Fine-grained Systems Research Articles

Grain-size variability of point-bar deposits from a fine-grained dryland river terminus, Southern Altiplano, Bolivia

Point-bar deposits exhibiting fining-upward grain-size trends are widely documented from both modern rivers and ancient preserved successions. However, in some mud-dominated sedimentary systems this common pattern may not occur; rather, alternative coarsening-upward trends can develop. We investigated point-bar deposits in the mud-dominated river terminus (median value (D50) < 55 μm) of the meandering Río Colorado in Bolivia's southern Altiplano, a semi-arid endorheic basin. Grain-size trends were investigated using double random grain-size distribution (GSD) measurements and end-member modelling analyses (EMMA), as well as detailed mineralogical analyses (XRD and LOI). These methods revealed an upward-coarsening trend of point-bar deposits in terms of D50 of GSD and in proportions of coarse sediment for two (C2 and C0) of three chronologically different meandering channels in the river terminus. Those of another abandoned channel (C1) were, by contrast, dominated by a fining-upward trend. The deposits in channel C2 were characterised by a lower content of organic matter and carbonate compared with those of the other two channels. A novel conceptual model for the formation of coarsening-upward point-bar deposits is proposed. The model implies the action of low-frequency, high-magnitude floods during an overall hyper-arid period when sediment supply to the river terminus is dominated by clay and fine silt. A temporal trend toward higher magnitude and longer duration flood events allows for the transport coarser-grained sediments further down system toward the river terminus where deposition occurs on the inner bend of meandering channels. Successive accretion layers within the point-bar deposits record a coarsening upward trend. Results demonstrate how a coarsening-upward succession of point-bar deposits in the muddy river terminus of a semi-arid endorheic basin can contribute to improved understanding of mechanisms of deposition in fine-grained fluvial systems. Our results contribute to an improved understanding of the varied processes and sedimentology of very fine-grained meandering river terminus systems in semi-arid or arid endorheic basins; the results additionally provide insight to enable improved interpretations of rock record examples of pre-vegetation rivers on Earth and other planetary bodies.

Neural Named Entity Boundary Detection

In this paper, we focus on named entity boundary detection, which is to detect the start and end boundaries of an entity mention in text, without predicting its type. The detected entities are input to entity linking or fine-grained typing systems for semantic enrichment. We propose BdryBot, a recurrent neural network encoder-decoder framework with a pointer network to detect entity boundaries from a given sentence. The encoder considers both character-level representations and word-level embeddings to represent the input words. In this way, BdryBot does not require any hand-crafted features. Because of the pointer network, BdryBot overcomes the problem of variable size output vocabulary and the issue of sparse boundary tags. We conduct two sets of experiments, in-domain detection and cross-domain detection, on six datasets. Our results show that BdryBot achieves state-of-the-art performance against five baselines. In addition, our proposed approach can be further enhanced when incorporating contextualized language embeddings into token representations.

On the expressiveness and semantics of information flow types

Information Flow Control (IFC) is a form of dependence analysis that tracks and prohibits dependence of public outputs on secret inputs. Such a dependence analysis is often carried out using a type system. IFC type systems can track dependence (via confidentiality labels) at varying levels of granu larity. On one extreme, there are fine-grained type systems that track dependence at the level of individual values. They label individual values. On the other extreme, there are coarse-grained type systems that track dependence at the level of entire computations. These type systems do not label individual values but instead label entire sub-computations. An important foundational question is one of the relative expressiveness of these two classes of IFC type systems. In this paper we show that, despite the glaring differences in how they track dependence, the two classes of type systems are actually equally expressive. We do this by showing translations from FG, a fine-grained IFC type system derived from SLAM (In Proceedings of the ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (POPL) (1998)), to SLIO∗, a coarse-grained IFC type system derived from HLIO (In Proceedings of the ACM SIGPLAN International Conference on Functional Programming (ICFP) (2015)), and vice-versa. The translation from SLIO∗ to FG is straightforward since FG tracks dependence at a granularity finer than SLIO∗ does. However, the translation from FG to SLIO∗ is quite involved and relies extensively on label quantification. We further examine the reason for this complexity using a slight variant of SLIO∗, called CG, to which FG can be translated more easily. As a separate, more foundational contribution we show how to extend logical relation models of information flow to languages with higher-order state. Specifically, we build world-indexed (Kripke) logical relations for FG, SLIO∗ and CG, which we use to prove these type systems sound and also to prove the translations between them correct.

