, with negligible computational and structural overheads, suggests that generative models provide a natural way for alleviating catastrophic forgetting, which is a major drawback of discriminative models. The code is publicly available at https://github.com/aminbana/GeMCL .
IV.—Geology of West Galway and S.W. Mayo, Ireland

Having received permission from the Director-General of the Geological Survey of the United Kingdom, I exhibited before the Geological Section of the British Association at Belfast in August last, the Maps and Sections of the rocks forming the hill-country of West Galway and S.W. Mayo, and gave a description of the district, of which the following is an epitome.
Workload-Aware Game-Theoretic Framework for Wireless Federated Learning

—Federated learning (FL) has been proposed as a popular learning framework to protect the users’ data privacy but it has difﬁculties in motivating the users to participate in task training. This paper proposes a Bertrand-game-based framework for FL in wireless networks, where the model server as a resource buyer can issue an FL task, whereas the employed user equipment (UEs) as the resource sellers can help train the model by using their local data. Specially, the inﬂuence of time-varying task load and channel quality on UE’s motivation to participate in FL is considered. Firstly, we adopt the ﬁnite-state discrete-time Markov chain (FSDT-MC) method to predict the existing task load and channel gain of a UE during a FL task. Depending on the performance metrics set by the model server and the estimated overall energy cost for engaging in the FL task, each UE seeks the best price to maximize its own proﬁt in the game. To this end, the Nash equilibrium (NE) of the game is obtained in closed form, and a distributed iterative algorithm is also developed to ﬁnd the NE. Simulation result veriﬁes the effectiveness of the proposed approach.