Fine Grained Named Entity Recognition via Seq2seq Framework

Fine-grained Named entity recognition (NER) is crucial to natural language processing (NLP) applications like relation extraction and knowledge graph construction. Most existing fine-grained NER systems suffer from inefficiency problem as they use manually annotated training datasets. To address such issue, our NER system could automatically generate datasets from Wikipedia in distant supervision paradigm through mapping hyperlinks in Wikipedia documents to Freebase. In addition, previous NER models can not effectively process fine-grained labels with more than 100 types. So we introduce a ‘BIO’ tagging strategy which can identify the position and type attributes simultaneously. Such tagging scheme transfers NER problem into a sequence-to-sequence (seq2seq) based issue. We propose a seq2seq framework to comprehend the input sentence in a comprehensive way. Specifically, we adopt a Bi-LSTM as the encoder to equally process the past and future information of the input. Then we add a self-attention mechanism to handle the long-term dependency problem in a long sequence. When classifying the entity tags, we choose CRF model as it adds more constraints to avoid position logical problem. Experiments are performed on large-scale datasets for fine-grained NER tasks. Experimental results verify the effectiveness of FSeqC, and it outperforms other state-of-the-art alternatives consistently and significantly.

Characterisation of pore fluid salinity dependent evaporative dewatering of kaolin using dielectric spectroscopy

Realising the importance of pore fluid salinity on the dewatering behaviour of fine-grained porous systems, the present study systematically investigated such impacts on temporal moisture dynamics of kaolin subjected to evaporative dewatering. A detailed discussion is provided pertaining to the background processes dictating evaporative dewatering response and corresponding alterations in the dielectric behaviour of kaolin. Frequency dependent dielectric spectra of soil, which can be considered as the fingerprint of the transient changes in the condition of water phase within the pore system of the soil and associated densification, are monitored in real time during dewatering using an open-ended coaxial probe with a vector network analyser. The spatial sensitivity of the coaxial probe has been quantified through layered media approach. Combining the results of volume change behaviour of the material along with its moisture loss response, the study characterised the hydro-mechanical response of the material from the windows of frequency dependent dielectric spectroscopy.

Edge Computing: A Survey On the Hardware Requirements in the Internet of Things World

In today’s world, ruled by a great amount of data and mobile devices, cloud-based systems are spreading all over. Such phenomenon increases the number of connected devices, broadcast bandwidth, and information exchange. These fine-grained interconnected systems, which enable the Internet connectivity for an extremely large number of facilities (far beyond the current number of devices) go by the name of Internet of Things (IoT). In this scenario, mobile devices have an operating time which is proportional to the battery capacity, the number of operations performed per cycle and the amount of exchanged data. Since the transmission of data to a central cloud represents a very energy-hungry operation, new computational paradigms have been implemented. The computation is not completely performed in the cloud, distributing the power load among the nodes of the system, and data are compressed to reduce the transmitted power requirements. In the edge-computing paradigm, part of the computational power is moved toward data collection sources, and, only after a first elaboration, collected data are sent to the central cloud server. Indeed, the “edge” term refers to the extremities of systems represented by IoT devices. This survey paper presents the hardware architectures of typical IoT devices and sums up many of the low power techniques which make them appealing for a large scale of applications. An overview of the newest research topics is discussed, besides a final example of a complete functioning system, embedding all the introduced features.

Network telemetry

Network telemetry is about understanding what is happening in the current network. It serves as the basis for making a variety of management decisions for improving the performance, availability, security, and efficiency of networks. However, it is challenging to build real-time and fine-grained network telemetry systems because of the need to support a variety of measurement queries, handle a large amount of traffic for large networks, while staying within the resource constraints at hosts and switches. Today, most operators take a bottom-up approach by passively collecting data from individual devices and infer the network-wide information they need. They are often limited by the monitoring tools device vendors provide and find it hard to extract useful information. In this paper, we argue for a top-down approach: We should provide a high-level declarative abstraction for operators to specify measurement queries, programmable measurement primitives at switches and hosts, and a runtime that translates the high-level queries into low-level API calls. We discuss a few recent works taking this top-down approach and call for more research in this direction.

From fine- to coarse-grained dynamic information flow control and back

We show that fine-grained and coarse-grained dynamic information-flow control (IFC) systems are equally expressive. To this end, we mechanize two mostly standard languages, one with a fine-grained dynamic IFC system and the other with a coarse-grained dynamic IFC system, and prove a semantics-preserving translation from each language to the other. In addition, we derive the standard security property of non-interference of each language from that of the other, via our verified translation. This result addresses a longstanding open problem in IFC: whether coarse-grained dynamic IFC techniques are less expressive than fine-grained dynamic IFC techniques (they are not!). The translations also stand to have important implications on the usability of IFC approaches. The coarse- to fine-grained direction can be used to remove the label annotation burden that fine-grained systems impose on developers, while the fine- to coarse-grained translation shows that coarse-grained systems---which are easier to design and implement---can track information as precisely as fine-grained systems and provides an algorithm for automatically retrofitting legacy applications to run on existing coarse-grained systems.

From Signal to Image: Enabling Fine-Grained Gesture Recognition with Commercial Wi-Fi Devices.

Gesture recognition acts as a key enabler for user-friendly human-computer interfaces (HCI). To bridge the human-computer barrier, numerous efforts have been devoted to designing accurate fine-grained gesture recognition systems. Recent advances in wireless sensing hold promise for a ubiquitous, non-invasive and low-cost system with existing Wi-Fi infrastructures. In this paper, we propose DeepNum, which enables fine-grained finger gesture recognition with only a pair of commercial Wi-Fi devices. The key insight of DeepNum is to incorporate the quintessence of deep learning-based image processing so as to better depict the influence induced by subtle finger movements. In particular, we make multiple efforts to transfer sensitive Channel State Information (CSI) into depth radio images, including antenna selection, gesture segmentation and image construction, followed by noisy image purification using high-dimensional relations. To fulfill the restrictive size requirements of deep learning model, we propose a novel region-selection method to constrain the image size and select qualified regions with dominant color and texture features. Finally, a 7-layer Convolutional Neural Network (CNN) and SoftMax function are adopted to achieve automatic feature extraction and accurate gesture classification. Experimental results demonstrate the excellent performance of DeepNum, which recognizes 10 finger gestures with overall accuracy of 98% in three typical indoor scenarios.

Analytic surveillance: Big data business models in the time of privacy awareness

Massive data collection and analysis is at the heart of many business models today. New technologies allow for fine-grained recommendation systems that help companies make accurate market predictions while also providing clients with highly personalized services. Because of this, extreme care must be taken when it comes to storing and managing personal (often highly sensitive) information. In this paper we focus on the influence of big data management in media business content platforms, mainly in well-known OTT (Over the Top) services. In addition, we comment on the implications of data management in social networks. We discuss the privacy and security risks associated with this novel scenario, and briefly comment on tools that aid in securing the privacy of business intelligence within this context.

Fine-Grained Access Control Systems Suitable for Resource-Constrained Users in Cloud Computing

For the sake of practicability of cloud computing, fine-grained data access is frequently required in the sense that users with different attributes should be granted different levels of access privileges. However, most of existing access control solutions are not suitable for resource-constrained users because of large computation costs, which linearly increase with the complexity of access policies. In this paper, we present an access control system based on ciphertext-policy attribute-based encryption. The proposed access control system enjoys constant computation cost and is proven secure in the random oracle model under the decision Bilinear Diffie-Hellman Exponent assumption. Our access control system supports AND-gate access policies with multiple values and wildcards, and it can efficiently support direct user revocation. Performance comparisons indicate that the proposed solution is suitable for resource-constrained environment.

Spatial subdivision of complex indoor environments for 3D indoor navigation

ABSTRACTAs we realize that we spend most of our time in increasingly complex indoor environments, applications to assist indoor activities (e.g. guidance) have gained a lot of attention in the recent years. The advances in ubiquitous computing made possible the development of several spatial models intending to support context-aware and fine-grained indoor navigation systems. However, the available models often rely on simplified representations (e.g. 2D plans) and ignore the indoor features (e.g. furniture), thereby missing to reflect the complexity of the indoor environment. In this paper, we introduce the Flexible Space Subdivision framework (FSS) that allows to automatically identify the spaces that can be used for indoor navigation purpose. We propose a classification of indoor objects based on their ability to autonomously change location and we define a spatial subdivision of the indoor environment based on the classified objects and their functions. The framework can consider any 3D indoor configuration, the static and dynamic activities it hosts and it enables the possibility to consider all types of locomotion (e.g. walking, flying, etc.). It relies on input 3D models with geometric, semantic and topological information and identifies a set of subspaces with dedicated properties. We assess the framework against criteria defined in previous researches and we provide an example.

Performance differences between the episode-based DBC and diagnosis-related DRG case mix systems

This paper explores the question how much detail a cost system needs to have in order to provide reliable cost information at a reasonable price. In general, fine-grained cost systems with a lot of detail (in product definition, in cost drivers and in cost pools) are expected to provide more reliable cost information than coarse- grained cost systems with less detail. This paper takes as an example the DBC cost system that has been developed for the Dutch hospital sector. The fine-grained DBC system with over 40,000 health care products appears to outperform lowergrained DRG systems with “only” 15,000 and 6,000 health care products on cost homogeneity and predictive validity. It does so however at the cost of a high number of products with measurement and specification errors, caused by a large number of outliers and by a low number of observations in product groups. The cost-effectiveness of the DBC system is not very high: only 3% of all DBC-codes explains 80% of total costs, whereas the lower-grained DRG system uses 14% of the codes to explain 80% of total costs. Combined with the high administration cost of the DBCsystem, it was from an economic perspective, a sensible idea to replace the finegrained DBC-system by the coarse-grained DOT system.

Iterative Reconstruction of Memory Kernels.

In recent years, it has become increasingly popular to construct coarse-grained models with non-Markovian dynamics to account for an incomplete separation of time scales. One challenge of a systematic coarse-graining procedure is the extraction of the dynamical properties, namely, the memory kernel, from equilibrium all-atom simulations. In this article, we propose an iterative method for memory reconstruction from dynamical correlation functions. Compared to previously proposed noniterative techniques, it ensures by construction that the target correlation functions of the original fine-grained systems are reproduced accurately by the coarse-grained system, regardless of time step and discretization effects. Furthermore, we also propose a new numerical integrator for generalized Langevin equations that is significantly more accurate than the more commonly used generalization of the velocity Verlet integrator. We demonstrate the performance of the above-described methods using the example of backflow-induced memory in the Brownian diffusion of a single colloid. For this system, we are able to reconstruct realistic coarse-grained dynamics with time steps about 200 times larger than those used in the original molecular dynamics simulations.

The exceptional sediment load of fine-grained dispersal systems: Example of the Yellow River, China

Sedimentary dispersal systems with fine-grained beds are common, yet the physics of sediment movement within them remains poorly constrained. We analyze sediment transport data for the best-documented, fine-grained river worldwide, the Huanghe (Yellow River) of China, where sediment flux is underpredicted by an order of magnitude according to well-accepted sediment transport relations. Our theoretical framework, bolstered by field observations, demonstrates that the Huanghe tends toward upper-stage plane bed, yielding minimal form drag, thus markedly enhancing sediment transport efficiency. We present a sediment transport formulation applicable to all river systems with silt to coarse-sand beds. This formulation demonstrates a remarkably sensitive dependence on grain size within a certain narrow range and therefore has special relevance to silt-sand fluvial systems, particularly those affected by dams.

Flexible dynamic information flow control in the presence of exceptions

AbstractWe describe a language-based, dynamic information flow control (IFC) system called LIO. Our system presents a new design point for IFC, influenced by the challenge of implementing IFC as a Haskell library, as opposed to the more typical approach of modifying the language runtime system. In particular, we take a coarse-grained, floating-label approach, previously used by IFC Operating Systems, and associate a single, mutable label—thecurrent label—with all the data in a computation's context. This label is always raised to reflect the reading of sensitive information and it is used to restrict the underlying computation's effects. To preserve the flexibility of fine-grained systems, LIO also provides programmers with a means for associating an explicit label with a piece of data. Interestingly, these labeled values can be used to encapsulate the results of sensitive computations which would otherwise lead to the creeping of the current label. Unlike other language-based systems, LIO also bounds the current label with acurrent clearance, providing a form of discretionary access control that LIO programs can use to deal with covert channels. Moreover, LIO provides programmers with mutable references and exceptions. The latter, exceptions, are used in LIO to encode and recover from monitor failures, all while preserving data confidentiality and integrity—this addresses a longstanding concern that dynamic IFC is inherently prone to information leakage due to monitor failure.

Fine-grained and coarse-grained Paleogene sublacustrine fan systems in Fushan Depression, Beibuwan Basin, South China Sea: implications for sedimentary characteristics and depositional processes

The Fushan Depression is a hydrocarbon-bearing half-graben rift sub-basin located in the southeast of the Beibuwan Basin, South China Sea. The sublacustrine fan systems in the Paleogene Liushagang Formation have recently become important targets in this depression, while the depositional process and detailed characterization of these systems are little known. Analysis of drilled cores, wire-line logs, and 3-D seismic survey suggest that the Fushan Depression develops two different types of sublacustrine fan systems. The fine-grained sublacustrine fan system located in the western area is mainly composed of fine- to medium-grained light gray sandstones interbedded with layers of silt-rich shales. This kind of sediment slumped from the distal bar of the delta front due to high sediment pore pressures at the slope edge as well as the activity of the Meitai Fault. By contrast, the coarse-grained sublacustrine fan system formed in the eastern area is characterized by medium to coarse gray sandstones and conglomerates with the development of various deformed lenticular beddings, deformed pillow structures, and micro-faults. Further study suggests that its sedimentation process is closely related to the geomorphology of flexure slope-break belt. The slope changed abruptly from 3°~5° to 7°~9° across the flexure slope point, increasing the accommodation space under the flexure belt. The coarse-grained sublacustrine fan formed when coarse-grained sediment spillover the slope-break toward the increased accommodation space. As the reservoir quality of these sublacustrine fan systems is well controlled by the sedimentary characteristics and sedimentation processes, our results should be of great significance for the development of future exploration.

Mud-Rich Density-Driven Flows Along an Early Ordovician Storm-Dominated Shoreline: Implications for Shallow-Marine Facies Models

Abstract Dynamic physical and biological seafloor processes shape the three-dimensional stratal architecture of modern and ancient mud-dominated continental shelves. When evaluating a mudstone unit as a potential source, reservoir, or seal, it is imperative to understand the physical, biological, and chemical seafloor processes that control mudstone depositional fabric and diagenesis. This study presents sedimentological, ichnological, and geochemical data of exceptionally preserved mudstone originating from hyperpycnal and possible combined-flow deposits in the Lower Ordovician (Tremadocian) Beach Formation, Bell Island Group, Newfoundland. Seven mudstone facies are described, based on textural, compositional, and ichnological characteristics. Mudstone deposits interpreted to originate from hyperpycnal flows are well cemented, exhibit high chlorite­:illite ratios, and contain sharp grain-size changes, commonly forming beds with tripartite subdivisions. Deposits of wave-enhanced sediment gravity flows are, in contrast, poorly cemented illitic mudstone. These mudstone facies exhibit decimeter-thick wave or combined-flow structures within siltstone and very fine sandstone beds as well as laterally discontinuous, nonbioturbated mudstone layers. Beds of this facies have abundant mudstone-on-mudstone and mudstone-on-sandstone erosional contacts. Low organic carbon loading from the nonvegetated early Paleozoic hinterland, combined with a high reworking frequency on a shallow marine wave-dominated shelf, is inferred to have resulted in high remineralization efficiency, and low preservation potential of reactive organic carbon. Burial efficiency and bioavailability of organic matter are considered to be the critical variables controlling infaunal colonization and bioturbation in mud-dominated open-coastline paleoenvironments. This work demonstrates that ancient fine-grained coastal systems are incompletely incorporated into sequence stratigraphic models owing to their atypical proximal-to-distal facies relationships. It is proposed that the spatial organization of fine-grained sediment and facies architecture in such systems are controlled by: 1) frequency of sediment supply events, 2) direction of mud transport, 3) diagenetic reactivity of minerals and bioavailable organic carbon, and (4) residence time of mineral grains and organic matter close to the sediment–water interface.

Constraining the sedimentology and stratigraphy of submarine intraslope lobe deposits using exhumed examples from the Karoo Basin, South Africa

Intraslope lobe deposits provide a process record of the infill of accommodation on submarine slopes and their recognition enables the accurate reconstruction of the stratigraphic evolution of submarine slope systems. Extensive exposures of discrete sand-prone packages in Units D/E and E, Fort Brown Formation, Karoo Basin, South Africa, permit analysis of the sedimentology and stacking patterns of three intraslope lobe complexes and their palaeogeographic reconstruction via bed-scale analysis and physical correlation of key stratal surfaces. The sand-prone packages comprise tabular, aggradationally to slightly compensationally stacked lobe deposits with constituent facies associations that can be attributed to lobe axis, lobe off-axis, lobe-fringe and distal lobe-fringe environments. Locally, intraslope lobe deposits are incised by low aspect ratio channels that mark basinward progradation of the deepwater system. The origin of accommodation on the slope for lobe deposition is interpreted to be due to differential compaction or healing of scars from mass wasting processes. The stacking patterns and sedimentary facies arrangement identified in this study are distinct from those of more commonly recognized basin-floor lobe deposits, thereby enabling the establishment of recognition criteria for intraslope lobe deposits in other less well exposed and studied fine-grained systems. Compared to basin floor lobes, intraslope lobes are smaller in volume, influenced by higher degrees of confinement, and tend to show aggradational stacking patterns.

An Improved Cloud Data Sharing Scheme with Hierarchical Attribute Structure

Cloud computing is an emerging computing paradigm that can provide storage resources and computing capacities services over the Internet. However, some new security issues arise when users' sensitive data are outsourced and shared in un- trusted cloud. The traditional techniques to protect the confidentiality of sensitive data stored in cloud are encryption and related cryptographic tools. And the correspond- ing private keys to access and decrypt the files are disclosed to only authorized users. However, these traditional solutions are not scalable because the computational cost of encryption and other access control is heavy for devices with limited computation ability. In this paper, we present a new way to implement scalable and fine-grained access control systems, which can be applied for big data in untrusted cloud computing en- vironment. The solution is based on symmetric, efficient broadcast encryption and fine-grained attribute-based encryption (ABE). In this access control system, users are able to join and revoked with broadcast encryption. An outsourced Hierarchical ABE scheme is first proposed in this paper to construct the access control system. The security analysis is also presented under the security model.